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18_Python_Finance.ipynb	###Markdown Web Scraping do Site da B3 Site: www.b3.com.br ###Code import pandas as pd pd.set_option('display.min_rows', 50) pd.set_option('display.max_rows', 200) url = 'http://bvmf.bmfbovespa.com.br/indices/ResumoCarteiraTeorica.aspx?Indice=IBOV&idioma=pt-br' pd.read_html(url, decimal=',', thousands='.', index_col='Código')[0][:-1] def busca_carteira_teorica(indice): url = 'http://bvmf.bmfbovespa.com.br/indices/ResumoCarteiraTeorica.aspx?Indice={}&idioma=pt-br'.format(indice.upper()) return pd.read_html(url, decimal=',', thousands='.', index_col='Código')[0][:-1] ibov = busca_carteira_teorica('ibov') ibov.sort_values('Part. (%)', ascending=False) ###Output _____no_output_____ ###Markdown Índices de Segmentos e Setoriais Índice BM&FBOVESPA Financeiro (IFNC B3) ###Code ifnc = busca_carteira_teorica('ifnc') ifnc ###Output _____no_output_____ ###Markdown Índice de BDRs Não Patrocinados-GLOBAL (BDRX B3) ###Code bdrx = busca_carteira_teorica('bdrx') bdrx ###Output _____no_output_____ ###Markdown Índice de Consumo (ICON B3) ###Code icon = busca_carteira_teorica('icon') icon ###Output _____no_output_____ ###Markdown Índice de Energia Elétrica (IEE B3) ###Code iee = busca_carteira_teorica('iee') iee ###Output _____no_output_____ ###Markdown Índice de Fundos de Investimentos Imobiliários (IFIX B3) ###Code ifix = busca_carteira_teorica('ifix') ifix ###Output _____no_output_____ ###Markdown Índice de Materiais Básicos BM&FBOVESPA (IMAT B3) ###Code imat = busca_carteira_teorica('imat') imat ###Output _____no_output_____ ###Markdown Índice Dividendos BM&FBOVESPA (IDIV B3) ###Code idiv = busca_carteira_teorica('idiv') idiv ###Output _____no_output_____ ###Markdown Índice do Setor Industrial (INDX B3) ###Code indx = busca_carteira_teorica('indx') indx iee = busca_carteira_teorica('iee') iee ###Output _____no_output_____ ###Markdown Índice Imobiliário (IMOB B3) ###Code imob = busca_carteira_teorica('imob') imob ###Output _____no_output_____ ###Markdown Índice MidLarge Cap (MLCX B3) ###Code mlcx = busca_carteira_teorica('mlcx') mlcx ###Output _____no_output_____ ###Markdown Índice Small Cap (SMLL B3) ###Code smll = busca_carteira_teorica('smll') smll ###Output _____no_output_____ ###Markdown Índice Utilidade Pública BM&FBOVESPA (UTIL B3 ###Code util = busca_carteira_teorica('util') util ###Output _____no_output_____ ###Markdown Índice Valor BM&FBOVESPA (IVBX 2 B3) ###Code ivbx = busca_carteira_teorica('ivbx') ivbx ###Output _____no_output_____ ###Markdown Índices amplos Índice Bovespa (Ibovespa B3) ###Code ibov = busca_carteira_teorica('ibov') ibov ###Output _____no_output_____ ###Markdown Índice Brasil 100 (IBrX 100 B3) ###Code ibrx = busca_carteira_teorica('ibrx') ibrx ###Output _____no_output_____ ###Markdown Índice Brasil 50 (IBrX 50 B3) ###Code ibxl = busca_carteira_teorica('ibxl') ibxl ###Output _____no_output_____ ###Markdown Índice Brasil Amplo (IBrA B3) ###Code ibra = busca_carteira_teorica('ibra') ibra ###Output _____no_output_____ ###Markdown Índices de Governança Índice de Ações com Governança Corporativa Diferenciada (IGC B3) ###Code igc = busca_carteira_teorica('igc') igc ###Output _____no_output_____ ###Markdown Índice de Ações com Tag Along Diferenciado (ITAG B3) ###Code itag = busca_carteira_teorica('itag') itag ###Output _____no_output_____ ###Markdown Índice de Governança Corporativa Trade (IGCT B3) ###Code igct = busca_carteira_teorica('igct') igct ###Output _____no_output_____ ###Markdown Índice de Governança Corporativa – Novo Mercado (IGC-NM B3) ###Code ignm = busca_carteira_teorica('ignm') ignm ###Output _____no_output_____ ###Markdown Índices de Sustentabilidade Índice Carbono Eficiente (ICO2 B3) ###Code ico2 = busca_carteira_teorica('ico2') ico2 ###Output _____no_output_____ ###Markdown Índice de Sustentabilidade Empresarial (ISE B3) ###Code ise = busca_carteira_teorica('ise') ise ###Output _____no_output_____ ###Markdown Composição Índices Amplos ###Code pd.concat([ibov, ibrx, ibxl, ibra], keys=['IBOV', 'IBRX', 'IBXL', 'IBRA'], axis=1) ###Output _____no_output_____ ###Markdown Composição Índices de Governança ###Code pd.concat([ibov, igc, itag, igct, ignm], keys=['IBOV', 'IGC', 'ITAG', 'IGCT', 'IGNM'], axis=1) ###Output _____no_output_____ ###Markdown Composição Índices de Sustentabilidade ###Code pd.concat([ibov, ico2, ise], keys=['IBOV', 'ICO2', 'ISE'], axis=1) ###Output _____no_output_____ ###Markdown Composição Índices de Segmentos e Setoriais ###Code pd.concat([ibov, ifnc, bdrx, icon, iee, ifix, imat, idiv, indx, imob, mlcx, smll, util, ivbx], keys=['IBOV', 'IFNC', 'BDRX', 'ICON', 'IEE', 'IFIX', 'IMAT', 'IDIV', 'INDX', 'IMOB', 'MLCX', 'SMLL', 'UTIL', 'IVBX'], axis=1) ###Output _____no_output_____
cheatsheets/cuDF/cuDF_Properties.ipynb	###Markdown cuDF Cheat Sheets sample code(c) 2020 NVIDIA, Blazing SQLDistributed under Apache License 2.0 Imports ###Code import cudf import numpy as np ###Output _____no_output_____ ###Markdown Sample DataFrame ###Code df = cudf.DataFrame( [ (39, 6.88, np.datetime64('2020-10-08T12:12:01'), np.timedelta64(14378,'s'), 'C', 'D', 'data' , 'RAPIDS.ai is a suite of open-source libraries that allow you to run your end to end data science and analytics pipelines on GPUs.') , (11, 4.21, None, None , 'A', 'D', 'cuDF' , 'cuDF is a Python GPU DataFrame (built on the Apache Arrow columnar memory format)') , (31, 4.71, np.datetime64('2020-10-10T09:26:43'), np.timedelta64(12909,'s'), 'U', 'D', 'memory' , 'cuDF allows for loading, joining, aggregating, filtering, and otherwise manipulating tabular data using a DataFrame style API.') , (40, 0.93, np.datetime64('2020-10-11T17:10:00'), np.timedelta64(10466,'s'), 'P', 'B', 'tabular' , '''If your workflow is fast enough on a single GPU or your data comfortably fits in memory on a single GPU, you would want to use cuDF.''') , (33, 9.26, np.datetime64('2020-10-15T10:58:02'), np.timedelta64(35558,'s'), 'O', 'D', 'parallel' , '''If you want to distribute your workflow across multiple GPUs or have more data than you can fit in memory on a single GPU you would want to use Dask-cuDF''') , (42, 4.21, np.datetime64('2020-10-01T10:02:23'), np.timedelta64(20480,'s'), 'U', 'C', 'GPUs' , 'BlazingSQL provides a high-performance distributed SQL engine in Python') , (36, 3.01, np.datetime64('2020-09-30T14:36:26'), np.timedelta64(24409,'s'), 'T', 'D', None , 'BlazingSQL is built on the RAPIDS GPU data science ecosystem') , (38, 6.44, np.datetime64('2020-10-10T08:34:36'), np.timedelta64(90171,'s'), 'X', 'B', 'csv' , 'BlazingSQL lets you ETL raw data directly into GPU memory as a GPU DataFrame (GDF)') , (17, 5.28, np.datetime64('2020-10-09T08:34:40'), np.timedelta64(30532,'s'), 'P', 'D', 'dataframes' , 'Dask is a flexible library for parallel computing in Python') , (10, 8.28, np.datetime64('2020-10-03T03:31:21'), np.timedelta64(23552,'s'), 'W', 'B', 'python' , None) ] , columns = ['num', 'float', 'datetime', 'timedelta', 'char', 'category', 'word', 'string'] ) df['category'] = df['category'].astype('category') ###Output _____no_output_____ ###Markdown --- Properties--- DataFrame cudf.core.dataframe.DataFrame.at() ###Code df.at[3] df.at[3:7] df.at[2, 'string'] df.at[2:5, 'string'] df.at[2:5, ['string', 'float']] ###Output _____no_output_____ ###Markdown cudf.core.dataframe.DataFrame.columns() ###Code df.columns ###Output _____no_output_____ ###Markdown cudf.core.dataframe.DataFrame.dtypes() ###Code df.dtypes ###Output _____no_output_____ ###Markdown cudf.core.dataframe.DataFrame.iat() ###Code df.iat[3] df.iat[3:7] df.iat[2, 7] df.iat[2:5, 7] df.iat[2:5, 6:8] df.iat[2:5, [1,3,5]] ###Output _____no_output_____ ###Markdown cudf.core.dataframe.DataFrame.iloc() ###Code df.iloc[3] df.iloc[3:5] df.iloc[2, 7] df.iloc[2:5, 7] df.iloc[2:5, [4,5,7]] df.iloc[[1,2,7], [4,5,6]] ###Output _____no_output_____ ###Markdown cudf.core.dataframe.DataFrame.index() ###Code df.index ###Output _____no_output_____ ###Markdown cudf.core.dataframe.DataFrame.loc() ###Code df.loc[3] df.loc[3:6] df.loc[2, 'string'] df.loc[3:6, ['string', 'float']] ###Output _____no_output_____ ###Markdown cudf.core.dataframe.DataFrame.ndim() ###Code df.ndim ###Output _____no_output_____ ###Markdown cudf.core.dataframe.DataFrame.shape() ###Code df.shape ###Output _____no_output_____ ###Markdown cudf.core.dataframe.DataFrame.size() ###Code df.size ###Output _____no_output_____ ###Markdown cudf.core.dataframe.DataFrame.T() ###Code df[['num']].T ###Output _____no_output_____ ###Markdown cudf.core.dataframe.DataFrame.values() ###Code df[['num', 'float']].values ###Output _____no_output_____ ###Markdown Series cudf.core.series.Series.cat() ###Code df['category'].cat ###Output _____no_output_____ ###Markdown cudf.core.series.Series.data() ###Code df['num'].data ###Output _____no_output_____ ###Markdown cudf.core.series.Series.dt() ###Code df['datetime'].dt df['timedelta'].dt ###Output _____no_output_____ ###Markdown cudf.core.series.Series.dtype() ###Code df['num'].dtype ###Output _____no_output_____ ###Markdown cudf.core.series.Series.has_nulls() ###Code df['num'].has_nulls df['string'].has_nulls ###Output _____no_output_____ ###Markdown cudf.core.series.Series.iloc() ###Code df['num'].iloc[1] df['num'].iloc[1:4] ###Output _____no_output_____ ###Markdown cudf.core.series.Series.index() ###Code df['num'].index ###Output _____no_output_____ ###Markdown cudf.core.series.Series.is_monotonic_decreasing() ###Code df['num'].is_monotonic_decreasing ###Output _____no_output_____ ###Markdown cudf.core.series.Series.is_monotonic_increasing() ###Code df['num'].is_monotonic_decreasing ###Output _____no_output_____ ###Markdown cudf.core.series.Series.is_monotonic() ###Code df['num'].is_monotonic ###Output _____no_output_____ ###Markdown cudf.core.series.Series.is_unique() ###Code df['num'].is_unique ###Output _____no_output_____ ###Markdown cudf.core.series.Series.loc() ###Code df['num'].loc[3] df['num'].loc[3:6] ###Output _____no_output_____ ###Markdown cudf.core.series.Series.name() ###Code df['float'].name ###Output _____no_output_____ ###Markdown cudf.core.series.Series.ndim() ###Code df['float'].ndim ###Output _____no_output_____ ###Markdown cudf.core.series.Series.null_count() ###Code df['float'].null_count df['string'].null_count ###Output _____no_output_____ ###Markdown cudf.core.series.Series.nullable() ###Code df['num'].nullable df['string'].nullable ###Output _____no_output_____ ###Markdown cudf.core.series.Series.nullmask() ###Code df['datetime'].nullmask df['word'].nullmask ###Output _____no_output_____ ###Markdown cudf.core.series.Series.shape() ###Code df['num'].shape ###Output _____no_output_____ ###Markdown cudf.core.series.Series.size() ###Code df['float'].size df['word'].size ###Output _____no_output_____ ###Markdown cudf.core.series.Series.str() ###Code df['word'].str ###Output _____no_output_____ ###Markdown cudf.core.series.Series.valid_count() ###Code df['float'].valid_count df['word'].valid_count ###Output _____no_output_____ ###Markdown cudf.core.series.Series.values() ###Code df['num'].values ###Output _____no_output_____
03_classification_annotated.ipynb	###Markdown Chapter 3 – Classification_This notebook contains all the sample code and solutions to the exercises in chapter 3._ Run in Google Colab Setup First, let's import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures. We also check that Python 3.5 or later is installed (although Python 2.x may work, it is deprecated so we strongly recommend you use Python 3 instead), as well as Scikit-Learn ≥0.20. ###Code # Python ≥3.5 is required import sys assert sys.version_info >= (3, 5) # Scikit-Learn ≥0.20 is required import sklearn assert sklearn.__version__ >= "0.20" # Common imports import numpy as np import os # to make this notebook's output stable across runs np.random.seed(42) # To plot pretty figures %matplotlib inline import matplotlib as mpl import matplotlib.pyplot as plt mpl.rc('axes', labelsize=14) mpl.rc('xtick', labelsize=12) mpl.rc('ytick', labelsize=12) # Where to save the figures PROJECT_ROOT_DIR = "." CHAPTER_ID = "classification" IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID) os.makedirs(IMAGES_PATH, exist_ok=True) def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300): path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension) print("Saving figure", fig_id) if tight_layout: plt.tight_layout() plt.savefig(path, format=fig_extension, dpi=resolution) ###Output _____no_output_____ ###Markdown MNIST ###Code from sklearn.datasets import fetch_openml mnist = fetch_openml('mnist_784', version=1) mnist.keys() X, y = mnist["data"], mnist["target"] X.shape ## 70000 images, 28x28 pixels mnist # import json # def default(obj): # if type(obj).__module__ == np.__name__: # if isinstance(obj, np.ndarray): # return obj.tolist() # else: # return obj.item() # raise TypeError('Unknown type:', type(obj)) # js = json.dumps(mnist, default=default) # print(js) print(y.shape) print(np.unique(y)) %matplotlib inline import matplotlib as mpl import matplotlib.pyplot as plt some_digit = X[0] some_digit_image = some_digit.reshape(28, 28) plt.imshow(some_digit_image, cmap=mpl.cm.binary_r) plt.axis("off") save_fig("some_digit_plot") plt.show() y[0] y = y.astype(np.uint8) def plot_digit(data): image = data.reshape(28, 28) plt.imshow(image, cmap = mpl.cm.binary, interpolation="nearest") plt.axis("off") # EXTRA def plot_digits(instances, images_per_row=10, *options): size = 28 images_per_row = min(len(instances), images_per_row) images = [instance.reshape(size,size) for instance in instances] n_rows = (len(instances) - 1) // images_per_row + 1 row_images = [] n_empty = n_rows images_per_row - len(instances) images.append(np.zeros((size, size * n_empty))) for row in range(n_rows): rimages = images[row * images_per_row : (row + 1) * images_per_row] row_images.append(np.concatenate(rimages, axis=1)) image = np.concatenate(row_images, axis=0) plt.imshow(image, cmap = mpl.cm.binary, options) plt.axis("off") plt.figure(figsize=(9,9)) example_images = X[:100] plot_digits(example_images, images_per_row=10) save_fig("more_digits_plot") plt.show() y[0] X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:] ###Output _____no_output_____ ###Markdown Binary classifier ###Code y_train_5 = (y_train == 5) y_test_5 = (y_test == 5) ###Output _____no_output_____ ###Markdown Note*: some hyperparameters will have a different defaut value in future versions of Scikit-Learn, such as `max_iter` and `tol`. To be future-proof, we explicitly set these hyperparameters to their future default values. For simplicity, this is not shown in the book. [hinge loss](https://en.wikipedia.org/wiki/Hinge_loss)* [SGD](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) ###Code from sklearn.linear_model import SGDClassifier sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=42) sgd_clf.fit(X_train, y_train_5) sgd_clf.predict([some_digit]) from sklearn.model_selection import cross_val_score cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring="accuracy") from sklearn.model_selection import StratifiedKFold from sklearn.base import clone skfolds = StratifiedKFold(n_splits=3, shuffle=True, random_state=42) for train_index, test_index in skfolds.split(X_train, y_train_5): clone_clf = clone(sgd_clf) X_train_folds = X_train[train_index] y_train_folds = y_train_5[train_index] X_test_fold = X_train[test_index] y_test_fold = y_train_5[test_index] clone_clf.fit(X_train_folds, y_train_folds) y_pred = clone_clf.predict(X_test_fold) n_correct = sum(y_pred == y_test_fold) print(n_correct / len(y_pred)) ###Output 0.9669 0.91625 0.96785 ###Markdown Note: `shuffle=True` was omitted by mistake in previous releases of the book. ###Code from sklearn.base import BaseEstimator class Never5Classifier(BaseEstimator): def fit(self, X, y=None): pass def predict(self, X): return np.zeros((len(X), 1), dtype=bool) never_5_clf = Never5Classifier() cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring="accuracy") ###Output _____no_output_____ ###Markdown Warning: this output (and many others in this notebook and other notebooks) may differ slightly from those in the book. Don't worry, that's okay! There are several reasons for this:* first, Scikit-Learn and other libraries evolve, and algorithms get tweaked a bit, which may change the exact result you get. If you use the latest Scikit-Learn version (and in general, you really should), you probably won't be using the exact same version I used when I wrote the book or this notebook, hence the difference. I try to keep this notebook reasonably up to date, but I can't change the numbers on the pages in your copy of the book.* second, many training algorithms are stochastic, meaning they rely on randomness. In principle, it's possible to get consistent outputs from a random number generator by setting the seed from which it generates the pseudo-random numbers (which is why you will see `random_state=42` or `np.random.seed(42)` pretty often). However, sometimes this does not suffice due to the other factors listed here.* third, if the training algorithm runs across multiple threads (as do some algorithms implemented in C) or across multiple processes (e.g., when using the `n_jobs` argument), then the precise order in which operations will run is not always guaranteed, and thus the exact result may vary slightly.* lastly, other things may prevent perfect reproducibility, such as Python maps and sets whose order is not guaranteed to be stable across sessions, or the order of files in a directory which is also not guaranteed. ###Code from sklearn.model_selection import cross_val_predict y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3) sgd_clf.predict(X_train) y_train_pred from sklearn.metrics import confusion_matrix confusion_matrix(y_train_5, y_train_pred) ###Output _____no_output_____ ###Markdown \|predicted (-) \| predicted (+)\| \|\|----\|----\|---\|\|TN \| FP\| actual - \| \|FN \| TP\| actual + \|* precision: * accuracy of positive prediction $\equiv TP/(TP+FP)$ * when threshold moves down, both TP and FP might goes up so it's not guaranteed to be monotonic * recall/sensitivity/TPR(TPrate): * the ratio of TP among all actual positives$\equiv TP/(TP + FN)$ * total number of TP and FN is fixed, therefore recall is monotonically decreasing with higher threshold values. ###Code y_train_perfect_predictions = y_train_5 # pretend we reached perfection cm = confusion_matrix(y_train_5, y_train_perfect_predictions) print(cm) print(cm[0,0],cm[0,1],cm[1,0],cm[1,1]) from sklearn.metrics import precision_score, recall_score precision_score(y_train_5, y_train_pred) cm = confusion_matrix(y_train_5, y_train_pred) cm[1, 1] / (cm[0, 1] + cm[1, 1]) recall_score(y_train_5, y_train_pred) cm[1, 1] / (cm[1, 0] + cm[1, 1]) ###Output _____no_output_____ ###Markdown * harmonic mean of precision and recall gives f1 score. $\equiv 2/(precision^{-1} + recall^{-1})$ ###Code from sklearn.metrics import f1_score f1_score(y_train_5, y_train_pred) cm[1, 1] / (cm[1, 1] + (cm[1, 0] + cm[0, 1]) / 2) y_scores = sgd_clf.decision_function([some_digit]) y_scores threshold = 0 y_some_digit_pred = (y_scores > threshold) y_some_digit_pred threshold = 8000 y_some_digit_pred = (y_scores > threshold) y_some_digit_pred y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3, method="decision_function") y_scores.shape from sklearn.metrics import precision_recall_curve precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores) plt.plot(thresholds,'o') print(thresholds.shape) print(precisions.shape) print(recalls.shape) def plot_precision_recall_vs_threshold(precisions, recalls, thresholds): plt.plot(thresholds, precisions[:-1], "b--", label="Precision", linewidth=2) plt.plot(thresholds, recalls[:-1], "g-", label="Recall", linewidth=2) plt.legend(loc="center right", fontsize=16) # Not shown in the book plt.xlabel("Threshold", fontsize=16) # Not shown plt.grid(True) # Not shown plt.axis([-50000, 50000, 0, 1]) # Not shown recall_90_precision = recalls[np.argmax(precisions >= 0.90)] threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)] plt.figure(figsize=(8, 4)) # Not shown plot_precision_recall_vs_threshold(precisions, recalls, thresholds) plt.plot([threshold_90_precision, threshold_90_precision], [0., 0.9], "r:") # Not shown plt.plot([-50000, threshold_90_precision], [0.9, 0.9], "r:") # Not shown plt.plot([-50000, threshold_90_precision], [recall_90_precision, recall_90_precision], "r:")# Not shown plt.plot([threshold_90_precision], [0.9], "ro") # Not shown plt.plot([threshold_90_precision], [recall_90_precision], "ro") # Not shown save_fig("precision_recall_vs_threshold_plot") # Not shown plt.show() (y_train_pred == (y_scores > 0)).all() def plot_precision_vs_recall(precisions, recalls): plt.plot(recalls, precisions, "b-", linewidth=2) plt.xlabel("Recall", fontsize=16) plt.ylabel("Precision", fontsize=16) plt.axis([0, 1, 0, 1]) plt.grid(True) plt.figure(figsize=(8, 6)) plot_precision_vs_recall(precisions, recalls) plt.plot([recall_90_precision, recall_90_precision], [0., 0.9], "r:") plt.plot([0.0, recall_90_precision], [0.9, 0.9], "r:") plt.plot([recall_90_precision], [0.9], "ro") save_fig("precision_vs_recall_plot") plt.show() threshold_90_precision = thresholds[np.argmax(precisions >= 0.90)] threshold_90_precision y_train_pred_90 = (y_scores >= threshold_90_precision) y_train_pred_90 precision_score(y_train_5, y_train_pred_90) recall_score(y_train_5, y_train_pred_90) ###Output _____no_output_____ ###Markdown ROC (receiver operating characteristics) curves. (recall/TPR vs. FPR)* FPR: ratio of false positives among all actual negatives $\equiv FP/(FP+TN)$; in other words, ratio of negative instances that are incorrectly classified as positive.* when to use which: As a rule of thumb, you should prefer the PR curve whenever the positive class is rare or when you care more about the FPs than the FNs, and the ROC curve otherwise. * For example, looking at the previous ROC curve (and the ROC AUC score), you may think that the classifier is really good. But this is mostly because there are few positives (5s) compared to the negatives (non-5s). In contrast, the PR curve makes it clear that the classifier has room for improvement (the curve could be closer to the top- right corner). ###Code from sklearn.metrics import roc_curve fpr, tpr, thresholds = roc_curve(y_train_5, y_scores) def plot_roc_curve(fpr, tpr, label=None): plt.plot(fpr, tpr, linewidth=2, label=label) plt.plot([0, 1], [0, 1], 'k--') # dashed diagonal plt.axis([0, 1, 0, 1]) # Not shown in the book plt.xlabel('False Positive Rate (Fall-Out)', fontsize=16) # Not shown plt.ylabel('True Positive Rate (Recall)', fontsize=16) # Not shown plt.grid(True) # Not shown plt.figure(figsize=(8, 6)) # Not shown plot_roc_curve(fpr, tpr) fpr_90 = fpr[np.argmax(tpr >= recall_90_precision)] # Not shown plt.plot([fpr_90, fpr_90], [0., recall_90_precision], "r:") # Not shown plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], "r:") # Not shown plt.plot([fpr_90], [recall_90_precision], "ro") # Not shown save_fig("roc_curve_plot") # Not shown plt.show() from sklearn.metrics import roc_auc_score roc_auc_score(y_train_5, y_scores) ###Output _____no_output_____ ###Markdown Note: we set `n_estimators=100` to be future-proof since this will be the default value in Scikit-Learn 0.22. ###Code from sklearn.ensemble import RandomForestClassifier forest_clf = RandomForestClassifier(n_estimators=100, random_state=42) y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3, method="predict_proba") y_probas_forest.shape y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest) recall_for_forest = tpr_forest[np.argmax(fpr_forest >= fpr_90)] plt.figure(figsize=(8, 6)) plt.plot(fpr, tpr, "b:", linewidth=2, label="SGD") plot_roc_curve(fpr_forest, tpr_forest, "Random Forest") plt.plot([fpr_90, fpr_90], [0., recall_90_precision], "r:") plt.plot([0.0, fpr_90], [recall_90_precision, recall_90_precision], "r:") plt.plot([fpr_90], [recall_90_precision], "ro") plt.plot([fpr_90, fpr_90], [0., recall_for_forest], "r:") plt.plot([fpr_90], [recall_for_forest], "ro") plt.grid(True) plt.legend(loc="lower right", fontsize=16) save_fig("roc_curve_comparison_plot") plt.show() roc_auc_score(y_train_5, y_scores_forest) y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3) precision_score(y_train_5, y_train_pred_forest) recall_score(y_train_5, y_train_pred_forest) ###Output _____no_output_____ ###Markdown Multiclass(multinomial) classification* OvA(one vs all): one binary classifier for each class; run all for prediction and take the highest score one.* OvO (one vs one): pairwise binary classifier n/(n-1)/2 such classifiers in n-class problem. favors SVM as it scales poorly over sample size;; it seem sklearn default this to be OvR though. ###Code from sklearn.svm import SVC import time start_time = time.time() svm_clf = SVC(gamma="auto", random_state=42, decision_function_shape='ovo') svm_clf.fit(X_train[:1000], y_train[:1000]) # y_train, not y_train_5 print(time.time() - start_time) svm_clf.predict([some_digit]) some_digit_scores = svm_clf.decision_function([some_digit]) print(some_digit_scores) print(some_digit_scores.shape) import itertools x = [0,1,2,3,4,5,6,7,8,9] y = np.zeros([10,10]) indices = list(itertools.combinations(x, 2)) for i, index in enumerate(indices): y[index[0],index[1]] = some_digit_scores[0,i] plt.imshow(y, cmap='hot', interpolation='nearest') plt.ylabel('positive') plt.xlabel('negative') plt.colorbar() plt.show() print(np.argmax(some_digit_scores)) print(some_digit_scores[0,37]) svm_clf.classes_ svm_clf.classes_[5] ###Output _____no_output_____ ###Markdown * using OvR or default ###Code start_time = time.time() svm_clf = SVC(gamma="auto", random_state=42, decision_function_shape='ovr') svm_clf.fit(X_train[:1000], y_train[:1000]) # y_train, not y_train_5 print(time.time() - start_time) svm_clf.predict([some_digit]) some_digit_scores = svm_clf.decision_function([some_digit]) print(some_digit_scores) print(some_digit_scores.shape) from sklearn.multiclass import OneVsRestClassifier start_time = time.time() ovr_clf = OneVsRestClassifier(SVC(gamma="auto", random_state=42)) ovr_clf.fit(X_train[:1000], y_train[:1000]) print(time.time() - start_time) ovr_clf.predict([some_digit]) len(ovr_clf.estimators_) ###Output _____no_output_____ ###Markdown * use linear sgd classifier (takes really long time to run full dataset) ###Code from sklearn.linear_model import SGDClassifier start_time = time.time() sgd_clf = SGDClassifier(max_iter=1000, tol=1e-3, random_state=42) sgd_clf.fit(X_train, y_train) print(time.time() - start_time) sgd_clf.predict([some_digit]) sgd_clf.decision_function([some_digit]) from sklearn.model_selection import cross_val_score cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring="accuracy") from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_train_scaled = scaler.fit_transform(X_train.astype(np.float64)) cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring="accuracy") X_train_scaled.shape print(np.min(X_train_scaled), np.max(X_train_scaled)) ###Output -1.2742078920823614 244.94693302836035 ###Markdown * Question: what scaler did they use here? ###Code plt.imshow(X_train_scaled[0,:].reshape([28,28])) plt.colorbar() plt.imshow(15/254.X_train[0,:].reshape([28,28])) plt.colorbar() print(X_train[0,:]) print(X_train_scaled[10,:]) from sklearn.model_selection import cross_val_predict from sklearn.metrics import confusion_matrix start_time = time.time() y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3) print(time.time() - start_time) conf_mx = confusion_matrix(y_train, y_train_pred) conf_mx # since sklearn 0.22, you can use sklearn.metrics.plot_confusion_matrix() def plot_confusion_matrix(matrix): """If you prefer color and a colorbar""" fig = plt.figure(figsize=(8,8)) ax = fig.add_subplot(111) cax = ax.matshow(matrix) fig.colorbar(cax) plt.matshow(conf_mx, cmap=plt.cm.gray) save_fig("confusion_matrix_plot", tight_layout=False) plt.show() row_sums = conf_mx.sum(axis=1, keepdims=True) norm_conf_mx = conf_mx / row_sums np.fill_diagonal(norm_conf_mx, 0) plt.matshow(norm_conf_mx, cmap=plt.cm.gray) save_fig("confusion_matrix_errors_plot", tight_layout=False) plt.show() cl_a, cl_b = 3, 5 X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)] X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)] X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)] X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)] plt.figure(figsize=(8,8)) plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5) plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5) plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5) plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5) save_fig("error_analysis_digits_plot") plt.show() ###Output Saving figure error_analysis_digits_plot ###Markdown error analysis https://medium.com/apprentice-journal/evaluating-multi-class-classifiers-12b2946e755b* Kappa = (observed accuracy - expected accuracy)/(1 - expected accuracy);``` Cats DogsCats\| 22 \| 9 \|Dogs\| 7 \| 13 \|Ground truth: Cats (29), Dogs (22)Machine Learning Classifier: Cats (31), Dogs (20)Total: (51)Observed Accuracy: ((22 + 13) / 51) = 0.69Expected Accuracy: ((29 * 31 / 51) + (22 * 20 / 51)) / 51 = 0.51 29 = 22 + 7 from ground truth, 31 = 22 + 9 pediction. for Cats class similar for dogs classKappa: (0.69 - 0.51) / (1 - 0.51) = 0.37``` * slightly different than confusion maxtrix shown above, here columns are actual truth, while rows are prediction.