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codeparrot
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github-jupyter

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Y-Fujikawa/study_meeting_office
ruby_notebook/2015-07-22_2-7-1_2-7-5.ipynb
1
7359
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "2-7 主な組み込みクラス \n", "2-7-1 数値（Numeric）" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "[Fixnum, Integer, Numeric, Comparable, Object, PP::ObjectMixin, Kernel, BasicObject]\n", "[Float, Numeric, Comparable, Object, PP::ObjectMixin, Kernel, BasicObject]\n" ] }, { "data": { "text/plain": [ "[Float, Numeric, Comparable, Object, PP::ObjectMixin, Kernel, BasicObject]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 復習\n", "p 1\n", "p Fixnum.ancestors\n", "p Float.ancestors" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n", "170\n", "6\n", "1000000000\n", "100000000000\n" ] }, { "data": { "text/plain": [ "100000000000" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 10進数\n", "p 0d5\n", "\n", "# 16進数\n", "p 0xAA\n", "\n", "# 2進数\n", "p 0b110\n", "\n", "# 大きな数字を見やすくする\n", "p 1_000_000_000\n", "\n", "# アンダースコアはどこでも入るの？ ※できるけど、アンチパターン！\n", "p 1_0_0_00_000_0000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2-7-2 文字列（String）\n", "ダブルクォートでは #{...} を用いた式展開ができます。" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"result:\\\\t\\#{1+1}\"\n", "\"result:\\t2\"\n", "\"It's mine\"\n", "\"99 test, 99 hoge.\"\n", "\" 1行目\\n 2行目\\n\"\n", "\"\\#{foo}\\\\t\\#{bar}\\n\"\n", "\"It's \\#{weather}\"\n", "\"It's rainy\"\n", "\"It's rainy\"\n" ] }, { "data": { "text/plain": [ "\"It's rainy\"" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p 'result:\\t#{1+1}'\n", "p \"result:\\t#{1+1}\"\n", "\n", "p 'It\\'s mine'\n", "\n", "paragraph = \"99 test, \\\n", "99 hoge.\"\n", "p paragraph\n", "\n", "# 上記は使わずに << 演算子を用いる\n", "str = <<-EOS\n", " 1行目\n", " 2行目\n", "EOS\n", "p str\n", "\n", "str2 = <<'EOS'\n", "#{foo}\\t#{bar}\n", "EOS\n", "p str2\n", "\n", "weather = 'rainy'\n", "\n", "# 式展開とバックスラッシュ記法は無効\n", "p %q(It's #{weather})\n", "# 式展開できる\n", "p %(It's #{weather})\n", "p %Q(It's #{weather})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2-7-3 シンボル（Symbol） \n", "「:ruby」のように先頭にコロンをつけた文字の並びはシンボルリテラルです。 \n", "シンボルは文字列に似ていますが、読み書きのしやすさから「attr_accessor :lenght」のように \n", "識別子やキーワード的な単語を表現するのに適しています。" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ":ruby\n", ":ruby=\n" ] }, { "data": { "text/plain": [ ":ruby=" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p :ruby\n", "p :ruby=" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2-7-4 配列（Array） \n", "配列はブラケット（[]）の中に要素となる値をカンマ区切りで記述します。" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[\"Alice\", 4423, 3.14, nil, false]\n", "\"Alice\"\n", "\"Alice\"\n", "nil\n", "\"Carol\"\n", "\"Carol\"\n", "[\"Alice\", \"Bob\", \"Hoge\"]\n", "[\"Alice\", \"Bob\", \"Hoge\", nil, nil, \"Piyo\"]\n", "[\"Alice\", \"Bob\", \"Hoge\"]\n", "[\"Alice Bob\", \"Hoge Piyo\"]\n", "[:red, :bule, :green]\n" ] }, { "data": { "text/plain": [ "[:red, :bule, :green]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 異なる要素を入れる\n", "array = ['Alice', 4423, 3.14, nil, false]\n", "p array\n", "p array[0]\n", "\n", "# 配列の要素の取り方\n", "people = ['Alice', 'Bob', 'Carol']\n", "\n", "p people[0]\n", "p people[10]\n", "p people[2]\n", "p people[-1]\n", "\n", "# 途中に新たな要素を格納\n", "people[2] = 'Hoge'\n", "p people\n", "\n", "people[5] = 'Piyo'\n", "p people\n", "\n", "# %を使った記法\n", "hoge2 = %w(Alice Bob Hoge)\n", "p hoge2\n", "hoge2 = %w(Alice\\ Bob Hoge\\ Piyo)\n", "p hoge2\n", "\n", "hoge3 = %i(red bule green)\n", "p hoge3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2-7-5 ハッシュ（Hash） \n", "ハッシュはいわゆる連想配列です。{ キー => 要素}" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\"piyo\"\n", "nil\n", "\"hoge\"\n", "\"piyo\"\n" ] }, { "data": { "text/plain": [ "\"piyo\"" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "colors = {'red' => 'ff0000', 'green' => 'hoge', 'blue' => 'piyo'}\n", "\n", "# 要素を参照\n", "p colors['blue']\n", "p colors['black']\n", "\n", "# キーはシンボルがよく用いられる\n", "colors2 = {:red => 'ff0000', :green => 'hoge', :blue => 'piyo'}\n", "p colors2[:green]\n", "\n", "# 1.9以降では新しいリテラルが追加された。\n", "colors3 = {red: 'ff0000', green: 'hoge', blue: 'piyo'}\n", "p colors3[:blue]" ] } ], "metadata": { "kernelspec": { "display_name": "Ruby 2.2.0", "language": "ruby", "name": "ruby" }, "language_info": { "file_extension": "rb", "mimetype": "text/ruby", "name": "ruby", "version": "2.2.0" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
newworldnewlife/TensorFlow-Tutorials
12_Adversarial_Noise_MNIST.ipynb
1
712622
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow Tutorial #12\n", "# Adversarial Noise for MNIST\n", "\n", "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "The previous Tutorial #11 showed how to find so-called adversarial examples for a state-of-the-art neural network, which caused the network to mis-classify images even though they looked identical to the human eye. For example, an image of a parrot became mis-classified as a bookcase when adding the adversarial noise, but the image looked completely unchanged to the human eye.\n", "\n", "The adversarial noise in Tutorial #11 was found through an optimization process for each individual image. Because the noise was specialized for each image, it may not generalize and have any effect on other images.\n", "\n", "In this tutorial we will instead find adversarial noise that causes nearly all input images to become mis-classified as a desired target-class. The MNIST data-set of hand-written digits is used as an example. The adversarial noise is now clearly visible to the human eye, but the digits are still easily identified by a human, while the neural network mis-classifies nearly all the images.\n", "\n", "In this tutorial we will also try and make the neural network immune to adversarial noise.\n", "\n", "Tutorial #11 used NumPy for the adversarial optimization. In this tutorial we will show how to implement the optimization process directly in TensorFlow. This might be faster, especially when using a GPU, because it does not need to copy data to and from the GPU in each iteration.\n", "\n", "It is recommended that you first study Tutorial #11. You should also be familiar with TensorFlow in general, see e.g. Tutorials #01 and #02." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Flowchart" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below.\n", "\n", "This example shows an input image with a hand-written 7-digit. The adversarial noise is then added to the image. Red noise-pixels are positive and make the input image darker in those pixels, while blue noise-pixels are negative and make the input lighter in those pixels.\n", "\n", "The noisy image is then fed to the neural network which results in a predicted class-number. In this case the adversarial noise fools the network into believing that the 7-digit shows a 3-digit. The noise is clearly visible to humans, but the 7-digit is still easily identified by a human.\n", "\n", "The remarkable thing here, is that a single noise-pattern causes the neural network to mis-classify almost all input images to a desired target-class.\n", "\n", "There are two separate optimization procedures in this neural network. First we optimize the variables of the neural network so as to classify images in the training-set. This is the normal optimization procedure for neural networks. Once the classification accuracy is good enough, we switch to the second optimization procedure, which tries to find a single pattern of adversarial noise, that causes all input images to be mis-classified as the given target-class.\n", "\n", "The two optimization procedures are completely separate. The first procedure only modifies the variables of the neural network, while the second procedure only modifies the adversarial noise." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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OrWrYsh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\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pOIiIjEDfvn3xwQcfAAA0Gg2GDh2KrKwswamIiIiIDEtCQgLy8vJExyAiolJIrVZj4sSJ\n8Pf3lwvEVSoVVqxYgY0bN7JAnIiIDFJeXh4SEhLe+P5HjhzB5s2bAeRfRL1s2TJeAEJERFSCuLu7\ny/sXL14UmISIiIjIsAQFBeHnn3+GmZkZAODPP/9EmzZtcOHCBcHJiIiI6N94eXnh7Nmz8oQuWVlZ\n8Pf3R2BgIHJycgSnE+P27dto0aJFgQLxgIAAhIeHs0Cc3pi1tTUOHToEPz8/uW/16tX4+OOPkZqa\nKjCZeC9evEDXrl0LFIgPGjQIu3btQpkyZQQmIzJ+6enpOHfuHABAqVQabZE4VxInIjIS33//PY4d\nO4akpCRcv34ds2fPxvTp00XHIqISzsrKCra2tlAqlQVmKZMkCQDeauYyQ531TKPRQKFQGGy+tyFJ\nEiRJglL5ZnNF/fW8GyJjfU2+7XOmb0V5Tfzb97c43m/x8fFIS0sDAFy6dAnJyclwdHTU29cjIiL6\nu7i4OPTu3RvHjh2T+6pXr44tW7bAw8NDYDIiIqLXO3PmDB48eIBevXoV+b65ubkYPXq03O7Tpw9a\ntWqly3hERESkZywSJyIiInq1/v37w97eHr169UJGRgbi4uLg7e2NXbt2oW3btqLjERER0d/UrFkT\nx48fR5cuXeTzHCtXrkR0dDR+++031KxZU3DC4rN161YMGTJEnuAeAEaMGIGlS5dysl96a2XKlMHW\nrVvx1VdfITQ0FACwa9cuNGjQABs3biyVnxceOHAAAwYMwPPnzwHkX0s7Z84cBAcHC05GVDpERUUh\nNzcXANCgQQPY29sLTqQfLBInIjISzs7OmDNnDkaOHAkAmDNnDj788EM0adJEcDIiKsmsrKxQpUoV\n1KpVCyqVCkD+KoiXL19G5cqV36jQUqFQoHz58rCwsNBqvLW1tTz78utIkoS0tDT5j3htclhZWRU4\ndl5eHsLDw+Hi4oIqVapodZyS5MGDB7h69Sp8fHxgalq0fwU0Gg1iY2MLnBg0FG/7mgQAGxsb1K5d\n2+CKsV/1nOXk5CA5OVmrY6hUKtjY2Oi8EFuSJDx69Ajp6emFjlUoFKhUqRIsLS3lPl283yRJQkZG\nxmtXB9+xYwfOnj0LAMjOzn6jr0NERPSmTp06hYCAADx69Eju69SpE9avXw8nJyeByYiIiAoXFhaG\nx48fv1GR+JIlS3D16lUAgK2tLebPn6/reERERKRnfy8SlyTJICdbJSIiIhLF19cXERER6N69O168\neIGkpCR06tQJa9euRc+ePUXHIyIior+pUKECwsLCMGDAAOzfvx8AEB0dDS8vL6xduxadOnUSnFC/\n8vLyMH36dMyaNQsajQYAYGpqiunTp2PixIk870M6o1AoEBISAisrK3z77beQJAkPHjyAj48P5s2b\nh88++0x0xGKh0WgwZ84cTJ06Vb7G1cTEBAsXLiw13wMiQxAZGSnvG/NEFSwSJyIyIoGBgdi2bRvC\nwsKQl5eHIUOG4MyZM1oVVxIR/RuFQoFatWrBy8sLZcuWBZB/oigtLQ2urq6oVq3aGx2zXr16sLW1\n1Wqsk5MTypQpU+hYSZIQHx+PrKwsrXM4OjoWOHZubi7i4+Ph4+ODRo0aaXWckuTChQtQqVTo27dv\nkX83qNVqnDp1Ck+fPtVTujf3tq9JAChfvjxatmxpcDNhvuo5y8jIwLNnz7Raybts2bIoX768zk/i\najQaXLlyBQkJCYWOVSqVaNCgARwcHOQ+XbzfJElCYmLia4u///jjD7lIXKVSFXmCBCIiojcVGhqK\nSZMmyR/0KJVKTJ48GZMnTza4vzmIiIj+TXh4OOLi4op8v8ePH2Pq1Klye/LkyShfvrwOkxEREVFx\nqFKlChwdHREfH4+kpCQ8ePAAVatWFR2LiIiIyKA0b94ckZGR6NKlC+7fv4/s7Gx88sknSEhIQGBg\noOh4RERE9DdOTk7Yt28fVq5cidGjRyMnJwfPnj1D586d4evriyVLlqB69eqiY+rcvn37EBQUhNjY\nWLmvVq1a2Lx5M5o2bSowGRmzSZMmoVGjRhg0aBDi4+OwRfwLAAAgAElEQVSRnZ2N0aNHIzw8HMuW\nLYOzs7PoiHpz584dDB8+HGFhYXJfxYoVsWHDBrRr105cMKJS6NChQ/J+hw4dBCbRL14dT0RkRBQK\nBVauXAk3Nzekp6fjwoULWLBgASZMmCA6GhGVUAqFAiqVCmXLli1QJG5qagpzc3O5ryiUSiUsLS1h\nbW2t1de3sbHRukg8JydH6+Lnfzt2Tk4OzM3NYWVlBRsbG62OU5JYWVnB3Nwc1tbW8srw2lKr1bC0\ntHyj51zf3vY1CQCWlpawsbExuIKtVz1npqamyMjI0KpI3MLCQi8riWs0GlhZWWm1OrdSqYS1tXWB\n95Uu3m+SJCEvL++1r+eXfyYoFArOeEpERHqXnJyMQYMGYefOnXKfg4MDfv31V3Tv3l1gMiIiIu2l\npaXh999/R05ODh49eoRKlSppfd/g4GCkpqYCANzc3BAUFKSvmERERKRnrq6uOHbsGAAgJiaGReJE\nRERE/6J+/fo4ffo0unbtipiYGKjVaowYMQJ37txBSEiIzr5OSkqKUV7LQkREJMLw4cPRtGlT9OrV\nC7dv3wYA7NmzB+Hh4ZgwYQImTpyo1XWrhu7u3bsYPXo09uzZU6C/Z8+e+PHHH/m3Bemdr68vrl69\niv79+8uFmjt37sTBgweN6r32l9TUVEyaNAnLly9Hbm6u3O/v748ff/xRqwXWiEh3kpOT5YXGlEol\nvL29BSfSH6XoAESiaDQarTe1Wo28vDytN20pFAqYmJjA1NS00M3QCpbIcNWoUaPAKi3ffvstrl27\nJi4QERERERERUSlx+fJleHp6FigQb968Of744w8WiBMRUYkSGRmJnJwcAJALw7Rx9OhRrF+/HkD+\nZyDLli2DqSnnrCYiIiqp3N3d5f2LFy8KTEJERERk2JydnXH06FG0atVK7gsNDcVnn30GjUajk69x\n8uRJHD58WCfHIiIiIqBp06b4/fff0a9fP3nhkczMTEybNg0eHh7YsWOHVou4GKKUlBTMnDkTDRs2\nLFAgbmNjg4ULF2Lz5s0sEKdi884772DPnj348ssv//Fea9q0KSIiIgQn1I3du3ejUaNGWLx4sVwg\nbmJighkzZmDz5s0sECcS4OjRo3KdZ+PGjeHg4CA4kf7wqgwqlTQaDW7duoWkpCStxufm5mLPnj2I\niYkpdKwkSUhMTISlpWWhY83MzODt7Y0GDRoUOtbZ2ZmF4qS1L774Atu3b8fp06eRnZ2NIUOG4MSJ\nE1AqOTcIERXdy5Ol/NU2Zkql0mhXGlYoFEb7u8BYnzdjfs4A/Txvf0309NcJel194E5ERFSYzZs3\nY+jQoUhLS5P7hg8fjsWLF8Pc3FxgMiIioqJ7+WKMY8eOoU+fPoXeJzc3F5999pn8/1hAQADatGmj\nt4xERESkfywSJyIiItKevb09Dh06hICAAOzduxcAsGzZMjx+/BgbNmx46xUSraysMHjwYFy6dMmo\nVlskIiISycnJCWvXrsXYsWMxcuRIebXRK1euwM/PDzVq1MDEiRMxePDgEjEp7pMnTxASEoLVq1cj\nPT1d7lcqlfjss88wZcoUoy6QI8NlZmaGuXPnokePHhg1ahQuXboEALh69Srat2+Pbt26YdKkSfDy\n8hKctOiOHDmC6dOn/2Pi7ebNm2P58uVo0qSJoGREFB4eLu/7+PgITKJ/hv9XCpGeJCUl4fnz51qN\nzcnJwd27d3H79m2txpcpU0arfwLMzMzg7OyMWrVqFTrWzs7OKAufSD+USiVWrFgBDw8P5OTk4PTp\n0/jhhx8watQo0dGIqAS6fPky0tLSCvxuy8jIgJmZmcBU+qFUKuHq6mq0J8EcHBzg6upqdEXHSqUS\nlStXhpWVlegoOmeszxmgn/ebRqPBjRs3cPbsWXlCqGvXruns+ERERP8mJycHo0ePxsqVK+U+CwsL\nLF++HAMGDBCYjIiI6M0dOnRI3n/5g9PXWb58Oa5cuQIg/6LlefPm6SUbERERFR8WiRMREREVjYWF\nBXbt2oXAwECsXr0aALBjxw5069YNO3fufKsVO62srBAbG4s5c+Zg2rRpuopMREREyF9V/OTJk5g/\nfz5mzpwpF1jfuXMHgYGBWLhwIcaNGwd/f3+DXA04NjYWa9aswdKlS5GSklLgtrp162Lp0qXo2LGj\noHRE/6d169a4cOEC1q1bhy+++ALx8fEAgH379mHfvn1o2bIlJk6cCF9fX4Oun5IkCVu3bsXMmTP/\nsRhp+fLlMXfuXPTv39+gHwNRaRAWFibvG3uRuPFVGhAREQDAzc0NX331ldyeOHEi7t+/LzAREZVU\nrq6u6Ny5M3x9feWtV69eqFSpkuhoOmdqaopu3bqhatWqoqPoRdWqVdGtW7cSMaNlUSiVSjRu3BiO\njo6io+icsT5ngH7eb0qlEvXq1UOfPn0wcuRIjBw5EvXr19fZ8YmIqORKSkqCWq3W+XHv3buH1q1b\nFygQr1WrFk6ePMkCcSIiKrHi4uLk2fsB4Pbt24WeW37y5AkmT54stydNmoTKlSvrLSMREREVD1dX\nV/n8dGxsLNLS0gQnIiIiIjJ8JiYmWLVqFYKDg+W+iIgItG/fHs+ePXvj4/41cX5ISAgnSyciItID\nMzMzTJw4Ebdu3cK4ceMKLFpz7do1DB06FBUqVIC/vz927NiB7OxsgWnzP89ZtGgRWrRogTp16mD2\n7NkFCsTr1KmDH3/8EZcuXWKBOBkUpVKJAQMG4MKFC+jdu3eBRZROnTqFHj16oHXr1li9ejWSk5MF\nJv2nhIQEfP/992jWrBkCAgIKFIibmJhg8ODBuHTpEgYMGMACcSLBHj58KP/vbG5ujtatWwtOpF8s\nEiciMmJff/01XF1dAQCpqakYMWKE4EREVBKZmJjA1NS0wGZiYmK0/7wqlUqjfWwKhcIoV6QG8h+b\nMT5vxvycAfp5vymVSpiamsLMzAxmZmZG/f0jIiLtSJKE0aNHw8TERKfHPXz4MDw8PHD27Fm5z8/P\nD+fPn0ejRo10+rWIiIiK05EjRyBJUoG+Y8eOvfY+X3/9tXzh0bvvvovPP/9cb/mIiIio+Jibm6Nu\n3boAAI1GgytXrghORERERFQyKBQKhISEYOHChfJn1tHR0WjRogVu3br1Rse0trYGAOTk5GDEiBH/\nOH9DREREulGxYkXMmzcP9+7dw5QpU+Dg4CDflpWVhW3btsHPzw8VKlRA37598fPPP+PRo0d6z6XR\naHD+/HmEhobCx8cHlStXxtixYxEVFVVgnLu7OzZu3Ihr165hyJAhUKlUes9G9CYqV66MjRs34vLl\nyxgwYADMzMzk206dOoWhQ4eiYsWK6NOnD/bv36+XxSG0kZOTg507d8LPzw8VK1bEp59+iujoaPl2\nMzMz/Oc//8H169exevVqlCtXTkhOIiroyJEj8r6XlxcsLCwEptE/41uOjoiIZCqVCqtXr0bLli2h\nVquxf/9+rF27Fv379xcdjYiMkIWFhVaFN0qlEhYWFihTpkyhYxUKBdRqNXJycgodK0kSTE1NtTru\nX8cuScWj2dnZSExMFPYhnyRJsLS0RMWKFbUam5iYKHymTIVCAXt7e5ibmxc61srKCgkJCVoVLKtU\nKlhbWwstSjcxMYGFhYVWrwdzc3OtXzdFfUx/fQitzXGLcrI5KytLqxN6kiRBqVS+9jnWdUEgERGV\nPIsXL9bpihoajQbTp0/HzJkz5d9XJiYmmDVrFiZMmGCUE9cQEVHpEh4e/o++iIiIV55XPn78OH75\n5Re5vWTJEl5wREREZETc3d1x9epVAMDFixfh6ekpOBERERFRyREUFARHR0cMHjwYubm5uHPnDtq2\nbYt9+/ahcePGRTqWpaWlvB8ZGYlNmzbhk08+0XVkIiIi+v8cHBwwdepUjB8/Hr/++ivWrVuH06dP\ny7cnJSVhw4YN2LBhAwDAxcUFHTt2RLNmzVC7dm3UqVOnQIF5UWg0Gjx48ACxsbG4efMmIiMjER4e\njufPn//r+LJly6JHjx4YNGgQOnfuzOsWqESpX78+fvnlF0ybNg3//e9/8dNPPyErKwsAkJmZiY0b\nN2Ljxo2wtbVF27Zt4ePjA29vb7i5uenlta5Wq3HhwgUcOXIEEREROH78ONLS0v4xTqVSYeDAgfjq\nq69Qo0YNnecgorfz8nUP3t7eApMUDxaJExEZuebNmyMoKAjfffcdAGDs2LHo1KkTypcvLzgZERkT\nhUKB8uXLw8rKqtCxSqUSFSpU0GqsJElISUlBenq6VjlsbW2LdBFySToRlpiYiJMnTwqbCc/ExARe\nXl5a/f5Qq9U4efIk4uLiiiHZqymVSri6uqJChQqFjk1ISEBMTAw0Gk2hY52cnODu7i709WNubq71\n73JJkrR6XED+86zt41IqlahevbpWYwHt32+SJOHFixdave8VCgWcnZ0LfBj+d9pMEkBERMYrKioK\nX375JXr06KGT4yUmJmLAgAHYs2eP3FehQgVs2rQJ7733nk6+BhERkWhhYWH/6IuIiPjXsWq1GkFB\nQfLkZH5+fujQoYNe8xEREVHxatiwITZu3AgAiImJEZyGiIiIqOTp168fKlSoAD8/P6SmpiIuLg5t\n27bF9u3b0bFjR62PY2lpCYVCIZ+H+eKLL9C1a1fY2dnpKzoREREhfwGaUaNGYdSoUYiNjcX69eux\nfv163Lp1q8C4q1evyhPt/cXBwQF16tRBlSpVYGlpCSsrK1hZWcHe3h5A/grFaWlpSEpKQlpaGtLS\n0nD79m3ExsYWukiPUqlEu3bt0L9/f/j5+cHGxka3D5yomFWvXh3Lli3DzJkzsXHjRvzyyy84c+aM\nfHtycjJ2796N3bt3AwDKlSuH5s2bo379+qhXrx7effdd1K9fH46Ojlp/zWfPnuHq1au4ceMGbty4\ngWvXriEqKgqJiYmvvI+Hhwf69u2L3r17a3V9MhEVP41Gg4MHD8rtovzvXVKxSJyIqBSYMWMG/ve/\n/yE2NhYJCQkYPXo0fvvtN9GxiMjIaLsyt0KhkDdtSJKk1SrIRT1uSSNJEtRqtbAicSD/e6ztqsyG\n8jwolUqtMisUCmg0Gq2KqbUtuNY3Q/gea/OefxPavu8BGPX7noiI3k5KSgr69u2L3NxcnczYe/bs\nWfj7++P+/ftyX8uWLfHbb7+hUqVKb318IiIiQ3D79u0Cv+v+cvfuXdy/fx9Vq1Yt0L9y5Ur88ccf\nAPIvVF64cGGx5CQiIqLi4+7uLu9fvHhRYBIiIiKikqtDhw4IDw9H9+7d8fz5c6SlpeH999/Hr7/+\nioCAAK2OoVQqYWlpKa9iGBcXh/Hjx+PHH3/UZ3QiIiJ6Se3atTFlyhRMmTIFFy5cwOHDhxEWFobj\nx48jMzPzH+MTEhIQFRWFqKgonXz9ChUqoGPHjujQoQM6derEAlUySvb29vLEDFevXsUvv/yCTZs2\n/eMzzOfPn2Pv3r3Yu3dvgX5ra2t5QgYbGxvY2NjAxMQEeXl5SE1NRXJysjwpw7+tEP5vateujV69\neqFv376oX7++zh4rEenH2bNn8fTpUwBA+fLl4enpKTiR/rFInIioFLCwsMCqVavQvn17SJKELVu2\nYPv27fDz8xMdjYiIiIiIiEgvxo4di9u3bwMAqlWr9lbHWrVqFcaMGYOsrCy5Lzg4GDNnzoSpKU+x\nEhGR8Xh5Nu2/O3LkCAYNGiS3nz9/jm+++UZuT5w4EVWqVNFnPCIiIhLg5SLxmJgYSJLEiTuJiIiI\n3kCzZs0QGRmJzp074/79+8jOzkafPn2QkJCAESNGaHUMKyurAoUsP/30E/r27Qtvb299xSYiIqJX\naNSoERo1aoQvv/wSWVlZOHnyJI4dO4br16/j1q1buHnzJjIyMt74+OXKlUO9evVQt25duLm5oUOH\nDnB1ddXhIyAyfC4uLggNDUVoaChiY2MREREhb3Fxcf96n9TUVKSmpr7V161cuTK8vb3Rvn17eHt7\nv/V1R0RUvPbv3y/vd+7cWW+LohkSXsFIRFRKtGvXDkOHDsWqVasAAJ9++im8vb1hb28vOBkRERER\nERGRbq1fvx5r1qyR22+6knhGRgZGjBiBtWvXyn22trZYs2YNPvroo7fOSUREZGjCw8NfeVtERESB\nIvFvvvkGiYmJAIC6detiwoQJ+o5HREREAjg7O+Odd97Bs2fPkJKSgrt3777x/9lEREREpd27776L\n33//HV27dsXFixehVqsxcuRI3L17FyEhIYXe39raukAxjCRJGD16NM6fPw+VSqXP6ERERPQaZcqU\ngY+PD3x8fAr0P3jwALdu3UJ8fDxSUlKQkZGBFStW4MqVKwAAf39/eHh4wNbWFhYWFrC0tETVqlVR\np04d2NnZiXgoRAardu3aqF27NoYNGwYAuHHjBq5du4abN2/KEzPcuHFDXj1YG5UqVULdunVRp04d\n1K1bF3Xr1oWLiwtq1aqlr4dBRMVg37598n63bt0EJik+LBInIipF5s2bh/379+Phw4eIi4vD+PHj\nsXr1atGxiIiIiIiIiHTm2rVrCAwMLNBXs2bNIh/nzz//hL+/P/744w+5z9XVFVu3bkW9evXeOicR\nEZGh0Wg0OHbs2Ctvj4yMlPejoqIKnFtesmTJG1+InJGRAQsLize6LxERERUPNzc3eTKZixcvskic\niIiI6C1UrFgRERER6NGjB06cOAEACA0NxbNnz7By5UqYmr760m4rK6t/9F25cgVLlizBuHHj9JaZ\niIiI3kyVKlVQpUqVAn0RERFykfgnn3wCPz8/EdGISrx69eq98vqdxMREJCcnIzk5Gc2aNUNubi4A\n4MyZM3jnnXdgZ2cHW1vb4oxLRMXk6dOniI6OBgCYmpqiU6dOghMVD+NfK51KHUmStNrUajVyc3O1\n2vLy8iBJktYZlEqlzjeFQqHH7xqVFjY2Nvjhhx/k9k8//YSDBw8KTERERERERESkO5mZmQgICEB6\nerrcp1AoUL169SIdZ8eOHWjcuHGBAvFevXrh9OnTLBAnIiKjde7cOcTHx7/y9rt37+L27dvQaDT4\n9NNPodFoAAA9evQo0gerDx48wMqVKzFgwAB4enri2rVrb52diIiI9Mvd3V3ev3jxosAkRERERMbB\n3t4eYWFhBYrC1qxZg549eyIzM/OV9/u3InEAmDx5Mv7880+d5yQiIiIiKons7e1RvXp1uLu7Q6n8\nv9JJNzc3VKtWjQXiREbswIED8rUMXl5esLe3F5yoeHAlcTIqeXl5yMzMLLSgOzc3F3v27NH6w0uN\nRoP79+9DrVYXOlapVKJWrVpwdHQsdKxKpUKDBg20urjY1NSUheKkE927d8cnn3yCjRs3AgACAwNx\n6dIlWFtbC05GRERERERE9HbGjRuHy5cvF+grX748ypYtq9X91Wo1vvnmG8ydO1c+v6RSqbBkyRIM\nHz5c53mJiIgMSURERKFjIiMjERERIc+8XaZMGSxYsOC190lNTUVkZCSOHj2KiIgIXLhwAWq1GuXK\nlcOBAwfQpEkTneQnIiIi/WnYsKG8HxMTIzAJERERkfEwNzfHb7/9hpEjR2LVqlUAgJ07d6Jbt27Y\nuXPnvxauvOoav8zMTIwaNQoHDhzQa2YiIiIiIiIiQ7Z//355v1u3bgKTFC8WiZNRkSQJubm5hRaJ\nZ2dn4969e1qvTiFJElJTU7VeTdzW1hblypUrdJyZmRns7e1hZ2en1XGJdGXx4sUIDw/Hs2fPcO/e\nPUyaNAmLFi0SHYuIiIiIiIjojW3atAnLly//R7+2q4g/ffoUvXv3xtGjR+W+atWqYcuWLWjWrJmO\nUhIRERmusLCwQsccOHCgwLjg4GDUrFmzwJj09HScOHFCLgqPjo5GXl5egTGVK1fG4cOH8e677+om\nPBEREekVVxInIiIi0g8TExOsWLECzs7OmDZtGgDg6NGjaN26NQ4cOIBKlSoVGP+qlcQB4ODBg9iy\nZQt69uyp18xEREREREREhigvLw+HDx+W2126dBGYpngpRQcgIqLi5+TkVGB1l6VLl+LEiRMCExER\nERERERG9uYcPH+Kzzz7719u0KRI/ffo0PDw8ChSId+zYEefOnWOBOBERlQoZGRk4fvx4oeP27NmD\nhIQEAECNGjUQHByMxMREbNmyBYGBgWjQoAFsbGzQpUsXhISEICoq6h8F4m5ubjhz5gwLxImIiEoQ\nFxcXqFQqAMCff/6JlJQUwYmIiIiIjIdCocDUqVOxePFiKJX5l3VfvnwZbdq0wa1btwqMfV2ROACM\nGTMGSUlJestKREREREREZKgiIyPl6xmqVKlSYAJcY8cicSKiUqpPnz748MMPAQAajQZDhw5FVlaW\n4FRERERERERERZOTk4OPP/4Y8fHx/3r731c3/bvQ0FC0bdsWDx8+BAAolUpMmTIF+/fvh5OTk87z\nEhERGaLTp08jOzu70HEZGRnyvqenJ7p27YqKFSsiICAAK1euxNWrV6HRaF55fzc3Nxw6dAgVK1bU\nSW4iIiIqHiqVCvXq1QMASJKEy5cvC05EREREZHxGjx6NtWvXwszMDABw584dtGnTBufPn5fHFFYk\nHhcXh8mTJ+s1JxEREREREZEh2rp1q7zv5+cHhUIhME3xMhUdgIiIxFm2bBmOHj2KpKQk3LhxAzNm\nzMCsWbNExyIiA6NQKKBQKOTZil81xszMDObm5lod73XH+jtTU1NIkqR1TtHUajWysrK0ymxqaooy\nZcoUQ6q3J0mSPLNWYTQajVYXlhsSlUoFJyen117I/hdra2vk5eVp9TpWKBQwMTHRRcS3YgjvjaLQ\n9n1hKN9fIiISa8qUKThz5swrb3/VSuLp6ekYNmwYNm7cKPfZ29vj119/ha+vr65jEhERGbSwsLAi\n32fTpk1FGu/p6Yl9+/bBwcGhyF+LiIiIxHN3d8elS5cAABcvXkTLli0FJyIiIiIyPn369EH58uXx\n0UcfITU1FU+fPsV7772Hbdu2oVOnTrC2ti70GN9//z369u2LFi1aFENiIiIiIiIiIvE0Gg127dol\nt/9aVLW0YJE4EVEp5uzsjJCQEIwYMQIAMHfuXPj5+aFp06aCkxGRIVEqlTA1NYWp6av/dFQqlbCx\nsdHqIt+/Csq1oVAoYGFhoVXhrqEUi2ZlZeHRo0daZbayskKlSpVKRAGvRqPB5cuXtc6qzeM3JNbW\n1nB3d9dqbF5entYTAZiZmcHCwuJt470VQ3lvaEuhUBRp1daS8P4hIiL9OXToEObOnfvaMTVq1PhH\n35UrV+Dv74/r16/Lfc2aNcOWLVtQrVo1neckIiIydOHh4Xo9vre3N3bt2qXVhcxERERkmNzd3bFu\n3ToA+UXiRERERKQfPj4+OHLkCLp164bnz58jLS0N77//Pn799VdYWloWen+NRoPAwEBER0e/9lof\nIiIiIiIiImMRFRWFx48fAwDKlSuHNm3aCE5UvLRfwpGIiIzS8OHD0aFDBwD5RW9DhgxBbm6u4FRE\nVBL9tZK3Nps+jm0oJEmCRqOBJElabSWJRqOBWq3Waitpj+2vFe613bR9fCXt+2Ao9PnzhIiIjMeT\nJ0/Qv3//Qien+ftK4r/99hu8vLwKFIgPHToUkZGRLBAnIqJS6cWLF4iOjtbb8QMCAnDgwAEWiBMR\nEZVwDRs2lPdjYmLe6Bjx8fG6ikNERERk1Dw8PHD6/7F35+FNlfn7x+8k3WhpoWXYQRYR2cqmMiPF\n0QKFBnDEDdm+KvsmAoKKKKOyiCAOCMhQVsEdYQQHFcUqLqgIyNIiyL4vshQo0DXJ7w9+ZEDaJm2T\nnjR9v64r15VzznOec5OQnCY5n+f56SfdfPPNkqTMzEx169ZNGzZscGv/bdu2adasWd6MCAAAAACA\nz/j444+d9zt16lSsJljzBIrEAaCEM5lMmjt3rnOU0a1bt2rq1KkGpwIAAAAAIHc2m03du3fXH3/8\nkWc7i8Wim266SZKUlZWlAQMG6JFHHlFqaqokKTQ0VIsXL9a8efMUEhLi9dwAAPiib7/91uWgKwXV\nvXt3vfPOOwoKCvJK/wAAoOg0adLEeX/btm35/vth/fr1+uijjzwdCwAAwG/dfPPN+v7779W0aVNJ\nVwapv/aid1f++c9/6siRI96KBwAAAACAz7j283Lnzp0NTGIMisQBAKpVq5Zefvll5/LLL7+s3377\nzcBEAAAAAADkbsqUKVq7dq3LdlWqVFFQUJCOHTum1q1ba+7cuc5ttWvX1g8//KBHH33Ui0kBAPB9\na9as8Uq/I0eO1DvvvKPAwECv9A8AAIpWxYoVVbFiRUnSpUuXtG/fPrf3TUtL0+OPP67y5ct7Kx4A\nAIBfqlChgj7++GO1aNEi3/umpqZq1KhRXkgFAAAAAIDvSE5O1p49eyRJYWFhiouLMzhR0QswOgAA\nwDeMGDFC//nPf/Tjjz8qIyNDffr00bp162Q2M54IAAAAAMB3rFu3Tv/85z/dalurVi199dVX6t69\nu06dOuVcf//99+utt95SRESEt2ICAFBsfPXVVx7v89VXX9Wzzz7r8X4BAICxmjRpoi+//FKStHXr\nVtWpU8et/UaPHq2dO3eqcuXK3owHAADgU06dOqV9+/YpNTVV58+f16VLl3Tp0iVduHDhhuULFy7o\n4sWLNyynp6cXKsOHH36oXr16qX379h76VwEAAAAA4FuunUU8Li5OpUqVMjCNMSgSR7HgcDjcanfu\n3Dlt2bJFdrs9z3ZZWVk6deqU0tLS3D5+YGCgTCaTy7YBAQGqXr26atWq5VbbsLAwtzIA3mY2mzV/\n/nw1a9ZMGRkZ+vnnnzV79mw98cQTRkcDAAAAAECSdObMGXXr1k3Z2dlutT9//rzi4+Nls9kkSRaL\nRRMnTtQzzzzj1vc8AAD4u/3792vv3r0e689sNmvGjBkaMmSIx/oEAAC+489F4g8++KDLfVavXq2Z\nM2dKEkXiAACgRImKitKaNWs0ceJE/fbbb4blGGhKVFIAACAASURBVDJkiJKSkkrkRfIAAAAAAP/3\n4YcfOu+787uFP6JIHD7P4XC4LPq+avfu3XrllVeUmZmZZzu73a7ff/9d586dc6tfk8mkihUrKjQ0\n1GXboKAgxcfH67bbbnOrX34EhS+pX7++Ro8erZdfflnSlRHdO3TooNq1axucDAAAAABQ0jkcDj3+\n+OM6fPiw2/ts3brVeb9ixYr64IMPdM8993ghHQAAxZMnZxEPCAjQokWL1LNnT4/1CQAAvMfhcOR7\nALUmTZo471/7mTs3Fy5c0KBBg5wTA1SqVCl/IQEAAIoxi8Wi7t27q2vXrlq2bJkmTJigpKSkIs+x\nd+9eTZgwQRMnTizyYwMAAAAA4E1bt27V9u3bJUlhYWF64IEHDE5kDLPRAQBPcjgcyszMdOtmt9vl\ncDjcvplMJrdvFotFAQEBbt2YtQq+ZsyYMYqOjpYkXbp0Sf369XP+aA8AAAAAgFEWLFigVatWFWjf\npk2bat26dRSIAwDwJ4mJiR7pJzg4WMuWLaNAHACAYmTy5Mk6duxYvvZp3Lix8/62bdtcth8xYoQO\nHDggSSpbtiyzVwIAgBLJbDarS5cu2rJli5YvX37dwDtFZerUqYbOZg4AgD/KyMgw5Br79PT0Ij8m\nAAC+6tpZxDt16uTWBMH+iCJxAMB1goKCtGDBAlksFknS119/rcWLFxucCgAAAABQkm3fvl3Dhg0r\n8P5btmxRnTp1VKVKFcXExKhnz54aO3asFi5cqLVr1+rgwYOy2WweTAwAgO+z2+0emUm8dOnS+uyz\nz3Tfffd5IBUAACgqf//731W/fn3NnTvX7X3q1aun4OBgSdLBgwd17ty5XNsuXbpUCxcudC5Xrly5\n4GEBAAD8gNls1gMPPKAtW7bo+++/V+vWrYvs2JmZmerVq5fsdnuRHRMAAH9nMpk0fPjwIj2/7t+/\nX6+99lqRHQ/wJfv27Svyv2cdDof27NlTpMcE4D6Hw6EPPvjAudytWzcD0xiLInEAwA3uuOMODR8+\n3Lk8fPhwHT161MBEAAAAAICSKi0tTV27dtXly5cL3dfx48f1448/6t1339WECRPUp08fxcbGqmbN\nmipVqpTq1KmjuLg49e/fX5MmTdIHH3yg9evX648//vDAvwQAAN+ydetWnTlzplB9lClTRp999lmR\nXtQMAAA8o2XLlmrevLkGDBigjh076vjx4y73CQwMVP369SVdufgqKSkpx3bHjx/X4MGDr1tHkTgA\nAMD/tGrVSomJifr+++/Vpk2bIjnmL7/8onnz5hXJsQAAKAmCgoJ08OBB9evXr0gKVw8cOKDY2Fg1\naNDA68cCfNHV64eys7OL5Hh2u139+vXjmiHAh/3yyy/av3+/JCkqKkpWq9XgRMYJMDoAAMA3jR8/\nXitXrtSePXt0/vx5DRs2TMuWLTM6FgADREREqGLFigoNDc21jdl8ZeyhtLQ0l/2ZTCaFhIS4fXyT\nyeR2W19gs9mUmprq1pd+WVlZslgsbv0bz549K4fD4VaG4OBgRUZGFrvHztPMZrNzRhNPMplMCgwM\ndOv5sFgsHj9+SZCZmen2F+dBQUHO9yA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ARc2ox02S9u/f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\nzm0ZTjBIRI5y/vx5+Pr6OjoMIiJygOTkZEeHQEQCnDdJvF49YNQooFEjwNw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qKiqyKLNq1SoNGjRIP/30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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image('images/12_adversarial_noise_flowchart.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import tensorflow as tf\n", "import numpy as np\n", "from sklearn.metrics import confusion_matrix\n", "import time\n", "from datetime import timedelta\n", "import math\n", "\n", "# We also need PrettyTensor.\n", "import prettytensor as pt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'0.12.0-rc0'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "PrettyTensor version:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'0.7.1'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pt.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting data/MNIST/train-images-idx3-ubyte.gz\n", "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n", "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n", "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "data = input_data.read_data_sets('data/MNIST/', one_hot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Size of:\n", "- Training-set:\t\t55000\n", "- Test-set:\t\t10000\n", "- Validation-set:\t5000\n" ] } ], "source": [ "print(\"Size of:\")\n", "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n", "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n", "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data.test.cls = np.argmax(data.test.labels, axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Dimensions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We know that MNIST images are 28 pixels in each dimension.\n", "img_size = 28\n", "\n", "# Images are stored in one-dimensional arrays of this length.\n", "img_size_flat = img_size * img_size\n", "\n", "# Tuple with height and width of images used to reshape arrays.\n", "img_shape = (img_size, img_size)\n", "\n", "# Number of colour channels for the images: 1 channel for gray-scale.\n", "num_channels = 1\n", "\n", "# Number of classes, one class for each of 10 digits.\n", "num_classes = 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function for plotting images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image. If the noise is supplied then it is added to all images." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_images(images, cls_true, cls_pred=None, noise=0.0):\n", " assert len(images) == len(cls_true) == 9\n", " \n", " # Create figure with 3x3 sub-plots.\n", " fig, axes = plt.subplots(3, 3)\n", " fig.subplots_adjust(hspace=0.3, wspace=0.3)\n", "\n", " for i, ax in enumerate(axes.flat):\n", " # Get the i'th image and reshape the array.\n", " image = images[i].reshape(img_shape)\n", " \n", " # Add the adversarial noise to the image.\n", " image += noise\n", " \n", " # Ensure the noisy pixel-values are between 0 and 1.\n", " image = np.clip(image, 0.0, 1.0)\n", "\n", " # Plot image.\n", " ax.imshow(image,\n", " cmap='binary', interpolation='nearest')\n", "\n", " # Show true and predicted classes.\n", " if cls_pred is None:\n", " xlabel = \"True: {0}\".format(cls_true[i])\n", " else:\n", " xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n", "\n", " # Show the classes as the label on the x-axis.\n", " ax.set_xlabel(xlabel)\n", " \n", " # Remove ticks from the plot.\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " \n", " # Ensure the plot is shown correctly with multiple plots\n", " # in a single Notebook cell.\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot a few images to see if data is correct" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2059353828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get the first images from the test-set.\n", "images = data.test.images[0:9]\n", "\n", "# Get the true classes for those images.\n", "cls_true = data.test.cls[0:9]\n", "\n", "# Plot the images and labels using our helper-function above.\n", "plot_images(images=images, cls_true=cls_true)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TensorFlow Graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The computational graph for the neural network will now be constructed using TensorFlow and PrettyTensor. As usual, we need to create placeholder variables for feeding images into the graph and then we add the adversarial noise to the images. The noisy images are then used as input to a convolutional neural network.\n", "\n", "There are two separate optimization procedures for this network. A normal optimization procedure for the variables of the neural network itself, and another optimization procedure for the adversarial noise. Both optimization procedures are implemented directly in TensorFlow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Placeholder variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Placeholder variables provide the input to the computational graph in TensorFlow that we may change each time we execute the graph. We call this feeding the placeholder variables.\n", "\n", "First we define the placeholder variable for the input images. This allows us to change the images that are input to the TensorFlow graph. This is a so-called tensor, which just means that it is a multi-dimensional array. The data-type is set to `float32` and the shape is set to `[None, img_size_flat]`, where `None` means that the tensor may hold an arbitrary number of images with each image being a vector of length `img_size_flat`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_true_cls = tf.argmax(y_true, dimension=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adversarial Noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pixels in the input image are float-values between 0.0 and 1.0. The adversarial noise is a number that is added or subtracted from the pixels in the input image.\n", "\n", "The limit of the adversarial noise is set to 0.35 so the noise will be between &plusmn;0.35." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "noise_limit = 0.35" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The optimizer for the adversarial noise will try and minimize two loss-measures: (1) The normal loss-measure for the neural network, so we will find the noise that gives the best classification accuracy for the adversarial target-class; and (2) the so-called L2-loss-measure which tries to keep the noise as low as possible.\n", "\n", "The following weight determines how important the L2-loss is compared to the normal loss-measure. An L2-weight close to zero usually works best." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "noise_l2_weight = 0.02" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When we create the new variable for the noise, we must inform TensorFlow which variable-collections that it belongs to, so we can later inform the two optimizers which variables to update.\n", "\n", "First we define a name for our new variable-collection. This is just a string." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ADVERSARY_VARIABLES = 'adversary_variables'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we create a list of the collections that we want the new noise-variable to belong to. If we add the noise-variable to the collection `tf.GraphKeys.VARIABLES` then it will also get initialized with all the other variables in the TensorFlow graph, but it will not get optimized. This is a bit confusing." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "collections = [tf.GraphKeys.VARIABLES, ADVERSARY_VARIABLES]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can create the new variable for the adversarial noise. It will be initialized to zero. It will not be trainable, so it will not be optimized along with the other variables of the neural network. This allows us to create two separate optimization procedures." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x_noise = tf.Variable(tf.zeros([img_size, img_size, num_channels]),\n", " name='x_noise', trainable=False,\n", " collections=collections)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The adversarial noise will be limited / clipped to the given \n", "&plusmn; noise-limit that we set above. Note that this is actually not executed at this point in the computational graph, but will instead be executed after the optimization-step, see further below." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_noise_clip = tf.assign(x_noise, tf.clip_by_value(x_noise,\n", " -noise_limit,\n", " noise_limit))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The noisy image is just the sum of the input image and the adversarial noise." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_noisy_image = x_image + x_noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When adding the noise to the input image, it may overflow the boundaries for a valid image, so we clip / limit the noisy image to ensure its pixel-values are between 0 and 1." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x_noisy_image = tf.clip_by_value(x_noisy_image, 0.0, 1.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convolutional Neural Network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use PrettyTensor to construct the convolutional neural network. First we need to wrap the tensor for the noisy image in a PrettyTensor-object, which provides functions that construct the neural network." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_pretty = pt.wrap(x_noisy_image)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have wrapped the input image in a PrettyTensor object, we can add the convolutional and fully-connected layers in just a few lines of source-code." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with pt.defaults_scope(activation_fn=tf.nn.relu):\n", " y_pred, loss = x_pretty.\\\n", " conv2d(kernel=5, depth=16, name='layer_conv1').\\\n", " max_pool(kernel=2, stride=2).\\\n", " conv2d(kernel=5, depth=36, name='layer_conv2').\\\n", " max_pool(kernel=2, stride=2).\\\n", " flatten().\\\n", " fully_connected(size=128, name='layer_fc1').\\\n", " softmax_classifier(num_classes=num_classes, labels=y_true)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that `pt.defaults_scope(activation_fn=tf.nn.relu)` makes `activation_fn=tf.nn.relu` an argument for each of the layers constructed inside the `with`-block, so that Rectified Linear Units (ReLU) are used for each of these layers. The `defaults_scope` makes it easy to change arguments for all of the layers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optimizer for Normal Training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a list of the variables for the neural network that will be trained during the normal optimization procedure. Note that `'x_noise:0'` is not in the list, so the adversarial noise is not being optimized in the normal procedure." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "['layer_conv1/weights:0',\n", " 'layer_conv1/bias:0',\n", " 'layer_conv2/weights:0',\n", " 'layer_conv2/bias:0',\n", " 'layer_fc1/weights:0',\n", " 'layer_fc1/bias:0',\n", " 'fully_connected/weights:0',\n", " 'fully_connected/bias:0']" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[var.name for var in tf.trainable_variables()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Optimization of these variables in the neural network is done with the Adam-optimizer using the loss-measure that was returned from PrettyTensor when we constructed the neural network above.\n", "\n", "Note that optimization is not performed at this point. In fact, nothing is calculated at all, we just add the optimizer-object to the TensorFlow graph for later execution." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optimizer for Adversarial Noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the list of variables that must be optimized in the second procedure for the adversarial noise." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [], "source": [ "adversary_variables = tf.get_collection(ADVERSARY_VARIABLES)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Show the list of variable-names. There is only one, which is the adversarial noise variable that we created above." ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "['x_noise:0']" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[var.name for var in adversary_variables]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will combine the loss-function for the normal optimization with a so-called L2-loss for the noise-variable. This should result in the minimum values for the adversarial noise along with the best classification accuracy.\n", "\n", "The L2-loss is scaled by a weight that is typically set close to zero." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "l2_loss_noise = noise_l2_weight * tf.nn.l2_loss(x_noise)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Combine the normal loss-function with the L2-loss for the adversarial noise." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "loss_adversary = loss + l2_loss_noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now create the optimizer for the adversarial noise. Because this optimizer is not supposed to update all the variables of the neural network, we must give it a list of the variables that we want updated, which is the variable for the adversarial noise. Also note the learning-rate is much greater than for the normal optimizer above." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "optimizer_adversary = tf.train.AdamOptimizer(learning_rate=1e-2).minimize(loss_adversary, var_list=adversary_variables)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have now created two optimizers for the neural network, one for the variables of the neural network and another for the single variable with the adversarial noise." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performance Measures\n", "\n", "We need a few more operations in the TensorFlow graph which will make it easier for us to display the progress to the user during optimization.\n", "\n", "First we calculate the predicted class number from the output of the Neural Network `y_pred`, which is a vector with 10 elements. The class number is the index of the largest element." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "y_pred_cls = tf.argmax(y_pred, dimension=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we create a vector of booleans telling us whether the predicted class equals the true class of each image." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "correct_prediction = tf.equal(y_pred_cls, y_true_cls)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The classification accuracy is calculated by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then taking the average of these numbers." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TensorFlow Run" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create TensorFlow session\n", "\n", "Once the TensorFlow graph has been created, we have to create a TensorFlow session which is used to execute the graph." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "session = tf.Session()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Initialize variables\n", "\n", "The variables for `weights` and `biases` must be initialized before we start optimizing them." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "session.run(tf.global_variables_initializer())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a helper-function for initializing / resetting the adversarial noise to zero." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def init_noise():\n", " session.run(tf.variables_initializer([x_noise]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Call the function to initialize the adversarial noise." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [], "source": [ "init_noise()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function to perform optimization iterations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n", "\n", "If your computer crashes or becomes very slow because you run out of RAM, then you may try and lower this number, but you may then need to perform more optimization iterations." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "train_batch_size = 64" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is the function for performing a number of optimization iterations so as to gradually improve the variables of the neural network. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples. The progress is printed every 100 iterations.\n", "\n", "This function is similar to the previous tutorials, except that it now takes an argument for the adversarial target-class. When this target-class is set to an integer, it will be used instead of the true class-number for the training-data. The adversarial optimizer is also used instead of the normal optimizer, and after each step of the adversarial optimizer, the noise will be limited / clipped to the allowed range. This optimizes the adversarial noise and ignores the other variables of the neural network." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def optimize(num_iterations, adversary_target_cls=None):\n", " # Start-time used for printing time-usage below.\n", " start_time = time.time()\n", "\n", " for i in range(num_iterations):\n", "\n", " # Get a batch of training examples.\n", " # x_batch now holds a batch of images and\n", " # y_true_batch are the true labels for those images.\n", " x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n", "\n", " # If we are searching for the adversarial noise, then\n", " # use the adversarial target-class instead.\n", " if adversary_target_cls is not None:\n", " # The class-labels are One-Hot encoded.\n", " \n", " # Set all the class-labels to zero.\n", " y_true_batch = np.zeros_like(y_true_batch)\n", "\n", " # Set the element for the adversarial target-class to 1.\n", " y_true_batch[:, adversary_target_cls] = 1.0\n", " \n", " # Put the batch into a dict with the proper names\n", " # for placeholder variables in the TensorFlow graph.\n", " feed_dict_train = {x: x_batch,\n", " y_true: y_true_batch}\n", "\n", " # If doing normal optimization of the neural network.\n", " if adversary_target_cls is None:\n", " # Run the optimizer using this batch of training data.\n", " # TensorFlow assigns the variables in feed_dict_train\n", " # to the placeholder variables and then runs the optimizer.\n", " session.run(optimizer, feed_dict=feed_dict_train)\n", " else:\n", " # Run the adversarial optimizer instead.\n", " # Note that we have 'faked' the class above to be\n", " # the adversarial target-class instead of the true class.\n", " session.run(optimizer_adversary, feed_dict=feed_dict_train)\n", " \n", " # Clip / limit the adversarial noise. This executes\n", " # another TensorFlow operation. It cannot be executed\n", " # in the same session.run() as the optimizer, because\n", " # it may run in parallel so the execution order is not\n", " # guaranteed. We need the clip to run after the optimizer.\n", " session.run(x_noise_clip)\n", "\n", " # Print status every 100 iterations.\n", " if (i % 100 == 0) or (i == num_iterations - 1):\n", " # Calculate the accuracy on the training-set.\n", " acc = session.run(accuracy, feed_dict=feed_dict_train)\n", "\n", " # Message for printing.\n", " msg = \"Optimization Iteration: {0:>6}, Training Accuracy: {1:>6.1%}\"\n", "\n", " # Print it.\n", " print(msg.format(i, acc))\n", "\n", " # Ending time.\n", " end_time = time.time()\n", "\n", " # Difference between start and end-times.\n", " time_dif = end_time - start_time\n", "\n", " # Print the time-usage.\n", " print(\"Time usage: \" + str(timedelta(seconds=int(round(time_dif)))))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-functions for getting and plotting the noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function gets the adversarial noise from inside the TensorFlow graph." ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_noise():\n", " # Run the TensorFlow session to retrieve the contents of\n", " # the x_noise variable inside the graph.\n", " noise = session.run(x_noise)\n", "\n", " return np.squeeze(noise)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function plots the adversarial noise and prints some statistics." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_noise():\n", " # Get the adversarial noise from inside the TensorFlow graph.\n", " noise = get_noise()\n", " \n", " # Print statistics.\n", " print(\"Noise:\")\n", " print(\"- Min:\", noise.min())\n", " print(\"- Max:\", noise.max())\n", " print(\"- Std:\", noise.std())\n", "\n", " # Plot the noise.\n", " plt.imshow(noise, interpolation='nearest', cmap='seismic',\n", " vmin=-1.0, vmax=1.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function to plot example errors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function for plotting examples of images from the test-set that have been mis-classified." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_example_errors(cls_pred, correct):\n", " # This function is called from print_test_accuracy() below.\n", "\n", " # cls_pred is an array of the predicted class-number for\n", " # all images in the test-set.\n", "\n", " # correct is a boolean array whether the predicted class\n", " # is equal to the true class for each image in the test-set.\n", "\n", " # Negate the boolean array.\n", " incorrect = (correct == False)\n", " \n", " # Get the images from the test-set that have been\n", " # incorrectly classified.\n", " images = data.test.images[incorrect]\n", " \n", " # Get the predicted classes for those images.\n", " cls_pred = cls_pred[incorrect]\n", "\n", " # Get the true classes for those images.\n", " cls_true = data.test.cls[incorrect]\n", "\n", " # Get the adversarial noise from inside the TensorFlow graph.\n", " noise = get_noise()\n", " \n", " # Plot the first 9 images.\n", " plot_images(images=images[0:9],\n", " cls_true=cls_true[0:9],\n", " cls_pred=cls_pred[0:9],\n", " noise=noise)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function to plot confusion matrix" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_confusion_matrix(cls_pred):\n", " # This is called from print_test_accuracy() below.\n", "\n", " # cls_pred is an array of the predicted class-number for\n", " # all images in the test-set.\n", "\n", " # Get the true classifications for the test-set.\n", " cls_true = data.test.cls\n", " \n", " # Get the confusion matrix using sklearn.\n", " cm = confusion_matrix(y_true=cls_true,\n", " y_pred=cls_pred)\n", "\n", " # Print the confusion matrix as text.\n", " print(cm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function for showing the performance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Function for printing the classification accuracy on the test-set.\n", "\n", "It takes a while to compute the classification for all the images in the test-set, that's why the results are re-used by calling the above functions directly from this function, so the classifications don't have to be recalculated by each function.\n", "\n", "Note that this function can use a lot of computer memory, which is why the test-set is split into smaller batches. If you have little RAM in your computer and it crashes, then you can try and lower the batch-size." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Split the test-set into smaller batches of this size.\n", "test_batch_size = 256\n", "\n", "def print_test_accuracy(show_example_errors=False,\n", " show_confusion_matrix=False):\n", "\n", " # Number of images in the test-set.\n", " num_test = len(data.test.images)\n", "\n", " # Allocate an array for the predicted classes which\n", " # will be calculated in batches and filled into this array.\n", " cls_pred = np.zeros(shape=num_test, dtype=np.int)\n", "\n", " # Now calculate the predicted classes for the batches.\n", " # We will just iterate through all the batches.\n", " # There might be a more clever and Pythonic way of doing this.\n", "\n", " # The starting index for the next batch is denoted i.\n", " i = 0\n", "\n", " while i < num_test:\n", " # The ending index for the next batch is denoted j.\n", " j = min(i + test_batch_size, num_test)\n", "\n", " # Get the images from the test-set between index i and j.\n", " images = data.test.images[i:j, :]\n", "\n", " # Get the associated labels.\n", " labels = data.test.labels[i:j, :]\n", "\n", " # Create a feed-dict with these images and labels.\n", " feed_dict = {x: images,\n", " y_true: labels}\n", "\n", " # Calculate the predicted class using TensorFlow.\n", " cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\n", "\n", " # Set the start-index for the next batch to the\n", " # end-index of the current batch.\n", " i = j\n", "\n", " # Convenience variable for the true class-numbers of the test-set.\n", " cls_true = data.test.cls\n", "\n", " # Create a boolean array whether each image is correctly classified.\n", " correct = (cls_true == cls_pred)\n", "\n", " # Calculate the number of correctly classified images.\n", " # When summing a boolean array, False means 0 and True means 1.\n", " correct_sum = correct.sum()\n", "\n", " # Classification accuracy is the number of correctly classified\n", " # images divided by the total number of images in the test-set.\n", " acc = float(correct_sum) / num_test\n", "\n", " # Print the accuracy.\n", " msg = \"Accuracy on Test-Set: {0:.1%} ({1} / {2})\"\n", " print(msg.format(acc, correct_sum, num_test))\n", "\n", " # Plot some examples of mis-classifications, if desired.\n", " if show_example_errors:\n", " print(\"Example errors:\")\n", " plot_example_errors(cls_pred=cls_pred, correct=correct)\n", "\n", " # Plot the confusion matrix, if desired.\n", " if show_confusion_matrix:\n", " print(\"Confusion Matrix:\")\n", " plot_confusion_matrix(cls_pred=cls_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Normal optimization of neural network\n", "\n", "First we perform 1000 optimization iterations with the normal optimizer. This finds the variables that makes the neural network perform well on the training-set.\n", "\n", "The adversarial noise is not effective yet because it has only been initialized to zero above and it is not being updated during this optimization." ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization Iteration: 0, Training Accuracy: 12.5%\n", "Optimization Iteration: 100, Training Accuracy: 90.6%\n", "Optimization Iteration: 200, Training Accuracy: 84.4%\n", "Optimization Iteration: 300, Training Accuracy: 84.4%\n", "Optimization Iteration: 400, Training Accuracy: 89.1%\n", "Optimization Iteration: 500, Training Accuracy: 87.5%\n", "Optimization Iteration: 600, Training Accuracy: 93.8%\n", "Optimization Iteration: 700, Training Accuracy: 93.8%\n", "Optimization Iteration: 800, Training Accuracy: 93.8%\n", "Optimization Iteration: 900, Training Accuracy: 96.9%\n", "Optimization Iteration: 999, Training Accuracy: 92.2%\n", "Time usage: 0:00:03\n" ] } ], "source": [ "optimize(num_iterations=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The classification accuracy is now about 96-97% on the test-set. (This will vary each time you run this Python Notebook)." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Test-Set: 96.3% (9633 / 10000)\n", "Example errors:\n" ] }, { "data": { "image/png": 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JkyZNiqaIRABCx/m0r1qdNm2ayQ8++KDJ9mkIu1vMfkyTPfBGtOzH5NmPhmrX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\n3bNnj8l23Z4+fbrJ/isg+/f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"text/plain": [ "<matplotlib.figure.Figure at 0x7f20571306a0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print_test_accuracy(show_example_errors=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Find the adversarial noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before we start optimizing the adversarial noise, we first initialize it to zero. This was already done above but it is repeated here in case you want to re-run this code with another target-class." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "init_noise()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now perform optimization of the adversarial noise. This uses the adversarial optimizer instead of the normal optimizer, which means that it only optimizes the variable for the adversarial noise, while ignoring all the other variables of the neural network." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Optimization Iteration: 0, Training Accuracy: 6.2%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", "Optimization Iteration: 200, Training Accuracy: 96.9%\n", "Optimization Iteration: 300, Training Accuracy: 98.4%\n", "Optimization Iteration: 400, Training Accuracy: 95.3%\n", "Optimization Iteration: 500, Training Accuracy: 96.9%\n", "Optimization Iteration: 600, Training Accuracy: 100.0%\n", "Optimization Iteration: 700, Training Accuracy: 98.4%\n", "Optimization Iteration: 800, Training Accuracy: 95.3%\n", "Optimization Iteration: 900, Training Accuracy: 93.8%\n", "Optimization Iteration: 999, Training Accuracy: 100.0%\n", "Time usage: 0:00:03\n" ] } ], "source": [ "optimize(num_iterations=1000, adversary_target_cls=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The adversarial noise has now been optimized and it can be shown in a plot. The red pixels show positive noise-values and the blue pixels show negative noise-values. This noise-pattern is added to every input image. The positive (red) noise-values makes the pixels darker and the negative (blue) noise-values makes the pixels brighter. Examples of this are shown below." ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Noise:\n", "- Min: -0.35\n", "- Max: 0.35\n", "- Std: 0.195455\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2056bad3c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_noise()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When this noise is added to all the images in the test-set, the result is typically a classification accuracy of 10-15% depending on the target-class that was chosen. We can also see from the confusion matrix that most images in the test-set are now classified as the desired target-class - although some of the target-classes require more adversarial noise than others.\n", "\n", "So we have found adversarial noise that makes the neural network mis-classify almost all images in the test-set as our desired target-class.\n", "\n", "We can also show some examples of mis-classified images with the adversarial noise. The noise is clearly visible but the digits are still easily identified by the human eye." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Test-Set: 13.2% (1323 / 10000)\n", "Example errors:\n" ] }, { "data": { "image/png": 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GOwF448aNofPZicL2OuyE5ebNm4fKkeJTam8CYX9++umnqpcsWaLaTla3oyHn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\nX+EcNpnyu+++q9que2fXrZs0aZLqV19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I2bMVwN8BnJCr30UATgJwbwnrUNeo8/4UkYMA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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f2056ba3e80>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix:\n", "[[ 85 0 0 895 0 0 0 0 0 0]\n", " [ 0 0 0 1135 0 0 0 0 0 0]\n", " [ 0 0 46 986 0 0 0 0 0 0]\n", " [ 0 0 0 1010 0 0 0 0 0 0]\n", " [ 0 0 0 959 20 0 0 0 3 0]\n", " [ 0 0 0 847 0 45 0 0 0 0]\n", " [ 0 0 0 914 0 1 42 0 1 0]\n", " [ 0 0 0 977 0 0 0 51 0 0]\n", " [ 0 0 0 952 0 0 0 0 22 0]\n", " [ 0 0 1 1006 0 0 0 0 0 2]]\n" ] } ], "source": [ "print_test_accuracy(show_example_errors=True,\n", " show_confusion_matrix=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Adversarial noise for all target-classes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a helper-function for finding the adversarial noise for all target-classes. The function loops over all the class-numbers from 0 to 9 and runs the optimization above. The results are then stored in an array." ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def find_all_noise(num_iterations=1000):\n", " # Adversarial noise for all target-classes.\n", " all_noise = []\n", "\n", " # For each target-class.\n", " for i in range(num_classes):\n", " print(\"Finding adversarial noise for target-class:\", i)\n", "\n", " # Reset the adversarial noise to zero.\n", " init_noise()\n", "\n", " # Optimize the adversarial noise.\n", " optimize(num_iterations=num_iterations,\n", " adversary_target_cls=i)\n", "\n", " # Get the adversarial noise from inside the TensorFlow graph.\n", " noise = get_noise()\n", "\n", " # Append the noise to the array.\n", " all_noise.append(noise)\n", "\n", " # Print newline.\n", " print()\n", " \n", " return all_noise" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Finding adversarial noise for target-class: 0\n", "Optimization Iteration: 0, Training Accuracy: 9.4%\n", "Optimization Iteration: 100, Training Accuracy: 90.6%\n", "Optimization Iteration: 200, Training Accuracy: 92.2%\n", "Optimization Iteration: 299, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 1\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 62.5%\n", "Optimization Iteration: 200, Training Accuracy: 62.5%\n", "Optimization Iteration: 299, Training Accuracy: 75.0%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 2\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", "Optimization Iteration: 200, Training Accuracy: 95.3%\n", "Optimization Iteration: 299, Training Accuracy: 96.9%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 3\n", "Optimization Iteration: 0, Training Accuracy: 6.2%\n", "Optimization Iteration: 100, Training Accuracy: 98.4%\n", "Optimization Iteration: 200, Training Accuracy: 96.9%\n", "Optimization Iteration: 299, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 4\n", "Optimization Iteration: 0, Training Accuracy: 12.5%\n", "Optimization Iteration: 100, Training Accuracy: 81.2%\n", "Optimization Iteration: 200, Training Accuracy: 82.8%\n", "Optimization Iteration: 299, Training Accuracy: 82.8%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 5\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 96.9%\n", "Optimization Iteration: 200, Training Accuracy: 96.9%\n", "Optimization Iteration: 299, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 6\n", "Optimization Iteration: 0, Training Accuracy: 6.2%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", "Optimization Iteration: 200, Training Accuracy: 92.2%\n", "Optimization Iteration: 299, Training Accuracy: 96.9%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 7\n", "Optimization Iteration: 0, Training Accuracy: 12.5%\n", "Optimization Iteration: 100, Training Accuracy: 98.4%\n", "Optimization Iteration: 200, Training Accuracy: 93.8%\n", "Optimization Iteration: 299, Training Accuracy: 92.2%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 8\n", "Optimization Iteration: 0, Training Accuracy: 4.7%\n", "Optimization Iteration: 100, Training Accuracy: 96.9%\n", "Optimization Iteration: 200, Training Accuracy: 93.8%\n", "Optimization Iteration: 299, Training Accuracy: 96.9%\n", "Time usage: 0:00:01\n", "\n", "Finding adversarial noise for target-class: 9\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 84.4%\n", "Optimization Iteration: 200, Training Accuracy: 87.5%\n", "Optimization Iteration: 299, Training Accuracy: 90.6%\n", "Time usage: 0:00:01\n", "\n" ] } ], "source": [ "all_noise = find_all_noise(num_iterations=300)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the adversarial noise for all target-classes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a helper-function for plotting a grid with the adversarial noise for all target-classes 0 to 9." ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def plot_all_noise(all_noise): \n", " # Create figure with 10 sub-plots.\n", " fig, axes = plt.subplots(2, 5)\n", " fig.subplots_adjust(hspace=0.2, wspace=0.1)\n", "\n", " # For each sub-plot.\n", " for i, ax in enumerate(axes.flat):\n", " # Get the adversarial noise for the i'th target-class.\n", " noise = all_noise[i]\n", " \n", " # Plot the noise.\n", " ax.imshow(noise,\n", " cmap='seismic', interpolation='nearest',\n", " vmin=-1.0, vmax=1.0)\n", "\n", " # Show the classes as the label on the x-axis.\n", " ax.set_xlabel(i)\n", "\n", " # Remove ticks from the plot.\n", " ax.set_xticks([])\n", " ax.set_yticks([])\n", " \n", " # Ensure the plot is shown correctly with multiple plots\n", " # in a single Notebook cell.\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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7FMWpBPqFLIQQQmQATchCCCFEBmi3ZM3BLqrIDXHLgEudekM5IgHncysX8jxk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\njxaWLJCmG81MXrYUX+3Tq9ESUmzfb7I3K6VDhph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"text/plain": [ "<matplotlib.figure.Figure at 0x7f205683b2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_all_noise(all_noise)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Red pixels show positive noise values, and blue pixels show negative noise values.\n", "\n", "In some of these noise-images you can see traces of the numbers. For example, the noise for target-class 0 shows a red circle surrounded by blue. This means that a little noise will be added to the input image in the shape of a circle, and it will dampen the other pixels. This is sufficient for most input images in the MNIST data-set to be mis-classified as a 0. Another example is the noise for 3 which also shows traces of the number 3 with red pixels. But the noise for the other classes is less obvious." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Immunity to adversarial noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now try and make the neural network immune to adversarial noise. We do this by re-training the neural network to ignore the adversarial noise. This process can be repeated a number of times." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Helper-function to make a neural network immune to noise\n", "\n", "This is the helper-function for making the neural network immune to adversarial noise. First it runs the optimization to find the adversarial noise. Then it runs the normal optimization to make the neural network immune to that noise." ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def make_immune(target_cls, num_iterations_adversary=500,\n", " num_iterations_immune=200):\n", "\n", " print(\"Target-class:\", target_cls)\n", " print(\"Finding adversarial noise ...\")\n", "\n", " # Find the adversarial noise.\n", " optimize(num_iterations=num_iterations_adversary,\n", " adversary_target_cls=target_cls)\n", "\n", " # Newline.\n", " print()\n", " \n", " # Print classification accuracy.\n", " print_test_accuracy(show_example_errors=False,\n", " show_confusion_matrix=False)\n", "\n", " # Newline.\n", " print()\n", "\n", " print(\"Making the neural network immune to the noise ...\")\n", "\n", " # Try and make the neural network immune to this noise.\n", " # Note that the adversarial noise has not been reset to zero\n", " # so the x_noise variable still holds the noise.\n", " # So we are training the neural network to ignore the noise.\n", " optimize(num_iterations=num_iterations_immune)\n", "\n", " # Newline.\n", " print()\n", " \n", " # Print classification accuracy.\n", " print_test_accuracy(show_example_errors=False,\n", " show_confusion_matrix=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make immune to noise for target-class 3\n", "\n", "First try and make the neural network immune to the adverserial noise for targer-class 3.\n", "\n", "First we find the adversarial noise that causes the neural network to mis-classify most of the images in the test-set. Then we run the normal optimization which fine-tunes the variables of the neural network to ignore this noise and this brings the classification accuracy for the noisy images up to 95-97% again." ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target-class: 3\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 3.1%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", "Optimization Iteration: 200, Training Accuracy: 93.8%\n", "Optimization Iteration: 300, Training Accuracy: 96.9%\n", "Optimization Iteration: 400, Training Accuracy: 96.9%\n", "Optimization Iteration: 499, Training Accuracy: 96.9%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 14.4% (1443 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 42.2%\n", "Optimization Iteration: 100, Training Accuracy: 90.6%\n", "Optimization Iteration: 199, Training Accuracy: 89.1%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 95.3% (9529 / 10000)\n" ] } ], "source": [ "make_immune(target_cls=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now try and run it again. It is now more difficult to find adversarial noise for the target-class 3. The neural network seems to have become somewhat immune to adversarial noise." ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target-class: 3\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 32.8%\n", "Optimization Iteration: 200, Training Accuracy: 32.8%\n", "Optimization Iteration: 300, Training Accuracy: 29.7%\n", "Optimization Iteration: 400, Training Accuracy: 34.4%\n", "Optimization Iteration: 499, Training Accuracy: 26.6%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 72.1% (7207 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 75.0%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", "Optimization Iteration: 199, Training Accuracy: 92.2%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 95.2% (9519 / 10000)\n" ] } ], "source": [ "make_immune(target_cls=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make immune to noise for all target-classes\n", "\n", "Now try and make the neural network immune to adversarial noise for all target-classes. Unfortunately this does not seem to work so well." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target-class: 0\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 4.7%\n", "Optimization Iteration: 100, Training Accuracy: 73.4%\n", "Optimization Iteration: 200, Training Accuracy: 75.0%\n", "Optimization Iteration: 300, Training Accuracy: 85.9%\n", "Optimization Iteration: 400, Training Accuracy: 81.2%\n", "Optimization Iteration: 499, Training Accuracy: 90.6%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 23.3% (2326 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 34.4%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 95.6% (9559 / 10000)\n", "\n", "Target-class: 1\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 12.5%\n", "Optimization Iteration: 100, Training Accuracy: 57.8%\n", "Optimization Iteration: 200, Training Accuracy: 62.5%\n", "Optimization Iteration: 300, Training Accuracy: 62.5%\n", "Optimization Iteration: 400, Training Accuracy: 67.2%\n", "Optimization Iteration: 499, Training Accuracy: 67.2%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 42.2% (4218 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 59.4%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 95.5% (9555 / 10000)\n", "\n", "Target-class: 2\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 6.2%\n", "Optimization Iteration: 100, Training Accuracy: 43.8%\n", "Optimization Iteration: 200, Training Accuracy: 57.8%\n", "Optimization Iteration: 300, Training Accuracy: 70.3%\n", "Optimization Iteration: 400, Training Accuracy: 68.8%\n", "Optimization Iteration: 499, Training Accuracy: 71.9%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 46.4% (4639 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 59.4%\n", "Optimization Iteration: 100, Training Accuracy: 96.9%\n", "Optimization Iteration: 199, Training Accuracy: 92.2%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 95.5% (9545 / 10000)\n", "\n", "Target-class: 3\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 6.2%\n", "Optimization Iteration: 100, Training Accuracy: 48.4%\n", "Optimization Iteration: 200, Training Accuracy: 46.9%\n", "Optimization Iteration: 300, Training Accuracy: 53.1%\n", "Optimization Iteration: 400, Training Accuracy: 50.0%\n", "Optimization Iteration: 499, Training Accuracy: 48.4%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 56.5% (5648 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 54.7%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", "Optimization Iteration: 199, Training Accuracy: 96.9%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 95.8% (9581 / 10000)\n", "\n", "Target-class: 4\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 9.4%\n", "Optimization Iteration: 100, Training Accuracy: 85.9%\n", "Optimization Iteration: 200, Training Accuracy: 85.9%\n", "Optimization Iteration: 300, Training Accuracy: 87.5%\n", "Optimization Iteration: 400, Training Accuracy: 95.3%\n", "Optimization Iteration: 499, Training Accuracy: 92.2%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 15.6% (1557 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 18.8%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", "Optimization Iteration: 199, Training Accuracy: 96.9%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 95.6% (9557 / 10000)\n", "\n", "Target-class: 5\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 18.8%\n", "Optimization Iteration: 100, Training Accuracy: 71.9%\n", "Optimization Iteration: 200, Training Accuracy: 90.6%\n", "Optimization Iteration: 300, Training Accuracy: 95.3%\n", "Optimization Iteration: 400, Training Accuracy: 89.1%\n", "Optimization Iteration: 499, Training Accuracy: 92.2%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 17.4% (1745 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 15.6%\n", "Optimization Iteration: 100, Training Accuracy: 96.9%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.0% (9601 / 10000)\n", "\n", "Target-class: 6\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 10.9%\n", "Optimization Iteration: 100, Training Accuracy: 81.2%\n", "Optimization Iteration: 200, Training Accuracy: 93.8%\n", "Optimization Iteration: 300, Training Accuracy: 92.2%\n", "Optimization Iteration: 400, Training Accuracy: 89.1%\n", "Optimization Iteration: 499, Training Accuracy: 92.2%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 17.6% (1762 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 20.3%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 95.7% (9570 / 10000)\n", "\n", "Target-class: 7\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 14.1%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", "Optimization Iteration: 200, Training Accuracy: 98.4%\n", "Optimization Iteration: 300, Training Accuracy: 100.0%\n", "Optimization Iteration: 400, Training Accuracy: 96.9%\n", "Optimization Iteration: 499, Training Accuracy: 100.0%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 12.8% (1281 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 12.5%\n", "Optimization Iteration: 100, Training Accuracy: 98.4%\n", "Optimization Iteration: 199, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 95.9% (9587 / 10000)\n", "\n", "Target-class: 8\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 4.7%\n", "Optimization Iteration: 100, Training Accuracy: 64.1%\n", "Optimization Iteration: 200, Training Accuracy: 81.2%\n", "Optimization Iteration: 300, Training Accuracy: 71.9%\n", "Optimization Iteration: 400, Training Accuracy: 78.1%\n", "Optimization Iteration: 499, Training Accuracy: 84.4%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 24.9% (2493 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 25.0%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", "Optimization Iteration: 199, Training Accuracy: 96.9%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.0% (9601 / 10000)\n", "\n", "Target-class: 9\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 9.4%\n", "Optimization Iteration: 100, Training Accuracy: 48.4%\n", "Optimization Iteration: 200, Training Accuracy: 50.0%\n", "Optimization Iteration: 300, Training Accuracy: 53.1%\n", "Optimization Iteration: 400, Training Accuracy: 64.1%\n", "Optimization Iteration: 499, Training Accuracy: 65.6%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 45.5% (4546 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 51.6%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.2% (9615 / 10000)\n", "\n" ] } ], "source": [ "for i in range(10):\n", " make_immune(target_cls=i)\n", " \n", " # Print newline.\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make immune to all target-classes (double runs)\n", "\n", "Now try and use double-runs to make the neural network immune to adversarial noise for all target-classes. Unfortunately this does not seem to work so well either.\n", "\n", "Making the neural network immune to one adversarial target-class appears to cancel the immunity towards the other target-classes." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Target-class: 0\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 53.1%\n", "Optimization Iteration: 200, Training Accuracy: 73.4%\n", "Optimization Iteration: 300, Training Accuracy: 79.7%\n", "Optimization Iteration: 400, Training Accuracy: 84.4%\n", "Optimization Iteration: 499, Training Accuracy: 95.3%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 29.2% (2921 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 29.7%\n", "Optimization Iteration: 100, Training Accuracy: 96.9%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.2% (9619 / 10000)\n", "\n", "Target-class: 0\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 1.6%\n", "Optimization Iteration: 100, Training Accuracy: 12.5%\n", "Optimization Iteration: 200, Training Accuracy: 7.8%\n", "Optimization Iteration: 300, Training Accuracy: 18.8%\n", "Optimization Iteration: 400, Training Accuracy: 9.4%\n", "Optimization Iteration: 499, Training Accuracy: 9.4%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 94.4% (9437 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 89.1%\n", "Optimization Iteration: 100, Training Accuracy: 98.4%\n", "Optimization Iteration: 199, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.4% (9635 / 10000)\n", "\n", "Target-class: 1\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 42.2%\n", "Optimization Iteration: 200, Training Accuracy: 60.9%\n", "Optimization Iteration: 300, Training Accuracy: 75.0%\n", "Optimization Iteration: 400, Training Accuracy: 70.3%\n", "Optimization Iteration: 499, Training Accuracy: 85.9%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 28.7% (2875 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 39.1%\n", "Optimization Iteration: 100, Training Accuracy: 98.4%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.4% (9643 / 10000)\n", "\n", "Target-class: 1\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 15.6%\n", "Optimization Iteration: 200, Training Accuracy: 18.8%\n", "Optimization Iteration: 300, Training Accuracy: 12.5%\n", "Optimization Iteration: 400, Training Accuracy: 9.4%\n", "Optimization Iteration: 499, Training Accuracy: 12.5%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 94.3% (9428 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 95.3%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", "Optimization Iteration: 199, Training Accuracy: 92.2%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.9% (9685 / 10000)\n", "\n", "Target-class: 2\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 6.2%\n", "Optimization Iteration: 100, Training Accuracy: 60.9%\n", "Optimization Iteration: 200, Training Accuracy: 64.1%\n", "Optimization Iteration: 300, Training Accuracy: 71.9%\n", "Optimization Iteration: 400, Training Accuracy: 75.0%\n", "Optimization Iteration: 499, Training Accuracy: 82.8%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 34.3% (3427 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 31.2%\n", "Optimization Iteration: 100, Training Accuracy: 100.0%\n", "Optimization Iteration: 199, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.6% (9657 / 10000)\n", "\n", "Target-class: 2\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 6.2%\n", "Optimization Iteration: 100, Training Accuracy: 9.4%\n", "Optimization Iteration: 200, Training Accuracy: 14.1%\n", "Optimization Iteration: 300, Training Accuracy: 10.9%\n", "Optimization Iteration: 400, Training Accuracy: 7.8%\n", "Optimization Iteration: 499, Training Accuracy: 17.2%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 94.3% (9435 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 96.9%\n", "Optimization Iteration: 100, Training Accuracy: 98.4%\n", "Optimization Iteration: 199, Training Accuracy: 96.9%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.6% (9664 / 10000)\n", "\n", "Target-class: 3\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 14.1%\n", "Optimization Iteration: 100, Training Accuracy: 20.3%\n", "Optimization Iteration: 200, Training Accuracy: 40.6%\n", "Optimization Iteration: 300, Training Accuracy: 57.8%\n", "Optimization Iteration: 400, Training Accuracy: 54.7%\n", "Optimization Iteration: 499, Training Accuracy: 64.1%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 48.4% (4837 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 54.7%\n", "Optimization Iteration: 100, Training Accuracy: 98.4%\n", "Optimization Iteration: 199, Training Accuracy: 100.0%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.5% (9650 / 10000)\n", "\n", "Target-class: 3\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 4.7%\n", "Optimization Iteration: 100, Training Accuracy: 10.9%\n", "Optimization Iteration: 200, Training Accuracy: 17.2%\n", "Optimization Iteration: 300, Training Accuracy: 15.6%\n", "Optimization Iteration: 400, Training Accuracy: 1.6%\n", "Optimization Iteration: 499, Training Accuracy: 9.4%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 95.7% (9570 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 95.3%\n", "Optimization Iteration: 100, Training Accuracy: 90.6%\n", "Optimization Iteration: 199, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.7% (9667 / 10000)\n", "\n", "Target-class: 4\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 67.2%\n", "Optimization Iteration: 200, Training Accuracy: 78.1%\n", "Optimization Iteration: 300, Training Accuracy: 79.7%\n", "Optimization Iteration: 400, Training Accuracy: 81.2%\n", "Optimization Iteration: 499, Training Accuracy: 96.9%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 23.7% (2373 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 26.6%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", "Optimization Iteration: 199, Training Accuracy: 96.9%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.3% (9632 / 10000)\n", "\n", "Target-class: 4\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 4.7%\n", "Optimization Iteration: 100, Training Accuracy: 7.8%\n", "Optimization Iteration: 200, Training Accuracy: 12.5%\n", "Optimization Iteration: 300, Training Accuracy: 15.6%\n", "Optimization Iteration: 400, Training Accuracy: 7.8%\n", "Optimization Iteration: 499, Training Accuracy: 14.1%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 92.0% (9197 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 92.2%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.3% (9632 / 10000)\n", "\n", "Target-class: 5\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 4.7%\n", "Optimization Iteration: 100, Training Accuracy: 57.8%\n", "Optimization Iteration: 200, Training Accuracy: 76.6%\n", "Optimization Iteration: 300, Training Accuracy: 85.9%\n", "Optimization Iteration: 400, Training Accuracy: 89.1%\n", "Optimization Iteration: 499, Training Accuracy: 85.9%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 23.0% (2297 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 28.1%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", "Optimization Iteration: 199, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.6% (9663 / 10000)\n", "\n", "Target-class: 5\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 6.2%\n", "Optimization Iteration: 100, Training Accuracy: 10.9%\n", "Optimization Iteration: 200, Training Accuracy: 18.8%\n", "Optimization Iteration: 300, Training Accuracy: 18.8%\n", "Optimization Iteration: 400, Training Accuracy: 20.3%\n", "Optimization Iteration: 499, Training Accuracy: 21.9%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 88.2% (8824 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 93.8%\n", "Optimization Iteration: 100, Training Accuracy: 93.8%\n", "Optimization Iteration: 199, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.7% (9665 / 10000)\n", "\n", "Target-class: 6\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 40.6%\n", "Optimization Iteration: 200, Training Accuracy: 53.1%\n", "Optimization Iteration: 300, Training Accuracy: 51.6%\n", "Optimization Iteration: 400, Training Accuracy: 56.2%\n", "Optimization Iteration: 499, Training Accuracy: 62.5%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 44.0% (4400 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 39.1%\n", "Optimization Iteration: 100, Training Accuracy: 96.9%\n", "Optimization Iteration: 199, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.4% (9642 / 10000)\n", "\n", "Target-class: 6\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 4.7%\n", "Optimization Iteration: 100, Training Accuracy: 17.2%\n", "Optimization Iteration: 200, Training Accuracy: 12.5%\n", "Optimization Iteration: 300, Training Accuracy: 14.1%\n", "Optimization Iteration: 400, Training Accuracy: 20.3%\n", "Optimization Iteration: 499, Training Accuracy: 7.8%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 94.6% (9457 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 93.8%\n", "Optimization Iteration: 100, Training Accuracy: 100.0%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.8% (9682 / 10000)\n", "\n", "Target-class: 7\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 4.7%\n", "Optimization Iteration: 100, Training Accuracy: 65.6%\n", "Optimization Iteration: 200, Training Accuracy: 89.1%\n", "Optimization Iteration: 300, Training Accuracy: 82.8%\n", "Optimization Iteration: 400, Training Accuracy: 85.9%\n", "Optimization Iteration: 499, Training Accuracy: 90.6%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 18.1% (1809 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 23.4%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", "Optimization Iteration: 199, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 96.8% (9682 / 10000)\n", "\n", "Target-class: 7\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 12.5%\n", "Optimization Iteration: 100, Training Accuracy: 10.9%\n", "Optimization Iteration: 200, Training Accuracy: 18.8%\n", "Optimization Iteration: 300, Training Accuracy: 18.8%\n", "Optimization Iteration: 400, Training Accuracy: 28.1%\n", "Optimization Iteration: 499, Training Accuracy: 18.8%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 84.1% (8412 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 84.4%\n", "Optimization Iteration: 100, Training Accuracy: 100.0%\n", "Optimization Iteration: 199, Training Accuracy: 100.0%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 97.0% (9699 / 10000)\n", "\n", "Target-class: 8\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 48.4%\n", "Optimization Iteration: 200, Training Accuracy: 46.9%\n", "Optimization Iteration: 300, Training Accuracy: 71.9%\n", "Optimization Iteration: 400, Training Accuracy: 70.3%\n", "Optimization Iteration: 499, Training Accuracy: 75.0%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 36.8% (3678 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 48.4%\n", "Optimization Iteration: 100, Training Accuracy: 96.9%\n", "Optimization Iteration: 199, Training Accuracy: 93.8%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 97.0% (9699 / 10000)\n", "\n", "Target-class: 8\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 7.8%\n", "Optimization Iteration: 100, Training Accuracy: 14.1%\n", "Optimization Iteration: 200, Training Accuracy: 12.5%\n", "Optimization Iteration: 300, Training Accuracy: 7.8%\n", "Optimization Iteration: 400, Training Accuracy: 4.7%\n", "Optimization Iteration: 499, Training Accuracy: 9.4%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 96.2% (9625 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 96.9%\n", "Optimization Iteration: 100, Training Accuracy: 98.4%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 97.2% (9720 / 10000)\n", "\n", "Target-class: 9\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 9.4%\n", "Optimization Iteration: 100, Training Accuracy: 23.4%\n", "Optimization Iteration: 200, Training Accuracy: 43.8%\n", "Optimization Iteration: 300, Training Accuracy: 37.5%\n", "Optimization Iteration: 400, Training Accuracy: 45.3%\n", "Optimization Iteration: 499, Training Accuracy: 39.1%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 64.9% (6494 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 67.2%\n", "Optimization Iteration: 100, Training Accuracy: 95.3%\n", "Optimization Iteration: 199, Training Accuracy: 98.4%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 97.5% (9746 / 10000)\n", "\n", "Target-class: 9\n", "Finding adversarial noise ...\n", "Optimization Iteration: 0, Training Accuracy: 9.4%\n", "Optimization Iteration: 100, Training Accuracy: 7.8%\n", "Optimization Iteration: 200, Training Accuracy: 10.9%\n", "Optimization Iteration: 300, Training Accuracy: 15.6%\n", "Optimization Iteration: 400, Training Accuracy: 12.5%\n", "Optimization Iteration: 499, Training Accuracy: 4.7%\n", "Time usage: 0:00:02\n", "\n", "Accuracy on Test-Set: 97.1% (9709 / 10000)\n", "\n", "Making the neural network immune to the noise ...\n", "Optimization Iteration: 0, Training Accuracy: 98.4%\n", "Optimization Iteration: 100, Training Accuracy: 100.0%\n", "Optimization Iteration: 199, Training Accuracy: 95.3%\n", "Time usage: 0:00:01\n", "\n", "Accuracy on Test-Set: 97.7% (9768 / 10000)\n", "\n" ] } ], "source": [ "for i in range(10):\n", " make_immune(target_cls=i)\n", " \n", " # Print newline.\n", " print()\n", " \n", " make_immune(target_cls=i)\n", "\n", " # Print newline.\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the adversarial noise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have now performed many optimizations of both the neural network and the adversarial noise. Let us see how the adversarial noise looks now." ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Noise:\n", "- Min: -0.35\n", "- Max: 0.35\n", "- Std: 0.270488\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2057188518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_noise()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interestingly, the neural network now has a higher classification accuracy on noisy images than we had on clean images before all these optimizations." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Test-Set: 97.7% (9768 / 10000)\n", "Example errors:\n" ] }, { "data": { "image/png": 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OnFzLz7KyMt95mJ9Vj01uet8LYpubQGwuTJ06NeEytWvX1vHYsWN1/K9//Sth+wD7/DRz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\nVOVqwjmr/MaY9z4Atyil3nQ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f20572d0080>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix:\n", "[[ 972 0 1 0 0 0 2 1 3 1]\n", " [ 0 1119 4 0 0 2 2 0 8 0]\n", " [ 3 0 1006 9 1 1 1 5 4 2]\n", " [ 1 0 1 997 0 5 0 4 2 0]\n", " [ 0 1 3 0 955 0 3 1 2 17]\n", " [ 1 0 0 9 0 876 3 0 2 1]\n", " [ 6 4 0 0 3 6 934 0 5 0]\n", " [ 2 4 18 3 1 0 0 985 2 13]\n", " [ 4 0 4 3 4 1 1 3 950 4]\n", " [ 6 6 0 7 4 5 0 4 3 974]]\n" ] } ], "source": [ "print_test_accuracy(show_example_errors=True,\n", " show_confusion_matrix=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Performance on clean images\n", "\n", "Now let us see how the neural network performs on clean images so we reset the adversarial noise to zero." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [], "source": [ "init_noise()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The neural network now performs worse on clean images compared to noisy images." ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy on Test-Set: 92.2% (9222 / 10000)\n", "Example errors:\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f2056abcdd8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix:\n", "[[ 970 0 1 0 0 1 8 0 0 0]\n", " [ 0 1121 5 0 0 0 9 0 0 0]\n", " [ 2 1 1028 0 0 0 1 0 0 0]\n", " [ 1 0 27 964 0 13 2 2 1 0]\n", " [ 0 2 3 0 957 0 20 0 0 0]\n", " [ 3 0 2 2 0 875 10 0 0 0]\n", " [ 4 1 0 0 1 1 951 0 0 0]\n", " [ 10 21 61 3 14 3 0 913 3 0]\n", " [ 29 2 91 7 7 26 70 1 741 0]\n", " [ 20 18 10 12 150 65 11 12 9 702]]\n" ] } ], "source": [ "print_test_accuracy(show_example_errors=True,\n", " show_confusion_matrix=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Close TensorFlow Session" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now done using TensorFlow, so we close the session to release its resources." ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# This has been commented out in case you want to modify and experiment\n", "# with the Notebook without having to restart it.\n", "# session.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discussion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We saw in the experiments above that we were able to make the neural network immune to adversarial noise for a single target-class. This made it impossible to find adversarial noise that caused mis-classification to that target-class. But it was apparently not possible to make the neural network immune to all target-classes simultaneously. Perhaps this would be possible using another method.\n", "\n", "One suggestion would be to interleave the immunity-training for different target-classes, instead of doing a full optimization for each target-class in turn. This should be possible with minor modifications to the source-code above.\n", "\n", "Another suggestion would be to have two tiers of neural networks with 11 networks in total. The network in the first tier is used for classifying the input image. This network has not been made immune to adversarial noise. Another neural network from the second tier is then selected depending on the predicted class from the first tier. The networks in the second tier have been made immune to adversarial noise for their respective target-classes. So an adversary could fool the network in the first tier with adversarial noise, but the network in the second tier would be immune to noise for that particular target-class.\n", "\n", "This might work for a small number of classes but it would become infeasible for larger numbers, e.g. the ImageNet data-set has 1000 classes so we would need to train 1000 neural networks for the second tier, which is not practical." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "This tutorial showed how to find adversarial noise for the MNIST data-set of hand-written digits. A single noise-pattern was found for each target-class, which caused almost all input images to become mis-classified as that target-class.\n", "\n", "The noise-patterns for the MNIST data-set were clearly visible to the human eye. But it is possible that more subtle noise-patterns can be found for larger neural networks that work on higher-resolution images, e.g. the ImageNet data-set.\n", "\n", "This tutorial also experimented with methods for making the neural network immune to adversarial noise. This worked well for a single target-class but the tested methods were not able to make the neural network simultaneously immune to all adversarial target-classes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n", "\n", "You may want to backup this Notebook before making any changes.\n", "\n", "* Try using fewer or more optimization iterations for the adversarial noise.\n", "* Why does this tutorial require more optimization iterations than Tutorial #11, which needed less than 30 iterations to find the adversarial noise?\n", "* Try different settings for `noise_limit` and `noise_l2_weight`. How does it affect the adversarial noise and classification accuracy?\n", "* Try finding the adversarial noise for target-class 1. Does it work as well as for target-class 3?\n", "* Can you find a better way to make the neural network immune to adversarial noise?\n", "* Is the neural network immune to adversarial noise generated for individual images, as was done in Tutorial #11?\n", "* Try making another neural network with a different configuration. Does the adversarial noise for one network also work on another network?\n", "* Try using the CIFAR-10 data-set instead of MNIST. You may re-use some of the code from Tutorial #06.\n", "* How would you find the adversarial noise for the Inception model and the ImageNet data-set?\n", "* Explain to a friend how this program works." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## License (MIT)\n", "\n", "Copyright (c) 2016 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", "\n", "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n", "\n", "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n", "\n", "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE." ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:tf-gpu]", "language": "python", "name": "conda-env-tf-gpu-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
swc2124/starcat
notebooks/heatmap selector.ipynb
1
7590
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import ebf\n", "import numpy as np\n", "from matplotlib import pyplot as plt\n", "from c_functions import bin as _bin, integerize as _int" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "halo_ebf_dir = os.path.join(os.environ['HOMEPATH'], 'Desktop', 'halo_ebf')\n", "halo_filehandels = os.listdir(halo_ebf_dir)\n", "halo_filehandels" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "halo_fh = halo_filehandels[-2]\n", "halo = ebf.read(os.path.join(halo_ebf_dir, halo_fh))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def fix_rslice(grid, rslices=[4]):\n", " \n", " #center = grid.shape[0] / 2\n", " ratio = (2.0 * 300.0 / 600.0) #(1.0 * Config.getint('grid_options', 'size'))\n", " #x_bump, y_bump = np.nonzero(grid[295:305, 295:305, 0] == grid[295:305, 295:305, 0].max())\n", " x_center = (grid.shape[1] / 2) #+ (10 - x_bump[0])\n", " y_center = (grid.shape[0] / 2) #+ (10 - y_bump[0])\n", " # if VERBOSE:\n", " # print('fixing radial data slice')\n", " # print('ratio: ', ratio)\n", " # print('slices: ', rslices)\n", " # print('center:', center)\n", " for r in rslices:\n", " for i in range(grid.shape[0]):\n", " for q in range(grid.shape[1]):\n", " value = np.sqrt(\n", " (np.square(i - y_center) + np.square(q - x_center)))\n", " value /= ratio\n", " if value > 300.0:\n", " value = 0.0\n", " grid[i, q, r] = value\n", " return grid" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data_dir = ''\n", "for pth in os.path.abspath(os.path.curdir).split('\\\\')[:-1]:\n", " data_dir = os.path.join(data_dir, pth)\n", "data_dir = os.path.join(data_dir, 'data')\n", "data_dir" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "px, py = _int(halo['px'].astype(np.float64), halo['py'].astype(np.float64))\n", "ab_mags = halo['wfirst-hst_h158'].astype(np.float64)\n", "ap_mags = ab_mags + (5 * np.log10(4.0 * 1e5))\n", "r_proj = np.sqrt(np.square(halo['px']) + np.square(halo['py'])).astype(np.float64)\n", "lims = np.array([27.0, 28.0, 29.0], dtype=np.float64)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "grid = _bin(px, py, ab_mags, ap_mags, r_proj, lims, halo['satid'].astype(np.int32))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# box coord set making function\n", "def make_boxe(xy, size=100):\n", " '''\n", "\n", " '''\n", " x, y = xy\n", " x += 300\n", " y += 300\n", " segment = (size * 0.5)\n", " return ((x - segment, x + segment), (y - segment, y + segment))\n", "\n", "# box placing function\n", "def place_box(_ax, _box, _boxid):\n", " '''\n", " _ax is the subplot to plot on\n", " _box is ((x0, x1), (y0, y1))\n", " _boxid is the box ID\n", " \n", " make a single box from a set of numbers like this: box = ((x0, x1), (y0, y1))\n", " _ax.vlines(x0, y0, y1)\n", " _ax.vlines(x1, y0, y1)\n", " _ax.hlines(y0, x0, x1)\n", " _ax.hlines(y1, x0, x1)\n", " '''\n", " # unpack box tuples\n", " _age, _feh = _box\n", " \n", " # find box center\n", " a_center = _age[0] + ((_age[1] - _age[0]) * 0.5)\n", " f_center = _feh[1] + ((_feh[1] - _feh[0]) * 0.5)\n", " \n", " # set box label\n", " box_label = 'R ' + str(_boxid)\n", " _ax.text(a_center, f_center, box_label, \n", " ha='center', \n", " fontsize=10, \n", " color='k', \n", " bbox={'facecolor':'white', 'alpha':0.75, 'pad':2})\n", " \n", " # params\n", " lwidth = 3\n", " lstyle = 'dashed'\n", " lclr = 'r'\n", " aph = .3\n", " \n", " # set lines\n", " _ax.vlines(_age[0], _feh[0], _feh[1], \n", " colors=lclr, \n", " linestyles=lstyle,\n", " alpha = aph,\n", " linewidth=lwidth)\n", " _ax.vlines(_age[1], _feh[0], _feh[1], \n", " colors=lclr, \n", " linestyles=lstyle,\n", " alpha = aph,\n", " linewidth=lwidth)\n", " _ax.hlines(_feh[0], _age[0], _age[1], \n", " colors=lclr, \n", " linestyles=lstyle,\n", " alpha = aph,\n", " linewidth=lwidth)\n", " _ax.hlines(_feh[1], _age[0], _age[1], \n", " colors=lclr, \n", " linestyles=lstyle,\n", " alpha = aph,\n", " linewidth=lwidth)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig = plt.figure(figsize=(10, 10))\n", "\n", "ax = fig.add_subplot(111)\n", "ax.set_title(halo_fh)\n", "ticks = [str(i) for i in np.linspace(-150, 150, 7)]\n", "\n", "ax.set_xlabel('KPC')\n", "ax.set_xticklabels(ticks)\n", "ax.set_ylabel('KPC')\n", "ax.set_yticklabels(ticks)\n", "\n", "# fill radius gridslice\n", "grid = fix_rslice(grid)\n", "\n", "#arr = np.log10(grid[8:, :-3, 0])\n", "arr = grid[8:, :-3, 3]\n", "\n", "ax.pcolormesh(arr)#, cmap=plt.cm.bone_r)#, vmin=1.0, vmax=4.5)\n", "cp = ax.contour(grid[:, :, 4], [50, 100, 150, 300], colors='k',linewidths=1.5, alpha=.25, linestyles='dashed')\n", "cl = ax.clabel(cp, [50, 100, 150, 300], inline=1, fmt='%s Kpc', fontsize=10, color='k', linewidth=50, alpha=1)\n", "\n", "# limits\n", "center = grid.shape[0] / 2\n", "include = center / 2\n", "lim0 = center + include\n", "lim1 = center - include\n", "ax.set_xlim([lim1, lim0])\n", "ax.set_ylim([lim1, lim0])\n", "\n", "# boxes\n", "box = make_boxe((-53,45), size=10)\n", "place_box(ax, box, 1)\n", "\n", "ax.axes.grid(alpha=.4, linestyle='dashed',color='grey')\n", "#fig.savefig(os.path.join(os.path.curdir, halo_fh.split('.')[0] + '.png'), dpi=800)\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
ENCODE-DCC/pyencoded-tools
jupyter_notebooks/keenan/ENCD-3757-read-depth-distributions.ipynb
1
1882452
null
mit
d-grossman/pelops
pelops/analysis/makeFeatureFiles.ipynb
3
6566
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pelops.datasets.veri import VeriDataset\n", "from pelops.analysis.unsorted.recompute.extract_feats_from_chips import extract_feats_from_chips\n", "import pelops.utils as utils\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-body_type.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-body_type.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',\n", " set_type=utils.SetType.TRAIN.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/image_body_type',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-color.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-color.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',\n", " set_type=utils.SetType.TRAIN.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/image_color_type',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-color_body_type.model2.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-color_body_type.weights2.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',\n", " set_type=utils.SetType.TRAIN.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/image_color_body_type',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-make_model.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet-make_model.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',\n", " set_type=utils.SetType.TRAIN.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/image_make_model_type',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/dgcars_resenet.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',set_type=utils.SetType.TRAIN.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/resnet50',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/compcars_resenet-make_model.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/compcars_resenet-make_model.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',set_type=utils.SetType.TRAIN.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/compcars_make_model',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "model_output_file = '/local_data/dgrossman/model_save_dir/compcars_resenet-colors.model.json'\n", "weights_output_file = '/local_data/dgrossman/model_save_dir/compcars_resenet-colors.weights.hdf5'\n", "layer = 'avg_pool'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "veri = VeriDataset('/local_data/dgrossman/VeRi',set_type=utils.SetType.TRAIN.value)\n", "extract_feats_from_chips(veri, '/local_data/dgrossman/compcars_color',\n", " model_output_file,\n", " weights_output_file,\n", " layer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "1+1\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
drusk/pml-applications
student_records/notebooks/signed_pca.ipynb
1
3804
{ "metadata": { "name": "signed_pca" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from pml.api import *\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "data = load(\"../dataset_ext2.csv\")\n", "data = data.drop_empty_samples()\n", "data.fill_missing_with_feature_means()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "data.get_label_value_counts()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 3, "text": [ "f 30\n", "s 26\n", "p 16" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# Get first principal component\n", "princomp = pca(data, 1)\n", "signed_weights = pd.Series(princomp.weights[:, 0], \n", " index=princomp._original_features)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "print signed_weights.order(ascending=False)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ENGR120 0.002989\n", "ENGR110 0.002226\n", "ENGL135 -0.089390\n", "CHEM150 -0.242886\n", "MATH101 -0.260933\n", "ELEC199 -0.268938\n", "MATH100 -0.271468\n", "PHYS125 -0.287089\n", "PHYS122 -0.288521\n", "MECH141 -0.320300\n", "CSC115 -0.335191\n", "CSC111 -0.339158\n", "MATH110 -0.472499\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "min_weight = signed_weights.min()\n", "max_weight = signed_weights.max()\n", "print min_weight, max_weight" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "-0.472498927759 0.00298931311672\n" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "normalized_weights = ((signed_weights - min_weight) \n", " / (max_weight - min_weight))\n", "print normalized_weights.order(ascending=False)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "ENGR120 1.000000\n", "ENGR110 0.998394\n", "ENGL135 0.805716\n", "CHEM150 0.482898\n", "MATH101 0.444945\n", "ELEC199 0.428110\n", "MATH100 0.422787\n", "PHYS125 0.389935\n", "PHYS122 0.386925\n", "MECH141 0.320089\n", "CSC115 0.288773\n", "CSC111 0.280429\n", "MATH110 0.000000\n" ] } ], "prompt_number": 21 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
nwfpug/python-primer
notebooks/02-operators.ipynb
1
11231
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Operators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Arithmetic" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a + b = 14\n", "a / b = 3\n", "a / d = 2.2\n", "c ** b = 64\n", "a % c = 3\n", "a // b = 3\n", "a // e = -4\n" ] } ], "source": [ "a = 11; b = 3; c = 4; d = 5.0; e=-3\n", "print \"a + b = \", a+b # addition\n", "print \"a - d = \", a-d # subtraction\n", "print \"a * e = \", a*e # multiplication\n", "print \"a / b = \", a/b # division of integers\n", "print \"a / d = \", a/d # division of float\n", "print 'c ** b = ', c**b # exponentiation\n", "print 'a % c = ', a%c # remainder\n", "print 'a // b = ', a//b # floor division\n", "print 'a // e = ', a//e # floor division" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Relational" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "is a == b? False\n", "is a != b? True\n", "is a <> b? True\n", "is a > b? True\n", "is a >= b? True\n", "is a < b? False\n", "is a <= b? False\n" ] } ], "source": [ "a = 11; b = 3; c = 4; d = 5.0; e=-3\n", "print 'is a == b? ', a == b # equality\n", "print 'is a != b? ', a != b # not equal\n", "print 'is a <> b? ', a <> b # not equal\n", "print 'is a > b? ', a > b # greater than\n", "print 'is a >= b? ', a >= b # greater then or equal to\n", "print 'is a < b? ', a < b # less than\n", "print 'is a <= b? ', a <= b # less then or equal to" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Assignment" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a += b = 14\n", "a -= d = 9.0\n", "a *= e = -27.0\n", "a /= b = -9.0\n", "a /= d = -1.8\n", "c **= b = 64\n", "a %= c = 62.2\n", "a //= b = 20.0\n", "a //= e = -7.0\n" ] } ], "source": [ "a = 11; b = 3; c = 4; d = 5.0; e=-3\n", "a+=b\n", "print \"a += b = \", a # same as a = a + b\n", "a-=d\n", "print \"a -= d = \", a # same as a = a - d\n", "a*=e\n", "print \"a *= e = \", a # same as a = a * e\n", "a/=b\n", "print \"a /= b = \", a # same as a = a / b (dividing integers)\n", "a/=d\n", "print \"a /= d = \", a # same as a = a / d\n", "c**=b\n", "print 'c **= b = ', c # c = c ** b\n", "a%=c\n", "print 'a %= c = ', a # a = a % c\n", "a//=b\n", "print 'a //= b = ', a # a = a // b\n", "a//=e\n", "print 'a //= e = ', a # a = a // e" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Logical " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n", "True\n", "True\n" ] } ], "source": [ "# and\n", "# or \n", "# not\n", "c = True\n", "b = False\n", "\n", "print b and c\n", "print b or c\n", "print not b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bitwise" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a = 28 ( 0b11100 )\n", "b = 45 ( 0b101101 )\n", "a & b = ( 12 0b1100 )\n", "a | b = ( 61 0b111101 )\n", "a ^ b = ( 49 0b110001 )\n", "~a = -29 ( -0b11101 )\n", "a << 2 = 112 ( 0b1110000 )\n", "a >> 2 = 7 ( 0b111 )\n" ] } ], "source": [ "#& -- logical and\n", "# | -- logical or\n", "# ^ -- XOR\n", "# ~ -- One's Complement\n", "# >> -- left shift\n", "# << -- right shift\n", "\n", "\n", "a = 0b00011100\n", "b = 0b00101101\n", "print 'a =', a, '(', bin(a), ')'\n", "print 'b =', b, '(', bin(b), ')'\n", "print 'a & b =', '(', a & b, bin(a & b), ')'\n", "print 'a | b =', '(', a | b, bin(a | b), ')'\n", "print 'a ^ b =', '(', a ^ b, bin(a ^ b), ')'\n", "print '~a =', ~a, '(', bin(~a), ')'\n", "print 'a << 2 =', a << 2, '(', bin(a << 2), ')'\n", "print 'a >> 2 =', a >> 2, '(', bin(a >> 2), ')'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Membership" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "False\n", "False\n" ] } ], "source": [ "# in \n", "# not in\n", "a = ['1', 2, 'this is an element', 'x']\n", "\n", "print 'x' in a\n", "print 'x' not in a\n", "print 5 in a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Identity" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "False\n" ] } ], "source": [ "# is\n", "# is not\n", "\n", "a = 1\n", "b = a\n", "\n", "print b is a\n", "print b is not a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extra" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(2, 4)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "divmod(20,8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Variable Types" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*We'll talk about lists, dictionaries, strings, & tuples in the following notebooks*" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x = 786 # decimal int\n", "m = 060 # octal int\n", "s = 0x69 # hex int\n", "y = 0122L # long\n", "z = -21.9 # float\n", "f = 9.322e-36j # complex" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-9.322e-36j" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# print the conjugate of the complex number\n", "f.conjugate()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "786\n", "48\n", "105\n", "82\n", "-21.9\n", "9.322e-36j\n" ] } ], "source": [ "# print all the example variables\n", "print x\n", "print m\n", "print s\n", "print y\n", "print z\n", "print f" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "numeber 600\n", "number is: 600\n", "string 1 is not the same as string 2\n", "string 1 is not the same as string 2\n", "1.7 plus 11.9 equals 13.6\n" ] } ], "source": [ "number = 600 \n", "print 'numeber ', number\n", "print \"number is: %d\" % (number, ) \n", "str1 = \"string 1\" \n", "str2 = \"string 2\" \n", "print str1 + \" is not the same as \" + str2 \n", "print \"%s is not the same as %s\" % (str1, str2) \n", "print '%s plus %s equals %s' % (1.7, 11.9, 13.6) \n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the binary value of 10 is 0b1010\n", "Number of bytes : 4\n" ] } ], "source": [ "# write a decimal number in binary\n", "x=10\n", "print \"the binary value of\", x, \"is \", bin(x)\n", "print \"Number of bytes : \", x.bit_length()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "x = 3.5\n", "x as the ratio of two integers: (7, 2)\n", "get the integer value of x: 3\n" ] } ], "source": [ "# write a float as the ration of two integers\n", "x=3.5\n", "print \"x = \", x\n", "print \"x as the ratio of two integers:\", x.as_integer_ratio()\n", "print \"get the integer value of x:\", int(x)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7.0\n" ] } ], "source": [ "y = 42/6.0\n", "print y" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y.is_integer()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.14\n" ] } ], "source": [ "# some more formatting\n", "x=3.1415926\n", "print '{:.2f}'.format(x)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" }, "widgets": { "state": {}, "version": "1.1.1" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
ahwkuepper/stdme
code/CDC_and_census_data_exploration_and_cleaning_Gonorrhea.ipynb
1
856630
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# STD & me" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Analysis of 2014 data from the CDC on the prevalence of STD's in U.S. counties. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import re\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Always display all the columns\n", "pd.set_option('display.width', 5000) \n", "pd.set_option('display.max_columns', 200) \n", "\n", "# Plain Seaborn figures with matplotlib color codes mapped to the default seaborn palette \n", "sns.set(style=\"white\", color_codes=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CDC data on Gonorrhea" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.read_csv(\"../data/cdc/gonorrhea.csv\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(3228, 12)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['Disease', 'Area', 'State Abbreviation', 'FIPS', 'Year', 'Race', 'Sex', 'Age group', 'Transmission Category', 'Population', 'Cases', 'Rate'], dtype='object')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Disease object\n", "Area object\n", "State Abbreviation object\n", "FIPS int64\n", "Year int64\n", "Race object\n", "Sex object\n", "Age group object\n", "Transmission Category object\n", "Population object\n", "Cases object\n", "Rate object\n", "dtype: object" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n", " if __name__ == '__main__':\n" ] }, { "data": { "text/plain": [ "Disease object\n", "Area object\n", "State Abbreviation object\n", "FIPS int64\n", "Year int64\n", "Race object\n", "Sex object\n", "Age group object\n", "Transmission Category object\n", "Population float64\n", "Cases float64\n", "Rate float64\n", "dtype: object" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_test = df.convert_objects(convert_numeric=True)\n", "df_test.dtypes" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Disease</th>\n", " <th>Area</th>\n", " <th>State Abbreviation</th>\n", " <th>FIPS</th>\n", " <th>Year</th>\n", " <th>Race</th>\n", " <th>Sex</th>\n", " <th>Age group</th>\n", " <th>Transmission Category</th>\n", " <th>Population</th>\n", " <th>Cases</th>\n", " <th>Rate</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Gonorrhea</td>\n", " <td>Autauga County</td>\n", " <td>AL</td>\n", " <td>1001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>NaN</td>\n", " <td>48</td>\n", " <td>86.9</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Gonorrhea</td>\n", " <td>Baldwin County</td>\n", " <td>AL</td>\n", " <td>1003</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>NaN</td>\n", " <td>153</td>\n", " <td>78.2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Gonorrhea</td>\n", " <td>Barbour County</td>\n", " <td>AL</td>\n", " <td>1005</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>NaN</td>\n", " <td>52</td>\n", " <td>192.1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Gonorrhea</td>\n", " <td>Bibb County</td>\n", " <td>AL</td>\n", " <td>1007</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>NaN</td>\n", " <td>22</td>\n", " <td>97.7</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Gonorrhea</td>\n", " <td>Blount County</td>\n", " <td>AL</td>\n", " <td>1009</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>NaN</td>\n", " <td>6</td>\n", " <td>10.4</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Disease Area State Abbreviation FIPS Year Race Sex Age group Transmission Category Population Cases Rate\n", "0 Gonorrhea Autauga County AL 1001 2014 All races/ethnicities Both sexes All age groups All transmission categories NaN 48 86.9\n", "1 Gonorrhea Baldwin County AL 1003 2014 All races/ethnicities Both sexes All age groups All transmission categories NaN 153 78.2\n", "2 Gonorrhea Barbour County AL 1005 2014 All races/ethnicities Both sexes All age groups All transmission categories NaN 52 192.1\n", "3 Gonorrhea Bibb County AL 1007 2014 All races/ethnicities Both sexes All age groups All transmission categories NaN 22 97.7\n", "4 Gonorrhea Blount County AL 1009 2014 All races/ethnicities Both sexes All age groups All transmission categories NaN 6 10.4" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_test.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['Population'] = df['Population'].str.replace(',','')\n", "df['Cases'] = df['Cases'].str.replace(',','')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n", " if __name__ == '__main__':\n" ] }, { "data": { "text/plain": [ "Disease object\n", "Area object\n", "State Abbreviation object\n", "FIPS int64\n", "Year int64\n", "Race object\n", "Sex object\n", "Age group object\n", "Transmission Category object\n", "Population float64\n", "Cases float64\n", "Rate float64\n", "dtype: object" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.convert_objects(convert_numeric=True)\n", "df.dtypes" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Disease</th>\n", " <th>Area</th>\n", " <th>State Abbreviation</th>\n", " <th>FIPS</th>\n", " <th>Year</th>\n", " <th>Race</th>\n", " <th>Sex</th>\n", " <th>Age group</th>\n", " <th>Transmission Category</th>\n", " <th>Population</th>\n", " <th>Cases</th>\n", " <th>Rate</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Gonorrhea</td>\n", " <td>Autauga County</td>\n", " <td>AL</td>\n", " <td>1001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>55246</td>\n", " <td>48</td>\n", " <td>86.9</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Gonorrhea</td>\n", " <td>Baldwin County</td>\n", " <td>AL</td>\n", " <td>1003</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>195540</td>\n", " <td>153</td>\n", " <td>78.2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Gonorrhea</td>\n", " <td>Barbour County</td>\n", " <td>AL</td>\n", " <td>1005</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>27076</td>\n", " <td>52</td>\n", " <td>192.1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Gonorrhea</td>\n", " <td>Bibb County</td>\n", " <td>AL</td>\n", " <td>1007</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>22512</td>\n", " <td>22</td>\n", " <td>97.7</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Gonorrhea</td>\n", " <td>Blount County</td>\n", " <td>AL</td>\n", " <td>1009</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>57872</td>\n", " <td>6</td>\n", " <td>10.4</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>Gonorrhea</td>\n", " <td>Bullock County</td>\n", " <td>AL</td>\n", " <td>1011</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>10639</td>\n", " <td>27</td>\n", " <td>253.8</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>Gonorrhea</td>\n", " <td>Butler County</td>\n", " <td>AL</td>\n", " <td>1013</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>20265</td>\n", " <td>29</td>\n", " <td>143.1</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>Gonorrhea</td>\n", " <td>Calhoun County</td>\n", " <td>AL</td>\n", " <td>1015</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>116736</td>\n", " <td>229</td>\n", " <td>196.2</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>Gonorrhea</td>\n", " <td>Chambers County</td>\n", " <td>AL</td>\n", " <td>1017</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>34162</td>\n", " <td>73</td>\n", " <td>213.7</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>Gonorrhea</td>\n", " <td>Cherokee County</td>\n", " <td>AL</td>\n", " <td>1019</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>26203</td>\n", " <td>24</td>\n", " <td>91.6</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>Gonorrhea</td>\n", " <td>Chilton County</td>\n", " <td>AL</td>\n", " <td>1021</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>43951</td>\n", " <td>44</td>\n", " <td>100.1</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>Gonorrhea</td>\n", " <td>Choctaw County</td>\n", " <td>AL</td>\n", " <td>1023</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>13426</td>\n", " <td>8</td>\n", " <td>59.6</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>Gonorrhea</td>\n", " <td>Clarke County</td>\n", " <td>AL</td>\n", " <td>1025</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>25207</td>\n", " <td>28</td>\n", " <td>111.1</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>Gonorrhea</td>\n", " <td>Clay County</td>\n", " <td>AL</td>\n", " <td>1027</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>13486</td>\n", " <td>14</td>\n", " <td>103.8</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>Gonorrhea</td>\n", " <td>Cleburne County</td>\n", " <td>AL</td>\n", " <td>1029</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>14994</td>\n", " <td>6</td>\n", " <td>40.0</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>Gonorrhea</td>\n", " <td>Coffee County</td>\n", " <td>AL</td>\n", " <td>1031</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>50938</td>\n", " <td>64</td>\n", " <td>125.6</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>Gonorrhea</td>\n", " <td>Colbert County</td>\n", " <td>AL</td>\n", " <td>1033</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>54520</td>\n", " <td>62</td>\n", " <td>113.7</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>Gonorrhea</td>\n", " <td>Conecuh County</td>\n", " <td>AL</td>\n", " <td>1035</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>12887</td>\n", " <td>9</td>\n", " <td>69.8</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>Gonorrhea</td>\n", " <td>Coosa County</td>\n", " <td>AL</td>\n", " <td>1037</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>10898</td>\n", " <td>11</td>\n", " <td>100.9</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>Gonorrhea</td>\n", " <td>Covington County</td>\n", " <td>AL</td>\n", " <td>1039</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>37886</td>\n", " <td>25</td>\n", " <td>66.0</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>Gonorrhea</td>\n", " <td>Crenshaw County</td>\n", " <td>AL</td>\n", " <td>1041</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>13986</td>\n", " <td>18</td>\n", " <td>128.7</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>Gonorrhea</td>\n", " <td>Cullman County</td>\n", " <td>AL</td>\n", " <td>1043</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>80811</td>\n", " <td>21</td>\n", " <td>26.0</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>Gonorrhea</td>\n", " <td>Dale County</td>\n", " <td>AL</td>\n", " <td>1045</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>49884</td>\n", " <td>60</td>\n", " <td>120.3</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>Gonorrhea</td>\n", " <td>Dallas County</td>\n", " <td>AL</td>\n", " <td>1047</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>41996</td>\n", " <td>60</td>\n", " <td>142.9</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>Gonorrhea</td>\n", " <td>Dekalb County</td>\n", " <td>AL</td>\n", " <td>1049</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>71013</td>\n", " <td>43</td>\n", " <td>60.6</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>Gonorrhea</td>\n", " <td>Elmore County</td>\n", " <td>AL</td>\n", " <td>1051</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>80902</td>\n", " <td>97</td>\n", " <td>119.9</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>Gonorrhea</td>\n", " <td>Escambia County</td>\n", " <td>AL</td>\n", " <td>1053</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>37983</td>\n", " <td>52</td>\n", " <td>136.9</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>Gonorrhea</td>\n", " <td>Etowah County</td>\n", " <td>AL</td>\n", " <td>1055</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>103931</td>\n", " <td>185</td>\n", " <td>178.0</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>Gonorrhea</td>\n", " <td>Fayette County</td>\n", " <td>AL</td>\n", " <td>1057</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>16909</td>\n", " <td>5</td>\n", " <td>29.6</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>Gonorrhea</td>\n", " <td>Franklin County</td>\n", " <td>AL</td>\n", " <td>1059</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>31532</td>\n", " <td>8</td>\n", " <td>25.4</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>47</th>\n", " <td>Gonorrhea</td>\n", " <td>Marshall County</td>\n", " <td>AL</td>\n", " <td>1095</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>94760</td>\n", " <td>25</td>\n", " <td>26.4</td>\n", " </tr>\n", " <tr>\n", " <th>48</th>\n", " <td>Gonorrhea</td>\n", " <td>Mobile County</td>\n", " <td>AL</td>\n", " <td>1097</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>414079</td>\n", " <td>715</td>\n", " <td>172.7</td>\n", " </tr>\n", " <tr>\n", " <th>49</th>\n", " <td>Gonorrhea</td>\n", " <td>Monroe County</td>\n", " <td>AL</td>\n", " <td>1099</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>22236</td>\n", " <td>11</td>\n", " <td>49.5</td>\n", " </tr>\n", " <tr>\n", " <th>50</th>\n", " <td>Gonorrhea</td>\n", " <td>Montgomery County</td>\n", " <td>AL</td>\n", " <td>1101</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>226659</td>\n", " <td>959</td>\n", " <td>423.1</td>\n", " </tr>\n", " <tr>\n", " <th>51</th>\n", " <td>Gonorrhea</td>\n", " <td>Morgan County</td>\n", " <td>AL</td>\n", " <td>1103</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>119787</td>\n", " <td>111</td>\n", " <td>92.7</td>\n", " </tr>\n", " <tr>\n", " <th>52</th>\n", " <td>Gonorrhea</td>\n", " <td>Perry County</td>\n", " <td>AL</td>\n", " <td>1105</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>10020</td>\n", " <td>13</td>\n", " <td>129.7</td>\n", " </tr>\n", " <tr>\n", " <th>53</th>\n", " <td>Gonorrhea</td>\n", " <td>Pickens County</td>\n", " <td>AL</td>\n", " <td>1107</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>19401</td>\n", " <td>27</td>\n", " <td>139.2</td>\n", " </tr>\n", " <tr>\n", " <th>54</th>\n", " <td>Gonorrhea</td>\n", " <td>Pike County</td>\n", " <td>AL</td>\n", " <td>1109</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>33339</td>\n", " <td>90</td>\n", " <td>270.0</td>\n", " </tr>\n", " <tr>\n", " <th>55</th>\n", " <td>Gonorrhea</td>\n", " <td>Randolph County</td>\n", " <td>AL</td>\n", " <td>1111</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>22727</td>\n", " <td>11</td>\n", " <td>48.4</td>\n", " </tr>\n", " <tr>\n", " <th>56</th>\n", " <td>Gonorrhea</td>\n", " <td>Russell County</td>\n", " <td>AL</td>\n", " <td>1113</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>59585</td>\n", " <td>119</td>\n", " <td>199.7</td>\n", " </tr>\n", " <tr>\n", " <th>57</th>\n", " <td>Gonorrhea</td>\n", " <td>St. Clair County</td>\n", " <td>AL</td>\n", " <td>1115</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>86308</td>\n", " <td>54</td>\n", " <td>62.6</td>\n", " </tr>\n", " <tr>\n", " <th>58</th>\n", " <td>Gonorrhea</td>\n", " <td>Shelby County</td>\n", " <td>AL</td>\n", " <td>1117</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>204180</td>\n", " <td>94</td>\n", " <td>46.0</td>\n", " </tr>\n", " <tr>\n", " <th>59</th>\n", " <td>Gonorrhea</td>\n", " <td>Sumter County</td>\n", " <td>AL</td>\n", " <td>1119</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>13361</td>\n", " <td>22</td>\n", " <td>164.7</td>\n", " </tr>\n", " <tr>\n", " <th>60</th>\n", " <td>Gonorrhea</td>\n", " <td>Talladega County</td>\n", " <td>AL</td>\n", " <td>1121</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>81096</td>\n", " <td>212</td>\n", " <td>261.4</td>\n", " </tr>\n", " <tr>\n", " <th>61</th>\n", " <td>Gonorrhea</td>\n", " <td>Tallapoosa County</td>\n", " <td>AL</td>\n", " <td>1123</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>41203</td>\n", " <td>54</td>\n", " <td>131.1</td>\n", " </tr>\n", " <tr>\n", " <th>62</th>\n", " <td>Gonorrhea</td>\n", " <td>Tuscaloosa County</td>\n", " <td>AL</td>\n", " <td>1125</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>200821</td>\n", " <td>302</td>\n", " <td>150.4</td>\n", " </tr>\n", " <tr>\n", " <th>63</th>\n", " <td>Gonorrhea</td>\n", " <td>Walker County</td>\n", " <td>AL</td>\n", " <td>1127</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>65998</td>\n", " <td>77</td>\n", " <td>116.7</td>\n", " </tr>\n", " <tr>\n", " <th>64</th>\n", " <td>Gonorrhea</td>\n", " <td>Washington County</td>\n", " <td>AL</td>\n", " <td>1129</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>16877</td>\n", " <td>18</td>\n", " <td>106.7</td>\n", " </tr>\n", " <tr>\n", " <th>65</th>\n", " <td>Gonorrhea</td>\n", " <td>Wilcox County</td>\n", " <td>AL</td>\n", " <td>1131</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>11307</td>\n", " <td>14</td>\n", " <td>123.8</td>\n", " </tr>\n", " <tr>\n", " <th>66</th>\n", " <td>Gonorrhea</td>\n", " <td>Winston County</td>\n", " <td>AL</td>\n", " <td>1133</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>24146</td>\n", " <td>2</td>\n", " <td>8.3</td>\n", " </tr>\n", " <tr>\n", " <th>67</th>\n", " <td>Gonorrhea</td>\n", " <td>Aleutians East Borough</td>\n", " <td>AK</td>\n", " <td>2013</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>3092</td>\n", " <td>4</td>\n", " <td>129.4</td>\n", " </tr>\n", " <tr>\n", " <th>68</th>\n", " <td>Gonorrhea</td>\n", " <td>Aleutians West Census Area</td>\n", " <td>AK</td>\n", " <td>2016</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>5511</td>\n", " <td>1</td>\n", " <td>18.1</td>\n", " </tr>\n", " <tr>\n", " <th>69</th>\n", " <td>Gonorrhea</td>\n", " <td>Anchorage Municipality</td>\n", " <td>AK</td>\n", " <td>2020</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>300950</td>\n", " <td>782</td>\n", " <td>259.8</td>\n", " </tr>\n", " <tr>\n", " <th>70</th>\n", " <td>Gonorrhea</td>\n", " <td>Bethel Census Area</td>\n", " <td>AK</td>\n", " <td>2050</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>17758</td>\n", " <td>79</td>\n", " <td>444.9</td>\n", " </tr>\n", " <tr>\n", " <th>71</th>\n", " <td>Gonorrhea</td>\n", " <td>Bristol Bay Borough</td>\n", " <td>AK</td>\n", " <td>2060</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>960</td>\n", " <td>1</td>\n", " <td>104.2</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td>Gonorrhea</td>\n", " <td>Denali Borough</td>\n", " <td>AK</td>\n", " <td>2068</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>1867</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>73</th>\n", " <td>Gonorrhea</td>\n", " <td>Dillingham Census Area</td>\n", " <td>AK</td>\n", " <td>2070</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>5010</td>\n", " <td>23</td>\n", " <td>459.1</td>\n", " </tr>\n", " <tr>\n", " <th>74</th>\n", " <td>Gonorrhea</td>\n", " <td>Fairbanks North Star Borough</td>\n", " <td>AK</td>\n", " <td>2090</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>100436</td>\n", " <td>84</td>\n", " <td>83.6</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>Gonorrhea</td>\n", " <td>Haines Borough</td>\n", " <td>AK</td>\n", " <td>2100</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>2592</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>76</th>\n", " <td>Gonorrhea</td>\n", " <td>Hoonah-Angoon Census Area</td>\n", " <td>AK</td>\n", " <td>2105</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>77 rows × 12 columns</p>\n", "</div>" ], "text/plain": [ " Disease Area State Abbreviation FIPS Year Race Sex Age group Transmission Category Population Cases Rate\n", "0 Gonorrhea Autauga County AL 1001 2014 All races/ethnicities Both sexes All age groups All transmission categories 55246 48 86.9\n", "1 Gonorrhea Baldwin County AL 1003 2014 All races/ethnicities Both sexes All age groups All transmission categories 195540 153 78.2\n", "2 Gonorrhea Barbour County AL 1005 2014 All races/ethnicities Both sexes All age groups All transmission categories 27076 52 192.1\n", "3 Gonorrhea Bibb County AL 1007 2014 All races/ethnicities Both sexes All age groups All transmission categories 22512 22 97.7\n", "4 Gonorrhea Blount County AL 1009 2014 All races/ethnicities Both sexes All age groups All transmission categories 57872 6 10.4\n", "5 Gonorrhea Bullock County AL 1011 2014 All races/ethnicities Both sexes All age groups All transmission categories 10639 27 253.8\n", "6 Gonorrhea Butler County AL 1013 2014 All races/ethnicities Both sexes All age groups All transmission categories 20265 29 143.1\n", "7 Gonorrhea Calhoun County AL 1015 2014 All races/ethnicities Both sexes All age groups All transmission categories 116736 229 196.2\n", "8 Gonorrhea Chambers County AL 1017 2014 All races/ethnicities Both sexes All age groups All transmission categories 34162 73 213.7\n", "9 Gonorrhea Cherokee County AL 1019 2014 All races/ethnicities Both sexes All age groups All transmission categories 26203 24 91.6\n", "10 Gonorrhea Chilton County AL 1021 2014 All races/ethnicities Both sexes All age groups All transmission categories 43951 44 100.1\n", "11 Gonorrhea Choctaw County AL 1023 2014 All races/ethnicities Both sexes All age groups All transmission categories 13426 8 59.6\n", "12 Gonorrhea Clarke County AL 1025 2014 All races/ethnicities Both sexes All age groups All transmission categories 25207 28 111.1\n", "13 Gonorrhea Clay County AL 1027 2014 All races/ethnicities Both sexes All age groups All transmission categories 13486 14 103.8\n", "14 Gonorrhea Cleburne County AL 1029 2014 All races/ethnicities Both sexes All age groups All transmission categories 14994 6 40.0\n", "15 Gonorrhea Coffee County AL 1031 2014 All races/ethnicities Both sexes All age groups All transmission categories 50938 64 125.6\n", "16 Gonorrhea Colbert County AL 1033 2014 All races/ethnicities Both sexes All age groups All transmission categories 54520 62 113.7\n", "17 Gonorrhea Conecuh County AL 1035 2014 All races/ethnicities Both sexes All age groups All transmission categories 12887 9 69.8\n", "18 Gonorrhea Coosa County AL 1037 2014 All races/ethnicities Both sexes All age groups All transmission categories 10898 11 100.9\n", "19 Gonorrhea Covington County AL 1039 2014 All races/ethnicities Both sexes All age groups All transmission categories 37886 25 66.0\n", "20 Gonorrhea Crenshaw County AL 1041 2014 All races/ethnicities Both sexes All age groups All transmission categories 13986 18 128.7\n", "21 Gonorrhea Cullman County AL 1043 2014 All races/ethnicities Both sexes All age groups All transmission categories 80811 21 26.0\n", "22 Gonorrhea Dale County AL 1045 2014 All races/ethnicities Both sexes All age groups All transmission categories 49884 60 120.3\n", "23 Gonorrhea Dallas County AL 1047 2014 All races/ethnicities Both sexes All age groups All transmission categories 41996 60 142.9\n", "24 Gonorrhea Dekalb County AL 1049 2014 All races/ethnicities Both sexes All age groups All transmission categories 71013 43 60.6\n", "25 Gonorrhea Elmore County AL 1051 2014 All races/ethnicities Both sexes All age groups All transmission categories 80902 97 119.9\n", "26 Gonorrhea Escambia County AL 1053 2014 All races/ethnicities Both sexes All age groups All transmission categories 37983 52 136.9\n", "27 Gonorrhea Etowah County AL 1055 2014 All races/ethnicities Both sexes All age groups All transmission categories 103931 185 178.0\n", "28 Gonorrhea Fayette County AL 1057 2014 All races/ethnicities Both sexes All age groups All transmission categories 16909 5 29.6\n", "29 Gonorrhea Franklin County AL 1059 2014 All races/ethnicities Both sexes All age groups All transmission categories 31532 8 25.4\n", ".. ... ... ... ... ... ... ... ... ... ... ... ...\n", "47 Gonorrhea Marshall County AL 1095 2014 All races/ethnicities Both sexes All age groups All transmission categories 94760 25 26.4\n", "48 Gonorrhea Mobile County AL 1097 2014 All races/ethnicities Both sexes All age groups All transmission categories 414079 715 172.7\n", "49 Gonorrhea Monroe County AL 1099 2014 All races/ethnicities Both sexes All age groups All transmission categories 22236 11 49.5\n", "50 Gonorrhea Montgomery County AL 1101 2014 All races/ethnicities Both sexes All age groups All transmission categories 226659 959 423.1\n", "51 Gonorrhea Morgan County AL 1103 2014 All races/ethnicities Both sexes All age groups All transmission categories 119787 111 92.7\n", "52 Gonorrhea Perry County AL 1105 2014 All races/ethnicities Both sexes All age groups All transmission categories 10020 13 129.7\n", "53 Gonorrhea Pickens County AL 1107 2014 All races/ethnicities Both sexes All age groups All transmission categories 19401 27 139.2\n", "54 Gonorrhea Pike County AL 1109 2014 All races/ethnicities Both sexes All age groups All transmission categories 33339 90 270.0\n", "55 Gonorrhea Randolph County AL 1111 2014 All races/ethnicities Both sexes All age groups All transmission categories 22727 11 48.4\n", "56 Gonorrhea Russell County AL 1113 2014 All races/ethnicities Both sexes All age groups All transmission categories 59585 119 199.7\n", "57 Gonorrhea St. Clair County AL 1115 2014 All races/ethnicities Both sexes All age groups All transmission categories 86308 54 62.6\n", "58 Gonorrhea Shelby County AL 1117 2014 All races/ethnicities Both sexes All age groups All transmission categories 204180 94 46.0\n", "59 Gonorrhea Sumter County AL 1119 2014 All races/ethnicities Both sexes All age groups All transmission categories 13361 22 164.7\n", "60 Gonorrhea Talladega County AL 1121 2014 All races/ethnicities Both sexes All age groups All transmission categories 81096 212 261.4\n", "61 Gonorrhea Tallapoosa County AL 1123 2014 All races/ethnicities Both sexes All age groups All transmission categories 41203 54 131.1\n", "62 Gonorrhea Tuscaloosa County AL 1125 2014 All races/ethnicities Both sexes All age groups All transmission categories 200821 302 150.4\n", "63 Gonorrhea Walker County AL 1127 2014 All races/ethnicities Both sexes All age groups All transmission categories 65998 77 116.7\n", "64 Gonorrhea Washington County AL 1129 2014 All races/ethnicities Both sexes All age groups All transmission categories 16877 18 106.7\n", "65 Gonorrhea Wilcox County AL 1131 2014 All races/ethnicities Both sexes All age groups All transmission categories 11307 14 123.8\n", "66 Gonorrhea Winston County AL 1133 2014 All races/ethnicities Both sexes All age groups All transmission categories 24146 2 8.3\n", "67 Gonorrhea Aleutians East Borough AK 2013 2014 All races/ethnicities Both sexes All age groups All transmission categories 3092 4 129.4\n", "68 Gonorrhea Aleutians West Census Area AK 2016 2014 All races/ethnicities Both sexes All age groups All transmission categories 5511 1 18.1\n", "69 Gonorrhea Anchorage Municipality AK 2020 2014 All races/ethnicities Both sexes All age groups All transmission categories 300950 782 259.8\n", "70 Gonorrhea Bethel Census Area AK 2050 2014 All races/ethnicities Both sexes All age groups All transmission categories 17758 79 444.9\n", "71 Gonorrhea Bristol Bay Borough AK 2060 2014 All races/ethnicities Both sexes All age groups All transmission categories 960 1 104.2\n", "72 Gonorrhea Denali Borough AK 2068 2014 All races/ethnicities Both sexes All age groups All transmission categories 1867 0 0.0\n", "73 Gonorrhea Dillingham Census Area AK 2070 2014 All races/ethnicities Both sexes All age groups All transmission categories 5010 23 459.1\n", "74 Gonorrhea Fairbanks North Star Borough AK 2090 2014 All races/ethnicities Both sexes All age groups All transmission categories 100436 84 83.6\n", "75 Gonorrhea Haines Borough AK 2100 2014 All races/ethnicities Both sexes All age groups All transmission categories 2592 0 0.0\n", "76 Gonorrhea Hoonah-Angoon Census Area AK 2105 2014 All races/ethnicities Both sexes All age groups All transmission categories NaN NaN NaN\n", "\n", "[77 rows x 12 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head(77)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 3220.000000\n", "mean 99360.995963\n", "std 318648.364529\n", "min 90.000000\n", "25% 11267.750000\n", "50% 26165.500000\n", "75% 66834.250000\n", "max 10017068.000000\n", "Name: Population, dtype: float64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Population'].describe()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "207" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Population'].idxmax()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Disease Gonorrhea\n", "Area Los Angeles County\n", "State Abbreviation CA\n", "FIPS 6037\n", "Year 2014\n", "Race All races/ethnicities\n", "Sex Both sexes\n", "Age group All age groups\n", "Transmission Category All transmission categories\n", "Population 1.00171e+07\n", "Cases 15316\n", "Rate 152.9\n", "Name: 207, dtype: object" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[207]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "image/png": 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k5efnq23btmrfvr0+++wzud1uFRcX6+DBg0pJSQlmLAAAAKMCOqn/EpvNJkl68sknNXv2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\nQgYAAGDY/wOuuuicQh2GQAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f42f3c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10, 6))\n", "data = np.log10(df['Population'])\n", "ax = data.plot.hist(50)\n", "ax.set_xlabel(\"log(Population)\")\n", "ax.set_title(\"Gonorrhea\")\n", "plt.savefig('../graphics/county_population_gonorrhea.png', bbox_inches='tight', dpi=150)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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MTNSCBQtkWZYkaceOHUpPT5ckZWZmqry8XJ988onS0tJkt9sVGxurxMRE7d69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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10ec6a2b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10, 6))\n", "data = np.log10(df['Cases']+1)\n", "ax = data.plot.hist(50)\n", "ax.set_xlabel(\"log(Cases)\")\n", "ax.set_title(\"Gonorrhea\")\n", "plt.savefig('../graphics/county_cases_gonorrhea.png', bbox_inches='tight', dpi=150)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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TnZIUFBQku90uh8OhevXqFWt3OBy+KgsAAKDaCCjvBY899ph69OihTp06KTAw8II7zsrK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\nxMREeTweJSUl1Yo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"text/plain": [ "<matplotlib.figure.Figure at 0x10ec6a278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(10, 6))\n", "ax = df['Rate'].plot.hist(100)\n", "ax.set_xlabel(\"Rate (per 100,000 inhabitants)\")\n", "ax.set_title(\"Gonorrhea\")\n", "plt.savefig('../graphics/county_rate_gonorrhea.png', bbox_inches='tight', dpi=150)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "outliers = df[df['Rate']<10]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Disease</th>\n", " <th>Area</th>\n", " <th>State Abbreviation</th>\n", " <th>FIPS</th>\n", " <th>Year</th>\n", " <th>Race</th>\n", " <th>Sex</th>\n", " <th>Age group</th>\n", " <th>Transmission Category</th>\n", " <th>Population</th>\n", " <th>Cases</th>\n", " <th>Rate</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>66</th>\n", " <td>Gonorrhea</td>\n", " <td>Winston County</td>\n", " <td>AL</td>\n", " <td>1133</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>24146</td>\n", " <td>2</td>\n", " <td>8.3</td>\n", " </tr>\n", " <tr>\n", " <th>72</th>\n", " <td>Gonorrhea</td>\n", " <td>Denali Borough</td>\n", " <td>AK</td>\n", " <td>2068</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>1867</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>75</th>\n", " <td>Gonorrhea</td>\n", " <td>Haines Borough</td>\n", " <td>AK</td>\n", " <td>2100</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>2592</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>88</th>\n", " <td>Gonorrhea</td>\n", " <td>Prince of Wales - Outer Ketchikan</td>\n", " <td>AK</td>\n", " <td>2201</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>5786</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>93</th>\n", " <td>Gonorrhea</td>\n", " <td>Valdez-Cordova Census Area</td>\n", " <td>AK</td>\n", " <td>2261</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>9763</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>96</th>\n", " <td>Gonorrhea</td>\n", " <td>Wrangell-Petersburg Census Area</td>\n", " <td>AK</td>\n", " <td>2280</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>6174</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td>Gonorrhea</td>\n", " <td>Yakutat City and Borough</td>\n", " <td>AK</td>\n", " <td>2282</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>642</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>125</th>\n", " <td>Gonorrhea</td>\n", " <td>Cleburne County</td>\n", " <td>AR</td>\n", " <td>5023</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>25686</td>\n", " <td>1</td>\n", " <td>3.9</td>\n", " </tr>\n", " <tr>\n", " <th>138</th>\n", " <td>Gonorrhea</td>\n", " <td>Fulton County</td>\n", " <td>AR</td>\n", " <td>5049</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>12304</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>157</th>\n", " <td>Gonorrhea</td>\n", " <td>Madison County</td>\n", " <td>AR</td>\n", " <td>5087</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>15701</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>158</th>\n", " <td>Gonorrhea</td>\n", " <td>Marion County</td>\n", " <td>AR</td>\n", " <td>5089</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>16430</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>164</th>\n", " <td>Gonorrhea</td>\n", " <td>Newton County</td>\n", " <td>AR</td>\n", " <td>5101</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>8064</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>166</th>\n", " <td>Gonorrhea</td>\n", " <td>Perry County</td>\n", " <td>AR</td>\n", " <td>5105</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>10345</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>170</th>\n", " <td>Gonorrhea</td>\n", " <td>Polk County</td>\n", " <td>AR</td>\n", " <td>5113</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>20406</td>\n", " <td>2</td>\n", " <td>9.8</td>\n", " </tr>\n", " <tr>\n", " <th>190</th>\n", " <td>Gonorrhea</td>\n", " <td>Alpine County</td>\n", " <td>CA</td>\n", " <td>6003</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>1159</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>251</th>\n", " <td>Gonorrhea</td>\n", " <td>Baca County</td>\n", " <td>CO</td>\n", " <td>8009</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>3682</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>256</th>\n", " <td>Gonorrhea</td>\n", " <td>Cheyenne County</td>\n", " <td>CO</td>\n", " <td>8017</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>1890</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>259</th>\n", " <td>Gonorrhea</td>\n", " <td>Costilla County</td>\n", " <td>CO</td>\n", " <td>8023</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>3518</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>262</th>\n", " <td>Gonorrhea</td>\n", " <td>Delta County</td>\n", " <td>CO</td>\n", " <td>8029</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>30483</td>\n", " <td>1</td>\n", " <td>3.3</td>\n", " </tr>\n", " <tr>\n", " <th>264</th>\n", " <td>Gonorrhea</td>\n", " <td>Dolores County</td>\n", " <td>CO</td>\n", " <td>8033</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>2029</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>266</th>\n", " <td>Gonorrhea</td>\n", " <td>Eagle County</td>\n", " <td>CO</td>\n", " <td>8037</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>52460</td>\n", " <td>5</td>\n", " <td>9.5</td>\n", " </tr>\n", " <tr>\n", " <th>273</th>\n", " <td>Gonorrhea</td>\n", " <td>Gunnison County</td>\n", " <td>CO</td>\n", " <td>8051</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>15507</td>\n", " <td>1</td>\n", " <td>6.4</td>\n", " </tr>\n", " <tr>\n", " <th>274</th>\n", " <td>Gonorrhea</td>\n", " <td>Hinsdale County</td>\n", " <td>CO</td>\n", " <td>8053</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>813</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>276</th>\n", " <td>Gonorrhea</td>\n", " <td>Jackson County</td>\n", " <td>CO</td>\n", " <td>8057</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>1365</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>278</th>\n", " <td>Gonorrhea</td>\n", " <td>Kiowa County</td>\n", " <td>CO</td>\n", " <td>8061</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>1423</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>279</th>\n", " <td>Gonorrhea</td>\n", " <td>Kit Carson County</td>\n", " <td>CO</td>\n", " <td>8063</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>8037</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>285</th>\n", " <td>Gonorrhea</td>\n", " <td>Logan County</td>\n", " <td>CO</td>\n", " <td>8075</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>22450</td>\n", " <td>1</td>\n", " <td>4.5</td>\n", " </tr>\n", " <tr>\n", " <th>287</th>\n", " <td>Gonorrhea</td>\n", " <td>Mineral County</td>\n", " <td>CO</td>\n", " <td>8079</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>721</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>290</th>\n", " <td>Gonorrhea</td>\n", " <td>Montrose County</td>\n", " <td>CO</td>\n", " <td>8085</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>40713</td>\n", " <td>1</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>292</th>\n", " <td>Gonorrhea</td>\n", " <td>Otero County</td>\n", " <td>CO</td>\n", " <td>8089</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>18703</td>\n", " <td>1</td>\n", " <td>5.3</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>3177</th>\n", " <td>Gonorrhea</td>\n", " <td>Guànica Municipio</td>\n", " <td>PR</td>\n", " <td>72055</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>17852</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3178</th>\n", " <td>Gonorrhea</td>\n", " <td>Guayama Municipio</td>\n", " <td>PR</td>\n", " <td>72057</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>43467</td>\n", " <td>2</td>\n", " <td>4.6</td>\n", " </tr>\n", " <tr>\n", " <th>3179</th>\n", " <td>Gonorrhea</td>\n", " <td>Guayanilla Municipio</td>\n", " <td>PR</td>\n", " <td>72059</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>20148</td>\n", " <td>1</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>3180</th>\n", " <td>Gonorrhea</td>\n", " <td>Guaynabo Municipio</td>\n", " <td>PR</td>\n", " <td>72061</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>92799</td>\n", " <td>6</td>\n", " <td>6.5</td>\n", " </tr>\n", " <tr>\n", " <th>3182</th>\n", " <td>Gonorrhea</td>\n", " <td>Hatillo Municipio</td>\n", " <td>PR</td>\n", " <td>72065</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>41618</td>\n", " <td>2</td>\n", " <td>4.8</td>\n", " </tr>\n", " <tr>\n", " <th>3186</th>\n", " <td>Gonorrhea</td>\n", " <td>Jayuya Municipio</td>\n", " <td>PR</td>\n", " <td>72073</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>15693</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3189</th>\n", " <td>Gonorrhea</td>\n", " <td>Lajas Municipio</td>\n", " <td>PR</td>\n", " <td>72079</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>24465</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3190</th>\n", " <td>Gonorrhea</td>\n", " <td>Lares Municipio</td>\n", " <td>PR</td>\n", " <td>72081</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>28208</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3191</th>\n", " <td>Gonorrhea</td>\n", " <td>Las MarÕas Municipio</td>\n", " <td>PR</td>\n", " <td>72083</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>9158</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3196</th>\n", " <td>Gonorrhea</td>\n", " <td>Maricao Municipio</td>\n", " <td>PR</td>\n", " <td>72093</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>6022</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3197</th>\n", " <td>Gonorrhea</td>\n", " <td>Maunabo Municipio</td>\n", " <td>PR</td>\n", " <td>72095</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>11565</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3199</th>\n", " <td>Gonorrhea</td>\n", " <td>Moca Municipio</td>\n", " <td>PR</td>\n", " <td>72099</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>38461</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3200</th>\n", " <td>Gonorrhea</td>\n", " <td>Morovis Municipio</td>\n", " <td>PR</td>\n", " <td>72101</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>32194</td>\n", " <td>1</td>\n", " <td>3.1</td>\n", " </tr>\n", " <tr>\n", " <th>3202</th>\n", " <td>Gonorrhea</td>\n", " <td>Naranjito Municipio</td>\n", " <td>PR</td>\n", " <td>72105</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>29602</td>\n", " <td>1</td>\n", " <td>3.4</td>\n", " </tr>\n", " <tr>\n", " <th>3203</th>\n", " <td>Gonorrhea</td>\n", " <td>Orocovis Municipio</td>\n", " <td>PR</td>\n", " <td>72107</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>22392</td>\n", " <td>1</td>\n", " <td>4.5</td>\n", " </tr>\n", " <tr>\n", " <th>3204</th>\n", " <td>Gonorrhea</td>\n", " <td>Patillas Municipio</td>\n", " <td>PR</td>\n", " <td>72109</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>18261</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3205</th>\n", " <td>Gonorrhea</td>\n", " <td>PeÐuelas Municipio</td>\n", " <td>PR</td>\n", " <td>72111</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>22365</td>\n", " <td>1</td>\n", " <td>4.5</td>\n", " </tr>\n", " <tr>\n", " <th>3207</th>\n", " <td>Gonorrhea</td>\n", " <td>Quebradillas Municipio</td>\n", " <td>PR</td>\n", " <td>72115</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>25042</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3210</th>\n", " <td>Gonorrhea</td>\n", " <td>Sabana Grande Municipio</td>\n", " <td>PR</td>\n", " <td>72121</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>24121</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3211</th>\n", " <td>Gonorrhea</td>\n", " <td>Salinas Municipio</td>\n", " <td>PR</td>\n", " <td>72123</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>29881</td>\n", " <td>2</td>\n", " <td>6.7</td>\n", " </tr>\n", " <tr>\n", " <th>3212</th>\n", " <td>Gonorrhea</td>\n", " <td>San Germàn Municipio</td>\n", " <td>PR</td>\n", " <td>72125</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>33725</td>\n", " <td>1</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>3215</th>\n", " <td>Gonorrhea</td>\n", " <td>San Sebastiàn Municipio</td>\n", " <td>PR</td>\n", " <td>72131</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>39969</td>\n", " <td>1</td>\n", " <td>2.5</td>\n", " </tr>\n", " <tr>\n", " <th>3216</th>\n", " <td>Gonorrhea</td>\n", " <td>Santa Isabel Municipio</td>\n", " <td>PR</td>\n", " <td>72133</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>22860</td>\n", " <td>1</td>\n", " <td>4.4</td>\n", " </tr>\n", " <tr>\n", " <th>3217</th>\n", " <td>Gonorrhea</td>\n", " <td>Toa Alta Municipio</td>\n", " <td>PR</td>\n", " <td>72135</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>74837</td>\n", " <td>7</td>\n", " <td>9.4</td>\n", " </tr>\n", " <tr>\n", " <th>3220</th>\n", " <td>Gonorrhea</td>\n", " <td>Utuado Municipio</td>\n", " <td>PR</td>\n", " <td>72141</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>31050</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3221</th>\n", " <td>Gonorrhea</td>\n", " <td>Vega Alta Municipio</td>\n", " <td>PR</td>\n", " <td>72143</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>39236</td>\n", " <td>3</td>\n", " <td>7.6</td>\n", " </tr>\n", " <tr>\n", " <th>3222</th>\n", " <td>Gonorrhea</td>\n", " <td>Vega Baja Municipio</td>\n", " <td>PR</td>\n", " <td>72145</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>56166</td>\n", " <td>2</td>\n", " <td>3.6</td>\n", " </tr>\n", " <tr>\n", " <th>3224</th>\n", " <td>Gonorrhea</td>\n", " <td>Villalba Municipio</td>\n", " <td>PR</td>\n", " <td>72149</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>24389</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3225</th>\n", " <td>Gonorrhea</td>\n", " <td>Yabucoa Municipio</td>\n", " <td>PR</td>\n", " <td>72151</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>35879</td>\n", " <td>3</td>\n", " <td>8.4</td>\n", " </tr>\n", " <tr>\n", " <th>3226</th>\n", " <td>Gonorrhea</td>\n", " <td>Yauco Municipio</td>\n", " <td>PR</td>\n", " <td>72153</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>38782</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>672 rows × 12 columns</p>\n", "</div>" ], "text/plain": [ " Disease Area State Abbreviation FIPS Year Race Sex Age group Transmission Category Population Cases Rate\n", "66 Gonorrhea Winston County AL 1133 2014 All races/ethnicities Both sexes All age groups All transmission categories 24146 2 8.3\n", "72 Gonorrhea Denali Borough AK 2068 2014 All races/ethnicities Both sexes All age groups All transmission categories 1867 0 0.0\n", "75 Gonorrhea Haines Borough AK 2100 2014 All races/ethnicities Both sexes All age groups All transmission categories 2592 0 0.0\n", "88 Gonorrhea Prince of Wales - Outer Ketchikan AK 2201 2014 All races/ethnicities Both sexes All age groups All transmission categories 5786 0 0.0\n", "93 Gonorrhea Valdez-Cordova Census Area AK 2261 2014 All races/ethnicities Both sexes All age groups All transmission categories 9763 0 0.0\n", "96 Gonorrhea Wrangell-Petersburg Census Area AK 2280 2014 All races/ethnicities Both sexes All age groups All transmission categories 6174 0 0.0\n", "97 Gonorrhea Yakutat City and Borough AK 2282 2014 All races/ethnicities Both sexes All age groups All transmission categories 642 0 0.0\n", "125 Gonorrhea Cleburne County AR 5023 2014 All races/ethnicities Both sexes All age groups All transmission categories 25686 1 3.9\n", "138 Gonorrhea Fulton County AR 5049 2014 All races/ethnicities Both sexes All age groups All transmission categories 12304 0 0.0\n", "157 Gonorrhea Madison County AR 5087 2014 All races/ethnicities Both sexes All age groups All transmission categories 15701 0 0.0\n", "158 Gonorrhea Marion County AR 5089 2014 All races/ethnicities Both sexes All age groups All transmission categories 16430 0 0.0\n", "164 Gonorrhea Newton County AR 5101 2014 All races/ethnicities Both sexes All age groups All transmission categories 8064 0 0.0\n", "166 Gonorrhea Perry County AR 5105 2014 All races/ethnicities Both sexes All age groups All transmission categories 10345 0 0.0\n", "170 Gonorrhea Polk County AR 5113 2014 All races/ethnicities Both sexes All age groups All transmission categories 20406 2 9.8\n", "190 Gonorrhea Alpine County CA 6003 2014 All races/ethnicities Both sexes All age groups All transmission categories 1159 0 0.0\n", "251 Gonorrhea Baca County CO 8009 2014 All races/ethnicities Both sexes All age groups All transmission categories 3682 0 0.0\n", "256 Gonorrhea Cheyenne County CO 8017 2014 All races/ethnicities Both sexes All age groups All transmission categories 1890 0 0.0\n", "259 Gonorrhea Costilla County CO 8023 2014 All races/ethnicities Both sexes All age groups All transmission categories 3518 0 0.0\n", "262 Gonorrhea Delta County CO 8029 2014 All races/ethnicities Both sexes All age groups All transmission categories 30483 1 3.3\n", "264 Gonorrhea Dolores County CO 8033 2014 All races/ethnicities Both sexes All age groups All transmission categories 2029 0 0.0\n", "266 Gonorrhea Eagle County CO 8037 2014 All races/ethnicities Both sexes All age groups All transmission categories 52460 5 9.5\n", "273 Gonorrhea Gunnison County CO 8051 2014 All races/ethnicities Both sexes All age groups All transmission categories 15507 1 6.4\n", "274 Gonorrhea Hinsdale County CO 8053 2014 All races/ethnicities Both sexes All age groups All transmission categories 813 0 0.0\n", "276 Gonorrhea Jackson County CO 8057 2014 All races/ethnicities Both sexes All age groups All transmission categories 1365 0 0.0\n", "278 Gonorrhea Kiowa County CO 8061 2014 All races/ethnicities Both sexes All age groups All transmission categories 1423 0 0.0\n", "279 Gonorrhea Kit Carson County CO 8063 2014 All races/ethnicities Both sexes All age groups All transmission categories 8037 0 0.0\n", "285 Gonorrhea Logan County CO 8075 2014 All races/ethnicities Both sexes All age groups All transmission categories 22450 1 4.5\n", "287 Gonorrhea Mineral County CO 8079 2014 All races/ethnicities Both sexes All age groups All transmission categories 721 0 0.0\n", "290 Gonorrhea Montrose County CO 8085 2014 All races/ethnicities Both sexes All age groups All transmission categories 40713 1 2.5\n", "292 Gonorrhea Otero County CO 8089 2014 All races/ethnicities Both sexes All age groups All transmission categories 18703 1 5.3\n", "... ... ... ... ... ... ... ... ... ... ... ... ...\n", "3177 Gonorrhea Guànica Municipio PR 72055 2014 All races/ethnicities Both sexes All age groups All transmission categories 17852 0 0.0\n", "3178 Gonorrhea Guayama Municipio PR 72057 2014 All races/ethnicities Both sexes All age groups All transmission categories 43467 2 4.6\n", "3179 Gonorrhea Guayanilla Municipio PR 72059 2014 All races/ethnicities Both sexes All age groups All transmission categories 20148 1 5.0\n", "3180 Gonorrhea Guaynabo Municipio PR 72061 2014 All races/ethnicities Both sexes All age groups All transmission categories 92799 6 6.5\n", "3182 Gonorrhea Hatillo Municipio PR 72065 2014 All races/ethnicities Both sexes All age groups All transmission categories 41618 2 4.8\n", "3186 Gonorrhea Jayuya Municipio PR 72073 2014 All races/ethnicities Both sexes All age groups All transmission categories 15693 0 0.0\n", "3189 Gonorrhea Lajas Municipio PR 72079 2014 All races/ethnicities Both sexes All age groups All transmission categories 24465 0 0.0\n", "3190 Gonorrhea Lares Municipio PR 72081 2014 All races/ethnicities Both sexes All age groups All transmission categories 28208 0 0.0\n", "3191 Gonorrhea Las MarÕas Municipio PR 72083 2014 All races/ethnicities Both sexes All age groups All transmission categories 9158 0 0.0\n", "3196 Gonorrhea Maricao Municipio PR 72093 2014 All races/ethnicities Both sexes All age groups All transmission categories 6022 0 0.0\n", "3197 Gonorrhea Maunabo Municipio PR 72095 2014 All races/ethnicities Both sexes All age groups All transmission categories 11565 0 0.0\n", "3199 Gonorrhea Moca Municipio PR 72099 2014 All races/ethnicities Both sexes All age groups All transmission categories 38461 0 0.0\n", "3200 Gonorrhea Morovis Municipio PR 72101 2014 All races/ethnicities Both sexes All age groups All transmission categories 32194 1 3.1\n", "3202 Gonorrhea Naranjito Municipio PR 72105 2014 All races/ethnicities Both sexes All age groups All transmission categories 29602 1 3.4\n", "3203 Gonorrhea Orocovis Municipio PR 72107 2014 All races/ethnicities Both sexes All age groups All transmission categories 22392 1 4.5\n", "3204 Gonorrhea Patillas Municipio PR 72109 2014 All races/ethnicities Both sexes All age groups All transmission categories 18261 0 0.0\n", "3205 Gonorrhea PeÐuelas Municipio PR 72111 2014 All races/ethnicities Both sexes All age groups All transmission categories 22365 1 4.5\n", "3207 Gonorrhea Quebradillas Municipio PR 72115 2014 All races/ethnicities Both sexes All age groups All transmission categories 25042 0 0.0\n", "3210 Gonorrhea Sabana Grande Municipio PR 72121 2014 All races/ethnicities Both sexes All age groups All transmission categories 24121 0 0.0\n", "3211 Gonorrhea Salinas Municipio PR 72123 2014 All races/ethnicities Both sexes All age groups All transmission categories 29881 2 6.7\n", "3212 Gonorrhea San Germàn Municipio PR 72125 2014 All races/ethnicities Both sexes All age groups All transmission categories 33725 1 3.0\n", "3215 Gonorrhea San Sebastiàn Municipio PR 72131 2014 All races/ethnicities Both sexes All age groups All transmission categories 39969 1 2.5\n", "3216 Gonorrhea Santa Isabel Municipio PR 72133 2014 All races/ethnicities Both sexes All age groups All transmission categories 22860 1 4.4\n", "3217 Gonorrhea Toa Alta Municipio PR 72135 2014 All races/ethnicities Both sexes All age groups All transmission categories 74837 7 9.4\n", "3220 Gonorrhea Utuado Municipio PR 72141 2014 All races/ethnicities Both sexes All age groups All transmission categories 31050 0 0.0\n", "3221 Gonorrhea Vega Alta Municipio PR 72143 2014 All races/ethnicities Both sexes All age groups All transmission categories 39236 3 7.6\n", "3222 Gonorrhea Vega Baja Municipio PR 72145 2014 All races/ethnicities Both sexes All age groups All transmission categories 56166 2 3.6\n", "3224 Gonorrhea Villalba Municipio PR 72149 2014 All races/ethnicities Both sexes All age groups All transmission categories 24389 0 0.0\n", "3225 Gonorrhea Yabucoa Municipio PR 72151 2014 All races/ethnicities Both sexes All age groups All transmission categories 35879 3 8.4\n", "3226 Gonorrhea Yauco Municipio PR 72153 2014 All races/ethnicities Both sexes All age groups All transmission categories 38782 0 0.0\n", "\n", "[672 rows x 12 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "outliers" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "not_exclude_list = df[\"Cases\"]<0\n", "exclude_list = [not i for i in not_exclude_list]\n", "df_sig = df[exclude_list].copy()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = df_sig.copy()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "880 0.0\n", "1654 0.0\n", "1655 0.0\n", "3083 0.0\n", "1234 0.0\n", "3089 0.0\n", "3090 0.0\n", "1658 0.0\n", "660 0.0\n", "1659 0.0\n", "1660 0.0\n", "2807 0.0\n", "1651 0.0\n", "1661 0.0\n", "2423 0.0\n", "1665 0.0\n", "310 0.0\n", "1190 0.0\n", "308 0.0\n", "305 0.0\n", "675 0.0\n", "303 0.0\n", "302 0.0\n", "1671 0.0\n", "1676 0.0\n", "1664 0.0\n", "1650 0.0\n", "1649 0.0\n", "1235 0.0\n", "620 0.0\n", " ... \n", "1152 451.9\n", "148 456.3\n", "1149 458.3\n", "73 459.1\n", "2713 462.8\n", "2948 478.7\n", "81 485.4\n", "1600 486.2\n", "2375 508.6\n", "1983 528.9\n", "159 529.9\n", "1995 567.1\n", "2944 633.1\n", "2384 716.1\n", "2424 801.4\n", "2379 830.4\n", "2420 835.8\n", "1643 844.9\n", "2032 850.4\n", "2035 857.8\n", "94 877.5\n", "76 NaN\n", "83 NaN\n", "86 NaN\n", "87 NaN\n", "90 NaN\n", "95 NaN\n", "2919 NaN\n", "3146 NaN\n", "3148 NaN\n", "Name: Rate, dtype: float64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"Rate\"].sort_values()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1962" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df['Area'].unique())" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(3228, 12)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Disease</th>\n", " <th>Area</th>\n", " <th>State Abbreviation</th>\n", " <th>FIPS</th>\n", " <th>Year</th>\n", " <th>Race</th>\n", " <th>Sex</th>\n", " <th>Age group</th>\n", " <th>Transmission Category</th>\n", " <th>Population</th>\n", " <th>Cases</th>\n", " <th>Rate</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2319</th>\n", " <td>Gonorrhea</td>\n", " <td>Abbeville County</td>\n", " <td>SC</td>\n", " <td>45001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>25007</td>\n", " <td>43</td>\n", " <td>172.0</td>\n", " </tr>\n", " <tr>\n", " <th>1116</th>\n", " <td>Gonorrhea</td>\n", " <td>Acadia Parish</td>\n", " <td>LA</td>\n", " <td>22001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>62204</td>\n", " <td>106</td>\n", " <td>170.4</td>\n", " </tr>\n", " <tr>\n", " <th>2823</th>\n", " <td>Gonorrhea</td>\n", " <td>Accomack County</td>\n", " <td>VA</td>\n", " <td>51001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>33148</td>\n", " <td>28</td>\n", " <td>84.5</td>\n", " </tr>\n", " <tr>\n", " <th>554</th>\n", " <td>Gonorrhea</td>\n", " <td>Ada County</td>\n", " <td>ID</td>\n", " <td>16001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>416464</td>\n", " <td>205</td>\n", " <td>49.2</td>\n", " </tr>\n", " <tr>\n", " <th>996</th>\n", " <td>Gonorrhea</td>\n", " <td>Adair County</td>\n", " <td>KY</td>\n", " <td>21001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>18732</td>\n", " <td>6</td>\n", " <td>32.0</td>\n", " </tr>\n", " <tr>\n", " <th>1486</th>\n", " <td>Gonorrhea</td>\n", " <td>Adair County</td>\n", " <td>MO</td>\n", " <td>29001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>25572</td>\n", " <td>8</td>\n", " <td>31.3</td>\n", " </tr>\n", " <tr>\n", " <th>792</th>\n", " <td>Gonorrhea</td>\n", " <td>Adair County</td>\n", " <td>IA</td>\n", " <td>19001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>7472</td>\n", " <td>2</td>\n", " <td>26.8</td>\n", " </tr>\n", " <tr>\n", " <th>2134</th>\n", " <td>Gonorrhea</td>\n", " <td>Adair County</td>\n", " <td>OK</td>\n", " <td>40001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>22194</td>\n", " <td>11</td>\n", " <td>49.6</td>\n", " </tr>\n", " <tr>\n", " <th>1657</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>NE</td>\n", " <td>31001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>31610</td>\n", " <td>8</td>\n", " <td>25.3</td>\n", " </tr>\n", " <tr>\n", " <th>700</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>IN</td>\n", " <td>18001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>34614</td>\n", " <td>6</td>\n", " <td>17.3</td>\n", " </tr>\n", " <tr>\n", " <th>555</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>ID</td>\n", " <td>16003</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>3828</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1404</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>MS</td>\n", " <td>28001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>32090</td>\n", " <td>56</td>\n", " <td>174.5</td>\n", " </tr>\n", " <tr>\n", " <th>793</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>IA</td>\n", " <td>19003</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>3894</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>598</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>IL</td>\n", " <td>17001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>67130</td>\n", " <td>33</td>\n", " <td>49.2</td>\n", " </tr>\n", " <tr>\n", " <th>2046</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>OH</td>\n", " <td>39001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>28105</td>\n", " <td>2</td>\n", " <td>7.1</td>\n", " </tr>\n", " <tr>\n", " <th>1993</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>ND</td>\n", " <td>38001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>2360</td>\n", " <td>1</td>\n", " <td>42.4</td>\n", " </tr>\n", " <tr>\n", " <th>247</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>CO</td>\n", " <td>8001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>469193</td>\n", " <td>233</td>\n", " <td>49.7</td>\n", " </tr>\n", " <tr>\n", " <th>3051</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>WI</td>\n", " <td>55001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>20480</td>\n", " <td>3</td>\n", " <td>14.6</td>\n", " </tr>\n", " <tr>\n", " <th>2957</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>WA</td>\n", " <td>53001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>19067</td>\n", " <td>10</td>\n", " <td>52.4</td>\n", " </tr>\n", " <tr>\n", " <th>2247</th>\n", " <td>Gonorrhea</td>\n", " <td>Adams County</td>\n", " <td>PA</td>\n", " <td>42001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>101546</td>\n", " <td>18</td>\n", " <td>17.7</td>\n", " </tr>\n", " <tr>\n", " <th>2809</th>\n", " <td>Gonorrhea</td>\n", " <td>Addison County</td>\n", " <td>VT</td>\n", " <td>50001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>36791</td>\n", " <td>4</td>\n", " <td>10.9</td>\n", " </tr>\n", " <tr>\n", " <th>3149</th>\n", " <td>Gonorrhea</td>\n", " <td>Adjuntas Municipio</td>\n", " <td>PR</td>\n", " <td>72001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>18900</td>\n", " <td>2</td>\n", " <td>10.6</td>\n", " </tr>\n", " <tr>\n", " <th>3150</th>\n", " <td>Gonorrhea</td>\n", " <td>Aguada Municipio</td>\n", " <td>PR</td>\n", " <td>72003</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>40329</td>\n", " <td>3</td>\n", " <td>7.4</td>\n", " </tr>\n", " <tr>\n", " <th>3151</th>\n", " <td>Gonorrhea</td>\n", " <td>Aguadilla Municipio</td>\n", " <td>PR</td>\n", " <td>72005</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>57290</td>\n", " <td>7</td>\n", " <td>12.2</td>\n", " </tr>\n", " <tr>\n", " <th>3152</th>\n", " <td>Gonorrhea</td>\n", " <td>Aguas Buenas Municipio</td>\n", " <td>PR</td>\n", " <td>72007</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>27473</td>\n", " <td>2</td>\n", " <td>7.3</td>\n", " </tr>\n", " <tr>\n", " <th>3153</th>\n", " <td>Gonorrhea</td>\n", " <td>Aibonito Municipio</td>\n", " <td>PR</td>\n", " <td>72009</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>24561</td>\n", " <td>3</td>\n", " <td>12.2</td>\n", " </tr>\n", " <tr>\n", " <th>2320</th>\n", " <td>Gonorrhea</td>\n", " <td>Aiken County</td>\n", " <td>SC</td>\n", " <td>45003</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>164176</td>\n", " <td>280</td>\n", " <td>170.5</td>\n", " </tr>\n", " <tr>\n", " <th>1317</th>\n", " <td>Gonorrhea</td>\n", " <td>Aitkin County</td>\n", " <td>MN</td>\n", " <td>27001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>15742</td>\n", " <td>5</td>\n", " <td>31.8</td>\n", " </tr>\n", " <tr>\n", " <th>323</th>\n", " <td>Gonorrhea</td>\n", " <td>Alachua County</td>\n", " <td>FL</td>\n", " <td>12001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>253451</td>\n", " <td>411</td>\n", " <td>162.2</td>\n", " </tr>\n", " <tr>\n", " <th>1893</th>\n", " <td>Gonorrhea</td>\n", " <td>Alamance County</td>\n", " <td>NC</td>\n", " <td>37001</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>154378</td>\n", " <td>262</td>\n", " <td>169.7</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>3225</th>\n", " <td>Gonorrhea</td>\n", " <td>Yabucoa Municipio</td>\n", " <td>PR</td>\n", " <td>72151</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>35879</td>\n", " <td>3</td>\n", " <td>8.4</td>\n", " </tr>\n", " <tr>\n", " <th>1991</th>\n", " <td>Gonorrhea</td>\n", " <td>Yadkin County</td>\n", " <td>NC</td>\n", " <td>37197</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>38038</td>\n", " <td>13</td>\n", " <td>34.2</td>\n", " </tr>\n", " <tr>\n", " <th>2995</th>\n", " <td>Gonorrhea</td>\n", " <td>Yakima County</td>\n", " <td>WA</td>\n", " <td>53077</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>247044</td>\n", " <td>409</td>\n", " <td>165.6</td>\n", " </tr>\n", " <tr>\n", " <th>97</th>\n", " <td>Gonorrhea</td>\n", " <td>Yakutat City and Borough</td>\n", " <td>AK</td>\n", " <td>2282</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>642</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1484</th>\n", " <td>Gonorrhea</td>\n", " <td>Yalobusha County</td>\n", " <td>MS</td>\n", " <td>28161</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>12373</td>\n", " <td>18</td>\n", " <td>145.5</td>\n", " </tr>\n", " <tr>\n", " <th>2246</th>\n", " <td>Gonorrhea</td>\n", " <td>Yamhill County</td>\n", " <td>OR</td>\n", " <td>41071</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>100725</td>\n", " <td>18</td>\n", " <td>17.9</td>\n", " </tr>\n", " <tr>\n", " <th>1992</th>\n", " <td>Gonorrhea</td>\n", " <td>Yancey County</td>\n", " <td>NC</td>\n", " <td>37199</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>17566</td>\n", " <td>2</td>\n", " <td>11.4</td>\n", " </tr>\n", " <tr>\n", " <th>2429</th>\n", " <td>Gonorrhea</td>\n", " <td>Yankton County</td>\n", " <td>SD</td>\n", " <td>46135</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>22696</td>\n", " <td>8</td>\n", " <td>35.2</td>\n", " </tr>\n", " <tr>\n", " <th>1892</th>\n", " <td>Gonorrhea</td>\n", " <td>Yates County</td>\n", " <td>NY</td>\n", " <td>36123</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>25156</td>\n", " <td>2</td>\n", " <td>8.0</td>\n", " </tr>\n", " <tr>\n", " <th>3226</th>\n", " <td>Gonorrhea</td>\n", " <td>Yauco Municipio</td>\n", " <td>PR</td>\n", " <td>72153</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>38782</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>112</th>\n", " <td>Gonorrhea</td>\n", " <td>Yavapai County</td>\n", " <td>AZ</td>\n", " <td>4025</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>215133</td>\n", " <td>51</td>\n", " <td>23.7</td>\n", " </tr>\n", " <tr>\n", " <th>1485</th>\n", " <td>Gonorrhea</td>\n", " <td>Yazoo County</td>\n", " <td>MS</td>\n", " <td>28163</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>27883</td>\n", " <td>103</td>\n", " <td>369.4</td>\n", " </tr>\n", " <tr>\n", " <th>188</th>\n", " <td>Gonorrhea</td>\n", " <td>Yell County</td>\n", " <td>AR</td>\n", " <td>5149</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>21893</td>\n", " <td>8</td>\n", " <td>36.5</td>\n", " </tr>\n", " <tr>\n", " <th>1403</th>\n", " <td>Gonorrhea</td>\n", " <td>Yellow Medicine County</td>\n", " <td>MN</td>\n", " <td>27173</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>10143</td>\n", " <td>1</td>\n", " <td>9.9</td>\n", " </tr>\n", " <tr>\n", " <th>1656</th>\n", " <td>Gonorrhea</td>\n", " <td>Yellowstone County</td>\n", " <td>MT</td>\n", " <td>30111</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>154162</td>\n", " <td>101</td>\n", " <td>65.5</td>\n", " </tr>\n", " <tr>\n", " <th>2776</th>\n", " <td>Gonorrhea</td>\n", " <td>Yoakum County</td>\n", " <td>TX</td>\n", " <td>48501</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>8184</td>\n", " <td>5</td>\n", " <td>61.1</td>\n", " </tr>\n", " <tr>\n", " <th>245</th>\n", " <td>Gonorrhea</td>\n", " <td>Yolo County</td>\n", " <td>CA</td>\n", " <td>6113</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>204593</td>\n", " <td>194</td>\n", " <td>94.8</td>\n", " </tr>\n", " <tr>\n", " <th>2917</th>\n", " <td>Gonorrhea</td>\n", " <td>York County</td>\n", " <td>VA</td>\n", " <td>51199</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>66269</td>\n", " <td>34</td>\n", " <td>51.3</td>\n", " </tr>\n", " <tr>\n", " <th>2364</th>\n", " <td>Gonorrhea</td>\n", " <td>York County</td>\n", " <td>SC</td>\n", " <td>45091</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>239363</td>\n", " <td>311</td>\n", " <td>129.9</td>\n", " </tr>\n", " <tr>\n", " <th>1749</th>\n", " <td>Gonorrhea</td>\n", " <td>York County</td>\n", " <td>NE</td>\n", " <td>31185</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>13883</td>\n", " <td>5</td>\n", " <td>36.0</td>\n", " </tr>\n", " <tr>\n", " <th>1195</th>\n", " <td>Gonorrhea</td>\n", " <td>York County</td>\n", " <td>ME</td>\n", " <td>23031</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>199431</td>\n", " <td>35</td>\n", " <td>17.5</td>\n", " </tr>\n", " <tr>\n", " <th>2313</th>\n", " <td>Gonorrhea</td>\n", " <td>York County</td>\n", " <td>PA</td>\n", " <td>42133</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>438965</td>\n", " <td>294</td>\n", " <td>67.0</td>\n", " </tr>\n", " <tr>\n", " <th>2777</th>\n", " <td>Gonorrhea</td>\n", " <td>Young County</td>\n", " <td>TX</td>\n", " <td>48503</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>18341</td>\n", " <td>11</td>\n", " <td>60.0</td>\n", " </tr>\n", " <tr>\n", " <th>246</th>\n", " <td>Gonorrhea</td>\n", " <td>Yuba County</td>\n", " <td>CA</td>\n", " <td>6115</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>73340</td>\n", " <td>86</td>\n", " <td>117.3</td>\n", " </tr>\n", " <tr>\n", " <th>98</th>\n", " <td>Gonorrhea</td>\n", " <td>Yukon-Koyukuk Census Area</td>\n", " <td>AK</td>\n", " <td>2290</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>5695</td>\n", " <td>20</td>\n", " <td>351.2</td>\n", " </tr>\n", " <tr>\n", " <th>113</th>\n", " <td>Gonorrhea</td>\n", " <td>Yuma County</td>\n", " <td>AZ</td>\n", " <td>4027</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>201201</td>\n", " <td>124</td>\n", " <td>61.6</td>\n", " </tr>\n", " <tr>\n", " <th>310</th>\n", " <td>Gonorrhea</td>\n", " <td>Yuma County</td>\n", " <td>CO</td>\n", " <td>8125</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>10151</td>\n", " <td>0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2778</th>\n", " <td>Gonorrhea</td>\n", " <td>Zapata County</td>\n", " <td>TX</td>\n", " <td>48505</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>14390</td>\n", " <td>1</td>\n", " <td>6.9</td>\n", " </tr>\n", " <tr>\n", " <th>2779</th>\n", " <td>Gonorrhea</td>\n", " <td>Zavala County</td>\n", " <td>TX</td>\n", " <td>48507</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>12156</td>\n", " <td>5</td>\n", " <td>41.1</td>\n", " </tr>\n", " <tr>\n", " <th>2430</th>\n", " <td>Gonorrhea</td>\n", " <td>Ziebach County</td>\n", " <td>SD</td>\n", " <td>46137</td>\n", " <td>2014</td>\n", " <td>All races/ethnicities</td>\n", " <td>Both sexes</td>\n", " <td>All age groups</td>\n", " <td>All transmission categories</td>\n", " <td>2834</td>\n", " <td>11</td>\n", " <td>388.1</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3228 rows × 12 columns</p>\n", "</div>" ], "text/plain": [ " Disease Area State Abbreviation FIPS Year Race Sex Age group Transmission Category Population Cases Rate\n", "2319 Gonorrhea Abbeville County SC 45001 2014 All races/ethnicities Both sexes All age groups All transmission categories 25007 43 172.0\n", "1116 Gonorrhea Acadia Parish LA 22001 2014 All races/ethnicities Both sexes All age groups All transmission categories 62204 106 170.4\n", "2823 Gonorrhea Accomack County VA 51001 2014 All races/ethnicities Both sexes All age groups All transmission categories 33148 28 84.5\n", "554 Gonorrhea Ada County ID 16001 2014 All races/ethnicities Both sexes All age groups All transmission categories 416464 205 49.2\n", "996 Gonorrhea Adair County KY 21001 2014 All races/ethnicities Both sexes All age groups All transmission categories 18732 6 32.0\n", "1486 Gonorrhea Adair County MO 29001 2014 All races/ethnicities Both sexes All age groups All transmission categories 25572 8 31.3\n", "792 Gonorrhea Adair County IA 19001 2014 All races/ethnicities Both sexes All age groups All transmission categories 7472 2 26.8\n", "2134 Gonorrhea Adair County OK 40001 2014 All races/ethnicities Both sexes All age groups All transmission categories 22194 11 49.6\n", "1657 Gonorrhea Adams County NE 31001 2014 All races/ethnicities Both sexes All age groups All transmission categories 31610 8 25.3\n", "700 Gonorrhea Adams County IN 18001 2014 All races/ethnicities Both sexes All age groups All transmission categories 34614 6 17.3\n", "555 Gonorrhea Adams County ID 16003 2014 All races/ethnicities Both sexes All age groups All transmission categories 3828 0 0.0\n", "1404 Gonorrhea Adams County MS 28001 2014 All races/ethnicities Both sexes All age groups All transmission categories 32090 56 174.5\n", "793 Gonorrhea Adams County IA 19003 2014 All races/ethnicities Both sexes All age groups All transmission categories 3894 0 0.0\n", "598 Gonorrhea Adams County IL 17001 2014 All races/ethnicities Both sexes All age groups All transmission categories 67130 33 49.2\n", "2046 Gonorrhea Adams County OH 39001 2014 All races/ethnicities Both sexes All age groups All transmission categories 28105 2 7.1\n", "1993 Gonorrhea Adams County ND 38001 2014 All races/ethnicities Both sexes All age groups All transmission categories 2360 1 42.4\n", "247 Gonorrhea Adams County CO 8001 2014 All races/ethnicities Both sexes All age groups All transmission categories 469193 233 49.7\n", "3051 Gonorrhea Adams County WI 55001 2014 All races/ethnicities Both sexes All age groups All transmission categories 20480 3 14.6\n", "2957 Gonorrhea Adams County WA 53001 2014 All races/ethnicities Both sexes All age groups All transmission categories 19067 10 52.4\n", "2247 Gonorrhea Adams County PA 42001 2014 All races/ethnicities Both sexes All age groups All transmission categories 101546 18 17.7\n", "2809 Gonorrhea Addison County VT 50001 2014 All races/ethnicities Both sexes All age groups All transmission categories 36791 4 10.9\n", "3149 Gonorrhea Adjuntas Municipio PR 72001 2014 All races/ethnicities Both sexes All age groups All transmission categories 18900 2 10.6\n", "3150 Gonorrhea Aguada Municipio PR 72003 2014 All races/ethnicities Both sexes All age groups All transmission categories 40329 3 7.4\n", "3151 Gonorrhea Aguadilla Municipio PR 72005 2014 All races/ethnicities Both sexes All age groups All transmission categories 57290 7 12.2\n", "3152 Gonorrhea Aguas Buenas Municipio PR 72007 2014 All races/ethnicities Both sexes All age groups All transmission categories 27473 2 7.3\n", "3153 Gonorrhea Aibonito Municipio PR 72009 2014 All races/ethnicities Both sexes All age groups All transmission categories 24561 3 12.2\n", "2320 Gonorrhea Aiken County SC 45003 2014 All races/ethnicities Both sexes All age groups All transmission categories 164176 280 170.5\n", "1317 Gonorrhea Aitkin County MN 27001 2014 All races/ethnicities Both sexes All age groups All transmission categories 15742 5 31.8\n", "323 Gonorrhea Alachua County FL 12001 2014 All races/ethnicities Both sexes All age groups All transmission categories 253451 411 162.2\n", "1893 Gonorrhea Alamance County NC 37001 2014 All races/ethnicities Both sexes All age groups All transmission categories 154378 262 169.7\n", "... ... ... ... ... ... ... ... ... ... ... ... ...\n", "3225 Gonorrhea Yabucoa Municipio PR 72151 2014 All races/ethnicities Both sexes All age groups All transmission categories 35879 3 8.4\n", "1991 Gonorrhea Yadkin County NC 37197 2014 All races/ethnicities Both sexes All age groups All transmission categories 38038 13 34.2\n", "2995 Gonorrhea Yakima County WA 53077 2014 All races/ethnicities Both sexes All age groups All transmission categories 247044 409 165.6\n", "97 Gonorrhea Yakutat City and Borough AK 2282 2014 All races/ethnicities Both sexes All age groups All transmission categories 642 0 0.0\n", "1484 Gonorrhea Yalobusha County MS 28161 2014 All races/ethnicities Both sexes All age groups All transmission categories 12373 18 145.5\n", "2246 Gonorrhea Yamhill County OR 41071 2014 All races/ethnicities Both sexes All age groups All transmission categories 100725 18 17.9\n", "1992 Gonorrhea Yancey County NC 37199 2014 All races/ethnicities Both sexes All age groups All transmission categories 17566 2 11.4\n", "2429 Gonorrhea Yankton County SD 46135 2014 All races/ethnicities Both sexes All age groups All transmission categories 22696 8 35.2\n", "1892 Gonorrhea Yates County NY 36123 2014 All races/ethnicities Both sexes All age groups All transmission categories 25156 2 8.0\n", "3226 Gonorrhea Yauco Municipio PR 72153 2014 All races/ethnicities Both sexes All age groups All transmission categories 38782 0 0.0\n", "112 Gonorrhea Yavapai County AZ 4025 2014 All races/ethnicities Both sexes All age groups All transmission categories 215133 51 23.7\n", "1485 Gonorrhea Yazoo County MS 28163 2014 All races/ethnicities Both sexes All age groups All transmission categories 27883 103 369.4\n", "188 Gonorrhea Yell County AR 5149 2014 All races/ethnicities Both sexes All age groups All transmission categories 21893 8 36.5\n", "1403 Gonorrhea Yellow Medicine County MN 27173 2014 All races/ethnicities Both sexes All age groups All transmission categories 10143 1 9.9\n", "1656 Gonorrhea Yellowstone County MT 30111 2014 All races/ethnicities Both sexes All age groups All transmission categories 154162 101 65.5\n", "2776 Gonorrhea Yoakum County TX 48501 2014 All races/ethnicities Both sexes All age groups All transmission categories 8184 5 61.1\n", "245 Gonorrhea Yolo County CA 6113 2014 All races/ethnicities Both sexes All age groups All transmission categories 204593 194 94.8\n", "2917 Gonorrhea York County VA 51199 2014 All races/ethnicities Both sexes All age groups All transmission categories 66269 34 51.3\n", "2364 Gonorrhea York County SC 45091 2014 All races/ethnicities Both sexes All age groups All transmission categories 239363 311 129.9\n", "1749 Gonorrhea York County NE 31185 2014 All races/ethnicities Both sexes All age groups All transmission categories 13883 5 36.0\n", "1195 Gonorrhea York County ME 23031 2014 All races/ethnicities Both sexes All age groups All transmission categories 199431 35 17.5\n", "2313 Gonorrhea York County PA 42133 2014 All races/ethnicities Both sexes All age groups All transmission categories 438965 294 67.0\n", "2777 Gonorrhea Young County TX 48503 2014 All races/ethnicities Both sexes All age groups All transmission categories 18341 11 60.0\n", "246 Gonorrhea Yuba County CA 6115 2014 All races/ethnicities Both sexes All age groups All transmission categories 73340 86 117.3\n", "98 Gonorrhea Yukon-Koyukuk Census Area AK 2290 2014 All races/ethnicities Both sexes All age groups All transmission categories 5695 20 351.2\n", "113 Gonorrhea Yuma County AZ 4027 2014 All races/ethnicities Both sexes All age groups All transmission categories 201201 124 61.6\n", "310 Gonorrhea Yuma County CO 8125 2014 All races/ethnicities Both sexes All age groups All transmission categories 10151 0 0.0\n", "2778 Gonorrhea Zapata County TX 48505 2014 All races/ethnicities Both sexes All age groups All transmission categories 14390 1 6.9\n", "2779 Gonorrhea Zavala County TX 48507 2014 All races/ethnicities Both sexes All age groups All transmission categories 12156 5 41.1\n", "2430 Gonorrhea Ziebach County SD 46137 2014 All races/ethnicities Both sexes All age groups All transmission categories 2834 11 388.1\n", "\n", "[3228 rows x 12 columns]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort_values(by='Area')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Washington County 30\n", "Jefferson County 25\n", "Franklin County 24\n", "Jackson County 23\n", "Lincoln County 23\n", "Madison County 19\n", "Montgomery County 18\n", "Clay County 18\n", "Marion County 17\n", "Monroe County 17\n", "Union County 17\n", "Wayne County 16\n", "Grant County 14\n", "Warren County 14\n", "Greene County 14\n", "Carroll County 13\n", "Clark County 12\n", "Marshall County 12\n", "Lee County 12\n", "Polk County 12\n", "Lake County 12\n", "Johnson County 12\n", "Adams County 12\n", "Douglas County 12\n", "Morgan County 11\n", "Scott County 11\n", "Calhoun County 11\n", "Crawford County 11\n", "Fayette County 11\n", "Lawrence County 11\n", " ..\n", "Dearborn County 1\n", "Harnett County 1\n", "Gentry County 1\n", "Prince Edward County 1\n", "Petersburg Census Area 1\n", "Loving County 1\n", "East Carroll Parish 1\n", "Ouachita County 1\n", "Alger County 1\n", "Pickaway County 1\n", "Rockland County 1\n", "Torrance County 1\n", "Union Parish 1\n", "Kimball County 1\n", "Vanderburgh County 1\n", "Aleutians East Borough 1\n", "Pima County 1\n", "Clarion County 1\n", "Wrangell City and Borough 1\n", "Reynolds County 1\n", "La Paz County 1\n", "Kosciusko County 1\n", "Natrona County 1\n", "Bremer County 1\n", "Galveston County 1\n", "Refugio County 1\n", "Estill County 1\n", "Nome Census Area 1\n", "Charlevoix County 1\n", "Schleicher County 1\n", "Name: Area, dtype: int64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Area'].value_counts()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().values.any()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "null_list = df[\"Population\"].isnull()\n", "not_null_list = [not i for i in null_list]\n", "df_clean = df[not_null_list].copy()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "null_list = df_clean[\"Rate\"].isnull()\n", "not_null_list = [not i for i in null_list]\n", "df_completely_clean = df_clean[not_null_list].copy()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_completely_clean[\"Rate\"].isnull().values.any()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model the Gonorrhea rate" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_merged = pd.read_csv(\"../data/chlamydia_cdc_census.csv\")" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "48.0" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_completely_clean[df_completely_clean[\"FIPS\"]==1001].Cases[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Replace the number of Chlamydia cases with the number of Gonorrhea cases" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#print(df_merged[df_merged[\"FIPS\"]==1001].Cases[0])\n", "#df_merged.set_value(1, \"FIPS\", 10)\n", "for county in df_merged[\"FIPS\"]:\n", " rowlist = df_merged[df_merged['FIPS'] == county].index.tolist()\n", " gonorrhea_cases = df_completely_clean[df_completely_clean[\"FIPS\"] == county].Cases.tolist()\n", " df_merged.set_value(rowlist[0], 'Cases', gonorrhea_cases[0])\n", "# print(df_merged[\"FIPS\"][rowlist[0]])\n", " #df_merged[df_merged[\"FIPS\"]==county]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>FIPS</th>\n", " <th>Population</th>\n", " <th>Cases</th>\n", " <th>hd01s001</th>\n", " <th>hd02s002</th>\n", " <th>hd02s005</th>\n", " <th>hd02s006</th>\n", " <th>hd02s007</th>\n", " <th>hd02s008</th>\n", " <th>hd02s009</th>\n", " <th>hd02s010</th>\n", " <th>hd02s011</th>\n", " <th>hd02s013</th>\n", " <th>hd02s015</th>\n", " <th>hd01s020</th>\n", " <th>hd02s026</th>\n", " <th>hd02s051</th>\n", " <th>hd02s078</th>\n", " <th>hd02s079</th>\n", " <th>hd02s080</th>\n", " <th>hd02s081</th>\n", " <th>hd02s089</th>\n", " <th>hd02s095</th>\n", " <th>hd02s107</th>\n", " <th>hd02s131</th>\n", " <th>hd02s132</th>\n", " <th>hd02s133</th>\n", " <th>hd02s134</th>\n", " <th>hd02s135</th>\n", " <th>hd02s136</th>\n", " <th>hd02s143</th>\n", " <th>hd02s151</th>\n", " <th>hd02s152</th>\n", " <th>hd02s153</th>\n", " <th>hd02s154</th>\n", " <th>hd02s159</th>\n", " <th>hd01s167</th>\n", " <th>hd01s168</th>\n", " <th>hd02s181</th>\n", " <th>hd02s184</th>\n", " <th>hd01vd01</th>\n", " <th>d002</th>\n", " <th>d014</th>\n", " <th>d019</th>\n", " <th>d024</th>\n", " <th>d029</th>\n", " <th>lnd110210d</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1001</td>\n", " <td>55246</td>\n", " <td>48</td>\n", " <td>4.736962</td>\n", " <td>21.8</td>\n", " <td>7.9</td>\n", " <td>5.6</td>\n", " <td>5.8</td>\n", " <td>6.1</td>\n", " <td>7.6</td>\n", " <td>7.5</td>\n", " <td>15.0</td>\n", " <td>10.7</td>\n", " <td>12.0</td>\n", " <td>37.0</td>\n", " <td>48.7</td>\n", " <td>51.3</td>\n", " <td>78.5</td>\n", " <td>17.7</td>\n", " <td>0.4</td>\n", " <td>0.9</td>\n", " <td>0.1</td>\n", " <td>1.6</td>\n", " <td>2.4</td>\n", " <td>99.2</td>\n", " <td>37.1</td>\n", " <td>20.8</td>\n", " <td>31.8</td>\n", " <td>23.6</td>\n", " <td>6.1</td>\n", " <td>0.8</td>\n", " <td>74.5</td>\n", " <td>34.9</td>\n", " <td>56.2</td>\n", " <td>25.3</td>\n", " <td>25.5</td>\n", " <td>2.68</td>\n", " <td>3.13</td>\n", " <td>75.4</td>\n", " <td>24.6</td>\n", " <td>52475</td>\n", " <td>0.562138</td>\n", " <td>0.003017</td>\n", " <td>0.020029</td>\n", " <td>0.002868</td>\n", " <td>0.017704</td>\n", " <td>92.781808</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1003</td>\n", " <td>195540</td>\n", " <td>153</td>\n", " <td>5.260703</td>\n", " <td>19.0</td>\n", " <td>6.4</td>\n", " <td>5.2</td>\n", " <td>5.6</td>\n", " <td>5.9</td>\n", " <td>6.3</td>\n", " <td>6.6</td>\n", " <td>14.8</td>\n", " <td>13.5</td>\n", " <td>16.9</td>\n", " <td>41.1</td>\n", " <td>48.9</td>\n", " <td>51.1</td>\n", " <td>85.7</td>\n", " <td>9.4</td>\n", " <td>0.7</td>\n", " <td>0.7</td>\n", " <td>0.0</td>\n", " <td>1.5</td>\n", " <td>4.4</td>\n", " <td>98.7</td>\n", " <td>40.2</td>\n", " <td>21.9</td>\n", " <td>26.8</td>\n", " <td>20.1</td>\n", " <td>5.6</td>\n", " <td>1.3</td>\n", " <td>69.9</td>\n", " <td>28.0</td>\n", " <td>54.5</td>\n", " <td>19.9</td>\n", " <td>30.1</td>\n", " <td>2.46</td>\n", " <td>2.93</td>\n", " <td>72.5</td>\n", " <td>27.5</td>\n", " <td>50183</td>\n", " <td>0.545409</td>\n", " <td>0.002747</td>\n", " <td>0.023886</td>\n", " <td>0.003444</td>\n", " <td>0.020292</td>\n", " <td>122.920831</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1005</td>\n", " <td>27076</td>\n", " <td>52</td>\n", " <td>4.438653</td>\n", " <td>18.0</td>\n", " <td>6.3</td>\n", " <td>6.5</td>\n", " <td>7.3</td>\n", " <td>6.6</td>\n", " <td>6.6</td>\n", " <td>6.6</td>\n", " <td>14.7</td>\n", " <td>13.2</td>\n", " <td>14.3</td>\n", " <td>39.0</td>\n", " <td>53.1</td>\n", " <td>46.9</td>\n", " <td>48.0</td>\n", " <td>46.9</td>\n", " <td>0.4</td>\n", " <td>0.4</td>\n", " <td>0.1</td>\n", " <td>0.9</td>\n", " <td>5.1</td>\n", " <td>88.4</td>\n", " <td>35.8</td>\n", " <td>15.6</td>\n", " <td>25.7</td>\n", " <td>17.7</td>\n", " <td>7.8</td>\n", " <td>11.6</td>\n", " <td>68.4</td>\n", " <td>27.4</td>\n", " <td>43.7</td>\n", " <td>14.4</td>\n", " <td>31.6</td>\n", " <td>2.47</td>\n", " <td>3.01</td>\n", " <td>66.8</td>\n", " <td>33.2</td>\n", " <td>35634</td>\n", " <td>0.437169</td>\n", " <td>0.002342</td>\n", " <td>0.019348</td>\n", " <td>0.003666</td>\n", " <td>0.022200</td>\n", " <td>30.563959</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1007</td>\n", " <td>22512</td>\n", " <td>22</td>\n", " <td>4.360120</td>\n", " <td>18.4</td>\n", " <td>6.7</td>\n", " <td>6.5</td>\n", " <td>7.0</td>\n", " <td>7.2</td>\n", " <td>7.6</td>\n", " <td>7.1</td>\n", " <td>14.8</td>\n", " <td>11.9</td>\n", " <td>12.6</td>\n", " <td>37.8</td>\n", " <td>53.7</td>\n", " <td>46.3</td>\n", " <td>75.8</td>\n", " <td>22.0</td>\n", " <td>0.3</td>\n", " <td>0.1</td>\n", " <td>0.1</td>\n", " <td>0.9</td>\n", " <td>1.8</td>\n", " <td>90.3</td>\n", " <td>34.7</td>\n", " <td>18.2</td>\n", " <td>26.8</td>\n", " <td>18.7</td>\n", " <td>7.5</td>\n", " <td>9.7</td>\n", " <td>72.3</td>\n", " <td>29.5</td>\n", " <td>52.5</td>\n", " <td>20.1</td>\n", " <td>27.7</td>\n", " <td>2.60</td>\n", " <td>3.09</td>\n", " <td>75.6</td>\n", " <td>24.4</td>\n", " <td>37984</td>\n", " <td>0.524582</td>\n", " <td>0.001886</td>\n", " <td>0.020244</td>\n", " <td>0.002012</td>\n", " <td>0.020370</td>\n", " <td>36.101222</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1009</td>\n", " <td>57872</td>\n", " <td>6</td>\n", " <td>4.758321</td>\n", " <td>20.2</td>\n", " <td>7.0</td>\n", " <td>5.4</td>\n", " <td>6.0</td>\n", " <td>6.0</td>\n", " <td>6.8</td>\n", " <td>7.0</td>\n", " <td>14.1</td>\n", " <td>12.6</td>\n", " <td>14.6</td>\n", " <td>39.0</td>\n", " <td>49.5</td>\n", " <td>50.5</td>\n", " <td>92.6</td>\n", " <td>1.3</td>\n", " <td>0.5</td>\n", " <td>0.2</td>\n", " <td>0.1</td>\n", " <td>1.2</td>\n", " <td>8.1</td>\n", " <td>99.1</td>\n", " <td>37.6</td>\n", " <td>22.8</td>\n", " <td>29.2</td>\n", " <td>21.3</td>\n", " <td>6.4</td>\n", " <td>0.9</td>\n", " <td>75.0</td>\n", " <td>31.1</td>\n", " <td>60.6</td>\n", " <td>24.1</td>\n", " <td>25.0</td>\n", " <td>2.63</td>\n", " <td>3.07</td>\n", " <td>80.6</td>\n", " <td>19.4</td>\n", " <td>44409</td>\n", " <td>0.606034</td>\n", " <td>0.001946</td>\n", " <td>0.017981</td>\n", " <td>0.003707</td>\n", " <td>0.013440</td>\n", " <td>89.615659</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " FIPS Population Cases hd01s001 hd02s002 hd02s005 hd02s006 hd02s007 hd02s008 hd02s009 hd02s010 hd02s011 hd02s013 hd02s015 hd01s020 hd02s026 hd02s051 hd02s078 hd02s079 hd02s080 hd02s081 hd02s089 hd02s095 hd02s107 hd02s131 hd02s132 hd02s133 hd02s134 hd02s135 hd02s136 hd02s143 hd02s151 hd02s152 hd02s153 hd02s154 hd02s159 hd01s167 hd01s168 hd02s181 hd02s184 hd01vd01 d002 d014 d019 d024 d029 lnd110210d\n", "0 1001 55246 48 4.736962 21.8 7.9 5.6 5.8 6.1 7.6 7.5 15.0 10.7 12.0 37.0 48.7 51.3 78.5 17.7 0.4 0.9 0.1 1.6 2.4 99.2 37.1 20.8 31.8 23.6 6.1 0.8 74.5 34.9 56.2 25.3 25.5 2.68 3.13 75.4 24.6 52475 0.562138 0.003017 0.020029 0.002868 0.017704 92.781808\n", "1 1003 195540 153 5.260703 19.0 6.4 5.2 5.6 5.9 6.3 6.6 14.8 13.5 16.9 41.1 48.9 51.1 85.7 9.4 0.7 0.7 0.0 1.5 4.4 98.7 40.2 21.9 26.8 20.1 5.6 1.3 69.9 28.0 54.5 19.9 30.1 2.46 2.93 72.5 27.5 50183 0.545409 0.002747 0.023886 0.003444 0.020292 122.920831\n", "2 1005 27076 52 4.438653 18.0 6.3 6.5 7.3 6.6 6.6 6.6 14.7 13.2 14.3 39.0 53.1 46.9 48.0 46.9 0.4 0.4 0.1 0.9 5.1 88.4 35.8 15.6 25.7 17.7 7.8 11.6 68.4 27.4 43.7 14.4 31.6 2.47 3.01 66.8 33.2 35634 0.437169 0.002342 0.019348 0.003666 0.022200 30.563959\n", "3 1007 22512 22 4.360120 18.4 6.7 6.5 7.0 7.2 7.6 7.1 14.8 11.9 12.6 37.8 53.7 46.3 75.8 22.0 0.3 0.1 0.1 0.9 1.8 90.3 34.7 18.2 26.8 18.7 7.5 9.7 72.3 29.5 52.5 20.1 27.7 2.60 3.09 75.6 24.4 37984 0.524582 0.001886 0.020244 0.002012 0.020370 36.101222\n", "4 1009 57872 6 4.758321 20.2 7.0 5.4 6.0 6.0 6.8 7.0 14.1 12.6 14.6 39.0 49.5 50.5 92.6 1.3 0.5 0.2 0.1 1.2 8.1 99.1 37.6 22.8 29.2 21.3 6.4 0.9 75.0 31.1 60.6 24.1 25.0 2.63 3.07 80.6 19.4 44409 0.606034 0.001946 0.017981 0.003707 0.013440 89.615659" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_merged.head()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>FIPS</th>\n", " <th>Population</th>\n", " <th>Cases</th>\n", " <th>hd01s001</th>\n", " <th>hd02s002</th>\n", " <th>hd02s005</th>\n", " <th>hd02s006</th>\n", " <th>hd02s007</th>\n", " <th>hd02s008</th>\n", " <th>hd02s009</th>\n", " <th>hd02s010</th>\n", " <th>hd02s011</th>\n", " <th>hd02s013</th>\n", " <th>hd02s015</th>\n", " <th>hd01s020</th>\n", " <th>hd02s026</th>\n", " <th>hd02s051</th>\n", " <th>hd02s078</th>\n", " <th>hd02s079</th>\n", " <th>hd02s080</th>\n", " <th>hd02s081</th>\n", " <th>hd02s089</th>\n", " <th>hd02s095</th>\n", " <th>hd02s107</th>\n", " <th>hd02s131</th>\n", " <th>hd02s132</th>\n", " <th>hd02s133</th>\n", " <th>hd02s134</th>\n", " <th>hd02s135</th>\n", " <th>hd02s136</th>\n", " <th>hd02s143</th>\n", " <th>hd02s151</th>\n", " <th>hd02s152</th>\n", " <th>hd02s153</th>\n", " <th>hd02s154</th>\n", " <th>hd02s159</th>\n", " <th>hd01s167</th>\n", " <th>hd01s168</th>\n", " <th>hd02s181</th>\n", " <th>hd02s184</th>\n", " <th>hd01vd01</th>\n", " <th>d002</th>\n", " <th>d014</th>\n", " <th>d019</th>\n", " <th>d024</th>\n", " <th>d029</th>\n", " <th>lnd110210d</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>30492.802614</td>\n", " <td>100423.684967</td>\n", " <td>103.278758</td>\n", " <td>4.460522</td>\n", " <td>19.163007</td>\n", " <td>6.908072</td>\n", " <td>5.977222</td>\n", " <td>5.819314</td>\n", " <td>5.695294</td>\n", " <td>5.899739</td>\n", " <td>6.297484</td>\n", " <td>15.002190</td>\n", " <td>13.250327</td>\n", " <td>15.985556</td>\n", " <td>40.491667</td>\n", " <td>50.003856</td>\n", " <td>49.996144</td>\n", " <td>84.076340</td>\n", " <td>8.121307</td>\n", " <td>1.550719</td>\n", " <td>1.165033</td>\n", " <td>0.082418</td>\n", " <td>1.966765</td>\n", " <td>8.417353</td>\n", " <td>96.604379</td>\n", " <td>39.254118</td>\n", " <td>20.403791</td>\n", " <td>27.047647</td>\n", " <td>20.409281</td>\n", " <td>5.134248</td>\n", " <td>3.395654</td>\n", " <td>67.828627</td>\n", " <td>27.814542</td>\n", " <td>52.057712</td>\n", " <td>19.173268</td>\n", " <td>32.171438</td>\n", " <td>2.476042</td>\n", " <td>2.988451</td>\n", " <td>72.576307</td>\n", " <td>27.423791</td>\n", " <td>46800.265359</td>\n", " <td>0.520577</td>\n", " <td>0.002492</td>\n", " <td>0.030613</td>\n", " <td>0.003221</td>\n", " <td>0.024522</td>\n", " <td>239.640924</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>15071.256920</td>\n", " <td>323873.098080</td>\n", " <td>492.184847</td>\n", " <td>0.634640</td>\n", " <td>2.846254</td>\n", " <td>1.135197</td>\n", " <td>2.546279</td>\n", " <td>1.223101</td>\n", " <td>0.950620</td>\n", " <td>0.878120</td>\n", " <td>0.830619</td>\n", " <td>1.506377</td>\n", " <td>2.129781</td>\n", " <td>4.166765</td>\n", " <td>4.959052</td>\n", " <td>2.204346</td>\n", " <td>2.204346</td>\n", " <td>15.045667</td>\n", " <td>13.038226</td>\n", " <td>5.071524</td>\n", " <td>2.533382</td>\n", " <td>0.967483</td>\n", " <td>1.549707</td>\n", " <td>13.303866</td>\n", " <td>4.451890</td>\n", " <td>3.484761</td>\n", " <td>2.711350</td>\n", " <td>3.484574</td>\n", " <td>3.001770</td>\n", " <td>2.163983</td>\n", " <td>4.451875</td>\n", " <td>4.990887</td>\n", " <td>4.766401</td>\n", " <td>5.948292</td>\n", " <td>4.289568</td>\n", " <td>4.990872</td>\n", " <td>0.203247</td>\n", " <td>0.181387</td>\n", " <td>7.350396</td>\n", " <td>7.350283</td>\n", " <td>12025.134705</td>\n", " <td>0.059487</td>\n", " <td>0.001291</td>\n", " <td>0.007625</td>\n", " <td>0.001295</td>\n", " <td>0.007228</td>\n", " <td>1610.657866</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1001.000000</td>\n", " <td>90.000000</td>\n", " <td>0.000000</td>\n", " <td>1.913814</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.300000</td>\n", " <td>2.300000</td>\n", " <td>2.400000</td>\n", " <td>1.200000</td>\n", " <td>2.800000</td>\n", " <td>6.300000</td>\n", " <td>4.000000</td>\n", " <td>3.500000</td>\n", " <td>22.600000</td>\n", " <td>43.200000</td>\n", " <td>27.900000</td>\n", " <td>14.200000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.100000</td>\n", " <td>0.000000</td>\n", " <td>45.000000</td>\n", " <td>17.600000</td>\n", " <td>7.500000</td>\n", " <td>4.400000</td>\n", " <td>0.000000</td>\n", " <td>0.700000</td>\n", " <td>0.000000</td>\n", " <td>18.800000</td>\n", " <td>0.000000</td>\n", " <td>11.600000</td>\n", " <td>0.000000</td>\n", " <td>13.400000</td>\n", " <td>1.260000</td>\n", " <td>2.000000</td>\n", " <td>1.400000</td>\n", " <td>10.200000</td>\n", " <td>19146.000000</td>\n", " <td>0.115942</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.069674</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>19006.500000</td>\n", " <td>11208.000000</td>\n", " <td>2.000000</td>\n", " <td>4.051722</td>\n", " <td>17.400000</td>\n", " <td>6.300000</td>\n", " <td>4.700000</td>\n", " <td>5.100000</td>\n", " <td>5.100000</td>\n", " <td>5.400000</td>\n", " <td>5.800000</td>\n", " <td>14.200000</td>\n", " <td>12.000000</td>\n", " <td>13.300000</td>\n", " <td>37.700000</td>\n", " <td>48.900000</td>\n", " <td>49.600000</td>\n", " <td>76.575000</td>\n", " <td>0.500000</td>\n", " <td>0.200000</td>\n", " <td>0.300000</td>\n", " <td>0.000000</td>\n", " <td>1.100000</td>\n", " <td>1.600000</td>\n", " <td>96.300000</td>\n", " <td>37.400000</td>\n", " <td>18.800000</td>\n", " <td>25.000000</td>\n", " <td>18.600000</td>\n", " <td>3.500000</td>\n", " <td>1.100000</td>\n", " <td>65.200000</td>\n", " <td>25.000000</td>\n", " <td>48.800000</td>\n", " <td>16.500000</td>\n", " <td>29.300000</td>\n", " <td>2.350000</td>\n", " <td>2.880000</td>\n", " <td>69.200000</td>\n", " <td>22.600000</td>\n", " <td>38810.000000</td>\n", " <td>0.488003</td>\n", " <td>0.001797</td>\n", " <td>0.025435</td>\n", " <td>0.002469</td>\n", " <td>0.020005</td>\n", " <td>17.078483</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>29188.000000</td>\n", " <td>25987.000000</td>\n", " <td>10.000000</td>\n", " <td>4.414815</td>\n", " <td>19.100000</td>\n", " <td>6.800000</td>\n", " <td>5.500000</td>\n", " <td>5.600000</td>\n", " <td>5.600000</td>\n", " <td>5.900000</td>\n", " <td>6.300000</td>\n", " <td>15.100000</td>\n", " <td>13.100000</td>\n", " <td>15.700000</td>\n", " <td>40.500000</td>\n", " <td>49.500000</td>\n", " <td>50.500000</td>\n", " <td>89.450000</td>\n", " <td>1.900000</td>\n", " <td>0.400000</td>\n", " <td>0.500000</td>\n", " <td>0.000000</td>\n", " <td>1.600000</td>\n", " <td>3.300000</td>\n", " <td>98.200000</td>\n", " <td>39.500000</td>\n", " <td>20.700000</td>\n", " <td>27.100000</td>\n", " <td>20.400000</td>\n", " <td>4.900000</td>\n", " <td>1.800000</td>\n", " <td>68.100000</td>\n", " <td>27.450000</td>\n", " <td>52.500000</td>\n", " <td>18.700000</td>\n", " <td>31.900000</td>\n", " <td>2.450000</td>\n", " <td>2.970000</td>\n", " <td>73.800000</td>\n", " <td>26.200000</td>\n", " <td>45009.500000</td>\n", " <td>0.524803</td>\n", " <td>0.002367</td>\n", " <td>0.030222</td>\n", " <td>0.003160</td>\n", " <td>0.024365</td>\n", " <td>45.094109</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>45085.500000</td>\n", " <td>67582.250000</td>\n", " <td>43.000000</td>\n", " <td>4.825413</td>\n", " <td>20.700000</td>\n", " <td>7.300000</td>\n", " <td>6.500000</td>\n", " <td>6.400000</td>\n", " <td>6.200000</td>\n", " <td>6.400000</td>\n", " <td>6.800000</td>\n", " <td>15.800000</td>\n", " <td>14.300000</td>\n", " <td>18.300000</td>\n", " <td>43.400000</td>\n", " <td>50.400000</td>\n", " <td>51.100000</td>\n", " <td>95.625000</td>\n", " <td>9.400000</td>\n", " <td>0.800000</td>\n", " <td>1.000000</td>\n", " <td>0.100000</td>\n", " <td>2.300000</td>\n", " <td>8.400000</td>\n", " <td>98.900000</td>\n", " <td>41.300000</td>\n", " <td>22.225000</td>\n", " <td>29.000000</td>\n", " <td>22.000000</td>\n", " <td>6.500000</td>\n", " <td>3.700000</td>\n", " <td>70.700000</td>\n", " <td>30.100000</td>\n", " <td>55.700000</td>\n", " <td>21.000000</td>\n", " <td>34.800000</td>\n", " <td>2.570000</td>\n", " <td>3.070000</td>\n", " <td>77.400000</td>\n", " <td>30.800000</td>\n", " <td>52013.500000</td>\n", " <td>0.556712</td>\n", " <td>0.002993</td>\n", " <td>0.035313</td>\n", " <td>0.003841</td>\n", " <td>0.028793</td>\n", " <td>114.277352</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>56045.000000</td>\n", " <td>10017068.000000</td>\n", " <td>15316.000000</td>\n", " <td>6.992050</td>\n", " <td>33.600000</td>\n", " <td>18.300000</td>\n", " <td>28.100000</td>\n", " <td>16.100000</td>\n", " <td>11.700000</td>\n", " <td>9.700000</td>\n", " <td>11.900000</td>\n", " <td>24.500000</td>\n", " <td>28.100000</td>\n", " <td>43.400000</td>\n", " <td>62.700000</td>\n", " <td>72.100000</td>\n", " <td>56.800000</td>\n", " <td>99.200000</td>\n", " <td>84.400000</td>\n", " <td>75.500000</td>\n", " <td>43.900000</td>\n", " <td>48.900000</td>\n", " <td>29.500000</td>\n", " <td>95.700000</td>\n", " <td>100.000000</td>\n", " <td>76.700000</td>\n", " <td>28.800000</td>\n", " <td>42.800000</td>\n", " <td>34.400000</td>\n", " <td>16.000000</td>\n", " <td>55.000000</td>\n", " <td>86.600000</td>\n", " <td>50.300000</td>\n", " <td>79.200000</td>\n", " <td>41.700000</td>\n", " <td>81.200000</td>\n", " <td>3.680000</td>\n", " <td>4.050000</td>\n", " <td>89.800000</td>\n", " <td>98.600000</td>\n", " <td>123966.000000</td>\n", " <td>0.792199</td>\n", " <td>0.022064</td>\n", " <td>0.080335</td>\n", " <td>0.017631</td>\n", " <td>0.077751</td>\n", " <td>68239.991607</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " FIPS Population Cases hd01s001 hd02s002 hd02s005 hd02s006 hd02s007 hd02s008 hd02s009 hd02s010 hd02s011 hd02s013 hd02s015 hd01s020 hd02s026 hd02s051 hd02s078 hd02s079 hd02s080 hd02s081 hd02s089 hd02s095 hd02s107 hd02s131 hd02s132 hd02s133 hd02s134 hd02s135 hd02s136 hd02s143 hd02s151 hd02s152 hd02s153 hd02s154 hd02s159 hd01s167 hd01s168 hd02s181 hd02s184 hd01vd01 d002 d014 d019 d024 d029 lnd110210d\n", "count 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000\n", "mean 30492.802614 100423.684967 103.278758 4.460522 19.163007 6.908072 5.977222 5.819314 5.695294 5.899739 6.297484 15.002190 13.250327 15.985556 40.491667 50.003856 49.996144 84.076340 8.121307 1.550719 1.165033 0.082418 1.966765 8.417353 96.604379 39.254118 20.403791 27.047647 20.409281 5.134248 3.395654 67.828627 27.814542 52.057712 19.173268 32.171438 2.476042 2.988451 72.576307 27.423791 46800.265359 0.520577 0.002492 0.030613 0.003221 0.024522 239.640924\n", "std 15071.256920 323873.098080 492.184847 0.634640 2.846254 1.135197 2.546279 1.223101 0.950620 0.878120 0.830619 1.506377 2.129781 4.166765 4.959052 2.204346 2.204346 15.045667 13.038226 5.071524 2.533382 0.967483 1.549707 13.303866 4.451890 3.484761 2.711350 3.484574 3.001770 2.163983 4.451875 4.990887 4.766401 5.948292 4.289568 4.990872 0.203247 0.181387 7.350396 7.350283 12025.134705 0.059487 0.001291 0.007625 0.001295 0.007228 1610.657866\n", "min 1001.000000 90.000000 0.000000 1.913814 0.000000 0.000000 1.300000 2.300000 2.400000 1.200000 2.800000 6.300000 4.000000 3.500000 22.600000 43.200000 27.900000 14.200000 0.000000 0.000000 0.000000 0.000000 0.100000 0.000000 45.000000 17.600000 7.500000 4.400000 0.000000 0.700000 0.000000 18.800000 0.000000 11.600000 0.000000 13.400000 1.260000 2.000000 1.400000 10.200000 19146.000000 0.115942 0.000000 0.000000 0.000000 0.000000 0.069674\n", "25% 19006.500000 11208.000000 2.000000 4.051722 17.400000 6.300000 4.700000 5.100000 5.100000 5.400000 5.800000 14.200000 12.000000 13.300000 37.700000 48.900000 49.600000 76.575000 0.500000 0.200000 0.300000 0.000000 1.100000 1.600000 96.300000 37.400000 18.800000 25.000000 18.600000 3.500000 1.100000 65.200000 25.000000 48.800000 16.500000 29.300000 2.350000 2.880000 69.200000 22.600000 38810.000000 0.488003 0.001797 0.025435 0.002469 0.020005 17.078483\n", "50% 29188.000000 25987.000000 10.000000 4.414815 19.100000 6.800000 5.500000 5.600000 5.600000 5.900000 6.300000 15.100000 13.100000 15.700000 40.500000 49.500000 50.500000 89.450000 1.900000 0.400000 0.500000 0.000000 1.600000 3.300000 98.200000 39.500000 20.700000 27.100000 20.400000 4.900000 1.800000 68.100000 27.450000 52.500000 18.700000 31.900000 2.450000 2.970000 73.800000 26.200000 45009.500000 0.524803 0.002367 0.030222 0.003160 0.024365 45.094109\n", "75% 45085.500000 67582.250000 43.000000 4.825413 20.700000 7.300000 6.500000 6.400000 6.200000 6.400000 6.800000 15.800000 14.300000 18.300000 43.400000 50.400000 51.100000 95.625000 9.400000 0.800000 1.000000 0.100000 2.300000 8.400000 98.900000 41.300000 22.225000 29.000000 22.000000 6.500000 3.700000 70.700000 30.100000 55.700000 21.000000 34.800000 2.570000 3.070000 77.400000 30.800000 52013.500000 0.556712 0.002993 0.035313 0.003841 0.028793 114.277352\n", "max 56045.000000 10017068.000000 15316.000000 6.992050 33.600000 18.300000 28.100000 16.100000 11.700000 9.700000 11.900000 24.500000 28.100000 43.400000 62.700000 72.100000 56.800000 99.200000 84.400000 75.500000 43.900000 48.900000 29.500000 95.700000 100.000000 76.700000 28.800000 42.800000 34.400000 16.000000 55.000000 86.600000 50.300000 79.200000 41.700000 81.200000 3.680000 4.050000 89.800000 98.600000 123966.000000 0.792199 0.022064 0.080335 0.017631 0.077751 68239.991607" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_merged.describe()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(3060, 47)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_merged.shape" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x10effb048>" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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Ql1E86bx81e8WM9RRQ/ux9+mtP4DbMURywUqSZ99B94lPiElfQOLM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\nSmNpYFUxe5iTE/96magBqTRWHCEyYQRtjRW0NpR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"text/plain": [ "<matplotlib.figure.Figure at 0x10effb5c0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(np.log10(df_merged[\"Population\"]), df_merged[\"Cases\"]/df_merged[\"Population\"])" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x10f053eb8>" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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0pmiITqtHb6S/vihoGdFr7ylAWqK7N+YtIJrNZjz40xc087eA94vSzsIS9HJz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\n6upqzJ8/HytXrkR6ejqysrKwYcMGhIeHIz8/3+9ykyZNQnZ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"text/plain": [ "<matplotlib.figure.Figure at 0x10f053198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(df_merged[\"FIPS\"], df_merged[\"Cases\"]/df_merged[\"Population\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot the correlation matrix" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_all = df_merged.copy()\n", "df_all[\"Rate\"] = df_all[\"Cases\"]/df_all[\"Population\"]\n", "corr = df_all.corr()" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "FIPS -0.074673\n", "Population 0.200290\n", "Cases 0.299517\n", "hd01s001 0.326293\n", "hd02s002 0.177230\n", "hd02s005 0.180993\n", "hd02s006 0.261563\n", "hd02s007 0.389383\n", "hd02s008 0.305542\n", "hd02s009 0.185169\n", "hd02s010 0.048447\n", "hd02s011 -0.264997\n", "hd02s013 -0.303107\n", "hd02s015 -0.312399\n", "hd01s020 -0.366802\n", "hd02s026 -0.156963\n", "hd02s051 0.156963\n", "hd02s078 -0.677478\n", "hd02s079 0.638579\n", "hd02s080 0.142858\n", "hd02s081 0.137539\n", "hd02s089 -0.004756\n", "hd02s095 0.167221\n", "hd02s107 0.086671\n", "hd02s131 -0.083829\n", "hd02s132 -0.214553\n", "hd02s133 -0.583597\n", "hd02s134 0.172910\n", "hd02s135 -0.016189\n", "hd02s136 0.539012\n", "hd02s143 0.083823\n", "hd02s151 -0.037727\n", "hd02s152 0.143635\n", "hd02s153 -0.523363\n", "hd02s154 -0.166232\n", "hd02s159 0.037720\n", "hd01s167 0.179159\n", "hd01s168 0.279396\n", "hd02s181 -0.419810\n", "hd02s184 0.419816\n", "hd01vd01 -0.149611\n", "d002 -0.523460\n", "d014 0.176792\n", "d019 -0.130580\n", "d024 0.159114\n", "d029 0.258884\n", "lnd110210d 0.144497\n", "Rate 1.000000\n", "Name: Rate, dtype: float64" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pearsonr = corr[\"Rate\"]\n", "pearsonr" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mask = np.zeros_like(corr, dtype=np.bool)\n", "mask[np.triu_indices_from(mask)] = True" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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W0O/NZjOBQICof9zj3HPP5ZZbbiE2NpZvfOMbR8QOOcxwBjZU9jjQ3trays6d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\nI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"text/plain": [ "<matplotlib.figure.Figure at 0x10ec362e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Set up the matplotlib figure\n", "f, ax = plt.subplots(figsize=(11, 9))\n", "\n", "# Generate a custom diverging colormap\n", "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n", "\n", "# Draw the heatmap with the mask and correct aspect ratio\n", "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3,\n", " square=True, xticklabels=2, yticklabels=2,\n", " linewidths=.5, cbar_kws={\"shrink\": .5}, ax=ax)\n", "\n", "plt.savefig('../graphics/cross-correlation_gonorrhea.png', bbox_inches='tight', dpi=150)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make principal component analysis" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.decomposition import PCA" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>FIPS</th>\n", " <th>Population</th>\n", " <th>Cases</th>\n", " <th>hd01s001</th>\n", " <th>hd02s002</th>\n", " <th>hd02s005</th>\n", " <th>hd02s006</th>\n", " <th>hd02s007</th>\n", " <th>hd02s008</th>\n", " <th>hd02s009</th>\n", " <th>hd02s010</th>\n", " <th>hd02s011</th>\n", " <th>hd02s013</th>\n", " <th>hd02s015</th>\n", " <th>hd01s020</th>\n", " <th>hd02s026</th>\n", " <th>hd02s051</th>\n", " <th>hd02s078</th>\n", " <th>hd02s079</th>\n", " <th>hd02s080</th>\n", " <th>hd02s081</th>\n", " <th>hd02s089</th>\n", " <th>hd02s095</th>\n", " <th>hd02s107</th>\n", " <th>hd02s131</th>\n", " <th>hd02s132</th>\n", " <th>hd02s133</th>\n", " <th>hd02s134</th>\n", " <th>hd02s135</th>\n", " <th>hd02s136</th>\n", " <th>hd02s143</th>\n", " <th>hd02s151</th>\n", " <th>hd02s152</th>\n", " <th>hd02s153</th>\n", " <th>hd02s154</th>\n", " <th>hd02s159</th>\n", " <th>hd01s167</th>\n", " <th>hd01s168</th>\n", " <th>hd02s181</th>\n", " <th>hd02s184</th>\n", " <th>hd01vd01</th>\n", " <th>d002</th>\n", " <th>d014</th>\n", " <th>d019</th>\n", " <th>d024</th>\n", " <th>d029</th>\n", " <th>lnd110210d</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " <td>3060.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>30492.802614</td>\n", " <td>100423.684967</td>\n", " <td>103.278758</td>\n", " <td>4.460522</td>\n", " <td>19.163007</td>\n", " <td>6.908072</td>\n", " <td>5.977222</td>\n", " <td>5.819314</td>\n", " <td>5.695294</td>\n", " <td>5.899739</td>\n", " <td>6.297484</td>\n", " <td>15.002190</td>\n", " <td>13.250327</td>\n", " <td>15.985556</td>\n", " <td>40.491667</td>\n", " <td>50.003856</td>\n", " <td>49.996144</td>\n", " <td>84.076340</td>\n", " <td>8.121307</td>\n", " <td>1.550719</td>\n", " <td>1.165033</td>\n", " <td>0.082418</td>\n", " <td>1.966765</td>\n", " <td>8.417353</td>\n", " <td>96.604379</td>\n", " <td>39.254118</td>\n", " <td>20.403791</td>\n", " <td>27.047647</td>\n", " <td>20.409281</td>\n", " <td>5.134248</td>\n", " <td>3.395654</td>\n", " <td>67.828627</td>\n", " <td>27.814542</td>\n", " <td>52.057712</td>\n", " <td>19.173268</td>\n", " <td>32.171438</td>\n", " <td>2.476042</td>\n", " <td>2.988451</td>\n", " <td>72.576307</td>\n", " <td>27.423791</td>\n", " <td>46800.265359</td>\n", " <td>0.520577</td>\n", " <td>0.002492</td>\n", " <td>0.030613</td>\n", " <td>0.003221</td>\n", " <td>0.024522</td>\n", " <td>239.640924</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>15071.256920</td>\n", " <td>323873.098080</td>\n", " <td>492.184847</td>\n", " <td>0.634640</td>\n", " <td>2.846254</td>\n", " <td>1.135197</td>\n", " <td>2.546279</td>\n", " <td>1.223101</td>\n", " <td>0.950620</td>\n", " <td>0.878120</td>\n", " <td>0.830619</td>\n", " <td>1.506377</td>\n", " <td>2.129781</td>\n", " <td>4.166765</td>\n", " <td>4.959052</td>\n", " <td>2.204346</td>\n", " <td>2.204346</td>\n", " <td>15.045667</td>\n", " <td>13.038226</td>\n", " <td>5.071524</td>\n", " <td>2.533382</td>\n", " <td>0.967483</td>\n", " <td>1.549707</td>\n", " <td>13.303866</td>\n", " <td>4.451890</td>\n", " <td>3.484761</td>\n", " <td>2.711350</td>\n", " <td>3.484574</td>\n", " <td>3.001770</td>\n", " <td>2.163983</td>\n", " <td>4.451875</td>\n", " <td>4.990887</td>\n", " <td>4.766401</td>\n", " <td>5.948292</td>\n", " <td>4.289568</td>\n", " <td>4.990872</td>\n", " <td>0.203247</td>\n", " <td>0.181387</td>\n", " <td>7.350396</td>\n", " <td>7.350283</td>\n", " <td>12025.134705</td>\n", " <td>0.059487</td>\n", " <td>0.001291</td>\n", " <td>0.007625</td>\n", " <td>0.001295</td>\n", " <td>0.007228</td>\n", " <td>1610.657866</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>1001.000000</td>\n", " <td>90.000000</td>\n", " <td>0.000000</td>\n", " <td>1.913814</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.300000</td>\n", " <td>2.300000</td>\n", " <td>2.400000</td>\n", " <td>1.200000</td>\n", " <td>2.800000</td>\n", " <td>6.300000</td>\n", " <td>4.000000</td>\n", " <td>3.500000</td>\n", " <td>22.600000</td>\n", " <td>43.200000</td>\n", " <td>27.900000</td>\n", " <td>14.200000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.100000</td>\n", " <td>0.000000</td>\n", " <td>45.000000</td>\n", " <td>17.600000</td>\n", " <td>7.500000</td>\n", " <td>4.400000</td>\n", " <td>0.000000</td>\n", " <td>0.700000</td>\n", " <td>0.000000</td>\n", " <td>18.800000</td>\n", " <td>0.000000</td>\n", " <td>11.600000</td>\n", " <td>0.000000</td>\n", " <td>13.400000</td>\n", " <td>1.260000</td>\n", " <td>2.000000</td>\n", " <td>1.400000</td>\n", " <td>10.200000</td>\n", " <td>19146.000000</td>\n", " <td>0.115942</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.069674</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>19006.500000</td>\n", " <td>11208.000000</td>\n", " <td>2.000000</td>\n", " <td>4.051722</td>\n", " <td>17.400000</td>\n", " <td>6.300000</td>\n", " <td>4.700000</td>\n", " <td>5.100000</td>\n", " <td>5.100000</td>\n", " <td>5.400000</td>\n", " <td>5.800000</td>\n", " <td>14.200000</td>\n", " <td>12.000000</td>\n", " <td>13.300000</td>\n", " <td>37.700000</td>\n", " <td>48.900000</td>\n", " <td>49.600000</td>\n", " <td>76.575000</td>\n", " <td>0.500000</td>\n", " <td>0.200000</td>\n", " <td>0.300000</td>\n", " <td>0.000000</td>\n", " <td>1.100000</td>\n", " <td>1.600000</td>\n", " <td>96.300000</td>\n", " <td>37.400000</td>\n", " <td>18.800000</td>\n", " <td>25.000000</td>\n", " <td>18.600000</td>\n", " <td>3.500000</td>\n", " <td>1.100000</td>\n", " <td>65.200000</td>\n", " <td>25.000000</td>\n", " <td>48.800000</td>\n", " <td>16.500000</td>\n", " <td>29.300000</td>\n", " <td>2.350000</td>\n", " <td>2.880000</td>\n", " <td>69.200000</td>\n", " <td>22.600000</td>\n", " <td>38810.000000</td>\n", " <td>0.488003</td>\n", " <td>0.001797</td>\n", " <td>0.025435</td>\n", " <td>0.002469</td>\n", " <td>0.020005</td>\n", " <td>17.078483</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>29188.000000</td>\n", " <td>25987.000000</td>\n", " <td>10.000000</td>\n", " <td>4.414815</td>\n", " <td>19.100000</td>\n", " <td>6.800000</td>\n", " <td>5.500000</td>\n", " <td>5.600000</td>\n", " <td>5.600000</td>\n", " <td>5.900000</td>\n", " <td>6.300000</td>\n", " <td>15.100000</td>\n", " <td>13.100000</td>\n", " <td>15.700000</td>\n", " <td>40.500000</td>\n", " <td>49.500000</td>\n", " <td>50.500000</td>\n", " <td>89.450000</td>\n", " <td>1.900000</td>\n", " <td>0.400000</td>\n", " <td>0.500000</td>\n", " <td>0.000000</td>\n", " <td>1.600000</td>\n", " <td>3.300000</td>\n", " <td>98.200000</td>\n", " <td>39.500000</td>\n", " <td>20.700000</td>\n", " <td>27.100000</td>\n", " <td>20.400000</td>\n", " <td>4.900000</td>\n", " <td>1.800000</td>\n", " <td>68.100000</td>\n", " <td>27.450000</td>\n", " <td>52.500000</td>\n", " <td>18.700000</td>\n", " <td>31.900000</td>\n", " <td>2.450000</td>\n", " <td>2.970000</td>\n", " <td>73.800000</td>\n", " <td>26.200000</td>\n", " <td>45009.500000</td>\n", " <td>0.524803</td>\n", " <td>0.002367</td>\n", " <td>0.030222</td>\n", " <td>0.003160</td>\n", " <td>0.024365</td>\n", " <td>45.094109</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>45085.500000</td>\n", " <td>67582.250000</td>\n", " <td>43.000000</td>\n", " <td>4.825413</td>\n", " <td>20.700000</td>\n", " <td>7.300000</td>\n", " <td>6.500000</td>\n", " <td>6.400000</td>\n", " <td>6.200000</td>\n", " <td>6.400000</td>\n", " <td>6.800000</td>\n", " <td>15.800000</td>\n", " <td>14.300000</td>\n", " <td>18.300000</td>\n", " <td>43.400000</td>\n", " <td>50.400000</td>\n", " <td>51.100000</td>\n", " <td>95.625000</td>\n", " <td>9.400000</td>\n", " <td>0.800000</td>\n", " <td>1.000000</td>\n", " <td>0.100000</td>\n", " <td>2.300000</td>\n", " <td>8.400000</td>\n", " <td>98.900000</td>\n", " <td>41.300000</td>\n", " <td>22.225000</td>\n", " <td>29.000000</td>\n", " <td>22.000000</td>\n", " <td>6.500000</td>\n", " <td>3.700000</td>\n", " <td>70.700000</td>\n", " <td>30.100000</td>\n", " <td>55.700000</td>\n", " <td>21.000000</td>\n", " <td>34.800000</td>\n", " <td>2.570000</td>\n", " <td>3.070000</td>\n", " <td>77.400000</td>\n", " <td>30.800000</td>\n", " <td>52013.500000</td>\n", " <td>0.556712</td>\n", " <td>0.002993</td>\n", " <td>0.035313</td>\n", " <td>0.003841</td>\n", " <td>0.028793</td>\n", " <td>114.277352</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>56045.000000</td>\n", " <td>10017068.000000</td>\n", " <td>15316.000000</td>\n", " <td>6.992050</td>\n", " <td>33.600000</td>\n", " <td>18.300000</td>\n", " <td>28.100000</td>\n", " <td>16.100000</td>\n", " <td>11.700000</td>\n", " <td>9.700000</td>\n", " <td>11.900000</td>\n", " <td>24.500000</td>\n", " <td>28.100000</td>\n", " <td>43.400000</td>\n", " <td>62.700000</td>\n", " <td>72.100000</td>\n", " <td>56.800000</td>\n", " <td>99.200000</td>\n", " <td>84.400000</td>\n", " <td>75.500000</td>\n", " <td>43.900000</td>\n", " <td>48.900000</td>\n", " <td>29.500000</td>\n", " <td>95.700000</td>\n", " <td>100.000000</td>\n", " <td>76.700000</td>\n", " <td>28.800000</td>\n", " <td>42.800000</td>\n", " <td>34.400000</td>\n", " <td>16.000000</td>\n", " <td>55.000000</td>\n", " <td>86.600000</td>\n", " <td>50.300000</td>\n", " <td>79.200000</td>\n", " <td>41.700000</td>\n", " <td>81.200000</td>\n", " <td>3.680000</td>\n", " <td>4.050000</td>\n", " <td>89.800000</td>\n", " <td>98.600000</td>\n", " <td>123966.000000</td>\n", " <td>0.792199</td>\n", " <td>0.022064</td>\n", " <td>0.080335</td>\n", " <td>0.017631</td>\n", " <td>0.077751</td>\n", " <td>68239.991607</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " FIPS Population Cases hd01s001 hd02s002 hd02s005 hd02s006 hd02s007 hd02s008 hd02s009 hd02s010 hd02s011 hd02s013 hd02s015 hd01s020 hd02s026 hd02s051 hd02s078 hd02s079 hd02s080 hd02s081 hd02s089 hd02s095 hd02s107 hd02s131 hd02s132 hd02s133 hd02s134 hd02s135 hd02s136 hd02s143 hd02s151 hd02s152 hd02s153 hd02s154 hd02s159 hd01s167 hd01s168 hd02s181 hd02s184 hd01vd01 d002 d014 d019 d024 d029 lnd110210d\n", "count 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000 3060.000000\n", "mean 30492.802614 100423.684967 103.278758 4.460522 19.163007 6.908072 5.977222 5.819314 5.695294 5.899739 6.297484 15.002190 13.250327 15.985556 40.491667 50.003856 49.996144 84.076340 8.121307 1.550719 1.165033 0.082418 1.966765 8.417353 96.604379 39.254118 20.403791 27.047647 20.409281 5.134248 3.395654 67.828627 27.814542 52.057712 19.173268 32.171438 2.476042 2.988451 72.576307 27.423791 46800.265359 0.520577 0.002492 0.030613 0.003221 0.024522 239.640924\n", "std 15071.256920 323873.098080 492.184847 0.634640 2.846254 1.135197 2.546279 1.223101 0.950620 0.878120 0.830619 1.506377 2.129781 4.166765 4.959052 2.204346 2.204346 15.045667 13.038226 5.071524 2.533382 0.967483 1.549707 13.303866 4.451890 3.484761 2.711350 3.484574 3.001770 2.163983 4.451875 4.990887 4.766401 5.948292 4.289568 4.990872 0.203247 0.181387 7.350396 7.350283 12025.134705 0.059487 0.001291 0.007625 0.001295 0.007228 1610.657866\n", "min 1001.000000 90.000000 0.000000 1.913814 0.000000 0.000000 1.300000 2.300000 2.400000 1.200000 2.800000 6.300000 4.000000 3.500000 22.600000 43.200000 27.900000 14.200000 0.000000 0.000000 0.000000 0.000000 0.100000 0.000000 45.000000 17.600000 7.500000 4.400000 0.000000 0.700000 0.000000 18.800000 0.000000 11.600000 0.000000 13.400000 1.260000 2.000000 1.400000 10.200000 19146.000000 0.115942 0.000000 0.000000 0.000000 0.000000 0.069674\n", "25% 19006.500000 11208.000000 2.000000 4.051722 17.400000 6.300000 4.700000 5.100000 5.100000 5.400000 5.800000 14.200000 12.000000 13.300000 37.700000 48.900000 49.600000 76.575000 0.500000 0.200000 0.300000 0.000000 1.100000 1.600000 96.300000 37.400000 18.800000 25.000000 18.600000 3.500000 1.100000 65.200000 25.000000 48.800000 16.500000 29.300000 2.350000 2.880000 69.200000 22.600000 38810.000000 0.488003 0.001797 0.025435 0.002469 0.020005 17.078483\n", "50% 29188.000000 25987.000000 10.000000 4.414815 19.100000 6.800000 5.500000 5.600000 5.600000 5.900000 6.300000 15.100000 13.100000 15.700000 40.500000 49.500000 50.500000 89.450000 1.900000 0.400000 0.500000 0.000000 1.600000 3.300000 98.200000 39.500000 20.700000 27.100000 20.400000 4.900000 1.800000 68.100000 27.450000 52.500000 18.700000 31.900000 2.450000 2.970000 73.800000 26.200000 45009.500000 0.524803 0.002367 0.030222 0.003160 0.024365 45.094109\n", "75% 45085.500000 67582.250000 43.000000 4.825413 20.700000 7.300000 6.500000 6.400000 6.200000 6.400000 6.800000 15.800000 14.300000 18.300000 43.400000 50.400000 51.100000 95.625000 9.400000 0.800000 1.000000 0.100000 2.300000 8.400000 98.900000 41.300000 22.225000 29.000000 22.000000 6.500000 3.700000 70.700000 30.100000 55.700000 21.000000 34.800000 2.570000 3.070000 77.400000 30.800000 52013.500000 0.556712 0.002993 0.035313 0.003841 0.028793 114.277352\n", "max 56045.000000 10017068.000000 15316.000000 6.992050 33.600000 18.300000 28.100000 16.100000 11.700000 9.700000 11.900000 24.500000 28.100000 43.400000 62.700000 72.100000 56.800000 99.200000 84.400000 75.500000 43.900000 48.900000 29.500000 95.700000 100.000000 76.700000 28.800000 42.800000 34.400000 16.000000 55.000000 86.600000 50.300000 79.200000 41.700000 81.200000 3.680000 4.050000 89.800000 98.600000 123966.000000 0.792199 0.022064 0.080335 0.017631 0.077751 68239.991607" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_merged.describe()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1151f4518>]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x114e93780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_new = df_merged.drop([\"FIPS\",\"Cases\", \"d002\", \"hd02s051\", \"hd02s143\", \"hd02s159\", \"hd02s184\", \"hd01s001\"], axis=1)\n", "X = df_new.values\n", "\n", "columns = X.shape[1]\n", "for column in np.arange(columns):\n", " mean_temp = X[:,column].mean()\n", " std_temp = X[:,column].std()\n", " X[:,column] = (X[:,column]-mean_temp)/std_temp\n", "\n", "plt.plot((np.std(X, axis=0)))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(3060, 39)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.shape" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 2.94525564e-01, 1.64308375e-01, 7.98070500e-02,\n", " 6.79178134e-02, 6.47768303e-02, 4.95135898e-02,\n", " 4.30534407e-02, 3.07112910e-02, 2.94860465e-02,\n", " 2.71764298e-02, 2.12387110e-02, 1.65600184e-02,\n", " 1.57898743e-02, 1.35634732e-02, 1.22893006e-02,\n", " 1.20671080e-02, 8.77515194e-03, 8.08329827e-03,\n", " 6.89497673e-03, 5.86716605e-03, 4.85048055e-03,\n", " 4.22251140e-03, 3.79732597e-03, 3.26146886e-03,\n", " 2.58523806e-03, 2.24116473e-03, 1.79765723e-03,\n", " 1.48926992e-03, 1.15747422e-03, 9.54360190e-04,\n", " 3.18175827e-04, 2.56011170e-04, 1.86629727e-04,\n", " 1.76638055e-04, 1.58593900e-04, 7.52418315e-05,\n", " 4.39366076e-05, 1.35256555e-05, 8.78706740e-06])" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pca = PCA(n_components=39)\n", "pca.fit(X)\n", "pca.explained_variance_ratio_" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x115e925f8>" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x114e8ae10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(pca.components_[:,0], pca.components_[:,1])" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x115ef6630>" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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7XvQRJjqqPsQ73I8paSaK161OCWsrf4eAz4cxJpHUguC/2SrfR6c3Mmxvw9lW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\nABToIiIiBqBAFxERMQAFuoiIiAH8f8okSnQ6UfDPAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11523b160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "X_trans = pca.transform(X)\n", "Y = df_merged[\"Cases\"]/df_merged[\"Population\"]\n", "plt.scatter(X_trans[:,0],Y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear regression" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import linear_model" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x115227fd0>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x115e9f828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df_new[\"hd02s002\"].hist()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Population</th>\n", " <th>hd02s002</th>\n", " <th>hd02s005</th>\n", " <th>hd02s006</th>\n", " <th>hd02s007</th>\n", " <th>hd02s008</th>\n", " <th>hd02s009</th>\n", " <th>hd02s010</th>\n", " <th>hd02s011</th>\n", " <th>hd02s013</th>\n", " <th>hd02s015</th>\n", " <th>hd01s020</th>\n", " <th>hd02s026</th>\n", " <th>hd02s078</th>\n", " <th>hd02s079</th>\n", " <th>hd02s080</th>\n", " <th>hd02s081</th>\n", " <th>hd02s089</th>\n", " <th>hd02s095</th>\n", " <th>hd02s107</th>\n", " <th>hd02s131</th>\n", " <th>hd02s132</th>\n", " <th>hd02s133</th>\n", " <th>hd02s134</th>\n", " <th>hd02s135</th>\n", " <th>hd02s136</th>\n", " <th>hd02s151</th>\n", " <th>hd02s152</th>\n", " <th>hd02s153</th>\n", " <th>hd02s154</th>\n", " <th>hd01s167</th>\n", " <th>hd01s168</th>\n", " <th>hd02s181</th>\n", " <th>hd01vd01</th>\n", " <th>d014</th>\n", " <th>d019</th>\n", " <th>d024</th>\n", " <th>d029</th>\n", " <th>lnd110210d</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>55246</td>\n", " <td>21.8</td>\n", " <td>7.9</td>\n", " <td>5.6</td>\n", " <td>5.8</td>\n", " <td>6.1</td>\n", " <td>7.6</td>\n", " <td>7.5</td>\n", " <td>15.0</td>\n", " <td>10.7</td>\n", " <td>12.0</td>\n", " <td>37.0</td>\n", " <td>48.7</td>\n", " <td>78.5</td>\n", " <td>17.7</td>\n", " <td>0.4</td>\n", " <td>0.9</td>\n", " <td>0.1</td>\n", " <td>1.6</td>\n", " <td>2.4</td>\n", " <td>99.2</td>\n", " <td>37.1</td>\n", " <td>20.8</td>\n", " <td>31.8</td>\n", " <td>23.6</td>\n", " <td>6.1</td>\n", " <td>74.5</td>\n", " <td>34.9</td>\n", " <td>56.2</td>\n", " <td>25.3</td>\n", " <td>2.68</td>\n", " <td>3.13</td>\n", " <td>75.4</td>\n", " <td>52475</td>\n", " <td>0.003017</td>\n", " <td>0.020029</td>\n", " <td>0.002868</td>\n", " <td>0.017704</td>\n", " <td>92.781808</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>195540</td>\n", " <td>19.0</td>\n", " <td>6.4</td>\n", " <td>5.2</td>\n", " <td>5.6</td>\n", " <td>5.9</td>\n", " <td>6.3</td>\n", " <td>6.6</td>\n", " <td>14.8</td>\n", " <td>13.5</td>\n", " <td>16.9</td>\n", " <td>41.1</td>\n", " <td>48.9</td>\n", " <td>85.7</td>\n", " <td>9.4</td>\n", " <td>0.7</td>\n", " <td>0.7</td>\n", " <td>0.0</td>\n", " <td>1.5</td>\n", " <td>4.4</td>\n", " <td>98.7</td>\n", " <td>40.2</td>\n", " <td>21.9</td>\n", " <td>26.8</td>\n", " <td>20.1</td>\n", " <td>5.6</td>\n", " <td>69.9</td>\n", " <td>28.0</td>\n", " <td>54.5</td>\n", " <td>19.9</td>\n", " <td>2.46</td>\n", " <td>2.93</td>\n", " <td>72.5</td>\n", " <td>50183</td>\n", " <td>0.002747</td>\n", " <td>0.023886</td>\n", " <td>0.003444</td>\n", " <td>0.020292</td>\n", " <td>122.920831</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>27076</td>\n", " <td>18.0</td>\n", " <td>6.3</td>\n", " <td>6.5</td>\n", " <td>7.3</td>\n", " <td>6.6</td>\n", " <td>6.6</td>\n", " <td>6.6</td>\n", " <td>14.7</td>\n", " <td>13.2</td>\n", " <td>14.3</td>\n", " <td>39.0</td>\n", " <td>53.1</td>\n", " <td>48.0</td>\n", " <td>46.9</td>\n", " <td>0.4</td>\n", " <td>0.4</td>\n", " <td>0.1</td>\n", " <td>0.9</td>\n", " <td>5.1</td>\n", " <td>88.4</td>\n", " <td>35.8</td>\n", " <td>15.6</td>\n", " <td>25.7</td>\n", " <td>17.7</td>\n", " <td>7.8</td>\n", " <td>68.4</td>\n", " <td>27.4</td>\n", " <td>43.7</td>\n", " <td>14.4</td>\n", " <td>2.47</td>\n", " <td>3.01</td>\n", " <td>66.8</td>\n", " <td>35634</td>\n", " <td>0.002342</td>\n", " <td>0.019348</td>\n", " <td>0.003666</td>\n", " <td>0.022200</td>\n", " <td>30.563959</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>22512</td>\n", " <td>18.4</td>\n", " <td>6.7</td>\n", " <td>6.5</td>\n", " <td>7.0</td>\n", " <td>7.2</td>\n", " <td>7.6</td>\n", " <td>7.1</td>\n", " <td>14.8</td>\n", " <td>11.9</td>\n", " <td>12.6</td>\n", " <td>37.8</td>\n", " <td>53.7</td>\n", " <td>75.8</td>\n", " <td>22.0</td>\n", " <td>0.3</td>\n", " <td>0.1</td>\n", " <td>0.1</td>\n", " <td>0.9</td>\n", " <td>1.8</td>\n", " <td>90.3</td>\n", " <td>34.7</td>\n", " <td>18.2</td>\n", " <td>26.8</td>\n", " <td>18.7</td>\n", " <td>7.5</td>\n", " <td>72.3</td>\n", " <td>29.5</td>\n", " <td>52.5</td>\n", " <td>20.1</td>\n", " <td>2.60</td>\n", " <td>3.09</td>\n", " <td>75.6</td>\n", " <td>37984</td>\n", " <td>0.001886</td>\n", " <td>0.020244</td>\n", " <td>0.002012</td>\n", " <td>0.020370</td>\n", " <td>36.101222</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>57872</td>\n", " <td>20.2</td>\n", " <td>7.0</td>\n", " <td>5.4</td>\n", " <td>6.0</td>\n", " <td>6.0</td>\n", " <td>6.8</td>\n", " <td>7.0</td>\n", " <td>14.1</td>\n", " <td>12.6</td>\n", " <td>14.6</td>\n", " <td>39.0</td>\n", " <td>49.5</td>\n", " <td>92.6</td>\n", " <td>1.3</td>\n", " <td>0.5</td>\n", " <td>0.2</td>\n", " <td>0.1</td>\n", " <td>1.2</td>\n", " <td>8.1</td>\n", " <td>99.1</td>\n", " <td>37.6</td>\n", " <td>22.8</td>\n", " <td>29.2</td>\n", " <td>21.3</td>\n", " <td>6.4</td>\n", " <td>75.0</td>\n", " <td>31.1</td>\n", " <td>60.6</td>\n", " <td>24.1</td>\n", " <td>2.63</td>\n", " <td>3.07</td>\n", " <td>80.6</td>\n", " <td>44409</td>\n", " <td>0.001946</td>\n", " <td>0.017981</td>\n", " <td>0.003707</td>\n", " <td>0.013440</td>\n", " <td>89.615659</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Population hd02s002 hd02s005 hd02s006 hd02s007 hd02s008 hd02s009 hd02s010 hd02s011 hd02s013 hd02s015 hd01s020 hd02s026 hd02s078 hd02s079 hd02s080 hd02s081 hd02s089 hd02s095 hd02s107 hd02s131 hd02s132 hd02s133 hd02s134 hd02s135 hd02s136 hd02s151 hd02s152 hd02s153 hd02s154 hd01s167 hd01s168 hd02s181 hd01vd01 d014 d019 d024 d029 lnd110210d\n", "0 55246 21.8 7.9 5.6 5.8 6.1 7.6 7.5 15.0 10.7 12.0 37.0 48.7 78.5 17.7 0.4 0.9 0.1 1.6 2.4 99.2 37.1 20.8 31.8 23.6 6.1 74.5 34.9 56.2 25.3 2.68 3.13 75.4 52475 0.003017 0.020029 0.002868 0.017704 92.781808\n", "1 195540 19.0 6.4 5.2 5.6 5.9 6.3 6.6 14.8 13.5 16.9 41.1 48.9 85.7 9.4 0.7 0.7 0.0 1.5 4.4 98.7 40.2 21.9 26.8 20.1 5.6 69.9 28.0 54.5 19.9 2.46 2.93 72.5 50183 0.002747 0.023886 0.003444 0.020292 122.920831\n", "2 27076 18.0 6.3 6.5 7.3 6.6 6.6 6.6 14.7 13.2 14.3 39.0 53.1 48.0 46.9 0.4 0.4 0.1 0.9 5.1 88.4 35.8 15.6 25.7 17.7 7.8 68.4 27.4 43.7 14.4 2.47 3.01 66.8 35634 0.002342 0.019348 0.003666 0.022200 30.563959\n", "3 22512 18.4 6.7 6.5 7.0 7.2 7.6 7.1 14.8 11.9 12.6 37.8 53.7 75.8 22.0 0.3 0.1 0.1 0.9 1.8 90.3 34.7 18.2 26.8 18.7 7.5 72.3 29.5 52.5 20.1 2.60 3.09 75.6 37984 0.001886 0.020244 0.002012 0.020370 36.101222\n", "4 57872 20.2 7.0 5.4 6.0 6.0 6.8 7.0 14.1 12.6 14.6 39.0 49.5 92.6 1.3 0.5 0.2 0.1 1.2 8.1 99.1 37.6 22.8 29.2 21.3 6.4 75.0 31.1 60.6 24.1 2.63 3.07 80.6 44409 0.001946 0.017981 0.003707 0.013440 89.615659" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_new.head()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>FIPS</th>\n", " <th>Population</th>\n", " <th>Cases</th>\n", " <th>hd01s001</th>\n", " <th>hd02s002</th>\n", " <th>hd02s005</th>\n", " <th>hd02s006</th>\n", " <th>hd02s007</th>\n", " <th>hd02s008</th>\n", " <th>hd02s009</th>\n", " <th>hd02s010</th>\n", " <th>hd02s011</th>\n", " <th>hd02s013</th>\n", " <th>hd02s015</th>\n", " <th>hd01s020</th>\n", " <th>hd02s026</th>\n", " <th>hd02s051</th>\n", " <th>hd02s078</th>\n", " <th>hd02s079</th>\n", " <th>hd02s080</th>\n", " <th>hd02s081</th>\n", " <th>hd02s089</th>\n", " <th>hd02s095</th>\n", " <th>hd02s107</th>\n", " <th>hd02s131</th>\n", " <th>hd02s132</th>\n", " <th>hd02s133</th>\n", " <th>hd02s134</th>\n", " <th>hd02s135</th>\n", " <th>hd02s136</th>\n", " <th>hd02s143</th>\n", " <th>hd02s151</th>\n", " <th>hd02s152</th>\n", " <th>hd02s153</th>\n", " <th>hd02s154</th>\n", " <th>hd02s159</th>\n", " <th>hd01s167</th>\n", " <th>hd01s168</th>\n", " <th>hd02s181</th>\n", " <th>hd02s184</th>\n", " <th>hd01vd01</th>\n", " <th>d002</th>\n", " <th>d014</th>\n", " <th>d019</th>\n", " <th>d024</th>\n", " <th>d029</th>\n", " <th>lnd110210d</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1001</td>\n", " <td>55246</td>\n", " <td>48</td>\n", " <td>4.736962</td>\n", " <td>21.8</td>\n", " <td>7.9</td>\n", " <td>5.6</td>\n", " <td>5.8</td>\n", " <td>6.1</td>\n", " <td>7.6</td>\n", " <td>7.5</td>\n", " <td>15.0</td>\n", " <td>10.7</td>\n", " <td>12.0</td>\n", " <td>37.0</td>\n", " <td>48.7</td>\n", " <td>51.3</td>\n", " <td>78.5</td>\n", " <td>17.7</td>\n", " <td>0.4</td>\n", " <td>0.9</td>\n", " <td>0.1</td>\n", " <td>1.6</td>\n", " <td>2.4</td>\n", " <td>99.2</td>\n", " <td>37.1</td>\n", " <td>20.8</td>\n", " <td>31.8</td>\n", " <td>23.6</td>\n", " <td>6.1</td>\n", " <td>0.8</td>\n", " <td>74.5</td>\n", " <td>34.9</td>\n", " <td>56.2</td>\n", " <td>25.3</td>\n", " <td>25.5</td>\n", " <td>2.68</td>\n", " <td>3.13</td>\n", " <td>75.4</td>\n", " <td>24.6</td>\n", " <td>52475</td>\n", " <td>0.562138</td>\n", " <td>0.003017</td>\n", " <td>0.020029</td>\n", " <td>0.002868</td>\n", " <td>0.017704</td>\n", " <td>92.781808</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1003</td>\n", " <td>195540</td>\n", " <td>153</td>\n", " <td>5.260703</td>\n", " <td>19.0</td>\n", " <td>6.4</td>\n", " <td>5.2</td>\n", " <td>5.6</td>\n", " <td>5.9</td>\n", " <td>6.3</td>\n", " <td>6.6</td>\n", " <td>14.8</td>\n", " <td>13.5</td>\n", " <td>16.9</td>\n", " <td>41.1</td>\n", " <td>48.9</td>\n", " <td>51.1</td>\n", " <td>85.7</td>\n", " <td>9.4</td>\n", " <td>0.7</td>\n", " <td>0.7</td>\n", " <td>0.0</td>\n", " <td>1.5</td>\n", " <td>4.4</td>\n", " <td>98.7</td>\n", " <td>40.2</td>\n", " <td>21.9</td>\n", " <td>26.8</td>\n", " <td>20.1</td>\n", " <td>5.6</td>\n", " <td>1.3</td>\n", " <td>69.9</td>\n", " <td>28.0</td>\n", " <td>54.5</td>\n", " <td>19.9</td>\n", " <td>30.1</td>\n", " <td>2.46</td>\n", " <td>2.93</td>\n", " <td>72.5</td>\n", " <td>27.5</td>\n", " <td>50183</td>\n", " <td>0.545409</td>\n", " <td>0.002747</td>\n", " <td>0.023886</td>\n", " <td>0.003444</td>\n", " <td>0.020292</td>\n", " <td>122.920831</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1005</td>\n", " <td>27076</td>\n", " <td>52</td>\n", " <td>4.438653</td>\n", " <td>18.0</td>\n", " <td>6.3</td>\n", " <td>6.5</td>\n", " <td>7.3</td>\n", " <td>6.6</td>\n", " <td>6.6</td>\n", " <td>6.6</td>\n", " <td>14.7</td>\n", " <td>13.2</td>\n", " <td>14.3</td>\n", " <td>39.0</td>\n", " <td>53.1</td>\n", " <td>46.9</td>\n", " <td>48.0</td>\n", " <td>46.9</td>\n", " <td>0.4</td>\n", " <td>0.4</td>\n", " <td>0.1</td>\n", " <td>0.9</td>\n", " <td>5.1</td>\n", " <td>88.4</td>\n", " <td>35.8</td>\n", " <td>15.6</td>\n", " <td>25.7</td>\n", " <td>17.7</td>\n", " <td>7.8</td>\n", " <td>11.6</td>\n", " <td>68.4</td>\n", " <td>27.4</td>\n", " <td>43.7</td>\n", " <td>14.4</td>\n", " <td>31.6</td>\n", " <td>2.47</td>\n", " <td>3.01</td>\n", " <td>66.8</td>\n", " <td>33.2</td>\n", " <td>35634</td>\n", " <td>0.437169</td>\n", " <td>0.002342</td>\n", " <td>0.019348</td>\n", " <td>0.003666</td>\n", " <td>0.022200</td>\n", " <td>30.563959</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1007</td>\n", " <td>22512</td>\n", " <td>22</td>\n", " <td>4.360120</td>\n", " <td>18.4</td>\n", " <td>6.7</td>\n", " <td>6.5</td>\n", " <td>7.0</td>\n", " <td>7.2</td>\n", " <td>7.6</td>\n", " <td>7.1</td>\n", " <td>14.8</td>\n", " <td>11.9</td>\n", " <td>12.6</td>\n", " <td>37.8</td>\n", " <td>53.7</td>\n", " <td>46.3</td>\n", " <td>75.8</td>\n", " <td>22.0</td>\n", " <td>0.3</td>\n", " <td>0.1</td>\n", " <td>0.1</td>\n", " <td>0.9</td>\n", " <td>1.8</td>\n", " <td>90.3</td>\n", " <td>34.7</td>\n", " <td>18.2</td>\n", " <td>26.8</td>\n", " <td>18.7</td>\n", " <td>7.5</td>\n", " <td>9.7</td>\n", " <td>72.3</td>\n", " <td>29.5</td>\n", " <td>52.5</td>\n", " <td>20.1</td>\n", " <td>27.7</td>\n", " <td>2.60</td>\n", " <td>3.09</td>\n", " <td>75.6</td>\n", " <td>24.4</td>\n", " <td>37984</td>\n", " <td>0.524582</td>\n", " <td>0.001886</td>\n", " <td>0.020244</td>\n", " <td>0.002012</td>\n", " <td>0.020370</td>\n", " <td>36.101222</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1009</td>\n", " <td>57872</td>\n", " <td>6</td>\n", " <td>4.758321</td>\n", " <td>20.2</td>\n", " <td>7.0</td>\n", " <td>5.4</td>\n", " <td>6.0</td>\n", " <td>6.0</td>\n", " <td>6.8</td>\n", " <td>7.0</td>\n", " <td>14.1</td>\n", " <td>12.6</td>\n", " <td>14.6</td>\n", " <td>39.0</td>\n", " <td>49.5</td>\n", " <td>50.5</td>\n", " <td>92.6</td>\n", " <td>1.3</td>\n", " <td>0.5</td>\n", " <td>0.2</td>\n", " <td>0.1</td>\n", " <td>1.2</td>\n", " <td>8.1</td>\n", " <td>99.1</td>\n", " <td>37.6</td>\n", " <td>22.8</td>\n", " <td>29.2</td>\n", " <td>21.3</td>\n", " <td>6.4</td>\n", " <td>0.9</td>\n", " <td>75.0</td>\n", " <td>31.1</td>\n", " <td>60.6</td>\n", " <td>24.1</td>\n", " <td>25.0</td>\n", " <td>2.63</td>\n", " <td>3.07</td>\n", " <td>80.6</td>\n", " <td>19.4</td>\n", " <td>44409</td>\n", " <td>0.606034</td>\n", " <td>0.001946</td>\n", " <td>0.017981</td>\n", " <td>0.003707</td>\n", " <td>0.013440</td>\n", " <td>89.615659</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " FIPS Population Cases hd01s001 hd02s002 hd02s005 hd02s006 hd02s007 hd02s008 hd02s009 hd02s010 hd02s011 hd02s013 hd02s015 hd01s020 hd02s026 hd02s051 hd02s078 hd02s079 hd02s080 hd02s081 hd02s089 hd02s095 hd02s107 hd02s131 hd02s132 hd02s133 hd02s134 hd02s135 hd02s136 hd02s143 hd02s151 hd02s152 hd02s153 hd02s154 hd02s159 hd01s167 hd01s168 hd02s181 hd02s184 hd01vd01 d002 d014 d019 d024 d029 lnd110210d\n", "0 1001 55246 48 4.736962 21.8 7.9 5.6 5.8 6.1 7.6 7.5 15.0 10.7 12.0 37.0 48.7 51.3 78.5 17.7 0.4 0.9 0.1 1.6 2.4 99.2 37.1 20.8 31.8 23.6 6.1 0.8 74.5 34.9 56.2 25.3 25.5 2.68 3.13 75.4 24.6 52475 0.562138 0.003017 0.020029 0.002868 0.017704 92.781808\n", "1 1003 195540 153 5.260703 19.0 6.4 5.2 5.6 5.9 6.3 6.6 14.8 13.5 16.9 41.1 48.9 51.1 85.7 9.4 0.7 0.7 0.0 1.5 4.4 98.7 40.2 21.9 26.8 20.1 5.6 1.3 69.9 28.0 54.5 19.9 30.1 2.46 2.93 72.5 27.5 50183 0.545409 0.002747 0.023886 0.003444 0.020292 122.920831\n", "2 1005 27076 52 4.438653 18.0 6.3 6.5 7.3 6.6 6.6 6.6 14.7 13.2 14.3 39.0 53.1 46.9 48.0 46.9 0.4 0.4 0.1 0.9 5.1 88.4 35.8 15.6 25.7 17.7 7.8 11.6 68.4 27.4 43.7 14.4 31.6 2.47 3.01 66.8 33.2 35634 0.437169 0.002342 0.019348 0.003666 0.022200 30.563959\n", "3 1007 22512 22 4.360120 18.4 6.7 6.5 7.0 7.2 7.6 7.1 14.8 11.9 12.6 37.8 53.7 46.3 75.8 22.0 0.3 0.1 0.1 0.9 1.8 90.3 34.7 18.2 26.8 18.7 7.5 9.7 72.3 29.5 52.5 20.1 27.7 2.60 3.09 75.6 24.4 37984 0.524582 0.001886 0.020244 0.002012 0.020370 36.101222\n", "4 1009 57872 6 4.758321 20.2 7.0 5.4 6.0 6.0 6.8 7.0 14.1 12.6 14.6 39.0 49.5 50.5 92.6 1.3 0.5 0.2 0.1 1.2 8.1 99.1 37.6 22.8 29.2 21.3 6.4 0.9 75.0 31.1 60.6 24.1 25.0 2.63 3.07 80.6 19.4 44409 0.606034 0.001946 0.017981 0.003707 0.013440 89.615659" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_merged.