* micro-averaging vs. macro-averaging * A macro-average calculates the metric autonomously for each class to calculate the average. * the micro-average calculates average metric from the aggregate contributions of all classes. Micro -average is used in unbalanced datasets as this method takes the frequency of each class into consideration. Multilabel classification ###Code from sklearn.neighbors import KNeighborsClassifier y_train_large = (y_train >= 7) y_train_odd = (y_train % 2 == 1) y_multilabel = np.c_[y_train_large, y_train_odd] knn_clf = KNeighborsClassifier() knn_clf.fit(X_train, y_multilabel) y_multilabel.shape knn_clf.predict([some_digit]) # np.c_[np.array([[1,2,3]]), np.array([[4,5,6]]), np.array([[0,0,0]])] # a = np.array([[1,2,3,4]]) # a = np.array([1,2,3,4]) # print(a.shape) ###Output (4,) ###Markdown Warning: the following cell may take a very long time (possibly hours depending on your hardware). ###Code y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3) f1_score(y_multilabel, y_train_knn_pred, average="macro") ###Output _____no_output_____ ###Markdown Multioutput classification ###Code noise = np.random.randint(0, 100, (len(X_train), 784)) X_train_mod = X_train + noise noise = np.random.randint(0, 100, (len(X_test), 784)) X_test_mod = X_test + noise y_train_mod = X_train y_test_mod = X_test some_index = 0 plt.subplot(121); plot_digit(X_test_mod[some_index]) plt.subplot(122); plot_digit(y_test_mod[some_index]) save_fig("noisy_digit_example_plot") plt.show() knn_clf.fit(X_train_mod, y_train_mod) clean_digit = knn_clf.predict([X_test_mod[some_index]]) plot_digit(clean_digit) save_fig("cleaned_digit_example_plot") ###Output Saving figure cleaned_digit_example_plot ###Markdown Extra material Dummy (ie. random) classifier ###Code from sklearn.dummy import DummyClassifier dmy_clf = DummyClassifier(strategy="prior") y_probas_dmy = cross_val_predict(dmy_clf, X_train, y_train_5, cv=3, method="predict_proba") y_scores_dmy = y_probas_dmy[:, 1] fprr, tprr, thresholdsr = roc_curve(y_train_5, y_scores_dmy) plot_roc_curve(fprr, tprr) ###Output _____no_output_____ ###Markdown KNN classifier ###Code from sklearn.neighbors import KNeighborsClassifier knn_clf = KNeighborsClassifier(weights='distance', n_neighbors=4) knn_clf.fit(X_train, y_train) y_knn_pred = knn_clf.predict(X_test) from sklearn.metrics import accuracy_score accuracy_score(y_test, y_knn_pred) from scipy.ndimage.interpolation import shift def shift_digit(digit_array, dx, dy, new=0): return shift(digit_array.reshape(28, 28), [dy, dx], cval=new).reshape(784) plot_digit(shift_digit(some_digit, 5, 1, new=100)) X_train_expanded = [X_train] y_train_expanded = [y_train] for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)): shifted_images = np.apply_along_axis(shift_digit, axis=1, arr=X_train, dx=dx, dy=dy) X_train_expanded.append(shifted_images) y_train_expanded.append(y_train) X_train_expanded = np.concatenate(X_train_expanded) y_train_expanded = np.concatenate(y_train_expanded) X_train_expanded.shape, y_train_expanded.shape knn_clf.fit(X_train_expanded, y_train_expanded) y_knn_expanded_pred = knn_clf.predict(X_test) accuracy_score(y_test, y_knn_expanded_pred) ambiguous_digit = X_test[2589] knn_clf.predict_proba([ambiguous_digit]) plot_digit(ambiguous_digit) ###Output _____no_output_____ ###Markdown Exercise solutions 1. An MNIST Classifier With Over 97% Accuracy Warning: the next cell may take hours to run, depending on your hardware. ###Code from sklearn.model_selection import GridSearchCV param_grid = [{'weights': ["uniform", "distance"], 'n_neighbors': [3, 4, 5]}] knn_clf = KNeighborsClassifier() grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3) grid_search.fit(X_train, y_train) grid_search.best_params_ grid_search.best_score_ from sklearn.metrics import accuracy_score y_pred = grid_search.predict(X_test) accuracy_score(y_test, y_pred) ###Output _____no_output_____ ###Markdown 2. Data Augmentation ###Code from scipy.ndimage.interpolation import shift def shift_image(image, dx, dy): image = image.reshape((28, 28)) shifted_image = shift(image, [dy, dx], cval=0, mode="constant") return shifted_image.reshape([-1]) image = X_train[1000] shifted_image_down = shift_image(image, 0, 5) shifted_image_left = shift_image(image, -5, 0) plt.figure(figsize=(12,3)) plt.subplot(131) plt.title("Original", fontsize=14) plt.imshow(image.reshape(28, 28), interpolation="nearest", cmap="Greys") plt.subplot(132) plt.title("Shifted down", fontsize=14) plt.imshow(shifted_image_down.reshape(28, 28), interpolation="nearest", cmap="Greys") plt.subplot(133) plt.title("Shifted left", fontsize=14) plt.imshow(shifted_image_left.reshape(28, 28), interpolation="nearest", cmap="Greys") plt.show() X_train_augmented = [image for image in X_train] y_train_augmented = [label for label in y_train] for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)): for image, label in zip(X_train, y_train): X_train_augmented.append(shift_image(image, dx, dy)) y_train_augmented.append(label) X_train_augmented = np.array(X_train_augmented) y_train_augmented = np.array(y_train_augmented) shuffle_idx = np.random.permutation(len(X_train_augmented)) X_train_augmented = X_train_augmented[shuffle_idx] y_train_augmented = y_train_augmented[shuffle_idx] knn_clf = KNeighborsClassifier(*grid_search.best_params_) knn_clf.fit(X_train_augmented, y_train_augmented) y_pred = knn_clf.predict(X_test) accuracy_score(y_test, y_pred) ###Output _____no_output_____ ###Markdown By simply augmenting the data, we got a 0.5% accuracy boost. :) 3. Tackle the Titanic dataset The goal is to predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked and so on. First, login to [Kaggle](https://www.kaggle.com/) and go to the [Titanic challenge](https://www.kaggle.com/c/titanic) to download `train.csv` and `test.csv`. Save them to the `datasets/titanic` directory. Next, let's load the data: ###Code import os TITANIC_PATH = os.path.join("datasets", "titanic") import pandas as pd def load_titanic_data(filename, titanic_path=TITANIC_PATH): csv_path = os.path.join(titanic_path, filename) return pd.read_csv(csv_path) train_data = load_titanic_data("train.csv") test_data = load_titanic_data("test.csv") ###Output _____no_output_____ ###Markdown The data is already split into a training set and a test set. However, the test data does not* contain the labels: your goal is to train the best model you can using the training data, then make your predictions on the test data and upload them to Kaggle to see your final score. Let's take a peek at the top few rows of the training set: ###Code train_data.head() ###Output _____no_output_____ ###Markdown The attributes have the following meaning:* Survived: that's the target, 0 means the passenger did not survive, while 1 means he/she survived.* Pclass: passenger class.* Name, Sex, Age: self-explanatory* SibSp: how many siblings & spouses of the passenger aboard the Titanic.* Parch: how many children & parents of the passenger aboard the Titanic.* Ticket: ticket id* Fare: price paid (in pounds)* Cabin: passenger's cabin number* Embarked: where the passenger embarked the Titanic Let's get more info to see how much data is missing: ###Code train_data.info() ###Output <class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 PassengerId 891 non-null int64 1 Survived 891 non-null int64 2 Pclass 891 non-null int64 3 Name 891 non-null object 4 Sex 891 non-null object 5 Age 714 non-null float64 6 SibSp 891 non-null int64 7 Parch 891 non-null int64 8 Ticket 891 non-null object 9 Fare 891 non-null float64 10 Cabin 204 non-null object 11 Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.7+ KB ###Markdown Okay, the Age, Cabin and Embarked attributes are sometimes null (less than 891 non-null), especially the Cabin (77% are null). We will ignore the Cabin for now and focus on the rest. The Age attribute has about 19% null values, so we will need to decide what to do with them. Replacing null values with the median age seems reasonable. The Name and Ticket attributes may have some value, but they will be a bit tricky to convert into useful numbers that a model can consume. So for now, we will ignore them. Let's take a look at the numerical attributes: ###Code train_data.describe() ###Output _____no_output_____ ###Markdown * Yikes, only 38% Survived. :( That's close enough to 40%, so accuracy will be a reasonable metric to evaluate our model.* The mean Fare was £32.20, which does not seem so expensive (but it was probably a lot of money back then).* The mean Age was less than 30 years old. Let's check that the target is indeed 0 or 1: ###Code train_data["Survived"].value_counts() ###Output _____no_output_____ ###Markdown Now let's take a quick look at all the categorical attributes: ###Code train_data["Pclass"].value_counts() train_data["Sex"].value_counts() train_data["Embarked"].value_counts() ###Output _____no_output_____ ###Markdown The Embarked attribute tells us where the passenger embarked: C=Cherbourg, Q=Queenstown, S=Southampton. Note: the code below uses a mix of `Pipeline`, `FeatureUnion` and a custom `DataFrameSelector` to preprocess some columns differently. Since Scikit-Learn 0.20, it is preferable to use a `ColumnTransformer`, like in the previous chapter. Now let's build our preprocessing pipelines. We will reuse the `DataframeSelector` we built in the previous chapter to select specific attributes from the `DataFrame`: ###Code from sklearn.base import BaseEstimator, TransformerMixin class DataFrameSelector(BaseEstimator, TransformerMixin): def __init__(self, attribute_names): self.attribute_names = attribute_names def fit(self, X, y=None): return self def transform(self, X): return X[self.attribute_names] ###Output _____no_output_____ ###Markdown Let's build the pipeline for the numerical attributes: ###Code from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer num_pipeline = Pipeline([ ("select_numeric", DataFrameSelector(["Age", "SibSp", "Parch", "Fare"])), ("imputer", SimpleImputer(strategy="median")), ]) num_pipeline.fit_transform(train_data) ###Output _____no_output_____ ###Markdown We will also need an imputer for the string categorical columns (the regular `SimpleImputer` does not work on those): ###Code # Inspired from stackoverflow.com/questions/25239958 class MostFrequentImputer(BaseEstimator, TransformerMixin): def fit(self, X, y=None): self.most_frequent_ = pd.Series([X[c].value_counts().index[0] for c in X], index=X.columns) return self def transform(self, X, y=None): return X.fillna(self.most_frequent_) from sklearn.preprocessing import OneHotEncoder ###Output _____no_output_____ ###Markdown Now we can build the pipeline for the categorical attributes: ###Code cat_pipeline = Pipeline([ ("select_cat", DataFrameSelector(["Pclass", "Sex", "Embarked"])), ("imputer", MostFrequentImputer()), ("cat_encoder", OneHotEncoder(sparse=False)), ]) cat_pipeline.fit_transform(train_data) ###Output _____no_output_____ ###Markdown Finally, let's join the numerical and categorical pipelines: ###Code from sklearn.pipeline import FeatureUnion preprocess_pipeline = FeatureUnion(transformer_list=[ ("num_pipeline", num_pipeline), ("cat_pipeline", cat_pipeline), ]) ###Output _____no_output_____ ###Markdown Cool! Now we have a nice preprocessing pipeline that takes the raw data and outputs numerical input features that we can feed to any Machine Learning model we want. ###Code X_train = preprocess_pipeline.fit_transform(train_data) X_train ###Output _____no_output_____ ###Markdown Let's not forget to get the labels: ###Code y_train = train_data["Survived"] ###Output _____no_output_____ ###Markdown We are now ready to train a classifier. Let's start with an `SVC`: ###Code from sklearn.svm import SVC svm_clf = SVC(gamma="auto") svm_clf.fit(X_train, y_train) ###Output _____no_output_____ ###Markdown Great, our model is trained, let's use it to make predictions on the test set: ###Code X_test = preprocess_pipeline.transform(test_data) y_pred = svm_clf.predict(X_test) ###Output _____no_output_____ ###Markdown And now we could just build a CSV file with these predictions (respecting the format excepted by Kaggle), then upload it and hope for the best. But wait! We can do better than hope. Why don't we use cross-validation to have an idea of how good our model is? ###Code from sklearn.model_selection import cross_val_score svm_scores = cross_val_score(svm_clf, X_train, y_train, cv=10) svm_scores.mean() ###Output _____no_output_____ ###Markdown Okay, over 73% accuracy, clearly better than random chance, but it's not a great score. Looking at the [leaderboard](https://www.kaggle.com/c/titanic/leaderboard) for the Titanic competition on Kaggle, you can see that you need to reach above 80% accuracy to be within the top 10% Kagglers. Some reached 100%, but since you can easily find the [list of victims](https://www.encyclopedia-titanica.org/titanic-victims/) of the Titanic, it seems likely that there was little Machine Learning involved in their performance! ;-) So let's try to build a model that reaches 80% accuracy. Let's try a `RandomForestClassifier`: ###Code from sklearn.ensemble import RandomForestClassifier forest_clf = RandomForestClassifier(n_estimators=100, random_state=42) forest_scores = cross_val_score(forest_clf, X_train, y_train, cv=10) forest_scores.mean() ###Output _____no_output_____ ###Markdown That's much better! Instead of just looking at the mean accuracy across the 10 cross-validation folds, let's plot all 10 scores for each model, along with a box plot highlighting the lower and upper quartiles, and "whiskers" showing the extent of the scores (thanks to Nevin Yilmaz for suggesting this visualization). Note that the `boxplot()` function detects outliers (called "fliers") and does not include them within the whiskers. Specifically, if the lower quartile is $Q_1$ and the upper quartile is $Q_3$, then the interquartile range $IQR = Q_3 - Q_1$ (this is the box's height), and any score lower than $Q_1 - 1.5 \times IQR$ is a flier, and so is any score greater than $Q3 + 1.5 \times IQR$. ###Code plt.figure(figsize=(8, 4)) plt.plot([1]10, svm_scores, ".") plt.plot([2]10, forest_scores, ".") plt.boxplot([svm_scores, forest_scores], labels=("SVM","Random Forest")) plt.ylabel("Accuracy", fontsize=14) plt.show() ###Output _____no_output_____ ###Markdown To improve this result further, you could:* Compare many more models and tune hyperparameters using cross validation and grid search,* Do more feature engineering, for example: * replace SibSp and Parch with their sum, * try to identify parts of names that correlate well with the Survived attribute (e.g. if the name contains "Countess", then survival seems more likely),* try to convert numerical attributes to categorical attributes: for example, different age groups had very different survival rates (see below), so it may help to create an age bucket category and use it instead of the age. Similarly, it may be useful to have a special category for people traveling alone since only 30% of them survived (see below). ###Code train_data["AgeBucket"] = train_data["Age"] // 15 * 15 train_data[["AgeBucket", "Survived"]].groupby(['AgeBucket']).mean() train_data["RelativesOnboard"] = train_data["SibSp"] + train_data["Parch"] train_data[["RelativesOnboard", "Survived"]].groupby(['RelativesOnboard']).mean() ###Output _____no_output_____ ###Markdown 4. Spam classifier First, let's fetch the data: ###Code import os import tarfile import urllib DOWNLOAD_ROOT = "http://spamassassin.apache.org/old/publiccorpus/" HAM_URL = DOWNLOAD_ROOT + "20030228_easy_ham.tar.bz2" SPAM_URL = DOWNLOAD_ROOT + "20030228_spam.tar.bz2" SPAM_PATH = os.path.join("datasets", "spam") def fetch_spam_data(spam_url=SPAM_URL, spam_path=SPAM_PATH): if not os.path.isdir(spam_path): os.makedirs(spam_path) for filename, url in (("ham.tar.bz2", HAM_URL), ("spam.tar.bz2", SPAM_URL)): path = os.path.join(spam_path, filename) if not os.path.isfile(path): urllib.request.urlretrieve(url, path) tar_bz2_file = tarfile.open(path) tar_bz2_file.extractall(path=SPAM_PATH) tar_bz2_file.close() fetch_spam_data() ###Output _____no_output_____ ###Markdown Next, let's load all the emails: ###Code HAM_DIR = os.path.join(SPAM_PATH, "easy_ham") SPAM_DIR = os.path.join(SPAM_PATH, "spam") ham_filenames = [name for name in sorted(os.listdir(HAM_DIR)) if len(name) > 20] spam_filenames = [name for name in sorted(os.listdir(SPAM_DIR)) if len(name) > 20] len(ham_filenames) len(spam_filenames) ###Output _____no_output_____ ###Markdown We can use Python's `email` module to parse these emails (this handles headers, encoding, and so on): ###Code import email import email.policy def load_email(is_spam, filename, spam_path=SPAM_PATH): directory = "spam" if is_spam else "easy_ham" with open(os.path.join(spam_path, directory, filename), "rb") as f: return email.parser.BytesParser(policy=email.policy.default).parse(f) ham_emails = [load_email(is_spam=False, filename=name) for name in ham_filenames] spam_emails = [load_email(is_spam=True, filename=name) for name in spam_filenames] ###Output _____no_output_____ ###Markdown Let's look at one example of ham and one example of spam, to get a feel of what the data looks like: ###Code print(ham_emails[1].get_content().strip()) print(spam_emails[6].get_content().strip()) ###Output Help wanted. We are a 14 year old fortune 500 company, that is growing at a tremendous rate. We are looking for individuals who want to work from home. This is an opportunity to make an excellent income. No experience is required. We will train you. So if you are looking to be employed from home with a career that has vast opportunities, then go: http://www.basetel.com/wealthnow We are looking for energetic and self motivated people. If that is you than click on the link and fill out the form, and one of our employement specialist will contact you. To be removed from our link simple go to: http://www.basetel.com/remove.html 4139vOLW7-758DoDY1425FRhM1-764SMFc8513fCsLl40 ###Markdown Some emails are actually multipart, with images and attachments (which can have their own attachments). Let's look at the various types of structures we have: ###Code def get_email_structure(email): if isinstance(email, str): return email payload = email.get_payload() if isinstance(payload, list): return "multipart({})".format(", ".join([ get_email_structure(sub_email) for sub_email in payload ])) else: return email.get_content_type() from collections import Counter def structures_counter(emails): structures = Counter() for email in emails: structure = get_email_structure(email) structures[structure] += 1 return structures structures_counter(ham_emails).most_common() structures_counter(spam_emails).most_common() ###Output _____no_output_____ ###Markdown It seems that the ham emails are more often plain text, while spam has quite a lot of HTML. Moreover, quite a few ham emails are signed using PGP, while no spam is. In short, it seems that the email structure is useful information to have. Now let's take a look at the email headers: ###Code for header, value in spam_emails[0].items(): print(header,":",value) ###Output Return-Path : <[email protected]> Delivered-To : [email protected] Received : from localhost (localhost [127.0.0.1]) by phobos.labs.spamassassin.taint.org (Postfix) with ESMTP id 136B943C32 for <zzzz@localhost>; Thu, 22 Aug 2002 08:17:21 -0400 (EDT) Received : from mail.webnote.net [193.120.211.219] by localhost with POP3 (fetchmail-5.9.0) for zzzz@localhost (single-drop); Thu, 22 Aug 2002 13:17:21 +0100 (IST) Received : from dd_it7 ([210.97.77.167]) by webnote.net (8.9.3/8.9.3) with ESMTP id NAA04623 for <[email protected]>; Thu, 22 Aug 2002 13:09:41 +0100 From : [email protected] Received : from r-smtp.korea.com - 203.122.2.197 by dd_it7 with Microsoft SMTPSVC(5.5.1775.675.6); Sat, 24 Aug 2002 09:42:10 +0900 To : [email protected] Subject : Life Insurance - Why Pay More? Date : Wed, 21 Aug 2002 20:31:57 -1600 MIME-Version : 1.0 Message-ID : <0103c1042001882DD_IT7@dd_it7> Content-Type : text/html; charset="iso-8859-1" Content-Transfer-Encoding : quoted-printable ###Markdown There's probably a lot of useful information in there, such as the sender's email address ([email protected] looks fishy), but we will just focus on the `Subject` header: ###Code spam_emails[0]["Subject"] ###Output _____no_output_____ ###Markdown Okay, before we learn too much about the data, let's not forget to split it into a training set and a test set: ###Code import numpy as np from sklearn.model_selection import train_test_split X = np.array(ham_emails + spam_emails, dtype=object) y = np.array([0] * len(ham_emails) + [1] * len(spam_emails)) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) ###Output _____no_output_____ ###Markdown Okay, let's start writing the preprocessing functions. First, we will need a function to convert HTML to plain text. Arguably the best way to do this would be to use the great [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/) library, but I would like to avoid adding another dependency to this project, so let's hack a quick & dirty solution using regular expressions (at the risk of [un̨ho͞ly radiańcé destro҉ying all enli̍̈́̂̈́ghtenment](https://stackoverflow.com/a/1732454/38626)). The following function first drops the `` section, then converts all `` tags to the word HYPERLINK, then it gets rid of all HTML tags, leaving only the plain text. For readability, it also replaces multiple newlines with single newlines, and finally it unescapes html entities (such as `>` or ` `): ###Code import re from html import unescape def html_to_plain_text(html): text = re.sub('<head.?>.?</head>', '', html, flags=re.M \| re.S \| re.I) text = re.sub('<a\s.?>', ' HYPERLINK ', text, flags=re.M \| re.S \| re.I) text = re.sub('<.?>', '', text, flags=re.M \| re.S) text = re.sub(r'(\s\n)+', '\n', text, flags=re.M \| re.S) return unescape(text) ###Output _____no_output_____ ###Markdown Let's see if it works. This is HTML spam: ###Code html_spam_emails = [email for email in X_train[y_train==1] if get_email_structure(email) == "text/html"] sample_html_spam = html_spam_emails[7] print(sample_html_spam.get_content().strip()[:1000], "...") ###Output <HTML><HEAD><TITLE></TITLE><META http-equiv="Content-Type" content="text/html; charset=windows-1252"><STYLE>A:link {TEX-DECORATION: none}A:active {TEXT-DECORATION: none}A:visited {TEXT-DECORATION: none}A:hover {COLOR: #0033ff; TEXT-DECORATION: underline}</STYLE><META content="MSHTML 6.00.2713.1100" name="GENERATOR"></HEAD> <BODY text="#000000" vLink="#0033ff" link="#0033ff" bgColor="#CCCC99"><TABLE borderColor="#660000" cellSpacing="0" cellPadding="0" border="0" width="100%"><TR><TD bgColor="#CCCC99" valign="top" colspan="2" height="27"> <font size="6" face="Arial, Helvetica, sans-serif" color="#660000"> <b>OTC</b></font></TD></TR><TR><TD height="2" bgcolor="#6a694f"> <font size="5" face="Times New Roman, Times, serif" color="#FFFFFF"> <b> Newsletter</b></font></TD><TD height="2" bgcolor="#6a694f"><div align="right"><font color="#FFFFFF"> <b>Discover Tomorrow's Winners </b></font></div></TD></TR><TR><TD height="25" colspan="2" bgcolor="#CCCC99"><table width="100%" border="0" ... ###Markdown And this is the resulting plain text: ###Code print(html_to_plain_text(sample_html_spam.get_content())[:1000], "...") ###Output OTC Newsletter Discover Tomorrow's Winners For Immediate Release Cal-Bay (Stock Symbol: CBYI) Watch for analyst "Strong Buy Recommendations" and several advisory newsletters picking CBYI. CBYI has filed to be traded on the OTCBB, share prices historically INCREASE when companies get listed on this larger trading exchange. CBYI is trading around 25 cents and should skyrocket to $2.66 - $3.25 a share in the near future. Put CBYI on your watch list, acquire a position TODAY. REASONS TO INVEST IN CBYI A profitable company and is on track to beat ALL earnings estimates! One of the FASTEST growing distributors in environmental & safety equipment instruments. Excellent management team, several EXCLUSIVE contracts. IMPRESSIVE client list including the U.S. Air Force, Anheuser-Busch, Chevron Refining and Mitsubishi Heavy Industries, GE-Energy & Environmental Research. RAPIDLY GROWING INDUSTRY Industry revenues exceed $900 million, estimates indicate that there could be as much as $25 billi ... ###Markdown Great! Now let's write a function that takes an email as input and returns its content as plain text, whatever its format is: ###Code def email_to_text(email): html = None for part in email.walk(): ctype = part.get_content_type() if not ctype in ("text/plain", "text/html"): continue try: content = part.get_content() except: # in case of encoding issues content = str(part.get_payload()) if ctype == "text/plain": return content else: html = content if html: return html_to_plain_text(html) print(email_to_text(sample_html_spam)[:100], "...") ###Output OTC Newsletter Discover Tomorrow's Winners For Immediate Release Cal-Bay (Stock Symbol: CBYI) Wat ... ###Markdown Let's throw in some stemming! For this to work, you need to install the Natural Language Toolkit ([NLTK](http://www.nltk.org/)). It's as simple as running the following command (don't forget to activate your virtualenv first; if you don't have one, you will likely need administrator rights, or use the `--user` option):`$ pip3 install nltk` ###Code try: import nltk stemmer = nltk.PorterStemmer() for word in ("Computations", "Computation", "Computing", "Computed", "Compute", "Compulsive"): print(word, "=>", stemmer.stem(word)) except ImportError: print("Error: stemming requires the NLTK module.") stemmer = None ###Output Computations => comput Computation => comput Computing => comput Computed => comput Compute => comput Compulsive => compuls ###Markdown We will also need a way to replace URLs with the word "URL". For this, we could use hard core [regular expressions](https://mathiasbynens.be/demo/url-regex) but we will just use the [urlextract](https://github.com/lipoja/URLExtract) library. You can install it with the following command (don't forget to activate your virtualenv first; if you don't have one, you will likely need administrator rights, or use the `--user` option):`$ pip3 install urlextract` ###Code # if running this notebook on Colab, we just pip install urlextract try: import google.colab !pip install -q -U urlextract except ImportError: pass # not running on Colab try: import urlextract # may require an Internet connection to download root domain names url_extractor = urlextract.URLExtract() print(url_extractor.find_urls("Will it detect github.com and https://youtu.be/7Pq-S557XQU?t=3m32s")) except ImportError: print("Error: replacing URLs requires the urlextract module.") url_extractor = None ###Output ['github.com', 'https://youtu.be/7Pq-S557XQU?t=3m32s'] ###Markdown We are ready to put all this together into a transformer that we will use to convert emails to word counters. Note that we split sentences into words using Python's `split()` method, which uses whitespaces for word boundaries. This works for many written languages, but not all. For example, Chinese and Japanese scripts generally don't use spaces between words, and Vietnamese often uses spaces even between syllables. It's okay in this exercise, because the dataset is (mostly) in English. ###Code from sklearn.base import BaseEstimator, TransformerMixin class EmailToWordCounterTransformer(BaseEstimator, TransformerMixin): def __init__(self, strip_headers=True, lower_case=True, remove_punctuation=True, replace_urls=True, replace_numbers=True, stemming=True): self.strip_headers = strip_headers self.lower_case = lower_case self.remove_punctuation = remove_punctuation self.replace_urls = replace_urls self.replace_numbers = replace_numbers self.stemming = stemming def fit(self, X, y=None): return self def transform(self, X, y=None): X_transformed = [] for email in X: text = email_to_text(email) or "" if self.lower_case: text = text.lower() if self.replace_urls and url_extractor is not None: urls = list(set(url_extractor.find_urls(text))) urls.sort(key=lambda url: len(url), reverse=True) for url in urls: text = text.replace(url, " URL ") if self.replace_numbers: text = re.sub(r'\d+(?:\.\d(?:[eE]\d+))?', 'NUMBER', text) if self.remove_punctuation: text = re.sub(r'\W+', ' ', text, flags=re.M) word_counts = Counter(text.split()) if self.stemming and stemmer is not None: stemmed_word_counts = Counter() for word, count in word_counts.items(): stemmed_word = stemmer.stem(word) stemmed_word_counts[stemmed_word] += count word_counts = stemmed_word_counts X_transformed.append(word_counts) return np.array(X_transformed) ###Output _____no_output_____ ###Markdown Let's try this transformer on a few emails: ###Code X_few = X_train[:3] X_few_wordcounts = EmailToWordCounterTransformer().fit_transform(X_few) X_few_wordcounts ###Output _____no_output_____ ###Markdown This looks about right! Now we have the word counts, and we need to convert them to vectors. For this, we will build another transformer whose `fit()` method will build the vocabulary (an ordered list of the most common words) and whose `transform()` method will use the vocabulary to convert word counts to vectors. The output is a sparse matrix. ###Code from scipy.sparse import csr_matrix class WordCounterToVectorTransformer(BaseEstimator, TransformerMixin): def __init__(self, vocabulary_size=1000): self.vocabulary_size = vocabulary_size def fit(self, X, y=None): total_count = Counter() for word_count in X: for word, count in word_count.items(): total_count[word] += min(count, 10) most_common = total_count.most_common()[:self.vocabulary_size] self.most_common_ = most_common self.vocabulary_ = {word: index + 1 for index, (word, count) in enumerate(most_common)} return self def transform(self, X, y=None): rows = [] cols = [] data = [] for row, word_count in enumerate(X): for word, count in word_count.items(): rows.append(row) cols.append(self.vocabulary_.get(word, 0)) data.append(count) return csr_matrix((data, (rows, cols)), shape=(len(X), self.vocabulary_size + 1)) vocab_transformer = WordCounterToVectorTransformer(vocabulary_size=10) X_few_vectors = vocab_transformer.fit_transform(X_few_wordcounts) X_few_vectors X_few_vectors.toarray() ###Output _____no_output_____ ###Markdown What does this matrix mean? Well, the 99 in the second row, first column, means that the second email contains 99 words that are not part of the vocabulary. The 11 next to it means that the first word in the vocabulary is present 11 times in this email. The 9 next to it means that the second word is present 9 times, and so on. You can look at the vocabulary to know which words we are talking about. The first word is "the", the second word is "of", etc. ###Code vocab_transformer.vocabulary_ ###Output _____no_output_____ ###Markdown We are now ready to train our first spam classifier! Let's transform the whole dataset: ###Code from sklearn.pipeline import Pipeline preprocess_pipeline = Pipeline([ ("email_to_wordcount", EmailToWordCounterTransformer()), ("wordcount_to_vector", WordCounterToVectorTransformer()), ]) X_train_transformed = preprocess_pipeline.fit_transform(X_train) ###Output _____no_output_____ ###Markdown Note: to be future-proof, we set `solver="lbfgs"` since this will be the default value in Scikit-Learn 0.22. ###Code from sklearn.linear_model import LogisticRegression from sklearn.model_selection import cross_val_score log_clf = LogisticRegression(solver="lbfgs", max_iter=1000, random_state=42) score = cross_val_score(log_clf, X_train_transformed, y_train, cv=3, verbose=3) score.mean() ###Output [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 \| elapsed: 0.1s remaining: 0.0s ###Markdown Over 98.5%, not bad for a first try! :) However, remember that we are using the "easy" dataset. You can try with the harder datasets, the results won't be so amazing. You would have to try multiple models, select the best ones and fine-tune them using cross-validation, and so on.But you get the picture, so let's stop now, and just print out the precision/recall we get on the test set: ###Code from sklearn.metrics import precision_score, recall_score X_test_transformed = preprocess_pipeline.transform(X_test) log_clf = LogisticRegression(solver="lbfgs", max_iter=1000, random_state=42) log_clf.fit(X_train_transformed, y_train) y_pred = log_clf.predict(X_test_transformed) print("Precision: {:.2f}%".format(100 * precision_score(y_test, y_pred))) print("Recall: {:.2f}%".format(100 * recall_score(y_test, y_pred))) ###Output _____no_output_____