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split data set into training/test and validation data" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((2400, 39), (1800, 39), (600, 39), (2400,), (1800,), (600,))" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cutoff = 1\n", "\n", "X = df_new[df_merged[\"Cases\"]>cutoff].values\n", "Y = df_merged[df_merged[\"Cases\"]>cutoff].Cases/(df_merged[df_merged[\"Cases\"]>cutoff].Population+1.0)\n", "\n", "X_full = df_new.values\n", "Y_full = df_merged.Cases/(df_merged.Population+1.0)\n", "\n", "#normalize all columns to the same normalization\n", "columns = X.shape[1]\n", "means = np.zeros(columns)\n", "stds = np.zeros(columns)\n", "for column in np.arange(columns):\n", " mean_temp = X[:,column].mean()\n", " std_temp = X[:,column].std()\n", " means[column] = mean_temp\n", " stds[column] = std_temp\n", " X[:,column] = (X[:,column]-mean_temp)/std_temp\n", " X_full[:,column] = (X_full[:,column]-mean_temp)/std_temp\n", "\n", "Ymean = Y_full.mean()\n", "Ystd = Y_full.std()\n", "\n", "Y = (Y-Ymean)/Ystd\n", "Y_full = (Y_full-Ymean)/Ystd\n", " \n", "ones = np.ones(round(0.75*len(X)), dtype=bool)\n", "zeros = np.zeros(len(X)-round(0.75*len(X)), dtype=bool)\n", "training_list = np.hstack((ones, zeros))\n", "np.random.shuffle(training_list)\n", "test_list = np.zeros(len(X),dtype=bool)\n", "test_list = np.array([not i for i in training_list])\n", "\n", "X_train = X[training_list]\n", "X_test = X[test_list]\n", "Y_train = Y[training_list]\n", "Y_test = Y[test_list]\n", "\n", "X.shape, X_train.shape, X_test.shape, Y.shape, Y_train.shape, Y_test.shape" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((2400, 39), (2400,))" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X.shape, Y.shape" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 600.000000\n", "mean 0.183267\n", "std 1.027183\n", "min -0.852713\n", "25% -0.517351\n", "50% -0.207258\n", "75% 0.565745\n", "max 6.344650\n", "dtype: float64" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Y_test.describe()" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#X_weights = df_merged.values\n", "#X_train_weights = X_weights[training_list]\n", "weights = 1 #X_train_weights[:,2]\n", "regr = linear_model.LinearRegression()\n", "regr.fit(X_train, Y_train, sample_weight=weights)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.06999576 -0.28355523 -0.23556967 -0.36628503 -0.20079447 -0.10982781\n", " -0.17001598 -0.14272377 -0.13889428 -0.38876025 -0.57277059 -0.35333905\n", " -0.09109317 -0.03240245 0.23917996 0.03822984 0.05814973 -0.07549794\n", " 0.06339112 0.00536592 0.1823018 0.63270391 -0.78847206 -0.85551799\n", " 0.26370773 -0.20390549 1.11361052 0.56902164 0.38174778 -1.08045281\n", " -1.05423933 1.08499758 -0.13462548 0.18570043 0.04571197 -0.09023666\n", " -0.01984907 -0.03387758 -0.08901632]\n" ] } ], "source": [ "print(regr.coef_)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.57055915646598554" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "1.0-np.sum((regr.predict(X_test)-Y_test)**2)/np.sum((Y_test-np.mean(Y_test))**2)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10f142588>]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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SHoCvU5THWC0mKo/uIjTuCgDSjmzHx30/Op+QZsfz8D31f/Zg1Hguri1zqU1t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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f142198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(Y,regr.predict(X))\n", "plt.plot(np.linspace(Y.min(),Y.max(),num=10),np.linspace(Y.min(),Y.max(),num=10),color='red')" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance score: 0.57056\t(0.59701)\n" ] } ], "source": [ "print('Variance score: %.5f\\t(%.5f)' % (regr.score(X_test, Y_test), regr.score(X_full, Y_full)))" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.51001046, 0.632968 , 0.6034146 , 0.60008855, 0.55867592])" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn import cross_validation\n", "cv = cross_validation.ShuffleSplit(len(Y_train), n_iter=5, test_size=0.2, random_state=0)\n", "scores_regression = cross_validation.cross_val_score(regr, X_train, Y_train, cv=cv)\n", "scores_regression\n", "#cross_val_score(regr, X_train, Y_train, cv=6, n_jobs=1)\n", "#scores" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.58 (+/- 0.09)\n" ] } ], "source": [ "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores_regression.mean(), scores_regression.std() * 2))" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import explained_variance_score" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.6014318615994938" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "explained_variance_score(Y_train, regr.predict(X_train))" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import mean_absolute_error" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.44396426675554196" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_absolute_error(Y_train, regr.predict(X_train))" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import mean_squared_error" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.41339284741867016" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean_squared_error(Y_train, regr.predict(X_train))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import median_absolute_error" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.31700773322247322" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "median_absolute_error(Y_train, regr.predict(X_train))" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.metrics import r2_score" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.6014318615994938" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r2_score(Y_train, regr.predict(X_train))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Polynomial regression" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.preprocessing import PolynomialFeatures" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": true }, "outputs": [], "source": [ "poly = PolynomialFeatures(degree=2)" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X_train_poly = poly.fit_transform(X_train)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=False, n_jobs=1, normalize=False)" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poly_regr = linear_model.LinearRegression(fit_intercept=False)\n", "poly_regr.fit(X_train_poly, Y_train)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x10f364828>]" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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IwiwiIhIgCrOIiEiAKMwiIiIBojCLiIgEiMIsIiISIAqziIhIgNgnesOrrrqKnJwcAHr3\n7s03v/lNZsyYgWmalJeXM2vWLAzDOGkDFRERORWcUJibmpoAmDNnTvqyb33rW0yfPp0RI0Ywa9Ys\nKioquOSSS07OKEVERE4RJzSV/f7779PY2Mgtt9zCtGnTWLlyJevWrWPEiBEAjBo1iqVLl57UgYqI\niJwKTmiLOTMzk1tuuYXrrruODz74gH/+539ucX1WVhbRaPSkDFBERORUckJhPv300ykrK0t/XVBQ\nwPr169PXx+Nx8vLyTs4IRURETiEnNJX9xz/+kYcffhiAPXv2EI/HOf/881m+fDkAixcvZvjw4Sdv\nlCIiIqeIE9pivvbaa5kxYwaTJk3CMAxmz55NQUEB9957L47j0K9fP8aNG3eyxyoiItLpnVCYQ6EQ\nP/3pTz9x+eFHaYuIiMjx0wlGREREAkRhFhERCRCFWUREJEAUZhERkQA54XNliwRdIpHghZdeA2DC\n+LFEIpEOHpGIyNEpzNIpJRIJbp/5GFVuPwAWvvUYv3zwu4qziASeprKlU3rhpdeocvthmBaGabEz\n1Te99SwiEmQKs4iISIAozNIpTRg/lp7WFjw3heem6GVvZcL4sR09LBGRo9I+ZumUIpEIv3zwu4cd\n/KX9yyLy+aAwS6cViUS44dqvdvQwRESOi6ayRUREAkRhFhERCRCFWUREJEAUZhERkQBRmEVERAJE\nYRYREQkQhVlERCRAFGYREZEAUZhFREQCRGEWEREJEIVZREQkQBRmERGRAFGYRUREAkRhFhERCRCF\nWUREJED095jbWSKR4IWXXgNgwvixRCKRDh6RiIgEicLcjhKJBLfPfIwqtx8AC996jF8++F3FWURE\n0jSV3Y5eeOk1qtx+GKaFYVrsTPVNbz2LiIiAwiwiIhIoCnM7mjB+LD2tLXhuCs9N0cveyoTxYzt6\nWCIiJyyRSDDv+QXMe34BiUSio4fTKWgfczuKRCL88sHvHnbwl/Yvi8jnl46baRsKczuLRCLccO1X\nO3oYx0xHkX86PS8iLY+bAdLHzbTpa5zrguM0/7NtyMxsu8fqIApzQBzvC317hCGo74YPrbvjOACE\nQqFPPAdt+fwc6XkBAhXrzvbm4fD1uWzsKP762mLg5K7b0Z6zj18Pn/4zD9xz73kfxcxxIJls8X1T\nLMbrFf/AdF0uOveLZJhmy+WP8K//2yu5ZksS2/ew3BSW6zBkmw9LFn76bT72uCf0z/c/Wq9QCJYs\ngREjOu65bQOG7x++lu2jsrKSMWPGUFFRQWlpaXs/fIdo7X/Uj7/Q97S2tBrAY1n+ZLwwzHt+Ac8s\ncdLvhlPJRgblbmHEsCEd9mJzaN0rk6exf8c7dO1zHtDyOTj8+fFcB7N6Cdd/bTTXXXX5MY25vr6e\n+3/8OAA/+uFt5Ofnt7j+48+L56a44VyDf7yzhSq3H6lkA/GtrzB8SH/u+sG3WLh4GXDkF/JP+1kd\n6eeXSCT47z+9zMr31nP2WQP52uVj+PPLFenvr7vqcgD+c+7z/H7+Irr0b/6+S/I96qp38OHO3ZxW\n2p2nH3uY4uLiFs/rocf78qhz+fGjvwHgh9/7Py3Gf2hsHx/DoUheNnbUp47n8KAeun7wwHJs2/7E\nG6sjPR+33fUou+mP5zrEd/yD3D5f/sTP/tPW51h/Vz/+/1UvcxO/+NG3iFgWOA6JaJQfPfRrapxS\nbC9FsbcZ2/Op83phuy7djR3cMe1r4Dj8+j/+RF1TIc7+D8mljm/feCVZodDJj9RR/vmOg590MH3v\nqOvfYUKh4/7nWha7a+tI5uTSY87viJx2WkevxTE51vYpzO2gtZAmEgnuuf+nLN/iktdjEKYVwnNT\nTLkg3GI66PAXGsdxeHY5LcJwzbAUa9/fDDS/mM78yX9SmTyN6J4N5Js1zH3qATIyMlp9c3DoxfbQ\nC+Y7K9/j7a0eph0iu1s/6na8S9e+zSG0apdx7bhz02FYsXINTUmH3bv3UnZaL+67+44WQfv4i/mw\nIQO46dt3k0ylmHr9eIqKuqeXDYVCfHnUufzbT/+d7dsr8Q2TPqf1YvptN/H9u2azba8Lvkv3gWOJ\nVW8CILNLGckP/sJZZ56B77u8vSVJQ+12wKTX0CsxrRDxD/5OXrgJHx8wMQwDyzIZc+F5bNi8jcqd\neygqKmTZu5vI7NqX+t3r8ZwEXQtzKSzIw7IzOP20ngwdPJA/rrDTz3+y8QA7lv0eDAvDCuE6CcKR\nfKxIDk0HdmNHmm9rJGtxXAffzCScVUiGH6NrYTZ766H74K81r/zepVw6cgBzX1hEuHAAud0HULvh\nZYrybWzLpr6hibq4Qc+zmn836ra8RoosuvU7H4Do1teo3bsdz7PoPfx69m1diu+5mKEsissvBKBm\n61ISB3Zz83Wjyc3NY9Wa9WzdFcfr2vyz3bv+Fbr1H4Nphdi7/hUK+43CtEKY1Uv46iXn8MIrS6iJ\nGXQ/4xIADmytIKv0S0T3bsSPVlFcPhbbS3Fg2zLO6JlFyIB9Ti+a9m7G2beRkJVN99PPwXZd4jtX\nU1DUj2J2cna/EgzXZcOGSqJeEbbn0pVd3HD5haxcsZr3tqWwfY9UfRVZWYXYnoftuViuQ1ezhnOG\nDML2PNxEgpUr38dxI9heiiwvxhllJZiHT4F+Shyb4g24yVTzfXouVpBjZttHjZdn22zfvY8GMxvX\ntLCtJAMG9sPKyMA1Td5ev42YUUBj4gBGbhGuFcI1LRzDZFBZmMFDzzymSCZ9n6Ur3sOzLM6/8Dwy\ncnKOPbK2DYZxXKt+vBsyQdIhYfY8j/vuu4+NGzcSCoV46KGHOO1T3sl0xjC3NsX1aSG94VyDxkSC\n3879C10HfQ3TCrHvg2UUlo0glWxk9ztz8THA9OndqwSHbBqMbjTW78aJVtJz2EQaaj8AICOvJzUb\nF9Lr7KvxXIfKFc9hRXKx7ExKBjaPZdfqF2isryLlgWmFsE2IZNj4rofnOjQmHTLyetL7i9cA8OGK\nP9JQX4lpWGBaeG6CUDgP122ePjZNC89L4bpNWKEIGZldCEXyaIruw/dcUk4UL+WAaWGaBp6bwg7n\n0PX0c8jrMYgP3/kjTfE9GJh4vovrOoTsCB7gu0kM08T3PCw7jGGGyMjphptK0KX0bLK79eXDd58n\nlajH93wwwLTCZBWUYpghGg/sIiOnK70Oxqtm61KSsX00xWpwnEbwPSw7g15nX8X+7e8Q3bcFz20i\nI6OA/NIhuE3x9M8qlJmP77nU71pPbskAktFq/MZafAOySgaTWVBK7bal9Dizecuw6r2/kEpEye52\nenprfve6v+E2NWBYIUw7TMmgS5t/Jmtfxvc8eg6+ghA+tpuCRIz9m16nV/loLNdl/+bFmMkEPb9w\nAaaTYN/6CvILe5NfWIbtpWja9wFerJquPc4k5IOZSuLs/xC3fhdhK0SXoi/g7K8kJ7e4xXRjqn43\nptNAbl4JXmwfWRk5HwXJdSBxgIidieU5ePH9hDDIsMOQiBIyDMKGieW52K6L5aWw3CS272O1//v8\nY3eUmNU1JKhJmLiWjWvapEyT4sIQJaU9IRRix55qtu430wGLN9Zh5Ranl3cMg8GnZ+DZFv+zZg+h\nLr1wrRAp0yZlGJw3MIMvnX/ukccQDqe/bvI8Zj36DDvph2taFIV28ON/vY1Ibu4xx+zTZnYOveE/\n/Lr6qvfIKxn0qcsFUWvrFXTH2r6Tuo/573//O47jMG/ePFatWsXDDz/Mk08+eTIfIpA2b97MNVN/\nQMJpAqeJWQ/+DN8AK5SNYRi4yQacZBwrlAG+j2EYzF6WT2HpFwl3HcCe9/9OvHYHvu9Rve0tQqEc\nMvN74iSiJBvr2LxtJ/7Bd+8GJq6bYOuSpzFMC9dJgGliWiE2LX4C3/Pw8THj+8G0MSybpugenEQM\nFwPDMMBz8cxMGh3wHAcPCGd1xfdcqta+TCS/hOziftjhTLoPHEt0zwbqdq4mI6eYkoGX4LkOeze8\nng7MztV/JrOgFz4+vufS86zxLZbxXIfKd+cTyiwAoGbLEsLZBZSdcwMA+7a9RVN0LyVnXoZphah6\n7y84jVHCmbn0OOuK9H31HnQtALve+wu9z76GiB2mfuubmE4TIcOguPcwLC9F7boaevT6IqF9H2J5\nKfpHulK7/V169BlJ45736VJUju25HHjrDxSWnEHSsynoVo7luSQ3vonR1EBGKILlOuTm98B2XZy6\nD8mOHiBVV4XpJMgr6Im1+1Wc2g/Jzi0itOnddNTc6F6yMwuwlj6P7bkYyThmqnk/XIYZwlr8X9he\nCsttXt6u+I9P/lK98V8tv1/2p9Z/CVce4UQ1G948ym/vulavTZkWDuDZGQf/G8bxfRw7RCKUScq0\nSJkWiWQcLxSBSC6uZR+MkUksuhfXsgl16X3Y5QaNiXqsvBIcw+BA7Q4ixf1x7RCx6F6sgl54dvjg\nYxucf2YGjg8Llm4ms8cgHKBm52ry+55HyrSp3b2e7NKhpEybK8/NxLMtnnvHx7XDuKZN0veZNCqT\nG6772hHXM5FIsOBPL/P8K8tIdTkHgF721uZjBw5uiRUnEjw48zF2pvoCUGJsxMBgl1+eXv6qg8ca\n3HPT/4WS0S3i0eOCMF86xnj86fkFrMkenr59vZvLCwuXtEl8crufwb6tb1HY59z0ekwY/92T/jhy\n7E5qmFesWMGFFzZPlw0dOpQ1a9aczLsPlENTswvf+AfLV28D3yAzp5jSsycAsPPdP4FpklPUPN0S\nr95KYb+RxKs3k99rCPu2vklB6VAAUokYfoFPOCs/PSW5a81fye5aRlnfiUDzVh++T6opjus00GPw\nFQeX+wv4YEdy6X7GmE9Es+q9BZSe3bwVvGfd3ygdNA7bc6lZ8zI9ykdheS51m/5Bj37nY3spYh++\ni79jLZlZ+XQpPJ3GFX+isKgcq2t/otuWUUgIt76KnNzu2GsqsL0UZsqkcc1CrFQTXYr7Y785D/fA\nbrIyC7Bef5pUXRW275Gd3RVrxzpSdVVkZxVib15xMFApSESxV1aQYYebt9ga6oiEMrGWvYiZaiIE\n2It+j+W6zbep+H+t/4Demv/Jy1Yt/ORlaxYdz4/9I7s2ffR1zY4WV6UwcOMHcC2LlGnj4OPgkzJM\nUhlZB7fGmoPW0LgfP5SFkdWFlGkRj9cQKuhFygqRsprj1pCop8lpIqPb6Qe3wCxisRpC+T1x7ebv\nUxhEo3tIukm8jCwaE1Fyew/DtUPU1X5AMuWQ1XNwOmTRup0UnX0lnhUmic++ne+RUzYc17Kp2vgG\nBQMuxg9F2L3uFcJ5xXQ9bTgHdq8jr2QQvu9Ru/1/KSxrDljttmWY4QgFvYawd+PrFPdv3vdbu305\nBaVfJLppAQUDr2kRqfpda8nvcSZ7NywkdNogCkvPxjAtPNehdtvyFpG4/WDs5tz1c5ZVNc/YfOG8\n4fiZDu9+4JA35HLqD+4CaigKM2H8WF7e8FFAe9lbmfDVI4emxTEJ+cMw97xx8JiEltOjn/y44/cB\nPvXjj3OfeoCpt/9bi8i3d+wmjB/Lwrc+9jwcHEPL6wwG9e3C6HOMg/v7gz0t3Np6dRYndSp75syZ\nXHrppYwaNQqAiy++mIqKCkyz5XlMPu9T2YlEgu/c9XPWbtpDcc0H9C6/CNtziX24ksLuAwhhsH/T\nYrIiuXQp+gKW55LY/T7J/ZWU9DmX+M736FoyENtv3kdmppIk9m4iwwodnHL0cQ/sISszH9tzsQ9O\nP3oNddi+i5GIk2GHsX0X02kibFiErdDBrbMGQqb10e0ObsXZnosd4H1mrmGQMqz0tGDSc/AzsnFN\nmybfxQ9nkTI/mmJsSjWRMi2I5BFvqCWU34PUoX1kQCy2l3BhGSnTom7fB2QWl9OQqMPK69E8FWk1\nb43VVW8mq+fg5uBZNingQH0VdTVb6TboK7h2+GBcob5uBw4GWSVntNga27FmAd2/eC2uaVG1YSFG\nKILvuRQPaA7Uvm1v4qWSpBIxTDtM94O7F/asf41wXjcKepzVYl95zeY3KBl0GfBR3PZuWkSPgZem\n47a/chUFPQe3iN3u91+luPxions34sRqSTVFKRl8OZ7rsGf938jq2geARF0VphnCw6X7gDHpMTqN\nB8jqUko0a4x/AAAOHElEQVROcTnxmi14nofrJDAsm0y3lnCvC6jbsYLCPuc23+e6v5HZ5TS+0MOi\ndv8BvG5fYv/OVVimjWGa5HY/AzC4ZliKFytWpCO1b9ubmOEsCswarr3iIkKhEG8s35je8iwxNjL6\nnAFHPSgM4PaZLV+gDz9241gP/mqrqdHPcgDmoTcLn7ZuJ2sMgTty/Dh8XsfeIfuYH374YYYOHcpl\nlzW/qFx00UW88cYbJzy4oJr3/AKe+O93+d6Kv3DN5uUdNg4Xo3l60DBwrTApy8LxfbxQBNe0m7fY\nDIsmzyFlGBDJ+2j/mWGScBpImSZGVmGLAz8aGutIOA0kXZfsnmemt8ocw6S2ehNZ3Qeyv3rTwelD\ni7p928jsPiA9xZjX90s4vk9N5Sqs/O64hknmwZgditz21S9QMuzrpEyb6h3vkIhX033QOEwrxJ73\nX6Nr35Ec2LkmHYG961+jZHDzftz0ltjejbiJGF4qiWHbdO3zpebrty3DSyWxM/OJVW8E1yeUnU9R\n/4vTYQHYteYlivp/mfqdq1rcNhHdjdPYQCgrJ32QVe22ZRScNoz9HyzHaTqQDuee9a9hRXKI7d2E\nYdpkF/SmsO951FWtJrZnI/m9hpBb3J9da/5CJK+EWM0HpBL1WBlZlJ59TfP6rn81PQOye91fScYP\nYNo2uSVnkFvcn+rNb+A2NYJh0POs8QBUvvs84XCY4jO/1mJ8icrFeI37KCzI45LRI9mwZTvvrNqI\nm90bJ76fZLyW3OwMTMvEzepN4/6d5BT1I7e4P+be/8Ho+eVPbNkO6p3JI/d+g7++tjj9EbVDDsUT\nSH+E7fDIpqeDD7v+8Nt91o8XtcWnD4Kyz/LzGh85sg4J86uvvsrrr7/O7NmzWblyJU8++SS//vWv\nT3hwQXUozINrdvC16g/TEXQMk4aGWqy8EmoqV5HX59z01KNjGOzd/g6Es8g+7YvU124ns+QMUqZ1\ncBrxn3BNm7r9O8gsLmf31rcgK4/c04aTMm1qKt8l4TTiZ+aRWzaC2j2b8A2DLr2Htpha9FyH6o2L\n0ltlu9a8RPeBX6F+9zp8J0Hh6c1R2rP+NYr6jwageuPrdB/YPPVdu20ZyYb9dCsfxf5ty0k5DfQ4\nGMTda1+mKbaP/F5D8XyHuh0r6db3vBZHk9fuWEFTfRWRgl5Ed28k2VhLJLc7WQW90kGs3vw/7N+5\nkszsbjhOA5HsInw3hZWRjdOwjx5DrsQOZ1G1egEN+z8E3yPlJojkdqdL72HkFvenavWfaYzuIZSR\ni2GYJBP1mGaI7MIywjndiO5aDyb4ngdOAynfwzRD+L6H5ybBhyangUgoC9/wMe1MLDOE6zZh+iks\nyyDpemTYYUIhm8amBF37jiKvxyBSyQZ2rf4zoZxudOt7Pvu2LCGcV4RpWqQSDXgHNlLaszu/eHgm\nf/37P/jDfy8g5TgUdMknHApx6ZcvIBQK8d7aDQCc0b8va9/fTNWuvYy9eCShUIjVa9/HoPkAnw2N\nA7BCETzXoX7XOkpCu/ntE7PTR9m39nlu+OQR8Yd/hOnw2142dhR3PvDr9FaavX85144795g/Znb4\n432egnKytk5FjqZDwuz7Pvfddx8bNjS/4MyePZs+ffqc8OCC6tBU9prNe/E8l+Lyi4Dm/cAHdr1P\nKCOLojMuoW77ckoOHq27a81LRPdtJ6dLb0wrRPEZlxDdu5Ho7vWUnHk5djiLnatfpLGuCtOKEMkt\noim2l0heCYZp0RSrIbfHGRT0HMKuNS/RFN+P5yaJ5BRT+sXmo7F3rHgWJ3EAP5Vq3n1ghsjvORC3\nKU5DXRV9zpvWYsp0+//OJZVswPB9DNNuPnjL9zF8D980MH0D3/fwDRPLMMH0cA2r+chbD5JugrzC\n0yn9YvNBWZXv/pHGAzsxDQPLClOQn0NubiY1NfU4qRTZmZnEGxrxDehVUkiDl0dOn+Y3EFWrX8Qw\nQ6Sa4lgZWfQuNLnuqsvTUUgkEvzXcy/y578upL6unrPO7M/M/3sbCxcvw3EcFr65jt3+F4ju3UiO\nu5O+vbuxt3o/l196EZO+fiXQ8rO0h5+cAvhEuD4tboe/eJs1b/KFXnkMGXwG/7NiC1WpPkT3bqTg\n4EfTPv7Z58/yu9ae0fi8RfVkOVXXW9qXPsfcxg5thfzvOytZv2ETOyp343kehhUiI7MrTmI/KbcR\nExsDaEzGMU2TDDuzeasNH8uwcL0UGAaGYYHvYQB2ZgFuMoaPh+EbmKZPaa/u7K+L05RooinZiGGF\nsQ2T3KwMwlnZGPjkZGfQ0Ohw1qD+fPOm65n2/Z/T/eCU6673FjDojD7sDw0GmreGfvPID9JhgyNv\ncR2+zh+PxKEpTiC9xXU8J0p54aXX+N8Vq1kf+wJWqHm5E5lKbK8zoX3aY9TX1zP19n/DLWyeETjZ\nn6tUNEQ6B4W5Axzaqnv51Tfo2aOYs88aSCgUIpVKsWb9phZnSTq0lbfXHAg0TyE3Hxk54BOfeT7R\n/V179+7l29PvA+DJn91HXl7eZ36Bby0SJ7qv7vM+lRjUfZQiEiwd8jnmU10kEuHmqddz89TrW13u\n0Av2dVdd/tEU6g3npff9/eOdk/NRgOLiYp5/puXnyD9rLNrij3Dor26JiHxEYe5AkUiEKROvZsrE\nlpd/XiP1WT5f+Hn7q1uHOxU+Vyki7UdhDqDPa6RO1S3fU3W9RaRtKMxyUn1e31R8VqfqeovIyWce\nfRERERFpLwqziIhIgCjMIiIiAaIwi4iIBIgO/pJA0VmuRORUpzBLYBz+d3EBFr712OfqDGAiIieD\nprIlMF546TWq3H4YpoVhWuxM9U1vPYuInCoUZhERkQBRmCUwJowfS09rC56bwnNTB09tObajhyUi\n0q60j1kCQ6e2FBFRmCVgdGpLETnVaSpbREQkQBRmERGRAFGYRUREAkRhFhERCRCFWUREJEAUZhER\nkQBRmEVERAJEYRYREQkQhVlERCRAFGYREZEAUZhFREQCRGEWEREJEIVZREQkQBRmERGRAFGYRURE\nAkRhFhERCRCFWUREJEAUZhERkQBRmEVERAJEYRYREQkQ+3hv8Lvf/Y7nn3+eLl26APDAAw9QVlbG\nrFmz2LhxI6FQiIceeojTTjvtpA9WRESkszvuMK9du5af/OQnDBo0KH3Zq6++iuM4zJs3j1WrVvHw\nww/z5JNPntSBioiInApOKMxPPfUUNTU1jB49mm984xusWLGCCy+8EIChQ4eyZs2akz5QERGRU8Fx\nh/mKK65g8uTJZGdnc9ttt7Fo0SJisRg5OTnpZSzLwvM8TPPTd2G7rgvA7t27T3DYIiIiny+Hmneo\ngUdyTGF+9NFHeeeddwD493//93SEL7roItatW0dOTg7xeDy9fGtRBqiurgZg8uTJx/LwIiIinUZ1\ndTVlZWVHvP6Ywvy9730PgFgsxvjx43n55ZfJzMzkrbfe4tprryWRSPD6669z2WWXsXLlSgYMGNDq\n/Q0ePJi5c+dSVFSEZVnHsToiIiKfT67rUl1dzeDBg1tdzvB93z+eO37xxReZM2cO4XCYkSNHcttt\nt+H7Pvfddx8bNmwAYPbs2fTp0+fERy8iInKKOu4wi4iISNvRCUZEREQCRGEWEREJEIVZREQkQBRm\nERGRAOmQML/00kt8/etfZ+LEicyaNQsdf3ZyeZ7Hj370I2644QamTJnCjh07OnpInY7jONx5551M\nnjyZ6667joULF3b0kDq1ffv2cdFFF7Ft27aOHkqn9Ktf/YobbriBq6++mueff76jh9PpOI7DD37w\nA2644QYmT57M1q1bW12+3cOcSCT4xS9+wZw5c/jDH/5ALBbj9ddfb+9hdGp///vf0+cu/5d/+Rce\nfvjhjh5Sp7NgwQIKCwuZO3cuTz/9NA888EBHD6nTchyHH/3oR2RmZnb0UDqlZcuW8e677zJv3jye\neeYZnZGxDbzxxhu4rsu8efP4zne+w6OPPtrq8u0e5oyMDJ599lkyMjIASKVSRCKR9h5Gp6Zzl7e9\ncePGcfvttwPNMxQ6UU7b+clPfsLEiRMpKirq6KF0SkuWLGHAgAF8+9vf5lvf+hajR4/u6CF1On36\n9MF1XXzfJxqNEgqFWl3+uM+V/VkZhkFhYSEAc+bMobGxkZEjR7b3MDq14z13uRy/rKwsoPm5vuOO\nO/j+97/fwSPqnObPn09hYSEXXHABv/rVr7Tbqw3U1taya9cufvWrX/Hhhx9y66238sorr3T0sDqV\nrKwsdu7cybhx46irq+Opp55qdfl2e6V+9NFHmTJlClOnTsXzPH784x/z5ptv8thjj7XXEE4Zx3vu\ncjkxu3btYtq0aUyYMIErrriio4fTKc2fP5+lS5cyZcoU3n//fWbMmEFNTU1HD6tT6dKlCxdccAG2\nbdOnTx8yMjKora3t6GF1Kr/73e+48MIL+dvf/saLL77IjBkzSCaTR1y+3baYD51vG2DmzJlkZGTw\nxBNPYBhGew3hlDFs2LDjOne5HL+amhpuvvlmZs2axXnnndfRw+m0nnnmmfTXU6ZM4f7776dbt24d\nOKLO55/+6Z/4/e9/z0033cSePXtobGykS5cuHT2sTiU/Px/bbs5tXl4ejuPged4Rl2/3U3KuXbuW\na6+9luHDh6cvmzZtGpdcckl7DqNT07nL296DDz7IK6+80uJ5ffrpp9PHTsjJdyjM+l0++R555BGW\nLVuG53n84Ac/4Pzzz+/oIXUqDQ0N3H333VRXV+M4DtOmTWt1lk3nyhYREQkQ7XgUEREJEIVZREQk\nQBRmERGRAFGYRUREAkRhFhERCRCFWUREJEAUZhERkQD5/60kNQoWvWueAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x10f0c59b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(Y,poly_regr.predict(poly.fit_transform(X)))\n", "plt.plot(np.linspace(Y_train.min(),Y_train.max(),num=10),np.linspace(Y_train.min(),Y_train.max(),num=10),color='red')" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance score: -99.89978\t(-24.62157)\n" ] } ], "source": [ "print('Variance score: %.5f\\t(%.5f)' % (poly_regr.score(poly.fit_transform(X_test), Y_test), poly_regr.score(poly.fit_transform(X), Y)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ridge regression" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import linear_model" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n", " normalize=False, random_state=None, solver='auto', tol=0.001)" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rregr = linear_model.Ridge(alpha=0.5)\n", "rregr.fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance score: 0.57282\t(0.59722)\n" ] } ], "source": [ "print('Variance score: %.5f\\t(%.5f)' % (rregr.score(X_test, Y_test), rregr.score(X_full, Y_full)))" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.58 (+/- 0.09)\n" ] } ], "source": [ "scores_rregression = cross_validation.cross_val_score(rregr, X_train, Y_train, cv=cv, n_jobs=1)\n", "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores_rregression.mean(), scores_rregression.std() * 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extra Trees Regression" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import ExtraTreesRegressor" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ExtraTreesRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", " max_features='sqrt', max_leaf_nodes=None, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=250, n_jobs=1, oob_score=True, random_state=None,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = ExtraTreesRegressor(n_estimators=250, bootstrap=True, oob_score=True, max_features='sqrt')\n", "clf.fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance score: 0.62724\t(0.80759)\n", "Out of bag error score: 0.60974\n" ] } ], "source": [ "print('Variance score: %.5f\\t(%.5f)\\nOut of bag error score: %.5f' % (clf.score(X_test, Y_test), clf.score(X_full, Y_full),clf.oob_score_))" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.62 (+/- 0.06)\n" ] } ], "source": [ "scores_etregression = cross_validation.cross_val_score(clf, X_train, Y_train, cv=cv, n_jobs=1)\n", "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores_etregression.mean(), scores_etregression.std() * 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ada Boost Regressor" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import AdaBoostRegressor" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "AdaBoostRegressor(base_estimator=None, learning_rate=0.01, loss='linear',\n", " n_estimators=500, random_state=None)" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = AdaBoostRegressor(n_estimators=500, learning_rate=0.01, loss='linear')\n", "clf.fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance score: 0.53526\t(0.57273)\n" ] } ], "source": [ "print('Variance score: %.5f\\t(%.5f)' % (clf.score(X_test, Y_test), clf.score(X_full, Y_full)))" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.55 (+/- 0.06)\n" ] } ], "source": [ "scores_adaregression = cross_validation.cross_val_score(clf, X_train, Y_train, cv=cv, n_jobs=1)\n", "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores_adaregression.mean(), scores_adaregression.std() * 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Bagging regressor" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import BaggingRegressor" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "BaggingRegressor(base_estimator=None, bootstrap=True,\n", " bootstrap_features=False, max_features=20, max_samples=1.0,\n", " n_estimators=250, n_jobs=1, oob_score=True, random_state=None,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 93, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = BaggingRegressor(n_estimators=250, bootstrap=True, oob_score=True, max_features=20)\n", "clf.fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance score: 0.65425\t(0.76777)\n", "Out of bag error score: 0.62089\n" ] } ], "source": [ "print('Variance score: %.5f\\t(%.5f)\\nOut of bag error score: %.5f' % (clf.score(X_test, Y_test), clf.score(X_full, Y_full),clf.oob_score_))" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.63 (+/- 0.07)\n" ] } ], "source": [ "scores_bagregression = cross_validation.cross_val_score(clf, X_train, Y_train, cv=cv, n_jobs=1)\n", "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores_bagregression.mean(), scores_bagregression.std() * 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gradient Boosting Regressor" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import GradientBoostingRegressor" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls',\n", " max_depth=5, max_features=10, max_leaf_nodes=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=250,\n", " presort='auto', random_state=None, subsample=1.0, verbose=0,\n", " warm_start=False)" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = GradientBoostingRegressor(n_estimators=250, max_features=10,max_depth=5)\n", "clf.fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance score: 0.66302\t(0.76559)\n" ] } ], "source": [ "print('Variance score: %.5f\\t(%.5f)' % (clf.score(X_test, Y_test), clf.score(X_full, Y_full)))" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x1154f37b8>]" ] }, "execution_count": 99, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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ZiatZp1/5w6/gMxaomQFXL7pzcu9NzuHVdPEKwaVHfwpP7eXkz3tYcvwQrrpS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\njUYjG9Y+TrahjGxD2VlvUQshxEgnvZoH4Vf8pExYENzOTc6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"text/plain": [ "<matplotlib.figure.Figure at 0x10f30f7f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(Y_full,clf.predict(X_full))\n", "plt.plot(np.linspace(Y_full.min(),Y_full.max(),num=10),np.linspace(Y_full.min(),Y_full.max(),num=10),color='red')" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.63 (+/- 0.10)\n" ] } ], "source": [ "scores_gradboostregression = cross_validation.cross_val_score(clf, X_train, Y_train, cv=cv, n_jobs=1)\n", "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores_gradboostregression.mean(), scores_gradboostregression.std() * 2))" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "GradientBoostingRegressor(alpha=0.9, init=None, learning_rate=0.1, loss='ls',\n", " max_depth=5, max_features=10, max_leaf_nodes=None,\n", " min_samples_leaf=1, min_samples_split=2,\n", " min_weight_fraction_leaf=0.0, n_estimators=250,\n", " presort='auto', random_state=None, subsample=1.0, verbose=0,\n", " warm_start=False)" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pickle" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open(\"../data/gradientboosting_params_gonorrhea.pickle\", \"wb\") as myfile:\n", " pickle.dump(clf, myfile)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random forest" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestRegressor" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", " max_features='sqrt', max_leaf_nodes=None, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=250, n_jobs=4, oob_score=True, random_state=None,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 105, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = RandomForestRegressor(n_estimators=250, oob_score=True, max_features='sqrt',min_samples_split=2, n_jobs=4)\n", "clf.fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance score: 0.65100\t(0.77673)\n", "Out of bag error score: 0.61952\n" ] } ], "source": [ "print('Variance score: %.5f\\t(%.5f)\\nOut of bag error score: %.5f' % (clf.score(X_test, Y_test), clf.score(X_full, Y_full),clf.oob_score_))" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.63 (+/- 0.08)\n" ] } ], "source": [ "scores_randomforest = cross_validation.cross_val_score(clf, X_train, Y_train, cv=cv, n_jobs=4)\n", "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores_randomforest.mean(), scores_randomforest.std() * 2))" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n", " max_features='sqrt', max_leaf_nodes=None, min_samples_leaf=1,\n", " min_samples_split=2, min_weight_fraction_leaf=0.0,\n", " n_estimators=250, n_jobs=4, oob_score=True, random_state=None,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 108, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = RandomForestRegressor(n_estimators=250, oob_score=True, max_features='sqrt',min_samples_split=2, n_jobs=4)\n", "clf.fit(X_train, Y_train)" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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5P3lwcLBMJpMsYe2VeWK3Mk/s1pHfmxa4w9zd+9G5Dx1AeZWqHD4lJUVvvPGG\ntmzZ4vokgYHq3bu3Jk6cqEaNGnl0gABQqXmo8D3/yXGLJU5fbC7+cywWi87+9pMiojtLkk4f2CiL\npXOpXxsAPMHt0Pnrr79q9OjRunDhgm699Va1bdtWkZGRunDhgrZu3aply5Zp27Zt+vLLL9WgQQNv\njhkA/J7ZbNaWl6eo88y/yfS72aXwPffjpd0fmffkuNls1rqfSt6PGRHd2bkkHx4dp5ycHM1PXOzy\nuu6MpbT7PwEgP7dD59/+9jddunRJ7733nvr06VPg4999952efvppzZgxQ2+88YZHBwkAlYn50iUt\ni0/Q4A3JMgfW0D8HjtbY559XcJ7AWZp+zMK405npOGRkcfmzr5b+z7kPdOXGmXrn5Yf07OtzShwL\nHZ0AysvtPZ0//vij4uPjCw2cktS3b1/17t1ba9as8djgAFRfpbkpx69cvKhTveI1eEOy0uvU17P3\nTtPiFoNd9j96an9kSfsx3dkHOmX6+26Pxd39nwBQGLdnOq1Wqxo3blzsYxo2bMhVmQDKzRMzgT5x\npfC96bZt2nHNDZo28HllhoRJ1hznQ8xm85WC9zZeH05Z9oECgLe4PdN5xx13aOnSpTp37lyhH8/K\nytKaNWt0++23e2xwAKqnSnlSet06qUMHads25Tz4oD4ccZ/O1ajlckd5bpjek3WdTh/c6JyBzDq8\nWv3iu5fr5YuaGc47OzliSP8Cd6e/8vx47lMHUCHcnumcOHGi/vznP2vYsGEaN26cOnXqpIYNG8ps\nNmv79u2aOXOmzp07p2HDhiklJcXlc7kWE0CVlq/wPXDcOP3N5XCOY5Z2fuJiHbe2lNFkVHjzTjqf\ntku/Z55Q5HXd9W3y2jJfL5l/Zjh5/d/Us1OMTCaTy8GgovZlslcTQEVwO3R27drV+etJkyYV+biH\nHnrI5fdciwmgtCrNSeliCt9LuqM8wGhSWOMbdSEgQAFGk8vHSnuyPe/MsM1q0e6DZ5XuGGCBrQmF\njYv71AFUBI9fg5kf12ICKK1KMfuWkSGNGCGtXu0ofF+0SIqOdnlI/vCYP0yfObhJda9t7xKqi9vP\n6k4YvZCeoogWV2uScrcmECoB+JrboXPatGneHAcAuPDr2Tc3Ct+LCo/vvPyQpkx/XzarVW2H3aqQ\nkBCXUJ131lK6GhoTBsQXGUZd7lq32Tz+5ZbmqkzuZgdQlDJdgwkA1VZSktSliyNwvvqqtGBBoTcM\nLUhaWuAY4CuoAAAgAElEQVQw1IKkpXr29TlKzW6jfZdjtXH7b26Hs+IOV+XODI/uFqSHE2LVSHs9\ndjAoNzzPW2/RvPUWPTFpZqEVVu4+DkD1RegEAHfYbNKUKY6ZTbtdSkyUJk923jAkXT1B/u/Pv9bn\nC1cUeIpfduwp8VR+/m5Nd0Nj7szw6HuH6v23Jmh0tyCN7hZU7qopd5sEKmXjAIAKVaq71wGgWrp4\nURozxnFQqFkzx/7Ndu1cHpJ/Of2MuaZ0YIMiortIkgLPbtbNcXFKLaEns6j9rKU5XOXXWxMAVFvM\ndALwez69nejwYem2266eTN+ypUDglArO9EVEd1FgcKjOp+3QiZRktWhUS4P693ZrFrOwm3/yLqF7\nYgbTXe7OvJZ1hhZA9cFMJwC/UdhBFJ/eTrRunWM5PSND+/r0089/elCDQ0Pl7ivbbFbZL5vVuE0f\n7bssPfv6HL3z8kP6NnmtpNKfyq/IGcy83wt3xlwpGgcA+JTBbrfbfT2I0jp69Kh69+6tFStWqEmT\nJr4eDlDp+cOp4/zhMsq43xli5q23OE9z26w5Gt0tyPvh60rhu13Sf3oM1Odt75MkGc9s0qfvvaiw\nsLBCx5+7/N3YsE+hQZe173JsxY+9nIr6XhAiARTG3VxW5ExnVlZWmV+8diEnOQH4J3+557yoqqAK\nl6/wfeXjz+jzczHOceXU66RRj7ysxE/ednmPCs70TdDCJcnat95S8V+DG4r7QaOo74W/h2UA/q3I\n0NmhQ4dSF7vb7XZuIAIqGX8PGBVxO1FuAAvKzNTgf82Rce1aZ+H7qZ92SustslktupCeIpvNJput\nbqHvUe7yd+7zWSwWNdJepVlbeW3sZeEvP2gAqF6KDJ0dO3asyHEAqOaKCpfe3iuYG8BM6YF6+Ztp\nMmae1OGOcWq4dImCIyOV0LixPls4SacvBKhes1t1IT1V2Wd+06VLNxb7fLmBrqHsGhlnuHIPeslj\nr4itDiX9oFFpriEFUKkUGTr//e9/V+Q4APiIvwSM4sKlNw/QLFySrGapGXp62XuqaTFrXtzdmnNt\nG7We9olz9m/oXZ20YItdZ4/8rIjmcQqLitXC7zfqj3cPLhAK8we6E9brZTKZXMZfVLD0lxlIDgUB\n8IYyVyadPHlS+/btkyRZLP65ZwlAyXxVxVPUWPJXBXmVzaYbF8zXS0velsFu11sDntPnne+WjIEu\ne0pr1qypC+mpimge56xEsobHlWnP6fnz53X3Q684b+4ZP/HvzhqoiipYd6feqMK/FwCqvFKFzuzs\nbL3zzjvq2rWrunfvrkGDBkmSPvnkE91///3av3+/VwYJwLuqZcC4eFG6+261/fI/yqhTT3+5+w39\nr2UnnTm8WaENW7s8NGFAvMICMgp9mvwdosUFOrPZrD8+9KJsEZ2dwTLN3koLkpZ6/cvNy59+0ABQ\nfbgdOi9evKhRo0bpn//8p4KCgtS0aVPlti2ZzWZt3rxZo0aN0tGjR702WADwiLyF7927q/bObYob\nEC1D+hrVbXKLJINLWAwODtZnH70u45lNLmGyX3z3AveNSyoy0C1ckqz089YCw/llh+PwZUUWrFfL\nHzQA+JTboXPWrFnavXu3XnrpJa1cudI5yylJTzzxhKZNm6bMzEx98MEHXhkogMrLlzcKFXjtdeuk\nDh2kbdukRx6RkpMV3LSpRgzpr3sG9dQNofs1rH2Obr+1pRYuSXaONywsTF/Mfs0lTH6bvLbQ5fDi\nAl2N0AY6fXCjM1ieObhJN7dtI4kZSABVm9s3En377bfq1q2bRo8eXejHExIStGzZMm3eXMLFwgCq\nFV8ejsn/2qffHaxHf1wpg+To4Rw3TpJjn+WoR17WeVukQhvGaPNXa2QPqi9DgFHJ63fqg7eeUnBw\ncLkPNCUMiFfy+p3a9etFnU/bJUlq3ayORgzp73wM96YDRfOHiyxQdm7PdJ48eVI33HBDsY+Jjo7W\nyZMnyz0oAFVHRR2OKe61A+12jVv1sR7buFyZAYH6felSZ+A0m826/4k3pUY9FRYVqzOHt8iiENVt\n0k6hDWO0dfteTZz8dqEztO4sh+edaZWk//fqo+p8Qx01Mp3QAwNaa9bbf+E/TsANuT9E5t3OUtEr\nJygft2c669WrV+JBoV9//VXh4eHlHhQAFKc0sx11sjP1wn/f1U1Hd+pgxLV6Lm6ohp03a+SVjy9c\nkixreJyz4shoqqV6TdvJbrfp7JGfFXXTQO27LD0xqeAMbUnVQvlnWpPX/00GGXRCbaX6bfXDtv26\nd4Qn3xlUBGbbfMPfL7JAydye6ezVq5dWrlypNWvWFPrxZcuWae3aterRo4fHBgeg8vP04ZjSzHYM\nad5Yf/tsgm46ulPrW8bpgS5DldUirsjntlktyj77myTpQnqKS0XSsZwWemnKuwX2pRa3fzP/LG/q\ncYtO6HqfzPrCM5htA8rO7dD52GOPqUGDBho3bpweeeQRrV+/XpI0c+ZMPfTQQ3ryyScVHh6uRx99\n1GuDBVD5ePpwjNvL9UlJqtGzpxpmnddnsZ30Qvu+qtE8TkGZW9UvvrvzYXlD8fm03Wp0Y1+dPrRJ\nNputwFNu3m8laFRzvtwuUt1VZLsDvMPt0Fm/fn19/vnn6tatm1avXq1ffvlFkvTBBx9o7dq16tix\noz777DM1atTIa4MFUDlVaD2PzSZNmSINHSrZ7VJiogb8b7nCjGeUdXKvLGHt9ezrc5yhMW8ojmtp\nVIDRpPBmHSXZlJ6y4uop80ObVKfxDW4HDbPZ7Lg448Rq5VzOls2ao5gokxppL/9pAmVAu0Pl5/ae\nTkmKiorSnDlzdPLkSe3evVuZmZkKCQlRTEyMmjZt6q0xAoBTsdd2XrwojRnj6N9s1kxatEhq107f\nJi6WGvVU3SL2guWG4n7x3XX/E28qp14nhTVuq0ZRe9Wzk0G/7NijPdfeqgCjya0xuuzlbNRTQWc2\naXjfOI0Y8pQkcb1kJeYv18ZWV7Q7VG6lCp25GjRooAYNGnh6LABQoiIP7xw+LA0e7Ojf7N5dSkyU\n6td3+3nNZrOefX2OLGHtdSFtl8ICMvT/PnpdYWFhGtT/vDOMSiUHjfwHHnLqdZLJZHIGTP7TrLy4\nlx4ouyJDZ1JSkgwGQ5meNCEhocwDAoCSFJjtWLdOGjZMOnXKUfg+Y4YUFOT8sKMf829KPWqRJMVE\nmZQw4CmX58wNikaTUXWbtJPNmqNvk9cqYUB8gTD6zkevezxocCK68mC2DSibIkPnxIkTy/SEBoOB\n0Amg4syZIz32mOPXeQrf8zPIoLpRsVd+vVe///67S8grSlFhtLjQUdolWF8W6ANARSkydL7wwgsu\nv7fZbJo7d66ysrKUkJCgm2++WXXr1tWlS5e0Y8cOJSYmql69enrqqaeKeEYA8CCLRZowwRE0IyIc\n+ziLqGxbuCTZWVUkSccuN9f9T7wpa7ijPmnlxpl65+WHtHLjHJeg2C/+IU1+8+86sSdNwWGNFdb4\nRkklrwCVdgmW/kEA1UGRoXPs2LEuv//oo4+UlZWlefPmKTY21uVj/fv31/Dhw3XPPfdo586d6tev\nn1cGC6B6yr/0rIwMne/bXw137ZAtNlYB33wjRUe7/XwXTu5V3ag4l5A3Zfr7eufl8fo2ea0kqV/8\nQ3r61Q+1++BFNWrTR5KUcWCDbmwRUWBpvjC5S7AsmwOAg9uVSfPnz1efPn0KBM5cLVu21F133aVF\nixZ5bHAAqjez2axP/v2l4of8WTM+/0Hvf/Gjnh48VhdubKuGu3ZoQ8s4PXnHPTI3blzs8yQMiFdD\npers0W06e3Sb6ii9wGM277fq2dfnKGFAvEYOH6hvk9dq73GLIqI7OzsZI6K7qGenGLeDo7tF4vQP\nAqgO3A6d586dU0hISLGPMRgMunTpUrkHBaByynvPeGnL0/N/rtls1viJf9dXPwcqtNVAWbLOqJ/Z\nrHdXLVL9zHP6LG6Epg16XgcC2uilKe+W+Hq5ezrrRsXKaKqp+tbdVzs4D29WncY3uNW/aTK5V5sk\nFV0knv9rpX8QQHXgdmVSTEyMvv/+e40bN04NGzYs8PFDhw5p2bJlatu2rUcHCMD/FLZkXJ7DMIV9\n7u23tnTuw7yYtlMTMs/pvo2zZQ6soTf6/0XL6oQpzBAgyabN+60ud6PnH1/+PZ22yC46mbZS1ze3\n6cdDUnizjgowmmSz5jjH5DjxvlO7D2xUeLRj72djwz4lDJhQrvfOYrEU+T6xhxNAVeb2TOeDDz6o\njIwMjRw5Up988om2bNmi3bt3a9OmTfrwww917733Kjs7W48/TkkuUJmUdnayqCXj8lwPWNjn/rJj\njySphsWs19Z/ofs2fqH0OvX17L3TtKFVZ9lstkJnKQsbn8ViKfCa59VQ7W+OVZumNSUZCixrBwcH\n64O3ntKjIzurdUiKRsYZ9P5bE1xCdEnvXWHL5pK4RhFAteT2TOedd96p119/XdOnT9f06dMLfDw8\nPFwzZsxQhw4dPDpAAN5TltnJok5ae9rNbdvI+P0mPfLNl2qRcUg/1W2kv454Q5khYWps2KdWze36\n8cB2GYNq6kJ6impFXicpqNDxSZLxzCZnufuZw5tVt8ktMplMxZ4yDw4O1uh7h2r0vQXH5857V9gp\ndsevC4ZgAKjqSnUj0YgRI3TXXXdpzZo1SklJUWZmpurUqaMbb7xRPXv2LHHPJwD/4smqnrJcD5g7\nQ2qxWNRIe5VmbeX83Lvr36L7Fn8iQ8YpfRN9oz7uN1FnM/Yr7MIvevej1yVJg8ZOUmjzOyRJFw6t\nUr/4qc7T53mZTCb9451nNGzM07pkr63IFrepaY0jzpBZlq+3sPduQdJS557Pok6qc40igOqq1Ndg\n1qlTRwMHDtTAgZ7fe2SxWPTiiy/q+PHjunz5ssaNG6devXp5/HUAf+fPNTtFhabSdlPmnylsKLtG\nxjk6MFut2C9T34mSwaAf//yI5tSOlyHAtZxdkkKb3+EMfbWb9dQ3S1c4nvzEauVEdlWA0eTs23z2\n9TkKaz1UYXLMer7z1osef1+/+Ga11KinpKvdn8++PqfAbGhZr1H0578XAFASg91ut5fmExYtWqTE\nxETt3btX2dnZqlu3rlq1aqXBgwdr0KBB5RrM119/rdTUVE2cOFHnz59XQkKCVq1aVeBxR48eVe/e\nvbVixQo1adKkXK8J+Jv8YSzKuN9rp5lzXytvgHTntTwRfuYnLta89Zarh3usObr3VquueffvumPb\nDzofHKq3u/9B1903RF/9HOjyuNHdHFdc5v38nMvZCsrc6ix8N57ZpOF94zRiSH8tXJJc4LVGdwsq\n18Gd/O9d4NnNsoS1l9EU7HyN1iEpSs1u45HXrci/FwBQGu7mMrdnOu12u5555hktXbpUkhQaGqqm\nTZvq/PnzWr9+vdavX681a9bo3XffLfOg+/btq7vuukuS4wYko9FY5ucC/F1Rwa0ib6cp7exk3s8r\naTylDaZ1sjPVedLruu63gzoY2UxvDH5RabUjtHXJSkVFNXVZes9djs4742rM2CBro55XQ2i9Tvpl\nxx6ZTKZCDxKVV/73zmKJ0xebPf4yTtxaBKCyczt0fv7551q6dKk6d+6sSZMm6brrrnN+7PDhw5oy\nZYr++9//qkuXLho+fHiZBpO7JzQrK0tPPvkkV2qiyvKnu7a9UdVT3NdX2D7OZhmHNWXxVEVmntWG\nlnH6W78JMgfVlKw5ylRD3dvp+jx7Ja++T3lDX3Z2N331s+s4Nu+3KjXbIsOpDQoICHDOguYG1/LO\n2OZ978xms9b95Dpr/Mrz4/Xs63PYvwkAKkVlUmJiopo2bapZs2a5BE5JatasmWbOnKkmTZroyy+/\nLNeA0tLSNGbMGCUkJOgPf/hDuZ4L8IbyFKDnKq5eqCrcTlNcKXpundEXmyW77Ho2YLNmJL6kyMyz\nynnpJU3vcrsuXenMPHN4s0IbXF/kTGVu6Bs5fKACAwN1+uDGq4XvhzapTuMbZAgwyhbZRaezg2VI\nX6ORcQa9N9UR/Ny5LchdhRW8h4WFeaz0vSr8vQBQvbk907l//34NHz5cNWvWLPTjISEh6t69uxYu\nXFjmwWRkZOj//u//NHnyZHXu3LnMzwN4S0XMUJZ1ybui5Z2xlBwnxEuaLXQJo3ab7vhhm3pu/EIK\nCZESExU4bJj6/3OePvjkc5lqRyqyxW06e2iLFp43yhbh+Ddh5caZmvrcGE3/+z8kSa88P15hYWEy\nmUwKu+YmnUxdocuXzumadoMVYLx6e5AxMEj2hj1kMpkUHBys+YmLPb5cXdissadmkivL3wsAKIrb\nodNoNJZ4xWV2drYMBkOZB/PRRx/pwoUL+uCDD/TBBx9Ikj7++GPVqFGjzM8JeJKn9tWVVJvjr7fT\n5A2aqzenKs3aQmd/+0kR0Z0lWZwhvF98d32++E1nL2bu17cgybEnvIbFrAnfvadu+zboYv0GqpW8\nXGrXTmazWf9ds13XdvyjJOlESrJsFots1/V3vudHfm+qwWMnKSKmryRHbdI3/99U9YvvrlmfTVLD\n1nfKZrUoY+8K1W/t2CN+5vBmhTfr6JX3Qir90nxZP9df/14AgDvcDp3t2rXTihUrdPTo0UJPJh05\nckTff/99ua7BnDRpkiZNmlTmzwcqi8o4a5V/lvf0wbMy1khRRHRnlxD+wuTpOp9tVMalGvo9PVkN\nw4x6Z86bkqTVm1Nl2nVIb29drhYZh5R6TbSa/bBGatpUkrQgaalsEVefr2HMnTqxe5nLOC6c3KuI\nmL4uVUlTpr+vju1vclYoGew2BYY2Uo1Ty/V7jkl1m3STZHAJ9+Xpy/T0lZ+cQvcNKqiAiuV26Pzz\nn/+sBx54QPfff7/Gjx+vjh07qnbt2kpPT9dPP/3knKV84IEHvDlewKc8Wexd1lkrX/1HmX+WN7x5\nnE6mrpCiXH/QXLf1qIJC6l2Z/ZROH9iorxZ9p10pvyr8N4v+unae6v1+SUkt2uvSq88p5krglKSf\nf9kpKTbfK9uUnrpCDa53dPYaLx0q8JjDvx1zPNJ6nQx2m84c3qKI5nG6LKmRUtWzU9CV5f+r4a48\nwb88M96cQvcPhH+g4rkdOm+77Ta99NJLmjZtml588UVJksFgUG7NZ2BgoF566SXdfvvt3hkp4Ad8\nPUPpb/9RmmpH6vSBjQqPdpwKP3N4sxQQ4DL7GR4dp08+T9KAk+f0zNZvJYNBH9zxoOaF1tWjgVf3\nXJrNZqUcOK5zliyFN7/yfAc3yZJ9QVE39VRm2i4FXDqkeR++pvueeFu1m/WUJJ1KWabwlt21ef8B\nmU9/o8CwZopoHud8/RNWx8n3wkIdy9XVF+EfqHilupFo9OjR6tmzp7755hulpKQoKytLtWrVUps2\nbTRo0CA1zTNjAXiCPy5/+TKo+PI/yvyzvI0N+zRs4I3asStVvx1N1onLDRTerKPOp+1y+TyjNUdP\n7UvV0P0/6nxwqKYNfE47m7ZVuDXH5XELkpbq7O+1JFmdz2G35eiam4foYsavV24julGr/7dFY4fe\nrv8uWyGb1arwlt11/vgORTSPU1hUrM7u/VY2q0XGgJJ7fsv696s8M95cgwmguir1NZhNmzbVY489\n5o2xAC78bVavMvFGWM8/y9svftyVKx5jpQaxCjy0SjZrjkIbxCjz4EqFNr9DdbIzNXHRZN2UfkT7\naodr2oipOlkvyvmcJpPJOdaFS5Yr/MqezAvpKbLZbAoMqSNJyj53XJJUs14zJX6309G3Wf9OGc9s\nUtbJfS4zm3Vb9ZUhfY1sDXtIKjrUlefvV3lmvH09Ww4Hwj9Q8Yq8BvP48eNlftKoqKiSH1QOXINZ\nPRR2TWJ5ry70BU8GQHeurayo6xIL+/60DklRx/Y3qV98d234+FPdPn2qap86qfUtOmpSTJzMQTWd\nS+eNDfv07qvjXO4mP31wo8Kbd1LAlZ7Oc0e3y5KVpgZtHCfV07d/pYY3DXN5zfMpX6tuG8ef2awW\nnU/brfbX2tSx/U3F1jhVlb9fKDt/XEkBKqNyX4PZq1cvZ/1Raa5nNxgM2rNnTymGClRdnp6tLW6W\nLPc/0C0/b9dxa5tyLcGX9T/jju1vcrxOUpL6TZ4oXbyo/8TdrdnNb1Ro41iF2G26kL5HNptNIxNi\n9W3y2gKHk86n7VJY4xuVnrpC19a16nKbqyfVa0TGFHjN9rEt9cOuFYq87nad++1nRbTorAM5kvkn\n7iZH8djTC1SsYpfX7Xa7QkJC1KFDB5lMJrfCZ3l6OoG8qsLylzf2YBb2H2XecHv+uFVhbi42FBYu\n3Q3KhX5/+j8mTZkiTZ4shYTof08/r//YOsl+YrckKcBoUlhUW9msOc5rLfPLPu9YZWnXuql6dopx\nuc88tMH1LifZA89uVse+cfr1skWn9q1Rw9Z3urzXC5KW5rk+0zU8V4W/XwBQmRQZOu+77z4tX75c\nJ0+e1NatW9W7d2/17dtXXbt2VVBQUEWOEdUUe9/clzfc1ml8g8uJ8tLuaSwsKL805V298cozLu9/\nge/PHf+n4DFjpMRE6dprpUWL1KF1a0VNmilr5HVFjin/4aSRY/o4l8Uludxnfu7Iz4ps2c05W/pw\nQpwG9e+tL755WTa7rcDX+MU3q6VGPV2+Pk9UJgEASq/IPZ2SY6Zz69atWrZsmZYvX660tDSFhoaq\nV69e6tu3r7p161bkbIU3sacTlUVRezAleXQvWf79iTmXs3VD6H51bH9Tqfc0Sirw55lpu9Smac2i\nl6sPH5YGD5a2bZNuv90RPBs0cL4HRV2XaTabtSBp6ZV+Tqn9zbEaMaR/gT2qeW9BOmG/3vlevvPy\nQ849oTarRWf3r1J4K0dYDTy7WZaw9jKagl2+Pk8up7InEAA8sKdTciyVt2/fXu3bt9fEiRO1fft2\nfffdd1q+fLkWLVqk2rVr64477lDfvn11++23MwMK5FPYbJokj5/Kz79U3LTGkQIzk3mZzWZt+Xm7\npDayWS3O0+IWiyP05X2u3Cskj+UYnFsD8oatIXVrqsYf/yidOiU9/LD03ntSIf8W5D/UkxvIj16+\nVmd/y1JEdGft2+yY2SwqmI8YYnZ5L/POyhoDjKrboqfzMJPFEueyNO9pntyvS3gFUB0UO9NZnF27\ndmnZsmVatmyZDh8+rFq1ajkD6J133unpcbpgphOVmbdOTbsbXPKGvTOHNstgMCqiheP2oEbaq/ff\nmiBJemnKu9q836raDVrpYsb+K8vZjlCaG7bu2rFcj676hwICAmR47z1p3LhCX6uwk/S570Pmid2q\n0+gGl/djZJxB637aX+IJ/OLeS3dO+peHp76P+d+jhkpVz04xxZ68BwB/4m4uCyjrC9x44416+umn\ntWzZMn355Zdq1aqVlixZoscfZyM+4Au5B4xGDh9YbFDJnR00moIVWLOOIlo4bg8yBBh1LKe57vvz\n03ppyrt6fsKDuj7KpHO/bVWdRjeoTsMYzf73QvUb+iftPZylR1bO0eMrZisrqJZWTXqtQODM+1pX\nn7+FMxgX55cde9z6vIQB8Yoy7pfNmiObNefKXtF45/vx3tTHNbpbkEZ3C/Lbk+x53yO73abdB87q\ni82OLQ5PTJops9ns6yECgEeUuhw+V1ZWllatWqXk5GStW7dO2dnZCgwMVNeuXT05PqDK8eap6aJm\nO/P+ee7eSkkKyHNrj81q0bnffpaxxZ1KzZYG/+kVjR7cRemKkf3KfeaGoEa6ttENeuG/f9VNR3fp\nYERTPRc3TE0u5ajrlXCU9/ULk/v6ue9DYYeMbm7bRqluLI2XdBjIm5U43vg+XkhPcf4QIHE1I9zH\nFg1UBqVaXj99+rRWrFih5cuXa+PGjcrJyVFwcLC6deumPn36qFevXqpdu7Y3xyuJ5XX4jqf+Yff0\nfxC5B3ISv9vkuK1HV5ekpat7SG1WiwzpayUZZGvQTZJ06eh61W7WU+fTdqluVKzLcnH61s/UqP19\nyjyxW3abTTcFheqVb6apYaaj8H1ii5tVJ9ZR3N5QqTLIoBO63vn677z8kJ55dZbS7K0kSWcObdIN\n0fX0wVtPuRwk+umXHTLI4DxIlDtmby2Ne4onvo95twEU9j2gsB4lqagLIYCiuJvLSgydx44dU3Jy\nspKTk7V161bZbDaFhISoZ8+e6tOnj7p3766QkBCPfwHFIXTCF/z1H/bcce05kq2wQgKL5FiqzZ2t\njLhyI5DxzCYN7+uoHPo2ea0SF34rc/07XT7/xO5lqls7SL+bGqj70T16ectC1bSY9Z/O9+iDa1op\nwGiSISBAoQ1b63zabmdgyr0ZqJHphOLv6Kp5yw84HycZCuy7LOw99cbMjb/OBuU9ob9m815nSPfX\nsA3/wu1a8LVyn16fNWuWkpOTtXu3o9S5Tp06GjRokPr06aNu3bpxUh3VjjeK3j07rt3FPu5CeorL\nHeWX69yiX3ZcvT2sf58e+scXy52VQ+kpyYq87nZlndijsT8u0IMHdirbaNKb/Z/RuhYdlL1nuaLa\nDpAknT60SYbAGpIcy/S54dasWP1/Xy9XWPNuMgWHXvl4TiFjL/ieurs0XtoDVJ5sDfCUvF9r/hP6\n/jA+APCEIkPnjBkzJEmRkZG688471blzZwUGBsput2vdunXFPmnv3r09O0qgmnMnWIU2bK3ThzYp\nvFknSQUL2M/Zrpan5+7fTG3RWambHXee17v2VrVs1kB7ts6TRcGq36qnLuz+TlN2r9edpw4rrWYd\nPd2ut3ZnHVdg6kpFtR1w9frKZp10dGuibJcvKTC4jku4DW8Vr/Stn6n+TXcrwGjy+B7WvFsH5n31\nnO4Z1LNA16fkvz805MfVjCgtbtdCZVHiQaKMjAzNnz9f8+fPd+sJuXsdVZWv/mEvaYYuYUC8vl//\nd6Wpleo2uUVpO5aocWSIps6c7Fyy7XzTtQoxpuhA2kZZw+N0Pm23y4EVx53nO3TWbFbj9qMlSfYf\nvz20NfwAACAASURBVNSclI1qmXFYP9drrGmDXtRvpw8qNCTMeVWlK7vqNrlZpw9skKJiXT9Us4GC\nMrdqeN84jRjiOvbyvKe5QdJut+nskZ8V0bynvsjT9cksIaoDbtdCZVFk6HzsscfK9ITcvY6qylf/\nsJc0QxccHKwena7XnIU7ZQgIUOO2f5DNmqMHn33Xeago48AWGYNCVDfgooa1z9GuFKNSs11fJ/t8\nmhq3uUt2u03RO77T61uWqN7vF7WoZUf97ea+Ondoo2rUaai6UbGq0zBGp/deXYo/c2iTrmk3RCdT\nVymi5W0up9HTU5JVv1UP5RiDZDKZir9Ks5D31J1Z3vxbBwqbxWQ2CFUZM+SoDIoMnfRtoirx1AES\nf/qHPe/XJEmhDWOUdWqfLqSnyGqxqF7TqyEsIrqLMtN2yRDVW4tWbNKn772oZ1+f4wxgpw/+INtl\ns2xWi7qvm6u/bF8hSXorpovW9XlStY0mnT21X/Vb3uZy+8+JrfMU0ugWhTfvpACjSQ3bxOvwlv8o\nqGZdHfrhX6pZL0r1W/VQYFCIbNYcl7qm/F9DYd8Xd2Z5828dKAqzQQDgW2Uuhwcqi9zgMm+9pcyF\n22azWfMTF2t+4uIKL+surAC9X3x3l69p1aYUXTi8xlHi3ugGXT73a4HnMQQEyBBgVE69Tvo2ea3e\nefkhtQ5J0fU1dqlTqxA1adNHY79+VS/8slyXgkL08rBX9b++zyjr1D4ZAowKbRTj8nwBRpNUo77C\nomIdv74iKDhUUW3/oLrX3ixTcB0FGINks+bozMFNLqHTne9LSeXyuUHy4YRYGc9sKrQkPi93C/QB\nAJ5H6ESVV9ZbcXJ5IrSWR96bdUbGGXT7rS01Zfr7Ll9TumKk4EbO30e2GaiA0xudIezMoU1X6ooc\nsrOzNeqRl7V5v1Wp2THKOJCmN755S3cfS9HByGv19Ki/amfTti7jCG1wvY7vWOLynMagEBlObXD+\n2cnUlWp0Yz/HbGhgkOpde6supO/RhfQ9qntte3397Xrne1fe70ve92f0vUP1xezX/P72IQCozsp8\nIxFQXfjq1HP+ped+8d016pGXdd4WKbvNqrqFVKHZrBbn8npdk0WZabtkt1ll//2sJINs1hwZz2zS\nV99ZpUY9FSap7rYl+uuPi9XoQobWt+iol1t3Vc3aEZI1R+l7klX/+p6Oovg9jrFkpu2SISBA4c07\nSTJo6K1W1awZJIvFop9qNNCvlx2znqENW+vMwc3OvZ1nDm9W3SZdne9d/qX2wpRmH2ZJWx/8taMT\nAKoLQieqvMp4gCR/FdC/FzyjCxcvqXaLvgozmpRxYIMyDmxQRHQXSVJjwz7Vb2hQyv4fZAoJU4DR\nqNMXAxTR4gYFGE3KudxGmalJUp3rZbMFq16Tm2UIMKrzvo16+n+fq6bFrO+69tCb1/SUzSBdTtsp\nySBT7XBdOLlXF0/tl8EQoFqR0QUK6LfvTNFbrz2n4OBgjRjSX+Mn/l1p1laSDLKYM3X+2HYFBJoU\n3qyjJIPz61u9OVWnD55V+JWy+saGfUoYMMHlfSjvPsy8peurN6c6ZoTlXkcnIRUAPIvldVR5eZen\ny7L0WtieyqLuFfeU/FVAhqjeqtNqoM4e+Vl2u00R0V0UEBSi1iEpGt0tSO+/NUFdb24hg9GosKhY\nRzA0GHU+backx/5LW+0WssuurJO/ymC36Z4N/9FLi6dJNqumdu6jU+OfUJ1rblJY41jnneyWrNMK\nCAhQSHhzRd00UHUa36DTh67unTxzcJM2H8hxbjnIPUmfmbZL59N2qEZoA10685tqRV4nyeB87xYu\nSVa6YlS36S06mbpCJ1KS1fWW6EK/L2Xdh5l3W8QXm6XdB8/Kbre5tZTv6y0VAFAVETpRLZTnAEl5\nQ2t55K0CMgQYFd6sky6kp0iSwgPP6Y1XnnF+TctXrldEdOerj42OU/b5E7JZcxSQ8YNyfr+oulFt\n1eKGu/Tk58/qvo1fKj20vp4bOU3LG1ynu3p3U0Ol6szBzarT6AbVjYpVYHCoakVep3qms5Ic4TW8\nWUedT9ulk6krVPfa9jIGBrmEOJPJpNCGMcoxZynAGKhakdEyH1mjkXEGl/fu/2fvzMOjqu/9/zoz\nmWSyzmSdhexANsIWhLCJLIKCqIiiaGu97W3tenu1Lv3Vra1L1Xq99Vq73ra31baKUFxQVgFRWRIk\nLNkTQvZMJvtkss/Mmd8fkxwySYAAYf++nqfPY86cOed7vjM073yW90d2OWirOYIh5UZMqcv4YGcO\nNpttzBq2htaMhsVnKnt3tu8913pTgUAgEJxEiE6BYBRcjK7nwR3yy5cuwEgJ8ghWQLIs01O1m9XL\n53kdNxkjh50brXOwNlMi3hhI5Ph5RHU08+q/fsaNDRXkjkvjR1/5LyoM4wlOWMzLr/0vC2clK6bx\nbreMjzaEwNbP+Mv/PKVEe0HC1dNBxIQFtNUc9mpQAk9kGOtnSJJKibr2uD0jMAdbHaka93oJamfo\nLL7ynacvaHRRluULHq2+lE4HAoFAcDkjRKdAcBkwNJ37yM9+h9PlwC07sRbtVNLZ6pYsQrDiF309\nGw9r+OFTv1aigxJuGsv2Kuc2l++nV/bh0+xijlSrmVRbwK/+8SiJjRW8n3gdT69+hvYAnbKGyqpa\n5b8H5qfrzen0RN7IU7/8G688/aDSQf+tO2egbtqHPno6ssuJZN2Dw+FQRJa/j2tY1HXwnHetVss9\nty0ctg82OeKM0cVTibqhx4eWRZikUr69Kn1U0epzLakQaXmBQCA4NZLb7XZf6kWcLTU1NSxZsoSd\nO3cSHT1CC6/goiAaLc6fgT08mHOMwo4JqDUDqWcn7ZZ89NFTkV0ObJYCZsaDGzeHKiRCTAMNQt34\nth8+OXmobC8+/iGea8gueu0NaIOjWNti5bu7/wSSxB9u+AZvBQbj7OvEkOoRUg3Fu4icuACT+gQS\nEkV1fYT0m80DBEZMIC24jJkZU5TP2maz8dNfvEZuaSP+cYsBMFKCGzfFNT3oo6d6NRytzZS4/97V\nXs/+w6dONnj5tGbj0GV47cH98329OtKHmsWrW7K46+ZMbluxhMee+6Ny3Kwu4/XnPc1ip/qOjsaY\n/my/3+9s2MTf9zq8nnvwMwy95unWJxAIBFcKo9VlQnQKzomhv/wHfslfbb80L6SwHrqHzeUHlMk+\ng0UnMExcNldk9ddWFqAf0k3eVnMM2dFNeOJs1C4nX/3gWdZU5WPTBvPkrDvY7bChHzeFEGMqnU1l\n/Sl8mdDo6Tj7ukkJKKGyqhZLt47whNme+504gMpXi840CVXjXlYvn8e+w+UU1/UN62Zvt+QTYkqj\npTyLsP73m6RS3njxodMKu+VLF3hNSRrnc2LYd2okUdduySdM23lGwXq6vR+r7+/pROfQew4I9IGO\n+qv135BAILj6Ga0uE+l1wTlxLTRaXOhU6UiNLjZLAbLLSZRcSAhWWmuO4uzrRt20D1fYyfpHffR0\n6os+QWovGXbdtuocwhNno+vt5Ln3nmNNVT7HdQYeXvsSh8KMRE+7k77OZlRqXwIjJmCvL6TbZqG5\n6iCtVV9S2pdOg9M4LD2OG1qrc8C4kP/7uJh6kpBUI/9fiEqtISwhE5sln5SAohEFJ3jXyup0unNq\n2JJUKpyhs7Bbi89570fz/R1Nrebp0vJD72lxT6SkznFO/4ZE3ahAILgSET6dAsEpuJCm8D09PWQd\nPIytDiVVDtBjqyMEK5JpHJJ5CXo8KeTbl89j42HPe2WXg7aqHEypy5BdDhoKtxI+cTH2hhLaa/MI\nMacR11TJMx++hKG9gX3jZ/FjowF3VxP66Om0VecQMX4+NksuPe0NxGSsAcBatJPIiQuQVGpUPpph\na+7rbMKQcmO/SPKIzWBDSn/UdRbgiWgazRrq+xuO0mL8eeGZ0UfvzmTwPtRztaUyu9//E3Sqpv5G\np7H3Yj3TDPjB67/Q891HuxaBQCC43BCRTsE5cSm8K68Wenp6+MFPXqO0z9PZ3VKejbOvm5byAxhS\nbqQdAw1SitJB3twTyMfbdhPpKuhPn+cSnjgbt1umo7EUdWAEDUWfoDenEztzLXPKsnnl7ccxtDfw\nj8w1PJI2n68/+BV+sGY6a2ZK6DUd2BtKcbvcGBURqSYqebESLQyMGE99wXbl820s2oZPgF55hmBD\nCs0nDgAS+ujpWHI/IskvnzdefIjfvPjwiNHKsYrOXT9jPEl++WDZjT56OgP+n//4/XOjjpKe7ff3\nbCKjp3I6GKmxKcmsOet/Q9dClkEgEFydiEin4JwYq4jO5dyMNDSqZpRKcDiSeWfDJmWt57L+9z/a\n0Z+a9kRQwxIyqS/agb/OqEQ8ob+DvDyL8MQ59JJO6fHtTJnQS0NLEX0R8TSV7iEwIhG1SoMpfQUq\nSeKeA+v5Su6ndKs1/Ch1PgfGJZEWG8h9d99Ob28vX/nO03SojIQYkuks3wZM9VpbZ1M5QVFJNJTu\nwT/EgM2Si0rlQ/jExXSXvoflWC3GybcxMG2oreYoao0vhrSbyJjmq+zJUMYiOud9jXSM5mIWzvJF\no9Eo37/RRqEvRkTyzPf0TF+6mGsQCASCS4loJBJcMk7XzHG5iNHBYxT3ZJdQT5Ky1ucff4Bv/Ofz\n2OQIgg3JRPtWjUpIjdRsYrPkA6AzTcIolSAhUVjdTWiMpwPcI0CzCU+cjexyUJPzL0JMqYSY0qgv\n2EZCyo38aPsbzCvdjzU4kv83dw0NKYtJCSjihWceAeCeb/8UV1gmsstBfcE2/EJMRAT0Ikd4Rmm2\nlGfhcvYgy06MKTcCJxuWQKLdkk9gRCJ1uZvQmSYRbEimtfIQ+tgM2qpy+N7a2ay5Y4XXZ2qgmIWz\nkjmSW0hxd+opu7pHw5k6wy8kQzvtR2pyulhcTmsRCAQCGL0uE5FOwSXjVDWTq1Yu9RIun+x9jRtm\nJfVHtC6uAB2Inr2zYZNXdLK6N4Z7v/Ms/nGL0eERZ+7o6aOq+Vy1cimf7B2YTw7N5fsJ9e0kJdFE\nxjSJNXc8DMDyO7/BQCTSbi1SUuqt1TnEzlzreW9FFpNMk3n6zR+SZG8md1waj09ZjCrpBgCmTU5V\nLJkcugyk/vebJ68EwN2wF411K+V1HQRGTcBXG4jePPlkFDZuFjZLPq7eDsLiPdOQYmfc4xWlbSje\nScSEBWg0Gq/PVHY5KChvxQrY6lzozGP2sSgczDmm7OmF/F5cisjolbAWgUAgOBtETafgvBnrTtqR\nunz/+H7eZWW2bW8owT9usfd4yoYSHA7HKfdiYJ/e/2gHr/7su6zNlEjyyycyGNTRSyntS+fzQ2WA\nR1jcf8/tNJcf8NT89U8mGjoW83qfIF5f/xRJ9mbeNSTw7xNnwPh5gIRRKmFPdglvftZFdpkLS95m\nLAVb8fENVGaQuyPn0izHEJOxBndfL27X8AlImo4iQmNneKX+B+MXYiTGr3pYPeKAUJZUas/M9hMH\nzqsGeGhNZEflpxR2TLho34uLMZXqSlyLQCAQjBYhOgXnxfnYCp1NM4ekUo1J08S5CuSha9VhHXaO\njgY+zS4ecS8GmocGTxxac8cKMmdOxx05V2kaKqju5qvf/BE2m4377r6dlNgQbJZ8ZNlJfcE2r7GY\nNx/bxi82/pygvm5enrSI5xNS+Ob9N3HfXE8zzcJZydS5EmitzkFnTmfc1NtRq/2QZWe/mHV47W1Y\nQiYzx6uIchcpzxklF7J65Y3UHtmIs6+bwIgJWPI+Vl73ac3m26vSlfSu1z4NWqtKrUEfm0FKQNE5\nz68fiPDdP9+XlIAiAmPmo9Zor/lmGmGfJBAIrhRETafgvDjfOrvBNZMAGo1mmEl4S0WWl2n6udbx\nna8h+OC17tpfQGFlG+EJnnpIqXE/ty6ext+3n0BSqfrnkUvKWt96eyPrsvHap1j3IYpKT+AMTiMo\naiJtNUcIj/eYv9srdvPhX5/Hz8+PN9/eyO/++gG+QSYcjk58JTVP1Vew8tg22nz9eSx9IbnmVFS+\nWiKDnKz7w8/RarW8s2ETv1l/eETzdmdPB5KvFrejx2tv12ZKfJpdTEmdA1l2ou5rJiRxCQCW/M30\ndbQSlbaMaVFNXtOJTrVPe7JLsLg9ZQRjWXt4Kes7LyeulSENAoHg8kaYwwuuCAaiY58fKmNdNvx9\nr4PHnvuj15zvtIRQQDpva6bBafuBqOKTz7466ujQQEpTo9FgxSOk2i35tFvyaWpqYdMuj8ALMabR\nUnlQiSQC5BzJG3a9L74sJjD5bnTmdKwF273S5kFxC3n25TcA+HD7AQzpt+JyduPfWsMf8j9n5bFt\nlOkM/HDN82QF6ejtbKK33UpDh5qv/PtDPPLECyxekEkw9cPuK6lUhCVkYvJtGLa3AFaS0UdPRe3j\nS0jiEmVNxrTl6GKm0mu3MjNjiiLwhkbZBvbp/ntX88aLD5212ftoEJZdHoR9kkAguJIQjUSC82Ko\nrdC5mHKP1FC0ZcdniqhZc0fPmDZNyC4HLZUHCY/PpLgbfvjU2dv32K3FRCTOPRlpM03CZslHP6gB\nR7LuYdXKXwKememeMZeeSGZLeRa+gWHK+7VhMdjq8ryipAN70+rUI9ceIzMqhacPvIexvZF942fx\nypLvUnXic2L7zd3rC7fT195Ib+xyirth5deeIlxrp/bYJqVxaLCZ+qqVy1hzxwqvvfX890mxPBJ6\nVROrVi4dlQ3S2dgYnQ2imUYgEAiuPESkU3BeDK6zG+to1uB7jEXTxEB0zGYp8Ioqnm10aNXKpai6\nKgCPgLXV5Sp1l4O557aFynpnTJtMaOwM7NZC7NZC9LEZ+AVHYavLpbXmCK4e+8koaXk2DQUfkxhr\nZsP7W+ixWVjhcPFf657A2N7I32feyXejDJQVbsWUfovyHIaUpQSExSg/R6bcRE2TA9Okm7HVHqPm\n8AbFTN1AMWvuWDFsbwdHEAMjJmAv36VEE1vKswjVtPOP3z+HVqu95FE20UwjIr4CgeDKQohOwXlz\nvr/8L9YvzgGBnDlefd7XWf/nl2jM/xBL7se4ZZkQQzKuDgvNVYdw9nUzzucEa+5YAaCknSXr5wRG\nTCDYkEpz+QH67I2EGNPQmyeDpKKt7hjt9QXoYzPo6bTz962F9EYs5mG7nac3/zfILp6et5Z/ZN6F\nyk+Pj8b/jGvVj5uCWqMlNDYD85Tbqcz6G/GqIyzKTDntHt0/35cHbgjkw7+9wNpMiZSAIr63djbr\n//Q8Op3uvPZPMHZcjD/6BAKBYKwQjUSCy4KLaQZ/JnPt0aylp6eHu7/5NHKkp5GouSILffR0OhpK\nCNN28ubrT6DT6YaloNUtWdy+JIPDxwood03F7ZaxW4uQZZmu5koCw+NwdNlw9LYTn7RIMXy3+Ifw\ni9U/pTwyHkvux/jpjIQYU2kq+wJDikeg1+dtxsc/hIjx8wFoKN5FeOIculoqAPALMdNZvY+wZI8Y\nHqnp5Gw+B2FSLhAIBAIYvS4TolNwTXIqcTXabuB3Nmzizc+66GgsBfCkohtKADcgkTlezQvPPML7\nH+0Yscv6YM4xCjsm0Fqdo3SsW/I3Y0hZSkt5NjpLIW+UHWZiWz1HIuP55R3PYA8KU65hLdqBv85M\nsCHZaw3t9QWofXwBj8hsKd+PadJyAKq+XEfsdfecsuP7XDqhL5fJUQKBQCC4dIiJRIJrjjMJoKGv\nj9TgcqopSUPPdTgctFYdIjxhNgDNJw7Q3VYDKjU+foFklRr4/k9+xcJZySOuddrkVHb/aRPmKbcq\n9zKmLcdmyWN6ax0vFexF193Oxrip/E/GSoIDdP2tRR76OpqQHb3ozOnozJMBj4jsbqnG1N80VHN4\nAzEZa5Trh5gnnXb/Rvvsg7lQjUICgUAguPoQNZ2Cq4IzmdSfj4n9qQhPmH1yIlFCJrLswl9nwpi6\nDH30VPLKmnA4HF71qj6t2TgcDm5bsQRTRMCwa67I28Wv968nsLeT3y75Nn9e9TT2Div1RZ8o12go\n3Ix56iq0YeOw5G9RjjeX70ej1dFae5T6gu34uLu9rh0clYS1eKfXWkTTiUAgEAguFkJ0Cq4KztRJ\nPdpO6+VLF2Cv2O01anH50gXDztNoho+E9A0M9RKiEYlz+Xjbbl55+kHWZkpI1j04dBm8vd/B2m/+\nGB93N81Fnuk+Ul8P3/jgeZ4o+IxO3wCevvNnbJnqSYsHhMbgGxSBtWgH1V+uIyM5ApVag4+PFv/Q\ncbRWH6Y6Zz2Obhs97fW4OuowTboJw7R7vURpY8mnhMXPxmbJx23ZyZuvP+EVDRad0AKBQCC4kIj0\nuuCic6nrAGWXA5ulgIM5akVUDazH4XDgb86koXgnAGEJc7w8QwcY6k9aX7AFf924YfdqcJp57Lk/\ncv2M8WBciOSWaS7bh6QOITxhNqqaI0gV2fxs37tkNFZwXBfFw5MX0SrJUHOU7tYaZEcPvkHhRE68\ngXZLEcWlxXT0VhKVfgcANUc2EmJIIcSURn3+ViJTViCp1KhVaqKSl2At2oFW6sRPctN8Yi9+wVGY\nomMAT23qwPMI70uBQCAQXEhEI5HgonKhxvaNpiP9h0/9mureGNqqcghP9NRiGinBjRsrntpLuW4n\nrb2BhCfMRnY5qM/fyg3XJfDizx8f1uX9z3c/4ONtu6mutxGWcgtNx79Aow0hLKHfAF4xYpfQNu6g\nvi+Kvo4mtCFGZTRleMEOXtq3HmN7A/smzOblhd+gpjIL0yRPh3l9wTYiJlxPW1UObkkicvw8AJpO\n7MMtu1CpfAhPPNlBL/n4ERY9zatZyGbJxdXTpTxzc0UWIaZ0tJ15uMIyx/RzEAgEAsG1hxiDKbgs\nuVCG4mfyKxx4PS24jPDEkylwi3siJXUO5WebK4LwhNm43TKt1TmYp9xKaV+6Vw1oT08PP/jJa2w8\nrKE3ahmqQDMdTWX4hkTSbauj6tC7WIt2oPY9WbPZ4DT3RywjlGOzSw/wu51/wtjewF/SFvL8Tf9B\neeE2TJNWnDR8T11GXd5HdNlqiRw/TzkenjAHZ28X4YlzTtaVxs1CckvUF+04aehemQ1IXs8cFjeL\nphN7cYWdu0G+QCAQCARni0ivCy4qDsfwEYsjHTsbRpuu12q1zMyYQvHeU99PUnsihHZrEaExGbTX\nF3jWGBrHk8++ysyMKTgcDupJQlKpkV0OJElNiCEFu7UYJImwsFCCE5cBnq52N3J/l7tEfdF2fNRa\nbt/y33yreC/dag2/uOVR9k2cgzXvYxy9ncPWpDOm0W2rG3bc0ds+7FhnSwWRExdQ9eU7uF09jJt+\nN53N5cPOC5A6TrkH58qlLpsQCAQCweWNiHQKLjrN5Qe8RiueD2fblT60WcYklZJk1ig/TzBItJ/Y\nidPZR2vVIUKMaYQY02gu20thxwT+vtfB+i0HcPZ1YavLpaF4F7roKbRW56AzpxMcNZHgxGVeXe0+\n2mBUak/jkb/TyQsH3+dbxXup9dXy+NqX2J88H0mlxjhpBSqNP00n9p3cn4osQkxpaHXGYfvmdrqw\n5G1WjtUXbAGXk9aKg8TNvJf42V+npfwAgc4aotxFXl3r//rbf49p09CFcAcQCAQCwdWFiHQKLioa\njUaZQQ6gj80YsRN8tJyLt+T1M8ZzJLeQaZNTWXPHQ8p1ALq6kig80Uy3tYTYGXcr1zWkLsVuLURn\nnowrLJO6nPXEzribEGMadcdO+m1KqpH+jpM8HeqHN/K3gr2Mb6riS10kD03IQB8Z7+W/GRYzjb6u\nVuqLtuPobsecfgsqtQadKZ3msv3YLPkA9HY2EhqmQztuLpa8jwkMj8eQshRL3sdEJS9S1h2VvBib\nJZ9FmSnKPq9a+fMxbxo6l89BIBAIBNcWQnQKLipK17chFaA/wvYfI5471ula7yamVOyHyrhtRS8f\nbt6piNDDx/JQ+fgSYhjZ1H0AnSlNEVgBEQnK8WBDCs0nDijNRE0n9tHVWsPEoj28VnIQXY+djfHT\nePPW/0cIUF+wHUOqJ8LYUpmNPnoabVWHMaXeBOARkSlLPZFSlcTAxKP502J46vEfcMt9D2OefKey\nFvOU22m35KOPnuq1Xo1GM0wAno+x+9DPRiAQCASCMyHS64KLyuCGn7WZEtfPGM/7H+0YloodTbq2\np6cHh8OBuiVrVGnikZqY7nvwSdZlQ3F3Kr9dd4AjBVWe+kuV5JXOthbuIDBiArLLSd2xDwgeJEoD\nwxOozP4HrTVHkV1OXK5eqnPepb5gG86edr7tG8r/5n5KYF8Xr864lb+sehqXRotaoyUyaSEVWW/S\nbsknLG4mnU3ejU7GSStoKN5JzZGNhMZeh86UTri2k18+/wRRUVF874Hbhz1nT1OxVxp+okGl7ElP\nTw/vbNjEOxs2nXP6e6TPZvnSBcLjUyAQCASnRYhOwZgyGlGj1WpZtXIpnx8qY102I4rK9e9tprC6\nm/b6AtxueVh39YDwWZcNDl0GknUPazOlUdv+eLw687HanLjdsqf+Mj6TLtlfOUet8aeheCc2Sy7h\n4+dhK/6Qdks+htRlNBR7DOSdfd00l+0lbtZX0JvTaTr+Oc6uVqKnrSY65UaeKP2SH+z6A51+gfxw\n9l28N/46r3Wo1Bp8tTqcvR2AhCzLw9ZqDPXDPPlW7A0lSNY9Xqbu9665bdjEo2/et5xEn2P4N+3k\nW3fO4He/fBStVjtmdZcjifctOz47rXuAQCAQCAQivS4YM4Z6cO468GtFfAxNx56uBrCnp4cNW7PQ\nmT0p6uaKLPTR0wFf5V6D369WqZENN6DRaE4rdAZS+15eneZ0mk/s70+HS/gFR0HDXpw9EhGJcz33\nP3GAprLPMaTcjlrjuX5k0kLqizzPY0pbNqyGUl1zhOcObmJKTT6lQWG8eNezWILCuS6gmBO1+3FF\neDwzG4p3YZp8C5JKjd1aiCw7qS/6BEPyEgCshTtInWBg2VxfNJrpw8oMBiLH69/bzLoPP8URNmWx\nlQAAIABJREFUOY9NeRrM6iD+/qefeJ17oesuxRx2gUAgEJwOEekUjBmn8uAcKcJ2Opuk9z/a4eUh\nGRY3C3XTvvNO12q1Wp5//AGac9/GRxt0MsKZMJvW6iM0FO8iyaThzpsziUic69WB3mOzolJrPBHS\nulzsDSUeG6Nu67D7JLZZ+cP23zOlJp994zP5yddeJ7/pOKqGz5kxbTJ/+u/HiJOOUJv9F3yDwlGp\nNZ5mIfNkQsdNpa+jmbaao9gsefiGRFFQ5rnH2rtuHSaqB8T8kdxCMC5ErdFecN9NMS5TIBAIBOeC\nEJ2CC85IYhQ4K+Fyz20LTzsn3Kc1G4fDcdp0sc1mY823nyNq+gPozOm0VB5EdnnEr91aRHjiHHzU\nGnx8hicAtH4STWV7aSnPJsSYht6cTqA+hohJq5EaT1ocTd73Fr/f+UfMnW38M/NuXrrtx3Rr/FCp\nNEjmJazLhtsfeJJDpXbM130NV28nzSdO1o42l+8nevpqnL12OpvKUat8MCQt4i/vfsJbb2/0er7B\nYn5/cRe2ujxsdbnKMw1lrMTimYz4BQKBQCAYCSE6BWPG2YgajUZzSuEy0nXW3LHC6/0DwmdtpoRk\n3YNDl8G6bIbVKQ6uMf3pL14jOH6RVwTVZimgpTKb6Ol30dFUhsU9kUNHcjFSoty/p2o36/78Xzg7\nLV5NPlHJi+loOM74ccFM8DnGV3e9yEsHP8DfV8NPp8/nn3PuwS2p+gXtoIlAyTfj6LZ5JgslzkHy\n1VKR/Ra22mOEJ3jOczl7GTflNnTmdFqrc9BGL+APG496Pd+AmHc5e+nraEZnTifEmEZLeTZRcuGw\nvR9LsTiQSh8p+ioQCAQCwUiImk7BmHEq70fFJmnQXPSB1wZqOIdaI43GQ1Kr1Xq8J40LUZ+iNnRw\njWl3ZSN+0Q7lXNnlwF5fRLDR04ne224FUzqHKiTGGxxg3U07UQSaZvOfT/6KcZGBuAbeZy1ClmW6\nGouoNt7Oj7b9mnnHD2ENjuAPq75KY3gAzeUHCIvPHLE5KCAiAbu1SEmp99ob0EdP9YzirMvFmHKj\nUnvpEcf59HY0UuucMqwOs+n45xgH1ZWGJWSi98s/5Z6JukuBQCAQXAqE6BSMKSOJmtOJyNM1H52v\nOBqIBLrdskckaqKw5b2HYcoaZJeDhuJPiMm4C/D4ZbocvTSX7yc8YTbHLQWEGK7HbS2k+cR+DClL\ncLgcWPO24BsQ6mlCAiJaanhl3RMkNlVyODKOh8ZPo68jgNC+Ttyymtbao/TYLHQ2Hcc82WNv1FKZ\nTYgp3TP/3OFA6ipnbnoCHX0FNLhTcTr7sNXlIalUBBtSAImupnICIuK8nm9AzFvcw0Wtqn+cp0Ag\nEAgElwtCdArGhDMZuZ9KRJ5LR/Xgey1fuoBdB/44LIo6gOxy0FqdQ3i8pxNepbGT6JdHdY0F06QV\nXhOHbLXHcPZ5Zp/LspOWimycfZ0YU5cpXfJanZnQGE9EclJNPj/Zuw5dd7ti+B6s1tBcfoCWLjW6\n6Km0VGRhnrQc2eWg5vB6giKTCTGl01y2F1Na/3z28m7K+tLwaTtEYtQRKvy6cUee7Jx39LSj0eoI\njkrxer4BMf/Pdz/gf9dtJ2yiJ53eUfkpz/z1+VF/dgKBQCAQXAyE6BScN6eLVl74e/2RV55+kC07\nPgO8o6irVi7l7/96nPD4hYq4dITOQK0uIz4uhuJu72urfDSEGq+jvugTtLINtGb8dWblddnloKfD\niq0uj7ubavnup38G4MXU+ey96WFPzSagj8mg9tgHdLXVEDvjHkWwRk9fg7VoB32djd7p8PhMbJY8\ndKZM9uftUEQueFLl8aojzJ45HY1GM6zUQKvV8o2v3cOdt9/Msy+/AcAzf30enU43xjsvEAgEAsH5\nIUSn4JSMFL0c6dhI0cr1720eNOt7eORz4DoOhwMjJVhcE4HhkcrR+Htu2fGZ8tr7H+1Q7qfVarnn\ntoWsy/ZcS3Y5aKvKoThxticCWnYyOnhyBGUOhqRF2K3FdDeVE2xKo+rLd9H4hyC7+ohOWcp3vniL\nW45tw6YN4rG0BXyhhkS87xGbsQZbXd6wPfXXmUecz97VXElwVDKSNPy1SouNV+9Yoez/Oxs2DdtX\nnU7Hq7948nQfp0AgEAgElxQhOq9RzpQOHyl6+crTD/LYc38cFtEciuxy8Nf1X+Aft9jrvFPVcRpw\nszZTGhbJG2kN188YP+x+DodjxLVu2fEZDoeD9vLPCY5fhM1SoHSRq1Vq9IkLsRVtpMvhiyHtJuwN\nJYTGzVDS8cGGZOoLthEYHkt4wmxCutt55N2fMKOpmvLwWL4Tl4p63teIdTmw5G3GOGm51z1CTGle\nc9jrcjfhq9UTljgba9FOopI9+9NSmY0hdRnWQs8c9sHv8YjhuYqYvlgRZYFAIBAIxhphmXQNMppx\niCN5az778hvDjq1/b/Ow+edNhZvwj1s8zCR+gKEjLuvdSRzJLcThcLD+vc3KCM3R+ns6nU7lPLdb\nJq/SzuqvP8nf9zrY8CXY2+1U57xLuyXf6xlVag0zpyZzw3UJJAcUkxErY7cWExqTQXt9AY2lewgM\njyc8YTbxzdX86u0fM6Opms+iU3n07hdo1JuQVGq6WiowpC7Fbi2kt73e6/r62Iz+UZr5+OvH4ZL7\n8G/azZxJ4bRb8rFbCwmLm4lKrcFfH4NK7Yujtx1b7TGv1071mVwoA/jzYSzmuwsEAoHg6kOIzmuQ\ncxUvsss17Ni6Dz/1mn+e5JePX+iEU17j5IjLfk/JfoP2A6W9/HbdAa9Z7CNNLRrq7/nK0w/yr81f\n9K/PQUvlQdQqH/zjFuN2y7RW5zBu6u3EzriHAH00TSdOGrnX520i+4ST4u5kOvr8aO9V09FwnNaq\nQ4QY09CGGAGYczyLV975fxjaG/hH5hqenHM3dbVHPdFRaxGAMlEoKnmxl9l7W1UOERMW4OrtQHb2\nEjlhAXfcdhO/fP4JUmP8CTakAhLqliyQ3NithUROvAFXX7fy2pU08Wes5rsLBAKB4OpDiE7BiKxa\nudTLIL2lIoviyhai3EVeU4CcYbNory+go7EUV8RcVOr+tHJFltd5A6JppBGX1oJtHqP0hNlnjGoO\nlAIMGJNv2fEZcuQ8miuysFkKCI3JoKe93jOdx5JHePzJe4UnzsVHG0Rr9WEqD/4DQ9pyQqOn0Xj8\nMwpquii1OAjoj2xKKjU6Ywr3H9nKkx/9Esnt5oUVj/JaaCQut4xaG4hKrcHlcBAYMYH6wu3ILicg\n0W2vp75gG3W5H9PX3UrT8c/Qx0wnPGEO9oYSZUb8YPH81q+fZFJcMMGGVFRqX9ISQ1mbKXmZuF8J\n4yevlGisQCAQCC4+oqbzGuRUZu2D0Wq1zJ2ewP99nI+kUhEWPwuQsNR+wn2rU9BoNHR1ZfC/Gw8R\nnuDxrGw+cYBJd87AfqwKd/R0bJZ89Kom3vz9c6etOwwIiz3la6MxiVepNYTFzcRauANXbweGlBsB\nj/dmm+xG5aMhMGI89oZSum0W5L4e4md9Fbdbpr2+AL9gA27ZSV9HE34hBgD8HD08vPV/mFe6n1q/\nAB7NWEF1UBBRpkxAwmbJp6UiC0d3O3a1RHjiXOpyNxEUkYgxdRnWgu0AmKas9OxNRRb66OmouypY\ntfJxZY8HW0N5P+vDI9pOjWY/RsOZanoFAoFAIBhrhOi8BhmteMkvOo7OnK50issuJ3aVCY1Gw9q7\nbuWttzcqUUHw2Pvk5udz/Yx0ADSa6UokbqDjeqivpkkqxRitpaCyQ5ngA9BSngWZs89oEj9YQPsF\nGxUPTdnlQKMNQR89FQBL/hYMKTeiN6dTd2zTMP9OS/4W/HQmOhrKCG+u4ZdHPyGxqYLD4dG8vOop\nKhvLCDN5nquheBd+IZGKEG+pysGSvwWdMZUQUxoqtYbAqAnoB+1dWNwsLEfWs/Xd108p8EZjiD/4\nnFN1sp+KwY4Be7JLqCcJGNuGpNH8QSMQCASCaxMhOq9RTiVwBkfAJqVM4LO3vbus9dHTlXM9lkje\ndZdfVkBpnyctPtDZfnpfzYfo7e3ltq8+TGDCTdithQDoYzMUy6Uzcf2M8RzJLUQer6a0z3Ns8Lxz\nAGPazdithejMkzGk3UTt0Q+JybjT6/V2Sz7X2Zt55dguQh09bIhJ49fXrcJhq6a3s5mukg3gdhOe\nuBKNNhgAZ183PbZaYjPWAJ6IZljcTMBTY9pRXwBAYMQE/uPfVxMVFTWqZzoTZ+uNOvh8W12e1x8T\nozHkP9NaBkdNxyoaKxAIBIKrCyE6BQpDhYyRclISI8jP/YiAiAT00dMxSsdxONJ4Z8OmYVHLloos\nwuJnedXydXV1kZNXhiSVEzHhemrdHl/NwQLn/Y92EJhwE63VOYTFzQJA3ZLFqpXPKusaLGoG3uNw\nOPg0uxiLKxG71UWQXIets4rQpOUjzjsfQKXW4OrrwlaXhxsZkACJ28uyefjwNtxumV8kz2Hf8kcJ\ndDmoPfo+wVFJ+JvSaK06RHvJHqJSlqBSa6g5vIG4mfcNm5Pu6GihoW03xv6pQ/aK3ax+fuymBJ3t\nJCfvWsuxK+U+lfgV890FAoFAMBQhOgUKQ4WMxTWRtfMlls2fypHcQialSOw77NNvtu7wiloezDlG\nYewMxd4HYO+BL8nKq8OYuhzw1FiGj58H+A6790Bdpt1aiCzLuNuqeXv9h7jdbjZu2YscOQ+VWsOO\nvb9CQlJSw01lTUhSC+GJc4B05LK92GqP0dF0gu7mSozpKwCwFu0gcuJCZJeTmsMbCIxMJNiQTGvV\nIaJir+Nbe/6Plce2YtMG8fjkRdTO+zel+z1mUBRTH5NBu6WQqkPvoB83Bd+A8GHPEqmqJT59HKV9\nJ6OJQXELFbF9qespgw0pXl6g55MCP5cxpgKBQCC4NhHd64LTotFouP/e1bz6iycJCAigniSvzuQB\nIfXCM48Q7VuldFZ3VH7K3iO1RKUuV843pC6l88TWYR3Xq1Yu9VgGIRFsSKXHVocueRX/yvHhfzce\nQo6cR2t1Dm63THGdw2sNPv46whPneHWoo1LhH2IiMnkRNYc3YrPkE544D2vhdtpqjuF2y0SOn0dH\nYykJxjSee+85Vh7bSnl4LGtS53BQHwn0p+jjvTvt7Q0ldLdWEWJIpq+zGUPKYprLD3g9919//0sy\nZ04fYTc9kcHv/+RX/Gb9YX6z/jBrvvkUNpvtrD+Xs+1kH3w+SCN2x18qhK+nQCAQXBsI0SlQOBdL\nHofDwTsbNvH+Rzt45ekHuX++LykBRQTGzMfl6Bp2vkblSXsPFhoAd92cSbsln4binUSMn09HYynt\n9QWeyKK1ELVvIA3Fu3CP4BU6lN52K+GJs9FogzFPWUlXUzmdzeUYUpfR29kIsucaiW31/PfbP2ZK\nTR77Jszm0btfoHvifJBlGsv2jpii72quwJB2Ex0NZRiSl6DWaAmL96TU/Zt28mH/3PNT7eX69zZT\nUN6KzpyOzpxOk13FvQ8+cdZia6jl0pmE49Dzf/Piw9x/72rW3nXreQnO87VxGirCv/+TXwnhKRAI\nBFcpktvtdl/qRZwtNTU1LFmyhJ07dxIdHX2pl3NVcbrU70D93kANp1Eq8Up1q1uyuOtmT8p2XTa0\n1OTQ196EIdUjQqyFO4iYMB+zpop6Sy12DAQbkon2rVJGbOZV2nE7egiNnYHdWkxHYxlqjb9SG9lU\n8BEREaG4wmZibyihs7EMH7WKqEkea6KW8iwkXy1h0dOUlK+zr5uG4t30dTYTGB6H2i+Q+ZVH+dmR\nbfg7evlH5hrezlxDc9WX/U1AEnVHP8A/Ig5HZwtRSZ5GqoaSXUSMn49K7Yu1aAdRSYvoaCwFPI1C\nD9wQ6JVWHmkvH3niBYq7U70cAeqLdvDYN2+5YlPS51Mu8NbbG/ntugNetlvfWzub++9dfUHWKhAI\nBIKxZ7S6TIhOwVkxWGA4HA7WZeMloNot+SSZNUhI1DrjaTlxgO4OK77+OiIn3oCPbwCyy6mIttbq\nHPTR07lvrqfO818fbqMn7HpsdbmKnVHTiX34aINQqXwIjJhATc6/8AsKx19vBqCzqZxuewMBehOo\n1Gi0enrb6zFPuQ2AttKtqBxtOIMn4uxo5uvF+/leZS7dag3PZ65ia6CegNBoxe5Idjmx9Y/MDI5K\novbIRlQ+WkzpK1CpNViLPiEsfhYtFVkYUz1iuLlkO1v/+TI6ne60+/fW2xuH7Vl9wTYWXhfHC888\nMkywXer6zwvNSCI8JaCIV3/x5CVemUAgEAhGy2h1mUivC87I0FT4wDSgkSyNJJWKencSN8xK4oEb\nAvnBVxewZHYqxtRl+PgGKOf568yK4LTVF/Dux/v4w8aj1LRK1B7e6D1JKGEOEiqCIidSX7AVR287\nGv8QJUXtFxSJxi8Q06QVmFJvwtHZjFZvpubwBsqz/kpgzFw65WBUbXW8duIY36vMpU4bxL/NWMEu\nw0R6Oxppqz12cvpSZTbBUUm01x7D3lDMuGmrCYoaT2fTcWyWfLRSB02ln2FMXXay3nPiUsUG6nSs\nuWOF11Qna+F2DClLKO5OHTYy8loYKTltcuqojgkEAoHgykeITsFpOZ3wGVrP11y+H1l2KVHCVSuX\notFomJKegtS4z0vUhZjSlMYcqb2URjvoo6diTF2GVmdCdnn7f7pxe+aoT7mNwDDPqMqBiUJqbRAa\nf50iAI2TluOj8ScmYw0hUck0ln6Gvr6Uv+ZsY97xA+SNm8Qj9/8Px3WRIDuIz7yfuJn3UV+wlZbq\nQ+ijp9NWc5hx0+9CpVIrHfk682R0pklIIRMJiIg/p/3UarX86uffJ8kvn67SfxGVvBi1RjviyMhr\nYaTkmjtWeI1bNUmlrLljxaVelkAgEAguAMIy6RrnTOnb01niDDSnrH9vM2+/vxPwR2+eDMDurCJ2\n7S+gUZ2G7HJTV9eIXPUuwYZkwuJmKmlsVccJJk+aQKV7unKPqOTF1BzeQPT0uwBPLahvSIQS/UTF\nsIlCve31yC6HIhAllcozP33cVGKy3uHVE8fQdbezMX4af1v5OE61Lz0djZgmLVfua558KzWHN9LX\n3oB/WAz2hhJk2emZjuTjQ2vNURwtxYQmLUet0fYbwXt8RUdrO9TT08Njz/2ROlc6ziC8LKauRbRa\nLW+8+NCg7+BDV10JgUAgEAg8iEjnNcxYpG+1Wi3337uae1ctISJxrhKVs5LMcauseF1GT72d2Bn3\n0NvecDLiWZFFbFwsR/KOD7tucFQSdmshdmshkUkL6bU3AR6xKbmhZkgKPip5CTZLgXLdYEMKssvB\n3E9e5/dHthHY28lvl3ybP9/+FM0NpVgLd6DrF8he9zUm4+zrQm+ejN6cjqOjmSBjCj2ttejN6USm\n30l3XRayy4k+ejqSdQ9rM6VR2w4NFvEhpjSaTxw4Zef3+XaGXykMTMc63056gUAgEFzeiEjnVcC5\nNpucydi7p6cHh8OBuiULZ+jIEb2Bex/JLQSG1+IN9roEMKQuxVq0g4DQaPQx0ymrOkxoyioay/YS\nkTgX8Mw2D0+cQ1dLBeARmp1NFVTnvIt/yDiMaTfRVnN02L2ajn9Bd20WmrAUpL4evr7lVe6sOIJN\nG8xLtz5OXsxkcDlprc4hdsY9/U1Bw8d8SuAVda3+8h1iZ97rZfSeElDEzIwprFr5y3MWSiq1Bn1s\nxqBreQvXgUiyGCkpEAgEgqsBITqvcM52Bve5XFfWZaCy7uGe2xay5o6T1/Y6xzWBzurdBMUtBDz2\nSY5uFz7+w7u5fQPD6W6rw1ZXgCl9BZJKjdrPn+qc9Ti62vDxkWgs7cM0aTmyy4G1cDsTrn8QgOby\nA7jdMrpxk72m6jSf2EdE4hzs1kJCO1p45p8/YpatkdKgMB7NWIHTnAouJ/UFW4mZfpfS1BSWMJvq\nQ+8SYp6EPno6TWWfEzlhgdd6NUFhw55hZsaUc7I4WrVyKbsOnLSdivGrHrFrfQCtVsuqlUt5/6Md\nvP/Rjquyg11weXC1OyUIBIJLj0ivX+GcT7PJ6dK3g6+r1mhxG25Ao9F4/SIaek5A9DxSAoq4f74v\nb/36SVLi9XS1VmPJ36zco/H45/S2WzGk3EhMxl00Hf+cxuJP6WmpJcSQjM48CX3cHKXWsqOxFFP6\nLSe7xOMzsVuLlChh9eENtNUeRZJU6KOnMj9uDn/bv55Ztkb2jc/kJ197Hasuitqj73Fi759xyzKt\nVYeU9TQU78LHLxAJ6Gw6TsT4+bRWnny9pTyLiMT51BduV475tGafNs19ugk7Z2vqfi10sAsuPeJ7\nJhAILgYi0nkNM1bpW9nlwG4tQpZlps1NV+aLq1RqzP3RyrrcTQT5uXH2qQgMT6C9voBgQwqRSYto\nKPoE8xRP1LC5/ABuhk8CGnwPWfaIv9aqL5GdvahUPoQmzmFO2UF+tPU1/B09/CVtIe/f9EPckorw\nxLlUf/k2EYmzCTYk01C8m5bqQ3RaSzFNuY2OxlK6bXUYUjxCMizAgV/jdhr6jDh6bHQ0luIXFEVD\n8U40QRF8d3XmKfdpNJHngRrG0SBmmwsuBuJ7JhAILgYi0nmFc77NJqdq4li+dAHqlqzTXnfVyqUY\nKKalPJsQYxp6czp7skuUNF2DlKJEQaOSl9DW3o3b5UBnTifEmEZL5UHaLYVKin0gktndWkfT8S+Q\nXU4CIyZgydvidY/ulipaKrPptTdhTLsZu6WAtQfe5clNLyG53byw4lH+L30Rbunk11s3bgo6czqt\n1TlEJi2k3VLIuOmr8fENQO3jS+TEhVR/+Q5tNce4c/ls7lh5E3qfVkLjM+ltbyA0ZhqGlBvROFu5\nbcWSU+7ntWBzdCUg5rkLBALB5YcQnVc4Q9O1rzz9IO9/tOOsftkO/QU9YOvj0GXQbslHsu7h+ccf\nYP17m3nkiRd46+2NyrVDfB2EJ85WRJbFPXGYyHL2ddFc9gX6cemMm7b6pMCMm4W9vmDYegJCo+nr\nbqOl+hAdDSX0dbV63cM8+Va6WqqISlmCqq2GX9eW8dUD67AGR/Lo3S+wycdNV0u1V5d8iCnt5D0b\nStAGRaFS+9JSfoBgQwoqtYaQcemEmFL5YGcO67IB40L66r7AmHbSBD4geh7PvvyG1/4O3j+HwzHs\nec6Ha6WDfSwRqeKzR3zPBALBxUCMwbyKGJraNavLRl0zOPg9188YP2xUo6t2Bw0tvQRGJBBsSMao\nKkOWXeSV1CqTeQbOvX++L8uXLmDZnd9DE55MV3Ml5sm30F5fQIgxzetcS8EWfP31hCfMATwd5APz\nzy15H2NKX4HNUoDenO71PpslH11dIa/l7maizcrB4DCeXfRNbAHB/XZJThqKd9NlqyEh82uoNVrl\nvdU57xIYmYjaxx9ZdhI6biot5VnoYzOg/jPU0Tcq92qtOarcW3Y5aCnPJjxxtrJXAzPjB/bPSAlu\n3NS7PfPox/mcUD6DUzVqDD0OnPZn0eBxet7ZsIm/73UM+06KVPHpEY1EAoHgXBmtLhM1nVcR51KX\nNfAet1vGbi2iTZYJUOcCk5U6Sqezj94258m6y4os3NHTsRZswzjpZprLDxDWb9I+zucEy5c+yI9+\n9lt8w5MBkJ09yC4HwYYUmk/sJyzBI9paKrIwpizFcuwjKrLeJCx2BvroaUrtpp/OSEPxLjQBYdQc\neY9xU1d53leZzXxJy08ObCTM2ceWycv4ZVImgTHT0PU/O0hIKom46+6loWS3Uq9ZX7AFU/ot+Prr\nlBnwNkseam0AtvoCfLq7CRm0P4HhCTSXbCds4lJslgIl4jqwv8++/AZ1rpOzwy2uiazNlJQRoQN1\nsqeq9QS8jn+y9zXcuLGS7HXe1SSYhLi5PDmbWmOBQCA4F4ToFAyb7nPCcoAIfT7FlXbCE2djq8tT\n6i4BwuJmYbPkExiRgFqjJSze87PBp47X//wa69/bTEF5K+H94tLZ04El92PMU29H8vXHWrQDf52Z\nsHiP96dWZ8IUt5KWimzaqg4Tnjgb2eWgoXg3xrRlALgc3dQd+5CAsHhWVeXzo8ObkSQVr2bcwqcL\nv0WAy+FlodRSkYUh5UZUag2RExdSX7AdZ68NjVaPj2+gJxJasouopEVKFLS15ihBiTd7TRrqqd5N\n6PibsFsL6W2vB3P6GfdTo9EM++V9qj8IAK/jFtdE2i356KOvzoaOC2XxNZihtlSjnRYlEAgEggvL\nZVXTKcsyzzzzDGvXruX++++nqqrqUi/piuJc6rJWrVyKqnEvoTEZtNXlYi36hKZuLQHqvkF1lMO/\nJn3NRQQbPNE4lVqDzjSJ2GgzWq2WI7mFhCecrMEMS8ikr9eGzZJPr70B36BIdOZ0VGqNxzw+cTZq\njRYf/xDlnh2NpV61lBGJc/FVafiP/W/z45yP6fIL4oez72JjYoayBn1sBvVFO6g+vIHQ2BnKiEmV\nWoOzt52YjLuJSl6E/fhH2GqPeeyRqnOU/dKrmlCpNYTFzcRuLcRmyScjLd7zfObJRCUv9pogZJJK\neebHPxC1cGfBxWi0OltbKoFAIBBcHC4r0fnJJ5/gcDh45513ePTRR3nppZcu9ZKuKM7ll61Wq2X1\n8nm0VB5Eb56MIeVG3I4e9ueUKOcEG1JoOrFPEVaqpv08cNdSWioPKseayr7gREUli265j+Nl5cgu\n74YabVAUbtkJsgt7XeHJ68mD7ZHc2OrysNXlIssur/eHdLfzu2M7WVtXyvGQSB5e+yKVM9fQ2VRO\na81Rz/+qviRq4kKQ3TSWfqasrS53E+OmrlI66YMnrETv04pK7es1yvIfv38Os7oMkAg2pJIW48/P\nnvhPRVSChBsZmyUPmyWfG2YlodPpRrXnp/qDYOhxk1RKklkjROx5IkZrCgQCweXHZdVI9NJLLzFl\nyhRWrFgBwIIFC/jss8+GnScaic6fwXV13d3d/CvHx6vxoqX6EH32RoxpN3saaEq245KZNzD0AAAg\nAElEQVR8AUhJjMLHR0NRRRs+/jpk2UlfWzWawEglpW4t2ExEkie9bS/fhb2zG7/AMGXUZX3BdtyS\nG42fDkdnM1GpS2mt+tL7ddmJcdJy4poqeeq9ZzF32dg3YTavLv0+ja0V+IfGYcndRIgxFTduulqq\nwOXCMOkmVGoN9flbkdQ++IeOQ2+e4vV83nWXp27qGajHfPLZV8kucxFiSkOl1pxTc8q5NhJdTaJp\nIL0+OPUtIpECgUBwZXNFNhJ1dHQQFBSk/KxWq5FlGdUI6V3BudPT08P3f/IrSuo80chgrKjMJ70n\nZZcDZ5cNQ8qNtFvyaa06QrBhApH9grLsxAFQO/DxD6HbVktPWz1hcTPQDeowj0pdjrZxBytvXsy7\ntTId3RIRiXOVhiWt3ozL0U2fvYHI5EXUHfuAmIw1XjPaa4++T/KuN3i+4AsCXE7+Ofse3plzDy5Z\nxunso6nkU2KvWwt4TOVNk5bTWLqHtpojhCfMxjR5JXXHPiQwfLzSDAQeoTN4nOdgRmqm0Gq1vPDM\nI/1iSRoUgTy7OsFTNWoMPn61N9mIefICgUBw7XJZic6goCA6OzuVn4XgvDAMbfRpOtFORMNenGEz\nabcU0ladgz52OpJKjT56Krb6kzWaAGEJmVRk/52EzPvRmydTd2zTiPeprrexcVs26uilqOzbhzUs\n1R77ENOk5ag1WnTmSV7vldwyP2is5Zv5n9Kt1vDY5MUUZK4BWaahZBey7MQ0+ZaTa4rPxG4tJCp5\nCTZLPnZrEcGGVPxDY2g6sZfv37eIgABPpHZALL6zYVP/z2cWdwNiaf17mzmSW8i0yannsvWn5WI0\n2VwOiC5pgUAguDa5rBRdRkaGkk4/cuQIycnJl3hFVydDG33CE+aQaArEp/VLQmOmkjD368iOHprL\nDyC7HGj8goddI3TcFOX9kcmL6Wwu92qyseRtxikF4AqbiaRS4xsciSV/C+HxmV4m7+0Wjzl8sCEF\na9FOZJcTTU8HP9rwFN/M340lQM+/ZSzns9jJ2GqPUX1oHWHxs/HXjTvtM8qyTEtFFr3tViKD4b67\nb1dq/IBzNg///FAZxd2prMuGe779Uy+j/PNFTDMSCAQCwdXMZRXpXLp0KXv37mXtWk/K9MUXX7zE\nK7o6mTY5leJs72NqtQ/uyLlekUObJR+bpQAf3wAsuR9jTPfU2tbnbcKQtlzx8exsriYqeQkdTcdp\nKN6Jb2AkWp2RsNgZ1BfuwMc/iPb6ItQq32Fraa3KQTduCiAhO/uQs//Jy4X7mNhWz+HwaB6bvBhN\n+s1EqH2xWwuJmXEPJ/b9mbC467w9P/vN3VvKs+jtbCQgPK7fkklivCbXK1p4Nn6mNpuNZ19+A4BJ\nKRO83ucMncUf38/j80NnNuG/XLna0/kCgUAguHy4rESnJEn8/Oc/v9TLuOoYKizW3LGCPdmvYXFN\nBMAklZIxLZ3S7OHv7W2vJyp5Mc6+biqy3iRAPw5NsIH6gq34BUYSnjibEGNav0H8LFTRmv5pQDuR\nVGoMKTdSfWgd/sEmfANDqS/YjlZvBsDR0QJAzeEN+PgFcV1HCy/lbCa0r4d/Rafy+qw76OxsJnLQ\nemSXA41/CCqVDyGxGdithbgcDno7m2go/ZTe9gaip69Gow3uP9+pNAydLTabjdv+7SmC4xcBsG/d\ndnTx85VrA0gq1Zh5aZ6rv+S5CsfTGdYLISoQCASCseayEp2CsWeosHh700958/UneOPFhwYJi4cA\nvIRoS3kWbreLqOTFqNQafHwl1GpfIiZcT0PpHpy9nRjTbh4WGdWZJtFSnoVfSJSyhhBzOi5nD33t\nDfj469Cb05FdDqzN20mY+3UA5u54nccLPseNm98s+habpyzDbS1C62OmPm8LfkGR6GKm0VC4g9iM\nuwFoPnEAfWwGrVVfEhAeC6jwk+00Hf8CQ6qnaaijYjfP/O0Frz0Zrbh79uU3CI5fdPIZJy6lvfg9\nQpLv8OyRMrJzbDiXJpvzqQMdKeK7/r3NfH6o7KqvKxUIBALBxeeyqukUjD1D6wSdobP4yneeBvDy\nMdRqtbz6s++S5JePf9NO7r0pCVdPG7LLSWvNUWoObyB84kIaindiSl1G7HVraa065OXH2W2r8xjA\ndzQQHOWZf95SmU1geAJdTRX468cROX6eYv5umnwLPm433939J57I34PdR8M3k2bxcfoSWioPEmJM\nQ29Ox0cbjK2+kLpjHyjNQwOm81Vfvk1o7HXoTJMJ13bywD3LyUwLo6tkAxrrdh5YfT1+fn709PTw\n1tsbeeSJF1j/3mZeefrBczIPv25KEmszJSTrHvTR0wFpTL00z9ZfcqzrQI/kFoq6UoFAIBBcEESk\n8ypitGnWNjmC9z/awaqVS5Xzly9dwGPP/ZE6VzpEpvP2R1vp6+mhofgTTJNWoDenU3nwn8TNvG/E\n6GbTiX34BUfRXl9Aj72RrgN/xddfR2TSYprL9hJiTKHbVue1jqCOZh7/8EUyGsopD4/hP5JmUe52\n4nP0Q2Iy7jxpv5S8mL6uDYQYh3eM60xpdDSU0NNcTOiEG/nrpmOEJ8wmMPk6rOUH2PAl7D38K2SX\ni6KqdsITZlOcDXuyf8cbLz50WmH3zI9/wG3/9hRBcQsB6Kj8lJ/99Xl0Oh1r7lhxxdv+jBTxHane\nVyAQCASCsUBEOq8SBtKsQzuyV61cirolS+kqb6nMJjgqCYfD4XX+1374/9u78/Aoq7v/4+9JMsmQ\nbbKQFUgIFpJAANkRQVGJC6AsJRSKQOtlbRV/LkWr1VZUEFq1VbGt2sc+eqHW+IgsghQEjcqugAgJ\nSUASluz7RjLJbL8/AiMx7GWMDJ/XX2TmnvucCXr58dznfL8LKWiJw+l0UFeyD0NANE5sxPQZ61r1\naj3w01b10Z0UfP0BNksdzbUleHv5Eh43iB5X/RJzTB9Ksz8mLGEYgZE9aTlWffxEvI1khxd/efu3\nDCzLZ8sVw5h1ZSrWKydgCooioHP3duP4+AUSFJVI5aHvvktZ7qc47FaCY3oTmTKZ6sNftW2/2X0Y\nDeUHyC2y8m2po817xc6eZ13BM5vNfPjmApL8c0jyz+HD44ETfjwdby6k9ekJp+pglTZprNp6ioiI\nW/yoOhKdK3Ukai996Sre3mxt03XnRMec2tpaZvzmj9Q4OhMU2YsuPocI8m3hQEtKm+urC3bjtLdg\nju1LZd5WTMFRbQq+21qaKNm3lti+rQdmSnPW42MyY8DpqvlZsu9jfIMisNQUEt27tTNQ8d6P8AuN\nJTC8BxXfbuTmpib+sH1pu4LvtcVZ4LCDlwGbpYHwhKsAqMzfipevCWdLCyFxA6kv209j5SGiklPx\nOn6qPSgqmbLcT4hKGtPmO9UWZwIGDNDmu1xIR6Hvuxgnv38s93Dn/URExLNdkh2JxD3MZjNL33iW\n95evYdfuTL4trCPXGow5tu11DcV76TpoOiX71hHbdzxOp4PKQ9sJix8KQFnuBkwhsdQWZ+Ll5U1E\nz9EU7llJ3PFOQraWRhy2Zry9jUQmXk/JvnX4h8cTmZzKkR3v4rQc48GGBmZuf58mL28WjnuIrYkj\nj4/uoL54HwYvHwKjehEWP4T60mwAQuMGU1+2HxsWynI/xT+0KzEpY13tKB0OBw2HPyMs4SrX4aL6\nsv00VuTjFxJD77ig1sfredsIS2gtTB9jOOA6QAXnH7QuRiH3i1UM/mIXW1fxdhERcQc9XvcQ5/KY\ndePOg+w4BM6IEQTH9G73qNonIIL60lwCwrsD4OVtJCx+CNWF35C/9Q1O/OPSUPYt9eUHKfxmBS0N\nldhaGrG1NFKW+ymx/W4lKCqRioObiO07npDYvtQc2UV0dDLP7PyImdvfpzQ4grlTn2G5vfG78fd/\nSnTKOLx8TICD8m+/ICgqmaCoZEr2rcXhsBEaN5h+SXEkxvoCre0ofaq/5NcTU/jwzQWkhBwBHx8q\nvt1ISGwKsf1uxd9Zx1+fvIdXnn2Ie6YNJ8k/h2nDDG32c55ua8KZXIwDPCoGLyIilxOtdHqIM5Xb\nsVgsPP70X8g63IylvhSA4JjehMUPoaZgDw3lB4hKTqUybxsVeVvpcuUkivauplNIN+w2C811xXQf\nNpP60lzqSvbh2ykMp9NBl0Gtq2GF36ykuaGCsPjBOJ0OGsoPEJ18o+tRdmJ4Dx5f/iQ9a0rZFRrD\nvGtnY+scj9F2jIKvlxEU3YvIXtcBBvxDuxDadQAOu5Xa4iyqD31F/wH9uOXaKzEajUwc/xBwch3J\np1zf85kn5jLll7+DuBtcY5viruM/679g2pRbmTl9MjOnf/c7O7G6+dWuPRTZk8+pWLyIiIhcGIVO\nD3Kqx6InVvEKWn6Cw7qTmOSbcNitFO/9iE6hcRyrzDseOLcQlTSG2uIsKvO20KXfbQCU5nxCVPKN\nrp7pQVGJHP7q3yQMn+0KabH9bqOuOAtzbAqVeVvxNgXgsFtpKNlH/7JDLNjyf5gt9SyL78+S236P\n09tIWdZ/8A2KAIMBc0xK61i563FYW1wdivysZcydczs/nzqh3SPnUwVCk8nEz24bzXvncPr65Efb\ntUX2dlsNzuZCC7lf7HuIiIhcKnSQyMOdOGBUU7QHA144cWKz1NO5xwgAivd+REtjNd0G/4zqo7vw\n8Q1od+CmNGc90ck34nQ6qDr81SmvqS/NxhzbF4fdxtFd/wcOO7/yDeXuz98Ap5O/9kvli+vuavOZ\ngq+X4RcYjo8pmIbSHLoMmAKAd8UWfnbbaNImjT2nvZUn78WE1r7qJwe5U+2TPPnglcNupSr/S9d+\nz9N95mxj/xgOAYmIiPzQdJBIXBx2K8315UQl3gBAZf42nE4HXt5GolPGUvD1UupLcwnvPoy6kn3t\nP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"text/plain": [ "<matplotlib.figure.Figure at 0x111abc5f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Set up the matplotlib figure\n", "f, ax = plt.subplots(figsize=(11, 9))\n", "\n", "#ax.text(-1.5, 3, r'$R^2 = $%.2f'%(clf.oob_score_), fontsize=30)\n", "\n", "\n", "ax.set_xlabel(\"CDC data\", fontsize=20)\n", "ax.set_ylabel(\"Model prediction\", fontsize=20)\n", "\n", "ax = plt.scatter(Y,clf.predict(X))\n", "ax2 = plt.plot(np.linspace(Y.min(),Y.max(),10),np.linspace(Y.min(),Y.max(),10),color='red')\n", "\n", "plt.savefig('../graphics/data_vs_model_gonorrhea.png', bbox_inches='tight', dpi=150)" ] }, { "cell_type": "code", "execution_count": 828, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/sklearn/ensemble/forest.py:687: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", " warn(\"Some inputs do not have OOB scores. \"\n", "/Users/akuepper/anaconda/lib/python3.5/site-packages/sklearn/ensemble/forest.py:687: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", " warn(\"Some inputs do not have OOB scores. \"\n", "/Users/akuepper/anaconda/lib/python3.5/site-packages/sklearn/ensemble/forest.py:687: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n", " warn(\"Some inputs do not have OOB scores. \"\n" ] }, { "data": { "image/png": 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5hdw17VtdOjqeEEKI7iWhf5pTVY33PzvEn9/aTpMnyIAMOzdfOY4pY9rW2gPV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\n5Dq3t7L6vKKuc5m974D69etHdHQ04eHh6HQ6YmJi8PHxYfbs2VgsFgI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"text/plain": [ "<matplotlib.figure.Figure at 0x1191b2cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from collections import OrderedDict\n", "RANDOM_STATE = 123\n", "ensemble_clfs = [\n", " (\"RandomForestRegressor, max_features='sqrt'\",\n", " RandomForestRegressor(warm_start=False, oob_score=True,\n", " max_features=\"sqrt\",\n", " random_state=RANDOM_STATE)),\n", " (\"RandomForestRegressor, max_features='log2'\",\n", " RandomForestRegressor(warm_start=False, max_features='log2',\n", " oob_score=True,\n", " random_state=RANDOM_STATE)),\n", " (\"RandomForestRegressor, max_features=None\",\n", " RandomForestRegressor(warm_start=False, max_features=None,\n", " oob_score=True,\n", " random_state=RANDOM_STATE))\n", "]\n", "\n", "# Map a classifier name to a list of (<n_estimators>, <error rate>) pairs.\n", "error_rate = OrderedDict((label, []) for label, _ in ensemble_clfs)\n", "\n", "# Range of `n_estimators` values to explore.\n", "min_estimators = 10\n", "max_estimators = 250\n", "\n", "for label, clf in ensemble_clfs:\n", " for i in range(min_estimators, max_estimators,10):\n", " clf.set_params(n_estimators=i)\n", " clf.fit(X_train, Y_train)\n", "\n", " # Record the OOB error for each `n_estimators=i` setting.\n", " oob_error = clf.oob_score_\n", " error_rate[label].append((i, oob_error))\n", "\n", "# Generate the \"OOB error rate\" vs. \"n_estimators\" plot.\n", "for label, clf_err in error_rate.items():\n", " xs, ys = zip(*clf_err)\n", " plt.plot(xs, ys, label=label)\n", "\n", "plt.xlim(min_estimators, max_estimators)\n", "plt.xlabel(\"n_estimators\")\n", "plt.ylabel(\"OOB score\")\n", "plt.legend(loc=\"lower right\")\n", "\n", "plt.savefig('../graphics/features_estimators_gonorrhea.png', bbox_inches='tight', dpi=150)" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feature_importance = (np.vstack((np.arange(len(clf.feature_importances_)), clf.feature_importances_)).T)\n", "ranking = feature_importance[feature_importance[:,1].argsort()[::-1]]" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hd02s078 0.131555315591\n", "hd02s079 0.131042669253\n", "hd02s153 0.077605262343\n", "hd02s133 0.0724125131037\n", "hd02s136 0.056609677503\n", "hd02s026 0.0381425939706\n", "hd02s181 0.0350510997217\n", "d019 0.0235904527678\n", "Population 0.0232923192023\n", "lnd110210d 0.0196776197322\n", "hd02s154 0.0195579171815\n", "hd02s002 0.0183205296601\n", "d029 0.0180897502352\n", "hd02s006 0.0178635196094\n", "hd02s011 0.017195152399\n", "hd02s007 0.0161211203852\n", "hd01vd01 0.0157915705716\n", "hd02s095 0.0156136762701\n", "hd01s020 0.015395242309\n", "hd02s107 0.0153760048708\n", "d024 0.0150625900875\n", "d014 0.015013875519\n", "hd02s134 0.0144646508373\n", "hd02s132 0.0143730075973\n", "hd02s151 0.0142209752962\n", "hd01s168 0.0138617799277\n", "hd02s135 0.012932051565\n", "hd02s080 0.0126747567138\n", "hd02s008 0.0123131135207\n", "hd02s010 0.012283776033\n", "hd02s131 0.0117112470565\n", "hd01s167 0.0114938183702\n", "hd02s152 0.0108361262187\n", "hd02s015 0.0100120041693\n", "hd02s009 0.00969465542139\n", "hd02s013 0.00955429544546\n", "hd02s005 0.00896322359447\n", "hd02s081 0.00893532837002\n", "hd02s089 0.00329471757626\n" ] } ], "source": [ "for rank, importance in ranking: \n", " print(df_new.columns[rank], importance)" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": false }, "outputs": [], "source": [ "labels_dict = {\n", " \"Population\": \"Population\",\n", " \"hd01s001\": \"log10(Population)\",\n", " \"hd02s002\": \"0-14\",\n", " \"hd02s005\": \"15-19\",\n", " \"hd02s006\": \"20-24\",\n", " \"hd02s007\": \"25-29\",\n", " \"hd02s008\": \"30-34\",\n", " \"hd02s009\": \"35-39\",\n", " \"hd02s010\": \"40-44\",\n", " \"hd02s011\": \"45-54\",\n", " \"hd02s013\": \"55-64\",\n", " \"hd02s015\": \"65+\",\n", " \"hd01s020\": \"Median age\",\n", " \"hd02s026\": \"Male percent\",\n", " \"hd02s051\": \"Female percent\",\n", " \"hd02s078\": \"White\",\n", " \"hd02s079\": \"Black\",\n", " \"hd02s080\": \"Native\",\n", " \"hd02s081\": \"Asian\",\n", " \"hd02s089\": \"Pacific/Hawaiian\",\n", " \"hd02s095\": \"Two or more races\",\n", " \"hd02s107\": \"Hispanic\",\n", " \"hd02s131\": \"In households\",\n", " \"hd02s132\": \"Householder\",\n", " \"hd02s133\": \"Spouse\",\n", " \"hd02s134\": \"Child\",\n", " \"hd02s135\": \"Child w own child under 18\",\n", " \"hd02s136\": \"Other relatives\",\n", " \"hd02s143\": \"In group quarters\",\n", " \"hd02s151\": \"Family households\",\n", " \"hd02s152\": \"Family households w own child under 18\",\n", " \"hd02s153\": \"Husband-wife family\",\n", " \"hd02s154\": \"Husband-wife family w own child under 18\",\n", " \"hd02s159\": \"Nonfamily households\",\n", " \"hd01s167\": \"Average household size\",\n", " \"hd01s168\": \"Average family size\",\n", " \"hd02s181\": \"Owner occupied housing units\",\n", " \"hd02s184\": \"Renter occupied housing units\",\n", " \"hd01vd01\": \"Median income\",\n", " \"d001\": \"Total households\",\n", " \"d002\": \"Husband-wife households\",\n", " \"d014\": \"Unmarried-partner households: - Male householder and male partner\",\n", " \"d019\": \"Unmarried-partner households: - Male householder and female partner\",\n", " \"d024\": \"Unmarried-partner households: - Female householder and female partner\",\n", " \"d029\": \"Unmarried-partner households: - Female householder and male partner\",\n", " \"lnd110210d\": \"Population density\"}" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "White 0.131555315591\n", "Black 0.131042669253\n", "Husband-wife family 0.077605262343\n", "Spouse 0.0724125131037\n", "Other relatives 0.056609677503\n", "Male percent 0.0381425939706\n", "Owner occupied housing units 0.0350510997217\n", "Unmarried-partner households: - Male householder and female partner 0.0235904527678\n", "Population 0.0232923192023\n", "Population density 0.0196776197322\n", "Husband-wife family w own child under 18 0.0195579171815\n", "0-14 0.0183205296601\n", "Unmarried-partner households: - Female householder and male partner 0.0180897502352\n", "20-24 0.0178635196094\n", "45-54 0.017195152399\n", "25-29 0.0161211203852\n", "Median income 0.0157915705716\n", "Two or more races 0.0156136762701\n", "Median age 0.015395242309\n", "Hispanic 0.0153760048708\n", "Unmarried-partner households: - Female householder and female partner 0.0150625900875\n", "Unmarried-partner households: - Male householder and male partner 0.015013875519\n", "Child 0.0144646508373\n", "Householder 0.0143730075973\n", "Family households 0.0142209752962\n", "Average family size 0.0138617799277\n", "Child w own child under 18 0.012932051565\n", "Native 0.0126747567138\n", "30-34 0.0123131135207\n", "40-44 0.012283776033\n", "In households 0.0117112470565\n", "Average household size 0.0114938183702\n", "Family households w own child under 18 0.0108361262187\n", "65+ 0.0100120041693\n", "35-39 0.00969465542139\n", "55-64 0.00955429544546\n", "15-19 0.00896322359447\n", "Asian 0.00893532837002\n", "Pacific/Hawaiian 0.00329471757626\n" ] } ], "source": [ "ranked_list = []\n", "ranked_labels = []\n", "for rank, importance in ranking: \n", " print(labels_dict[df_new.columns[rank]], importance)\n", " ranked_list.append(importance)\n", " ranked_labels.append(labels_dict[df_new.columns[rank]])" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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E5E7n5OgARERKEz8/P958802+//57+vTpQ4cOHfjqq68AaNy4MbGxsXTo0IHo6Gi+/vpr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\nlnh5efHzzz+za9cuvLy8CA8PL1Z73I973suVK8f06dPZuXMnvr6+fP/99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"text/plain": [ "<matplotlib.figure.Figure at 0x117dd3550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Set up the matplotlib figure\n", "f, ax = plt.subplots(figsize=(11, 9))\n", "\n", "ax.set_xlabel(\"Feature importance\", fontsize=20)\n", "ax.set_ylabel(\"Feature\", fontsize=20)\n", "\n", "ax = sns.barplot(x=ranked_list, y=ranked_labels)\n", "plt.savefig('../graphics/feature_importance_ranking_gonorrhea.png', bbox_inches='tight', dpi=150)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "39" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ranked_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Save model parameters for use in web app:" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pickle" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(\"../data/randomforest_params_gonorrhea.pickle\", \"wb\") as myfile:\n", " pickle.dump(clf, myfile)" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with open(\"../data/Ymean_gonorrhea.pickle\", \"wb\") as myfile:\n", " pickle.dump(Ymean, myfile)" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open(\"../data/Ystd_gonorrhea.pickle\", \"wb\") as myfile:\n", " pickle.dump(Ystd, myfile)" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false }, "outputs": [], "source": [ "deployed_model = pickle.load(open('../data/randomforest_params_gonorrhea.pickle', \"rb\" ))" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance score: 0.64313\t(0.77777)\n" ] } ], "source": [ "print('Variance score: %.5f\\t(%.5f)' % (deployed_model.score(X_test, Y_test), deployed_model.score(X_full, Y_full)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Suport Vector Regression" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.svm import SVR" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Variance score:\n", "\t0.49249\t(rbf)\n" ] } ], "source": [ "svr_rbf = SVR(kernel='rbf', C=15, gamma=0.0001, epsilon=0.0005, tol=0.00001)\n", "#svr_lin = SVR(kernel='linear', C=1, epsilon=0.001)\n", "#svr_poly = SVR(kernel='poly', C=1, degree=2, epsilon=0.001)\n", "svr_rbf.fit(X_train, Y_train)\n", "#svr_lin.fit(X_train, Y_train)\n", "#svr_poly.fit(X_train, Y_train)\n", "#print('Variance score:\\n\\t%.5f\\t(rbf)\\n\\t%.5f\\t(lin)\\n\\t%.5f\\t(poly)' % (svr_rbf.score(X_train, Y_train), svr_lin.score(X_train, Y_train),svr_poly.score(X_train, Y_train)))\n", "print('Variance score:\\n\\t%.5f\\t(rbf)' % (svr_rbf.score(X_test, Y_test)))" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.56 (+/- 0.05)\n" ] } ], "source": [ "scores_svm = cross_validation.cross_val_score(svr_rbf, X_train, Y_train, cv=cv, n_jobs=1)\n", "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores_svm.mean(), scores_svm.std() * 2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model comparison" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_scores = []\n", "model_errors = []\n", "model_names = []\n", "model_scores.append(np.mean(scores_regression))\n", "model_errors.append(np.std(scores_regression)*2)\n", "model_names.append(\"Linear\")\n", "model_scores.append(np.mean(scores_rregression))\n", "model_errors.append(np.std(scores_rregression)*2)\n", "model_names.append(\"Ridge\")\n", "model_scores.append(np.mean(scores_etregression))\n", "model_errors.append(np.std(scores_etregression)*2)\n", "model_names.append(\"Extra trees\")\n", "model_scores.append(np.mean(scores_adaregression))\n", "model_errors.append(np.std(scores_adaregression)*2)\n", "model_names.append(\"ADA boost\")\n", "model_scores.append(np.mean(scores_bagregression))\n", "model_errors.append(np.std(scores_bagregression)*2)\n", "model_names.append(\"Bagging\")\n", "model_scores.append(np.mean(scores_gradboostregression))\n", "model_errors.append(np.std(scores_gradboostregression)*2)\n", "model_names.append(\"Gradient boost\")\n", "model_scores.append(np.mean(scores_svm))\n", "model_errors.append(np.std(scores_svm)*2)\n", "model_names.append(\"Suport vector\")\n", "model_scores.append(np.mean(scores_randomforest))\n", "model_errors.append(np.std(scores_randomforest)*2)\n", "model_names.append(\"Random forest\")" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(['Linear',\n", " 'Ridge',\n", " 'Extra trees',\n", " 'ADA boost',\n", " 'Bagging',\n", " 'Gradient boost',\n", " 'Suport vector',\n", " 'Random forest'],\n", " [0.58456807197975214,\n", " 0.58724151623728538,\n", " 0.62247664219371257,\n", " 0.55762532394194042,\n", " 0.63789431535633934,\n", " 0.64292067288243726,\n", " 0.55546802562597519,\n", " 0.63547396612249241],\n", " [0.020748703236739038,\n", " 0.022872012731342393,\n", " 0.046991141818972265,\n", " 0.061103844940426887,\n", " 0.048239678568851994,\n", " 0.045742974864002715,\n", " 0.045432117257869865,\n", " 0.040558816555148106])" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model_names, model_scores, model_errors" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/akuepper/anaconda/lib/python3.5/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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Hjh1TcXGxzGazOnTooMDAQHdlBAAAgA+r99Ks0o93VDt37tzQWQAAAIAa6iys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\n586dqyti83ormLt37/7tGI1Gw/3799m1axfz58/PgVQitzt9+jTe3t7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pWIUQIgccOnTo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\nf/AVPA1dmoTyAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x124582f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Set up the matplotlib figure\n", "sns.set_context(\"poster\", font_scale=1)\n", "f, ax = plt.subplots(figsize=(11, 9))\n", "\n", "ax.set_xlabel(\"Model\", fontsize=20)\n", "ax.set_ylabel(\"Cross validation score\", fontsize=20)\n", "\n", "ax = sns.barplot(x=model_names, y=model_scores, yerr=model_errors)\n", "plt.xticks(rotation=90)\n", "plt.savefig('../graphics/model_performance_gonorrhea.png', bbox_inches='tight', dpi=150)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
hektor-monteiro/curso-python
integracao-2.ipynb
1
35233
{ "metadata": { "name": "", "signature": "sha256:94e819c9b8da5607d8036c67c9be6764add8434980af043a76f6c353994907c4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "def f(x):\n", " #return (a-3)*(a-5)*(a-7)+85\n", " return 1 + x**3 + np.sin(50.*x)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "x = np.linspace(0, 10, 400)\n", "y = f(x)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "a, b = 0., 2. # define os limnites de integra\u00e7\u00e3o\n", "\n", "ndiv = 100 # numero de intervalos a serem utilizados\n", "xint = np.linspace(a,b,ndiv) # cria um vetor com ndiv elementos entre os limites de integra\u00e7\u00e3o\n", "yint = f(xint) # calcula o valor da fun\u00e7\u00e3o nos pontos do vetor acima\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ " plt.plot(x, y, lw=2,color='r')\n", "plt.axis([a-1, b+1, 0, max([f(a),f(b)])])\n", "plt.plot(xint,yint,'bo')\n", "\n", "\n", "for i in range(ndiv-1):\n", " xfit = np.array([xint[i],(xint[i]+xint[i+1])/2,xint[i+1]])\n", " yfit = f(xfit)\n", "\n", " # calculate polynomial\n", " z = np.polyfit(xfit, yfit, 2)\n", " #print(\"Fit coeficients\", z)\n", " fit = np.poly1d(z)\n", " xnew = np.linspace(xfit[0], xfit[2], 50)\n", " ynew = fit(xnew)\n", " \n", " plt.plot(xnew,ynew,color='b')\n", " plt.fill_between(xnew, 0, ynew, facecolor='gray', alpha=0.4)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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WjcpEnw8vRZjzAqTyFKt8+/Y9/O+bU/BwOqHUqTnXz8vLnVUpCP1ISVhBOEzO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\nQ2Gyf38vTuf/KK7ZuZFG34dE4ujRFsxmPSPkM0I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TySSqq6tFdXW12L17d9zFMxY/4yGe\nx48fFzU1NcJkMoklS5aIH//4x0KIT/a3Lg/pSCQSSQIiZ1hKJBJJAiKTt0QikSQgMnlLJBJJAiKT\nt0QikSQgMnlLJBJJAiKTt0QikSQgMnlLJBJJAiKTt0QikSQg/w+tzX7btWczkAAAAABJRU5ErkJg\ngg==\n", "text": [ "<matplotlib.figure.Figure at 0x7f5b6b0be5d0>" ] } ], "prompt_number": 54 }, { "cell_type": "code", "collapsed": false, "input": [ "from scipy.integrate import quad, trapz, simps\n", "integral, error = quad(f, a, b)\n", "print(\"Integral por quadratura:\", integral, \"+/-\", error)\n", "\n", "print(\"Integral usando \", len(xint), \"trapezoides:\", trapz(yint, xint))\n", "print(\"erro relativo:\",(integral-trapz(yint, xint))/integral)\n", "\n", "print(\"Integral usando Simpson\", simps(yint, xint))\n", "print(\"erro relativo:\",(integral-simps(yint, xint))/integral)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "('Integral por quadratura:', 6.002753622554247, '+/-', 1.917556928130934e-10)\n", "('Integral usando ', 100, 'trapezoides:', 6.0029235358789599)\n", "('erro relativo:', -2.8305896826226489e-05)\n", "('Integral usando Simpson', 6.003114979663227)\n", "('erro relativo:', -6.019855747913912e-05)\n" ] } ], "prompt_number": 55 }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Exerc\u00edcios" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Exerc\u00edcio 1: Escreva uma fun\u00e7\u00e3o em python que calcule a integral pelo m\u00e9todo de Simpson, da mesma maneira que foi feito para o m\u00e9todo dos trap\u00e9zios. Com essa fun\u00e7\u00e3o calcule a integrais da fun\u00e7\u00e3o abaixo:\n", "\n", "$$b)~f_2(x) = 1 + x^3 + \\sin(kx)~~~com~a=0~e~b=2$$\n", " \n", " onde $k$ \u00e9 um parametro que para este exerc\u00edcio deve ser adotado como $k=50$.\n", " \n", " - Fa\u00e7a uma an\u00e1lise dos erros cometidos a medida que se aumenta o numero de intervalos na integra\u00e7\u00e3o da fun\u00e7\u00e3o acima no intervalo [0,2]. Para o numero de intervalos utilize a seginte sequencia: \n", " \n", " [2, 4, 6, 8, 10, 20, 40, 80, 100,200, 300, 600, 1000, 2000]\n", " \n", " Utilize gr\u00e1ficos para mostrar seus resultados." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-2.0
bbci/wyrm
examples/BCI Competition 3, Data Set 2 (P300 Speller).ipynb
2
13615
{ "metadata": { "name": "", "signature": "sha256:4d24c8ded105574ea6223737c60341d7fa99a270de0c90752d4327cfdc09e43f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Getting the Dataset\n", "\n", "This example uses the [Data Set 2][bcicomp3ds2] from the BCI Competition 3. After downloading and copying it into a directory called `data` next to this script, you should be able to follow this example.\n", "\n", "[bcicomp3ds2]: http://www.bbci.de/competition/iii/#data_set_ii\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import division\n", "\n", "import numpy as np\n", "import scipy as sp\n", "from matplotlib import pyplot as plt\n", "from matplotlib import ticker\n", "import matplotlib as mpl\n", "\n", "from wyrm import plot\n", "plot.beautify()\n", "from wyrm.types import Data\n", "from wyrm import processing as proc\n", "from wyrm.io import load_bcicomp3_ds2" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "TRAIN_A = 'data/BCI_Comp_III_Wads_2004/Subject_A_Train.mat'\n", "TRAIN_B = 'data/BCI_Comp_III_Wads_2004/Subject_B_Train.mat'\n", "\n", "TEST_A = 'data/BCI_Comp_III_Wads_2004/Subject_A_Test.mat'\n", "TEST_B = 'data/BCI_Comp_III_Wads_2004/Subject_B_Test.mat'\n", "\n", "TRUE_LABELS_A = 'WQXPLZCOMRKO97YFZDEZ1DPI9NNVGRQDJCUVRMEUOOOJD2UFYPOO6J7LDGYEGOA5VHNEHBTXOO1TDOILUEE5BFAEEXAW_K4R3MRU'\n", "TRUE_LABELS_B = 'MERMIROOMUHJPXJOHUVLEORZP3GLOO7AUFDKEFTWEOOALZOP9ROCGZET1Y19EWX65QUYU7NAK_4YCJDVDNGQXODBEV2B5EFDIDNR'\n", "\n", "MATRIX = ['abcdef',\n", " 'ghijkl',\n", " 'mnopqr',\n", " 'stuvwx',\n", " 'yz1234',\n", " '56789_']\n", "\n", "MARKER_DEF_TRAIN = {'target': ['target'], 'nontarget': ['nontarget']}\n", "MARKER_DEF_TEST = {'flashing': ['flashing']}\n", "\n", "SEG_IVAL = [0, 700]\n", "\n", "JUMPING_MEANS_IVALS_A = [150, 220], [200, 260], [310, 360], [550, 660] # 91%\n", "JUMPING_MEANS_IVALS_B = [150, 250], [200, 280], [280, 380], [480, 610] # 91%" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "def preprocessing_simple(dat, MRK_DEF, *args, **kwargs):\n", " \"\"\"Simple preprocessing that reaches 97% accuracy.\n", " \"\"\"\n", " fs_n = dat.fs / 2\n", " b, a = proc.signal.butter(5, [10 / fs_n], btype='low')\n", " dat = proc.filtfilt(dat, b, a)\n", " \n", " dat = proc.subsample(dat, 20)\n", " epo = proc.segment_dat(dat, MRK_DEF, SEG_IVAL)\n", " fv = proc.create_feature_vectors(epo)\n", " return fv, epo" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def preprocessing(dat, MRK_DEF, JUMPING_MEANS_IVALS):\n", " dat = proc.sort_channels(dat)\n", " \n", " fs_n = dat.fs / 2\n", " b, a = proc.signal.butter(5, [30 / fs_n], btype='low')\n", " dat = proc.lfilter(dat, b, a)\n", " b, a = proc.signal.butter(5, [.4 / fs_n], btype='high')\n", " dat = proc.lfilter(dat, b, a)\n", " \n", " dat = proc.subsample(dat, 60)\n", " epo = proc.segment_dat(dat, MRK_DEF, SEG_IVAL)\n", " \n", " fv = proc.jumping_means(epo, JUMPING_MEANS_IVALS)\n", " fv = proc.create_feature_vectors(fv)\n", " return fv, epo" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "epo = [None, None]\n", "acc = 0\n", "for subject in range(2):\n", " if subject == 0:\n", " training_set = TRAIN_A\n", " testing_set = TEST_A\n", " labels = TRUE_LABELS_A\n", " jumping_means_ivals = JUMPING_MEANS_IVALS_A\n", " else:\n", " training_set = TRAIN_B\n", " testing_set = TEST_B\n", " labels = TRUE_LABELS_B\n", " jumping_means_ivals = JUMPING_MEANS_IVALS_B\n", " \n", " # load the training set\n", " dat = load_bcicomp3_ds2(training_set)\n", " fv_train, epo[subject] = preprocessing(dat, MARKER_DEF_TRAIN, jumping_means_ivals)\n", " \n", " # train the lda\n", " cfy = proc.lda_train(fv_train)\n", " \n", " # load the testing set\n", " dat = load_bcicomp3_ds2(testing_set)\n", " fv_test, _ = preprocessing(dat, MARKER_DEF_TEST, jumping_means_ivals)\n", " \n", " # predict\n", " lda_out_prob = proc.lda_apply(fv_test, cfy)\n", " \n", " # unscramble the order of stimuli\n", " unscramble_idx = fv_test.stimulus_code.reshape(100, 15, 12).argsort()\n", " static_idx = np.indices(unscramble_idx.shape)\n", " lda_out_prob = lda_out_prob.reshape(100, 15, 12)\n", " lda_out_prob = lda_out_prob[static_idx[0], static_idx[1], unscramble_idx]\n", " \n", " #lda_out_prob = lda_out_prob[:, :5, :]\n", " \n", " # destil the result of the 15 runs\n", " #lda_out_prob = lda_out_prob.prod(axis=1)\n", " lda_out_prob = lda_out_prob.sum(axis=1)\n", " \n", " # \n", " lda_out_prob = lda_out_prob.argsort()\n", " \n", " cols = lda_out_prob[lda_out_prob <= 5].reshape(100, -1)\n", " rows = lda_out_prob[lda_out_prob > 5].reshape(100, -1)\n", " text = ''\n", " for i in range(100):\n", " row = rows[i][-1]-6\n", " col = cols[i][-1]\n", " letter = MATRIX[row][col]\n", " text += letter\n", " print\n", " print 'Result for subject %d' % (subject+1)\n", " print 'Constructed labels: %s' % text.upper()\n", " print 'True labels : %s' % labels\n", " a = np.array(list(text.upper()))\n", " b = np.array(list(labels))\n", " accuracy = np.count_nonzero(a == b) / len(a)\n", " print 'Accuracy: %.1f%%' % (accuracy * 100)\n", " acc += accuracy\n", "print\n", "print 'Overal accuracy: %.1f%%' % (100 * acc / 2)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:wyrm.processing:Subsampling led to loss of 2 samples, in an online setting consider using a BlockBuffer with a buffer size of a multiple of 4 samples.\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "WARNING:wyrm.processing:Subsampling led to loss of 2 samples, in an online setting consider using a BlockBuffer with a buffer size of a multiple of 4 samples.\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Result for subject 1\n", "Constructed labels: WQXPLZCOMRKOW7YFZDEZ1DPI9NN2GRKDJCUJRMEUOCOJD2UFYPOO6J7LDGYEGOA5VHNEKBW4OO1TDOILUEE5BFAEEXAW_K3R3MRU\n", "True labels : WQXPLZCOMRKO97YFZDEZ1DPI9NNVGRQDJCUVRMEUOOOJD2UFYPOO6J7LDGYEGOA5VHNEHBTXOO1TDOILUEE5BFAEEXAW_K4R3MRU\n", "Accuracy: 91.0%\n", "\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Result for subject 2\n", "Constructed labels: MERMIROOMUZJPXJOHUVFBORZP3GLOO7AUFDKEFTWEOOALZOP9R1CGZE11Y19EWX65QUYU7NAK_1ACJDVDNGQXOJBEV2B5EFDIDTR\n", "True labels : MERMIROOMUHJPXJOHUVLEORZP3GLOO7AUFDKEFTWEOOALZOP9ROCGZET1Y19EWX65QUYU7NAK_4YCJDVDNGQXODBEV2B5EFDIDNR\n", "Accuracy: 91.0%\n", "\n", "Overal accuracy: 91.0%\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analysis of the data\n", "\n", "The following part shows how to visualize interesting information of the data." ] }, { "cell_type": "code", "collapsed": false, "input": [ "avgs = [None, None]\n", "fig, axes = plt.subplots(2, 3, sharex=True, sharey=True, figsize=(9, 6))\n", "for idx, file in enumerate([TRAIN_A, TRAIN_B]):\n", " avgs[idx] = proc.calculate_classwise_average(epo[idx])\n", " #avgs[idx] = proc.correct_for_baseline(avgs[idx], [0, 50])\n", " \n", " d = proc.select_channels(avgs[idx], [\"fcz\", \"cz\", \"oz\"])\n", " for i in range(3):\n", " axes[idx, i].plot(d.axes[-2], d.data[..., i].T)\n", " axes[idx, i].grid()\n", "\n", "for i in range(3): \n", " axes[0, i].set_title(d.axes[-1][i])\n", " \n", "axes[1, 1].set_xlabel('time [ms]')\n", "for i in range(2):\n", " axes[i, 0].set_ylabel(u'voltage [a.u.]')\n", "\n", "for i in range(2):\n", " axes[i, 2].yaxis.set_label_position(\"right\")\n", " axes[i, 2].set_ylabel('Subject %s' % 'AB'[i])\n", "\n", "axes[0, -1].legend(d.class_names)\n", "plt.tight_layout()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "def plot_scalps(epo, ivals):\n", " # ratio scalp to colorbar width\n", " scale = 10\n", " dat = proc.jumping_means(epo, ivals)\n", " n_classes = epo.data.shape[0]\n", " n_ivals = len(ivals)\n", " for class_idx in range(n_classes):\n", " vmax = np.abs(dat.data).max()\n", " vmax = round(vmax)\n", " vmin = -vmax\n", " for ival_idx in range(n_ivals):\n", " ax = plt.subplot2grid((n_classes, scale*n_ivals+1), (class_idx, scale*ival_idx), colspan=scale)\n", " plot.ax_scalp(dat.data[class_idx, ival_idx, :], epo.axes[-1], vmin=vmin, vmax=vmax)\n", " if class_idx == 1:\n", " ax.text(0, -1.5, ivals[ival_idx], horizontalalignment='center')\n", " if ival_idx == 0:\n", " ax.text(-1.5, 0, ['nontarget', 'target'][class_idx], color='bm'[class_idx], rotation='vertical', verticalalignment='center')\n", " \n", " # colorbar\n", " ax = plt.subplot2grid((n_classes, scale*n_ivals+1), (0, scale*n_ivals), rowspan=n_classes)\n", " plot.ax_colorbar(vmin, vmax, label='voltage [a.u.]', ticks=[vmin, 0, vmax])" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "for subj_idx in range(2):\n", " fig = plt.figure(figsize=(11, 6))\n", " ivals = [JUMPING_MEANS_IVALS_A, JUMPING_MEANS_IVALS_B][subj_idx]\n", " plot_scalps(avgs[subj_idx], ivals)\n", " plt.tight_layout()\n", " fig.subplots_adjust(left=.06, bottom=.10, right=None, top=None, wspace=0, hspace=0)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "fig, axes = plt.subplots(2, 1, sharex=True, sharey=True)\n", "for i in range(2):\n", " r2 = proc.calculate_signed_r_square(epo[i])\n", " # switch the sign to make the plot more consistent with the timecourse. This is equivalent to reordering the classidices and calculating r2\n", " r2 *= -1\n", " \n", " max = np.max(np.abs(r2))\n", " im = axes[i].imshow(r2.T, aspect='auto', interpolation='None', vmin=-max, vmax=max)\n", " \n", " axes[i].set_ylabel('%s' % (epo[i].names[-1]))\n", " axes[i].grid()\n", " axes[i].set_title(\"Subject %s\" % \"AB\"[i])\n", " cb = plt.colorbar(im, ax=axes[i])\n", " cb.set_label('[a.u.]')\n", "\n", "axes[1].yaxis.set_major_formatter(ticker.IndexFormatter(epo[i].axes[-1]))\n", "mask = map(lambda x: True if x.lower().endswith('z') else False, epo[i].axes[-1])\n", "axes[1].yaxis.set_major_locator(ticker.FixedLocator(np.nonzero(mask)[0]))\n", "axes[1].xaxis.set_major_formatter(ticker.IndexFormatter(['%d' % j for j in epo[i].axes[-2]]))\n", "axes[1].xaxis.set_major_locator(ticker.MultipleLocator(6))\n", "axes[1].set_xlabel('%s [%s]' % (epo[i].names[-2], epo[i].units[-2]))\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 9 } ], "metadata": {} } ] }
mit
gnublet/py_explorations
sklearn/clustering1.ipynb
1
101255
{ "cells": [ { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Automatically created module for IPython interactive environment\n" ] } ], "source": [ "print(__doc__)\n", "\n", "#%matplotlib inline #way too small\n", "\n", "import time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn import cluster, datasets\n", "from sklearn.neighbors import kneighbors_graph\n", "from sklearn.preprocessing import StandardScaler\n", "\n", "np.random.seed(0)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Generate datasets\n", "n_samples = 1000\n", "#large circle containing smaller circle\n", "#(factor is scale factor between inner and outer circle)\n", "noisy_circles = datasets.make_circles(n_samples = n_samples, factor = .5, noise = .05)\n", "#double moon (classic)\n", "noisy_moons = datasets.make_moons(n_samples=n_samples, noise = .5)\n", "blobs = datasets.make_blobs(n_samples=n_samples, random_state=7)\n", "no_structure = np.random.rand(n_samples,2), None#2d\n", "swiss_roll = datasets.make_swiss_roll(n_samples = n_samples, random_state = 7)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#colors\n", "colors = np.array([x for x in 'bgrcmykbgrcmykbgrcmykbgrcmyk'])\n", "colors = np.hstack([colors]*20)#stack arrays horizontally" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#clustering names\n", "clustering_names = ['MiniBatchKMeans', 'AffinityPropagation', 'MeanShift', 'SpectralClustering', 'Ward', 'AgglomerativeClustering', 'DBSCAN', 'Birch']" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f774da0c780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (len(clustering_names)*2+3, 9.5))\n", "plot_num=1" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "datasets = [noisy_circles, noisy_moons, blobs, no_structure, swiss_roll]\n", " " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/kevin/anaconda3/lib/python3.5/site-packages/sklearn/manifold/spectral_embedding_.py:229: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.\n", " warnings.warn(\"Graph is not fully connected, spectral embedding\"\n", "/home/kevin/anaconda3/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py:193: UserWarning: the number of connected components of the connectivity matrix is 2 > 1. Completing it to avoid stopping the tree early.\n", " connectivity, n_components = _fix_connectivity(X, connectivity)\n", "/home/kevin/anaconda3/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py:418: UserWarning: the number of connected components of the connectivity matrix is 2 > 1. Completing it to avoid stopping the tree early.\n", " connectivity, n_components = _fix_connectivity(X, connectivity)\n", "/home/kevin/anaconda3/lib/python3.5/site-packages/sklearn/manifold/spectral_embedding_.py:229: UserWarning: Graph is not fully connected, spectral embedding may not work as expected.\n", " warnings.warn(\"Graph is not fully connected, spectral embedding\"\n", "/home/kevin/anaconda3/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py:193: UserWarning: the number of connected components of the connectivity matrix is 3 > 1. Completing it to avoid stopping the tree early.\n", " connectivity, n_components = _fix_connectivity(X, connectivity)\n", "/home/kevin/anaconda3/lib/python3.5/site-packages/sklearn/cluster/hierarchical.py:418: UserWarning: the number of connected components of the connectivity matrix is 3 > 1. Completing it to avoid stopping the tree early.\n", " connectivity, n_components = _fix_connectivity(X, connectivity)\n" ] }, { "data": { "image/png": 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UnVTXqXieYcEDC2hZqWWW69ml9Y03YNCgXBDyyNVQ+nfXNJdxkXHsf3I/xROK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+LJGp3FggULuO666wAY9NQgxi0ZJwlhw+E3YA8k3BYdrl6ULl2au+9+hDff/E+OqBIpKbB9+2ka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\nSnl+gMIaUJYC7d6Xl3XGSGFM9kI4+3WY/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"text/plain": [ "<matplotlib.figure.Figure at 0x7f774f09ffd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i_dataset, dataset in enumerate(datasets):\n", " #print(dataset)#i_dataset = 0,1,2,3,4\n", " X,y = dataset\n", " #normalize\n", " X = StandardScaler().fit_transform(X)\n", " \n", " #estimate bandwidth for mean shift\n", " bandwidth = cluster.estimate_bandwidth(X, quantile=.3)\n", " \n", " #connectivity matrix for structured ward\n", " connectivity = kneighbors_graph(X, n_neighbors=10, include_self = False)\n", " #make connectivity symmetric\n", " connectivity = .5*(connectivity + connectivity.T)\n", " \n", " #create clustering estimators\n", " ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)\n", " two_means= cluster.MiniBatchKMeans(n_clusters=2)\n", " ward = cluster.AgglomerativeClustering(n_clusters=2, linkage='ward', connectivity = connectivity)\n", " #arpack for large sparse matrices\n", " spectral = cluster.SpectralClustering(n_clusters=2, eigen_solver='arpack', affinity = \"nearest_neighbors\")\n", " \n", " dbscan=cluster.DBSCAN(eps=.2)#need to tune eps and minpts\n", " affinity_propagation = cluster.AffinityPropagation(damping=.9, preference=-200)\n", " \n", " average_linkage = cluster.AgglomerativeClustering(\n", " linkage=\"average\", affinity=\"cityblock\", n_clusters=2, connectivity = connectivity )\n", " \n", " birch = cluster.Birch(n_clusters=2)\n", " clustering_algorithms = [\n", " two_means, affinity_propagation, ms, spectral, ward, average_linkage, dbscan, birch]\n", " \n", " for name, algorithm in zip(clustering_names, clustering_algorithms):\n", " #predict cluster memberships\n", " t0 = time.time()\n", " algorithm.fit(X)\n", " t1 = time.time()\n", " if hasattr(algorithm, 'labels_'):\n", " y_pred = algorithm.labels_.astype(np.int)\n", " else:\n", " y_pred = algorithm.predict(X)\n", " \n", " #plot\n", " plt.subplot(5, len(clustering_algorithms), plot_num)\n", " if i_dataset == 0:\n", " plt.title(name, size=18)\n", " plt.scatter(X[:,0], X[:,1], color=colors[y_pred].tolist(), s=10)\n", " \n", " if hasattr(algorithm, 'cluster_centers_'):\n", " centers = algorithm.cluster_centers_\n", " center_colors = colors[:len(centers)]\n", " plt.scatter(centers[:,0], centers[:,1], s=100, c=center_colors)\n", " plt.xlim(-2,2)\n", " plt.ylim(-2,2)\n", " plt.xticks(())\n", " plt.yticks(())\n", " plt.text(.99, .01, ('%.2fs' % (t1-t0)).lstrip('0'),\n", " transform=plt.gca().transAxes, size=15,\n", " horizontalalignment='right')\n", " plot_num+=1\n", " \n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function dbscan in module sklearn.cluster.dbscan_:\n", "\n", "dbscan(X, eps=0.5, min_samples=5, metric='minkowski', algorithm='auto', leaf_size=30, p=2, sample_weight=None, n_jobs=1)\n", " Perform DBSCAN clustering from vector array or distance matrix.\n", " \n", " Read more in the :ref:`User Guide <dbscan>`.\n", " \n", " Parameters\n", " ----------\n", " X : array or sparse (CSR) matrix of shape (n_samples, n_features), or array of shape (n_samples, n_samples)\n", " A feature array, or array of distances between samples if\n", " ``metric='precomputed'``.\n", " \n", " eps : float, optional\n", " The maximum distance between two samples for them to be considered\n", " as in the same neighborhood.\n", " \n", " min_samples : int, optional\n", " The number of samples (or total weight) in a neighborhood for a point\n", " to be considered as a core point. This includes the point itself.\n", " \n", " metric : string, or callable\n", " The metric to use when calculating distance between instances in a\n", " feature array. If metric is a string or callable, it must be one of\n", " the options allowed by metrics.pairwise.pairwise_distances for its\n", " metric parameter.\n", " If metric is \"precomputed\", X is assumed to be a distance matrix and\n", " must be square. X may be a sparse matrix, in which case only \"nonzero\"\n", " elements may be considered neighbors for DBSCAN.\n", " \n", " algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional\n", " The algorithm to be used by the NearestNeighbors module\n", " to compute pointwise distances and find nearest neighbors.\n", " See NearestNeighbors module documentation for details.\n", " \n", " leaf_size : int, optional (default = 30)\n", " Leaf size passed to BallTree or cKDTree. This can affect the speed\n", " of the construction and query, as well as the memory required\n", " to store the tree. The optimal value depends\n", " on the nature of the problem.\n", " \n", " p : float, optional\n", " The power of the Minkowski metric to be used to calculate distance\n", " between points.\n", " \n", " sample_weight : array, shape (n_samples,), optional\n", " Weight of each sample, such that a sample with a weight of at least\n", " ``min_samples`` is by itself a core sample; a sample with negative\n", " weight may inhibit its eps-neighbor from being core.\n", " Note that weights are absolute, and default to 1.\n", " \n", " n_jobs : int, optional (default = 1)\n", " The number of parallel jobs to run for neighbors search.\n", " If ``-1``, then the number of jobs is set to the number of CPU cores.\n", " \n", " Returns\n", " -------\n", " core_samples : array [n_core_samples]\n", " Indices of core samples.\n", " \n", " labels : array [n_samples]\n", " Cluster labels for each point. Noisy samples are given the label -1.\n", " \n", " Notes\n", " -----\n", " See examples/cluster/plot_dbscan.py for an example.\n", " \n", " This implementation bulk-computes all neighborhood queries, which increases\n", " the memory complexity to O(n.d) where d is the average number of neighbors,\n", " while original DBSCAN had memory complexity O(n).\n", " \n", " Sparse neighborhoods can be precomputed using\n", " :func:`NearestNeighbors.radius_neighbors_graph\n", " <sklearn.neighbors.NearestNeighbors.radius_neighbors_graph>`\n", " with ``mode='distance'``.\n", " \n", " References\n", " ----------\n", " Ester, M., H. P. Kriegel, J. Sander, and X. Xu, \"A Density-Based\n", " Algorithm for Discovering Clusters in Large Spatial Databases with Noise\".\n", " In: Proceedings of the 2nd International Conference on Knowledge Discovery\n", " and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996\n", "\n" ] } ], "source": [ "help(cluster.dbscan)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
derrowap/MA490-MachineLearning-FinalProject
ProjectReportSupplement.ipynb
1
4277
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Supplement" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import math\n", "\n", "def evenParity(n):\n", " \"\"\"Determines if n is an even parity.\n", "\n", " Calculates the number of 1 bits in the binary representation of the given \n", " integer, n. An even parity is true if the number of 1 bits in the\n", " representation is even.\n", "\n", " Args:\n", " n: integer to check for even parity.\n", "\n", " Returns:\n", " A 0 if n is an even parity, else a 1.\n", " \"\"\"\n", " binary = \"{0:b}\".format(n)\n", " return binary.count('1') % 2\n", "\n", "def adder(n):\n", " \"\"\"Adds 42 to a given number.\n", "\n", " Args:\n", " n: integer to add to.\n", "\n", " Returns:\n", " The value of n added by 42.\n", " \"\"\"\n", " return n + 42\n", "\n", "def multiply(n, m):\n", " \"\"\"Returns n multiplied by m.\n", "\n", " Args:\n", " n: integer to be multiplied by\n", " m: integer to be multiplied by\n", "\n", " Returns:\n", " The product of n and m.\n", " \"\"\"\n", " return n * m\n", "\n", "def sine(x):\n", " \"\"\"Returns the sin of x radians.\n", "\n", " Args:\n", " x: number of radians\n", "\n", " Returns:\n", " The sine of x radians.\n", " \"\"\"\n", " return math.sin(x)\n", "\n", "def fib(n):\n", " \"\"\"Approximates the nth Fibonacci number.\n", "\n", " Since this uses Binet's formula, it is only exactly accurate from 1 to 70.\n", " Past n values of 70, it is only an approximation.\n", "\n", " Binet's formula is defined as:\n", " F(n) = ((Phi ^ n) - ((- Phi) ^ -n)) / sqrt(5)\n", " = (((1 + sqrt(5)) / 2) ^ n) - ((1 - sqrt(5)) / 2) ^ n)) / sqrt(5))\n", "\n", " Args:\n", " n: integer for the nth number in the Fibonacci sequence\n", "\n", " Returns:\n", " The output of Binet's forumula with input n, an approximation of the nth\n", " number in the Fibonacci sequence.\n", " \"\"\"\n", " return round(((((1 + math.sqrt(5)) / 2) ** n) - (((1 - math.sqrt(5)) / 2) ** n)) / math.sqrt(5))\n", "\n", "def testFibAccuracy():\n", " \"\"\"Calculates when the function fib(n) becomes inaccurate.\n", "\n", " Exactly up to 70.\n", " 71 - 72 are off by 1.\n", " 73 and on are off by more than 1.\n", "\n", " Returns:\n", " fn: the last Fibonacci number found by using the recursive function.\n", " count: the number of Fibonacci numbers calculated.\n", " fib(count): an approximation for the last Fibonacci number.\n", " \"\"\"\n", " fn = f1 = f2 = 1\n", " count = 2\n", " while (fn == fib(count)) | (fn == fib(count) + 1) | (fn == fib(count) - 1):\n", " fn = f1 + f2\n", " f2, f1 = f1, fn\n", " count+=1\n", " return fn, count, fib(count)\n", "\n", "def determinant(m):\n", " \"\"\"Calculates the determinant of a matrix.\n", "\n", " Args:\n", " m: array of arrays that represents a matrix.\n", "\n", " Returns:\n", " The determinant of a matrix.\n", " \"\"\"\n", " return int(round(np.linalg.det(m)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
MachinesWhoLearn/lectures
2016-2017.Meetings/spring/01.keras_tutorial_duplicate_questions/03.train_model.ipynb
1
215656
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from __future__ import print_function\n", "try:\n", " import cPickle as pickle\n", "except:\n", " import pickle\n", "\n", "import keras.backend as K\n", "\n", "from keras.preprocessing import sequence\n", "from keras.models import Model\n", "from keras.layers import Input, Embedding, LSTM, Bidirectional, Lambda\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# load the indexed data\n", "indexed_question_1s = pickle.load(open(\"./data/processed/02.indexed_question_1s_train.pkl\", \"rb\"))\n", "indexed_question_2s = pickle.load(open(\"./data/processed/02.indexed_question_2s_train.pkl\", \"rb\"))\n", "labels_list = pickle.load(open(\"./data/processed/02.labels_train.pkl\", \"rb\"))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# load the word to index dictionary\n", "word_indices = pickle.load(open(\"./data/processed/02.word_indices.pkl\", \"rb\"))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Padding\n", "We're almost ready to train our model. There's just one hitch though: neural networks take as input fixed-length vectors. What are we to do, since our questions are sequences of ints with variable length?\n", "\n", "The answer is to pad the shorter instances to the length of the longest instance, thus making them all the same length! We'll pad with the `0` character -- this is why we set the padding character to have a 0 index in the word to index dictionary. Keras will automatically figure out that these 0's are padding, and not take them into account when doing model computations (this is called masking).\n", "\n", "It's common to also truncate sequences. For example, say that the average length of our questions is 10 words, but there's one outlier with 900 words. Padding all of the other questions to 900 words would be a huge waste of space, when we could simply truncate that one outlier with 900 words to 10 words. Thus, we'll set a max length of 100 words; if a question is less than 100 words, it'll be padded up, and if it's longer it'll be truncated.\n", "\n", "Note that since the two questions are actually separate inputs to the model, as you'll see later, their max length could be set to different values if you wanted. This is useful if you're comparing, say, a question and a document -- you'd expect the question to be much shorter than the document, and adjust your lengths accordingly." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "maxlen = 100\n", "max_training_instances=10000" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# It takes a long time to train on all 400,000 samples on CPU (5 hours/epoch) --- let's cut it down to \n", "# max_training_instances size. The dataset itself is a bit unbalanced, around 67% non-duplicate \n", "# / 33% duplicate. We can use this opportunity to make it more balanced as well.\n", "indices_with_0 = [index for index,value in enumerate(labels_list) if value==0]\n", "indices_with_1 = [index for index,value in enumerate(labels_list) if value==1]\n", "\n", "reduced_indexed_question_1s = []\n", "reduced_indexed_question_2s = []\n", "reduced_labels_list = []\n", "\n", "for i in range(max_training_instances):\n", " # if i is even (~50%), pull something from indices_with_0 and add it to \n", " # the truncated dataset. Else, pull something from indices_with_1 and \n", " # add it to the truncatd dataset. If any of the list of indices are empty,\n", " # use the other one.\n", " # TODO: I'm pretty sure this if can be refactored, but it's late and I can't think\n", " # right now.\n", " if i % 2 == 0:\n", " if indices_with_0:\n", " index = indices_with_0.pop()\n", " else:\n", " index = indices_with_1.pop()\n", " else:\n", " if indices_with_1:\n", " index = indices_with_1.pop()\n", " else:\n", " index = indices_with_0.pop()\n", " reduced_indexed_question_1s.append(indexed_question_1s[index])\n", " reduced_indexed_question_2s.append(indexed_question_2s[index])\n", " reduced_labels_list.append(labels_list[index])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000\n", "10000\n", "10000\n" ] } ], "source": [ "print(len(reduced_indexed_question_1s))\n", "print(len(reduced_indexed_question_2s))\n", "print(len(reduced_labels_list))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Now we want to pad / truncate our instances to a max length.\n", "# Keras has a handy function to do this, but it isn't hard to implement yourself as well.\n", "padded_question_1s = sequence.pad_sequences(reduced_indexed_question_1s, maxlen=maxlen)\n", "padded_question_2s = sequence.pad_sequences(reduced_indexed_question_2s, maxlen=maxlen)\n", "\n", "padded_question_1s_shape = padded_question_1s.shape\n", "padded_question_2s_shape = padded_question_2s.shape\n", "\n", "# We also want to convert our list of labels to a numpy array for use in the model.\n", "labels = np.array(reduced_labels_list)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Let's inspect the shapes of our padded questions." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "padded_question_1s_shape: (10000, 100)\n", "padded_question_2s_shape: (10000, 100)\n", "labels shape: (10000,)\n" ] } ], "source": [ "print(\"padded_question_1s_shape: {}\".format(padded_question_1s_shape))\n", "print(\"padded_question_2s_shape: {}\".format(padded_question_1s_shape))\n", "print(\"labels shape: {}\".format(labels.shape))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Great, each of our questions is now of length `maxlen`." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "At this point, we need to get the \"vocabulary\" of the training data. This is the number of unique indices in the data, so in this case it's easy to calculate by taking the length of the `word_indices` dictionary." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vocabulary size: 104472\n" ] } ], "source": [ "vocabulary_size = len(word_indices)\n", "print(\"Vocabulary size: {}\".format(vocabulary_size))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "The `batch_size` controls how many training instances we process (do a gradient update on) at once, since it's impossible to train on all of the data at once. 32 is a fairly standard number." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "batch_size = 32" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Building the model\n", "Now that we have our data sorted out, we can finally build our Keras model. As a computation graph framework, Keras has a nice \"functional API\"; the notion is that you \"construct\" layers, and then \"apply\" these layers to tensors by calling them. This probably sounds quite abstract, but hopefully the code below illustrates." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Setting up input layers\n", "At the beginning of every model in Keras, you need \"input\" layers which indicate what the shape of the incoming data arrays are going to be, and provides a means for the other layers to interface with it." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "question_1_input Tensor(\"input_1:0\", shape=(?, 100), dtype=float32)\n", "question_2_input Tensor(\"input_2:0\", shape=(?, 100), dtype=float32)\n" ] } ], "source": [ "# We are passed in two matrices, one of shape (batch_size, question_1_length) and \n", "# (batch_size, question_2_length). In this case, these are both (32, 100) by default. \n", "# Note that the input layer's shape argument does not include the batch size, and it is a \n", "# tuple with a value of (maxlen,)\n", "question_1_input = Input(shape=(padded_question_1s_shape[-1:]))\n", "question_2_input = Input(shape=(padded_question_2s_shape[-1:]))\n", "\n", "print(\"question_1_input {}\".format(question_1_input))\n", "print(\"question_2_input {}\".format(question_2_input))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "In the above printed representation of our tensor, you'll notice that the shape is a weird `(?, maxlen)`. In this case, the `?` refers to a dimension that can be of any size. Since that is our batch_size, we can vary the batch size to be whatever we want and the model will still work." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## The Embedding Layer\n", "Now that our questions are in the graph, we want to use an embedding layer to project each int index (which actually represents one word) into a higher-dimensional space. The way we do this is by using an `Embedding` layer. This layer replaces each index with a vector, and the vector should ideally represent the semantic meaning of the index. In this way, the model can get some notion of \"meaning\" between the indices.\n", "\n", "In this model, the Embedding layer is randomly initialized --- every index is assigned a random vector at first. As the model trains, it will tweak the vector assigned to each word in order to minimize the loss. However, this naturally leads to a lot more parameters to tune, which makes the model harder to learn. \n", "\n", "It's thus common practice in the field to use *pre-trained embeddings*. Pre-trained embeddings are what they sound like, embeddings for a word that already have gotten to a pretty good representation. By using these pretrained embeddings and not updating them (so keeping them fixed and not letting the model change them), you drastically lower the amount of parameters the model has to fiddle with and also prevent the model from overfitting (as it can make the embeddings overly domain-specific, while the pretrained embeddings are quite general)." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "question_1_embedded Tensor(\"embedding_1/Gather:0\", shape=(?, 100, 128), dtype=float32)\n", "question_2_embedded Tensor(\"embedding_2/Gather:0\", shape=(?, 100, 128), dtype=float32)\n" ] } ], "source": [ "# Embedding layer for question 1. For each word in the question, it'll\n", "# transform it into a fixed-length vector of size 128.\n", "embedding_layer_1 = Embedding(input_dim=vocabulary_size, output_dim=128, \n", " mask_zero=True, input_length=maxlen)\n", "\n", "# Embedding layer for question 2. For each word in the question, it'll \n", "# transform it into a fixed-length vector of size 128.\n", "embedding_layer_2 = Embedding(vocabulary_size, 128, \n", " mask_zero=True, input_length=maxlen)\n", "\n", "# Now, we apply the embedding layers that we constructed to the input\n", "# shape: (batch_size, question_1_length, embedding_output_dim) or (32, 100, 128) by default\n", "question_1_embedded = embedding_layer_1(question_1_input)\n", "print(\"question_1_embedded {}\".format(question_1_embedded))\n", "\n", "# shape: (batch_size, question_2_length, embedding_output_dim) or (32, 100, 128) by default\n", "question_2_embedded = embedding_layer_2(question_2_input)\n", "print(\"question_2_embedded {}\".format(question_2_embedded))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Encoding the words\n", "\n", "Now, our data consists of matrices of shape `(batch_size, maxlen, embedding_dimension)`. It might be hard to intuitively think about what this means, but you've intuitively replaced each \"int\" index in the sentence with a vector of size `embedding dimension` (so from `(batch_size, maxlen)` to `(batch_size, maxlen, embedding_dimension)`).\n", "\n", "Now that we have embedded our questions, it's time to encode them. A popular choice in modern NLP is to use a recurrent neural networks, especially the Bidirectional LSTM (biLSTM). An LSTM essentially takes a single question as input (something of shape `(maxlen, embedding_dimension)` in this case), and squeezes it into a fixed-length vector of size `(LSTM_output_units)`. In this manner, you can think of the LSTM as \"encoding\" the question. into a single vector.\n", "\n", "The \"Bidirectional\" part comes from the idea that you should run the question (a sequence of vectors) through the LSTM, and then reverse the question and run it through another LSTM. Then, you take the vector that was outputted from both and concatenate it. This intuitively lets the LSTM \"read from both directions\"." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "question_1_encoded: Tensor(\"bidirectional_1/concat_2:0\", shape=(?, 128), dtype=float32)\n", "question_2_encoded: Tensor(\"bidirectional_2/concat_2:0\", shape=(?, 128), dtype=float32)\n" ] } ], "source": [ "# Now we take the embedded questions, and we encode them with a bidirectional LSTM.\n", "# Think of a LSTM as converting/encoding a sequence of vectors into a fixed length vector.\n", "# In this case, it takes in a single question of size (100, 128) and returns something of \n", "# size (2*LSTM_output_units). Since it is batched, we go from (32, 100, 128) to (32, 2*LSTM_output_units)\n", "\n", "# Bidirectional LSTM encoder for question_1_embedded\n", "question_1_encoder = Bidirectional(LSTM(units=64))\n", " \n", "# Bidirectional LSTM encoder for question_2_embedded\n", "question_2_encoder = Bidirectional(LSTM(units=64))\n", "\n", "# Now, we apply the Bidirectional LSTM encoders to our embedded questions.\n", "# shape: (batch_size, 2*LSTM_output_units), or (32, 128) by default\n", "question_1_encoded = question_1_encoder(question_1_embedded)\n", "print(\"question_1_encoded: {}\".format(question_1_encoded))\n", "\n", "# shape: (batch_size, 2*LSTM_output_units), or (32, 128) by default\n", "question_2_encoded = question_2_encoder(question_2_embedded)\n", "print(\"question_2_encoded: {}\".format(question_2_encoded))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Getting an output probability\n", "Lastly, we compute a similarity metric between each of the two vectors, over the batch. Our similarity metric will be: exp(-||question_1_encoded-question_2_encoded||), or in words, e to the power of the negative L1 norm (a.k.a manhattan distance). With this metric, for each question pair (vector of size LSTM_units*2) we get a value between 0 and 1, with questions having a larger L1 norm being closer to 0 and questions having a smaller L1 norm being closer to 1. We can intuitively interpret this as the probability that two sentences are semantically the same, assuming that if two sentences have the same semantic meaning, they are probably duplicate questions." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# The L1 Norm/Manhattan distance formula is simple: subtract vector 1 from vector 2, and add up the \n", "# absolute value of the resulting vector.\n", "\n", "# We'll first write a function to calculate our similarity metric given two tensors.\n", "def l1_similarity(vectors):\n", " vector_1, vector_2 = vectors\n", " # Note that vector_1 and vector_2 are of shape (batch_size, LSTM_units*2)\n", " # First, take the absolute value of the difference. shape(batch_size, LSTM_units*2)\n", " abs_diff = K.abs(vector_1-vector_2)\n", " \n", " # Now, sum across the \"first\" axis and negate it (which thus negates every element of it). \n", " # This is roughly analogous to summing the rows.\n", " # keepdims=True does not reduce the dimensionality, and just leaves it as 1.\n", " # shape: (batch_size, 1)\n", " negative_l1_distance = -K.sum(abs_diff, axis=1, keepdims=True)\n", " \n", " # Finally, apply the exponential function and return the output.\n", " # shape: (batch_size, 1), where the \"1\" is a value in [0, 1] that\n", " # describes the probability of the two vectors being semantically similar.\n", " return K.exp(negative_l1_distance)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "duplicate_probabilities: Tensor(\"lambda_1/Exp:0\", shape=(?, 1), dtype=float32)\n" ] } ], "source": [ "# We now want to pass our two encoded questions to our similarity function.\n", "# To do so, we'll use a keras Lambda layer, which lets us wrap an arbitrary\n", "# function in a Lambda object. Note that _ALL_ operations on keras tensors \n", "# in the Model class _must_ be a layer; we thus cannot call the function directly.\n", "\n", "# Here, we're creating a layer and using it in one line.\n", "# output shape: (batch_size, 1)\n", "duplicate_probabilities = Lambda(l1_similarity)([question_1_encoded, question_2_encoded])\n", "print(\"duplicate_probabilities: {}\".format(duplicate_probabilities))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Wrapping up the model\n", "\n", "Now that we've successfully strung together a bunch of our layers and inputs to get a final probability, we can create a keras `Model` to seamlessly take the input numpy arrays, run them through the computation graph we built in the way we specified, to get an output probability that it will automatically compare to the label in order to adjust the loss.\n", "\n", "To do all of this, we just need to create an instance of the `Model` class and specify which `Input` layers are our inputs, and what value from the graph is our final output. Note that since we have multiple inputs, we need to pass a list of `Input` tensors." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# These duplicate probabilties are what we want to output from our model, so we'll create\n", "# the model now.\n", "duplicate_questions_model = Model(inputs=[question_1_input, question_2_input], outputs=duplicate_probabilities)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Compiling the model\n", "\n", "Now, we compile our model into a Tensorflow/Theano graph. Keras handles this for us, but we need to specify an optimization algorithm to use, as well as a loss function. `adam` is generally a good choice of optimizer, and `binary_crossentropy` is appropriate for a binary classification task like the one we have. \n", "\n", "We can also specify a list of metrics to be printed during training and testing, so we'll print the accuracy." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "duplicate_questions_model.compile('adam', 'binary_crossentropy', metrics=['accuracy'])" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "input_1 (InputLayer) (None, 100) 0 \n", "____________________________________________________________________________________________________\n", "input_2 (InputLayer) (None, 100) 0 \n", "____________________________________________________________________________________________________\n", "embedding_1 (Embedding) (None, 100, 128) 13372416 \n", "____________________________________________________________________________________________________\n", "embedding_2 (Embedding) (None, 100, 128) 13372416 \n", "____________________________________________________________________________________________________\n", "bidirectional_1 (Bidirectional) (None, 128) 98816 \n", "____________________________________________________________________________________________________\n", "bidirectional_2 (Bidirectional) (None, 128) 98816 \n", "____________________________________________________________________________________________________\n", "lambda_1 (Lambda) (None, 1) 0 \n", "====================================================================================================\n", "Total params: 26,942,464.0\n", "Trainable params: 26,942,464.0\n", "Non-trainable params: 0.0\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "# Print a summary of the layers of our model and their inputs and outputs\n", "duplicate_questions_model.summary()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Training our model\n", "\n", "Now, we can finally pass in our input arrays and output labels and watch the model train!" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train on 9000 samples, validate on 1000 samples\n", "Epoch 1/4\n", "9000/9000 [==============================] - 427s - loss: 0.6605 - acc: 0.5999 - val_loss: 0.6197 - val_acc: 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"Epoch 2/4\n", "9000/9000 [==============================] - 446s - loss: 0.4942 - acc: 0.8378 - val_loss: 0.6072 - val_acc: 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"Epoch 3/4\n", "9000/9000 [==============================] - 439s - loss: 0.3912 - acc: 0.9071 - val_loss: 0.6339 - val_acc: 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"Epoch 4/4\n", "9000/9000 [==============================] - 443s - loss: 0.3257 - acc: 0.9388 - val_loss: 0.7801 - val_acc: 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] }, { "data": { "text/plain": [ "<keras.callbacks.History at 0x13704c0b8>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Now, we can finally fit our model on training data!\n", "# Note that the order of the input x matters.\n", "duplicate_questions_model.fit(x=[padded_question_1s, padded_question_2s], y=labels, \n", " batch_size=batch_size, epochs=4, validation_split=0.1)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Some quick analysis\n", "\n", "Great, you can see that the model is definitely learning the task since the training accuracy (denoted by `acc`) goes up for each epoch. However, the validation accuracy seems to peak at epoch 2 and goes down afterwords, perhaps because we are overfitting on the data (remember that this is a relatively small slice of the actual dataset)." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Next Steps\n", "\n", "This is a quick model to perform the task, and there are *a lot* of different ways to make it better. For example, you could:\n", "\n", "- Try using pretrained embeddings (i.e. word2vec or GloVe). [There's a good blog post on how to do so here](https://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html). I'd expect this to give sizeable gains.\n", "- Try adding early stopping, so the model can automatically stop training after a certain decrease in the validation accuracy.\n", "- Try visualizing your model with TensorBoard (Tensorflow backend only).\n", "- Try tweaking the output dimensionality of some of the layers. 64\\*2 dimensions is a bit small for a BiLSTM, but we used it here to keep training times manageable. 128\\*2 is more common (but will also drastically increase train time).\n", "- Gated Recurrent Units (GRUs) are a popular alternative to LSTMs --- try using GRUs in the model.\n", "- Right now, we're encoding question_1 and question_2 with different biLSTM encoders. This doesn't make a lot of intuitive sense though --- there's nothing intrinsically different about question_1 vs question_2 (i.e. if we were to flip question_1 and question_2, we'd probably get different results). Try using the same encoder to encoder both of the questions (this is also known as sharing weights between the encoders).\n", "- Manhattan distance might not be optimal, maybe play around with Euclidean distance / cosine similarity. Make sure that you always output a number between 0 and 1, though!\n", "- Try training on all the data (be prepared to wait if you don't have access to a GPU)\n", "- Build your own model, and try a completely different architecture! The world is your oyster at this point :)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
google-research/google-research
collocated_irradiance_network/Collocated_Irradiance_Network_data_loading.ipynb
1
981374
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Collocated Irradiance Network data loading.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "cells": [ { "cell_type": "code", "metadata": { "id": "EdSKW2mFsYjT" }, "source": [ "_ = \"\"\"\n", "Copyright 2021 The Collocated Irradiance Network Authors.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); \n", "you may not use this file except in compliance with the License. \n", "You may obtain a copy of the License at \n", " \n", " https://www.apache.org/licenses/LICENSE-2.0 \n", " \n", "Unless required by applicable law or agreed to in writing, software \n", "distributed under the License is distributed on an \"AS IS\" BASIS, \n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n", "See the License for the specific language governing permissions and \n", "limitations under the License.\n", "\"\"\"" ], "execution_count": 1, "outputs": [] }, { "cell_type": "code", "source": [ "!pip install xarray[complete]" ], "metadata": { "id": "oyzU-xcupeZ9" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "!pip install gcsfs" ], "metadata": { "id": "_9DAl_nsw6-D" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import xarray\n", "import matplotlib.pyplot as plt" ], "metadata": { "id": "mYS8kjx6qxOp" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Read irradiances in zarr format" ], "metadata": { "id": "CObUz7I5vhJ6" } }, { "cell_type": "code", "source": [ "CERES_GOES_VERSION = '2022_04_25_1650910589'" ], "metadata": { "id": "11iqD1Y2w_E9" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "coin = xarray.open_zarr(f'gs://upwelling_irradiance/ceres_goes/{CERES_GOES_VERSION}/2018/021/16/s20180211630395_olr', consolidated=True)\n", "coin" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 350 }, "id": "sdYvf1krwmDT", "outputId": "b42f1a44-f0d8-43b7-a15f-d1faa6a730bd" }, "execution_count": 7, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<xarray.Dataset>\n", "Dimensions: (x: 5424, x_image_bounds: 2, y: 5424, y_image_bounds: 2)\n", "Coordinates:\n", " t datetime64[ns] ...\n", " * x (x) float32 -0.1518 -0.1518 ... 0.1518 0.1518\n", " x_image float32 ...\n", " * x_image_bounds (x_image_bounds) float64 -0.1519 0.1519\n", " * y (y) float32 0.1518 0.1518 0.1517 ... -0.1518 -0.1518\n", " y_image float32 ...\n", " * y_image_bounds (y_image_bounds) float64 0.1519 -0.1519\n", "Data variables:\n", " goes_imager_projection int32 ...\n", " predicted_olr (y, x) float32 dask.array<chunksize=(339, 678), meta=np.ndarray>" ], "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 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"}\n", "\n", ".xr-var-list,\n", ".xr-var-item {\n", " display: contents;\n", "}\n", "\n", ".xr-var-item > div,\n", ".xr-var-item label,\n", ".xr-var-item > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-even);\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-var-list > li:nth-child(odd) > div,\n", ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", "}\n", "\n", ".xr-var-name {\n", " grid-column: 1;\n", "}\n", "\n", ".xr-var-dims {\n", " grid-column: 2;\n", "}\n", "\n", ".xr-var-dtype {\n", " grid-column: 3;\n", " text-align: right;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2 {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n", "Dimensions: (x: 5424, x_image_bounds: 2, y: 5424, y_image_bounds: 2)\n", "Coordinates:\n", " t datetime64[ns] ...\n", " * x (x) float32 -0.1518 -0.1518 ... 0.1518 0.1518\n", " x_image float32 ...\n", " * x_image_bounds (x_image_bounds) float64 -0.1519 0.1519\n", " * y (y) float32 0.1518 0.1518 0.1517 ... -0.1518 -0.1518\n", " y_image float32 ...\n", " * y_image_bounds (y_image_bounds) float64 0.1519 -0.1519\n", "Data variables:\n", " goes_imager_projection int32 ...\n", " predicted_olr (y, x) float32 dask.array&lt;chunksize=(339, 678), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-5c8fe708-4002-46cd-a966-5450fb3da705' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5c8fe708-4002-46cd-a966-5450fb3da705' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 5424</li><li><span class='xr-has-index'>x_image_bounds</span>: 2</li><li><span class='xr-has-index'>y</span>: 5424</li><li><span class='xr-has-index'>y_image_bounds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-07d42fb8-6f57-4e0a-8973-a98ead1df2f6' class='xr-section-summary-in' type='checkbox' checked><label for='section-07d42fb8-6f57-4e0a-8973-a98ead1df2f6' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-638513b5-fc28-498d-833f-18d53834f538' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-638513b5-fc28-498d-833f-18d53834f538' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cb5ba42-5b38-4564-9892-6fed63023d61' class='xr-var-data-in' type='checkbox'><label for='data-5cb5ba42-5b38-4564-9892-6fed63023d61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;2018-01-21T16:30:39.500000000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.1518 -0.1518 ... 0.1518 0.1518</div><input id='attrs-9480fcbc-2023-4eaa-b9e8-6e89fcfab68b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9480fcbc-2023-4eaa-b9e8-6e89fcfab68b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d717185-32a9-4daf-9cf8-3594ebb985c3' class='xr-var-data-in' type='checkbox'><label for='data-0d717185-32a9-4daf-9cf8-3594ebb985c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.151844, -0.151788, -0.151732, ..., 0.151732, 0.151788, 0.151844],\n", " dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_image</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b2717761-6812-4953-a9b1-8c68805053d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2717761-6812-4953-a9b1-8c68805053d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b06652f-fb1a-4be0-96e9-91b3d2905365' class='xr-var-data-in' type='checkbox'><label for='data-9b06652f-fb1a-4be0-96e9-91b3d2905365' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x_image_bounds</span></div><div class='xr-var-dims'>(x_image_bounds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.1519 0.1519</div><input id='attrs-eb0886de-c349-435f-9b7a-38f6fc0d7b78' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb0886de-c349-435f-9b7a-38f6fc0d7b78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9cfaaea-abea-4f69-bd40-d7b72d4beaef' class='xr-var-data-in' type='checkbox'><label for='data-f9cfaaea-abea-4f69-bd40-d7b72d4beaef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-0.151872, 0.151872])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1518 0.1518 ... -0.1518 -0.1518</div><input id='attrs-9b32d4cd-a327-4a1b-bac9-ee31ec057356' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b32d4cd-a327-4a1b-bac9-ee31ec057356' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a915fbd-fac4-4b61-a82b-cc746ae1b36e' class='xr-var-data-in' type='checkbox'><label for='data-9a915fbd-fac4-4b61-a82b-cc746ae1b36e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.151844, 0.151788, 0.151732, ..., -0.151732, -0.151788, -0.151844],\n", " dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_image</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-caa65ed7-fa13-4337-99cd-531e49d833ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-caa65ed7-fa13-4337-99cd-531e49d833ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15dc246b-660a-4fb1-82b1-0e723232babe' class='xr-var-data-in' type='checkbox'><label for='data-15dc246b-660a-4fb1-82b1-0e723232babe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(0., dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_image_bounds</span></div><div class='xr-var-dims'>(y_image_bounds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1519 -0.1519</div><input id='attrs-86ef1470-3a17-47e7-b570-ddfd1a7a55a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86ef1470-3a17-47e7-b570-ddfd1a7a55a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d31bfb1a-93b7-40ab-8c9d-28d3d079ce54' class='xr-var-data-in' type='checkbox'><label for='data-d31bfb1a-93b7-40ab-8c9d-28d3d079ce54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.151872, -0.151872])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-27013188-4c7b-4ace-b4b8-0c1707618fb2' class='xr-section-summary-in' type='checkbox' checked><label for='section-27013188-4c7b-4ace-b4b8-0c1707618fb2' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>goes_imager_projection</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-53140536-e9ca-45b8-b973-dd56ea9464a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53140536-e9ca-45b8-b973-dd56ea9464a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f53354c3-9150-40f8-b9e4-93b31868b5bd' class='xr-var-data-in' type='checkbox'><label for='data-f53354c3-9150-40f8-b9e4-93b31868b5bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>geostationary</dd><dt><span>inverse_flattening :</span></dt><dd>[298.2572221]</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>[0.0]</dd><dt><span>long_name :</span></dt><dd>GOES-R ABI fixed grid projection</dd><dt><span>longitude_of_projection_origin :</span></dt><dd>[-75.0]</dd><dt><span>perspective_point_height :</span></dt><dd>[35786023.0]</dd><dt><span>semi_major_axis :</span></dt><dd>[6378137.0]</dd><dt><span>semi_minor_axis :</span></dt><dd>[6356752.31414]</dd><dt><span>sweep_angle_axis :</span></dt><dd>x</dd></dl></div><div class='xr-var-data'><pre>array(-2147483648, dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>predicted_olr</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(339, 678), meta=np.ndarray&gt;</div><input id='attrs-702bd261-01f9-4d1a-b844-fbf5fbbe1698' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-702bd261-01f9-4d1a-b844-fbf5fbbe1698' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-573c6843-eed3-461c-87cb-4e3e3ecdb574' class='xr-var-data-in' type='checkbox'><label for='data-573c6843-eed3-461c-87cb-4e3e3ecdb574' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n", "<tr>\n", "<td>\n", "<table>\n", " <thead>\n", " <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n", " </thead>\n", " <tbody>\n", " <tr><th> Bytes </th><td> 117.68 MB </td> <td> 919.37 kB </td></tr>\n", " <tr><th> Shape </th><td> (5424, 5424) </td> <td> (339, 678) </td></tr>\n", " <tr><th> Count </th><td> 129 Tasks </td><td> 128 Chunks </td></tr>\n", " <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n", " </tbody>\n", "</table>\n", "</td>\n", "<td>\n", "<svg width=\"170\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"7\" x2=\"120\" y2=\"7\" />\n", " <line x1=\"0\" y1=\"15\" x2=\"120\" y2=\"15\" />\n", " <line x1=\"0\" y1=\"22\" x2=\"120\" y2=\"22\" />\n", " <line x1=\"0\" y1=\"30\" x2=\"120\" y2=\"30\" />\n", " <line x1=\"0\" y1=\"37\" x2=\"120\" y2=\"37\" />\n", " <line x1=\"0\" y1=\"45\" x2=\"120\" y2=\"45\" />\n", " <line x1=\"0\" y1=\"52\" x2=\"120\" y2=\"52\" />\n", " <line x1=\"0\" y1=\"60\" x2=\"120\" y2=\"60\" />\n", " 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class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>" ] }, "metadata": {}, "execution_count": 7 } ] }, { "cell_type": "code", "source": [ "plt.figure(figsize=(15,15))\n", "plt.imshow(coin.predicted_olr, cmap='magma')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 884 }, "id": "kMXFNXPisjFo", "outputId": "9086ab68-ed77-48cb-ea01-3606232e699a" }, "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7ff9bbca1b50>" ] }, "metadata": {}, "execution_count": 8 }, { "output_type": "display_data", "data": { "text/plain": [ "<Figure size 1080x1080 with 1 Axes>" ], "image/png": 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "markdown", "source": [ "# Read tfrecord training/validation data" ], "metadata": { "id": "1_uMEbO0vaAf" } }, { "cell_type": "code", "source": [ "import tensorflow as tf" ], "metadata": { "id": "Xf8yZtdCv4-m" }, "execution_count": 9, "outputs": [] }, { "cell_type": "code", "source": [ "tf.io.FixedLenFeature([], tf.string).dtype == tf.string" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dguFCdLuzh_A", "outputId": "31279674-2efa-41bc-fbde-60db39c8b012" }, "execution_count": 10, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "code", "source": [ "spec = {\n", " 'surface_type_index': tf.io.FixedLenFeature([], tf.string),\n", " 'surface_type_percent_coverage': tf.io.FixedLenFeature([], tf.string),\n", " 'psf_weights': tf.io.FixedLenFeature([], tf.string),\n", " 'goes_lats': tf.io.FixedLenFeature([], tf.string),\n", " 'goes_lngs': tf.io.FixedLenFeature([], tf.string),\n", " 'goes_timestamp': tf.io.FixedLenFeature([], tf.int64),\n", " 'ceres_timestamp': tf.io.FixedLenFeature([], tf.int64),\n", " 'ceres_olr': tf.io.FixedLenFeature([], tf.float32),\n", " 'ceres_rsr': tf.io.FixedLenFeature([], tf.float32),\n", "}\n", "for channel in range(1, 17):\n", " spec[f'goes_channel_{channel}'] = tf.io.FixedLenFeature([], tf.string)\n", "\n", "def _parse_examples(serialized):\n", " return tf.io.parse_example(serialized, spec)\n", "\n", "validation_dataset = tf.data.TFRecordDataset(\n", " f'gs://upwelling_irradiance/ceres_goes/{CERES_GOES_VERSION}/training_and_validation_data/before_20190402/validation.tfrecords-00000-of-00100')\n", "validation_dataset = validation_dataset.map(_parse_examples, num_parallel_calls=tf.data.AUTOTUNE)\n", "\n", "# Meta features are useful for analysis of model bias on subsets of the data.\n", "META_FEATURE_NAMES = ('ceres_latitude', 'ceres_longitude',\n", " 'ceres_viewing_zenith', 'ceres_relative_azimuth',\n", " 'ceres_solar_zenith', 'calculated_solar_zenith')\n", "\n", "for feature_name in META_FEATURE_NAMES:\n", " spec[feature_name] = tf.io.FixedLenFeature([], tf.float32)\n", "\n", "\n", "def _parse_tensors(features):\n", " for feature_name in features:\n", " if spec[feature_name].dtype == tf.string:\n", " features[feature_name] = tf.io.parse_tensor(\n", " features[feature_name], out_type=tf.float32)\n", " \n", " return features\n", "\n", "validation_dataset = validation_dataset.map(_parse_tensors, num_parallel_calls=tf.data.AUTOTUNE)\n", "\n", "for features in validation_dataset.take(1):\n", " break\n", "\n", "features" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gL8X971Zu64w", "outputId": "42c1db66-f862-4fed-db82-70833475076c" }, "execution_count": 11, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'ceres_olr': <tf.Tensor: shape=(), dtype=float32, numpy=202.84843>,\n", " 'ceres_rsr': <tf.Tensor: shape=(), dtype=float32, numpy=208.16934>,\n", " 'ceres_timestamp': <tf.Tensor: shape=(), dtype=int64, numpy=1554675073>,\n", " 'goes_channel_1': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([185.211 , 168.76587, 166.32953, ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_10': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([8.463301, 8.643673, 8.403177, ..., 0. , 0. , 0. ],\n", " dtype=float32)>,\n", " 'goes_channel_11': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([22.381832, 23.515997, 23.149061, ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_12': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([24.53735 , 25.299511, 24.86399 , ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_13': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([43.35296 , 45.593674, 44.49618 , ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_14': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([51.48542 , 53.564095, 52.178314, ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_15': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([59.937134, 62.0481 , 60.25378 , ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_16': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([59.550335, 61.310917, 59.550335, ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_2': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([130.45213, 115.85174, 112.30322, ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_3': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([81.15398, 70.97734, 68.90432, ..., 0. , 0. , 0. ],\n", " dtype=float32)>,\n", " 'goes_channel_4': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([3.753168, 3.116588, 3.753168, ..., 0. , 0. , 0. ],\n", " dtype=float32)>,\n", " 'goes_channel_5': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([16.052494, 14.184393, 13.777243, ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_6': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([4.304532 , 3.9434707, 3.823117 , ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_7': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([0.30968592, 0.32689378, 0.31125027, ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_8': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([1.6865051, 1.7149241, 1.7078195, ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_channel_9': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([4.8337145, 4.94641 , 4.8111753, ..., 0. , 0. ,\n", " 0. ], dtype=float32)>,\n", " 'goes_lats': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([ -42.48015 , -42.47658 , -42.473022, ..., -999. ,\n", " -999. , -999. ], dtype=float32)>,\n", " 'goes_lngs': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([-118.27679, -118.23336, -118.18998, ..., -999. , -999. ,\n", " -999. ], dtype=float32)>,\n", " 'goes_timestamp': <tf.Tensor: shape=(), dtype=int64, numpy=1554675025>,\n", " 'psf_weights': <tf.Tensor: shape=(1700,), dtype=float32, numpy=\n", " array([9.6741974e-07, 3.6154586e-06, 4.0361615e-06, ..., 0.0000000e+00,\n", " 0.0000000e+00, 0.0000000e+00], dtype=float32)>,\n", " 'surface_type_index': <tf.Tensor: shape=(8,), dtype=float32, numpy=array([17., nan, nan, nan, nan, nan, nan, nan], dtype=float32)>,\n", " 'surface_type_percent_coverage': <tf.Tensor: shape=(8,), dtype=float32, numpy=array([100., nan, nan, nan, nan, nan, nan, nan], dtype=float32)>}" ] }, "metadata": {}, "execution_count": 11 } ] } ] }
apache-2.0
google/or-tools
examples/notebook/contrib/set_covering.ipynb
1
5437
{ "cells": [ { "cell_type": "markdown", "id": "google", "metadata": {}, "source": [ "##### Copyright 2021 Google LLC." ] }, { "cell_type": "markdown", "id": "apache", "metadata": {}, "source": [ "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may not use this file except in compliance with the License.\n", "You may obtain a copy of the License at\n", "\n", " http://www.apache.org/licenses/LICENSE-2.0\n", "\n", "Unless required by applicable law or agreed to in writing, software\n", "distributed under the License is distributed on an \"AS IS\" BASIS,\n", "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "See the License for the specific language governing permissions and\n", "limitations under the License.\n" ] }, { "cell_type": "markdown", "id": "basename", "metadata": {}, "source": [ "# set_covering" ] }, { "cell_type": "markdown", "id": "link", "metadata": {}, "source": [ "<table align=\"left\">\n", "<td>\n", "<a href=\"https://colab.research.google.com/github/google/or-tools/blob/master/examples/notebook/contrib/set_covering.ipynb\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/colab_32px.png\"/>Run in Google Colab</a>\n", "</td>\n", "<td>\n", "<a href=\"https://github.com/google/or-tools/blob/master/examples/contrib/set_covering.py\"><img src=\"https://raw.githubusercontent.com/google/or-tools/master/tools/github_32px.png\"/>View source on GitHub</a>\n", "</td>\n", "</table>" ] }, { "cell_type": "markdown", "id": "doc", "metadata": {}, "source": [ "First, you must install [ortools](https://pypi.org/project/ortools/) package in this colab." ] }, { "cell_type": "code", "execution_count": null, "id": "install", "metadata": {}, "outputs": [], "source": [ "!pip install ortools" ] }, { "cell_type": "code", "execution_count": null, "id": "code", "metadata": {}, "outputs": [], "source": [ "# Copyright 2010 Hakan Kjellerstrand [email protected]\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# http://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License.\n", "\"\"\"\n", "\n", " Set covering in Google CP Solver.\n", "\n", " Placing of firestations, from Winston 'Operations Research', page 486.\n", "\n", " Compare with the following models:\n", " * MiniZinc: http://www.hakank.org/minizinc/set_covering.mzn\n", " * ECLiPSe : http://www.hakank.org/eclipse/set_covering.ecl\n", " * Comet : http://www.hakank.org/comet/set_covering.co\n", " * Gecode : http://www.hakank.org/gecode/set_covering.cpp\n", " * SICStus : http://www.hakank.org/sicstus/set_covering.pl\n", "\n", "\n", " This model was created by Hakan Kjellerstrand ([email protected])\n", " Also see my other Google CP Solver models:\n", " http://www.hakank.org/google_or_tools/\n", "\n", "\"\"\"\n", "from ortools.constraint_solver import pywrapcp\n", "\n", "\n", "\n", "# Create the solver.\n", "solver = pywrapcp.Solver(\"Set covering\")\n", "\n", "#\n", "# data\n", "#\n", "min_distance = 15\n", "num_cities = 6\n", "\n", "distance = [[0, 10, 20, 30, 30, 20], [10, 0, 25, 35, 20, 10],\n", " [20, 25, 0, 15, 30, 20], [30, 35, 15, 0, 15, 25],\n", " [30, 20, 30, 15, 0, 14], [20, 10, 20, 25, 14, 0]]\n", "\n", "#\n", "# declare variables\n", "#\n", "x = [solver.IntVar(0, 1, \"x[%i]\" % i) for i in range(num_cities)]\n", "\n", "#\n", "# constraints\n", "#\n", "\n", "# objective to minimize\n", "z = solver.Sum(x)\n", "\n", "# ensure that all cities are covered\n", "for i in range(num_cities):\n", " b = [x[j] for j in range(num_cities) if distance[i][j] <= min_distance]\n", " solver.Add(solver.SumGreaterOrEqual(b, 1))\n", "\n", "objective = solver.Minimize(z, 1)\n", "\n", "#\n", "# solution and search\n", "#\n", "solution = solver.Assignment()\n", "solution.Add(x)\n", "solution.AddObjective(z)\n", "\n", "collector = solver.LastSolutionCollector(solution)\n", "solver.Solve(\n", " solver.Phase(x + [z], solver.INT_VAR_DEFAULT, solver.INT_VALUE_DEFAULT),\n", " [collector, objective])\n", "\n", "print(\"z:\", collector.ObjectiveValue(0))\n", "print(\"x:\", [collector.Value(0, x[i]) for i in range(num_cities)])\n", "\n", "print(\"failures:\", solver.Failures())\n", "print(\"branches:\", solver.Branches())\n", "print(\"WallTime:\", solver.WallTime())\n", "\n" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }
apache-2.0
esa-as/2016-ml-contest
LA_TEAM_FRESH/So Meta.ipynb
2
12847
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Stop, meta time" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![title](well_log.jpg)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "from keras.utils import np_utils\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "E:\\Anaconda3\\lib\\site-packages\\keras\\utils\\np_utils.py:23: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future\n", " Y[i, y[i]] = 1.\n" ] } ], "source": [ "houmath = pd.read_csv(\"../HouMath/Prediction6.csv\", delimiter=\",\")\n", "houmath_f1 = 0.63\n", "houmath_facies = np_utils.to_categorical(houmath[\"Facies\"]-1, nb_classes=9)\n", "\n", "\n", "ar4 = pd.read_csv(\"../ar4/ar4_predicted_facies_submission002.csv\", delimiter=\",\")\n", "ar4_f1 = 0.606\n", "ar4_facies = np_utils.to_categorical(ar4[\"Facies\"]-1, nb_classes=9)\n", "\n", "bestagini = pd.read_csv(\"../ispl/well_data_with_facies_try02.csv\", delimiter=\",\")\n", "bestagini_f1 = 0.604\n", "bestagini_facies = np_utils.to_categorical(bestagini[\"Facies\"]-1, nb_classes=9)\n", "\n", "birdteam = pd.read_csv(\"../Bird_Team/XmasPreds_4.csv\", delimiter=\",\")\n", "birdteam_f1 = 0.598\n", "birdteam_facies = np_utils.to_categorical(birdteam[\"Facies\"]-1, nb_classes=9)\n", "\n", "sum_f1 = ar4_f1+bestagini_f1+houmath_f1+birdteam_f1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Unnamed: 0 Formation Well Name Depth GR ILD_log10 DeltaPHI PHIND \\\n", "0 0 A1 SH STUART 2808.0 66.276 0.630 3.3 10.65 \n", "1 1 A1 SH STUART 2808.5 77.252 0.585 6.5 11.95 \n", "2 2 A1 SH STUART 2809.0 82.899 0.566 9.4 13.60 \n", "3 3 A1 SH STUART 2809.5 80.671 0.593 9.5 13.25 \n", "4 4 A1 SH STUART 2810.0 75.971 0.638 8.7 12.35 \n", "\n", " PE NM_M RELPOS Facies \n", "0 3.591 1 1.000 3 \n", "1 3.341 1 0.978 3 \n", "2 3.064 1 0.956 3 \n", "3 2.977 1 0.933 3 \n", "4 3.020 1 0.911 3 \n", " Unnamed: 0 Formation Well Name Depth GR ILD_log10 DeltaPHI PHIND \\\n", "0 0 A1 SH STUART 2808.0 66.276 0.630 3.3 10.65 \n", "1 1 A1 SH STUART 2808.5 77.252 0.585 6.5 11.95 \n", "2 2 A1 SH STUART 2809.0 82.899 0.566 9.4 13.60 \n", "3 3 A1 SH STUART 2809.5 80.671 0.593 9.5 13.25 \n", "4 4 A1 SH STUART 2810.0 75.971 0.638 8.7 12.35 \n", "\n", " PE NM_M RELPOS Facies \n", "0 3.591 1 1.000 3 \n", "1 3.341 1 0.978 3 \n", "2 3.064 1 0.956 3 \n", "3 2.977 1 0.933 3 \n", "4 3.020 1 0.911 3 \n", " Unnamed: 0 Formation Well Name Depth GR ILD_log10 DeltaPHI PHIND \\\n", "0 0 A1 SH STUART 2808.0 66.276 0.630 3.3 10.65 \n", "1 1 A1 SH STUART 2808.5 77.252 0.585 6.5 11.95 \n", "2 2 A1 SH STUART 2809.0 82.899 0.566 9.4 13.60 \n", "3 3 A1 SH STUART 2809.5 80.671 0.593 9.5 13.25 \n", "4 4 A1 SH STUART 2810.0 75.971 0.638 8.7 12.35 \n", "\n", " PE NM_M RELPOS Facies \n", "0 3.591 1 1.000 3 \n", "1 3.341 1 0.978 3 \n", "2 3.064 1 0.956 3 \n", "3 2.977 1 0.933 3 \n", "4 3.020 1 0.911 3 \n", " Unnamed: 0 Depth GR ILD_log10 DeltaPHI PHIND PE NM_M \\\n", "0 0 2808.0 66.276 0.630 3.3 10.65 3.591 1 \n", "1 1 2808.5 77.252 0.585 6.5 11.95 3.341 1 \n", "2 2 2809.0 82.899 0.566 9.4 13.60 3.064 1 \n", "3 3 2809.5 80.671 0.593 9.5 13.25 2.977 1 \n", "4 4 2810.0 75.971 0.638 8.7 12.35 3.020 1 \n", "\n", " RELPOS formation_size ... sh_Formation_lag_2 a1_Formation_lead_2 \\\n", "0 1.000 43 ... 1 1 \n", "1 0.978 43 ... 1 1 \n", "2 0.956 43 ... 1 1 \n", "3 0.933 43 ... 1 1 \n", "4 0.911 43 ... 1 1 \n", "\n", " b1_Formation_lead_2 b2_Formation_lead_2 b3_Formation_lead_2 \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " b4_Formation_lead_2 b5_Formation_lead_2 lm_Formation_lead_2 \\\n", "0 0 0 0 \n", "1 0 0 0 \n", "2 0 0 0 \n", "3 0 0 0 \n", "4 0 0 0 \n", "\n", " sh_Formation_lead_2 Facies \n", "0 1 2.0 \n", "1 1 3.0 \n", "2 1 3.0 \n", "3 1 3.0 \n", "4 1 3.0 \n", "\n", "[5 rows x 439 columns]\n" ] } ], "source": [ "print( houmath.head())\n", "print( ar4.head())\n", "print( bestagini.head())\n", "print( birdteam.head())" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 0. 0. 1. ..., 0. 0. 0. ]\n", " [ 0. 0. 1. ..., 0. 0. 0. ]\n", " [ 0. 0. 1. ..., 0. 0. 0. ]\n", " ..., \n", " [ 0. 0.25840853 0.74159147 ..., 0. 0. 0. ]\n", " [ 0. 0.25840853 0.74159147 ..., 0. 0. 0. ]\n", " [ 0.49302707 0.50697293 0. ..., 0. 0. 0. ]]\n" ] } ], "source": [ "meta_facies = (bestagini_f1*bestagini_facies + ar4_f1*ar4_facies \n", " + birdteam_f1*bestagini_facies + houmath_f1*houmath_facies)/(sum_f1)\n", "print(meta_facies)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 2 2 2 2 2 2 2\n", " 2 3 3 3 3 3 8 8 8 8 8 8 6 6 6 6 6 4 4 4 4 4 4 4 4 6 6 6 6 6 6 6 6 8 8 8 8\n", " 8 8 8 8 8 8 8 8 8 8 8 8 8 6 6 6 6 6 6 6 6 6 6 6 4 4 4 4 4 4 4 4 6 6 6 6 8\n", " 8 8 8 8 8 8 8 8 8 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3\n", " 3 3 8 8 8 8 8 8 8 8 8 8 8 8 8 8 7 7 7 7 6 6 6 6 6 6 6 6 6 6 8 8 8 8 3 3 3\n", " 3 3 3 3 3 3 2 2 2 3 3 3 3 3 3 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 8 8 8 8 6 6\n", " 6 6 6 6 6 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 8 8 8 8 8 8 8 8 6 6\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 8 8 8 8 8 8 8 8 8 8 8 6 6 3 3 3 3 8\n", " 8 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 6 6 6 6 6 6 6 8 9 9 9 9 8 8 8 6 6 6 6 6 6\n", " 6 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3\n", " 3 3 3 5 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 5 5 5 5 7 7 8 8 8 8 8 5\n", " 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 4 4 4 4 4 4 4 4\n", " 4 4 4 4 4 4 4 4 4 6 6 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 8 8 8 8 8 8\n", " 8 8 8 8 7 7 7 7 7 7 7 7 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 6 6 6 6 6 4 4 4\n", " 4 8 8 8 8 8 8 6 6 6 6 6 6 8 8 8 6 6 6 6 6 6 6 4 4 4 4 4 7 7 8 8 7 7 7 7 7\n", " 7 7 8 8 8 8 8 7 6 6 4 4 4 6 6 6 8 8 8 8 8 8 8 8 8 8 6 6 6 6 8 8 8 8 8 8 8\n", " 8 8 8 8 8 8 8 6 6 6 6 6 6 6 6 6 6 6 6 6 8 8 8 8 8 8 6 6 3 2 2 2 2 2 2 2 2\n", " 2 2 2 2 2 2 2 2 2 3 3 8 8 8 8 8 8 8 8 8 8 8 7 7 7 8 8 7 7 7 7 7 7 7 7 7 8\n", " 8 6 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 8 8 8 8 8 8 8 8 8 8 8 8 6 6 6 6\n", " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 5 8 8 8 8 8 8 8 8 6 6 6 6 6 6 6 6\n", " 2 2 2 2 2 2 2 2 2 3 3 3 3 8 8 8 8 8 8 8 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7\n", " 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 6 6 6 6 8 8 8 3 3\n", " 3 3 3 2 2 2 2 2 2 2 2 2 3 3 3 2]\n" ] } ], "source": [ "metasubmission = np.argmax(meta_facies, axis=1) + 1\n", "print (metasubmission)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Formation Well Name Depth GR ILD_log10 DeltaPHI PHIND PE \\\n", "0 A1 SH STUART 2808.0 66.276 0.630 3.3 10.65 3.591 \n", "1 A1 SH STUART 2808.5 77.252 0.585 6.5 11.95 3.341 \n", "2 A1 SH STUART 2809.0 82.899 0.566 9.4 13.60 3.064 \n", "3 A1 SH STUART 2809.5 80.671 0.593 9.5 13.25 2.977 \n", "4 A1 SH STUART 2810.0 75.971 0.638 8.7 12.35 3.020 \n", "\n", " NM_M RELPOS \n", "0 1 1.000 \n", "1 1 0.978 \n", "2 1 0.956 \n", "3 1 0.933 \n", "4 1 0.911 \n", " Formation Well Name Depth GR ILD_log10 DeltaPHI PHIND PE \\\n", "0 A1 SH STUART 2808.0 66.276 0.630 3.3 10.65 3.591 \n", "1 A1 SH STUART 2808.5 77.252 0.585 6.5 11.95 3.341 \n", "2 A1 SH STUART 2809.0 82.899 0.566 9.4 13.60 3.064 \n", "3 A1 SH STUART 2809.5 80.671 0.593 9.5 13.25 2.977 \n", "4 A1 SH STUART 2810.0 75.971 0.638 8.7 12.35 3.020 \n", "\n", " NM_M RELPOS Facies \n", "0 1 1.000 3 \n", "1 1 0.978 3 \n", "2 1 0.956 3 \n", "3 1 0.933 3 \n", "4 1 0.911 3 \n" ] } ], "source": [ "test_data = pd.read_csv(\"../validation_data_nofacies.csv\", delimiter=\",\")\n", "print(test_data.head())\n", "test_data[\"Facies\"] = metasubmission\n", "\n", "test_data.to_csv(\"the_meta_submission.csv\")\n", "\n", "print(test_data.head())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
bjshaw/phys202-2015-work
assignments/assignment01/Codecademy.ipynb
1
145006
{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Codecademy Completion" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "This problem will be used for verifying that you have completed the Python course on http://www.codecademy.com/.\n", "\n", "Here are the steps to do this verification:\n", "\n", "1. Go to the page on http://www.codecademy.com/ that shows your percent completion.\n", "2. Take a screen shot of that page.\n", "3. Name the file `codecademy.png` and upload it to this folder.\n", "4. Run the following cells to display the image in this notebook." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "nbgrader": {} }, "outputs": [], "source": [ "from IPython.display import Image" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": false, "nbgrader": { "checksum": "54f5a91240cc5d5ed59b3d29b228bb9a", "grade": true, "grade_id": "codecademy", "points": 10 } }, "outputs": [ { "data": { "image/png": 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QlycMtMeW5034dWdZNvJrh84Wje11g72ZwSzYT+a9eSa7yK8d+l9EvHesitrL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\nkT4I+VL9aErDWhB0Dhe2pOS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ERERERE7EJ1CKARERERERERERER\nsQvVIYBGRERERERERERExC5UhwAaEREREREREREREbtQHQJoREREREREREREROxCdQigERERQ+Qe\nAAAAuHA8LhERETEw1SGARkREDJGynw0AAAAWHI9LREREDEx1CKARERFDpOxnAwAAgAXH4xIRERED\nUx0CaERExBAp+9kAAABgwfG4RERExMBUhwAaERExRMp+NgAAAFhwPC4RERExMNUhgEZERAyRsp8N\nAAAAFhyPS0RERAxMdQigERERQ6TsZwMAAIAFx+MSERERA1MdAmhERMQQKfvZAAAAYMHxuERERMTA\nVIcAGhERMUTKfjYAAABYcDwuERERMTDVIYBGREQMkbKfDQAAABYcj0tEREQMTHUIoBEREUOk7GcD\nAACABcfjEhEREQNTHQJoRETEECn72QAAAGDB8bhERETEwFSHABoRETFEyn42AAAAWHA8LhERETEw\n1SGARkREDJGynw0AAAAWHI9LREREDEx1CKARERFDpOxnAwAAgAXH4xIREREDUx0CaERExBAp+9kA\nAABgwfG4RERExMBUhwAaERExRMp+NgAAAFhwPC4RERExMNUhgEZERAyRsp8NAAAAFhyPS0RERAxM\ndQigERERQ6TsZwMAAIAFx+MSERERA1MdAmhERMQQKfvZAAAAYMHxuERERMTAVIcAGhERMUTKfjYA\nAABYcDwuERERMTDVIYBGREQMkbKfDf3F5x9cvPaJfA0AAAeA43GJiIiIgakOATQiImKIlP1s6AVf\nLr90+tiJlk8Nvnrp7T9sy6Xt2L6Z+8Xn8rXGzfljM7fk6wPm7ocrF78X/5uTRpnPPD386sU3P/is\nLpc+PvVbb7z7hXwNABA+HI9LREREDEx1CKARERFDpOxnQy/4cvmli8t35BvBgzu3fvzSmadnbj2Q\nH/hyY+b0pZvytUaPAujGjZlzx4d/tHz7y2YJH9S/uP32mz95/yv5/rHpYYYOAPAYOB6XiIiIGJjq\nEEAjIiKGSNnPhl7gDKA17rzzwsn5G/KNH438pSAC6MavL5545Q3ryOv948H7rxNAA0CYcTwuERER\nMTDVIYBGREQMkbKfDb3AK4Deu3XpxPyNvS/eeN4d9d66dPLVtz57b3z43FOtiTv0tNoIoO/d/MmF\nV/TJMc48d2Hltn1ajLsfvvny86PHxSYnR5+79I516Y2ZM+mbe9ufvnNRrnDuXPaWx0wg9955wTic\nN7v5S2cuvt+Q7yTiHEd/fNt4cfGte6IYi+cGz4iSH3/2lYtvG3NufPWL78eflnN6aMpsvf5FPvvD\nYX1lo8zmRB9iV+cu3dw13uhoh37h2pfyHQDAAeB4XCIiImJgqkMAjYiIGCJlPxt6gVcA/fnKM88u\n3t7b23771b/N2H9X8Ob88e+9p+fCYkPXCOjv/Wj8+Sv5z43896sbmVeOX/igGSJvXbv41PCV/B25\n9Pa1Hz09OH/DzKBvzJwen5l/4bUPtozljS+WXzrz8rvuWTW+eOP50UsfWpNfO7cX/1aW0OTOOy/o\np6OX+cKlmR++vPzJXeMo925eGjynZ9Mad69ddI6A/vyDn1y7JYu099X1mXPHL/1aTv0hdjvcKv+D\n919/6qV37sp3AAAHguNxiYiIiIGpDgE0IiJiiJT9bOgFjgC6cffTD9IvmiN57713Tka3khszZ15+\n1wh/vQLokz/8xT35TkObLuOHvzAiWrErx9K9vduZc82A+8bMaWtarXFz3vmJwb2bP37p3NMvvf6T\nax/c+PTLbZkON/nkx8+++pblQHevXTSPopX5aXukblnqFUA7uJMbtlTI1rWLzxiTZd/74GXX2QEA\n7DuOxyUiIiIGpjoE0IiIiCFS9rOhF3y5/NKZ482pJ06OPvPiD3/yYXMeicZ124wWty6dfP26fOcV\nQDdHB0u+eGNYpttatutc2hpqLbgxc9o5dcadd15oM6a48eVnNz9Yzl55+cX404OvXLz2h9ZQ6+VX\nnls2JtYQiHI2JxIRr+POSUUsU1d3EUA7JgD56hcXzl368ItfXBC15D8oGwBgn3A8LhERETEw1SGA\nRkREDJGynw294EuvOaAt3F7822ZwfHP+qVaI7BVAO9Pb1s5vzJz2mh/55iVziLRYwbY3QfsA2oY2\n3UerbPfeOze88pnxWuzk+dyW8drzZDsG0Pf+cP3am+nXfjg8HH9mePS4Ywbq+q8vnjz9t6/ddGbr\nAAAHgONxiYiIiIGpDgE0IiJiiJT9bOgFnQJobc5lOaPFjZkzlow4VAG0fboPbeC2nNn57rWLluOq\nBtCN229eePrFK8sf/uFufVeLmJ0joPVPTp55mgAaAHqC43GJiIiIgakOATQiImKIlP1s6AUdA2jt\npwj1DPfWpUHrfNBqAXQ3U3A8UQAtinfCciK3F5/WCiMKYJ0PWjGAFgWwT4HtCqDFDuPpm1/dmIl7\n/V4iAMA+43hcIiIiYmCqQwCNiIgYImU/G3qBVybroPHriy/mPru9+Jzt5/vEhgoBdDc/QvhEAfSn\nbz5z0hoNf/HGi69fv/Peue+9Z/kZQ8UA+ub8cXsBtpYvHrME0OKt/EnD+q1Lwxffal+NAABPjONx\niYiIiIGpDgE0IiJiiJT9bOgFXQTQWjp87rnnXzEmtTBp5C+dfkH+1l9DG9rcPoDe29u6dvGpF+ev\n3zF+afCr29d+9PTg/A05aUbXAXTj1o9f+uFP3r712R19Tgz9o7u33xkfPOOY4uPutYuizC+/a/1t\nwA4B9IP3Xz/2kpww+oEoZuPXF09e/MltY2izVuDhS6+fawbQtxeftgwJf3Bz/pmX3jEnmwYAOBAc\nj0tEREQMTHUIoBEREUOk7GdDL+gqgN778Mqx5s/6NbnzzsuDZ46dOH38WX1oc6cAWnD3w8Vz+ibH\nTp4799p7n5nps0BhBHT9i+vXFl9+Mf43J09ruzpx5unvXVm+6ZoB405u+MTreSPulnQIoMUKb104\n95RWvNGX39Z2+ODTdy4+bxY4c/Pu3q20Mc66fuvS4CtvfK5vZHI7c+6FZfc81wAA+4bjcYmIiIiB\nqQ4BNCIiYoiU/WwIDbczo8/Jwc59w/bbrx53zzoNANDPOB6XiIiIGJjqEEAjIiKGSNnPhpCgzURh\n/Sm/vuCLN54ftc8ZAgDQ9zgel4iIiBiY6hBAIyIihkjZz4Yw0Pgyf+ncc2/21/Dnr26/efEphj8D\nwKHD8bhERETEwFSHABoRETFEyn42BEv9g5e1uY9fefnNW9vyo/DzyY+f1WZwfuG1D/g9QAA4fDge\nl4iIiBiY6hBAIyIihkjZzwYAAAALjsclIiIiBqY6BNCIiIghUvazAQAAwILjcYmIiIiBqQ4BNCIi\nYoiU/WwAAACw4HhcIiIiYmCqQwCNiIgYImU/GwAAACw4HpeIiIgYmOoQQCMiIoZI2c8GAAAAC47H\nJSIiIgamOgTQiIiIIVL2syFQauXNakO+PgxUVpMLJfn6CagX5mbWavLN3lYuubgPO90X6lvFcrNg\n0AmqqwNhurfBguNxiYiIiIGpDgE0IiJiiJT9bDhgqrnxgYFTHiZWq2JhYjxXlWvaabMoIIpzA5lN\n+dqPbtbxZTMzMFfUX4lKS7ROvvW5Hb/PD5InOsG+Q9Rw646NDI2lFgpqcfLRqq7HIIh7GLrA8bhE\nRETEwFSHABoRETFEyn52WNl+2Pj5vcr/vPO7lz7dED7/8fp/K/5cKF4Yn4hFYgWxmtygD3BnyvuV\nMtdyiS5jrM2MFnwfGPsUOHYXQHfJvp5yvyaqj1cJ9ppvbOXTI9Fs+wG79gMRQHeAADqkOB6XiIiI\nGJjqEEAjIiKGSNnPDhl/fHD/3+6WX/p0w4ibu1GsLDYRG8pdhJeDC6C3lka7i7Ea19NHLYDe31Pu\n00T1MSvBVfNiP0Pz7RJox4EIoDtAAB1SHI9LREREDEx1CKARERFDpOxnh4bth43/eed3jnBZSbF5\nuAdE+wXQtUJ2fChyamBgMJZeN5dbV66XFiai2gqnItGJmfyW7STrhUx8JGLOk6DlttXVhD1tlHlu\nZTU5OtxcMyNzr638jLHzwaHE7Fq5bnyqF2CuuFfbmBkTm2grW8PEenlNbqUVKVs0J2bwDByrV+OJ\n1dbUDfX11MCp9EbrJIqZQf1tK4xTnYJDrC9KWCvMJ4YGtSLFpnPGiXicsj5Et15eSYnPzRquby6l\nxuQlSC3appmoby4k9eqNjCSzhVrBPEG/SpZUC1mzVmPTefNjs4Tah2sV+aFJPT817BheXMqONCuq\nkp9NRvWzGxqfaV0nV+W4L4Hadbfirnn9k/LiaLI1RbdGbS0Zu1pxH0gvTL28mo7Jki+UrAfyK4O8\nRrm0du8NRIYToublIl+qhcVUTD96ZDiWXq14/iWoFRZS5j5FUYpzrUvWOtxIcmGzWRTfW8tAtN2E\ndtDI0Fh6bUt+6HGPiQYjL59+I22ap+N3bx8Z/uub3Z36/YfyXXhwPC4RERExMNUhgEZERAyRsp8d\nAozo+f/efNsRKD+GYichjqE9A+jh6OjEUsnIo2qFzEhsxYixWivX8pPR9LrxW4WN+lbJ41fd7DFW\nm2zUuUgcZTDRPHxpdWq0WUKx6HImc9lcaE82q5trRZmDNypXE5FpGZN6BtB7peyQuYJYf2M6kZ2f\naq1WXYlP5rVMr3UWjxFAx5KTU6Ks+lHq5cVEdLGsL3Kd8mYmPpuZms03f/yxvpmJjqTl+0a1MBcf\nnSvKjLGUbS2qlXKX49GRLgJoW602quWKvrd6cW40Pm9k9fXyslZpliRTIGrGPsHFZsasWLHzSGK5\neZ1yU2fNY7kqx/vfALq/7lZcO6+tJUeXK3tbK7EJawKt1b9x3zoOJAoTHYk3717rrdKuDOLEx5Pi\nGskUuZpPj0zpt4g/jdLGetm4/LZ2ZKGhXeipNeO+bZTXLk8lrJfsrLlIO1xsSW7e7tYSd87oefmv\nL/XyUmIwLa+o6x7bqxbXNuXZNLZWtDWNN3739tHh0Te7O9v1+82aCgmOxyUiIiIGpjoE0IiIiCFS\n9rOD5o8P7jfnd94vxQ5DOSNH1SuAboZWOvpgYX2V1srFzKA5atUPe4zVJhu1L2psTEfS163BXl3/\nRA+DRAFO2ca5eofLgsZG2jwvn3UspyBWPr9SqeZS5kwO9fWULJItUFYNoO0nUmtVtbM2xFa2gcaV\nlbFm2mhQWRoz4st6ftKsDQNR+MGOAbS7VnW2VmJjS5ZRz+K4lnM0KGWjraReVKY8euN62pFW1/VP\ntGWuyukigG573a3Ydt6obC4kh40y19YmLAmvuFXGVoxTc1SLKExs2TrUuzAjh2C3LYM4rq2utKrw\nvvd8qCyPutYXZbafo3Y1vS+ZVuGdby3nnVO5Gmvdyc7B7FbE4cxL77p8R5BHD3bFIylkEbTjcYmI\niIiBqQ4BNCIiYoiU/exA+fjrv+zLwGe3Yrdi5/IwYcEzgLZ/0ppC17KouppKLRZboynd2GMs/2zU\nscic+8JCK3pzlc0z2WzUBflOAXRjY3pURnWbGT2RrCyNy4SuOGemeK2zeIwA2rK+RjHTqj17bTj2\nVsslztqyTkFleVSfM6SYsU0VIqjnJzsG0B61KhDra2OHLXjVVWXlfHPbUnZEpvbNJLqFFp7qS12V\n00UA3fa6WxE7H4zoE61os2SMTrSm/hDrx6/K06mtJZs7d1SLqzCVpbPGSbUtg+ukxG697isv9Duy\nvBx3rS+u5kxBvjZoBsGuC926+f1vLbGO485pFttV/hZGgyGAtvGocV88knYbj+T7EGB9ViIiImKQ\nqkMAjYiIGCJlPzs4/v1exZEa77viEPJgocAV6Xp8os8eq71wLKqV88vp1PjUQsG2usQeY/lno64g\nsjkjcJPmrlxls4aJ2ly650dHJ9KZuczS2lLSXNMngNaGOafWteyyNB8zgufSfELPnStLY2YZWmfR\nwwDafQlam3jUT/ME/SvZq1b19bUY165RJ1YqV2Ny2HkpGzXL6VWr5lFcleN5CRSuuxW/mhc0NtIj\nxpWsrU20Kt9RLa7CiFoyDt22DK7jit163lct9EmWR8/GU7OZzGJuZdY1AlpcaI+r2bxkzktjDmH2\nv7W0Hbq2ck4mYyIazGR89GwyPZfJLK8tNWvMuqZ4be7HfmsdAR49uC+eSvcfhCaCdjwuERERMTDV\nIYBGREQMkbKfHRA9SJ8Nw5RBu7NO9yd+AbRJ5Woy6Q6n7IGXfzbqCiIfbwR0KTs6aZuco5n2eqaf\nGltLo9qUEa2Bz/JMa7lEc9KJ1lnYUz93nGfgt77Gfo2Ado+ZlSfoX8nurTRc6/tQW0vq0ys3k3pB\nkCOgPWteR5bQLLCB4zTbBdD7OQK6lp+MZy2/b+i1fnP2jybWEdCuNFzif2v5tFANZ/lL2bMp+2w2\n5j7b1vDRomFMxBGSCNrxuERERMTAVIcAGhERMUTKfnYQfPz1Xxwx8YEamrk49iOA9v7cHmNVVxPx\nq9ZVqivnZeBlzwcb7ebhdR2oGSY6Qsa9UnbIXNM3gBYrjWVLW0uxZjkbG+mJter1dGsUcOssehhA\nd5gDeiizaUlJtdi3VQk+leyuVR1RS7Yf7vNDbB5f2dKrS36iJ7Nt5oCetcXdhdmOAXTb627Fr+YN\ntlbi0xvV9ZR1pnBHbfsH0G3L4Dqu2K3PfWXgSJBraxPuShAfDrXqVKBdTeOSaRfatqhFm1urlB22\nTZLewlF+Z1MSG5r7bF/DR4y/3t/Z2dkNxyhox+MSERERA1MdAmhERMQQKfvZPeePD+4f0LzPforD\nheM3Cd3ZsfsTjwC6XrperNSNcLBWWk54jlSNTORaUdjWSmwknZeTRjeq6+mo+RtxtdXEkCMaG0ws\nlYxN9Z03y+MqWzNMrK9PRcbNHZZyU5cTzTWrV+OxxbL+0klxLpaetsaI9fxkSnzSCn8tYVxhNpJu\nBtPVlfjYgsdOW+u3C6Cdp+yK/OqbmWizuhrV/HQ0Olc0jt2wLhKnejkZGzOTTf9Kttdqo7ZV1fdW\ny09G4nMFc4taubBhGbPbonE9HRtPxMwfadQRJxhJLDev05LYvzw9PUU1j2Usco4sFihcdysd4lHt\ndxQT47YQ1nEg/wC6bRlcx7UE0KXscEqf58JKeWEkminonzaqhblUctz9I4TuqxmPjpjVWMpGR6Zy\nZjU2tkprm8bFbHtrraci5zMeV9RR/np+ajAhf7NRO+5UorlPv3v7iPLw6/r2zu43IYigmw9KRERE\nDFh1CKARERFDpOxn95bth43nP153BMQ9UBxUHFoWIjBcka7HJ14joLUJl8eGtN+CGxyKTefM34Gz\nUs1NaitEhuX/9K+Xc+nmJpNLMk8V1IvZsciAWHNkQQacW/mZcWPNaHI235qMwlU2S5hYLy4mhgYH\nBk5F9PKUspPmmrWNGW3/kSFbfqrRuJ4esGejtbWkOdOujjW2K68khrX96OdT25iN6WdnH6XaWr9d\nSug8Za9Qtb65lDLWGY6lllu1JagVsskRfdFYWpxqebmVbPpWsqBayE5EZa3OF82EtlaYT0a1ehOF\nSWbXKx5XUqOUHXYPyK3kZ2WdRydm8tYh281juRc16f66W/GqKyvaFTRmPW5iP1C7AFrgVwbXcVsB\ndD2fcjQZg621tLyCiWyhVltPuQNoQetqjiQXNuuF5lQYjqs5vWJes7a3lmWH+oXOyyvqKn99c0G/\nn+VdVJpPmbvwubePLA+/3t3Z3v36oXwbGI7HJSIiIgamOgTQiIiIIVL2s3vL/J2PHdFwzxSHloUA\nCDW1tWSfxJGl+SH37ygeLKVsYtk7LX8MCrOOcBmC5+HX9Z2detARtONxiYiIiIGpDgE0IiJiiJT9\n7B6y/bDR48k3rIpDh2AQNEAnamvJ2NV9y1gPklJ2xGO6jwOlcT1rn7D7SSgvnO11+aELHn69u70T\n8Chox+MSERERA1MdAmhERMQQKfvZPSTA4c+GDIKGsNPYWtEmce7tqOLHolaYi8b2bzByr2lUCnPx\nUddEMRAKHn2zu7Md6Chox+MSERERA1MdAmhERMQQKfvZveKPD+474uBADMevEQK4KWWHzDmC5Sch\npZ6f1CZKTswXzLmt+4b6ekqbr1mfA3pmLewVfZR51Njd2dkJLoJ2PC4RERExMNUhgEZERAyRsp/d\nK/7tbtmRBe+7r/z5L/9ZvzXo+tyqKIYsEAAAhJVHjfvbO/Xdv8q3PcbxuERERMTAVIcAGhERMUTK\nfnav+B+f/sqRBe+vr/z5L+KUduqlF1yLrIpiyAIBAEB4efTgvnhU3X/wSL7vJY7HJSIiIgamOgTQ\niIiIIVL2s3vC9sOGIwjeX830ucPwZ0N+ihAAoB/46259Z/t+o/cRtONxiYiIiIGpDgE0IiJiiJT9\n7J7w7/cqjhR4H1VKn4WiMLJYAAAQZh5+vbOzs9vzCNrxuERERMTAVIcAGhERMUTKfnZPmL/zsSMF\n3i9V02ehKIwsFgAAhJuHX9e3d3a/6W0E7XhcIiIiYmCqQwCNiIgYImU/uycc0ATQj5E+C5kGev+p\nlYtVJjbxplbePHp1U68UyzX5uslWLrlYkq8Buufh17s7272NoB2PS0RERAxMdQigERERQ6TsZ/eE\ngwigHy99FhJA7zvV3HgiV5VvHFRXE5lN+TowqrnEuF8BDxjPQ29mBuaK8vWhxPMED/1Zw4Hx8Ov6\nzk7964fy7cHjeFwiIiJiYKpDAI2IiBgiZT+7Jzz/8bojBfbwo3e7j5IfO30WisLIYsE+0S6AfgJq\nufFMSALL4tzjnuCTZN+bmcRqMLG5weOfNQE07DOPvtnd3tntWQTteFwiIiJiYKpDAI2IiBgiZT+7\nJzgiYC8//Pk3/9VloPwk6bOhLBbsEwcUQFeWzoYkgG5sTAcQQDeupwMNoJ/grAmgYd959I02EUeP\nImjH4xIREREDUx0CaERExBAp+9k9wZH/evrKvfqDLmLlJ0+fhbJYPUMfSVovLiaGBgcGTkVi07ly\nXS6qribsIWMzzBUvMsW92sZsLHJqQJvFoppLzBX3ttbSY5GBUwOR4US2YJ1mt15cTsWG9UVjqQXr\nonppYSIqdiIOHZ2YyW+ZMxLXy7lpbecDg9HkYtEsURsq+dlkVD+FofGZteY5GGUuN/c2lJhvHd46\nBYc4oCz8SHJh03ZAfdGQLMxyqVEvZM4bZdb0y2Hrm0spuVUstdg6qFHh5aspUeHatvYU2LsYRvXW\nCtlxucP0qnGGldzEqH7hdM0I1f9cKmvTMW39wSHtQpfbT8Ehr3Jh3nlvVFaTo/rV1JVBfNuSb8xo\ni/Q19aHT9c2FhLwf0q0bzlhUXkmJRWbBvHbrfdaievR9RobG0mtbxmc69aK8x8TlE1e/0EUAXS+v\nydtJO252U149ee1W0zG9QobGbbd5rbCQMoo6nFgo1ZtjtP3bkfa6sGg2jcsFKHAAAJe5SURBVGFx\nWSutKbnF7ibNm3axVLcNOTcvinYd1yrywyb1/OWhrH1G69J8NH3d2LdPS3FF8MU5vWm7cDYH+eHa\nTKshZ4uyWnxvocPHo8buzs5OTyJox+MSERERA1MdAmhERMQQKfvZPeG52790RMCedsyg9yV9Hvk4\nL4vVM4pzkdGzyUzB+DG6Wmk5ETGzv7YB9FRmbmqpZMZv1VxiZDR+ecX4oFHNp0eGMpsymyrORaPT\neeMAjWohc340I5PEWn4yml6XS+pbJfPH4cT+R6fWjDCump+OxpZdIZsNsX4ksSxLUyvlps62Ar7c\n+FD0bNIsqn6C0xvG4VsBtCj/2ak1I/7WCh9bMhPMxmYmOpKWpRebl439FjNm8OpJ3bqVOOe5+Oic\njNGLc/GZuakZedb6oZspsF8xxOdjydRlcRL6onp5YTy6UNYXaSfYPFkd33MRtd2sJe06xM9GhzoE\n0LHkpHaV5VEXE9FF86iOe6NNyS9nMlrJ9beCzUzk7GhyrnnDLSUGzfJvZuKzmalZs7IFvufiPGtR\n4aPnZe5ZL4t9puU13itlR5r3WK20OhUfiXYOoKvFtU2ZBTe2VrS96W9EY0lMiGsnF1XX09HLeeM4\n+n1iFrVRXrs8lTBL2C6AbpQ21stG/e7VCpmR2Ipxgo1iZiRqNgGxu6mp8eZORIMajc+b52q5n5s0\nrqej89YEupiRpyAO7dNSugugfZrDXnVzrWic+16jclUrkv6m3S106Hj0YHd7p37/4CNox+MSERER\nA1MdAmhERMQQKfvZPaH7HyFsk0HvS/osDOBHCItzA0O2rKqaG5fDJ9sG0APJtWamqH2QOGX7oLGZ\nGZrUs7mtldjYki0/3lqKja3onxQzgx4xbuN62papNTbSXqs1ca6/t1fXPzEjMHtR9yorYzLmMwPo\nxsZ0JH3dckCxuQzjtJWbYbSF9gG0e6vKknlQZ4WLqpMpsH8xxDqtRFWj1ro0zYti4L+TUnZIVruk\ncjXWHGjcwhZA23a1V2tl5fZ7o23J7TeGtv9h2/BcsStZIc5Fba6L46ydFS5OzShefT1l3gkG2j47\nB9A2xCbyWOLa2f8tpHkD19YmRFFtx0mbwXq7ANpOZXnUyHxra0l3seVOnA1KnLt7b6XsiAzNNTYz\nxt60CvRrKV0F0H7NwY44d3mftLuFDhuPHtzv0SwcjsclIiIiBqY6BNCIiIghUvaze0L3AbTQM4Pe\nr/RZGEwA7YiZqlfjRtTVNoC2Z16tFLVJYUaPaGuriVHn+OXK0tlETg8lq6up1GKxNeJVRxTJluX5\nB3YGxTl79idoZdZi2/iKfdvK8mhiVTu8GUAXM6csaZ2geTq1XOKsPT2XtA2gvbZqHtRZ4a2q8y+G\nu3pbEzI4Ksd3J+JknReilO00AtpR7cWMub793lAruTPqra7EjXWci/x36yib+NxR4eauXPeSFkk7\nCyBwl8qgURfkLQG0vbGIYhi3gSjqTEH/yKQVW3cVQOsHKi/Hjf2LAzl3Z0667b6OrlJpVK7Gmyde\nmo+au/VvKa4a8Nitb3NoYVSYJYD2vYUOF/rw5x5NAu14XCIiImJgqkMAjYiIGCJlP7sn/L9f/Icj\nBW6vI4Pex/RZOH/nY1msnuGOmaqriYg+ItU/OHPlSu6cUYvktKGvrp0I7JvXyvnldGp8aqEgPxJF\nktP7tnTOaWvFK4ATR28G0M6wr1kk8ULfUKzsONyAHIrrcV4Gzf1rWAqsH8trq+ZBnaVtraxSjHYB\ntPdOPC6E59m1gkh31bUJoL0P6llyZ9Qr1hnUV3Yu8t+to2xiD47VhPoAfI97wzNrdnyozb8cHz2b\nTM9lMstrSxPyWK69iWLot4FWgNb9YNDVHND6ZNOjZ+Op2UxmMbcya4yAFit4FNvYidib80xPDaTW\nrcOadbZWYhPG0PNSdkSWrV1LcVWLx8qeN4yONv/1+dHRiXRmLrO0tpSUq7W7hQ4Tjx7s7uzUe/Qb\nhATQiIiIoVEdAmhERMQQKfvZPeHf71UcKXBHmxn0D/Y1fRaKwshi9Yyia6zlY46APu8YZ9zVCGgr\nlavJpH44r5isHaojoEvzQ64R0M70UOJxXgb+mwgefwS0fzFcMa55aRzXwncn4mTjV+2nsrU06k4D\nW0Fku/TQfm+olXxg1nHDtRkB7VfJ9rK5j2Livr0b19OdAuhS9mzKOm1IM0p23ZmiGEYJ5d1uoZsR\n0LX8ZDxbamXH5g25V5h1NgHrCGj73vyorU3ok5+UsjFzypcnHQHt1xxK2dFJW4WZ90m7W+jw8Oib\n3R5NvmHgeFwiIiJiYKpDAI2IiBgiZT+7J2w/bDhS4G40MmjBPqbPQlEYfa89pNh2Dmh7ZFldOW/E\nSa5cqeo1B7QxALPdHNB2zBixvp6yF6kD7Wa21YracQ7oen7Sb4S136S3bQNoj61sc0Dbcr1WeOpf\nDHfA6htA++/ENeFGF3NA+6aH9hhUreTmKGaJ2FVrDmhbBtrmujjKVsoO2y+yiXYvzRUtraqLOaCd\nZRY7l8fyD6BraxP2omrBrtzKvx057iKxE7n/2lrS3gQsc0CL6yiHNndAtIv41UppPtYsWLuWImrA\n/g8D7hDcrznYbwbrndbuFjosPPqmd5NvGDgel4iIiBiY6hBAIyIihkjZz+4VStNAN33lz3/5z31N\nnwOYAFpQnBuIjiQzBWMe5lppORFpJkRbK7GRdF7O0NyorqejMjh25UrVXGI4Gr+8UtKDsUY1nx6J\nZjaNDevFuWh0Wu7GXGSEYPXS9WKlbqymH1qGgKXsSHRq1diZ2KZSWisagbVPNi3KE0ksy/VrpaWE\nmf0ZRU1MJJeaC8VRJvPGGzOA1g4YHZnKNQ+4VVrblAl5fTMTtVRCpWqsU8wMJt2DuJvYthLnPB2N\nzhWNc/YPoP2L0S6AruXGh2w79D0Xay01qoVM/HzssUdA11YTQ9YYV6XkAyPR5FzzhrNcLGcA3eZc\nnGddW09Fzpt38V6jVi5sGCOLG8XMSDS9bh5tdSo5FvMIoKsr8bGFsvG6np8aTBj/WiC2yF2eSpj1\n4B9Aa//i0rri2lbx6Ih5Xr7tqLwg2kLBKGe1MJdKjssfIXQXOz4SbV7x/GQkblag9VydNDbSY4nE\nmDUY928pemJuNhNjkX0Cbh3P5lBfn4qMy39SEqc+dTlhXvR2t9Dh4OHX9R5OvmFgfVYiIiJikKpD\nAI2IiBgiZT+7V/zb3bIjCw5EUQxZoF6iZWqFWmE+MTQ4MDA4FJvOlS1hVr2cS48NRU7piyaXZIbq\nzpX0nLGsrRwZODUQGUlmCzLJ0qkXl1Mxsf9TkaGx1JJMnzW0qWOb+7ceWhx4OqYVSdskvbIp99b8\nOTUXlfysfgqnItGJmXxrkKYeEep7M46SmDenmhbLmgG040ynV4qW4jcXRYZj6dWKkb1VV1NGjdkm\na7BQ31xKGbUxHEstmzXXPoD2K0a7AFocKGvUbXRRJo2+56LXg1FsrR7qG+aPxVnoLoAWlzQrr/WC\ncdTuSz4wJ264bGJYbK5d3NZVdwfQ/ufiPmuxz+SIVqSBwWhyPl9p1rhYMBHV9yAuX7leXhp1B9B7\ntY1Z7Q6J6KOz65sLevEGInrxSvMp4xzaBNCCZgHE/b+wWS+YE3cIbGfRakd7e1trsskMJ0STqa2n\nWvtvFTuaXCzWC60rri2bT0a109fb2nrrXB2U5odc/2Dj11LE2ZhHdC+y0DwXS3OoFxflPvVWXMpO\nGqd+2APoh19rk29880i+7RGOxyUiIiIGpjoE0IiIiCFS9rN7xR8f3HdkwYEoiiEL1Etcmdpj4c4Z\nD4R6ftJjVOZjYw2goUd4pcyHksKsI3t9MgoztmkuusI1MQjsJw+/1ibf6HX8TACNiIgYHtUhgEZE\nRAyRsp/dQ+bvfOyIg3usKIAsSo/pqwC6sjS/j/kzAXQQHJUAurxwdj//saS8OOr8/cCO1NaSnpOt\nw35gTL7R+/iZABoRETE8qkMAjYiIGCJlP7uHbD9s/PfNtx2hcM8Uhw7g5wcN+iqA3l/q+cv7ce6g\nxFEIoBuVwlx8VOWHNNsidpeJn1UcytyorIxH0tf9JueAJ+Ph/frO9u6DAOJnAmhERMTwqA4BNCIi\nYoiU/ezeEuAg6MCGPwtKq2m/yV4VqG1kLdMc9wGFGW0C4vGlUkC5/9GllEuvH85RufX1lDYDtT4v\n88yadSr1x6KeT+l70+aAnlXaXSk7bE54LT+B/eXh1/VAJt8wcDwuERERMTDVIYBGREQMkbKf3Vu2\nHzZGPs47ouEeKA4qSwAAACHn4dc7Ozu7jYDiZwJoRETE8KgOATQiImKIlP3snvPHB/d7PBFHkJNv\nAACAGn/dre9s3w8ufiaARkREDI/qEEAjIiKGSNnPDoKPv/6LIyM+UMXh5IEBACDcNO7Xt3fuBzP3\ns4njcYmIiIiBqQ4BNCIiYoiU/eyA+Pd7FUdMfECKA8lDAgBAyGncF4+nACffMLA+KxERETFI1SGA\nRkREDJGynx0cB51B//fNt0mfAQD6hkeNXfF4CnTyDQPH4xIREREDUx0CaERExBAp+9mB8vHXfzmg\n+aDFbpl5I8TUC5nZtZp809/Ut4rl9mdyiE4W4AB59OC+eDYFPPmGgeNxiYiIiIGpDgE0IiJiiJT9\n7KD544P7Ix/nHfHxEyp2eDR+dbC2MTMWGTg1EJneqMuP+oRqLjGeq8o3/U1xbiCzKV9704cnW5xL\nHJLL48lmZmCuKF93TecLDU/EIzn5hnwbLNZnJSIiIgapOgTQiIiIIVL2s0PA9sPG/J2P92UotNiJ\n2JXc76GnOBeZ6puRtbXceKaV+QWTydrLsE8cygD68anlEurZbkjZzCRWj8p1Cxxz8g35Nmgcj0tE\nREQMTHUIoBEREUOk7GeHBiOGdgTKSorNj8bAZ0k1N95Hw1QrS2cDD6DtZdgnCKBtbC2NHpYAunE9\nTQDdI/TJN+qhiZ8JoBEREcOjOgTQiIiIIVL2s0PGHx/c/7e75f/x6a8c4XIbxcpiE7Gh3EX4cCXF\n1kSymk+PDUVODQwMDsUml4rmVBr1ci5tTK8xklzYdE2wUVtLDQ6IpYZmAFrJzyaj2ueRofGZtbK5\nlTjcXNGcr8MjgdWPZZQhmlwumSGQz95c0xe0Elh9xGir5MOJbEEfoF0vZM5Htf3raqGeK5P1O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3ZB7dOGKbALpSXC+bB6/mp6OueTwAAMKP43GJiIiIgakOATQiImKIlP1s6AVba+mxyMCpgchw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\niIGpDgE0IiJiiJT97KD544P7Ix/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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 2, "metadata": { "image/png": { "width": "100%" } }, "output_type": "execute_result" } ], "source": [ "Image(filename='codecademy.png', width='100%')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.0" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
wasit7/PythonDay
notebook/01 Installation.ipynb
1
4186
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Welcome to PythonDay Tutorial\n", "====\n", "Python is crossplatform this means you can get it run in almost every where. Here is an installation guideline for the major OS.\n", "\n", "OS X and Ubuntu\n", "--\n", "For OS X and Ubuntu, you already get Python come along with the OS.\n", "\n", "Windows OS\n", "--\n", "For Windows, you can download original installer file from the <a href=\"https://www.python.org/downloads/\">official page</a> or using the <a href=\"http://docs.continuum.io/anaconda/install#anaconda-install\">conda package</a>. Anyway, I normally use conda because some modules and package dependency that I'm using have been managed better.\n", "\n", "Please, add the directory /Scripts into you system path. \n", "```shell \n", "set PATH=%PATH%;C:\\Anaconda\\Scripts\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython\n", "---\n", "Next we need IPython in this tutorial. IPython is an interactive python it comes along with many useful feature, e.g. auto completetion and this notebook. To install Ipython, you may use the following commands;\n", "\n", "```shell\n", "pip install ipython\n", "```\n", "or\n", "```shell\n", "conda update conda\n", "conda update ipython\n", "```\n", "IPython had Ipython Notebook but recently the project has been move to Jupyter, see more detail http://ipython.org/.\n", "IPython has many *magic commad*, plese check example below.\n", "<a href=\"http://jupyter.cs.brynmawr.edu/hub/dblank/public/Jupyter%20Notebook%20Users%20Manual.ipynb#4.-Using-Markdown-Cells-for-Writing\">Jupyter help</a>\n", "<a href=\"https://ipython.org/ipython-doc/3/interactive/tutorial.html\">magic commad</a>" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Python 2.7.11 :: Anaconda 2.1.0 (64-bit)\n" ] } ], "source": [ "! python --version" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Pinging www.bbc.net.uk [212.58.246.92] with 32 bytes of data:\n", "Reply from 212.58.246.92: bytes=32 time=206ms TTL=48\n", "Reply from 212.58.246.92: bytes=32 time=206ms TTL=48\n", "Reply from 212.58.246.92: bytes=32 time=209ms TTL=48\n", "Reply from 212.58.246.92: bytes=32 time=209ms TTL=48\n", "\n", "Ping statistics for 212.58.246.92:\n", " Packets: Sent = 4, Received = 4, Lost = 0 (0% loss),\n", "Approximate round trip times in milli-seconds:\n", " Minimum = 206ms, Maximum = 209ms, Average = 207ms\n" ] } ], "source": [ "! ping www.bbc.co.uk" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10000 loops, best of 3: 25.7 µs per loop\n" ] } ], "source": [ "%timeit range(1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Git\n", "==\n", "All codes in this tutorial will be posted on Github.\n", "Please install Git or Github Desktop.\n", "https://desktop.github.com/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
quantalea/AleaNotebooks
AleaGPU_PairwiseDist.ipynb
1
2021643
null
mit
irsisyphus/machine-learning
3 Kernel, Bayes and Models.ipynb
1
55686
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Assignment 3 - basic classifiers\n", "\n", "Math practice and coding application for main classifiers introduced in Chapter 3 of the Python machine learning book. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Weighting\n", "\n", "Note that this assignment is more difficult than the previous ones, and thus has a higher weighting 3 and longer duration (3 weeks). Each one of the previous two assignments has a weighting 1.\n", "\n", "Specifically, the first 3 assignments contribute to your continuous assessment as follows:\n", "\n", "Assignment weights: $w_1 = 1, w_2 = 1, w_3 = 3$\n", "\n", "Assignment grades: $g_1, g_2, g_3$\n", "\n", "Weighted average: $\\frac{1}{\\sum_i w_i} \\times \\sum_i \\left(w_i \\times g_i \\right)$\n", "\n", "Future assignments will be added analogously." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# RBF kernel (20 points)\n", "\n", "Show that a Gaussian RBF kernel can be expressed as a dot product:\n", "$$\n", "K(\\mathbf{x}, \\mathbf{y}) \n", "= e^\\frac{-|\\mathbf{x} - \\mathbf{y}|^2}{2} \n", "= \\phi(\\mathbf{x})^T \\phi(\\mathbf{y})\n", "$$\n", "by spelling out the mapping function $\\phi$.\n", "\n", "For simplicity\n", "* you can assume both $\\mathbf{x}$ and $\\mathbf{y}$ are 2D vectors\n", "$\n", "x =\n", "\\begin{pmatrix}\n", "x_1 \\\\\n", "x_2\n", "\\end{pmatrix}\n", ", \\;\n", "y =\n", "\\begin{pmatrix}\n", "y_1 \\\\\n", "y_2\n", "\\end{pmatrix}\n", "$\n", "* we use a scalar unit variance here\n", "\n", "even though the proof can be extended for vectors $\\mathbf{x}$ $\\mathbf{y}$ and general covariance matrices.\n", "\n", "Hint: use Taylor series expansion of the exponential function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Answer\n", "We denote $e^x$ as exp($x$). Since $\n", "\\mathbf x =\n", "\\begin{pmatrix}\n", "x_1 \\\\\n", "x_2\n", "\\end{pmatrix}\n", ", \\;\n", "\\mathbf y =\n", "\\begin{pmatrix}\n", "y_1 \\\\\n", "y_2\n", "\\end{pmatrix}\n", "$, we have\n", "\n", "$$\n", "\\begin{align}\n", "K(\\mathbf{x}, \\mathbf{y}) = \\text{exp}(\\frac{-||\\mathbf{x} - \\mathbf{y}||^2}{2}) & = \\text{exp}(\\frac{-||(x_1-y_1, x_2-y_2)||^2}{2} )\\\\\n", "& = \\text{exp}(\\frac{-(x_1-y_1)^2-(x_2-y_2)^2}{2})\\\\\n", "& = \\text{exp}(\\frac{-{x_1}^2-{x_2}^2-{y_1}^2-{y_2}^2+2 x_1 y_1+2 x_2 y_2}{2}) \\\\\n", "& = \\text{exp}(\\frac{-||\\mathbf{x}||^2}{2}) \\text{ exp}(\\frac{-||\\mathbf{y}||^2}{2}) \\text{ exp}(x_1 y_1 + x_2 y_2)\n", "\\end{align}\n", "$$\n", "\n", "<br>By Taylor series of $f(x)$ on $a$, $e^x$ at $a=0$ can be expressed as $\\sum_{n=0}^{\\infty} \\frac{x^n}{n!} = 1 + x + \\frac{x^2}{2!} +$ ... for $x \\in \\mathbb{R}^1$. Therefore,<br><br>\n", "\n", "$$K(\\mathbf{x}, \\mathbf{y}) = \\text{exp}(\\frac{-||\\mathbf{x}||^2}{2}) \\text{ exp}(\\frac{-||\\mathbf{y}||^2}{2}) \\sum_{n=0}^{\\infty} \\frac{(x_1 y_1 + x_2 y_2)^n}{n!}$$\n", "\n", "<br>By binomial expansion, we have\n", "\n", "$$ (x_1 y_1 + x_2 y_2)^n = \\sum_{i = 0}^{n} \\binom{n}{i} (x_1 y_1)^{n-i} (x_2 y_2)^i = \\sum_{i = 0}^{n} \\sqrt{\\binom{n}{i}} (x_1^{n-i} x_2^i) \\sqrt{\\binom{n}{i}} (y_1^{n-i} y_2^i)$$\n", "\n", "<br>We let $\\xi_{n}(\\mathbf{x}) = \\xi_{n}(x_1, x_2) = \\left[\\sqrt{\\binom{n}{i}} (x_1^{n-i} x_2^i) \\right] = \\left[\\sqrt{\\binom{n}{0}} (x_1^{n} x_2^0), \\sqrt{\\binom{n}{1}} (x_1^{n-1} x_2^1), ..., \\sqrt{\\binom{n}{n}} (x_1^{0} x_2^n)\\right] \\in \\mathbb{R}^{\\text{n}}$. <br>\n", "\n", "<br>Hence we have <br>\n", "\n", "$$ \\left\\{\n", "\\begin{aligned}\n", "\\phi(\\mathbf{x}) & = \\text{exp}(\\frac{-||\\mathbf{x}||^2}{2}) \\left[1, \\frac{\\xi_{1}(\\mathbf{x})}{\\sqrt{1!}}, \\frac{\\xi_{2}(\\mathbf{x})}{\\sqrt{2!}} , \\frac{\\xi_{3}(\\mathbf{x})}{\\sqrt{3!}} , ... \\right]^T \\\\\n", "\\phi(\\mathbf{y}) & = \\text{exp}(\\frac{-||\\mathbf{y}||^2}{2}) \\left[1, \\frac{\\xi_{1}(\\mathbf{y})}{\\sqrt{1!}}, \\frac{\\xi_{2}(\\mathbf{y})}{\\sqrt{2!}} , \\frac{\\xi_{3}(\\mathbf{y})}{\\sqrt{3!}} , ... \\right]^T\n", "\\end{aligned}\n", "\\right.\n", "$$\n", "\n", "<br>The mapping function is therefore $\\phi(\\mathbf{x}) = \\text{exp}(\\frac{-||\\mathbf{x}||^2}{2}) \\left[1, \\frac{\\xi_{1}(\\mathbf{x})}{\\sqrt{1!}}, \\frac{\\xi_{2}(\\mathbf{x})}{\\sqrt{2!}} , \\frac{\\xi_{3}(\\mathbf{x})}{\\sqrt{3!}} , ... \\right]^T$, where $\\xi_{n}(\\mathbf{x}) = \\left[\\sqrt{\\binom{n}{0}} (x_1^{n} x_2^0), \\sqrt{\\binom{n}{1}} (x_1^{n-1} x_2^1), ..., \\sqrt{\\binom{n}{n}} (x_1^{0} x_2^n)\\right]$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Kernel SVM complexity (10 points)\n", "\n", "How would the complexity (in terms of number of parameters) of a trained kernel SVM change with the amount of training data, and why?\n", "Note that the answer may depend on the specific kernel used as well as the amount of training data.\n", "Consider specifically the following types of kernels $K(\\mathbf{x}, \\mathbf{y})$.\n", "* linear:\n", "$$\n", "K\\left(\\mathbf{x}, \\mathbf{y}\\right) = \\mathbf{x}^T \\mathbf{y}\n", "$$\n", "* polynomial with degree $q$:\n", "$$\n", "K\\left(\\mathbf{x}, \\mathbf{y}\\right) =\n", "(\\mathbf{x}^T\\mathbf{y} + 1)^q\n", "$$\n", "* RBF with distance function $D$:\n", "$$\n", "K\\left(\\mathbf{x}, \\mathbf{y} \\right) = e^{-\\frac{D\\left(\\mathbf{x}, \\mathbf{y} \\right)}{2s^2}}\n", "$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Answer\n", "\n", "For all examples, we assume $\\mathbf{x}, \\mathbf{y} \\in \\mathbb{R}^\\text{d}$.\n", "\n", "### Linear:\n", "For linear kernal, the mapping function $\\phi(\\mathbf{x}) = \\mathbf{x}$, which mapps $\\mathbb{R}^\\text{d}$ to $\\mathbb{R}^\\text{d}$, therefore the size of data is unchanged.<br>\n", "There are not explicit parameters, therefore the time cost increase linearly with the dimension of data, or the amount of data increase $n$ times, the time cost simply increase $O(n)$ time. Both changes in dimension or data amount will not afftect any parameters.<br>\n", "\n", "### Polynomial with degree $q$:\n", "For simplicity we write $1 = x_{d+1} y_{d+1}$. Then\n", "\n", "$$K\\left(\\mathbf{x}, \\mathbf{y}\\right) =(\\mathbf{x}^T\\mathbf{y} + 1)^q = (\\sum_{i=1}^{d+1} x_i y_i)^q = \\sum_{k_1 + k_2 + ... + k_{d+1} = q} \\binom{q}{k_1, k_2, ..., k_{d+1}} \\prod_{t=1}^{d+1} (x_t y_t)^{k_t} = \\sum_{\\sum_{i=1}^{d+1} k_i = q} \\frac{q!}{\\prod_{i=1}^{d+1} k_i!} \\prod_{t=1}^{d+1} (x_t y_t)^{k_t}$$\n", "\n", "by Multinomial theorem. Therefore the mapping function is\n", "\n", "$$\\phi(\\mathbf{x}) = \\left[\\sqrt{\\frac{q!}{\\prod_{i=1}^{d+1} k_i!}} \\prod_{t=1}^{d+1} (x_t)^{k_t}\\right]_{\\sum_{i=1}^{d+1} k_i = q}^T,$$\n", "\n", "which maps $\\mathbb{R}^\\text{d}$ to $\\mathbb{R}^\\binom{p+(d+1)-1}{(d+1)-1} = \\mathbb{R}^\\binom{p+d}{d} = \\mathbb{R}^\\frac{(p+d)!}{p! d!}$, computed using the stars and bars method. <br>\n", "\n", "* If $p=1$, only one useless dimension is added, where $x_{d+1} = 1$. In this case the actual dimension remains. <br>\n", "* If $p>2$, then the dimension increases from $d$ to $\\binom{p+d}{d}$, where actural dimension is $\\binom{p+d}{d} - 1$ since we always have a $x_{d+1}^q = 1$ term.<br><br>\n", "\n", "Now we consider the parameters.\n", "* For each entry in $K\\left(\\mathbf{x}, \\mathbf{y}\\right)$, we have a parameter $\\frac{q!}{\\prod_{i=1}^{d+1} k_i!} = \\binom{q}{k_1, k_2, ..., k_{d+1}}$, which takes $O(q \\prod_{t=1}^{d+1} k_t)$ to compute in brute force. Considering the dimension analysis we discuss above, the greater the dimension or the greater $q$ is, the more parameters and greater time complexity we will have in the kernal function.<br>\n", "* However, since $q$ and $k_i$ are identical for any set of input data, increasing amount of data will not change number of parameter to be calculated (because they only need to be calculated once), although multiplying them to each term of $x$ and $y$ takes constant time.<br>\n", "* If we do $\\mathbf{x}^T \\mathbf{y} + 1$ first and then do the power function, then the parameter analysis is the same as the linear function, except that we need an extra power $q$ after the $\\mathbf{x}^T \\mathbf{y} + 1$.<br>\n", "\n", "### RBF with distance function $D$:\n", "\n", "Assume $D(\\mathbf{x}, \\mathbf{y}) = \\omega(\\mathbf{x}) \\omega(\\mathbf{y})$. For $K(\\mathbf{x}, \\mathbf{y} ) = e^{-\\frac{D\\left(\\mathbf{x}, \\mathbf{y} \\right)}{2s^2}} = e^{-\\frac{\\omega(\\mathbf{x}) \\omega(\\mathbf{y})}{2s^2}} = e^{-\\frac{1}{2s^2} \\omega(\\mathbf{x}) \\omega(\\mathbf{y})}$, we have the mapping function as\n", "\n", "$$\n", "\\phi(\\mathbf{x}) = e^{-\\frac{1}{4s^2}} \\left[1, \\frac{\\omega(\\mathbf{x})}{\\sqrt {1!}}, \\frac{\\omega(\\mathbf{x})^2}{\\sqrt {2!}}, \\frac{\\omega(\\mathbf{x})^3}{\\sqrt {3!}}, ... \\right]^T,\n", "$$\n", "\n", "which maps $\\mathbb{R}^\\text{d}$ to $\\mathbb{R}^\\infty$. That is, RBF essentially projects the original vector to an infinite dimensional space.<br><br>\n", "\n", "Now we consider the parameters.\n", "* We first clarify that although RBF maps $\\mathbb{R}^\\text{d}$ to $\\mathbb{R}^\\infty$, the dimension actually used is determined by the explicit function $K(\\mathbf{x}, \\mathbf{y})$, because we don't have to separate the mapping function. Instead, we can just compute the kernal $K$ directly.<br>\n", "* As calculating exp($\\mathbf{x}$) is simply mapping to an exponential function, the main cost in terms of dimension of data is at the distance function $D(\\mathbf{x}, \\mathbf{y})$, which varies with different distance functions we choose. For example, if we choose simple metrics such as Taxicab distance and Euclidean distance, the cost is relatively small. However, if we choose complex metrics for some reason, then the time cost could be huge.<br>\n", "* Also, if all input data share the same set of parameters, then the parameters only need to be computed once, and applyed to each set of input data with constant time. However, if parameters change with different sets of input data in a specific kernal, then the number of parameters as well as time complexity also increase linearly with the increase of amount of data." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Gaussian density Bayes (30 points)\n", "\n", "$$\n", "p\\left(\\Theta | \\mathbf{X}\\right)\n", "= \n", "\\frac{p\\left(\\mathbf{X} | \\Theta\\right) p\\left(\\Theta\\right)}{p\\left(\\mathbf{X}\\right)}\n", "$$\n", "\n", "Assume both the likelihood and prior have Gaussian distributions:\n", "\n", "$$\n", "\\begin{align}\n", "p(\\mathbf{X} | \\Theta)\n", "&=\n", "\\frac{1}{(2\\pi)^{N/2}\\sigma^N} \\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right)\n", "\\\\\n", "p(\\Theta)\n", "&=\n", "\\frac{1}{\\sqrt{2\\pi}\\sigma_0} \\exp\\left( -\\frac{(\\Theta - \\mu_0)^2}{2\\sigma_0^2} \\right)\n", "\\end{align}\n", "$$\n", "\n", "Derive $\\Theta$ from the dataset $\\mathbf{X}$ via the following methods:\n", "\n", "### ML (maximum likelihood) estimation \n", "$$\n", "\\Theta_{ML} = argmax_{\\Theta} p(\\mathbf{X} | \\Theta)\n", "$$\n", "\n", "### MAP estimation\n", "$$\n", "\\begin{align}\n", "\\Theta_{MAP} \n", "&= \n", "argmax_{\\Theta} p(\\Theta | \\mathbf{X})\n", "\\\\\n", "&=\n", "argmax_{\\Theta} p(\\mathbf{X} | \\Theta) p(\\Theta)\n", "\\end{align}\n", "$$\n", "\n", "### Bayes estimation\n", "\n", "$$\n", "\\begin{align}\n", "\\Theta_{Bayes} \n", "&= \n", "E(\\Theta | \\mathbf{X})\n", "\\\\\n", "&= \n", "\\int \\Theta p(\\Theta | \\mathbf{X}) d\\Theta\n", "\\end{align}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Answer\n", "\n", "### 1. ML (maximum likelihood) estimation \n", "\n", "To maximize $p(\\mathbf{X} | \\Theta)$, we set $\\nabla_\\Theta p(\\mathbf{X} | \\Theta) = \\nabla_\\Theta \\left(\\frac{1}{(2\\pi)^{N/2}\\sigma^N} \\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right)\\right) = 0$. <br>\n", "By Chain rule we get<br>\n", "$$\n", "\\begin{align}\n", "\\nabla_\\Theta p(\\mathbf{X} | \\Theta) & = \\frac{1}{(2\\pi)^{N/2}\\sigma^N} \\frac{\\partial \\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right)}{\\partial \\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right)} \\frac{\\partial \\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right)}{\\partial \\Theta}\\\\\n", "0 & = \\frac{1}{(2\\pi)^{N/2}\\sigma^N} \\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right) \\left( - \\frac{\\sum_{t=1}^N -2(\\mathbf{x}^{(t)} - \\Theta)}{2\\sigma^2} \\right) \\\\\n", "\\end{align}\n", "$$\n", "Note that $p(\\mathbf{X} | \\Theta) = \\frac{1}{(2\\pi)^{N/2}\\sigma^N} \\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right)$ is non-zero, because $e^y$ is always positive for $y \\in \\mathbb{R}$, and the constant $\\frac{1}{(2\\pi)^{N/2}\\sigma^N}$ is positive. <br>\n", "Then we have<br>\n", "$$\n", "\\begin{align}\n", "0 & = \\frac{\\sum_{t=1}^N 2(\\mathbf{x}^{(t)} - \\Theta)}{2\\sigma^2} = \\frac{\\sum_{t=1}^N \\mathbf{x}^{(t)} - \\Theta}{\\sigma^2} \\\\\n", "0 & = \\frac{(\\sum_{t=1}^N \\mathbf{x}^{(t)}) - N\\Theta}{\\sigma^2} \\\\\n", "N\\Theta & = \\sum_{t=1}^N \\mathbf{x}^{(t)} \\\\\n", "\\Theta & = \\frac{\\sum_{t=1}^N \\mathbf{x}^{(t)}}{N}\n", "\\end{align}\n", "$$\n", "Hence $$\\Theta_{ML} = argmax_{\\Theta} p(\\mathbf{X} | \\Theta) = \\frac{\\sum_{t=1}^N \\mathbf{x}^{(t)}}{N}.$$\n", "\n", "### 2. MAP estimation\n", "To maximize $p(\\mathbf{X} | \\Theta) p(\\Theta)$, we set $\\nabla_\\Theta (p(\\mathbf{X} | \\Theta) p(\\Theta)) = p(\\Theta)\\nabla_\\Theta p(\\mathbf{X} | \\Theta) + p(\\mathbf{X} | \\Theta)\\nabla_\\Theta p(\\Theta) = 0$. <br><br>\n", "We get $p(\\Theta)\\nabla_\\Theta p(\\mathbf{X} | \\Theta) = - p(\\mathbf{X} | \\Theta)\\nabla_\\Theta p(\\Theta) $ and therefore <br><br>\n", "$$\n", "\\begin{align}\n", "\\frac{1}{\\sqrt{2\\pi}\\sigma_0} \\exp\\left( -\\frac{(\\Theta - \\mu_0)^2}{2\\sigma_0^2} \\right) \\nabla_\\Theta \\left(\\frac{1}{(2\\pi)^{N/2}\\sigma^N} \\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right)\\right) & \\\\\n", "= - \\frac{1}{(2\\pi)^{N/2}\\sigma^N} \\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right) \\nabla_\\Theta & \\left(\\frac{1}{\\sqrt{2\\pi}\\sigma_0} \\exp\\left( -\\frac{(\\Theta - \\mu_0)^2}{2\\sigma_0^2} \\right)\\right)\n", "\\end{align}\n", "$$\n", "<br><br>By Removing the constants we get<br><br>\n", "$$\n", "\\begin{align}\n", "\\exp\\left( -\\frac{(\\Theta - \\mu_0)^2}{2\\sigma_0^2} \\right) \\nabla_\\Theta \\left(\\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right)\\right) & = - \\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right) \\nabla_\\Theta \\left(\\exp\\left( -\\frac{(\\Theta - \\mu_0)^2}{2\\sigma_0^2} \\right)\\right) \\\\\n", "\\end{align}\n", "$$\n", "<br>By Chain rule we get<br><br>\n", "$$\n", "\\begin{align}\n", "\\exp\\left( -\\frac{(\\Theta - \\mu_0)^2}{2\\sigma_0^2} \\right) \\frac{\\partial \\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right)}{\\partial \\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right)} \\frac{\\partial \\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right)}{\\partial \\Theta} & = - \\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right) \\frac{\\partial \\exp\\left( -\\frac{(\\Theta - \\mu_0)^2}{2\\sigma_0^2} \\right)}{\\partial \\left(-\\frac{(\\Theta - \\mu_0)^2}{2\\sigma_0^2}\\right)} \\frac{\\partial \\left(-\\frac{(\\Theta - \\mu_0)^2}{2\\sigma_0^2}\\right)}{\\partial \\Theta}\\\\\n", "\\exp\\left( -\\frac{(\\Theta - \\mu_0)^2}{2\\sigma_0^2} \\right) \\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right) \\left( - \\frac{\\sum_{t=1}^N -2(\\mathbf{x}^{(t)} - \\Theta)}{2\\sigma^2} \\right) & = -\\exp\\left(-\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)^2}{2\\sigma^2}\\right) \\exp\\left( -\\frac{(\\Theta - \\mu_0)^2}{2\\sigma_0^2} \\right) \\left(-\\frac{2(\\Theta - \\mu_0)}{2\\sigma_0^2}\\right)\n", "\\end{align}\n", "$$\n", "<br>Since $e^y$ is always positive for $y \\in \\mathbb{R}$, the first two terms of both sides are non-zero. Dividing them on both sides we have<br>\n", "$$\n", "\\begin{align}\n", "- \\frac{\\sum_{t=1}^N -2(\\mathbf{x}^{(t)} - \\Theta)}{2\\sigma^2} & = \\frac{2(\\Theta - \\mu_0)}{2\\sigma_0^2}\\\\\n", "\\frac{\\sum_{t=1}^N (\\mathbf{x}^{(t)} - \\Theta)}{\\sigma^2} & = \\frac{\\Theta -\\mu_0}{\\sigma_0^2}\\\\\n", "\\frac{(\\sum_{t=1}^N \\mathbf{x}^{(t)}) - N\\Theta}{\\sigma^2} & = \\frac{\\Theta -\\mu_0}{\\sigma_0^2}\\\\\n", "(\\sum_{t=1}^N \\mathbf{x}^{(t)})\\sigma_0^2 - N\\Theta\\sigma_0^2 & = \\sigma^2 \\Theta - \\sigma^2 \\mu_0 \\\\\n", "(\\sum_{t=1}^N \\mathbf{x}^{(t)})\\sigma_0^2 + \\sigma^2 \\mu_0 & = (N\\sigma_0^2 + \\sigma^2)\\Theta \\\\\n", "\\end{align}\n", "$$\n", "<br>Hence <br>\n", "$$\n", "\\begin{align}\n", "\\Theta_{MAP} & = argmax_{\\Theta} p(\\Theta | \\mathbf{X}) \\\\\n", "& = argmax_{\\Theta} p(\\mathbf{X} | \\Theta) p(\\Theta) \\\\\n", "& = \\frac{(\\sum_{t=1}^N \\mathbf{x}^{(t)})\\sigma_0^2 + \\sigma^2 \\mu_0}{N\\sigma_0^2 + \\sigma^2} \\\\\n", "\\end{align}\n", "$$<br>\n", "Furthurmore, since $\\Theta_{ML} = \\frac{\\sum_{t=1}^N \\mathbf{x}^{(t)}}{N}$, we have<br>\n", "$$\\Theta_{MAP} = \\frac{(\\sum_{t=1}^N \\mathbf{x}^{(t)})\\sigma_0^2 + \\sigma^2 \\mu_0}{N\\sigma_0^2 + \\sigma^2}\n", "= \\frac{N\\Theta_{ML}\\sigma_0^2 + \\sigma^2 \\mu_0}{N\\sigma_0^2 + \\sigma^2} = \\frac{N/\\sigma^2}{N/\\sigma^2 + 1/\\sigma_0^2} \\Theta_{ML} +\\frac{1/\\sigma_0^2}{N/\\sigma^2 + 1/\\sigma_0^2} \\mu_0$$\n", "\n", "### 3. Bayes estimation\n", "\n", "For $\\Theta_{Bayes} = E(\\Theta | \\mathbf{X}) = \\int \\Theta p(\\Theta | \\mathbf{X}) d\\Theta \\\\$, since $p(\\Theta | \\mathbf{X}) = \\frac{p(\\mathbf{X}| \\Theta) p(\\Theta)}{p(\\mathbf{X})}$ and $p(\\mathbf{X})$ is a constant for given $\\mathbf{X}$, our interest is in $p(\\mathbf{X}| \\Theta) p(\\Theta)$. We denote $\\phi(x, \\mu, \\sigma^2)$ as the normal distribution with input $x$, mean $\\mu$ and standard deviation $\\sigma$. Then $$p(\\mathbf{X}| \\Theta) p(\\Theta) = \\phi(\\Theta, \\mu_0, \\sigma_0^2) \\prod_{i=1}^N \\phi(\\Theta, \\mathbf{x}^{(i)}, \\sigma^2).$$\n", "\n", "Notice that $$\\phi(x, \\mu_1, \\sigma_1^2) \\phi(x, \\mu_2, \\sigma_2^2) = \\phi(\\mu_1, \\mu_2, \\sigma_1^2 + \\sigma_2^2) \\phi(x, \\mu_i, \\sigma_i^2)$$ where $$\\mu_i = \\frac{1 / \\sigma_1^2}{1/ \\sigma_1^2 + 1/ \\sigma_2^2}\\mu_1 + \\frac{1 / \\sigma_2^2}{1/ \\sigma_1^2 + 1/ \\sigma_2^2}\\mu_2 \\text{ and } \\sigma_i^2 = \\frac{\\sigma_1^2 \\sigma_2^2}{\\sigma_1^2 + \\sigma_2^2}.$$<br>\n", "\n", "We will prove the formula later on. Using this formuma, we have $$\\phi(\\Theta, \\mathbf{x}^{(a)}, \\sigma^2) \\phi(\\Theta, \\mathbf{x}^{(b)}, \\sigma^2) = \\phi(\\mathbf{x}^{(a)}, \\mathbf{x}^{(b)}, 2\\sigma^2) \\phi(\\Theta, \\frac{\\mathbf{x}^{(a)}+\\mathbf{x}^{(b)}}{2}, \\frac{\\sigma^2}{2}) = C_0 \\phi(\\Theta, \\frac{\\mathbf{x}^{(a)}+\\mathbf{x}^{(b)}}{2}, \\frac{\\sigma^2}{2}),$$ where $C_0$ is some constant since all variables of $\\phi(\\mathbf{x}^{(a)}, \\mathbf{x}^{(b)}, 2\\sigma^2)$ are set. Following similar steps we get\n", "\n", "$$\\prod_{i=1}^N \\phi(\\Theta, \\mathbf{x}^{(i)}, \\sigma^2) = C_1 \\phi(\\Theta, \\frac{\\sum_{t=1}^N \\mathbf{x}^{(t)}}{N}, \\frac{\\sigma^2}{N}),$$ where $C_1$ is some constant.<br>\n", "\n", "Hence, $$p(\\mathbf{X}| \\Theta) p(\\Theta) = C_1 \\phi(\\Theta, \\frac{\\sum_{t=1}^N \\mathbf{x}^{(t)}}{N}, \\frac{\\sigma^2}{N}) \\phi(\\Theta, \\mu_0, \\sigma_0^2) = C_2 \\phi(\\Theta, \\mu_\\text{new}, \\sigma_\\text{new}^2),$$ where $C_2$ is some constant and by the formula, $\\mu_\\text{new} = \\frac{N/\\sigma^2}{N/\\sigma^2 + 1/\\sigma_0^2} \\Theta_{ML} +\\frac{1/\\sigma_0^2}{N/\\sigma^2 + 1/\\sigma_0^2} \\mu_0 $, where $\\Theta_{ML} = \\frac{\\sum_{t=1}^N \\mathbf{x}^{(t)}}{N}.$ <br>\n", "\n", "Notice that for a given normal distribution, multiplying the probability density function by a constant will not change its mean value. Therefore the expectation of $p(\\Theta | \\mathbf{X})$ is exactly the expectation of the non-constant normal distribution part. Hence,\n", "$$\n", "E(\\Theta | \\mathbf{X}) = \\mu_\\text{new} = \\frac{N/\\sigma^2}{N/\\sigma^2 + 1/\\sigma_0^2} \\Theta_{ML} +\\frac{1/\\sigma_0^2}{N/\\sigma^2 + 1/\\sigma_0^2} \\mu_0 = \\Theta_{MAP},\n", "$$\n", "where $\\Theta_{ML} = \\frac{\\sum_{t=1}^N \\mathbf{x}^{(t)}}{N}.$ <br>\n", "\n", "Finally we want to prove the formula $\\phi(x, \\mu_1, \\sigma_1^2) \\phi(x, \\mu_2, \\sigma_2^2) = \\phi(\\mu_1, \\mu_2, \\sigma_1^2 + \\sigma_2^2) \\phi(x, \\mu_i, \\sigma_i^2)$:<br><br>\n", "$$\n", "\\begin{align}\n", "\\phi(x, \\mu_1, \\sigma_1^2) \\phi(x, \\mu_2, \\sigma_2^2) & = \\frac{1}{\\sqrt{2\\pi}\\sigma_1} \\exp\\left( -\\frac{(x - \\mu_1)^2}{2\\sigma_1^2} \\right) \\frac{1}{\\sqrt{2\\pi}\\sigma_2} \\exp\\left( -\\frac{(x - \\mu_2)^2}{2\\sigma_2^2} \\right) \\\\\n", "& = \\frac{1}{2\\pi \\sigma_1 \\sigma_2} \\exp\\left( -\\frac{(x - \\mu_1)^2}{2\\sigma_1^2} - \\frac{(x - \\mu_2)^2}{2\\sigma_2^2}\\right) \\\\\n", "& = \\frac{1}{2\\pi \\sigma_1 \\sigma_2} \\exp\\left( -\\frac{(\\sigma_1^2+\\sigma_2^2) x^2 -2(\\mu_1 \\sigma_2^2 +\\mu_2 \\sigma_1^2)x +(\\mu_1^2 \\sigma_2^2+\\mu_2^2 \\sigma_1^2)}{2\\sigma_1^2 \\sigma_2^2} \\right)\\\\\n", "& = \\frac{1}{2\\pi \\sigma_1 \\sigma_2} \\exp\\left( -\\frac{x^2 -2\\frac{\\mu_1 \\sigma_2^2 +\\mu_2 \\sigma_1^2}{\\sigma_1^2+\\sigma_2^2}x + \\frac{\\mu_1^2 \\sigma_2^2+\\mu_2^2 \\sigma_1^2}{\\sigma_1^2+\\sigma_2^2}}{2\\frac{\\sigma_1^2 \\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}} \\right) \\\\\n", "& = \\frac{1}{2\\pi \\sigma_1 \\sigma_2} \\exp\\left( -\\frac{x^2 -2\\frac{\\mu_1 \\sigma_2^2 +\\mu_2 \\sigma_1^2}{\\sigma_1^2+\\sigma_2^2}x + \\frac{\\mu_1^2 \\sigma_2^4+\\mu_2^2 \\sigma_1^4 + 2\\mu_1 \\sigma_2^2 \\mu_2 \\sigma_1^2}{(\\sigma_1^2+\\sigma_2^2)^2}}{2\\frac{\\sigma_1^2 \\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}} \\right) \\\\\n", "& \\times \\exp\\left(\\frac{ - (\\sigma_1^2+\\sigma_2^2)(\\mu_1^2 \\sigma_2^2+\\mu_2^2 \\sigma_1^2) + (\\mu_1^2 \\sigma_2^4+\\mu_2^2 \\sigma_1^4 + 2\\mu_1 \\sigma_2^2 \\mu_2 \\sigma_1^2) }{2 \\sigma_1^2 \\sigma_2^2 (\\sigma_1^2+\\sigma_2^2)}\\right) \\\\\n", "& = \\frac{1}{2\\pi \\sqrt{\\frac{\\sigma_1^2 \\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}} \\sqrt{\\sigma_1^2+\\sigma_2^2}} \\exp\\left( -\\frac{(x -\\frac{\\mu_1 \\sigma_2^2 +\\mu_2 \\sigma_1^2}{\\sigma_1^2+\\sigma_2^2})^2}{2\\frac{\\sigma_1^2 \\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}} \\right) \\exp\\left( -\\frac{(\\mu_1 - \\mu_2)^2}{2 \\sigma_1^2 + \\sigma_2^2} \\right)\\\\\n", "& = \\frac{1}{\\sqrt{2\\pi} \\sqrt{\\frac{\\sigma_1^2 \\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}}} \\exp\\left( -\\frac{(x -\\frac{\\mu_1 \\sigma_2^2 +\\mu_2 \\sigma_1^2}{\\sigma_1^2+\\sigma_2^2})^2}{2\\frac{\\sigma_1^2 \\sigma_2^2}{\\sigma_1^2+\\sigma_2^2}} \\right) \\frac{1}{\\sqrt{2\\pi} \\sqrt{\\sigma_1^2+\\sigma_2^2}} \\exp\\left( -\\frac{(\\mu_1 - \\mu_2)^2}{2 \\sigma_1^2 + \\sigma_2^2} \\right)\\\\\n", "& = \\phi(x, \\mu_i, \\sigma_i^2) \\phi(\\mu_1, \\mu_2, \\sigma_1^2 + \\sigma_2^2)\\\\\n", "\\end{align}\n", "$$\n", "where $$\\mu_i = \\frac{1 / \\sigma_1^2}{1/ \\sigma_1^2 + 1/ \\sigma_2^2}\\mu_1 + \\frac{1 / \\sigma_2^2}{1/ \\sigma_1^2 + 1/ \\sigma_2^2}\\mu_2 \\text{ and } \\sigma_i^2 = \\frac{\\sigma_1^2 \\sigma_2^2}{\\sigma_1^2 + \\sigma_2^2}.$$<br>\n", "Hence we complete the proof and we validate that\n", "$$\\Theta_{Bayes} = \\mu_\\text{new} = \\frac{N/\\sigma^2}{N/\\sigma^2 + 1/\\sigma_0^2} \\Theta_{ML} +\\frac{1/\\sigma_0^2}{N/\\sigma^2 + 1/\\sigma_0^2} \\mu_0.$$\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Hand-written digit classification (40 points)\n", "\n", "In the textbook sample code we applied different scikit-learn classifers for the Iris data set.\n", "\n", "In this exercise, we will apply the same set of classifiers over a different data set: hand-written digits.\n", "Please write down the code for different classifiers, choose their hyper-parameters, and compare their performance via the accuracy score as in the Iris dataset.\n", "Which classifier(s) perform(s) the best and worst, and why?\n", "\n", "The classifiers include:\n", "* perceptron\n", "* logistic regression\n", "* SVM\n", "* decision tree\n", "* random forest\n", "* KNN\n", "* naive Bayes\n", "\n", "The dataset is available as part of scikit learn, as follows." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "last updated: 2016-10-17 \n", "\n", "CPython 3.5.2\n", "IPython 4.2.0\n", "\n", "numpy 1.11.1\n", "pandas 0.18.1\n", "matplotlib 1.5.1\n", "scipy 0.17.1\n", "sklearn 0.18\n" ] } ], "source": [ "%load_ext watermark\n", "%watermark -a '' -u -d -v -p numpy,pandas,matplotlib,scipy,sklearn\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true, "slideshow": { "slide_type": "skip" } }, "outputs": [], "source": [ "# Added version check for recent scikit-learn 0.18 checks\n", "from distutils.version import LooseVersion as Version\n", "from sklearn import __version__ as sklearn_version" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Load data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1797, 64)\n", "(1797,)\n" ] } ], "source": [ "from sklearn.datasets import load_digits\n", "digits = load_digits()\n", "\n", "X = digits.data # training data\n", "y = digits.target # training label\n", "\n", "print(X.shape)\n", "print(y.shape)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Visualize data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1140eb668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import pylab as pl\n", "\n", "num_rows = 4\n", "num_cols = 5\n", "\n", "fig, ax = plt.subplots(nrows=num_rows, ncols=num_cols, sharex=True, sharey=True)\n", "ax = ax.flatten()\n", "for index in range(num_rows*num_cols):\n", " img = digits.images[index]\n", " label = digits.target[index]\n", " ax[index].imshow(img, cmap='Greys', interpolation='nearest')\n", " ax[index].set_title('digit ' + str(label))\n", "\n", "ax[0].set_xticks([])\n", "ax[0].set_yticks([])\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Date Preprocessing\n", "Hint: How you divide training and test data set? And apply other techinques we have learned if needed.\n", "You could take a look at the Iris data set case in the textbook." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "scikit-learn version: 0.18\n", "1. Complete removing the mean and scaling to unit variance.\n", "2. Complete splitting with 1257(70%) training data and 540(30%) test data.\n" ] } ], "source": [ "from sklearn.preprocessing import StandardScaler\n", "if Version(sklearn_version) < '0.18':\n", " from sklearn.cross_validation import train_test_split\n", "else:\n", " from sklearn.model_selection import train_test_split\n", "print ('scikit-learn version: ' + str(Version(sklearn_version)))\n", "\n", "# 1. Standardize features by removing the mean and scaling to unit variance\n", "X_std = StandardScaler().fit_transform(X) # fit_transform(X) will fit to data, then transform it.\n", "print ('1. Complete removing the mean and scaling to unit variance.')\n", "\n", "# 2. splitting data into 70% training and 30% test data: \n", "split_ratio = 0.3\n", "X_train, X_test, y_train, y_test = train_test_split(X_std, y, test_size=split_ratio, random_state=0)\n", "print('2. Complete splitting with ' + str(y_train.shape[0]) + \\\n", " '(' + str(int((1-split_ratio)*100)) +'%) training data and ' + \\\n", " str(y_test.shape[0]) + '(' + str(int(split_ratio*100)) +'%) test data.')\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Classifier #1 Perceptron" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Misclassified samples: 39 out of 540\n", "Accuracy: 0.928\n" ] } ], "source": [ "from sklearn.linear_model import Perceptron\n", "from sklearn.metrics import accuracy_score\n", "\n", "# Training\n", "ppn = Perceptron(n_iter=800, eta0=0.1, random_state=0)\n", "ppn.fit(X_train, y_train)\n", "# Testing\n", "y_pred = ppn.predict(X_test)\n", "# Results\n", "print('Misclassified samples: %d out of %d' % ((y_test != y_pred).sum(), y_test.shape[0]))\n", "print('Accuracy: %.3f' % accuracy_score(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Classifier #2 Logistic Regression" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Misclassified samples: 25 out of 540\n", "Accuracy: 0.954\n" ] } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", "# Training\n", "lr = LogisticRegression(C=1.0, random_state=0) # we observe that changing C from 0.0001 to 1000 has ignorable effect\n", "lr.fit(X_train, y_train)\n", "# Testing\n", "y_pred = lr.predict(X_test)\n", "# Results\n", "print('Misclassified samples: %d out of %d' % ((y_test != y_pred).sum(), y_test.shape[0]))\n", "print('Accuracy: %.3f' % accuracy_score(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Classifier #3 SVM" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. Using linear kernel:\n", " Misclassified samples: 14 out of 540\n", " Accuracy: 0.974\n", "2. Using rbf kernel:\n", " Misclassified samples: 8 out of 540\n", " Accuracy: 0.985\n" ] } ], "source": [ "from sklearn.svm import SVC\n", "\n", "# 1. Using linear kernel\n", "\n", "# Training\n", "svm = SVC(kernel='linear', C=1.0, random_state=0)\n", "svm.fit(X_train, y_train)\n", "# Testing\n", "y_pred = svm.predict(X_test)\n", "# Results\n", "print('1. Using linear kernel:')\n", "print(' Misclassified samples: %d out of %d' % ((y_test != y_pred).sum(), y_test.shape[0]))\n", "print(' Accuracy: %.3f' % accuracy_score(y_test, y_pred))\n", "\n", "\n", "# 2. Using rbf kernel\n", "\n", "# Training\n", "svm = SVC(kernel='rbf', C=1.0, random_state=0)\n", "svm.fit(X_train, y_train)\n", "# Testing\n", "y_pred = svm.predict(X_test)\n", "# Results\n", "print('2. Using rbf kernel:')\n", "print(' Misclassified samples: %d out of %d' % ((y_test != y_pred).sum(), y_test.shape[0]))\n", "print(' Accuracy: %.3f' % accuracy_score(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Classifier #4 Decision Tree" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. Using entropy criterion:\n", " Misclassified samples: 71 out of 540\n", " Accuracy: 0.869\n", "2. Using Gini criterion:\n", " Misclassified samples: 77 out of 540\n", " Accuracy: 0.857\n" ] } ], "source": [ "from sklearn.tree import DecisionTreeClassifier\n", "\n", "# 1. Using entropy criterion\n", "\n", "# Training\n", "tree = DecisionTreeClassifier(criterion='entropy', random_state=0)\n", "tree.fit(X_train, y_train)\n", "# Testing\n", "y_pred = tree.predict(X_test)\n", "# Results\n", "print('1. Using entropy criterion:')\n", "print(' Misclassified samples: %d out of %d' % ((y_test != y_pred).sum(), y_test.shape[0]))\n", "print(' Accuracy: %.3f' % accuracy_score(y_test, y_pred))\n", "\n", "# 2. Using Gini criterion\n", "\n", "# Training\n", "tree = DecisionTreeClassifier(criterion='gini', random_state=0)\n", "tree.fit(X_train, y_train)\n", "# Testing\n", "y_pred = tree.predict(X_test)\n", "# Results\n", "print('2. Using Gini criterion:')\n", "print(' Misclassified samples: %d out of %d' % ((y_test != y_pred).sum(), y_test.shape[0]))\n", "print(' Accuracy: %.3f' % accuracy_score(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Classifer #5 Random Forest" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. Using entropy criterion:\n", " Misclassified samples: 32 out of 540\n", " Accuracy: 0.941\n", "2. Using Gini criterion:\n", " Misclassified samples: 27 out of 540\n", " Accuracy: 0.950\n" ] } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "# 1. Using entropy criterion\n", "\n", "# Training\n", "forest = RandomForestClassifier(criterion='entropy', n_estimators=10, random_state=1, n_jobs=2)\n", "forest.fit(X_train, y_train)\n", "# Testing\n", "y_pred = forest.predict(X_test)\n", "# Results\n", "print('1. Using entropy criterion:')\n", "print(' Misclassified samples: %d out of %d' % ((y_test != y_pred).sum(), y_test.shape[0]))\n", "print(' Accuracy: %.3f' % accuracy_score(y_test, y_pred))\n", "\n", "# 2. Using Gini criterion\n", "\n", "# Training\n", "forest = RandomForestClassifier(criterion='gini', n_estimators=10, random_state=1, n_jobs=2)\n", "forest.fit(X_train, y_train)\n", "# Testing\n", "y_pred = forest.predict(X_test)\n", "# Results\n", "print('2. Using Gini criterion:')\n", "print(' Misclassified samples: %d out of %d' % ((y_test != y_pred).sum(), y_test.shape[0]))\n", "print(' Accuracy: %.3f' % accuracy_score(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Classifier #6 KNN" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Misclassified samples: 15 out of 540\n", "Accuracy: 0.972\n" ] } ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "# Training\n", "knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')\n", "knn.fit(X_train, y_train)\n", "# Testing\n", "y_pred = knn.predict(X_test)\n", "# Results\n", "print('Misclassified samples: %d out of %d' % ((y_test != y_pred).sum(), y_test.shape[0]))\n", "print('Accuracy: %.3f' % accuracy_score(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "## Classifier #7 Naive Bayes" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Misclassified samples: 123 out of 540\n", "Accuracy: 0.772\n" ] } ], "source": [ "from sklearn.naive_bayes import GaussianNB\n", "\n", "# Training\n", "gnb = GaussianNB()\n", "gnb.fit(X_train, y_train)\n", "# Testing\n", "y_pred = gnb.predict(X_test)\n", "# Results\n", "print('Misclassified samples: %d out of %d' % ((y_test != y_pred).sum(), y_test.shape[0]))\n", "print('Accuracy: %.3f' % accuracy_score(y_test, y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hence in this example, the accuracy of the predictions is ranked as (in descending order):\n", "* SVM (rbf kernel) - 0.985 <br>\n", "* SVM (linear kernel) - 0.974 <br>\n", "* KNN - 0.972 <br>\n", "* Logistic Regression - 0.954 <br>\n", "* Random forest (Gini criterion) - 0.950 <br>\n", "* Random forest (entropy criterion) - 0.941 <br>\n", "* Perceptron - 0.928 <br>\n", "* Decision Tree (entropy criterion) - 0.869 <br>\n", "* Decision Tree (Gini criterion) - 0.857 <br>\n", "* Naive Bayes - 0.772 <br>\n", "\n", "The best is SVM (using rbf kernel). Because SVM maximize margins to nearest samples (called support vectors), which is considered as an effective way of classifying spacially separated samples. Also, SVM is more robust against outliers and offers slack variables as a solution to not linearly-separable samples. Moreover, using rbf kernel, the dimension is mapped to infinity and therefore samples are highly likely to be separated by some hyperplanes.\n", "\n", "The worst is Naive Bayes. Because it assumes that all data are independent, which leads to high bias and low variance. When the input samples are not generally independent, the assumption fails and therefore the accuracy is low. Also, Naive Bayes cannot deal with outliers or noise, therefore unideal samples may not be correctly classified." ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
mmcco/bioinformatics
ipython-notebooks/bloom-filter.ipynb
1
22481
{ "metadata": { "name": "", "signature": "sha256:7d9d545b4d6567e32cf9242163c466159b0ddab4b71d435b4675bebaf265f1be" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "As you probably know, disk access is painfully slow and should be avoided at all costs. Often, programs use a large set, a group of unique values, that cannot fit in RAM and must be stored on disk. In a simple set implementation, we must access the full set data in order to add a value (regardless of whether it's already a member) and to check whether a value is a member.\n", "\n", "In many cases, it would be useful to have a smaller data structure stored in memory that estimates whether a value is a member of our set. Because this structure would be much smaller than the full set, thereby storing much less information, it could not give a definitive answer. However, instead of returning \u201cTrue\u201d or \u201cFalse\u201d when asked if a set contains a value, our \u201cfirst-pass filter\u201d could return \u201cPossibly\u201d or \u201cFalse\u201d. When it returns \u201cPossibly\u201d, the full set must then be searched to determine whether the value is actually a member. When it returns \u201cFalse\u201d, though, we need not access disk.\n", "\n", "A simple solution would be storing each value\u2019s [hash](https://en.wikipedia.org/wiki/Hash_function) in a list. Hash functions are many-to-one; multiple values can have the same hash, but each value has only one valid hash. If a value\u2019s hash exists in our list, the value might be in the set, or the set might just contain another value with the same hash (two values having the same hash is known as a *collision*). If the value\u2019s hash isn\u2019t in the list, the value definitely isn\u2019t in the set.\n", "\n", "A simply Python implementation of this filter is below." ] }, { "cell_type": "code", "collapsed": false, "input": [ "class ListFilter:\n", " \n", " def __init__(self):\n", " self.hashes = []\n", " \n", " \n", " def add(self, val):\n", " valHash = hash(val)\n", " if valHash not in self.hashes:\n", " self.hashes.insert(valHash)\n", " \n", " \n", " def check(self, val):\n", " return hash(val) in self.hashes" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 56 }, { "cell_type": "markdown", "metadata": {}, "source": [ "`True` returned from `check` implies \"Probably\" because the filter can't definitively tell us whether a value is in the actual set.\n", "\n", "For simplicity and modularity, we didn't incorporate the actual set into the filter. This means that, after adding a value to the filter with `add`, a program would have to also add the value to the set. Similarly, if a program calls `check` and `True` is returned, it will have to check the set to see if the value is actually a member.\n", "\n", "Our first-pass list filter is a decent solution, but it isn\u2019t quite ideal. Ignoring collisions, it grows in proportion to the set, and therefore becomes unwieldy for really big sets. Assuming we don\u2019t want to use extra memory to make a hash table of the hashes, finding a hash is $O(\\log_2{n})$ if the list is sorted and $O(n)$ if it isn\u2019t.\n", "\n", "Bloom filters, an alternative, offer constant space and $O(1)$ checking. While the rate of *false positives* (\u201cProbably\u201d responses for values not in the set) increases modestly as the set grows, relatively small Bloom filters have low ($\\leq1\\%$) false positive rates for huge data sets.\n", "\n", "Below is an implementation of a Bloom filter." ] }, { "cell_type": "code", "collapsed": false, "input": [ "class BloomFilter:\n", "\n", " def __init__(self, m, k):\n", " self.m = m\n", " self.k = k\n", " self.bitArray = [0 for _ in range(m)]\n", "\n", " \n", " def getIndices(self, val):\n", " indices = []\n", " for i in range(self.k):\n", " hash_ = hash(str(i) + val)\n", " index = hash_ % self.m\n", " indices.append(index)\n", " return indices\n", "\n", " \n", " def add(self, val):\n", " indices = self.getIndices(val)\n", " for index in indices:\n", " self.bitArray[index] = 1\n", "\n", " \n", " def check(self, val):\n", " indices = self.getIndices(val)\n", " for index in indices:\n", " if self.bitArray[index] == 0:\n", " return False\n", " return True" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "markdown", "metadata": {}, "source": [ "A Bloom filter is stores an array of $m$ bits (`bitArray`, in our code), all of which are initially set to $0$. We use an integer list here for clarity. In a real implementation, each array index is only one bit. For readability, our below examples will use $m=16$. Typical real-world bloom filters use an $m$ value about $9.6$ times larger than the the expected number of elements in the set. This gives a false positive rate of about $1\\%$.\n", "\n", "The filter uses $k$ different hash functions. Often, these are just the same hash function with a different *salt*, a value prepended to the value to be hashed. In our implementation, the first hash function prepends `\"0\"`, the second prepends `\"1\"`, and so on. The hashing algorithm used is Python's default: `hash`.\n", "\n", "Each hash function\u2019s output must map to an index of the bit array. One way to do this is to ensure that the function\u2019s output is $\\log_2 m$ bits, mapping each hash value directly to an index of the bit array. If no available hash functions have our desired output size, we can use $hash \\bmod{m}$ to map the hash value to an array index:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hash(\"0\" + \"hopkins\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 1, "text": [ "4028870035670873848" ] } ], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "filter = BloomFilter(m = 16, k = 3)\n", "print hash(\"0\" + \"hopkins\") % filter.m\n", "print hash(\"1\" + \"hopkins\") % filter.m\n", "print hash(\"2\" + \"hopkins\") % filter.m" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "8\n", "15\n", "6\n" ] } ], "prompt_number": 54 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `getIndices` method, which does the hashing in our implementation, uses the $\\bmod$ solution. It returns a list of $k$ indices, each generated by a salted hash function:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "filter.getIndices(\"hopkins\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "[8, 15, 6]" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "metadata": {}, "source": [ "When a value is added to a Bloom filter, the bit array index indicated by each hash value is set to $1$. Our code uses the `add` method, calling `getIndices` and updating `bitArray` accordingly." ] }, { "cell_type": "code", "collapsed": false, "input": [ "filter.getIndices(\"hopkins\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "[8, 15, 6]" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "filter.bitArray" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]" ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "filter.add(\"hopkins\")\n", "filter.bitArray" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "[0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1]" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To check if a value is possibly in the Bloom filter's set, we compute its indices using `getIndices`. If the bit array values at any of these indices are $0$, then the value cannot be in the set:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "for index in filter.getIndices(\"johns\"):\n", " print filter.bitArray[index]" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n", "0\n", "0\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "filter.check(\"johns\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 55, "text": [ "False" ] } ], "prompt_number": 55 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The possibility of a false positive remains (especially with an $m$ value as unrealistically small as $16$):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "filter.getIndices(\"bootstrap\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "[8, 15, 6]" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "filter.check(\"bootstrap\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "True" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using simple probability laws, we can estimate the probability of a false positive. We will assume that the hashing algorithm used is effectively random, which implies that the probability of a given $0$ bit being flipped to $1$ during a single bit assignment is $\\frac{1}{m}$. The probability of this bit remaining $0$ for a single index assignment is therefore $1 - \\frac{1}{m}$. By exponential decay, the probability that it will remain $0$ when a new value is added to the filter is:\n", "\n", "$(1 - \\frac{1}{m})^k$.\n", "\n", "Again by exponential decay, the probability that it will remain $0$ after $n$ values are added is:\n", "\n", "$((1 - \\frac{1}{m})^k)^n = (1 - \\frac{1}{m})^{kn}$\n", "\n", "This implies that the probability of a bit being $1$ after $n$ values are added to the filter is:\n", "\n", "$1 - (1 - \\frac{1}{m})^{kn}$\n", "\n", "For a false positive, all $k$ of a value's bit array indices must already be $1$ by chance. The probability of a false positive is therefore:\n", "\n", "$(1 - (1 - \\frac{1}{m})^{kn})^k$\n", "\n", "If we use five hash functions ($k=5$) and two gigabytes of RAM ($m=1.6 \\times 10^{10}$) for a Bloom filter and store a billion values in the set ($n=10^9$), the likelihood of a false positive is only:\n", "\n", "$(1 - (1 - \\frac{1}{m})^{kn})^k = (1 - (1 - \\frac{1}{1.6 \\times 10^10}) ^{5 \\times 10^9}) ^5 \\approx 0.0014 = 0.14\\%$\n", "\n", "It's clear why Bloom filters are used for massive-scale database software like Google BigTable, Apache Hadoop, and Apache Cassandra.\n", "\n", "---\n", "\n", "There are situations in which we can further improve our filter's performance. We may, for example, load a set of $k$-character-long strings into a Bloom filter and then search for these substrings in a larger string. Similarly, we may want to find if and where a genome sequence read differs from a reference genome. We could do this by storing all the reference genome's *$k$-mers* (sequences that are $k$ nucleotides long) in a set and checking which $k$-mers in the sequencing reads don't exist in the reference genome.\n", "\n", "These problems are best treated with frame shifting; to search for 4-long substrings in the string `\"refrigerator\"`, you would check `\"refr\"`, then `\"efri\"`, then `\"frig\"`, and so on. No matter the value of $k$, each substring differs from the one before it by only a deletion at the front and an insertion at the back. They are very similar, yet our current Bloom filter implementation doesn't reuse any computation when calculating their hashes.\n", "\n", "A family of hash functions known as *rolling hashes* exist for this purpose. They can convert a substring's hash to that of its successor frame, such as `hash(\"refr\")` to `hash(\"efri\")`, in $O(1)$ time. We will use the rolling hash function traditionally associated with the [Rabin\u2013Karp algorithm](https://en.wikipedia.org/wiki/Rabin-Karp_algorithm), an efficient method of searching for substrings. The hash value of a string of length $m$ is:\n", "\n", "$\\sum_{i=m-1}^{0} a_i \\times b^i$\n", "\n", "where $b$ is an arbitrary constant (typically prime) and $a_i$ is the ASCII value of the $i$th character of the string $a$. For example, the hash value of the string \"deck\" where $b=73$ (relevant ASCII values: $d=100$, $e=101$, $c=99$, $k=107$) is:\n", "\n", "$(100 \\times 73^3) + (101 \\times 73^2) + (99 \\times 73^1) + (107 \\times 73^0) = 39,447,263$\n", "\n", "A Python implementation is below." ] }, { "cell_type": "code", "collapsed": false, "input": [ "class RKHash:\n", " \n", " def __init__(self, b):\n", " self.b = b\n", " self.prevFrame = None\n", " self.prevHash = None\n", " \n", " \n", " def getHash(self, frame):\n", " \n", " m = len(frame)\n", " \n", " if self.prevFrame and self.prevHash:\n", " tailHash = self.prevHash - (ord(self.prevFrame[0]) * self.b ** (m-1))\n", " thisHash = (tailHash * self.b) + ord(frame[-1])\n", " \n", " else:\n", " thisHash = 0\n", " for i in range(m):\n", " thisHash += ord(frame[i]) * (self.b ** (m - (i + 1)))\n", " \n", " self.prevFrame = frame\n", " self.prevHash = thisHash\n", " return thisHash\n", " \n", " \n", " def clear(self):\n", " self.prevFrame = None\n", " self.prevHash = None\n", " \n", " \n", "hasher = RKHash(6)\n", "firstHash = hasher.getHash(\"tomat\")\n", "print firstHash\n", "nextHash = hasher.getHash(\"omato\")\n", "print nextHash" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "178934\n", "171699\n" ] } ], "prompt_number": 62 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Logically, `prevFrame` and `prevHash` are initialized to `None`, and the first frame's hash value is computed from scratch. This is executed by the `else` clause in `getHash`, which simply computes the summation equation given above.\n", "\n", "The `if` clause is executed for all but the first frame. It converts `prevHash`, the previous frame's hash, to `thisHash`, the current frame's hash, in three simple $O(1)$ operations. We will observe these operations with an example `prevFrame` of length $4$, composed of characters $a_0$ through $a_3$. The algorithm converts its hash to that of its successor, `frame`, which is comprised of characters $a_1$ through $a_4$. Initially:\n", "\n", "$prevHash = (a_0 \\times b^3) + (a_1 \\times b^2) + (a_2 \\times b^1) + (a_3 \\times b^0)$\n", "\n", "We then subtract $a_0 \\times b^3$, obtaining the hash of `prevHash`'s tail (tail refers to all but the first character; the tail of `\"couch\"` is `\"ouch\"`):\n", "\n", "$(a_1 \\times b^2) + (a_2 \\times b^1) + (a_3 \\times b^0)$\n", "\n", "We then multiply by $b$:\n", "\n", "$(a_1 \\times b^3) + (a_2 \\times b^2) + (a_3 \\times b^1)$\n", "\n", "And add $a_4 \\times b^0 = a_4$ obtaining `thisHash`:\n", "\n", "$(a_1 \\times b^3) + (a_2 \\times b^2) + (a_3 \\times b^1) + (a_4 \\times b^0) = thisHash$\n", "\n", "Regardless of the length of the substrings processed, the algorithm uses only these three $O(1)$ arithmetic operations.\n", "\n", "The `clear` function simply resets the `RKHasher` instance, allowing us to start with a new first frame.\n", "\n", "We omitted exception statements checking soundness and sanity, so remember that `getHash` assumes that `len(frame) == len(prevFrame)`, that `prevFrame[1:] == frame[:-1]`, and that `b != 0`.\n", "\n", "Below is a modified version of our above Bloom filter implementation that uses Rabin-Karp hash functions:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import random\n", "\n", "class BloomFilter:\n", "\n", " def __init__(self, m, k):\n", " self.m = m\n", " self.k = k\n", " self.bitArray = [0 for _ in range(m)]\n", " \n", " # random.sample(population, x) a list of x unique\n", " # elements from the supplied population list\n", " randBs = random.sample(range(1, 200), k)\n", " self.hashFuncs = [RKHash(b) for b in randBs]\n", " \n", "\n", " def getIndices(self, val):\n", " hashes = [f.getHash(val) for f in self.hashFuncs]\n", " return map((lambda hash_: hash_ % self.m), hashes)\n", " \n", " \n", " def add(self, val):\n", " indices = self.getIndices(val)\n", " for index in indices:\n", " self.bitArray[index] = 1\n", "\n", " \n", " def check(self, val):\n", " indices = self.getIndices(val)\n", " for index in indices:\n", " if self.bitArray[index] == 0:\n", " return False\n", " return True\n", " \n", "bloomFilter = BloomFilter(1024, 3)\n", "print \"randomly selected b's for Rabin-Karp hash functions:\", [f.b for f in bloomFilter.hashFuncs]\n", "print \"indices returned for the string \\\"johns\\\":\", bloomFilter.getIndices(\"johns\")\n", "bloomFilter.add(\"johns\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "randomly selected b's for Rabin-Karp hash functions: [101, 3, 145]\n", "indices returned for the string \"johns\": [478, 676, 978]\n" ] } ], "prompt_number": 67 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The value of $b$ for each Rabin-Karp hashing algorithm is unique and pseudorandomly selected. Higher values of $b$ more heavily weight earlier characters relative to later ones, so two values that collide in one hash function<!-- ($hash_x(a) = hash_x(b)$)--> are unlikely to collide in any other<!-- ($hash_y(a) = hash_y(b)$)-->:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "hasher6 = RKHash(6)\n", "hasher7 = RKHash(7)\n", "\n", "print 'hasher6.getHash(\"kudos\") =', hasher6.getHash(\"kudos\")\n", "hasher6.clear()\n", "print 'hasher6.getHash(\"locus\") =', hasher6.getHash(\"locus\")\n", "print 'hasher7.getHash(\"kudos\") =', hasher7.getHash(\"kudos\")\n", "hasher7.clear()\n", "print 'hasher7.getHash(\"locus\") =', hasher7.getHash(\"locus\")" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "hasher6.getHash(\"kudos\") = 168325\n", "hasher6.getHash(\"locus\") = 168325\n", "hasher7.getHash(\"kudos\") = 302830\n", "hasher7.getHash(\"locus\") = 303166\n" ] } ], "prompt_number": 66 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
vzg100/Post-Translational-Modification-Prediction
.ipynb_checkpoints/Phosphorylation Sequence Tests -MLP -dbptm+ELM -EnzymeBenchmarks-VectorAvr.-checkpoint.ipynb
1
167838
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Template for test" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n" ] } ], "source": [ "from pred import Predictor\n", "from pred import sequence_vector\n", "from pred import chemical_vector" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.\n", "\n", "Included is N Phosphorylation however no benchmarks are available, yet. \n", "\n", "\n", "Training data is from phospho.elm and benchmarks are from dbptm. " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 175317\n", "Test Data Points: 75137\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9305041712005804\n", "Specificity : 0.9977017864128309\n", "Accuracy: 0.9853733846174322\n", "ROC 0.964102978807\n", "TP 12827 FP 141 TN 61211 FN 958\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98528707 0.98522689 0.98528678 0.98584548 0.98512677]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1744 FN 64\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 175317\n", "Test Data Points: 75137\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9292520935604967\n", "Specificity : 0.9979113975687363\n", "Accuracy: 0.9852536034177569\n", "ROC 0.963581745565\n", "TP 12872 FP 128 TN 61157 FN 980\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98584604 0.98430856 0.98562616 0.98548612 0.98532641]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1744 FN 64\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 284485\n", "Test Data Points: 121923\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9217376968098261\n", "Specificity : 0.9835150486387674\n", "Accuracy: 0.9527816736792893\n", "ROC 0.952626372724\n", "TP 55908 FP 1010 TN 60258 FN 4747\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96109888 0.95121921 0.93903864 0.9413147 0.93396981]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Sensitivity: 0.09375\n", "Specificity : 0.9873853211009175\n", "Accuracy: 0.9557522123893806\n", "ROC 0.54056766055\n", "TP 6 FP 22 TN 1722 FN 58\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 284485\n", "Test Data Points: 121923\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9287236679058241\n", "Specificity : 0.9729144271800384\n", "Accuracy: 0.9509772561370701\n", "ROC 0.950819047543\n", "TP 56211 FP 1663 TN 59735 FN 4314\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95879827 0.9299845 0.94441505 0.93912476 0.94564535]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Sensitivity: 0.078125\n", "Specificity : 0.9776376146788991\n", "Accuracy: 0.9457964601769911\n", "ROC 0.527881307339\n", "TP 5 FP 39 TN 1705 FN 59\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 283216\n", "Test Data Points: 121379\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9422324357629348\n", "Specificity : 0.9945864857813723\n", "Accuracy: 0.9686848631147068\n", "ROC 0.968409460772\n", "TP 56582 FP 332 TN 60996 FN 3469\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.97062531 0.96866002 0.96823984 0.96809155 0.96809115]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 5 TN 1739 FN 64\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 283274\n", "Test Data Points: 121404\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9403129000615159\n", "Specificity : 0.9941557699528217\n", "Accuracy: 0.9674804784026886\n", "ROC 0.967234335007\n", "TP 56557 FP 358 TN 60899 FN 3590\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96919827 0.96655382 0.96812257 0.96793723 0.96724532]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 6 TN 1738 FN 64\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9315998555435175\n", "Specificity : 0.9962406015037594\n", "Accuracy: 0.9639050475123749\n", "ROC 0.963920228524\n", "TP 12898 FP 52 TN 13780 FN 947\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95843269 0.96320182 0.96450249 0.95680217 0.96271003]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1744 FN 64\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9302275189599133\n", "Specificity : 0.9961683053788317\n", "Accuracy: 0.9631824258409509\n", "ROC 0.963197912169\n", "TP 12879 FP 53 TN 13779 FN 966\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96249729 0.96390635 0.95962497 0.94325203 0.96243902]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1744 FN 64\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 164000\n", "Test Data Points: 70286\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9304398232012173\n", "Specificity : 0.9991856245020801\n", "Accuracy: 0.985687050052642\n", "ROC 0.964812723852\n", "TP 12841 FP 46 TN 56439 FN 960\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98617098 0.98606428 0.98666183 0.98478317 0.98555148]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1744 FN 64\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 164014\n", "Test Data Points: 70292\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9316617146170012\n", "Specificity : 0.9994512594480732\n", "Accuracy: 0.9861435156205542\n", "ROC 0.965556487033\n", "TP 12856 FP 31 TN 56462 FN 943\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98638556 0.9851052 0.98615083 0.98585147 0.98583013]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1744 FN 64\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9466955579631636\n", "Specificity : 0.9855407750144592\n", "Accuracy: 0.9661090436102179\n", "ROC 0.966118166489\n", "TP 13107 FP 200 TN 13632 FN 738\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96520702 0.96764578 0.96699545 0.96428184 0.80205962]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Sensitivity: 0.15625\n", "Specificity : 0.7706422018348624\n", "Accuracy: 0.7488938053097345\n", "ROC 0.463446100917\n", "TP 10 FP 400 TN 1344 FN 54\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9588299024918743\n", "Specificity : 0.9744071717755928\n", "Accuracy: 0.9666148787802146\n", "ROC 0.966618537134\n", "TP 13275 FP 354 TN 13478 FN 570\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.97469109 0.96639931 0.97295686 0.96205962 0.93311653]\n", "Number of data points in benchmark 1808\n", "Benchmark Results \n", "Sensitivity: 0.453125\n", "Specificity : 0.6496559633027523\n", "Accuracy: 0.6426991150442478\n", "ROC 0.551390481651\n", "TP 29 FP 611 TN 1133 FN 35\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 175317\n", "Test Data Points: 75137\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9282633697527315\n", "Specificity : 0.998121682319314\n", "Accuracy: 0.9851870583068262\n", "ROC 0.963192526036\n", "TP 12914 FP 115 TN 61110 FN 998\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98556656 0.98606536 0.98532671 0.98602515 0.98476742]\n", "Number of data points in benchmark 1148\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 1 TN 1066 FN 81\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 175317\n", "Test Data Points: 75137\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9297529694673177\n", "Specificity : 0.9980460481323477\n", "Accuracy: 0.9855730199502243\n", "ROC 0.9638995088\n", "TP 12759 FP 120 TN 61294 FN 964\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98610557 0.98482761 0.98602543 0.98506688 0.98568577]\n", "Number of data points in benchmark 1148\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1067 FN 81\n", "\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "None\n", "y ADASYN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 284485\n", "Test Data Points: 121923\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9044532409698169\n", "Specificity : 0.9906677760918865\n", "Accuracy: 0.9477949197444289\n", "ROC 0.947560508531\n", "TP 54837 FP 572 TN 60721 FN 5793\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9584538 0.9464088 0.94793371 0.93426508 0.94800753]\n", "Number of data points in benchmark 1148\n", "Benchmark Results \n", "Sensitivity: 0.04938271604938271\n", "Specificity : 0.9925023430178069\n", "Accuracy: 0.9259581881533101\n", "ROC 0.520942529534\n", "TP 4 FP 8 TN 1059 FN 77\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 284485\n", "Test Data Points: 121923\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9096285314224528\n", "Specificity : 0.9832728935933875\n", "Accuracy: 0.9466056445461479\n", "ROC 0.946450712508\n", "TP 55219 FP 1024 TN 60194 FN 5486\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95808472 0.94915233 0.93409284 0.93614744 0.94904098]\n", "Number of data points in benchmark 1148\n", "Benchmark Results \n", "Sensitivity: 0.07407407407407407\n", "Specificity : 0.9765698219306467\n", "Accuracy: 0.9128919860627178\n", "ROC 0.525321948002\n", "TP 6 FP 25 TN 1042 FN 75\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 283347\n", "Test Data Points: 121435\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9437953899589517\n", "Specificity : 0.9894877738239038\n", "Accuracy: 0.9668464610697081\n", "ROC 0.966641581891\n", "TP 56791 FP 644 TN 60618 FN 3382\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96797105 0.96687089 0.96792085 0.96654973 0.96750086]\n", "Number of data points in benchmark 1148\n", "Benchmark Results \n", "Sensitivity: 0.024691358024691357\n", "Specificity : 0.9906279287722587\n", "Accuracy: 0.9224738675958188\n", "ROC 0.507659643398\n", "TP 2 FP 10 TN 1057 FN 79\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 283281\n", "Test Data Points: 121407\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9443502261239691\n", "Specificity : 0.9915772978796337\n", "Accuracy: 0.9681814063439506\n", "ROC 0.967963762002\n", "TP 56797 FP 516 TN 60747 FN 3347\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96952026 0.96863031 0.96792567 0.96824691 0.96701138]\n", "Number of data points in benchmark 1148\n", "Benchmark Results \n", "Sensitivity: 0.012345679012345678\n", "Specificity : 0.985941893158388\n", "Accuracy: 0.9172473867595818\n", "ROC 0.499143786085\n", "TP 1 FP 15 TN 1052 FN 80\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced 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0.961246527749\n", "TP 12861 FP 89 TN 13743 FN 984\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96406894 0.96374377 0.96108823 0.96357724 0.96281843]\n", "Number of data points in benchmark 1148\n", "Benchmark Results \n", "Sensitivity: 0.024691358024691357\n", "Specificity : 0.9943767572633552\n", "Accuracy: 0.9259581881533101\n", "ROC 0.509534057644\n", "TP 2 FP 6 TN 1061 FN 79\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 164014\n", "Test Data Points: 70292\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9320240597144721\n", "Specificity : 0.9995220646805799\n", "Accuracy: 0.9862715529505491\n", "ROC 0.965773062198\n", "TP 12861 FP 27 TN 56466 FN 938\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98572404 0.98578806 0.98583074 0.98529663 0.98610755]\n", "Number of data points in benchmark 1148\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1067 FN 81\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 164023\n", "Test Data Points: 70297\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.92971523802623\n", "Specificity : 0.9990087793826111\n", "Accuracy: 0.9854047825654011\n", "ROC 0.964362008704\n", "TP 12831 FP 56 TN 56440 FN 970\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98523418 0.98653579 0.98623677 0.98493481 0.98587372]\n", "Number of data points in benchmark 1148\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1067 FN 81\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished 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0.9630913687251715\n", "Specificity : 0.9701417004048583\n", "Accuracy: 0.9666148787802146\n", "ROC 0.966616534565\n", "TP 13334 FP 413 TN 13419 FN 511\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96195534 0.97116844 0.96412313 0.90254743 0.92926829]\n", "Number of data points in benchmark 1148\n", "Benchmark Results \n", "Sensitivity: 0.4691358024691358\n", "Specificity : 0.5866916588566073\n", "Accuracy: 0.578397212543554\n", "ROC 0.527913730663\n", "TP 38 FP 441 TN 626 FN 43\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 175317\n", "Test Data Points: 75137\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9311368573497466\n", "Specificity : 0.9973910349438257\n", "Accuracy: 0.9852136763511985\n", "ROC 0.964263946147\n", "TP 12859 FP 160 TN 61167 FN 951\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98544678 0.985167 0.98586572 0.98492713 0.98552605]\n", "Number of data points in benchmark 741\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 710 FN 31\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 175317\n", "Test Data Points: 75137\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9305675597810429\n", "Specificity : 0.9973878830424632\n", "Accuracy: 0.9850406590627787\n", "ROC 0.963977721412\n", "TP 12920 FP 160 TN 61093 FN 964\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98554659 0.98496736 0.98588569 0.98558595 0.9854462 ]\n", "Number of data points in benchmark 741\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 710 FN 31\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 284485\n", "Test Data Points: 121923\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9185917865222345\n", "Specificity : 0.9876637768072486\n", "Accuracy: 0.9533558065336318\n", "ROC 0.953127781665\n", "TP 55629 FP 757 TN 60607 FN 4930\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96080361 0.94579366 0.93237042 0.94067494 0.93561841]\n", "Number of data points in benchmark 741\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 5 TN 705 FN 31\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 284485\n", "Test Data Points: 121923\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9093087435757605\n", "Specificity : 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"\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96461088 0.96482766 0.96130501 0.96178862 0.96205962]\n", "Number of data points in benchmark 741\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 710 FN 31\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 164026\n", "Test Data Points: 70298\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9294978624737338\n", "Specificity : 0.9992388976405827\n", "Accuracy: 0.9855472417422971\n", "ROC 0.964368380057\n", "TP 12828 FP 43 TN 56454 FN 973\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98617335 0.98551158 0.98540489 0.98627945 0.98521253]\n", "Number of data points in benchmark 741\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 710 FN 31\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 163972\n", "Test Data Points: 70275\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9315267009636983\n", "Specificity : 0.9994687820944151\n", "Accuracy: 0.9861259338313767\n", "ROC 0.965497741529\n", "TP 12856 FP 30 TN 56444 FN 945\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9852508 0.98633938 0.98514408 0.98644582 0.98589054]\n", "Number of data points in benchmark 741\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 710 FN 31\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.942578548212351\n", "Specificity : 0.9893001735106999\n", "Accuracy: 0.9659283881923619\n", "ROC 0.965939360862\n", "TP 13050 FP 148 TN 13684 FN 795\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95940819 0.97290267 0.95816172 0.96482385 0.96081301]\n", "Number of data points in benchmark 741\n", "Benchmark Results \n", "Sensitivity: 0.0967741935483871\n", "Specificity : 0.8295774647887324\n", "Accuracy: 0.7989203778677463\n", "ROC 0.463175829169\n", "TP 3 FP 121 TN 589 FN 28\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9563741422896352\n", "Specificity : 0.976576055523424\n", "Accuracy: 0.9664703544459299\n", "ROC 0.966475098907\n", "TP 13241 FP 324 TN 13508 FN 604\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9654238 0.96412313 0.96471927 0.96650407 0.94525745]\n", "Number of data points in benchmark 741\n", "Benchmark Results \n", "Sensitivity: 0.22580645161290322\n", "Specificity : 0.643661971830986\n", "Accuracy: 0.6261808367071525\n", "ROC 0.434734211722\n", "TP 7 FP 253 TN 457 FN 24\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 175317\n", "Test Data Points: 75137\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9272374658813389\n", "Specificity : 0.9977129788450543\n", "Accuracy: 0.9846546974193806\n", "ROC 0.962475222363\n", "TP 12909 FP 140 TN 61075 FN 1013\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98558652 0.98580583 0.98500729 0.98658415 0.9849471 ]\n", "Number of data points in benchmark 2971\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 1 TN 2808 FN 162\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 175317\n", "Test Data Points: 75137\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9277875329236172\n", "Specificity : 0.9974621353853161\n", "Accuracy: 0.984787787641242\n", "ROC 0.962624834154\n", "TP 12681 FP 156 TN 61313 FN 987\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98474806 0.98562616 0.9860454 0.98578559 0.98572569]\n", "Number of data points in benchmark 2971\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 2809 FN 162\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 284485\n", "Test Data Points: 121923\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 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"\n", "\n", "None\n", "Cross: Validation: [ 0.95988091 0.94968136 0.94176991 0.94247118 0.94279106]\n", "Number of data points in benchmark 2971\n", "Benchmark Results \n", "Sensitivity: 0.030864197530864196\n", "Specificity : 0.9889640441438234\n", "Accuracy: 0.9367216425445978\n", "ROC 0.509914120837\n", "TP 5 FP 31 TN 2778 FN 157\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 283280\n", "Test Data Points: 121407\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9430542854767645\n", "Specificity : 0.9926055303450753\n", "Accuracy: 0.96805785498365\n", "ROC 0.967829907911\n", "TP 56720 FP 453 TN 60809 FN 3425\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96998975 0.96866699 0.96593647 0.96840753 0.96867934]\n", "Number of data points in benchmark 2971\n", "Benchmark Results \n", "Sensitivity: 0.018518518518518517\n", "Specificity : 0.9943040227839088\n", "Accuracy: 0.9410972736452373\n", "ROC 0.506411270651\n", "TP 3 FP 16 TN 2793 FN 159\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 283299\n", "Test Data Points: 121415\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9419933554817276\n", "Specificity : 0.9943804623049906\n", "Accuracy: 0.96840588065725\n", "ROC 0.968186908893\n", "TP 56708 FP 344 TN 60871 FN 3492\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.97068344 0.96759448 0.96897817 0.96823652 0.96768056]\n", "Number of data points in benchmark 2971\n", "Benchmark Results \n", "Sensitivity: 0.012345679012345678\n", "Specificity : 0.9967960128159488\n", "Accuracy: 0.9431167956916863\n", "ROC 0.504570845914\n", "TP 2 FP 9 TN 2800 FN 160\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9317443120260022\n", "Specificity : 0.9952284557547716\n", "Accuracy: 0.9634714745095205\n", "ROC 0.96348638389\n", "TP 12900 FP 66 TN 13766 FN 945\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96574897 0.96298504 0.96200954 0.96411924 0.96292683]\n", "Number of data points in benchmark 2971\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 5 TN 2804 FN 162\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9282773564463705\n", "Specificity : 0.9917582417582418\n", "Accuracy: 0.9600028904866857\n", "ROC 0.960017799102\n", "TP 12852 FP 114 TN 13718 FN 993\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96314763 0.9573488 0.96298504 0.96243902 0.96531165]\n", "Number of data points in benchmark 2971\n", "Benchmark Results \n", "Sensitivity: 0.024691358024691357\n", "Specificity : 0.9971520113919544\n", "Accuracy: 0.9441265567149109\n", "ROC 0.510921684708\n", "TP 4 FP 8 TN 2801 FN 158\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 164017\n", "Test Data Points: 70294\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9312368668937033\n", "Specificity : 0.9991503372099199\n", "Accuracy: 0.9858167126639542\n", "ROC 0.965193602052\n", "TP 12852 FP 48 TN 56445 FN 949\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98570301 0.98519088 0.98585238 0.98668402 0.98568106]\n", "Number of data points in benchmark 2971\n", "Benchmark Results \n", "Sensitivity: 0.006172839506172839\n", "Specificity : 0.9989320042719829\n", "Accuracy: 0.9447997307303938\n", "ROC 0.502552421889\n", "TP 1 FP 3 TN 2806 FN 161\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 164005\n", "Test Data Points: 70288\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9297101449275362\n", "Specificity : 0.999522022376434\n", "Accuracy: 0.9858155019348964\n", "ROC 0.964616083652\n", "TP 12830 FP 27 TN 56461 FN 970\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9862353 0.98531766 0.98531766 0.98595757 0.98597892]\n", "Number of data points in benchmark 2971\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 2 TN 2807 FN 162\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9521849042975804\n", "Specificity : 0.9864806246385194\n", "Accuracy: 0.9693247100480543\n", "ROC 0.969332764468\n", "TP 13183 FP 187 TN 13645 FN 662\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96363538 0.96385216 0.9634186 0.96271003 0.92227642]\n", "Number of data points in benchmark 2971\n", "Benchmark Results \n", "Sensitivity: 0.3333333333333333\n", "Specificity : 0.6863652545389819\n", "Accuracy: 0.6671154493436553\n", "ROC 0.509849293936\n", "TP 54 FP 881 TN 1928 FN 108\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9499458288190683\n", "Specificity : 0.9864806246385194\n", "Accuracy: 0.9682046464573473\n", "ROC 0.968213226729\n", "TP 13152 FP 187 TN 13645 FN 693\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.97290267 0.98108606 0.96428571 0.96065041 0.90314363]\n", "Number of data points in benchmark 2971\n", "Benchmark Results \n", "Sensitivity: 0.25925925925925924\n", "Specificity : 0.7408330366678534\n", "Accuracy: 0.714574217435207\n", "ROC 0.500046147964\n", "TP 42 FP 728 TN 2081 FN 120\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 175317\n", "Test Data Points: 75137\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9331200930503053\n", "Specificity : 0.9976377054788941\n", "Accuracy: 0.9858258913717609\n", "ROC 0.965378899265\n", "TP 12836 FP 145 TN 61236 FN 920\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98504751 0.98506718 0.98574594 0.9853863 0.98624476]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1378 FN 76\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 175317\n", "Test Data Points: 75137\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9295641417448883\n", "Specificity : 0.9975078997947682\n", "Accuracy: 0.9850805861293371\n", "ROC 0.96353602077\n", "TP 12775 FP 153 TN 61241 FN 968\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98530704 0.98446827 0.9858258 0.98654422 0.98604512]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1378 FN 76\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 284485\n", "Test Data Points: 121923\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9259265365338961\n", "Specificity : 0.9794832454665644\n", "Accuracy: 0.9528390869647236\n", "ROC 0.952704891\n", "TP 56163 FP 1257 TN 60010 FN 4493\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9580109 0.94655643 0.93716859 0.94523935 0.93169375]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Sensitivity: 0.039473684210526314\n", "Specificity : 0.9854862119013063\n", "Accuracy: 0.9360385144429161\n", "ROC 0.512479948056\n", "TP 3 FP 20 TN 1358 FN 73\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 284485\n", "Test Data Points: 121923\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9258198340576848\n", "Specificity : 0.9855179064711748\n", "Accuracy: 0.9557753664197896\n", "ROC 0.955668870264\n", "TP 56238 FP 886 TN 60293 FN 4506\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96034841 0.94140154 0.93806671 0.94287718 0.9380421 ]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Sensitivity: 0.02631578947368421\n", "Specificity : 0.9912917271407837\n", "Accuracy: 0.9408528198074277\n", "ROC 0.508803758307\n", "TP 2 FP 12 TN 1366 FN 74\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 260250\n", "Test Data Points: 111536\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9424995110502641\n", "Specificity : 0.9903547229972101\n", "Accuracy: 0.9640295509969875\n", "ROC 0.966427117024\n", "TP 57828 FP 484 TN 49696 FN 3528\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96021948 0.96434767 0.965047 0.96411905 0.96424008]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Sensitivity: 0.039473684210526314\n", "Specificity : 0.9854862119013063\n", "Accuracy: 0.9360385144429161\n", "ROC 0.512479948056\n", "TP 3 FP 20 TN 1358 FN 73\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 283304\n", "Test Data Points: 121417\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9495011639507815\n", "Specificity : 0.9834358731661146\n", "Accuracy: 0.9666274080235882\n", "ROC 0.966468518558\n", "TP 57103 FP 1015 TN 60262 FN 3037\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9671876 0.96753311 0.9691268 0.96743428 0.96845968]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Sensitivity: 0.039473684210526314\n", "Specificity : 0.9840348330914369\n", "Accuracy: 0.9346629986244842\n", "ROC 0.511754258651\n", "TP 3 FP 22 TN 1356 FN 73\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9305164319248826\n", "Specificity : 0.9964574898785425\n", "Accuracy: 0.9634714745095205\n", "ROC 0.963486960902\n", "TP 12883 FP 49 TN 13783 FN 962\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96152179 0.96385216 0.9634186 0.96330623 0.96411924]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1378 FN 76\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9323221379559408\n", "Specificity : 0.9964574898785425\n", "Accuracy: 0.9643747515988005\n", "ROC 0.964389813917\n", "TP 12908 FP 49 TN 13783 FN 937\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9644483 0.96461088 0.96325602 0.96054201 0.96260163]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1378 FN 76\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 164007\n", "Test Data Points: 70289\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9289078918762229\n", "Specificity : 0.9990971853425386\n", "Accuracy: 0.9853177595356315\n", "ROC 0.964002538609\n", "TP 12818 FP 51 TN 56439 FN 981\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98482714 0.98531797 0.98693982 0.98580819 0.9860216 ]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Sensitivity: 0.013157894736842105\n", "Specificity : 1.0\n", "Accuracy: 0.9484181568088033\n", "ROC 0.506578947368\n", "TP 1 FP 0 TN 1378 FN 75\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 164000\n", "Test Data Points: 70286\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9321063691036882\n", "Specificity : 0.9989023634593255\n", "Accuracy: 0.9857866431437271\n", "ROC 0.965504366282\n", "TP 12864 FP 62 TN 56423 FN 937\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98659781 0.985296 0.98664049 0.98486853 0.98563684]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1378 FN 76\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9508847959552185\n", "Specificity : 0.9800462695199538\n", "Accuracy: 0.9654586841059364\n", "ROC 0.965465532738\n", "TP 13165 FP 276 TN 13556 FN 680\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9705723 0.96385216 0.96943421 0.76764228 0.92189702]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Sensitivity: 0.3026315789473684\n", "Specificity : 0.6988388969521045\n", "Accuracy: 0.6781292984869326\n", "ROC 0.50073523795\n", "TP 23 FP 415 TN 963 FN 53\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 64579\n", "Test Data Points: 27677\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9621524015890213\n", "Specificity : 0.9674667437825333\n", "Accuracy: 0.9648083246016548\n", "ROC 0.964809572686\n", "TP 13321 FP 450 TN 13382 FN 524\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.96433991 0.96878387 0.9705181 0.95533875 0.91604336]\n", "Number of data points in benchmark 1454\n", "Benchmark Results \n", "Sensitivity: 0.42105263157894735\n", "Specificity : 0.5761973875181422\n", "Accuracy: 0.5680880330123796\n", "ROC 0.498625009549\n", "TP 32 FP 584 TN 794 FN 44\n", "\n", "\n", "\n", "None\n" ] } ], "source": [ "par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n", "benchmarks = [\"Data/Benchmarks/phos_CDK1.csv\", \"Data/Benchmarks/phos_CK2.csv\", \"Data/Benchmarks/phos_MAPK1.csv\", \"Data/Benchmarks/phos_PKA.csv\", \"Data/Benchmarks/phos_PKC.csv\"]\n", "for j in benchmarks:\n", " for i in par:\n", " print(\"y\", i, \" \", j)\n", " y = Predictor()\n", " y.load_data(file=\"Data/Training/clean_s_filtered.csv\")\n", " y.process_data(vector_function=\"sequence\", amino_acid=\"S\", imbalance_function=i, random_data=0)\n", " y.supervised_training(\"mlp_adam\")\n", " y.benchmark(j, \"S\")\n", " del y\n", " print(\"x\", i, \" \", j)\n", " x = Predictor()\n", " x.load_data(file=\"Data/Training/clean_s_filtered.csv\")\n", " x.process_data(vector_function=\"sequence\", amino_acid=\"S\", imbalance_function=i, random_data=1)\n", " x.supervised_training(\"mlp_adam\")\n", " x.benchmark(j, \"S\")\n", " del x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y Phosphorylation " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 8836\n", "Test Data Points: 3788\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.022598870056497175\n", "Specificity : 0.999169205206314\n", "Accuracy: 0.9535374868004224\n", "ROC 0.510884037631\n", "TP 4 FP 3 TN 3608 FN 173\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95368171 0.95405941 0.95287129 0.9548336 0.9540412 ]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ADASYN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 16953\n", "Test Data Points: 7266\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9456673168013374\n", "Specificity : 0.9970084307859668\n", "Accuracy: 0.9716487751169832\n", "ROC 0.971337873794\n", "TP 3394 FP 11 TN 3666 FN 195\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.875129 0.98100743 0.99174236 0.99112121 0.98843692]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y SMOTEENN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 16841\n", "Test Data Points: 7219\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9359619686800895\n", "Specificity : 0.9849025528410651\n", "Accuracy: 0.960659371104031\n", "ROC 0.960432260761\n", "TP 3347 FP 55 TN 3588 FN 229\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.88905049 0.98524829 0.97859518 0.98482644 0.98254001]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y random_under_sample Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. 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Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 8078\n", "Test Data Points: 3463\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.041916167664670656\n", "Specificity : 0.9984830097087378\n", "Accuracy: 0.9523534507652325\n", "ROC 0.520199588687\n", "TP 7 FP 5 TN 3291 FN 160\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94846254 0.94974003 0.95190641 0.95103986 0.95060659]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y near_miss Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 827\n", "Test Data Points: 355\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5257142857142857\n", "Specificity : 0.7333333333333333\n", "Accuracy: 0.6309859154929578\n", "ROC 0.629523809524\n", "TP 92 FP 48 TN 132 FN 83\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.60084034 0.66949153 0.65677966 0.65254237 0.6440678 ]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y pass Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 8836\n", "Test Data Points: 3788\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.0736196319018405\n", "Specificity : 0.9975172413793103\n", "Accuracy: 0.9577613516367476\n", "ROC 0.535568436641\n", "TP 12 FP 9 TN 3616 FN 151\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95249406 0.95485149 0.95485149 0.95562599 0.95522979]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ADASYN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 16953\n", "Test Data Points: 7266\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9493036211699164\n", "Specificity : 0.9972796517954298\n", "Accuracy: 0.9735755573905863\n", "ROC 0.973291636483\n", "TP 3408 FP 10 TN 3666 FN 182\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.86769866 0.98926507 0.99112304 0.98926285 0.99215362]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y SMOTEENN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 16843\n", "Test Data Points: 7219\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9348863317429132\n", "Specificity : 0.9852297592997812\n", "Accuracy: 0.960382324421665\n", "ROC 0.960058045521\n", "TP 3331 FP 54 TN 3602 FN 232\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.89382921 0.98150841 0.97984625 0.97942643 0.97963001]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y random_under_sample Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 827\n", "Test Data Points: 355\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.46285714285714286\n", "Specificity : 0.65\n", "Accuracy: 0.5577464788732395\n", "ROC 0.556428571429\n", "TP 81 FP 63 TN 117 FN 94\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.53361345 0.58050847 0.58474576 0.59745763 0.6059322 ]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y ncl Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 8068\n", "Test Data Points: 3459\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.041916167664670656\n", "Specificity : 0.9984811664641555\n", "Accuracy: 0.9522983521248916\n", "ROC 0.520198667064\n", "TP 7 FP 5 TN 3287 FN 160\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94755093 0.95184382 0.94967462 0.95140998 0.95140998]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y near_miss Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 827\n", "Test Data Points: 355\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5371428571428571\n", "Specificity : 0.7611111111111111\n", "Accuracy: 0.6507042253521127\n", "ROC 0.649126984127\n", "TP 94 FP 43 TN 137 FN 81\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.6302521 0.68220339 0.59322034 0.69067797 0.65677966]\n", "Number of data points in benchmark 0\n", "Benchmark not relevant\n", "y pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py:395: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " DeprecationWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 8836\n", "Test Data Points: 3788\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.03428571428571429\n", "Specificity : 0.9994464433988375\n", "Accuracy: 0.954857444561774\n", "ROC 0.516866078842\n", "TP 6 FP 2 TN 3611 FN 169\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95328583 0.95326733 0.95287129 0.9544374 0.9548336 ]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 1 TN 567 FN 1\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 8836\n", "Test Data Points: 3788\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.058823529411764705\n", "Specificity : 0.9991708126036484\n", "Accuracy: 0.9569693769799367\n", "ROC 0.528997171008\n", "TP 10 FP 3 TN 3615 FN 160\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95486936 0.95405941 0.95287129 0.9540412 0.9544374 ]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 1 TN 567 FN 1\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 16953\n", "Test Data Points: 7266\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9455706748125521\n", "Specificity : 0.9964529331514325\n", "Accuracy: 0.9712358932012112\n", "ROC 0.971011803982\n", "TP 3405 FP 13 TN 3652 FN 196\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.87203302 0.99112304 0.99277457 0.99153417 0.98327483]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.7464788732394366\n", "Accuracy: 0.7469244288224957\n", "ROC 0.87323943662\n", "TP 1 FP 144 TN 424 FN 0\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 16953\n", "Test Data Points: 7266\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9546726357022943\n", "Specificity : 0.9972914409534128\n", "Accuracy: 0.9763281034957335\n", "ROC 0.975982038328\n", "TP 3412 FP 10 TN 3682 FN 162\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.87781218 0.98596201 0.99463254 0.99029527 0.99029527]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.8116197183098591\n", "Accuracy: 0.81195079086116\n", "ROC 0.905809859155\n", "TP 1 FP 107 TN 461 FN 0\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 16839\n", "Test Data Points: 7217\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9399105645612074\n", "Specificity : 0.9887331684528716\n", "Accuracy: 0.9645281973119024\n", "ROC 0.964321866507\n", "TP 3363 FP 41 TN 3598 FN 215\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.89422278 0.98545303 0.97984206 0.98004158 0.97900208]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.03873239436619718\n", "Accuracy: 0.040421792618629174\n", "ROC 0.519366197183\n", "TP 1 FP 546 TN 22 FN 0\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 16841\n", "Test Data Points: 7218\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9409614843969637\n", "Specificity : 0.9863425293635618\n", "Accuracy: 0.9639789415350513\n", "ROC 0.96365200688\n", "TP 3347 FP 50 TN 3611 FN 210\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.89216705 0.98108894 0.98067332 0.98254001 0.9850343 ]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.051056338028169015\n", "Accuracy: 0.05272407732864675\n", "ROC 0.525528169014\n", "TP 1 FP 539 TN 29 FN 0\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 827\n", "Test Data Points: 355\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.48\n", "Specificity : 0.6666666666666666\n", "Accuracy: 0.5746478873239437\n", "ROC 0.573333333333\n", "TP 84 FP 60 TN 120 FN 91\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.55462185 0.63135593 0.54661017 0.58050847 0.62711864]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.6883802816901409\n", "Accuracy: 0.6889279437609842\n", "ROC 0.844190140845\n", "TP 1 FP 177 TN 391 FN 0\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 827\n", "Test Data Points: 355\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.49142857142857144\n", "Specificity : 0.6555555555555556\n", "Accuracy: 0.5746478873239437\n", "ROC 0.573492063492\n", "TP 86 FP 62 TN 118 FN 89\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.58403361 0.55508475 0.61864407 0.59745763 0.58474576]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.704225352112676\n", "Accuracy: 0.7047451669595782\n", "ROC 0.852112676056\n", "TP 1 FP 168 TN 400 FN 0\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 8066\n", "Test Data Points: 3458\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.023952095808383235\n", "Specificity : 0.9984807049529019\n", "Accuracy: 0.951417004048583\n", "ROC 0.511216400381\n", "TP 4 FP 5 TN 3286 FN 163\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95143105 0.94967462 0.95010846 0.94965278 0.95225694]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 1.0\n", "Accuracy: 1.0\n", "ROC 1.0\n", "TP 1 FP 0 TN 568 FN 0\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 8070\n", "Test Data Points: 3459\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.0658682634730539\n", "Specificity : 0.996962332928311\n", "Accuracy: 0.9520092512286789\n", "ROC 0.531415298201\n", "TP 11 FP 10 TN 3282 FN 156\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95231903 0.9509974 0.94839549 0.9483731 0.95097614]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.9982394366197183\n", "Accuracy: 0.9982425307557118\n", "ROC 0.99911971831\n", "TP 1 FP 1 TN 567 FN 0\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 827\n", "Test Data Points: 355\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5142857142857142\n", "Specificity : 0.7611111111111111\n", "Accuracy: 0.6394366197183099\n", "ROC 0.637698412698\n", "TP 90 FP 43 TN 137 FN 85\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.6512605 0.69067797 0.62288136 0.62288136 0.66949153]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.5\n", "Accuracy: 0.5008787346221442\n", "ROC 0.75\n", "TP 1 FP 284 TN 284 FN 0\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 827\n", "Test Data Points: 355\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.5257142857142857\n", "Specificity : 0.8\n", "Accuracy: 0.6647887323943662\n", "ROC 0.662857142857\n", "TP 92 FP 36 TN 144 FN 83\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.63445378 0.62288136 0.67372881 0.6440678 0.6779661 ]\n", "Number of data points in benchmark 569\n", "Benchmark Results \n", "Sensitivity: 1.0\n", "Specificity : 0.5352112676056338\n", "Accuracy: 0.5360281195079086\n", "ROC 0.767605633803\n", "TP 1 FP 264 TN 304 FN 0\n", "\n", "\n", "\n", "None\n" ] } ], "source": [ "par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n", "benchmarks = [\"Data/Benchmarks/phos_CDK1.csv\", \"Data/Benchmarks/phos_CK2.csv\", \"Data/Benchmarks/phos_MAPK1.csv\", \"Data/Benchmarks/phos_PKA.csv\", \"Data/Benchmarks/phos_PKC.csv\"]\n", "for j in benchmarks:\n", " for i in par:\n", " try:\n", " print(\"y\", i, \" \", j)\n", " y = Predictor()\n", " y.load_data(file=\"Data/Training/clean_Y_filtered.csv\")\n", " y.process_data(vector_function=\"sequence\", amino_acid=\"Y\", imbalance_function=i, random_data=0)\n", " y.supervised_training(\"bagging\")\n", " y.benchmark(j, \"Y\")\n", " del y\n", " print(\"x\", i, \" \", j)\n", " x = Predictor()\n", " x.load_data(file=\"Data/Training/clean_Y_filtered.csv\")\n", " x.process_data(vector_function=\"sequence\", amino_acid=\"Y\", imbalance_function=i, random_data=1)\n", " x.supervised_training(\"bagging\")\n", " x.benchmark(j, \"Y\")\n", " del x\n", " except:\n", " print(\"Benchmark not relevant\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "T Phosphorylation " ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "y pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 58032\n", "Test Data Points: 24872\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8983126110124334\n", "Specificity : 0.9990180675569521\n", "Accuracy: 0.9807816018012222\n", "ROC 0.948665339285\n", "TP 4046 FP 20 TN 20348 FN 458\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98124472 0.98142564 0.98166466 0.9800965 0.97870929]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1153 FN 43\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 58032\n", "Test Data Points: 24872\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9098434004474273\n", "Specificity : 0.9967160082344868\n", "Accuracy: 0.9811032486330009\n", "ROC 0.953279704341\n", "TP 4067 FP 67 TN 20335 FN 403\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98064166 0.97901339 0.97985525 0.9814234 0.98281062]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 2 TN 1151 FN 43\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 95846\n", "Test Data Points: 41077\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9076254826254826\n", "Specificity : 0.8902588790096773\n", "Accuracy: 0.8990189156949144\n", "ROC 0.898942180818\n", "TP 18806 FP 2234 TN 18123 FN 1914\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9255459 0.85711156 0.84593193 0.82544552 0.85670465]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.11627906976744186\n", "Specificity : 0.9124024284475282\n", "Accuracy: 0.8837792642140468\n", "ROC 0.514340749107\n", "TP 5 FP 101 TN 1052 FN 38\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 95846\n", "Test Data Points: 41077\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8549805391379559\n", "Specificity : 0.9524819895391295\n", "Accuracy: 0.9030844511527132\n", "ROC 0.903731264339\n", "TP 17793 FP 963 TN 19303 FN 3018\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.92488863 0.86054409 0.82862255 0.86985101 0.84023517]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.023255813953488372\n", "Specificity : 0.9748482220294883\n", "Accuracy: 0.9406354515050167\n", "ROC 0.499052017991\n", "TP 1 FP 29 TN 1124 FN 42\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 94235\n", "Test Data Points: 40387\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9440486273728265\n", "Specificity : 0.9689899586532782\n", "Accuracy: 0.9565949439175973\n", "ROC 0.956519293013\n", "TP 18948 FP 630 TN 19686 FN 1123\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95238626 0.95624884 0.95327589 0.95736146 0.96029565]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.11627906976744186\n", "Specificity : 0.9627059843885516\n", "Accuracy: 0.9322742474916388\n", "ROC 0.539492527078\n", "TP 5 FP 43 TN 1110 FN 38\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 94252\n", "Test Data Points: 40394\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9239856445020437\n", "Specificity : 0.986720440684635\n", "Accuracy: 0.9555627073327722\n", "ROC 0.955353042593\n", "TP 18537 FP 270 TN 20062 FN 1525\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9527293 0.96052729 0.95562405 0.95629247 0.95859329]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.13953488372093023\n", "Specificity : 0.9791847354726799\n", "Accuracy: 0.9489966555183946\n", "ROC 0.559359809597\n", "TP 6 FP 24 TN 1129 FN 37\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9007717750826902\n", "Specificity : 0.9884135472370766\n", "Accuracy: 0.9443644020835642\n", "ROC 0.94459266116\n", "TP 4085 FP 52 TN 4436 FN 450\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95129654 0.94165559 0.94329897 0.94280013 0.94812105]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1153 FN 43\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9020948180815876\n", "Specificity : 0.9959893048128342\n", "Accuracy: 0.9487975174553918\n", "ROC 0.949042061447\n", "TP 4091 FP 18 TN 4470 FN 444\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95013298 0.9481383 0.94795477 0.93382108 0.94013967]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 1 TN 1152 FN 43\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 54641\n", "Test Data Points: 23418\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8984090909090909\n", "Specificity : 0.9993690188242718\n", "Accuracy: 0.9803996925441968\n", "ROC 0.948889054867\n", "TP 3953 FP 12 TN 19006 FN 447\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98014475 0.9805931 0.9783486 0.97937352 0.98129524]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1153 FN 43\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 54634\n", "Test Data Points: 23415\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8949522510231923\n", "Specificity : 0.9985802176999526\n", "Accuracy: 0.9791159513132607\n", "ROC 0.946766234362\n", "TP 3936 FP 27 TN 18990 FN 462\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9813593 0.98039844 0.97821769 0.98135691 0.9798834 ]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1153 FN 43\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9042998897464167\n", "Specificity : 0.9886363636363636\n", "Accuracy: 0.9462484761165909\n", "ROC 0.946468126691\n", "TP 4101 FP 51 TN 4437 FN 434\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.80934176 0.95246011 0.95327569 0.95111407 0.93116063]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.09302325581395349\n", "Specificity : 0.8725065047701648\n", "Accuracy: 0.8444816053511706\n", "ROC 0.482764880292\n", "TP 4 FP 147 TN 1006 FN 39\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_CDK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9095920617420066\n", "Specificity : 0.9877450980392157\n", "Accuracy: 0.9484650338025047\n", "ROC 0.948668579891\n", "TP 4125 FP 55 TN 4433 FN 410\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.78839761 0.95545213 0.94379781 0.94845361 0.8689724 ]\n", "Number of data points in benchmark 1196\n", "Benchmark Results \n", "Sensitivity: 0.09302325581395349\n", "Specificity : 0.8673026886383348\n", "Accuracy: 0.8394648829431438\n", "ROC 0.480162972226\n", "TP 4 FP 153 TN 1000 FN 39\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 58032\n", "Test Data Points: 24872\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.903606411398041\n", "Specificity : 0.9990186457311089\n", "Accuracy: 0.9817867481505307\n", "ROC 0.951312528565\n", "TP 4059 FP 20 TN 20360 FN 433\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98118442 0.98034013 0.98190591 0.9805187 0.97901086]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 704 FN 15\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 58032\n", "Test Data Points: 24872\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9001774622892635\n", "Specificity : 0.9991651934786879\n", "Accuracy: 0.981223866194918\n", "ROC 0.949671327884\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "TP 4058 FP 17 TN 20347 FN 450\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.97991798 0.97979737 0.98069964 0.98045838 0.98293124]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 704 FN 15\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 95846\n", "Test Data Points: 41077\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9074154694529096\n", "Specificity : 0.9010978239560871\n", "Accuracy: 0.9042773328139835\n", "ROC 0.904256646704\n", "TP 18759 FP 2018 TN 18386 FN 1914\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.93909297 0.8636845 0.86378907 0.86583406 0.84563979]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.2\n", "Specificity : 0.8835227272727273\n", "Accuracy: 0.8692628650904033\n", "ROC 0.541761363636\n", "TP 3 FP 82 TN 622 FN 12\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 95846\n", "Test Data Points: 41077\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8577846480846336\n", "Specificity : 0.9553886925795053\n", "Accuracy: 0.9062005501862356\n", "ROC 0.906586670332\n", "TP 17757 FP 909 TN 19467 FN 2944\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.92222303 0.85364251 0.82161116 0.84107508 0.8404908 ]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 36 TN 668 FN 15\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 94183\n", "Test Data Points: 40365\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9272292279320141\n", "Specificity : 0.9830558565658556\n", "Accuracy: 0.9553078161773814\n", "ROC 0.955142542249\n", "TP 18603 FP 344 TN 19958 FN 1460\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94541266 0.95923449 0.96116541 0.95897283 0.95685458]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.06666666666666667\n", "Specificity : 0.9857954545454546\n", "Accuracy: 0.9666203059805285\n", "ROC 0.526231060606\n", "TP 1 FP 10 TN 694 FN 14\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 94168\n", "Test Data Points: 40359\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9352686351640691\n", "Specificity : 0.9781021897810219\n", "Accuracy: 0.956787829232637\n", "ROC 0.956685412473\n", "TP 18783 FP 444 TN 19832 FN 1300\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95640378 0.96160708 0.96405873 0.95755436 0.95621632]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.06666666666666667\n", "Specificity : 0.9730113636363636\n", "Accuracy: 0.9541029207232267\n", "ROC 0.519839015152\n", "TP 1 FP 19 TN 685 FN 14\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.903638368246968\n", "Specificity : 0.9895276292335116\n", "Accuracy: 0.9463593040008866\n", "ROC 0.94658299874\n", "TP 4098 FP 47 TN 4441 FN 437\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9424867 0.93101729 0.92334553 0.94728966 0.94728966]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 15 TN 689 FN 15\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.898566703417861\n", "Specificity : 0.9964349376114082\n", "Accuracy: 0.9472459270752521\n", "ROC 0.947500820515\n", "TP 4075 FP 16 TN 4472 FN 460\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94647606 0.94481383 0.94413036 0.94845361 0.94662454]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 704 FN 15\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 54644\n", "Test Data Points: 23420\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9013636363636364\n", "Specificity : 0.9991587802313354\n", "Accuracy: 0.9807856532877882\n", "ROC 0.950261208297\n", "TP 3966 FP 16 TN 19004 FN 434\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98213142 0.98046625 0.98136049 0.97899052 0.97777351]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 704 FN 15\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 54643\n", "Test Data Points: 23419\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9022505114798818\n", "Specificity : 0.9998948475289169\n", "Accuracy: 0.9815534395149238\n", "ROC 0.951072679504\n", "TP 3969 FP 2 TN 19018 FN 430\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98040095 0.97892782 0.98078401 0.98014348 0.97975916]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 704 FN 15\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9206174200661521\n", "Specificity : 0.982397504456328\n", "Accuracy: 0.9513465587941926\n", "ROC 0.951507462261\n", "TP 4175 FP 79 TN 4409 FN 360\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.84840426 0.95246011 0.95527103 0.94978384 0.94812105]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.2\n", "Specificity : 0.7911931818181818\n", "Accuracy: 0.7788595271210014\n", "ROC 0.495596590909\n", "TP 3 FP 147 TN 557 FN 12\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_CK2.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9219404630650496\n", "Specificity : 0.9663547237076648\n", "Accuracy: 0.9440319184306771\n", "ROC 0.944147593386\n", "TP 4181 FP 151 TN 4337 FN 354\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.83892952 0.95229388 0.94878617 0.95360825 0.94546059]\n", "Number of data points in benchmark 719\n", "Benchmark Results \n", "Sensitivity: 0.2\n", "Specificity : 0.7173295454545454\n", "Accuracy: 0.7065368567454798\n", "ROC 0.458664772727\n", "TP 3 FP 199 TN 505 FN 12\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 58032\n", "Test Data Points: 24872\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8951401050788091\n", "Specificity : 0.9990642237982663\n", "Accuracy: 0.9799774847217755\n", "ROC 0.947102164439\n", "TP 4089 FP 19 TN 20285 FN 479\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98076227 0.98118442 0.97937274 0.98015682 0.98238842]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 395 FN 26\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 58032\n", "Test Data Points: 24872\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9035454745650737\n", "Specificity : 0.99734395750332\n", "Accuracy: 0.9802187198456095\n", "ROC 0.950444716034\n", "TP 4103 FP 54 TN 20277 FN 438\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9800989 0.98015921 0.98226779 0.97961399 0.98106152]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 395 FN 26\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 95846\n", "Test Data Points: 41077\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8774552491533624\n", "Specificity : 0.9330621845445191\n", "Accuracy: 0.9050807020960635\n", "ROC 0.905258716849\n", "TP 18137 FP 1366 TN 19041 FN 2533\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.91159717 0.85685594 0.84322962 0.83669296 0.83198218]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.11538461538461539\n", "Specificity : 0.9265822784810127\n", "Accuracy: 0.8764845605700713\n", "ROC 0.520983446933\n", "TP 3 FP 29 TN 366 FN 23\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 95846\n", "Test Data Points: 41077\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8492820661077383\n", "Specificity : 0.9444963834079614\n", "Accuracy: 0.8963897071353799\n", "ROC 0.896889224758\n", "TP 17626 FP 1128 TN 19195 FN 3128\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.93467465 0.8686142 0.84786737 0.85465966 0.84717353]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 18 TN 377 FN 26\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 94217\n", "Test Data Points: 40379\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9462028466208818\n", "Specificity : 0.9614493468079862\n", "Accuracy: 0.9538621560712252\n", "ROC 0.953826096714\n", "TP 19013 FP 782 TN 19503 FN 1081\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95635215 0.95969539 0.95798507 0.95753928 0.95594026]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 22 TN 373 FN 26\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 94179\n", "Test Data Points: 40363\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9334163967106902\n", "Specificity : 0.9783722534239827\n", "Accuracy: 0.9560240814607438\n", "ROC 0.955894325067\n", "TP 18729 FP 439 TN 19859 FN 1336\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95774648 0.95778364 0.95514345 0.9590828 0.96023487]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 9 TN 386 FN 26\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.915986769570011\n", "Specificity : 0.9725935828877005\n", "Accuracy: 0.9441427463149729\n", "ROC 0.944290176229\n", "TP 4154 FP 123 TN 4365 FN 381\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94232048 0.95229388 0.94762221 0.94296641 0.94645826]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.038461538461538464\n", "Specificity : 0.9670886075949368\n", "Accuracy: 0.9097387173396675\n", "ROC 0.502775073028\n", "TP 1 FP 13 TN 382 FN 25\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9040793825799338\n", "Specificity : 0.9855169340463458\n", "Accuracy: 0.9445860578521555\n", "ROC 0.944798158313\n", "TP 4100 FP 65 TN 4423 FN 435\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94547872 0.94414894 0.93781177 0.95044895 0.94263385]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 3 TN 392 FN 26\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 54646\n", "Test Data Points: 23421\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8995454545454545\n", "Specificity : 0.999158824457179\n", "Accuracy: 0.9804448998761794\n", "ROC 0.949352139501\n", "TP 3958 FP 16 TN 19005 FN 442\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.97937748 0.98014602 0.98072119 0.97854352 0.97969641]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 395 FN 26\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 54639\n", "Test Data Points: 23417\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9036363636363637\n", "Specificity : 0.9978966188147447\n", "Accuracy: 0.9801853354400649\n", "ROC 0.950766491226\n", "TP 3976 FP 40 TN 18977 FN 424\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98084807 0.98091212 0.97860483 0.98065467 0.97982063]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 395 FN 26\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9206174200661521\n", "Specificity : 0.9750445632798574\n", "Accuracy: 0.9476892386124349\n", "ROC 0.947830991673\n", "TP 4175 FP 112 TN 4376 FN 360\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.87599734 0.95495346 0.94795477 0.94861989 0.94895244]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.15384615384615385\n", "Specificity : 0.7924050632911392\n", "Accuracy: 0.7529691211401425\n", "ROC 0.473125608569\n", "TP 4 FP 82 TN 313 FN 22\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_MAPK1.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9214994487320838\n", "Specificity : 0.9777183600713012\n", "Accuracy: 0.9494624847611659\n", "ROC 0.949608904402\n", "TP 4179 FP 100 TN 4388 FN 356\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.78390957 0.95445479 0.95144662 0.93814433 0.9501164 ]\n", "Number of data points in benchmark 421\n", "Benchmark Results \n", "Sensitivity: 0.11538461538461539\n", "Specificity : 0.7746835443037975\n", "Accuracy: 0.7339667458432304\n", "ROC 0.445034079844\n", "TP 3 FP 89 TN 306 FN 23\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 58032\n", "Test Data Points: 24872\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8971837282074206\n", "Specificity : 0.9985782919894107\n", "Accuracy: 0.9803393374075265\n", "ROC 0.947881010098\n", "TP 4014 FP 29 TN 20369 FN 460\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98202871 0.98034013 0.98003619 0.98154403 0.97913148]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 2101 FN 29\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 58032\n", "Test Data Points: 24872\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9010915571396748\n", "Specificity : 0.9990678506598636\n", "Accuracy: 0.9813846896108074\n", "ROC 0.9500797039\n", "TP 4045 FP 19 TN 20364 FN 444\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98124472 0.98118442 0.98208685 0.98094089 0.97834741]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 2101 FN 29\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 95846\n", "Test Data Points: 41077\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9308731307284129\n", "Specificity : 0.8119624514670467\n", "Accuracy: 0.8719721498648879\n", "ROC 0.871417791098\n", "TP 19297 FP 3826 TN 16521 FN 1433\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.93584313 0.86686142 0.86002775 0.83851884 0.8586766 ]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.4827586206896552\n", "Specificity : 0.8581627796287482\n", "Accuracy: 0.8530516431924883\n", "ROC 0.670460700159\n", "TP 14 FP 298 TN 1803 FN 15\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 95846\n", "Test Data Points: 41077\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8686412155014905\n", "Specificity : 0.9523152029192761\n", "Accuracy: 0.9099496068359423\n", "ROC 0.91047820921\n", "TP 18066 FP 967 TN 19312 FN 2732\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9299277 0.86350192 0.84604148 0.85721589 0.83607216]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.2413793103448276\n", "Specificity 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on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 94170\n", "Test Data Points: 40359\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9239655429965643\n", "Specificity : 0.990234760307753\n", "Accuracy: 0.9572586040288411\n", "ROC 0.957100151652\n", "TP 18556 FP 198 TN 20078 FN 1527\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95495596 0.95822648 0.95551013 0.95666233 0.95896673]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.034482758620689655\n", "Specificity : 0.991432651118515\n", "Accuracy: 0.9784037558685446\n", "ROC 0.51295770487\n", "TP 1 FP 18 TN 2083 FN 28\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting 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"ROC 0.947722482298\n", "TP 4076 FP 15 TN 4473 FN 459\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94265293 0.94680851 0.94645826 0.9469571 0.95177918]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.034482758620689655\n", "Specificity : 0.9985721085197525\n", "Accuracy: 0.9854460093896713\n", "ROC 0.51652743357\n", "TP 1 FP 3 TN 2098 FN 28\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 54646\n", "Test Data Points: 23421\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9036363636363637\n", "Specificity : 0.9995794122285895\n", "Accuracy: 0.9815550147303702\n", "ROC 0.951607887932\n", "TP 3976 FP 8 TN 19013 FN 424\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98072243 0.98065838 0.9799526 0.9802088 0.97969641]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 2101 FN 29\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 54640\n", "Test Data Points: 23418\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.895\n", "Specificity : 0.9993164370596277\n", "Accuracy: 0.9797164574259117\n", "ROC 0.94715821853\n", "TP 3938 FP 13 TN 19005 FN 462\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9803369 0.97963105 0.98110307 0.98167958 0.9783486 ]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 2101 FN 29\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9106945975744212\n", "Specificity : 0.982174688057041\n", "Accuracy: 0.9462484761165909\n", "ROC 0.946434642816\n", "TP 4130 FP 80 TN 4408 FN 405\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.80402261 0.95445479 0.94961756 0.94246758 0.91004323]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.27586206896551724\n", "Specificity : 0.857686815801999\n", "Accuracy: 0.8497652582159625\n", "ROC 0.566774442384\n", "TP 8 FP 299 TN 1802 FN 21\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_PKA.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9173098125689085\n", "Specificity : 0.981951871657754\n", "Accuracy: 0.9494624847611659\n", "ROC 0.949630842113\n", "TP 4160 FP 81 TN 4407 FN 375\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.7997008 0.95246011 0.95344197 0.95410708 0.94363153]\n", "Number of data points in benchmark 2130\n", "Benchmark Results \n", "Sensitivity: 0.27586206896551724\n", "Specificity : 0.774869109947644\n", "Accuracy: 0.7680751173708921\n", "ROC 0.525365589457\n", "TP 8 FP 473 TN 1628 FN 21\n", "\n", "\n", "\n", "None\n", "y pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 58032\n", "Test Data Points: 24872\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8968397291196388\n", "Specificity : 0.9988259465805694\n", "Accuracy: 0.9806609842393053\n", "ROC 0.94783283785\n", "TP 3973 FP 24 TN 20418 FN 457\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.97991798 0.9829333 0.97973462 0.98069964 0.98045838]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1036 FN 28\n", "\n", "\n", "\n", "None\n", "x pass Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Finished working with Data\n", "Training Data Points: 58032\n", "Test Data Points: 24872\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8926905132192846\n", "Specificity : 0.9988218545972215\n", "Accuracy: 0.9796156320360244\n", "ROC 0.945756183908\n", "TP 4018 FP 24 TN 20347 FN 483\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.97853094 0.98118442 0.98154403 0.97889023 0.97895054]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1036 FN 28\n", "\n", "\n", "\n", "None\n", "y ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 95846\n", "Test Data Points: 41077\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8432670015455951\n", "Specificity : 0.9597015657978697\n", "Accuracy: 0.9010151666382648\n", "ROC 0.901484283672\n", "TP 17459 FP 821 TN 19552 FN 3245\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.93953115 0.84779989 0.834721 0.8190184 0.82960853]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.17857142857142858\n", "Specificity : 0.9652509652509652\n", "Accuracy: 0.9445488721804511\n", "ROC 0.571911196911\n", "TP 5 FP 36 TN 1000 FN 23\n", "\n", "\n", "\n", "None\n", "x ADASYN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 95846\n", "Test Data Points: 41077\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8389543873212215\n", "Specificity : 0.9661940042196163\n", "Accuracy: 0.9020863256810381\n", "ROC 0.90257419577\n", "TP 17363 FP 689 TN 19692 FN 3333\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.9240853 0.8487128 0.8508253 0.822305 0.84301052]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.14285714285714285\n", "Specificity : 0.9806949806949807\n", "Accuracy: 0.9586466165413534\n", "ROC 0.561776061776\n", "TP 4 FP 20 TN 1016 FN 24\n", "\n", "\n", "\n", "None\n", "y SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 85474\n", "Test Data Points: 36632\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9513513513513514\n", "Specificity : 0.9520943373049994\n", "Accuracy: 0.9516815898667832\n", "ROC 0.951722844328\n", "TP 19360 FP 780 TN 15502 FN 990\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94406682 0.95467202 0.95528439 0.95450637 0.96191646]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 60 TN 976 FN 28\n", "\n", "\n", "\n", "None\n", "x SMOTEENN Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 94214\n", "Test Data Points: 40378\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.937596725076132\n", "Specificity : 0.978817516095739\n", "Accuracy: 0.9583684184456882\n", "ROC 0.958207120586\n", "TP 18781 FP 431 TN 19916 FN 1250\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.95687061 0.95913667 0.95653466 0.95839215 0.95579166]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.03571428571428571\n", "Specificity : 0.9806949806949807\n", "Accuracy: 0.9558270676691729\n", "ROC 0.508204633205\n", "TP 1 FP 20 TN 1016 FN 27\n", "\n", "\n", "\n", "None\n", "y random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9058434399117972\n", "Specificity : 0.9950980392156863\n", "Accuracy: 0.9502382799512358\n", "ROC 0.950470739564\n", "TP 4108 FP 22 TN 4466 FN 427\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94414894 0.94797207 0.94080479 0.94978384 0.93531759]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 3 TN 1033 FN 28\n", "\n", "\n", "\n", "None\n", "x random_under_sample Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8974641675854466\n", "Specificity : 0.9959893048128342\n", "Accuracy: 0.9464701318851824\n", "ROC 0.946726736199\n", "TP 4070 FP 18 TN 4470 FN 465\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.94581117 0.9481383 0.94396408 0.94778849 0.94845361]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 1 TN 1035 FN 28\n", "\n", "\n", "\n", "None\n", "y ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 54636\n", "Test Data Points: 23416\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.901318781264211\n", "Specificity : 0.9993690188242718\n", "Accuracy: 0.9809531943969935\n", "ROC 0.950343900044\n", "TP 3964 FP 12 TN 19006 FN 434\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98027032 0.97982192 0.98129404 0.97918001 0.98001281]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 1 TN 1035 FN 28\n", "\n", "\n", "\n", "None\n", "x ncl Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 54650\n", "Test Data Points: 23422\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.8966144058168598\n", "Specificity : 0.9995794122285895\n", "Accuracy: 0.9802322602681239\n", "ROC 0.948096909023\n", "TP 3946 FP 8 TN 19013 FN 455\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.98014729 0.9818764 0.97950557 0.98021007 0.97918535]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Failed\n", "TP 0 FP 0 TN 1036 FN 28\n", "\n", "\n", "\n", "None\n", "y near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9206174200661521\n", "Specificity : 0.9696969696969697\n", "Accuracy: 0.9450293693893383\n", "ROC 0.945157194882\n", "TP 4175 FP 136 TN 4352 FN 360\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.75615027 0.95162899 0.94995012 0.95377453 0.94562687]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.21428571428571427\n", "Specificity : 0.7461389961389961\n", "Accuracy: 0.7321428571428571\n", "ROC 0.480212355212\n", "TP 6 FP 263 TN 773 FN 22\n", "\n", "\n", "\n", "None\n", "x near_miss Data/Benchmarks/phos_PKC.csv\n", "Loading Data\n", "Loaded Data\n", "Working on Data\n", "Balancing Data\n", "Balanced Data\n", "Finished working with Data\n", "Training Data Points: 21051\n", "Test Data Points: 9023\n", "Starting Training\n", "Done training\n", "Test Results\n", "Sensitivity: 0.9124586549062844\n", "Specificity : 0.9828431372549019\n", "Accuracy: 0.9474675828438435\n", "ROC 0.947650896081\n", "TP 4138 FP 77 TN 4411 FN 397\n", "\n", "\n", "\n", "None\n", "Cross: Validation: [ 0.88098404 0.95728059 0.94895244 0.95194546 0.94130362]\n", "Number of data points in benchmark 1064\n", "Benchmark Results \n", "Sensitivity: 0.14285714285714285\n", "Specificity : 0.7953667953667953\n", "Accuracy: 0.7781954887218046\n", "ROC 0.469111969112\n", "TP 4 FP 212 TN 824 FN 24\n", "\n", "\n", "\n", "None\n" ] } ], "source": [ "par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n", "benchmarks = [\"Data/Benchmarks/phos_CDK1.csv\", \"Data/Benchmarks/phos_CK2.csv\", \"Data/Benchmarks/phos_MAPK1.csv\", \"Data/Benchmarks/phos_PKA.csv\", \"Data/Benchmarks/phos_PKC.csv\"]\n", "for j in benchmarks:\n", " for i in par:\n", " print(\"y\", i, \" \", j)\n", " y = Predictor()\n", " y.load_data(file=\"Data/Training/clean_t_filtered.csv\")\n", " y.process_data(vector_function=\"sequence\", amino_acid=\"T\", imbalance_function=i, random_data=0)\n", " y.supervised_training(\"mlp_adam\")\n", " y.benchmark(j, \"T\")\n", " del y\n", " print(\"x\", i, \" \", j)\n", " x = Predictor()\n", " x.load_data(file=\"Data/Training/clean_t_filtered.csv\")\n", " x.process_data(vector_function=\"sequence\", amino_acid=\"T\", imbalance_function=i, random_data=1)\n", " x.supervised_training(\"mlp_adam\")\n", " x.benchmark(j, \"T\")\n", " del x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
olgabot/prettyplotlib
ipython_notebooks/fill_between.ipynb
2
108789
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%load_ext autoreload\n", "%autoreload 2" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "import prettyplotlib as ppl\n", "\n", "# prettyplotlib imports \n", "import matplotlib.pyplot as plt\n", "from prettyplotlib import mpl\n", "from prettyplotlib import brewer2mpl\n", "\n", "# Set the random seed for consistency\n", "np.random.seed(12)\n", "\n", "fig, ax = plt.subplots(1)\n", "\n", "# Show the whole color range\n", "for i in range(8):\n", " y1 = np.random.normal(size=1000).cumsum()\n", " y2 = np.random.normal(size=1000).cumsum()\n", " x = np.arange(1000)\n", "\n", " plt.fill_between(x, y1, y2, label=str(i))\n", " \n", "ppl.legend()\n", "\n", "fig.savefig('fill_between_matplotlib_default.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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Kf4tikqrv1S0EZn5U/S4mMDKEdLJIr2dbixYt4uzZs/PVFvlBsRMA2dnZbNiwIW+99VaS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"text": [ "<matplotlib.figure.Figure at 0x10569b790>" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "import prettyplotlib as ppl\n", "\n", "# prettyplotlib imports \n", "import matplotlib.pyplot as plt\n", "from prettyplotlib import mpl\n", "from prettyplotlib import brewer2mpl\n", "\n", "# Set the random seed for consistency\n", "np.random.seed(12)\n", "\n", "fig, ax = plt.subplots(1)\n", "\n", "# Show the whole color range\n", "for i in range(8):\n", " y1 = np.random.normal(size=1000).cumsum()\n", " y2 = np.random.normal(size=1000).cumsum()\n", " x = np.arange(1000)\n", "\n", " ppl.fill_between(x, y1, y2, label=str(i))\n", " \n", "ppl.legend()\n", "\n", "fig.savefig('fill_between_prettyplotlib_default.png')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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MtAQtSJkpRCmDJEnUumV2h1RqLLkl54c8AhMj/QSIEB7tZnJ0gJBLZXdIpJiYBqXK5fOn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TAAAg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\nC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"text": [ "<matplotlib.figure.Figure at 0x105676450>" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "%pdb" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Automatic pdb calling has been turned OFF\n" ] } ], "prompt_number": 18 }, { "cell_type": "code", "collapsed": false, "input": [ "ax.lines" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "[]" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "ax.fill_between?" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
SwissDataScienceCenter/renga
docs/_static/zhbikes/ZHBikes.ipynb
1
851224
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Zürich bike counter data\n", "\n", "Contents:\n", "- Read input files\n", "- Remove non relevant columns\n", "- Remove NaNs\n", "- Translate column names to English\n", "- Convert datetime strings to datetime objects\n", "- Store data in feather format\n", "- Explore the dataset, make visualizations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "from glob import glob\n", "\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pre-process the dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read input files" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [ "parameters" ] }, "outputs": [], "source": [ "input_folder = '../data/zhbikes'" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "input_files = glob('{}/*.csv'.format(input_folder))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "dataframes = [pd.read_csv(file)[['fk_zaehler','datum','velo_in','velo_out','fuss_in','fuss_out','objectid']] for file in input_files]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df = pd.concat(dataframes)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2610991, 7)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>fk_zaehler</th>\n", " <th>datum</th>\n", " <th>velo_in</th>\n", " <th>velo_out</th>\n", " <th>fuss_in</th>\n", " <th>fuss_out</th>\n", " <th>objectid</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Y2G14045587</td>\n", " <td>2016-01-14T10:15:00</td>\n", " <td>4.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3600767</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Y2G14045587</td>\n", " <td>2016-01-14T10:30:00</td>\n", " <td>6.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3600768</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Y2G14045587</td>\n", " <td>2016-01-14T10:45:00</td>\n", " <td>5.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3600769</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Y2G14045587</td>\n", " <td>2016-01-14T11:00:00</td>\n", " <td>4.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3600770</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Y2G14045587</td>\n", " <td>2016-01-14T11:15:00</td>\n", " <td>6.0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>3600771</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " fk_zaehler datum velo_in velo_out fuss_in fuss_out \\\n", "0 Y2G14045587 2016-01-14T10:15:00 4.0 NaN NaN NaN \n", "1 Y2G14045587 2016-01-14T10:30:00 6.0 NaN NaN NaN \n", "2 Y2G14045587 2016-01-14T10:45:00 5.0 NaN NaN NaN \n", "3 Y2G14045587 2016-01-14T11:00:00 4.0 NaN NaN NaN \n", "4 Y2G14045587 2016-01-14T11:15:00 6.0 NaN NaN NaN \n", "\n", " objectid \n", "0 3600767 \n", "1 3600768 \n", "2 3600769 \n", "3 3600770 \n", "4 3600771 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "fk_zaehler object\n", "datum object\n", "velo_in float64\n", "velo_out float64\n", "fuss_in float64\n", "fuss_out float64\n", "objectid int64\n", "dtype: object" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dtypes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remove non relevant columns" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "df.drop(['fuss_in','fuss_out'], axis=1, inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remove NaNs" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "df.dropna(inplace=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Translate column names to English" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "df.columns = ['counting_station','datetime','velo_in', 'velo_out', 'objectid']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Convert datetime strings to datetime objects" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df['datetime'] = df['datetime'].apply(pd.Timestamp)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>counting_station</th>\n", " <th>datetime</th>\n", " <th>velo_in</th>\n", " <th>velo_out</th>\n", " <th>objectid</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>132</th>\n", " <td>Y2G13124875</td>\n", " <td>2016-01-29 00:00:00</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>3600899</td>\n", " </tr>\n", " <tr>\n", " <th>133</th>\n", " <td>Y2G13124875</td>\n", " <td>2016-01-29 00:15:00</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>3600900</td>\n", " </tr>\n", " <tr>\n", " <th>134</th>\n", " <td>Y2G13124875</td>\n", " <td>2016-01-29 00:30:00</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>3600901</td>\n", " </tr>\n", " <tr>\n", " <th>135</th>\n", " <td>Y2G13124875</td>\n", " <td>2016-01-29 00:45:00</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>3600902</td>\n", " </tr>\n", " <tr>\n", " <th>136</th>\n", " <td>Y2G13124875</td>\n", " <td>2016-01-29 01:00:00</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>3600903</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " counting_station datetime velo_in velo_out objectid\n", "132 Y2G13124875 2016-01-29 00:00:00 0.0 0.0 3600899\n", "133 Y2G13124875 2016-01-29 00:15:00 0.0 0.0 3600900\n", "134 Y2G13124875 2016-01-29 00:30:00 0.0 0.0 3600901\n", "135 Y2G13124875 2016-01-29 00:45:00 0.0 0.0 3600902\n", "136 Y2G13124875 2016-01-29 01:00:00 0.0 0.0 3600903" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore the data\n", "### Question: How many stations do we find in the data?" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "stations = df['counting_station'].unique()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(22,)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stations.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Let's plot the data for each station, binning on a weekly basis" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "def sum_by_week(station):\n", " df_station = df[df['counting_station'] == station]\n", " aggregated = df_station.set_index('datetime').resample('1W').agg({'velo_in' : 'sum', 'velo_out': 'sum'}).reset_index()\n", " aggregated['velo_all'] = aggregated['velo_out'] + aggregated['velo_in']\n", " aggregated['velo_out'] = -aggregated['velo_out']\n", " aggregated['counting_station'] = station\n", " return aggregated" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "dataframes_weekly = [sum_by_week(station) for station in stations]\n", "df_weekly = pd.concat(dataframes_weekly).reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x7fed4b7ef898>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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4r3/9K9/jlMZy8ODBQr0miYgKipnOPma60q+8n5u7du2amJqaavd+KdOo1Wrx0aNHVvcX5txcZGRkjuexVq9eLarVarF58+ZiXFycw8at0+nEpk2b2j3fKIr/vHdffPFFq/ukfNq4cWPx6tWrVvf/97//FdVqtfjaa6/ZzIk5+fzzz0W1Wi2+8847+VqPCsb+xM5EZdzt27cxffp0dOnSBS1atMBLL72EXr16ITAwEFeuXLG5ztWrVzF+/Hh06NABzZo1wyuvvIJhw4Zh3759dvfTqFEjNGrUSG5vmt2jR4/kZbIbNGgQGjVqhC1btiAjIwNLly5Fly5d0Lx5c7Rt2xZfffUV7t27Z3N70hzoW7dulbdvayz2xrd06VI0atQIEydOlLfTr18/tGrVCq1bt8agQYNy7bhw69YtfPnll2jbti2aN2+Orl27IigoCDqdzmr7hREfH4958+ahZ8+eaNmyJfz9/dGhQwcMHDgQS5YsQVRUVIEem7t372LZsmUYPHgwOnXqBH9/f7z00kvo378/QkJCkJGRYTWWTp06YfDgwQDMbaWzb3/Lli3yslmfX1s0Gg2WLl2K3r17o1WrVmjVqhV69eqFoKAgiykvsnLE81YaVKlSBZMnTwYA/O9//8PVq1fl+2JiYuQrdEaOHIkGDRpYrNu6dWsolUqb2xUEAe+88w4A8+tBmnJDEh0djdDQUADA0KFD0bt371zH+vzzz2Pq1Kk296VQFM2fUS8vL7l9dkxMjNX99o4/L1566SW797m6uqJ79+4AkOuUGWlpaZg1axbc3d3l5zIn0vRqAODn52dzGakjS3p6eq7by2rr1q0AgMqVK+epaw0RlR3MczmPj3mOea4kMc/ljHnOsXnu8OHDuH79OlxdXTF16lSb08gRUenCHJfz+JjjmOPKsgULFkAURfj5+WHGjBk55ilBEDB48GD07NlTvi1rngsICJCziy0NGjTA9OnTAQAXLlzAgQMHrJYpTK7auXMnAKBXr16oXr261f1S15LIyEjcuXPH4r79+/dDr9fDy8sLAwYMsFq3c+fOqFu3LkRRxK5duwo8xoLw9/cHYJ4eOTk5Wb7daDTKr9GAgACbnX+GDh0KQRCg1Wod/nhn9ccff+DgwYPo2LEj3nzzTYdsk4gcj5ku5/Ex0zHTlaTyfm6ucePGNqe2lWTtKmfrHJkjMmS9evVs5pgBAwbAy8sLGRkZ2L9/v8V9hRl3cnIyDAYDgNzPzWVkZEAURYv7duzYAQD417/+hSZNmlitO2zYMAiCgNjYWJw6dcruGLOTunlnHz8VHU7dS8+k0NBQzJkzR54P3t3dHYIg4MaNG7hx4wauX78u/wGSrF+/HjNmzIDJZAIAeHt7IzU1FceOHcOxY8fQu3dvzJ0712EfZLPSaDQICAjA1atX4ezsDIVCgYSEBOzZswcnTpzAxo0b8fzzzwMw/9GpUqUKtFottFotXFxc5Nb8kvyOccqUKdi0aROUSiXc3Nyg0WgQHh6OiIgI/PDDD+jSpYvVOidOnMBnn30GnU4HAPD09MSjR4/w448/4vjx42jTpk0BHw1LUVFRGDBggNyiV6lUwtPTE9HR0Xj69CnOnz+PatWqISAgIN+Pzbhx4+Q/kC4uLnB3d0dycjIuXryIixcvYvfu3VizZo3FH9uKFStCo9EgOTkZCoUClSpVsti+q6trno7r/v37+Oijj+Tg7ObmBgDya3Tr1q1YtWqV/OWgLQV53kqTt99+G3v27MGRI0cwefJkbNq0CU5OTpg5cyZSUlLg5+eH4cOH53u7Pj4+8s/S+1myefNmGAwGqFSqHNsLl7TExET5g6etKUmKkvT4ZX/ssgsKCsKTJ08wbtw41KpVK9ft+vr6yj9fvXoVarXaahnp/WgvnNqSmZkpnyzt2bMnnJwYbYieFcxzzHPMc8xzAPNcQTyLeU46gdquXTuL1wcRlU7MccxxzHGlP8cV1Llz53Djxg0AwPDhw/N8HiZrkf6WLVtgMBigVCoxYsSIXNft1q0bfvjhB9y7dw/r16932GOr0Wjk16CtaXkBoGXLlvDy8kJqaipOnjyJF154Qb7v9OnTAMwXYUhT1GXXrl073Lt3L19flDrC+fPnAZhf25UrV5ZvT0xMlC/IqFevns11PT09Ua1aNURHR+PEiRPo16+fw8en1Woxa9Ys+SIOIiqdmOmY6ZjpSn+m47k5+7Ieg/R7zFGkHGgvQ7q6uuKll17C4cOHcerUKQQEBOR52zmNu3LlynB1dUVGRgauXr2Kjh07Wq0vvR+bNGlidaFsbuOuXr06GjZsiBs3buDUqVNo3759nsa8a9cu+TWR9QIfKjrs6EfPnL179yIwMBBGoxFdunTBnj17cP78eZw5cwanT5/GggUL5Epmyblz5+Tg2aVLFxw9ehRnzpzBmTNn8OWXX0IQBOzYsQPBwcFFMualS5ciOTkZ//vf/3DhwgWcP38ev/76K2rUqIGkpCQsWrRIXrZmzZo4fvw4Pv74YwBA9+7dcfz4cYt/NWvWzPO+//jjD+zcuRMzZszA2bNncfbsWRw8eBAvv/wyTCYTZs2ahczMTIt1EhIS8NVXX0Gn06F58+bYuXMnzp49i/Pnz2PhwoW4ceMGfv/9d4c8NsuWLUNsbCzq1KmDX3/9FVeuXEF4eDguXbqEnTt34vPPP0fVqlUL9Ni0aNECgYGBOHToEC5duoTTp0/j0qVLWLFiBerWrYsrV65YPPaAObwsXbrUYn9Z/+V0BapEr9dj9OjRiIqKQs2aNRESEoLz58/j/PnzWL16NWrVqoXHjx/jiy++gF6vt7mNgjxvpdF3330HLy8vXLt2DStXrsSePXtw8OBBODk5Yfbs2QUq2goPDwdgvpKlYsWKNu9r1qwZqlSpUvgDcCBRFBEfH48jR45g2LBh0Gq18PDwKPYrH6THqGHDhnaXuXr1KkJDQ1G/fn189NFHedpu1apV5cA5Z84chIWFya/vlJQU/Pjjj9i6dSs8PT0xevToPI/3zz//lLvL5OWxGjBgAFq3bo3mzZujU6dOGD9+PCIiIvK8PyIqHsxzzHPMc8xzAPNcQT2Lee7ChQsAzCcpo6OjMW3aNLz++uto1qwZOnTogP/85z+4fv16nvdJREWHOY45jjmu7OS4gpC+HFQqlXjjjTcKtA0pqzRt2hQ1atTI0zpSp5Rz58457LG9c+eO3OUke5cbiUKhkAvibt++bXHfrVu3AOScuerXry+vm72jimTnzp3o2LEjmjVrhjZt2mDgwIFYuXIlNBpNvo4nIyMDd+7cwZIlS/DLL78AAN5//32LL3iz/pzTRSHSl8rSMTrakiVL8OTJE3z66afFflEMEeUNMx0zHTNd2cl0PDdn25kzZ+SfbV2sWlCiKOLu3bsA7GdIwDIH5kdO41YoFHjvvfcAAMHBwdi4caPcNTM9PR3r1q1DcHAwVCoVxo8fb7FufHy83J3R0ePetm0bAKB9+/ZWhbtUNNj2hp4pBoMBc+bMAWDuBJA9OPj4+KB3795WLWSXLFkCk8mE1q1b4/vvv5evRPDw8MDnn3+O9PR0BAcHY+XKlRg8eHCO7VQLQq/XY9WqVahTp45820svvYTJkydjzJgxOHToEPR6vc1W+oWVkpKCBQsWWDwmtWvXxqJFi/Dmm28iNjYW58+fx8svvyzf/3//939ISkpC5cqV8csvv8Db2xsAoFKp0KtXLyiVSnz11VcOGd/FixcBAF9++aXFFFXOzs5Qq9WF+sP87bffWt3m7OyMTp06oWHDhujatSu2bt2Kb775Rr4KxBH27NmD69evQ6VS4eeff7Y4hrZt2+Lnn39Gnz59cPPmTezYsUP+g51VQZ633GzZsgWTJk0q0DH16dMHc+fOzfd61atXx4QJEzB16lQsX74cHh4eAMxXBNtqGZyb6Oho+YNPnz59rK5UkEKJIwNdYS1fvhxLliyxuv25557D4sWLizUkR0ZG4uDBgwCAvn372lzGZDJh2rRpMBqN+Pbbb6FSqfK8/dmzZ2P06NGIiIjA2LFjoVAo4OHhgdTUVDg5OaFz5874+uuv5RCZF9I0b40bN87Ta+bChQvy1WdRUVGIiorCzp07MXjwYEyePJnTwBGVAsxz+cc8Z4l5jnmuuDHPFW2e0+l0ePr0KQDz++btt99GYmIinJ2d4erqiqdPn2LHjh3Yu3cv5s2bhx49euR530TkWMxx+cccZ4k5rnhzHACEhITkWETQsGFDrF69Wv5/KYvVrl0b7u7uBdqnVDzWuHHjPK8jTdWYnp6Ox48fyx2ZCiMmJkb+uVq1anaXk+6TuiBJpP/Py7parRZpaWk2f3/dv38fKpUK7u7uSElJkYsU1q1bhxUrVuT6OPn5+Vl1e3FyckJAQIDV7wIfHx+4u7tDq9Xi1q1beOutt6y2l5SUhLi4OACWj5GjSBec1K1bF5988onDt09EhcdMl3/MdJaY6XhurqSZTCYEBQUBMHdozs95qtxoNBpotVoABcuQOcnLuMePH4/Y2Fjs27cPU6dOxdSpU+UO1IIgoG3bthg9ejRat25tsV7WcThy3NevX5e7CNo7F0mOx45+9Ew5efIkoqOjoVQq8c033+RpnaSkJPlKxE8//dRmK+bhw4fDxcUFWq0WR48edeiYAaBLly4WwVPSqVMnCIIAvV6PBw8eOHy/AFCrVi306tXL6vbq1aujefPmACBPxyA5cOAAAKB///5y8Myqe/fuqF27tkPGJwX9ojipkJPatWujQYMGSE9Px7Vr1xy67X379gEwP7+2QlDDhg3lVtB79+61uY2CPG+5cXV1RZUqVQr0rzAfyPr164fXXnsNer0eiYmJaNiwIUaOHJnv7WRmZmL8+PHQarWoVasWPv30U6tlkpOTAQAVKlSwu51evXrhtddes/p37ty5fI8pL9zd3a2uiPH19cXUqVPRokWLItmnLRqNBuPHj4fRaETTpk3tTsshXe3Vq1cvvPLKK/naR6VKlRAcHCx/aDKZTEhNTQVgvlJYq9UiMTExz9tLSkrC4cOHAeTczc/FxQXvv/8+fv31V5w7dw4RERG4ePEitmzZInelWbt2bZFdSUhE+cM8l3/Mc7YxzzHPMc89G3kuJSVF/jk0NBQGgwHff/89zp8/j4iICOzcuRMtWrSAwWDApEmT5Kuqiaj4McflH3OcbcxxxZfjtFot4uLi7P7L/ndd6gKSdUqx/JLyXH62kTVnSWMoLGkKWyDnKQKl+6QvdLOvn9O6WQsasq/v5+eHGTNm4MiRI7h06RLCw8MRHh6OmTNnwtvbG48fP8Ynn3ySa7aSXgdZxxEQEIBPP/3U6oIOpVKJV199FQCwbt06qzEBwMqVK+Wf09LSctx3fplMJkyfPh1GoxHTp08vkmIbIio8Zrr8Y6azjZmO5+aK69xcdkuWLEFkZCScnJwwZcoUh247a37KSw7MT57Ky7jd3NywYMECiwsmpHNzoigiLS3NZn4sqnFL3fwqVqyIDh065GkdKjx29KNninRFQuPGjVG9evU8rXPt2jWIoghBEOxW5Ht5eaFp06Y4d+4cIiMjHd4hwN/f3+btKpUKlStXRlxcnPxH09GaNWtmt4OV9Bhm/WJFr9fLV12++OKLdrf74osv4uHDh4UeX/v27XHx4kUsXLgQ9+/fR5cuXdCyZcsc/wDlx/Hjx7F582ZcunQJsbGxcnvbrBwdfK9evQoAOX6p9uqrr2LXrl3ystnl93nLi+7du+epHXZR+Oabb/D2228DAL766qsCneSZNWsWwsPDoVKpsHDhQrlrW34lJCTIV61mZTAYCrS93AwdOhRDhw4FYD5BGB4ejoULF+Kzzz5Djx49MG/evHx1WSkIKbjfuXMH3t7eWLx4sc1W3tHR0fjhhx/g5eWFCRMm5Hs/Fy5cwMiRI5GWloZx48ahS5cuqFq1Kh48eIDVq1dj69atOHPmDIKCgtCpU6dct7d7924YDAY4OTnZ/DAmqVq1qtVVZYIgoGnTpvjpp58wduxYhIWFITg4GO+//77ND9VEVHyY5/KPeY55TsI8Z8Y892zluazTyplMJkycONHida5Wq7F8+XL8+9//hlarxZo1azBjxox8HxsRFR5lWe0OAAAgAElEQVRzXP4xxzHHSUoqx33xxRcYPXp0se+XgMGDB1vd5u3tjYEDB6J58+bo378/YmNjsWrVKnz99dd2t/Pnn38CMH+x++TJE4SGhmLt2rXYuXMnli5dijZt2lgs/9lnn+Ho0aOIjY3F8OHDMWHCBDRu3BjJycnYsGEDQkJCoFKpYDAYoFA4tlfIr7/+isuXL6Nbt2547bXXHLptInIcZrr8Y6ZjppPw3JxZcZ+by2rXrl1yY4+vv/5aLtos7fI67rt37+Kzzz7Dw4cP8fHHH+O9996Tp6retGkT1qxZg1GjRuHbb79FQEBAkY7ZaDRi586dAMwdYIv6/Cf9g4V+9EyR/mDUrFkzz+skJCQAMAdMqZWtLTVq1ACAfHUnyKuc9uvi4gLA/MVNUcjvvpOTk+UvWqpWrWp33ZxavubHiBEjEBkZiUOHDmHdunVYt24dnJyc4O/vj86dO9u90iUvAgMDERoaKv+/SqWCj4+P/IVYcnIyDAaDxdWdjiC95nL6gCTdl5SUJH84yqokXzNFIetVKgW5YmXx4sX4/fffoVQqsXDhQrsfjCpUqICYmJgcP8wdP35c/jkzMxNNmzbN93gKys3NDR06dMCLL76I3r17Y/fu3WjevLn8xXFRkL4sPXz4MNzc3LBixQrUrVvX5rKBgYHQaDSYOnVqju9/WzQaDT777DMkJiZi/vz58ocNwHzCYO7cuVAoFNi8eTNmzZqFdu3a5fohRJrm7fXXX0flypXzNZ6sxo8fj7CwMGi1Wpw8eVK+youISgbzXNHvm3mu8JjnrDHPmTHPOT7PZX2veHl52ZwGpEqVKujVqxfWr1+PkydP5uu4iMhxmOOKft/McYXHHFc4Uhe+wnTVk/JcfraR9b2fU/eY/MjabS8jI8NuhpWKFbJPVezm5gaDwWCzmEGS9fWbn6mO/fz80KNHD2zbtg2HDx/OsdBPIggCatWqhQkTJqBWrVoIDAzEuHHjsH//fotjbdGiBb777jt8++23iIiIsOoC3bRpUzRr1gzr16936MWw0gUnHh4eBZ7SkIiKBzNd0e+bma7wmOms8dwccOTIEUycOBGiKGLQoEEYNmyYw/eRNdPlJQfm9DqT5HXcRqMRI0eOxL179zB27FiLro3169fHhAkT4OnpiaCgIMyfPx9dunRBpUqVimzcx44dk6f4zWnmNXI8Tt1L9De9Xl/SQyAbnJ2dsWLFCqxfvx6ffPIJWrZsCUEQcP78eSxYsABdunTBX3/9le/tHj16FKGhoVAqlRg9ejQOHDiAy5cv4/Tp0zh+/DiOHz8uV8qLoujowwLA15yjrFixAsHBwRAEAbNmzULXrl3tLlu/fn0A+W+1Xdw8PT3lQLR58+Yi248oipgxYwZ27twJlUqFZcuW4aWXXrK57KlTp7B//340bNgQ77zzDtLS0iz+SVfhSG2h09LSLD4Abd++HYmJiahYsaLFl8JZSV+AP3782O4VVpLbt2/j8uXLAAofHmvXri0HXUdcHUdEJYd/W0sn5jnKDfNcwZWHPOfh4SGfjHz++edtTgEFAPXq1QMAPH36NMf9ElHpxL+ppRNzHGUnZbGHDx/anPY1P9vIz2vn+vXrAMzFdbVq1SrQfrPLWjyRU5ch6b7shRjS+nlZ193dPd9fuEvvgYKcqxowYACcnZ0RExMjd/zL6r333sO2bdswYMAAqNVq1KxZEy1atMD48ePx22+/ye8PW1NgFtTixYuh0WjwySefwNPT0yqLSu91g8Eg30ZEZQ//vpZOzHSUm2fh3NzJkycxZswYGAwG9O3b1+FT9ko8PT3l81QFyZDZ5Wfcx44dw507dyAIAoYMGWJzGel2qbmJpLDZ1xbpAl61Wl2sF1oTO/rRM6ZKlSoAzF8q5JVU3JGRkYGEhAT5/7OTviyoWLGixe1KpRJGoxE6nc7mehqNJs9jKQsqVKgAhUIBk8mE2NhYNG7c2OZyUvW2o7Rs2RItW7YEYP7DdPjwYSxcuBCPHz/GlClT8v3lWVhYGADzSY0vvvjC5jLx8fGFG7QdlSpVwtOnT3N8nUZHRwMwXyVrr220o+3Zswf//e9/C7Rut27dMHXqVAePKHerV6/GDz/8AACYMmUK3n333RyXb9OmDU6ePIkrV64gLi5O/p1RGklXGj148KDI9vHf//4X69evh5OTE77//nu0a9fO7rLS6/XmzZt2vzyWlmvdujUAYM6cOXLHlTt37gAAnnvuObvr1q5dW/45KipKfs/bsmXLFgDm90jHjh3tLkdEZQ/zXNFjnis85jnHYZ4rnPKQ5wRBQMOGDeXpo3JTXO83IrLGHFf0mOMKjzmucKTp8YxGI44cOVKgqeleeeUVnDx5EpGRkXj69Knc3Sknf/zxBwCgVatWDpsS7IUXXoAgCBBFEbdu3cILL7xgtYzJZMLdu3cB/POltqR+/fq4desWbt68aXcft2/ftrluUXN2doaPjw9iYmLsZtGGDRviu+++s3lfZGQkAOSY5fJLes8tWbIES5YssbtccHCwPG2dVOBJRMWLma7oMdMVHjOd4zwL5+YiIiLw+eefQ6fToVu3bggMDCyy51wQBLzwwgu4cuWKPAW3LXnJgfkdt7TNihUr2u245+npiUqVKiEhIQFRUVHy7ZUqVULFihWRmJiIW7du4fXXXy/wuAHztNZSRn/nnXdyXJYcjx396JnSokULAOYPgNIf79w0adJE/oV56tQpm8ukpqbKH26zVyNLc9Pb25/UocDRpDEX1RUQ9jg7O6NBgwYAgLNnz9pdLiIiosjG4O7ujh49esgnIiIjIy2uIM3LYyM9X35+fjbvj4qKwv37923ep1Aoct1+TqR9nj592u4y0mvR3viKQkZGBuLi4gr0ryQ+ZK1btw5z5swBAIwbNw6DBg3KdZ2+fftCpVLBYDDgl19+KeohFsqjR48A5G9aj/xYuHAhQkNDoVAoMHfuXPz73/8ukv1IpPflkydP7C6T9QNZTi2hjUYjduzYAQDo0aNHrlPC5ebhw4dym/ecvrgmouLBPFf0mOeY55jnigfznOPyXNu2bQGYiyaNRqPNZaRCRF9f3xy3RURFhzmu6DHHMceVdI5r3bo11Go1AGDlypV5ns4u6/Ml5Tmj0Yiff/4513X37t2Le/fuATB3qnMUT09PNGvWDIDl9HJZXbx4EampqQD+ySMSqejx7NmzdgtTpO1mXzcvLl26BKBg56rS0tLkaTHzm0Vv3rwpd+jp1atXvvdNRGUfM13RY6ZjpivpTCd5Fs7NXbp0CZ9++inS09PRsWNHLFiwwO5sEI4i5UB7GVKn08nvX3s5sCDjlt43SUlJdqffTU9Pl3Ng9nNzuY07Ojpavoglt/y6e/du6PV6KJVK9O7dO8dlyfFY6EfPlLZt26J69eowGo2YP39+ntbx8fGRf6n973//g8lkslpm5cqV0Ol0cHd3R4cOHSzuk05sSBXLWen1eqxZsya/h5EnUqv/lJSUItl+Tjp37gwA2Lhxo3yiI6t9+/Y5bPrLnFouu7q6AjCHQGmaKSBvj420jL22wosXL7YbLqV1bR17XnTp0gUA8Oeff9qczurmzZvYt28fAPPVG8Wlb9++uH79eoH+zZ07t9jGCZhbAUsfPkaNGoURI0bkab0aNWrIIXX16tXyl4vFLbeToAkJCXKHk5y6rRTUjz/+iJUrV0IQBHz33Xd5OmmX2+tDulrL19dXvk3q/gJAviItLi4Ohw4dsrmPDRs2ADB/gPT397c7lhMnTsito/NylUhuHxS///57AObfKa+++mqu2yOiosU8VzyY55jnmOcKh3muePNc7969oVAokJqaKj+uWcXFxWHXrl0AgPbt2+e6PSIqGsxxxYM5jjmupHPc+PHjIQgCrl69ipkzZ9p830pEUURoaKj8dxowdz3+8MMPAQC//fYb9uzZY3f927dvy5mxefPmDr+woWfPngCAnTt32pzCLCQkBIC5ICV7x7+33noLzs7OSElJwcaNG63WPXToEO7evQtBENCjRw+L+3I7V/XXX39h9+7dAGD1ew/IPYuuXbtWfl/mJ4vq9Xr58W7fvr3dDlMFERoamuPrWLpY44svvpBvI6KSwUxXPJjpmOlKOtOV9XNzgDkzffLJJ9BoNHjttdcQFBTksO7POZEy5J07d3D48GGr+zds2IDU1FS4urrazK8FHXejRo0AmLtO28qfgPl3ivS+k6bOlkjnD48fP25zuu5Vq1ZBFEVUrVpV/p1uz7Zt2wAA7dq1y9M0v+RYLPSjZ4pKpcLEiRMBALt27cLYsWPl9qKAubp5w4YNCAwMtFhv7NixUCgUiIyMxFdffSW3jk5LS8NPP/0kX1k4YsQIOXhIpHCwYcMGbN68WQ5LN2/exPDhw3Oc47wwGjZsCAA4d+6cfEVjcRk0aBAqVKiAuLg4DB8+XK7szszMxO7duzFp0iR4e3s7ZF+9evXC4sWLcenSJfmxFUURly5dwqxZswAA/v7+qFChgrxOXh6b1157DQCwfv16bNq0Sd7248ePMWHCBOzevdtim1nVqVMHKpUKqampckjMj+7du8t/iEeNGoUTJ07If3BPnjyJESNGwGAwoGHDhqyAt2Hfvn2YMmUKRFHEsGHDMGbMmHyt//XXX6Nt27YwmUz4z3/+g7Fjx+LkyZMWV95KV1pMmTIlx22lpqYiISFB/id9eE1PT7e4PfuHqJ9//hkTJ07EyZMnkZaWJt+u1Wqxf/9+BAQEIDY2Fk5OTvj000+t9msymSy2n/WDUFJSksV92a1evRpBQUEAgGnTpqFfv355eNQKr2vXrnI7/kmTJmHLli3yscfHx2PRokVYu3YtAHNXl8qVK9vdlhQeGzRoYBVSbRk0aBCCg4Nx48YNuQOMKIq4evUqRo0aJZ84HT58OHx8fAp+kETkEMxzxYN5jnmuJDHPMc/lN8/Vr18f7733HgBg7ty52LNnj/wF982bNzFq1ChotVpUqFABQ4cOLfAxElHhMMcVD+Y45riS1qFDB4wcORKA+b03cOBAHDx40CITJSQkYNu2bejbty8CAwMtCgcAc5578cUXYTKZMG7cOHz33XcWvy8SEhKwZs0aDBw4EAkJCahSpQoWL15ss8uJXq+3yE7p6ekAzO8Je3lLMnDgQPj6+iItLQ2fffaZPP2aRqPB/PnzsX//fnm82VWtWhWDBw8GACxYsADbtm2TzzsdPXoUkyZNAmDORdkL5rZv344xY8bgjz/+QFJSknx7amoqNmzYgCFDhsBgMKBy5coYNmyY1b579OiB0NBQPHjwwKKA4s6dOwgMDJSnxv33v/8tv96z+u677xARESF3bjKZTIiIiMCQIUMQHh6OSpUqYebMmVbrAYDBYLB4XKVtiKJocXtycrLN9Ymo9GOmKx7MdMx0JelZODd3584dfPzxx0hOTkabNm2wfPnyfM0AVphzc35+fvLvrYkTJ+Lo0aMAzDNYbNu2DQsXLgQADB061Or8WGHG3aZNG3lK3YULFyIkJEQuxk1JSUFISAgWLVoEAGjVqpXVRbhvvvkmWrRoAZPJhFGjRuHChQsAzHk6JCRELqoeM2ZMjmO6e/euvC6n7S0ZTiU9ACJH6969O6KjozF//nyEhYUhLCwM7u7ucHJykn/RtWnTxmKd1q1b49tvv8XMmTMRFhaGffv2wdvbGxqNRv5w3qtXL5uV7P369cO2bdtw8eJFTJ48GdOnT4erqys0Gg18fHwwe/ZsjBo1yuHH2aZNGzz//PN48OCB/IWLm5sbAHOb3Ro1ajh8n5JKlSph0aJFGDlyJM6fP4+ePXvCy8sLOp0Oer0erVu3xksvvYSff/650FNqxsfHIzg4GMHBwVAqlfDy8kJaWpp8cqhixYpWHyby8tj06dMHW7ZswYULFzBlyhRMnz4dHh4e8mtkzJgxOHXqFMLDw63GJLWz3rZtG8aMGQMvLy85bH/zzTfo2rVrjsfk7OyMpUuX4qOPPkJUVBQ++ugjeXzSiahatWph6dKlhX78nkXz58+X35fbt2/H9u3b7S67dOlStG7d2uI2lUqFlStXYt68eVi3bp38e0IQBHh6esqdQqQg6ebmhmHDhqFly5ZW2x85cqTN18i8efMwb948+f/nzJlj0Q3FaDRi69at2Lp1q7xfpVKJlJQUeb+enp6YM2eOPIVIVo8fP8abb75p85izdzDJfvWrdEWQQqHA8uXLsXz5cpvbAYBNmzahZs2adu/PD09PTwQFBWHkyJFISkrCpEmTMGnSJHh4eFicCG7evDlmzJhhdzsajQYHDx4EkPfw+PjxYyxevBiLFy+GSqWCh4cHMjIyLNpaDxo0qEh+VxNRwTDPMc8xzz3bmOeY5/Kb5wBg6tSpePDgAU6dOoWvvvoKLi4ucHZ2lk/Eenl5ISgoCNWrVy/YARKRQzDHMccxx5UtISEh+P3333NcZsqUKejevbvFbWPGjEG1atWwYMECXLx4UX6feXl5ITMzU34sAXOXpuxfMjo7OyMkJARTpkzBrl278Ouvv+LXX3+Fi4sLVCqVxdR1TZs2xZIlS1C7dm2b49u1a5dcVJdVZGSkxZRjbdq0QWhoqMUyrq6uWL58OYYMGYLIyEj06NEDnp6e0Gq1MJlMEAQBX3/9Ndq1a2dz319++SVu3ryJo0ePYsKECZg2bRqUSqV8/P7+/jYL5kwmE/bt2ycXNnh4eEClUiE5OVkuUqhVqxaWLVuGSpUqWa1/7949BAYGIjAwEM7OzvDw8EB6errFua727dtb5NmspMcbALy9vZGeni6/r319ffHTTz+hVq1aNtc9d+6cXOCYVXx8vMXj7evra7cLNBGVfsx0zHTMdM+2Z+Hc3MqVKxEfHw/AfO7M3nk2APj444+tLp4ozLk5AAgMDMSDBw8QGRmJESNGwM3NDUajUS5I7NixI0aPHm21XmHGrVQqERQUhI8++ggxMTHyY5T93FydOnWwePFiq+0JgoCgoCB88MEHePToEQYMGAB3d3fo9Xr5gtqBAweif//+dscE/HMBb4UKFeTupFS8WOhHz6SPPvoIbdu2xZo1a3D69GnExsZCpVKhUaNGeOWVV9CnTx+rdQYOHAh/f3+EhIQgPDwciYmJ8PLyQtOmTdG/f3+7gUKlUiEkJATLly9HWFgYYmJi4ObmhrfeeqtIi0ZUKhVWr16NJUuW4PTp04iLi5MrynNr3e8Ir7/+OjZv3oxly5bh9OnTSE9Px3PPPYcePXpgxIgRWLBgAQDzyZ3CWL58OY4dO4YzZ87gyZMniIuLg0qlwgsvvIAOHTrYrITPy2Pj7OyMVatWYcWKFdi7dy+ePn0KpVKJ1157DYMGDULHjh1x6tQpu+OaOXMmqlevjgMHDiAqKgpRUVEAIF/BmJs6depg+/btWLVqFQ4cOCC331ar1ejcuTM+/vjjQj92z6qsV6nGxcXluGz2q4UlKpUKU6dOxaBBg7BlyxacPn0aDx8+RHJyMlQqFXx9feHn54d//etf6NGjh8Ofi3fffRfe3t44deoU7ty5g7i4OGg0GlSoUAEvvPAC2rVrh379+hVJq2Pp8TOZTLk+flLId5Q2bdpg9+7dCA0NxbFjx/DgwQPodDr4+PigUaNG6N69O959990c21Pv3bsXGRkZUCgUeb4K6z//+Q9OnDiBS5cuIS4uTn6e69Wrh9atW2PAgAFo0aKFow6TiByEeY55jnnu2cU8VzjlMc8BgIuLC1atWoUNGzZg27ZtuHXrFnQ6HerUqYP27dtj2LBhDitqJKLCYY5jjmOOKzu0Wm2uj1vW4rGsBg4ciK5du2Ljxo04duwYbt++jaSkJKhUKtStWxfNmzdH9+7d0b59e5ud+FxdXbFo0SI5z4WHhyMmJgYGgwG1atWCv78/unbtim7dukEQBIccry2NGzfGrl27EBwcjCNHjiA6Oho+Pj5o3rw5hg4dalG8lp1KpcJPP/2EDRs2YOvWrbh16xZMJhOaNGmCHj16YMiQITaLC1555RWMHTsW586dw927d5GYmAiNRoOKFStCrVajU6dOePfdd606XklWrFiBkydP4ty5c4iJiUFiYiKcnJxQp04d+Pv7o3fv3jan/JWMHz8ep06dwq1btxAfHw8PDw/Uq1cPb731Ft5//315GkciKt+Y6ZjpmOmeXc/Cubmsx5BbJ+G8vqbyw9PTE7///jtWr16N3bt348GDB3B2doafnx/69u2L/v3728ywhR13gwYN5AtlDh8+jLt370Kr1cLb2xv169dH586dERAQAA8PD5vbrFGjBrZv346VK1di//79iIqKgoeHBxo3boyAgIBcp7o2mUxyYWi3bt1YSFtCBNHexOhERIXw/vvv4+zZs1bV9URERERUNjDPEREREZVNzHFEREREZR8zHRER2aIo6QEQ0bPn/PnzOHv2LBQKRY5XPBIRERFR6cQ8R0RERFQ2MccRERERlX3MdEREZA+n7iWiAlm/fj0SExPRvXt3+Pr6QqlUIi0tDQcOHMCcOXMAmNu1cuokIiIiotKJeY6IiIiobGKOIyIiIir7mOmIiKggOHUvERXI999/j59++gkAoFQq4eXlhZSUFJhMJgBAkyZNEBISgkqVKpXkMImIiIjIDuY5IiIiorKJOY6IiIio7GOmIyKigmBHPyIqkB49ekCn0yE8PBzR0dFITk6Gp6cn6tevj65du2LgwIFwdXWVl//ll18QEhKSr30cP37c0cMmIiIior8xzxERERGVTcxxRERERGUfMx0RERUEC/2IqEDUajUmTpyY5+W1Wi3i4uKKcERERERElB/Mc0RERERlE3McERERUdnHTEdERAXBqXvLiPh4DUwmERUruiMxUVvSwyl25fW4JeX5+Hns5e/Yy+txS8rz8fPYy96xV63qVdJDKFOY58rncUvK8/Hz2MvfsZfX45aU5+PnsZe9Y2eey5/4eA0qVHArk8+1I5TV17mjlOfj57GXv2Mvr8cNlO9jB8r38ZfFY2eWyz/mubL3Onek8nz8PPbyd+zl9biB8n3sQPk+/rJ47AXJc4oiGAcVIScnZUkPoUSU1+OWlOfj57GXP+X1uCXl+fh57FRelNfnu7wet6Q8Hz+Pvfwpr8ctKc/Hz2On8qA8P9fl+diB8n38PPbyp7weN1C+jx0o38dfno+9vCnPz3V5PnagfB8/j738Ka/HDZTvYwfK9/GXl2NnoR8RERERERERERERERERERERERFRKcZCPyIiIiIiIiIiIiIiIiIiIiIiIqJSjIV+RERERERERERERERERERERERERKUYC/2IiIiIiIiIiIiIiIiIiIiIiIiISjEW+hERERERERERERERERERERERUbkliiISMkzQGMSSHgqRXSz0IyIiIiIiIiIiIiIiIiIiIiKiciteJyJBJyJRZyrpoRDZxUI/IiIiIiIiIiIiIiIiIiIiIiIqlxJ1JiTpRCgFQGcETCK7+lHpxEI/IiIiIiIiIiIiIiIiIiIiIiIqd1L0JsRniPBUCajqZi6j0hlLeFBEdrDQj4iIiIiIiIiIiIiIiIiIiOgZYDCJSNKZILIjGVGu0gwiYtJFuDkB1d0EuCrNt2cY+f6h0omFfkRERERERERERERERERERETPgGSdiLgMEdrMkh4JUemWniniqdYEFyVQ010BQRDgpBCgUgAZmSz0o9KJhX5EREREREREREREREREREREhWAURcSkm2As4U56Uiey+Ax29SOyRxRFxKaboFQAtdwVUAiCfJ+rUkCGETm+f0x8b1EJYaEfERERERERERERERERERERUSGk6kWk6EVoDSVXAGQSRWQYAZUC0JuANHb1I7Ip3Wh+j1RyEaBUCBb3uToBRhEwmGyvqzOKuJNiQjq7/lEJYKEfERERERERERERERERERERUSGk/l3gZ684qDjojOb/VnZVwFkBJLCrH5FNyToTFALgqRKs7nNVmm+TumNmp5Hf63xvUfFjoR8RERERERERERERERERERFRARlMolxkpy/BQj+pw5ibEqjkqoDeBMRpDCU3IKJSyGAUkZYJVHAWLKbslTgrzMVUGUbrdUVRlAv97NQBEhUpFvoRERERERERERERERERERERFZBU+KNSmIuISkqGUYSzAlAqBHg4mQuWHiVksKsfURZJevP7oYKzdZEfAAiCAFcnIMPG1Lx60z9dO1noRyWBhX5EREREREREREREREREREREBaQxiHBRAu5OAvQmlEhhnSiKyMgEXJ3MxUuCIKCSqwIZBpM8rTBReWcSRaTqRXiqBDgpbBf6Aebpe/UmwJjtvSwV9QoATCXYvZPKLxb6ERERERERERERERERERERERWA3miettdTJcBZAYgAbDQCK/pxmAATzNP2SjycAA8XJRIyRHb1IwKQohdhAuBjp5ufRCqYzcj85zZp2l43pbl7Z/YiQKLiwEI/IiIiIiIiIiIiIiIiIiIiogKQOnx5qgSolObiIEMJdPpK/7u6UCpQAsxd/WpXckWmCKSwqx+VE6IoItNk/XoXRRHJenP3zazvE1tc/y6YzcgyP680ba+nSoBC4NS9VDJY6EdERERERERERERERERERERUABqDCFcloFKYO/oB5i5/xS3DCCgFIHv9ko+7E1yVQFy6iNh0Eww2CqCInhWiKCI2Q8S9VBOeak0WBX/aTHOhXm7d/ABAIQhwUQAZWdpzSkW9HioBSgHgW4lKAgv9iIiIiIiIiIiIiIiIiIiIiPJJbxSh/7vDF2AutBNQ/B39RFFEeqYINycBgmBZxCQIAqq7K+CpEpCsF3E/1YRoralEihGJipIoirjxVIsUvQh3JyDNYH69J+pMf3fzM0Ep/PN+zY2rk4AMI+Rpr6Vpe50UApSCwI5+VCKcSnoARERERERERERERERERERERGVN1ml7AXNRnbMS0OfQ6stgEpFpAtxymcVjvLwAACAASURBVDo0PzJF8zSi0nSj2akUAqq7C6hkEpGkE5GiF5FqMBdDVXBWwN0JVgWCRGWJSRTxJM2EdKMJlV0FVHRRwGAUEZthQnyG+TVvMAGVXKyLYe1xVQLJME/ZC5jX93E1r6tQmN9zoijyvUPFioV+RERERERERERERERERERERHakGUQIAuCerThPmrbXSfHP7c4KAemZ9gv9EjLMRXbPeyrgrHRMgZA0vWhuxYMqhYCqbgIquYhI0puLn55oTVApAG9nAd7O5k5lRGWJ0STisdYEnRFoUM0d0OkAACqlgJruCmgzgdh0EwSYX+d55eokADB3y5S693lk6d4JACLMXTyJigsL/YiIiIiIiIiIiIiIiIiIiIhsEEURT7UmiAC8VAKquApQKgTo/p62t6qrZZmPSgGkiuYOYwobRXNSEWCCTkQNd8eUCKUbAQUAZ0XellcqBFR2NRf8aQwikvUi4jNEJGSIqOAioJKLYHPsRCVJFM2v06wdM0UABqO5u15NdwWqejsjNlYn3y8IAjxUgJuTAibRsig3N06CuaAvw2iepjtrUa9U6GcUgXxskqjQWOhHREREREREREREREREREREZIPOaC4mcncCUg0i0jJFVHEVYDCZ75c6fEnMXfrM03y6ZJtKN9MkIlM0FxBpDCIyMsW/u4YVjnk7+Z9+VxAEeDkL8HIGdEbztL5JOnPxXzVXBdxVrGCi0kObCSTpRagUlsV1KgVQ3VWRY0dLhSDkuyBPEAS4OQlIM4gQAVTJUtRr7nwpwmgy75+ouLDQj4iIiIiIiIiIiIiIiIiIiMiG9L/n7KzmZu4IFpNuQkz631PlKq07hElFP3qjCJdsU/NK3fyquSsQrTUhPsOEWh6KfBfoZWU0mTsLehayKM9FKaC6uwCvTBGx6SY81prkDoYKAcgUAb0R0JtEmESgIrv+lSvRWhPSMkV5mloBgCAAlVwEeOW1lWQhmLv5maeZft6zcO+Z/HBVAhqD+ees7zFFlo5+RMWJhX5ERERERERERERERERERERENmRkmjuISQV9vh4KpBikaW6tC5zkQj+TjW0ZzQVSbkpzoVxchghtJuChKsT4jOb/5tTNLD/cnQTU9lQgUSci8e/ufoC5q2F2lV1Z6Fce6IwiUg0i3JwAlUIARPPrIT1TRIJOhKdKLPLCO43BXNBa3U0otiI/AHD9u0Ona7aiXqmG1ySKAPg+oOLDQj8iIiIiIiIiIiIiIiIiIiKibERRRLoR8MhSRCcIAio4C6jgbHsdhSDASQF5at+sMozmgiHzNoBkvblLmbtTwTuUSR0Hs08TXBgKQUBlVwFeKnOxn0IAnBXmaYmdFUBchvl2L5X491TF9CxL1Jk7+dVwV/w9Za1Zit7c3TLDCLgVYfWRKJoLCp0Vhe9cmV8uSvNrv4Kz5X6V7OhHJaRYC/0MBgMiIiJw9OhRhIeH4969e9Dr9ahYsSJatWqFDz74AK+88orVehMnTsTWrVvtbrdevXoICwuzeZ/JZMJvv/2GzZs34+7du1AoFGjUqBHef/999OzZM8fx7ty5E7/99huuX78Ok8mEevXq4d1330VAQAAUCvutR//880+sXr0aV65cgU6nQ+3atdGjRw8MGzYMzs52/toTERERERERERERERERERFRqWEwASYx/0VMzgrz1L1ZmUQROqO5kx9gLvar5CIgOt3cLc3buWAFTBmZIlyUKJJpdJ3/ns43u8qu5g5rcRkm1PJwYIUhlToGk7mro4+zYFHkB5iL7mLTRaToRYd1lLQl1SDCYDIXGhZnNz/A/D593sv6Nf6sTt0riiKMorkjqcEowgRzkSOn6S49irXQ78yZM/joo48AAFWrVsXLL78MNzc33L59G/v27cO+ffswcuT/s3enQZLd9Znvv/+z5Vr72qtardaOALE0SIxlG8ugwYa5FuMYY1syEza2wzMRExPGL67HMyMcE+GJMR4cvCBMwLCEsJiFMTb2xWDMvshINhI0UgstqKVea6/KPc/2vy9OZvVW3V3VXd2VVfV8IjpKqsyTeU7mOSersp58fr/Dv/t3/27F5V/zmtdw3XXXnff9sbGxFa+fJAn/9t/+W77yla9QLpd505veRBiGPProo/zu7/4uTz75JH/wB3+w4rLve9/7eOSRR8jlctx11114nsejjz7KH/7hH/Loo4/ywQ9+cMWw30c+8hHe//7347ouBw8epL+/n8cff5w//dM/5Wtf+xqf+MQnKBQKq33IREREREREREREREREREREZAN02/Lya2yt8x1DM7ZYe3qkaXfE7pm3VfYNi2E2Brjs2zWHaVKbtakNXmZI8HJ5jmE4b5hrWeqRpXSNW9bk2lloZ8fAYO7859gxhj7fUI0sY3bt++9qdNv8cg6UemhmqTEGx2RB4K0gsZapRkorhnPLSBuRZWfp2ocsZWXX9DAwxvDWt76VBx98kNe97nVnXfb5z3+e9773vXzoQx/iDW94A2984xvPW/4Xf/EXuf/++1d9f5/85Cf5yle+woEDB/jkJz/J6OgoAEeOHOFXfuVXePjhh3njG9/Ivffee9ZyX/ziF3nkkUcYGxvjU5/6FPv27QNgdnaWBx98kC996Us8/PDD/Nqv/dpZyx06dIg/+ZM/oVAo8MlPfpJXvepVANTrdX7rt36Lxx9/nA984AP8/u///qq3QURERERERERERERERERERK69Vpw1d/kXHvi3osAFC8QWuhm4VtwJDZ6R0jDGMJJ3OFFPWQrtctvfalXC7m1e+wDOYGCohJaZVkrBc9T4tQXFqaUaWvp9g+es/Pz2BYZKlLVSnjvedj1UIkucwtgGtPldimu2RqOftZaZpqURQ3+QjecOHIPvQjO2TDezf+MFeu452I7W+HJ0Ze666y4++MEPnhfyA3jb297GL/zCLwDwuc997orvK0kSPvrRjwLw0EMPLYf8APbt28d73/teAP7sz/7svGU//OEPA/De9753OeQHMDo6ykMPPQRkzX1penaO9SMf+QjWWn7jN35jOeQHUCqV+KM/+iMcx+GRRx6hUqlc8faJiIiIiIiIiIiIiIiIiIjI1dNKLAV37eGWoBOKipKzbytwOG/8adEzFD2Yb1naa0gNLbRTZluWgrcxTWfGGMYKDnEKi+0tkHaS8yyFFsvKbX5deTcbVd0Nna6n1FoWWtlo6mIPtfl1OSZrwtvsqlE2nnk4ZxgvOAzmHIq+wXcM/YHDcC5rbZzXcd4TrmnQ71Juu+02AKampq74tp544gnm5uaYnJzk9a9//XmX33ffffi+z6FDh866v1OnTvHUU0/h+z733XffecsdPHiQiYkJZmZmePLJJ5e/H4Yh3/jGNwB4xzvecd5ye/bs4dWvfjVRFPH1r3/9irdPREREREREREREREREREREro44tUTp5bXldRsAw85cT2strfjCtzVecHAMnGqklwwOWWuZbabMtSxl37BzA5vOip6h7BsW2pZoq8wwFSAL2S21LSUPgouMrjbG0BcY2glrCqquRiW0xBZG8r3X5gdZo1967pzbTSZMsja/gssFG0WHcob+znG+FG7yDd4Ceirod+TIEQDGxsZWvPy73/0uf/RHf8R//I//kT/90z/lm9/85nmtel2HDx8G4I477ljx8kKhwIEDB866LsDTTz8NwI033kg+n19x2e5tnrnciy++SLPZZHBwkL179150ue59iIiIiIiIiIiIiIiIiIiIyJUJE0uYWOw6tmu1Om18hYuEnC7ENVkYI+rEGcIUUrL2s5V4jmGy6BClMN1IL7gd1mYjNBfDbEzqRMFseABqJJ/d/2xTAaCtZCm0pMBQ7tKxov7OfOr1bPVLrWWhbcm7ULjAcbPRXGM29eheay1TzRRjYOIigeGsvTNrHp1pWurRJt7oLaBnyi1nZmb47Gc/C8Bb3vKWFa/zl3/5l+d978CBA/z3//7fufnmm8/6/rFjxwDYuXPnBe9zx44dHD58ePm6a1nuzOue+d/dy1bSvc3jx49f8DoiIiIiIiIiIiIiIiIiIiKyOlFqebl2OmQWOFmjXuAa+oNs/OTlaMYWA+QuI2RkjMF3swAiQCvOvhYu0g5Y8AwjecNcKwvynduuFaWWmWZKI84atoZzGx/yA/Adw1DOMN+2NGN70W2UzcFay2I7a3lbTaOl6xhKXjYCdjRv12W/rIaWxMJEj7b5QXd070avxeWba1vaCUwWHbxLnCeNycLIx2sppxopI3nDQLD2c1CSWlpJNsq8lVjCJAsZFnXeWLWeCPrFcczv/d7vUa1Wueuuu3jzm9981uW33HILf/AHf8Ddd9/Njh07qNVqPP3003zgAx/gmWee4V//63/NZz/7WSYmJpaXaTQaQNbcdyHFYhGAer2+puVKpdJlLbfS/YmIiIiIiIiIiIiIiIiIiMj5UmuxNgsSXchSO0vajOUNkYUosYQp1OOsEWwgMAwma2+bayVZm9jlhowCx9DoBPxaSdbyd6ksy2BgaMWWuZYl72ahuSjJtqPSadEazRsGV9Gydi0N5gyV0DLbStld6t1glqzOYidkt5o2v67+wKHeSKlFlr7gyp5/22nzy/Vwmx9kx7QlW9/Nts834izM2e9n47dXwzGGHSWH6WbKbMtSiyzjBee80c7WZmPP20n2NU6zoHKUQnxGMDJwILVZqFNBv9XriaDff/7P/5lHH32UHTt28Md//MfnXf7ud7/7rP8vFouMj49z991388ADD/Dkk0/y4Q9/mP/0n/7TNVrja29kpLz832NjfRu4Jhtnu25313befm379rNdt7trO2+/tl22Mv08t323u2s7b7+2ffvZrtvdtZ23X9suW1n357nt/Fxv522H7b392vbtZ7tuN2zvbYftvf3bedu3C/08d/W2PUktjXZCvZ1QDxOaYcLEQI6xvuCKbtday6FjNaIk5c69fTgrhP2S1PLikQrDJZ8DO0pnXdaOUo7Ot5iphnzvSIWdgzl2DOUv2VzVvd3nl5bYNZRjbOTCZT8XE863qM63GB4pc7ReZaDoMT5euuRyw4nlB8eqTLcsA0WX2VqEMTA5ELBzME/OX3vI71rs9ybf5oXpJm4xz0j5yp779bRdj/nL2e4ktbw402SulTBQ8Ni7o7TqANuotcy9VKGFy/6x8qUXuIiZSkhcaXDDeInhkr/m5a/Vc54stZmfaTI4XCbweid8u5rtf+KlCnnf4ZY9fRcNUq9k0lpmqiFHZpscq6fsHSkw3h9QacYsNCIWGzHt+HS42ncNOc+l6DuUci7lvEc55+I6huenGszXI0ZGyzjrEJbcDsf7hgf9/st/+S985jOfYWxsjE984hOMjY2tetkgCPjN3/xNfud3foevf/3rZ13Wbc9rNpsXXL7bwtdt6Fvtct1GvrUut9L9rdbcXI00tYyN9TEzU13z8pvddt3uru28/dr27bft23W7u7bz9mvbN9+2b4cflteTfp7bntvdtZ23X9u+/bZ9u25313befm375tt2/Ty3NnNzNUZGypvyuV4Pm3U/Xy/befu17dtv27frdsP23nbY3tu/GbddP8utnX6euzr7+XQjXW6ag2yUpQM8P9WgVm2tuilqJZUwpd5p63vhRGXFFrulMCVJLQWSFbdvwIFC2aFmXY4ttDm52GZH0bnkONJuE1/ajpiZiS9r/cPO43L0VJV2nFL27Kqfg/EcHKtZ5qoRg4FhMGfwSKgsrn164DU7x1lL4MCL0w2SRqsnGs424/l9PVzOdrdiy6lmSpx2RkP7KbOztTXdRsmFhWbMianKZY/MtjYbxR04ENebzDRaa1r+Wj7nzc4xPj1bI+du/P4Oq9v+xFpaUTZ+d35ubc9xlwH2dNr9jsw2OTLbXP5+wcsaVvOewXc4I8CX1fuFtYj5zt26iSVJLUdPVa+41W8zHu+X8/Pchgb9/ut//a88/PDDDA8P84lPfIJ9+/at+Tb2798PwNTU1Fnf37VrFwAnTpy44LKnTp0667rrsdzJkycvuFz3sjOXExERERERERERERERERER2WyiNBsnW/YNfb4h554eZXminnKqkbKz6FC8jLBfarPxtTk3Cw4utC39gT2r8clay1I7C5flLzLeM3ANN4+VOHqywqlGyol6yo6SQ+EioZJWJ+hXuIJERdDJJVbCrNmqsIYgUM417Ck7uObiY4t7iTGGkbzDyUbKUmgZzG2O9d7uumNy59sWz8CuSxwbF9MfmGzMdGgZyV/ebdRjiFKYKJieCIteTPfQTOzFr9droiT7erlhzC7PMewoOtQiSzuBgmcoeKypma/oZeHAeqTxvau1Yd2R/+2//Tc+/vGPMzg4yMc//nEOHDhwWbezuLgInN+Sd9tttwFw6NChFZdrNps899xzZ133zP9+7rnnaLVWTgZ3b/PWW29d/t7+/fvJ5/MsLi7y8ssvr7jcD37wg/OWExERERERERERERERERER2WyqYZZuGckbSr7Bc7JgjmMMO0oOgQMnG+lyaK4rtZalMGWqkRKnKydkFtqWxMJY3mEk75BYWGyffd1mAmEKg7nVBYLynmFX2cF1siBiI75wOqeZZAHCKxkl2Z2wW4+zIEvuImHElQSu2TQhv66iBwUX5tuWxF5e+qkeWY5WE1qbLT21SS2GWciv7Bv29F1+yA+y4FjJg6XQkl7G82+tZaGV4jtcURvotdLN7l7gNNazws4KB+uQGDPG0Bc4jBYcSr5Z8znTMYailx339jLPGdvNhgT93v/+9/M//sf/YGBggI9//OPccsstl31bf/u3fwvAK17xirO+f+eddzI8PMypU6d4/PHHz1vuC1/4AlEUcccddzAxMbH8/R07dnD77bcTRRFf+MIXzlvuscce49SpU4yNjXHnnXcufz8IAu655x4APve5z5233NGjR3nyySfxfZ+f+qmfuqxtFRERERERERERERERERER2WjWWqqRJe+u3ArlGsPOkoPnwIlGSjuxRKlltplypJoy08yWP1HPRu+eKUoti53gUd7L/pW8LOh3ZnhsqZ3imLUFgnzHsKvk4Dtwsp7SiM4PllhracVcUeAJsgBMN+yXc+n5drL1YIxhpOCQrhDMvBRrs/3jZCOlncLSGpeXteu2YhZcmCw6uOuwjw7ksue/tsKxdSmNGNqd0cGb4XjpBv3OPYf1uk7J6PL5aaOVfENss+deLu2aP20f+MAH+MhHPkJ/fz8f+9jHzmrTW8nhw4f56le/SpIkZ30/jmM+9rGP8fDDDwPw7ne/+6zLXdflN37jNwB46KGHmJubW77syJEj/Mmf/AkAv/3bv33eff7mb/4mkAUSX3rppeXvz83N8b73vQ+A97znPTjO2Q/fe97zHowxfPSjH11u7wOo1+v8/u//Pmma8su//Mv09/dfdJtFRERERERERERERERERER6VTvJxmv2BxcO43hOFvYzwLFaykvVlMUwCxXtKjnsLDlEKRw/J+w31zrdFNg1nHdIOR0ei1JLPYaBYO0NUt4ZYb8TjZRqmJ7VJNVOs/HDFxsHvFrdIM2VhgY3k7xrKPuGxba9YGPjuaLUcqye7R8DQbZ8Pbq8VjhZvXoMsc3Ceeul4GZNcYvttTe0LbRTPAN9m6DNDzbz6F6L7/RO+LjUOT/WLyMcuh1dwUT5tfvyl7/Mn/3ZnwGwd+9ePvWpT614vf379y+H7Y4fP86/+Tf/hsHBQW677TaGh4dZXFzk2WefZXp6Gsdx+L3f+z1+4id+4rzbefe7383jjz/OV7/6Vd7ylrdw1113Eccx3/nOd2i32zzwwAPce++95y1333338a53vYtPf/rTvP3tb+fuu+/G8zweffRRarUa9957L7/6q7963nKvfOUr+d3f/V3e//7380u/9Eu88Y1vpK+vj8cff5y5uTle9apX8e///b+/kodQRERERERERERERERERERkQ1UjiyFrYrqYboPedDMl7xkGAnNWA+Bk0eFkI+VEI82Cf0nWBDaUO/t6uTPCYwOBXW57G7hI0PBi3M56nWikTDUtMy1L0ctGSEadVqn1COcFjqGBJe/2RqDmWhnJG2qRZbZlmShcPFBUjyxTzRRrYaKQjQFtxJZaZGnEUPZXf79zrZQwsYyvUzvdVrfUCdaV1jE5ZIxhMGeYblqaSTbOeTWasaWVwGh+c7T5QTZ21rAZR/euz9je9eI6hoKbnQtG8hu9Nr3vmgb9lpaWlv/7hz/8IT/84Q9XvN7BgweXg34333wzDz74IIcOHeL5559ncXERYwyTk5Pcf//9/Mqv/Mp5Y3u7XNflQx/6EI888gh/8Rd/wbe+9S0cx+H222/nl3/5l3n7299+wXV96KGHeO1rX8uf//mf89hjj5GmKfv37+ed73wn73rXu85r8+t6z3vew80338zHP/5xDh06RLvdZs+ePTzwwAP8+q//OkEQrPbhEhERERERERERERERERERueastUQpBCsE1Lpje0u+WVWYKnANu8sr1+OVfMOOTtjvZD3Fko3DHMqdf7vDuSw8Nt+y1OJstK+3wtjg1eqG/eqdQFkWLssu8wxXdNtdBc9kI46vaTJj4/mOYThnmG9bosQyUXTO25fiNAsC1iJLzoGJ0unrFNxsP6iGKWV/ddWKtciy0AmAHqul7Cj2UJKpB7WTLIg3chWCdWXfMNuyLLVTit7qnr+FdoprLt4S2otcs7ka/brn9rWMPL8WSp19Jkzsiq875+q2RW6WUOh6uqYvJ/fffz/333//mpbZs2cP/+E//IfLvk/HcfjVX/3VFRv4LuXtb3/7RcOAF3LPPfdwzz33rHk5ERERERERERERERERERGRjTbVzAJYO0sOxXOa7Rpx1mC1XuM1S75hsuhwqpFV6Y0XVh7HG7iGft9Qia6sze9MjjH0BYa+IAuOhGkW+AvWIeQH2bZdv8qg2lYznHcIXMt0M+VoLWUkb5afs0pkmWtaUrIA51Du7LCZMVmD41JoSVKLe4nnI06z+8k5MFLI9qVj9ZRyI7qam7ipLYVZK2f/VQh8OSZ7rhfalii1Z7VzrqQVZ2HbkfzaR3FvtCzot3mSft3GUr/HcrDdoF89vnjQrxs0n29bsLCn7Fzy/LDV9NhTJyIiIiIiIiIiIiIiIiIiIhulOzYVYKqRkpwzl7IaWRyz+pGcq1HuhP0GAnPRAOFQPrss50B+nfNzxhhyrmEo51xyJLGsTtk37C07FDyYbVlONFKO11NmmpacC3vLDsN5Z8VWrr5OKLAWXzxEZa1lqtEZ/VvMgql7yg6egcMn6iyFaRbiTCzVMGW2mXK8njDXSok328zVdZJYSzXMWjGvVkiqG+rsjtm+mPl2imPWJ7x7rTnO5hrdG3aCfusVZl4vvmMInGx870qszV6XjtZSppsWB4gtnGqmy+1+28U2K4gVERERERERERERERERERGRlVhrmWmm+A6MFxyO11OmmymTxSyMlVhLPbL0B1dn3Oelxkn6jmFn0cF1tufIxs3Ic7LxzJXIMtu0GJO1Nvb5F9+Hck7WOlYNLQPBhW9/sZ2NoB0vmOUmMN8x7Co7LMQOM42Y2aalGwUyZLe7EFsW25a+wDAYmFWNC90qqmH2eAysMCJ7vXhOdjxXQstw3l6wqa+VZG1+w7nN1+YH4BpDexMl/cLOnOGgB4tGy3427jtO7Vmj05uxZbaV0k6yY3ey6FDysmbQmWbW7jeS33z7zuVS0E9EREREREREREREREREREQ6ozZhR9Gh4BlG8oa5lqUSWgZyhlqUBYTWa2zv5SiqbW/TMZ1RriUvC/q5qwh0GZOFAecvMv61FVvm2lkz3bn7pGsMt+wo8dzxCnEKORdybtYaZowhTCyLYdZsVwktRS8Lt3o91nS23qy1LIVZo2L+KocbB4LsnFHtnD9WstBKcbi6ocOrKRvdu9FrsXpRmq1zL4YqS53jvRFnYfLE2uXXH2+FgHC/D604e93Ku5axDV7/a0VBPxERERERERERERERERERkW0uSi0LndBUd3TtYGBoxJbZliXvZYEd38lCUyJrtdYQXV+QBX9qoV0e29yVWstUM8UzMFZYuR3QmGwU80oC1zBeMAznsuDbYttyop6yu+z0ZAhqvTTjLOw1Ubj625h3s3PFYmjpD+x5z1E7sdRjGMqZVYU/e5FjstG91p6/fb0oTG1PtvkBBA54BmqRxTFZu2xis8DoSP78xkdjDGOFbD+aaqZMRskGrfm1tfIZTURERERERERERERERERERLaF7shegNEzAlXGGCYKDo6BU42UVsIlR66KrBffMeRdqEYWa0/XpsVpFsqLUpgoOlcUEvMcw0jeYbLoEKZwspGedV+bRSOyLLZTKmHKXC2kHlmasSVMLOkZ27MYpriGS47JXg/GZGORoxSmmmevB2RNbAYY3KRtfpC140EW9ut11lrCBIIeba00JguZN+Ls9cY1sLvkMFa4cPjWMYbJogMWnj3VOG8f24rU6CciIiIiIiIiIiIiIiIiIrKN1WNoxDCSN+e1rnmOYbzgcLKRBQH7gt4MicjW1OcbZlqWsDN+txlbTjVSUguTnRHT66HkZw1/003LdNMyXmDTBFrrkV0+PgGmm43zrmMAz8na/IZy1y6sW/YNcQpzbUuUWCZLDr6TjU6uRXZTt/nB6aBfYqFHi/KWJRYs4PdwJVx/YKhHloHAMLjK/TRwDeNFh1ONhNk0a/nbLMfu5VDQT0REREREREREREREREREpEdc6xGQqbXMNlMCJxvVu5KSbxjOGWKbtayJXCvlTtCvGlqaTjZG2ndgZ8kh567vvtgfOERpykLb4jlZ8LXXhYllqpGSc2BHycECg4MlZufrpBYSa0ksxGkW9PKdLER1rRhjGMobAjdbz6O1lMmiQyXstPlt8uBw1jSXPca9LuxkQYN1Pm7WU8417Otfe2Sy7Bt2DeU4vtAmcDd300+cbwAAIABJREFUS+SlKOgnIiIiIiIiIiIiIiIiIiLSA5qx5Xg9xTMQuFnoIft39QJ2C21LbGFX0blowHA438M1ULJluY6h6MFimCWpSh6MX+G43osZzmUNdAtti++k9Ae9u9+nNms3xMBkyVlu4yzm3DOaDnsj8FTyDbvLWTPoiXqWOBsMDO4mDw5vptG9YSeN2MO79BXZM5xnsdruhIEtpWswnnojbNGnT0REREREREREREREREREZHNZaKc4BvLe6bDRqUbKS9WU2WZKatc3TRKllsW2peybdRuBKrLeBjrJpOGcYfIqhvwga6AbLxgKHkw3LVGPJriszUYMhylMFpxN0bQZuIY9ZYeSB47ZGq1rbid1lazzuflqiLJMKD1c6HdFjDFMFB0CB6Ya6XKwcatRo5+IiIiIiIiIiIiIiIiIiMgGixJLI4ahnGGk056X2izIU2lbFkNLPbaMF5x1C+XNtbIgxGYYUSrbV8k37O93OmNSr74s7OfwUjWlGlqGe/D4WAottcgynDMUN1FzmWMMO0ruNR9RfrV085WbIVMWppbAZUs87heS7V8Ox2opJxope0rOpm+NPJca/URERERERERERERERERERDZYdzTpQHA6lOAYQ941jBcddpYcrIXj9ZSZdWj3a8VZUGgwZzZFG5hsb9cq5NflO4a8C7VoYxNccWqphulZ/5baKbMtS8nLgsGb0VYJm3VDVz1a/HiWMIFgG5zrfcewo+iQpHCykWI3QdviWqjRT0REREREREREREREREREZAOl1lINsxG63gWCGEXPsLfPYa5lWQot1Si7fp+fBZJWCs6k1q4YkLLWMttKcc3mDQqJXG1l3zDbsoSJJdiAeaftxHKinq7YFuc7MFF0tkxgbrMyxuCa3m/0S60lsdl+sx3kvWwE91TTcryeMlZwyG2RmcUK+omIiIiIiIiIiIiIiIiIiGygSmhJgcHg4kEExxjGCoY+PxvlWw0tldDiGujzDZ4DYZqNAQ7TLHxScKFvMDnrdmqRpZXAWMFc86Y0kc2iG/SrRpaRaxwSakSWk40Ux8DOkkN3Wnc3T+Y7177lUFbmGkh6vNIv7LwEbERgdaP0BQ6QMtOyHK2lDASGkfzmf81T0E9ERERERERERERERERERGQdhZ16p9WEKqzNGvpybtZCtBp5zzDpGVJrqUdZEKk7+tcxWQio6GVNU5XQ8v2XqwznDAOBwQJzLUvgQL+/uQMPIleT5xgKnfG9wzl7zdrzKmHKdDM7RneWnAu2fEpvcDZBo1/YCSIG26TRr6svcCh6lrl29jpbiyyjeUPZN5u2DVNBPxERERERERERERERERERkXU01Uhpp1lj3sAlkhWNGKIUJgprDx04xtAXGPoCSKwFC+45oaCBnGUxcZhtxNSjbARpbGGnxn6KXFI5MMw0s4bMnHt178tay0LbMt+2FFyYLDm4OkZ7nmuyc3gv667fdhndeybXycb49vuWmVbKVNMy07KUPEPJNxS9zdWOqaCfiIiIiIiIiIiIiIiIiIjIOkmspZ1mLU8zTUs7SRnLX7g9aDFMcU02JvRKuMbACjfhO4Zbx0v8+ESF2aalmViKXtb4JyIXV/YMM2RjsnOXEcZdDWst9RgW2intJBvDPV7YvI1j241rDC3b25V+YWLxHbb1PpX3DLtLDvU4a+nstuEClDyYKDqbIvCnoJ+IiIiIiIiIiIiIiIiIiMg6acXZ18mCQyPORuqGiWWyeP4IzjCxNGMYzl3dUI8xhv7AoeBZltqWgVzvhxlEeoHrZI1ftcgykl/f8b3WWiqRZbFtidKsbW2sYOjfxGNFtyPHyUb3WnvtxjuvVZhuv7G9KzHGUPazYL21lmYC9Sgb61sNN8dro4J+IiIiIiIiIiIiIiIiIiIi66SVZA1BeQ+KvkPOTZluWo7WUgZzBt8x+E4W6lkMLQYYCK5NuMB3DKNXqZVMZKsq+4bp2NJKoLBOKZtmbDnVSEks5JxsdHdZAb9Nye08ZZYVS1U3nLVZkLR0ha2xW40xWYi34GbHYyWyDOQ2eq0uTUE/ERERERERERERERERERGRddKKLTmH5RGAfYFD4GahnrmWJYuDnNbnG1xHAQyRXlX2DTNNSy2yFNZh5HUtskw1UjwnGxdacLf3SNXNrhv0S2w2sr3XRGn2VY1+K8sabw2zLUs7seTcHnwSz6Cgn4iIiIiIiIiIiIiIiIiIyDqwNmv96j+noS/nGvaWHVKbhS6i1BJbiFMY3ASjAkW2M8ecHt87eoXjexfbKbMtS86FnUVHId8tIAt1WxIL/kavzArC5aCf9rUL6QsMcy1LJbSM9XjrrYJ+IiIiIiIiIiIiIiIiIiIi66CdZn19eff8y4wxuAZcB/I9OeBRRC6kL3CoN1KaCRQvI2ljrWW+bVloW0pe1uTnqMVvS+gWwKUpsMK5f6NFadYi6/fguvUK1xhKvqEaWkbytqePTRUzioiIiIiIiIiIiIiIiIiIrINWnAUq1mO8p4j0jqIHBqiF9pLXPVNqLY3YMtXMQn79gWFSIb8t5fTo3rXtG9dKmGTr6Gqfu6iBwJAC9ag3n8cuNfqJiIiIiIiIiIiIiIiIiIisg1YCngFPIxJFthSn2/gVWQYSS8698DEeJpZ6ZGkkllactXwCDOcMQzlzRaN/pfecDvpt7HpcSDuxBGrzu6S8C74DS6GlL9jotbkwBf1ERERERERk3USJ5Zn5JjnX0B+4lAYSrLV680rkDKm1PH6yzjNzTRwDnjG4jsFzDOMljzfs6MO/yJvFIiIiIiIi0pustTRjqzY/kS1qJG9oxpYT9ZRdJYdghfdvqmHKVDNLfAUO9AeGomcoeKjFb4vq5rrTHgz6xaklTGEk0L53KcYY+gPDXMsSJnbF47sXKOgnIiIiIiKyBby42OK7J2q8YWeZ6wfzG7IO0/WIz/xojplGfPqbT07jO4bRosdrJkq8crxI4Dobsn4iveBkLeRvnl/gRC1ioujju4ZGmpJYS5RYDs00+N6pOvftH+Sm4cJZyyap5fBck+9P1ykHLrcMF9g/mFcoUEREREREpEfENmt0yqs5SWRL8h3DrpLD8XrK8XrK7pKz/L6MtZbFtmWubcm7MFl01Oy5TRhjcOjNRr9GZ5x8UQH0Venzs6BfJbKM9uh7rgr6iYiIiIiIbGInaiFfObLEC4ttAF6uhPzWneMM5K7dr3vWWr43VecLP14i5xp+6dYRSr7DUpiQeB4n5hu8tNTm/3thkS+/tMRrJkoc3FFmIL+2dbTWUgkTZhoxO8s+RV/vmm8X9TBhqhFR8Jzlf4Hbe2NOrLXM1kN+vNii4Dn0By5F38EYQztO+erLFR47UaPoO7zz5mFuHy2ctw0vLrb4/AuLfPrpOW4ezvPW/YPkXMM/narz+Mk61TBhMOdytBLy5FQD3zEcGMpz41CenGdIUkisJbGWsu9y03C+5x4nERERERGRrarVCVSo0U9k6wpcw84zwn67yg6egZlmFg4q+4bxglF73zbjOr0Z9GvG2WjhQJ+9XxXPMZQ8qIaWkVxvTipS0E9ERERERKTHhUnKXDPGAtaCxRInlsdP1Xl6tknBc3jL9QPsH8zzsR9M85ln5nn3HWO4F/jEaJLaC162Vq045W+eX+Cp2Sb7B3P8wk3DlIMsgLcbGBvrY2YmwFrL0UrId0/UePR49m9Pf0DezT716jsGzzUEjiFwHXJu9jVwDfPNmBO1kOPVkFqUAtmbE7eMFLhzosT+wVxP/sItV64Zp3z7WJXHTtSIzpl94RjY3Rfwpt193Dh09cJs1lpO1CKenW/y3EKLVpxS9ByKfhbiK3gOtTBhphkz14yI07OXdw3051zCxFKPUl47WeLefQPkvZXfXbt+MM9v3znBP5yo8fWXK3zoe6cAiFPYP5jj5w8McuNQntTCkaU2z8w1+dF8k8NzzRVv71/cOMSrJ0rr+piIiIiIiIjIyloJGBSoENnqcp2w34layol6iudkgaqhnGE413sfTpWrzzHZh297ibWWRmecvPbJ1esPHOqNlHoMZX+j1+Z8CvqJiIiIiIisE2stcWppJ5YotfQH7hUH6k7VQv7n4TmW2sl5l/mO4Z49fdy9q49cJzT0jgNDfOZH83z5pSXecv3gWddvxyl/88ICP5xpsqsv4JbhPLeMFBgtXvi31TBJWWwlLLRiFloxtSilESU0opRGlDLXimlGKT+zr5837eq74BsGxhj2DuTYO5BjsRXz+MkaR6shlTAhSrPHK0osYZKu+MnHkYLH/sE8O/sCRgsez863ODTT4KnZJgM5l1dPlHjNRJH+a9hkKFdPmKR890SNbx+r0k4srxgr8OrxElFqaUYpjTjbD5+abfLpp+eYLPn8sz193DpSWJdPS1trObLU5tBMg+fmW9SiFAPs7g/Y1RfQjFJqYcJ0I6LZCf6NFn2uH8hx/XgZP45pxZZKGFNpJ1Ta2X7+pt197OnPXfL+Xcfwpt19vGKswNdfruIYOLijzHjp9LHqGrhhKM8NQ3neZgeZbcakNvu+6xgcA//3R/P83YtL3DScVwOmiIiIiIjINdCMLXkPBSpEtoH8Gc1+UQpjBcOAUr7blmt6r9EvTLN1Kuot8zUpetnzWQlTyj34nqqeThERERER2dSszYJ1S+2YpXbCUjuhL3C5YTCP717Zm6qptYSJvWDzVmotT882+e6JGjONiDCxnPm7vOcY9vQF3Lazxahn2N0X4K0h+Hd4tslnn50n7zn8wk3D5FyDMdknw40x7Cj7lM75RfP2sSIvVdo8erzG3v4ct4wUAJiuR/yfZ+aYa8a8erzIdCPiyy9V+PJLFUYKHrv7guWQYpikhImlGibUo7PryRwDRd+h5GVtZvsHcxzcUV5VeKlrMO/xs+eEEM+UpNnj3u6sR3/OPe85uGEoz89eP8CP5po8MVXn6y9X+MbLFW4azvO6HWVuUMvfppCklmfnWyy1Y+pRSj3K9rlj1ZBGlHLzcJ6fvq6fiVKw4vJvvm6AQzMNvnWsymeemWek4HHvvoHl/X6tusf0d45VOVmPyLmdsbjD2Wjc1YTlshbL6mXd/7kGch7vuHHoktczxjC2QmD3528Y4sNPTvGlF5f4FzcNr8s6iYiIiIiIyMoSawlTGPb1foTIdpH3DLvLDtZm/y3bl2sMYY8l/RqdcfJF7ZtrYoxhMDAshb31fHYp6CciIiIiIpvSqVrIN45WeWGxteIv0L5juGk4z60jBW4czhO4WVDM2qw9LrVcMMCXpJZDMw2+ebTKfCtmR8nnxuE8Nw0X2Fn2CRPLE1N1/uFEjaV2wkjB41XjJQLXLI+c9RzDVD3i5Uqbzx+exZJ9Cmxvf44bhvIcGMozXvRWDKNZa/nm0SpffbnCrr6Af3XrCH3B6j859pbrBzlWDfnLZ+f5rTsnOFoJ+ZvnFwhcwwOvGOX6wTwAS+2YH821+NF8kx8vts9a//6cw2TZZzjvMdT5N5h3KXrOVQ/QuY6h4BgK/sU/Aes5htvHitw+VmShFfNPp+o8MVXnR/OzDOVdXjtZ5s6J4qrCWWGS8sJCm2fnm7y41OaGwTxv3T+wvN+sxlIr5lQ9YndfQGkNz9d2NVWP+Mtn5zlVj4AswFryHUqBy3X9Oe7eVWb3JQKkrmN49USJV44XOTzX5BsvV/hfh+d4xViBf75/cNUtdlGS8k+nzj6mf/7AIK8aL60pnNtrxks+d+3q49vHqrxqvMi+zrEvIiIiIiIi668VZ18V9hHZXnJX+GFz2RqcHmz0a8SWwGFTv7+5UQZzhoFcbz5uCvqJiIiIiEhPsdby0lKbI5WQ8aLHnv7cWSG36XrE11+u8PRck5xreOVYkaG8x0DeZSDn0h94zDQiDs81eWauyVOzTVwDOdchTLPRul1DeZf9g3luGMxz/WAO3zE8OV3nW0erLLYTJks+9+zp48WlNt88WuUbR6uUfGe5+e66/oB/vn+Qm4bzFw2/lQaKPPHiHC8utvnxYou/P7LE3x9Zoi9w2DeQpz/nUu4EnMq+w/dO1fnhbJM7xoq848ahNf8i7jmGX7xlhA8/McVHn5ymEafs7Q/4lzeP0Jc7/VgO5DwO7ixzcGd5Tbffi4byWZPbT+3t5/Bck388WePvjyzxtZeXeMVokYM7y+won26Fi1PLVD3iaKXNC4ttXlxskdhs5MauvoDvTdV5qdLmX948zGR55Ta5roVWzDePVvn+dJ3u7jVW9Lh1ss5k4DCUd2nG2ajjRuerJQujLv9zDUXfoT9w6Qvc5VHMW1FqLd85XuVrL1XIew7/8pZhrh/IUbiCEKljDLePFrlluMC3jlX5xtEKLy62edsNg9w2WrzgcrbT4Pd3R5aotBP2rvKY3kx+ck8fT800+JsXFvntOyf0xp6IiIiIiMhV0uokPPL67J+IyLbjGrBk7zf2wvuKqbW0YugPNn5dNiNjDL36yCnoJyIiIiIiPWG2EfGDmQY/mG6w1E7Oumww57KnP0dqLU/NNglcwz17+njjrj4KKwSi+nMuNwzledsNg7xcCXl2vkmU2uVQVbel7WilzaGZBv90qo4ha/hrxik7yz7//IZBbhzKwj4/DTSihOcX2jy30MTBcHBnmV19Fw+AdRUDl5uGC9w0nI0TrbRjXlho88Jii5eW2tSihPScT/v9zHX9vGl332W/KTCU9/h/bhrmM8/McfeuMj+zbwCnB95guNo8x3DHWJE7xopM1yMeO1njB9MNnpxusLsvYGc54EQt5GQtXP6E5VDe5fU7ytw8nGdPfw7XMby42OIvnp3no9+f5mevH+DgjvJ5z8VcM+JbR6t8f7qBY+C1kyVuHSlwvBpyZKnNP7y0eNnjGgLXMJjzeOv+Afb3eAubtZbj1ZC5VkycWvKViIVKkziFwDGUA5dy4FD2XRJr+fwLixyrhtw6UuDnDgyeN376SriO4Sf39nPLSJ6/enaB//PMPLeNNnntZImd5eCsFs+pesjfvrDIS5WQyZLP/TcNc93A6kdQbxa+6/BzB4b486dm+faxKj+5t3+jV0lERERERGRLasWWnMO2eP9FRETO1i12TCz0QrFrK8mChxrbu/Uo6HeV/PVf/zWf/vSn+dGPfkSaplx//fW8853v5F3veheOs3WbGURERERkczteDfnm0QquYxjOewwXPEY6X0v++o9sjVPLU7MN/vFknWPVEAPcMJTjZ/YNcONQntlmxNFKyNFKyI87I3rftLuPu3eVVzWS0zGGfQM59l0wvNNHklqOVbPbn2vGvHqixA2DufO2tei7vHK8yCvHL9wOtlr9OY87Jz3unCwBWVCqGafUo5RamFDwnEu2yK3GLSMF/t+7duFu0wav8ZLPzx8Y4t59Azw5Vefxk3W+N1VnZ9nn4M4yu/sCdvcF9OfO/9X4+sE8v33nBH/17AJf+PESzy+0GCn4LLVjKu2EpXZCPUrxnCz0+aZdfcttidcP5vlne2BopMz3X5yjFiYU/WzscdF3Os112f4fJdko6Si11MKEaphSDROqYcLzCy0eeWqWd948wq2jhWv98F1U2mnezJozW1TD5NILdeQ9w/03D/OK0cJV+3TrRCng1181zrePV/nGyxWenm0CMJz32NXn4xrD96cb5D2Hn7thkNdMlrb0H2IODOW5fbTAN49WeMVYgZGCv9GrtOEq7ZivvFSh4Dm8cWeZgfyl3yKLkpSZZsx0PaIVpxwYyjNa1GMpIiIiIiLZezutRM1JIiLbVc4zgGUxtIzmN/61oBFlH0AvKBW25egpvQre97738cgjj5DL5bjrrrvwPI9HH32UP/zDP+TRRx/lgx/8oMJ+IiIiItJT2nHKl19a4vGTdYq+Q951ODzb5MwussDNwn8jhSz4N5T36A9c+nMu/WeMGg2TlMVWwlI7Zqmd4BrDAcfFjZLlcF61nfCPp2r806k69ShlpODxs/sGuGO8eNaY3t19OXb35bhrV/aGqWX9PxXtOobrBnIb2uRljMmCYL7L2DqHRrZryO9Mec/hjbv6eENnRPFqw2Ul3+Vdt43w2Mkaf3+kwsuVsDMe2mWyFDBc8HjVeJFysHLo1HMMe/svvF8FruHMRc997ptxyiNPzfJ/npnj7QeGloOh10I7TlloxSx0juXuyOFmZ/zwdD2iEWdBxwNDOW4d6WdXX4DvOEyMlaks1PEcQzvJAozdEGsrSblpuHDWcX61uI7hnj39vH6yzIlayPFayMlqyEtLIdUw4XU7Svz03gEK/vb4/fyt+wd5fqHF/3x6jp3lAMdk51PHsLwvryZAvdSOO2PI20zVQ24cLnBwR5n+3OaZTXVopsHnn18gtpCklsdO1njFaJG7d5eZKGUh6yhJOVHLxnsfr4ZMN2LmW/FZt/PFF5cYLXjcPFLgLtej2COjWURERERE5Nprp1lzksb2iohsT3nX0OcbFtuWft8SuBv7HlEjthRctcxuRQr6rbMvfvGLPPLII4yNjfGpT32Kffv2ATA7O8uDDz7Il770JR5++GF+7dd+bWNXVERERES2LGst3z5e5bn5FpOlgF19Abv6fIbz3nkBBGsth+eafOHHi1TDlNfvKPHm6wbIew5JallqJ8y3YuaaMfPNmPlWxIlayNPnhAAhCy25xtCM0/NX6vkFAIqew2De5VQ9IrVw03CegzvK7F+hQe9cxhj0K6lcicsJ4BhjeMPOPl6/o4y5zNu4XAXP4YFXjPK/D8/xuecXaMYpd+/uW7fbr4UJM42IhVbSCfXFy+G+c49jAxR8h6LnUPAdDgzluXkkz4Gh/PIo7K5yzqPZ+V7eM+Q9h9F1W+u1K/gONwzluWHo9AjkJLXbLgTbF7i848YhvvpShZcrbVKyZsYkzUKlXz6yxCvGirxussSuvgBjDNZaZpsxx6shxzojqeeaWdit5DuMFjy+c6zKo8er3D5a4D7Po0j22lJpJ8vtd404peA5Z/wz+K7TCRtmbzgaoBGlLLYTFtsxS62ESphQ9ByGCl6nZdZlOO91WjHX/vw145TPv7DAD2ea7O4L+IWbhnEM/MOJGt87VecHMw32DeQIk3T5dQpgpOAxWfZ55XiRsaLHeNHHcwzPzrd4Zq7Jd45V+faxKoM5l4M7y7xmorQcfhcRERERke2hFWe/QOQ1IlFEZNsazRvqkWWmmbKztP4TklYrTi1hCsM5vSZtRQr6rbMPf/jDALz3ve9dDvkBjI6O8tBDD/HAAw/wkY98hAceeECtfiIi25i12ai+ZpTQjlPaSUoYW062U6ZnazjG4DoGzzF4nU98zNVDpqoh07U2U9WQ+XpEM05oRimtKPtqgJFSwGjJZ7QcMFIKKAduJ5jQCcmYbPzkQN5jsOAzUPAYyHvkLvMPppfj5YUmf3VoiuFSwG0TZW6ZuHYNRSJbXZJa/vr5Bb4/3WCs6PHEVJ3HTtaA7I3GwZyXnQ/IzglRYpluREyWfP7VraPs6js9LtZ1DMOd9r4DQ+ffz1I7Gy9aaWdhjEo7IbGWwVznHJNzGch5xKklDnxeOFVhthMYPLijzOt3lBlWb7xsEhv1ycfAdXjXbaN89tl5vnRkiXqU8ObrBi47pLbYinl6tsnTsw2O16Ll7zsGBnMeg3mX20YDhvIeQ3mXobzHYM4j75kt1VS23UJ+XbeNFrlt9Pzx41P1iH88WeMHMw2+P91gsuRT8ByO10LCJPtjVeAaruvP8drJEvsH84wXs/D4QivmsRM1vjdV59DXjjBS8KiGyfJykL3mnBsOv5SS79AfuEw3In4w0zjrMtdAKXAp+w7lIBuLTec+rKXT/gq+awgcQ+A6uAYeP1mnFiX89N5+/tmevuXj+r79g/zk3n7+8WSNJ6ca9Odc7t7Vx57+bLz3hZoOD+4sc3BnmWacciqCrz83y9+9uMTXXq5w50SJN+wsM7SKkcAiIiIiIrI15F3wt+nvmyIikr3nOJI3zLQstcjSt0Hj3Jud8HlR4fMtSe82rqNTp07x1FNP4fs+991333mXHzx4kImJCaampnjyySd5zWteswFrKb0mSS3TtTbNKCVwHQIv+yNE4Dp4bhbyuVxhknJiqc2xxSYnK23mGxFz9Yi5RshcPSJKUjzHwXdN55/D7sE8t0+WuX2yzN6hwvIfPqy1LDRjji+2mK2HFILsD/j9OY++vEcpcM8KDXS3rRUltOKUVpTSilOiJCVJLUmnOSK1lh39OXb0X7rF58zH7GvPz/H9E1XynkPBdykGLgXfyYJKZEEmx3QDDCmNMAtBNcJsfco5l9FSwGg5YKwTiupfoeVIeluSWhYaESkWtxOMcx1DnFiOL7V4eaHF0cUmLy9k+22UpMSJJUotcWrxHcNouROKKwWMlgJ29OfYPZhn10CeYA0tHElqmW9ETNfazNRCpqshc42Iajum1o6pthJqYUy9ne2DzShZbghZK9cxjJUCRko+QwWfnf3Z/p/3XVJrmauHzNQijswvMd+IVv1HVcdAwc9uq+C79Oc93nbbGD99YGTd/hhea8f8+T+d4K9+OI1rIErs8h9ib5oos28wj9M5bqPUEiVZm9BA3mewcDqY6DqGhUbEQjPOvjayc1o3HOk62Xkt77mUguwcUQyykZhBp0HG61yn2z7WihIaYfY1Si2+a8h5zlnnZGuzR7P7mA7mffYM5RnQ+UN6RCtO+d+H53hxqc1P7e3nnj19WGCmcbqNqR4mWRCC7PU97xrunBjg4M7ymoJMZ4YAV2NsrMy4e5knPpFtznUM9988TN5b5DvHa/xwpsndu7PWMN+98M8rSWqZb8XMNCKmGzHPzTc50Qn37Sj5vPm6bNzucN6jP+dqjMM2NlHy+bkDQ9y7b4BDMw2emKrTSlJeOV5kVzlrhh0trPzzzlDe4637B/mpvf08W485dKzC/sEc40WfsaLPWDFr4ItSSzPOxj834+xnvdRmvxNaC6nN2heiyW1EAAAgAElEQVSzkLh71r4dJZbFdhYUX2jFVM8YB73UTjhVj5YbX7sfcElttlyYpnQLKkcLHr9+6zg7zwi1dxU8h5/Y089P7Olf8+NX8BwO7ujj+oLDiVrIPxyv8fjJGo+dqLGz7DPUGXc/mMsCtKlleRx2I0poxZaxosee/oCxoq9jUUREZBOwNntPK7Wnf/5Y/nmkR1/Luz93bYZ1FdmMBnMOA4He+xIR2e76A0MltMy2LCXfbsj7PI04+9trTuPktyQF/dbR008/DcCNN95IPp9f8Tp33HEHU1NTHD58eMsG/bohiGvxC2IYp1RaMZV2TDNMOn80SGhFKe04xTHZH8V8x8F1DBZ7OuxWz8Ju1TAmTcGS/ZHBWosxhtwZobucly3vGLN8m64xFAOXvpxLOefRl/PI+w4LjSgL+dSy1q1qO8maBnIufZ3rpdZystLmZKXNVLV9ybCPY+i0ejn057ws7FL0Gcx79Oc9EmsJ4ywQEyWWajvm2GKLU+fcds5zGC35jBQDbh4vkfccosQSpdly7Tjle0eX+PKzcwCUA5cbx0pU2zEnllo0ohXG8K2TgbzHzeMlbp0oc/N4iZvGS/Tlzj5FxUnKV56b59PfO8GJSpuBvEeSWhprCExlwR+Henj+Mr5jGO4EvoaLPkOdcUzeGcEhYwxhnBImpx+zZpRQbSfU2jG1dhbmKvgu4+Ug+9cXMFbKEXjd0GR2f6m11MOERphQC5PlAFi3wa2/FBCHSXa/dNvYwGBwHfCc00Elz8kCSXnPJd8JPOZ9h8m+HMNFf8PfsLE2C9a14pR2J/TZOqeJLuyEQItHKyxVWiTdKg44/c6ThZl6yImlFscW25yotIiSiz/5Oc9h90Cenf05As/B7xxLnmNoxylz9ZCjiy2eOF6lESbLyxlgsj/H7oE8fXmXwHXwO+cF1xgqrZj5RsRCM1oOnCXn7FRF36Evnx335ZzHdaUCpcAl77udkGoWzst5zvI5Z3ykTKPWArI/TCapJU5TUgsjRZ/xznO62uBdnGTnw9OhntP73lIzZqkVdb7GnUBs9nw0o4SXF1r88Vde5H89cZIHX7eLN+0fuuwfRpPU8nc/muXj3z1GpRVz361jvPvgLjzHcHiqztNTNZ6ba/LokcVO+0oWQg5ch9TC060aS634vOPWwPI5MXCzMaNJJ8gZd8LGjc5rwtXUl3PZM1hg92Aex0CjExrsPqbdc0b0/7P3pjFypOmd3++NOyPvzMo6WReLN/tid0+r75me6bmnNZc0Ws1K1uoYYQ3DK6wFS5bXhkcLLeAPgmHAkA3vwoAxOsaQNBqpZ/pknySbZPPq5n2TRbLuu/LOjMsfIjIri0c3ySabxWb8gKrMyCMy4o0n3oh44v8+f8elHogbU4ZC2lTJmCo92Sg6HlFdJqbJRHVfQG2qMqYmBQJMGU3+bFVTCrm1LNZs/u7oDDMVm2+vTfNQUClT4As4OqIqD3eG1TNDQu5WJCH45lCKDdkI2y/lefXcItsuFXiiO8bmnEm+5jBbsZmtWE277dnK8mNnV0zlSwMJNmXNsJpmyFXRFYlHu2I82hW7qe9+cU2W+5NXiujArwioyRJJ/caXS5VFIBpUb/zL+Oe/luN9KudS3TGN763P8PxAkn0TRUbydUYLdY5OX2l530CVBJa7VDlxVVxjTdrg8e5YeO4XEhISErLicT0Px1sufPPwB3nXHJeq41IN8qiW6+Hif9Z/9Cv1arIUVOEN8vJBRV5dFqhBXv6jcNylgb111xf6+4J//zXHWxp4bgfJ2UauXwqe28H5ArNlylUbu+VEuvXn/fkE63yN5WnMU5bwBycLmm3THORwje/Kwv+OEtyD0GVBLqLSHlExrmNQsud51ByPguVQtByKlkvJ8gdJVJwrf1Xgn4toskCTJOKLVVzLz5F6zX9+GzTWpXF/pNEurVuntcJyIyY8b/n7V11uf+Gb7/sDg4PqzC3L5+KvXz0Y0FF3PBRJYMgCQ5YwgjxrIy4bRQdcz7/P0tzul21/Sfj3m1zvztyMD/nsEJ6/h4SEhIQIIchFJEZKLnNVj7bIp3ts8DyPsu1hfsYcWkKWCDP7t5CRkREAuru7r/mZrq6uZZ9dKbieX/3q9HSZ09MlzkyXmS7VMRQpqJTmCw0MRUYJrG+UQIRRrjvMBMK5mVKd+YqN63ooQYU4NRBJNS/6RCBaAqRAPCUHF70C4V94N0UaLp4HqiohggsxWRJULZfFqn3Twg1VFk0xV2dcX3Zx54/894VztUDQVa5Y2K6HG1SgcwPRUrnuXF0wJvvVtnIxjcGMRrnuUKg6jC3WKNZsALoSOutyUT4/lKE7qWNqMpbtUXf836zZbrMtNEMlX6hhOS75ms1CxWayUOPUVIlC1UaWRFPApAXioaE2ky+szdKbMuhN+SInU5M/tjP3PI+xfI1jE0WOjBc4M1MmGVHY3NlGT9KgJ2WQi2pULKcpsswHAiH/+0sX65KgKWgyVP8iW5OlZtW1hoDt4nyF45MlTkwV2XNxsbks3Qmd+3uT9CV0NFnwT4cmGc/XWNNm8r98dQ2PD6SQhF9hq+7426PuuMEyNISb/vZoVClrVIVoVIKbKdWZLtWZKfrV1xoC0POzFT4YyS+Lx8Z6yY3EQrAPGKov+ExHVHpTBjFdoWw5TBfqHJ8sse3c/BUCsFakwEY1qvuCL9cFy3VxgZrlxwGtibIgMWF/xDxbMVWJVakIvWk/DhpCM18Y6G+Pprir7lC2HCzHW1YZTRIEokpfANYQ1taDOLUcf3/1hUxeIGpaEjZVbfemq9ddjioJupI6PUmDx/qSdCZ0FEksJdlcPxnTndTpTUVoj2vXnZwp1R3G81VGFqpcmq9yaaHK6GKVkcVqU6DVWOeEofiC0IjKYMYkY6q0xwNxZ0wnF9MwtRsfJpHLxZmeLtzw966FIksoV6n0k4qo9CQ/+ruu5/HeuXl+sm+Uv9h6lqGsyW9s6WJ1W4SOmP6xFQ8XKhaHxwscGSuwbyTPyEKVzZ0x/uun+libWxIbfa4vyef6kh+77q7nUajaTVFlxvQrcV6P6NFy3GZVz1bRnRXETKOKYUSVMVW/zeqO2xT21myvuR+3htNc2eLifIVLQczsu+T3YWaLSC8Z0dGDPlpV/GQ1+NaJc2WL83MVPhjNU6w5Vyz35UhB8lsJ+qCGEDkZUWiLak3b5lUpg8f7U+EJ/D1Evmbz/xycou54/OvNbaxOXX3QSUhIyN2NEII1aYM1aYMLizV2jOR584L/10ASkDEUshGF9ZkIOVMhZ6pkIwraR1T/Cwn5LCMJgf4pW5UkdJkv9i+dcLueb3m/ULX9gYOqhBlcJwtgoeZwKV/jUqHOSL7OrtECD3dEP/XlDrm7aVwX24EARVwmYGjk5KC1ktPl09eOOS/Iibkey0QzzeeBgKXh4OAE+ZmG+4PU8nsNkUur2Kcx78brXrBcje83BkFKNNYnyOU1P+PnFptt0bJMrRWsWtvBaVmfRs6lub4tj54Her5GpepXxm1cizXynrIQwTp5y9bFCgQmdktuqbGckliyNm/8fuN7jXmrLb+xrM2CtvWukmsRYmnerU4dzcF/wVq5Hi2CHA/bpbmcdhBLjTbWJot4jrusCpgfN/7UcvHM0jZuDDpkmSCqVTzli4Ya21RqERE1ni+JcZbP3w2qwbotOcBG9q7x23IweLrpAiGWhFaN2GgKw5zGo4vjeU3HEH2ugmXZ0Gz/pe3cWPemu8hSwwQtE8xD8sVrekPEJgsUsZQXV4L9s7E/NGLRW9ZaBMuwJF5qjV3nsv2zdV9ygo0gBcsjB23qQTAIfOmvMR8xXsCyXdxgCRrfba2c24iV6835KQJUSVpyYgm2qb8N7Kbo/Wqo0lJct+7DjZj9uLsEDfFc4z4EXCm6k4Pliyr+MVptrPBlgr5WoVsjRhvx1zrI1rls+0DLvgLXzFc2+/KgjedrNuNlv+9JaTIdpkpSU5b1c3ZQOTlfdygEThWt7R5VZTKGgqlITdFhqyCv7rjN/WChYlG1nJbjRdCPtMbex7T3x3GtI40I/jXedy9r+6uhCD8vfEtSz0FeTxJ+zCliSWjajP/GgnpL4s3Gr+uyRESRiASCw6QmE1XDMjohISEhISH3IoYiSKiChbpHXPPQ5U8vv1OpuzgemKEa7DOL/OMf//jHd3ohPivs2rWL3bt3s3nzZr7yla9c9TP79+9n3759rFu3jueee+66533kyFHS6QyGofDTn/4tQgja2nJYlsUvfvEzZFkhm22jVqvxy1/+E6qqkclkqVQqvPTSzzEMg1QqQ7lc4qWX/hnTNEkm0xQKBX7+4j/xv++c5G8O5/ng3BiJyb1YismqXIaYV6FtZj95V2esIjM7O010fB+H5gQHp22GR8dJTu5n2jExzShrzRqbreM8tKaP9d1ZBvUy/YUjdHV20ZVJ0CMX6SseIdfRQ3sqRgfz9OSPEEl3YRgGaXuWVYWjxHM9dKSi9Mnz9JeP0zOwmlREJ+dM05U/Rlv3APf3JNkSzbPRPs23n3mEr2/q4NH4Imvrp/h3336a39jSzRZznsHqKf7bX/0839zUzoPGDBvd8/zHHz7Pdx/opMcexZw7yY++8RTPDmVIFIcxF07z+197kufWZsmWh+msXeDf/eozfGtzOz31YQa9cf677zzDdx/oZLVzgS2RWf74O0/zzU3trLbO83hikf/515/lOw90Eps7Tg8z/P5XHuMbm3Lk8id4LFXmv//OU3xjUzti/CBZd44vPryZ/nSES0d3YdRm+fKjm9ncGWf25G5yosBvf+1X2JAxKJx5n/XxOr/z3Ba+tbmd5NQBvr5a54+/9Rg/eKiL+OR+nuvT+dHzW3h2KMPE4Xdo0xw2Dw2gKRKvvPIvlMtlOjt9wekvf/lP1Os12ts7AXjxxX/EdR2GensYajOZ/HAr39zUzm88uZGHe+Kc3P0KPakIQ73dJHXBgW0vsbY9wdObBlif1bl4YCuPDrTx3H0DrMuoXPrgDR4eaOOxdb10RjwO73yNtd0ZNvZ1EhV19m9/hcHOLI+u6WFzRqBeep/ffXodT69fRY9RJzG5j7MFia3nypy4MMYW5xi/9vg6/pvn1mM6BbZufZm2thzRaIzZ6Ul2vPsag6t6aE8nKM5NsWfHVtb299KWjDMzOco7b79OV1cPhmEwOnKR9997kwfXr2FtZ4pobZrF0+/zwy88zDfu6+bBeJGOxaP8D999mt95vJ8n0mV6S8f5D7/2LP/m8T4eiRfoLBzjT773LN99sItBaRpt8hC/+9UneXwgTbY2hjlzhD/7wRf5V1u62KBMscY6y7//zuf53gMdPKhPsd69wH/4wRf5ncd62ChP0G9d4I+/+3m+fX8HA84Im9RJ/uS7z/CvtnSxjhE2q1P8T7/+eX74SDfrvYs8HJnlf/y1Z/nBli7WOMNsMef5w689zlc3tLG6fpYHzAWeeGATbTENffoIxelLvDKqsO9SnuL5/Zw+d45/OAdvn5kjf2YvZ4aHeWVU5tBYAWX8A0ZGR3ljXOaD0TzexX2cHxlj+5TCyEKVzOyH1EsLLMgpXM+jc/5DIm4FNZEjE1UZyH9Ij+kxsGoV69pjbCwf4sEOjafuG+LpwTRD+QM8OxDne49v5IXN7XTP7uGFzW38/nP384dfHCJy7l1+/aFO/u2XHuD79+dIT+zmB1u6+NFz9/HDLR1I53fwpfUdfPnB1fQnFU7sfpXN3Wme2NDHQELmzN7XGepIsaojR6VSvqLfe+WVfyEajZFMplhcXODVV18kHk+QTacQtSIn977Js5v6+caD/TzeIWGO7+UPvrCZ3316LV/uU8jNfsC//dL9/Pqjg2xOWpTO7OIrW9byYH8OqTzHvp1vsKq7h0gkwujoJd5667Vm7F26dIG3336dnp4+dF3n4sXzvP32Vvr6BlBVjZGRYV577RX6+1ejqipnz55m27Y3GRxcg6IonD59ku3b32JoaB2yLHPy5DF27HibtWs3IEkSJ04cZceOd9iwYTMAx44dYteu7axfvyk4nnzInj07WbduIwAHDx7gwIH3Wbt2AwAffLCPgwf3sWbNeoQQzA4fZsAd4+mH72f/SJ6zx/Zz8PBB/rcPLP7p4AQf7N/Fjv0f8l+OOvzs4AT79rzHtv2H+KtDdf52/zizp/dTmL6EmV3Fv3msh83uWUqzo/T1DQKwbdubjIxcoq9vgGhU5+WXX2J8fIze3n4A3nrrNaanJ+np6UMIwfZ3XsOr5rlvzSARVeaNrS+zuLhAV1cPAK+++iKFQoGuLl90//LL/0y5XKa7qxtdkXhn64tEZJcNA720xTTef/sX5KIyGwd6SUZUtr78MzRForO9A0V4bH3l5yQMlTW93cRV2PHmi7TFI6xZ1YUpuxzY9hIPrMrw9S1DPNMfxRjZxW/9ygC/8/QGvjBgUj+zg2880Mu3tqzm/jaFuWPv8oWNq/jS/f08kJVwzu/kt55Yy599/3M83QFtMwf44eNr+daWfh5pg1WLH/L5+wb43FAnGxM2g8XDrOvrZqA9TY9aZlX+MMlMjjoai7OTSJf28saIx+tnC9yfqLJ7xxvXHXvDw2d55503bij2+vpWXf+JUshtPZ979ZV/QdKj/MaWAeJepdmvJRJJ5ufneO21X5BMponH48zOzvD6678knc4Si8WYmZni9ddfoq0th2lGmZyc4I03XiaX68A0TcbHx3jzzVfo6Oi6qX6tEVsbN27Asrw72q8BHDiwh8OHP2RoaB0A+/bt5ujRQwwNrQVgz573OHnyOIODawDYtWs7Z8+eYmBgCID33nuX4eGz9PevBmDHjre5eHH4qv0awDvvbGV8fIz169dSLteX9WsAW7e+zPz8LN3d/v70+usvXVe/9lHnc45jk8t1APDP//z3ALS1teM4Di+++I+3NPaudUxtjb329hyKYtyx2LuTx9T9+/cyOLj2tsTekX3bSVWn+PJDG+mMqsTHDrBGXuC/evoBfqU7zszR94hUZnl0wxrimsy777z+qcVeNKrzd3/3N3c89u5Uv7eSzuc+7X7v487nPsv9XmdnjtHRiWvGXjwWp7Qww95trzG0qou2ZIK5mSnefOMV2ts7ySRiuPkZLh54h28+tJYvrmlncnzktvd74fncjXHkyFF6erooFCq3PLb+5Zc/50xVcNHSOD42xY43X+JcTeFcXeHYyCTvvfUyw5bKcF3m2Mg4O996hQuOzvmaxJGLo+x8+1UueCbDNYkTl0bYv30rY1KcizWJ4xcusmfbVi6JBOcrcOTcMO9v38olOcXZssfhs+fYvf0NLippzpRcDp4+w+7tbzKiZjhbdDh48hS7drzFSTvB2aLNoZMn2LfzHSYjnVws2xw/dYJD729jPtHDeMXh7OkTHN+3g1K6j+mqw7nTxzm5/z2K2QEmyhZnThzhxAe7KGX6marYnDtxmDOH9lDN9jNXs7l04iAXjh3Aaetnoe5w4fiHXDh+kGqml9mqzdmjBzh/4jCl1CqmKhanDu3lzMkj5BM9TJQtTnz4PudOH6eY7GamanP2w91cGj5NJdnNbNXm9Ac7uTh8jlKi05/fB+8xMXIBL91FyXY5/8EOpsdHIZge3r+duelx5HQXNcfl/L53mZ2ZohbPMVO1Ob7rTUanpsmbWaYrNid2vcHE3BwlM0vRcji3eyulUhEz04EHnNn5OtVqGSWZo+a6nHnvVax6nWi6HVkIzu14GddxkeIZ8pbD0Xd+wWzVpqgnWajUObntJQq2ixVJka/WObX9ZcquwIokyZernNz+MkVHUNLiTOeLnNjxCnlXpqzFmMsXOf3eq1QkHUuPkS/kObPrdRw1gmLGsSsFLr7/BqlkklwqBZUCF/a8QSaTQTNiVBfnGN7zFmYig2ZGqSzOMrz3LfREBqFFWJyZ5OyetyCWwVUNFmcmOL/3bbRUDqHqLE6PcWHfOxiZTlB05sZHOL/vXUh1UvZkxkeGOb1vG+V4B3OW4MLwOU7seZfFWCeTNY+z505z5P1tzMe6mKi6nD1zkuN7t1NIrWK65jB85iQn9++gnOljpubH3vF975HP9DNaqnPi2GGO7N/JYrqf8bLF6eOHOB3EXtFymDxzmMmTH5LtW4MQMHnqEOMnDxLrGsT1YPLkQWbOHyW7ajWqJJg6+QGzwydJ9wzieTB6bB+zF08R7ezH8WD0yB5mLp3Fy/QwVbU5eWAnZ8+dZdbs4GKxzgfvb+PY2bOM6m2cWayyd+c7HDt/nnG9jfOFGvt3vsWpC5eY0LMMF2rs3/EGp0fHmNazjJTqHHxvKxcnp5iPZJmsWBzb+QZjs3MUzSxzNZvTO19ndnERO95G0XI5+d6rzBaKFCMZZqs2x7e/zEKpgoi34Xpw7r2XkXFJt7WTMDVOv/siUUWmI9eOLnmcfe9lDEUmlWlDx+XirlfImjoDnR2kZZeLu16lOxFlqKuDjGRzYfdrrGlLsqWviz7d5fTO17ivK8NDvZ1khcWxHa+yqTPLg70d5KhydvdWHuxpZ2NXjphdZPj9NxjsaKMzncIrLXJp75u0ZdtIxeLYxTku7XuLzlw7XekERnmeiQPvsLF3FWvbU8Qqc4wdeIen1g/yUGcaszTDhf3v8tSGITbkUsiLkwzvf5dnNq1jY3sSbXGcCwe28+x963l0TScLF85yYu+7/MrGDfQmTcoTFzi1bxtP3reJnkSExZFzHN+7jcc2b6bN1Jm+cIpj+3bw+P33kzM1Zi+c4tSB9/jCw1voimosXDjBhUPv84WHH6LT1Jg+d4zhI3t5+qEHaDdVxk8fZuT4AZ568H46TY2JU4eYOXOI57Y8wGBCZ/bMQRbOHyfWNcBoyeLYh3s4dcLv96YqFmcO7WHs3ElS3QNkDYXy6f1IMxf54gObWJ+OMHL4fapTF3lkwzoyhur30VMjPLx+LW0RlZP7tuPOT/LUpnU8OtjGiR1vEinP8fnN61mdNBje/y7x+iKf37SOtakIF/a+TdYt8dSGIYaSBhf2vkWOKk+tX81Q0uD8+2/QJVs8s2E161IGZ3e9xirN49kNq1mXinBqxysMmBJPrxtkXSrCiW0vMRRXeXLtAGtTEY6+8wvWpiI8MdTLQEzl+LZf0pcw2Liqm05DcP69V1iXifLE0Cr6IhInd7zMumyCTas6SUo253a+Sm86zvruDtokmwu7X2ddLsXG7nbSXo3h3a8zmMuwuiNLzC5zcc8b9OUybBzoQhTnubTnTbLpDLFYnOriHOf2vIkRTyMbJsX5Wc7veRMtkUUxIlTmZ7i4722i6RyWojMyNsrx3W8xp6W4VBNESzO39XwuGr2Jctn3OLfzfG4lX6eG+bkwPxfm525Pfu56Y+9O5Eju5fxcKhXj2LHjdzz2Th/dT1vvOvJ1jwMH9nDw0IcYnUMs1Dz27d3N4SOH0Dv86T17dnL0+FH09tUs1Dze372D46dOouUGWKh57N65jZNnz6C29bNQ89j53jucPn8eNdvHQs3jve1vcfbCRZRsL1OFOkd2v0lxZoy+MD+34vu9mzmfCzWcdwmxmE4uF8dxHFRVJpGIkMvFsSwrmDbI5eJUqyqqKpNM+u+Xy/KyzxeLInjfJJeLo2kukiz5lfEE6KpETFIY7EmyZqiDuVmF0YJKvDNBp5KgVnCITMqszkTp1hJIFZfYokJuVZLurjbijsZETeOh9TkiiQxT4x6n8hqr17ahRpPMTVqMlVQ6+5JIepziTIW5kkLvqiSSblKdrVCqq2xenSEWj1OaqjJe1RjqS4FqMDNSYKamMjiUJRWPUpwoMFbSeHh1G7lUlHPyLJVpjftX55BlmepCjKlRlYfW5gAQxUsUZxTa2xNBuxpomkIuF2+28+XTCwtqczoa1SkUlt43TY1KRWVwVRqAmYtR8nmnOX/T1BDCWfZ5yxLN6UjEtzRqTBuGiqqqy6ZbP2MYKqapL3vfNLXmtK4rRKN6y7TajB0ATVOuMm00p1VVvmI6HjduW+ypqkwq5U8rio2qynTl4jzW10M6YvPORZXeeIQJW4YqaIpELu1/3nXLqKpMOh0ll4tTr+dRVZlMJhosj9mcbmuLUyr509lsjHQ6TqHQmI6STMZZXFx6PxaLMTcXQVVl2tpimKbJ9LThzy8bJR41mZz0p3O5OKqqMj4eaU7Lsszo6NL7AJ3ZKLMTKptXtwFQno1RmNMY6EkBcP6qscfHxl53p18h4nRcx3M11vencT2Yv2hSLgsGV2eJTpU4OaniuS6mIy+zp70cAX5lQVtCkRS6TN+GNFqSMRSN+7MJIppCalpFjhj096bRFAnrtIaeMMkMtWFqMuMlnVQuzvoHe4ioMu/PG3T1JNn8QDeKJHjjvM66VUmeeqgHIQTDBzR6czHuC9rHNBQ6slGGetM4jsNOXaEzG2NtX+a2xV5jWohaEFv+tOdVlk07Tuk2xx7LYm9mZikWDcO44diLxa6cvvF+T+XZ+7s5Nl3m7KGleClbLjXPQ8a3DAeCqpouRc+PMyEgbaqsWZ3hiQ3tHM1fX7+3vJ/7bPd7qZQJQEdbnIiusLY3Ta6znWHTYeq0ypruJEYyw9SkxakxhfaOOMJMkp+tMj8rE0tFUN0I85aKUhbNCrjJROQT9XvXE3shN8btPJ/TVIVcXKO7Iw5VscL6NT+WwN/3V0a/duUxdWlax7Zry94Pz+c+2TG1se53Kvbu7DH148/nPmnsPTCY5QHgpUsGqqrSEVyL3KvH1JXR763E87lPv9+712IPIJOJrohjbng+d/uIxfzkq9+Gtza2hCRhBxXg7NZKaJc9+hWirnx+O/Dwq1zdbOUmRRLI8pLTh19Z1mtWazMUCSEENVmiLgSxoCp+TZGoS76DguuBHFQAUwJnCEWAI3zLcAEUJYErfPcFv4qbwJMEEUVCkgQlRUJ2JaKajOt65IXAFb5VtiJJlDhEPnUAACAASURBVGTJr4gb9fu3RUVGSDIJQ8UD5mWBovhV4IWQUWUJRZGI6Qp1x11Wlau1YqISWI9KwWM0ovrV22QJQ5VJmCp4fiV+U1fIxn3HgguKTCyikI0Z1ByHMclvq4SuIOExKwuiukImqmFbgqnAbSKqK9jCRZYEuioT1xUsT2FeEpiaQtJQsYXNnCyI6QppU6OOxqwkETdUMlEdW9QYlyUSEY1MIoLiVFBlCV2RScZ05LqGIgsSpko8pqPUVVRZkIioRKM6UkVlRpZImRqJuEGhqjOvyHQkDGIpk7mazqIq05WMYCZMpsoai4pEW0xHieiIvEpRFiQNFaEpOIpEsbHtP6IAhl+9kGalR12RkCSJynU4ADRi1VAkarKMJQniEd8qfkGVqEmCVFTD82BRlbElCUNX/FgLnEsiuoIQAk2VwZJJmn4szasSkuu3r2w5LHD79tfLaVYda1R1bClFJgm/kpvAd9xB8vcZELgIHCEo1R2/DxACJ7BLBUAWCEVCCAmEQJJlZE1BkvxkhFBlhKaAq4AkkDQFxdRxFRdZkYjFI2TbYlccU5EsFEXCjOnEkxFKdR1JlpA1BaHKuLK/cT1JAkVCkv3+Q1ZlFF31PycL1IiKbmqoERVJltCjOpGESSRvICsyyXSUtkzsI4+pAInEnb2WiEY1NE0hnY1RtRyQZTzJ3zbXwgMcIXBlyW8nSUBEQ9MVjIiKY1/f+Zznec3vpDK+I4emKWiGRjQZwXY9JEVGUhXUmO8mICl+HGgxw6/GqcpoEY1EOoos/GOeGdVIZ6MI/GkjohNNmX71UFlCaCqSqfuRJglQZbyIhmfZIPmxoJoaqiMhKxIRUyeVieFaNRRFRjc19JiObFt+rMgyrirj2jKeELiKDLqKZPmxYRgq0ZiBZVeQJAlFU6lYDrKqICSBosqomoKiyn7fIguQJDxJ4Am/rR0hcIK2d4W4ogqnEJBM3t78XMiNczvP51b2dWqYnwvzc2F+7k7G3r2WI7nT/R7c+fM509SwEM1K9Y3zhMb19a2Y5lrvBy/Kl8XivRB7cPfl524G4bX6EYR8In7yk5/wn/7Tf+L555/nr/7qr676mb/4i7/gr//6r/m93/s9/vRP//S65z07W8R1PXK5W2vp2GAisGo9PlXk+GSRc7OVJYtCfOFPQlcwNXnJUkASSBIUaw6ThRqF67AcvN2osiATUUkHdo51x7dpLNddypZD3XYxVN+OOKrJTWvFxvNoMI3AtzFtWJRavnWj7XrYgZ2B7bpUG59psYOM6wqpiEra9B/juhJYfvrWj1aQAMxGVdqiWmC1qKIrvuCyZvs2p1XLxXJdbMdDj6gs5KvUHY+K5dsFN2yDq5bT7Mgbl/maItGfjjCYMRnIRhjMRIjpN6brrVoOJ6ZKnJoqoasS6aBd0xGVuC5jux7VYHkbfw2r1oYtpiwEccNPLMYNhbiuEA1iqJVy3eHEVJFjE8UgDktNMVpvJsLm9ijJiMqrx6dZrNpsaI/ygy1dPBFY994MrudRCdqwWHcoVG0mCjXG8/7f2GKNmVK9adtpOUvl/3VFIqb5Vrtx3V+vhKGQNILHiELVcpkJbIBnSnXmypY/j6YFrz83Q12y9zRVGU2RcAKrDEmWKFeXbKObFhyuL6aq2n781Sz3I60tABKGwupshP50hLiuENF8e9LGPl2uO5RqfluUajZV221amLiBFUg9sD6tNmx+Lae53euOd91Wwq1IAqKaTExXiKgSuiIRNVRwPXTFt72oX2YFnDQUuhI6XUnfirgzoWOqMmrDLjywNL0eO9erUajZjC/WGMtXGc/XmMjXqNlu05KoYemRMVVyMW2ZXW/GVD/WzvbjuF39/M1StRz+8eAEf//hBK7r8fVNOda3R1mVNOhOGiSMq/ctddvl2GSRD0byfDCa5/R0CUkIfvW+dn74SDfxq/RJ17vutuOnl9WbsB9sHBeKNYdizaZY9/t50WoNFNw8arXKdi87XWpYLRVqNtPFJQvwmVId8PsJVZZ8ax41eFSW/mTJT5oXajbFmk3NhblijWLNf81yrm9/EsFvNayjczGd9phGX9rg2aHMTfeR10vjhD3k+rid53PTZYv/8uEUqiT43voMQ+mVZ9270vq3T5t7ef3Ddb896161XS4s1hherFG0HHKmSmdUpSOqktDkO2rffi9vc7i31z9c97tv3cPzuRtjdrZINhu77dvabbFtdC5P3TbuElz2mu0tt9+0Xa9p3SoFlpWttpsC0fJ8ue2tuOznGqRSJgsL5eb0kr1kq5VoYInKkg3t1fBabBYb1pGX07BCbbXtXf75JfvUxnGvseyN3EurNaXUMr+G0Klhq+s/Lv9tcdn8FFWmHuSrZMm3i2xY7MoNMVXL8jfm3dgeVpBfabW/Ffht5bhLFpyO6zXnq8kCTZIC614Pp6VdG3bEEi3br2W5W+0lG5azXkvbN7ZNw4Z2qS2WW3NKgdCOy5bdn71Y2laNuBKXWTE37Zlbtnnw2LA+beSaW61QZalhMSua267xvDGPxva/fL0a061WwHLrd1u+5wb7jm/dG+S4WvY7RZFxbGdpbS9rgwbeZQHQzNeKwLpXkTBa7Hvly5bN31RL29a96l5xme21t2Q/e7mNb+NGX7PdveVW3SIQJbmtsddiYa2oMrW6w7UMURtxYrdYeF++7h9HY7s2bIavhS4L9EDwu6wtWG43fSOpyattw4atripL4HlLFreNmGp8jiX744ala9O611uKq6ta97LcyrwZs8395Nrtp0qCjKGQNRRiqrzMKt32PGqOR8lyKNsuJcvBvmxGhuwLoJeOB0sWyJa7FP+Wd3Vb8JVK48b55a9xldc/jmb/I5asnqVGX0SrRXmjz/da+lZfQN5q053QZGLq7bXuDc/lbpzbeT7XOH/7NO0Zb5S79brlVnEvr3+47vfeut+r6w13ft3Llsdkxb/XntEFaV18qrnatrYYHwwvYrvQF7v5e+Z3I3d6298MN3M+F1b0u4X09PhlLMfGxq75mYmJiWWfXSl0BmKZL67LAr6wY7ZkETeuLsy6GhXLYapQZ6pYx/O8puCmIb5RJP+5EiSn/CTGkrAiyBdRt11qjkc9ELy5nkdHW4zCYgVV9r9nOR6luu0LNeq+WGOhYrNQtpirWMyXLebKFroikTRUuhK+kE+ThS/Ks5ymsGmqUPfFgIF4qRVVFpiqjBEIkBRpafkVWZAxVUzNIKLKRFRf1FGo+csxX7E4OVWiWLPRZAlNkdBkCV0RWK7H8ckii1X7hraTJFgSJur+Y9JQr0hylOoO756Z46X6dPO1mOYLoZSWbRHVZLJRjYypkjVVUhGVkcUqR8YLnJ4u35Rw63pQguSWL4QRTBfrfqITGMhGeG5Nhvu74jzQHWfDQLbZGf/rR7p5/eQMPzs4wX987QwxTUaRxfJEpbeUaHaDhBP4o1Ol4FEANfvq6TJJQHtMpyuhszqbRA+2m6pIqMF+UKovCYSKgcjnzEyJxepycY4iCbKmSjaqMZCJoAUjzVsTylXbF6JWLIfxii8oU4LR7hFdAdcf6a4qLUkF4W8/I4g7Q/FHgiuSCEYuLyXxu5M6gxmTbFS97ScQjusL8loFsFXLoWL7glXb9cWyjutRs/19uBQIrkp1m7LlYtkurgfFqk3dcZsj/jXZF0LKuvD3rTMlih9TmTBt+mLaXEwlF9XIRLWgrfzYMxSZiuUwulhlbLHG6GKV0cXqFaLlVETxf7vZbwk8rr0PxzSZTFQlY6rBvuWLebNRrbmfRXU5iN+PFqp5jZsmrp9WjdxgYshxPUYWqjieR0dMI/oRgl/H9ZZEnLZD1XI5N1vmJ3tHmSlZPLM6zR883ktn4vrKB2uKxEM9CR7qSfC7wGypzk/2jvLzQ5NsPTnDbz/awzc35ZAkwfBchUNjBU7MDHNsdBHwjw2NfUEgmv12xXKoB/uZoUiBiNgXiyqSaAqyG/FWC4TT1eCY4tymfi1pKORiGrmYhhDCP5bZLguWRa3oUrc9arbTFHO7HpiqRFxX/EoKcY3eVIS4viSOjgX9fOMY4z/KGEogHgz6pTsp5AhZOeRMlR891M4/nJjlb47O8IW+BM/2xsP4CAn5jJGvOewaLTC8WGOiZAH+zaiYJnNkutL8XESRSBsyuuyfK+qyf/6dMRS64yqdUc2/YR8SErIisRyPyVKdVdd57h3y2UMSAkkWrLQ6Pbm4jlSt3+nFuGPcjTcMPgme5zWvJ+61dW/lXl33W7HejudRCwa/1xyXapCv8YXAS4NpG2JSVVoSz+qNwZOyuO5BjO5VxLVWiwiwmS/2ri5fbIjzNF2hVKk3hYOXi6FbRayNgdKSEEgIhOR/riEGawjGWgWqDWGfB8tyxQ1x9jIRNn7bpHXlhgbzeJ5H3fUoW64v/LOd5nPb8/Bcf2ka7aBKgpgqo+mCVNygXrGuELUKaN7TaaybuEwy2cjRO8328ZbEtyz981jKczfm16jIKVp+sZHjbhXF2m4QV07j0UWVJUxFIqL4j1qQL2sKYlmKt+Zy4jW3VeM32tsT9+T+HnLrWKx7zNc8+uNSeM0dEhIScg/ieR4zVY/FuocqQVdUwrgD4m8hBO0RiUtFl5mqR4cZHpM+a4RCv1vIpk2+H/fp06epVqsYxpWVVA4fPgzAxo0bP9Vlu1EMVaYndWOCkogq05+J0J/5ZNYvqiwRvey1XFuUaW+5CC+H9ol+52o4rkfZcvACAcbHiXA+KXXbZbZcZ6ZoUXfcpmBLVyQ0xa9KpsoSXR1x5mdLN6S29jyP6VKd4dkK5+cqzJTqywQwluNRqDlcnK/w4Ui+KZpSJMG6XJTvPdDB/V1xNnTEcD2PubIvoJyvWBRrjj86rVmdyhdRaoFwT5X8R8fzKFT96lSFmk2+alOxXF/sEghv6o5Ld8JgU2eMDR0xotq1405XJF7Y3M43NubYfm6Og2P+RXdjJCT4iaHmKN0g8QCBlU1LEsZQJKJBhcFGdb6OuE5HTLvp7e55vqhooWJjqBIJQ/lE1bTutkSiLAkiki9ESn+C2xHXu975qs14vspkod6sgGkFIuGK7TJXqjNdrHNpocqBkfwVQt5lvxnT6EnqPDOUoTuh0500/KqBCf0jxXU122W6WGeyUGtWcJwL9pO5ksXRiWKzmuPVaFSHlCThj94PRsxbgSDt8kqNcV1mVcpgVSrCqqROR1xHiGAUfjDaO1+xOTdX4fxsmQvzlWW/HdP8OG+Pa3hAvmKzWLVYrPrC6auxLmfyZ88PcV/XJxsdmo1q/PsvDPKr93Xwn3dd5P987yI/OzhBxXbJB4LJnpTBxs5YU7DXbAMPIkEFSlOViWgyAoK+xRfcFmp+/6JIQWVISUaWfeudxp8eCOZimtIUW0Z1f54eLG2D1iRtI9kYJGobI3XBF+vGdJm2qHbDlRwb829wt+3vISuTnKnyBw+289KZBd65mOdSvsY3htKUbYf5qsN8xWa+alNzvCuqt6yK6zzYbqJ/wqqkISEht4/hhSr/eHKOqu3Sm9D5fF+CgaTOqrgv2qvZLpMli4mSxWTJIl+3qdoexYpF1farcTfOLSQB7aZKd0wjHZFJaDLx4C+hy4G1YkhIyJ1gqmTxs5OzTJdt/uTxbozw2BwSEnKHCAcNhXxSZCEwFRnzU7oLJYmgCuYnLKD2WcjRCCGaVeXSN/jdu2H941zfRhYNkaA/dTsXKSQEgLgmmKt5zNU82iNhzIWEhITcaxQsX+SX1ARZ4/oHrNwOdNmvJDhf84jbHqYSHpc+S4RCv1tIV1cXmzdv5ujRo7z66qt85zvfWfb+nj17mJiYIJfLsWXLlju0lCEfhSyJq9pJ3i40RaIrYdCV+Gh7PV25vqqKrQghaI/ptMd0HutPfezna7bLfNkibapXvcmfiqiQvaFFuG3IkuALa7J8Yc0KWaAAIfwqe523uRx/iE/CUEgYMda3X9/n/cpuTtNyumq5aIqgK2HctLBFV6RAeHftfdjzfFHtbKnOTMkiX7UCi2SHYlCZ1PUILGogHtOpVa1lFVDVoHLleKHGyEKV/ZcW2XrSuuZvZkyVwUyEb9/XwWA2giZLTBZqTBZ8UeLYYg1ZgoShsqYtSjKikAiEr4bqC44NxRerbuqM3dIT0aE2k//1W+t5/+IiPz80SS6m8WC3X8HzvtVtKz6ReKu4kyf3IZ9tNFniO+vS9CU1Xjm7wP+xf2LZ+zHNH+Heai1jex6Hpyu8eWGRB9tNPtcVI2deKdh2PY+S5VJo2N7X/YoAvsW6b/PTEBdH1JbR9KqE7YI9U2V8rsxizSFfd8iZCvfnTNZlIuEo55CQj8DzPHaNFnljeJFMROF37s9ddR/VFYm+pE5f8uoVwDzPI193GCtYjBXrjBXrHJstU73c0ws/EZTUZRJB5ZCELhNVJaJq8KhJxLVQEBgScivxPI/9EyVeO7+ALkv8cHNbKPILCQkJCQkJCQkJuYtQJUFSEyzWPdK613SJCgkJCQm5N6g5/tCCNmNluHGldUHR8pgqu/TFpfDe5A3iVwwH7Q5UZfw4QqHfLeYP//AP+aM/+iP+8i//ki1bttDf3w/A7Owsf/7nfw7Aj370IyQpTNaGrCx0RbpuS86QkLuRRgXK5Kf8u0KIQJSoMHgd2tTrHTVbrjvMlOrBb+DbgwgwNdkX5q5ghBA83p/i8esQIYeEhNw4Qgge6YyxKq4zvFgjpcukDYW0IaNeQ5QzWqizd7zIgYkSe8dL9Cc0IqrkW/sEf5WPsL1XJRFYrfvC5EpQXXXZcuFXFk3qMjlTYSRf58RsFV0WbMxGuL/dZCCphxebISuWfFDV+tMU3tRslxfPzHNspsLGbIRvr03f9AAFIQRJXSGpK2xsW6rCXrN9AW/jL193yNeW/saLdUpXqYwsgK6YSl9Cpz+p05fQMD9mwIvr+cLgOyle8jyPhWDdVsW1Gx5QFRJyO6hYLi+emePEbJWhlM531mWIfdJyRCEhISEhISEhISEhnzppXZCve8yFVokhIfcMrufbdrsepHQRinzvYeqOhyavnOroUmDhO1pymat6tIXVZq8bx/MYK7o4HgwkVl6OLhT63WK+9rWv8Zu/+Zv89Kc/5YUXXuDJJ59EURR27dpFsVjk+eef57d+67fu9GKGhISEhNzlmJpMn/bJrNJDQkI+23REVTqi1yf87Ylr9MQzfHkwyYGJEoeny5Rtl6gq026qmKpfmS8WWHvGNIm4KhPV5GtW43M93yq0YrtIQjDUk2Jutrjs/fMLNQ5Plzk2W+HDqTKmKrEhE2FTW4SBpI4siaYoZ3ixxoXFGhXL5dGuKGvSxi2/YJ6r2EyVLVan9LBS2WcU1/OalXSvl2Ld4fXzixyeLgNgKCIQzypkIwr3tZm0X+e+9lF4nkfZcpmu2MyULabLNmfmq8xXbZ4fSPJkT+y2JIkagyHarlIlsIHj+stWshxKlkvJcpmpWFxcrLF3vMjuMX/fjqq+4NfQppFcF0US2K5H2XapWC7VoOrnhqzBt9akiX4KlbBdz+PkbJWRQo3xosV4qd6sYpjSZZ7ujfNgezSsLBryqeB5HpMli9mKzXzNZqHqsFC1GS9aVB2XLw8keeI27eshISEhISEhISEhIbcfJajqt1D3SDveiqwCFBIScmvwPI+i5TFT9QhSXk3b1rQuwlzTPYbnedRciK0wi9yIIkio/nEppnkY4XHpY3E9j/GSS82FLnNl3icKhX63gR//+Mc88sgj/O3f/i179uzBdV1Wr17N97//fX7zN38zrOYXEhISEhISEhKyIomqMs/0JnimN/GJ5yUJganKzQpfl1fNkoRgKG0wlDb4puNxer7CsZkKR2bKHJgsYSiC3rjOZMkiX3cAMBUJWYK/O1alM6ryTG+cjdnIxwoiqrbLRMlCAqKabz+qBxe040WLE3MVTs5WmCrbgG9b+nBnlMe6YqSMpUumuuNydr7GqbkKQsATPfGrWqjeCPmaL/BwPfCCmokekNIVeuLaJ5o3+CK17ZcKPNoV/cTLerdiOS5nF2qcnK1waq5KxXbJRBTaTZV2U6E9qtIZ1Ugb8rJYcj2PveMl3r6wiO16PNkTI6rKgTjHZrJkcWK2wvZLBVbFNbZ0RLkvF0GTJTzPY65qc+7iAsdH8+TrTotltr+lbdevbme5HnXHpRbYYDdQJUF7VOVba9oYTBmffsO1IEuCuC4T168U5tmux1ihzoV8jYWag+14CFWmVKk3q/elI0pg5S1jOS7vjxX5vw5M8sKaNOuzt2/gwsV8jVfOLjBRspCFL4De1GbSFVXRFYndowV+eWaBdy8WeGpVjIc7otesfBoScrPMV23OLVQ5t1Dj/EJtWcXbiCKRMmQGUzpP9sTpvgX9fkhISEhISEhISEjInSWt+/a9czWPzrCqX0jIZ5K64zFdcak4oEu+GEiWYL7msVj3yNc9EprADERfrU45hnxjg5BD7g4cD1wPVqJBQzYiKNkeMxWXVbEVuIArCM/zmCi7VB3oiAii6srcV0Oh323ihRde4IUXXrjTixESEhISEhISEhKy4lFlwaY2k01tJpbjcXahyvGZCiOFOr0Jjf6kTn9CJ2cquB4cmiqzY6TAP5yYoy2i8EC7iSZLKBIoQiBLgnzNYaxYZ7xoMVe1r/jNhuVwzfEQQH9S56uDUdpMlQ8nS+weLbJ7tMiGbIT+pMbZ+RrnFqo4nl/RzXHhg8kyG7MRnumN0xW7PnFG2fKrE55bqDG8WGO2cuWyNfiV7hhfHkjetLWo5Xj89NgMY0WLDyZL/OraNPflzJua192G5bicmK1yZKbMuYUqtusLONdlDFKGwnTZYrJkcXy20vyOoQi6YxrdMY1sROH9sSITJYuhlM7Xh1JkI1cKJcuWw8GpMgcmSvzizDyvnV+gO6Yx0VI1TpMFGUNBCN/uVgiBwE/oJTS/Ap4q+/bXKV2mzVTJRRQSunxXVPVSJEFfUqcvqTdfy+XiTE8XrvmdB9pNfn5ynv/v+CxbOky+Opi6aUviq5GvObwx7FdhTGgy31+fYWM2csW+dF9bhHMLNbZfyvPquUW2XSzwUIfJI50xMpEwXRJy88xWLI5MVzg8XW7283FNZl3GYDCp0xlTSenKLY37kJCQkJCQkJCQkJCVgSwJUrpgvuZRc7zmYNPLcTyPqg2VYOBfQpOIrVBBQUhIyBKLdZfpiocE5AxBQhPNHF57RJDWPearvuBvse5ddR6mAglNIqqsHJvXkE9Gza9VcM0+/04iC/+4NFv1AnvhlbeMKwHP85iseJRtyEUEcW3l5u3CzHVISEhISEhISEhIyIpBlQUbshE2XKPKlyxgS2eUBztMjs1U2H4pz1sX8lf9bFKX6Y5pPNRh0hXTELDMerRqu6yKa6zLGM3KgwBr0gaLNZu94yX2TxQ5Plshpcs82hVjQ8agL6lTtV12jxXZM+a/vzZtsCZtoEgCWfLFT7IQLNZsZso2+RNzjC1WKVp+JSdNFvQndB7pjNIb11AkgZ/T8S+yD0yUeH+syKV8je+vz96w6MjzPP759BxjRYtvrUlxcKrMz07OcTFf46uDqWWCp+myxcGpMvNVm3ZTpSum0hXViGnSXZVo8jyPC4s1Dk75dtB1xyOhyzzcEWN91qA/oV8h9Ko7LtNlm4lSndGCxXixznsjBTx8Uc6vb8h8ZNVIU5V5oifO490xLhXqHJgoMVWy2Jg1WRXXeKA/jVStId1F7fhp0BHV+IOH2nnnYp6dIwXOzFcDMaQvgpQERFSJx7pi9Cb0j51fA9v12D1aYNulAq7n8WxvnKdWxa9pxS1aKoteWKyxe6zArtEiO0eLrE7pPNwZZUPmSoHg3cxEsc5c1b6uaqj3Io7rcXCqTESR6E1oxG5gGPZ8xWL3aIHD02XGihYAA0mdx7pirE7pZCNK2OYhISEhISEhISEh9wgpXbBY85itunRH/esKz/OoOlC0PCq2R32p2DeygImyS0b3LT/Da4eQkJVJ0fKYrniYCrRHpKtW5lMlQbspyLgetguIRsYXPA+Ktkeh7lcNkwXEVYGpCgyZMId4F1N3g0HfK1QbFld9oV/e8mgLhX5X4Hq+DXfR8sgaguRK3ZABodAvJCQkJCQkJCQkJOSuQxKC+3Imm9siWK6HHSROHM9/bqq+Re/NktQVnh9I8vneOEXLJXVZdTVTlflif5Ine+LsHS+ya7TI6fnqVeely4KuhC8EbDNV+hJ+1biPEg99fSjFQFLnxdNz/N8f+hanN1KN7+2LeY7NVPjyQJJHOmM81B7ljeFFdo8VGStafHMoxYXFGoemy4wXLQS+MPLYzFKFu6gqkTYU9KDaXOMxZch0RFU6TJXoCvAiKFkOByZK7J8osVhz0GTBprYID7VH6UtoH5kc12SJnrhGT1zjkU7/NcvxmKlYZCPKNQVilyOEoC+h03eZKC2X0Jmu1W963T7LKJLg+YEk6zIGOy4VqDsuruc1LY7HijWOTFdYndJ5tjdBf/Lagj/P8zgxW+X18wss1Bw2ZAy+sjpF2rj+lEd/Uqc/qVOoOXwwWWL/ZIl/PDFHXJN4sifOI513t63vfNXm7Qt5Dk+XAXiyJ8bzA8nw5lELFdvl74/PMrxYa76WNmR64zo9cY2UIZPQZOKajKlKOJ5vD31mvsqZ+SrTgf17Z1TlywNJ7stFSOhh2i0kJCQkJCQkJCTkXqRRPWmu5rFQc6m7ULI8HM8X/BgyZHSBofjiHoDpim/3W3M8Okzplgt+PM+jbrtUbD935ngQU0VoIRoScp1UbI/JsosuQ+d17KOKJLhaIX9DEWR1v2pYvu6yUPdYqHvNviGi+Ja/uhxW+7ubqDu+aHulDhhWJIGpQKHukdW9eza2PM8X2lcdj7oDlus/BuY8pDRBWl/5OeAw4xgSEhISEhISEhISctcihECTBbdLb6bKEumPEPcYT1JKEAAAIABJREFUisQzvQme7IlTtV1fcOh5OIHoMK7JRFWJ9vbER1qZXo2NbRG6Yh387OQcPzs5x56xIlFVQlckdFlCVwQdUZU1KWOZ/ePByRLbLxXY0mHyRE8M8BMMX12doi+h8y+n5/jPH04B0BVV+epgkvtyJjFNpma7TJYsxksWE8U6+bpDzfHI1y1qjkfNdqk5S5YTUVWiM6ryRE+cobRxQ+tnu15QXcxhrmozX7GZr9p4QEyViGkysUBUk9Bl0oZMUleaCejxYp09Y0UOT5dxPBhM6nxpIMmGjPGJBFmqLK7bijnkk9OX0Pnh5itFfHXHZd94iZ2jBf7fw9MMJHU+1xUlYyikDAUjiPmJYp3Xzi8yvFij3VT47fvaWJ26sVhsJa7LPNuX4OneOGfmq+wcKfDa+UV2jBR4oifO57qi1y0AvRV4nsepOV9EPBRUDb0WluMhCb8iYiNZV7Yctl0qsHe8iCQET6+KU7Fddo4Wqbse31idumpib7FqYyjSPWMtO1ex+btjM8xXbb69Nk1bROFivs5Ioc7ZhSqHAoFkg0Y7266fxO1P6jy9OkO3Jmgzr7T5DgkJCQkJCQkJCQm590jpgsW6XyFIwP/P3p2HR1Xe/R//3Gf2bAQImyCC0rAILrhra91axMetPq2WqmgV/Ylb3Vp7abW2pah1q4r2sa0r1S4KVrC4UbeqBRRZFBAUZYsQAtmXWc/9++NkBkIWEowkIe/XdXER5sx9zrlPZiY3k898v8oOGGX7vb+bCgj1jUghn7QlarWh2tWAbEd+I7mSUq4XQki5Vq6VXCulrLfN+/e22936D9ClpY+UtJIqG3bFKI16lYu2bz0KoLF4ympjrSu/I+3VDkFcY4yyA1J2wCfXWtUlpdqkV+2zNOb9cYyU5fdeN7L8ptMGyODxWrV39Fm0LC/oaFOtq9qklN1N3r6y1gvV1iWtoimrWGrbz0gjrwJj2G8UdLyCDVldJEHXRU4TAAAAADovn2O+lup2+WG/LhzTR//ZUKXPy6MqjaYUSyUUS7mKJa2svJDJfvlhjegdUVbA0azPyjSkR0j/s1/PRm/SjiyIqF92P60srdN++WH13eF/9CG/o8E9QhrcQvW0mnhKxbUJFdcktLkmoTUVMf1l2RYdsZdXIWxnnwQviyb1wcZqLSquVV1yW5+avJBPvcJ+OUYqjSa1tjLeYHtaTtBR2OdoS11SAcfo4H7ZOnyvHPUhXLNHCfocHT3IC9Yt3FSjdzdU6dlPSjPbQz6jvJBPW2q9QNop++XrkP7Z7VbxwDFGhb0iKuwV0dqKmN5eX6m5ayr07oYq7ZMXzPyCxQv2WkX8jvJCPuWF/BpUk5LiCSVSVnVJN/PHtVZj+mS1KkhqrdVnZVH9e22limu8NrBhn9HIgohG98nSkB4hpVyrtZVxfV4e1RflMW2qv5+R1x484DOK1VdqOKhfto4bnKe8kE/WWoV8Ru8VVSuRsjr9Gz0z121zTUJvr6/Usi11Gpgb1I/H9Nnj30heVxnT35dvlZU0cXSfTPXIQfUVOq21qoqnVBlLqSruel/HU0q5VkPzQxrSI6Sgz1GfPrltDnQDAAAA2HM5xmhAtqOUK0X8O2/JaeqrAAZ9XkvPdVXeeyK2hTE+s+2DSI6kgOMdZ/sj2e3u27NHWNGamAKOd/uWOlclUauqhFXfiKMg7RyBRpKuF/KTpAFZTru/T+JkQn8mc7y6pBdOqk1aVSckycpvvOetlTIdMYKO1DNklBPoWmHddPgq7lrl7wFB43SVuCx/555Htt/7WVAZd79SN6SuwLVWlXGrirhVov5XDCGflBf0KumGfV7Fza762CPoBwAAAACdmM8xOm5wno4bnNfgdtdarauM65Otdfpka51W1bcO7h3x6+wRvZt906lXxK+jBubu8vlkB33aN+jLVE1LpKxeW1Ou+V9W6/PyqM4q7KX+OwSZkq7VmoqY3v+yWqvKojKSRvSOaEyfLBVk+dUz7G8yIJhyrarjKVXEUiqPJVUe9f6uirs6pH+2DuqXnanshj1TwOfoyIG5OnRAjoprEiqPJVVR/zioiKU0rGdY39o7T5Gv8XGwT4+Qzu/RR+srY3p3Q5VKo0n5HK+9kd8YhQJGtQlXm2oSqkm40rrKRvtwjBfA+29RtfbJC+rIgbkq7BVu8hc96ypi+vfaCq2rjKtn2KezCnsp7Df6uKROy7bUaVFxrbL8jmIpVynrvUG3d15Ix+6dK59jlEjZ+rYTVj5HOnxAwyCsMV7b5JDf0RtrKxVPWR0zKFfvbqjSiq11CvqMRhdE9PGWOr21vlIn7NOj3a5lXcLVks01WlxcK7/P6DtDerTYlnlH1lqVRpOqTbiKp6xiKe/vpGvld7xwY6D+e5NyrcpjKZVHvcdKebpiaLpaaMCRjNE76yvVI+TXj/bvrd6RxoFhY4zyQn7a8AIAAABos7DPSG3MUmT5jfbOdjKtPH2O5K9vB+kz28J9Rm0PKPTpEVJJPJ75917ZjqoSVlvqrNZXu+oZMuoZajl0Y633wdP2bi0MdEYJ1wveJl1pYPbuCcP6HaPcoFFu0Hu+xVJe4C/hpp/39ZU6jVSbsCqub/vdM2SU28kDf02Fr+qStlWtkDuz+HZBss7MGO8xUh73PrjcmT/cu6vnF0+62hp1VRH3Kt2mW21n+7tuqK8pvEsJAAAAAF2QY4yG9PCqSY0b2kObahJaXR7V6IIsRQK7L/wW8Bmdsl9PFfaK6IVVpfrzks06ZlCufMZoc21CW5dsVnFVXFZeq99v7Z2rQ/tntyo043OMeoT96hH2a7BaHwbCnsfvGA3MDWpgbse1Vd47L6Qfjmr5cZh0rQI5Ya3dWKmAzyjid5QVcBRwjGIpqw831WjBxmr9fcVW9Qz7NKogS7GUq5q4q+pEStXxlMqiKeUEvCqFY/tlZ97U+kaviBIpq1VldVq5NarcoKN988ManBdsc7tqY4yO3TtPQcfolS8qtGJrnUI+o2P3ztWRe+UqEnDk/7RU/1lfpX3zwxrSyjCetVYltV5lwXQQ0u8YldQmtHBTjZZvrVXSlfbKCag6ntITH5VoVO+IvjO0h/LDzb8mxJKulmyu1cJN1dpcm2zTXH1G6hHyKz/sk2OkilhKRVVxL5QpaZ+8oM4e2VtZe/gnmQEAAAB0HQGfUZ/I1x9IMMZr25vl91oMl8a8EE7vcOMKYbY+oFMW82oEDszx/q/bERKu1eZaV0GfUUG4cweb0H7iKbstALtd6DXoMwq1UwDPWqtoSqpJWNUmbSbA1T/LUbgDqrUZYxT2q9lj25BVTVIqjbraXGdVGrXKDhj5jaTKuGqTVj6zrd130rVKWa8iYH7I7LQzS3tJWauyqPca4soLX/WLGLmSSuqsitLtyjtx8Kwl8ZT3utgVqqLmBr2gX1XCKj/UOc+3Iu6qpM4qyy/1CrXuuRdPeT+fVldUer+H8Ev5IUdh354V8Esj6AcAAAAAXZwxRgNygq1qCfp1GdYzrMlj+2n2p2V6e73XvjI/5NPePSMa1iOkvXKC+kavcJd9wwZoDb9jVJAdlG0iGBf2Gx09KFdHDszRii11mvdltd7dUKWI31FO0FF2wKeBuUEdNiCoQ/tnNxneC/iM9i/I0v4FWe1yvkcOzFVu0KfyWEqH9G9YIXP8vvlaVxHX8ytLddnB/XYaIE66Vo+9X6RFRU23rw35jA7ql61D+mWrf05QiZTVe0VVendDlVaW1unoQbka0SvSYEw85eqjkjp9VFKrhGs1ICegU/bLV8+wX0HHeG/m+x35jFHS9Sr7Jer/doyUH/IrJ+g0+YZeyvVaK2cHmt4OAAAAAN2F3zHqn2VUm7DaEnVVXOcFqgrCXkiiMuGFdJJWCvukeEr6ssbVwDYGc6y1Lf7/K5ayqohZJa1Vfshpsg1mTcKquNaVK6ku5f3fr3eY/9N1JQnXC+u15bGTdK2+rPE6GxhJboOtVv0iRrnBtn/wOWWtYkkpmrLen+S2fUd83mMr2286bYDLGKOcgJTtd1SblMpirqrqw3RbN9e2OLY6YTUg22m3kGRzEq7VxhpXcVfKCRjlB02D4JbfeFUTN1S72ms3VU1sb+lA6C48BHe7kM8o5JMq41Y9gi2/JneEWMqrMht0pGhK2lDjKtsv9Qo3fqymg7llMVe1Se+1oW9eUCE32SU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\n", "text/plain": [ "<Figure size 2592x1296 with 22 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set(font_scale=2)\n", "grid = sns.FacetGrid(df_weekly, col=\"counting_station\", hue=\"counting_station\", palette=\"tab20c\",\n", " col_wrap=4, height=3, aspect=3)\n", "grid.map(plt.plot, \"datetime\", \"velo_in\")\n", "grid.map(plt.plot, \"datetime\", \"velo_out\")\n", "grid.map(plt.axhline, y=0, ls=\":\", c=\".5\")\n", "grid.set_xticklabels(rotation=30)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7fed4b0199b0>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1080x720 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set(rc={'figure.figsize':(15, 10)})\n", "sns.lineplot(x=\"datetime\", y=\"velo_all\", hue=\"counting_station\", data=df_weekly)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
rdipietro/jupyter-notebooks
tensorflow_scan_examples/tensorflow_scan_examples.ipynb
1
359332
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow Scan Examples\n", "#### By [Rob DiPietro](http://rdipietro.github.io) – Version 0.32 – April 28, 2016." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Post or Jupyter Notebook?\n", "\n", "This work is available both as a [post](http://rdipietro.github.io/tensorflow-scan-examples/) and as a [Jupyter notebook](https://github.com/rdipietro/jupyter-notebooks/tree/master/tensorflow-scan-examples). If you see any mistakes or have any questions, please open a [GitHub issue](https://github.com/rdipietro/jupyter-notebooks/issues)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Contents\n", "\n", "- [Overview](#overview)\n", "- [Preliminaries](#preliminaries)\n", "- [Hard Coding the Cumulative Sum](#hard-coding-the-cumulative-sum)\n", "- [Learning the Cumulative Sum](#learning-the-cumulative-sum)\n", " - [Generating Inputs and Targets](#generating-inputs-and-targets)\n", " - [Defining the RNN Model from Scratch](#defining-the-rnn-model-from-scratch)\n", " - [Defining an Optimizer](#defining-an-optimizer)\n", " - [Training](#training)\n", " - [Testing Qualitatively](#testing-qualitatively)\n", " - [Ideas for Playing with the Code](#ideas-for-playing-with-the-code)\n", "- [Some Final Thoughts](#some-final-thoughts)\n", "- [Acknowledgements](#acknowledgements)\n", "- [About Me](#about-me)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"overview\"></a>\n", "## Overview\n", "\n", "`scan` was [recently made available](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/ops/functional_ops.py) in TensorFlow.\n", "\n", "`scan` lets us write loops inside a computation graph, allowing backpropagation and all.\n", "We could explicitly unroll the loops ourselves, creating new graph nodes for each loop\n", "iteration, but then the number of iterations is fixed instead of dynamic, and graph\n", "creation can be [extremely slow](https://github.com/tensorflow/tensorflow/issues/511).\n", "\n", "Let's go over two examples. First, we'll create a simple cumulative-sum operation using\n", "`scan`. For example, `[1, 2, 2, 2]` as input will produce `[1, 3, 5, 7]` as output. Second,\n", "we'll build a toy RNN from scratch, and we'll have it *learn* the cumulative-sum operation\n", "from example input, target sequences. For example, the RNN will learn to map `[1, 2, 2, 2]`\n", "to `[1, 3, 5, 7]` (approximately)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"hard-coding-the-cumulative-sum\"></a>\n", "## Hard Coding the Cumulative Sum\n", "\n", "Let's just start with code:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1. 3. 5. 7.]\n" ] } ], "source": [ "from __future__ import division, print_function\n", "import tensorflow as tf\n", "\n", "def fn(previous_output, current_input):\n", " return previous_output + current_input\n", "\n", "elems = tf.Variable([1.0, 2.0, 2.0, 2.0])\n", "elems = tf.identity(elems)\n", "initializer = tf.constant(0.0)\n", "out = tf.scan(fn, elems, initializer=initializer)\n", "\n", "with tf.Session() as sess:\n", " sess.run(tf.initialize_all_variables())\n", " print(sess.run(out))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example shows how `scan` is used: it loops over the first dimension of `elems`, at each step applying `fn`, which takes in the previous step's output and the current step's input. The very first step's previous output is given by `initializer`:\n", "- Iteration 0: `fn(0.0, 1.0) == 1.0`\n", "- Iteration 1: `fn(1.0, 2.0) == 3.0`\n", "- Iteration 2: `fn(3.0, 2.0) == 5.0`\n", "- Iteration 3: `fn(5.0, 2.0) == 7.0`\n", "\n", "And why the `elems = tf.identity(elems)` line? `scan` is new, and this is just a temporary workaround for [a bug](https://github.com/tensorflow/tensorflow/issues/1725)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"learning-the-cumulative-sum\"></a>\n", "## Learning the Cumulative Sum\n", "\n", "Now a more complex example: we'll build a recurrent neural network and *learn* the cumulative-sum function from data." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%reset -f" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "from __future__ import division, print_function\n", "\n", "%matplotlib inline\n", "from IPython.display import set_matplotlib_formats\n", "set_matplotlib_formats('svg')\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "import os\n", "import shutil\n", "import numpy as np\n", "import tensorflow as tf\n", "from tensorflow.python.ops import functional_ops" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"generating-inputs-and-targets\"></a>\n", "### Generating Inputs and Targets\n", "\n", "First let's write a function for generating input, target sequences, one pair at a time. We'll limit ourselves to inputs with independent time steps, drawn from a standard normal distribution.\n", "\n", "Each sequence is 2-D with time on the first axis and inputs or targets on the second. (This way it'd be easy to generalize to the case of multiple inputs/targets per time step.)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def input_target_generator(min_duration=5, max_duration=50):\n", " \"\"\" Generate toy input, target sequences.\n", " \n", " Each input sequence has values that are drawn from the standard normal\n", " distribution, and each target sequence is the corresponding cumulative sum.\n", " Sequence durations are chosen at random using a discrete uniform\n", " distribution over `[min_duration, max_duration]`.\n", " \n", " Args:\n", " min_duration: A positive integer. The minimum sequence duration.\n", " max_duration: A positive integer. The maximum sequence duration.\n", "\n", " Yields:\n", " A tuple,\n", " inputs: A 2-D float32 NumPy array with shape `[duration, 1]`.\n", " targets: A 2-D float32 NumPy array with shape `[duration, 1]`.\n", " \"\"\"\n", " \n", " while True:\n", " duration = np.random.randint(min_duration, max_duration)\n", " inputs = np.random.randn(duration).astype(np.float32)\n", " targets = np.cumsum(inputs).astype(np.float32)\n", " yield inputs.reshape(-1, 1), targets.reshape(-1, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"defining-the-rnn-model-from-scratch\"></a>\n", "### Defining the RNN Model from Scratch\n", "\n", "Next let's define the RNN model. The code is a bit verbose because it's meant to be self explanatory, but the pieces are simple:\n", "- The update for the vanilla RNN is $h_t = \\tanh( W_h h_{t-1} + W_x x_t + b )$.\n", "- `_vanilla_rnn_step` is the core of the vanilla RNN: it applies this update by taking in a previous hidden state along with a current input and producing a new hidden state. (The only difference below is that both sides of the equation are transposed, and each variable is replaced with its transpose.)\n", "- `_compute_predictions` applies `_vanilla_rnn_step` to all time steps using `scan`, resulting in hidden states for each time step, and then applies a final linear layer to each state to yield final predictions.\n", "- `_compute_loss` just computes the mean squared Euclidean distance between the ground-truth targets and our predictions." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Model(object):\n", " \n", " def __init__(self, hidden_layer_size, input_size, target_size, init_scale=0.1):\n", " \"\"\" Create a vanilla RNN.\n", " \n", " Args:\n", " hidden_layer_size: An integer. The number of hidden units.\n", " input_size: An integer. The number of inputs per time step.\n", " target_size: An integer. The number of targets per time step.\n", " init_scale: A float. All weight matrices will be initialized using\n", " a uniform distribution over [-init_scale, init_scale].\n", " \"\"\"\n", " \n", " self.hidden_layer_size = hidden_layer_size\n", " self.input_size = input_size\n", " self.target_size = target_size\n", " self.init_scale = init_scale\n", " \n", " self._inputs = tf.placeholder(tf.float32, shape=[None, input_size],\n", " name='inputs')\n", " self._targets = tf.placeholder(tf.float32, shape=[None, target_size],\n", " name='targets')\n", " \n", " initializer = tf.random_uniform_initializer(-init_scale, init_scale)\n", " with tf.variable_scope('model', initializer=initializer):\n", " self._states, self._predictions = self._compute_predictions()\n", " self._loss = self._compute_loss()\n", " \n", " def _vanilla_rnn_step(self, h_prev, x):\n", " \"\"\" Vanilla RNN step.\n", "\n", " Args:\n", " h_prev: A 1-D float32 Tensor with shape `[hidden_layer_size]`.\n", " x: A 1-D float32 Tensor with shape `[input_size]`.\n", "\n", " Returns:\n", " The updated state `h`, with the same shape as `h_prev`.\n", " \"\"\"\n", "\n", " h_prev = tf.reshape(h_prev, [1, self.hidden_layer_size])\n", " x = tf.reshape(x, [1, self.input_size])\n", "\n", " with tf.variable_scope('rnn_block'):\n", " W_h = tf.get_variable(\n", " 'W_h', shape=[self.hidden_layer_size, self.hidden_layer_size])\n", " W_x = tf.get_variable(\n", " 'W_x', shape=[self.input_size, self.hidden_layer_size])\n", " b = tf.get_variable('b', shape=[self.hidden_layer_size],\n", " initializer=tf.constant_initializer(0.0))\n", " h = tf.tanh( tf.matmul(h_prev, W_h) + tf.matmul(x, W_x) + b )\n", " h = tf.reshape(h, [self.hidden_layer_size], name='h')\n", " \n", " return h\n", "\n", " def _compute_predictions(self):\n", " \"\"\" Compute vanilla-RNN states and predictions. \"\"\"\n", "\n", " with tf.variable_scope('states'):\n", " initial_state = tf.zeros([self.hidden_layer_size],\n", " name='initial_state')\n", " states = tf.scan(self._vanilla_rnn_step, self.inputs,\n", " initializer=initial_state, name='states')\n", "\n", " with tf.variable_scope('predictions'):\n", " W_pred = tf.get_variable(\n", " 'W_pred', shape=[self.hidden_layer_size, self.target_size])\n", " b_pred = tf.get_variable('b_pred', shape=[self.target_size],\n", " initializer=tf.constant_initializer(0.0))\n", " predictions = tf.add(tf.matmul(states, W_pred), b_pred, name='predictions')\n", " \n", " return states, predictions\n", "\n", " def _compute_loss(self):\n", " \"\"\" Compute l2 loss between targets and predictions. \"\"\"\n", "\n", " with tf.variable_scope('loss'):\n", " loss = tf.reduce_mean((self.targets - self.predictions)**2, name='loss')\n", " return loss\n", " \n", " @property\n", " def inputs(self):\n", " \"\"\" A 2-D float32 placeholder with shape `[dynamic_duration, input_size]`. \"\"\"\n", " return self._inputs\n", " \n", " @property\n", " def targets(self):\n", " \"\"\" A 2-D float32 placeholder with shape `[dynamic_duration, target_size]`. \"\"\"\n", " return self._targets\n", " \n", " @property\n", " def states(self):\n", " \"\"\" A 2-D float32 Tensor with shape `[dynamic_duration, hidden_layer_size]`. \"\"\"\n", " return self._states\n", " \n", " @property\n", " def predictions(self):\n", " \"\"\" A 2-D float32 Tensor with shape `[dynamic_duration, target_size]`. \"\"\"\n", " return self._predictions\n", " \n", " @property\n", " def loss(self):\n", " \"\"\" A 0-D float32 Tensor. \"\"\"\n", " return self._loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"defining-an-optimizer\"></a>\n", "### Defining an Optimizer\n", "\n", "Next let's write an optimizer class. We'll use vanilla gradient descent after gradient \"clipping,\" according to the method described by [Pascanu, Mikolov, and Bengio](http://arxiv.org/abs/1211.5063).\n", "\n", "The gradient-clipping method is simple and could instead be called gradient scaling: if the global norm is smaller than `max_global_norm`, do nothing. Otherwise, rescale all gradients so that the global norm becomes `max_global_norm`.\n", "\n", "What is the global norm? It's just the norm over *all* gradients, as if they were concatenated together to form one global vector." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class Optimizer(object):\n", " \n", " def __init__(self, loss, initial_learning_rate, num_steps_per_decay,\n", " decay_rate, max_global_norm=1.0):\n", " \"\"\" Create a simple optimizer.\n", " \n", " This optimizer clips gradients and uses vanilla stochastic gradient\n", " descent with a learning rate that decays exponentially.\n", " \n", " Args:\n", " loss: A 0-D float32 Tensor.\n", " initial_learning_rate: A float.\n", " num_steps_per_decay: An integer.\n", " decay_rate: A float. The factor applied to the learning rate\n", " every `num_steps_per_decay` steps.\n", " max_global_norm: A float. If the global gradient norm is less than\n", " this, do nothing. Otherwise, rescale all gradients so that\n", " the global norm because `max_global_norm`.\n", " \"\"\"\n", " \n", " trainables = tf.trainable_variables()\n", " grads = tf.gradients(loss, trainables)\n", " grads, _ = tf.clip_by_global_norm(grads, clip_norm=max_global_norm)\n", " grad_var_pairs = zip(grads, trainables)\n", " \n", " global_step = tf.Variable(0, trainable=False, dtype=tf.int32)\n", " learning_rate = tf.train.exponential_decay(\n", " initial_learning_rate, global_step, num_steps_per_decay,\n", " decay_rate, staircase=True)\n", " optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n", " self._optimize_op = optimizer.apply_gradients(grad_var_pairs,\n", " global_step=global_step)\n", " \n", " @property\n", " def optimize_op(self):\n", " \"\"\" An Operation that takes one optimization step. \"\"\"\n", " return self._optimize_op" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"training\"></a>\n", "### Training\n", "\n", "Next let's define and run our training function. This is where we'll run the main optimization loop and export TensorBoard summaries." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train(sess, model, optimizer, generator, num_optimization_steps,\n", " logdir='./logdir'):\n", " \"\"\" Train.\n", " \n", " Args:\n", " sess: A Session.\n", " model: A Model.\n", " optimizer: An Optimizer.\n", " generator: A generator that yields `(inputs, targets)` tuples, with\n", " `inputs` and `targets` both having shape `[dynamic_duration, 1]`.\n", " num_optimization_steps: An integer.\n", " logdir: A string. The log directory.\n", " \"\"\"\n", " \n", " if os.path.exists(logdir):\n", " shutil.rmtree(logdir)\n", " \n", " tf.scalar_summary('loss', model.loss)\n", " \n", " ema = tf.train.ExponentialMovingAverage(decay=0.99)\n", " update_loss_ema = ema.apply([model.loss])\n", " loss_ema = ema.average(model.loss)\n", " tf.scalar_summary('loss_ema', loss_ema)\n", " \n", " summary_op = tf.merge_all_summaries()\n", " summary_writer = tf.train.SummaryWriter(logdir=logdir, graph=sess.graph)\n", " \n", " sess.run(tf.initialize_all_variables())\n", " for step in xrange(num_optimization_steps):\n", " inputs, targets = generator.next()\n", " loss_ema_, summary, _, _ = sess.run(\n", " [loss_ema, summary_op, optimizer.optimize_op, update_loss_ema],\n", " {model.inputs: inputs, model.targets: targets})\n", " summary_writer.add_summary(summary, global_step=step)\n", " print('\\rStep %d. Loss EMA: %.6f.' % (step+1, loss_ema_), end='')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can train our model:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step 45000. Loss EMA: 0.024024." ] } ], "source": [ "generator = input_target_generator()\n", "model = Model(hidden_layer_size=256, input_size=1, target_size=1, init_scale=0.1)\n", "optimizer = Optimizer(model.loss, initial_learning_rate=1e-2, num_steps_per_decay=15000,\n", " decay_rate=0.1, max_global_norm=1.0)\n", "\n", "sess = tf.Session()\n", "train(sess, model, optimizer, generator, num_optimization_steps=45000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After running `tensorboard --logdir ./logdir` and navigating to [http://localhost:6006](http://localhost:6006), we can view our loss summaries. Here the exponential moving average is especially helpful because our raw losses correspond to individual sequences (and are therefore very noisy estimates).\n", "\n", "![Loss (Raw)](images/loss_raw.png)\n", "\n", "![Loss (EMA)](images/loss_ema.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"testing-qualitatively\"></a>\n", "### Testing Qualitatively\n", "\n", "Finally let's write a function to test the trained RNN qualitatively: we'll plot the original inputs (random real numbers), the ground-truth target (the cumulative sum), and our trained RNN's predictions (hopefully matching the cumulative sum)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def test_qualitatively(sess, model, generator, num_examples=5, figsize=(10, 3)):\n", " \"\"\" Test qualitatively.\n", " \n", " Args:\n", " sess: A Session.\n", " model: A Model.\n", " generator: A generator that yields `(inputs, targets)` tuples, with\n", " `inputs` and `targets` both having shape `[dynamic_duration, 1]`.\n", " num_examples: An integer. The number of examples to plot.\n", " figsize: A tuple `(width, height)`, the size of each example's figure.\n", " \"\"\"\n", " \n", " for i in xrange(num_examples):\n", " \n", " inputs, targets = generator.next()\n", " predictions = sess.run(model.predictions, {model.inputs: inputs})\n", " \n", " fig, ax = plt.subplots(nrows=2, sharex=True, figsize=figsize)\n", " ax[0].plot(inputs.flatten(), label='inputs')\n", " ax[0].legend()\n", " ax[1].plot(targets.flatten(), label='targets')\n", " ax[1].plot(predictions.flatten(), 'o', label='predictions')\n", " ax[1].legend()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"144pt\" 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Here are a few questions to help spark a some ideas:\n", "\n", "- **How many hidden units are needed?** The cumulative sum can be computed using a *linear* transformation at each time step with just one hidden unit: we just initialize it to $0.0$ and then add $x_t$ at each time step. This is *not* the solution that the RNN learned (we used 256 hidden units, and each is squashed between -1.0 and 1.0 at each time step). Try reducing the hidden layer size by factors of 2, and see if/when performance takes a hit.\n", "- **How sensitive is training to the initial learning rate?** Try varying the initial learning rate above by one order of magnitude at a time. Would you say that performance depends heavily on this parameter?\n", "- **How sensitive is training to the initial learning rate without gradient clipping?** In the implementation above, we clipped our gradients (or in reality we scaled them globally). Try training without scaled gradients and see if you can find an initial learning rate that works. Is convergence more sensitive or less sensitive to the initial learning rate?\n", "- **Does the RNN generalize to other inputs?** Above, we trained and tested the RNN using inputs with time steps that were drawn from a standard normal distribution. See what happens if you test this trained model using other inputs, for example a sine wave or a function that doesn't have an average value of 0.0." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"some-final-thoughts\"></a>\n", "## Some Final Thoughts\n", "\n", "Aside the simple `scan` example, we built and trained a vanilla RNN from scratch. It is written with simplicity in mind and is largely a toy example. Here are some reasons why:\n", "\n", "- **We're using a batch size of 1.** This is inefficient: it's analogous to performing 20 different vector-vector multiplies to multiply one vector by twenty others, instead of performing one matrix-vector multiply.\n", "- **We're using one hidden layer.** We could easily extend the above code to handle multiple layers: we'd run `scan` to get the first layer, then run it again to get the second layer, and so on. (The states of the previous layer become the inputs to the next layer.)\n", "- **The vanilla RNN isn't often used because it has trouble learning.** In practice we'd replace the vanilla-RNN block with a long short-term memory (LSTM) block or a gated recurrent unit (GRU) block, both of which were designed to avoid the vanishing gradient problem, or perhaps one could use the recent [identity-initialization-plus-ReLU modification of the vanilla RNN](http://arxiv.org/abs/1504.00941).\n", "- **We're using full backpropagation through time, performing a complete forward, backward pass for *each* gradient update.** This means that the number of updates per time is low, leading to longer training times. An alternative is to use truncated backpropagation through time, updating after shorter forward-backward passes in time, but still capturing long-term dependencies by carrying the hidden states over from pass to pass." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"acknowledgements\"></a>\n", "## Acknowledgements\n", "\n", "I'd like to thank my advisor, [Gregory D. Hager](http://www.cs.jhu.edu/~hager/), for being supportive of this work." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<a id=\"About Me\"></a>\n", "## About Me\n", "\n", "![Rob](images/rob_small.jpg)\n", "\n", "I'm [Rob DiPietro](http://rdipietro.github.com), a PhD student in the [Department of Computer Science at Johns Hopkins](https://www.cs.jhu.edu/), where I'm advised by [Gregory D. Hager](http://www.cs.jhu.edu/~hager/). I'm part of the [Computational Interaction and Robotics Laboratory](http://cirl.lcsr.jhu.edu/) and the [Malone Center for Engineering in Healthcare](http://malonecenter.jhu.edu/). Previously, I was an associate research-staff member at [MIT Lincoln Laboratory](http://www.ll.mit.edu/) and a BS/MS student at [Northeastern University](http://www.northeastern.edu/).\n", "\n", "You can find my other tutorials [here](http://rdipietro.github.io/#tutorials)." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
tlkh/Generating-Inference-from-3D-Printing-Jobs
hdbscan test.ipynb
1
5575
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# hdbscan test" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import hdbscan\n", "import csv\n", "import pandas as pd\n", "from IPython.display import display\n", "import itertools\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "%run 'preprocessor.ipynb' #our own preprocessor functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Distance metric to fuse different types of data (Gower)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def gower_distance(df,types):\n", " \"\"\"\n", " Treat each type of data with an appropriate distance measure\n", " Inputs:\n", " df: dataframe containing all data\n", " types: list containing type of data (length = number of data columns in df)\n", " categorical, rating (ordinal), continuous\n", " Outputs:\n", " pairwise_distance_matrix: full pairwise distance matrix between all rows and columns\n", " \"\"\"\n", " \n", " return pairwise_distance_matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prepare Dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "with open('data_w1w4.csv', 'r') as f:\n", " reader = csv.reader(f)\n", " data = list(reader)\n", " \n", "matrix = obtain_data_matrix(data)\n", "samples = len(matrix)\n", "\n", "print(\"Number of samples: \" + str(samples))\n", "print(\"First entry: \" + str(matrix[0]))\n", "\n", "df = pd.DataFrame(matrix)\n", "df.columns = ['uid',\n", " \"Test Fit (tolerances)\",\n", " \"Test Strength\",\n", " \"Test Ergonomics\",\n", " \"Wearout\",\n", " \"Integration\",\n", " \"Ornamental / Design or Ornamental / Gift or Design (Look)\",\n", " \"Others\",\n", " \"Filament Used\",\n", " \"Print Time\",\n", " \"Satisfaction\",\n", " \"Print Failed\"\n", " ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Perform clustering with hdbscan" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#create clusterer\n", "#TODO: distance metric appropriate to our mixed data? Alternatively, scale data?\n", "clusterer = hdbscan.HDBSCAN()\n", "\n", "#specify columns to ignore during clustering\n", "remove_cols = ['uid']\n", "\n", "#apply clusterer to data\n", "clusterer.fit(df.drop(remove_cols,axis=1))\n", "\n", "#add cluster labels and cluster probabilities to dataframe\n", "df['clusters'] = clusterer.labels_\n", "df['probabilities'] = clusterer.probabilities_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Make all pairwise 2D plots" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "Plot every pairwise combination of data. \n", "Points are colored according to cluster and \n", "faded according to probability of cluster membership.\n", "\"\"\"\n", "\n", "#remove entries that were not assigned to a cluster\n", "df_noiseless = df[df['clusters'] != -1]\n", "\n", "#generate color palette for noiseless dataframe (from hdbscan tutorial)\n", "color_palette = sns.color_palette('colorblind', max(df_noiseless['clusters'])+1)\n", "cluster_colors = [color_palette[x] if x >= 0\n", " else (0.5, 0.5, 0.5)\n", " for x in df_noiseless['clusters']]\n", "cluster_member_colors = [sns.desaturate(x, p) for x, p in\n", " zip(cluster_colors, df_noiseless['probabilities'])]\n", "\n", "#make all the plots! (most will be nonsense)\n", "for header in itertools.permutations(list(df.columns),2):\n", " plt.scatter(*np.array(df_noiseless[[header[0],header[1]]]).T, s=50, linewidth=0, c=cluster_member_colors, alpha=0.25)\n", " plt.xlabel(header[0])\n", " plt.ylabel(header[1])\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dump Dataframe" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print full dataframe\n", "with pd.option_context('display.max_rows', None, 'display.max_columns', None):\n", " display(df.sort_values('clusters'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "distributed_ideation" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
cernbox/entf
ROOT_testing/converted_notebooks/math/CrystalBall.C.nbconvert.ipynb.output.ipynb
1
23923
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Crystal Ball\n", "<hr style=\"border-top-width: 4px; border-top-color: #34609b;\">\n", "Example of CrystalBall Function and its distribution (pdf and cdf)\n", "\n", "\n", "\n", "\n", "**Author:** Lorenzo Moneta \n", "<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-mirror/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer (Beta)</a> from the macro found in the ROOT repository on Thursday, January 19, 2017 at 04:32 PM.</small></i>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "auto c1 = new TCanvas();\n", "c1->Divide(1,3);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Crystal ball function" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "c1->cd(1);\n", "\n", "auto f1 = new TF1(\"f1\",\"crystalball\",-5,5);\n", "f1->SetParameters(1, 0, 1, 2, 0.5);\n", "f1->SetLineColor(kRed);\n", "f1->Draw();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use directly the functionin root::math note that the parameters definition is different is (alpha, n sigma, mu)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "auto f2 = new TF1(\"f2\",\"ROOT::Math::crystalball_function(x, 2, 1, 1, 0)\",-5,5);\n", "f2->SetLineColor(kGreen);\n", "f2->Draw(\"same\");\n", "auto f3 = new TF1(\"f3\",\"ROOT::Math::crystalball_function(x, 2, 2, 1, 0)\",-5,5);\n", "f3->SetLineColor(kBlue);\n", "f3->Draw(\"same\");\n", "\n", "auto legend = new TLegend(0.7,0.6,0.9,1.);\n", "legend->AddEntry(f1,\"N=0.5 alpha=2\",\"L\");\n", "legend->AddEntry(f2,\"N=1 alpha=2\",\"L\");\n", "legend->AddEntry(f3,\"N=2 alpha=2\",\"L\");\n", "legend->Draw();\n", "\n", "c1->cd(2);\n", "auto pdf1 = new TF1(\"pdf\",\"crystalballn\",-5,5);\n", "pdf1->SetParameters(2, 0, 1, 2, 3);\n", "pdf1->Draw();\n", "auto pdf2 = new TF1(\"pdf\",\"ROOT::Math::crystalball_pdf(x, 3, 1.01, 1, 0)\",-5,5);\n", "pdf2->SetLineColor(kBlue);\n", "pdf2->Draw(\"same\");\n", "auto pdf3 = new TF1(\"pdf\",\"ROOT::Math::crystalball_pdf(x, 2, 2, 1, 0)\",-5,5);\n", "pdf3->SetLineColor(kGreen);\n", "pdf3->Draw(\"same\");\n", "\n", "legend = new TLegend(0.7,0.6,0.9,1.);\n", "legend->AddEntry(pdf1,\"N=3 alpha=2\",\"L\");\n", "legend->AddEntry(pdf2,\"N=1.01 alpha=3\",\"L\");\n", "legend->AddEntry(pdf3,\"N=2 alpha=3\",\"L\");\n", "legend->Draw();\n", "\n", "c1->cd(3);\n", "auto cdf = new TF1(\"cdf\",\"ROOT::Math::crystalball_cdf(x, 1.2, 2, 1, 0)\",-5,5);\n", "auto cdfc = new TF1(\"cdfc\",\"ROOT::Math::crystalball_cdf_c(x, 1.2, 2, 1, 0)\",-5,5);\n", "cdf->SetLineColor(kRed-3);\n", "cdf->SetMinimum(0.);\n", "cdf->SetMaximum(1.);\n", "cdf->Draw();\n", "cdfc->SetLineColor(kMagenta);\n", "cdfc->Draw(\"Same\");\n", "\n", "legend = new TLegend(0.7,0.7,0.9,1.);\n", "legend->AddEntry(cdf,\"N=1.2 alpha=2\",\"L\");\n", "legend->AddEntry(cdfc,\"N=1.2 alpha=2\",\"L\");\n", "legend->Draw();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Draw all canvases " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gROOT->GetListOfCanvases()->Draw()" ] } ], "metadata": { "kernelspec": { "display_name": "ROOT C++", "language": "c++", "name": "root" }, "language_info": { "codemirror_mode": "text/x-c++src", "file_extension": ".C", "mimetype": " text/x-c++src", "name": "c++" } }, "nbformat": 4, "nbformat_minor": 0 }
agpl-3.0
tpin3694/tpin3694.github.io
machine-learning/calibrate_predicted_probabilities.ipynb
2
5220
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: Calibrate Predicted Probabilities \n", "Slug: calibrate_predicted_probabilities \n", "Summary: How to calibrate predicted probabilities of naive bayes classifer in Scikit-Learn \n", "Date: 2017-09-22 12:00 \n", "Category: Machine Learning \n", "Tags: Naive Bayes \n", "Authors: Chris Albon " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Class probabilities are a common and useful part of machine learning models. In scikit-learn, most learning algortihms allow us to see the predicted probabilities of class membership using `predict_proba`. This can be extremely useful if, for instance, we want to only predict a certain class if the model predicts the probability that they are that class is over 90%. However, some models, including naive Bayes classifiers output probabilities that are not based on the real world. That is, `predict_proba` might predict an observation has a 0.70 chance of being a certain class, when the reality is that it is 0.10 or 0.99. Specifically in naive Bayes, while the ranking of predicted probabilities for the different target classes is valid, the raw predicted probabilities tend to take on extreme values close to 0 and 1. \n", "\n", "To obtain meaningful predicted probabilities we need conduct what is called calibration. In scikit-learn we can use the `CalibratedClassifierCV` class to create well calibrated predicted probabilities using k-fold cross-validation. In `CalibratedClassifierCV` the training sets are used to train the model and the test sets is used to calibrate the predicted probabilities. The returned predicted probabilities are the average of the k-folds." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preliminaries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load libraries\n", "from sklearn import datasets\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.calibration import CalibratedClassifierCV" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Iris Flower Dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load data\n", "iris = datasets.load_iris()\n", "X = iris.data\n", "y = iris.target" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Naive Bayes Classifier" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create Gaussian Naive Bayes object\n", "clf = GaussianNB()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Calibrator" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create calibrated cross-validation with sigmoid calibration\n", "clf_sigmoid = CalibratedClassifierCV(clf, cv=2, method='sigmoid')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Classifier With Calibrated Probabilities" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "CalibratedClassifierCV(base_estimator=GaussianNB(priors=None), cv=2,\n", " method='sigmoid')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Calibrate probabilities\n", "clf_sigmoid.fit(X, y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create Previously Unseen Observation" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create new observation\n", "new_observation = [[ 2.6, 2.6, 2.6, 0.4]]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## View Calibrated Probabilities" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.31859969, 0.63663466, 0.04476565]])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# View calibrated probabilities\n", "clf_sigmoid.predict_proba(new_observation)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
sspickle/vpython-jupyter
Demos/Stonehenge.ipynb
1
16055
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "require.undef(\"nbextensions/jquery-ui.custom.min\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require.undef(\"nbextensions/glow.2.1.min\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require.undef(\"nbextensions/glowcomm\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require.undef(\"nbextensions/pako.min\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require.undef(\"nbextensions/pako_deflate.min\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require.undef(\"nbextensions/pako_inflate.min\");" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "require([\"nbextensions/glowcomm\"], function(){console.log(\"glowcomm loaded\");})" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<div id=\"glowscript\" class=\"glowscript\"></div>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "window.__context = { glowscript_container: $(\"#glowscript\").removeAttr(\"id\")}" ], "text/plain": [ "<IPython.core.display.Javascript object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from vpython import *\n", "import time\n", "\n", "# Bruce Sherwood\n", "\n", "# A surreal scene that illustrates many of the features of GlowScript\n", "\n", "# Add instructions below the display\n", "s = \"<b>Fly through the scene:</b> With the mouse button down near the center of the canvas,<br><br>\"\n", "s += \" move the mouse or your finger above or below the center of the scene to move forward or backward;<br><br>\"\n", "s += \" move the mouse or your finger right or left to turn your direction of motion.<br><br>\"\n", "s += \"(Normal VPython rotate and zoom are turned off in this program.)\"\n", "scene.caption = s\n", "\n", "ycenter = 2\n", "scene.width = 800\n", "scene.height = 400\n", "scene.range = 12\n", "scene.center = vector(0,ycenter,0)\n", "scene.userspin = False\n", "scene.userzoom = False\n", "scene.background = color.gray(0.5)\n", "scene.ambient = color.gray(0.4)\n", "\n", "scene.title = \"<b>Surreal Stonehenge</b>\"\n", "\n", "def hourminute():\n", " now = time.localtime(time.time())\n", " hour = now[3] % 12\n", " minute = now[4]\n", " return [hour, minute]\n", "\n", "class analog_clock:\n", " def __init__(self, pos, radius, axis):\n", " self.pos = pos\n", " self.axis = axis\n", " self.radius = radius\n", " self.spheres = []\n", " self.hour = 0\n", " self.minute = -1\n", " for n in range(12):\n", " sp = sphere(pos=pos+(radius*scene.up).rotate(angle=-2*pi*n/12, axis=axis),\n", " size=(2*radius/20)*vector(1,1,1),\n", " color=color.hsv_to_rgb(vector(n/12,1,1)) )\n", " self.spheres.append(sp)\n", " self.hand = arrow(pos=self.pos, axis=0.95*radius*scene.up,\n", " shaftwidth=radius/10, color=color.cyan)\n", " self.update()\n", " \n", " def update(self):\n", " hour, minute = hourminute()\n", " hour = hour % 12\n", " if self.hour == hour and self.minute == minute: return\n", " self.hand.axis = (0.95*self.radius*scene.up).rotate(\n", " axis=vector(0,0,1), angle=-2*pi*minute/60)\n", " self.spheres[self.hour].size = (2*self.radius/20)*vector(1,1,1)\n", " self.spheres[hour].size = (2*self.radius/10)*vector(1,1,1)\n", " self.hour = hour\n", " self.minute = minute\n", "\n", "grey = color.gray(0.8)\n", "Nslabs = 8\n", "R = 10\n", "w = 5\n", "d = 0.5\n", "h = 5\n", "photocenter = 0.15*w\n", "\n", "# The floor, central post, and ball atop the post\n", "floor = box(pos=vector(0,-0.1,0),size=vector(.2,24,24), axis=vector(0,1,0), texture=textures.wood)\n", "pole= cylinder(pos=vector(0,0,0),axis=vector(0,1,0), size=vector(h,0.4,0.4), color=color.red)\n", "sphere(pos=vector(0,h,0), size=vector(1,1,1), color=color.red)\n", "\n", "# Set up the gray slabs, including a portal\n", "for i in range(Nslabs):\n", " theta = i*2*pi/Nslabs\n", " c = cos(theta)\n", " s = sin(theta)\n", " xc = R*c\n", " zc = R*s\n", " if i == 2: # Make a portal\n", " box(pos=vector(-3.*w/8.,0.75*h/2.,R),\n", " size=vector(0.5*w/2,0.75*h,d), color=grey)\n", " box(pos=vector(3.*w/8.0,0.75*h/2.,R),\n", " size=vector(0.5*w/2,0.75*h,d), color=grey)\n", " box(pos=vector(0,0.85*h,R),\n", " size=vector(w,0.3*h,d), color=grey)\n", " else:\n", " slab = box(pos=vector(R*c, h/2., R*s), axis=vector(c,0,s),\n", " size=vector(d,h,w), color=grey)\n", " if i != 6:\n", " T = textures.flower\n", " if (i == 7 or i == 4): T = textures.rug\n", " box(pos=slab.pos,\n", " size=vec(1.1*d,0.9*4*photocenter,0.9*4*photocenter), axis=vec(c,0,s),\n", " texture=T)\n", "\n", "# Decorate back slab with a gold box and a clock\n", "box(pos=vector(0,h/2.,-R+d/2+0.1), size=vector(w/2.,w/2.,0.2), texture=textures.wood)\n", "clock = analog_clock(vector(0,h/2.,-R+d/2+0.2+0.2*h/10), 0.2*w, vector(0,0,1))\n", "\n", "# Draw guy wires from the top of the central post\n", "Nwires = 32\n", "for i in range(Nwires):\n", " theta = i*2*pi/Nwires\n", " L = vector(R * cos(theta), -h - 0.1, R * sin(theta))\n", " cylinder(pos=vector(0,h,0), axis=L, size=vector(mag(L),.04,.04), color=vector(1,0.7,0))\n", "\n", "# Display a pyramid\n", "pyramid(pos=vector(-4,0,-5), size=vector(2,2,2), axis=vector(0,3,0), color=vector(0,.5,0), texture=textures.rough)\n", "\n", "# Display smoke rings rising out of a black tube\n", "smoke = []\n", "Nrings = 20\n", "x0, y0, z0 = -5, 1.5, -2\n", "r0 = 0.075\n", "spacing = 0.2\n", "thick = r0/3\n", "dr = 0.0075\n", "dthick = thick/Nrings\n", "gray = 1\n", "cylinder(pos=vector(x0,0,z0), axis=vector(0,y0+r0,0), radius=1.5*(r0+thick), color=color.black)\n", "\n", "# Create the smoke rings\n", "for i in range(Nrings):\n", " smoke.append(ring(pos=vector(x0,y0+spacing*i,z0), axis=vector(0,1,0),\n", " radius=r0+dr*i, thickness=thick-dthick*i))\n", "y = 0\n", "dy = spacing/20\n", "top = Nrings-1\n", "\n", "# Log rolls back and forth between two stops\n", "rlog = 1\n", "wide = 4\n", "zpos = 2\n", "zface = 5\n", "tlogend = 0.2\n", "v0 = 0.3\n", "v = v0\n", "omega = -v0 / rlog\n", "theta = 0\n", "dt = 0.1\n", "tstop = 0.3\n", "logcyl = cylinder(pos=vector(-wide, rlog, zpos), size=vector(zface - zpos, 2, 2),\n", " axis=vector(0, 0, 1), texture=textures.granite)\n", "leftstop = box(pos=vector(-wide-rlog-tstop/2,0.6*rlog,(zpos+zface)/2),\n", " size=vector(tstop, 1.2*rlog, (zface-zpos)), color=color.red, emissive=True)\n", "rightstop = box(pos=vector(wide+rlog+tstop/2,0.6*rlog,(zpos+zface)/2),\n", " size=vector(tstop, 1.2*rlog, (zface-zpos)), color=color.red, emissive=True)\n", "\n", "# Run a ball up and down the pole\n", "y1 = 0.2*h\n", "y2 = 0.7*h\n", "rball = 0.4\n", "Dband = 1.3 * pole.size.y\n", "cylinder(pos=vector(0,y1-0.9*rball,0), axis=vector(0,1,0), size=vector(0.1,Dband,Dband), color=color.green)\n", "cylinder(pos=vector(0,y2+0.9*rball,0), axis=vector(0,1,0), size=vector(0.1,Dband,Dband), color=color.green)\n", "vball0 = 0.3*v0\n", "vball = vball0\n", "ballangle = 0.05*pi\n", "ball = []\n", "ball.append(sphere(pos=vector(0,0,0), size=2*rball*vector(1,1,1), color=color.blue))\n", "for nn in range(4):\n", " cc = cone(pos=vector(0,0,0)+vector(0.8*rball,0,0), axis=vector(3*rball,0,0), size=rball*vector(3,1,1), color=color.yellow)\n", " cc.rotate(angle=0.5*nn*pi, axis=vector(0,1,0), origin=vector(0,0,0))\n", " ball.append(cc)\n", "ball = compound(ball)\n", "ball.pos = vector(0,y1,0)\n", "\n", "# A table with a mass-spring object sliding on it\n", "table = cone(pos=vector(0.4*R, h/4, -.3*R), size=vector(h/4, 0.6 * R, 0.6 * R), \n", " axis=vector(0, -1, 0), texture=dict(file=textures.wood_old, turn=1))\n", "tabletop = table.pos\n", "rspring = 0.02 * h\n", "Lspring = .15 * R\n", "Lspring0 = .1 * R\n", "hmass = 4 * rspring\n", "post = cylinder(pos=tabletop, axis=vector(0, 1, 0), size=vector(2 * hmass, .4, .4), color=color.gray(.6))\n", "spring = helix(pos=post.pos + vector(0, hmass/2, 0), size=vector(Lspring, 2 * rspring, 2 * rspring),\n", " color=color.orange, thickness=rspring)\n", "mass = cylinder(pos=post.pos + vector(Lspring, 0, 0), axis=vector(0, 1, 0),\n", " size=vector(hmass, .04 * R, .04 * R), color=color.orange)\n", "mass.p = vector(10, 0, 5)\n", "mass.m = 1\n", "kspring = 200\n", "deltat = .01\n", "\n", "# Display an ellipsoid\n", "Rcloud = 0.8*R\n", "omegacloud =3*v0/Rcloud\n", "cloud = sphere(pos=vector(0,0.7*h,-Rcloud), size=vector(5,2,2),\n", " color=color.green, opacity=0.3)\n", "\n", "rhairs = 0.025 # half-length of crosshairs\n", "dhairs = 2 # how far away the crosshairs are\n", "maxcosine = dhairs/sqrt(rhairs**2+dhairs**2) # if ray inside crosshairs, don't move\n", "haircolor = color.black\n", "roam = 0\n", "\n", "# Decorate the front entrance slab (GlowScript currently lacks a 3D text object)\n", "#text(pos=(0, 0.77*h, R+d/2), text=\"No Exit\", color=color.yellow,\n", "# depth=0.3, height=0.7, align=\"center\")\n", "\n", "# Display extruded text\n", "##text( pos=(2.0,-0.3*sin(pi/10),-2.0), text='A', height=2.0, depth=0.5,\n", "## color=(0,0,1.0), up=(0,cos(pi/3.4),-sin(pi/3.4)), axis=(0.5,0,1))\n", "##text( pos=(3.2,0,-2), text='B', height=2.0, depth=0.5,\n", "## color=(1.0,1.0,0), axis=(1,0,0.3))\n", "##text( pos=(5.0,-0.6*sin(pi/18),-1.4), text='C', height=2.0, depth=0.5,\n", "## color=(1.0,0,1.0), axis=(1,0,0.6), up=(0,cos(pi/8),sin(pi/8)) )\n", "\n", "scene.visible = False\n", "#scene.waitfor(\"textures\")\n", "scene.visible = True\n", "\n", "roam = False\n", "\n", "def setroam(evt):\n", " global roam\n", " roam = not roam\n", " \n", "scene.bind(\"mousedown mouseup\", setroam)\n", "\n", "while True:\n", " rate(30)\n", "\n", " # If in roaming mode, change camera position and direction according to mouse position\n", " if roam:\n", " ray = scene.mouse.ray\n", " if abs(ray.dot(scene.forward)) < maxcosine: # do something only if outside crosshairs\n", " newray = norm(vector(ray.x, 0, ray.z))\n", " angle = asin(scene.forward.cross(newray).dot(scene.up))\n", " scene.camera.rotate(angle=angle/30, axis=scene.up)\n", " scene.camera.pos = scene.camera.pos + (ray.y/4)*norm(scene.camera.axis)\n", "\n", " # Roll the log\n", " theta = theta + omega*dt\n", " logcyl.pos.x = logcyl.pos.x+v*dt\n", " logcyl.rotate(angle=omega*dt, axis=vector(0,0,1))\n", " if logcyl.pos.x >= wide:\n", " v = -v0\n", " omega = -v/rlog\n", " if rightstop.color.equals(color.red):\n", " rightstop.color = color.cyan\n", " else:\n", " rightstop.color = color.red\n", " if logcyl.pos.x <= -wide:\n", " v = +v0\n", " omega = -v/rlog\n", " if leftstop.color.equals(color.red):\n", " leftstop.color = color.cyan\n", " else:\n", " leftstop.color = color.red\n", "\n", " # Move the cloud\n", " cloud.rotate(angle=omegacloud*dt, origin=vector(0,0,0), axis=vector(0,1,0))\n", "\n", " # Run the ball up and down\n", " ball.pos.y = ball.pos.y+vball*dt\n", " ball.rotate(angle=ballangle, axis=vector(0,1,0), origin=vector(0,0,0))\n", " if ball.pos.y >= y2:\n", " vball = -vball0\n", " ballangle = -ballangle\n", " if ball.pos.y <= y1:\n", " vball = +vball0\n", " ballangle = -ballangle\n", "\n", " # Move the smoke rings\n", " for i in range(Nrings):\n", " # Raise the smoke rings\n", " smoke[i].pos = smoke[i].pos+vector(0,dy,0)\n", " smoke[i].radius = smoke[i].radius+(dr/spacing)*dy\n", " smoke[i].thickness = smoke[i].thickness - (dthick/spacing)*dy\n", " y = y+dy\n", " if y >= spacing:\n", " # Move top ring to the bottom\n", " y = 0\n", " smoke[top].pos = vector(x0, y0, z0)\n", " smoke[top].radius = r0\n", " smoke[top].thickness = thick\n", " top = top-1\n", " if top < 0:\n", " top = Nrings-1\n", " \n", " # Update the mass-spring motion\n", " F = -kspring * (spring.size.x - Lspring0) * spring.axis.norm()\n", " mass.p = mass.p + F * deltat\n", " mass.pos = mass.pos + (mass.p / mass.m) * deltat\n", " spring.axis = mass.pos + vector(0, hmass / 2, 0) - spring.pos\n", "\n", " # Update the analog clock on the back slab\n", " clock.update()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "VPython", "language": "python", "name": "vpython" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
cochoa0x1/integer-programming-with-python
01-introduction/vitamin-problem.ipynb
1
16107
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from pulp import *\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from IPython.display import HTML\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#TODO hard code the vitamin list for easier reproduction\n", "import re\n", "\n", "with open('vitamins.txt','r') as f:\n", " data = f.read()\n", " \n", "matcher = re.compile(r'(.+)\\s[0-9\\.]+\\s(mg|mcg|IU)')\n", "ingredients = [ m[0] for m in matcher.findall(data)]\n", "\n", "np.random.seed(42)\n", "df = pd.DataFrame( list(zip(ingredients,np.random.rand(len(ingredients)))), columns = ['vitamin','cost'])\n", "df.cost = df.cost.round(2)\n", "\n", "df.to_csv('vitamin_costs.csv', index=False)" ] }, { "cell_type": "markdown", "metadata": { "variables": { "HTML(df.to_html(index=False)) ": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>vitamin</th>\n <th>cost</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>Vitamin A</td>\n <td>0.37</td>\n </tr>\n <tr>\n <td>Vitamin C</td>\n <td>0.95</td>\n </tr>\n <tr>\n <td>Vitamin D</td>\n <td>0.73</td>\n </tr>\n <tr>\n <td>Vitamin E</td>\n <td>0.60</td>\n </tr>\n <tr>\n <td>Vitamin K</td>\n <td>0.16</td>\n </tr>\n <tr>\n <td>Thiamin (B1)</td>\n <td>0.16</td>\n </tr>\n <tr>\n <td>Riboflavin (B2)</td>\n <td>0.06</td>\n </tr>\n <tr>\n <td>Niacin</td>\n <td>0.87</td>\n </tr>\n <tr>\n <td>Vitamin B6</td>\n <td>0.60</td>\n </tr>\n <tr>\n <td>Folic Acid</td>\n <td>0.71</td>\n </tr>\n <tr>\n <td>Vitamin B12</td>\n <td>0.02</td>\n </tr>\n <tr>\n <td>Biotin</td>\n <td>0.97</td>\n </tr>\n <tr>\n <td>Pantothenic Acid</td>\n <td>0.83</td>\n </tr>\n <tr>\n <td>Calcium</td>\n <td>0.21</td>\n </tr>\n <tr>\n <td>Iron</td>\n <td>0.18</td>\n </tr>\n <tr>\n <td>Magnesium</td>\n <td>0.18</td>\n </tr>\n <tr>\n <td>Zinc</td>\n <td>0.30</td>\n </tr>\n <tr>\n <td>Selenium</td>\n <td>0.52</td>\n </tr>\n <tr>\n <td>Copper</td>\n <td>0.43</td>\n </tr>\n <tr>\n <td>Magnesium</td>\n <td>0.29</td>\n </tr>\n <tr>\n <td>Chromium</td>\n <td>0.61</td>\n </tr>\n <tr>\n <td>Lycopene</td>\n <td>0.14</td>\n </tr>\n </tbody>\n</table>" } }, "source": [ "# A new high performance and optimally cheap multivitamin. Saving the world one mixed integer program at a time\n", "\n", "You have been tasked with developing a new superior multivitamin. You have been given free reign to select the ingredients and their relative amounts in the vitamin but you have been asked to keep the cost of the raw materials as low as possible.\n", "\n", "Further complicating things, you must abide by some restrictions in the formula.\n", "\n", "1. The formula should not have more than 20% of any one vitamin\n", "2. The formula must have at least 10% Iron, Zinc, or Magnesium\n", "3. The formula must have at least 20% Vitamin A, Vitamin C, or Vitamin D\n", "4. Each vitamin that is used must account for at least 5% of the total\n", "5. The formula may contain as few as 5 vitamins but no more than 10 vitamins\n", "6. If the formula contains Magnesium it must also contain Calcium and Zinc\n", "7. The formula must have one of the B vitamins either B6 or B12 but not both\n", "\n", "#### The possible Ingredients and their per mg cost\n", "\n", "{{HTML(df.to_html(index=False)) }}\n", "\n", "You might see a simple greedy strategy to solve this, but the vitamin B constrains and the Magnesium/Zinc/Calcium constraints make things a bit more complicated. Instead of trial and error we can try and create this vitamin by writing a fairly simple mixed integer linear program. First we need to come up with an expression that captures our ultimate goal, in this case, to minimize the cost of raw materials.\n", "\n", "We can calculate the total cost of the formula by adding up the individual cost of each vitamin in it.\n", "\n", "$$\\text{Total Cost} = \\text{ sum over all the vitamins (cost of vitamin) * (percent of vitamin) } $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First lets load the data we have and organize it so we can easily grab the cost of particular vitamin" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>vitamin</th>\n", " <th>cost</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Vitamin A</td>\n", " <td>0.37</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Vitamin C</td>\n", " <td>0.95</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Vitamin D</td>\n", " <td>0.73</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Vitamin E</td>\n", " <td>0.60</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Vitamin K</td>\n", " <td>0.16</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " vitamin cost\n", "0 Vitamin A 0.37\n", "1 Vitamin C 0.95\n", "2 Vitamin D 0.73\n", "3 Vitamin E 0.60\n", "4 Vitamin K 0.16" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('vitamin_costs.csv')\n", "vitamins = df.vitamin.values\n", "vitamin_cost = df.set_index('vitamin').to_dict()['cost']\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets model the percent each vitamin is included as by a variable u. so if Vitamin C is included at 15% then u[Vitamin C] = .15. We can use pulp.LpVariable.dicts to return a dictionary of variables that are indexed by the vitamin names. This will make referring to variables later on very easy. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "u = LpVariable.dicts('percent', vitamins, 0, 1, LpContinuous)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next thing we need to do is create an instance of the ```pulp.LpProblem``` class. This creates a problem variable that will hold the cost and constraints and tells PuLP that we want to minimize our cost. We pass in a name and either ```LpMinimize``` or ```LpMaximize``` for either minimizing our cost or maximizing it." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "prob = LpProblem('super-awesome-vitamin', LpMinimize)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can define our cost. We will use ```pulp.lpSum``` instead of the regular python sum function for efficiency. In this problem it won't make a difference so feel free to experiment. This returns a ```pulp.LpAffineExpression``` which we will discuss in detail later on. To add our cost expression to the problem we literally just add it to the problem." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cost = lpSum([ u[v]*vitamin_cost[v] for v in vitamins])\n", "prob += cost" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now start adding our constraints. The first few are very straightforward. Like with the cost, each constraint just gets added to the problem variable. The key difference is that the constraint will be an inequality." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1: The formula should not have more than 20% of any one vitamin" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#no more than 20% of any one vitamin\n", "for v in vitamins:\n", " prob += u[v] <= .2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2: The formula must have at least 10% Iron, Zinc, or Magnesium" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#The formula must have at least 10% Iron, Zinc, or Magnesium\n", "prob += u['Iron'] + u['Zinc'] + u['Magnesium'] >= .1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3: The formula must have at least 20% Vitamin A, Vitamin C, or Vitamin D" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#The formula must have at least 20% Vitamin A, Vitamin C, or Vitamin D\n", "prob += u['Vitamin A'] + u['Vitamin C'] + u['Vitamin D'] >= .2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These were fairly straightforward, the code for the constraints and the description of the constraint are almost identical. However the next constraint is a bit stranger.\n", "\n", "\"4. Each vitamin that is used must account for at least 5% of the total\"\n", "\n", "We basically need the u variables to be at least 5% or 0%. To model this we need to introduce some new variables that will track in simple yes/no manner if the vitamin is included in the final formula. Once we have these variables we will link them with the u variables somehow and satisfy the rest of the constraints. For now lets just look at the code." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#binary variable that captures if this vitamin will be used in the formula\n", "b = LpVariable.dicts('use',vitamins,0,1, LpBinary)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4: Each vitamin that is used must account for at least 5% of the total" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Each vitamin that is used must account for at least 5% of the total\n", "for v in vitamins:\n", " #if we don't use this vitamin then the percent must be zero\n", " prob += u[v] <= b[v] \n", " #likewise if we do use this vitamin, then the percent must not be zero\n", " prob += u[v] >= .05 -100*(1-b[v]) # > .05 or > .05 -100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That looks a bit confusing but its actually a very common modeling technique we will explore in detail later on. These new b variables make the rest of the constraints really easy to model.\n", "\n", "5: The formula may contain as few as 5 vitamins but no more than 10 vitamins" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#The formula may contain as few as 5 vitamins but no more than 10 vitamins\n", "prob += lpSum([ b[v] for v in vitamins]) >= 5\n", "prob += lpSum([ b[v] for v in vitamins]) <= 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "6: If the formula contains Magnesium it must also contain Calcium and Zinc" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#If the formula contains Magnesium it must also contain Calcium and Zinc\n", "prob += 2*b['Magnesium'] <= b['Calcium'] + b['Zinc'] " ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "7: The formula must have one of the B vitamins either B6 or B12 but not both" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#The formula must have one of the B vitamins either B6 or B12 but not both\n", "prob += b['Vitamin B12'] + b['Vitamin B6'] == 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, while it wasn't stated as a constraint, our u variables are supposed to be percents, so they must add up to 100%" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#the percentages must add up to 100\n", "prob += lpSum([ u[v] for v in vitamins]) == 1.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now solve the problem and relax knowing we have made the best possible multivitamin (with our highly customized and formalized definition of \"best\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Optimal'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "LpStatus[prob.solve()]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "total cost: $0.15\n" ] } ], "source": [ "print('total cost: $%.2f'%prob.objective.value())" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Vitamin A 20% at unit cost of: $0.37\n", "Thiamin (B1) 10% at unit cost of: $0.16\n", "Riboflavin (B2) 20% at unit cost of: $0.06\n", "Vitamin B12 20% at unit cost of: $0.02\n", "Iron 10% at unit cost of: $0.18\n", "Lycopene 20% at unit cost of: $0.14\n" ] } ], "source": [ "for v in vitamins:\n", " if value(u[v]) >0:\n", " print( '%s %.0f%% at unit cost of: $%.2f' %(v, 100*value(u[v]), vitamin_cost[v]))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
daviddesancho/Cossio
schutte/notebooks/.ipynb_checkpoints/schutte-checkpoint.ipynb
1
93168
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Run Brownian dynamics in a modified version of the Schutte potential (Schutte et al. JCP 2011)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "nbpresent": { "id": "fd29919c-4d19-4132-b23b-5258df819e89" } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.cm as cm\n", "%matplotlib inline\n", "%load_ext autoreload\n", "%autoreload 2\n", "import h5py\n", "import seaborn as sns\n", "sns.set(style=\"ticks\", color_codes=True, font_scale=1.5)\n", "sns.set_style({\"xtick.direction\": \"in\", \"ytick.direction\": \"in\"})" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import itertools\n", "from scipy.stats import norm\n", "import time\n", "import schutte" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def f(x):\n", " \"\"\"\n", " Functional form of the potential. NOTE\n", " it is a MODIFIED version to have deeper\n", " minima in the intermediate region.\n", "\n", " x : float\n", " Value of coordinate x.\n", "\n", " \"\"\"\n", " if x <= 0.0:\n", " return (1.-x*x)*(1.-x*x)\n", " elif x >= 8.0:\n", " return (1.-(x-8.)*(x-8.))*(1.-(x-8.)*(x-8.))\n", " else:\n", " return 2.7/5. + 2.3/5. * np.cos(x*np.pi)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(numpy.ndarray, 9.9, -1.9, 1000)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = np.linspace(-1.9,9.9,num=1000)\n", "type(x),max(x),min(x),len(x)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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MQSkDAAybrJPcEkKCS8gHB/aXp24FIWOFfHCgbgUhroV8cGB/eQoOhNxidzAwWwI3zyEog4NSLoVIBAybqeZACItdUhDSwUEsFkGlkMJImQMhowK5IhMI0uAAAGEKKXUrCLkN+3kI+eCgUsqoW0HIbYwB3OgFCOLgEKakzIGQ27Gby6oCtN4oeIMD1RwIGcNI3QqnMOpWEDLG0EhwCFOFfHCgbgUhtxsyOr8sw0eWFwgtaIODioIDIWOwa43CVSEeHMIUMhjNNjgczGQ3hZCgMGSyQS6TQCoJzMc2eIPDyHANnZlJiNOQ0Qp1gOoNwF0QHKhrQYjTkMk6up1BIARvcFDQng6E3G7YaA1YMRII4uDAzgIbpj0dCAHgzBwCVYwEgjg4jO4jSd0KQgAAQ0bb6OciEII4OIx0KyhzIAQAZQ6jwtgNX4xUcyAEoJrDKDZCDlJwIARWmx0WmyNgU6eBIA4OYUopxGIRBQdC4Kw3AICaMgdAJBIhXCnD4LBlsptCyKRjh/TDqObgpA6TYXCYMgdChkyBXXQFBHtwUMmoW0EIbluRSZmDkyZMjkEjdSsIGd3LgeY5OKlV1K0gBAj8Xg4AwCkM6fV6vPvuu7hw4QLq6+sxPDyMDz/8EKWlpYI2LjxMhgEKDoRgYMiZQWvC5QF7Tk6ZQ2trK/bs2YOwsDAsWLBA6DaNUqtkGDJZwTC0pwMJbQPDFkglYijlkoA9J6fMobi4GKdOnQIAHDx4EIcPHxa0USxNmBwOBwOj2RbQpaqEBJuBYSu04TKIRKKAPSenzEEsnpzShHqkMktdCxLqBoYtUIcFrksBBHtBMmxkCjVNhCIhzjBkgSbAwUGwcRG9Xj/uNq1WC61Wy/kx2Egp1FwHu92BXUebcajqGpRyCf51VTYWFCQK8lxc2/O3w004euY6tOFylN2fi9mZcZPWHovVjh376nG69ibio8Lw+AMzkZUaNWntGTJa8cHeOpxt6ERKghr/tiYfqQmaSWtP34AZ7316AXWXuzFdp8UP1uQhJV6Y9gwMW5AcpxbksSciWOZQVlaGlStXjvn3wQcfePUYagEXXzkcDF7eUY3399QhUq2AyWLHL9+rwOGqa7w/FxcMw+D1nTXYsa8eMRFK9BhMeP4Pp1B1sX1S2mO3O/DL9yrwjy8uITE2HFfbB/DvbxxH49XeSWmPyWzDL94+hc9PtyI1QYP6K7342dZjuNY+MCntGRy24N/fPI5jZ28gPSkCdZe7sfn1L3HlpkGw55symcOOHTug0+nG3OZN1gAAatVI5iBAt2LX0WYcP9eGHzyQh2+vyILVZsf/eec03vzbWWRPixTsG2Ai+8uv4kj1dZTdn4tHV+dg2GTFv795HK/sqMa2/70CURplQNvz98NNONPQgZ98Zw7uX5iGvgEzNv3uS/zqg0ps+9mKgJ26xPrws4touNqL//hBMRbOSkJ7zzA2vX4UL2+vxstP3wuZNLA95Df+fg7tPUP4r/WLUJARi46eYWzZegy/fK8cWzcth5LH14dhGBiGrNCEBbYoL9grqtPpkJKSMuaft8GBfTH4Lkh29A7jz/vrUZKnw9rlmQAAmVSCzWVFkEjEeG93Ha/P58mg0YoP9tQhf0YMHlmVDcC52c3P/td8GM02bP+sPqDt6ew14uNDTVg8Own3L0wDAERqFNj8vSJ09Rnxt0ONAW1Pq96AT4+34IHF6Vg4KwkAkBAdhp98pxAtbf3Yf/pKQNtzrqkTJ8614ZHVOSjIiAUAxEeHYfNjRdB3D2PnQX5fH5PFDpvdEfDMIagLkkqFFHKZBP2DZl4fd+eBRjAOBusfmjVmaChKq8TDK7NQUadH07XApc+fnbyMgWELfvRgwZj2pMRr8MDidBysaEV7z3DA2vOPL5rgcDjww2/kj7l9Zno07i1MxqfHWwK65mXngUYo5RJ872u5Y25fUKBDQUYM/nqwERarPWDt+euBBsREKLF2WeaY22dlxGLZvBTsPt6CvgH+3rMDw4GfAAV4ERz27duHffv2oaamBgBQWVmJffv24ejRo4I1DgAi1HIYhvjrVnT3G3G46ipWlUxDfHTYuPsfWJwOlUKKfx5t4e053bFY7fh/x1owLzceGSmR4+7/1tJMiEQi7D4WmPYYhiw4UHEV985Ncfn6fHtFFoxmOz47eTkg7bnZNYTj527ggcUzoL3jwyESifDoqhz0DZhx7OyNgLSnobUHtc3d+NbSDMhl4yckPbI6GxarHbuOXuLtOUdnRwa4W8G5Y/T000+P+f9bt24FACQnJws6KSoiXI4+HjOHPScuw+Fg8NAdUZ8VppThvtLp+PR4C374zXxEa4Xt6588fxN9A2Y8tDTD5f1xUSosmZOM/eWteOz+XF77sq4crLgKs8WOb03QnhnJEZiTFYu9Jy7j28uzIBYLOynnQEUrRADWLEl3ef/srFikJmjw6fEWrJifKvgkob0nryBMKcV9pdNd3p8Sr8GiWUnYX96K730t12UA8dZo5hCs3YqGhgaX/4SeLRmhVsDAU3CwOxgcqryGebkJ0MWET3jdvyxKg93B4OiZ67w8rzuHKq8iPjrM7ZDl/Qunw2i24XTtTUHbwjAMDlddRc60KKQnRUx43ddK09DVb8L5S12Ctsdud+BQ5VXMy01ATITK5TUikQgPLErDpev9aLnRL2h7jGYbTn7VhnsKk93O2P2XhWkYGLbi5FdtvDzvwJCzCxe03YrJEqFWoJ+nbkVNQwd6DCasKpnm9rrkODWyp0XiSLWww5pdfUaca+rEiqJUt9/AeekxiI9S4Ui1sMHqcpsBrfoBLJ+f6va6kgIdwpVSHBb49alu6ECPwTzhtzTrnrkpkIhF+LJG2K7FiXNtMFnsWOHh9ZmVGYvE2HDsL7/Ky/MOGIM8c5gs2nA5+gfMvCy+OlR5FdpwOUrydB6vXV6UisttBsHGrQHg6JnrYBh4fLOJxSIsL0rF2cYOdPcbBWvPkeprkEpEuHdustvrFDIJlhQm4+RXbTAJeHTA6fM3EaaUYv7MBLfXacPlmJcbjy9rrgt68PKR6mtIjA3HzLRot9eJxSIsn5eC2pYuXv5e/SPFzTtrLkIL+uAQqVbAYnPAZPGvGm2y2FB5sR2LZydxGhO/pzAZYrFI0EJXRZ0eM5IjkBg7cReHtXReChyM8wMjBIZhcOr8TRRmx3P6hrp3bjJMFjvONHQI0h6Hg0HVxXbMy4nn9PdaOjcFXf0m1F3uFqQ9hiELalu6sWROEqe6xpLCZDAMcIKHrkXvgBmaMHnATtdmBX1wiFA736j+DmfWNHTCbLFj0Wxu06Mj1ArkpUejXKB+vmHIgvorPSjOc/+tyEpN0CA5To3TF8ZPS+fD9Y5BtPcMc25PfnoMNGFynBLo9Wm+0YfeATOKOWR5AFCSr4NMKsbpWmFen6qLejgcDOfp9akJGqQlanH8rP/BoW/QjCitwu/H8VbQBwet2vmi+BscTp1vg1olG520wkVpfiJa9QPQdw/59dyunKlvh4MBij2kzLdbUKDD+UtdgswxqKxzTtP2lMKzJBIxSvITUFnXDpvdwXt7Ki60QywCinLjOV2vUkgxJysOFRf0guz/cbpWj5gIJTJdDDdPZElhEi5e6fG7a9FrMCFSTcFhnIiRfpY/RUmrzYGKunaU5Ou8Ss0WFDi/tYT4Nqq82I4ItdyrhUwLChJhH0m3+VZ1sR1piVrER42f2zCRhQWJGDJaBRm1qLyoR870aER48aEoydfhZvcQ7+stTBYbqus7UJqv82rotjTfmWVUXfSv69U3aA749HngbggOI28Of4Yz61q6MWS0YuEs71Zc6mLCMV2nQfkFflNnu92B6voOzJ+Z4NWbLXtaFCI1Ct67OoNGKy5c7ubcpWAV5sRDIZfwPsTa3W9E8/V+r9tTMnJ9Oc9dr3ONnbBY7V6v2J2u0yA2UoWqi/61p2/AjEgNZQ7jsMGhb9D3zKGmsQNSiQhzsrxf/lxakIi6lm5eZ2nWt/ZiyGhF8Uxu/WmWWCxCab4O1fXtsNr4my5c09ABh4Ph3KVgKWQSzMmMQ1V9B6+pPJsZcRlVul1MhAqZqZGo4Dk4lF/QI1wp9apLCjjnYBTPTMDZxk6f/15Gsw0mi52CgytKuQQKuQS9AyafH6OmoRO5adE+rSQszdfBwThrBHyprNNDIhZhbo73waokTwej2Y4LLfxV5Svr9NCEyZAz3f0QnStFM+PR0TOM6x2DPLanHfFRKkzTeb8ytiRPh4arvbytbRgdNclN8Gnl5/y8BJgsdpxv9u3vxf4eURQcxhOJRIjRKtHd71tw6B0woaWtH3OzuRW27pSZEolItWK0YMeHyovtyJ8R49O+mLOzYiGXilHBU3vsDgbV9R0oyk2AxIep0EW5zmyjup6fIU2z1Y6axk6U5Ol8mgpdnJcAhgGqeQrml9v60Ttg5lwYvdPsTOffy9c6EfulSJnDBGIiVOjxMTica+wEAJ++pQFnKj9/ZgKqGzp4qcq39wzjqn6A8xDdnZRyKWbzWJVvutoLw5DF6/49KyE6DKkJat4+jOcvdcFitfv8+mQkRyBaq+QtmLNBb56PwUEpl2JWZqzPweFW5kAFSZdiIpQ+DwfVNHZCEybHjORIn5+/OC8BQ0YrLl7p8fkxWFV1zv5wiY8fRvZn23lK5SsvtkMsFmFejm9vfsCZPdQ2d/MyW7KiTg+lXIKCjBiffl4kcgbzmsYOWG3+B/Oqi+3ITInw68NZlJuAm11DuNnl/ZA4u+iQMocJsNumeTs1lmEY1DR0oDA7zqeUmVWYHQepRMTLt1HFxXYkxYYjyY/9AOePFDIr6/wvvFXW6TEzLdqvnY2LcuNhszvwlZ9DmgzDoLKuHYXZcX6tZizOS8Cwyeb3bMnBYQsaWntGu06+YrskvtStegwmiEW3hvQD6S4JDirY7IzXIwat+gH0DpgxN9u/TVrDlDIUzIj1+8NoMttw/lKXzykzKy5KhfQkrd91h64+Iy63GbyaiOVK/owYKOUSVPnZtbhy04CuPqPfr8+crDhIJb7381k1jZ1wMPA7OCTGhkMXE4ZqH6aad/UZEaVVQhLgqdPAXRMcnCldl5ddi5qRP8ZcP1JmVnFeAq53DPqUGrLONXXCanP43L8f2x4dLl7pGV3r74vKkQ+Pv+2RSSWYkxWHaj+HNNnMzN9gpVJIMTsz1u9Mr7q+HZowGbKn+7fjtkjk7LZ9danL6yHNzl4jYiNdL1cX2l0VHLwtStY0dCA1QcPLi8t+m1X6MaGl8mI7VAop8tJ960+PbU8CHA4GZ/wYJaiqa0d8dBgv27sX5fo/pFlRp0dWaiSieNhgZ/7MBNzoHERbl2/tcYyM4szNjverS8oqyk2A2WJHXYt3dauuPiPiKDhMjN3ow5uipMXqnAvg6yjFnRJjw5ESr/b524jtT3NdZehJdmoUItRyVPjY1TFb7Tjb1ImSmQm87J50a0jT96p849Vev7sULDYbqvLx79XS1o++ATOKZvqfdQLOPR6kErFXXQuGYdDVR5mDW1EaBcQieDXXoe5yNyw2h8/zG1wpztOhtrkLwybvFz613OhHj8HES5cCuG2Itb4Ddh+GWNkhw/k8tYfNQKp9XEdQXd8OhvG/i8PSxYQjNcH3YM4GuXk5/LRHpZAif0a0V8HTMGSBxeZAXBQFhwlJJGJEa5Xo6OW+A3NNQyekEjEKZvifwrOK8xJgszM4OzJ3whsVde0Qifwvbo1tj87nIdaKC84hw1leTgl2Z/7MBNS2dPsUPCvq9IjWKpGRPPH2dN4qnqlDbYtvwbz6YgcyUyN5HUKcl5OAq/oBdPZyy4A7+5zXUbfCA11sOPTd3IPDmYYO5KVH87oh68y0aISrZD59G5VfuInc6dG8vtnm+jjEyjAMyi/oMTcnnpcNUFml+TrYRhaVecNitePMyKpHPjeIne9jMDcMsUOY/GWdwG1Dmhy7FmwQoW6FB4kx4Zz3VegxmHDlpoGXUYrbSSViFOXEo6q+3as5F529zlWGpfn89KdZYUrn/hTeFkmbrzu7OHy3JzctGtpwuderNL+61AWTxY7SAn7bwwZzb4c0K+v0cDDAgnx+z02dptMgJkKJMw3c2sO+391thiykuyY46GLC0Ttg5jQL72zjyBCmn/MbXCnOS0DfgBmXrvdx/pmKkSXfJTx/GNn2XGv3boi1/IIeYhH3jV24koysGq262O7V7MTTtTdHhx/5JJWIMS8nHlUXvQvmp87fRGykChkp/HVxAOeQZlGuc5Uml6n4bV1D0ITJAr6xLOuuCQ6JI9FTz+HkpzP1nYhUK9xur+6rebkJEIvgVSp/+oIeyXHhgpwIXezDbMmKC3rkpnm3kQpXCwoSMWyy4Xwzt9mSDoezizN/ZgJkUv66OKzivAT0DpjRfKOP0/Umsw01DR1YUMBvF4c1LzcewyYbGlo9n6jW1jmIpNjAnqx9u7smOOhinTsUefqGdDgY1DR2oDAnTpADV7ThcuRMj+acyg8araht7kIJzykqKzHWu6r8za4htLT1j+5SxLc52XFebQDTOLK8mu8uDmteTjxEIu5DmmcaOmCxObzeGIirOVnO9yWXUYu2riEkxk1OlwK4i4JD4kgEbet0P6ml5UY/DEMWvxYSeVKcl4Dm6/3o6vNcdT5xrg02O4Mlc5KEa48XVfkva5xnX9xT6H77eV8pZBLMy4nH6fM3OQ2xfnn2BmRSMe9dHFaEWoHc6dE48VUbp9mbx8+1QRMmRz4PE9VcUatkyJ0e5bEoabba0dVnpMyBC7VKhpgIJa7o3Z8jwb7ohQLUG1iLZzs/6FxOxPrizDUkx4UjKzVSsPaU5OtgszMe97pkGAZfnLmO/Bkxgo6dL5uXgt4BM842uR8lsNkd+LLmOkrydQhXCXcO5PKiFLTqBzyeiDVotOJ07U0snZss6FqGebnxaL7e73YDIzZDTuJwbIFQ7prgAADTE7Vo9XDIzJmGDsxI8m+JrSdJcWrMTIvGwcqrbr+NOnqHUdvcjWVFwp7hmJcejcTYcByoaHV7XcuNflzvGMTSeSmCtQVwzr/QhMlxqNL9iVhnGjrQP2jBiiL3h/r4a0lhMqQSMQ5XuW/PiXM3YLU5PJ745S92rktNw8TB83KbM5ClJWkFbYs7d1VwSNNpca19cMJKb/+gGRcvdwsyKnCn1SXTcL1j0G1h6UD5VYhEzm9SIYlEIqwumYba5m7ccNPt+uzUFchlEkG7OAAgk4qxdF4yTtfedLsw7EB56+hpVULShMlRkp+AL85ch8U68cKngxVXkZqgFjTLA4AZSRGIVCvcrotpudEPmVSMFD+W9vvrrgoO0xO1sNkdE9YdKi6MjE/zPF7uyuI5SVAppNh9rMXl/RarHZ+duozimbqAjFOvLJ4GsViEvScvu7x/YNiCI9XXsWxeSkCGxr62IA1WmwOfnbzi8v62rkGUX9DjawumB+Qkp39ZmAbDkAVfTNAVrL/Sg/rWXty/ME3wk7rFI/uHnmnogH2CIdbLbf2YnqidlKXarLsqOLBTa5uu9bm8/1TtTcRHqTCDxym4EwlTyvD1RWk4fu6Gy2B1qOoa+gct+OY9MwRvCwBEa5VYOjcZ+061uuzLfnqsBRarfcKj7PmWlqjFvNx47D7WArOLb+tdR5shEYuxZklgXp85WXGYkRSBT7645PID+Y8vLkGtkmF1iftDe/kyLzcBA8MWNLuYL8MwDFpuGHidSu6Luyo4pCZooFbJXO68PGS04mxjJxYUJAoe+VkPLs2AVCLGh3svjrndaLbhL5/XY2ZaNGZn8Tuxx51HVufAZrPj4wONY27vNZjwydFmLJyVKMjcj4k8vCILfYNm7Pri0pjbr7UPYP/pVqwqmYZoHpZncyESifDwqixc7xjEwTtqM3WXu3Hq/E18454ZPu1Q7ou52XEQiVxvzHuzawgDwxZkeHG6lhA4BYehoSG8+OKLWLJkCWbPno21a9fi0KFDQrdtHLFYhLz0GJfB4dhZZzFJ6GLb7aI0Svzr6myc+KptdOSCYRi89Y+v0Ddoxg+/mR+wQAUAyXFqfH1ROvacvDw6S9TuYPD6zhrY7Q48/vWZAWsLABRkxGLxnCR8fLARTdectRmrzY7X/noGSrkEZV/LDWh7Fs9OQv6MGLz3ad1otjdktOJ3O88iNkKJtcsyA9YWdoj12Nkb44ra7ASyWT7uo8kXTsFhw4YN2L17N55++mn84Q9/QGZmJjZs2ICjR48K3b5x8mfEoK1raNzeDgcrr2KaTiN4MelOa5dlIS89Gq/99Qw+3FuHX39UhcNV1/Do6hzk+nAOhL8efyAPqQkavPheBf7yeT3+64+nUV3fgR9+swAp8fzP0PTkxw/NRqRGgV+8fRofH2zEz986icarfXj60bkB3zRVJBLhmUfnQiwS4bnfn8DfDzfhP7adgL57CJvKinhdpMfFqpJpuNY+gPorY4vaZxs7EaVRIHkSi5EAh+Bw9OhRnDx5Ei+++CIefvhhLFy4EP/zP/+DwsJCvPTSS4Fo4xjsev+TX92agXfpeh8aWnuxumRaQL+pAWdl/vknFqAoNwF/O9SE8gt6fO++HHz3vpyAtoOlUkjx4vpFyJkWhT/vb0Dd5W6s+9YsPLA4MLWGO0VqFPiv9YuQGBuGjz67iGvtg3j2e/OwcJawIyYT0cWE48UfL0KYSoYP9tShb9CM//i3Eq9Ps+LDPYXJUCkk2HvqVhHZbLWj6qLzXNdAv5fv5DFUHjhwABqNBitXrhy9TSQS4aGHHsLzzz+PS5cuITMzcOkYe7T5oaqrWLMkHSKRCH/d34BwlQz3lQammHQntUqGn/+wFINGK+RSMa/LoH0RpVXil08txsCwBUq5lJedp/yRFKfGK08vRf+gGeEqWUBGJ9yZkRyBNzYvh2HIArVKNmkjAiqFFPeVpmH3sWb868pspCZocPKrNpgsdsGHm7nw+Ko0NTUhMzMTYvHYS3NynN+MjY2Nrn5MUN+4Zwaar/fj+Lk2HDt7A+UX9HhoWYZPJ0gBgMFgwNatW2EwuJ9g5YlaJZv0wHA7TZh80gPD7UQOM36/7U2/X2de2iISIUKtmNShQgB4eGUWFHIJtv3fcxg0WrHzQCOm6zSYnenbDF++3ssAh+DQ19eHiIjxFW72tr6+Ppc/p9frcf369TH/+HpTrJifiqzUSLy8vQq/2V6FnGlRWLssy+fHMxgMeOONN4LiTTuV0es8XoRagR+vnYPa5m6UvfAZ2roG8cNvFPi8aJDP15hTBcZd32ei+8rKysbdtmHDBmzcuJFj0yYmlYjxiycX4B9HLkEmFWPt8syg+oYkxBsr5qdCGy7HqfM3sWh2ouAzRrnyGBwiIyNdZgf9/c65366yCgDYsWMHdLqxMxW1Wv7miUeoFfi3b+Tz9niETKb5MxMEW5nqK4/BITMzE/v374fD4RhTd2BrDdnZ2S5/TqfTISUlcHMOCCH88hgcVq9ejb///e84fPgwVq1aNXr7rl27kJ6ePm6kwm53TpXV6/0/xzFQ2LbeTW2+G9HrLDwurzF7H/tZnYiI8bADBsMw+P73v4+GhgZs2bIFKSkp2LVrF3bt2oVt27ZhxYoVY66vqqpyWW8ghASXHTt2YP78+RPe7zE4AMDg4CB++9vf4vPPP4fBYEBmZiZ+8pOfjMkkWCaTCbW1tYiLi4NEEjzDeoQQJ7vdjs7OThQUFECpnHhtC6fgQAgJPTT+RwhxKbArTYLM0NAQXn31Vezbt29Md+n2qeLEP+Xl5Xj88cdd3rd3715kZGQEuEV3P71ej3fffRcXLlxAfX09hoeH8eGHH6K0tHTctSdOnMDrr7+O+vp6hIeHY/Xq1di8eTOnaQUhHRw2bNiAuro6bN68GSkpKfjkk0+wYcMGvPXWW1i6dOlkN29K2bx5M4qLi8fcRkPdvmltbcWePXuQl5eHBQsW4PDhwy6vKy8vx7p167By5Uo888wz6OjowMsvv4zGxkb8+c9/HrckYhwmRH3xxRdMdnY2s3///tHbHA4H8+ijjzL333//JLZsajl9+jSTnZ3NHDhwYLKbMmXY7fbR/z5w4ACTnZ3NnD59etx13/72t5kHH3xwzPXHjx9nsrOzmT179nh8npCtObhbbdrS0oJLly65+WlCJo/Hb3wA7e3tOH/+PB588MEx1y9evBgJCQn4/PPPPT+PX628iwXjatOp7IUXXkBeXh6Kioqwfv161NbWTnaTpjT2/ZuVNX5BYnZ2Npqamjw+RsjWHPr6+pCWljbudk+rTYl3NBoNvv/976OkpASRkZFobm7G22+/je9+97vYvn075syZM9lNnJLY9+9EK6rr6uo8PkbIBgfAt9WmxDt5eXnIy8sb/f/z58/HihUrsGbNGrz66qt4//33J69xIWCi9zGX93fIdit8XW1K/BcXF4clS5bg3Llzk92UKSsyMhKA6wy4v7+f0/s7ZINDZmYmmpub4XCMPT3L02pTwo87X3fCL7bW4Kq20NjY6LIWcaeQDQ6rV6+GwWAYN0Y80WpTwp/Ozk6cPHkShYWFk92UKUun06GgoAC7d+8eE4hPnTqF9vZ23HfffR4fI2RrDkuXLkVpaSmee+459PX1ja42ra6uxrZt2ya7eVPGpk2bkJqaivz8fGi1WrS0tOCdd96ByWTCs88+O9nNu2vt27cPAHD+/HkAQGVlJXp7e6FSqUYn8G3evBlPPPEEnn32WTzyyCNob2/Hyy+/jDlz5uD+++/3+BwhvfDKm9WmxDdvv/029uzZgxs3bsBoNCIyMhIlJSV46qmnqOvmB3bI/U7JycljsuEvv/wSW7duHZ0+vWrVKmzZsoVTzSGkgwMhZGIhW3MghLhHwYEQ4hIFB0KISxQcCCEuUXAghLhEwYEQ4hIFB0KISxQcCCEuUXAghLj0/wHsosi7QIlYQQAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 288x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(4,4))\n", "fplot = [f(y) for y in x]\n", "#dx = [cossio.dGqxdx(0, y, barrier=5.) for y in x]\n", "ax.plot(x, fplot)\n", "ax.set_ylim(-0.1,5)\n", "#ax[0].set_ylabel('$G(x)$', fontsize=20)\n", "#ax[1].plot(x, dGqxdx)\n", "#ax[1].set_xlabel('$x$', fontsize=20)\n", "#ax[1].set_ylabel('$\\partial G(x)/\\partial x$', fontsize=20)\n", "#ax[1].hlines(0, -10, 10, linestyle='dashed', linewidth=0.5)\n", "\n", "plt.tight_layout(h_pad=0)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "def schutte_runner(inp):\n", " np.random.seed()\n", " numsteps = inp[0]\n", " x = inp[1]\n", " dt = inp[2]\n", "\n", " tt, xk = schutte.run_brownian(x0=x, dt=dt, \\\n", " numsteps=numsteps, fwrite=int(1./dt))\n", " data = np.column_stack((tt,xk))\n", "\n", " h5file = \"data/schutte_x0%g_num%g_dt%g.h5\"%(x,numsteps,dt)\n", " with h5py.File(h5file, \"w\") as hf:\n", " hf.create_dataset(\"data\", data=data)\n", "\n", " return h5file, tt, xk" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "h5file, t, x = schutte_runner([1e7, 5., 5e-4])" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "h5file = \"data/schutte_x05_num1e+07_dt0.0005.h5\"\n", "file = h5py.File(h5file, 'r')\n", "data = np.array(file['data'])\n", "file.close()" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(5001, 5001)" ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(t),len(x)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 288x216 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(2,figsize=(4,3))\n", "#fplot = [f(y) for y in x]\n", "#ax.plot(x, fplot)\n", "ax[0].plot(t,x)\n", "ax[1].plot(t,x)\n", "ax[1].set_xlim(0,15)\n", "#ax.set_ylim(-0.1,5)\n", "\n", "plt.tight_layout(h_pad=0)" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [], "source": [ "lev = np.linspace(-1.9,9.9,num=1000)\n", "fplot = [f(y) for y in lev]" ] }, { "cell_type": "code", "execution_count": 86, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f11831a2a50>]" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 288x288 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(4,4))\n", "axt = ax.twinx()\n", "axt.hist(x, bins=np.linspace(-2,10,20), density=True, alpha=0.5)\n", "#axt.hist(data[hdb.labels_==0, 0], bins=np.linspace(-10,10,30), density=True, alpha=0.5)\n", "#axt.hist(data[hdb.labels_==-1, 0], bins=np.linspace(-10,10,30), density=True, alpha=0.5)\n", "ax.plot(lev,fplot, '.', color='k')" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "#RUN LONG to obtain EQUILIBRIUM DISTRIBUTION and compare with previous\n", "h5file2, t2, x2 = schutte_runner([1e8, 5., 5e-4])\n", "# too small dt: h5file3, t3, x3 = schutte_runner([1e10, 5., 1e-4])" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f11831d8750>]" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x288 with 4 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1,2,figsize=(14,4))\n", "axt = ax[0].twinx()\n", "axt.hist(x, bins=np.linspace(-2,10,40), density=True, alpha=0.5)\n", "ax[0].plot(lev,fplot, '.', color='k')\n", "ax[0].set_title('numsteps=1e7')\n", "axt = ax[1].twinx()\n", "axt.hist(x2, bins=np.linspace(-2,10,40), density=True, alpha=0.5)\n", "ax[1].plot(lev,fplot, '.', color='k')\n", "ax[0].set_title('numsteps=1e8')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### STOP HERE !!!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", 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mit
awfuldynne/data-512-a2
hcds-a2-bias.ipynb
1
42832
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignment 2 - Bias in Data\n", "Sean Miller (<[email protected]>)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview\n", "\n", "This notebook outlines an analysis of English Wikipedia articles on political figures from many countries. We seek to explore the ratio of articles compared to population of the country and the percent of those articles that are high quality to understand how the English Wikipedia might be biased.\n", "\n", "#### Libraries\n", "\n", "All of the following code was written and tested against the default packages present in **Anaconda3 v4.4.0**. You can find a download for Anaconda and its latest versions at <https://repo.continuum.io/archive/>." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Preparation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "import os\n", "import pandas as pd\n", "import requests\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we'll prepare our folder structure for our analysis. Any data sets we've downloaded or will scrape from the web will be stored in the *raw_data* folder, any data sets that have been processed by our code will be stored in *clean_data* and any visualizations or tables used for our final analysis will be stored in the *outputs* folder." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# If the folder raw_data doesn't already exist, create it\n", "# raw_data is where any initial data sets are stored\n", "if not os.path.exists(\"raw_data\"):\n", " os.makedirs(\"./raw_data\")\n", " \n", "# If the folder clean_data doesn't already exist, create it\n", "# clean_data is where any processed data sets are stored\n", "if not os.path.exists(\"clean_data\"):\n", " os.makedirs(\"./clean_data\")\n", "\n", "# If the folder outputs doesn't already exist, create it\n", "# The outputs folder is where visualizations for our analysis will be stored\n", "if not os.path.exists(\"outputs\"):\n", " os.makedirs(\"./outputs\")" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Reading in the Data\n", "\n", "To perform this analysis, we'll be joining data from three different data sets. These data sets and relevant information are listed below.\n", "\n", "|Data Set | File Name | URL | Documentation | License |\n", "|--------------------------------|\n", "| EN-Wikipedia Articles On Politicians | page_data.csv | [Figshare](https://figshare.com/articles/Untitled_Item/5513449) | Same as URL | [CC-BY-SA 4.0](https://figshare.com/articles/Untitled_Item/5513449) |\n", "| Country Population Data (Mid-2015) | Population Mid-2015.csv | [Population Research Bureau website](http://www.prb.org/DataFinder/Topic/Rankings.aspx?ind=14) | Same as URL | I have no idea |\n", "| Wikipedia ORES | N/A | [ORES](https://www.mediawiki.org/wiki/ORES) | [ORES Swagger](https://ores.wikimedia.org/v3/#!/scoring/get_v3_scores_context_revid_model) | [CC-BY-SA 3.0](https://wikimediafoundation.org/wiki/Terms_of_Use/en#7._Licensing_of_Content) |\n", "\n", "For the first two data sets, we'll be manually downloading the data from the provided links, copying the files to the *raw_data* folder and reading in the csv files with the [pandas](http://pandas.pydata.org/) library." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Location</th>\n", " <th>Location Type</th>\n", " <th>TimeFrame</th>\n", " <th>Data Type</th>\n", " <th>Data</th>\n", " <th>Footnotes</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Afghanistan</td>\n", " <td>Country</td>\n", " <td>Mid-2015</td>\n", " <td>Number</td>\n", " <td>32247000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Albania</td>\n", " <td>Country</td>\n", " <td>Mid-2015</td>\n", " <td>Number</td>\n", " <td>2892000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Algeria</td>\n", " <td>Country</td>\n", " <td>Mid-2015</td>\n", " <td>Number</td>\n", " <td>39948000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Andorra</td>\n", " <td>Country</td>\n", " <td>Mid-2015</td>\n", " <td>Number</td>\n", " <td>78000</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Location Location Type TimeFrame Data Type Data Footnotes\n", "0 Afghanistan Country Mid-2015 Number 32247000 NaN\n", "1 Albania Country Mid-2015 Number 2892000 NaN\n", "2 Algeria Country Mid-2015 Number 39948000 NaN\n", "3 Andorra Country Mid-2015 Number 78000 NaN" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Paths to files\n", "population_data_file = \"./raw_data/Population Mid-2015.csv\"\n", "politician_file_path = \"./raw_data/page_data.csv\"\n", "\n", "# Read in population data\n", "# We skip the first line using header=1 as we're uninterested in information before the column headers\n", "population_df = pd.read_csv(population_url, header=1)\n", "\n", "# Remove \",\" characters and cast the population column Data to a numeric value\n", "population_df[\"Data\"] = population_df[\"Data\"].str.replace(\",\", \"\")\n", "population_df[\"Data\"] = population_df[\"Data\"].apply(pd.to_numeric)\n", "\n", "# Write the data our to a csv\n", "population_df.to_csv(population_file_path, index=False)\n", "\n", "# Read in Wikipedia politician data\n", "politician_df = pd.read_csv(politician_file_path)\n", "\n", "# Print out sample of population DataFrame\n", "population_df.head(4)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>page</th>\n", " <th>country</th>\n", " <th>rev_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Template:ZambiaProvincialMinisters</td>\n", " <td>Zambia</td>\n", " <td>235107991</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Bir I of Kanem</td>\n", " <td>Chad</td>\n", " <td>355319463</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Template:Zimbabwe-politician-stub</td>\n", " <td>Zimbabwe</td>\n", " <td>391862046</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Template:Uganda-politician-stub</td>\n", " <td>Uganda</td>\n", " <td>391862070</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " page country rev_id\n", "0 Template:ZambiaProvincialMinisters Zambia 235107991\n", "1 Bir I of Kanem Chad 355319463\n", "2 Template:Zimbabwe-politician-stub Zimbabwe 391862046\n", "3 Template:Uganda-politician-stub Uganda 391862070" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Print out sample of politician DataFrame\n", "politician_df.head(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ORES\n", "\n", "After reading in our initial two data sets, we'll want to map the rev_id column of the politician DataFrame to a corresponding article quality using the [ORES API](https://www.mediawiki.org/wiki/ORES). The predicted article quality can map to one of the six following values. Documentation for how to format the URLs for this API can be found at the [ORES Swagger](https://ores.wikimedia.org/v3/#!/scoring/get_v3_scores_context_revid_model).\n", "\n", "[HCDS Fall 2017 - Assignment 2 - Article Ratings](https://wiki.communitydata.cc/HCDS_(Fall_2017%29/Assignments#Getting_article_quality_predictions)\n", "\n", "1. FA - Featured article\n", "2. GA - Good article\n", "3. B - B-class article\n", "4. C - C-class article\n", "5. Start - Start-class article\n", "6. Stub - Stub-class article\n", "\n", "**To Note**\n", "\n", "You can submit up to 50 articles at a time to be evaluated by the ORES API.\n", "\n", "If a page has been deleted, the ORES API will return \"RevisionNotFound: Could not find revision\". Within this function we handle that by outputting the JSON blob of the article that could not be found.\n", "\n", "As part of the Terms and conditions from the [Wikimedia REST API](https://www.mediawiki.org/wiki/REST_API), we agree to send a unique User-Agent header in our requests so Wikimedia can contact us if any problem arises from our script." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\n", " \"enwiki\": {\n", " \"models\": {\n", " \"wp10\": {\n", " \"version\": \"0.5.0\"\n", " }\n", " },\n", " \"scores\": {\n", " \"391862070\": {\n", " \"wp10\": {\n", " \"score\": {\n", " \"prediction\": \"Stub\",\n", " \"probability\": {\n", " \"B\": 0.03460022211051763,\n", " \"C\": 0.10152025001080041,\n", " \"FA\": 0.022405202755090857,\n", " \"GA\": 0.004661806667863751,\n", " \"Start\": 0.12578014679847194,\n", " \"Stub\": 0.7110323716572554\n", " }\n", " }\n", " }\n", " }\n", " }\n", " }\n", "}\n" ] } ], "source": [ "# ORES API endpoint Example code\n", "endpoint = \"https://ores.wikimedia.org/v3/scores/{project}/?models={model}&revids={revids}\"\n", "# Create user-agent header\n", "headers = {\"User-Agent\": \"https://github.com/awfuldynne\", \"From\": \"[email protected]\"}\n", "\n", "params = \\\n", " {\n", " \"project\": \"enwiki\",\n", " \"model\": \"wp10\",\n", " \"revids\": \"391862070\"\n", " }\n", "\n", "api_call = requests.get(endpoint.format(**params), headers=headers)\n", "response = api_call.json()\n", "print(json.dumps(response, indent=4, sort_keys=True))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'wp10': {'error': {'message': 'RevisionNotFound: Could not find revision ({revision}:807367030)', 'type': 'RevisionNotFound'}}}\n", "{'wp10': {'error': {'message': 'RevisionNotFound: Could not find revision ({revision}:807367166)', 'type': 'RevisionNotFound'}}}\n" ] } ], "source": [ "def get_ores_page_quality_prediction(rev_ids, batch_size=50):\n", " \"\"\"Method to get the wp10 model\"s prediction of page quality for a list of Wikipedia pages identified by revision ID\n", " https://en.wikipedia.org/wiki/Wikipedia:WikiProject_assessment#Grades\n", "\n", " :param rev_ids: List of revision IDs for Wikipedia pages.\n", " :type rev_ids: list of int.\n", " :param batch_size: Number of pages to send to ORES per iteration.\n", " :type batch_size: int.\n", " :returns: Pandas Dataframe -- DataFrame with columns rev_id and article_quality\n", " \"\"\"\n", " # ORES API endpoint\n", " endpoint = \"https://ores.wikimedia.org/v3/scores/{project}/?models={model}&revids={revids}\"\n", " \n", " # Create user-agent header\n", " headers = {\"User-Agent\": \"https://github.com/awfuldynne\", \"From\": \"[email protected]\"}\n", "\n", " # Create column list\n", " columns = [\"rev_id\", \"article_quality\"]\n", " \n", " # Create empty DataFrame for article quality result set\n", " df = pd.DataFrame(columns=columns)\n", "\n", " # Indexes to keep track of what subset in the rev_id list we should be processing\n", " start_index = 0\n", " end_index = start_index + batch_size\n", " done_processing = False\n", "\n", " # Iterate through our list of revision IDs appending to df as we process the results\n", " while not done_processing:\n", " params = \\\n", " {\n", " \"project\": \"enwiki\",\n", " \"model\": \"wp10\",\n", " # Create a string of revision IDs like \"123123|123124\"\n", " \"revids\": \"|\".join(str(rev) for rev in rev_ids[start_index:end_index])\n", " }\n", "\n", " api_call = requests.get(endpoint.format(**params), headers=headers)\n", " response = api_call.json()\n", " for quality_score in response[\"enwiki\"][\"scores\"]:\n", " # Create a new Series to append to the DataFrame\n", " new_row = pd.Series(index=columns)\n", " new_row.rev_id = quality_score\n", " try:\n", " new_row.article_quality = response[\"enwiki\"][\"scores\"][quality_score][\"wp10\"][\"score\"][\"prediction\"]\n", " df = df.append(new_row, ignore_index=True)\n", " except:\n", " # The target article no longer exists in wikipedia. Print each data point that \n", " # couldn't be retrieved\n", " print(response[\"enwiki\"][\"scores\"][quality_score])\n", "\n", " # Update indexes\n", " start_index += batch_size\n", " end_index += batch_size\n", " # If start_indexd is greater then the length of rev_ids we are finished processing our list\n", " done_processing = start_index >= len(rev_ids)\n", "\n", " return df\n", "\n", "article_quality_df = get_ores_page_quality_prediction(politician_df.rev_id.tolist())\n", "article_quality_df.to_csv(\"./raw_data/article_quality_data.csv\", index=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After creating the mapping of revision ID to article quality, we then want to join this to the politician DataFrame." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_article_quality(rev_id):\n", " \"\"\"Method used to map a Wikipedia revision ID to an article quality within article_quality_df\n", "\n", " :param rev_id: Wikipedia Revision ID\n", " :type rev_id: int.\n", " :return: str -- Article quality from article_quality_df if exists, None if not\n", " \"\"\"\n", " article_quality = None\n", " # If the revision ID exists in the article quality DataFrame, set article quality to the mapped value\n", " if (article_quality_df.rev_id == rev_id).any():\n", " article_quality = article_quality_df.loc[article_quality_df.rev_id == rev_id].article_quality.iloc[0]\n", " return article_quality\n", "\n", "# Join the politician DataFrame to the article quality DataFrame\n", "politician_df[\"article_quality\"] = politician_df.apply(lambda row: get_article_quality(row.rev_id), axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a similar fashion, we also want to join the population data to the politician DataFrame." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_country_population(country_name):\n", " \"\"\"Method used to map country name to a population within population_df\n", "\n", " :param country_name: Country name\n", " :type country_name: str.\n", " :return: int -- Population value from population_df if exists, None if not\n", " \"\"\"\n", " population = None\n", " # If the country exists in the population DataFrame, set population to the mapped value\n", " if (population_df.Location == country_name).any():\n", " population = population_df.loc[population_df.Location == country_name].Data.iloc[0]\n", " return population\n", "\n", "# Join the politician DataFrame to the country population DataFrame\n", "politician_df[\"population\"] = \\\n", " politician_df.apply(lambda row: get_country_population(row.country), axis=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Cleaning our Analysis DataFrame\n", "\n", "To simplify our analysis, any row without a corresponding country population or a corresponding article quality will be removed from the data set. We perform some additional cleaning by ordering our rows, renaming our columns and representing population as an integer before writing it out to the *clean_data* directory.\n", "\n", "Our DataFrame will look like the following:\n", "\n", "| Column | Value |\n", "|-----------------------|\n", "| country | Name of the Country the article belongs to |\n", "| article_name | Name of the Wikipedia article |\n", "| revision_id | Integer ID that maps to the given Wikipedia page's last edit |\n", "| article_quality | Quality of the Article as determined by ORES |\n", "| population | Number of people living in the country in mid-2015 |" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1400 rows were removed\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>country</th>\n", " <th>article_name</th>\n", " <th>revision_id</th>\n", " <th>article_quality</th>\n", " <th>population</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Zambia</td>\n", " <td>Template:ZambiaProvincialMinisters</td>\n", " <td>235107991</td>\n", " <td>Stub</td>\n", " <td>15473900</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Chad</td>\n", " <td>Bir I of Kanem</td>\n", " <td>355319463</td>\n", " <td>Stub</td>\n", " <td>13707000</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Zimbabwe</td>\n", " <td>Template:Zimbabwe-politician-stub</td>\n", " <td>391862046</td>\n", " <td>Stub</td>\n", " <td>17354000</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Uganda</td>\n", " <td>Template:Uganda-politician-stub</td>\n", " <td>391862070</td>\n", " <td>Stub</td>\n", " <td>40141000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " country article_name revision_id article_quality \\\n", "0 Zambia Template:ZambiaProvincialMinisters 235107991 Stub \n", "1 Chad Bir I of Kanem 355319463 Stub \n", "2 Zimbabwe Template:Zimbabwe-politician-stub 391862046 Stub \n", "3 Uganda Template:Uganda-politician-stub 391862070 Stub \n", "\n", " population \n", "0 15473900 \n", "1 13707000 \n", "2 17354000 \n", "3 40141000 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Filter out any countries without a population or without an article quality\n", "df = politician_df[(pd.notnull(politician_df.population)) & (pd.notnull(politician_df.article_quality))]\n", "\n", "print(\"{} rows were removed\".format(politician_df.shape[0] - df.shape[0]))\n", "\n", "# Reorder columns\n", "df = df[[\"country\", \"page\", \"rev_id\", \"article_quality\", \"population\"]]\n", "\n", "# Rename columns to match assignment definition\n", "df.columns = [\"country\", \"article_name\", \"revision_id\", \"article_quality\", \"population\"]\n", "\n", "# Change population column to integer\n", "df.loc[:, \"population\"] = df[\"population\"].astype(int)\n", "\n", "# Write analysis data set out to file\n", "cleaned_data_file_path = \"./clean_data/en-wikipedia_politician_article_quality.csv\"\n", "df.to_csv(cleaned_data_file_path, index=False)\n", "\n", "# Print example of analysis DataFrame\n", "df.head(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analysis\n", "\n", "As mentioned at the start of this notebook, our analysis seeks to understand bias on Wikipedia through two metrics:\n", "\n", "1. The percent of articles-per-poulation for each country\n", "2. The percent of high quality articles for each country\n", "\n", "We also output population and the number of articles within the aggregate DataFrame for readability." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>articles_per_population_percent</th>\n", " <th>number_of_articles</th>\n", " <th>percent_high_quality_article</th>\n", " <th>population</th>\n", " </tr>\n", " <tr>\n", " <th>Country</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Afghanistan</th>\n", " <td>0.001014</td>\n", " <td>327.000000</td>\n", " <td>5.810398</td>\n", " <td>32247000.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Albania</th>\n", " <td>0.015906</td>\n", " <td>460.000000</td>\n", " <td>1.086957</td>\n", " <td>2892000.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Algeria</th>\n", " <td>0.000298</td>\n", " <td>119.000000</td>\n", " <td>2.521008</td>\n", " <td>39948000.000000</td>\n", " </tr>\n", " <tr>\n", " <th>Andorra</th>\n", " <td>0.043590</td>\n", " <td>34.000000</td>\n", " <td>0.000000</td>\n", " <td>78000.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " articles_per_population_percent number_of_articles \\\n", "Country \n", "Afghanistan 0.001014 327.000000 \n", "Albania 0.015906 460.000000 \n", "Algeria 0.000298 119.000000 \n", "Andorra 0.043590 34.000000 \n", "\n", " percent_high_quality_article population \n", "Country \n", "Afghanistan 5.810398 32247000.000000 \n", "Albania 1.086957 2892000.000000 \n", "Algeria 2.521008 39948000.000000 \n", "Andorra 0.000000 78000.000000 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Group our DataFrame by country\n", "country_group = df.groupby(\"country\")\n", "\n", "# Returns the number of articles as a percent of the population\n", "def articles_per_population(group):\n", " articles = group.article_name.nunique()\n", " population = group.population.max()\n", " return articles * 100 / float(population)\n", "\n", "# Returns the proportion of articles which are ranked FA or GA in quality\n", "def high_quality_articles(group):\n", " high_quality_rating_list = [\"FA\", \"GA\"]\n", " article_count = group.shape[0]\n", " high_quality_article_count = group[group.article_quality.isin(high_quality_rating_list)].shape[0]\n", " return high_quality_article_count * 100 / article_count\n", "\n", "# Returns the population for a given country.\n", "def population(group):\n", " return group.population.max()\n", "\n", "# Returns the number of articles a country has\n", "def number_of_articles(group):\n", " return group.shape[0]\n", "\n", "# https://stackoverflow.com/questions/40532024/pandas-apply-multiple-functions-of-multiple-columns-to-groupby-object\n", "# Aggregate method which generates our four aggregate metrics\n", "def get_aggregate_stats(group):\n", " return pd.Series({\"articles_per_population_percent\": articles_per_population(group),\n", " \"population\": population(group),\n", " \"percent_high_quality_article\": high_quality_articles(group),\n", " \"number_of_articles\": number_of_articles(group)})\n", "\n", "agg_df = country_group.apply(get_aggregate_stats)\n", "agg_df.index.name = \"Country\"\n", "\n", "# Print example of aggregate DataFrame\n", "agg_df.head(4)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Next we create our four DataFrames to look at the top and bottom 10 countries for both of these metrics." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Top 10 Countries - Percent of Articles-Per-Population\n", " Percent of Articles-Per-Population\n", "Country \n", "Nauru 0.488029\n", "Tuvalu 0.466102\n", "San Marino 0.248485\n", "Monaco 0.105020\n", "Liechtenstein 0.077189\n", "Marshall Islands 0.067273\n", "Iceland 0.062268\n", "Tonga 0.060987\n", "Andorra 0.043590\n", "Federated States of Micronesia 0.036893\n", "\n", "\n", "Bottom 10 Countries - Percent of Articles-Per-Population\n", " Percent of Articles-Per-Population\n", "Country \n", "India 0.000075\n", "China 0.000083\n", "Indonesia 0.000084\n", "Uzbekistan 0.000093\n", "Ethiopia 0.000107\n", "Korea, North 0.000156\n", "Zambia 0.000168\n", "Thailand 0.000172\n", "Congo, Dem. Rep. of 0.000194\n", "Bangladesh 0.000202\n", "\n", "\n", "Top 10 Countries - Percent of Articles that are High Quality\n", " Percent of High Quality Articles\n", "Country \n", "Korea, North 23.076923\n", "Romania 12.931034\n", "Saudi Arabia 12.605042\n", "Central African Republic 11.764706\n", "Qatar 9.803922\n", "Guinea-Bissau 9.523810\n", "Vietnam 9.424084\n", "Bhutan 9.090909\n", "Ireland 8.136483\n", "United States 7.832423\n", "\n", "\n", "Bottom 10 Countries - Percent of Articles that are High Quality\n", " Percent of High Quality Articles\n", "Country \n", "Sao Tome and Principe 0.000000\n", "Turkmenistan 0.000000\n", "Marshall Islands 0.000000\n", "Guyana 0.000000\n", "Comoros 0.000000\n", "Tunisia 0.000000\n", "Djibouti 0.000000\n", "Dominica 0.000000\n", "Macedonia 0.000000\n", "Tonga 0.000000\n", "\n", "\n" ] } ], "source": [ "# Suppress scientific notation\n", "# SO Post: https://stackoverflow.com/questions/21137150/format-suppress-scientific-notation-from-python-pandas-aggregation-results\n", "pd.set_option('display.float_format', lambda x: '%.6f' % x)\n", "\n", "# Top 10 of Articles per Population\n", "print(\"Top 10 Countries - Percent of Articles-Per-Population\")\n", "top_10_article_per_pop = \\\n", " agg_df.sort_values(by=[\"articles_per_population_percent\"], ascending=False).head(10)[[\"articles_per_population_percent\"]]\n", "top_10_article_per_pop.columns = [\"Percent of Articles-Per-Population\"]\n", "print(top_10_article_per_pop)\n", "print(\"\\n\")\n", "\n", "# Bottom 10 of Articles per Population\n", "print(\"Bottom 10 Countries - Percent of Articles-Per-Population\")\n", "bottom_10_article_per_pop = \\\n", " agg_df.sort_values(by=[\"articles_per_population_percent\"], ascending=True).head(10)[[\"articles_per_population_percent\"]]\n", "bottom_10_article_per_pop.columns = [\"Percent of Articles-Per-Population\"]\n", "print(bottom_10_article_per_pop)\n", "print(\"\\n\")\n", "\n", "# Top 10 of High Quality Articles\n", "print(\"Top 10 Countries - Percent of Articles that are High Quality\")\n", "top_10_high_quality_articles = \\\n", " agg_df.sort_values(by=[\"percent_high_quality_article\"], ascending=False).head(10)[[\"percent_high_quality_article\"]]\n", "top_10_high_quality_articles.columns = [\"Percent of High Quality Articles\"]\n", "print(top_10_high_quality_articles)\n", "print(\"\\n\")\n", "\n", "# Bottom 10 of High Quality Articles\n", "print(\"Bottom 10 Countries - Percent of Articles that are High Quality\")\n", "bottom_10_high_quality_articles = \\\n", " agg_df.sort_values(by=[\"percent_high_quality_article\"], ascending=True).head(10)[[\"percent_high_quality_article\"]]\n", "bottom_10_high_quality_articles.columns = [\"Percent of High Quality Articles\"]\n", "print(bottom_10_high_quality_articles)\n", "print(\"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Appendix\n", "\n", "##### Population Data\n", "\n", "For those that are interested in how to download the population data directly from the [Population Research Bureau website](http://www.prb.org/DataFinder/Topic/Rankings.aspx?ind=14) the following code downloads the file and writes it out to the *raw_data* directory." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Location</th>\n", " <th>Location Type</th>\n", " <th>TimeFrame</th>\n", " <th>Data Type</th>\n", " <th>Data</th>\n", " <th>Footnotes</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Afghanistan</td>\n", " <td>Country</td>\n", " <td>Mid-2015</td>\n", " <td>Number</td>\n", " <td>32247000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Albania</td>\n", " <td>Country</td>\n", " <td>Mid-2015</td>\n", " <td>Number</td>\n", " <td>2892000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Algeria</td>\n", " <td>Country</td>\n", " <td>Mid-2015</td>\n", " <td>Number</td>\n", " <td>39948000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Andorra</td>\n", " <td>Country</td>\n", " <td>Mid-2015</td>\n", " <td>Number</td>\n", " <td>78000</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Angola</td>\n", " <td>Country</td>\n", " <td>Mid-2015</td>\n", " <td>Number</td>\n", " <td>25000000</td>\n", " <td>NaN</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Location Location Type TimeFrame Data Type Data Footnotes\n", "0 Afghanistan Country Mid-2015 Number 32247000 NaN\n", "1 Albania Country Mid-2015 Number 2892000 NaN\n", "2 Algeria Country Mid-2015 Number 39948000 NaN\n", "3 Andorra Country Mid-2015 Number 78000 NaN\n", "4 Angola Country Mid-2015 Number 25000000 NaN" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "population_file_path = \"./raw_data/Population Mid-2015.csv\"\n", "population_url = \"http://www.prb.org/RawData.axd?ind=14&fmt=14&tf=76&loc=34235%2c249%2c250%2c251%2c252%2c253%2c254%2\" \\\n", " \"c34227%2c255%2c257%2c258%2c259%2c260%2c261%2c262%2c263%2c264%2c265%2c266%2c267%2c268%2c269%2c270%2\" \\\n", " \"c271%2c272%2c274%2c275%2c276%2c277%2c278%2c279%2c280%2c281%2c282%2c283%2c284%2c285%2c286%2c287%2c2\" \\\n", " \"88%2c289%2c290%2c291%2c292%2c294%2c295%2c296%2c297%2c298%2c299%2c300%2c301%2c302%2c304%2c305%2c306\" \\\n", " \"%2c307%2c308%2c311%2c312%2c315%2c316%2c317%2c318%2c319%2c320%2c321%2c322%2c324%2c325%2c326%2c327%2\" \\\n", " \"c328%2c34234%2c329%2c330%2c331%2c332%2c333%2c334%2c336%2c337%2c338%2c339%2c340%2c342%2c343%2c344%2\" \\\n", " \"c345%2c346%2c347%2c348%2c349%2c350%2c351%2c352%2c353%2c354%2c358%2c359%2c360%2c361%2c362%2c363%2c3\" \\\n", " \"64%2c365%2c366%2c367%2c368%2c369%2c370%2c371%2c372%2c373%2c374%2c375%2c377%2c378%2c379%2c380%2c381\" \\\n", " \"%2c382%2c383%2c384%2c385%2c386%2c387%2c388%2c389%2c390%2c392%2c393%2c394%2c395%2c396%2c397%2c398%2\" \\\n", " \"c399%2c400%2c401%2c402%2c404%2c405%2c406%2c407%2c408%2c409%2c410%2c411%2c415%2c416%2c417%2c418%2c4\" \\\n", " \"19%2c420%2c421%2c422%2c423%2c424%2c425%2c427%2c428%2c429%2c430%2c431%2c432%2c433%2c434%2c435%2c437\" \\\n", " \"%2c438%2c439%2c440%2c441%2c442%2c443%2c444%2c445%2c446%2c448%2c449%2c450%2c451%2c452%2c453%2c454%2\" \\\n", " \"c455%2c456%2c457%2c458%2c459%2c460%2c461%2c462%2c464%2c465%2c466%2c467%2c468%2c469%2c470%2c471%2c4\" \\\n", " \"72%2c473%2c474%2c475%2c476%2c477%2c478%2c479%2c480\"\n", "\n", "# Use pandas read_csv function to read the file directly from the website\n", "# We skip the first line using header=1 as we're uninterested in information before the column headers\n", "population_df = pd.read_csv(population_url, header=1)\n", "\n", "# Remove \",\" characters and cast the population column Data to a numeric value\n", "population_df[\"Data\"] = population_df[\"Data\"].str.replace(\",\", \"\")\n", "population_df[\"Data\"] = population_df[\"Data\"].apply(pd.to_numeric)\n", "\n", "# Write the data out to a csv\n", "population_df.to_csv(population_file_path, index=False)\n", "\n", "# Print a few lines of the data set\n", "population_df.head(4)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