notebooks/Exploration of player data with week by week differencing.ipynb	###Markdown We can use difflib.get_close_matches() in order to replace player name strings in the old datasets. ###Code temp = copy.copy(df_1_Rec) temp.Player = [difflib.get_close_matches(df_1_Rec.Player.iloc[x] , df_2_Rec.Player.unique(), 1)[0] for x in range(len(df_1_Rec))] temp = pd.merge(temp,df_4_Rec, how='outer', on=['Player']) ###Output _____no_output_____ ###Markdown Based on our calculation above, there is only one issue with the player names between these two sets now. This might have to be fixed manually, or modifying the difflib function might help. ###Code temp temp2 = copy.copy(temp.iloc[:, 1:4]) for i in [0,1,2,3,5,6,7,8,9,10]: temp2 = pd.concat([temp2, pd.to_numeric(temp.iloc[:,18+i].fillna(0), errors='coerce') - pd.to_numeric(temp.iloc[:,4+i].fillna(0), errors='coerce')], axis=1) temp2.columns = ['Player', 'Team', 'Position', 'Rec', 'Yds', 'Avg', 'Yds/G', 'TDs', '20+', '40+', '1st', '1st%', 'Fumbles'] temp2 = temp2.iloc[:,[0,1,2,3,4,7,12]] temp2["FFP"] = temp2.Rec+temp2.Yds0.1+temp2.TDs6-temp2.Fumbles2 temp2 temp2.to_csv('WEEK4_DATA/WEEK4_2018_NFL_RECEIVING', index=False) df = pd.DataFrame(pd.read_csv('WEEK3_DATA/WEEK3_2018_NFL_RECEIVING')) df.columns temp = temp.iloc[:,[1,2,3,4,5,9,-1]] temp.columns = ['Player', 'Team', 'Position', 'Rec', 'Yds', 'TDs', 'Fumbles'] temp['FFP'] = temp.Rec+temp.Yds0.1+temp.TDs6-temp.Fumbles2 temp.to_csv('WEEK1_DATA/WEEK1_2018_NFL_RECEIVING', index=False) ###Output _____no_output_____
FashionMnist_usingMLP.ipynb	###Markdown shape of data ###Code print(x_train.shape) print(x_test.shape) print(y_train.shape) print(y_test.shape) ###Output (10000,) ###Markdown 2) preprocessing stage Normalize all the images ###Code x_train=x_train/255.0 x_test=x_test/255.0 def fashion_model(): #method for building the MLP model model=tf.keras.models.Sequential([ tf.keras.layers.Flatten(), tf.keras.layers.Dense(256,activation='relu'), tf.keras.layers.Dense(128,activation='relu'), tf.keras.layers.Dense(10,activation='softmax') ]) model.compile(loss='sparse_categorical_crossentropy',optimizer='sgd',metrics=['accuracy']) history=model.fit(x_train,y_train,epochs=1) model.save('fashion_model.h5') return model,history.epoch, history.history['accuracy'][-1] fashion_model() def load_fashion_and_predict(): model = keras.models.load_model('/content/fashion_model.h5') prediction=model.predict([1][0]) return prediction load_fashion_and_predict() from keras.preprocessing.image import load_img from keras.preprocessing.image import img_to_array from keras.models import load_model # load and prepare the image def load_image(filename): # load the image img = load_img(filename, grayscale=True, target_size=(28, 28)) # convert to array img = img_to_array(img) # reshape into a single sample with 1 channel img = img.reshape(1, 28, 28, 1) # prepare pixel data img = img.astype('float32') img = img / 255.0 return img # load an image and predict the class def run_example(): # load the image img = load_image('/content/sample_image.png') # load model model = load_model('/content/fashion_model.h5') # predict the class result = model.predict(img) print(np.argmax(result[0])) # entry point, run the example run_example() ###Output _____no_output_____
Notes on feature ranking with CART and Random Forests in sklearn.ipynb	###Markdown Introduction In this notebook I'm reviewing heuristics for ranking features in decision trees and random forests, and their implementation in sklearn. In the text I sometimes use variable as a synonym of feature.Feature selection is a step embedded in the process of growing decision trees. An attractive side effect is that once model is built, we can retrive the relative importance of each feature in the decision making procees. This not only increases the general interpretability of the model, but can help both in exploratory data analysis as well as in feature engineering piepelines. A nice example of how tree based models can be used to improve feature engineering can be found in the winner recap notes of the Criteo Kaggle competition (http://machine-learning-notes.blogspot.nl/2014/12/kaggle-criteo-winner-method-recap.html). Tree based models, and ensambles of trees, are very powerful and well understood methods for supervised learning. Ensamles such as Random Forests are roboust and stable methods for both classification and regression, while decision trees allow for conceptually simple and easilly interpretable models. For an overview of tree models in classification and regression in sklean, there is an excellent talk from Gilles Louppe at PyData Paris 2015 (http://www.slideshare.net/glouppe/slides-46767187). A well regarded paper from the same author that provides a thorough analysis of the mathematics of feature selection in tree ensamles is "Understanding variable importances in forests of randomized trees" (Louppe et al. 2014). With this notebook I'm attempting to fill some gaps and bridge literature review to implementation (sklearn). Data I'll be using the Boston dataset (http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html) for regression and the Iris dataset (http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html) for classification examples. ###Code boston = load_boston() iris = load_iris() ###Output _____no_output_____ ###Markdown Tree models for regression and classification Tree based models are non parametric methods that recursively partition the feature space into rectangular disjoint subsets, and fit a model in each of them. Assuming the space is partitioned in M regions, a regression tree would predict a response $Y$ given inputs $X = \{x_1, x_2, .. x_n\}$ as $$f(x_i) = \sum\limits_{m=1}^M c_mI(x_i\in R_m)$$ where $I$ is the indicator function and $c_m$ a constant that models $Y$ in region $R_m$. This model can be represented by a tree that has $R_1..R_m$ as its terminal nodes. In both the regression and classification cases, the algorithm decides the splitting variables and split points, and tree topology. Essentially, at each split point, the algorithm performs a feature selection step using an heuristic to estimate information gain; this is represented by a numerical value known as the Gini importance of a feature (not to be confused with the Gini index!).We can exploit this very same process to rank features (features engineering) and to explain their imporance with regards to the models we want to learn from data. The main difference between the regression and classification case is the criterion employed to evaluate the quality of a split when growing a tree. Regardless of this aspect, in sklearn, the importance of a variable is calculated as the Gini Importance or "mean decreased impurity" (http://stackoverflow.com/questions/15810339/how-are-feature-importances-in-randomforestclassifier-determined). See"Classification and regression trees" (Breiman, Friedman, 1984). Gini importance (MDI) of a variable Gini importance, or mean decreased impurity is defined as the total decrease in node impurity $i(s, n)$, at split $s$ for some impurity function $i(n)$ . That is$$ \Delta i(s, n) = i(n) - p_L i(t_L) - p_R i(t_R) $$Where $p_L$ and $p_R$ are the proportion of $N_L$ and $N_R$ samples in the left and right splits, over the total number of samples $N_n$ for node $n$. [Under the hood](https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/tree/_tree.pyxL3340), sklearn calculates MDI as follows: ```pythoncdef class Tree: [...] cpdef compute_feature_importances(self, normalize=True): [...] while node != end_node: if node.left_child != _TREE_LEAF: ... and node.right_child != _TREE_LEAF: left = &nodes[node.left_child] right = &nodes[node.right_child] importance_data[node.feature] += ( node.weighted_n_node_samples * node.impurity - left.weighted_n_node_samples * left.impurity - right.weighted_n_node_samples * right.impurity) node += 1 importances /= nodes[0].weighted_n_node_samples [...]``` This method looks at a node and its left, right children. A list of split variables (node.feature objects) and the associated importance score is kept in importance_data. The node impurity is weighted by the probability of reaching that node, approximated by the proportion of samples (weighted_n_node_samples) reaching that node. ```pythonnode.weighted_n_node_samples * node.impurity -left.weighted_n_node_samples * left.impurity -right.weighted_n_node_samples * right.impurity``` The impurity criteria are defined by implementations of the Criterion interface. For classification trees, impurity criteria are Entropy - cross entropy - and Gini, the Gini index. For regression trees the impurity criteria is MSE (mean squared error). Regression trees As said, sklearn.tree.DecisionTreeRegressor uses mean squared error (MSE) to determine the quality of a split; that is, an estimate $\hat{c}_m$ for $c_m$ is calcuated so to minimize the sum of squares $\sum (y_i - \hat{f}(x_i))^2$ with $y_i$ being the target variable and $\hat{f}(x_i)$ its predicted value. The best (proof!) estimator is obtained by taking $\hat{f}({x_i}) = avg(y_i \| x_i \in R_m)$. By bias variance decompostion of the squared error , we have $Var[\hat{f}(x_i)] = E[(\hat{f}(x_i) - E[\hat{f}(x_i)])^2]$. So, for a split node, the mean squared error can then be calculated as the sum of variance of the left and right split: $MSE = Var_{left} + Var_{right}$ ###Code # To keep the diagram tractable, restrict the tree to at most 5 leaf nodes reg = DecisionTreeRegressor(max_leaf_nodes=5) reg.fit(boston.data, boston.target) dot_data = StringIO() export_graphviz(reg, out_file="/tmp/tree.dot", feature_names=boston.feature_names) # pydot.graph_from_dot_data is broken in my env !dot -Tpng /tmp/tree.dot -o /tmp/tree.png from IPython.display import Image Image(filename='/tmp/tree.png') ###Output _____no_output_____ ###Markdown In the example above, with 5 terminal nodes, we identify three split variables: RM, DIS and LSTAT. For each non terminal node, the diagram shows the split variables and split value, the MSE and the number of datapoints (samples) contained in the resulting partitioned region. Terminal nodes, on the other hand, report the value for the response we want to predict.We can retrieve the Gini importance of each feature in the fitted model with the feature\_importances_ property. ###Code reg = DecisionTreeRegressor(max_leaf_nodes=5) reg.fit(boston.data, boston.target) plt = pd.DataFrame(zip(boston.feature_names, reg.feature_importances_), columns=['feature', 'Gini importance']).plot(kind='bar', x='feature') _ = plt.set_title('Regression tree on the Boston dataset (5 leaf nodes)') ###Output _____no_output_____ ###Markdown Things become a bit more interesting when we grow larger trees. ###Code reg = DecisionTreeRegressor() reg.fit(boston.data, boston.target) plt = pd.DataFrame(zip(boston.feature_names, reg.feature_importances_), columns=['feature', 'Gini importance']).plot(kind='bar', x='feature') _ = plt.set_title('Regression tree on the Boston dataset') ###Output _____no_output_____ ###Markdown Classification trees Assume a classification task where the target $k$ classes take values in $0,1,...,K−1$. If node $m$ represents a region $R_m$ with $N_m$ observations, then let $p_{mk} = \frac{1}{N_m} \sum_{x_i \in R_m} I(y_i = k)$ be the proportion of class $k$ observations in node $m$. $sklearn.DecisionTreeClassifier$ allows two impurity criteria for determining splits in such a setting. On the Iris dataset the two criteria agree on which features are important. Experimental results suggest that for tree induction purposes both impurity measures generally lead to similar results (Tan et. al, Introduction to Data Mining). This is not entirely surprising given that both mesaures are particular cases of Tsallis entropy $H_\beta = \frac{1}{\beta-1}(1 - \sum_{k=0}^{K-1} p^{\beta}_{mk})$. For Gini index $\beta=2$, while cross entroy is recovered by taking $\beta \rightarrow 1$.Cross entropy might give higher scores to balanced partitions when there are many classes though. See "Technical Note: Some Properties of Splitting Criteria" (Breiman , 1996). On the other hand working in log scale might introduce computational overhead. Gini Index Gini Index is defined as $\sum_{k=0}^{K-1} p_{mk} (1 - p_{mk}) = 1 - \sum_{k=0}^{K-1} p_{mk}^2$. It is a measure of statistical dispersion commonly used to measure inequality among values of frequency distributions. An interpretation of the Gini index is popular in social sciences as a way to represent levels of income http://en.wikipedia.org/wiki/Gini_coefficient of a nation's residents. In other words, it is the area under the Lorentz curve (http://en.wikipedia.org/wiki/Lorenz_curve). ###Code cls = DecisionTreeClassifier(criterion='gini', splitter='best') est = cls.fit(iris.data, iris.target) zip(iris.feature_names, cls.feature_importances_) plt = pd.DataFrame(zip(iris.feature_names, cls.feature_importances_), columns=['feature', 'Gini importance']).plot(kind='bar', x='feature') _ = plt.set_title('Classification tree on the Iris dataset (criterion=gini)') ###Output _____no_output_____ ###Markdown Cross Entropy Cross-entropy is defined as $- \sum_{k=0}^{K-1} p_{mk} \log(p_{mk})$. Intutively it tells us the amount of information contained at each split node. ###Code cls = DecisionTreeClassifier(criterion='entropy', splitter='best') est = cls.fit(iris.data, iris.target) zip(iris.feature_names, cls.feature_importances_) plt = pd.DataFrame(zip(iris.feature_names, cls.feature_importances_), columns=['feature', 'Gini importance']).plot(kind='bar', x='feature') _ = plt.set_title('Classification tree on the Iris dataset (criterion=entropy)') ###Output _____no_output_____
.ipynb_checkpoints/Create EMA Dataset-checkpoint.ipynb	###Markdown Once we have the data downloaded we can start to create our dataset. We will begin by using data for the last 12 seasons. This should give us enough data to make good predictions, going any further back and the data might not as relevant. Due to the nature of football data being time-series data (ie: matches occur over the course of a season) we will be using full seasons (or two seasons) for our test data. We can also use a repeated K-fold to check our accuracy. We will first load our data as seperate seasons. We are removing any rows containing NaNs and converting Date to a datetime object, we are also adding a gameId column so that we can process our data easier. ###Code # Run this once to concatenate all seasons together # df1 = pd.read_csv(os.path.join(DATA_PATH, 'season0708.csv')) # df2 = pd.read_csv(os.path.join(DATA_PATH, 'season0809.csv')) # df3 = pd.read_csv(os.path.join(DATA_PATH, 'season0910.csv')) # df4 = pd.read_csv(os.path.join(DATA_PATH, 'season1011.csv')) # df5 = pd.read_csv(os.path.join(DATA_PATH, 'season1112.csv')) # df6 = pd.read_csv(os.path.join(DATA_PATH, 'season1213.csv')) # df7 = pd.read_csv(os.path.join(DATA_PATH, 'season1314.csv')) # df8 = pd.read_csv(os.path.join(DATA_PATH, 'season1415.csv')) # df9 = pd.read_csv(os.path.join(DATA_PATH, 'season1516.csv')) # df10 = pd.read_csv(os.path.join(DATA_PATH, 'season1617.csv')) # df11 = pd.read_csv(os.path.join(DATA_PATH, 'season1718.csv')) # df12 = pd.read_csv(os.path.join(DATA_PATH, 'season1819.csv')) # df13 = pd.read_csv(os.path.join(DATA_PATH, 'season1920.csv')) # df = pd.concat([df1, df2, df3, df4, df5, df6, df7, # df8, df9, df10, df11, df12, df13], # ignore_index=True, sort=False) # df.to_csv(os.path.join(DATA_PATH, 'all_seasons_joined.csv')) def create_df(path): """ Function to convert date to datetime and add 'Id' column """ df = (pd.read_csv(path, dtype={'season': str}) .assign(Date=lambda df: pd.to_datetime(df.Date)) .pipe(lambda df: df.dropna(thresh=len(df) - 2, axis=1)) # Drop cols with NAs .dropna(axis=0) # Drop rows with NAs .rename(columns={'Unnamed: 0': 'gameId'}) .sort_values('gameId') .reset_index(drop=True) ) return df df = create_df(os.path.join(DATA_PATH, 'all_seasons_joined.csv')) df.columns ###Output _____no_output_____ ###Markdown In order to add exponential moving averages we first need to restructure our dataset so that every row is a seperate team, rather than a match. ###Code # Define a function which restructures our DataFrame def create_multiline_df_stats(old_stats_df): # Create a list of columns we want and their mappings to more interpretable names home_stats_cols = ['Date', 'season', 'HomeTeam', 'FTHG', 'FTAG', 'HTHG', 'HTAG', 'HS', 'AS', 'HST', 'AST', 'HF', 'AF', 'HC', 'AC', 'HY', 'AY', 'HR', 'AR'] away_stats_cols = ['Date', 'season', 'AwayTeam', 'FTAG', 'FTHG', 'HTAG', 'HTHG', 'AS', 'HS', 'AST', 'HST', 'AF', 'HF', 'AC', 'HC', 'AY', 'HY', 'AR', 'HR'] stats_cols_mapping = ['Date', 'season', 'Team', 'goalsFor', 'goalsAgainst', 'halfTimeGoalsFor', 'halfTimeGoalsAgainst', 'shotsFor', 'shotsAgainst', 'shotsOnTargetFor', 'shotsOnTargetAgainst', 'freesFor', 'freesAgainst', 'cornersFor', 'cornersAgainst', 'yellowsFor', 'yellowsAgainst', 'redsFor', 'redsAgainst'] # Create a dictionary of the old column names to new column names home_mapping = {old_col: new_col for old_col, new_col in zip(home_stats_cols, stats_cols_mapping)} away_mapping = {old_col: new_col for old_col, new_col in zip(away_stats_cols, stats_cols_mapping)} # Put each team onto an individual row multi_line_stats = (old_stats_df[['gameId'] + home_stats_cols] # Filter for only the home team columns .rename(columns=home_mapping) # Rename the columns .assign(homeGame=1) # Assign homeGame=1 so that we can use a general function later .append((old_stats_df[['gameId'] + away_stats_cols]) # Append the away team columns .rename(columns=away_mapping) # Rename the away team columns .assign(homeGame=0), sort=True) .sort_values(by='gameId') # Sort the values .reset_index(drop=True)) return multi_line_stats # Define a function which creates an EMA DataFrame from the stats DataFrame def create_stats_features_ema(stats, span): # Create a restructured DataFrames so that we can calculate EMA multi_line_stats = create_multiline_df_stats(stats) # Create a copy of the DataFrame ema_features = multi_line_stats[['Date', 'season', 'gameId', 'Team', 'homeGame']].copy() # Get the columns that we want to create EMA for feature_names = multi_line_stats.drop(columns=['Date', 'season', 'gameId', 'Team', 'homeGame']).columns # Loop over the features for feature_name in feature_names: feature_ema = (multi_line_stats.groupby('Team')[feature_name] # Calculate the EMA .transform(lambda row: row.ewm(span=span, min_periods=2) .mean() .shift(1))) # Shift the data down 1 so we don't leak data ema_features[feature_name] = feature_ema # Add the new feature to the DataFrame return ema_features # Add weighted average to each row with a span of 50. df = create_stats_features_ema(df, 50) df.tail() df.columns pd.DataFrame(df.groupby('Team') .goalsFor .mean() .sort_values(ascending=False)[:10]) ###Output _____no_output_____ ###Markdown We now need to restructure our dataset back to having a match on each row as this will be a much easier format for machine learning. ###Code def restructure_stats_features(stats_features): non_features = ['homeGame', 'Team', 'gameId'] stats_features_restructured = (stats_features.query('homeGame == 1') .rename(columns={col: 'f_' + col + 'Home' for col in stats_features.columns if col not in non_features}) .rename(columns={'Team': 'HomeTeam'}) .pipe(pd.merge, (stats_features.query('homeGame == 0') .rename(columns={'Team': 'AwayTeam'}) .rename(columns={col: 'f_' + col + 'Away' for col in stats_features.columns if col not in non_features})), on=['gameId']) .dropna()) return stats_features_restructured df = restructure_stats_features(df) df.tail() df.shape df.to_csv(os.path.join(DATA_PATH, 'EMA_data.csv')) ###Output _____no_output_____
scripts/Pytorch-Geom-HRG-GCN.ipynb	###Markdown Reference: https://pytorch-geometric.readthedocs.io/en/latest/notes/introduction.htmllearning-methods-on-graphs ###Code from torch_geometric.data import Data import json from collections import Counter import torch from tqdm import tqdm def read_json_to_list(pth): res = [] with open(pth, "r") as fin: for line in fin: res.append(json.loads(line.strip())) return res hrgs = read_json_to_list("/usr0/home/amadaan/data/audio/LJSpeech-1.1/TTS/hrg.jsonl") len(hrgs) ###Output _____no_output_____ ###Markdown Make vocab, init random embeddings ###Code def get_tokens(hrgs): tokens = [] for hrg in hrgs: for word_rep in hrg["hrg"]: tokens.extend(get_tokens_from_word_rep(word_rep)) return tokens def get_tokens_from_word_rep(word_rep): tokens = [] tokens.append(word_rep["word"]) for daughter in word_rep["daughters"]: tokens.append(daughter["syll"]) return tokens def make_vocab(hrgs): tokens = Counter(list(get_tokens(hrgs))) tokens = [w[0] for w in tokens.items() if w[1] > 1] tokens.extend([str(i) for i in range(20)]) # position tokens.extend(["<W>", "<SYLL>", "<UNK>"]) tok2id = {w:i for i, w in enumerate(tokens)} id2tok = {i:w for w, i in tok2id.items()} return tok2id, id2tok tok2id, id2tok = make_vocab(hrgs) def get_tok2id(tok): if tok in tok2id: return tok2id[tok] return tok2id["<UNK>"] n_embed = 64 hrgs[0] embeddings = torch.rand(len(tok2id), n_embed) ###Output _____no_output_____ ###Markdown Convert HRGs to PyTorchGeom Objects ###Code def hrg_to_graph(hrg): """ Converts the HRG to graph, Returns: Edge index: (num_edges, 2) Node features: (num_nodes, feature_dim) """ words, sylls = [], [] node_idx = {} edges = [] node_features = [] syll_node_idxs = set() for i, word_rep in enumerate(hrg["hrg"]): word_node = f"{word_rep['word']}-{i}" word_node_id = get_tok2id(word_rep['word']) node_idx[word_node] = len(node_idx) node_features.append(embeddings[word_node_id, :]) for j, syll in enumerate(word_rep["daughters"]): syll_node = f"{syll['syll']}-{i}-{j}" syll_node_id = get_tok2id(syll['syll']) node_idx[syll_node] = len(node_idx) syll_node_idxs.add(node_idx[syll_node]) node_features.append(embeddings[syll_node_id, :]) edges.append([node_idx[word_node], node_idx[syll_node]]) return torch.tensor(edges, dtype=torch.long), torch.stack(node_features).float(),\ torch.tensor(list(syll_node_idxs), dtype=torch.long) hrg_to_graph(hrgs[0])[2] py_geom_graphs = [] for hrg in tqdm(hrgs, total=len(hrgs)): edge_index, node_features, syll_nodes = hrg_to_graph(hrg) data = Data(x=node_features, edge_index=edge_index.t().contiguous(), syll_nodes=syll_nodes) py_geom_graphs.append(data) ?? from torch_geometric.data import DataLoader loader = DataLoader(py_geom_graphs, batch_size=32, shuffle=True) py_geom_graphs[1] batches = [] for batch in loader: batches.append(batch) batches[0].num_graphs type(batches[0]) from torch_geometric.data.batch import Batch Batch.from_data_list(batches[0].to_data_list()) batches[0] x = 0 for g in batches[0].to_data_list(): x += g.x.shape[0] print(x) ###Output _____no_output_____ ###Markdown Sample GCN ###Code import torch import torch.nn.functional as F from torch_geometric.nn import GCNConv class Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = GCNConv(n_embed, 16) self.conv2 = GCNConv(16, 200) def forward(self, data): x, edge_index = data.x, data.edge_index x = self.conv1(x, edge_index) x = F.relu(x) x = F.dropout(x, training=self.training) x = self.conv2(x, edge_index) return F.log_softmax(x, dim=1) conv1 = GCNConv(n_embed, 16).cuda() x, edge_index = batches[0].x, batches[0].edge_index type(batch.x) edge_index.shape x.shape x = conv1(x.cuda(), edge_index.cuda()) x.shape batches[0].to_data_list()[0].syll_nodes.shape batches[0].to_data_list() x.shape batch_graphs = batches[0].to_data_list() batch_graphs[0].x.shape[0] batch_graphs[0].syll_nodes.shape offset = 0 x[offset + batch_graphs[0].syll_nodes] offset += batch_graphs[0].x.shape[0] x[offset + batch_graphs[1].syll_nodes].shape ??torch_geometric.data.batch.Batch def break_into_utterances(x, batch): offset = 0 res = [] batch_graphs = batch.to_data_list() for graph in batch_graphs: res.append(x[offset + graph.syll_nodes]) offset += graph.x.shape[0] return res res = break_into_utterances(x, batches[0]) res ###Output _____no_output_____
files/spring2020/12-intro-modeling-2/01-matrix-regression-gradient-decent-python.ipynb	###Markdown [![AnalyticsDojo](https://github.com/rpi-techfundamentals/spring2019-materials/blob/master/fig/final-logo.png?raw=1)](http://rpi.analyticsdojo.com)Linear Regressionrpi.analyticsdojo.com Adopted from Hands-On Machine Learning with Scikit-Learn and TensorFlow Chapter 4 – Training Linear Models. [You can access the book here.](http://proquestcombo.safaribooksonline.com.libproxy.rpi.edu/book/programming/9781491962282.) Origional Material has been released under this license.Apache LicenseVersion 2.0, January 2004http://www.apache.org/licenses/ in [this repository](https://github.com/ageron/handson-ml). Setup First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures: ###Code # To support both python 2 and python 3 #from __future__ import division, print_function, unicode_literals # Common imports import numpy as np import os # to make this notebook's output stable across runs np.random.seed(42) # To plot pretty figures %matplotlib inline import matplotlib import matplotlib.pyplot as plt plt.rcParams['axes.labelsize'] = 14 plt.rcParams['xtick.labelsize'] = 12 plt.rcParams['ytick.labelsize'] = 12 # Let's generate some random data. import numpy as np X = 2 * np.random.rand(100, 1) y = 4 + 3 * X + np.random.randn(100, 1) plt.plot(X, y, "b.") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.axis([0, 2, 0, 15]) ###Output _____no_output_____ ###Markdown Linear Regression - Linear regression involves fitting the optimal values for \theta that minimize the error.$$h_0(x) = \theta_0 + \theta_1x$$Below, we are just adding a constant using the [numpy concatenate function](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.c_.html). ###Code #This will add a 1 to the X matrix X_b = np.c_[np.ones((100, 1)), X] # add x0 = 1 to each instance X_b ###Output _____no_output_____ ###Markdown Linear regression using the Normal EquationUsing matrix calculus, we can actually solve for the optimal value for theta. The regression question below calculates the optimal theta. These are the coefficients relevant to understand. $$ \theta = (X^T X)^{-1}X^T \vec{y} $$In order to calculate this, we are using the `dot` product function for Numpy and `T` to transpose matrix. `linalg.inv(a)` takes the inverse. `np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y)` ###Code theta_best = np.linalg.inv(X_b.T.dot(X_b)).dot(X_b.T).dot(y) #This is the intercept and the coefficient. theta_best #This just Calcultes the line. X_new = np.array([[0], [2]]) X_new_b = np.c_[np.ones((2, 1)), X_new] # add x0 = 1 to each instance y_predict = X_new_b.dot(theta_best) y_predict ###Output _____no_output_____ ###Markdown The figure in the book actually corresponds to the following code, with a legend and axis labels: ###Code plt.plot(X_new, y_predict, "r-", linewidth=2, label="Predictions") plt.plot(X, y, "b.") plt.xlabel("$x_1$", fontsize=18) plt.ylabel("$y$", rotation=0, fontsize=18) plt.legend(loc="upper left", fontsize=14) plt.axis([0, 2, 0, 15]) # We can also do this much easier with the linear regression model. from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(X, y) lin_reg.intercept_, lin_reg.coef_ lin_reg.predict(X_new) ###Output _____no_output_____ ###Markdown Linear regression using batch gradient descentWhere `m` is the number of iteratations(1) $$Gradient = \frac{2}{m}X^T(X\theta - y)$$(2) $$\theta = \theta - \eta Gradient$$(3) $$\theta := \theta - \eta\frac{2}{m}X^T(X\theta - y)$$ ###Code eta = 0.1#learning rate n_iterations = 1000 m = 100 #size of training set theta = np.random.randn(2,1) #Starting point. for iteration in range(n_iterations): gradients = 2/m * X_b.T.dot(X_b.dot(theta) - y) theta = theta - eta * gradients print("Ending:", theta) # theta X_new_b.dot(theta) ###Output _____no_output_____
03_knn.ipynb	###Markdown 设特征空间$\mathcal{X}$是$n$维实数向量空间$R^{n}$，$x_{i},x_{j} \in \mathcal{X},x_{i} = \left( x_{i}^{\left( 1 \right)},x_{i}^{\left( 2 \right) },\cdots,x_{i}^{\left( n \right) } \right)^{T},x_{j} = \left( x_{j}^{\left( 1 \right)},x_{j}^{\left( 2 \right) },\cdots,x_{j}^{\left( n \right) } \right)^{T}$，$x_{i},x_{j}$的$L_{p}$距离或Minkowski（闵科夫斯基）距离\begin{align} \\ & L_{p} \left( x_{i},x_{j} \right) = \left( \sum_{l=1}^{N} \left\| x_{i}^{\left(l\right)} - x_{j}^{\left( l \right)} \right\|^{p} \right)^{\dfrac{1}{p}}\end{align} 其中，$p \geq 1$。当$p=2$时，称为欧氏距离，即\begin{align} \\ & L_{2} \left( x_{i},x_{j} \right) = \left( \sum_{l=1}^{N} \left\| x_{i}^{\left(l\right)} - x_{j}^{\left( l \right)} \right\|^{2} \right)^{\dfrac{1}{2}}\end{align} 当$p=1$时，称为曼哈顿距离，即\begin{align} \\ & L_{1} \left( x_{i},x_{j} \right) = \sum_{l=1}^{N} \left\| x_{i}^{\left(l\right)} - x_{j}^{\left( l \right)} \right\| \end{align} 当$p=\infty$时，称为切比雪夫距离，是各个坐标距离的最大值，即\begin{align} \\ & L_{\infty} \left( x_{i},x_{j} \right) = \max_{l} \left\| x_{i}^{\left(l\right)} - x_{j}^{\left( l \right)} \right\| \end{align} ###Code def minkowski_distance_p(x, y, p=2): """ Parameters: ------------ x: (M, K) array_like y: (M, K) array_like p: float, 1<= p <= infinity ------------ 计算M个K维向量的距离，但是该距离没有开p次根号 """ #把输入的array_like类型的数据转换成numpy中的ndarray x = np.asarray(x) y = np.asarray(y) ##axis=-1沿最后一个坐标轴，0，1沿着第一，第二个坐标轴 if p == np.inf: return np.max(np.abs(x-y), axis=-1) else: return np.sum(np.abs(x-y)p, axis=-1) def minkowski_distance(x, y, p=2): if p==np.inf: return minkowski_distance_p(x, y, np.inf) else: return minkowski_distance_p(x, y, p)(1./p) ###Output _____no_output_____ ###Markdown 平衡kd树构造算法：输入：$k$维空间数据集$T = \left\{ x_{1}, x_{2}, \cdots, x_{N} \right\}$，其中$x_{i} = \left( x_{i}^{\left(1\right)}, x_{i}^{\left(1\right)},\cdots,x_{i}^{\left(k\right)} \right)^{T}, i = 1, 2, \cdots, N$；输出：kd树 1. 开始：构造根结点，根结点对应于包涵$T$的$k$维空间的超矩形区域。选择$x^{\left( 1 \right)}$为坐标轴，以$T$中所欲实例的$x^{\left( 1 \right)}$坐标的中位数为切分点，将根结点对应的超矩形区域切分成两个子区域。切分由通过切分点并与坐标轴$x^{\left( 1 \right)}$垂直的超平面实现。由根结点生成深度为1的左、右子结点：坐子结点对应坐标$x^{\left( 1 \right)}$小于切分点的子区域，右子结点对应于坐标$x^{\left( 1 \right)}$大与切分点的子区域。将落在切分超平面上的实例点保存在跟结点。2. 重复：对深度为$j$的结点，选择$x^{\left( l \right)}$为切分坐标轴，$l = j \left(\bmod k \right) + 1 $，以该结点的区域中所由实例的$x^{\left( l \right)}$坐标的中位数为切分点，将该结点对应的超矩形区域切分为两个子区域。切分由通过切分点并与坐标轴$x^{\left( l \right)}$垂直的超平面实现。由根结点生成深度为$j+1$的左、右子结点：坐子结点对应坐标$x^{\left( l \right)}$小于切分点的子区域，右子结点对应于坐标$x^{\left( l \right)}$大与切分点的子区域。将落在切分超平面上的实例点保存在跟结点。3. 直到两个子区域没有实例存在时停止。关于kd树划分的几点说明：1. 切分的粒度不用那么细，叶子结点上可以保留多个值，规定叶子结点的大小就好了。2. 对划分坐标轴的选取可以采用维度最大间隔的方法,使用第d个坐标，该坐标点的最大值和最小值差最大。$argmax\{d\} = max\{max\{x^d\}-min\{x^d\}\}$3. 使用平均值而不是采用中位数作为切分点最后两点的结合以及一些细节在论文[Analysis of Approximate Nearest NeighborSearching with Clustered Point Sets](https://arxiv.org/abs/cs/9901013)中称为 Sliding-midpoint split。最后的实现也采用了这种方法，以及大量的参考了[Scipy中kd树实现代码](https://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.spatial.KDTree.htmlscipy.spatial.KDTree) ###Code """ 定义一个超矩形区域 """ class Rectangle(object): """Hyperrectangle class. Represents a Cartesian product of intervals. """ def __init__(self, maxes, mins): """Construct a hyperrectangle.""" self.maxes = np.maximum(maxes,mins).astype(np.float) self.mins = np.minimum(maxes,mins).astype(np.float) self.m, = self.maxes.shape def __repr__(self): return "<Rectangle %s>" % list(zip(self.mins, self.maxes)) def volume(self): """Total volume.""" return np.prod(self.maxes-self.mins) def split(self, d, split): """ Produce two hyperrectangles by splitting. In general, if you need to compute maximum and minimum distances to the children, it can be done more efficiently by updating the maximum and minimum distances to the parent. Parameters ---------- d : int Axis to split hyperrectangle along. split : Input. """ mid = np.copy(self.maxes) mid[d] = split less = Rectangle(self.mins, mid) mid = np.copy(self.mins) mid[d] = split greater = Rectangle(mid, self.maxes) return less, greater ###Output _____no_output_____ ###Markdown kd树的最近邻搜索算法：输入：kd树；目标点$x$ 输出：$x$的最近邻 1. 在kd树中找出包含目标点$x$的叶结点：从跟结点出发，递归地向下访问kd树。若目标点$x$当前维的坐标小于切分点的坐标，则移动到左子结点，否则移动到右子结点。直到子结点为叶结点为止。 2. 以此叶结点为“当前最近点”。3. 递归地向上回退，在每个结点进行以下操作： 3.1 如果该结点保存的实例点比当前最近点距离目标点更近，则以该实例点为“当前最近点”。 3.2 当前最近点一定存在于该结点一个子结点对应的区域。检查该子结点的父结点的另一子结点对应的区域是否有更近的点。具体地，检查另一子结点对应的区域是否与以目标点为球心、以目标点与“当前最近点”间的距离为半径的超球体相交。如果相交，可能在另一个子结点对应的区域内存在距目标点更近的点，移动到另一个子结点。接着，递归地进行最近邻搜索；如果不相交，向上回退。 4. 当回退到根结点时，搜索结束。最后的“当前最近点”即为$x$的当前最近邻点。 kd树的k近邻搜索算法，需要使用优先队列$neighbors$保存$k$个搜索结果，搜索的过程也可以不使用递归，用另外一个优先队列$q$来存储接下来需要搜索的结点 ###Code class KDTree(object): def __init__(self, data, leafsize=10): """ """ self.data= np.asarray(data) self.n, self.m = self.data.shape self.leafsize = leafsize if self.leafsize < 1: raise ValueError("leafsize must be at least 1") self.maxes = np.max(self.data, axis=0) self.mins = np.min(self.data, axis=0) self.tree = self.__build(np.arange(self.n), self.maxes, self.mins) """ 定义结点类，作为叶子结点和内部结点的父类是必须重写比较运算符吗？？？ """ class node(object): def __it__(self, other): return id(self) < id(other) def __gt__(self, other): return id(self) > id(other) def __le__(self, other): return id(self) <= id(other) def __ge__(self, other): return id(self) >= id(other) def __eq__(self, other): return id(self) == id(other) """ 定义叶子结点 """ class leafnode(node): def __init__(self, idx): self.idx = idx self.children = len(self.idx) """ 定义内部结点 """ class innernode(node): def __init__(self, split_dim, split, less, greater): """ split_dim: 在某个维度上进行的划分 split：在该维度上的划分点 """ self.split_dim = split_dim self.split = split self.less = less self.greater = greater self.children = less.children+greater.children """ 仅开头带双下划线__的命名用于对象的数据封装，以此命名的属性或者方法为类的私有属性或者私有方法。如果在外部直接访问私有属性或者方法,是不可行的，这就起到了隐藏数据的作用。但是这种实现机制并不是很严格，机制是通过自动"变形"实现的，类中所有以双下划线开头的名称__name都会自动变为"_类名__name"的新名称。使用"_类名__name"就可以访问了，如._KDTree__build()。同时机制可以阻止继承类重新定义或者更改方法的实现。 """ def __build(self, idx, maxes, mins): if len(idx) <= self.leafsize: return KDTree.leafnode(idx) else: #在第d维上进行划分，选自第d维的依据是该维度的间隔最大 d = np.argmax(maxes-mins) #第d维上的数据 data = self.data[idx][d] #第d维上的区间端点 maxval, minval = maxes[d], mins[d] if maxval == minval: #所有的点值都相同 return KDTree.leafnode(idx) """ Splitting Methods sliding midpoint rule; see Maneewongvatana and Mount 1999""" split = (maxval + minval) / 2 #分别返回小于等于，大于split值的元素的索引 less_idx = np.nonzero(data <= split)[0] greater_idx = np.nonzero(data>split)[0] #对于极端的划分情况进行调整 if len(less_idx) == 0: split = np.min(data) less_idx = np.nonzero(data <= split)[0] greater_idx = np.nonzero(data > split)[0] if len(greater_idx) == 0: split = np.max(data) less_idx = np.nonzero(data < split)[0] greater_idx = np.nonzero(data >= split)[0] if len(less_idx) == 0: # _still_ zero? all must have the same value if not np.all(data == data[0]): raise ValueError("Troublesome data array: %s" % data) split = data[0] less_idx = np.arange(len(data)-1) greater_idx = np.array([len(data)-1]) #递归调用左边和右边 lessmaxes = np.copy(maxes) lessmaxes[d] = split greatermins = np.copy(mins) greatermins[d] = split return KDTree.innernode(d, split, self.__build(idx[less_idx],lessmaxes,mins), self.__build(idx[greater_idx],maxes,greatermins)) def query(self, x, k=1, p=2, distance_upper_bound=np.inf): x = np.asarray(x) #距离下界，形象化的思考一下哈，点x出现在mins和maxes的位置分三种情况 side_distances = np.maximum(0,np.maximum(x-self.maxes,self.mins-x)) if p != np.inf: side_distances = p min_distance = np.sum(side_distances) else: min_distance = np.amax(side_distances) if p != np.inf and distance_upper_bound != np.inf: distance_upper_bound = distance_upper_boundp q, neighbors = [(min_distance, tuple(side_distances), self.tree)], [] """ q: 维护搜索的优先队列 # entries are: (minimum distance between the cell and the target, distances between the nearest side of the cell and the target, the head node of the cell) neighbors: priority queue for the nearest neighbors 用于保存k近邻结果的优先队列，heapq默认是最小堆，为了立即能够得到队列中点的最大距离来更新距离上界upper bound，可以存储距离的相反数。 #entries are (-distancep, index) """ while q: min_distance, side_distances, node = heappop(q) if isinstance(node, KDTree.leafnode): # 对于叶子结点，就一个个暴力排除 data = self.data[node.idx] # 把x沿x-轴扩充，然后和叶子结点上的点比较大小 ds = minkowski_distance_p(data,x[np.newaxis,:],p) for i in range(len(ds)): if ds[i] < distance_upper_bound: if len(neighbors) == k: heappop(neighbors) heappush(neighbors, (-ds[i], node.idx[i])) if len(neighbors) == k: #更新距离上界 distance_upper_bound = -neighbors[0][0] else: # we don't push cells that are too far onto the queue at all, # but since the distance_upper_bound decreases, we might get # here even if the cell's too far if min_distance > distance_upper_bound: # since this is the nearest cell, we're done, bail out break # compute minimum distances to the children and push them on if x[node.split_dim] < node.split: near, far = node.less, node.greater else: near, far = node.greater, node.less # near child is at the same distance as the current node heappush(q,(min_distance, side_distances, near)) # 对于far child需要进行距离判断，用新的距离替代原来的距离，然后和距离上界比较 sd = list(side_distances) if p == np.inf: min_distance = max(min_distance, abs(node.split-x[node.split_dim])) elif p == 1: sd[node.split_dim] = np.abs(node.split-x[node.split_dim]) min_distance = (min_distance - side_distances[node.split_dim]) + sd[node.split_dim] else: sd[node.split_dim] = np.abs(node.split-x[node.split_dim])p min_distance = (min_distance - side_distances[node.split_dim]) + sd[node.split_dim] if min_distance <= distance_upper_bound: heappush(q,(min_distance, tuple(sd), far)) if p == np.inf: return sorted([(-d,i) for (d,i) in neighbors]) else: return sorted([((-d)**(1./p),i) for (d,i) in neighbors]) data = [[2,3],[5,4],[9,6],[4,7],[8,1],[7,2]] kd = KDTree(data) ans = kd.query([2, 1], k=2) for item in ans: print("距离：%f，索引:%d" %(item[0], item[1])) np.random.random((2,3)) from time import clock t0 = clock() kd2 = KDTree(np.random.random(( 400000, 3))) # 构建包含四十万个3维空间样本点的kd树 ret2 = kd2.query([0.1,0.5,0.8]) # 四十万个样本点中寻找离目标最近的点 t1 = clock() print ("time: ",t1-t0, "s") print (ret2) ###Output time: 0.08968533333330697 s [(0.23022348089627068, 1)]
examples/colab/component_examples/sequence2sequence/T5_question_answering.ipynb	###Markdown ![JohnSnowLabs](https://nlp.johnsnowlabs.com/assets/images/logo.png)[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/nlu/blob/master/examples/colab/component_examples/sequence2sequence/T5_question_answering.ipynb) `Open book` and `Closed book` question answering with Google's T5 With the latest NLU release and Google's T5 you can answer general knowledge based questions given no context and in addition answer questions on text databases. These questions can be asked in natural human language and answerd in just 1 line with NLU!. What is a `open book question`? You can imagine an `open book` question similar to an examen where you are allowed to bring in text documents or cheat sheets that help you answer questions in an examen. Kinda like bringing a history book to an history examen. In `T5's` terms, this means the model is given a `question` and an additional piece of textual information or so called `context`.This enables the `T5` model to answer questions on textual datasets like `medical records`,`newsarticles` , `wiki-databases` , `stories` and `movie scripts` , `product descriptions`, 'legal documents' and many more.You can answer `open book question` in 1 line of code, leveraging the latest NLU release and Google's T5. All it takes is : ```pythonnlu.load('answer_question').predict("""Where did Jebe die?context: Ghenkis Khan recalled Subtai back to Mongolia soon afterwards, and Jebe died on the road back to Samarkand""")>>> Output: Samarkand```Example for answering medical questions based on medical context``` pythonquestion ='''What does increased oxygen concentrations in the patient’s lungs displace? context: Hyperbaric (high-pressure) medicine uses special oxygen chambers to increase the partial pressure of O 2 around the patient and, when needed, the medical staff. Carbon monoxide poisoning, gas gangrene, and decompression sickness (the ’bends’) are sometimes treated using these devices. Increased O 2 concentration in the lungs helps to displace carbon monoxide from the heme group of hemoglobin. Oxygen gas is poisonous to the anaerobic bacteria that cause gas gangrene, so increasing its partial pressure helps kill them. Decompression sickness occurs in divers who decompress too quickly after a dive, resulting in bubbles of inert gas, mostly nitrogen and helium, forming in their blood. Increasing the pressure of O 2 as soon as possible is part of the treatment.'''Predict on text data with T5nlu.load('answer_question').predict(question)>>> Output: carbon monoxide ```Take a look at this example on a recent news article snippet : ```pythonquestion1 = 'Who is Jack ma?'question2 = 'Who is founder of Alibaba Group?'question3 = 'When did Jack Ma re-appear?'question4 = 'How did Alibaba stocks react?'question5 = 'Whom did Jack Ma meet?'question6 = 'Who did Jack Ma hide from?' from https://www.bbc.com/news/business-55728338 news_article_snippet = """ context:Alibaba Group founder Jack Ma has made his first appearance since Chinese regulators cracked down on his business empire.His absence had fuelled speculation over his whereabouts amid increasing official scrutiny of his businesses.The billionaire met 100 rural teachers in China via a video meeting on Wednesday, according to local government media.Alibaba shares surged 5% on Hong Kong's stock exchange on the news.""" join question with context, works with Pandas DF aswell!questions = [ question1+ news_article_snippet, question2+ news_article_snippet, question3+ news_article_snippet, question4+ news_article_snippet, question5+ news_article_snippet, question6+ news_article_snippet,]nlu.load('answer_question').predict(questions)```This will output a Pandas Dataframe similar to this : \|Answer\|Question\|\|-----\|---------\|Alibaba Group founder\| Who is Jack ma? \| \|Jack Ma \|Who is founder of Alibaba Group? \| Wednesday \| When did Jack Ma re-appear? \| surged 5% \| How did Alibaba stocks react? \| 100 rural teachers \| Whom did Jack Ma meet? \| Chinese regulators \|Who did Jack Ma hide from?\| What is a `closed book question`? A `closed book question` is the exact opposite of a `open book question`. In an examen scenario, you are only allowed to use what you have memorized in your brain and nothing else. In `T5's` terms this means that T5 can only use it's stored weights to answer a `question` and is given no aditional context. `T5` was pre-trained on the [C4 dataset](https://commoncrawl.org/) which contains petabytes of web crawling data collected over the last 8 years, including Wikipedia in every language.This gives `T5` the broad knowledge of the internet stored in it's weights to answer various `closed book questions` You can answer `closed book question` in 1 line of code, leveraging the latest NLU release and Google's T5. You need to pass one string to NLU, which starts which a `question` and is followed by a `context:` tag and then the actual context contents. All it takes is : ```pythonnlu.load('en.t5').predict('Who is president of Nigeria?')>>> Muhammadu Buhari ``````pythonnlu.load('en.t5').predict('What is the most spoken language in India?')>>> Hindi``````pythonnlu.load('en.t5').predict('What is the capital of Germany?')>>> Berlin``` ###Code import os ! apt-get update -qq > /dev/null # Install java ! apt-get install -y openjdk-8-jdk-headless -qq > /dev/null os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64" os.environ["PATH"] = os.environ["JAVA_HOME"] + "/bin:" + os.environ["PATH"] ! pip install nlu pyspark==2.4.7 > /dev/null import nlu ###Output _____no_output_____ ###Markdown Closed book question answering example ###Code t5_closed_book = nlu.load('en.t5') t5_closed_book.predict('What is the capital of Germany?') t5_closed_book.predict('Who is president of Nigeria?') t5_closed_book.predict('What is the most spoken language in India?') ###Output _____no_output_____ ###Markdown Open Book question examplesYour context must be prefixed with `context:` ###Code t5_open_book = nlu.load('answer_question') t5_open_book.predict("""Where did Jebe die? context: Ghenkis Khan recalled Subtai back to Mongolia soon afterwards, and Jebe died on the road back to Samarkand""" ) ###Output _____no_output_____ ###Markdown Open Book question example on a Story ###Code question1 = 'What does Jimmy like to eat for breakfast usually?' question2 = 'Why was Jimmy suprised?' story = """ context: Once upon a time, there was a squirrel named Joey. Joey loved to go outside and play with his cousin Jimmy. Joey and Jimmy played silly games together, and were always laughing. One day, Joey and Jimmy went swimming together 50 at their Aunt Julie’s pond. Joey woke up early in the morning to eat some food before they left. He couldn’t find anything to eat except for pie! Usually, Joey would eat cereal, fruit (a pear), or oatmeal for breakfast. After he ate, he and Jimmy went to the pond. On their way there they saw their friend Jack Rabbit. They dove into the water and swam for several hours. The sun was out, but the breeze was cold. Joey and Jimmy got out of the water and started walking home. Their fur was wet, and the breeze chilled them. When they got home, they dried off, and Jimmy put on his favorite purple shirt. Joey put on a blue shirt with red and green dots. The two squirrels ate some food that Joey’s mom, Jasmine, made and went off to bed. """ questions = [ question1+ story, question2+ story,] t5_open_book.predict(questions) ###Output _____no_output_____ ###Markdown Open book question example on news article ###Code question1 = 'Who is Jack ma?' question2 = 'Who is founder of Alibaba Group?' question3 = 'When did Jack Ma re-appear?' question4 = 'How did Alibaba stocks react?' question5 = 'Whom did Jack Ma meet?' question6 = 'Who did Jack Ma hide from?' # from https://www.bbc.com/news/business-55728338 news_article_snippet = """ context: Alibaba Group founder Jack Ma has made his first appearance since Chinese regulators cracked down on his business empire. His absence had fuelled speculation over his whereabouts amid increasing official scrutiny of his businesses. The billionaire met 100 rural teachers in China via a video meeting on Wednesday, according to local government media. Alibaba shares surged 5% on Hong Kong's stock exchange on the news. """ questions = [ question1+ news_article_snippet, question2+ news_article_snippet, question3+ news_article_snippet, question4+ news_article_snippet, question5+ news_article_snippet, question6+ news_article_snippet,] t5_open_book.predict(questions) # define Data, add additional context tag between sentence question =''' What does increased oxygen concentrations in the patient’s lungs displace? context: Hyperbaric (high-pressure) medicine uses special oxygen chambers to increase the partial pressure of O 2 around the patient and, when needed, the medical staff. Carbon monoxide poisoning, gas gangrene, and decompression sickness (the ’bends’) are sometimes treated using these devices. Increased O 2 concentration in the lungs helps to displace carbon monoxide from the heme group of hemoglobin. Oxygen gas is poisonous to the anaerobic bacteria that cause gas gangrene, so increasing its partial pressure helps kill them. Decompression sickness occurs in divers who decompress too quickly after a dive, resulting in bubbles of inert gas, mostly nitrogen and helium, forming in their blood. Increasing the pressure of O 2 as soon as possible is part of the treatment. ''' #Predict on text data with T5 t5_open_book.predict(question) ###Output _____no_output_____
dgl_smell.ipynb	###Markdown ###Code pip install dgl pip install ogb pip install rdkit-pypi !wget https://raw.githubusercontent.com/napoles-uach/DGL_Smell/main/train.csv import pandas as pd df=pd.read_csv('train.csv') df.head() import dgl from dgl.data import DGLDataset import torch import torch as th import os from ogb.utils.features import (allowable_features, atom_to_feature_vector, bond_to_feature_vector, atom_feature_vector_to_dict, bond_feature_vector_to_dict) from rdkit import Chem import numpy as np def smiles2graph(smiles_string): """ Converts SMILES string to graph Data object :input: SMILES string (str) :return: graph object """ mol = Chem.MolFromSmiles(smiles_string) A = Chem.GetAdjacencyMatrix(mol) A = np.asmatrix(A) nnodes=len(A) nz = np.nonzero(A) edge_list=[] src=[] dst=[] for i in range(nz[0].shape[0]): src.append(nz[0][i]) dst.append(nz[1][i]) u, v = src, dst g = dgl.graph((u, v)) bg=dgl.to_bidirected(g) return bg def feat_vec(smiles_string): """ Returns atom features for a molecule given a smiles string """ # atoms mol = Chem.MolFromSmiles(smiles_string) atom_features_list = [] for atom in mol.GetAtoms(): atom_features_list.append(atom_to_feature_vector(atom)) x = np.array(atom_features_list, dtype = np.int64) return x # this block uses the column SENTENCE to build one hot encoding for each sentence # All sentences are stored in a list, repetitions are eliminated and ordered alphabetically # this gives 109 strings whose indexes are used to build one hot encoding vectors on 109 entries. # Example: fresh,ethereal,fruity gives a vector with ones in entries 42, 47, and 48. lista_senten=df['SENTENCE'].to_list() olores=[] for olor in lista_senten: olor=olor.split(",") olores.append(olor) # da formato correcto a las sentencias en forma de lista from pandas.core.common import flatten olores=list(flatten(olores)) #crea una sola lista con los olores olores = list(dict.fromkeys(olores)) #elimina repeticiones olores.sort() #ordena alfabeticamente inoh=[] for olor in lista_senten: olor=olor.split(",") indices=[] for t in olor: if t in olores: indices.append(olores.index(t)) inoh.append(indices) onehot=[] for i in inoh: ohv=np.zeros(len(olores)) ohv[i]=1 onehot.append(ohv) lista_senten[1] onehot[1] # This block makes a list of graphs lista_mols=df['SMILES'].to_list() j=0 graphs=[] execptions=[] for mol in lista_mols: g_mol=smiles2graph(mol) try: g_mol.ndata['feat']=torch.tensor(feat_vec(mol)) except: execptions.append(j) graphs.append(g_mol) #agrega grafo de mol j+=1 labels=onehot # Some smiles are not well processed, so they are droped for i in execptions: graphs.pop(i) labels.pop(i) class SyntheticDataset(DGLDataset): def __init__(self): super().__init__(name='synthetic') def process(self): #edges = pd.read_csv('./graph_edges.csv') #properties = pd.read_csv('./graph_properties.csv') self.graphs = graphs self.labels = torch.LongTensor(labels) def __getitem__(self, i): return self.graphs[i], self.labels[i] def __len__(self): return len(self.graphs) dataset = SyntheticDataset() graph, label = dataset[0] print(graph, label) ###Output Graph(num_nodes=14, num_edges=28, ndata_schemes={'feat': Scheme(shape=(9,), dtype=torch.int64)} edata_schemes={}) tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
crossValidation.ipynb	###Markdown Cross ValidationThis section documents how to do cross-validation in Scikit-Learn. Cross validation is ourcritical model evaluation system. It tries to simulate how a model would perform on clean data by splitting it into training and testing samples. To keep things simple we will stickwith the basic linear model that we used for monte-carlo examples in class. Also,the only model fit will be a basic linear regression. Everything that is done here caneasily be extended to any of the models in the Scikit-learn family of ML models. ###Code # Load helpers # Will try to just load what I need on this %matplotlib inline import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression from sklearn.model_selection import cross_val_score from sklearn.model_selection import cross_validate from sklearn.model_selection import ShuffleSplit ###Output _____no_output_____ ###Markdown Linear model data generationThis model is from the class notes, and generates a simple linear model with M predictors. We used it to generate overfitting even in linear model space. ###Code # Function to generate linear data experiments def genLinData(N,M,noise): # y = x_1 + x_2 .. x_M + eps # X's scaled so the variance of explained part is same order as noise variance (if std(eps) = 1) sigNoise = np.sqrt(1./M) X = np.random.normal(size=(N,M),loc=0,scale=sigNoise) eps = np.random.normal(size=N,loc=0,scale=noise) y = np.sum(X,axis=1)+eps return X,y ###Output _____no_output_____ ###Markdown Over fitting in one run using train_test_split ###Code # Basic overfitting example X, y = genLinData(200,50,1.0) X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25) # Now run regression # print score, which is R-squared (fit) lr = LinearRegression() lr.fit(X_train, y_train) print(lr.score(X_train,y_train)) print(lr.score(X_test,y_test)) ###Output 0.6490379774856974 0.2253399639542668 ###Markdown Raw Python for the appropriate simulation of many test scores ###Code nmc = 100 X, y = genLinData(200,50,1.0) scoreVec = np.zeros(nmc) for i in range(nmc): X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25) # Now run regression # print score, which is R-squared (fit) lr = LinearRegression() lr.fit(X_train, y_train) scoreVec[i] = lr.score(X_test,y_test) print(np.mean(scoreVec)) print(np.std(scoreVec)) print(np.mean(scoreVec<0)) ###Output 0.44224466961870573 0.1097046822176599 0.0 ###Markdown Automate this by building a function ###Code # A function to automate MC experiments def MCtraintest(nmc,X,y,modelObj,testFrac): trainScore = np.zeros(nmc) testScore = np.zeros(nmc) for i in range(nmc): X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=testFrac) modelObj.fit(X_train,y_train) trainScore[i] = modelObj.score(X_train,y_train) testScore[i] = modelObj.score(X_test,y_test) return trainScore,testScore nmc = 100 lr = LinearRegression() trainS, testS = MCtraintest(nmc,X,y,lr,0.25) print(np.mean(trainS)) print(np.std(trainS)) print(np.mean(testS)) print(np.std(testS)) ###Output 0.761162190524635 0.02293850535797592 0.46753101981207146 0.12839692764931585 ###Markdown Scikit-learn functions* Scikit-learn has many built in functions for cross validation. * Here are a few of them. cross-validate* This general functions does many things. * This first example uses it on a data set, and performs an even more basic cross-validation than we have been doing. * This is called k-fold cross-validation.* It splits the data set into k parts. Then trains on k-1 parts, and tests on the remaining 1 part.* This is a very standard cross-validation system* It returns a rich dictionary of results ###Code # X, y = genLinData(200,50,1.0) lr = LinearRegression() CVInfo = cross_validate(lr, X, y, cv=5,return_train_score=True) print(np.mean(CVInfo['train_score'])) print(np.mean(CVInfo['test_score'])) ###Output _____no_output_____ ###Markdown ShuffleSplit* ShuffleSplit function can add a randomized train/test split to cross-validate* Here is how you do it ###Code # X, y = genLinData(200,50,1.0) lr = LinearRegression() shuffle = ShuffleSplit(n_splits=100, test_size=.25, random_state=0) CVInfo = cross_validate(lr, X, y, cv=shuffle,return_train_score=True) print(np.mean(CVInfo['train_score'])) print(np.mean(CVInfo['test_score'])) ###Output _____no_output_____ ###Markdown cross_val_score* This is a very basic cross validation system* It returns a simple vector of test set (only) scores* Also, uses ShuffleSplit ###Code # X, y = genLinData(200,50,1.0) lr = LinearRegression() shuffle = ShuffleSplit(n_splits=100, test_size=.25, random_state=0) CVScores = cross_val_score(lr, X, y, cv=shuffle) print(np.mean(CVScores)) print(CVScores) ###Output _____no_output_____