GoogleCloudPlatform/asl-ml-immersion
notebooks/introduction_to_tensorflow/solutions/2d_loading_tfrecords.ipynb
1
139273
{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "3pkUd_9IZCFO" }, "source": [ "# TFRecord and tf.Example\n", "\n", "**Learning Objectives**\n", "\n", "1. Understand the TFRecord format for storing data\n", "2. Understand the tf.Example message type\n", "3. Read and Write a TFRecord file\n", "\n", "\n", "## Introduction \n", "\n", "In this notebook, you create, parse, and use the `tf.Example` message, and then serialize, write, and read `tf.Example` messages to and from `.tfrecord` files. To read data efficiently it can be helpful to serialize your data and store it in a set of files (100-200MB each) that can each be read linearly. This is especially true if the data is being streamed over a network. This can also be useful for caching any data-preprocessing.\n", "\n", "\n", "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](../labs/tfrecord-tf.example.ipynb) -- try to complete that notebook first before reviewing this solution notebook. \n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Ac83J0QxjhFt" }, "source": [ "### The TFRecord format \n", "\n", "The TFRecord format is a simple format for storing a sequence of binary records. [Protocol buffers](https://developers.google.com/protocol-buffers/) are a cross-platform, cross-language library for efficient serialization of structured data. Protocol messages are defined by `.proto` files, these are often the easiest way to understand a message type.\n", "\n", "The `tf.Example` message (or protobuf) is a flexible message type that represents a `{\"string\": value}` mapping. It is designed for use with TensorFlow and is used throughout the higher-level APIs such as [TFX](https://www.tensorflow.org/tfx/).\n", "Note: While useful, these structures are optional. There is no need to convert existing code to use TFRecords, unless you are using [`tf.data`](https://www.tensorflow.org/guide/datasets) and reading data is still the bottleneck to training. See [Data Input Pipeline Performance](https://www.tensorflow.org/guide/performance/datasets) for dataset performance tips." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "WkRreBf1eDVc" }, "source": [ "## Load necessary libraries \n", "We will start by importing the necessary libraries for this lab." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": {}, "colab_type": "code", "id": "Ja7sezsmnXph" }, "source": [ "import IPython.display as display\n", "import numpy as np\n", "import tensorflow as tf\n", "\n", "print(\"TensorFlow version: \", tf.version.VERSION)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "e5Kq88ccUWQV" }, "source": [ "## `tf.Example`" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VrdQHgvNijTi" }, "source": [ "### Data types for `tf.Example`" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "lZw57Qrn4CTE" }, "source": [ "Fundamentally, a `tf.Example` is a `{\"string\": tf.train.Feature}` mapping.\n", "\n", "The `tf.train.Feature` message type can accept one of the following three types (See the [`.proto` file](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto) for reference). Most other generic types can be coerced into one of these:\n", "\n", "1. `tf.train.BytesList` (the following types can be coerced)\n", "\n", " - `string`\n", " - `byte`\n", "\n", "1. `tf.train.FloatList` (the following types can be coerced)\n", "\n", " - `float` (`float32`)\n", " - `double` (`float64`)\n", "\n", "1. `tf.train.Int64List` (the following types can be coerced)\n", "\n", " - `bool`\n", " - `enum`\n", " - `int32`\n", " - `uint32`\n", " - `int64`\n", " - `uint64`" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_e3g9ExathXP" }, "source": [ "In order to convert a standard TensorFlow type to a `tf.Example`-compatible `tf.train.Feature`, you can use the shortcut functions below. Note that each function takes a scalar input value and returns a `tf.train.Feature` containing one of the three `list` types above:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": {}, "colab_type": "code", "id": "mbsPOUpVtYxA" }, "outputs": [], "source": [ "# TODO 1a\n", "# The following functions can be used to convert a value to a type compatible\n", "# with tf.Example.\n", "\n", "\n", "def _bytes_feature(value):\n", " \"\"\"Returns a bytes_list from a string / byte.\"\"\"\n", " if isinstance(value, type(tf.constant(0))):\n", " value = (\n", " value.numpy()\n", " ) # BytesList won't unpack a string from an EagerTensor.\n", " return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n", "\n", "\n", "def _float_feature(value):\n", " \"\"\"Returns a float_list from a float / double.\"\"\"\n", " return tf.train.Feature(float_list=tf.train.FloatList(value=[value]))\n", "\n", "\n", "def _int64_feature(value):\n", " \"\"\"Returns an int64_list from a bool / enum / int / uint.\"\"\"\n", " return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Wst0v9O8hgzy" }, "source": [ "Note: To stay simple, this example only uses scalar inputs. The simplest way to handle non-scalar features is to use `tf.serialize_tensor` to convert tensors to binary-strings. Strings are scalars in tensorflow. Use `tf.parse_tensor` to convert the binary-string back to a tensor." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "vsMbkkC8xxtB" }, "source": [ "Below are some examples of how these functions work. Note the varying input types and the standardized output types. If the input type for a function does not match one of the coercible types stated above, the function will raise an exception (e.g. `_int64_feature(1.0)` will error out, since `1.0` is a float, so should be used with the `_float_feature` function instead):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": {}, "colab_type": "code", "id": "hZzyLGr0u73y" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bytes_list {\n", " value: \"test_string\"\n", "}\n", "\n", "float_list {\n", " value: 2.7182817459106445\n", "}\n", "\n", "int64_list {\n", " value: 1\n", "}\n", "\n", "int64_list {\n", " value: 1\n", "}\n", "\n" ] } ], "source": [ "print(_bytes_feature(b\"test_string\"))\n", "\n", "print(_float_feature(np.exp(1)))\n", "\n", "print(_int64_feature(True))\n", "print(_int64_feature(1))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "nj1qpfQU5qmi" }, "source": [ "All proto messages can be serialized to a binary-string using the `.SerializeToString` method:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": {}, "colab_type": "code", "id": "5afZkORT5pjm" }, "outputs": [ { "data": { "text/plain": [ "b'\\x12\\x06\\n\\x04T\\xf8-@'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TODO 1b\n", "feature = _float_feature(np.exp(1))\n", "\n", "feature.SerializeToString()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "laKnw9F3hL-W" }, "source": [ "### Creating a `tf.Example` message" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "b_MEnhxchQPC" }, "source": [ "Suppose you want to create a `tf.Example` message from existing data. In practice, the dataset may come from anywhere, but the procedure of creating the `tf.Example` message from a single observation will be the same:\n", "\n", "1. Within each observation, each value needs to be converted to a `tf.train.Feature` containing one of the 3 compatible types, using one of the functions above.\n", "\n", "1. You create a map (dictionary) from the feature name string to the encoded feature value produced in #1.\n", "\n", "1. The map produced in step 2 is converted to a [`Features` message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto#L85)." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "4EgFQ2uHtchc" }, "source": [ "In this notebook, you will create a dataset using NumPy.\n", "\n", "This dataset will have 4 features:\n", "\n", "* a boolean feature, `False` or `True` with equal probability\n", "* an integer feature uniformly randomly chosen from `[0, 5]`\n", "* a string feature generated from a string table by using the integer feature as an index\n", "* a float feature from a standard normal distribution\n", "\n", "Consider a sample consisting of 10,000 independently and identically distributed observations from each of the above distributions:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": {}, "colab_type": "code", "id": "CnrguFAy3YQv" }, "outputs": [], "source": [ "# The number of observations in the dataset.\n", "n_observations = int(1e4)\n", "\n", "# Boolean feature, encoded as False or True.\n", "feature0 = np.random.choice([False, True], n_observations)\n", "\n", "# Integer feature, random from 0 to 4.\n", "feature1 = np.random.randint(0, 5, n_observations)\n", "\n", "# String feature\n", "strings = np.array([b\"cat\", b\"dog\", b\"chicken\", b\"horse\", b\"goat\"])\n", "feature2 = strings[feature1]\n", "\n", "# Float feature, from a standard normal distribution\n", "feature3 = np.random.randn(n_observations)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "aGrscehJr7Jd" }, "source": [ "Each of these features can be coerced into a `tf.Example`-compatible type using one of `_bytes_feature`, `_float_feature`, `_int64_feature`. You can then create a `tf.Example` message from these encoded features:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": {}, "colab_type": "code", "id": "RTCS49Ij_kUw" }, "outputs": [], "source": [ "def serialize_example(feature0, feature1, feature2, feature3):\n", " \"\"\"\n", " Creates a tf.Example message ready to be written to a file.\n", " \"\"\"\n", " # Create a dictionary mapping the feature name to the tf.Example-compatible\n", " # data type.\n", " feature = {\n", " \"feature0\": _int64_feature(feature0),\n", " \"feature1\": _int64_feature(feature1),\n", " \"feature2\": _bytes_feature(feature2),\n", " \"feature3\": _float_feature(feature3),\n", " }\n", "\n", " # Create a Features message using tf.train.Example.\n", "\n", " example_proto = tf.train.Example(\n", " features=tf.train.Features(feature=feature)\n", " )\n", " return example_proto.SerializeToString()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "XftzX9CN_uGT" }, "source": [ "For example, suppose you have a single observation from the dataset, `[False, 4, bytes('goat'), 0.9876]`. You can create and print the `tf.Example` message for this observation using `create_message()`. Each single observation will be written as a `Features` message as per the above. Note that the `tf.Example` [message](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto#L88) is just a wrapper around the `Features` message:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": {}, "colab_type": "code", "id": "N8BtSx2RjYcb" }, "outputs": [ { "data": { "text/plain": [ "b'\\nR\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x04\\n\\x14\\n\\x08feature2\\x12\\x08\\n\\x06\\n\\x04goat\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04[\\xd3|?'" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# This is an example observation from the dataset.\n", "\n", "example_observation = []\n", "\n", "serialized_example = serialize_example(False, 4, b\"goat\", 0.9876)\n", "serialized_example" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "_pbGATlG6u-4" }, "source": [ "To decode the message use the `tf.train.Example.FromString` method." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": {}, "colab_type": "code", "id": "dGim-mEm6vit" }, "outputs": [ { "data": { "text/plain": [ "features {\n", " feature {\n", " key: \"feature0\"\n", " value {\n", " int64_list {\n", " value: 0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"feature1\"\n", " value {\n", " int64_list {\n", " value: 4\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"feature2\"\n", " value {\n", " bytes_list {\n", " value: \"goat\"\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"feature3\"\n", " value {\n", " float_list {\n", " value: 0.9876000285148621\n", " }\n", " }\n", " }\n", "}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TODO 1c\n", "example_proto = tf.train.Example.FromString(serialized_example)\n", "example_proto" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "o6qxofy89obI" }, "source": [ "## TFRecords format details\n", "\n", "A TFRecord file contains a sequence of records. The file can only be read sequentially.\n", "\n", "Each record contains a byte-string, for the data-payload, plus the data-length, and CRC32C (32-bit CRC using the Castagnoli polynomial) hashes for integrity checking.\n", "\n", "Each record is stored in the following formats:\n", "\n", " uint64 length\n", " uint32 masked_crc32_of_length\n", " byte data[length]\n", " uint32 masked_crc32_of_data\n", "\n", "The records are concatenated together to produce the file. CRCs are\n", "[described here](https://en.wikipedia.org/wiki/Cyclic_redundancy_check), and\n", "the mask of a CRC is:\n", "\n", " masked_crc = ((crc >> 15) | (crc << 17)) + 0xa282ead8ul\n", "\n", "Note: There is no requirement to use `tf.Example` in TFRecord files. `tf.Example` is just a method of serializing dictionaries to byte-strings. Lines of text, encoded image data, or serialized tensors (using `tf.io.serialize_tensor`, and\n", "`tf.io.parse_tensor` when loading). See the `tf.io` module for more options." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "y-Hjmee-fbLH" }, "source": [ "## TFRecord files using `tf.data`" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "GmehkCCT81Ez" }, "source": [ "The `tf.data` module also provides tools for reading and writing data in TensorFlow." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "1FISEuz8ubu3" }, "source": [ "### Writing a TFRecord file\n", "\n", "The easiest way to get the data into a dataset is to use the `from_tensor_slices` method.\n", "\n", "Applied to an array, it returns a dataset of scalars:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": {}, "colab_type": "code", "id": "mXeaukvwu5_-" }, "outputs": [ { "data": { "text/plain": [ "<TensorSliceDataset shapes: (), types: tf.int64>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf.data.Dataset.from_tensor_slices(feature1)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "f-q0VKyZvcad" }, "source": [ "Applied to a tuple of arrays, it returns a dataset of tuples:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": {}, "colab_type": "code", "id": "H5sWyu1kxnvg" }, "outputs": [ { "data": { "text/plain": [ "<TensorSliceDataset shapes: ((), (), (), ()), types: (tf.bool, tf.int64, tf.string, tf.float64)>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features_dataset = tf.data.Dataset.from_tensor_slices(\n", " (feature0, feature1, feature2, feature3)\n", ")\n", "features_dataset" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": {}, "colab_type": "code", "id": "m1C-t71Nywze" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor(False, shape=(), dtype=bool)\n", "tf.Tensor(1, shape=(), dtype=int64)\n", "tf.Tensor(b'dog', shape=(), dtype=string)\n", "tf.Tensor(-0.6086492521118764, shape=(), dtype=float64)\n" ] } ], "source": [ "# Use `take(1)` to only pull one example from the dataset.\n", "for f0, f1, f2, f3 in features_dataset.take(1):\n", " print(f0)\n", " print(f1)\n", " print(f2)\n", " print(f3)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "mhIe63awyZYd" }, "source": [ "Use the `tf.data.Dataset.map` method to apply a function to each element of a `Dataset`.\n", "\n", "The mapped function must operate in TensorFlow graph mode—it must operate on and return `tf.Tensors`. A non-tensor function, like `serialize_example`, can be wrapped with `tf.py_function` to make it compatible.\n", "\n", "Using `tf.py_function` requires to specify the shape and type information that is otherwise unavailable:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": {}, "colab_type": "code", "id": "apB5KYrJzjPI" }, "outputs": [], "source": [ "# TODO 2a\n", "def tf_serialize_example(f0, f1, f2, f3):\n", " tf_string = tf.py_function(\n", " serialize_example,\n", " (f0, f1, f2, f3), # pass these args to the above function.\n", " tf.string,\n", " ) # the return type is `tf.string`.\n", " return tf.reshape(tf_string, ()) # The result is a scalar" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": {}, "colab_type": "code", "id": "lHFjW4u4Npz9" }, "outputs": [ { "data": { "text/plain": [ "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03dog\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04p\\xd0\\x1b\\xbf'>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf_serialize_example(f0, f1, f2, f3)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "CrFZ9avE3HUF" }, "source": [ "Apply this function to each element in the dataset:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": {}, "colab_type": "code", "id": "VDeqYVbW3ww9" }, "outputs": [ { "data": { "text/plain": [ "<MapDataset shapes: (), types: tf.string>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TODO 2b\n", "serialized_features_dataset = features_dataset.map(tf_serialize_example)\n", "serialized_features_dataset" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": {}, "colab_type": "code", "id": "DlDfuh46bRf6" }, "outputs": [], "source": [ "def generator():\n", " for features in features_dataset:\n", " yield serialize_example(*features)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": {}, "colab_type": "code", "id": "iv9oXKrcbhvX" }, "outputs": [], "source": [ "serialized_features_dataset = tf.data.Dataset.from_generator(\n", " generator, output_types=tf.string, output_shapes=()\n", ")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "colab": {}, "colab_type": "code", "id": "Dqz8C4D5cIj9" }, "outputs": [ { "data": { "text/plain": [ "<FlatMapDataset shapes: (), types: tf.string>" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "serialized_features_dataset" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "p6lw5VYpjZZC" }, "source": [ "And write them to a TFRecord file:" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": {}, "colab_type": "code", "id": "vP1VgTO44UIE" }, "outputs": [], "source": [ "filename = \"test.tfrecord\"\n", "writer = tf.data.experimental.TFRecordWriter(filename)\n", "writer.write(serialized_features_dataset)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6aV0GQhV8tmp" }, "source": [ "### Reading a TFRecord file" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "o3J5D4gcSy8N" }, "source": [ "You can also read the TFRecord file using the `tf.data.TFRecordDataset` class.\n", "\n", "More information on consuming TFRecord files using `tf.data` can be found [here](https://www.tensorflow.org/guide/datasets#consuming_tfrecord_data).\n", "\n", "Using `TFRecordDataset`s can be useful for standardizing input data and optimizing performance." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": {}, "colab_type": "code", "id": "6OjX6UZl-bHC" }, "outputs": [ { "data": { "text/plain": [ "<TFRecordDatasetV2 shapes: (), types: tf.string>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# TODO 2c\n", "filenames = [filename]\n", "raw_dataset = tf.data.TFRecordDataset(filenames)\n", "raw_dataset" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6_EQ9i2E_-Fz" }, "source": [ "At this point the dataset contains serialized `tf.train.Example` messages. When iterated over it returns these as scalar string tensors.\n", "\n", "Use the `.take` method to only show the first 10 records.\n", "\n", "Note: iterating over a `tf.data.Dataset` only works with eager execution enabled." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": {}, "colab_type": "code", "id": "hxVXpLz_AJlm" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03dog\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04p\\xd0\\x1b\\xbf\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x01'>\n", "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nS\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x03\\n\\x15\\n\\x08feature2\\x12\\t\\n\\x07\\n\\x05horse\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\xa6\\xbf\\xba\\xbe'>\n", "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nS\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x03\\n\\x15\\n\\x08feature2\\x12\\t\\n\\x07\\n\\x05horse\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\xaa\\x05/@'>\n", "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03dog\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04C\\x96\\n?\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x01'>\n", "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03cat\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04^\\x06\\x96>\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x00'>\n", "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03dog\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\x057\\x8c\\xbe'>\n", "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nS\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x03\\n\\x15\\n\\x08feature2\\x12\\t\\n\\x07\\n\\x05horse\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\xbco\\xab\\xbe\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00'>\n", "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03cat\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04p[|\\xbd'>\n", "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nU\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x02\\n\\x17\\n\\x08feature2\\x12\\x0b\\n\\t\\n\\x07chicken\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\xba.\\xb6\\xbf'>\n", "<tf.Tensor: shape=(), dtype=string, numpy=b'\\nQ\\n\\x14\\n\\x08feature3\\x12\\x08\\x12\\x06\\n\\x04\\x96tf?\\n\\x11\\n\\x08feature0\\x12\\x05\\x1a\\x03\\n\\x01\\x01\\n\\x11\\n\\x08feature1\\x12\\x05\\x1a\\x03\\n\\x01\\x00\\n\\x13\\n\\x08feature2\\x12\\x07\\n\\x05\\n\\x03cat'>\n" ] } ], "source": [ "for raw_record in raw_dataset.take(10):\n", " print(repr(raw_record))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "W-6oNzM4luFQ" }, "source": [ "These tensors can be parsed using the function below. Note that the `feature_description` is necessary here because datasets use graph-execution, and need this description to build their shape and type signature:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": {}, "colab_type": "code", "id": "zQjbIR1nleiy" }, "outputs": [], "source": [ "# Create a description of the features.\n", "feature_description = {\n", " \"feature0\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n", " \"feature1\": tf.io.FixedLenFeature([], tf.int64, default_value=0),\n", " \"feature2\": tf.io.FixedLenFeature([], tf.string, default_value=\"\"),\n", " \"feature3\": tf.io.FixedLenFeature([], tf.float32, default_value=0.0),\n", "}\n", "\n", "\n", "def _parse_function(example_proto):\n", " # Parse the input `tf.Example` proto using the dictionary above.\n", " return tf.io.parse_single_example(example_proto, feature_description)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gWETjUqhEQZf" }, "source": [ "Alternatively, use `tf.parse example` to parse the whole batch at once. Apply this function to each item in the dataset using the `tf.data.Dataset.map` method:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": {}, "colab_type": "code", "id": "6Ob7D-zmBm1w" }, "outputs": [ { "data": { "text/plain": [ "<MapDataset shapes: {feature0: (), feature2: (), feature1: (), feature3: ()}, types: {feature0: tf.int64, feature2: tf.string, feature1: tf.int64, feature3: tf.float32}>" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parsed_dataset = raw_dataset.map(_parse_function)\n", "parsed_dataset" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "sNV-XclGnOvn" }, "source": [ "Use eager execution to display the observations in the dataset. There are 10,000 observations in this dataset, but you will only display the first 10. The data is displayed as a dictionary of features. Each item is a `tf.Tensor`, and the `numpy` element of this tensor displays the value of the feature:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": {}, "colab_type": "code", "id": "x2LT2JCqhoD_" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'dog'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-0.60864925>}\n", "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'horse'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=3>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-0.3647434>}\n", "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'horse'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=3>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=2.7347207>}\n", "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'dog'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=0.5413553>}\n", "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'cat'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=0.29301733>}\n", "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'dog'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-0.27385727>}\n", "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'horse'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=3>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-0.33483684>}\n", "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'cat'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-0.06161064>}\n", "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'chicken'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=2>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=-1.423301>}\n", "{'feature0': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'feature2': <tf.Tensor: shape=(), dtype=string, numpy=b'cat'>, 'feature1': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'feature3': <tf.Tensor: shape=(), dtype=float32, numpy=0.90021646>}\n" ] } ], "source": [ "for parsed_record in parsed_dataset.take(10):\n", " print(repr(parsed_record))" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Cig9EodTlDmg" }, "source": [ "Here, the `tf.parse_example` function unpacks the `tf.Example` fields into standard tensors." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "jyg1g3gU7DNn" }, "source": [ "## TFRecord files in Python" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "3FXG3miA7Kf1" }, "source": [ "The `tf.io` module also contains pure-Python functions for reading and writing TFRecord files." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "CKn5uql2lAaN" }, "source": [ "### Writing a TFRecord file" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "LNW_FA-GQWXs" }, "source": [ "Next, write the 10,000 observations to the file `test.tfrecord`. Each observation is converted to a `tf.Example` message, then written to file. You can then verify that the file `test.tfrecord` has been created:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": {}, "colab_type": "code", "id": "MKPHzoGv7q44" }, "outputs": [], "source": [ "# Write the `tf.Example` observations to the file.\n", "with tf.io.TFRecordWriter(filename) as writer:\n", " for i in range(n_observations):\n", " example = serialize_example(\n", " feature0[i], feature1[i], feature2[i], feature3[i]\n", " )\n", " writer.write(example)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "colab": {}, "colab_type": "code", "id": "EjdFHHJMpUUo" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "984K\ttest.tfrecord\n" ] } ], "source": [ "!du -sh {filename}" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2osVRnYNni-E" }, "source": [ "### Reading a TFRecord file\n", "\n", "These serialized tensors can be easily parsed using `tf.train.Example.ParseFromString`:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": {}, "colab_type": "code", "id": "U3tnd3LerOtV" }, "outputs": [ { "data": { "text/plain": [ "<TFRecordDatasetV2 shapes: (), types: tf.string>" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filenames = [filename]\n", "raw_dataset = tf.data.TFRecordDataset(filenames)\n", "raw_dataset" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "colab": {}, "colab_type": "code", "id": "nsEAACHcnm3f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "features {\n", " feature {\n", " key: \"feature0\"\n", " value {\n", " int64_list {\n", " value: 0\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"feature1\"\n", " value {\n", " int64_list {\n", " value: 1\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"feature2\"\n", " value {\n", " bytes_list {\n", " value: \"dog\"\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"feature3\"\n", " value {\n", " float_list {\n", " value: -0.6086492538452148\n", " }\n", " }\n", " }\n", "}\n", "\n" ] } ], "source": [ "for raw_record in raw_dataset.take(1):\n", " example = tf.train.Example()\n", " example.ParseFromString(raw_record.numpy())\n", " print(example)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "S0tFDrwdoj3q" }, "source": [ "## Walkthrough: Reading and writing image data" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "rjN2LFxFpcR9" }, "source": [ "This is an end-to-end example of how to read and write image data using TFRecords. Using an image as input data, you will write the data as a TFRecord file, then read the file back and display the image.\n", "\n", "This can be useful if, for example, you want to use several models on the same input dataset. Instead of storing the image data raw, it can be preprocessed into the TFRecords format, and that can be used in all further processing and modelling.\n", "\n", "First, let's download [this image](https://commons.wikimedia.org/wiki/File:Felis_catus-cat_on_snow.jpg) of a cat in the snow and [this photo](https://upload.wikimedia.org/wikipedia/commons/f/fe/New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg) of the Williamsburg Bridge, NYC under construction." ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "5Lk2qrKvN0yu" }, "source": [ "### Fetch the images" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": {}, "colab_type": "code", "id": "3a0fmwg8lHdF" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg\n", "24576/17858 [=========================================] - 0s 0us/step\n", "Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\n", "16384/15477 [===============================] - 0s 0us/step\n" ] } ], "source": [ "cat_in_snow = tf.keras.utils.get_file(\n", " \"320px-Felis_catus-cat_on_snow.jpg\",\n", " \"https://storage.googleapis.com/download.tensorflow.org/example_images/320px-Felis_catus-cat_on_snow.jpg\",\n", ")\n", "williamsburg_bridge = tf.keras.utils.get_file(\n", " \"194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\",\n", " \"https://storage.googleapis.com/download.tensorflow.org/example_images/194px-New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\",\n", ")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "colab": {}, "colab_type": "code", "id": "7aJJh7vENeE4" }, "outputs": [ { "data": { "image/jpeg": 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\n", 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "<a \"href=https://commons.wikimedia.org/wiki/File:New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\">From Wikimedia</a>" ], "text/plain": [ "<IPython.core.display.HTML object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display.display(display.Image(filename=williamsburg_bridge))\n", "display.display(\n", " display.HTML(\n", " '<a \"href=https://commons.wikimedia.org/wiki/File:New_East_River_Bridge_from_Brooklyn_det.4a09796u.jpg\">From Wikimedia</a>'\n", " )\n", ")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "VSOgJSwoN5TQ" }, "source": [ "### Write the TFRecord file" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "Azx83ryQEU6T" }, "source": [ "As before, encode the features as types compatible with `tf.Example`. This stores the raw image string feature, as well as the height, width, depth, and arbitrary `label` feature. The latter is used when you write the file to distinguish between the cat image and the bridge image. Use `0` for the cat image, and `1` for the bridge image:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "colab": {}, "colab_type": "code", "id": "kC4TS1ZEONHr" }, "outputs": [], "source": [ "image_labels = {\n", " cat_in_snow: 0,\n", " williamsburg_bridge: 1,\n", "}" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "colab": {}, "colab_type": "code", "id": "c5njMSYNEhNZ" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "features {\n", " feature {\n", " key: \"depth\"\n", " value {\n", " int64_list {\n", " value: 3\n", " }\n", " }\n", " }\n", " feature {\n", " key: \"height\"\n", " value {\n", " int64_list {\n", " value: 213\n", " }\n", "...\n" ] } ], "source": [ "# This is an example, just using the cat image.\n", "image_string = open(cat_in_snow, \"rb\").read()\n", "\n", "label = image_labels[cat_in_snow]\n", "\n", "# Create a dictionary with features that may be relevant.\n", "def image_example(image_string, label):\n", " image_shape = tf.image.decode_jpeg(image_string).shape\n", "\n", " feature = {\n", " \"height\": _int64_feature(image_shape[0]),\n", " \"width\": _int64_feature(image_shape[1]),\n", " \"depth\": _int64_feature(image_shape[2]),\n", " \"label\": _int64_feature(label),\n", " \"image_raw\": _bytes_feature(image_string),\n", " }\n", "\n", " return tf.train.Example(features=tf.train.Features(feature=feature))\n", "\n", "\n", "for line in str(image_example(image_string, label)).split(\"\\n\")[:15]:\n", " print(line)\n", "print(\"...\")" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2G_o3O9MN0Qx" }, "source": [ "Notice that all of the features are now stored in the `tf.Example` message. Next, functionalize the code above and write the example messages to a file named `images.tfrecords`:" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": {}, "colab_type": "code", "id": "qcw06lQCOCZU" }, "outputs": [], "source": [ "# Write the raw image files to `images.tfrecords`.\n", "# First, process the two images into `tf.Example` messages.\n", "# Then, write to a `.tfrecords` file.\n", "record_file = \"images.tfrecords\"\n", "with tf.io.TFRecordWriter(record_file) as writer:\n", " for filename, label in image_labels.items():\n", " image_string = open(filename, \"rb\").read()\n", " tf_example = image_example(image_string, label)\n", " writer.write(tf_example.SerializeToString())" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "colab": {}, "colab_type": "code", "id": "yJrTe6tHPCfs" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36K\timages.tfrecords\n" ] } ], "source": [ "!du -sh {record_file}" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "jJSsCkZLPH6K" }, "source": [ "### Read the TFRecord file\n", "\n", "You now have the file—`images.tfrecords`—and can now iterate over the records in it to read back what you wrote. Given that in this example you will only reproduce the image, the only feature you will need is the raw image string. Extract it using the getters described above, namely `example.features.feature['image_raw'].bytes_list.value[0]`. You can also use the labels to determine which record is the cat and which one is the bridge:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": {}, "colab_type": "code", "id": "M6Cnfd3cTKHN" }, "outputs": [ { "data": { "text/plain": [ "<MapDataset shapes: {depth: (), height: (), width: (), image_raw: (), label: ()}, types: {depth: tf.int64, height: tf.int64, width: tf.int64, image_raw: tf.string, label: tf.int64}>" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_image_dataset = tf.data.TFRecordDataset(\"images.tfrecords\")\n", "\n", "# Create a dictionary describing the features.\n", "image_feature_description = {\n", " \"height\": tf.io.FixedLenFeature([], tf.int64),\n", " \"width\": tf.io.FixedLenFeature([], tf.int64),\n", " \"depth\": tf.io.FixedLenFeature([], tf.int64),\n", " \"label\": tf.io.FixedLenFeature([], tf.int64),\n", " \"image_raw\": tf.io.FixedLenFeature([], tf.string),\n", "}\n", "\n", "\n", "def _parse_image_function(example_proto):\n", " # Parse the input tf.Example proto using the dictionary above.\n", " return tf.io.parse_single_example(example_proto, image_feature_description)\n", "\n", "\n", "parsed_image_dataset = raw_image_dataset.map(_parse_image_function)\n", "parsed_image_dataset" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "0PEEFPk4NEg1" }, "source": [ "Recover the images from the TFRecord file:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "colab": {}, "colab_type": "code", "id": "yZf8jOyEIjSF" }, "outputs": [ { "data": { "image/jpeg": 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GIQdpjL+kgszkrkcsDWMnDkildNFuLh9ph5rSS8tL3/H4oprvU3iRIYZ96W8cYO1RICC8jFmLEengDNJpRiop2EW+16FZ/DN/oeluBqSppgRp1DsTNH13Jv8A8vzjNO+zTaHlYOI6f4z1PTdWe806fbFECIopDlM/5se9erBuCSOKaUm2Xk/2meMLyAB9RManBykYVR26+3Wm+V+xKCrRU6jretagq3F9qN5NvGOZSFVPoOAen8qn8jb2NwRqt7+/mCs5kLHG52JJJPX/AL1omZ0WMREEBtYgQxBO4E8fP9+9ZPLs0WAtzLlYoU3BgMAL1A7/ANaaQN+haeJbSQKedoy3qyQfbNO7FoYMUbw7pAEXHAzyam2iqTEhYr5JcZRc9WPJFX3I6DWj3Wr+Gr9dR0O8uLO6To8eMkY5ypyCPqKa5EwcJI719lH7QVsiiz8eTXKTHhb7ZuQk/wCYKMgfODWvYikzur30Ws2UV3YXMU9nJhkkjbKuPg961jSMpJ+RxSn3bbCMNjk0vIeAaXH3YKE5bvTqw0WIvGEW6Q/lU9SrMx3nmqdvNHULF5pTGCwXmmkSVs0r7DIzk/FXQMErCXgj1fSnoWyl1XRZnJeE8ntWkeT2ZS434AaPY3kBcSIAuaJyTHBNG0WUnkkOeNo6Vi8mqY6uomVCMZqeo7FbjUPLjYvgCqUbE3RWaddedclnGFz0q3GkTF2B+7lQHTOaLJotNLkZQGfOfmokiosuxeKUweDWfU0sBJtk5zgU9CF51XyiAOaoQAsRb4x6qBGdMRg5Mnc0SBFjqRBtDj2qFst6NDukWTekgyrZBBFdNWjDRyjxN4XmsL2BDepDo0j52FMHHXYW+vTIrg5eLrL6OuHJ2X2JXXhpZdQ/w8G4aJx5kU0mPTnnp8cc4rN8dOkV3xbKDS59SsEXw59xlXWEmMHkYw7EjIbB6qeu7PSuLm+PfJk9Di5UuHvZ2m38HyWVvDp95I8l1JEgmZVLKJCoG4t8Yx7HFc645QfUUuRTXYDq+g6lolzHJpl9Zyyhdr/eY2G9sclAuf5DJq5cfUiM09o0b7VdeOmaE6tOp1GeMxRrECoTPUjPT8/eq4oXJBOWDg8MXn3EEU4VpG/E+cE//L5/nxXc5YbOZR0jZNQgVYIYUJC5VckEMf8AjmueLt2dElgDrkam3WCHhcAMQcEADP55GKvjeSJrBqoVQscjE53GRhj8IHGPk11HMWSysbcMq7VJHPc49v8AtUqORtg7IiKYFxmRhuY/9PtVMSHDIrweZ5aksAAxXOAD7e9SkOwcsMb24klMhBG4ntS80PxZGSVVVdsY3AjOB/Sl1bKUkhuAfeoAxU89MKTWbfU0rsip1CyVDuwykdDjIP51rHksylx0bJ9n32g694Luh9yuZJLEt++s3YFJB8ccHp0rVS66Irthnr/wV4s0nxR4fTUdGuUmAAE0YyGhfHKsCOP9a3TUtGUouOx+3k82Z2BzVtUiFkO2+ZwD0pDLiwiVIiCOcVEmUirurzNyYUGatLBNmVtw+C36UWFBobcBzxSbAMYgAc0rHQvDGz5OOAadgZu4VMZxw2KExFNFOYSyfxVdWSmJ6pdAxlSfVnpVwjkmbBaYxUbz0FVMUTb47FRGpYVzdjWiLoqqdoAAoATeUtkDqKoRODe7DOcUmA+8WEzSsYBBufBximAWQpGuR1pAJPOzZDfhqqARS1Sa4yV4FVdIirZYz6Tb3Vm8MsYaNhyp9+1ZtlpHMtX0k+HrlYJXLQknynBJYDuST8np8Vl/JpsS07TFOvwa3b6sw1CC3a3QSIpHllt2D36/NZyp5KVm6xa7FdJmYo9yn4tgOOOuCevviueSjdmqbqjVfFPiC30/zZZnUXXAjkPKtu6ce2Pbn9K5ZtWdEE6OFeN7mXU9UJvztlB3SoTwoA4IPcY6VfHjI5o1y3hluL/zIozljkrjt2OO9aydRyZxVsevpFkCjfsQTBVJOQfn8+azii5MDq0v7q4RT+Ecs3O4nn++1awRnN4KG9tttpGoVSW2qm0+3UV0JnO0HClbq3iAA2rk4HBpgAuIijzSDgBcBemP9/rQB8wcQRxhiWYAscgkD2x+nzQIF98ciSJhlRgKp/l/P+lFeQssLFFEcpnO0KdrMD1PcD498f0qOTWDTjXsvdLhX7kJpUKJv2hcEcdOv+v9K5J3Z1Rqiv1RmFw0GQQTk5Pf61pB4siWcGv3UCxysICElGSBng57D3/Ot1L2YSj6Ng8EeJ77RNVW8024+7zYxIu4hXHHDDoc9qabhlBiSpnrvwJq0GvaTFqNpKrxvw4HVH7qR2/Ou1TUlaOVxcXRuKRBIw+OamxmLm68qNmzjihIG6K6yiPmNI/LNzmrfolIcDgEgnmpGPW6kpkDmpZSGHjUL80rHRXmTy2ZQfTVE2LPMJOFPNOgKSdXSd5GGABxWq1RkynZWmmLHlc1rpGZcWkIkQbfwrWUmapG2PKQMdq5zUTkfcCKskWijJkJxRYh62AzgUmMncyYwi0JDYvnac0xA2JdvigCEo5AoEM2UA37j0pNjSHZp44xheoFSlZV0al4xs3v9LmMcYkmj/eIv+bHaiUbjSCLp5Of6bPGpaYyqW53enGMDoPjr+tcTfs6Cqv/ABEn+KPbWpIDYQOcFPUMA/JBx+mPauScrdI6IwxbNU1O8+/TNBM4K2b7k3ndujbufocfI3Gs/DNVtGnzyxXk7ysR5SHywWPXAyFB/nWqTikiG7tgbOBY2JZfL85eSeMqDx+p6CnKVglQKRB5GwsPMR92GOOc9v76U1sGVV/LukVjtZmyDjOeB1+tdMVRzSdgbELKLaNgCqguMjGTxVkE4woklmOMseOM8Y68f6UAwUOwxSFhuDOWUEZzjp/r8UxCKMB5hcnDEKABk59l9v8AemIJDEEaSNtvmMAd3J2gg9uu7jA+tDYIsIbaGzEkczkxxvlyycs2eVUZ5/hzz/rWbdmkcF0oyiFtq4XAwDhFOcAe/HT6VzyRvF+ANxFBawnIzcDsy7vnB/v3ojkcmUGpoXkWfaDt5RSeGHuO/XPb3+lbxeKMZLNi01rFJ6o1kV8cqxxz1789KpNoTSeUb99jPjOTwv4pgiuZQmn3ZEU27pnsTnvz1rXjl1ZEl2VM9kC7V4FAxyOOa3owsSuYzIVPO0GqToQzkJtUd6QApkMb7veiwLS0mCw89alotMhd3BEW5T+dCQmyr8wlSfxZ71dEg7eFlkZ15+Pam2CMajD58BUdaIumKStFJa2siSFHB2561q5Gaiy8toVSLatYt2apUh4SE8GpoYLH7zNMQYcdKQGQ208UAYY4bcaABsdxyOaYGVHIoAzOmAMUkwMrNhcDiigIE5JJpgDl3NhccUCOU+PYLfw9rUQjkWFL0F4iRwrA+ofQZzXJzwSydHE2zQJdVspJlVJI2ZJA/o4A28kk/kPn9a89xzZ2J4o1u4uZlsdQuio8u5LKDjBA3AgcdPw/rRGNsqTpFVHBJLboHDLFnexJyHPye3firdJkLKLeWyhjTe8mZVXK55GD0+n0rNOzR4Nb1wyQh2iY7WI9Pv8Al7V0caTZjySaRRJJv3MyqwGcAD4/r/tXTRz3ZYx7Y5pXwMJDkZHbv/zU7wMkIGS0WMfix6z3APOB8/7UWIjIoVmUBQkQ9ee3HSqQhSUBbdZGiG8/+mnGAPp70woYmg+6G3uy0atCqttJx1HGfnjpU3Y0EtUuby4gBICIC+CoIRc9AOmSP1pPCKRsVhbQvcPKXKhSWRWyA2MDHJHX88DHHOBzytm8aRWXkYvpUkMkaQAhi7EZzjcRjOM9sdeABVRVbJk7YW1i+8LG0EVvFFkjkcFSSeuOwbAYdjUylRUY2VU0OJ13ZbYFR++Mk8HHtjk1SlaCqYskcXmRiaAyRSBv3UcoRg5BAGSCOuDjHI44zmtYszkj079gPiKfXPDcdhqF0ZrvTsRs0h9bJ/CT746Zrr4p9onNyRp2dmMMbQ8AfFOyaKrUPMimi2qTngVaJY22EhMk/GBSBmvm7ubq62xHZDnr8VpSiiE22XL3KGD7tH6j/Eais2aX4BXCGOJQvpHehCGNOQzDEY496JYGshmtGMpDdBU2FGGtkQHcOTRYADEI1LKelMRCJT1agA20sMjigCYdEQ7qQCb3H734qqFZJmaU4Xp70hhVQxr9aACRlQOetAH0jhgQBzSoACRMzZY8e1MQcwrjrikMUluCJfKAzjvVUK/BzL7XLFb/AFyxiuEzD93IVs9CW5rn5VZrB0jncvhTTIEIgMsMcmQSpJduCWP8gPqa8/kdbOyGdGveJSrCDT4l8vYNpOegz+I/yohjI3qiutlMsi2ihkfdgFu4A56fGf1pSwuw45wT1R1ktwVBQwjB5OOPcduKnjWSpukatd3YfeJDwWwM/wBDx/fNdkY0cspWV1m4+8zKrYVlJ4HUd8j++laMhFzaRqY8yjBZUBAO7qeT9agocuifMKrklcHcOnHAB980kDAXMQKrbkKuB5kpB4Yjtn+/inYhTzSs+ZFXdI+5OM4x3pgNabF97s91yoWIOCqHOSBkhQe/Tp9MVLZSD6pGxszGIsMMZJU5fIyM8ntjv70IBqBUFje2aySTTNGpjw4cjJUDjrkjIHtUNFpgJ9v3C22H9zETkBcbpByQDjkH2+D2xT0F2yx+8zWdkzJJIvqEaqg3mNSOTk9s7j+WBWTVs0TwLSxBmLKhLllSPC7mkYdCe+CRzzjke1Kh2JXlmQgaG3YHjgsd4OTgA/JB69AKuONkSydB+wbWDZeJYreYHyblDANxUtGxORzxkZ4P1FdPBKpV7MuRXGz08sz27Ks/pB6AmurejmLKRRPAjKBuWp0x7BSJFcwOknToaMpg1Zq96q20hETEKDjFbRd7MngtLBolhBA9R7mobtmiQ3H5UtrKJcF+wpWG0Rs5Bbw+o7cdqHkA33sMNynJpUFg5riDyBJPMB8A069AyuXVklLQxwOd3RyOMUU7DwWKqFJ3dRSsQKScbtqnFMBa4k6AdKaEyDqGAJHAo0GxmBlEfXBpDJmQMvJoATknaOQgAkU0iWR+9tngH86KHZiO8kz7/SgD6S5kmbYmQaEqC7CLbyJF5h5J/WhsKrJr/i/SZNSsBOqt59t6kPx3/lUyVoqLOW6jLJboJI4wHHpUnkKvfJPfHPHxXncsHZ18cjn1xBcuJrySRWFw5cOBgAbsAY7cCs1jBo85JHbDdh4VbftPlg/p/pUvKoqOHZSX+pylQZU4LYXn3+lbcfHRlOdlLexL92kMBYyRjPBzkfH0rdGJU6bIv35QMeWcrk9Bn3pyWBLZfQekyxNhMAFGPHBOMf0qPBbH23BTMzMSWAGADk/1465oELXBN1ZSsFAZ2Kh2PUDv+n9aNAzEb27H7zlGUSeVGxGd2DgZH0GfyxToLAO1xdefDbHbGCEU7Op/07jAoryF+B+wuN8dwPMdXWRYfVjdICpx8YyBz2OKGsgtGbeLyZZJlRlnm8p9o9IEYzuPTPxn5PfFJjWSwtbVDY3lnb7cwRB1m9OQoH4QR05PzzWMpXk2iqClI553RwrJaoCY9jESck4B6kAbj0BwDQAKdblwkhZkYhSk3LYxgjntjpge546mpKEb2B3szcW8s9tPFJhk2kD3Oe3YHr9KcbTrwJ01Yx4anMerfepLX7sUx5Mg/eBGBHA6Hkd8nBx75q3+tUyUeuNO1l9U0+zubiItIyBiT3OOtejHKtHFLDDre3DSsC/lqP4RVUibYU3biILnaCfzpUMrLuAzXqsWOwdapOlRLVuyxlnRoxHEuFUdazUadstu9AorwRNtBOaqrEBvL6GwU3eqXMcVp7u2AKTaSBLJrT/aX4W82NF1WJVlbai4OW+fgfJpKaK6su7F7fVJfNguUe2XvG2Qa0vGCOrvJZeZHkJEMk8CkkAe5ebO0fiNQhmbaJiQJeD3psBnUZLVQgGAR1qY2N0J+YhTOcrVCPnnjdML1FFALzCTYGU8e1ABUEojMkm0KO1AC8ErXU+xUwpPLVTjRKlbG7qNIFxERnvSWStH1hCQvmz429qGJBnu0J2orbfelQ7IXe7yPR6if4RQgZoninwy13E91bKu8As8Z6N/ftWfJxqWioTo5d4nWG0sJ/OfYSMhQm1V+B844rhnCmdUJWc8uNUOUkSJxNGm1WUEhsUKA3LGBVZU1CJDKzCdfWo75Hz+n8q0qjO7KycX9tK0zQ7ldvUK0VEFdbDzL2RwuY2GcZx3x+XNN6BGyaeGknyjBoyMqdo4PHJB7dP5VBRbTQr5UokOEQFRtHq56ge5NJgVRgmur8oX2xxoF2qDsUEcjPdumew6/FO6DYtKbOAebFFPdPE4G4EABPYAcde/NJNsdE3uJor+2QiNI0JLscgnGSAQOnbPuR8VdkBHCtKEkVpLNpEkC/OO5xnJB6YqdZKLqWP96LtAJDHhSeq7h6lBJ75wDj3z9M26waJFmFL3dqEmha4uJFHlOCAckl1bPDcgnIPBPesdGiB6mw/xBoEeQGVwXYnaMKrcHHB5bGR/OhaDyHu4AlgqIWFvCGV07MQPyyOe3c0IGUrpKtwDCFSYbNwBIXgEdPYqOo9xzVr7E/orIbm6tL4SgI8eeHA3Ng9QV689c1dJqiU2mepPs41K31HwlYfdQMopDAe+fbt/3rs4ZXE5+VVI3FLeKIkyZZiPwitLZlgHBa/vgbjGzqE7079BRiSNQ+wLyT+gosCOoG3srYyzyLGoxkk4+P8AWlY6OQeO/tj0/SM2mjot1cyEhXGNqqDjP1ODj9ahz9DUTjnjbxFrfiiRZ9TvHECnKWseQkSk9h3PyaycrZfWjTrfd5nLBR1Ungn2I+KGxo9R/s7WkieGbiS7LCORwUBBxj4zx+lbQ0Lk9HU2SMO7xHCr0+tWZBYo5HmeY5VR0JpBtkI0nuJdyk7c8k09C2Ykga7k2qPQp5b3oToHnBM2LtN5cIIjH4mPejsFeEQktN0vlxDpwW7Udga9Gb0CARwL6n7n2oi7yDxgmkIk2x7iT35pN1kKIusUN3HFGcJ/Ef8AShO1YPGENS2yTyAk7YB1PvSuhtWSZoZm27wlunH1oysgTaGKfb5XphXueM0Wwr0ElSOKLZAC0hPLe1Kx0DihgGc4du9O2FHPvH/ge28Q2s7KqCZf4Rxkd8Y71nOCkVGXU4lqWiRWEU1qsJRoPSVJ5BrjknFnRFpo0m40+GW6SaOQAcF17Ee4q1LAmic86xo6ttyuOh+Ohpogo7byfvsgibKOmFz/AAtu6VpQiy0jdG6xnjYwUE/yP9/FSxo3O6t/L0sPCTvPHPqwx7/NKWAWTUQsly06I4gs41ClsjJ+BnuT3NIYzHbpHYG3s5ZFCbgu0Zfkg4H1HxUt5spaE7ospWGRpZVQMA7sAo74xjg9CT1yfbFNAxzSHMcWzKLG6FnnYkFVxk4J6Ejj/wC3xRLLCKL6O3CxyrdJJ5Qk3CPLMn4cc49jhh35I4zWMmaRQ2S8kBBRLlo/3sOGJdTgsin8ieO2SKzbo0ooJEu73VriUvHlweG5APTBx2AAH0x71phRJV2bbHA0mj2kccqzzAFWKj1bwAdxHtnr/wA0rTdiarBrN+p3XrTKcKjJD6fSnJwVU8kY3HH0z0NWvBNlBrCW8iSG2hlDRtlZEDD4IP8AkJ4GDxWkbsmR1f8AZ58bWttqp0u5mfz7rEUXnZCs3YewPOBjriteNdZU9MmT7RtbR6GW4e0kciLzZXPU9BXTVnPdFjCES3eWQK8xGfzqG80VWLNL8U+OtE8KrF9+lEuoTybBbocsuBk59uw/MUpPwCRxbxf4t1HxPf26zgrazI06QxnCoMggnHcAY+euKxcrNFGtnIfEGjPZ6gpibc+0SSqqkLHnkKCeTxjn3OKaeBVQ9p9wl1bmF2ZznG0rgZ+e/wCZpMpZOn/Zn9kp1aYahrcrW2lqdw7mX4AI61pGF7FJqJ6J0jRLPTLJViEVpYqMIgPatbrCMnnLCS2a3R3RSeXbk4Vs9aLFQSTUd8oQpthHJ9zT6+RWGuL4NB+5AjTp80lH2O/Qt98JQCIhAOvuadCsanuDNaDy5RH7461KWR3aFh53khUIC+/c1QhdOSwdgHHU96YHzkgqImwT2FAArxWGA52KB1oQmN2l03kBNu9B3NJoEwaopuQzjdk4AHQU/AeT68a7F9HHFIDFjJCihVQndjr3Epj2qMDoc9ami7At5YX1OU9wDTEfQqpZiDtHb5oYHPPtL8PJJZXWpwYVo4yZFxy2O9Y8sOys0hKnR5Na6uDJIqxP5PmFQ+Dlc9visqVF3kzeWzWpDNdEBupLZPvgc007EyvmljEjm2PXn6fPxVoRcaHeRz3sC/h81thUnOGPAz8dKmSGjrsPhy/g0qY3seFByBj8IH9/pRKDoFJWcturW4uLuPTbVxEItzzMOBuPX88VC1ZTD/drWyhSGGaSW5l3AJFgvJ7tu+vGD7ce9J5BMakFpcW8nnTmKQeoxRpvQIOqk/Ujv2PxWeUabDwIYMsLqCWFkEqsigjcSV4zwQOPYZ9zScilEvrRGE9vFKVkhaQbpBnCsygkdgRk8Z9vyrBy2aqIaeC4aG1igkdYyXUeW2Cgzwp6gjG3nrj6mknY9AJYDDkqi/PPvVuyVQHw7fCC9uEn9RcbYyOijnJxjnn+eKqKwKWRa4tA12st1GI1tX4Rs4kYYyBn3x/U1qsIxspbqWfUGmngMhMjfvZckDLc+r3GSfV3wM06rYjWLwLC6i2lEz4J3pkEDjrzWyzszf0dq+z77f3stNj07xjZ3d7JbIFS/tcNI6jp5qkjJA/iBye4J5rZT9ktWWfiX7dpLnTb1/CunyxRrGcXd4ByThRtRT7+5544qJTEonnm81a+vtYkv76eSW7kkMjs553H+/5UqK0dR0+5Gp29gnm7jHgOiDBdiBtJb2AGMH3rB7Ni5sNJGty+UfMm3ZCq5wvyR2+mMY96EFHQ/Bf2R6ZZXEd/qkUDTp6ooiMBB2z7/SuqEKyzGU6wjpLRboQcF0j4BA9IArUyYxBAl26rPIWXrk9BRoNjRWIyBY3ztOBjoKQCaMqM6lc/NMQtcqzKdjVSEyFhEYGLSncxpydiiqDtcrIphYYz7Uq8jswrPGAm47T0opMNAzHmTJUkimIO5yBHHw4GeO1IYWOPdb5mBdhzipe8AtZPovMETEKAp6ChjC29tIpdlPqIobBIzE/kjeFy/SkAM+Y0o3th27DoKYH0tqIZfUxlJ5oUhURSWaGYnZ5gPAHZaMNBmzSvtl11NJ0CO2aaNJ74kMSQAka9f1JA/WseWXVUjWCt2zhdlbafJpN3c22GlLGafHII6AiuV20brZz7VPu9/eNhiVUY2j+GtI2kRLZSyR+TPtjwvPDH+VaEmw/Z/avc+KdLjjCEy3KjLjcu4f8AcdD1zTA9oNp0NxbyWuVdyoQ+w4x+lbtYyY3k8y/at4fuvDGp3EcRKvcuyxN0DhsZ5+mRXI49XRunaNMWKPTWjjBEMhCiWZ2A6jk568fHfpRdj0WkmrabbNKryzyxl8GRAUJOAMlfkZ6HPfvWLhJmqkkXmnXUNsUjMb/d5HZjuIjPvlQx45I69ckdK55RbNoySL+302IQNLG/mWdxL5pyPcjj8vUMfSs4pydFyaSsGJEslAXkszMQ3fIHf8q6Ix6oycnJlNd3BuFRmbiR8nHGM/8ANIdCtlF5l2gBIVztJzxnuP8AX61ajZDkbb9oXheaxtoroBmtn8tpW255HBJ+P04Jrolx9TBSs5/qK/4jrkdlEfLtUQKzrgDaWPXPGOM5981K1Y/opdfcedHZWsSJI3AKuT6QTj6EjB+h7VUfZLKjVYHtxDarFsc/i4wWOehPfmrsRuen26QaRbozxqsSMW3MpwxGMHpyecdc5GKybyaVgp9R8KSRAvEzy5GIgq7c4IGOmcgduuOeaa5ET1oL4dTULPUkiljTazZ6KUU++B2+mKGlLRUW0em/AuhRx6bFdzpHLLIAcR8R+/51tx8SWWZz5PCOhxRw3EWLlDEcgKue1bN1oz3sdl+72eUkJ8g/hUd6m2weBi3ubRLPapRfak7Hiij1qX7nDHNA+XJ/CB1NaRzhmcnWUElkijZvMYD2ApFAdglG6N8k9Fp3RNWRjim3klN2O1Fjo+EG5uIiH+aLCkNQ6dcoFlbB56Gl2TCjFyZzMMqqGhDFzG25mUnd3agBmBJCMqSe3vmkBO8gliQFty8cAULIMHppllkPmkqo446mqaSJTbGZlRQW3YUdeaRVmIZEL5IyooaFdkzdQtkIoBFKgsgsiLGwjA5Oc0DOFfbs8N3rKW86rIkduow3QZJNcnyZVJHRwq0zhemyLY6vHbmRfukpMUgAYYVuOOcEjNEf2QpKmJavpN1Z39xHZxMsCHAYcEimpWsiaEVtbl32zegYzuP9f5/zq7Ebj9j1nKftI0JInRiJtzK3RgATn6+w+KcctCej2C5lLHIC564HWuijI1zxx4XtPFWnx22pAs0TeZDIvBRsdf6UpQUkNSaZ5m8UeF7mx8avaalbsscUe+MtzHKAcE5rjknBUdCalkqVgvNkS2qRtOymUnOwIu4kDJPXjjB5AqG15Gr8BbZLOKGL77sJ2eaWcByc/PX8vntUO3/JpGl/Ru+g3NmPD90LI4jiKk7VOPV0wOgrKKkpZLk1WCuvtQQwSMPWFTJIPBPXGf771TjYk6Kq+WW1jXEkWY2wpUnDp03KTyRlWz8cVURSbH/DIdtStDFAZYPNDFFbBfJxj69MVakk0ierZ6ouNKstS8NSQThN8icCTja2OOB84rtavBy6ycI8afZzcWGoyT6fH58EqgnaDhdpyAPf3IrGXG4lxkmc5vbI2+u4WJjNPCUjdjgRnPfv8dRxUJ4KrJnxH4fTUPMuLWTZLEod1GSFwoB47nOORxzn3qVOhuN5DeE7s3Ajs9Qt7aOeH0208ahTgjpkcH8/c0pryi4u9m7ad4YZbjfGn49rKMFiBng8d+uT1+tRd4Ko6j4V+zGxtGW/uLdDNwd4cgZznIxjmuvjhWWYTl4R0NrSKAqB6Yjgknk1tZkLXKvPIZLdsIOAxpr7E/oLdSebHFHEAzgdX6mhIHklKsSW6xkDzG6vjofakBJtMhhjFxKw3AcAnv70dvA68lNNctPsSNM/OK0SohuyVraSeZveUg9gD0obEo+yxRRG2fPOfrUFhw5BB8wUgCPduy7TMMD2pUOwHnKHBLBj80xGTMOcqB8UAfRShPwLj6GgCcs7P1Ut9TQAN3KkeWBnHNAAXSRzlgCD2p2FH2yXcB0FFgGlt0XmFz85pWAIQNg+rFAHnv7brgW/ijUCZARGqKBnphR+tcfPmdHTxOo2cGkuUkuCZC6xu4O/3XPJH/FapUZtm3a8x0+GOe4vvvMc8aSwYHLKQcH+WPyrNr9qRSeLZqP32aaSSRQIoud21evwauqJOh/s92dzN9ptldQp50EEckkzDH7oFcZwfkgcfrVw2KWj1k78gnJroMgMkrgYCEjtRgWSu1jRrLV7Yw6hbJMCpXJHK59j2pSSkqZStHAfE3hoeD9Z+5BJJ7UW4lgdlB3YOMcnqMniuDng0dPFI554hn8+8lKD059GRyo6jPseaXGqQ5u2b39kmntrMPiCxEb4eySVT1y6OCR+eSKaj2lX0DdRv7KsLc6kx+5whLSBjGzMPxHIzj6YI/OsZNR2aQTlkFeaXE8j3TykxCcqg5y65wMDtgZ/XNQuSsRL6J5Za+FrZpNVsY7e+gZC2djgDaM9xxnpt9uacXclaG1SwendPtkSCEOD6VGQW3YNeusI815ZYTyxyJsMalcEYx8YpUBqmt+C9F1SUSTWyCRQQGXgkE5P86l8aZSk0a/H9mFpvVhcP5iPlW6ek4yDjvkde9YvgRa5aJ2v2R6D5gmuPMaUMWyDjOeoPuPrTjxVsb5PRvulaXpumW6xwxocfxba1UEtIhzb2PyXUZ24JwOnsKqiAEt4mCXJbPvQkFgLIGefbFGEibkux4pvAIvdO0u1+8eb5qvMBjg9KhydFJLZbLpFu6nem4nqajuPqiB0G0JywY9+WJo7h1RzhXmI9JC10mNBV3Yy7t+VLAwqS7TwuT7mkMOZsj1HFIZISx455pAFjki7Ak/SgAyevnac/SkBMwqeCMUASWAHgZoAkLUZ5BoAmIMng9PmgCYtRkZJP50ASWOEHBIJ+tAExFEWG4DHwaQHkj9o+EweOtRVmKwSMspDZyVKggD865n/AORm6/hHJl0nUJWsgluXe9z5Kq4JbB5yM+nr3xxz0rSiLOmeM9Dhk8O+DbmcBpTosfpB6kO3X3wCBU8lxa+yoZs0O4WJV8pwDgnCL6QP1/rSQM61+zvpsH+NS3cWVMULEOq5BJIHX2q+PMiZ/wAnoYSomOc10UZWSFyh425NFDsIsoJ4iNKgs539uUUcnheCVkfek2AqpuyCpzkDr/3rDn/k04tnmW8Zmcjq6kqzAnHuMn36/l9Kxias7h+zRZOLnWbtwFVIkgA98nJ+nQ1XDnkf0h8uONfbM+NbGOx1vVLOCMQxs29PYBueP51x/LVTNvju4nPNQjWzt5oHZ32tvyTjGQR/U1EH20azVbLLwLp8cviLR0SKYr95UbBLucnp1B9utaxbc0jNpdWeoo7SfzBGmHHXg9PivVs86jEsTxSlJFOfinYURRuc7RQB9v8ATx1oAiHJXkgjpigCG5y+1Bk4yc9qYENsjAkqQKAMiLzFG/8ABRdBRMWqM2WYsF7dgKOwqGLJIrZG2yupJyccUm7GlQ9HqWzkTTke26pcUOz6XXJS52NIMds0dBWVMsEECFnGW7AVoskt0ZtvMkg8tvRATyo6n86GkhJsfeG3SIDylIx1PWoLK+a39X7tBk+/aqQmx63toYlJnG4YqWBGRRI+2BNkf+duv6U69hd6CBNq4DEkdzSGTVX/AM3P0ooCRVwhLvgD2FFILPrWPzizksyj3NDwJFtDFD5QCglj2CVDKFJ9LluAdpeJO/qpqSQqsPb6YsKeogn6UnIdDf3ONEyUycdalyHR4z/aP8y7+1HUraWeKG3aeOIOSWEaqigkgZPGaxSubZq8RRze3eQSK/3lJEgBniEzhXZcgAYycscfhz05zg1qQegvtN8O3GnfZF4W1S9jjQ2tvbx3SxpjyFlTjH/xO0Yo5P2ivoIYbPOt9EYJmaCQTyOwXcTkYP8AfWoQ2d2/ZzCS6nfWcgYyJaLIzKuFTL9PzyPir4sNsU8o9Ax2UZAVQuT71tZnRCS1EJ/hJ+KfYVGQrnhQAPegZzj7edsHhazWWQ/vLgkDdj8KHnr9K5vkv9UacX9Hm6/yLqRZGYRqcjuG6Ege5II5x7VhHRs9nfP2bLUReFdQnVDNPc3ARCpznHOSfqa2+OrlJ/4TzyqMV/prP2k6qJfGl8wljEUbGElG4IUYznucg1yfJfaTo2+OqVnNfFN+l9bJFEGDD1s4HyeCf75qeDjcXbL5p2qOn/szaEb3W73Wpy8i2KeTESuAZ5B6iT7qn/8AfxXdwwy5HNOVRr2eh5LUZ4dkI/ymtznFpLYk8yMT7d6YEDEc84GPcUAYwGIBkG48YXvTAyIAjbejdM0gMiPaCVwSO9AEfLkIxjApgSMBA5Ix1+lFgR4BIHTpQBHBI9hQB8y7fxfpQAKRgqjJBzxTEQKsXbMcjHdgkjgH2p2KgqmYsGC7Yx2xUjGBcQuArMu72HJNSMVfUrSIlC0zlc52xMapJsm0M6Zcw3aNLOJrePO2MOuC5+lKVrQ1T2T80tcOqK2FIyW6Y70h+QiSAquxd5PJfI29aLAYjEfl72njBJIG3+H60uwUKzyRPgLcxOTjKn04/wB6pNixobspnWHYmw98gcc9PpzUsaAx+I0hljA9TEhWXZ37jilVjsvYdbjklSBYQ0jjIVX5I9+ajqOwseo2jyMkhdGHQFCfy470UwslLqNuo4V3i7uBwB+dS0x2eTv2ofDtinii08Q208zadfuBdrGATFKBg4zx6gAeTjg0q6u35LvssFH9kXg6fx740tr7VLWO38K2RUXO44jYJykSsRmRmYZY+xPI9IrTLyRo9V+PLGy8T+C9c0uW4j23lrIqt1wQNwIHwVpNYBPJ4Hv7b7s5NsuxUKlgF/ET9TyBWcXZbVHav2XpTLqfiFUcvOYLf0oMKBvbjH6VrAiR6DAij5nuFVccguAM1paIJWzW7NuhlSUf9DbqE09ANGSKIhZGCuezcUAedP2rdaW2vdEtIDumktmkOeiqX4/XFYci7SRpB0cJj1CWRWZwI2cY3H+Iew98kH+xUdaLs9J+DvEKeF/srtk05ozf3ZIizyETaN8nHyeM9/pWXFyOMZe7NeaPZxX0cn1KOfU74w6fHLcXihikEZDSSsFLHC8EnAJwOTg8Vgk5S/01xGJrGj293r2q21jYwl7ueRYo4wp5YnAOPjvn2NdUY1hGF9nbPZXgDw5beDfDltpVlvkEeZJZnHqllb8T47fHwBXYkkqOeUrZtqKTDvyBu/iPagQswG5iv9aYiC+UdwLqzY6e1FgSt4lllZYNpKjJ5A/nQ2CRiaJ45cSKVI6ZFCYBABtCoBjvzmgDDRK2DyXPVs0ADmjDKFOFA9upoQC4tmDk7i0fPA4qgBta7s5Zs+4c8UCJm3LCNT6Qo7D/AFoAxDAkLSZIYuMerkj4A7UN2CNkmswofAYbhzjoR81imWUf3eFQEkBDluQTlVHatKJskunQgMY1ijaQcsCQ2KWQwfX1gPKCGLqQRI3Uf7Uk2NpUL3bXHnAOdmwD1FPjr37U0AvI/mRHzpY2DHHrYgqKSEAtbiysJFliQ+jjjLr9QDVNOWBYjkPaztqk5fDBWY7pQo4+g7fWjr1QdrC6pZRpDHHNGpySzSqDnGejGkm7G1aE02RALaukag7vKjLbD9fmhu9hVaHpMyDEDLHgg9AFY+5zS1sZP7w0Rc+e7yY5ZcLx7CirAClxIZ/U8u1lJyuTtx2/4pNAZ2vcIyKXZCQpDkkfPAphRLUbSxYbJxDcwjACzwbunTjHvU234HoDfyOLLyXnW2t8ekMQFJ7gAirWXZLwUOv+LNO0LR7q5fVLY3v3V1gt3cCSVipAAXqMms5ySVMcc6PIPiJJI3ZojkY2knPbvjp0HxWMDWR0z9l6dLfxTrNvPAN8+nrMGfvskH8jkHn2FaReaRMliz0FPcxSFkZ0e4YgKuzBY+wJFa0vJnfouNLhgiRJLhmDFsshChQvdgByTnqahtL+Sv8ATUvGnjux8ExHzriKd7oyPChUGR1HBYscBFzx3+KJySWVkEn4Z5G+1DxLdeMPFE2szpiIosEezJRFXICg/wB5696hW8suq0axB6A55zjGKGB2Tw9dTJ4asoL+NoJ7eFkMbghk9WcsMZGeK4eX9W0jrj+1P6NF8UXcb3kaW8gEq/vGkVRuD9sMDwTx9MfNacMWssz5ZJukdx/Zc8LqFn8Yaqs+yQyWunhlyJT/AO7MCef+nPvu+a64xezFypV7O/LfWgcxbnjtgvCoBlz8nuat2Z4J7oJbj0XUVvEi8GYhm5/6en59aMhg+kS0uLZbfTZoLqVG9UjrhBzk7j/tRbWWGHhBl02Muwea0igUZMcABJP+lH5A6i9nprTl5gkzw/8AtbVXJ9+ppudYBR8k76ymtYo3MJlc5Cxphm6fNCkmJpoSW0v7vBkt5osDqwC7R9arskFNkUhv1McccJbPAkRWZRj++tFoKZFVnSVxc7w0eC25dgGew96LVYCgri48rzoYSYxx6hgA/JNFrTCmJS3V9Exb/DJZEXA37wA2f8vPP5Va6+yG5LwG0q4fVJZoHtbi0dMZWZSjOD3GcYFKS65sat7RsVvYxptBWOMYwMEc1m5F0VV7qE8xIiGz2B5qkktiZSTWkruXnuGxjnjFaqSWkZ9fbHbe/giJEZ3A4HPPA+TUOLY7SHYtQRyUiidoz/mOc1PVoqxsaq2NjWkeQMHLdKXUdiczpIy/+XQFehzz+tNIRhYISxY28ak9h0pgWFk9urqsq7F+Omal2NF2kCugKsrL271lZVFJeeHYixKJkHoAcY/3q1ITRXtoUkSqAuCT25/4ppoVAPuE8eRi3ePeSzvlHUewHIPPxTEA1ewvrez8/TZ47p0IMsWxt4UfiZAPxHHbGe/PSiLTdMbTrBrsup317O40YvfQoB5kc1v5ZHwTgH9K0UEv7wZuTf8AI81/qk1kmbW3srsFMlZJHJXPIIIO3Ax71PSKe7RVyayB1Cy1zUtHuYvvelxXUisscjrIxQkH1cL1Gfb564qZJV+o1fk5ZN9i+tNh0v8AT2lB3K370/r6c1z/AIpXho3U4lbqX2HeLLm4BXUdFht3YuwcTk4z1/8AT7jPGf8AenHirbE5rwbn9nH2T3HhXWL3U7/U4bm6mh+7xiNdiJHkHkEk7uABjgD3q4xURSl2OlRQSQxBYvu7oWyS0hLNj3z/AKUUTZhXaB55ttusj4BfJYqOwH/FOmFnN/tZ+zT/AMcXljfDVUs7mGDyUVE8xW9W4bh1GCT+VHWwTPvAv2Wz6PNex3d1BfaRdmNZrNdOEcJZUK4G+Rl9WRk4zwMEZNLrjY7zY1B9jnhO2Bkg0BjMvmfvpLh2EYZdvAY4G3qp7Nz8VLj9j7fRr+ufY3ol5NLJFreq200q7SseydRjg54U8jGSWyTlicmj8SH+Qt/Bf2b+FvD+iy2Oo6BZeILh3Zmv7q22SMrdBt3HZj3U/PBqnF+Bd/RudrJHZafb2FjpohtIYRBFFJOSsaDhQBjij9hOmOJcL5cUccaQyKAHJLNvx1J9s005eROjPmujyfdp0cSNtxMGOF65Jz/T9aeWLCPvNd7uIuw2gbNqgBB/1Y/l/vRkeBq3NxGpeS5S1WYFSYogSfb4Ix+YpZCgcs91DM6C7mlj24C7GQD2GM8fWnV+BCkkYa9BuVmleT1Mzs3pwBjkH25xj4+apXWBNItlubxrVULb0K7d/mbGRR0GOhP15qKyU2BnubgQARzXgKAejzzg/H54p0IB9+1NpswWTl5CN8rTejj3zk88cimo+2H+F3Hb396haPf0AUXMu4LwMnaCQe9ThDoWutOWd4hPetC9s+VwqgH5zzgd8flVJixsObgvKkVzLFfW0eWDxpkrx1x1z/LmlXlBfsBfmOWQmBCJOAkaqV3KP4vamrWxMJOLhkIhjC56k8k1aryIqbywu58mV1jU9cnOa0jKKJcWxNbERygTTCRB0RBVd70T0otbWWUKQkYUHgZ7Cs2UhiGKXkk5J+KTGhmOCQ8k8/SpGFW0f6596LAkbSbtgUrGFhjuIMmNip+DSdMB2HUmHoulI/6lqOvoqxvNvPypD0ZAGLeIZO3gn45p2IG8cAUs64HuGxQBWTXFuf3MDygg8kDOPrVdWKxO5WP1PJGbgAerD4GPp1poQSLzGtVjtLWLyySQijgfPNH+jBGzmDSCSNUkf1BmGAvxgf1owGQVjZ3lzcSKzegcLuUhc59wemKHSQKwjWEsEZe4gVZA5LKr7l2/GeaVrwANJbfzSkUU1vcYyZgw6f5en8qbixWgE9nJdMAskkb4xlT/AL5xQsD2F+5wi7aaW2lYsArhpN2eCDgduvPei3QUHa+mQCK3jaK2BA2AAHA7Z/2/Wl1vLCyV1cwTQENA5Bbo5DcY/v3pdXY7K42czyrLBEVgBBaMRhg3/wBvaqX2LJKex3YbyYIsk5V59xH1osBY28Hmui+tgvJ83I/MY/1pgShsyxchcrGM7whwaAAJNFE8vIyPxexP0p0xWT3hgcRiYkdEAz/M0UAbRN1yZfMga1ii/CpIJJI7YOBSnFIcWOsHcZR8sBgArgdM4JpVQ7MRFmGHhVnxgFnx27Y6UUKxmO3jVlMgUIqkhVUEfnk80DEyIfvax2sqyXTcKr5AzjOD78c06YvOCamKLJugQqnaREgwD7cfX+dKn4HY47WCjYY51/zAhUqaY7RX3yRXCqsUUionKrJLyD71UU0S8icsV9I6mCeGJVUBWjGCCM8+5PNVjyLIrPaapO8iy3sxj/EYWkOw++4dD7+1NOK8EuLOhRxLgcVjZofS20bqdyg/lRYynm0+FJTtGM/FWpMTVE47VOKLENR26ZpWNIZjhUUrCgyxKKQwqxikB8YV70WAN7aN1IYD2osCruIjaShoXINVdiYLz5XJV3Y/Q4p0hMG+DlzuJ/CctnNOxEPKRXBI3KeNvQZ96dgQuG8rCADDAk8ULImxWDU5mv1jT0Z4yKbiqsFIflnlPDSEjpz+lTRRGSSW3LRrKdo5wvApUnkBC5mJIZtzHqMtwD1qkkIjbo10yhnKDGfSKbwAWW4WO5FuqHOAN+7mlV5Cy1SE4lDuW2bSO3BqCqI6hBGLBpUUBtuRnnn3oi8gxKwst9x92aVi2CxkI549varbrIki6i0mJohI7uzMMZNZORfUrpdJji8wrI2FOAABVqRLQCQRRhUaPcVG4tnGadiE7p97LGqIqOOvJYEnrknH8qaQgCP5cjSIqbugyMgY9vrVAV+pXbwKx2xmSUgbgoG0e3H9aqMUyW6LOJY1WCNIkVWIU4HPp7596zbyWsobubgxLtRF6ZyfgUkFgrBnuEYs+0g/wihug2MpEvnIMvnfjIPxSsYd4Y0jDIGBUnBzz+R7UKTChbyAtwj7iWOQaq/Aq8g76JINzct5eGIz+Ie30oTE8ZGLBGvtMW9DeUnOIQAwGD7mlJ9XQ0sWDtnLjzW5JwTmmwQWONbiVwcgEHvmlpCP/9k=\n", 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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for image_features in parsed_image_dataset:\n", " image_raw = image_features[\"image_raw\"].numpy()\n", " display.display(display.Image(data=image_raw))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2020 Google Inc.\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", "http://www.apache.org/licenses/LICENSE-2.0\n", "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." ] } ], "metadata": { "colab": { "collapsed_sections": [ "pL--_KGdYoBz" ], "name": "tfrecord.ipynb", "private_outputs": true, "provenance": [], "toc_visible": true }, "environment": { "kernel": "python3", "name": "tf2-gpu.2-6.m82", "type": "gcloud", "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-6:m82" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
google/floq-client
samples/notebooks/PennyLane_Cirq_with_Floq_Tutorial.ipynb
1
5477
{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "PennyLane-Cirq with Floq Tutorial.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "5sAFtvqiiM3t" }, "source": [ "Copyright 2021 Floq authors.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", "metadata": { "id": "QAIEPJm3iY50" }, "source": [ "#@title Copyright 2021 Floq authors, All Rights Reserved.\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "-sSF6cwjia-s" }, "source": [ "In this tutorial, we introduce how to use Pennylane with Floq backend. Please make sure that the versions of all libraries should match for the successful execution." ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "D10rprCSRac3", "outputId": "e3201f1d-dada-4b2c-af3b-662e6d45fabc" }, "source": [ "!pip install pennylane==0.14.0 --quiet\n", "!pip install pennylane-cirq==0.14.0 --quiet\n", "!pip install floq-client==0.1.1 --quiet\n", "!pip install cirq==0.10.0 --quiet" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "\u001b[K |████████████████████████████████| 481kB 8.3MB/s \n", "\u001b[K |████████████████████████████████| 1.5MB 7.8MB/s \n", "\u001b[K |████████████████████████████████| 389kB 42.8MB/s \n", "\u001b[K |████████████████████████████████| 1.3MB 42.7MB/s \n", "\u001b[K |████████████████████████████████| 51kB 4.0MB/s \n", "\u001b[K |████████████████████████████████| 1.8MB 14.8MB/s \n", "\u001b[K |████████████████████████████████| 61kB 7.7MB/s \n", "\u001b[K |████████████████████████████████| 51kB 6.3MB/s \n", "\u001b[K |████████████████████████████████| 3.2MB 31.4MB/s \n", "\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n", "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n", "\u001b[?25h" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "__kLfziVXIOV", "outputId": "aed5c9d8-4ea7-41d8-cc32-744ec6248a5b" }, "source": [ "import floq.client\n", "import pennylane as qml\n", "import numpy as np\n", "import sys\n", "\n", "wires = 26 # The minimum number of qubits is 26 for Floq.\n", "np.random.seed(0)\n", "\n", "weights = np.random.randn(1, wires, 3)\n", "API_KEY = \"YOUR_API_KEY\"\n", "client = floq.client.CirqClient(API_KEY)\n", "sim = client.simulator\n", "\n", "dev = qml.device(\"cirq.simulator\", wires=wires, simulator=sim, analytic=False)\n", "# dev = qml.device(\"cirq.simulator\", wires=wires, analytic=False)\n", "\n", "@qml.qnode(dev)\n", "def my_circuit(weights):\n", "\tqml.templates.layers.StronglyEntanglingLayers(weights, wires=range(wires))\n", "\treturn qml.expval(qml.PauliZ(0))\n", "\n", "print(my_circuit(weights))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "6.2515027821064e-08\n" ], "name": "stdout" } ] } ] }
apache-2.0
luwei0917/awsemmd_script
notebook/GlpG_paper/pick_structure.ipynb
1
2459895
null
mit
Kaggle/learntools
notebooks/time_series/raw/ex1.ipynb
1
15451
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction #\n", "\n", "Run this cell to set everything up!" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Setup feedback system\n", "from learntools.core import binder\n", "binder.bind(globals())\n", "from learntools.time_series.ex1 import *\n", "\n", "# Setup notebook\n", "from pathlib import Path\n", "from learntools.time_series.style import * # plot style settings\n", "\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "from sklearn.linear_model import LinearRegression\n", "\n", "\n", "data_dir = Path('../input/ts-course-data/')\n", "comp_dir = Path('../input/store-sales-time-series-forecasting')\n", "\n", "book_sales = pd.read_csv(\n", " data_dir / 'book_sales.csv',\n", " index_col='Date',\n", " parse_dates=['Date'],\n", ").drop('Paperback', axis=1)\n", "book_sales['Time'] = np.arange(len(book_sales.index))\n", "book_sales['Lag_1'] = book_sales['Hardcover'].shift(1)\n", "book_sales = book_sales.reindex(columns=['Hardcover', 'Time', 'Lag_1'])\n", "\n", "ar = pd.read_csv(data_dir / 'ar.csv')\n", "\n", "dtype = {\n", " 'store_nbr': 'category',\n", " 'family': 'category',\n", " 'sales': 'float32',\n", " 'onpromotion': 'uint64',\n", "}\n", "store_sales = pd.read_csv(\n", " comp_dir / 'train.csv',\n", " dtype=dtype,\n", " parse_dates=['date'],\n", " infer_datetime_format=True,\n", ")\n", "store_sales = store_sales.set_index('date').to_period('D')\n", "store_sales = store_sales.set_index(['store_nbr', 'family'], append=True)\n", "average_sales = store_sales.groupby('date').mean()['sales']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "--------------------------------------------------------------------------------\n", "\n", "One advantage linear regression has over more complicated algorithms is that the models it creates are *explainable* -- it's easy to interpret what contribution each feature makes to the predictions. In the model `target = weight * feature + bias`, the `weight` tells you by how much the `target` changes on average for each unit of change in the `feature`.\n", "\n", "Run the next cell to see a linear regression on *Hardcover Sales*." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.plot('Time', 'Hardcover', data=book_sales, color='0.75')\n", "ax = sns.regplot(x='Time', y='Hardcover', data=book_sales, ci=None, scatter_kws=dict(color='0.25'))\n", "ax.set_title('Time Plot of Hardcover Sales');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1) Interpret linear regression with the time dummy\n", "\n", "The linear regression line has an equation of (approximately) `Hardcover = 3.33 * Time + 150.5`. Over 6 days how much on average would you expect hardcover sales to change? After you've thought about it, run the next cell." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# View the solution (Run this line to receive credit!)\n", "q_1.check()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Uncomment the next line for a hint\n", "#_COMMENT_IF(PROD)_\n", "q_1.hint()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------------------------------------------------------------------------\n", "\n", "Interpreting the regression coefficients can help us recognize serial dependence in a time plot. Consider the model `target = weight * lag_1 + error`, where `error` is random noise and `weight` is a number between -1 and 1. The `weight` in this case tells you how likely the next time step will have the same sign as the previous time step: a `weight` close to 1 means `target` will likely have the same sign as the previous step, while a `weight` close to -1 means `target` will likely have the opposite sign.\n", "\n", "# 2) Interpret linear regression with a lag feature\n", "\n", "Run the following cell to see two series generated according to the model just described." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(11, 5.5), sharex=True)\n", "ax1.plot(ar['ar1'])\n", "ax1.set_title('Series 1')\n", "ax2.plot(ar['ar2'])\n", "ax2.set_title('Series 2');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of these series has the equation `target = 0.95 * lag_1 + error` and the other has the equation `target = -0.95 * lag_1 + error`, differing only by the sign on the lag feature. Can you tell which equation goes with each series?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# View the solution (Run this cell to receive credit!)\n", "q_2.check()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Uncomment the next line for a hint\n", "#_COMMENT_IF(PROD)_\n", "q_2.hint()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------------------------------------------------------------------------\n", "\n", "Now we'll get started with the *Store Sales - Time Series Forecasting* competition data. The entire dataset comprises almost 1800 series recording store sales across a variety of product families from 2013 into 2017. For this lesson, we'll just work with a single series (`average_sales`) of the average sales each day.\n", "\n", "# 3) Fit a time-step feature\n", "\n", "Complete the code below to create a linear regression model with a time-step feature on the series of average product sales. The target is in a column called `'sales'`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "df = average_sales.to_frame()\n", "\n", "# YOUR CODE HERE: Create a time dummy\n", "time = ____\n", "\n", "#_UNCOMMENT_IF(PROD)_\n", "#df['time'] = time \n", "\n", "# YOUR CODE HERE: Create training data\n", "X = ____ # features\n", "y = ____ # target\n", "\n", "# Train the model\n", "#_UNCOMMENT_IF(PROD)_\n", "#model = LinearRegression()\n", "#_UNCOMMENT_IF(PROD)_\n", "#model.fit(X, y)\n", "\n", "# Store the fitted values as a time series with the same time index as\n", "# the training data\n", "#_UNCOMMENT_IF(PROD)_\n", "#y_pred = pd.Series(model.predict(X), index=X.index)\n", "\n", "\n", "# Check your answer\n", "q_3.check()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Lines below will give you a hint or solution code\n", "#_COMMENT_IF(PROD)_\n", "q_3.hint()\n", "#_COMMENT_IF(PROD)_\n", "q_3.solution()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "from sklearn.linear_model import LinearRegression\n", "\n", "df = average_sales.to_frame()\n", "\n", "time = np.ones_like(df.index)\n", "\n", "df['time'] = time\n", "\n", "X = df.loc[:, ['time']]\n", "y = df.loc[:, 'sales']\n", "\n", "model = LinearRegression()\n", "model.fit(X, y)\n", "\n", "y_pred = pd.Series(model.predict(X), index=X.index)\n", "\n", "q_3.assert_check_failed()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "from sklearn.linear_model import LinearRegression\n", "\n", "df = average_sales.to_frame()\n", "\n", "time = np.arange(len(df.index))\n", "\n", "df['time'] = time\n", "\n", "X = df.loc[:, ['sales']]\n", "y = df.loc[:, 'time']\n", "\n", "model = LinearRegression()\n", "model.fit(X, y)\n", "\n", "y_pred = pd.Series(model.predict(X), index=X.index)\n", "\n", "q_3.assert_check_failed()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "from sklearn.linear_model import LinearRegression\n", "\n", "df = average_sales.to_frame()\n", "\n", "time = np.arange(len(df.index))\n", "\n", "df['time'] = time\n", "\n", "X = df.loc[:, ['time']]\n", "y = df.loc[:, 'sales']\n", "\n", "model = LinearRegression()\n", "model.fit(X, y)\n", "\n", "y_pred = pd.Series(model.predict(X), index=X.index)\n", "\n", "q_3.assert_check_passed()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run this cell if you'd like to see a plot of the result." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ax = y.plot(**plot_params, alpha=0.5)\n", "ax = y_pred.plot(ax=ax, linewidth=3)\n", "ax.set_title('Time Plot of Total Store Sales');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "-------------------------------------------------------------------------------\n", "\n", "# 4) Fit a lag feature to Store Sales\n", "\n", "Complete the code below to create a linear regression model with a lag feature on the series of average product sales. The target is in a column of `df` called `'sales'`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "df = average_sales.to_frame()\n", "\n", "# YOUR CODE HERE: Create a lag feature from the target 'sales'\n", "lag_1 = ____\n", "\n", "#_UNCOMMENT_IF(PROD)_\n", "#df['lag_1'] = lag_1 # add to dataframe\n", "\n", "#_UNCOMMENT_IF(PROD)_\n", "#X = df.loc[:, ['lag_1']].dropna() # features\n", "#_UNCOMMENT_IF(PROD)_\n", "#y = df.loc[:, 'sales'] # target\n", "#_UNCOMMENT_IF(PROD)_\n", "#y, X = y.align(X, join='inner') # drop corresponding values in target\n", "\n", "# YOUR CODE HERE: Create a LinearRegression instance and fit it to X and y.\n", "model = ____\n", "\n", "# YOUR CODE HERE: Create Store the fitted values as a time series with\n", "# the same time index as the training data\n", "y_pred = ____\n", "\n", "\n", "# Check your answer\n", "q_4.check()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Lines below will give you a hint or solution code\n", "q_4.hint()\n", "q_4.solution()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "df = average_sales.to_frame()\n", "\n", "lag_1 = df['sales']\n", "\n", "df['lag_1'] = lag_1\n", "\n", "X = df.loc[:, ['lag_1']]\n", "X.dropna(inplace=True) # drop missing values in the feature set\n", "y = df.loc[:, 'sales'] # create the target\n", "y, X = y.align(X, join='inner') # drop corresponding values in target\n", "\n", "model = LinearRegression()\n", "model.fit(X, y)\n", "\n", "y_pred = pd.Series(model.predict(X), index=X.index)\n", "\n", "q_4.assert_check_failed()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "df = average_sales.to_frame()\n", "\n", "lag_1 = df['sales'].shift(-1)\n", "\n", "df['lag_1'] = lag_1\n", "\n", "X = df.loc[:, ['lag_1']]\n", "X.dropna(inplace=True) # drop missing values in the feature set\n", "y = df.loc[:, 'sales'] # create the target\n", "y, X = y.align(X, join='inner') # drop corresponding values in target\n", "\n", "model = LinearRegression()\n", "model.fit(X, y)\n", "\n", "y_pred = pd.Series(model.predict(X), index=X.index)\n", "\n", "q_4.assert_check_failed()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "df = average_sales.to_frame()\n", "\n", "lag_1 = df['sales'].shift(1)\n", "\n", "df['lag_1'] = lag_1\n", "\n", "X = df.loc[:, ['sales']]\n", "X.dropna(inplace=True) # drop missing values in the feature set\n", "y = df.loc[:, 'lag_1'] # create the target\n", "y.dropna(inplace=True)\n", "y, X = y.align(X, join='inner') # drop corresponding values in target\n", "\n", "model = LinearRegression()\n", "model.fit(X, y)\n", "\n", "y_pred = pd.Series(model.predict(X), index=X.index)\n", "\n", "q_4.assert_check_failed()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#%%RM_IF(PROD)%%\n", "df = average_sales.to_frame()\n", "\n", "lag_1 = df['sales'].shift(1)\n", "\n", "df['lag_1'] = lag_1\n", "\n", "X = df.loc[:, ['lag_1']]\n", "X.dropna(inplace=True) # drop missing values in the feature set\n", "y = df.loc[:, 'sales'] # create the target\n", "y, X = y.align(X, join='inner') # drop corresponding values in target\n", "\n", "model = LinearRegression()\n", "model.fit(X, y)\n", "\n", "y_pred = pd.Series(model.predict(X), index=X.index)\n", "\n", "q_4.assert_check_passed()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the next cell if you'd like to see the result." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.plot(X['lag_1'], y, '.', color='0.25')\n", "ax.plot(X['lag_1'], y_pred)\n", "ax.set(aspect='equal', ylabel='sales', xlabel='lag_1', title='Lag Plot of Average Sales');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Keep Going #\n", "\n", "[**Model trend**](#$NEXT_NOTEBOOK_URL$) in time series with moving average plots and the time dummy." ] } ], "metadata": { "jupytext": { "formats": "md,ipynb" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 5 }
apache-2.0
MohamedAbdultawab/FOC_RiceUniv
algorithmic-thinking-1/module-1-project-and-application/02_application-1-analysis-of-citation-graphs/working.ipynb
2
15112
{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded graph with 27770 nodes\n" ] }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x7f2c397c34e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from collections import Counter, OrderedDict\n", "import matplotlib.pyplot as plt\n", "\n", "def load_graph(file_name):\n", " \"\"\"\n", " Function that loads a graph given a text\n", " representation of the graph\n", "\n", " Returns a dictionary that models a graph\n", " \"\"\"\n", " graph_file = open(file_name)\n", " graph_text = graph_file.read()\n", " graph_lines = graph_text.split('\\n')\n", " graph_lines = graph_lines[:-1]\n", "\n", " print(\"Loaded graph with\", len(graph_lines), \"nodes\")\n", "\n", " answer_graph = OrderedDict()\n", " for line in graph_lines:\n", " neighbors = line.split(' ')\n", " node = int(neighbors[0])\n", " answer_graph[node] = set([])\n", " for neighbor in neighbors[1:-1]:\n", " answer_graph[node].add(int(neighbor))\n", "\n", " return answer_graph\n", "\n", "\n", "citation_graph = load_graph('alg_phys-cite.txt')\n", "\n", "\n", "def compute_in_degrees(digraph):\n", " \"\"\"Returns a dictionary of in-degrees of the nodes\n", " of the input digraph\"\"\"\n", " degrees = OrderedDict.fromkeys(digraph, 0)\n", " edges = []\n", " for value in digraph.values():\n", " edges += list(value)\n", " for edge in edges:\n", " degrees[edge] += 1\n", " return degrees\n", "\n", "\n", "def in_degree_distribution(digraph):\n", " \"\"\"Returns the degree distribution of a digraph\"\"\"\n", " num_edges = float(len(digraph))\n", " degrees = compute_in_degrees(digraph)\n", " normalized_degrees = OrderedDict(Counter(degrees.values()))\n", " for degree in iter(normalized_degrees):\n", " normalized_degrees[degree] /= num_edges\n", " return normalized_degrees\n", "\n", "\n", "normalized_degree_dist = in_degree_distribution(citation_graph)\n", "\n", "# plt.loglog(normalized_degree_dist.keys(), normalized_degree_dist.values(), 'o', color='#634017')\n", "# plt.title(\"Log-log plot of the normalized\\ndegree distribution of paper's citations\", fontsize=18, color='#ff8800')\n", "# plt.xlabel(\"Number of Citations\", fontsize=14, color='#ff8800')\n", "# plt.ylabel(\"Papers\", fontsize=14, color='#ff8800')\n", "\n", "plt.savefig('Q1.jpg', dpi=300, format='png', transparent=False, orientation='landscape', bbox_inches='tight', pad_inches=0.3)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'nx' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-1-0d92ffbb9583>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# plt.plot(normalized_degree_dist.keys(), normalized_degree_dist.values(), 'o', color='#634017')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merdos_renyi_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdirected\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mnodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0medges\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'nx' is not defined" ] } ], "source": [ "# normalized_degree_dist = normalized_in_degree_distribution(er)\n", "# plt.loglog(normalized_degree_dist.keys(), normalized_degree_dist.values(), 'o', color='#634017')\n", "\n", "# plt.plot(normalized_degree_dist.keys(), normalized_degree_dist.values(), 'o', color='#634017')\n", "\n", "er = nx.erdos_renyi_graph(100, .5, directed=True)\n", "nodes = er.nodes()\n", "edges = er.edges()\n", "\n", "from math import log, e\n", "from collections import Counter, OrderedDict\n", "import matplotlib.pyplot as plt\n", "import networkx as nx\n", "\n", "def make_digraph(vertices, edges):\n", " \"\"\"Returns a digraph from a list of nodes\n", " and a list of edges represented as tuples\"\"\"\n", " graph = dict()\n", " for node in vertices:\n", " graph[node] = set()\n", " for edge in edges:\n", " graph[edge[0]].add(edge[1])\n", " return graph\n", "\n", "\n", "er = make_digraph(nodes, edges)\n", "\n", "def compute_in_degrees(digraph):\n", " \"\"\"Returns a dictionary of in-degrees of the nodes\n", " of the input digraph\"\"\"\n", " degrees = OrderedDict.fromkeys(digraph, 0)\n", " edges = []\n", " for value in digraph.values():\n", " edges += list(value)\n", " for edge in edges:\n", " degrees[edge] += 1\n", " return degrees\n", "\n", "\n", "def in_degree_distribution(digraph):\n", " \"\"\"Returns the degree distribution of a digraph\"\"\"\n", " degrees = compute_in_degrees(digraph)\n", " return dict(Counter(degrees.values()))\n", "\n", "# def normalized_in_degree_distribution(digraph):\n", "# \"\"\"Returns the degree distribution of a digraph\"\"\"\n", "# num_edges = float(len(digraph))\n", "# degrees = compute_in_degrees(digraph)\n", "# normalized_degrees = OrderedDict(Counter(degrees.values()))\n", "# for degree in iter(normalized_degrees):\n", "# normalized_degrees[degree] /= num_edges\n", "# return normalized_degrees\n", "\n", "\n", "degree_dist = in_degree_distribution(er)\n", "# plt.loglog(degree_dist.keys(), degree_dist.values(), 'o', color='#000000')\n", "# plt.title(\"Log-log plot of the normalized\\ndegree distribution of ER graph\", fontsize=18, color='#ff8800')\n", "\n", "# plt.plot(degree_dist.keys(), degree_dist.values(), 'o', color='#ff0000')\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12.703204897371265\n", "Loaded graph with 27770 nodes\n", "352768\n", "27770\n" ] }, { "data": { "text/plain": [ "12.703204897371265" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n = 27770\n", "m = 352768/n\n", "print(m)\n", "\n", "def compute_out_degrees(digraph):\n", " \"\"\"Returns a dictionary of out-degrees of the nodes\n", " of the input digraph\"\"\"\n", " degrees = dict()\n", " for node in digraph:\n", " degrees[node] = len(digraph[node])\n", " return degrees\n", "\n", "from collections import Counter, OrderedDict\n", "\n", "def load_graph(file_name):\n", " \"\"\"\n", " Function that loads a graph given a text\n", " representation of the graph\n", "\n", " Returns a dictionary that models a graph\n", " \"\"\"\n", " graph_file = open(file_name)\n", " graph_text = graph_file.read()\n", " graph_lines = graph_text.split('\\n')\n", " graph_lines = graph_lines[:-1]\n", "\n", " print(\"Loaded graph with\", len(graph_lines), \"nodes\")\n", "\n", " answer_graph = OrderedDict()\n", " for line in graph_lines:\n", " neighbors = line.split(' ')\n", " node = int(neighbors[0])\n", " answer_graph[node] = set([])\n", " for neighbor in neighbors[1:-1]:\n", " answer_graph[node].add(int(neighbor))\n", "\n", " return answer_graph\n", "\n", "\n", "citation_graph = load_graph('alg_phys-cite.txt')\n", "\n", "out_degrees = compute_out_degrees(citation_graph)\n", "\n", "print(sum(out_degrees.values()))\n", "print(len(out_degrees))\n", "sum(out_degrees.values())/ len(out_degrees)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "Provided code for application portion of module 1\n", "\n", "Helper class for implementing efficient version\n", "of DPA algorithm\n", "\"\"\"\n", "\n", "# general imports\n", "import random\n", "from collections import OrderedDict, Counter\n", "from matplotlib import pyplot as plt\n", "\n", "\n", "class DPATrial:\n", " \"\"\"\n", " Simple class to encapsulate optimized trials for DPA algorithm\n", "\n", " Maintains a list of node numbers with multiple instances of each number.\n", " The number of instances of each node number are\n", " in the same proportion as the desired probabilities\n", "\n", " Uses random.choice() to select a node number from this list for each trial.\n", " \"\"\"\n", " def __init__(self, num_nodes):\n", " \"\"\"\n", " Initialize a DPATrial object corresponding to a\n", " complete graph with num_nodes nodes\n", "\n", " Note the initial list of node numbers has num_nodes copies of\n", " each node number\n", " \"\"\"\n", " self._num_nodes = num_nodes\n", " self._node_numbers = [node for node in range(num_nodes) for dummy_idx in range(num_nodes)]\n", "\n", " def run_trial(self, num_nodes):\n", " \"\"\"\n", " Conduct num_node trials using by applying random.choice()\n", " to the list of node numbers\n", "\n", " Updates the list of node numbers so that the number of instances of\n", " each node number is in the same ratio as the desired probabilities\n", "\n", " Returns:\n", " Set of nodes\n", " \"\"\"\n", "\n", " # compute the neighbors for the newly-created node\n", " new_node_neighbors = set()\n", " for dummy_idx in range(num_nodes):\n", " new_node_neighbors.add(random.choice(self._node_numbers))\n", "\n", " # update the list of node numbers so that each node number\n", " # appears in the correct ratio\n", " self._node_numbers.append(self._num_nodes)\n", " self._node_numbers.extend(list(new_node_neighbors))\n", "\n", " # update the number of nodes\n", " self._num_nodes += 1\n", " return new_node_neighbors\n", "\n", "\n", "def make_digraph(vertices, edges):\n", " \"\"\"Returns a digraph from a list of nodes\n", " and a list of edges represented as tuples\"\"\"\n", " graph = dict()\n", " for node in vertices:\n", " graph[node] = set()\n", " for edge in edges:\n", " graph[edge[0]].add(edge[1])\n", " return graph\n", "\n", "\n", "def make_complete_graph(num_nodes):\n", " \"\"\"Returns a complete graph\"\"\"\n", " nodes = list(range(num_nodes))\n", " edges = [(node_1, node_2) for node_1 in nodes for node_2 in nodes if node_1 != node_2]\n", " return make_digraph(nodes, edges)\n", "\n", "\n", "def compute_in_degrees(digraph):\n", " \"\"\"Returns a dictionary of in-degrees of the nodes\n", " of the input digraph\"\"\"\n", " degrees = dict.fromkeys(digraph, 0)\n", " edges = []\n", " for value in digraph.values():\n", " edges += list(value)\n", " for edge in edges:\n", " degrees[edge] += 1\n", " return degrees\n", "\n", "\n", "def normalized_in_degree_distribution(digraph):\n", " \"\"\"Returns the degree distribution of a digraph\"\"\"\n", " num_edges = float(len(digraph))\n", " degrees = compute_in_degrees(digraph)\n", " normalized_degrees = OrderedDict(Counter(degrees.values()))\n", " for degree in iter(normalized_degrees):\n", " normalized_degrees[degree] /= num_edges\n", " return normalized_degrees\n", "\n", "\n", "graph = make_complete_graph(13)\n", "trials = DPATrial(13)\n", "for node in range(13, 27769):\n", " new_nodes = trials.run_trial(13)\n", " graph[node] = new_nodes\n", "\n", "\n", "normalized_degree_dist = normalized_in_degree_distribution(graph)\n", "\n", "plt.loglog(normalized_degree_dist.keys(), normalized_degree_dist.values(), 'o', color='#634017')\n", "plt.title(\"Log-log plot of the normalized\\nin-degree distribution of the DPA graph\\nwith 27770 node and num of fixed edges\", fontsize=18, color='#ff8800')\n", "plt.xlabel(\"Number of Citations\", fontsize=14, color='#ff8800')\n", "plt.ylabel(\"Papers\", fontsize=14, color='#ff8800')\n", "\n", "plt.savefig('Q4.png', dpi=300, format='png', transparent=False, orientation='landscape', bbox_inches='tight', pad_inches=0.3)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
sr320/sr320.github.io
jupyter/Ssalar/Blast Proteins - Uniprot-gf.ipynb
1
2279
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ">NP_001116995.1 dj-1 protein [Salmo salar]\r\n", "METVIPVDVMRRAGIAVTLAGLTGKDPVQCSRDIYLVPDSSLEDARKQGPYDVILLPGGALGAQNLSESPAVKEVLKDQE\r\n", "GRKGLIAAICAGPTALLAHGIAYGSTVTTHPGAKDKMMTGGHYTYSEARVQKDCHLITSRGPGTSFEFALAIVEELMGAE\r\n", "VAATVKAPLVLKD\r\n", ">NP_001116996.1 serine/threonine protein kinase RAF1c [Salmo salar]\r\n", "MEHIQGAWKTLSNGLGLKDSGFDDSCMSPTIMKGFPYQRRSSDDGKMADPKTSSTIRVFLPNKQCTVVNARPGMTLHNCL\r\n", "IKAMKVXGLQPECCAVFRLHSAQSRKSRMDWNTDATSLIGEELLVDVLDHVPLTTHNFVRNTFLKLAFCDICQKFLLNGF\r\n", "RCQTCGYKFHEHCSTKVPTMCVDWSNIRQLLLFPTPGETGGPSLPPLTSRRLRDSLSRFPVRHSTLHAFNQAPGSVLGAG\r\n", "PAVLSHRLRSTSTPNVHMVSTTLPLDSTAFKDAMRSHESESPSDLSLTCWSQSVSSRAPAPMHKERAGSSNTQVKNRMRP\r\n", "RXKRASSYYWEIEASEVVLQNRIGSGSFGTVYKGKWHGDVAVKILXVVDPTPEQFQAFRNEVAVLRKTRHVNILLFMGYM\r\n" ] } ], "source": [ "!head data/GCF_000233375.1_ICSASG_v2_protein.faa" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!/Users/Shared/Apps/ncbi-blast-2.2.29\\+/bin/blastp \\\n", "-query data/GCF_000233375.1_ICSASG_v2_protein.faa \\\n", "-db /Users/Steven/Documents/git-repos/course-fish546-2016/data/uniprot_sprot_r2016-09 \\\n", "-out ../analyses/2016-10-24/ICSASG-blastp-uniprot01.tab \\\n", "-num_threads 4 \\\n", "-max_target_seqs 1 \\\n", "-max_hsps 1 \\\n", "-outfmt 6\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
Open-Network-Insight/spot-docker
spot-demo/assets/spot-oa/ipynb/dns/20160708/Threat_Investigation.ipynb
1
14137
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h4> Save for Storyboard </h4>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] }, { "ename": "IOError", "evalue": "[Errno 2] No such file or directory: '/etc/duxbay.conf'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIOError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-1-dade28cb694b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 14\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mIPython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdisplay\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mHTML\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclear_output\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mJavascript\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'/etc/duxbay.conf'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mconf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 17\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mconf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m\"DBNAME=\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mDBNAME\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"=\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'\\n'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"'\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mIOError\u001b[0m: [Errno 2] No such file or directory: '/etc/duxbay.conf'" ] } ], "source": [ "import struct, socket\n", "import numpy as np \n", "import csv, json \n", "import os \n", "import urllib2 \n", "import datetime\n", "import operator\n", "import itertools\n", "\n", "try:\n", " import ipywidgets as widgets # For jupyter/ipython >= 1.4\n", "except ImportError:\n", " from IPython.html import widgets\n", "from IPython.display import display, HTML, clear_output, Javascript \n", "\n", "with open('/etc/duxbay.conf') as conf:\n", " for line in conf.readlines():\n", " if \"DBNAME=\" in line: DBNAME = line.split(\"=\")[1].strip('\\n').replace(\"'\",\"\"); \n", " elif \"IMPALA_DEM=\" in line: IMPALA_DEM = line.split(\"=\")[1].strip('\\n').replace(\"'\",\"\"); \n", "\n", "path = os.getcwd().split(\"/\") \n", "t_date = path[len(path)-1] \n", "dpath = '/'.join(['data' if var == 'ipynb' else var for var in path]) + '/'\n", "t_date = path[len(path)-1] \n", "sconnect = dpath + 'dns_scores.csv' \n", "threat_f = dpath + \"threats.csv\"\n", "anchor = ''\n", "anchor_type = ''\n", "top_results = 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Interface**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "##Expanded search\n", "susp_box = widgets.HBox(width=500, height=150) \n", "susp_h_box = widgets.Box(width=500, height=200) \n", "susp_title = widgets.HTML(value='<h4>Expanded search</h4>')\n", "susp_select = widgets.Select(width=300)\n", "\n", "##Search Results\n", "result_title = widgets.HTML() \n", "result_html = widgets.HTML(width=500)\n", "result_box = widgets.Box(width=500)\n", "result_box.children = [result_title, result_html] \n", "\n", "#Threat Summary\n", "tc_box_main = widgets.Box(width=500, height=200) \n", "threat_container = widgets.HBox(width=500, height=150)\n", "tc_box_separator = widgets.Box(width=500, height=15) \n", "\n", "yy = t_date[0:4]\n", "mm = t_date[4:6] \n", "dd = t_date[6:8]\n", "\n", "ip_sev={}\n", "dns_sev={}\n", "\n", "def start_investigation():\n", " ips_query = {} \n", " c_ips=[]\n", " c_dns=[]\n", "\n", " display(Javascript(\"$('.widget-area > .widget-subarea > *').remove();\")) \n", " clear_output() \n", " \n", " if os.path.isfile(threat_f) and not file_is_empty(threat_f):\n", " with open(threat_f, 'r') as th:\n", " t_read = csv.reader(th, delimiter='|')\n", " t_read.next()\n", " for row in t_read: \n", " if row[0] != '' : c_ips.append(row[0])\n", " if row[1] != '' : c_dns.append(row[1])\n", " \n", " with open(sconnect, 'r') as f:\n", " reader = csv.reader(f, delimiter=',')\n", " reader.next()\n", " for row in reader:\n", " # frame_time, frame_len, ip_dst, dns_qry_name, dns_qry_class, dns_qry_type, dns_qry_rcode, domain, subdomain, \n", " # 0 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ,\n", " #subdomain_length, num_periods, subdomain_entropy, top_domain, word, score, query_rep, hh, \n", " # 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16, \n", " #ip_sev, dns_sev, dns_qry_class_name, dns_qry_type_name, dns_qry_rcode_name, network_context, unix_tstamp\n", " # 17 , 18 , 19 , 20 , 21 , 22 , 23\n", " if row[2] not in ips_query and row[2] not in c_ips and row[17] == '1': \n", " ips_query[row[2]]='i'\n", " if row[3] not in ips_query and row[3] not in c_dns and row[18] == '1':\n", " ips_query[row[3]]='q' \n", " \n", " if row[2] not in ip_sev: \n", " ip_sev[row[2]] = row[14]\n", " if row[3] not in dns_sev: \n", " dns_sev[row[3]] =row[14]\n", " \n", " if len(ips_query) == 0:\n", " result_html = widgets.HTML(value=\"There are not high risk results.\", width=500)\n", " result_box = widgets.Box(width=500, height=150)\n", " result_box.children = [result_html] \n", " display(result_box) \n", " else: \n", " sorted_dict = sorted(ips_query.items(), key=operator.itemgetter(0)) \n", " display_controls(sorted_dict) \n", "\n", " \n", "def display_controls(ip_list): \n", " \n", " susp_title = widgets.HTML(value='<h4>Expanded search</h4>')\n", " search_btn = widgets.Button(description='Search')\n", " susp_box.children = [susp_select,search_btn]\n", " susp_h_box.children = [susp_title,susp_box] \n", "\n", " susp_select.options = ip_list\n", " susp_select.height=150\n", " susp_select.selected_label = ip_list[0][0]\n", " \n", " display(susp_h_box)\n", " \n", " def search_ip(b): \n", " global anchor \n", " global anchor_type\n", " anchor = ''\n", " anchor_type = ''\n", " anchor = susp_select.selected_label \n", " anchor_type = susp_select.value \n", " removeWidget(2)\n", " removeWidget(1) \n", " clear_output()\n", " global ir_f\n", " ir_f = dpath + 'threat-dendro-' + anchor + \".csv\" \n", " \n", " table = \"<table border=1><th>IP</th><th>QUERY</th><th>TOTAL</th>\"\n", "\n", " if not os.path.isfile(ir_f) or (os.path.isfile(ir_f) and file_is_empty(ir_f)):\n", " if anchor_type == 'i':\n", " imp_query = (\"\\\" SELECT COUNT(dns_qry_name) as total, dns_qry_name, ip_dst, 0 as sev FROM \"+DBNAME+\".dns \" +\n", " \" WHERE y=\"+ yy +\" AND m=\"+ mm +\" AND d=\"+ dd +\" AND ip_dst='\"+ anchor +\"' GROUP BY dns_qry_name, ip_dst\" +\n", " \" ORDER BY total DESC LIMIT 10000\\\" \") \n", " elif anchor_type == 'q':\n", " imp_query = (\"\\\" SELECT COUNT(ip_dst) as total, dns_qry_name, ip_dst, 0 as sev FROM \"+DBNAME+\".dns \" + \n", " \" WHERE y=\"+ yy +\" AND m=\"+ mm +\" AND d=\"+ dd +\" AND dns_qry_name='\"+ anchor +\"'\" +\n", " \" GROUP BY ip_dst, dns_qry_name ORDER BY total DESC LIMIT 10000\\\"\") \n", " \n", " !impala-shell -i $IMPALA_DEM --print_header -B --output_delimiter=',' -q $imp_query -o $ir_f\n", "\n", " height=80\n", " clear_output() \n", "# total, dns_qry_name, ip_dst, sev\n", " with open(ir_f, 'r') as f:\n", " reader = itertools.islice(csv.reader(f, delimiter=','), top_results) \n", " if reader!= '':\n", " reader.next()\n", " for row in reader: \n", " table += \"<tr><td>\" + row[2] + \"</td><td>\" + row[1] + \"</td><td align='center'>\" + str(row[0]) + \"</td></tr>\" \n", " height += 20\n", " \n", " table += \"</table>\" \n", " \n", " result_html.value=table\n", " result_title.value='<h4>Displaying top {0} search results</h4>'.format(top_results)\n", " result_box.height=max(200, height)\n", " display_threat_box(anchor)\n", " display(result_box)\n", " \n", " search_btn.on_click(search_ip)\n", "\n", " \n", "def display_threat_box(ip): \n", " tc_div_label = widgets.HTML(value='<h4>Threat summary for ' + anchor +'</h4>')\n", " \n", " tc_txt_title = widgets.Text(value='',width=300, placeholder='Threat Title')\n", " tc_txa_summary = widgets.Textarea(value='', width=300, height=100)\n", " tc_btn_save = widgets.Button(description='Save')\n", " threat_container.children = [tc_txa_summary,tc_btn_save]\n", " tc_box_main.children = [tc_div_label, tc_txt_title, tc_box_separator, threat_container] \n", "\n", " display(tc_box_main) \n", " \n", " def save_threat_summary(b):\n", " global anchor\n", " anchor_ip =''\n", " anchor_dns ='' \n", " if anchor != '': \n", " if anchor_type == 'i':\n", " anchor_ip = anchor\n", " elif anchor_type == 'q':\n", " anchor_dns = anchor\n", " \n", " global threat_f\n", " if not os.path.exists(threat_f): \n", " with open(threat_f, 'w') as comment:\n", " comment.write('ip_dst|dns_qry_name|title|summary\\n')\n", " \n", " with open(threat_f, 'a') as comment:\n", " comment.write(anchor_ip + '|' + anchor_dns + '|' + tc_txt_title.value + '|' +\n", " tc_txa_summary.value.replace('\\n', '\\\\n') + '\\n') \n", " \n", " removeWidget(2)\n", " removeWidget(1) \n", " display(Javascript(\"$(\\\"option[data-value='\" + anchor +\"']\\\").remove();\")) \n", " response = \"Successfully saved\"\n", " else:\n", " response = \"No data selected\"\n", " \n", " save_html = widgets.HTML(value=response, width=500)\n", " save_box = widgets.Box(width=500, height=150)\n", " save_box.children = [save_html] \n", " susp_select.selected_label = susp_select.options[0][0]\n", " display(save_box) \n", " \n", " \n", " tc_btn_save.on_click(save_threat_summary)\n", " \n", " \n", "def file_is_empty(path):\n", " return os.stat(path).st_size==0\n", "\n", "def removeWidget(index):\n", " js_command = \"$('.widget-area > .widget-subarea > .widget-box:eq({0})').remove();\".format(index) \n", " display(Javascript(js_command))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "start_investigation()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
balarsen/pymc_learning
Logit/Reeves_Denton_Dst_MinL-1nTbins-Copy1_20160907.ipynb
1
3504529
null
bsd-3-clause
openworm/ChannelWorm
channelworm/fitter/examples/SLO-2.ipynb
2
56339
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Parameter Extraction for SLO-2 Ion Channel" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "\"\"\"\n", "Example of using cwFitter to generate a HH model for SLO-2 ion channel\n", "Based on experimental data from doi:10.1083/jcb.200203055\n", "\"\"\"\n", "\n", "import os.path\n", "import sys\n", "import time\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "sys.path.append('..')\n", "sys.path.append('../..')\n", "sys.path.append('../../..')\n", "from channelworm.fitter import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "userData = dict()\n", "\n", "cwd=os.getcwd()\n", "\n", "csv_path_VC_1 = os.path.dirname(cwd)+'/examples/slo-2-data/slo-2-VClamp/1.csv'\n", "csv_path_VC_2 = os.path.dirname(cwd)+'/examples/slo-2-data/slo-2-VClamp/2.csv'\n", "csv_path_VC_3 = os.path.dirname(cwd)+'/examples/slo-2-data/slo-2-VClamp/3.csv'\n", "csv_path_VC_4 = os.path.dirname(cwd)+'/examples/slo-2-data/slo-2-VClamp/4.csv'\n", "x_var_VC = {'type':'Time','unit':'ms','toSI':1e-3}\n", "y_var_VC = {'type':'Current','unit':'nA','toSI':1e-9,'adjust':-0.82}\n", "traces_VC = [{'vol':110e-3,'csv_path':csv_path_VC_1,'x_var':x_var_VC,'y_var':y_var_VC},\n", " {'vol':40e-3,'csv_path':csv_path_VC_2,'x_var':x_var_VC,'y_var':y_var_VC},\n", " {'vol':-140e-3,'csv_path':csv_path_VC_3,'x_var':x_var_VC,'y_var':y_var_VC},\n", " {'vol':-90e-3,'csv_path':csv_path_VC_4,'x_var':x_var_VC,'y_var':y_var_VC}]\n", "ref_VC = {'fig':'6a','doi':'10.1038/77670'}\n", "VClamp = {'ref':ref_VC,'traces':traces_VC}\n", "# #\n", "# csv_path_VC = os.path.dirname(cwd)+'/examples/slo-2-data/SLO-2-2000-VClamp.csv'\n", "# x_var_VC = {'type':'Time','unit':'ms','toSI':1e-3}\n", "# y_var_VC = {'type':'Current','unit':'nA','toSI':1e-9,'adjust':-0.82}\n", "# traces_VC = [{'csv_path':csv_path_VC,'x_var':x_var_VC,'y_var':y_var_VC}]\n", "# ref_VC = {'fig':'6a','doi':'10.1038/77670'}\n", "# VClamp = {'ref':ref_VC,'traces':traces_VC}\n", "#\n", "csv_path = os.path.dirname(cwd)+'/examples/slo-2-data/SLO-2-2000-IV.csv'\n", "ref = {'fig':'6a','doi':'10.1038/77670'}\n", "x_var = {'type':'Voltage','unit':'mV','toSI':1e-3}\n", "y_var = {'type':'Current','unit':'pA','toSI':1e-12}\n", "IV = {'ref':ref,'csv_path':csv_path,'x_var':x_var,'y_var':y_var}\n", "\n", "csv_path_POV = os.path.dirname(cwd)+'/examples/slo-2-data/SLO-2-2000-GV.csv'\n", "ref_POV = {'fig':'6b','doi':'10.1038/77670'}\n", "x_var_POV = {'type':'Voltage','unit':'mV','toSI':1e-3}\n", "y_var_POV = {'type':'G/Gmax','unit':'','toSI':1}\n", "POV = {'ref':ref_POV,'csv_path':csv_path_POV,'x_var':x_var_POV,'y_var':y_var_POV}\n", "\n", "# userData['samples'] = {'IV':IV,'POV':POV}\n", "userData['samples'] = {'VClamp':VClamp}\n", "# userData['samples'] = {'IV':IV,'POV':POV,'VClamp':VClamp}\n", "\n", "# args = {'weight':{'start':20,'peak':10,'tail':30,'end':30,4:50}}" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "myInitiator = initiators.Initiator(userData)\n", "sampleData = myInitiator.get_sample_params()\n", "bio_params = myInitiator.get_bio_params()\n", "sim_params = myInitiator.get_sim_params()\n", "myEvaluator = evaluators.Evaluator(sampleData,sim_params,bio_params)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# bio parameters for SLO-2\n", "bio_params['cell_type'] = 'Xenopus oocytes'\n", "bio_params['channel_type'] = 'SLO-2'\n", "bio_params['ion_type'] = 'K'\n", "bio_params['val_cell_params'][0] = 200e-9 # C_mem DOI: 10.1074/jbc.M605814200\n", "bio_params['val_cell_params'][1] = 20e-6 # area DOI: 10.1101/pdb.top066308\n", "bio_params['gate_params'] = {'vda': {'power': 1}}\n", "\n", "bio_params['channel_params'] = ['g_dens','e_rev']\n", "bio_params['unit_chan_params'] = ['S/m2','V']\n", "bio_params['min_val_channel'] = [1e-4,-5e-3]\n", "bio_params['max_val_channel'] = [10, 5e-3]\n", "\n", "bio_params['channel_params'].extend(['v_half_a','k_a','T_a'])\n", "bio_params['unit_chan_params'].extend(['V','V','s'])\n", "bio_params['min_val_channel'].extend([-0.15, 0.001, 0.0001])\n", "bio_params['max_val_channel'].extend([ 0.15, 0.1, 0.01])\n", "\n", "# Simulation parameters for SLO-2 VClamp and I/V\n", "sim_params['v_hold'] = -110e-3\n", "sim_params['I_init'] = 0\n", "sim_params['pc_type'] = 'VClamp'\n", "sim_params['deltat'] = 1e-5\n", "sim_params['duration'] = 0.059\n", "sim_params['start_time'] = 0.0029\n", "sim_params['end_time'] = 0.059\n", "sim_params['protocol_start'] = -140e-3\n", "sim_params['protocol_end'] = 110e-3\n", "sim_params['protocol_steps'] = 10e-3" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "opt = '-pso'\n", "# opt = '-ga'\n", "# opt = None\n", "if len(sys.argv) == 2:\n", " opt = sys.argv[1]\n", "\n", "if ('IV' or 'POV') in sampleData and opt is not None:\n", " while True:\n", " q = raw_input(\"\\n\\nTry fitting curves (y,n):\")\n", " if q == \"n\":\n", " break # stops the loop\n", " elif q == \"y\":\n", "\n", " # Find initial guess for parameters using curve_fit, leastsq\n", "\n", " popt = None\n", " best_candidate = np.asarray(bio_params['min_val_channel']) + np.asarray(bio_params['max_val_channel']) / 2\n", "\n", " best_candidate_params = dict(zip(bio_params['channel_params'],best_candidate))\n", " cell_var = dict(zip(bio_params['cell_params'],bio_params['val_cell_params']))\n", " mySimulator = simulators.Simulator(sim_params,best_candidate_params,cell_var,bio_params['gate_params'])\n", " bestSim = mySimulator.patch_clamp()\n", "\n", " if 'IV' in sampleData:\n", "\n", " popt , p0 = mySimulator.optim_curve(params= bio_params['channel_params'],\n", " best_candidate= best_candidate,\n", " target= [sampleData['IV']['V'],sampleData['IV']['I']])\n", "\n", " print 'Params after IV minimization:'\n", " print p0\n", " IV_fit_cost = myEvaluator.iv_cost(popt)\n", " print 'IV cost:'\n", " print IV_fit_cost\n", " if 'POV' in sampleData:\n", " POV_fit_cost = myEvaluator.pov_cost(popt)\n", " print 'POV cost:'\n", " print POV_fit_cost\n", " if 'VClamp' in sampleData:\n", " VClamp_fit_cost = myEvaluator.vclamp_cost(popt)\n", " print 'VClamp cost:'\n", " print VClamp_fit_cost\n", " vData = np.arange(-0.140, 0.110, 0.001)\n", " Iopt = mySimulator.iv_act(vData,*popt)\n", " plt.plot([x*1e3 for x in bestSim['V_ss']],bestSim['I_ss'], label = 'Initial parameters', color='y')\n", " plt.plot([x*1e3 for x in sampleData['IV']['V']],sampleData['IV']['I'], '--ko', label = 'sample data')\n", " plt.plot([x*1e3 for x in vData],Iopt, color='r', label = 'Fitted to IV curve')\n", " plt.legend()\n", " plt.title(\"IV Curve Fit\")\n", " plt.xlabel('V (mV)')\n", " plt.ylabel('I (A)')\n", " plt.show()\n", "\n", " elif 'POV' in sampleData:\n", "\n", " popt , p0 = mySimulator.optim_curve(params= bio_params['channel_params'],\n", " best_candidate= best_candidate,\n", " target= [sampleData['POV']['V'],sampleData['POV']['PO']],curve_type='POV')\n", "\n", " print 'Params after POV minimization:'\n", " print p0\n", " POV_fit_cost = myEvaluator.pov_cost(popt)\n", " print 'POV cost:'\n", " print POV_fit_cost\n", " if 'VClamp' in sampleData:\n", " VClamp_fit_cost = myEvaluator.vclamp_cost(popt)\n", " print 'VClamp cost:'\n", " print VClamp_fit_cost\n", " vData = np.arange(-0.140, 0.110, 0.001)\n", " POopt = mySimulator.pov_act(vData,*popt)\n", " plt.plot([x*1e3 for x in bestSim['V_PO_ss']],bestSim['PO_ss'], label = 'Initial parameters', color='y')\n", " plt.plot([x*1e3 for x in sampleData['POV']['V']],sampleData['POV']['PO'], '--ko', label = 'sample data')\n", " plt.plot([x*1e3 for x in vData],POopt, color='r', label = 'Fitted to G/Gmax vs V curve')\n", " plt.legend()\n", " plt.title(\"G/Gmax vs V Curve Fit\")\n", " plt.xlabel('V (mV)')\n", " plt.ylabel('G/Gmax')\n", " plt.show()\n", "\n", " if popt is not None:\n", " if opt == '-pso':\n", " bio_params['min_val_channel'][0:4] = popt[0:4] - abs(popt[0:4]/2)\n", " bio_params['max_val_channel'][0:4] = popt[0:4] + abs(popt[0:4]/2)\n", " else:\n", " bio_params['min_val_channel'][0:4] = popt[0:4]\n", " bio_params['max_val_channel'][0:4] = popt[0:4]\n", "\n", " best_candidate_params = dict(zip(bio_params['channel_params'],popt))\n", " cell_var = dict(zip(bio_params['cell_params'],bio_params['val_cell_params']))\n", " mySimulator = simulators.Simulator(sim_params,best_candidate_params,cell_var,bio_params['gate_params'])\n", " bestSim = mySimulator.patch_clamp()\n", "\n", " myModelator = modelators.Modelator(bio_params,sim_params)\n", " myModelator.compare_plots(sampleData,bestSim,show=True)\n", " myModelator.ss_plots(bestSim,show=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No constraints given.\n", "Best after iteration 1: [ 7.05254465e+00 -3.28326507e-03 1.48931903e-01 4.11664768e-03\n", " 5.99266569e-03] 1.36527947323e-17\n", "New best for swarm at iteration 2: [ 5.56330454e+00 -1.93592666e-04 1.50000000e-01 4.56178489e-03\n", " 1.00000000e-04] 1.08913920968e-17\n", "Best after iteration 2: [ 5.56330454e+00 -1.93592666e-04 1.50000000e-01 4.56178489e-03\n", " 1.00000000e-04] 1.08913920968e-17\n", "Best after iteration 3: [ 5.56330454e+00 -1.93592666e-04 1.50000000e-01 4.56178489e-03\n", " 1.00000000e-04] 1.08913920968e-17\n", "Best after iteration 4: [ 5.56330454e+00 -1.93592666e-04 1.50000000e-01 4.56178489e-03\n", " 1.00000000e-04] 1.08913920968e-17\n", "Best after iteration 5: [ 5.56330454e+00 -1.93592666e-04 1.50000000e-01 4.56178489e-03\n", " 1.00000000e-04] 1.08913920968e-17\n", "Best after iteration 6: [ 5.56330454e+00 -1.93592666e-04 1.50000000e-01 4.56178489e-03\n", " 1.00000000e-04] 1.08913920968e-17\n", "Best after iteration 7: [ 5.56330454e+00 -1.93592666e-04 1.50000000e-01 4.56178489e-03\n", " 1.00000000e-04] 1.08913920968e-17\n", "New best for swarm at iteration 8: [5.64338535e+00 2.71415905e-03 1.50000000e-01 4.87972123e-03\n", " 4.37965156e-03] 1.01541654486e-17\n", "New best for swarm at iteration 8: [ 6.13871248e+00 -1.57042757e-04 1.50000000e-01 4.82675641e-03\n", " 7.92289690e-04] 9.90112785959e-18\n", "Best after iteration 8: [ 6.13871248e+00 -1.57042757e-04 1.50000000e-01 4.82675641e-03\n", " 7.92289690e-04] 9.90112785959e-18\n", "New best for swarm at iteration 9: [5.93828981e+00 7.95592016e-04 1.50000000e-01 4.72717126e-03\n", " 1.19210163e-03] 9.68944187574e-18\n", "New best for swarm at iteration 9: [ 6.25194828e+00 -1.20832762e-04 1.50000000e-01 4.68872208e-03\n", " 5.13570897e-04] 9.63290958067e-18\n", "Best after iteration 9: [ 6.25194828e+00 -1.20832762e-04 1.50000000e-01 4.68872208e-03\n", " 5.13570897e-04] 9.63290958067e-18\n", "Best after iteration 10: [ 6.25194828e+00 -1.20832762e-04 1.50000000e-01 4.68872208e-03\n", " 5.13570897e-04] 9.63290958067e-18\n", "New best for swarm at iteration 11: [ 5.16601319e+00 -1.44341476e-04 1.50000000e-01 4.85142976e-03\n", " 8.76135392e-04] 9.60962102108e-18\n", "New best for swarm at iteration 11: [5.84796761e+00 4.49961984e-04 1.50000000e-01 4.77912607e-03\n", " 4.90203496e-04] 9.59042689057e-18\n", "New best for swarm at iteration 11: [ 6.09813369e+00 -7.39887679e-05 1.50000000e-01 4.74316260e-03\n", " 5.54087406e-04] 9.5861160321e-18\n", "Best after iteration 11: [ 6.09813369e+00 -7.39887679e-05 1.50000000e-01 4.74316260e-03\n", " 5.54087406e-04] 9.5861160321e-18\n", "Best after iteration 12: [ 6.09813369e+00 -7.39887679e-05 1.50000000e-01 4.74316260e-03\n", " 5.54087406e-04] 9.5861160321e-18\n", "Best after iteration 13: [ 6.09813369e+00 -7.39887679e-05 1.50000000e-01 4.74316260e-03\n", " 5.54087406e-04] 9.5861160321e-18\n", "New best for swarm at iteration 14: [ 5.98898675e+00 -3.29395303e-05 1.50000000e-01 4.74479793e-03\n", " 4.75853736e-04] 9.58570294663e-18\n", "Best after iteration 14: [ 5.98898675e+00 -3.29395303e-05 1.50000000e-01 4.74479793e-03\n", " 4.75853736e-04] 9.58570294663e-18\n", "New best for swarm at iteration 15: [ 6.23102329e+00 -1.13481172e-04 1.50000000e-01 4.73057833e-03\n", " 3.90864050e-04] 9.585533283e-18\n", "Best after iteration 15: [ 6.23102329e+00 -1.13481172e-04 1.50000000e-01 4.73057833e-03\n", " 3.90864050e-04] 9.585533283e-18\n", "New best for swarm at iteration 16: [ 5.97749274e+00 -6.09272947e-05 1.50000000e-01 4.75287289e-03\n", " 4.17021760e-04] 9.58487296867e-18\n", "Best after iteration 16: [ 5.97749274e+00 -6.09272947e-05 1.50000000e-01 4.75287289e-03\n", " 4.17021760e-04] 9.58487296867e-18\n", "New best for swarm at iteration 17: [ 6.03718719e+00 -7.91869268e-05 1.50000000e-01 4.74292531e-03\n", " 4.71467106e-04] 9.58485070167e-18\n", "Best after iteration 17: [ 6.03718719e+00 -7.91869268e-05 1.50000000e-01 4.74292531e-03\n", " 4.71467106e-04] 9.58485070167e-18\n", "New best for swarm at iteration 18: [ 6.00352694e+00 -1.12704790e-07 1.50000000e-01 4.74662498e-03\n", " 4.45330389e-04] 9.58471201559e-18\n", "New best for swarm at iteration 18: [ 5.97821235e+00 -8.73575958e-05 1.50000000e-01 4.74909152e-03\n", " 4.59517601e-04] 9.58467586668e-18\n", "New best for swarm at iteration 18: [5.90298383e+00 1.38971810e-04 1.50000000e-01 4.75922634e-03\n", " 4.53967242e-04] 9.58455780298e-18\n", "Best after iteration 18: [5.90298383e+00 1.38971810e-04 1.50000000e-01 4.75922634e-03\n", " 4.53967242e-04] 9.58455780298e-18\n", "Best after iteration 19: [5.90298383e+00 1.38971810e-04 1.50000000e-01 4.75922634e-03\n", " 4.53967242e-04] 9.58455780298e-18\n", "Best after iteration 20: [5.90298383e+00 1.38971810e-04 1.50000000e-01 4.75922634e-03\n", " 4.53967242e-04] 9.58455780298e-18\n", "Best after iteration 21: [5.90298383e+00 1.38971810e-04 1.50000000e-01 4.75922634e-03\n", " 4.53967242e-04] 9.58455780298e-18\n", "Best after iteration 22: [5.90298383e+00 1.38971810e-04 1.50000000e-01 4.75922634e-03\n", " 4.53967242e-04] 9.58455780298e-18\n", "Best after iteration 23: [5.90298383e+00 1.38971810e-04 1.50000000e-01 4.75922634e-03\n", " 4.53967242e-04] 9.58455780298e-18\n", "New best for swarm at iteration 24: [5.89913460e+00 1.35277816e-04 1.50000000e-01 4.75929828e-03\n", " 4.53142784e-04] 9.58455694034e-18\n", "Stopping search: Swarm best objective change less than 1e-24\n", "----------------------------------------------------\n", "\n", "Ran in 3009.586560 seconds (50.159776 mins)\n", "\n" ] }, { "ename": "KeyError", "evalue": "'x_var'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-8-e4505142351b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0mmyModelator\u001b[0m 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"\u001b[0;32m/Users/gopalsarma/git/openworm_base/ChannelWorm/channelworm/fitter/modelators.pyc\u001b[0m in \u001b[0;36mcompare_plots\u001b[0;34m(self, sampleData, simData, show, path)\u001b[0m\n\u001b[1;32m 190\u001b[0m \u001b[0mvc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 191\u001b[0m \u001b[0mref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msampleData\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'VClamp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ref'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m \u001b[0mx_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msampleData\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'VClamp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'x_var'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 193\u001b[0m \u001b[0my_var\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msampleData\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'VClamp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y_var'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 194\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtrace\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msampleData\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'VClamp'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'traces'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyError\u001b[0m: 'x_var'" ] }, { "data": { "text/plain": [ "<Figure size 432x288 with 0 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "start = time.time()\n", "\n", "if opt == '-ga':\n", " ga_args = myInitiator.get_opt_params()\n", " best_candidate, score = myEvaluator.ga_evaluate(min=bio_params['min_val_channel'],\n", " max=bio_params['max_val_channel'],\n", " args=ga_args)\n", "elif opt == '-pso':\n", " pso_args = myInitiator.get_opt_params(type='PSO')\n", " pso_args['minstep'] = 1e-24\n", " pso_args['minfunc'] = 1e-24\n", " pso_args['swarmsize'] = 100\n", " pso_args['maxiter'] = 100\n", " best_candidate, score = myEvaluator.pso_evaluate(lb=bio_params['min_val_channel'],\n", " ub=bio_params['max_val_channel'],\n", " args=pso_args)\n", "else:\n", " # best_candidate = [2.678373586024887e-08, -0.004343196320916513, -0.15148699378068883, 0.04457177073153084, 0.0006512657782666903]\n", " best_candidate = [2.6713432536911465e-08, -0.0043477407996737093, -0.077423632426596764, 0.030752484500400822, 0.0007076266889846564]\n", " best_candidate[0] = 2.6713432536911465e-08 / bio_params['val_cell_params'][1]\n", "\n", "secs = time.time()-start\n", "print(\"----------------------------------------------------\\n\\n\"\n", " +\"Ran in %f seconds (%f mins)\\n\"%(secs, secs/60.0))\n", "\n", "best_candidate_params = dict(zip(bio_params['channel_params'],best_candidate))\n", "cell_var = dict(zip(bio_params['cell_params'],bio_params['val_cell_params']))\n", "mySimulator = simulators.Simulator(sim_params,best_candidate_params,cell_var,bio_params['gate_params'])\n", "bestSim = mySimulator.patch_clamp()\n", "\n", "myModelator = modelators.Modelator(bio_params,sim_params)\n", "myModelator.compare_plots(sampleData,bestSim,show=True)\n", "myModelator.ss_plots(bestSim,show=True)\n", "\n", "print 'best candidate after optimization:'\n", "print best_candidate_params\n", "\n", "# Only for tau_max\n", "if 'VClamp' in sampleData:\n", " for trace in sampleData['VClamp']['traces']:\n", " if 'vol' in trace:\n", " if trace['vol'] is None:\n", " pass\n", " elif trace['vol'] == 110e-3:\n", " end = sim_params['protocol_end']\n", " start = sim_params['protocol_start']\n", " sim_params['protocol_end'] = trace['vol']\n", " sim_params['protocol_start'] = trace['vol']\n", "\n", " x = np.asarray(trace['t'])\n", " on = sim_params['start_time']\n", " off = sim_params['end_time']\n", " onset = np.abs(x-on).argmin()\n", " offset = np.abs(x-off).argmin()\n", " t_sample_on = trace['t'][onset+1:offset]\n", " I_sample_on = trace['I'][onset+1:offset]\n", "\n", " vcSim = simulators.Simulator(sim_params,best_candidate_params,cell_var,bio_params['gate_params'])\n", " pcSim = vcSim.patch_clamp()\n", " popt , p0 = vcSim.optim_curve(params= bio_params['channel_params'],\n", " best_candidate= best_candidate,\n", " target= [t_sample_on,I_sample_on],curve_type='VClamp')\n", " vcEval = evaluators.Evaluator(sampleData,sim_params,bio_params)\n", "\n", " print 'Params after VClamp minimization:'\n", " print p0\n", "\n", " if 'IV' in sampleData:\n", " IV_fit_cost = vcEval.iv_cost(popt)\n", " print 'IV cost:'\n", " print IV_fit_cost\n", " if 'POV' in sampleData:\n", " POV_fit_cost = vcEval.pov_cost(popt)\n", " print 'POV cost:'\n", " print POV_fit_cost\n", " # VClamp_fit_cost = vcEval.vclamp_cost(popt)\n", " # print 'VClamp cost:'\n", " # print VClamp_fit_cost\n", " tData = np.arange(on, off, sim_params['deltat'])\n", " Iopt = vcSim.patch_clamp(tData,*popt)\n", " # plt.plot(pcSim['t'],pcSim['I'][0], label = 'Initial parameters', color='y')\n", " plt.plot(t_sample_on,I_sample_on, '--ko', label = 'sample data')\n", " plt.plot(tData,Iopt, color='r', label = 'Fitted to VClamp trace')\n", " plt.legend()\n", " plt.title('VClamp Curve Fit for holding potential %i (mV)'%(trace['vol']*1e3))\n", " plt.xlabel('T (s)')\n", " plt.ylabel('I (A)')\n", " plt.show()\n", "\n", " sim_params['protocol_end'] = end\n", " sim_params['protocol_start'] = start\n", "\n", " # best_candidate_params = dict(zip(bio_params['channel_params'],popt))\n", " best_candidate_params['T_a'] = popt[4]\n", " cell_var = dict(zip(bio_params['cell_params'],bio_params['val_cell_params']))\n", " mySimulator = simulators.Simulator(sim_params,best_candidate_params,cell_var,bio_params['gate_params'])\n", " bestSim = mySimulator.patch_clamp()\n", "\n", " myModelator = modelators.Modelator(bio_params,sim_params)\n", " myModelator.compare_plots(sampleData,bestSim,show=True)\n", " myModelator.ss_plots(bestSim,show=True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Unknown cell_type: Xenopus oocytes\n", "Currently unknown: <<<Unknown cell_type: Xenopus oocytes\n", ">>>\n", "Written NeuroML 2 channel file to: /Users/gopalsarma/git/openworm_base/ChannelWorm/channelworm/fitter/examples/slo-2-data/SLO-2.channel.nml\n", "Validating /Users/gopalsarma/git/openworm_base/ChannelWorm/channelworm/fitter/examples/slo-2-data/SLO-2.channel.nml against /Users/gopalsarma/git/openworm_base/ChannelWorm/src/libneuroml/neuroml/nml/NeuroML_v2beta5.xsd\n", "It's valid!\n", "pyNeuroML >>> \n", "pyNeuroML >>> Analysing channels from files: ['/Users/gopalsarma/git/openworm_base/ChannelWorm/channelworm/fitter/examples/slo-2-data/SLO-2.channel.nml']\n", "pyNeuroML >>> \n", "pyNeuroML >>> Reloading data specified in LEMS file: /Users/gopalsarma/git/openworm_base/ChannelWorm/channelworm/fitter/examples/LEMS_Test_ChannelWorm_SLO2_4_1.xml (/Users/gopalsarma/git/openworm_base/ChannelWorm/channelworm/fitter/examples/LEMS_Test_ChannelWorm_SLO2_4_1.xml), base_dir: ., cwd: /Users/gopalsarma/git/openworm_base/ChannelWorm/channelworm/fitter/examples\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/gopalsarma/anaconda/envs/py35/lib/python2.7/site-packages/matplotlib/cbook/deprecation.py:107: MatplotlibDeprecationWarning: Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n", " warnings.warn(message, mplDeprecation, stacklevel=1)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Generate NeuroML2 file\n", "model_params = {}\n", "model_params['channel_name'] = 'SLO2'\n", "model_params['channel_id'] = '4'\n", "model_params['model_id'] = '1'\n", "model_params['contributors'] = [{'name': 'Vahid Ghayoomi','email': '[email protected]'}]\n", "model_params['references'] = [{'doi': '10.1038/77670',\n", " 'PMID': '10903569',\n", " 'citation': 'SLO-2, a K+ channel with an unusual Cl- dependence. '\n", " '(Yuan A; Dourado M; Butler A; Walton N; Wei A; Salkoff L. Nat. Neurosci., 3(8):771-9)'}]\n", "model_params['file_name'] = cwd+'/slo-2-data/SLO-2.channel.nml'\n", "\n", "nml2_file = myModelator.generate_channel_nml2(bio_params,best_candidate_params,model_params)\n", "run_nml_out = myModelator.run_nml2(model_params['file_name'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
maxis42/ML-DA-Coursera-Yandex-MIPT
2 Supervised learning/Homework/5 data preparation and logistic regression/Preprocessing_LR.ipynb
1
302146
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Предобработка данных и логистическая регрессия для задачи бинарной классификации" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Programming assignment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "В задании вам будет предложено ознакомиться с основными техниками предобработки данных, а так же применить их для обучения модели логистической регрессии. Ответ потребуется загрузить в соответствующую форму в виде 6 текстовых файлов.\n", "\n", "Для выполнения задания требуется Python версии 2.7, а также актуальные версии библиотек:\n", "- NumPy: 1.10.4 и выше\n", "- Pandas: 0.17.1 и выше\n", "- Scikit-learn: 0.17 и выше" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib\n", "from matplotlib import pyplot as plt\n", "matplotlib.style.use('ggplot')\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Описание датасета" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Задача: по 38 признакам, связанных с заявкой на грант (область исследований учёных, информация по их академическому бэкграунду, размер гранта, область, в которой он выдаётся) предсказать, будет ли заявка принята. Датасет включает в себя информацию по 6000 заявкам на гранты, которые были поданы в университете Мельбурна в период с 2004 по 2008 год.\n", "\n", "Полную версию данных с большим количеством признаков можно найти на https://www.kaggle.com/c/unimelb." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6000, 39)\n" ] } ], "source": [ "data = pd.read_csv('data.csv')\n", "print data.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Выделим из датасета целевую переменную Grant.Status и обозначим её за y\n", "Теперь X обозначает обучающую выборку, y - ответы на ней" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6000L,)\n" ] } ], "source": [ "X = data.drop('Grant.Status', 1)\n", "y = data['Grant.Status']\n", "print y.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Теория по логистической регрессии" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "После осознания того, какую именно задачу требуется решить на этих данных, следующим шагом при реальном анализе был бы подбор подходящего метода. В данном задании выбор метода было произведён за вас, это логистическая регрессия. Кратко напомним вам используемую модель.\n", "\n", "Логистическая регрессия предсказывает вероятности принадлежности объекта к каждому классу. Сумма ответов логистической регрессии на одном объекте для всех классов равна единице.\n", "\n", "$$ \\sum_{k=1}^K \\pi_{ik} = 1, \\quad \\pi_k \\equiv P\\,(y_i = k \\mid x_i, \\theta), $$\n", "\n", "где:\n", "- $\\pi_{ik}$ - вероятность принадлежности объекта $x_i$ из выборки $X$ к классу $k$\n", "- $\\theta$ - внутренние параметры алгоритма, которые настраиваются в процессе обучения, в случае логистической регрессии - $w, b$\n", "\n", "Из этого свойства модели в случае бинарной классификации требуется вычислить лишь вероятность принадлежности объекта к одному из классов (вторая вычисляется из условия нормировки вероятностей). Эта вероятность вычисляется, используя логистическую функцию:\n", "\n", "$$ P\\,(y_i = 1 \\mid x_i, \\theta) = \\frac{1}{1 + \\exp(-w^T x_i-b)} $$\n", "\n", "Параметры $w$ и $b$ находятся, как решения следующей задачи оптимизации (указаны функционалы с L1 и L2 регуляризацией, с которыми вы познакомились в предыдущих заданиях):\n", "\n", "L2-regularization:\n", "\n", "$$ Q(X, y, \\theta) = \\frac{1}{2} w^T w + C \\sum_{i=1}^l \\log ( 1 + \\exp(-y_i (w^T x_i + b ) ) ) \\longrightarrow \\min\\limits_{w,b} $$\n", "\n", "L1-regularization:\n", "\n", "$$ Q(X, y, \\theta) = \\sum_{d=1}^D |w_d| + C \\sum_{i=1}^l \\log ( 1 + \\exp(-y_i (w^T x_i + b ) ) ) \\longrightarrow \\min\\limits_{w,b} $$\n", "\n", "$C$ - это стандартный гиперпараметр модели, который регулирует то, насколько сильно мы позволяем модели подстраиваться под данные." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Предобработка данных" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Из свойств данной модели следует, что:\n", "- все $X$ должны быть числовыми данными (в случае наличия среди них категорий, их требуется некоторым способом преобразовать в вещественные числа)\n", "- среди $X$ не должно быть пропущенных значений (т.е. все пропущенные значения перед применением модели следует каким-то образом заполнить)\n", "\n", "Поэтому базовым этапом в предобработке любого датасета для логистической регрессии будет кодирование категориальных признаков, а так же удаление или интерпретация пропущенных значений (при наличии того или другого)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Grant.Status</th>\n", " <th>Sponsor.Code</th>\n", " <th>Grant.Category.Code</th>\n", " <th>Contract.Value.Band...see.note.A</th>\n", " <th>RFCD.Code.1</th>\n", " <th>RFCD.Percentage.1</th>\n", " <th>RFCD.Code.2</th>\n", " <th>RFCD.Percentage.2</th>\n", " <th>RFCD.Code.3</th>\n", " <th>RFCD.Percentage.3</th>\n", " <th>...</th>\n", " <th>Dept.No..1</th>\n", " <th>Faculty.No..1</th>\n", " <th>With.PHD.1</th>\n", " <th>No..of.Years.in.Uni.at.Time.of.Grant.1</th>\n", " <th>Number.of.Successful.Grant.1</th>\n", " <th>Number.of.Unsuccessful.Grant.1</th>\n", " <th>A..1</th>\n", " <th>A.1</th>\n", " <th>B.1</th>\n", " <th>C.1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>21A</td>\n", " <td>50A</td>\n", " <td>A</td>\n", " <td>230202.0</td>\n", " <td>50.0</td>\n", " <td>230203.0</td>\n", " <td>30.0</td>\n", " <td>230204.0</td>\n", " <td>20.0</td>\n", " <td>...</td>\n", " <td>3098.0</td>\n", " <td>31.0</td>\n", " <td>Yes</td>\n", " <td>&gt;=0 to 5</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>4.0</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>4D</td>\n", " <td>10A</td>\n", " <td>D</td>\n", " <td>320801.0</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>2553.0</td>\n", " <td>25.0</td>\n", " <td>Yes</td>\n", " <td>&gt;=0 to 5</td>\n", " <td>3.0</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>320602.0</td>\n", " <td>50.0</td>\n", " <td>321004.0</td>\n", " <td>30.0</td>\n", " <td>321015.0</td>\n", " <td>20.0</td>\n", " <td>...</td>\n", " <td>2813.0</td>\n", " <td>25.0</td>\n", " <td>NaN</td>\n", " <td>Less than 0</td>\n", " <td>1.0</td>\n", " <td>5.0</td>\n", " <td>0.0</td>\n", " <td>7.0</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0</td>\n", " <td>51C</td>\n", " <td>20C</td>\n", " <td>A</td>\n", " <td>291503.0</td>\n", " <td>60.0</td>\n", " <td>321402.0</td>\n", " <td>40.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>2553.0</td>\n", " <td>25.0</td>\n", " <td>NaN</td>\n", " <td>more than 15</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " <td>5.0</td>\n", " <td>6.0</td>\n", " <td>9.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>24D</td>\n", " <td>30B</td>\n", " <td>NaN</td>\n", " <td>380107.0</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>...</td>\n", " <td>2923.0</td>\n", " <td>25.0</td>\n", " <td>NaN</td>\n", " <td>Less than 0</td>\n", " <td>0.0</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 39 columns</p>\n", "</div>" ], "text/plain": [ " Grant.Status Sponsor.Code Grant.Category.Code \\\n", "0 1 21A 50A \n", "1 1 4D 10A \n", "2 0 NaN NaN \n", "3 0 51C 20C \n", "4 0 24D 30B \n", "\n", " Contract.Value.Band...see.note.A RFCD.Code.1 RFCD.Percentage.1 \\\n", "0 A 230202.0 50.0 \n", "1 D 320801.0 100.0 \n", "2 NaN 320602.0 50.0 \n", "3 A 291503.0 60.0 \n", "4 NaN 380107.0 100.0 \n", "\n", " RFCD.Code.2 RFCD.Percentage.2 RFCD.Code.3 RFCD.Percentage.3 ... \\\n", "0 230203.0 30.0 230204.0 20.0 ... \n", "1 0.0 0.0 0.0 0.0 ... \n", "2 321004.0 30.0 321015.0 20.0 ... \n", "3 321402.0 40.0 0.0 0.0 ... \n", "4 0.0 0.0 0.0 0.0 ... \n", "\n", " Dept.No..1 Faculty.No..1 With.PHD.1 \\\n", "0 3098.0 31.0 Yes \n", "1 2553.0 25.0 Yes \n", "2 2813.0 25.0 NaN \n", "3 2553.0 25.0 NaN \n", "4 2923.0 25.0 NaN \n", "\n", " No..of.Years.in.Uni.at.Time.of.Grant.1 Number.of.Successful.Grant.1 \\\n", "0 >=0 to 5 2.0 \n", "1 >=0 to 5 3.0 \n", "2 Less than 0 1.0 \n", "3 more than 15 2.0 \n", "4 Less than 0 0.0 \n", "\n", " Number.of.Unsuccessful.Grant.1 A..1 A.1 B.1 C.1 \n", "0 0.0 0.0 4.0 2.0 0.0 \n", "1 1.0 0.0 2.0 0.0 0.0 \n", "2 5.0 0.0 7.0 2.0 0.0 \n", "3 1.0 5.0 6.0 9.0 1.0 \n", "4 2.0 0.0 0.0 0.0 0.0 \n", "\n", "[5 rows x 39 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Видно, что в датасете есть как числовые, так и категориальные признаки. Получим списки их названий:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Country.of.Birth.1',\n", " 'SEO.Code.4',\n", " 'SEO.Code.5',\n", " 'B.1',\n", " 'SEO.Code.1',\n", " 'SEO.Code.2',\n", " 'SEO.Code.3',\n", " 'C.1',\n", " 'No..of.Years.in.Uni.at.Time.of.Grant.1',\n", " 'With.PHD.1',\n", " 'Sponsor.Code',\n", " 'RFCD.Code.5',\n", " 'RFCD.Code.4',\n", " 'RFCD.Code.3',\n", " 'RFCD.Code.2',\n", " 'RFCD.Code.1',\n", " 'Home.Language.1',\n", " 'A..1',\n", " 'Person.ID.1',\n", " 'Grant.Category.Code',\n", " 'Faculty.No..1',\n", " 'Role.1',\n", " 'Dept.No..1',\n", " 'Contract.Value.Band...see.note.A',\n", " 'A.1']" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numeric_cols = ['RFCD.Percentage.1', 'RFCD.Percentage.2', 'RFCD.Percentage.3', \n", " 'RFCD.Percentage.4', 'RFCD.Percentage.5',\n", " 'SEO.Percentage.1', 'SEO.Percentage.2', 'SEO.Percentage.3',\n", " 'SEO.Percentage.4', 'SEO.Percentage.5',\n", " 'Year.of.Birth.1', 'Number.of.Successful.Grant.1', 'Number.of.Unsuccessful.Grant.1']\n", "categorical_cols = list(set(X.columns.values.tolist()) - set(numeric_cols))\n", "categorical_cols" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Также в нём присутствуют пропущенные значения. Очевидны решением будет исключение всех данных, у которых пропущено хотя бы одно значение. Сделаем это:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(213, 39)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.dropna().shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Видно, что тогда мы выбросим почти все данные, и такой метод решения в данном случае не сработает.\n", "\n", "Пропущенные значения можно так же интерпретировать, для этого существует несколько способов, они различаются для категориальных и вещественных признаков.\n", "\n", "Для вещественных признаков:\n", "- заменить на 0 (данный признак давать вклад в предсказание для данного объекта не будет)\n", "- заменить на среднее (каждый пропущенный признак будет давать такой же вклад, как и среднее значение признака на датасете)\n", "\n", "Для категориальных:\n", "- интерпретировать пропущенное значение, как ещё одну категорию (данный способ является самым естественным, так как в случае категорий у нас есть уникальная возможность не потерять информацию о наличии пропущенных значений; обратите внимание, что в случае вещественных признаков данная информация неизбежно теряется)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Задание 0. Обработка пропущенных значений.\n", "1. Заполните пропущенные вещественные значения в X нулями и средними по столбцам, назовите полученные датафреймы X_real_zeros и X_real_mean соответственно. Для подсчёта средних используйте описанную ниже функцию calculate_means, которой требуется передать на вход вещественные признаки из исходного датафрейма.\n", "2. Все категориальные признаки в X преобразуйте в строки, пропущенные значения требуется также преобразовать в какие-либо строки, которые не являются категориями (например, 'NA'), полученный датафрейм назовите X_cat.\n", "\n", "Для объединения выборок здесь и далее в задании рекомендуется использовать функции\n", "\n", " np.hstack(...)\n", " np.vstack(...)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def calculate_means(numeric_data):\n", " means = np.zeros(numeric_data.shape[1])\n", " for j in range(numeric_data.shape[1]):\n", " to_sum = numeric_data.iloc[:,j]\n", " indices = np.nonzero(~numeric_data.iloc[:,j].isnull())[0]\n", " correction = np.amax(to_sum[indices])\n", " to_sum /= correction\n", " for i in indices:\n", " means[j] += to_sum[i]\n", " means[j] /= indices.size\n", " means[j] *= correction\n", " return pd.Series(means, numeric_data.columns)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6000, 13)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>RFCD.Percentage.1</th>\n", " <th>RFCD.Percentage.2</th>\n", " <th>RFCD.Percentage.3</th>\n", " <th>RFCD.Percentage.4</th>\n", " <th>RFCD.Percentage.5</th>\n", " <th>SEO.Percentage.1</th>\n", " <th>SEO.Percentage.2</th>\n", " <th>SEO.Percentage.3</th>\n", " <th>SEO.Percentage.4</th>\n", " <th>SEO.Percentage.5</th>\n", " <th>Year.of.Birth.1</th>\n", " <th>Number.of.Successful.Grant.1</th>\n", " <th>Number.of.Unsuccessful.Grant.1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>50.0</td>\n", " <td>30.0</td>\n", " <td>20.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1965.0</td>\n", " <td>2.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1965.0</td>\n", " <td>3.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>50.0</td>\n", " <td>30.0</td>\n", " <td>20.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>60.0</td>\n", " <td>20.0</td>\n", " <td>20.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1955.0</td>\n", " <td>1.0</td>\n", " <td>5.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>60.0</td>\n", " <td>40.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>60.0</td>\n", " <td>40.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1950.0</td>\n", " <td>2.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>100.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>50.0</td>\n", " <td>50.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1970.0</td>\n", " <td>0.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RFCD.Percentage.1 RFCD.Percentage.2 RFCD.Percentage.3 RFCD.Percentage.4 \\\n", "0 50.0 30.0 20.0 0.0 \n", "1 100.0 0.0 0.0 0.0 \n", "2 50.0 30.0 20.0 0.0 \n", "3 60.0 40.0 0.0 0.0 \n", "4 100.0 0.0 0.0 0.0 \n", "\n", " RFCD.Percentage.5 SEO.Percentage.1 SEO.Percentage.2 SEO.Percentage.3 \\\n", "0 0.0 100.0 0.0 0.0 \n", "1 0.0 100.0 0.0 0.0 \n", "2 0.0 60.0 20.0 20.0 \n", "3 0.0 60.0 40.0 0.0 \n", "4 0.0 50.0 50.0 0.0 \n", "\n", " SEO.Percentage.4 SEO.Percentage.5 Year.of.Birth.1 \\\n", "0 0.0 0.0 1965.0 \n", "1 0.0 0.0 1965.0 \n", "2 0.0 0.0 1955.0 \n", "3 0.0 0.0 1950.0 \n", "4 0.0 0.0 1970.0 \n", "\n", " Number.of.Successful.Grant.1 Number.of.Unsuccessful.Grant.1 \n", "0 2.0 0.0 \n", "1 3.0 1.0 \n", "2 1.0 5.0 \n", "3 2.0 1.0 \n", "4 0.0 2.0 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#numerical X, NA filled with zeros\n", "X_real_zeros = X[numeric_cols].fillna(0)\n", "print X_real_zeros.shape\n", "X_real_zeros.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6000, 13)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>RFCD.Percentage.1</th>\n", " <th>RFCD.Percentage.2</th>\n", " <th>RFCD.Percentage.3</th>\n", " <th>RFCD.Percentage.4</th>\n", " <th>RFCD.Percentage.5</th>\n", " <th>SEO.Percentage.1</th>\n", " <th>SEO.Percentage.2</th>\n", " <th>SEO.Percentage.3</th>\n", " <th>SEO.Percentage.4</th>\n", " <th>SEO.Percentage.5</th>\n", " <th>Year.of.Birth.1</th>\n", " <th>Number.of.Successful.Grant.1</th>\n", " <th>Number.of.Unsuccessful.Grant.1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1899</th>\n", " <td>20.000000</td>\n", " <td>40.000000</td>\n", " <td>40.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>30.00000</td>\n", " <td>40.00000</td>\n", " <td>30.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1960.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " </tr>\n", " <tr>\n", " <th>327</th>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>100.00000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1955.0</td>\n", " <td>9.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>3081</th>\n", " <td>40.000000</td>\n", " <td>30.000000</td>\n", " <td>30.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>70.00000</td>\n", " <td>30.00000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1960.0</td>\n", " <td>1.0</td>\n", " <td>2.0</td>\n", " </tr>\n", " <tr>\n", " <th>5996</th>\n", " <td>74.832348</td>\n", " <td>17.677593</td>\n", " <td>6.933011</td>\n", " <td>0.437937</td>\n", " <td>0.119112</td>\n", " <td>71.48324</td>\n", " <td>20.64688</td>\n", " <td>6.926704</td>\n", " <td>0.730804</td>\n", " <td>0.212192</td>\n", " <td>1975.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2420</th>\n", " <td>100.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>100.00000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1960.0</td>\n", " <td>0.0</td>\n", " <td>4.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " RFCD.Percentage.1 RFCD.Percentage.2 RFCD.Percentage.3 \\\n", "1899 20.000000 40.000000 40.000000 \n", "327 100.000000 0.000000 0.000000 \n", "3081 40.000000 30.000000 30.000000 \n", "5996 74.832348 17.677593 6.933011 \n", "2420 100.000000 0.000000 0.000000 \n", "\n", " RFCD.Percentage.4 RFCD.Percentage.5 SEO.Percentage.1 \\\n", "1899 0.000000 0.000000 30.00000 \n", "327 0.000000 0.000000 100.00000 \n", "3081 0.000000 0.000000 70.00000 \n", "5996 0.437937 0.119112 71.48324 \n", "2420 0.000000 0.000000 100.00000 \n", "\n", " SEO.Percentage.2 SEO.Percentage.3 SEO.Percentage.4 SEO.Percentage.5 \\\n", "1899 40.00000 30.000000 0.000000 0.000000 \n", "327 0.00000 0.000000 0.000000 0.000000 \n", "3081 30.00000 0.000000 0.000000 0.000000 \n", "5996 20.64688 6.926704 0.730804 0.212192 \n", "2420 0.00000 0.000000 0.000000 0.000000 \n", "\n", " Year.of.Birth.1 Number.of.Successful.Grant.1 \\\n", "1899 1960.0 0.0 \n", "327 1955.0 9.0 \n", "3081 1960.0 1.0 \n", "5996 1975.0 0.0 \n", "2420 1960.0 0.0 \n", "\n", " Number.of.Unsuccessful.Grant.1 \n", "1899 1.0 \n", "327 2.0 \n", "3081 2.0 \n", "5996 0.0 \n", "2420 4.0 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#numerical X, NA filled with mean columns values\n", "X_real_mean = X[numeric_cols].fillna(value=calculate_means(X[numeric_cols]))\n", "print X_real_mean.shape\n", "X_real_mean.sample(5)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6000, 25)\n" ] }, { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Country.of.Birth.1</th>\n", " <th>SEO.Code.4</th>\n", " <th>SEO.Code.5</th>\n", " <th>B.1</th>\n", " <th>SEO.Code.1</th>\n", " <th>SEO.Code.2</th>\n", " <th>SEO.Code.3</th>\n", " <th>C.1</th>\n", " <th>No..of.Years.in.Uni.at.Time.of.Grant.1</th>\n", " <th>With.PHD.1</th>\n", " <th>...</th>\n", " <th>RFCD.Code.1</th>\n", " <th>Home.Language.1</th>\n", " <th>A..1</th>\n", " <th>Person.ID.1</th>\n", " <th>Grant.Category.Code</th>\n", " <th>Faculty.No..1</th>\n", " <th>Role.1</th>\n", " <th>Dept.No..1</th>\n", " <th>Contract.Value.Band...see.note.A</th>\n", " <th>A.1</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>4231</th>\n", " <td>Australia</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>4.0</td>\n", " <td>730113.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>4.0</td>\n", " <td>&gt;=0 to 5</td>\n", " <td>Yes</td>\n", " <td>...</td>\n", " <td>321006.0</td>\n", " <td>NA</td>\n", " <td>5.0</td>\n", " <td>75972.0</td>\n", " <td>NA</td>\n", " <td>25.0</td>\n", " <td>CHIEF_INVESTIGATOR</td>\n", " <td>2603.0</td>\n", " <td>NA</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>5736</th>\n", " <td>Australia</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>4.0</td>\n", " <td>730106.0</td>\n", " <td>730104.0</td>\n", " <td>0.0</td>\n", " <td>3.0</td>\n", " <td>more than 15</td>\n", " <td>Yes</td>\n", " <td>...</td>\n", " <td>320702.0</td>\n", " <td>NA</td>\n", " <td>10.0</td>\n", " <td>9067.0</td>\n", " <td>10A</td>\n", " <td>25.0</td>\n", " <td>CHIEF_INVESTIGATOR</td>\n", " <td>2538.0</td>\n", " <td>NA</td>\n", " <td>15.0</td>\n", " </tr>\n", " <tr>\n", " <th>4288</th>\n", " <td>Australia</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>20.0</td>\n", " <td>730211.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>20.0</td>\n", " <td>&gt;5 to 10</td>\n", " <td>Yes</td>\n", " <td>...</td>\n", " <td>321204.0</td>\n", " <td>NA</td>\n", " <td>0.0</td>\n", " <td>3182.0</td>\n", " <td>10B</td>\n", " <td>25.0</td>\n", " <td>CHIEF_INVESTIGATOR</td>\n", " <td>2523.0</td>\n", " <td>A</td>\n", " <td>6.0</td>\n", " </tr>\n", " <tr>\n", " <th>3358</th>\n", " <td>Western Europe</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>1.0</td>\n", " <td>670401.0</td>\n", " <td>730213.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>&gt;5 to 10</td>\n", " <td>Yes</td>\n", " <td>...</td>\n", " <td>321206.0</td>\n", " <td>English</td>\n", " <td>1.0</td>\n", " <td>13682.0</td>\n", " <td>10A</td>\n", " <td>34.0</td>\n", " <td>CHIEF_INVESTIGATOR</td>\n", " <td>1258.0</td>\n", " <td>NA</td>\n", " <td>15.0</td>\n", " </tr>\n", " <tr>\n", " <th>5873</th>\n", " <td>Australia</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>3.0</td>\n", " <td>630101.0</td>\n", " <td>670101.0</td>\n", " <td>0.0</td>\n", " <td>3.0</td>\n", " <td>&gt;=0 to 5</td>\n", " <td>Yes</td>\n", " <td>...</td>\n", " <td>270105.0</td>\n", " <td>NA</td>\n", " <td>0.0</td>\n", " <td>9227.0</td>\n", " <td>50A</td>\n", " <td>34.0</td>\n", " <td>CHIEF_INVESTIGATOR</td>\n", " <td>1258.0</td>\n", " <td>NA</td>\n", " <td>2.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 25 columns</p>\n", "</div>" ], "text/plain": [ " Country.of.Birth.1 SEO.Code.4 SEO.Code.5 B.1 SEO.Code.1 SEO.Code.2 \\\n", "4231 Australia 0.0 0.0 4.0 730113.0 0.0 \n", "5736 Australia 0.0 0.0 4.0 730106.0 730104.0 \n", "4288 Australia 0.0 0.0 20.0 730211.0 0.0 \n", "3358 Western Europe 0.0 0.0 1.0 670401.0 730213.0 \n", "5873 Australia 0.0 0.0 3.0 630101.0 670101.0 \n", "\n", " SEO.Code.3 C.1 No..of.Years.in.Uni.at.Time.of.Grant.1 With.PHD.1 ... \\\n", "4231 0.0 4.0 >=0 to 5 Yes ... \n", "5736 0.0 3.0 more than 15 Yes ... \n", "4288 0.0 20.0 >5 to 10 Yes ... \n", "3358 0.0 0.0 >5 to 10 Yes ... \n", "5873 0.0 3.0 >=0 to 5 Yes ... \n", "\n", " RFCD.Code.1 Home.Language.1 A..1 Person.ID.1 Grant.Category.Code \\\n", "4231 321006.0 NA 5.0 75972.0 NA \n", "5736 320702.0 NA 10.0 9067.0 10A \n", "4288 321204.0 NA 0.0 3182.0 10B \n", "3358 321206.0 English 1.0 13682.0 10A \n", "5873 270105.0 NA 0.0 9227.0 50A \n", "\n", " Faculty.No..1 Role.1 Dept.No..1 \\\n", "4231 25.0 CHIEF_INVESTIGATOR 2603.0 \n", "5736 25.0 CHIEF_INVESTIGATOR 2538.0 \n", "4288 25.0 CHIEF_INVESTIGATOR 2523.0 \n", "3358 34.0 CHIEF_INVESTIGATOR 1258.0 \n", "5873 34.0 CHIEF_INVESTIGATOR 1258.0 \n", "\n", " Contract.Value.Band...see.note.A A.1 \n", "4231 NA 3.0 \n", "5736 NA 15.0 \n", "4288 A 6.0 \n", "3358 NA 15.0 \n", "5873 NA 2.0 \n", "\n", "[5 rows x 25 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#categorical X, NA filled with 'NA'\n", "X_cat = X[categorical_cols].fillna('NA').astype(str)\n", "print X_cat.shape\n", "X_cat.sample(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Преобразование категориальных признаков." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "В предыдущей ячейке мы разделили наш датасет ещё на две части: в одной присутствуют только вещественные признаки, в другой только категориальные. Это понадобится нам для раздельной последующей обработке этих данных, а так же для сравнения качества работы тех или иных методов.\n", "\n", "Для использования модели регрессии требуется преобразовать категориальные признаки в вещественные. Рассмотрим основной способ преоборазования категориальных признаков в вещественные: one-hot encoding. Его идея заключается в том, что мы преобразуем категориальный признак при помощи бинарного кода: каждой категории ставим в соответствие набор из нулей и единиц.\n", "\n", "Посмотрим, как данный метод работает на простом наборе данных." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Исходные данные:\n", "\n", " nationality sex\n", "0 American male\n", "1 European female\n", "2 Asian male\n", "3 European female\n", "\n", "Закодированные данные:\n", "\n", "[[ 1. 0. 0. 0. 1.]\n", " [ 0. 0. 1. 1. 0.]\n", " [ 0. 1. 0. 0. 1.]\n", " [ 0. 0. 1. 1. 0.]]\n" ] } ], "source": [ "from sklearn.linear_model import LogisticRegression as LR\n", "from sklearn.feature_extraction import DictVectorizer as DV\n", "\n", "categorial_data = pd.DataFrame({'sex': ['male', 'female', 'male', 'female'], \n", " 'nationality': ['American', 'European', 'Asian', 'European']})\n", "print('Исходные данные:\\n')\n", "print(categorial_data)\n", "encoder = DV(sparse = False)\n", "encoded_data = encoder.fit_transform(categorial_data.T.to_dict().values())\n", "print('\\nЗакодированные данные:\\n')\n", "print(encoded_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Как видно, в первые три колонки оказалась закодированна информация о стране, а во вторые две - о поле. При этом для совпадающих элементов выборки строки будут полностью совпадать. Также из примера видно, что кодирование признаков сильно увеличивает их количество, но полностью сохраняет информацию, в том числе о наличии пропущенных значений (их наличие просто становится одним из бинарных признаков в преобразованных данных).\n", "\n", "Теперь применим one-hot encoding к категориальным признакам из исходного датасета. Обратите внимание на общий для всех методов преобработки данных интерфейс. Функция\n", "\n", " encoder.fit_transform(X)\n", " \n", "позволяет вычислить необходимые параметры преобразования, впоследствии к новым данным можно уже применять функцию\n", "\n", " encoder.transform(X)\n", " \n", "Очень важно применять одинаковое преобразование как к обучающим, так и тестовым данным, потому что в противном случае вы получите непредсказуемые, и, скорее всего, плохие результаты. В частности, если вы отдельно закодируете обучающую и тестовую выборку, то получите вообще говоря разные коды для одних и тех же признаков, и ваше решение работать не будет.\n", "\n", "Также параметры многих преобразований (например, рассмотренное ниже масштабирование) нельзя вычислять одновременно на данных из обучения и теста, потому что иначе подсчитанные на тесте метрики качества будут давать смещённые оценки на качество работы алгоритма. Кодирование категориальных признаков не считает на обучающей выборке никаких параметров, поэтому его можно применять сразу к всему датасету." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(6000L, 5593L)\n" ] } ], "source": [ "encoder = DV(sparse = False)\n", "X_cat_oh = encoder.fit_transform(X_cat.T.to_dict().values())\n", "print X_cat_oh.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Для построения метрики качества по результату обучения требуется разделить исходный датасет на обучающую и тестовую выборки.\n", "\n", "Обращаем внимание на заданный параметр для генератора случайных чисел: random_state. Так как результаты на обучении и тесте будут зависеть от того, как именно вы разделите объекты, то предлагается использовать заранее определённое значение для получение результатов, согласованных с ответами в системе проверки заданий." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4200L,)\n", "(1800L,)\n", "(4200, 13)\n", "(1800, 13)\n", "(4200L, 5593L)\n", "(1800L, 5593L)\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "(X_train_real_zeros, \n", " X_test_real_zeros, \n", " y_train, y_test) = train_test_split(X_real_zeros, y, \n", " test_size=0.3, \n", " random_state=0)\n", "(X_train_real_mean, \n", " X_test_real_mean) = train_test_split(X_real_mean, \n", " test_size=0.3, \n", " random_state=0)\n", "(X_train_cat_oh,\n", " X_test_cat_oh) = train_test_split(X_cat_oh, \n", " test_size=0.3, \n", " random_state=0)\n", "print y_train.shape\n", "print y_test.shape\n", "print X_train_real_mean.shape\n", "print X_test_real_mean.shape\n", "print X_train_cat_oh.shape\n", "print X_test_cat_oh.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Описание классов" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Итак, мы получили первые наборы данных, для которых выполнены оба ограничения логистической регрессии на входные данные. Обучим на них регрессию, используя имеющийся в библиотеке sklearn функционал по подбору гиперпараметров модели\n", " \n", " optimizer = GridSearchCV(estimator, param_grid)\n", "\n", "где:\n", "- estimator - обучающий алгоритм, для которого будет производиться подбор параметров\n", "- param_grid - словарь параметров, ключами которого являются строки-названия, которые передаются алгоритму estimator, а значения - набор параметров для перебора\n", "\n", "Данный класс выполняет кросс-валидацию обучающей выборки для каждого набора параметров и находит те, на которых алгоритм работает лучше всего. Этот метод позволяет настраивать гиперпараметры по обучающей выборке, избегая переобучения. Некоторые опциональные параметры вызова данного класса, которые нам понадобятся:\n", "- scoring - функционал качества, максимум которого ищется кросс валидацией, по умолчанию используется функция score() класса estimator\n", "- n_jobs - позволяет ускорить кросс-валидацию, выполняя её параллельно, число определяет количество одновременно запущенных задач\n", "- cv - количество фолдов, на которые разбивается выборка при кросс-валидации\n", "\n", "После инициализации класса GridSearchCV, процесс подбора параметров запускается следующим методом:\n", "\n", " optimizer.fit(X, y)\n", " \n", "На выходе для получения предсказаний можно пользоваться функцией\n", "\n", " optimizer.predict(X)\n", " \n", "для меток или\n", "\n", " optimizer.predict_proba(X)\n", " \n", "для вероятностей (в случае использования логистической регрессии).\n", " \n", "Также можно напрямую получить оптимальный класс estimator и оптимальные параметры, так как они является атрибутами класса GridSearchCV:\n", "- best\\_estimator\\_ - лучший алгоритм\n", "- best\\_params\\_ - лучший набор параметров\n", "\n", "Класс логистической регрессии выглядит следующим образом:\n", "\n", " estimator = LogisticRegression(penalty)\n", " \n", "где penalty принимает либо значение 'l2', либо 'l1'. По умолчанию устанавливается значение 'l2', и везде в задании, если об этом не оговорено особо, предполагается использование логистической регрессии с L2-регуляризацией." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Задание 1. Сравнение способов заполнения вещественных пропущенных значений.\n", "1. Составьте две обучающие выборки из вещественных и категориальных признаков: в одной вещественные признаки, где пропущенные значения заполнены нулями, в другой - средними. Рекомендуется записывать в выборки сначала вещественные, а потом категориальные признаки.\n", "2. Обучите на них логистическую регрессию, подбирая параметры из заданной сетки param_grid по методу кросс-валидации с числом фолдов cv=3. В качестве оптимизируемой функции используйте заданную по умолчанию.\n", "3. Постройте два графика оценок точности +- их стандратного отклонения в зависимости от гиперпараметра и убедитесь, что вы действительно нашли её максимум. Также обратите внимание на большую дисперсию получаемых оценок (уменьшить её можно увеличением числа фолдов cv).\n", "4. Получите две метрики качества AUC ROC на тестовой выборке и сравните их между собой. Какой способ заполнения пропущенных вещественных значений работает лучше? В дальнейшем для выполнения задания в качестве вещественных признаков используйте ту выборку, которая даёт лучшее качество на тесте.\n", "5. Передайте два значения AUC ROC (сначала для выборки, заполненной средними, потом для выборки, заполненной нулями) в функцию write_answer_1 и запустите её. Полученный файл является ответом на 1 задание.\n", "\n", "Информация для интересующихся: вообще говоря, не вполне логично оптимизировать на кросс-валидации заданный по умолчанию в классе логистической регрессии функционал accuracy, а измерять на тесте AUC ROC, но это, как и ограничение размера выборки, сделано для ускорения работы процесса кросс-валидации." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\Anaconda3\\envs\\python2\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "C:\\ProgramData\\Anaconda3\\envs\\python2\\lib\\site-packages\\sklearn\\grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n" ] } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.metrics import roc_auc_score\n", "\n", "def plot_scores(optimizer):\n", " scores = [[item[0]['C'], \n", " item[1], \n", " (np.sum((item[2]-item[1])**2)/(item[2].size-1))**0.5] for item in optimizer.grid_scores_]\n", " scores = np.array(scores)\n", " plt.semilogx(scores[:,0], scores[:,1])\n", " plt.fill_between(scores[:,0], scores[:,1]-scores[:,2], \n", " scores[:,1]+scores[:,2], alpha=0.3)\n", " plt.show()\n", " \n", "def write_answer_1(auc_1, auc_2):\n", " auc = (auc_1 + auc_2)/2\n", " with open(\"preprocessing_lr_answer1.txt\", \"w\") as fout:\n", " fout.write(str(auc))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#stacking numerical and categorical features\n", "X_train_zeros = np.hstack( (X_train_real_zeros, X_train_cat_oh) )\n", "X_train_mean = np.hstack( (X_train_real_mean, X_train_cat_oh) )\n", "X_test_zeros = np.hstack( (X_test_real_zeros, X_test_cat_oh) )\n", "X_test_mean = np.hstack( (X_test_real_mean, X_test_cat_oh) )" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#GridSearchCV parameters\n", "param_grid = {'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]}\n", "cv = 3\n", "estimator = LogisticRegression()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GridSearchCV(cv=3, error_score='raise',\n", " estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]},\n", " pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)\n", "Wall time: 9.36 s\n" ] } ], "source": [ "%%time\n", "#GridSearchCV with zero fillna\n", "optimizer_zeros = GridSearchCV(estimator, param_grid, cv=cv)\n", "optimizer_zeros.fit(X_train_zeros, y_train)\n", "print optimizer_zeros" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Wall time: 11.5 s\n" ] } ], "source": [ "%%time\n", "#GridSearchCV with mean fillna\n", "optimizer_mean = GridSearchCV(estimator, param_grid, cv=cv)\n", "optimizer_mean.fit(X_train_mean, y_train)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xbc380b8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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w5AA+28k1ZTtp5/C1daEiqK2/UiHU1oVXXTjnausv/zv3/ORrrBKpMBMdwEsysHJ1ojuA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\n+I0xpspY4DfGmCrz/wFh776nNtvvWgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0xd655390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_scores(optimizer_zeros)\n", "plot_scores(optimizer_mean)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best parameter for GridSearchCV with zero fillna {'C': 0.1}\n", "roc_auc_score_zeros 0.886855921727\n", "Best parameter for GridSearchCV with mean fillna {'C': 0.05}\n", "roc_auc_score_mean 0.885529436949\n" ] } ], "source": [ "#GridSearchCV with zero fillna\n", "print 'Best parameter for GridSearchCV with zero fillna', optimizer_zeros.best_params_\n", "roc_auc_score_zeros = roc_auc_score(y_test, optimizer_zeros.best_estimator_.predict_proba(X_test_zeros)[:, 1])\n", "print 'roc_auc_score_zeros', roc_auc_score_zeros\n", "#GridSearchCV with mean fillna\n", "print 'Best parameter for GridSearchCV with mean fillna', optimizer_mean.best_params_\n", "roc_auc_score_mean = roc_auc_score(y_test, optimizer_mean.best_estimator_.predict_proba(X_test_mean)[:, 1])\n", "print 'roc_auc_score_mean', roc_auc_score_mean\n", "\n", "write_answer_1(roc_auc_score_zeros, roc_auc_score_mean)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Масштабирование вещественных признаков." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Попробуем как-то улучшить качество классификации. Для этого посмотрим на сами данные:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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+xI5opPyXSDvm/ejrsGH1ngf++C/cwsKM40V1SXS6MWZCRLFFpE5SstaopYvw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"text/plain": [ "<matplotlib.figure.Figure at 0xd7441d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pandas.plotting import scatter_matrix\n", "\n", "data_numeric = pd.DataFrame(X_train_real_zeros, columns=numeric_cols)\n", "list_cols = ['Number.of.Successful.Grant.1', 'SEO.Percentage.2', 'Year.of.Birth.1']\n", "scatter_matrix(data_numeric[list_cols], alpha=0.5, figsize=(10, 10))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Как видно из графиков, разные признаки очень сильно отличаются друг от друга по модулю значений (обратите внимание на диапазоны значений осей x и y). В случае обычной регрессии это никак не влияет на качество обучаемой модели, т.к. у меньших по модулю признаков будут большие веса, но при использовании регуляризации, которая штрафует модель за большие веса, регрессия, как правило, начинает работать хуже.\n", "\n", "В таких случаях всегда рекомендуется делать стандартизацию (масштабирование) признаков, для того чтобы они меньше отличались друг друга по модулю, но при этом не нарушались никакие другие свойства признакового пространства. При этом даже если итоговое качество модели на тесте уменьшается, это повышает её интерпретабельность, потому что новые веса имеют смысл \"значимости\" данного признака для итоговой классификации.\n", "\n", "Стандартизация осуществляется посредством вычета из каждого признака среднего значения и нормировки на выборочное стандартное отклонение:\n", "\n", "$$ x^{scaled}_{id} = \\dfrac{x_{id} - \\mu_d}{\\sigma_d}, \\quad \\mu_d = \\frac{1}{N} \\sum_{i=1}^l x_{id}, \\quad \\sigma_d = \\sqrt{\\frac{1}{N-1} \\sum_{i=1}^l (x_{id} - \\mu_d)^2} $$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Задание 1.5. Масштабирование вещественных признаков.\n", "\n", "1. По аналогии с вызовом one-hot encoder примените масштабирование вещественных признаков для обучающих и тестовых выборок X_train_real_zeros и X_test_real_zeros, используя класс \n", "\n", " StandardScaler\n", " \n", " и методы \n", "\n", " StandardScaler.fit_transform(...)\n", " StandardScaler.transform(...)\n", "2. Сохраните ответ в переменные X_train_real_scaled и X_test_real_scaled соответственно" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.preprocessing import StandardScaler\n", "\n", "encoder = StandardScaler()\n", "\n", "X_train_real_scaled = encoder.fit_transform(X_train_real_zeros)\n", "X_test_real_scaled = encoder.fit_transform(X_test_real_zeros)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Сравнение признаковых пространств." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Построим такие же графики для преобразованных данных:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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fAg5c+DPcXfZstl3yhafwP3gT+g/COe0CEiWf+z0siJc2v0xz89athsvOBj8J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"text/plain": [ "<matplotlib.figure.Figure at 0xbc09ac8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data_numeric_scaled = pd.DataFrame(X_train_real_scaled, columns=numeric_cols)\n", "list_cols = ['Number.of.Successful.Grant.1', 'SEO.Percentage.2', 'Year.of.Birth.1']\n", "scatter_matrix(data_numeric_scaled[list_cols], alpha=0.5, figsize=(10, 10))\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Как видно из графиков, мы не поменяли свойства признакового пространства: гистограммы распределений значений признаков, как и их scatter-plots, выглядят так же, как и до нормировки, но при этом все значения теперь находятся примерно в одном диапазоне, тем самым повышая интерпретабельность результатов, а также лучше сочетаясь с идеологией регуляризации." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Задание 2. Сравнение качества классификации до и после масштабирования вещественных признаков.\n", "1. Обучите ещё раз регрессию и гиперпараметры на новых признаках, объединив их с закодированными категориальными.\n", "2. Проверьте, был ли найден оптимум accuracy по гиперпараметрам во время кроссвалидации.\n", "3. Получите значение ROC AUC на тестовой выборке, сравните с лучшим результатом, полученными ранее.\n", "4. Запишите полученный ответ в файл при помощи функции write_answer_2." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def write_answer_2(auc):\n", " with open(\"preprocessing_lr_answer2.txt\", \"w\") as fout:\n", " fout.write(str(auc))" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#stacking numerical and categorical features\n", "X_train_scaled = np.hstack( (X_train_real_scaled, X_train_cat_oh) )\n", "X_test_scaled = np.hstack( (X_test_real_scaled, X_test_cat_oh) )" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GridSearchCV(cv=3, error_score='raise',\n", " estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]},\n", " pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)\n", "Wall time: 5.32 s\n" ] } ], "source": [ "%%time\n", "#GridSearchCV with zero fillna\n", "optimizer_zeros.fit(X_train_scaled, y_train)\n", "print optimizer_zeros" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best parameter for GridSearchCV with scaled num parameters {'C': 0.05}\n", "roc_auc_score 0.884358863628\n" ] } ], "source": [ "#GridSearchCV with zero fillna\n", "print 'Best parameter for GridSearchCV with scaled num parameters', optimizer_zeros.best_params_\n", "roc_auc_score_scaled = roc_auc_score(y_test, optimizer_zeros.best_estimator_.predict_proba(X_test_scaled)[:, 1])\n", "print 'roc_auc_score', roc_auc_score_scaled\n", "\n", "write_answer_2(roc_auc_score_scaled)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Балансировка классов." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Алгоритмы классификации могут быть очень чувствительны к несбалансированным классам. Рассмотрим пример с выборками, сэмплированными из двух гауссиан. Их мат. ожидания и матрицы ковариации заданы так, что истинная разделяющая поверхность должна проходить параллельно оси x. Поместим в обучающую выборку 20 объектов, сэмплированных из 1-й гауссианы, и 10 объектов из 2-й. После этого обучим на них линейную регрессию, и построим на графиках объекты и области классификации." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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ECkbBLiJSMAp2EZGCUbCLiBSMgl1EpGAU7CIiBaNgFxEpGAW7iEjBKNhFRApm\n/zi/7Jy7Gvgw8CbwEnCx974viYaJiEhj4vbYr/fev8N7Pw34CfD1BNokIiIxxAp27/3rJTc7gCBe\nc0REJK5YQzEAzrlrgf8NvAa8d5jHzQJmAXjvOe/Id8Z9ahERqcCCYPhOtnPufmBShbsWeO/vLnnc\nZcBY7/2VIz2pc+4x7/276m1sq9Dra216fa2t6K+vFiP22L3359S4ryXAPcCIwS4iIumJNcbunJta\ncvPDwH/Fa46IiMQVd4z9OufccYTljuuAz9f4e4tiPm/e6fW1Nr2+1lb01zeiEcfYRUSktejMUxGR\nglGwi4gUTOw69kYVfTkC59z1wAeBAeAZ4BLv/avZtio5zrmPAlcBxwMzvPePZdui+JxzM4GbgFHA\nd73312XcpEQ5574HnAe85L0/Kev2JMk5dyTwA2Ai4YmSi7z3N2Xbquxk2WMv+nIE9wEnee/fATwF\nXJZxe5K2CrgAWJZ1Q5LgnBsF3AKcC5wAfMI5d0K2rUrcYmBm1o1IyS7gS977E4BTgbkF/Pxqllmw\nF305Au/9z7z3u6Kby4HuLNuTNO/9au/9mqzbkaAZwFrv/bPe+wHgTsJvlIXhvV8GvJJ1O9Lgve/3\n3v8m+vcbwGqgK9tWZSezoRiofTmCAvgscFfWjZBhdQEvltzuAU7JqC0Sg3PuKOBk4FcZNyUzqQb7\nSMsReO8XAAui5Qi+QIudtVrLcgvOuQWEXxOXNLNtSah1OQmRvHDOHQT8CPizslGBtpJqsBd9OYKR\nXp9z7mLCyaqzvfctN9RUx+dXBL3AkSW3u6Nt0iKcc6MJQ32J9/7HWbcnS5mNsRd9OYKowuIrwIe8\n91uzbo+M6NfAVOfc0c65McDHgX/LuE1SI+ecAbcDq733N2bdnqxlduapc+5HwJDlCLz3hekhOefW\nAgcAL0eblnvva11yIfeccx8B/haYALwKrPTevz/bVsXjnPsA8B3Ccsfvee+vzbhJiXLO/RA4ExgP\nbACu9N7fnmmjEuKcOx14BPgtYaYAXO69vye7VmVHSwqIiBSMzjwVESkYBbuISMEo2EVECkbBLiJS\nMAp2EZGCUbCLiBSMgl1EpGD+PyNbW5eomckxAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0xde71eb8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "AUC: 0.906667\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x9da5b38>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "AUC: 0.895000\n" ] } ], "source": [ "np.random.seed(0)\n", "\"\"\"Сэмплируем данные из первой гауссианы\"\"\"\n", "data_0 = np.random.multivariate_normal([0,0], [[0.5,0],[0,0.5]], size=40)\n", "\"\"\"И из второй\"\"\"\n", "data_1 = np.random.multivariate_normal([0,1], [[0.5,0],[0,0.5]], size=40)\n", "\"\"\"На обучение берём 20 объектов из первого класса и 10 из второго\"\"\"\n", "example_data_train = np.vstack([data_0[:20,:], data_1[:10,:]])\n", "example_labels_train = np.concatenate([np.zeros((20)), np.ones((10))])\n", "\"\"\"На тест - 20 из первого и 30 из второго\"\"\"\n", "example_data_test = np.vstack([data_0[20:,:], data_1[10:,:]])\n", "example_labels_test = np.concatenate([np.zeros((20)), np.ones((30))])\n", "\"\"\"Задаём координатную сетку, на которой будем вычислять область классификации\"\"\"\n", "xx, yy = np.meshgrid(np.arange(-3, 3, 0.02), np.arange(-3, 3, 0.02))\n", "\"\"\"Обучаем регрессию без балансировки по классам\"\"\"\n", "optimizer = GridSearchCV(LogisticRegression(), param_grid, cv=cv, n_jobs=-1)\n", "optimizer.fit(example_data_train, example_labels_train)\n", "\"\"\"Строим предсказания регрессии для сетки\"\"\"\n", "Z = optimizer.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Pastel2)\n", "plt.scatter(data_0[:,0], data_0[:,1], color='red')\n", "plt.scatter(data_1[:,0], data_1[:,1], color='blue')\n", "\"\"\"Считаем AUC\"\"\"\n", "auc_wo_class_weights = roc_auc_score(example_labels_test, optimizer.predict_proba(example_data_test)[:,1])\n", "plt.title('Without class weights')\n", "plt.show()\n", "print('AUC: %f'%auc_wo_class_weights)\n", "\"\"\"Для второй регрессии в LogisticRegression передаём параметр class_weight='balanced'\"\"\"\n", "optimizer = GridSearchCV(LogisticRegression(class_weight='balanced'), param_grid, cv=cv, n_jobs=-1)\n", "optimizer.fit(example_data_train, example_labels_train)\n", "Z = optimizer.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Pastel2)\n", "plt.scatter(data_0[:,0], data_0[:,1], color='red')\n", "plt.scatter(data_1[:,0], data_1[:,1], color='blue')\n", "auc_w_class_weights = roc_auc_score(example_labels_test, optimizer.predict_proba(example_data_test)[:,1])\n", "plt.title('With class weights')\n", "plt.show()\n", "print('AUC: %f'%auc_w_class_weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Как видно, во втором случае классификатор находит разделяющую поверхность, которая ближе к истинной, т.е. меньше переобучается. Поэтому на сбалансированность классов в обучающей выборке всегда следует обращать внимание.\n", "\n", "Посмотрим, сбалансированны ли классы в нашей обучающей выборке:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2316\n", "1884\n" ] } ], "source": [ "print(np.sum(y_train==0))\n", "print(np.sum(y_train==1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Видно, что нет.\n", "\n", "Исправить ситуацию можно разными способами, мы рассмотрим два:\n", "- давать объектам миноритарного класса больший вес при обучении классификатора (рассмотрен в примере выше)\n", "- досэмплировать объекты миноритарного класса, пока число объектов в обоих классах не сравняется" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Задание 3. Балансировка классов.\n", "1. Обучите логистическую регрессию и гиперпараметры с балансировкой классов, используя веса (параметр class_weight='balanced' регрессии) на отмасштабированных выборках, полученных в предыдущем задании. Убедитесь, что вы нашли максимум accuracy по гиперпараметрам.\n", "2. Получите метрику ROC AUC на тестовой выборке.\n", "3. Сбалансируйте выборку, досэмплировав в неё объекты из меньшего класса. Для получения индексов объектов, которые требуется добавить в обучающую выборку, используйте следующую комбинацию вызовов функций:\n", " np.random.seed(0)\n", " indices_to_add = np.random.randint(...)\n", " X_train_to_add = X_train[y_train.as_matrix() == 1,:][indices_to_add,:]\n", " После этого добавьте эти объекты в начало или конец обучающей выборки. Дополните соответствующим образом вектор ответов.\n", "4. Получите метрику ROC AUC на тестовой выборке, сравните с предыдущим результатом.\n", "5. Внесите ответы в выходной файл при помощи функции write_asnwer_3, передав в неё сначала ROC AUC для балансировки весами, а потом балансировки выборки вручную." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def write_answer_3(auc_1, auc_2):\n", " auc = (auc_1 + auc_2) / 2\n", " with open(\"preprocessing_lr_answer3.txt\", \"w\") as fout:\n", " fout.write(str(auc))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#GridSearchCV parameters\n", "param_grid = {'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]}\n", "cv = 3\n", "estimator = LogisticRegression(class_weight='balanced')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GridSearchCV(cv=3, error_score='raise',\n", " estimator=LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n", " fit_intercept=True, intercept_scaling=1, max_iter=100,\n", " multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n", " solver='liblinear', tol=0.0001, verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]},\n", " pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)\n", "Wall time: 4.83 s\n" ] } ], "source": [ "%%time\n", "#GridSearchCV with balanced weights\n", "optimizer = GridSearchCV(estimator, param_grid, cv=cv)\n", "optimizer.fit(X_train_scaled, y_train)\n", "print optimizer" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best parameter for GridSearchCV with balanced weights {'C': 0.1}\n", "roc_auc_score 0.886692585915\n" ] } ], "source": [ "#GridSearchCV with balanced weights\n", "print 'Best parameter for GridSearchCV with balanced weights', optimizer.best_params_\n", "roc_auc_score_bal1 = roc_auc_score(y_test, optimizer.best_estimator_.predict_proba(X_test_scaled)[:, 1])\n", "print 'roc_auc_score', roc_auc_score_bal1" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(432L,)\n", "(4200L,)\n", "(4632L, 5606L) (4200L, 5606L)\n", "(4632L,) (4200L,)\n" ] } ], "source": [ "#generating new indices for class 1\n", "np.random.seed(0)\n", "num_of_indices = np.sum(y_train==0) - np.sum(y_train==1)\n", "indices_to_add = np.random.randint(np.sum(y_train==1)+1, size=num_of_indices)\n", "X_train_to_add = X_train_scaled[y_train.as_matrix() == 1,:][indices_to_add,:]\n", "y_train_to_add = y_train[indices_to_add]\n", "print y_train_to_add.shape\n", "print y_train.shape\n", "\n", "#new X, y train\n", "X_train_balanced = np.vstack( (X_train_scaled, X_train_to_add) )\n", "y_train_balanced = np.append(y_train, y_train_to_add)\n", "print X_train_balanced.shape, X_train_scaled.shape\n", "print y_train_balanced.shape, y_train.shape" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GridSearchCV(cv=3, error_score='raise',\n", " estimator=LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n", " fit_intercept=True, intercept_scaling=1, max_iter=100,\n", " multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n", " solver='liblinear', tol=0.0001, verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]},\n", " pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)\n" ] } ], "source": [ "#GridSearchCV with balanced weights\n", "optimizer = GridSearchCV(estimator, param_grid, cv=cv)\n", "optimizer.fit(X_train_scaled, y_train)\n", "print optimizer" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best parameter for GridSearchCV with balanced weights {'C': 0.1}\n", "roc_auc_score 0.886692585915\n" ] } ], "source": [ "#GridSearchCV with balanced weights\n", "print 'Best parameter for GridSearchCV with balanced weights', optimizer.best_params_\n", "roc_auc_score_bal2 = roc_auc_score(y_test, optimizer.best_estimator_.predict_proba(X_test_scaled)[:, 1])\n", "print 'roc_auc_score', roc_auc_score_bal2" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_answer_3(roc_auc_score_bal1, roc_auc_score_bal2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Стратификация выборок." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Рассмотрим ещё раз пример с выборками из нормальных распределений. Посмотрим ещё раз на качество классификаторов, получаемое на тестовых выборках:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('AUC ROC for classifier without weighted classes', 0.90666666666666662)\n", "('AUC ROC for classifier with weighted classes: ', 0.89500000000000002)\n" ] } ], "source": [ "print('AUC ROC for classifier without weighted classes', auc_wo_class_weights)\n", "print('AUC ROC for classifier with weighted classes: ', auc_w_class_weights)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Насколько эти цифры реально отражают качество работы алгоритма, если учесть, что тестовая выборка так же несбалансирована, как обучающая? При этом мы уже знаем, что алгоритм логистический регрессии чувствителен к балансировке классов в обучающей выборке, т.е. в данном случае на тесте он будет давать заведомо заниженные результаты. Метрика классификатора на тесте имела бы гораздо больший смысл, если бы объекты были разделы в выборках поровну: по 20 из каждого класса на обучени и на тесте. Переформируем выборки и подсчитаем новые ошибки:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xeeba4a8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "('AUC ROC for stratified samples: ', 0.91000000000000003)\n" ] } ], "source": [ "\"\"\"Разделим данные по классам поровну между обучающей и тестовой выборками\"\"\"\n", "example_data_train = np.vstack([data_0[:20,:], data_1[:20,:]])\n", "example_labels_train = np.concatenate([np.zeros((20)), np.ones((20))])\n", "example_data_test = np.vstack([data_0[20:,:], data_1[20:,:]])\n", "example_labels_test = np.concatenate([np.zeros((20)), np.ones((20))])\n", "\"\"\"Обучим классификатор\"\"\"\n", "optimizer = GridSearchCV(LogisticRegression(class_weight='balanced'), param_grid, cv=cv, n_jobs=-1)\n", "optimizer.fit(example_data_train, example_labels_train)\n", "Z = optimizer.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)\n", "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Pastel2)\n", "plt.scatter(data_0[:,0], data_0[:,1], color='red')\n", "plt.scatter(data_1[:,0], data_1[:,1], color='blue')\n", "auc_stratified = roc_auc_score(example_labels_test, optimizer.predict_proba(example_data_test)[:,1])\n", "plt.title('With class weights')\n", "plt.show()\n", "print('AUC ROC for stratified samples: ', auc_stratified)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Как видно, после данной процедуры ответ классификатора изменился незначительно, а вот качество увеличилось. При этом, в зависимости от того, как вы разбили изначально данные на обучение и тест, после сбалансированного разделения выборок итоговая метрика на тесте может как увеличиться, так и уменьшиться, но доверять ей можно значительно больше, т.к. она построена с учётом специфики работы классификатора. Данный подход является частным случаем т.н. метода стратификации." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Задание 4. Стратификация выборки.\n", "\n", "1. По аналогии с тем, как это было сделано в начале задания, разбейте выборки X_real_zeros и X_cat_oh на обучение и тест, передавая в функцию \n", " train_test_split(...)\n", " дополнительно параметр \n", " stratify=y\n", " Также обязательно передайте в функцию переменную random_state=0.\n", "2. Выполните масштабирование новых вещественных выборок, обучите классификатор и его гиперпараметры при помощи метода кросс-валидации, делая поправку на несбалансированные классы при помощи весов. Убедитесь в том, что нашли оптимум accuracy по гиперпараметрам.\n", "3. Оцените качество классификатора метрике AUC ROC на тестовой выборке.\n", "4. Полученный ответ передайте функции write_answer_4" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def write_answer_4(auc):\n", " with open(\"preprocessing_lr_answer4.txt\", \"w\") as fout:\n", " fout.write(str(auc))" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4200, 13) (1800, 13) (4200L,)\n" ] } ], "source": [ "(X_train_real_zeros, \n", " X_test_real_zeros, \n", " y_train, y_test) = train_test_split(X_real_zeros, y, \n", " test_size=0.3, \n", " random_state=0, stratify=y)\n", "print X_train_real_zeros.shape, X_test_real_zeros.shape, y_train.shape\n", "\n", "(X_train_real_mean, \n", " X_test_real_mean, \n", " y_train, y_test) = train_test_split(X_real_mean, y, \n", " test_size=0.3, \n", " random_state=0, stratify=y)\n", "\n", "(X_train_cat_oh,\n", " X_test_cat_oh) = train_test_split(X_cat_oh, \n", " test_size=0.3, \n", " random_state=0, stratify=y)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "encoder = StandardScaler()\n", "\n", "X_train_real_scaled = encoder.fit_transform(X_train_real_zeros)\n", "X_test_real_scaled = encoder.fit_transform(X_test_real_zeros)\n", "\n", "#stacking numerical and categorical features\n", "X_train_scaled = np.hstack( (X_train_real_scaled, X_train_cat_oh) )\n", "X_test_scaled = np.hstack( (X_test_real_scaled, X_test_cat_oh) )" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#GridSearchCV parameters\n", "param_grid = {'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]}\n", "cv = 3\n", "estimator = LogisticRegression(class_weight='balanced')" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GridSearchCV(cv=3, error_score='raise',\n", " estimator=LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n", " fit_intercept=True, intercept_scaling=1, max_iter=100,\n", " multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n", " solver='liblinear', tol=0.0001, verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]},\n", " pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)\n", "Wall time: 4.6 s\n" ] } ], "source": [ "%%time\n", "optimizer = GridSearchCV(estimator, param_grid, cv=cv)\n", "optimizer.fit(X_train_scaled, y_train)\n", "print optimizer" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best parameter for GridSearchCV with balanced weights {'C': 0.1}\n", "roc_auc_score 0.879407176869\n" ] } ], "source": [ "#GridSearchCV\n", "print 'Best parameter for GridSearchCV with balanced weights', optimizer.best_params_\n", "roc_auc_score_strat = roc_auc_score(y_test, optimizer.best_estimator_.predict_proba(X_test_scaled)[:, 1])\n", "print 'roc_auc_score', roc_auc_score_strat" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_answer_4(roc_auc_score_strat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Теперь вы разобрались с основными этапами предобработки данных для линейных классификаторов.\n", "Напомним основные этапы:\n", "- обработка пропущенных значений\n", "- обработка категориальных признаков\n", "- стратификация\n", "- балансировка классов\n", "- масштабирование\n", "\n", "Данные действия с данными рекомендуется проводить всякий раз, когда вы планируете использовать линейные методы. Рекомендация по выполнению многих из этих пунктов справедлива и для других методов машинного обучения." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Трансформация признаков.\n", "\n", "Теперь рассмотрим способы преобразования признаков. Существует достаточно много различных способов трансформации признаков, которые позволяют при помощи линейных методов получать более сложные разделяющие поверхности. Самым базовым является полиномиальное преобразование признаков. Его идея заключается в том, что помимо самих признаков вы дополнительно включаете набор все полиномы степени $p$, которые можно из них построить. Для случая $p=2$ преобразование выглядит следующим образом:\n", "\n", "$$ \\phi(x_i) = [x_{i,1}^2, ..., x_{i,D}^2, x_{i,1}x_{i,2}, ..., x_{i,D} x_{i,D-1}, x_{i,1}, ..., x_{i,D}, 1] $$\n", "\n", "Рассмотрим принцип работы данных признаков на данных, сэмплированных их гауссиан:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xbc29048>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.preprocessing import PolynomialFeatures\n", "\n", "\"\"\"Инициализируем класс, который выполняет преобразование\"\"\"\n", "transform = PolynomialFeatures(2)\n", "\"\"\"Обучаем преобразование на обучающей выборке, применяем его к тестовой\"\"\"\n", "example_data_train_poly = transform.fit_transform(example_data_train)\n", "example_data_test_poly = transform.transform(example_data_test)\n", "\"\"\"Обращаем внимание на параметр fit_intercept=False\"\"\"\n", "optimizer = GridSearchCV(LogisticRegression(class_weight='balanced', fit_intercept=False), param_grid, cv=cv, n_jobs=-1)\n", "optimizer.fit(example_data_train_poly, example_labels_train)\n", "Z = optimizer.predict(transform.transform(np.c_[xx.ravel(), yy.ravel()])).reshape(xx.shape)\n", "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Pastel2)\n", "plt.scatter(data_0[:,0], data_0[:,1], color='red')\n", "plt.scatter(data_1[:,0], data_1[:,1], color='blue')\n", "plt.title('With class weights')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Видно, что данный метод преобразования данных уже позволяет строить нелинейные разделяющие поверхности, которые могут более тонко подстраиваться под данные и находить более сложные зависимости. Число признаков в новой модели:" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(40L, 6L)\n" ] } ], "source": [ "print(example_data_train_poly.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Но при этом одновременно данный метод способствует более сильной способности модели к переобучению из-за быстрого роста числа признаком с увеличением степени $p$. Рассмотрим пример с $p=11$:" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xbc292e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "transform = PolynomialFeatures(11)\n", "example_data_train_poly = transform.fit_transform(example_data_train)\n", "example_data_test_poly = transform.transform(example_data_test)\n", "optimizer = GridSearchCV(LogisticRegression(class_weight='balanced', fit_intercept=False), param_grid, cv=cv, n_jobs=-1)\n", "optimizer.fit(example_data_train_poly, example_labels_train)\n", "Z = optimizer.predict(transform.transform(np.c_[xx.ravel(), yy.ravel()])).reshape(xx.shape)\n", "plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Pastel2)\n", "plt.scatter(data_0[:,0], data_0[:,1], color='red')\n", "plt.scatter(data_1[:,0], data_1[:,1], color='blue')\n", "plt.title('Corrected class weights')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Количество признаков в данной модели:" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(40L, 78L)\n" ] } ], "source": [ "print(example_data_train_poly.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Задание 5. Трансформация вещественных признаков.\n", "\n", "1. Реализуйте по аналогии с примером преобразование вещественных признаков модели при помощи полиномиальных признаков степени 2\n", "2. Постройте логистическую регрессию на новых данных, одновременно подобрав оптимальные гиперпараметры. Обращаем внимание, что в преобразованных признаках уже присутствует столбец, все значения которого равны 1, поэтому обучать дополнительно значение $b$ не нужно, его функцию выполняет один из весов $w$. В связи с этим во избежание линейной зависимости в датасете, в вызов класса логистической регрессии требуется передавать параметр fit_intercept=False. Для обучения используйте стратифицированные выборки с балансировкой классов при помощи весов, преобразованные признаки требуется заново отмасштабировать.\n", "3. Получите AUC ROC на тесте и сравните данный результат с использованием обычных признаков.\n", "4. Передайте полученный ответ в функцию write_answer_5." ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def write_answer_5(auc):\n", " with open(\"preprocessing_lr_answer5.txt\", \"w\") as fout:\n", " fout.write(str(auc))" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "transform = PolynomialFeatures(2)\n", "\n", "data_train_poly = transform.fit_transform(X_train_real_zeros)\n", "data_test_poly = transform.transform(X_test_real_zeros)\n", "\n", "encoder = StandardScaler()\n", "\n", "data_train_poly_scaled = encoder.fit_transform(data_train_poly)\n", "data_test_poly_scaled = encoder.fit_transform(data_test_poly)\n", "\n", "#stacking numerical and categorical features\n", "data_train_poly_full = np.hstack( (data_train_poly_scaled, X_train_cat_oh) )\n", "data_test_poly_full = np.hstack( (data_test_poly_scaled, X_test_cat_oh) )" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#GridSearchCV parameters\n", "param_grid = {'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]}\n", "cv = 3\n", "estimator = LogisticRegression(class_weight='balanced', fit_intercept=False)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GridSearchCV(cv=3, error_score='raise',\n", " estimator=LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n", " fit_intercept=False, intercept_scaling=1, max_iter=100,\n", " multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n", " solver='liblinear', tol=0.0001, verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]},\n", " pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)\n", "Wall time: 14.3 s\n" ] } ], "source": [ "%%time\n", "optimizer = GridSearchCV(estimator, param_grid, cv=cv)\n", "optimizer.fit(data_train_poly_full, y_train)\n", "print optimizer" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best parameter for GridSearchCV with balanced weights {'C': 0.1}\n", "roc_auc_score 0.887567233393\n" ] } ], "source": [ "#GridSearchCV\n", "print 'Best parameter for GridSearchCV with balanced weights', optimizer.best_params_\n", "roc_auc_score_poly = roc_auc_score(y_test, optimizer.best_estimator_.predict_proba(data_test_poly_full)[:, 1])\n", "print 'roc_auc_score', roc_auc_score_poly" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "write_answer_5(roc_auc_score_poly)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Регрессия Lasso.\n", "К логистической регрессии также можно применить L1-регуляризацию (Lasso), вместо регуляризации L2, которая будет приводить к отбору признаков. Вам предлагается применить L1-регуляцию к исходным признакам и проинтерпретировать полученные результаты (применение отбора признаков к полиномиальным так же можно успешно применять, но в нём уже будет отсутствовать компонента интерпретации, т.к. смысловое значение оригинальных признаков известно, а полиномиальных - уже может быть достаточно нетривиально). Для вызова логистической регрессии с L1-регуляризацией достаточно передать параметр penalty='l1' в инициализацию класса." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Задание 6. Отбор признаков при помощи регрессии Lasso.\n", "1. Обучите регрессию Lasso на стратифицированных отмасштабированных выборках, используя балансировку классов при помощи весов.\n", "2. Получите ROC AUC регрессии, сравните его с предыдущими результатами.\n", "3. Найдите номера вещественных признаков, которые имеют нулевые веса в итоговой модели.\n", "4. Передайте их список функции write_answer_6." ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def write_answer_6(features):\n", " with open(\"preprocessing_lr_answer6.txt\", \"w\") as fout:\n", " fout.write(\" \".join([str(num) for num in features]))" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "encoder = StandardScaler()\n", "\n", "data_train_lasso_scaled = encoder.fit_transform(X_train_real_zeros)\n", "data_test_lasso_scaled = encoder.fit_transform(X_test_real_zeros)\n", "\n", "#stacking numerical and categorical features\n", "data_train_lasso_full = np.hstack( (data_train_lasso_scaled, X_train_cat_oh) )\n", "data_test_lasso_full = np.hstack( (data_test_lasso_scaled, X_test_cat_oh) )" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#GridSearchCV parameters\n", "param_grid = {'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]}\n", "cv = 3\n", "estimator = LogisticRegression(class_weight='balanced', fit_intercept=False, penalty='l1')" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GridSearchCV(cv=3, error_score='raise',\n", " estimator=LogisticRegression(C=1.0, class_weight='balanced', dual=False,\n", " fit_intercept=False, intercept_scaling=1, max_iter=100,\n", " multi_class='ovr', n_jobs=1, penalty='l1', random_state=None,\n", " solver='liblinear', tol=0.0001, verbose=0, warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'C': [0.01, 0.05, 0.1, 0.5, 1, 5, 10]},\n", " pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)\n", "Wall time: 6.14 s\n" ] } ], "source": [ "%%time\n", "optimizer = GridSearchCV(estimator, param_grid, cv=cv)\n", "optimizer.fit(data_train_lasso_full, y_train)\n", "print optimizer" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best parameter for GridSearchCV with balanced weights {'C': 1}\n", "roc_auc_score 0.876669328636\n" ] } ], "source": [ "#GridSearchCV\n", "print 'Best parameter for GridSearchCV with balanced weights', optimizer.best_params_\n", "roc_auc_score_lasso = roc_auc_score(y_test, optimizer.best_estimator_.predict_proba(data_test_lasso_full)[:, 1])\n", "print 'roc_auc_score', roc_auc_score_lasso" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.00898779 0.03993311 -0.08878523 ..., 1.89625806 0. 0.34706998]\n", "13\n", "[4, 6, 7]\n" ] } ], "source": [ "print optimizer.best_estimator_.coef_.ravel()\n", "print X_train_real_zeros.shape[1]\n", "zero_coefs = [index for index, value in enumerate(optimizer.best_estimator_.coef_[0][:13]) if value == 0]\n", "print zero_coefs" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true }, "outputs": [], "source": [ "write_answer_6(zero_coefs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
luwei0917/awsemmd_script
notebook/GlpG_paper/May_second_long.ipynb
1
5178100
null
mit
paix120/First-API-NPR-data-sci-stories
First Python API Usage.ipynb
1
76760
{ "metadata": { "name": "", "signature": "sha256:546e8ad6fedc87a21622bf3392d8bc9a576e1c537695645253395f6faec21f7f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Renee's First IPython Notebook and First API Usage \"In the Wild\" (outside of a course)" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Import the \"requests\" library and set up the parameters to pass in the request. We're going to create a list of the 10 most recent stories matching the search term \"data science\"." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import requests \n", "#key-value pairs of parameters\n", "p = {'searchTerm':'\"data science\"','numResults':'10'}\n", "r = requests.get(\"http://api.npr.org/query?apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\", params=p)\n", "#view the generated url with the parameters in the querystring (it's clickable to see the xml) and the \n", "#response status and content-type\n", "print(r.url + '\\nstatus:' + str(r.status_code) + '\\ncontent-type:' + r.headers['content-type'])\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "http://api.npr.org/query?apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001&searchTerm=%22data+science%22&numResults=10\n", "status:200\n", "content-type:text/xml;charset=UTF-8\n" ] } ], "prompt_number": 2 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Parse the XML tree and loop through it." ] }, { "cell_type": "code", "collapsed": true, "input": [ "import lxml.etree as etree\n", "#parse the response xml\n", "tree = etree.XML(r.content)\n", "\n", "#iterate through the entire tree and display each item's tag and text\n", "for item in tree:\n", " #separate the stories\n", " if item.tag == 'story':\n", " print('\\n---------------------------------------------------------')\n", " #print everything under the story tag but the paragraphs and fullText (to save space)\n", " for subitems in item.iter(\"*\"):\n", " if subitems.tag != 'paragraph' and subitems.tag != 'fullText':\n", " print(subitems.tag + ': ' + str(subitems.text))\n", " \n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "list: \n", " \n", "title: NPR: Stories from NPR\n", "teaser: Assorted stories from NPR\n", "miniTeaser: Custom NPR News Feed API. Visit http://www.npr.org/templates/apidoc/index.php for more information.\n", "link: http://api.npr.org/query?searchTerm=%22data%20science%22&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "story: \n", " \n", "link: http://www.npr.org/sections/13.7/2015/03/24/395012901/what-if-web-search-results-were-based-on-accuracy?ft=nprml&f=\n", "link: http://api.npr.org/query?id=395012901&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "link: http://n.pr/1BKkKLc\n", "title: What If Web Search Results Were Based On Accuracy?\n", "subtitle: None\n", "shortTitle: None\n", "teaser: Google has been researching the possibility of ranking search results based on established facts. Were this to become the norm, it would have huge implications for future discourse, says Adam Frank.\n", "miniTeaser: None\n", "slug: 13.7: Cosmos And Culture\n", "thumbnail: \n", " \n", "medium: http://media.npr.org/assets/img/2015/03/24/google_sq-6994360b215eb7bb08fafa418c6e742a90f1a600.jpg?s=13\n", "large: http://media.npr.org/assets/img/2015/03/24/google_sq-6994360b215eb7bb08fafa418c6e742a90f1a600.jpg?s=11\n", "provider: iStockphoto\n", "storyDate: Tue, 24 Mar 2015 07:55:00 -0400\n", "pubDate: Tue, 24 Mar 2015 13:24:00 -0400\n", "lastModifiedDate: Tue, 31 Mar 2015 04:07:20 -0400\n", "audioRunByDate: None\n", "keywords: None\n", "priorityKeywords: None\n", "organization: \n", " \n", "name: NPR\n", "website: http://www.npr.org/\n", "parent: \n", " \n", "title: Commentary\n", "link: http://www.npr.org/sections/commentary/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=1060&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: civil society\n", "link: http://www.npr.org/tags/395014619/civil-society?ft=nprml&f=\n", "link: http://api.npr.org/query?id=395014619&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: accuracy\n", "link: http://www.npr.org/tags/395014606/accuracy?ft=nprml&f=\n", "link: http://api.npr.org/query?id=395014606&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: web search\n", "link: http://www.npr.org/tags/395014604/web-search?ft=nprml&f=\n", "link: http://api.npr.org/query?id=395014604&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: facts\n", "link: http://www.npr.org/tags/395014602/facts?ft=nprml&f=\n", "link: http://api.npr.org/query?id=395014602&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Culture\n", "link: http://www.npr.org/sections/13.7/126355748/culture/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=126355748&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Google\n", "link: http://www.npr.org/tags/125099562/google?ft=nprml&f=\n", "link: http://api.npr.org/query?id=125099562&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: 13.7: Cosmos And Culture\n", "link: http://www.npr.org/sections/13.7/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=114424647&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Commentary\n", "link: http://www.npr.org/sections/commentary/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=1060&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Digital Life\n", "link: http://www.npr.org/sections/digital-life/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=1049&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Technology\n", "link: http://www.npr.org/sections/technology/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=1019&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Business\n", "link: http://www.npr.org/sections/business/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=1006&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Home Page Top Stories\n", "link: http://www.npr.org/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=1002&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "byline: \n", " \n", "name: Adam Frank\n", "link: http://www.npr.org/people/336050847/adam-frank?ft=nprml&f=\n", "link: http://api.npr.org/query?id=336050847&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "image: \n", " \n", "title: Google search page.\n", "caption: None\n", "link: None\n", "producer: Matjaz Boncina\n", "provider: iStockphoto\n", "copyright: None\n", "enlargement: \n", " \n", "caption: None\n", "crop: None\n", "crop: None\n", "crop: None\n", "crop: None\n", "text: \n", " \n", "textWithHtml: \n", " \n", "story: \n", " \n", "link: http://www.npr.org/sections/alltechconsidered/2015/03/23/394827451/now-algorithms-are-deciding-whom-to-hire-based-on-voice?ft=nprml&f=\n", "link: http://api.npr.org/query?id=394827451&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "link: http://n.pr/18T09hi\n", "title: Now Algorithms Are Deciding Whom To Hire, Based On Voice\n", "subtitle: None\n", "shortTitle: None\n", "teaser: If you're trying out for a job, the one judging you may not be a person \u2014 it could be a computer. Algorithms are evaluating human voices to determine which ones are engaging, calming and trustworthy.\n", "miniTeaser: None\n", "slug: All Tech Considered\n", "thumbnail: \n", " \n", "medium: http://media.npr.org/assets/img/2015/03/23/gettyimages-106683221edit_sq-3f2aa467193d4740564c3d4e9b1041d9bbb134e6.jpg?s=13\n", "large: http://media.npr.org/assets/img/2015/03/23/gettyimages-106683221edit_sq-3f2aa467193d4740564c3d4e9b1041d9bbb134e6.jpg?s=11\n", "provider: Ikon Images/Getty Images\n", "storyDate: Mon, 23 Mar 2015 16:40:00 -0400\n", "pubDate: Wed, 27 May 2015 08:58:00 -0400\n", "lastModifiedDate: Wed, 27 May 2015 08:58:41 -0400\n", "audioRunByDate: Sat, 28 Mar 2015 01:00:00 -0400\n", "show: \n", " \n", "program: All Things Considered\n", "showDate: Mon, 23 Mar 2015 16:00:00 -0400\n", "segNum: 9\n", "keywords: None\n", "priorityKeywords: None\n", "organization: \n", " \n", "name: NPR\n", "website: http://www.npr.org/\n", "transcript: \n", " \n", "link: http://api.npr.org/transcript?id=394827451&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Technology\n", "link: http://www.npr.org/sections/technology/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=1019&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: All Things Considered for March 23, 2015\n", "link: http://www.npr.org/programs/all-things-considered/2015/03/23/394807955?ft=nprml&f=\n", "link: http://api.npr.org/query?id=394807955&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: All Tech Considered Featured Post Two\n", "link: http://www.npr.org/series/241677706/all-tech-considered-featured-post-two?ft=nprml&f=\n", "link: http://api.npr.org/query?id=241677706&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Science\n", "link: http://www.npr.org/sections/alltechconsidered/211399851/science?ft=nprml&f=\n", "link: 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"link: http://www.npr.org/2012/12/20/167702665/geoff-nunbergs-word-of-the-year-big-data?ft=nprml&f=\n", "link: http://api.npr.org/query?id=167702665&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "relatedLink: \n", " \n", "caption: Even Dictionaries Grapple With Getting 'Marriage' Right\n", "link: http://www.npr.org/2013/04/04/176235479/even-dictionaries-grapple-with-getting-marriage-right?ft=nprml&f=\n", "link: http://api.npr.org/query?id=176235479&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "relatedLink: \n", " \n", "caption: 'Horrific' And 'Surreal': The Words We Use To Bear Witness\n", "link: http://www.npr.org/2013/04/26/179021100/horrific-and-surreal-the-words-we-use-to-bear-witness?ft=nprml&f=\n", "link: http://api.npr.org/query?id=179021100&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "pullQuote: \n", " \n", "text: Whether or not you think the government should be sweeping this stuff up, calling it metadata doesn't make the process any less intrusive. Tell me where you've been and who you've been talking to, and I'll tell you about your politics, your health, your sexual orientation, your finances. \n", "person: None\n", "date: None\n", "text: \n", " \n", "textWithHtml: \n", " \n", "story: \n", " \n", "link: http://www.npr.org/2013/03/24/174982423/former-bush-aide-pushes-conservative-case-for-gay-marriage?ft=nprml&f=\n", "link: http://api.npr.org/query?id=174982423&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "link: http://n.pr/YrQlSA\n", "title: Former Bush Aide Pushes 'Conservative Case' For Gay Marriage\n", "subtitle: None\n", "shortTitle: None\n", "teaser: Ken Mehlman, the political director for the George W. Bush White House, compares the right to marry to other fundamental rights conservatives embrace. He rounded up a group of 131 prominent Republicans to sign a legal brief that's at odds with the House GOP leadership and the party's platform.\n", "miniTeaser: Ken Mehlman compares the right to marry to other fundamental rights conservatives embrace.\n", "slug: Same-Sex Marriage And The Supreme Court\n", "thumbnail: \n", " \n", "medium: http://media.npr.org/assets/img/2013/03/21/56633856_sq-abd9085b957eb2e00f61c6236663eb856c9e0654.jpg?s=13\n", "large: http://media.npr.org/assets/img/2013/03/21/56633856_sq-abd9085b957eb2e00f61c6236663eb856c9e0654.jpg?s=11\n", "provider: Getty Images\n", "storyDate: Sun, 24 Mar 2013 06:05:00 -0400\n", "pubDate: Sun, 24 Mar 2013 08:05:00 -0400\n", "lastModifiedDate: Sun, 24 Mar 2013 18:46:54 -0400\n", "audioRunByDate: None\n", "show: \n", " \n", "program: Weekend Edition Sunday\n", "showDate: Sun, 24 Mar 2013 08:00:00 -0400\n", "segNum: 11\n", "keywords: None\n", "priorityKeywords: None\n", "organization: \n", " \n", "name: NPR\n", "website: http://www.npr.org/\n", "transcript: \n", " \n", "link: http://api.npr.org/transcript?id=174982423&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Weekend Edition Sunday for March 24, 2013\n", "link: http://www.npr.org/programs/weekend-edition-sunday/2013/03/24/174983550/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=174983550&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Same-Sex Marriage And The Supreme Court\n", "link: http://www.npr.org/series/174965583/same-sex-marriage-and-the-supreme-court?ft=nprml&f=\n", "link: http://api.npr.org/query?id=174965583&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Law\n", "link: http://www.npr.org/sections/law/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=1070&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Law\n", "link: http://www.npr.org/sections/law/?ft=nprml&f=\n", "link: 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http://www.npr.org/programs/weekend-edition-sunday/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=10&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "audio: \n", " \n", "title: None\n", "duration: 306\n", "description: None\n", "format: \n", " \n", "mp3: http://api.npr.org/m3u/1175173098-1b63a1.m3u?orgId=1&topicId=1070&aggIds=174965583&d=306&p=10&story=174982423&t=progseg&e=174983550&seg=11&ft=nprml&f=\n", "wm: http://www.npr.org/templates/dmg/dmg_wmref_em.php?id=175173098&type=1&mtype=WM&orgId=1&topicId=1070&aggIds=174965583&d=306&p=10&story=174982423&t=progseg&e=174983550&seg=11&ft=nprml&f=\n", "mp4: http://pd.npr.org/npr-mp4/npr/wesun/2013/03/20130324_wesun_11.mp4?orgId=1&topicId=1070&aggIds=174965583&d=306&p=10&story=174982423&t=progseg&e=174983550&seg=11&ft=nprml&f=\n", "mediastream: rtmp://flash.npr.org/ondemand/mp3:anon.npr-mp3/npr/wesun/2013/03/20130324_wesun_11.mp3\n", "region: all\n", "rightsHolder: None\n", "permissions: \n", " \n", "download: None\n", "stream: None\n", "embed: None\n", "stream: None\n", "byline: \n", " \n", "name: Nina Totenberg\n", "link: http://www.npr.org/people/2101289/nina-totenberg?ft=nprml&f=\n", "link: http://api.npr.org/query?id=2101289&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "container: \n", " \n", "title: Related Stories\n", "introText: None\n", "colSpan: 2\n", "displayOptions: Display Both\n", "link: None\n", "link: None\n", "link: None\n", "image: \n", " \n", "title: Ken Mehlman, then chairman of the Republican National Committee, speaks during a meeting at the Capitol Hilton in January 2006 in Washington, D.C.\n", "caption: Ken Mehlman, then chairman of the Republican National Committee, speaks during a meeting at the Capitol Hilton in January 2006 in Washington, D.C.\n", "link: None\n", "producer: Chip Somodevilla\n", "provider: Getty Images\n", "copyright: None\n", "enlargement: \n", " \n", "caption: None\n", "crop: None\n", "crop: None\n", "crop: None\n", "crop: None\n", "relatedLink: \n", " \n", "caption: Timeline: Gay Marriage In Law, Pop Culture And The Courts\n", "link: http://www.npr.org/2013/03/21/174732431/timeline-gay-marriage-in-law-pop-culture-and-the-courts?ft=nprml&f=\n", "link: http://api.npr.org/query?id=174732431&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "relatedLink: \n", " \n", "caption: Meet The 83-Year-Old Taking On The U.S. Over Same-Sex Marriage\n", "link: http://www.npr.org/2013/03/21/174944430/meet-the-83-year-old-taking-on-the-u-s-over-same-sex-marriage?ft=nprml&f=\n", "link: http://api.npr.org/query?id=174944430&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "relatedLink: \n", " \n", "caption: As Gay Marriage Heads To Court, A Look Back At The Bumpy Ride\n", "link: http://www.npr.org/2013/03/21/174879832/as-gay-marriage-heads-to-court-a-look-back-at-the-bumpy-ride?ft=nprml&f=\n", "link: http://api.npr.org/query?id=174879832&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "pullQuote: \n", " \n", "text: You can't change the past, but you can try to, as much as you can going forward, be helpful and be constructive.\n", "person: Ken Mehlman\n", "date: None\n", "text: \n", " \n", "textWithHtml: \n", " \n", "story: \n", " \n", "link: http://www.npr.org/sections/13.7/2013/03/12/174028759/big-data-is-the-steam-engine-of-our-time?ft=nprml&f=\n", "link: http://api.npr.org/query?id=174028759&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "link: http://n.pr/Y65cyZ\n", "title: Big Data Is The Steam Engine Of Our Time\n", "subtitle: None\n", "shortTitle: None\n", "teaser: Big Data is no flash in the pan. It's a revolution that will transform how we live. It's another in a long line of human attempts to bring order to the universe. As such, commentator Adam Frank also says it will change how we do science.\n", "miniTeaser: Like it or not, Big Data is not going away. It's only going to get bigger (and bigger).\n", "slug: 13.7: Cosmos And Culture\n", "thumbnail: \n", " \n", "medium: http://media.npr.org/assets/img/2013/03/12/160313744-tokyo-stocks_sq-3ceba7291ac649b7c2de903e9272bc57db02da8b.jpg?s=13\n", "large: http://media.npr.org/assets/img/2013/03/12/160313744-tokyo-stocks_sq-3ceba7291ac649b7c2de903e9272bc57db02da8b.jpg?s=11\n", "provider: AFP/Getty Images\n", "storyDate: Tue, 12 Mar 2013 12:28:00 -0400\n", "pubDate: Sat, 13 Apr 2013 13:48:00 -0400\n", "lastModifiedDate: Sat, 13 Apr 2013 13:47:59 -0400\n", "audioRunByDate: None\n", "keywords: None\n", "priorityKeywords: None\n", "organization: \n", " \n", "name: NPR\n", "website: http://www.npr.org/\n", "parent: \n", " \n", "title: Commentary\n", "link: http://www.npr.org/sections/commentary/?ft=nprml&f=\n", "link: http://api.npr.org/query?id=1060&meta=inherit&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "parent: \n", " \n", "title: Emanuel Derman\n", "link: 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numbers\n", "caption: Big Data: trying to make sense of the numbers\n", "link: None\n", "producer: Kazuhiro Nogi\n", "provider: AFP/Getty Images\n", "copyright: None\n", "enlargement: \n", " \n", "caption: None\n", "crop: None\n", "crop: None\n", "crop: None\n", "crop: None\n", "crop: None\n", "relatedLink: \n", " \n", "caption: Self-Tracking Apps To Help You 'Quantify' Yourself\n", "link: http://www.npr.org/sections/alltechconsidered/2013/03/12/174058272/self-tracking-apps-to-help-you-quantify-yourself?ft=nprml&f=\n", "link: http://api.npr.org/query?id=174058272&apiKey=MDE5Mzg3Mjc2MDE0MzMyMjM3NjM5ZTI2Ng001\n", "text: \n", " \n", "textWithHtml: \n", " \n" ] } ], "prompt_number": 18 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Now that we see how the output looks, we can format and print the output as we want it to appear." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import lxml.etree as etree\n", "#parse the API response xml\n", "tree = etree.XML(r.content)\n", "\n", "#library to format the date later\n", "from dateutil import parser as dtp\n", "\n", "#define the list that the selected stories will go into\n", "storylist = []\n", "#find all of the elements in the tree that make up a story\n", "#the story tag is 2 \"layers\" down into the tree\n", "stories = [subitem for item in tree for subitem in item if subitem.tag=='story']\n", "for story in stories:\n", " #define dictionary for story parts\n", " storypart = {}\n", " for element in story:\n", " #print(element.tag) #for testing\n", " if element.tag == 'title':\n", " storypart[\"title\"] = element.text\n", " if element.tag == 'teaser':\n", " storypart[\"teaser\"] = element.text\n", " if element.tag == 'link' and element.attrib['type'] == 'html':\n", " storypart[\"htmllink\"] = element.text\n", " for subelement in element.iter(\"*\"):\n", " if subelement.tag == 'program':\n", " storypart[\"program\"] = subelement.text\n", " if subelement.tag == 'mp3':\n", " storypart[\"mp3link\"] = subelement.text\n", " if subelement.tag == 'storyDate':\n", " dt = dtp.parse(subelement.text)\n", " storypart[\"date\"] = dt.strftime(\"%x %I:%M%p\")\n", " #print(storypart) #for testing\n", " #we're creating a list of dictionaries here\n", " storylist.append(storypart)\n", "#format for display\n", "#iterate through list of stories, then output items in each story's dictionary in desired display order\n", "for story in storylist:\n", " #check to see if dict key exists, because not every story has every element\n", " #and error is thrown if you refer to nonexistent key\n", " if \"title\" in story:\n", " print(\"\\n***** \" + story[\"title\"] + \" *****\")\n", " print()\n", " if \"teaser\" in story:\n", " print(story[\"teaser\"],\"\\n\")\n", " if \"htmllink\" in story:\n", " print (\"Story Link: \" + story[\"htmllink\"]) \n", " if \"date\" in story:\n", " print(\"Published: \" + story[\"date\"])\n", " if \"program\" in story:\n", " print(\"Program: \" + story[\"program\"])\n", " if \"mp3link\" in story:\n", " print(\"Download MP3: \" + story[\"mp3link\"])\n", " print(\"\\n\")\n", "\n", " " ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "***** What If Web Search Results Were Based On Accuracy? *****\n", "\n", "Google has been researching the possibility of ranking search results based on established facts. Were this to become the norm, it would have huge implications for future discourse, says Adam Frank. \n", "\n", "Story Link: http://www.npr.org/sections/13.7/2015/03/24/395012901/what-if-web-search-results-were-based-on-accuracy?ft=nprml&f=\n", "Published: 03/24/15 07:55AM\n", "\n", "\n", "\n", "***** Now Algorithms Are Deciding Whom To Hire, Based On Voice *****\n", "\n", "If you're trying out for a job, the one judging you may not be a person \u2014 it could be a computer. Algorithms are evaluating human voices to determine which ones are engaging, calming and trustworthy. \n", "\n", "Story Link: http://www.npr.org/sections/alltechconsidered/2015/03/23/394827451/now-algorithms-are-deciding-whom-to-hire-based-on-voice?ft=nprml&f=\n", "Published: 03/23/15 04:40PM\n", "Program: All Things Considered\n", "Download MP3: http://api.npr.org/m3u/1394906629-e8c9fa.m3u?orgId=1&topicId=1019&d=267&p=2&story=394827451&t=progseg&e=394807955&seg=9&ft=nprml&f=\n", "\n", "\n", "\n", "***** A Data Analyst's Blog Is Transforming How New Yorkers See Their City *****\n", "\n", "Ben Wellington tracked down two of the city's costliest parking spots and its leafiest neighborhoods, while pointing out questionable health grades for restaurants and odd charges for subway riders. \n", "\n", "Story Link: http://www.npr.org/sections/alltechconsidered/2014/11/28/367046864/a-data-analysts-blog-is-transforming-how-new-yorkers-see-their-city?ft=nprml&f=\n", "Published: 11/28/14 08:03AM\n", "\n", "\n", "\n", "***** Parties Compete To Build The Best Voter-Turnout Machine *****\n", "\n", "A week ahead of Election Day, both parties are still scrambling to identify and turn out every one of their voters. These get-out-the-vote operations are as expensive and high-tech as every other bit of modern campaigning. \n", "\n", "Story Link: http://www.npr.org/2014/10/27/359233263/parties-compete-to-build-the-best-voter-turnout-machine?ft=nprml&f=\n", "Published: 10/27/14 04:36AM\n", "Program: Morning Edition\n", "Download MP3: http://api.npr.org/m3u/1359233264-106fb3.m3u?orgId=1&topicId=1014&d=432&p=3&story=359233263&t=progseg&e=359233262&seg=1&ft=nprml&f=\n", "\n", "\n", "\n", "***** In San Diego, A Boot Camp For Data Junkies *****\n", "\n", "Natasha Balac runs a two-day boot camp out of the San Diego Supercomputer Center for people from all types of industries to learn the tools and algorithms to help them analyze data and spot patterns. \n", "\n", "Story Link: http://www.npr.org/2014/09/20/350155825/in-san-diego-a-bootcamp-for-data-junkies?ft=nprml&f=\n", "Published: 09/20/14 04:55PM\n", "Program: All Things Considered\n", "Download MP3: http://api.npr.org/m3u/1350155828-c6e491.m3u?orgId=1&topicId=1019&d=247&p=2&story=350155825&t=progseg&e=350108710&seg=7&ft=nprml&f=\n", "\n", "\n", "\n", "***** Facebook Apologizes For Manipulation; Data Research Likely To Go On *****\n", "\n", "A mood study that Facebook conducted on unwitting users has been criticized. Data science plays an integral role at Facebook \u2014 for bottom line reasons, and in collaboration with academic researchers. \n", "\n", "Story Link: http://www.npr.org/2014/07/04/328373960/facebook-apologizes-for-manipulation-data-research-likely-to-go-on?ft=nprml&f=\n", "Published: 07/04/14 05:05AM\n", "Program: Morning Edition\n", "Download MP3: http://api.npr.org/m3u/1328373961-eb7891.m3u?orgId=1&topicId=1006&d=112&p=3&story=328373960&t=progseg&e=328373945&seg=4&ft=nprml&f=\n", "\n", "\n", "\n", "***** The Occupy Movement At 2: Many Voices, Many Messages *****\n", "\n", "Demonstrators packed lower Manhattan on Tuesday, two years after the launch of the Occupy Wall Street movement. While Occupy's prominence has faded since becoming a household name in 2011, its supporters say the group's concerns have helped prompt a national conversation about income inequality. \n", "\n", "Story Link: http://www.npr.org/2013/09/17/223356184/the-occupy-movement-at-two-many-voices-many-messages?ft=nprml&f=\n", "Published: 09/17/13 05:10PM\n", "Program: All Things Considered\n", "Download MP3: http://api.npr.org/m3u/1223472694-83d6af.m3u?orgId=1&topicId=1003&d=238&p=2&story=223356184&t=progseg&e=223381920&seg=8&ft=nprml&f=\n", "\n", "\n", "\n", "***** Calling It 'Metadata' Doesn't Make Surveillance Less Intrusive *****\n", "\n", "Whether it's logs of phone calls or GPS data, commentator Geoff Nunberg says it still says a lot about who you are: \"Tell me where you've been and who you've been talking to, and I'll tell you about your politics, your health, your sexual orientation, your finances,\" he says. \n", "\n", "Story Link: http://www.npr.org/2013/06/21/193578367/calling-it-metadata-doesnt-make-surveillance-less-intrusive?ft=nprml&f=\n", "Published: 06/21/13 01:25PM\n", "Program: Fresh Air\n", "Download MP3: http://api.npr.org/m3u/1193954334-10056c.m3u?orgId=1&topicId=1060&d=351&p=13&story=193578367&t=progseg&e=193925921&seg=2&ft=nprml&f=\n", "\n", "\n", "\n", "***** Former Bush Aide Pushes 'Conservative Case' For Gay Marriage *****\n", "\n", "Ken Mehlman, the political director for the George W. Bush White House, compares the right to marry to other fundamental rights conservatives embrace. He rounded up a group of 131 prominent Republicans to sign a legal brief that's at odds with the House GOP leadership and the party's platform. \n", "\n", "Story Link: http://www.npr.org/2013/03/24/174982423/former-bush-aide-pushes-conservative-case-for-gay-marriage?ft=nprml&f=\n", "Published: 03/24/13 06:05AM\n", "Program: Weekend Edition Sunday\n", "Download MP3: http://api.npr.org/m3u/1175173098-1b63a1.m3u?orgId=1&topicId=1070&aggIds=174965583&d=306&p=10&story=174982423&t=progseg&e=174983550&seg=11&ft=nprml&f=\n", "\n", "\n", "\n", "***** Big Data Is The Steam Engine Of Our Time *****\n", "\n", "Big Data is no flash in the pan. It's a revolution that will transform how we live. It's another in a long line of human attempts to bring order to the universe. As such, commentator Adam Frank also says it will change how we do science. \n", "\n", "Story Link: http://www.npr.org/sections/13.7/2013/03/12/174028759/big-data-is-the-steam-engine-of-our-time?ft=nprml&f=\n", "Published: 03/12/13 12:28PM\n", "\n", "\n" ] } ], "prompt_number": 28 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-2.0
IsacLira/data-science-cookbook
2016/spark/lesson-3/Algoritmos_Classificacao_Regressao.ipynb
2
6567
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Algoritmos de Classificação e Regressão" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### - SVM" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Erro = 0.38198757764\n" ] } ], "source": [ "from pyspark.mllib.classification import SVMWithSGD, SVMModel\n", "from pyspark.mllib.regression import LabeledPoint\n", "\n", "# Constroi estrutura LabeledPoint a partir dos dados\n", "def parsePoint(line):\n", " values = [float(x) for x in line.split(' ')]\n", " return LabeledPoint(values[0], values[1:])\n", "\n", "data = sc.textFile(\"data/sample_svm_data.txt\")\n", "parsedData = data.map(parsePoint)\n", "\n", "# Constroi o modelo SVM utilizando Gradiente Descendente Estocástico\n", "model = SVMWithSGD.train(parsedData, iterations=100)\n", "\n", "# Faz as predições\n", "labelsAndPreds = parsedData.map(lambda p: (p.label, model.predict(p.features)))\n", "trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count())\n", "print(\"Erro = \" + str(trainErr))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### - Decision Tree" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Porcentagem de Erro = 0.0769230769231\n", "Modelos de classificação gerados:\n", "DecisionTreeModel classifier of depth 1 with 3 nodes\n", " If (feature 406 <= 20.0)\n", " Predict: 0.0\n", " Else (feature 406 > 20.0)\n", " Predict: 1.0\n", "\n" ] } ], "source": [ "from pyspark.mllib.tree import DecisionTree, DecisionTreeModel\n", "from pyspark.mllib.util import MLUtils\n", "\n", "# Carrega os dados\n", "data = MLUtils.loadLibSVMFile(sc, 'data/sample_libsvm_data.txt')\n", "# Divide os dados em treino (70%) e teste (30%)\n", "(trainingData, testData) = data.randomSplit([0.7, 0.3])\n", "\n", "# Treina um classificador DecisionTree.\n", "# categoricalFeaturesInfo vazio indica que todas as features são valores contínuos\n", "# caso houvesse alguma feature categórica, categoricalFeaturesInfo = {0:2}, \n", "# indicando que a feature 0 tem 2 categorias\n", "model = DecisionTree.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={},\n", " impurity='gini', maxDepth=5, maxBins=32)\n", "\n", "# Faz as predições\n", "predictions = model.predict(testData.map(lambda x: x.features))\n", "labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)\n", "testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(testData.count())\n", "print('Porcentagem de Erro = ' + str(testErr))\n", "print('Modelos de classificação gerados:')\n", "print(model.toDebugString())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### - Random Forest" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Error = 0.030303030303\n", "Learned classification forest model:\n", "TreeEnsembleModel classifier with 3 trees\n", "\n", " Tree 0:\n", " If (feature 511 <= 0.0)\n", " If (feature 402 <= 0.0)\n", " Predict: 1.0\n", " Else (feature 402 > 0.0)\n", " Predict: 0.0\n", " Else (feature 511 > 0.0)\n", " Predict: 0.0\n", " Tree 1:\n", " If (feature 401 <= 0.0)\n", " If (feature 370 <= 0.0)\n", " Predict: 1.0\n", " Else (feature 370 > 0.0)\n", " Predict: 0.0\n", " Else (feature 401 > 0.0)\n", " Predict: 0.0\n", " Tree 2:\n", " If (feature 399 <= 0.0)\n", " If (feature 494 <= 0.0)\n", " If (feature 434 <= 0.0)\n", " Predict: 0.0\n", " Else (feature 434 > 0.0)\n", " Predict: 1.0\n", " Else (feature 494 > 0.0)\n", " If (feature 606 <= 0.0)\n", " Predict: 0.0\n", " Else (feature 606 > 0.0)\n", " Predict: 1.0\n", " Else (feature 399 > 0.0)\n", " Predict: 0.0\n", "\n" ] } ], "source": [ "from pyspark.mllib.tree import RandomForest, RandomForestModel\n", "from pyspark.mllib.util import MLUtils\n", "\n", "data = MLUtils.loadLibSVMFile(sc, 'data/sample_libsvm_data.txt')\n", "# Divide os dados em treino (70%) e teste (30%)\n", "(trainingData, testData) = data.randomSplit([0.7, 0.3])\n", "\n", "# Treina um classificador DecisionTree.\n", "# Número de Decision Tree a serem geradas: 3. \n", "model = RandomForest.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={},\n", " numTrees=3, featureSubsetStrategy=\"auto\",\n", " impurity='gini', maxDepth=4, maxBins=32)\n", "\n", "# Faz a predição\n", "predictions = model.predict(testData.map(lambda x: x.features))\n", "labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)\n", "testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(testData.count())\n", "print('Porcentagem de Erro = ' + str(testErr))\n", "print('Modelos de classificação gerados:')\n", "print(model.toDebugString())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Pyspark (Py 2)", "language": "", "name": "pyspark" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
maasg/spark-notebook
notebooks/misc/Using JavaScript with Playground.snb.ipynb
10
2481
{ "metadata" : { "name" : "Using JavaScript with Playground", "user_save_timestamp" : "2014-12-23T01:03:45.877Z", "auto_save_timestamp" : "2014-12-23T01:03:15.549Z", "language_info" : { "name" : "Scala", "file_extension" : "scala", "codemirror_mode" : "text/x-scala" }, "trusted" : true, "customLocalRepo" : null, "customRepos" : null, "customDeps" : null, "customImports" : null, "customArgs" : null, "customSparkConf" : null }, "cells" : [ { "metadata" : { "trusted" : true, "collapsed" : false }, "cell_type" : "code", "source" : "import notebook._\nimport notebook.front._\nimport notebook.front.widgets._\nimport notebook.JsonCodec._\nimport play.api.libs.json._\n\nval d = \"update da DOM\" \nnew Playground(Seq(\"ok\", \"nok\"), List(Script(\"consoleDir\", JsObject(Nil))), \n List(\n s\"\"\"\n function(dataObs, elem) { \n console.warn(arguments); \n $$(elem).append(\"<p>$d</p>\");\n }\n \"\"\"\n ) \n )", "outputs" : [ ] }, { "metadata" : { "trusted" : true, "collapsed" : false }, "cell_type" : "code", "source" : "case class Data(name:String, payload:Int)", "outputs" : [ ] }, { "metadata" : { "trusted" : true, "collapsed" : false }, "cell_type" : "code", "source" : "import notebook.JsonCodec._\nimplicit val dataCodec = new Codec[JsValue, Data] {\n val c:Codec[JsValue, Int] = ints // `ints` is a predefined Codec[JValue, Int]\n \n def encode(x:JsValue):Data = Data(\"<unknown>\", c.encode(x))\n def decode(x:Data):JsValue = c.decode(x.payload)\n}\nval data = Seq(Data(\"test1\", 1), Data(\"test2\", 2))\nnew Playground(data, List(Script(\"consoleDir\", JsObject(Nil))), List(s\"\"\" \n function(dataO, e) {\n dataO.subscribe(function(d) {\n //do something with new data `d`\n })\n }\n \"\"\"))", "outputs" : [ ] } ], "nbformat" : 4 }
apache-2.0
sbalanovich/ReinforcementHalma
JC-SB-Halma3-Small-Copy1.ipynb
2
1241990
null
mit
grfiv/predict-blood-donations
bagged_scikit_neuralnetwork.ipynb
1
140700
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# bagged_scikit_neuralnetwork" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division\n", "from IPython.display import display\n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import random, sys, os, re\n", "\n", "from sknn.mlp import Classifier, Layer\n", "\n", "from sklearn.cross_validation import StratifiedKFold\n", "from sklearn.grid_search import RandomizedSearchCV, GridSearchCV\n", "from sklearn.cross_validation import cross_val_predict, permutation_test_score" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "SEED = 97\n", "scale = True \n", "minmax = False\n", "norm = False\n", "nointercept = True\n", "engineering = True\n", "\n", "N_CLASSES = 2\n", "max_epochs = 100\n", "\n", "submission_filename = \"../submissions/submission_bagged_scikit_nn.csv\"" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "scrolled": true }, "source": [ "# Load the training data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from load_blood_data import load_blood_data\n", "\n", "y_train, X_train = load_blood_data(train=True, SEED = SEED, \n", " scale = scale,\n", " minmax = minmax,\n", " norm = norm,\n", " nointercept = nointercept,\n", " engineering = engineering)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fit the model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "StatifiedCV = StratifiedKFold(y = y_train, \n", " n_folds = 10, \n", " shuffle = True, \n", " random_state = SEED)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"\n", "[\"Bagging Predictors\" Department of Statistics UC Berkeley 1994; Leo Breiman]\n", "\"\"\"\n", "\n", "from sklearn.base import BaseEstimator, ClassifierMixin, clone\n", "\n", "\n", "class BaggedScikitClassifier(BaseEstimator, ClassifierMixin):\n", " \"\"\"\n", " Runs a scikit-learn base estimator 'n_estimators' times, fitting with\n", " the X_train indices drawn from a random sample of size X_train.shape[0] \n", " with replacement\n", " \n", " sklearn.ensemble.BaggingClassifier is more convenient and has more tunable\n", " features but it requires the base estimator to have a 'fit' method with a\n", " 'sample_weight' parameter and many estimators do not \n", " (as of sklearn.__version__ == '0.17').\n", " \n", " \n", " Usage\n", " -----\n", " clf = BaggedScikitClassifier(base_estimator, n_estimators)\n", " \n", " base_estimator is an instance of a scikit-learn object\n", " n_estimators is the number of bagged replicas to use\n", " \n", " clf.fit(X_train, y_train)\n", " \n", " a no-op; merely stores X_train and y_train for use in the\n", " predict and predict_proba methods\n", " \n", " clf.predict(X_test)\n", " \n", " the value returned is the majority vote of each of the\n", " fitted base_estimators' class predictions\n", " \n", " clf.predict_proba(X_test)\n", " \n", " the value returned is the average of each of the\n", " fitted base_estimators' probabilities\n", " \"\"\"\n", " def __init__(self, base_estimator, n_estimators=15):\n", " self.n_estimators = n_estimators\n", " self.base_estimator = base_estimator\n", "\n", " \n", " def fit(self, X_train, y_train):\n", " \"\"\"\n", " The actual fitting is done in the predict & predict_proba methods\n", " \"\"\"\n", " self.X_train = X_train\n", " self.y_train = y_train\n", " \n", " return self\n", " \n", " \n", " def predict(self, X_test):\n", " \"\"\"\n", " Return the majority vote of 'n_estimators' fits\n", " \"\"\"\n", " predictions = []\n", " for i in range(self.n_estimators):\n", " clf = clone(self.base_estimator)\n", " idx = np.random.choice(a = range(self.X_train.shape[0]), \n", " size = self.X_train.shape[0], \n", " replace=True, p=None)\n", "\n", " clf.fit(self.X_train[idx,:], self.y_train[idx])\n", " predictions.append(clf.predict(X_test))\n", " \n", " from scipy.stats import mode\n", " \n", " return mode(predictions)[0][0]\n", " \n", " def predict_proba(self, X_test):\n", " \"\"\"\n", " Return the average probability matrix of 'n_estimators' fits\n", " \"\"\"\n", " completed_bags = 0\n", " predictions = np.empty((X_test.shape[0], N_CLASSES), dtype=np.float32)\n", " for i in range(self.n_estimators):\n", " clf = clone(self.base_estimator)\n", " idx = np.random.choice(a = range(self.X_train.shape[0]), \n", " size = self.X_train.shape[0], \n", " replace=True, p=None)\n", " \n", " clf.fit(self.X_train[idx,:], self.y_train[idx])\n", " predictions += clf.predict_proba(X_test)\n", " completed_bags += 1\n", " \n", " return predictions / float(completed_bags)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n", "Wall time: 133 µs\n" ] } ], "source": [ "%%time\n", "\n", "random.seed(SEED)\n", "\n", "# ----------------------------------- base estimator -------------------------------\n", "nn_layers = [\n", " Layer(type = 'Rectifier', name = 'hidden', \n", " units = 100,\n", " weight_decay = None,\n", " pieces = None,\n", " dropout = None),\n", " \n", " Layer(type = 'Softmax', name = 'output')\n", " ]\n", "\n", "base = Classifier(layers = nn_layers,\n", " learning_rate = 0.01,\n", " learning_rule = 'nesterov',\n", " learning_momentum = 0.9,\n", " loss_type = u'mse',\n", " mutator = None, # data augmentation function\n", " \n", " regularize = None,\n", " weight_decay = None,\n", " dropout_rate = None,\n", " \n", " batch_size = 10,\n", " \n", " valid_size = None, \n", " valid_set = None,\n", " \n", " n_stable = 10, # early stopping after ...\n", " f_stable = 0.001, # validation error change threshold\n", " n_iter = max_epochs, \n", " \n", " random_state = SEED, \n", " debug = False, \n", " verbose = True) \n", "\n", "# ----------------------------------- BaggedScikitClassifier ------------------------------- \n", "clf = BaggedScikitClassifier(base_estimator = base, \n", " n_estimators = 15)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# from sklearn_utilities import GridSearchHeatmap\n", "\n", "# GridSearchHeatmap(grid_clf, y_key='learning_rate', x_key='n_estimators')\n", "\n", "# from sklearn_utilities import plot_validation_curves\n", "\n", "# plot_validation_curves(grid_clf, param_grid, X_train, y_train, ylim = (0.0, 1.05))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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gGuXpRYt4/PbbKWlooLG8vOWkwOlzvTU1NR8bvKYmbpkekVXoP1cREel26jHL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\nIqIhUphFRERENEQKs4iIiIiG+B/r9Y0l7m0qFQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8c90e67e10>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 468 ms, sys: 104 ms, total: 572 ms\n", "Wall time: 5min 43s\n" ] } ], "source": [ "%%time\n", "\n", "try:\n", " from sklearn_utilities import plot_learning_curve\n", "except:\n", " import imp, os\n", " util = imp.load_source('sklearn_utilities', os.path.expanduser('~/Dropbox/Python/sklearn_utilities.py'))\n", " from sklearn_utilities import plot_learning_curve\n", "\n", "plot_learning_curve(estimator = clf, \n", " title = 'Learning Curves', \n", " X = X_train.values.astype(np.float32), \n", " y = y_train, \n", " ylim = (0.0, 1.10), \n", " cv = StatifiedCV, \n", " train_sizes = np.linspace(.1, 1.0, 5),\n", " n_jobs = -1)\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training set predictions" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 100 ms, sys: 40 ms, total: 140 ms\n", "Wall time: 1min\n" ] } ], "source": [ "%%time\n", "\n", "train_preds = cross_val_predict(estimator = clf, \n", " X = X_train.values.astype(np.float32), \n", " y = y_train, \n", " cv = StatifiedCV, \n", " n_jobs = -1, \n", " verbose = 0, \n", " fit_params = None, \n", " pre_dispatch = '2*n_jobs')\n", "\n", "y_true, y_pred = y_train, train_preds" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[400 38]\n", " [ 78 60]]\n" ] }, { "data": { "image/png": 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ihs+4cV2DerXaiLYt2L4b6JK0ctOuLwB/Li0A5wFr9HKKJYGrbb8a+DPwgbL9B8DJtjcF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\n92SqGemeBX7VndAknUX1paRZd81+BlV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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8c90e79b90>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Accuracy 0.7986, mis-class rate 0.2014\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "cm = confusion_matrix(y_true, y_pred, labels=None)\n", "print cm\n", "\n", "try:\n", " from sklearn_utilities import plot_confusion_matrix\n", "except:\n", " import imp, os\n", " util = imp.load_source('sklearn_utilities', os.path.expanduser('~/Dropbox/Python/sklearn_utilities.py'))\n", " from sklearn_utilities import plot_confusion_matrix\n", "\n", "plot_confusion_matrix(cm, ['Did not Donate','Donated'])\n", "\n", "accuracy = round(np.trace(cm)/float(np.sum(cm)),4)\n", "misclass = 1 - accuracy\n", "print(\"Accuracy {}, mis-class rate {}\".format(accuracy,misclass))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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cOJBt27YxcuRILeIX8VEo15Q1d86965zb4Jz7b/Zry9EuMrNM4K/AXGADMMnM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\niYmsXr1a21yEkV27djF//nx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fmHPOVq1aZWbe1GGzZs3sggsuKPK65ORki4iIsPvuu6/I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\nffZZ/vKXvwAwc+ZMNmzYwIQJE/wcVXDavn07ISEhNG7c2Ku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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8c90e79990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import roc_curve\n", "from sklearn.metrics import roc_auc_score\n", "from sklearn.metrics import log_loss\n", "from sklearn.metrics import f1_score\n", "\n", "fpr, tpr, thresholds = roc_curve(y_true, y_pred, pos_label=None)\n", "\n", "\n", "plt.figure(figsize=(10,6))\n", "plt.plot([0, 1], [0, 1], 'k--')\n", "plt.plot(fpr, tpr)\n", "\n", "AUC = roc_auc_score(y_true, y_pred, average='macro')\n", "plt.text(x=0.6,y=0.4,s=\"AUC {:.4f}\"\\\n", " .format(AUC),\n", " fontsize=16)\n", "\n", "plt.text(x=0.6,y=0.3,s=\"accuracy {:.2f}%\"\\\n", " .format(accuracy*100),\n", " fontsize=16)\n", "\n", "logloss = log_loss(y_true, y_pred)\n", "plt.text(x=0.6,y=0.2,s=\"LogLoss {:.4f}\"\\\n", " .format(logloss),\n", " fontsize=16)\n", "\n", "f1 = f1_score(y_true, y_pred)\n", "plt.text(x=0.6,y=0.1,s=\"f1 {:.4f}\"\\\n", " .format(f1),\n", " fontsize=16)\n", "\n", "plt.xlabel('False positive rate')\n", "plt.ylabel('True positive rate')\n", "plt.title('ROC curve')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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mzdM333yTb4rY/v378y12/Nxzzx313Bo1aqT4+Ph8vwfz5s0Led+l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\nXLmyqlevrn//+9/q1q2bOnbsqKefflo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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8c8d4a8e90>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3min 10s, sys: 1.33 s, total: 3min 12s\n", "Wall time: 1h 23min 30s\n" ] } ], "source": [ "%%time\n", "\n", "# score : The true score without permuting targets.\n", "# permutation_scores : array, shape (n_permutations,)\n", "# The scores obtained for each permutation.\n", "# pvalue : The returned value equals \n", "# > p-value if scoring returns bigger numbers for better scores (e.g., accuracy_score). \n", "# > If scoring is a loss function (i.e. when lower is better such as with mean_squared_error) \n", "# then this is actually the complement of the p-value: 1 - p-value.\n", "\n", "score, permutation_scores, pvalue = permutation_test_score(estimator = clf, \n", " X = X_train.values.astype(np.float32), \n", " y = y_train, \n", " cv = StatifiedCV, \n", " labels = None,\n", " random_state = SEED,\n", " verbose = 0,\n", " n_permutations = 100, \n", " scoring = None,\n", " n_jobs = -1) \n", "# find mean and stdev of the scores\n", "from scipy.stats import norm\n", "mu, std = norm.fit(permutation_scores)\n", "\n", "\n", "plt.figure(figsize=(20,8))\n", "plt.hist(permutation_scores, 20, label='Permutation scores')\n", "\n", "ylim = plt.ylim()\n", "plt.plot(2 * [score], ylim, '--g', linewidth=3,\n", " label='Classification Score (pvalue {:.4f})'.format(pvalue))\n", " \n", "plt.plot(2 * [1. / N_CLASSES], ylim, 'r', linewidth=7, label='Luck')\n", "\n", "plt.ylim(ylim)\n", "plt.title('mu={:.4f}, std={:.4f}'.format(mu,std), fontsize=20)\n", "plt.legend(loc='center',fontsize=16)\n", "plt.xlabel('Score')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "bagged_scikit_nn , , 0.7986 , 6.9558 , 0.6740 , 0.5085 , 0.7463 , 0.0065\n" ] } ], "source": [ "# format for scores.csv file\n", "import re\n", "algo = re.search(r\"submission_(.*?)\\.csv\", submission_filename).group(1)\n", "print(\"{: <26} , , {:.4f} , {:.4f} , {:.4f} , {:.4f} , {:.4f} , {:.4f}\"\\\n", " .format(algo,accuracy,logloss,AUC,f1,mu,std))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Predict the leaderboard score with linear regression" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "clear all but functions rm(list = setdiff(ls(), lsf.str()))\n", "R functions in Dropbox:\n", " [1] \"confusion.matrix_ROC.R\" \"eda.R\" \"knitr_settings.R\" \n", " [4] \"lm_utilities.R\" \"lsp.R\" \"matrix.power.R\" \n", " [7] \"metrics.R\" \"multiplot.R\" \"my.special.functions.R\"\n", "[10] \"NOT_IN.R\" \"SDAFE.R\" \"signOnToTwitter.R\" \n", "[13] \"source.dropbox.R\" \"stem_plot.R\" \n", "to load these functions: source(\"/home/george/Dropbox/R_functions/*.R\")\n", "\n", "To save graphical parameters: opar = par(no.readonly=TRUE)\n", "to restore graphical parameters: par(opar)\n" ] } ], "source": [ "# load the R extension\n", "%load_ext rpy2.ipython\n", "\n", "# see http://ipython.readthedocs.org/en/stable/config/extensions/index.html?highlight=rmagic\n", "# see http://rpy.sourceforge.net/rpy2/doc-2.4/html/interactive.html#module-rpy2.ipython.rmagic" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import python variables into R\n", "%R -i accuracy,logloss,AUC,f1,mu,std" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ " fit lwr upr\n", "1 0.8084 0.0429 1.5738\n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R\n", "# read in the scores.csv file and perform a linear regression with it using this process's variables\n", "\n", "score_data = read.csv('../input/scores.csv')\n", "\n", "lm.fit = lm(leaderboard_score ~ accuracy + logloss + AUC + f1 + mu + std, \n", " data = score_data, \n", " na.action = na.omit)\n", "\n", "slm.fit = step(lm.fit, direction = \"both\", trace=0)\n", "\n", "predicted_leaderboard_score = predict(object = slm.fit, \n", " newdata = data.frame(accuracy,logloss,AUC,f1,mu,std),\n", " interval = \"prediction\", level = 0.99)\n", "\n", "print(round(predicted_leaderboard_score,4))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# --------------------------------------------------------------------------------------------" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Test Set Predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Re-fit with the full training set" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "BaggedScikitClassifier(base_estimator=Classifier(batch_size=10, debug=False, dropout_rate=None, f_stable=0.001,\n", " hidden=<sknn.nn.Layer `Rectifier`: name='hidden', units=100>,\n", " layers=[<sknn.nn.Layer `Rectifier`: name='hidden', units=100>, <sknn.nn.Layer `Softmax`: name='output'>],\n", " learning_momentum=0.9, le...e=97,\n", " regularize=None, valid_set=None, valid_size=None, verbose=True,\n", " weight_decay=None),\n", " n_estimators=15)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#clf.set_params(**clf_params)\n", "clf.fit(X_train.values.astype(np.float32), y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the test data" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from load_blood_data import load_blood_data\n", "\n", "X_test, IDs = load_blood_data(train=False, SEED = SEED, \n", " scale = scale,\n", " minmax = minmax,\n", " norm = norm,\n", " nointercept = nointercept,\n", " engineering = engineering)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Predict the test set with the fitted model" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 0 0 0 1 1 0 0 0 0]\n", "[[ 5.32215059e-01 4.67784971e-01]\n", " [ 8.90477240e-01 1.09522872e-01]\n", " [ 8.65536153e-01 1.34463772e-01]\n", " [ 8.86783600e-01 1.13216370e-01]\n", " [ 4.22802091e-01 5.77197909e-01]\n", " [ 1.89969927e-01 8.10029984e-01]\n", " [ 7.37736285e-01 2.62263745e-01]\n", " [ 9.58714843e-01 4.12850343e-02]\n", " [ 9.99967933e-01 3.19885330e-05]\n", " [ 9.84968185e-01 1.50317950e-02]]\n", "[0.46778497, 0.10952287, 0.13446377, 0.11321637, 0.57719791, 0.81002998, 0.26226375, 0.041285034, 3.1988533e-05, 0.015031795]\n" ] } ], "source": [ "y_pred = clf.predict(X_test.values.astype(np.float32)).ravel()\n", "print(y_pred[:10])\n", "\n", "try:\n", " y_pred_probs = clf.predict_proba(X_test.values.astype(np.float32))\n", " print(y_pred_probs[:10])\n", " donate_probs = [prob[1] for prob in y_pred_probs]\n", "except Exception,e:\n", " print(e)\n", " donate_probs = [0.65 if x>0 else 1-0.65 for x in y_pred]\n", " \n", "print(donate_probs[:10])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Create the submission file" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert len(IDs)==len(donate_probs)\n", "\n", "f = open(submission_filename, \"w\")\n", "\n", "f.write(\",Made Donation in March 2007\\n\")\n", "for ID, prob in zip(IDs, donate_probs):\n", " f.write(\"{},{}\\n\".format(ID,prob))\n", " \n", "f.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
KeltyAllen/Amazon-Reviews-Project
nltk_scratchwork.ipynb
1
35518