Lecture 03/6.006.L3.ipynb	###Markdown SortingLecture by Srini Devdas at MITVideo link: [https://www.youtube.com/watch?v=Kg4bqzAqRBM&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=3](https://www.youtube.com/watch?v=Kg4bqzAqRBM&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=3) ###Code import random, time def generateArray(n: int=10, min_range: int=-100, max_range: int=100) -> list: ''' Helper function to generate an array of size n where elements are in the (min-range, max_range) range ''' array=[random.randint(min_range, max_range) for i in range(n)] return array ###Output _____no_output_____ ###Markdown Insertion Sort ```For i=1..n The first element is sorted by default insert A[i] into sorted array A[0:i-1] pairwise swaps down to the correct position``` ###Code def insertionSort(array: list, ascending: bool= True, verbose: int = 1) -> None: ''' This function sorts the array in-place and does not return anything ascending: if True, the array will be returned in ascending order, else in descending order verbose: 0 - doesn't show any ouptut. 1 - displays array before and after sorting and the number of swaps 2 - displays every time a swap is made ''' assert verbose in [0,1,2], "Invalid value for verbose" if verbose: print("Before sorting") print(array) if ascending: mult_factor=1 else: mult_factor=-1 swap_count=0 for key_idx in range(1,len(array)): for inner_idx in range(key_idx): key,el=array[key_idx]mult_factor, array[inner_idx]mult_factor if key<el: array[inner_idx],array[key_idx]=array[key_idx],array[inner_idx] swap_count+=1 if verbose>1: print(f"After swap #{swap_count}", array) if verbose: print(f"After sorting. Total number of swaps: {swap_count}") print(array) ###Output _____no_output_____ ###Markdown Improvement to insertion sort- Observation: All elements before the current one are already sorted- Action: Use binary search instead of pairwise swaps. Note: The number of swaps does not decrease since all elements have to be moved to put the key in the correct position TO-DO Implement ascending/descending for binary insertion sort ###Code def rotateElements(arr,start,end) -> None: assert start<len(arr) and end<len(arr) and start<=end, "Cannot move elements" for i in range(end, start, -1): arr[i],arr[i-1]=arr[i-1],arr[i] return def binarySearch(arr, val, start, end): if start==end: temp=arr[start] if temp>val: return start else: return start+1 if start>end: return start mid=(start+end)//2 temp=array[mid] if temp>val: return binarySearch(arr, val,start, mid-1) elif temp<val: return binarySearch(arr, val, mid+1, end) else: return mid def improvedInsertionSort(array: list, ascending: bool= True, verbose: int = 1) -> None: ''' This function sorts the array in-place and does not return anything ascending: if True, the array will be returned in ascending order, else in descending order verbose: 0 - doesn't show any ouptut. 1 - displays array before and after sorting and the number of swaps 2 - displays every time a swap is made ''' assert verbose in [0,1,2], "Invalid value for verbose" if verbose: print("Before sorting") print(array) swap_count=0 for key_idx in range(1,len(array)): lo, hi= 0, key_idx-1 key=array[key_idx] pos=binarySearch(array,key,lo,hi) if pos!=key_idx: rotateElements(array, pos, key_idx) swap_count+=key_idx-pos if verbose>1: print(array) if verbose: print(f"After sorting. Total number of swaps: {swap_count}") print(array) ###Output _____no_output_____ ###Markdown Merge Sort ```Break down list into single elements, which by definition are sortedMerge each of these one-element lists into a sorted list``` ###Code def mergeSort(array): ''' Breaks down the list into single-element lists ''' if len(array)>1: mid=len(array)//2 left_arr=array[:mid] right_arr=array[mid:] mergeSort(left_arr) mergeSort(right_arr) return merge(left_arr, right_arr, array) def merge(left_arr, right_arr, array): ''' Takes two sorted lists as input and merges them in O(\|left_arr\| + \|right_arr\|) time ''' i,j,k=0,0,0 while(i<len(left_arr) and j<len(right_arr)): if left_arr[i]<right_arr[j]: array[k]=left_arr[i] i+=1 else: array[k]=right_arr[j] j+=1 k+=1 while i<len(left_arr): array[k]=left_arr[i] i+=1 k+=1 while j<len(right_arr): array[k]=right_arr[j] j+=1 k+=1 ###Output _____no_output_____
art_tweets.ipynb	###Markdown Number of Nodes ###Code %cypher match (n) return count() as num_nodes %cypher match (n:tweet) return count () as num_tweets %cypher match (n:user) return count () as num_users %cypher match (n:hashtag) return count () as num_hashtags ###Output 1 rows affected. ###Markdown Number of Edges ###Code %cypher match (n)-[r]->() return count(*) as num_edges ###Output 1 rows affected. ###Markdown Top Tweets ###Code top_tweets = %cypher match (n:tweet)-[r]-(m:tweet) return n.tid, n.text, count(r) as deg order by deg desc limit 10 top_tweets.get_dataframe() ###Output _____no_output_____ ###Markdown Top Hashtags ###Code top_tags = %cypher match (n:hashtag)-[r]-(m) return n.hashtag, count(r) as deg order by deg desc limit 10 top_tags.get_dataframe() ###Output _____no_output_____ ###Markdown Top Users ###Code top_users = %cypher match (n:user)-[r]-(m) return n.uid, n.screen_name, count(r) as deg order by deg desc limit 10 top_users.get_dataframe() ###Output _____no_output_____ ###Markdown Top Languages ###Code top_langs = %cypher match (n:tweet) where n.lang is not null return distinct n.lang, count(n.lang) as num_tweets order by num_tweets desc top_langs.get_dataframe().head(20) ###Output _____no_output_____ ###Markdown Top Cities ###Code top_locs = %cypher match (n:tweet) where n.full_name is not null return distinct n.full_name, count(n.full_name) as num_tweets order by num_tweets desc top_locs.get_dataframe().head(20) ###Output _____no_output_____ ###Markdown Top Countries ###Code top_countries = %cypher match (n:tweet) where n.country is not null return distinct n.country, count(n.country) as num_tweets order by num_tweets desc top_countries.get_dataframe().head(20) ###Output _____no_output_____
2_Neural_network/intro_to_pytorch/Part 2 - Neural Networks in PyTorch .ipynb	###Markdown Neural networks with PyTorchDeep learning networks tend to be massive with dozens or hundreds of layers, that's where the term "deep" comes from. You can build one of these deep networks using only weight matrices as we did in the previous notebook, but in general it's very cumbersome and difficult to implement. PyTorch has a nice module `nn` that provides a nice way to efficiently build large neural networks. ###Code # Import necessary packages %matplotlib inline %config InlineBackend.figure_format = 'retina' import numpy as np import torch import helper import matplotlib.pyplot as plt ###Output _____no_output_____ ###Markdown Now we're going to build a larger network that can solve a (formerly) difficult problem, identifying text in an image. Here we'll use the MNIST dataset which consists of greyscale handwritten digits. Each image is 28x28 pixels, you can see a sample belowOur goal is to build a neural network that can take one of these images and predict the digit in the image.First up, we need to get our dataset. This is provided through the `torchvision` package. The code below will download the MNIST dataset, then create training and test datasets for us. Don't worry too much about the details here, you'll learn more about this later. ###Code ### Run this cell from torchvision import datasets, transforms # Define a transform to normalize the data transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)), ]) # Download and load the training data trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True, train=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True) ###Output _____no_output_____ ###Markdown We have the training data loaded into `trainloader` and we make that an iterator with `iter(trainloader)`. Later, we'll use this to loop through the dataset for training, like```pythonfor image, label in trainloader: do things with images and labels```You'll notice I created the `trainloader` with a batch size of 64, and `shuffle=True`. The batch size is the number of images we get in one iteration from the data loader and pass through our network, often called a batch. And `shuffle=True` tells it to shuffle the dataset every time we start going through the data loader again. But here I'm just grabbing the first batch so we can check out the data. We can see below that `images` is just a tensor with size `(64, 1, 28, 28)`. So, 64 images per batch, 1 color channel, and 28x28 images. ###Code dataiter = iter(trainloader) images, labels = dataiter.next() print(type(images)) print(images.shape) print(labels.shape) ###Output <class 'torch.Tensor'> torch.Size([64, 1, 28, 28]) torch.Size([64]) ###Markdown This is what one of the images looks like. ###Code plt.imshow(images[1].numpy().squeeze(), cmap='Greys_r'); ###Output _____no_output_____ ###Markdown First, let's try to build a simple network for this dataset using weight matrices and matrix multiplications. Then, we'll see how to do it using PyTorch's `nn` module which provides a much more convenient and powerful method for defining network architectures.The networks you've seen so far are called fully-connected or dense networks. Each unit in one layer is connected to each unit in the next layer. In fully-connected networks, the input to each layer must be a one-dimensional vector (which can be stacked into a 2D tensor as a batch of multiple examples). However, our images are 28x28 2D tensors, so we need to convert them into 1D vectors. Thinking about sizes, we need to convert the batch of images with shape `(64, 1, 28, 28)` to a have a shape of `(64, 784)`, 784 is 28 times 28. This is typically called flattening, we flattened the 2D images into 1D vectors.Previously you built a network with one output unit. Here we need 10 output units, one for each digit. We want our network to predict the digit shown in an image, so what we'll do is calculate probabilities that the image is of any one digit or class. This ends up being a discrete probability distribution over the classes (digits) that tells us the most likely class for the image. That means we need 10 output units for the 10 classes (digits). We'll see how to convert the network output into a probability distribution next.> Exercise: Flatten the batch of images `images`. Then build a multi-layer network with 784 input units, 256 hidden units, and 10 output units using random tensors for the weights and biases. For now, use a sigmoid activation for the hidden layer. Leave the output layer without an activation, we'll add one that gives us a probability distribution next. ###Code ## Solution def activation(x): return 1/(1+torch.exp(-x)) # Flatten the input images inputs = images.view(images.shape[0], -1) # Create parameters w1 = torch.randn(784, 256) b1 = torch.randn(256) w2 = torch.randn(256, 10) b2 = torch.randn(10) h = activation(torch.mm(inputs, w1) + b1) out = torch.mm(h, w2) + b2 ###Output _____no_output_____ ###Markdown Now we have 10 outputs for our network. We want to pass in an image to our network and get out a probability distribution over the classes that tells us the likely class(es) the image belongs to. Something that looks like this:Here we see that the probability for each class is roughly the same. This is representing an untrained network, it hasn't seen any data yet so it just returns a uniform distribution with equal probabilities for each class.To calculate this probability distribution, we often use the [softmax function](https://en.wikipedia.org/wiki/Softmax_function). Mathematically this looks like$$\Large \sigma(x_i) = \cfrac{e^{x_i}}{\sum_k^K{e^{x_k}}}$$What this does is squish each input $x_i$ between 0 and 1 and normalizes the values to give you a proper probability distribution where the probabilites sum up to one.> Exercise: Implement a function `softmax` that performs the softmax calculation and returns probability distributions for each example in the batch. Note that you'll need to pay attention to the shapes when doing this. If you have a tensor `a` with shape `(64, 10)` and a tensor `b` with shape `(64,)`, doing `a/b` will give you an error because PyTorch will try to do the division across the columns (called broadcasting) but you'll get a size mismatch. The way to think about this is for each of the 64 examples, you only want to divide by one value, the sum in the denominator. So you need `b` to have a shape of `(64, 1)`. This way PyTorch will divide the 10 values in each row of `a` by the one value in each row of `b`. Pay attention to how you take the sum as well. You'll need to define the `dim` keyword in `torch.sum`. Setting `dim=0` takes the sum across the rows while `dim=1` takes the sum across the columns. ###Code ## Solution def softmax(x): return torch.exp(x)/torch.sum(torch.exp(x), dim=1).view(-1, 1) probabilities = softmax(out) # Does it have the right shape? Should be (64, 10) print(probabilities.shape) # Does it sum to 1? print(probabilities.sum(dim=1)) ###Output torch.Size([64, 10]) tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000]) ###Markdown Building networks with PyTorchPyTorch provides a module `nn` that makes building networks much simpler. Here I'll show you how to build the same one as above with 784 inputs, 256 hidden units, 10 output units and a softmax output. ###Code from torch import nn class Network(nn.Module): def __init__(self): super().__init__() # Inputs to hidden layer linear transformation self.hidden = nn.Linear(784, 256) # Output layer, 10 units - one for each digit self.output = nn.Linear(256, 10) # Define sigmoid activation and softmax output self.sigmoid = nn.Sigmoid() self.softmax = nn.Softmax(dim=1) def forward(self, x): # Pass the input tensor through each of our operations x = self.hidden(x) x = self.sigmoid(x) x = self.output(x) x = self.softmax(x) return x ###Output _____no_output_____ ###Markdown Let's go through this bit by bit.```pythonclass Network(nn.Module):```Here we're inheriting from `nn.Module`. Combined with `super().__init__()` this creates a class that tracks the architecture and provides a lot of useful methods and attributes. It is mandatory to inherit from `nn.Module` when you're creating a class for your network. The name of the class itself can be anything.```pythonself.hidden = nn.Linear(784, 256)```This line creates a module for a linear transformation, $x\mathbf{W} + b$, with 784 inputs and 256 outputs and assigns it to `self.hidden`. The module automatically creates the weight and bias tensors which we'll use in the `forward` method. You can access the weight and bias tensors once the network (`net`) is created with `net.hidden.weight` and `net.hidden.bias`.```pythonself.output = nn.Linear(256, 10)```Similarly, this creates another linear transformation with 256 inputs and 10 outputs.```pythonself.sigmoid = nn.Sigmoid()self.softmax = nn.Softmax(dim=1)```Here I defined operations for the sigmoid activation and softmax output. Setting `dim=1` in `nn.Softmax(dim=1)` calculates softmax across the columns.```pythondef forward(self, x):```PyTorch networks created with `nn.Module` must have a `forward` method defined. It takes in a tensor `x` and passes it through the operations you defined in the `__init__` method.```pythonx = self.hidden(x)x = self.sigmoid(x)x = self.output(x)x = self.softmax(x)```Here the input tensor `x` is passed through each operation a reassigned to `x`. We can see that the input tensor goes through the hidden layer, then a sigmoid function, then the output layer, and finally the softmax function. It doesn't matter what you name the variables here, as long as the inputs and outputs of the operations match the network architecture you want to build. The order in which you define things in the `__init__` method doesn't matter, but you'll need to sequence the operations correctly in the `forward` method.Now we can create a `Network` object. ###Code # Create the network and look at it's text representation model = Network() model ###Output _____no_output_____ ###Markdown You can define the network somewhat more concisely and clearly using the `torch.nn.functional` module. This is the most common way you'll see networks defined as many operations are simple element-wise functions. We normally import this module as `F`, `import torch.nn.functional as F`. ###Code import torch.nn.functional as F class Network(nn.Module): def __init__(self): super().__init__() # Inputs to hidden layer linear transformation self.hidden = nn.Linear(784, 256) # Output layer, 10 units - one for each digit self.output = nn.Linear(256, 10) def forward(self, x): # Hidden layer with sigmoid activation x = F.sigmoid(self.hidden(x)) # Output layer with softmax activation x = F.softmax(self.output(x), dim=1) return x ###Output _____no_output_____ ###Markdown Activation functionsSo far we've only been looking at the softmax activation, but in general any function can be used as an activation function. The only requirement is that for a network to approximate a non-linear function, the activation functions must be non-linear. Here are a few more examples of common activation functions: Tanh (hyperbolic tangent), and ReLU (rectified linear unit).In practice, the ReLU function is used almost exclusively as the activation function for hidden layers. Your Turn to Build a Network> Exercise: Create a network with 784 input units, a hidden layer with 128 units and a ReLU activation, then a hidden layer with 64 units and a ReLU activation, and finally an output layer with a softmax activation as shown above. You can use a ReLU activation with the `nn.ReLU` module or `F.relu` function.It's good practice to name your layers by their type of network, for instance 'fc' to represent a fully-connected layer. As you code your solution, use `fc1`, `fc2`, and `fc3` as your layer names. ###Code ## Solution class Network(nn.Module): def __init__(self): super().__init__() # Defining the layers, 128, 64, 10 units each self.fc1 = nn.Linear(784, 128) self.fc2 = nn.Linear(128, 64) # Output layer, 10 units - one for each digit self.fc3 = nn.Linear(64, 10) def forward(self, x): ''' Forward pass through the network, returns the output logits ''' x = self.fc1(x) x = F.relu(x) x = self.fc2(x) x = F.relu(x) x = self.fc3(x) x = F.softmax(x, dim=1) return x model = Network() model ###Output _____no_output_____ ###Markdown Initializing weights and biasesThe weights and such are automatically initialized for you, but it's possible to customize how they are initialized. The weights and biases are tensors attached to the layer you defined, you can get them with `model.fc1.weight` for instance. ###Code print(model.fc1.weight) print(model.fc1.bias) ###Output Parameter containing: tensor([[ 0.0303, 0.0247, 0.0203, ..., -0.0096, 0.0325, -0.0308], [ 0.0286, 0.0208, 0.0282, ..., 0.0155, 0.0251, 0.0305], [-0.0327, 0.0202, -0.0023, ..., -0.0312, -0.0091, -0.0203], ..., [ 0.0236, 0.0162, -0.0151, ..., 0.0218, 0.0133, 0.0050], [ 0.0152, -0.0154, 0.0147, ..., 0.0254, 0.0016, -0.0102], [-0.0142, -0.0009, -0.0167, ..., -0.0344, 0.0329, 0.0084]], requires_grad=True) Parameter containing: tensor([-4.9410e-03, 3.1008e-02, -1.2816e-02, 3.3149e-02, 1.8155e-02, -2.7854e-02, 1.5113e-02, 1.4183e-02, -3.1788e-02, 2.0937e-02, -2.5365e-02, 2.2258e-02, 2.1894e-02, 3.0449e-02, 9.6907e-03, 1.2307e-02, -2.3984e-02, 2.2405e-02, -2.3917e-03, 1.7656e-03, 2.7852e-02, -1.0032e-02, 3.4842e-02, 9.7149e-03, -3.0658e-02, -1.1512e-02, 2.8253e-02, 8.2238e-03, 6.2211e-03, -2.6598e-02, -2.7131e-02, 1.6534e-02, 5.5935e-03, -2.2016e-02, -1.6198e-03, 1.5000e-02, 2.6743e-03, -3.6234e-03, -3.1462e-02, 1.5911e-02, 1.7786e-02, 5.5933e-03, 2.9720e-02, -1.9199e-02, 2.8142e-02, -2.6229e-02, 7.6062e-03, -3.1366e-02, 2.4301e-02, 3.1553e-02, -4.3286e-03, 3.3555e-02, 1.5849e-02, -5.0726e-03, -2.1366e-02, 3.5055e-02, 3.1942e-02, 3.3061e-03, -1.9475e-03, -1.4672e-02, -1.5711e-02, 3.0720e-02, -3.1663e-03, -9.8440e-03, 5.2348e-03, 1.3381e-02, 3.8120e-03, 4.5848e-03, -3.5209e-03, 4.3512e-03, 1.7837e-02, 2.7730e-02, -2.7186e-02, 2.6336e-02, 1.1688e-02, -6.3765e-03, -1.6544e-02, -1.3445e-02, -2.4220e-02, -1.9764e-02, 3.1500e-02, 2.8748e-04, 1.0275e-02, 3.2774e-04, -1.1330e-02, 9.7137e-03, -2.1778e-02, 2.5887e-02, 2.8984e-02, -2.4236e-02, 3.2555e-02, -4.7531e-05, 3.4844e-02, 1.9684e-02, -1.7230e-02, -2.8936e-02, 3.4231e-03, -1.8061e-04, 2.5501e-02, -2.3185e-02, 3.4177e-02, 7.2736e-05, 5.8774e-03, 2.2466e-02, -5.0424e-03, -3.0295e-02, -8.8438e-03, 2.7634e-02, 1.6712e-02, -2.8233e-03, 2.8857e-02, -3.3587e-03, 1.3444e-02, 1.9077e-02, 2.2042e-02, 2.3956e-03, -3.8368e-03, -3.1524e-02, 3.2758e-02, 3.1700e-02, 1.5248e-02, 1.2044e-02, 9.6497e-03, -1.7745e-03, -2.8037e-02, 5.2726e-03, 1.2630e-02, -6.0728e-03], requires_grad=True) ###Markdown For custom initialization, we want to modify these tensors in place. These are actually autograd Variables, so we need to get back the actual tensors with `model.fc1.weight.data`. Once we have the tensors, we can fill them with zeros (for biases) or random normal values. ###Code # Set biases to all zeros model.fc1.bias.data.fill_(0) # sample from random normal with standard dev = 0.01 model.fc1.weight.data.normal_(std=0.01) ###Output _____no_output_____ ###Markdown Forward passNow that we have a network, let's see what happens when we pass in an image. ###Code # Grab some data dataiter = iter(trainloader) images, labels = dataiter.next() # Resize images into a 1D vector, new shape is (batch size, color channels, image pixels) images.resize_(64, 1, 784) # or images.resize_(images.shape[0], 1, 784) to automatically get batch size # Forward pass through the network img_idx = 0 ps = model.forward(images[img_idx,:]) img = images[img_idx] helper.view_classify(img.view(1, 28, 28), ps) ###Output _____no_output_____ ###Markdown As you can see above, our network has basically no idea what this digit is. It's because we haven't trained it yet, all the weights are random! Using `nn.Sequential`PyTorch provides a convenient way to build networks like this where a tensor is passed sequentially through operations, `nn.Sequential` ([documentation](https://pytorch.org/docs/master/nn.htmltorch.nn.Sequential)). Using this to build the equivalent network: ###Code # Hyperparameters for our network input_size = 784 hidden_sizes = [128, 64] output_size = 10 # Build a feed-forward network model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]), nn.ReLU(), nn.Linear(hidden_sizes[0], hidden_sizes[1]), nn.ReLU(), nn.Linear(hidden_sizes[1], output_size), nn.Softmax(dim=1)) print(model) # Forward pass through the network and display output images, labels = next(iter(trainloader)) images.resize_(images.shape[0], 1, 784) ps = model.forward(images[0,:]) helper.view_classify(images[0].view(1, 28, 28), ps) ###Output Sequential( (0): Linear(in_features=784, out_features=128, bias=True) (1): ReLU() (2): Linear(in_features=128, out_features=64, bias=True) (3): ReLU() (4): Linear(in_features=64, out_features=10, bias=True) (5): Softmax(dim=1) ) ###Markdown The operations are availble by passing in the appropriate index. For example, if you want to get first Linear operation and look at the weights, you'd use `model[0]`. ###Code print(model[0]) model[0].weight ###Output Linear(in_features=784, out_features=128, bias=True) ###Markdown You can also pass in an `OrderedDict` to name the individual layers and operations, instead of using incremental integers. Note that dictionary keys must be unique, so _each operation must have a different name_. ###Code from collections import OrderedDict model = nn.Sequential(OrderedDict([ ('fc1', nn.Linear(input_size, hidden_sizes[0])), ('relu1', nn.ReLU()), ('fc2', nn.Linear(hidden_sizes[0], hidden_sizes[1])), ('relu2', nn.ReLU()), ('output', nn.Linear(hidden_sizes[1], output_size)), ('softmax', nn.Softmax(dim=1))])) model ###Output _____no_output_____ ###Markdown Now you can access layers either by integer or the name ###Code print(model[0]) print(model.fc1) ###Output Linear(in_features=784, out_features=128, bias=True) Linear(in_features=784, out_features=128, bias=True)
Beginners_Guide_Math_LinAlg/Math/I_05_LU_decomposition_of_A.ipynb	###Markdown LU Decomposition+ This notebook is part of lecture 4 Factorization into LU in the OCW MIT course 18.06 [1]+ Created by me, Dr Juan H Klopper + Head of Acute Care Surgery + Groote Schuur Hospital + University Cape Town + Email me with your thoughts, comments, suggestions and corrections Linear Algebra OCW MIT18.06 IPython notebook [2] study notes by Dr Juan H Klopper is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.+ [1] OCW MIT 18.06+ [2] Fernando Pérez, Brian E. Granger, IPython: A System for Interactive Scientific Computing, Computing in Science and Engineering, vol. 9, no. 3, pp. 21-29, May/June 2007, doi:10.1109/MCSE.2007.53. URL: http://ipython.org ###Code from IPython.core.display import HTML, Image css_file = 'style.css' HTML(open(css_file, 'r').read()) import numpy as np from sympy import * import matplotlib.pyplot as plt import seaborn as sns from IPython.display import Image from warnings import filterwarnings init_printing(use_latex = 'mathjax') %matplotlib inline filterwarnings('ignore') ###Output _____no_output_____ ###Markdown LU decomposition of a matrix A * We will decompose the matrix A into and upper and lower triangular matrix, such that multiplying these will result back into A$$ A = LU $$ Turning the matrix of coefficients into Upper triangular form * Consider the following matrix of coefficients$$ \begin{bmatrix} 1 & -2 & 1 \\ 3 & 2 & -2 \\ 6 & -1 & -1 \end{bmatrix} $$* Successive elementary row operation follow * Which is nothing other than matrix multiplication of the elementary matrices * An elementary matrix is an identity matrix on which one elementary row operation was performed ###Code A = Matrix([[1, -2, 1], [3, 2, -2], [6, -1, -1]]) A eye(3) ###Output _____no_output_____ ###Markdown * We have to get a -3 in the first pivot (the 1 in row 1, column 1) to get rid of the 3 in position row 2, column 1 (we call the resulting matrix E21, referring to the row 2, column 1)* Then we add the new row 1 to row two Row one of the identity matrix is then (-3,0,0) (but we leave it (1,0,0) in E21) and adding this to row 2 leaves (-3,1,0) ###Code E21 = Matrix([[1, 0, 0], [-3, 1, 0], [0, 0, 1]]) E21 E21 * A # The resulting matrix after multiplication by E21 ###Output _____no_output_____ ###Markdown * We do the same to get rid of the 6 in row 3, column 1 * Multiplying row 1 (of the identity matrix) by -6 and adding this new row to row 3 (but again leaving row 1 as (1,0,0) in E31) ###Code E31 = Matrix([[1, 0, 0], [0, 1, 0], [-6, 0, 1]]) E31 E31 * E21 * A # This got rid of the leading 6 in row 3 ###Output _____no_output_____ ###Markdown * Now the 8 in row 2, column 2 is the pivot and we need to get rid of the 11 in row 3, column 2* Unfortunately we have an 8 and an 11 to deal with* We will have to do two elementary row operations * -11 times row 2 of the identity matrix (0,-11,0) * Added to 8 times row 3 (0,0,8) &8756; (0,-11,8) ###Code E32 = Matrix([[1, 0 , 0], [0, 1, 0], [0, -11, 8]]) E32 U = E32 * E31 * E21 * A U # We call is U for upper triangular ###Output _____no_output_____ ###Markdown * The matrix is now in upper triangular form$$ \left( { E }_{ 32 } \right) \left( { E }_{ 31 } \right) \left( { E }_{ 21 } \right) A=U $$ Calculating the Lower triangular from * Note, to reverse this process we would have to do the following:$$ { \left( { E }_{ 21 } \right) }^{ -1 }{ \left( { E }_{ 31 } \right) }^{ -1 }{ \left( { E }_{ 32 } \right) }^{ -1 }\left( { E }_{ 32 } \right) \left( { E }_{ 31 } \right) \left( { E }_{ 21 } \right) A=A $$* The inverse of a matrix can be calculated using the sympy method .inv() * We can check this with a Boolean request ###Code E21.inv() * E31.inv() * E32.inv() * E32 * E31 * E21 * A == A # The Boolean double equal signs asks: Is the # left-hand side equal to the right-hand side? ###Output _____no_output_____ ###Markdown * Indeed, we will be back with the identity matrix just multiplying the inverse elementary matrices and the elementary matrices ###Code E21.inv() * E31.inv() * E32.inv() * E32 * E31 * E21 ###Output _____no_output_____ ###Markdown * Multiplying the inverse elementary matrices on the left, must also have it happen on the right$$ { \left( { E }_{ 21 } \right) }^{ -1 }{ \left( { E }_{ 31 } \right) }^{ -1 }{ \left( { E }_{ 32 } \right) }^{ -1 }\left( { E }_{ 32 } \right) \left( { E }_{ 31 } \right) \left( { E }_{ 21 } \right) A={ \left( { E }_{ 21 } \right) }^{ -1 }{ \left( { E }_{ 31 } \right) }^{ -1 }{ \left( { E }_{ 32 } \right) }^{ -1 }U $$ * The multiplication of these inverse elementary matrices is lower triangular* We can call in L$$ A=LU $$ ###Code L = E21.inv() * E31.inv() * E32.inv() L A == L * U # Checking this with a Boolean question A, L * U # They are identical ###Output _____no_output_____ ###Markdown Doing this in one go using sympy ###Code L, U, _ = A.LUdecomposition() L U # Note the difference from the U above L * U # Back to A ###Output _____no_output_____ ###Markdown What's special about L? * This only works when no row interchange happens* It also actually only works when doing the conventional subtracting the scalar multiplication of a row from another row, leaving the positive scalar as opposed to the negatives I use, allowing me to add the two rows (as opposed to subtraction)* Note the 3 (in row 2, column 1) and the 6 (in row 3, column 1)* They are the row multiplications we have to do for E21 and E31* The &185;&185; / &8328; is what we did for E32 (we just did it in two steps so as not to use fractions) Row exchanges * We have to allow row exchanges if the pivot contains a zero * For an example, from a 3&215;3 identity matrix we could have: ###Code eye(3) ###Output _____no_output_____ ###Markdown * Exchanging rows one and two would be: ###Code Matrix([[0, 1, 0], [1, 0, 0], [0, 0, 1]]) A, Matrix([[0, 1, 0], [1, 0, 0], [0, 0, 1]]) * A # Showing row exchange ###Output _____no_output_____ ###Markdown * How many permutations of row exchanges are there?$$ {n!} $$ * In a 3&215;3 matrix there are 3! = 6 permutations * Multiplying any of them will result in one of the 6 * They are inverses of each other * The inverse are the transposes* For 4&215;4 there are 4! = 24 Example problems Example problem 01 * Perform LU decomposition of:$$ \begin{bmatrix} 1 & 0 & 1 \\ a & a & a \\ b & b & a \end{bmatrix} $$* For which values of a and b does L and U exist? Solution ###Code a, b = symbols('a b') A = Matrix([[1, 0, 1], [a, a, a], [b, b, a]]) A L,U, _ = A.LUdecomposition() L, U ###Output _____no_output_____ ###Markdown * Checking ###Code L * U == A ###Output _____no_output_____ ###Markdown * For existence: * a &8800; 0* It's easy to see why, since if a equals zero, we will have a zero in a pivot position and we will have to do row exchange, which is not allowed for LU-decomposition Hints and tips ###Code E21, E21.inv() # To take the inverse of an elementary matrix, simply change the sign of the off-diagonal elements and # multiply each element by 1 over the determinant # The determinant is easy to do for these n = 3 square matrices, since the top row is (1,0,0) E31, E31.inv() E32, E32.inv() ###Output _____no_output_____
Universal Sentence Encoder.ipynb	###Markdown Based on https://tfhub.dev/google/universal-sentence-encoder-large/5 ###Code import numpy as np import tensorflow as tf import tensorflow_hub as hub tf.get_logger().setLevel('ERROR') # load the model embed = hub.load("/Users/nicholasdinicola/Desktop/ASTON/Dissertation /universal-sentence-encoder-large_5") texts = [ "shares crashed", "stock tumbled", "shares popped", "stock jumped" ] embeddings = embed(texts) print(embeddings) ###Output tf.Tensor( [[ 0.03329532 -0.02210531 0.03445317 ... -0.03690788 0.01125785 0.03272377] [ 0.01449671 -0.03980014 0.01547467 ... -0.07442201 0.01560574 -0.03663752] [ 0.00608954 -0.03756524 0.01019685 ... -0.07720214 0.02829976 0.02770101] [ 0.05248757 -0.06430706 -0.01321932 ... -0.09114028 -0.02422299 0.04228854]], shape=(4, 512), dtype=float32) ###Markdown SimilaritySince the values are normalized, the inner product of encodings can be treated as a similarity matrix. ###Code sim_matrix = np.inner(embeddings, embeddings) sim_matrix for i, t in enumerate(texts): print(t) most_similar_idx = (-sim_matrix[i]).argsort()[1:2][0] print(">", texts[most_similar_idx]) print("-"10) texts = [ "After the earnings report the stock crashed", "After the earnings report the tumbled", "After the earnings report the popped", "After the earnings report the jumped" ] embeddings = embed(texts) sim_matrix = np.inner(embeddings, embeddings) for i, t in enumerate(texts): print(t) most_similar_idx = (-sim_matrix[i]).argsort()[1:2][0] print(">", texts[most_similar_idx]) print("-"10) ###Output After the earnings report the stock crashed > After the earnings report the tumbled ---------- After the earnings report the tumbled > After the earnings report the jumped ---------- After the earnings report the popped > After the earnings report the tumbled ---------- After the earnings report the jumped > After the earnings report the tumbled ----------
01-intro-pytorch/bow_pytorch.ipynb	###Markdown ###Code from google.colab import drive drive.mount('/gdrive') %cd "/gdrive/MyDrive/Colab Notebooks/git/nn4nlp-code/01-intro-pytorch/" %pwd !ls ###Output bow.py cbow.py model.py bow-pytorch.ipynb deep_cbow.py new_file_add_on_colab
notebooks/fig5-diet-cron/analysis-caloric-restriction-dbbact.ipynb	###Markdown Load the data ###Code # supress warning about samples without metadata ca.set_log_level('ERROR') dat=ca.read_amplicon('./all.cron.biom','./map.cron.txt',normalize=10000,min_reads=1000) ca.set_log_level('INFO') ###Output _____no_output_____ ###Markdown remove reverse read sequences ###Code badseqs=[] for cseq in dat.feature_metadata.index.values: if cseq[:2]=='CC': badseqs.append(cseq) print('%d bad sequences out of %d'% (len(badseqs), len(dat.feature_metadata))) dat=dat.filter_ids(badseqs,negate=True) datc=dat.cluster_features(10) ###Output 2021-06-06 14:05:43 INFO After filtering, 1980 remain. ###Markdown Get just the human samples ###Code hum=datc.filter_samples('type','Hum').cluster_features(10) hum=hum.sort_samples('BMI category') ###Output _____no_output_____ ###Markdown let's look at the data ###Code cu.splot(hum,'diet',barx_fields=['BMI category']) ###Output creating logger ###Markdown Identify differentially abundant bacteria The significance level ###Code alpha=0.01 ###Output _____no_output_____ ###Markdown Only compare lean individuals, and choose one random sample per individual if more than one exists ###Code tt=hum.filter_samples('BMI category','lean') # tt=tt.aggregate_by_metadata('human_donor',agg='random') tt=tt.downsample('human_donor',axis='s',keep=1,random_seed=2018) ###Output _____no_output_____ ###Markdown Number of samples in each group ###Code tt.sample_metadata['diet'].value_counts() ###Output _____no_output_____ ###Markdown Get the bacteria which are different ###Code dd=tt.diff_abundance('diet','CRON','AMER',random_seed=2018, alpha=alpha) ###Output 2021-06-06 14:05:54 INFO 99 samples with both values 2021-06-06 14:05:54 INFO After filtering, 1332 remain. 2021-06-06 14:05:54 INFO 33 samples with value 1 (['CRON']) 2021-06-06 14:05:55 INFO number of higher in CRON: 141. number of higher in AMER : 28. total 169 ###Markdown What we get ###Code f=dd.sort_samples('diet').plot(sample_field='diet',gui='jupyter',barx_fields=['diet']) f.save_figure('./caloric-restriction-diff-bact.pdf') ###Output _____no_output_____ ###Markdown The rotated heatmaps ###Code import numpy as np # scale 100, sort by subject tt=dd.normalize(100).sort_samples('diet') # lets flip the order tt=tt.reorder(np.arange(len(tt.feature_metadata)-1,0,-1), axis='f') # and rotate tt=rotate_exp(tt) tt=flip_data(tt,'s') tt=flip_data(tt,'f') f=tt.plot(feature_field=None,gui='jupyter',clim=[0,100]) f.save_figure('./caloric-restriction-diff-bact-rotated.pdf') ###Output _____no_output_____ ###Markdown have a look to see these are not bmi related (even though we compared only to lean bmi) ###Code f=hum.filter_ids(dd.feature_metadata.index).sort_samples('diet').plot(sample_field='diet',gui='qt5',barx_fields=['BMI category']) ###Output _____no_output_____ ###Markdown Plot the enriched terms the dbBact release to use ###Code # max_id = 3925 # dbbact release 10-20 max_id = 6237 # dbbact release 2021.05 ###Output _____no_output_____ ###Markdown The number of terms to show ###Code numterms=6 db=ca.database._get_database_class('dbbact') import matplotlib matplotlib.rc('ytick', labelsize=15) f,res = dd.plot_diff_abundance_enrichment(max_show=numterms,ignore_exp=True, use_term_pairs=False, colors=['green','red'], num_results_needed=numterms, min_appearances=2, random_seed=2018, max_id=max_id) f.set_xlim([-1,1]) f.figure.set_size_inches(6.3,2) f.figure # save the csv table of the terms res.feature_metadata.to_csv('./terms.csv') f.figure.savefig('caloric-restriction-diff-terms.pdf') f.figure.savefig('caloric-restriction-diff-terms.svg') # save the tsv table of the terms res.feature_metadata.to_csv('./terms-list.tsv',sep='\t') ###Output _____no_output_____ ###Markdown Create also the full enriched terms list for the supplementary tablewe use min_appearances=1 so we'll get even the terms enriched in only 1 experiment (as opposed to the bar plot where we showed only the terms enriched in 2 experiments ###Code positive = dd.feature_metadata['_calour_stat'] > 0 positive = dd.feature_metadata.index.values[positive.values] enriched, term_features, features = dd.enrichment(features=positive, dbname='dbbact', ignore_exp=True, random_seed=2018,max_id=max_id, min_appearances=1) enriched.to_csv('./all-enriched-terms.tsv',sep='\t') ###Output _____no_output_____ ###Markdown And the B/W per-term heatmap ###Code tres=flip_data(res,'f') tres=flip_data(tres,'s') f=tres.plot(gui='jupyter',clim=[0,500], feature_field='term',yticklabel_kwargs={'rotation':0},yticklabel_len=35, cmap='Greys',norm=None) f.figure.set_size_inches(6.3,2) f.figure f.save_figure('./caloric-restriction-diff-terms-heatmap.pdf') f=tres.plot(gui='jupyter',clim=[0,500], cmap='Greys',norm=None) f.save_figure('./caloric-restriction-diff-terms-heatmap-no-labels.pdf') ###Output _____no_output_____ ###Markdown The BMI term zoom ###Code db=ca.database._get_database_class('dbbact') %matplotlib inline f=db.plot_term_venn_all('low bmi',dd,max_id=max_id,use_exact=True) f.savefig('./venn-low-bmi.pdf') f=db.plot_term_venn_all('high bmi',dd,max_id=max_id,use_exact=True) f.savefig('./venn-high-bmi.pdf') ###Output _____no_output_____ ###Markdown Also let's view this as a heatmap for the term annotations for sequences in both groups ###Code db.show_term_details_diff('low bmi',dd,gui='qt5') ###Output _____no_output_____
colabs/run_sort_tracker_on_colab.ipynb	###Markdown Tracking people in a room using YOLOv5 and Deep SORTOne must use CPU for inference! Part 1. Download github repository and install requirements ###Code !git clone https://github.com/maxmarkov/track_and_count !pip3 install -r track_and_count/requirements.txt --quiet %cd track_and_count/yolov5 !./weights/download_weights.sh %cd .. ###Output /root/track_and_count/yolov5 Downloading https://github.com/ultralytics/yolov5/releases/download/v3.0/yolov5s.pt to weights/yolov5s.pt... 100% 14.5M/14.5M [00:00<00:00, 98.9MB/s] Downloading https://github.com/ultralytics/yolov5/releases/download/v3.0/yolov5m.pt to weights/yolov5m.pt... 100% 41.9M/41.9M [00:00<00:00, 91.6MB/s] Downloading https://github.com/ultralytics/yolov5/releases/download/v3.0/yolov5l.pt to weights/yolov5l.pt... 100% 91.6M/91.6M [00:01<00:00, 92.4MB/s] Downloading https://github.com/ultralytics/yolov5/releases/download/v3.0/yolov5x.pt to weights/yolov5x.pt... 100% 170M/170M [00:01<00:00, 94.4MB/s] /root/track_and_count ###Markdown Part 2. Run tracker on example. ###Code !python3 track_yolov5_sort.py --source example/running.mp4 --weights yolov5/weights/yolov5s.pt --conf 0.4 --max_age 50 --min_hits 10 --iou_threshold 0.3 ###Output _____no_output_____ ###Markdown Download video with inference ###Code from google.colab import files f = 'inference/output/running.mp4' files.download(f) ###Output _____no_output_____
docs/tutorials/10 - Duramat Webinar Simulations.ipynb	###Markdown Duramat Webinar: US NREL Electric Futures 2021This journal simulates the Reference and High Electrification scenarios from Electrification Futures, and comparing to a glass baseline with High bifacial future projection. Installed Capacity considerations from bifacial installations are not considered here.Results from this journal were presented during Duramat's webinar April 2021 – “The Impacts of Module Reliability and Lifetime on PV in the Circular Economy" presented by Teresa Barnes, Silvana Ayala, and Heather Mirletz, NREL. ###Code import os from pathlib import Path testfolder = str(Path().resolve().parent.parent / 'PV_ICE' / 'TEMP') # Another option using relative address; for some operative systems you might need '/' instead of '\' # testfolder = os.path.abspath(r'..\..\PV_DEMICE\TEMP') print ("Your simulation will be stored in %s" % testfolder) MATERIALS = ['glass','silver','silicon', 'copper','aluminium_frames'] MATERIAL = MATERIALS[0] MODULEBASELINE = r'..\baselines\ElectrificationFutures_2021\baseline_modules_US_NREL_Electrification_Futures_2021_basecase.csv' MODULEBASELINE_High = r'..\baselines\ElectrificationFutures_2021\baseline_modules_US_NREL_Electrification_Futures_2021_LowREHighElec.csv' import PV_ICE import matplotlib.pyplot as plt import pandas as pd import numpy as np PV_ICE.__version__ plt.rcParams.update({'font.size': 22}) plt.rcParams['figure.figsize'] = (12, 5) r1 = PV_ICE.Simulation(name='Simulation1', path=testfolder) r1.createScenario(name='base', file=MODULEBASELINE) for mat in range (0, len(MATERIALS)): MATERIALBASELINE = r'..\baselines\baseline_material_'+MATERIALS[mat]+'.csv' r1.scenario['base'].addMaterial(MATERIALS[mat], file=MATERIALBASELINE) r1.createScenario(name='high', file=MODULEBASELINE_High) for mat in range (0, len(MATERIALS)): MATERIALBASELINE = r'..\baselines\baseline_material_'+MATERIALS[mat]+'.csv' r1.scenario['high'].addMaterial(MATERIALS[mat], file=MATERIALBASELINE) r2 = PV_ICE.Simulation(name='bifacialTrend', path=testfolder) r2.createScenario(name='base', file=MODULEBASELINE) MATERIALBASELINE = r'..\baselines\PVSC_2021\baseline_material_glass_bifacialTrend.csv' r2.scenario['base'].addMaterial('glass', file=MATERIALBASELINE) for mat in range (1, len(MATERIALS)): MATERIALBASELINE = r'..\baselines\baseline_material_'+MATERIALS[mat]+'.csv' r2.scenario['base'].addMaterial(MATERIALS[mat], file=MATERIALBASELINE) r2.createScenario(name='high', file=MODULEBASELINE_High) MATERIALBASELINE = r'..\baselines\PVSC_2021\baseline_material_glass_bifacialTrend.csv' r2.scenario['high'].addMaterial('glass', file=MATERIALBASELINE) for mat in range (1, len(MATERIALS)): MATERIALBASELINE = r'..\baselines\baseline_material_'+MATERIALS[mat]+'.csv' r2.scenario['high'].addMaterial(MATERIALS[mat], file=MATERIALBASELINE) IRENA= False ELorRL = 'EL' if IRENA: if ELorRL == 'RL': weibullInputParams = {'alpha': 5.3759} # Regular-loss scenario IRENA if ELorRL == 'EL': weibullInputParams = {'alpha': 2.49} # Regular-loss scenario IRENA r1.calculateMassFlow(weibullInputParams=weibullInputParams, weibullAlphaOnly=True) r2.calculateMassFlow(weibullInputParams=weibullInputParams, weibullAlphaOnly=True) title_Method = 'Irena_'+ELorRL else: r1.calculateMassFlow() r2.calculateMassFlow() title_Method = 'PVICE' ###Output Working on Scenario: base ****************** Finished Area+Power Generation Calculations ==> Working on Material : glass ==> Working on Material : silver ==> Working on Material : silicon ==> Working on Material : copper ==> Working on Material : aluminium_frames Working on Scenario: high **************** Finished Area+Power Generation Calculations ==> Working on Material : glass ==> Working on Material : silver ==> Working on Material : silicon ==> Working on Material : copper ==> Working on Material : aluminium_frames Working on Scenario: base **************** Finished Area+Power Generation Calculations ==> Working on Material : glass ==> Working on Material : silver ==> Working on Material : silicon ==> Working on Material : copper ==> Working on Material : aluminium_frames Working on Scenario: high ****************** Finished Area+Power Generation Calculations ==> Working on Material : glass ==> Working on Material : silver ==> Working on Material : silicon ==> Working on Material : copper ==> Working on Material : aluminium_frames ###Markdown Creating Summary of results ###Code objects = [r1, r2] scenarios = ['base', 'high'] USyearly=pd.DataFrame() keyword='mat_Total_Landfilled' materials = ['glass', 'silicon', 'silver', 'copper', 'aluminium_frames'] # Loop over objects for kk in range(0, len(objects)): obj = objects[kk] # Loop over Scenarios for jj in range(0, len(scenarios)): case = scenarios[jj] for ii in range (0, len(materials)): material = materials[ii] foo = obj.scenario[case].material[material].materialdata[keyword].copy() foo = foo.to_frame(name=material) USyearly["Waste_"+material+'_'+obj.name+'_'+case] = foo[material] filter_col = [col for col in USyearly if (col.startswith('Waste') and col.endswith(obj.name+'_'+case)) ] USyearly['Waste_Module_'+obj.name+'_'+case] = USyearly[filter_col].sum(axis=1) # Converting to grams to Tons. USyearly.head(20) keyword='mat_Total_EOL_Landfilled' materials = ['glass', 'silicon', 'silver', 'copper', 'aluminium_frames'] # Loop over objects for kk in range(0, len(objects)): obj = objects[kk] # Loop over Scenarios for jj in range(0, len(scenarios)): case = scenarios[jj] for ii in range (0, len(materials)): material = materials[ii] foo = obj.scenario[case].material[material].materialdata[keyword].copy() foo = foo.to_frame(name=material) USyearly["Waste_EOL_"+material+'_'+obj.name+'_'+case] = foo[material] filter_col = [col for col in USyearly if (col.startswith('Waste') and col.endswith(obj.name+'_'+case)) ] USyearly['Waste_EOL_Module_'+obj.name+'_'+case] = USyearly[filter_col].sum(axis=1) # Converting to grams to Tons. USyearly.head(20) keyword='mat_Virgin_Stock' materials = ['glass', 'silicon', 'silver', 'copper', 'aluminium_frames'] # Loop over objects for kk in range(0, len(objects)): obj = objects[kk] # Loop over Scenarios for jj in range(0, len(scenarios)): case = scenarios[jj] for ii in range (0, len(materials)): material = materials[ii] foo = obj.scenario[case].material[material].materialdata[keyword].copy() foo = foo.to_frame(name=material) USyearly["VirginStock_"+material+'_'+obj.name+'_'+case] = foo[material] filter_col = [col for col in USyearly if (col.startswith('VirginStock_') and col.endswith(obj.name+'_'+case)) ] USyearly['VirginStock_Module_'+obj.name+'_'+case] = USyearly[filter_col].sum(axis=1) ###Output _____no_output_____ ###Markdown Converting to grams to METRIC Tons. ###Code USyearly = USyearly/1000000 # This is the ratio for Metric tonnes #907185 -- this is for US tons UScum = USyearly.copy() UScum = UScum.cumsum() UScum.head() ###Output _____no_output_____ ###Markdown Adding Installed Capacity to US ###Code keyword='Installed_Capacity_[W]' materials = ['glass', 'silicon', 'silver', 'copper', 'aluminium_frames'] # Loop over SF Scenarios for kk in range(0, len(objects)): obj = objects[kk] # Loop over Scenarios for jj in range(0, len(scenarios)): case = scenarios[jj] foo = obj.scenario[case].data[keyword] foo = foo.to_frame(name=keyword) UScum["Capacity_"+obj.name+'_'+case] = foo[keyword] UScum.tail(20) ###Output _____no_output_____ ###Markdown Mining Capacity ###Code USyearly.index = r1.scenario['base'].data['year'] UScum.index = r1.scenario['base'].data['year'] mining2020_aluminum = 65267000 mining2020_silver = 22260 mining2020_copper = 20000000 mining2020_silicon = 8000000 objects = [r1, r2] scenarios = ['base', 'high'] ###Output _____no_output_____ ###Markdown PLOTTING GALORE ###Code plt.rcParams.update({'font.size': 10}) plt.rcParams['figure.figsize'] = (12, 8) keyw='VirginStock_' materials = ['glass', 'silicon', 'silver', 'copper', 'aluminium_frames'] fig, axs = plt.subplots(1,1, figsize=(4, 6), facecolor='w', edgecolor='k') fig.subplots_adjust(hspace = .3, wspace=.2) # Loop over CASES name2 = 'Simulation1_high' name0 = 'Simulation1_base' # ROW 2, Aluminum and Silicon: g- 4 aluminum k - 1 silicon orange - 3 copper gray - 2 silver axs.plot(USyearly[keyw+materials[2]+'_'+name2]100/mining2020_silver, color = 'gray', linewidth=2.0, label='Silver') axs.fill_between(USyearly.index, USyearly[keyw+materials[2]+'_'+name0]100/mining2020_silver, USyearly[keyw+materials[2]+'_'+name2]100/mining2020_silver, color='gray', lw=3, alpha=.3) axs.plot(USyearly[keyw+materials[1]+'_'+name2]100/mining2020_silicon, color = 'k', linewidth=2.0, label='Silicon') axs.fill_between(USyearly.index, USyearly[keyw+materials[1]+'_'+name0]100/mining2020_silicon, USyearly[keyw+materials[1]+'_'+name2]100/mining2020_silicon, color='k', lw=3, alpha=.5) axs.plot(USyearly[keyw+materials[4]+'_'+name2]100/mining2020_aluminum, color = 'g', linewidth=2.0, label='Aluminum') axs.fill_between(USyearly.index, USyearly[keyw+materials[4]+'_'+name0]100/mining2020_aluminum, USyearly[keyw+materials[4]+'_'+name2]100/mining2020_aluminum, color='g', lw=3, alpha=.3) axs.plot(USyearly[keyw+materials[3]+'_'+name2]100/mining2020_copper, color = 'orange', linewidth=2.0, label='Copper') axs.fill_between(USyearly.index, USyearly[keyw+materials[3]+'_'+name0]100/mining2020_copper, USyearly[keyw+materials[3]+'_'+name2]100/mining2020_copper, color='orange', lw=3, alpha=.3) axs.set_xlim([2020,2050]) axs.legend() #axs.set_yscale('log') #axs.set_ylabel('Virgin material needs as a percentage of 2020 global mining production capacity [%]') fig.savefig(title_Method+' Fig_1x1_MaterialNeeds Ratio to Production_NREL2018.png', dpi=600) plt.rcParams.update({'font.size': 15}) plt.rcParams['figure.figsize'] = (15, 8) keyw='VirginStock_' materials = ['glass', 'silicon', 'silver', 'copper', 'aluminium_frames'] f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) ######################## # SUBPLOT 1 ######################## ####################### # loop plotting over scenarios name2 = 'Simulation1_high' name0 = 'Simulation1_base' # SCENARIO 1 ************* modulemat = (USyearly[keyw+materials[0]+'_'+name0]+USyearly[keyw+materials[1]+'_'+name0]+ USyearly[keyw+materials[2]+'_'+name0]+USyearly[keyw+materials[3]+'_'+name0]+ USyearly[keyw+materials[4]+'_'+name0]) glassmat = (USyearly[keyw+materials[0]+'_'+name0]) modulemat = modulemat/1000000 glassmat = glassmat/1000000 a0.plot(USyearly.index, modulemat, 'k.', linewidth=5, label='S1: '+name0+' module mass') a0.plot(USyearly.index, glassmat, 'k', linewidth=5, label='S1: '+name0+' glass mass only') a0.fill_between(USyearly.index, glassmat, modulemat, color='k', alpha=0.3, interpolate=True) # SCENARIO 2 *********** modulemat = (USyearly[keyw+materials[0]+'_'+name2]+USyearly[keyw+materials[1]+'_'+name2]+ USyearly[keyw+materials[2]+'_'+name2]+USyearly[keyw+materials[3]+'_'+name2]+ USyearly[keyw+materials[4]+'_'+name2]) glassmat = (USyearly[keyw+materials[0]+'_'+name2]) modulemat = modulemat/1000000 glassmat = glassmat/1000000 a0.plot(USyearly.index, modulemat, 'c.', linewidth=5, label='S2: '+name2+' module mass') a0.plot(USyearly.index, glassmat, 'c', linewidth=5, label='S2: '+name2+' glass mass only') a0.fill_between(USyearly.index, glassmat, modulemat, color='c', alpha=0.3, interpolate=True) a0.legend() a0.set_title('Yearly Virgin Material Needs by Scenario') a0.set_ylabel('Mass [Million Tonnes]') a0.set_xlim([2020, 2050]) a0.set_xlabel('Years') ######################## # SUBPLOT 2 ######################## ####################### # Calculate cumulations2050 = {} for ii in range(0, len(materials)): matcum = [] matcum.append(UScum[keyw+materials[ii]+'_'+name0].loc[2050]) matcum.append(UScum[keyw+materials[ii]+'_'+name2].loc[2050]) cumulations2050[materials[ii]] = matcum dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes dfcumulations2050['bottom1'] = dfcumulations2050['glass'] dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminium_frames'] dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] ## Plot BARS Stuff ind=np.arange(2) width=0.35 # width of the bars. p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') p1 = a1.bar(ind, dfcumulations2050['aluminium_frames'], width, bottom=dfcumulations2050['bottom1']) p2 = a1.bar(ind, dfcumulations2050['silicon'], width, bottom=dfcumulations2050['bottom2']) p3 = a1.bar(ind, dfcumulations2050['copper'], width, bottom=dfcumulations2050['bottom3']) p4 = a1.bar(ind, dfcumulations2050['silver'], width, bottom=dfcumulations2050['bottom4']) a1.yaxis.set_label_position("right") a1.yaxis.tick_right() a1.set_ylabel('Virgin Material Cumulative Needs 2020-2050 [Million Tonnes]') a1.set_xlabel('Scenario') a1.set_xticks(ind, ('S1', 'S2')) #plt.yticks(np.arange(0, 81, 10)) a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'aluminium_frames', 'Silicon','Copper','Silver')) f.tight_layout() f.savefig(title_Method+' Fig_2x1_Yearly Virgin Material Needs by Scenario and Cumulatives_NREL2018.png', dpi=600) print("Cumulative Virgin Needs by 2050 Million Tones by Scenario") dfcumulations2050[['glass','silicon','silver','copper','aluminium_frames']].sum(axis=1) ###Output C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:89: MatplotlibDeprecationWarning: Passing the minor parameter of set_ticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later. ###Markdown Bonus: Bifacial Trend Cumulative Virgin Needs (not plotted, just values) ###Code name2 = 'bifacialTrend_high' name0 = 'bifacialTrend_base' cumulations2050 = {} for ii in range(0, len(materials)): matcum = [] matcum.append(UScum[keyw+materials[ii]+'_'+name0].loc[2050]) matcum.append(UScum[keyw+materials[ii]+'_'+name2].loc[2050]) cumulations2050[materials[ii]] = matcum dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes print("Cumulative Virgin Needs by 2050 Million Tones by Scenario for Bifacial Trend") dfcumulations2050[['glass','silicon','silver','copper','aluminium_frames']].sum(axis=1) ###Output Cumulative Virgin Needs by 2050 Million Tones by Scenario for Bifacial Trend ###Markdown Waste by year ###Code plt.rcParams.update({'font.size': 15}) plt.rcParams['figure.figsize'] = (15, 8) keyw='Waste_' materials = ['glass', 'silicon', 'silver', 'copper', 'aluminium_frames'] f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) ######################## # SUBPLOT 1 ######################## ####################### # loop plotting over scenarios name2 = 'Simulation1_high' name0 = 'Simulation1_base' # SCENARIO 1 *********** modulemat = (USyearly[keyw+materials[0]+'_'+name0]+USyearly[keyw+materials[1]+'_'+name0]+ USyearly[keyw+materials[2]+'_'+name0]+USyearly[keyw+materials[3]+'_'+name0]+ USyearly[keyw+materials[4]+'_'+name0]) glassmat = (USyearly[keyw+materials[0]+'_'+name0]) modulemat = modulemat/1000000 glassmat = glassmat/1000000 a0.plot(USyearly.index, modulemat, 'k.', linewidth=5, label='S1: '+name0+' module mass') a0.plot(USyearly.index, glassmat, 'k', linewidth=5, label='S1: '+name0+' glass mass only') a0.fill_between(USyearly.index, glassmat, modulemat, color='k', alpha=0.3, interpolate=True) # SCENARIO 2 *********** modulemat = (USyearly[keyw+materials[0]+'_'+name2]+USyearly[keyw+materials[1]+'_'+name2]+ USyearly[keyw+materials[2]+'_'+name2]+USyearly[keyw+materials[3]+'_'+name2]+ USyearly[keyw+materials[4]+'_'+name2]) glassmat = (USyearly[keyw+materials[0]+'_'+name2]) modulemat = modulemat/1000000 glassmat = glassmat/1000000 a0.plot(USyearly.index, modulemat, 'c.', linewidth=5, label='S2: '+name2+' module mass') a0.plot(USyearly.index, glassmat, 'c', linewidth=5, label='S2: '+name2+' glass mass only') a0.fill_between(USyearly.index, glassmat, modulemat, color='c', alpha=0.3, interpolate=True) a0.legend() a0.set_title('Yearly Material Waste by Scenario') a0.set_ylabel('Mass [Million Tonnes]') a0.set_xlim([2020, 2050]) a0.set_xlabel('Years') ######################## # SUBPLOT 2 ######################## ####################### # Calculate cumulations2050 = {} for ii in range(0, len(materials)): matcum = [] matcum.append(UScum[keyw+materials[ii]+'_'+name0].loc[2050]) matcum.append(UScum[keyw+materials[ii]+'_'+name2].loc[2050]) cumulations2050[materials[ii]] = matcum dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes dfcumulations2050['bottom1'] = dfcumulations2050['glass'] dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminium_frames'] dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] ## Plot BARS Stuff ind=np.arange(2) width=0.35 # width of the bars. p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') p1 = a1.bar(ind, dfcumulations2050['aluminium_frames'], width, bottom=dfcumulations2050['bottom1']) p2 = a1.bar(ind, dfcumulations2050['silicon'], width, bottom=dfcumulations2050['bottom2']) p3 = a1.bar(ind, dfcumulations2050['copper'], width, bottom=dfcumulations2050['bottom3']) p4 = a1.bar(ind, dfcumulations2050['silver'], width, bottom=dfcumulations2050['bottom4']) a1.yaxis.set_label_position("right") a1.yaxis.tick_right() a1.set_ylabel('Cumulative Waste by 2050 [Million Tonnes]') a1.set_xlabel('Scenario') a1.set_xticks(ind, ('S1', 'S2')) #plt.yticks(np.arange(0, 81, 10)) a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'aluminium_frames', 'Silicon','Copper','Silver')) f.tight_layout() f.savefig(title_Method+' Fig_2x1_Yearly WASTE by Scenario and Cumulatives_NREL2018.png', dpi=600) print("Cumulative Waste by 2050 Million Tones by case") dfcumulations2050[['glass','silicon','silver','copper','aluminium_frames']].sum(axis=1) plt.rcParams.update({'font.size': 15}) plt.rcParams['figure.figsize'] = (15, 8) keyw='Waste_EOL_' materials = ['glass', 'silicon', 'silver', 'copper', 'aluminium_frames'] f, (a0, a1) = plt.subplots(1, 2, gridspec_kw={'width_ratios': [3, 1]}) ######################## # SUBPLOT 1 ######################## ####################### # loop plotting over scenarios name2 = 'Simulation1_high' name0 = 'Simulation1_base' # SCENARIO 1 *********** modulemat = (USyearly[keyw+materials[0]+'_'+name0]+USyearly[keyw+materials[1]+'_'+name0]+ USyearly[keyw+materials[2]+'_'+name0]+USyearly[keyw+materials[3]+'_'+name0]+ USyearly[keyw+materials[4]+'_'+name0]) glassmat = (USyearly[keyw+materials[0]+'_'+name0]) modulemat = modulemat/1000000 glassmat = glassmat/1000000 a0.plot(USyearly.index, modulemat, 'k.', linewidth=5, label='S1: '+name0+' module mass') a0.plot(USyearly.index, glassmat, 'k', linewidth=5, label='S1: '+name0+' glass mass only') a0.fill_between(USyearly.index, glassmat, modulemat, color='k', alpha=0.3, interpolate=True) # SCENARIO 2 ************* modulemat = (USyearly[keyw+materials[0]+'_'+name2]+USyearly[keyw+materials[1]+'_'+name2]+ USyearly[keyw+materials[2]+'_'+name2]+USyearly[keyw+materials[3]+'_'+name2]+ USyearly[keyw+materials[4]+'_'+name2]) glassmat = (USyearly[keyw+materials[0]+'_'+name2]) modulemat = modulemat/1000000 glassmat = glassmat/1000000 a0.plot(USyearly.index, modulemat, 'c.', linewidth=5, label='S2: '+name2+' module mass') a0.plot(USyearly.index, glassmat, 'c', linewidth=5, label='S2: '+name2+' glass mass only') a0.fill_between(USyearly.index, glassmat, modulemat, color='c', alpha=0.3, interpolate=True) a0.legend() a0.set_title('Yearly Material Waste by Scenario') a0.set_ylabel('Mass [Million Tonnes]') a0.set_xlim([2020, 2050]) a0.set_xlabel('Years') ######################## # SUBPLOT 2 ######################## ####################### # Calculate cumulations2050 = {} for ii in range(0, len(materials)): matcum = [] matcum.append(UScum[keyw+materials[ii]+'_'+name0].loc[2050]) matcum.append(UScum[keyw+materials[ii]+'_'+name2].loc[2050]) cumulations2050[materials[ii]] = matcum dfcumulations2050 = pd.DataFrame.from_dict(cumulations2050) dfcumulations2050 = dfcumulations2050/1000000 # in Million Tonnes dfcumulations2050['bottom1'] = dfcumulations2050['glass'] dfcumulations2050['bottom2'] = dfcumulations2050['bottom1']+dfcumulations2050['aluminium_frames'] dfcumulations2050['bottom3'] = dfcumulations2050['bottom2']+dfcumulations2050['silicon'] dfcumulations2050['bottom4'] = dfcumulations2050['bottom3']+dfcumulations2050['copper'] ## Plot BARS Stuff ind=np.arange(2) width=0.35 # width of the bars. p0 = a1.bar(ind, dfcumulations2050['glass'], width, color='c') p1 = a1.bar(ind, dfcumulations2050['aluminium_frames'], width, bottom=dfcumulations2050['bottom1']) p2 = a1.bar(ind, dfcumulations2050['silicon'], width, bottom=dfcumulations2050['bottom2']) p3 = a1.bar(ind, dfcumulations2050['copper'], width, bottom=dfcumulations2050['bottom3']) p4 = a1.bar(ind, dfcumulations2050['silver'], width, bottom=dfcumulations2050['bottom4']) a1.yaxis.set_label_position("right") a1.yaxis.tick_right() a1.set_ylabel('Cumulative EOL Only Waste by 2050 [Million Tonnes]') a1.set_xlabel('Scenario') a1.set_xticks(ind, ('S1', 'S2')) #plt.yticks(np.arange(0, 81, 10)) a1.legend((p0[0], p1[0], p2[0], p3[0], p4[0] ), ('Glass', 'aluminium_frames', 'Silicon','Copper','Silver')) f.tight_layout() f.savefig(title_Method+' Fig_2x1_Yearly EOL Only WASTE by Scenario and Cumulatives_NREL2018.png', dpi=600) print("Cumulative Eol Only Waste by 2050 Million Tones by case") dfcumulations2050[['glass','silicon','silver','copper','aluminium_frames']].sum(axis=1) ###Output C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:89: MatplotlibDeprecationWarning: Passing the minor parameter of set_ticks() positionally is deprecated since Matplotlib 3.2; the parameter will become keyword-only two minor releases later.