{ "metadata": { "name": "", "signature": "sha256:ba3123c0dcb9d5fad5668c63888b947f4831ddf0b1b87d2359f2e237cba82e78" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from sklearn.hmm import MultinomialHMM\n", "import MySQLdb\n", "import matplotlib.pyplot as plt\n", "import pylab as pl\n", "from nltk.tag.hmm import HiddenMarkovModelTrainer, HiddenMarkovModelTagger\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 135 }, { "cell_type": "code", "collapsed": false, "input": [ "# Normalizes the time so that the average wait time for the next review is 1. \n", "def normalized(time):\n", " review_rate = len(time)/(time[len(time)-1]-time[0])\n", " normalized_time = np.zeros(len(time))\n", " for k in range(len(time)):\n", " normalized_time[k] = (time[k]-time[0])*review_rate\n", " return normalized_time" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def GetTrainingSet(PID, tablename, cursor):\n", " sql = \"Select RTime, RScore From \" +tablename + \" Where PID = \" + '\"' + PID +'\";'\n", " cursor.execute(sql)\n", " data = cursor.fetchall()\n", " data = sorted(data)\n", " rating = np.array(zip(*data)[1], dtype = int)\n", " time = np.array(zip(*data)[0], dtype = float)\n", " normalized_time = normalized(time)\n", " normalized_time\n", " discrete_time = np.zeros(len(time))\n", " #ratings 1-5, mult by 2 if short wait for a rating,\n", " data_encoded = np.zeros(len(time))\n", " #data_encoded.append('temp')\n", " for k in range(1, len(time)):\n", " #rating[k] = rating[k]-1\n", " if (normalized_time[k] - normalized_time[k-1])> 1:\n", " discrete_time[k]=2\n", " #data_encoded[k] = \n", " #data_encoded.append('f' + str(rating[k])) #ok let's try strings instead\n", " else:\n", " discrete_time[k]=1\n", " #data_encoded.append('s' + str(rating[k]))\n", " data_encoded[k] = rating[k]*2 - discrete_time[k]\n", " rating[k] = rating[k]-1\n", " #print discrete_time\n", " discrete_time[0] = discrete_time[1]\n", " data_encoded[0] = rating[0]*2 - discrete_time[0]\n", " rating[0]=rating[0]-1\n", " data_string = ''\n", " for k in range(len(time)):\n", " data_string = data_string + str(int(data_encoded[k]))\n", " return data_string, rating, time \n", " #return np.column_stack([rating, discrete_time]), time" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 87 }, { "cell_type": "code", "collapsed": false, "input": [ "def running_avg(data): #now assuming rating is 1 less than actual because sklearn hmm is so dumb\n", " avg = np.zeros(len(data), dtype = float)\n", " total = 0\n", " for k in range(len(data)):\n", " #avg[k] = np.mean(data[:k])\n", " total += data[k]+1\n", " avg[k] = float(total)/float((k+1))\n", " return avg" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 88 }, { "cell_type": "code", "collapsed": false, "input": [ "db = MySQLdb.connect(host=\"localhost\", user=\"root\", db = \"home_kitchen\")\n", "cursor = db.cursor()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 89 }, { "cell_type": "code", "collapsed": false, "input": [ "tablename = 'all_hk'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 90 }, { "cell_type": "code", "collapsed": false, "input": [ "PID1 = ' B0000X7CMQ' #zojirushi, it breaks\n", "PID2 = ' B000GXZ2GS' #Later input PIDs from terminal or website or whatever\n", "PID3 = ' B000GTR2F6'\n", "PID4 = ' B000AQSMPO'\n", "PID5 = ' B00005MF9C'\n", "PID6 = ' B0000E2PEI'\n", "PID7 = ' B0006SFFAQ'\n", "PID8 = ' B00005AQ9Q'\n", "PID9 = ' B00005R19P'\n", "PID10 = ' B000FFQ554'\n", "PID11 = ' B0006ZUHR0'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 91 }, { "cell_type": "code", "collapsed": false, "input": [ "#Getting the Data:" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 92 }, { "cell_type": "code", "collapsed": false, "input": [ "#X1, T1 = GetTrainingSet(PID1, tablename, cursor)\n", "#print X1, T1\n", "#X2, T2 = GetTrainingSet(PID2, tablename, cursor)\n", "#X3, T3 = GetTrainingSet(PID3, tablename, cursor)\n", "D1, X1, T1 = GetTrainingSet(PID1, tablename, cursor)\n", "D2, X2, T2 = GetTrainingSet(PID2, tablename, cursor)\n", "D3, X3, T3 = GetTrainingSet(PID3, tablename, cursor)\n", "D4, X4, T4 = GetTrainingSet(PID4, tablename, cursor)\n", "D5, X5, T5 = GetTrainingSet(PID5, tablename, cursor)\n", "D6, X6, T6 = GetTrainingSet(PID6, tablename, cursor)\n", "D7, X7, T7 = GetTrainingSet(PID7, tablename, cursor)\n", "D8, X8, T8 = GetTrainingSet(PID8, tablename, cursor)\n", "D9, X9, T9 = GetTrainingSet(PID9, tablename, cursor)\n", "D10, X10, T10 = GetTrainingSet(PID10, tablename, cursor)\n", "D11, X11, T11 = GetTrainingSet(PID11, tablename, cursor)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 124 }, { "cell_type": "code", "collapsed": false, "input": [ "R1 = running_avg(X1)\n", "#R2 = running_avg(X2)\n", "#R3 = running_avg(X3)\n", "#R4 = running_avg(X4)\n", "#R5 = running_avg(X5)\n", "#R6 = running_avg(X6)\n", "#R7 = running_avg(X7)\n", "#R8 = running_avg(X8)\n", "#R9 = running_avg(X9)\n", "#R10 = running_avg(X10)\n", "#R11 = running_avg(X11)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 125 }, { "cell_type": "code", "collapsed": false, "input": [ "#print R1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 126 }, { "cell_type": "code", "collapsed": false, "input": [ "#print D1" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 127 }, { "cell_type": "code", "collapsed": false, "input": [ "#### HMM Time" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 128 }, { "cell_type": "code", "collapsed": false, "input": [ "n_components = 4 #? number of states in the model. I'm just guessing here. " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 129 }, { "cell_type": "code", "collapsed": false, "input": [ "states = ['a', 'b', 'c', 'd']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 130 }, { "cell_type": "code", "collapsed": false, "input": [ "symbols = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 131 }, { "cell_type": "code", "collapsed": false, "input": [ "example = HiddenMarkovModelTrainer(states, symbols)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 132 }, { "cell_type": "code", "collapsed": false, "input": [ "ataggermaybe = example.train_unsupervised([D1, D2, D3, D4, D5, D6, D7])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "iteration 0 logprob -39577.4513225\n", "iteration" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 1 logprob -31576.9896627\n", "iteration" ] }, { "output_type": "stream", "stream": "stdout", "text": [ " 2 logprob -31576.9896627\n" ] } ], "prompt_number": 152 }, { "cell_type": "code", "collapsed": false, "input": [ "ataggermaybe.tag(D4)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 153, "text": [ "[('8', 'a'),\n", " ('8', 'a'),\n", " ('8', 'a'),\n", " ('4', 'a'),\n", " ('4', 'a'),\n", " ('8', 'a'),\n", " ('8', 'a'),\n", " ('6', 'a'),\n", " ('8', 'a'),\n", " ('9', 'a'),\n", " ('8', 'a'),\n", " ('9', 'a'),\n", " ('8', 'a'),\n", " ('9', 'a'),\n", " ('6', 'a'),\n", " ('6', 'a'),\n", " ('8', 'a'),\n", " ('8', 'a'),\n", " ('8', 'a'),\n", " ('9', 'a'),\n", " ('6', 'a'),\n", " ('9', 'a'),\n", " ('8', 'a'),\n", " ('8', 'a'),\n", " ('9', 'a'),\n", " ('7', 'a'),\n", " ('9', 'a'),\n", " ('8', 'a'),\n", " ('9', 'a'),\n", " ('8', 'a'),\n", " ('6', 'a'),\n", " ('8', 'a'),\n", " ('8', 'a'),\n", " ('4', 'a'),\n", " ('8', 'a'),\n", " ('8', 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" ('1', 'a'),\n", " ('0', 'a'),\n", " ('9', 'a'),\n", " ('2', 'a'),\n", " ('5', 'a'),\n", " ('2', 'a'),\n", " ('1', 'a'),\n", " ('1', 'a'),\n", " ('3', 'a'),\n", " ('9', 'a'),\n", " ('9', 'a'),\n", " ('9', 'a'),\n", " ('2', 'a'),\n", " ('1', 'a'),\n", " ('0', 'a'),\n", " ('2', 'a'),\n", " ('9', 'a'),\n", " ('8', 'a'),\n", " ('8', 'a'),\n", " ('0', 'a'),\n", " ('9', 'a'),\n", " ...]" ] } ], "prompt_number": 153 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
jamesmcm/cryptopals
basic/vigniere.ipynb
1
29157
{ "metadata": { "name": "", "signature": "sha256:754124156d6196ce7ab828668f3e2a76904078ea73c19208d53b8db8212110e0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "b64d={0:\"A\",16:\"Q\",32:\"g\",48:\"w\",1:\"B\",17:\"R\",33:\"h\",49:\"x\",2:\"C\",18:\"S\",34:\"i\",50:\"y\",3:\"D\",19:\"T\",35:\"j\",51:\"z\",4:\"E\",20:\"U\",36:\"k\",52:\"0\",5:\"F\",21:\"V\",37:\"l\",53:\"1\",6:\"G\",22:\"W\",38:\"m\",54:\"2\",7:\"H\",23:\"X\",39:\"n\",55:\"3\",8:\"I\",24:\"Y\",40:\"o\",56:\"4\",9:\"J\",25:\"Z\",41:\"p\",57:\"5\",10:\"K\",26:\"a\",42:\"q\",58:\"6\",11:\"L\",27:\"b\",43:\"r\",59:\"7\",12:\"M\",28:\"c\",44:\"s\",60:\"8\",13:\"N\",29:\"d\",45:\"t\",61:\"9\",14:\"O\",30:\"e\",46:\"u\",62:\"+\",15:\"P\",31:\"f\",47:\"v\",63:\"/\"}\n", "nb64d= dict (zip(b64d.values(),b64d.keys()))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def b642ascii(s):\n", " out=[]\n", " for i in xrange(len(s)/4):\n", " c=s[4*i:(4*i)+4]\n", " #print c\n", " n=0\n", " nulls=0\n", " for z in c:\n", " if z!=\"=\":\n", " n=n<<6 | nb64d[z]\n", " else:\n", " nulls+=1\n", " n=n<<6 | 0 \n", " c1=(n&16711680)>>16\n", " c2=(n&65280)>>8\n", " c3=n&255\n", " \n", " cs=[c1,c2,c3]\n", " for i in range(3-nulls):\n", " out.append(chr(cs[i]))\n", "\n", " return \"\".join(out)\n", "\n", "b642ascii(\"YW55IGNhcm5hbCBwbGVhcw==\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ "'any carnal pleas'" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "def hamming(s1,s2):\n", " b1=str2bin(s1)\n", " b2=str2bin(s2)\n", " b=b1^b2\n", " return ones(b)\n", " \n", " \n", "def str2bin(s):\n", " o=0\n", " for c in s:\n", " o=o << 8 | ord(c)\n", " return o\n", "\n", "def ones(n):\n", " w = 0\n", " while (n):\n", " w += 1\n", " n &= n - 1\n", " return w" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "hamming(\"this is a test\",\"wokka wokka!!!\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "37" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "out=\"\"\n", "f=open(\"6.txt\",\"r\")\n", "for line in f:\n", " out+=line.strip()\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "from numpy import mean\n", "s=b642ascii(out)\n", "ksd={}\n", "for keysize in xrange(1,40):\n", " numbytes=8*keysize\n", " numchars=(1+(keysize/4))*4\n", " c1=s[:keysize]\n", " c2=s[keysize:2*keysize]\n", " c3=s[2*keysize:3*keysize]\n", " c4=s[3*keysize:4*keysize]\n", " c5=s[4*keysize:5*keysize]\n", " diff=mean([hamming(c1,c2)/float(keysize),hamming(c1,c3)/float(keysize),hamming(c2,c3)/float(keysize),hamming(c4,c5)/float(keysize),hamming(c2,c4)/float(keysize),hamming(c1,c5)/float(keysize)])\n", " ksd[keysize]=diff" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 44 }, { "cell_type": "code", "collapsed": false, "input": [ "print ksd" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{1: 3.6666666666666665, 2: 2.9166666666666665, 3: 3.3333333333333335, 4: 3.25, 5: 2.6666666666666665, 6: 3.3888888888888893, 7: 3.2619047619047623, 8: 3.3333333333333335, 9: 3.1481481481481484, 10: 3.1499999999999999, 11: 3.4090909090909087, 12: 3.0138888888888893, 13: 3.3205128205128207, 14: 3.2261904761904767, 15: 3.2222222222222219, 16: 3.2916666666666665, 17: 3.1960784313725488, 18: 3.2037037037037037, 19: 3.1140350877192979, 20: 3.0500000000000003, 21: 3.285714285714286, 22: 3.3636363636363633, 23: 3.2826086956521738, 24: 3.0347222222222228, 25: 3.2866666666666666, 26: 3.108974358974359, 27: 3.3950617283950617, 28: 3.339285714285714, 29: 2.7643678160919536, 30: 3.2611111111111111, 31: 3.3118279569892475, 32: 3.2395833333333335, 33: 3.2222222222222219, 34: 3.2009803921568629, 35: 3.361904761904762, 36: 3.3287037037037037, 37: 3.1756756756756754, 38: 3.2324561403508771, 39: 3.1923076923076921}\n" ] } ], "prompt_number": 45 }, { "cell_type": "code", "collapsed": false, "input": [ "print len(b642ascii(out))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "2876\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "#key length 5 - maybe 2?" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "s=b642ascii(out)\n", "#s=\"teststringnew\"\n", "keysize=29\n", "blocks=[]\n", "for i in xrange((len(s)/keysize)+1):\n", " if i!=len(s)/keysize:\n", " blocks.append(s[i*keysize:(i+1)*keysize])\n", " else:\n", " if len(s[i*keysize:])>0:\n", " blocks.append(s[i*keysize:])\n", "print len(blocks)\n" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "100\n" ] } ], "prompt_number": 46 }, { "cell_type": "code", "collapsed": false, "input": [ "newblocks=[]\n", "for i in xrange(keysize):\n", " newblocks.append([])\n", "for block in blocks:\n", " for j in xrange(len(block)):\n", " newblocks[j].append(block[j])\n", "print len(newblocks)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "29\n" ] } ], "prompt_number": 47 }, { "cell_type": "code", "collapsed": false, "input": [ "lf={\n", "\"a\":0.08167,\n", "\"b\":0.01492,\n", "\"c\":0.02782,\n", "\"d\":0.04253,\n", "\"e\":0.12702,\n", "\"f\":0.02228,\n", "\"g\":0.02015,\n", "\"h\":0.06094,\n", "\"i\":0.06966,\n", "\"j\":0.00153,\n", "\"k\":0.00772,\n", "\"l\":0.04025,\n", "\"m\":0.02406,\n", "\"n\":0.06749,\n", "\"o\":0.07507,\n", "\"p\":0.01929,\n", "\"q\":0.00095,\n", "\"r\":0.05987,\n", "\"s\":0.06327,\n", "\"t\":0.09056,\n", "\"u\":0.02758,\n", "\"v\":0.00978,\n", "\"w\":0.02360,\n", "\"x\":0.00150,\n", "\"y\":0.01974,\n", "\"z\":0.00074}" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "from math import sqrt\n", "def computehistogram(block):\n", " myhist={}\n", " chars=0\n", " for k in lf:\n", " myhist[k]=0\n", " for c in block:\n", " c=c.lower()\n", " if c in myhist:\n", " chars+=1\n", " myhist[c]+=1\n", " for k in myhist:\n", " myhist[k]=myhist[k]/float(chars)\n", " return(myhist)\n", "\n", "def comparehist(hist):\n", " rmse=0\n", " for k in hist:\n", " rmse+=(lf[k]-hist[k])**2\n", " return rmse" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "h= computehistogram(newblocks[0])\n", "print h\n", "total=0\n", "for k in h:\n", " total+=h[k]\n", "print total\n", "print comparehist(h)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "{'a': 0.08928571428571429, 'c': 0.03571428571428571, 'b': 0.047619047619047616, 'e': 0.08333333333333333, 'd': 0.011904761904761904, 'g': 0.041666666666666664, 'f': 0.0, 'i': 0.08928571428571429, 'h': 0.023809523809523808, 'k': 0.023809523809523808, 'j': 0.005952380952380952, 'm': 0.05357142857142857, 'l': 0.005952380952380952, 'o': 0.06547619047619048, 'n': 0.1488095238095238, 'q': 0.0, 'p': 0.011904761904761904, 's': 0.05952380952380952, 'r': 0.06547619047619048, 'u': 0.005952380952380952, 't': 0.07142857142857142, 'w': 0.011904761904761904, 'v': 0.0, 'y': 0.03571428571428571, 'x': 0.005952380952380952, 'z': 0.005952380952380952}\n", "1.0\n", "0.0172590407698\n" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "key=[]\n", "keyds=[]\n", "for block in newblocks:\n", " minscore=float(\"infinity\")\n", " bestc=None\n", " keyd={}\n", " for keyc in range(32,123):\n", " decrypt=map(lambda x: chr(ord(x)^keyc),block)\n", " score=comparehist(computehistogram(decrypt))\n", " keyd[chr(keyc)]=score\n", " #print score\n", " if score<minscore:\n", " minscore=score\n", " bestc=chr(keyc)\n", " key.append(bestc)\n", " keyds.append(keyd)\n", "print key" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['T', 'E', 'R', 'M', 'I', 'N', 'A', 'T', 'O', 'R', ' ', 'X', ':', ' ', 'B', 'R', 'I', 'N', 'G', ' ', 'T', 'H', 'E', ' ', 'N', 'O', 'I', 'S', 'E']\n" ] } ], "prompt_number": 51 }, { "cell_type": "code", "collapsed": false, "input": [ "key=[]\n", "keyds=[]\n", "for i in xrange(keysize):\n", " key.append([])\n", "blocknum=1\n", "for block in newblocks:\n", " minscore=float(\"infinity\")\n", " bestc=None\n", " keyd={}\n", " for i in range(32,122):\n", " cur=map(chr,map(lambda x: ord(x)^i, block))\n", " if sum(map(lambda x: (x>=65 and x<=90) or (x>=97 and x<=122), map(ord, cur)))/float(len(cur))>0.76000:\n", " #print \"Key: \" + str(chr(i))\n", " key[blocknum-1].append(chr(i))\n", " blocknum+=1\n", "print key\n", "keylist=key" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[], [], [], [], [], ['M', 'N', 'm', 'n'], [], ['R', 'T', 'V', 'r', 't', 'v'], [], ['R', 'r'], [], ['X', '[', 'x'], [], [], [], [], [], [], [], [' '], ['T', 't'], [], [], [], ['N', 'n'], [], [], [], []]\n" ] } ], "prompt_number": 50 }, { "cell_type": "code", "collapsed": false, "input": [ "key=[]\n", "keyds=[]\n", "for i in xrange(keysize):\n", " key.append([])\n", "blocknum=1\n", "for block in newblocks:\n", " minscore=float(\"infinity\")\n", " bestc=None\n", " keyd={}\n", " for i in range(32,91):\n", " cur=map(chr,map(lambda x: ord(x)^i, block))\n", " if sum(map(lambda x: x>=65 and x<=122, map(ord, cur)))/float(len(cur))>0.6200:\n", " #print blocknum\n", " #print \"Key: \" + str(chr(i))\n", " key[blocknum-1].append(chr(i))\n", " blocknum+=1\n", "print key\n", "keylist=key\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "import operator\n", "sorts=[]\n", "for d in keyds:\n", " sorted_x = sorted(d.items(), key=operator.itemgetter(1))\n", " sorts.append(sorted_x[:10])\n", " " ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 244 }, { "cell_type": "code", "collapsed": false, "input": [ "from hex2b64 import hexstring2ascii, repeatkeyxor\n", "def decryptxor(k,s):\n", " return repeatkeyxor(k,s,tohex=False)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 20 }, { "cell_type": "code", "collapsed": false, "input": [ "print sorts" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[[('I', 0.014088403671853555), ('i', 0.014088403671853555), (' ', 0.017259040769841267), ('T', 0.018203819662517322), ('t', 0.018203819662517322), ('O', 0.02025311879202975), ('o', 0.02025311879202975), ('N', 0.02340262026798269), ('n', 0.02340262026798269), ('R', 0.023426310579554948)], [(' ', 0.00849006935774702), ('R', 0.01782790152866874), ('r', 0.01782790152866874), ('H', 0.018912455055555555), ('h', 0.018912455055555555), ('T', 0.01926750918988594), ('t', 0.01926750918988594), ('I', 0.019317728428336626), ('i', 0.019317728428336626), ('O', 0.019639911800701855)], [(' ', 0.010544657422419465), ('N', 0.014131370757550573), ('n', 0.014131370757550573), ('T', 0.016749924481738492), ('t', 0.016749924481738492), ('O', 0.020421133555670667), ('o', 0.020421133555670667), ('I', 0.02157208456893604), ('i', 0.02157208456893604), ('R', 0.023048573656089654)], [(' ', 0.008566330006245891), ('T', 0.02059146432232067), ('t', 0.02059146432232067), ('O', 0.021028289061436715), ('o', 0.021028289061436715), ('N', 0.021458865906273324), ('n', 0.021458865906273324), ('E', 0.0221260841943371), ('e', 0.0221260841943371), ('I', 0.024556969275948786)], [(' ', 0.013595172667044597), ('E', 0.01781714817096177), ('e', 0.01781714817096177), ('N', 0.01834125362528237), ('n', 0.01834125362528237), ('I', 0.022073862280064024), ('i', 0.022073862280064024), ('_', 0.024739254388346777), ('R', 0.025194021034800708), ('r', 0.025194021034800708)]]\n" ] } ], "prompt_number": 245 }, { "cell_type": "code", "collapsed": false, "input": [ "#print hexstring2ascii(repeatkeyxor(\"\".join(key),s))\n", "print \"\".join(key)\n", "#print s\n", "key=['i', 'n', 'n', 'e', 'r']\n", "for i in range(len(sorts[0])):\n", " for j in range(len(sorts[0])):\n", " for k in range(len(sorts[0])):\n", " for l in range(len(sorts[0])):\n", " for m in range(len(sorts[0])):\n", " key=[sorts[0][i][0],sorts[1][j][0],sorts[2][k][0],sorts[3][l][0],sorts[4][m][0]]\n", " temp=decryptxor(\"\".join(key),s)\n", " if \" the \" in temp:\n", " print key\n", " print temp" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "inner\n" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-184-66724a257a8a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mm\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msorts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 10\u001b[0m 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11\u001b[1;33m \u001b[0mtemp\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdecryptxor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 12\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m\" the \"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtemp\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 13\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m<ipython-input-111-98910ae91673>\u001b[0m in \u001b[0;36mdecryptxor\u001b[1;34m(k, s)\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mhex2b64\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mhexstring2ascii\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrepeatkeyxor\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdecryptxor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mhexstring2ascii\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrepeatkeyxor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/home/jamesmcm/Dropbox/crypto/hex2b64.py\u001b[0m in \u001b[0;36mrepeatkeyxor\u001b[1;34m(key, s)\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 101\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msl\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m^\u001b[0m\u001b[0mord\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m%\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[1;34m\"\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mascii2hex\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mb642ascii\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/jamesmcm/Dropbox/crypto/hex2b64.py\u001b[0m in \u001b[0;36mascii2hex\u001b[1;34m(c)\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 91\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mascii2hex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 92\u001b[1;33m \u001b[0mo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mencodehex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 93\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 94\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"0\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/jamesmcm/Dropbox/crypto/hex2b64.py\u001b[0m in \u001b[0;36mencodehex\u001b[1;34m(n)\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[0mtrigger\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m64\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m \u001b[1;32mif\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m16\u001b[0m\u001b[1;33m**\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m63\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m>=\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mtrigger\u001b[0m\u001b[1;33m==\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 65\u001b[0m \u001b[0mtrigger\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[1;31m#print i, n\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "prompt_number": 184 }, { "cell_type": "code", "collapsed": false, "input": [ "#key=[\"I\",\"I\",\"N\",\"I\",\"O\"]\n", "#key=[\"t\",\"t\",\"r\",\"t\",\"o\"]\n", "#for x in ['c', 'd', 'e', 'h', 'i', 'n', 'o', 's', 't', 'x']:\n", "#key[0]=x\n", "print decryptxor(\"Terminator X: Bring the noise\",s)\n", "#print \"------\"" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "I'm back and I'm ringin' the bell \n", "A rockin' on the mike while the fly girls yell \n", "In ecstasy in the back of me \n", "Well that's my DJ Deshay cuttin' all them Z's \n", "Hittin' hard and the girlies goin' crazy \n", "Vanilla's on the mike, man I'm not lazy. \n", "\n", "I'm lettin' my drug kick in \n", "It controls my mouth and I begin \n", "To just let it flow, let my concepts go \n", "My posse's to the side yellin', Go Vanilla Go! \n", "\n", "Smooth 'cause that's the way I will be \n", "And if you don't give a damn, then \n", "Why you starin' at me \n", "So get off 'cause I control the stage \n", "There's no dissin' allowed \n", "I'm in my own phase \n", "The girlies sa y they love me and that is ok \n", "And I can dance better than any kid n' play \n", "\n", "Stage 2 -- Yea the one ya' wanna listen to \n", "It's off my head so let the beat play through \n", "So I can funk it up and make it sound good \n", "1-2-3 Yo -- Knock on some wood \n", "For good luck, I like my rhymes atrocious \n", "Supercalafragilisticexpialidocious \n", "I'm an effect and that you can bet \n", "I can take a fly girl and make her wet. \n", "\n", "I'm like Samson -- Samson to Delilah \n", "There's no denyin', You can try to hang \n", "But you'll keep tryin' to get my style \n", "Over and over, practice makes perfect \n", "But not if you're a loafer. \n", "\n", "You'll get nowhere, no place, no time, no girls \n", "Soon -- Oh my God, homebody, you probably eat \n", "Spaghetti with a spoon! Come on and say it! \n", "\n", "VIP. Vanilla Ice yep, yep, I'm comin' hard like a rhino \n", "Intoxicating so you stagger like a wino \n", "So punks stop trying and girl stop cryin' \n", "Vanilla Ice is sellin' and you people are buyin' \n", "'Cause why the freaks are jockin' like Crazy Glue \n", "Movin' and groovin' trying to sing along \n", "All through the ghetto groovin' this here song \n", "Now you're amazed by the VIP posse. \n", "\n", "Steppin' so hard like a German Nazi \n", "Startled by the bases hittin' ground \n", "There's no trippin' on mine, I'm just gettin' down \n", "Sparkamatic, I'm hangin' tight like a fanatic \n", "You trapped me once and I thought that \n", "You might have it \n", "So step down and lend me your ear \n", "'89 in my time! You, '90 is my year. \n", "\n", "You're weakenin' fast, YO! and I can tell it \n", "Your body's gettin' hot, so, so I can smell it \n", "So don't be mad and don't be sad \n", "'Cause the lyrics belong to ICE, You can call me Dad \n", "You're pitchin' a fit, so step back and endure \n", "Let the witch doctor, Ice, do the dance to cure \n", "So come up close and don't be square \n", "You wanna battle me -- Anytime, anywhere \n", "\n", "You thought that I was weak, Boy, you're dead wrong \n", "So come on, everybody and sing this song \n", "\n", "Say -- Play that funky music Say, go white boy, go white boy go \n", "play that funky music Go white boy, go white boy, go \n", "Lay down and boogie and play that funky music till you die. \n", "\n", "Play that funky music Come on, Come on, let me hear \n", "Play that funky music white boy you say it, say it \n", "Play that funky music A little louder now \n", "Play that funky music, white boy Come on, Come on, Come on \n", "Play that funky music \n", "\n" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-2.0
pligor/predicting-future-product-prices
04_time_series_prediction/27_price_history_generate_train_test_and_baseline.ipynb
2
196382
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -*- coding: UTF-8 -*-\n", "#%load_ext autoreload\n", "%reload_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/studenthp/anaconda2/envs/dis/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n", " from pandas.core import datetools\n" ] } ], "source": [ "from __future__ import division\n", "import tensorflow as tf\n", "from os import path\n", "import numpy as np\n", "import pandas as pd\n", "import csv\n", "from sklearn.model_selection import StratifiedShuffleSplit\n", "from time import time\n", "from matplotlib import pyplot as plt\n", "import seaborn as sns\n", "from mylibs.jupyter_notebook_helper import show_graph\n", "from tensorflow.contrib import rnn\n", "from tensorflow.contrib import learn\n", "import shutil\n", "from tensorflow.contrib.learn.python.learn import learn_runner\n", "from IPython.display import Image\n", "from IPython.core.display import HTML\n", "from mylibs.tf_helper import getDefaultGPUconfig\n", "from sklearn.metrics import r2_score\n", "from mylibs.py_helper import factors\n", "from fastdtw import fastdtw\n", "from scipy.spatial.distance import euclidean\n", "from statsmodels.tsa.stattools import coint\n", "from sklearn.linear_model import LinearRegression\n", "from scipy.signal import detrend\n", "from common import get_or_run_nn\n", "from sklearn.metrics import mean_squared_error\n", "from statsmodels.tsa.stattools import coint\n", "from models.my_27_baseline import MyBaseline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dtype = tf.float32\n", "seed = 16011984\n", "random_state = np.random.RandomState(seed=seed)\n", "config = getDefaultGPUconfig()\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_path = '../../../../Dropbox/data'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ph_data_path = data_path + '/price_history'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "npz_train_reduced = ph_data_path + '/price_history_mobattrs_date_dp_60to30_62020_6000_train.npz'\n", "npz_train = ph_data_path + '/price_history_mobattrs_date_dp_60to30_62020_train.npz'\n", "npz_test = ph_data_path + '/price_history_mobattrs_date_dp_60to30_62020_test.npz'\n", "assert path.isfile(npz_train_reduced)\n", "assert path.isfile(npz_train)\n", "assert path.isfile(npz_test)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(55820, 6000, 6200)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Our batch size is 64 that is why we want train and test size that will be divided exactly by 64\n", "batch_size = 100\n", "train_full_size = len(np.load(npz_train)['inputs'])\n", "train_size = len(np.load(npz_train_reduced)['inputs'])\n", "test_size = len(np.load(npz_test)['inputs'])\n", "assert train_size % batch_size == 0 and test_size % batch_size == 0\n", "\n", "typical_split_size = 5 #for cross validation\n", "assert train_size % typical_split_size == 0 and (train_size / typical_split_size) % batch_size == 0\n", "\n", "train_full_size, train_size, test_size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Baseline is static, a straight line for each input - Test" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bltest = MyBaseline(npz_path=npz_test)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0073356416100968606" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bltest.getMSE()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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UPM/T/p1D2r9zSC8XSvrGE8f0g2dP6TMPWX3x0SP6+du36y2vnaIpDgAA1znP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\nJCzf9zW/XNfh6WUdmVk+p9q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"text/plain": [ "<matplotlib.figure.Figure at 0x7f49a02c0e90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bltest.renderMSEs()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.005181441272113646" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bltest.getHuberLoss()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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yAl59+OI5umBRsSKxuJ5967je3tukRJKVGQEAwOkoZQAwRVyWpRXnFenGS+cq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\n7T/erkhHXD6PW16PSzl+r7w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"text/plain": [ "<matplotlib.figure.Figure at 0x7f493daf6f10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bltest.renderHuberLosses()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 38.1 s, sys: 136 ms, total: 38.2 s\n", "Wall time: 37.9 s\n" ] }, { "data": { "text/plain": [ "1.1328810436818912" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "bltest.get_dtw()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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eeZB4q1ZUXP+3Oj2n8qJLE4PEn30Kx08/NkoOERGRptCqFZx6apRVq5wsW6a3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\nAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f493d98b150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bltest.renderRandomTargetVsPrediction()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Baseline is static, a straight line for each input - Train (small)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bltrainsmall = MyBaseline(npz_path=npz_train_reduced)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0067634888342177667" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bltrainsmall.getMSE()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb924a35690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bltrainsmall.renderMSEs()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0057794194246570891" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bltrainsmall.getHuberLoss()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "image/png": 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jlDLgNm3ralRTrU9nL0/rUnTG6TgAAAAoU5Qy4DaZpqF9O9pkGNLB48NKphlj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\nPAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb9249c97d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bltrainsmall.renderHuberLosses()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.023131977074719937" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# myran = lambda: np.random.randn() * 1e-3\n", "# dtw_scores = [fastdtw(bltrainsmall.targets[ind], bltrainsmall.targets[ind] + myran())[0] \n", "# for ind in range(len(bltrainsmall.targets))]\n", "# np.mean(dtw_scores)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 51.8 s, sys: 172 ms, total: 51.9 s\n", "Wall time: 51.8 s\n" ] }, { "data": { "text/plain": [ "1.097277832655853" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "bltrainsmall.get_dtw()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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d7spc82Eu9swBzEqYAwAWX+Pg75Yqc5ZZAkxLmAMAFt1EIJvPnrnxBigODQeY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\nSUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb924c96990>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bltrainsmall.renderRandomTargetVsPrediction()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Baseline is static, a straight line for each input - Train Full" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": true }, "outputs": [], "source": [ "bltrain = MyBaseline(npz_path=npz_train)" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0070081309903258383" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bltrain.getMSE()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb926e8ffd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bltrain.renderMSEs()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0053788250983214415" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bltrain.getHuberLoss()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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LB+u6TMcBAAAAJFHKkGCWLPJqcV662vwjavMPm44DAAAAUMqQWCzL0ublPlmS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\naV7YzYaJiIiIiIg8CvZk8WgRERERERHZmlbCExE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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb92aa97dd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bltrain.renderHuberLosses()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 7min 34s, sys: 1.67 s, total: 7min 36s\n", "Wall time: 7min 34s\n" ] }, { "data": { "text/plain": [ "1.0970981352082814" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%time\n", "bltrain.get_dtw()" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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eh4YDwMhNTfXDXCnbc8ghIx4Me218PDn4YCF8udMAhcapHBoOACM3Odl/XLdu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\ntnT1D88iwgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fb924b0f750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bltrain.renderRandomTargetVsPrediction()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 2 }
agpl-3.0
poppy-project/community-notebooks
tutorials-education/Documentation Snap! & Poppy/Dictionnaire des blocs/C1.ipynb
2
1084
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### C1 : Réinitialiser la simulation v-rep - Réinitialiser la connection _Poppy-Snap!_\n", "<img src=\"image/Dico/reset-simulation.png\" alt=\"Blablabla\" style=\"height: 25px;\"/>\n", "\n", "**_Help :_**\n", "Reset the simulation in V-rep.\n", "It is usefull if you have connection issues with v-rep.\n", "\n", "** _Aide :_ ** \n", "Cette instruction permet de réinitialiser la simulation. Elle est utile pour revenir à zéro après une mauvaise manipulation ou en cas de problème de connexions." ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.9" } }, "nbformat": 4, "nbformat_minor": 0 }
lgpl-3.0
NYUDataBootcamp/Projects
UG_S17/Schnell-Patel-LongShort (1).ipynb
2
122873
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Go Long or Short?\n", "## How does volatility affect returns of S&P 500 stocks?\n", " \n", " **Krupa Patel and Melanie Schnell**\n", " \n", "This Research Project was conducted at NYU Stern's DataBootcamp course.\n", "For further inquireies please reach out to Krupa Patel at [[email protected]](kp1524nyu.edu) and Melanie Schnell as [[email protected]]([email protected]).\n", "\n", "There is a common saying that states, \"the higher the risk, the bigger the return.\" However, in the past volatile markets have yielded negative returns for many investors. This leads us to explore the question: Is it profitable to go long or short when volatility is high?\n", "\n", "We will use 57 years of S&P index to find the returns and volatilty. Then, we will normalize this times series, plot the data in a scatterplot, and run a regression to see if we get a positive or negative correlation between increasing volatility and returns.\n", "\n", "If there is a positive slope it signifies that when volatility is high the returns are also high. However, if we get a negative slope it signifies that when volatility is high, returns are low.\n", "\n", "Our data can easily be accessed [here](https://finance.yahoo.com/quote/%5EGSPC/history?period1=-630961200&period2=1494475200&interval=1d&filter=history&frequency=1d)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 1: Install Packages\n", "To begin, we installed the necessary packages to read, manipulate, and plot our dataframe." ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd #Data Management Package\n", "import pandas_datareader as pd_rd #Data Reader Package\n", "import numpy as np #Numerical Computing Package\n", "import matplotlib.pyplot as plt #Graphics Package\n", "%matplotlib inline\n", "import seaborn as sns #Graphics Package to Complement Matplotlib\n", "import statsmodels.formula.api as smf #Statistical Computing Package" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 2: Import Data\n", "We found our data on [Yahoo Finance](https://finance.yahoo.com/quote/%5EGSPC/history?period1=-630961200&period2=1494475200&interval=1d&filter=history&frequency=1d) and click the download button to save a csv file. The file is named S&P_data.csv and saved to a Databootcamp folder. Our data did not need any changes. Next, we just needed to read in the data with the code below." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "path= \"/Users/MelanieSchnellAccount/Desktop/Senior_Year/Data_Bootcamp/S&P_data.csv\"\n", "df=pd.read_csv(path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The path will vary based on what type of machine is processing the data, and where the file is saved on the computer. Be sure to update this code with your unique file path. The file path can be easily copied and pasted from file information page.\n", "\n", "## Step 3: Arrange the Data\n", "\n", "Next we reversed the dataframe to put the date in order by index, old to new. We also renamed the column \"Adj Close\" to \"close\" to make it better for slicing." ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = df.rename(columns={'Adj Close': 'close'})\n", "reversed_df = df.loc[::-1]\n", "rdf=pd.DataFrame(reversed_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 4: Calculations\n", "**1 Day Return Calculation**\n", "\n", "This calculates the return after the close of one day. We calculated this by finding the difference between consecutive \"close\" column values, before dividing that difference by the newer value." ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "return1 = (rdf.close[1:].values - rdf.close[:-1].values) / rdf.close[:-1].values\n", "\n", "return1 = pd.Series(return1, index=rdf.index[1:])\n", "\n", "rdf[\"return1\"]=return1\n", "# calculates the return for the close column" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**20 Day Return Calculation**\n", "\n", "This calculates the return over the course of 20 days. We calculated this by finding the difference between \"close\" values that are 20 days appart, before dividing that differnce by the older value." ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ret20 = (rdf.close[20:].values - rdf.close[:-20].values)/rdf.close[:-20].values\n", "\n", "ret20 = pd.Series(ret20, index=rdf.index[20:])\n", "rdf[\"ret20\"]=ret20\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**20 Day Volatility Calculaion**\n", "\n", "This applies a standard deviation to the volatility values over 20 days." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [], "source": [ "vol20= rdf[\"return1\"].rolling(window=20).apply(np.std)\n", "rdf[\"vol20\"]=vol20\n", "\n", "#Apply standard deviation to the volitility over 20 days" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Normalized 20 Day Return**\n", "\n", "We used a 250 day frame and normalized the data since it is a times series." ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "zret20=((rdf.ret20[270:].values)-(rdf.ret20[270:].rolling(window=250).apply(np.mean)))/(rdf.ret20[270:].rolling(window=250).apply(np.std))\n", "zret20=pd.Series(zret20, index=rdf.index[270:])\n", "rdf[\"zret20\"]=zret20\n", "\n", "# Remove the effects of imflation, make the data comparable" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Normalized 20 Day Volatility**\n", "\n", "This makes the volatility data comparable as well by making the data normalized." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "zvol20=((rdf.vol20[270:].values)-(rdf.vol20[270:].rolling(window=250).apply(np.mean)))/(rdf.vol20[270:].rolling(window=250).apply(np.std))\n", "zvol20=pd.Series(zvol20, index=rdf.index[270:])\n", "rdf[\"zvol20\"]=zvol20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 5: Create the Data Table\n", "**Table Dimensions**" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(16930, 12)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rdf.shape\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Table Formatting**\n", "\n", "First, we created a new Dataframe called zvol20ret20 by copying the normalized 20 day volatiliy and returns columns. We then renamed the columns \"Volatility\" and \"Returns\" to add clarity. Next, we eliminated the values that were nullified in the process of normalization, and sorted the rows by ascending order of volatility." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "zvol20ret20=rdf[[\"zvol20\", \"zret20\"]].copy()\n", "zvol20ret20 = zvol20ret20.rename(columns={'zvol20' : 'Volatility', 'zret20' : 'Returns'})" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "zvol20ret20 = zvol20ret20[zvol20ret20.Volatility.notnull()]\n", "zvol20ret20 = zvol20ret20[zvol20ret20.Returns.notnull()] \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 6: Plot the Data\n", "**Scatter Plot**\n", "\n", "We used seaborn to plot the data in a scatter plot to visually display the relationship between volatility and returns. The regression line demonstrates that (while points tend to cluster between -2 and 3 Volatility, and -3 and 2 Returns) there is a general trend towards lower returns during high market volatility." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x1196c5048>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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KQzi4AaXoaIvR05EMxHi5kmiCPdq/PTHC3701Rm9Hkt6uJOOZIm5V+EzT4K2L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qsGrO2xSMmp4+/GVy1BT6d9g7k0yOeSPGPn49LrlpQ1ytcq/TvidHXo0uv1SNeLz3rF1pxNbY9P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\nTKEqNNrTE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"text/plain": [ "<matplotlib.figure.Figure at 0x1196c5748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='Volatility',y='Returns',data=zvol20ret20,fit_reg=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Regression Results**\n", "\n", "The R-squared value (0.085) is very close to 0, showing that there is a weak association between volatility and return. However, the negative slope coefficient (-0.33) of the regression indicates that as volatility increases, returns tend to decrease. Also, while there is a cluster of positive returns between volatility levels of -3 and 4, there are no points that indicate a positive return after a volatility level of 6. This means that it is very unlikely to recieve a positive return during times of high market volatility." ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>OLS Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>Volatility</td> <th> R-squared: </th> <td> 0.085</td> \n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.085</td> \n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 1520.</td> \n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Thu, 11 May 2017</td> <th> Prob (F-statistic):</th> <td>5.25e-318</td>\n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>16:54:27</td> <th> Log-Likelihood: </th> <td> -25861.</td> \n", "</tr>\n", "<tr>\n", " <th>No. Observations:</th> <td> 16411</td> <th> AIC: </th> <td>5.173e+04</td>\n", "</tr>\n", "<tr>\n", " <th>Df Residuals:</th> <td> 16409</td> <th> BIC: </th> <td>5.174e+04</td>\n", "</tr>\n", "<tr>\n", " <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n", "</tr>\n", "<tr>\n", " <th>Intercept</th> <td> 0.0304</td> <td> 0.009</td> <td> 3.323</td> <td> 0.001</td> <td> 0.012 0.048</td>\n", "</tr>\n", "<tr>\n", " <th>Returns</th> <td> -0.3310</td> <td> 0.008</td> <td> -38.984</td> <td> 0.000</td> <td> -0.348 -0.314</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <th>Omnibus:</th> <td>3107.093</td> <th> Durbin-Watson: </th> <td> 0.166</td>\n", "</tr>\n", "<tr>\n", " <th>Prob(Omnibus):</th> <td> 0.000</td> <th> Jarque-Bera (JB): </th> <td>6140.265</td>\n", "</tr>\n", "<tr>\n", " <th>Skew:</th> <td> 1.147</td> <th> Prob(JB): </th> <td> 0.00</td>\n", "</tr>\n", "<tr>\n", " <th>Kurtosis:</th> <td> 4.927</td> <th> Cond. No. </th> <td> 1.09</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Volatility R-squared: 0.085\n", "Model: OLS Adj. R-squared: 0.085\n", "Method: Least Squares F-statistic: 1520.\n", "Date: Thu, 11 May 2017 Prob (F-statistic): 5.25e-318\n", "Time: 16:54:27 Log-Likelihood: -25861.\n", "No. Observations: 16411 AIC: 5.173e+04\n", "Df Residuals: 16409 BIC: 5.174e+04\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "Intercept 0.0304 0.009 3.323 0.001 0.012 0.048\n", "Returns -0.3310 0.008 -38.984 0.000 -0.348 -0.314\n", "==============================================================================\n", "Omnibus: 3107.093 Durbin-Watson: 0.166\n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 6140.265\n", "Skew: 1.147 Prob(JB): 0.00\n", "Kurtosis: 4.927 Cond. No. 1.09\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reg = smf.ols(formula='Volatility ~ Returns', data=zvol20ret20).fit()\n", "reg.params\n", "reg.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Negative Correlation**\n", "\n", "We calculated that the correlation between volatility and returns is -0.291. Because the value is negative, we see that high volatility yeilds low returns." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Volatility</th>\n", " <th>Returns</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Volatility</th>\n", " <td>1.000000</td>\n", " <td>-0.291146</td>\n", " </tr>\n", " <tr>\n", " <th>Returns</th>\n", " <td>-0.291146</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Volatility Returns\n", "Volatility 1.000000 -0.291146\n", "Returns -0.291146 1.000000" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "zvol20ret20.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step 7: Quintiles\n", "\n", "**Quintile Table**\n", "\n", "Because the points were clustered in the scatter plot, and the results showed a weak R-squared value, we grouped the data by quintiles. This showed a much clearer result with a stronger R-squared value. To accomplish this, we sorted the rows by volatility, from low to high. Because there are 16,411 rows in the dataframe, we took the mean of Returns in groups of 3,283 (one fifth of the total number of rows) and created a new dataframe with these values. In the new dataframe we assigned the volatiliy values one through five (one being the lowest volatility and five being the highest)." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "zvol20ret20=zvol20ret20.sort_values('Volatility', ascending=True)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "zvol20ret20 = zvol20ret20.reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Ret=zvol20ret20.Returns.groupby(np.arange(len(zvol20ret20.Returns))//3283).mean().tolist()\n", "Return= pd.Series(Ret)\n", "quintileframe= pd.DataFrame({\"Volatility\":[1,2,3,4,5]})\n", "quintileframe['Return'] = Return.values" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Volatility</th>\n", " <th>Return</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1</td>\n", " <td>0.246311</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>2</td>\n", " <td>0.127676</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>3</td>\n", " <td>0.025283</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4</td>\n", " <td>-0.088610</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5</td>\n", " <td>-0.523899</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Volatility Return\n", "0 1 0.246311\n", "1 2 0.127676\n", "2 3 0.025283\n", "3 4 -0.088610\n", "4 5 -0.523899" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quintileframe\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quintile Plot**\n", "\n", "We used seaborn to create a scatter plot with a regression line of the quintile data. The graph shows a clearer association between increasing volatility and decreasing returns. " ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x1197a6198>" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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KpeL+674ej4HHF2VqZoX+qJ/BgYGWfJ9KpUq5rF8661QPJ9XDSfU4nG65WPCjwDeBLwG/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\nS5ttTDObXkjz/JU5rt5Y3pJmdm06wbX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"text/plain": [ "<matplotlib.figure.Figure at 0x1195ffdd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.lmplot(x='Volatility',y='Return',data=quintileframe,fit_reg=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Quintile Regression Results**\n", "\n", "When we break the data into quintiles, there is a much higher R-squared value (0.88), demonstrating a stronger association between volatility and returns. We are aware that five points are not enough to run an accurate regression; however, because of the strong correlation value (-0.937) and the negative coefficient (-5) we are reassured that on average high volatility does lead to lower returns." ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/MelanieSchnellAccount/anaconda/lib/python3.5/site-packages/statsmodels/stats/stattools.py:72: UserWarning: omni_normtest is not valid with less than 8 observations; 5 samples were given.\n", " \"samples were given.\" % int(n))\n" ] }, { "data": { "text/html": [ "<table class=\"simpletable\">\n", "<caption>OLS Regression Results</caption>\n", "<tr>\n", " <th>Dep. Variable:</th> <td>Volatility</td> <th> R-squared: </th> <td> 0.880</td>\n", "</tr>\n", "<tr>\n", " <th>Model:</th> <td>OLS</td> <th> Adj. R-squared: </th> <td> 0.839</td>\n", "</tr>\n", "<tr>\n", " <th>Method:</th> <td>Least Squares</td> <th> F-statistic: </th> <td> 21.92</td>\n", "</tr>\n", "<tr>\n", " <th>Date:</th> <td>Thu, 11 May 2017</td> <th> Prob (F-statistic):</th> <td>0.0184</td> \n", "</tr>\n", "<tr>\n", " <th>Time:</th> <td>16:46:10</td> <th> Log-Likelihood: </th> <td> -3.5349</td>\n", "</tr>\n", "<tr>\n", " <th>No. Observations:</th> <td> 5</td> <th> AIC: </th> <td> 11.07</td>\n", "</tr>\n", "<tr>\n", " <th>Df Residuals:</th> <td> 3</td> <th> BIC: </th> <td> 10.29</td>\n", "</tr>\n", "<tr>\n", " <th>Df Model:</th> <td> 1</td> <th> </th> <td> </td> \n", "</tr>\n", "<tr>\n", " <th>Covariance Type:</th> <td>nonrobust</td> <th> </th> <td> </td> \n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[95.0% Conf. Int.]</th> \n", "</tr>\n", "<tr>\n", " <th>Intercept</th> <td> 2.7865</td> <td> 0.287</td> <td> 9.711</td> <td> 0.002</td> <td> 1.873 3.700</td>\n", "</tr>\n", "<tr>\n", " <th>Return</th> <td> -5.0072</td> <td> 1.069</td> <td> -4.682</td> <td> 0.018</td> <td> -8.411 -1.604</td>\n", "</tr>\n", "</table>\n", "<table class=\"simpletable\">\n", "<tr>\n", " <th>Omnibus:</th> <td> nan</td> <th> Durbin-Watson: </th> <td> 1.643</td>\n", "</tr>\n", "<tr>\n", " <th>Prob(Omnibus):</th> <td> nan</td> <th> Jarque-Bera (JB): </th> <td> 0.518</td>\n", "</tr>\n", "<tr>\n", " <th>Skew:</th> <td> 0.431</td> <th> Prob(JB): </th> <td> 0.772</td>\n", "</tr>\n", "<tr>\n", " <th>Kurtosis:</th> <td> 1.680</td> <th> Cond. No. </th> <td> 3.78</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<class 'statsmodels.iolib.summary.Summary'>\n", "\"\"\"\n", " OLS Regression Results \n", "==============================================================================\n", "Dep. Variable: Volatility R-squared: 0.880\n", "Model: OLS Adj. R-squared: 0.839\n", "Method: Least Squares F-statistic: 21.92\n", "Date: Thu, 11 May 2017 Prob (F-statistic): 0.0184\n", "Time: 16:46:10 Log-Likelihood: -3.5349\n", "No. Observations: 5 AIC: 11.07\n", "Df Residuals: 3 BIC: 10.29\n", "Df Model: 1 \n", "Covariance Type: nonrobust \n", "==============================================================================\n", " coef std err t P>|t| [95.0% Conf. Int.]\n", "------------------------------------------------------------------------------\n", "Intercept 2.7865 0.287 9.711 0.002 1.873 3.700\n", "Return -5.0072 1.069 -4.682 0.018 -8.411 -1.604\n", "==============================================================================\n", "Omnibus: nan Durbin-Watson: 1.643\n", "Prob(Omnibus): nan Jarque-Bera (JB): 0.518\n", "Skew: 0.431 Prob(JB): 0.772\n", "Kurtosis: 1.680 Cond. No. 3.78\n", "==============================================================================\n", "\n", "Warnings:\n", "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n", "\"\"\"" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quintilereg=smf.ols(formula='Volatility ~ Return', data=quintileframe).fit()\n", "quintilereg.params\n", "quintilereg.summary()\n" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Volatility</th>\n", " <th>Return</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>Volatility</th>\n", " <td>1.000000</td>\n", " <td>-0.937878</td>\n", " </tr>\n", " <tr>\n", " <th>Return</th>\n", " <td>-0.937878</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Volatility Return\n", "Volatility 1.000000 -0.937878\n", "Return -0.937878 1.000000" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "quintileframe.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion\n", "\n", "Through our analysis of S&P 500 data from the last 57 years, we conclude that at times of high volatility, returns tend to be negative. Therefore, when there is high volatility, it is safer to go short." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
cerrno/neurokernel
notebooks/multi.ipynb
1
11840
{ "metadata": { "name": "", "signature": "sha256:7cad297b60a7b9a0e526ba92617fd75fffa23fa3d5b33a07ec31d0125aee6c6c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Mapping a Network of LPUs onto Multiple GPUs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook illustrates how to connect and execute several generic LPUs on multiple GPUs." ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Background" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Neurokernel's architecture enables one to specify complex networks of LPUs that interact via different connectivity patterns and map the LPUs to individual GPUs. This functionality is essential both to express models of the entire fly brain in terms of their constituent processing units and to the development of future resource allocation mechanisms that will be able to take advantage of available GPU resources in an automated manner." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src='files/files/lpu-network.jpg' />" ] }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Constructing an LPU Network" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since each LPU instance in a multi-LPU model must possess a unique identifier, construction of an LPU network is a matter of instantiating connectivity patterns between those pairs of LPUs that one wishes to connect and populating them with data describing the connections between ports exposed by the respective LPUs.\n", "\n", "In the example below, we first create an input signal and instantiate N generic LPUs containing fixed numbers of local and projection neurons. Each LPU is configured to run on a different GPU (where the at least N GPUs are assumed to be available). Notice that only one LPU receives the input signal:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%cd -q ~/neurokernel/examples/multi/data\n", "\n", "import itertools\n", "import random\n", "\n", "import gen_generic_lpu as g\n", "\n", "%cd -q ~/neurokernel/examples/multi\n", "\n", "import neurokernel.core as core\n", "from neurokernel.tools.comm import get_random_port\n", "\n", "import neurokernel.pattern as pattern\n", "from neurokernel.LPU.LPU import LPU\n", "\n", "# Execution parameters:\n", "dt = 1e-4\n", "dur = 1.0\n", "start = 0.3\n", "stop = 0.6\n", "I_max = 0.6\n", "steps = int(dur/dt)\n", "\n", "N_sensory = 30 # number of sensory neurons\n", "N_local = 30 # number of local neurons\n", "N_output = 30 # number of projection neurons\n", "\n", "N = 3\n", "\n", "# Only LPU 0 receives input and should therefore be associated with a population \n", "# of sensory neurons: \n", "neu_dict = {i: [0, N_local, N_output] for i in xrange(N)}\n", "neu_dict[0][0] = N_sensory\n", "\n", "# Create input signal for LPU 0: \n", "in_file_name_0 = 'data/generic_input.h5'\n", "g.create_input(in_file_name_0, neu_dict[0][0], dt, dur, start, stop, I_max)\n", "\n", "# Store info for all instantiated LPUs in the following dict: \n", "lpu_dict = {}\n", "\n", "# Create several LPUs: \n", "port_data = get_random_port()\n", "port_ctrl = get_random_port()\n", "\n", "for i, neu_num in neu_dict.iteritems():\n", " lpu_entry = {}\n", "\n", " if i == 0:\n", " in_file_name = in_file_name_0\n", " else:\n", " in_file_name = None\n", " lpu_file_name = 'data/generic_lpu_%s.gexf.gz' % i\n", " out_file_name = 'generic_output_%s.h5' % i\n", "\n", " id = 'lpu_%s' % i\n", " \n", " g.create_lpu(lpu_file_name, id, *neu_num)\n", " (n_dict, s_dict) = LPU.lpu_parser(lpu_file_name)\n", "\n", " lpu = LPU(dt, n_dict, s_dict, input_file=in_file_name,\n", " output_file=out_file_name,\n", " port_ctrl=port_ctrl, port_data=port_data,\n", " device=i, id=id,\n", " debug=False)\n", "\n", " lpu_entry['lpu_file_name'] = lpu_file_name\n", " lpu_entry['in_file_name'] = in_file_name\n", " lpu_entry['out_file_name'] = out_file_name\n", " lpu_entry['lpu'] = lpu\n", " lpu_entry['id'] = id\n", "\n", " lpu_dict[i] = lpu_entry" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each LPU exposes input and output communication ports. The generic LPU generator invoked above associates an output port with each projection neuron in an LPU and an input port with a node connected to a synapse that is in turn connected to some neuron in the LPU.\n", "\n", "Once the LPUs have been instantiated, we use information about the ports exposed by each LPU to define connectivity patterns between those LPUs we wish to connect. Notice that since the ``Pattern`` class enables one to specify connections in both directions between two LPUs, it is only necessary to consider combinations of LPUs without regard to their order. In the example below, we define connections between all pairs of LPUs in the network, i.e., the graph of all LPUs is complete, and we only connect spiking neurons exposed by the LPUs:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "man = core.Manager(port_data, port_ctrl)\n", "man.add_brok()\n", "\n", "random.seed(0)\n", "\n", "# Since each connectivity pattern between two LPUs contains the synapses in both\n", "# directions, create connectivity patterns between each combination of LPU\n", "# pairs:\n", "for id_0, id_1 in itertools.combinations(lpu_dict.keys(), 2):\n", "\n", " lpu_0 = lpu_dict[id_0]['lpu']\n", " lpu_1 = lpu_dict[id_1]['lpu']\n", "\n", " # Find all output and input port selectors in each LPU: \n", " out_ports_0 = lpu_0.interface.out_ports().to_selectors()\n", " out_ports_1 = lpu_1.interface.out_ports().to_selectors()\n", "\n", " in_ports_0 = lpu_0.interface.in_ports().to_selectors()\n", " in_ports_1 = lpu_1.interface.in_ports().to_selectors()\n", "\n", " out_ports_spk_0 = lpu_0.interface.out_ports().spike_ports().to_selectors()\n", " out_ports_gpot_0 = lpu_0.interface.out_ports().gpot_ports().to_selectors()\n", "\n", " out_ports_spk_1 = lpu_1.interface.out_ports().spike_ports().to_selectors()\n", " out_ports_gpot_1 = lpu_1.interface.out_ports().gpot_ports().to_selectors()\n", "\n", " in_ports_spk_0 = lpu_0.interface.in_ports().spike_ports().to_selectors()\n", " in_ports_gpot_0 = lpu_0.interface.in_ports().gpot_ports().to_selectors()\n", "\n", " in_ports_spk_1 = lpu_1.interface.in_ports().spike_ports().to_selectors()\n", " in_ports_gpot_1 = lpu_1.interface.in_ports().gpot_ports().to_selectors()\n", "\n", " # Initialize a connectivity pattern between the two sets of port \n", " # selectors: \n", " pat = pattern.Pattern(','.join(out_ports_0+in_ports_0),\n", " ','.join(out_ports_1+in_ports_1))\n", " \n", " # Create connections from the ports with identifiers matching the output\n", " # ports of one LPU to the ports with identifiers matching the input\n", " # ports of the other LPU. First, define connections from LPU0 to LPU1:\n", " N_conn_spk_0_1 = min(len(out_ports_spk_0), len(in_ports_spk_1))\n", " N_conn_gpot_0_1 = min(len(out_ports_gpot_0), len(in_ports_gpot_1))\n", " for src, dest in zip(random.sample(out_ports_spk_0, N_conn_spk_0_1),\n", " random.sample(in_ports_spk_1, N_conn_spk_0_1)):\n", " pat[src, dest] = 1\n", " pat.interface[src, 'type'] = 'spike'\n", " pat.interface[dest, 'type'] = 'spike'\n", " for src, dest in zip(random.sample(out_ports_gpot_0, N_conn_gpot_0_1),\n", " random.sample(in_ports_gpot_1, N_conn_gpot_0_1)):\n", " pat[src, dest] = 1\n", " pat.interface[src, 'type'] = 'gpot'\n", " pat.interface[dest, 'type'] = 'gpot'\n", "\n", " # Next, define connections from LPU1 to LPU0:\n", " N_conn_spk_1_0 = min(len(out_ports_spk_1), len(in_ports_spk_0))\n", " N_conn_gpot_1_0 = min(len(out_ports_gpot_1), len(in_ports_gpot_0))\n", " for src, dest in zip(random.sample(out_ports_spk_1, N_conn_spk_1_0),\n", " random.sample(in_ports_spk_0, N_conn_spk_1_0)):\n", " pat[src, dest] = 1\n", " pat.interface[src, 'type'] = 'spike'\n", " pat.interface[dest, 'type'] = 'spike'\n", " for src, dest in zip(random.sample(out_ports_gpot_1, N_conn_gpot_1_0),\n", " random.sample(in_ports_gpot_0, N_conn_gpot_1_0)):\n", " pat[src, dest] = 1\n", " pat.interface[src, 'type'] = 'gpot'\n", " pat.interface[dest, 'type'] = 'gpot'\n", " \n", " man.connect(lpu_0, lpu_1, pat, 0, 1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once all of the connections are in place, the entire network may be executed as follows:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "man.start(steps=steps)\n", "man.stop()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generated output for each LPU is stored in HDF5 files." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assuming that the Neurokernel source code has been cloned to ``~/neurokernel``, the above demo can also be run in script form as follows. The parameters below specify a model comprising 30 sensory neurons connected to one LPU in a network of 3 LPUs connected to each other, each of which contains 30 local neurons and 30 output neurons:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%cd -q ~/neurokernel/examples/multi\n", "%run multi_demo.py -y 30 -n 30 -o 30 -u 3" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 } ], "metadata": {} } ] }
bsd-3-clause
AtmaMani/pyChakras
islr/verifying_central_limit_theorem.ipynb
2
44710
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Verifying Central Limit Theorem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Central Limit Theorem states that the sampling distribution of the sampling means approaches a normal distribution as the sample size gets larger — no matter what the shape of the population distribution. This fact holds especially true for sample sizes over 30. All this is saying is that as you take more samples, especially large ones, your graph of the sample means will look more like a normal distribution." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate 1k random integers\n", "Let us use NumPy to generate `1000` random integers between the range `0-100`. Our objective is to calculate the population mean and verify if the mean obtained using CLT comes close to population mean." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "rand_1k = np.random.randint(0,100,1000)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1000" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rand_1k.size" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/atma6951/anaconda3/envs/pychakras/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1a19f2c048>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.distplot(rand_1k)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thus the population follows a `uniform` distribution, **not** a `normal` distribution. Still, we will see the distribution of our means will follow a `normal` distribution." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate population mean" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "48.826" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(rand_1k)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Try out creating a subset and finding its mean" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "100" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subset_100 = np.random.choice(rand_1k, size=100, replace=False)\n", "subset_100.size" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "43.2" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(subset_100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mean of this subset of `100` integers is `43.2`. Not close enough." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Apply CLT.\n", "We will generate `50` samples with `100` items each and find their means." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# generate 50 random samples of size 100 each\n", "subset_means = []\n", "for i in range(0,50):\n", " current_subset = np.random.choice(rand_1k, size=100, replace=False)\n", " subset_means.append(np.mean(current_subset))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the mean of means (its meta :))" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "48.9768" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clt_mean = np.mean(subset_means)\n", "clt_mean" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the SD of the means" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.657234983963594" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "subset_sd = np.std(subset_means)\n", "subset_sd" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/atma6951/anaconda3/envs/pychakras/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "text/plain": [ "<matplotlib.lines.Line2D at 0x1a1ac5f908>" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x288 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = sns.distplot(subset_means, bins=10)\n", "# draw mean in black\n", "ax.axvline(clt_mean, color='black', linestyle='dashed')\n", "\n", "# draw mean +- 1 SD\n", "ax.axvline(clt_mean + subset_sd, color='red', linestyle='dotted')\n", "ax.axvline(clt_mean - subset_sd, color='red', linestyle='dotted')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Difference between mean of means and the population mean" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.15079999999999671" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(rand_1k) - clt_mean" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
km-Poonacha/python4phd
Session 3/ipython/Lesson 7- Sklearn and Randomforest.ipynb
2
8039
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ensemble methods\n", "The goal of ensemble methods is to combine the predictions of several base estimators built with a given learning algorithm in order to improve generalizability / robustness over a single estimator.\n", "\n", "Two families of ensemble methods are usually distinguished:\n", "\n", "* Averaging methods: Here the driving principle is to build several estimators independently and then to average their predictions. On average, the combined estimator is usually better than any of the single base estimator because its variance is reduced. Example: Random forest\n", "\n", "* Boosting methods: Here base estimators are built sequentially and one tries to reduce the bias of the combined estimator. The motivation is to combine several weak models to produce a powerful ensemble. Example: Adaboost\n", "\n", "### Random Forest classifier\n", "In random forests, each tree in the ensemble is built from a sample drawn with replacement from the training set. In addition, when splitting a node during the construction of the tree, the split that is chosen is no longer the best split among all features. Instead, the split that is picked is the best split among a random subset of the features. As a result of this randomness, the bias of the forest usually slightly increases (with respect to the bias of a single non-random tree) but, due to averaging, its variance also decreases, usually more than compensating for the increase in bias, hence yielding an overall better model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sklearn\n", "High level module built on NumPy, SciPy, and matplotlib. Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Step 1: Import data\n", "*Create dataframes from the csv data*" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "\n", "TRAIN_CSV = \"C:\\\\Users\\kmpoo\\Dropbox\\HEC\\Teaching\\Python for PhD May 2019\\python4phd\\Session 3\\Sent\\sentence_review.csv\"\n", "dataframe = pd.read_csv(TRAIN_CSV, sep=\",\",error_bad_lines=False,header= 0, low_memory=False, encoding = \"Latin1\")\n", "print(dataframe)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Step 2: Manipulate the dataframe and add new features\n", "*Create a new feature \"length of the sentence\"*\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dataframe = dataframe.assign(nWords = lambda x : x['review_text'].str.split().str.len() )\n", "dataframe['bi_senti'] = [ \"positive\" if x >= 4 else \"negative\" for x in dataframe['sentiment']]\n", "print(dataframe)\n", "print(dataframe['bi_senti'].value_counts())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Step 3: Split into train and test samples" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.utils import shuffle #To shuffle the dataframe\n", "from sklearn.model_selection import train_test_split\n", "\n", "dataframe = shuffle(dataframe)\n", "df_train, df_test = train_test_split(dataframe, test_size=0.2)\n", "print(\"size of trainig data \", len(df_train))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Step 4: Tokenization and veectorize the sentence\n", "Text data requires special preparation before you can start using it for predictive modeling.\n", "\n", "The text must be parsed to remove words, called tokenization. Then the words need to be encoded as integers or floating point values for use as input to a machine learning algorithm, called feature extraction (or vectorization).\n", "##### Convert sentences to TF-IDF vectors \n", "tf-idf ( term frequency–inverse document frequency), is a numerical statistic that is intended to reflect how important a word is to a document in a collection or corpus. It is often used as a weighting factor in searches of information retrieval, text mining, and user modeling. The tf–idf value increases proportionally to the number of times a word appears in the document and is offset by the number of documents in the corpus that contain the word, which helps to adjust for the fact that some words appear more frequently in general." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.feature_extraction.text import TfidfVectorizer\n", "\n", "\n", "def tfidf_vectorizer(corpus):\n", " \"\"\"This function converts the input sentence into a sparse matrix of tfidf vectors\"\"\"\n", " tokenizer = TfidfVectorizer( strip_accents = \"unicode\", analyzer=\"word\", stop_words=\"english\", ngram_range=(1,2), max_features=20000)\n", " tokenizer.fit(corpus)\n", " return tokenizer\n", "\n", "\n", "vectorizer = tfidf_vectorizer(dataframe['review_text']) \n", "train_x = vectorizer.transform(df_train['review_text'])\n", "test_x = vectorizer.transform(df_test['review_text'])\n", "\n", "print(train_x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Step 5:Generate the classifier" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "\n", "rfc = RandomForestClassifier(n_estimators=10)\n", "classifier = rfc.fit(train_x, df_train['bi_senti'])\n", "acc = classifier.score(test_x,df_test['bi_senti'])\n", "print(\"accuracy of rfc is = \", acc)\n", "rfc.predict_proba(test_x)[0:10]\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Step 6: Use the model to predict the sentiment of your sentence" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = pd.Series(\"his movie was absolutely horrible. A boring, random, nonsensical mess from start to finish. The film is incompetently directed from a very poor script. It feels more like a superhero movie from the early 2000's such as Catwoman or Daredevil. Watching it makes it clear that the people involved had no idea what they were doing, and should never have been put in charge of a project this size to begin with. The story makes no sense, and the whole reason Batman wants to kill Superman is contrived. Batman and Superman hate each other because they both cause collateral damage and human death, and neither one ever sees fit to point out their similarities, or try and talk to each other about their different perspectives. Apparently that would have been too interesting, so of course Snyder didn't include it.\")\n", "x = vectorizer.transform(s)\n", "print(rfc.predict_proba(x))\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
wcmckee/signinlca
wcusersdata.ipynb
1
37999
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "wcUsersData\n", "\n", "Script to get users logged into the system.\n", "\n", "Saves the output of users as the hostname of the system.\n", "Convert to a dict and json object, merging with other hostname output.\n", "\n", "Runs script every min and if user is found then - time from their account \n", "\n", "File with, usernames, time remaining on user.\n", "\n", "time file is a json object.\n", "{'username' : 'wcmckee', 'time' : 320 }\n", "\n", "time is amount of time left on account.\n", "\n", "script to check this object and if 0 - lock account. ELSE, allow login. \n", "\n", "Script to add time to acoun,, auto add time to certain groups/user a day. \n", "\n", "\n", "\n", "username 45" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import socket\n", "import json" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "usertimedict = dict()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "lisho = os.listdir('/home')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for ish in lisho:\n", " usertimedict.update({ish : 0})" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'blag': 0,\n", " 'blah': 0,\n", " 'charle': 0,\n", " 'checkthis': 0,\n", " 'clittle': 0,\n", " 'figlet': 0,\n", " 'gerty': 0,\n", " 'jblog': 0,\n", " 'jchick': 0,\n", " 'joecheck': 0,\n", " 'joeman': 0,\n", " 'joemanz': 0,\n", " 'pjohns': 0,\n", " 'poi': 0,\n", " 'point': 0,\n", " 'poiu': 0,\n", " 'pytest': 0,\n", " 'qwe': 0,\n", " 'red': 0,\n", " 'signinlca.py': 0,\n", " 'sjohns': 0,\n", " 'ssung': 0,\n", " 'tnow': 0,\n", " 'wblack': 0,\n", " 'wcm': 0,\n", " 'wcmckee': 0,\n", " 'webmck': 0,\n", " 'wez': 0,\n", " 'wkee': 0,\n", " 'wmen': 0}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "usertimedict" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "add how much time: 15\n" ] } ], "source": [ "addtime = (raw_input('add how much time: '))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#letim = len(addtime)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#letim" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'15'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "addtime" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "str" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(addtime)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "intime = int(addtime)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for ish in lisho:\n", " usertimedict.update({ish : intime})" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'blag': 15,\n", " 'blah': 15,\n", " 'charle': 15,\n", " 'checkthis': 15,\n", " 'clittle': 15,\n", " 'figlet': 15,\n", " 'gerty': 15,\n", " 'jblog': 15,\n", " 'jchick': 15,\n", " 'joecheck': 15,\n", " 'joeman': 15,\n", " 'joemanz': 15,\n", " 'pjohns': 15,\n", " 'poi': 15,\n", " 'point': 15,\n", " 'poiu': 15,\n", " 'pytest': 15,\n", " 'qwe': 15,\n", " 'red': 15,\n", " 'signinlca.py': 15,\n", " 'sjohns': 15,\n", " 'ssung': 15,\n", " 'tnow': 15,\n", " 'wblack': 15,\n", " 'wcm': 15,\n", " 'wcmckee': 15,\n", " 'webmck': 15,\n", " 'wez': 15,\n", " 'wkee': 15,\n", " 'wmen': 15}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "usertimedict" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "myhn = socket.gethostname()" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Which user to edit time: point\n", "How much time to add: 100\n" ] } ], "source": [ "#Edit just one person\n", "\n", "edione = raw_input('Which user to edit time: ')\n", "timedi = raw_input('How much time to add: ')\n", "\n", "usertimedict.update({edione : timedi})" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "jstim = json.dumps(usertimedict)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'{\"wcmckee\": 15, \"joemanz\": 15, \"point\": 15, \"qwe\": 15, \"wkee\": 15, \"checkthis\": 15, \"poiu\": 15, \"blah\": 15, \"charle\": 15, \"poi\": 15, \"tnow\": 15, \"clittle\": 15, \"wblack\": 15, \"ssung\": 15, \"wmen\": 15, \"blag\": 15, \"signinlca.py\": 15, \"sjohns\": 15, \"gerty\": 15, \"webmck\": 15, \"joeman\": 15, \"joecheck\": 15, \"jchick\": 15, \"pytest\": 15, \"jblog\": 15, \"pjohns\": 15, \"wez\": 15, \"figlet\": 15, \"wcm\": 15, \"red\": 15}'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "jstim" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "opus = open('/home/wcmckee/sellcoffee/hostnames/' + myhn, 'w')\n", "\n", "opus.write(jstim)\n", "opus.close()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\"wcmckee\": 15, \"joemanz\": 15, \"point\": 15, \"qwe\": 15, \"wkee\": 15, \"checkthis\": 15, \"poiu\": 15, \"blah\": 15, \"charle\": 15, \"poi\": 15, \"tnow\": 15, \"clittle\": 15, \"wblack\": 15, \"ssung\": 15, \"wmen\": 15, \"blag\": 15, \"signinlca.py\": 15, \"sjohns\": 15, \"gerty\": 15, \"webmck\": 15, \"joeman\": 15, \"joecheck\": 15, \"jchick\": 15, \"pytest\": 15, \"jblog\": 15, \"pjohns\": 15, \"wez\": 15, \"figlet\": 15, \"wcm\": 15, \"red\": 15}\n" ] } ], "source": [ "rdopuw = open('/home/wcmckee/sellcoffee/hostnames/localhost', 'r')\n", "\n", "print rdopuw.read()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'point'" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "edione" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "limedi = int(timedi)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "limedi" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "osscm = ('users > ' + myhn + '.txt')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'users > naplesyellow.txt'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "osscm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "os.chdir('/home/wcmckee/sellcoffee/')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.system(osscm)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ophos = open('/home/wcmckee/sellcoffee/' + myhn + '.txt')" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'noby wcmckee wcmckee wcmckee wcmckee wcmckee\\n'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print ophos.read()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ophos.close()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['IPython.config.re',\n", " 'IPython.core.error',\n", " 'curses.sys',\n", " 'zmq.sugar.weakref',\n", " 'IPython.kernel.connect',\n", " 'ctypes.os',\n", " 'pexpect.select',\n", " 'runpy',\n", " 'gc',\n", " 'pty',\n", " 'IPython.utils.pprint',\n", " 'logging.weakref',\n", " 'pprint',\n", " 'IPython.kernel.inprocess.zmq',\n", " 'IPython.kernel.blocking.client',\n", " 'IPython.kernel.comm.uuid',\n", " 'zmq',\n", " 'IPython.terminal.sys',\n", " 'string',\n", " 'IPython.config.sys',\n", " 'IPython.utils.logging',\n", " 'IPython.config.json',\n", " 'encodings.utf_8',\n", " 'IPython.kernel.zmq.serialize',\n", " 'IPython.html.IPython',\n", " 'IPython.kernel.channels',\n", " 'json.encoder',\n", " 'datetime',\n", " 'zmq.devices.threading',\n", " 'zmq.backend.cython.utils',\n", " 'IPython.utils.pickleshare',\n", " 'IPython.utils.errno',\n", " 'IPython.core.debugger',\n", " 'IPython.kernel.inprocess.manager',\n", " 'IPython.core.displayhook',\n", " 'curses.os',\n", " 'IPython.lib.IPython',\n", " 'IPython.core.magics.auto',\n", " 'IPython.lib.select',\n", " 'shlex',\n", " 'IPython.config',\n", " 'IPython.core.ultratb',\n", " 'IPython.kernel.launcher',\n", " 'multiprocessing',\n", " 'IPython.utils.resource',\n", " 'dis',\n", " 'IPython.kernel.IPython',\n", " 'logging.threading',\n", " 'IPython.core.splitinput',\n", " 'IPython.lib.types',\n", " 'IPython.terminal.ipapp',\n", " 'IPython.core.excolors',\n", " 'IPython.utils.tempfile',\n", " 'IPython.extensions.textwrap',\n", " 'IPython.core.inspect',\n", " 'IPython.utils.version',\n", " 'IPython.lib.getpass',\n", " 'abc',\n", " 'IPython.core.displaypub',\n", " 'zmq.utils.sys',\n", " 'bdb',\n", " 'tarfile',\n", " 'IPython.core.mimetypes',\n", " 'IPython.external.path.hashlib',\n", " 'UserDict',\n", " 'zmq.sugar.warnings',\n", " 'zmq.devices.monitoredqueue',\n", " 'IPython.utils.jsonutil',\n", " 'IPython.utils.openpy',\n", " 'IPython.core.alias',\n", " 'inspect',\n", " 'IPython.core.datetime',\n", " 'ctypes.tempfile',\n", " 'simplejson.decoder',\n", " 'IPython.external.path.functools',\n", " 'IPython.lib.collections',\n", " 'IPython.kernel.zmq.cPickle',\n", " 'exceptions',\n", " 'json.scanner',\n", " 'IPython.utils.zmq',\n", " 'codecs',\n", " 'IPython.core.shadowns',\n", " 'IPython.kernel.subprocess',\n", " 'importlib',\n", " 'IPython.core.magics.extension',\n", " 'IPython.utils.shutil',\n", " 'StringIO',\n", " 'weakref',\n", " 'pexpect.termios',\n", " 'IPython.utils.datetime',\n", " 'IPython.kernel.zmq',\n", " 'IPython.core.display',\n", " 'IPython.utils.eventful',\n", " 'IPython.terminal',\n", " 'encodings.binascii',\n", " 'base64',\n", " '_sre',\n", " 'IPython.core.json',\n", " 'sqlite3.dbapi2',\n", " 'IPython',\n", " 'logging.re',\n", " 'select',\n", " 'IPython.external.path.os',\n", " '_heapq',\n", " 'IPython.core.display_trap',\n", " 'zmq.sugar.constants',\n", " 'zmq.backend.cython.time',\n", " 'binascii',\n", " 'pexpect.time',\n", " 'zmq.backend.cython._poll',\n", " 'IPython.core.logger',\n", " 'IPython.config.collections',\n", " 'tokenize',\n", " 'IPython.kernel.kernelspec',\n", " 'cPickle',\n", " 'simplejson.sys',\n", " 'IPython.core.magics.errno',\n", " 'IPython.utils.encoding',\n", " 'IPython.core.ast',\n", " 'IPython.core.keyword',\n", " 'IPython.lib.os',\n", " 'IPython.core.magics.itertools',\n", " 'zmq.backend.cython.cPickle',\n", " 'IPython.core.getipython',\n", " 'IPython.core.magics.cProfile',\n", " 'IPython.core.magics.display',\n", " 'IPython.core.page',\n", " '_ast',\n", " 'IPython.utils.process',\n", " 'zmq.sugar.tracker',\n", " 'zmq.backend.cython.codecs',\n", " 'encodings.aliases',\n", " 'pexpect.resource',\n", " 'fnmatch',\n", " 'sre_parse',\n", " 'tornado.concurrent',\n", " 'pickle',\n", " 'IPython.kernel.blocking.IPython',\n", " 'logging.cStringIO',\n", " 'simplejson.simplejson',\n", " 'numbers',\n", " 'IPython.utils.shlex',\n", " 'IPython.core.magics.signal',\n", " 'IPython.kernel.zmq.heartbeat',\n", " 'IPython.html.nbextensions',\n", " 'IPython.utils.platform',\n", " 'strop',\n", " 'IPython.kernel.manager',\n", " 'IPython.core.history',\n", " 'IPython.external.decorator',\n", " 'IPython.utils.ipstruct',\n", " 'IPython.kernel.channelsabc',\n", " 'zmq.utils.sixcerpt',\n", " 'IPython.extensions.__future__',\n", " 'IPython.config.application',\n", " 'zipfile',\n", " 'htmlentitydefs',\n", " 'zmq.backend.cython.threading',\n", " 'IPython.external.path.fnmatch',\n", " 'pexpect.os',\n", " 'codeop',\n", " 'tornado.platform.interface',\n", " 'IPython.core.magics.osm',\n", " 'os.path',\n", " 'IPython.core.magics.time',\n", " 'IPython.utils.socket',\n", " 'argparse',\n", " 'IPython.kernel.io',\n", " 'simplegeneric',\n", " 'pexpect.struct',\n", " '_weakrefset',\n", " 'zmq.backend.select',\n", " 'IPython.external.pexpect',\n", " 'IPython.kernel.zmq.ipkernel',\n", " 'tty',\n", " 'functools',\n", " 'sysconfig',\n", " 'IPython.terminal.embed',\n", " 'IPython.kernel.inprocess.client',\n", " 'zmq.devices',\n", " 'sqlite3.collections',\n", " 'IPython.external.path.__future__',\n", " 'uuid',\n", " 'tempfile',\n", " 'IPython.kernel.zmq.thread',\n", " 'IPython.config.configurable',\n", " 'imp',\n", " 'multiprocessing.os',\n", " 'IPython.utils.tempdir',\n", " 'IPython.lib.deepreload',\n", " 'multiprocessing.itertools',\n", " 'IPython.kernel.zmq.IPython',\n", " 'IPython.core.magics.logging',\n", " 'pexpect.codecs',\n", " 'zmq.zmq',\n", " 'IPython.utils.pickleutil',\n", " 'IPython.core.traceback',\n", " 'IPython.html.sys',\n", " 'IPython.core.__main__',\n", " 'IPython.extensions',\n", " 'IPython.utils.hashlib',\n", " 'IPython.config.IPython',\n", " 'zmq.eventloop.__future__',\n", " 'decorator',\n", " 'IPython.core.zipimport',\n", " 'IPython.core.magics.execution',\n", " 'IPython.terminal.interactiveshell',\n", " 'zmq.backend.cython.context',\n", " 'IPython.core.warnings',\n", " 'IPython.utils.imp',\n", " 'logging.handlers',\n", " 'token',\n", " 'IPython.kernel.json',\n", " 'IPython.testing.sys',\n", " 'IPython.core.pylabtools',\n", " 'IPython.utils.warnings',\n", " 'IPython.core.magics.gc',\n", " 'cStringIO',\n", " 'cmd',\n", " 'IPython.kernel.zmq.signal',\n", " 'IPython.core.io',\n", " 'IPython.core.magics.namespace',\n", " 'IPython.kernel.inprocess.Queue',\n", " 'zmq.os',\n", " 'zmq.sugar.frame',\n", " 'zmq.backend.zmq',\n", " 'multiprocessing.process',\n", " 'ctypes.errno',\n", " 'encodings',\n", " 'IPython.kernel.zmq.kernelapp',\n", " 'IPython.core.magics.history',\n", " 'IPython.core.__future__',\n", " 'sqlite3._sqlite3',\n", " 'zmq.backend.cython.struct',\n", " 'IPython.kernel.zmq.atexit',\n", " 'IPython.core.interactiveshell',\n", " 'IPython.utils.base64',\n", " 'zmq.sugar.poll',\n", " 'IPython.core.operator',\n", " 'IPython.core.crashhandler',\n", " 'IPython.utils',\n", " 'IPython.core.magics.ast',\n", " 're',\n", " 'IPython.kernel.zmq.sys',\n", " 'IPython.core.magics.IPython',\n", " 'IPython.utils.capture',\n", " 'sqlite3.time',\n", " 'math',\n", " 'IPython.core.IPython',\n", " 'IPython.kernel.comm.sys',\n", " 'IPython.utils.distutils',\n", " 'IPython.core.importlib',\n", " 'ast',\n", " 'IPython.lib.sys',\n", " 'simplejson.scanner',\n", " 'zmq.utils.constant_names',\n", " 'IPython.external.path.codecs',\n", " 'ctypes.struct',\n", " 'IPython.core.errno',\n", " '_sysconfigdata_nd',\n", " '_locale',\n", " 'IPython.core.logging',\n", " 'logging',\n", " 'IPython.kernel.zmq.uuid',\n", " 'thread',\n", " 'traceback',\n", " 'IPython.utils.tokenutil',\n", " 'IPython.core.builtin_trap',\n", " 'zmq.backend.platform',\n", " 'IPython.utils.coloransi',\n", " 'multiprocessing.util',\n", " 'IPython.utils._process_posix',\n", " 'IPython.core.completerlib',\n", " 'pexpect.fcntl',\n", " 'ctypes.re',\n", " '_collections',\n", " 'IPython.html.os',\n", " 'zmq.sugar.codecs',\n", " 'multiprocessing.sys',\n", " 'array',\n", " 'IPython.kernel.blocking.channels',\n", " 'IPython.utils.locale',\n", " 'IPython.core.magics.pdb',\n", " 'zmq.devices.time',\n", " 'ctypes.sys',\n", " 'IPython.core.magics.config',\n", " 'pexpect',\n", " 'posixpath',\n", " 'ctypes.util',\n", " 'IPython.external.simplegeneric',\n", " 'zmq.zipimport',\n", " 'IPython.utils._sysinfo',\n", " 'zmq.eventloop.ioloop',\n", " 'IPython.lib.imp',\n", " 'types',\n", " 'zmq.backend.cython._device',\n", " 'IPython.utils.textwrap',\n", " 'IPython.core.linecache',\n", " 'json._json',\n", " 'IPython.lib.StringIO',\n", " '_codecs',\n", " 'zmq.backend.cython.zmq',\n", " 'json.sys',\n", " 'IPython.utils.os',\n", " 'IPython.extensions.inspect',\n", " 'IPython.core.magics.code',\n", " 'copy',\n", " 'hashlib',\n", " 'zmq.error',\n", " 'keyword',\n", " 'IPython.extensions.IPython',\n", " 'posix',\n", " 'IPython.core.hashlib',\n", " 'IPython.config.logging',\n", " 'IPython.utils.codeutil',\n", " '_curses',\n", " 'IPython.utils.generics',\n", " 'IPython.kernel.zmq.zmq',\n", " 'sre_compile',\n", " '_hashlib',\n", " 'IPython.utils.ulinecache',\n", " 'IPython.external.path.errno',\n", " 'IPython.core.sqlite3',\n", " 'getpass',\n", " 'logging.collections',\n", " 'IPython.core.threading',\n", " 'zmq.eventloop.zmq',\n", " '__main__',\n", " 'multiprocessing.atexit',\n", " 'calendar',\n", " 'pexpect.pty',\n", " 'IPython.kernel.zmq.iostream',\n", " 'IPython.core.pprint',\n", " 'encodings.codecs',\n", " 'IPython.utils.__builtin__',\n", " 'IPython.terminal.os',\n", " 'zmq.utils.jsonapi',\n", 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'IPython.utils.functools',\n", " 'readline',\n", " 'zmq.utils.interop',\n", " 'IPython.kernel.zmq.hmac',\n", " 'sitecustomize',\n", " 'IPython.kernel.zmq.pprint',\n", " 'IPython.html.__future__',\n", " 'IPython.kernel.client',\n", " 'IPython.core.magics.deprecated',\n", " 'IPython.utils.signatures',\n", " 'IPython.core.tempfile',\n", " 'IPython.config.copy',\n", " 'IPython.kernel.inprocess.IPython',\n", " 'IPython.utils.py3compat',\n", " 'tornado.ioloop']" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sys.modules.keys()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": 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mit
tensorflow/docs
site/en/guide/tensor_slicing.ipynb
1
18269
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "tuOe1ymfHZPu" }, "outputs": [], "source": [ "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in compliance with the License.\n", "# You may obtain a copy of the License at\n", "#\n", "# https://www.apache.org/licenses/LICENSE-2.0\n", "#\n", "# Unless required by applicable law or agreed to in writing, software\n", "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", "# See the License for the specific language governing permissions and\n", "# limitations under the License." ] }, { "cell_type": "markdown", "metadata": { "id": "VZ-KA8k5kybx" }, "source": [ "# Introduction to tensor slicing" ] }, { "cell_type": "markdown", "metadata": { "id": "MfBg1C5NB3X0" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td>\n", " <a target=\"_blank\" href=\"https://www.tensorflow.org/guide/tensor_slicing\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/guide/tensor_slicing.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n", " </td>\n", " <td>\n", " <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/guide/tensor_slicing.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n", " </td>\n", " <td>\n", " <a href=\"https://storage.googleapis.com/tensorflow_docs/docs/site/en/guide/tensor_slicing.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n", " </td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "AixIdVeRk3CO" }, "source": [ "When working on ML applications such as object detection and NLP, it is sometimes necessary to work with sub-sections (slices) of tensors. For example, if your model architecture includes routing, where one layer might control which training example gets routed to the next layer. In this case, you could use tensor slicing ops to split the tensors up and put them back together in the right order.\n", "\n", "In NLP applications, you can use tensor slicing to perform word masking while training. For example, you can generate training data from a list of sentences by choosing a word index to mask in each sentence, taking the word out as a label, and then replacing the chosen word with a mask token. \n", "\n", "In this guide, you will learn how to use the TensorFlow APIs to:\n", "\n", "* Extract slices from a tensor\n", "* Insert data at specific indices in a tensor\n", "\n", "This guide assumes familiarity with tensor indexing. Read the indexing sections of the [Tensor](https://www.tensorflow.org/guide/tensor#indexing) and [TensorFlow NumPy](https://www.tensorflow.org/guide/tf_numpy#indexing) guides before getting started with this guide." ] }, { "cell_type": "markdown", "metadata": { "id": "FcWhWYn7eXkF" }, "source": [ "## Setup\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "m6uvewqi0jso" }, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": { "id": "K-muS4ej5zoN" }, "source": [ "## Extract tensor slices\n", "\n", "Perform NumPy-like tensor slicing using `tf.slice`.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wZep0cjs0Oai" }, "outputs": [], "source": [ "t1 = tf.constant([0, 1, 2, 3, 4, 5, 6, 7])\n", "\n", "print(tf.slice(t1,\n", " begin=[1],\n", " size=[3]))" ] }, { "cell_type": "markdown", "metadata": { "id": "Vh3xI3j0DRJ2" }, "source": [ "Alternatively, you can use a more Pythonic syntax. Note that tensor slices are evenly spaced over a start-stop range." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "P1MtEyKuWuDD" }, "outputs": [], "source": [ "print(t1[1:4])" ] }, { "cell_type": "markdown", "metadata": { "id": "cjq1o8D2wKKs" }, "source": [ "<img src=\"images/tf_slicing/slice_1d_1.png\">" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "UunuLTIuwDA-" }, "outputs": [], "source": [ "print(t1[-3:])" ] }, { "cell_type": "markdown", "metadata": { "id": "EHvRB-XTwRTd" }, "source": [ "<img src=\"images/tf_slicing/slice_1d_2.png\">" ] }, { "cell_type": "markdown", "metadata": { "id": "SW1zFFTnUpCQ" }, "source": [ "For 2-dimensional tensors,you can use something like:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "kThZhmpAVAQw" }, "outputs": [], "source": [ "t2 = tf.constant([[0, 1, 2, 3, 4],\n", " [5, 6, 7, 8, 9],\n", " [10, 11, 12, 13, 14],\n", " [15, 16, 17, 18, 19]])\n", "\n", "print(t2[:-1, 1:3])" ] }, { "cell_type": "markdown", "metadata": { "id": "xA5Xt4OdVUui" }, "source": [ "<img src=\"images/tf_slicing/slice_2d_1.png\">" ] }, { "cell_type": "markdown", "metadata": { "id": "iJPggqsH15fI" }, "source": [ "You can use `tf.slice` on higher dimensional tensors as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Re5eX1OXnKOZ" }, "outputs": [], "source": [ "t3 = tf.constant([[[1, 3, 5, 7],\n", " [9, 11, 13, 15]],\n", " [[17, 19, 21, 23],\n", " [25, 27, 29, 31]]\n", " ])\n", "\n", "print(tf.slice(t3,\n", " begin=[1, 1, 0],\n", " size=[1, 1, 2]))" ] }, { "cell_type": "markdown", "metadata": { "id": "x-O5FNV9qOJK" }, "source": [ "You can also use `tf.strided_slice` to extract slices of tensors by 'striding' over the tensor dimensions." ] }, { "cell_type": "markdown", "metadata": { "id": "b9FhvrOnJsJb" }, "source": [ "Use `tf.gather` to extract specific indices from a single axis of a tensor." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TwviZrrIj2h7" }, "outputs": [], "source": [ "print(tf.gather(t1,\n", " indices=[0, 3, 6]))\n", "\n", "# This is similar to doing\n", "\n", "t1[::3]" ] }, { "cell_type": "markdown", "metadata": { "id": "oKyjGi2zyzEC" }, "source": [ "<img src=\"images/tf_slicing/slice_1d_3.png\">" ] }, { "cell_type": "markdown", "metadata": { "id": "obrjeKy1WfTN" }, "source": [ "`tf.gather` does not require indices to be evenly spaced." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LjJcwcZ0druw" }, "outputs": [], "source": [ "alphabet = tf.constant(list('abcdefghijklmnopqrstuvwxyz'))\n", "\n", "print(tf.gather(alphabet,\n", " indices=[2, 0, 19, 18]))" ] }, { "cell_type": "markdown", "metadata": { "id": "mSHmUXIyeaJG" }, "source": [ "<img src=\"images/tf_slicing/gather_1.png\">" ] }, { "cell_type": "markdown", "metadata": { "id": "XsxMx49SOaVu" }, "source": [ "To extract slices from multiple axes of a tensor, use `tf.gather_nd`. This is useful when you want to gather the elements of a matrix as opposed to just its rows or columns." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mT52NFWVdiTe" }, "outputs": [], "source": [ "t4 = tf.constant([[0, 5],\n", " [1, 6],\n", " [2, 7],\n", " [3, 8],\n", " [4, 9]])\n", "\n", "print(tf.gather_nd(t4,\n", " indices=[[2], [3], [0]]))" ] }, { "cell_type": "markdown", "metadata": { "id": "87NN7YQhh2-a" }, "source": [ "<img src=\"images/tf_slicing/gather_2.png\">" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_z6F2WcPJ9Rh" }, "outputs": [], "source": [ "t5 = np.reshape(np.arange(18), [2, 3, 3])\n", "\n", "print(tf.gather_nd(t5,\n", " indices=[[0, 0, 0], [1, 2, 1]]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gyIjhm7cV2N0" }, "outputs": [], "source": [ "# Return a list of two matrices\n", "\n", "print(tf.gather_nd(t5,\n", " indices=[[[0, 0], [0, 2]], [[1, 0], [1, 2]]]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "368D4ciDWB3r" }, "outputs": [], "source": [ "# Return one matrix\n", "\n", "print(tf.gather_nd(t5,\n", " indices=[[0, 0], [0, 2], [1, 0], [1, 2]]))" ] }, { "cell_type": "markdown", "metadata": { "id": "od51VzS2SSPS" }, "source": [ "## Insert data into tensors\n", "\n", "Use `tf.scatter_nd` to insert data at specific slices/indices of a tensor. Note that the tensor into which you insert values is zero-initialized." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "jlALYLWm1KhN" }, "outputs": [], "source": [ "t6 = tf.constant([10])\n", "indices = tf.constant([[1], [3], [5], [7], [9]])\n", "data = tf.constant([2, 4, 6, 8, 10])\n", "\n", "print(tf.scatter_nd(indices=indices,\n", " updates=data,\n", " shape=t6))" ] }, { "cell_type": "markdown", "metadata": { "id": "CD5vd-kxksW7" }, "source": [ "Methods like `tf.scatter_nd` which require zero-initialized tensors are similar to sparse tensor initializers. You can use `tf.gather_nd` and `tf.scatter_nd` to mimic the behavior of sparse tensor ops.\n", "\n", "Consider an example where you construct a sparse tensor using these two methods in conjunction." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xyK69QgRmrlW" }, "outputs": [], "source": [ "# Gather values from one tensor by specifying indices\n", "\n", "new_indices = tf.constant([[0, 2], [2, 1], [3, 3]])\n", "t7 = tf.gather_nd(t2, indices=new_indices)" ] }, { "cell_type": "markdown", "metadata": { "id": "_7V_Qfa4qkdn" }, "source": [ "<img src=\"images/tf_slicing/gather_nd_sparse.png\">" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "QWT1E1Weqjx2" }, "outputs": [], "source": [ "# Add these values into a new tensor\n", "\n", "t8 = tf.scatter_nd(indices=new_indices, updates=t7, shape=tf.constant([4, 5]))\n", "\n", "print(t8)" ] }, { "cell_type": "markdown", "metadata": { "id": "NUyYjnvCn_vu" }, "source": [ "This is similar to:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "LeqFwUgroE4j" }, "outputs": [], "source": [ "t9 = tf.SparseTensor(indices=[[0, 2], [2, 1], [3, 3]],\n", " values=[2, 11, 18],\n", " dense_shape=[4, 5])\n", "\n", "print(t9)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5MaF6RlJot33" }, "outputs": [], "source": [ "# Convert the sparse tensor into a dense tensor\n", "\n", "t10 = tf.sparse.to_dense(t9)\n", "\n", "print(t10)" ] }, { "cell_type": "markdown", "metadata": { "id": "4sf3F3Xk56Bt" }, "source": [ "To insert data into a tensor with pre-existing values, use `tf.tensor_scatter_nd_add`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "mte2ifOb6sQO" }, "outputs": [], "source": [ "t11 = tf.constant([[2, 7, 0],\n", " [9, 0, 1],\n", " [0, 3, 8]])\n", "\n", "# Convert the tensor into a magic square by inserting numbers at appropriate indices\n", "\n", "t12 = tf.tensor_scatter_nd_add(t11,\n", " indices=[[0, 2], [1, 1], [2, 0]],\n", " updates=[6, 5, 4])\n", "\n", "print(t12)" ] }, { "cell_type": "markdown", "metadata": { "id": "2dQYyROU09G6" }, "source": [ "Similarly, use `tf.tensor_scatter_nd_sub` to subtract values from a tensor with pre-existing values." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ac6_i6uK1EI6" }, "outputs": [], "source": [ "# Convert the tensor into an identity matrix\n", "\n", "t13 = tf.tensor_scatter_nd_sub(t11,\n", " indices=[[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [2, 1], [2, 2]],\n", " updates=[1, 7, 9, -1, 1, 3, 7])\n", "\n", "print(t13)" ] }, { "cell_type": "markdown", "metadata": { "id": "B_2DuzRRwVc8" }, "source": [ "Use `tf.tensor_scatter_nd_min` to copy element-wise minimum values from one tensor to another." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "T_4FrHrHlkHK" }, "outputs": [], "source": [ "t14 = tf.constant([[-2, -7, 0],\n", " [-9, 0, 1],\n", " [0, -3, -8]])\n", "\n", "t15 = tf.tensor_scatter_nd_min(t14,\n", " indices=[[0, 2], [1, 1], [2, 0]],\n", " updates=[-6, -5, -4])\n", "\n", "print(t15)" ] }, { "cell_type": "markdown", "metadata": { "id": "PkaiKyrF0WtX" }, "source": [ "Similarly, use `tf.tensor_scatter_nd_max` to copy element-wise maximum values from one tensor to another." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "izJu0nXi0GDq" }, "outputs": [], "source": [ "t16 = tf.tensor_scatter_nd_max(t14,\n", " indices=[[0, 2], [1, 1], [2, 0]],\n", " updates=[6, 5, 4])\n", "\n", "print(t16)" ] }, { "cell_type": "markdown", "metadata": { "id": "QAffUOa-85lF" }, "source": [ "## Further reading and resources\n", "\n", "In this guide, you learned how to use the tensor slicing ops available with TensorFlow to exert finer control over the elements in your tensors.\n", "\n", "* Check out the slicing ops available with TensorFlow NumPy such as `tf.experimental.numpy.take_along_axis` and `tf.experimental.numpy.take`.\n", "\n", "* Also check out the [Tensor guide](https://www.tensorflow.org/guide/tensor) and the [Variable guide](https://www.tensorflow.org/guide/variable)." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "tensor_slicing.ipynb", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
gmr/RabbitMQ-in-Depth
notebooks/7.2.2 Send with Message Headers.ipynb
1
1493
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import stomp\n", "import time" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "conn = stomp.Connection()\n", "conn.start()\n", "conn.connect()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "conn.send(body='Example message with Headers', \n", " destination='/queue/stomp-messages', \n", " headers={'app-id': '7.2.2 Example', \n", " 'priority': 5,\n", " 'reply-to': 'reply-to-example',\n", " 'timestamp': int(time.time())})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "conn.disconnect()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
longyangking/ML
Deep Learning.ipynb
1
579
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Deep Learning\n", "## Convolution Neural Network" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
lgpl-3.0
samueljrowell/UVM-ME249-CFD
ME249-Lecture-3.ipynb
2
340429
{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "Text provided under a Creative Commons Attribution license, CC-BY. All code is made available under the FSF-approved MIT license. (c) Yves Dubief, 2015. NSF for support via NSF-CBET award #1258697." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture 3: Accuracy in Fourier's Space" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline \n", "# plots graphs within the notebook\n", "%config InlineBackend.figure_format='svg' # not sure what this does, may be default images to svg format\n", "\n", "from IPython.display import Image\n", "\n", "from IPython.core.display import HTML\n", "def header(text):\n", " raw_html = '<h4>' + str(text) + '</h4>'\n", " return raw_html\n", "\n", "def box(text):\n", " raw_html = '<div style=\"border:1px dotted black;padding:2em;\">'+str(text)+'</div>'\n", " return HTML(raw_html)\n", "\n", "def nobox(text):\n", " raw_html = '<p>'+str(text)+'</p>'\n", " return HTML(raw_html)\n", "\n", "def addContent(raw_html):\n", " global htmlContent\n", " htmlContent += raw_html\n", " \n", "class PDF(object):\n", " def __init__(self, pdf, size=(200,200)):\n", " self.pdf = pdf\n", " self.size = size\n", "\n", " def _repr_html_(self):\n", " return '<iframe src={0} width={1[0]} height={1[1]}></iframe>'.format(self.pdf, self.size)\n", "\n", " def _repr_latex_(self):\n", " return r'\\includegraphics[width=1.0\\textwidth]{{{0}}}'.format(self.pdf)\n", "\n", "class ListTable(list):\n", " \"\"\" Overridden list class which takes a 2-dimensional list of \n", " the form [[1,2,3],[4,5,6]], and renders an HTML Table in \n", " IPython Notebook. \"\"\"\n", " \n", " def _repr_html_(self):\n", " html = [\"<table>\"]\n", " for row in self:\n", " html.append(\"<tr>\")\n", " \n", " for col in row:\n", " html.append(\"<td>{0}</td>\".format(col))\n", " \n", " html.append(\"</tr>\")\n", " html.append(\"</table>\")\n", " return ''.join(html)\n", " \n", "font = {'family' : 'serif',\n", " 'color' : 'black',\n", " 'weight' : 'normal',\n", " 'size' : 18,\n", " }\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Discrete Fourier Series</h1>\n", "\n", "Consider a function $f$ periodic over a domain $0\\leq x\\leq 2\\pi$, discretized by $N_x$ points. The longest wavelength wave that can be contained in the domain is $L_x$. A phyiscal understanding of Fourier series is the representation of a system as the sum of many waves fo wavelengths smaller or equal to $L_x$. In a discrete sense, the series of wave used to decompose the system is defined as:\n", "$$\n", "a_n\\exp\\left(\\hat{\\jmath}\\frac{2\\pi n}{Lx}\\right)\n", "$$\n", "such that\n", "<p class='alert alert-danger'>\n", "$$\n", "f(x) = \\sum_{n=-\\infty}^{\\infty}a_n\\exp\\left(\\hat{\\jmath}\\frac{2\\pi nx}{Lx}\\right)\n", "$$\n", "</p>\n", "and \n", "<p class='alert alert-danger'>\n", "$$\n", "a_n = \\frac{1}{L_x}\\int_Lf(x)\\exp\\left(-\\hat{\\jmath}\\frac{2\\pi nx}{Lx}\\right)dx\n", "$$\n", "</p>\n", "Often the reduction to wavenumber is used, where\n", "<p class='alert alert-danger'>\n", "$$\n", "k_n = \\frac{2\\pi n}{L_x}\n", "$$\n", "</p>\n", "Note that if $x$ is time instead of distance, $L_x$ is a time $T$ and the smallest frequency contained in the domain is $f_0=1/T_0$ and the wavenumber $n$ is $k_n=2\\pi f_0n=2\\pi f_n$ with $f_n$ for $\\vert n\\vert >1$ are the higher frequencies. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Discrete Fourier Transform (DFT)</h1>\n", "\n", "In scientific computing we are interested in applying Fourier series on vectors or matrices, containing a integer number of samples. The DFT is the fourier series for the number of samples. DFT functions available in python or any other language only care about the number of samples, therefore the wavenumber is \n", "<p class='alert alert-danger'>\n", "$$\n", "k_n=\\frac{2\\pi n}{N_x}\n", "$$\n", "</p>\n", "Consider a function $f$ periodic over a domain $0\\leq x\\leq 2\\pi$, discretized by $N_x$ points. The nodal value is $f_i$ located at $x_i=(i+1)\\Delta x$ with $\\Delta x=L_x/Nx$. The DFT is defined as\n", "<p class='alert alert-danger'>\n", "$$\n", "\\hat{f}_k=\\sum_{i=0}^{N_x-1}f_i\\exp\\left(-2\\pi\\hat{\\jmath}\\frac{ik}{N_x}\\right)\n", "$$\n", "</p>\n", "The inverse DFT is defined as\n", "<p class='alert alert-danger'>\n", "$$\n", "f_i=\\sum_{k=0}^{N_x-1}\\hat{f}_k\\exp\\left(2\\pi\\hat{\\jmath}\\frac{ik}{N_x}\\right)\n", "$$\n", "</p>\n", "\n", "<h1>Fast Fourier Transform (FFT)</h1>\n", "Using symmetries, the FFT reduces computational costs and stores in the following way:\n", "<p class='alert alert-danger'>\n", "$$\n", "\\hat{f}_k=\\sum_{i=-Nx/2+1}^{N_x/2}f_i\\exp\\left(-2\\pi\\mathbf{j}\\frac{ik}{N_x}\\right)\n", "$$\n", "</p>\n", "<p class='alert alert-info'>\n", "Compared to the Fourier series, DFT or FFT assumes that the system can be accurately captured by a finite number of waves. It is up to the user to ensure that the number of computational points is sufficient to capture the smallest scale, or smallest wavelength or highest frequence. Remember that the function on which FT is applied must be periodic over the domain and the grid spacing must be uniform.\n", "</p>\n", "There are FT algorithms for unevenly space data, but this is beyond the scope of this notebook.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Example 1: Filtering</h1>\n", "\n", "The following provides examples of low- and high-pass filters based on Fourier transform. A ideal low-(high-) pass filter passes frequencies that are lower than a threshold without attenuation and removes frequencies that are higher than the threshold. \n", "\n", "When applied to spatial data (function of $x$ rather than $t$-time), the FT (Fourier Transform) of a variable is function of wavenumbers\n", "$$\n", "k_n=\\frac{2\\pi n}{L_x}\n", "$$\n", "or wavelengths\n", "$$\n", "\\lambda_n=\\frac{2\\pi}{k_n}\n", "$$" ] }, { "cell_type": "code", "execution_count": 302, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"278pt\" version=\"1.1\" viewBox=\"0 0 388 278\" width=\"388pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", " <defs>\n", " <style 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np.linspace(Lx/Nx,Lx,Nx)\n", "Nwave = 32\n", "uwave = np.zeros((Nx,Nwave),dtype='float64')\n", "duwave = np.zeros((Nx,Nwave),dtype='float64')\n", "dduwave = np.zeros((Nx,Nwave),dtype='float64')\n", "#ampwave = np.array([0., 1.0, 2.0, 3.0])\n", "ampwave = np.random.random(Nwave)\n", "#print(ampwave)\n", "#phasewave = np.array([0.0, 0.0, np.pi/2, np.pi/2])\n", "phasewave = np.random.random(Nwave)*2*np.pi\n", "#print(phasewave)\n", "for iwave in range(Nwave):\n", " uwave[:,iwave] = ampwave[iwave]*np.cos(k_0*iwave*x+phasewave[iwave])\n", " duwave[:,iwave] = -k_0*iwave*ampwave[iwave]*np.sin(k_0*iwave*x+phasewave[iwave])\n", " dduwave[:,iwave] = -(k_0*iwave)**2*ampwave[iwave]*np.cos(k_0*iwave*x+phasewave[iwave])\n", "u = np.sum(uwave,axis=1)\n", "#print(u)\n", "plt.plot(x,u,lw=2)\n", "plt.xlim(0,Lx)\n", "plt.legend(loc=3, bbox_to_anchor=[0, 1],\n", " ncol=3, shadow=True, fancybox=True)\n", "plt.xlabel('$x$', fontdict = font)\n", "plt.ylabel('$u$', fontdict = font)\n", "plt.show()\n", 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as\n", "<p class='alert alert-danger'>\n", "$$\n", "F(k_n) = \\hat{f}_n.\\hat{f}_n^* \n", "$$\n", "</p>\n", "which can be interpreted as the energy contained in the $k_n$ wavenumber. This is helpful when searching for the most energitic scales or waves in our system. Thanks to the symmetries of the FFT, the spectrum is defined over $n=0$ to $N_x/2$" ] }, { "cell_type": "code", "execution_count": 305, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"302pt\" version=\"1.1\" viewBox=\"0 0 412 302\" width=\"412pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", " <defs>\n", " <style type=\"text/css\">\n", "*{stroke-linecap:butt;stroke-linejoin:round;}\n", " </style>\n", " </defs>\n", " <g id=\"figure_1\">\n", " <g id=\"patch_1\">\n", " <path d=\"\n", "M8.88178e-16 302.268\n", "L412.51 302.268\n", "L412.51 0\n", "L8.88178e-16 0\n", "L8.88178e-16 302.268\" style=\"fill:none;\"/>\n", " </g>\n", " <g id=\"axes_1\">\n", " <g 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"metadata": {}, "source": [ "<h2>Low-Pass Filter</h2>\n", "\n", "The following code filters the original signal by half the wavenumbers using FFT and compares to exact filtered function" ] }, { "cell_type": "code", "execution_count": 316, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"300pt\" version=\"1.1\" viewBox=\"0 0 437 300\" width=\"437pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", " <defs>\n", " <style type=\"text/css\">\n", "*{stroke-linecap:butt;stroke-linejoin:round;}\n", " </style>\n", " </defs>\n", " <g id=\"figure_1\">\n", " <g id=\"patch_1\">\n", " <path d=\"\n", "M0 300.912\n", "L437.519 300.912\n", "L437.519 0\n", "L0 0\n", "L0 300.912\" 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"plt.plot(x,u,'g:',lw=2,label='original')\n", "plt.legend(loc=3, bbox_to_anchor=[0, 1],\n", " ncol=3, shadow=True, fancybox=True)\n", "plt.xlabel('$x$', fontdict = font)\n", "plt.ylabel('$u$', fontdict = font)\n", "plt.show()\n", "print('error:',np.linalg.norm(v-u_filter_exact,np.inf))" ] }, { "cell_type": "code", "execution_count": 317, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"302pt\" version=\"1.1\" viewBox=\"0 0 412 302\" width=\"412pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", " <defs>\n", " <style type=\"text/css\">\n", "*{stroke-linecap:butt;stroke-linejoin:round;}\n", " </style>\n", " </defs>\n", " <g id=\"figure_1\">\n", " <g 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np.hstack((np.arange(0,Nx/2+1),np.arange(-Nx/2+1,0)))\n", "plt.loglog(k[0:Nx/2+1],F,'r-',lw=2,label='F_u')\n", "plt.loglog(k[0:Nx/2+1],F_filter,'b-',lw=2,label='F_v')\n", "plt.legend(loc=3, bbox_to_anchor=[0, 1],\n", " ncol=3, shadow=True, fancybox=True)\n", "plt.xlabel('$k$', fontdict = font)\n", "plt.ylabel('$F_u(k)$', fontdict = font)\n", "plt.show()\n", "plt.show" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> High-Pass Filter</h2>\n", "\n", "From the example below, develop a function for a high-pass filter." ] }, { "cell_type": "code", "execution_count": 261, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"256pt\" version=\"1.1\" viewBox=\"0 0 378 256\" width=\"378pt\" 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"nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
NYUDataBootcamp/Projects
UG_F16/ZillowDataReportFinal.ipynb
1
4875152
null
mit
DamienIrving/ocean-analysis
development/multifile_loading.ipynb
1
67177
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "infile1 = \"/g/data1/r87/drstree/CMIP5/GCM/CSIRO-QCCCE/CSIRO-Mk3-6-0/historicalMisc/mon/ocean/thetao/r1i1p1/thetao_Omon_CSIRO-Mk3-6-0_historicalMisc_r1i1p1_185001-185912.nc\"\n", "infile2 = \"/g/data1/r87/drstree/CMIP5/GCM/CSIRO-QCCCE/CSIRO-Mk3-6-0/historicalMisc/mon/ocean/thetao/r1i1p1/thetao_Omon_CSIRO-Mk3-6-0_historicalMisc_r1i1p1_186001-186912.nc\"\n", "allfiles = \"/g/data1/r87/drstree/CMIP5/GCM/CSIRO-QCCCE/CSIRO-Mk3-6-0/historicalMisc/mon/ocean/thetao/r1i1p1/thetao_Omon_CSIRO-Mk3-6-0_historicalMisc_r1i1p1_*.nc\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading multiple files with xarray" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From [the documentation](http://xarray.pydata.org/en/stable/dask.html): `xarray` uses Dask, which divides arrays into many small pieces, called chunks, each of which is presumed to be small enough to fit into memory.\n", "\n", "Unlike NumPy, which has eager evaluation, operations on dask arrays are lazy. Operations queue up a series of tasks mapped over blocks, and no computation is performed until you actually ask values to be computed (e.g., to print results to your screen or write to disk)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import xarray" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ds = xarray.open_mfdataset(allfiles) #chunks={'lev': 1, 'time': 1956})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the default chunking will have each file in a separate chunk. You can't change this with the chunk option (i.e. the commented code above still chunks along the time axis (as well as the level axis)), so you have to rechunk later on (see below)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<xarray.Dataset>\n", "Dimensions: (bnds: 2, lat: 189, lev: 31, lon: 192, time: 1956)\n", "Coordinates:\n", " * lev (lev) float64 5.0 15.0 28.25 42.02 59.66 78.54 102.1 127.9 ...\n", " * lat (lat) float64 -88.2 -87.24 -86.3 -85.36 -84.42 -83.49 -82.55 ...\n", " * lon (lon) float64 0.0 1.875 3.75 5.625 7.5 9.375 11.25 13.12 15.0 ...\n", " * bnds (bnds) int64 0 1\n", " * time (time) datetime64[ns] 1850-01-16T12:00:00 1850-02-15 ...\n", "Data variables:\n", " time_bnds (time, bnds) float64 0.0 31.0 31.0 59.0 59.0 90.0 90.0 120.0 ...\n", " lat_bnds (time, lat, bnds) float64 -90.0 -87.71 -87.71 -86.77 -86.77 ...\n", " lon_bnds (time, lon, bnds) float64 -0.9375 0.9375 0.9375 2.812 2.812 ...\n", " lev_bnds (time, lev, bnds) float64 0.0 10.0 10.0 21.62 21.62 35.14 ...\n", " thetao (time, lev, lat, lon) float64 nan nan nan nan nan nan nan nan ...\n", "Attributes:\n", " institution: Australian Commonwealth Scientific and Industrial Research Organization (CSIRO) Marine and Atmospheric Research (Melbourne, Australia) in collaboration with the Queensland Climate Change Centre of Excellence (QCCCE) (Brisbane, Australia)\n", " institute_id: CSIRO-QCCCE\n", " experiment_id: historicalMisc\n", " source: CSIRO-Mk3-6-0 2010 atmosphere: AGCM v7.3.5 (T63 spectral, 1.875 degrees EW x approx. 1.875 degrees NS, 18 levels); ocean: GFDL MOM2.2 (1.875 degrees EW x approx. 0.9375 degrees NS, 31 levels)\n", " model_id: CSIRO-Mk3-6-0\n", " forcing: Ant\n", " parent_experiment_id: piControl\n", " parent_experiment_rip: r1i1p1\n", " branch_time: 29200.0\n", " contact: Project leaders: Stephen Jeffrey ([email protected]) & Leon Rotstayn ([email protected]). Project team: Mark Collier ([email protected]: diagnostics & post-processing), Stacey Dravitzki ([email protected]: post-processing), Carlo Hamalainen ([email protected]: post-processing), Steve Jeffrey ([email protected]: modeling & post-processing), Chris Moeseneder ([email protected]: post-processing), Leon Rotstayn ([email protected]: modeling...\n", " comment: Model output post-processed by the CSIRO-QCCCE CMIP5 Data post-processor for the IPCC Fifth Assessment. Dataset version: 1.0\n", " references: a) Rotstayn, L., Collier, M., Dix, M., Feng, Y., Gordon, H., O\\'Farrell, S., Smith, I. and Syktus, J. 2010. Improved simulation of Australian climate and ENSO-related climate variability in a GCM with an interactive aerosol treatment. Int. J. Climatology, vol 30(7), pp1067-1088, DOI 10.1002/joc.1952 b) Please refer to online documentation at: http://cmip-pcmdi.llnl.gov/cmip5/\n", " initialization_method: 1\n", " physics_version: 1\n", " tracking_id: f05321f0-090f-4dfb-becb-60ada90a142d\n", " product: output\n", " experiment: other historical forcing\n", " frequency: mon\n", " creation_date: 2011-05-14T02:26:03Z\n", " history: Attribution experiment not explicitly defined by CMIP5: anthropogenic-only. Historical period was extended to Dec 2012 using RCP 4.5 forcing data. 2011-05-14T02:26:03Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.\n", " Conventions: CF-1.4\n", " project_id: CMIP5\n", " table_id: Table Omon (27 April 2011) 9e1a53e4873bf6f26879903e165fe4a0\n", " title: CSIRO-Mk3-6-0 model output prepared for CMIP5 anthropogenic-only\n", " parent_experiment: piControl\n", " modeling_realm: ocean\n", " realization: 1\n", " cmor_version: 2.5.9\n", " version_number: v20110518\n" ] } ], "source": [ "print ds" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "16.406008556485176" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.nbytes * (2 ** -30)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So there's 16.4 GB of data, according to the conversion that Stephan Hoyer does at [this blog post](https://www.continuum.io/content/xray-dask-out-core-labeled-arrays-python)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Frozen(SortedKeysDict({u'lat': (189,), u'bnds': (2,), u'lon': (192,), u'lev': (31,), u'time': (120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 120, 36)}))" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds.chunks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can re-chunk your data like so, where the number represents the size of each individual chunk. This might be useful when you want each chunk to contain the entire time axis." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rechunked = ds.chunk({'time': 1956, 'lev': 1})" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Frozen(SortedKeysDict({u'lat': (189,), u'bnds': (2,), u'lon': (192,), u'lev': (1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), u'time': (1956,)}))" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rechunked.chunks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Eager evaluation\n", "\n", "From [the documentation](http://xarray.pydata.org/en/stable/dask.html): You can convert an xarray data structure from lazy dask arrays into eager, in-memory numpy arrays using the `load()` method (i.e. `ds.load()`), or make it a numpy array using the `values` method of `numpy.asarray()`." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "darray = ds['thetao']" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<xarray.DataArray 'thetao' (time: 1956, lev: 31, lat: 189, lon: 192)>\n", "dask.array<concate..., shape=(1956, 31, 189, 192), dtype=float64, chunksize=(120, 31, 189, 192)>\n", "Coordinates:\n", " * lev (lev) float64 5.0 15.0 28.25 42.02 59.66 78.54 102.1 127.9 ...\n", " * lat (lat) float64 -88.2 -87.24 -86.3 -85.36 -84.42 -83.49 -82.55 ...\n", " * lon (lon) float64 0.0 1.875 3.75 5.625 7.5 9.375 11.25 13.12 15.0 ...\n", " * time (time) datetime64[ns] 1850-01-16T12:00:00 1850-02-15 ...\n", "Attributes:\n", " standard_name: sea_water_potential_temperature\n", " long_name: Sea Water Potential Temperature\n", " units: K\n", " original_name: Temp\n", " comment: Data is stored on the native ocean T-grid on which the data was generated. (MOM2 uses a rectangular staggered grid with T cells and U cells.)\n", " original_units: celsius\n", " cell_methods: time: mean\n", " cell_measures: area: areacello volume: volcello\n", " associated_files: baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_ocean_fx_CSIRO-Mk3-6-0_historicalMisc_r0i0p1.nc areacello: areacello_fx_CSIRO-Mk3-6-0_historicalMisc_r0i0p1.nc volcello: volcello_fx_CSIRO-Mk3-6-0_historicalMisc_r0i0p1.nc\n" ] } ], "source": [ "print darray" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "ename": "MemoryError", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mMemoryError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-12-4db4e79f64dd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m 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"\u001b[1;32m/g/data/r87/dbi599/miniconda2/envs/default/lib/python2.7/site-packages/numpy/core/numeric.pyc\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order)\u001b[0m\n\u001b[0;32m 472\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 473\u001b[0m \"\"\"\n\u001b[1;32m--> 474\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 475\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 476\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m 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divide\n", " x = np.divide(x1, x2, out)\n" ] } ], "source": [ "%%time \n", "\n", "climatology_lazy.to_netcdf(\"/g/data/r87/dbi599/lazy.nc\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notice that the computation used only 25 seconds of wall clock time, but 47 seconds of CPU time. It's definitely using 2 cores." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Applying a function in a lazy manner" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simple numpy functions are available through xarray (see [here](http://xarray.pydata.org/en/stable/dask.html) for the notes on `import xarray.ufuncs as xu`), so doing something like a mean or standard deviation is trivial. For more complex functions, you need to use the `map_blocks()` method associted with dask arrays. Below I'll try this for the task of fitting a cubic polynomial:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "% matplotlib inline" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7fb0452f3350>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "x = [2.53240, 1.91110, 1.18430, 0.95784, 0.33158,\n", " -0.19506, -0.82144, -1.64770, -1.87450, -2.2010]\n", "\n", "y = [-2.50400, -1.62600, -1.17600, -0.87400, -0.64900,\n", " -0.477000, -0.33400, -0.20600, -0.10100, -0.00600]\n", "\n", "coefficients = numpy.polyfit(x, y, 3)\n", "polynomial = numpy.poly1d(coefficients)\n", "xs = numpy.arange(-2.2, 2.6, 0.1)\n", "ys = polynomial(xs)\n", "\n", "plt.plot(x, y, 'o')\n", "plt.plot(xs, ys)\n", "plt.ylabel('y')\n", "plt.xlabel('x')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def cubic_fit(data_series):\n", " \"\"\"Fit a cubic polynomial to a 1D numpy array.\"\"\"\n", " \n", " x = numpy.arange(0, len(data_series))\n", " \n", " coefficients = numpy.polyfit(x, data_series, 3)\n", " polynomial = numpy.poly1d(coefficients)\n", "\n", " return polynomial(x)\n", "\n", "\n", "def cubic_fit_ds(dataset):\n", " \"\"\"Fit a cubic polynomial to an xarray dataset.\"\"\"\n", "\n", " return numpy.apply_along_axis(cubic_fit, 0, dataset)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import dask.array as da\n", "#dask_array = da.from_array(rechunked['thetao'], chunks=(1956, 1, 189, 192))\n", "dask_array = rechunked['thetao'].data" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dask.array<rechunk..., shape=(1956, 31, 189, 192), dtype=float64, chunksize=(1956, 1, 189, 192)>\n" ] } ], "source": [ "print dask_array" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cubic_data = dask_array.map_blocks(cubic_fit_ds)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "((1956,),\n", " (1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1),\n", " (189,),\n", " (192,))" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cubic_data.chunks" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1956, 31, 189, 192)" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cubic_data.shape" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": true }, "outputs": [], "source": [ "new_ds = xarray.Dataset({'thetao': (('time', 'lev', 'lat', 'lon',), cubic_data)})" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "must supply lists of the same length for the datasets, paths and groups arguments to save_mfdataset", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-22-b797dae192e6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfile_nums\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m31\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mpaths\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'/g/data/r87/dbi599/dask_%s.nc'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[0mf\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfile_nums\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mxarray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_mfdataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_ds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/g/data/r87/dbi599/miniconda2/envs/default/lib/python2.7/site-packages/xarray/backends/api.pyc\u001b[0m in \u001b[0;36msave_mfdataset\u001b[1;34m(datasets, paths, mode, format, groups, engine)\u001b[0m\n\u001b[0;32m 428\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 429\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpaths\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgroups\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 430\u001b[1;33m raise ValueError('must supply lists of the same length for the '\n\u001b[0m\u001b[0;32m 431\u001b[0m \u001b[1;34m'datasets, paths and groups arguments to '\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 432\u001b[0m 'save_mfdataset')\n", "\u001b[1;31mValueError\u001b[0m: must supply lists of the same length for the datasets, paths and groups arguments to save_mfdataset" ] } ], "source": [ "file_nums = range(0,31)\n", "paths = ['/g/data/r87/dbi599/dask_%s.nc' %f for f in file_nums]\n", "xarray.save_mfdataset(new_ds, paths)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "### A simple example\n", "\n", "To try and figure out if `save_mfdataset()` accepts dask arrays..." ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ds_small = xarray.open_mfdataset(infile2) " ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<xarray.Dataset>\n", "Dimensions: (bnds: 2, lat: 189, lev: 31, lon: 192, time: 120)\n", "Coordinates:\n", " * time (time) datetime64[ns] 1860-01-16T12:00:00 1860-02-15 ...\n", " * lev (lev) float64 5.0 15.0 28.25 42.02 59.66 78.54 102.1 127.9 ...\n", " * lat (lat) float64 -88.2 -87.24 -86.3 -85.36 -84.42 -83.49 -82.55 ...\n", " * lon (lon) float64 0.0 1.875 3.75 5.625 7.5 9.375 11.25 13.12 15.0 ...\n", " * bnds (bnds) int64 0 1\n", "Data variables:\n", " time_bnds (time, bnds) float64 3.65e+03 3.681e+03 3.681e+03 3.709e+03 ...\n", " lev_bnds (lev, bnds) float64 0.0 10.0 10.0 21.62 21.62 35.14 35.14 ...\n", " lat_bnds (lat, bnds) float64 -90.0 -87.71 -87.71 -86.77 -86.77 -85.83 ...\n", " lon_bnds (lon, bnds) float64 -0.9375 0.9375 0.9375 2.812 2.812 4.688 ...\n", " thetao (time, lev, lat, lon) float64 nan nan nan nan nan nan nan nan ...\n", "Attributes:\n", " institution: Australian Commonwealth Scientific and Industrial Research Organization (CSIRO) Marine and Atmospheric Research (Melbourne, Australia) in collaboration with the Queensland Climate Change Centre of Excellence (QCCCE) (Brisbane, Australia)\n", " institute_id: CSIRO-QCCCE\n", " experiment_id: historicalMisc\n", " source: CSIRO-Mk3-6-0 2010 atmosphere: AGCM v7.3.5 (T63 spectral, 1.875 degrees EW x approx. 1.875 degrees NS, 18 levels); ocean: GFDL MOM2.2 (1.875 degrees EW x approx. 0.9375 degrees NS, 31 levels)\n", " model_id: CSIRO-Mk3-6-0\n", " forcing: Ant\n", " parent_experiment_id: piControl\n", " parent_experiment_rip: r1i1p1\n", " branch_time: 29200.0\n", " contact: Project leaders: Stephen Jeffrey ([email protected]) & Leon Rotstayn ([email protected]). Project team: Mark Collier ([email protected]: diagnostics & post-processing), Stacey Dravitzki ([email protected]: post-processing), Carlo Hamalainen ([email protected]: post-processing), Steve Jeffrey ([email protected]: modeling & post-processing), Chris Moeseneder ([email protected]: post-processing), Leon Rotstayn ([email protected]: modeling...\n", " comment: Model output post-processed by the CSIRO-QCCCE CMIP5 Data post-processor for the IPCC Fifth Assessment. Dataset version: 1.0\n", " references: a) Rotstayn, L., Collier, M., Dix, M., Feng, Y., Gordon, H., O\\'Farrell, S., Smith, I. and Syktus, J. 2010. Improved simulation of Australian climate and ENSO-related climate variability in a GCM with an interactive aerosol treatment. Int. J. Climatology, vol 30(7), pp1067-1088, DOI 10.1002/joc.1952 b) Please refer to online documentation at: http://cmip-pcmdi.llnl.gov/cmip5/\n", " initialization_method: 1\n", " physics_version: 1\n", " tracking_id: 9f52a027-c29a-43c9-bdc6-67297d8ce808\n", " product: output\n", " experiment: other historical forcing\n", " frequency: mon\n", " creation_date: 2011-05-14T02:27:05Z\n", " history: Attribution experiment not explicitly defined by CMIP5: anthropogenic-only. Historical period was extended to Dec 2012 using RCP 4.5 forcing data. 2011-05-14T02:27:05Z CMOR rewrote data to comply with CF standards and CMIP5 requirements.\n", " Conventions: CF-1.4\n", " project_id: CMIP5\n", " table_id: Table Omon (27 April 2011) 9e1a53e4873bf6f26879903e165fe4a0\n", " title: CSIRO-Mk3-6-0 model output prepared for CMIP5 anthropogenic-only\n", " parent_experiment: piControl\n", " modeling_realm: ocean\n", " realization: 1\n", " cmor_version: 2.5.9\n", " version_number: v20110518\n" ] } ], "source": [ "print ds_small" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "dask.array.core.Array" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(ds_small['thetao'].data)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cubic_data_small = ds_small['thetao'].data.map_blocks(cubic_fit_ds)" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "ename": "TypeError", "evalue": "DataArray.name or Dataset key must be either a string or None for serialization to netCDF files", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-58-8bdd88b24ae9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mfile_nums\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mpaths\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'/g/data/r87/dbi599/dask_%s.nc'\u001b[0m \u001b[1;33m%\u001b[0m\u001b[0mf\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mfile_nums\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mxarray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_mfdataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcubic_data_small\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpaths\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/g/data/r87/dbi599/miniconda2/envs/default/lib/python2.7/site-packages/xarray/backends/api.pyc\u001b[0m in \u001b[0;36msave_mfdataset\u001b[1;34m(datasets, paths, mode, format, groups, engine)\u001b[0m\n\u001b[0;32m 434\u001b[0m \u001b[0mwriter\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mArrayWriter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 435\u001b[0m stores = [to_netcdf(ds, path, mode, format, group, engine, writer)\n\u001b[1;32m--> 436\u001b[1;33m for ds, path, group in zip(datasets, paths, groups)]\n\u001b[0m\u001b[0;32m 437\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 438\u001b[0m \u001b[0mwriter\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msync\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/g/data/r87/dbi599/miniconda2/envs/default/lib/python2.7/site-packages/xarray/backends/api.pyc\u001b[0m in \u001b[0;36mto_netcdf\u001b[1;34m(dataset, path, mode, format, group, engine, writer, encoding)\u001b[0m\n\u001b[0;32m 336\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 337\u001b[0m \u001b[1;31m# validate Dataset keys and DataArray names\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 338\u001b[1;33m \u001b[0m_validate_dataset_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 339\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/g/data/r87/dbi599/miniconda2/envs/default/lib/python2.7/site-packages/xarray/backends/api.pyc\u001b[0m in \u001b[0;36m_validate_dataset_names\u001b[1;34m(dataset)\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdataset\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m \u001b[0mcheck_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/g/data/r87/dbi599/miniconda2/envs/default/lib/python2.7/site-packages/xarray/backends/api.pyc\u001b[0m in \u001b[0;36mcheck_name\u001b[1;34m(name)\u001b[0m\n\u001b[0;32m 72\u001b[0m 'serialization to netCDF files')\n\u001b[0;32m 73\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 74\u001b[1;33m raise TypeError('DataArray.name or Dataset key must be either a '\n\u001b[0m\u001b[0;32m 75\u001b[0m 'string or None for serialization to netCDF files')\n\u001b[0;32m 76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mTypeError\u001b[0m: DataArray.name or Dataset key must be either a string or None for serialization to netCDF files" ] } ], "source": [ "file_nums = range(0,1)\n", "paths = ['/g/data/r87/dbi599/dask_%s.nc' %f for f in file_nums]\n", "xarray.save_mfdataset([cubic_data_small], paths)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "How do you write a dask array to a netCDF file??? Perhaps manually convert each chuck to a numpy array / xarray dataArray?" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Reading multiple files with iris" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import iris" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/g/data/r87/dbi599/miniconda2/envs/default/lib/python2.7/site-packages/iris/fileformats/cf.py:794: UserWarning: Missing CF-netCDF measure variable u'areacello', referenced by netCDF variable u'thetao'\n", " warnings.warn(message % (variable_name, nc_var_name))\n", "/g/data/r87/dbi599/miniconda2/envs/default/lib/python2.7/site-packages/iris/fileformats/cf.py:794: UserWarning: Missing CF-netCDF measure variable u'volcello', referenced by netCDF variable u'thetao'\n", " warnings.warn(message % (variable_name, nc_var_name))\n", "/g/data/r87/dbi599/miniconda2/envs/default/lib/python2.7/site-packages/iris/fileformats/cf.py:1139: UserWarning: NetCDF default loading behaviour currently does not expose variables which define reference surfaces for dimensionless vertical coordinates as independent Cubes. This behaviour is deprecated in favour of automatic promotion to Cubes. To switch to the new behaviour, set iris.FUTURE.netcdf_promote to True.\n", " warnings.warn(msg)\n" ] }, { "ename": "ConstraintMismatchError", "evalue": "failed to merge into a single cube.\n cube.attributes values differ for keys: 'history', 'creation_date', 'tracking_id'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mConstraintMismatchError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-3-4d4b7eb4e3d9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcube\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_cube\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minfile1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfile2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;32m/g/data/r87/dbi599/miniconda2/envs/default/lib/python2.7/site-packages/iris/__init__.pyc\u001b[0m in \u001b[0;36mload_cube\u001b[1;34m(uris, constraint, callback)\u001b[0m\n\u001b[0;32m 339\u001b[0m \u001b[0mcube\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcubes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge_cube\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 340\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMergeError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 341\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mConstraintMismatchError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 342\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 343\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0miris\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mConstraintMismatchError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'no cubes found'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mConstraintMismatchError\u001b[0m: failed to merge into a single cube.\n cube.attributes values differ for keys: 'history', 'creation_date', 'tracking_id'" ] } ], "source": [ "cube = iris.load_cube([infile1, infile2])" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "history = []\n", "\n", "def edit_attributes(cube, field, filename):\n", " cube.attributes.pop('creation_date', None)\n", " cube.attributes.pop('tracking_id', None)\n", " history.append(cube.attributes['history'])\n", " cube.attributes.pop('history', None)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cubes = iris.load([infile1, infile2], 'sea_water_potential_temperature', callback=edit_attributes)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: sea_water_potential_temperature / (K) (time: 120; depth: 31; latitude: 189; longitude: 192)\n", "1: sea_water_potential_temperature / (K) (time: 120; depth: 31; latitude: 189; longitude: 192)\n" ] } ], "source": [ "print cubes" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#iris.util.unify_time_units(cubes)\n", "cubes = cubes.concatenate_cube()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sea_water_potential_temperature / (K) (time: 240; depth: 31; latitude: 189; longitude: 192)\n", " Dimension coordinates:\n", " time x - - -\n", " depth - x - -\n", " latitude - - x -\n", " longitude - - - x\n", " Attributes:\n", " Conventions: CF-1.4\n", " associated_files: baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_ocean_fx_CSIRO-Mk3-6-0_historicalMisc_r0i0p1.nc...\n", " branch_time: 29200.0\n", " cmor_version: 2.5.9\n", " comment: Data is stored on the native ocean T-grid on which the data was generated....\n", " contact: Project leaders: Stephen Jeffrey ([email protected]) & Leon Rotstayn...\n", " experiment: other historical forcing\n", " experiment_id: historicalMisc\n", " forcing: Ant\n", " frequency: mon\n", " initialization_method: 1\n", " institute_id: CSIRO-QCCCE\n", " institution: Australian Commonwealth Scientific and Industrial Research Organization...\n", " model_id: CSIRO-Mk3-6-0\n", " modeling_realm: ocean\n", " original_name: Temp\n", " original_units: celsius\n", " parent_experiment: piControl\n", " parent_experiment_id: piControl\n", " parent_experiment_rip: r1i1p1\n", " physics_version: 1\n", " product: output\n", " project_id: CMIP5\n", " realization: 1\n", " references: a) Rotstayn, L., Collier, M., Dix, M., Feng, Y., Gordon, H., O\\'Farrell,...\n", " source: CSIRO-Mk3-6-0 2010 atmosphere: AGCM v7.3.5 (T63 spectral, 1.875 degrees...\n", " table_id: Table Omon (27 April 2011) 9e1a53e4873bf6f26879903e165fe4a0\n", " title: CSIRO-Mk3-6-0 model output prepared for CMIP5 anthropogenic-only\n", " version_number: v20110518\n", " Cell methods:\n", " mean: time\n" ] } ], "source": [ "print cubes" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "[\"2011-05-14T02:26:03Z altered by CMOR: Converted units from 'celsius' to 'K'. 2011-05-14T02:26:03Z altered by CMOR: replaced missing value flag (-7.77778e+06) with standard missing value (1e+20).\", \"2011-05-14T02:27:05Z altered by CMOR: Converted units from 'celsius' to 'K'. 2011-05-14T02:27:05Z altered by CMOR: replaced missing value flag (-7.77778e+06) with standard missing value (1e+20).\"]\n" ] } ], "source": [ "print len(history)\n", "print history" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, (240, 189, 192))\n", "(1, (240, 189, 192))\n", "(2, (240, 189, 192))\n", "(3, (240, 189, 192))\n", "(4, (240, 189, 192))\n", "(5, (240, 189, 192))\n", "(6, (240, 189, 192))\n", "(7, (240, 189, 192))\n", "(8, (240, 189, 192))\n", "(9, (240, 189, 192))\n", "(10, (240, 189, 192))\n", "(11, (240, 189, 192))\n", "(12, (240, 189, 192))\n", "(13, (240, 189, 192))\n", "(14, (240, 189, 192))\n", "(15, (240, 189, 192))\n", "(16, (240, 189, 192))\n", "(17, (240, 189, 192))\n", "(18, (240, 189, 192))\n", "(19, (240, 189, 192))\n", "(20, (240, 189, 192))\n", "(21, (240, 189, 192))\n", "(22, (240, 189, 192))\n", "(23, (240, 189, 192))\n", "(24, (240, 189, 192))\n", "(25, (240, 189, 192))\n", "(26, (240, 189, 192))\n", "(27, (240, 189, 192))\n", "(28, (240, 189, 192))\n", "(29, (240, 189, 192))\n", "(30, (240, 189, 192))\n" ] } ], "source": [ "for i, x_slice in enumerate(cubes.slices(['time', 'latitude', 'longitude'])):\n", " print(i, x_slice.shape)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "coord_names = [coord.name() for coord in cubes.coords()]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[u'time', u'depth', u'latitude', u'longitude']\n" ] } ], "source": [ "print coord_names" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "()" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cubes.aux_coords" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'days since 1850-01-01 00:00:00'" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "str(cubes.coord('time').units)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
WomensCodingCircle/CodingCirclePython
Lesson14_NumpyAndMatplotlib/matplotlib.ipynb
1
20153
{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Plotting with matplotlib"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## 1. Getting Started"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 1.1 What is matplotlib?\n", "\n", "Matplotlib is the most popular and mature library for plotting data using\n", "Python. It has all of the functionality you would expect, including the ability to control\n", "the formatting of plots and figures at a very fine level.\n", "\n", "The official matplotlib documentation is at http://matplotlib.org/ \n", "The matplotlib gallery is at http://matplotlib.org/gallery.html"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 1.2 Importing matplotlib\n", "\n", "Matplotlib is often used through 'pyplot', which provides a high-level interface for\n", "plotting."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# In IPython or the IPython notebook, it's easiest to use the pylab magic, which\n", "# imports matplotlib, numpy, and scipy.\n", "\n", "# The matplotlib notebook flag means that plots will be shown interactively in the\n", "# notebooks, rather than in pop-up windows.\n", "\n", "%matplotlib notebook\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## 2. Creating Figures\n", "\n", "There are two major challenges with creating figures. First is understanding the\n", "syntax to actually make the basic plot appear. Second is formatting the basic plot to look\n", "exactly how you would like it to look. In general, the formatting will probably take you\n", "longer...\n", "\n", "Within pyplot (currently imported as 'plt'), there are two basic ways to go about making\n", "plots - using the Matlab-like clone, and using the object-oriented approach. The latter\n", "provides better control over plot features, while only requiring slightly more typing. It's\n", "easy to quickly outgrow the Matlab clone, so we'll go right to the object-oriented syntax."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 2.1 A first plot\n", "\n", "In simple matplotlib plotting, there are two concepts to distinguish:\n", "\n", "- __Figure__ - the entire figure, like what you might see in a journal, including all\n", "subplots, axes, lines, labels, etc. The whole enchilada. \n", " \n", "- __Subplot/Axes__ - one of the sub-sections of the figure, labeled (a), (b), etc. in\n", "articles. Each subplot will contain one Axes object, which is the container where all of the\n", "useful stuff, such as actual lines, legends, labels, etc., are actually housed.\n", "\n", "For example, here's how to make one figure with two subplots, the second of which contains\n", "two lines."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": ["# First we make some data to plot\n", "x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["First, create an empty figure with 2 subplots\n", "using the subplots method\n", " \n", " figure, axes = plt.subplots(rows, columns)\n", "\n", "- The arguments (1, 2) indicate 1 row and 2 cols\n", "- The function plt.subplots returns an object for the figure and for each axes\n", "- There are multiple ways to accomplish this same goal, but this is probably the simplest - notice that each subplot is associated with one of the axes objects."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["fig, axes = plt.subplots(1,2)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Now let's actually plot the data using the plot method on an axis\n", "\n", " axis.plot(x, y)\n", " \n", "You can plot multiple lines on an axis "]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["\n", "fig, axes = plt.subplots(1,2)\n", "\n", "# We plot one line on the first axis\n", "axes[0].plot(x, y1)\n", "# and both lines on the second axis\n", "axes[1].plot(x, y1)\n", "axes[1].plot(x, y2);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Many of the basic formatting problems you have will be solved by the magic of `tight_layou`t. Before you start tweaking how you figure looks, try it out.\n", "\n", " plt.tight_layout()"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1)\n", "axes[1].plot(x, y1)\n", "axes[1].plot(x, y2)\n", "\n", "plt.tight_layout();"]}, {"cell_type": "markdown", "metadata": {}, "source": ["To save your figure you can use the `savefig` command:\n", "\n", " fig.savefig('fileanme', format='png')\n", " \n", "Format options include png, pdf, ps, eps and svg"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1)\n", "axes[1].plot(x, y1)\n", "axes[1].plot(x, y2)\n", "\n", "fig.savefig('first_plot.png', format='png');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Create a line graph plotting the function f(x) = x^3 for values of x 0-10."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## 3. Formatting Figures\n", "\n", "The formatting of figures often takes longer than actually setting them up and adding data. There are many different approaches to formatting figures in matplotlib (many goals can be accomplished in different ways, using different commands), and you will come across many of these as you learn more. The tips below give a few simple ways to get started.\n", "\n", "### 3.1 Line formatting\n", "\n", "The plot method has several available keyword arguments that you can use to change the line formatting.\n", "\n", "* color - Chages color of line. examples: 'red', 'blue', 'r', 'k', 0.5, '#ffaa00', (0,0.5,0.75)\n", "* linewidth - Weight of line. Takes float value in points (like font)\n", "* linestyle - Solid, dashed, or other. examples: -, --, -."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1, color='r', linewidth=5)\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", "axes[1].plot(x, y2, color='green', linestyle='-.');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 3.2 Tick marks\n", "\n", "You can set the values where the ticks are located using the `xticks` and `yticks` methods.\n", "\n", "They take a list of values\n", "\n", " plt.xticks([val1, val2, val3])\n", " plt.yticks([yval1, yval2, yval3])\n", " "]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false, "scrolled": true}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1, color='r', linewidth=5)\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", "axes[1].plot(x, y2, color='green', linestyle='-.')\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", "plt.yticks([-1, 0, 1]);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Oh no! That changed it for the last plot but not for the first plot. \n", "\n", "To set each plot individually, you need to set the current axis to the subplot you are interested in using the method `sca`. Then you can use `xticks` and `yticks`.\n", "\n", " plt.sca(axis)\n", " plt.xticks([val1, val2, val3])\n", " plt.yticks([yval1, yval2, yval3])\n", " "]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1, color='r', linewidth=5)\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", "axes[1].plot(x, y2, color='green', linestyle='-.')\n", "\n", "# Set the current axis to the first subplot\n", "fig.sca(axes[0])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", "plt.yticks([-1, 0, 1])\n", "\n", "# Set the current axis to the second subplot\n", "fig.sca(axes[1])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", "plt.yticks([-1, 0, 1]);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 3.3 Axis limits\n", "\n", "Setting the limits of the axes is very similar to setting the ticks. The command to set the limits are `xlim` and `ylim`. Remember if you have more than one subplot, you will need to set the current axis before you set that axis' limits.\n", "\n", " plt.sca(axis)\n", " plt.xlim(xmin, xmax)\n", " plt.ylim(ymin, ymax)\n", " \n", " "]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1, color='r', linewidth=5)\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", "axes[1].plot(x, y2, color='green', linestyle='-.')\n", "\n", "# Set the current axis to the first subplot\n", "fig.sca(axes[0])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", "plt.yticks([-1, 0, 1])\n", "\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1)\n", "\n", "# Set the current axis to the second subplot\n", "fig.sca(axes[1])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n", "plt.yticks([-1, 0, 1])\n", "\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 3.4 Setting tick labels\n", "\n", "To set the tick labels, you pass a second parameter to the `xticks` and `yticks` methods with a list of labels for that axis. \n", "\n", " plt.sca(axis)\n", " plt.xticks([tickvalues], [ticklabels])\n", " plt.yticks([tickvalues], [ticklabels])\n", " "]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "axes[0].plot(x, y1, color='r', linewidth=5)\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--')\n", "axes[1].plot(x, y2, color='green', linestyle='-.')\n", "\n", "# Set the current axis to the first subplot\n", "fig.sca(axes[0])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", "# You probably don't want to set the labels when you just want the exact numbers.\n", "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", "\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1)\n", "\n", "# Set the current axis to the second subplot\n", "fig.sca(axes[1])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", "\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 3.5 Legend\n", "\n", "When you create a line on a plot, you can pass it a keyword argument `label` and then create a legend that will use that label using the `legend` method. The `legend` method takes an optional parameter of `loc` for the location of the legend. You can see the values allowed here: http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.legend This is another one of those commands that you need to set the current axis if you have more than one subplot.\n", "\n", " plt.plot(x,y, label='my_label')\n", " plt.legend(loc='best')"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["x = np.linspace(-2*np.pi, 2*np.pi)\n", "y1 = np.sin(x)\n", "y2 = np.cos(x)\n", "\n", "fig, axes = plt.subplots(1,2)\n", "# Let's set labels here\n", "axes[0].plot(x, y1, color='r', linewidth=5, label='sin(x)')\n", "axes[1].plot(x, y1, color='#ffaa00', linewidth=0.5, linestyle='--', label='sin(x)')\n", "axes[1].plot(x, y2, color='green', linestyle='-.', label='cos(x)')\n", "\n", "# Set the current axis to the first subplot\n", "fig.sca(axes[0])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", "# You probably don't want to set the labels when you just want the exact numbers.\n", "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", "plt.legend(loc='best')\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1)\n", "\n", "# Set the current axis to the second subplot\n", "fig.sca(axes[1])\n", "# set x and y ticks\n", "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi], ['-pi', '-pi/2', '0', 'pi/2', 'pi'])\n", "plt.yticks([-1, 0, 1], ['-1', '0', '1'])\n", "plt.legend(loc='upper right')\n", "\n", "# set x and y limits\n", "plt.xlim(-np.pi, np.pi)\n", "plt.ylim(-1, 1);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Go back to your plot of f(x) = x^3 for values of x 0-10. Try out some of the formatting options you just learned to make your plot look \"just right\""]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## 4. Other types of plots\n", "\n", "Matplotlib is more than just line plots, let's see what else it can do.\n"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 1.1 Other plots\n", "\n", "In the examples above, we used the plot method to make line plots. There are also methods to\n", "make scatter plots, barplots, histograms, loglog plots, semilog plots, etc.\n", "\n", " # Bar graph\n", " ax.bar(x, y)\n", " \n", " # Scatter plot\n", " ax.scatter(x,y)\n", " \n", " # Horizontal bar plot\n", " ax.barh(x,y)\n", " \n", " # Boxplot\n", " ax.boxplot(x)\n", " \n", " # Log-log plot\n", " ax.loglog(x,y)\n", " \n", " # Semilog plot\n", " ax.semilogx(x,y)\n", " ax.semilogy(x,y)\n", " \n", "Plots too squished? Check out plt.tight_layout()"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# Make some data to plot\n", "x = np.arange(0, 100)\n", "y = np.random.rand(100) # 100 random numbers\n", "\n", "# Make a figure with 6 subplots and axes\n", "# Notice that we are doing some arguement unpacking to get six subplots. You can use indexing instead if you prefer\n", "fig, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2)\n", "\n", "# Add data to each axis. Optional arguments to each method will customize each plot.\n", "ax1.bar(x,y)\n", "ax2.scatter(x,y)\n", "ax3.barh(x,y)\n", "ax4.boxplot(x)\n", "ax5.loglog(x,y)\n", "ax6.semilogx(x,y)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Many of the same formatting options as the line plot are available for these additional plots. There are also some other options. The gallery (section 5) is the best place to find all the options. \n", "\n", "Let's try changing the marker on the scatter plot: http://matplotlib.org/exmples/lines_bars_and_markers/marker_reference.html\n", "\n"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["fig, ax = plt.subplots(1,1)\n", "ax.scatter(x, y, marker='x')\n", "ax.scatter(x, y + 2, marker='>', color='#00aaff')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 4.2 Plotting images\n", "\n", "Matplotlib also makes it easy to plot images. For this, you can use the plot method `imshow`\n", "(syntax borrowed from Matlab).\n", "\n", "To load in an image we use the `imread` function. This takes a file path and reads the file into a numpy ndarray\n", "\n", "To plot an image, you can use `imshow` function giving it an array. \n", "\n", "A 1D array will be rendered as grayscale and a 3D or (4D with transparency) array will be a full color image.\n", "\n", "To set the colormap of a grayscale image, you can use the optional `cmap` key word argument to `imshow`. Options available are listed here: http://matplotlib.org/examples/color/colormaps_reference.html"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# Read image from file and display it\n", "img1 = plt.imread('astronaut.png')\n", "# Uncomment following line to prove it still works without the alpha channel\n", "# img1 = img1[:,:, 0:3]\n", "fig, ax = plt.subplots(1,1)\n", "ax.imshow(img1)"]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# We can plot random noise in the viridis colormap.\n", "img2 = np.random.rand(128, 128)\n", "\n", "fig, ax = plt.subplots(1,1)\n", "ax = ax.imshow(img2, cmap='viridis')"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Plot the cubes from 1-10 in a vertical bar chart and in a scatter plot (2 separate subplots). Change the colors of the bars to green. Change the marker of the scatter plot to plus signs."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": []}, {"cell_type": "markdown", "metadata": {}, "source": ["## 5. The matplotlib gallery\n", "\n", "It can be very intimidating to try to craft exactly the figure that you want, especially if\n", "you are used to being able to adjust things visually using a program like Excel.\n", "\n", "If you get stuck and don't know where to start, or just want to learn more about what\n", "matplotlib can do, a great option is to have a look at the matplotlib gallery, which can be\n", "found at http://matplotlib.org/gallery.html. A good way to get started is to find a figure\n", "here that sort of looks like what you want, copy the code, and modify it for your own needs."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### 5.1 Exploring the matplotlib gallery\n", "\n", "Have a look at the matplotlib gallery. If find a cool looking figure you can copy the code into a code line. You can of course do this manually but you can also use IPython \"load magic\" Type %loadpy and then the URL of the py file containing the code, and it will automatically copy it into a cell below. Run the cell with the code to see the\n", "figure. Now you can make small (or large) tweaks to get your perfect figure.\n", "\n", "\n", "Note that some of the examples might require packages that are not installed on your machine (in particular those that make maps) - if this is the case, pick another example for the purposes of this exercise.\n", "\n", "**Hint** to get the raw python url right click on the source code link towards the top and pick copy link."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": false}, "outputs": [], "source": ["# %load http://matplotlib.org/mpl_examples/pylab_examples/contour_demo.py"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### TRY IT\n", "Find an example from the gallery and run it here."]}, {"cell_type": "code", "execution_count": null, "metadata": {"collapsed": true}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 2", "language": "python", "name": "python2"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 2}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11"}}, "nbformat": 4, "nbformat_minor": 0}
mit
facemelters/data-science
Atlas/Youtube Analytics.ipynb
1
9326
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "pd.set_option('display.mpl_style', 'default')\n", "plt.rcParams['figure.figsize'] = (15, 3)\n", "plt.rcParams['font.family'] = 'sans-serif'" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "ERROR: An unexpected error occurred while tokenizing input\n", "The following traceback may be corrupted or invalid\n", "The error message is: ('EOF in multi-line string', (1, 0))\n", "\n" ] }, { "ename": "NameError", "evalue": "name '__file__' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-8-963ff31c22c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0mFor\u001b[0m \u001b[0mmore\u001b[0m \u001b[0minformation\u001b[0m \u001b[0mabout\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mclient_secrets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m \u001b[0mfile\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplease\u001b[0m \u001b[0mvisit\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 52\u001b[0m \u001b[0mhttps\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m//\u001b[0m\u001b[0mdevelopers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcom\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mlibrary\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mpython\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mguide\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0maaa_client_secrets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m \"\"\" % os.path.abspath(os.path.join(os.path.dirname(__file__),\n\u001b[0m\u001b[1;32m 54\u001b[0m CLIENT_SECRETS_FILE))\n\u001b[1;32m 55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name '__file__' is not defined" ] } ], "source": [ "# %load youtube-analytics-example.py\n", "#!/usr/bin/python\n", "\n", "from datetime import datetime, timedelta\n", "import httplib2\n", "import os\n", "import sys\n", "\n", "from apiclient.discovery import build\n", "from apiclient.errors import HttpError\n", "from oauth2client.client import flow_from_clientsecrets\n", "from oauth2client.file import Storage\n", "from oauth2client.tools import argparser, run_flow\n", "\n", "\n", "# The CLIENT_SECRETS_FILE variable specifies the name of a file that contains\n", "# the OAuth 2.0 information for this application, including its client_id and\n", "# client_secret. You can acquire an OAuth 2.0 client ID and client secret from\n", "# the Google Developers Console at\n", "# https://console.developers.google.com/.\n", "# Please ensure that you have enabled the YouTube Data and YouTube Analytics\n", "# APIs for your project.\n", "# For more information about using OAuth2 to access the YouTube Data API, see:\n", "# https://developers.google.com/youtube/v3/guides/authentication\n", "# For more information about the client_secrets.json file format, see:\n", "# https://developers.google.com/api-client-library/python/guide/aaa_client_secrets\n", "CLIENT_SECRETS_FILE = \"client_secrets.json\"\n", "\n", "# These OAuth 2.0 access scopes allow for read-only access to the authenticated\n", "# user's account for both YouTube Data API resources and YouTube Analytics Data.\n", "YOUTUBE_SCOPES = [\"https://www.googleapis.com/auth/youtube.readonly\",\n", " \"https://www.googleapis.com/auth/yt-analytics.readonly\"]\n", "YOUTUBE_API_SERVICE_NAME = \"youtube\"\n", "YOUTUBE_API_VERSION = \"v3\"\n", "YOUTUBE_ANALYTICS_API_SERVICE_NAME = \"youtubeAnalytics\"\n", "YOUTUBE_ANALYTICS_API_VERSION = \"v1\"\n", "\n", "# This variable defines a message to display if the CLIENT_SECRETS_FILE is\n", "# missing.\n", "MISSING_CLIENT_SECRETS_MESSAGE = \"\"\"\n", "WARNING: Please configure OAuth 2.0\n", "\n", "To make this sample run you will need to populate the client_secrets.json file\n", "found at:\n", "\n", " %s\n", "\n", "with information from the Developers Console\n", "https://console.developers.google.com/\n", "\n", "For more information about the client_secrets.json file format, please visit:\n", "https://developers.google.com/api-client-library/python/guide/aaa_client_secrets\n", "\"\"\" % os.path.abspath(os.path.join(os.path.dirname(__file__),\n", " CLIENT_SECRETS_FILE))\n", "\n", "\n", "def get_authenticated_services(args):\n", " flow = flow_from_clientsecrets(CLIENT_SECRETS_FILE,\n", " scope=\" \".join(YOUTUBE_SCOPES),\n", " message=MISSING_CLIENT_SECRETS_MESSAGE)\n", "\n", " storage = Storage(\"%s-oauth2.json\" % sys.argv[0])\n", " credentials = storage.get()\n", "\n", " if credentials is None or credentials.invalid:\n", " credentials = run_flow(flow, storage, args)\n", "\n", " http = credentials.authorize(httplib2.Http())\n", "\n", " youtube = build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION,\n", " http=http)\n", " youtube_analytics = build(YOUTUBE_ANALYTICS_API_SERVICE_NAME,\n", " YOUTUBE_ANALYTICS_API_VERSION, http=http)\n", "\n", " return (youtube, youtube_analytics)\n", "\n", "def get_channel_id(youtube):\n", " channels_list_response = youtube.channels().list(\n", " mine=True,\n", " part=\"id\"\n", " ).execute()\n", "\n", " return channels_list_response[\"items\"][0][\"id\"]\n", "\n", "def run_analytics_report(youtube_analytics, channel_id, options):\n", " # Call the Analytics API to retrieve a report. For a list of available\n", " # reports, see:\n", " # https://developers.google.com/youtube/analytics/v1/channel_reports\n", " analytics_query_response = youtube_analytics.reports().query(\n", " ids=\"channel==%s\" % channel_id,\n", " metrics=options.metrics,\n", " dimensions=options.dimensions,\n", " start_date=options.start_date,\n", " end_date=options.end_date,\n", " max_results=options.max_results,\n", " sort=options.sort\n", " ).execute()\n", "\n", " print \"Analytics Data for Channel %s\" % channel_id\n", "\n", " for column_header in analytics_query_response.get(\"columnHeaders\", []):\n", " print \"%-20s\" % column_header[\"name\"],\n", " print\n", "\n", " for row in analytics_query_response.get(\"rows\", []):\n", " for value in row:\n", " print \"%-20s\" % value,\n", " print\n", "\n", "if __name__ == \"__main__\":\n", " now = datetime.now()\n", " one_day_ago = (now - timedelta(days=1)).strftime(\"%Y-%m-%d\")\n", " one_week_ago = (now - timedelta(days=7)).strftime(\"%Y-%m-%d\")\n", "\n", " argparser.add_argument(\"--metrics\", help=\"Report metrics\",\n", " default=\"views,comments,favoritesAdded,favoritesRemoved,likes,dislikes,shares\")\n", " argparser.add_argument(\"--dimensions\", help=\"Report dimensions\",\n", " default=\"video\")\n", " argparser.add_argument(\"--start-date\", default=one_week_ago,\n", " help=\"Start date, in YYYY-MM-DD format\")\n", " argparser.add_argument(\"--end-date\", default=one_day_ago,\n", " help=\"End date, in YYYY-MM-DD format\")\n", " argparser.add_argument(\"--max-results\", help=\"Max results\", default=10)\n", " argparser.add_argument(\"--sort\", help=\"Sort order\", default=\"-views\")\n", " args = argparser.parse_args()\n", "\n", " (youtube, youtube_analytics) = get_authenticated_services(args)\n", " try:\n", " channel_id = get_channel_id(youtube)\n", " run_analytics_report(youtube_analytics, channel_id, args)\n", " except HttpError, e:\n", " print \"An HTTP error %d occurred:\\n%s\" % (e.resp.status, e.content)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-2.0
yihaochen/FLASHtools
heat_pump/yt_Spitzer_conduction_300Myr_jet1E-3.ipynb
1
16115
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import os\n", "import yt\n", "yt.mylog.setLevel(\"WARNING\")\n", "import numpy as np\n", "import matplotlib\n", "matplotlib.rcParams['font.family'] = 'stixgeneral'\n", "matplotlib.rcParams['figure.dpi'] = 150\n", "import matplotlib.pyplot as plt\n", "\n", "from yt_conduction_fields import *" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ds = yt.load('/home/ychen/Mount/gdrive/2015_production_runs/1022_L45_M10_b1_h1_10Myr/data/MHD_Jet_10Myr_hdf5_plt_cnt_1380')\n", "sp1 = ds.sphere([0,0,0], (180, 'kpc'))\n", "sp2 = ds.sphere([0,0,0], (0.5, 'kpc'))\n", "sp = sp1 - sp2\n", "\n", "ds.current_time.in_units('Myr')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'jet ')\n", "plot.zoom(4)\n", "plot.set_zlim('jet ', 1E-7, 1)\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'xray_emissivity_0.1_100_keV')\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.zoom(4)\n", "plot.show()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'xray_cooling_time', )\n", "plot.set_unit('xray_cooling_time', 'Gyr')\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.zoom(4)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'temperature')\n", "plot.set_unit('temperature', 'keV', equivalency='thermal')\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.zoom(4)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'entropy', data_source=sp)\n", "plot.set_zlim('entropy', 30, 250)\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.zoom(4)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'H_nuclei_density')\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.zoom(4)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'temperature_gradient_magnitude', data_source=sp, width=(240, 'kpc'))\n", "plot.set_unit('temperature_gradient_magnitude', 'K/pc')\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'temperature_gradient_x', data_source=sp, width=(240, 'kpc'))\n", "plot.set_unit('temperature_gradient_x', 'K/pc')\n", "plot.set_cmap('temperature_gradient_x', 'seismic')\n", "plot.set_zlim('temperature_gradient_x', -1E3, 1E3)\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'spitzer_conduction_coefficient', data_source=sp, width=(240, 'kpc'))\n", "plot.set_zlim('spitzer_conduction_coefficient', 1E29, 1E32)\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'spitzer_heat_flux_x', data_source=sp, width=(240, 'kpc'))\n", "plot.set_log('spitzer_heat_flux_x' , True, linthresh=1E-3)\n", "plot.set_cmap('spitzer_heat_flux_x', 'seismic')\n", "plot.set_zlim('spitzer_heat_flux_x', -1E-1, 1E-1)\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'spitzer_heat_flux_divergence', data_source=sp, width=(240, 'kpc'))\n", "\n", "plot.set_log('spitzer_heat_flux_divergence' , True, linthresh=1E-25)\n", "plot.set_cmap('spitzer_heat_flux_divergence', 'seismic_r')\n", "plot.set_zlim('spitzer_heat_flux_divergence', -1E-23, 1E-23)\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'spitzer_heating_rate', data_source=sp, width=(240, 'kpc'))\n", "\n", "plot.set_log('spitzer_heating_rate' , True, linthresh=1E40)\n", "plot.set_cmap('spitzer_heating_rate', 'seismic')\n", "plot.set_zlim('spitzer_heating_rate', -1E42, 1E42)\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'total_heating_rate', data_source=sp, width=(240, 'kpc'))\n", "\n", "plot.set_log('total_heating_rate' , True, linthresh=1E40)\n", "plot.set_cmap('total_heating_rate', 'seismic')\n", "plot.set_zlim('total_heating_rate', -1E42, 1E42)\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot = yt.SlicePlot(ds, 'y', 'total_cooling_time', data_source=sp, width=(240, 'kpc'))\n", "\n", "plot.set_log('total_cooling_time' , True, linthresh=1E2)\n", "plot.set_cmap('total_cooling_time', 'seismic')\n", "plot.set_zlim('total_cooling_time', -1E4, 1E4)\n", "plot.annotate_contour('jet ', ncont=2, clim=(1E-3, 1E-2), take_log=True)\n", "plot.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "extrema = {'entropy': (20, 250)}\n", "logs = {'entropy': True}\n", "fields = ['spitzer_heating_rate', 'xray_luminosity_0.1_100_keV', 'cell_mass']\n", "prof_entropy = yt.create_profile(sp, 'entropy', fields, weight_field=None, \n", " extrema=extrema, logs=logs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(6,4))\n", "plt.step(prof_entropy.x, -prof_entropy['xray_luminosity_0.1_100_keV'],\n", " label='X-ray Cooling', linestyle='dotted')\n", "plt.step(prof_entropy.x, prof_entropy['spitzer_heating_rate'],\n", " label='Spitzer Thermal Conduction', linestyle='dashed')\n", "plt.step(prof_entropy.x, prof_entropy['spitzer_heating_rate']-prof_entropy['xray_luminosity_0.1_100_keV'], \n", " label='Total $f_{\\mathrm{Sp}}$ = 1', linewidth=1)\n", "plt.step(prof_entropy.x, 0.1*prof_entropy['spitzer_heating_rate']-prof_entropy['xray_luminosity_0.1_100_keV'], \n", " label=r'Total $f_{\\mathrm{Sp}}$ = 0.1', linewidth=2)\n", "plt.ylim(-1E45, 1E45)\n", "plt.yscale('symlog', linthreshy=1E42)\n", "yticks = [-1E45, -1E44, -1E43, -1E42, 0, 1E42, 1E43, 1E44, 1E45]\n", "\n", "plt.yticks(yticks, yticks)\n", "plt.ylabel('Heating(+)/Cooling(-) rate (erg/s)')\n", "plt.semilogx()\n", "plt.xlim(20, 250)\n", "entropy_ticks = [20, 30, 40, 60, 80, 100, 150, 200]\n", "plt.xticks(entropy_ticks, entropy_ticks)\n", "plt.xlabel(r'Entropy (cm$^2\\ $ keV)')\n", "plt.axhline(0, ls='-', lw=1, color='k')\n", "plt.text(170, 3E44, '%.0f Myr' % ds.current_time.in_units('Myr'))\n", "plt.legend(loc=3, fontsize=9, ncol=2, columnspacing=0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fig = plt.figure(figsize=(6,4))\n", "plt.step(prof_entropy.x, prof_entropy['xray_luminosity_0.1_100_keV'],\n", " label='X-ray Cooling', linestyle='dashed', color='C0')\n", "plt.step(prof_entropy.x, prof_entropy['spitzer_heating_rate'],\n", " label='Spitzer Conduction', linewidth=1, color='C1')\n", "plt.step(prof_entropy.x, -prof_entropy['spitzer_heating_rate'],\n", " linestyle='dotted', linewidth=1, color='C1')\n", "plt.step(prof_entropy.x, 0.1*prof_entropy['spitzer_heating_rate'],\n", " label='10% Spitzer Conduction', linewidth=2, color='C3')\n", "plt.step(prof_entropy.x, -0.1*prof_entropy['spitzer_heating_rate'],\n", " linestyle='dotted', linewidth=2, color='C3')\n", "\n", "#plt.step(prof_entropy.x, prof_entropy['spitzer_heating_rate']-prof_entropy['xray_luminosity_0.1_100_keV'], \n", "# label='Total $f_{\\mathrm{Sp}}$ = 1', linewidth=1)\n", "#plt.step(prof_entropy.x, 0.1*prof_entropy['spitzer_heating_rate']-prof_entropy['xray_luminosity_0.1_100_keV'], \n", "# label=r'Total $f_{\\mathrm{Sp}}$ = 0.1', linewidth=2)\n", "plt.ylim(1E41, 1E45)\n", "plt.yscale('log')\n", "yticks = [1E41, 1E42, 1E43, 1E44, 1E45]\n", "\n", "plt.yticks(yticks, yticks)\n", "plt.ylabel('Heating(solid)/Cooling(dotted) rate (erg/s)')\n", "plt.semilogx()\n", "plt.xlim(20, 250)\n", "entropy_ticks = [20, 30, 40, 60, 80, 100, 150, 200]\n", "plt.xticks(entropy_ticks, entropy_ticks)\n", "plt.xlabel(r'Entropy (cm$^2\\ $ keV)')\n", "plt.axhline(0, ls='-', lw=1, color='k')\n", "plt.text(170, 4E44, '%.0f Myr' % ds.current_time.in_units('Myr'))\n", "plt.legend(loc=2, fontsize=9, ncol=2, columnspacing=0.5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "extrema = {'entropy': (20, 250), 'spherical_radius': ((10, 'kpc'), (150, 'kpc'))}\n", "fields = ['spitzer_heating_rate', 'xray_luminosity_0.1_100_keV', 'cell_mass']\n", "prof = yt.create_profile(sp, ['entropy', 'spherical_radius'], fields, weight_field=None,\n", " extrema=extrema)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "prof.save_as_dataset('300Myr_prof_entropy_r')\n", "prof_entropy.save_as_dataset('300Myr_prof_entropy')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "pp = yt.PhasePlot.from_profile(prof, figure_size=6)\n", "pp.set_unit('spherical_radius', 'kpc')\n", "pp.set_cmap('spitzer_heating_rate', 'RdBu_r')\n", "pp.set_log('spitzer_heating_rate', False)\n", "pp.set_zlim('spitzer_heating_rate', -1E44, 1E44)\n", "pp.set_ylim(20, 150)\n", "pp.set_xlim(20, 250)\n", "\n", "plot2 = pp['spitzer_heating_rate']\n", "\n", "r_ticks = [30, 40, 50, 60, 80, 100, 150]\n", "\n", "plot2.axes.set_yticks(r_ticks)\n", "#plot2.axes.yaxis.set_ticklabels(r_ticks)\n", "plot2.axes.set_yticklabels(r_ticks)\n", "plot2.axes.set_yticklabels([], minor=True)\n", "#print(plot2.axes.yaxis.get_ticklabels())\n", "\n", "entropy_ticks = [20, 30, 40, 60, 80, 100, 150, 200]\n", "#plot2.axes.set_xticks(entropy_ticks)\n", "#plot2.axes.set_xticklabels(entropy_ticks)\n", "\n", "plot2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "divider = make_axes_locatable(plot2)\n", "axHist = divider.append_axes(\"bottom\", size=1.5, pad=0.1, sharex=plot2.axes)\n", "axHist.step(prof_entropy.x, prof_entropy['xray_luminosity_0.1_100_keV'],\n", " label='X-ray Cooling', linestyle='dashed', color='C0')\n", "plot2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "extrema = {'spherical_radius': ((10, 'kpc'), (150, 'kpc'))}\n", "logs = {'spherical_radius': True}\n", "fields = ['spitzer_heating_rate', 'xray_luminosity_0.1_100_keV', 'cell_mass']\n", "prof_radius = yt.create_profile(sp, 'spherical_radius', fields, weight_field=None, \n", " extrema=extrema, logs=logs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "plot2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from yt.visualization.plot_container import FieldTransform\n", "from yt.visualization.tick_locators import LogLocator\n", "\n", "linthresh = 1E43\n", "\n", "symlog_transform = FieldTransform('symlog', None, LogLocator()) \n", "pp._field_transform['spitzer_heating_rate'] = symlog_transform\n", "pp._field_transform['spitzer_heating_rate'].func = linthresh\n", "pp._setup_plots()\n", "pp.annotate_text(12, 200, '%.0f Myr' % ds.current_time.in_units('Myr'))\n", "print(pp._field_transform['spitzer_heating_rate'])\n", "pp.set_zlim('spitzer_heating_rate', -5E44, 5E44)\n", "plot2 = pp['spitzer_heating_rate']\n", "entropy_ticks = [20, 30, 40, 50, 60, 70, 80, 90, 100, 200]\n", "plot2.axes.set_yticks(entropy_ticks)\n", "plot2.axes.set_yticklabels(entropy_ticks)\n", "r_ticks = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n", "plot2.axes.set_xticks(r_ticks)\n", "plot2.axes.set_xticklabels(r_ticks)\n", "plot2\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "keV = yt.units.keV\n", "cm = yt.units.cm\n", "kappa = 1*(keV**(5/2)*cm**3)\n", "kappa.convert_to_base()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "heating_rate = prof['spitzer_heating_rate'].flatten()\n", "null = plt.hist(np.log10(heating_rate[heating_rate>0]), bins=20, histtype='step', label='heating')\n", "null = plt.hist(np.log10(-heating_rate[heating_rate<0]), bins=20, histtype='step', label='cooling')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-2.0
sixy6e/geospatial-hdf5
examples/notebooks/sieve_example.ipynb
1
13914
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy\n", "from scipy import ndimage\n", "import pandas\n", "from geoh5 import kea\n", "from geoh5.kea import common as kc\n", "\n", "# https://github.com/sixy6e/image-processing\n", "from image_processing.segmentation import Segments" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example we'll create a segmented array, and compute some basic statistics for every segment\n", "(min, max, mean, standard deviation, total, area), and output both the segmented array and the\n", "associated dataframe (as a raster attribute table) to disk.\n", "\n", "The sieving filter will remove segements containing < 30 pixels." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# data dimensions and datatype\n", "dims = (1000, 1000)\n", "dtype = 'int32'\n", "\n", "# create some random data and segment via value > 5000\n", "seg_data = numpy.random.randint(0, 10001, dims).astype('uint32')\n", "seg_data, nlabels = ndimage.label(seg_data > 5000)\n", "\n", "# create some random data to calculate stats against\n", "data = numpy.random.ranf(dims)\n", "\n", "# create a segments class object\n", "seg = Segments(seg_data, include_zero=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of segments: 66341\n" ] } ], "source": [ "# initial number of segments\n", "print \"Number of segments: {}\".format(seg.n_segments)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of segments: 3759\n" ] } ], "source": [ "# remove segments containing < 30 pixels\n", "seg.sieve(30)\n", "print \"Number of segments: {}\".format(seg.n_segments)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# basic stats (min, max, mean, standard deviation, total, area)\n", "stats_table = seg.basic_statistics(data, dataframe=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# join via segment id, specifying 'outer' will account for empty segments\n", "df = pandas.DataFrame({\"Histogram\": seg.histogram})\n", "df[\"Segment_IDs\"] = df.index\n", "stats_table = pandas.merge(df, stats_table, how='outer', on=\"Segment_IDs\")\n", "nrows = stats_table.shape[0]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# assign random colours to each segment\n", "stats_table.insert(1, \"Red\", numpy.random.randint(0, 256, (nrows)))\n", "stats_table.insert(2, \"Green\", numpy.random.randint(0, 256, (nrows)))\n", "stats_table.insert(3, \"Blue\", numpy.random.randint(0, 256, (nrows)))\n", "stats_table.insert(4, \"Alpha\", 255)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# define the output image specifications\n", "kwargs = {'width': dims[1],\n", " 'height': dims[0],\n", " 'count': 1,\n", " 'compression': 4,\n", " 'chunks': (100, 100),\n", " 'blocksize': 100,\n", " 'dtype': seg_data.dtype.name}" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "with kea.open('sieve-example.kea', 'w', **kwargs) as src:\n", " src.write(seg.array, 1)\n", " \n", " # define the layer type as thematic (labelled, classified etc)\n", " src.write_layer_type(1, kc.LayerType.thematic)\n", " \n", " # write the stats table as an attribute table\n", " usage = {\"Red\": \"Red\",\n", " \"Green\": \"Green\",\n", " \"Blue\": \"Blue\",\n", " \"Alpha\": \"Alpha\",\n", " \"Histogram\": \"PixelCount\"}\n", " \n", " src.write_rat(stats_table, 1, usage=usage)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with kea.open('sieve-example.kea') as ds:\n", " tbl = ds.read_rat()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Histogram</th>\n", " <th>Red</th>\n", " <th>Green</th>\n", " <th>Blue</th>\n", " <th>Alpha</th>\n", " <th>Segment_IDs</th>\n", " <th>Mean</th>\n", " <th>Max</th>\n", " <th>Min</th>\n", " <th>StdDev</th>\n", " <th>Total</th>\n", " <th>Area</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>742125</td>\n", " <td>131</td>\n", " <td>162</td>\n", " <td>108</td>\n", " <td>255</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>38</td>\n", " <td>218</td>\n", " <td>190</td>\n", " <td>73</td>\n", " <td>255</td>\n", " <td>1</td>\n", " <td>0.524985</td>\n", " <td>0.970592</td>\n", " <td>0.015172</td>\n", " <td>0.281338</td>\n", " <td>19.949436</td>\n", " <td>38.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>39</td>\n", " <td>74</td>\n", " <td>75</td>\n", " <td>197</td>\n", " <td>255</td>\n", " <td>2</td>\n", " <td>0.453546</td>\n", " <td>0.989691</td>\n", " <td>0.036141</td>\n", " <td>0.299307</td>\n", " <td>17.688289</td>\n", " <td>39.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>69</td>\n", " <td>238</td>\n", " <td>230</td>\n", " <td>245</td>\n", " <td>255</td>\n", " <td>3</td>\n", " <td>0.471219</td>\n", " <td>0.970443</td>\n", " <td>0.001869</td>\n", " <td>0.294685</td>\n", " <td>32.514120</td>\n", " <td>69.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>52</td>\n", " <td>107</td>\n", " <td>65</td>\n", " <td>156</td>\n", " <td>255</td>\n", " <td>4</td>\n", " <td>0.480466</td>\n", " <td>0.998480</td>\n", " <td>0.026320</td>\n", " <td>0.309385</td>\n", " <td>24.984221</td>\n", " <td>52.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Histogram Red Green Blue Alpha Segment_IDs Mean Max \\\n", "0 742125 131 162 108 255 0 NaN NaN \n", "1 38 218 190 73 255 1 0.524985 0.970592 \n", "2 39 74 75 197 255 2 0.453546 0.989691 \n", "3 69 238 230 245 255 3 0.471219 0.970443 \n", "4 52 107 65 156 255 4 0.480466 0.998480 \n", "\n", " Min StdDev Total Area \n", "0 NaN NaN NaN NaN \n", "1 0.015172 0.281338 19.949436 38.0 \n", "2 0.036141 0.299307 17.688289 39.0 \n", "3 0.001869 0.294685 32.514120 69.0 \n", "4 0.026320 0.309385 24.984221 52.0 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tbl.head(5)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Histogram</th>\n", " <th>Red</th>\n", " <th>Green</th>\n", " <th>Blue</th>\n", " <th>Alpha</th>\n", " <th>Segment_IDs</th>\n", " <th>Mean</th>\n", " <th>Max</th>\n", " <th>Min</th>\n", " <th>StdDev</th>\n", " <th>Total</th>\n", " <th>Area</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>742125</td>\n", " <td>131</td>\n", " <td>162</td>\n", " <td>108</td>\n", " <td>255</td>\n", " <td>0</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>38</td>\n", " <td>218</td>\n", " <td>190</td>\n", " <td>73</td>\n", " <td>255</td>\n", " <td>1</td>\n", " <td>0.524985</td>\n", " <td>0.970592</td>\n", " <td>0.015172</td>\n", " <td>0.281338</td>\n", " <td>19.949436</td>\n", " <td>38.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>39</td>\n", " <td>74</td>\n", " <td>75</td>\n", " <td>197</td>\n", " <td>255</td>\n", " <td>2</td>\n", " <td>0.453546</td>\n", " <td>0.989691</td>\n", " <td>0.036141</td>\n", " <td>0.299307</td>\n", " <td>17.688289</td>\n", " <td>39.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>69</td>\n", " <td>238</td>\n", " <td>230</td>\n", " <td>245</td>\n", " <td>255</td>\n", " <td>3</td>\n", " <td>0.471219</td>\n", " <td>0.970443</td>\n", " <td>0.001869</td>\n", " <td>0.294685</td>\n", " <td>32.514120</td>\n", " <td>69.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>52</td>\n", " <td>107</td>\n", " <td>65</td>\n", " <td>156</td>\n", " <td>255</td>\n", " <td>4</td>\n", " <td>0.480466</td>\n", " <td>0.998480</td>\n", " <td>0.026320</td>\n", " <td>0.309385</td>\n", " <td>24.984221</td>\n", " <td>52.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Histogram Red Green Blue Alpha Segment_IDs Mean Max \\\n", "0 742125 131 162 108 255 0 NaN NaN \n", "1 38 218 190 73 255 1 0.524985 0.970592 \n", "2 39 74 75 197 255 2 0.453546 0.989691 \n", "3 69 238 230 245 255 3 0.471219 0.970443 \n", "4 52 107 65 156 255 4 0.480466 0.998480 \n", "\n", " Min StdDev Total Area \n", "0 NaN NaN NaN NaN \n", "1 0.015172 0.281338 19.949436 38.0 \n", "2 0.036141 0.299307 17.688289 39.0 \n", "3 0.001869 0.294685 32.514120 69.0 \n", "4 0.026320 0.309385 24.984221 52.0 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats_table.head(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
balouf/INF674
2016-2017/09-Kleinberg/09-Kleinberg-TP.ipynb
1
20400
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# INF674 S7: Kleinberg's grid\n", "\n", "## Céline Comte & Fabien Mathieu\n", "\n", "## 2016 - 2017" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The goal of this mini-project is to verify John Kleinberg's theorem on small-world navigability, stated in the article [The Small-World Phenomenon: An Algorithmic Perspective][kleinberg].\n", "\n", "[kleinberg]: http://www.cs.cornell.edu/home/kleinber/swn.pdf \"Jon Kleinberg. 2000. The small-world phenomenon: an algorithmic perspective. In Proceedings of the thirty-second annual ACM symposium on Theory of computing (STOC '00). ACM, New York, NY, USA, 163-170.\"\n", "\n", "Consider an $n \\times n$ flat grid, where each node has up to $4$ **regular** neighbors (West, East, North, South).\n", "Additionally, each node has a special **shortcut** picked up at random among the other nodes of the grid.\n", "Denoting by $d(a,b) = |a_x - b_x| + |a_y - b_y|$ the Manhattan distance between two nodes $a = (a_x, a_y)$ and $b = (b_x, b_y)$,\n", "the probability to choose $b$ as the shortcut of $a$ is proportional to $1 / d(a,b)^r$ for some $r > 0$ which does not depend on $a$ an $b$.\n", "The vector from a node to its shortcut is called the **shortcut offset**." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<div style=\"float: center; text-align=center; display: inline;\">\n", "<figure style=\"width:350px; margin: 30px; display:inline-block;\">\n", " <img width=300px src=\"http://www.lincs.fr/wp-content/uploads/2016/11/grid.png\">\n", " <figcaption> <center> Kleinberg's grid with $n=6$ </center> </figcaption>\n", "</figure>\n", "<figure style=\"width:350px; margin: 30px; display:inline-block;\">\n", " <img width=300px src=\"http://www.lincs.fr/wp-content/uploads/2016/11/shortcuts.png\">\n", " <figcaption> <center> Neighbors of a node $a$ </center> </figcaption>\n", "</figure>\n", "<figcaption> <center> Inspired by the figures of [The Small-World Phenomenon: An Algorithmic Perspective][kleinberg] </center> </figcaption>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We are interested in the **average routing time**. It is defined as the mean number of hops necessary to link up two nodes chosen uniformly and independently at random in a grid where the shortcuts are generated according to the previous distribution. The routing is **decentralized** in the sense that we always choose the next hope as (one of) the neighbor(s) which is the closest to the destination for the Manhattan distance.\n", "In general, the routing time between a source and a destination is not equal to the distance between these nodes in the graph representing the grid.\n", "Kleinberg's result states that\n", "- if $r = 2$, then the routing time is \"short\": it requires $O(\\log^2(n))$ steps on average,\n", "- if $r \\neq 2$, then the routing time is \"long\": it requires $O(n^\\alpha)$ steps on average, for some $\\alpha > 0$ which depends on $r$.\n", "\n", "This result is often interpreted as follows: shortcuts can turn graphs of large diameter into navigable small-worlds, but this works only if the shortcuts follow a precise distribution." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Your mission: validate this result through simulations. The questions below aim at helping you to identify the difficulties of the problem and to develop solutions. For this assignment, we expect that you send us\n", "- the present notebook completed with the answers to the questions and a working program,\n", "- *and* a report where you discuss your methodology and the results obtained.\n", "\n", "The report can be either included into the notebook (recommended) or written in an external pdf file. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "You will not be judged on your raw programming skills but on the following criteria:\n", "- **Algorithmic**: optimize the time and space complexity of your simulator,\n", "- **Explications**: justify the choice of the parameters, interpret you results, draw plots and/or figures,\n", "- **Research approach**: give intuitions on the results of Kleinberg (you *must* quote any external reference that you use, including [The Small-World Phenomenon: An Algorithmic Perspective][kleinberg], which is of course recommended),\n", "- **Initiatives**: if you try something that was not suggested or you make a \"good\" mistake that helped you to get a better grasp of the problem, discuss it instead of hiding it under the carpet!\n", "\n", "Even if Python 3.X is recommended, you can use other languages if your choice is justified and the code is extensively commented.\n", "\n", "[kleinberg]: http://www.cs.cornell.edu/home/kleinber/swn.pdf \"Jon Kleinberg. 2000. The small-world phenomenon: an algorithmic perspective. In Proceedings of the thirty-second annual ACM symposium on Theory of computing (STOC '00). ACM, New York, NY, USA, 163-170.\"" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Last remarks and hints:\n", "- For comparison, you can remind the average path length between departure and arrival nodes in absence of shortcuts.\n", "- Running 10 or 100 simulations is sufficient to get rough estimates for debugging and seeking directions. 10,000 runs are slow and better executed in background, but they give precise results (\"1%\" error margin).\n", "- The code of the correction, probably not optimal, is less than 2K in size. For $n = $ 10,000, it computes 10,000 trials for each $\\sf r$ $\\sf in$ $\\sf range(.1,3,.1)$ in a few hours on a laptop. All the \"tricks\" used are suggested in the questions below.\n", "- You can save and load your results with $\\sf np.save$ and $\\sf np.load$ (see practicals of S5, S6 or S8 for examples)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 1. Time and space complexity" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Random number generation has a much lower time complexity per sample when executed in bulk, especially for custom distributions (using for instance $\\sf np.random.choice$). More precisely, if you draw $k$ values in bulk from a random variable which can take $n$ distinct values, the cost is $O(n + k\\log(k))$. If you plan to use a given distribution a lot, it is best to draw $k$ values at once (say $k =$ 1,000,000) and to draw them again anytime you stock is depleted." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "1\\. What is the time and the space complexity to draw and memorize all the shortcuts in the grid? Discuss this considering the values indicated above (10,000 runs with $n =$ 10,000)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "**Answer:**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "2\\. What would the time complexity become if we substituted the grid with a torus as a first approximation?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "**Answer:**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The space complexity can be made constant by applying the **principle of deferred decisions**:\n", "\n", "\" When there are several random variables [...], it often helps to think of some of them as being set at one point in the algorithm with the rest of them being left random - or deferred - until some further point in the analysis. Formally, this corresponds to conditioning on the revealed values; when some of the random variables are revealed, we must condition on the revealed values of the rest of the analysis.\"\n", "[Probability and Computing: Randomized Algorithms and Probabilistic Analysis by Mitzenmacher and Upfal (Cambridge University Press, New York, NY, USA, 2005)][mitzenmacher]\n", "\n", "[mitzenmacher]: https://books.google.fr/books?id=0bAYl6d7hvkC&printsec=frontcover&dq=probability+and+computing+mitzenmacher&hl=en&sa=X&ved=0ahUKEwjdmNXsoKXQAhWRyRoKHYpRAr8Q6AEIHTAA#v=onepage&q=probability%20and%20computing%20mitzenmacher&f=false \"Probability and Computing: Randomized Algorithms and Probabilistic Analysis by Mitzenmacher and Upfal (Cambridge University Press, New York, NY, USA, 2005)\"\n", "\n", "With the decentralized routing algorithm described above, the distance between the current node and the destination strictly decreases at each step. In particular, each node can be visited at most once during the routing, so that we don't need to memorize the shortcut of a node once we have used it. Thus, instead of drawing and memorizing all shortcuts at the beginning, we can thus draw them \"on the fly\" as we visit the nodes.\n", "\n", "In order to keep both the space *and* the time complexity low, we decide to draw at once a large number $k$ of shortcut offsets which fits into memory. We redraw it any time the shortcut offsets have all been used. The time complexity will be kept low by applying the following method." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 2. *Tant que je perds, je joue.* (\"As long as I loose, I play again.\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "We will use a simple version of the **rejection sampling** method, explained on <a href=\"https://en.wikipedia.org/wiki/Rejection_sampling\">this Wikipedia page</a>. It consists in drawing shortcut offsets in a grid which is larger than the initial one, and to reject them afterwards if it turns out that the shortcut they produce for the node considered is not in the initial grid. With an appropriate choice of the covering grid, it is possible to draw in a bulk a large number of shortcut offsets (*all* with the same probability distribution) and to use a rejection criterion adapted to the node considered as we do the routing.\n", "\n", "After recalling the longest possible shortcut offset(s) in the $n \\times n$ grid and an example of situation where it can arise, give the smallest covering grid we can use to implement this method." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "**Answer:**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 3. Polar coordinates" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Following on from the method proposed in Question 2, we consider a covering grid which is strictly larger than the smallest covering grid but has the advantage of significantly facilitating the drawing of shortcut offsets. Specifically, we want to draw points in a ball with some radius $R$ *for the Manhattan distance*, so that a node $a = (a_x, a_y)$ with norm $|a| = |a_x| + |a_y| \\le R$ is drawn with a probability which is proportional to $1 / |a|^r$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "1\\. What is the smallest value we can use for $R$?" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "**Answer:**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "2\\. Give the number of nodes with norm $i$, for each $i = 1,\\ldots,R$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "**Answer:** " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "3\\. Considering some discretised version of the polar coordinates in $\\mathbb{R}^2$, explain how you we can efficiently draw points in a ball of radius $R$ for the Manhattan distance, so that a node $a$ is drawn with a probability which is proportional to $1 / |a|^r$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "**Answer:**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# 4. Sampling the radii (Bonus)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "The distribution of the radii is a power law. Even though the function $\\sf random$.$\\sf choice$ is faster when we draw radii in bulks, it may be interesting to use even more efficient solutions in order to reach higher values of $n$. Below we propose two such solutions." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "<img style=\"float: right; width:35vw; margin: 0px\" src=\"http://www.lincs.fr/wp-content/uploads/2016/11/rejection.png\">\n", "\n", "1\\. **Rejection sampling method:** Here we consider only the method of Question 3 with $r > 1$.\n", "The distribution of the shortcut radii is proportional to\n", "$$\n", "a_i = \\frac1{i^{r-1}},\n", "\\quad \\forall i = 1,\\ldots,R.\n", "$$\n", "Denote by $f$ the function defined on $[1,R]$ by\n", "$$\n", "f(x) = \\frac1{x^{r-1}},\n", "\\quad \\forall x \\in (0,R].\n", "$$\n", "For each $i = 2,\\ldots,R$, we have $a_i \\le f(x)$ for all $x \\in [i-1,i]$\n", "with an equality when $x = i$.\n", "\n", "Using this observation, explain how you can apply the method of rejection sampling to draw efficiently a large number of radii. You can distinguish the case $i = 1$ since the area under the curve of function $f$ is not finite on $(0,1]$.\n", "\n", "A similar approach can be applied when $r \\ge 1$." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Hint: You can use the method of *inversion sampling* to draw a random variable $X$ with probability distribution function defined on $[1,R]$ by\n", "$$\n", "x \\mapsto \\frac{f(x)}{\\int_1^R f(t) dt}.\n", "$$\n", "This method consists in drawing a random variable $U$ uniformly distribued on $[0,1]$ and computing\n", "$$\n", "X = F^{-1}\\left( U \\times \\int_1^R f(t) dt \\right),\n", "$$\n", "where $F$ is the primitive of $f$ such that $F(1) = 0$. The random variable $X$ has the desired probability distribution function.\n", "\n", "More details about this method are provided in [\"Inverse Transform Method\" by Sigman (2010)][sigman] and [\"Intro to Sampling Methods, Collins (2010)\"][collins]. The first resource gives rigorous details whereas the second is more graphical.\n", "\n", "[sigman]: http://www.columbia.edu/~ks20/4404-Sigman/4404-Notes-ITM.pdf \"Inverse Transform Method, Sigman (2010)\"\n", "\n", "[collins]: http://www.cse.psu.edu/~rtc12/CSE586Spring2010/lectures/cse586sampling1_6pp.pdf \"Intro to Sampling Methods, Collins (2010)\"" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "**Answer and code:**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "2\\. **Alias method:** This second approach doesn't use the explicit expression of the radii distribution but it turns out to give much better results than the first one. The following resource [\"Darts, Dice, and Coins: Sampling from a Discrete Distribution\", Schwarz (2011)][schwarz] explains the details of the method and gives an algorithm to implement it.\n", "\n", "[schwarz]: http://www.keithschwarz.com/darts-dice-coins/ \"Darts, Dice, and Coins: Sampling from a Discrete Distribution, Schwarz (2011)\"" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "**Answer and code:**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Implementation" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "1\\. Using the insights given in the previous questions, write a function $\\sf make$\\_$\\sf shortcut$\\_$\\sf offsets$ which computes $k$ shortcut offsets in bulk in a ball with the radius given in Question 3. **It is strongly recommended to use the rejection sampling-based method introduced in Exercices 2 and 3. If you decide not to apply it, you will need to find another solution to reduce the time complexity of your simulator.** Approximating the grid with a torus, as discussed in Question 1.2, is an option. If you do so, you are expected to compare the results you obtain with the ones you would have expected for the initial grid." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "**Answer and code:**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "2\\. Write a function $\\sf kleinberg$\\_$\\sf distance$ which returns the mean number of hops of the decentralized routing algorithm over $runs$ realizations of Kleinberg's grid, for some size $n$ of the grid and exponent $r$ of the shortcut distribution which are fixed. Each run corresponds to an independent realization of Kleinberg's grid. Hence, the shortcut of a node is redrawn independently at each run, which spares you from memorizing the shortcuts once they are used. The departure and arrival nodes are also drawn uniformly and independently at random at each run." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "**Answer and code:**" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Discussion\n", "\n", "Your turn now!" ] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
frazer-lab/cardips-ipsc-eqtl
notebooks/Figure. CNV eQTL Examples.ipynb
1
181104
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Figure. CNV eQTL Examples" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import copy\n", "import cPickle\n", "import os\n", "import subprocess\n", "\n", "import cdpybio as cpb\n", "import matplotlib as mpl\n", "import matplotlib.gridspec as gridspec\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "pd.options.mode.chained_assignment = None # default='warn'\n", "import pybedtools as pbt\n", "import scipy.stats as stats\n", "import seaborn as sns\n", "\n", "import ciepy\n", "import cardipspy as cpy\n", "\n", "%matplotlib inline\n", "%load_ext rpy2.ipython\n", "\n", "dy_name = 'figure_cnv_eqtl_examples'\n", " \n", "outdir = os.path.join(ciepy.root, 'output', dy_name)\n", "cpy.makedir(outdir)\n", "\n", "private_outdir = os.path.join(ciepy.root, 'private_output', dy_name)\n", "cpy.makedir(private_outdir)\n", "\n", "import socket\n", "if socket.gethostname() == 'fl-hn1' or socket.gethostname() == 'fl-hn2':\n", " dy = os.path.join(ciepy.root, 'sandbox', 'tmp', dy_name)\n", " cpy.makedir(dy)\n", " pbt.set_tempdir(dy)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fn = os.path.join(ciepy.root, 'output', 'cnv_analysis', 'cnv_gene_variants.pickle')\n", "cnv_gv = pd.read_pickle(fn)\n", "\n", "fn = os.path.join(ciepy.root, 'output', 'cnv_analysis', 'lead_variants.pickle')\n", "lead_vars = pd.read_pickle(fn)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fn = os.path.join(ciepy.root, 'output', 'mcnv_analysis', 'reg_results.tsv')\n", "mcnv_results = pd.read_table(fn, index_col=0)\n", "mcnv_sig = mcnv_results[mcnv_results.bh_sig]\n", "\n", "fn = os.path.join(ciepy.root, 'private_output', 'mcnv_analysis', 'filtered_mcnvs.tsv')\n", "mcnv_genotypes = pd.read_table(fn, index_col=0)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cnv_lead_vars = cnv_gv[cnv_gv.cnv_is_lead]\n", "cnv_lead_vars = cnv_lead_vars.sort_values(by='pvalue').drop_duplicates(subset=['gene_id'])" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(162, 131)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mcnv_genotypes.shape" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "CNV_17_44566797_44580424 11\n", "CNV_17_44336433_44367851 10\n", "CNV_17_44311202_44328032 10\n", "CNV_17_43650217_43655844 7\n", "CNV_1_16885240_16950375 7\n", "CNV_7_143951166_143953316 7\n", "CNV_17_43655545_43662029 6\n", "CNV_10_46945989_47151257 6\n", "CNV_8_11979660_12009126 6\n", "CNV_8_12427702_12432588 5\n", "CNV_1_16951816_16969312 5\n", "CNV_8_12395839_12427801 5\n", "Name: cnv, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mcnv_sig.cnv.value_counts().head(12)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mCNV length is 2,150.\n" ] } ], "source": [ "n = 143953316 - 143951166\n", "print('mCNV length is {:,}.'.format(n))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "c = 'CNV_7_143951166_143953316'\n", "fn = os.path.join(ciepy.root, 'output', 'mcnv_analysis', 'CNV_7_143951166_143953316_data.tsv')\n", "data = pd.read_table(fn, index_col=0)\n", "data.columns = list(data.columns[0:-1]) + ['Gene']" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sns.set_style('whitegrid')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1340 AC004889.1\n", "1701 OR2A1-AS1\n", "1808 ARHGEF34P\n", "2063 OR2A7\n", "2352 ARHGEF5\n", "2449 CTAGE15\n", "2879 RP4-545C24.1\n", "Name: gene_name, dtype: object" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mcnv_sig[mcnv_sig.cnv == 'CNV_7_143951166_143953316'].gene_name" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "legend_colors = [\n", " np.array((255,0,0)) / 255.,\n", " np.array((255,105,105)) / 255.,\n", " np.array((250,202,0)) / 255.,\n", " np.array((255,252,4)) / 255.,\n", " np.array((10,190,254)) / 255.,\n", " np.array((0,176,80)) / 255.,\n", " np.array((0,176,80)) / 255.,\n", " np.array((153,255,102)) / 255.,\n", " np.array((245,245,245)) / 255.,\n", " ]\n", "ind = [\n", " 'Active promoter',\n", " 'Weak promoter',\n", " 'Strong enhancer',\n", " 'Weak/poised enhancer',\n", " 'Insulator',\n", " 'Transcriptional transition',\n", " 'Transcriptional elongation',\n", " 'Weak transcribed',\n", " 'Heterochromatin',\n", "]\n", "legend_colors = pd.Series(legend_colors, index=ind)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Effect sizes are different with p=7.017e-10, Mann Whitney U.\n" ] } ], "source": [ "s,p = stats.mannwhitneyu(cnv_lead_vars.drop_duplicates('gene_id').beta.abs(),\n", " lead_vars[lead_vars.cnv_sig == False].drop_duplicates('gene_id').beta.abs())\n", "print('Effect sizes are different with p={:.3e}, Mann Whitney U.'.format(p))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "cnv_abs_beta_pdf = pd.Series(index=np.arange(0, 3 + 0.1, 0.1))\n", "se = cnv_lead_vars.drop_duplicates('gene_id').beta.abs()\n", "density = stats.gaussian_kde(se)\n", "cnv_abs_beta_pdf = pd.Series(density(cnv_abs_beta_pdf.index), index=cnv_abs_beta_pdf.index)\n", "snv_abs_beta_pdf = pd.DataFrame(index=np.arange(0, 3 + 0.005, 0.005))\n", "se = lead_vars[lead_vars.cnv_sig == False].drop_duplicates('gene_id').beta.abs()\n", "density = stats.gaussian_kde(se)\n", "snv_abs_beta_pdf = pd.Series(density(snv_abs_beta_pdf.index), index=snv_abs_beta_pdf.index)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fn = os.path.join(ciepy.root, 'output', 'cnv_analysis', 'sig_not_genic_vs_not_sig_roadmap_res.tsv')\n", "intergenic_res = pd.read_table(fn, index_col=0)\n", "\n", "repressive = ['H3K9me3', 'H3K27me3']\n", "transcribed = ['H3K36me3']\n", "intergenic_res['type'] = 'Active'\n", "for i in repressive:\n", " intergenic_res.ix[intergenic_res.mark == i, 'type'] = 'Repressive'\n", "for i in transcribed:\n", " intergenic_res.ix[intergenic_res.mark == i, 'type'] = 'Transcribed'\n", "\n", "intergenic_res['mark_mean'] = np.nan\n", "for m in set(intergenic_res.mark):\n", " ind = intergenic_res[intergenic_res.mark == m].index\n", " intergenic_res.ix[ind, 'mark_mean'] = intergenic_res.ix[ind, 'neg_log_pvalue'].mean()\n", "\n", "intergenic_res.sort_values(by=['type', 'mark_mean'], inplace=True, ascending=[True, False])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data['CNV_7_143951166_143953316'] = data.CNV_7_143951166_143953316.astype(int)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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b2xuAI0eOsGzZMpYuXVqq3fvbCAkJKVHHp7CwkBUrVhAeHo6TkxM9e/bkzTff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\npZLqnFWXfLQKIYSFkcQvhBAWRhK/EEJ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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa196d1edd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(x='CNV_7_143951166_143953316', y='exp', hue='Gene', data=data, width=0.5,\n", " fliersize=0, linewidth=0.5);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the purposes of plotting I'll group 7 and 8 and call it 7+. I have a feeling the\n", "8 calls are really seven. I also think the one calls are really 2 but I'll leave it." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_f = data.copy(deep=True)\n", "data_f.ix[data.CNV_7_143951166_143953316 == 8, 'CNV_7_143951166_143953316'] = 7" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "49 lead intergenic CNV eGenes.\n", "Effect sizes for intergenic lead CNVs are biased (p=9.264e-06, binomial test).\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/frazer01/home/cdeboever/software/anaconda/envs/cie/lib/python2.7/site-packages/matplotlib/gridspec.py:302: UserWarning: This figure includes Axes that are not compatible with tight_layout, so its results might be incorrect.\n", " warnings.warn(\"This figure includes Axes that are not \"\n" ] }, { "data": { "image/png": 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d9j6BfBG6h09DdnE6MoRJncfiXWwpaSFqtZoPvz5PPYdsOnTQ30Kt62zfvl0r\nmpGRkdy4cYNTp05pyyuSFx70708rEJRFzU8zWU7CYjSD6spUV6aPbk8LX8MrVEyBRCKhX9PurBu6\nlI4N2nIl4SazDi9n69mvuZscXmI7v4j4dM5ejWPf6RRmbTjBuWtxpabZqOssX76cd999l169eiGV\nSrVpRgqpSF54KHt/Wkslv0DF3ahUohLSAU2qjbtRqSSnlT5kERGfzt2o0tNwmILE1GzuRqVqJpv0\nkJ2bz92oVG0sdIoil7tRqSgylVXmV1VSJ5bEqFRqjly+AFYwuE0H+nXyrXYf3B1cmd9rKn9HXeC7\nywf4/f5f/H7/L7wcPencsB0dG7QhwLMFTbyd+fe/+rNp119cCU/jvc/PEODnzqvDAwlo6l7tflsq\nKpWKtWvXolKp2LZtGzKZbnhO0bzwgwcPLrfd8nxZWcrkXkZOAWv3xtLQw4Y3htQn+PAZgv9K1nv+\nis/PAPDu2LKjPowNY9p7/A57j98ptSwvL4+QkBAiHubyn6MPtcd/ORPOL2fCebarGx2alx0hcPv2\nbQAePnxoMZOZdUJAfw97QLokFtsCFyYPr7qt8gwhkUjo7tuJrg07cCH2Cn9FnOdczCUO3vqNg7d+\nw97Kjrbe/nT0acuQJ+2YNLIr3xy5zt9X4ngQmyYEtAgvvfQSnp6eDB06VJtCpOgOYPfv3zdqb4by\nbIRjKZN7qem5sDcW+aNsok2bNoUyBLSQsuqsTBhTWVhbW9OpUyfs7ydBEQEtxM+vCZ066c/iGhIS\nQsuWLeF4IvXq1bIwJkvHyi0eiUzFkFbdkFUyYN4USKVSOjcMonPDIJQFeVxLuM2F2MuExlzhbFQY\nZ6PCAGia9ifN2/oxqAUk2V/k+6vXsZZa4Whjj7e8Ht5O9cnLssHL3bHOjZUWplkuOnlZFGP3ZrDk\ndNwCy8MsAqpvKWdVZOTMVxXw482jSCQSBrXsWWl7psZGZk17n9a092mNusNoYtPjuRB7hRM3TxOR\nGs39lMgyr1cXyJDkOmGT74Kd2hW51B1XWzfmju6Bo42DTosqI0vJy+/+jKO9FU4ONjg52ODqZEsD\nT0deGda6RomwoYnLiuzNoO99FAgMYRYB1beU05iMnKnpuaRnKUlNz+Xa/SSSFDlMe6EdarWaxKxk\n/nfpByLTYujftIfBNMbmRiKR0MDZmwbO3vhkuNE6KJCkrBRy8nNRFuSRr8onT5VPem4GcRkPiUiJ\n5WpMONmZzWnMAAAgAElEQVT2qSglqSgJRwHEAK/t34tMIsXZzgnrAiuOZPyF3EaOu7+C/FxrUrNl\nxKVJUCXa4BTjyLB+XlhJrZBJpcikMqwkMjKyCtjwvzAc7KxxsLMiPS2FK3HXcHOy5Z+lbDqdX6Ai\nMTUbe1srbG1kWFvJkJlJlDt27Mhnn33GX3/9xUsvvVTmufreR4HAEGYRUH1LOdVqNTKZDJlMVu6M\nnG/8OBckjwb+JWpAzYndoC6yb3Zz9ya88mjD45qEvbUdjVx8DJ6XryogPuMhUYpYwlNiSMpKIT0v\nA0VOOmk5ClJy0kiIS9Kc7PjoB7B+dL0SeOvgsdKNu4FaLQG1BJwkXEoDiUJC8F4rJGjEUQIgkaBW\nQ2ZW0d/b43hXJwdr7VGZVMakhqPK+xgqzKVLl2jUqBHLli3TeGFgXFPf+ygQGMIsAqpvKaednV2F\nMnICLPCfUK7zrl+6Vma5pcysVsaOFdAcb5pbeWs+2JvEjRrHH3/8UeJYUFCQ3vP1vY/6sKR3peiM\nulwdb3CGvTx1llVeHvuG7JZuI5GQkMQyr1dnRj66Nt9iZuElajMEGSYmJmqXcrZp04abN2+yYMEC\nQkND+f7778nPz+e1116rlel9BZZH8fexcIJKIDCEWQRUIBAIagPmj+kRCASCGooQUIFAIDASIaAC\ngUBgJEJABQKBwEhqzFJOU61eMnVCu9L2kDR2RVVptiril770vMb4o89WRZ9TeHg4GzduxN3dncDA\nQO0KoqpYdVYd7Nq1i9u3b5Odnc2MGTO08aPGsHnzZlJTU0lJSWH8+PFlhlqVh/3793P58mWWLFli\n1PWmXpFliv1VTZlyWt+7WCnUNYSNGzeqQ0ND1Wq1Wv3GG2+o8/Pzdf6fk5OjnjJlitF2Fi1apH7n\nnXfUCxYsUN+/f79cPj18+FC9du1a9dixY3WOV9SnsmxVxK/jx4+rMzMz1Wq1Wv3aa69Vyh99tir6\nnK5cuaKOiopS5+XlqadOnVopnyyBI0eOqNVqtfrw4cPq4ODgStk6ePCgWq3WPKN169ZVytahQ4fU\nO3bsUC9btsxoG/r+NoxB3/tcUfS9h8ag712sDDWmBWqq1UumTGhXuIdkaSlzK7qiSp+tivhVVnre\nivqjz1ZFn1NgYCDx8fFMnjyZrl27Vsonc/Ddd99pd7OXSCQsWrSIo0ePsmXLFtavX19pW0lJSXz+\n+ecsWLCg0raCgoIqtRmKKVdklfW3URFMmXK6rHfRWGqMgJpq9ZIpE9oVoi4WSmtra1vhFVX6bFXE\nL33peY3xR5+tij6nGzdu4O3tzeeff87bb79Neno6Tk5OlXpG1cno0aMZPXq09vPp06cZNGgQ3bt3\nZ8GCBWzevNloWyEhIXz55ZcsXbq0wkJV3BYYzkVmiIquyCoPxd/nilLRlNNloe9drAw1JpDeVKuX\n9Nn58ccfOXfuHKAZk2vXrvz7Db7xxht8+umnrFy5stIrqorbqohfixYtIiIiggYNGiCVSpHL5Ub7\no89WRZ/TpUuX+Oyzz/D29sba2prc3Nwavepsy5YtxMXFkZ+fz9NPP03Pnsbt8JWRkcGQIUPo3r07\nUqmUrl27GswlZojo6Gg+/fRT3n33XaOur4oVWYXvs7EUfQ9lMpnBlNNlUfguenl5YWNjw7x584y2\nVUiNEVCBQCCwNEQYk0AgEBiJEFCBQCAwEiGgAoFAYCRCQAUCgcBIhIAKBAKBkQgBFQgEAiMRAioQ\nCARGUmNWItUFDh06xLBhwwA4f/48VlZWREZGYmVlxT/+8Q8zeyeoC5w/fx6ZTEZUVFSll07WBUQL\n1IIoKCgA4ObNmwQFBdGoUSNSU1PJz883s2eCukDhe+fr60tqaqq53akRCAG1QFQqFaGhoaxZs4Yh\nQ4aY2x1BHUG8dxVHdOEtECsrK7p27Up2djahoaHmdkdQRyj63oWFhTF48GBzu2TxiBaoBaFWq4mM\njMTd3R2Ay5cvV3qTXYGgPEREROi8d23btjWzRzUD0QK1MHJzcwkPD0cmkxEQEICPj0+ltwQTCAyR\nm5tLZGQkUqlU+94JDCME1MJQKpX06tXL3G4I6hhKpdLorfnqMqILb0FYWVmVuju7lZX4nhNULSLS\nwzjEfqACgUBgJKIFKhAIBEYiBFQgEAiMpFwCqlKp2LFjB0OGDCEoKIiBAwfy0UcfkZWVVdX+CQQC\ngcVSrtmJpUuXEhwczPjx4+natStnzpzh888/JzIykk2bNlW1jwKBQGCRGBTQqKgogoODGTZsmDZ3\n9VNPPYWvry8eHh5V7qBAIBBYKgYF9OrVq6jVajp27KhzfNy4cVXmlEAgENQEDI6BFu4QpFKpqtwZ\ngUAgqEkYFNDWrVsDEBYWpnN88uTJLFu2rGq8EggEghqAwS68n58f//znPzl48CBeXl507tyZ3377\njT/++EN04wUCQZ2mXCuRCgoK2LZtG3v37uXhw4fUr1+f5557jjfffBOJRFIdflYZISEh5nZBIBBU\nM506dTKJnTq/lFMjoJqHef36dQICAsziR3nqPnBgOcOHm37YxNLvu7bWL+ouHWdnaNYwF9VvpwCQ\nDujJvWhbFApTeRBiMgG1iF0q9u3bx/fff0/Dhg3Jz8+nU6dOXLlyBQ8PD+bOnQvAxIkT+eyzz8zs\nad1CqczFysoatVqFSqXC2trG3C4J6gBebkpUv51Cde3u42Pde6FQWN77ZxECCpqwqMKEaosXLyY/\nP5+oqCiOHz9Ov379tEMFq1evRiaTcf/+fRYsWMDt27c5efIkmZmZvPzyy9ja2vLtt98ikUjw8fFh\n0qRJ5rwtiyMm5h7Xr58lNzeHfv1G4uAg11P2AuHh19m9ex3+/k8yatTb2vOcnTUvOUB8io0JWwYC\nQc3CItfCBwYG8ueff7JgwQL++9//EhUVBWhCqQYPHkzPnj1xcXEhLCyMuLg4rK2tefrpp/Hz82PH\njh04Ojri5OREWFiYCL8qxtGju+jRYzht2nTjxIngMsr2AhKWLt3Fiy/ORibTfNcWdq/sT5/E/vRJ\nmjXMxdnZDDciqLXEp9ggHdATaevmmp8BPYlPsbzWJ1hQC7QoYWFh9OnTB2tra1auXMk777xDfn4+\n8fHxbN++nUmTJtGyZUtAMxjcp08ffvvtN86ePYtKpWLMmDE0atSIPXv2IJVa5HeEUbRs2Vfn8507\nFzl//jeCgnoSEXGToUNfISrqNlFRt3XOc3Wtj79/ZwBSUx9iby/HxcWTuLgHOucVLYuNvU/TpoFc\nvnySxMRYevYcjp2dY43qXglqJgoF3MMWr+6ajcXjoy23l2MxArpr1y5OnDiBUqmkffv23LhxAwBv\nb28mTJjAe++9h52dHSqVimPHjhEdHU3Hjh0JDw/n6NGjuLi40Lt3b3x8fHj//fdxd3fHz8/PvDdl\nYp54op/OZw8PbwoK8mjduivHjn0HQKNGLWnUqKVeG4VDISqVCqlUprdMJrPSiu69e5c5deoAAwb8\nn6luRSAoE4WCGvGlbBECOmLECEaMGKG3vHfv3hw9ehSAzz//vET5P/7xD53PmzdvNq2DFoqjoyv2\n9o6o1WptFzsi4iYRETd1znN396J1664AuLnVR6nMISUlHk/PhjrnFS/78ccdPPXUKNLSkrC2tgU0\n3atmAx6nfpAO6El8tOW/6AJBVWARAiowjvv3r5Cbm8OxY98xatQMABo3foLGjZ/Qe83AgWM4efJH\n0tNTeOqpUdy7d4Xz538hICCgRFl2dgZ3714iNvYBgwaNBWpW90ogqGqEgNZg7t69zNChr+DmVr/c\n1/j4NMXHp6n2s7OzO7m5Mr1lXl6Nad9ed+y1pnSvBIKqpvbMsNQxsrIyCAs7QXZ2hrld0YuzM7Tw\ny6CFX4bFzNRbok+CmotogdYgbt48rp1IcnCQs2jRTvM6VAbOzuDXKJ3UM2sB8Os6lwdRTmV2952d\nob675gshIVleoaGB8lzr7Q3enukkn37sU1SUvOSJAkE5MYuA7tu3D1tbW23g/MKFC+nVqxenTp1C\nrVYzfvx4nJyc2LFjB8uXLycsLIzt27ezatUqdu3aRWJiIjKZjFdffZVGjRqZ4xbMwu3bJ0rMxBdS\nGfGpCuq7Z5B6Zi2Zd39+fKztPBSK0gXLGMGtyLXOzuDlkUby6XU6Pj3R4k1iE4y4QQvD0n7/dQWL\naYFKJBJWrFjBtWvXOHr0KM8//zwAv/76K7///jubNm3C2tqas2fP8tVXX5GRkcGKFSuYNm0a27dv\nx9HREYVCwQcffMAXX3xBTEwMcXFxLF68mPr1yz9GWBOpjPhYChUV3IpeW989g3xFZKV8tFSRqg2/\n/5qK2QT0f//7H3/++ScAFy5cYO7cuVy4cIHVq1ezaNEiAE6ePElsbCzdunXD2toagDFjxrBo0SKa\nNGlCTk4ODg4OPP/88zx8+JD169ejVCoJCwtjw4YNJCQkIJPJ9PpQW6iM+FQVCcly/LrO1X527TqX\nB2buLufGheHW5S3tZ/fuczlxKgUXF8PXWrJIWeLvv65gNgEdM2aMThf+9OnTDB8+nODgYCZOnMiK\nFSvo0qUL77//PgsWLODXX39l4MCBZGdns3r1aqKjo4mOjubAgQNkZ2fTv39/XF1dycvL09aRm5tL\ndna2yN1kBhQKeBDlRP228wB4EFV2i60yglueaxOS5fi1fJbM2z/g1HoUVs6+xCU6ERMTVS4BFSIl\nKA2L6cJHRUUxf/587Ozs+Oc//wmAnZ0dAO+99x4TJ06kYcOGKJVKFi1aRFZWFnPmzOHSpUv88ssv\npKSkkJ2dTX5+PgEBAaxcuZKkpCRta7Y2Y4mtPSgMdyqfHxUV3Ipeqz2n4XPkApGRltMFryyW+vuv\nC4j9QGvQfqBFZ+GLU5nxOUu/b0uov3gX3tUEXXhT3ntFf/91+Xde6/YDFZQPfeIJFWvtCSpOZVrI\n1YH4/ZsHIaCCGouzM7h7aFpdyUlVL2hCpATFEQIq0KG6RclYnJ3Bt3E6IVc+AqBTm3lERphuVrym\nPAeBeakSAa1MoLy7uzuxsbGMGzeOY8eOVahepVLJkiVLcHd3RyqVMm/evKq4vVpLWaJUVFBysuXY\n2ZtXXNw9Mgi58hEPoh/Pivs3/ZdJWohFn4ObcyuaNX6G3FwZCfFCSAW6VFsL1FCg/MaNG7GxsSEn\nJ4dt27bh6+sLaMT4xIkTNGnShMjISPz9/QkNDWXJkiXavUALCgpo06YN/fv3Z/To0XTq1Ik333yT\nvLw8bfxoTaa6WkP6RAnkOsLape087oT/QIrilslbftVNw4Zy/JrqPtvC56DMy8DPdwghVz4GTN/K\nFdR8qkxAKxoob2Oj2d1n7dq1TJs2jXfeeUdrq3fv3rzwwgu89NJLrFu3jr1793Lx4kV2795NUFAQ\nAGfPnmXUqFF4eHiwdOlSpFJpjU+5DKbpqlZWgEsT1sYNBhJ6/ROg9JafvjqLHzeW5CQ5ndo87mFo\nnkvF7Dk7Q0BrZ0KvfVjEhpO2vEWT5wi9+kmVtHIFtYMqE1BjAuV9fX2Jjo5my5Yt3L17ly+++AIX\nFxfs7e2Bx3GhEokEtVpNQUEBU6dOxdbWln379vHw4UMUCgXvvfceX3/9NSdOnGDAgAFVdYvVQmW7\nqhURYH2iVCh45UVfnVDyuLGbeSgUEBnh9KiFDJERxn0xhF77uMSzLXwO6ZkPjPJNUHeoti58eQLl\nFy5cyNatWwF44403mDBhAvv27dPaKN6inDJlCvPnz8fBwYFevXphY2PD5s2bqVevHunp6WXucl9X\nqIgA6xclXWHVdOH349dwSKktP/1DAZQ43qLlWyTEG3dvVTUrXvgcvLxb0Llt5Vq5gtpNlQhoceFa\ns2ZNqee9++67ANjY2PDVV1/plH366aclbJV2rGfPnjrXrV+/3jinLRRTdFUrQmmiVFxYHz6U4+Ux\nAi8P41p+lkJykpwOredoPxd9tprnYI+zM5Vq5QpqNyKMycIpKl5JSQ8qPP5pKgEuKaz6bZRVZ/Hj\nf5VzM4+qQKGAqL8VBLXTL5Ai9lNQFkJALQxnZ5B7ZgOQkWj/6A9Y80d84EAww4e3qZA9U4wVVpSy\n6ix+vLybeVQV0dEZODv7ms8BQY1GCKgF4ewMDfxyuJ+tGRRs6ucDD2y14lM8L3zhNcUFtzjmaEXp\nq1O06AS1CSGgFoSHdy656jz2RGvCv2Y0fw4Pb1AoNCmFi6+Fd3aG+k2yWHc3GIDZzUdCuCZiwZCo\nCgSCylPlAlrZ9B2pqamkpKQwfvx4oqKitLGlJ0+e5ODBgzjXgsxgzs7Q/an6SG3zWX93Pz8nhGjL\nZvg9D9iWep3cM5t1d4N1zn+r0UhsbNUlRFWIqEBgeszSAi1v+o5mzZoxbNgwrl69yi+//MKsWbMY\nNmwYX331FUOHDsXZ2Zl///vfZGRkEBkZqc2RtGHDBmxsbGjcuDGvv/66OW6x3DxuRR5ilFPvEuUF\n+RVLnCqzUrHu7l4dUZ3iPRKFwt5o/0RrtrZQAGg2HLexUQE5ZvGieuu2BqouK0W1CKix6TuGDRtG\nUlISn3/+OQsWLABAoVBw9epVXnnlFQC6d++OUqnkxIkTnD59mry8PF599VVatWrFtWvXquP2KkXR\nVqQiP4slT4zVls1pPpL4cF3hKypoqix7TQvzEbObj6Qg13SZqvUNEQgRrYmocXdPwcdHM/4cGNjc\nbJ5UZ92xsSkkJ3sAVbMqsVoE1Nj0HSEhIXz//fcsXboUV1dXAIKDgxk5UiMaubm5rF+/nmnTphEQ\nEEBERIROSo/o6Ghat25dHbdoEk4nX+dQ/Dlm+D1Pbo6U+GJiVZqgZTy0Z4q35nkkPBLb4qKaEG5c\n67O0IYLKtGYF5iSPevUctItX6gr16qlITs4DbKrEvlm68OVZleTi4sLMmTPp3r07q1evpmvXrrzw\nwguEhITw4osvAiCVSnFwcODEiROkpaXh6OjIhAkT2LhxI/b29jRr1swct1chMhJ1W5HPeHUnQU8r\nT+YQy7q7v5YQtJh7xQQtXFdURYtRACqsrEzXO6lQzSoVycnJeHp6VnvdmntWVZl9kdLDAlJ6ODuD\n1CEJBweHMscZbV2usyfvvFZAh9TvVLqAVhB9961p8Waz7u73wOPWrCkF2dzpHepOaoscAgMfN1RC\nQgycroeyMmGcPHmSzZs38+233+ocP3fuHA8ePMDLywt3d3fatKlYLHNlyMnJ4epVgKItb5HSo1ah\nUMD1MwkG/5gunrnA7FGm6Z6X1y/RmhWUl4MHD9K7d2/Onz/PuXPnSE5Oxt7eHgcHB8LCwhg6dCgF\nBQUsW7aM5cuX8+677zJ16lS2bdsGQFBQUI3bv0IIqIlwdgYnT834a3qidbmEpug1aWmOBs+/c+cO\nCXoEzZj6y0PhmnCBoCxSU1NRKpU8//zzrFq1Ci8vL5YsWcLt27dJTU3Vdt/t7OywsrIiKysL0ETe\nKJVKPDw8CAsLq3ECap5BkVqGszPUb5zH9ujLbI++TP3GeRgKTy1+TcceXgavAY2gxdyzJ+aernhW\ntH59PjVslkfDZsZdL6i7/Pjjj6SmprJt2zbOnTuHSqUZd0xISACg6Ehhnz59WL9+Pb1790atVjN0\n6FBmz55N165dzeJ7ZagSAd23bx+HDh3Sfl64cCEHDx5k0aJFLFy4kBs3bhAdHc2yZcsACAsLY+rU\nqSQnJwMQGxtL//79jao7IyODt99+m0uXLlX6PsorKE6eeWy4fZmjcdEcjYtmw+3L2tZgea/ZeOeq\nwWtMWX9xyhJhIawCQ5w8eVK7GGblypXs2bOHlStXcuHCBerXr8+hQ4dQKpWAZge1P/74gz59+jBs\n2DD27NnD4sWLzXwHxlFrUnq0bduWkSNHsnXrVu0GzJWhUFA23L4MwMyWbSHCdF3jivhRKIZ9+1Zd\n96aoCBcyuWFbwNoinoPAtJhoDkXLjh07tP8fNGgQV65c0SnfuXOnzueff9bsCevu7s6mTZtM60w1\nUmtSepw5c4aRI0cyb948Nm/eXGn/9QmKQlEyx1J6orVGWB4xs2VbEiJKnldUDAuydK+Z0SKQh5HW\nJc6RexYRr+5tyUgEmYPuWGd56zcGJ888DsaG09+rAQAHYsMZ4Nmk1OcgENQ1alVKD3OhUAAR1o9a\nbJBQSguttBZtRuLjay78FYuvb2Odc2Y90ZYD0eE6Ij6zVRs23LqitaFtDRqo3xD6RNjTO5+hDXz5\n5NZVAKa3CkSSW/NzTQkEpqBWpfQwJRVt1Wlmq/WX62vRRt/TXBMTk0nrDiXPeaFRUx07kVmZpbaK\nDdVvCH0i7OqlZsutqzp1TmvcVo8VgaBuUetSegC89dZbFXe6GKZo1ZkCXwdHBnk3BDQt0p+iw6us\nrtJEuKCg5HmlHRMI6iIiDrQMKtuqK0p5WrSlnZPx8LGIpydYM9ynCXcyFHptmJqqHF8VmBETL0Xa\nvHkzDx48QC6Xk5iYyOzZs6ttKXV0dDR79uxh5syZ1VJfUYSAVhPladHqP0cjWDdvHqdLl37V2iq2\nlJa4wPKZMWMGvr6+hIWF8euvvxIXF0d2djbjxo3ju+++o379+iQmJvL222+zfPlyHB0defHFF9m7\ndy8qlYoBAwaQmZnJmTNnyMzM5P3332f16tVIpVIaNmzIiBEj2LhxI6BZtdShQwc2bNiAo6Mj9evX\nN8s9CwEFOqH5NnbgOgFkVV1Fikc/gHexIm3dZZxz4PYJxj7hpLdcB2dnMj01L5VjYgLFt3UqWuZA\nSNn3XYZPlaXKn7kF11+ddWt23wys0jo++ugjFAoFbm5u5OTk0LJlS2xtbTl//jwqlYpJkyZx//59\nDh06REFBAatXr+bDDz/Ezs4OGxsbQkJCcHV1xdXVlWeeeQapVEpMTAwDBw6kc+fOHDx4ULtqKTQ0\nlDt37rB06VJSU1P56aef9PoVyNViK+FNhxDQ2oizM+mNm7LhRhoAs/ybIo+4rxHR0sqioszpraCW\nMG/ePFxdXZk9ezZ2dnZMnjyZnJwcbt++zZ07d1Cr1eTm5iKTyXBwcAA0K5RGjx5No0aN+OOPP/D1\n9UUikbB9+3YcHByYPn06iYmJvP/++/Tq1YuhQ4fSu3dvDh48yI0bN1Cr1Uil5ltQaRIBtaS0Hb/+\n+ivHjh3T1uvv72+KW6xRZHrWZ8ONNH6Nedy6md24Po4KRallU1r6Q3yMcZWV1dIV1DmcnJyYNm0a\nGzZsYMmSJUgkEqZNm0ZBQQFr164lLS2NxYsXExoaCsD//d//sW7dOuzs7Bg5ciS3b9/m1KlTyGQy\nfHx8WLt2Lfb29gQGBvL000+zbNkyfv75Z3r06MHo0aNZvXo1zs7O2v2Cq5sqa4GaMm3H2LFj6d69\nO9euXaN169bk5+ejVquZOXMmH330Efn5+WRkZDB//vwS9VZYQC1NEIr406JFizLLjfXXxs6WzGYt\nStowZLuslq7AsjHxUqSikS8dOnTgyy+/LHHOwoULta3FDz/8EAA/P78SK5GGDx+u/f+qVat0yoqf\n+/HHH1fO8UpiMgGtyrQdUqmU6dOnExwcjEwm47nnnmPixImcPHmSO3fuEBAQQFZWFhcvXmTAgAGc\nO3dOp97yIm/YsPoEobg46TmnqD9vjhgJ0RE6Aleav46JCczyfxw/OsvfBceI+9q6ipbNbe1KXoGK\ndTcVOjYAg8+irJauQFAUfaGMNR2TCWhVpe2AxwH3UqlUu+QTNOMnQUFBTJ8+nfPnz+Ph4cHp06fp\n3r27tt4uXbqU+x7sWvpXjyCUcxzSkEBlevmUXn7vDvKI+8xu/EigiwqfQqFTpsrOZuP93BI2ACGO\nAoEBqqwLb6q0HVByBVLhscLB5FWrVpGYmMiqVas4f/58iXotDZOMQzo7U2BnD+SWXq5Q6Be7ImWZ\nLZ8oWS6TlStavqyWrkBQFxApPUJCKBwNikhLw71nH9ZrW4YuVdKFz2zWgnUR+VoBHdjAgSkeSuoV\nF1BnZzIaNy3Vn8xmLfgxRcKgBo5svZkKwMwAV5zC71XI35xW/mTb2LH5hsbGW/6u2CtzsIuL0Vt3\ncR8rMwYrUnpUT905AIGB2kZMTIxxk4YNGjQocWzz5s3cuHGD8PBwAgICmDNnDl5eXpXw9jHBwcH0\n6tWLtWvX8tFHHxk8f968eTrn5eTkwNWSYUwipUcVkBkTQ2N9XV8TUlrLLfnUH+Diontise52cX/u\npOcRdiWZZ3zluNlIsc7NqbC/Bfn5HHmYyVPemrCSI9GZ/NNNbbDuoj6Kbn3d5q233iImJoY9e/bQ\no0cPFixYwLBhw4iKikKhUJCTk8OaNWt4/vnnGTRoEAkJCbzzzjssWrQIOzs7nnzySdq1a8fWrVvJ\nzs7mrbfe4t1336Vp06Z4eHiQm5tLZGQka9asQalUsmzZMtavX09mZib29vZMnz6dxYsX4+zsTHp6\nerXeu9iRvjgKBY737uB4707VzSZrxcmK2Y2tkEfcJ1Nfi0CPPxoRdkFuLeX3uCyaOMiwi6t4q8Ix\nMYGnfez4PS6L3+OyeKahw+NJrep4FoJagVqt1v489dRTjBo1ir59+9K3b1/u3LkDQKNGjZg6dSpZ\nWVnk5eWRlJTEk08+SceOHdm9ezdz585lxYoVODo64uLiwnvvvYdMJgM0Ld+FCxfi6+vLhQsXuHTp\nEg4ODkRGRvLnn3/y1FNPsXjxYmxtbav1vkUL1BDOzuS5a7oj1snxALqfS+nSllleSGVbbuVtIZbD\nTtrfp5jdvmPl7AgEj3BwcCA7O5udO3fy+uuva5dZFoqbVCpFIpEwb948wsPDWb9+vTZnklqtJiEh\nQRtoX0jh5LFUKkUul9OmTRtmz57N4cOHkclk2pQh1R1ULwS0EGdnGvQdQJ6D42OhrN8Ata094bcK\nUCSrade9GWo1XPpbM8ES1LUZ1lH3dMKK8ho149IZPeWV5PjNm/R7osikj4m6zxnR0fiKXB2CSiKR\nSF127E0AACAASURBVLQTvjKZDJVKxc8//0xcXBzp6ek6k8FSqZTt27fj4eFBUFAQPXr04KOPPiIv\nL48333yzhO3o6Gi2b9+OQqGgVatWpKWlsXLlSuzs7JgxYwZLly4lNDSUgmreKqxKJpEquzIpMTER\nmUzGq6++SqNGjcpdr1KpZMmSJbi7uyOVSpk3b57Ba0JCQuhUTPja9ZChRkJ+HkgkIJNJuHs1n/qN\npNy/VkDUXc15jZrLCGqbg/WD2wDk+bXk0mU7veVlUZ4JheUHDrCsSJCxqagrEymWVr85J5HqCrVm\nEqm8K5POnj3LV199RUZGBitWrKBbt27lyonUpk0b+vfvz+jRo+nUqRNvvvkmeXl52oD9sshz9+LS\nGY0wevlKUaulqFRw7ZwmdUbbbtY0b2NFdkadDlgQCATFsJicSIVCN2bMGBYtWkSTJk3IycnRxnsa\nyol09uxZRo0ahYeHB0uXLtWOs1SUJk9YkZ6m0mlpArQMssJeDkHdZNpjQV1lWEfFa8c9JdZWpZcL\nBIJaiUXlRBo4cCDZ2dmsXr2a6OhooqM1aSTKmxPp4cOHKBQK3nvvPb7++mtOnDjBgAEDDPpqnRxP\nUFfN5q+2ehJ6WtsA2dlYJ8QQ1PbRJNEjcdTp/neHoA55kF+gKTfVhIyzM2PHvEKeh0fZk1MCQSlY\nAWnJyeDubm5XqpXM5GRcDJ9mNBaVE6lhw4YolUoWLVpEVlYWc+bM4fz581obhnIi2djYsHnzZurV\nq0d6enqJ1CJ6USiwjrpHi+ZyHO1cUNvb0abr465/m27WSKVqrMNjNOcWEa88v5ba7n8hQW0LyjXu\nWW4ejdEqzhRwKcb0k1OC2o8V4BIbS35sLAB3796lefPmZvGlOut2oWpFTqxEKrISSTuo7+1NXj0f\n8pWQpwR7J7BOiIW4uBLXV2biqChlTSiYqg5j6q5qxCSSqLu6qZGTSDWKuDiss7LA3QsrwPqB/i5z\n0e4/WOC4p7MzaldNHJ4k1QK25xMIahFCQPVRrKte5nlR93THRU0sUkaLtLMz6gZNyTusWedu/Y+m\nSBCB8gKBqRACCsT4+Gj+TU3F5dH/K0xuhuZfR0fNT0V9MFC3bVYqT3TS2FVkpZNbRj22trY4Wjtg\nLbNCFZpJwaXHuz6pBjci2TGlQnVXJeas29z1i7rNxKNxYFNg9pQeR44c4fbt22RnZzNjxgzkcjkf\nfPABTk5OFBQUVHhT5Nqa0iM3N5fcXD1b1xXB1tYWN3tX8g+lUABYD3VD9SAX1a2cqnfShBR+CQBk\n5mWV694FgurG7Ck9PDw8GDt2LEeOHOH06dOkpqbi5uZGamoqPXr0AKjelB7VQGni4OHhgbvcTeeY\nMThaO2jEs0ir06qvCwV2UqyGuZGSnVr5G6hiin4JALgNcyOFVCGiAovDZCvv//e//7Fw4UIWLlzI\nhQsX6Nq1KxcuXGDp0qV069YN0ATOf/fddzRv3lwbOD9kyBCOHj3Kli1baNeuHREREbRr144VK1bw\n/fffk5OTo03pMXDgQHx9fZk1axbXrl3TpvSwt7dHKpVqU3oUr9cYbG1tcZe74S53K/cOL4ausbW1\nxdPVA3cHV6S/ZCL9JRM3e1fN5ggtA3WO2dralrAXEmJkQlZ3KarBjqRka0TImHurTop+CRRcyiL/\nUIr2C0cgsCRMJqBjxoxhzZo1rFmzho4dO3L69Gm6dOlCcHAwW7duBaBLly7s2LGDW7du8euvvwJw\n+vRpBg0axK5du1i/fj316tVDLpcD4OrqikQiMZjSY/bs2YwYMYLGjRuXWm9FKWwBFRe0ylxTWC5L\ngryDxcRBZo/qcJrOMSd7eQl75dnrMDMvC6thbsiCHJAFOWA1zI3UHAXJGSla8Sxu18PDw6jnVBux\n9C8XgWVh1pQeDRs2JDQ0lMOHD5Ofn8+YMWN44oknWLVqFT///DNt27bF1ta22lN6lNYNdhzsWGYX\n0tA1heWywPK1pGRqKfmHde1172i4RZ2bm0sKqTgO1kwwFbY6y/KzcU9fctSW0z3OzMvCbZib9nN1\nDT2IoQNBRRGB9CEh+DyaESwM8HWXuyH95fHstSzIAdVgR5IzUvTaMXRNYbk6W4XNs+7k/aIRBKth\nbmTkZ+LwqBVaeCy/IB9+TtexF91Gga1z5XbTKc3PjJ4SswmovqDq6ppEKlq/Mb93U9Vd3dTVugFi\nY2NFIH1VYkwLyNA1heX5h1LIv5SpFcnCFuK9hHv4D9bs9Vl4XXF7f+37maeeesrk9xZx67J201tL\nobxRBwKBORECWgqGusHGXFO8XFGsPCkpieT6ui2d4vbu3LlTaQEtzc+kpCSLE1BzYK6hA0HNRXTh\njZ3ZFggENRZTdeHrvIAKBAKBsYisnAKBQGAkQkAFAoHASISACgQCgZEIARUIBAIjEQIqEAgERiLi\nQIH4+Hg++OADXF1dad68OePGjavW+sPDw5k5cyb79u2r1npDQ0P59ttvkcvluLu78+abb1Zb3eHh\n4WzcuBF3d3cCAwPLn7/KhMydO5f+/ftrt2GsLmJiYpg2bRoBAQHUr1+fWbNmVVvd0dHRbNmyBblc\njqurK1On/j979x1XZf3/f/xx2FOQ5UAcqbnKkZQzyY/ZMEea+snM1ERJM7cmarhRsfTjN1IzVz/T\n3LMsS6zM1MiBC9EcCOIAZO9xzu8POpcgoHCdwzBf99utW4fDua7rfY7w5Lqu9/v1fo8st2Nv3LiR\n8+fPk52dzalTpzh06FC5HRvyfs8DAwOVuTY+/vhjg/cpZ6DAli1beO+99/Dz8+O3334jNzf30RsZ\nSWxsLNu3b8fGpvxnG0pKSmLmzJl88sknnDp1qlyPnZKSwsSJE5k6dSo///xzuR4bYP369diqmPja\nGP766y9cXV0BaNWqVbkee926ddSuXZukpCSjjYUsqYEDB7JgwQKqV6/O8uXLy/XYANeuXeP48ePc\nuXMHR0dHo+xTApS8ENPXw1epUqVEsx4Zi4uLCxMnTqyQAPXy8sLGxoaVK1fSo0ePcj12s2bNMDMz\nw8fHh5YtW5brsQ8dOoS9vX25H1evefPmLFiwAH9/f9atW1euf7Bv3LiBl5cXc+fOJTAwsNyOq3ft\n2jVyc3MrZJ7e6tWrs27dOpYuXUpwcLBRSoUlQIGaNWty558VN5OSkqhSpUq5t6Ei6hlSU1OZPn06\nLVu25M033yzXY4eFhWFpacmaNWs4f/58uf7R2rdvH+fOnWPXrl3s2LGDxMTEcjs25E2mkZWVhUaj\nwdbWFq1WW27HdnV1xdbWFjMzM+VStjxt2rSJQYMGlftxIe8Wgv7nzM7OjpycHIP3KfdAgb59+7Jg\nwQLs7Ozo2rUrJibl/3elqCn7ytr8+fOJiIhg586d7NmzhwULFpTbsbOysvDz86NatWp4eHhgb29f\nbsdeunQpALt378bCwgIHB4dyOzZAnTp1CAgIwNnZGS8vL2Vy8fIwbNgwPvvsM+zt7cv93i/krQlf\ns2bNcj8uQJ8+fVi2bBnu7u60aNHCKLdwpJRTCCFUkkt4IYRQSQJUCCFUkgAVQgiVJECFEEIlCVAh\nhFBJAlQIIVSSABVCCJUkQEWZ+PPPP7l48WJFN0OIMiUBKsqEs7MzQUFBFd2MMpOVlcW9e/cquhmi\ngkmAijJhbW1dIcfdsWMHV69eLfR8YGAgZ86cMdpx9u/fz59//mm0/Rm7faJ8SC28KFPXrl3j0qVL\nmJubU7t2bXJzczlx4gSRkZE0bdqUN998k59++olXXnmFY8eOYWZmhrW1Nc8884yyj7CwMDZv3oxG\no6FGjRpkZWURFxeHvb09Go2GcePGERgYyJUrVzhx4gTLly/n8uXLrFu3Dp1OR4cOHYC8KeysrKxw\nd3cHIDo6GgsLC1JTU6lXrx6nTp1iyZIlREREFDjeiBEjCAwMLHDMqKgoMjMzefnll7GwsODMmTME\nBQXRoUMHLl26xHvvvae0f8KECcyZMwcTExOmTZvGkCFD2LlzJ6amplhZWSnzUn7++eeMGTOG5s2b\n4+3tzerVqwu99xEjRpTjv554FAlQYZDY2FhOnjyJRqNBp9Oh0WioX78+VlZWAKxevRp/f38AZsyY\nQePGjWnatCl///03Xbt25fbt2xw/fpxXXnmFS5cuMWTIENavX18gQFetWkWNGjUwNTUlJCSERo0a\n8corr9C2bVuGDRumvK5v3740aNAAnU7HqlWrmD17Nra2toSGhnLjxg3eeustOnbsyLBhw2jVqhXd\nunWjTZs2DB48mAULFvD5559z5coV1q9fX+B4+tmS9Md8//336dmzJxYWFlhYWAB5U6VlZ2fTpk0b\ntm7dWuAz6tWrFz/88AOmpqZ0794dJycnevXqxa1bt1i3bp3yuvwTyugfP/jetVpthUx2I4r2yACN\nioqiS5cu9zcwM8PDw4Nhw4bRt2/fMm2cqPxcXFx49dVXCz0fFRUF5E3Tp/+lT05OpmnTpsTGxvL6\n669ja2uLra0tbm5uAGRnZwMUmqdRq9UyYMAAatWqxdatW4mOjlbmT80fJvlndMrJyVGmCNS3RT9N\noT6cbGxs0Gg0WFpaKs/r26s/3rZt25RjFHVMPUdHR2xtbdHpdJiZmbF8+XKuXr3KW2+9xYsvvsjk\nyZMBCAgIYMGCBTRq1IjnnntOCWCA1q1bk52djU6nU6bYK64tonIo8RnoK6+8woABA0hJSWHjxo3M\nmDEDW1tbXn/99bJsn3hMnTx5ktDQUCZOnMiuXbuwtrbGx8eH7OxsDh8+TI0aNahVqxYeHh5K0OkD\n6sHp3UaMGMHChQtxcnKibt26xR5TH4wajYZhw4Yxd+5cTExM6NixY5GvK24fPj4+Dz2eRqOhVq1a\nrFixgk6dOmFnZ8f58+fJyMhg69atjB07ttCUbfXq1SMrKwtTU1Nq1KjBqVOnlHlB9e//hRdeYPny\n5dSrV0/5LEr63kXFeOR0dvoz0MGDB+Pr6wvkLcfw0ksv8dRTTxW6XBHiYdauXcugQYNITU0lMDAQ\nb29vAgIC+Pjjj7ly5QqQ14NfETOWG2L9+vV069ZNOZsWTwZV90Dt7Oxo0KABly5dMnZ7xL9c7dq1\nCQ4ORqPR8Oyzz1K9enWWLFkCQLVq1Sq4deqkpKTw22+/0alTJwnQJ4xBnUjluRSB+Hd4+eWXK7oJ\nRmdnZ1egM0g8OVTdkc7OzubatWs8/fTTxm6PEEI8Nkp8Bnr79m2OHTtGRkYGO3fuJDk5maFDh5Zl\n24QQolIrUSdS/ssuMzMz6tSpw+DBg+nXr1+ZN1AIISqrSrWo3I0bN1i2bBlOTk40a9aM3r17V3ST\nhBCiWJUqQC9cuICjoyPVqlVjzJgxLF++vKKbJIQQxapUZQ3NmjXDzMwMHx8fWrZsWdHNEUKIh6pU\ntfBhYWFUr16dNWvWMGbMGJKTkwuU5+V38uTJcm6dEP8urVu3rugmPPYqVYBmZWXh5+dHtWrV8PDw\nKDY89eQHQAh15ATEOCpVgDZv3pz/+7//q+hmCCFEiRjlHqhUJAkhnkQGn4GuWrWKkJAQOnfuTHJy\nMu+//74x2iWEEJWewWegt2/fpkGDBvTr14/w8HAjNEkIIR4PBp+B6nQ6cnNzuXnzJnFxccZokxCq\nHD16lLCwsIdeBR09epRff/2Vl156ifbt25dj60ou//vI316gRG1fu3YtjRs3LvX702q1REdHq2qz\nm5vbEznZs8EB+vbbb7Nq1So+/fRTRo0aZYw2CaFK+/bti1xQrqjXVNbwhILv48H2lqTt5ubmqt5f\ndHQ0V1ZvwsnaplTbxaWngfc7VK9evdD3cnJyWLhwIRkZGSQlJTFgwABmzZpFu3btyMnJwcnJifHj\nxzN79mxlIcKPP/4YnU5H9+7dCQgIoFmzZnz99deEhYURHh5Oz549GTBgQKnfX1kwOEBDQkKU+RwN\ncfr0aTZv3oydnR1OTk58+OGHBu9TCFE6TtY2uNrYGW1/27dvp3PnznTo0AGtVsvIkSNp0aIFs2bN\nAmD8+PGkpqbSvXt3PD09mTRpEgC///47vXr1YsuWLcyZM4fBgweTnZ3N3LlzK014ghEC9Pjx49Sq\nVUtZb6Z58+aq9pOUlMTMmTOxsbEpsFCYEGoY+1K9vC79N321moy4BKJiotmUsZp3hnsD8OWa/xGb\nEEVsdCJpa+7xbJMXimzP6rWbuHw5nMVL1lLV0Qrv998ps7aWRFhYGD179gTy1pJ66qmnCAoKYvLk\nyVy9ehVvb2/s7Ozw9PTkl19+oVmzZgDs2bMHPz8/pk6dqhTUfP/997zyyisV+XYKMfimRYMGDQgJ\nCeHw4cMcPnxY9X68vLywsbFh5cqV9OjRw9BmiSdc+/bt8fDwMFrYGXt/xcmIS6BHlep8UL85GXEJ\nyvOxCVGYuZ+leqsbxCZEFdue+IQMmr3wAW51exCfkFGmbS2Jhg0bEhISonx98+ZNWrZsyeLFi3nr\nrbdISMh7j9u2bePWrVsMHTqUuLg4zp07x9KlS0lLS2Pnzp0A/PXXX4XWt6poBp+Bjh49mrS0NLRa\nLXZ26k/9U1NT8ff3p0ePHrRt27ZE20g1hXjQ7du3lZ+L/I+Le43afed34MABPDw8aNq0aekb/IAr\nETfYaXYXgJNxEdz9ZCSx8alEpyVhq+/fSb/DyZMni2xPxI0r3I3NC5zM1Bulfp9x6WmlbnNcehpO\nxXyvf//+LFiwgH379pGRkcHAgQOVQBw4cCBjxoyhXbt2rFixAk9PT86cOUPTpk2ZPn06Xl5eZGZm\n4u3tzeDBg8nIqPg/CA8yOEDXrVvHyZMnMTExwdPTk/fee0/VfubPn09ERAQ7d+5kz549LFiw4JHb\nSCmneLDHOTQ0VPm5yP84v+Kef9CDl+0P29+gQYMeuX1JnDn4Cz2q5HXGnP3zGtqUcJzMIVZjTr1W\nea/JiapO69ati2zPod/O4FY37wouOnxfse+zqGB1c3MD79Jf8jvpty2Cubk5fn5+BZ7Lf4Kkrzw8\ndOhQkdtbWlqyYcMGAD777LNSt62sGRyg4eHhBAYGArBw4ULV+/H39ze0KeIJ8GAoqe1xLonieuxL\nGoxqevytnBxZeeksAE5VbejUNBOAqAtmQKbyumVrVxF6+RLnltzE3dGZse+PAKCqoxUXglcC8PTT\n9Ut8XMi7R1lUT7oonsEBmpycTHJyMgD37t0zuEFCPExlGIaUvw3FBZla+k4jgMCAKUB8ka+LSrjH\nzRc8uAlEHb7AB1OnAdCk4dMELPzEoDaIkjM4QEeMGMHMmTMBGD58uMENEuJBhvaAG7r9118tIzk+\nijsxiQQGnMO+qjsmVnl3/fIHGeEVcwKRmpJGgnXVvC/+vqx6PzKQvvQMDtC///4bb29voqKiuHbt\nGk2aNDFGu4QRlPXQG7UVL6Xd3tCzTjXb5x9OlJV7m1eaxtOiKkAU287dxNTajojPznLrbhrUbVho\n+9VrNxGfkEFMdBSr124y+nCixOhYtLH3O1XMWrcDwCEiTPU+o6OjObRuGPbWpdsuOR3+M3TNQy//\njxw5QmBgIJs3b+btt9+mcePGaLVatFot8+bNY/LkySxevBgAX19f5s+fz5UrV/jyyy+xtrYmMzOT\nSZMmcfToUfbu3YurqysAH330EcuXLycnJweNRkPjxo1p06YN33zzDQCTJ0/GycmJq1ev4u/vz5o1\na/Dx8cHBwQGNRkPVqlWZOnWqug8MIwToH3/8QY8ePWjatCm+vr688cYbhu5SGElZX+4aev+xLO9f\nGurqpcu4ZetwA84mFuyZzjHJwL1V3tlmdpRHkdvHJ2TgVrcHbnXzOnPUsK/qzpl4uBOTiKlZEpCq\nfM/BzQWTunlJZ3YkkmxVRyjimNbgaKsp5VaPXhXo+++/p1OnTpw4cYLatWsrA+lnzpxJUlJS4T3q\ndHzxxRd8+umnmJubEx8fz927eaMTRowYQbt27Qq8ftGiRcoZ8OnTp5k+fTq///47p0+f5sUXX2Tb\ntm04OeVdNVSpUoWAgAAAxo4dW8r3WpDBAarRaEhNTUWn0xllWrsbN24wbtw4du3aZfC+RMVQU79d\nXjZ+uZHrl66yJvIrrJxtGOgzsMjXubu6Kb3hERdukr8Dp7wMHj5W+SxTE8OBoueasNTm4HLiNwBq\nNny6/BpYQgkJCWRlZdG7d28+++wzIiMjmTp1KpcvX6Z3795UqVKFGzdu4OvrC8CpU6fQaDRoNBrM\nzc05ePAgQUFB1KhRAw8PD1avXs3evXsBmDdvHgDTpk1Do9Hw9ttv06pVK0JCQli3bh1Lly5l9erV\nDB06VOnFT0pKUo7VqVMng96bwQE6dOhQZs6cSU5OjsEVRLGxsWzfvh0bm9LV4orKpbj67fKq5lm2\nZj23EhNJjL5L3Jr1jB02RPlexr00RtQYCsD3934y+FjZSeloT/wNQKLWyuD9Pah9+/a0b98+rxIp\nyoHY6EQaP+1OYmK68hr32tUJmOBr9GMby969e0lISGDlypWcOnWKBg0asHDhQn755RdlOFWdOnWU\noYu+vr7odDoyMzNJSkri5ZdfplOnTsycOZPatWvj7e1d6AzU399fOQM9d+4czZo1Y9WqVSxZsoS7\nd+9y9+5dzp49y59//omDg0OJhkmWhMEBmp6ezieffMLKlSuJiYkxaF8uLi5MnDhROqMeQyWZCam8\netBvJSYSVrsx1G6s+p6glZMj++LuEBUTTWJGLodDc5XvpSbnKI/Nq1iT6Zl3D9QhPF35HGKio5QB\n7Sm3zjNnwiwSUhJxreGG7+zSh53PsHHKH6Bnm7zA1eO/qXpfj5KcDiW5JC+8TfGOHDnCqlWrMDU1\n5eeff2bMmDEAdO7cmSNHjhASEoJGU/C2gUajYdy4cfj6+mJpaamcoF27do2vvvqKnTt3otFoihx/\nm5KSwrRp07CwsOC///2vUl4+ZcoU2rRpw44dO0r1/h7G4GWNJ02axBtvvMH169eJjIxUeuQN4e3t\nzerVqx/6GqlCKpmgoCC6dOlSLvvO/3VpHxfnj59+Rpeazt34OKpVdeJmQgRacnGpasvNhGRyNRoc\nnWy5Ha/FxtmF1HsJZJhYk9khr2a66q8Hqe1Wi/h7d6nqXA3NrXhG1s87A90ct5MX+z78Ei4oKIjc\n5Ot0rB2pPLfqD7CtlnfX8U6ME9nd2wBgc+gyrq41SY27h52JIy+8mDc72ZUfv+DDhiU/Zkms/n4H\nEc/kDV6vfT4a7zfeKvJ1oaGhREZG8uqrrxb63oOD7KUXvvQMPgO1tbXljz/+YNCgQSxbtswYbSr0\n16g4Uon0aPpqlbK4fH6wEqa4KqCSPC7OmYO/0MO9Ibjnff2/hMu81iIZiOPbZAvqtc3rWLke7EHs\nM26AG9ZH7oedtYU99Z+53wN+/fZK5bG9vd0jjx8aGkoisQWes3e0oE6rvACND7ZVOnB0FtZENXse\nAMs/fuNC8EqSk+Kxyr5/jlKSY5bEkTMn0QZfwsHNBfdadYvc58YvN5J1L4Oc2CzCToQVuN9b1AmI\nDKQvPYMDtGPHjty5c4eff/6Zrl27GqNNfPXVV0bZz5NCzeWzoUOQnmRmWivunHbBxc0Bc7P7PfQ5\nSUnknDwGgIklyoD2ORNmsetaXk98jHVs4R2qMPb9EWzYsKHIS1i9jHtpvGH+CtQwzv1eUZjBAaoP\nzYkTJ+Lt7f2IV4uyUJKJhB+kZghRRczmHhUTzc5bd+9/HZum3I/Mfy+yPD1dqxYONZ7Nu+pau4rQ\nf84Eo5ycSSliPGb9Rg3JuJdGVGwU9RsVHjMqHl9GW9b4qaeeMtauRCVlzE4gNcOJACJyb9KpaV6A\nXj4O10/nPZ+ddL8nI/+ZYGpSwVLIuNR7JT4bPHr0KJGRkaTFJ/4ziL6w/GeCHy9ZRlFdVvr3tmHD\nBgYOKvp9lpa+bUePHjXaHzO5B1p6Bgfo7t276dGjBwMGDCAyMhIPj6IHFguRf7LfzOu3GVHbsOFE\ntvZm1GmVNz4zPthaGalpVqUKun/OBG2P/llgGydbZ3o/lTdb0ffZDz+ufghRXk16lPL8nZhEknIK\nh1dNBwcSy2k8pr5txhQdHc2yLYOxLOUowsw0GPvfrytNJVL79u2ZN28eHh4e9O7dmxdeeEH1Z/Io\nBgfozZs3+fzzz/H29mbZsmXMnj3bGO16IpXmErm4+5759/FX2PkCE104mZeyRs/I9JP9Alz/e+Uj\nXm0cqUn3OHdip/J1Vqrh9erVXR0YPWVaoefzjzd9UFmcMT5KVGwUu9JLd+/V0gas7Y3flvKsRNq6\ndSuurq6YmprSoEED47+ZfAwO0NzcXD744AO++OKLIodKPI5KG2TGui9Ymkvk4u575t/H7uO/FZjo\nwsm1FlB42Yemjes+shMK8urDwy9dZu2tT7kecxWdRktgwDmux8ThVL0qsdGJ/PHxX1SpVoPE6FgO\nf3wep2o1SYy+y/kly8i8k4pbXRUfTD46bDhwuQrVXR0wNYskf3ljUWyrOPOsZx/l6+vxpQ/u/CWV\n1V0dsK/qXup9lMUZ46O4u7jndSLx6LPtslTelUitW7emW7du3Lt3jzVr1jB58uQye28GB6i+lnT8\n+PH8/vvvBu3r7t27LFq0CEdHR+rXr8/Agca5X1RaaoLscerNzn8mGB2+r8SdUBlxCXxQP29Q8v8i\nT/4znCiK0FsWmLlHUt0dTgV7EFnXGup6YH0kkuh/BrTfBmre/POh+y9K/gHt7q5uPNOiHblWlgwa\nNIgv1/yPsNOhuLg5oE2NUSqCNInpNI4IIzH6LpZmhi+QNnh43s/4o3q9RdHKuxLpu+++w9XVFXt7\ne6OUlz+M0TqRTExM8PLyMmgfW7Zs4b333qNly5aMGDGCt99+G1NTUyO18MkWFXGHpOhko50JGqqk\nnTn6+THzh5d+hnKfYeOU56csWcCFfybXqBeezqIJY9mwYQN3YowzzUZFXIJXhMzSr+jxyG3KxLBr\nbQAAIABJREFUuxLJ3d2duXPnYm5uXuar+xotQI0hNjaWGjVqAHkzpiQnJ+Po6FjBrfp3yDQxI8XT\nq9CZYOy1cNb4f0VUbBQb0zYW2xteUvlrw3OSsop9XWk6cyqDirgEN5SVsw3f3/upxMOn3NzcGPvf\nr1Udq7glPQBWrVqlPO7atSsXL15Uvv7kk7yxsi1btlSe05+JNmrUiC+++KLAvlq0aEHv3r0LPPdg\nXXurVq1o1apVKd+BOgYFaFEz0Ds7O6veX82aNblz5w7VqlUjKSlJWSq5OGVZzlmahcdK8triFh57\nsNRO7XFv377NvEUzSEmPJSEulRu3LpGcqQXyzspyc+/XcicnxCgdKxkJCbxRZxjUgM3hOx967PwL\nnt1Luj8XZXJCljKcSKvVYvJPbbj2u3Pk/jOcCCD29o37x02+P29CcnLKI99zcYvF6R8npyQr7zU5\nJVlZdC0lw5L8v9pJ2jSWXVtBNcdqaOxMjPIzpHaRurLW2LMxAPeC4mns2fiRbZRKpNIzKEA7depE\nmzZtqFatmvKcIbOc9O3blwULFmBnZ0fXrl0fOa6srEo5N365kYw7aYQcOPXQMYp6JSlJLG7hsdat\nW7Nhw4ZSlTfqJ/vNjIll97erqeZmT1JMIom6JGp5xlGVvIXH7M3vr5VoamqK/m6QvaOr0rFyPf7+\nuL9HlRk+uOCZfkC7ien9Bc/ylzZaODqS3fr+vSqXTBNVx4VHl4Nu+e3+Way9nb2y6NqDl/A1GjWk\nuqu5Ue9llnSRuopw9OhRXFxcCrWvMgb+48igAD1w4AC7du0iNjaWzp07G3wP1MXFpVKsvGfsKc+M\nTb92OFWq878L39OiaiQtqsK3FyyK3eZhg8vVcLSzKHbBs4rg7ugM4fdIjI7F/elGFdqWyqQ0tx5k\nIH3pGRSgtWrV4qOPPkKr1bJ582Zee+01Dhw4YKy2PdZKMrxJ7bIP+csbE1KKv89Y3LIPDw4uf5wU\n15mjX8xNesrVi46O5v2dK8DWsnQbpmayts/IIi//c3JyWLhwIRkZGSQlJTFgwABmzZpFu3btyMnJ\nwcnJifHjxzN79mysrfNuwXz88cfodDq6d+9OQEAAzZo1A+D48eN89913ytAlvZSUFN544w127NiB\ni4sLp0+f5ptvvsHGxoY6derg7e1NZmYm48ePx9fX16jFPgYFaHZ2NgcPHuTgwYM4OzsrazyLhw9v\n0g+Cj09AWfYh9NDnrLlTss6c/OWN+S+l70Vnof3nXqQ1iThUq/fIZR9KU9pYEtZaHXXC0/PC29wM\np3+GEzm4VSPT7P6PW7ppNqtur8PdxR0r55KXvjyOnTmPFVtLNPalK0V62HyY27dvp3PnznTo0AGt\nVsvIkSNp0aKFMpB+/PjxpKam0r17dzw9PZk0aRIAv//+O7169WLLli3MmTOHiIgILl68SFZW4ROG\nffv28d5777F161ZGjRrFoUOHGD58OI0bN+aHH34A4Ntvvy2TidoNOuf28vJiz549dOjQgZYtW5Z6\nQouycvToUfz9/Tl69GilPK5+PfP8rHPNecP8FUbUGErGvZKPJcm7lDalU1NTnN0sqNcK6rUCFzeH\nYrfJ1WVwIXgl0eH7cHNwpfdTPej9VA/cXUo+QDxvQLs7Z+KbYm3uRE5Uc+6crkO7Z58lYIIvHZu1\n5KtFi1g0YSwdmjVh0YSxuOc7Q3F5qi71WtVn2LThBvf8V7Ts7Oxy/1l7XISFhSk94iYmJjz11FOE\nhIQwefJk+vTpQ9euXbGzs8PT05NffvlFOdvcs2cP//3vf4mJiSE5OZnatWszdOjQIo/x66+/MmTI\nEI4fP45Op2PUqFH89ttvzJ07l9jYvJOCIUOGULduXaO/P4POQCdPnoxGo1EGvVYWJR3cbuzZhfIf\n98s1/yPscigRn53FxdEdG4vCoxPyz1pemhLD/IPLTcxL/09Y1aMaz7g6MWjQINb4l3zqQCsnR1Ze\nOltoQLveoy6fqzpacSF4Ja5u7lR1tAJyi32tsVR1tCI6fB8x0VFldtxHVXA9yRo2bEhISIjy+3Xz\n5k1atmxJQEAAGzduJCEhAYBt27aRlZXF0KFDiYuL49y5cyxdupS0tDR27NjBkCFDCux35syZpKWl\nMWzYMG7dusWcOXNIS0sjKCiIGzduMHz4cExMTJg7dy4xMTFK7byxGRSgN2/eJDg4mCFDhhhl1vPd\nu3dz7tw5ZWxYWVLWzUlK4a+LlwsEaElriPMvfbspY7Uy6BsgNiGK6q1uAHDseByWVo5KTbr+fp2r\nmztudfPGQpamxDD/4PLE2wCP7hTSLzzm4FaNmg7Fn50+6rhFDWgvKe/331G1vZpB7Ppt9H8cDWn3\nEyU1s5QLeuRtU5z+/fuzYMEC9u3bR0ZGBgMHDmTnzryThoEDBzJmzBjatWvHihUr8PT05MyZMzRt\n2pTp06fj5eVFZmYm3t7ehQJUP+eGv78/S5cupUGDBsTExODn58f777/Phx9+iL29PXZ2dmUWnmBg\ngIaHh7NhwwamTp1qcID+8MMPxMTEFBivqNbXXy3j78uhBAacw76qu1KKl9/Fvy8TqbEAjQX8fbnA\n90paQ5y/N3xf3J1iX5duoiHmn5r0qMMX+HjJMhKj72KltVMqgoxxL9JMa0VOVP1iFx571rXWYxki\nD7vvqb98fvD7cq+09Nzc3FjbZ6TqbYtibm6On59fgefatm2rPNb3mxw6dKjI7S0tLQv8rOqXI9ab\nNu3+pC6urq6sWLECgOeff77QvkaPHv2wt6CKQQFq9k+ngJrhC1u3bmXfvrzA0Gg0TJs2jebNm6ue\njT7/5XhyfBSvPh0FRHEmvujZ15s0fBqHf1ZubFKOS8FmmpgpC57VzNcbrrYyJ/9EF62fbcrg4WML\nlDc+SmmrVfIrLrzKk1w+G48MpC89gy/h9+/fT2RkJPv37wegW7duJdq2f//+9O/fv8BzUVFRxby6\naPkHA1taWmJmZoalpSXJycnwzwS4ycnJpBGLpaVlgdd3bPksoaGhnL19EycL0wLfS05OAaf7j4sb\ndJy/MudkXAR3PxlJbHwqNy8fJTIphnr/9MnkP6vO/zg9K5mr5zcRf+8uTrr7YzhLUpkDeRUw+Rdo\ne+a5jkoFzsmTJ7HM0eH822VsnR2xtLIrsoKntNUq+ffRokWLvPf+kAqhkj42luL2WdbHFU8mgwK0\nb9++ZGZm0qdPnwrpRHqwukJfEbJr8yplaM/VxBgcXHP58dcYXBzd8Rk2rtjt9UIOnFIeP6xKJn9l\nTsSFm3krN9YGiCPsuIlS3pgVm6LUh+tS7w/DqPZULeb8M+lFVuT98ZolXXjsUdU5+iqn/JftxVU8\nlbSa5mGvK4tF5UqruH2W9XEfN/IHxDgMCtDo6Gh8fHyM1Rbc3d2V8WGlkb8zZ+2tT9HkpNGpad4s\nTlEXtEpnTmwJT3CtnG1Ydal0YxQTUrIKrB0OJvfLG7MdyP2nPpz9F5SKoERd8YPgH2f5O32AJ2IW\no38DqUQqPYMCtLKM+8zfmQOwNPIiBy67U93VgfS0q8qZoDWJJdrfQJ+Bpa5oyV/aCMWXN+ZfbiL/\nwmOlvRd59OhRsrONM1WbsT3YgVNewVmZP5PHQXR0NMO370djW7o5VHWpKXzVt1uR90+zsrJYuHAh\n2dnZxMfHY2lpSWZmJjdu3KBJkyZMnDgRW1vbApVEKSkpLFy4EK1WS1ZWFn379sXDw4MPPviAJk2a\nAPDGG2/g5eVVoDopKCiILVu24OzszPvvv0/DhmW/gJ9BAXrs2DGGDx8O5E3Br9FoKsWSxA08mmBe\nsxqDBg1i/meTMXM/C0BOlEOJlgCuCKVdeCx/SElw5JGed8NpbO3Q2Ksb5laUTZs28fLLL9O+fXvi\n4+M5c+YMTz/9NNu2bVMmY//2228LVBKtWLGCYcOGUa9ePSAvZwBefvllZRugUHXS2bNncXV1xcrK\nitq1axvtPTyMQQHatm1bZSGoysrF0Z2w04m4uDng4uiuaglgQ1lrdTgGR+Lg5kJ8vvJGYy08Vl7B\n8bDxmGpCvDL04ouyde3aNfr16wdA1apVeemllwp1Fv/6668sX76coUOH8sEHHxAVFUW9evWUyZMz\nMjKYNGkShw4d4s6dvOGC+jPMoUOHMmXKFABeffVVnn76af7880927NjBO++UbG4JQxgUoPq/EBXt\nwbXDL6Yn4ZabxdGjRwvMWr5s7SqmLFlAYnQscWvTlQHtYHhVkg4bzsR7KOvm5F+vp6GHE7Xdmpeq\nasfYjDGj+sOCWk2IV8RVwJMys3xloa9E6tChA3FxcezcuZPXX38dnS5vuH5YWFiBSqJDhw5Ro0YN\nzp49S/PmzVmwYAETJ05Eo9HQuXNnxo0bV+gY+n2dO3eOxo0bU6VKlTJfykPPoAAdNWqUsdphkAfX\nDt+XdIf3P56kfK0/04lKuJe37ENdDxzCC5ZO5i/DLOkvWf6SymdatCtQqZN/vR4Xx9IvQmZsDwbc\nk3r2J5f5D6dLTTHqNv3792fWrFns3buX9PR0JkyYAKAs4bFjx45ClUSLFy9m3rx5rF27Fq1Wy0sv\nvYROpyMoKIhbt24B0KFDB3r16lVgX05OTsyYMQMTExM+/vjjUr8PNSrNkh6BgYEkJCQQHx/P4MGD\nad68udH2rT/T+XrvDmV6t0StlfL9/NPK6VeqzF/hUJz8JZXvPHA2mf/MV/+aohj7/mVJw1/O/sSD\n3Nzc+KpvycZxF7VtUSwtLYucZF1/L3P69OnKc/kriRYuXFhoG33hzYP01Uldu3ala9eupWu4gQwO\n0N27d9O1a1dsbW2JjIzk66+/5tlnn1X+OpTUU089Rbdu3bhw4QI//fRTqQL0wZUbrZyKXkfJwc1F\nmd7NIfx+mePly1fRmriBiRt3Y7OJiS7dgH5DGPuM6MHOpcoUWHL2V7lJJVLpGRygoaGhnDp1itdf\nf51Tp04xevRoQkNDH7ldUaWc+nWcp06dWqo2FLVyY1GKm7U8/6QekLfU77+BBJYQZcvgANUvlHbv\n3j1u376Nra2tclP3YR4s5Tx58iRff/01fn5+JV6J88FqikeV6HVskVd9EhQURMcWrZXX5uakKCWV\nVZ2rYWOlKVWlhpqyxZKUHJYX/cJ2/+bqFCnffDQZSF96Bgeop6cnJ0+epGnTpvTr148NGzaUegxW\nSkqKMq2Vv78/bdq04a233nrkdsWVcj7Kg6/TP1bbM66mbLEkJYfl5Ukoa5TyzYKK+mMSHR3NNzuv\nYG3rVMQWxUtPjePdPjz08v/IkSMEBgayefNmzp8/z9dff42ZmRk2NjbMmDGDwMBAkpKScHFxwcfH\nh7lz56LRaHj66aeVEy1fX1969uzJs88+y8KFC7G0tKRp06a0a9eO5cuXo9PpeO2110hOTubIkSPY\n2dkxbty4AjPR79u3j+vXrytr0xvK4AA1NTUlLCyMv/76Cx8fH1WdE3Z2dvzxxx8GtaOk9/uK67Qp\nz/uFlaENQhTF2tYJO3vjz5/5/fff06lTJ06cOMH69etZsmQJFhYWHD58mJiYGO7cucP8+fOZOXMm\nYWFhVK1aldGjRzNp0iT69+/Pjz/+qOzr7t27BAcHU7t2bV5//XWuXr1KaGgo9vb21KpViy+++IKA\ngAAuXrzIrl27GDgwrzAlJCSEGzdulOgKuaQMDtCIiAhlkaf9+/cXWve8vJT0fl9xryvP+4WVoQ1C\nlJeEhASysrLo3bs3n332GZaWllhY5M0+1qlTJ+7evYuzc96KDY6OjqSnpyuTIGs0Gm7dusWNGzdo\n06YNkHfCtX79euzt7VmyZAnvvvsuX3/9NbGxsXz//fcMGjSI6dOn4+7urgxxAmjZsiWurq5s377d\naO/N4JsW+jMprVZLenr6I17975P/rLG4xyDr5lSkB/8tRPnau3cvCQkJrFy5klOnThEbG6uUX+7e\nvZv4+HhlaY/4+Hjs7e2JiYkB8nLl0KFDREREsHPnTrZu3cr+/fuJiYnB1taW9PR0vv32WzIzM6la\ntSqJiYnExMQwf/58WrduTY0aNcr0vRl8BtqoUSOmTZuGTqfD29v70Rv8yzxs4oz8jytb7f2TRM7s\nSy49NU7lNsXfNz1y5AirVq3C1NSUgwcPcvXqVcaPH4+9vT22tra8+eab1KpVi3nz5uHh4UGDBg1I\nTU1lzpw5dOjQgT59+gB5YVutWjVq1arFokWLcHBwoH///piamjJnzhxsbW3x8fHhzp07TJ06FZ1O\nx5w5c9izZw/PPPMM9evXV/uxFEujU3FD4OLFiwQFBRV6PjQ0lOXLlxulYY9y8uRJ6RQQQqWifn+k\nF770VJ2BJiUl8dprr2FtbV3geUNmQNm0aRN///036enpjB07tsxPvYUQBclA+tJTFaD6m7kPcndX\nX/Pt7OzMO++8w48//sixY8eU03YhhKisKqwWvqhKpJ9//pnly5ezdOnSimqWEEKUmKp7oGXh2LFj\ntGvXjpSUFKZOnUpgYOBDXy9VJUIYRu6BGq7SzMZ0+vRpfvjhB3JychgwYMAjXy8dSEIYV3R0NJc/\nv0BVy5KVUuvFZybAR0VXIgUGBhIeHo6dnR0xMTFYWFiQnZ2Nvb09aWlpLFq0CCsrK3bv3k14eHiB\n+T6LWqLj+PHj7N69m4ULF7Jo0SJl+NO8efM4evQo+/btw8TEhFq1avHhhx8ye/Zspa9GP8WdvqKp\nXbt2yrFu377NlClTip01rTiVJkAry9yiQjzJqlo64mrtYtR9jh07Fg8PD0JCQhg+fDg7d+7Ew8OD\n1atX8/fff+Pg4MD58+ext7cvsN2DS3QkJCRw/PhxZbLk69evU716daXv5dtvv1VGAX3//fekpqbS\nvXt3PD09mTQpb37g/BVN+W3YsIEqVaqU+r1VmgAVQvw7LV68mKSkJKpWrUqvXr1YvHgxdnZ2ODk5\n0ahRIxYvXszgwYPZsWNHge30S3QEBwezY8cOIiMjGT16tLJy78iRI2nRogVLly7lzJkz1KlTR9n2\njTfeAPLm6vjll19o1qyZUtH0wgsvFGrjlClTlKVBSkMCVAhRpiZPnoyjoyMTJkygWrVqTJ48GQ8P\nDwBOnTpFTEwMy5Yt4/r163Ts2JFNmzbRrl07dDodjRs3xt7entzcXCIiIggICODcuXNcunSJCxcu\n0KJFC6pUqYKNjQ03b95Ujrl69Wr69OlDUFAQWVlZDB06lA0bNhAREcEff/yBs7NzgUt4QFWNvASo\nEEIRn5mgahtXHj6E0d7enlGjRvHdd98VeP65557jueee49atW2zbtg1PT088PT0B+Pnnnwss0aGf\nKW3KlCk0atSIH3/8EX9/f8zNzWncuDFvvvkmH330EdbW1tSuXZukpCRWrFiBp6cnZ86cUWau11c0\nxcXFsXfvXoYMGQJQoG6+pCpNL7wQomJJL3zpPXEBeuPGDcaNG8euXbvK5XinT59m8+bNyj2fDz/8\nsFyOe+PGDZYtW4aTkxPNmjWjd+/e5XJcvUmTJvGf//yHbt3UrbFTWrdu3WLUqFE0adIENzc3xo8f\nX+bHjIqKYvny5djZ2eHo6MjIkSPL9HgbN27k/PnzZGdnc+rUKQ4dOsTvv/9OgwYNpHKvgjxRl/Cx\nsbFs3769wASrZS0pKYmZM2diY2PDsGHDyu24KSkpTJw4kWrVqjFmzJhyDdD169dja2tbbscD+Ouv\nv5Qp0Fq1alUux1y3bh21a9cmPDycLl26lPnx9PNafvrpp0pv85kzZ6hSpYoEaAV5os65XVxcmDhx\nYrkGqJeXFzY2NqxcuZIePXo8egMjadasGWZmZvj4+NCyZctyO+6hQ4ewt7cv12MCyhri/v7+rFu3\njtzc3DI/5o0bN/Dy8mLu3LmPLPwwlmvXrpGbm0u9evXw9fXl4MGDrFixQlnNUpSvJ+oMVK8871qk\npqbi7+9Pjx49aNu2bbkdNywsjOrVq7NmzRrGjBlDcnJyoXF2ZWHfvn04ODhw7do1zM3N6dChAw4O\nDmV+3IsXL9KyZUs0Gg22trZotVpMTU3L9Jiurq7Y2tpiZmaGnZ1dmR5Lb9OmTbz//vvKcsGBgYF0\n6tTJqMuAi5J7IgNUTW+bWvPnz1cmg92zZ0+Ra2SXhaysLPz8/KhWrRoeHh7lEp6AMo/B7t27sbCw\nKJfwBKhTpw4BAQE4Ozvj5eWFubl5mR9z2LBhfPbZZ9jb25fbvd6rV69Ss2ZN5evRo0eXy3FF0Z64\nTiQhhDCWJ+oeqBBCGJMEqBBCqCQBKoQQKkmACiGEShKgQgihkgTov8SuXbsYOHAgU6ZMYcKECWza\ntAmARYsWFfl6X19f7t27V+j5HTt2cPXqVeXrqKgoZs6cWTaNNlBgYCBnzpyp6GaIJ9gTOQ7032rg\nwIHKeMRPPvmEiIgIrly5AuTNrdivXz/Cw8MZO3asss3atWsJDw8nNTWV4cOHc/v2bRo2bMiOHTs4\nceIEJiYmBcZU5uTksHDhQkxMTEhJSWH27NnMnj0bKysrEhMTmTlzJpMmTaJZs2YkJyfTuXNnjh07\nxn/+8x9atmzJqFGjlDLEqKgoJk6cSOfOnblw4QLz589n/PjxrF69mtjYWD777DN69+7NypUradWq\nFVevXqVRo0ZcvnyZoUOHArBmzRrc3NyoUaMGQ4cOZfHixeTk5JCSksLHH3/M1KlTcXZ2pmfPnsUu\nhiiEWhKg/1JNmzYtcCbp7u6Ot7c3YWFhbNmyBcgLw9OnT/P5558r4degQQN0Oh0//fQTX375JVev\nXi2wzMGff/5J3bp1effdd7ly5QqHDx+mYcOGDB48mIMHD7Jnzx50Oh0ffvghGo2GMWPG4Ofnx5Il\nS0hMTMTLy6tAO+vUqYOPjw8rVqzg0qVLhYocNBoNLVu25KOPPmLIkCH4+PgQHBxMcHAwAAMGDKBd\nu3Z88MEHNGzYkCtXrtCkSRPS0tI4c+YMGRkZ+Pn5YWlpWVYftXiCySX8v1RISAgNGjRQvtbXhqen\np2NhYaE8nz+w8tdU6J9/sBwyOztb+V5cXBwZGRmFQk+n05Gbm0t2djYmJia4urpibm7O1q1befPN\nNwu8Vr9ejampqbJUg06nU9a60el0ymvMzMwwMTHBxMREea3+exYWFuTk5NC8eXMmTJhA79698fDw\nwNzcXMJTlBk5A/0X2bRpE7/99htZWVm0bNlSmfUbICIigoULF3Lv3j2mTZtGQEAAZmZmtGjRgrlz\n55KRkcHo0aM5cOAAGo2Gbt26MWPGDMzMCv6IdOzYET8/P+bNm0dWVhaffPIJs2fPZtGiRaSmpjJl\nyhQOHjxIQEAACQkJygxUXbt25a+//io2zDQaDRqNhq5duzJx4kRldqH84VzU423btnH06FHatGnD\nSy+9xI8//sj8+fOJjY1l3rx5xvlghSiGlHI+IYYPH85XX31VIccKDg5m3bp1zJ8/Hycnp3JpgxDl\nQQJUCCFUknugQgihkgSoEEKoJAEqhBAqSYAKIYRKEqBCCKHSI8eBRkVFFbnioEajYfv27TRr1qxM\nGiaEEJVdiQfSv/rqqwwYMECpVtFoNNSrV6/MGiaEEJVdiQPU1dWV5557rsBz+UsChRDiSfPIgfT6\nS3iNRlOgVtrd3Z2goKAyb6AQQlRWJT4D7datG4MHD1a+lrNPIcSTrsQB6uLiQvPmzcuyLUII8Vgp\ncYDevn2bY8eOFXiuYcOGuLi4GL1RQgjxOCjRPdCXX365yO8FBATQo0ePMmmYEEJUdjIbkxBCqCSV\nSEIIoZIEqBBCqCQBKoQQKkmACiGEShKgQgihkgSoEEKoJAEqhBAqSYAKIYRKEqBCCKGSBKgQQqgk\nASqEECpJgAohhEoSoEIIoZIEqBBCqCQBKoQQKkmACiGEShKgQgihkgSoEEKoJAEqhBAqSYAKIYRK\nEqBCCKGSBKgQQqgkASqEECpJgAohhEoSoEIIoZIEqBBCqCQBKoQQKkmACiGEShKgQgihkgSoEEKo\nJAEqhBAqSYAKIYRKEqBCCKGSBKgQQqgkASqEECpJgAohhEoSoEIIoZIEqBBCqCQBKoQQKkmACiGE\nShKgQgihkgSoEEKoJAEqhBAqSYAKIYRKEqBCCKGSBKgQQqgkASqEECpJgAohhEoSoEIIoZIEqBBC\nqCQBKoQQKkmACiGEShKgQgihkgSoEEKoJAEqhBAqSYAKIYRKEqBCCKGSBKgQQqgkASqEECpJgAoh\nhEoSoEIIoZIEqBBCqCQBKoQQKkmACiGEShKgQgihkgSoEEKoJAEqhBAqSYAKIYRKEqBCCKGSBKgQ\nQqgkASqEECpJgAohhEoSoEIIoZIEqBBCqCQBKoQQKkmACiGEShKgQgihkgSoEEKoJAEqhBAqSYAK\nIYRKEqBCCKGSBKgQQqgkASqEECpJgAohhEoSoEIIoZIEqBBCqCQBKoQQKkmACiGEShKgQgihkgSo\nEEKoJAEqhBAqSYAKIYRKEqBCCKGSBKgQQqgkASqEECpJgAohhEoSoEIIoZIEqBBCqCQBKoQQKkmA\nCiGEShKgQgihkgSoEEKoJAEqhBAqSYAKIYRKEqBCCKGSBKgQQqgkASqEECpJgAohhEoSoEIIoZIE\nqBBCqCQBKoQQKkmACiGEShKgQgihkgSoEEKoJAEqhBAqSYAKIYRKEqBCCKGSBKgQQqgkASqEECpJ\ngAohhEoSoEIIoZIEqBBCqCQBKoQQKkmACiGEShKgQgihkgSoEEKoJAEqhBAqSYAKIYRKEqBCCKGS\nBKgQQqgkASqEECpJgAohhEoSoEIIoZIEqBBCqCQBKoQQKkmACiGEShKgQgihkgSoEEKoJAEqhBAq\nSYAKIYRKEqBCCKGSBKgQQqgkASqEECpJgAohhEoSoEIIoZIEqBBCqCQBKoQQKkmACiGEShKgQgih\nkgSoEEKoJAEqhBAqSYAKIYRKEqBCCKGSBKgQQqgkASqEECpJgAohhEoSoEIIoZIEqBCuECL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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa0fbfc99d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(4.5, 8), dpi=300)\n", "\n", "gs = gridspec.GridSpec(1, 1)\n", "ax = fig.add_subplot(gs[0, 0])\n", "ax.text(0, 1, 'Figure 4',\n", " size=16, va='top')\n", "ciepy.clean_axis(ax)\n", "ax.set_xticks([])\n", "ax.set_yticks([])\n", "gs.tight_layout(fig, rect=[0, 0.92, 1, 1])\n", "\n", "# SNV/indel lead vs. CNV lead effect size\n", "gs = gridspec.GridSpec(1, 1)\n", "ax = fig.add_subplot(gs[0, 0])\n", "cnv_abs_beta_pdf.plot(label='CNV', ax=ax, linestyle='--')\n", "snv_abs_beta_pdf.plot(label='Not CNV', ax=ax)\n", "for t in ax.get_xticklabels() + ax.get_yticklabels():\n", " t.set_fontsize(8)\n", "ax.set_xlabel('$\\\\left|\\\\beta\\\\right|$', fontsize=8)\n", "ax.set_ylabel('Density', fontsize=8)\n", "ax.legend(fontsize=7, frameon=True, fancybox=True)\n", "gs.tight_layout(fig, rect=[0, 0.77, 0.52, 0.94])\n", "\n", "# Lead variant CNV not overlapping gene\n", "gs = gridspec.GridSpec(1, 1)\n", "ax = fig.add_subplot(gs[0, 0])\n", "g = cnv_lead_vars.ix[cnv_lead_vars.cnv_overlaps_gene_cons, 'gene_id']\n", "t = cnv_lead_vars[cnv_lead_vars.gene_id.apply(lambda x: x not in g.values)]\n", "bins = np.arange(-3, 3.1, 0.1)\n", "t.drop_duplicates('gene_id').beta.hist(bins=bins, histtype='stepfilled', lw=0)\n", "p = stats.binom_test((t.drop_duplicates('gene_id').beta > 0).value_counts())\n", "print('{:,} lead intergenic CNV eGenes.'.format(t.drop_duplicates('gene_id').shape[0]))\n", "print('Effect sizes for intergenic lead CNVs are biased '\n", " '(p={:.3e}, binomial test).'.format(p))\n", "for t in ax.get_xticklabels() + ax.get_yticklabels():\n", " t.set_fontsize(8)\n", "ax.set_xlabel('$\\\\beta$', fontsize=8)\n", "ax.set_ylabel('Number of\\nlead variants', fontsize=8)\n", "ax.set_xlim(-3, 3)\n", "gs.tight_layout(fig, rect=[0.48, 0.77, 1, 0.94])\n", "\n", "# Functional annotation enrichment\n", "gs = gridspec.GridSpec(1, 1)\n", "ax = fig.add_subplot(gs[0, 0])\n", "sns.stripplot(x='neg_log_pvalue', y='mark', data=intergenic_res, jitter=0.3, \n", " ax=ax, orient='h', size=4.5)\n", "for t in ax.get_xticklabels():\n", " t.set_fontsize(8)\n", "ax.set_xlabel('$-\\\\log_{10}$ enrichment $p$-value', fontsize=8)\n", "xmin,xmax = ax.get_xlim()\n", "ax.set_xlim(-0.1, xmax)\n", "ymin,ymax = ax.get_ylim()\n", "ax.vlines(-np.log10(0.05), ymin, ymax, linestyle='--', color='grey',\n", " linewidth=1)\n", "ax.text(-np.log10(0.05) + 0.1, ymax, '$p=0.05$', ha='left', va='top', fontsize=8)\n", "ax.axhspan(-0.5, 4.5, facecolor='blue', alpha=0.2, label='Active', lw=0)\n", "ax.axhspan(4.5, 6.5, facecolor='red', alpha=0.2, label='Repressed', lw=0)\n", "ax.axhspan(6.5, 7.5, facecolor='grey', alpha=0.2, label='Transcribed', lw=0)\n", "ax.legend(frameon=True, fancybox=True, loc=[0.74, 0.17], fontsize=7)\n", "ax.set_ylabel('')\n", "for t in ax.get_xticklabels() + ax.get_yticklabels():\n", " t.set_fontsize(8)\n", "gs.tight_layout(fig, rect=[0, 0.57, 1, 0.79])\n", "\n", "# mCNV eQTL gene expression\n", "gs = gridspec.GridSpec(1, 1)\n", "ax = fig.add_subplot(gs[0, 0])\n", "sns.boxplot(x='CNV_7_143951166_143953316', y='exp', hue='Gene', data=data_f, \n", " width=0.75, fliersize=0, linewidth=0.5)\n", "ax.set_xticklabels([str(x) for x in range(1,7)] + ['7+'])\n", "for t in ax.get_xticklabels() + ax.get_yticklabels():\n", " t.set_fontsize(8)\n", "ax.set_ylabel('$\\log$ TPM $z$-score', fontsize=8)\n", "ax.set_xlabel('Diploid copy number', fontsize=8)\n", "ax.legend(fontsize=7, loc='upper left', bbox_to_anchor=(1, 1))\n", "gs.tight_layout(fig, rect=[0, 0.4, 0.8, 0.6])\n", "\n", "# mCNV legend\n", "gs = gridspec.GridSpec(1, 1)\n", "ax = fig.add_subplot(gs[0, 0])\n", "ciepy.clean_axis(ax)\n", "rects = []\n", "labels = []\n", "for k in legend_colors.index:\n", " labels.append(k)\n", " rects.append(plt.Rectangle((0, 0), 0, 0, fc=legend_colors[k]))\n", "lgd = ax.legend(rects, labels, loc='center', prop={'size':7}, ncol=3)\n", "for p in lgd.get_patches():\n", " p.set_linewidth(0)\n", "gs.tight_layout(fig, rect=[0, 0, 1, 0.05])\n", "\n", "t = fig.text(0.005, 0.915, 'A', weight='bold', \n", " size=12)\n", "t = fig.text(0.5, 0.915, 'B', weight='bold', \n", " size=12)\n", "t = fig.text(0.005, 0.77, 'C', weight='bold', \n", " size=12)\n", "t = fig.text(0.005, 0.58, 'D', weight='bold', \n", " size=12)\n", "t = fig.text(0.005, 0.4, 'E', weight='bold', \n", " size=12)\n", "\n", "plt.savefig(os.path.join(outdir, 'cnv_examples_skeleton.pdf'))" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%R\n", "\n", "suppressPackageStartupMessages(library(Gviz))\n", "suppressPackageStartupMessages(library(GenomicFeatures))" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "chrom = 'chr7'\n", "start = 143851293\n", "end = 144061217\n", "fontsize = 6\n", "cnv_color = '#000000'\n", "cepbp_color = \"#20B2AA\"\n", "dnase_color = \"#663399\"\n", "cnvs = os.path.join(ciepy.root, 'output', 'cnv_processing', 'gs_cnvs.bed')" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/frazer01/home/cdeboever/software/anaconda/envs/cie/lib/python2.7/site-packages/rpy2/robjects/functions.py:106: UserWarning: Note that the behaviour of the 'setPar' method has changed. You need to reassign the result to an object for the side effects to happen. Pass-by-reference semantic is no longer supported.\n", "\n", " res = super(Function, self).__call__(*new_args, **new_kwargs)\n" ] } ], "source": [ "%%R -i data,chrom,start,end,fontsize,cepbp_color,dnase_color,cnv_color,cnvs\n", "\n", "cnvTrack <- AnnotationTrack(\n", " range=cnvs, \n", " genome=\"hg19\",\n", " chromosome=chrom, \n", " start=start, \n", " end=end,\n", " collapse=FALSE,\n", " stacking=\"dense\",\n", " fontsize=fontsize,\n", " name=\"CNVs\",\n", " fontcolor.legend='black', \n", " col.axis='black', \n", " col.title='black',\n", " background.title='transparent', \n", " cex=1,\n", " cex.id=1, \n", " cex.axis=1, \n", " cex.title=1,\n", " fontface=1, \n", " fontface.title=1,\n", " lwd=0,\n", " fontface=1, \n", " fontface.title=1,\n", " rotation.title=0\n", " )\n", "\n", "ideoTrack <- IdeogramTrack(\n", " genome=\"hg19\", \n", " fontsize=fontsize, \n", " fontsize.legend=fontsize,\n", " fontcolor='black', \n", " cex=1, \n", " cex.id=1, \n", " cex.axis=1, \n", " cex.title=1,\n", " fontface=1, \n", " fontface.title=1\n", ")\n", "\n", "gtrack <- GenomeAxisTrack(\n", " col=\"black\", \n", " cex=1, \n", " fontsize=8, \n", " col.id=\"black\", \n", " fontcolor=\"black\", \n", " fontface=1,\n", " fontface.group=1,\n", " lwd=1,\n", ")\n", "\n", "biomTrack <- BiomartGeneRegionTrack(\n", " genome=\"hg19\", \n", " chromosome=chrom, \n", " start=start, \n", " end=end,\n", " name=\"\", \n", " fontsize=fontsize,\n", " collapseTranscripts='meta',\n", " fontcolor.legend='black', \n", " col.axis='black', \n", " col.title='black', \n", " fontcolor.legend=\"black\",\n", " background.title='transparent', \n", " cex=1, \n", " cex.id=1, \n", " cex.axis=1, \n", " cex.title=1,\n", " fontface=1, \n", " fontface.title=1, \n", " geneSymbols=TRUE,\n", " cex.group=1,\n", " fontcolor.group=\"black\",\n", " fontface.group=1,\n", " fontface.title=1, \n", " alpha.title=1,\n", " lwd=0.8,\n", ")\n", "\n", "hmmTrack <- UcscTrack(\n", " track=\"Broad ChromHMM\", \n", " table=\"wgEncodeBroadHmmH1hescHMM\",\n", " genome=\"hg19\", \n", " chromosome=chrom,\n", " from=start, \n", " to=end, \n", " trackType=\"AnnotationTrack\",\n", " shape=\"box\",\n", " start=\"chromStart\",\n", " end=\"chromEnd\",\n", " feature=\"itemRgb\", \n", " id=\"name\", \n", " collapse=FALSE,\n", " stacking=\"dense\",\n", " fontsize=fontsize,\n", " name=\"chromHMM\",\n", " fontcolor.legend='black', \n", " col.axis='black', \n", " col.title='black',\n", " background.title='transparent', \n", " cex=1,\n", " cex.id=1, \n", " cex.axis=1, \n", " cex.title=1,\n", " fontface=1, \n", " fontface.title=1,\n", " lwd=0,\n", " fontface=1, \n", " fontface.title=1,\n", " rotation.title=0\n", ")\n", "\n", "feat <- unique(feature(hmmTrack))\n", "featCol <- setNames(as.list(rgb(t(sapply(strsplit(feat, \",\"),\n", "as.numeric)), maxColorValue=255)), feat)\n", "displayPars(hmmTrack) <- featCol\n", "\n", "cebpbTrack <- UcscTrack(\n", " track=\"Uniform TFBS\", \n", " table=\"wgEncodeAwgTfbsSydhH1hescCebpbIggrabUniPk\",\n", " genome=\"hg19\", \n", " chromosome=chrom,\n", " from=start, \n", " to=end, \n", " trackType=\"AnnotationTrack\",\n", " shape=\"box\",\n", " start=\"chromStart\",\n", " end=\"chromEnd\",\n", " feature=\"itemRgb\", \n", " id=\"name\", \n", " collapse=FALSE,\n", " stacking=\"dense\",\n", " fontsize=fontsize,\n", " name=\"CEBPB\",\n", " fontcolor.legend='black', \n", " col.axis='black', \n", " col.title='black',\n", " background.title='transparent', \n", " cex=1,\n", " cex.id=1, \n", " cex.axis=1, \n", " cex.title=1,\n", " fontface=1, \n", " fontface.title=1,\n", " lwd=0,\n", " fontface=1, \n", " fontface.title=1,\n", " rotation.title=0\n", ")\n", "\n", "dnaseTrack <- UcscTrack(\n", " track=\"Uniform DNaseI HS\", \n", " table=\"wgEncodeAwgDnaseUwdukeH1hescUniPk\",\n", " genome=\"hg19\", \n", " chromosome=chrom,\n", " from=start, \n", " to=end, \n", " trackType=\"AnnotationTrack\",\n", " shape=\"box\",\n", " start=\"chromStart\",\n", " end=\"chromEnd\",\n", " feature=\"itemRgb\", \n", " id=\"name\", \n", " collapse=FALSE,\n", " stacking=\"dense\",\n", " fontsize=fontsize,\n", " name=\"DHS\",\n", " fontcolor.legend='black', \n", " col.axis='black', \n", " col.title='black',\n", " background.title='transparent', \n", " cex=1,\n", " cex.id=1, \n", " cex.axis=1, \n", " cex.title=1,\n", " fontface=1, \n", " fontface.title=1,\n", " lwd=0,\n", " fontface=1, \n", " fontface.title=1,\n", " rotation.title=0\n", ")\n", "\n", "cnvTrack = setPar(cnvTrack, \"fill\", cnv_color)\n", "cebpbTrack = setPar(cebpbTrack, \"fill\", cepbp_color)\n", "dnaseTrack = setPar(dnaseTrack, \"fill\", dnase_color)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fn = os.path.join(outdir, 'CNV_7_143951166_143953316_region.pdf')" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "png \n", " 2 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%%R -i fn,chrom,start,end\n", "\n", "pdf(fn, 4.5, 3)\n", "plotTracks(c(gtrack, biomTrack, cnvTrack, cebpbTrack, dnaseTrack, hmmTrack), chromosome=chrom, \n", " from=start, to=end, col.title=\"black\", sizes=c(0.22, 1, 0.12, 0.12, 0.12, 0.12))\n", "dev.off()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "gene_info = pd.read_table(cpy.gencode_gene_info, index_col=0)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>gene_name</th>\n", " <th>gene_type</th>\n", " <th>chrom</th>\n", " <th>start</th>\n", " <th>end</th>\n", " <th>strand</th>\n", " <th>gene_status</th>\n", " <th>source</th>\n", " <th>level</th>\n", " </tr>\n", " <tr>\n", " <th>gene_id</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>ENSG00000176227.9</th>\n", " <td>CTAGE15</td>\n", " <td>pseudogene</td>\n", " <td>chr7</td>\n", " <td>143268818</td>\n", " <td>143271478</td>\n", " <td>+</td>\n", " <td>KNOWN</td>\n", " <td>ENSEMBL</td>\n", " <td>3</td>\n", " </tr>\n", " <tr>\n", " <th>ENSG00000271079.1</th>\n", " <td>CTAGE15</td>\n", " <td>protein_coding</td>\n", " <td>chr7</td>\n", " <td>143268893</td>\n", " <td>143271480</td>\n", " <td>+</td>\n", " <td>KNOWN</td>\n", " <td>HAVANA</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " gene_name gene_type chrom start end \\\n", "gene_id \n", "ENSG00000176227.9 CTAGE15 pseudogene chr7 143268818 143271478 \n", "ENSG00000271079.1 CTAGE15 protein_coding chr7 143268893 143271480 \n", "\n", " strand gene_status source level \n", "gene_id \n", "ENSG00000176227.9 + KNOWN ENSEMBL 3 \n", "ENSG00000271079.1 + KNOWN HAVANA 2 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gene_info[gene_info.gene_name == 'CTAGE15']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Presentation" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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x48Zlj21paeE//uM/cs7vzoPtGWY6HPPnz+fBBx/khhtuIJ1Oc9555x2xtP5w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j33XF71qlfx1re+lU9/+tNMT0/PbRMEAf/6r//KaaedxoknnsgHPvABtmzZsk9777jjDk477bQD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\ndEcI0dEouq63XRHGZfDiiy/y2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"text/plain": [ "<matplotlib.figure.Figure at 0x7fa10bf2f6d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig,ax = plt.subplots(1, 1)\n", "\n", "fs = 14\n", "\n", "for i,g in enumerate(sorted(list(set(data.Gene)))):\n", " t = data[data.Gene == g]\n", " sns.regplot(x='CNV_7_143951166_143953316', y='exp', data=t,\n", " x_jitter=0.2, fit_reg=False, ax=ax, label=g,\n", " color=sns.color_palette('husl', 7)[i], scatter_kws={'alpha':0.35, 's':40})\n", "lgd = ax.legend(fontsize=fs, loc='upper left', bbox_to_anchor=(1, 1))\n", "ax.set_ylabel('$\\log$ TPM $z$-score', fontsize=fs)\n", "ax.set_xlabel('Diploid copy number', fontsize=fs)\n", "for t in ax.get_xticklabels() + ax.get_yticklabels():\n", " t.set_fontsize(fs)\n", " \n", "fig.tight_layout()\n", "fig.savefig(os.path.join(outdir, 'CNV_7_143951166_143953316_reg.pdf'), bbox_extra_artists=(lgd,), bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Environment (cie)", "language": "", "name": "cie" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
fabriziocosta/pyMotif
Parameter Optimization.ipynb
1
15753
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1> Parameter Optimization for different noise levels in artificial datasets </h1>" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from smod_wrapper import SMoDWrapper\n", "from sklearn.cluster import KMeans\n", "import numpy as np\n", "from sklearn.metrics import roc_auc_score\n", "import datetime\n", "import time" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from eden.util import configure_logging\n", "import logging\n", "logger = logging.getLogger()\n", "configure_logging(logger,verbosity=1)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "def random_string(length,alphabet_list):\n", " rand_str = ''.join(random.choice(alphabet_list) for i in range(length))\n", " return rand_str\n", "\n", "def perturb(seed,alphabet_list,p=0.5):\n", " seq=''\n", " for c in seed:\n", " if random.random() < p: c = random.choice(alphabet_list)\n", " seq += c\n", " return seq\n", "\n", "def make_artificial_dataset(alphabet='ACGT', motives=None, motif_length=6, \n", " sequence_length=100, n_sequences=1000, n_motives=2, p=0.2,\n", " random_state=1):\n", " random.seed(random_state)\n", "\n", " alphabet_list=[c for c in alphabet]\n", " \n", " if motives is None:\n", " motives=[]\n", " for i in range(n_motives):\n", " motives.append(random_string(motif_length,alphabet_list))\n", " else:\n", " motif_length = len(motives[0])\n", " n_motives = len(motives)\n", " \n", " sequence_length = sequence_length / len(motives)\n", " flanking_length = (sequence_length - motif_length ) / 2\n", " n_seq_per_motif = n_sequences\n", "\n", " counter=0\n", " seqs=[]\n", " for i in range(n_seq_per_motif):\n", " total_seq = ''\n", " total_binary_seq=''\n", " for j in range(n_motives):\n", " left_flanking = random_string(flanking_length,alphabet_list)\n", " right_flanking = random_string(flanking_length,alphabet_list)\n", " noisy_motif = perturb(motives[j],alphabet_list,p)\n", " seq = left_flanking + noisy_motif + right_flanking\n", " total_seq += seq\n", " seqs.append(('ID%d'%counter,total_seq))\n", " counter += 1\n", " binary_skeleton = '0' * flanking_length + '1' * motif_length + '0' * flanking_length\n", " binary_seq = binary_skeleton * n_motives\n", " return motives, seqs, binary_seq" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def score_seqs(seqs, n_motives, tool):\n", " scores = []\n", " if tool is None:\n", " return scores\n", " \n", " for j in range(len(seqs)):\n", " seq_scr = []\n", " iters = tool.nmotifs\n", " for k in range(iters):\n", " scr=tool.score(motif_num=k+1, seq=seqs[j][1])\n", " seq_scr.append(scr)\n", "\n", " # taking average over all motives for a sequence\n", " if len(seq_scr) > 1:\n", " x = np.array(seq_scr[0])\n", " for l in range(1, iters):\n", " x = np.vstack((x, seq_scr[l]))\n", " seq_scr = list(np.mean(x, axis=0))\n", " scores.append(seq_scr)\n", " elif len(seq_scr) == 1:\n", " scores.append(np.array(seq_scr[0]))\n", " else:\n", " raise ValueError(\"no sequence score\")\n", " return scores" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_dataset(sequence_length=200,\n", " n_sequences=200,\n", " motif_length=10,\n", " n_motives=2, \n", " p=0.2,\n", " random_state=1):\n", " \n", " motives, pos_seqs, binary_seq = make_artificial_dataset(alphabet='ACGT',\n", " sequence_length=sequence_length,\n", " n_sequences=n_sequences,\n", " motif_length=motif_length,\n", " n_motives=n_motives,\n", " p=p, \n", " random_state=random_state)\n", "\n", " from eden.modifier.seq import seq_to_seq, shuffle_modifier\n", " neg_seqs = seq_to_seq(pos_seqs, modifier=shuffle_modifier, times=2, order=2)\n", " neg_seqs = list(neg_seqs)\n", "\n", " block_size=n_sequences/8\n", "\n", " pos_size = len(pos_seqs)\n", " train_pos_seqs = pos_seqs[:pos_size/2]\n", " test_pos_seqs = pos_seqs[pos_size/2:]\n", "\n", " neg_size = len(neg_seqs)\n", " train_neg_seqs = neg_seqs[:neg_size/2]\n", " test_neg_seqs = neg_seqs[neg_size/2:]\n", "\n", " true_score = [float(int(i)) for i in binary_seq]\n", " return (block_size, train_pos_seqs, train_neg_seqs, test_pos_seqs, n_motives, true_score)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def test_on_datasets(n_sets = 5, param_setting=None, p=0.2, max_roc=0.5, std_roc=0.01):\n", " dataset_score = []\n", " seeds = [i * 2000 for i in range(1, n_sets + 1)]\n", " for k in range(n_sets):\n", " # Generate data set\n", " seed = seeds[k]\n", " data = get_dataset(sequence_length=40,\n", " n_sequences=50,\n", " motif_length=10,\n", " n_motives=2,\n", " p=p,\n", " random_state=seed)\n", " block_size = data[0]\n", " train_pos_seqs = data[1]\n", " train_neg_seqs = data[2]\n", " test_pos_seqs = data[3]\n", " n_motives = data[4]\n", " true_score = data[5]\n", "\n", " smod = SMoDWrapper(alphabet = 'dna',\n", " scoring_criteria = 'pwm',\n", "\n", " complexity = 5,\n", " n_clusters = 10,\n", " min_subarray_size = 8,\n", " max_subarray_size = 12,\n", " clusterer = KMeans(),\n", " pos_block_size = block_size,\n", " neg_block_size = block_size,\n", " # sample_size = 300,\n", " p_value = param_setting['p_value'],\n", " similarity_th = param_setting['similarity_th'],\n", " min_score = param_setting['min_score'],\n", " min_freq = param_setting['min_freq'],\n", " min_cluster_size = param_setting['min_cluster_size'],\n", " regex_th = param_setting['regex_th'],\n", " freq_th = param_setting['freq_th'],\n", " std_th = param_setting['std_th']) \n", "\n", " \n", "\n", " try:\n", " smod.fit(train_pos_seqs, train_neg_seqs)\n", " scores = score_seqs(seqs = test_pos_seqs,\n", " n_motives = n_motives,\n", " tool = smod)\n", " except:\n", " continue\n", "\n", " mean_score = np.mean(scores, axis=0)\n", " roc_score = roc_auc_score(true_score, mean_score)\n", "\n", "\n", " # if a parameter setting performs poorly, don't test on other datasets\n", " # z-score = (x - mu)/sigma\n", " # if ((roc_score - max_roc)/std_roc) > 2:\n", " if roc_score < 0.6:\n", " break\n", "\n", " dataset_score.append(roc_score)\n", " return dataset_score" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def check_validity(key, value, noise):\n", " if key == 'min_score': # atleast greater than (motif_length)/2\n", " if value >= 5:\n", " return True, int(round(value))\n", " elif key == 'min_cluster_size':\n", " if value >= 3:\n", " return True, int(round(value))\n", " elif key == 'min_freq': # atmost (1 - noise_level)\n", " if value > 0 and value <= (1 - noise):\n", " return True, value\n", " elif key == 'p_value':\n", " if value <= 1.0 and value >= 0.0:\n", " return True, value\n", " elif key == 'similarity_th':\n", " if value <= 1.0 and value >= 0.8:\n", " return True, value\n", " elif key == 'regex_th':\n", " if value > 0 and value <= 0.3:\n", " return True, value\n", " elif key == 'freq_th':\n", " if value <= 1.0 and value > 0:\n", " return True, value\n", " elif key == 'std_th':\n", " if value <= 1.0 and value > 0:\n", " return True, value\n", " else:\n", " raise ValueError('Invalid key: ', key)\n", " return False, value\n", "\n", "def random_setting(parameters=None, best_config=None, noise=None):\n", " parameter_setting = {}\n", " MAX_ITER = 1000\n", " if not parameters['min_score']: # use best_configuration of last run as initial setting\n", " for key in parameters.keys():\n", " parameters[key].append(best_config[key])\n", " parameter_setting[key] = best_config[key]\n", " else:\n", " for key in parameters.keys():\n", " success = False\n", " n_iter = 0\n", " mu = np.mean(parameters[key])\n", " sigma = np.mean(parameters[key])\n", " if sigma == 0:\n", " sigma == 0.1\n", " while not success:\n", " if n_iter == MAX_ITER: # if max_iterations exceeded, return mean as value\n", " value = mu\n", " if key in ['min_score', 'min_cluster_size']:\n", " value = int(round(value))\n", " break\n", " value = np.random.normal(mu, 2 * sigma)\n", " n_iter += 1\n", " success, value = check_validity(key, value, noise)\n", " parameter_setting[key] = value\n", " return parameter_setting" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "07:26:42 Starting experiment...\n", "\n", "07:26:48 Better Configuration found at perturbation prob = 0.1\n", "ROC: 0.7\n", "Parameter Configuration: {'min_freq': 0.1, 'min_score': 6, 'p_value': 0.1, 'min_cluster_size': 3, 'std_th': 0.2, 'regex_th': 0.3, 'similarity_th': 0.8, 'freq_th': 0.05}\n", "\n" ] } ], "source": [ "%%time\n", "\n", "print datetime.datetime.fromtimestamp(time.time()).strftime('%H:%M:%S'),\n", "print \"Starting experiment...\\n\"\n", "\n", "best_config = {'min_score':6, # atleast motif_length/2\n", " 'min_freq':0.1, # can not be more than (1- noise level)\n", " 'min_cluster_size':3, # atleast 3\n", " 'p_value':0.1, # atleast 0.1\n", " 'similarity_th':0.8, # 0.8 \n", " 'regex_th':0.3, # max 0.3 \n", " 'freq_th':0.05, # 0.05 \n", " 'std_th':0.2} # 0.2\n", "\n", "# Final results\n", "param = [0.1, 0.2, 0.3]#, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n", "results_dic = {}\n", "\n", "reps = 30 #0 # different settings to be tried\n", "\n", "for i in param:\n", " parameters = {'min_freq': [],\n", " 'min_cluster_size': [],\n", " 'p_value': [],\n", " 'similarity_th': [],\n", " 'min_score': [],\n", " 'regex_th': [],\n", " 'freq_th': [],\n", " 'std_th': []}\n", " max_roc = 0.5\n", " std_roc = 0.01\n", " #parameters = generate_dist(parameters, best_config)\n", " for j in range(reps):\n", " param_setting = random_setting(parameters, best_config, i) # Randomize Parameter setting\n", " n_sets = 5 # Different data sets\n", " dataset_score = test_on_datasets(n_sets=n_sets, \n", " param_setting=param_setting, \n", " p=i, \n", " max_roc=max_roc,\n", " std_roc=std_roc)\n", " mean_roc = np.mean(dataset_score)\n", " std = np.std(dataset_score)\n", "\n", " if mean_roc > max_roc:\n", " max_roc = mean_roc\n", " std_roc = std\n", " print datetime.datetime.fromtimestamp(time.time()).strftime('%H:%M:%S'),\n", " print \"Better Configuration found at perturbation prob = \", i\n", " print \"ROC: \", mean_roc\n", " print \"Parameter Configuration: \", param_setting\n", " print\n", " best_config = param_setting\n", " param_setting[\"ROC\"] = mean_roc\n", " results_dic[i] = param_setting\n", " \n", "print datetime.datetime.fromtimestamp(time.time()).strftime('%H:%M:%S'),\n", "print \" Finished experiment...\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
sz2472/foundations-homework
homework12/311_time_series_homework.ipynb
1
303594
{ "cells": [ { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "# import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('ggplot')\n", "import dateutil.parser" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> First, I made a mistake naming the data set! **It's 2015 data, not 2014 data.** But yes, still use `311-2014.csv`. You can rename it.\n", "\n", "# Importing and preparing your data\n", "\n", "Import your data, but **only the first 200,000 rows**. You'll also want to change the index to be a datetime based on the **Created Date** column - you'll want to check if it's already a datetime, and parse it if not." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (8) have mixed types. Specify dtype option on import or set low_memory=False.\n", " interactivity=interactivity, compiler=compiler, result=result)\n" ] } ], "source": [ "df=pd.read_csv(\"311-2014.csv\",nrows=20000)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unique Key</th>\n", " <th>Created Date</th>\n", " <th>Closed Date</th>\n", " <th>Agency</th>\n", " <th>Agency Name</th>\n", " <th>Complaint Type</th>\n", " <th>Descriptor</th>\n", " <th>Location Type</th>\n", " <th>Incident Zip</th>\n", " <th>Incident Address</th>\n", " <th>...</th>\n", " <th>Bridge Highway Name</th>\n", " <th>Bridge Highway Direction</th>\n", " <th>Road Ramp</th>\n", " <th>Bridge Highway Segment</th>\n", " <th>Garage Lot Name</th>\n", " <th>Ferry Direction</th>\n", " <th>Ferry Terminal Name</th>\n", " <th>Latitude</th>\n", " <th>Longitude</th>\n", " <th>Location</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>31015465</td>\n", " <td>07/06/2015 10:58:27 AM</td>\n", " <td>07/22/2015 01:07:20 AM</td>\n", " <td>DCA</td>\n", " <td>Department of Consumer Affairs</td>\n", " <td>Consumer Complaint</td>\n", " <td>Demand for Cash</td>\n", " <td>NaN</td>\n", " <td>11360</td>\n", " <td>27-16 203 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.773540</td>\n", " <td>-73.788237</td>\n", " <td>(40.773539552542, -73.78823697228408)</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>30997660</td>\n", " <td>07/03/2015 01:26:29 PM</td>\n", " <td>07/03/2015 02:08:20 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Vending</td>\n", " <td>In Prohibited Area</td>\n", " <td>Residential Building/House</td>\n", " <td>10019</td>\n", " <td>200 CENTRAL PARK SOUTH</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.767021</td>\n", " <td>-73.979448</td>\n", " <td>(40.76702142171206, -73.97944780718524)</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>31950223</td>\n", " <td>11/09/2015 03:55:09 AM</td>\n", " <td>11/09/2015 08:08:57 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Blocked Driveway</td>\n", " <td>No Access</td>\n", " <td>Street/Sidewalk</td>\n", " <td>10453</td>\n", " <td>1993 GRAND AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.852671</td>\n", " <td>-73.910608</td>\n", " <td>(40.85267061877697, -73.91060771362552)</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>31000038</td>\n", " <td>07/03/2015 02:18:32 AM</td>\n", " <td>07/03/2015 07:54:48 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Commercial</td>\n", " <td>Loud Music/Party</td>\n", " <td>Club/Bar/Restaurant</td>\n", " <td>11372</td>\n", " <td>84-16 NORTHERN BOULEVARD</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.755774</td>\n", " <td>-73.883262</td>\n", " <td>(40.755773786469966, -73.88326243225418)</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>30995614</td>\n", " <td>07/04/2015 12:03:27 AM</td>\n", " <td>07/04/2015 03:33:09 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Street/Sidewalk</td>\n", " <td>Loud Talking</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11216</td>\n", " <td>1057 BERGEN STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.676175</td>\n", " <td>-73.951269</td>\n", " <td>(40.67617516102934, -73.9512690004692)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 53 columns</p>\n", "</div>" ], "text/plain": [ " Unique Key Created Date Closed Date Agency \\\n", "0 31015465 07/06/2015 10:58:27 AM 07/22/2015 01:07:20 AM DCA \n", "1 30997660 07/03/2015 01:26:29 PM 07/03/2015 02:08:20 PM NYPD \n", "2 31950223 11/09/2015 03:55:09 AM 11/09/2015 08:08:57 AM NYPD \n", "3 31000038 07/03/2015 02:18:32 AM 07/03/2015 07:54:48 AM NYPD \n", "4 30995614 07/04/2015 12:03:27 AM 07/04/2015 03:33:09 AM NYPD \n", "\n", " Agency Name Complaint Type \\\n", "0 Department of Consumer Affairs Consumer Complaint \n", "1 New York City Police Department Vending \n", "2 New York City Police Department Blocked Driveway \n", "3 New York City Police Department Noise - Commercial \n", "4 New York City Police Department Noise - Street/Sidewalk \n", "\n", " Descriptor Location Type Incident Zip \\\n", "0 Demand for Cash NaN 11360 \n", "1 In Prohibited Area Residential Building/House 10019 \n", "2 No Access Street/Sidewalk 10453 \n", "3 Loud Music/Party Club/Bar/Restaurant 11372 \n", "4 Loud Talking Street/Sidewalk 11216 \n", "\n", " Incident Address ... \\\n", "0 27-16 203 STREET ... \n", "1 200 CENTRAL PARK SOUTH ... \n", "2 1993 GRAND AVENUE ... \n", "3 84-16 NORTHERN BOULEVARD ... \n", "4 1057 BERGEN STREET ... \n", "\n", " Bridge Highway Name Bridge Highway Direction Road Ramp \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 NaN NaN NaN \n", "4 NaN NaN NaN \n", "\n", " Bridge Highway Segment Garage Lot Name Ferry Direction Ferry Terminal Name \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "3 NaN NaN NaN NaN \n", "4 NaN NaN NaN NaN \n", "\n", " Latitude Longitude Location \n", "0 40.773540 -73.788237 (40.773539552542, -73.78823697228408) \n", "1 40.767021 -73.979448 (40.76702142171206, -73.97944780718524) \n", "2 40.852671 -73.910608 (40.85267061877697, -73.91060771362552) \n", "3 40.755774 -73.883262 (40.755773786469966, -73.88326243225418) \n", "4 40.676175 -73.951269 (40.67617516102934, -73.9512690004692) \n", "\n", "[5 rows x 53 columns]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['Unique Key', 'Created Date', 'Closed Date', 'Agency', 'Agency Name',\n", " 'Complaint Type', 'Descriptor', 'Location Type', 'Incident Zip',\n", " 'Incident Address', 'Street Name', 'Cross Street 1', 'Cross Street 2',\n", " 'Intersection Street 1', 'Intersection Street 2', 'Address Type',\n", " 'City', 'Landmark', 'Facility Type', 'Status', 'Due Date',\n", " 'Resolution Description', 'Resolution Action Updated Date',\n", " 'Community Board', 'Borough', 'X Coordinate (State Plane)',\n", " 'Y Coordinate (State Plane)', 'Park Facility Name', 'Park Borough',\n", " 'School Name', 'School Number', 'School Region', 'School Code',\n", " 'School Phone Number', 'School Address', 'School City', 'School State',\n", " 'School Zip', 'School Not Found', 'School or Citywide Complaint',\n", " 'Vehicle Type', 'Taxi Company Borough', 'Taxi Pick Up Location',\n", " 'Bridge Highway Name', 'Bridge Highway Direction', 'Road Ramp',\n", " 'Bridge Highway Segment', 'Garage Lot Name', 'Ferry Direction',\n", " 'Ferry Terminal Name', 'Latitude', 'Longitude', 'Location'],\n", " dtype='object')" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 20000 entries, 0 to 19999\n", "Data columns (total 53 columns):\n", "Unique Key 20000 non-null int64\n", "Created Date 20000 non-null object\n", "Closed Date 18788 non-null object\n", "Agency 20000 non-null object\n", "Agency Name 20000 non-null object\n", "Complaint Type 20000 non-null object\n", "Descriptor 19745 non-null object\n", "Location Type 18372 non-null object\n", "Incident Zip 18779 non-null object\n", "Incident Address 15073 non-null object\n", "Street Name 15069 non-null object\n", "Cross Street 1 13224 non-null object\n", "Cross Street 2 13179 non-null object\n", "Intersection Street 1 2992 non-null object\n", "Intersection Street 2 2965 non-null object\n", "Address Type 18095 non-null object\n", "City 18788 non-null object\n", "Landmark 22 non-null object\n", "Facility Type 9699 non-null object\n", "Status 20000 non-null object\n", "Due Date 17403 non-null object\n", "Resolution Description 19838 non-null object\n", "Resolution Action Updated Date 18630 non-null object\n", "Community Board 20000 non-null object\n", "Borough 20000 non-null object\n", "X Coordinate (State Plane) 18093 non-null float64\n", "Y Coordinate (State Plane) 18093 non-null float64\n", "Park Facility Name 20000 non-null object\n", "Park Borough 20000 non-null object\n", "School Name 20000 non-null object\n", "School Number 19991 non-null object\n", "School Region 19597 non-null object\n", "School Code 19597 non-null object\n", "School Phone Number 20000 non-null object\n", "School Address 20000 non-null object\n", "School City 20000 non-null object\n", "School State 20000 non-null object\n", "School Zip 20000 non-null object\n", "School Not Found 17987 non-null object\n", "School or Citywide Complaint 0 non-null float64\n", "Vehicle Type 6 non-null object\n", "Taxi Company Borough 65 non-null object\n", "Taxi Pick Up Location 494 non-null object\n", "Bridge Highway Name 395 non-null object\n", "Bridge Highway Direction 395 non-null object\n", "Road Ramp 394 non-null object\n", "Bridge Highway Segment 395 non-null object\n", "Garage Lot Name 18 non-null object\n", "Ferry Direction 6 non-null object\n", "Ferry Terminal Name 17 non-null object\n", "Latitude 18093 non-null float64\n", "Longitude 18093 non-null float64\n", "Location 18093 non-null object\n", "dtypes: float64(5), int64(1), object(47)\n", "memory usage: 8.1+ MB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dateutil.parser.parse('07/16/1990').month" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unique Key</th>\n", " <th>Created Date</th>\n", " <th>Closed Date</th>\n", " <th>Agency</th>\n", " <th>Agency Name</th>\n", " <th>Complaint Type</th>\n", " <th>Descriptor</th>\n", " <th>Location Type</th>\n", " <th>Incident Zip</th>\n", " <th>Incident Address</th>\n", " <th>...</th>\n", " <th>Bridge Highway Name</th>\n", " <th>Bridge Highway Direction</th>\n", " <th>Road Ramp</th>\n", " <th>Bridge Highway Segment</th>\n", " <th>Garage Lot Name</th>\n", " <th>Ferry Direction</th>\n", " <th>Ferry Terminal Name</th>\n", " <th>Latitude</th>\n", " <th>Longitude</th>\n", " <th>Location</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>31015465</td>\n", " <td>2015-07-06 10:58:27</td>\n", " <td>07/22/2015 01:07:20 AM</td>\n", " <td>DCA</td>\n", " <td>Department of Consumer Affairs</td>\n", " <td>Consumer Complaint</td>\n", " <td>Demand for Cash</td>\n", " <td>NaN</td>\n", " <td>11360</td>\n", " <td>27-16 203 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.773540</td>\n", " <td>-73.788237</td>\n", " <td>(40.773539552542, -73.78823697228408)</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>30997660</td>\n", " <td>2015-07-03 13:26:29</td>\n", " <td>07/03/2015 02:08:20 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Vending</td>\n", " <td>In Prohibited Area</td>\n", " <td>Residential Building/House</td>\n", " <td>10019</td>\n", " <td>200 CENTRAL PARK SOUTH</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.767021</td>\n", " <td>-73.979448</td>\n", " <td>(40.76702142171206, -73.97944780718524)</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>31950223</td>\n", " <td>2015-11-09 03:55:09</td>\n", " <td>11/09/2015 08:08:57 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Blocked Driveway</td>\n", " <td>No Access</td>\n", " <td>Street/Sidewalk</td>\n", " <td>10453</td>\n", " <td>1993 GRAND AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.852671</td>\n", " <td>-73.910608</td>\n", " <td>(40.85267061877697, -73.91060771362552)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3 rows × 53 columns</p>\n", "</div>" ], "text/plain": [ " Unique Key Created Date Closed Date Agency \\\n", "0 31015465 2015-07-06 10:58:27 07/22/2015 01:07:20 AM DCA \n", "1 30997660 2015-07-03 13:26:29 07/03/2015 02:08:20 PM NYPD \n", "2 31950223 2015-11-09 03:55:09 11/09/2015 08:08:57 AM NYPD \n", "\n", " Agency Name Complaint Type Descriptor \\\n", "0 Department of Consumer Affairs Consumer Complaint Demand for Cash \n", "1 New York City Police Department Vending In Prohibited Area \n", "2 New York City Police Department Blocked Driveway No Access \n", "\n", " Location Type Incident Zip Incident Address \\\n", "0 NaN 11360 27-16 203 STREET \n", "1 Residential Building/House 10019 200 CENTRAL PARK SOUTH \n", "2 Street/Sidewalk 10453 1993 GRAND AVENUE \n", "\n", " ... Bridge Highway Name \\\n", "0 ... NaN \n", "1 ... NaN \n", "2 ... NaN \n", "\n", " Bridge Highway Direction Road Ramp Bridge Highway Segment Garage Lot Name \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 NaN NaN NaN NaN \n", "\n", " Ferry Direction Ferry Terminal Name Latitude Longitude \\\n", "0 NaN NaN 40.773540 -73.788237 \n", "1 NaN NaN 40.767021 -73.979448 \n", "2 NaN NaN 40.852671 -73.910608 \n", "\n", " Location \n", "0 (40.773539552542, -73.78823697228408) \n", "1 (40.76702142171206, -73.97944780718524) \n", "2 (40.85267061877697, -73.91060771362552) \n", "\n", "[3 rows x 53 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def parse_date (str_date):\n", " return dateutil.parser.parse(str_date)#dateutil is a module, import parser class, then transform a string into a python time object\n", "df['Created Date']= df['Created Date'].apply(parse_date)\n", "df.head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What was the **most popular type of complaint**, and how many times was it filed?" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Blocked Driveway 2534\n", "Illegal Parking 2410\n", "Noise - Street/Sidewalk 1584\n", "Street Condition 1216\n", "Noise - Commercial 1162\n", "Consumer Complaint 705\n", "Benefit Card Replacement 701\n", "Broken Muni Meter 690\n", "Derelict Vehicle 538\n", "Noise - Vehicle 530\n", "Taxi Complaint 489\n", "Damaged Tree 420\n", "Overgrown Tree/Branches 365\n", "Highway Condition 364\n", "HEAT/HOT WATER 359\n", "Maintenance or Facility 333\n", "Food Establishment 325\n", "Animal Abuse 292\n", "Graffiti 269\n", "SCRIE 235\n", "Dead Tree 224\n", "Construction 219\n", "UNSANITARY CONDITION 204\n", "Indoor Air Quality 203\n", "Root/Sewer/Sidewalk Condition 189\n", "Sidewalk Condition 170\n", "Homeless Encampment 160\n", "Fire Safety Director - F58 159\n", "PAINT/PLASTER 147\n", "Traffic 145\n", " ... \n", "DOF Property - Reduction Issue 2\n", "Hazardous Materials 2\n", "Drinking Water 2\n", "Boilers 2\n", "Open Flame Permit 2\n", "Missed Collection (All Materials) 2\n", "Beach/Pool/Sauna Complaint 2\n", "Noise 2\n", "Derelict Vehicles 2\n", "Litter Basket / Request 2\n", "New Tree Request 2\n", "Bus Stop Shelter Placement 2\n", "OUTSIDE BUILDING 1\n", "Special Projects Inspection Team (SPIT) 1\n", "Public Toilet 1\n", "Unsanitary Animal Facility 1\n", "ELEVATOR 1\n", "Highway Sign - Missing 1\n", "Window Guard 1\n", "Other Enforcement 1\n", "Unsanitary Pigeon Condition 1\n", "Senior Center Complaint 1\n", "Municipal Parking Facility 1\n", "Highway Sign - Dangling 1\n", "Compliment 1\n", "Hazmat Storage/Use 1\n", "Air Quality 1\n", "X-Ray Machine/Equipment 1\n", "DOF Property - Owner Issue 1\n", "DOF Parking - Tax Exemption 1\n", "Name: Complaint Type, dtype: int64" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Complaint Type'].value_counts()\n", "# the most popular type of complaint is blocked driveway, and it was filed 2534 times" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make a horizontal bar graph of the **top 5 most frequent complaint types**." ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x112010fd0>" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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T22VUGl/uz5d7gyrcX0iotyvwOQplKaE8lzdUVXYfvyzDl/vz5d7A9/uT8tP0tYiIiEko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\nRkbicrk4efIk4eH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"text/plain": [ "<matplotlib.figure.Figure at 0x111f18b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df['Complaint Type'].value_counts().head(5).sort_values().plot(kind='barh')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which borough has the **most complaints per capita?** Since it's only 5 boroughs, you can do the math manually." ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "BROOKLYN 5761\n", "QUEENS 5500\n", "MANHATTAN 4491\n", "BRONX 2446\n", "Unspecified 988\n", "STATEN ISLAND 814\n", "Name: Borough, dtype: int64" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Borough'].value_counts()" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true }, "outputs": [], "source": [ "people_bronx= 1438159\n", "people_queens= 2321580\n", "people_manhattan=1636268\n", "people_brooklyn= 2621793\n", "people_staten_island= 473279" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.020588822237318682" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" } ], "source": [ "complaints_per_capita_bronx= 29610/people_bronx\n", "complaints_per_capita_bronx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to your selection of data, **how many cases were filed in March?** How about May?" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[numpy.datetime64('2015-07-06T10:58:27.000000000'),\n", " numpy.datetime64('2015-07-03T13:26:29.000000000'),\n", " numpy.datetime64('2015-11-09T03:55:09.000000000'),\n", " numpy.datetime64('2015-07-03T02:18:32.000000000'),\n", " numpy.datetime64('2015-07-04T00:03:27.000000000'),\n", " numpy.datetime64('2015-07-09T00:00:00.000000000'),\n", " 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numpy.datetime64('2015-09-14T12:04:07.000000000'),\n", " numpy.datetime64('2015-09-14T21:36:26.000000000'),\n", " numpy.datetime64('2015-09-15T16:37:18.000000000'),\n", " numpy.datetime64('2015-05-08T19:29:12.000000000'),\n", " numpy.datetime64('2015-05-15T11:42:20.000000000'),\n", " numpy.datetime64('2015-09-15T19:22:22.000000000'),\n", " numpy.datetime64('2015-09-15T11:58:38.000000000'),\n", " numpy.datetime64('2015-09-15T14:51:52.000000000'),\n", " ...]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[x for x in list(df['Created Date'].values)]# if np.datetime64(x, 'M') == 3]" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "date_index = pd.DatetimeIndex(df['Created Date'].values)#for dataframe, each column is a series(object), call a values method" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2361" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len([x for x in date_index.month if x == 3])" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [], "source": [ "iterable = filter(lambda x: x == 3, list(date_index.month))" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2361" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "march_days = 0\n", "for x in iterable:\n", " march_days += 1\n", "march_days" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I'd like to see all of the 311 complaints **called in on April 1st.**\n", "\n", "> **Surprise!** We couldn't do this in class, but it was just a limitation of our data set" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unique Key</th>\n", " <th>Created Date</th>\n", " <th>Closed Date</th>\n", " <th>Agency</th>\n", " <th>Agency Name</th>\n", " <th>Complaint Type</th>\n", " <th>Descriptor</th>\n", " <th>Location Type</th>\n", " <th>Incident Zip</th>\n", " <th>Incident Address</th>\n", " <th>...</th>\n", " <th>Bridge Highway Name</th>\n", " <th>Bridge Highway Direction</th>\n", " <th>Road Ramp</th>\n", " <th>Bridge Highway Segment</th>\n", " <th>Garage Lot Name</th>\n", " <th>Ferry Direction</th>\n", " <th>Ferry Terminal Name</th>\n", " <th>Latitude</th>\n", " <th>Longitude</th>\n", " <th>Location</th>\n", " </tr>\n", " <tr>\n", " <th>Created Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " 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<td>Vending</td>\n", " <td>In Prohibited Area</td>\n", " <td>Residential Building/House</td>\n", " <td>10019</td>\n", " <td>200 CENTRAL PARK SOUTH</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.767021</td>\n", " <td>-73.979448</td>\n", " <td>(40.76702142171206, -73.97944780718524)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-11-09 03:55:09</th>\n", " <td>31950223</td>\n", " <td>2015-11-09 03:55:09</td>\n", " <td>11/09/2015 08:08:57 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Blocked Driveway</td>\n", " <td>No Access</td>\n", " <td>Street/Sidewalk</td>\n", " <td>10453</td>\n", " <td>1993 GRAND AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.852671</td>\n", " <td>-73.910608</td>\n", " <td>(40.85267061877697, -73.91060771362552)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-07-03 02:18:32</th>\n", " <td>31000038</td>\n", " <td>2015-07-03 02:18:32</td>\n", " <td>07/03/2015 07:54:48 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Commercial</td>\n", " <td>Loud Music/Party</td>\n", " <td>Club/Bar/Restaurant</td>\n", " <td>11372</td>\n", " <td>84-16 NORTHERN BOULEVARD</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.755774</td>\n", " <td>-73.883262</td>\n", " <td>(40.755773786469966, -73.88326243225418)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-07-04 00:03:27</th>\n", " <td>30995614</td>\n", " <td>2015-07-04 00:03:27</td>\n", " <td>07/04/2015 03:33:09 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Street/Sidewalk</td>\n", " <td>Loud Talking</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11216</td>\n", " <td>1057 BERGEN STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.676175</td>\n", " <td>-73.951269</td>\n", " <td>(40.67617516102934, -73.9512690004692)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 53 columns</p>\n", "</div>" ], "text/plain": [ " Unique Key Created Date Closed Date \\\n", "Created Date \n", "2015-07-06 10:58:27 31015465 2015-07-06 10:58:27 07/22/2015 01:07:20 AM \n", "2015-07-03 13:26:29 30997660 2015-07-03 13:26:29 07/03/2015 02:08:20 PM \n", "2015-11-09 03:55:09 31950223 2015-11-09 03:55:09 11/09/2015 08:08:57 AM \n", "2015-07-03 02:18:32 31000038 2015-07-03 02:18:32 07/03/2015 07:54:48 AM \n", "2015-07-04 00:03:27 30995614 2015-07-04 00:03:27 07/04/2015 03:33:09 AM \n", "\n", " Agency Agency Name \\\n", "Created Date \n", "2015-07-06 10:58:27 DCA Department of Consumer Affairs \n", "2015-07-03 13:26:29 NYPD New York City Police Department \n", "2015-11-09 03:55:09 NYPD New York City Police Department \n", "2015-07-03 02:18:32 NYPD New York City Police Department \n", "2015-07-04 00:03:27 NYPD New York City Police Department \n", "\n", " Complaint Type Descriptor \\\n", "Created Date \n", "2015-07-06 10:58:27 Consumer Complaint Demand for Cash \n", "2015-07-03 13:26:29 Vending In Prohibited Area \n", "2015-11-09 03:55:09 Blocked Driveway No Access \n", "2015-07-03 02:18:32 Noise - Commercial Loud Music/Party \n", "2015-07-04 00:03:27 Noise - Street/Sidewalk Loud Talking \n", "\n", " Location Type Incident Zip \\\n", "Created Date \n", "2015-07-06 10:58:27 NaN 11360 \n", "2015-07-03 13:26:29 Residential Building/House 10019 \n", "2015-11-09 03:55:09 Street/Sidewalk 10453 \n", "2015-07-03 02:18:32 Club/Bar/Restaurant 11372 \n", "2015-07-04 00:03:27 Street/Sidewalk 11216 \n", "\n", " Incident Address \\\n", "Created Date \n", "2015-07-06 10:58:27 27-16 203 STREET \n", "2015-07-03 13:26:29 200 CENTRAL PARK SOUTH \n", "2015-11-09 03:55:09 1993 GRAND AVENUE \n", "2015-07-03 02:18:32 84-16 NORTHERN BOULEVARD \n", "2015-07-04 00:03:27 1057 BERGEN STREET \n", "\n", " ... \\\n", "Created Date ... \n", "2015-07-06 10:58:27 ... \n", "2015-07-03 13:26:29 ... \n", "2015-11-09 03:55:09 ... \n", "2015-07-03 02:18:32 ... \n", "2015-07-04 00:03:27 ... \n", "\n", " Bridge Highway Name Bridge Highway Direction Road Ramp \\\n", "Created Date \n", "2015-07-06 10:58:27 NaN NaN NaN \n", "2015-07-03 13:26:29 NaN NaN NaN \n", "2015-11-09 03:55:09 NaN NaN NaN \n", "2015-07-03 02:18:32 NaN NaN NaN \n", "2015-07-04 00:03:27 NaN NaN NaN \n", "\n", " Bridge Highway Segment Garage Lot Name Ferry Direction \\\n", "Created Date \n", "2015-07-06 10:58:27 NaN NaN NaN \n", "2015-07-03 13:26:29 NaN NaN NaN \n", "2015-11-09 03:55:09 NaN NaN NaN \n", "2015-07-03 02:18:32 NaN NaN NaN \n", "2015-07-04 00:03:27 NaN NaN NaN \n", "\n", " Ferry Terminal Name Latitude Longitude \\\n", "Created Date \n", "2015-07-06 10:58:27 NaN 40.773540 -73.788237 \n", "2015-07-03 13:26:29 NaN 40.767021 -73.979448 \n", "2015-11-09 03:55:09 NaN 40.852671 -73.910608 \n", "2015-07-03 02:18:32 NaN 40.755774 -73.883262 \n", "2015-07-04 00:03:27 NaN 40.676175 -73.951269 \n", "\n", " Location \n", "Created Date \n", "2015-07-06 10:58:27 (40.773539552542, -73.78823697228408) \n", "2015-07-03 13:26:29 (40.76702142171206, -73.97944780718524) \n", "2015-11-09 03:55:09 (40.85267061877697, -73.91060771362552) \n", "2015-07-03 02:18:32 (40.755773786469966, -73.88326243225418) \n", "2015-07-04 00:03:27 (40.67617516102934, -73.9512690004692) \n", "\n", "[5 rows x 53 columns]" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index=df['Created Date']\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What was the most popular type of complaint on April 1st?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What were the **most popular three types of complaint** on April 1st" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Unique Key</th>\n", " <th>Created Date</th>\n", " <th>Closed Date</th>\n", " <th>Agency</th>\n", " <th>Agency Name</th>\n", " <th>Complaint Type</th>\n", " <th>Descriptor</th>\n", " <th>Location Type</th>\n", " <th>Incident Zip</th>\n", " <th>Incident Address</th>\n", " <th>...</th>\n", " <th>Bridge Highway Name</th>\n", " <th>Bridge Highway Direction</th>\n", " <th>Road Ramp</th>\n", " <th>Bridge Highway Segment</th>\n", " <th>Garage Lot Name</th>\n", " <th>Ferry Direction</th>\n", " <th>Ferry Terminal Name</th>\n", " <th>Latitude</th>\n", " <th>Longitude</th>\n", " <th>Location</th>\n", " </tr>\n", " <tr>\n", " <th>Created Date</th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2015-04-01 21:37:42</th>\n", " <td>30311691</td>\n", " <td>2015-04-01 21:37:42</td>\n", " <td>04/01/2015 10:49:33 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Illegal Parking</td>\n", " <td>Blocked Sidewalk</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11234</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.609810</td>\n", " <td>-73.922498</td>\n", " <td>(40.60980966645303, -73.92249759633725)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 23:12:04</th>\n", " <td>30307701</td>\n", " <td>2015-04-01 23:12:04</td>\n", " <td>04/01/2015 11:32:40 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Commercial</td>\n", " <td>Loud Music/Party</td>\n", " <td>Store/Commercial</td>\n", " <td>11205</td>\n", " <td>700 MYRTLE AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.694644</td>\n", " <td>-73.955504</td>\n", " <td>(40.694643700748486, -73.95550356170298)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 13:10:35</th>\n", " <td>30313389</td>\n", " <td>2015-04-01 13:10:35</td>\n", " <td>04/07/2015 04:01:08 PM</td>\n", " <td>DPR</td>\n", " <td>Department of Parks and Recreation</td>\n", " <td>Root/Sewer/Sidewalk Condition</td>\n", " <td>Trees and Sidewalks Program</td>\n", " <td>Street</td>\n", " <td>11422</td>\n", " <td>245-16 149 AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.653016</td>\n", " <td>-73.738626</td>\n", " <td>(40.653016256598534, -73.73862588133056)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 17:37:38</th>\n", " <td>30314393</td>\n", " <td>2015-04-01 17:37:38</td>\n", " <td>04/03/2015 11:40:54 AM</td>\n", " <td>DPR</td>\n", " <td>Department of Parks and Recreation</td>\n", " <td>Maintenance or Facility</td>\n", " <td>Hours of Operation</td>\n", " <td>Park</td>\n", " <td>11211</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 12:32:40</th>\n", " <td>30309207</td>\n", " <td>2015-04-01 12:32:40</td>\n", " <td>04/17/2015 01:06:49 AM</td>\n", " <td>DCA</td>\n", " <td>Department of Consumer Affairs</td>\n", " <td>Consumer Complaint</td>\n", " <td>Installation/Work Quality</td>\n", " <td>NaN</td>\n", " <td>11423</td>\n", " <td>90-71 198 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.714299</td>\n", " <td>-73.761158</td>\n", " <td>(40.71429859671565, -73.76115807774032)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 18:44:50</th>\n", " <td>30311759</td>\n", " <td>2015-04-01 18:44:50</td>\n", " <td>06/24/2015 11:27:00 AM</td>\n", " <td>DPR</td>\n", " <td>Department of Parks and Recreation</td>\n", " <td>Damaged Tree</td>\n", " <td>Entire Tree Has Fallen Down</td>\n", " <td>Street</td>\n", " <td>10467</td>\n", " <td>862 EAST 213 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.878028</td>\n", " <td>-73.860237</td>\n", " <td>(40.87802828144708, -73.86023734606933)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 16:30:15</th>\n", " <td>30309690</td>\n", " <td>2015-04-01 16:30:15</td>\n", " <td>04/01/2015 11:27:22 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Animal Abuse</td>\n", " <td>Neglected</td>\n", " <td>Residential Building/House</td>\n", " <td>11368</td>\n", " <td>107-15 NORTHERN BOULEVARD</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.757811</td>\n", " <td>-73.861677</td>\n", " <td>(40.757811195752154, -73.86167714731972)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 09:04:07</th>\n", " <td>30307990</td>\n", " <td>2015-04-01 09:04:07</td>\n", " <td>04/06/2015 09:17:10 AM</td>\n", " <td>DOF</td>\n", " <td>Senior Citizen Rent Increase Exemption Unit</td>\n", " <td>SCRIE</td>\n", " <td>Miscellaneous</td>\n", " <td>Senior Address</td>\n", " <td>10027</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 07:46:58</th>\n", " <td>30308253</td>\n", " <td>2015-04-01 07:46:58</td>\n", " <td>04/01/2015 09:32:31 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Blocked Driveway</td>\n", " <td>No Access</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11370</td>\n", " <td>32-51 80 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.756412</td>\n", " <td>-73.887405</td>\n", " <td>(40.75641194675221, -73.88740503059863)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 17:12:17</th>\n", " <td>30314214</td>\n", " <td>2015-04-01 17:12:17</td>\n", " <td>04/09/2015 02:20:11 PM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Highway Condition</td>\n", " <td>Pothole - Highway</td>\n", " <td>Highway</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>Long Island Expwy</td>\n", " <td>West/Manhattan Bound</td>\n", " <td>Roadway</td>\n", " <td>Clearview Expwy (I-295) (Exit 27 S-N) - Utopia...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 21:30:48</th>\n", " <td>30307111</td>\n", " <td>2015-04-01 21:30:48</td>\n", " <td>NaN</td>\n", " <td>DOHMH</td>\n", " <td>Department of Health and Mental Hygiene</td>\n", " <td>Food Establishment</td>\n", " <td>Food Temperature</td>\n", " <td>Restaurant/Bar/Deli/Bakery</td>\n", " <td>11215</td>\n", " <td>709 5 AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.660699</td>\n", " <td>-73.994082</td>\n", " <td>(40.660699296661825, -73.99408169463258)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 15:51:04</th>\n", " <td>30311571</td>\n", " <td>2015-04-01 15:51:04</td>\n", " <td>04/14/2015 09:23:30 AM</td>\n", " <td>DPR</td>\n", " <td>Department of Parks and Recreation</td>\n", " <td>Maintenance or Facility</td>\n", " <td>Hours of Operation</td>\n", " <td>Park</td>\n", " <td>11210</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.621474</td>\n", " <td>-73.950711</td>\n", " <td>(40.62147413119333, -73.95071097029123)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 10:43:28</th>\n", " <td>30313817</td>\n", " <td>2015-04-01 10:43:28</td>\n", " <td>NaN</td>\n", " <td>DPR</td>\n", " <td>Department of Parks and Recreation</td>\n", " <td>Damaged Tree</td>\n", " <td>Branch Cracked and Will Fall</td>\n", " <td>NaN</td>\n", " <td>10009</td>\n", " <td>620 EAST 12TH STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.727725</td>\n", " <td>-73.978204</td>\n", " <td>(40.72772462544187, -73.97820435916094)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 15:12:46</th>\n", " <td>30308922</td>\n", " <td>2015-04-01 15:12:46</td>\n", " <td>06/01/2015 06:25:48 AM</td>\n", " <td>DOHMH</td>\n", " <td>Department of Health and Mental Hygiene</td>\n", " <td>Food Establishment</td>\n", " <td>Letter Grading</td>\n", " <td>Restaurant/Bar/Deli/Bakery</td>\n", " <td>11238</td>\n", " <td>663 FRANKLIN AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.675746</td>\n", " <td>-73.956122</td>\n", " <td>(40.67574618440852, -73.9561218336512)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 06:15:42</th>\n", " <td>30311132</td>\n", " <td>2015-04-01 06:15:42</td>\n", " <td>04/01/2015 10:28:30 AM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Highway Condition</td>\n", " <td>Pothole - Highway</td>\n", " <td>Highway</td>\n", " <td>10304</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>Staten Island Expwy</td>\n", " <td>East/Brooklyn Bound</td>\n", " <td>Roadway</td>\n", " <td>Clove Rd/Richmond Rd (Exit 13) - Lily Pond Ave...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.606875</td>\n", " <td>-74.085408</td>\n", " <td>(40.60687536641399, -74.0854077221027)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 11:28:02</th>\n", " <td>30308180</td>\n", " <td>2015-04-01 11:28:02</td>\n", " <td>04/01/2015 11:42:53 AM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Highway Condition</td>\n", " <td>Pothole - Highway</td>\n", " <td>Highway</td>\n", " <td>11432</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>Grand Central Pkwy</td>\n", " <td>West/Toward Triborough Br</td>\n", " <td>Ramp</td>\n", " <td>168th St (Exit 17)</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.719228</td>\n", " <td>-73.791963</td>\n", " <td>(40.71922760413319, -73.791962929951)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 17:35:18</th>\n", " <td>30313207</td>\n", " <td>2015-04-01 17:35:18</td>\n", " <td>06/01/2015 06:25:54 AM</td>\n", " <td>DOHMH</td>\n", " <td>Department of Health and Mental Hygiene</td>\n", " <td>Food Establishment</td>\n", " <td>Rodents/Insects/Garbage</td>\n", " <td>Restaurant/Bar/Deli/Bakery</td>\n", " <td>10011</td>\n", " <td>140 WEST 13 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.737182</td>\n", " <td>-73.998585</td>\n", " <td>(40.737182358685516, -73.99858548189518)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 13:54:54</th>\n", " <td>30310017</td>\n", " <td>2015-04-01 13:54:54</td>\n", " <td>04/06/2015 10:11:11 AM</td>\n", " <td>DOF</td>\n", " <td>Senior Citizen Rent Increase Exemption Unit</td>\n", " <td>SCRIE</td>\n", " <td>Miscellaneous</td>\n", " <td>Senior Address</td>\n", " <td>11435</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 23:49:33</th>\n", " <td>30306774</td>\n", " <td>2015-04-01 23:49:33</td>\n", " <td>04/02/2015 12:20:59 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Commercial</td>\n", " <td>Loud Music/Party</td>\n", " <td>Store/Commercial</td>\n", " <td>10003</td>\n", " <td>36 SAINT MARKS PLACE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.728733</td>\n", " <td>-73.988011</td>\n", " <td>(40.72873338955463, -73.98801059255561)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 07:50:49</th>\n", " <td>30313339</td>\n", " <td>2015-04-01 07:50:49</td>\n", " <td>07/08/2015 02:19:25 PM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Street Condition</td>\n", " <td>Rough, Pitted or Cracked Roads</td>\n", " <td>Street</td>\n", " <td>11385</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.703414</td>\n", " <td>-73.862854</td>\n", " <td>(40.70341423569781, -73.86285397616253)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 13:50:29</th>\n", " <td>30312146</td>\n", " <td>2015-04-01 13:50:29</td>\n", " <td>06/01/2015 06:25:49 AM</td>\n", " <td>DOHMH</td>\n", " <td>Department of Health and Mental Hygiene</td>\n", " <td>Food Establishment</td>\n", " <td>Rodents/Insects/Garbage</td>\n", " <td>Restaurant/Bar/Deli/Bakery</td>\n", " <td>10028</td>\n", " <td>1291 LEXINGTON AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.780069</td>\n", " <td>-73.955158</td>\n", " <td>(40.78006850471446, -73.95515761412761)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 16:14:19</th>\n", " <td>30313259</td>\n", " <td>2015-04-01 16:14:19</td>\n", " <td>04/01/2015 04:21:53 PM</td>\n", " <td>HRA</td>\n", " <td>HRA Benefit Card Replacement</td>\n", " <td>Benefit Card Replacement</td>\n", " <td>Medicaid</td>\n", " <td>NYC Street Address</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 19:27:34</th>\n", " <td>30308920</td>\n", " <td>2015-04-01 19:27:34</td>\n", " <td>04/01/2015 08:45:17 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Street/Sidewalk</td>\n", " <td>Loud Music/Party</td>\n", " <td>Street/Sidewalk</td>\n", " <td>10017</td>\n", " <td>210 EAST 46 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.753104</td>\n", " <td>-73.972096</td>\n", " <td>(40.75310402468627, -73.97209629231209)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 05:30:02</th>\n", " <td>30314164</td>\n", " <td>2015-04-01 05:30:02</td>\n", " <td>04/01/2015 02:57:31 PM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Highway Condition</td>\n", " <td>Pothole - Highway</td>\n", " <td>Highway</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>BQE/Gowanus Expwy</td>\n", " <td>East/Queens Bound</td>\n", " <td>Roadway</td>\n", " <td>Williamsburg Br / Metropolitan Ave (Exit 32) -...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 10:33:26</th>\n", " <td>30311790</td>\n", " <td>2015-04-01 10:33:26</td>\n", " <td>04/01/2015 11:19:12 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Illegal Parking</td>\n", " <td>Blocked Sidewalk</td>\n", " <td>Street/Sidewalk</td>\n", " <td>10033</td>\n", " <td>2284 AMSTERDAM AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.843149</td>\n", " <td>-73.934539</td>\n", " <td>(40.84314882753921, -73.93453937669832)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 11:47:38</th>\n", " <td>30310940</td>\n", " <td>2015-04-01 11:47:38</td>\n", " <td>04/06/2015 09:23:32 AM</td>\n", " <td>DOF</td>\n", " <td>Senior Citizen Rent Increase Exemption Unit</td>\n", " <td>SCRIE</td>\n", " <td>Miscellaneous</td>\n", " <td>Senior Address</td>\n", " <td>11355</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 11:01:27</th>\n", " <td>30310409</td>\n", " <td>2015-04-01 11:01:27</td>\n", " <td>04/17/2015 01:06:42 AM</td>\n", " <td>DCA</td>\n", " <td>Department of Consumer Affairs</td>\n", " <td>Consumer Complaint</td>\n", " <td>Exchange/Refund/Return</td>\n", " <td>NaN</td>\n", " <td>10455</td>\n", " <td>2997 3 AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.819111</td>\n", " <td>-73.913908</td>\n", " <td>(40.819110789789214, -73.91390802507868)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 08:51:52</th>\n", " <td>30310350</td>\n", " <td>2015-04-01 08:51:52</td>\n", " <td>04/03/2015 04:33:46 PM</td>\n", " <td>DCA</td>\n", " <td>Department of Consumer Affairs</td>\n", " <td>Consumer Complaint</td>\n", " <td>Cars Parked on Sidewalk/Street</td>\n", " <td>NaN</td>\n", " <td>11223</td>\n", " <td>1701 WEST 8 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.605657</td>\n", " <td>-73.981194</td>\n", " <td>(40.60565667868274, -73.98119372058547)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 14:58:55</th>\n", " <td>30313106</td>\n", " <td>2015-04-01 14:58:55</td>\n", " <td>04/06/2015 10:06:35 AM</td>\n", " <td>DOF</td>\n", " <td>Senior Citizen Rent Increase Exemption Unit</td>\n", " <td>SCRIE</td>\n", " <td>Rent Discrepancy</td>\n", " <td>Senior Address</td>\n", " <td>11201</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 16:59:19</th>\n", " <td>30309324</td>\n", " <td>2015-04-01 16:59:19</td>\n", " <td>04/01/2015 07:48:33 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Blocked Driveway</td>\n", " <td>Partial Access</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11210</td>\n", " <td>650 EAST 24 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.634497</td>\n", " <td>-73.954167</td>\n", " <td>(40.63449684441219, -73.95416735372353)</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 12:14:11</th>\n", " <td>30308181</td>\n", " <td>2015-04-01 12:14:11</td>\n", " <td>04/16/2015 03:52:40 PM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Highway Condition</td>\n", " <td>Unsafe Worksite</td>\n", " <td>Highway</td>\n", " <td>11103</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>Grand Central Pkwy</td>\n", " <td>East/Long Island Bound</td>\n", " <td>Roadway</td>\n", " <td>31st (Exit 3) - Brooklyn-Queens Expwy (I-278) ...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.769309</td>\n", " <td>-73.912236</td>\n", " <td>(40.76930913453694, -73.91223589513348)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 10:00:57</th>\n", " <td>30312096</td>\n", " <td>2015-04-01 10:00:57</td>\n", " <td>04/03/2015 02:35:36 PM</td>\n", " <td>DOF</td>\n", " <td>Senior Citizen Rent Increase Exemption Unit</td>\n", " <td>SCRIE</td>\n", " <td>Copy of Approval Order</td>\n", " <td>Senior Address</td>\n", " <td>10025</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 08:56:10</th>\n", " <td>30314369</td>\n", " <td>2015-04-01 08:56:10</td>\n", " <td>04/01/2015 11:12:26 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Blocked Driveway</td>\n", " <td>No Access</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11377</td>\n", " <td>31-36 68 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.757216</td>\n", " <td>-73.899106</td>\n", " <td>(40.7572160209837, -73.89910584068605)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 18:19:48</th>\n", " <td>30310395</td>\n", " <td>2015-04-01 18:19:48</td>\n", " <td>04/17/2015 01:07:04 AM</td>\n", " <td>DCA</td>\n", " <td>Department of Consumer Affairs</td>\n", " <td>Consumer Complaint</td>\n", " <td>False Advertising</td>\n", " <td>NaN</td>\n", " <td>11372</td>\n", " <td>80-13 37 AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.749584</td>\n", " <td>-73.885951</td>\n", " <td>(40.74958432631206, -73.88595125985013)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 16:23:18</th>\n", " <td>30313528</td>\n", " <td>2015-04-01 16:23:18</td>\n", " <td>04/17/2015 01:07:00 AM</td>\n", " <td>DCA</td>\n", " <td>Department of Consumer Affairs</td>\n", " <td>Consumer Complaint</td>\n", " <td>Exchange/Refund/Return</td>\n", " <td>NaN</td>\n", " <td>11226</td>\n", " <td>850 FLATBUSH AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.651629</td>\n", " <td>-73.959035</td>\n", " <td>(40.651628886860884, -73.95903518064264)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 11:41:29</th>\n", " <td>30312050</td>\n", " <td>2015-04-01 11:41:29</td>\n", " <td>04/06/2015 09:09:45 AM</td>\n", " <td>DOF</td>\n", " <td>Senior Citizen Rent Increase Exemption Unit</td>\n", " <td>SCRIE</td>\n", " <td>Application Renewal</td>\n", " <td>Senior Address</td>\n", " <td>10034</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 23:47:24</th>\n", " <td>30314231</td>\n", " <td>2015-04-01 23:47:24</td>\n", " <td>07/28/2015 01:03:24 PM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Street Condition</td>\n", " <td>Rough, Pitted or Cracked Roads</td>\n", " <td>Street</td>\n", " <td>11377</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.743456</td>\n", " <td>-73.914836</td>\n", " <td>(40.74345557431229, -73.91483581341043)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 08:16:38</th>\n", " <td>30311103</td>\n", " <td>2015-04-01 08:16:38</td>\n", " <td>04/09/2015 09:33:26 AM</td>\n", " <td>TLC</td>\n", " <td>Taxi and Limousine Commission</td>\n", " <td>Taxi Complaint</td>\n", " <td>Driver Complaint</td>\n", " <td>NaN</td>\n", " <td>11209</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.634792</td>\n", " <td>-74.032318</td>\n", " <td>(40.63479238458042, -74.03231826494591)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 07:04:27</th>\n", " <td>30310725</td>\n", " <td>2015-04-01 07:04:27</td>\n", " <td>04/01/2015 03:47:39 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Illegal Parking</td>\n", " <td>Posted Parking Sign Violation</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11419</td>\n", " <td>104-22 110 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.684012</td>\n", " <td>-73.831954</td>\n", " <td>(40.68401163822402, -73.83195428896114)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 23:36:08</th>\n", " <td>30311907</td>\n", " <td>2015-04-01 23:36:08</td>\n", " <td>04/02/2015 07:29:05 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Illegal Parking</td>\n", " <td>Blocked Hydrant</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11228</td>\n", " <td>1343 78 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.617990</td>\n", " <td>-74.008220</td>\n", " <td>(40.617990283460536, -74.00821981214455)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 14:48:14</th>\n", " <td>30308202</td>\n", " <td>2015-04-01 14:48:14</td>\n", " <td>04/01/2015 02:49:13 PM</td>\n", " <td>HRA</td>\n", " <td>HRA Benefit Card Replacement</td>\n", " <td>Benefit Card Replacement</td>\n", " <td>Food Stamp</td>\n", " <td>NYC Street Address</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 15:26:42</th>\n", " <td>30308819</td>\n", " <td>2015-04-01 15:26:42</td>\n", " <td>04/01/2015 05:19:41 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Illegal Parking</td>\n", " <td>Posted Parking Sign Violation</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11101</td>\n", " <td>43-10 CRESCENT STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.748707</td>\n", " <td>-73.942316</td>\n", " <td>(40.74870685388612, -73.94231592971958)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 19:08:07</th>\n", " <td>30313580</td>\n", " <td>2015-04-01 19:08:07</td>\n", " <td>04/07/2015 11:25:05 AM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Street Condition</td>\n", " <td>Defective Hardware</td>\n", " <td>Street</td>\n", " <td>11208</td>\n", " <td>880 GLENMORE AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.675876</td>\n", " <td>-73.877688</td>\n", " <td>(40.67587618287245, -73.87768812152434)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 22:06:37</th>\n", " <td>30313046</td>\n", " <td>2015-04-01 22:06:37</td>\n", " <td>04/02/2015 12:40:09 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Noise - Street/Sidewalk</td>\n", " <td>Loud Music/Party</td>\n", " <td>Street/Sidewalk</td>\n", " <td>10454</td>\n", " <td>592 OAK TERRACE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.808939</td>\n", " <td>-73.914543</td>\n", " <td>(40.80893932182981, -73.91454250715576)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 00:09:40</th>\n", " <td>30298884</td>\n", " <td>2015-04-01 00:09:40</td>\n", " <td>04/01/2015 02:17:16 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Blocked Driveway</td>\n", " <td>No Access</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11433</td>\n", " <td>150-38 107 AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.694510</td>\n", " <td>-73.800763</td>\n", " <td>(40.69451003870482, -73.80076336778066)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 11:43:50</th>\n", " <td>30311054</td>\n", " <td>2015-04-01 11:43:50</td>\n", " <td>04/06/2015 09:12:54 AM</td>\n", " <td>DOF</td>\n", " <td>Senior Citizen Rent Increase Exemption Unit</td>\n", " <td>SCRIE</td>\n", " <td>Copy of Approval Order</td>\n", " <td>Senior Address</td>\n", " <td>10003</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 13:30:29</th>\n", " <td>30307424</td>\n", " <td>2015-04-01 13:30:29</td>\n", " <td>04/13/2015 12:20:33 PM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Street Condition</td>\n", " <td>Failed Street Repair</td>\n", " <td>Street</td>\n", " <td>11364</td>\n", " <td>67-07 BELL BOULEVARD</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.743972</td>\n", " <td>-73.759771</td>\n", " <td>(40.74397211975241, -73.75977055909947)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 07:33:41</th>\n", " <td>30314352</td>\n", " <td>2015-04-01 07:33:41</td>\n", " <td>04/13/2015 12:27:12 PM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Street Condition</td>\n", " <td>Cave-in</td>\n", " <td>Street</td>\n", " <td>11357</td>\n", " <td>14-51 143 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.785713</td>\n", " <td>-73.826140</td>\n", " <td>(40.7857127748661, -73.82614011947928)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 12:28:15</th>\n", " <td>30312905</td>\n", " <td>2015-04-01 12:28:15</td>\n", " <td>04/01/2015 02:29:53 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Illegal Parking</td>\n", " <td>Double Parked Blocking Traffic</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11372</td>\n", " <td>72 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.749789</td>\n", " <td>-73.893794</td>\n", " <td>(40.74978944638325, -73.89379359227247)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 12:17:19</th>\n", " <td>30311302</td>\n", " <td>2015-04-01 12:17:19</td>\n", " <td>04/17/2015 01:06:56 AM</td>\n", " <td>DCA</td>\n", " <td>Department of Consumer Affairs</td>\n", " <td>Consumer Complaint</td>\n", " <td>Illegal Tow</td>\n", " <td>NaN</td>\n", " <td>11232</td>\n", " <td>14 53 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.648964</td>\n", " <td>-74.021255</td>\n", " <td>(40.648963544502585, -74.02125458310132)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 12:08:19</th>\n", " <td>30314467</td>\n", " <td>2015-04-01 12:08:19</td>\n", " <td>04/09/2015 02:43:13 PM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Street Condition</td>\n", " <td>Failed Street Repair</td>\n", " <td>Street</td>\n", " <td>11428</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.722832</td>\n", " <td>-73.748158</td>\n", " <td>(40.72283183191531, -73.74815780857023)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 17:43:25</th>\n", " <td>30311231</td>\n", " <td>2015-04-01 17:43:25</td>\n", " <td>04/01/2015 10:50:39 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Blocked Driveway</td>\n", " <td>No Access</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11435</td>\n", " <td>143-30 LAKEWOOD AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.689215</td>\n", " <td>-73.803877</td>\n", " <td>(40.68921522366862, -73.80387663789386)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 18:30:35</th>\n", " <td>30307426</td>\n", " <td>2015-04-01 18:30:35</td>\n", " <td>04/13/2015 12:15:11 PM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Street Condition</td>\n", " <td>Failed Street Repair</td>\n", " <td>Street</td>\n", " <td>11362</td>\n", " <td>NaN</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.750641</td>\n", " <td>-73.739344</td>\n", " <td>(40.75064138697133, -73.7393436538413)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 13:30:31</th>\n", " <td>30308409</td>\n", " <td>2015-04-01 13:30:31</td>\n", " <td>04/13/2015 12:19:28 PM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Street Condition</td>\n", " <td>Failed Street Repair</td>\n", " <td>Street</td>\n", " <td>11379</td>\n", " <td>79-17 68 ROAD</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.710496</td>\n", " <td>-73.872661</td>\n", " <td>(40.71049602255762, -73.8726613318581)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 01:28:45</th>\n", " <td>30298825</td>\n", " <td>2015-04-01 01:28:45</td>\n", " <td>04/01/2015 02:36:49 AM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Blocked Driveway</td>\n", " <td>No Access</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11232</td>\n", " <td>4001 8 AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.646630</td>\n", " <td>-73.997960</td>\n", " <td>(40.646629679609966, -73.99796038095705)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 08:52:11</th>\n", " <td>30307104</td>\n", " <td>2015-04-01 08:52:11</td>\n", " <td>04/02/2015 04:34:46 PM</td>\n", " <td>DCA</td>\n", " <td>Department of Consumer Affairs</td>\n", " <td>Consumer Complaint</td>\n", " <td>Installation/Work Quality</td>\n", " <td>NaN</td>\n", " <td>10469</td>\n", " <td>3033 YOUNG AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.870105</td>\n", " <td>-73.847979</td>\n", " <td>(40.870105314232546, -73.84797875606117)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 11:12:49</th>\n", " <td>30310013</td>\n", " <td>2015-04-01 11:12:49</td>\n", " <td>04/06/2015 11:47:20 AM</td>\n", " <td>DOT</td>\n", " <td>Department of Transportation</td>\n", " <td>Street Sign - Damaged</td>\n", " <td>Street Cleaning - ASP</td>\n", " <td>Street</td>\n", " <td>10026</td>\n", " <td>17 LENOX AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.798932</td>\n", " <td>-73.951952</td>\n", " <td>(40.7989317549172, -73.9519520651255)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 16:18:23</th>\n", " <td>30311889</td>\n", " <td>2015-04-01 16:18:23</td>\n", " <td>04/01/2015 11:11:11 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Derelict Vehicle</td>\n", " <td>With License Plate</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11226</td>\n", " <td>485 EAST 17 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.638946</td>\n", " <td>-73.962055</td>\n", " <td>(40.638946273235284, -73.96205520207174)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 13:16:44</th>\n", " <td>30312450</td>\n", " <td>2015-04-01 13:16:44</td>\n", " <td>04/01/2015 02:30:55 PM</td>\n", " <td>NYPD</td>\n", " <td>New York City Police Department</td>\n", " <td>Blocked Driveway</td>\n", " <td>No Access</td>\n", " <td>Street/Sidewalk</td>\n", " <td>11372</td>\n", " <td>37-18 73 STREET</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.748262</td>\n", " <td>-73.892616</td>\n", " <td>(40.748262273356396, -73.89261586191228)</td>\n", " </tr>\n", " <tr>\n", " <th>2015-04-01 20:27:33</th>\n", " <td>30313471</td>\n", " <td>2015-04-01 20:27:33</td>\n", " <td>04/17/2015 01:07:09 AM</td>\n", " <td>DCA</td>\n", " <td>Department of Consumer Affairs</td>\n", " <td>Consumer Complaint</td>\n", " <td>Overcharge</td>\n", " <td>NaN</td>\n", " <td>11226</td>\n", " <td>3008 CHURCH AVENUE</td>\n", " <td>...</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>NaN</td>\n", " <td>40.650810</td>\n", " <td>-73.949370</td>\n", " <td>(40.6508098378492, -73.94937030940775)</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>147 rows × 53 columns</p>\n", "</div>" ], "text/plain": [ " Unique Key Created Date Closed Date \\\n", "Created Date \n", "2015-04-01 21:37:42 30311691 2015-04-01 21:37:42 04/01/2015 10:49:33 PM \n", "2015-04-01 23:12:04 30307701 2015-04-01 23:12:04 04/01/2015 11:32:40 PM \n", "2015-04-01 13:10:35 30313389 2015-04-01 13:10:35 04/07/2015 04:01:08 PM \n", "2015-04-01 17:37:38 30314393 2015-04-01 17:37:38 04/03/2015 11:40:54 AM \n", "2015-04-01 12:32:40 30309207 2015-04-01 12:32:40 04/17/2015 01:06:49 AM \n", "2015-04-01 18:44:50 30311759 2015-04-01 18:44:50 06/24/2015 11:27:00 AM \n", "2015-04-01 16:30:15 30309690 2015-04-01 16:30:15 04/01/2015 11:27:22 PM \n", "2015-04-01 09:04:07 30307990 2015-04-01 09:04:07 04/06/2015 09:17:10 AM \n", "2015-04-01 07:46:58 30308253 2015-04-01 07:46:58 04/01/2015 09:32:31 AM \n", "2015-04-01 17:12:17 30314214 2015-04-01 17:12:17 04/09/2015 02:20:11 PM \n", "2015-04-01 21:30:48 30307111 2015-04-01 21:30:48 NaN \n", "2015-04-01 15:51:04 30311571 2015-04-01 15:51:04 04/14/2015 09:23:30 AM \n", "2015-04-01 10:43:28 30313817 2015-04-01 10:43:28 NaN \n", "2015-04-01 15:12:46 30308922 2015-04-01 15:12:46 06/01/2015 06:25:48 AM \n", "2015-04-01 06:15:42 30311132 2015-04-01 06:15:42 04/01/2015 10:28:30 AM \n", "2015-04-01 11:28:02 30308180 2015-04-01 11:28:02 04/01/2015 11:42:53 AM \n", "2015-04-01 17:35:18 30313207 2015-04-01 17:35:18 06/01/2015 06:25:54 AM \n", "2015-04-01 13:54:54 30310017 2015-04-01 13:54:54 04/06/2015 10:11:11 AM \n", "2015-04-01 23:49:33 30306774 2015-04-01 23:49:33 04/02/2015 12:20:59 AM \n", "2015-04-01 07:50:49 30313339 2015-04-01 07:50:49 07/08/2015 02:19:25 PM \n", "2015-04-01 13:50:29 30312146 2015-04-01 13:50:29 06/01/2015 06:25:49 AM \n", "2015-04-01 16:14:19 30313259 2015-04-01 16:14:19 04/01/2015 04:21:53 PM \n", "2015-04-01 19:27:34 30308920 2015-04-01 19:27:34 04/01/2015 08:45:17 PM \n", "2015-04-01 05:30:02 30314164 2015-04-01 05:30:02 04/01/2015 02:57:31 PM \n", "2015-04-01 10:33:26 30311790 2015-04-01 10:33:26 04/01/2015 11:19:12 AM \n", "2015-04-01 11:47:38 30310940 2015-04-01 11:47:38 04/06/2015 09:23:32 AM \n", "2015-04-01 11:01:27 30310409 2015-04-01 11:01:27 04/17/2015 01:06:42 AM \n", "2015-04-01 08:51:52 30310350 2015-04-01 08:51:52 04/03/2015 04:33:46 PM \n", "2015-04-01 14:58:55 30313106 2015-04-01 14:58:55 04/06/2015 10:06:35 AM \n", "2015-04-01 16:59:19 30309324 2015-04-01 16:59:19 04/01/2015 07:48:33 PM \n", "... ... ... ... \n", "2015-04-01 12:14:11 30308181 2015-04-01 12:14:11 04/16/2015 03:52:40 PM \n", "2015-04-01 10:00:57 30312096 2015-04-01 10:00:57 04/03/2015 02:35:36 PM \n", "2015-04-01 08:56:10 30314369 2015-04-01 08:56:10 04/01/2015 11:12:26 AM \n", "2015-04-01 18:19:48 30310395 2015-04-01 18:19:48 04/17/2015 01:07:04 AM \n", "2015-04-01 16:23:18 30313528 2015-04-01 16:23:18 04/17/2015 01:07:00 AM \n", "2015-04-01 11:41:29 30312050 2015-04-01 11:41:29 04/06/2015 09:09:45 AM \n", "2015-04-01 23:47:24 30314231 2015-04-01 23:47:24 07/28/2015 01:03:24 PM \n", "2015-04-01 08:16:38 30311103 2015-04-01 08:16:38 04/09/2015 09:33:26 AM \n", "2015-04-01 07:04:27 30310725 2015-04-01 07:04:27 04/01/2015 03:47:39 PM \n", "2015-04-01 23:36:08 30311907 2015-04-01 23:36:08 04/02/2015 07:29:05 AM \n", "2015-04-01 14:48:14 30308202 2015-04-01 14:48:14 04/01/2015 02:49:13 PM \n", "2015-04-01 15:26:42 30308819 2015-04-01 15:26:42 04/01/2015 05:19:41 PM \n", "2015-04-01 19:08:07 30313580 2015-04-01 19:08:07 04/07/2015 11:25:05 AM \n", "2015-04-01 22:06:37 30313046 2015-04-01 22:06:37 04/02/2015 12:40:09 AM \n", "2015-04-01 00:09:40 30298884 2015-04-01 00:09:40 04/01/2015 02:17:16 AM \n", "2015-04-01 11:43:50 30311054 2015-04-01 11:43:50 04/06/2015 09:12:54 AM \n", "2015-04-01 13:30:29 30307424 2015-04-01 13:30:29 04/13/2015 12:20:33 PM \n", "2015-04-01 07:33:41 30314352 2015-04-01 07:33:41 04/13/2015 12:27:12 PM \n", "2015-04-01 12:28:15 30312905 2015-04-01 12:28:15 04/01/2015 02:29:53 PM \n", "2015-04-01 12:17:19 30311302 2015-04-01 12:17:19 04/17/2015 01:06:56 AM \n", "2015-04-01 12:08:19 30314467 2015-04-01 12:08:19 04/09/2015 02:43:13 PM \n", "2015-04-01 17:43:25 30311231 2015-04-01 17:43:25 04/01/2015 10:50:39 PM \n", "2015-04-01 18:30:35 30307426 2015-04-01 18:30:35 04/13/2015 12:15:11 PM \n", "2015-04-01 13:30:31 30308409 2015-04-01 13:30:31 04/13/2015 12:19:28 PM \n", "2015-04-01 01:28:45 30298825 2015-04-01 01:28:45 04/01/2015 02:36:49 AM \n", "2015-04-01 08:52:11 30307104 2015-04-01 08:52:11 04/02/2015 04:34:46 PM \n", "2015-04-01 11:12:49 30310013 2015-04-01 11:12:49 04/06/2015 11:47:20 AM \n", "2015-04-01 16:18:23 30311889 2015-04-01 16:18:23 04/01/2015 11:11:11 PM \n", "2015-04-01 13:16:44 30312450 2015-04-01 13:16:44 04/01/2015 02:30:55 PM \n", "2015-04-01 20:27:33 30313471 2015-04-01 20:27:33 04/17/2015 01:07:09 AM \n", "\n", " Agency Agency Name \\\n", "Created Date \n", "2015-04-01 21:37:42 NYPD New York City Police Department \n", "2015-04-01 23:12:04 NYPD New York City Police Department \n", "2015-04-01 13:10:35 DPR Department of Parks and Recreation \n", "2015-04-01 17:37:38 DPR Department of Parks and Recreation \n", "2015-04-01 12:32:40 DCA Department of Consumer Affairs \n", "2015-04-01 18:44:50 DPR Department of Parks and Recreation \n", "2015-04-01 16:30:15 NYPD New York City Police Department \n", "2015-04-01 09:04:07 DOF Senior Citizen Rent Increase Exemption Unit \n", "2015-04-01 07:46:58 NYPD New York City Police Department \n", "2015-04-01 17:12:17 DOT Department of Transportation \n", "2015-04-01 21:30:48 DOHMH Department of Health and Mental Hygiene \n", "2015-04-01 15:51:04 DPR Department of Parks and Recreation \n", "2015-04-01 10:43:28 DPR Department of Parks and Recreation \n", "2015-04-01 15:12:46 DOHMH Department of Health and Mental Hygiene \n", "2015-04-01 06:15:42 DOT Department of Transportation \n", "2015-04-01 11:28:02 DOT Department of Transportation \n", "2015-04-01 17:35:18 DOHMH Department of Health and Mental Hygiene \n", "2015-04-01 13:54:54 DOF Senior Citizen Rent Increase Exemption Unit \n", "2015-04-01 23:49:33 NYPD New York City Police Department \n", "2015-04-01 07:50:49 DOT Department of Transportation \n", "2015-04-01 13:50:29 DOHMH Department of Health and Mental Hygiene \n", "2015-04-01 16:14:19 HRA HRA Benefit Card Replacement \n", "2015-04-01 19:27:34 NYPD New York City Police Department \n", "2015-04-01 05:30:02 DOT Department of Transportation \n", "2015-04-01 10:33:26 NYPD New York City Police Department \n", "2015-04-01 11:47:38 DOF Senior Citizen Rent Increase Exemption Unit \n", "2015-04-01 11:01:27 DCA Department of Consumer Affairs \n", "2015-04-01 08:51:52 DCA Department of Consumer Affairs \n", "2015-04-01 14:58:55 DOF Senior Citizen Rent Increase Exemption Unit \n", "2015-04-01 16:59:19 NYPD New York City Police Department \n", "... ... ... \n", "2015-04-01 12:14:11 DOT Department of Transportation \n", "2015-04-01 10:00:57 DOF Senior Citizen Rent Increase Exemption Unit \n", "2015-04-01 08:56:10 NYPD New York City Police Department \n", "2015-04-01 18:19:48 DCA Department of Consumer Affairs \n", "2015-04-01 16:23:18 DCA Department of Consumer Affairs \n", "2015-04-01 11:41:29 DOF Senior Citizen Rent Increase Exemption Unit \n", "2015-04-01 23:47:24 DOT Department of Transportation \n", "2015-04-01 08:16:38 TLC Taxi and Limousine Commission \n", "2015-04-01 07:04:27 NYPD New York City Police Department \n", "2015-04-01 23:36:08 NYPD New York City Police Department \n", "2015-04-01 14:48:14 HRA HRA Benefit Card Replacement \n", "2015-04-01 15:26:42 NYPD New York City Police Department \n", "2015-04-01 19:08:07 DOT Department of Transportation \n", "2015-04-01 22:06:37 NYPD New York City Police Department \n", "2015-04-01 00:09:40 NYPD New York City Police Department \n", "2015-04-01 11:43:50 DOF Senior Citizen Rent Increase Exemption Unit \n", "2015-04-01 13:30:29 DOT Department of Transportation \n", "2015-04-01 07:33:41 DOT Department of Transportation \n", "2015-04-01 12:28:15 NYPD New York City Police Department \n", "2015-04-01 12:17:19 DCA Department of Consumer Affairs \n", "2015-04-01 12:08:19 DOT Department of Transportation \n", "2015-04-01 17:43:25 NYPD New York City Police Department \n", "2015-04-01 18:30:35 DOT Department of Transportation \n", "2015-04-01 13:30:31 DOT Department of Transportation \n", "2015-04-01 01:28:45 NYPD New York City Police Department \n", "2015-04-01 08:52:11 DCA Department of Consumer Affairs \n", "2015-04-01 11:12:49 DOT Department of Transportation \n", "2015-04-01 16:18:23 NYPD New York City Police Department \n", "2015-04-01 13:16:44 NYPD New York City Police Department \n", "2015-04-01 20:27:33 DCA Department of Consumer Affairs \n", "\n", " Complaint Type \\\n", "Created Date \n", "2015-04-01 21:37:42 Illegal Parking \n", "2015-04-01 23:12:04 Noise - Commercial \n", "2015-04-01 13:10:35 Root/Sewer/Sidewalk Condition \n", "2015-04-01 17:37:38 Maintenance or Facility \n", "2015-04-01 12:32:40 Consumer Complaint \n", "2015-04-01 18:44:50 Damaged Tree \n", "2015-04-01 16:30:15 Animal Abuse \n", "2015-04-01 09:04:07 SCRIE \n", "2015-04-01 07:46:58 Blocked Driveway \n", "2015-04-01 17:12:17 Highway Condition \n", "2015-04-01 21:30:48 Food Establishment \n", "2015-04-01 15:51:04 Maintenance or Facility \n", "2015-04-01 10:43:28 Damaged Tree \n", "2015-04-01 15:12:46 Food Establishment \n", "2015-04-01 06:15:42 Highway Condition \n", "2015-04-01 11:28:02 Highway Condition \n", "2015-04-01 17:35:18 Food Establishment \n", "2015-04-01 13:54:54 SCRIE \n", "2015-04-01 23:49:33 Noise - Commercial \n", "2015-04-01 07:50:49 Street Condition \n", "2015-04-01 13:50:29 Food Establishment \n", "2015-04-01 16:14:19 Benefit Card Replacement \n", "2015-04-01 19:27:34 Noise - Street/Sidewalk \n", "2015-04-01 05:30:02 Highway Condition \n", "2015-04-01 10:33:26 Illegal Parking \n", "2015-04-01 11:47:38 SCRIE \n", "2015-04-01 11:01:27 Consumer Complaint \n", "2015-04-01 08:51:52 Consumer Complaint \n", "2015-04-01 14:58:55 SCRIE \n", "2015-04-01 16:59:19 Blocked Driveway \n", "... ... \n", "2015-04-01 12:14:11 Highway Condition \n", "2015-04-01 10:00:57 SCRIE \n", "2015-04-01 08:56:10 Blocked Driveway \n", "2015-04-01 18:19:48 Consumer Complaint \n", "2015-04-01 16:23:18 Consumer Complaint \n", "2015-04-01 11:41:29 SCRIE \n", "2015-04-01 23:47:24 Street Condition \n", "2015-04-01 08:16:38 Taxi Complaint \n", "2015-04-01 07:04:27 Illegal Parking \n", "2015-04-01 23:36:08 Illegal Parking \n", "2015-04-01 14:48:14 Benefit Card Replacement \n", "2015-04-01 15:26:42 Illegal Parking \n", "2015-04-01 19:08:07 Street Condition \n", "2015-04-01 22:06:37 Noise - Street/Sidewalk \n", "2015-04-01 00:09:40 Blocked Driveway \n", "2015-04-01 11:43:50 SCRIE \n", "2015-04-01 13:30:29 Street Condition \n", "2015-04-01 07:33:41 Street Condition \n", "2015-04-01 12:28:15 Illegal Parking \n", "2015-04-01 12:17:19 Consumer Complaint \n", "2015-04-01 12:08:19 Street Condition \n", "2015-04-01 17:43:25 Blocked Driveway \n", "2015-04-01 18:30:35 Street Condition \n", "2015-04-01 13:30:31 Street Condition \n", "2015-04-01 01:28:45 Blocked Driveway \n", "2015-04-01 08:52:11 Consumer Complaint \n", "2015-04-01 11:12:49 Street Sign - Damaged \n", "2015-04-01 16:18:23 Derelict Vehicle \n", "2015-04-01 13:16:44 Blocked Driveway \n", "2015-04-01 20:27:33 Consumer Complaint \n", "\n", " Descriptor \\\n", "Created Date \n", "2015-04-01 21:37:42 Blocked Sidewalk \n", "2015-04-01 23:12:04 Loud Music/Party \n", "2015-04-01 13:10:35 Trees and Sidewalks Program \n", "2015-04-01 17:37:38 Hours of Operation \n", "2015-04-01 12:32:40 Installation/Work Quality \n", "2015-04-01 18:44:50 Entire Tree Has Fallen Down \n", "2015-04-01 16:30:15 Neglected \n", "2015-04-01 09:04:07 Miscellaneous \n", "2015-04-01 07:46:58 No Access \n", "2015-04-01 17:12:17 Pothole - Highway \n", "2015-04-01 21:30:48 Food Temperature \n", "2015-04-01 15:51:04 Hours of Operation \n", "2015-04-01 10:43:28 Branch Cracked and Will Fall \n", "2015-04-01 15:12:46 Letter Grading \n", "2015-04-01 06:15:42 Pothole - Highway \n", "2015-04-01 11:28:02 Pothole - Highway \n", "2015-04-01 17:35:18 Rodents/Insects/Garbage \n", "2015-04-01 13:54:54 Miscellaneous \n", "2015-04-01 23:49:33 Loud Music/Party \n", "2015-04-01 07:50:49 Rough, Pitted or Cracked Roads \n", "2015-04-01 13:50:29 Rodents/Insects/Garbage \n", "2015-04-01 16:14:19 Medicaid \n", "2015-04-01 19:27:34 Loud Music/Party \n", "2015-04-01 05:30:02 Pothole - Highway \n", "2015-04-01 10:33:26 Blocked Sidewalk \n", "2015-04-01 11:47:38 Miscellaneous \n", "2015-04-01 11:01:27 Exchange/Refund/Return \n", "2015-04-01 08:51:52 Cars Parked on Sidewalk/Street \n", "2015-04-01 14:58:55 Rent Discrepancy \n", "2015-04-01 16:59:19 Partial Access \n", "... ... \n", "2015-04-01 12:14:11 Unsafe Worksite \n", "2015-04-01 10:00:57 Copy of Approval Order \n", "2015-04-01 08:56:10 No Access \n", "2015-04-01 18:19:48 False Advertising \n", "2015-04-01 16:23:18 Exchange/Refund/Return \n", "2015-04-01 11:41:29 Application Renewal \n", "2015-04-01 23:47:24 Rough, Pitted or Cracked Roads \n", "2015-04-01 08:16:38 Driver Complaint \n", "2015-04-01 07:04:27 Posted Parking Sign Violation \n", "2015-04-01 23:36:08 Blocked Hydrant \n", "2015-04-01 14:48:14 Food Stamp \n", "2015-04-01 15:26:42 Posted Parking Sign Violation \n", "2015-04-01 19:08:07 Defective Hardware \n", "2015-04-01 22:06:37 Loud Music/Party \n", "2015-04-01 00:09:40 No Access \n", "2015-04-01 11:43:50 Copy of Approval Order \n", "2015-04-01 13:30:29 Failed Street Repair \n", "2015-04-01 07:33:41 Cave-in \n", "2015-04-01 12:28:15 Double Parked Blocking Traffic \n", "2015-04-01 12:17:19 Illegal Tow \n", "2015-04-01 12:08:19 Failed Street Repair \n", "2015-04-01 17:43:25 No Access \n", "2015-04-01 18:30:35 Failed Street Repair \n", "2015-04-01 13:30:31 Failed Street Repair \n", "2015-04-01 01:28:45 No Access \n", "2015-04-01 08:52:11 Installation/Work Quality \n", "2015-04-01 11:12:49 Street Cleaning - ASP \n", "2015-04-01 16:18:23 With License Plate \n", "2015-04-01 13:16:44 No Access \n", "2015-04-01 20:27:33 Overcharge \n", "\n", " Location Type Incident Zip \\\n", "Created Date \n", "2015-04-01 21:37:42 Street/Sidewalk 11234 \n", "2015-04-01 23:12:04 Store/Commercial 11205 \n", "2015-04-01 13:10:35 Street 11422 \n", "2015-04-01 17:37:38 Park 11211 \n", "2015-04-01 12:32:40 NaN 11423 \n", "2015-04-01 18:44:50 Street 10467 \n", "2015-04-01 16:30:15 Residential Building/House 11368 \n", "2015-04-01 09:04:07 Senior Address 10027 \n", "2015-04-01 07:46:58 Street/Sidewalk 11370 \n", "2015-04-01 17:12:17 Highway NaN \n", "2015-04-01 21:30:48 Restaurant/Bar/Deli/Bakery 11215 \n", "2015-04-01 15:51:04 Park 11210 \n", "2015-04-01 10:43:28 NaN 10009 \n", "2015-04-01 15:12:46 Restaurant/Bar/Deli/Bakery 11238 \n", "2015-04-01 06:15:42 Highway 10304 \n", "2015-04-01 11:28:02 Highway 11432 \n", "2015-04-01 17:35:18 Restaurant/Bar/Deli/Bakery 10011 \n", "2015-04-01 13:54:54 Senior Address 11435 \n", "2015-04-01 23:49:33 Store/Commercial 10003 \n", "2015-04-01 07:50:49 Street 11385 \n", "2015-04-01 13:50:29 Restaurant/Bar/Deli/Bakery 10028 \n", "2015-04-01 16:14:19 NYC Street Address NaN \n", "2015-04-01 19:27:34 Street/Sidewalk 10017 \n", "2015-04-01 05:30:02 Highway NaN \n", "2015-04-01 10:33:26 Street/Sidewalk 10033 \n", "2015-04-01 11:47:38 Senior Address 11355 \n", "2015-04-01 11:01:27 NaN 10455 \n", "2015-04-01 08:51:52 NaN 11223 \n", "2015-04-01 14:58:55 Senior Address 11201 \n", "2015-04-01 16:59:19 Street/Sidewalk 11210 \n", "... ... ... \n", "2015-04-01 12:14:11 Highway 11103 \n", "2015-04-01 10:00:57 Senior Address 10025 \n", "2015-04-01 08:56:10 Street/Sidewalk 11377 \n", "2015-04-01 18:19:48 NaN 11372 \n", "2015-04-01 16:23:18 NaN 11226 \n", "2015-04-01 11:41:29 Senior Address 10034 \n", "2015-04-01 23:47:24 Street 11377 \n", "2015-04-01 08:16:38 NaN 11209 \n", "2015-04-01 07:04:27 Street/Sidewalk 11419 \n", "2015-04-01 23:36:08 Street/Sidewalk 11228 \n", "2015-04-01 14:48:14 NYC Street Address NaN \n", "2015-04-01 15:26:42 Street/Sidewalk 11101 \n", "2015-04-01 19:08:07 Street 11208 \n", "2015-04-01 22:06:37 Street/Sidewalk 10454 \n", "2015-04-01 00:09:40 Street/Sidewalk 11433 \n", "2015-04-01 11:43:50 Senior Address 10003 \n", "2015-04-01 13:30:29 Street 11364 \n", "2015-04-01 07:33:41 Street 11357 \n", "2015-04-01 12:28:15 Street/Sidewalk 11372 \n", "2015-04-01 12:17:19 NaN 11232 \n", "2015-04-01 12:08:19 Street 11428 \n", "2015-04-01 17:43:25 Street/Sidewalk 11435 \n", "2015-04-01 18:30:35 Street 11362 \n", "2015-04-01 13:30:31 Street 11379 \n", "2015-04-01 01:28:45 Street/Sidewalk 11232 \n", "2015-04-01 08:52:11 NaN 10469 \n", "2015-04-01 11:12:49 Street 10026 \n", "2015-04-01 16:18:23 Street/Sidewalk 11226 \n", "2015-04-01 13:16:44 Street/Sidewalk 11372 \n", "2015-04-01 20:27:33 NaN 11226 \n", "\n", " Incident Address \\\n", "Created Date \n", "2015-04-01 21:37:42 NaN \n", "2015-04-01 23:12:04 700 MYRTLE AVENUE \n", "2015-04-01 13:10:35 245-16 149 AVENUE \n", "2015-04-01 17:37:38 NaN \n", "2015-04-01 12:32:40 90-71 198 STREET \n", "2015-04-01 18:44:50 862 EAST 213 STREET \n", "2015-04-01 16:30:15 107-15 NORTHERN BOULEVARD \n", "2015-04-01 09:04:07 NaN \n", "2015-04-01 07:46:58 32-51 80 STREET \n", "2015-04-01 17:12:17 NaN \n", "2015-04-01 21:30:48 709 5 AVENUE \n", "2015-04-01 15:51:04 NaN \n", "2015-04-01 10:43:28 620 EAST 12TH STREET \n", "2015-04-01 15:12:46 663 FRANKLIN AVENUE \n", "2015-04-01 06:15:42 NaN \n", "2015-04-01 11:28:02 NaN \n", "2015-04-01 17:35:18 140 WEST 13 STREET \n", "2015-04-01 13:54:54 NaN \n", "2015-04-01 23:49:33 36 SAINT MARKS PLACE \n", "2015-04-01 07:50:49 NaN \n", "2015-04-01 13:50:29 1291 LEXINGTON AVENUE \n", "2015-04-01 16:14:19 NaN \n", "2015-04-01 19:27:34 210 EAST 46 STREET \n", "2015-04-01 05:30:02 NaN \n", "2015-04-01 10:33:26 2284 AMSTERDAM AVENUE \n", "2015-04-01 11:47:38 NaN \n", "2015-04-01 11:01:27 2997 3 AVENUE \n", "2015-04-01 08:51:52 1701 WEST 8 STREET \n", "2015-04-01 14:58:55 NaN \n", "2015-04-01 16:59:19 650 EAST 24 STREET \n", "... ... \n", "2015-04-01 12:14:11 NaN \n", "2015-04-01 10:00:57 NaN \n", "2015-04-01 08:56:10 31-36 68 STREET \n", "2015-04-01 18:19:48 80-13 37 AVENUE \n", "2015-04-01 16:23:18 850 FLATBUSH AVENUE \n", "2015-04-01 11:41:29 NaN \n", "2015-04-01 23:47:24 NaN \n", "2015-04-01 08:16:38 NaN \n", "2015-04-01 07:04:27 104-22 110 STREET \n", "2015-04-01 23:36:08 1343 78 STREET \n", "2015-04-01 14:48:14 NaN \n", "2015-04-01 15:26:42 43-10 CRESCENT STREET \n", "2015-04-01 19:08:07 880 GLENMORE AVENUE \n", "2015-04-01 22:06:37 592 OAK TERRACE \n", "2015-04-01 00:09:40 150-38 107 AVENUE \n", "2015-04-01 11:43:50 NaN \n", "2015-04-01 13:30:29 67-07 BELL BOULEVARD \n", "2015-04-01 07:33:41 14-51 143 STREET \n", "2015-04-01 12:28:15 72 STREET \n", "2015-04-01 12:17:19 14 53 STREET \n", "2015-04-01 12:08:19 NaN \n", "2015-04-01 17:43:25 143-30 LAKEWOOD AVENUE \n", "2015-04-01 18:30:35 NaN \n", "2015-04-01 13:30:31 79-17 68 ROAD \n", "2015-04-01 01:28:45 4001 8 AVENUE \n", "2015-04-01 08:52:11 3033 YOUNG AVENUE \n", "2015-04-01 11:12:49 17 LENOX AVENUE \n", "2015-04-01 16:18:23 485 EAST 17 STREET \n", "2015-04-01 13:16:44 37-18 73 STREET \n", "2015-04-01 20:27:33 3008 CHURCH AVENUE \n", "\n", " ... \\\n", "Created Date ... \n", "2015-04-01 21:37:42 ... \n", "2015-04-01 23:12:04 ... \n", "2015-04-01 13:10:35 ... \n", "2015-04-01 17:37:38 ... \n", "2015-04-01 12:32:40 ... \n", "2015-04-01 18:44:50 ... \n", "2015-04-01 16:30:15 ... \n", "2015-04-01 09:04:07 ... \n", "2015-04-01 07:46:58 ... \n", "2015-04-01 17:12:17 ... \n", "2015-04-01 21:30:48 ... \n", "2015-04-01 15:51:04 ... \n", "2015-04-01 10:43:28 ... \n", "2015-04-01 15:12:46 ... \n", "2015-04-01 06:15:42 ... \n", "2015-04-01 11:28:02 ... \n", "2015-04-01 17:35:18 ... \n", "2015-04-01 13:54:54 ... \n", "2015-04-01 23:49:33 ... \n", "2015-04-01 07:50:49 ... \n", "2015-04-01 13:50:29 ... \n", "2015-04-01 16:14:19 ... \n", "2015-04-01 19:27:34 ... \n", "2015-04-01 05:30:02 ... \n", "2015-04-01 10:33:26 ... \n", "2015-04-01 11:47:38 ... \n", "2015-04-01 11:01:27 ... \n", "2015-04-01 08:51:52 ... \n", "2015-04-01 14:58:55 ... \n", "2015-04-01 16:59:19 ... \n", "... ... \n", "2015-04-01 12:14:11 ... \n", "2015-04-01 10:00:57 ... \n", "2015-04-01 08:56:10 ... \n", "2015-04-01 18:19:48 ... \n", "2015-04-01 16:23:18 ... \n", "2015-04-01 11:41:29 ... \n", "2015-04-01 23:47:24 ... \n", "2015-04-01 08:16:38 ... \n", "2015-04-01 07:04:27 ... \n", "2015-04-01 23:36:08 ... \n", "2015-04-01 14:48:14 ... \n", "2015-04-01 15:26:42 ... \n", "2015-04-01 19:08:07 ... \n", "2015-04-01 22:06:37 ... \n", "2015-04-01 00:09:40 ... \n", "2015-04-01 11:43:50 ... \n", "2015-04-01 13:30:29 ... \n", "2015-04-01 07:33:41 ... \n", "2015-04-01 12:28:15 ... \n", "2015-04-01 12:17:19 ... \n", "2015-04-01 12:08:19 ... \n", "2015-04-01 17:43:25 ... \n", "2015-04-01 18:30:35 ... \n", "2015-04-01 13:30:31 ... \n", "2015-04-01 01:28:45 ... \n", "2015-04-01 08:52:11 ... \n", "2015-04-01 11:12:49 ... \n", "2015-04-01 16:18:23 ... \n", "2015-04-01 13:16:44 ... \n", "2015-04-01 20:27:33 ... \n", "\n", " Bridge Highway Name Bridge Highway Direction Road Ramp \\\n", "Created Date \n", "2015-04-01 21:37:42 NaN NaN NaN \n", "2015-04-01 23:12:04 NaN NaN NaN \n", "2015-04-01 13:10:35 NaN NaN NaN \n", "2015-04-01 17:37:38 NaN NaN NaN \n", "2015-04-01 12:32:40 NaN NaN NaN \n", "2015-04-01 18:44:50 NaN NaN NaN \n", "2015-04-01 16:30:15 NaN NaN NaN \n", "2015-04-01 09:04:07 NaN NaN NaN \n", "2015-04-01 07:46:58 NaN NaN NaN \n", "2015-04-01 17:12:17 Long Island Expwy West/Manhattan Bound Roadway \n", "2015-04-01 21:30:48 NaN NaN NaN \n", "2015-04-01 15:51:04 NaN NaN NaN \n", "2015-04-01 10:43:28 NaN NaN NaN \n", "2015-04-01 15:12:46 NaN NaN NaN \n", "2015-04-01 06:15:42 Staten Island Expwy East/Brooklyn Bound Roadway \n", "2015-04-01 11:28:02 Grand Central Pkwy West/Toward Triborough Br Ramp \n", "2015-04-01 17:35:18 NaN NaN NaN \n", "2015-04-01 13:54:54 NaN NaN NaN \n", "2015-04-01 23:49:33 NaN NaN NaN \n", "2015-04-01 07:50:49 NaN NaN NaN \n", "2015-04-01 13:50:29 NaN NaN NaN \n", "2015-04-01 16:14:19 NaN NaN NaN \n", "2015-04-01 19:27:34 NaN NaN NaN \n", "2015-04-01 05:30:02 BQE/Gowanus Expwy East/Queens Bound Roadway \n", "2015-04-01 10:33:26 NaN NaN NaN \n", "2015-04-01 11:47:38 NaN NaN NaN \n", "2015-04-01 11:01:27 NaN NaN NaN \n", "2015-04-01 08:51:52 NaN NaN NaN \n", "2015-04-01 14:58:55 NaN NaN NaN \n", "2015-04-01 16:59:19 NaN NaN NaN \n", "... ... ... ... \n", "2015-04-01 12:14:11 Grand Central Pkwy East/Long Island Bound Roadway \n", "2015-04-01 10:00:57 NaN NaN NaN \n", "2015-04-01 08:56:10 NaN NaN NaN \n", "2015-04-01 18:19:48 NaN NaN NaN \n", "2015-04-01 16:23:18 NaN NaN NaN \n", "2015-04-01 11:41:29 NaN NaN NaN \n", "2015-04-01 23:47:24 NaN NaN NaN \n", "2015-04-01 08:16:38 NaN NaN NaN \n", "2015-04-01 07:04:27 NaN NaN NaN \n", "2015-04-01 23:36:08 NaN NaN NaN \n", "2015-04-01 14:48:14 NaN NaN NaN \n", "2015-04-01 15:26:42 NaN NaN NaN \n", "2015-04-01 19:08:07 NaN NaN NaN \n", "2015-04-01 22:06:37 NaN NaN NaN \n", "2015-04-01 00:09:40 NaN NaN NaN \n", "2015-04-01 11:43:50 NaN NaN NaN \n", "2015-04-01 13:30:29 NaN NaN NaN \n", "2015-04-01 07:33:41 NaN NaN NaN \n", "2015-04-01 12:28:15 NaN NaN NaN \n", "2015-04-01 12:17:19 NaN NaN NaN \n", "2015-04-01 12:08:19 NaN NaN NaN \n", "2015-04-01 17:43:25 NaN NaN NaN \n", "2015-04-01 18:30:35 NaN NaN NaN \n", "2015-04-01 13:30:31 NaN NaN NaN \n", "2015-04-01 01:28:45 NaN NaN NaN \n", "2015-04-01 08:52:11 NaN NaN NaN \n", "2015-04-01 11:12:49 NaN NaN NaN \n", "2015-04-01 16:18:23 NaN NaN NaN \n", "2015-04-01 13:16:44 NaN NaN NaN \n", "2015-04-01 20:27:33 NaN NaN NaN \n", "\n", " Bridge Highway Segment \\\n", "Created Date \n", "2015-04-01 21:37:42 NaN \n", "2015-04-01 23:12:04 NaN \n", "2015-04-01 13:10:35 NaN \n", "2015-04-01 17:37:38 NaN \n", "2015-04-01 12:32:40 NaN \n", "2015-04-01 18:44:50 NaN \n", "2015-04-01 16:30:15 NaN \n", "2015-04-01 09:04:07 NaN \n", "2015-04-01 07:46:58 NaN \n", "2015-04-01 17:12:17 Clearview Expwy (I-295) (Exit 27 S-N) - Utopia... \n", "2015-04-01 21:30:48 NaN \n", "2015-04-01 15:51:04 NaN \n", "2015-04-01 10:43:28 NaN \n", "2015-04-01 15:12:46 NaN \n", "2015-04-01 06:15:42 Clove Rd/Richmond Rd (Exit 13) - Lily Pond Ave... \n", "2015-04-01 11:28:02 168th St (Exit 17) \n", "2015-04-01 17:35:18 NaN \n", "2015-04-01 13:54:54 NaN \n", "2015-04-01 23:49:33 NaN \n", "2015-04-01 07:50:49 NaN \n", "2015-04-01 13:50:29 NaN \n", "2015-04-01 16:14:19 NaN \n", "2015-04-01 19:27:34 NaN \n", "2015-04-01 05:30:02 Williamsburg Br / Metropolitan Ave (Exit 32) -... \n", "2015-04-01 10:33:26 NaN \n", "2015-04-01 11:47:38 NaN \n", "2015-04-01 11:01:27 NaN \n", "2015-04-01 08:51:52 NaN \n", "2015-04-01 14:58:55 NaN \n", "2015-04-01 16:59:19 NaN \n", "... ... \n", "2015-04-01 12:14:11 31st (Exit 3) - Brooklyn-Queens Expwy (I-278) ... \n", "2015-04-01 10:00:57 NaN \n", "2015-04-01 08:56:10 NaN \n", "2015-04-01 18:19:48 NaN \n", "2015-04-01 16:23:18 NaN \n", "2015-04-01 11:41:29 NaN \n", "2015-04-01 23:47:24 NaN \n", "2015-04-01 08:16:38 NaN \n", "2015-04-01 07:04:27 NaN \n", "2015-04-01 23:36:08 NaN \n", "2015-04-01 14:48:14 NaN \n", "2015-04-01 15:26:42 NaN \n", "2015-04-01 19:08:07 NaN \n", "2015-04-01 22:06:37 NaN \n", "2015-04-01 00:09:40 NaN \n", "2015-04-01 11:43:50 NaN \n", "2015-04-01 13:30:29 NaN \n", "2015-04-01 07:33:41 NaN \n", "2015-04-01 12:28:15 NaN \n", "2015-04-01 12:17:19 NaN \n", "2015-04-01 12:08:19 NaN \n", "2015-04-01 17:43:25 NaN \n", "2015-04-01 18:30:35 NaN \n", "2015-04-01 13:30:31 NaN \n", "2015-04-01 01:28:45 NaN \n", "2015-04-01 08:52:11 NaN \n", "2015-04-01 11:12:49 NaN \n", "2015-04-01 16:18:23 NaN \n", "2015-04-01 13:16:44 NaN \n", "2015-04-01 20:27:33 NaN \n", "\n", " Garage Lot Name Ferry Direction Ferry Terminal Name \\\n", "Created Date \n", "2015-04-01 21:37:42 NaN NaN NaN \n", "2015-04-01 23:12:04 NaN NaN NaN \n", "2015-04-01 13:10:35 NaN NaN NaN \n", "2015-04-01 17:37:38 NaN NaN NaN \n", "2015-04-01 12:32:40 NaN NaN NaN \n", "2015-04-01 18:44:50 NaN NaN NaN \n", "2015-04-01 16:30:15 NaN NaN NaN \n", "2015-04-01 09:04:07 NaN NaN NaN \n", "2015-04-01 07:46:58 NaN NaN NaN \n", "2015-04-01 17:12:17 NaN NaN NaN \n", "2015-04-01 21:30:48 NaN NaN NaN \n", "2015-04-01 15:51:04 NaN NaN NaN \n", "2015-04-01 10:43:28 NaN NaN NaN \n", "2015-04-01 15:12:46 NaN NaN NaN \n", "2015-04-01 06:15:42 NaN NaN NaN \n", "2015-04-01 11:28:02 NaN NaN NaN \n", "2015-04-01 17:35:18 NaN NaN NaN \n", "2015-04-01 13:54:54 NaN NaN NaN \n", "2015-04-01 23:49:33 NaN NaN NaN \n", "2015-04-01 07:50:49 NaN NaN NaN \n", "2015-04-01 13:50:29 NaN NaN NaN \n", "2015-04-01 16:14:19 NaN NaN NaN \n", "2015-04-01 19:27:34 NaN NaN NaN \n", "2015-04-01 05:30:02 NaN NaN NaN \n", "2015-04-01 10:33:26 NaN NaN NaN \n", "2015-04-01 11:47:38 NaN NaN NaN \n", "2015-04-01 11:01:27 NaN NaN NaN \n", "2015-04-01 08:51:52 NaN NaN NaN \n", "2015-04-01 14:58:55 NaN NaN NaN \n", "2015-04-01 16:59:19 NaN NaN NaN \n", "... ... ... ... \n", "2015-04-01 12:14:11 NaN NaN NaN \n", "2015-04-01 10:00:57 NaN NaN NaN \n", "2015-04-01 08:56:10 NaN NaN NaN \n", "2015-04-01 18:19:48 NaN NaN NaN \n", "2015-04-01 16:23:18 NaN NaN NaN \n", "2015-04-01 11:41:29 NaN NaN NaN \n", "2015-04-01 23:47:24 NaN NaN NaN \n", "2015-04-01 08:16:38 NaN NaN NaN \n", "2015-04-01 07:04:27 NaN NaN NaN \n", "2015-04-01 23:36:08 NaN NaN NaN \n", "2015-04-01 14:48:14 NaN NaN NaN \n", "2015-04-01 15:26:42 NaN NaN NaN \n", "2015-04-01 19:08:07 NaN NaN NaN \n", "2015-04-01 22:06:37 NaN NaN NaN \n", "2015-04-01 00:09:40 NaN NaN NaN \n", "2015-04-01 11:43:50 NaN NaN NaN \n", "2015-04-01 13:30:29 NaN NaN NaN \n", "2015-04-01 07:33:41 NaN NaN NaN \n", "2015-04-01 12:28:15 NaN NaN NaN \n", "2015-04-01 12:17:19 NaN NaN NaN \n", "2015-04-01 12:08:19 NaN NaN NaN \n", "2015-04-01 17:43:25 NaN NaN NaN \n", "2015-04-01 18:30:35 NaN NaN NaN \n", "2015-04-01 13:30:31 NaN NaN NaN \n", "2015-04-01 01:28:45 NaN NaN NaN \n", "2015-04-01 08:52:11 NaN NaN NaN \n", "2015-04-01 11:12:49 NaN NaN NaN \n", "2015-04-01 16:18:23 NaN NaN NaN \n", "2015-04-01 13:16:44 NaN NaN NaN \n", "2015-04-01 20:27:33 NaN NaN NaN \n", "\n", " Latitude Longitude \\\n", "Created Date \n", "2015-04-01 21:37:42 40.609810 -73.922498 \n", "2015-04-01 23:12:04 40.694644 -73.955504 \n", "2015-04-01 13:10:35 40.653016 -73.738626 \n", "2015-04-01 17:37:38 NaN NaN \n", "2015-04-01 12:32:40 40.714299 -73.761158 \n", "2015-04-01 18:44:50 40.878028 -73.860237 \n", "2015-04-01 16:30:15 40.757811 -73.861677 \n", "2015-04-01 09:04:07 NaN NaN \n", "2015-04-01 07:46:58 40.756412 -73.887405 \n", "2015-04-01 17:12:17 NaN NaN \n", "2015-04-01 21:30:48 40.660699 -73.994082 \n", "2015-04-01 15:51:04 40.621474 -73.950711 \n", "2015-04-01 10:43:28 40.727725 -73.978204 \n", "2015-04-01 15:12:46 40.675746 -73.956122 \n", "2015-04-01 06:15:42 40.606875 -74.085408 \n", "2015-04-01 11:28:02 40.719228 -73.791963 \n", "2015-04-01 17:35:18 40.737182 -73.998585 \n", "2015-04-01 13:54:54 NaN NaN \n", "2015-04-01 23:49:33 40.728733 -73.988011 \n", "2015-04-01 07:50:49 40.703414 -73.862854 \n", "2015-04-01 13:50:29 40.780069 -73.955158 \n", "2015-04-01 16:14:19 NaN NaN \n", "2015-04-01 19:27:34 40.753104 -73.972096 \n", "2015-04-01 05:30:02 NaN NaN \n", "2015-04-01 10:33:26 40.843149 -73.934539 \n", "2015-04-01 11:47:38 NaN NaN \n", "2015-04-01 11:01:27 40.819111 -73.913908 \n", "2015-04-01 08:51:52 40.605657 -73.981194 \n", "2015-04-01 14:58:55 NaN NaN \n", "2015-04-01 16:59:19 40.634497 -73.954167 \n", "... ... ... \n", "2015-04-01 12:14:11 40.769309 -73.912236 \n", "2015-04-01 10:00:57 NaN NaN \n", "2015-04-01 08:56:10 40.757216 -73.899106 \n", "2015-04-01 18:19:48 40.749584 -73.885951 \n", "2015-04-01 16:23:18 40.651629 -73.959035 \n", "2015-04-01 11:41:29 NaN NaN \n", "2015-04-01 23:47:24 40.743456 -73.914836 \n", "2015-04-01 08:16:38 40.634792 -74.032318 \n", "2015-04-01 07:04:27 40.684012 -73.831954 \n", "2015-04-01 23:36:08 40.617990 -74.008220 \n", "2015-04-01 14:48:14 NaN NaN \n", "2015-04-01 15:26:42 40.748707 -73.942316 \n", "2015-04-01 19:08:07 40.675876 -73.877688 \n", "2015-04-01 22:06:37 40.808939 -73.914543 \n", "2015-04-01 00:09:40 40.694510 -73.800763 \n", "2015-04-01 11:43:50 NaN NaN \n", "2015-04-01 13:30:29 40.743972 -73.759771 \n", "2015-04-01 07:33:41 40.785713 -73.826140 \n", "2015-04-01 12:28:15 40.749789 -73.893794 \n", "2015-04-01 12:17:19 40.648964 -74.021255 \n", "2015-04-01 12:08:19 40.722832 -73.748158 \n", "2015-04-01 17:43:25 40.689215 -73.803877 \n", "2015-04-01 18:30:35 40.750641 -73.739344 \n", "2015-04-01 13:30:31 40.710496 -73.872661 \n", "2015-04-01 01:28:45 40.646630 -73.997960 \n", "2015-04-01 08:52:11 40.870105 -73.847979 \n", "2015-04-01 11:12:49 40.798932 -73.951952 \n", "2015-04-01 16:18:23 40.638946 -73.962055 \n", "2015-04-01 13:16:44 40.748262 -73.892616 \n", "2015-04-01 20:27:33 40.650810 -73.949370 \n", "\n", " Location \n", "Created Date \n", "2015-04-01 21:37:42 (40.60980966645303, -73.92249759633725) \n", "2015-04-01 23:12:04 (40.694643700748486, -73.95550356170298) \n", "2015-04-01 13:10:35 (40.653016256598534, -73.73862588133056) \n", "2015-04-01 17:37:38 NaN \n", "2015-04-01 12:32:40 (40.71429859671565, -73.76115807774032) \n", "2015-04-01 18:44:50 (40.87802828144708, -73.86023734606933) \n", "2015-04-01 16:30:15 (40.757811195752154, -73.86167714731972) \n", "2015-04-01 09:04:07 NaN \n", "2015-04-01 07:46:58 (40.75641194675221, -73.88740503059863) \n", "2015-04-01 17:12:17 NaN \n", "2015-04-01 21:30:48 (40.660699296661825, -73.99408169463258) \n", "2015-04-01 15:51:04 (40.62147413119333, -73.95071097029123) \n", "2015-04-01 10:43:28 (40.72772462544187, -73.97820435916094) \n", "2015-04-01 15:12:46 (40.67574618440852, -73.9561218336512) \n", "2015-04-01 06:15:42 (40.60687536641399, -74.0854077221027) \n", "2015-04-01 11:28:02 (40.71922760413319, -73.791962929951) \n", "2015-04-01 17:35:18 (40.737182358685516, -73.99858548189518) \n", "2015-04-01 13:54:54 NaN \n", "2015-04-01 23:49:33 (40.72873338955463, -73.98801059255561) \n", "2015-04-01 07:50:49 (40.70341423569781, -73.86285397616253) \n", "2015-04-01 13:50:29 (40.78006850471446, -73.95515761412761) \n", "2015-04-01 16:14:19 NaN \n", "2015-04-01 19:27:34 (40.75310402468627, -73.97209629231209) \n", "2015-04-01 05:30:02 NaN \n", "2015-04-01 10:33:26 (40.84314882753921, -73.93453937669832) \n", "2015-04-01 11:47:38 NaN \n", "2015-04-01 11:01:27 (40.819110789789214, -73.91390802507868) \n", "2015-04-01 08:51:52 (40.60565667868274, -73.98119372058547) \n", "2015-04-01 14:58:55 NaN \n", "2015-04-01 16:59:19 (40.63449684441219, -73.95416735372353) \n", "... ... \n", "2015-04-01 12:14:11 (40.76930913453694, -73.91223589513348) \n", "2015-04-01 10:00:57 NaN \n", "2015-04-01 08:56:10 (40.7572160209837, -73.89910584068605) \n", "2015-04-01 18:19:48 (40.74958432631206, -73.88595125985013) \n", "2015-04-01 16:23:18 (40.651628886860884, -73.95903518064264) \n", "2015-04-01 11:41:29 NaN \n", "2015-04-01 23:47:24 (40.74345557431229, -73.91483581341043) \n", "2015-04-01 08:16:38 (40.63479238458042, -74.03231826494591) \n", "2015-04-01 07:04:27 (40.68401163822402, -73.83195428896114) \n", "2015-04-01 23:36:08 (40.617990283460536, -74.00821981214455) \n", "2015-04-01 14:48:14 NaN \n", "2015-04-01 15:26:42 (40.74870685388612, -73.94231592971958) \n", "2015-04-01 19:08:07 (40.67587618287245, -73.87768812152434) \n", "2015-04-01 22:06:37 (40.80893932182981, -73.91454250715576) \n", "2015-04-01 00:09:40 (40.69451003870482, -73.80076336778066) \n", "2015-04-01 11:43:50 NaN \n", "2015-04-01 13:30:29 (40.74397211975241, -73.75977055909947) \n", "2015-04-01 07:33:41 (40.7857127748661, -73.82614011947928) \n", "2015-04-01 12:28:15 (40.74978944638325, -73.89379359227247) \n", "2015-04-01 12:17:19 (40.648963544502585, -74.02125458310132) \n", "2015-04-01 12:08:19 (40.72283183191531, -73.74815780857023) \n", "2015-04-01 17:43:25 (40.68921522366862, -73.80387663789386) \n", "2015-04-01 18:30:35 (40.75064138697133, -73.7393436538413) \n", "2015-04-01 13:30:31 (40.71049602255762, -73.8726613318581) \n", "2015-04-01 01:28:45 (40.646629679609966, -73.99796038095705) \n", "2015-04-01 08:52:11 (40.870105314232546, -73.84797875606117) \n", "2015-04-01 11:12:49 (40.7989317549172, -73.9519520651255) \n", "2015-04-01 16:18:23 (40.638946273235284, -73.96205520207174) \n", "2015-04-01 13:16:44 (40.748262273356396, -73.89261586191228) \n", "2015-04-01 20:27:33 (40.6508098378492, -73.94937030940775) \n", "\n", "[147 rows x 53 columns]" ] }, "execution_count": 84, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['2015-04-01']" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Street Condition 18\n", "Illegal Parking 15\n", "Consumer Complaint 12\n", "Name: Complaint Type, dtype: int64" ] }, "execution_count": 85, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['2015-04-01']['Complaint Type'].value_counts().head(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What month has the most reports filed?** How many? Graph it." ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11544af28>" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AQkND6dixI3a7HaDa29+zsrIYNmwYVquVqKgo2rdvT25uLoWFhVy4cIGYmBgAkpKS2LZt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\ndGJwcBCNjY1QqVSYmJj4dHP6s3gZ+yleYmK/Wm5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"text/plain": [ "<matplotlib.figure.Figure at 0x1156b3f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.resample('M').count().plot(y=\"Unique Key\",legend=False)\n", "#http://pandas.pydata.org/pandas-docs/stable/timeseries.html#up-and-downsampling\n", "#resample is a time-based groupby, followed by a reduction method on each of its groups" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x1156f2198>" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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JiF7Rvxzo2gsuXUCaLuuXlGIVVWgURXEJMi0F0fuXQiPc3BC9o5GpqvvM2alCoyiK05Om\nLMjJhpBulY6LfoPUMGcXoAqNoihOT6YdQIT3QxiMlR8Ij4Qfv0fmXdUnMcUqqtAoiuL0ZFoKVOg2\nKyc8PCG8L/LQfh2yUqylCo2iuDB5JoP8eX/UOw27kiXX4Xgaome/ah8Xkar7zNmpQqMoLkx+kUTp\noW+Q537UOxX7OZ4GwZ0QTX2rfVj0joGT6cjCAgcnplhLFRpFcVEyOwuZfhCPuHHIA1/rnY7dyMMp\nlYc130A08YYuEebuNcUpqUKjKC5KbtuAiB2Bx/DbkSm79U7HLqSUVYY1V0dEDgTVfea0VKFRFBck\niwqRuzcjRow3r/tVVNgwu88yz4JWBu063fRpou8A5NGDyOvFjslLqRVVaBTFBcm9W6FLBCKwtXnd\nr6jYBtl9Vt5tJoS46fOEb3PoEApHUx2UmVIbbo4KVFJSwqxZsygtLaW0tJTo6Ggeeugh8vPzeeON\nN8jKyiIoKIjExES8vb0BSEpKYtu2bRiNRhISEujTpw8Ap06dYvny5ZSUlBAZGUlCQoKjLkNRdCc1\nDbn5MwyPPms5JqIGo61eCnc9pGNmtifTUjCMmmDVc8tHn4m+A+yclVJbDmvRuLu7M2vWLF599VVe\ne+010tPTOXbsGOvXr6dXr14sXryYiIgIkpKSADh79ix79+5l0aJFTJ8+nZUrV1q2bl25ciXTpk1j\n8eLFXLhwgdRU9SlGaUTSUqCJN3QJ/+VYSLcG130mC67BmQzo3tuq54vIAcjDycjSUjtnptSWQ7vO\nPD09AXPrRtM0fHx8SElJYdiwYQDExcWRnJwMQEpKCrGxsRiNRoKCgmjTpg0ZGRnk5ORQWFhIWFgY\nAEOHDrW8RlEaA+2rTxC33VWpO6lBdp8dPQhh4Yif/9+4FeEfCEFt4MQROyem1JZDC42mabz44otM\nnTqViIgIgoODyc3Nxc/PDwA/Pz9yc3MBMJlMtGzZ0vJaf39/TCYTJpOJgIAAy/GAgABMJpMjL0NR\ndCN/PAUXzyGiB1d5TEQNblCjz+ThW482u5GIVFsHOCOH3aMBMBgMvPrqqxQUFDB37lzS09OrPOdW\nN/1qIz09vVKM+Ph4fH2rn/RlTx4eHrrE1TN2Y4vrqNjXdmzEY8w9eLXwrxJX9onmanER3jmXMbbv\nbNc8boxta1LTuHr0ID6THsdYzflrilv2q5Hk/+13+Ez9A8Jgn8/RjfF3G2DNmjWWryMiIoiIiLD6\ntQ4tNOW8vb2JjIzk+++/x8/Pj5ycHMvfzZs3B8wtmMuXf9lnIjs7G39/f/z9/cnOzq5yvDrVvRl5\neXl2uKKb8/X11SWunrEbW1xHxJa5V9BSvqZs7j8oqRCnUtx+g8jf+SUGBw0KsNc1yx9OIL19KGjS\nFKo5f41xff2QTX3JO5SCCOth87xuGtvO9P7djo+Pr/PrHdZ1dvXqVQoKzEtEXL9+nbS0NDp37kxU\nVBTbt28HYPv27URHm5vK0dHR7Nmzh9LSUi5dukRmZiZhYWH4+fnh7e1NRkYGUkp27txJTEyMoy5D\nUXQjt29ERA9B+DSr8TkNpfusLt1m5czdZ2qPGmfisBZNTk4Oy5YtM8/0lZIhQ4bQq1cvOnfuzKJF\ni9i2bRuBgYEkJiYCEBwczKBBg0hMTMTNzY0pU6ZYutUmT57MsmXLLMOb+/bt66jLUBRdyJLryB0b\nMbww7+ZPrDD6TLTr4Jjk7ECmpWC4/7d1eq2IHIj21nzkxASbdsUrdeewQtOhQwcWLFhQ5biPjw9/\n+ctfqn3NPffcwz333FPleEhICK+//rrNc1QUZyX3bYeOYYg2wTd9XsXRZ6Kda86pkTkmyLoAod3r\ndoL2nUFKOHva/LWiO7UygKI4OSklcvOnGEbdZdXzXb37TKalIMIjEW51+xwshFA7bzoZVWgUxdl9\nd8j8dw8ru4hdfPKmTEuBm6zWbA01zNm5qEKjKE5O2/xplQmaN+PKkzdlSQkcO4zoFVW/E4V0g/yr\nyMxztklMqRdVaBTFickLZ+H0ScSAYbV6nct2n51Mh9bB5kUy60EYDIjIgWr0mZNQhUZRnJjc8ili\n2FiEh3XLsFi4aPeZNXvPWEt1nzkPVWgUxUnJ/KvI5F2IuNtr/VpX7T4zbwtgo3lxXXvCpQtIU5Zt\nzqfUmSo0iuKk5K4vEX0GIJq3qNPrXa37TF48D8VF0CHEJucTbm6I3jHIg/ttcj6l7lShURQnJEtL\nkVs/R9xm3ZDmapV3n513je4zmZaM6BVl00mWop/qPnMGqtAoihOSB3ZDq7aIeny6t3SfuUirpnw3\nTZsK7ws/fo/My7XteZVaUYVGUZyMlBL51ScY6tOa+Zm5+8z579PIogI4dQLC+9j0vMLDExEeiUxV\n3Wd6sqrQfP3115w9exaA8+fPM2vWLGbPns25c2qMuqLYXMZ3UHgNetvgprirdJ8dPQQhXRFe3rY/\ndz+1yKberCo0H330ET4+PgCsXr2a0NBQevTowcqVK+2anKI0RtrmTxEj77TJfiqu0n1mHtZsn1XY\nRa9oOJlu3hpa0YVVv8lXr17Fz8+P69evc/z4cSZNmsTEiRM5ffq0ndNTlMZFZmXCiTRE7EibndPZ\nu8+klMi0AzabP3Mj0cQbukSYl7ZRdGFVoWnWrBmZmZmkpqYSGhqKu7s7JSUl9s5NURodufVzROxt\nCK8mtjups3ef/XgKvJoggtraLYQafaYvqwrNfffdxx//+EdWrFjBXXeZb1CmpaXRsWNHuyanKI2J\nLCxA7t2KGDHepud19u4z87Bm+7Rmyok+A+BoKvJ6sV3jKNWzah3uuLg4Bg0aBICnp3kpjC5duvC7\n3/3OfpkpSiMjd3+F6NEHERBo83OLqMFoq5fCXZNsfu76kodTMNzzG7vGEL7NoEMoHD0IfQfaNZZS\nlVUtmhdffBFPT09LkQFo3rw58+fPt1tiitKYSK0MuWVD/SZo3oyTdp/JqzmQeQ66hNs9lnmPGjX6\nTA9WFZrMzMwqx6SUXLx40eYJKUqjlPoNNPND1HVXyVtw1u4zeeQA9OiNcHO3eyzRdyDycDKytNTu\nsZTKbtp1tnTpUgBKS0stX5fLysqiffv29stMURoRbfMn9mvN/Mwpu8/ssRpADYR/SwhqAyeOmFcM\nUBzmpoWmVatW1X4thKBbt26W+zaKotSdPJMB2ZcQ/WLtG6hC95lo28G+sawgS0uR36VimDTVYTHL\ntw4QqtA41E0Lzf333w+Yb/z37at+MIpiD/KrTxAjxiOMRrvGqdh9Ju7Sv9Dw/XcQ2KbOq1PXhYgc\niPban5CTnrDJhFjFOlaNOuvbty/nz5/n9OnTFBUVVXpsxIgRdklMURoDeSUbmXYAw0NPOCSeM3Wf\n2WURzVsQrduBTzM4dRzCejg0dmNmVaFZt24d//3vf+nYsWOlkWegCo2i1Ifc/j/EgGEIbx/HBHSi\n7jOZloLhseccHtfSfaYKjcNYVWj+97//8corr9RrgmZ2djZLly4lNzcXIQS33XYb48aNY+3atWzZ\nsoXmzc17hE+aNMnSTZeUlMS2bdswGo0kJCTQp495ZddTp06xfPlySkpKiIyMJCEhoc55KYpeZHEx\ncucXGF561WExnaX7TGZlQv5V6Bjm8Nii3yC05a8gJz5m071vlJpZVWg8PDxo165dvQIZjUYeffRR\nOnXqRFFREX/84x/p3bs3AOPHj2f8+Mqzoc+ePcvevXtZtGgR2dnZvPzyyyxZsgQhBCtXrmTatGmE\nhYUxb948UlNT1T0kxeXIfdsgtDuilf2WXqmOM3SfybQURM8ofe6TBHcCIeCnH2y2m6dyc1b9lB94\n4AHefvttrly5gqZplf5Yy8/Pj06dOgHg5eVFu3btMJlMgHlOzo1SUlKIjY3FaDQSFBREmzZtyMjI\nICcnh8LCQsLCzJ+Ehg4dSnJystV5KIozkJqG3PypTfacqTUnmLxpXq3ZsfdnygkhLN1nimNY1aJZ\nvnw5AFu2bKny2EcffVTroJcuXeLMmTN06dKFY8eOsWnTJnbu3EloaCiPPPII3t7emEwmunbtanmN\nv78/JpMJo9FIQECA5XhAQIClYCmKy0g/CG7u0K2Xw0Pr3X0mi4vg5Hfw+AsOj11O9BtkbtXd/Wvd\ncmhMrCo0N07WrI+ioiIWLlxIQkICXl5ejBkzhokTJyKE4MMPP2T16tVMmzbNJrHS09NJT0+3fB8f\nH4+vr69Nzl0bHh4eusTVM3Zji1vb2PnbP8frzng8mjVzaNxypUNGUfB/r+H76/rNYalL7JITaRSH\ndsOnVWuHxq1I9o7iauE1vPOuYKzloIjG+LsNsGbNGsvXERERREREWP1aqwpNYKBtFvkrKyvj9ddf\nZ+jQocTEmDc5albhH9rIkSNZsGABYG7BXL582fJYdnY2/v7++Pv7k52dXeV4dap7M/Ly8mxyLbXh\n6+urS1w9Yze2uLWJLc/9iHbme7Rp0ym2Qa51uWbZuj3atWtcPZ5er9FndYmt7d8F4ZH1+jnZ5Ofc\nZwD5X2/BMG6i42PXgd6/2/Hx8XV+fY2F5h//+AdPPGEe2//mm2/WODrjmWeesTrYihUrCA4O5vbb\nb7ccy8nJwc/PD4D9+/dblrWJjo5myZIljB8/HpPJRGZmJmFhYQgh8Pb2JiMjg9DQUHbu3Mm4ceOs\nzkFR9Ca3fIqIG4dwt//6XjXRq/vMvMlZCobE2Q6LWRPRbxDautVQy0Kj1F6NhSYoKMjydevWdW/i\nljt27Bi7du2iQ4cOvPjiiwghmDRpEl9//TWnT59GCEFgYCBTp5qb8sHBwQwaNIjExETc3NyYMmWK\npdhNnjyZZcuWWYY3qxFniquQebnIA7sxvLxC71T0GX127jQYjdA62HExa9IlAi5nIrOz7LI1g/IL\nIasb8tWAnT9/3uEx9W7yNqZmvrO/19qGDyE7C8Ojzzo0bnWkpqH9cTKGxNl17j6rbWztf2shx1Tv\nlRBs9XPWVi2GDiEYRt7p8Ni1pefvdtu29RuCb/Ug9tLSUn788UeOHDlS6Y+iKNaRJSXI7Rvtvkqz\ntYTBgIge7NCtA/Qc1lwd8x41apizvVk1GODYsWMsXLiQkpISCgsLadKkCUVFRQQEBNh0RJqiNGQy\neRe064ho5zxboIuowWj/XuaQ7jOZfxXOndFlSHeNwvvCvxYh83IRvs31zqbBsqpF8+6773LXXXex\natUqmjRpwqpVq7jvvvsYPXq0vfNTlAZBSonc/AmG2+7WO5XKQrpBwTWHTN6U6Qeha0+Eu4fdY1lL\nuHsgIiKRqfv1TqVBs6rQnD9/vtJIMYAJEybw+eef2yUpxTZkVma1qy4oOjhxBEpKICJS70wqcWj3\n2eFkp+o2s+g3CHlQbfFsT1YVGm9vbwoLCwHzUjJnz54lPz+/ypYBivOQl86j/XkaxZ+v1TsVBdC+\n+gQx8k6n3ANFRA1GHrBvoZFlZcj0g4iezldoRK8oOJmOLLimdyoNllW/9QMGDODgwYMADB8+nNmz\nZ/PSSy8xcOBAuyan1J38fC1i8G0Uf74WeegbvdNp1OSl8/D9McQgJ91SwxHdZ6eOQ4uW5u2UnYzw\n8oauPZFpKXqn0mBZNRig4jL8d911F126dKGoqMiybL/iXOSl88jD32CY+w+8R99N/oLpGH7/MqJ9\nZ71Ta5Tklg2IIaMRN+zl5Cwqdp/Za/KmTHPSbrOfiX4/L7I5YJjeqTRIdWrH9+jRg8jISAxO2A2g\n/NyaGX4HwtsHty7hiIeeQFs6B5l7Re/UGh1ZkI/ctx0x/A69U7kpe3ef6bGbZm2I3v3haCryerHe\nqTRINbZoZs6cadWmQLNn67+UhPKLiq2ZcoaYIWgXzqItm4vhD3MRHs75ybohkru+QvSKQrQIuPWT\n9VSh+8zWO2/K7CzINUFI11s/WSfCtxl0CIWjB6GvuiVgazUWGrVFs2uq2JqpSNz5IFw8h3xnCTz+\nB7WzoAPIsjLk1g0YnnxJ71RuyZ7dZzItBRHRD2Ew2vS8tlY+eVOoQmNzNRaauLg4B6ah2EJ1rZly\nQgh49Fm012bAZx8g7npIhwwbF/ntXvAPRHTqoncqVrHX5E2ZloLoP9Sm57QH0Xcg2qcfIEtLEW5W\n3b5WrGTfnxNFAAAgAElEQVT1u7l161Z2797NlStXaNGiBYMHD2b48OHqk7ETqak1U054eGJ4egba\nvBfQWrXDoG582pXc8imG0RP0TsN6dug+k9eL4cQRxG9/Z5Pz2ZPwbwlBbeBEGoQ713wnV2fV3fz3\n3nuPTz75hAEDBvDwww8zYMAAPvvsM/7zn//YOz/FSvLSBeThb265jpZo3gLDMzOQH/4f8vtjDsqu\n8ZGnjkPuFeg7QO9UrGaXyZvHj0BwZ0RT/Tbsqg219pl9WFVotm/fzsyZMxk9ejT9+vVj9OjRzJgx\ng23bttk7P8VK8n9rbtqaqUgEd8aQ8BzaivnI7EsOyK7xkZs/RYwc7/T3JW5k69Fnzj6s+UYichAy\ndT9SK9M7lQbFqkLTpEkTmjRpUuWYt7e3XZJSakdeuoA8dOvWTEWiTwxi9AS0N19GFhXYMbvGR2Zn\nIY+mIgaP0juV2rPh5E0ppdMPa76RaNUWfJqZJ5gqNmNVobn99tt57bXXOHz4MGfPnuXQoUMsXLiQ\nO+64g4sXL1r+KPqoTWumIjHqbkRIN7T/e119grMhuW0DYtBwRBPX+yBm0+6zCz+B1MCJVqu2huo+\nsz2rBgO88847AKSnp1c6fuTIEVatWmX5/qOPPrJdZopVylsz1Y00uxUhBDw0DfnGLOTH7yDiJ9sh\nw8ZFFhUid2/G8KfX9U6lzmw1+kymmVszrjZgSPQbhLZ0LvL+37pc7s7KqkKjCojzqmtrppxwc8Pw\n5Etor7yA1joYw9AxNs6wcZF7t5qXwg+s//bnurHR6DN5OAXD6HtsmJiDtOsEBgP8dMo8iVOpN7WG\njAury72Z6oimvhie/Qty/XvIY4dtlF3jIzUNufkz59tzppZs0X0mC/Lhx++he28bZuYYQgjVfWZj\nVrVoLl++zNq1azl9+nSVrQEWL15sl8SUW6tva6Yi0bodhsf/gPbPv2N4cT6idTsbZNi4lB7cB028\nIayH3qnUW327z2R6KoSFO+1CorciIgehvfsmTHhY71QaBKsKzcKFC2nbti3x8fF4eDjP7niNWX3u\nzdRE9OiDmPAw2psvY/jT311m7oOzKP7fx+YBFg2hX7++3WcuNqy5is5dofAaMvMsonWw3tm4PKsK\nzblz55gzZ45ardmJ2LI1U5Fh6Bi0zLNoK+Zj+N1stRSHleSPp9Au/ISIitU7FZuoz9pnUitDph3A\n4MLLHAmDARE5EHlwH2LcRL3TcXlWVY6oqCiOHj1q71wUK9nq3kxNxMQE8PRCvv+W2graSnLHRjxH\n3Y1wc9c7FZup8+TNH06Cb3NEy1a2T8qBRKS6T2MrVn1cfeyxx/jzn/9M69atad68eaXHnnrqKasC\nZWdns3TpUnJzcxFCMHLkSG6//Xby8/N54403yMrKIigoiMTERMtE0KSkJLZt24bRaCQhIcGy0dqp\nU6dYvnw5JSUlREZGVtqYrTGwV2umnDAYMTz+PNr8P8LmTxGjXPvmtr1JTUMe+gb3e39Did7J2FId\nu89kWgqid4wdE3OQrj3hciYyOwsREKh3Ni7NqhbNihUrMBqNtGvXDn9//0p/rGU0Gnn00UdZuHAh\nc+fO5YsvvuDcuXOsX7+eXr16sXjxYiIiIkhKSgLg7Nmz7N27l0WLFjF9+nRWrlxp+XS9cuVKpk2b\nxuLFi7lw4QKpqal1uHTXZO/WTDnh5W0eifZFEvJQsl1jubwz34O3D8YGNoCirqPPzIXGhe/P/EwY\njYg+/ZGp+/ROxeVZVWiOHDnC3LlzeeSRR3jwwQcr/bGWn58fnTp1AsDLy4t27dqRnZ1NSkoKw4aZ\nVxGOi4sjOdn8n1pKSgqxsbEYjUaCgoJo06YNGRkZ5OTkUFhYSFhYGABDhw61vKYxkP9bg4izX2um\nIhEQZJ5j8+4S5Nkf7B7PVcnDLn7j+yZq230mc7Lh8iUIdf2RdwAiMlZ1n9mAVYWmY8eO5OXl2Szo\npUuXOHPmDF27diU3Nxc/Pz/AXIxyc3MBMJlMtGzZ0vIaf39/TCYTJpOJgIBfdisMCAjAZDLZLDdn\n5qjWTEUitDvigSnmmdJX1VbQ1TEXmgbQVVSdWq59JtMOICIiEUbXWky0RuF94KcfkFdz9M7EpVl1\njyYiIoK5c+cSFxdX5R5NbXfiLCoqYuHChSQkJODl5VXlcVsODU1PT6+0bE58fDy+vo4fsuvh4WGT\nuAX/WY4Ycw9NWrdxbOzbxlN45TKlby3A5y8LrdoK2lbXXFuOjquZssjLvoRv3/4N9poLBw1HpKXg\n1S3ilrGvfZeKe8wQPOz8Pjjyvb7WJwa344fxHHGHw2NXpFfccmvWrLF8HRERQURE1d+HmlhVaI4f\nP46/vz+HD1edNV6bQlNWVsbrr7/O0KFDiYkxfwL08/MjJyfH8nd5IfP39+fy5cuW12ZnZ1vuC2Vn\nZ1c5Xp3q3gxbtsys5evrW++48tIFtJTdGOb8g9JanMsWsQHkmHuRP57i6tJXEFOev+UHAlvFrS1H\nx9X27oCISPILChBGY4O8Ztk7Bu3fyygZc+9NY8uSErQj31I26QmK7fw+OPLnrPWOoWTPVq7HDHV4\n7Ir0ilseOz4+vs6vt6rQzJo1q84BKlqxYgXBwcHcfvvtlmNRUVFs376dCRMmsH37dqKjzX3d0dHR\nLFmyhPHjx2MymcjMzCQsLAwhBN7e3mRkZBAaGsrOnTsZN26cTfJzZpZ7M03tf2+mOkIISPh/5q2g\nN3yEuNP6+3MNmTycjIgZonca9mXt6LOTR6BNe4Rv85qf44JEryjk6qXIgmsI76Z6p+OSrJ6Nl5+f\nz4EDBzCZTPj7+xMVFYWPj/X/6R07doxdu3bRoUMHXnzxRYQQTJo0iQkTJrBo0SK2bdtGYGAgiYmJ\nAAQHBzNo0CASExNxc3NjypQplk/RkydPZtmyZZbhzX379q3lZbsWyyoAc2y3CkBdWLaCfuUPaK3b\nYWjo/8HegrxeDMfTEI89p3cqdmXt5E1X23vGWsLLG7r2NI+mU9uf14mQVszIO3HiBPPmzaNdu3a0\nbNmS7Oxszp49y/Tp0+natasj8rSZ8+fPOzxmfZu82jtLoEVLDHfXfqa1PZrb8qcf0Bb+BcP/m4no\nXP3PvzF0L8i0A2gb12J8cb7DY1fkiLgy4zu0fy/DOHtpjbHLZjyB4YkXEQ5Y8djhXaS7NyMPp2B8\n8qUG/XOuSdu2bev1eqv3o5kyZQqDBw+2HNuzZw+rVq1i3rx59UpAuTmZlYk8tF/31kxFon1nDAn/\nD235Kxhe+nujnczWoEeb3egW3Wcy8xxcL4b2ITokZ3+id3/kRyuRxcWg4w15V2XV8OYLFy4waNCg\nSscGDhxIZmamXZJSfiE/1/feTE1En/6IUXejLZ2DLCrUOx2HM29T3HgKza0mb7rqJmfWEr7NoGMY\nHD2odyouyapC07p1a/bs2VPp2N69e2nVyrXXMnJ25a0ZR86bqQ0xagKiUxjayka4FfS5M+bNsdq0\n1zsTh7nZ5M3yQtOQqT1q6s6qrrOEhATmz5/Pxo0badmyJVlZWVy4cIGXXnrJ3vk1as7amiknhIBf\nT0O+8Vfkf1cj7n9M75Qcprw101A/wVerhu4zWVQAp07AU3/SMTn7E30Hoq3/D7K0Qa1o5xBWtWi6\ndevGm2++yZgxYwgJCWHs2LG8+eabdOvWzd75NVrO3popJ9zcMTz5EjJ1H9quL/VOx2EaU7dZuRq7\nz46mQmg3hFcTfRJzENEiANp3pmTfdr1TcTk3LTTXr1/nxx/NS0/4+PgwdOhQ7r77boYOHYrJZOL6\n9esOSbIxcvbWTEWWraCT/o08nqZ3OnYn867C+R/Nq/s2MtV1nzXUYc3VMYy9j6JPPlDbZ9TSTQvN\nJ598wrZt26p9bPv27Xz66ad2Saqxc5XWTEWidbB5K+h/vIq86Pgh5I4kjxyA7r0R7g1n7xmr3bD2\nmdQ05JEDDXZR0SrK13FLS9E7E5dy00KzZ88e7rzzzmofGz9+PLt312FTJOWWXKk1U5F5K+hfo735\nMlq+PuP9HaIRdpuVu7H7rOyHk+DljQiq3zwLVyGEwPOuSWgb/6t3Ki7lpoWmfBWA6pSvpqzYliu2\nZioyDB2L6NGborWr9E7FLmRpKfLowUbTVVSdit1npQf3Nbr3wn3AMMg1IU+qXYetddNC4+XlVWlh\ny4ouX76Mp+etV/FVasdVWzMVidH3ULJnK7K0VO9UbC/jKAS1RTRvoXcm+qnQfVZycF/j6Tb7mTAa\nEWPuRdukWjXWummhiYyM5IMPPqj2sQ8//JB+/frZJanGytVbM+VEYGsMbdtDesOb3NYYR5vdyNJ9\ntu1/lF34CbqE652Sw4nYEXDme+TZ03qn4hJuWmgefPBBjh07xgsvvMDatWvZvHkza9eu5YUXXuDY\nsWO12mFTubWG0Jop5/GrUcj92/VOw+bk4ZRGX2jg5+6zHRtx79kP4db4BkUIdw/EyDuRX6zTOxWX\ncNNC4+fnx4IFC4iKiiI1NZXPPvuM1NRUoqKimD9/vmVnTKX+Gkprppz7wGHIIweQhQV6p2IzMvMc\nFBdCh4a5nlethHSD5v64RQ7UOxPdiGFjzb/jly/qnYrTu+XKAD4+Pjz44IOq9WJn5tbM7Q2iNQNg\n8G0O3Xohv92LGDxS73RsoqGv51UbwmDA8PuX8QgJ43phkd7p6EJ4N0UMGY38cj3ioSf0TsepWbUy\ngGJfMisTmdpwWjPlDAPjGlT3mbo/U5loE9wou80qEiPvQu7fgbyao3cqTk0VGicgP1+DGH47omkD\nW368d4z5humV7Fs/18nJgmtw+iT06KN3KooTEc1bIGJ+hdy6Qe9UnJoqNDpraPdmKhLuHuYVb7/Z\nqXcq9Xf0IISFIzy99M5EcTJi9D3IHZvMi4sq1aqx0MyYMcPy9dq1ax2STGMk/7f253szDaw18zMx\nMA7ZABYhlIdUt5lSPRHUBtGjD3LnF3qn4rRqLDTnz5+3LJq5YYNqFtqD+d7MvgbZmrHoEgHX8lx6\nvoHUypBHUhrdxETFemLsfcivPkGWqC0EqlPjqLOYmBiee+45goKCuH79OrNmzar2ebNnz7Zbcg1d\nQ2/NwM+T+wYMQ+7fgQjupHc6dXPqBDT3RwQE6Z2J4qREhxAI7oTctw0xZLTe6TidGgvNU089xbFj\nx7h06RIZGRkMHz7ckXk1eOWtGcOct/ROxe7EwDi0xbOR9/wGYXC924JqtJliDcPYiWj/XoYcPBJh\nMOqdjlO56Tya7t270717d0pLS4mLi3NQSo1DY2jNlBPtOkJTXziZDt166Z1OrcnDyRgefkrvNBRn\n1zUCmvrAwf0QFat3Nk7Fqq2cR4wYQXp6Ojt27ODKlSu0aNGCoUOH0rNn49v4yRYaU2umXPmgAOFi\nhUZmX4LcKxDSVe9UFCcnhMAw7j60DWsw9BukJvZWYFWh2bJlCx988AEjRoygS5cuXL58mcWLF/PA\nAw9w2223WRVoxYoVfPvttzRv3pzXXnsNMI9m27JlC82bNwdg0qRJ9O3bF4CkpCS2bduG0WgkISGB\nPn3M8xdOnTrF8uXLKSkpITIykoSEhNpes+4aU2umnOg/FO2vzyIfegLh7qF3OlYz7x4ZpbpCFOv0\n7g/r/g3fHYLwvnpn4zSsKjSffvopf/7zn+nUqZPlWGxsLK+//rrVhWb48OGMGzeOpUuXVjo+fvx4\nxo8fX+nY2bNn2bt3L4sWLSI7O5uXX36ZJUuWIIRg5cqVTJs2jbCwMObNm0dqaqqlOLmCxtiagZ/3\nW+8YCoeTIWqw3ulYTR5OxtBAltBR7E8YDIix5i0EjKrQWFh1ZzYvL4/g4OBKx9q2bUt+fr7Vgbp3\n707Tpk2rHK9u7+2UlBRiY2MxGo0EBQXRpk0bMjIyyMnJobCwkLCwMACGDh1KcnKy1Tk4g8bYmikn\nBsShudCcGllcZN5/JjxS71QUFyL6D4WL55A/nNQ7FadhVaHp3r07q1evpri4GICioiL+/e9/07Vr\n/futN23axAsvvMBbb71FQYF5Zq3JZKJly5aW55Tv5mkymQgICLAcDwgIcKldPhvFvJmbEP0GwfE0\nZP5VvVOxzneHoFMXhHfVD0iKUhPh5o4YNUFtjFaBVV1njz/+OG+88QYJCQn4+PiQn59P165dee65\n5+oVfMyYMUycOBEhBB9++CGrV69m2rRp9TpnRenp6aSnp1u+j4+Px9fX8S0JDw8PfH19KXj/LcTo\nCTRp7bj91ctjO1q1cX19udZ3AG5HDuA5yj7F1pbXW/BdKoaYX+Fl5fmc6r1u4LGd/ZrluHu5uvFj\nvPOuYGzbwWFx7WnNmjWWryMiIoiIiLD6tVYVmhYtWjB79myys7Mto84qtizqqlmzZpavR44cyYIF\nCwBzC6biFtLZ2dn4+/vj7+9PdnZ2leM1qe7NyMvLq3feteXr68vVUyfRkndhmPMWpQ7MwdfXV7dr\nri6ujPoVJZs+5vpA+8zLstX1SinRDuzFMOJOSqw8n7O91w05tktc87Bx5K97D8Ojzzo2rh34+voS\nHx9f59fXavZcQEAAYWFhdS4yUspK92Rycn5ZWnv//v20b98egOjoaPbs2UNpaSmXLl0iMzOTsLAw\n/Pz88Pb2JiMjAyklO3fuJCbGNSbSNeZ7M5VERELmOWRWpt6Z3NyPp8CrCaKV41qfSsMiRtyB/HZv\ng1i9vL6satHYwuLFizl69Ch5eXk8+eSTxMfHk56ezunTpxFCEBgYyNSpUwEIDg5m0KBBJCYm4ubm\nxpQpUyxj0idPnsyyZcssw5tdYcRZ2cXzjXKkWXWEm5t5WfX9OxDjH9A7nRqZVwNQa5spdSd8miFi\nRyA3f4K4/7d6p6MrIasb9tWAnT9/3uExje+/RUlTXwx3/9rhsZ2xa0N+fwztncUY/rbc5pPabHW9\nZXOfx3Dfo4juvR0eu7b07lJR11wzacpCm/0chlf+Ue/eDD2vuW3b+rXsb9l1pmkaR44cobS0tF6B\nGiuZlUlJyteNdqRZtUK6QVkZnMnQO5NqydwrcOk8hIXrnYri4oR/IKLvAOS2/+mdiq5uWWgMBgOv\nvvoqbm4O62VrMKSUaO+twPP2+9W9mQqEEE69T41MS0GERyLU77xiA2LsvcitG5A/Tw9pjKwaDNCj\nRw9OnDhh71waHLljIxTk43nng3qn4nTEgDhk8i5kWZneqVQhDyebt6FWFBsQbdpDaA/k7q/0TkU3\nVn1kCwwMZN68eURHRxMQEFCpX/2BB5z3hq6e5KXzyE/+g+HFBeqTcTVEq7YQEATfpULPKL3TsZAl\nJXDsMOI3z+iditKAGMbei/Z/ryGHjm2U/x9Y1aK5fv06MTExCCEwmUxkZ2db/ihVSa0M7e03EOMf\nRLQJvvULGimn7D47cQTadkD4Nrv1cxXFSiK0O7RshUzZpXcqurCqtD71lNqLozbkF0ng5o4Yfofe\nqTg1ETMEbf1/kEWFCK8meqcDqE3OFPsxjL0Pbe3byP7DXHIDwPqw+mrPnTvHxx9/zL/+9S/APEz4\nzJkzdkvMVcmffkB+uR7DY79rdL9MtSV8m0NYD2TqPr1TAX6eUKwKjWIvEZFgNELaAb0zcTir/ifc\nu3cvM2fOxGQysXPnTgAKCwtZvXq1XZNzNbKkBO1fCxH3P4YICNQ7HZfgVN1nF34CTYN2HfXORGmA\nhBCIcRPRNn2sdyoOZ1WhWbNmDX/5y1+YOnUqhp8/pXfs2JHTp0/bMzeXIz99HwLbIAaN0DsVlyH6\nDIAfTpjnruisvDWjdkZU7EX0i4XcK8iTR/VOxaGsKjS5ubl07Fj5U54QQv2DrEBmHEXu3YrhN0+p\n96UWhKcnos8AZPJOvVNR3WaK3QmjETHmXrSNjatVY1WhCQkJsXSZldu9e7dlA7LGThYVor39BoaH\npiGa+emdjssxd5/t0DUHeS0PfvoBuvfSNQ+l4ROxI+DH75Fnf9A7FYexqtA89thjfPjhh8yaNYvi\n4mLmzp3LRx99xKOPPmrv/FyC/HgVIizcvLGXUnvde0GOCXnhJ91SkEe+hW69EO4euuWgNA7C3QMx\n8i7kpnV6p+IwVg1vbteuHW+88QYHDhwgKiqKgIAAoqKi8PLysnd+Tk+mHUCmHcAwa4neqbgsYTAi\nBgxF7tuBuOdhfZI49I3qNlMcRgwbi/anqcisTERga73TsTurx996enrSvXt3wsPD6dGjhyoymLtb\ntNVLMST8P7Xdbz2JAXHI/duRmubw2LK0FJl+ENFLbQugOIbwbooYOhr51Xq9U3EIq1o0ly9fZsmS\nJZw8eZKmTZty7do1unTpwrPPPktgYOMdxivf/wciKhbRo4/eqbi+9p3B0wu+PwZdHLxq8vfHoGUr\nRIv67xqrKNYSI+9Cm/k0cvyDDf7erlUtmmXLlhESEsKqVatYuXIlq1atIiQkhGXLltk7P6elJe9C\n/vg94p5H9E6lQdBzRWc12kzRg2jewrwJ4JYNeqdid1YVmlOnTvHwww9busu8vLx4+OGHOXXqlF2T\nc1YyJxv5wT8x/DYR4empdzoNhug/DPntbvPClg6kCo2iFzH6HuTOjcjCAr1TsSurCk2XLl3IyKi8\nSdX3339P165d7ZKUM5NSor27FDFsHKJz47t+exIBgdC2Ixxx3BId8tJ5KMiHjqEOi6ko5URQG0SP\nvsidX+idil3VeI/mo48+snzdqlUr5s2bR79+/QgICCA7O5uDBw/yq1/9yiFJOhO56wu4moO4I17v\nVBokMTAObd92jJEDHRJPHk5B9IpW69IpuhFj70N782/IEeMR7u56p2MXNf7rqrgVQElJCQMGDMDd\n3Z2rV6/i7u5O//79uX79uiNz1Z3MykQmvYdhcmKj3FPCEURULHyXiizId0g81W2m6E10CIHgTsh9\n2/ROxW5q/N9SbQ1QmWWPmXETEW076J1OgyW8faBHX+SBPYgho+0aSxYWwKkT8NR0u8ZRlFsxjJ2I\ntnopcvBIhMGodzo2Z/XH8uLiYjIzMykqKqp0vFu3bjZPyhnJrz4BgwFx2116p9LgGQbGoW3+FOxc\naDiaCqHdEV7e9o2jKLfSNQJ8fOHgPogarHc2NmdVodmxYwdvv/02bm5ueHhUXqJjxYoVdknMmciz\np5Gb1mGY8brqy3eEnlGw+k1kdpZdt1tQ3WaKsxBCYBh3H9qGNRj6xTa4hXmtKjTvvfcezz//PL17\n965zoBUrVvDtt9/SvHlzXnvtNQDy8/N54403yMrKIigoiMTERLy9zZ8uk5KS2LZtG0ajkYSEBPr0\nMU+KPHXqFMuXL6ekpITIyEgSEhLqnJM1ZGkJ2r8WIe57FNGylV1jKWbC3R3RbzDymx2IcRPtEkNq\nGjItBcP4B+xyfkWptd79Yd2/4btDEN5X72xsyqqP525uboSH12+29vDhw5kxY0alY+vXr6dXr14s\nXryYiIgIkpKSADh79ix79+5l0aJFTJ8+nZUrVyKlBGDlypVMmzaNxYsXc+HCBVJTU+uV163Izz4C\n/5aIwbfZNY5SmRgYh9y7zfJzt7nTJ8GnWaNYZ0pxDcJgQIy9F23Tf/VOxeasKjQPPPAAq1ev5urV\nq3UO1L17d5o2rbweWEpKCsOGDQMgLi6O5ORky/HY2FiMRiNBQUG0adOGjIwMcnJyKCwstGxPMHTo\nUMtr7EF+fwz59ZcYHnmmwTVlnV5od7hebF663w5Ut5nijET/oXDxHPKHk3qnYlNWdZ21adOGjz76\niC++qDqpqOJ8m9rKzc3Fz8+8xo+fnx+5ubkAmEymSpNB/f39MZlMGI1GAgJ+WY8qICAAk8lU5/g3\nI4uLftljpnkLu8RQaiYMBstCm6JDiM3PLw8nY3hwqs3Pqyj1IdzcEaMmoG36L8YnX9I7HZuxqtAs\nW7aMYcOGERsbW2UwgC3ZutWQnp5Oenq65fv4+Hh8fX2tem3B2reRXSNoGjem3nl4eHhYHdfW9Ipt\ni7hlI28nf87z+CQ8Y/WQT2viatlZ5F3JxrdvDMJou6Gkrvxeu1rshnzNcty9XN34Md55VzBWmEqh\n5zUDrFmzxvJ1REQEERERVr/WqkKTl5fHAw88YPNC4OfnR05OjuXv5s2bA+YWzOXLly3Py87Oxt/f\nH39/f7Kzs6scr0l1b0ZeXt4t85JHD6KlfI1h1hKrnn8rvr6+NjmPK8W2Sdxm/shmLchL3oOw8uao\nNXG1vdshPJL8AtuuL+XS77WLxW7w1zxsHPnr3sPw6LOOjVsDX19f4uPrvhqKVfdo4uLiqmzlXBdS\nyko3d6Oioti+fTsA27dvJzravB9IdHQ0e/bsobS0lEuXLpGZmUlYWBh+fn54e3uTkZGBlJKdO3cS\nE2PbfnZ5LR/tnTcxPPqsefKgoisxcJjNV3SWh5Oht9p7RnFeYsQdyG/3Ik2Xb/1kF2BViyYjI4NN\nmzaxbt06yz2VcrNnz7Yq0OLFizl69Ch5eXk8+eSTxMfHM2HCBBYtWsS2bdsIDAwkMTERgODgYAYN\nGkRiYiJubm5MmTLF0pqaPHkyy5Ytswxv7tvXtsMA5Yf/RPTtjwiPtOl5lboRMUPRPvsQWVxsk5Wy\nZXExnDiC+G2iDbJTFPsQPs0QsSOQmz9BxE/WO516E9KK8aPlrY7qxMXF2TAd+zt//nyNj8kDu9HW\n/RvDzDcQnrbbQVTvJq+rd22UvTELETsSQ/+h9Y4rDyejfZGE8YVXbJJbbWLbi/r9apixpSkLbfZz\nGF75B6Kpr67X3LZt23q93qoWjasVk7qQuVfQ3v8Hhqf+ZNMio9SfZUM0KwrNrahhzYqrEP6BiL4D\nkNs+R4x/UO906sWqQrN169YaHxsxYoTNktGLlBJt9VLEr0YhQrvrnY5yA9F3IPL9fyLzchG+zet8\nHikl8nAKhsS/2TA7RbEfMfZetL//CTlqAug44qy+rCo0u3btqvR9Tk4OmZmZdO/evWEUmt2bwXQZ\n0e4twcUAABZ3SURBVIDGrTckwqsJonc0MnkXYsT4up/o7Glwc4PW7WyWm6LYk2jTHkJ7IL/eDBMm\n6Z1OnVlVaGbNmlXl2NatWzl37pzNE3I0mZWJ/O+7GJ6fg3BrmJsONQRiYBzapx9APQpNebeZWuVB\ncSWGsfei/fPvyPH3651KndV5KeK4uLibdqm5AqlpaO8sRoy9FxHcSe90lJvp0ReyLyEv1jyY41bU\n/RnFFYnQ7hDYmutfb9Y7lTqzqtBomlbpT1FREZs3b66ydpmrkZs/BU0iRt2tdyrKLQijEREzBLl/\ne51eL6/mwIWz5n0/FMXFGO75DUUf/LNeH7T0ZFXX2aRJVfsG/f39eeKJJ2yekKPIcz8iN67F8KfX\nG+SOdg2RGBhn7kK4c1Ktu79k2gHo0Ud1jyouSYR2x/P+BAqXzcUw/e+IJq61WZ9VhWbp0qWVvvf0\n9KRZs2Z2ScgRZGkp2tuLEPf8Ri0T70o6hoHRCKeOm1d3rgXVbaa4Os/b7qLoxHdo/1ponobhQpsw\nWpVpYGBgpT+uXGQA5OdroJkfYkj9F8xUHEcIYVnRuTZkaQl8l4ro1c8+iSmKg4hJj8O1fORnH+id\nSq3ctEVzq+VlhBDMnDnTpgnZm/zhBHLHRgwzF6vRRy5IDBiG9sofkPFTEG5WNcjhRDq0DkY0U9s9\nKK5NuLljePKPaHOfR7bvjOgXq3dKVrnpv9QhQ4ZUe9xkMrFx40aKi4vtkpQ9aW8vQkx6AuFX86rP\nivMSga3N82DSD0If67rCVLeZ0pCIZi0wPDkdbfFsDEFtXWLE7E0LzY2TMfPy8khKSmLLli3ExsYy\ncaJ99nO3J9E+BEPMr/ROQ6kHy4ZoVhQa82oAyRimqcm4SsMhOnVBPDAZbfkrGGa8jmjq3KsGWNX3\nUFBQwKeffsoXX3xBv379WLBgAa1bu+ZNdPHraXqnoNSTiB6Mtu5dZGHBrUffZJ6DkhJo39kxySmK\ngxgGDkf76Qe0f7yK4bm/2nQTP1u76WCA69evk5SUxLPPPsu5c+f429/+xrPPPuuyRQZw+sqv3Jrw\naQbdeiG/3XvL56rVAJSGTNz7KAiB/O87eqdyUzdt0Tz99NNomsZdd91FaGgoubm55ObmVnpOz549\n7ZqgolTHMDAObccmGDzyps+Th5MxjL7HQVkpimMJoxHD1BfQ5j6P1j4Ew6DheqdUrZsWGg8PDwC+\n/PLLah8XQlSZY6MoDtE7BlYvQ17JRrQIqPYp8lo+nPkeuvd2cHKK4jiiqS+Gp2egvTYD2SYY0amL\n3ilVcdNCs2zZMkfloSi1Itw9EP0GIb/ZiRhTfYtFpn8LXSNssjOnojgz0a4jht88jbZinnlwgJMN\n5XedqaWKcgPLhmg1UcOalUZE9BuEiL0NbcV88yRlJ6IKjeK6ukTAtTzk2dNVHpJlZcgj3yJ6Rzs+\nL0XRibjzQfBphvzg//ROpRJVaBSXJQwGxIBhyP07qj546ji0aInwD3R8YoqiE2EwYPhtIvJkOtr2\njXqnY6EKjeLSxMA45P4dSE2rdFytBqA0VqKJN4anZyA/fR95Il3vdABVaBQXJ9p1hKa+cLLyPyhz\noVHdZkrjJFq1xfDb35m31TBl6Z2OKjSK67txUIDMyoS8XOjsfMM8FcVRRM8oxKi70Ja9gryu77qU\nVi5/a19PP/003t7eCCEwGo3MmzeP/Px83njjDbKysggKCiIxMRFvb/NyI0lJSWzbtg2j0UhCQgJ9\n+vTR+QoUPYn+Q9H++izyIfNGfDItxfyPTG1opzRyYvQ98OMp5OqlMPn3uq2Q4RQtGiEEs2bN4tVX\nX2XevHkArF+/nl69erF48WIiIiJISkoC4OzZs+zdu5dFixYxffp0Vq5ciZRSz/QVnYkWAdAxFA4n\nAz93m1m5srOiNGRCCMQjzyIv/IT8ar1ueThFoZFSVikWKSkpDBs2DIC4uDiSk5Mtx2NjYzEajQQF\nBdGmTRsyMjIcnrPiXMSAOLR925FFhZBxDMIj9U5JUZyC8PTE8NQM5JfrkekHdcnBKQqNEII5c+Yw\nffp0tmzZAkBubi5+fn4A+Pn5WdZYM5lMtGzZ0vJaf39/TCaT45NWnIroNwiOp3F9z1YI6epye6or\nij2JgEDzmmj/Woi8dMHh8Z3iHs3LL79MixYtuHr1KnPmzKFt27ZVnlOXvsX09HTS038ZjRQfH4+v\nr+NXb/bw8NAlrp6xHR7X15drfQdQ9P4/8br3N3g1hmvWOa6esdU110HUIIrvT6B4xTx8Xl5W6w9j\na9assXwdERFBRESE1a91ikLTooV5XZ5mzZoRExNDRkYGfn5+/P/27jwmqrvf4/j7HBYBBdqpEm0p\nVqTWiiIodUMRxQbtbnLFtmm0j3CvvdqkWkVsTVTaioIa41PRNK7RuCb2NlqtXkWswtRStdbCSLCW\n1hUEN1C2gXPuH17mkYdByyxMke8rMTJzDr/P73dm5nzPxpnbt29b/vf39wfu78GUlZVZfvfGjRsY\nDNa/LdPawqioqHDSKJrn6+vrklxXZrsiVx84HP2HLGpf6Ie5nYzZlbmuzJYx20YfMhr9/Dnu/PNz\n1A/moqh/7aCWr68v8fHxNue6/NBZTU0N1dXVAFRXV3P27FmCgoIYOHAgR48eBeDo0aNERt7/m4jI\nyEiMRiN1dXVcv36d4uJiQkJCXNV98XcSGoH3f85CCWi6RyyE+P+LA96ZCuW30fftevQvOIjL92ju\n3LnD0qVLURSF+vp6RowYQf/+/enZsycrVqwgKyuLLl26MHPmTAACAwMZOnQoM2fOxN3dncTERPlS\nKwGA4u5Oh9jXqHXRlq4QbYHi4YH6wVy01Nnozz6HEj7E+Zl6O7s2+OrVq62eKbv5j3+uK7NlzO0j\n29G5elEh2j8/Q52divJM0EPntXbevCVcfuhMCCFE61N69EKZMAVt9aL7XxLoRFJohBCinVKHjUYJ\ne+n+PdG0euflOK1lIYQQf3vKf/wDdA39681Oy5BCI4QQ7Zji5ob6X0nop4xo1r7byQGk0AghRDun\ndPJDnf4p+o616H9ecHj7UmiEEEKgBPZAfe+/0VanopffdmjbUmiEEEIAoAyMQhkyCu2rNPS6Ooe1\nK4VGCCGEhfLmu+Dlg75zncPalEIjhBDCQlFV1ISP0Qt+QTv+vw5pUwqNEEKIRhSfjqjT56H/zxb0\n387Z3Z4UGiGEEE0oXQNR//ER2lfpdrclhUYIIYRVSr9I1KRFdrcjhUYIIUSzHPG1G1JohBBCOJUU\nGiGEEE4lhUYIIYRTSaERQgjhVFJohBBCOJUUGiGEEE4lhUYIIYRTSaERQgjhVFJohBBCOJW7qztg\nqzNnzrBp0yZ0XWfUqFG89dZbru6SEEIIK9rkHo2maaxfv5558+axfPlycnJyuHLliqu7JYQQwoo2\nWWh+++03unXrRpcuXXB3dycqKoqffvrJ1d0SQghhRZssNDdv3uSpp56yPDYYDNy8edOFPRJCCNGc\nNllohBBCtB1t8mIAg8FAWVmZ5fHNmzcxGAxN5svPzyc/P9/yOD4+nqeftv+W17bw9fV1Sa4rs9tb\nriuzZcztI9uVY961a5fl59DQUEJDQ//y77bJPZqQkBCKi4spLS2lrq6OnJwcIiMjm8wXGhpKfHy8\n5d+DC6o1uSrXldntLdeV2TLm9pHt6jE/uC5tSZGBNrpHo6oqCQkJfPHFF+i6zujRowkMDHR1t4QQ\nQljRJgsNQHh4OCtXrnR1N4QQQjyC28KFCxe6uhOtKSAgoF3lujK7veW6MlvG3D6y2+qYFV3XdQf2\nRQghhGikTV4MIIQQou2QQiOEEMKpHstzNJMmTWL8+PGtljdx4kROnjzJoUOHOHz4MBEREfj4+Fid\n12QysX79eoYPH+6Q3JKSEgYNGgTcvwdcYmIiBQUFDmn/UXJzc/n444+Jiopqlev7XT3eBq39/mpp\nfkpKCkFBQTz55JN2Z7X2a/ygr7/+mnXr1nH48GEyMzPp0aOH1b+Xc4abN2/y5ZdfsmvXLr777jtK\nSkro168fqmp923z//v10794dNzc3m/ImTpxIdXU1/fv3B2Dv3r38+uuv9OnTx+YxtCT75MmTHDhw\ngMzMTGpqanj++edRFMVhGW32qrOHceQC+iu8vLxIS0v7y/M7qn8dOnTg0qVLmM1mPDw8OHv2LJ07\nd25RG5qmNfvheRSj0ciAAQPIyclhwoQJTs90xHgdobXfX67Mt/U1tldhYSE///wz6enpuLm5cffu\nXerq6lotf9myZcTFxTFy5Eh0Xeerr75i+/btvPfee1bn37dvH9HR0Xh6etqU5+7uTm5uLuPHj6dT\np072dL3FHlx/lZeXs3LlSiorK4mPj3dYxmNZaABqampIT0/n3r171NfXM3HiRCIjIyktLSU1NZXe\nvXtTWFiIwWBgzpw5eHh42Jxl7XoKTdPYtm0bJpMJs9lMXFwcY8aMAaCyspIlS5ZQXFxM3759SUxM\ntDk7IiKC06dPM3jwYLKzs4mKiuLcuXPA/ZuPbtq0CbPZjKenJ9OmTaNbt24cPXqU3Nxcqqur0XWd\nBQsWtDi3urqa8+fPk5KSwqJFi5gwYQImk4mdO3fi7e3dZGyTJk1izJgx5OXlkZCQwAsvvNBq412w\nYAFTpkyhe/fuAMyfP5/ExESCgoJs6oOu65hMJvbs2cPcuXMB2LBhAz179mTkyJFMnz6dkSNHcurU\nKTRNY+bMmQ69I8Wj8h2lude4udzTp0+zZcsWvLy86NWrFyUlJZb5Wur27dv4+vpa9hAaVr6///47\nmzdvpqamBl9fX6ZNm8YTTzxBSkoK3bt3x2QyoWkaH3zwASEhITZl5+Xl4enpaVmWiqIwefJkPvzw\nQ+Lj49mxYwe//PILqqoSGxuLruvcunWLlJQUfH19mT9/fosz3dzciI2N5dtvv+Xtt99uNK20tJQ1\na9ZQUVGBn58f06ZNw9vbm6SkJDIyMoD767sZM2aQkZFh84YjgJ+fH1OnTuWTTz4hPj7+oeuxb775\nhuzsbFRVJTw8nHfffbfZdh/bczQeHh4kJSWxZMkS5s+fz+bNmy3TiouLGTduHMuXL8fHx4cff/zR\nrqza2lqSk5OZM2cOy5YtA+DIkSP4+PiQmprK4sWLyczMpLS0FIALFy6QkJDAihUrKC4utjlfURSG\nDRtGTk4OZrOZixcvNvpwBQYG8tlnn5GWlkZ8fDzbtm2zTCsqKmL27Nk2FRmAkydP0r9/fzp37oyf\nnx9FRUUPHVtNTQ29evUiPT3d5iJj63hjY2PJysoC4Nq1a5jNZpuLzL/3pzn+/v6kpaXx8ssvs2fP\nHruzWprvCM29xtZyzWYza9euZd68eSxevJjy8nK7+hcWFkZZWRkzZsxg3bp1mEwm6uvr2bhxI7Nm\nzWLx4sXExMSwfft2y+/U1taSnp5OQkICa9assTn70qVLBAcHN3rO29ubzp07c/jwYcrKyli2bBlL\nly5lxIgRjBs3DoPBwIIFC2wqMnB/mY4dO5bjx49TVVXVaNqGDRuIiYlh6dKlDB8+nA0bNuDj48Nz\nzz2HyWQC4NSpU4SHh9tVZBoEBASgaRrl5eXNrsfOnDnDqVOnWLx4Menp6bz55psPbfOx3aMB2Lp1\nKwUFBSiKwq1bt7hz5w5wf0E2rGiCg4O5fv26XTkdOnRocujs7NmzXLx4kRMnTgBQVVXFtWvXcHd3\nJyQkhC5dugAQFRVFQUEBgwcPtik7KCiI0tJScnJyGDBgQKNp9+7dY9WqVVy7dg1FUaivr7dMCwsL\na/Y80l+RnZ3Na6+9BsDQoUPJzs5m4MCBzY5NVVWbx/ggW8Y7ZMgQdu/ezaRJk8jKyiImJsbufjxK\nw3mk4OBgcnNznZ7nDM29xtZcuXKFrl27Wg5lRkVFkZmZaXN2w+Gcc+fOkZeXx8qVKxk/fjwXL160\n3BFE1/VG56GioqIAePHFF6murqaystKu97g1JpOJuLg4SxHt2LEjYP2oRkt5eXkxcuRI9u/f3+gQ\nXGFhIUlJSQBER0ezdetW4P5rYjQa6dOnD0ajkbi4OLv78O+aW4+dPXuWUaNGWY4ENSyH5jyWhUbX\ndY4dO0ZFRQVpaWmoqsr06dMxm80AjQ6Tqapqed7RfZgyZQphYWGNnjeZTE229OzdMh04cCBbtmxh\n4cKFVFRUWJ7fuXMnffv2Zfbs2ZSWlpKSkmKZ1qFDB5vz7t69S35+PpcuXUJRFDRNQ1GUJit++NfY\nPD09HbYF3tLxenp60q9fP3Jzc/nhhx9adD6tOW5ubmiaZnlcW1vbaHrDe0xV1UYF3lEelW+v5l7j\nl156qdlcR/9JnqIo9OnThz59+hAUFMTBgwcJCgri888/b3b+B/ti6/stMDDQsmJtUFVVRVlZmWUj\nylleeeUVkpOTGTVqlOW55sYRGRnJjh07uHv3LkVFRfTt29chfSgpKUFVVfz8/Jpdj505c6ZFbT62\nh86qqqrw9/dHVVXy8vIa3e3Z0R8Ia+3179+fgwcPWlYy165ds3woz58/T2lpKZqmYTQa6d27t125\no0ePZsKECTz77LONpldWVlqu0mk4dOQIJ06cIDo6moyMDFatWsXq1asJCAjg3LlzXLhwwerYHLHM\n7Rnv6NGj2bhxIyEhIXZv5SqKQpcuXbh8+TJ1dXXcu3ePvLw8u9r8u+U39xprmsaVK1ea5D799NNc\nv37d8jkzGo125V+9epXi4mLL4z/++IPAwEDKy8spLCwEoL6+nsuXL1vmacgsKCigY8eOeHt725Td\nr18/amtrOXbsGHD/fOvmzZuJiYkhPDycQ4cOWYrt3bt3AfDx8aGystKmPPjXe7tTp04MHTqUI0eO\nWKb16tWL7OxsAI4fP275THl5eREcHMymTZsYMGCAzYX1wc9meXk569atY9y4cYD19VhNTQ1hYWFk\nZWVZ1mkNy6E5j90ejaZpeHh4MGLECJYsWUJSUhLBwcE888wzlnkcfWzbWnuxsbGUlpaSnJyMruv4\n+/tbdn9DQkJYv349JSUlhIaGWg6z2JprMBgYO3Zsk+lvvPEGGRkZ7N692+rehq2MRmOTY7KDBg3i\n0KFD9OzZ0+rYHLHM7RlvcHAwPj4+jbYUbaFpGu7u7hgMBoYOHcqsWbMICAigR48eTfrpDH8l3xGs\nvcaDBw/GaDRazfX09CQxMZFFixbh5eVFz5497VoO1dXVbNy4kcrKSlRVpWvXrkydOpUxY8awYcMG\nKisr0TSNV1991XJDXQ8PD5KTk6mvr2fatGm2Dx5ISkpi7dq17N69G13XiYiI4J133kFVVa5evcrs\n2bNxd3cnNjaWuLg4YmNjSU1NxWAw2HSe5sFl9frrr3Pw4EHL4ylTprB69Wr27t1ruRigwbBhw1ix\nYkWjoxUtZTabSU5Opq6uDjc3N6Kjoy2HTJtbj4WHh/Pnn38yd+5cPDw8iIiIaHIRQyP6Y6aoqEj/\n9NNPXd2Ndik/P19fsmSJq7th1Y0bN/SPPvrI7nZc/f5ydf7DVFVVWX5eu3atvm/fvlbLXrhwoX7h\nwoVWyxMt81jt0Rw6dIgDBw7w/vvvu7or4m/k2LFj7Nixg8mTJ9vVjqvfX67Of5TMzEy+//576urq\n6NGjh+UyWCHkpppCCCGc6rG9GEAIIcTfgxQaIYQQTiWFRgghhFNJoRFCCOFUUmiEEEI4lRQaIYQQ\nTvV/zQC/nwjvEHUAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x115f3ca20>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax= df.groupby(df.index.month).count().plot(y='Unique Key', legend=False)\n", "ax.set_xticks([1,2,3,4,5,6,7,8,9,10,11, 12])\n", "ax.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n", "ax.set_ylabel(\"Number of Complaints\")\n", "ax.set_title(\"311 complains filed monthly in 2015\")\n", "#september has the most complaints cases filed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What week of the year has the most reports filed?** How many? Graph the weekly complaints." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Noise complaints are a big deal. Use `.str.contains` to select noise complaints, and make an chart of when they show up annually. **Then** make a chart about when they show up every day (cyclic)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Which were the top five days of the year for filing complaints?** How many on each of those days? Graph it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**What hour of the day are the most complaints?** Graph a day of complaints." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One of the hours has an odd number of complaints. What are the most common complaints at that hour, and what are the most common complaints the hour before and after?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So odd. What's the **per-minute breakdown** of complaints between 12am and 1am? You don't need to include 1am." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like midnight is a little bit of an outlier. Why might that be? Take the 5 most common agencies and graph the times they file reports at (all day, not just midnight)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Graph those same agencies on an annual basis - make it **weekly**. When do people like to complain? When does the NYPD have an odd number of complaints?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maybe the NYPD deals with different issues at different times? Check the most popular complaints in July and August vs the month of May. Also check the most common complaints for the Housing Preservation Bureau (HPD) in winter vs. summer." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
AntArch/Presentations_Github
20160202_Nottingham_GIServices_Lecture3_Beck_InteroperabilitySemanticsAndOpenData/.ipynb_checkpoints/20151008_OpenGeo_Reuse_under_licence-checkpoint.ipynb
2
55933
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "![](https://dl.dropboxusercontent.com/u/393477/ImageBank/ForOGC/20151008_Re-UseUnderLicence.png)\n", "\n", "\n", "Go down for licence and other metadata about this presentation" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Preamble\n", "\n", "## Licence\n", "\n", "Unless stated otherwise all content is released under a [CC0]+BY licence. I'd appreciate it if you reference this but it is not necessary.\n", "\n", "![](https://dl.dropboxusercontent.com/u/393477/SharedPresentations/Shared_HTML5/Images/CC_BY.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Using Ipython for presentations\n", "\n", "A short video showing how to use Ipython for presentations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"400\"\n", " height=\"300\"\n", " src=\"https://www.youtube.com/embed/F4rFuIb1Ie4\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x7f13c80794d0>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('F4rFuIb1Ie4')" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ipython nbconvert --to markdown 20151008_OpenGeo_Reuse_under_licence.ipynb\n", "ipython nbconvert --to markdown 20151008_OpenGeo_Reuse_under_licence.ipynb\n" ] }, { "data": { "text/plain": [ "0" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## PDF output using pandoc\n", "\n", "import os\n", "\n", "\n", "### Export this notebook as markdown\n", "commandLineSyntax = 'ipython nbconvert --to markdown 20151008_OpenGeo_Reuse_under_licence.ipynb'\n", "print (commandLineSyntax)\n", "\n", "os.system(commandLineSyntax)\n", "\n", "### Export this notebook and the document header as PDF using Pandoc\n", "\n", "commandLineSyntax = 'pandoc -f markdown -t latex -N -V geometry:margin=1in DocumentHeader.md 20151008_OpenGeo_Reuse_under_licence.md --filter pandoc-citeproc --latex-engine=xelatex --toc -o interim.pdf '\n", "\n", "os.system(commandLineSyntax)\n", "\n", "### Remove cruft from the pdf\n", "\n", "commandLineSyntax = 'pdftk interim.pdf cat 1-3 16-end output 20151008_OpenGeo_Reuse_under_licence.pdf'\n", "\n", "os.system(commandLineSyntax)\n", "\n", "### Remove the interim pdf\n", "\n", "commandLineSyntax = 'rm interim.pdf'\n", "\n", "os.system(commandLineSyntax)\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## The environment\n", "\n", "In order to replicate my environment you need to know what I have installed!" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "### Set up watermark\n", "\n", "This describes the versions of software used during the creation. \n", "\n", "Please note that critical libraries can also be watermarked as follows:\n", "\n", "```python\n", "%watermark -v -m -p numpy,scipy\n", "```" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "slideshow": { "slide_type": "skip" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Installed watermark.py. To use it, type:\n", " %load_ext watermark\n" ] } ], "source": [ "%install_ext https://raw.githubusercontent.com/rasbt/python_reference/master/ipython_magic/watermark.py\n", "%load_ext watermark\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Anthony Beck 29/06/2015 \n", "\n", "CPython 2.7.10\n", "IPython 3.2.0\n", "\n", "compiler : GCC 4.4.7 20120313 (Red Hat 4.4.7-1)\n", "system : Linux\n", "release : 3.13.0-37-generic\n", "machine : x86_64\n", "processor : x86_64\n", "CPU cores : 4\n", "interpreter: 64bit\n", "Git hash : \n" ] } ], "source": [ "%watermark -a \"Anthony Beck\" -d -v -m -g" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "# packages in environment at /home/arb/LocalPython/Anaconda27/anaconda:\n", "#\n", "_license 1.1 py27_0 \n", "abstract-rendering 0.5.1 np19py27_0 \n", "accelerate 1.11.0 np19py27_p0 \n", "affine 1.1.0 py27_0 \n", "alabaster 0.7.3 py27_0 \n", "anaconda 2.2.0 np19py27_0 \n", "argcomplete 0.8.4 py27_0 \n", "arrow 0.5.4 <pip>\n", "astroid 1.3.6 <pip>\n", "astropy 1.0.2 np19py27_0 \n", "babel 1.3 py27_0 \n", "backports.ssl-match-hostname 3.4.0.2 <pip>\n", "basemap 1.0.7 np19py27_0 \n", "bcolz 0.8.1 np19py27_0 \n", "beautiful-soup 4.3.2 py27_0 \n", "beautifulsoup4 4.3.2 <pip>\n", "binstar 0.10.3 py27_0 \n", "bitarray 0.8.1 py27_0 \n", "blaze 0.8.0 <pip>\n", "blaze-core 0.8.0 np19py27_0 \n", "blz 0.6.2 np19py27_0 \n", "bokeh 0.8.2 np19py27_0 \n", "boto 2.38.0 py27_0 \n", "bottle 0.12.7 py27_0 \n", "brewer2mpl 1.4 py27_0 \n", "cairo 1.12.18 4 \n", "cartopy 0.10.0 np18py27_0 \n", "cdecimal 2.3 py27_0 \n", "certifi 14.05.14 py27_0 \n", "cffi 0.9.2 py27_0 \n", "click 4.0 py27_0 \n", "cligj 0.1.0 py27_0 \n", "cloog 0.18.0 0 \n", "clyent 0.3.4 py27_0 \n", "colorama 0.3.3 py27_0 \n", "conda 3.14.0 py27_0 \n", "conda-build 1.12.1 py27_0 \n", "conda-env 2.2.3 py27_0 \n", "configobj 5.0.6 py27_0 \n", "cryptography 0.8.2 py27_0 \n", "cudatoolkit 6.0 p0 \n", "curl 7.38.0 0 \n", "cython 0.22 py27_0 \n", "cytoolz 0.7.2 py27_0 \n", "datashape 0.4.5 np19py27_0 \n", "dateutil 2.4.1 py27_0 \n", "decorator 3.4.0 py27_0 \n", "descartes 1.0.1 py27_0 \n", "docutils 0.12 py27_0 \n", "dynd-python 0.6.5 np19py27_0 \n", "enum 0.4.4 <pip>\n", "enum34 1.0.4 py27_0 \n", "fastcache 1.0.2 py27_0 \n", "fiona 1.5.1 np19py27_0 \n", "flask 0.10.1 py27_1 \n", "fontconfig 2.11.1 4 \n", "freetype 2.5.2 2 \n", "funcsigs 0.4 py27_0 \n", "futures 2.2.0 py27_0 \n", "gdal 1.11.2 np19py27_2 \n", "geopandas 0.1.1 py27_0 \n", "geopy 1.10.0 <pip>\n", "geos 3.3.3 0 \n", "gevent 1.0.1 py27_0 \n", "gevent-websocket 0.9.3 py27_0 \n", "glib 2.43.0 2 \n", "gmp 5.1.2 2 \n", "greenlet 0.4.6 py27_0 \n", "grin 1.2.1 py27_1 \n", "h5py 2.5.0 np19py27_0 \n", "harfbuzz 0.9.35 6 \n", "hdf5 1.8.14 0 \n", "html5lib 0.99999 <pip>\n", "ipython 3.2.0 py27_0 \n", "ipython-notebook 3.2.0 py27_0 \n", "ipython-qtconsole 3.1.0 py27_0 \n", "isl 0.12.2 0 \n", "itsdangerous 0.24 py27_0 \n", "jdcal 1.0 py27_0 \n", "jedi 0.8.1 py27_0 \n", "jinja2 2.7.3 py27_1 \n", "jpeg 8d 0 \n", "jsonschema 2.4.0 py27_0 \n", "libdynd 0.6.5 0 \n", "libffi 3.0.13 0 \n", "libgcc 4.8.4 1 \n", "libgdal 1.11.2 0 \n", "libnetcdf 4.3.2 1 \n", "libpng 1.6.17 0 \n", "libsodium 0.4.5 0 \n", "libtiff 4.0.2 1 \n", "libxml2 2.9.0 0 \n", "libxslt 1.1.28 0 \n", "llvmlite 0.4.0 py27_0 \n", "logilab-common 0.63.2 <pip>\n", "lxml 3.4.4 py27_0 \n", "mapnik 0.1 <pip>\n", "markupsafe 0.23 py27_0 \n", "matplotlib 1.4.3 np19py27_2 \n", "mistune 0.6 py27_0 \n", "mkl 11.1 np19py27_p3 \n", "mkl-rt 11.1 p0 \n", "mkl-service 1.0.0 py27_p1 \n", "mklfft 2.0 np19py27_p0 \n", "mock 1.0.1 py27_0 \n", "mpc 1.0.1 0 \n", "mpfr 3.1.2 0 \n", "multipledispatch 0.4.7 py27_0 \n", "ncurses 5.9 4 \n", "networkx 1.9.1 py27_0 \n", "nltk 3.0.2 np19py27_0 \n", "nose 1.3.6 py27_0 \n", "notedown 1.4.4 <pip>\n", "numba 0.18.2 np19py27_1 \n", "numbapro 0.18.0 np19py27_p2 \n", "numbapro_cudalib 0.2 0 \n", "numexpr 2.3.1 np19py27_p0 [mkl]\n", "numpy 1.9.2 py27_p0 [mkl]\n", "odo 0.3.2 np19py27_0 \n", "openpyxl 2.0.2 py27_0 \n", "openssl 1.0.1k 1 \n", "pandana 0.1.2 py27_0 \n", "pandas 0.16.2 np19py27_0 \n", "pandasql 0.6.2 np19py27_0 \n", "pandoc-attributes 0.1.7 <pip>\n", "pandocfilters 1.2.4 <pip>\n", "pango 1.36.8 3 \n", "patchelf 0.6 0 \n", "patsy 0.3.0 np19py27_0 \n", "pcre 8.31 0 \n", "pep8 1.6.2 py27_0 \n", "pillow 2.7.0 py27_1 \n", "pip 7.0.3 py27_0 \n", "pixman 0.26.2 0 \n", "plotly 1.6.17 <pip>\n", "ply 3.6 py27_0 \n", "prettyplotlib 0.1.7 <pip>\n", "prettytable 0.7.2 py27_0 \n", "proj4 4.8.0 0 \n", "psutil 2.2.1 py27_0 \n", "ptyprocess 0.4 py27_0 \n", "py 1.4.26 py27_0 \n", "py2cairo 1.10.0 py27_2 \n", "pyasn1 0.1.7 py27_0 \n", "pycosat 0.6.1 py27_0 \n", "pycparser 2.12 py27_0 \n", "pycrypto 2.6.1 py27_0 \n", "pycurl 7.19.5.1 py27_0 \n", "pyflakes 0.8.1 py27_0 \n", "pygments 2.0.2 py27_0 \n", "pylint 1.4.3 <pip>\n", "pymc 2.3.4 np19py27_p0 [mkl]\n", "pyopenssl 0.15.1 py27_0 \n", "pyparsing 2.0.3 py27_0 \n", "pyproj 1.9.3 py27_0 \n", "pyqt 4.11.3 py27_1 \n", "pysal 1.6.0 np19py27_1 \n", "pyshp 1.2.1 <pip>\n", "pytables 3.1.1 np19py27_2 \n", "pytest 2.7.0 py27_0 \n", "python 2.7.10 0 \n", "python-dateutil 2.4.2 py27_0 \n", "pytz 2015.4 py27_0 \n", "pyyaml 3.11 py27_1 \n", "pyzmq 14.7.0 py27_0 \n", "qt 4.8.6 3 \n", "r 3.1.3 0 \n", "r-base 3.1.3 2 \n", "r-boot 1.3_16 r3.1.3_0 \n", "r-class 7.3_12 r3.1.3_0 \n", "r-cluster 1.15.3 0 \n", "r-codetools 0.2_11 r3.1.3_0 \n", "r-foreign 0.8_63 r3.1.3_0 \n", "r-kernsmooth 2.23_14 r3.1.3_0 \n", "r-lattice 0.20_31 r3.1.3_0 \n", "r-mass 7.3.37 0 \n", "r-matrix 1.2_1 r3.1.3_0 \n", "r-mgcv 1.8_6 r3.1.3_0 \n", "r-nlme 3.1.118 0 \n", "r-nlme 3.1_120 r3.1.3_0 \n", "r-nnet 7.3_9 r3.1.3_0 \n", "r-recommended 3.1.3 0 \n", "r-rpart 4.1_9 r3.1.3_0 \n", "r-spatial 7.3_9 r3.1.3_0 \n", "r-survival 2.38_2 r3.1.3_0 \n", "rasterio 0.15.1 py27_0 \n", "readline 6.2 2 \n", "redis 2.6.9 0 \n", "redis-py 2.10.3 py27_0 \n", "requests 2.7.0 py27_0 \n", "rope 0.9.4 py27_1 \n", "rpy2 2.5.6 py27_0 \n", "runipy 0.1.3 py27_0 \n", "scikit-image 0.11.3 np19py27_0 \n", "scikit-learn 0.16.1 np19py27_p0 [mkl]\n", "scipy 0.15.1 np19py27_p0 [mkl]\n", "seaborn 0.5.1 <pip>\n", "setuptools 17.1.1 py27_0 \n", "shapely 1.5.9 <pip>\n", "simplejson 3.6.3 py27_0 \n", "singledispatch 3.4.0.3 py27_1 \n", "sip 4.16.5 py27_0 \n", "six 1.9.0 py27_0 \n", "snowballstemmer 1.2.0 py27_0 \n", "snuggs 1.3.1 <pip>\n", "sockjs-tornado 1.0.1 py27_0 \n", "sphinx 1.3.1 py27_0 \n", "sphinx-rtd-theme 0.1.7 <pip>\n", "sphinx_rtd_theme 0.1.7 py27_0 \n", "spyder 2.3.4 py27_1 \n", "spyder-app 2.3.4 py27_0 \n", "sqlalchemy 1.0.3 py27_0 \n", "sqlite 3.8.4.1 1 \n", "sqlparse 0.1.14 py27_0 \n", "ssl_match_hostname 3.4.0.2 py27_0 \n", "statsmodels 0.6.1 np19py27_0 \n", "sympy 0.7.6 py27_0 \n", "system 5.8 2 \n", "tables 3.1.1 <pip>\n", "terminado 0.5 py27_0 \n", "theano 0.7.0 np19py27_0 \n", "tk 8.5.18 0 \n", "toolz 0.7.2 py27_0 \n", "tornado 4.2 py27_0 \n", "tweepy 2.3 py27_0 \n", "twitter 1.17.0 <pip>\n", "ujson 1.33 py27_0 \n", "unicodecsv 0.9.4 py27_0 \n", "urbansim 2.0.1 <pip>\n", "util-linux 2.21 0 \n", "werkzeug 0.10.4 py27_0 \n", "xlrd 0.9.3 py27_0 \n", "xlsxwriter 0.7.2 py27_0 \n", "xlwt 1.0.0 py27_0 \n", "yaml 0.1.6 0 \n", "zeromq 4.0.5 0 \n", "zlib 1.2.8 0 \n" ] } ], "source": [ "#List of installed conda packages\n", "!conda list" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abstract-rendering (0.5.1)\n", "affine (1.1.0)\n", "alabaster (0.7.3)\n", "argcomplete (0.8.4)\n", "arrow (0.5.4)\n", "astroid (1.3.6)\n", "astropy (1.0.2)\n", "Babel (1.3)\n", "backports.ssl-match-hostname (3.4.0.2)\n", "basemap (1.0.7)\n", "bcolz (0.8.1)\n", "beautifulsoup4 (4.3.2)\n", "binstar (0.10.3)\n", "bitarray (0.8.1)\n", "blaze (0.8.0)\n", "blz (0.6.2)\n", "bokeh (0.8.2)\n", "boto (2.38.0)\n", "bottle (0.12.7)\n", "brewer2mpl (1.4.1)\n", "Cartopy (0.10.0)\n", "cdecimal (2.3)\n", "certifi (14.5.14)\n", "cffi (0.9.2)\n", "click (4.0)\n", "cligj (0.1.0)\n", "clyent (0.3.4)\n", "colorama (0.3.3)\n", "conda (3.14.0)\n", "conda-build (1.12.1)\n", "conda-env (2.2.3)\n", "configobj (5.0.6)\n", "cryptography (0.8.2)\n", "Cython (0.22)\n", "cytoolz (0.7.2)\n", "datashape (0.4.5)\n", "decorator (3.4.0)\n", "descartes (1.0.1)\n", "docutils (0.12)\n", "enum (0.4.4)\n", "enum34 (1.0.4)\n", "fastcache (1.0.2)\n", "Fiona (1.5.1)\n", "Flask (0.10.1)\n", "funcsigs (0.4)\n", "futures (2.2.0)\n", "GDAL (1.11.2)\n", "geopandas (0.1.1)\n", "geopy (1.10.0)\n", "gevent (1.0.1)\n", "gevent-websocket (0.9.3)\n", "greenlet (0.4.6)\n", "grin (1.2.1)\n", "h5py (2.5.0)\n", "html5lib (0.99999)\n", "ipython (3.2.0)\n", "itsdangerous (0.24)\n", "jdcal (1.0)\n", "jedi (0.8.1)\n", "Jinja2 (2.7.3)\n", "jsonschema (2.4.0)\n", "llvmlite (0.4.0)\n", "logilab-common (0.63.2)\n", "lxml (3.4.4)\n", "mapnik (0.1)\n", "MarkupSafe (0.23)\n", "matplotlib (1.4.3)\n", "mistune (0.6)\n", "mklfft (0.0.0)\n", "mock (1.0.1)\n", "multipledispatch (0.4.7)\n", "networkx (1.9.1)\n", "nltk (3.0.2)\n", "nose (1.3.6)\n", "notedown (1.4.4)\n", "numba (0.18.2)\n", "numbapro (0.18.0)\n", "numexpr (2.3.1)\n", "numpy (1.9.2)\n", "odo (0.3.2)\n", "openpyxl (2.0.2)\n", "pandana (0.1.2)\n", "pandas (0.16.2)\n", "pandasql (0.6.2)\n", "pandoc-attributes (0.1.7)\n", "pandocfilters (1.2.4)\n", "patsy (0.3.0)\n", "pep8 (1.6.2)\n", "Pillow (2.7.0)\n", "pip (7.0.3)\n", "plotly (1.6.17)\n", "ply (3.6)\n", "prettyplotlib (0.1.7)\n", "prettytable (0.7.2)\n", "psutil (2.2.1)\n", "ptyprocess (0.4)\n", "py (1.4.26)\n", "pyasn1 (0.1.7)\n", "pycosat (0.6.1)\n", "pycparser (2.12)\n", "pycrypto (2.6.1)\n", "pycurl (7.19.5.1)\n", "pyflakes (0.8.1)\n", "Pygments (2.0.2)\n", "pylint (1.4.3)\n", "pymc (2.3.4)\n", "pyOpenSSL (0.15.1)\n", "pyparsing (2.0.3)\n", "pyproj (1.9.4)\n", "PySAL (1.6.0)\n", "pyshp (1.2.1)\n", "pytest (2.7.0)\n", "python-dateutil (2.4.2)\n", "pytz (2015.4)\n", "PyYAML (3.11)\n", "pyzmq (14.7.0)\n", "rasterio (0.23.0)\n", "redis (2.10.3)\n", "requests (2.7.0)\n", "rope (0.9.4)\n", "rpy2 (2.5.6)\n", "runipy (0.1.3)\n", "scikit-image (0.11.3)\n", "scikit-learn (0.16.1)\n", "scipy (0.15.1)\n", "seaborn (0.5.1)\n", "setuptools (17.1.1)\n", "Shapely (1.5.9)\n", "simplejson (3.6.3)\n", "singledispatch (3.4.0.3)\n", "six (1.9.0)\n", "snowballstemmer (1.2.0)\n", "snuggs (1.3.1)\n", "sockjs-tornado (1.0.1)\n", "Sphinx (1.2.3)\n", "sphinx-rtd-theme (0.1.7)\n", "spyder (2.3.4)\n", "SQLAlchemy (1.0.3)\n", "sqlparse (0.1.14)\n", "statsmodels (0.6.1)\n", "sympy (0.7.6)\n", "tables (3.1.1)\n", "terminado (0.5)\n", "Theano (0.7.0)\n", "toolz (0.7.2)\n", "tornado (4.2)\n", "tweepy (2.3.0)\n", "twitter (1.17.0)\n", "ujson (1.33)\n", "unicodecsv (0.9.4)\n", "urbansim (2.0.1)\n", "Werkzeug (0.10.4)\n", "xlrd (0.9.3)\n", "XlsxWriter (0.7.2)\n", "xlwt (1.0.0)\n" ] } ], "source": [ "#List of installed pip packages\n", "!pip list" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Running dynamic presentations\n", "\n", "You need to install the [RISE Ipython Library](https://github.com/damianavila/RISE) from [Damián Avila](https://github.com/damianavila) for dynamic presentations" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "To convert and run this as a static presentation run the following command:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/arb/LocalPython/Anaconda27/anaconda/lib/python2.7/site-packages/IPython/nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead.\n", " \"You should import from ipython_nbconvert instead.\", ShimWarning)\n", "[NbConvertApp] Converting notebook 20151008_OpenGeo_Reuse_under_licence.ipynb to slides\n", "[NbConvertApp] Writing 257994 bytes to 20151008_OpenGeo_Reuse_under_licence.slides.html\n", "[NbConvertApp] Redirecting reveal.js requests to https://cdn.jsdelivr.net/reveal.js/2.6.2\n", "Serving your slides at http://127.0.0.1:8000/20151008_OpenGeo_Reuse_under_licence.slides.html\n", "Use Control-C to stop this server\n", "Created new window in existing browser session.\n", "WARNING:tornado.access:404 GET /custom.css (127.0.0.1) 3.88ms\n", "WARNING:tornado.access:404 GET /favicon.ico (127.0.0.1) 1.86ms\n", "WARNING:tornado.access:404 GET /favicon.ico (127.0.0.1) 0.83ms\n", "WARNING:tornado.access:404 GET /favicon.ico (127.0.0.1) 0.88ms\n", "WARNING:tornado.access:404 GET /favicon.ico (127.0.0.1) 0.92ms\n", "WARNING:tornado.access:404 GET /custom.css (127.0.0.1) 1.01ms\n", "WARNING:tornado.access:404 GET /style.min.css.map (127.0.0.1) 1.37ms\n", "WARNING:tornado.access:404 GET /style.min.css.map (127.0.0.1) 0.92ms\n", "WARNING:tornado.access:404 GET /custom.css (127.0.0.1) 1.11ms\n" ] } ], "source": [ "!ipython nbconvert 20151008_OpenGeo_Reuse_under_licence.ipynb --to slides --post serve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To close this instances press *control 'c'* in the *ipython notebook* terminal console\n", "\n", "Static presentations allow the presenter to see *speakers notes* (use the 's' key)\n", "\n", "If running dynamically run the scripts below" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Pre load some useful libraries" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "#Future proof python 2\n", "from __future__ import print_function #For python3 print syntax\n", "from __future__ import division\n", "\n", "# def\n", "import IPython.core.display\n", "\n", "# A function to collect user input - ipynb_input(varname='username', prompt='What is your username')\n", "\n", "def ipynb_input(varname, prompt=''):\n", " \"\"\"Prompt user for input and assign string val to given variable name.\"\"\"\n", " js_code = (\"\"\"\n", " var value = prompt(\"{prompt}\",\"\");\n", " var py_code = \"{varname} = '\" + value + \"'\";\n", " IPython.notebook.kernel.execute(py_code);\n", " \"\"\").format(prompt=prompt, varname=varname)\n", " return IPython.core.display.Javascript(js_code)\n", " \n", "# inline\n", "\n", "%pylab inline" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## About me\n", "\n", " \n", "\n", "![It's all about me - details about Anthony Beck](https://dl.dropboxusercontent.com/u/393477/ImageBank/Geolytics_ARB_Banner.png)\n", "\n", "* Research Fellow, University of Nottingham: [orcid](http://orcid.org/0000-0002-2991-811X)\n", "* Director, Geolytics Limited - A spatial data analytics consultancy\n", "\n", "## About this presentation\n", "\n", "* [Available on GitHub](https://github.com/AntArch/Presentations_Github/tree/master/20151008_OpenGeo_Reuse_under_licence) - https://github.com/AntArch/Presentations_Github/\n", "* [Fully referenced PDF](https://github.com/AntArch/Presentations_Github/blob/master/20150916_OGC_Reuse_under_licence/20151008_OpenGeo_Reuse_under_licence.pdf)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# A potted history of mapping" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "source": [ "## In the beginning was the geoword\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "and the word was ***cartography***\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![The lens of cartography @TheLensOfCartography_beck_2015](https://upload.wikimedia.org/wikipedia/commons/thumb/8/85/TheLensOfCartography.svg/1024px-TheLensOfCartography.svg.png)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "![A static map (public domain)](https://upload.wikimedia.org/wikipedia/commons/thumb/f/f0/Claudius_Ptolemy-_The_World.jpg/1024px-Claudius_Ptolemy-_The_World.jpg)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Cartography was king. Static representations of spatial knowledge with the cartographer deciding what to represent." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "# And then there was data .........\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "![Data @Data_types_en_beck_2015](https://upload.wikimedia.org/wikipedia/commons/thumb/6/6d/Data_types_-_en.svg/738px-Data_types_-_en.svg.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "![But the data was siloed (restricted use)](https://dl.dropboxusercontent.com/u/393477/ImageBank/ForOGC/TheEndOfThe20thCentury.png)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false, "slideshow": { "slide_type": "notes" } }, "source": [ "Restrictive data" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![Concerted efforts to de-silo data and make data interoperable (restricted use)](https://dl.dropboxusercontent.com/u/393477/ImageBank/ForOGC/TheCatalyst.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Making data interoperable and open" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Technical interoperability - levelling the field\n", "\n", "![Interoperable integration of spatial data - the technological issues @TechnicalInteroperableIntegrationOfSpatialData_beck_2015](https://upload.wikimedia.org/wikipedia/commons/thumb/5/53/TechnicalInteroperableIntegrationOfSpatialData.svg/1024px-TechnicalInteroperableIntegrationOfSpatialData.svg.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Facilitating data driven visualization\n", "\n", "![From Map to Model The changing paradigm of map creation from cartography to data driven visualization @FromMapToModel_beck_2015](https://upload.wikimedia.org/wikipedia/commons/thumb/a/a0/FromMapToModel.svg/1024px-FromMapToModel.svg.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "From Map to Model The changing paradigm of map creation from cartography to data driven visualization" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![Local To Global integration of data to create multiple generic products @Local_To_Global_integration_of_data_to_create_multiple_generic_products_beck_2015](https://upload.wikimedia.org/wikipedia/commons/thumb/1/19/Local_To_Global_integration_of_data_to_create_multiple_generic_products.svg/1024px-Local_To_Global_integration_of_data_to_create_multiple_generic_products.svg.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![A new working paradigm (public domain)](https://dl.dropboxusercontent.com/u/393477/ImageBank/ForOGC/ANewWorkingParadigm.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "![Cartography is no longer key. Spatial mapping is now about the the formal and informal data stack. Elements such as provenance, credibility are much more important for use and re-use of this data. @CartographyNoLongerKing_beck_2015](https://upload.wikimedia.org/wikipedia/commons/thumb/1/1c/CartographyNoLongerKing.svg/1024px-CartographyNoLongerKing.svg.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# So where are these new data products?\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Non-technical interoperability issues?\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![The full stack that enables interoperable integration of spatial data @InteroperableIntegrationOfSpatialData_beck_2015](https://upload.wikimedia.org/wikipedia/commons/thumb/5/5d/InteroperableIntegrationOfSpatialData.svg/500px-InteroperableIntegrationOfSpatialData.svg.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Issues surrounding non-technical interoperability include:\n", " \n", "* Policy interoperabilty\n", "* Licence interoperability\n", "* Legal interoperability\n", "* Social interoperability\n", "\n", "We will focus on licence interoperability\n", " " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![The modern data landscape (restricted)](https://dl.dropboxusercontent.com/u/393477/ImageBank/ForOGC/20151008_Re-UseUnderLicence.png)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "notes" } }, "source": [ "There is a multitude of formal and informal data. " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## What is a licence?\n", "\n", "[Wikipedia state:](https://en.wikipedia.org/wiki/License)\n", "\n", "> A license may be granted by a party (\"licensor\") to another party (\"licensee\") as an element of an agreement between those parties. \n", "\n", "> A shorthand definition of a license is \"an authorization (by the licensor) to use the licensed material (by the licensee).\"\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![Some licences @rdflicense_2015](https://dl.dropboxusercontent.com/u/393477/ImageBank/ForOGC/SomeLicences_FromRDFLicences.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Each of these data objects can be licenced in a different way. This shows some of the licences described by the RDFLicence ontology" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![A licence describes what you can and cannot do to a data object @licence_classification_2015](https://dl.dropboxusercontent.com/u/393477/ImageBank/ForOGC/Licence_Constraints.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "Concepts (derived from Formal Concept Analysis) surrounding licences" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Two lead organisations have developed legal frameworks for content licensing:\n", "\n", "* [Creative Commons (CC)](https://creativecommons.org/) and \n", "* [Open Data Commons (ODC)](http://opendatacommons.org/). \n", "\n", "Until the release of [CC version 4](https://wiki.creativecommons.org/4.0), published in November 2013, the CC licence did not cover data. Between them, CC and ODC licences can cover all forms of digital work.\n", "\n", "* **There are many other licence types**\n", "* Many are bespoke\n", " * Bespoke licences are difficult to manage\n", " * Many legacy datasets have bespoke licences" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![Creative Commons @love_CC_2008](https://farm4.staticflickr.com/3148/2732488224_aedf36e837_b_d.jpg)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "I'll describe CC in more detail" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Creative Commons Zero \n", "\n", "Creative Commons Zero (CC0) is essentially public domain which allows:\n", " \n", "* Reproduction\n", "* Distribution\n", "* Derivations\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### Constraints on CC0\n", "\n", "The following clauses constrain CC0:\n", "\n", "* Permissions\n", " * ND – No derivatives: the licensee can not derive new content from the resource.\n", "* Requirements\n", " * BY – By attribution: the licensee must attribute the source.\n", " * SA – Share-alike: if the licensee adapts the resource, it must be released under the same licence.\n", "* Prohibitions\n", " * NC – Non commercial: the licensee must not use the work commercially without prior approval.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "### CC license combinations\n", "\n", "License|Reproduction|Distribution|Derivation|ND|BY|SA|NC\n", "----|----|----|----|----|----|----|----\n", "CC0|X|X|X||||\n", "CC-BY-ND|X|X||X|X||\n", "CC-BY-NC-ND|X|X||X|X||X\n", "CC-BY|X|X|X||X||\n", "CC-BY-SA|X|X|X||X|X|\n", "CC-BY-NC|X|X|X||X||X\n", "CC-BY-NC-SA|X|X|X||X|X|X\n", "\n", "Table: [Creative Commons license combinations](https://docs.google.com/spreadsheets/d/17aT7Dj6QtE88XPS44oPQ7mVeSdY1YnZ1rlpjPvXNz0E/pub?single=true&gid=0&output=html)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Why are licenses important?\n", "\n", "* They tell you what you can and can't do with 'stuff'\n", "* Very significant when multiple datasets are combined\n", " * It then becomes an issue of license compatibility\n", "\n", "\n", "![Compatibility of common open-source software licenses @Floss-license-slide-image_Wheeler_2007](https://upload.wikimedia.org/wikipedia/commons/1/1d/Floss-license-slide-image.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Which is important when we mash up data\n", "\n", "Certain licences when combined:\n", " \n", "* Are incompatible\n", " * Creating data islands\n", "* Inhibit commercial exploitation (NC)\n", "* Force the adoption of certain licences\n", " * If you want people to commercially exploit your stuff don't incorporate CC-BY-NC-SA data!\n", "* Stops the derivation of *new works*\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![A conceptual licence processing workflow @ConceptualLicenceProcessingWorkflow_beck_2015](https://upload.wikimedia.org/wikipedia/commons/thumb/b/b4/ConceptualLicenceProcessingWorkflow.svg/1024px-ConceptualLicenceProcessingWorkflow.svg.png)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "notes" } }, "source": [ "A conceptual licence processing workflow. The licence processing service analyses the incoming licence metadata and determines if the data can be legally integrated and any resulting licence implications for the derived product." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# A rudimentry logic example\n", "\n", "```\n", "Data1 hasDerivedContentIn NewThing.\n", "\n", "Data1 hasLicence a cc-by-sa.\n", "\n", "What hasLicence a cc-by-sa? #reason here\n", "\n", "If X hasDerivedContentIn Y and hasLicence Z then Y hasLicence Z. #reason here\n", "\n", "Data2 hasDerivedContentIn NewThing.\n", "\n", "Data2 hasLicence a cc-by-nc-sa.\n", "\n", "What hasLicence a cc-by-nc-sa? #reason here\n", "\n", "Nothing hasLicence a cc-by-nc-sa and hasLicence a cc-by-sa. #reason here\n", "```\n", "\n", "\n", "And processing this within the Protege reasoning environment" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"400\"\n", " height=\"300\"\n", " src=\"https://www.youtube.com/embed/jUzGF401vLc\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x7f3ad00bc150>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('jUzGF401vLc')" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Here's something I prepared earlier\n", "\n", "A live presentation (for those who weren't at the event)....." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "slideshow": { "slide_type": "fragment" } }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"400\"\n", " height=\"300\"\n", " src=\"https://www.youtube.com/embed/tkRB5Rp1_W4\"\n", " frameborder=\"0\"\n", " allowfullscreen\n", " ></iframe>\n", " " ], "text/plain": [ "<IPython.lib.display.YouTubeVideo at 0x7f3ad00bc3d0>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import YouTubeVideo\n", "YouTubeVideo('tkRB5Rp1_W4')\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# A more robust logic\n", "\n", "* Would need to decouple licence incompatibility from licence name into licence clause (see table below)\n", "* Deal with all licence type\n", "* Provide recommendations based on desired derivative licence type\n", "* Link this through to the type of process in a workflow:\n", " * data derivation is, from a licence position, very different to contextual display\n", "* etc..... for discussion?\n", "\n", "License|Reproduction|Distribution|Derivation|ND|BY|SA|NC\n", "----|----|----|----|----|----|----|----\n", "CC0|X|X|X||||\n", "CC-BY-ND|X|X||X|X||\n", "CC-BY-NC-ND|X|X||X|X||X\n", "CC-BY|X|X|X||X||\n", "CC-BY-SA|X|X|X||X|X|\n", "CC-BY-NC|X|X|X||X||X\n", "CC-BY-NC-SA|X|X|X||X|X|X\n", "\n", "Table: [Creative Commons license combinations](https://docs.google.com/spreadsheets/d/17aT7Dj6QtE88XPS44oPQ7mVeSdY1YnZ1rlpjPvXNz0E/pub?single=true&gid=0&output=html)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# OGC and Licence interoperability\n", "\n", "* The geo business landscape is increasingly based on integrating heterogeneous data to develop new products\n", "* Licence heterogeneity is a barrier to data integration and interoperability\n", "* A licence calculus can help resolve and identify heterogenties leading to\n", " * legal compliance\n", " * confidence\n", "* Use of standards and collaboration with organisations is crucial\n", " * [Open Data Licensing ontology](https://github.com/theodi/open-data-licensing)\n", " * [The Open Data Institute](http://theodi.org/)\n", "* Failure to do this could lead to breaches in data licenses\n", " * and we all know where that puts us........\n", " \n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "subslide" } }, "source": [ "![Breaching a data license can be serious (restricted = randomly!)](https://dl.dropboxusercontent.com/u/393477/ImageBank/Jail_Bars_Icon.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "# Questions\n", "\n", "In terms of discussion I'm interested in how these issues affect you.........\n", "\n", "![Processing transparency between open and closed systems](https://upload.wikimedia.org/wikipedia/commons/thumb/d/d5/Processing_transparency_between_open_and_closed_systems.svg/630px-Processing_transparency_between_open_and_closed_systems.svg.png)" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "\\newpage\n", "\n", "# References" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }
cc0-1.0
jadnohra/connect
ddq_1/notebooks/fol_lang_demo.ipynb
1
21543
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys\n", "sys.path.append('../..')\n", "\n", "import ddq.fol.lang as lang" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Print tree helper function\n", "\n", "def print_tree(root, children_func=iter, name_func=str):\n", " \"\"\"Pretty print a tree, in the style of gnu tree\"\"\"\n", " \n", " # Inspired by https://stackoverflow.com/questions/9727673\n", " \n", " # prefix components:\n", " space = ' '\n", " branch = '│ '\n", " # pointers:\n", " tee = '├── '\n", " last = '└── '\n", " \n", " def tree(dir_path, children_func, name_func, prefix: str=''):\n", " \"\"\"A recursive generator, given a tree\n", " will yield a visual tree structure line by line\n", " with each line prefixed by the same characters\n", " \"\"\" \n", " contents = children_func(dir_path)\n", " # contents each get pointers that are ├── with a final └── :\n", " pointers = [tee] * (len(contents) - 1) + [last]\n", " for pointer, path in zip(pointers, contents):\n", " yield prefix + str(pointer) + name_func(path)\n", " if len(children_func(path)): # extend the prefix and recurse:\n", " extension = branch if pointer == tee else space \n", " # i.e. space because last, └── , above so no more |\n", " yield from tree(path, children_func, name_func, prefix=prefix+extension)\n", "\n", " for line in tree(root, children_func, name_func):\n", " print(line)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "└── PrimitiveSymbol [ primitive symbol: ? ]\n", " ├── ImproperSymbol [ improper symbol: ? ]\n", " │ ├── LeftParenSymbol [ left parenthesis: ( ]\n", " │ ├── RightParenSymbol [ right parenthesis: ) ]\n", " │ ├── ConjSymbol [ conjunction: ^ ]\n", " │ ├── DisjSymbol [ disjunction: v ]\n", " │ ├── NegSymbol [ negation: ~ ]\n", " │ ├── ImplSymbol [ material implication: => ]\n", " │ ├── EquivSymbol [ material equivalence: == ]\n", " │ ├── UniversalSymbol [ universal quantifier: ∀ ]\n", " │ └── ExistentialSymbol [ existential quantifier: ∃ ]\n", " ├── IndividualSymbol [ individual: ? ]\n", " │ ├── Constant [ constant: c ]\n", " │ └── Variable [ variable: ? ]\n", " │ ├── IndividualVariable [ individual variable: p ]\n", " │ └── PropositionalVariable [ propositional variable: X ]\n", " ├── Function [ function: f ]\n", " └── Predicate [ predicate: P ]\n" ] } ], "source": [ "# Print the language's symbol hierarchy\n", "\n", "def print_symbol_hierarchy(root_class):\n", " \"\"\"Print the hierarchy and include a canonical example for each class\"\"\"\n", " print_tree(root_class, \n", " lambda cls: cls.__subclasses__(), \n", " lambda cls: cls.__name__ \n", " + \" [ \" + cls.canonical_instance().symbol_type() \n", " + \": \" + cls.canonical_instance().expression()\n", " + \" ]\")\n", "\n", "print_symbol_hierarchy(lang.Symbol)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Note\n", "What about adding our own differentiation between Specific and Arbitrary indiv var, \n", "maybe not as part of the language though :(, but then, as part of what?" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'Term' object has no attribute 'expression'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-dd4dda22915b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m + \" ]\")\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mprint_symbol_hierarchy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlang\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTerm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-4-dd4dda22915b>\u001b[0m in \u001b[0;36mprint_symbol_hierarchy\u001b[0;34m(root_class)\u001b[0m\n\u001b[1;32m 5\u001b[0m print_tree(root_class, \n\u001b[1;32m 6\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__subclasses__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mlambda\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" [ \"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanonical_instance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexpression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m + \" ]\")\n", "\u001b[0;32m<ipython-input-2-a2e1335c1f47>\u001b[0m in \u001b[0;36mprint_tree\u001b[0;34m(root, children_func, name_func)\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchildren_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprefix\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mprefix\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mextension\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 29\u001b[0m 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cls.__name__\n", " + \" [ \" + cls.canonical_instance().expression()\n", " + \" ]\")\n", "\n", "print_symbol_hierarchy(lang.Term)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "descriptor '__init__' requires a 'super' object but received a 'list'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-4-f6d10dcea2e0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m + \" ]\")\n\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mprint_symbol_hierarchy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlang\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mWff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", 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\u001b[0mcanonical_instance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m\"PropVarWff\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mPropVarWff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mPropositionalVariable\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcanonical_instance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/github/connect/ddq/fol/lang.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, var)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mPropVarWff\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mWff\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 301\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mPropositionalVariable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 303\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 304\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mis_atomic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: descriptor '__init__' requires a 'super' object but received a 'list'" ] } ], "source": [ "# Print the language's Wff hierarchy\n", "\n", "def print_symbol_hierarchy(root_class):\n", " \"\"\"Print the hierarchy and include a canonical example for each class\"\"\"\n", " print_tree(root_class, \n", " lambda cls: cls.__subclasses__(), \n", " lambda cls: cls.__name__\n", " + \" [ \" + cls.canonical_instance().expression()\n", " + \" ]\")\n", "\n", "print_symbol_hierarchy(lang.Wff)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.5" } }, "nbformat": 4, "nbformat_minor": 4 }
unlicense
mikecroucher/notebook
GPy/sparse_gp_regression.ipynb
1
337121
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First we set up the notebook with a fixed random seed, and import GPy." ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'svg'\n", "import GPy\n", "import numpy as np\n", "np.random.seed(101)" ], "language": "python", "metadata": { "slideshow": { "slide_type": "slide" } }, "outputs": [], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sample Function\n", "\n", "Now we'll sample a Gaussian process regression problem directly from a Gaussian process prior. We'll use an exponentiated quadratic covariance function with a lengthscale and variance of 1 and sample 50 equally spaced points. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "N = 50\n", "noise_var = 0.05\n", "\n", "X = np.linspace(0,10,50)[:,None]\n", "k = GPy.kern.RBF(1)\n", "y = np.random.multivariate_normal(np.zeros(N),k.K(X)+np.eye(N)*np.sqrt(noise_var)).reshape(-1,1)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Full Gaussian Process Fit\n", "\n", "Now we use GPy to optimize the parameters of a Gaussian process given the sampled data. Here, there are no approximations, we simply fit the full Gaussian process." ] }, { "cell_type": "code", "collapsed": false, "input": [ "m_full = GPy.models.GPRegression(X,y)\n", "m_full.optimize('bfgs')\n", "m_full.plot()\n", "print m_full" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Name : GP regression\n", "Log-likelihood : -50.0803453316\n", "Number of Parameters : 3\n", "Parameters:\n", " GP_regression. | Value | Constraint | Prior | Tied to\n", " \u001b[1mrbf.variance \u001b[0;0m | 1.6628455219 | +ve | | \n", " \u001b[1mrbf.lengthscale \u001b[0;0m | 1.1131954895 | +ve | | \n", " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.236017716477 | +ve | | \n" ] }, { "metadata": {}, "output_type": "display_data", "svg": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created 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This model uses the inducing variable approximation and initialises the inducing variables in two 'clumps'. Our initial fit uses the *correct* covariance function parameters, but a badly placed set of inducing points. " ] }, { "cell_type": "code", "collapsed": false, "input": [ "Z = np.hstack((np.linspace(2.5,4.,3),np.linspace(7,8.5,3)))[:,None]\n", "m = GPy.models.SparseGPRegression(X,y,Z=Z)\n", "m.likelihood.variance = noise_var\n", "m.plot()\n", "print m" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Name : sparse gp\n", "Log-likelihood : -260.813337782\n", "Number of Parameters : 9\n", "Parameters:\n", " sparse_gp. | Value | Constraint | Prior | Tied to\n", " \u001b[1minducing inputs \u001b[0;0m | (6, 1) | | | \n", " \u001b[1mrbf.variance \u001b[0;0m | 1.0 | +ve | | \n", " \u001b[1mrbf.lengthscale \u001b[0;0m | 1.0 | +ve | | \n", " \u001b[1mGaussian_noise.variance\u001b[0;0m | 0.05 | +ve | | \n" ] }, { "metadata": {}, "output_type": "display_data", "svg": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " 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"cell_type": "markdown", "metadata": {}, "source": [ "\n", "Notice how the fit is reasonable where there are inducing points, but bad elsewhere. \n", "\n", "### Optimizing Covariance Parameters\n", "\n", "Next, we will try and find the optimal covariance function parameters, given that the inducing inputs are held in their current location. 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The lengthscale is much longer than the full GP, and the noise variance is larger. This is because in this case the Kullback Leibler term in the objective free energy is dominating, and requires a larger lengthscale to improve the quality of the approximation. This is due to the poor location of the inducing inputs. \n", "\n", "### Optimizing Inducing Inputs\n", "\n", "Firstly we try optimzing the location of the inducing inputs to fix the problem, however we still get a larger lengthscale than the Gaussian process we sampled from (or the full GP fit we did at the beginning)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "m.randomize()\n", "m.Z.unconstrain()\n", "m.optimize('bfgs')\n", "m.plot()" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ "{'dataplot': [<matplotlib.lines.Line2D at 0x4b90350>],\n", " 'gpplot': [[<matplotlib.lines.Line2D at 0x4b8aed0>],\n", " [<matplotlib.patches.Polygon at 0x4b8e150>],\n", " [<matplotlib.lines.Line2D at 0x4b8e790>],\n", " [<matplotlib.lines.Line2D at 0x4b8ecd0>]],\n", " 'inducing_inputs': [<matplotlib.lines.Line2D at 0x4b8ae90>]}" ] }, { "metadata": {}, "output_type": "display_data", "svg": [ "<?xml 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We can try increasing the number of the inducing points.\n", "\n", "### Train with More Inducing Points\n", "\n", "Now we try 12 inducing points, rather than the original six. We then compare with the full Gaussian process likelihood." ] }, { "cell_type": "code", "collapsed": false, "input": [ "Z = np.random.rand(12,1)*12\n", "m = GPy.models.SparseGPRegression(X,y,Z=Z)\n", "\n", "m.optimize('bfgs')\n", "m.plot()\n", "m_full.plot()\n", "print m.log_likelihood(), m_full.log_likelihood()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[-50.09435833] -50.0803453316\n" ] }, { "metadata": {}, "output_type": "display_data", "svg": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\n", "<svg height=\"251pt\" version=\"1.1\" viewBox=\"0 0 371 251\" width=\"371pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n", " <defs>\n", " <style type=\"text/css\">\n", 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This is verified by the fact that the bound on the marginal likelihood is tight, which means that our variational approximation must be good (the difference between the bound and the true likelihood is the Kullback Leibler divergence between the approximation and the truth). " ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 } ], "metadata": {} } ] }
bsd-3-clause
intel-analytics/analytics-zoo
apps/variational-autoencoder/using_variational_autoencoder_to_generate_faces.ipynb
1
209269
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Variational Autoencoder to Generate Faces" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we are going to use VAE to generate faces. The dataset we are going to use is [CelebA](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). The dataset consists of more than 200K celebrity face images. You have to download the Align&Cropped Images from the above website to run this example." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from bigdl.nn.layer import *\n", "from bigdl.nn.criterion import *\n", "from bigdl.optim.optimizer import *\n", "from bigdl.dataset import mnist\n", "import datetime as dt\n", "from glob import glob\n", "import os\n", "import numpy as np\n", "\n", "from utils import *\n", "import imageio\n", "\n", "image_size = 148\n", "Z_DIM = 128\n", "ENCODER_FILTER_NUM = 32\n", "\n", "#download the data CelebA, and may repalce with your own data path\n", "DATA_PATH = os.getenv(\"ANALYTICS_ZOO_HOME\") + \"/apps/variational-autoencoder/img_align_celeba\"\n", "\n", "from zoo.common.nncontext import *\n", "sc = init_nncontext(\"Variational Autoencoder Example\")\n", "sc.addFile(os.getenv(\"ANALYTICS_ZOO_HOME\")+\"/apps/variational-autoencoder/utils.py\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we define a slightly more complicate CNN networks using convolution, batchnorm, and leakyRelu." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def conv_bn_lrelu(in_channels, out_channles, kw=4, kh=4, sw=2, sh=2, pw=-1, ph=-1):\n", " model = Sequential()\n", " model.add(SpatialConvolution(in_channels, out_channles, kw, kh, sw, sh, pw, ph))\n", " model.add(SpatialBatchNormalization(out_channles))\n", " model.add(LeakyReLU(0.2))\n", " return model\n", "\n", "def upsample_conv_bn_lrelu(in_channels, out_channles, out_width, out_height, kw=3, kh=3, sw=1, sh=1, pw=-1, ph=-1):\n", " model = Sequential()\n", " model.add(ResizeBilinear(out_width, out_height))\n", " model.add(SpatialConvolution(in_channels, out_channles, kw, kh, sw, sh, pw, ph))\n", " model.add(SpatialBatchNormalization(out_channles))\n", " model.add(LeakyReLU(0.2))\n", " return model" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def get_encoder_cnn():\n", " input0 = Input()\n", " \n", " #CONV\n", " conv1 = conv_bn_lrelu(3, ENCODER_FILTER_NUM)(input0) # 32 * 32 * 32\n", " conv2 = conv_bn_lrelu(ENCODER_FILTER_NUM, ENCODER_FILTER_NUM * 2)(conv1) # 16 * 16 * 64\n", " conv3 = conv_bn_lrelu(ENCODER_FILTER_NUM * 2, ENCODER_FILTER_NUM * 4)(conv2) # 8 * 8 * 128\n", " conv4 = conv_bn_lrelu(ENCODER_FILTER_NUM * 4, ENCODER_FILTER_NUM * 8)(conv3) # 4 * 4 * 256\n", " view = View([4*4*ENCODER_FILTER_NUM*8])(conv4)\n", " \n", " inter = Linear(4*4*ENCODER_FILTER_NUM*8, 2048)(view)\n", " inter = BatchNormalization(2048)(inter)\n", " inter = ReLU()(inter)\n", " \n", " # fully connected to generate mean and log-variance\n", " mean = Linear(2048, Z_DIM)(inter)\n", " \n", " log_variance = Linear(2048, Z_DIM)(inter)\n", " \n", " model = Model([input0], [mean, log_variance])\n", " return model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def get_decoder_cnn():\n", " input0 = Input()\n", " linear = Linear(Z_DIM, 2048)(input0)\n", " linear = Linear(2048, 4*4*ENCODER_FILTER_NUM * 8)(linear)\n", " reshape = Reshape([ENCODER_FILTER_NUM * 8, 4, 4])(linear)\n", " bn = SpatialBatchNormalization(ENCODER_FILTER_NUM * 8)(reshape)\n", " \n", " # upsampling\n", " up1 = upsample_conv_bn_lrelu(ENCODER_FILTER_NUM*8, ENCODER_FILTER_NUM*4, 8, 8)(bn) # 8 * 8 * 128\n", " up2 = upsample_conv_bn_lrelu(ENCODER_FILTER_NUM*4, ENCODER_FILTER_NUM*2, 16, 16)(up1) # 16 * 16 * 64\n", " up3 = upsample_conv_bn_lrelu(ENCODER_FILTER_NUM*2, ENCODER_FILTER_NUM, 32, 32)(up2) # 32 * 32 * 32\n", " up4 = upsample_conv_bn_lrelu(ENCODER_FILTER_NUM, 3, 64, 64)(up3) # 64 * 64 * 3\n", " output = Sigmoid()(up4)\n", " \n", " model = Model([input0], [output])\n", " return model" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def get_autoencoder_cnn():\n", " input0 = Input()\n", " encoder = get_encoder_cnn()(input0)\n", " sampler = GaussianSampler()(encoder)\n", " \n", " decoder_model = get_decoder_cnn()\n", " decoder = decoder_model(sampler)\n", " \n", " model = Model([input0], [encoder, decoder])\n", " return model, decoder_model" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "creating: createInput\n", "creating: createInput\n", "creating: createSequential\n", "creating: createSpatialConvolution\n", "creating: createSpatialBatchNormalization\n", "creating: createLeakyReLU\n", "creating: createSequential\n", "creating: createSpatialConvolution\n", "creating: createSpatialBatchNormalization\n", "creating: createLeakyReLU\n", "creating: createSequential\n", "creating: createSpatialConvolution\n", "creating: createSpatialBatchNormalization\n", "creating: createLeakyReLU\n", "creating: createSequential\n", "creating: createSpatialConvolution\n", "creating: createSpatialBatchNormalization\n", "creating: createLeakyReLU\n", "creating: createView\n", "creating: createLinear\n", "creating: createBatchNormalization\n", "creating: createReLU\n", "creating: createLinear\n", "creating: createLinear\n", "creating: createModel\n", "creating: createGaussianSampler\n", "creating: createInput\n", "creating: createLinear\n", "creating: createLinear\n", "creating: createReshape\n", "creating: createSpatialBatchNormalization\n", "creating: createSequential\n", "creating: createResizeBilinear\n", "creating: createSpatialConvolution\n", "creating: createSpatialBatchNormalization\n", "creating: createLeakyReLU\n", "creating: createSequential\n", "creating: createResizeBilinear\n", "creating: createSpatialConvolution\n", "creating: createSpatialBatchNormalization\n", "creating: createLeakyReLU\n", "creating: createSequential\n", "creating: createResizeBilinear\n", "creating: createSpatialConvolution\n", "creating: createSpatialBatchNormalization\n", "creating: createLeakyReLU\n", "creating: createSequential\n", "creating: createResizeBilinear\n", "creating: createSpatialConvolution\n", "creating: createSpatialBatchNormalization\n", "creating: createLeakyReLU\n", "creating: createSigmoid\n", "creating: createModel\n", "creating: createModel\n" ] } ], "source": [ "model, decoder = get_autoencoder_cnn()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the Dataset" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def get_data():\n", " data_files = glob(os.path.join(DATA_PATH, \"*.jpg\"))\n", " \n", " rdd_train_images = sc.parallelize(data_files[:100000]) \\\n", " .map(lambda path: inverse_transform(get_image(path, image_size)).transpose(2, 0, 1))\n", " rdd_train_sample = rdd_train_images.map(lambda img: Sample.from_ndarray(img, [np.array(0.0), img]))\n", " return rdd_train_sample" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "train_data = get_data()\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the Training Objective" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "creating: createParallelCriterion\n", "creating: createKLDCriterion\n", "creating: createBCECriterion\n" ] }, { "data": { "text/plain": [ "<bigdl.nn.criterion.ParallelCriterion at 0x7fd9a40eed90>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "criterion = ParallelCriterion()\n", "criterion.add(KLDCriterion(), 1.0) # You may want to twick this parameter\n", "criterion.add(BCECriterion(size_average=False), 1.0 / 64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the Optimizer" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "creating: createAdam\n", "creating: createMaxEpoch\n", "creating: createDistriOptimizer\n", "creating: createTrainSummary\n", "creating: createSeveralIteration\n", "creating: createEveryEpoch\n", "('saving logs to ', 'vea-20180514-113346')\n" ] } ], "source": [ "batch_size = 100\n", "\n", "\n", "# Create an Optimizer\n", "optimizer = Optimizer(\n", " model=model,\n", " training_rdd=train_data,\n", " criterion=criterion,\n", " optim_method=Adam(0.001, beta1=0.5),\n", " end_trigger=MaxEpoch(1),\n", " batch_size=batch_size)\n", "\n", "\n", "app_name='vea-'+dt.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n", "train_summary = TrainSummary(log_dir='/tmp/vae',\n", " app_name=app_name)\n", "\n", "train_summary.set_summary_trigger(\"LearningRate\", SeveralIteration(10))\n", "train_summary.set_summary_trigger(\"Parameters\", EveryEpoch())\n", "\n", "\n", "optimizer.set_train_summary(train_summary)\n", "\n", "print (\"saving logs to \",app_name)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spin Up the Training" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This could take a while. It took about 2 hours on a desktop with a intel i7-6700 cpu and 40GB java heap memory. You can reduce the training time by using less data (some changes in the \"Load the Dataset\" section), but the performce may not as good." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "redire_spark_logs()\n", "show_bigdl_info_logs()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "def gen_image_row():\n", " decoder.evaluate()\n", " return np.column_stack([decoder.forward(np.random.randn(1, Z_DIM)).reshape(3, 64,64).transpose(1, 2, 0) for s in range(8)])\n", "\n", "def gen_image():\n", " return np.row_stack([gen_image_row() for i in range(8)])" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "creating: createMaxEpoch\n" ] } ], "source": [ "for i in range(1, 6):\n", " optimizer.set_end_when(MaxEpoch(i))\n", " trained_model = optimizer.optimize()\n", " image = gen_image()\n", " if not os.path.exists(\"./images\"):\n", " os.makedirs(\"./images\")\n", " if not os.path.exists(\"./models\"):\n", " os.makedirs(\"./models\")\n", " # you may change the following directory accordingly and make sure the directory\n", " # you are writing to exists\n", " imageio.imwrite(\"./images/image_%s.png\" % i , image)\n", " decoder.saveModel(\"./models/decoder_%s.model\" % i, over_write = True)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['Normalize', 'imread']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "import matplotlib\n", "matplotlib.use('Agg')\n", "%pylab inline\n", "\n", "import numpy as np\n", "import datetime as dt\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fab6a077c10>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fab6a0c5d50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "loss = np.array(train_summary.read_scalar(\"Loss\"))\n", "\n", "plt.figure(figsize = (12,12))\n", "plt.plot(loss[:,0],loss[:,1],label='loss')\n", "plt.xlim(0,loss.shape[0]+10)\n", "plt.grid(True)\n", "plt.title(\"loss\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Sample Some Images" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fab69ff7950>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fab6a0ed690>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from matplotlib.pyplot import imshow\n", "img = gen_image()\n", "imshow(img)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.15" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
jcchoiling/learningPython
coursera/python-intro/wk2/assignment2.ipynb
1
92425
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignment 2 - Pandas Introduction\n", "All questions are weighted the same in this assignment.\n", "## Part 1\n", "The following code loads the olympics dataset (olympics.csv), which was derrived from the Wikipedia entry on [All Time Olympic Games Medals](https://en.wikipedia.org/wiki/All-time_Olympic_Games_medal_table), and does some basic data cleaning. \n", "\n", "The columns are organized as # of Summer games, Summer medals, # of Winter games, Winter medals, total # number of games, total # of medals. Use this dataset to answer the questions below." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "nbgrader": { "grade": false, "grade_id": "1", "locked": false, "solution": false } }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('olympics.csv', index_col=0, skiprows=1)\n", "\n", "for col in df.columns:\n", " if col[:2]=='01':\n", " df.rename(columns={col:'Gold'+col[4:]}, inplace=True)\n", " if col[:2]=='02':\n", " df.rename(columns={col:'Silver'+col[4:]}, inplace=True)\n", " if col[:2]=='03':\n", " df.rename(columns={col:'Bronze'+col[4:]}, inplace=True)\n", " if col[:1]=='№':\n", " df.rename(columns={col:'#'+col[1:]}, inplace=True)\n", "\n", "names_ids = df.index.str.split('\\s\\(') # split the index by '('\n", "\n", "df.index = names_ids.str[0] # the [0] element is the country name (new index) \n", "df['ID'] = names_ids.str[1].str[:3] # the [1] element is the abbreviation or ID (take first 3 characters from that)\n", "\n", "df = df.drop('Totals')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 0 (Example)\n", "\n", "What is the first country in df?\n", "\n", "*This function should return a Series.*" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "# Summer 13\n", "Gold 0\n", "Silver 0\n", "Bronze 2\n", "Total 2\n", "# Winter 0\n", "Gold.1 0\n", "Silver.1 0\n", "Bronze.1 0\n", "Total.1 0\n", "# Games 13\n", "Gold.2 0\n", "Silver.2 0\n", "Bronze.2 2\n", "Combined total 2\n", "ID AFG\n", "Name: Afghanistan, dtype: object" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# You should write your whole answer within the function provided. The autograder will call\n", "# this function and compare the return value against the correct solution value\n", "def answer_zero():\n", " # This function returns the row for Afghanistan, which is a Series object. The assignment\n", " # question description will tell you the general format the autograder is expecting\n", " return df.iloc[0]\n", "\n", "# You can examine what your function returns by calling it in the cell. If you have questions\n", "# about the assignment formats, check out the discussion forums for any FAQs\n", "answer_zero()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1\n", "Which country has won the most gold medals in summer games?\n", "\n", "*This function should return a single string value.*" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "nbgrader": { "grade": false, "locked": false, "solution": false } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "United States\n" ] } ], "source": [ "def answer_one():\n", " max_gold = df['Gold'].max()\n", " ret = df[df['Gold'] == max_gold]\n", " ans = ret.index.values\n", " return ans[0]\n", "\n", "print(answer_one())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 2\n", "Which country had the biggest difference between their summer and winter gold medal counts?\n", "\n", "*This function should return a single string value.*" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "United States\n" ] } ], "source": [ "def answer_two():\n", " df2 = df.copy()\n", " df2['Gold_diff'] = df['Gold'] - df['Gold.1']\n", "\n", " score = []\n", "\n", " for row in df2['Gold_diff']:\n", " if row < 0:\n", " row = row * -1\n", " score.append(row)\n", " else:\n", " score.append(row)\n", "\n", " df2['score'] = score\n", "\n", " max_score = df2['score'].max()\n", " name = df2[df2['score'] == max_score]\n", " country_name = name.index.values\n", " return country_name[0]\n", "\n", "print(answer_two())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3\n", "Which country has the biggest difference between their summer gold medal counts and winter gold medal counts relative to their total gold medal count? \n", "\n", "$$\\frac{Summer~Gold - Winter~Gold}{Total~Gold}$$\n", "\n", "Only include countries that have won at least 1 gold in both summer and winter.\n", "\n", "*This function should return a single string value.*" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bulgaria\n" ] } ], "source": [ "def answer_three():\n", " df2 = df.copy()\n", " df2 = df2[(df2['Gold'] > 0) & (df2['Gold.1'] > 0)]\n", "\n", " df2['Gold_diff'] = (df2['Gold'] - df2['Gold.1']) / df2['Gold.2']\n", "\n", " score = []\n", "\n", " for row in df2['Gold_diff']:\n", " if row < 0:\n", " row = row * -100\n", " score.append(row)\n", " else:\n", " row = row * 100\n", " score.append(row)\n", "\n", " df2['score'] = score\n", "\n", " df3 = df2[['Gold','Gold.1','Gold.2','score',]]\n", "\n", " max_score = df3['score'].max()\n", " name = df3[df3['score'] == max_score]\n", " country_name = name.index.values\n", "\n", " return country_name[0]\n", "\n", "print(answer_three())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 4\n", "Write a function to update the dataframe to include a new column called \"Points\" which is a weighted value where each gold medal (`Gold.2`) counts for 3 points, silver medals (`Silver.2`) for 2 points, and bronze medals (`Bronze.2`) for 1 point. The function should return only the column (a Series object) which you created.\n", "\n", "*This function should return a Series named `Points` of length 146*" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Afghanistan 2\n", "Algeria 27\n", "Argentina 130\n", "Armenia 16\n", "Australasia 22\n", "Australia 923\n", "Austria 569\n", "Azerbaijan 43\n", "Bahamas 24\n", "Bahrain 1\n", "Barbados 1\n", "Belarus 154\n", "Belgium 276\n", "Bermuda 1\n", "Bohemia 5\n", "Botswana 2\n", "Brazil 184\n", "British West Indies 2\n", "Bulgaria 411\n", "Burundi 3\n", "Cameroon 12\n", "Canada 846\n", "Chile 24\n", "China 1120\n", "Colombia 29\n", "Costa Rica 7\n", "Ivory Coast 2\n", "Croatia 67\n", "Cuba 420\n", "Cyprus 2\n", " ... \n", "Spain 268\n", "Sri Lanka 4\n", "Sudan 2\n", "Suriname 4\n", "Sweden 1217\n", "Switzerland 630\n", "Syria 6\n", "Chinese Taipei 32\n", "Tajikistan 4\n", "Tanzania 4\n", "Thailand 44\n", "Togo 1\n", "Tonga 2\n", "Trinidad and Tobago 27\n", "Tunisia 19\n", "Turkey 191\n", "Uganda 14\n", "Ukraine 220\n", "United Arab Emirates 3\n", "United States 5684\n", "Uruguay 16\n", "Uzbekistan 38\n", "Venezuela 18\n", "Vietnam 4\n", "Virgin Islands 2\n", "Yugoslavia 171\n", "Independent Olympic Participants 4\n", "Zambia 3\n", "Zimbabwe 18\n", "Mixed team 38\n", "Name: Points, dtype: int64\n" ] } ], "source": [ "def answer_four():\n", " df2 = df.copy()\n", " df2['Points'] = df2['Gold.2']*3 + df2['Silver.2']*2 + df2['Bronze.2']*1\n", " df3 = df2[['Gold.2','Silver.2','Bronze.2','Points']]\n", " return df3['Points']\n", "\n", "print(answer_four())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2\n", "For the next set of questions, we will be using census data from the [United States Census Bureau](http://www.census.gov/popest/data/counties/totals/2015/CO-EST2015-alldata.html). Counties are political and geographic subdivisions of states in the United States. This dataset contains population data for counties and states in the US from 2010 to 2015. [See this document](http://www.census.gov/popest/data/counties/totals/2015/files/CO-EST2015-alldata.pdf) for a description of the variable names.\n", "\n", "The census dataset (census.csv) should be loaded as census_df. Answer questions using this as appropriate.\n", "\n", "### Question 5\n", "Which state has the most counties in it? (hint: consider the sumlevel key carefully! You'll need this for future questions too...)\n", "\n", "*This function should return a single string value.*" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>SUMLEV</th>\n", " <th>REGION</th>\n", " <th>DIVISION</th>\n", " <th>STATE</th>\n", " <th>COUNTY</th>\n", " <th>STNAME</th>\n", " <th>CTYNAME</th>\n", " <th>CENSUS2010POP</th>\n", " <th>ESTIMATESBASE2010</th>\n", " <th>POPESTIMATE2010</th>\n", " <th>...</th>\n", " <th>RDOMESTICMIG2011</th>\n", " <th>RDOMESTICMIG2012</th>\n", " <th>RDOMESTICMIG2013</th>\n", " <th>RDOMESTICMIG2014</th>\n", " <th>RDOMESTICMIG2015</th>\n", " <th>RNETMIG2011</th>\n", " <th>RNETMIG2012</th>\n", " <th>RNETMIG2013</th>\n", " <th>RNETMIG2014</th>\n", " <th>RNETMIG2015</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>40</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>Alabama</td>\n", " <td>Alabama</td>\n", " <td>4779736</td>\n", " <td>4780127</td>\n", " <td>4785161</td>\n", " <td>...</td>\n", " <td>0.002295</td>\n", " <td>-0.193196</td>\n", " <td>0.381066</td>\n", " <td>0.582002</td>\n", " <td>-0.467369</td>\n", " <td>1.030015</td>\n", " <td>0.826644</td>\n", " <td>1.383282</td>\n", " <td>1.724718</td>\n", " <td>0.712594</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>Alabama</td>\n", " <td>Autauga County</td>\n", " <td>54571</td>\n", " <td>54571</td>\n", " <td>54660</td>\n", " <td>...</td>\n", " <td>7.242091</td>\n", " <td>-2.915927</td>\n", " <td>-3.012349</td>\n", " <td>2.265971</td>\n", " <td>-2.530799</td>\n", " <td>7.606016</td>\n", " <td>-2.626146</td>\n", " <td>-2.722002</td>\n", " <td>2.592270</td>\n", " <td>-2.187333</td>\n", " </tr>\n", " <tr>\n", " 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<td>-10.543299</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>7</td>\n", " <td>Alabama</td>\n", " <td>Bibb County</td>\n", " <td>22915</td>\n", " <td>22919</td>\n", " <td>22861</td>\n", " <td>...</td>\n", " <td>-5.527043</td>\n", " <td>-5.068871</td>\n", " <td>-6.201001</td>\n", " <td>-0.177537</td>\n", " <td>0.177258</td>\n", " <td>-5.088389</td>\n", " <td>-4.363636</td>\n", " <td>-5.403729</td>\n", " <td>0.754533</td>\n", " <td>1.107861</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>9</td>\n", " <td>Alabama</td>\n", " <td>Blount County</td>\n", " <td>57322</td>\n", " <td>57322</td>\n", " <td>57373</td>\n", " <td>...</td>\n", " <td>1.807375</td>\n", " <td>-1.177622</td>\n", " <td>-1.748766</td>\n", " <td>-2.062535</td>\n", " <td>-1.369970</td>\n", " <td>1.859511</td>\n", " <td>-0.848580</td>\n", " <td>-1.402476</td>\n", " 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<td>-1.821092</td>\n", " <td>4.701181</td>\n", " <td>3.781439</td>\n", " <td>-1.290228</td>\n", " <td>2.346901</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>19</td>\n", " <td>Alabama</td>\n", " <td>Cherokee County</td>\n", " <td>25989</td>\n", " <td>25986</td>\n", " <td>25976</td>\n", " <td>...</td>\n", " <td>6.339327</td>\n", " <td>1.113180</td>\n", " <td>5.488706</td>\n", " <td>-0.076806</td>\n", " <td>-3.239866</td>\n", " <td>6.416167</td>\n", " <td>1.420264</td>\n", " <td>5.757384</td>\n", " <td>0.230419</td>\n", " <td>-2.931307</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>21</td>\n", " <td>Alabama</td>\n", " <td>Chilton County</td>\n", " <td>43643</td>\n", " <td>43631</td>\n", " <td>43665</td>\n", " <td>...</td>\n", " <td>-1.372935</td>\n", " <td>-2.653369</td>\n", " <td>0.480044</td>\n", " <td>0.456017</td>\n", " <td>-2.253483</td>\n", " <td>-0.823761</td>\n", " <td>-2.447504</td>\n", " <td>0.868651</td>\n", " <td>0.957636</td>\n", " <td>-1.752709</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>23</td>\n", " <td>Alabama</td>\n", " <td>Choctaw County</td>\n", " <td>13859</td>\n", " <td>13858</td>\n", " <td>13841</td>\n", " <td>...</td>\n", " <td>-15.455274</td>\n", " <td>-0.737028</td>\n", " <td>-8.766391</td>\n", " <td>-1.274984</td>\n", " <td>-5.291205</td>\n", " <td>-15.528177</td>\n", " <td>-0.737028</td>\n", " <td>-8.766391</td>\n", " <td>-1.274984</td>\n", " <td>-5.291205</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>25</td>\n", " <td>Alabama</td>\n", " <td>Clarke County</td>\n", " <td>25833</td>\n", " <td>25840</td>\n", " <td>25767</td>\n", " <td>...</td>\n", " <td>-6.194363</td>\n", " <td>-17.667705</td>\n", " <td>-0.318345</td>\n", " <td>-8.686428</td>\n", " <td>-5.613667</td>\n", " <td>-6.077488</td>\n", " <td>-17.509958</td>\n", " <td>-0.159172</td>\n", " <td>-8.486280</td>\n", " <td>-5.411736</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>27</td>\n", " <td>Alabama</td>\n", " <td>Clay County</td>\n", " <td>13932</td>\n", " <td>13932</td>\n", " <td>13880</td>\n", " <td>...</td>\n", " <td>-10.744102</td>\n", " <td>-13.345130</td>\n", " <td>4.902871</td>\n", " <td>5.702648</td>\n", " <td>3.912450</td>\n", " <td>-10.816697</td>\n", " <td>-13.345130</td>\n", " <td>4.977157</td>\n", " <td>5.776708</td>\n", " <td>3.986270</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>29</td>\n", " <td>Alabama</td>\n", " <td>Cleburne County</td>\n", " <td>14972</td>\n", " <td>14972</td>\n", " <td>14973</td>\n", " <td>...</td>\n", " <td>-3.673524</td>\n", " <td>-5.151880</td>\n", " <td>7.345821</td>\n", " <td>3.654485</td>\n", " <td>-3.123961</td>\n", " <td>-3.673524</td>\n", " <td>-5.151880</td>\n", " <td>7.345821</td>\n", " <td>3.654485</td>\n", " <td>-3.123961</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>31</td>\n", " <td>Alabama</td>\n", " <td>Coffee County</td>\n", " <td>49948</td>\n", " <td>49948</td>\n", " <td>50177</td>\n", " <td>...</td>\n", " <td>0.377640</td>\n", " <td>7.675579</td>\n", " <td>-13.146535</td>\n", " <td>-3.602859</td>\n", " <td>2.214774</td>\n", " <td>2.166460</td>\n", " <td>11.513368</td>\n", " <td>-10.438741</td>\n", " <td>-0.767822</td>\n", " <td>5.350738</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>33</td>\n", " <td>Alabama</td>\n", " <td>Colbert County</td>\n", " <td>54428</td>\n", " <td>54428</td>\n", " <td>54514</td>\n", " <td>...</td>\n", " <td>-0.073423</td>\n", " <td>1.065051</td>\n", " <td>1.762390</td>\n", " <td>1.835688</td>\n", " <td>-0.110260</td>\n", " <td>0.513964</td>\n", " <td>1.469035</td>\n", " <td>2.276420</td>\n", " <td>2.533249</td>\n", " <td>0.588052</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>35</td>\n", " <td>Alabama</td>\n", " <td>Conecuh County</td>\n", " <td>13228</td>\n", " <td>13228</td>\n", " <td>13208</td>\n", " <td>...</td>\n", " <td>-4.861559</td>\n", " <td>-7.504690</td>\n", " <td>-6.107224</td>\n", " <td>-14.645416</td>\n", " <td>2.684140</td>\n", " <td>-4.861559</td>\n", " <td>-7.504690</td>\n", " <td>-6.107224</td>\n", " <td>-14.645416</td>\n", " <td>2.684140</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>37</td>\n", " <td>Alabama</td>\n", " <td>Coosa County</td>\n", " <td>11539</td>\n", " <td>11758</td>\n", " <td>11758</td>\n", " <td>...</td>\n", " <td>-33.930581</td>\n", " <td>-10.291443</td>\n", " <td>-4.313831</td>\n", " <td>-22.958017</td>\n", " <td>-5.387581</td>\n", " <td>-34.017138</td>\n", " <td>-10.380162</td>\n", " <td>-4.403703</td>\n", " <td>-23.049483</td>\n", " <td>-5.387581</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>39</td>\n", " <td>Alabama</td>\n", " <td>Covington County</td>\n", " <td>37765</td>\n", " <td>37765</td>\n", " <td>37796</td>\n", " <td>...</td>\n", " <td>6.696899</td>\n", " <td>-4.612668</td>\n", " <td>0.740271</td>\n", " <td>3.697932</td>\n", " <td>-0.316945</td>\n", " <td>6.881460</td>\n", " <td>-4.559952</td>\n", " <td>0.793147</td>\n", " <td>3.750759</td>\n", " <td>-0.264121</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>41</td>\n", " <td>Alabama</td>\n", " <td>Crenshaw County</td>\n", " <td>13906</td>\n", " <td>13906</td>\n", " <td>13853</td>\n", " <td>...</td>\n", " <td>1.729792</td>\n", " <td>3.950156</td>\n", " <td>-1.864936</td>\n", " <td>3.084648</td>\n", " <td>3.439504</td>\n", " <td>2.666763</td>\n", " <td>5.099293</td>\n", " <td>-0.502098</td>\n", " <td>4.734577</td>\n", " <td>5.087600</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>43</td>\n", " <td>Alabama</td>\n", " <td>Cullman County</td>\n", " <td>80406</td>\n", " <td>80410</td>\n", " <td>80473</td>\n", " <td>...</td>\n", " <td>-1.404233</td>\n", " <td>-1.019628</td>\n", " <td>4.071247</td>\n", " <td>5.087142</td>\n", " <td>7.915406</td>\n", " <td>-1.031427</td>\n", " <td>-0.634159</td>\n", " <td>4.542916</td>\n", " <td>5.593387</td>\n", " <td>8.417777</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>45</td>\n", " <td>Alabama</td>\n", " <td>Dale County</td>\n", " <td>50251</td>\n", " <td>50251</td>\n", " <td>50358</td>\n", " <td>...</td>\n", " <td>-10.749798</td>\n", " <td>-5.277150</td>\n", " <td>-15.236079</td>\n", " <td>-11.979785</td>\n", " <td>-5.107706</td>\n", " <td>-9.575283</td>\n", " <td>-0.776637</td>\n", " <td>-12.640155</td>\n", " <td>-9.503292</td>\n", " <td>-1.998668</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>47</td>\n", " <td>Alabama</td>\n", " <td>Dallas County</td>\n", " <td>43820</td>\n", " <td>43820</td>\n", " <td>43803</td>\n", " <td>...</td>\n", " <td>-15.635599</td>\n", " <td>-11.308243</td>\n", " <td>-16.745678</td>\n", " <td>-9.344789</td>\n", " <td>-14.687232</td>\n", " <td>-15.727573</td>\n", " <td>-11.378047</td>\n", " <td>-16.792849</td>\n", " <td>-9.368689</td>\n", " <td>-14.711389</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>49</td>\n", " <td>Alabama</td>\n", " <td>DeKalb County</td>\n", " <td>71109</td>\n", " <td>71115</td>\n", " <td>71142</td>\n", " <td>...</td>\n", " <td>0.294677</td>\n", " <td>-9.302391</td>\n", " <td>-1.748807</td>\n", " <td>0.267830</td>\n", " <td>0.028141</td>\n", " <td>1.375159</td>\n", " <td>-8.656001</td>\n", " <td>-1.029539</td>\n", " <td>1.198187</td>\n", " <td>0.956790</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>51</td>\n", " <td>Alabama</td>\n", " <td>Elmore County</td>\n", " <td>79303</td>\n", " <td>79296</td>\n", " <td>79465</td>\n", " <td>...</td>\n", " <td>3.235576</td>\n", " <td>0.822717</td>\n", " <td>1.760531</td>\n", " <td>-1.507057</td>\n", " <td>2.067820</td>\n", " <td>3.674511</td>\n", " <td>1.558176</td>\n", " <td>2.306047</td>\n", " <td>-0.951175</td>\n", " <td>2.757093</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>53</td>\n", " <td>Alabama</td>\n", " <td>Escambia County</td>\n", " <td>38319</td>\n", " <td>38319</td>\n", " <td>38309</td>\n", " <td>...</td>\n", " <td>-3.449988</td>\n", " <td>-3.855889</td>\n", " <td>-4.822706</td>\n", " <td>-1.189831</td>\n", " <td>1.190902</td>\n", " <td>-3.397716</td>\n", " <td>-3.803428</td>\n", " <td>-4.769999</td>\n", " <td>-1.136950</td>\n", " <td>1.243830</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>55</td>\n", " <td>Alabama</td>\n", " <td>Etowah County</td>\n", " <td>104430</td>\n", " <td>104427</td>\n", " <td>104442</td>\n", " <td>...</td>\n", " <td>-1.015919</td>\n", " <td>2.062637</td>\n", " <td>-1.931884</td>\n", " <td>-1.726932</td>\n", " <td>-2.082234</td>\n", " <td>-0.632554</td>\n", " <td>2.446383</td>\n", " <td>-1.518596</td>\n", " <td>-1.234901</td>\n", " <td>-1.588308</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>50</td>\n", " <td>3</td>\n", " <td>6</td>\n", " <td>1</td>\n", " <td>57</td>\n", " <td>Alabama</td>\n", " <td>Fayette County</td>\n", " <td>17241</td>\n", " <td>17241</td>\n", " <td>17231</td>\n", " <td>...</td>\n", " <td>-5.015601</td>\n", " <td>-0.646640</td>\n", " <td>-3.725937</td>\n", " <td>0.296745</td>\n", " <td>-2.797536</td>\n", " <td>-5.132243</td>\n", " <td>-0.705426</td>\n", " <td>-3.785079</td>\n", " <td>0.237396</td>\n", " <td>-2.857058</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>3163</th>\n", " <td>50</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>55</td>\n", " <td>131</td>\n", " <td>Wisconsin</td>\n", " <td>Washington County</td>\n", " <td>131887</td>\n", " <td>131885</td>\n", " <td>131967</td>\n", " <td>...</td>\n", " <td>-0.794876</td>\n", " <td>0.785279</td>\n", " <td>-2.215465</td>\n", " <td>1.601149</td>\n", " <td>-0.434498</td>\n", " <td>-0.431504</td>\n", " <td>1.162817</td>\n", " <td>-1.763330</td>\n", " <td>2.104796</td>\n", " <td>0.059931</td>\n", " </tr>\n", " <tr>\n", " <th>3164</th>\n", " <td>50</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>55</td>\n", " <td>133</td>\n", " <td>Wisconsin</td>\n", " <td>Waukesha County</td>\n", " <td>389891</td>\n", " <td>389938</td>\n", " <td>390076</td>\n", " <td>...</td>\n", " <td>-0.765799</td>\n", " <td>2.128860</td>\n", " <td>0.038132</td>\n", " <td>0.760109</td>\n", " <td>-0.719858</td>\n", " <td>0.102448</td>\n", " <td>3.180527</td>\n", " <td>1.189727</td>\n", " <td>2.077633</td>\n", " <td>0.593567</td>\n", " </tr>\n", " <tr>\n", " <th>3165</th>\n", " <td>50</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>55</td>\n", " <td>135</td>\n", " <td>Wisconsin</td>\n", " <td>Waupaca County</td>\n", " <td>52410</td>\n", " <td>52410</td>\n", " <td>52422</td>\n", " <td>...</td>\n", " <td>3.111756</td>\n", " <td>-2.241873</td>\n", " <td>6.292687</td>\n", " <td>-0.441031</td>\n", " <td>-0.480617</td>\n", " <td>3.359933</td>\n", " <td>-2.011937</td>\n", " <td>6.561277</td>\n", " <td>-0.134227</td>\n", " <td>-0.173022</td>\n", " </tr>\n", " <tr>\n", " <th>3166</th>\n", " <td>50</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>55</td>\n", " <td>137</td>\n", " <td>Wisconsin</td>\n", " <td>Waushara County</td>\n", " <td>24496</td>\n", " <td>24496</td>\n", " <td>24506</td>\n", " <td>...</td>\n", " <td>4.930022</td>\n", " <td>-2.404973</td>\n", " <td>-4.097017</td>\n", " <td>-4.906711</td>\n", " <td>-4.397793</td>\n", " <td>5.174486</td>\n", " <td>-2.160399</td>\n", " <td>-3.810226</td>\n", " <td>-4.535615</td>\n", " <td>-4.024395</td>\n", " </tr>\n", " <tr>\n", " <th>3167</th>\n", " <td>50</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>55</td>\n", " <td>139</td>\n", " <td>Wisconsin</td>\n", " <td>Winnebago County</td>\n", " <td>166994</td>\n", " <td>166994</td>\n", " <td>167059</td>\n", " <td>...</td>\n", " <td>0.316712</td>\n", " <td>2.889873</td>\n", " <td>0.833819</td>\n", " <td>-2.406192</td>\n", " <td>-4.557985</td>\n", " <td>0.842573</td>\n", " <td>3.502335</td>\n", " <td>1.531624</td>\n", " <td>-1.545153</td>\n", " <td>-3.685304</td>\n", " </tr>\n", " <tr>\n", " <th>3168</th>\n", " <td>50</td>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>55</td>\n", " <td>141</td>\n", " <td>Wisconsin</td>\n", " <td>Wood County</td>\n", " <td>74749</td>\n", " <td>74749</td>\n", " <td>74807</td>\n", " <td>...</td>\n", " <td>-4.081523</td>\n", " <td>-5.019090</td>\n", " <td>-6.901200</td>\n", " <td>-5.596471</td>\n", " <td>-3.958322</td>\n", " <td>-3.733590</td>\n", " <td>-4.562809</td>\n", " <td>-6.442917</td>\n", " <td>-5.040889</td>\n", " <td>-3.414223</td>\n", " </tr>\n", " <tr>\n", " <th>3169</th>\n", " <td>40</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>0</td>\n", " <td>Wyoming</td>\n", " <td>Wyoming</td>\n", " <td>563626</td>\n", " <td>563767</td>\n", " <td>564516</td>\n", " <td>...</td>\n", " <td>-0.381530</td>\n", " <td>9.636214</td>\n", " <td>4.487115</td>\n", " <td>-4.788275</td>\n", " <td>-3.221091</td>\n", " <td>0.289680</td>\n", " <td>10.694870</td>\n", " <td>5.440390</td>\n", " <td>-3.727831</td>\n", " <td>-2.091573</td>\n", " </tr>\n", " <tr>\n", " <th>3170</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>1</td>\n", " <td>Wyoming</td>\n", " <td>Albany County</td>\n", " <td>36299</td>\n", " <td>36299</td>\n", " <td>36428</td>\n", " <td>...</td>\n", " <td>3.708956</td>\n", " <td>2.637812</td>\n", " <td>-3.544634</td>\n", " <td>-3.334877</td>\n", " <td>-9.911169</td>\n", " <td>6.736119</td>\n", " <td>6.433032</td>\n", " <td>0.719587</td>\n", " <td>1.429233</td>\n", " <td>-5.166460</td>\n", " </tr>\n", " <tr>\n", " <th>3171</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>3</td>\n", " <td>Wyoming</td>\n", " <td>Big Horn County</td>\n", " <td>11668</td>\n", " <td>11668</td>\n", " <td>11672</td>\n", " <td>...</td>\n", " <td>4.868258</td>\n", " <td>2.804930</td>\n", " <td>16.815908</td>\n", " <td>-8.026420</td>\n", " <td>5.095861</td>\n", " <td>4.868258</td>\n", " <td>3.144921</td>\n", " <td>17.236306</td>\n", " <td>-7.608378</td>\n", " <td>5.513554</td>\n", " </tr>\n", " <tr>\n", " <th>3172</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>5</td>\n", " <td>Wyoming</td>\n", " <td>Campbell County</td>\n", " <td>46133</td>\n", " <td>46133</td>\n", " <td>46244</td>\n", " <td>...</td>\n", " <td>-2.843479</td>\n", " <td>15.601020</td>\n", " <td>-5.895711</td>\n", " <td>-8.550911</td>\n", " <td>10.916963</td>\n", " <td>-2.649606</td>\n", " <td>15.558684</td>\n", " <td>-5.916543</td>\n", " <td>-8.509402</td>\n", " <td>10.978525</td>\n", " </tr>\n", " <tr>\n", " <th>3173</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>7</td>\n", " <td>Wyoming</td>\n", " <td>Carbon County</td>\n", " <td>15885</td>\n", " <td>15885</td>\n", " <td>15837</td>\n", " <td>...</td>\n", " <td>-7.581980</td>\n", " <td>-13.081441</td>\n", " <td>3.178134</td>\n", " <td>-2.970641</td>\n", " <td>-23.300971</td>\n", " <td>-7.392431</td>\n", " <td>-12.636926</td>\n", " <td>3.623073</td>\n", " <td>-2.338590</td>\n", " <td>-22.600668</td>\n", " </tr>\n", " <tr>\n", " <th>3174</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>9</td>\n", " <td>Wyoming</td>\n", " <td>Converse County</td>\n", " <td>13833</td>\n", " <td>13833</td>\n", " <td>13826</td>\n", " <td>...</td>\n", " <td>-12.847499</td>\n", " <td>15.493820</td>\n", " <td>19.035533</td>\n", " <td>-20.550587</td>\n", " <td>-0.070403</td>\n", " <td>-12.774915</td>\n", " <td>16.502720</td>\n", " <td>20.093063</td>\n", " <td>-19.358233</td>\n", " <td>1.126443</td>\n", " </tr>\n", " <tr>\n", " <th>3175</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>11</td>\n", " <td>Wyoming</td>\n", " <td>Crook County</td>\n", " <td>7083</td>\n", " <td>7083</td>\n", " <td>7114</td>\n", " <td>...</td>\n", " <td>-1.544618</td>\n", " <td>-4.202564</td>\n", " <td>1.397819</td>\n", " <td>6.378258</td>\n", " <td>18.629317</td>\n", " <td>-0.982939</td>\n", " <td>-3.642222</td>\n", " <td>2.096729</td>\n", " <td>7.071547</td>\n", " <td>19.309219</td>\n", " </tr>\n", " <tr>\n", " <th>3176</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>13</td>\n", " <td>Wyoming</td>\n", " <td>Fremont County</td>\n", " <td>40123</td>\n", " <td>40123</td>\n", " <td>40222</td>\n", " <td>...</td>\n", " <td>2.747083</td>\n", " <td>7.782673</td>\n", " <td>-4.990688</td>\n", " <td>-12.331633</td>\n", " <td>-13.673610</td>\n", " <td>3.093562</td>\n", " <td>8.027411</td>\n", " <td>-4.747240</td>\n", " <td>-12.013555</td>\n", " <td>-13.352750</td>\n", " </tr>\n", " <tr>\n", " <th>3177</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>15</td>\n", " <td>Wyoming</td>\n", " <td>Goshen County</td>\n", " <td>13249</td>\n", " <td>13247</td>\n", " <td>13408</td>\n", " <td>...</td>\n", " <td>14.293649</td>\n", " <td>3.961413</td>\n", " <td>-8.079028</td>\n", " <td>-7.017803</td>\n", " <td>-11.899450</td>\n", " <td>14.886132</td>\n", " <td>4.841727</td>\n", " <td>-6.903896</td>\n", " <td>-5.761986</td>\n", " <td>-10.635133</td>\n", " </tr>\n", " <tr>\n", " <th>3178</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>17</td>\n", " <td>Wyoming</td>\n", " <td>Hot Springs County</td>\n", " <td>4812</td>\n", " <td>4812</td>\n", " <td>4813</td>\n", " <td>...</td>\n", " <td>3.322604</td>\n", " <td>6.208609</td>\n", " <td>3.095336</td>\n", " <td>-6.017222</td>\n", " <td>-5.454164</td>\n", " <td>5.191569</td>\n", " <td>6.001656</td>\n", " <td>2.888981</td>\n", " <td>-6.224712</td>\n", " <td>-5.663940</td>\n", " </tr>\n", " <tr>\n", " <th>3179</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>19</td>\n", " <td>Wyoming</td>\n", " <td>Johnson County</td>\n", " <td>8569</td>\n", " <td>8569</td>\n", " <td>8581</td>\n", " <td>...</td>\n", " <td>4.995063</td>\n", " <td>-4.058912</td>\n", " <td>-0.812583</td>\n", " <td>-10.715742</td>\n", " <td>0.933652</td>\n", " <td>5.227392</td>\n", " <td>-4.058912</td>\n", " <td>-0.812583</td>\n", " <td>-10.715742</td>\n", " <td>0.933652</td>\n", " </tr>\n", " <tr>\n", " <th>3180</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>21</td>\n", " <td>Wyoming</td>\n", " <td>Laramie County</td>\n", " <td>91738</td>\n", " <td>91881</td>\n", " <td>92271</td>\n", " <td>...</td>\n", " <td>-1.200428</td>\n", " <td>15.547274</td>\n", " <td>4.787847</td>\n", " <td>-1.226133</td>\n", " <td>0.278940</td>\n", " <td>-0.973320</td>\n", " <td>17.914554</td>\n", " <td>6.003143</td>\n", " <td>-0.207819</td>\n", " <td>1.673640</td>\n", " </tr>\n", " <tr>\n", " <th>3181</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>23</td>\n", " <td>Wyoming</td>\n", " <td>Lincoln County</td>\n", " <td>18106</td>\n", " <td>18106</td>\n", " <td>18091</td>\n", " <td>...</td>\n", " <td>-9.802564</td>\n", " <td>-11.566801</td>\n", " <td>13.564556</td>\n", " <td>6.125989</td>\n", " <td>1.555544</td>\n", " <td>-9.691801</td>\n", " <td>-11.566801</td>\n", " <td>13.619696</td>\n", " <td>6.234414</td>\n", " <td>1.662823</td>\n", " </tr>\n", " <tr>\n", " <th>3182</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>25</td>\n", " <td>Wyoming</td>\n", " <td>Natrona County</td>\n", " <td>75450</td>\n", " <td>75450</td>\n", " <td>75472</td>\n", " <td>...</td>\n", " <td>7.189319</td>\n", " <td>23.066162</td>\n", " <td>24.322042</td>\n", " <td>-0.958472</td>\n", " <td>-0.061057</td>\n", " <td>7.689674</td>\n", " <td>23.749508</td>\n", " <td>25.085233</td>\n", " <td>-0.110593</td>\n", " <td>0.793743</td>\n", " </tr>\n", " <tr>\n", " <th>3183</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>27</td>\n", " <td>Wyoming</td>\n", " <td>Niobrara County</td>\n", " <td>2484</td>\n", " <td>2484</td>\n", " <td>2492</td>\n", " <td>...</td>\n", " <td>-0.401849</td>\n", " <td>0.806452</td>\n", " <td>29.066295</td>\n", " <td>-12.603387</td>\n", " <td>7.492114</td>\n", " <td>-0.401849</td>\n", " <td>0.806452</td>\n", " <td>29.066295</td>\n", " <td>-12.603387</td>\n", " <td>7.492114</td>\n", " </tr>\n", " <tr>\n", " <th>3184</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>29</td>\n", " <td>Wyoming</td>\n", " <td>Park County</td>\n", " <td>28205</td>\n", " <td>28205</td>\n", " <td>28259</td>\n", " <td>...</td>\n", " <td>4.582951</td>\n", " <td>8.057765</td>\n", " <td>7.641997</td>\n", " <td>-9.252437</td>\n", " <td>-2.878980</td>\n", " <td>6.486639</td>\n", " <td>11.127389</td>\n", " <td>10.877797</td>\n", " <td>-5.585731</td>\n", " <td>0.856839</td>\n", " </tr>\n", " <tr>\n", " <th>3185</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>31</td>\n", " <td>Wyoming</td>\n", " <td>Platte County</td>\n", " <td>8667</td>\n", " <td>8667</td>\n", " <td>8678</td>\n", " <td>...</td>\n", " <td>4.373094</td>\n", " <td>5.392073</td>\n", " <td>2.634593</td>\n", " <td>6.055759</td>\n", " <td>4.662270</td>\n", " <td>4.373094</td>\n", " <td>4.933173</td>\n", " <td>2.176403</td>\n", " <td>5.598720</td>\n", " <td>4.207414</td>\n", " </tr>\n", " <tr>\n", " <th>3186</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>33</td>\n", " <td>Wyoming</td>\n", " <td>Sheridan County</td>\n", " <td>29116</td>\n", " <td>29116</td>\n", " <td>29146</td>\n", " <td>...</td>\n", " <td>0.958559</td>\n", " <td>8.425487</td>\n", " <td>4.546373</td>\n", " <td>3.678069</td>\n", " <td>-3.298406</td>\n", " <td>2.122524</td>\n", " <td>9.342778</td>\n", " <td>5.523001</td>\n", " <td>4.781489</td>\n", " <td>-2.198937</td>\n", " </tr>\n", " <tr>\n", " <th>3187</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>35</td>\n", " <td>Wyoming</td>\n", " <td>Sublette County</td>\n", " <td>10247</td>\n", " <td>10247</td>\n", " <td>10244</td>\n", " <td>...</td>\n", " <td>-23.741784</td>\n", " <td>15.272374</td>\n", " <td>-40.870074</td>\n", " <td>-16.596273</td>\n", " <td>-22.870900</td>\n", " <td>-21.092907</td>\n", " <td>16.828794</td>\n", " <td>-39.211861</td>\n", " <td>-14.409938</td>\n", " <td>-20.664059</td>\n", " </tr>\n", " <tr>\n", " <th>3188</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>37</td>\n", " <td>Wyoming</td>\n", " <td>Sweetwater County</td>\n", " <td>43806</td>\n", " <td>43806</td>\n", " <td>43593</td>\n", " <td>...</td>\n", " <td>1.072643</td>\n", " <td>16.243199</td>\n", " <td>-5.339774</td>\n", " <td>-14.252889</td>\n", " <td>-14.248864</td>\n", " <td>1.255221</td>\n", " <td>16.243199</td>\n", " <td>-5.295460</td>\n", " <td>-14.075283</td>\n", " <td>-14.070195</td>\n", " </tr>\n", " <tr>\n", " <th>3189</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>39</td>\n", " <td>Wyoming</td>\n", " <td>Teton County</td>\n", " <td>21294</td>\n", " <td>21294</td>\n", " <td>21297</td>\n", " <td>...</td>\n", " <td>-1.589565</td>\n", " <td>0.972695</td>\n", " <td>19.525929</td>\n", " <td>14.143021</td>\n", " <td>-0.564849</td>\n", " <td>0.654527</td>\n", " <td>2.408578</td>\n", " <td>21.160658</td>\n", " <td>16.308671</td>\n", " <td>1.520747</td>\n", " </tr>\n", " <tr>\n", " <th>3190</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>41</td>\n", " <td>Wyoming</td>\n", " <td>Uinta County</td>\n", " <td>21118</td>\n", " <td>21118</td>\n", " <td>21102</td>\n", " <td>...</td>\n", " <td>-17.755986</td>\n", " <td>-4.916350</td>\n", " <td>-6.902954</td>\n", " <td>-14.215862</td>\n", " <td>-12.127022</td>\n", " <td>-18.136812</td>\n", " <td>-5.536861</td>\n", " <td>-7.521840</td>\n", " <td>-14.740608</td>\n", " <td>-12.606351</td>\n", " </tr>\n", " <tr>\n", " <th>3191</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>43</td>\n", " <td>Wyoming</td>\n", " <td>Washakie County</td>\n", " <td>8533</td>\n", " <td>8533</td>\n", " <td>8545</td>\n", " <td>...</td>\n", " <td>-11.637475</td>\n", " <td>-0.827815</td>\n", " <td>-2.013502</td>\n", " <td>-17.781491</td>\n", " <td>1.682288</td>\n", " <td>-11.990126</td>\n", " <td>-1.182592</td>\n", " <td>-2.250385</td>\n", " <td>-18.020168</td>\n", " <td>1.441961</td>\n", " </tr>\n", " <tr>\n", " <th>3192</th>\n", " <td>50</td>\n", " <td>4</td>\n", " <td>8</td>\n", " <td>56</td>\n", " <td>45</td>\n", " <td>Wyoming</td>\n", " <td>Weston County</td>\n", " <td>7208</td>\n", " <td>7208</td>\n", " <td>7181</td>\n", " <td>...</td>\n", " <td>-11.752361</td>\n", " <td>-8.040059</td>\n", " <td>12.372583</td>\n", " <td>1.533635</td>\n", " <td>6.935294</td>\n", " <td>-12.032179</td>\n", " <td>-8.040059</td>\n", " <td>12.372583</td>\n", " <td>1.533635</td>\n", " <td>6.935294</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3193 rows × 100 columns</p>\n", "</div>" ], "text/plain": [ " SUMLEV REGION DIVISION STATE COUNTY STNAME CTYNAME \\\n", "0 40 3 6 1 0 Alabama Alabama \n", "1 50 3 6 1 1 Alabama Autauga County \n", "2 50 3 6 1 3 Alabama Baldwin County \n", "3 50 3 6 1 5 Alabama Barbour County \n", "4 50 3 6 1 7 Alabama Bibb County \n", "5 50 3 6 1 9 Alabama Blount County \n", "6 50 3 6 1 11 Alabama Bullock County \n", "7 50 3 6 1 13 Alabama Butler County \n", "8 50 3 6 1 15 Alabama Calhoun County \n", "9 50 3 6 1 17 Alabama Chambers County \n", "10 50 3 6 1 19 Alabama Cherokee County \n", "11 50 3 6 1 21 Alabama Chilton County \n", "12 50 3 6 1 23 Alabama Choctaw County \n", "13 50 3 6 1 25 Alabama Clarke County \n", "14 50 3 6 1 27 Alabama Clay County \n", "15 50 3 6 1 29 Alabama Cleburne County \n", "16 50 3 6 1 31 Alabama Coffee County \n", "17 50 3 6 1 33 Alabama Colbert County \n", "18 50 3 6 1 35 Alabama Conecuh County \n", "19 50 3 6 1 37 Alabama Coosa County \n", "20 50 3 6 1 39 Alabama Covington County \n", "21 50 3 6 1 41 Alabama Crenshaw County \n", "22 50 3 6 1 43 Alabama Cullman County \n", "23 50 3 6 1 45 Alabama Dale County \n", "24 50 3 6 1 47 Alabama Dallas County \n", "25 50 3 6 1 49 Alabama DeKalb County \n", "26 50 3 6 1 51 Alabama Elmore County \n", "27 50 3 6 1 53 Alabama Escambia County \n", "28 50 3 6 1 55 Alabama Etowah County \n", "29 50 3 6 1 57 Alabama Fayette County \n", "... ... ... ... ... ... ... ... \n", "3163 50 2 3 55 131 Wisconsin Washington County \n", "3164 50 2 3 55 133 Wisconsin Waukesha County \n", "3165 50 2 3 55 135 Wisconsin Waupaca County \n", "3166 50 2 3 55 137 Wisconsin Waushara County \n", "3167 50 2 3 55 139 Wisconsin Winnebago County \n", "3168 50 2 3 55 141 Wisconsin Wood County \n", "3169 40 4 8 56 0 Wyoming Wyoming \n", "3170 50 4 8 56 1 Wyoming Albany County \n", "3171 50 4 8 56 3 Wyoming Big Horn County \n", "3172 50 4 8 56 5 Wyoming Campbell County \n", "3173 50 4 8 56 7 Wyoming Carbon County \n", "3174 50 4 8 56 9 Wyoming Converse County \n", "3175 50 4 8 56 11 Wyoming Crook County \n", "3176 50 4 8 56 13 Wyoming Fremont County \n", "3177 50 4 8 56 15 Wyoming Goshen County \n", "3178 50 4 8 56 17 Wyoming Hot Springs County \n", "3179 50 4 8 56 19 Wyoming Johnson County \n", "3180 50 4 8 56 21 Wyoming Laramie County \n", "3181 50 4 8 56 23 Wyoming Lincoln County \n", "3182 50 4 8 56 25 Wyoming Natrona County \n", "3183 50 4 8 56 27 Wyoming Niobrara County \n", "3184 50 4 8 56 29 Wyoming Park County \n", "3185 50 4 8 56 31 Wyoming Platte County \n", "3186 50 4 8 56 33 Wyoming Sheridan County \n", "3187 50 4 8 56 35 Wyoming Sublette County \n", "3188 50 4 8 56 37 Wyoming Sweetwater County \n", "3189 50 4 8 56 39 Wyoming Teton County \n", "3190 50 4 8 56 41 Wyoming Uinta County \n", "3191 50 4 8 56 43 Wyoming Washakie County \n", "3192 50 4 8 56 45 Wyoming Weston County \n", "\n", " CENSUS2010POP ESTIMATESBASE2010 POPESTIMATE2010 ... \\\n", "0 4779736 4780127 4785161 ... \n", "1 54571 54571 54660 ... \n", "2 182265 182265 183193 ... \n", "3 27457 27457 27341 ... \n", "4 22915 22919 22861 ... \n", "5 57322 57322 57373 ... \n", "6 10914 10915 10887 ... \n", "7 20947 20946 20944 ... \n", "8 118572 118586 118437 ... \n", "9 34215 34170 34098 ... \n", "10 25989 25986 25976 ... \n", "11 43643 43631 43665 ... \n", "12 13859 13858 13841 ... \n", "13 25833 25840 25767 ... \n", "14 13932 13932 13880 ... \n", "15 14972 14972 14973 ... \n", "16 49948 49948 50177 ... \n", "17 54428 54428 54514 ... \n", "18 13228 13228 13208 ... \n", "19 11539 11758 11758 ... \n", "20 37765 37765 37796 ... \n", "21 13906 13906 13853 ... \n", "22 80406 80410 80473 ... \n", "23 50251 50251 50358 ... \n", "24 43820 43820 43803 ... \n", "25 71109 71115 71142 ... \n", "26 79303 79296 79465 ... \n", "27 38319 38319 38309 ... \n", "28 104430 104427 104442 ... \n", "29 17241 17241 17231 ... \n", "... ... ... ... ... \n", "3163 131887 131885 131967 ... \n", "3164 389891 389938 390076 ... \n", "3165 52410 52410 52422 ... \n", "3166 24496 24496 24506 ... \n", "3167 166994 166994 167059 ... \n", "3168 74749 74749 74807 ... \n", "3169 563626 563767 564516 ... \n", "3170 36299 36299 36428 ... \n", "3171 11668 11668 11672 ... \n", "3172 46133 46133 46244 ... \n", "3173 15885 15885 15837 ... \n", "3174 13833 13833 13826 ... \n", "3175 7083 7083 7114 ... \n", "3176 40123 40123 40222 ... \n", "3177 13249 13247 13408 ... \n", "3178 4812 4812 4813 ... \n", "3179 8569 8569 8581 ... \n", "3180 91738 91881 92271 ... \n", "3181 18106 18106 18091 ... \n", "3182 75450 75450 75472 ... \n", "3183 2484 2484 2492 ... \n", "3184 28205 28205 28259 ... \n", "3185 8667 8667 8678 ... \n", "3186 29116 29116 29146 ... \n", "3187 10247 10247 10244 ... \n", "3188 43806 43806 43593 ... \n", "3189 21294 21294 21297 ... \n", "3190 21118 21118 21102 ... \n", "3191 8533 8533 8545 ... \n", "3192 7208 7208 7181 ... \n", "\n", " RDOMESTICMIG2011 RDOMESTICMIG2012 RDOMESTICMIG2013 RDOMESTICMIG2014 \\\n", "0 0.002295 -0.193196 0.381066 0.582002 \n", "1 7.242091 -2.915927 -3.012349 2.265971 \n", "2 14.832960 17.647293 21.845705 19.243287 \n", "3 -4.728132 -2.500690 -7.056824 -3.904217 \n", "4 -5.527043 -5.068871 -6.201001 -0.177537 \n", "5 1.807375 -1.177622 -1.748766 -2.062535 \n", "6 -30.953709 -5.180127 -1.130263 14.354290 \n", "7 -14.032727 -11.684234 -5.655413 1.085428 \n", "8 -6.155670 -4.611706 -5.524649 -4.463211 \n", "9 -2.731639 3.849092 2.872721 -2.287222 \n", "10 6.339327 1.113180 5.488706 -0.076806 \n", "11 -1.372935 -2.653369 0.480044 0.456017 \n", "12 -15.455274 -0.737028 -8.766391 -1.274984 \n", "13 -6.194363 -17.667705 -0.318345 -8.686428 \n", "14 -10.744102 -13.345130 4.902871 5.702648 \n", "15 -3.673524 -5.151880 7.345821 3.654485 \n", "16 0.377640 7.675579 -13.146535 -3.602859 \n", "17 -0.073423 1.065051 1.762390 1.835688 \n", "18 -4.861559 -7.504690 -6.107224 -14.645416 \n", "19 -33.930581 -10.291443 -4.313831 -22.958017 \n", "20 6.696899 -4.612668 0.740271 3.697932 \n", "21 1.729792 3.950156 -1.864936 3.084648 \n", "22 -1.404233 -1.019628 4.071247 5.087142 \n", "23 -10.749798 -5.277150 -15.236079 -11.979785 \n", "24 -15.635599 -11.308243 -16.745678 -9.344789 \n", "25 0.294677 -9.302391 -1.748807 0.267830 \n", "26 3.235576 0.822717 1.760531 -1.507057 \n", "27 -3.449988 -3.855889 -4.822706 -1.189831 \n", "28 -1.015919 2.062637 -1.931884 -1.726932 \n", "29 -5.015601 -0.646640 -3.725937 0.296745 \n", "... ... ... ... ... \n", "3163 -0.794876 0.785279 -2.215465 1.601149 \n", "3164 -0.765799 2.128860 0.038132 0.760109 \n", "3165 3.111756 -2.241873 6.292687 -0.441031 \n", "3166 4.930022 -2.404973 -4.097017 -4.906711 \n", "3167 0.316712 2.889873 0.833819 -2.406192 \n", "3168 -4.081523 -5.019090 -6.901200 -5.596471 \n", "3169 -0.381530 9.636214 4.487115 -4.788275 \n", "3170 3.708956 2.637812 -3.544634 -3.334877 \n", "3171 4.868258 2.804930 16.815908 -8.026420 \n", "3172 -2.843479 15.601020 -5.895711 -8.550911 \n", "3173 -7.581980 -13.081441 3.178134 -2.970641 \n", "3174 -12.847499 15.493820 19.035533 -20.550587 \n", "3175 -1.544618 -4.202564 1.397819 6.378258 \n", "3176 2.747083 7.782673 -4.990688 -12.331633 \n", "3177 14.293649 3.961413 -8.079028 -7.017803 \n", "3178 3.322604 6.208609 3.095336 -6.017222 \n", "3179 4.995063 -4.058912 -0.812583 -10.715742 \n", "3180 -1.200428 15.547274 4.787847 -1.226133 \n", "3181 -9.802564 -11.566801 13.564556 6.125989 \n", "3182 7.189319 23.066162 24.322042 -0.958472 \n", "3183 -0.401849 0.806452 29.066295 -12.603387 \n", "3184 4.582951 8.057765 7.641997 -9.252437 \n", "3185 4.373094 5.392073 2.634593 6.055759 \n", "3186 0.958559 8.425487 4.546373 3.678069 \n", "3187 -23.741784 15.272374 -40.870074 -16.596273 \n", "3188 1.072643 16.243199 -5.339774 -14.252889 \n", "3189 -1.589565 0.972695 19.525929 14.143021 \n", "3190 -17.755986 -4.916350 -6.902954 -14.215862 \n", "3191 -11.637475 -0.827815 -2.013502 -17.781491 \n", "3192 -11.752361 -8.040059 12.372583 1.533635 \n", "\n", " RDOMESTICMIG2015 RNETMIG2011 RNETMIG2012 RNETMIG2013 RNETMIG2014 \\\n", "0 -0.467369 1.030015 0.826644 1.383282 1.724718 \n", "1 -2.530799 7.606016 -2.626146 -2.722002 2.592270 \n", "2 17.197872 15.844176 18.559627 22.727626 20.317142 \n", "3 -10.543299 -4.874741 -2.758113 -7.167664 -3.978583 \n", "4 0.177258 -5.088389 -4.363636 -5.403729 0.754533 \n", "5 -1.369970 1.859511 -0.848580 -1.402476 -1.577232 \n", "6 -16.167247 -29.001673 -2.825524 1.507017 17.243790 \n", "7 -6.529805 -13.936612 -11.586865 -5.557058 1.184103 \n", "8 -3.376322 -5.791579 -4.092677 -5.062836 -3.912834 \n", "9 1.349468 -1.821092 4.701181 3.781439 -1.290228 \n", "10 -3.239866 6.416167 1.420264 5.757384 0.230419 \n", "11 -2.253483 -0.823761 -2.447504 0.868651 0.957636 \n", "12 -5.291205 -15.528177 -0.737028 -8.766391 -1.274984 \n", "13 -5.613667 -6.077488 -17.509958 -0.159172 -8.486280 \n", "14 3.912450 -10.816697 -13.345130 4.977157 5.776708 \n", "15 -3.123961 -3.673524 -5.151880 7.345821 3.654485 \n", "16 2.214774 2.166460 11.513368 -10.438741 -0.767822 \n", "17 -0.110260 0.513964 1.469035 2.276420 2.533249 \n", "18 2.684140 -4.861559 -7.504690 -6.107224 -14.645416 \n", "19 -5.387581 -34.017138 -10.380162 -4.403703 -23.049483 \n", "20 -0.316945 6.881460 -4.559952 0.793147 3.750759 \n", "21 3.439504 2.666763 5.099293 -0.502098 4.734577 \n", "22 7.915406 -1.031427 -0.634159 4.542916 5.593387 \n", "23 -5.107706 -9.575283 -0.776637 -12.640155 -9.503292 \n", "24 -14.687232 -15.727573 -11.378047 -16.792849 -9.368689 \n", "25 0.028141 1.375159 -8.656001 -1.029539 1.198187 \n", "26 2.067820 3.674511 1.558176 2.306047 -0.951175 \n", "27 1.190902 -3.397716 -3.803428 -4.769999 -1.136950 \n", "28 -2.082234 -0.632554 2.446383 -1.518596 -1.234901 \n", "29 -2.797536 -5.132243 -0.705426 -3.785079 0.237396 \n", "... ... ... ... ... ... \n", "3163 -0.434498 -0.431504 1.162817 -1.763330 2.104796 \n", "3164 -0.719858 0.102448 3.180527 1.189727 2.077633 \n", "3165 -0.480617 3.359933 -2.011937 6.561277 -0.134227 \n", "3166 -4.397793 5.174486 -2.160399 -3.810226 -4.535615 \n", "3167 -4.557985 0.842573 3.502335 1.531624 -1.545153 \n", "3168 -3.958322 -3.733590 -4.562809 -6.442917 -5.040889 \n", "3169 -3.221091 0.289680 10.694870 5.440390 -3.727831 \n", "3170 -9.911169 6.736119 6.433032 0.719587 1.429233 \n", "3171 5.095861 4.868258 3.144921 17.236306 -7.608378 \n", "3172 10.916963 -2.649606 15.558684 -5.916543 -8.509402 \n", "3173 -23.300971 -7.392431 -12.636926 3.623073 -2.338590 \n", "3174 -0.070403 -12.774915 16.502720 20.093063 -19.358233 \n", "3175 18.629317 -0.982939 -3.642222 2.096729 7.071547 \n", "3176 -13.673610 3.093562 8.027411 -4.747240 -12.013555 \n", "3177 -11.899450 14.886132 4.841727 -6.903896 -5.761986 \n", "3178 -5.454164 5.191569 6.001656 2.888981 -6.224712 \n", "3179 0.933652 5.227392 -4.058912 -0.812583 -10.715742 \n", "3180 0.278940 -0.973320 17.914554 6.003143 -0.207819 \n", "3181 1.555544 -9.691801 -11.566801 13.619696 6.234414 \n", "3182 -0.061057 7.689674 23.749508 25.085233 -0.110593 \n", "3183 7.492114 -0.401849 0.806452 29.066295 -12.603387 \n", "3184 -2.878980 6.486639 11.127389 10.877797 -5.585731 \n", "3185 4.662270 4.373094 4.933173 2.176403 5.598720 \n", "3186 -3.298406 2.122524 9.342778 5.523001 4.781489 \n", "3187 -22.870900 -21.092907 16.828794 -39.211861 -14.409938 \n", "3188 -14.248864 1.255221 16.243199 -5.295460 -14.075283 \n", "3189 -0.564849 0.654527 2.408578 21.160658 16.308671 \n", "3190 -12.127022 -18.136812 -5.536861 -7.521840 -14.740608 \n", "3191 1.682288 -11.990126 -1.182592 -2.250385 -18.020168 \n", "3192 6.935294 -12.032179 -8.040059 12.372583 1.533635 \n", "\n", " RNETMIG2015 \n", "0 0.712594 \n", "1 -2.187333 \n", "2 18.293499 \n", "3 -10.543299 \n", "4 1.107861 \n", "5 -0.884411 \n", "6 -13.193961 \n", "7 -6.430868 \n", "8 -2.806406 \n", "9 2.346901 \n", "10 -2.931307 \n", "11 -1.752709 \n", "12 -5.291205 \n", "13 -5.411736 \n", "14 3.986270 \n", "15 -3.123961 \n", "16 5.350738 \n", "17 0.588052 \n", "18 2.684140 \n", "19 -5.387581 \n", "20 -0.264121 \n", "21 5.087600 \n", "22 8.417777 \n", "23 -1.998668 \n", "24 -14.711389 \n", "25 0.956790 \n", "26 2.757093 \n", "27 1.243830 \n", "28 -1.588308 \n", "29 -2.857058 \n", "... ... \n", "3163 0.059931 \n", "3164 0.593567 \n", "3165 -0.173022 \n", "3166 -4.024395 \n", "3167 -3.685304 \n", "3168 -3.414223 \n", "3169 -2.091573 \n", "3170 -5.166460 \n", "3171 5.513554 \n", "3172 10.978525 \n", "3173 -22.600668 \n", "3174 1.126443 \n", "3175 19.309219 \n", "3176 -13.352750 \n", "3177 -10.635133 \n", "3178 -5.663940 \n", "3179 0.933652 \n", "3180 1.673640 \n", "3181 1.662823 \n", "3182 0.793743 \n", "3183 7.492114 \n", "3184 0.856839 \n", "3185 4.207414 \n", "3186 -2.198937 \n", "3187 -20.664059 \n", "3188 -14.070195 \n", "3189 1.520747 \n", "3190 -12.606351 \n", "3191 1.441961 \n", "3192 6.935294 \n", "\n", "[3193 rows x 100 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "census_df = pd.read_csv('census.csv')\n", "census_df" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Texas\n" ] } ], "source": [ "def answer_five():\n", " maximum_country = census_df.groupby([\"STNAME\"]).size().max()\n", " g2 = census_df.groupby([\"STNAME\"]).size()\n", " df3 = g2.reset_index()\n", " name = df3[df3[0] == maximum_country]\n", " name = name.set_index('STNAME').index.values[0]\n", "\n", " return name\n", "\n", "print(answer_five())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 6\n", "Only looking at the three most populous counties for each state, what are the three most populous states (in order of highest population to lowest population)? Use `CENSUS2010POP`.\n", "\n", "*This function should return a list of string values.*" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Los Angeles County', 'Cook County', 'Harris County']\n" ] } ], "source": [ "def answer_six():\n", " df = census_df.copy()\n", " df=df[df['SUMLEV'] == 50]\n", " df = df[['CTYNAME', 'CENSUS2010POP']]\n", " df = df.set_index('CTYNAME')\n", " idx = df.sum(axis=1).sort_values(ascending=False).head(3).index\n", " # df1 = df.ix[idx]\n", " df1 = list(idx.values)\n", "\n", " return idx\n", "\n", "print(answer_six())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 7\n", "Which county has had the largest absolute change in population within the period 2010-2015? (Hint: population values are stored in columns POPESTIMATE2010 through POPESTIMATE2015, you need to consider all six columns.)\n", "\n", "e.g. If County Population in the 5 year period is 100, 120, 80, 105, 100, 130, then its largest change in the period would be |130-80| = 50.\n", "\n", "*This function should return a single string value.*" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Harris County\n" ] } ], "source": [ "def answer_seven():\n", " df = census_df.copy()\n", " df=df[df['SUMLEV'] == 50]\n", " df = df[['STNAME','CTYNAME','POPESTIMATE2015','POPESTIMATE2014','POPESTIMATE2013','POPESTIMATE2012','POPESTIMATE2011','POPESTIMATE2010']]\n", " df = df.set_index(['STNAME', 'CTYNAME'])\n", " df1 = df.apply(lambda x: x.max() - x.min(),axis=1)\n", "\n", " df2 = df1.reset_index()\n", " df2 = df2.sort_values([0],ascending=[0])\n", " df3 = df2.set_index('CTYNAME').index.values\n", "\n", " return df3[0]\n", "\n", "print(answer_seven())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 8\n", "In this datafile, the United States is broken up into four regions using the \"REGION\" column. \n", "\n", "Create a query that finds the counties that belong to regions 1 or 2, whose name starts with 'Washington', and whose POPESTIMATE2015 was greater than their POPESTIMATE 2014.\n", "\n", "*This function should return a 5x2 DataFrame with the columns = ['STNAME', 'CTYNAME'] and the same index ID as the census_df (sorted ascending by index).*" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " STNAME CTYNAME\n", "896 Iowa Washington County\n", "1419 Minnesota Washington County\n", "2345 Pennsylvania Washington County\n", "2355 Rhode Island Washington County\n", "3163 Wisconsin Washington County\n" ] } ], "source": [ "def answer_eight():\n", " df = census_df.copy()\n", " df = df[(df['REGION'] == 1) | (df['REGION'] == 2)]\n", " df = df[df['CTYNAME'] == 'Washington County']\n", " df = df[df['POPESTIMATE2015'] > df['POPESTIMATE2014']]\n", "\n", " return df[['STNAME','CTYNAME']]\n", "\n", "print(answer_eight())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "" ] } ], "metadata": { "anaconda-cloud": {}, "coursera": { "course_slug": "python-data-analysis", "graded_item_id": "tHmgx", "launcher_item_id": "Um6Bz", "part_id": "OQsnr" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