.ipynb_checkpoints/MaxCut_Isaac-checkpoint.ipynb	###Markdown This is a quick and inefficient MaxCut prototype using QuTiP ###Code # Number of qubits N= 4 # Define Operators def gen_cij(edge): i,j = edge Id = [qeye(2) for n in range(N)] si_n = tensor(Id) Id[i] = sigmaz() Id[j] = sigmaz() zij = tensor(Id) return 0.5(si_n - zij) def gen_B(): b_op = 0tensor([qeye(2) for j in range(N)]) for i in range(N): Id = [qeye(2) for j in range(N)] Id[i] = sigmax() b_op += tensor(Id) return b_op def gen_init(): init = tensor([basis(2,0) for i in range(N)]) x_all = tensor([hadamard_transform(1) for i in range(N)]) return (x_allinit).unit() ψ_init = gen_init() edges = [[0,1],[1,2],[2,3],[3,0]] C = np.sum(gen_cij(edge) for edge in edges) B = gen_B() C_vals, C_vecs = C.eigenstates() def gen_U(angles): L = len(angles) γs = angles[:int(L/2)] βs = angles[int(L/2):] U = np.prod([(-1jβs[i]B).expm()(-1jγs[i]C).expm() for i in range(int(L/2))]) return U def cost(angles,num_measures=0): """ The cost function of MaxCut QAOA Args: angles (list): The list of angles. The first half contains the γs and the second half has βs. num_measures (int): The number of measurement shots. Returns: float: The negative of the energy of the C expression. """ U_temp = gen_U(angles) ψ_temp = U_tempψ_init if num_measures == 0: energy = -expect(C,ψ_temp) else: sim_counts = np.random.multinomial(num_measures,expect(ket2dm(ψ_temp),C_vecs))/num_measures energy = - np.dot(C_vals,sim_counts) return energy cost([1,2],0) ###Output _____no_output_____ ###Markdown Comparing some optimization routines Single layer Nelder-Mead ###Code def averaged(x): # Given a list of arrays, outputs their average and standard deviation # First, find the maximum length among the arrays. maxlength = 0 for array in x: if len(array) > maxlength: maxlength = len(array) # Pad elements at the end of each arrays, so that all of them have the same length. for array in x: array += [array[-1]] (maxlength - len(array)) # Take the mean and standard deviation. mean = [] std = [] for i in range(maxlength): data = [x[j][i] for j in range(len(x))] mean.append(np.mean(data)) std.append(np.std(data)) return mean, std num_layers = 2 n_measures_list = [100,1000,10000,100000] n_trials = 10 stats_NM = [] def callback_NM(x): # The callback function records the "noiseless" value of the cost function. stats_NM_temp.append(cost(x)) for n_measures in n_measures_list: stats_NM = [] for i in range(n_trials): # Initialize the history array stats_NM_temp = [] # Record the data for n_trials iterations sol_NM = minimize(cost,np.random.rand(2num_layers),callback=callback_NM,args = (n_measures),method='Nelder-Mead') #sol_NM = sparse_minimize(cost, np.random.rand(2num_layers), cutoff=100,samples=50, args= 100) stats_NM.append(stats_NM_temp) # Take the number of average and standard deviation mean, std = averaged(stats_NM) plt.errorbar(x=[i * n_measures for i in range(len(mean))], y=mean, yerr=std) plt.title('n_measures ={}'.format(n_measures)) plt.xlabel('# samples') plt.ylabel('cost') plt.show() for i in range(n_trials): #plt.plot(stats_NM[i],label=f'trial #{i}') plt.plot(stats_NM[i]) plt.legend() plt.xlabel('# steps') plt.ylabel('cost'); end = [] for i in range(len(stats_NM)): end.append(stats_NM[i][:-1]) a=np.hstack(end) plt.hist(a) plt.show() ###Output _____no_output_____ ###Markdown Sparse Optimization. PreliminarySparse optimization works very well! We first begin with a frequency cutoff of $50$. We only sample $30$ data points. This choice is arbitrary. ###Code n_measures_list = [100,1000,10000] n_trials = 10 stats_so = [] n_samples = 30 n_cutoff=50 for n_measures in n_measures_list: stats_so = [] for i in range(n_trials): # Record the data for n_trials iterations x0, value, history = sparse_minimize(cost, np.random.rand(2num_layers), cutoff=n_cutoff,samples=n_samples, args= n_measures) stats_so.append(history) # Take the number of average and standard deviation print("Trial={}".format(i)) mean, std = averaged(stats_so) plt.errorbar(x=[i n_measures * n_samples for i in range(len(mean))], y=mean, yerr=std) plt.title('n_measures ={}'.format(n_measures)) plt.xlabel('# samples') plt.ylabel('cost') plt.show() ###Output itt=19, which=3, cost=-3.914753201908698, x0=[4.0212386 4.64955713 5.52920307 5.02654825]Trial=0 itt=19, which=3, cost=-3.8780348810131997, x0=[2.38761042 4.52389342 0.75398224 1.13097336]Trial=1 itt=19, which=3, cost=-3.814543090814257, x0=[2.136283 4.77522083 2.26194671 4.39822972]Trial=2 itt=19, which=3, cost=-3.856281352264909, x0=[1.25663706 3.89557489 5.15221195 3.64424748]Trial=3 itt=19, which=3, cost=-3.9603930578132873, x0=[1.13097336 0.75398224 5.15221195 5.27787566]Trial=4 itt=19, which=3, cost=-3.7573044510065983, x0=[4.0212386 4.1469023 0.50265482 5.15221195]Trial=5 itt=19, which=3, cost=-3.9984457819530115, x0=[1.13097336 4.0212386 5.15221195 5.27787566]Trial=6 itt=19, which=3, cost=-3.875858935182515, x0=[1.00530965 1.25663706 2.38761042 0.37699112]Trial=7 itt=19, which=3, cost=-3.9618390342343694, x0=[3.89557489 1.50796447 3.89557489 0.37699112]Trial=8 itt=19, which=3, cost=-3.9617078794297225, x0=[3.89557489 1.50796447 3.89557489 2.0106193 ]Trial=9 ###Markdown Sparse optimization. Fixing the total sample size and changing the sample per each data point.Given a fixed number of samples, we vary the number of samples per each data point. With a sample per angle of $100$, one reaches the minimum at ~$200000$ samples, whereas with a sample per angle of $1000$, one reaches the minimum at ~$1300000$ samples. It gets worse with a sample per angle of $10000$. ###Code n_measures_total = 600000 n_measures_list = [100,1000,10000] n_trials = 10 stats_so = [] n_samples = 30 n_cutoff=50 for n_measures in n_measures_list: stats_so = [] for i in range(n_trials): # Record the data for n_trials iterations x0, value, history = sparse_minimize(cost, np.random.rand(2num_layers), cutoff=n_cutoff,samples=n_samples, itt = int(n_measures_total/n_measures/n_samples), args= n_measures) stats_so.append(history) # Take the number of average and standard deviation print("Trial={}".format(i)) mean, std = averaged(stats_so) plt.errorbar(x=[i n_measures * n_samples for i in range(len(mean))], y=mean, yerr=std) plt.title('n_measures ={}'.format(n_measures)) plt.xlabel('# samples') plt.ylabel('cost') plt.show() ###Output itt=199, which=3, cost=-3.863922854877589, x0=[0.62831853 4.90088454 2.26194671 3.64424748]Trial=0 itt=199, which=3, cost=-3.998445781953009, x0=[1.13097336 4.0212386 2.0106193 2.136283 ]Trial=1 itt=199, which=3, cost=-3.9125278140884547, x0=[1.13097336 3.89557489 3.64424748 5.27787566]Trial=2 itt=199, which=3, cost=-3.5872245719211624, x0=[1.00530965 1.63362818 0.87964594 0.25132741]Trial=3 itt=199, which=3, cost=-3.957548690259249, x0=[4.0212386 1.38230077 2.26194671 5.15221195]Trial=4 itt=199, which=3, cost=-3.9911593278919137, x0=[3.89557489 1.63362818 5.52920307 0.37699112]Trial=5 itt=199, which=3, cost=-3.912527814088449, x0=[5.15221195 2.38761042 2.63893783 4.1469023 ]Trial=6 itt=199, which=3, cost=-3.970043616382802, x0=[1.13097336 1.00530965 0.50265482 5.27787566]Trial=7 itt=199, which=3, cost=-3.9765064702886628, x0=[3.89557489 4.77522083 0.75398224 5.15221195]Trial=8 itt=199, which=3, cost=-3.9765064702886543, x0=[2.38761042 1.50796447 2.38761042 4.27256601]Trial=9 ###Markdown Sparse-SPSASame setting. n_cutoff=50, n_samples=30. Not great. ###Code n_measures_list = [100,1000,10000] n_trials = 10 stats_so = [] n_samples = 30 n_cutoff=50 for n_measures in n_measures_list: stats_so = [] for i in range(n_trials): # Record the data for n_trials iterations x0, value, history = sparse_spsa(cost, np.random.rand(2num_layers), cutoff=n_cutoff,samples=n_samples, itt = 50, args= n_measures) stats_so.append(history) # Take the number of average and standard deviation print("Trial={}".format(i)) mean, std = averaged(stats_so) plt.errorbar(x=[i n_measures * n_samples for i in range(len(mean))], y=mean, yerr=std) plt.title('n_measures ={}'.format(n_measures)) plt.xlabel('# samples') plt.ylabel('cost') plt.show() ###Output itt=49, cost=-3.977889414396259, x0=[5.43943588 4.70428669 2.32529753 2.75145479]Trial=0 itt=49, cost=-3.7561854217162844, x0=[5.04161744 5.65814039 2.63007965 4.1683354 ]Trial=1 itt=49, cost=-3.6789989288815748, x0=[2.38056479 1.36102291 5.69567369 2.56902823]Trial=2 itt=49, cost=-3.91368333045663, x0=[0.76202293 1.62948287 3.87813319 3.49089011]Trial=3 itt=49, cost=-3.8754781405016483, x0=[0.63122875 4.89395494 3.86737186 3.63126827]Trial=4 itt=49, cost=-3.9204567200784, x0=[5.26695984 5.18435503 2.689149 2.61946215]Trial=5 itt=49, cost=-3.1476781743534685, x0=[4.534174 0.75466943 2.92977918 3.43053978]Trial=6 itt=49, cost=-3.942996911205269, x0=[0.68858303 1.60588269 0.83740183 1.97229072]Trial=7 itt=49, cost=-3.7798814414875, x0=[2.13981726 5.21380335 5.72601048 5.87773258]Trial=8 itt=49, cost=-3.2186424036900734, x0=[3.66357507 5.79151136 0.44819753 0.82418211]Trial=9 ###Markdown n_cutoff=100, n_samples ###Code n_measures_list = [100,1000,10000] n_trials = 10 stats_so = [] n_samples = 60 n_cutoff=100 for n_measures in n_measures_list: stats_so = [] for i in range(n_trials): # Record the data for n_trials iterations x0, value, history = sparse_spsa(cost, np.random.rand(2num_layers), cutoff=n_cutoff,samples=n_samples, itt = 50, args= n_measures) stats_so.append(history) # Take the number of average and standard deviation print("Trial={}".format(i)) mean, std = averaged(stats_so) plt.errorbar(x=[i n_measures * n_samples for i in range(len(mean))], y=mean, yerr=std) plt.title('n_measures ={}'.format(n_measures)) plt.xlabel('# samples') plt.ylabel('cost') plt.show() ###Output itt=49, cost=-3.944653181757502, x0=[5.46540037 1.66856834 3.83965652 2.75946609]Trial=0 itt=49, cost=-3.961472591382087, x0=[4.25904834 4.09869731 3.6284231 3.64472822]Trial=1 itt=49, cost=-3.942542166005555, x0=[2.04791019 2.06251503 2.68660073 2.60460223]Trial=2 itt=49, cost=-3.9604005348375635, x0=[2.30297516 4.79480474 2.26205358 2.78913436]Trial=3 itt=49, cost=-3.788334595180927, x0=[2.13600564 5.44941645 1.04101855 5.66863069]Trial=4 itt=49, cost=-3.9317123422653477, x0=[5.12083702 5.39123418 1.04986884 4.21217541]Trial=5 itt=49, cost=-3.9549452576982516, x0=[2.45756856 1.5409327 0.74153699 5.89255567]Trial=6 itt=49, cost=-3.794729523752649, x0=[2.09196111 2.11931361 5.6965216 5.6794406 ]Trial=7 itt=49, cost=-3.6935761035660053, x0=[4.10053262 4.1466945 0.72028291 1.95323457]Trial=8 itt=49, cost=-3.9599779307234777, x0=[4.21197455 1.03869489 2.06770273 5.21942071]Trial=9 ###Markdown BFGS It seems that BFGS doesn't work with noise! ###Code stats_BFGS = [] n_measures = 10000 def callback_BFGS(x): stats_BFGS.append(cost(x)) sol_BFGS = minimize(cost,np.random.rand(2num_layers),args = (n_measures),callback=callback_BFGS,method='BFGS') sol_BFGS.x,sol_BFGS.fun #Noisy cost of the optimal solution cost(sol_BFGS.x,0) # Noiseless cost of the optimal solution plt.plot(stats_BFGS) # plt.ylim([-1,-3]) ###Output _____no_output_____ ###Markdown Two layers Nelder-Mead ###Code num_layers = 2 stats_NM = [] n_measures = 10000 def callback_NM(x): # The callback function records the "noiseless" value of the cost function. stats_NM.append(cost(x)) sol_NM = minimize(cost,np.random.rand(2num_layers),callback=callback_NM,args = (n_measures),method='Nelder-Mead') plt.plot(stats_NM) sol_NM.x,sol_NM.fun cost(sol_NM.x,0) # Noiseless cost of the optimal solution stats_BFGS = [] n_measures = 10000 def callback_BFGS(x): stats_BFGS.append(cost(x)) sol_BFGS = minimize(cost,np.random.rand(2num_layers),args = (n_measures),callback=callback_BFGS,method='BFGS') plt.plot(stats_BFGS) sol_BFGS.x,sol_BFGS.fun cost(sol_BFGS.x,0) # Noiseless cost of the optimal solution ###Output _____no_output_____ ###Markdown Comparing with Rigetti's solution ###Code ψ_init = gen_init() angle_list = [2.35394,1.18] #[γ,β] U_mat = gen_U(angle_list) ψ = U_matψ_init energy = expect(C,ψ) print(f"The optimized value is {energy}") ###Output The optimized value is 2.9999608711896224 ###Markdown Plotting the wavefuntion weights ###Code plt.bar(np.arange(16),np.abs(ψ.full()).flatten()) ###Output _____no_output_____
AIND-sudoku/sudoku_sol.ipynb	###Markdown sudoku solution ###Code from utils import * row_units = [cross(r, cols) for r in rows] column_units = [cross(rows, c) for c in cols] square_units = [cross(rs, cs) for rs in ('ABC','DEF','GHI') for cs in ('123','456','789')] diag_unit_01=[a+b for a,b in zip("ABCDEFGHI","123456789")] diag_unit_02=[a+b for a,b in zip("ABCDEFGHI","987654321")] unitlist = row_units + column_units + square_units unitlist.append(diag_unit_01) unitlist.append(diag_unit_02) # TODO: Update the unit list to add the new diagonal units unitlist = unitlist units = dict((s, [u for u in unitlist if s in u]) for s in boxes) peers = dict((s, set(sum(units[s],[]))-set([s])) for s in boxes) def naked_twins(values): potential_twins = [item for item in values.keys() if len(values[item]) == 2] # Collect boxes that have the same elements naked_twins = [[x,y] for x in potential_twins for y in peers[x] if set(values[x])==set(values[y]) ] # For each pair of naked twins, for i in range(len(naked_twins)): box1 = naked_twins[i][0] box2 = naked_twins[i][1] # 1- compute intersection of peers peers1 = set(peers[box1]) peers2 = set(peers[box2]) peers_int = peers1 & peers2 # 2- Delete the two digits in naked twins from all common peers. for peer_val in peers_int: if len(values[peer_val])>2: for rm_val in values[box1]: values = assign_value(values, peer_val, values[peer_val].replace(rm_val,'')) return values """Eliminate values using the naked twins strategy. Parameters ---------- values(dict) a dictionary of the form {'box_name': '123456789', ...} Returns ------- dict The values dictionary with the naked twins eliminated from peers Notes ----- Your solution can either process all pairs of naked twins from the input once, or it can continue processing pairs of naked twins until there are no such pairs remaining -- the project assistant test suite will accept either convention. However, it will not accept code that does not process all pairs of naked twins from the original input. (For example, if you start processing pairs of twins and eliminate another pair of twins before the second pair is processed then your code will fail the PA test suite.) The first convention is preferred for consistency with the other strategies, and because it is simpler (since the reduce_puzzle function already calls this strategy repeatedly). """ # TODO: Implement this function! raise NotImplementedError def eliminate(values): for item in values.keys(): if len(values[item])==1: val=values[item] for peer in peers[item]: if peer in values: values[peer]=values[peer].replace(val,"") """Apply the eliminate strategy to a Sudoku puzzle The eliminate strategy says that if a box has a value assigned, then none of the peers of that box can have the same value. Parameters ---------- values(dict) a dictionary of the form {'box_name': '123456789', ...} Returns ------- dict The values dictionary with the assigned values eliminated from peers """ # TODO: Copy your code from the classroom to complete this function return values raise NotImplementedError def only_choice(values): for unit in unitlist: for i in "123456789": s=[] for box in unit: if i in values[box]: s.append(box) if len(s)==1: values[s[0]]=i """Apply the only choice strategy to a Sudoku puzzle The only choice strategy says that if only one box in a unit allows a certain digit, then that box must be assigned that digit. Parameters ---------- values(dict) a dictionary of the form {'box_name': '123456789', ...} Returns ------- dict The values dictionary with all single-valued boxes assigned Notes ----- You should be able to complete this function by copying your code from the classroom """ # TODO: Copy your code from the classroom to complete this function return values raise NotImplementedError def reduce_puzzle(values): stalled = False while not stalled: # Check how many boxes have a determined value solved_values_before = len([box for box in values.keys() if len(values[box]) == 1]) values=eliminate(values) values=only_choice(values) # Your code here: Use the Eliminate Strategy # Your code here: Use the Only Choice Strategy # Check how many boxes have a determined value, to compare solved_values_after = len([box for box in values.keys() if len(values[box]) == 1]) # If no new values were added, stop the loop. stalled = solved_values_before == solved_values_after # Sanity check, return False if there is a box with zero available values: if len([box for box in values.keys() if len(values[box]) == 0]): return False return values raise NotImplementedError def search(values): values = reduce_puzzle(values) if values is False: return False ## Failed earlier if all(len(values[s]) == 1 for s in boxes): return values ## Solved! # Choose one of the unfilled squares with the fewest possibilities n,s = min((len(values[s]), s) for s in boxes if len(values[s]) > 1) # Now use recurrence to solve each one of the resulting sudokus, and print(n,s) for value in values[s]: new_sudoku = values.copy() new_sudoku[s] = value attempt = search(new_sudoku) if attempt: return attempt return values raise NotImplementedError def solve(grid): """Find the solution to a Sudoku puzzle using search and constraint propagation Parameters ---------- grid(string) a string representing a sudoku grid. Ex. '2.............62....1....7...6..8...3...9...7...6..4...4....8....52.............3' Returns ------- dict or False The dictionary representation of the final sudoku grid or False if no solution exists. """ values = grid2values(grid) values = search(values) return values if __name__ == "__main__": diag_sudoku_grid = '2.7...........62....1....7...6..8...3...9...7...6..4...4....8....52.............3' display(grid2values(diag_sudoku_grid)) result = solve(diag_sudoku_grid) display(result) try: import PySudoku PySudoku.play(grid2values(diag_sudoku_grid), result, history) except SystemExit: pass except: print('We could not visualize your board due to a pygame issue. Not a problem! It is not a requirement.') ###Output 2 123456789 7 \|123456789 123456789 123456789 \|123456789 123456789 123456789 123456789 123456789 123456789 \|123456789 123456789 6 \| 2 123456789 123456789 123456789 123456789 1 \|123456789 123456789 123456789 \|123456789 7 123456789 ------------------------------+------------------------------+------------------------------ 123456789 123456789 6 \|123456789 123456789 8 \|123456789 123456789 123456789 3 123456789 123456789 \|123456789 9 123456789 \|123456789 123456789 7 123456789 123456789 123456789 \| 6 123456789 123456789 \| 4 123456789 123456789 ------------------------------+------------------------------+------------------------------ 123456789 4 123456789 \|123456789 123456789 123456789 \| 8 123456789 123456789 123456789 123456789 5 \| 2 123456789 123456789 \|123456789 123456789 123456789 123456789 123456789 123456789 \|123456789 123456789 123456789 \|123456789 123456789 3 2 6 7 \|9 4 5 \|3 8 1 8 5 3 \|7 1 6 \|2 4 9 4 9 1 \|8 2 3 \|5 7 6 ------+------+------ 5 7 6 \|4 3 8 \|1 9 2 3 8 4 \|1 9 2 \|6 5 7 1 2 9 \|6 5 7 \|4 3 8 ------+------+------ 6 4 2 \|3 7 9 \|8 1 5 9 3 5 \|2 8 1 \|7 6 4 7 1 8 \|5 6 4 \|9 2 3 We could not visualize your board due to a pygame issue. Not a problem! It is not a requirement.