letsgoexploring/economicData
us-housing-prices/python/housing_price_index_data.ipynb
1
3359
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import fredpy as fp\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Import data from Shiller's website, using only date and housing price index columns\n", "data = pd.read_excel('http://www.econ.yale.edu/~shiller/data/Fig3-1.xls',sheet_name='Data',skiprows=6,index_col=0)['From fig2.1Revised2011.xls']\n", "\n", "# Drop missing data from end\n", "data = data.dropna()\n", "\n", "# Parse date numbers into strings and set index\n", "data.index = pd.DatetimeIndex([str(int(1+round((d-np.floor(d))*12))).zfill(2)+'-01-'+str(int(np.floor(d))) for d in data.index])\n", "\n", "# Start data in first ful year available.\n", "data = data.loc['1954-01-01':]\n", "\n", "# Reset index to monthly frequency\n", "data.index = pd.date_range(start=data.index[0], end=data.index[-1], freq='MS')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Import CPI data\n", "cpi = fp.series('CPIAUCSL')\n", "\n", "# Import PCE deflator data\n", "pce = fp.series('PCEPI')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Create DataFrame with monthly data\n", "df = pd.concat([data,cpi.data,pce.data],axis=1).dropna()\n", "cpi_col = 'cpi'+' ('+cpi.units.split(' ')[1]+')'\n", "pce_col = 'pce'+' ('+pce.units.split(' ')[1]+')'\n", "df.columns = ['hpi',cpi_col,pce_col]\n", "\n", "df['real hpi (cpi)'] = df.hpi/df[cpi_col]*100\n", "df['real hpi (pce)'] = df.hpi/df[pce_col]*100\n", " \n", "# Export data\n", "df.to_csv('hpi_monthly.csv',index=False)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Plot real house price index, with pce delfated series normalized to CPI base year\n", "fig = plt.figure(figsize=(12,4))\n", "ax = fig.add_subplot(1,1,1)\n", "(df['real hpi (pce)']*pce.data.loc['1983-08-01']/100).plot(lw=2)\n", "df['real hpi (cpi)'].plot(lw=2)\n", "\n", "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", "\n", "ax.set_title('Both real HPI measures w common base: '+cpi.units.split(' ')[1])\n", "ax.grid(ls=':')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Create DataFrame with quarterly data\n", "df = df.asfreq('QS')\n", " \n", "# Export data\n", "df.to_csv('hpi_quarterly.csv',index=False)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
unnati-xyz/ensemble-package
.ipynb_checkpoints/MNIST - Images-checkpoint.ipynb
1
3233
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] } ], "source": [ "import numpy as np\n", "from keras.datasets import mnist\n", "from keras.models import Sequential\n", "from keras.layers import Dense, Dropout, Activation, Flatten\n", "from keras.layers import Convolution2D, MaxPooling2D\n", "from keras.utils import np_utils\n", "from PIL import Image\n", "from numpy import array\n", "import math" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Loading the training and testing data\n", "(X_train, y_train), (X_test, y_test) = mnist.load_data()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "img = Image.fromarray(X_train[0])\n", "img.show()\n", "print(y_train[0])" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "no_of_examples = 71" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_im = Image.new('RGB', (280,28))\n", "concat_img = Image.new('RGB', (280,int(math.ceil(no_of_examples/10)*28)))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x_offset = 0\n", "concat_x_offset = 0 " ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in range(0,no_of_examples):\n", " img = Image.fromarray(X_train[i])\n", " new_im.paste(img, (x_offset,0))\n", " x_offset += img.size[0]\n", " if ((i%10==0 and i!=0) or (i == no_of_examples-1)):\n", " #new_im.show()\n", " #concat_img = new_im\n", " concat_img.paste(new_im,(0,concat_x_offset))\n", " concat_x_offset += new_im.size[1]\n", " new_im = Image.new('RGB', (280,28))\n", " x_offset = 0" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "concat_img.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
INRA/ODAM
Jupyter/PyODAM_demo.ipynb
1
48712
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### import the PyODAM module" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from PyODAM import Odam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Using the API" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>index</th>\n", " <th>LinkID</th>\n", " <th>Subset</th>\n", " <th>Identifier</th>\n", " <th>Description</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>plants</td>\n", " <td>PlantID</td>\n", " <td>Plant features</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>samples</td>\n", " <td>SampleID</td>\n", " <td>Sample features</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>2</td>\n", " <td>aliquots</td>\n", " <td>AliquotID</td>\n", " <td>Aliquots features</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>3</td>\n", " <td>cellwall_metabo</td>\n", " <td>AliquotID</td>\n", " <td>Cell wall Compound quantifications</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>3</td>\n", " <td>cellwall_metaboFW</td>\n", " <td>AliquotID</td>\n", " <td>Cell Wall Compound quantifications (FW)</td>\n", " </tr>\n", " <tr>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>3</td>\n", " <td>activome</td>\n", " <td>AliquotID</td>\n", " <td>Activome Features</td>\n", " </tr>\n", " <tr>\n", " <td>6</td>\n", " <td>7</td>\n", " <td>2</td>\n", " <td>pools</td>\n", " <td>PoolID</td>\n", " <td>Pools of remaining pools</td>\n", " </tr>\n", " <tr>\n", " <td>7</td>\n", " <td>8</td>\n", " <td>7</td>\n", " <td>qMS_metabo</td>\n", " <td>PoolID</td>\n", " <td>MS Compounds quantification</td>\n", " </tr>\n", " <tr>\n", " <td>8</td>\n", " <td>9</td>\n", " <td>7</td>\n", " <td>qNMR_metabo</td>\n", " <td>PoolID</td>\n", " <td>NMR Compounds quantification</td>\n", " </tr>\n", " <tr>\n", " <td>9</td>\n", " <td>10</td>\n", " <td>3</td>\n", " <td>plato_hexosesP</td>\n", " <td>AliquotID</td>\n", " <td>Hexoses Phosphate</td>\n", " </tr>\n", " <tr>\n", " <td>10</td>\n", " <td>11</td>\n", " <td>3</td>\n", " <td>lipids_AG</td>\n", " <td>AliquotID</td>\n", " <td>Lipids AG</td>\n", " </tr>\n", " <tr>\n", " <td>11</td>\n", " <td>12</td>\n", " <td>3</td>\n", " <td>AminoAcid</td>\n", " <td>AliquotID</td>\n", " <td>Amino Acids</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index LinkID Subset Identifier \\\n", "0 1 0 plants PlantID \n", "1 2 1 samples SampleID \n", "2 3 2 aliquots AliquotID \n", "3 4 3 cellwall_metabo AliquotID \n", "4 5 3 cellwall_metaboFW AliquotID \n", "5 6 3 activome AliquotID \n", "6 7 2 pools PoolID \n", "7 8 7 qMS_metabo PoolID \n", "8 9 7 qNMR_metabo PoolID \n", "9 10 3 plato_hexosesP AliquotID \n", "10 11 3 lipids_AG AliquotID \n", "11 12 3 AminoAcid AliquotID \n", "\n", " Description \n", "0 Plant features \n", "1 Sample features \n", "2 Aliquots features \n", "3 Cell wall Compound quantifications \n", "4 Cell Wall Compound quantifications (FW) \n", "5 Activome Features \n", "6 Pools of remaining pools \n", "7 MS Compounds quantification \n", "8 NMR Compounds quantification \n", "9 Hexoses Phosphate \n", "10 Lipids AG \n", "11 Amino Acids " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the subset list of a dataset\n", "po = Odam('https://pmb-bordeaux.fr','frim1')\n", "meta = po.getDataFromODAM()\n", "meta[['index', 'LinkID','Subset','Identifier', 'Description']]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['index', 'PlantID', 'Rank', 'PlantNum', 'Treatment', 'SampleID',\n", " 'Truss', 'DevStage', 'FruitAge', 'HarvestDate', 'HarvestHour',\n", " 'FruitPosition', 'FruitDiameter', 'FruitHeight', 'FruitFW', 'FruitDW',\n", " 'DW', 'AliquotID', 'PGM', 'cFBPase', 'PyrK', 'CitS', 'PFP', 'Aconitase',\n", " 'PFK', 'FruK', 'pFBPase', 'GluK', 'NAD_ISODH', 'Enolase', 'NADP_ISODH',\n", " 'PEPC', 'Aldolase', 'Succ_CoA_ligase', 'NAD_MalDH', 'AlaAT', 'Fumarase',\n", " 'AspAT', 'NADP_GluDH', 'NAD_GAPDH', 'NADP_GAPDH', 'NAD_GluDH', 'TPI',\n", " 'PGK', 'Neutral_Inv', 'Acid_Inv', 'G6PDH', 'UGPase', 'SuSy', 'NAD_ME',\n", " 'ShiDH', 'NADP_ME', 'PGI', 'StarchS', 'AGPase', 'SPS', 'PoolID',\n", " 'glucose', 'saccharose', 'fructose', 'galactose', 'mannose', 'rhamnose',\n", " 'acetate', 'chlorogenate', 'citrate', 'fumarate', 'galacturonate',\n", " 'malate', 'quinate', 'alanine', 'asparagine', 'aspartate', 'GABA',\n", " 'glutamine', 'glutamate', 'isoleucine', 'phenylalanine', 'tryptophane',\n", " 'tyrosine', 'valine', 'pyroglutamate', 'trigonelline', 'choline',\n", " 'inositol'],\n", " dtype='object')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get all values of a merged data subsets ( both activome & qNMR_metabofor) the specific 'sample' entry equal to 365\n", "subset = 'activome,qNMR_metabo'\n", "df = po.getSubsetFromODAM(subset,'sample/365?limit=10')\n", "data = df['data']\n", "\n", "# View all merged subset columns\n", "data.columns" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>index</th>\n", " <th>PlantID</th>\n", " <th>Rank</th>\n", " <th>PlantNum</th>\n", " <th>Treatment</th>\n", " <th>SampleID</th>\n", " <th>Truss</th>\n", " <th>DevStage</th>\n", " <th>FruitAge</th>\n", " <th>HarvestDate</th>\n", " <th>...</th>\n", " <th>glutamate</th>\n", " <th>isoleucine</th>\n", " <th>phenylalanine</th>\n", " <th>tryptophane</th>\n", " <th>tyrosine</th>\n", " <th>valine</th>\n", " <th>pyroglutamate</th>\n", " <th>trigonelline</th>\n", " <th>choline</th>\n", " <th>inositol</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>A17</td>\n", " <td>A</td>\n", " <td>17</td>\n", " <td>Control</td>\n", " <td>365</td>\n", " <td>T6</td>\n", " <td>FR.02</td>\n", " <td>47DPA</td>\n", " <td>09/02/2010</td>\n", " <td>...</td>\n", " <td>45.478818</td>\n", " <td>2.750447</td>\n", " <td>5.153496</td>\n", " <td>0.345287</td>\n", " <td>1.624778</td>\n", " <td>1.372135</td>\n", " <td>24.046419</td>\n", " <td>1.078428</td>\n", " <td>5.936098</td>\n", " <td>65.898711</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>A8</td>\n", " <td>A</td>\n", " <td>8</td>\n", " <td>Control</td>\n", " <td>365</td>\n", " <td>T6</td>\n", " <td>FR.02</td>\n", " <td>47DPA</td>\n", " <td>09/02/2010</td>\n", " <td>...</td>\n", " <td>45.478818</td>\n", " <td>2.750447</td>\n", " <td>5.153496</td>\n", " <td>0.345287</td>\n", " <td>1.624778</td>\n", " <td>1.372135</td>\n", " <td>24.046419</td>\n", " <td>1.078428</td>\n", " <td>5.936098</td>\n", " <td>65.898711</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>D3</td>\n", " <td>D</td>\n", " <td>210</td>\n", " <td>Control</td>\n", " <td>365</td>\n", " <td>T6</td>\n", " <td>FR.02</td>\n", " <td>47DPA</td>\n", " <td>09/02/2010</td>\n", " <td>...</td>\n", " <td>45.478818</td>\n", " <td>2.750447</td>\n", " <td>5.153496</td>\n", " <td>0.345287</td>\n", " <td>1.624778</td>\n", " <td>1.372135</td>\n", " <td>24.046419</td>\n", " <td>1.078428</td>\n", " <td>5.936098</td>\n", " <td>65.898711</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>E35</td>\n", " <td>E</td>\n", " <td>311</td>\n", " <td>Control</td>\n", " <td>365</td>\n", " <td>T6</td>\n", " <td>FR.02</td>\n", " <td>47DPA</td>\n", " <td>09/02/2010</td>\n", " <td>...</td>\n", " <td>45.478818</td>\n", " <td>2.750447</td>\n", " <td>5.153496</td>\n", " <td>0.345287</td>\n", " <td>1.624778</td>\n", " <td>1.372135</td>\n", " <td>24.046419</td>\n", " <td>1.078428</td>\n", " <td>5.936098</td>\n", " <td>65.898711</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>H11</td>\n", " <td>H</td>\n", " <td>356</td>\n", " <td>Control</td>\n", " <td>365</td>\n", " <td>T6</td>\n", " <td>FR.02</td>\n", " <td>47DPA</td>\n", " <td>09/02/2010</td>\n", " <td>...</td>\n", " <td>45.478818</td>\n", " <td>2.750447</td>\n", " <td>5.153496</td>\n", " <td>0.345287</td>\n", " <td>1.624778</td>\n", " <td>1.372135</td>\n", " <td>24.046419</td>\n", " <td>1.078428</td>\n", " <td>5.936098</td>\n", " <td>65.898711</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 85 columns</p>\n", "</div>" ], "text/plain": [ " index PlantID Rank PlantNum Treatment SampleID Truss DevStage FruitAge \\\n", "0 1 A17 A 17 Control 365 T6 FR.02 47DPA \n", "1 2 A8 A 8 Control 365 T6 FR.02 47DPA \n", "2 3 D3 D 210 Control 365 T6 FR.02 47DPA \n", "3 4 E35 E 311 Control 365 T6 FR.02 47DPA \n", "4 5 H11 H 356 Control 365 T6 FR.02 47DPA \n", "\n", " HarvestDate ... glutamate isoleucine phenylalanine tryptophane \\\n", "0 09/02/2010 ... 45.478818 2.750447 5.153496 0.345287 \n", "1 09/02/2010 ... 45.478818 2.750447 5.153496 0.345287 \n", "2 09/02/2010 ... 45.478818 2.750447 5.153496 0.345287 \n", "3 09/02/2010 ... 45.478818 2.750447 5.153496 0.345287 \n", "4 09/02/2010 ... 45.478818 2.750447 5.153496 0.345287 \n", "\n", " tyrosine valine pyroglutamate trigonelline choline inositol \n", "0 1.624778 1.372135 24.046419 1.078428 5.936098 65.898711 \n", "1 1.624778 1.372135 24.046419 1.078428 5.936098 65.898711 \n", "2 1.624778 1.372135 24.046419 1.078428 5.936098 65.898711 \n", "3 1.624778 1.372135 24.046419 1.078428 5.936098 65.898711 \n", "4 1.624778 1.372135 24.046419 1.078428 5.936098 65.898711 \n", "\n", "[5 rows x 85 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert both data and time in MS Excel format into String\n", "data.HarvestDate = po.convertDateToStr(data.HarvestDate)\n", "data.HarvestHour = po.convertTimeToStr(data.HarvestHour)\n", "data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>index</th>\n", " <th>Subset</th>\n", " <th>Attribute</th>\n", " <th>Description</th>\n", " <th>Type</th>\n", " <th>CV_Term_ID</th>\n", " <th>CV_Term_Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>plants</td>\n", " <td>Treatment</td>\n", " <td>Treatment applied on plants</td>\n", " <td>string</td>\n", " <td>http://www.ebi.ac.uk/efo/EFO_0000469</td>\n", " <td>environmental factor</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>samples</td>\n", " <td>DevStage</td>\n", " <td>fruit development stage</td>\n", " <td>string</td>\n", " <td>http://purl.obolibrary.org/obo/PO_0001002</td>\n", " <td>fruit development stage</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>samples</td>\n", " <td>FruitAge</td>\n", " <td>fruit age (dpa)</td>\n", " <td>string</td>\n", " <td>http://purl.obolibrary.org/obo/PO_0001002</td>\n", " <td>fruit development stage</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index Subset Attribute Description Type \\\n", "0 1 plants Treatment Treatment applied on plants string \n", "1 2 samples DevStage fruit development stage string \n", "2 3 samples FruitAge fruit age (dpa) string \n", "\n", " CV_Term_ID CV_Term_Name \n", "0 http://www.ebi.ac.uk/efo/EFO_0000469 environmental factor \n", "1 http://purl.obolibrary.org/obo/PO_0001002 fruit development stage \n", "2 http://purl.obolibrary.org/obo/PO_0001002 fruit development stage " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display the variable list within the 'factor' category of a merged data subset\n", "df['factor']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>index</th>\n", " <th>Subset</th>\n", " <th>Attribute</th>\n", " <th>Description</th>\n", " <th>Type</th>\n", " <th>CV_Term_ID</th>\n", " <th>CV_Term_Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>plants</td>\n", " <td>PlantID</td>\n", " <td>Plant identifier</td>\n", " <td>string</td>\n", " <td>http://edamontology.org/data_0842</td>\n", " <td>identifier</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>samples</td>\n", " <td>SampleID</td>\n", " <td>Pool of several harvests</td>\n", " <td>numeric</td>\n", " <td>http://edamontology.org/data_0842</td>\n", " <td>identifier</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>aliquots</td>\n", " <td>AliquotID</td>\n", " <td>Aliquot Identifier</td>\n", " <td>numeric</td>\n", " <td>http://edamontology.org/data_0842</td>\n", " <td>identifier</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>activome</td>\n", " <td>AliquotID</td>\n", " <td>Aliquot Identifier</td>\n", " <td>numeric</td>\n", " <td>http://edamontology.org/data_0842</td>\n", " <td>identifier</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>pools</td>\n", " <td>PoolID</td>\n", " <td>Pool of several samples</td>\n", " <td>string</td>\n", " <td>http://edamontology.org/data_0842</td>\n", " <td>identifier</td>\n", " </tr>\n", " <tr>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>qNMR_metabo</td>\n", " <td>PoolID</td>\n", " <td>Pool of several samples</td>\n", " <td>string</td>\n", " <td>http://edamontology.org/data_0842</td>\n", " <td>identifier</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index Subset Attribute Description Type \\\n", "0 1 plants PlantID Plant identifier string \n", "1 2 samples SampleID Pool of several harvests numeric \n", "2 3 aliquots AliquotID Aliquot Identifier numeric \n", "3 4 activome AliquotID Aliquot Identifier numeric \n", "4 5 pools PoolID Pool of several samples string \n", "5 6 qNMR_metabo PoolID Pool of several samples string \n", "\n", " CV_Term_ID CV_Term_Name \n", "0 http://edamontology.org/data_0842 identifier \n", "1 http://edamontology.org/data_0842 identifier \n", "2 http://edamontology.org/data_0842 identifier \n", "3 http://edamontology.org/data_0842 identifier \n", "4 http://edamontology.org/data_0842 identifier \n", "5 http://edamontology.org/data_0842 identifier " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display the variable list within the 'identifier' category of a merged data subset\n", "df['identifier']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>index</th>\n", " <th>Subset</th>\n", " <th>Attribute</th>\n", " <th>Description</th>\n", " <th>Type</th>\n", " <th>CV_Term_ID</th>\n", " <th>CV_Term_Name</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>samples</td>\n", " <td>FruitDiameter</td>\n", " <td>Fruit diameter (mm)</td>\n", " <td>numeric</td>\n", " <td>http://aims.fao.org/aos/agrovoc/c_16072</td>\n", " <td>Diameter</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>2</td>\n", " <td>samples</td>\n", " <td>FruitHeight</td>\n", " <td>Fruit height (mm)</td>\n", " <td>numeric</td>\n", " <td>http://aims.fao.org/aos/agrovoc/c_3536</td>\n", " <td>Height</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>3</td>\n", " <td>samples</td>\n", " <td>FruitFW</td>\n", " <td>Fruit Fresh Weight(g)</td>\n", " <td>numeric</td>\n", " <td>http://aims.fao.org/aos/agrovoc/c_8349</td>\n", " <td>Weight</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>4</td>\n", " <td>samples</td>\n", " <td>FruitDW</td>\n", " <td>Fruit Dry Weight(g)</td>\n", " <td>numeric</td>\n", " <td>http://aims.fao.org/aos/agrovoc/c_8349</td>\n", " <td>Weight</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>5</td>\n", " <td>samples</td>\n", " <td>DW</td>\n", " <td>Percentage of dry matter (% DW), measured afte...</td>\n", " <td>numeric</td>\n", " <td></td>\n", " <td></td>\n", " </tr>\n", " <tr>\n", " <td>5</td>\n", " <td>6</td>\n", " <td>activome</td>\n", " <td>PGM</td>\n", " <td>Phosphoglucomutase (nmol/gFW/min)</td>\n", " <td>numeric</td>\n", " <td>http://purl.obolibrary.org/obo/GO_0004614</td>\n", " <td>EC 5.4.2.2 Phosphoglucomutase</td>\n", " </tr>\n", " <tr>\n", " <td>6</td>\n", " <td>7</td>\n", " <td>activome</td>\n", " <td>cFBPase</td>\n", " <td>cytosolic Fru-1,6-bisphosphatase (nmol/gFW/min)</td>\n", " <td>numeric</td>\n", " <td>http://purl.obolibrary.org/obo/GO_0042132</td>\n", " <td>EC 3.1.3.11 fructose-bisphosphatase</td>\n", " </tr>\n", " <tr>\n", " <td>7</td>\n", " <td>8</td>\n", " <td>activome</td>\n", " <td>PyrK</td>\n", " <td>Pyruvate Kinase (nmol/gFW/min)</td>\n", " <td>numeric</td>\n", " <td>http://purl.obolibrary.org/obo/GO_0004743</td>\n", " <td>EC 2.7.1.40 pyruvate kinase</td>\n", " </tr>\n", " <tr>\n", " <td>8</td>\n", " <td>9</td>\n", " <td>activome</td>\n", " <td>CitS</td>\n", " <td>Citrate Synthase Total (nmol/gFW/min)</td>\n", " <td>numeric</td>\n", " <td>http://purl.obolibrary.org/obo/GO_0036440</td>\n", " <td>EC 2.3.3.1 citrate synthase</td>\n", " </tr>\n", " <tr>\n", " <td>9</td>\n", " <td>10</td>\n", " <td>activome</td>\n", " <td>PFP</td>\n", " <td>6-phosphofructokinase (nmol/gFW/min)</td>\n", " <td>numeric</td>\n", " <td>http://purl.obolibrary.org/obo/GO_0047334</td>\n", " <td>EC 2.7.1.90 6-phosphofructokinase</td>\n", " </tr>\n", " <tr>\n", " <td>10</td>\n", " <td>11</td>\n", " <td>activome</td>\n", " <td>Aconitase</td>\n", " <td>Aconitase (nmol/gFW/min)</td>\n", " <td>numeric</td>\n", " <td>http://purl.obolibrary.org/obo/GO_000394</td>\n", " <td>EC 4.2.1.3 Aconitase</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " index Subset Attribute \\\n", "0 1 samples FruitDiameter \n", "1 2 samples FruitHeight \n", "2 3 samples FruitFW \n", "3 4 samples FruitDW \n", "4 5 samples DW \n", "5 6 activome PGM \n", "6 7 activome cFBPase \n", "7 8 activome PyrK \n", "8 9 activome CitS \n", "9 10 activome PFP \n", "10 11 activome Aconitase \n", "\n", " Description Type \\\n", "0 Fruit diameter (mm) numeric \n", "1 Fruit height (mm) numeric \n", "2 Fruit Fresh Weight(g) numeric \n", "3 Fruit Dry Weight(g) numeric \n", "4 Percentage of dry matter (% DW), measured afte... numeric \n", "5 Phosphoglucomutase (nmol/gFW/min) numeric \n", "6 cytosolic Fru-1,6-bisphosphatase (nmol/gFW/min) numeric \n", "7 Pyruvate Kinase (nmol/gFW/min) numeric \n", "8 Citrate Synthase Total (nmol/gFW/min) numeric \n", "9 6-phosphofructokinase (nmol/gFW/min) numeric \n", "10 Aconitase (nmol/gFW/min) numeric \n", "\n", " CV_Term_ID \\\n", "0 http://aims.fao.org/aos/agrovoc/c_16072 \n", "1 http://aims.fao.org/aos/agrovoc/c_3536 \n", "2 http://aims.fao.org/aos/agrovoc/c_8349 \n", "3 http://aims.fao.org/aos/agrovoc/c_8349 \n", "4 \n", "5 http://purl.obolibrary.org/obo/GO_0004614 \n", "6 http://purl.obolibrary.org/obo/GO_0042132 \n", "7 http://purl.obolibrary.org/obo/GO_0004743 \n", "8 http://purl.obolibrary.org/obo/GO_0036440 \n", "9 http://purl.obolibrary.org/obo/GO_0047334 \n", "10 http://purl.obolibrary.org/obo/GO_000394 \n", "\n", " CV_Term_Name \n", "0 Diameter \n", "1 Height \n", "2 Weight \n", "3 Weight \n", "4 \n", "5 EC 5.4.2.2 Phosphoglucomutase \n", "6 EC 3.1.3.11 fructose-bisphosphatase \n", "7 EC 2.7.1.40 pyruvate kinase \n", "8 EC 2.3.3.1 citrate synthase \n", "9 EC 2.7.1.90 6-phosphofructokinase \n", "10 EC 4.2.1.3 Aconitase " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display the variable list within the 'quantitative' category of a merged data subset\n", "quantitative = df['quantitative']\n", "quantitative.loc[ 0:10, ]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>PGM</th>\n", " <th>cFBPase</th>\n", " <th>PyrK</th>\n", " <th>CitS</th>\n", " <th>PFP</th>\n", " <th>Aconitase</th>\n", " <th>PFK</th>\n", " <th>FruK</th>\n", " <th>pFBPase</th>\n", " <th>GluK</th>\n", " <th>...</th>\n", " <th>G6PDH</th>\n", " <th>UGPase</th>\n", " <th>SuSy</th>\n", " <th>NAD_ME</th>\n", " <th>ShiDH</th>\n", " <th>NADP_ME</th>\n", " <th>PGI</th>\n", " <th>StarchS</th>\n", " <th>AGPase</th>\n", " <th>SPS</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>NA</td>\n", " <td>10.92</td>\n", " <td>304.68</td>\n", " <td>6.12</td>\n", " <td>576.56</td>\n", " <td>145.05</td>\n", " <td>73.97</td>\n", " <td>73.88</td>\n", " <td>44.38</td>\n", " <td>26.46</td>\n", " <td>...</td>\n", " <td>109.01</td>\n", " <td>3233.62</td>\n", " <td>NA</td>\n", " <td>535.3</td>\n", " <td>67.44</td>\n", " <td>219.78</td>\n", " <td>384.85</td>\n", " <td>77.01</td>\n", " <td>80.18</td>\n", " <td>64.33</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>NA</td>\n", " <td>10.92</td>\n", " <td>304.68</td>\n", " <td>6.12</td>\n", " <td>576.56</td>\n", " <td>145.05</td>\n", " <td>73.97</td>\n", " <td>73.88</td>\n", " <td>44.38</td>\n", " <td>26.46</td>\n", " <td>...</td>\n", " <td>109.01</td>\n", " <td>3233.62</td>\n", " <td>NA</td>\n", " <td>535.3</td>\n", " <td>67.44</td>\n", " <td>219.78</td>\n", " <td>384.85</td>\n", " <td>77.01</td>\n", " <td>80.18</td>\n", " <td>64.33</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>NA</td>\n", " <td>10.92</td>\n", " <td>304.68</td>\n", " <td>6.12</td>\n", " <td>576.56</td>\n", " <td>145.05</td>\n", " <td>73.97</td>\n", " <td>73.88</td>\n", " <td>44.38</td>\n", " <td>26.46</td>\n", " <td>...</td>\n", " <td>109.01</td>\n", " <td>3233.62</td>\n", " <td>NA</td>\n", " <td>535.3</td>\n", " <td>67.44</td>\n", " <td>219.78</td>\n", " <td>384.85</td>\n", " <td>77.01</td>\n", " <td>80.18</td>\n", " <td>64.33</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>NA</td>\n", " <td>10.92</td>\n", " <td>304.68</td>\n", " <td>6.12</td>\n", " <td>576.56</td>\n", " <td>145.05</td>\n", " <td>73.97</td>\n", " <td>73.88</td>\n", " <td>44.38</td>\n", " <td>26.46</td>\n", " <td>...</td>\n", " <td>109.01</td>\n", " <td>3233.62</td>\n", " <td>NA</td>\n", " <td>535.3</td>\n", " <td>67.44</td>\n", " <td>219.78</td>\n", " <td>384.85</td>\n", " <td>77.01</td>\n", " <td>80.18</td>\n", " <td>64.33</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>NA</td>\n", " <td>10.92</td>\n", " <td>304.68</td>\n", " <td>6.12</td>\n", " <td>576.56</td>\n", " <td>145.05</td>\n", " <td>73.97</td>\n", " <td>73.88</td>\n", " <td>44.38</td>\n", " <td>26.46</td>\n", " <td>...</td>\n", " <td>109.01</td>\n", " <td>3233.62</td>\n", " <td>NA</td>\n", " <td>535.3</td>\n", " <td>67.44</td>\n", " <td>219.78</td>\n", " <td>384.85</td>\n", " <td>77.01</td>\n", " <td>80.18</td>\n", " <td>64.33</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 38 columns</p>\n", "</div>" ], "text/plain": [ " PGM cFBPase PyrK CitS PFP Aconitase PFK FruK pFBPase GluK \\\n", "0 NA 10.92 304.68 6.12 576.56 145.05 73.97 73.88 44.38 26.46 \n", "1 NA 10.92 304.68 6.12 576.56 145.05 73.97 73.88 44.38 26.46 \n", "2 NA 10.92 304.68 6.12 576.56 145.05 73.97 73.88 44.38 26.46 \n", "3 NA 10.92 304.68 6.12 576.56 145.05 73.97 73.88 44.38 26.46 \n", "4 NA 10.92 304.68 6.12 576.56 145.05 73.97 73.88 44.38 26.46 \n", "\n", " ... G6PDH UGPase SuSy NAD_ME ShiDH NADP_ME PGI StarchS AGPase \\\n", "0 ... 109.01 3233.62 NA 535.3 67.44 219.78 384.85 77.01 80.18 \n", "1 ... 109.01 3233.62 NA 535.3 67.44 219.78 384.85 77.01 80.18 \n", "2 ... 109.01 3233.62 NA 535.3 67.44 219.78 384.85 77.01 80.18 \n", "3 ... 109.01 3233.62 NA 535.3 67.44 219.78 384.85 77.01 80.18 \n", "4 ... 109.01 3233.62 NA 535.3 67.44 219.78 384.85 77.01 80.18 \n", "\n", " SPS \n", "0 64.33 \n", "1 64.33 \n", "2 64.33 \n", "3 64.33 \n", "4 64.33 \n", "\n", "[5 rows x 38 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Select the variables from the merged data belongings to the 'activome' data subset\n", "data[quantitative[quantitative.Subset=='activome']['Attribute']]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style scoped>\n", " .dataframe tbody tr th:only-of-type {\n", " vertical-align: middle;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: right;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>glucose</th>\n", " <th>saccharose</th>\n", " <th>fructose</th>\n", " <th>galactose</th>\n", " <th>mannose</th>\n", " <th>rhamnose</th>\n", " <th>acetate</th>\n", " <th>chlorogenate</th>\n", " <th>citrate</th>\n", " <th>fumarate</th>\n", " <th>...</th>\n", " <th>glutamate</th>\n", " <th>isoleucine</th>\n", " <th>phenylalanine</th>\n", " <th>tryptophane</th>\n", " <th>tyrosine</th>\n", " <th>valine</th>\n", " <th>pyroglutamate</th>\n", " <th>trigonelline</th>\n", " <th>choline</th>\n", " <th>inositol</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>756.688509</td>\n", " <td>33.907515</td>\n", " <td>975.485366</td>\n", " <td>1.544911</td>\n", " <td>4.150709</td>\n", " <td>4.02276</td>\n", " <td>4.296019</td>\n", " <td>2.428279</td>\n", " <td>182.777927</td>\n", " <td>0.079999</td>\n", " <td>...</td>\n", " <td>45.478818</td>\n", " <td>2.750447</td>\n", " <td>5.153496</td>\n", " <td>0.345287</td>\n", " <td>1.624778</td>\n", " <td>1.372135</td>\n", " <td>24.046419</td>\n", " <td>1.078428</td>\n", " <td>5.936098</td>\n", " <td>65.898711</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>756.688509</td>\n", " <td>33.907515</td>\n", " <td>975.485366</td>\n", " <td>1.544911</td>\n", " <td>4.150709</td>\n", " <td>4.02276</td>\n", " <td>4.296019</td>\n", " <td>2.428279</td>\n", " <td>182.777927</td>\n", " <td>0.079999</td>\n", " <td>...</td>\n", " <td>45.478818</td>\n", " <td>2.750447</td>\n", " <td>5.153496</td>\n", " <td>0.345287</td>\n", " <td>1.624778</td>\n", " <td>1.372135</td>\n", " <td>24.046419</td>\n", " <td>1.078428</td>\n", " <td>5.936098</td>\n", " <td>65.898711</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>756.688509</td>\n", " <td>33.907515</td>\n", " <td>975.485366</td>\n", " <td>1.544911</td>\n", " <td>4.150709</td>\n", " <td>4.02276</td>\n", " <td>4.296019</td>\n", " <td>2.428279</td>\n", " <td>182.777927</td>\n", " <td>0.079999</td>\n", " <td>...</td>\n", " <td>45.478818</td>\n", " <td>2.750447</td>\n", " <td>5.153496</td>\n", " <td>0.345287</td>\n", " <td>1.624778</td>\n", " <td>1.372135</td>\n", " <td>24.046419</td>\n", " <td>1.078428</td>\n", " <td>5.936098</td>\n", " <td>65.898711</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>756.688509</td>\n", " <td>33.907515</td>\n", " <td>975.485366</td>\n", " <td>1.544911</td>\n", " <td>4.150709</td>\n", " <td>4.02276</td>\n", " <td>4.296019</td>\n", " <td>2.428279</td>\n", " <td>182.777927</td>\n", " <td>0.079999</td>\n", " <td>...</td>\n", " <td>45.478818</td>\n", " <td>2.750447</td>\n", " <td>5.153496</td>\n", " <td>0.345287</td>\n", " <td>1.624778</td>\n", " <td>1.372135</td>\n", " <td>24.046419</td>\n", " <td>1.078428</td>\n", " <td>5.936098</td>\n", " <td>65.898711</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>756.688509</td>\n", " <td>33.907515</td>\n", " <td>975.485366</td>\n", " <td>1.544911</td>\n", " <td>4.150709</td>\n", " <td>4.02276</td>\n", " <td>4.296019</td>\n", " <td>2.428279</td>\n", " <td>182.777927</td>\n", " <td>0.079999</td>\n", " <td>...</td>\n", " <td>45.478818</td>\n", " <td>2.750447</td>\n", " <td>5.153496</td>\n", " <td>0.345287</td>\n", " <td>1.624778</td>\n", " <td>1.372135</td>\n", " <td>24.046419</td>\n", " <td>1.078428</td>\n", " <td>5.936098</td>\n", " <td>65.898711</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>5 rows × 28 columns</p>\n", "</div>" ], "text/plain": [ " glucose saccharose fructose galactose mannose rhamnose \\\n", "0 756.688509 33.907515 975.485366 1.544911 4.150709 4.02276 \n", "1 756.688509 33.907515 975.485366 1.544911 4.150709 4.02276 \n", "2 756.688509 33.907515 975.485366 1.544911 4.150709 4.02276 \n", "3 756.688509 33.907515 975.485366 1.544911 4.150709 4.02276 \n", "4 756.688509 33.907515 975.485366 1.544911 4.150709 4.02276 \n", "\n", " acetate chlorogenate citrate fumarate ... glutamate isoleucine \\\n", "0 4.296019 2.428279 182.777927 0.079999 ... 45.478818 2.750447 \n", "1 4.296019 2.428279 182.777927 0.079999 ... 45.478818 2.750447 \n", "2 4.296019 2.428279 182.777927 0.079999 ... 45.478818 2.750447 \n", "3 4.296019 2.428279 182.777927 0.079999 ... 45.478818 2.750447 \n", "4 4.296019 2.428279 182.777927 0.079999 ... 45.478818 2.750447 \n", "\n", " phenylalanine tryptophane tyrosine valine pyroglutamate \\\n", "0 5.153496 0.345287 1.624778 1.372135 24.046419 \n", "1 5.153496 0.345287 1.624778 1.372135 24.046419 \n", "2 5.153496 0.345287 1.624778 1.372135 24.046419 \n", "3 5.153496 0.345287 1.624778 1.372135 24.046419 \n", "4 5.153496 0.345287 1.624778 1.372135 24.046419 \n", "\n", " trigonelline choline inositol \n", "0 1.078428 5.936098 65.898711 \n", "1 1.078428 5.936098 65.898711 \n", "2 1.078428 5.936098 65.898711 \n", "3 1.078428 5.936098 65.898711 \n", "4 1.078428 5.936098 65.898711 \n", "\n", "[5 rows x 28 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Select the variables from the merged data belongings to the 'qNMR_metabo' data subset\n", "data[quantitative[quantitative.Subset=='qNMR_metabo']['Attribute']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### 0ct, 4 2019 - Daniel Jacob INRA UMR 1332 - MetaboHub Bordeaux" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernel_info": { "name": "python3" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "nteract": { "version": "0.15.0" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
matthew-sochor/fish.io.ai
modeling/Fish CNN modeling.ipynb
1
1000197
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "from keras.preprocessing.image import ImageDataGenerator\n", "from keras.models import Sequential\n", "from keras.layers import Dropout, Flatten, Dense\n", "from keras import applications\n", "import os\n", "import tqdm" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(4, 224, 224, 3)" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "auggen = ImageDataGenerator(rotation_range=10,\n", " width_shift_range=0.1,\n", " height_shift_range=0.1,\n", " horizontal_flip=True)\n", "\n", "imread('data/raw/train/walleye/005597a253936bb45edf70392d89b403ee7d2285.jpg')\n", "next(auggen.flow(np.tile(np.empty((1, 224, 224, 3)), (4, 1, 1, 1)),\n", " np.empty((4, 4)), batch_size=4))[0].shape" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(4, 256, 256, 3)" ] }, "execution_count": 73, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.tile(imread('data/raw/train/walleye/005597a253936bb45edf70392d89b403ee7d2285.jpg'), (4, 1, 1, 1)).shape\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\r", "0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Converting from: \"data/raw/train\"\n", "Saving to: \"data/arr/train\"\n", "Iterating over all categories\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "4it [00:03, 1.12it/s]\n", "0it [00:00, ?it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Converting from: \"data/raw/test\"\n", "Saving to: \"data/arr/test\"\n", "Iterating over all categories\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "4it [00:01, 2.82it/s]\n" ] } ], "source": [ "import os\n", "import subprocess\n", "\n", "import tqdm\n", "\n", "import numpy as np\n", "\n", "from keras.preprocessing.image import load_img\n", "\n", "\n", "def imgdir_to_arr(data_dir, arr_dir):\n", " print('Converting from: \"{}\"'.format(data_dir))\n", " print('Saving to: \"{}\"'.format(arr_dir))\n", "\n", " subprocess.call(['mkdir', '-p', arr_dir])\n", "\n", " cats = sorted(os.listdir(data_dir))\n", " cat_nbr = len(cats)\n", " print('Iterating over all categories')\n", " \n", " for cat_idx, cat in tqdm.tqdm(enumerate(cats)):\n", " cat_path = os.path.join(data_dir, cat)\n", " img_files = sorted(os.listdir(cat_path))\n", " for img_idx, img_file in enumerate(img_files):\n", " img_path = os.path.join(cat_path, img_file)\n", " img = (img_path)\n", " img_name = '{:04d}-img-{}-{}'.format(img_idx, cat, cat_idx)\n", " lab_name = '{:04d}-lab-{}-{}'.format(img_idx, cat, cat_idx)\n", " lab = np.eye(cat_nbr, dtype=np.float32)[cat_idx]\n", " arr_path = os.path.join(arr_dir, img_name)\n", " lab_path = os.path.join(arr_dir, lab_name)\n", " np.save(arr_path, img)\n", " np.save(lab_path, lab)\n", "\n", "\n", "if __name__ == '__main__':\n", " imgdir_to_arr('data/raw/train', 'data/arr/train')\n", " imgdir_to_arr('data/raw/test', 'data/arr/test')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([[[215, 200, 167],\n", " [204, 189, 156],\n", " [229, 214, 181],\n", " ..., \n", " [147, 156, 173],\n", " [121, 128, 144],\n", " [181, 188, 204]],\n", " \n", " [[210, 197, 163],\n", " [211, 198, 164],\n", " [224, 211, 177],\n", " ..., \n", " [144, 153, 170],\n", " [126, 135, 152],\n", " [175, 184, 201]],\n", " \n", " [[202, 189, 155],\n", " [207, 194, 160],\n", " [208, 195, 161],\n", " ..., \n", " [133, 144, 162],\n", " [129, 140, 158],\n", " [162, 174, 190]],\n", " \n", " ..., \n", " [[139, 128, 96],\n", " [143, 132, 102],\n", " [144, 133, 103],\n", " ..., \n", " [168, 159, 116],\n", " [155, 146, 103],\n", " [147, 139, 93]],\n", " \n", " [[136, 127, 94],\n", " [141, 132, 101],\n", " [143, 132, 102],\n", " ..., \n", " [156, 148, 101],\n", " [146, 138, 91],\n", " [146, 138, 91]],\n", " \n", " [[135, 126, 93],\n", " [140, 131, 100],\n", " [142, 133, 102],\n", " ..., \n", " [151, 140, 94],\n", " [132, 124, 77],\n", " [129, 121, 74]]], dtype=uint8),\n", " array([ 1., 0., 0., 0.], dtype=float32))" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arr_dir='data/arr/test'\n", "\n", "def gen_XY_from_dir(arr_dir):\n", " arr_files = sorted(os.listdir(arr_dir))\n", " arr_names = list(filter(lambda x: r'-img-' in x, arr_files))\n", " lab_names = list(filter(lambda x: r'-lab-' in x, arr_files))\n", "\n", " assert len(arr_names) == len(lab_names), '# labels != images'\n", "\n", " for arr_name, lab_name in zip(arr_names, lab_names):\n", " X = np.load(os.path.join(arr_dir, arr_name))\n", " Y = np.load(os.path.join(arr_dir, lab_name))\n", " yield X, Y\n", "\n", "next(gen_XY_from_dir('data/arr/test'))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n", "\n", "0it [00:00, ?it/s]\u001b[A\u001b[A" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Found 1244 images belonging to 4 classes.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n", "\n", "1it [00:00, 4.79it/s]\u001b[A\u001b[A\n", "\n", "2it [00:00, 4.87it/s]\u001b[A\u001b[A\n", "\n", "3it [00:00, 4.93it/s]\u001b[A\u001b[A\n", "\n", "4it [00:00, 4.98it/s]\u001b[A\u001b[A\n", "\n", "5it [00:01, 4.96it/s]\u001b[A\u001b[A\n", "\n", "6it [00:01, 4.88it/s]\u001b[A\u001b[A\n", "\n", "7it [00:01, 4.67it/s]\u001b[A\u001b[A\n", "\n", "8it [00:01, 4.59it/s]\u001b[A\u001b[A\n", 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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-6-fd515402f99f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maug_round\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m333\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m save_to_dir=data_gen_dir)):\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/thenome/anaconda3/lib/python3.5/site-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 717\u001b[0m \"\"\", fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m 718\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 719\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 720\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;31m# Update and print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/thenome/anaconda3/lib/python3.5/site-packages/keras/preprocessing/image.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 725\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 726\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__next__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 729\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/thenome/anaconda3/lib/python3.5/site-packages/keras/preprocessing/image.py\u001b[0m in \u001b[0;36mnext\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 971\u001b[0m \u001b[0mhash\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 972\u001b[0m format=self.save_format)\n\u001b[0;32m--> 973\u001b[0;31m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave_to_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 974\u001b[0m \u001b[0;31m# build batch of labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 975\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclass_mode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'sparse'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/thenome/anaconda3/lib/python3.5/site-packages/PIL/Image.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m 1683\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1684\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1685\u001b[0;31m \u001b[0msave_handler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1686\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1687\u001b[0m \u001b[0;31m# do what we can to clean up\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/home/thenome/anaconda3/lib/python3.5/site-packages/PIL/JpegImagePlugin.py\u001b[0m in \u001b[0;36m_save\u001b[0;34m(im, fp, filename)\u001b[0m\n\u001b[1;32m 708\u001b[0m \u001b[0mbufsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mImageFile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMAXBLOCK\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbufsize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"exif\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mb\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 709\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 710\u001b[0;31m 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bufsize)\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_to_pyfd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 496\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 497\u001b[0;31m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_to_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbufsize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 498\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 499\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mIOError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"encoder error %d when writing image file\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "import os\n", "import subprocess\n", "\n", "import numpy as np\n", "\n", "from keras.preprocessing.image import ImageDataGenerator\n", "\n", "\n", "aug_rounds = 4\n", "\n", "\n", "# TODO: replace this listdir with a mapping tbl/json\n", "CATS = os.listdir('data/raw/train')\n", "def cat_from_int(cat_int):\n", " return CATS[cat_int]\n", "\n", "\n", "def gen_XY_from_dir(arr_dir):\n", " arr_files = sorted(os.listdir(arr_dir))\n", " arr_names = list(filter(lambda x: r'-img-' in x, arr_files))\n", " lab_names = list(filter(lambda x: r'-lab-' in x, arr_files))\n", "\n", " assert len(arr_names) == len(lab_names), '# labels != images'\n", "\n", " for arr_name, lab_name in zip(arr_names, lab_names):\n", " X = np.load(os.path.join(arr_dir, arr_name))\n", " Y = np.load(os.path.join(arr_dir, lab_name))\n", " yield X, Y\n", "\n", "\n", "def augment_XY(x, y, aug_rounds):\n", " auggen = ImageDataGenerator(rotation_range=10,\n", " width_shift_range=0.1,\n", " height_shift_range=0.1,\n", " horizontal_flip=True)\n", "\n", " X_aug, Y_aug = next(auggen.flow([x], [y],\n", " target_size=(img_width, img_height),\n", " batch_size=aug_rounds))\n", "\n", " for x_aug, y_aug in zip(X_aug, Y_aug):\n", " yield x_aug, y_aug\n", "\n", "\n", "def arrs_to_aug(arr_dir, aug_dir):\n", " \n", " subprocess.call(['mkdir', '-p', aug_dir])\n", "\n", " for img_idx, (x, y) in enumerate(gen_XY_from_dir(arr_dir)):\n", " for aug_idx, (x_aug, y_aug) in augment_XY(x, y, aug_rounds):\n", " cat_idx = np.argmax(y_aug)\n", " cat = cat_from_int(cat_idx)\n", " img_name = '{:04d}-{:02d}-img-{}-{}'.format(img_idx, aug_idx cat, cat_idx)\n", " lab_name = '{:04d}-{:02d}-lab-{}-{}'.format(img_idx, aug_idx cat, cat_idx)\n", " arr_path = os.path.join(arr_dir, img_name)\n", " lab_path = os.path.join(arr_dir, lab_name)\n", " np.save(aug_path, x_aug)\n", " np.save(aug_path, y_aug)\n", "\n", "\n", "if __name__ == '__main__':\n", " arrs_to_aug('data/train/arr', 'data/train/aug')\n", " arrs_to_aug('data/test/arr', 'data/test/aug')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7f5baf775828>" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rmLPh0O+MqRLl4/RPLBAbTU6HJgbitag2vWCk5y12thVyQasT0ajlJOSfY0GngJd7+zDx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YghO4yn2TCsvP2j+vlJfedHmeayZSCQud+lMt5HgW/aFZCjHUu/HdhGadGQ/ZALE0JYyQLkJLOo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\nYZtcWNnfnOr7QXSavNuB879gaxdFkKkN1wwMqC+5TpHjRVrv6HMi0gQx+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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5baf848ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "\n", "from matplotlib import pyplot as plt\n", "\n", "plt.imshow(np.random.randint(0, 255, size=(500, 100, 3)))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(dtype('float32'), dtype('uint8'))" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#plt.imshow(np.load('data/aug/test/0530-00-img-carp-1.npy').astype(np.uint8))\n", "np.load('data/aug/test/0530-00-img-carp-1.npy').dtype, np.load('data/arr/test/0091-img-carp-0.npy').dtype\n", "#plt.imshow(np.load('data/arr/test/0091-img-carp-0.npy'))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(array([ 0.03595813, 1.89400518, 0.14984652, ..., 1.57549405,\n", " 0. , 0.50150651], dtype=float32),\n", " array([ 0.00758399, 1.12199354, 0.08758638, ..., 0.25394434,\n", " 0.00889139, 0.72352391], dtype=float32))" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.load('data/emb/train/1243-03-img-carp-1.npy'), np.load('data/emb/test/0112-img-white_perch-2.npy')" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "8192" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2048*4" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n", "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "input_1 (InputLayer) (None, 224, 224, 3) 0 \n", "____________________________________________________________________________________________________\n", "zero_padding2d_1 (ZeroPadding2D) (None, 230, 230, 3) 0 \n", "____________________________________________________________________________________________________\n", "conv1 (Conv2D) (None, 112, 112, 64) 9472 \n", "____________________________________________________________________________________________________\n", "bn_conv1 (BatchNormalization) (None, 112, 112, 64) 256 \n", "____________________________________________________________________________________________________\n", "activation_1 (Activation) (None, 112, 112, 64) 0 \n", "____________________________________________________________________________________________________\n", "max_pooling2d_1 (MaxPooling2D) (None, 55, 55, 64) 0 \n", "____________________________________________________________________________________________________\n", "res2a_branch2a (Conv2D) (None, 55, 55, 64) 4160 \n", "____________________________________________________________________________________________________\n", "bn2a_branch2a (BatchNormalizatio (None, 55, 55, 64) 256 \n", "____________________________________________________________________________________________________\n", "activation_2 (Activation) (None, 55, 55, 64) 0 \n", "____________________________________________________________________________________________________\n", "res2a_branch2b (Conv2D) (None, 55, 55, 64) 36928 \n", "____________________________________________________________________________________________________\n", "bn2a_branch2b (BatchNormalizatio (None, 55, 55, 64) 256 \n", "____________________________________________________________________________________________________\n", "activation_3 (Activation) (None, 55, 55, 64) 0 \n", "____________________________________________________________________________________________________\n", "res2a_branch2c (Conv2D) (None, 55, 55, 256) 16640 \n", "____________________________________________________________________________________________________\n", "res2a_branch1 (Conv2D) (None, 55, 55, 256) 16640 \n", "____________________________________________________________________________________________________\n", "bn2a_branch2c (BatchNormalizatio (None, 55, 55, 256) 1024 \n", "____________________________________________________________________________________________________\n", "bn2a_branch1 (BatchNormalization (None, 55, 55, 256) 1024 \n", "____________________________________________________________________________________________________\n", "add_1 (Add) (None, 55, 55, 256) 0 \n", "____________________________________________________________________________________________________\n", "activation_4 (Activation) (None, 55, 55, 256) 0 \n", "____________________________________________________________________________________________________\n", "res2b_branch2a (Conv2D) (None, 55, 55, 64) 16448 \n", "____________________________________________________________________________________________________\n", "bn2b_branch2a (BatchNormalizatio (None, 55, 55, 64) 256 \n", "____________________________________________________________________________________________________\n", "activation_5 (Activation) (None, 55, 55, 64) 0 \n", "____________________________________________________________________________________________________\n", "res2b_branch2b (Conv2D) (None, 55, 55, 64) 36928 \n", "____________________________________________________________________________________________________\n", "bn2b_branch2b (BatchNormalizatio (None, 55, 55, 64) 256 \n", "____________________________________________________________________________________________________\n", "activation_6 (Activation) (None, 55, 55, 64) 0 \n", "____________________________________________________________________________________________________\n", "res2b_branch2c (Conv2D) (None, 55, 55, 256) 16640 \n", "____________________________________________________________________________________________________\n", "bn2b_branch2c (BatchNormalizatio (None, 55, 55, 256) 1024 \n", "____________________________________________________________________________________________________\n", "add_2 (Add) (None, 55, 55, 256) 0 \n", "____________________________________________________________________________________________________\n", "activation_7 (Activation) (None, 55, 55, 256) 0 \n", "____________________________________________________________________________________________________\n", "res2c_branch2a (Conv2D) (None, 55, 55, 64) 16448 \n", "____________________________________________________________________________________________________\n", "bn2c_branch2a (BatchNormalizatio (None, 55, 55, 64) 256 \n", "____________________________________________________________________________________________________\n", "activation_8 (Activation) (None, 55, 55, 64) 0 \n", "____________________________________________________________________________________________________\n", "res2c_branch2b (Conv2D) (None, 55, 55, 64) 36928 \n", "____________________________________________________________________________________________________\n", "bn2c_branch2b (BatchNormalizatio (None, 55, 55, 64) 256 \n", "____________________________________________________________________________________________________\n", "activation_9 (Activation) (None, 55, 55, 64) 0 \n", "____________________________________________________________________________________________________\n", "res2c_branch2c (Conv2D) (None, 55, 55, 256) 16640 \n", "____________________________________________________________________________________________________\n", "bn2c_branch2c (BatchNormalizatio (None, 55, 55, 256) 1024 \n", "____________________________________________________________________________________________________\n", "add_3 (Add) (None, 55, 55, 256) 0 \n", "____________________________________________________________________________________________________\n", "activation_10 (Activation) (None, 55, 55, 256) 0 \n", "____________________________________________________________________________________________________\n", "res3a_branch2a (Conv2D) (None, 28, 28, 128) 32896 \n", "____________________________________________________________________________________________________\n", "bn3a_branch2a (BatchNormalizatio (None, 28, 28, 128) 512 \n", "____________________________________________________________________________________________________\n", "activation_11 (Activation) (None, 28, 28, 128) 0 \n", "____________________________________________________________________________________________________\n", "res3a_branch2b (Conv2D) (None, 28, 28, 128) 147584 \n", "____________________________________________________________________________________________________\n", "bn3a_branch2b (BatchNormalizatio (None, 28, 28, 128) 512 \n", "____________________________________________________________________________________________________\n", "activation_12 (Activation) (None, 28, 28, 128) 0 \n", "____________________________________________________________________________________________________\n", "res3a_branch2c (Conv2D) (None, 28, 28, 512) 66048 \n", "____________________________________________________________________________________________________\n", "res3a_branch1 (Conv2D) (None, 28, 28, 512) 131584 \n", "____________________________________________________________________________________________________\n", "bn3a_branch2c (BatchNormalizatio (None, 28, 28, 512) 2048 \n", "____________________________________________________________________________________________________\n", "bn3a_branch1 (BatchNormalization (None, 28, 28, 512) 2048 \n", "____________________________________________________________________________________________________\n", "add_4 (Add) (None, 28, 28, 512) 0 \n", "____________________________________________________________________________________________________\n", "activation_13 (Activation) (None, 28, 28, 512) 0 \n", "____________________________________________________________________________________________________\n", "res3b_branch2a (Conv2D) (None, 28, 28, 128) 65664 \n", "____________________________________________________________________________________________________\n", "bn3b_branch2a (BatchNormalizatio (None, 28, 28, 128) 512 \n", "____________________________________________________________________________________________________\n", "activation_14 (Activation) (None, 28, 28, 128) 0 \n", "____________________________________________________________________________________________________\n", "res3b_branch2b (Conv2D) (None, 28, 28, 128) 147584 \n", "____________________________________________________________________________________________________\n", "bn3b_branch2b (BatchNormalizatio (None, 28, 28, 128) 512 \n", "____________________________________________________________________________________________________\n", "activation_15 (Activation) (None, 28, 28, 128) 0 \n", "____________________________________________________________________________________________________\n", "res3b_branch2c (Conv2D) (None, 28, 28, 512) 66048 \n", "____________________________________________________________________________________________________\n", "bn3b_branch2c (BatchNormalizatio (None, 28, 28, 512) 2048 \n", "____________________________________________________________________________________________________\n", "add_5 (Add) (None, 28, 28, 512) 0 \n", "____________________________________________________________________________________________________\n", "activation_16 (Activation) (None, 28, 28, 512) 0 \n", "____________________________________________________________________________________________________\n", "res3c_branch2a (Conv2D) (None, 28, 28, 128) 65664 \n", "____________________________________________________________________________________________________\n", "bn3c_branch2a (BatchNormalizatio (None, 28, 28, 128) 512 \n", "____________________________________________________________________________________________________\n", "activation_17 (Activation) (None, 28, 28, 128) 0 \n", "____________________________________________________________________________________________________\n", "res3c_branch2b (Conv2D) (None, 28, 28, 128) 147584 \n", "____________________________________________________________________________________________________\n", "bn3c_branch2b (BatchNormalizatio (None, 28, 28, 128) 512 \n", "____________________________________________________________________________________________________\n", "activation_18 (Activation) (None, 28, 28, 128) 0 \n", "____________________________________________________________________________________________________\n", "res3c_branch2c (Conv2D) (None, 28, 28, 512) 66048 \n", "____________________________________________________________________________________________________\n", "bn3c_branch2c (BatchNormalizatio (None, 28, 28, 512) 2048 \n", "____________________________________________________________________________________________________\n", "add_6 (Add) (None, 28, 28, 512) 0 \n", "____________________________________________________________________________________________________\n", "activation_19 (Activation) (None, 28, 28, 512) 0 \n", "____________________________________________________________________________________________________\n", "res3d_branch2a (Conv2D) (None, 28, 28, 128) 65664 \n", "____________________________________________________________________________________________________\n", "bn3d_branch2a (BatchNormalizatio (None, 28, 28, 128) 512 \n", "____________________________________________________________________________________________________\n", "activation_20 (Activation) (None, 28, 28, 128) 0 \n", "____________________________________________________________________________________________________\n", "res3d_branch2b (Conv2D) (None, 28, 28, 128) 147584 \n", "____________________________________________________________________________________________________\n", "bn3d_branch2b (BatchNormalizatio (None, 28, 28, 128) 512 \n", "____________________________________________________________________________________________________\n", "activation_21 (Activation) (None, 28, 28, 128) 0 \n", "____________________________________________________________________________________________________\n", "res3d_branch2c (Conv2D) (None, 28, 28, 512) 66048 \n", "____________________________________________________________________________________________________\n", "bn3d_branch2c (BatchNormalizatio (None, 28, 28, 512) 2048 \n", "____________________________________________________________________________________________________\n", "add_7 (Add) (None, 28, 28, 512) 0 \n", "____________________________________________________________________________________________________\n", "activation_22 (Activation) (None, 28, 28, 512) 0 \n", "____________________________________________________________________________________________________\n", "res4a_branch2a (Conv2D) (None, 14, 14, 256) 131328 \n", "____________________________________________________________________________________________________\n", "bn4a_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_23 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4a_branch2b (Conv2D) (None, 14, 14, 256) 590080 \n", "____________________________________________________________________________________________________\n", "bn4a_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_24 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4a_branch2c (Conv2D) (None, 14, 14, 1024) 263168 \n", "____________________________________________________________________________________________________\n", "res4a_branch1 (Conv2D) (None, 14, 14, 1024) 525312 \n", "____________________________________________________________________________________________________\n", "bn4a_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 \n", "____________________________________________________________________________________________________\n", "bn4a_branch1 (BatchNormalization (None, 14, 14, 1024) 4096 \n", "____________________________________________________________________________________________________\n", "add_8 (Add) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "activation_25 (Activation) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "res4b_branch2a (Conv2D) (None, 14, 14, 256) 262400 \n", "____________________________________________________________________________________________________\n", "bn4b_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_26 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4b_branch2b (Conv2D) (None, 14, 14, 256) 590080 \n", "____________________________________________________________________________________________________\n", "bn4b_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_27 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4b_branch2c (Conv2D) (None, 14, 14, 1024) 263168 \n", "____________________________________________________________________________________________________\n", "bn4b_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 \n", "____________________________________________________________________________________________________\n", "add_9 (Add) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "activation_28 (Activation) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "res4c_branch2a (Conv2D) (None, 14, 14, 256) 262400 \n", "____________________________________________________________________________________________________\n", "bn4c_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_29 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4c_branch2b (Conv2D) (None, 14, 14, 256) 590080 \n", "____________________________________________________________________________________________________\n", "bn4c_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_30 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4c_branch2c (Conv2D) (None, 14, 14, 1024) 263168 \n", "____________________________________________________________________________________________________\n", "bn4c_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 \n", "____________________________________________________________________________________________________\n", "add_10 (Add) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "activation_31 (Activation) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "res4d_branch2a (Conv2D) (None, 14, 14, 256) 262400 \n", "____________________________________________________________________________________________________\n", "bn4d_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_32 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4d_branch2b (Conv2D) (None, 14, 14, 256) 590080 \n", "____________________________________________________________________________________________________\n", "bn4d_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_33 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4d_branch2c (Conv2D) (None, 14, 14, 1024) 263168 \n", "____________________________________________________________________________________________________\n", "bn4d_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 \n", "____________________________________________________________________________________________________\n", "add_11 (Add) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "activation_34 (Activation) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "res4e_branch2a (Conv2D) (None, 14, 14, 256) 262400 \n", "____________________________________________________________________________________________________\n", "bn4e_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_35 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4e_branch2b (Conv2D) (None, 14, 14, 256) 590080 \n", "____________________________________________________________________________________________________\n", "bn4e_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_36 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4e_branch2c (Conv2D) (None, 14, 14, 1024) 263168 \n", "____________________________________________________________________________________________________\n", "bn4e_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 \n", "____________________________________________________________________________________________________\n", "add_12 (Add) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "activation_37 (Activation) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "res4f_branch2a (Conv2D) (None, 14, 14, 256) 262400 \n", "____________________________________________________________________________________________________\n", "bn4f_branch2a (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_38 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4f_branch2b (Conv2D) (None, 14, 14, 256) 590080 \n", "____________________________________________________________________________________________________\n", "bn4f_branch2b (BatchNormalizatio (None, 14, 14, 256) 1024 \n", "____________________________________________________________________________________________________\n", "activation_39 (Activation) (None, 14, 14, 256) 0 \n", "____________________________________________________________________________________________________\n", "res4f_branch2c (Conv2D) (None, 14, 14, 1024) 263168 \n", "____________________________________________________________________________________________________\n", "bn4f_branch2c (BatchNormalizatio (None, 14, 14, 1024) 4096 \n", "____________________________________________________________________________________________________\n", "add_13 (Add) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "activation_40 (Activation) (None, 14, 14, 1024) 0 \n", "____________________________________________________________________________________________________\n", "res5a_branch2a (Conv2D) (None, 7, 7, 512) 524800 \n", "____________________________________________________________________________________________________\n", "bn5a_branch2a (BatchNormalizatio (None, 7, 7, 512) 2048 \n", "____________________________________________________________________________________________________\n", "activation_41 (Activation) (None, 7, 7, 512) 0 \n", "____________________________________________________________________________________________________\n", "res5a_branch2b (Conv2D) (None, 7, 7, 512) 2359808 \n", "____________________________________________________________________________________________________\n", "bn5a_branch2b (BatchNormalizatio (None, 7, 7, 512) 2048 \n", "____________________________________________________________________________________________________\n", "activation_42 (Activation) (None, 7, 7, 512) 0 \n", "____________________________________________________________________________________________________\n", "res5a_branch2c (Conv2D) (None, 7, 7, 2048) 1050624 \n", "____________________________________________________________________________________________________\n", "res5a_branch1 (Conv2D) (None, 7, 7, 2048) 2099200 \n", "____________________________________________________________________________________________________\n", "bn5a_branch2c (BatchNormalizatio (None, 7, 7, 2048) 8192 \n", "____________________________________________________________________________________________________\n", "bn5a_branch1 (BatchNormalization (None, 7, 7, 2048) 8192 \n", "____________________________________________________________________________________________________\n", "add_14 (Add) (None, 7, 7, 2048) 0 \n", "____________________________________________________________________________________________________\n", "activation_43 (Activation) (None, 7, 7, 2048) 0 \n", "____________________________________________________________________________________________________\n", "res5b_branch2a (Conv2D) (None, 7, 7, 512) 1049088 \n", "____________________________________________________________________________________________________\n", "bn5b_branch2a (BatchNormalizatio (None, 7, 7, 512) 2048 \n", "____________________________________________________________________________________________________\n", "activation_44 (Activation) (None, 7, 7, 512) 0 \n", "____________________________________________________________________________________________________\n", "res5b_branch2b (Conv2D) (None, 7, 7, 512) 2359808 \n", "____________________________________________________________________________________________________\n", "bn5b_branch2b (BatchNormalizatio (None, 7, 7, 512) 2048 \n", "____________________________________________________________________________________________________\n", "activation_45 (Activation) (None, 7, 7, 512) 0 \n", "____________________________________________________________________________________________________\n", "res5b_branch2c (Conv2D) (None, 7, 7, 2048) 1050624 \n", "____________________________________________________________________________________________________\n", "bn5b_branch2c (BatchNormalizatio (None, 7, 7, 2048) 8192 \n", "____________________________________________________________________________________________________\n", "add_15 (Add) (None, 7, 7, 2048) 0 \n", "____________________________________________________________________________________________________\n", "activation_46 (Activation) (None, 7, 7, 2048) 0 \n", "____________________________________________________________________________________________________\n", "res5c_branch2a (Conv2D) (None, 7, 7, 512) 1049088 \n", "____________________________________________________________________________________________________\n", "bn5c_branch2a (BatchNormalizatio (None, 7, 7, 512) 2048 \n", "____________________________________________________________________________________________________\n", "activation_47 (Activation) (None, 7, 7, 512) 0 \n", "____________________________________________________________________________________________________\n", "res5c_branch2b (Conv2D) (None, 7, 7, 512) 2359808 \n", "____________________________________________________________________________________________________\n", "bn5c_branch2b (BatchNormalizatio (None, 7, 7, 512) 2048 \n", "____________________________________________________________________________________________________\n", "activation_48 (Activation) (None, 7, 7, 512) 0 \n", "____________________________________________________________________________________________________\n", "res5c_branch2c (Conv2D) (None, 7, 7, 2048) 1050624 \n", "____________________________________________________________________________________________________\n", "bn5c_branch2c (BatchNormalizatio (None, 7, 7, 2048) 8192 \n", "____________________________________________________________________________________________________\n", "add_16 (Add) (None, 7, 7, 2048) 0 \n", "____________________________________________________________________________________________________\n", "activation_49 (Activation) (None, 7, 7, 2048) 0 \n", "____________________________________________________________________________________________________\n", "avg_pool (AveragePooling2D) (None, 1, 1, 2048) 0 \n", "____________________________________________________________________________________________________\n", "global_average_pooling2d_1 (Glob (None, 2048) 0 \n", "====================================================================================================\n", "Total params: 23,587,712.0\n", "Trainable params: 23,534,592.0\n", "Non-trainable params: 53,120.0\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "from keras.layers import Input\n", "from keras.applications.resnet50 import ResNet50, preprocess_input\n", "\n", "\n", "\n", "# TODO: allow for this to be parameterized\n", "img_width = 224\n", "img_height = 224\n", "\n", "\n", "arr_input = Input(shape=(img_height, img_width, 3))\n", "model = ResNet50(include_top=False, weights='imagenet', input_tensor=arr_input, pooling='avg')\n", "\n", "model.save()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/10\n", "12s - loss: 0.6211 - categorical_accuracy: 0.7725 - val_loss: 0.2950 - val_categorical_accuracy: 0.8891\n", "Epoch 2/10\n", "11s - loss: 0.3951 - categorical_accuracy: 0.8506 - val_loss: 0.2246 - val_categorical_accuracy: 0.9267\n", "Epoch 3/10\n", "11s - loss: 0.3487 - categorical_accuracy: 0.8699 - val_loss: 0.1670 - val_categorical_accuracy: 0.9417\n", "Epoch 4/10\n", "11s - loss: 0.3253 - categorical_accuracy: 0.8765 - val_loss: 0.1313 - val_categorical_accuracy: 0.9586\n", "Epoch 5/10\n", "12s - loss: 0.3016 - categorical_accuracy: 0.8869 - val_loss: 0.1635 - val_categorical_accuracy: 0.9539\n", "Epoch 6/10\n", "11s - loss: 0.2969 - categorical_accuracy: 0.8865 - val_loss: 0.1379 - val_categorical_accuracy: 0.9549\n", "Epoch 7/10\n", "12s - loss: 0.2919 - categorical_accuracy: 0.8869 - val_loss: 0.1162 - val_categorical_accuracy: 0.9718\n", "Epoch 8/10\n", "11s - loss: 0.2827 - categorical_accuracy: 0.8941 - val_loss: 0.1118 - val_categorical_accuracy: 0.9737\n", "Epoch 9/10\n", "11s - loss: 0.2789 - categorical_accuracy: 0.8975 - val_loss: 0.1155 - val_categorical_accuracy: 0.9633\n", "Epoch 10/10\n", "11s - loss: 0.2688 - categorical_accuracy: 0.9009 - val_loss: 0.1001 - val_categorical_accuracy: 0.9737\n" ] } ], "source": [ "import os\n", "import subprocess\n", "\n", "import numpy as np\n", "\n", "batch_size = 8\n", "\n", "from keras.layers import Dense, Dropout, Input, BatchNormalization\n", "from keras.models import Model\n", "from keras.optimizers import Adam\n", "\n", "\n", "# TODO: replace this listdir with a mapping tbl/json\n", "CATS = os.listdir('data/raw/train')\n", "def cat_from_int(cat_int):\n", " return CATS[cat_int]\n", "\n", "input_dims = (2048,)\n", "nbr_classes = len(CATS)\n", "\n", "\n", "def gen_minibatches(arr_paths):\n", " # TODO: refactor this to be more performative HHD\n", " # reading pattern if necessary\n", "\n", " # reset seed for multiprocessing issues\n", " np.random.seed()\n", "\n", " arr_files = sorted(os.listdir(arr_dir))\n", " arr_names = list(filter(lambda x: r'-img-' in x, arr_files))\n", " lab_names = list(filter(lambda x: r'-lab-' in x, arr_files))\n", "\n", " xy_names = list(zip(arr_names, lab_names))\n", " \n", " while True:\n", " # in place shuffle\n", " np.random.shuffle(xy_names)\n", " xy_names_mb = xy_names[:batch_size]\n", "\n", " X = []\n", " Y = []\n", " for arr_name, lab_name in xy_names_mb:\n", " x = np.load(os.path.join(arr_dir, arr_name))\n", " y = np.load(os.path.join(arr_dir, lab_name))\n", " X.append(x)\n", " Y.append(y)\n", "\n", " yield np.array(X), np.array(Y)\n", "\n", "\n", "def train_model():\n", " model_name = 'resnet50_1layer.h5'\n", "\n", " model_dir = 'data/models'\n", " subprocess.call(['mkdir', '-p', model_dir])\n", " \n", " nbr_trn_samples = len(os.listdir('data/emb/train'))\n", " nbr_tst_samples = len(os.listdir('data/emb/test'))\n", " \n", " gen_trn = gen_minibatches('data/emb/train')\n", " gen_tst = gen_minibatches('data/emb/test')\n", "\n", "\n", " x_in = Input(shape=input_dims)\n", " x = BatchNormalization()(x_in)\n", " x = Dropout(0.2)(x)\n", " x = Dense(nbr_classes, activation='softmax')(x)\n", "\n", "\n", " model = Model(x_in, x)\n", "\n", " model.compile(optimizer=Adam(lr=1e-3, decay=1e-3),\n", " loss='categorical_crossentropy',\n", " metrics=['categorical_accuracy'])\n", "\n", " model.fit_generator(gen_trn, steps_per_epoch=(nbr_trn_samples // batch_size),\n", " epochs=10, verbose=2, validation_data=gen_tst,\n", " validation_steps=(nbr_tst_samples // batch_size),\n", " initial_epoch=0)\n", " \n", " model.save(os.path.join(model_dir, model_name))\n", " \n", " return model\n", "\n", "\n", "model = train_model()" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": true }, "outputs": [], "source": [ "model_dir = 'data/models'\n", "model_name = 'resnet50_1layer_moreopts.h5'\n", "model.save(os.path.join(model_dir, model_name))" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "------------------------------\n", "img_name: walleye_1.jpg\n", "y_pred: [ 0.036 0.866 0.07 0.028]\n", "sorted cat list: ['carp', 'walleye', 'white_perch', 'yellow_perch']\n", "predicted class: walleye\n", "------------------------------\n" ] } ], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "\n", "from keras.preprocessing.image import load_img\n", "from keras.applications.resnet50 import ResNet50, preprocess_input\n", "\n", "\n", "# TODO: allow for this to be parameterized\n", "img_width = 224\n", "img_height = 224\n", "\n", "\n", "# TODO: replace this listdir with a mapping tbl/json\n", "CATS = sorted(os.listdir('data/raw/train'))\n", "def cat_from_int(cat_int):\n", " return CATS[cat_int]\n", "\n", "input_dims = (2048,)\n", "nbr_classes = len(CATS)\n", "\n", "\n", "# res50 = ResNet50(include_top=False, weights='imagenet',\n", "# input_shape=(img_height, img_width, 3), pooling='avg')\n", "\n", "img_name = 'walleye_1.jpg'\n", "\n", "img_path = os.path.join('test_data', img_name)\n", "\n", "x = np.array(load_img(img_path, target_size=(img_height, img_width)))\n", "X = preprocess_input(x[np.newaxis].astype(np.float32))\n", "\n", "X_fea = res50.predict_on_batch(X)\n", "\n", "y_pred = np.squeeze(model.predict_on_batch(X_fea), axis=0)\n", "\n", "print('-' * 30)\n", "print('img_name:', img_name)\n", "print('y_pred:', np.round(y_pred, 3))\n", "print('sorted cat list:', CATS)\n", "print('predicted class:', cat_from_int(np.argmax(y_pred)))\n", "print('-' * 30)\n", "# plt.imshow(x)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0. 0.99900001 0.001 ]\n", "['carp', 'walleye', 'white_perch', 'yellow_perch']\n", "white_perch\n" ] }, { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fbdbc79cda0>" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rSQvjfBb1UvlVWHb5Zy/hAoiXPdhPUTQltCvvCQKaoEhieKD2kAuzixFWGzia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4hSBV85PZHPDAU2qw0PFXvf9hvpNkyjwHZVtNMvTreUKtj5Ow/fEI69wi9939FvNpjGcV5fELp+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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbdd9da4588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "\n", "from keras.preprocessing.image import load_img\n", "from keras.applications.resnet50 import ResNet50, preprocess_input\n", "\n", "\n", "# TODO: allow for this to be parameterized\n", "img_width = 224\n", "img_height = 224\n", "\n", "\n", "# TODO: replace this listdir with a mapping tbl/json\n", "CATS = sorted(os.listdir('data/raw/train'))\n", "def cat_from_int(cat_int):\n", " return CATS[cat_int]\n", "\n", "input_dims = (2048,)\n", "nbr_classes = len(CATS)\n", "\n", "\n", "res50 = ResNet50(include_top=False, weights='imagenet',\n", " input_shape=(img_height, img_width, 3), pooling='avg')\n", "\n", "\n", "img_path = os.path.join(os.path.expanduser('~'), 'Desktop/test/white_perch_1.jpg')\n", "\n", "x = np.array(load_img(img_path, target_size=(img_height, img_width)))\n", "X = preprocess_input(x[np.newaxis].astype(np.float32))\n", "\n", "x_fea = res50.predict_on_batch(X)\n", "\n", "y_pred = np.squeeze(model.predict_on_batch(x_fea), axis=0)\n", "\n", "print(np.round(y_pred, 3))\n", "print(CATS)\n", "print(cat_from_int(np.argmax(y_pred)))\n", "\n", "plt.imshow(x)" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0.028 0.002 0.97000003]\n", "['carp', 'walleye', 'white_perch', 'yellow_perch']\n", "yellow_perch\n" ] }, { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fbdbed8e9b0>" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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XfuYjN1hdUtb6BcnSCOdyvMI4F65upTx/PuGtm0JZwg9eh8s3lhiMg3ZrkofpL59jafUk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BJ8NKb5DOccWQf6vYTBkqHjJFVRkY/2aVyfRqVICb1ehMdjWww2iRWnTq3xhc99EoCmLNndP0TP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3/lIRIjIXIFMgcs7noJk0lrumpTQGESPBaBSRde/oVz0xBOatxaLQdSr4BBnpncNlO6LBdoQw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2G8/xcAlO0Tyn6FOUom943D3rGEvX0QCvOwprhn4B/8ffj6N+HkCP7V7/L+08c8Hmr+9re+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\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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbdec506550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "\n", "from keras.preprocessing.image import load_img\n", "from keras.applications.resnet50 import ResNet50, preprocess_input\n", "\n", "\n", "# TODO: allow for this to be parameterized\n", "img_width = 224\n", "img_height = 224\n", "\n", "\n", "# TODO: replace this listdir with a mapping tbl/json\n", "CATS = sorted(os.listdir('data/raw/train'))\n", "def cat_from_int(cat_int):\n", " return CATS[cat_int]\n", "\n", "input_dims = (2048,)\n", "nbr_classes = len(CATS)\n", "\n", "\n", "# res50 = ResNet50(include_top=False, weights='imagenet',\n", "# input_shape=(img_height, img_width, 3), pooling='avg')\n", "\n", "\n", "img_path = os.path.join(os.path.expanduser('~'), 'Desktop/test/yellow_perch_1.jpg')\n", "\n", "x = np.array(load_img(img_path, target_size=(img_height, img_width)))\n", "X = preprocess_input(x[np.newaxis].astype(np.float32))\n", "\n", "x_fea = res50.predict_on_batch(X)\n", "\n", "y_pred = np.squeeze(model.predict_on_batch(x_fea), axis=0)\n", "\n", "print(np.round(y_pred, 3))\n", "print(CATS)\n", "print(cat_from_int(np.argmax(y_pred)))\n", "\n", "plt.imshow(x)" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0. 0.007 0.073 0.92000002]\n", "['carp', 'walleye', 'white_perch', 'yellow_perch']\n", "yellow_perch\n" ] }, { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fbdbed6f3c8>" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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fjgmDgNlsRpHljC8uKHUATDiC2TQjyyqapuHs7Bw/cCjL3P6m4zjW0lIPa3NBgBAurrVC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GK1VXnyWk/JiWiAHFlER6ri/aA7mTef9CFSqTBUgUbqOpOfOf3ML5jp/pzjKxWot3db7q7uUIBr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x4hu/84I//jNPudz0tf51CsJvY3j8uLDIpydQw5kE1ScxQqj3qRbvJLeONnsgs4fh\nnfcIzqM5cvzuNxDv6B4/BCB++/eQ3Q3rOqKlWCBITdFNLJNauiNFCPe4y7bFL7XqZvoRjs3mAler\n6Ou+cHer3NaW8qHvCM4RaxDxzhuXtb7/fn9kd9zRrTrOAZ67/cjr21QD6gonPX0w3Hu7vYBxIqt1\nP+6PM7uDg8ZmwWiFoZ6jgqv1nO3jKK15yIp7HsVX70OMO5tl2QkIViBM0T5HyZkc45JRO2fO5mfS\n3qjCnMqCa/tg7Jh05m1oEENY3jPOka6e1NzAHethWAqDx8MOfLBA6sB7Ry6WiTc6HlZXqvenZsFi\n12euKLYbaDKnSzuNllOwdi1RqAEtCz55ch2f4rDrCBsEIeY9x7LH+QkRCMxs3A1fevf7fPW9O+Ie\nnt4Jl92enEdQT96+x+byK/SdabT46XuMd8L+zhb5wTsmF5jUriTqyMQIqx6cxwkEcRzHRMmR7IS8\nusR1V9Bf2Wdq3pFLoSQjTghda6LpUFaEytBIc6KkjK/Pn/Md3eYBqjOaZ1JxaNpwd/c9dvMtXgpf\nuLji8YOZhxf1Rjvh5c2B731sC07KsNmueHphgZqpY06Ob39gtEvvPevNjic/e8l6uCTKwEUYuMue\nOdlCqG4L+CU+lCx1wa7zWoWSM8ejnXM1PGS42NL3W5wIN4fC//kbv8/77z5kc7E16KtN1B9y/DjB\n+jP1vM+Oz47Pjs+O/58fn6KMWmkcH5WKdzZ35VqdbrAHKN4pTk0dqwmWhQcPCT5Q0szuxe/Q9x2r\nwaoxI4lSprN+f7VOs8bLdfVnzfVDoDit8Mcpo3beLdeDD2j25uwBHI4zh8N0amWNkTjPi6Gr9x7f\ndRyPlolOKZNSxW7x5KyM48zHL295eT3ixHGxfUbRBl2AOE9WZV/94m72L3l5NzBGw3bFB9abgMhs\n112qAH/FjaQ6mzvnzYy1UsZMr7oWzyqrpVHuRBQtkbgIJmVSmq3hQM3X0DjjcspSFVIxCAMxwaCY\nUlWDo1IHT9iy+SCevs85k1NiCIY1F1VSUUJn+KI4gyJitOtoRV1fPRL9CTHhBJidYJOiVF9IaSUJ\nWpKWczYz24Udgel4L237ASmdYfsogUInmaW5XSBJ4Pn1Gi+Zzg08++p7hO33GOP3EO/o8wqVnlyb\nZiI9MTyg29r4lDBzSBPMp6ak4grSz4CHnNE443QFbk2RgVEfogy14UpwvrJoSjWFDb1x+utYZTGm\nhfiqptdlIJoMgyquslw0rCnSUbIQj4GeFZ2PBFdY9xPBC6VyzLPfMnXCVDXa58mKux9dX9dnYIsb\nntE9vlhEsA4iyOwIElEnxOEA7govPYuConTgmnhURCWjsTaSRQ+l5+rS4L9h7cnuQGSNAPtZef4q\n8c3vvOT1/shm6Pjquw8Wxlc7PiEt8FnDy1uOc06X2IO5tHs7WcTja+kHTzkVExWUgr96iLu4osSJ\n/MElMqwYqlLY3AEuLYUTETF9DYPBaLvk0iyb3AnHerMZo8EtrTjXJpCqAwJdPWfOhmk3WyJXA9ru\nsKtSpUrKA4eDq0a3wjgFpsmTigUcY2t5fK2ii3PMMXO7ty3yRy/h9WGNrOoWOvQmUGR94ZRciGfM\nFFEhOE9YXLUN+ijlVFDLxRaoVrwx1+6TZGyuJrULrXiBkE6uz80M1TBpJSWDPRZpVOcoMTJVKCQX\nWwQXpkWFQlZdbw+U2lJl3ZaeViSMcTYbsGblVe+l1IVX62JRtBi2XO9DPjMTOD2gFrFjzPggtfYA\neCi+UJq9FAFXPF0NTqqJQmGW3jjAOPbq+Ma3nvDB8w1Xl2t+6V/7CmX7DL/+HuI6Vv4hveuQUOVq\n3YrcP8ENjdr5iv38qsrZmsyvua2MpqoXZ+Juz8X6fYJfk2VNCp/Dh0sIvW3vvbFcXKU6dW6w96hj\nRc7Q9XSyqnMnEtyRebxB1Z6DiFLCyv4mF0Kf2LrAUHqCK1xu1/QrB8Hu406vuPNrprUtDlFuiPGa\nF7cWqDerDZuLK7p3voiKI6kQ1XHo/VJLkdwhTgku18U1GrS3PGcjJSVKVexLc0B0zeWFfc6wchS/\nZ6ZD1KFz4fnLwj/7rQ+5ugw8e7Dh/Ucb+i6cceXv/9/O81mgfstRqRxWAar9FmfVeSwYW4JUDN+i\nPZEWEFxXfRE7T/fuU0IUqpUgq+0KXXniaDSh0A9IGE7UpFJATy4m1MKSD4GWUZunXitGQZwTnRe6\nSvV59Pgp240jRmMDpJjIKVvxB7i+vuY73/k+H308k1JGpEfLU7797Y/Y7Sa6bsXTJ1/i/S++xxc7\nayJ5/r3XeD+wXltRVItwOI7c3O3qsF2xvXhI2Jor+RgTU4qs+x6pATKnqTIxrMjYeW82Y2bLDCqk\nvIwuKVmxkFw71rSgOVHyXH8fySkyDANNjKkUtUBb74l3gVQiTZck54I4R1eLhb4LjONxcd3ug2cI\nYcGoRy1M00gfqnCVD/TDyjjilRaklOp8o6xWtUV5jpScrM1cWqNL8+2ThX6nnBp+RMJSp7D7NsFK\nTGoWKEEpXab0dT4mh0RloFqwEel9YM+lyY+qcD06/tFvelyc2D4QfidFbrr3+PzPJkQ8F+59QpdB\nXtk5hwdk/4io9jk0z5RyaypxCr0PdC5wN11TJBPHkePNHarvG1Om39Jt3idsHuLWK/soKeH8sMia\nevGLaziA4hAPLtc6wXxkSiO76YaSYy22Onz/GJGO0Ec+d7XnIkaGnPGiPFw5Qr+l1FrDq+str+Yn\nxMHooz7sCPOH5PGbAByOUGTPxc/+HK5b0fVXrC6+jM4BLUJJkXl3DTJC4zy7G8ugsYd5nO847ibm\nO1v0B7eh7y5wzuoULhQIEylPWBLiYPR883c/ou8iX/rcI/7cn/gSwZ9YQ61L8a1+rn+A41MUqIGz\nh8W+/QnEvVtDg5wyrB9+6I981dubJ/QTv5f69en72vG2XE/7u8oiOa0J9/4JxjzQ8slznK75B1yj\nvOVny1V/cqv3tumo6P1TvPmic3ZOY1Isu6G3j2Ub5XsZy1te98MKOHL2mjf/9m2f/975f2QXmiUH\nb6oUwlKLfet7t5B3eo92n63LD3XkUncpsGjJnEww3ni3N7fftB/d1wdvXMi2s1uuV1oR+e1zpxVm\nT9/pJ8fubUFqmce60Gff9gwsnbBNabCJZ937oLqMgZorA42d08SlTte78HLu/f35WJym3huf840X\ntIarovrWufeHOT49gfpsZJuErHtj/riltlqpZxVbBfBaX1cx0eHiHSQmxsqGyI/fR9bbswllFKyl\ng0osW1WXlmmQs5rmB+1ee9DEIjKiCTTSdJsLMyrRtLQBlUxMM1PlPO8OR+72kf1Rq/ZEIsUDc8lk\nURyZMe2QNKDOkzNkccSSmSbLFh496Ljsey5Xle7kNuxTx7EyS0QLnasNHEI1bQ1U2NGuU+3cZCwz\nlbZTqeOcTAtaXcOsbXK39mapxrbiQkU5hNB5VE9c6pgiRfOyCxIH4gWp2VxKkRznBS/tnEnoHyo9\nLcWIC97cRpyzhhuEUNSMd7U+rpkz+iG2S/ACVGlakaVrroky5SrNKe60eKYzoSvfd+CFWLWxvR9w\nKrhmTq6KuMLUaJviDOstNjdMZVdwa4f0gdwJH7/qOMaHlGBNHMV7ostLu7aq4DXg9aLO5wd47lBn\nNMysQMmsSwbJdJdXbN//WbL+LIUL1K2IusIlj6T6ucIGJwNdrhCO7pnmA3Nl70xTJMaCq5rsUk0W\nwnBprJn61InrEQl0knnoMpeh0LuqlOeVm7LibrKGl9fugth3eLGdkhwnMh56M54tnYPtJSsucblH\nZoHdKzyX9nwVxfk1KWPUQ1ECRzMnsA0d+ZDRKS/QpPcF5/MyfyV7iM7meGVnFS8cS0fMntej53e+\nf8vPfP4RV9uVLQaa7sWTpLLsMP8gx6coUJ+yieWxeyODaW4Yel4EBFCla6gJxm3ePPkKMR4Z63bd\nPbzE+1NBQuvDHVrLsjqysyBgQdiU31wLTvWBt8XAHDBciaiMBhMAKR/IZVoC95xm7g57bm6OKPDy\n5R0fvTxwfZPJuTDHif3+wJgKxQtRMnfzNW5+CKzJpZCdY04zhyr2vpYLHq1XPL4w/u1+vGI+rrir\nxcWtLzwIoGKuLuKEECBTRZTEUVCypuq4jemcWDS2sUoZvKM0J+lsZgClppYhWCfg0vbtHaHrmaNZ\nlGUtHOYJ76qesVANi0+Bej6OpGnEV07f4D1SCre16CROCH0Pqw1adxclZ9OfsFtOihkySHALlc50\nJfzCj7du0JOzzNLkIi3A2yIUcySmhIiwvXqAdO5MqnMgqFt8LMzUonBcJmDDVlPNpRUJCpdWJI4q\nfPf5wCE9Qzamz5H8ETRRcu2QE08nA6HKgTotKEeifwGoURtL5Ik6Asq7n/sif+LP/dv8xu92vN4J\nJQl67fHJ4ZLa4rbZ4HLATzbmh/Ka/eEF+wqbzVMkpULomjXXQL9es9o+WTJ+oc4NtTLlI4Qr19HV\ndGZH4sP0iOfJGncOfkPoRi4wEf84ZRI9efUVm0sbT7rcsOExvgQkTcj+Y9wq41yPE4/vNkxALAGh\n4N2I5kJqRfq7hKZMV+mzXbCawrIRiL6WeGsnaQjk3rEvKyQ6uPP86r/4kMvthquLLSZYO4O6haIa\nVUifYdT/co7zgkDfW+GkZRMWaFvnISAe1J3pVxQK+aR/IY4uCDlPC85aSqLzajrAampsMc7M0R7Z\n8XjkeDwy16Lbzc0dL1/d8uqVYZmvrnc8/+iWaTQcM8bMcYyI7wmhdlMidC6z7mZyUVY+4pgpFS+O\naTY2STy5dw9dwPVWhPLznjgdyFWa1D6q0IdhgWAkdKivW041RoGKnrb4rmK7TfgpRnJKS2BDT1rN\ninU5xnisPo2YuE5b2GqgDq7y3Zu0ako4cQxru24tmXmaTsFfrIAcYzRJU4TBedbDmuA9MUam2g13\n3mVmGb2eBeb274Q9vlkwakyf5Q84wVbnrzv/+sfh2IordGHHxWUmuA7VwnSX8K5jvbF6xjQ7UlRy\nbdrQTiluYJwf1M9V8P4SVhtwypP3/zh/5t/8d/gnv/t3+eDFc6Q4urThonN0a1P/0yEx3+wZb2yc\nPr55yXEakXqj+9Cz3V6wvbBz+L7HDwNa6xlaqtdhVZaMuuF5+Sq7cEPvRkqB1zfKHNZc1tb/bSqE\necUQzRpuzLfsZM9NDbLr5Fir0ImJSGUGZoEx7UHucB76ATarnhb+nGyZj8IhWgG9OMV3vtaPQHyV\nKtCWdFXmTg3krjUx2Wab8TjzW9+85Ws//97/x96bxFqWZed539p7n+be+5pos88slotFlYqdREIE\nBUMANZQBA4INw66Rm6FnHHjmuWeGJx4aHhmGDdgDD6zyQBbsgWTZlESLRRazisXKqmwiM6N53e3O\nObvxYO19zrnvvcjMKFaRhPF2IOLFfffe0+6z9lr/+te/eOtxft+ZrJypw2IwzJzBLxl3hvoVxyRb\nqJ4UhWIF2XBM8MmNHjI3gU9GTDlpyXXZx7hRJjZI6UxRGn0OPtIPYVS+6/vAMERCpkvFSNZ1lhGq\n0O0rPTCRKPp3E64dOUx0qNFwY3WKYplKi5tYDSMEIKKY0piQSyU3OgOBD69FUfG7YZqy16V98YpA\n1eFlPLhW6WCjBwljbqsOm/8+O+bG5Icu07bgGtZ77R6WZrVfxbiOuHHe3s9LtEdQYX9jtPAnxtzz\nU6aekQrTJpIp+BQZ8ilR5CjEAibh6gWro1MS2gDXJIMjgESMUU12TCCmTotsgGHo8IOfepNCbtOW\nDaK1WGsJWVZBA65AufgJURohNSKRAPRBiLYat2mJuGRwMYuGpSzrOsvHmAy8F+xa27JpLxq911Fb\nreWmw1Br4vga7n4jB3NLHmfE7Zn+ppjo9gPe64yN4zyasPTrqPqXjTtD/YpjupeqxTy3E1oiXQx1\n+TlPVkwtpCZ7rpVdYhJCIMaBPuiECr4jhAHvNeT1PtJ1nqtNxgD7iPdC3+s+tN2fyR5LLuVOWZYj\nQzfee7bbDXHYKcZqrIrp5Iep84ltH9lnalIfEj4ljJt41mILU0XxOZNV+ARdDJKRTFlTXC/EkBOX\naXZl0shjJyqveswZZMM3XrnSfSVbTJX9lJGnqtfeENKs7DepvnEpIY/9oBWD88ScMeOCUh6imKbO\nMSH4UaNkrmt9w1jHNHYRLw948bgn2uX0KI/ts67PkBt0vtk+bgmTD71whWeMOESiYtNGG0zpRoOW\nQmcgXNfthmqpAl8xeGzckapAlMg+wOdnl+z7gI/qARY4MOXIZr/d4Xc7Qpc7uoiZGjigEJaxRVCD\n8bzni/fhWSUGBjqEmGrtbNPUJAO+dPJBCAh9PndvINg05m2MEQxujJimO1uSkEIcLBLM6DxYk3DJ\njFLE0Vp1nF6SmE4pQQBr85wY56eM0GYwFS/Ot3z85AXOCu+93uo8Ko2Qqe4M9c97TGEtM8/PakhT\nxO7Lg5gO6qIpRRDqwnhSpnulBJiIyB6MNrOtzMBmfU633+VNeHzfaVIM2O86zs93PHmq5d0pGnZb\nwzrTiPY7g/cVIeoDEVLSPokiGKNJrv1ux6eXF0jsMNbx6K1v0C4XuIWGyBf7wOdXAy/02eM8RHY2\nsVwolczVCwyRfvC527Z2ZqmqGiOl00ukHwaVYU1JJSyZkrfW5BZN+byIAQMjJqi/mrjZIQT6ftAH\nPyfonMnXP98bK44Q/NiNOoWAc5Zllmu93G1zA9T8wFstMU/ZkJTtBB9IEhmGnn23Y7E6GluE6RyY\nRJsKiybGiDM3w9iYP6ByrTIKMY39JmddrF+FrjVS/8bvisp7Sos4oxrioQJbY00urZY1wfVEl1UQ\nTUUlD6iPlB8fhitS/xSkJUngRWf5F9/7Mc+uOnbeUFnDSVOP1yjGyNmLM4azC9jk+bo4ZtGstFs6\nULmaql5gil61yV3UR73mItulT0+gZ5/O6dIRNq1ADNXpKWG4wnud85UYvDGEbDp7B0PFqDdSWUsd\nW6wziJVcPGaQwWoyPxr8VTsW3YiAW4AzjiYfd8fUPgwYdV0mDr4u5NbaLF8sWfkyF0WJwdkl3/vT\nT/n0k6eslhUP//7fxDk75pucs9hXsNR3hvpQmm+1AAAgAElEQVQVxqjXwCRcXn6W8GbMDCey3EWp\nflSRGWcGDbfjQLdfM/TnxNjjfcfZiycsakddVSrx+PwFm6stQ27ntdms2fWBPmfy+y7Q94mj1X0A\nnO2xsuNqvSHGSFvVHLUVZxdn7IcBYw1HiwVLs8ChPOfFwmGJDHt9gLexJpmWoxNN3my2jm5IhF3m\nhxOVk7w8Uu8kRsIQ6Pb7bJBU5nTUzBChrjXZVjzoMHiCH8bu3Ua0yMQWFkhO7GX/EklQuwrnqhtY\ncKGWSRad91kI34p2MS/3o+96+q4fm7navD+fywWNsVSuIgyBkFIWX9IO13ZWKGOt00RwTiJPkM7M\nY8aMC3TMGD0ZB5eRTzvBZK9KElV8e27cBWgwLiAYjI0sFkuFonJXdmtqWgtkTr7vhL6vsV7x46Hv\nccbyzW//bdqmYufhn//Bn+KHhqPV61TWslgdsVlf0Z1vSCT2PlGnI9rMwY9thamqUejKGafFUZKd\nGXGqI5ITc/p4xILw4YzleFXhd/eIwzHGwNFSePR4yb0jvUZPnpzz4kXPJubik7TCxopVVM+idTW1\nDSS/06hCHOIWDGEgxgFJiWg7sNo31IhB2mOGEOi9PgPJAFFUNhcoJaWVLaJiUb3orJEeM9Y+Rmu2\nJtVH/OjDj/lxvOT0ZMG/+bu/zPFJNUZ4Wjcw16//4nFnqG8Zt4m9TyNDGInRSORvXcNdy7bKa2V8\nGLRxQEieEDq835FiT9/tuLx4hjk5wtklIQS22yvWVxuGrD62Xm8YIkhVlMI8wavgvdLChLoOOLsj\nyhSGp+jxvsPhcG5JYx2NAGKprYZrY2l11OapVc7UWwvSM/Y7xCSMGKq6QoxVISkfcvPZIvKv2GTx\nRBUWmRoFhLK/Euqbgg0XQ30dJy+l2zIWBRUP50CYPgthcfAe4zZDjGNBTMEWR6yRUlE4HPRlHDnJ\n040csc/pzjJCKAfzIb8fs+dmMtQyBuMjF/jlhnrKiczD8Ovwi0BSdUKFpYL2bVQgWW8bhmTsCGH5\nLhKCRaIq2YWggvzHpw9YLVuGszXPnz8hpQdUVa3VprbGe8Vfc/SPmGoUFwvWINbOSuPtwYI0nnvR\n7p6dlwi6KFaGuK9IqUViwonneGF5dE8N8/lzMCYysl6D02gsL4wVFisp0zKTKvolq1FHkrzPSMxR\nZhRUqiHF0UufsSrLLdd9XUsqM3ulz0/OJUkiGcfVtiPsr4gp0odImkVuMaSpU8ZXGHeG+mcc03Mz\nYdBpdnel/C7N3s/yncro6Am+1wqtqD3hnBVSDPihV+H9pApvpV1fVVUkP1X4WWu063bK7bKin/0N\nuUGrNl+N0WCtwZqkovT5GS6tlmL2dod+YOgiQ58Nc9RqvtJL0EnR6QijFzu2xir4bf5/weNTboNU\nXs9ZHXqtStJwnsyZ8gEjm2T28ByMNCUk5zKlkA4w6+n3M0OeRlOLEcHPjmPEsOdHK9eONXFgRDNg\nfuMQJ6RUvtAwH3znKycbs86IRJCQf0atxBuxcgPJjFGN5q4jMnYIV3x/fbUmDAPb7Z6UlfVK5KIz\nL47X0lhtfDGSdfI/k6N//VxvOR+ZpdhEG9KKmNIECFLAEqit3sdlm2jqxNUu31e02e2QNJIySaiS\ndmyRXMsgkpuE5JySxBwRi3Ybj6nLKoBpvJpzZs/U3SfPHXO9yEa4PjVjUFVFsRpFbPc9wxCoKplv\n6iuPO0P9CmNaUKfbMt6clGBsUlt+aKYZIKaA9x2b9efE4Amxox/OIV5BUtGf46XQ76+42JyrFxUT\ny6YhVWokj5ZHbPZ7LrfKU21WDX6A8zOlR/muY+jW+OGcGAJVrT0TH9+vCCEL+0vkyCRaox5ljAOh\n2xMG3cfltuP5FTy7ysaqfcDD5THtMpeY+w7f77m6usr010QKqj1tZnY69v34oMahn95Aw12TPSvI\n+hpmMirF+JUH3hgtt849AmYmb3pQfPBEX3IA4JoGSYn9XilXBRIpnVmscyBmhFjEVdR1RbfZapSQ\nElVTY6vc8CB7iQZNHhU8Uox6kAdaDgcPoeYtNMJwM9jji73ogyq4GxHeoVlIRAY2WLvBmB4xkWR3\n+F7wvlzEhhQdsUjJbntS143iUma4Yr+/4A//xR9hjZDcktA8wjUVlakRSfjYEWJHTD0ihqPVgsbZ\nsUl0PygjaYSJxGFshRSaWxbKkpxQNNbgpMJkGCRi8MMKU1U4lz81rDmye948zvS7dyMpBJ6eKRQ3\nSEsvniEpP771D/HDilM50d6eNtDYHtcs1KuO0Pdaem5Mj5Do/IfgFzifdVDw+OjxmStviyRCLvm3\nJTF5oNM75WBSjGy2O5qqpa7vI3XF+z9+wv2V5ZfevJ/nkR8ToF9l3BnqVxqFypZfCePrkSM8GurE\nwRtJoY+6siSb1HBGS+9zmB0D0Scq66hsRUpgY6SLkSEbMOcq6srgbDZGtqIyhrDK3q6BFCK1c6Rk\naZqa45MVm7XD+0AMid1uIKaaPZLbX1m6IGTiCPvQ0rsWd5Q7ZrglXqqx4CXFQBgS+91+TJYpC6Ms\nBIbKVUhdTaGiaAIq+OLdqkddaFuFCueL8p1k6lRGk1R9TzHBAgNYa2ehaSJET0phdOCsgeT9mJiF\nRF3XY4NS4xQ/XTUtIqrlPQw9682Gft9pn8y2GSOJcRGJKZ+H5GM1WXyqRAFlgTk0xHOaWvHG5hj1\nbXSw+Th4bwYZjNfDgcNiktP5hFEKGqX3ZYP3e7pOF/kHp8K7b53y7W++DcDZsyWfflhxvl6r0mB9\nhFk95tl5pBs0CbvzHcZ62lY1XFSQSgjZYLk6e8RMUMfIiMj3QHL3k3LuxhgVb0rKLKlNSzCGKDpH\nhn7F87Xhgw91/r331oLf/GbP6UrPY9edcnZ2yocf6oJM22KOBLHqKAQ/MOx3xHBKotIWbNtL7j88\nYbF8ABLx5pIu9vSDbrOKQJh71NrzfL5YHkBwgFYogjZlhhg3KspUHTEIvP/jp7z3+n3efKyaOZWx\nB8nkLxt3hvol49aH5sYCOPdq0hSeM8GfY5A/Vt05tHlmJISKocvhd4ToE3Xlskh+giEwmH5G21Kv\n3uaV2BoNr9omG5MgDDU0ucNL01ScnDRIivjB4oeA7weGYPFYEsIQLbto6PLxDTSkaoVzxbuoGKLQ\n9ZmhkRLRJ4ZcBl4MtXYvNyBJPRCnlL9xcsc0Nm+N2VDPQ8o5Lp0kZU76AdKryZ0Ch8ikTZEoMMtE\n8TMo+6QYf9BEYBFCwqrBaLPwkxZfePquo+s6nHO0y8VoUCYue5hkXbnp/eZLxPzIy+fG8z2gBHLd\npn/5mDnUI4/XCiZZdEkr3GSb+36CweJjImSPerVo+dq7S37rtx4hInz2kbC0HT/9VO+tqVbY1RFn\nV+ekXmUMYtwhAlVtszHKWmOjeqPT5OU8VzPnr0cUbrAzvRqRDLup6EIjjs4CtuhmtFztI08zZPPL\n71U8OBWarAq4XTuexCM2H2veZqhrhhZEBo3mYke/2xGDlqB3PrJebzi+9wixpyQC0UV6c8ZOCgvJ\nYON0UyY0b3ZiMxwupUQULe3Xz0cig0qnWkuUyLOzDVfrHh/0WlVVmii6X2HcGepXGGn8h/FhuW67\nJ6nS24aMf8sDXrilkiupQCYO8TV89KBgorw9+2mtwVUu85fTyHCYG4SRYpQypp4f9DHBLYeTMnGI\ndZJm5vFLKGVz7vGNic789TUc+CXjdoj69mMYMwOzfdxce28a2PkhTboth5j0dcmdAk3Mk343rs0r\nGuNXVVcrc1FpvNP1nC9+83smopi8y1V3zlmsK8m/SbN7dpKzY9MTkhsYNHkf0zHdGLPFV+bXuNBA\nrp+/aMwas+H0XvOEZUGubKKtPctWF+S+GXBVn90QwaaINIah18VcXORooRKnEgcgIrmJaHppcu+W\nezo++2my2ZLnOtO1jlFx8JSLz4oGe3KzufYVxp2hfoWRYlacyyFOjvnzm5rQKX0B8zQZoREjgGkx\n7kg/ZxpIRmlAodeEx/HA+flzNusNINRVRb0UxTSAqqlJrqfu9HVTO5yrR4+6bo5p2rfoujUpRYbB\ns9nu6Paz1lJLx8WLHVe7DmMcx/cbUmjGlX7vE3s/kBmBdF1g2/XssidWdDgY+uksxeTqR9HGB06I\nRJ38aDdxn8vEgYzwSVYCIhf7yFh+L2LAMHaJz5d3tAnGkJsATFKiMcMeY4FLDIpbF9ioclrCnJvb\nJhEVf6q0O3rfBXbb7QifGGNGNo0Wv5S+lEHbSY2MFocRi49FO/r6AqbUP2NcZmQoPJLfun2eXWN5\n3Hj/xuchDkCqScmpbkpotOTeZN60BAZ2BMp9bHnwoOVv/IryqB+frKhNxeWwZ7fvuFgnPvjgpyR7\nStu2xBTohp4wxFGoatkeg52oiKqXEolDhrCqmmRnmsxG6YmuKli9PiG7YciJSouwwAdPFwdEoD6q\n2exq+rVGBt/73pZvvW34xrvK+7//5jnttz5l8zvvA7BNwkXvuDhvidHQNq9xuvo13v/+Jeu1Z3Hi\n+fpv7vin/9cP+PEHQsKxiV8jdLDPc7xdtpkdlLF2p4tZl89rgjhKuz59vn1mmkBCTM9mG9juROer\nF5483fPBTxVL//q7R7la8quNO0P9CmPK2B/CHDrCgUdmpITFpTLMYKwwdnqxAyIV1rpcrRQh7ln2\nYMwCEWgXFYlAiCVBZthtau18DLTNkso1U3hvIj7sOLu4xIeBGBL9MLDZ7ei7ADiMWbGs9znzHVlI\nj7UDrtaTeffRY+rFYxVdAjabLRdXa662m3yW2qR01y80mRiyXgOZH1wqHataH+IExqlof0lYphAI\nQxz1RFxtsM6OyZoYI5GYBZAKNJQwVSkqKde+PCyayFWKX6aK+QEf/GjdXVVR1c1IGYw5WbnbbRES\nflD2ysnJseKlztEuFvS5uOPAu57PiYxHJV/uffam8vslvpoipmk2cbAQzb3dl9NDD+iI05cVI0UQ\nHIZA1dSEuKGPitHXZoFtHIuklYi7reGjn+x5/49eIAJPPnnK9//kR/zxDz5h3/WEtGRIr0FSHrRG\nX46qKg0vZIS+Ssyf8n26XsU58udDIgYPRo20s5a6rqicJRpNHgb2iBJJ9XySI9R7klWe9KcsGZ41\nfLQpxSWf0dQbjo6U92+JSIisNxUpCffvC4/fuuKddweGXaRd7njv3oekX1nxNx62hOR43l/wB+93\nfP/HHhBMFTG1jItxiXSHwtG3JjsEUlAsrUxN0+ywDPQBQtL+kxch8tEnF5zkfpKP7n0N4ssX4+vj\nzlC/wpgXWNyM1tPBj/wNppup9KDMidOwK4GzWhQbo8d3wmJ1jzpLjLYLi3EqOwlgqsRiUWlDA6Bp\nllSuHifQdnfJ0xfP+OzpE4ahz0UcLevNhm6v6mHL5oij2rA0+sCL7KlNTZsn5TfffcTjN75B3WZZ\nyfNzXpydcX6pzJJt13O27Xi2d4QkDP3AbrNlGLTsXYzBVI6q1U4wKSV8bjIaXFYB7AflkBfZz5Tl\nUqvSPX0gei0SKdrHAhNbolzjUEJMNdalDRjA0HdaeGOKV5STeWO7Ky1c6DYbCq3QiOX4aKWfEwHr\n6LyKQRVs24jkSrWU2QuMi8kIP0wAsn5uhLmu61zLtZ95Cn2JR10+M3uF4DFSaXd4hLpxdEMg9Gqo\nk61wbkFt1BPdbjZ88Gdb/uXqCSLCT376Af/v9/41P/r0jN4Hjk7f5O1f+hb7PjcQlgTOUVWGylrV\ns9j1GkVl40MweU5N+DMw0jKH4BmS4hbGGKgcNJNSYkqeEK+wnGJQR4HBweISabQycRvf4PnZEWd/\nrvt8cf6CGOGtN/4NAO47z6M6UGK31zpo3nrByb0li1OLFc/u4hO+9bWv0X7zBB+FT7srnj4P/Pn3\n9VqakHK3n6KTog325kJcpQhK8nprrMxkEiKG0vHHEmNi23uePbvi40qf5e32jSl/9RXGK8DZd+Nu\n3I27cTf+KsadR/0qIy+AhRA/FxrS3nFpLItGLEjphTgLbbMAdQSiOA0tyfq2NmIag8RBcVwbSKkb\nkxx93xO8sDjKGe6+Y7+5GIVe9tsd3dWe7YtA10XlDLcttYBUIXvYgbAVYucQY1iuHLsQ6HulN732\naMl7v3RKlXUd3nn7Dbx/jC9NUGNg7wNnSSlLZ2dr/uzPPuanH75gu+2JEbZ9YHMx8az33S4fe6bn\nZVKCKSL/KUGIVFapc8kkTG1Acq2YJLCRkBQDVDqdIQ6TQiB9DexGTNaKIHVLyt6edUvEWZLdTscQ\nVPMClGXinCOaCo+WniefsKbwgLOXSGa7lPK1HNqLTB5ummfMULjLzIqGRunb0msyF9bE6RsZUpAb\nQdtICUvTZ7Xoo6ZI8SeEmBzWLKnHQqVGMf8MmwVnebLu+e7/82cA7DZb1v4B9x4/JgFVsyQmhcSK\n5xdTRVIGMAjYlhwgFsw9KvukVCZaiMkTc/GU9guNDLvc3KCqVBfDVRhjkWgQjjBVQuyWlIRuaIm7\nY9Kgc94tE/XxjtMcKYX6IbvtgiFrmuyGj9j7n/LG/cdY47j8zPDf/w8N9997QL1Y4swx96oNbx8f\ncVIvSJLoXeTpbstQ6dxYVC1i7XifSoFMnTVLjBFMyvFUiaQKPp0vRTSJxiXaTCntk+WjF1vON6r7\n/mu/8XUqf4dR/2LGmDi8+UsjRjuOpPnjZkYcO+VkY+Eea6m1UHSrMRFxNSZGfY1WTBENKQs9xWAQ\ncTStvg6hJ+ERUaGnvt+yubpEksOJYFIFwdE2S+o6tziyibqpsWiirKpbtDZFDXFdDxyvIqtjNdTW\n1AhuLCEvdLC9iURJXFwe8fpJxVsPTthse7yPnF12bLteKXwkBr/Uh7RQkUPCh0Sfk4m7fcdu12e+\nbqZ8JYO4amQGiLUEPyUHh+gJXa8FLkmobIsPWioPYEyLmJqYcgIPg49xxPcLd9mWIgarym8kOxo8\n1S7Re3FAB8mGNaeNcqZzSl9ounSCP1TJbSpykVKZOWMIvZSQMk2g6b0b38tqiaTM5skUBFQYX09G\nwKSR0YEVdgN8/vlVfj/hZMlytVKNFmO0A4qdinmsVMQUCT5L37pJPVIPS/M0ZsafTylO9z3z+Xyn\nHOcYVFe8XUx4MLaBFLU5AVDFPWFoibusK86Ouo0s72cIq3/ENvY0VjvVvLG84ldOn/DNtw21c/zw\nI+FP/qzh0/Aaoa6oXcWj01/m8+eGpTEkAp18zmebhD3JHPt6UvzLZwYkbVKRr38KmkfJZ0YgjvdM\nDIhLGL/PUttCSA3PL3d8Pmiu50//7GPePNlev+svHXeG+mccc9zxtlE8aDN7QIm5/Q9kaxRHjAs0\nIZG7+2SeqjInSgGBtTVgMFmqsqoaLQHO5bMhvmC9uWR1tABE2QURnK31wUuRIfQsj2tk5fL0azAm\n4lz2yvdnDN0lzf17eh4IIUSGLPJvjaVywlJLZlgdWx59+x1++e3XGbx22D672LLrupwFh6o2ufpQ\nz2PoPc+f73jyueKnTz57yocffconn2rXDt8nBm9ol8datGEtVb1g2CveGUJgt+uQeI6kLcYYTu89\nZNsNdLm0uKoEkiXlBGbvdxgTxs7fRgw4ZR2TWR7WVblzV07mGclKcTNvORvdaSJce6bzVZsP1Tph\niq5mc+hlQ24339c+M08uppF9ETNmH2PIzXlVFEySGduGGRGauiIs1BONhfeWBQo0LxZz9KPzvK5r\nuqEbt+mcnXHhoZRdz5F3M6/aLEcZC7Et0fcGJEcb1sCqxWxrZDAIkdP2nBjA7/OCGysqsdxb5fvo\nLEfmgoX5AIBff+OCv//tS3753Zq6srzzKPHkmfDPPjnh8sUJfrHAr97js7XAkAix5/OLTzD1guVD\nzcsYY3IOYkyGKMujCPSmIoXrx/M4uOeixTvDZqcMGDHY5h77XU+/V67299//EfJO9YX3dz7uMOq7\ncTfuxt34az7uPOqf0yge9LwbC0ylpkpdU2yrMAW0nqV0dokkgv4tLAYxiK1Gip/6JQMwaQr7mNhc\n5QalMdAsGs4vLvA+YE1NVa3wQ6cekihWasSB1UKWq6ueZBxNbnXk/YauO8cPj/I+lhipcKVxgGRW\nQoHiRXBiOG5rQlRvaVFZfFiMoaFzk4h7uTav3zvivTezKuDmAeeX73B2rh72+eWOz55e8vzsihC0\nUm7XXWGXpajE4B6e8Jvf+jpvPF6SBLw1/Kvv/YQ/+eFHACxX90lxwX6TcfCQiHFPyt1vjKtw1uFj\nP4t8ioc6ebLFYy1eYYmQxpZeo2xp6ZVYaFrl+0r10zL5icY2bTvPH17uP0/FNrdT9nRMeKfk+wxp\n9H4FQ5q3gjL2Bjcd9HsiKvHZd3ua5XKmx52U9WIKVz1izBQplcrUg2snUwWmGKPC+lFzD0UzZZL7\njJjUAc14Nfa7BQRDpM/b8Ox94vlVxuNdi292fPRCIZx3HkAfjjTKjAMPToS/86uG3eqM59uOkE7Y\nbL5GMqJYeFTYxRmhKbBQ7oY0dYdPhBQovUpVVG3i1s9ipPHHsC/VskqbrOqKFFdIlp29PN+zvj98\nwf08HHeG+uc0rhc6qOxnnE1iydQuDatSnsS5WQ8QSckrpldachmbiyUm6CRFIRXFM+MAw77rSICt\nKk7v3+ezzy+JJIwRbFWx33VjZ2wbwceOlIz2pLuqWK4qjpc5kRf37LcvOH/xKQCro9doF/dHMfii\nUCdRS4VFLNYYFvVk4I6XTV6AijEjnx/jtTi4doAP0A36+xcXaz765Bkf/ORTvPfsu57zy01uFyVY\n61itVvze7/5Nvv7OYwKR8167rX92psmaqrlPHJbUmeMbomfwif0+J4RspXTAMIWtKRVKloxVZbrQ\nTsdqrR1DXz23oluRE4gpF/MUpNhkSdjr3Ghh7Apzc8jBj/HgvmhkbYzREFuDmETMcyUEQQtWM/br\nqgypxen7JrdtM4Y4ePq+Y3l8RFW7EYorGixQCo6ygc7XIsUZPa/8LYvcNahQIZaiepiQFGEY8oKn\n96mPFUk8yWzzNgcGH9ntdJ+rxhCPE88/18//5LzlTz9uef2BZ9Ek2mXid387Mhw/5/n6kournn/5\nvQFZWWxbKgUjNgh15jUPRvLzWS694tEl+RszkSBNAj/qVGUcLCWVL6mrCldbxDjaRUtllzRule/H\nU4bhLpn4Cx+3NTGdj9LOqRhqm/vFTTkiGbtzq75w1JZVacoeO6slvWVp995rv8RUPBBDXbcsVwVb\ns6yO7nO1TvS9x9mG2h3z/OyKvvf44Llarzm7uGLXdYDFxsc0rVGRdaB2jt3mnB89V97q177+t2nb\nh6OdSJIZD7KYHImUIXdQfM7l/8wsTYgTn1Y5qJ4SGRAjDnCNTsflazVvv/YOv/O33s2bV7w9MImz\nJ4k0scYlxY3tkeP1hyvefEOx9cv1ij7WLJZ5n+4I74WL81x8gSEkyWp66tkNg6duW4xRY9z3A66q\nMCYelFMfiPOUXpTFk55xiCXf97qu8SERRnGirKA34rsmJ5Cn786B7y/WRx+3ODJPRMDlZqrjTUqa\naBxV/5IyXWLOPZR2cMYWCdBIiAPWmWyoE0Ovi0vKTVlLc4V5B3ZrzRyEZ+qINIsa8rUykr1Z5xTL\nxmP2V0TZEY1TvvXJEYPv6XO7r+ShCQtWrRq8anGELBzLjPf+yaf/mk8+/IDV8TH3jgxvvhH4jV/f\ncx7WnF/B58+EH/3pGbFZYpeGYegZdhsMR8SUMeOVgZlkSUyREP3E6EoaoRR2izpTVcb/pYQ03Dux\ntI02gQ5pBXUFC/3Om/cWHJ+cf+E9nY87Q/1zHjcFem4WL4yFEuPrW4kk42dvyVblfZHnxewBz29M\n+5xpCo/0rdtCaZn9Oz+BWw5qfgAvsR9fVrBxPadeNjffdkmoFXZFGl9rYkfSTe2TAyH6g43qd287\nrhv3Z2YY5dpnvqivYTFAMns9T6O9dL+3XOPxe3LTCfiiMa0Xctsd+8rbuH6caXaM8oXHdJumym37\nuHnN9TrMeIe3/bzx/cP7mpDMGpochTIXsrLvOItuHuf8wftqV28+327OL5mdFwfP5avenztD/RcY\ncyN8GwvkhreR9XgLPW/q+D11LMFMHonyYdPBaw27c6WiKaXOR2iWH/qhz9Q0z74LfPbp55ydb3KP\nw4T3A7tdpPe6v5NlRbtoaZrc0cXU2UDm3oNxIIUw0ty08C9lZkGebKPnppM/3vIQH2bGtQtMKiW0\nyWajNpWIq07IzAMFTCkXz0+bidmVl4QxcP/eMV979w0A3v9hYh+jKgxCbloLWU4El9SrtTbDFEnb\nbxUPL4RASv34cB0KTM3KpEX9xPkZJ2bY9Yz184VG/pVGmv7NvOokE4Vwvp/DhWZSF5y2M4djJDMy\nlLnhnEYWei2yVkyM4/0tpdWTjKlus9x+7VRvRmxXGx7PPivlnwKNWKp0jJcR/CN4lWw1RqG5kAIM\nAl3OBZkdYh2ntXrD9eoRZniPP/qTD2grz69+q+Y3fvWYi2d7nj2PvLjwbPqnpPAGzi/pg7BPCUyg\nN4WCqgqX0/UkR7UTFm9FNaqLsJq1BUrKdsBYSIEwBGISNvs1cXBIyLo8jxvq+quzPu4M9c84rnvK\nRcZy/gymlEYdjsI7zZ/OONaE4wI55HYjtDG2XCpOxrWGqGIFa2rqVkX9d7uO3b6nHxyDF87Od/zg\nBx9xddXhfVS8tHKqcyEKxbTLhtXRklWGT2q3VOZwypxm3xP6HjLZv0T6gaLiL7nLh3Z+SeVhTDJz\nJPNiNrYZT6QohDglSRXntOXCzRYxvRIGcKOhFsQaRBsSqqG28PYbj4gZA3zyycdcXl5iar2WfQ8+\nGWwu4TVJMFjttyql/ZdqgtRVPYb1RpReVqKQsUXX6MGVfw/x59L9xBqX4a8wJRspWP/MoF7zxMZu\nQTfWvDEgL5dKve6o1yFlr7SUN4+d2qrhwjUAACAASURBVLNRLH0pzeyz5X1BxusAUNc1MUvFCoJz\n9VgHACpIVRJreizzQrBy19MI3ZUO77NwZ/ovIDgqViR2QE9C8IMKJ5lsqrzviUnGRHU/rKkby2tL\nhby2997lYp/4J//sj0nDFfvhMf/2P3iDZ5+e8+TJwIttxXn3BBlOccMK74VdimACndWEZS1Ok/6j\noc4YPBMWXzlt6CwZxrLGjiCP5PMK/Y59NxBj4uzSE3qHycnso1//Bstle/3mvnTcGeqfccz5o2WI\nTIbaWu0neF3qU0XmRdWNvMkmKCEYrKnz5/QhjTEovzfvy1h34HH33ZbLy3M+++wFAC+eX/DRh095\n8tElwxDp+wisshHWh3AYeq0ItAZnDa6amsoCNO0RVR1hm7vGhAEfeqpsAMuDJVH5trnZEXpC+QFW\nN5uia0IqP/NOEqqYZwovVfUkfOHX5iIJI6q5YZgMzmgTfT6YMoND5MHRClcrL/itx+dcrrd0edFz\nZkkQw379NG8ntxTLibCYUlZ3EzCiTJIQtNvJ7NBHPPogcroZQVin/G8jh4s3uTimzIlyTTn4iGp8\n38B1xghDDXjZVEqJPGX0K8XIzA48xnBguHUDuTwTnbvGaKfuHCblRWyKBoahJ4mhKETq7ydPUzWr\nDxUsYkpjKeZUnTsBQvPfhwTbZIhBOVCC0NZHbLZb1nk+iktQN2xzJGG9Z+ESuc9F1nM5YRN+Be93\nnPdv8bz7LZpHl5zagd3FQPfhCwzKegkJ2sZSiwdKwrJFpBqTpHINBrPGUlWOyrr5SWgLvJT7cw49\n3fYMP+xICPtYI8lhs9E/Oak4WjV81XFnqH9OYyo8kGu/u/HJW35/E29LxROVGQ5mJNOusic6GGJM\nY8ugvvfs9z37biD4iB9AVftcDssCMFAajo4Ly8E+zEF4POks33YmwOhHXMMWZxjhiGWP28iSRqPB\nmltxNUClum5Ceadqtwnzm30BwRlLkx/YymXp1ZHUoMZ1rmsnxTu8DS+l6ILP9JUPjpJD/PGWcbte\n8yuM2TUbUQI4OJ7Dz07e+u3bmxZHCgX0C3Y/R1ELzAI6D+cY9WTMv2Bj3HLtrv2+lOzMt2OSgYCK\ndFH8lAkUC/kezdIzIJaQGiW8ppaQVoj12Npj3J4k9uDM9TbNC1xuF8UyI+Y9y/tImSezxheZIRK8\nJ3if8yuzKlHA2qmZ81cZd4b6LzgOJ+z0+8IvneN3B/KPGb8kFTW3lEN+DiLeOSadQmIY9nivZajr\nq0suLi7Z52on7yOg3aiLUfIhYkRLh1NmljiE7DgSvEckUdUTVzah9DXQzP6+29GsDg2pGqlMRIt6\nBlIsy4ypwi2moFD8Jk+0eCwTV1v/M5VDTwnQg4vPqAlsBGegzu+vFoJzkf1V1v5wS608LA9bvq4h\nc3pjjMrKGfHolzys1yKpNPt3Ol0Z77+2ETscc09y/LrMPWyZtlvWv9kCVd6Xce4VmETK2s4hWS97\n6cUNpyzAU6PasnBPxkYXmYlXrlQ242wOKq5j0y8f13ngtyW99TORKB3GBgoHxsiO41VkudBKxKo1\n9B7Wm03ehmFvLJte73NPwi0sq0evE/xAb495/ydrzGLJSWPZmD3Hqz27kBi2W2IYqIKjshZb8jCj\nNzAZ5jmEB5I54D7fOGXIaAStTZz9fq8KjoN2mrFVS13bUXulbR3O3hnqX+i4nlyafgdj+G8tVVWN\nEqQw46HmYayDVJMJ0sQUiVFGbQQk9wnMK3HoPdvtBVeXzwD4/OlnvDg7H439fueBBjHaODPS03Vb\nqkoTWiF49v2ek2ZJUzlEoN/vMDZyfKp4WZJAxFI3JwBcbbaIe8r91zRJp+Lwoi2eFMwmhaBmukAe\nprrd05yF/CEOhFxOL7mEeIR49Ipk3qoaipgvbfGzjQghDvoZEZxpWAgUyeq3XjP88EPPDz/SkPnB\na8dUdY1/kSlVIZD6ga1X7LiqKlar1Xh8KU3FSuX/5T7DrPtJ4ka+wqBNdF1VZfRBMYlxG7cZ+2uL\nMzGNdcMjhi1mhDpKJKB5AaUvGjHK1Seh7bjmiczs8eXDjNHntmIZs7dKkQu5x6EYg8Fo/0qxWgDT\n9zTGItnYhBBuwddni1k+zuvJ5fIdU+RO84ji6e0VC1PjxGFIuPScN19b8vihzsflkeUnH53zB//q\nCQCeN9kPNX2jRV+tcyxea3j9/t8BDBfpjP/uux/x7/07v8u7bz2g+XTHuz9s+dGPP+Hi8hxJiUXX\nsmgamqDPQMSASTO4zUKSybGIWlymQmUqxNYPO/php5o2IRB2HaHrSMFjrOXo/j3unyw4yqXvjx4s\naF/B/N4Z6lcY1ylJN7t5TKCgMWp85vzbGCP7rgPSCGMYUR5vioFh6Ilhgj2ERNdv6bpcsXf+nGHY\nk3ukZqNu6HI7lq5PhCDEIBkrtFRNTYy5iMAkjk5bmspSGVUBOz055eHDezx6rMnEdukQU1OjIji7\n3Z59f0XfPc/ntcC61dSzl0gkYIrYkUSEQEgyeq9a6KAeE0CSAcFQSca9Yea5MYaSxVCNPrVM/qsf\nAdlizQJOzKjV/d57R7z77AE/+kz3WbULeu/p87ppfUT8QLfviSnhnGOxWIz4qzGGuq7oe3/gORbj\nPSaJddkYE6XF97cu975M4EM8MGjzar7bRozaT88wN4ATuXLk4KcJs07JZOzXaBhfFr8cGaXgEcK4\n3ziyGvJ8RSOavtOmxVVds1hq5+4Q1MjrfI7E1I/36baf85Hm75czKYZaStRRztMQpCbaFkxFjMLm\n2TF7nhBQlb9FesK3Vkve+lXl2P/0+RUfXu45f6Hb6FcNcuxIRvM/fdcwXKz4g3/6AT86/ojOw4Ml\nfBx3XGw2JAQjNXt2bFFjv+IhFSsmzyLoM5XhFx8jMQx4v9MlMfT03Zqu3xBjwBnLSbPCuaX2VDCW\nuql5cHrEo0c65x8/WhHWd4b6L31cx3EnFshh+BmKB4Ng3YQTx9EzgjEzRCJ4zzDkRp6bDSH0LFfZ\n+80zP+SVPsbr21B4pYS5IozJQ4U+tOCjrmuatqjIqeemAlCQ2GfvS4/BmHoKzfOznkSTWRNkq5WX\nY8QrwAwDTCibwr50+s1YI+OCMHmDBTsesXW1BhiZ8OblsmK5rKlL13FrSd5TbK7RrI96ldkrVhZD\nyRMUOOBmeHq9AGWO8xen1eT7P080HnJuZ6+vwRTjVZx56uN3MnZ+e9A85Tcmr3WCk6YFcR7Ec/C/\nWOh3Sa/ZKMmao4rIjNXxlfD3OcBfooAJvy/sFH2dhY/EgnGkCL5vCd1A6rU4xA6fsHIPOTn9JQDW\nm4HPLwOpz92BGkdIMUM3hpgMqbe8eLrGbyCJpamXWIKWDyKIE4J4BrSoJubEeDm7yKFTFmPAe5+F\nyhLe9wxDxzB0mrS1FXYBlTgsKjrljKWuHcuFzsemdnTuq0st3RnqVxnz5NOYuCnj+gOUSCkQvHrD\nxswx6elhiUHbcKkM6ECKvSrspQRphx92hEEnkJUw0uP0tdKtCmPPWXA2UblMv4oRI57aCgkNi50T\n6qxtYIxR/BpGTNkYR8Lk0Dl7zFGr9kA9ZlUGTeM5XLsQYwpwbgwOfUNz+Jtrz/v1xz+bztHMv7RU\nICmnGqB2ltoabO71YUWvV8Fkp1L94vWrB9rnllxxLIUuZzOd721Gekq6lh6JChlgIkX+c/raHJfV\nhW7E95mM6sGppflCqB+a+6+SzynEXBokjHO0fCIlRiN70/mdFj+FYZJqcNiJ4SEiBe2iYPE3tnPN\neM/L7cs2SjKuQAspL5RJlAJnnMUkxfaT9AQRPGrgtv2KytQsat3mg/uBtxOki7yPFpKr6Pojvcch\natQZAjsfSUZIAwQka58UvN9gM100hUgIfowajRRmVOGDe2LoiF51YlL0ECNWCiXTkpuCTedsDM5A\nleenFV4+j28Zd4b6VUYIucBCwDmSCLFglhEkCa5Q9SUQ/BWbiw8gQdMK7aKB9DqgRHlLYrM9w/se\nCKS4xndXxNCTUmS3eUG3X+OzoV5Yi6nr8XCWlSUtKupcnNK7SBU96TQQQmS77Ri2F5wc3cO5avSw\n6tbgnIbJznp1GYI+CG19j0hgnz2YGHfs9wMvnmtYeHJa07SHhsSUetviZpvc8PZgHma6HgDupvWe\nDRHBzShgLxtFzkqTZD1kXW6Ax8cnvLY6Y4Vqf7TuBAmO5DUyGEIHocOaGpNES/GPTlk/e0bf95DA\n2gpnAkbSCKmkpKGvzUU+ugBD1/kR627bBW2zwtU13ns2myFDWpMxPtALkZjlXKcHmyl9On7W2Pxw\n54c8Fc9ewIrS55SGJyxXC9UyMWV/Ap5JACklJE1NAUJSxyLlvp4hBTa7DYvlKuPXgrUVMU6Ys3rf\ncYI0jB0XVX1/IPiQ53fmH9uKyrpR81owGVrR56eJNQtZUDc1MQZ29mN2znDhFOp48dmSrz2O/Ma3\nNHn47d/a4IaO9/+5zpdnFXwYFnz0Y8H3Fd2QuOgbPtlucGEAapBj1jjSQnMIfkjUsWXh9RnYb3qG\nbqDKGONqqTmdIehzGPZr+vU53b47WPFOFitNSBsHsqAzGilbazlaLjhpLQ8bdRyOnGgDhq847gz1\nKwwpgvHFW5l5Pk5yNign9nyArjfs9vnBiIEqeWwbELHEoedyu6EfrrKGQIC0JoY1KfWKZw8XIHuq\ntsSfHckOWKevq6anJeJavY21W0GyfLJ4ih88u33FcpmIQQ2ZiDYUddURxtSoUa2p65q2VUy663ow\niUV+3feefkjs9roYLJbhcHrdkkzKv+allvhL3/qSCTyDQEjZoGVVshKrW7GcHre8+859PQ8a9t1A\n4xSL74fI4PccHR9jjGW5XCo8M6Nc+eCJQZOJoXjUebGz+SE2OXQ3Jjv0Gp4QQyAOnhgCglZOFj65\ns1Y52+V0stDRmJjOGLYZO6prUcnQe4p2hhGlIxaqXMjQ1uECOIv85teKklyMM6+8VNjl7iwFkksR\nSTFDQjkyeMltPIRyJqiv4PljQ4K8qo8NoMv6ZRLUO/bUDIMBPO5Bx7kPXHycE83bd7habznbakL9\nzUc9D1cRFgp9PHow8MA+5+kfPiVs4PH9Jb/+64/59FnFfm0IqWeb/pxh/ym9X2Okojn+FTyGfcbe\nrSSqlLI+Nyxqx3LRss+9Szdrz3azzrGWUFU1q9VqgonQmgBbt1inSXJnLEfLmgf3lOffVFnH5yuO\nO0P9KsMcPAUwe2UyRb+Ii8co+GjwQT/hiUSbcI0+1D54tpsNKkwUSHigJ6UBTZUFYlKqUlXrNvuh\nR9sd5SpB53Ep4XJF32rZ0lQL1ldXKjDUGATPZr0hePW0nNM2VsbUhZ+Ccy6LE8F+6DDO0GY1varq\n8MGPgkK3lYf/1Y0ZgHpdWj1BU1fcv6dVm+fb3J3bKr7fsyYGaJsW6xxVrUndYohjisScRJufsq4/\ns3ZTAkLM8p8TBBFDIIpRRozkQpMSSl9jO8yhtPFX5jDRVmQ1izG3BqWUZRw8zhaSg+3McyQ3Lt3h\nic2Pa0x+prKAJE1e34JTHeDc1yDpNJNRKOeR8oIxsQszuCSRVPUMyWvTCQk0q8jmPLG5yJBWd8yw\nh81ejf/z88ibj4Q3H6kpu78cuLfYY7onpE1g9fCUb7zl2L94yFXf0KeBXXxKGp4SwwZMg2ksQzLs\nc1eZFfqoj/Ff7pYehgyNpEQ/9DSu0WjGWhbtkm23H8+PGKmMxVXNuJhXlWW5LDmTa/Dfl4w7Q/0K\nI936/5cYruxRzrVA9OOZu5qTjyPVavad8vnCbWXchtXwMsMC1jiCCaMqXXlQikTnVM47Ya1jWHpt\nn7d5Q+OpyCEWOUbwfw2M9jwvcFA4I2CsUGe+npBGw1uGYocTZlpw4OkD5Z+b53kg2nTtO7cn2Yok\nqIzJz/nO5sc1nxK3MSnGqTQzorrra3PttmOY/T+Ds9N5HO5l9rX5saYbtz29ZJGYvv7y63fwWvJ5\nq4XPTKHDMKGcbSzVkFhChN7r+10vdEZzLdaSOwspEwk8RgJ1ZbR/ZBL9O8+YMqL1s+RhJHg/RgYp\nxQxBzY5rPq8gJ9enwhjIC+EM3nppYdIt485Qv8KIHDYgFSJ21jcNDEUCUkRwVcVq9QBIuHBFHNZ0\nuw1IR0weS5Y2jQFjEq6qKfzXROL49CEiYby5SwPOtlRWvcSUAn2/4+ryDABnK4Y+sL7SZrLdfmB9\nuaOpGpqm6BRYtl1kGLTBaN1UuKpmucrbFEPnOzbbLp+Ho67qcUrFELUpgZtU1OYsiL+44NBXHyl1\nRJTql2RB8kLMXhFN4J03T2hPvwXAd//xD3j+2cekTu9XbRa0x47j4xOs1ZZSvu9x1iGNIXgV1JFa\niEMcsV2VF0kMocgGRQxR5T2Buq5YLhqcM5liB85A5+MYldjcvKFcqlKNV65xCB6fAkMo90wFvdq2\nLSeuieoQcsGS6CIuBsn5wBhT9pInD3kujJVGczSNQ5ZIoRBmyCSlkZmUT3zc5ujl5+s4Rpm3sJ7K\nn8nsQqmSjDHQD3sMWwxaOdpwD1sP2GWuvk0RpCWZr+k+k2F36fnh0zUAR99P3KfhwZvf4OHbhkev\n95w+es6iOaOzcO+o4be//Rr+/+zYPbcgFd3ZgG8jpsqGeejxREo3hc+efEzwnt229PSMrBYrXN2M\nDtV6t8t0SKe66cbRtkfUi5Xywe3AvftL3n3vMQCLVfUqDvWrG2oR+XvAfwb8NvAm8A9TSv/L7P3/\nFvgPr33tuymlf2v2mQb4L4F/H23n8L8B/2lK6fNXPZ5f1LjNC1BlBPWwXIqYFJjn3rWMtM8vAi71\nHGfaWxxafDcw7DJ30xQqnAObQyOrzVhjzi4vFydzmBFXV1S2xYnix4Jnby/ZbQunN+GHDUMfGboI\nybJoT+l3Rcdaz+tiu2Xfe6xzvPveI1arYxYL5XdebbaECLbKamXR54amuegmzLnjs6D3L9FAT/vM\ny2KJThTo1DdTz7IxvNnqebz7+jGvP2h58bkmF4011O2Kpq5VlyUEuv1eDVyGBca6SKMCTpCZ4Cnm\npg1A8pASxmmC2DiHcU6hiKjFNGIsYgIl2RqTalNLKuYqqcBWYVhI8S6zUY2JKGnsd1jioyLpNc7A\nAleUBRSZYc7Xr55+0444aT6mWYSlRnf6olINJyBWqdhThamQwJiR/50yaF8KfBTOyQvKDO6JuRgn\nCbjYaPm/UW86bFekfk0lyp46eWwJQ2KzzlCcMVSuZejV0Xi+X/JkWPDonsFa+Dxs+Ww/sO96YpMI\nVc+QNjhZsjAtCcswOKq6pzK6D2ci4gMha3X3fU/f9QzZo65cTdO02n9TSgm5INZmCQaLqVuquqV2\nC3XopOPeacvbb2vOpF0Ykkw1Fl82fhaPegX8IfDfAP/zSz7zj4D/iOlJ7q69/18B/wD4d4FL4L8G\n/ifg7/0Mx/OXNiLazJmMvSFxOkMxyifOjWbFd7i4x+SFsxfHEBuG/Y4UE85ZKtsi1qFl34qFDaHS\nThegTI0sJQlQ1w3ONtgicE6P82BM5lWHXg2pIis4U7M4OuLTixfsdz0xQvBwvt2yHzqquqZplywW\ny5Fv7EMkRFhUus1h2OFDIGYP8nrZ8F+FgS5DDVfG+lLxAvW9QMKkSJ0P781Hx7zz5invv/8xAGJr\nnKuxRnFe7z1d3+v2REhxgqgQRtVCveWCddkYBfU4JSf2MJaIyZBTmhlqM5Z8J3JRy+h6poMEpdrO\nSeQ/RWXxlGSnso1nic/ZNZmXZIvMDfXhfSqQkTBBWzFFhNKSbPKIFa657rjoeccQxgUleDXUk56I\n/r4kRQtlcSx0QUZaYzkeR0trtfVbisJ+U5NCxGZD/fBhxW4bOXuhryOGuj7GWW0d17kFl0PL2bMr\nhISERP2DI77xSztWq0iSxOfn5wzhIbVb5bxShcgWY7LcgHGqipOvfxgGgvfjAmREKXhFwlajIb33\nYgxiHFVT42yFFYcQsCayWlY8vK8LSiX+Fwt9pJS+C3wXQF7+lHYppae3vSEiJ8B/AvwHKaX/I//u\nPwa+LyK/k1L6v1/1mO7G3bgbd+P/z+MXhVH/noh8BpwB/zvwn6eUXuT3fjvv9x+XD6eU3heRnwJ/\nF/hra6gzYqcjei1pLiW5YvB+T79VvDhtnkK/pl4oHcdXS1jep/UVRMXj9vuBpq1HHesYQLSWSbcR\nLNY1uEq506HobGTaENIx+C0xbXLyY4+4NY/faAm+Zn3V8eTjj/Gp0tA8CWIawkahD7E1r7/xJk27\n4OLqMm/TkJJwtd7m15a6aukLvmoOZVFvG39ZXrZQAY6EUtxFEqYucIIQYiBE9bzee+uIv/Vrb/Dn\nP9VekJdrz9Uu4v1AjEa91+w9l3NomhrPQApp9Kb6GPDB48cCl4Q1FlfViBEqV+Eqhx9U8jMBPgZ8\niPisiWItGOdGbzdhSDHgh6IQV0qs9VgimvwdQm7EK4IzBmdd1vYAn8gFVRM8YYxR2U802khpEu6P\nufGqdRP0oVo0JSHN+DqVhsxxFkFScqITHzx4j5epkCXGTA0tYlvGqqiTzRzqTK2QMD1XYsB7FYAS\nhLbxSJ/oet3m+bM1KVpOcnUuxhDp2A1/rMcQDYMkdn4LKVGJwdQP+fBZh30RsS6xeDbwfLdm074A\nLNa9QRd6uswsaeMeZslDEJq6meAuBO8jRpTFb6zBuooQlNLpKkOVn1nvPdZEjo8rlq3gpOihxymJ\n+RXGL8JQ/yMUxvgx8A3gvwD+VxH5u0ljpDeAPqV0ee17n+X3/srHbfg06BwtZRgacEuJU7UQot+y\nv8ow+/oJ1m9x9ev6zXqFWZxSxwZJib7vWF9dMATBZPsgJmJdyQxncXyD8kvJVYxRkKK4JwFjo+Jd\n5NBTEvcfHpFSwlaW58939H2lD1s0DN6RcFrm6mpOTu9RNTV9Fo8yziKDZdcp1u6coaoajo914jVt\nQyG+Xk8g/mXDIKWjsya7olYBFs2NqFV0pQDpwUnDW68f8frrubmo2XG+27HfdxijuLGIEFLMhIiE\nNULMxAA7Zu4TxiRsRCESJmjEZP0Wa3Pn+VkSzkWbSQKiOiA58QQlmRjn0h2ZH2Yy6q77CDJxH2KM\nRIn5GPRvwaf1gCJiDS7TLjUvGEcRphiC4jhY3WFhWORznxoCzJkNMUNMEwOk7B2m8vM0wi8KCxRs\n3RqrkrsjPj0V7JTDjhLHPIFIwtY7xEasU2hus97jnOPoSCGEbojshysCn+d9Nhha7Z6SBJHAIB0X\nvYVYIQJ229KHgWC0hDzGPUM/4LNUQ5cT/TNcExGLy7TY/FCOlYcJbVptbebJVzVVVSlkFTtsLTx+\nvOLkqMEWvZprDKQvGz93Q51S+h9nL/9YRP4I+BHwe8A/+Yts+/d///c5PT09+N13vvMdvvOd7/xF\nNvuVx9yjHrtPZz5tCgO+X9NdKeJj9+dYE0a8LzqHrY9YiHZQMbst2/1A73ekpFKjziXayvL/sfcu\nz5JkW3rXbz/cPSLOOZmVVXXrPltNixaiDdOrGwmZGGKY8VcwYAjGAAYMGMCYAZIZcwbMwTDMGGiK\nYZgwgZDoVtPq1+17b9WtW1WZlY/ziAh333svBmvt7R6RmVWZ91UpOLssKzMiPDzct7uvvda3vvWt\nEPXhCT3gj2Sxxp6uaBNNsbZZEWIf8SbqP06CjxMhVOrVhu/9YMdf/PCaOSdSgbsxQ+jYbHYMmy1d\n3+Pjos8bY4f3uWlipJQZ+sAHHygG2Pc9rKlmfIM4tSx/O6/GqXqMDhN4N4dxQhsl7Hb6sHXDSHHC\ncTzgcPgQiENPmqel1RQo/ixiLbsgBmX21PTZnBNJVAZATWrBeZpsrBQheK9Gv1RPt5aY6z4DVYfZ\nHuJGUcMShMoUqcwSKYWSCmmukZXDxa4xMdYeta8eNWZoq6Eues+pBoymDP26DVxdZMpCI31ZgEwT\niNXzV1/eneyDVUKzilGJrQvCkuDW7wszGej0iKUg+Y4YaY0r7o4ZFzZsdmoH5tsDMt22IjCySqRu\nL9/H+8iYvuT2+GNC/ADveqR4Ut4h+QOcKLV1HI9QjnQtlSbgQ6stmOeihU92D3SxI/YDx5TaOedS\n2O4uiF3USs4QVYs6zRADP/2zP+A//5//Ple7vt0XL17c8KbjV07PE5G/cM49AX4bNdSfAb1z7sGZ\nV/1t++y14x/8g3/A7/7u7/7qDvZ+3I93aHxzadp/2ca7P1O/9/f+Xf6z//Q/4N/4y+rw5DHzT/7Z\nH/Bv/d2/90bf/5UbaufcD4APgJ/ZW/8EbaT07wD/o23zV4G/BPyjX/Xx/DxjoUll43xqWCo4zfoD\n5XBDuXlGtpZBfezpLi+I2w90J2GrEpFOv+e6ns3FJXd3EzkVFbJPCT+bxKeD2PfknJmywhKx03A5\nNJwxkeaZyRiBJXeQLzkelGb27OmRH/7oC56+gHmyakl6tttLfNiw2+3Ybrd45xkN+hDz4uoIIZjH\nXQX6WXofvsGc1fEr8bqr8+g0E59l1RarzASPyciqB3c8TPzsU5VrfXGTmCVTZvXqhsERQ+Ruv28t\n1CQn0jjjCuSonpALWrpdIa/NsMF3nnmekZKYjxNf3DxnTgmxoiPFW2sDX0cIUQWbbE622y39ZrBI\nSlXr8KGxArT+Y02JVO5uqX0Y0QTHuiydygqpDIxKI6sVpkUhHKebKmMmqO7GqUe8hgDqOG2G0Ro4\newdlKYipGtq+etQxnnKr5eX7BFHaalA8CecGchlJ5Wi/oaX/Xz79ss2NMisUNU3MzCVTDrc2RzPe\nX+HyDimdMlXSTD5mky3NSHpG74u21gJy8Cre1BpZONNisc+BNE4cZpUPjrFj02luIkbzqL1nSnvm\ncWLoB7777Yc8vNy1qXNEeAM9mzp+Hh71Beod16v3l51zfwN4an/+SxSj/sy2+6+AP0G50ojItXPu\nvwX+vnPuGXAD/DfA//auMz6cTbfvSwAAIABJREFU5JrxQ9xG+a2WcEg3Tyk3XyJ7Jd779z8kXn4H\nhg8Ah5OIHyfKsNMbtesYLi+Yy4ifRxUfP05MUyJnTcj0fachcc1pBMu8uNp49sA8ToymJ5KSY7/3\nfP75SEqZx49v+fiTF8x5Q5EAeEQKH3z4iIdXG4bNRosoHEuSSZw1Ia34cyDGroWBrQjiXfBiZOnT\niFNx96XBqBkcezAOd4lnX+z59BNN9u59VA7tlEAU0tkMA9c3uiCXnBnHA/NxglSougxd32mnGMvZ\ndUOkL4HDfm+NYGfu7m6tClINUUqJEPrWL1MNtVXGAWnaMUwKQ4E22NXkZOVVq+Y1rOh4fumwosnU\nTMBbeydlgAfHCT1vbailFFOSrdi7GqOyWhBqsrQOnYNqxRcsu3WJaVj2sg/nXYNfQtA+ks1QG6RS\nF1wH+AKdN9E+gSLBmtnWBgeQppmbG2sUMGgz4iDvA1DkllyuyXk0SEwI8QrKBiSYc/OCdBTKDI6E\nDzfEODBUeYHoVV3Pro/zogVFlkzMpTCnREnGtI+Oru+JXUcXo1UPe+b5wPF4x26z47vffsCDq127\n5t69nen9eTzqfxOFMGp6+b+29/874D8E/jrw7wPvAZ+iBvq/kEow1vGfoAvTf48WvPxD4D/6OY7l\n1zpMvRAczMA8zeSjrvT+9ikcXhCKWdV4SR4+Iot2pmAccekON1j22Hui63nv/fcRKUzTyOOfjUzT\nhKMYB1YYNls2nSZOUipM8w3ZqbKdSCblI0cTTLq7PfLF58/5oz/+MeM0M46FOUVmy9qXkhnnG779\n3b/Cb/3m91vrqZxT6982p0xKuWlciygmd3WlYkYlZ9I8E2L/iiKKX/OQyVYxB25r3FxLXIVOWQoW\n8Xz+6Z6/+PMbnj8zXPcyEi8i050WrIQQuLi4YHd3g/fKYJjmI1ISx/2e40HZIzhPEuFwUDxTZEZk\nZhi0Si3GyMXFlgcXF8QYKUVU6KrJ26pVcurGAvDi2ZfMT/KyGAw924tLrh480urRfmCz3TV9EWVk\n5NbEoIiQ5oTenN488UwI6+hHFkyfamSXC7gucDmpxLckS13+9P0aYYolxaoin9Mpr8beKT7fmsSe\n6Jc4aqu1lv9EcCkTJFu3bgESMYDzmkyUowOX6KI1hPBJtcVNU4esFJI+muiYFGSOFpFkyEem6TEp\ngRRtsrAdAtJHZsP3ZymIdbcBwOtc5VpLIFqivrNuLcN24OrqSnnwdb6cY5oOHPfXTJvC1a5n2/e0\njPErWrR91fh5eNT/Cy8p4JyMf+8N9jEC/7H9+ZdqVKmGNx5nUILuhJOdOLeErK/yVd8GNqirJ9Sk\n1Cs2sqTTyX6/5ie+ycKWNx+O157SysB8xdf1L2NPvPaM1wZvBROsE3l1P80jXXmvrzq+tZzGVx3l\nuly/HetXsAfe5LK9JOR0dnDu/DPeTqdi5UCfvHdyr58/E6cvXz7GE+bJ63535RW3rc+nS84OcDWf\n7YJ81fyeP9ur12fn84uMe62PNxjOaZi57l5cSqKM16Q7zYd20x2SxqYd7cJM6Gdma0Hvg4Y7Jc8g\n2TyYgGCqbUEYtpekwy2SZ/VsciGnsZWluxhwYaFDTePEzd0tT75Uivp4zByOiRA3dBKZpol5PpJF\nKKIe09V2w8Vuy3anHmgWkCwrXRo14MG8t4uLC6NC1ZvZW/FexUpf+5j88i7Aa4aIh2KVhL4qU9fw\nOyJOKOZR/vjJl/zw8Zds3tOcQfbKh459p06O94xTYp6LZfktPHUOH6DvFzwx5IJE81C7YFl+pd+F\nGFXvAa3ybOXFujrazKjiXbbcQwiBodLW0H1Iyezt3hpjxzQezGv35rFpJaN3jiAeIZLFoQ3pHT4M\nCEXphoALDnxqvHIVPepwvla5RsDjfFhYJy0CcIZF184nNdrSdmm++m3Wt+FkwXG0qk7VNWV1a6ho\n2IKoKM1NHBTL02hzYGnebOV3t05JUbU1sjWIEOvOon8XVeTzGSkjlKJ/54nogKDNNDbdFhd6sjW3\nLZJOFupQueU1WhbAKzurQjvFKUSjHeQEZObRhee9ruP9Rx2bIIgkRqt2DOHV3YNeN+4N9SvGq8pt\nATKBQtSExHzLfPOY8UstSWZ6gaQj+HoDzbi4J436ugsX+P6SOU0a7oWIH7bgNXzyXeTi6gMOqTCn\nW5xAmWYSI0raRcvHmchmuO8Otzx59pRPP1cOaU6ecfT4MBDp8QFyKpYcg9B1PHh4xcOHD3j4vmJ6\nqQgUgw9Q4+J9YGsCQO89fMjDhw/VKKKbaels+Mob7dfigIsHieYlZTuFyq3uSEUY7dr9849/yh98\n/CkXH/4WAOP+GbJ/Rtjs1PDFyP4wMY6Z2RKMwath9NExVKy3FGSGYJCK7zriMDRKn/Oa8M3WDFVE\ny5zDKgGpi/BMMkPd9Vt87BbpU6fNU2+vn1KphwqpXJlOdaDrB3a7XeMlb/uewyxMqeqe9GhnGdOB\n8eB8oog6Ds4FgutwFSt1ASFQ15Mq1Vl1OUQUf/dtkZZKKlzgFdPjrjBaQReIliT1nqag4mjJVicm\n8IVDfKB4wJUGx5SiiXD01xTqyaa7IR3FeaaiUFQ2qmRhNi0VwflMKTfaQSnPuPmo3X+8Lkzb4RGT\nRKZUT0PwsixI3srEczXUzhNCpN90CufEjuwCqeiseBFKOvJXPrrgw4stV1c7rgaPlMRk+4ixa8Vy\nbzLuDfVbjFKLiUTw08Th6WOeffwXADzohQePLvnBD74HwKEbuH56y2Se526j8opYBZxIZmYixE6N\ngXNsNxuOIbQmWKSJ6IsJ1MD19Y1i1JMmUo7HI8f9Xr090aq5nA4c9kfVLi7Cw4dbbl7sSbO2+rq7\necFmGPjgg29RSuHLL79kHOcGnR2nmc12x0cfqefZ9wPznIhxMTLhLW6wX+kIoVZyMOeRAMSVjOT+\nOPP4uT7AL14cGceJPlnEQ6HvOuuYo9FNyWlx+ET1PhqfedWBWqMO36ClUkozwtWQRAwvdzTx+Fr4\nIVm7fK8TfWvlOl9517GrvivgmOep/e5hf+Bwe6ctnmLkwdX7bLqBTd8hUnhx84IwBLqt8XZzhgJD\nt7PJc3gfyXkCHC53+LOWWRVrrcfWdZ1VTeo20zQyz1Mzml5MkMgKQ2LUis0mNmVj3fDZsVRi6jTX\nKOSct10veSDGgMgyd6UUZmMt5ZTJKTe4wntVLyx5piRl5jivYv9diDjnraIwM5vw2NBHzRvNiwft\nnSdYtWHXDXSbHX2/1XkKHig4q0QtubA/jPzO7/xN/tpf+S5DH7i8vFINcXMk/NdAVufj3lC/zVjP\nq9G3cr1Bgt7Y3WDtfIq1GLKyw1dek1bUoC+dlZIvH1evRUfO2i0jV6ZJSoiURZTe1Sx8bje7hljm\n4RqVqj7ci+jPQqkqRUt3l9Ljl/vivSt4tUKKVdjHcOLVh6UIyR6+kqs29zmG7JrXWE/UvYTBvhro\nP5mGmos4yz0s13fpHHPyeTuR5bVbbV+xWvUP9F/K3oBEWincFUUWvFOBfymI+JO+hODaYlGPX7H1\n5RwX6tyCYawLm9RoL+exLoSRIrXBUT0hu/e+7n55nfBTPZRVk9/632qfp1req3uhXY9VIlVo39eF\n0dn8nl+XldiVmIpijYicbwVLzvulJRqGrRu7Zhg2XF5d0cX6W9Kux9s+QfeG+i1GtYeIMN7d4nPi\ncqg8XfUsqk6xuIj3XSsTFinM89hkHouo7rAPpsEB1s9wixQ1wONhVGNsCak0HZmnkWwrfVZAkqGv\nx+AoF1BSoGRhHDPP04EuOCSbcLlpU/jQUQpcX99xPB5pzQhip/SwaF3Jjfu6Hs0gvgsG20Ln4AMO\naeGpd5H9fuRnnynfdp6L6mrnRYbWe0eMBuGIo+SyoNxOVob/zAA3w6KvVCGvfXjqCTrdfs12KCeq\n5rxkeOp2dd5FMFbXojaXV2qGUoTbm2u6TSJE1Q4f+kAfAzVU8jj1dg2Tzjlr1aSjMS/Or+m5MW6c\n7XacyzVo26/OvXZi/ypJBiqMImYsa67kzHA2Q+jd2X0nbV5O9luPy6HaOrl62sXgd29/lkWtQZxl\naXCsv+9xMTaP2ncdLgQ10HYIZcUnL6Kl9Nn+hHVn+3aM0lQy32TcG+q3GFpAoRfi+vNPGcY937NW\nT4+ff8F+PNKZsI4MHX23A6MVzWnmLj3n/UcfEUNknhPzYQ8xGi7oiLHn4aMPKA8fkNPMJz95xvF4\nIJpB3u9fMI63mpAEUp7wXnj40PQrUubhlfDdb6m2x4sXd/wo/Yx5H4imC+EcxG6g63fkcuSnP/2M\n29s9vUUC3//+D9htL7m40H16H0+kVqu34N4F+MMEmR0wxIG5HEnWgDT6LZ998ZR//H/8EQC3N4UH\nuysO0x0A3hViDGw3KiU7T4n9eMQhBK8wF6Lq4x5PLX6ojRFDNXhoQ4AaRsvKaLTkoHlgwQdERGEo\ncY0nXVnS56L9nWmCVzGlaoqUr73qOFIK18+esL24pBs2hBD43m/8BsEHsulXdD7Sxy3eqHT7fNBk\nqjdj4RfDWuGEXCMw51h7znqM1btlMa5S1OjX8w6BEMJL89H24xRGW++7ItHr+20NC7naSzKt7sf1\n90V1WVpzmCIIiTTN5HkERBOePhi/2zFlTR7Xw5qnGdU60deh7wmbHcOFUm1dCIpLs5x3mTNVFrcU\nhVLGlNnPmYxQ8KoRYyFHkax1GW847g31W4wsok1OHXz43e+w/9EXPPlUCy6vvvs+/uEjJrvYEY8X\nKMYC6QPgHddPfooIxNiz3V1xnPYUQSsOL65U0MYHfOf49nd/g9vrjsNeWR0Pr94jbXuO49GOKOFd\nIhqndLjcMHQDhzttQtp7uH3/Clc806QJxZIC0UfSLHjX8bf+1t/mn/6zf8Yf//EfA/Cd73yPzWaj\nPGRo0EKFSapewzsxzhyr4EOrRHx+e8cXX97w+EvrHC07dtsO35nXk9QwbYcO7wOHkrnLVs2IdpdP\nDqL3SPCk0vCJE8NRYQnvNaln6bUVXKD6xM4vlYjV6C7elZrqsDqhc4jEG95d4YvgNDmpnn2BFBjT\nxHEecc5x+OGRR+894uGl8t+7TrXM52PtUqL389HKWns/E/vSksT+zICCsj60kMcZNp/xztPXqNE7\nnI+rBcfunVVz27AqePE2l3pvSdvmqwI175d2cthce5YGCOI9xQUq0QRJWpS28l6dD+TimDIgwlyK\n0e31SyllZeJY04l+e0m/ucCZRy040pKW0LyN00a+2qYLQnT8wR/+EZ9/+iMeXG34y9/7t7nY7pZr\nXk6bjnzduDfUbzFqRw2AYbvh4GAyo9n1PX4zcLT7oaGlq9DTOeE4jip0MwjBXyHr4hIqLqiqaZvN\njvEwcLQ7oo8d3g3NaLqKATLjHPRdYLvtLWmi4e/Qd2w2XcueZytAqJjle4/eJ8aOuzt9gEVUNMit\nw+41DrnCC7/x4dr/7OVSMj2lzHGaGSeLcKJ1YLfts13M4FXsKJjB804NjlhMqzjxGpqoSnX2SpZi\nEG2F9TIGfdLRRJagfsGol6RdHbL6/ASjtXDbhQCVeSMFSNpCyoziXBK77RYxlTnvNJm5srvGZFCq\nqFYsSsOUXw1XmBiXLHi5gzbnpSzGt81VhTTOz9nOe9nvCj45286d7fNlgHclrdpQ7DbdBnes4Shv\nmP/6/l6dpUE8FbYMIWrS3yKgUpTO2hQ0VwC4M/jGOXj+4hqZbxmPO+0OczKlp/mSrxv3hvotRrs/\n7A7wIRINMqhTXmUl9fnJi0aDBNMOLstN7uyur/+uiSIzooobuxaCeRfpun7psuJm9X5WyaiUZsW1\ni1KYqm4FdkOO02SKaBUHVa70+0bX6/uhedHrs14bindtnOSLzHiP08ycStNnEEvMNS1tr7rbjdrr\nVjkIzt5bx/eVh9GeMXtQ2xdX2DXr75tROMO7XznWz/75ua7tzeq64zwxdhS7rqHTnozjqKyXlBOl\nZHKufONymug7+c1XQBVnC0l9X6rFrzt56fY4NYKvO6dzB2ANj7yE+a+OpUIlp4b+xOoqVNe+rots\ngZWDdHrQPihHulaCKq2Q9iwvi8mKsro22XYPzilzGBPD2HEcM6lA75cjeZtxb6jfZthT54CSMpvL\nS/z3fwDA5KHzgUcfafPK2+uJw+2dlrcCXRzoYo/IrKGv6/AxE63FkvdFi2jK4u32cUdhYDLR9EcP\nrrjYRSvugFIOiOyJ1kLo5uYFz54+4XhQhsP19Z7r6+ekuQM80zTz6WdPGKdENH1fgL/6r/0O3/rw\n2wBcXF4qVa1FikItjX7nxurBzjMQfNNj+PTz5zy9HtlcaY+6wzHjS+Jqo+Hr5GZShk3UxSoFiK6o\nTIBob8zZQQmOEhyWFrAGr2daxd48efO+vaCFG6gB9cF0SFYGzbmwwl0tggkrSGkdqju3dI237/vg\nG9yioYHjcrtDvDI9dpeX7G9v+ewzE6ScRubjLYejatGIF1zniVUwyfnFxWwLlLEaAKR6i0tcKUkb\nM9P0QCInBugVtujlpOkqYqsB5QoX0kKzM+pijO1+9N4pJNPEpgpSCtEEp6TMlHnURbrCVjGQijBb\n/YBzxmm2hPpus2EYNq1vqI8Dxanhpc6Fd8QKEzm9d8gGFzkQD0+e3/CkHHlxd+Qnn93x0fvv8cGV\nPXfWWuxNxzv49L27o3baQAQXO+Z+xzRogiFeRLIbePJEOc59t2N3eUmelgRGnhK7q0vF+Jzj+fVz\nXNio5yzZWCCD4axiYelA3yvOGMOOXBIHwxlDyMSwKpN1gRh6Nhs1DOPo6Yc7Hn95zf44k5Iwi6rM\n5ZbRdlw9eMB2p/xaFb5feK8Lhe3d86TLCkbwwTEmYbIu44+fTrzYZzBcMZYJnxPRPBqJioUGld8h\nusIQMWobJOeI0SHZkb1rtQm1R2K1m+JUyCpnE25yohWsou9741znXBY6ZA2bT2CARVtCNa3dojUh\nqoldC2ic5TGMTKD/c5HQDa3JLq4HFrx3Gm/I6YahU0x6Spk8aWEngAs743rXnRrNs2Hj5lmWxXst\nUgtW6uvMuhCq9hZsp+jcqecscho4tLcXLz2vkpmgOt8xBnJ1HKRQcmEyrN2JqDh/yYgTcpqZxoOy\ngaSA9/jitHjNFoZ+6Ind0DzobujwsaOC0EWUNVLJOk2re9Yd6HUQJCtbRHAEAsUrA2fMgf/z//5T\nvvVez6N//ft2uSPyq1TP+//1kKV7RfGBFDrmoIag77fMCLe3ulK+996O7WZgboI1mojpthucD0zT\nxN3dHdutNvJ0NUSLrmXKc8o4AjFqlaD3HSUnjhbO9t0SrutheULo8F6LOPo+03UD+3Hi5u5IKVgZ\n/Cq0BGtyu3jYpZxieu+ikYaK8tUklCMXYbJs7t0hM87Sqr98MG+3VTNX2EMbxSptccGbRZ1UNdIr\n+GJBLhYIZGF6iFruwArL1S9Vo7aE5i9jtTWUrlCDX78WsUpBWmR3auQskWf3YzFnoDYbKGVEypHe\nnviUJlKGkvqGc/vVopwrDu1W0YPdbA63BngXA1tE6waWg8J5t+p0TjufirVXg3yCx6+203Zga4qf\nOykWKlkjnLoIBgBvi53o4pHSjFSORhFEop5fxbOtmrRRUkM03N2e9QrxtFGgKFPEOe0C5JwVPrXz\n0QSz5js8n/zsMc+vb6t4HlUN8U3HvaF+y+HW/3B+KQN13i5OrZiyC9GecH/yviaYVvX+FjZXLLm+\ntw7v5eyGqbzTk2IAV6vmNOxuz7is2bIv3yCv47rWz95NYy0nx3bKL170SwC1mutTaLjzYjxX+T5z\nVKsBPf3iCZzr2m5aMnhtaKrDW1+vfrx55XWbkytgNv9knGPXr7gmZuObMa2ypl2MuE1PH3QHc0qk\nViV7Sr1reO9qp6vpsvvw7EdX5/XLHufsk5Nn5PwYVu1m1rzs+voUkzco81URY/1+feHW+5ITZ6dd\n4xphtENYOpSnVJhTZrauPJ0/O/6vGfeG+i3G0DwZz8F3uMtH9JstoDZ76x27y0oTCmQXCDv9XLJW\nC8bNA+WCdgnCQBc682QCIUYthEnK3w0hagmuhe8vrm9wYSRYkU2RiVSErZUFdz1st4HN5j2c8xzH\nJ9wcP+ZuLOxH0YUhDPgQW8UiKB2pJpmiFbhUTHCatKHqxrQ/3qWh/FRdibyH65sDXzxWWOiLJ0f2\nYyFsatwuuFkIVsI7z5lcEqXMVghU2A4dkzWldTj66JEcyDHQd0tLKwdIrg6lkETIZhB9oHGsAVtI\n/ckiUk15M29nhqJIbUJrHqxTnWlx6rEp5S+cGEv1gANIsEU6kKeZca/CTr/3N36b3/hoR+cUbP/D\nP/xT/sWf/ITrG210kcKO+TKpuJSv5fHanLYaLI3wq9FejNXJMnZ2LiIKp8HiIa+htWrYqp3VytlT\nrzqXolg44GYMBir1C5yuyLpQzfMIIqQ0InkilayG2gdC7AnDBhdUviEMW2I3NLw+l9IqWnV/gvNl\nlRRWj7pC1uqgOaout3eeIfZkIhnPOAt//uMv+MM/+oT3L/VZ/e3f/JDylSKkp+PeUL/FcGJd8UTI\n4nHDJf3OMOqciEjDQGfRNjaJaN5SQbKQk/Fpi3aviKFTJoI9fG5dXIHgo6cf1FCPSW/6YWsC52Mh\n5ZlxVCN7HBO3t0e+fPoYwfHZZ0958vQG8QP9piMX4e6YmUs5EapVEZ1FOwEWb6GGnc3IONdEd77p\nUYXsq9l7/vyWn3z8BIAX15m5eKJ1JQ8odFCsMLF6iVoFqhWbfafC+cUYODF6cvLEGCjd8qg4XMN2\nJRf8WrvCOQ2hrfLtJd65JaLWXOCaSKxU7WyhvFsl0Lz37bUzUaC1p+mtJ2OZ1KB48eyGDdsPNJn6\n8KqjC3tm4+R//zsX7Lrf5vPP1XBfz5Hn+71WopoRVghu3fGlUKluWvikDRZaniCqiuBSUake/avK\nzc+LaNrrstxn9e+SM6kaamd4fbJmzCz3af08l0RKk8ETJiRlIUsVURo2W0K/NTisB6sW1jvJazXw\n2osveu76sopR+RZ5OZb1QsQcM2V4UxDGlPmTH/4UmXRhvNr+nUW3+w3GvaF+q2HxqCjiFeJAN1ij\n2XkmlERT3NKtmcsCNYgI86hZci9Ch8f1wTwM89ZdhSyshZNzRDMSuQvEDgbz4lM6Ms3C4TjhHBwO\nIzc3e754fEPOwpMvb3h+fYuPV2y6jnGcObx4Rqpl7nZWPoTm5ZxXetVjq4Zan4d3x1B7CzkhcXd7\naMnc41iQEFq3IxeguELOi9FYOMhaydZFxzypF6uJMMV4K9caoJSgOalqVGHhThvspJRAZYE0I7sC\nuauGRzuPYEbY3izmRbp6jBVPXxkv5cWXJXEXA9PECqsNPNhuuXzPFmC54/mzz7j58hMAvv+93+Z7\nH/4l3rvQfMeff3bNZz+7YZuSVcsas6RI07Ko0Ftlf2jn9KViMnp3UtACGhk0t2NllKu2xwmIYl68\n4/RcqycOCtk4KYjxxWt3+OalS9Jy8VyLXAo+OIKZdBcCIXb0w4Zu2CosmJ2xeez4agJ0bZhl8epF\nVII1rGQBHJ6i8Z3+ySqLjA/ghNBv+fyLpxyeqdrm3/29v9qaWrzJeEdKzP6/OX415uxN9vpuGNL7\n8YuO++t4P3Tce9RvOLz3DZfDeS76rXpeqXpo3jApfV3lUOPKgxMn2kCzeWwaLuKrFJBTjnUxjQkP\nQz8QzWsYDxOHwzPmWfWnvSt4cdwZ0+Tm9sDTFwem7CglgO/Ybrbc7kfVwMiFy0Hoa/MBsWawbqno\ng/McpvoI1aNsSaX24psbXsSgKLjZw2fP9/zksYb2oXuPbRzgYJ5w3ijEEY0QnY8gCdzWil8KqSRS\nyeScKaJYaYyergvkXLHtRM4zOdUQPbDpB1LSe8MTKDPEPpoAl1NxfAwysryUemIGbTh3wlxAxDi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ZOFUL/0KgP/cvXryiyf2f6Xu/JgXGHXPkfkRCUx54T3dZHxi4GtgUSDYxaYR2w/DuWNn3ia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\nf3txzFf4Z33d4z6nt2hO/hbwvz7gb97pc/JuP5c//rTfJ090RC0iP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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbdec506898>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "\n", "from keras.preprocessing.image import load_img\n", "from keras.applications.resnet50 import ResNet50, preprocess_input\n", "\n", "\n", "# TODO: allow for this to be parameterized\n", "img_width = 224\n", "img_height = 224\n", "\n", "\n", "# TODO: replace this listdir with a mapping tbl/json\n", "CATS = sorted(os.listdir('data/raw/train'))\n", "def cat_from_int(cat_int):\n", " return CATS[cat_int]\n", "\n", "input_dims = (2048,)\n", "nbr_classes = len(CATS)\n", "\n", "\n", "# res50 = ResNet50(include_top=False, weights='imagenet',\n", "# input_shape=(img_height, img_width, 3), pooling='avg')\n", "\n", "\n", "img_path = os.path.join(os.path.expanduser('~'), 'Desktop/test/yellow_perch_2.jpg')\n", "\n", "x = np.array(load_img(img_path, target_size=(img_height, img_width)))\n", "X = preprocess_input(x[np.newaxis].astype(np.float32))\n", "\n", "x_fea = res50.predict_on_batch(X)\n", "\n", "y_pred = np.squeeze(model.predict_on_batch(x_fea), axis=0)\n", "\n", "print(np.round(y_pred, 3))\n", "print(CATS)\n", "print(cat_from_int(np.argmax(y_pred)))\n", "\n", "plt.imshow(x)" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1. 0. 0. 0.]\n", "['carp', 'walleye', 'white_perch', 'yellow_perch']\n", "carp\n" ] }, { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x7fbdbcacada0>" ] }, "execution_count": 80, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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K9C2z6pLtm2Pa1qGkJIokdVuGsVOKXq9DVTW0bUuSJFyeTxiNtrl9cBuA05ML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swTbDKLFDFRpPFCA4V0egP26hF5YQJ5JV7x7OszstUSrSQ1HkdHpzx/9rQ1Dgj9AHd7TK/XRwrB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2ttt00eE3mGj6+XxOGIY2kfr6+pq37664vRVO7cuXL3lyfsrV1TVKwe3dNW/fvuby4hLP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"text/plain": [ "<matplotlib.figure.Figure at 0x7fbdec506748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib import pyplot as plt\n", "\n", "from keras.preprocessing.image import load_img\n", "from keras.applications.resnet50 import ResNet50, preprocess_input\n", "\n", "\n", "# TODO: allow for this to be parameterized\n", "img_width = 224\n", "img_height = 224\n", "\n", "\n", "# TODO: replace this listdir with a mapping tbl/json\n", "CATS = sorted(os.listdir('data/raw/train'))\n", "def cat_from_int(cat_int):\n", " return CATS[cat_int]\n", "\n", "input_dims = (2048,)\n", "nbr_classes = len(CATS)\n", "\n", "\n", "# res50 = ResNet50(include_top=False, weights='imagenet',\n", "# input_shape=(img_height, img_width, 3), pooling='avg')\n", "\n", "\n", "img_path = os.path.join(os.path.expanduser('~'), 'Desktop/test/carp_1.jpg')\n", "\n", "x = np.array(load_img(img_path, target_size=(img_height, img_width)))\n", "X = preprocess_input(x[np.newaxis].astype(np.float32))\n", "\n", "x_fea = res50.predict_on_batch(X)\n", "\n", "y_pred = np.squeeze(model.predict_on_batch(x_fea), axis=0)\n", "\n", "print(np.round(y_pred, 3))\n", "print(CATS)\n", "print(cat_from_int(np.argmax(y_pred)))\n", "\n", "plt.imshow(x)" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda root]", "language": "python", "name": "conda-root-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
oudalab/fajita
otherHelperCode/jupyter_ldacluster/lda_cluster_prodfinal_english.ipynb
1
5071
{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2h 52min 49s, sys: 33min 51s, total: 3h 26min 41s\n", "Wall time: 2h 50min 56s\n" ] } ], "source": [ "%%time\n", "from pymongo import MongoClient\n", "from nltk.tokenize import RegexpTokenizer\n", "from stop_words import get_stop_words\n", "from nltk.stem.porter import PorterStemmer\n", "from gensim import corpora, models\n", "import gensim\n", "from sklearn.cluster import KMeans\n", "import numpy as np\n", "import pickle\n", "import time\n", "\n", "#start_time = time.time()\n", "\n", "client=MongoClient()\n", "client=MongoClient('mongodb://localhost:/')\n", "db=client['eventData']\n", "sen=db.documents_english\n", "\n", "tokenizer = RegexpTokenizer(r'\\w+')\n", "\n", "# create English stop words list\n", "en_stop = get_stop_words('en')\n", "\n", "# Create p_stemmer of class PorterStemmer\n", "p_stemmer = PorterStemmer()\n", "\n", "texts = []\n", "docIds=[]\n", "actuallyTrained=0;\n", "for i in sen.find():\n", " try:\n", " raw = ''.join(i['document']).lower()\n", " tokens = tokenizer.tokenize(raw)\n", " stopped_tokens = [i for i in tokens if not i in en_stop]\n", " stemmed_tokens = [p_stemmer.stem(i) for i in stopped_tokens]\n", " texts.append(stemmed_tokens)\n", " docIds.append(i['_id'])\n", " actuallyTrained=actuallyTrained+1\n", " except:\n", " pass\n", "\n", "dictionary = corpora.Dictionary(texts)\n", "corpus = [dictionary.doc2bow(text) for text in texts]\n", "ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=20, id2word = dictionary, passes=1)\n", "\n", "dim=20 \n", "result=[]\n", "for i in range(0,actuallyTrained):\n", " feature=[]\n", " previousindex=0\n", " for item in ldamodel[corpus[i]]:\n", " index=item[0]\n", " #print(index)\n", " for beforeindex in range(previousindex,index):\n", " feature.append(0)\n", " feature.append(item[1])\n", " previousindex=index+1\n", " while (len(feature)<dim):\n", " feature.append(0); #add in 0 at the end\n", " result.append(feature)\n", " \n", "kmeanstest=np.array(result)\n", "kmeans = KMeans(n_clusters=20, random_state=0).fit(kmeanstest)\n", "\n", "#and before building the dictionary test if the size of docIds and cluster result dimensions are the same.\n", "try:\n", " assert(len(docIds)==kmeans.labels_.size)\n", " dictionary_cocId_topicClusterItBelongs={}\n", " for i in range(0,actuallyTrained):\n", " dictionary_cocId_topicClusterItBelongs.update({docIds[i]:kmeans.labels_[i]})\n", "except:\n", " print(\"the docIds size is different from the topic # cluster size\")\n", "\n", "with open('traingrst_english.pkl', 'wb') as output:\n", " pickle.dump(dictionary_cocId_topicClusterItBelongs,output)\n", "\n", "#print(\"--- %s seconds ---\" % (time.time() - start_time))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1161388\n", "CPU times: user 3min 32s, sys: 24 s, total: 3min 56s\n", "Wall time: 5min 57s\n" ] } ], "source": [ "%%time\n", "dic=pickle.load(open('traingrst_english.pkl', 'rb'))\n", "topic_set=0;\n", "for i in sen.find(modifiers={\"$snapshot\": True}):\n", " try:\n", " #find the topic from the dictionary\n", " docid=i['_id']\n", " topic=dic[docid]\n", " #print(topic)\n", " sen.update_many(\n", " {\"_id\": str(docid)},\n", " {\n", " \"$set\": {\n", " #this need to cast to int, otherwise it has a can't encode object error after it use pickle to load.\n", " \"topic\":int(topic)\n", " }\n", " }\n", " )\n", " topic_set=topic_set+1\n", " except Exception as e:\n", " print(str(e))\n", " pass\n", "print(topic_set)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
statsmodels/statsmodels.github.io
v0.12.1/examples/notebooks/generated/categorical_interaction_plot.ipynb
5
19299
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot Interaction of Categorical Factors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we will visualize the interaction between categorical factors. First, we will create some categorical data. Then, we will plot it using the interaction_plot function, which internally re-codes the x-factor categories to integers." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "from statsmodels.graphics.factorplots import interaction_plot" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(12345)\n", "weight = pd.Series(np.repeat(['low', 'hi', 'low', 'hi'], 15), name='weight')\n", "nutrition = pd.Series(np.repeat(['lo_carb', 'hi_carb'], 30), name='nutrition')\n", "days = np.log(np.random.randint(1, 30, size=60))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 432x432 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(figsize=(6, 6))\n", "fig = interaction_plot(x=weight, trace=nutrition, response=days, \n", " colors=['red', 'blue'], markers=['D', '^'], ms=10, ax=ax)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
palcu/python-for-competitive-programming
python-for-competitive-programming.ipynb
1
25357
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python for competitive programming" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello World!\n" ] } ], "source": [ "# This is a comment in Python\n", "\n", "print(\"Hello World!\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numbers" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2 + 2" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "x = 5" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.5" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x / 2" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x // 2" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1024" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2 ** 10" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "19950631168807583848837421626835850838234968318861924548520089498529438830221946631919961684036194597899331129423209124271556491349413781117593785932096323957855730046793794526765246551266059895520550086918193311542508608460618104685509074866089624888090489894838009253941633257850621568309473902556912388065225096643874441046759871626985453222868538161694315775629640762836880760732228535091641476183956381458969463899410840960536267821064621427333394036525565649530603142680234969400335934316651459297773279665775606172582031407994198179607378245683762280037302885487251900834464581454650557929601414833921615734588139257095379769119277800826957735674444123062018757836325502728323789270710373802866393031428133241401624195671690574061419654342324638801248856147305207431992259611796250130992860241708340807605932320161268492288496255841312844061536738951487114256315111089745514203313820202931640957596464756010405845841566072044962867016515061920631004186422275908670900574606417856951911456055068251250406007519842261898059237118054444788072906395242548339221982707404473162376760846613033778706039803413197133493654622700563169937455508241780972810983291314403571877524768509857276937926433221599399876886660808368837838027643282775172273657572744784112294389733810861607423253291974813120197604178281965697475898164531258434135959862784130128185406283476649088690521047580882615823961985770122407044330583075869039319604603404973156583208672105913300903752823415539745394397715257455290510212310947321610753474825740775273986348298498340756937955646638621874569499279016572103701364433135817214311791398222983845847334440270964182851005072927748364550578634501100852987812389473928699540834346158807043959118985815145779177143619698728131459483783202081474982171858011389071228250905826817436220577475921417653715687725614904582904992461028630081535583308130101987675856234343538955409175623400844887526162643568648833519463720377293240094456246923254350400678027273837755376406726898636241037491410966718557050759098100246789880178271925953381282421954028302759408448955014676668389697996886241636313376393903373455801407636741877711055384225739499110186468219696581651485130494222369947714763069155468217682876200362777257723781365331611196811280792669481887201298643660768551639860534602297871557517947385246369446923087894265948217008051120322365496288169035739121368338393591756418733850510970271613915439590991598154654417336311656936031122249937969999226781732358023111862644575299135758175008199839236284615249881088960232244362173771618086357015468484058622329792853875623486556440536962622018963571028812361567512543338303270029097668650568557157505516727518899194129711337690149916181315171544007728650573189557450920330185304847113818315407324053319038462084036421763703911550639789000742853672196280903477974533320468368795868580237952218629120080742819551317948157624448298518461509704888027274721574688131594750409732115080498190455803416826949787141316063210686391511681774304792596709376" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2 ** 10000" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "30103" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(str(2 ** 100000))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Strings" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = \"python\"" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'pythonpython'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s + s" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'pythonpythonpython'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s * 3" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "infoclock\n" ] } ], "source": [ "s = input() # type infoclock" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'i'" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[0]" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'n'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[1]" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'k'" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[-1]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'c'" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[-2]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'nfoclock'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[1:]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'i'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[:1]" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'foclock'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[2:]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'in'" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[:2]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'infocloc'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[:-1]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'infoclo'" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[:-2]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'foclo'" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[2 : -2]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'fco'" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[2 : -2 : 2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lists" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [], "source": [ "l = [2, 3, 5, 7, 11]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "22" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l[0] * l[-1]" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 3, 5, 7, 11, 13]\n" ] } ], "source": [ "l.append(13)\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[2, 3, 5, 7, 11, 13, 2, 4, 6, 8]" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "l2 = [2, 4, 6, 8]\n", "l + l2" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n" ] } ], "source": [ "v = [0] * 10\n", "print(v)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[33, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n" ] } ], "source": [ "v = [1] * 10\n", "v[0] = 33\n", "print(v)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# s[0] = 'x' # this will fail" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['python', 'is', 'awesome']\n" ] } ], "source": [ "s = \"python is awesome\"\n", "v = s.split()\n", "print(v)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['python', 'is', 'awesome', 100]\n" ] } ], "source": [ "v.append(100)\n", "print(v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dictionaries" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{3: 42, 'f': 'fmi', 'a': 'alex'}\n" ] } ], "source": [ "d = {}\n", "d['a'] = 'alex'\n", "d['f'] = 'fmi'\n", "d[3] = 42\n", "print(d)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Key: 3; Value: 42\n", "Key: f; Value: fmi\n", "Key: a; Value: alex\n" ] } ], "source": [ "for key in d:\n", " print ('Key: {0}; Value: {1}'.format(key, d[key]))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "43\n" ] } ], "source": [ "d[3] += 1\n", "print(d[3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conditionals and loops" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I want 1 apples.\n", "I want 2 apples.\n", "I want 3 apples.\n", "I want 4 apples.\n", "I want 5 apples.\n" ] } ], "source": [ "for x in range(5):\n", " print(\"I want \" + str(x + 1) + \" apples.\")" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the answer to life, the universe and everything is 42\n" ] } ], "source": [ "this_is_true = True\n", "if this_is_true:\n", " print(\"the answer to life, the universe and everything is 42\")\n", "else:\n", " whatever # change this_is_true to False" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "I am number 2\n", "I am number 3\n", "I am number 4\n" ] } ], "source": [ "x = 0\n", "while x <= 5:\n", " x += 1\n", " \n", " if x == 1:\n", " continue\n", "\n", " print(\"I am number\", x)\n", " \n", " if x == 4:\n", " break" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## List Comprehension" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4, 6, 10, 14, 22]\n" ] } ], "source": [ "v = [2, 3, 5, 7, 11]\n", "doubles = [x*2 for x in v]\n", "print(doubles)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 9, 25, 49, 81, 121, 169, 225, 289, 361, 441, 529, 625, 729, 841]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[x*x for x in range(30) if x % 2 == 1]" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "N = 10\n", "[0] * N" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[[0]*N for i in range(N)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sexy stuff" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[25, 63, 96, 81, 90, 39, 23, 54, 8, 86]\n" ] } ], "source": [ "# Syntax for importing\n", "from random import randint\n", "\n", "v = [randint(1, 100) for i in range(10)]\n", "print(v)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[8, 23, 25, 39, 54, 63, 81, 86, 90, 96]\n" ] } ], "source": [ "v.sort()\n", "print(v)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'1 3 7 13 2'" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "line = \"1 3 7 13 2\\n\"\n", "line.strip()" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['1', '3', '7', '13', '2']" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "line.strip().split()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 3, 7, 13, 2]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[int(x) for x in line.strip().split()]" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 7, 13]" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sorted([int(x) for x in line.strip().split()])" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 13\n" ] } ], "source": [ "[alfa, beta, gamma, epsilon, omega] = sorted([int(x) for x in line.strip().split()])\n", "print(alfa, omega)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7\n" ] } ], "source": [ "beta, epsilon = epsilon, beta\n", "print(beta)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Smart stuff" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 8\n", "2 10\n", "3 10\n", "4 12\n", "5 8\n", "6 7\n", "7 8\n", "8 10\n", "9 10\n", "10 17\n" ] } ], "source": [ "# How to count number of appearances\n", "from collections import defaultdict\n", "\n", "v = [randint(1, 10) for i in range(100)]\n", "d = defaultdict(int)\n", "\n", "for x in v:\n", " d[x] += 1\n", "\n", "for key in d:\n", " print(key, d[key])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Combinatorics" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A B C D\n", "A B D C\n", "A C B D\n", "A C D B\n", "A D B C\n", "A D C B\n", "B A C D\n", "B A D C\n", "B C A D\n", "B C D A\n", "B D A C\n", "B D C A\n", "C A B D\n", "C A D B\n", "C B A D\n", "C B D A\n", "C D A B\n", "C D B A\n", "D A B C\n", "D A C B\n", "D B A C\n", "D B C A\n", "D C A B\n", "D C B A\n" ] } ], "source": [ "v = 'ABCD'\n", "\n", "from itertools import permutations\n", "\n", "for p in permutations(v):\n", " print(\" \".join(p))" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A B\n", "A C\n", "A D\n", "B C\n", "B D\n", "C D\n" ] } ], "source": [ "from itertools import combinations\n", "for c in combinations(v, 2):\n", " print(\" \".join(c))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The End" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Zen of Python, by Tim Peters\n", "\n", "Beautiful is better than ugly.\n", "Explicit is better than implicit.\n", "Simple is better than complex.\n", "Complex is better than complicated.\n", "Flat is better than nested.\n", "Sparse is better than dense.\n", "Readability counts.\n", "Special cases aren't special enough to break the rules.\n", "Although practicality beats purity.\n", "Errors should never pass silently.\n", "Unless explicitly silenced.\n", "In the face of ambiguity, refuse the temptation to guess.\n", "There should be one-- and preferably only one --obvious way to do it.\n", "Although that way may not be obvious at first unless you're Dutch.\n", "Now is better than never.\n", "Although never is often better than *right* now.\n", "If the implementation is hard to explain, it's a bad idea.\n", "If the implementation is easy to explain, it may be a good idea.\n", "Namespaces are one honking great idea -- let's do more of those!\n" ] } ], "source": [ "import this" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
remenska/iSDM
notebooks/old/Species mapping.ipynb
1
3376076
null
apache-2.0
chappers/sklearn-recipes
streaming/getting_perf-uci.ipynb
1
9233
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import glob\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from grafting_classifier import GraftingClassifier\n", "from sklearn.linear_model import SGDClassifier\n", "from ogfs_classifier import OGFSClassifier\n", "from osfs_classifier import OSFSClassifier\n", "from dpp_classifier import DPPClassifier\n", "from dpp_classifier_dppsample import DPPClassifier as DPPClassifier0\n", "from dpp_classifier_mitra import DPPClassifier as DPPClassifier2\n", "from dpp_classifier_ogfs_dppsample import DPPClassifier as DPPClassifier3\n", "\n", "from sklearn.metrics import log_loss, accuracy_score\n", "\n", "#import dask.dataframe as dd\n", "#import dask.array as da" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['uci\\\\Ionosphere_train.csv', 'uci\\\\spambase_train.csv', 'uci\\\\spectf_train.csv', 'uci\\\\wdbc_train.csv']\n" ] } ], "source": [ "class_train = glob.glob(\"uci/*_train.csv\")\n", "print(class_train)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train_label(fname):\n", " targetname = fname.replace(\".csv\", \".labels\")\n", " return pd.read_csv(targetname)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_performance(mod, fpath, mod_name):\n", " train1 = pd.read_csv(fpath).fillna(0)\n", " y = np.array(train_label(fpath)).flatten()\n", " \n", " # simulate streaming...\n", " # try splitting into groups of ~10,\n", " # if there is no splits, try ~5.\n", " train1_cols = np.array_split(range(train1.shape[1]), int(train1.shape[1]/10.0) + 1)\n", " if len(train1_cols) == 1:\n", " train1_cols = np.array_split(range(train1.shape[1]), int(train1.shape[1]/5.0) + 1)\n", " all_cols = []\n", "\n", " #mod = GraftingClassifier(max_iter=5)\n", " if mod_name == 'Base':\n", " mod.fit(train1, y)\n", " results = {'accuracy': accuracy_score(y, mod.predict(train1)), \n", " 'logloss': log_loss(y, mod.predict_proba(train1)), \n", " 'feat_dim': mod.coef_.flatten().shape}\n", " return results\n", " \n", " # lets normalise the dataset...\n", " train1 = (train1 - train1.mean())/(np.maximum(train1.std(), 1))\n", " for idx, collist in enumerate(train1_cols):\n", " if idx == 0:\n", " column_list = list(np.array(list(train1.columns))[collist])\n", " mod.fit(train1[column_list], y)\n", " all_cols.extend(list(collist))\n", " else:\n", " all_cols.extend(list(collist))\n", " column_list = list(np.array(list(train1.columns))[all_cols])\n", " mod.partial_fit(train1[column_list], y)\n", " \n", " # debugging\n", " print_cond = True if idx % int((len(train1_cols)/10)+1) == 0 else False\n", " if mod_name in ['Fast_OSFS', 'DPP', 'DPP3', 'OGFS'] and print_cond:\n", " print(\"\\tmodel: {}, iter: {}\".format(mod_name, idx))\n", " \n", " # for fast osfs\n", " if mod_name == 'Fast_OSFS':\n", " mod._redundancy(train1, y, mode='all')\n", " \n", " results = {'accuracy': accuracy_score(y, mod.predict(train1)), \n", " 'logloss': log_loss(y, mod.predict_proba(train1)), \n", " 'feat_dim': mod.coef_.flatten().shape}\n", " return results" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_models():\n", " return [\n", " #('Grafting', GraftingClassifier(max_iter=5, random_state=42)), \n", " #('DPP', DPPClassifier(max_iter=5, random_state=42)), \n", " #('DPP0', DPPClassifier0(max_iter=5, random_state=42)), \n", " #('DPP2', DPPClassifier2(max_iter=5, random_state=42)),\n", " ('DPP3', DPPClassifier3(max_iter=5, random_state=42)),\n", " #('OGFS', OGFSClassifier(max_iter=5, random_state=42)),\n", " #('OSFS', OSFSClassifier(max_iter=5, random_state=42, fast_osfs=False)),\n", " #('Fast_OSFS', OSFSClassifier(max_iter=5, random_state=42)),\n", " ('Base', SGDClassifier(loss='log', max_iter=5, random_state=42))\n", "]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "#ex_dat = class_train[0]\n", "#print(ex_dat, pd.read_csv(ex_dat).shape)\n", "#models = create_models()\n", "#for nm, mod in models:\n", "# print(nm, get_performance(mod, ex_dat, mod_name=nm))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [], "source": [ "#ex_dat = class_train[1]\n", "#print(ex_dat, pd.read_csv(ex_dat).shape)\n", "#models = create_models()\n", "#for nm, mod in models:\n", "# print(nm, get_performance(mod, ex_dat, mod_name=nm))\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "uci\\spectf_train.csv (267, 44)\n", "\tmodel: DPP3, iter: 0\n", "\tmodel: DPP3, iter: 1\n", "\tmodel: DPP3, iter: 2\n", "\tmodel: DPP3, iter: 3\n", "\tmodel: DPP3, iter: 4\n", "DPP3 {'accuracy': 0.79400749063670417, 'logloss': 0.73235612201213274, 'feat_dim': (28,)}\n", "Base {'accuracy': 0.79400749063670417, 'logloss': 7.1147292199254215, 'feat_dim': (44,)}\n" ] } ], "source": [ "ex_dat = class_train[2]\n", "print(ex_dat, pd.read_csv(ex_dat).shape)\n", "models = create_models()\n", "for nm, mod in models:\n", " print(nm, get_performance(mod, ex_dat, mod_name=nm))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "uci\\wdbc_train.csv (569, 30)\n", "\tmodel: DPP3, iter: 0\n", "\tmodel: DPP3, iter: 1\n", "\tmodel: DPP3, iter: 2\n", "\tmodel: DPP3, iter: 3\n", "DPP3 {'accuracy': 0.85237258347978906, 'logloss': 0.4160592889007918, 'feat_dim': (16,)}\n", "Base {'accuracy': 0.91564147627416526, 'logloss': 2.9136401879713767, 'feat_dim': (30,)}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\chapm\\Anaconda3\\envs\\skrecipe\\lib\\site-packages\\scipy\\stats\\morestats.py:2397: UserWarning: Warning: sample size too small for normal approximation.\n", " warnings.warn(\"Warning: sample size too small for normal approximation.\")\n", "C:\\Users\\chapm\\Anaconda3\\envs\\skrecipe\\lib\\site-packages\\scipy\\stats\\morestats.py:2422: RuntimeWarning: invalid value encountered in double_scalars\n", " z = (T - mn - correction) / se\n", "C:\\Users\\chapm\\Anaconda3\\envs\\skrecipe\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n", " return (self.a < x) & (x < self.b)\n", "C:\\Users\\chapm\\Anaconda3\\envs\\skrecipe\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n", " return (self.a < x) & (x < self.b)\n", "C:\\Users\\chapm\\Anaconda3\\envs\\skrecipe\\lib\\site-packages\\scipy\\stats\\_distn_infrastructure.py:1818: RuntimeWarning: invalid value encountered in less_equal\n", " cond2 = cond0 & (x <= self.a)\n", "C:\\Users\\chapm\\Anaconda3\\envs\\skrecipe\\lib\\site-packages\\sklearn\\linear_model\\base.py:340: RuntimeWarning: overflow encountered in exp\n", " np.exp(prob, prob)\n" ] } ], "source": [ "ex_dat = class_train[3]\n", "print(ex_dat, pd.read_csv(ex_dat).shape)\n", "models = create_models()\n", "for nm, mod in models:\n", " print(nm, get_performance(mod, ex_dat, mod_name=nm))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:skrecipe]", "language": "python", "name": "conda-env-skrecipe-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
MarioAboytes/HS_SQC
.ipynb_checkpoints/HS_SQC(pre-data)-checkpoint.ipynb
1
827
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Pre-Proccesing for Hub-and-Spoke Network with Stochastic Quality Costs (HS_SQC)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This jupiter nootbook contains all the necessary routines to generate the data required in the optimization model HS_SQC" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.6.0-rc2", "language": "julia", "name": "julia-0.6" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
gabicfa/RedesSociais
encontro02/1-introducao.ipynb
1
129348
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Encontro 02, Parte 1: Revisão de Grafos\n", "\n", "Este guia foi escrito para ajudar você a atingir os seguintes objetivos:\n", "\n", "* formalizar conceitos básicos de teoria dos grafos;\n", "* usar funcionalidades básicas da biblioteca da disciplina.\n", "\n", "\n", "## Grafos não-dirigidos\n", "\n", "Um **grafo não-dirigido** (*undirected graph*) é um par\n", "\n", "$(N, E)$,\n", "\n", "onde $N$ é um conjunto qualquer e $E$ é um conjunto de pares *não-ordenados* de elementos de $N$, ou seja,\n", "\n", "$E \\subseteq \\{\\{n, m\\} \\colon n \\in N \\textrm{ e } m \\in N\\}$.\n", "\n", "Um elemento de $N$ chama-se **nó** (*node*) e um elemento de $E$ chama-se **aresta** (*edge*). Em alguns trabalhos, usa-se $V$ e *vértice* em vez de $N$ e nó.\n", "\n", "\n", "## Grafos dirigidos\n", "\n", "Formalmente, um **grafo dirigido** (*directed graph*) é um par\n", "\n", "$(N, E)$,\n", "\n", "onde $N$ é um conjunto qualquer e $E$ é um conjunto de pares *ordenados* de elementos de *N*, ou seja,\n", "\n", "$E \\subseteq \\{(n, m) \\colon n \\in N \\textrm{ e } m \\in N\\}$.\n", "\n", "Um elemento de $N$ chama-se **nó** (*node*) e um elemento de $E$ chama-se **aresta** (*edge*). Em alguns trabalhos, usa-se $V$ e *vértice* em vez de $N$ e nó e usa-se $A$ e *arco* em vez de $E$ e aresta.\n", "\n", "\n", "## Instalando as dependências\n", "\n", "Antes de continuar, instale as duas dependências da biblioteca da disciplina:\n", "\n", "> `pip install networkx plotly`\n", "\n", "Em algumas distribuições Linux você deve usar o comando `pip3`, pois o comando `pip` está associado a Python 2 por padrão.\n", "\n", "\n", "## Importando a biblioteca\n", "\n", "Não mova ou renomeie os arquivos do repositório, a menos que você esteja disposto a adaptar os notebooks de acordo.\n", "\n", "Vamos importar a biblioteca da disciplina no notebook:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>" ], "text/vnd.plotly.v1+html": [ "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import sys\n", "sys.path.append('..')\n", "\n", "import socnet as sn" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Configurando a biblioteca\n", "\n", "A `socnet` disponibiliza variáveis de módulo que permitem configurar propriedades visuais. Os nomes são auto-explicativos e os valores abaixo são padrão." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sn.graph_width = 800\n", "sn.graph_height = 450\n", "\n", "sn.node_size = 20\n", "sn.node_color = (255, 255, 255)\n", "\n", "sn.edge_width = 2\n", "sn.edge_color = (0, 0, 0)\n", "\n", "sn.node_label_position = 'middle center'\n", "sn.edge_label_distance = 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Uma variável de cor armazena uma tupla contendo três inteiros entre *0* e *255* que representam intensidades de *vermelho*, *verde* e *azul* respectivamente.\n", "\n", "Uma variável de posição armazena uma string contendo duas palavras separadas por um espaço:\n", "* a primeira representa o alinhamento vertical e pode ser `top`, `middle` ou `bottom`;\n", "* a segunda representa o alinhamento horizontal e pode ser `left`, `center` ou `right`.\n", "\n", "## Carregando grafos\n", "\n", "Vamos carregar dois grafos no [formato GML](http://www.fim.uni-passau.de/fileadmin/files/lehrstuhl/brandenburg/projekte/gml/gml-technical-report.pdf):" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ug = sn.load_graph('5-kruskal.gml', has_pos=True)\n", "dg = sn.load_graph('4-dijkstra.gml', has_pos=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Abra esses arquivos em um editor de texto e note como o formato é auto-explicativo.\n", "\n", "## Visualizando grafos\n", "\n", "Vamos visualizar o primeiro grafo, que é não-dirigido:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hoverinfo": "none", "line": { "color": "rgb(0, 0, 0)", "width": 2 }, "mode": "lines", "x": [ 0, 0.25, null, 0, 0.25, null, 0.25, 0.5, null, 0.25, 0.25, null, 0.5, 0.75, null, 0.5, 0.75, null, 0.5, 0.375, null, 0.75, 1, null, 0.75, 0.75, null, 1, 0.75, null, 0.75, 0.5, null, 0.5, 0.25, null, 0.5, 0.375, null, 0.25, 0.375, null ], "y": [ 0.5, 1, null, 0.5, 0, null, 1, 1, null, 1, 0, null, 1, 1, null, 1, 0, null, 1, 0.5, null, 1, 0.5, null, 1, 0, null, 0.5, 0, null, 0, 0, null, 0, 0, null, 0, 0.5, null, 0, 0.5, null ] }, { "hoverinfo": "none", "marker": { "color": "rgb(255, 255, 255)", "line": { "color": "rgb(0, 0, 0)", "width": 2 }, "size": 20 }, "mode": "markers+text", "text": [], "textfont": { "color": "rgb(0, 0, 0)" }, "textposition": "middle center", "x": [ 0, 0.25, 0.5, 0.75, 1, 0.75, 0.5, 0.25, 0.375 ], "y": [ 0.5, 1, 1, 1, 0.5, 0, 0, 0, 0.5 ] } ], "layout": { "height": 180, "margin": { "b": 0, "l": 0, "r": 0, "t": 0 }, "showlegend": false, "width": 320, "xaxis": { "showgrid": false, "showticklabels": false, "zeroline": false }, "yaxis": { "showgrid": false, "showticklabels": false, "zeroline": false } } }, "text/html": [ "<div id=\"cc55e5f1-b626-4c7f-a089-1991e956d437\" style=\"height: 180px; 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width: 320px;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"cc55e5f1-b626-4c7f-a089-1991e956d437\", [{\"x\": [0.0, 0.25, null, 0.0, 0.25, null, 0.25, 0.5, null, 0.25, 0.25, null, 0.5, 0.75, null, 0.5, 0.75, null, 0.5, 0.375, null, 0.75, 1.0, null, 0.75, 0.75, null, 1.0, 0.75, null, 0.75, 0.5, null, 0.5, 0.25, null, 0.5, 0.375, null, 0.25, 0.375, null], \"y\": [0.5, 1.0, null, 0.5, 0.0, null, 1.0, 1.0, null, 1.0, 0.0, null, 1.0, 1.0, null, 1.0, 0.0, null, 1.0, 0.5, null, 1.0, 0.5, null, 1.0, 0.0, null, 0.5, 0.0, null, 0.0, 0.0, null, 0.0, 0.0, null, 0.0, 0.5, null, 0.0, 0.5, null], \"hoverinfo\": \"none\", \"mode\": \"lines\", \"line\": {\"width\": 2, \"color\": \"rgb(0, 0, 0)\"}}, {\"x\": [0.0, 0.25, 0.5, 0.75, 1.0, 0.75, 0.5, 0.25, 0.375], \"y\": [0.5, 1.0, 1.0, 1.0, 0.5, 0.0, 0.0, 0.0, 0.5], \"text\": [], \"textposition\": \"middle center\", \"hoverinfo\": \"none\", \"mode\": \"markers+text\", \"marker\": {\"size\": 20, \"color\": \"rgb(255, 255, 255)\", \"line\": {\"width\": 2, \"color\": \"rgb(0, 0, 0)\"}}, \"textfont\": {\"color\": \"rgb(0, 0, 0)\"}}], {\"showlegend\": false, \"width\": 320, \"height\": 180, \"margin\": {\"b\": 0, \"l\": 0, \"r\": 0, \"t\": 0}, \"xaxis\": {\"showgrid\": false, \"zeroline\": false, \"showticklabels\": false}, \"yaxis\": {\"showgrid\": false, \"zeroline\": false, \"showticklabels\": false}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"displayModeBar\": false})});</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sn.graph_width = 320\n", "sn.graph_height = 180\n", "\n", "sn.show_graph(ug)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Essa é a representação mais comum de grafos não-dirigidos: círculos como nós e retas como arestas. 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Se uma seta *sai* do círculo que representa $n$ e *entra* no círculo que representa $m$, ela representa a aresta $(n, m)$.\n", "\n", "Note que as duas primeiras linhas não são necessárias se você rodou a célula anterior, pois os valores atribuídos a `graph_width` e `graph_height` são exatamente iguais.\n", "\n", "\n", "## Atributos de nós e arestas\n", "\n", "Na estrutura de dados usada pela `socnet`, os nós são *inteiros* e cada nó é asssociado a um *dicionário* que armazena seus atributos. Vamos modificar e imprimir o atributo `color` do nó $0$ do grafo `ug`. Esse atributo existe por padrão." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 0, 255)\n" ] } ], "source": [ "ug.node[0]['color'] = (0, 0, 255)\n", "\n", "print(ug.node[0]['color'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cada aresta também é asssociada a um dicionário que armazena seus atributos. Vamos modificar e imprimir o atributo `color` da aresta $\\{1, 2\\}$ do grafo `ug`. Esse atributo existe por padrão." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 255, 0)\n" ] } ], "source": [ "ug.edge[1][2]['color'] = (0, 255, 0)\n", "\n", "print(ug.edge[1][2]['color'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note que a ordem dos nós não importa, pois `ug` é um grafo não-dirigido." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0, 255, 0)\n" ] } ], "source": [ "ug.edge[2][1]['color'] = (0, 255, 0)\n", "\n", "print(ug.edge[1][2]['color'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Os atributos `color` são exibidos na visualização." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hoverinfo": "none", "line": { "color": "rgb(0, 0, 0)", "width": 2 }, "mode": "lines", "x": [ 0, 0.25, null, 0, 0.25, null, 0.25, 0.25, null, 0.5, 0.75, null, 0.5, 0.75, null, 0.5, 0.375, null, 0.75, 1, null, 0.75, 0.75, null, 1, 0.75, null, 0.75, 0.5, null, 0.5, 0.25, null, 0.5, 0.375, null, 0.25, 0.375, null ], "y": [ 0.5, 1, null, 0.5, 0, null, 1, 0, null, 1, 1, null, 1, 0, null, 1, 0.5, null, 1, 0.5, null, 1, 0, null, 0.5, 0, null, 0, 0, null, 0, 0, null, 0, 0.5, null, 0, 0.5, null ] }, { "hoverinfo": "none", "line": { "color": "rgb(0, 255, 0)", "width": 2 }, "mode": "lines", "x": [ 0.25, 0.5, null ], "y": [ 1, 1, null ] }, { "hoverinfo": "none", "marker": { "color": "rgb(0, 0, 255)", "line": { "color": "rgb(0, 0, 0)", "width": 2 }, "size": 20 }, "mode": "markers+text", "text": [], "textfont": { "color": "rgb(255, 255, 255)" }, "textposition": "middle center", "x": [ 0 ], "y": [ 0.5 ] }, { "hoverinfo": "none", "marker": { "color": "rgb(255, 255, 255)", "line": { "color": "rgb(0, 0, 0)", "width": 2 }, "size": 20 }, "mode": "markers+text", "text": [], "textfont": { "color": "rgb(0, 0, 0)" }, "textposition": "middle center", "x": [ 0.25, 0.5, 0.75, 1, 0.75, 0.5, 0.25, 0.375 ], "y": [ 1, 1, 1, 0.5, 0, 0, 0, 0.5 ] } ], "layout": { "height": 180, "margin": { "b": 0, "l": 0, "r": 0, "t": 0 }, "showlegend": false, "width": 320, "xaxis": { "showgrid": false, "showticklabels": false, "zeroline": false }, "yaxis": { "showgrid": false, "showticklabels": false, "zeroline": false } } }, "text/html": [ "<div id=\"51dfc482-44f3-4283-80df-0c594378458e\" style=\"height: 180px; 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\"?\", \"?\", \"?\", \"?\", \"?\", \"?\"], \"textposition\": \"middle center\", \"hoverinfo\": \"none\", \"mode\": \"text\", \"textfont\": {\"color\": \"rgb(0, 0, 0)\"}}], {\"showlegend\": false, \"width\": 320, \"height\": 180, \"margin\": {\"b\": 0, \"l\": 0, \"r\": 0, \"t\": 0}, \"xaxis\": {\"showgrid\": false, \"zeroline\": false, \"showticklabels\": false}, \"yaxis\": {\"showgrid\": false, \"zeroline\": false, \"showticklabels\": false}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"displayModeBar\": false})});</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sn.show_graph(ug, nlab=True, elab=True)\n", "sn.show_graph(dg, nlab=True, elab=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Vizinhos, predecessores e sucessores\n", "\n", "Considere um grafo $(N, E)$ e um nó $n$. Suponha que esse grafo é não-dirigido.\n", "\n", "Nesse caso, dizemos que $n$ é **vizinho** (*neighbor*) de $m$ se $\\{n, m\\} \\in E$. Denotamos por $\\mathcal{N}(n)$ o conjunto dos vizinhos de $n$." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 7]\n" ] } ], "source": [ "print(ug.neighbors(0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suponha agora que o grafo $(N, E)$ é dirigido.\n", "\n", "Nesse caso, dizemos que $n$ é **predecessor** de $m$ se $(n, m) \\in E$ e dizemos que $n$ é **sucessor** de $m$ se $(m, n) \\in E$. Denotamos por $\\mathcal{P}(n)$ o conjunto dos predecessores de $n$ e denotamos por $\\mathcal{S}(n)$ o conjunto dos sucessores de $n$." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 3]\n", "[0, 3]\n" ] } ], "source": [ "print(dg.successors(0))\n", "print(dg.predecessors(1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Passeios, trilhas e caminhos\n", "\n", "Se $(N, E)$ é um grafo não-dirigido:\n", "\n", "* um **passeio** (*walk*) é uma sequência de nós $\\langle n_0, n_1, \\ldots, n_{k-1} \\rangle$ tal que, para todo $i$ entre $0$ e $k-2$, temos que $\\{n_i, n_{i + 1}\\} \\in E$;\n", "\n", "* uma **trilha** (*trail*) é um passeio $\\langle n_0, n_1, \\ldots, n_{k-1} \\rangle$ no qual não existem índices $i$ e $j$ entre $0$ e $k-2$ tais que $i \\neq j$ e $\\{n_i, n_{i+1}\\} = \\{n_j, n_{j+1}\\}$;\n", "\n", "* um **caminho** (*path*) é um passeio $\\langle n_0, n_1, \\ldots, n_{k-1} \\rangle$ no qual não existem índices $i$ e $j$ entre $0$ e $k-1$ tais que $i \\neq j$ e $n_i = n_j$.\n", "\n", "Se $(N, E)$ é um grafo dirigido:\n", "\n", "* um **passeio** (*walk*) é uma sequência de nós $\\langle n_0, n_1, \\ldots, n_{k-1} \\rangle$ tal que, para todo $i$ entre $0$ e $k-2$, temos que $(n_i, n_{i + 1}) \\in E$;\n", "\n", "* uma **trilha** (*trail*) é um passeio $\\langle n_0, n_1, \\ldots, n_{k-1} \\rangle$ no qual não existem índices $i$ e $j$ entre $0$ e $k-2$ tais que $i \\neq j$ e $(n_i, n_{i+1}) = (n_j, n_{j+1})$;\n", "\n", "* um **caminho** (*path*) é um passeio $\\langle n_0, n_1, \\ldots, n_{k-1} \\rangle$ no qual não existem índices $i$ e $j$ entre $0$ e $k-1$ tais que $i \\neq j$ e $n_i = n_j$.\n", "\n", "Pode-se dizer que uma trilha é um passeio que não repete arestas e um caminho é um passeio que não repete nós.\n", "\n", "### Exercício 1\n", "\n", "Dê um exemplo de passeio que não é trilha no grafo `ug`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Um passeio que não é trilha é o seguinte:\n", " - 0, 1, 7, 8, 6, 7, 1, 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercício 2\n", "\n", "Dê um exemplo de passeio que não é trilha no grafo `dg`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Um exemplo de passeio que não é trilha é o seguinte:\n", " - 0, 1, 3, 4, 0, 1, 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercício 3\n", "\n", "Dê um exemplo de trilha que não é caminho no grafo `ug`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Um exemplo de trilha que não é caminho é o seguinte:\n", " - 0, 1, 2, 5, 6, 8, 2, 3, 4, 5, 3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercício 4\n", "\n", "Dê um exemplo de trilha que não é caminho no grafo `dg`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Um exemplo de trilha que não é caminho é o seguinte:\n", " - 0, 1, 3, 2, 4, 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercício 5\n", "\n", "Use cores para dar um exemplo de caminho no grafo `ug`." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hoverinfo": "none", "line": { "color": "rgb(0, 255, 0)", "width": 2 }, "mode": "lines", "x": [ 0, 0.25, null, 0.25, 0.5, null, 0.5, 0.75, null, 0.75, 1, null, 1, 0.75, null ], "y": [ 0.5, 1, null, 1, 1, null, 1, 1, null, 1, 0.5, null, 0.5, 0, null ] }, { "hoverinfo": "none", "line": { "color": "rgb(0, 0, 0)", "width": 2 }, "mode": "lines", "x": [ 0, 0.25, null, 0.25, 0.25, null, 0.5, 0.75, null, 0.5, 0.375, null, 0.75, 0.75, null, 0.75, 0.5, null, 0.5, 0.25, null, 0.5, 0.375, null, 0.25, 0.375, null ], "y": [ 0.5, 0, null, 1, 0, null, 1, 0, null, 1, 0.5, null, 1, 0, null, 0, 0, null, 0, 0, null, 0, 0.5, null, 0, 0.5, null ] }, { "hoverinfo": "none", "marker": { "color": "rgb(0, 0, 255)", "line": { "color": "rgb(0, 0, 0)", 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"display_data" } ], "source": [ "dg.node[0]['color'] = (0, 0, 255)\n", "dg.edge[0][1]['color'] = (0, 255, 0)\n", "dg.node[1]['color'] = (0, 0, 255)\n", "dg.edge[1][3]['color'] = (0, 255, 0)\n", "dg.node[3]['color'] = (0, 0, 255)\n", "dg.edge[3][2]['color'] = (0, 255, 0)\n", "dg.node[2]['color'] = (0, 0, 255)\n", "dg.edge[2][4]['color'] = (0, 255, 0)\n", "dg.node[4]['color'] = (0, 0, 255)\n", "\n", "sn.show_graph(dg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posicionamento dos nós\n", "\n", "Para encerrar, vamos carregar o grafo do encontro anterior. O próprio arquivo atribui `label` aos nós, portanto não é necessário criá-los." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hoverinfo": "none", "line": { "color": "rgb(0, 0, 0)", "width": 2 }, "mode": "lines", "x": [ 0.4963144963144963, 0.05405405405405406, null, 0.4963144963144963, 0.10319410319410319, null, 0.4963144963144963, 0.25061425061425063, null, 0.4963144963144963, 0.9115479115479116, null, 0.4963144963144963, 0.9877149877149877, null, 0.4963144963144963, 0.941031941031941, null, 0.4963144963144963, 0.8304668304668305, null, 0.3022113022113022, 0.009828009828009828, null, 0.3022113022113022, 0.10319410319410319, null, 0.3022113022113022, 0.4004914004914005, null, 0.3022113022113022, 0.742014742014742, null, 0.3022113022113022, 1, null, 0.15233415233415235, 0.6953316953316954, null, 0.15233415233415235, 1, null, 0.15233415233415235, 0.742014742014742, null, 0.05405405405405406, 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plot.ly\", \"displayModeBar\": false})});</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sn.graph_width = 450\n", "sn.graph_height = 450\n", "\n", "sn.node_label_position = 'hover' # easter egg!\n", "\n", "g = sn.load_graph('1-introducao.gml', has_pos=True)\n", "\n", "sn.show_graph(g, nlab=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Usamos o argumento `has_pos` para indicar que os atributos `x` e `y` devem ser usados para posicionar os nós. Esse argumento é `False` por padrão, pois nem todo arquivo atribui essas coordenadas.\n", "\n", "Se elas não forem usadas, a visualização usa um tipo de [force-directed graph drawing](https://en.wikipedia.org/wiki/Force-directed_graph_drawing)." ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { "hoverinfo": "none", "line": { "color": "rgb(0, 0, 0)", "width": 2 }, "mode": "lines", "x": [ 0.801376067707108, 0.8446722574007478, null, 0.801376067707108, 0.6203751999566156, null, 0.801376067707108, 0.9421518295635388, null, 0.801376067707108, 0.926758005881293, null, 0.801376067707108, 0.70420429066877, null, 0.801376067707108, 1, null, 0.801376067707108, 0.8404251425182095, null, 0.32623726345893717, 0.10375791438372685, null, 0.32623726345893717, 0.6203751999566156, null, 0.32623726345893717, 0.20005814554607704, null, 0.32623726345893717, 0.23442568121102605, null, 0.32623726345893717, 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"ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
QuantCrimAtLeeds/PredictCode
examples/Case Study Chicago South Side/SEPP.ipynb
1
587276
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SEPP\n", "\n", "This is the full-blown, self-excited point process model with non-parametric kernels." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GDAL_DATA not set and failed to find suitable location... This is probably not a problem on linux.\n" ] } ], "source": [ "%matplotlib inline\n", "from common import *\n", "datadir = os.path.join(\"//media\", \"disk\", \"Data\")\n", "#datadir = os.path.join(\"..\", \"..\", \"..\", \"..\", \"..\", \"Data\")" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import open_cp.sepp as sepp\n", "import open_cp.predictors\n", "\n", "import open_cp.logger\n", "open_cp.logger.log_to_true_stdout()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(numpy.datetime64('2011-03-01T00:01:00.000'),\n", " numpy.datetime64('2012-01-06T19:00:00.000'))" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "south_side, points = load_data(datadir)\n", "points.time_range" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "masked_grid = grid_for_south_side()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train\n", "\n", "Here we experiment with using the `StocasticDecluster` class directly, to tune parameters. I also ran the convergence algorithm without \"bandwidths\". This is very (very!) slow, but checks that our bandwidths are appropriate." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "flipped = open_cp.data.TimedPoints.from_coords(points.timestamps, points.ycoords, points.xcoords)\n", "region = masked_grid.region()\n", "flipped_region = open_cp.data.RectangularRegion(xmin=region.ymin, xmax=region.ymax,\n", " ymin=region.xmin, ymax=region.xmax)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "trainer = sepp.SEPPTrainer()\n", "trainer.data = points" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sd = trainer.make_stocastic_decluster()\n", "sd.initial_time_bandwidth = 10000\n", "p = sd.initial_p_matrix()\n", "backgrounds, aftershocks = sepp.sample_points(sd.points, p)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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sAYjI5g0kItx7YJDxqRSz6Tx98TBHdg0ykIg0e2gi0iKcc+9Y5/ZDK64/ATzh55hERETq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\na/T7df14XJddH31urdu5HPd96jXAO4AHi3M+Af4Ax6jZZcD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"text/plain": [ "<matplotlib.figure.Figure at 0x7f84878b8ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_space_time(aftershocks):\n", " fig, ax = plt.subplots(ncols=2, figsize=(16,6))\n", "\n", " ax[0].scatter(aftershocks[1], aftershocks[2], alpha=.2)\n", " ax[0].set_title(\"Triggered events in space\")\n", "\n", " ax[1].hist(aftershocks[0] / 60 / 24)\n", " ax[1].set_title(\"Triggered events in time\")\n", " ax[1].set_xlabel(\"Days\")\n", " None\n", " \n", "plot_space_time(aftershocks)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "result = sd.run_optimisation(40)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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ERES2Sj2xAsCDxwdYqgUs1QJKtYhk0jg+mOUb7x4GWj2tT1xdopDxuWu4sNLTulgNwOD0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"text/plain": [ "<matplotlib.figure.Figure at 0x7f84c4546c88>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "backgrounds, aftershocks = sepp.sample_points(sd.points, result.p)\n", "plot_space_time(aftershocks)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Conclusions\n", "\n", "- Changing the \"initial bandwidths\" leads to a different initial estimate.\n", "- But the final estimate we converge to seems unchanged.\n", "- By removing the convergence bandwidth cutoffs, we obtain a \"trigger kernel\" which has a much long time tail than expected (up to 200 days).\n", "- But the overall pattern, and spatial distribution, looks the same.\n", "\n", "Which is frustrating, as it suggests converging to this very \"North-South\" dominated distribution seems stable." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Back to the usual procedure\n", "\n", "Let us now return to using the main library code, even though we know that the \"bandwidths\" set are not entirely correct." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer = sepp.SEPPTrainer()\n", "trainer.data = points\n", "predictor = trainer.train(iterations=40)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_density(predictor, ax, region):\n", " kernel_array = open_cp.predictors.grid_prediction_from_kernel(predictor.background_kernel.space_kernel,\n", " region, masked_grid.xsize)\n", " ax.set(xlim=[region.xmin, region.xmax], ylim=[region.ymin, region.ymax])\n", " mesh = ax.pcolormesh(*kernel_array.mesh_data(), kernel_array.intensity_matrix * 10000, cmap=\"Blues\")\n", " fig.colorbar(mesh, ax=ax)\n", " return kernel_array\n", "\n", "# Time unit is minutes\n", "def plot_time(points, kernel):\n", " total_time = points.time_deltas()[-1]\n", " tc = np.linspace(-total_time * 0.1, total_time * 1.1, 100)\n", " def actual(t):\n", " return ((t>=0) & (t<=total_time)) / total_time\n", " days_tc = tc / 60 / 24\n", " ax[1].plot(days_tc, actual(tc) * 10000, linewidth=1, color=\"r\")\n", " ax[1].scatter(days_tc, kernel.time_kernel(tc) * 10000)\n", " ax[0].set_title(\"Estimated background risk in space\")\n", " ax[1].set_title(\"Estimated background risk in time\")\n", " ax[1].set_xlabel(\"Days from start\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Ll+zz5TauT5LUZfbsSr1XltBpWAPE5c73laRWVc7ZjYh9ImK/xZ+BlwPXF6tf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\n8NbMzIjYi9p8rGcD9wKvycz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W4Kd61D6NMoNdadma9dLON8n+ObSZkAfBiA9jNtiVJLUs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\n+OpX4R2/OSHPWU6NXRERERkVd3czG7GbwMyuJBkazYknnliz55zbWaC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K4O6PhP1PAba5\n+xeB7wKvrrII/wG8w8xaQ1neCNwzyrq8zN03uftnSYZDv4JkWPaRJZvF6leqfD+R3FFjV0RERESa\nTaEYegi4nWR48KdD3j8Al5vZgySNxQO9nWGY8aMkC1IVvR3YbGYPAKeT9P5W4zvAQ8CDwB3Ax939\nl9VXCYCPmtlmM3sIGAC+H449FEIsfYxI/Uq5+zMkvcObzWz1KMsjMqm0QJWIiIiISAXC4k6bgLPc\n/bnJLo+IxKlnV0REREQkg5n9Bkmv7v9RQ1ckH9SzKyIiIiIiIg1HPbsiIiIiIiLScNTYFRERERER\nkYajxq6IiIiIiIg0HDV2RUREREREpOGosSsiIiIiIiIN5/8DElObBxJeHCcAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f84871ea978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=2, figsize=(16,7))\n", "kernel_array = plot_density(predictor, ax[0], masked_grid.region())\n", "plot_time(points, predictor.background_kernel)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stocastically decluster\n", "\n", "Using the computed kernels, we take a sample of which points are estimated to be \"background\" and what the triggering events look like.\n", "\n", "The most obvious problem is that the \"triggered\" events are aligned almost vertically North/South. We would expect a much more homogeneous pattern; or at least also a strong east/west bias, reflecting the grid layout of Chicago." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "backgrounds, aftershocks, triggers = sepp.sample_offsets(trainer.as_time_space_points(), predictor.result.p)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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JRERERGSPKIiVQ+1O82/eaX2cFVSjW6fSCX2jnWjmCRERERGRvaAgVmQdpcCj0c1oxRlJ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\nXWe2EVMUjlrk8+TJMYrS8b5z40zXY16/1WGlmxKFPqH5vOP0OLXY582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"text/plain": [ "<matplotlib.figure.Figure at 0x7f84871ea358>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=2, figsize=(16,6))\n", "\n", "ax[0].scatter(aftershocks[1], aftershocks[2], alpha=0.1)\n", "ax[0].set_title(\"Triggered events in space\")\n", "\n", "ax[1].hist(aftershocks[0] / 60 / 24)\n", "ax[1].set_title(\"Triggered events in time\")\n", "ax[1].set_xlabel(\"Days\")\n", "None" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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6zp37F3housJVGwd503VbWKh36PjdJ6QjmCim2TaSZXKuThc4uNBg71yNriqD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\nmH1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"text/plain": [ "<matplotlib.figure.Figure at 0x7f84871f3550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=2, figsize=(16,6))\n", "\n", "ax[0].scatter(backgrounds[1], backgrounds[2], alpha=0.2)\n", "ax[0].set_title(\"Location of background events\")\n", "ax[0].set_aspect(1)\n", "\n", "ax[1].hist(backgrounds[0] / 60 / 24)\n", "ax[1].set_title(\"Background event intensity in time\")\n", "ax[1].set_xlabel(\"Days\")\n", "None" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Wl8sLhbeSkCS0jQ/eI8Me634MOEVEREREDjMFcLuslUTk3YyRRrRirptKD0VE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\nOKWUUkoppZQ6JjSBU0oppZRSSqlj4v8DG9R/mpGDb+QAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8487113cc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=2, figsize=(16,6))\n", "\n", "ax[0].scatter(triggers[1], triggers[2], alpha=0.05)\n", "ax[0].set_title(\"Location of triggering events\")\n", "ax[0].set_aspect(1)\n", "\n", "ax[1].hist(triggers[0] / 60 / 24)\n", "ax[1].set_title(\"Triggering event intensity in time\")\n", "ax[1].set_xlabel(\"Days\")\n", "None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "Immediately above we have plotted the location of \"trigger\" events-- those events which (under our stocastic estimate) directly give rise to other events.\n", "- Notice that these are strongly clustered in the South East corner of the region.\n", "- (We have plotted this with a much smaller `alpha` parameter than for the backgrounds).\n", "- This, perhaps, shows a flaw in the algorithm. While the background intensity and triggering intensity are estimated in a non-parametric way, the model assumes that the triggering kernel is the same for all time and space.\n", "- So, again conjecturally, what could be happening is that the South East corner of the data is dominating, and in this region it is appropriate that the triggering kernel is North/South dominated. As the kernel cannot vary, we are forced into this even though it may be inappropriate for other regions of the data set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Try North side" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(numpy.datetime64('2011-03-01T06:30:00.000'),\n", " numpy.datetime64('2012-01-06T21:00:00.000'))" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "north_side, points = load_data(datadir, side=\"North\")\n", "points.time_range" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "masked_grid = grid_for_side(side=\"North\")" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trainer = sepp.SEPPTrainer()\n", "trainer.data = points\n", "predictor = trainer.train(iterations=40)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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3qAbfUqYekiRJ0hBZrul4mi3XHLeroZvW2m7GCQ/TNVqK5uuwa2b2qUC3bjnu\nUQ2HqtmYJUkCbNmVhlm7wG161wzHbPl0z8a1jkUwn60Dlk0nTQ1Uq283rbXddDde6vjeYdHqOrRi\n5mm1YrArSeqOse7AiIgzgXcD48AHM3NrU3kU5a8AHgd+JTP/KSKOBD5CLW9HApdm5ruLbX4feANw\nX7Gb383Ma1bgdLQC2gVuwB6ti9Bdl9Hm7tHNgW7d3btmVrQrdS90mxyqanfj5RrfO2iqnq+Zp9WK\n3ZglSV2JiJ58tLwiYhx4H3AWcDxwbkQc37TaWcCxxed84P3F8jngtzPzeGo5ON7YtO27MvPE4mOg\nO0RadbNttpguo1Vb5xL47U/+84p0pe6VTlMCVbHtlmlO23odx2z5NKdtve6pbspOJ1RT5XzNPK12\nbNmVJGk4nQLsrM+MEBGfAM4Gvt6wztnARzIzgRsjYn1EHJ6Z9wD3ABRTD34DmGraVkOouZtt6/bX\n7lsXu1m/U6tvv1pscqhOrdhLnU5okLqCd9LqOkyMBfvvu4Zdj88O9Llp+RnsSpIqs1V2oEwBdzZ8\nfxdwaoV1pigCXYCIOBo4CbipYb3/GBGvA26m1gL8UPPBI+J8imkJjzrqqMWeg1ZBY+B22tbrusoe\n3E677tHjLcbudtrHsGk3LvfNV9zK1PpJ/v0Lp/i7b97XdcA6aF3BOxm1aZXUW3ZjliR1xW7MoyMi\n9gc+Bbw5M39QLH4/8BzgRGpB8R+32jYzL83MkzPz5EMPPXRF6qve6yZ78GL288evfn6lNADD2k21\nU2v19K4ZPvWVaTZvPI5vb30lN2w5vXKAt1JZtVfKppOmuGHL6V1fB8lgV5Kk4TQNHNnw/RHFskrr\nRMQEtUD3LzPzyvoKmfn9zJzPzAXgz6h1l9YAazdmFJY+HrXKftq12I5HLOmYg6CstXqxAarJraQa\nuzFLkrpiq+zA+DJwbEQcQy2AfQ3wi03rXA28qRjPeyrwcGbeU2Rp/hDwjcz8k8YNGsb0AvwccPty\nnoSWV5Xurosdj9qs3X7ajU0d1gC3Uatzb7aYALWb6ZCkYWbLriSpO9Gjj5ZVZs4BbwK2A98APpmZ\nX4uICyLigmK1a4A7gJ3UWml/s1h+GvBa4PSIuLX4vKIoe2dE3BYRXwVeBrxlhU5Jy6Afurv2qvV4\nEDWeezuLCVB71f1cGnS27EqSNKSKaYGuaVp2ScPXCbyxxXZfpM0ricx8bY+rqVW0nN1du8kG3KvW\n40FUP/cQmoLaAAAgAElEQVTmVnZYfIBqUiepZuiC3QQ6JfXLtkn0G9YpWWWhZIWKSQU7Kmv0qNKN\ncCV6GpZdz6Veiyo/r9n5zus8XjKv36O750qPcf/jsx3Lv/dI5/LHJhdKj/HMdZ3XWVcy7+G+453L\noTb+qZOxkvIqP4/5hc7rlBQD5ddqdq5zPefXdO60UvZ/GCBL/heOcsOk3Zil4bFc3V2HKRvwSul1\ngDrKLxCkuqELdiVJyygMdqVh0u1crlVbazt1jzYAa88AVeotg11JkqQR1U1rYjettWYDltQPDHYl\nSZUFKzNEQtLKqdqa2E1r7bBnA+5mPLKk1WM2ZklSF4KI3nwkDZZuWmuHORtwvYV7etcMydMt3I3z\nE0vqDwa7kiRJKtWuVbbV8mGeTqgfpmuSVI3dmCVJXbFRVhpN3SazGtZkS4M4Htlu1xpVBruSpK7Y\nBVkaTc7dWjNo45GdBkqjzGBXkiRJlQxra203um3hXm2DOA2ULdHqFYNdSVJ1YTdmSe2NQpAyaC3c\ng9bt2pZo9ZLBriSpsgDGxox2pUG3HEHpKAUpg9TCPWjdrrttiR6FFyxaPINdSZKkEdLLoLQx0BiL\nYD5zj/J+7y47Cgat23U3LdGj9IJFi+PUQ5KkrkT05iNpdfRq6pzm+WabA926fu0uOyoGbRqobqa4\nchoolbFlV5LUFbMxS4OtV2M4WwUarfRrd9lRMkjdrrtpiR608chaebbsSpIkjZBuWs46qRJQ9HN3\nWfWnblqie3Uva3jZsitJqs4uyNLA69UYznaJj8YjWMgcuGRBJjrqH1VbogdtPLJW3lAGu0nrMSMA\nCwvl28+VrDQ7337/APMl5UAtpWkH4yXZTtdUyIY6VvJEWvbA2mbozZ7rdLjWVfZRVoeJ8fLOBxNr\nOu9kn7nO+5gYKz/GIftNdCxfW1LPhZLrBLB7ruxadu4qts/4XOkx1s2W/Jcv+Xk8NLO79Bg3TT/c\nsXy/fTpfq+MP2b/0GBvGO7+xXSi58Srd2yXrZHReIcou5oAK7MYsDbpeTZ3TLtDo5/Gg7ZjoaDAN\n2jRQWnlDGexKkiSpvV6M4RymQKPb6W7UPwZpPLJWnsGuJKkLYcuu1Cf6odvtsAQaJjqShpPBriSp\nK8a60uqz221vtRt/bKIjabAZ7EqSJA2AxpbcsYi95rXt1O22H1qB+9moJTryftCoMNiVJHXFbszS\nymtuyW0OdOumd81wzJZP7xHA2Apcrsr442EJEL0fNEoMdiVJ1a3w1EMRcRnwM8C9mfm8FuW/BPxO\nrWY8AvxGZv5zUfadYtk8MJeZJ69UvaVea5VAqZ1kzwDG5EvVdBp/PEwBoveDRonBriSpn10OvBf4\nSJvybwMvycyHIuIs4FLg1Ibyl2Xm/ctbRY2sL30JvvzlFTnUy6+7vcIkdnv7yk1/w+ltygLgGd9Y\nfKVGyL98bgc/PzO79/KvXgNnDFZX53b3kvfDEIqAV70KDj10tWuyagx2JUmVrfQ8u5l5fUQc3aH8\nHxq+vRE4YrnrJD3lHe+Axx6DH/7hZT/U8x+5m8ee3LtlNwJIFhUIr1s7Dl8vn2te8Kw7v0urcCEA\nvl6txb1ftLuXvB+G0LXXwgEHwC//8mrXZNUY7EqSutLDWPeQiLi54ftLM/PSJezv9cBnGr5P4PMR\nMQ98YIn7lvY2Nwe/8Rtw9tnLfqjxW6a5qEUCpYvOOYFNJ01x2tbrWmYTbqe+LXZbreSSNtd3av0k\nr93Sru28P3W6l7wfhszrXgfzg/UyptcMdiVJq+X+Xo2jjYiXUQt2X9yw+MWZOR0RzwKujYhvZub1\nvTieBNSC3TXL+yjVmBTpwMkJ9p0YY9fjs3slSGqVTbiVgIFOrrRahilbc5VkXBoSa9bUfk+NMINd\nSVJX+i0bc0T8KPBB4KzMfKC+PDOni3/vjYirgFMAg131zjIHu81JkXbNzDI5Mc67fuHEvQKTxgCm\nXQvv1PpJbhiwVsh+MWwBYqdkXBoiBrsGu5Kk7vRTrBsRRwFXAq/NzP/dsHwdMJaZjxRfnwG8fZWq\nqWG1zMFut1lz6wFMc5AMg9sKuZpaTTXkywINFINdg11JUv+KiI8DL6U2vvcu4K3ABEBmXgL8HvBM\n4E+LFuf6FEOHAVcVy9YAH8vMz674CWi4zc/D+Piy7f7uNi207ZbXDVsr5GoYpqmGNMLGxx2zu9oV\nkCQNkFjxbMznlpT/GvBrLZbfATx/ueolAcvesrth/WTLLskb1k+Wbms31aVxLloNBVt2Mb+4JKmy\n2tRDvflIA2+Zg93NG49jcmLPlmO7I6+MxbaqS33FYHdIW3Y7TDa3kOUz0c3OdV6nLNPhEyXlAAsL\nncv3WdP5PURZOcCa8c5Pk2XPmlWuVZkoPUpnE+Pl57lubefbuOxaP7q7/JdA2bVYKJnhcHa+yrXs\nvM4jT3Yu/+5DT5Ye4cB9H+9Y/qz9JzqWf/3e8j/y//CNezuWH7D/2o7l4yeUHoID9ulcz+aHw2b7\nrCm5KYA12fneHSspr/ITN+CTBtwyB7t2R149S2lVl/qGwe6QBruSpGUSfZeNWVo1KzD1kN2RV8cw\nTTVURatkXN53Q8Bg12BXktQdY12psALBrlbHKLWqm4xriBnsGuxKkiQtyjIEu7aw9Y9RaVXvl2Rc\n3vvLYM0aeLJ8mNswM9iVJHXFbsxSocdTD9nCptXQD8m4vPeXiVMPmY1ZktSFHmViNl7WUOhxy26n\nFjZpubRLurWSybi895eJ3ZgNdiVJkhalx8FuP7SwafT0wxRX3vvLxGDXYFeSVF1tnt3oyUcaeD0O\ndvuhhU2jZ9NJU1x0zglMrZ8kgKn1k1x0zgkr2n3Ye3+ZGOw6ZleS1B0DVanQ42B31Ka7Uf9Y7WRc\n3vvLxGDXYFeSJGlRehzsjtJ0N1Ij7/1lYrBrsCtJ6o4Nu1JhGaYeWu0WNmm19OredwqjBga7BruS\npO7YjVkq9GDqIR/M1W8G+Z50CqMmTj1ULUFVRHwnIm6LiFsj4uZi2YkRcWN9WUSc0rD+j0bEP0bE\n14rt9i2Wv7D4fmdEvCeKJ6aIWBsRVxTLb4qIoxv2dV5EfKv4nNfLk5ckSVq0Jbbs1h/Mp3fNkDz9\nYL7tlune1VHqQrf35LZbpjlt63Ucs+XTnLb1ulW/d53CqIktu11lY35ZZp6YmScX378TeFtmngj8\nXvE9EbEG+Avggsz8EeClwGyxzfuBNwDHFp8zi+WvBx7KzOcC7wLeUezrYOCtwKnAKcBbI+KgRZyn\nJKkXnGdXetoSg10fzNVvurknWwXGm//qnznp7Z9bteDXKYyaGOwuaeqhBA4ovj4QuLv4+gzgq5n5\nzwCZ+UBmzkfE4cABmXljZibwEWBTsc3ZwIeLr/8aeHnR6rsRuDYzH8zMh4BreTpAliStsKA30w7Z\nFXplRMSZEbGj6Dm1pUV5FD2tdkbEVyPiBcXyIyPi7yLi60Uvrd9q2ObgiLi26HF17ci+hF5YqP07\ntvhHKR/M1W+q3JP11tw3X3HrXoHx7ELy0OOzq9ZTwSmMmhjsVh6zm8DnI2Ie+EBmXgq8GdgeEX9E\nLWj+8WLdfwNkRGwHDgU+kZnvBKaAuxr2eVexjOLfOwEycy4iHgae2bi8xTZPiYjzgfMBjjzyKBay\n/YksZIfCwnzJOrNzCx3LZ54s7xv/xGznfUyMd34Q3Hef8jFCa9csbRrlsusA1O6MDsbGOp/HmpLz\nHK/wQDxWsk5JFSq1MHX+acHuuc4X4pEK98TsfMl91+nGBnY9Xv7L7LHdnc+k7Bhl2wNMTHS+N8t+\nXvc+Un4e9zzjiY7lkyV1WFtSDjAx3vlalN93Vf7/dN5Hv8aD/Vov7SkixoH3AT9N7e/nlyPi6sz8\nesNqZ/F0b6tTqfXAOhWYA347M/8pIp4BfCUiri223QL8bWZuLQLoLcDvrNiJ9YseJKfasH6S6RbB\nxcg+mGvVtbsnxyI4ZsunOXBygsd2z5U+s9TVW4VXarysUxg1Mdit3LL74qK78lnAGyPiJ4HfAN6S\nmUcCbwE+VKy7Bngx8EvFvz8XES/vbbX3lJmXZubJmXnyIYceupyHkiRpUJwC7MzMOzJzN/AJaj2p\nGp0NfCRrbgTWR8ThmXlPZv4TQGY+AnyDp182N/bG+jBP99IaLT0IdjdvPG6vl3Mj/WCuVdfqnoRa\nA0gCu2ZmKwe6dSvZU2HTSVNcdM4JTK2fJICp9ZNcdM4Jo5mcCgx2qdiym5nTxb/3RsRV1P6AngfU\nuzX9FfDB4uu7gOsz836AiLgGeAG1cbxHNOz2CKDer2EaOBK4qxjzeyDwQLH8pU3bfKHy2UmSeq6s\nVVt9o1XvqFMrrDMF3FNfUCSNPAm4qVh0WGbWy78HHNbq4I29ro466qjF1L+/9SDYdW5R9Zvme3Is\nolpPvw5WuqeC03c1MNgtD3YjYh0wlpmPFF+fAbyd2hjdl1ALPk8HvlVssh34TxGxH7C7WOddmXlP\nRPwgIl5E7Q/m64D/XmxzNbXg+R+Bnweuy8x6V+g/bBgPdAZw4RLPWZK0BMa6oyMi9gc+Bbw5M3/Q\nXF78rW75JFwMeboU4OSTT17a03I/6sG0Q+CDufpP4z15zJZPV95ufYsuzvZUWGVOPVSpZfcw4Koi\nmcga4GOZ+dmIeBR4d9ES+wTF29vMfCgi/gT4MrURnddkZv1/ym8ClwOTwGeKD9S6QH80InYCDwKv\nKfb1YET8QbEvgLdn5oNLOF9JkkZFvddUXWOPqtJ1ImKCWqD7l5l5ZcM63693dS6ST97b85oPgkW2\n7A7yHKYaPe3G8DaanBh/qqtw8/39sn97KBdv38FbrrjV+3012LJbHuxm5h3A81ss/yLwwjbb/AW1\nbsvNy28Gntdi+RPAq9rs6zLgsrJ6SpKWX23aIJt2B8SXgWMj4hhqAexrgF9sWudq4E0R8QlqXZwf\nLoLYoPYi+huZ+ScttjkP2Fr8+zfLeA79axHBbn2qlnrynHq2WsAAQH2pVcKnibFg/33XsOvx2b0C\n2MZWYe/3PmCwWzkbsyRJQHmGc/WHYnaDN1EbXjQOXJaZX4uIC4ryS4BrgFcAO4HHgV8tNj8NeC1w\nW0TcWiz73cy8hlqQ+8mIeD3wXeDVK3VOfWURwW6nOUx9+Fc/Wsq48rL73V4OK8Bg12BXkqRhVQSn\n1zQtu6Th6wTe2GK7LwItX2tk5gPAss6yMBAWEew6r64G0WLHlXe63231XSEGu5WnHpIkCah1Y+7F\nRxpoiwh222WldV5dDaNO93unVl/1kMGuwa4kqTu1cbtL/0gDbRHBrvPqapR0ut/t5bBCDHbtxixJ\nktS1RUw95Ly6GiXN9/uBkxNEwFuuuLXt/L32cugxpx4y2JUkVRdAtB7KKY2WRU495Ly6GiX1+715\njG6rQNdeDjU9Tdxly67BriSpO2Zjllh0sCuNolZjdAHGI1jItJdDoVXirrdccStvvuJWphZzjQx2\nDXYlSZK6ZrArVdZuLO5CJt/e+soVrk3/avVSoN4GvqiM1Qa7JqiSJHWhR5mYzcasgWewK1VmJvJq\nyhJ0dZ2x2mDXYFeS1B2zMUsY7EpdMBN5e9tumea0rddxzJZPM1bhj2NXGasNdoevG3MmzC/sPei9\nbna+fVndXMk6cx32X/UYM3OdM6M9WZI4raQKQPl5jJW86lhYKD9G0vkYa0oOkllSiQqJLst+L0zu\n03knP7T/vqXHOHDtRMfyH+w/27H8vsefLD3Gric6/zIqO8/9nlX+dnS/ic7/5fdb0/lavfCHyt+P\n/fQPH9SxvOzWrTIe9IB9Ov88xsc778RAS9KSGexKlZmJvLUqibuaddUavmaN2ZhXuwKSpMERUOnN\nszT0uph6qKfZVaUBZSbyvXVK3DWfSbBnI0HXreHj4yPfsms3ZklSV+zGLFG5ZbfecjO9a4bk6SQz\n226ZXv46SuprnRJ3fWfrK3nXL5zI1PpJAphaP8lF55xgNuYu2bIrSZLUrYrBbquWm3qSGVu5pNG2\nYf0k0y0C3npX5SW3hhvs2rIrSeqO2ZglKge77VpuukoyI2koLXvirvHxWhKeKol4hpQtu5KkyuyC\nLBUqBrtlLTeSRteyJ+6KqAW88/PlmWmHlMGuJElStyoGu5s3HrdHtlVwyhVplLVKWHfDltMXvW1p\nYFzvyjzReSaLYWWwK0nqitmYJSoHu065IqmueaqhesI6oPR3wqK3HfHphwx2JUldMdSV6GrqIadc\nkVZPP039tZSEdYvedsSnHzLYlSRJ6lbFll1Jq2cpLanLYSkJ6xa97YhnZB7NkcqSpEUzG7OEwa40\nADq1hq6GdonpqiSsW/S2BruSJFUTwFj05lPpeBGXRcS9EXF7m/KIiPdExM6I+GpEvKCh7MyI2FGU\nbenJBZDqDHalvtcvU39tu2Wa07Zex/Sumb2GAlVNWLfoaYoMdiVJ6luXA2d2KD8LOLb4nA+8HyAi\nxoH3FeXHA+dGxPHLWlONFoNdqe8tpSW1V+pdqetTkCVP576YWj/JReecUKlL9aaTprjonBOYWj9J\ndLPtiAe7/paWJFW3wl2QM/P6iDi6wypnAx/JzARujIj1EXE4cDSwMzPvAIiITxTrfn15a6yRYbAr\n9b1+mPqrVVfqpBasVp1yqG5Rye4MdiVJqq6Hse4hEXFzw/eXZualXe5jCriz4fu7imWtlp+6qFpK\nrRjsSn1vtab+aswAnW3WWbGu1E49JEnSqrg/M09e7UpIi9LF1EOSVs9KT/3VnAG6nRXrSu3UQ8Ml\nSXbPLbQt/8ETs6X72PVY53UenNndsfzR2fIbqtbjrr2Jsc7DqecWOm8PsH92/vGuGe/cPBMVZtMs\na+EpO8+y8vny02S8pBJrxjtfy3X7lp/n2onODzT7r+18rQ/db9/SY8zNt79vAeZLrtVshYs1l52P\nUfYz33dN+TD/NWNrS9fppOyeANinpB77lvy8xitkRzJZcHt9lkl5Gjiy4fsjimUTbZZLvWHLrqRC\nY0vuWETpM9uKdqW2G7MkSdXUszH3kauBNxVjck8FHs7MeyLiPuDYiDiGWpD7GuAXV7GeGjZzc7Df\nfqtdC0mrpB7g1jMs18PbToFuwIp1pX6Kwa4kSf0pIj4OvJTa+N67gLdSa7UlMy8BrgFeAewEHgd+\ntSibi4g3AduBceCyzPzaip+Ahpctu9LIau6qXKEj4qISUvWEwa4kSdWtcDbmc0vKE3hjm7JrqAXD\nUu8Z7Eojpduuyo1WOgP0Hgx2JUmqrr96MUurxGBXGhnNLblVAt3xCBYyV77bcjODXUmSJHWlQ7Db\n2AK06g+6kpas1Vy5nUxOjHPROSf0x/97px6SJKmaCBjrr2zM0upoM/VQcwvQ9K4ZLrzyNoD+ePCV\n1LUqc+LWk1RN9dsLLqcekiSpOmNdibYtu61agGZm57l4+47+efiVVKrKGN2+6arcid2YJUmS1JU2\nwW67FqAqLUOSllfVIQZVxuj2VVflTgx2JUmqbiWzMUt9q02wu2H9JNMtAtsN6ydXolaS2uhmiEG7\nMboD0ZLbzGBXkqTqjHUl2ga7mzcet8cDNazytCOSgO6GGLTribGQybe3vnLZ6titSi3VBruSJEnq\nSptgt/6gaTZmqb9UGWJQDx7bTSzUTz00KrdUG+xKklRNEGZjlqDj1EObTpoyuJX6TNkQg+bgsVm/\n9dCo3FLt1EOSJFUUdmOWgLZTD0nqT62GGEyMBY/vnuOYLZ9um3EZ+nA6IbpIhufUQ5IkSepKh5Zd\nSf2neYjBgZMTPLZ7jocenwVaZ1yG2vy5N2w5faWqWVnlZHgj3o15bLUrIEkaLBHRk4800Ax2pYGz\n6aQpbthyOt/e+krWrV3D7Hy70blP66dxuo02bzyOyYk9e5e07Go94sHu0P2WzoT5hfY37uNPlvdZ\n/5ddj3Ysv3n6kY7l373/8dJjPNlmPEDdurWdfzTPOXRd6TGed9h+Hcs3rOtcvs94+buQNeOdH1jL\nxvZ1+FHVti//HdT2TVxVS9y8puS5fazCa6Xxsp0slOygwnnMldz+CyUXo8ofhczOFZ1d6Fxe5ee5\nf8mvrn1LbqyFshuP8vsiyy54VgjmBjTe8y2phMGuNOCqzH3db+N0G1VOhmewK0mSpK4Y7EoDrV03\n4EGaS7dSMjyDXUmSqgmwC7IEBrvSgGs3J/ZF55zQ1wFu18zGLElSdWPGupLBrjTgRmZObFt2JUmq\nzmBXwqmHpCEwEnNij/jUQ+YZkSRJ6pYtu5IGwYi37BrsSpIqi3DqoUESEWdGxI6I2BkRW1qUR0S8\npyj/akS8oKHssoi4NyJub9rm9yNiOiJuLT6vWIlz6TsGu5IGgcGuJEnVjUVvPlpeETEOvA84Czge\nODcijm9a7Szg2OJzPvD+hrLLgTPb7P5dmXli8bmmpxUfFAa7kgaBwa4kSRpCpwA7M/OOzNwNfAI4\nu2mds4GPZM2NwPqIOBwgM68HHlzRGg8Sg11Jg8BgV5Kk6mpdmZf+0bKbAu5s+P6uYlm367TyH4tu\nz5dFxEGtVoiI8yPi5oi4+b777uum3oPBYFfSIBjxqYcMdiVJlQUwFtGTjwbW+4HnACcC9wB/3Gql\nzLw0M0/OzJMPPfTQlazfyjDYlTQIbNmVJElDaBo4suH7I4pl3a6zh8z8fmbOZ+YC8GfUukuPHqce\nkjQInHqoXER8JyJuK7Iu3lwsOzEibqwvi4hTiuVHR8RMQ5bGSxr288JiPzuL7I9RLF8bEVcUy2+K\niKMbtjkvIr5VfM7r5clLkro31qOPlt2XgWMj4piI2Ad4DXB10zpXA68rsjK/CHg4M+/ptNP6mN7C\nzwG3t1t3qNmyK2kQjHjLbje/pV+Wmfc3fP9O4G2Z+Zli2oF3Ai8tyv4lM09ssY/3A28AbgKuoZbl\n8TPA64GHMvO5EfEa4B3AL0TEwcBbgZOBBL4SEVdn5kNd1FuS1EP2QB4MmTkXEW8CtgPjwGWZ+bWI\nuKAov4Ta3+JXADuBx4FfrW8fER+n9nf9kIi4C3hrZn4IeGdEnEjt7/J3gF9fsZPqJwa7kgaBwe6i\nJXBA8fWBwN2dVi7eBB9QZHskIj4CbKIW7J4N/H6x6l8D7y1afTcC12bmg8U211ILkD++hHpLkjQS\nimmBrmladknD1wm8sc2257ZZ/tpe1nFgGexKGgQGu5Uk8PmImAc+kJmXAm8GtkfEH1HrkfbjDesf\nExG3Ag8D/zkz/55adse7GtZpzPj4VDbI4k30w8AzqZglMiLOpzY/IFNHHlXxlCRJ3QqTS0k1BruS\nBoHBbiUvzszpiHgWcG1EfBP4eeAtmfmpiHg18CHgp6hlZjwqMx+IiBcC2yLiR5al9oUi+L4U4KQX\nnJxrJ9qPBjto3UTp/o6df0bH8jsenOlY/tEb7ig9xq4v/13nFQ4/tmPxMSf+u/Jj/NiRHctffHTn\n7Q/ad5/SY+xX8oe+7JF4zXjnNRaytAqlD969eCxPOldk99xCx/L7H3+y9Bj3VVink3UT5f+d9y1J\npjJR8vOoYq7kh/bkfOdrVXatAeZLjrFQUl7htlIHxroSBruSBoNTD5XLzOni33uBq6hlXjwPuLJY\n5a+KZWTmk5n5QPH1V4B/Af4NteyORzTstjHj41PZICNiDbVu0Q+wiCyRkiRJyyoTFhZgrPYYte2W\naU7beh3HbPk0p229jm23+KgiqU+MeMtuabAbEesi4hn1r4EzqGVevBt4SbHa6cC3inUOjYjx4uvn\nAMcCdxTZHX8QES8qxuO+DvibYvurqQXPUGsxvq4YR7QdOCMiDiomrT+jWCZJWiVj0ZuPNLDq0w5F\nsO2WaS688jamd82QwPSuGS688jYDXkn9YcSnHqrS/+Yw4KpilqA1wMcy87MR8Sjw7qIl9gmKMbPA\nTwJvj4hZYAG4oJ5gCvhN4HJgklpiqs8Uyz8EfDQidgIPUpsegcx8MCL+gNr0CQBvb9iXJGmFBeVD\nB6Sh19CF+eLtO5iZ3bOL4MzsPBdv38Gmk/ZKMyJJK2vEW3ZLg93MvAN4fovlXwRe2GL5p4BPtdnX\nzcDzWix/AnhVm20uAy4rq6ckSdKKaAh2797VOo9Hu+WStKJGPNitNGZXkqS6iN58pIHVEOxuWD/Z\ncpV2yyVpRRnsSpJUUY/G6zpmVwOtIdjdvPE4Jif2zHQ/OTHO5o3HrUbNJI2otonyRjzYNWe+JElS\nNxqC3fq43Iu37+DuXTNsWD/J5o3HOV5X0oqpJ8qr5w+oJ8oD2DTiUw8Z7EqSuhI9mb1aGmBNc+xu\nOmnK4FbSqumYKO9l62zZlSSpilo25tWuhbTK6lMPSVIf6Jgob/yAkQ52HbMrSZLUjaaWXUlaTR0T\n5Y34mF2DXUlSV0xQpZFnsCupj3RMlDfiwa6/qSVJXQnnDdKoM9iV1Ec6Jsr75iMGu5IkSarIYFdS\nn2mbKM+WXUmSqjFBlYTBrqTBMeJTDzlmV5JUXUD06FPpcBFnRsSOiNgZEVtalG+OiFuLz+0RMR8R\nBxdl34mI24qym3t7ITTSDHYlDQpbdodLBEyMt4/h160tP+X5/TuXv+SoZ3Ys/8GmE0qP8bmjDu5Y\n/uijuzuWH3ZYSSWByX06v8uYz+xYXlJcaZ2yYywslOygQhPSWMk6a8Y7l1d55p5b6LzWfMl5VJmX\n9HslP/PHdi90LF8/WT4NxoH7dr7/1010Lp8c71wHgImS611mTZS/gxsv+ZmXlfeiYbL0Z2rr55JF\nxDjwPuCngbuAL0fE1Zn59fo6mXkxcHGx/s8Cb8nMBxt287LMvH8Fq61R4NRDkgbF+LjBriRJVY2t\nXIKqU4CdmXkHQER8Ajgb+Hqb9c8FPr5CddMos2VX0qAY8ZZduzFLkiqrj9nt0dRDh0TEzQ2f85sO\nNwXc2fD9XcWyvesVsR9wJvCphsUJfD4ivtJi39LiGexKGhQjHuz6m1qStFruz8yTe7SvnwVuaOrC\n/FnX+PoAACAASURBVOLMnI6IZwHXRsQ3M/P6Hh1Po8xgV9KgGPFg15ZdSVJXVjBB1TRwZMP3RxTL\nWnkNTV2YM3O6+Pde4Cpq3aKlpTPYlTQozMYsSVJVwViPPhV8GTg2Io6JiH2oBbRX71WjiAOBlwB/\n07BsXUQ8o/41cAZwew8ugGSwK2lwjHjLrr+pJUl9KTPnIuJNwHZgHLgsM78WERcU5ZcUq/4c8LnM\nfKxh88OAq6LWhLwG+Fhmfnblaq+hZrAraVDUszFnVp/3b4j4m1qSVFmwsn8rM/Ma4JqmZZc0fX85\ncHnTsjuA5y9z9TSqnHpI0qAYG6v94V5YGMnfWwa7kqTqotL019Jws2VX0iCpd2UewWDXMbuSJEnd\nMNiVNEhGeNyuv6klSV0ZG8ExP9IeDHYlDRKDXUmSyq30mF2pLxnsShokIzz9kN2YJUmSumGwK2mQ\n2LIrSVI1dmPWyDPYlTRIDHYlSarGWFcjz6mHJA2S+ly7I8huzJIkSd2wZVfSILFld7h0mgOySve7\npbZaTB24T+k6P3PKER3Ly+q5frL8jfIzJyc672Nt53pOjJW/C5ldWOhYvvvJzoPh5+ezY/m6fctv\n0fGSST/LrmXZ9gBrxsuO0flaB+XHKLve98480bH8kd3lv8TmFjpf790lyQsmKlyrfUp+rawd73ye\nayu0lqyd6LzOUu8JsPWyncC3pJLBrqSBYrArSVIFAeGbAI06g11Jg8RgV5IkSZXMzfHN+x7n9Vuv\n4+5dM2xYP8nmjcex6aSp1a6ZJO1thKceMtiVJHXFdl2Nuh13PcT2nbuYPmgGgOldM1x45W0ABryS\n+s8It+w69EqSVFlQG/Pci480qL60816eaHrtMzM7z8Xbd6xSjSSpA4NdSZIkVTEzs5v52PsR6u5d\nM6tQG0kqMcJTD9mNWZLUFdtkNeoOnAi+P7Z3VvgN6ydXoTaStLdtt0xz8fYd3L1rhm3fe4wffP0e\nfuL/Wu1arTxbdiVJXYnozUcaVD92xDOIiT3bCyYnxtm88bhVqpEkPW3bLdNceOVtTO+aIYGZBfjg\nF77FtlumV7tqK85gV5IkqQvPOWhffuYFRzG1fpIAptZPctE5J5icSlJfuHj7DmZmn86+PDc2zvzu\n2ZHMK2A3ZklSF8J5dqW5OU485hBueMPpq10TSdpLc/6A+bFxxnNhJPMK2LIrSaosqP3h6MVHGlhz\nc7XsppLUh5rzB8yNjTO+MD+SeQV83pAkdSUievKRBpbBrqQ+tnnjcUxOPJ1Eb35snP3GciTzChjs\nSpI0pCLizIjYERE7I2JLi/KIiPcU5V+NiBc0lF0WEfdGxO1N2xwcEddGxLeKfw9aiXPpK/Pztak8\nJKkPbTppiovOOeGpvAL77DPB635saiTzChjsSpK6Ej36aHlFxDjwPuAs4Hjg3Ig4vmm1s4Bji8/5\nwPsbyi4Hzmyx6y3A32bmscDfFt+PFlt2JfW5TSdNccOW0/n21lfy0h85nFOOPHC1q7QqDHYlSdWF\n3ZgHyCnAzsy8IzN3A58Azm5a52zgI1lzI7A+Ig4HyMzr/0979x4lWVUfevz7m37M9DBA8xKZAQQj\nGeMTwlzwKte3DJjcMBo15CXJNSFGzVVXnAjxrqhJVoJiYsw1kaByg64kGg3iGNEJxjxJUFDAAXXi\nCCTSoCjD8NAGZrp/9486DTVN1zmnuqsfp+r7mVVrqvY+j312VZ0+v9r77A3snmO7ZwGXFs8vBbYs\nSulXMoNdSU0yPNw6bw2g/jxTl1xD1bm+mprO0vydd99bmv93X/1e5T42HLa2NP/ko9eV5q8eWvjv\nFA9NTZfmZ3k1ADA5NVWa/8C+8vy1w+XdwI6i+kb6qnJWHedIjbocHir/4AxVfLDGRqu7ux12wGhp\n/qqKz+7Y0N7KfVR9/g8aHSnfx0j1cQyvKq/PpPwNq/O5m65Y6MF9Fe/5cHk+wPBUxXu+qqqgBnNa\ndhuAb7W9vg04tcYyG4A7SrZ7ZGbO5H8bOHKuhSLiXFqtxRx77LH1S90EBruSmmSAg11bdiVJtTka\ns9plZsLcv2Bl5sWZuSkzNx1xxBFLXLJFZrArqUmGh1tjDQwgz9SSpK7YBbkxJoBj2l4fXaR1u8xs\n34mIozLzjqLL850LLmnTGOxKahJbdiVJUp+5BjghIo6PiFHgbGDbrGW2Aa8sRmV+BnBPWxflTrYB\n5xTPzwE+2ctCN4LBrqQmMdiVJKkeR2NuhszcB7wO2A58DfjrzLwpIl4dEa8uFrsCuBnYBbwfeM3M\n+hHxV8C/Axsj4raIeFWRdQHwooj4BvDC4vVgceohSU0yNDSwwa4/S0qSumIv5ubIzCtoBbTtaRe1\nPU/gtR3W/ekO6XcBL+hhMZvHll1JTWLLriRJkmox2JXUJAMc7HqmliTV1hqN2aZdDTiDXUlN4mjM\nkiTVYzdmDTyDXUlNMsAtu3ZjliRJ6obBrqQmGeBg1zO1JKkLQdiNWYPOYFdSkwwPwwMPLHcploVn\naklSV+zGrIHn1EOSmmSApx6yG7MkSVI3bNmV1CQD3I25VrAbEbdGxI6IuD4iri3SToyIq2fSIuKU\nWescGxH3R8Sb2tJOLrazKyL+OKLVPhARqyPio0X6FyLiuLZ1zomIbxSPc3px0JKk+ZkZjbkXD6mx\nDHYlNYnBbi3Py8wTM3NT8fqdwNsz80Tgt4rX7f4Q+MystPcBvwycUDzOKNJfBdydmU8A3g28AyAi\nDgXeCpwKnAK8NSIO6aLMkqReilY35l48pMYy2JXUJAM89dBCujEncFDx/GDg9pmMiNgC3ALc1JZ2\nFHBQZl6dmQl8CNhSZJ8FXFo8/zjwgqLVdzNwZWbuzsy7gSt5JECWJElaega7kppkgFt2656pE/hc\nREwBf5aZFwNvALZHxLtoBc3PBIiIdcCbgRcBb2rbxgbgtrbXtxVpM3nfAsjMfRFxD3BYe/oc6zws\nIs4FzgU45thjS0cKDbLyYEeGypscnjB+YGn+s56wt3If9z04XZp/6+4HS/P3Tlcfx2MPHCnNX31Q\n+eAaozVaXkZWlS80NFL+ERurGOBjKquP874Hyuv7gYpfsurs4+DR0dL8A9aUH8eakeqBTEaGyn97\nWltRl72wZnjhA6784KHyk+k9D5W/X/dV5AOMrCqvq0PXrC7Nn67x/Rmq+GwPV5wnVtVoumxq62ZT\nyy31jMGupCYZ4GC3bsvuaUV35TOB10bEs4FfBd6YmccAbwQ+WCz7NuDdmXl/rwvbSWZenJmbMnPT\n4YcfsVS7laSBFD36JzWWwa6kJhngYLfWmTozJ4r/74yIT9C6f/Yc4PXFIh8DPlA8PxV4WUS8ExgH\npiPiAeBvgKPbNns0MFE8nwCOAW6LiGFa3aLvKtKfO2udf6x/eJIkSQt3+XUTXLh9J7fvmeTrD+5l\n+45v8xP//QnLXSxJqubUQ51FxAERceDMc+B04EZa9+g+p1js+cA3ADLzf2TmcZl5HPBHwO9l5nsz\n8w7g3oh4RnE/7iuBTxbrb6MVPAO8DPh8cV/vduD0iDikGJjq9CJNkrQMAlgVvXlITXH5dROcf9kO\nJvZMksCq6Sne8qmvc/l1E5XrStKys2W31JHAJ4pZgoaBv8zMz0bE/cB7ipbYByjuma3wGuDPgTFa\nIzXPjNb8QeDDEbEL2A2cDZCZuyPid4BriuV+OzN31zkwSdLiWMouyBFxBvAeYAj4QGZeMCv/ubR+\nOL2lSLosM3+7zrpSXRdu38nk3mL8h0xGpqe4fyq5cPtOtpz0qKFEJGllGR7m9u/dx8sv+Dy375lk\n/fgYWzdvHIjzV2Wwm5k3A0+fI/1fgZMr1n3brNfXAk+ZY7kHgJd32MYlwCVV5ZQk9ZeIGAL+hNaA\nh7cB10TEtsz86qxF/yUzf3ye60qVbt8z+fDzVTnNVKwiY9V+6ZK0Un3hv+7hvtv2MFGcsyb2THL+\nZTsA+j7gXcjUQ5KkAbSE8+yeAuzKzJsz8yHgI7SmqlvsdaX9rB8fe/j58PQ0+4oR4dvTJWml+vgN\n3yam9u/GPLl3igu371ymEi0dg11JUld6OBrz4RFxbdtj9u0wtaafA54ZEV+JiM9ExJO7XFeqtHXz\nRsaKKeSGpqeYWjXE2MgQWzdvXOaSSVK1OyenGJ5+9FScg9A7xXHzJUnL5XuZuWmB2/gycGxm3h8R\nLwYuB05YeNGkR8x087tw+07u+873mV41zO+/9Kl93/1PUn8YP2iMoTmC3UHonWKwK0mqbWY05iUy\nMy3djPYp6wDIzHvbnl8REX8aEYfXWVfqxpaTNrSC29274dJRA11JjfHyUx7H6JXT+6UNSu8UuzFL\nkrrQq07MtSLma4ATIuL4iBilNVL/tv1KE/HYYjo7IuIUWn/X7qqzrjQv+/a1pvGQpIY47UeO4ocO\nXcOG8TEC2DA+NjC9UzxbS5JWpMzcFxGvozW/+hBwSWbeFBGvLvIvojU3+69GxD5gEji7mKd9znWX\n5UDUXwx2JTXN8DCHrV7FVec9f7lLsuQ8W0uS6qs/knJPZOYVwBWz0i5qe/5e4L1115UWzGBXUtMM\nD8PUo+/ZHQSerSVJXVnCWFdaeQx2JTXN8HDr3DWAvGdXkiSpLoNdSU0zwMGuZ2tJUm2t0Zht29UA\nM9iV1DQGu4MjalykDQ+VN3gfuKa82o4+eHXlPq67/ful+Xd/f29pfp1rzUPHhkrzhys2MjZcvj7A\n2gV+hFZX1PV0ZuU29jxYXlcT95dPmP3diroGGKmYa2X9QeXv+Q8fcmDlPsbXjpTmH7S2vK5X763u\nqHH/A+UnulvvLf9c/sst91TuY9e37y3NP3jtaGn+k9ZX19VTj1xXml/12a36jkN1V92qj2aNjy4Z\nVRspL8VyxZyGuhpoU1MwVP33UZJWjKGhgQ127cYsSZJUly27kprGll1JkmqyaVeDzGBXUtMY7EqS\nVE8Y7WqQGexKapoBnnrIbsySJEl1GexKahpbdiVJqsfBmDXQDHYlNY3BriRJ9RjraqAZ7EpqmgEO\ndu3GLEmSVJdTD0lqmgGeesifJiVJ3bFpV4PMll1JTTPALbuerSVJtQWOxqwBZ7ArqWkcjVmSJEmV\nDHYlNc2qVTA93XoMGM/WkqT6wtGYNeAMdiU1TcQjrburBqut07O1JKkrxroaBJdfN8GF23dy+55J\n1o+PsXXzRractMFgV1Izzdy3OzKy3CVZUp6tJUmS2lx+3QTnX7aDyb2te9wm9kxy/mU7ANhisCup\nidoGqer4Y14fGqx2bEnSwkWPHtIKdeH2nQ8HujMm905x4fadTj0kqZmK6Ydmfsyb2DNJ8siPeZdf\nN7HcJVwUBruSpC5Ez/5JK9XteyY7p9uyK6mJipbd0h/z+lDfna2D8sFT6gyssqpimb37sjT/2tvu\nr9zHP91wR2n+vfc+WJp/xBFrK/dx3GHrS/OHVsAoM/umy+uyThGHK96wdaPlv8A/OFU9Mt3EPXtL\n8/9zz32l+ZP7qod7P/nIQ0rzD103Wpo/OlT929V0VtR3xUe36v0CeHDvwkb6e8y66tPSY9euKc0/\naKx8G2tGq+tqqOpEUCGpriuyYh/L/xWVBtL68TEm5gh414+PGexKaqZigKrSH/P6kC27kqSuRPTm\nIa1UWzdvZGxk/x9Kx0aG2Lp5o8GupGYqWnbXj4/Nmd0pvekMdiVJtfXqdl1jXa1kW07awO+/9Kls\nGB8jgA3jY/z+S5/qaMySmqsIdkt/zOtDnq0lSd0xUtUA2HLShrlHJzXYldRERbC75aTjAAZmNGbP\n1pIk9amIOAN4DzAEfCAzL5iVH0X+i4EfAL+QmV8uWzci3gb8MvDdYjO/mZlXLP7RrBAGu5KaqG3q\noY4/5vUhz9aSpK44knIzRMQQ8CfAi4DbgGsiYltmfrVtsTOBE4rHqcD7gFNrrPvuzHzXEh3KyuLU\nQ5KaqJh6aNB4z64kqSsOUNUYpwC7MvPmzHwI+Ahw1qxlzgI+lC1XA+MRcVTNdQeTLbuSmqitZXeQ\nGOxKktSfNgDfant9W5FWZ5mqdX8tIr4SEZdExJzzpkXEuRFxbURc+93vfneuRZrJYFdSExVTDw0a\ng11JUlccjXngvQ94PHAicAfwB3MtlJkXZ+amzNx0xBFHLGX5FpfBrqQmGtCWXc/WkqT6jFSbZAI4\npu310UVanWVGOq2bmd+ZSYyI9wN/27siN4DBrqQmGtBg15ZdSZL60zXACRFxfESMAmcD22Ytsw14\nZbQ8A7gnM+8oW7e4p3fGS4AbF/tAVhSDXUlNNKDBrmdrSVJXHI25GTJzX0S8DthOa/qgSzLzpoh4\ndZF/EXAFrWmHdtGaeugXy9YtNv3OiDgRSOBW4FeW7qhWAINdSU1ksCtJUrnAkZSbpJj/9opZaRe1\nPU/gtXXXLdJ/vsfFbBanHpLURE49JEmSpFK27EpqIlt2JUmqZsOuBprBrqQmGtCphzxbS5K6Y7Sr\nQWawK6mJbNkdDHWu0YZXlffuPuSAkdL803/osMp9HLSm/H6fr99xf/n6a0cr9/GYdeXlvOm75fv4\nrz0PVu7jhw5bU5r/w4ceUJo/vrr8OFYPVfe0XzO8ekH7OOqA6i/+IWsmS/OvuvXe0vxP3XBn5T7u\n3Vj+a9sLjy+fp/KwA6s/E+MVn5sfWXVQaf7aGhd4Gx8zVpp/34Plxzk2Un0v3N7p6fL8qSzNH63I\nB5gaKl8mqzZRvQuy4oRkTCktrcuvm+DC7Tu5fc8k68fH2Lp5I1tO2rD/Qga7kpqoJNitde5rKM/W\nkqSuOBqz+tHl101w/mU7mNzb+kFuYs8k51+2A2D/iz6DXUlN1CHYrX3uaygHqJIkdSWiNw9pJblw\n+86HL/ZmTO6d4sLtO/df0GBXUhN1CHZrn/saymBXkiQNvNv3zH27yqPSnXpIUhN1mHqo9rmvoQx2\nJUldiR49pJVk/fjc4w08Kt2WXUlN1KFlt/a5r6EMdiVJ3THaVR/aunnjowbIGxsZYuvmjfsvaLAr\nqYk6TD1U+9zXUJ6tJUnSwJsZiMXRmCX1pQ4tu7XPfQ3l2VqSVFurUdZmWfWnLSdtqL7AM9iV1EQl\nUw/VOvc1lN2YJUn19Wgk5rqjMUfEGRGxMyJ2RcR5c+T/bER8JSJ2RMS/RcTT2/JuLdKvj4hre1cJ\nGmgGu5KaqCTY7WeerSVJK1JEDAF/ArwIuA24JiK2ZeZX2xa7BXhOZt4dEWcCFwOntuU/LzO/t2SF\nVv8z2JXURAa7kiRVW8JOzKcAuzLzZoCI+AhwFvBwsJuZ/9a2/NXA0UtXPA0kpx6S1EQdph7qd3Zj\nliR1p3ejMR8eEde2Pc6dtacNwLfaXt9WpHXyKuAzba8T+FxEfGmObUvzY8uupCbqMBpzv6sV7M51\n31NEnBgRV8+kRcQpRfopRdr1EXFDRLykbTsnF9vZFRF/HNG6aysiVkfER4v0L0TEcW3rnBMR3yge\n5/Ty4CVJy+p7mbmp7XHxfDcUEc+jFey+uS35tMw8ETgTeG1EPHuB5ZUMdiU1k92YK82+7+mdwNsz\n8zMR8eLi9XOBG4FNmbkvIo4CboiIT2XmPuB9wC8DXwCuAM6g9Sv8q4C7M/MJEXE28A7gpyLiUOCt\nwCZav9B/qbhf6+4FHLMkad5iKUdjngCOaXt9dJG2f4kingZ8ADgzM++aSc/MieL/OyPiE7S6Rf/z\nopZY/c9gV1ITdRHsXn7dRN9MRbSQbswJHFQ8Pxi4HSAzf1AEtgBriuUoAt+DMvPqzEzgQ8CWYrmz\ngEuL5x8HXlC0+m4GrszM3UWAeyWtAFmStEyWcDTma4ATIuL4iBgFzga27V+WOBa4DPj5zPyPtvQD\nIuLAmefA6bR+jJUWxmBXUhPVDHYvv26C8y/bwcSeSRKY2DPJ+Zft4PLrHvVbcyPUPVvP3Pc0BfxZ\n0dXsDcD2iHgXraD5mTMLR8SpwCXA42hdgOyLiA207rea0X7v1cP3ZRXL3gMcRs37tYp7sc4FOObY\nY2sekiRpJSv+HrwO2A4MAZdk5k0R8eoi/yLgt2j9vfjT4s6YfZm5CTgS+ESRNgz8ZWZ+dhkOQyvY\nvFovDHYlNVHNYPfC7TuZ3Lv/vb2Te6e4cPvORrbu1j1bn5aZExHxGODKiPg68DLgjZn5NxHxCuCD\nwAsBMvMLwJMj4keASyPiMx233ANF8H0xwMknb8rShWu0JowMlS900NhIaf4Tjzywch/HjK8tzd/3\nxOnKbVSZfKj8JvQv7dtTmr9udfVok+sPWl2a/9gD1pTmj1XsY3SouvPBqlXl79d0ln8kDthXfZxr\nqy5sjivP/rcan7s9k+Xv10P7yj8TUaOp7IDV5fU5VFWXC/9YsufBh0rz141Wn5ZGVy1sbL3p8o9E\nrWWqPlfZg66+SUUhcgnHRS48MrbU0sjMK2jd9tKedlHb818CfmmO9W4Gnj47XZox03oxc1E303oB\nlF/QGexKaqKawe7teya7Sl/pal0xtt/3BMzc93QOra5jAB8r0mav9zXgfuAptO6zap8Sov3eq4fv\ny4qIYVrdou+i5v1akqQl1LvRmKVlU9Z6UcqphyQ1Uc2ph9aPj3WVvtJVBrsl9z3dDjynWOz5wDeK\nZY4vAlYi4nHAE4FbM/MO4N6IeEZxP+4rgU8W62+jFTxDq8X488V9vduB0yPikIg4pNj39gUesyRJ\nGnDzbr2wZVdSE9Wcemjr5o2Mjez/g97YyBBbN29crJItqjpn6znve4qI+4H3FIHtAxT3zAKnAedF\nxF5gGnhN2yjOrwH+HBijNQrzTPfmDwIfjohdwG5ag5CQmbsj4ndoDVIC8NuZuXu+BytJWrglHI1Z\nWjTrx8eYmCOwrWy9MNiV1EQ1uzHP3MbRL6MxV56tO933lJn/Cpw8R/qHgQ932Na1tLo0z05/AHh5\nh3UuoTXYlSRpBag5krK0om3dvHG/e3ahZuuFwa6kJupi6qEtJ21obHA7m2drSZI0cObdemGwK6mJ\nugh2+4lna0lSV2zYVb+YV+uFwa6kJppnsDuvKdpWEM/WkqT6wm7MGmAz868tcPozSVpy8wh25z1F\n2wpisCtJkgbCglsonHZIUlPVnHqoXdkUbQa7kqQ+ZdOumqcnLRR2YZbUVDWnHmo37ynaVhD74UiS\nagta3Zh78ZCWUlkLRW0Gu5Kaah7dmDtNxVY5RdsKYrArSZL6Xk9aKAx2JTXVPILdrZs3Mjay/60b\ntaZoW0E8Y0uSumKjrJpo/fgYE3MEtl21UBjsSmqqeQS7856ibQXxjC1J6opdkNVEWzdv3O+eXZhH\nC4XBrqSmmufUQ7OnaLv8ugmedcHnGxP8esaWJEl9ryctFAa7kppqnsFuuyZOReQZW5LUlbAjsxpk\nwdMNtXPqIUlNNY+ph2Zr4lREAxfs1rlIG6oYtmvNaPkCw0Mjlfs4YHV51U9llm+gIhtg33T5Qo8d\nX7OwMgCjFZU1PFRe31HRH3JVjWvqqm1U1VWd4zxsXfkyGw4tr8vnHH9E5T6qjmPNSHldjw5XjzdX\nVVVDFRU+NV392c4sv//t4AfLtzFUo4/sujXl35+x0fKL0dUVdQkwUvHZraqrOuFg1TJV56tl605s\nrKuG6HkrhC27kppqHlMPzdbEqYgcjVmSJPWlnkw31M5gV1JT9aAbc6cB/VZFcPx5n+ZZF3yey6+b\nWNA+es1gV5LUlejRQ1psPW+FMNiV1FQ9CHbnmooIWr0kk0d6z6ykgNcztiSptghHY9bK1n6P7qqI\nOW9V6Wq6oXYGu5KaqgfB7uyB/uY6x660e3ht2ZUkSX1h5h7diT2TJHOPydD1dEPtDHYlNVUPgl1o\nBbxXnfd8brngx5juMO7NxJ7JFdOl2TO2JKkrjsaslWque3ShNfDddObCR2M22JXUVD0KdtutHx9j\nosNtIStlWiLP2JKk7hjragVp77bcadz86UxuueDHFr4zpx6S1FQ9mHpotq2bN+434v1sK6FLs8Gu\nJElqpNlTC3Uy73t0Z7NlV1JT9WDqodna7+Eta+E9/rxPL7xnzTx5xpYkdcWGXS23mdbcThdX7RZ0\nj+5sBruSmmoRujFDK+DdctIGnnXB5zuek9tHap5ZZ6k4QJUkqSszIzIv9CHNR/sgVGUC2DA+xu+/\n9Km9u7Ay2JXUVIsU7M7oNC1RuwXNcz5PnrElSV0IB6hqkIg4A3gPMAR8IDMvmJUfRf6LgR8Av5CZ\nXy5bNyIOBT4KHAfcCrwiM+9eiuOBzoNQtdswPsZV5z2/9zs32JXUVIsc7M6elqjTGArznud8nmzZ\nlSSpD0XEEPAnwJnAk4CfjognzVrsTOCE4nEu8L4a654H/H1mngD8ffF6yVRdKPW02/JsBruSmmqR\ng13Yf1qiDR3GSujZGAo1GexKkmoL7MbcIKcAuzLz5sx8CPgIcNasZc4CPpQtVwPjEXFUxbpnAZcW\nzy8Ftiz2gbQru1Dqebfl2Qx2JTXV0FBrgKoOc+P22lzdmhf1x8gOPGNLktSfNgDfant9G3BqjWU2\nVKx7ZGbeUTz/NnBkrwpcx8xUF+/62O/yYzuvevQC5y9yAc49d5F3IEmLIAIOPxxWLU1b5xYe+SX0\n9f/zTVz7zDMdjVmSJDVHZmZEzNlMEBHn0uoazbHHHtuzfc5cKP3e2rfzuj2TyzadhSQ1zp13Lstu\n3wPL1qVr4ILdOvUcVQtVtP4P1djJaEXNV/UwyKpCLJF+GKimF9+9yk3U+dwtQV1WfW6qynDQWPUp\nY3S4fBvj+0ZL81fVqIbhofKFhit+taxav1WO8mWqfhitWh9o7Bw+dkFujAngmLbXRxdpdZYZKVn3\nOxFxVGbeUXR5nvPqKTMvBi4G2LRpU0//aM1MdSFJ6sIA/gH3nl1JUleiR/+06K4BToiI4yNiFDgb\n2DZrmW3AK6PlGcA9RRflsnW3AecUz88BPrnYByJJ0nwMXMuuJEmDIDP3RcTrgO20pg+6JDNvgckW\nHAAADGlJREFUiohXF/kXAVfQmnZoF62ph36xbN1i0xcAfx0RrwL+E3jFEh6WJEm1GexKkupzJOVG\nycwraAW07WkXtT1P4LV11y3S7wJe0NuSSpLUewa7kqTagsbeaixJkgaM9+xKkiRJkvqOLbuSpO7Y\ntCtJkhrAYFeS1BVHUpYkSU1gN2ZJkiRJUt+xZVeS1BVHY5YkSU1gsCtJ6oqxriRJagK7MUuSJEmS\n+o4tu5Kk7ti0K0mSGsBgV5LUFUdjliRJTWA3ZkmSJElS37FlV5JUW+BozJIkqRkiM5e7DD0VEd8F\n/nOBmzkc+F4PitMPrIsW6+ER1kVLU+rhcZl5RK82FhGfpXXsvfC9zDyjR9vSCjbH3+amfH86aXr5\nofnH0PTyQ/OPoenlh+YfQ9PLD/M/hlrXN30X7PZCRFybmZuWuxwrgXXRYj08wrposR6k+Wv696fp\n5YfmH0PTyw/NP4amlx+afwxNLz8s/jF4z64kSZIkqe8Y7EqSJEmS+o7B7twuXu4CrCDWRYv18Ajr\nosV6kOav6d+fppcfmn8MTS8/NP8Yml5+aP4xNL38sMjH4D27kiRJkqS+Y8uuJEmSJKnvGOxKkiRJ\nkvpOXwW7EbEmIr4YETdExE0R8fYi/W0RMRER1xePF7etc35E7IqInRGxuS395IjYUeT9cUREkb46\nIj5apH8hIo5rW+eciPhG8Thn6Y58f93WQ0S8KCK+VBzvlyLi+W3bamw9FGXp+jNR5B8bEfdHxJva\n0hpbF/P8bjwtIv69WH5HRKwp0htbD0VZuv1+jETEpcUxfy0izm/bVqPrQlpKFeebOf8Wr0QRcUZR\nzl0Rcd5yl6eOiLi1OFddHxHXFmmHRsSVxbnoyog4ZLnL2S4iLomIOyPixra0jmVeaZ+hDuVvzHcg\nIo6JiH+IiK8WfytfX6Q36T3odAxNeh86XbM04n3o9pqryOtt+TOzbx5AAOuK5yPAF4BnAG8D3jTH\n8k8CbgBWA8cD3wSGirwvFusG8BngzCL9NcBFxfOzgY8Wzw8Fbi7+P6R4fkhD6uEkYH3x/CnARFte\nY+thPnXRtt7HgY+1L9PkupjHZ2IY+Arw9OL1Yf3w3ZhnXfwM8JHi+VrgVuC4fqgLHz6W8lHyHev4\nt3ilPYChonyPB0aLcj9puctVo9y3AofPSnsncF7x/DzgHctdzlnlezbwo8CNVWVeiZ+hDuVvzHcA\nOAr40eL5gcB/FOVs0nvQ6Ria9D50umZpxPtQUv4lew/6qmU3W+4vXo4Uj7IRuM6idRH7YGbeAuwC\nTomIo4CDMvPqbNX8h4AtbetcWjz/OPCCojVnM3BlZu7OzLuBK4Ezenl8dXVbD5l5XWbeXry8CRgr\nWqYaXQ8wr88EEbEFuIVWXcykNbou5lEPpwNfycwbivXvysypptcDzKsuEjggIoaBMeAh4N5+qAtp\nhZjzb/Eyl6mTU4BdmXlzZj4EfIRW+Zuo/Tx1KY+cv1aEzPxnYPes5E5lXnGfoQ7l72Qllv+OzPxy\n8fw+4GvABpr1HnQ6hk5W4jF0umZpxPvQq9hsIWXoq2AXICKGIuJ64E5aF5VfKLJ+LSK+UnQrmWnq\n3wB8q23124q0DcXz2en7rZOZ+4B7aLV6ddrWsuiyHtr9JPDlzHyQPqgH6K4uImId8Gbg7bM20/i6\n6PIz8cNARsT2iPhyRPxGkd74eoCu6+LjwPeBO4D/At6Vmbvpk7qQllg3f4tXoiaVtV0Cn4vWrUrn\nFmlHZuYdxfNvA0cuT9G60qnMTXpfGvcdiNatOCfRapVr5Hsw6xigQe9Dh2uWxrwPPYrN5q3vgt3M\nnMrME4GjabXSPgV4H60uRyfSumD9g2Us4pKYTz1ExJOBdwC/ssTFXVRd1sXbgHe3/QrVN7qsh2Hg\nNOBni/9fEhEvWPpSL44u6+IUYApYT6tLza9HxOOXvtTSyhcRn4uIG+d4nMUA/i1eQU4rznlnAq+N\niGe3Zxa9Uxo1F2UTy0wDvwNFI8DfAG/IzHvb85ryHsxxDI16Hzpcs7Tnr+j3Ybljs74Ldmdk5h7g\nH4AzMvM7RUVPA+/nkebwCeCYttWOLtImiuez0/dbp+jWeDBwV8m2llXNeiAijgY+AbwyM79ZJPdN\nPUDtujgVeGdE3Aq8AfjNiHgdfVQXNevhNuCfM/N7mfkD4Apa9x71TT1A7br4GeCzmbk3M+8ErgI2\n0Wd1IfVCZr4wM58yx+OT8/hbvBI1qawPy8yJ4v87af2tPwX4TnE7xsytOncuXwlr61TmRrwvTfsO\nRMQIrSDxLzLzsiK5Ue/BXMfQtPdhRvs1Cw17H2DBsdm89VWwGxFHRMR48XwMeBHw9ZkPQ+ElwMzI\neNuAs4v7U48HTgC+WHQLuDcinlHcZ/dK4JNt68yMoPoy4PPFLyrbgdMj4pCiKf70Im3JdVsPxbKf\npnWj+1UzCzS9HqD7usjM/5GZx2XmccAfAb+Xme9tel3M47uxHXhqRKwtgrXnAF9tej3AvOriv4Dn\nF8sfQGtgha/3Q11IS6nbv8VLXb6argFOiIjjI2KU1gB025a5TKUi4oCIOHDmOa3zzo3sf546h0fO\nXytZpzI34jPUpO9A8Xftg8DXMvMP27Ia8x50OoaGvQ9zXrPQkPehV7HZggqRyzjCWK8fwNOA62iN\nInsj8FtF+oeBHUX6NuCotnXeQmukr50UI6kW6ZuKbXwTeC8QRfoaWqP07ioq//Ft6/yvIn0X8ItN\nqQfg/9C6J/H6tsdjml4P8/1MtK37NvYfjbmxdTHP78bP0Rqk60bgnf1QD/P8fqwrjusm4KvA1n6p\nCx8+lvJRcb6Z82/xSnwAL6Y1qus3gbcsd3lqlPfxtEY3vaE4j72lSD8M+HvgG8DngEOXu6yzyv1X\ntLo37qXV2+hVZWVeaZ+hDuVvzHeA1i1MWZR15trwxQ17DzodQ5Peh07XLI14H0rKv2TvwcyFmSRJ\nkiRJfaOvujFLkiRJkgQGu5IkSZKkPmSwK0mSJEnqOwa7kiRJkqS+Y7ArSZIkSeo7BruSJEkaKBEx\nFRHXR8RNEXFDRPx6RCzadXFEvDwivhYR/7BY+yjZ93ER8TPzWG88Il6zGGWSlorBriRJkgbNZGae\nmJlPBl4EnAm8dRH39yrglzPzee2JETG8iPuccRzQVbBblGscMNhVoznPriRJkgZKRNyfmevaXj8e\nuAY4HHgc8GHggCL7dZn5bxHxIeCyzLy8WOcvgL8GdgH/Dxil1ZD0k5n5jbZt/xbwG8AEsA24CXgp\nsA4YAp4LvJNWwJ3A72bmRyPiucDbgT3AU4t97QBeD4wBWzLzm7OO6znAe4qXCTwbuBL4EeAW4FLg\nEx2O77nA7wB3A08EvgycBewErszMrXXrV1opDHYlSZI0UGYHu0XaHmAjcB8wnZkPRMQJwF9l5qYi\nkHxjZm6JiIOB64ETgHcDV2fmX0TEKDCUmZOztv2PwJsy89qI+AXgd4GnZebuiPhJ4NXAGbSC7WuA\nU4uyXE4rUN0N3Ax8IDPfGhGvB47PzDfM2s+ngAsy86qIWAc8AJxW7PvHi2XWdji+5wKfBp6SmbdE\nxHHA32bmUxZQ1dKyshuzJEmS9IgR4P0RsQP4GPAkgMz8J+CEiDgC+GngbzJzH/DvwG9GxJuBx80O\ndDu4MjN3F89PoxVwTmXmd4B/Av5bkXdNZt6RmQ8C3wT+rkjfQat78mxXAX8YEf8bGC/KV+v4Cl/M\nzFtqlF9qBINdSZIkDbSiG/MUcCfwRuA7wNOBTbS6J8/4EPBzwC8ClwBk5l8CPwFMAldExPNr7PL7\nNYv2YNvz6bbX08Cj7vfNzAuAX6LVzfmqiHjiHNssO7665ZIawWBXkiRJA6toqb0IeG+27u87GLgj\nM6eBn6d1X+2MPwfeAJCZXy3Wfzxwc2b+MfBJ4GldFuFfgJ+KiKGiLM8GvjjPY/mhzNyRme+g1R36\nibS6ZR/YtljZ8bWbvZ7UOAa7kiRJGjRjM1MPAZ+j1T347UXenwLnRMQNtILFh1s7i27GX6M1INWM\nVwA3RsT1wFNotf524xPAV4AbgM8Dv5GZ3+7+kAB4Q0TcGBFfAfYCnym2PVVMsfRGSo6vXWbeRat1\n+MaIuHCe5ZGWlQNUSZIkSTUUgzvtAH40M+9Z7vJIKmfLriRJklQhIl5Iq1X3/xroSs1gy64kSZIk\nqe/YsitJkiRJ6jsGu5IkSZKkvmOwK0mSJEnqOwa7kiRJkqS+Y7ArSZIkSeo7/x9RMpduI4893wAA\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f84870bc860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=2, figsize=(16,7))\n", "kernel_array = plot_density(predictor, ax[0], masked_grid.region())\n", "plot_time(points, predictor.background_kernel)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "backgrounds, aftershocks, triggers = sepp.sample_offsets(trainer.as_time_space_points(), predictor.result.p)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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kSStnEitJ0jaUmZ8ATixo+53MbBdPPwkcLB7fBXwwMxuZ+RTwBHD7pgUrSdIqVAYdgKSt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\nSZIkSZ1hI1aSJEmS1Bn/D0UuBFYH8EeZAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f8487228940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=2, figsize=(16,6))\n", "\n", "ax[0].scatter(aftershocks[1], aftershocks[2], alpha=0.1)\n", "ax[0].set_title(\"Triggered events in space\")\n", "\n", "ax[1].hist(aftershocks[0] / 60 / 24)\n", "ax[1].set_title(\"Triggered events in time\")\n", "ax[1].set_xlabel(\"Days\")\n", "None" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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DNsYYY4wxZi1YsGvMGqhnUvsjAY7OZzmezIPWGIpFKFZq7B2Nky9Vuf3RE1y1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H2wlRJIGmCIIo5o3bXZolnacWylyYs3nnbu8+IayGpdFx8wWmNBM8tVimdavN/ihXvNYVCS9K\n+Wh/yGrdnC6sPUyJfLvnc2WxPG4rSNnouPm7+TFVAnzWKg0KCgoKCgoKPj2KYPdT4LhJdMVUqRgq\nL5+9l4U9KkRTNtT7BGRKhoITxvhRgq5IyEKwWjNZrBiHRGaubva4sT9CkPHUQpmhH3O2bh069kur\nNYI4JU6yaTDuRQmqBFVDZRTEDP2Ira7L7bZL3dZOXVqbZ7v0e1kiIMuy+wK2jhPylUvz/PR2m4/2\nHaqmwtm6RWsUsNPz6HsRC2WD187VD5VBvnSmBjBVWN7pe1xolqbbvXGnwxfW6vzWa2en4/vutX2i\nJM0DHkPDNlS6I5+eF1FVVM4vWTy/Upvep5KuMPQjSsaT+8l+tD9ksWxgzgZSYczVzT47PZ8beyOW\nKjqKIlM1NLwwZm+Qn9coiFEkQXMsCnRcwLZSM5kr6bx2rjk9vhvGBHFC2VDx44S+H/PMYgVDzW2W\nOk7ISs3k5v6QxYrBctVk6Ef8Xz++w4tna9iacmxgeNqS3OP4eZWbHvI6jk1GfkzJUGiUtGkv6j0R\ntAFv3OnyrecWDwX1pzmvo4HVwIsZehF/+OEezyxVpgrfs8/5pC/9bsedLvxoisR33ttlqWrcdx0m\n1+ijgxF3Wg6XF8tAHpgGcXKo6mGy/fduHND3It683SNOM6qmSrOko0oSIz/P5N9puaw18nOUJXHf\n36SGrdP3I9wwxtYknCCmbqpUDIWmbeBFMV0nyq3EJHGqzGneb5/SKBnjfvv8PDpOgCKLB+57Wj6L\nlQYFBQUFBQUFnw5FsPspcNwkumqqCDjk8Vmz1HH/7PG+n3faDu9vD9jueahS3serqxKyJLA1ZWpj\n8/0bB2x23XFJruD97QFxkmIo8qHg04tygaefrLe52x7RLBnESUIQZaw1bYSAa7sDBCAEx6obn5Q9\nOW32b+BFXJiz6XshFUMjTjNMVRr3HmbTsm6Al89ouGGMpuQlt5NsmRvEKGPBpStLZV4+U59uNzsZ\nr5gqqizR92PqloqGhFwyadg6F+ZsrHG58+Q+1S2NTmeNigAAIABJREFUra7L2Yb9xH6yebH4YfsY\nN4zZ7LqosmCxrOOFGXdaDvMVnY4bcLft8MxKBU2W2Bv6bHVdNLl57GLDSYHa5PevjPskJ4Hm86vV\naaXAYsWgaukAKOMe53fu9vhXX1w5dlHjOEGykyoK4Ggvcr4o8aQ90McxyTBOFn0mTNS+r+0OGAUx\ncZKRZim/9/YW/85Xzj/Sec0GVkM/oj0KkIRAAFGScW13yFrDOtSLvd3z2Ot7mJpCxcjVoD/aG7BY\nNXl2uXLoOkzGa2kKzyyWud1y+HC7zzMrVRRJ4EcJaw370Ht0dbPHZtejZmpEaYrIMjRVRpMFiiRR\nMRXcKKHnhdP3MYiTQ8+6FyVI42qJjhNi6Qp9P2aurNO0NbYHPn4U8/JalafmS8Rpdqp7lvfba9PS\ncsjbEA5GAU8tlh/1Fh/LZ7HSoKCgoKCgoODToQh2PwWOm0R//fI8wKHPvnbMZ5PJ9p22w+++uUlJ\nl2naKm6Y8OHugJfP1qiY6tQKRJYEPTeiOlZkBRBCMPSjQzY9XpSwP/QRwKtrDd6+26XlBJBllAyV\nKEmQJXkcouXlmcepG5+UPTlt9m8yUU0zwVMLuYWSH8WossTQjxBH1Jsn3zmbYXPCZFoi+aCxXZiz\n2eq6JGnKyAvRVIVRELFYNmjYOo2Sdqj/sFHS+ParZ+g44amCugdxeaHEB9t9hBDoSu5HeqfjcnGu\nRMPWUCSBNwgQmcDxI7Isw9AUqqaGpkg0bY00zU7sH37UAHTCwdA/pJbbGvroisydtsObGz1Keh70\nH13UeFjp6oTZEt7ZRYnHsXi603b40c32WKn8dL3jkAdcm12HvaFPWdewdUGYpNwde9AuV81Dmeaj\nwfIsk+c1STN+eLOFGyb0vYjFqp7b+UQx6+0Rr50/O91n5MdIMyriQz9CV2SSNGMUxGyPy473BwHL\nNeNQ5vjrl+f5Fz/b5QcftVitmSxUDNwo5rW5e2N8d7PHyE/oOhFBkpIJiVGYMPJdqqaGrkqoQqI+\nXtAwVRk/Sk58Xs417eniyHfe26XnhVyaK7FSMykbuQf3aXu87/Xb5zZVuiKP++051G//JDxJpUFB\nQUFBQUHBLxZFsPspcVJwcNrPfnSzTdVUqVo6FVPj3c0emiqz2/f4yqW56ST0ve0++TRU4k57hBum\nmKpAV2XO1e2pTU/FVKma6lSkZqLo3HYCVFnw1EKZq3d7LFYMVut57+hx6sYnZU9OG3xNJqqKDH6U\nIET+77mmza2D5ER/0dkM20SUKB9bfOLYapbG1y7PI0sS372+j5ZkXJq3OVO3kcaT7+Pu02kCqofx\n0pkaAy+i50b0vRBVlijpCs+vVKaLEUsVnb4bsTsIkETGv/HKKiu1vPT8jTudh/YPT8Y+yaS+c7c3\nDeCAY0V87HGptiLnNjfvb/cZelFuWWTkIk93Og5+nEwXAh6lBPlxFiWOY7LYUzXVabn17765ybdf\nPfPQ+1MxVW4eOJQNdVoVIBDYmsT3rh/w7VfOnFrYaCIG9u5Wn3e3ekRxSiYEigx7Q585W8PSlUP7\nH2096LkRipzbXl3bHWKoMnO2xt2ex4/W29ha3qO71rBYqpica9jsDXxW6yaqLB16JXpuyN2uR1lX\nKOkqTUvjo70RUZxgqIIwTej0A64slHlx7Hc7eTcetmBRszR+44Wl6XNjqnIuVPcIgeSFOZueG7LW\nsOk4AQcjH1mSDvXbPymPu9BTUFBQUFBQ8ItHEex+TpnNwNmaQtVUWarotJ1omiEzVRlBRpRm3G67\nlHUVW5dx/ISO4/PCavVQ1uq71/anIjW5IJY67av9lacXKBsqYZyO1Y25T934YZPeo5PpSZbvaLD0\nylodWRJTcamnF8vIknhgWfd6yzmivDwgiJKHjq1mafzmi8t85VLzE7UqmQTas9+51rRyJWRN4cpS\nhe2eR5xmvFq3WK4Z6Mo9AaHZ/uGhH00XJmqmdsjGpeeGfP/GAT03IkpSVFni+u4AJ4yJkoymrU8z\ndABLVZOrd7ts9jxGfszNgyFpCs2Sjhvm1zOI8v7hx1G8fZxFieOYXewBpv/+6Gb7ocHuhTmbMEkJ\no7y0PYqzXAFdQJYd7qU9Lot99D62nYA/We/ghAmWJtMsaThBggDWmvY0oJ6wUjMxFJmtnsvNgxEd\nJ6BiqAgBhmpiqDIdJ6A19HH9iDTJqBoqN/dHXN8bUrc0qqY27Xk1VWU6xvWWw3LV4N3NPn6c4vgx\nNUvBCXKrL02WOFO3eHa1ylLFeORg9bhAcqlaGj/HvYe+O7P7K3IuejXZ/uPs3z5tpUFBQUFBQUHB\nLzZFsPsZ4lEme/Nlg6EfTSf5libTc0Lq1r1A4WAYIBB8uNNn5McoDYsMhSCJadj3BxSzJZnbvXtC\nSGvNPJvYsDVe/2CXkR8TpxlxkuLHKc+vVNAU6b5Jb8PO1ZaPO5+H2YP8ytMLvHSmNr0eli49sKz7\nwhzT0sWSrrDWsFlvO1Pl5Ydldj7uyfFp7uVJwf/kHM42LJqlez2nE9GhOM4Ik5SuE1C3dT7c6SMJ\nMfVY/t6NA6qmSpbBzYMhfTdiqWpiaRpdN+CdzQGykPil8w3CJOXa7oArSxVKuoKlySzXDH5yu41A\nomyoqLLM3sDjxt6AZ5YrpFmGO15YeFTF2/vtoE63KHGUo+XWkC/Q7PS9h+5bszS+8fQCP7jZInIj\nqobCUtXg6laPkpb70lYMNS8tb4/wY/PEUuaeG/L96y2aJZ1KkrI/zMuP50u5z/GVpfL0fGZ7ld/f\n7tMaBViKQsPW6ToBwyBmrWHhk7Hd98hSAMG1vSFtJ6Jm5334e32PL1+aP3aM273cxzpKEiQEQRwj\nEMyVNP7K1y+NS7RDru0NHjvrOfvcPo7Nz3HvWmEXVFBQUFBQUPDzoAh2PyMcnewdDAPeuNNluWpM\nvStnJ31fvtTkd9/cBPJJfprBrZbDcysVPtzpoym5bc8LqzW8MOFg6Oc2NKY67tuTubrZB/Js1iQ4\nfXezNxWzSpKUmy2XrV6e6UrTDFtTeHujhxMm2JrMVy/NMVfSUSQx9YVtWBo9N+KPr+3zwmoNQ5V4\nf6vP964f8IW12jSIfViw9Cil3kczTo2SxmvnP7mJ8iSQubE35MPdPnuDkDN1k1fX6hiqfKqJ+4PK\nL3tuiBsk3G17jIKQkq6xVDXoOAFRCk1bZaWWB3/X9/MA96UzNW63XISA+bKJEIKBl1AzNfYGAWGS\nW1a5YcwPb7YoGSo1U+Xa7ohX1xqUDY2dvosXJblq8d6ASwsl4jQXWfpwJ+85TjNBSZdZrhr4UXLi\n+cHhfsrHWZSYcHSxB/Le1/my8YC97vGVS02SND2U8bZUmdWaNe2lNVSFIEoY+SfbAq23HII4JclS\nTFVlsaLTc0O2ej5LNWN6z2ff75qpst3zuNN2kSWBpkjMlzQsTebG/oiXzuTK1+sth74ToqsSQz+k\n7fiEUcILqzUatn7sGEd+jBsmXF6s4AR5b3CUptQtnYEXsVw1UWSJV9YaD+xFPi3HvccjPz5RWfpR\njjP5vMjQFhQUFBQUFDwuRbD7GWF2sjf0IzY6DooAN4gJ4/S+YOlc0+bbr57hRzfb3Go5OGHEn3lh\nGVUWdNyInX6fV9caLFQMum5I3dY407DZ7rlcnLdz65Guywfb8OJqjTBOuXUwQpIEJUOl78e0hgHn\nGzamKvHORg9DlWg5IStVE1tXcIKEmwcjbF3hu9cPON+waJQMgjjhw83cx3Oz55KmWd6HWNLZ6Lgk\naYYbJoesj+DJ7UF+3qWLJ2Vr75Vjx7y10aXvRZCBFyZ899oBv3plgaqlcnWzR9lQHynbO+HqZo+u\nG3B+zkJXynTdgFsHI7pexPmmTZLmys5uGFM1VMIkRYg8kAqilKubPWqWxv7Ap2GrVEwFf1wGvtPz\n8eKEupULm211PS7O56XANVvH63nTYMkJEwQZZ+sW1/ZGqLLEUwsl+l6u2LxY1R8Y4HxcixJHF3uG\nfkTfi/jms4un2v+4MnJNkRgF93ppgzglJe+xPYmBF1GzVHb7PqYKmiLRsDVaacCFOWt6XrPv91sb\nHbqjEF2WsHSZpm0wDCIUOaVma5xtWNxuOwzdECeIWarlpc2jIGKn6zEMIm7sDaZBuq0rzI9V1UtG\nnv0VCOIspWIqDIOIsi4zHNsIfZxiTUdtfoZ+xJ32iCjlPmXp05a3TyjsggoKCgoKCgqelCLY/Yww\nO9nb7nkYqkycZlzbG+KEKYoMsiT4lacXpvuca9qca+aZskkv7YQf3GwRjLNsk3LRg6E/DooEO32f\n8w0bS5PZ6ftcWaoAsN4a8cVzTa7vDamZGoYqk2UZThgjiXwivbBo5II+hkzXSbmxP8Qbiw2Jscps\nkoEfJnSckEvzJQxVIcsyBn6MpSnsD4LPlT3Ig8osJ4HMm3e6lHUNP0pRZIkoSSnrGu9u9fjyxSZv\nb/T40sXmY5Vp3tgfUTFUDFXBCWJ2BwEA+32fOSv/zjTNeHerR93SppnPuZLOT9bb6IrCSs2ADG4e\njPjKpTmuLJX54c0WXpzSsHSeWa5SNlTOzVlsdj0sTcVQJOZKGhsdF02WKOsKF+dL3NgfkmYZPS/k\nBzdaCDkPTkq6fezizCwfx6LE7GLPTt9jvmzwzWcXH0k87LhxdEa50vbAjynpMgtl+5Bt0FEqZp5R\nH/kxUZIQJeQ2PLbKM0vV6Xaz7/dGx0WSBYaQSLN8QaIsVAZuyMV5hdsth9sth54fYWkKiiRy4asM\nLE2dWgRNlMnDOKE8DsjLhoIiC4IoQQgJTZGoy/m/KTxS9vxh9NyQ3b7Ph7uDae/3ds9DEoKmrSKE\nODFDe3ThSAg+V38PCgoKCgoKCj4fFMHuZ4TZftkPdweEcUZr5LM4VsH1o4Qf3mwD98qOJ9mzo16f\n2z2Pg0Huw7pat8ZiUxXWWw6aLKHKgrmyPt1noqZsqjIZAi9KGAUJFeOeB2fd0hj68XTi7UQJt1sj\nvDAhTlKeWa4QxCnGWOCqaqi0nABTldEVGWdsqTIMQlojHy9MGAbRIY/Vg6FPxVT57rX9T0QkapaH\n9dgezbzP2sOUDIWzdYuuGzFX0jACiTjN8CNYrEi0RiHrbYeGrT12maYgX6SAvBdbl2W6YUjV1lAV\niSBOuL7no0jQ9yIuL5a5tjsABHVbIwXcIKFeVklFhiTlZcQlQ6VuqdNAF+Crl+b4J+9s4cUxQQR+\nnGJrMg07t9MBcMMEVRZEsWDPDVmsGliajBeln1gJ6mSx5+NiohR8tmEdEkB7kCXOhTmbubKOKkvs\nDXw6boghJC7N2wyDPNN/Yc6+3/s1hY4bISFy/1sZwiQvR/7iuQaSACeMcfwEN0zQZImGrWOpMglw\ntmFPs88973D2c6lq0nMiSroCAnpOwFzZ4N989czH9j5NFn/mSjpOGDPyIz7cCRmM/25MSurh/gzt\ncQtHAy8iI2KhbBR2QQUFBQUFBQUfG0Ww+xlhYmFyY39EexiwM/CJkoQgSklSkCXYG3js9HxsXRr3\nbOosVg3+ZL1LnKas1EzSFOp2/rs7HY+rm11eXK2hyBJrTZsLTZuFigG7A4I4BbJ8UkyeSbm8UMIN\n4/usf15crfG9GweUDZW9kcetfQdVkalbKm0nojUK2e55rNRMdEVCVyTiJGOhqdNxfO52fcI4JUlT\n1kdD4gQqhsoHOwMuxjZlI89YzvZQbnVdvnZ5/r4J+uP6q57EacRxJgsKQz86ZA/TGgsLGeNr4QYp\nNVvnbtvB0GScIMHWZTqjgNfONaaB8ihIsDUJS1dO1Tv51EKZ97cHCCHwwpgkS7nddZgr6UiSoO+F\nuFHCpTmbtptnBLMsZb015OJcibKhkGZ5gFs2FLZ6Ll03pDb2ZJ4EupBnPH/tyiK3Wg4bbQcy+Fcu\nz6FIAieIubY7YKfnMfQi4lSgKbn3bs8N+Jk7YL6kn6p/97PG41jW1CyNbz23xOsf7FEyFJ5XJDpu\nhKlKPLNYnma5L86XuHUwAqBpa7zphSgCqpbKKIgJ44SzTYvVmomlKazW8//f7fu50nrVZBTE9ASc\na1hs91y6bkTdUnlhtUqWV7GTZfDL5xvcPBix0XEBwdNLZebKxqHzeFLl49nFH1OTp4s/YZxyrmEf\nep6OZmiP68+dL+ftD7NWaIVdUEFBQUFBQcGTUgS7nxFqloYkCXb6HqMwpjUKkCWBIMLSAoZ+zMHQ\nZ6lmcn6uSscJ+Wfv5QJWL6xW2ei4vLnRpWGppJnFbt+n7US8tdHl99/d5ZfO1/j1Z5cI4hQ3jFmu\nGlzd6iPIeHG1xv7AZ72d25ZIQjBwIz7aHzJfNnn1XI2qpfL8aoWDYcDbd7uYqoyt532OX3uqyt7A\nY+CFrDVMDkYBmirxl758jq4b8gfvbmPpKmSCPSdCkSRWGwZBnFI2lWkJZtcNqZoalqYRxAmbXZer\nm71DpdtP4q96EieJ48z22O72fXrjazL0E2qWQtlQadp5hny9PeKphTI/utVCjyVqlgIIWkOfb72w\nRElX8KOUjY6LocpUDIWBF9L340NWQSfx0pkafS/vS3WjmP1BQM1QOFM1USUJJ4i5NF/m/FyJ1STP\nuo78DEWGZ5Yq+QLHGDeMWajk4kmTQH/Wzulg6KOrEt94ep6NtoUz7mNdrVmMggQvitnoOOiyTAb4\ncfL/s/emMZLc6Znf7x93ZOSddVd1dVd3s5tsNq/haIYaDOeQTI0sGbuyIUtrwLaMFbyA98vanywD\nBrxeG8ba8BfDBmwv7MVqtfZ6d7XWSIY9Erg6RhpJnBlxSDbJJpt9V9edlXdmZNzhD5GZnVWVVZ3d\nbM40yfgBRHdXZkZFRkQm/0887/u8fO96lVJW4+xMFj+MeHezyaWlwvFv6AnlUUqsT1csXrs0zxs3\na1y51yBraJypWGy3nFGiuSyJkZAOYzg1k/TNG7JM2ZIS0z5m5CLnDJWXT5f5/Xe3uVnt4gcx5+ay\nWLqCkASLeZO1mUQYfu/mPvM5Y1QOfD+Aqgwk53t8/NHjSD4eryYZH1N2r2EjSZPHg0167RBTlXH8\n8LGEZqWkpKSkpKSkDEnF7hPErWoPQ5G5OJ/HCyKa/SRZdb+X9GfmDBUJkISEF8TIMux1XCpZA0NT\nqPVc9rsuDdtHlSWqnSR92fVDWnbAn35U5RefXxqU2IY8u5T06Tb7PtutPmuVLIYq8e5mEyEJvnV5\nkWrX5dpOmxdXS/z85UUAtlt9LFXBMhRmcgaWplDKqFzdaTOXNw7MzgToOAG2G/CHH+yS1VVmCwaG\nLNHzQgqGyo29DjFi1JMKjHp8r+91D4jdjzNfdRJN2+NH6w2kwfEdzpwNwoi31xt8+ezMwLX0+eMP\nd1EUieWCge0G7LUdvvH0PLM5HScIWSqauEGJOzU7mbFazoxc56bt8dtv3kMRYlB+GhIDaxVrqnLf\nYkbj1UGgUsv2EEIwn9Np2j5eEKMOSsUdP+TiQo6ckfR2np2zBoFgk8XHJDczb6roikxGU+h5EXkz\nufnQdnwuLuT4i5v7yFIy5qhuewgRo6syEtD3kms2HpRcN22PKxtNru91EcScn8vx/ErxM+XYNW2P\nW9UuZ2aspKw/ivjh7RpLxQx+GNHqe7y/1eR0xeKl1RLtvs9Lp4rcqHa5V7cBWCmZdN0QRU5Eacfx\nadoeL6wWsb2QnKEiS7BsGux3XSA5p+t1myiMyOkyXhBNVQ78OJKPj5Rlkzi4wxtX7221Ruf7sIg+\n7rVpf25KSkpKSkrK4yYVu08Q9a6HLidloVldBSEIwwgBOEGMOngMwA0iRJwkrgJYmsL52Sw39rog\nBD0vIKurKLKEoUoIIeFHMVe32vzql1YP/N5h711GU7i206ZgaoCg4wS8sFIaOUPDBesLKyX6XnBg\n7EsYxby4UuLrF+c4zFLRxAsiVsqZQYlzsjA3VRmIiREHelLvI3C8YJB0nJRbrtd7nJ3JHnjW+HzV\nhynPHDpcuiIhIfDHZs6+v92i54V8sN0hq8vYXsCFhRy3qz1a/YBiRmW+YNLu+xQGIUUvrZaOdaaK\nGY3FgontBrSdpJ/ydMUiqytHEmePew9D17Hd93nxVBIyJssSthtwfjbLftelbGlsNmzqtocsSbx2\naZ6CqZ5YmnvYzfzutb3BuYGsLuMGEboi03Z8coZKzlB59akZ7tVtTukZDFUmzEXsdhwsPQkxe+Vs\nhWbf53vXq6NRViB4f6tNq+/z6oTy9E8r4+IxayjcqnYJ45gf3K4NbhxImJrC61d3+eWXV8ibKl4Q\n8YXVMl8Yc1/dIHHVm7bP7f3kc1yxNL5ybmZ08+K9rRbPLRfZbjncrLbJaDJLxSxhdHw58OH511vN\n/sdOQh8fITUU1XsdB0EyFuqnTpdHQnua16b9uSkpKSkpKSmfBKnYfYIoZ5PRMHoQkTMUvCBip+si\nSYK8rhBEMbIEW02bvY5DzXZZLSblkNWOy17HZaftUM5oOF5IRpMJooisobDfdug6En0v4FuXFw4I\njfF+1A932ghACLD9kK4bYGnygd7SS0t5/skbd1HkLpWMhqHJ+EHEy2fKB4TpUKQNF7dzeZ3Nuo2v\nxETAYkGn7fijctdhT+oweGen7aDKAi+IRuWWthuy23ZYLN5frA/nqz5seeZQpJydyY76cHVF4r2t\nJh9stnl2uUDeUHCDiGs7HZ5eyLE2m8VUZQxVRpMF+z2XSlabaqE+FP3jjpbtBQccrWnew1AsDUOA\nuqqMIglOVyyqXYcwgnJGo2zp3Kp2TxThkxh33pIk7w6uH2DpyUxeWYJTJYt612en3Wen3UeRZJ5Z\nKHBuLsvdWo/3tlqDmwIKBVMbOfZCCFp9/zM1P7Xd91EkwbWdNnsdlw922nQdH1lORG7fC8hoMV03\n4PZ+71ixd3Y2y5WNJgz65POGBvH9G0CmKiOIUWSJiwt5um5A3lBxg4g4Drm206brBERE/FtfOHVk\nvu/wetpu9TEU+UBp+8M6q5MqAgpjFQFwvGP8KL3RKSkpKSkpKSmPQip2nxCatoelybT6Pl03IIpi\nmrZLKaPx9EIOQ1X40492qfeStOQgDAmCmLrt8ubdGjNZA1WWmM8b7LUd2v2AriqT0WWqbRchC7KB\njBCC335zg9cu3R/TkjdVqh2X9XoPVZHw/Yh7dZsgirFUhZ7roygSl5cLFEyV/a7LV87NcH2vw27H\nxdQkvnJ2hu/frhNGEeWMRhDGNG1vJNJeWi0hS4Km7ROGMeXBwng+b/D8ShFg1JPa7ocoikCVEwF3\nr27TdQOyusKFhRzv3GuQ0eQj81UftjxzKPKFECwXTd7dbNLoeTT7Hk/N5ZL+SCEwVJmCqXGnbvPM\nQn40YmW/51E0tal7HadxtKZ5D8Mws6FjqskSLcenYftcWjzan/uwwnJ8P7O6wmo5w+1al4yuoCkS\nr11a4C9v7vPRbgcEzFgGbcel7fi8v9kkqyuosoQmC+5Uu1xaKo62rSsS7X6Svvs4+LhBS48DIeDd\nzSYFU2Mhb2CpCht1m5mcgSwEp8oWYRgSDMqMjxN7t/d7zOUMzlSyGIqMH0aAYKvZ5+JCcgPi/Fxu\n5P7eq/dACDKqgqnKqLKMpgiiWD4yFmv8elqrZLld65E1lI/lrJ5UETDkOMf4k56JnZKSkpKSkpIC\nqdh9Ihi6L6tlC9sNaNge63WbMzMWuipTyurMZnW+cn6W2zUbxwtYLmX4yrkZrmy0+GC7zVLRZ7lo\nslzKYGoy6/s2G80+QSspaVRlmTiCnz6XRxHw+tUdfvnlUyPn9c27DRQBSwWT7360R6PnEYYRbhhy\npmwxlzN4/eouF+azZDSFmazB2dmknHiv7fBnN6qcKWcoZ5MyyvV6j9Xy/X7UYkbjaxfmeH6leKw4\nGfakDh+7sddhv+NgasrAwQrpewFPLxYwVfnIfNU7+72JwTfHlWeOj3vabPZZKmZYLBh8uNNBkSUa\nPY+SBboiU7YU3t+0KZ3VyOoKp8oZKtnphS5M52gdF95zr2GPHs+bKpIkyBoqXghZXeKFlSLvbjap\n99wDYvdhy1Mn7Wc5q/HymVOj/WzaHtWOS86UWa/ZCElgqTLNnocQcGmpwPm53GDUVMBms8+F+cQ1\ndIMIRRGPpT/zcQQtPS6SHuXEhc0NXM6SqbBYNPCDGB9QFGn0vieJvXa/OTr3w9nYuiLRdZL+3MPu\nbyWrJ6nYjs+FuRwQ4wYRFxfyyJIYfZYOX0/DHvPHnXyc9uKmpKSkpKSkPGmkYvcJYNIYj44bMJsz\nRv16AF0n6es7PzeLocr0vABVbnGmYnFxIY8fRvTcgNVyhjtVm/mczm7bxYsi8hmZS0tZYmLypka1\n6x4QoouD0KV7jT71nofrh8iSwAtihEjCo/a7Ln9ybZfVUpbsYJZmzlCp91z6Xkje1AZOaHJZ1Xsu\ninywD/c4R2eSQ/fRTgdJCMIY1us9bC8Rpsslg1/90troNXf2e9QHQuthFttDB3Oz2R/Mj03Ewrm5\nLHEUI0kCVZZoOz6GqvDa5QXKWe1jCYQHOVqTBEO1445KT+s9l7fWm7y/2eCZxSKrlczoPJQzGnX7\noGP6MGJjWpd0mChcMDWeW9HoOgHNvke94/LlczOjFOClIuy2He7WevRLPiBoOT7lTOLIf9x5yo8j\naOlxEMfw/HKB7ZZD2wmYyWqUMmVuVm0aPZ+CKVO2NDRFPnFm7+FzL0mCD3c6yLLg7Jx136lVFVw/\nItDgqXmF97dabLf6PLNYoGLp3Njr8NFuh92Ow1xW53TF4vJycfQ90vfDUY/5NEx7XZQtjdev7o6q\nO8qWjiSR9uKmpKSkpKSk/MRIxe4TwHFjPOq2f2BepaIIem7I7f0Oex2XetfHD0IqOY2+nwRVZXWF\nu/t9FgoGFxZybDf73K7ZrJZNZCGwvQg3iChn1AOlpEtFk3rX48OdDqfKGWodD0WCKBZEUcwbt2r0\nXJ+OE7Be76PLCqWMwtcuzFG3fWZzOm4QYQx+c8YqAAAgAElEQVTKGHVFptp1OD+fG/2O4xbNxzl0\nQkDPC9nb741KY93Ap9pxuVvrcavaPfCaaZJoxxk6mLf2e6M05mFp9x99sMP7W+1BCbPKC6cKfOXc\nzCfqGDZtj47j8/Z6k7KVOO6KLHG71mM2ayTJu3HS+6mqMtf3OhQyKh0n4OJCjrKl03L8E8e+nPS7\np3VJ232fIIiQgKyhkTeS8K2rcQsvjEbPyxkqTy/kMTWJfhAhiDldyRBGMboij/bxUd3Y41zwh3Wy\nPy7DHuqLC0m6ecfxubLR4OUzRXRZ4tZ+D6/j8OqFuVG/8iThOLz5stNyeOdenY4T0PcjVkoZPtrt\ncLpisdXss9t2MFWFvJGEm/XcEDdI3N+7NTspc+65mJpMs+/z7rvb/NGHezy3nOfycom8qRx7TRz+\njJYt7cjnbNL5GiZSr1Us6j2Xuu3Tcnxeu7Rw7Hl9EkrQU1JSUlJSUj7bpGL3CWCSm1e2dHbbDu/c\na+CHEaos4YcRHcdnv+tiaYnLZ/sBXS+gYGjM5HRsL6Rhu1yYz9LpB9huSM/x2W1L6LKgbGk4fshq\nOXPA8VMkwf/91gb3ajbljErfC9EUQSGjcrvWw3YCcoPnN22PuZyg2o34Vx/scKqc4am5HPVeIjKS\nvswkDXjoZJ0kpo5z6OLYRZUFlq7gR2CqEqtlC12VeONmjTMz1oHXTEqifZD7WsxofGG1dCA46sZe\nh3c320gy5A0FL4r4we0GXzxT+cQW4+PH5+XTJW7Xerx5t86LqyUWCwb7HYdqx2Or2UeWBPM5g3t1\nm/2ey6lihlv7XZaLJl9eq3B1q0214zCbM3jl3HT7PH4OOo7PVrNPreey13aPBJrlTRVFkXCCEC+I\nUAc3YWayOpI4OGNVkhiFJUGS/O0FEWEU89Fu58Ac2vERU9Pw4yybPUmYHe7FliXBSimDJAlu7HZ5\nai4ZxeX4Ed/+0QaXl4vM5vQjwrGY0Tg7m+Uffe82HS85tmVLIwgjvCDi9au7yAIkSCo73ICdtkPO\nUGg7PnfrNtvNPnEcowwS3a/vdTCVJJF9vdan7YT8u6+cnnhNTPqMvn51h7VK9oHu+fj1Myyjt72A\nes+bOBLsSSpBT0lJSUlJSfnskordJ4BJwUV9P2AmpxNEyWAeBHTdgNWyxfVqh+22g65JWIZJGAbs\nd10qOR0nTIRsVld5+14TVRZcXs5zbbfLVt+naKkULRXbD3h5pkTT9vjdtzf49pubNFwfBRAiQggJ\nTdHo9EPCOEJVJII4ZqWcIUZguz6aIug6AefmkoTirK5Q77lUuy6yxAFX56SS0+McuqyhsNt2WCkl\n423cIMLxQ9YqyfF6ZjF/5DWOHz5U8vCk4/8nH1XJ6jIXFgqjwJ161+E77+3w3ErxyOsfh0N1+Pi8\nsKKNRj4BfPdah9mcDgIkkZQ2LxVN/CDCDUIi4OxsdjTv9ZnFPH0/5Fa1S8FUH+iu/fn1KvN5g0Im\nGV1kqDIzls5+zzsiQoY93posaNket/ddgijk0mKBpxayR242AKOU7hvVLiuDgC9DlckbKo4f8PZ6\ng9MVi3rPm/o4/rhG2DxImE3qxf7qoP+8Yumjc7rdalMwVRq2x1zemCgc6z2PhaJB3g0JI9AUCS+I\ncP0IWRI4YUxGlXD8gGrHQcRg6YnA7LnB6LmXV7Jc3+1iaQpxnNzUmcvpLBUMrm61J17Hkz6jYcRU\nfeAP67I/KSXoKSkpKSkpKZ9tUrH7BDBpsZw3VWZzxgHX6i9u7tOyfc7NWNR6Hn0/otP30Q2DxaLB\nXE7nqfksiiTxpx/tslg0sTSJthNSMFXOz2XJGwoZVUGQpB//5c19fu/tLXRdYUZOZuv2vJhTJY1C\nRsHxIqJIIMkSUTxYlMYgIVgqJE6qpSm8cCoJnlJkwfn53BGhctJi+DiHLulFVViv27SdgKwuc7qS\nQ5YEsznjsbl6h49/2/a4MJ89kCxbyKhsNPqjfw9F4lazz3bLYa1iMZvTqXZc3rzbYLFgsFQ0pxa+\nJx0fIUBXJfwgxFAlXD+CGGRJ4umFPKfKGTRFot7zHkpAjIu4+bxBtePyp9f3yeoyczmTvKlQsTSi\nCP7gvR0WCsZIhL52aZ7fe3uThh2wWjapZDWCKCKKYsqWNnpPVzaatPvJaKhSRkNXJL53Y58zFWts\nHJGEocm8fnWX55YLUzt9P64RNtMIsyTo7f7Nm+G1caqUGTnlb99rUM5o2H7ExYVk24cFYbvvU85o\nbLfalMzkODRsl/2uN0hllzldydKwPWo9l4qlkTc1CoPrXpUEN6pdqh2XzaaNpSmYuoQiMUowH86k\nPsyka7CcUakfEqyTPmcP67I/KSXoKSkpKSkpKZ9tUrH7hDDNGI9yRmWzYSNFyTgTgaDteNR7Hook\n4YUhrz61RDGjEcUxthvQ80K0rsf5uSyljEbbCXjhVAnbC3jjZo3b+z0EgoKh4IcKfhTjhzFtxyeM\nI87N5HhxtUSr7/HG7Rp9L0CRk1LNrhuwXDLJD5zDhw1eGi6GH+TQhVGchHeNPfbKuQq3qt1jXzOJ\nSf2Ik5zE79+uYzs+lj7YzyDkbq2LhOCt9caBPkbbDVBEEqAVxjGbDRtFgD1w2d5ab3B2NvtAx/Kk\n49Pu+3z1/Aw/uFNHDI5HydJw/UQwVDsOeVPlyr0m83mD5VJm1Os9rbtWMDXeWm8m+6BK9L2Qasfh\np86UuVvv4YfRyC0evidLlzFUCT9KnP9LcwX6XlL6+txykVJG48pmk67jU7Z0hBCcncly5V6T/U4y\nVmvo1uuyhDNWSj6t0/fjGGEzjTA7bp5tEMbUex6GKlOxNFq2T88L6ThJP/5hQZg3VYIwRhZQ67rs\n91zato8fRzRtn7wZU+06BFGMoSSlzLoiszSocri+28bxIyR8NFnQcXyiWEbKw0zOODCT+nA1wuFr\nsOP4NGyPO/s9LE0Z9ZBP+pw9rMv+sOI47e9NSUlJSUlJeRSkn/QOpExmuBgcp2zpzOYMwjim6/i4\nfogfxJyfy/LTgxTc4QJwqWiyWrF4+XSZSjZx1G7sddho2FzbaROEEdWOQ88JKJgKbhCjKRIVS0eX\nBH4Yc3m5xH/w1TXypsJyMcO5mSw9N6TedcnpEkVLZbFonJgwO2RtxhqNT4nj+6NUhovWl1ZLo/JX\nTZGOlIgefqxgqsiS4P2tJj+8W8cNwoku4FCE/L9XtvjtN+9R73qUMhr1rse3f7Qx+vdQmDZtj3/9\n8gLNvk+969BzPa7vtGn3fX7m6blB7+QOUZQIsp4XkTc1DFXm3Y1WUppravS8RLhFUTLmyQuiI79n\n2uMzvJnws0/P8/LpMk8v5sloMkulDKoiiGE0s7jadfnDD3b5s+tVru20qXbcE9214Q2VtuNzbi6b\n3ACwfTK6xLm5LLf2u0hAZSBWx9+TH8ILK0XOzWaJohhISl7DwbERQhCEkDdUtpr33cS8ofLRXoer\n2y38MOTiQg4niClPEJSPax7vx2HSZ/GwMBu/cTA8TmuVLG/dayCI0RWJvKnhhRGVbFIqPn6Oh6zN\nWEgSPL9SYrfVZ6vpEIuYp+ZyKBKEIVzf7XCv1qfZ99lo9mk5PnEcI0uCnKHyxTMl5vIGMzkDTZEo\nWzpFUyMIQlp9n0tL+VHv9Pg1Wba00T61+x5XNhqEMXzj4hwIePOEz9lJn+FJnHS9Dznus3vcZygl\nJSUlJSUl5TCps/uEctgpqXZcrm63cQazZtcbHos5nWcW86yUkgXy4UXz8PUQc3W7gypLnJ/L4ocx\nVzZbWLqCZSjMRDrrtUSMyFKMpsnkdJlf/alTnK5YFEyV2/s9nl7MU7JUbC8ko8mcn8vx/EpxKofl\nQSWnJzl0hx8bd9G+eLoycpHGHz9cYpw4sIL1uo2pJa7c8f2TJX791bN8570drm61KJoqv/jc0ihZ\neryPMasnvcS6ItOwXdZmLNwgIqsPen3HxB8c71iedHzWZhi93wvzOU6VM9heMHq+rsiJO5vReGu9\ngSZLOF5A15HYbNj80hdWJh7XcXet64aUMhoX53NstfrM5w00WeKj3Q6ljMpS0Ry9rt5z6TohYRSx\n3XQoZhRyA0Fbt70DojWry3hBRNcN6Dg+13bamJqMqSb7eKfWo2BqyFJyM2ecJ2VG6zSu5XHzbC1N\nwdKTAKmCqfKNp+dp2R677SSp/HDZ9fh1kM1oZHSVfEallFGZyRnc2GuzXrN57dICFxdyifNa6/EX\nt2qsljL0/YgzlQw/daYyKlm+cq/JdqvPubkc33xmflTuPikkbPi739tqIYkk2Gq75ZDVFZ5dSsYX\nnVxW/nhmTjdtj+9dr9K0fW7vdwGBF0SYWmlUtZD296akpKSkpKQ8iFTsPqGMLwbvNWxuVbsEUcxy\nwWS5YHC31qfjeOx1XAxVOZK6O/76rhsgC1gtm1iaghuECGIWCiamJvNXdz1WKyY7LYe9tkfJ0vib\nXz07SlEdLmInBT8Nhec05YWPq+T0pB7KcWE4XmLsBBHzOQM3iNhq9um6ATlDpePed+zGS1OfWyny\n3EqR717bo5RJ5gcPGe9jXCqaXNvp4PoBRVOj3feIEZyuJML4sPg7/HumOT4nCYOt5v6oXL3W9Vgq\nZvDDkFrPZ7VicapsHZuIOy7iLC1J0JYEfHmtQrvvs99zqWR1TleyB0ZgbTb7tPs+y6UMfd/BdiM6\njk21I+P6IX0vRAjBUtEkb6j82fUqkoD9rosAtpoOBUOn43o4HQ/HC/l3vnya/a77SGOTPmmm6Q0+\nrix3tZxhtWId+HnBVDk/nzs2SG14Hby1XieOoOMG2F7Ifsdho2FjqvKo37ls6dhewDsbTfKGghBQ\n7bp03YCLC3kWCyYFUx05rQB39nsokuCj3c6RkLDnV4q8tFpKRhy1+qiyhK7IuINSfieYfj7vdMd1\n8vfBlY0mGw07uREiJFRFsNtyuKF3eGm1nPb3/hgRQtwBOkAIBHEcf1EIUQb+GXAGuAP8ShzHjeO2\nkZKSkpKS8pMiFbtPMOOLwYbtIQspGTniBbhBgKkrnKlYrFasiam7w9e3+z4vnRJstxzajk9WV3hu\nuUgQxfz0uUXypspf3amxVDL5+sVZfvaZhYni6DAnpdQCD9Vj9zA9edd3O6zXbZq2Tymj8txykYWC\nQWOwjaEQHpYYu0FIvefjmiG6Ig/CrpIRO9kxETdt8E4yzzYpu8zqCqvlDLdrXU7PZGj1fdYqFlld\nwfYCZEl6LI7lJGHQtD22W30UIcibGndqNn0fFgsmp8oWFxfyxHF8rCgYF3EZXaHlBKOgrYKpUslq\no4TncRHa7PvM5XTKlp7MU+64bLZc+p7Dz11apNZz6To+b93t03YDvDBEkQUfbrWIYihaGiVLY66g\n0+knzt13P6ry/EoBNwhxBsfnkwibelQedKPmOPf3uN7yhUL2gTeJFgomf/zhLqaq4Poh6/WAO9Ue\nL566LzZ7XsA79xp0nID9jksQxYmLO5tls2GzWrGO3DTImyrvbyYl9+MhYeWsPnJLu06AJARhBHdr\nNn0/JIrCAzd9Pkmu73XJGyqGqpDRZYIwmSG+Xrd5abX8xLj+nyO+Gcfx/ti/fwP4wziO/74Q4jcG\n//5PfzK7lpKSkpKScjyp2P0U0O77BEFMxkxarG/sdrhTs+k4PtW2w79hask83GPK+vKmihdEXFy4\nP6rH9gIyukQxo/ELzy3xC88tPfR+HeewXtloEkYx0aDc98OdNm/ebfDapXlOV6yJQVHDwKcHJfHe\nrfV4824DU5GYyWrYbsSfXNvjy2crrFYybDX7A6czotZ1CCONUkYjoydjiVw/xNKT37PZsDlVtojj\n+FgncZKIkSR47VJSDtqwPcpZjZfPJLNkh+9t6AC+dmn+iFh8XI7l7f0ea5Us63UbN4goGDItO0n+\nHY5lepAoGHftx/ddCJAlwZ39HrIkDojQZ5fydPo+jh+Q0WQWColLu1I0OTubZTans9Xs88FOh7bt\n8cW1CqWMxnWjwxu3a1i6gqZI9IOQnbaDpSs0ex66Io/Ks58UkTstJ7m/wzaA4c8XCtmprvesrlC2\ndO7V+kgCLF1moWCy23Go95KAr5t7HXZbLmdmLLKGih9EtGyPD7fb3BwkqB+u+libsfizj6rMZHXi\nOB6FhF2Yz456pLOGwn7XZa9jk9UVVEnQ8WP2Oi5N2/vEz0/SiZ4I65mcwd39HsCB/t4nwfX/HPPX\ngW8M/v6bwJ+Qit2UlJSUlCeQqcTuMWVMLwL/C2AAAfC34zj+gRDiS8A/GL4U+LtxHP/OYDsvA/8I\nMIH/D/g7cRzHQggd+MfAy0AN+NU4ju8MXvNrwH8+2N5/Hcfxb36sd/wpJG+qKIrADSK6js+7my1M\nTSavK0hI/MH72zw1lyOIIuB+7+5QUArBaPzL4xRcx6XUvr/V5HQ5y3q9h6HKzGYN2n2P16/u8Nql\nhSML/dev7rBWyY7EchjFXNlo8i/fvIcfRXSdAEtLSj8lEXO6kmGn5bDR6BNGMV4Q8P1b+7y4ep43\n7zZo2z477T57HQfbC3lhpcSF+SxlS+d2LXExy1mNX/rCykiwHuckniRiJrnfkxzAw2LncTmWyTnV\nMTU5mVurKXTcgJyhjJzlhznPw30fd+yH18tex6EwSIaOokSAeEE0qhTImxrLhWQWa85Qubig8uF2\nG0O972wvlzJId2pUO0m/arXjAMm2GIQ6wcm9mE9yKu/JZej3fz48tg/q445jWCoYyEIiiGJMVebi\nfI73t9rsdRxkIdhpOcznDebzJgJBGIMXRvhBzKtPzU6s+ihmNF5cLU4c6ZXRkxtqS0WTzUYfS5Px\nw5iMJnE6a6Er0mPplX3QeTw/l7xPIQQZVWahYHCn3mN+ELr1JLn+nwNi4F8JIULgf43j+B8A83Ec\nbw8e3wHmf2J7l5KSkpKScgIP4+weLmP674D/Mo7j7wghfmHw728A7wFfjOM4EEIsAu8IIf6fOI4D\n4H8G/kPg+yRi9+eB7wC/DjTiOD4vhPgbwH8L/OqgL+i/AL5I8j/cN4UQv/d56w1am7HYaNhsNPp8\ntNtBVwRBFJO3NEpZje1mn/e3W3ztqVm8IOJ716vEwNxgtmnfD4nxj5SIAlP3207iuD7FGEG95xLF\nsNtOBKepSggheONmjTMz1oGF/njgU8fx+Yub+3yw1cL2ksRoIQnyRki2pXB1q8U3LsyNfl9MjKEo\ntPoBd2s9LE3mex9VyeoKC3mTvbbDD2/XWC6ZnJ/XePnMwUXyNOXaH7fX+JMajzM8/kNxeRHYazvs\nd92PJawPO/ZhFLPR6NOyfZ5fKRKEMe9tNrm8XOTCfG5U2ny4XNsLI6yxa8PSFC7M5fhgq817m03u\nNWxkBBt1m+dWCklZua4cW3bdtD3+7HqVVt9nt+Vwr2HTcwNOlU2eXijw1IT5zk8i086YzZsqziBt\nfVg+7PgBL60WCWNYm7VYb3QpmXpSLh5ENHouMQJEzHIpc6yQfn6lOHGk1/B7IXF/91gpmRiqMvru\nWJuxjiRkP+wNiJPaH4ave36lSKvv0+r7tPshuirxpTNlXn1q9ok/v59BvhrH8aYQYg54XQjx4fiD\ngxvW8aQXCiH+FvC3AFZXVz/5PU1JSUlJSTnExyljjoFhXWwB2AKI49gee44xeB4D4ZuP4/iNwb//\nMfBLJGL3rwN/d/Ca3wb+J5Gs7r4FvB7HcX3wmtdJBPI//Rj7/amjmNF49alZrmw0+cGtGnlTASSW\n8gZdL0ASMXfrfd7ZbHFtt4MXRKyUMpypZIFEUM4NHJFhP+00C84HcVyf4lNzWX60nsxXNRQZS1fo\nOQFBFOKH8ajEdkg5o7LZcrD9kDfvNLi93yWjyTRtn6yhoCkyjh9wr9YDIXj9g12+eXGemXLiJHad\nxL2+vtel0fVYrWTwwwjHj5nL65RzOqYqf6xgnSfRUTyuxPpblxc+1r4dFmNbzT4FQ8ULI4QQzOUN\nLlNkv+uiyOJAufZeOymx3RwkYSuSQAhYLJookkTeVJnN6QRhTLI8jhGSoOcmAUkXF/KUsyeFFvUJ\nw5iPdjoAI4HYd5NE7KbtPfFl0NPOmF2bsfje9Srvb7eQEciKIKsrnCpl6HtJsNpKKUPTDljIG4NQ\nMQ91kKp80qzladLRX1wtsV6zR+796Yp1wP2FR/seOSlgbijIh995T9pn7vNIHMebgz/3hBC/A3wJ\n2BVCLMZxvD34f/veMa/9Bwwqvb74xS9OFMQpKSkpKSmfJNOK3UllTP8x8AdCiP+eZF7vV4ZPFkJ8\nGfiHwGng3xu4vMvAxtg2N4Dlwd+XgXsAg+e2gMr4zye8ZsTn4e5xMaPxtQtzbDcd+l6Aosjsdxzu\n1HrUey6aIlEwVCDm9qBH9PJy4dgF7zQLzmn2adKCGeCPP9gjjmNUXcUPYmIBM1mDrhceWehrisz6\nfpeqJtP3Atp9n0bPw/ZDTpVNojim5wZ4skQ5o/L+Vpt3Nxs8s5hHk2W6rs/XL86x2bSpdh0WCyaS\nSBbkXhAhiXhUMvsoTLOgP04MP4xIvlvr8cbNpMx3NmfwyrnKic5zMZMESB1+zaMIgvH93Gk5BGE8\ncto/3OkQhImD3HF8ckYiWBVZ8PWLcwe28/rVXbpuQNvxeXohlyQ3Oz7NLY8zM1myusrl5SJ36z00\nRcLxI7J6Mpu23nO5Xevx8pnSxON2Y69DwVB5Z6OBIkvsd13cIGa/67FYMLmx1+GrA4H0JI+kmWaU\n0ZC5nM5ex0UCTEmmbQdc7bf4qTNJH/Tpcpa9do0wlillNM7NWhiqzPMrxdE2juvbPi70bLz9oZhR\nT2x/OKlvP2eoE6/7dt9HkQTXdtp03STkbbFg4ByaZfxJVUSkTI8QwgKkOI47g7//HPD3gN8Dfg34\n+4M/f/cnt5cpKSkpKSnHM63YnVTG9MvAfxLH8b8UQvwK8L8D/xpAHMffB54VQjwD/KYQ4jufxM4P\n+SzdPX6QOHrlXIVv/2iDggmnShlu7vWIY4+n5/PoSjLbNWuouH7MVrPPxYVkkXt4wTttKeU0+zfJ\nMX1mKc+HO20afZ+CobBgGQgBMzl9NBN3uICudt0kuKrv0XEDdEVCVST8Tky17ZM3YuJIEEsQx4JT\n5QySJHFlo8XLp0t84+I8hYyKqcncq/Vp9jxKlo4fxrhhSMlMHnvUku0H3Rg4TgwPk4ynDd5KzqvK\nYsGk4/h8+0cb/NIXVg4I3sNipN33OTNj8cxinr4fTkzlfhCH939YpnzGzVLrucTExHFMMaNxbafD\nxYWkv1OIg2XwHcfnueUC9+o2fk7HUBWKGY2dVh8vjNhu96lYOqoiqFhJz7MTROw2bTabfXpuQNHS\nafV9rmw0aQ2C2RRFsNGwsb0QQ5HZ73oEQZTMN5aTkv5W38f2o0/FSJppRhk1bY8/eG+HZt/nqbmk\nQiOKBdtNm3JWYy6fVDXM5Q2eWSxwbbdDRo1YGMyMliVxYvDaJCZdx5PaH8b386BwDcnqMjlD4dpO\nhy+frUy87oWAdzebqLJMu+9zp2bzzkaTr5ybecxHOuUxMA/8zqCMXgH+zziOf18I8UPgnwshfh24\nC/zKT3AfU1JSUlJSjmUqsXtMGdOvAX9n8JR/AfxvE173gRCiC1wGNoGVsYdXBj9j8OcpYEMIoZCU\nRdcGP//Godf8yTT7/GlkGgfxdMXil76wwhs3a2y3+uRNiRWRzNKM4xg/jMmbKrYbJELl0IJ3KJZu\nVLvoisTZmfszVB+U3Ht4/6odlzfvNlgsGCwVzQMC8qn5HBVLp2F7o0VwKaNRzmqszVgHFvqLBYM9\nAVEMzy4XubvfY6vZJ6fJ1B0PrxdgqhIFzcANY169MIcfRDh+wIX5HIWMOkrxLWU0/o837lDt9Clb\nOqWBCKtkdbwgeqSS7fEbAx0nSTvuOgER0ei9TBLDk/qTYbJ7/sbNGgVTpZBJ+l6Hf75xszYSu4eP\n/5XNpFS8bOlJkM8juPPD50cR3KvbdN0QSSRJuK9f3aVsqSwVE3c9o8nEMdza71I0VWJAV+TRMX17\nvcnLp0t03YD84JrSFAnbC3l2KRkrpCoS17bbKLKEHySBal0nRJVlZEnQ6fv8sx/eI4wiZCHwwxhV\nFoNxUz3qPZ9qxyWrK1i6jOvHzOZ1wihGi6NPzUiak1zLYW/y1e0WmiShqBJZXeGlUyXiwXscuqKS\ngI4TUDAVfvrszChMrN7z2Gr1EcScn8tNtU+TruPD7Q+HEQKubLYomhp5Q8ENIr53fZ9TFfPE677v\nR2w3B+dRk2n2Am7sdX8sSc8p0xPH8S3ghQk/rwE/++Pfo5SUlJSUlIfjgWL3hDKmLeDrJOLzZ4Dr\ng+evAfcG5cingaeBO3Ec7wsh2kKIV0gCqv594H8c/JphSdRfkjjGfzQIvfgD4L8RQgxXWj8H/GeP\n4X0/kUxbWny6Yo0E0FvrDX54p0azF9DzQkxV5kwlQxjFFEztSHnxUCw9PZ/j3c0m72w0eX65gCJL\nD3SAxvev4/is13soAuxBOM64gCxbGm/erRNGSU9uKaMhSYwE8eGF/mbD5ka1iypL5DIKC7FOtQOL\nukzXDdBkiXJW44WVEqfKFvVeEsK0206SfYeOUzGj8R9986lRaW8lq7M2m6VsaVOJzknO9bDHMoxi\nru10MFQZTRF0vZjffnODRs/lTMViuZQ5UDZe7ThH+pOPcx6rnaT8epycobLd6h97fQQh5A31gINv\nqjL3Gknb/LQu9lazz27bwVQVZAlu7PZAxBiqxNMLOdwgYrmUod336Tg+EUnfqR/EA4GclKIamsTt\nWo+sngQaGarC7WqXvh/y0V6HnK7y3HKBPctlp50kae91HGo9h4KZXDNzeYPvXttDkwVPLxbJGoJq\nx+Gt9QZRDCslg44TUO24eIFKwVRQJQkvCFkpmZ+JkTTD3mRLUxECBIK9tsv1vQ5+FLHd7FPKaOQN\nlRt7HVp9jwsL+dENj4yqcLvW5bnl4mSATI4AACAASURBVKh6YpqbO4erPTqOz2bD5k7N5qOdDllD\nOXJTC4ZjgoYFNTFuEKLJ0oFtj1/3cZx8J/hBjD9ImX56qYDtBU98CXpKSkpKSkrKp4tpnN3jypi6\nwP8wcGIdBj2zwFeB3xBC+EBEMpJomOL8t7k/eug7g/8gKYH+LSHEDaAO/A2AOI7rQoj/Cvjh4Hl/\nbxhW9VnkUUqLh0nNYdQf9ey2HZ+VUoavHkouPTzy5PmVEreqXa7ttnlptfzA5N7x/dtq9jFUGV2R\naTvBAQG5NgO3ql3WKlnqPZe67dFyAl67ND9x+2VLo9Hz8IMQRQKiJLn56xfneOlUCVURfLTbRRGQ\nN7VkXm4QkjcVZEk6sr3xmwEA3722h6nKDzyu486p44f8xc199toO5+eyzGQN/ChCVyQgptn3iQFL\nBSGSgKVhiW/OSMTxbM6YKogIYDaX9McOHV1IxMZszph4/AGyuowXRHTdYPSzasdlu9VnJqsfcbGH\n52dYAg2J8Hh/q01WkylldO7W+uQMFS8IsQkQQsJQBe2+z8WFPHtth7v1Lr/79iadvsdy2eLcTBY/\njPCCiKbd5+XVMndrXRq2x81ql9WSiSIJihmVzabNs0t5dEUQI/hgp0XOUMhnVCRJouP4OEGIGwo0\nJTm3uy0HQYyqyJyfLwASu3ofx4vImxp5U6WS01mrWE90ONU0/dt3az3++Q/v4fghOUNBVSTmsiaW\nJnNzr8tsTqdk6Qxn0Np+dOQzUO+5hBFT3dwZZzw4q+P4XNvp0PeD5D8voOcFGIdCwOIYnlsust1y\nRkFWzy7lcQau/ZDx636YMr1YNKh1XWwvYKsZMp/XjyQ9p6SkpKSkpKR8HB4odk8oY/oeyVzcwz//\nLeC3jtnWX5GUNB/+uQP828e85h+ShF195pk2pfXwovn5lSIFM3F5YgSXlgo8v1I8spA+LJZyRvLa\nxmDxetz212YsWn2f79+uU+u6zOWSxfbajIUbRGT1REgOBeS4AznsLbS9gHrPmxi4VO95fPFMhUJG\n40frDVQJTpVM8oaKJCVjSE5XLF6/ukO162IoAsePyAiJZxdzB1xlYKIzW+24E0uqx7my0WSz2Wev\n5fLhdpuFgsliwaDe8+k6AbIsU7FUcoaKpStosjToF/Vo9FxMVUYSMU8vJi7VK+cq3Kp2R8fmpP7J\nYS/28Lx0nGTsyjefuT++ctxh3mr2qXZdthp9TpXNUbn67VrvwMzi8cCg4agZRRJc2WwhiHluuYim\nSNyr2+iqhO2GqIogiiMWixkcP0RXEhG613b4qzs1NEUiihLxWW27hCFcWMiR1RQymkw5q+EEJu9v\ntVkoGORMjaVSBktTcPzElf3K+dnB+YrZa7vkDQ1VEfhBjOtHCAHeoOy55QSEEeRNBVOROT+fI6NJ\n9NyQr56f5Zml/KiM/UkWug9qURj2bQdhRMFUiWLYb7uYqoIgRpYFZ2ezFE11IC4DcrpM0TSIx5IK\n6rZH+RH68ceDszYbNoKYWs9juZihkNFx/JCG7XGqnBkJ57yp4gURFxfuVzDstR1u13rYXnDsWKM/\nv17lw1qXYkZDlSW6bkDD9jkrjh63NJE5JSUlJSUl5VE5aoul/MRYm7GwvQDbC4jjePT3tZmDAUVv\nrTdG/adeEHGr2uX5lSJ/86vn+PWvnuVrF+aOLAibtsdOy+Evb+1zbadNx0kclMNietL2f/+9bf6v\n798lq8tkdZlO3+fDnRa397s4fshS0TywrXbfn+ikHufatPs+szmdszNZTpczhHHEdqvPtd02thdy\ne79HwVT55ZdP8aW1MmEcM5fTeX6lRN7URsL6ykbzyL6/td5AkQTvDfpbc7pM1/F5b7NJ2ToYCPT2\nehMJwU67j6bKdByfGIEsCebzBlEcc2mpwMWFPHGczJ+9sdslp2s8vVhAlWXeudfEDUJeWi1xeuA0\naopEw/ZGvY+TFuvDXmxTU9hu9TE15Ug41dqMxV7H4Z2NJl4QUTAUylkNN4i417DRFInFgsFs7uC8\nW1OVubHXGR2n7ZZD0dQomBrbLYeVosmpSoam7RMRE8WwWMywWs5wcSGX/AzY77qULI2FgknOUFEl\nCVWW6LkBWw2bCJjPm7y0WuIXn1/iy2tl/toLy8zmDGSRBCbFcXJzY3hNZzSZMIoYL4MtWSoFQyWK\nY7qOT0aTMVSFmWzyvkxFpmBqZHWViOjE4/qkMH4DaFRurCnc3u+NnjPs2z4zY+H6EVEMQRzzwXaL\nnZZDRpPpOgGOn4jLl0+X+OlzM3hRjDIIpLK9AFmSjsw8nqaXeRicpSkSu20HS1dGFQIAuiLRdcMD\nn+VJ31mSBK9dmj/2ui9mNM7OZZFkiZ4boUiwWs5gHPrOmPRd9NZ6g+YTHkCWkpKSkpKS8uTwcebs\npjxmpklpfZSRQcNF40xWp+cFdB2fD7c9ZnIGex2XxULivh4XtnR7v0cQxiwWMuRNjf2OQ9cPubXf\n5cVTJbK6MlrwXlxI9n/a8l1IHMvb+z3+9PoeQRgjIah2PWwvQpHEAef2pdXSyKEW4r4NZKoy7281\neXapeOTYXN1qc3k5cbA7bkDWUAd9v/ed5tv7PcpWkhTbcUOKpkIYJb20K4Ne3Oqg5BLA0mQ+3O2A\ngKWiiaUrrFYEy6VECI4v7KftQTxcfn2YYkajYKq0bB8vTEb2fHmtgiyJAyFCk459jBjdgOi6IXkj\nebzt+FyYz9Hue8iSxCtnK7y72cQLIxYLBrIkWC4mAvade01sL0BXZMqWhjZw5Fp9Hy/UOF22Drjl\n912/HFvNPm0nQJHhxdX7VQfzeZO+F9Fzk+tSlSWeWy7SdX2WSiZBEJPVVa5sNlBlgeMH+GFE3w/4\n6XNlvnV58YkWuUOmaVEY9m0rSvLzjYZDENx3UzOqgqHKvLfZ5DJFZnM6siRYKSUBdcPvjOHMY9sL\nCMKI27Ue9a7Li6ulBwZAjV+vXpDMVR72Xw+rOMY/yyd9Z510LVuaws9fWmC75YyqLRYLBkF036J+\nHOPRUlJSUlJSUj7fpGL3CeNB4mjavt7Dc1NnsjpzeQNTk9lq9tlo2Kw36rx6fo7ZnD4qq7S9kFOl\nzIFt9ZwAQ02KACxNwapkOVXKcGu/RzmrHVnkrs0w9RxRSET2v/jhOi3bo5TR6Tg+iixQJfirO3X+\n2otJiPd46eSDBN34sRkGRQ1LqgHiOD5wzNp9n7UZi492O5iqRN+PUCVB1w2YGfTTrpat0cI+oyvY\nrs+52SwZTcbxAxw/HAjH6fsOH7ZMM46Tsu5xoT/+Xo6b4frUXHZ0zLK6jBskbmpWV8gZKqcrWfa7\nLkGUuNcAQRST0aXRec2bKqos4QYhMzmDntslbyThY8tlcxRANn5eh6W7F+Zzo30ZnwG7VDQxBuJu\nvMRcVcSBOa0/88wcP7xT48ZeF12V+ebT83zl3MynQujCdC0K433bxUySML1R77NczPDSanmQTh1x\nebnIftdFkQV5U+XVQ735AAVT5cpGk7fXkwqGl0+XUWRp6hTy4bkrZTTu1rq4fkgEzOWsRw4Bm/Sd\nNF7+bHsBGf1+sdGjjkdLSUlJSUlJSRmSit1PGdMsmg/3B36406Hn+piaTM5QubiQjCkyx3pqh9vb\na7tHtm8ZCkF4cHxxIv4yE0eSTONQH36+G8aYisxex6Xe85jJaWQ1ZZQsPL7InUbQjR+baYKixnsP\n3SDijZs1MrrMailDEISj/tnhzYiXVkvkDIX1mj0K5jldsZAlcWDBfhLT9HEe5kHn/7hjD/dvQCwW\njAM9u8PS029dXjhRBK3NWGw2bDYaNnlDZSFvcKduk9UVVsuZI33i01wHazMWzYFzOX4uJ/WcPzcm\nkj9tHHfNjovG8b7tKIoomSr7qsdXzs9gaQpxHNN2Ai7M6yhyEuAG96+jSTdMylmNIITtlsNS0RxV\najzIGR0/d05g0nUCsoOy+fHtT3sNHzfLeehQTzoe02YYpKSkpKSkpKQcRyp2P2VMs2g+XP5XsTS6\ng/mwwxE1x4XYZA1lVKo73P7ajMXd/R4t2z02POkwD1O+C2Ao0HVgPm+gKwIviKl2PQrm0RnA0wi6\n8WMzTVDUuAv5lXMzLBVN3rrbQFUkTE3hm8/MHynLfH6lOAp9msbBPsyjlGlOc/6PO/Yj8eKHPLuU\nOGqH3duTKGY0vvrULFc2mlzf6yIGvZmThOmD9uXg49PfGPm0Ms37HJ+hbbsBWUPjlXMVcoOS80ll\nxEMRGUVJCvOHO23evNvgy2tl3l5vMpPVyRvJjaIPtttULA1dk6cKehq/sXMc017Dh583lze4zEGH\netKNkIepEElJSUlJSUlJOUwqdj9lTLNoPlz+t1Q0+XDHp9ZzR6m9x4XYDOdojm//5y8v0ur7vHGz\nxnarz2zOmCj+Pg5LJYuNRi0pszVUtht9+n7I+TnrQD/wweNwvKA7fGwKg77g447Z4eN6qpzhaxeO\nloeO83GF2qOUaX6c3/mwNyCO28bXLszxtQtzH2s7h7f5eejBnOZ9Dvu2v3V5YSRiTyojvr3fI4rg\nw902XTcgDGJCYv7p9+9yZtZCCLD9kJ1WH1kIOk5APqMe674+bPLxtNfwpOfN5g461JOP14Ov9TSx\nOSUlJSUlJeU4UrH7KeRBi+bD5X85Q+V02WK/604MsTnsmkza/oMCZz4uZyoWthuw3XLoeyGVrI6u\nSeRNDU2Zznkc7uekYzON0HgU0fVxhNqjlml+XsTh55lpy4jbfZ97jR57bZecrmIYAs+PuNbocqqc\njI7a67jJzOJY0Ox7vDoziyyJA+7ro5TUw/TX8Cd1rT/qfqekpKSkpKR8PkjF7meQSeV/k3oyH+R2\n/jg5GFQUkNWV0Szck8ooP82kZZopJzFNGXHeVLlV7ZEz1ETQAkIIZrPJWKmfv7zIZnMXCYEiw8WF\nHDlDPRLQ9qjJx9New4efd3u/x1v3Gliawkc7HV45V3mkm2lpYnNKSkpKSkrKSaRi9zPItOV/T5JD\neFxQ0Xi672eNz0u/asonx9qMhRdGeEGIJkv4YYwbhmRUiR/caXBnv4csC56azTGbMzg/lwOOuqqP\nmnxczGicnc3yxs0a1Y7DbM7glXOVid81w+d9tNvhTr3HswsFTlcydByfb/9o48hc6WlIE5tTUlJS\nUlJSTiIVu59RniQhOw2fV+H3aTtPKU8WSQ/1LH95Yx8/iikYKpEf89a9Jgs5nfm8wU7b4c9v7vNv\nvrR8ZCb2sN/1RrWLrkicncmSM46Gwh1H0/a4Ve1yZsbimcU8fT/kVrVLwVQPfHbHn3evYXOqYNL3\nA/pBRCGTZAe8cbM2qjaZtv82TWxOSUlJSUlJOYnpZqSkpPwYGArer1+cS3vuUlKm5NmlAvN5gziK\n6bo+V7fazOQ0nlkuslyyePl0hedXClzdadOwPTRFGpVGv7XewAsinp7P0XV83tlo0u57I0H8oMqK\n8TJiIcTo77f3e8c+r2n7FC0NXZHZ7zhAkiuwXrdH+1PKaHhBxFvrDZonuLRrM/cD7OI4nnq/U1JS\nUlJSUv7/9u49xu7zrvP4+zMX2+PrOM61dq7bhCVNUm/XChWUgsomTVntJl12IQXagCrKahsECHZp\nWQnaXQkVVNoVWy4qaiDp9rIRpUtE00atKJdCSRoH5+JcqEncxEls1/Etvsx4Lt/94/zGGZsZZ8ad\nmTPnzPsljeac5/x+v/M83+dodL7zXH5Lg8muJHWoiRHT775oHddsXMf5a1dwYOgEq5f1s/vgEC8e\nau1qfv6a5aTg9Re37lX88HMHue+x3YyPt9a5rh1YxnWb1rNmeR9P7Tl8MiGeyW7MA/29p5QN9Pdy\n+PjItMetX9nPseFxRqvY+dJRnnjxMI/vPszQyOiMEufJJv5Btqyv55RE3n+USZIkcBqzJHWM02+z\n8/LQyMmk8Py1K3h5aIS/fPLb7D82zMbBPvYcGmLnvmOsXh7OX7vilJ2Ln9z9MkeHRxhY1suaFf2s\nWdHPdZsGOXDsxIw3hTub3Ziv3TjIfdt3c+jYMOsGllE1zuHjJ1i9vJ/RsfFTzptu/a23G5IkSTPh\nyK4kdYCJ2+xMnua77dkDpySILxw8zhsuHeTQ8VGe238UUhRj7Dk8zPL+3pMjuUnYsGoZPQkvHDx+\n8vzZrned6TTiycdduG4Fl5yzkr6+Xnp6e1jR38sN330R/+K81Tzz0qmjuFPVZ6o4vNp0Z0mStDQ5\nsitJHWCq2+ycs3o5z+w7yusvbo1qHhkeY+PgANdtGmTPoSEOD42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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8487287f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=2, figsize=(16,6))\n", "\n", "ax[0].scatter(backgrounds[1], backgrounds[2], alpha=0.2)\n", "ax[0].set_title(\"Location of background events\")\n", "ax[0].set_aspect(1)\n", "\n", "ax[1].hist(backgrounds[0] / 60 / 24)\n", "ax[1].set_title(\"Background event intensity in time\")\n", "ax[1].set_xlabel(\"Days\")\n", "None" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Gfgf4p/BcvwX8VlhHMT5OME51wMyetg7vLP4UaCW4J18g+MJgLGxPH8HY1Y8QpIM/C7in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\nykpzdkVERERERCRxlOyKiIhIJJnZdWb2gpm9ZGa3hh1PJZjZXjPbaWZ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"text/plain": [ "<matplotlib.figure.Figure at 0x7f8481e5c5f8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(ncols=2, figsize=(16,6))\n", "\n", "ax[0].scatter(triggers[1], triggers[2], alpha=0.2)\n", "ax[0].set_title(\"Location of triggering events\")\n", "ax[0].set_aspect(1)\n", "\n", "ax[1].hist(triggers[0] / 60 / 24)\n", "ax[1].set_title(\"Triggering event intensity in time\")\n", "ax[1].set_xlabel(\"Days\")\n", "None" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }
artistic-2.0
calebjordan/Data-Analysis
SLUG/Chips for Ed.ipynb
1
5569316
null
mit
markdewing/qmc_algorithms
Diffusion/DMC_propagator.ipynb
1
46687
{ "cells": [ { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sympy import *\n", "init_printing()\n", "import numpy as np\n", "import sys\n", "import math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Diffusion Monte Carlo propagators\n", "Most of the equations taken from Chapter 24 (\"Projector quantum Monte Carlo\") in \"Interacting Electrons\" (2016) by R.M. Martin, L. Reining, and D.M. Ceperley." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Trotter breakup" ] }, { "cell_type": "code", "execution_count": 189, "metadata": { "collapsed": false }, "outputs": [], "source": [ "T_op = Symbol('That') # Kinetic energy operator\n", "V_op = Symbol('Vhat') # Potential energy operator\n", "tau = Symbol('tau') # Projection time\n", "n = Symbol('n',isinteger=True) # Number of timestep divisions\n", "dt = Symbol(r'\\Delta\\tau') # Time for individual timestep" ] }, { "cell_type": "code", "execution_count": 209, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAS4AAAAmBAMAAACISmupAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nBZkjLX3p2sWlh0SNeCyqo8bPKBL4Pz+OTWl80vM7AAAAAElFTkSuQmCC\n", "text/latex": [ "$$e^{- \\tau \\left(\\hat{T} + \\hat{V}\\right)} = \\lim_{n \\to \\infty}\\left(e^{- \\hat{T} \\Delta\\tau} e^{- \\hat{V} \\Delta\\tau}\\right)$$" ], "text/plain": [ " -τ⋅(T̂ + V̂) -T̂⋅\\Delta\\tau -V̂⋅\\Delta\\tau\n", "ℯ = lim ℯ ⋅ℯ \n", " n─→∞ " ] }, "execution_count": 209, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Eq. 24.7\n", "Eq(exp(-tau *(T_op + V_op)),\n", " Limit(exp(-dt*T_op) * exp(-dt*V_op),n,oo))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## In coordinate space, no importance sampling" ] }, { "cell_type": "code", "execution_count": 211, "metadata": { "collapsed": false }, "outputs": [], "source": [ "R = Symbol('R')\n", "Rp = Symbol(\"R'\")\n", "Rpp = Symbol(\"R''\")\n", "ET = Symbol(\"E_T\") # Trial energy\n", "N = Symbol('N',isinteger=True) # number of particles\n", "V = Symbol('V') # potential energy\n", "bracket = lambda a,b,c : Symbol(r'\\left\\langle{%s}\\left|{%s}\\right|{%s}\\right\\rangle'%(latex(a),latex(b),latex(c)))" ] }, { "cell_type": "code", "execution_count": 212, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\left\\langle{R}\\left|{e^{- \\hat{T} \\Delta\\tau}}\\right|{R''}\\right\\rangle = \\left(2 \\pi \\Delta\\tau\\right)^{- \\frac{3 N}{2}} e^{- \\frac{\\left(R - R''\\right)^{2}}{2 \\Delta\\tau}}$$" ], "text/plain": [ " \n", " \n", " \n", " \n", "\\left\\langle{R}\\left|{e__{- \\hat{T} \\Delta\\tau}}\\right|{R''}\\right\\rangle = (2\n", "\n", " 2 \n", " -3⋅N -(R - R'') \n", " ───── ────────────\n", " 2 2⋅\\Delta\\tau\n", "⋅π⋅\\Delta\\tau) ⋅ℯ " ] }, "execution_count": 212, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Kinetic energy - Eq. 24.8\n", "Eq(bracket(R, exp(-dt*T_op), Rpp),\n", " (2 *pi*dt)**(-3*N/2) * exp(-(R-Rpp)**2/(2*dt)))" ] }, { "cell_type": "code", "execution_count": 213, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\left\\langle{R''}\\left|{e^{- \\Delta\\tau \\left(- E_{T} + \\hat{V}\\right)}}\\right|{R'}\\right\\rangle = e^{- \\Delta\\tau V{\\left (R' \\right )}} \\delta\\left(- R' + R''\\right)$$" ], "text/plain": [ " \n", "\\left\\langle{R''}\\left|{e_{T} + \\hat{V}\\right)}}\\right|{R'}\\right\\rangle__{- \\\n", "\n", " -\\Delta\\tau⋅V(R') \n", "Delta\\tau \\left(- E = ℯ ⋅DiracDelta(-R' + R'')" ] }, "execution_count": 213, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Potential energy - Eq. 24.9\n", "Eq(bracket(Rpp, exp(-dt*(V_op-ET)),Rp),\n", " exp(-dt*V(Rp))*DiracDelta(Rpp-Rp))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## In coordinate space, with importance sampling" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [], "source": [ "F = Symbol('F_i')\n", "psiG = Symbol('Psi_G',commutative=False)\n", "EL = Symbol(\"E_L\") # Local energy\n", "H_op = Symbol(\"Hhat\",commutative=False)\n", "gradient = lambda x: Symbol(r'\\nabla{%s}'%latex(x))\n", "gradient_with_index = lambda x,i : Symbol(r'\\nabla_{%s}{%s}'%(latex(i),latex(x)))" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/latex": [ "$$\\operatorname{F_{i}}{\\left (R \\right )} = 2 \\nabla_{i}{\\log{\\left (\\Psi_{G} \\right )}}$$" ], "text/plain": [ "Fᵢ(R) = 2⋅\\nabla_{i}{\\log{\\left (\\Psi_{G} \\right )}}" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Quantum force\n", "Eq(F(R), 2*gradient_with_index(log(psiG),Symbol('i')))" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAK8AAAAaBAMAAAAzuxGAAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nZnUAAAAASUVORK5CYII=\n", "text/latex": [ "$$\\operatorname{E_{L}}{\\left (R \\right )} = \\Psi_{G}^{-1} \\hat{H} \\Psi_{G}$$" ], "text/plain": [ " -1 \n", "E_L(R) = Ψ_G ⋅Ĥ⋅Ψ_G" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Local energy\n", "Eq(EL(R), psiG**-1 * H_op * psiG)" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAANsAAAAaBAMAAADMGTRiAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n0GpG//8q0+/s7BqZvnimyl8Lut69J0oLqQAAAABJRU5ErkJggg==\n", "text/latex": [ "$$e^{- \\frac{1}{2 \\Delta\\tau} \\left(- R + R' - \\frac{\\Delta\\tau}{2} \\operatorname{F_{i}}{\\left (R \\right )}\\right)^{2}}$$" ], "text/plain": [ " 2 \n", " ⎛ \\Delta\\tau⋅Fᵢ(R)⎞ \n", " -⎜-R + R' - ────────────────⎟ \n", " ⎝ 2 ⎠ \n", " ───────────────────────────────\n", " 2⋅\\Delta\\tau \n", "ℯ " ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drift_diffusion = exp(-(Rp-R-S.Half*dt*F(R))**2/(2*dt))\n", "drift_diffusion" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAJsAAAAVBAMAAACj9YEXAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEIl2mSJE3e9UMqtm\nzbsXyEShAAAACXBIWXMAAA7EAAAOxAGVKw4bAAACTUlEQVQ4Ea1Uv2sTcRx910ua5i5ez6TiINhD\nh4BQCDSjYIUM4g+8QXANLg7+yuDkIAG7CIoniFOLt7gErBkFkcY/QAiouBj4VndNNApthvg+l9xd\nrjZbHi+ffN77vHwu3PcSYArS7pTBPjvMZQFyKvLRZHNQufQnUoC2vV75qUKDuduDC99htIT7YHmh\nsRM20LtApKq0LYV0LRxzkqsj4+AUhEls1sd6vhQNTAU4oWIv67DGd4HkGDBrOA5hEmd/jbXlRoMF\nx7BboVJsLGV4T8eG5DIlLDiwfGECunvGBq4Oh38zsb9cNWOh2Foq54WO5JZ97S2gl4QJFJBpAueP\nHcar2H9RuQ98ONFfcegpvqy72x7y/SrbIHdn9ZzPc20KE3CQ3YMGi6cV+0UUeK+MuibXVnxZat5L\noxckJLeCVJt3UQkDZH8QOzA8oEPjGnCDlxuZ+IQjWsvLNbWq2ek87nzlOtiF8Trm8A25LmC0hZNI\nUbzmyovAVuzvAnM2zFLgKFY52efjdZLbRarOb9cUTmKj0WhscXIPOBr5Bh/hZzw+P3AUK9dpn7lO\nY8+c0eNz8hI5RziJJ0PiN1Djp+1wUOxX3u3x+EZa8Vdxeb3ypst1Oi3migPX+FiCWRVCO1l2R9HJ\nqrcmFftbI60iu4cr7OPcIYDEdTftRJm4UXEr3dwXJzDWgsqS75cfSK+kCDYCph9iyQ90stxMyqkq\nzGlNkDAfrb4/KJv1D3L/98KcboPEIlfOEIvODJcB/C9AaXYb9TaW3NmtQ/l0a3bb/gHa0pb1nECH\nWQAAAABJRU5ErkJggg==\n", "text/latex": [ "$$e^{- \\Delta\\tau \\left(- E_{T} + \\operatorname{E_{L}}{\\left (R \\right )}\\right)}$$" ], "text/plain": [ " -\\Delta\\tau⋅(-E_T + E_L(R))\n", "ℯ " ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "branching = exp(-dt*(EL(R)-ET))\n", "branching" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGUAAAAdBAMAAABBI79QAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nrkJggg==\n", "text/latex": [ "$$\\left(2 \\pi \\Delta\\tau\\right)^{- \\frac{3 N}{2}}$$" ], "text/plain": [ " -3⋅N \n", " ─────\n", " 2 \n", "(2⋅π⋅\\Delta\\tau) " ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prefactor = (2*pi*dt)**(-3*N/2)\n", "prefactor" ] }, { "cell_type": "code", "execution_count": 214, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeUAAAAfBAMAAAArYMiaAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nSUVORK5CYII=\n", "text/latex": [ "$$\\left(2 \\pi \\Delta\\tau\\right)^{- \\frac{3 N}{2}} e^{- \\frac{1}{2 \\Delta\\tau} \\left(- R + R' - \\frac{\\Delta\\tau}{2} \\operatorname{F_{i}}{\\left (R \\right )}\\right)^{2}} e^{- \\Delta\\tau \\left(- E_{T} + \\operatorname{E_{L}}{\\left (R \\right )}\\right)}$$" ], "text/plain": [ " 2 \n", " ⎛ \\Delta\\tau⋅Fᵢ(R)⎞ \n", " -⎜-R + R' - ────────────────⎟ \n", " -3⋅N ⎝ 2 ⎠ \n", " ───── ─────────────────────────────── \n", " 2 2⋅\\Delta\\tau -\\Delta\\tau⋅(-E_T + E_\n", "(2⋅π⋅\\Delta\\tau) ⋅ℯ ⋅ℯ \n", "\n", " \n", " \n", " \n", " \n", " \n", "L(R))\n", " " ] }, "execution_count": 214, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Eq. 24.18\n", "prefactor*drift_diffusion*branching" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling the drift-diffusion term" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": true }, "outputs": [], "source": [ "chi = Symbol('chi') # gaussian random sample with zero mean and variance delta tau\n", "r = Symbol('r')\n", "rp = Symbol(\"r'\")" ] }, { "cell_type": "code", "execution_count": 158, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKYAAAAWBAMAAAC4bPoxAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nwCiVAAAAAElFTkSuQmCC\n", "text/latex": [ "$$r' = F_{i} \\Delta\\tau + \\chi + r$$" ], "text/plain": [ "r' = Fᵢ⋅\\Delta\\tau + χ + r" ] }, "execution_count": 158, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Sample new positions with this formula (Eq 23.13)\n", "# Question - how to detemine sampling formula from evolution equation/distribution above?\n", "sample_drift_diffusion = Eq(rp, r + dt * F + chi)\n", "sample_drift_diffusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scaling the drift\n", "\n", "In QMCPACK, the drift term is scaled. (From C. J. Umrigar, M. P. Nightingale, K. J. Runge \"A diffusion Monte Carlo algorithm with very small time-step errors\" JCP 99, 2865 (1993) doi: 10.1063/1.465195 )" ] }, { "cell_type": "code", "execution_count": 288, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Fmag = Symbol('Fmag^2')\n", "epsilon = Symbol('epsilon')" ] }, { "cell_type": "code", "execution_count": 289, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAABRCAMAAAD4td0XAAAAOVBMVEX///8AAAAAAAAAAAAAAAAA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\n5IZWF1ncBnOR7V03DzuI72qIrvd/P5xUEsXskksAAAAASUVORK5CYII=\n", "text/latex": [ "$$\\begin{cases} \\tau & \\text{for}\\: \\left|{F}\\right|^{2} < \\epsilon \\\\\\frac{1}{\\left|{F}\\right|^{2}} \\left(\\sqrt{2 \\left|{F}\\right|^{2} \\tau + 1} - 1\\right) & \\text{otherwise} \\end{cases}$$" ], "text/plain": [ "⎧ τ for |F|² < ε\n", "⎪ \n", "⎪ ______________ \n", "⎨╲╱ 2⋅|F|²⋅τ + 1 - 1 \n", "⎪──────────────────── otherwise \n", "⎪ |F|² \n", "⎩ " ] }, "execution_count": 289, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drift_scale = Piecewise( (tau,Fmag < epsilon ),\n", " ((sqrt(1 + 2*Fmag*tau)-1)/Fmag, True))\n", "drift_scale" ] }, { "cell_type": "code", "execution_count": 290, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAABRCAMAAAD2MGE9AAAAOVBMVEX///8AAAAAAAAAAAAAAAAA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\nI6+BBW/sCYSCPEuyCveLS/K+zuv/bIdV9G9Dpu4AAAAASUVORK5CYII=\n", "text/latex": [ "$$F_{i} \\begin{cases} \\tau & \\text{for}\\: \\left|{F}\\right|^{2} < \\epsilon \\\\\\frac{1}{\\left|{F}\\right|^{2}} \\left(\\sqrt{2 \\left|{F}\\right|^{2} \\tau + 1} - 1\\right) & \\text{otherwise} \\end{cases}$$" ], "text/plain": [ " ⎛⎧ τ for |F|² < ε⎞\n", " ⎜⎪ ⎟\n", " ⎜⎪ ______________ ⎟\n", "Fᵢ⋅⎜⎨╲╱ 2⋅|F|²⋅τ + 1 - 1 ⎟\n", " ⎜⎪──────────────────── otherwise ⎟\n", " ⎜⎪ |F|² ⎟\n", " ⎝⎩ ⎠" ] }, "execution_count": 290, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scaled_drift = F*drift_scale\n", "scaled_drift" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Values for Testing" ] }, { "cell_type": "code", "execution_count": 205, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class SymPrinter(printing.lambdarepr.NumPyPrinter):\n", " def _print_Symbol(self, expr):\n", " if expr.name == r'\\Delta\\tau':\n", " return 'dt'\n", " return expr.name" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ -1.17741002e+00, 3.84734139e-16, -1.17741002e+00],\n", " [ 3.84734139e-16, -1.17741002e+00, 3.84734139e-16]])" ] }, "execution_count": 206, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# RNG corresponding to src/ParticleBase/RandomSeqGenerator.h\n", "def gaussian_rng_list(n):\n", " input_rng = [0.5]*(n+1)\n", " slightly_less_than_one = 1.0 - sys.float_info.epsilon\n", " vals = []\n", " for i in range(0,n,2):\n", " temp1 = math.sqrt(-2.0 * math.log(1.0- slightly_less_than_one*input_rng[i]))\n", " temp2 = 2*math.pi*input_rng[i+1]\n", " vals.append(temp1*math.cos(temp2))\n", " vals.append(temp2*math.sin(temp2))\n", " if n%2 == 1:\n", " temp1 = math.sqrt(-2.0 * math.log(1.0- slightly_less_than_one*input_rng[n-1]))\n", " temp2 = 2*math.pi*input_rng[n]\n", " vals.append(temp1*math.cos(temp2))\n", " return vals\n", "\n", "chi_vals = np.array(gaussian_rng_list(6)).reshape((2,3))\n", "chi_vals\n", " " ] }, { "cell_type": "code", "execution_count": 240, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r_vals = np.array( [ [1.0, 0.0, 0.0],\n", " [0.0, 0.0, 1.0]])\n", "tau_val = 0.1\n", "scaled_chi_vals = chi_vals * math.sqrt(tau_val)" ] }, { "cell_type": "code", "execution_count": 241, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 6.27670259e-01 1.21663617e-16 -3.72329741e-01]\n", "[ 1.21663617e-16 -3.72329741e-01 1.00000000e+00]\n" ] } ], "source": [ "drift_diffuse_func = lambdify((r, F, chi, dt),sample_drift_diffusion.rhs, printer=SymPrinter)\n", "scaled_drift_func = lambdify((tau, Fmag, F), scaled_drift.subs(epsilon, sys.float_info.epsilon) )\n", "# For a constant wavefunction, gradient is zero\n", "for r_val, chi_val in zip(r_vals, scaled_chi_vals):\n", " rp_val = np.zeros(3)\n", " rp_val = drift_diffuse_func(r_val, np.zeros(3), chi_val, tau_val)\n", " print rp_val" ] }, { "cell_type": "code", "execution_count": 286, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['0.695481606677082', '0.135622695565971', '-0.168895697756948']\n", "['0.0678113477829853', '-0.236707045539933', '1.20343404334896']\n" ] } ], "source": [ "# For a linear wavefunction, gradient is constant\n", "grad_coeff = np.array([ 1.0, 2.0, 3.0])\n", "for r_val, chi_val in zip(r_vals, scaled_chi_vals):\n", " rp_val = np.zeros(3)\n", " # Scaled drift is already multiplied by dt, accomodate by setting dt param to 1.0\n", " rp_val = drift_diffuse_func(r_val, scaled_drift_func(tau_val, np.dot(grad_coeff, grad_coeff),grad_coeff), chi_val, 1.0)\n", " print ['%.15g'%v for v in rp_val]" ] }, { "cell_type": "code", "execution_count": 287, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAALQAAAAPBAMAAAC/7vi3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n", "text/latex": [ "$$0.0678113477829852$$" ], "text/plain": [ "0.0678113477829852" ] }, "execution_count": 287, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Compute scaled drift\n", "drift_scale.subs({epsilon:sys.float_info.epsilon, tau:tau_val, Fmag:np.dot(grad_coeff, grad_coeff)})" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
maxrose61/GA_DS
maxrose_hw/oct13/06_yelp_votes_homework.ipynb
2
92816
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear regression homework with Yelp votes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "This assignment uses a small subset of the data from Kaggle's [Yelp Business Rating Prediction](https://www.kaggle.com/c/yelp-recsys-2013) competition.\n", "\n", "**Description of the data:**\n", "\n", "- `yelp.json` is the original format of the file. `yelp.csv` contains the same data, in a more convenient format. Both of the files are in this repo, so there is no need to download the data from the Kaggle website.\n", "- Each observation in this dataset is a review of a particular business by a particular user.\n", "- The \"stars\" column is the number of stars (1 through 5) assigned by the reviewer to the business. (Higher stars is better.) In other words, it is the rating of the business by the person who wrote the review.\n", "- The \"cool\" column is the number of \"cool\" votes this review received from other Yelp users. All reviews start with 0 \"cool\" votes, and there is no limit to how many \"cool\" votes a review can receive. In other words, it is a rating of the review itself, not a rating of the business.\n", "- The \"useful\" and \"funny\" columns are similar to the \"cool\" column." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 1\n", "\n", "Read `yelp.csv` into a DataFrame." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>business_id</th>\n", " <th>date</th>\n", " <th>review_id</th>\n", " <th>stars</th>\n", " <th>text</th>\n", " <th>type</th>\n", " <th>user_id</th>\n", " <th>cool</th>\n", " <th>useful</th>\n", " <th>funny</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>9yKzy9PApeiPPOUJEtnvkg</td>\n", " <td>2011-01-26</td>\n", " <td>fWKvX83p0-ka4JS3dc6E5A</td>\n", " <td>5</td>\n", " <td>My wife took me here on my birthday for breakf...</td>\n", " <td>review</td>\n", " <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " business_id date review_id stars \\\n", "0 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 fWKvX83p0-ka4JS3dc6E5A 5 \n", "\n", " text type \\\n", "0 My wife took me here on my birthday for breakf... review \n", "\n", " user_id cool useful funny \n", "0 rLtl8ZkDX5vH5nAx9C3q5Q 2 5 0 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# access yelp.csv using a relative path\n", "import pandas as pd\n", "yelp = pd.read_csv('../data/yelp.csv')\n", "yelp.head(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 1 (Bonus)\n", "\n", "Ignore the `yelp.csv` file, and construct this DataFrame yourself from `yelp.json`. This involves reading the data into Python, decoding the JSON, converting it to a DataFrame, and adding individual columns for each of the vote types." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# read the data from yelp.json into a list of rows\n", "# each row is decoded into a dictionary named \"data\" using using json.loads()\n", "import json\n", "with open('../data/yelp.json', 'rU') as f:\n", " data = [json.loads(row) for row in f]\n", " " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{u'business_id': u'9yKzy9PApeiPPOUJEtnvkg',\n", " u'date': u'2011-01-26',\n", " u'review_id': u'fWKvX83p0-ka4JS3dc6E5A',\n", " u'stars': 5,\n", " u'text': u'My wife took me here on my birthday for breakfast and it was excellent. The weather was perfect which made sitting outside overlooking their grounds an absolute pleasure. Our waitress was excellent and our food arrived quickly on the semi-busy Saturday morning. It looked like the place fills up pretty quickly so the earlier you get here the better.\\n\\nDo yourself a favor and get their Bloody Mary. It was phenomenal and simply the best I\\'ve ever had. I\\'m pretty sure they only use ingredients from their garden and blend them fresh when you order it. It was amazing.\\n\\nWhile EVERYTHING on the menu looks excellent, I had the white truffle scrambled eggs vegetable skillet and it was tasty and delicious. It came with 2 pieces of their griddled bread with was amazing and it absolutely made the meal complete. It was the best \"toast\" I\\'ve ever had.\\n\\nAnyway, I can\\'t wait to go back!',\n", " u'type': u'review',\n", " u'user_id': u'rLtl8ZkDX5vH5nAx9C3q5Q',\n", " u'votes': {u'cool': 2, u'funny': 0, u'useful': 5}}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# show the first review\n", "data[0]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>business_id</th>\n", " <th>date</th>\n", " <th>review_id</th>\n", " <th>stars</th>\n", " <th>text</th>\n", " <th>type</th>\n", " <th>user_id</th>\n", " <th>votes</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>9yKzy9PApeiPPOUJEtnvkg</td>\n", " <td>2011-01-26</td>\n", " <td>fWKvX83p0-ka4JS3dc6E5A</td>\n", " <td>5</td>\n", " <td>My wife took me here on my birthday for breakf...</td>\n", " <td>review</td>\n", " <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n", " <td>{u'funny': 0, u'useful': 5, u'cool': 2}</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ZRJwVLyzEJq1VAihDhYiow</td>\n", " <td>2011-07-27</td>\n", " <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n", " <td>5</td>\n", " <td>I have no idea why some people give bad review...</td>\n", " <td>review</td>\n", " <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n", " <td>{u'funny': 0, u'useful': 0, u'cool': 0}</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " business_id date review_id stars \\\n", "0 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 fWKvX83p0-ka4JS3dc6E5A 5 \n", "1 ZRJwVLyzEJq1VAihDhYiow 2011-07-27 IjZ33sJrzXqU-0X6U8NwyA 5 \n", "\n", " text type \\\n", "0 My wife took me here on my birthday for breakf... review \n", "1 I have no idea why some people give bad review... review \n", "\n", " user_id votes \n", "0 rLtl8ZkDX5vH5nAx9C3q5Q {u'funny': 0, u'useful': 5, u'cool': 2} \n", "1 0a2KyEL0d3Yb1V6aivbIuQ {u'funny': 0, u'useful': 0, u'cool': 0} " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert the list of dictionaries to a DataFrame\n", "ydata = pd.DataFrame(data)\n", "ydata.head(2)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>business_id</th>\n", " <th>date</th>\n", " <th>review_id</th>\n", " <th>stars</th>\n", " <th>text</th>\n", " <th>type</th>\n", " <th>user_id</th>\n", " <th>votes</th>\n", " <th>cool</th>\n", " <th>funny</th>\n", " <th>useful</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>9yKzy9PApeiPPOUJEtnvkg</td>\n", " <td>2011-01-26</td>\n", " <td>fWKvX83p0-ka4JS3dc6E5A</td>\n", " <td>5</td>\n", " <td>My wife took me here on my birthday for breakf...</td>\n", " <td>review</td>\n", " <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n", " <td>{u'funny': 0, u'useful': 5, u'cool': 2}</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ZRJwVLyzEJq1VAihDhYiow</td>\n", " <td>2011-07-27</td>\n", " <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n", " <td>5</td>\n", " <td>I have no idea why some people give bad review...</td>\n", " <td>review</td>\n", " <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n", " <td>{u'funny': 0, u'useful': 0, u'cool': 0}</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " business_id date review_id stars \\\n", "0 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 fWKvX83p0-ka4JS3dc6E5A 5 \n", "1 ZRJwVLyzEJq1VAihDhYiow 2011-07-27 IjZ33sJrzXqU-0X6U8NwyA 5 \n", "\n", " text type \\\n", "0 My wife took me here on my birthday for breakf... review \n", "1 I have no idea why some people give bad review... review \n", "\n", " user_id votes cool \\\n", "0 rLtl8ZkDX5vH5nAx9C3q5Q {u'funny': 0, u'useful': 5, u'cool': 2} 2 \n", "1 0a2KyEL0d3Yb1V6aivbIuQ {u'funny': 0, u'useful': 0, u'cool': 0} 0 \n", "\n", " funny useful \n", "0 0 5 \n", "1 0 0 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add DataFrame columns for cool, useful, and funny\n", "x = pd.DataFrame.from_records(ydata.votes)\n", "ydata= pd.concat([ydata, x], axis=1)\n", "ydata.head(2)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>business_id</th>\n", " <th>date</th>\n", " <th>review_id</th>\n", " <th>stars</th>\n", " <th>text</th>\n", " <th>type</th>\n", " <th>user_id</th>\n", " <th>cool</th>\n", " <th>funny</th>\n", " <th>useful</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>9yKzy9PApeiPPOUJEtnvkg</td>\n", " <td>2011-01-26</td>\n", " <td>fWKvX83p0-ka4JS3dc6E5A</td>\n", " <td>5</td>\n", " <td>My wife took me here on my birthday for breakf...</td>\n", " <td>review</td>\n", " <td>rLtl8ZkDX5vH5nAx9C3q5Q</td>\n", " <td>2</td>\n", " <td>0</td>\n", " <td>5</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>ZRJwVLyzEJq1VAihDhYiow</td>\n", " <td>2011-07-27</td>\n", " <td>IjZ33sJrzXqU-0X6U8NwyA</td>\n", " <td>5</td>\n", " <td>I have no idea why some people give bad review...</td>\n", " <td>review</td>\n", " <td>0a2KyEL0d3Yb1V6aivbIuQ</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " business_id date review_id stars \\\n", "0 9yKzy9PApeiPPOUJEtnvkg 2011-01-26 fWKvX83p0-ka4JS3dc6E5A 5 \n", "1 ZRJwVLyzEJq1VAihDhYiow 2011-07-27 IjZ33sJrzXqU-0X6U8NwyA 5 \n", "\n", " text type \\\n", "0 My wife took me here on my birthday for breakf... review \n", "1 I have no idea why some people give bad review... review \n", "\n", " user_id cool funny useful \n", "0 rLtl8ZkDX5vH5nAx9C3q5Q 2 0 5 \n", "1 0a2KyEL0d3Yb1V6aivbIuQ 0 0 0 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# drop the votes column and then display the head\n", "ydata.drop(\"votes\", axis=1, inplace=True)\n", "ydata.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 2\n", "\n", "Explore the relationship between each of the vote types (cool/useful/funny) and the number of stars." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th>stars</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>cool</th>\n", " <td>0.576769</td>\n", " <td>0.719525</td>\n", " <td>0.788501</td>\n", " <td>0.954623</td>\n", " <td>0.944261</td>\n", " </tr>\n", " <tr>\n", " <th>funny</th>\n", " <td>1.056075</td>\n", " <td>0.875944</td>\n", " <td>0.694730</td>\n", " <td>0.670448</td>\n", " <td>0.608631</td>\n", " </tr>\n", " <tr>\n", " <th>useful</th>\n", " <td>1.604806</td>\n", " <td>1.563107</td>\n", " <td>1.306639</td>\n", " <td>1.395916</td>\n", " <td>1.381780</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "stars 1 2 3 4 5\n", "cool 0.576769 0.719525 0.788501 0.954623 0.944261\n", "funny 1.056075 0.875944 0.694730 0.670448 0.608631\n", "useful 1.604806 1.563107 1.306639 1.395916 1.381780" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# treat stars as a categorical variable and look for differences between groups by comparing the means of the groups\n", "ydata.groupby(['stars'])['cool','funny','useful'].mean().T" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11ac5d8d0>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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BcHFxsXJzc3X8+HFlZ2ertLTU7roAAA7ihjnEoALx3//+t372s5/JMAwNHDhQSUlJdtcF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\nAEnS/wJl53XNNk0IJwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11ac5d110>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# display acorrelation matrix of the vote types (cool/useful/funny) and stars\n", "%matplotlib inline\n", "import seaborn as sns\n", "sns.heatmap(yelp.corr())" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.PairGrid at 0x11db6c150>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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69HaMQtnC0bNJjE/M4vJsaz+G+IAXo8MxHN4bQyS4RpYAISIiIiKiZWEYQdQjFoYQ/biw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\n/gNzj0cMGIXiJgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11db6cb10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# display multiple scatter plots (cool, useful, funny) with linear regression line\n", "feat_cols = ['cool', 'useful', 'funny']\n", "sns.pairplot(ydata, x_vars=feat_cols, y_vars='stars', kind='reg', size=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 3\n", "\n", "Define cool/useful/funny as the feature matrix X, and stars as the response vector y." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X = ydata[['cool', 'useful', 'funny']]\n", "y = ydata['stars']\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 4\n", "\n", "Fit a linear regression model and interpret the coefficients. Do the coefficients make intuitive sense to you? Explore the Yelp website to see if you detect similar trends." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.83989479278\n", "[ 0.27435947 -0.14745239 -0.13567449]\n" ] }, { "data": { "text/plain": [ "[(u'cool', 0.27435946858853977),\n", " (u'useful', -0.14745239099401466),\n", " (u'funny', -0.13567449053706701)]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "lr = LinearRegression()\n", "lr.fit(X, y)\n", "\n", "# print the coefficients\n", "print lr.intercept_\n", "print lr.coef_\n", "zip(X, lr.coef_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 5\n", "\n", "Evaluate the model by splitting it into training and testing sets and computing the RMSE. Does the RMSE make intuitive sense to you?" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split\n", "from sklearn import metrics\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[ cool useful funny\n", " 9174 1 2 1\n", " 4379 1 2 1\n", " 541 0 0 0\n", " 7251 0 0 0\n", " 1135 7 8 10\n", " 9978 0 1 0\n", " 3536 2 1 1\n", " 5891 0 0 0\n", " 3906 3 3 2\n", " 4348 0 1 0\n", " 889 0 0 0\n", " 3927 0 0 0\n", " 2017 1 1 0\n", " 2497 0 0 0\n", " 3779 2 1 0\n", " 7652 2 3 1\n", " 7513 1 2 1\n", " 8765 0 0 0\n", " 5234 0 0 0\n", " 2995 0 1 0\n", " 2350 1 1 1\n", " 466 0 0 0\n", " 5758 2 3 1\n", " 1299 0 0 0\n", " 9717 0 0 0\n", " 9256 0 0 0\n", " 4109 0 1 0\n", " 2092 0 0 0\n", " 3628 1 3 3\n", " 1057 0 0 0\n", " ... ... ... ...\n", " 7344 0 0 0\n", " 6077 0 0 0\n", " 6648 0 0 0\n", " 6740 0 0 0\n", " 9998 0 0 0\n", " 39 1 1 0\n", " 6484 1 1 1\n", " 1847 0 1 0\n", " 7985 0 0 0\n", " 1092 0 0 0\n", " 3325 2 1 1\n", " 2894 6 5 4\n", " 1363 0 1 0\n", " 3481 0 0 0\n", " 111 0 2 0\n", " 6368 0 1 0\n", " 942 0 1 0\n", " 5664 0 1 1\n", " 4169 0 0 0\n", " 4143 0 1 0\n", " 6782 0 1 0\n", " 6257 0 0 0\n", " 96 1 1 0\n", " 5857 0 0 0\n", " 7382 0 0 0\n", " 9785 0 0 0\n", " 7763 0 0 0\n", " 5218 0 0 0\n", " 1346 0 0 0\n", " 3582 1 1 1\n", " \n", " [7500 rows x 3 columns], cool useful funny\n", " 2656 0 0 0\n", " 445 2 2 0\n", " 9505 0 0 0\n", " 332 0 1 0\n", " 4168 0 0 0\n", " 2364 1 1 0\n", " 6097 1 2 0\n", " 7 0 1 0\n", " 7752 1 2 1\n", " 4453 0 0 0\n", " 4743 0 2 1\n", " 6243 0 0 0\n", " 3126 0 1 0\n", " 4056 0 1 0\n", " 5852 0 0 0\n", " 628 0 0 0\n", " 8545 0 2 0\n", " 2481 0 1 0\n", " 9743 2 2 0\n", " 1335 0 0 0\n", " 2683 1 2 0\n", " 4566 0 1 0\n", " 2514 1 1 0\n", " 9353 0 0 0\n", " 1907 0 0 0\n", " 2802 1 1 1\n", " 6890 0 0 0\n", " 784 0 0 0\n", " 9489 4 5 8\n", " 6893 0 1 0\n", " ... ... ... ...\n", " 4649 1 5 1\n", " 3645 1 0 0\n", " 9751 0 1 0\n", " 4129 1 1 0\n", " 9561 0 1 0\n", " 7183 0 1 1\n", " 1479 0 0 0\n", " 2109 0 1 0\n", " 6523 0 1 0\n", " 9912 1 2 0\n", " 6474 1 2 1\n", " 2069 1 1 0\n", " 4026 1 2 1\n", " 8740 0 1 1\n", " 7827 1 2 1\n", " 4613 0 1 0\n", " 3516 9 8 6\n", " 8400 1 5 1\n", " 2322 0 0 1\n", " 582 6 8 2\n", " 8951 0 1 0\n", " 5153 0 0 0\n", " 1667 2 3 5\n", " 1516 0 0 0\n", " 4990 0 2 0\n", " 397 1 1 0\n", " 5552 2 2 1\n", " 1453 4 5 2\n", " 4469 0 0 0\n", " 2413 0 0 0\n", " \n", " [2500 rows x 3 columns], 9174 4\n", " 4379 4\n", " 541 4\n", " 7251 5\n", " 1135 4\n", " 9978 5\n", " 3536 3\n", " 5891 3\n", " 3906 4\n", " 4348 4\n", " 889 3\n", " 3927 4\n", " 2017 4\n", " 2497 4\n", " 3779 4\n", " 7652 4\n", " 7513 1\n", " 8765 5\n", " 5234 4\n", " 2995 1\n", " 2350 4\n", " 466 4\n", " 5758 4\n", " 1299 1\n", " 9717 3\n", " 9256 5\n", " 4109 2\n", " 2092 4\n", " 3628 1\n", " 1057 5\n", " ..\n", " 7344 5\n", " 6077 4\n", " 6648 1\n", " 6740 5\n", " 9998 2\n", " 39 4\n", " 6484 4\n", " 1847 4\n", " 7985 4\n", " 1092 4\n", " 3325 4\n", " 2894 4\n", " 1363 5\n", " 3481 3\n", " 111 5\n", " 6368 4\n", " 942 1\n", " 5664 4\n", " 4169 2\n", " 4143 5\n", " 6782 5\n", " 6257 5\n", " 96 4\n", " 5857 3\n", " 7382 1\n", " 9785 5\n", " 7763 4\n", " 5218 2\n", " 1346 3\n", " 3582 4\n", " Name: stars, dtype: int64, 2656 3\n", " 445 5\n", " 9505 4\n", " 332 2\n", " 4168 4\n", " 2364 4\n", " 6097 5\n", " 7 4\n", " 7752 4\n", " 4453 4\n", " 4743 1\n", " 6243 5\n", " 3126 5\n", " 4056 3\n", " 5852 2\n", " 628 4\n", " 8545 1\n", " 2481 3\n", " 9743 3\n", " 1335 4\n", " 2683 5\n", " 4566 5\n", " 2514 2\n", " 9353 5\n", " 1907 4\n", " 2802 4\n", " 6890 3\n", " 784 5\n", " 9489 5\n", " 6893 4\n", " ..\n", " 4649 4\n", " 3645 5\n", " 9751 2\n", " 4129 5\n", " 9561 5\n", " 7183 2\n", " 1479 5\n", " 2109 4\n", " 6523 2\n", " 9912 4\n", " 6474 4\n", " 2069 3\n", " 4026 4\n", " 8740 4\n", " 7827 1\n", " 4613 5\n", " 3516 5\n", " 8400 3\n", " 2322 5\n", " 582 4\n", " 8951 4\n", " 5153 2\n", " 1667 1\n", " 1516 4\n", " 4990 3\n", " 397 3\n", " 5552 4\n", " 1453 4\n", " 4469 4\n", " 2413 5\n", " Name: stars, dtype: int64]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# define a function that accepts a list of features and returns testing RMSE\n", "# define a function that accepts a list of features and returns testing RMSE\n", "def train_test_rmse(feat_cols):\n", " X = ydata[feat_cols]\n", " y = ydata.stars\n", " X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)\n", " linreg = LinearRegression()\n", " linreg.fit(X_train, y_train)\n", " y_pred = linreg.predict(X_test)\n", " return np.sqrt(metrics.mean_squared_error(y_test, y_pred))\n", "\n", "train_test_split(X, y, random_state=123)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.17336862742\n" ] } ], "source": [ "# calculate RMSE with all three features\n", "print train_test_rmse(['cool', 'funny', 'useful'])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 6\n", "\n", "Try removing some of the features and see if the RMSE improves." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.17336862742\n", "1.1851949299\n", "1.20049049928\n" ] } ], "source": [ "print train_test_rmse(['cool', 'funny', 'useful'])\n", "print train_test_rmse(['cool', 'funny'])\n", "print train_test_rmse(['cool'])\n", "\n", "### RMSE is best with all 3 features\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 7 (Bonus)\n", "\n", "Think of some new features you could create from the existing data that might be predictive of the response. Figure out how to create those features in Pandas, add them to your model, and see if the RMSE improves." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# new feature: Number of reviews per business_id. More reviews = more favored by reviewer? \n", "# Adding # of occurs for business_id\n", "ydata['review_freq']= ydata.groupby(['business_id'])['stars'].transform('count')\n", "\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# new features: \n", "# add 0 if occurs < 4 or 1 if >= 4\n", "ydata[\"favored\"] = [1 if x > 3 else 0 for x in ydata.review_freq]\n" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.16823194233\n" ] } ], "source": [ "# add new features to the model and calculate RMSE\n", "print train_test_rmse(['cool', 'funny', 'useful','review_freq'])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Task 8 (Bonus)\n", "\n", "Compare your best RMSE on the testing set with the RMSE for the \"null model\", which is the model that ignores all features and simply predicts the mean response value in the testing set." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3.7808, 3.7808, 3.7808, ..., 3.7808, 3.7808, 3.7808])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)\n", "\n", "# create a NumPy array with the same shape as y_test\n", "y_null = np.zeros_like(y_test, dtype=float)\n", "\n", "# fill the array with the mean value of y_test\n", "y_null.fill(y_test.mean())\n", "y_null\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1.2019781029619465" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.sqrt(metrics.mean_squared_error(y_test, y_null))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NUll model worse than slight;y improved model with added features from task 7" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [Root]", "language": "python", "name": "Python [Root]" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
jpallas/beakerx
test/ipynb/python/EasyFormPythonTest.ipynb
1
7995
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from beakerx import *\n", "f1 = EasyForm(\"Legend name\")\n", "f1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "f2 = EasyForm(\"form2\")\n", "f2.addTextField(\"field name2\")\n", "f2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f2['field name2']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f2['field name2'] = '2text from code2'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f2b = EasyForm(\"form2\")\n", "f2b.addTextField(\"field name2\", width = 10)\n", "f2b" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f3 = EasyForm(\"form3\")\n", "f3.addTextArea(\"field name3\")\n", "f3" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f3['field name3']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f3['field name3'] = '3text from code3'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f3b = EasyForm(\"form3\")\n", "f3b.addTextArea(\"field name3\", width = 20, height = 5)\n", "f3b" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f3c = EasyForm(\"form3\")\n", "f3c.addTextArea(\"field name3\", value = \"3c initial value 3c\")\n", "f3c" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f4 = EasyForm(\"form4\")\n", "f4.addCheckBox(\"field name4\")\n", "f4" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f4['field name4']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f4['field name4'] = False" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f4b = EasyForm(\"form4\")\n", "f4b.addCheckBox(\"field name4\", value = True)\n", "f4b" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f5 = EasyForm(\"form5\")\n", "f5.addComboBox(\"field name5\", [\"onef5\", \"twof5\", \"threef5\"])\n", "f5" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f5['field name5']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f5['field name5'] = 'threef5'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f6 = EasyForm(\"form6\")\n", "f6.addList(\"field name6\", [\"onef6\", \"twof6\", \"threef6\"], rows = 3)\n", "f6" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f6['field name6']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f6['field name6'] = ['onef6']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f6b = EasyForm(\"form6b\")\n", "f6b.addList(\"field name6\", [\"onef6b\", \"twof6b\", \"threef6b\"], multi=False)\n", "f6b" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f6b['field name6']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f6c = EasyForm(\"form6c\")\n", "f6c.addList(\"field name6c\", [\"onef6c\", \"twof6c\", \"threef6c\", \"threef6c\"], rows=2)\n", "f6c" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f7 = EasyForm(\"form7\")\n", "f7.addCheckBoxes(\"field name7\", [\"onef7\", \"twof7\", \"threef7\", \"fourf7\"])\n", "f7" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f7['field name7']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f7['field name7'] = ['onef7']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f7b = EasyForm(\"form7b\")\n", "f7b.addCheckBoxes(\"field name7\", [\"onef7b\", \"twof7b\", \"threef7b\"], orientation=EasyForm.HORIZONTAL)\n", "f7b" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f8 = EasyForm(\"form8\")\n", "f8.addRadioButtons(\"field name8\", [\"onef8\", \"twof8\", \"threef8\"])\n", "f8" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f8['field name8']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f8['field name8'] = 'threef8'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f8b = EasyForm(\"form8\")\n", "f8b.addRadioButtons(\"field name8\", [\"onef8b\", \"twof8b\", \"threef8b\"], orientation=EasyForm.HORIZONTAL)\n", "f8b" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f9 = EasyForm(\"form9\")\n", "f9.addDatePicker(\"field name9\")\n", "f9" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f9['field name9']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f9['field name9'] = '20170527'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from beakerx import *\n", "\n", "def setField4():\n", " f11[\"field4\"] = \"from actionPerformed\"\n", "\n", "f10 = EasyForm(\"form10\")\n", "f10.addButton(\"run tag\", tag=\"tag1\")\n", "button11 = f10.addButton(\"actionPerformed\")\n", "button11.actionPerformed = lambda: setField4()\n", "f10" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f11['field2'] = \"test text\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "tags": [ "tag1" ] }, "outputs": [], "source": [ "f11 = EasyForm(\"form11\")\n", "f11.addTextField(\"field2\")\n", "f11.addTextField(\"field4\")\n", "f11" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Tags", "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }
apache-2.0
norsween/data-science
springboard-answers-to-exercises/Mini_Project_Logistic_Regression-Answers.ipynb
1
492454
{ "cells": [ { "cell_type": "markdown", "metadata": { "hide": true }, "source": [ "# Classification\n", "$$\n", "\\renewcommand{\\like}{{\\cal L}}\n", "\\renewcommand{\\loglike}{{\\ell}}\n", "\\renewcommand{\\err}{{\\cal E}}\n", "\\renewcommand{\\dat}{{\\cal D}}\n", "\\renewcommand{\\hyp}{{\\cal H}}\n", "\\renewcommand{\\Ex}[2]{E_{#1}[#2]}\n", "\\renewcommand{\\x}{{\\mathbf x}}\n", "\\renewcommand{\\v}[1]{{\\mathbf #1}}\n", "$$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "hide": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib as mpl\n", "import matplotlib.cm as cm\n", "from matplotlib.colors import ListedColormap\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "pd.set_option('display.width', 500)\n", "pd.set_option('display.max_columns', 100)\n", "pd.set_option('display.notebook_repr_html', True)\n", "import seaborn as sns\n", "sns.set_style(\"whitegrid\")\n", "sns.set_context(\"poster\")\n", "import sklearn.cross_validation\n", "\n", "c0=sns.color_palette()[0]\n", "c1=sns.color_palette()[1]\n", "c2=sns.color_palette()[2]\n", "\n", "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n", "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n", "cm = plt.cm.RdBu\n", "cm_bright = ListedColormap(['#FF0000', '#0000FF'])\n", "\n", "def points_plot(ax, Xtr, Xte, ytr, yte, clf, mesh=True, colorscale=cmap_light, \n", " cdiscrete=cmap_bold, alpha=0.1, psize=10, zfunc=False, predicted=False):\n", " h = .02\n", " X=np.concatenate((Xtr, Xte))\n", " x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n", " y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n", " xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n", " np.linspace(y_min, y_max, 100))\n", "\n", " #plt.figure(figsize=(10,6))\n", " if zfunc:\n", " p0 = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 0]\n", " p1 = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n", " Z=zfunc(p0, p1)\n", " else:\n", " Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n", " ZZ = Z.reshape(xx.shape)\n", " if mesh:\n", " plt.pcolormesh(xx, yy, ZZ, cmap=cmap_light, alpha=alpha, axes=ax)\n", " if predicted:\n", " showtr = clf.predict(Xtr)\n", " showte = clf.predict(Xte)\n", " else:\n", " showtr = ytr\n", " showte = yte\n", " ax.scatter(Xtr[:, 0], Xtr[:, 1], c=showtr-1, cmap=cmap_bold, \n", " s=psize, alpha=alpha,edgecolor=\"k\")\n", " # and testing points\n", " ax.scatter(Xte[:, 0], Xte[:, 1], c=showte-1, cmap=cmap_bold, \n", " alpha=alpha, marker=\"s\", s=psize+10)\n", " ax.set_xlim(xx.min(), xx.max())\n", " ax.set_ylim(yy.min(), yy.max())\n", " return ax,xx,yy\n", "\n", "def points_plot_prob(ax, Xtr, Xte, ytr, yte, clf, colorscale=cmap_light, \n", " cdiscrete=cmap_bold, ccolor=cm, psize=10, alpha=0.1):\n", " ax,xx,yy = points_plot(ax, Xtr, Xte, ytr, yte, clf, mesh=False, \n", " colorscale=colorscale, cdiscrete=cdiscrete, \n", " psize=psize, alpha=alpha, predicted=True) \n", " Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n", " Z = Z.reshape(xx.shape)\n", " plt.contourf(xx, yy, Z, cmap=ccolor, alpha=.2, axes=ax)\n", " cs2 = plt.contour(xx, yy, Z, cmap=ccolor, alpha=.6, axes=ax)\n", " plt.clabel(cs2, fmt = '%2.1f', colors = 'k', fontsize=14, axes=ax)\n", " return ax " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A Motivating Example Using `sklearn`: Heights and Weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use a dataset of heights and weights of males and females to hone our understanding of classifiers. We load the data into a dataframe and plot it." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Gender</th>\n", " <th>Height</th>\n", " <th>Weight</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Male</td>\n", " <td>73.847017</td>\n", " <td>241.893563</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Male</td>\n", " <td>68.781904</td>\n", " <td>162.310473</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Male</td>\n", " <td>74.110105</td>\n", " <td>212.740856</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Male</td>\n", " <td>71.730978</td>\n", " <td>220.042470</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Male</td>\n", " <td>69.881796</td>\n", " <td>206.349801</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Gender Height Weight\n", "0 Male 73.847017 241.893563\n", "1 Male 68.781904 162.310473\n", "2 Male 74.110105 212.740856\n", "3 Male 71.730978 220.042470\n", "4 Male 69.881796 206.349801" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflog = pd.read_csv(\"data/01_heights_weights_genders.csv\")\n", "dflog.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x15cac34c978>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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My4gEt19KUlwveyIvVjHgz8aYhhSr+2OSg5wjL6ZYDgLxvsRaWxu1gLX2v8aYT5C4ulR0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\n9dxKM3scOAw4EEj33xla5HTp8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F5EKpiCHhHpCY8S+9ccSG70pljQk+7XcyCxY/Pf3P2v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\noEdERESqgoIeERERqQr/H4zIjzVglk2AAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x15ca7bfef98>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Checkup Exercise Set I:\n", "# Create a scatter plot of Weight vs. Height\n", "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "plt.scatter(dflog.Weight, dflog.Height)\n", "plt.xlabel(\"Weight\")\n", "plt.ylabel(\"Height\")\n", "plt.title(\"Relationship between Weight and Height\")\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x15cac4432b0>" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ENWrUuO79mzDhVRo0WImnZzWkffpKHI6N7N7dkC5dXr7u+cqdx031+TAMowVQ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jVAeChqSSSERkWQk5eHsZ4+lcqWI3D4aIL2bAdgzrYN5OTlscd3Ll/iY/Fcv4LHm/YQ8uwRbgE+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"text/plain": [ "<matplotlib.figure.Figure at 0x15cac345b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Checkup Exercise Set I:\n", "# Color the points differently by Gender.\n", "plt.scatter(dflog.Weight, dflog.Height, c=['Blue' if gender == 'Male' else 'Pink' for gender in dflog['Gender']])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we try a basic Logistic Regression:\n", "\n", "* Split the data into a training and test (hold-out) set\n", "* Train on the training set, and test for accuracy on the testing set" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9252\n" ] } ], "source": [ "from sklearn.cross_validation import train_test_split\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import accuracy_score\n", "\n", "# Split the data into a training and test set.\n", "Xlr, Xtestlr, ylr, ytestlr = train_test_split(dflog[['Height','Weight']].values, \n", " (dflog.Gender == \"Male\").values,random_state=5)\n", "\n", "clf = LogisticRegression()\n", "\n", "# Fit the model on the training data.\n", "clf.fit(Xlr, ylr)\n", "\n", "# Print the accuracy score of the testing data.\n", "print(accuracy_score(clf.predict(Xtestlr), ytestlr))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tuning the Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We use the following `cv_score` function to perform K-fold cross-validation and apply a scoring function to each test fold. In this incarnation we use accuracy score as the default scoring function." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.cross_validation import KFold\n", "from sklearn.metrics import accuracy_score\n", "\n", "def cv_score(clf, x, y, score_func=accuracy_score):\n", " result = 0\n", " nfold = 5\n", " for train, test in KFold(nfold).split(x): # split data into train/test groups, 5 times\n", " clf.fit(x[train], y[train]) # fit\n", " result += score_func(clf.predict(x[test]), y[test]) # evaluate score function on held-out data\n", " return result / nfold # average" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is an example of using the `cv_score` function for a basic logistic regression model without regularization." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", " intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n", " penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n", " verbose=0, warm_start=False)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# First, we try a basic Logistic Regression:\n", "# Split the data into a training and test set\n", "# Train on the training set, and test for accuracy on the testing set\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.cross_validation import train_test_split\n", "from sklearn.metrics import accuracy_score\n", "\n", "Xlr, Xtestlr, ylr, ytestlr = train_test_split(dflog[['Height','Weight']].values, \n", " (dflog.Gender==\"Male\").values,random_state=5)\n", "clf = LogisticRegression()\n", "clf.fit(Xlr,ylr)\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "For c value of 0.001000 score is: 0.916933\n", "For c value of 0.100000 score is: 0.916800\n", "For c value of 1.000000 score is: 0.916800\n", "For c value of 10.000000 score is: 0.916800\n", "For c value of 100.000000 score is: 0.916800\n", "Best c value is: 0.001000 with a score of: 0.916933\n" ] } ], "source": [ "#the grid of parameters to search over\n", "Cs = [0.001, 0.1, 1, 10, 100]\n", "\n", "# your turn\n", "# Checkup Exercise Set II\n", "# Find the best model parameters based only on training set.\n", "# For each C: \n", "# 1) Create a logistic regression model with that value of C \n", "# 2) Find the average score for this model using the cv_score \n", "# function only on the training set (Xlr, ylr) \n", "# 3) Pick the C with the highest average score \n", "results = []\n", "max_score = 0\n", "for c in Cs:\n", " clf = LogisticRegression(C=c)\n", " clf.fit(Xlr, ylr)\n", " score = accuracy_score(clf.predict(Xlr),ylr)\n", " print(\"For c value of %f score is: %f\" % (c, score))\n", " if (score > max_score):\n", " max_score = score\n", " best_c = c\n", "\n", "print(\"Best c value is: %f with a score of: %f\" % (best_c, max_score))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.9256\n" ] } ], "source": [ "# your turn\n", "# Checkup Exercise Set III\n", "# 1) Use the C obtained from procedure above and train\n", "# a Logistic Regression on the training data.\n", "clf = LogisticRegression(C=best_c)\n", "clf.fit(Xlr, ylr)\n", "\n", "# 2) Calculate the accurace on the test data.\n", "accuracy = accuracy_score(clf.predict(Xtestlr), ytestlr)\n", "print(accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Black Box Grid Search in `sklearn`" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Grid scores:\n", "0.916 (+/-0.002) for {'C': 0.001}\n", "0.917 (+/-0.002) for {'C': 0.1}\n", "0.917 (+/-0.002) for {'C': 1}\n", "0.917 (+/-0.002) for {'C': 10}\n", "0.917 (+/-0.002) for {'C': 100}\n", "Accuracy is: 0.925200\n", "\n", "Classification report:\n", "\n", "The best model of training set based on GridSearchCV:\n", "\n", " precision recall f1-score support\n", "\n", " False 0.92 0.93 0.92 1232\n", " True 0.93 0.92 0.93 1268\n", "\n", "avg / total 0.93 0.93 0.93 2500\n", "\n" ] } ], "source": [ "# your turn\n", "# Checkup Exercise Set IV\n", "# 1) Use GridSearchCV to find the best model over the training set\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.metrics import classification_report\n", "\n", "# GridSearchCV(estimator, param_grid, scoring=None, fit_params=None, n_jobs=1, iid=True, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score='raise')[source]¶\n", "param_grid = {'C': [0.001, 0.1, 1, 10, 100]}\n", "clf = GridSearchCV(LogisticRegression(C=1), param_grid=param_grid)\n", "clf.fit(Xlr, ylr)\n", "\n", "print(\"Grid scores:\")\n", "for params, mean_score, scores in clf.grid_scores_:\n", " print(\"%0.3f (+/-%0.03f) for %r\"\n", " % (mean_score, scores.std() * 2, params))\n", " \n", "print(\"Accuracy is: %f\" % accuracy_score(clf.predict(Xtestlr), ytestlr))\n", "print()\n", "\n", "print(\"Classification report:\")\n", "print()\n", "\n", "print(\"The best model of training set based on GridSearchCV:\")\n", "print()\n", "y_true, y_pred = ytestlr, clf.predict(Xtestlr)\n", "print(classification_report(y_true, y_pred))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A Walkthrough of the Math Behind Logistic Regression" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def cv_optimize(clf, parameters, Xtrain, ytrain, n_folds=5):\n", " gs = sklearn.grid_search.GridSearchCV(clf, param_grid=parameters, cv=n_folds)\n", " gs.fit(Xtrain, ytrain)\n", " print(\"BEST PARAMS\", gs.best_params_)\n", " best = gs.best_estimator_\n", " return best" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "hide": true }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split\n", "\n", "def do_classify(clf, parameters, indf, featurenames, targetname, target1val, standardize=False, train_size=0.8):\n", " subdf=indf[featurenames]\n", " if standardize:\n", " subdfstd=(subdf - subdf.mean())/subdf.std()\n", " else:\n", " subdfstd=subdf\n", " X=subdfstd.values\n", " y=(indf[targetname].values==target1val)*1\n", " Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=train_size)\n", " clf = cv_optimize(clf, parameters, Xtrain, ytrain)\n", " clf=clf.fit(Xtrain, ytrain)\n", " training_accuracy = clf.score(Xtrain, ytrain)\n", " test_accuracy = clf.score(Xtest, ytest)\n", " print(\"Accuracy on training data: {:0.2f}\".format(training_accuracy))\n", " print(\"Accuracy on test data: {:0.2f}\".format(test_accuracy))\n", " return clf, Xtrain, ytrain, Xtest, ytest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logistic Regression: The Math" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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s8H3gHuDLxpiPeqxzB/CHgOPl9SLin2SWXoxDXU1UhIM+RSQihSDnJMMY0wp8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\nUd6Q7sGIlOujJyLFT1c6kQ0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"text/plain": [ "<matplotlib.figure.Figure at 0x15cac912a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "h = lambda z: 1. / (1 + np.exp(-z))\n", "zs=np.arange(-5, 5, 0.1)\n", "plt.plot(zs, h(zs), alpha=0.5);" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>Gender</th>\n", " <th>Height</th>\n", " <th>Weight</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Male</td>\n", " <td>73.847017</td>\n", " <td>241.893563</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Male</td>\n", " <td>68.781904</td>\n", " <td>162.310473</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Male</td>\n", " <td>74.110105</td>\n", " <td>212.740856</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Male</td>\n", " <td>71.730978</td>\n", " <td>220.042470</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Male</td>\n", " <td>69.881796</td>\n", " <td>206.349801</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Gender Height Weight\n", "0 Male 73.847017 241.893563\n", "1 Male 68.781904 162.310473\n", "2 Male 74.110105 212.740856\n", "3 Male 71.730978 220.042470\n", "4 Male 69.881796 206.349801" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dflog.head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BEST PARAMS {'C': 0.01}\n", "Accuracy on training data: 0.92\n", "Accuracy on test data: 0.92\n" ] } ], "source": [ "clf_l, Xtrain_l, ytrain_l, Xtest_l, ytest_l = do_classify(LogisticRegression(), \n", " {\"C\": [0.01, 0.1, 1, 10, 100]}, \n", " dflog, ['Weight', 'Height'], 'Gender','Male')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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guj0EIYGuV8hkFGKxeba2/gnwPtPTeSKReUxzl2vXNvjggyQ3b+4yMbHEzs4m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\n57HpOKiGMQ5UlGUalsVUuYyTSjE9Pc39/X0O4hhtNKL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ALEb8xRfXZR9raKD5+HHGGxtJ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\n/wM8RN+oceXPYAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x15cacb38cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "ax=plt.gca()\n", "points_plot(ax, Xtrain_l, Xtest_l, ytrain_l, ytest_l, clf_l, alpha=0.2);" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0.05426815, 0.94573185],\n", " [ 0.12727578, 0.87272422],\n", " [ 0.07551666, 0.92448334],\n", " ..., \n", " [ 0.96633645, 0.03366355],\n", " [ 0.00324165, 0.99675835],\n", " [ 0.98605732, 0.01394268]])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf_l.predict_proba(Xtest_l)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Discriminative vs Generative Classifier" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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RcZ4RZSlLT39h0ktCkqRJ8eNcp8Pv/ea3+bWvvcyfv/4OP/rJT0nShD/+j/6AWq1G3ZBh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\nJs1StGtwjNsl7HQ58dxLQ16Gu3/ksY3FnE68kccskHsZzAHJaKQcCoAEoFB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hUCi2hTIa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\nAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x15cacb98ef0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "ax = plt.gca()\n", "points_plot_prob(ax, Xtrain_l, Xtest_l, ytrain_l, ytest_l, clf_l, psize=20, alpha=0.1);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
gpl-3.0
turbomanage/training-data-analyst
courses/machine_learning/asl/open_project/time_series_anomaly_detection/tf_anomaly_detection_model_selection/model_selection_anomaly_detection_run_module_gcp_labeled_tune.ipynb
2
247595
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Run model module on GCP with labeled threshold tuning" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "PROJECT = \"PROJECT\" # REPLACE WITH YOUR PROJECT ID\n", "BUCKET = \"BUCKET\" # REPLACE WITH A BUCKET NAME\n", "REGION = \"us-central1\" # REPLACE WITH YOUR REGION e.g. us-central1\n", "\n", "# Import os environment variables\n", "os.environ[\"PROJECT\"] = PROJECT\n", "os.environ[\"BUCKET\"] = BUCKET\n", "os.environ[\"REGION\"] = REGION\n", "os.environ[\"TFVERSION\"] = \"1.13\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copy data over to bucket" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "gsutil -m cp -r data/* gs://${BUCKET}/anomaly_detection/data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import os environment variables for global sequence shape hyperparameters\n", "os.environ[\"SEQ_LEN\"] = str(30)\n", "os.environ[\"NUM_FEAT\"] = str(5)\n", "\n", "# Import os environment variables for global feature hyperparameters\n", "os.environ[\"FEAT_NAMES\"] = (\",\").join([\"tag_{}\".format(i) for i in range(int(os.environ[\"NUM_FEAT\"]))])\n", "os.environ[\"FEAT_DEFAULTS\"] = (\",\").join([(\";\").join([\"0.0\"] * int(os.environ[\"SEQ_LEN\"]))] * int(os.environ[\"NUM_FEAT\"]))\n", "\n", "# Import os environment variables for global training hyperparameters\n", "os.environ[\"START_DELAY_SECS\"] = str(60)\n", "os.environ[\"THROTTLE_SECS\"] = str(120)\n", "\n", "# Import os environment variables for global threshold hyperparameters\n", "os.environ[\"LABELED_TUNE_THRESH\"] = \"True\"\n", "os.environ[\"NUM_TIME_ANOM_THRESH\"] = str(300)\n", "os.environ[\"NUM_FEAT_ANOM_THRESH\"] = str(300)\n", "\n", "# Import global dense hyperparameters\n", "os.environ[\"ENC_DNN_HIDDEN_UNITS\"] = \"64,32,16\"\n", "os.environ[\"LATENT_VECTOR_SIZE\"] = str(8)\n", "os.environ[\"DEC_DNN_HIDDEN_UNITS\"] = \"16,32,64\"\n", "os.environ[\"TIME_LOSS_WEIGHT\"] = str(1.0)\n", "os.environ[\"FEAT_LOSS_WEIGHT\"] = str(1.0)\n", "\n", "# Import global lstm hyperparameters\n", "os.environ[\"REVERSE_LABELS_SEQUENCE\"] = \"True\"\n", "os.environ[\"ENC_LSTM_HIDDEN_UNITS\"] = \"64,32,16\"\n", "os.environ[\"DEC_LSTM_HIDDEN_UNITS\"] = \"16,32,64\"\n", "os.environ[\"LSTM_DROPOUT_OUTPUT_KEEP_PROBS\"] = \"0.9,0.95,1.0\"\n", "os.environ[\"DNN_HIDDEN_UNITS\"] = \"1024,256,64\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train reconstruction variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import os environment variables for reconstruction training hyperparameters\n", "os.environ[\"TRAIN_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/train_norm_seq.csv\".format(BUCKET)\n", "os.environ[\"EVAL_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/val_norm_1_seq.csv\".format(BUCKET)\n", "os.environ[\"PREVIOUS_TRAIN_STEPS\"] = str(0)\n", "os.environ[\"RECONSTRUCTION_EPOCHS\"] = str(1.0)\n", "os.environ[\"TRAIN_EXAMPLES\"] = str(64000)\n", "os.environ[\"LEARNING_RATE\"] = str(0.1)\n", "os.environ[\"TRAINING_MODE\"] = \"reconstruction\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dense Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/dense_labeled\n", "JOBNAME=job_anomaly_detection_reconstruction_dense_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gsutil -m rm -rf ${OUTDIR}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n", " --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=./tmp \\\n", " --seq_len=${SEQ_LEN} \\\n", " --num_feat=${NUM_FEAT} \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n", " --reconstruction_epochs=${RECONSTRUCTION_EPOCHS} \\\n", " --train_examples=${TRAIN_EXAMPLES} \\\n", " --learning_rate=${LEARNING_RATE} \\\n", " --start_delay_secs=${START_DELAY_SECS} \\\n", " --throttle_secs=${THROTTLE_SECS} \\\n", " --model_type=\"dense_autoencoder\" \\\n", " --enc_dnn_hidden_units=${ENC_DNN_HIDDEN_UNITS} \\\n", " --latent_vector_size=${LATENT_VECTOR_SIZE} \\\n", " --dec_dnn_hidden_units=${DEC_DNN_HIDDEN_UNITS} \\\n", " --time_loss_weight=${TIME_LOSS_WEIGHT} \\\n", " --feat_loss_weight=${FEAT_LOSS_WEIGHT} \\\n", " --training_mode=${TRAINING_MODE} \\\n", " --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n", " --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n", " --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LSTM Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/lstm_labeled\n", "JOBNAME=job_anomaly_detection_reconstruction_lstm_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gsutil -m rm -rf ${OUTDIR}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n", " --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=./tmp \\\n", " --seq_len=${SEQ_LEN} \\\n", " --num_feat=${NUM_FEAT} \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n", " --reconstruction_epochs=${RECONSTRUCTION_EPOCHS} \\\n", " --train_examples=${TRAIN_EXAMPLES} \\\n", " --learning_rate=${LEARNING_RATE} \\\n", " --start_delay_secs=${START_DELAY_SECS} \\\n", " --throttle_secs=${THROTTLE_SECS} \\\n", " --model_type=\"lstm_enc_dec_autoencoder\" \\\n", " --reverse_labels_sequence=${REVERSE_LABELS_SEQUENCE} \\\n", " --enc_lstm_hidden_units=${ENC_LSTM_HIDDEN_UNITS} \\\n", " --dec_lstm_hidden_units=${DEC_LSTM_HIDDEN_UNITS} \\\n", " --lstm_dropout_output_keep_probs=${LSTM_DROPOUT_OUTPUT_KEEP_PROBS} \\\n", " --dnn_hidden_units=${DNN_HIDDEN_UNITS} \\\n", " --training_mode=${TRAINING_MODE} \\\n", " --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n", " --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n", " --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PCA Autoencoder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reconstruction" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/pca_labeled\n", "JOBNAME=job_anomaly_detection_reconstruction_pca_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gsutil -m rm -rf ${OUTDIR}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n", " --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=./tmp \\\n", " --seq_len=${SEQ_LEN} \\\n", " --num_feat=${NUM_FEAT} \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n", " --reconstruction_epochs=1.0 \\\n", " --train_examples=${TRAIN_EXAMPLES} \\\n", " --eval_examples=6400 \\\n", " --start_delay_secs=${START_DELAY_SECS} \\\n", " --throttle_secs=${THROTTLE_SECS} \\\n", " --model_type=\"pca\" \\\n", " --training_mode=${TRAINING_MODE} \\\n", " --autotune_principal_components=\"False\" \\\n", " --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n", " --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n", " --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Autotune principal components" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/pca_labeled\n", "JOBNAME=job_anomaly_detection_reconstruction_pca_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=${EVAL_FILE_PATTERN} \\\n", " --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=./tmp \\\n", " --seq_len=${SEQ_LEN} \\\n", " --num_feat=${NUM_FEAT} \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=2000 \\\n", " --reconstruction_epochs=1.0 \\\n", " --train_examples=6400 \\\n", " --eval_examples=6400 \\\n", " --start_delay_secs=${START_DELAY_SECS} \\\n", " --throttle_secs=${THROTTLE_SECS} \\\n", " --model_type=\"pca\" \\\n", " --training_mode=${TRAINING_MODE} \\\n", " --autotune_principal_components=\"True\" \\\n", " --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n", " --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n", " --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hyperparameter tuning of reconstruction hyperparameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dense Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile hyperparam_reconstruction_dense.yaml\n", "trainingInput:\n", " scaleTier: STANDARD_1\n", " hyperparameters:\n", " hyperparameterMetricTag: rmse\n", " goal: MINIMIZE\n", " maxTrials: 30\n", " maxParallelTrials: 1\n", " params:\n", " - parameterName: enc_dnn_hidden_units\n", " type: CATEGORICAL\n", " categoricalValues: [\"64 32 16\", \"256 128 16\", \"64 64 64\"]\n", " - parameterName: latent_vector_size\n", " type: INTEGER\n", " minValue: 8\n", " maxValue: 16\n", " scaleType: UNIT_LINEAR_SCALE\n", " - parameterName: dec_dnn_hidden_units\n", " type: CATEGORICAL\n", " categoricalValues: [\"16 32 64\", \"16 128 256\", \"64 64 64\"]\n", " - parameterName: train_batch_size\n", " type: INTEGER\n", " minValue: 8\n", " maxValue: 512\n", " scaleType: UNIT_LOG_SCALE\n", " - parameterName: learning_rate\n", " type: DOUBLE\n", " minValue: 0.001\n", " maxValue: 0.1\n", " scaleType: UNIT_LINEAR_SCALE" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/hyperparam_reconstruction_dense_labeled\n", "JOBNAME=job_anomaly_detection_hyperparam_reconstruction_dense_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gsutil -m rm -rf ${OUTDIR}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --config=hyperparam_reconstruction_dense.yaml \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=gs://${BUCKET}/anomaly_detection/data/train_norm_seq.csv \\\n", " --eval_file_pattern=gs://${BUCKET}/anomaly_detection/data/val_norm_1_seq.csv \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=${OUTDIR} \\\n", " --seq_len=30 \\\n", " --num_feat=5 \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=0 \\\n", " --reconstruction_epochs=1.0 \\\n", " --train_examples=64000 \\\n", " --start_delay_secs=60 \\\n", " --throttle_secs=120 \\\n", " --training_mode=\"reconstruction\" \\\n", " --labeled_tune_thresh=True \\\n", " --num_time_anom_thresh=300 \\\n", " --num_feat_anom_thresh=300" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LSTM Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile hyperparam_reconstruction_lstm.yaml\n", "trainingInput:\n", " scaleTier: STANDARD_1\n", " hyperparameters:\n", " hyperparameterMetricTag: rmse\n", " goal: MINIMIZE\n", " maxTrials: 30\n", " maxParallelTrials: 1\n", " params:\n", " - parameterName: enc_lstm_hidden_units\n", " type: CATEGORICAL\n", " categoricalValues: [\"64 32 16\", \"256 128 16\", \"64 64 64\"]\n", " - parameterName: dec_lstm_hidden_units\n", " type: CATEGORICAL\n", " categoricalValues: [\"16 32 64\", \"16 128 256\", \"64 64 64\"]\n", " - parameterName: lstm_dropout_output_keep_probs\n", " type: CATEGORICAL\n", " categoricalValues: [\"0.9 1.0 1.0\", \"0.95 0.95 1.0\", \"0.95 0.95 0.95\"]\n", " - parameterName: dnn_hidden_units\n", " type: CATEGORICAL\n", " categoricalValues: [\"256 128 64\", \"256 128 16\", \"64 64 64\"]\n", " - parameterName: train_batch_size\n", " type: INTEGER\n", " minValue: 8\n", " maxValue: 512\n", " scaleType: UNIT_LOG_SCALE\n", " - parameterName: learning_rate\n", " type: DOUBLE\n", " minValue: 0.001\n", " maxValue: 0.1\n", " scaleType: UNIT_LINEAR_SCALE" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/hyperparam_reconstruction_lstm_labeled\n", "JOBNAME=job_anomaly_detection_hyperparam_reconstruction_lstm_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gsutil -m rm -rf ${OUTDIR}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --config=hyperparam_reconstruction_lstm.yaml \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=gs://${BUCKET}/anomaly_detection/data/train_norm_seq.csv \\\n", " --eval_file_pattern=gs://${BUCKET}/anomaly_detection/data/val_norm_1_seq.csv \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=${OUTDIR} \\\n", " --seq_len=30 \\\n", " --num_feat=5 \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=0 \\\n", " --reconstruction_epochs=1.0 \\\n", " --train_examples=64000 \\\n", " --start_delay_secs=60 \\\n", " --throttle_secs=120 \\\n", " --training_mode=\"reconstruction\" \\\n", " --labeled_tune_thresh=True \\\n", " --num_time_anom_thresh=300 \\\n", " --num_feat_anom_thresh=300" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PCA Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%writefile hyperparam_reconstruction_pca.yaml\n", "trainingInput:\n", " scaleTier: STANDARD_1\n", " hyperparameters:\n", " hyperparameterMetricTag: rmse\n", " goal: MINIMIZE\n", " maxTrials: 30\n", " maxParallelTrials: 1\n", " params:\n", " - parameterName: k_principal_components_time\n", " type: INTEGER\n", " minValue: 2\n", " maxValue: 10\n", " scaleType: UNIT_LINEAR_SCALE\n", " - parameterName: k_principal_components_feat\n", " type: INTEGER\n", " minValue: 2\n", " maxValue: 10\n", " scaleType: UNIT_LINEAR_SCALE" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/hyperparam_reconstruction_pca_labeled\n", "JOBNAME=job_anomaly_detection_hyperparam_reconstruction_pca_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gsutil -m rm -rf ${OUTDIR}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --config=hyperparam_reconstruction_pca.yaml \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=gs://${BUCKET}/anomaly_detection/data/train_norm_seq.csv \\\n", " --eval_file_pattern=gs://${BUCKET}/anomaly_detection/data/val_norm_1_seq.csv \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=${OUTDIR} \\\n", " --seq_len=30 \\\n", " --num_feat=5 \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=0 \\\n", " --reconstruction_epochs=1.0 \\\n", " --train_examples=64000 \\\n", " --eval_examples=6400 \\\n", " --start_delay_secs=60 \\\n", " --throttle_secs=120 \\\n", " --training_mode=\"reconstruction\" \\\n", " --labeled_tune_thresh=True \\\n", " --num_time_anom_thresh=300 \\\n", " --num_feat_anom_thresh=300" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train error distribution variables" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import os environment variables for error dist training hyperparameters\n", "os.environ[\"TRAIN_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/val_norm_1_seq.csv\".format(BUCKET)\n", "os.environ[\"EVAL_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/val_norm_1_seq.csv\".format(BUCKET)\n", "os.environ[\"PREVIOUS_TRAIN_STEPS\"] = str(2000)\n", "os.environ[\"TRAIN_EXAMPLES\"] = str(6400)\n", "os.environ[\"TRAINING_MODE\"] = \"calculate_error_distribution_statistics\"\n", "os.environ[\"EPS\"] = \"1e-12\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dense Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/dense_labeled\n", "JOBNAME=job_anomaly_detection_calculate_error_distribution_statistics_dense_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n", " --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=./tmp \\\n", " --seq_len=${SEQ_LEN} \\\n", " --num_feat=${NUM_FEAT} \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n", " --train_examples=${TRAIN_EXAMPLES} \\\n", " --start_delay_secs=${START_DELAY_SECS} \\\n", " --throttle_secs=${THROTTLE_SECS} \\\n", " --model_type=\"dense_autoencoder\" \\\n", " --enc_dnn_hidden_units=${ENC_DNN_HIDDEN_UNITS} \\\n", " --latent_vector_size=${LATENT_VECTOR_SIZE} \\\n", " --dec_dnn_hidden_units=${DEC_DNN_HIDDEN_UNITS} \\\n", " --time_loss_weight=${TIME_LOSS_WEIGHT} \\\n", " --feat_loss_weight=${FEAT_LOSS_WEIGHT} \\\n", " --training_mode=${TRAINING_MODE} \\\n", " --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n", " --eps=${EPS} \\\n", " --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n", " --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LSTM Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/lstm_labeled\n", "JOBNAME=job_anomaly_detection_calculate_error_distribution_statistics_lstm_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n", " --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=./tmp \\\n", " --seq_len=${SEQ_LEN} \\\n", " --num_feat=${NUM_FEAT} \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n", " --train_examples=${TRAIN_EXAMPLES} \\\n", " --start_delay_secs=${START_DELAY_SECS} \\\n", " --throttle_secs=${THROTTLE_SECS} \\\n", " --model_type=\"lstm_enc_dec_autoencoder\" \\\n", " --reverse_labels_sequence=${REVERSE_LABELS_SEQUENCE} \\\n", " --enc_lstm_hidden_units=${ENC_LSTM_HIDDEN_UNITS} \\\n", " --dec_lstm_hidden_units=${DEC_LSTM_HIDDEN_UNITS} \\\n", " --lstm_dropout_output_keep_probs=${LSTM_DROPOUT_OUTPUT_KEEP_PROBS} \\\n", " --dnn_hidden_units=${DNN_HIDDEN_UNITS} \\\n", " --training_mode=${TRAINING_MODE} \\\n", " --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n", " --eps=${EPS} \\\n", " --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n", " --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PCA Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/pca_labeled\n", "JOBNAME=job_anomaly_detection_calculate_error_distribution_statistics_pca_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n", " --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=./tmp \\\n", " --seq_len=${SEQ_LEN} \\\n", " --num_feat=${NUM_FEAT} \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=2200 \\\n", " --train_examples=${TRAIN_EXAMPLES} \\\n", " --start_delay_secs=${START_DELAY_SECS} \\\n", " --throttle_secs=${THROTTLE_SECS} \\\n", " --model_type=\"pca\" \\\n", " --training_mode=${TRAINING_MODE} \\\n", " --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n", " --eps=${EPS} \\\n", " --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n", " --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tune anomaly thresholds" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import os environment variables for tune threshold training hyperparameters\n", "os.environ[\"PREVIOUS_TRAIN_STEPS\"] = str(2200)\n", "os.environ[\"TRAIN_EXAMPLES\"] = str(12800)\n", "os.environ[\"TRAINING_MODE\"] = \"tune_anomaly_thresholds\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Labeled" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Import os environment variables for labeled tune threshold training hyperparameters\n", "os.environ[\"TRAIN_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/labeled_val_mixed_seq.csv\".format(BUCKET)\n", "os.environ[\"EVAL_FILE_PATTERN\"] = \"gs://{}/anomaly_detection/data/labeled_val_mixed_seq.csv\".format(BUCKET)\n", "os.environ[\"MIN_TIME_ANOM_THRESH\"] = str(1.0)\n", "os.environ[\"MAX_TIME_ANOM_THRESH\"] = str(20.0)\n", "os.environ[\"MIN_FEAT_ANOM_THRESH\"] = str(20.0)\n", "os.environ[\"MAX_FEAT_ANOM_THRESH\"] = str(80.0)\n", "os.environ[\"F_SCORE_BETA\"] = str(0.05)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dense Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/dense_labeled\n", "JOBNAME=job_anomaly_detection_tune_anomaly_thresholds_dense_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n", " --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=./tmp \\\n", " --seq_len=${SEQ_LEN} \\\n", " --num_feat=${NUM_FEAT} \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n", " --train_examples=${TRAIN_EXAMPLES} \\\n", " --start_delay_secs=${START_DELAY_SECS} \\\n", " --throttle_secs=${THROTTLE_SECS} \\\n", " --model_type=\"dense_autoencoder\" \\\n", " --enc_dnn_hidden_units=${ENC_DNN_HIDDEN_UNITS} \\\n", " --latent_vector_size=${LATENT_VECTOR_SIZE} \\\n", " --dec_dnn_hidden_units=${DEC_DNN_HIDDEN_UNITS} \\\n", " --time_loss_weight=${TIME_LOSS_WEIGHT} \\\n", " --feat_loss_weight=${FEAT_LOSS_WEIGHT} \\\n", " --training_mode=${TRAINING_MODE} \\\n", " --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n", " --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n", " --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH} \\\n", " --min_time_anom_thresh=${MIN_TIME_ANOM_THRESH} \\\n", " --max_time_anom_thresh=${MAX_TIME_ANOM_THRESH} \\\n", " --min_feat_anom_thresh=${MIN_FEAT_ANOM_THRESH} \\\n", " --max_feat_anom_thresh=${MAX_FEAT_ANOM_THRESH} \\\n", " --f_score_beta=${F_SCORE_BETA}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LSTM Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/lstm_labeled\n", "JOBNAME=job_anomaly_detection_tune_anomaly_thresholds_lstm_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n", " --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=./tmp \\\n", " --seq_len=${SEQ_LEN} \\\n", " --num_feat=${NUM_FEAT} \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=${PREVIOUS_TRAIN_STEPS} \\\n", " --train_examples=${TRAIN_EXAMPLES} \\\n", " --start_delay_secs=${START_DELAY_SECS} \\\n", " --throttle_secs=${THROTTLE_SECS} \\\n", " --model_type=\"lstm_enc_dec_autoencoder\" \\\n", " --reverse_labels_sequence=${REVERSE_LABELS_SEQUENCE} \\\n", " --enc_lstm_hidden_units=${ENC_LSTM_HIDDEN_UNITS} \\\n", " --dec_lstm_hidden_units=${DEC_LSTM_HIDDEN_UNITS} \\\n", " --lstm_dropout_output_keep_probs=${LSTM_DROPOUT_OUTPUT_KEEP_PROBS} \\\n", " --dnn_hidden_units=${DNN_HIDDEN_UNITS} \\\n", " --training_mode=${TRAINING_MODE} \\\n", " --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n", " --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n", " --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH} \\\n", " --min_time_anom_thresh=${MIN_TIME_ANOM_THRESH} \\\n", " --max_time_anom_thresh=${MAX_TIME_ANOM_THRESH} \\\n", " --min_feat_anom_thresh=${MIN_FEAT_ANOM_THRESH} \\\n", " --max_feat_anom_thresh=${MAX_FEAT_ANOM_THRESH} \\\n", " --f_score_beta=${F_SCORE_BETA}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PCA Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "OUTDIR=gs://${BUCKET}/anomaly_detection/trained_model/pca_labeled\n", "JOBNAME=job_anomaly_detection_tune_anomaly_thresholds_pca_labeled_$(date -u +%y%m%d_%H%M%S)\n", "echo ${OUTDIR} ${REGION} ${JOBNAME}\n", "gcloud ml-engine jobs submit training ${JOBNAME} \\\n", " --region=${REGION} \\\n", " --module-name=trainer.task \\\n", " --package-path=$PWD/anomaly_detection_module/trainer \\\n", " --job-dir=${OUTDIR} \\\n", " --staging-bucket=gs://${BUCKET} \\\n", " --scale-tier=STANDARD_1 \\\n", " --runtime-version=${TFVERSION} \\\n", " -- \\\n", " --train_file_pattern=${TRAIN_FILE_PATTERN} \\\n", " --eval_file_pattern=${EVAL_FILE_PATTERN} \\\n", " --output_dir=${OUTDIR} \\\n", " --job-dir=./tmp \\\n", " --seq_len=${SEQ_LEN} \\\n", " --num_feat=${NUM_FEAT} \\\n", " --feat_names=${FEAT_NAMES} \\\n", " --feat_defaults=${FEAT_DEFAULTS} \\\n", " --train_batch_size=32 \\\n", " --eval_batch_size=32 \\\n", " --previous_train_steps=2400 \\\n", " --train_examples=${TRAIN_EXAMPLES} \\\n", " --start_delay_secs=${START_DELAY_SECS} \\\n", " --throttle_secs=${THROTTLE_SECS} \\\n", " --model_type=\"pca\" \\\n", " --training_mode=${TRAINING_MODE} \\\n", " --labeled_tune_thresh=${LABELED_TUNE_THRESH} \\\n", " --num_time_anom_thresh=${NUM_TIME_ANOM_THRESH} \\\n", " --num_feat_anom_thresh=${NUM_FEAT_ANOM_THRESH} \\\n", " --min_time_anom_thresh=${MIN_TIME_ANOM_THRESH} \\\n", " --max_time_anom_thresh=${MAX_TIME_ANOM_THRESH} \\\n", " --min_feat_anom_thresh=${MIN_FEAT_ANOM_THRESH} \\\n", " --max_feat_anom_thresh=${MAX_FEAT_ANOM_THRESH} \\\n", " --f_score_beta=${F_SCORE_BETA}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Deploy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dense Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "MODEL_NAME=\"anomaly_detection_dense_labeled\"\n", "MODEL_VERSION=\"v1\"\n", "MODEL_LOCATION=$(gsutil ls gs://${BUCKET}/anomaly_detection/trained_model/dense_labeled/export/exporter/ | tail -1)\n", "echo \"Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes\"\n", "#gcloud ml-engine versions delete ${MODEL_VERSION} --model ${MODEL_NAME}\n", "#gcloud ml-engine models delete ${MODEL_NAME}\n", "gcloud ml-engine models create ${MODEL_NAME} --regions ${REGION}\n", "gcloud ml-engine versions create ${MODEL_VERSION} --model ${MODEL_NAME} --origin ${MODEL_LOCATION} --runtime-version ${TFVERSION}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LSTM Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "MODEL_NAME=\"anomaly_detection_lstm_labeled\"\n", "MODEL_VERSION=\"v1\"\n", "MODEL_LOCATION=$(gsutil ls gs://${BUCKET}/anomaly_detection/trained_model/lstm_labeled/export/exporter/ | tail -1)\n", "echo \"Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes\"\n", "#gcloud ml-engine versions delete ${MODEL_VERSION} --model ${MODEL_NAME}\n", "#gcloud ml-engine models delete ${MODEL_NAME}\n", "gcloud ml-engine models create ${MODEL_NAME} --regions ${REGION}\n", "gcloud ml-engine versions create ${MODEL_VERSION} --model ${MODEL_NAME} --origin ${MODEL_LOCATION} --runtime-version ${TFVERSION}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PCA Autoencoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash\n", "MODEL_NAME=\"anomaly_detection_pca_labeled\"\n", "MODEL_VERSION=\"v1\"\n", "MODEL_LOCATION=$(gsutil ls gs://${BUCKET}/anomaly_detection/trained_model/pca_labeled/export/exporter/ | tail -1)\n", "echo \"Deleting and deploying $MODEL_NAME $MODEL_VERSION from $MODEL_LOCATION ... this will take a few minutes\"\n", "#gcloud ml-engine versions delete ${MODEL_VERSION} --model ${MODEL_NAME}\n", "#gcloud ml-engine models delete ${MODEL_NAME}\n", "gcloud ml-engine models create ${MODEL_NAME} --regions ${REGION}\n", "gcloud ml-engine versions create ${MODEL_VERSION} --model ${MODEL_NAME} --origin ${MODEL_LOCATION} --runtime-version ${TFVERSION}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prediction" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "UNLABELED_CSV_COLUMNS = [\"tag_{0}\".format(tag) for tag in range(0, 5)]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "labeled_test_mixed_sequences_array.shape = (12800, 6)\n" ] } ], "source": [ "import numpy as np\n", "labeled_test_mixed_sequences_array = np.loadtxt(\n", " fname=\"data/labeled_test_mixed_seq.csv\", dtype=str, delimiter=\",\")\n", "print(\"labeled_test_mixed_sequences_array.shape = {}\".format(\n", " labeled_test_mixed_sequences_array.shape))" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "labels = ['0' '0' '0' '0' '0' '0' '1' '1' '1' '0']\n" ] } ], "source": [ "number_of_prediction_instances = 10\n", "print(\"labels = {}\".format(\n", " labeled_test_mixed_sequences_array[0:number_of_prediction_instances, -1]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### GCloud ML-Engine prediction from deployed model" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "test_data_normal_string_list = labeled_test_mixed_sequences_array.tolist()[0:number_of_prediction_instances]" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "# Format dataframe to instances list to get sent to ML-Engine\n", "instances = [{UNLABELED_CSV_COLUMNS[i]: example[i]\n", " for i in range(len(UNLABELED_CSV_COLUMNS))} \n", " for example in labeled_test_mixed_sequences_array.tolist()[0:number_of_prediction_instances]]" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'tag_0': '0.66491856;1.9145195;1.75234653;-0.78400576;-0.75609776;0.84197907;2.33069302;0.67758695;-1.60418555;-0.16573821;1.65752209;1.38322258;-0.65379731;-0.98907577;0.85974301;2.28947191;1.30827902;-1.28121539;-0.84517686;1.16128253;2.54710653;-0.07715313;-0.99222116;-0.14657169;2.05595855;1.51383899;-0.53623582;-0.75514892;0.48173323;2.3100255',\n", " 'tag_1': '0.14611996;1.91715205;-0.37882894;0.21415584;2.12791802;-0.0619942;0.11572278;1.93012199;0.6541758;-0.68204207;1.56716207;0.75419849;-0.37778253;1.57716806;1.37025903;-1.14077495;1.15195495;1.68996712;-0.81952461;0.85177925;1.21750258;-0.15392611;-0.18813038;2.1498899;0.22421678;-0.30835851;1.58292183;-0.08034281;-0.51606627;2.0111497',\n", " 'tag_2': 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0.28628336, 0.52470708, 0.28861945, 0.74619109], [2.71393575, 1.53879333, 2.11374314, 1.45708077, 0.94730833]], 'mahalanobis_dist_time': [2.2050724201987797, 2.9204900130095925, 2.513823468247955, 2.27561362283683, 2.124023883200109, 1.2007023403848198, 2.8095994441924215, 2.710059230293343, 2.5792491042063816, 2.221194893902009, 2.5437116931864043, 3.1686912162241563, 2.281518245321126, 1.3691721076761314, 1.176027170351581, 2.614674353294886, 2.4945845864763716, 2.0207512205964266, 1.6791820975964626, 1.5007801946504218, 2.6894439061828606, 2.0870922428940006, 2.923944388847387, 2.1997790802470316, 3.2293004012523654, 2.142699392910762, 3.153213891977101, 1.4702002371930953, 1.6016431453509776, 2.7760841331241615], 'mahalanobis_dist_feat': [6.0770774907370875, 5.261981717892125, 5.360154030511367, 6.31381127807184, 6.469930908034346], 'feat_anom_flags': 0, 'X_feat_abs_recon_err': [[0.04002373, 0.77316403, 0.5064642, 0.00495428, 0.09601987], [2.77047149, 1.78993695, 1.73569998, 1.73058158, 1.79091122], [1.18899767, 0.00996767, 1.15883707, 0.32864859, 1.51241607], [0.60047226, 0.06333012, 0.32626716, 0.12331526, 0.15140355], [1.33342528, 1.74399522, 0.93921306, 1.38895531, 0.22029392], [0.9332609, 0.40204323, 0.61178983, 0.80194698, 0.35311483], [1.79713745, 0.3841756, 1.45841529, 0.70576781, 2.01294182], [0.12207074, 2.08135886, 1.66214296, 0.93858581, 0.74675243], [1.54002434, 0.05024028, 0.94871562, 2.34687482, 0.91219142], [0.12563447, 0.53364949, 0.40083787, 0.0124869, 0.11479409], [1.65582437, 2.07842082, 1.33746892, 0.42339452, 1.471586], [2.16662805, 0.61253916, 0.17665792, 2.1535986, 2.10590009], [0.27858593, 0.539925, 1.81168659, 0.88734299, 0.79785938], [1.33363261, 0.96458172, 1.70129509, 0.80289269, 0.51961445], [0.63004838, 0.93248708, 1.07749217, 0.40426268, 0.47316746], [2.60991971, 0.46253491, 0.68119423, 2.2888359, 0.80397399], [1.54024527, 1.1020334, 0.23273985, 0.22907384, 1.70220473], [0.56917252, 1.59779402, 0.39928992, 0.26897599, 0.79817527], [0.72655791, 0.86248362, 1.4249041, 1.54588075, 0.8511636], [1.15147733, 0.97942725, 1.33871969, 1.350909, 0.14538853], [2.50307969, 1.28409853, 0.37569222, 0.59548962, 1.10266323], [0.34091677, 0.41827883, 0.40994081, 0.19248556, 1.11418798], [1.78544364, 0.21428836, 0.21717019, 2.09446286, 0.05283848], [0.0469913, 1.3041642, 1.18499547, 0.0599024, 0.14234928], [2.3052051, 0.13853662, 2.23490559, 0.86764715, 0.33590364], [1.82387952, 0.37642735, 0.54602336, 1.94053306, 1.48447235], [0.12610546, 1.32348276, 0.17562612, 1.24092046, 1.6688788], [1.5179853, 0.03971839, 0.69806029, 1.21382899, 0.64356563], [1.08815115, 0.28628336, 0.52470708, 0.28861945, 0.74619109], [2.71393575, 1.53879333, 2.11374314, 1.45708077, 0.94730833]]}]}\n" ] } ], "source": [ "# Send instance dictionary to receive response from ML-Engine for online prediction\n", "from googleapiclient import discovery\n", "from oauth2client.client import GoogleCredentials\n", "import json\n", "\n", "credentials = GoogleCredentials.get_application_default()\n", "api = discovery.build(\"ml\", \"v1\", credentials = credentials)\n", "\n", "request_data = {\"instances\": instances}\n", "\n", "parent = \"projects/%s/models/%s/versions/%s\" % (PROJECT, \"anomaly_detection_dense_labeled\", \"v1\")\n", "response = api.projects().predict(body = request_data, name = parent).execute()\n", "print(\"response = {}\".format(response))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### LSTM Autoencoder" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "response = {'predictions': [{'time_anom_flags': 0, 'X_time_abs_recon_err': [[0.41491645314022535, 0.17814782047161393, 0.6791140559628012, 0.1871596458471131, 0.3532771634912568], [1.6645173931402253, 1.5928842695283862, 1.167116945962801, 1.262366685847113, 1.5737184865087435], [1.5023444231402254, 0.7030967204716139, 1.817456125962801, 0.5426792441528869, 0.5259311365087432], [1.0340078668597747, 0.11011194047161393, 0.4129033259628012, 0.517487364152887, 0.6041211734912568], [1.0060998668597747, 1.8036502395283862, 0.7120102940371988, 1.7537373558471132, 1.0682441434912568], [0.5919769631402254, 0.3862619804716139, 0.5945054840371988, 0.6186543458471131, 0.026214876508743212], [2.0806909131402254, 0.20854500047161392, 1.038939365962801, 1.263949314152887, 1.002613446508743], [0.4275848431402253, 1.605854209528386, 1.320479535962801, 0.20318665415288695, 0.7108267765087432], [1.8541876568597746, 0.32990801952838605, 1.365231725962801, 1.1130811458471133, 1.0890072034912568], [0.4157403168597747, 1.006309850471614, 0.7604224640371988, 0.5687900241528869, 0.6286095634912567], [1.4075199831402254, 1.242894289528386, 0.9446625540371988, 1.117074724152887, 0.6640726265087432], [1.1332204731402253, 0.42993070952838613, 0.47494078403719886, 1.0429314658471132, 1.6175212465087432], [0.9037994168597747, 0.7020503104716139, 1.4208801459628013, 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[1.7679874068597747, 0.3639861704716139, 0.9547695040371988, 1.581637614152887, 1.0881949034912568], [0.8381490431402254, 0.610551140471614, 0.2679978659628012, 0.07918917415288695, 1.1908203634912566], [2.4639336431402254, 1.2145255495283862, 1.857033925962801, 1.0892721458471128, 0.5026790565087432]]}]}\n" ] } ], "source": [ "# Send instance dictionary to receive response from ML-Engine for online prediction\n", "from googleapiclient import discovery\n", "from oauth2client.client import GoogleCredentials\n", "import json\n", "\n", "credentials = GoogleCredentials.get_application_default()\n", "api = discovery.build(\"ml\", \"v1\", credentials = credentials)\n", "\n", "request_data = {\"instances\": instances}\n", "\n", "parent = \"projects/%s/models/%s/versions/%s\" % (PROJECT, \"anomaly_detection_lstm_labeled\", \"v1\")\n", "response = api.projects().predict(body = request_data, name = parent).execute()\n", "print(\"response = {}\".format(response))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### PCA Autoencoder" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "response = {'predictions': [{'time_anom_flags': 0, 'X_time_abs_recon_err': [[0.1728792878886382, 0.38050960188316113, 0.4029056164752808, 0.014843864134493515, 0.37395442642699095], [0.4715622077460193, 1.347448091416575, 0.785070509241718, 0.8864118912527625, 0.5991388918543972], [0.07542798900316616, 0.9324050785330078, 1.4750373173605118, 0.842387971324521, 0.09149046337009026], [0.03565646553488011, 0.28264294850716104, 0.20999200847594207, 0.548774838816692, 0.03581776391927072], [0.04857788467463031, 1.6307784060092017, 0.9157590530057195, 1.7208385907098551, 0.43585294321592793], [0.08302349375710694, 0.594693984257074, 0.8856290524187558, 0.4176409110762759, 0.12889025356158673], [0.09530165447389516, 0.45055417216711735, 0.665313418520593, 1.623702553827909, 0.10392648557085626], 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prediction\n", "from googleapiclient import discovery\n", "from oauth2client.client import GoogleCredentials\n", "import json\n", "\n", "credentials = GoogleCredentials.get_application_default()\n", "api = discovery.build(\"ml\", \"v1\", credentials = credentials)\n", "\n", "request_data = {\"instances\": instances}\n", "\n", "parent = \"projects/%s/models/%s/versions/%s\" % (PROJECT, \"anomaly_detection_pca_labeled\", \"v1\")\n", "response = api.projects().predict(body = request_data, name = parent).execute()\n", "print(\"response = {}\".format(response))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
rastala/mmlspark
notebooks/samples/102 - Regression Example with Flight Delay Dataset.ipynb
1
5143
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 102 - Training Regression Algorithms with the L-BFGS Solver\n", "\n", "In this example, we run a linear regression on the *Flight Delay* dataset to predict the delay times.\n", "\n", "We demonstrate how to use the `TrainRegressor` and the `ComputePerInstanceStatistics` APIs.\n", "\n", "First, import the packages." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import mmlspark" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, import the CSV dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# load raw data from small-sized 30 MB CSV file (trimmed to contain just what we use)\n", "dataFile = \"On_Time_Performance_2012_9.csv\"\n", "import os, urllib\n", "if not os.path.isfile(dataFile):\n", " urllib.request.urlretrieve(\"https://mmlspark.azureedge.net/datasets/\"+dataFile, dataFile)\n", "flightDelay = spark.createDataFrame(\n", " pd.read_csv(dataFile, dtype={\"Month\": np.float64, \"Quarter\": np.float64,\n", " \"DayofMonth\": np.float64, \"DayOfWeek\": np.float64,\n", " \"OriginAirportID\": np.float64, \"DestAirportID\": np.float64,\n", " \"CRSDepTime\": np.float64, \"CRSArrTime\": np.float64}))\n", "# Print information on the dataset we loaded\n", "print(\"records read: \" + str(flightDelay.count()))\n", "print(\"Schema:\")\n", "flightDelay.printSchema()\n", "flightDelay.limit(10).toPandas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split the dataset into train and test sets." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "train,test = flightDelay.randomSplit([0.75, 0.25])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train a regressor on dataset with `l-bfgs`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from mmlspark import TrainRegressor, TrainedRegressorModel\n", "from pyspark.ml.regression import LinearRegression\n", "from pyspark.ml.feature import StringIndexer\n", "# Convert columns to categorical\n", "catCols = [\"Carrier\", \"DepTimeBlk\", \"ArrTimeBlk\"]\n", "trainCat = train\n", "testCat = test\n", "for catCol in catCols:\n", " simodel = StringIndexer(inputCol=catCol, outputCol=catCol + \"Tmp\").fit(train)\n", " trainCat = simodel.transform(trainCat).drop(catCol).withColumnRenamed(catCol + \"Tmp\", catCol)\n", " testCat = simodel.transform(testCat).drop(catCol).withColumnRenamed(catCol + \"Tmp\", catCol)\n", "lr = LinearRegression().setSolver(\"l-bfgs\").setRegParam(0.1).setElasticNetParam(0.3)\n", "model = TrainRegressor(model=lr, labelCol=\"ArrDelay\").fit(trainCat)\n", "model.write().overwrite().save(\"flightDelayModel.mml\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Score the regressor on the test data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "flightDelayModel = TrainedRegressorModel.load(\"flightDelayModel.mml\")\n", "scoredData = flightDelayModel.transform(testCat)\n", "scoredData.limit(10).toPandas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compute model metrics against the entire scored dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from mmlspark import ComputeModelStatistics\n", "metrics = ComputeModelStatistics().transform(scoredData)\n", "metrics.toPandas()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, compute and show per-instance statistics, demonstrating the usage\n", "of `ComputePerInstanceStatistics`." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from mmlspark import ComputePerInstanceStatistics\n", "evalPerInstance = ComputePerInstanceStatistics().transform(scoredData)\n", "evalPerInstance.select(\"ArrDelay\", \"Scores\", \"L1_loss\", \"L2_loss\").limit(10).toPandas()" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [default]", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
rajathkumarmp/BinPy
BinPy/examples/notebook/combinational/Encoder.ipynb
4
3420
{ "metadata": { "name": "", "signature": "sha256:867145e392b0fef82fe54fd7c733bff686f3f64a743b867ae87fb17b507b6171" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Example for Encoder class" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from __future__ import print_function\n", "from BinPy.combinational.combinational import *" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "# Initializing the Encoder class\n", "\n", "# Exacly 1 input must be 1\n", "\n", "encoder = Encoder(0, 1)\n", "\n", "# Output of encoder\n", "\n", "print (encoder.output())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[1]\n" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# Changing the number of inputs\n", "\n", "# No need to set the number, just change the inputs\n", "# Input must be power of 2\n", "#Inputs must be power of 2\n", "\n", "encoder.set_inputs(0, 0, 0, 1)\n", "\n", "# To get the input states\n", "\n", "print (encoder.get_input_states())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0, 0, 0, 1]\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "# New output of encoder\n", "\n", "print (encoder.output())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[1, 1]\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Using Connectors as the input lines\n", "# Take a Connector\n", "\n", "conn = Connector()\n", "\n", "# Set Output of decoder to Connector conn\n", "\n", "encoder.set_output(1, conn)\n", "\n", "# Put this connector as the input to gate1\n", "\n", "gate1 = AND(conn, 1)\n", "\n", "# Output of the gate1\n", "\n", "print (gate1.output())" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "1\n" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "# Information about encoder instance can be found by\n", "\n", "print (encoder)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Encoder Gate; Output: [1, 1]; Inputs: [0, 0, 0, 1];\n" ] } ], "prompt_number": 6 } ], "metadata": {} } ] }
bsd-3-clause
swucim/iris_tableau_python
iris_classiffier2.ipynb
1
19612
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/zhangjunwu/anaconda/envs/Tableau-Python-Server/lib/python2.7/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n", " \"This module will be removed in 0.20.\", DeprecationWarning)\n", "/Users/zhangjunwu/anaconda/envs/Tableau-Python-Server/lib/python2.7/site-packages/sklearn/grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n", " DeprecationWarning)\n" ] } ], "source": [ "import math\n", "import numpy as np\n", "import pandas as pd\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.linear_model import LogisticRegressionCV\n", "from sklearn.naive_bayes import GaussianNB\n", "from sklearn.cross_validation import cross_val_score, cross_val_predict, StratifiedKFold \n", "from sklearn import preprocessing, metrics, svm, ensemble\n", "from sklearn.metrics import accuracy_score, classification_report\n", "import tabpy_client " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sepal_length</th>\n", " <th>sepal_width</th>\n", " <th>petal_length</th>\n", " <th>petal_width</th>\n", " <th>Class</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5.4</td>\n", " <td>3.9</td>\n", " <td>1.7</td>\n", " <td>0.4</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>4.6</td>\n", " <td>3.4</td>\n", " <td>1.4</td>\n", " <td>0.3</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>5.0</td>\n", " <td>3.4</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>4.4</td>\n", " <td>2.9</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>setosa</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>4.9</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.1</td>\n", " <td>setosa</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width Class\n", "0 5.1 3.5 1.4 0.2 setosa\n", "1 4.9 3.0 1.4 0.2 setosa\n", "2 4.7 3.2 1.3 0.2 setosa\n", "3 4.6 3.1 1.5 0.2 setosa\n", "4 5.0 3.6 1.4 0.2 setosa\n", "5 5.4 3.9 1.7 0.4 setosa\n", "6 4.6 3.4 1.4 0.3 setosa\n", "7 5.0 3.4 1.5 0.2 setosa\n", "8 4.4 2.9 1.4 0.2 setosa\n", "9 4.9 3.1 1.5 0.1 setosa" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Iris dataset: http://aima.cs.berkeley.edu/data/iris.csv\n", "df = pd.read_csv('./iris2.csv', header=0)\n", "# Take a look at the structure of the file\n", "df.head(n=10)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>sepal_length</th>\n", " <th>sepal_width</th>\n", " <th>petal_length</th>\n", " <th>petal_width</th>\n", " <th>Class</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>5.4</td>\n", " <td>3.9</td>\n", " <td>1.7</td>\n", " <td>0.4</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>4.6</td>\n", " <td>3.4</td>\n", " <td>1.4</td>\n", " <td>0.3</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>5.0</td>\n", " <td>3.4</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>4.4</td>\n", " <td>2.9</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>4.9</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.1</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width petal_length petal_width Class\n", "0 5.1 3.5 1.4 0.2 0\n", "1 4.9 3.0 1.4 0.2 0\n", "2 4.7 3.2 1.3 0.2 0\n", "3 4.6 3.1 1.5 0.2 0\n", "4 5.0 3.6 1.4 0.2 0\n", "5 5.4 3.9 1.7 0.4 0\n", "6 4.6 3.4 1.4 0.3 0\n", "7 5.0 3.4 1.5 0.2 0\n", "8 4.4 2.9 1.4 0.2 0\n", "9 4.9 3.1 1.5 0.1 0" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use LabelEncoder to convert textual classifications to numeric. \n", "# We will use the same encoder later to convert them back.\n", "encoder = preprocessing.LabelEncoder()\n", "df['Class'] = encoder.fit_transform(df['Class'])\n", "\n", "# Check the result of the transform\n", "df.head(n=10)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "pass\n" ] } ], "source": [ "# Split columns into independent/predictor variables vs dependent/response/outcome variable\n", "X = np.array(df.drop(['Class'], 1))\n", "y = np.array(df['Class'])\n", "\n", "# Scale the data. We will use the same scaler later for scoring function\n", "scaler = preprocessing.StandardScaler().fit(X)\n", "X = scaler.transform(X)\n", "\n", "# 10 fold stratified cross validation\n", "kf = StratifiedKFold(y, n_folds=10, random_state=None, shuffle=True)\n", "\n", "# Define the parameter grid to use for tuning the Support Vector Machine\n", "parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],\n", " 'C': [1, 10, 100, 1000]},\n", " {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]\n", "\n", "# Pick accuracy as the goal you're optimizing\n", "scoringmethods = ['accuracy']\n", "\n", "print 'pass'" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "~~~ Hyper-parameter tuning for best accuracy ~~~\n", "Scores for different parameter combinations in the grid:\n", " 0.853 (+/-0.036) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.001}\n", " 0.853 (+/-0.036) for {'kernel': 'rbf', 'C': 1, 'gamma': 0.0001}\n", " 0.900 (+/-0.031) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.001}\n", " 0.853 (+/-0.036) for {'kernel': 'rbf', 'C': 10, 'gamma': 0.0001}\n", " 0.967 (+/-0.017) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.001}\n", " 0.900 (+/-0.031) for {'kernel': 'rbf', 'C': 100, 'gamma': 0.0001}\n", " 0.967 (+/-0.022) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.001}\n", " 0.967 (+/-0.017) for {'kernel': 'rbf', 'C': 1000, 'gamma': 0.0001}\n", " 0.973 (+/-0.022) for {'kernel': 'linear', 'C': 1}\n", " 0.967 (+/-0.022) for {'kernel': 'linear', 'C': 10}\n", " 0.973 (+/-0.022) for {'kernel': 'linear', 'C': 100}\n", " 0.980 (+/-0.021) for {'kernel': 'linear', 'C': 1000}\n", "\n", "Classification report:\n", " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 50\n", " 1 0.98 0.98 0.98 50\n", " 2 0.98 0.98 0.98 50\n", "\n", "avg / total 0.99 0.99 0.99 150\n", "\n", "Best model:\n", "SVC(C=1000, cache_size=200, class_weight=None, coef0=0.0,\n", " decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n", " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", " tol=0.001, verbose=False)\n", "Accuracy: 0.987\n", "\n" ] } ], "source": [ "# Iterate through different metrics looking for best parameter set\n", "for score in scoringmethods:\n", " print(\"~~~ Hyper-parameter tuning for best %s ~~~\" % score)\n", " \n", " # Setup for grid search with cross-validation for Support Vector Machine\n", " # n_jobs=-1 for parallel execution using all available cores\n", " svmclf = GridSearchCV(svm.SVC(C=1), parameters, cv=kf, scoring=score,n_jobs=1)\n", " svmclf.fit(X, y)\n", " \n", " # Show each result from grid search\n", " print(\"Scores for different parameter combinations in the grid:\")\n", " for params, mean_score, scores in svmclf.grid_scores_:\n", " print(\" %0.3f (+/-%0.03f) for %r\"\n", " % (mean_score, scores.std() / 2, params)) \n", " print(\"\")\n", "# Show classification report for the best model (set of parameters) run over the full dataset\n", "print(\"Classification report:\")\n", "y_pred = svmclf.predict(X)\n", "print(classification_report(y, y_pred))\n", " \n", "# Show the definition of the best model\n", "print(\"Best model:\")\n", "print(svmclf.best_estimator_)\n", " \n", "# Show accuracy \n", "print(\"Accuracy: %0.3f\" % accuracy_score(y, y_pred, normalize=True))\n", "print(\"\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classification report:\n", " precision recall f1-score support\n", "\n", " 0 0.79 0.96 0.86 50\n", " 1 0.85 0.70 0.77 50\n", " 2 0.96 0.92 0.94 50\n", "\n", "avg / total 0.87 0.86 0.86 150\n", "\n", "Accuracy: 0.860\n" ] } ], "source": [ "# Logistic regression with 10 fold stratified cross-validation using model specific cross-validation in scikit-learn\n", "lgclf = LogisticRegressionCV(Cs=list(np.power(10.0, np.arange(-10, 10))),penalty='l2',scoring='roc_auc',cv=kf)\n", "lgclf.fit(X, y)\n", "y_pred = lgclf.predict(X)\n", "\n", "# Show classification report for the best model (set of parameters) run over the full dataset\n", "print(\"Classification report:\")\n", "print(classification_report(y, y_pred))\n", "\n", "# Show accuracy\n", "print(\"Accuracy: %0.3f\" % accuracy_score(y, y_pred, normalize=True))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy: 0.947\n" ] } ], "source": [ "# Naive Bayes with 10 fold stratified cross-validation\n", "nbclf = GaussianNB()\n", "scores = cross_val_score(nbclf, X, y, cv=kf, scoring='accuracy')\n", "\n", "# Show accuracy statistics for cross-validation\n", "print(\"Accuracy: %0.3f\" % (scores.mean()))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "connect success\n" ] } ], "source": [ "# Connect to TabPy server using the client library\n", "connection = tabpy_client.Client('http://localhost:9004/')\n", "print 'connect success'" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "define success\n" ] } ], "source": [ "# The scoring function that will use the SVM Classifier to classify new data points\n", "def iris_classiffier2(sepal_length,sepal_width,petal_length,petal_width):\n", " X = np.column_stack([sepal_length,sepal_width,petal_length,petal_width])\n", " X = scaler.transform(X)\n", " return encoder.inverse_transform(svmclf.predict(X)).tolist()\n", "print 'define success'" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "deploy success!\n" ] } ], "source": [ "# Publish the function to TabPy server so it can be used from Tableau\n", "# Using the name Iris_Classiffier2 and a short description of what it does\n", "connection.deploy('Iris_Classiffier2',\n", " iris_classiffier2,\n", " 'Returns Iris dataset prediction',override=True)\n", "print 'deploy success!'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "tableau", "language": "python", "name": "tableau" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" }, "toc": { "colors": { "hover_highlight": "#DAA520", "navigate_num": "#000000", "navigate_text": "#333333", "running_highlight": "#FF0000", "selected_highlight": "#FFD700", "sidebar_border": "#EEEEEE", "wrapper_background": "#FFFFFF" }, "moveMenuLeft": true, "nav_menu": { "height": "12px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 1 }
mit
openradar/AMS-Short-Course-on-Open-Source-Radar-Software
5_Simple_proccessing_and_adding_a_field.ipynb
3
1148405
null
bsd-2-clause
herruzojm/udacity-deep-learning
dcgan-svhn/DCGAN_Exercises.ipynb
1
137179
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Deep Convolutional GANs\n", "\n", "In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a Deep Convolutional GAN, or DCGAN for short. The DCGAN architecture was first explored last year and has seen impressive results in generating new images, you can read the [original paper here](https://arxiv.org/pdf/1511.06434.pdf).\n", "\n", "You'll be training DCGAN on the [Street View House Numbers](http://ufldl.stanford.edu/housenumbers/) (SVHN) dataset. These are color images of house numbers collected from Google street view. SVHN images are in color and much more variable than MNIST. \n", "\n", "![SVHN Examples](assets/SVHN_examples.png)\n", "\n", "So, we'll need a deeper and more powerful network. This is accomplished through using convolutional layers in the discriminator and generator. It's also necessary to use batch normalization to get the convolutional networks to train. The only real changes compared to what [you saw previously](https://github.com/udacity/deep-learning/tree/master/gan_mnist) are in the generator and discriminator, otherwise the rest of the implementation is the same." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pickle as pkl\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy.io import loadmat\n", "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mkdir: cannot create directory ‘data’: File exists\r\n" ] } ], "source": [ "!mkdir data" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Getting the data\n", "\n", "Here you can download the SVHN dataset. Run the cell above and it'll download to your machine." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from urllib.request import urlretrieve\n", "from os.path import isfile, isdir\n", "from tqdm import tqdm\n", "\n", "data_dir = 'data/'\n", "\n", "if not isdir(data_dir):\n", " raise Exception(\"Data directory doesn't exist!\")\n", "\n", "class DLProgress(tqdm):\n", " last_block = 0\n", "\n", " def hook(self, block_num=1, block_size=1, total_size=None):\n", " self.total = total_size\n", " self.update((block_num - self.last_block) * block_size)\n", " self.last_block = block_num\n", "\n", "if not isfile(data_dir + \"train_32x32.mat\"):\n", " with DLProgress(unit='B', unit_scale=True, miniters=1, desc='SVHN Training Set') as pbar:\n", " urlretrieve(\n", " 'http://ufldl.stanford.edu/housenumbers/train_32x32.mat',\n", " data_dir + 'train_32x32.mat',\n", " pbar.hook)\n", "\n", "if not isfile(data_dir + \"test_32x32.mat\"):\n", " with DLProgress(unit='B', unit_scale=True, miniters=1, desc='SVHN Testing Set') as pbar:\n", " urlretrieve(\n", " 'http://ufldl.stanford.edu/housenumbers/test_32x32.mat',\n", " data_dir + 'test_32x32.mat',\n", " pbar.hook)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "These SVHN files are `.mat` files typically used with Matlab. However, we can load them in with `scipy.io.loadmat` which we imported above." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "trainset = loadmat(data_dir + 'train_32x32.mat')\n", "testset = loadmat(data_dir + 'test_32x32.mat')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Here I'm showing a small sample of the images. Each of these is 32x32 with 3 color channels (RGB). These are the real images we'll pass to the discriminator and what the generator will eventually fake." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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ScrHE8fER5kMWFpcXG6hiAgcJUkIlDas7EEe2fRjFPNyBYHhjHh8vcO0JNi+HDiklPGC8\n0DAfkFQJ+Xvsduz7VApDP8PSsN9JGfFrbrYjLq+yH/Ts2imGIc+LtRmmsvQZynAVLqEDj1kz5grE\nPsoGf1iEC7AXJVR7UqdANgvUpAHeUp1rRZQFZzlArBYlwFgLw24UZyMiC9GQIghJfFB7YNG9TmVR\nVv6k9wTW/ud/CQJLoVqRSk6eECJ8sxFCIBHm1losF0t+SaovrJR8J8aIGGr43Rgt/o4chq8nA7QS\n1O9isRDhZYyTCei6Ho7BM8Xp+GN/4SP4wz+8CwD4xX/8G3jT0xmx//1/7P14xzufAQD83b/1T/HS\na+fwBTuEEYlXPyUtzlxSSU4rojyJpiwupWWZ3HrpHIHHBeSx2WzRzzIAcDGf4/xu3hT9vMfiLGsL\nnbMiHKawA7GjNg9GdaJCK8SxRBkCehcxMVSjs0E0pBgJMRSnjgIVZ3fKC8Xy4rTGycLRGuhYOM+H\nGdysg3XlpLXix1HOwILD3DHB8mmutYZHxe7kB0IweGS1ADWVAgIFxJTHSSFhzs7n2azD2WnWSK+d\nnmDJAmuxWOJoeQxjssa1udiCAsMaxoTIIN6h69D3PTz7wTYpSsg+BBK/49HRDNfOTvPzrQaBMPEB\nrEISH1SEQipObDII0IjsdI8EbFmTW+9GXK2zb2pxvMS8ADlt1li6cjgZK4GKPZcwIAKYJ65+TFXw\ncEf2vuHDJH2CbjBzhKp5pixULM+BHnrswyMaSAxflBQhIco1+QDivyHWgBlSVkaKZtb6lhvsZhtI\neDhw8Ee1gw/r0A7t0B6b9nWYhEXGESQ03aRy7KfRAMZZ0QqM02KiKaXE7xVjNjOKD+r1Anc/AljQ\n2X3fy2FjjRNtq+/7vecAwNV6i3/4D34dAPDyC3cw77I5YYzGD/7p9wMAfvCHP4if/pv/HDsJ2wPJ\nlx44OFPMJIIq0ArKGlfBN1Ii7Lb5vZ770u1sdgHQwWDceQQ+cWaLOe7fy6kJm/VWXnEKvvq2oGCM\nxhGDY9Ggn1OT5KZ1QVeXcTJQqrx/1TKV0rBshtuZhTEWHZuRnXOC9jZKyed+ln10ZdqnGCXdw0xO\nMgoSIpTk5mns7t7PWlrxRYLExGnnBUrBOouur6lEc/YPLhYzzFmrms16iZ4OzmHmHKytP6NE77yX\n8L3VGpSSwAiQCEdHWWM7u3aKa2c5NarrO8myiCH7YDSn3CSloW3JdlBQtky0grMdrMt9iDEi8Fzv\npoTtVNwKUVwVxmgoTQKE7qwR+I3RdUy0RgaAsmaX0Owr1fqOq69NMgr8JFqVUkkijwTVRE8BKIgV\nooyB0SVXUVd4UIyI5bmaoTSsRSuja/QeJCDyxOZrsXaSQs1geSgz9OtpjySwiAienaxQ1QflnBPz\ncLfZgCiiY4em7azkHCVEMScACPbD+wmbzUb+1vUVefz6PkQRaH3n0PNiypNX46ctzAEA7ry2wr27\nF9L31Sb7GC4eXIgJ9q3veyve8rbfx+995oX8NaUQisCiERd38vdefnEtzmY/jQgTsJqyr+rbP/QW\nvPLlLIi+9OKXMVLZ0AazrhM0tLUWsyEv9s1qi7KzjdXwBakdA6y1uPHUk9LvmIo/JgrWjRBAaZIQ\n+55/gRqHbH6pPHZ9j8VyLn0YuirkS//KXBin4Rn5bVMSk9z7gMgSPVGqvjelcB95g6LxVZU0RtLU\npG4pGGhBvmsFCWj0fSepINZaQfJrxe/EUzzrBwnZIyZJGcmHosduN8p1xyfZV/XMMzdxepZdFdYY\nBD6kIvtpZss591Wj5zHqupkcCjHmlLG+y38bRy++tzY1LaUaXFIqp2TpFi8nfiuJB0BrDWoE2Ndm\nB6itTdhPlKCpjLOuARBkf3BxG2g4lPXnQkRXAkB+kL6Tzj5JqILD0nJoK70vvPb9aPu5wNRKzWKj\nln/fQDuYhId2aIf22LRH0rCUqlG3RBV1nVKqqGFtoCwkk945W6MTupoaOdxZNCyf86dK9ryuQNSv\n1OTUgKqJwoCEe9Me80MF9vmSLzUGvP2tTwAAvvPD75OT5nhQePapU3zmt7OGFdWIicPvF+cKP/NT\nHwMAfOp3Xq1o4hTRO4Mf/fHvztesLH7h/8z5g7sLIHX8jpEQfALxSW6gcbTMTuOr1RUmBnR2816C\nD0opWGtwxg75lAgjgxM3uwlgwGtIGkoZOO6T01qiMbG1rxVQlKi+t1jMB8xZkxj6ASUemwG6HHG1\nOpucnJirTA1HG6tBcNy3IOavaHRNuJyQBCVtGoeyUkUbLNHKNnpkBEmeqUk4YuUDwuRhOCBitRHI\nhLUGkWEpUIRx3EnQYrEYcMwm4enpCQYGokZK2LLZSCqDZgfWqkKKGMREnaOupwCKumpSOoCHDAYE\nJ6mhVClVkmPmCx6nVLNFtKr5eFoDSVVUeNv2tJSHfv9wU6juFWO0uAkoAZSqhqWgEU2ZUwPLpmLX\nd6JhRVCeipJgb5TsAWW0mK9gBH3rhBcNK6W9qH6JDJd/30h7ZB9WETgxJnhfM/jLwFhrQLpGn5wz\n1Z9kSNRSpRQUFUoRm/1OPEHOaDhbWATMnu+jwAgAgGJLcVOThpRKIF0Wev6dVgFlU1AMePNbspn1\n9JtOJaqXlMIEwJeEXgR88UsZA/W3f+qf44uffxkA4GkHHQp+acL3feQ78J4PvA0A8Hd/6mP44nOv\n5L53BrpEZkjjaj0KPs0QsGBTeeg6jJwQO/hB0NhXux2UUjIW3tfE4BinJpN+wnxw6LqC7rcoo6RD\nFEyb0VrQ8CVtQ6KEzoKoHEBBaGgSRcSoEERrrxvLmMp3lUhB+YrgznOSQKn6OUUQJy1+jYwHah0z\nSgBhKUEQ436KclAZBDgTYBh2nWISv1A/OEk2nqYR2+1a/CnL5RFO2CQchgGKJ2fyHqpEPk1hRcgv\n2akIp9lVob34eoLSiErDc6RWJS1ZA711sPxMP47wfBiRGwBNUOxjMtCCk1LWQhU3CKfXiJULiZMD\nVOEjSaU9qp86Q/VDi3posY20Z47WdLeWucI6B1tof1IAKUIqbgdTx0qZZlPmJ+x//CpUUQ/7u99I\nO5iEh3Zoh/bYtEfTsBpiOCLAmKLeaZGS2bkHSAJrJNRzWIkK3QpVZyxm3QDPKqtz1ZlprRVTJZ8Y\nJFGggpEq96uRFJLohtBZwEDMjgRsLgseJ2FYZJX/1p01/uAPbkmeoXWZkA8Azl/7NERd0oQwZVn/\n/vc9gx/80x/Cr/zT3wMA/OZvfg49jyopL4GFSAExGRQ+Oq2CnErGGHiOKiki4Sby2y07ZmsCayGg\nmyYvSaoUPbTuRBMz1lR/JhpKGRDKlCdlkPIxyR0ycjBSCqK6J8rKfZQJM9VZq01NXE2V1qgAS7Om\nVqKaUTQ4IlNBwpQyvE6oRhp8XkiS+L1DRZxrYzHEBM8/j8FLIm7fOaxXbDZvVvB+wulpxlhdf/Ia\nrjFpYjc4TD5jpbyPwq3VOwuAJJgw6yz8NgdU1mGHnml2+tkRFCB9QCKcHefnDH2H0WdaovMHDxpt\nzcEZI5ri0M0kH9k4h46BsLrrQEZXwkJFwsOlmlhbUjUyXAISLThbN3mFedz3o7QV96n2rimkBNY5\noTOKKYBQQcPaVC1Uq2oaP9wUUHMYG20rfZ0a1qP5sAAxz7RRcMzhFHxF1KYEUCAxV1JKNWJorSQj\nKwUhfENKUEQVUGcrCFQrLdny+WeIXuhbU3HPVtRwPAGBe2aMEhK0zs7whc9m8+7/+Qe/jXd981sA\nAB/71d/DCy+8BO1KyhDBlqx9KJDidBKf8NQTGQD6b/zZ78OXPvsKfvlnfye/iooY2c43FKs/hwjO\nVF8LpTrhJydHsCxYp2kSqIDW+SBYb/LGWm9GbCU1JIjQ7/uBQ9Tl3hFEYiBXsJ5x0p8QgNEnbMeS\nsOyhC9EcvzGQ02k6o2VjxZhE8CApPgiy7zIWU6WshphkThSRsGMaVf2QMeVk2yJUVVQIHFnd6q1A\nJULXoS/mLCl0eoRi0KoPU40yMnMFAOzGDaxzuHYtC5KnbtzA6WkWWNpF7HZZEIXo0bM5XXxXJSNg\nc3mJFPP4D53FglNxFDSMnksSd3Zn8PsZjYGZcDfrLTwDSrebHaJ1NfE7UeWkChGBI7EmZhJHleq6\nl6aAcrKoiiwSqaAapSDTGMumqIeE5C+IHb73gHLIZyZWHnOf/aKxbrK9y1oQaKEaz3ejryLKvr52\nMAkP7dAO7bFpj+h0p3q6UpXeSlG18VSOEAh76OSxXrEKPAxAXyKGGt6ztjBO8D5Irh4ASVkgBMSS\ntwcFMjUxulVrG1hO1sekO6p0C54BndvViB3ySfqzf/u30c1zVG/tt7A9UDyZKSQJEiQi0daGvsOf\n+fPfCQB453uv4dc++gBvfls+uf/gyyukKWuRo0tQbFoYraCQEDhS6YwWbbXrzV5Cd5ujFULCHU7h\n2Wwn4WcnShLNmc3m6DsHzRpkPphrNCaV9JEECZTsxh181GBLFH1n0bHG3A9GaIGtNug6V3nE4QXw\nSzFK5Kk1Ckg0g7SX7lPzSPfz0BIlUOHSIghvOqWYgaAAJtdh6BnACYvezeAKp7viG6FwO1Vm05OT\nYzx5/RoA4OR0CcsUQ1MIwuXv4wTHGpYxGThZaHhWFyt4Nh2dJWyX+bM2HRZzDSi2GHQNHBijxXSc\nzQdxwKcY4HMWcp6D3SimtnGd5DlC2axJsMslhSiJ1UpVTqqUokQcYzEZVTUD85qDjEtV2DiKXhKW\nsV+UpcXIidmXJ7ChWa6pdCntA1g1lLiBktJ799CN/v6QcviG2qMBRwEZLEo1cpFSAwjlpKXCyx3i\nhHFsOqzrxvRTZRRIKUHHGjIORRDFuKe6GltBhhmo19jBTfJogTgU08Nag299bzb9rp09gauLvPCe\nf+4ubt/l6khKYZq8mG1IgOcIkYpa8shuPnuGt73zzQCA3/2t+zh74gz/+V/5MwCA3/rEC/jZn/4l\nAMDty5oInGJAogmjL9zlnfTXWSchnJSi+AkoKPjgRWClkGCE6M9g6IsJ3aFzvUTKtLG1kEAi8Y9t\ntyM2Wy4O4AmrrceGN+asd5jP8+Zbph7DUPxU2acoq52SqOXU/r8NWZfNhRqlgq5RL2NMBU+m/QWb\nEolppFBzNlOIcijOh4AYgxyeWht5TiZyzN/r+x5nZyc45nxE6zSmKc/7ZrfFapWTykPy6GYseMo9\nFRP6mUFMunG3BSWmzF6s0HW9RPm0Nkip+BqjQBKGflbhJi4XxChkheM4iqltey/wC20zv34RRjkx\nvWYuGNQDfC8puox5+dwefmjhEAT2buafmDcsj3/DErF3PXKEsqHCSOKrrLCmh9tegF+pr+j3+oYJ\nLAUl6PZ8iLMDTluwzxIpEbQhYQ5YzAk75mDfbkepeBNDqLAIMPKbSdQI1Qdmm8VdSRjFAymftYKk\nDbQEbGUi3vyWa/h3/t2PAACC13jiekY5j+MFfv4ffRIA8H//499EIhLfOhmFFMppVR9765UtfuKv\n/zwA4PxyDQeFf+8/+UEAwB//ofdgWOTB+N//15/D5WU+xWNSmC96oTRRWlU/k1IS/o8+Acxe+oXP\nvYAUIza8sRIIM8x5vIxoL9ZaOFeR6rpNYG08CDFF0WqniWCaRNrgnZCwKQ0QsTajCMYYdIU8A5XX\nvF1nKaVaUaY4gpu5Sw1ywepK7BeJT+OScgSCFjQ05OSPsW4QooyVqulXNaCSqBIbzmZznJ2dYs7U\nMUQJq1UWWKvNGhdctQWasDheyBgBCstl9nsZXRODN+uwx30ewg5DX94vIvAc+qaElVJ6D9ektYbn\ngI1ZbzAJ+6hHKilhLkJrI2s4TBMCw2FIofLIx/hVBUVu+7iGh1PeqvyqgZlIUlep/FG+k6sbcQoe\nImIsCfbN3KTE9QbKZXUFauxrbLUU3xv3TB18WId2aIf22LRHhDUUzuZ88qmCekXD06QToAkWhbbE\nCAT98nIleV3T5MVssTYnSAttiVfQJYLoarhcawUyScy9qLQQgimjG+kbmoOBJb8n/KO/l5Off+Pj\nz+P7P/I+AMBf+A8+jB/7yz/AfTf4h3//4wjMn6RUgFYMr4BC8lk72m49XljnKKNzGip0+P3P3AIA\nfNf3vg3/hNr8AAAgAElEQVQf+u53AAC+9MUP4mf+3q/x+2r0bpDIT0xGkse0UeK/S5RDxgBw/cYJ\noIBSbzWSEnO19RvmV6w87oCGYW3T2g4dQ0SGPiAw1Yx1BGXcHszEM/BzN3qJzhFiLss1K5qhFjM7\n84Lnlrh0VPkMAH3vGnproK1VWa60xoISRPNDg4yOoaKgkzbougKLyPl+JZvC2q4BJyZJkj47O8PZ\n2TUh50vJY73N2uq9e3dx524u73Z0NIOi7OeKMeYMA8dkgUcdCvfZ5DcYi98rhOxDMgWDUamdJz9h\nt8uanIKGKRrW0KF3HUY2R1b9WhD2MTVaWcoRwuK/W11cYHVxzuNV6zamFCXpm5pIemmZWC9/1kqL\nb5ZIMZ6zmvPCcZb2NdmqwGWTUsxAis18pz3z8GENS9pDlDKtGftG2yObhKXQQUqVaH+fcZDxOU0v\nit8FWkEzxsNYkrJQ/azHMMyEQRMNb05MqRYCTRwkpcLLrUG2HYCy+ZQ424vpcefBFr/56S8CAF67\nvMJvfeozAIA//tq34OlncurLn/iRD+LTn/5DfO5zL+bndUCnuBwYObz7XZnMj0yPz30p+7120xY2\njQiBTb+Y0PV5oX3ow+/CP/nl/Jxp53NGP/tnOjIipEhVvNi4m7Cc5Q321M1TGK1xtOTk8VTNC6W1\nZPqP2x181zdFKIyMs+sJs7JojEbHaSY+JhBpSaeJkcQ3GGLEZleQ7ha9tZiXQpm6wfxANXCHKrDk\n4GlwN9kwaMy2Yg6z/0O4viMaM2bf3NkzCbkH5Xllw1lrpHjt8fERlkdzSUqepiDl3zbbjfjAWod5\nMb3KejZWo+f5sKbHxGj2wtQqrATka0Bjt8OOi6PMh4U8X1sL4xxsSYa2SkplAZB0loxKr3zt3nup\nkZCLEQe5QmpBllQYonrYtJGovWRqBZASd0lIsXGgRzlwMrax3j9RFWy5xFvrAytZFx7JVNdRmwiN\nRoC2aVuPIrEOJuGhHdqhPTbt0Tnd5RRPtYKOqiDIPfoIANBNZZUmR806DcN8RsvFAvP5bD/qwC3n\nPVU+HUWQsk1pVOhrEiNar2INdJTwaoIa+HcuYGRVfNxOxabC6bU5PvCBd+L3P/sSAMCMBooZFr/n\nT347/u2/+K8DAIahw8/8nY8DAP6vv/9r0NGgVDonqo7+s2s9blzPp72ihNVmg9Umf/HU9aIFphig\nmWr38t4KG5NP56NlB2MMrnHyc4xaKu2EEMSE2O52GLpeNFRtnPBedY4k9886jYFNO+8jQoSYiNMU\nasg6eqkwbTRzlIeiweg97blqaDVSLBqWUgIL0YTK19UCHglQROJqgDKiUTMTVH1YgahA7zlqY4zS\nd+sMOoY/nJ4eY7lYCLtpjKnhbYtS4XuxmAusJJuENYADVUuVKWskY5UA6KQll8/7Hdabor2NUt2I\ndEIsmiUISZP4OJQ1tUK3RlOxOVsYJYF96AfMGIhqjBZnNyGJVi288Y0+07Y8F3Uc20LIIVGuioPM\nGCwaFiAl2yKzj6ZUTdCqYTV8WGwSyp7NvoDy4NbTj30I/Btrj8zWUMOSTUhVafGfKM1DVtJnUsLI\nyO0xTPJizjlO0gWGxQzDfF5D8dHLsGc7WsnnzBLBURIfJZQPUoJp4djlXt+N0nAlekVJQsjjNEl0\nCzC4duMEtpQRCxGzRV78H/7e92LOZesjAt7zrW8CAHzU9ViPIxKVUlKExGq+cwb9rHBHRYSQZMFP\nfpIkU0qE9Zh9K8vFrHJtOw1rDa6dZWaJEJJEoq5WK2y3hZMrYPITfORSVUQCkzBKy2dtLFwqWC1C\njEr8VtPkMZX6hWMU8yYR5fqPAlVIUuUbCjWFJwR4nr9SWKFNK8oVBgWtUyEQlFNJiplJDdaHGpNy\nrynNbof8LiFN4sPpeif8a6dnJ5gv5k3FaStI9qOjBbTJkeKTkxOh8c6RR0Ik5tDSVopqZNdGSarP\nUV7NJmJKJP6tzXYDH0vUE3Utg/LlheXAaqnSnItHgJ+ZsUulMMcw1OiysVYgQynFfRMLZb/Q6z+3\nKT0pkwKUwyqXKCtVoGsGBZSqey0GTmCvGLtqOjam5x/VFEQ4tXURH4XY72ASHtqhHdpj0x65CIUk\nSRrdRKyqhkVIUKmJJjTYn8SVNwDA2C4j3wF0rmNupaIS2z38DRpTkahWqfVTQGStIjvauZt7fEsF\nm0RwjQpaCkp4H+R00VDonBJkMvmIIzahjmYdQolkkcKd21kjWgcPmFxEo/RXFzxOBEauSDy4BGuN\naG+ddRWXNPSS4B2TFwe8hoLRGqfM4TT6iO1UzUCAK0fHhBBrlImQ9oMgFQgjVkHW7jSsKUDJSmGc\nkpXk32zLkTj4k3aCqkaqRTdTAxyt8wWgcQbXE7mhBUpZC2hZOMWrkKqpkR3tTYClQXRnmpx8/Wzo\ncLTM2tJyuYS1VrSMYRhw7VqOBi6OerlmsVhK7l8pTppSAXEqaFe5okqjxGuurC+lK/VP8EhU8Iq1\nenWG+5sm39zK5JCBmIfgHEDhw9orSFFbxvKVe1crpA5go91RTWBPKZvyXipdBUxT0bCi7ENtauQ+\nO+MjKDUO/5LTqKrrZR+s+kc1ajTpbxAfVmsSKqWayidNWkZkqD6bgZHiXnnrMvjGmKYSSxZqbRqH\nmGkURQUufpJixmR7u6ioNbJICVKSXICLGoCr/ixfUod8xOV5pkveTRF3Li8wirmisWXTcbPbgmL2\nJf3+Z+/gn/zCb+d3Io9gFQIDTCkmEOMV7t2/wPn9+wCAN81O0OkEy0JhNgzwEi3SImxTnMTXY90M\nSml0HKYnnRDSyH/rYC0LrMIzZYpNAQEXaq2gGaZRmB4AsIA0VThqgmL6W20IRqKvhER1nJXWNboU\nvCy24D2SL/zkxc+WGt9m9e1lcGd1+mmoWq9Oa/FzZlxk6wvhvjNiutzDOSfQDYoJMx4vZy1nXuTr\n+r6D6zIfVlJzEep9NwjQud6/jlMZI9cI/IKELdEwYzoYzfdoaiPmn6vplMHXZcwbxoRGAIs7ryQ0\nG5LfaQ3UykeNcGjxI+LArV2IMUoZshgTYiRMvgqp8jmbzyV534hJGGNASkF8WGh8WK0/qrgtK6Ef\n9pqULkND8vcIFMlfRyFV7hiUpJ2Un/NrcGcEQR1geVC7rrIFWGsk3FuktyC19f5b1nJgaQ/z0baY\nmpppSr9OascYERnCHowH8atP2x3ckPMAJwJeeuE2qPgvOiPh6Vdeuo9nns2n88f/2afwhS8+n591\nomG2wMmccUpECEzq9qnfegG372f8zFNvWiJ6LzQmyiphL0ipcm8b7SQsXTa4HBJGyfW27+BYQ3WK\ncnkqXVJaInRhUVAV7ayMQhSKVsVUIsWnZeALq6gHJPFKqby0Gi5zSelIgFSlr/yI4ojWDXMA2lMY\ndY7zq1YEtDFGNmTeEK/3c4iGxbdOKTUwlsrnv91ssd1sxe+idKYMAoAIL7z8IURwDVp0XFTieJ61\ntEgJF5f5QJvGLY7EUT+D1lrK02vdoesG7oPDZp2ZILp+K/UUO2uy77ccSMaIMz0RYeK1hvkS2laS\nw8lPcuhrXVO6CGFfaAD7bA2ocI2Yqt82pIQQktSr3New6mFiyVaBlTJ5pNSPVOXQwx4jQ1luqrFm\nqsxoHPBUWT/SI2hYBx/WoR3aoT027ZF9WC1fd6nOnKN71T9BqD6KbAPnv/V9Jyd613U1Q16bh1C1\nWiJoUEZC5ylNCKFqWNrVpGEfvCSsZr/FvilBiDDMn2SCQV91e6nafPuVB3jucy/CFP+PIQSfv/hb\nn/h9vOd9bwMAvOWdN7Ccc3HONeHkWo+3fHMuzEqa8Ae/l2ERv/bRz7K2AqADrrbAxQVDFk4jVPGJ\nEJqqvxUQmlVmJZHBGBOC+CGoZugDXN24nmrVhKqoYygFK2SApVhtRe9VLSpIxFYblZ/ZRqFQPyvx\nl1Cl6y0ntDYSEdvTkBIJCSApvrZhcijfbPnoKdX3K/qF+McasOPkI+JV1m68D/Dei+bd9Q6O3QK7\nsMNqnasozeczXL+eKbNPT06zT43P8pgSqJi6UFLizHVmj2wQZKGYAUTByVpOIYqJn7LzqFiicNrI\nHooxCde9RmazKDVWU6p7IzXgSzSjUZo11XLJQ1tcK3X8KCXEECSPcbudMI3FJKTG1LaI7I7xfuRK\n3uU5qqliVDMwtFaMnJcO1xXWgldR/dwxfQN9WLLQTA0/K91we6e0l5VttJFwd2tGdq7DwCq0NTab\nkQXXhcpgSpGEeiT6CD95Mde7ftiruVdbddCWme4Gi6dvZuf1C59LuPlUhgpce/IYd+5nlf8Xf+E3\ncPu1CyjL/dCVzOxzn30ev/Rz/wIA8AN/4jvwl/6jHwIAvPzcHTz7ruu4+bZ8v4//6gv4f3/uEwCA\ne/du4U3PZJX/5tMnWK232O56HkyIeZdikgTxruH2jiEiEUlF4a33Nfk2BAk+aJszCJSpJnppERUW\nAmqDIwqJ6oYLPgmrgw++lqZCroUoAlUpSbVKkSoNUJPlL7RDDeNlWxcPRJXFQnGghqfPGC1yN6Ca\nT0hVOCrGRtW1X03WafTwzPZ5cXGB3W4r6P75YiaO9svNCnfvvgoAODk9ktJiR0cLUCJMU0WWl6yB\nfhiETLLre2jjpM4koKEY4qCdgyv1ChNhxwwZm9UWGPJo5JsrgUVoAzhOBzI6B0Tq1m7C/kpV/26q\n0IAiCDrjagVpgghLhUrLBCKkGKW+wG67wzQWqAQqqzC7agDATxNSjPVgMTWAZHUNVGlk4oFSo1G3\nAbDSqfJWBdMVDybhoR3aof3/sD2ihqVqtVgVazSHebmBrG0ZY8Q8cLZyM0EroUjubIfOFs4hk0Pi\nUtZIifQPU4DfcTRsCqBAgj63zkqo2WTeXQD7IMVy8ty5fYEf+FMfAAB807tv4ubNbAJcXE346Ecz\nvcxvf/LzoETQppTaISTW31NU+NgvZxrk127dxTd/x7sBAG/+pmdw/94Kv/7rObH6S59/ERvmAJ8f\nG3zgw+/K7zFOeP7523j723Ok0XZOEsaNtSDHfVc191JZgxQJq3XWGMYQMcbCMxaROJLXWZuBuK5G\nqSQxFZAITvKVjoSUQSISTrLVdqzVikM16buuyxWNSsFaKHG2AiT5pDFWFb/nZUUUoUqmuqrmSaLY\n5KFm1HprKpSzeK/QaAtUZkqaou131snJH0LEep0Tj8dxxG63k+rRgIbhyPTF+RXuMs+YUrV4bTaN\nEy4ustY9m82E43yYL0ST6LsOxjpEIV4zMAzunM8XUmUoBI8NA0oV8nNaTadEGa2x6DmIopzNa6M4\n11tc5UNA2tdDCOr47YHJ629RIAUCSYlVk2rjWSlVgG+5viUHrPQwzZ3Z9Kzg1Qo3omZf0r8cpLuS\nSrw5/FxMg2oOGEY3E4fSO2uxk7epUQuKScyJ/IIZLQ1kNbpENOLkhW0RiaBJS6n6rrOi4mtAwtRQ\ntV5hWcg//ZO/hHe//20AgOVijt/8RE6E/vznX8DtO3fz/ZyCtoTAia1RBSFfI6WwY9X1dz/9HH73\nU18CkEPqQUMgATRWVtanb97E5Xn2kzy4dQtPXDuSogqpwRgZZdANhZ88iEoeGWV+eZEF1i6M8DyA\nYwy5VhhyrUHjjqRkFxRhKoeErxgxvwviW0hQzEDK5uZuhGcudNJJ/IvOObiuF8YBqw3A2DKtCFo3\nnGZSKqokSlc/k4aGE5LCbHYCEPZSx4IkGLsHeegL9WmXmQ4AXnuJkArzhHGCh/JTECFsTIflciap\nOjFA+PGvLneYz/LhcXJyDf2Qw4RK5SrmD+4zV9Y1IwnKxrjqH9IWsTGptbESYVwsj6C5r5cXlxJp\n2+5GgLRwujtjYRmOYWwP43q5Nxr2jRztq9AI2fSq+mgrri1VW5nqdarxN5bP5eDRqsHqNZ+1UoIN\na+tFAoDVbbXupn/sEpJDjRo/cluIpGGH1w/L3D+iHUzCQzu0Q3ts2iPTy7iG3qQUuTSmYl80l4sS\njIy14oAjCuJAV4lQqA2tzY7dLVN/BB8EYTtNE+JUNBEL1xlYPjFd50TDatHSAIm6WnS4e3cf4GMf\nvZf7GCI083VBK/SMzxrDiA4aHUcTtSLMWNMga4Rb3lkHV3iynIabK7hZfl5vtZi61598Cl/83HMA\ngPXFXRwdKcwZOZ9iguF7B+8RijO8xaQQIYaIB5fZdNn6STRXaA3L9cSU7mCtFvOVQsBYeMd2Y6VI\n3ozwPJaRCInQVG0JkkDbL1yN5loH52xV+5WqycmqsR9QT3lbcg2p0bC0FqIvRwlyVvIJLJkNqZoK\n2eyz5XVh2TTTSrNDuTxfiRaeAY6lQG/OV5V8P4qCbUOqa9kYK5bW5AMoEbbrvBb72Q6O11uHroJi\nI5jllrF0ykCzSdgnBcVjMI6VGysXhiUo1H0jYGljoU1hebU5ivsV8yirI/0rV4CumLSCyuLLGra0\nHCMuIRqj2ohftUpstgH5Ipsr+ZSggzXoeI04o6AL1orSHv5LsYMfyBZU5NJ0KdSE9a+YL/pV2iNH\nCTsxOxSM5aRSH4R/OmU4vISFrSX0fI2fvFTB9WPA1uZFYXQG1I0jFxzwQSIIMdZCBsNswPJoCTOw\n3W+rDyvFirrORP25m0WFPzsDFDMiON1D8WZ3g4Yr5uu8w8L2mLkiEHdVDR5OhF0i0ITA003eIk4R\nMbHPLWzRcfRv9CvcfIoTcd/1LXjf+98p0cl2sXZdV8GxpGRTICSEGHB++YDHFnBcXr3veyy5nuLR\ncg5tKpd5jAHjmDfJdjeKD2VzuRVziVSGbQvHFIBhxkJYD2KK9X0Hawxi4YnSBsSo6OgrD7mPXua8\nljarUUKla03LfJtSg1Jl5LUU63A1RK6AAhkvPGtA9gXltKA2QXvkz14EKVHI9e8kub2i+a1BRbhS\nktJq47jN5IAcsdvtfK16nbQkflvXZd8j/ylTzhe0vpGCHkdHJ7U4SIwggqDyXd+j6xlUOvRCSAjt\nWIh7eT8pEkH7zCgPCy2Vqv9RUyW1JBZSAGBAMAoC6HYNq0bS9f5OVwPMOJP9UUVgOYO+lOUzGraY\nkZQyYFiGlqR+aIxeSAlT8E2xkTcusA4m4aEd2qE9Nu2RgaPF0WaTrmqk1iBdzYP9qrIGcy4+uVU7\nTD5H0MbdrjqAUwJSQqgYRBiUCJoSh692GqYz4viHalI8km7SFBqMDrcPfve3IqV8XQiEkTWi3W5C\n3LIZFj12YYUFM0weH1tcXGZN5cvP/SE2K0483uwwslN71s2w6HosmHrmySfP8Jansxb1tnfckCIF\nJ6en6FwHZh2BnwKcKdG0xrTwUUwdvx1BSNAMdpz1PWZLjkQt50L923UGzgCJSm5haMy1JFqK64xo\nNjFlE7rkKfZDj6Fob0MHK6BejTiOKGfbbrdDnCrlSFFlnascXDXv0DTpOLXyS9YuuXukEHQEE7vC\n6VpEVzWRxRw0KxpaLsXVpnQQCqVNFC0KiulQpHACIaTCKBrQlQrHBgD/3k8baGPQc2qO0jmNLH92\ngERADRKZmg5mkiS9G4JQYfd9iVCytQASl0Hf9xLZ7VwnDLSKzT5qzPCHAaJfvaXGWd/Cgl//WZze\n2NdcdPNvdQVkjbz8aLQWS8A0kWP10L2oTXJOUTBXeY2XINkb17DUo5SJHoaeliXpSqEC/GJsuKxy\n1ZGWz0oe1ti2LRauzgXV/xdTbO977BNpiNwkHKio2aQkvydSODpeouuHerM28psgwNZp8nmz8Yax\n1qBzTVS01n+vps5DcLiHl5V63YfmF83v9septjuv3UHflWpCzdVaCdtFPwx7WQQEQip0xS0hW8Nb\nRIS98ky6MQW0qZkGxhgYUwUPJRL/oveVlz+Glgs8IVLA9bMzeXZ+KSUP1zLG2T8mQk7VUuntuGRg\ncamElBgRX026SjrXgIaRaw1IH77imuMfm4n68ssv4vjkBK9rhL11vQ8pqBCAlEiAt9QkXxeO9YdZ\nJ77SZ/0Q64FsG6LGh5UqMQMRjAaunZ01NQbb/tFX+fz6w729rgWbtNko7V5usx+kBL3M2/7aLp8f\nzge+detlolqu/Ku2R9KwFos5fviHcqkso7WgfM/PL3D7Tib0v1qtQaQk4XSKvtYGVBbKV7yM4GqM\nZhFXCg4A4DQbClYEkbIeCTsUNaWLx6DA+KDOg2zGzigKIM/Z+8ni7Kkj/MiP/puVGiMBxJtz3EXc\nuZOd2l9++RauVhsYrsl37YlTPPumXJJ+OZthmhigQQmOHazO2kyIVoIOqNkAe9QuCmijEZk+hAWM\nVuILKuwF+SPhf/of/ga+7b0fApDpuwtVST+f4V3fnDFe3/wt78bbv+mtODnJYXofPNaXWZO9uH+J\n3YYhBGOSsfNTwGa9AbGfpO+1MHAeHx/h2vWcEH56dorj4yPBz01jxPmDPF63X72Nu3dyIOP8/jnW\nXI7MTxN+7Xc+iv/xv/0r4pck1ZT8igEz1pKfvP4Eut5htcnQja6b4ew0J5mnpDAypU9IHutNnt/t\naoXjxQAqmLRpxMiJwyGmqolpBzIWa37/FOshoVQVPkpBmEeM0fjz//5fwr/1l/9i/lkpFIUtpSTl\n5wJFKKXFPwVSwmK72e6w2W55vEKDicsFTItz3VonWnbXOYGS9Nbm1DXBPCqxRvzk5TmlAHF+N8Lz\nX/hd/Pd/9a/JmINL0nPvmwO9pMvxmjUNup2oYU0xIlxTysnSJQDmTI9UEqZjFFmwmzxG7+VwjykK\njU2MSfq72+1E8Gql8d/9tf/yk3gD7RGjhIAtqjcpmAKaSySk/JQcEimJPqlk9hxwqkQWFaDZMam6\nTDFsOJrjAQSuzecTCWaHaAdSQQRgbIRhnIIQ8iudYIqDkQvqBQpVbVWVxne7WeP8PDu1V+eX2I0e\nS5fNAYoRE0fbLv2Ey6uMqZrGSUB+y8USXTfIJBtt4Hjx987sRS4BQiwgzkYPwMMnVzmB+bQqm8TH\nWiDWpIBY+JdSxDSOwjumtZHqRK+8/CruvJJxZqvLLXo340cqrFaXSGwK9YPCUzev85i9CWcqaxjG\nKYQ0YcdRs4vzFV67nQtwvPLKLdy/l4XX5cUVVquMXfLsoM+WTBNd5PcaxxGBgwLzWYdp0tiywLF9\nh8UTWVh2w6IyB1iL1b383FsvfhmAx/woa/uaCOM2C7zVegNoNv1hMAaF2Zy/pxysKzTSWgIyezaM\nIo5c1lnbw0CV32dDQnjVgo/YMEXy1foKqxW7PkYv9NKErMVaNgOd68QsnS9m9TCDhrYkgSylIFi6\ncQrYlDqfmw2mkQV6CSyohrqnadQIqGwBpUr1QpD7xFgFllKV1TUlwHtC4BQesj127EoZpwlbPsxX\n6x12kxfqmEgBEvOAkTW629RCHekRnO6PDBwtXoS22GsGt5YyVQYhquI2yEK9CBJEOF4wpnfQJQPZ\nKBho4afutEJkTWCnI7DlDUuZNrbkoSmq+VGOILzozs3hDOcpug4RudpHU6BETr3tZoPVJVcA9iFH\nLIsoiRGxABzHiAf3sjZxcXEl0Z3j01MMw1zAkrO+F5+dNbMmMpZRvhXpW7WvfcW9UcuacloAAN2q\n0dU3pVSCMZDQcvAjLpiH66UXXsQrL+acuXHrcePJrDEeLY8xDEOpEgWlfK3qrRK6oaC7e8SUsBrz\nBrxzfh+vned7n29WAmR1izkWJQcyeuAWsgYiJ3f1YY2jx3aVhb/WhNXFPdn4w9k5bnOx3X5+Ivmm\nN07P4EI5nT3u3H0NhqO7vTWS1O1DgOWK2ElbjJOC67LAcv2AgQG63dCJ8MpaQOFHm/KR0Zg7rcBq\nspDztaw9jLsdNiw0N6sNVlf583a3q8IEGsZYOMeVk7qAUFwOpo4VSEPpKFFHpbRUzdnuRmwYyb/Z\nbLFlTa78XRNkXFr/kUJDoIgIoElQbgCw3gcUzDFIQaFAQoA4Rokyv7paY83ztN5usebsjqv1Dj4k\nKA4bkoaY+LPZkbgQxtFjvd7Jc95oO0QJD+3QDu2xaY8cJSxaDCUlLABkRhBrGMlMuTpIUWdDQl9K\nhw89ZjMGOw5KzJvJR0xjlJ+HRY++LwR0QU7+NEWQtjWooAiGU0P6vkfX55O063qR6gTCKm2gra5O\nYZ/E0b7djtX3EiZ0fY8Y86ml0UGnklaxxYqJ3NaXVxgdq+I+wnZr9EzENsxmWIwl4hSkRp6kEX0F\nf2ghsSs/lHFVJTJTLBeFesIjsV2SPzurYXhgtrstrs6zqXbvtTt4cO8+P9IKBuj6k9fhnEaMzP7g\nr7A8YcrqucOw7Plzh6vNBldjPk3vX53jfJ3HYaSInku8n82XDWU28Jnf+xXEQDVipY2k4VyeX+H+\n3WzepRiwuroAsbZzdfsS9z/5+fy9XYJiV8PNs1O8lU3Ws+MlgvdYcz8IQd5rvjjC8iSzchjbYTUG\nbO494DlYYMk1Ht2wgeu4v1ah8BZbk72pBSOklao+xWbatMo1HQsVkJ8CJjbVdpsdRtZ8xu1OTEKl\nDLqur8UzVC1GqnVTCUhpaGOFNZeo0oxvtltc8XpdrTbiz0opYa5YCyy+KmppiF9f7LQ4561x0ic/\nBaxXWfPZjl58VpOP2G1HTGP++e69c6xWpejGFjt2A4whwDiH5XF2qwzzGRRHRZMySCU9SxOmxIDa\n8A0yCQEg8iVBaakOE40DscNQOQVlAhIXFrU6SY7VctmhZ2ZOdBobdg7uVhN2Vx6Ohc8wn9WQf6fg\npnzNGGOmXJZCnSRUMP3gxFfRdb1MzpZRxpmfmuECIWLHCdWbcYftlDdjQgK0RfAMaFVLpAJI9CPA\nKF1NEYqFxW6zRVzv0PXst+m26Dh5+fhojSevZwfy6ckRTDfUyiioLI25q008RgDc+6pym6OlNIFU\nyUv0iH5C9JwzN00COVkul7hxI3/uuzne+tZnAQBvfvZZdL1FYnbVya/QL/KiPTlbikk4xjw+GxZY\nW9qZvkUAACAASURBVL9DKKXa+g4n17K/6fr1G8Ks6cQ0JBherASDEPJcnD+4wsWDfL+T02t48tlv\nwpY3xv2XbsuG/MJzr2Lizf5cdwuf4srPb7n5JN7zrnfixtM381j2PV66nYM+zz3/GpZLFiJB4f79\nK3z5hVs8tA5LBsdCr7G8xiyxxuJomd9j6GyWTIWjyVhBfmfoZXE25lzQErmMwSNyRkHyXji0ECtP\nWI7+GcnCAEHqCey2Y4VIWJMT35uIZKG7aYHA681WTEGAMO/ZfVDKx6XKRQdAcld98IgR0LyX1+uA\ni4vsf1ytdrhkc3Y3hpoJ4QPGaRIamlu37mCzYed/ICEuMJ3D0XyBo5N8uFy7fobFUXaRzGYzjHz9\ner1DSvn5241kG3/NdjAJD+3QDu2xaY+mYRFAVMF74gJWlYk0MW2FEtbOCDNkie9mFobzsiYCNlNR\nPSOQFLoCFTBGTmmtjajUYwigoCstiyH04hx26EtqiZ1hYi1pjBNgAIqVAgeJRH1fX62b4poKu+0W\nhew0+lALN1Cs0b/eifbj/YTdLshpo2yHBUevFrN5jbjoHP+TAgRKV3I11RTxaLTjcpq3kcavmMZA\nuXx5SZ+JYZKUpZPTEyzmGe5wfHSKNz2b6yk+/cxTuaiq5nSotIbtirZqYfvixN5h9DvhPx+nUUyJ\nrh9wcpqjiU/dfBJLPklLrcj5fC4RohCzGyF314jJvlgcoe+WeOm123k+1gngSkjRu+pMhsLIOZV+\nu8K1syW+5499HwDg7KlnsPvE7wIAPvmZX8V2m03Aceux2+5EI0mTgmHtcHmicHLjHQCA2fIEgU/7\nu/eYpaEE3XSdM2hVHcSJIQ+FpdV77KURmVJT0KDoBdpYGNdXvBwRQoH/TKOoD8ZaZqSoQZbi7PY+\nYmryJkt6UlmPucAH74lhwBVHts8vLkSDWx4dwcHhwb1sUt968R7u3s7vfX65EQ1r9AmL4zynZ0+c\nYXa8xIIj+XBHeO12DkLdu38pJmGvDeaLUzz95rcCAG7cvCYsr30/QPG8n99f4fIiO+rv313jjbZH\nNgmJfR4wVvwTaPK1dFKgqGDZ9zB0BgsGm3azhSSFTrsdRl+xGrPe4OSYTYpC1g8ADa0qUfYbFPym\n01XI9YOVckwwNmNukGl2FQAKsZpakbDd5EFar1bVnZQI292IxaLnH6sDqe97HB1lu9x1TqJr2+2E\n3TjhiosOKD0Jw6UxCl1D06J1rZhilKrgQqUlwlcog3lYobVGz2h0H4LQyVKMYmpYncs9lPt5P4nZ\nbK2V8b/+xA0cHWf/Tj8fMF/00IUZEwaqlPyygLYVM6U0SeWicdyKf3LoeympdXS0gLM1epvf30q4\nLaYkVNfj5IXvLDGd7nrDfj+POnchF3UFssDqOIp27foJnn37m/DsO94MAJgdH6GbZcF5/zzi3v21\n9F2rILQ2nkbceDKb6B/+nvfjO7/3u/L31ALbFVcjshF/8yf/6j4qXND6tXAINCGqWE305qQxxkgG\ngULF2xnjoK0TKy2EALYIM6Mnuxmcs3DOiFmvVMXpBT9JAjFRkvFtvQdFeXDOCR5ytVkLC+rRSQej\nB2y3WZjdevUcL385C5/1asLWF2XD4ugsr5eTJ55Bv3ACYr52M8LNs6k90otYvZpN8t1qxPJygyum\nqR4WThSA5VHE0VGeJ20qBXTxhb2R9oiwBoLlyiwRBB/5ch+h2HFmgoIOBjrmTTubd5gt8kvbYY5Y\n6rUpL6cLUUTnDDomses6DaNLlWXA6FKyyiIxYA/IrAkL9lv1w4AoVYZJUmCiyi+ZNaUCWg1YX2U/\nyW69kdM+JS63Lhqcg+MJ6qyCZgnhvZeN5OwWo0+44g0XQXAMipzNB6k03A8drFaCfcknaHG0Qzxa\nkX+uY65qGXVK0L5gtKozWA6O9ucK8hJtdZj3cJz0bayGdhq2K+/aSXpLAfACGTsXQ5JK0JOPoFQS\nl7uayGs7GZ9KBhelTzlkXwRRxFjoecctZgoi5Psh4pId1iF47PjzrOtx46lMuvjBf+19+I4PfRDD\nklO+JmD0ZYyORCMHTSCdkBKDUvuI7/zubwMA/MiPfgRP3MjC6+d/4ZP4zGf+AADw3ve+vRnJfYGl\nG1I9qP3UFWOtpPA458SJr5QRimVjLLS1IkRoAnQoSfNe0oZmfkAKAcmWdCOd06CQhXDVvCpDSdba\nyrgXrS3AlwRjghwY05TTl0YuTbfeRZxfFnYPQNmsOCzmCyyPcppZPztC0gErdvIfLU9wev1G/nx/\nhdsMvl5dbvHKq3cLFA7nl8c4Osn3u3HjmvTt/Hwj1NHFP/dG2sGHdWiHdmiPTXtkWEOh3WhzpPyU\nJAnWaCAEjY5F7KxzQuMREKSKCkjBBAaKJgsdLPyUJf6RHpAYNDftPEZOAdiFEV5NMHxSWH0Mq7L2\npuGk7mBUEb4gbYOGdXEvvy54jw2ngvjgJbqYSMFTEi7E0KrzzqCfsbbVdXC2kPMnGKUEOGpcLwUN\n/j/23uRXtiTNE/p9NpzBp3vvG2LKzKrsrM6qHhYI1BKwZocQGyTEAiEQzaIkekFvWi3xD/D30CxA\nAgSLUqkR3RI0tNTZWVVZkRGREW+4k7ufwSYW9tln5i+nd5eBrqWU4fe+6+7n2DnH7Bt+wziOGPk9\nxuiLHZmAykck8Qa+kAKufM2aGghtgrQAMQkElQiGf+77UTSpQvBiXLG6pUZPKiHEIKmMQlVKDdFX\n1xifMM8O81wUQquBhFY9wBFzAfoCuZuWfxegNWuLQWXFTWQaSUlbjscH7OOCBK5V6YTplKPf+9tb\nUU4NW5VrbgAOV3vYrsOJ65CBNiAGCnuvwY3hDIDtI0LM6cnf/OMf4e/9O38HAPD5D26wclbws5//\nAv/r//a/AwC++vpf4iLEbcfFtUkAVfmivjdwTBPLHpnFR1BDlTnQWcq5dN68qm4zKXq5P3MU1URS\nSm4EtJSazLtsorpQIQ75OEIDa4BkND4EKIq1Qzl7SclD0Oj5nLQZoE25vhEPx0fc3uf64OefVrVY\nrTsoVTi3GssacHeXa2LDhoSwH2LCwvfR7ds7HBl4unDH/mPG022+qAj4RSQurPuEarOtNaKKIi08\nbHpsGF2MvkNg4TyVNFDoc6uCTxrzmbmJ7ycJvdc1shstgBiRkqtGKiFhnZnbtCGwyxK0DjCMF9Eh\noWj1lbE4h5lDW1IaqqRc64oIdmsB4EKE44tso8paX/weoZCB2GmEa1V9XbCs1SKKJ/idxn68aAtR\nI4SnSSGpilTOJ8o/N5sEIqrEdACQlAjVWd3J3y3LIjpeQELXl4KsRVbirch7wdVFQkShZCQgWuhs\n94JB70FUTGN7rDMvPPcTCumuLHxaVzWD4CEie5m0W4vNy3ICStqvk0gKe+fhuelxd3uHn/88L2Sf\nvt7iJ3/0Y4yH/FCM+1Hqmm4N0jqPaUUIZ3Q232ivPrnCJ5/lBoQyCfPEG1NQOJ3ye3759Tc8vTwv\nKjWChanOFyUkqm4/xlh0TLmJCdCWoTdQsumBwBb3pQ7pqz6UCyjGqTF4bhIVlDpJQR8pSt1MKxJZ\n6s5aIOBCCx6oGmRaG3luUoxICoJun+YZ9495LokGgBf/CFQVkRhxPp1wd5sXrOnkYPjvzqdJNtJx\n3MJYBcMI/q4fcXWVISMvbl4JI8aFIAwAEVX8iPGcEj6P5/E8vjfjSRFWAiqpkSoRNyDWbi/lVZ5E\nPrVHb3NxNOkeTJxnlxX+3JjgQ5BO2zTN0KkADhU8iholoSNVU8w0YwrsUKMMil9B1ubKnYdEMwCT\nFXt4lzpPZ5w5b9BGy7Guq8vyGLwreWmCs5JqIYIi5g4icrveOYiUb2etdAbz7se7SIrQpCBFaDJI\nVIuN1UkFUkwPxYFE3E2i7EaJkhBi83+VhOVaVUmZZVlhu0IejyIpbTuCj06Q2im06QNBFaWPaGHV\nBkOXuzubISJysTauCvfvc1i/zl70uBIrTFprUIR4Y3BSuA+xoq7nZcbj6YiAYk6QRN45BsAwsyKG\nCe/e5q7WL7/8FvPZYeWi7W5fffHcughh3fkTVveIVT/yZwRRvzyeTzid831JtAWp/PrxlCOISjxo\n+Z9R7v8cDaXqp2iUaIh1BJhG4qW8DDGbikSJIFd4houkFGqJQExPU/kESd3b6DRHaqW4X3mBrYx5\nibCsMZICF6eicl7BB8liSNfyBFQbHRavwvyvb757h46f6+NxhePOojEdszrK8zBgv7sGALx+9anc\nA69enfD4kK/ft907fOx4OqyhhKaqWvbE6Ku+DWvjFLlXUg3EAAneCYxb2u2p60CI4kTrplixUSCE\nUt/ogcH2CJw+rHHFec0L1vU6oPfsPGMTPCPg45iR46QUvGf5j3kS2ZJh2EFzV68AzoUSoZQsXjED\ncsAnIlikmBRirE4+tuvQd9WBuowYAoQH/cG41GdTNeRvdM8BQCWq7soJspClmDWgBEGNiv1JKYn8\n7zzPeOTQ31jCaT7BlRY5JfRDqb1txMUmRQ2tBljFKXRSYqP1cD9Bm/d8rgqS46O41mipRcYYRSEj\na3GVGuCCeVmgVE4vlK7KA8FHuTaaekSmTM2zQ/AJrigMeC+LQH5wy8ObAF9rN9M0CaJ7s9vDr8XI\nd4AmlkSeisx2uTYtxjDxJsILFlWMlrYKNvFCofWF2kNZDJJzcCnAh2JYu4pTEVSSLqNmkYHLTiW/\nThAIUQhevj8ELcIEVOrEVA2NldagwhApkjHFUT0RdM8WY6qDEkULnd3VkW3pxnEUGItbT1XWO0R5\nHUOEUgHBs/JKW9KBQs8lic8++wxz3uvw1Zdv8LHjOSV8Hs/jeXxvxpOR7hQLkC/Wql3rcYa8O1jB\npBhJdwISOv79djtix5pSnVYgOMzsGXc6Bqyn/Hnz+YyF5WVMMNCdkYLwHBZEdtqhb+/wKubQc3y5\nwY65YZ0Zcf/NbRa1Y/mP0/GMks9a08FyUVpbA6VVgx6GRFWKSMCiwQV4XzBZEWvw0EVquO8x9LWb\nWFHNJZQvoX1oeGm1lq6o7oD5nwi2WNDHhKhrulkjKpL/5c9Q4jBjjJXI5uHhAb/6VUaU3z3c4/H4\ngIUZAaCEa+YFvn79Cbabgu/pQaSlqLqsDrdMrH58OMrO33VaZE1KIHr/eI9pLpFUVRXNHdV8Tsfj\nGW9+9jP84Z/8GwCA2/sH/IpFAUlrBI5AKKwiNnieTjivs5hDPNyfoah6+j0ci5Djgq7zII7Iv/76\na3z5i78GAHz+yQ/QcQNp6AyISmrMnL8iwqhImksRQV4XdcZSTtAEAUgSxSb6CA0uasW6LsIaWMMK\nHwvGkIQhYEy2gbfigUjwYixRQdoxBISGcaGBC6WWSzVhkuZNjoIqcNPFIFE4KQ1Xupg5NQEA7nZf\nyzG+uAl4PJao+xbLxNfmPGNdHE7HfF63dwpf/fIb/jsS8PV2c4Pr6/y8vnjxAh87npwStjruxPWA\n4sIBcJ6fSCa76xQUA0Kt0dBFEzsmcV/pO4Vh6LG7yjnxbop4ZCDbu7ceR65TJRCSVwhUdIw0TnLx\nHMY96+6kLXasxxOHFfe4xTqv+IZ1oe7e3cOagQ9YC1u8swM0zdX+CFIuyw99WZ9Dwsp0j3mdAZ1Q\nGAvGkogGgmpnzBoNq610fhCDPNg+xtqJQg3pCQpKKWxYCWLVTtRMjTHo+wKZyEDFAnMwDaD26nDA\nA4vJvXnzVojFIMLpfMLKaTJUwqeffZq/Z0347LP8WfudQUyElVvmD8dbvH2f5/H+/gGbkV2BzAED\nQziKcmbWeCoKAUHgFQlJukjGaES3QkmtK4ktHKlKWA8UpEO3hoDFrRUeUOhTyM9kUeLQiEiBoHhh\nevv2PX7+s78AAPz4D36Km5vyoCyY55wqTuc7ng/JCfHbYA45+Syd45p+xVg7vCFGoRcty4JpmkS4\nzjknOly9sWIUm5Hu1SSYlIIptxQlIWanEET9IBuoluMq313hI0RVthwxIvnQdG0BMkVeWyE092VI\nlVzdWwvHHfDPPn2F+/scYASnMfOCFXzA+bTgzMXq21sPxfSv+7v3ePGC/Q7+8KdQKn/Wzf43yFH/\nlvGcEj6P5/E8vjfjyb6EwuujRi42VbnVSAlRQSKOpFscS5V6necFE/ONOg1cX28xbFl2+LAB8W4z\nhQmPC+tVnROSqzuI7Yx05A6bA7ZMAUrU41Q8DucStqZLxw4pBge4svPHgKHvMHLRnGIQEKPXyB1K\nZD+3og8fg2PTBk4NTLXwbmiBDTCUf24wiEoRYgnrUjPHKUERYRwq+LQU8o02GPpCi7EXqqVaW2y3\nOfS+uXkhwNvb+3scuWAeYoTzDivv8CF5KMWaUptbjEN+v1YGibRI8JyWB8wup1w+Teg4wrp5ucUN\nh/jDMOCfIROvh6EUiB+xcqSRYhJ/P20SlPNNnZwq5KhJaSJV4nFIAef5lAnDAGznK3bLewE0AhFB\nB7n/vvvuDv/i/81aWz/60R/hT/52jih32w6vX+Z75z0szu8qrvAitmqbIymbREhWnkiwWz540VU/\nLwvO3P0+H0+YpglrKCBcLx3hznSC3+us5ei0aKsrwVtZreVeg2sK8M4DXY7AznPRx+8ER9UZg2CC\n/O08nyuxXJGUDXxa0ftirrvKDWyVxvblNeavWVn0/hbjmDFtn37yAstSXKhmnE9HuTd32xFnKfU8\nyjXdbW9wc52pPQf2IviY8eSUsDxxAUr0yR2SIMvX5OCVQixdOgUsBXm7evg1T/bpccId849UytZW\nLwo6fkvSsdpcHzBOpRt0Ak0xGz8C2PY99jf5gXnx8gaW1RpmRLzn1Of29h32QE7BRP2gQb17J3WE\nmDxMp6G5QxbcisdHlrPVhCuWPu67QWp20Qek4GF5kRp7i76kRKp2VUWpoMxjShd6R/JrahY5NiId\ni6xvMLCm3GQag9zgBq1Ym7EW211OCV+8eoWp8Pa8w8w3lgXh0HVwoQoUFoT6/cMRW65TgaV7i1he\njCuGsXAT9/jRj7Im1U/+xo/xisP9cRzxT5A5cKUzuiyzOCAv3gl/bPEu10wqPU4moJXhznzLAmcJ\nmNcVa+n0BicPXAyhIsYREJ2H4RT7dHT42c9+AQB49fKfw/QsF70f8G/9mxkB/3B3wH//5Z+JdlQC\nSd2WlMrkZTBGVquGPwnpgD8+nnF/nyEYx9MZEz+w67zARyfwBaMNel7w97ut2LYNncmeBKXzCS0a\na7v9RjqLMQZxS5+cw67L71fSJEzQkkpC5mX1K7xPQqhe11WkloPq0Yfy+2p44f0KtwQ8sDz2vHq8\neJU/bxgHEQzYDBY3N3tsDvnn65sRzrPe/sO91NEoJliBXHx8ovfkBcuXNioAn8oNFFGqCB4p46a4\ndqGthWdYQwpUCb/RSuvT+4DkNYwqmBsFxReoH0f0rNo5368AOXR8d1/3FtfXOfrYXCt4VhuIk8PC\nN8nx/SP21wbU1IysUUJRWf0srh7ndYWxBoHhEcsyVbXIsYdirA4SxP7dsUROz7I5w9ChZ0Iwkbqo\nIRBIjiGm6o4L1EJ7K9KXUv59x9GIpygrnlZKGhhaM6m6MPc1odvkY7i6OWDmB5u0xsL1HmUMdrud\nQAgeHh8E4gAF3LOhxBJXKCIcuZDt4oRxl4/ncNjj8x9kQvJnX7zCC64JlYgwxkrONZ2plu+2w7kw\nGZzPbjKp4KjWRsmhFrkTJSkorn7FvJ7hQsFbOVGTQFNP/bXHICm8e5MXkj//8/8D5ykfzw//8Ke4\n4vrpy6sfAq3lVq38FDaOXBuAJJqb51UiifP5LK/naa6OSMROOeXe7mpUtd/vq8+k1tAfRFhlQXfG\nCM7KmKqYUrIHAJWuo5VQ4bQm8Upc1xmL8wIj0qSwZbjC7JUEJdNcDVpOxxsgGty+y4Ymb2/fi5PS\nZ1/8GNsdN5p6hWu9wavX7Lr0covE+Lz7u1FEA7UhUacoLuUfM55rWM/jeTyP7814ItI9YeYdOpGC\nX4sGloZKDPxLwKg9BkZ+K+fR6wKkVFhYkoZSh+T4b5LKfLVCoDRAtAUmYQXUFuge0Tlc7XPEdXXY\n42bP+t1Dj6l4DT7OeORajVtKxy2iYyGxrjOSaqyuesidF4dhGOCHIvkBWFUNRY1AFIJEAevqYLte\nxAPHYRDgaIZ21AhLGVVrHjFJPUs10rutzZdilRhV9KWQpG2tVY3EWuE4IEdYljuV2/2AT1WWq726\nuZbMy/Q99vsrkTq5u7/H7X0O92/v3+PEtbv39+8QkhNN8TXMOHBr+rMvXuL1Z3knPVxvMW5KilpQ\n0VE8+MZhlJ0fiipBOmYJIF+ApN5Lly9REn4diZdejqjOp0lkrvtukRSTUJ2UFEdbke9ZY6um/tdf\nf4vz/GcAgNe/+BK7QlKn8jm1Dok22gpFPC8btK5cqzo9nmpUNS8NWJLE1ksrBWONOJd3nZXSx363\nlW6w0TpH4k0IXkCe2lcUuWmirRJ7kIJoySmlhKieHakK+d/DL1E007LlFz/LqnJcfQhS54oxQlNC\njAX+EKC4JrbdWGwGjgZR7OdYj00Trg65tvnysMPKyID94SDo/VIq+JjxZPJzaZ3n3J4nKSoQM9XJ\nh5x+8Vx3UGLBvUaNk5g/zFKYtDo3iDXXvZTlNjayL55tkOO+s7B8c/XbjbT2faOS7hMEo0OqB5Dr\nBiPXC3ZbByI2DHArfDHrXFaspKSAOG56bBiNf7i6wsAW9tPjWcjTPkZsukF06ztrYDgn0VrVVIVJ\ntIUyE2MUpv+Fg3CCpEcFn1XqKRcl4AbpjphJuErXhU4zrKMbLGwhn5OW9FwZi812JwtYPw7QbPwR\n4HGccgr4eDpiXk7wRc9eAS/63IY+3BywZxOKrjeSshWazbIsIM2igsFXdgAEBpc3vrBi4rRgWmeE\nVFDrJKk7IaJY0EcfcHyccD4tPOerpOiUUn34KAIUkFiXH8GL2ezmsMe4v+ZzPOHLL/8qf/Z6RguW\nB0FURbPrcvmoTI2qwnoBwZXja9VLqrpq11nYvkM35Gel7zv0RTttrBudIgKpeq1TTFL3sl0UQcdh\nGAXvZW0EsGYjVFVYDhFlkVKKBJunVXbeLvef4RQUAIyyMKU22ldCd/7IBH6UsemNULCcm8TMJCEg\nxrX6XVqNVzfcjOk7xIJ/7LZ4eOSU1LQto989nlPC5/E8nsf3ZjwZ1mCKq26obs+IBOKVk2JC9Cnv\nMsiyK1WiIonEr0srApUOHKBMgOKiuVUWZS31DdI7I65JpF20NQAX/nyK9XigJOUQ6qPtpLCIRDA6\ndy5CSGJ6uU4eOkEQ5N0wYM878v5wECukZXWSRiYimL6XSK/rO7E9zyKbdU8IvqLbE6prTsotMH6N\nCgBk406BYxAqu4BURTzHAKUSVEFa65oWmU6h6wugc8gOJwBCImirJH3Y6C0OzAU8r2fY95nftTqH\nu/t7ibC2uw00pyG73R4DpzFQBMfR5MrdrRBC4wozVaPWVAGNCRkGUNLA1S3CMwQpINViehkpBrhl\nFb5pCLHRuk+S0gARKmvj8iEG/PAHXwAA/t6/+2/j9ed/AAD45//3v8Q//bM/BwDcnaf8TRWJU3my\nEfI9yUf4tWr+twwFIiVRlTGEjlVrh80G3dhJg6brjOhNdUYLH1W16T7ARsPc7IrAMOTzGccV5WaJ\nMcHNK+upVTZFIXtrBQxc7I8DANLYDJzSbbcYN6yr7kl4mW6FRFHBO/S6x+efZCjCcd5gs2XurjuD\neI7HQSOsCYEZFMHPiNx1zKyJfLzn0xF373IDZCmkwo8YT+4SFhY/xVRb0bG21AFCCgnBl45Ogi2L\nmTIApwhRQTqJyhpoSyh5L6KG4ZanSknsk9bVZT+1UuPprPAhKEaIL3VSgl4vD4y1nbT6s9NtIY0S\nZqb+hDXBoxFF63tYedg7QbcfpxmuiAiaIbv5Fm35ho7TDiKmpjSk2pIJxlgVAVpaThllboMP1dLe\n1LUwpw+xdgktYDit7JWG5VTPdLp243zE6maZSyiNYZPPdX91QM/p7+I8fv4Xfy2LyN/5u38Lm01O\nCbfbK3Q9K3HAYlnrAgIAw7CRLqvRC5KQbZNod0Umzr79Li+Q0/kMXZS3o5P7RetUffVChIJq0uv6\nYNreVPszCvDkc2oIYLO1+OGP8gP3J3/yE3z62R/xnG/wq7/O33/39hacb/PFSXLclKje8yFmGeNi\nkxwTGmmPRsbboB/qgjVsBmECGKMrZi/VbiQXzmqnUimUDdyYJDWxru9rihoTKsuK7/0YxZmaSAk2\nSqsB1mqcZobiDIOUy/ziMPtcv+xsh5W74MGtGIYBP/3p3wQALOEM6lhkcyGsUz7Hm+sd1umE6Zy7\nzG++TYghL0iH/R7O5S96fJjxWAKFpUzq7x/PKeHzeB7P43sznkx+rvlKFFxHaz2ltIJvEO3eATYx\nLklbiapcCNWEQgFJKUEyp5jyz8hpRSmCr24FQpKCekhJLL+UttCsZkhRIXJk5RcHQEMbK9HLYlcY\nLnZuxhE75t0d+xkuJZE7VqSaiIfEammal2p0oAyMtlIw7Xp7UUynpmOTi7YlMjOyC7cpYa66fzDp\nDYq7hGWWLEiV7mAEKEqBOkbXBL8RsWhNqVBJvZQQnBNdL60JmndM23dQRRo3ZUPNch5dt0fXcWoN\ni1S4odCiaFnSfgBStO37voaECYLWjiFinVf85V/8JQDg7tHBxdLQCFUnjCARlXe5a+kZQLmEBQub\ntHaWAF3ONyKqqtCpLKEfec79int2xPYnB+WL3LTJ09+k6Krd1xvqAiVC6am42cEV/JjzEpUlraRR\npXW+V1SrQFoaRbGa80bSLDFT1Wol2qLaDVaqYrXET60xJ/Ehya8VFExfZLwNlDXYspTOMGhwtohO\nJ8zMjFApQPG1nM4nnM+PeMHPys12D2KA9X04YrfNn/3y1Q7TdCdZi/cT3rzJUdrt7Rt5xufJX0oz\n9AAAIABJREFUo+tyOeHTTz/Bx46nSyTzFcodqlorkL+gXD+amAzp1wjDWkdJGyR2Gp6mBTMvABtn\nAGgUWLTSFoFfL97hxAvWsgbomKpd1BLKs5xtqRpUdF0wPQANrTqpsZA2GBmkBygQtz50p2GUwdjn\niaQQEbhW5WwHx3rUbgkInFIaY9APvbgea2i5wYkIunRsQoA2Rn4mIunyUaqhbl4k5TQAZI318l+h\n7UQvbekYI4gg9JTz4yII9pgSRr7pNooAhpgk0uiGAVRE/7TFA7MDsht2nvOuH/HHf/vvysLz8tWn\nkk6/v3tENzCi/sUOm2Kzxg/16XSC5ZsSVOV8lVK1ZU8J3vmsoAHgfHJVZ15RtYJPCWUlJyJMy2OW\nVgawuhN8yA+FD2ekxD6EaYJWXmSq1xgFlrMsMwj5O3/1zVf47rtf8Zw3Sh15ohoVDMiiG6ByilgY\nE4uX0oVfnQByQUogMHZZsigeexlA1XpbcIvUJ/u+gzXmUr2BCsi60pCyDt2lUw4ReyzwZXAsF22N\nls9za8zzlQosBNhtrMxtxyWEoe+gONX+7s03mKd3+OKLzGb44otXMq/TNCNxnWo7ED775ArL0vMx\n1I00U8uKvpnF/pBLCzdXL/Gx48lF9yIhEpFgbIluUDTbkEDwCTiyQcD5POOqYFKUrrSJFKVdr7VB\nZ2yta8Bg4Yfi4TzjxJZaUVtQ8jixaenjwxm7a26rK5IalqKqQCmiZjDNTqcrE4RqG14bhX4YcMWt\n+qG3wh9UPsCXSG92smharWCtFjyM0roilKk+mOW2L1FRaoIqrXSty6EqjkYuTntZmEJjqlqlasrF\nKZiq6XzGiSkb6+ox8uYxuygLjO5GKNNlbX0A6xLwcJ8XrHfv7wTXtN0f8PLVp7Jgaa0wMxzlzZv3\nGMb8ebvdldjAGzEuCFDFr5KoFqKVEthLip6jCY4cFaEveLcmUiNA3r/ZjuisFWrOPJ3F1rfvEzo2\nhJ2XFQFOuHvBrfirX/wSAPDDH/whXl3nz3u4f4/zKddcVp63KtxT1y4Fkh8UqQtWAsVQeX3eY3Vl\noY3iBRBAsOuKpAtNK1bjD7cIdWwzjlmmiC3iut7ClszEVU/HdWlYAaHg1ZTU3xRUQ/OqkXuGkcxw\nXPQaLOHlodCCPEIqqrRWnvFlOuHb6R0M6+PvD73U32KM4qu53WiMw02zkHqJCHtrc6MMQN/16Dkw\nsPoD04XfMZ5rWM/jeTyP7814YkqYBFJgFEkHJ7vg8g6QYjZv5Db4/f0jDjd59+r0lYBDh94AO3ai\n3Y3Y9L2kS0uKeH/Oq/+v3r/HAyOIQyIkn8Ti+u0bjcOLvMObTkEVJLvV6G3R4+KoLYVG9lZV0Juv\nLXGtM3L4BQPdNpsetoDaYhIi6LLM0nnqO5N3QFu1s3WT+nzoyHvhEFKiN0WSRmWHlNoeT6m6Lpfd\nOP9bJbkqpn+V83DO4/hw4vk/QXf59fZ+wdXLjHrf7DV0RwLwO5/O+PKrLLT21S+/EUTyzYsX+OT1\npwJ/ePf2LY4nVm44TRg5wjrsrtDpguhuuG4NWtsIOLFxuvYO+02PgsmwZsGRI0I6zVJt0BqiobUZ\nRwzDCDczFKJbQRwVXO1HXDMT4iHOiA3Hclkn/IIjrE8/+UvQ32AA5qjxxRd5XpTx+Oarfy3nSwkN\n0r3WnBRluerSdVXNhQ6hGsUm57CGYlPnoY1CFIBtrFFkDOh0lZOOKVaNd6WQ9K9HWPMyS303Rj6y\nRLAcDRNqZzAhSUYTkwdUgGE2yWE/4ovPXvIcRWl2ktKiU+/jAO8nUVQ57Ldyz8cYq+9ALODokkk0\nCILmnHTju1BqkR8znkzNKYJeSVmookrQKVDx3EZWRSjYq7v7O3Tf5b97aYCBi92ffHqAY2v6680G\n3dbA8VTdTyt+9TYvct+9vcX5zFiSoLPiIqOLb++OeMOKD3bsMbKO/HbscXOTC8O0egALKz+WY7wU\nNisE1BQJ/VDdmjfjIAJ0yzTDrXnhjHEVSsVuN2AzWHkYSTXt6KboDiTWB6+z2eJ2WtyNQA3kWC+k\nDPJLqpsH6VwrK6jp3XYr9t/LfI/zXT7u93czeB/A/ipCmQ4z3/wPD3d48y639o/HEyzPyW67xc31\nXs5jms+Sii3Tgu/evOG5A+7e3/O85euqlJZNSFGQZkRrw44Ysd0OSLxwep0QeFPzlmqqQ4RC6nfL\njK9++TUe2JJ+t38Lt+Z77Pj+HSwv7J1RcMkKqVsri+NDnou/+PlfYWAThZurl/jjv/UTAMAP/uBz\n/JOv/rwW3RkLJ7NfXoeYU8DYPJiFtuODFOADkqg9rDFCGRK5m4hqKKEUAX3BOGbHbGowfFGgCh4L\niy7O84yFU9gYE8YuL6lVZJPQ870ck0f0RekD6DsNtc/nb/SAw4HT2ajFeCIkyD0WyWNeHtAzSv9w\ntUdXhBBigG+t2bwXaEvrOO2cq1AepUAFESJO1r9/PKeEz+N5PI/vzXgycJRYac6lJJ28bjuI6FeY\nAsgQDMvSHuczfvGr7wAAU4p49TqHnp99dg2li24UYY0edw+Zv/bVd4/4tthHPazQDALVKcJYYn4g\n8HB0+Mu/zHIXLhh88nleqfc3G/z4JznEnz/f4Vd/9XPM8yT64D4EWfW11tiwzlVnIoztmCia017p\nMKF2RYe+g2WBu+vrA3bbvuFDNSjuDzS1FSlxOckF1/J3wMXeIVXeksMm+XUFi0IK8FopaEWij9WP\nG0zcsv5G3+LxIUerp+kR0zl/5naXQaPF7uz+dC86S8oS9gzT2G4GdL2RVvz11V6iivv0gNvbDA14\n991b7HgeSxoSY5TUe3Urgi8CflHmwXYGY6eRmOO4dlVXyukgJHjbWYFdIEX8xb/6V9BUGgEbaNZ0\nn+aAiaVxoo+IKQhTwtitdLN++eXX8Nyl/uEPv8CWeYXetdcMF32Ni19H5gs28IdyuWOqNmYRCcUM\nILoVIVSQMGMjAOTSAqGTz2+BxW0pIYRQtbooSmTjVo+xGwBKwiggRHRFKjwAC1Wz2s5o9JyRbMYO\n/roQ+w0082JdcBIBRvKYZitdzePjvXRz28g/B6RNVkCQc1TqEgxL3MAz6eOXoad1CaFEs+q8LpiO\nrCT4cETgrqBNAVolYXmvEVjYHRjvzuAGE/rtSTSvyGhEH3F+zOHtw72DO3EYv9YOhAbXhErxRg+C\n7v3Fz7/GiWsrn/zwBt2Ww1q+6M6tcOIBV/W3u65DYewEH2GMlXpExk2VGkNCx92v3XbEyIqe14c9\nNputEEvFYgmXKWHu0lQSKyVTw2NfQ+LUPiFNaF8+43LBKgtZhFsXcREediPGgRdhu4Fb8qL+/rt7\n3N9xWmDewIUgkImoPbacIrx4fYMXN7nl/PLlNYa+Hut+P1ZNce9wesgp+f39LU4PpQvIcx9C1R1b\n16rCEKOk0L01eHmzw2HPHUaCiCF2Ftizusf2cEDHXUifPOZ5RmJ4QPRadJb2mwEjKwfMLsInhWo4\nUNVl1+WIu9tsdjEtj7C2wC9MvV4AiFN5uT7tZkT1epPWoouuG2VYRUDSjVVc9KJAAVU9EoyuKgxa\nm2zL1d6HTf3Siudfh0Xn58SJIl09xhAiOG5ACFVhxLGAX9mArSa5HjEmWF3SuWpOoVSmOZWyysPD\nXa1OqEZxV2eyd9XyguhmUdcJqVup6gqu6eMTveeU8Hk8j+fxvRlPdn4ueDi/BrgzFxaPMxKDKtOy\nIgaPsiF0ZOBdXhdP71YstyXt0NCszKm0QVgDHEshJ2hxl1HBoYC8omJsSSg6Wh1WDuGjTvj2bcYR\nPZ5WaEm9NF6+yHiQ1HTiShxDBMFQdZZgjBW9IsU7Yn5P3dnGYYMtq6AOfYfO2goI/TCFKFkdFJCU\ncDEjIN1ATUa6rECsMiwxZvBp4SamKgmidS1Ca8QsYcsRTJ+yjDMAHHYHbBh7RekOj3d5jhYX4XyQ\nNGt3PWLg91xfHfD6dVYPffXyCkA1Qd2NHXTgaMQ5nO7z6+nxAalEig0RuRDSY6gdsYiAVCzBNGG7\n2Ug0EX2SNLzrLQJfqasXL7DZMdhXU57PWFDrECCvUhpn7iy4AKhuECusdV3gC24JDqEU+sOKhUsa\n3gNfokpoI9UiditDTZqgjIExpVvcCyl5E4KAoD1BrMDIaASFxiEpSfTc9xZjX2SGewx9j64rKa8W\nwDZSQuTnxo9OsF85wglAUtWWLEUoTrdWHzBzBD4vK5bZCQDYqBUIhdmghNDuworAKbTpFFKcJara\nbTcXzaVWo0s1jtMFq5X/TknX+Neyj48cT16wSovWuYDAEH6sAbSWPN0jIQi1Q8eaTgTvMReyqAqg\non9FGslH6RoQJWjLoFJKQLkAKoCSqlpKSdf2MyI8L6AP8yRtbpU6vHyRP7OErURGQJY+1VqQMUbC\ncSCnLsWlF4DQb4gIgxhDZN/FC02rJq2jQhdie+fYaF21tB0h8iYSkb7EC5akF0hSKzNGQ5tKah7H\nDgOL9qnoseGO0xeffY7ItJPNeMAb7r4eTwsiJQysxX39aocbNmJ48eoKr17kms5hv0GMDr4Q0OGB\ngc/vCtD4EQDg9c1LoaMAwJf/zz8FUhDVjKi0WFI5CqJ3ZjqDt7d3olmekLDh1K8fe8kBUkpw57zY\nJtJ5/mJd/EvHNSmDvtBMFBDTGYY7umlUSKnYuxkAo3xnBVgC/9f/2SDHkWoJAlRTQJOgE2BT/uyB\ngMDnpzqLcVfMPRISXydlTdYsa+y7SwlAa43OFp/ELtdJrZV/S6mCbgvIVgMyb+sa8PD+0kH5ovtG\nlUBNUIgxChGZtJMOp3NRNsI8L6yoYjcYx23jflZTOkW2+mBaw4j2khIqeRqCD5dpLv9L8DXd/X3j\nOSV8Hs/jeXxvxtMirJTgeFX0KYlzrA9JCr4xeCSTEIvEB2YJ8bVtoiU0BOcUMyVFIqdYC3raI7Fs\nbUJAIkIo5pwpiQ6XdqEqUmoNUDFC8AByUbBQc2JSQrPJRcQStRgGcebvdj4IJ1FDoWdenDbUSNNW\nUi5QIyn+qbozE3GmVNJSJQYC7TtiSghthwWo6SZqAb4lvmqVd2cjqWOE5Q7Q9WGP9TVfs6AAFDLy\nGdAKmwPLgrw+4HDFDi5XA7ab4mRtEb1cNkQNWE6DUt/B3mSJ5Jv99YUmFZB5b0emUb27fcQqfDcS\nm69uGLPVGnPeXIhibgJUYCai9CCQUsxRlTjbJInkqOU88evI5YqomqbGhSZXG2GlFu2G9nLmhknh\n5iQoSzCFFE4c+SEDXIfC/0yxGkF0FtrohgMeWU44H6pm8OzQdVmdtEgnEQGpNJGoSkBTS4OKOcKi\n2KRYFcullIG1A8+RQg5q2HWoH2HZ1HSaVnZ5BkARjqPYzdij7ysomrSu35PqvJTuZpWI9nK+hXqX\n5zJVxVxqIsHfM+gp+ePhcJVevMwuKc5V4iWhPsDGZu1zJYoFNYhrvyqlJLUaEXRrGOllArLFewsT\nuIQKCAJAtRenyZsBvPnuWxzvHiUc1ap2caytNSvTWRhbU8IshnZ5zPU8UvMN9e8I6rJ12+T5aN6W\nUOWSU6p1q9QASgHg6y9/gcNuL1/VPljlPcXlRRYwrRv4xIejnsNlGvs7RqvV1FqS/Y63eLdmCeYG\nWNm+T5b01tvvg8+/BN7SxZTT5ZvqvZPa4+L7qoEeyBkTXbyWlwC++eYrbLleRhe6VATRz79820eN\nCk5pB/2GV+BbqkJlfttoUyznkbvXUgNN8oyGEKr4IN9jJJtf8+yQkgOJMcr7ZR4lJVRNPbXqv8mz\n0cz5bz/uen5f/fLLlFL6vRnfkyKs1598in/83/53AIBvv32LM1ugG6PxGducv379Ai9eHEQsz3ad\n1GBjqvfj6gMWrovMa/aVE9i+qQveui7SjvXeIcbQxC8kdamxt4L/6fuuCqMR8A/+/n+Bu3/9XqzA\nD7sDXr9i1vkPPscXP8r+dJ/96HPcfPIS26u8QNihk/pFTLFao3svi40ik5HBvLsq08EWffd+EMsv\no7PiXnmAvfeYucU+rwsWRmO7GCQqIyL8g//sP8J/8h/+xwAy7qbU3tbVYy5W40rDdlWgcLffY+Q6\nUFKo3ohN3SCmyAtboVe0iPpmMWO786IMEWKzgXy4MTYdh//5f/of8J//V3+K8zlDTdzqJIR2MUgd\npu8Nuk5BqBxN3cUYI5EiJVXQFEKhKiMiQpW2eoIU6mOM8I3US4hRalBa6TrPuiLyNRH+4X/zp/j3\n/4N/L/+bMnLfdIMGsSqEogirqUJLPngyU7PQRjkecCQo2yykrtQsUIqjlFYKCPKOdGkPJ3AEj//x\nf/kX+E//yz+V2vIyL2LP9vh4X70HQ4AxRhgdw9BVxdxukI3vfD7hzHVD51wu4kuTYMDhkJ+Tw+Eg\n94v3gRVgC1WnmZRGG0cpJUFDSgn/6B/+1/8MHzGemBICE4fux9Mk3ZihswJsyxGGbiKKqtgYY00j\n19mJCeQ8zxkA13jYlShnmmdh0AfvGDfEiwVICpUpjJJi5SL25U5mlEG5Udy6Ck4sIYlMbd8P6Ewn\naci6OKyxCO17UTBY10W6SEpbELsjA4DteuHXbSJEQSIaAlHFsXjvxcDAOd8s3otwFkPIC6Ofi4tM\nrDd/qAuG9D6pnn/Z9ZIiwfC0UVm+Xm0H5zICvlS7rOlTjJWTeRkSpOb3PM8uYOX75XQ+41hctKMX\nMOd+t8XhaieyweJqjEvNML/GhjeXDTyqFLUWmphVnTxwMV6m67azaJ8f6dalIJtRSNlr8JZpP0pX\nJY6+M+i6/He9BaIx6Apv9EPnimZypP8bifXJSH5fFiNFpspaI4mhSB41/SdKF4DNGvXXSP7xkYHC\nx6O4H93f3+PI8kEhBFhrBDA9jiMGdoqydpZvPZ0e8fjIfpTO5cRblxRxIxvIMGzlfpnnGeuywrla\nSJdj1zXjGPpBNiOR9PmI8eQuYclNl9lh5dqAbuRqc8NZXdQeJKoICZ5XrHVdhQd1Pp+xei+7RR8H\nuUnWecUyF+dnh9SYa6LZ+a21v4eTVFOKmJJwnbyPgnoPIWYZZj50hygmpGsr67GuHyxYGsT5edc7\naeUrMlKXyMdGokO+rIs475znGeeF52KeZCf03gMpwS/Vjkns0Lk3Vk6NUGuArc15jrD4dcOLU0kx\nObs+GPKei7RM54dENTeV7JIXK1z9LFmwHFZGk5/PE96+YVZCDGICG2NAP1jhcxpj5VHXxlSp7XXB\n4yNDMpYld0+L1HDfVSeZPkjHCpThK3JYWovoZAhBtLZ8CDV1DfmeOjLrAlQ1nGxHuOKaHyIhJZJN\n0ugqmdOOEKtQpQ9coyv1QLRzr0upC1ZraILAGqxVct1bKbFMjC91vMo3TeJaNOHhMfM7H4+Pci2I\nABWTSFrHOGHlqKzvR3n2zucFt7d5HsqzWhasc7fAL6XDWWXBp2nC6XjGkbOvZV3lPK5vrnB9nWue\n42CApob7seO5S/g8nsfz+N6MJ6eEjnley+KxckpjbZXHLZn4RYRVRPtCkEgsBC9p2TwvWJwTXAc1\nEZp3XowkUshFwLKbxOilUxT8IHmzagrQZQQkVAv42qEI3ksKNp0nkNFQS+FLRiyMw3LeS3TkVlfd\neHTkKKQYMAAkHDcrkiiReWdFduQ0TThxfed0PuE4seLmMouFeAjZdKKKHqbKio8eirFgVP5HlepQ\nI6wk6XFq/r8UtH8TeK/9fS4uV5nfi39rt8aLAnkezjk4VhY4Hc94y5LEIXpsOR3Zb7eIsRZutbIX\nVKTCP5ymRdKTaZoAIqlbGWOxZf/IdID4T3Zdd4EDKnMKZDVUkWmZZ4lqS/ewKFIo0k3urbCuVs5X\nwVahSKXkGaBGLM+tEUvxLgwJiwuCZQyJUDoOpAiWr9k4WlitQSgRkampsqIqeY0kcyD16hQxFLwg\nElauk4YYMHBd09oOWlm55qvzlUeqe1Fk0KaHjyXCDUAiiQLTuiD5TMvyLomTkvcep+MR727zvz0+\nPmLDsso/BuHmJnN8u35oGicfHzc9WdNdUkJXkdUhVGRw7kxU8mcWEfr1lkFMUT5rdQ7LsggI1Cgl\n6OLgvIDassgmVa6UD8KgCs7XdKfpAtbacRTQZmxrObGiuN0asC5O5EBcClgbCdpaD1H1ZBLlILx0\nTF1diJ1ztVaW8txIGng+S33heDrhxCTk2dX6WEq5m2OKeS3Vgmt2ninHE6EaMvSF1req0iRtEqIU\nQak6l6QgEBEkNIX/WhQuo0rztAXhVi6nFGDrZnA8HvH4wDUUBAysNqm0Ef5ZOa5y7U7nE85TWdTP\neOQajFsdlDZwzB1NiFj4wWzlfay1sNbK9c1gyXw9Hh4eZJE6nU+4v88PGCWCIsIV6z6RUlIf8inK\nNYtxgE5GUkKEVLtlIQpHc10djuV7pgXL6uFa7ahyz5sOm5HrsamD0QqBmQdRAX15uI2W5tRvrDmm\niHYdKMfUN1Z0fTdCEUnTZvYe5Et6WOus1lrsmDOrSXNlrvA3A1Z+z5u37yVtBgjLsmCZy6bbEMEj\nJMWPIVU54N/Zb74czynh83gez+N7M54sLxOKc4mPzPi+7MQABVtT25cCdgRJYTLvAmXlDYg+SCvY\nOw+nmdrgQxVxS5eFxuCj6J+HpnCaC9C843C3pXSBAI62WkVESVNzylqLp1Q/xxhpxUedIKqgSiHG\nDDIFcpwhEAAfpFsSE8GHIBHWslS4hnNOjAWQKrcxY6sqf3CeF4kQ0LS3CYTedNhyKjRuemhWg/Sp\nQjB8qP59AEGTwsi8sXEchVXvncf9/T1/53oRsVlr5Bi8d9JhtcY0na06t1527tgosWopkvd9h872\nUKqCJMtcLPOC04nlbx4fpKusQSCj4Ll6vbhZ9mhtaod6GAb0fV9Tn3XF6ZSLwcfjUSAX8zJhYf89\nhDzf1/scWSRdjTBWH7C4mvqHQAgsBxKIKnA2RmnqzPOCMyvmns8zvK9dwkAF5gAgOiwFJ0gKwSgQ\nSppWO5Uptxbz6wZNKzSvmCrcJJGY/3aGsOdoabvL5sDEkIfz+SRwGaVIzCqGoZfGyjLP+bnmjz4f\nT7jltC+GKkqotYZJBl2RGEok1/oC86h1EyF+PHD06YqjcvMnyW9jqqmD1rmlXlDX2ijE1oWZDzJz\n4Uo3rVRh+HsYPwMAcfVC8MzAyVhxNT5IXJxVDhuQW7mAxeUkVPxW/BBEV/5WaxhrRG2zN5W3qIyu\nOXeqHUckwuojpqmQaiE6XyCqqXEICDE26RkEutH1PcALzEgkXZXc7SKGZACd8ig2aYHCBcBUIQnh\nW2stNZSUKlanstCy5MfYdRLy7/cHdIwZW6alqe+s2UxC0j0lKYchI4TdYdOLQWhJE733zeIWmvZ2\nXbBGNlxoO5RTY2BSFs7jw4PwGftxh80w1IUorAI5Wc4L3MbJZ2WIS56z83TG/WN+yB4e7qXzFVKd\ny8jv2bMRSUhJ5gLritqt1wghoQC6jYHMf0wEz6uGb5xtwGRnsZHTRjZpgpIa7rqsiIHk87q+awC4\nkA2sxUXJIEIqztu+1oyz7lvenK6usnRQWVTv9SOCn/nzo2AYt9stBk5T/brwd+XPu32n8HDKC946\nO0H8a62BlGSBbeENMUaRqwkNtOUpfcInLVgxJsFeTdMidSbb9fKQ5hZ/3SUbjbMMFmtwGBu+sY6P\nZ16gGrAdT6Zba21qsxkAJMwMATjODitHDNuxl8iGiH6tBpMR87WY+2GRGagE1OLUq4cOiife2mor\nTlDVcdfHHIXovIuuLqAWEWqNqEAKyhkaYzCw6JztK0BV2epxWKhCRRjPGYOZNb1mv1SjYZ7jAgpt\nF2HStQamjRLowzD02G1G7Pf55j3sDgIHOKqzLChaK7h1rQ+WrmDdzlgcOBK5utqjZzpPEXnLNSyW\nU14WeXiMVU2E1cPaBjvlg0RBj4+PeGB4wXSeRat8fzhgt9tJ3cu5VWpY3nsh0yaupRa82/HhQcQM\nl3mSTasfeok045oJusULIMRaqFdkYCT8zhUdwVFpBWVLYbxSxpTR6LiIrYwCQYt1vTG2buYJ0mA4\nT6caeaEASWsk1TI2PnzUU6oCiN5FUU1RWosP4GYcQUphYov4oTcogbv3S3XRNgkd6/OjVzBKC23s\nfH4Uh+5lmaSxo/LKLc0AZZTc27mOWxbeiFLEegrb5rmG9Tyex/P43ownRlhR6gjLvGAcyi5sRLhe\nugjCQYo1+qIqB2OsbXLbQqosaVw1GQ3OS7TUmQ4gSF0keCcqon5xkipSQwGQz2xoKGglL2Klu8QU\nkYh3BeS2eMfRVjf01XuQFKIvub1DDIDRtXVd0ub2s8EgzVLHsV0Hy/AHMlrgD9oa8W7LfC2NFy8z\njWhdJjxylypNwNlVt5GUUq3LITU1xFpb0o0y5NB32O53uOYIK6eGBfDpK8mVsrdd2QQ1KZEOGsYR\nh6sMBHzx6gYblqpJDZq/pFPL4uS6dUMnaVHX9fmYSifOO6ktnc9neb2uq0RB2+0Wu91OPk9rLVHQ\nulavPreuWJYFE3/G8XjEqaC9YxDa1GF/kKjWrx4JSVyDIiCgzRYOQyBEXTW+SEHkfpSOiBzVBlhx\nl0oxwiiDoWfzB9NfROqcCWNZGHwtUZWCVY2nY/P7FqpThrwuRhkAEGvNUyuVI66iXNqZ6jHY1m1Q\n9bpydKlqx16lJjWVywdtFLQ2IuMcYoTtCvDW1mdIqYvv+djx5KL7Bfm2POghAUKL0dBaS7E7UcYC\n5RGaor2H4wfOBY8QgxQIU3kjclGyfGcMCUSV6pNTvNq+F32dUIue5b2mt2L9rTTVv40BK/P4Vr/C\nBVet1qlCBbITb1mwNLxgYVaEFOSB8cHLOVLUUrPSyOmVOC0nU/WsrIXlxVtZc3EZjbF48TpjV06n\nRwRelBa/yIKVF6uKdY7NcWutJL3pulr07DuL7WYrul6XTIGadiiVheqkNtwoLfT9BptCpgMRAAAg\nAElEQVTCXzwcMLLulGvSs1Kz8K4uWDHVm93wRnXBsSyb4roIjsi59QKukGk7/JA10JT834o4iyHI\n92baCMMfSMm5H7Y7jPx6mibEEHHPCxtBScPGrUFSXKU0QAmdLXVciyTcP4gpqo0AlYYCAKsN+iIU\noIxsfGtKcIJcyNxUJbVVVVkFVHePFvfWvtbFQV0pwZC1VLCweiRbFSoUNHQpGygt748xgk2yoBUh\nqVCvzVQ3y27oMI55Ed5st1w+KYFLkmPXRlU6BtV62K+rXv728ZwSPo/n8Ty+N+PJXMIqiZIk3Qkp\nNp223O2o3YtaAI7N+0P0Upgv3bNqDKqENJzThXoMBZgK5LC8MtoruTmlBpzMh9H3g8AVcrOgpJwO\na+HxTSf05x7KVrKmsO4VJPLSZOBcIfWecDwdcZqYO+U8qnKDlt1DaYKx5gKBWVI/bQ00Rz5QdUdP\nMXesCkI5hFUiMW0tkgAIMzu+FJGpIctqo6XrOLD0LgCMQ5+7QNw56mwnjAKtqu6RMVmdVTS6UA1N\n7dDXnXWsxpqrRCF1P8xOMhUGI9GgIiQF0YXyMcCXlDK46hCjSPTTIiWsIUgnDlSVQDujJYLpTQer\nKhwl+SgyzsYYdEWTyxp0cuz5fry/5whLKRTESRutGaNhNZBQlFIjyg2mDImxQoJCcWLXSqNTBqZ0\nXFMU5VUFJwV8rTN3sNwpupViaVD0hVWSp6CmdCJXbBuQaaig2WVdoJKp91mqpRyja4TlfT1fRQSk\nWBsi9yfhEo/9iB03X3b7HYzRAnNY1kVcfCJF+KITFkPVb3vCeCLSPVXCqPe1jRtizatVDvukhoJK\n60hI8gAn1NpAWdRKSqi1kRDVGVMhCEkxErfgsqretlb6AsvzoQ5VZ21FwocqkeKCw7KUzueZF6xG\npKw8dJRkgSUyokLweDri8XTEibE2LkTpXtnOimSL1gq2M80CWx8yGF27ZKjf49nSaSmps/do7ZOq\nIgZd1JnyQs43oKluLNZaGF4YdWcECV7+rny0buSXiQg+RoSS2iotKU6mHvECyvVFIMtOA8Bnn3yK\nMvwKnBnx7ZyrNbEircvft3qHhQ1r37x5I+fxxRef48WLXMsbhh4pRTwysdcHh5urjC169fIlPuXv\nHccxy3JzGvP4cI+HuwxrGDcb/MGPfgAA2G7GC7xYTAnzXFNHYUiEUFOSpEAaYlWlNEQWWFuC4lUq\nwUt3ulMGlgwUd9SiD4ixtPkdSkvPaC2LNvCbuoT197/WYUtJjiO7kDOsxweBhSzLAhOCbCzeV7+D\nGKvx6TyvmJmBQSmzWR64y/r+3R1OLEpwdbPDVjBeWxirZKOZp0lQ/9aYKp2sgUbHAh87nlPC5/E8\nnsf3Zjy96N5w6y66aw3eKMZ0YbbQduyowUfV1xk3JR00a6CpunCE0rGJIetSsblhTBV8SaQlrL1Q\niWy5cGUXoSpD7GIlo67OYVlXWC7Mmq4DlWirSQkJWiKseZqwzLMgpX2M0OBUI/QSESlN6Poup4V8\nzmVvjEhSTPfBC6bIOY8QIx6OBaA3VxBjqjyxkOJFn0URyby0KHVFSlKIyEJ8dWet3c3UKHhGMANA\nNMhUrZWqNnqGSBv7Ir2jlKRGH+IbW95jJvTycSAgoODpKmjzNJ/Rn0e5LkgsCoicMg22mIJuhAht\nteY54/YbajtLJYiuFKWGNZDysSrxAUywRVq76SAbSzBWwfal61v1rChFsULsupohaGikAIlWAUJC\n9XAs4FcfsolLdc2pbjNIESmSvFsirIbEXjr0Wlfc3Wk54/buPcrFcM7hgeV61gbcuaqAIsfsnMf9\nXe6wTqcT1mWVru3D8QjH37O/yp1WALg6XMEHj7Ur+loW5wculyyTZDMxbGGY2ygsj48YT04J24Xp\nN76OecESmkKrg02/ZcFCTiFrZ8vA6pKqWDEaLR2kaeLFwQVYVWkscphMki6fDbDyYdtokVNqLjrT\ndIT6c9H9C9kDCrlmVIifS/BYo4dr6nEFOBoblT2lCMZq6dKRIlnwnfcNFMAJAntdV8QYcTznC+6X\nBY47monqgkVCO6qwEP0bUojMVOB02me1jUIViiZJDatNEYg7jpXAeqkZLrQfX/uUvqkTCR1HU7N4\nV1cVrQiIEI2zFKJc7xCCEJTN/aNsYonTk9KBNLrCQlqSrxDoQ3GcroqrRCQqo0ppEH9/+f1+V3TN\nFVSqXbPykCqTBQS7vmhWabSUJAF3khbzUESV7y9ZC0mEHxM0PC9EzgMbW3W4lKoLTws+TilVVZRG\nXbgsdH1n5X3ruiKEovFFWJYVj8e8+ISYBDKy2+5lE1KUhKw8zx7L4jCv1Q5NGECpdXpufAyQ77lY\nzHqjR3HhaUX7PhD5+J3jOSV8Hs/jeXxvxtMirIbUTBVxnxVkCpGZoxKxH1TUYEhqYV3RJTCuLZSr\nJtpSiqSA553HeZqqTrhbBO8VGsKp8wGmRB2lEOyCpAD5FGo0JxbhxrCvGqejunUJUUglckoNmFDl\naKeVqyldkYQokQNR7oIWX0GlNMomEyKhiCn54IUyMU0TNzpajFcFh7Zz9OEQzBmRpCAuhUoZiYDz\nTgr6MQLJFVxdlOMpDjeiwRSTFJFjpKqX5D2K4W3R7FKq8t6ISLTLrCJ0HD1opQBCbaz4CLcUw08n\nvEKNMzpbqD/5Xihk7RxdtWWHel/FGBs9sdbsgwBV7OSV3NdAPubrq2v+DCB6BrXGIPw50iqblpiK\nzWukQCWyaWGYCkCiiitMsd7bCU0mwpOnGjxVbWqJoA0L+pT5rR03aZgoJWWMZZkRQqGPOSzLivNU\nZMIbPTLb1c9SSSLAEAHvq7YakZbjiBHSNS+FfRkfpNv1dROVfTwM6+k1rGoz9aGGeFOTQEXbxuaA\nFejCpqpK6qr8r0TN55WOYTULiCEIehngELPpGJYJCMELr7CYd3ofBRWsQRU0R0Y4asMwYBzHKgDH\nPDcg6xUVkTJAgYqyAQgBJI7YUmNCTgm9iLc5pBTkGIgq0NXHIC3ndZ6xsAbUfD6j2E7lYzCy6Hok\n6IbY2s6n1lokh5MhSdES1gorcSGfu2bgKNUaXVaPKPUdzTCREvJr4bl570UfSmsSsGDl3qFBZdfU\nW6uGHK80VKxE3BgrLAIh1ZZjqp/lWd5YFe39lCQNyqKHpR6W3a3dWtQz6mILUpWdoQwSCzCS0iCl\nsWPRuZiivB8OQlDOcJFeFtE8/2XOUK8zFGryxIuSpOj1Psz/bYCiVlfYizGNeUZd1GLMiPjyWfL7\nsnG79ULWuywqzocs2ucqC0NKBU3ZJrtLFTE/C208bFnwqT5vMSUB5Lp1zbCGshDHBsibYjMXdaQn\ndAmfGGGh0jx0s6gYLbudZfdkaqzXW3xVlNpIE1FRjqjEv9AYab/3XQ9neNLTBLR1CFUfklxgLLWC\nUCVeilNLCFAy2RWnpbWpiOf9HleHAzaMKdF9B9UV/7wettRGbC8SIcN2wbDZSN3k8fFR6i7OexGg\nG6cRO7dH15djrxSjZZlxPjEW7HjGzBFkWbiknpTqIndJ4M71QEFGKw0UE4pGBz7GBHDdh4jpLI0k\nj6hRtNQmgf/UemOJvpzzolJhzYKuqw48+XhT9Q5MsdawiIRoLWYZZbONUV63qPDtOArRmpTGqjQ8\nR2JudvI58zzLhmaMzQJy4ppTF/8LC7FGkz/x721fpHYcVsGIRZkjYy2M6QWWoXUlQlOD86MG4wTK\nNvJJ8GgcXSNHMPJssAClafCAJC5QNRIjVCmZxohNzv88TaJwG2O8FHi0BrZAjEjJ9Q7BCQzBWIsN\nE9qd28BYI/fsuk6yycYQMHNd2a0LtO5lY20XrJiC3AP568pG+gxreB7P43n8/3A8KcIioKlhURM5\nUUP81BcbdNsCSKgdprbbRJQjpNLp6YZOENnwAaH4Eq79BR9OKcJmLFZFvVh+qeY7y9CtrHFM0hpG\nIqiCqtfmom6ltRZ4gDY2t9MB2GFEQbOTsdmNunQTnZfQO8YA5xu55NVJKz4lls4B4BYPV87ReRQv\nNNXI4eT/xgZE2zimSIRaAa/S5kbduREh8AkilnUWdHxzbammLRmU2gAUG2nreZov7KcG5hKW+yJL\nUXNUVb4EBQ5Ro2tC27aFINM72yEM+bs22xE7hisQKSxa41g4fg3A8XQ6SY1zHMfcoSu81ouKUh1J\nkRx0qaeWnwMgnUEX/QUg19pqWKuh5Lzbb2jt5ogyJMRzpyyGxr0p1DhDGwXTyhmpJIyNtuueYmzK\nXjW6K6DXaa6SPn0/VJ0122MNsXaFQ7UVW/0Cn1aefyvO4GQylzJwl3qarNi2LdOCpWQVy4px6EU/\nLZOumy58W+tt7uWPHU8uupcTI001TM0lqDxUySh4Ulv8U2N5FKJvyLaZFV5IukPfi122CgmhPNjr\ngmHssYaiJ6Sw3VWNn3JB8qJZH1gg+9XV1CpK7o8QEUtRe/Vwq4Mp4kBWQ6EqJ5S0IaP5+QZXGspY\nmFLr6jpBj4dQmweBkcVa55tJuWoku65eNIwQIXURTSpDEUTTvvrnxVDbwjlVV3KDq1Y9oxEki6m+\nTsQ3TLNI/SYOan7Ek1zDhFqjWpa51jGCh/f5WpT6mdbVLNM7l48FWXGgpHo6KSRKQo+ap4o1m6ZJ\n5m8z9nhxc8OfazAvi1zPucGnHY/HahbSqFnkeXbyHtt3UnYgVVkHgSkDBaYyL04Mb93qpa5kTXdh\nRJufhTKBusJ60Kz9vPiX296FhNWXRlEUE4vOWN4sa4pealSJqFHETVJTk4WAIJSlGKpo4mazwZaV\nOfpxgxACzpzGnU5nSSld8NI00RrYbouhh8nYS57b44MSlP50PmNeqnrrNowCW8j/LfXJcPHMC9zj\n420Jn1PC5/E8nsf3ZzyxS5jQ1A/R1mhLKz/EkJHXBWiIhOINlBLEace5VXbAkFxOC4skrNUYuOip\nYhKAoPMjNsuAGAuK1mAv7rWD6O5cdGx4dTfa1E5XbMBtVLW4g8/dp7KLaB8EOBdilOiQQgQlyYdz\nhMUqkl2/ouuq8aspkVhS8C5gZv4V6YBZIokF81R2cSfRn9IdUmwkeqNHKI48wQvPkbi9XbmYNbKJ\nvkkbmqJ9RG5eFJJqUjUqJmp27BZNDf6bUnRfV+mCxuhFgdIU6Aa1nf7ax7KKZF7y31EVbw6pRr+A\ngDuNtmKtbowBUhJohKJKno6xFnZ9iogUJQ0OiCJ5nRShMQlq0u7c3ysS3cuyiJ2dcx7dpli697DG\nXjAKyr2WYpBiOmJCVb2lC66e9xFBQNGuNiuUzqav0jhJkDQwNSk+asWlzaqk4dRE4V0/4IqhGtv9\nHilmc9X8fYS5dGYREdjyK1IQzSxjuwyRKZxLv6B4zabo4JbSfXZotbfa0k+MqRbYKT0pFSzjaTUs\nyuL/AHLqJ1bDFesTygE2uXVplyciEZkLMQgCOQYPJNSJik7QsSG6TAwF4GKGBpR73RolgvmZ5FsU\nBlQtnZVWuq6dEDRCekpVBYUUI2LjCOycR1KcHi4OQbHOUyKQadr8SYvOlTEVaR2Vrm7MpHO9orjt\nIuBcnK+nCQuH58F7uUm00owUrtCNdkjXJyGnqA3+qNXDMpHZ9w3cX9L2ptwni1lsqVZ5AZAOrqq3\njHNOaCBKJcSQ04eyF2RMGiObYyPb3HboUFq2v94Fa0XsTCM4p62Gs1owbaRIdKVicHJf6ciQjCc8\nGJWxkX8OTDUDsvpGhbzYDD9oNanQnlP5wPazCSmS1E9DrIwJ772ob5R7VXrARLWDiItLJoGCRBKp\ndp+d97I4GmswDOwFuTvkY+ULtXjXmEiomqqlKDuOURpWa8Ti/Gy13I8xVpZD4vfI6Tcb1f/H3pv8\n2pal+UG/bzV7n+52773oMjMimypjF1VlCgNCFsWAORJ/AJInjPk3mIBAMsJjJpgSQgLbshlYFjAB\nl4CCwriysm8jIt+L19zunLP3Xs3HYK31feu8iHTGLcmDKN0lRea5795zzt5r773W1/ya/tj7c3iU\nSH4cj+Nx/KUcD4uwjBFnFBCk6G66gmPD2zSpF0sWiTQMbIBBMpDdN6WEFDMOlTM3DIOkaYf7I26u\ni4zIm9evcdjfIzcOmTOKKqeEpoBc9HjKa0Ggd9KvtgM0WmMkFTW1QdCK8GFZEOrivzBANToid0QT\nOTLOl+iw/i6GhMbrMsZL6uNMMd1sO15x2qmmA3uV8UXOcLtdPY/Ssby8PKu/S5ISHkPEUcLzVDsw\nioJvw1oLk2o02RXdDSquq3UiswIOe8u0woNWTyNLqg6aQxR9o5wHiYYaJi93jRVmxeNY65THhwoU\nlp9NH5xL6m2MURCjsTC0SIeOiIUwHTvyNJBByDCdjImyELpZonwaaXZgXYYGSavNBqsaYbXIXju1\nRssl6Dyguo5UTmW+W6GeeRHpmyVEDBvVOoMhUS0lQ50Uckd+7q65mNgaK42ZeVqkE7jZOuVYWg+A\nJVo8j+eI0v3b43goTZPz3Q62NlAM4YQ14DoXHgZjbl3awwEhhBPVU+mypojQOrspw8vc4UuPB6aE\nUGsuR7JAkNWwrumYZ2mf62J26vjRo95LfaEB1qbpKHWS/d0ed7fN6ukGx8NB6lN+cMr0zlnSryKA\nVoeAHIM2Mq22oJ0lsRvy1sqFAYAcgtBi8hKQTbkoiQzYaEfOkIVpTKDM9eGs2tlGU0/TEVxjiJir\nANrhsOD+vuoOgTFWIqrzhe3+7nvvlmNIUYCAwzSDb4o2UcQCkBKRTwjKJzdD38kqAXqTm44pibdi\njEr65nrdBBnBLKmntV66ZpvNBmdVLvmsLrilq9RsvqI8dKrhX252AsGdyPqSvL8NQ72WeVkMY2os\nAu1sFYBk20jNCeG7qFCUzyuy0mUkMDLpw8PMnb18llqS7azpnDOwljrFX3W+BrQemJPOVxvCNkia\nbqJTKzGmWHwRff5JZm7/U/6/pxmXjyG0BTIGlo6k905gP007rdXf1quViA3cTDOWrjzRaosGhBSS\nwDCc8dhsykbKmWThPR6OWELAuNKOfZv/yIoSiCmJYkov9PibxoOpOW0OrbNi5YOuNtVkZ9qBFZxN\n/bMTnpcuWH0BHygFyGN9eI7HA+bKrZvnCWFZpHZWIoG6UvQUIIIUbnMzgjwelD5U4Q9Azc07M1Bn\nnRxMziyYmRASApca1pKB2Or31ayy2SE5a+Dq96y86nyj1i5ahBVCxjLVBfowYzpUFQZkrMayeHFm\nWOtw9eRZfU+QGwPuHvcVb2NSOql15ax63eWCtclNXW2BkFkpMSkmwVfFqDspmECkCpIMxdytrYOr\nEffl1SWuqsDeVYUfcNLP4Zzl3imqlq0ZgdowUM6h1iKd7NTOOPgWmaBEGq0YPs9H5Lp4kWE432SK\nDKapN+dQagibDCYtSr9t4nBsVJOURcPeD4NKvjhbN6Q6zeihPB1EpI/WGCf68zFGwanBGjG/tbZE\nnEpdQ7dI9ZuOjv74xeBj9JiXpidvpS5XmgAabceYFFIxrsUE1Vqrn5vrBtzoUcEqzanVUIECDyF0\nc95Hf0YW/567+ZDxWMN6HI/jcXxlxoMjrLa7DoPvOnQKI2hodkFkN0U0AEV8rL48Ef1DdYWtHCbr\nNExkbelaY+C8ldB5tRqFBzgMXlUXrNXEp9WgpkkNQI1RVxNrMNb3jd5j8A6o5wVmhKiRY0uZ5pix\nVKDnEhMMDFYV7Dn6AasKr0jG6cYo59kgFPp5IUQsHTr+/r7U8sKylN2qCcghq/EmGY3eahcpJk3p\nZMPlDhkNknTLkAExgZOmLgqsNQD0s23bkevHjRVeMPgBm23pPD15eomz89I2XzficNaUJOcsUYLp\nIRRgdI1BEEGituKOo9r+jQmRalcxdt3cJAme8hRN67IKnxECbeHc0YW7NnwJZlj4eAxgGMo5DuPY\nkYGL9J5AGZg62IzWS3uWQOtYikNQykKat97A+zav/gT8jI4hYrrojbNGj33sMTQy/7jC3GA0hrp6\nYjmGlkaH0IG4zSnYuemlOTKw5ATAOodTV+82CiRGo0hmFlhT6Z62dULjxB6m8ZvGg8nPLYf3g9cb\nw9ou/zZKb2jHKPDq07BbPpZO83UCSRvdOS8h6nqzqQtT+a7ddiuFQ++Gt3LhNjE1BUtRLinl1B5H\neGsFme6HAatxBdMeGJAoIpiYYGphvUAT1NmaoLgn5zvUu/dCji0YKaMXLLPU19C1fnNO6sU3EUKI\n+PjTFwCAmINALpYYsDSbr/peJVPP8jvrnN6MmcUFehxGWHJad8lq/OEHLwXaYQ6FQsJNMsbKg3Vx\neYmLy6I0ud1t0OhKh6kpfHZqp8ZIOjUMgwj4pRRhBwNTIQrOO3ngxnGQupdzWgvx44BxXEl9cVkW\nDJUZcXF+2T34A8ZhJYob42olO9g4DKKGOg6DLDbzPBccVsPi+aE4c6OIA6pcDpfFivU+0wZG7mpT\nHUQixVK7kdQ9CRzAWneSbhpzCv3Q13rfWKIuANAFst2Lw7iCceVeijHj2KRfzILMsas7TeKmHkKQ\nhWi/34tCqGGCM16Q9Xf7A16/LgqmS1BYRDOaaITzyOpDwFlro3198iHjMSV8HI/jcXxlxoPJzy2S\nGgYvu7Xtds9xHE6cWsgQuEYjTLkL/7S966ozTgMleu+kEM7DCtjUdnlFqzcA4e5sI+TnXsQtVbcZ\n4IvDzVIgbZ1L1fhy1mLwHq52OEbrMNRd06cMVyMsN0f4ulu5JYBgsK5I99GrQ/TKK1HbeV9Ez+rO\nY42Bl/R6wBirOJ0BOKu2dwgRn/7qZT0XdVnJHUwgxAQ2fdH9dPdqMrrEnQhhLv+1cN4gKhE3atG+\npGwE03iUZGQ3TZwRmoBfyiJdEypQNSeWFIKIRE54GF0LxhA5YKRB0/XRi63Zer0SJD0TJAoYViOG\nYVAN+kzYrEpn8smTp5K2eb/CZp2xGqtj9HoD11zEvUJnnFHNtRhDqUfXCRiGQcGiw6BIbVSXcJF6\nAU7y/26oTlsuWm29TV3TnrLqym2tO02btblZQMId0Flvb81QRPLLGomgl3kRx5t5KmDs1nE+HPc4\n7o/1/GdM66qlxurgxKmBhsvn3e8PuK1d6hBmsalrhfogwpBJnsGYs4gBxJTk3jjpuP2G8eCU0FeN\nnmFwoKzhvuoCuVNSaIeC56yo95whyobDOMJZTTEH6+GaZdfo4GoHbj2sQFAbo/VmhZVgS1RxMaX8\nuQXLGXuifql0nIjU6kcpgQBZLO1qjbF+98jAqoaz+zliVUXddrFoJK2q7XkjrgI13RSEuCn1jHrO\nMUFqWAxIG3iJi0ABmIuu16FqbxcEcTuvBDJ1gckRTDipG8hDkbNq1Pf4qqYq2mAfJsiCdTweJV0I\nc8HISYrDEEG7aZ5hqk9dTEkeuJaZz8dJWuTF6buSvYM6Og+DL5g+0pSwtxhrVK7b+ztsrm/kuw7H\ng9BnNpstzivtxA8j3vYNaNg/Zx3m9gBPMw7V3fl2vZZja+h/9WW0QsQfhgGgZphQtPJ75HbroGWT\nRXGDOnx3zkm7r/VNvfqDFYaDwQmXumOlExn9uUPDt9FMYNq5tN/PywK+KfNn7RGZu+uxTFiqiQqD\nVecqZ1HBCEsEWOtgh8OE/b7clxmMdatrjiOss0I5agYzAE7qrKnfVB+R7o/jcTyOv4zjgSmhgiwH\n75Bls+ndcGz9rxFzS1EaKJilhkMCE1zFt6xXBAMS8JqBaXAZeEPgCpsiLlrZLZgaBieEZyLb1bC5\nr2eXz+lMJQmkMrLLovKu84IUkqRQ3nmYmk4MxmBoHZ0xYQwNsVvdmUVKWbFDlqTXJuBLYxvCVHXE\nh9EjpGaoqSBI5gxwKfiXt1CfkMh7Yo6ANQLEizmLLHLmJOTpGKPs2jMzFrdgqV0kZ6xEaMdpFtXU\nJSylmyW7YMK81Hk8qJvONE2S5rWo4/7+HnfVSmqeZ1XCPBxxV6ObYRxl123zJ80TQ4K1evXylRTZ\n1+s1pnmSdPHi4gJPnxTs12q1Uq2tKrU91qbNZrORY5jDguu72/o9Whgv0QULkXvwFt61+4HQGmIF\nKa8pl0UvUqzSxSAGQYUBCqi3pYSd2zlpx9FQqu/X8onmeRqN5C7e6JtWfSe/dVaPc5BmDmM6IYln\nZAmLvVOZIoI2H6ZpRgxJUr3jcZbrtt5usK3d4s12g2EcReXWui5i7viuBbspZ/elx4NrWEP98tUw\ninQs9a1zbouEpl8tMC1o5QaFYPTC+c56SasoUwc4hFw3wyUMb1LIxpCmv6brRoLV+KJ9vh9l8QBD\nvA6XY8B0aN2SA+ZpwirWNjYzrG2pinaV2CZY/9aCJSlht2BBb0KgLFjW15ty8PBjRYmnUa5a5qx1\ngwb9qKlPOdnWmg5IzfQhB/BgO9qDiggi587hV/WgOGUEY7GYRa5NW3yWRQGqSyym6f3NTbHVRSD2\nZGSgxhz1O47Hoyx8PUJ+WWbc3RWq/263wzLPJ5CUTVXg2G638pDN8yw1mMPhgHlZRM/q6dOn6go9\njlo/rcKG7fOePHkii/ehOzbOSTbVQp0xeP/dsgAO4waGGoF7FkdySw6GCb75GpzQZLIoaTBnuYfG\nwSPEJL6T3hK2qwIBWW1GDM3qnhgEdVYvwn/184COnAygOk+LuQazLNDb7RabTTU0mdThvDiKJ1mk\nnNdO+Th6rKsGFseEaSr/PpsFKUXMs7qQt41lvVljV1kO2+0Gq/VK7gXvvRwPGypUNgAgg1Na9Jcb\njynh43gcj+MrMx6Ow6rRg3cOKXSFRQ1uTmpoRclSehwQEKh1EgUZYzH4EeuafiGRhP+c82mEBY0q\ny57eUp+scT2xdAHb4m3IClgUjA68p8XHULmDavrLkrZZa0XF0+filAMUYKwx5oTeIxLLgJgwtAhL\nDEO76NAn7ZqB+KR4zinjWJ1ze8pDogSsqponJyCyYLRSjBLKg41EXikluTg5JmTF4w0AACAASURB\nVCRSorCBSpiEEIVaVa4PIKy1rlZaHGraoSkXNHVvbN3jy4sLAfmO67UUXVNKJe1rxGnvpSu32+0k\nCrq/uxdpo5gTQMDFecGAPXv2DGfnhdc2DCuJ3IuhqcFQO7ir1Vp2+8PxKOlhWBZJ7dbrLQwRhhr9\nDt5gaCmh7UsfBAcDK045+gxkVidrT8o/zAQk9jKJKWbR9FqNXkw8rEk1oOoiENKJbxgnEBQU25Xf\nV4Jb3GK1vqvzskeITYU2oUgsGZnz1aoex2qQ65RDkOvn3YyJTvGTThpwGkWNqxF+HEW1t4B/m8GH\n6ps9hD/YD3qIFs3V1RP+2je+IV/+RYHc5z+O8UV/eer68tZBmY7t3l0IQgl9uetYff678LnPffXy\ns5MLStR3FTvKKpXFUyeT9H3UoXQ/97301hl2tYf6OldH6dwB54QwnjTv7UG3xhjc311jW9v8vbbQ\nSRv7C/5Nfse9DpX+DbWV/6Tu15+fdpqMIdW2N72HH53Maz/vORMim24q+mPrrm47Pmo/kwIwu2Pn\nDjBJtYL09me2OevP6aSm0NV/uPuRWTXTjTE43L2Rh7a4VDe7Lb031Auyu9Y93OAL56UAQE23EHF/\nrHKc5T8nBPFOELOTXgb1M0kIc8D1fdR/6+fn5J497S6ecqw7DEWHR38b3P3rnN9RE71TIPjnD4JO\n/p0wT3fMzL9xFXtQhPXB17+Ov/s//I/lB6ufHbt2uXOu1KnkDuDOgmnBdW2tHu6P0kYv0ihJBNm2\nmzW2VWxsXI2yes/TjBefvsDr19cAgBySFPGLBbhGJmrhxPg7f/s/wb/6r/0ODOvueHVVdudy0RsW\nxmLcbrCuBUTjrArlkxEL9Zx6/IuBgRXjUry1EEtR+jjh5vYG97el2HzcT5hik5eZwPXYh2EQM4fd\nboN/+Pf/W/x7//bvld95K1roYIAq9IOzqS3supMDMhchhK7o2ZvVWhjrhBoSY5a6Vw8LSTlitR4k\ngjk720qjA9UEFWi1t7oYM+Of/vGf4b3f/ptI1dCAmNCMS53x8Ny03hdkAKZe492zZ9juivY4ZYOx\nftdmM2Cu8kMf/+iHuN3fALl8RuIB50/Le37397+N994r9ae7mwN+8N2f4Kc//mk9fwbbFrlHUdXI\nicH1e7yx+D/+8R/hb/7hvwMAOL+6wJOnT8qxXZxJ3bHR0prvvHNOFvU5LAhVjDJmxaKNxmMwA0w9\n9+P9LPdDWBahJA3bAX4knO3KoskcuromC4SIiASLNoeMf/L3/wl+evsNgRQ1hD/QcHcVXqFrf7mM\nRKBaCyPLsLapKKjuunMW63GUe3tZ1PMwhIAouKuIztMDlkhrzsigJorADC/1QML3/ux//hN8ifFY\nw3ocj+NxfGXGgyWSexJma53v7w9Sk1it1tislHNk3SA51Hy8x+2b0um5v99LaldAdwxbV/N0nJDO\nSvRxRU+w9qWmMe1nPP/0JX74/R+Wn6cFcanwhKB1G2OUw6bxrpHaBhELwtsPTl5nAkZikaxJkSXn\ntgbCKcuJpd2eApcuU9Oth+bn1lptC88TXr58jesaHXImuNpZHMeVCPPlnDHV3erMnVXSdOulaxu9\nREjNaqxASVpUmzIjtvSEu/NJ+SQoLxb09T3qqXoCX8kcT4CoKWU40R6HfhopabaFbdN0FB55zsVO\nqsylRaDqkpMWTDFjVx2o/8bv/hZ+53c/kmNvnzkMDrdvytz9o+vnuN/fIte53e3W+P3f+xoA4A9+\n9+t4clkjNGvw7Y8u8T/9gxKZ/eAnL+CrbRhSFhgIkS11HQAZC4gMttVS7Oz8HOtdIz97OeeWBrXI\n2ForHVMLAI1byhr5OnIgdri9Kcdz8+oGL18UFkNYAs6vShT7bHiKzXoj5qTWaApqjRPw8TzPONYO\n98tXb8qBWafOPuMg90RMGdzUKGNJlVXfX5qNsETwrtatBiN15nFw2G03ElH3hrXLPGOeyy9CqGuE\nkMy19lb8ACT3PynRfdnxYLUGTQUtjodSEL1+c4eWnzrjwSNLoX2ZFmmF3t7sRbTOGYNtFXpbrVZY\nlgmH+5IuFkOG8j0bv8XKlQVrOUYc9zPevCl/dzzOLSKvC5aiaxuRN3YPe5sr6hDGBKPhMNUHrz3s\nhhSiYLwusAngpS6uS0BkiAAciKQ4P46qLV8UM60sejGytJIBtX7iHE+UNNvvgdJKbjeAs16oTKY5\nFzekdc5YOmJwa6PHGD5PjWoa7WTFn9FaK5pStdIvOUTukdRJtd6/oJwE5ASb2iIKuSeYA6KtSgEc\nkSmDm55/XGTxMETIYroRBJayGjaI0YOrPtlq43FVU9YQAt68KWmWHz12Z1t861tfBwD86GevdMNl\nvTaRM3KDgZgEYwirTaMHjVg1QUXnlMjLhWYm90rWzYAJSrlxDg2Nx4mwzAkvPyuLy8c//wSf/eq5\nzJ9xpT787P0nWK0HzMfYrpQU+3Nm0f+/vr7Fi89eAQA+ffEZAKUtAcBmvZHzDSFgNg0ek5CDNrOI\nINfRe4tVTXtXKw/nazNiNeL8fCfCK8fjQaAvh6MT9sk0GcQlCFkeGVC9d70H8JvLVV84HlPCx/E4\nHsdXZjxcD6umCiEEkfW9vr4TE9SrK1NSjVYMnBZcX5c08PWbG2m9X53v8OxZkf49P7/E/nCL5zV6\nuH3zBvO+mjwORzhXQvKUCH414tn77wEoDSDvWgcNnQKiQWxI9jDjx9//X07lZtmIiqS1Rl1RjIG1\nmk5yl3ZZq8DMAqysiOlpQo4aURrrMIgDsqY0xhoMo8e67tYpsxx7QR237qEVqZUmL90OPSwL5noM\nPffSGAcyrtuFM+YaMSxLwOGoaHPlcFW3aNdMTwe4+nocRoA0+isgvzp1uXORMV/c6W1zPcUF7RZj\nsgL7j5QRmn1aMgBbpApGnY4BIbRCdpeyxiSpyuXlOXI2SHW/3R+OuLkt9+KTZ2eiEmsTYGwXsRpt\nJhhyHa81inEqE0DGYF31yofNSsobvaNPTnwCe0k5o6l1G2vBtv07CWd0f3PE9esbfPa8REW//MUn\n+OyzIh203qzx7tfK81AYHVaYIN5YcL0PD/sjXr0qz9PHH3+Kn/38lwCAl29u8OHTK2x2W+HXnu12\np0yEmoYzl/tAHY8gEdLgB2xrdLnbrTCMremxwuXFmRTTj8eVejDcOyHyW2Nw5AMWMUxmQRtlxmmH\ntM3llwcqPGzB6j93WQLu7xpGZo/zi5LeEbUWeFs8Mg77Rf7OtQzEOxW4dx7rcYtNrRvc3t5hX3Nz\ne3+AG7dykpvNFt/85jcBlIu8Xpffee9hTUu3IpaqrLA/7PGP/1E76hZQ9hAHxdaU1MqKgUahXiit\nqOG1jtOEfRXZOx4mxCVKB9GNI3a5HJO1RtQarDUY16OklTmzLBbzNJUHF6UpLAuWNeiVA3p5XWYS\nPI4xDGNZukO9GGKMurhO01FF3Op8Glc14ocVxlE7QsztQtXOYsMYdWRq6sTp3m57A8AhRqGyeGMg\nIGcCjPj2JeQEBNS5PU5y7Yxxqge+RJmXd9/fYn3ucf2mvOf2PuJnvyy1oKcfXOByVxkTMSOlgP2h\nLNjLckRCQX6bPKKdIki10SkW6EJ7UL2zXWre1WM+d84kC7JxHrFO2OEw4/XrUsJ48fw1rj+7xvOP\nyyJ1/foac71OG0MijGiGASFHDBVaYY0Vgcfb+wN++XFJI3/4w5/gF7/8VTm3kPHh0ytcXp0Lsv/i\n/ELqs/e393J/lEeTZZOwZLCqz+J2t8HF+a6+f4vNtqHm1zg/33Qp4Qr7lQoOCgskZaQQVFCAs3Qn\ne3OP07rVly9iPaaEj+NxPI6vzHiwa05bDafDglcvi+Lgy8+ucVk7M6io90b+DCEJATYsAZcX5e/O\nzrcCjAsxwDiDq6uCdzkcjnhzUxC686s3QI2ctmdbbC/OsDNlBzi/OsNQ06rt7gwtDJjngOlYOWM/\n23/BefRQtp5kWjlbQjTVYniOQVyb7+/2uLsrnzsdJ4QliJ2Sm5UEXIqWLSUsBdxmUJFTBksnzyA0\nMUhSM4ICWCTB3WBgdawGBJEfUwJlhjVa+O9TtV6CR0xtaweORINpwOA6Arc4xRj0hg8++RNgqzQv\njIJIBZPXRYGREnwzEDUJoVm8cQKTQ6oR1v5wh+O+OhJzEgxZDAfcV3fhp1fn+OaH7+Kz1z8BACzR\nYn/UlFpJxBnTYcLLF6XIfX9/ixpIICGKqYU1LK7ZZH2XJbQoqs4391pjVNV1NXoQTFtgMcl9+fI1\nPv74EwDAx798jptXt7i/vpcPbLZ5m90Gm7MSGVkPHOYDuJYPOAXpLP7q+Ut8+rwU2N/c7EHU+JQF\nV/jkyaXIVl9eXBZZGJQml8iYF78xxPo7S4RNjebOt1tcVgbB08sz7M4rr3O3wma7QkPcr1Zejt0Y\nUmu8ecE8TVhsxWghKtL6AanfrxsPrmEdWmfw5hY3FaIw7ycJ/QkESlm00G/v7nFbF58UZvih6bEP\nQkUoWACSFHG13sDW/OHu/ohhX7s+67EqNJSbxA0Esq0dPUn6FvOMaamCZKlZcOMkfI9iXAjVNa91\nsO6ebP9T0sza7Zxn1XMKIWAJAbG2jEPM8GNJO3ZnWwy5hvmWsF4P8L6lbUlqNdNkpF1u7KmUMBGk\nTjKOI+Bb2psQlkbxqCJpDaDXWUaBCLYumiNrp5OZS4ewToO1Ds62BUuPk9GgEV8sDtgjod8O7Sly\nw3YipABf/zYCWISyVGp0asDCONaa28uXr7Dblg3u4nKLpYJQnbP4G//G72C8uqzzl/Dh+4X8fLld\nA9z0xAg//cWn+NEnJQWzjpBMI+xnUKiLrcty6ERlwRpbucKYTtY3yaLcNmRhK0RI3SbmjLta0nj1\n5kYkrj/+5Dmm2z0MlXvCOwtXIQh2sLBDOx4CDGNJjTqU8OpNqXt99uqFgK9jYlzUTf6dZ+8A+Q4X\nF2fY1pTw8vwMy9Lc1VVpISwBnDJi0/wyBttKeD7bbnBeiczn52e4vCzBwXa3xrC2UmstChb1eoYo\n8IphPJxo0xX4R/vp1DWrl5f+suPBC1a7YedpwaEW3eclaNs7M0KMOE7lxA77Cfc1GlmvSCZzsxkF\n2R5ri1XMJTZrwSgdpmv4WoN4gqcYNyus1rUFO2gxOFGUXXKOC/Z1kZtDNRNgLV4DAlAGG4ENVRG8\n8l8bxjQ8CUu00F8E5gKhoLrzLEFlVFJKYDSPQgIZ02mZG7n4RAmtPlKK3yoeByj9YvCDRKXLHJBT\nY87ziV04GWXfO2RQFSFk4rdujs7GCSTzZzs6SowRPXqrr6mV75NCEN7eQjll0QJiAEvb4Sl3Khao\nKqpNwHDGVHe/H/30V3j92c8BAH/td34LH3yjLF6jD9hsCf/Wv15gAAYEk5sBx4RjhQP84qcv8Md/\n+l08vy4R1toZCFEgA20vo2SR0Qx5w4lpK5GK1vV8kiZd026eJUfc35eH9u5wxKvrghn71YtXuLmr\nuuoAMK5hWiMiR4GcTPMRc6hFcZPhxtb0AOIccX8om/7N7a1G99OMi4uC6n/2zlPcPb/D5eU5drUW\nfHV5KQsWEQQnximBOGNu2vsMEZpcDQPO2vsvzrHblYVsXDlY2ylyjA4hqMpwYz947+GdFzPd6BJi\ng09kpfpwh8N6m9j2LxqPNazH8Tgex1dmPDDCIqzGEiGthgHW1nSHFukSIGfEEBHnCis4LtLWXK3X\n2DXUsDVKBI5FhaDVK9w4iAsKEwkIkk1Bh/tVW2cTmjYAI6FxJ2OYsRfp3g79Xld603UGAaOtbhgU\nNypNF1v0lbMy43PKyAJjsPBkYCv4NKZFpHtjjKLrZS2V+lULiQ3Ax263kZ0nd3CFmnbU2o+1XtLF\nlAhNrjfVrqDWnZzWmawX5YGiWaXg2pygrf2U5byLsW1HTdVssyCkufHatC7S06DVMUnTKc4sCheZ\nMoba9ueUkDPE5WcJiyDQQ0j40Y9LhPXi+St8+1ul7f/hN57h4nyN9bohsr1AFw7HA374w18AAP70\nT3+A6/0BF2OZlxQSxrnM6ewSFmrdTtVmSihKEHILdJ1Zw5q6owoytogjLEFcZPbzgvt6/y1hUVqt\nsxiNR/NvDXOQUD+kgKVmAykmODfK9ciZcV9LMXf7A6a5WXdBEPlPn13h7vlPcXl5jrNdAdG+8+yp\n3IuDtzDtQYwRnKJEXOG4QN3KGbsK6bg6vxAhQ86VSUKtPkhSj3XOdG7bhGEYsapcYGTCUictLUEM\nbzNnYYc0NsmXGQ9csFjIt+v1DtttyW8Ph0lpHVQKsc0s4TjNkimsViuMY/OWWyT1yuwL/76mGtYZ\nrDr7LpF7yQXv5KjBDrLgdIiphpxAnBIO+3Lxm+BacaBuxWK1kuqjUWtdQS7bRoOwkt4Vcm9L+5QG\nZI0t5gpCgYiSBi7LIje7o+J625DR8zyLCUBMUciyQIE/ALWQzVqzzEziOB1zLjIrKKlnilkoIDYz\nXGMekN4Q5KhDmzPYJOQqCRNykoWRy4Ws88MAstzcucNhFZdvFfATTfMmnwK1b89B9cyNdUg1TyhW\n7hAN8LCoHdvoLSyV+2habvHP/r+SZn3vz3+Gs7PzQhVBIfk2aZv9/jWu37yucxxBLiLXOqeLA0K7\n58wCmCZsSMi1jrmgpChC/l6SNByMNcJ8YG/guHPO5lLmAIDtxQ5PnpXa0uXlhTQ9nn/2Ctev30jL\nHxykweIHr87kdOoSflyCpIGHaUbktgl67C7L4rTdlu/Yrkfs6uuLs60Q2mOYcXdTa2eDhbXUpVdZ\nZJAIrAuRdyDT1FIZOSZxy2bS8hB3mK72b0YaM+rvkCjhBFok9xi+9HhMCR/H43gcX5nx4KJ7Q5B7\n78SwElBDR0MFHd4Af8f5KMhj7zr+VpzBuePpGZLCtbWEVQWlWWexVJeWsAQg5+pMXL63RVsGVNIk\nAPMUMVdYw6a6EPeL+EnngvW3jMLNVD0hTQ1SyiJqF1JSgTwLRIqqHWggBWtXDTHbZ2Vi4QLGGNX5\nOasjNjkFIMKV9KqleilpOlaQ1pp6waikTkl5G4qRROuDiKo9WEF3pxTf0nvXNLnXCytSVA2ZntX5\nJWeodC9rzb3x9WJJEYCC3m6tbz5A3KyTZTjHGFohOgxS/D/beBBq8ZYZgZpMEeF+PoJe12J2WoB2\nj9kExCZBbDCnGagASYcgN4JnC3L1nJBaUI1VMmBmgamEECRKttYBvkUO6QQe40eDd9ZVV36zLTgW\nAGe7jTRAdrstvr9MePHmkzonCexLNOQcCch4HAYYBpbGrJhmcQOf50XlwznA1+NpxXFvCGOTMR+9\nUH8Hb0RcADmBUwRqFoQUgFybDGBA+JKpe26KQGQSyz6cCF8qULlkH7HTfRP+atfxJiaxi/uX1yXk\ngpsBSidJHyRVnrSupFVNvyctixBGAdXvMRkSehoqdSTpQIBFLYDYqCZ54LK4iVZTFjVIYhJTjBjU\n1bbl+ABONOSlTvUF4SgJnUbtwnMX9sYUsMTqupwyALXBMpbUGMPoRWmLekuF5iUIejkFFjQwddim\nVssSOgir5lfKrDUAZ8FQOyvbu08PRowIYBjU7MVTRIhBzS9Shury5Q57xFVADnUeAnK9uZmNsu8z\ndbiyVutjqdEUkcsufW8LIFVKi2k3+ISMcu1gCTCVeuUIV++Xa/nkyRXOL7eykNy9ucXNi9Lqv301\nYZbs+lCuy7FuZDnAiKKFBSq7oCQqde64dJ6lbmicUGSM6QwajKmdrlrDM2VDLu9JMHXR2G4GPLko\nx80p4cnlGV78vHauwVAP8k6cMSeArSz8x8MRhwobmI6zqFQMq1EQ+euajhKzyPMVd+r6mnNZpFAc\nqFNSjS0go/fp7HaertZLSCkVwxOUfSo2H8qwdLb1ETGmE6HKfj36nFggev7Abx6PKeHjeByP4ysz\nHqzpLmRPzmg7PxmS1MAwlU1HFBITclSTSqLGUTPyumDIsny2YyNdKcMWHFvhtiC9BQeUE9DMXK3D\nMtfC681RwuYGVD05DWM06us+j+rveiNO6dSxaq3HGBBjA/FkoCMBW2slaktQzaWUCSCWNHAJLKlu\n6lDvZM0JHgrQvZcZskVxpz9lrQMjK1HbO/i68w6DwzA2bauEmJpOWLmGCgRl6YCVLmF5XQrrWXbq\nnFVdtresAmd9E1qqFZBTAweTgCSNZUlNDRdtsuBrqosJlpq8yRW+89sfAAA++s5TfPRB6RKuVx7b\n3SDnf38IOMzl83716Wv8v//39wAAP/vpJ3DRItUUkYwB1RQq5gTXmhSsctVsDcAsRXcwn+iQSxqY\nM1KvpukIObcun2KMrCtqqQAwLWvstmvRpucUJWQgo6lYcUtOouJ53O+RatjYG6wWL4GaMrfoKSTR\nVothkdcpBLFFi2Euv6vPZf41MQ51xHtQbTwJU6L7zhTl2Ugp1Z+7e+Tk81t6WJo5Dx0PrmG1mkzm\nhOa35qwX5G+KRXNdLZ+0TkLWw7kGhUiqq4QEcIb1LY+2cvNbMwAV4ZxqG97UEL2HGjgyYtd1fXsn\n9YDtZvW5c8gESe/edskmLfmgt1ED1MaptLqrVhExTNJFL1dDCAAIwSOEcr7OrQq0smtVt85YzpBF\nqu+qlAeSTsJyGZYqErEedLeZWGsFqT6OaicW0wITG5XGwKBbkCl3Hnl0GrpzPlk0ey1vmTeG0u5b\nDcv2UAlVoTA2SarouNChWmYZgkUI5YdvfLTGO88+BAB4O4BqV++wn3C4B4axpcQe60oT+av/yjO8\n/14BmP7T/+3P8N3/58/1XnLAlMs9QpYxtl0xE+bYJI1r/UZcuVnKGMSdsgBVqIxv9ympPhlpPdY5\nwrp2fXcpYXO+wVhhA/M0SRpvijFhnb4C7GwLWFyW7n4zqrfmvQot1lRtPhxxrHOxv7uT903TEctc\nvSbnuSxaDWIQe0f2zik8Z4EClXvgrSHd6w7yU98vXVbWTbssUO2Zz9AF68vXsB5TwsfxOB7HV2Y8\nOMJqGCMiEmqNI6O4i5YmCliOBGvCMNIZLB0uISAW+eVuwW2dNmOd0GhyKu9rxWHnHIgb5mnGoSox\nZkDNCxqmqZNIodz1JThrMdhUQqvYjyl401h6K9qqaQ8nONbQNsaINNWi6GixnkuE572vJOamsWs6\nSWKjUsxWnVmam4wRTR6oemNSp5dckk9kUUpNoNr2Ms5Ip84wnV4n7juifSqMzw8hzmqXkDN3kSV/\n7h7gztzAEgkdp+zCdae1DA+H0BRq0z1iKDQUpHOEqov2i0+e48V16ZTd3wUwA+99rXTl/spvfQMX\n52WepyVjXcG1f/iHfxUby/iT/+u7AIA5BaUKpSxAYwMDJ7Sh2jlrgOSUEZp5Q06ixOqsh3FOGx2G\ndPsn7mhXBFPnYBwd1us1NmcFvxhyArVra+j0PcbAN7MJhgAu4zLJ3FnrTsoZADDNM/yh3PuH/UEi\nnXmZEWqmk5YFMQRJCTlTJ6MdEXIz6E1ofHpGaUI1I+RSwtHSiWQO1X1JIi7una61tFB78t3rLzce\nvmC1CTKQ0LQ85Fob6ZACpZvotBPSLM8djHYwwOX93XE3vzZr3EmL1BhdsGBIIA7HKeK22p+HEHFe\n5ZdbOE4ATmZf4tm3UpuyfJTXZNT9w5hTpYS+BpZ10QvLjIbujKtRHKYFSc66GjToXg/07eF8zf1n\nGBuQFAg1dZmXCUutzcS8IHLS58VAtKesIyFqL8ss3ZxlicV+vD4IwzAI+ZlgEeY+BSGpUczzLA/t\nuPKSvg6DCgq22YzE4snnjPoVMkYBvRIiCFZAk8s04/Z1uY63L+7xox98CgD45Sc3uK26ataVzeDF\ny5f1mBb89b/+WwCAzXqLpfJYh8HjD/7gr+DTqtbwvR9/jKb87KxBjq0emwSAbNu1r8da/C4bpEOR\n7t4NcGQwtHM2WtMBsuSOCTjZmLz3WG/Kvbk/zN09pWh7Jt0ogYoqb1pWIIFgWDKyCM3iYJOk+zzP\nsx57iB3ItdQlU0eEj9IBV1B0TEncoMgQ3OBhW5c1pVNGRtss6+c1+A53dnZg5bIStGz0L3fBUlq7\nwHYKIltPngBZVGwnVRJzEqrAOPousjEwBJlAQK3BQCwoayCJ+H/9pdR+wjIJViWEIOTV9v+cs0Qd\n4zgIxQUMxGrh3fJvoWJ4xSNlTgLpGIbu/SljvV4VlU4AZs+4q9r016/fCDJ6GFcYVx1pGiRa9zEm\nNRkdV533XVst63uIFSbRmcUaCxhi2cmt01ojURGxA8rN2M4thAUpRL1uieXJMtQvqgQL0vIU+CTC\nksEd8rn+k0kZqc75wklUMQxYouYUPLabAd/4sFl7zfjeP/+kzt++Iw5HkNXmDSePw335kB/84Je4\nqMjy3/7ORhaVaVqw2W7xe7//2wCAT5+/xFKNWYGEqT6Y3jglJKcGvajngJM9RueFGN71NUCIgCQT\ng1uUrlA1WMdFCcNWjXgatdCeLJjbPW8AQ9LYMQ5i/bYeBghqg4wQmI/7Mk8hJJGUCUuUe6SvTbVG\nSpbIx+gKbaAPbwfjMIYBw8gtKjVRzC56+E+D42ns1BfdO4I+eiL+Yw3rcTyOx/GXcPwF5GXqCyZJ\nXxK6NnBtj7c0cPBW0sWCiG37QxdhEQFQ/qEhKwt+b1VvjQUxC7eQus5KjEG6hDlkrYEJLCGLgeUw\nDPCDWnZRTZlydextkZTLmvIUSEZNZ52mPzEXO+8GUE05SaR3OE5Y78uOHpYFnJLyIlNWnmJmjCKy\nN4hgX4uStN6mnUpGljDAGII3DoMI/5kuwjKyk+Uu3E+xIN17xH9LTxwRuAcMArob5s6t+IRjqDCX\n9pFDdOBW8yBUkG0h2MYaDWYy2J0bfPj1wol7/QL4yU+Kztphyki185YRAIktAMPKkTzsF7x5U6Pr\nnOAFegMc5xnvfVB0sz549xK/+FmFHnACNekgo2ancyzo7labKij1FtWawPOFHAAAIABJREFUzl1m\nKCDpegwpJZHWhmEJBWIHujXMYLageh/mZAV4u0wZVB9H70aEEOX+8N5hXWWZjD0K2yFlxv6+RFZv\nXpWoPsUs4OQUWGqIuas3ilRxC6RIz9c6tQlz3ovLk7WACSpSmCwURGtdVxKq/b+utqkQEUUWmC7a\n+oL+468dD1ZrgEZ+SpbNilHKOcMQCR1hHEepY8QYEBqlBZoe5dwePk035XtylOJ+oRpQpxyg4XpR\nGZ3keJqWVL/gnJyJaJF3lBZjao1MlkuBCnjvBfFcWt09GVjzdOusOO6m1CGFc0m7JJ1KfCKmp2W0\n/nicfF89sbes7huZmGDIaE2BlALVK0FkZmlz5+rW3eqQPcLeeCebUYzpBE3RB/zM6aRYK39XvzwG\nhWREl2F9g6CQUEEigHFlMNY5Ww3ngC1F9/FyBNeHYp4d5oPq1DMCfP0emz2WmhJxmmFcs5n3SJzg\nq1b91z56Dx9/UojRKc+gCl1ICMiuEXRzLWvU6+6cbL7WqruzsaaQg6VYa0SNAsRSl+xTonJbO6m7\n5gAhOMcE5IopJDhYk8XxfBgGEbRkZmF+HA5HXF+XuVpXKluMxUKuvW5bP59sNPUwOxyfHZqQoIOp\nzytZEsS+sYDJBuIu3uEw+w/luqm25hyfFN07ND/pfcld0+o3jceU8HE8jsfxlRkPN1Kto8jB6G4t\noEowYAuBGShI6xa694DLvhlbdvq+g0Zd4U+7aG6wMBbgZlrKWULqmJIaqdZuDKDwiO7rSsH6hG9V\nv8qU9Ep5VZoqGLIajeC04EykCpWDXwkpPIQoaVZiYIlJuoZLWIQgXoKg7tw7pH2ZIEUUt5Q6xSAp\nFllzEiFRvSYAEFhtlkhauBp59WDTBtx1XtP1mCvHswt+JS3NGblFbNmqE079zMCA49ZtdKCahlM0\nyEvd0VOAiRY5VdkhG/Dhd4qN23d+60NsL0qqeH13jx/+4McAgB/+858AiSXqyIkx1QgrLwvM0LiY\nHiFqGWO7HrX7lxyY2rEFQXt7O4C7xssALx1rP46igZ+B0iFuUUImmNYxpFKgblMhfUBTsoh268SU\nVfmWHRiN+WFBhuTaDOOIXdW48uM1MpdzPdxPeIkinTxYg60vjIokMAzSxjhrfMMF9SrRovMaRRpn\nRF6JiSW1My2b0RAJGiG9lW6yFvS5YxEQFDLT30daJ/nN4y+8YDnv4Gp+ywaSb5v68Az1d+Ss3AzL\nsmgNJSWpI5W6CEu7nLk4ywKFXtCgCePg4LwROkTmzuF5yTgeyzFs1zupKeWsk6EBLKMJkRXnZ9Rj\nr2E76WLZrMi5q/Ew62JiKsWoLVJFFVgdmftFOXYYpphyh4HqunJk5D3GlgeiIeJjjEWxAqVN3Y7T\nmUJE712IG3zBGCOtbermQzqhRr+r707qa/2vnLuG+z0FI2XVVGptMXJJUvKSZtW0izMijnLq+7sJ\n+2M53otn5/jO06oltd0UQT0AZx9c4d2rvwYAGPOC7/6zT9RLEBFz3QjmY4QXMw4GLAlUwfV4q5y1\n45ojMjfyc0nxj1WAz5BeTzt4ofOAqEABJPXuFodOlSN3tcY2D1FoK9TVygZZ8DKXTbk9H6v1GruK\n3dpsN3BDNbFIhFCFMj97/hrbb1yBqlVdmReSe4tMt+EaWzfmdrxadsg5Cal5CQvs0koiBENZYYTI\nJ8ofDWnfqFsnGzq60detZTF97BI+jsfxOP4SjgdHWG31JlLsBXNGEsXMUmRs0dJ6sxIHEmYW9cYQ\nAlZiMkpIrAXqlFUgP6QANzTp5FIcFNWVROU/NBeZitPZkuCkepzMCR6k6dRyC9WbZA4pCRgnMbDs\ntM46GNP4jLULxI1T5mFNMzDoUgYuBfckoTNJ2EKs4T8695lWL29RKecMW+fcW6sYLz/AOsWMWah8\nSyF61SgoxhMNo5QSbCv8d7iYzJomE6hwKk9AtzKLpxwyKa7WP80LQmz3SwK4zEtAgh1qBJhXCDEh\n1I//8J0nOKsy2ikmQVAvh4jtqnzWtz96B7/48Q3mVJoske6RuEksJ5XUxgKKwOD0oK1tncosJGtO\nXtIlayxSytjf3bWzkOvEBhixah8EmNNoXKIMTorqN6nDsBkAyqG11ooM0DiupIySOSGnqO5J44Cx\n4vSK43eVu7EezYalJRJkLUgaA1YuWC/nzIZhHMFxwwh6cSv3g5OIcAkzcgfWXQ1WorKQg0RYRCpN\nVJR9+/6L3tsEU6K0+u9SwuAvHzc92PlZLN5NOvGko5O0BoJcHsdBuh0hsnQJRQQOJW8mNprbx4hl\nqR0/ZLlY48rX2lSbKCvSuEtKGmqz1phal6PRXOR1O1aTZaG0xlQKzmlHESjpnTqDDFKzijkgxiRu\nxSUKryF5NqJzpSJmWudpNQRmUpqD6RasmiqLWOCJ87AK+znn682u6U5zoibSjmYI4WTByswwbTFL\nWrdJtusMWoA6LatWo/j8665rWRcZR4xIrSuaUdcKRMeqY5YJKWYcp5IiegeMtQY1M8S5mDiXXjoA\nzg4GGbnanLEbBKnNoKadh4yMxEks3eZlRmwPmWVQ3exStpKem6rkcTwc5DqxwEuUDuXHAW4cdF4M\nIbWNhSOoOSylJAsI1Q5wW/yNV4dp642mkSmBHYqYI0oqKpLhq0H0zQazEmGBdo2NNQJL8OOAVGue\n07KIQ0/OAc4ZDEP5zPOLM1xdFZrT06eXWG+aAsSEuwrRyRyw2QwYBlVYydVOzTmDsQFb1wPGwSHM\nddHkrDqTYGEAGCbpDLsKuP0y44ECflqsLqVY3Skaipw51wvTUNgG68pOx7xoDSUnpelweXjbIjPN\nC45VgM9awlm1zl6tR5BhLVYTIUnuzfLQMynrvv3tF5yMvCLSh9uQ0doSFPU9DLrweq+NhFAfqnbj\nOO9lWSn4tC7C6rC9mblTvjhdRHuPvjZX5Q+TFLk5JWn5A1Uho6mZIsLatuXq2eROKTXGCCaFoxRZ\nkEbPSHLcRNwKe/XzGCe4kn/B1BZFiPY2IzutIQtTW/iWhqr0UKNjA2meMJM0DzhH1DIp3nx2gxxv\n4RvLYR4wTa1J4ECVYmQ4w1lCzuXa3BzuMFXJFoKRpkVOWa3dTChRba0NLXaBta0+62Fb5O493EkV\nuoOPgEFSOyWl33TzUj4EaKs4U6dwkPNJkdx5j915qcleXp7hza7g1HIA8lKjoZZd9PVH68SXM6aI\nWOe4YNAgRqir1Sia8JvtSpROABYqWIwzrMvSGLDGnsBqmnihdwbOKH0IpMKOvXelYTWx8O7zAcKv\nG481rMfxOB7HV2Y8uIYVpIMwi84NdR2HXO2AWiq22+0k3OTrmxMSbessAgRvLEL93f3+XiKs9XaL\nJ0/L+9ebsehhiYUVoXHrMwDXUrZhlIjAdKu78AI7eAWggLpU29kNwcusbX/mLO8fxhFD5Q7OU0AI\nUUJn56xEOt57tTw3VcK47irb7Ua6McZ4bCsh1nmvUY8pNbDc1aBijeQ4BonKrCmVDJGvzXqsZLS2\nlTqAa/ncrpvVOeCkzuHYmhpddZCQtmPmLqoDszjytMFRBRbJWeFE2o4TSRRA7FHpjjgcIpibWB2h\nYTJCCHjxprjm/PiXn2K/aO2RfEaur0OOGiGnqpNWgZYvr1+LQ/kKFrEKipGDWl5xuR9SFVeczALT\nUpcxFtNfAGlOCBRUFcNa0R0bjeu6tCQI9jc3NwghYKyCfpEXcYi+WtbIpN1golHqvX6w2FUb+298\n+D7mQzmfn/zklzjM+zr9WnIQFQ/O0qEvUKCuW4kMboJqnGSeiRlOlD4s1rGVc2pm0et3BS0HiBFt\npZE30jSD1NkKkE6x7W6VBkD/MuMvrNZQkNbl34iU/Jxy0XNui8d6NeCiYmmO04JQ7bcP8xHrRZG8\nKUXcH8rk39zdIVX8ztWTSzx5VmzI/WpEzJMoVxIsUrOtyizqi34cBFNkOoTwaR1G0xpp7+ZcYBnd\nIte/bqG9917qWdZapJixzFWqo/s867xIwxhrK0m0fMZqNSDGskhZ77Ae1bJKFlaq3nctVUiaeqaw\nnAitAaqxnXIS/Jn1Xhab3KlKGGuRQjglxCZ9YITcXesuPcUpd2mkEJ77Qn3DcFES0T6yUZyzDRGi\nq36RtAbSgGPVXZ8OC9biO6kO1sf9hJ/99DkA4MWLN+BswBUtny1hvW60mITDVO4jShnLkvDiRUG3\nv3k5o2aEMKT1rMyM3D1gBLUd4yVhrpAL5wOGVU39nYdxFqZLUlqjCeioLmRBppmSOJAFUk3NMi0n\nFmxNdHG9WmG1WuEwl8K/dQbbbfndO+9c4lDpXsdlAtWG1LGarhgikfepN4++7hsxnQpFn9iabmP3\n3iOv630ZLcjkjvIFCQoqoq9+Ap0W2qmTNCLIv1uoLsmXX64eU8LH8Tgex1do/AUkkhv6mgWBzTmK\nuWOIsQihsv79WdWmur054HbftJkWHGonhsjAOIfb27KjTMeDiJddXp7j4rK83xtCiBC0uHcKlhzH\nERdXF/IehTV8fv0upqhalJaCK067b72onTFKqB7HEevauZyGI2YOivTv4BnWGXivu6sbVPDNWds5\n3w4CPB1XXrtKpuxd7ViP0wH3d6XgmmLCWKNHcSxp0RIB3tU56rqxmbVLG2NCSir/G2NWpgBlGFON\nYgd/UiwFlKcIgjrldMVVAcuSypYQtNWP5EBU5i+RBbAgxHLt5+Ntc+mCGTddSZtwc1Mii5wcvFsL\nZ241erz7rFz71WDArYAPws2be/zkuz8HABxeHjE2WA7Pnc4aQdgFpiTDEnlGAyPshFDcmgGkMYGH\nDO4kpkMDTxt1vHHeSLrFOYKQZG6dZwFBe8cC16FqC+Dk/lDQ58X5Du+//6zM1bIAVaL5ZSU/m46k\nj65zy5mLhBBqhtHhWYuMgXbK2z06eC/PV4wOkWcRGeyDt9wFbwwqcyJdbyPQC9NlH4R2f+NBIdZf\nQK1BEbFL1ZE6HPdY7ctJ7o8TdseNEIDdsMHOlS7h2dkeUw3X4xJwc1Nu0v3+iHFcYakXPE0LfD3h\ns/Uam9r6NZSQyUin0ZLBurZTLy/OsVSlz8vLLbbV9qhddKDL8xWaVCdQH8b+J7K2S3OU7rMaR2x3\npWtTtIf2ggEzhkCuMtxhsV7XFvToi5djXfSyZ1mYYKyks9ZahZVTQc60NSdGlkWlqAPUm6ylhLII\nq5JBTFFJtNbBpQYJMUgxw9ffrVarDrbhZXEuapQd9SIzUqv5JRbybv+6dZINj10HNksKkZlgK0SB\nLEAmwVK9l26v8bKe49N3Haimh6vtiGfvFAT880/vERcv7fdvfuddfPTh++XYLSPXcsL93YTv/fAT\n/OzjX5XvJQK3hZgAX88pJO5YAgn9E5Q5i76UnWbMY7lHx9UKfhgwel1SG3bQWSuXMBnFcRECvEvY\nbsrcnp+ttAs9ElLtZk7TAZQCKoJIFol23Lt6bz95ssVcjS8WnoFJ03MAb2lgqWN3UUDhrqTV24Fp\n/cnbXq2kqKA05/GclVbUb3w595/71ugIz5pMPmw8poSP43E8jq/MeHiXsO42d3f3Ihvz/gfvCnJ8\nfzxgjufwtcuytlEkKa6eXGGsRcK7+xvs902j+x73d/eC/3j69CnOK9lze7YVPWtrCYMfpJsV4yLk\n5w/eewfvv1NsoMhZDE5R7Q8b/RrOUBljTYVW6zV2oe4oIQEMHKu0jfUerkaEIMZqVdPI1VjSwtYw\nIEKIFRxLkHSJ2UhU0h8FULqTZ2fnAAqPTwCEg0fmBJtUkXNs7kTOCtOgdCDLZzo3IsUsIMNh8AKG\ntcbJzppjQE5ZCuY5sRbxkxanU1LJXXW7joKG9jRo2sAJ4JLesSUYbAR1ffNmjx/8+DMAwLe+zfjm\nhyX9We8G/M7vfbvM5XaHV6/u8N57Refqww+fYqwpcJpn3Lwu99X3v/8xfvn8JZbaDcsuC882ZMBU\nHJK3avybOYCZMdf7PIGBqnQackLTgSfrqvx3046yAoD0VoHTrdAOALvdiGfPLrGEynndrdAm8/xs\ni/OzbZ3/Ehk3AK4ZLFzDOXkC1b9L/ARN89muLG6//8mJ5n5KURysSwlA5bqRIJrVnFg5oSkJHpKY\nJFJP1gKzQY4tcs8ItUsYlq57miv28Av5gb8uovrykdaDFiwCUPXAcL7ZYvfRpn4dCeJ8vV5jHFfa\nNUxJkuXBAabSLvxocX5eFqWcCgxVc+fhRAuorSGZU5E6bu1skLTfCxumFc6CyjfXmyWBpZXKRCKm\nX4CVrcVOtYNYA9aURXmgdMqadRaBd9p9McbB+1qP6wTQyDLGmtIMg69t+EoNsYPScZjF5gqUYdpD\nQVSpMeWX2+0WttYDY4zaCXQGMQeBnBAIg2spsVO7eGtQJang4BBc0jqa7fo2CXLNmlaXzEkHXTj9\ndwNuCP9W9wMXh2UAMTmpE1liZDQAJyEsAVOVzl7RgBevCnzh1Zs/w/311wAAH37zfVxclLrXb3/7\nHN/6aIOhznNKR7x5XRbAl59e4+c/Lt3El6/vARcBQdsHUIVZ5MRiSFF4uE35Aih1w/oAonPbZl2s\nrbOVqlX+YbVaYbst97YFpDYFZqkPbdYj3jGXWFWxxnfffRcNlj86jyf1eXDOIscAqt/rnJMFkBnY\nNFG9p1dYVcDn02dP8cff/6S4eYcKap4dQi3bhHmWskUMCSlmhAouDksQja15XjDP2hVtsI0QM5ZZ\nGR1LWHCs9ejpsEhtL4dUnmep6SYxiinifR0xXYtoX3o8poSP43E8jq/MeHBKuKlSrdY6xSgZIxGg\ncxbeOSEa01tuM83ya1yPqpNDpYiv7smnADXV2jGI3HUaDMuu3UcvXdNH6D/9YPCJlRZLwRAASHBN\nxFYLyEZlX5x1WFW2EaEUuBsnLAHFIgYlhW2dn6by0s4lcu60unSL+RyPscO7+GEQwniIUZoABIMl\nzbJbA0VmFyjuQ8JjNFD6Uk7IWE7wMqeaRu14bGXmsM65jLdcslv6XKNXD5XXiehAq8bAUsXHWUZK\nhOb+BhMxDuXvDvsjfvjnPwUAfPzzV7i4LCngekMYRsCgfMZ+mvDqdcFa7d8skqbaAUgUkGsXlXKS\ndNsZh/aliQOMmHGUc2pRUOSM0NRlodzGeZ4xH47wLWzPXExhAURvYGyjrTBc5UbmyiveNcydGdTI\ngQhD7RQzM3KO0ogxxokLdAxR0rRxHOFqenh2XqPbmJBjSwM7B5wQhV/KiU+K8KWZ0/imUSL1GCOo\npuoxMmJihNCisqhmFyG+5Q6VT+6ldjNxR9lDp7H2kAiLHqJFc3F+zu+99259Zw8W7d1ii36T1DwY\nUPajtrnBHSewVmn05u/SDvT5cEFcq4u4cvPMWwTs3lqIeCpGyQ3d3jUy+vfkXC5cu3jpxL+wm1WC\noqmpounfQtbXr5b3c0bHJCzToOJvnRVS91eEgnK+uLjSf9CJOfm0vttZPhunf3zy//3oQIZy3J2j\nyVtvoZ5XePI7PvkxxxlDB0s4Ze2/5SyNtwXg+va7wkVESZdwAupt0tble7TWSEQV1Ng9PN15nVyN\n7nj2t7f42tc+OJ2f7li72Xh7EuR75a+5XwyKRLg8K13q1H9MYyc0UT1mPgHoqrR2lmDAGIN5OlY/\ngQ7cqQelzAhri55dKylY+9bfduchi83pdSPTAaz7w5fr151Q//Lk2urr589fMPNvlm14UIT13rvv\n4u/8F/95+RJHmCtb++5wxP2h5LO3dzNuXk3Y35ffHY8McNk5rB+U1sEZ9/uCKQopgghY1bDFey/n\nGLsLXHBRFmFpu1yQzxvHEaNvxe6M0CDNOeC7/+d/j3/33/xdvHxd/OlyhtAFjLHSFJimiFevbvDi\n5Zt6XrPUYFLSKLLI0DSdb8IwDgVvA2AYVpKmhwjUjQdxSYgpieyLNVr3m+a9mFrmrNZbxhiMwz3+\nw//oPy6nVWtaZf4g6hTWulPRvV7WBmrqWYrd+sAa6E3c02xSDAKRSKk0FVQZQo0X+r2ub6EDwP/+\nv/49/Pv/wd+ShcR7j7FGod5ZOFlguIjGhearV0w+y2utrUzTgmVRSIbzarW2Wo9Yb5qixyCkXuuL\neKREDDlJw4b7zaiEnvLTf/1f/Zf4u3/035Tv+jUE53L+BK3JKB3KGo08l7Dg5ctSk/vk01/h5es3\nuL2rvou395jreTekAQAMqxGXT65wdlmwZTFGwSymGLBUZYtlmrCuz8xmvcbf++/+CPd3s6xU1PmF\nGmdFVHN7tsM77z3D03cKg+Ti6kLwgsxqFmutFckcZoYhK3XDcVTVCGOsUJtSjeqa8CUhy2bPGXJt\nl3nBUmEgKUb8Z//p3/4TfInx4JSwdZkWZhzrDXo/JdzclYm/vZlwe3/Ecmyrp8N2Wxass92ZFNNB\nhONSiowxJ1hH5WFH6bAITSQppYOpyL/e35cLdnN9h2O9oU/oNpxVIKiOTFloIss0K3XH+o7LlBGi\nmo0ackClVSxLlKK7cx65KadyxuV7V/jWNz8EALzz7rvYV5rE7e0eP/rxLwAAL+5eY/Cj7GpEGWfr\nbT2GrXSBgCQ3XEqMX3363Q782hlwAJrKcqHA2K6IL9zOjoJhiihL+SQuPDsJsHDKBWw3oKmR0Yni\naGsKnKSyGuVkAbpOsrgVWlBrlihlqZiKkAY+mbu0a8EyaTG4bVQMggmq7VUijXYceu4gFEccDbaV\nN9pFLSkndGttnZMvk3m8Fcc2/BOzgKqXJYisS0oRzlnsaho3DCPmeg7TErBMbWGNRd6mnscSI+5r\nRz3Os0jGIKtZhjhpk5WjytAICZzVnYaKnLN1rXs8wg+NbxqEeznP96p5BYa3DmPdJAp9qwYizumc\nV2qebLrQTbZEaKp6KjpjXyDn9OvGAxcsxlJ3qxnAoYYP+0PG9V2Ntu4CjscErq66m9HjqsrDvPfe\nu9jtWutWjVhTrT9ZAUyWvBgAIquIG2CxxIRXr2/r0QD5uiK/mZGbdHKMYh46tAuBLOTSjCQ7KlGS\nicuojrjC3WNk1vBaQv0MiRDG0eJyu8LVk1KX2J55uFGt5RtUg7lEfc3iabcb8dGHpQP29a+9I8Jy\nS5wghS82+If/4LvSiu/T9xgzUmPyWgCGxTUnhFnkg8OyqDaYMSpBE0KJK5v+elSTVSB3emarEvF2\nc9I2E0PavSoxXZ3/+v939wf5fUpjJwVGcHXOxdG4X7BqN2s+zpiq7PU8L1I/aXI8TjTddVFmZlnw\nmSC2XO2CKNGdEWJDsEf57C8ukfy6xevXLFhdil/4luog46wVLfrVCDHT9dOMfZWNPhwjwqTXbUkR\nx32JymJYZDOx1nbH27q1elQ5K8mcE+BaCkYAWQvjW5dZ3YBSzliq6/f9/UGyAGMI3jmJ6hlG+Je+\n4+kKBL4jY/elqn7z0ErFl1+wHruEj+NxPI6vzHhwhBW5ShdHg2UuS+T9MWF/KKv+3SFhmjKc4FNI\ncufzsxUuL2uX0VlZ1RfOyDnCNonkFDDHtmM4mMpRIOexLMp5e3N9g/v72plZEkKTXslJuntWdCwS\n+C0JDKCkZlFSqxIj9KF9i7aM0a4o5yS0id1ui4uLDXYVD+M9daayCWD1YQwhCCfI2w3eeVa6Xt/8\n6AM423bTWXhj1o+IMeFlralxt3OFmCVK3O62MMZiqtSm/f4ON7eFW3acZqTQ3GFIlV6ZYUFyfjFn\nwQ6NqxFnu4YJYhjLaHvb6dxBRt9lbPN0v7+Xug5zEtkSby24dTjJANQJGyZtesRZuXtxUcMLZgDG\nlOgYpdbV0hhjDYxvtB8DR67rhOr+nGIU5YvjtIgAXkutdC9/O7p6++cusmkSLZ26hXNWovzBW4Ar\nxxK1Bla/L8WEUIvs/z97b9IrW5alCX27O42Z3XvffY034e6RkVlZGdlIQCEVhUpCDBjBACZV/6Nm\nCIlB1ZQRYoDECDHnFyCVRCGYFCmkIiEhs4jMjAh3D3d/793WmnPO7hjstdfa577nzc1UDrx0txTx\nzK+ZHTvt3mt961vfd0IuHp6UJfjgMVdTluB5284a9t+sGFTbGRNzG/UmBIYvNZQWiW1jpFcxQ7PE\n9DQvomSqFfls1qgsQdvKBJDWnlLkanA+lSVdzw9A9/dw+75vPFIiOXG/U8gdFpKYPRwC7g9l56dF\nYZozOiMPPRM9dYQhfW3XCTCMJQLIqABN0bQmYqGW0N91FlplnoRybtO3hVm9WivUrMyyBlNT4XhH\nNqb+nW6/JqWoF1Urx6Frip5z8e12g7OLM2xIwtY6ywzg4/GAIzHgFdFcq4yuguIUTkGkSZR13FRq\nnEIIAa+/LszvYgtVe8OAoR/p2Aw6Yxm4P9wfsL8tKcT+dESYhVBat22tBjKYdR1C5Jt+lzI2447O\niQGgG4zkISZRr8XakBYA7m/3YmibMgaa5YPr+FwqbVZVMB8iwwFLU7Fte9S0LqA7g75Kc/l+nhdO\ndRT1ZbIMsc6gealMcnRtjscJU21qrue36a3jx0m1ZXkAWT2YtCvMIJ+z1mAg3HYcByAv4oqcMlIF\nsaMDYelIIWBZ5kY2SNLKxS+o5dKus0IToqAgZvFRTkoqpKrJ0rJSxUWHOTeGoY8QMxehlO4gNutF\n10upKirQwWjCsLTjfco5lIW/qS5ycTc3vadJemSb/vzvHU8p4dN4Gk/jRzMeF2HlDE/aH0tUWBbS\n1F6AhfzLvDfIquMVKibF4fZpOeE0UztFDtCkvV1K7yKTMs9SqXOd5QpVqUK1FaGWCwYBXpvqSQVd\n204ArbUA/LnhhtHecHirMk/pzlpRAvUJxpTVZbvdYrfd8Spuux5HslQ/HY5YTuS/Ry0addudc1xx\nsVbAUx+CuM9YUyKs14UUGUJkUqS1Di9fln1IsZw/zRIpUmVNPnJZv+0RtLYrXom0f37xXAJPEIty\n13XQRiNyRS6vuGbvGzXCOh4OXBV1WmOk4x37niMqhSqM2KRWTOSZJJjYAAAgAElEQVRtDTkb2R5t\ni9Bho2lfz8uiAywRGl0X4brcRD6QlNp7VrydphkTVSPLeVOcpqScqlAqNYJJhFXSWTmOYZAKWhVT\nVEphICmioe8QZi+RRc44JzrG4AwmErBUiFApINM2nLLYUeHqdp9wd1/SfR8n9NSrWjXZUxajjNxE\n1EgiB1MIv2v/QjaASQlQtT9ygNKVSlIyAltbvlwPS0RXax1yqkWymu5Vsm1T2U6Zq7E5K47q0t9a\nSphFmyknjYVSn2VJCEutPimkaDj3XeaAO6IhvL3Zs2ZToTA0eXdOqE7SPiysobPdjjCm9kCdipMv\nhZXOdLBUmlUqsiO0QmbcbKRUrRbUy+um3Ksyarky50QPiVQ4uOyvwXpCpfpXLvZmM2IcR3ETMVqq\nO9OCsFR+SkTwGR2lMWM/8ISVQsA9aYHtT3foR+oVc12RtaHysVKB+rEA23XohnrzOJjO8j5stltc\nUIpkOyeTv+3YIqrvLZZl4ZTmdJrEuHMYOd0yzhZ6wNJUoyoWpFscSzGGU09aaITG5nnGkSbHvu9w\ntpQHMCWS1K0TqZFJtSw6bV9lTQg0NWFXDKUhkSrBRo2h6pcSPCq3q1NTtRTDV+HnlW3LIlOSm7bM\nlSAtg6LEmlNkwisAJKIhlGpmhKpV1gx+0BUSuwVtOgurLDJNldo5jESB2W0ddiRPo51jGaWxr9CF\naYKFJCYsWq0kYJJi3QBaoisco6CJyuNsD62aCcsZ2GrE0g98v1hrmO6ka3pfL33TNZGBtWZaboKG\nHzgeOWGBJYmRVPMwJs57KzmxtsScloCr27JyRA3c7gtu0NsBC9P8I2Ja+GF0TjHJcLsdMY61Hchh\n9gm394I9VG6OD2CPQuMUxqF853x33hwB4SZt1RV44FaS+XOAPJApR774Ruvm4R7QdR3bn6WUuVQ9\nTQtTD2KIiD5D8wSrcSR79S/mO9zcvgEA7E97vPygKBQo49APHT75WWFdv3l9jRuidAy7AefPyQ34\nfIRyGpFCz2EzsGLES7yQg8hoIjxT8B66A778cuIJv+stBpo0tVXwISATDmm1bVqtFCJrNWXujK8U\nuNaByC8eB9K974zFiZq4d5tN0QmrE2nXoyMttUKIjXzNBPgvtJfqTOOsZV+9ruu4rabvBzjb8USX\nUmTszDQaZH0HVK1x8QykBSgn1KcvJQjjPJWQRVQeAiL5LiImBF+dkBNONFHPpwOO+4Ms+uUUltc5\nAyQfruEBRPS0UBUxgNpAPWIca9uaxcWWiKM0kaiGupGSuEznJG1FJVIXf0ReoOs+8f2v+S+AKoWO\nqsFmDEfhuqFXaLL/i7IT660ydrxu6/qh4wnDehpP42n8aMajI6xAWFWA5uimRBG1ipRp5qXP5YzD\niVK9fMLtnvAF7bkMH2aPlAJGWkXOzzdQqlZ2EqKvBpATbvdHlsq9vj9hv68O0Vkqelqjp5aF7faM\n9j0zbqWVlhQAgpmUhimRczFQLL9S0sNavsuCzThXzDe5YRqYyfHndDqKOqoxML3hBtewRLx587a8\njnvc3xNOlQM2FH3c395Da81y0dYYNvfoxwE9+T12QwdlhKAXFaBtZaNnXtVaidqUPUKcGd8C0EQc\nHVcwtcowKjPlZN0rmRlfhJLQv6ZqVolBaYqZjTqmaRap4RDhjBEagJEet2LaQd9HZh9BHyN8CExX\nGMYBrq/VK8Npb0mBrQQMWbS6rHXou3rNLJyt1dfCaOTjyoojxhQjE5K99/DzwtSIHD3jpSkk9o9M\nWSLuw/0e0+nAUZrREJdmazBQWne2G9Atgn11veMqsnYGSm/oGDrsKJOoOmutG3eMiftnkwKTvhcf\nMM+BCaLRi4xSSoBPlcTsOc2tvaCRZZZbeosSrDhpIGqKTCmtZ1CtyV5MMWMu+/a3lBKmlOGJHxV1\n5n690FhzaxWJe1N3UsFTsuxPEXquP6w4XFVJwdkOY1/y9IuzZzgn7SPjFPxcDuzm9hpvr+9we18m\nrGlOIHgGGZqxCyjNoLilsBop1+eoPBzMGRLeUGkkaHXcxZG57afT2fBDZa0t5N4k+E29YerNDBQs\nLUcNTUDnPC24pvQOZmbgcehHGMrTTscZOSccqfdsOU4wdC43XY8z0l/qh46woMq5EYpBC2dWG3EA\n8EvCNM2YCWxGBqcgQz/y5FXK+iLkJuph5UvS55mgHoDxXdfJ7+fm/YymtE/Aei2IKC14lrY84S3T\nzM4w0zxj9p4nrLPzHV+PYRx5/7Qu94Roh2t0dFyxH/haOJtYH74ysHOT0kizcuTJ53Q64nQ4YuJ+\nuJknVJVFDaT02dEEBcBpw9iRMZp7IIfNyMfz7PIM8zJhqJOw1dK4DzT3XoeO5McbpF1gtiTFg3Is\n9ZASUhJBvxilSJUTGMbw3vNnqnN654maQp4AAGBN60IFon/QNTC68d3Nq7lr7Xb+w8ZTSvg0nsbT\n+NGMR1rVA6FGWDYjVP0pRGhDQv0mwOpmxs4io5uSRaxEQwTu/LZGY+wtRwybYWDFzJQTThRGXd8e\ncHM74ThVoNeizrkpZw5l+2yRqTSbieimMpi0aZSSsFxZOCNVqZRkdTTQYjWfsoDBADdx931fmj3r\nKUqJCZiA4qhvGAYsp8hg7PF4QkgFdO+HjAvqANidbRkAvrs7IKWMw75EWDEEjLTq7sYRfSepYmia\ns4paAIGgrctNISwAAAIUwhLg59pIq+A0SS5by6lfqmR9VkFpwvqWRNmAvXWMNbpFAbzrfhhtpLoW\nE+k01cqWVPK00XxeT/OM27tqWnLEtMyc9mZkrgZvF4kKSqTb+EQqw+YogIYjxc3QyDtLr1vmf0Vy\nOAn1ZppwOp0wHQlQn45seNJ3rpAp8SDtQ4cUIxaqqBuLRuXDcAp4aXfwaWGfwpwz7u9r58LEUWtn\ne3S2dno0MjW10qkUTFXW1U2GoNQD+oi49yDnVYRVCbklOk7wvmwvBC8Fh6SZOApKFWvUpFUTYaXE\n1cjcQC/sxPUDxqPVGmoz6yFE3E/lgVv8JBc4zJiXyO4wwzCy2eM0iyvvdtNznn/5bIfnL87RVf0f\neNzclG3fHw9cFXx7fYfjFKBydZhxUNW8MgYYUmN49cFLbLclvaxtLZpYUACQWrY2SdYAxM+yhvEb\nwCJWygM0/310jpntQKku8sk34jjjrGWJWWMNNtsevqoPLBMOdP62O4dnz4uUyDhuiu0UgP3dETkm\nfihUNjjbUnVtt2laIBKAhOrxo7UuevcoDUl8w6gsXK2soZIq+AUKZUMzhUDcgnyYi053lCowO+oI\nPPbeURcCAMiNZpVzjtOxlFTRharOy4vnjoUYMlNYIuFWQMFWFu+haZ+W2TPXb1kCM+VjiMgxcSld\naTE4VUrDWpkkecICSsW9prptr8vDGbnlG6WAblOpBzt0lSOmpPK45IQQFhyOZeJVs3QazPMJO2qH\nunz+DEPfiwhlLMx3ALi+esvP2tnuGUZHaTLJOymlqrk4EkQRQRlJhzvr0FnbnIv1RWTLuigLrCpF\nQhEEjKGZsAyEz54JehBYpeVHMhQD4Q2aR0xYTynh03gaT+NHMx6vh0UzZIiRrb599lC0Ei6nPZbD\nidM9hBGGGO06WwxDiRAutgqbXWn+vXx+gcvLc16tp2XG3X1ZhdIxMVfo7HyHhAOH1CFNADdwKuzI\nA3C3c9xveEOyHLkBfTUa088HoanWhiuIJmtJoTI4wro4u8COIh3kjLh4jmJCFFNV1xmONA1tl4IC\nTIvn8Hi3O8cHH74CALx4/gy3tyXyOh5PJbKgilrnzMqGnBVba6Wqrj9a8eqlFTh1Uw3YXcTxAkcP\nRjfM56wwL9WIwJSUv7KS07p/sI6HEsvl9xqVS6uZjDr0g8hD51xkeZcSRU/zhBM1+s7z1FQxFVfC\nXNchpkbSRCnmGC1ehOGWeYK1Cq427DYdDkV6G3RMYACZTqREBbnR8SIlUKBEiZ2ziF3l33Wclo6b\nEc7WCDKyqUOIHofjHm+u3vJ/930VxBtweVl8F7uxw1aPHLR4H3C/L+fk9esr5nVtxnuMfYnK9sR1\ntEozqK+0YpBbNz2NfdeVCKueCz7oUlVWfP3yqqtEoTkvKXBU3GqdFa6fFHsUGtJjA1soSBr6CMz9\n8Uz3WvkK2TXKk5lvsul0Ql5mKGpeRpiZqT30O5wNJfU53zq8+KhMWJvdDnYwHFRa47AzhfCpuw6X\n7Lyb8PrqGtfXRcHxeDyxAoMxGhcXW/q+uOsOo9w49WTl3HCms7Qs1C57zQoDGjlKta1ek2EcsSHm\ncYrFTbnVJaoT3jAM6Mh4U2mFHBpVTqVYEeGzzz7Bp598AgDY7jZAKhSH7XaLmBK3kCgYvlnv7+5Z\n38t2FkM/VMcnoGkhyUkmtrAsnC7d3h1wdXODw3Gi86NwnISQCxBeZ3sgp9W1rkNrDVEzbWqH9YZP\nUu521rFb9jiMjCUVQ9fAaguHwxEnSoFPp5nTQ2MtRkq5tLboXM/V2M51PBEvi8fEtJIO2igoU4Uh\nO96f8i/hsQHcWSFEds4D+bhUs7hZZ4u9Wqo4XWKHpqQSV31jjgipivTNOByPuKXFeJ5nVkcd+gmK\n6BgfLr5AA017G102HCePm5uyCN/eLrC2vL6nCcsYA0MTeaeN4GfOoqfOiN51ZeGrpNIohFokca/q\nhwGb5nwplRhXSzE3zP4k3OSUi6pori1kBmwDkJuHLSVOu6VD4vvHU0r4NJ7G0/jRjEfKy2QEan42\nxmKkcPhOaVaGvN8fkGcPR/GS1WA+Sf/cwDjqcXMiO3M7HTDtA6/wOUb0tALv+pEMJwHjNHYXDheX\nZbev3t4wIJ1DxMW2/P3l5Zb92i6f1z48JVbdKjE/RenW6AAPtHlE0tUowFJqsRlGbMk9KIVTqRJS\n4XKzG+HYkUeARa01ksnc4mKswpbaKs7Ozjldcs5id1a2/ZOPPiqNpNyUnDETmP72+gqHmUD77Qbp\nLGEz0nlq1qEYM0doh/0ee0qR9/sT7u4PmGrqZzt2h5mXGY5IvC5aIGdpH2wA0veKcz4YtbjRWYdN\n1R8fNhiqvK4pTeWc0i1CKJ5nkVjphwGbLUmohBJ1SouLkIJjCBxhHQ8Oxhiu2nadE6DXWOHbqbZK\nRvvNaW8582VIJGFUaWCvEaZ1UpAKfoFv5Hi4QR8JsIadctIy4TTVVHiBJYnw/WGBsVLIUipjXqrS\nZwc3VDK0wjSRrE51CjKata2scVDsXWnhqiQyVWkTR1hSPVaQwsTQddzyZKxBjEHSOEi0tJLgyUUJ\nt54XDWm9aVviWg+B9EDO/LvG4zCs5uRbVciLZacUjjRx3N7toXxAX81TkeFoJ50Ghq6Kq2Vckcjc\n7bzgZj+R40fBV6p20tk44OVlSQ8//exDvHq+w7NLmoyebXD1denBu7u5x0jbfv5sB7upjdUiot8K\n89XGUpUSEhsxUPOztLWvKm/1Ju576Vc7+CNXmMop0jxBHA9HnE7Ugb/p0LsRkXCEGAFH+MXsJ1yT\nQcaynJgy8eLVcyilMVLFs3MdT6CH4xGnmUrqy4IYI1SuNlgDTzA5Ja6uTdOEwz1VX/cnHKcTMurD\nPDDe1uqFQRF5lsULm2PFerxjUZakvO2Mw0jnbDuObJ6gjUaKgbcbQmQy6zzN0IQbnm3PcE7uQUrp\nldrC4XjAaaJ7JwS+F7VRMFZjHGnRSsLUN0pIwSoDnLfw4YkYXz1SozQyTQZR6yIWWB2yc2gqhpL6\na605j+mGAbuzHWbC5fpxFChlWnhaPM0eg08rGoI1tT9yi24sz8O43eFAvbnL518CKJOUVN+aKqFu\nKL/0LDDmmDLYbTxnTilhDONcRhWohLtArFntH5sRE+m2tc5jEmlDyM2poYuEH54SPjrCmukh0dky\nXyj4wE4g9/sDugZcy8iIVVAtB8RYvr8/7XF3U3L562PE4bQgBsKOksZCXeyn4z0WAmT7jcXm7CNc\nnpcLtut7djIOk+dVtpzIesM0+9+IinF3esq1Z5qdS2RiSwxyq8YfUUOxmzJUASnrzern04odzNuK\nAXpQ6AZiWsfEN+jV1TX2+zJ5n5/t8NlPC551flEY3B98+CEAwHWOveCur6+YunA8HKCVQu/ELduw\nrI7iiEslWVVDKJNEdZ7Z7XYYCJezXSed+M4ipvhA5aBl0dfz0L5NOI8xDD73rmsirJFxzcrrYq35\nIOYSIUR0xGcahhHPnl3y8YUQGM/T1xoLRf7zMjEb3ViNYeiZV5QrZwEl8hPe/7uh4spEgW+ixNaP\nWhc8q/63VYYLFd4vjMtYY7jo1A89nj1/jo6wPB88c8turm+5pSilDCgDRxGXNQbn52UxWnxCR2IA\nZ89e4O11uW+qwIAzRiJHa5oHQHFEVFqMomBQMUrBJuXGEcpwoUOT92hdgGzrHKU1H2999AWqEv4d\nsmJ5mZREEqh6Gv6Q8YRhPY2n8TR+NOPRTPfTTNEOOhhFssDNahVzwhIiXF16FLh5dzN0HJ4vRsEQ\nBobjhGVOcKr6EvYQLfQZB7I/en1zg1fHC1xUgl6/wW5Tqo7G3iDS/Ls/TiS7DFhX0731oXDnX6N5\nBeTSZMs+appdR7QqFkhAkZqBInJiXEq0VXWNcsdyOBfPLnBHpNeyusi2AQNP0c6b61skShOePzvi\nFZnVPn8xwjqLDz8ulAdjLGM9rte4pWrp6XTCYX/Atp6XsBWMrsFdhs2IMzoRc4jIGTinaPXVy0tO\nPV0nWuFaEQWkrphNarHqH2tf0uve9ewaNI4ju4aP48jVOk+RXtXpDz7y66J5pfjYqwzwMGxKMzvd\nV4fjQZqsQ8RC0a5ZNJZGELFcn7qTTcQRIkslvYvLNU7jSrA8YxSSBqdPGWCbtaQ10yRSAip/1hkD\ntd1yVBtTZtwqJWCmXsllKVW2ir0N3YDzs3KdktL8HTeMyNfUBUFcBqslwlLarC202gjLByaFxhib\nqnJiDMtYkWBWWiEnMcO11rDjUeuDqeq9EiVLkRZSzVFdwS2JhBqkT/L7xqN5WDX1USmiI8Gx3djj\nnG72aXcBEyK2hIfsnMUHHxXDxp/+7Gf46LeKttNsR9zOdIHxFtMxQy3Es0mG07TJz0CqoXbAafH8\n0HZdD02tNwkOJxKZ++KbN6yMqmlqykraZLSScDYpi0iA5uQ95hAZ08pKsQidcRaXhKUNG8tOXJcv\nL2C00BqmacKGJuU//P2fo7PlvPzZn/8lrq7fYks3XgZwfVvSAa0TOtqfN1d3+M1Xhadzefm8pB00\nr+emrPxqeIWeJoMvPv8Sp+PM2M0SPBzfJaJqsDvfcap3f7eH1wvOd2WRuLjY8oQAlch4AkjBY+34\nK+lbUYp9D/JOD0xJ/WjCGkbmKXV9x5NI8qmYb9Y00IcV9YOZ+U13gjGF01axxqJ5VRemzCX1GELB\nx97Re8LKissvnpUkUgMkr18QDpTWW2KYISaMrqqH9swfi1FajRQ0OivUiJQyRk+CeNbilEpaN80H\npLDwvasQRZxxHBlHmydpV6qa9O0CvHLwhhSUqn08d0pkQcOLsmv5szXCW1NKASavU8LmvbZYpVkK\noWybFXSRhbuVBZjP+SklfBpP42n8GzgeWSUERzvLfIIlcmHfWbx4UQDRFIE4ewzVgspZnF+W9y6e\nP2P77cF0wLHMvHdnG+y3Ryy8EkYJvbVIUgBFsiXyp4CZIqCAjDffvKX9zALCEt1CDoBWiyqBqyTt\nywCtNvTbRrOq4jj0ePmyHMcHH73A5fPqKrNBxsKNvjFppgT0fcb5eQFFnbM4nESOxBggMzjqOPVZ\ngsdEKfC8BNqr2s+lgcZ1uaZtShXJ4Oip0rYE7mVrCwwxeG52jk2PWDlscWZROgNZ+gChmvRPtfIy\n3z36zsE5aaheSdTUCpH3CMEjBamwfVtExE3NKVLVlndJ5HRq0xvWUVk5RmnsTVG2Nx1nvi7pPRYu\nLWgs2xLwGiiR4dhXjXMD1ziGL9RzV+kDtZCjtZIoJ0XurQ3LhBQ9P2vIGkZT94QF9kSFeP3mDq+/\nKY5KR2LCr85dEgedrMC9ojlTf2nDjV3Zb63faM+E7PvDSK5GUbl0INRqbM7S8FwqiEIWbV//0PHo\nlLAyiqOfAFU5TsAZpRbeZ+xv98hUzYrQCFRNPC0JEz0oWmlkmlQ6G2FtxJHUC2IE3xy2yxhIt7q3\nGn3nEGmCOQaPqaaLcWGXWu9jwbEA3FMlsuxnc1O3qUb1rQP9XdUKo1q1Ypw/K+nc5eUFtmcl/I/h\nhJylkmS0hiZ3Zx8iVxNd30Hbia2/x2GL5+dlArTGYK6WU4c7Cd1rA2mdK3LmFMVqzRU4nUuDbK0a\nhhCQ2wknVdwATAVYgkcCeL/bMjcg1/mdrokkf/u+ics5ww3pUOIHsCxLsatCacVZFrl2OYtyQ1YZ\ntZbqo8dEDcB6tnAxsh5bys15doYrd33n4KwR0cIU2OYrxcQN04f9kdNpcb8GH3/r6CxVrtKeFmkb\nYZ6xVA2xvhe37piRSCjPpwhlDItCaqPKxAQgJ8/66RoRWmdU30WdA2ofiFYJd0QH+vzXn+PtN0Lr\nKfvfdDk0U89K9z6XSVNEGTVjihlVPQOY4sQtbtqU9DzzIgnEimFl0fNnueX6TwIaqY/3QwiPGE8p\n4dN4Gk/jRzMeFWEpBXS0Yk4hYF5KRJSSRU9g8G4cEaaIJVF1zBh4IjvezQEjOeiMGwVLM/6LyzMc\njgfMcwEdj/sFE7lKuqTRbQqzd+xssduqfWNxgk8UyiOy3EWMYDmUmGROXkVY8tcVSbIdKWau3oUu\niBtLZ0UxMycoreDpt422vJ1i0FCZ2sVQojacXVye49PPflKO0Rq8odA+hkm0rHSpuNS9UgBLzyIb\nBqRrSC7gZeLf1UpC7xAzZuoFXYKHglSU6nbKiyZFUFilVd8eV737d22Fr5ZyZIkUHRVHWMsyI0TP\nB6ltkfgp50/Sc+89kyyhSsTL6ViUwoLrHEd1fd/DdZYva4ytIamX7oz7PQ77I3/m4bFw1pbr/wGI\ngey8iGm+eChV7t9SCdP09wV7MhspDe9yTrTRHNkFH7hYAGoMrsFpTqH0+6Gk0HvSxrq+umKT3DBJ\nhMhpdJPCKtMo8hIfTTUE06rymlPi6+TDwiGNMRpWGy6ilLSyovOJI/96jpnFHsWFvTxnf7MI61ET\nltEaO0r9YixMaQCI3sLk8vfeOfT9gEwTRTbAnm6S1zf3MNXNRXd49oyY2VC4ur2BIUq86QI6OuCx\n6/CScK8PX7zAOI4cyt/vD7i9Lo3Cx7tbJl9qPfAD4FwHMtTlU8WletRJgP5O/80l/Jy5Itkyq4/T\nCTHWaknC4MTG3lrHk2SIEfX2cZ0j70KqrJ6N+OjjQl8YnIMnusjbN1YmnlzqS6Yp5rRzR72/q9Nu\nvTGMAj9lK6giiQBdTAnOWK5Yle3XH2p/BIzxlfOl3h/Wt8TRRuAv15QuLDjN8qBKJbBMPgNJBYcc\nuek3nSbOh0NYMBGbPecI6xxXn3yYOY03VjEdwLmi+cSHmMX2yi8LFjrn83TCRJNhxbiaw2/UBFSt\n1iPl0lpS25kSUpl4AYRkudVl8R73lO7f3h6KLLiW9KvSYaZTgF+EzpGCOO9EBa46TqcFhq7Py8tL\njNTqln3E9dv3TVh0zgFpNkap9jHRunmdtOZ7ZL/f8/k3pKfVefISTeJCHqx4h+ZUKBNVLDOlyHhW\nbcT+m4xHTVhaKWyIpXt7e48TGT+G0PHMO7gttqNGJtBxTgH3BPSm61vYao81noGI1cg6w7gBF2dl\nYjrbnXH0dbbd4YMXhYe02W4Q54zrmzJJvXn7FtdvC9B+8/YK04GitzNXgGwArn/fScrrqKWZvDTA\nQGjL3wk+cQn56998g2FDhgHbAdps+IKHWFj7AHBze4P9sfBkQg4wVrMd2HYz4pzMMa026KltxVrN\nT0i10NIs+5LhK77gJ9wRLQLEWK/9iGhkRRQyM6in5HFPSgHBR5ztBi4qJAWeKExzTugEvdt2g4cA\nb/tOpatIdLf4hJgqA93wvlpnMJpiUwYUH8UqepiQZMGICw7U5rSEpcjT0EQ6+2k1YdmubkvDOM0F\nnJQFaC+YX2XUL9wjWyYswS7jPCMb4R5Vq3altqVkT5PA0Isgnq1+iACydcjEQn99fQM/iyzO7f0t\nZuoHPdv2+OjDQv95/vwVnHENbUOzxv7F+YBhUxb6F89nfPllwbCWacJfgPBL1ONVHO1raJgaRJBF\nWV3KipktUUaUYdJi9J4VQZIphiK1gyKFwOYRIYNFA5ET9QlKxMWzmTGrJKbFBn/oeMKwnsbTeBo/\nmvFIDEsxcTGmhOOBIiw/YaRKXtcP6McOoXbP+wkzrV7hcER3U9I263o2VR2GEcpovHxeIimtFayt\nKZdDoHDh7dt73N/e45tvvgYA3N3dYTqVCGZaJu6Z88uCSBZCgRtSm2bPLPbZWjVW2lSEq/9dnOop\n2okJb16XaG5Z9uzq88HLF8g5sr56gsLVVYlivvzyDV6/prLzcULOBn1PIm9jzxrgKUU4ijCGsS9Y\nF0rKEGPC3R01UDeM4mkOrKvkY5EtdlRW106zKkRKokYRYkIiWkTnSr+gbikANfdpIjTgPZXC1Tvv\nwySoxG3kvZgD22VlJQxqY0uzrrY1bQDrR/nouS9QayBl+nvISIhN1JfguipzbZhJ3veu6DE1KWFO\njTtzg/lxSlNhnialft94aLhbpDnoZaegiFmcfRIfA92h2zh2K5+j4ExdN2BLXRvb8QxdN7IDc9c7\nrmr7oNnxu+syjsdyfqrCR6FQ1CqzpIQGAm+EEIqRKkXrOaXmeRC1hhYvZnFL7h8UazYNNDBCccp+\nn3R2gS3oOmeITtvfllqDUoq10rtu4JTptHgoQ42oeoTWCn3VZ9cdEmpjcMD1dbG28pNQ9nebM1xe\nXrBcTU6BmbIxBiy+Uvgzbq9ucEe4VQqe04wlepiOJF98QKP6g3AAACAASURBVOBZqALT7U2oWIUh\nQfHJVgSqygSm+b2UE25uSivM/hAxL9QxPwzYbEduE8mqiNABwM31Ne5uyTxg9rBdj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"text/plain": [ "<matplotlib.figure.Figure at 0x7faa91fcff28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "idx = np.random.randint(0, trainset['X'].shape[3], size=36)\n", "fig, axes = plt.subplots(6, 6, sharex=True, sharey=True, figsize=(5,5),)\n", "for ii, ax in zip(idx, axes.flatten()):\n", " ax.imshow(trainset['X'][:,:,:,ii], aspect='equal')\n", " ax.xaxis.set_visible(False)\n", " ax.yaxis.set_visible(False)\n", "plt.subplots_adjust(wspace=0, hspace=0)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Here we need to do a bit of preprocessing and getting the images into a form where we can pass batches to the network. First off, we need to rescale the images to a range of -1 to 1, since the output of our generator is also in that range. We also have a set of test and validation images which could be used if we're trying to identify the numbers in the images." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def scale(x, feature_range=(-1, 1)):\n", " # scale to (0, 1)\n", " x = ((x - x.min())/(255 - x.min()))\n", " \n", " # scale to feature_range\n", " min, max = feature_range\n", " x = x * (max - min) + min\n", " return x" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "class Dataset:\n", " def __init__(self, train, test, val_frac=0.5, shuffle=False, scale_func=None):\n", " split_idx = int(len(test['y'])*(1 - val_frac))\n", " self.test_x, self.valid_x = test['X'][:,:,:,:split_idx], test['X'][:,:,:,split_idx:]\n", " self.test_y, self.valid_y = test['y'][:split_idx], test['y'][split_idx:]\n", " self.train_x, self.train_y = train['X'], train['y']\n", " \n", " self.train_x = np.rollaxis(self.train_x, 3)\n", " self.valid_x = np.rollaxis(self.valid_x, 3)\n", " self.test_x = np.rollaxis(self.test_x, 3)\n", " \n", " if scale_func is None:\n", " self.scaler = scale\n", " else:\n", " self.scaler = scale_func\n", " self.shuffle = shuffle\n", " \n", " def batches(self, batch_size):\n", " if self.shuffle:\n", " idx = np.arange(len(dataset.train_x))\n", " np.random.shuffle(idx)\n", " self.train_x = self.train_x[idx]\n", " self.train_y = self.train_y[idx]\n", " \n", " n_batches = len(self.train_y)//batch_size\n", " for ii in range(0, len(self.train_y), batch_size):\n", " x = self.train_x[ii:ii+batch_size]\n", " y = self.train_y[ii:ii+batch_size]\n", " \n", " yield self.scaler(x), y" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Network Inputs\n", "\n", "Here, just creating some placeholders like normal." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def model_inputs(real_dim, z_dim):\n", " inputs_real = tf.placeholder(tf.float32, (None, *real_dim), name='input_real')\n", " inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n", " \n", " return inputs_real, inputs_z" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Generator\n", "\n", "Here you'll build the generator network. The input will be our noise vector `z` as before. Also as before, the output will be a $tanh$ output, but this time with size 32x32 which is the size of our SVHN images.\n", "\n", "What's new here is we'll use convolutional layers to create our new images. The first layer is a fully connected layer which is reshaped into a deep and narrow layer, something like 4x4x1024 as in the original DCGAN paper. Then we use batch normalization and a leaky ReLU activation. Next is a transposed convolution where typically you'd halve the depth and double the width and height of the previous layer. Again, we use batch normalization and leaky ReLU. For each of these layers, the general scheme is convolution > batch norm > leaky ReLU.\n", "\n", "You keep stacking layers up like this until you get the final transposed convolution layer with shape 32x32x3. Below is the archicture used in the original DCGAN paper:\n", "\n", "![DCGAN Generator](assets/dcgan.png)\n", "\n", "Note that the final layer here is 64x64x3, while for our SVHN dataset, we only want it to be 32x32x3. \n", "\n", ">**Exercise:** Build the transposed convolutional network for the generator in the function below. Be sure to use leaky ReLUs on all the layers except for the last tanh layer, as well as batch normalization on all the transposed convolutional layers except the last one." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def generator(z, output_dim, reuse=False, alpha=0.2, training=True):\n", " with tf.variable_scope('generator', reuse=reuse):\n", " # First fully connected layer\n", " x1 = tf.layers.dense(z, 4*4*512)\n", " # Reshape it to start the convolutional stack\n", " x1 = tf.reshape(x1, (-1, 4, 4, 512))\n", " x1 = tf.layers.batch_normalization(x1, training=training)\n", " x1 = tf.maximum(alpha * x1, x1)\n", " # 4x4x512 now\n", " \n", " x2 = tf.layers.conv2d_transpose(x1, 256, 5, strides=2, padding='same')\n", " x2 = tf.layers.batch_normalization(x2, training=training)\n", " x2 = tf.maximum(alpha * x2, x2)\n", " # 8x8x256 now\n", " \n", " x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')\n", " x3 = tf.layers.batch_normalization(x3, training=training)\n", " x3 = tf.maximum(alpha * x3, x3)\n", " # 16x16x128 now\n", " \n", " # Output layer\n", " logits = tf.layers.conv2d_transpose(x3, output_dim, 5, strides=2, padding='same')\n", " # 32x32x3 now\n", " \n", " out = tf.tanh(logits)\n", " \n", " return out" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Discriminator\n", "\n", "Here you'll build the discriminator. This is basically just a convolutional classifier like you've built before. The input to the discriminator are 32x32x3 tensors/images. You'll want a few convolutional layers, then a fully connected layer for the output. As before, we want a sigmoid output, and you'll need to return the logits as well. For the depths of the convolutional layers I suggest starting with 16, 32, 64 filters in the first layer, then double the depth as you add layers. Note that in the DCGAN paper, they did all the downsampling using only strided convolutional layers with no maxpool layers.\n", "\n", "You'll also want to use batch normalization with `tf.layers.batch_normalization` on each layer except the first convolutional and output layers. Again, each layer should look something like convolution > batch norm > leaky ReLU.\n", "\n", "Note: in this project, your batch normalization layers will always use batch statistics. (That is, always set `training` to `True`.) That's because we are only interested in using the discriminator to help train the generator. However, if you wanted to use the discriminator for inference later, then you would need to set the `training` parameter appropriately.\n", "\n", ">**Exercise:** Build the convolutional network for the discriminator. The input is a 32x32x3 images, the output is a sigmoid plus the logits. Again, use Leaky ReLU activations and batch normalization on all the layers except the first." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def discriminator(x, reuse=False, alpha=0.2):\n", " with tf.variable_scope('discriminator', reuse=reuse):\n", " # Input layer is 32x32x3\n", " x1 = tf.layers.conv2d(x, 64, 5, strides=2, padding='same')\n", " relu1 = tf.maximum(alpha * x1, x1)\n", " # 16x16x32\n", " \n", " x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')\n", " bn2 = tf.layers.batch_normalization(x2, training=True)\n", " relu2 = tf.maximum(alpha * bn2, bn2)\n", " # 8x8x128\n", " \n", " x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')\n", " bn3 = tf.layers.batch_normalization(x3, training=True)\n", " relu3 = tf.maximum(alpha * bn3, bn3)\n", " # 4x4x256\n", "\n", " # Flatten it\n", " flat = tf.reshape(relu3, (-1, 4*4*256))\n", " logits = tf.layers.dense(flat, 1)\n", " out = tf.sigmoid(logits)\n", " \n", " return out, logits" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Model Loss\n", "\n", "Calculating the loss like before, nothing new here." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def model_loss(input_real, input_z, output_dim, alpha=0.2):\n", " \"\"\"\n", " Get the loss for the discriminator and generator\n", " :param input_real: Images from the real dataset\n", " :param input_z: Z input\n", " :param out_channel_dim: The number of channels in the output image\n", " :return: A tuple of (discriminator loss, generator loss)\n", " \"\"\"\n", " g_model = generator(input_z, output_dim, alpha=alpha)\n", " d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)\n", " d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)\n", "\n", " d_loss_real = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))\n", " d_loss_fake = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))\n", " g_loss = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))\n", "\n", " d_loss = d_loss_real + d_loss_fake\n", "\n", " return d_loss, g_loss" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Optimizers\n", "\n", "Not much new here, but notice how the train operations are wrapped in a `with tf.control_dependencies` block so the batch normalization layers can update their population statistics." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def model_opt(d_loss, g_loss, learning_rate, beta1):\n", " \"\"\"\n", " Get optimization operations\n", " :param d_loss: Discriminator loss Tensor\n", " :param g_loss: Generator loss Tensor\n", " :param learning_rate: Learning Rate Placeholder\n", " :param beta1: The exponential decay rate for the 1st moment in the optimizer\n", " :return: A tuple of (discriminator training operation, generator training operation)\n", " \"\"\"\n", " # Get weights and bias to update\n", " t_vars = tf.trainable_variables()\n", " d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n", " g_vars = [var for var in t_vars if var.name.startswith('generator')]\n", "\n", " # Optimize\n", " with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n", " d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n", " g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n", "\n", " return d_train_opt, g_train_opt" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Building the model\n", "\n", "Here we can use the functions we defined about to build the model as a class. This will make it easier to move the network around in our code since the nodes and operations in the graph are packaged in one object." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "class GAN:\n", " def __init__(self, real_size, z_size, learning_rate, alpha=0.2, beta1=0.5):\n", " tf.reset_default_graph()\n", " \n", " self.input_real, self.input_z = model_inputs(real_size, z_size)\n", " \n", " self.d_loss, self.g_loss = model_loss(self.input_real, self.input_z,\n", " real_size[2], alpha=0.2)\n", " \n", " self.d_opt, self.g_opt = model_opt(self.d_loss, self.g_loss, learning_rate, beta1)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Here is a function for displaying generated images." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def view_samples(epoch, samples, nrows, ncols, figsize=(5,5)):\n", " fig, axes = plt.subplots(figsize=figsize, nrows=nrows, ncols=ncols, \n", " sharey=True, sharex=True)\n", " for ax, img in zip(axes.flatten(), samples[epoch]):\n", " ax.axis('off')\n", " img = ((img - img.min())*255 / (img.max() - img.min())).astype(np.uint8)\n", " ax.set_adjustable('box-forced')\n", " im = ax.imshow(img, aspect='equal')\n", " \n", " plt.subplots_adjust(wspace=0, hspace=0)\n", " return fig, axes" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "And another function we can use to train our network. Notice when we call `generator` to create the samples to display, we set `training` to `False`. That's so the batch normalization layers will use the population statistics rather than the batch statistics. Also notice that we set the `net.input_real` placeholder when we run the generator's optimizer. The generator doesn't actually use it, but we'd get an error without it because of the `tf.control_dependencies` block we created in `model_opt`. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def train(net, dataset, epochs, batch_size, print_every=10, show_every=100, figsize=(5,5)):\n", " saver = tf.train.Saver()\n", " sample_z = np.random.uniform(-1, 1, size=(72, z_size))\n", "\n", " samples, losses = [], []\n", " steps = 0\n", "\n", " with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for e in range(epochs):\n", " for x, y in dataset.batches(batch_size):\n", " steps += 1\n", "\n", " # Sample random noise for G\n", " batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n", "\n", " # Run optimizers\n", " _ = sess.run(net.d_opt, feed_dict={net.input_real: x, net.input_z: batch_z})\n", " _ = sess.run(net.g_opt, feed_dict={net.input_z: batch_z, net.input_real: x})\n", "\n", " if steps % print_every == 0:\n", " # At the end of each epoch, get the losses and print them out\n", " train_loss_d = net.d_loss.eval({net.input_z: batch_z, net.input_real: x})\n", " train_loss_g = net.g_loss.eval({net.input_z: batch_z})\n", "\n", " print(\"Epoch {}/{}...\".format(e+1, epochs),\n", " \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n", " \"Generator Loss: {:.4f}\".format(train_loss_g))\n", " # Save losses to view after training\n", " losses.append((train_loss_d, train_loss_g))\n", "\n", " if steps % show_every == 0:\n", " gen_samples = sess.run(\n", " generator(net.input_z, 3, reuse=True, training=False),\n", " feed_dict={net.input_z: sample_z})\n", " samples.append(gen_samples)\n", " _ = view_samples(-1, samples, 6, 12, figsize=figsize)\n", " plt.show()\n", "\n", " saver.save(sess, './checkpoints/generator.ckpt')\n", "\n", " with open('samples.pkl', 'wb') as f:\n", " pkl.dump(samples, f)\n", " \n", " return losses, samples" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Hyperparameters\n", "\n", "GANs are very sensitive to hyperparameters. A lot of experimentation goes into finding the best hyperparameters such that the generator and discriminator don't overpower each other. Try out your own hyperparameters or read [the DCGAN paper](https://arxiv.org/pdf/1511.06434.pdf) to see what worked for them.\n", "\n", ">**Exercise:** Find hyperparameters to train this GAN. The values found in the DCGAN paper work well, or you can experiment on your own. In general, you want the discriminator loss to be around 0.3, this means it is correctly classifying images as fake or real about 50% of the time." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "real_size = (32,32,3)\n", "z_size = 100\n", "learning_rate = 0.0002\n", "batch_size = 128\n", "epochs = 25\n", "alpha = 0.2\n", "beta1 = 0.5\n", "\n", "# Create the network\n", "net = GAN(real_size, z_size, learning_rate, alpha=alpha, beta1=beta1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Load the data and train the network here\n", "dataset = Dataset(trainset, testset)\n", "losses, samples = train(net, dataset, epochs, batch_size, figsize=(10,5))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "losses = np.array(losses)\n", "plt.plot(losses.T[0], label='Discriminator', alpha=0.5)\n", "plt.plot(losses.T[1], label='Generator', alpha=0.5)\n", "plt.title(\"Training Losses\")\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true, "scrolled": true }, "outputs": [], "source": [ "_ = view_samples(-1, samples, 6, 12, figsize=(10,5))" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", <<<<<<< HEAD "version": "3.5.2" }, "widgets": { "state": {}, "version": "1.1.2" ======= "version": "3.6.1" >>>>>>> refs/remotes/udacity/master } }, "nbformat": 4, "nbformat_minor": 2 }
mit
LorenzoBi/courses
CQD/assignment 6/Untitled.ipynb
1
188284
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import numpy.linalg as LA\n", "import matplotlib.pyplot as plt\n", "from pandas import *\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def Arnodi_algo(A, b, k):\n", " N = A.shape[0]\n", " Q = np.zeros((N, k), dtype='complex')\n", " H = np.zeros((k, k), dtype='complex')\n", " Q[:, [0]] = b / LA.norm(b)\n", " \n", " for j in range(k - 1):\n", " r = A.dot(Q[:,[j]])\n", " for i in range(j + 1):\n", " H[i][j] = Q[:, [i]].T.conj().dot(r)[0][0]\n", " r -= Q[:, [i]] * H[i, j]\n", " H[j + 1, j] = LA.norm(r)\n", " Q[:, [j + 1]] = r / H[j + 1, j]\n", " for i in range(k):\n", " H[i, k - 1] = Q[:, [i]].T.conj().dot(r)[0][0]\n", " r -= Q[:, [i]] * H[i, k - 1]\n", " return Q, H\n", "\n", "def Lanczos_algo(A, b, k):\n", " N = A.shape[0]\n", " Q = np.zeros((N, k), dtype='complex')\n", " alpha = np.zeros(k)\n", " beta = np.zeros(k - 1)\n", " Q[:, [0]] = b / LA.norm(b)\n", " r = np.dot(A, Q[:,[0]])\n", " for i in range(k - 1):\n", " alpha[i] = np.dot(Q[:, [i]].T.conj(), r)[0][0].real\n", " r -= Q[:, [i]] * alpha[i]\n", " beta[i] = LA.norm(r)\n", " Q[:, [i + 1]] = r / beta[i]\n", " r = np.dot(A, Q[:,[i + 1]]) - beta[i] * Q[:,[i]]\n", " alpha[k - 1] = np.dot(Q[:, [k - 1]].T.conj(), r)[0][0].real\n", " return Q, alpha, beta\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(13)\n", "N = 200\n", "k = 200\n", "A = np.random.randn(N, N) + 1j * np.random.randn(N, N)\n", "A = (A + A.T.conj()) / np.sqrt(2)\n", "b = (np.random.randn(N, 1) + 1j * np.random.randn(N, 1)) / np.sqrt(2)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "Q_ar, H_ar = Arnodi_algo(A, b, k)\n", "my_id_ar = np.dot(Q_ar.T.conj(), Q_ar)\n", "my_id_ar[np.absolute(my_id_ar) < 0.0001] = 0\n", "DataFrame(np.absolute(my_id_ar));" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dataframe thead th {\n", " text-align: left;\n", " }\n", "\n", " .dataframe tbody tr th {\n", " vertical-align: top;\n", " }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>0</th>\n", " <th>1</th>\n", " <th>2</th>\n", " <th>3</th>\n", " <th>4</th>\n", " <th>5</th>\n", " <th>6</th>\n", " <th>7</th>\n", " <th>8</th>\n", " <th>9</th>\n", " <th>...</th>\n", " <th>190</th>\n", " <th>191</th>\n", " <th>192</th>\n", " <th>193</th>\n", " <th>194</th>\n", " <th>195</th>\n", " <th>196</th>\n", " <th>197</th>\n", " <th>198</th>\n", " <th>199</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " 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0.000000 \n", "196 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 \n", "197 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 \n", "198 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 \n", "199 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 \n", "\n", "[200 rows x 200 columns]" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Q_lan, alpha, beta = Lanczos_algo(A, b, k)\n", "H_lan = np.diag(beta, -1) + np.diag(alpha) + np.diag(beta, 1)\n", "my_id_lan = np.dot(Q_lan.T.conj(), Q_lan)\n", "my_id_lan[np.absolute(my_id_lan) < 0.0001] = 0\n", "DataFrame(np.absolute(my_id_lan))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lam_true = LA.eigvalsh(A)\n", "lam_ar = LA.eigvals(H_ar)\n", "lam_lan = LA.eigvals(H_lan)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fdfe7eaef60>]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lF54kgS+Eh6ScT+GBxU+SlL2X5j738cfBVhxxJNM2pCoRiYu49fAm2tv2kaz9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\nNr3Gdwe+4/bGt/NKp1ewmqW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"text/plain": [ "<matplotlib.figure.Figure at 0x7fdfe7ec0828>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(lam_true, 'x')\n", "plt.plot(sorted(lam_ar.real))\n", "plt.plot(sorted(lam_lan.real))" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "A = np.random.randn(N, N) + 1j * np.random.randn(N, N)\n", "A = (A + A.T.conj()) / np.sqrt(2)\n", "b = (np.random.randn(N, 1) + 1j * np.random.randn(N, 1)) / np.sqrt(2)\n", "\n", "x = np.random.random(A.shape)\n", "\n", "A[(x + x.T) / 2 + np.eye(N) < .9] = 0" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [], "source": [ "Q_ar, H_ar = Arnodi_algo(A, b, k)\n", "my_id_ar = np.dot(Q_ar.T.conj(), Q_ar)\n", "my_id_ar[np.absolute(my_id_ar) < 0.0001] = 0\n", "DataFrame(np.absolute(my_id_ar));" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " 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0.000000 0.000000 \n", "185 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "186 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "187 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "188 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "189 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "190 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "191 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "192 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "193 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "194 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "195 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 0.000000 \n", "196 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 0.000000 \n", "197 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 0.000000 \n", "198 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 0.000000 \n", "199 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 1.000000 \n", "\n", "[200 rows x 200 columns]" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Q_lan, alpha, beta = Lanczos_algo(A, b, k)\n", "H_lan = np.diag(beta, -1) + np.diag(alpha) + np.diag(beta, 1)\n", "my_id_lan = np.dot(Q_lan.T.conj(), Q_lan)\n", "my_id_lan[np.absolute(my_id_lan) < 0.0001] = 0\n", "DataFrame(np.absolute(my_id_lan))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "lam_true = LA.eigvalsh(A)\n", "lam_ar = LA.eigvals(H_ar)\n", "lam_lan = LA.eigvals(H_lan)" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7fdfe7a81f98>]" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6iZ26PtqpIl/3bgyU34ekhZEeuxDijtiG+vv3vM+ToU8WW6jnDF+0raXHJ6fx7Nxodm9Z\ny2w9kWRLRfpfn8Dpqm0xurozukf9MrNHqb1Jj10IcVtSMlL4/eTvzDs4j/gr8bx/z/s8GHTDgXBF\nZrscwMz1x+gVWo3obZv5Z+XN9IhZxUUq0z9zAonGasx9rClw9/XSbdl1uOOtkuGOQpQtaaY0Fh9e\nzPr49exN3ItG09i7MS+2eJGOAR2L5Zz5Sy4AH8//gWdd19IiYxv+KgmTNrLC2p5Jpr/RLqxpmR7K\neCtudbijBLsQ4oZiU2IZEzWGuNQ4Qr1D6RTQic4BnWns3bjYZpDmlFxy1m2ZtS6Wsf676H1qClYM\nbLI2Ya21BbsqtONwmjuPhvkTFZNYbgM9x60Gu5RihBB/YdVWfj72M3P2z+HopaNUca3Clz2+pF3N\ndsV63vzDF1/u4ANr3mENUXjGX2GLbswo80tcsHrSxK8yR85eZmDbQH7Zfz7PqozlMdRvhwS7ECKP\nned38nH0x+xL2kdDr4aMCR9Dr7q9im344hdRcZy8mMZDzf1yA/2FToG87rmWXlHzqEwaK61t+dXa\nll8s4SiDEwPbBrBs51kibULdYs078ehuJqUYIQQApy6f4pMdn7D61GqquVVjVNgoHgp+CIOy/+A5\n2zAHGDF/Bxarpl3dqrRM30Tv87OoazjHJmsoE02DSHKvR+LVTEL9KnMqOZ1Zg1sB5Nbfy2vpJT+p\nsQshbsnFaxf5at9XLDmyBGeDM0OaDOGp0Keo6FSx2M65OS6JEfOzFoSdNbgVB86msnLlct5yXkxb\nw2GOan/eN0WyztqCe0J82HT0Yp7e+cz1x8rcPqT2IMEuhCjUdct11pxcwx9n/uC3E79h1mYeDXmU\nF1q84JAZo5AV7u/NX8W91i30VFtpqWJI0pX5xNyP7+mKWRtxNir6hPkT5Oue+yDVYuWu6qXbkmAX\nQhRIa81La18i6nQUlV0q06tuLwY2Gkhdz7rFel7b8ktEsA+knCT9swjcrFeJtfqzRN/H99bOXLJU\nwMmgeLNXgzKzAYajSLALIQo078A8JkdPZnSr0TzZ+MliXXmxsFp6h7qVefPca1TPOE7fzPeI0YEA\ndG3oS/tgb/615ihAnlr63RzoOSTYhRC5TFYT6+PXs+TwErad28a9gfcy7d5pxb6TUf5aetyJE5xd\n+yUDnVYToJJ4MfMl0us/DMCfcRdxMhpyw3z5nrPU9naXQLchwS6EICUjhSVHlrD0yFIuXLuAn7sf\njzd4nMiGkbg5uxXLOfOXXDbHJTF17hIGsZIHDFtxUWY2Wxozx9KLSs0f4pO/ZZVZNsclSZjfhExQ\nEuIudzTlKCPXjORc2jk6+HXg7fZv09G/Y7FvetEswJMZ646yYm8Cswa3olrsEr41TCCdCiyydOVb\n3Z3jhkAwgvOhC2yOSyIi2Cf3jyg6CXYhypljl46x6PAiVhxbQUWniizuvZgmPk2K9Zz5e+mzBrVk\n4TdfkPbNR4QYdrDO2pxXLaO4ZM0aQjn+/vqE+nkyYv4ORszfwazBrSTU7UhKMUKUI6tPrmbcxnFo\nrbm31r280vIV/Dz8iu18tvuM5tTSX763Nk33fEDb5P9yTlflO0tnZtKPR1rW5vzlDKmlF4GUYoS4\nixy6eIjP93zO+vj1NPNpxqf3fuqQbely1kafHhnGrEEtmffNLDquGU0Dw2k+Nz/MJ9a/YdEKVydD\nnpr78j1n78rldB1FeuxClGEXr13kX7v+xQ+xP1DJpRKDGg9iSJMhuBpdi+2ctr30FxftYmTnumxe\nu5zXnL6jifkAx6w1+Kc5kihDG+Y+0xogz8gYCfI757Aeu1IqEJgHVAc0MFtrPa2oxxVCFC4lI4Wl\nMUuZs38OGeYMnmz8JCOaj6CSS6ViPa9tL31mvxC+a3WQ9DVvMEwdI9HkyduWIXxnvZdMbcQ1eyRl\nRLAPswa3yu2lS7AXvyL32JVSNYGaWuudSqlKwA6gj9b6YGGfkR67EHfuh9gfmLhlIpnWTDoFdOL1\n8NcdMmvUtpc+ur0nHTY+SV0SOGwNZJ6lBz9YO5KhXRjXu2Hug1GQXro9OazHrrVOABKyv76ilDoE\n+AOFBrsQ4s7MPzifSdsn0a5mO95s/SYhVUOK7VwFLaf7cgdfhlfdSes/FlCdZAaaxrLJ2oRQv8pY\nLqQR2co/d4Eu6aWXHLs+PFVK1QHCgK32PK4QdzutNbP2zmLG7hncV+s+Pur0ES5Gl2I5l+1mFznj\n0Ud1C2GgTwy9o4bgoy6TggcjTK/yp25KRWcD43s3Bu7ufUZLE7sttKyU8gC+B17RWl8u4O+HK6Wi\nlVLRiYmJ9jqtEOVemimNCZsmMGP3DB4OfpjJnScXa6jn1NABvn4imEctv9Hi9/68dn4cyVSm3/V3\naHX9C5JrdMDd1YnRPernvn96ZBgWKzJ0sYTZZVSMUsoZWAH8qrWeerP3S41diFuzL3Efb/7xJmeu\nnmFEsxE81/w5u298kX+hrhcX7eKBUF+q7PqCl43f4YyZGKs/yywdWUAvMrQLzkbF10+3zn1/znK6\nEujFy5GjYhTwNXDoVkJdCHFz1y3X+WrfV3y19yt83HyY03MOraq3sus5Ciu53FcjnZ67XqSjcR8/\nW9owy9qHQ9TBZKHA5XRztqOTUC897DEq5h7gD2AfYM1+eZzWemVhn5EeuxAFM1vNrDy+kll7ZnHq\nyil6B/VmbJuxeLp62vU8OSWXnAedaM2sb+byoN7Ag8YtWDDwgXkgS6xd0VpxryynWyrI6o5ClDHx\nl+N564+32Ju0lwZVGzC61Wgi/CPseo6/TC7qEsTKNWt5z2kezc17uazdWGVpzVRzP7z96hB7IY3H\nW/nzy/7zuZtdyBIAJUeWFBCijDBZTCw4tICZe2biZHDio44f0atuL7uvlZ5nCYD+zZjbXRO18p8s\nUT9y1VSBv1ue5j+6K2kWJ1yMiqky0qXMkh67ECXIqq28su4V1sWvo0tgF8a1GUdNj5p2O35BD0Yf\nalyFrntep7NhNwC/WVox3jyUJO0pJZdSTkoxQpQBM3bP4Is9XzAmfAxPhj5pl2MWth1duyAvqpoT\n6XtyIu0Mh/ineQDLLe2l5FKGSClGiFLsWOoxpkRPYcPpDTwc/DCDGw8u8jELGuXyapcA+tZMIuXk\nflodjeUx4x84GSyMNo3kJ30Prs4GKbmUQ9JjF8KBrmReYeaemSw+tJiKThUZ1mwYgxoPwtngXKTj\n5h/l4pG4i6sr36OVOoyrMgNwVVcgytqMf5oj8awZzKnka4zqFvK/kTFIyaW0kx67EKXMmlNr+L8/\n/4+UjBQeq/cYo1qOwquC120fx7bUsvd0Ks0CPDEa4ONfY+he14XT80fQjzWcV1WZa+nJLms94lVN\n4vAnw2LAxaiYJL30ck167EI4wIbTGxi1dhQNvBrwTvt3CPUOva3PF1Y3b1Ddg4MJV/BSV3hMrWeI\nWo4nV/m35X6mW/uRripismT9f7yrPBgt8+ThqRClgFVbWRqzlEnbJxFSJYSve36Nu7P7LX++oK3n\nRnUL4c+4i5w4socnjFH0Nm4hUGWtv7TJ2oT3TZEcpg5aw70Ns3ZRku3oygcJdiFKWPyVeN7b/B7b\nzm2jbY22TOo86bZKL/nr5sqSyXfzP6exPkqY4RitDEcwawPrrc055xnG4ovBHNB1CfWrJKNcyikJ\ndiFK0Krjq3hn8zsYlIHXw1+nb72+tzzhqKDZofvXLOJt9W989EWuaRfitB8rdXt+ojPnrZ6YLBqj\nQeEii3OVaxLsQpSA65brfL77c+bsn0PLai35qNNH1HCvcdPP5YT53tOpub30kV2CSEhIIOjAZwxS\nq9hrrcunlifYSDMyLVn/kRjYNpCf9iRwLdOCs1Exukd9GeVSjkmwC+FgWxK2MHHLRE5ePknfen0Z\n33Y8zsabD2O0LbmM7BLEzPXHaO/vTN24BTzv8jMVrNf4xtKDjyyRXNfOeermFg1jetYn1M8zt9SS\n8x8ICfTyR4Y7CuEgJouJiVsnsix2GbUq1WJ299m092t/08/ZTijKKZt8seYgQ1zWMuDkd3g7X+FX\nczhTzf1wqhmKNXvruZy6+dCOQSzfcxaLNWvDaNuhijJs8e4mwS5EEaSb0nkt6jU2ntnIkCZDGNl8\nJBWcKtz0c3kW5IoMY0b/JvwyfyrL1VL8MpPZbmzOkGt92UcIrk4GvpZx5+I2SClGiDu0+8JuJmya\nwKnLp3i7/ds8Xv/xAt9nWz/PGYt+4GwqU3+L5bGWfiTv/C9vOy/Ez5rADms9vqkwiJ8u16OOtxsX\n0zJldqjIJaUYIYpJpiWTz3d/zr8P/JsabjX4uufXtK7RutD325ZaVuxN4L+7z1LVkMaE4AQq7fyU\nh41/csQcwBDz62TWvY+Nccl0a+jLrvjU3FCXXrq4HdJjF+I2pF5P5eV1L7Pj/A761uvLmNZjCpxw\nZNtLzxm6+MH85Qys8CeN0rfTTB3HoDQZ2pnZlt7MsDyGwcmFPmH+BPm654a5xYo8DBW5ZFSMEHZ2\n4OIBxv4xlvgr8bzf4X0eCHogz98XtPZ5rybV+WHHKUbX2M3AxGm4YGK3rscGS1M2WUPZq4Op5+cl\nC3KJWyKlGCHsxGQxMXvfbL7c+yXeFbyZ3X02O454sVkn5ambGw3w/Y4z/Hf3WdoFedGtxjXCd45l\nrDEaj6QMttGIV80vcsZSFSeDwqw1LkbFeHkwKuxMeuxC3MC5tHOMWjuKQ8mHaODehRFNRxN3zpo7\n7rxXk+p8F30GpcBfX2BU3VOcO3GYFoY4WqoYLBj5wXIPW2jCz+bWGIxOOBsNmK2ax1v589OeBEAW\n5BK3RkoxQhTR0ZSjPLf6Oa6artKpykvUq9QuzySisEBPEo9sYXjlLQRf20cjwykAMrUTh3Ugm61N\nWKzux8O3FgfOXiHUrzKxF67mTijKqb/LGi7iVjk02JVS9wPTACPwldb6wxu9X4JdlHY7z+9k+G/P\ng3ZmTPPJBHqE5NbMN+zYx6CKWwi7vpU2hiOkaVeOODdi1bVG/GYN56yhBhoDJktWqcXV2ZhndEtO\nHV1KLeJ2OSzYlVJGIAboDpwGtgMDtNYHC/uMBLsorbTWvPzzLP5Ino13hRqcjxkMZi9GdQvh9MGt\nhMQv4wnjelyViaOqDv/JbE+09yPsvGDNqptb8659PvnXGIyK3IW5cnrpUnIRd8KRD0/bAEe11sey\nT7wEeAQoNNiFKG2+iIqjmlcqcw9/zvH0rahrDXi41nhSuQr7lhLx+zpCDSfJNDrxg+Uelrk/wdbU\nKjTxq8z+s5dxdTKggRb+lTly7grbT6QwtGMQc59pzfI9Z//yQFR666I42SPY/YF4m+9PA23tcFwh\nHOKLqDhOZ+xixo5/4GxwwSulI2Ot1/BaO4IwwzGcjSb2WevwjukpfrJE0CSkDluPXqRbQ182xyXT\nItCTuMQ0RnULwWKFN+5vWGCYC+EoDhvuqJQaDgwHqFWrlqNOK0SBbMec+1RNZsaOD6muKjP6uOZ+\nvQiTduKIUzD/zuzBf3VHYqidWzMP9HJjfG9fZq4/xuge9QqcRCRhLkqSPWrs7YH3tNY9s78fC6C1\n/mdhn5Eauygpebea204d95Vc8N1ARauJxWfOYTJ78aOlA6s9HmHvJVcMitwt5mS/UFHSHPnw1Ims\nh6fdgDNkPTyN1FofKOwzEuzCkfLPCB2zcDOD6/7MzxmbOOGiCM0w0eV8CKuv30e0JYTAqhWJT8mg\njrcbZ1MzZIs5UWo4erjjA8CnZA13nKO1fv9G75dgF8Utf5iPmL8Di1UzvEYs7S5PZWQNd6qZDHin\nNGFXch+ukbXeyz0h3mzMrp/vik/NHZ4oW8yJ0kAmKIm7TmFh3r22gVZpf2C4cIBOrlEM9vPjivYg\n+dhonFVWoOfUz/u2CpBFuESpJcEu7gqFhXm7ulUJMB2n9qkfGWBcS0V1ne/dK/OhT1UytAuZp0eg\nr/tjtmipn4syQ4JdlFuFhnmQF57mizQ58Q09DNEEGhLJ1Eb+Q2s+9a3A9UrHsKbXJf3MExitXnSq\n70P7YO88KypK/VyUZhLsoly5UZg7W69zKXYrfYwb6WPchBMW1ltbsM7aglVuPmTWWAGGdK4n9uSe\nao8SEewpU84lAAAddElEQVQrvXNRJkmwi3Ih7/DEHQCM6hbCscN7CDr5HW0Mh2msTuKsLKRrV5Zb\n2jNb9+EUPhi8V+HivYGK2p8ghrEnzg0noyE3zKV3LsoaCXZR5uVs+JxbKtGa2d/8m8GspJtxF5na\niWhrfXbpEPZRjy06lEuWCiinFKqHfEeaOoY1tT2GlIeZNagdIGEuyjYJdlFm2fbSczafOLBmIa8Y\nv6OO5RSJ2pP55u78h25cpAomy/8W3kphJ7HWOSilGdboDcJ975UwF+WG7KAkyoT8e4PuPZ1KfHIa\n01bHMqZbLV6sdYIaq6cwTP3JYVMgY6zPsVJHkGbJ+tXt2jBr6v6fcUlsv/QtVP2N2p71CdbP4XI9\niIhgH5neL+46EuzC4WzD3Gj4396g01bHEhHsxdrD5xkTeISe657HX13kOs5MNfVjpvURzNrIvQ19\nAfgz7iLbT6QwfWBTKvv/wpqzv9HQ/V4W9vkYF6NLCV+lECVHSjHCIfKHec62cst2niUi2IvjR/Yw\n0nMLhrRzdHI+jK81iWOGOkzM6MdmayjBfj7EXkj7y/T+r3f8wiHTXK6YLzCg4QDeavMWBmUo4asV\nonhIKUaUGjkPQXPq5Tnbyv289SDPeR8gJG4LPV2jsV5TpDh5sccUyHaPp/kqpTlaGXF1Nvxlw+ed\npxIxVV7FtmtzCKkSwtQ279OuZrsSvlIhSgfpsYtiU9BD0JVr1vKg8w7qZBymo3EfLpi5SFV+NLfj\nt6oD2JboRGj25hV1vN24mJaZu61cTi991dHN7EyfzcnLJ+lbry9vtXmLCk4VSvJShXAI6bGLElFQ\n/fyFTgGMr7md4NVjGaZisWYqTjn5M9/UnY3u97EutQZN/Dw5cPYyXRv6suZwYu4iXLZ7he6Jv4TR\nay3/PT8DP3c/Zt03iwj/iJK+ZCFKHQl2UWSFPQxdtvMsHYMqU2f1SLoZdxGr/fmHeSDbKt3Hvkuu\nudvK5exEFNk2kGU7zzKwbSCBXu482zGIvadTmR4Zxt7TqXhU+5NJ2z+jV51evBvxLu7O7iV96UKU\nShLsokgKq5+v3HqAId5HaXZsPd2Mu3jb9DTzLd25J8SHfTbbyg1sG8gv+8/n7kT09dPhBe5EZHY9\nyKi1U+ga2JUPO30oD0iFuAGpsYs7UlD9fO6a3fR03U9I2m4ec9pIBTIxY+ATUz9mWvvg6mSgT5j/\nbS2Lm3A1gak7prLqxCqCPYNZ8MACPFw8HH25QpQKMvNUFJv8U/0NpnRiFr9BP9bipq5zDVdWmNvy\nq/uDbEitjp+3Z4EPQW+08JbWmgWHFjBt5zQAhjQZwjNNnqGiU0WHXacQpY0Eu7Cr/Ksr5tTRt++I\nZpbrNGqZT/KDtSO/ufVmTaofjfyq5tbPb3cnIrPVzJToKSw4tIAugV0Y12YcNT1qOupShSi1JNiF\nXRS0uuKrXfy5eGgjvmfWEmlczTVcecn0Etagrrnbym2OS+axln78sv/8be1EdDz1OG9veps9iXsY\n3Hgwr4e/LvV0IbJJsIsiyV9Dnx4ZRvzRfZg3TuchwyYqq2tYtOI7S2c+tTxOqpP3bdfPbZ29epZP\nd37Kryd+xcPZg3Ftx9E7qLcjLlWIMkOCXdyx/DV0t+RDnF3xPj3ZghknVljbstwSwQ5rfWr5VedU\n8rXbqp/n913Md0zePhmA/g3682Tok/hUlIW7hMhPJiiJ25bTS28W4MmLi3Yx6p5qnJ4/gidYTTAV\nmW15kG/0A1lL5VqzNn/OP9U/J9BvdUXFbw58w8fRHxPhF8G77d/Fz8OvOC9RiLtCkXrsSqnJwENA\nJhAHPKO1vnSzz0mPvfQoaHGukV2CSDx7kv4HX6AOCcyx3M/n1se4ojzsuvnz/IPzmbR9Ej1q9+Cj\nTh/hZJB+hhA34pBSjFKqB7BWa21WSn0EoLV+82afk2AvWTdaabFzXXdMR9cx0e1bKpmSGGZ6je00\nwWBQ3BPibZfNn7XWzDs4j4+jP6Z77e581OkjnA3OxXW5QpQbDinFaK1/s/l2C9CvKMcTjpFTaskZ\ngtirSXVWbT3AO1XW0+vkcqq4pJFs8uCpzDfJqBlORZsa+tCOQblT/G+n5JLjXNo5/rHlH2w4vYH7\nat0noS5EMbDbw1Ol1HLgW631gpu9V3rsjpd/pyKAMfOjiHSLpvqV/Txo3EIFMtlgaMuXGfeyTTfG\n4OTC10+3Bv5XQ7/ZGPSCmCwmNp/dzJaELXwX8x0Ar7Z6lQENB8hQRiFug91KMUqp1UCNAv5qvNb6\nv9nvGQ+EA4/pQg6olBoODAeoVatWq5MnT96sbcJObEe55PTSh9dN4qHYCfirJFK0B79ZwvnJ/TE2\npfoUulzu7dbQAdJN6YxaO4qt57bipJzoUacHL7d8WR6SCnEHHDbcUSn1NDAC6Ka1Tr+Vz0iP3THy\nj0UfdU81LkbNpq9aTx19mtPah1fMLxJtqUcTP887nilaEK01G89s5LNdnxGTEsOEdhN4MOhBWTdd\niCJwSI1dKXU/8AbQ+VZDXThOTi19emQYc7trAlb1xosrbLU0ZL71Sb4z30O6oRID2wbkLpdrO1PU\ntpZ+OzItmbz1x1v8fvJ3qrlVY2qXqXSt1bWYrlIIkV9RR8UcBVyBi9kvbdFaP3ezz0mPvfjZ9tYX\nLfyaT5jCGUsVRmW+SIJ7IxKvZhLqV5lTyel5hive6kzRgsRfiWfV8VWsP72evYl7ebnlyzzV+Cmc\njfJwVAh7cNSomJCifF7YX/5JRkvanWSa9SMOWgN5OvNNavoFknT2cm7vfFS3kNxeff410G/HmlNr\nmLBxAldNV/Fz9+ODez7goeCH7H15QohbIDNCyoGCdjAa2SWI1/33U3/Te2yyhDLC9CqZRnd6BHjy\nSJhfnvp5TsnlTgIdYOOZjby67lVCvUOZ0mWKPBgVooTJWLMyznYHo5yRLx39NDGrvuDxUxPZZm3A\nENMYuoeFMHdIG349cJ5QP0+mR4blPhSNCPa5o9ILZC3eNfaPsYRUDWHO/XMk1IUoBWQRsDIq/4iX\nVzp4k7xhNr0MW2lgPQbAYWsgA83vMLBLcxZsPVWkYYu2tNasjV/L3P1z2Zu0lwrGCix5cAl1PesW\n/cKEEIWSRcDKqfw19OmRYXzU+AQRUQNxV9fZZm7Aly6R/JzWkIMqGFcXZ9oFe9Mu2PsvtfQ7kW5K\n57Wo19h4ZiO1K9dmeLPh3F/nfgl1IUoR6bGXIfmX03XKSGbrt5N4Xi1lnw7mTdNQ0qvUJz4lw66T\njCCrl77zwk4+3v4xh5IPMab1GP7W4G+ycJcQDiQ99nKkoOV0D89/lUH8QhuDiV8srRltGkmrEP/c\nHYx2xafmhvqdLKdrK/pcNJOjJ3Pw4kEquVTi03s/pUtgF/tfqBDCLiTYS6nCRrq8WOsEvdYPw5dL\nLLN25Gvrg8TqQJydFYFebozv7VvgiJc76aWnm9KZHD2ZpTFLqeFeg79H/J1edXvJhtJClHIS7KWQ\n7UiXnKn9bf1dcfn1TZ5y+p0Y7c8w02gOqhAMBsWb99cn1O9/Nff8YX4nvfTdF3YzbuM4Tl85zTOh\nzzCyxUgJdCHKCKmxlyL5R7qM7BJE1JqfGWFcQaj5AF7qKl+ZezHZ/DdC/LyLvCVdftct15mzfw5R\n8VEcSj5ETfeaTOwwkfAaNy3pCSEcQPY8LWPyPxjFaubggjEMYTmJeLLTKYy56fewnUa4Ohnsspyu\nrZiUGN6IeoO41DhaVmtJ25ptebLxk3i4eNjrEoUQRSTBXkYU1EtftGY7nzjNoIV5L4vMXZnr8Swx\nl5TdR7pA1miXX47/wnt/voeHswf/1+H/uMf/HrtdnxDCfmRUTClW2IPR3o2qoFf/HytZicGkec30\nHOeDHiWmGEa6ABxIOsAH2z5gb+Jemvs255Mun+Dr5mvnqxVCOJr02B2soE0vWgZU4lrsej50W0yg\n+QQ/WDowzdyP80416RPmT5Cve56RLkVZgREgOSOZJYeX8OXeL/Gq4MXIFiN5JOQR2aJOiFJOSjGl\nTEEll6VrNjPA+Q/uy1xDgErigq7CG6bhJNboaPcHo2armU1nNvHD0R+Iio/CrM3cX+d+JrSbgKer\np92uUwhRfKQUUwoUVnL5Wz1Nw9XP8KvagzVTEW1sxkcZ/fldh6OcKvB178bA/95flJKL1prvYr5j\n5p6ZJF1LwquCFwMbDaRPSB9Cqsqqy0KUR9JjLyYFlVzCA9ypEPsz71eYh7aY+Mrcm00e3dmRWqlY\nHoyeuXqGGbtmsPzYcsKrhzO48WA6BnSUkosQZZT02EtYzvT/kZ3rsGfd90ytEE3jE1up5nKJQ+Za\njDS9TEBwE3bY+cFockYyPx39iT/O/EH0+WgUipHNR/Jc8+cwKFmlWYi7gfTY7Syn/BIR7EP03r14\nLhtIPU5xWbux09iceRkd2KBb4OzkZPcHo98c+IZ/7fwXmdZM6lWtR5eALjzR4AlquNcohisVQjia\n9Ngd7C/L6Q5oTuimMThzgVcyn2drxY4kpGnqeLtRMV/JxR5LAOy6sIsp0VPoGNCR11q9RlCVIHtf\nohCijJB/m9uB7douANMHtGDbvLfxPL+Fv5sGc7TGAySkabo19OVyhvkvJZei7GAEkJieyPiN4/Hz\n8GNSp0kS6kLc5STY7aBZgGduUM9a+C2+yx7nFcMSVllac6ZOXx5s7sf43g3ZFZ+aZ9XFoi4DkG5K\n55sD3/Dwjw9zPu08EztMxN3Z3Y5XJoQoi+xSilFKvQZ8DPhqrZPsccyyJCLYh+kDWpC8cAjf6A2k\npHkwwfIs6U0GcjA2mee71iMi2IdQP88il1xyHEk+wtDfhnLp+iUi/CIY13YctSvXttclCSHKsCIH\nu1IqEOgBnCp6c8qOL6LiOHkxjYea+xER7EOEdSfoDXxl7sWnlsdRrh7Mal2bfq1r5y6nGxHsU6Qw\nz6G15v2t72NQBhY8sIDmvs3tcEVCiPLCHqWYT4A3AMcPrykBX0TFsTkuiWYBnqzYm8CI+Tv4OuoQ\nZ799hThrTSZZBmBxcmNUt5D/1dyzH47ay6oTq9h1YRcvhb0koS6E+Isi9diVUo8AZ7TWe5RSdmpS\n6WX7kHR6ZBhf9m/EHwv/Sa+1q/BTyQyzvsHcoR0A+8waze/gxYN8ufdL1savpaFXQx4NebTIxxRC\nlD83DXal1GqgoIHQ44FxZJVhbkopNRwYDlCrVq3baGLJyz+UcWSXIJbN/4xx6hvaGVPYaAllnOVZ\nmnXplxvgRdmSLr9LGZf4cPuH/HzsZyq7VObp0KcZ3HgwRoOxyMcWQpQ/dzxBSSnVFFgDpGe/FACc\nBdporc/d6LNlaYKS7dIAM/o3oerptZxdP4euKprd1iA+sDzJXkNDAJyNBmYNbmWX3jnA6SunWXVi\nFYsOLSLlegrPhD7DM02eoZJLJbscXwhRthT7BCWt9T6gms0JTwDh5W1UTE4v/fnOtbm28Enas41q\neDDZ9ASzrQ9h1kbGZe85OmL+DkbM32GXcN+ftJ8hvw7hmvkaYdXC+Py+z2no1dBOVyWEKM9k5mkB\n8o94mT6gOacWvEBXtvGBaQBfWx6goV8V1IU0Ilv5584gnTW4Fcv3nM2ddHSnjqce54U1L+BVwYsv\nu39JYOVAO16dEKK8s1uwa63r2OtYJcW2lj5j3VFW7E1gdBd/wneNpz/r+cL8EF9aHqKCs4Hxdlxa\n19a2hG28uv5VjMrIzPtmSqgLIW6bzDzNln9ZgNmRTRhm+Q+PrOtJ6KUo/mEayJLKQ/Co4MToHvXz\nDGUs6gxSyBrxMuL3ETz727P4VPRh4QMLqetZt8jXJYS4+0gpJpvtiJcpC39imvMM2huP8pulFV+Y\nH8ItuD3rh7Zjc1ySXXvpFquF+QfnM23XNDxdPBkVNooBDQfg4eJhx6sTQtxN7vpgt11md3r/Zhxa\n9Bbf6v9yOaMiw8xjWKdbogxQ4XQqm+OSst5np6GMMSkxTNg4gUPJh+ga2JX/6/B/sk2dEKLI7upg\nzzPhqH9TIvZOIEIv43tLRz40RZKEJ+N6NyhwxEtRa+kHkg4w7PdhuBpdmdx5Mj1r9+RumOQlhCh+\nd3Ww5w5l7FSLy4uGgN7ER6b+LHN7nFRtKpYRL2tPreU/Mf8h+lw0PhV9+Lrn1/h7+NvxqoQQd7u7\nMtjzll+acnXRM/TQm/nANIDN1QeydVRHu9fSzVYzk7ZPYvHhxQR4BNAnpA9Dmw6V3Y2EEHZ31wV7\nnvLLgBZEHP4Q9GYmmgbya+V+pKVm2L2WrrVm4paJfB/7PYMbD+bVlq/ibJQNpYUQxeOuC/ac8ssL\nnQI5unA0EfonPjc/zJbqA0hLzWBklyC7LbO7P2k/c/bPIelaErsu7GJY02GMajnKjlcjhBB/ddcE\nu235ZfYj1am07G804ASLzF35xXcoKwoov9xJqJ9IPcH03dO5lHGJ7ee3U8W1Cv4e/gxtOpSXwl4q\nhisTQoi8yn2w51+ZcXafmjT5bSBmkhiWOZrDnh1Ju5xZ5PJL/JV4LqRfYEzUGDLMGdT1rEtkw0ie\nb/G8LNolhHCoch3s+ddPn9XHH5+lj2HiEoMyx1IpuB1pCZeLVH7RWjM5ejLzD84HwKuCF/N6zSOk\nakhxXZYQQtxQuQ5229mk7y1cw3yniXiQzODMsVQKbsuCoW3vuPxyIvUE38V8x9mrZ1l9ajV96/Wl\nvV97Wvi2oLp79WK+MiGEKFy5DPY8wxkjw/h04Y8s1P/E7foVnjW/Se0WXYiKSbrt8ovJamJf4j7O\np59n4paJXDNfo6JTRQY1GsQbrd+QCUZCiFKh3AV7/vJLRJVUQvXfuWY18jfTe5x0CWZUeCCPhwfe\nVvnFYrUwet1o1p9eD0CQZxAzus0goFKAA65KCCFuXbkLdtvyy7sL1zKXd6lotfKC8wecVNVyN5me\nHhmW21O/UagfSz3G2lNrOXjxIOtPr+elsJcIqxZGqHcobs5uDrwyIYS4NeUm2POXX+Yt/IZvrdOo\nqK/xTuWJfP/a4FueTZqckcyfZ/8kIS2BWXtmkWHJQKEY2nQow5sNL4GrE0KIW1cugv0v5RenWFpb\nP+CYtQbvV/w7+9MDbrmenno9lad+eYoTl08A0K5mOyZ2mIhXRS+cDTJbVAhR+pXpYM8/Rv3FjjVZ\nOn8GjfmSi1Zf3vWaTMwV50KHM2qtuWq6SpopjQ2nN3A89Ti7Luzi9NXTfNb1M4KrBBPgESAPRYUQ\nZUqZDvacQJ8eGcacXhXxW/EgQ0jhjPbmw6p/Z8krDxQ6nPFSxiVGrRvFrgu7co/n5uRGBacKvN/h\nfboEdimhqxJCiKIps8Ge01ufHhnGjIVL+VpNJEU7M8g0lmSfNpy7aimw/LLq+CrWxa/jwMUDJFxN\n4Pnmz1PZtTLh1cOpX7W+9M6FEGVemQv2/OWXzx+vxzTjNJIyXXni+jsEhTTkXCGzSfck7uGtP96i\nimsVfCr6MPO+mbSp2aakL0kIIeyqyMGulHoJeAGwAD9rrd8ocqtuwLb8Mn1Ac87OG0oblcDfMt8m\nyejLx/dmPRS1Lb9QMYbDyYdZGrOU6m7VWfrwUlm/RQhRbhUp2JVS9wKPAM211teVUtXs06yC2ZZf\n3lq4kRlus4kwbOJf5j4EtOjKq+GBvLAomhcfUIy4P5O4pGgqe8Ux4vfFALg7uzOj2wwJdSFEuVbU\nHvtI4EOt9XUArfWFojepcDm99Rn9m7DQYxrVU/fwrvkpLjd9hqiYJPq1CiCs1S98un/t/z50AQY1\nGsQLLV7A1clVhiwKIcq9ogZ7faCjUup9IAN4XWu9vejNKljOg9DY+aNoz05eMT3Pr251GVb7MINq\nw/O//B1r5SiGNR1G11pdAfBw9qCOZ53iapIQQpQ6Nw12pdRqoKCNOcdnf94LaAe0Bv6jlArSWusC\njjMcGA5Qq1at22qk7azSyue28CQ/M9fcg/3+oTh7TOLfMdmnqwzBbhG8FPaSjG4RQty1bhrsWuv7\nCvs7pdRIYFl2kG9TSlkBHyCxgOPMBmYDhIeH/yX4byS3BPO3UII2jCNeV2OqikQ7f0ll5yq82mQK\nRy9cZ2Db2jKhSAhx1ytqKeZH4F5gnVKqPuACJBW5VfnklGBmLn+ayz7XSaAaPpW/IDHjLGkJA/Fr\nW5d+99353qRCCFGeFDXY5wBzlFL7gUzgqYLKMPYQEezDYldfMq4nUs23MfWqV6Je1T40c+93x/uT\nCiFEeVSkYNdaZwKD7NSWG9ocl8SmpEgGta3Fgq2nGBselhvmHUJ8HdEEIYQoEwwl3YBbkbPey/TI\nMEb3aMD0yDBeXLSLzXF2r/oIIUSZVyaCfe/p1NylAYA8678IIYTISxVTSfyGwsPDdXR0tMPPK4QQ\nZZlSaofWOvxm7ysTPXYhhBC3ToJdCCHKGQl2IYQoZyTYhRCinJFgF0KIcqZERsUopRKBk3f4cR+K\nYdkCOyit7YLS2zZp1+0pre2C0tu28ta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"text/plain": [ "<matplotlib.figure.Figure at 0x7fdfe7a4d748>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(lam_true, 'x')\n", "plt.plot(sorted(lam_ar.real))\n", "plt.plot(sorted(lam_lan.real))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#DataFrame(A.real)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0.67971428, 0.33316919, 1.45052279],\n", " [ 0.33316919, 0.39737665, 0.94127939],\n", " [ 1.45052279, 0.94127939, 1.63564937]])" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.1" } }, "nbformat": 4, "nbformat_minor": 2 }
mit