Jupyter Notebook/.ipynb_checkpoints/svm-checkpoint.ipynb	###Markdown Read the CSV and Perform Basic Data Cleaning ###Code df = pd.read_csv("exoplanet_data.csv") # Drop the null columns where all values are null df = df.dropna(axis='columns', how='all') # Drop the null rows df = df.dropna() df.shape df.head() df.describe() df.keys() ###Output _____no_output_____ ###Markdown Select your features (columns) ###Code # Set features. This will also be used as your x values. #selected_features = df[['koi_disposition', 'koi_fpflag_nt', 'koi_fpflag_ss', 'koi_fpflag_co', # 'koi_fpflag_ec', 'koi_period', 'koi_time0bk', 'koi_duration', 'ra','dec',]] target = df["koi_disposition"] data = df.drop("koi_disposition", axis=1) feature_names = data.columns data.head() ###Output _____no_output_____ ###Markdown Create a Train Test SplitUse `koi_disposition` for the y values ###Code from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(data, target, random_state=42) X_train.head() ###Output _____no_output_____ ###Markdown Pre-processingScale the data using the MinMaxScaler and perform some feature selection ###Code from sklearn.preprocessing import MinMaxScaler # Scale your data X_scaler = MinMaxScaler().fit(X_train) X_train_scaled = X_scaler.transform(X_train) X_test_scaled = X_scaler.transform(X_test) # testing X_test_scaled ###Output _____no_output_____ ###Markdown Train the Model ###Code from sklearn.preprocessing import MinMaxScaler X_minmax = MinMaxScaler().fit(X_train) X_train_minmax = X_minmax.transform(X_train) X_test_minmax = X_minmax.transform(X_test) from sklearn.svm import SVC model = SVC(kernel='linear') model.fit(X_train_scaled, y_train) print(f"Training Data Score: {model.score(X_train_scaled, y_train)}") print(f"Testing Data Score: {model.score(X_test_scaled, y_test)}") plt.figure(figsize=(10,7)) sns.heatmap(df.corr(),annot=True) ###Output _____no_output_____ ###Markdown Hyperparameter TuningUse `GridSearchCV` to tune the model's parameters ###Code # Create the GridSearchCV model from sklearn.model_selection import GridSearchCV param_grid = {'C': [1, 5, 10], 'gamma': [0.0001, 0.0005, 0.001]} grid = GridSearchCV(model, param_grid, verbose=3) # Train the model with GridSearch grid.fit(X_train_scaled, y_train) print(grid.best_params_) print(grid.best_score_) grid.score(X_train_scaled, y_train) predictions = grid.predict(X_test_scaled) print(predictions) from sklearn.metrics import classification_report print(classification_report(y_test, predictions)) ###Output _____no_output_____ ###Markdown Save the Model ###Code # save your model by updating "your_name" with your name # and "your_model" with your model variable # be sure to turn this in to BCS # if joblib fails to import, try running the command to install in terminal/git-bash import joblib filename = '../Models/H_svm.sav' joblib.dump(model, filename) ###Output _____no_output_____
gabarito_aula_3.ipynb	###Markdown Curso básico de ferramentas computacionais para astronomiaContato: Julia Gschwend ([[email protected]](mailto:[email protected])) Github: https://github.com/linea-it/minicurso-jupyter Site: https://minicurso-ed2.linea.gov.br/ Última verificação: 26/08/2021 Exercícios Aula 3 - Acesso a dados pelo LIneA Science Server, Pandas DataFrame Parte 1 - repita os passos da aula 3 Sugestões: * Execute as células abaixo removendo os marcadores de comentário ("") para refazer todos os passos demonstrados na aula. * Experimente pequenas variações no código e compare os resultados. * Use as células tipo Markdown para adicionar seus comentários ou informações adicionais. Índice1.1 [SQL básico](sql)1.2 [Download dos dados](download)1.3 [Manipulação dos dados usando NumPy](numpy)1.4 [Manipulação dos dados usando Pandas](pandas) 1.1 SQL básico [slides aula 3](https://docs.google.com/presentation/d/1lK8XNvj1MG_oC39iNfEA16PiU10mzhgUkO0irmxgTmE/preview?slide=id.ge8847134d3_0_821) 1.2 Download dos dados (opcional)Se você ainda não tem acesso ao [LIneA Science Server](https://desportal2.cosmology.illinois.edu) e deseja pular essa etapa, vá para a [seção 1.3](numpy). Para exemplificar a leitura de dados a partir de um Jupyter Notebook, vamos criar um arquivo com dados baixados da ferramenta User Query da plataforma [LIneA Science Server](https://desportal2.cosmology.illinois.edu). Antes de executar a query que vai dar origem ao arquivo, vamos consultar o tamanho (número de linhas) da tabela que ela vai gerar utilizando a seguinte query: ```sqlSELECT COUNT()FROM DES_ADMIN.DR2_MAINWHERE ra > 35 and ra < 36AND dec > -10 and dec < -9AND mag_auto_g between 15 and 23AND CLASS_STAR_G < 0.5AND FLAGS_I <=4```Copie a qurery acima e cole no campo `SQL Sentence`. Em seguida pressione o botão `Preview`. O resultado esperado é de 8303 objetos. Substitua o texto pela query abaixo para criar a tabela selecionando as colunas que vamos utilizar na demonstração.```sqlSELECT coadd_object_id, ra ,dec, mag_auto_g, mag_auto_r, mag_auto_i, magerr_auto_g, magerr_auto_r, magerr_auto_i, flags_iFROM DES_ADMIN.DR2_MAINWHERE ra > 35 and ra < 36AND dec > -10 and dec < -9AND mag_auto_g between 15 and 23AND CLASS_STAR_G < 0.5AND FLAGS_I <=4```Clique no botão de `Play`(Excecute Query) no canto superior esquerdo, escolha um nome para a sua tabela e pressione `Start`. Você receberá um email de notificação quando a sua tabela estiver pronta e ela aparecerá no menu `My Tables`. Clique no botão de seta para baixo ao lado do nome da tabela para abrir o menu e clique em `Download`. Você receberá um email com um link para o download do catálogo em formato `.zip`. Os dados acessados pelo Science Server estão hospedados nos computadores do NCSA. Faremos o download deste subconjunto de dados no formato _comma-separated values_ (CSV) e em seguida o upload do arquivo gerado para o JupyterHub. Sugerimos nomear o arquivo como `galaxias.csv` e guardá-lo dentro da pasta `dados`. Nas células abaixo vamos praticar a leitura e escrita de arquivos e a manipulação dados usando duas bibliotecas diferentes. 1.2 NumpyDocumentação da biblioteca NumPy: https://numpy.org/doc/stable/index.htmlPara começar, importe a biblioteca. ###Code import numpy as np print('NumPy version: ', np.__version__) ###Output _____no_output_____ ###Markdown Leia o arquivo e atribua todo o seu conteúdo a uma variável usando a função `loadtxt`([doc](https://numpy.org/doc/stable/reference/generated/numpy.loadtxt.html)): ###Code tabela = np.loadtxt("dados/galaxias.csv", delimiter=",", skiprows=1) tabela ###Output _____no_output_____ ###Markdown Confira o tipo de objeto da variável. ###Code type(tabela) ###Output _____no_output_____ ###Markdown Consulte o tipo de dados dos elementos contidos no array. ###Code tabela.dtype ###Output _____no_output_____ ###Markdown Consulte as dimensões do array (linhas,colunas). ###Code tabela.shape tabela[0].dtype ###Output _____no_output_____ ###Markdown Usar o argumento `unpack` da função `loadtxt` para obter as colunas separadamente. Quando ativado, o argumento `unpack` traz a tabela transposta. ###Code tabela_unpack = np.loadtxt("dados/galaxias.csv", delimiter=",", unpack=True, skiprows=1) tabela_unpack.shape ###Output _____no_output_____ ###Markdown Se você já sabe a ordem das colunas, pode atribuir cada uma delas a uma variável. Dica: pegar os nomes do cabeçalho do arquivo. ###Code coadd_object_id,dec,flags_i,magerr_auto_g,magerr_auto_i,\ magerr_auto_r,mag_auto_g,mag_auto_i,mag_auto_r,meta_id,ra = tabela_unpack coadd_object_id ###Output _____no_output_____ ###Markdown Você pode fazer isso direto ao ler o arquivo. OBS: ao usar os mesmos nomes para as variáveis, está sobrescrevendo as anteriores. ###Code coadd_object_id,dec,flags_i,magerr_auto_g,magerr_auto_i,\ magerr_auto_r,mag_auto_g,mag_auto_i,mag_auto_r,meta_id,ra = np.loadtxt("dados/galaxias.csv", delimiter=",", unpack=True, skiprows=1) coadd_object_id ###Output _____no_output_____ ###Markdown Faça um filtros (ou máscara) para galáxias brilhantes (mag i < 20). ###Code mask = (mag_auto_i < 20) mask ###Output _____no_output_____ ###Markdown Confira o tamanho do array sem* filtro. ###Code len(mag_auto_i) ###Output _____no_output_____ ###Markdown Confira o tamanho do array com filtro. ###Code len(mag_auto_i[mask]) ###Output _____no_output_____ ###Markdown A "máscara" (ou filtro) tambem pode ser aplicada a qualquer outro array com as mesmas dimensões. Teste com outra coluna. ###Code len(ra[mask]) ###Output _____no_output_____ ###Markdown A máscara é útil para criar um subconjunto dos dados. Crie um subconjunto com 3 colunas e com as linhas que satisfazem a condição do filtro. ###Code subset_tabela = np.array([coadd_object_id[mask], mag_auto_i[mask], magerr_auto_i[mask]]) ###Output _____no_output_____ ###Markdown Salve este subconjunto em um arquivo usando a função `savetxt`. ###Code np.savetxt("subset_galaxias.csv", subset_tabela) ###Output _____no_output_____ ###Markdown Dê uma ohada no arquivo salvo (duplo clique no arquivo leva para o visualizador de tabelas). Algo de errado com o número de linhas? ###Code np.savetxt("subset_galaxias.csv", subset_tabela.T) ###Output _____no_output_____ ###Markdown E agora? Algo errado com o número de colunas? ###Code np.savetxt("subset_galaxias.csv", subset_tabela.T, delimiter=",") ###Output _____no_output_____ ###Markdown Como ficou a formatação? Que tal deixar mais legível e definir o número de espaços? ###Code np.savetxt("subset_galaxias.csv", subset_tabela.T, delimiter=",", fmt=["%12i", "%10.4f", "%10.4f"]) ###Output _____no_output_____ ###Markdown Que tal um cabeçalho para nomear as colunas? ###Code np.savetxt("subset_galaxias.csv", subset_tabela.transpose(), delimiter=",", fmt=["%12i", "%10.4f", "%10.4f"], header="objects id, magnitude, mag error") ###Output _____no_output_____ ###Markdown 1.2 Pandas Documentação da biblioteca Pandas: https://pandas.pydata.org/docs/ ###Code import pandas as pd print('Pandas version: ', pd.__version__) ###Output _____no_output_____ ###Markdown Pandas Serieshttps://pandas.pydata.org/docs/reference/api/pandas.Series.html Pandas DataFramehttps://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html ```python class pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=None)```_"Two-dimensional, size-mutable, potentially heterogeneous tabular data. Data structure also contains labeled axes (rows and columns). Arithmetic operations align on both row and column labels. Can be thought of as a dict-like container for Series objects. The primary pandas data structure."_ Exemplo de criação de um DataFrame a partir de um dicionário: ###Code dic = {"nomes": ["Huguinho", "Zezinho", "Luizinho"], "cores": ["vermelho", "verde", "azul"], "características": ["nerd", "aventureiro", "criativo"]} sobrinhos = pd.DataFrame(dic) sobrinhos ###Output _____no_output_____ ###Markdown Um DataFrame também é o resultado da leitura de uma tabela usando a função `read_csv` do Pandas. ###Code dados = pd.read_csv("dados/galaxias.csv") dados ###Output _____no_output_____ ###Markdown Vamos explorar algumas funções e atributos úteis de um objeto to tipo _DataFrame_, começando pela sua "ficha técnica". ###Code dados.info() ###Output _____no_output_____ ###Markdown Imprima as primeiras linhas. ###Code dados.head() ###Output _____no_output_____ ###Markdown Defina o número de linhas exibidas. ###Code dados.head(3) ###Output _____no_output_____ ###Markdown Imprima as últimas linhas. ###Code dados.tail() ###Output _____no_output_____ ###Markdown Renomeie a coluna `coadd_object_id` ###Code dados.rename(columns={"coadd_object_id":"ID"}) dados dados.rename(columns={"coadd_object_id":"ID"}, inplace=True) dados ###Output _____no_output_____ ###Markdown Use a coluna coadd_object_id IDs como índice no DataFrame. ###Code dados.set_index("ID") dados dados.set_index("ID", inplace=True) dados ###Output _____no_output_____ ###Markdown Imprima a descrição do _DataFrame_ (resumão com estatísticas básicas). ###Code dados.describe() ###Output _____no_output_____ ###Markdown Faça um filtro para selecionar objetos com fotometria perfeita (flags_i = 0). ###Code dados.query('flags_i == 0') ###Output _____no_output_____ ###Markdown Confira o tamanho do _DataFrame_ filtrado e compare com o original. ###Code dados.query('flags_i == 0').count() dados.count() ###Output _____no_output_____ ###Markdown Tratando dados qualitativos: flags de qualidade ###Code dados.flags_i type(dados.flags_i) ###Output _____no_output_____ ###Markdown Contagem dos valores de cada categoria (cada flag). ###Code dados.flags_i.value_counts() ###Output _____no_output_____ ###Markdown A classe _Series_ possui alguns gráficos simples embutidos, por exemplo, o gráfico de barras (histograma). ###Code %matplotlib inline dados.flags_i.value_counts().plot(kind='bar') ###Output _____no_output_____ ###Markdown Gráfico de pizza: ###Code dados.flags_i.value_counts().plot(kind='pie') ###Output _____no_output_____ ###Markdown ******* Parte 2 - pratique a manipulação de um _DataFrame_ com dados não sensíveis dos alunos Os dados não sensíveis dos alunos desta edição estão disponíveis no arquivo `alunos.csv`, dentro do diretório `dados`.As células abaixo contém instruções para treinar os comandos da biblioteca _Pandas_ vistos acima. Cria células novas caso ache necessário. Leia o arquivo e atribua o dataset resultante a uma variável. ###Code alunos = pd.read_csv("dados/alunos.csv") alunos.head() ###Output _____no_output_____ ###Markdown Imprima as informações do _DataFrame_ criado com o métofo `info`. Compare com as informações do _dataset_ de galáxias. ###Code alunos.info() ###Output _____no_output_____ ###Markdown Imprima as primeiras linhas. ###Code alunos.head() ###Output _____no_output_____ ###Markdown Imprima as 2 últimas linhas ###Code alunos.tail(2) ###Output _____no_output_____ ###Markdown Renomeie as colunas com letras maiúsculas. ###Code columns = {} for column in alunos.columns: columns[column] = column.upper() alunos.rename(columns=columns, inplace=True) alunos ###Output _____no_output_____ ###Markdown Imprima a descrição do _DataFrame_ (resumão com estatísticas básicas). Compare o resultado com o _DataFrame_ de galáxias. Por que os atributos são diferentes? ###Code alunos.describe() ###Output _____no_output_____ ###Markdown Faça um filtro (ou uma "query") para selecionar apenas os alunos do Minicurso Jupyter Notebook. ###Code alunos.query("MINICURSO != 'SS' ") ###Output _____no_output_____ ###Markdown Imprima o número de alunos do Minicurso Jupyter Notebook por estado. ###Code alunos.query("MINICURSO != 'SS' ")["UF"].value_counts() ###Output _____no_output_____ ###Markdown Faça um histograma dos estados dos alunos do Minicurso Jupyter Notebook usando o método `plot` da classe _Series_. ###Code alunos.query("MINICURSO != 'SS' ")["UF"].value_counts().plot(kind='bar') ###Output _____no_output_____ ###Markdown Faça um gráfico de pizza dos perídos dos alunos do Minicurso Jupyter Notebook usando o método `plot` da classe _Series_. ###Code alunos.query("MINICURSO != 'SS' ")["PERÍODO"].value_counts().plot(kind='pie') ###Output _____no_output_____
Lesson1/Numbers.ipynb	###Markdown Numbers IntegersWhole numbers, positive or negative. For example: 2 and -2 Floating Point NumbersHave a decimal point in them, or use an exponential (e) to define the number.For example 2.0 and -2.1 are examples of floating point numbers.4E2 (4 times 10 to the power of 2) is also an example of a floating point number in Python. ###Code 2+1 2-1 22 32 3/2 float(3)/2 23 40.5 2 + 10 * 10 + 3 (2 + 10) * (10 + 3) a = 5 a a + a a = 10 a a = a + a a my_income = 100 tax_rate = 0.1 my_taxes = my_income * tax_rate my_taxes ###Output _____no_output_____
2.NLP_and_Preprocessing/Tockenizer.ipynb	###Markdown Tokenize ###Code import numpy as np from konlpy.tag import * from sklearn.feature_extraction.text import CountVectorizer posToUse=["NNP","NNG","MAG","NP","VV","VV+EF",'XSV+EC'] def getTokens(s): global posToUse return [ i[0] for i in Mecab().pos(s) if i[1] in posToUse ] s="나는 너를 사랑한다" print( getTokens(s)) ###Output ['나', '너', '사랑', '한다'] ###Markdown 1. Simple Version by NLTK ###Code from nltk.tokenize import word_tokenize import nltk nltk.download('punkt') s="나는 너를 사랑한다" print(word_tokenize("나는 나를 사랑한다")) from nltk.tokenize import WordPunctTokenizer WordPunctTokenizer().tokenize(s) ###Output _____no_output_____ ###Markdown 2. Keras Tokenizer ###Code corpus = [ "고인물은 게임을 즐긴다.", "남중호는 게임을 잘한다.", "남중구는 게임을 못한다.", "나는 나란 놈 사랑한다.18", "너의 게임 나의 게임 사랑하라" ] s=" ".join(corpus) print('All string :' ,s) from tensorflow.keras.preprocessing.text import Tokenizer tokenizer = Tokenizer() sentences=[getTokens(r) for r in corpus] tokenizer.fit_on_texts (sentences) print(tokenizer.texts_to_sequences(sentences)) tokenizer.word_index ###Output _____no_output_____ ###Markdown 3. Keras pad_sequence ###Code from tensorflow.keras.preprocessing.sequence import pad_sequences sentences=[getTokens(r) for r in corpus] encoded = tokenizer.texts_to_sequences(sentences) pad_sequences(encoded) pad_sequences(encoded,padding = 'post') ###Output _____no_output_____ ###Markdown cf) Sentence Tokenizer ###Code from nltk.tokenize import sent_tokenize text="Mr. Nam은 게임을 즐긴다. 남중호는 게임을 잘한다...그런가 보다." print(sent_tokenize(text)) ?tokenizer.texts_to_sequences ###Output _____no_output_____
CATBoost-v2.ipynb	###Markdown CATBOOST ###Code df_train = pd.read_csv('../data/train_con_features_encoded.csv', index_col='Unnamed: 0') df_test = pd.read_csv('../data/test_con_features_encoded.csv', index_col='Unnamed: 0') display(df_train.head()) #Guardo y remuevo la columna id de los datos id_col = df_test['id'] df_train = df_train.drop(['id'], axis=1) df_test = df_test.drop(['id'], axis=1) #Separo features de entrenamiento del precio feature_cols = df_train.columns.tolist() feature_cols.remove('precio') X = df_train[feature_cols] y = df_train['precio'] feature_cols from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.25, random_state=0) print(X_train.shape, y_train.shape) print(X_test.shape, y_test.shape) from catboost import CatBoostRegressor '''CatBoost = CatBoostRegressor(loss_function='MAE') params = {'depth':[10,15], 'iterations':[1000], 'learning_rate':[0.05,0.1,0.12], 'l2_leaf_reg':[10, 15], } #border_count #ctr_border_count grid_search_result = CatBoost.grid_search(params, X=X_train, y=y_train, plot=True)''' iterations = 1000 #best=1000 MAE=551393 CATBOOST ENC(ciudades) + ONE HOT ENC ---> k=50 depth = 10 #best=10 MAE=550847 COUNT ENCODED ---> k=50 l2_leaf_reg = 6 #best=6 learning_rate = 0.07 #best=0.07 border_count = 128 #best=128 subsample = 0.9 #best=0.9 colsample_bylevel = 0.9 #best=0.9 CatBoost = CatBoostRegressor(iterations=iterations, loss_function='MAE', depth=depth, l2_leaf_reg=l2_leaf_reg, learning_rate=learning_rate, border_count=border_count, subsample=subsample, colsample_bylevel=colsample_bylevel) CatBoost_fit = CatBoost.fit(X_train, y_train) CatBoost_pred = CatBoost_fit.predict(X_test) from sklearn.metrics import mean_absolute_error CatBoost_mae = mean_absolute_error(y_test, CatBoost_pred) CatBoost_mae_train = mean_absolute_error(y_train, CatBoost_fit.predict(X_train)) print(f"MAE CATBoost (train): {CatBoost_mae_train:.5f}") print(f"MAE CATBoost: {CatBoost_mae:.5f}") print("------------------------------") CatBoost_fit.feature_importances_ features = pd.DataFrame(index=feature_cols) features['imp'] = CatBoost_fit.feature_importances_ features = features.sort_values(['imp'], ascending = False) features plt.style.use('default') plt.rcParams['figure.figsize'] = (10, 10) sns.set(style="whitegrid") g = sns.barplot(y=features.index, x=features.imp, \ palette=sns.color_palette("Reds_d", 10)); g.set_title('Importancia de Features de CATBoost', fontsize=15); g.set_xlabel('Valor'); g.set_ylabel('Nombre del Feature'); CatBoost_pred_submit = CatBoost.fit(X, y).predict(df_test) resultado_submit = pd.DataFrame(index=df_test.index) resultado_submit['id'] = id_col resultado_submit['target'] = CatBoost_pred_submit display(resultado_submit.head()) resultado_submit.to_csv('../data/submitCATBoost-v2.csv',index=False) ###Output _____no_output_____
code/evo_tune_201023/.ipynb_checkpoints/generate_subsample_pfamA_target_motor_toolkit-checkpoint.ipynb	###Markdown Filter out motor_toolkit n >= 5000 ###Code motor_toolkit = motor_toolkit.loc[motor_toolkit["Length"] < 5000,:] motor_toolkit = ###Output _____no_output_____
023 - Modelo de dados em Python/023 - Modelo de dados em Python.ipynb	###Markdown Códigos fortemente baseados no Capítulo 1 do livro de Luciano Ramalho: *Ramalho, L. (2015). Python fluente: Programação clara, concisa e eficaz. Novatec.* ###Code from IPython.display import Image Image(filename = 'C:/Users/User/Desktop/Python para Psicólogos/python-geral/023 - Modelo de dados em Python/Ramalho (2015).jpg') ###Output _____no_output_____ ###Markdown Um baralho pythônico: ilustrando os métodos especiais (ou métodos dunder) `__getitem__` e `__len__` ###Code import collections Card = collections.namedtuple('Card', ['rank', 'suit']) class FrenchDeck: # Cria um baralho; implementação atual não permite embaralhamento; ver Cap. 11 ranks = [str(n) for n in range(2, 11)] + list('JQKA') # valores suits = 'espadas ouros paus copas'.split() # naipes def __init__(self): self._cards = [Card(rank, suit) for suit in self.suits for rank in self.ranks] def __len__(self): return len(self._cards) def __getitem__(self, position): return self._cards[position] # Criando uma carta beer_card = Card('7', 'ouros') beer_card # Cria um objeto da classe FrenchDeck, que representará um baralho deck = FrenchDeck() len(deck) # Acessa cartas em posições específicas do baralho print(deck[0]) print(deck[-2]) # Selecionando cartas aleatórias do baralho from random import choice for i in range(0, 3): x = choice(deck) print(x) # o uso do método dunder __getitem__ possibilita fatiamento: print(deck[12::13]) print() # e torna o objeto da classe FrenchDeck iterável for card in deck: # for card in reversed(deck) --> mesma operação, mas na ordem inversa print(card) # sem o método dunder __contains__, o operador in leva a uma varredura sequencial print(Card('Q', 'copas') in deck) print(Card('22', 'paus') in deck) # usando a ordenação (valoração) do baralho suit_values = dict(espadas = 3, copas = 2, ouros = 1, paus = 0) def spades_high(card): rank_value = FrenchDeck.ranks.index(card.rank) return rank_value * len(suit_values) + suit_values[card.suit] # listando o baralho em ordem crescente de classificação for card in sorted(deck, key = spades_high): print(card) ###Output Card(rank='2', suit='paus') Card(rank='2', suit='ouros') Card(rank='2', suit='copas') Card(rank='2', suit='espadas') Card(rank='3', suit='paus') Card(rank='3', suit='ouros') Card(rank='3', suit='copas') Card(rank='3', suit='espadas') Card(rank='4', suit='paus') Card(rank='4', suit='ouros') Card(rank='4', suit='copas') Card(rank='4', suit='espadas') Card(rank='5', suit='paus') Card(rank='5', suit='ouros') Card(rank='5', suit='copas') Card(rank='5', suit='espadas') Card(rank='6', suit='paus') Card(rank='6', suit='ouros') Card(rank='6', suit='copas') Card(rank='6', suit='espadas') Card(rank='7', suit='paus') Card(rank='7', suit='ouros') Card(rank='7', suit='copas') Card(rank='7', suit='espadas') Card(rank='8', suit='paus') Card(rank='8', suit='ouros') Card(rank='8', suit='copas') Card(rank='8', suit='espadas') Card(rank='9', suit='paus') Card(rank='9', suit='ouros') Card(rank='9', suit='copas') Card(rank='9', suit='espadas') Card(rank='10', suit='paus') Card(rank='10', suit='ouros') Card(rank='10', suit='copas') Card(rank='10', suit='espadas') Card(rank='J', suit='paus') Card(rank='J', suit='ouros') Card(rank='J', suit='copas') Card(rank='J', suit='espadas') Card(rank='Q', suit='paus') Card(rank='Q', suit='ouros') Card(rank='Q', suit='copas') Card(rank='Q', suit='espadas') Card(rank='K', suit='paus') Card(rank='K', suit='ouros') Card(rank='K', suit='copas') Card(rank='K', suit='espadas') Card(rank='A', suit='paus') Card(rank='A', suit='ouros') Card(rank='A', suit='copas') Card(rank='A', suit='espadas') ###Markdown Como os métodos especiais são usados eles são chamados pelo interpretador, não pelo usuário. usuário: `for i in minha_lista:` implicitamente chama: `iter(minha_lista)` se o método `__iter__` estiver disponível para a instância `minha_lista`, então é chamado `minha_lista.__iter__()`. Emulando tipos numéricos ###Code from math import hypot class Vector(object): """Classe construtora de vetores euclidianos bidimensionais.""" def __init__(self, x = 0, y = 0): self.x = x self.y = y def __repr__(self): return f'Vector({self.x}, {self.y})' def __abs__(self): return hypot(self.x, self.y) def __bool__(self): return bool(abs(self)) def __add__(self, other): x = self.x + other.x y = self.y + other.y return Vector(x, y) def __mul__(self, scalar): return Vector(self.x * scalar, self.y * scalar) # Criando instâncias da classe Vector() v1 = Vector(2, 4) v2 = Vector(2, 1) # as chamadas a seguir não invocam explicitamente os métodos dunder # soma de vetores v3 = v1 + v2 print(v3) # retorna o comprimento da hipotenusa do vetor, considerando a origem (0, 0) v = Vector(3, 4) print(abs(v)) # multipla um vetor por um escalar v4 = v * 4 print(v4) print(abs(v * 4)) # O caso do método dunder __bool__ x = list() y = list('Planeta Terra') # listas não possuem o método x.__bool__() # neste caso, bool(x) invoca x.__len__() e retorna False se len == 0 print(bool(x)) # False --> a magnitude do vetor É zero print(bool(y)) # True --> a magnitude do vetor NÃO É zero ###Output False True
apps/Example - Abstract analysis without geoferenced locations.ipynb	###Markdown Workflow for Abstract Analysis without Geoferenced LocationsIn this application, we showcase the capability for pre-process network graphs and simplify their topologiesThe workflow is structured as follows:1. Import relevant modules / tools / packages2. Define Hydrogen Supply Chain Alternatives3. Run Supply Chain Model4. Postprocessing 1. Import relevant modules / tools / packages ###Code from HIM.workflow.scenarioExample import * #import processing as prc from HIM import dataHandling as sFun from HIM import optiSetup as optiFun from HIM import hscClasses as hscFun from HIM import plotFunctions as pFun import copy as copy import os pathPlot=None from HIM import hscAbstract as hscA ###Output _____no_output_____ ###Markdown 2. Define Hydrogen Supply Chain Alternatives ###Code hscPathways=[["Electrolyzer","None","Compressor", "GH2-Cavern","None","Compressor","Pipeline","Compressor","GH2-Truck","GH2 (Trailer)"], ["Electrolyzer","None","Compressor", "GH2-Cavern","None","Compressor","Pipeline","None","Pipeline","GH2 (Pipeline)"], ["Electrolyzer","None","Compressor", "GH2-Cavern","None","Compressor","GH2-Truck","None","None","GH2 (Trailer)"], ["Electrolyzer","None","Compressor", "GH2-Cavern","None","Liquefaction","LH2-Truck","None","None","LH2"], ["Electrolyzer","None","Compressor", "GH2-Cavern","None","Hydrogenation","LOHC-Truck","None","None","LOHC (NG)"], ["Electrolyzer","None","Liquefaction", "LH2-Tank","Evaporation","Compressor","Pipeline","Compressor","GH2-Truck","GH2 (Trailer)"], ["Electrolyzer","None","Liquefaction", "LH2-Tank","Evaporation","Compressor","Pipeline","None","Pipeline","GH2 (Pipeline)"], ["Electrolyzer","None","Liquefaction", "LH2-Tank","Evaporation","Compressor","GH2-Truck","None","None","GH2 (Trailer)"], ["Electrolyzer","Liquefaction","None", "LH2-Tank","None","None","LH2-Truck","None","None","LH2"], ["Electrolyzer","None","Hydrogenation", "LOHC-Tank","Dehydrogenation","Compressor","Pipeline","Compressor","GH2-Truck","GH2 (Trailer)"], ["Electrolyzer","None","Hydrogenation", "LOHC-Tank","Dehydrogenation","Compressor","Pipeline","None","Pipeline","GH2 (Pipeline)"], ["Electrolyzer","None","Hydrogenation", "LOHC-Tank","Dehydrogenation","Compressor","GH2-Truck","None","None","GH2 (Trailer)"], ["Electrolyzer","Hydrogenation","None", "LOHC-Tank","None","None","LOHC-Truck","None","None","LOHC (NG)"]] dfHSC=pd.DataFrame(hscPathways, columns=["Production", "Connector1", "Connector2", "Storage", "Connector3", "Connector4", "Transport1", "Connector5", "Transport2", "Station"]) nameHSC=[] for key, values in dfHSC.iterrows(): if values["Transport2"]=="None": nameHSC.append(values["Storage"]+"\n"+values["Transport1"]) elif values["Transport2"]=="Pipeline": nameHSC.append(values["Storage"]+"\nPipePipe") else: nameHSC.append(values["Storage"]+"\nPipe+"+values["Transport2"]) dfHSC["General"]=nameHSC dfHSC ###Output _____no_output_____ ###Markdown 3. Run Abstract Supply Chain Model ###Code res=100 ###Output _____no_output_____ ###Markdown Calculation of demand and distance arrays ###Code demArr, distArr = builtDemDistArray(demMax=100000, # maximal demand distMax=500, # maximal distance res=res) # Resolution Results={} for x in range(len(hscPathways)): Results[x]=hscA.HSCAbstract(demArr=demArr, distArr=distArr, dfTable=dfTable, listHSC=hscPathways[x]) Results[x].calcHSC() ###Output C:\ProgramData\Anaconda3\envs\him\lib\site-packages\pandas\core\indexing.py:1418: FutureWarning: Passing list-likes to .loc or [] with any missing label will raise KeyError in the future, you can use .reindex() as an alternative. See the documentation here: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike return self._getitem_tuple(key) ###Markdown Initialization ###Code totalCost = np.empty((len(distArr), demArr.shape[1], len(hscPathways))) minimalCost = np.empty((len(distArr), demArr.shape[1])) ###Output _____no_output_____ ###Markdown extract data ###Code for i in range(len(hscPathways)): # Read interesting Numbers totalCost[:, :, i] = Results[i].cumCost["Station"] ###Output _____no_output_____ ###Markdown Cut disturbing Data ###Code cuttedCost = copy.copy(totalCost) lineCost = copy.copy(totalCost) lineCost[:, :, :] = np.nan minimalLineCost = np.empty((len(distArr), demArr.shape[1])) minimalLineCost[:, :] = np.nan for i in range(len(distArr)): for j in range(len(demArr.T)): minimalCost[i, j] = min(cuttedCost[i, j, :]) for k in range(len(hscPathways)): if minimalCost[i, j] < cuttedCost[i, j, k]: cuttedCost[i, j, k] = np.nan if ( np.isnan(cuttedCost[i, j, k]) and np.isnan(cuttedCost[i - 1, j, k]) == False) or (np.isnan( cuttedCost[i, j, k]) == False and np.isnan(cuttedCost[i - 1, j, k]) == True): if not(i == 0): minimalLineCost[i, j] = minimalCost[i, j] minimalLineCost[i - 1, j] = minimalCost[i - 1, j] minimalLineCost[i, j - 1] = minimalCost[i, j - 1] elif (np.isnan(cuttedCost[i, j, k]) and np.isnan(cuttedCost[i, j - 1, k]) == False) or (np.isnan(cuttedCost[i, j, k]) == False and np.isnan(cuttedCost[i, j - 1, k]) == True): if not j == 0: minimalLineCost[i, j] = minimalCost[i, j] minimalLineCost[i - 1, j] = minimalCost[i - 1, j] minimalLineCost[i, j - 1] = minimalCost[i, j - 1] ###Output _____no_output_____ ###Markdown 4. Postprocessing ###Code pFun.trisurfplotMin( demArr/1000, distArr, cuttedCost, minimalCost, minimalLineCost, dfHSC, figSize=(7,4), zmax=10.5, savePath=os.path.join(os.getcwd(),'results','FigureComparison.png')) ###Output c:\users\l.kotzur\sciebo\fzj\04_public\him\HIM\plotFunctions.py:165: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect. plt.tight_layout()
tutorial_files/2_xbase_routing.ipynb	###Markdown Module 2: XBase Routing APIIn this module, you will learn the basics about the routing grid system in XBase. We will go over how tracks are defined, how to create wires, vias and pins, and how to define the size of a layout cell. XBase Routing Grid```yamlrouting_grid: layers: [4, 5, 6, 7] spaces: [0.06, 0.1, 0.12, 0.2] widths: [0.06, 0.1, 0.12, 0.2] bot_dir: 'x'```In XBase, all wires and vias have to be drawn on the routing grid, which is usually defined in a specification file, as shown above. On each layer, all wires must travel in the same direction (horizontal or vertical), and wire direction alternates between each layers. The routing grid usually starts on an intermediate layer (metal 4 in the above example), and lower layers are reserved for device primitives routing. As seen above, different layers can define different wire pitch, with the wire pitch generally increasing as you move up the metal stack.All layout cell dimensions in XBase must also be quantized to the routing grid, meaning that a layout cell must contain integer number of tracks on all metal layers it uses. Because of the difference in wire pitch, a layout cell that use more layers generally have coarser quantization compared with a layout cell that use fewer layers. XBase Routing TracksThe figure above shows some wires drawn in XBase. Track pitch is the sum of unit width and space, and track number 0 is defined as the wire that's half-pitch away from left or bottom boundary. From the figure, you can see spacing between wires follows the formula $S = sp + N \cdot p$, where $N$ is the number of tracks in between.XBase also supports drawing thicker wires by using multiple adjacent tracks. Wire width follows the formula $W = w + (N - 1)\cdot p$, where $N$ is the number of tracks a wire uses. One issue with this scheme is that even width wires wastes more space compared to odd width wires. For example, in the above figure, although tracks 1 and 3 are empty, no wire can be drawn there because it will violate minimum spacing rule to the wire centered on track 2. As a result, the wire on track 2 takes up 3 tracks although it is only 2 tracks wide.To work around this issues, XBase allows you to place wires on half-integer tracks. In the above figure, the 2 tracks wide wire is moved to track 1.5 from track 2, and thus wires can still be drawn on tracks 0 and 3, making the layout more space efficient. As an added benefit, track -0.5 is now on top of the left-most/bottom-most boundary, so it is now possible to share a wire with adjacent layout cells, such as supply wires in a custom digital standard cell. TrackID and WireArray```pythonclass TrackID(object): def __init__(self, layer_id, track_idx, width=1, num=1, pitch=0.0): type: (int, Union[float, int], int, int, Union[float, int]) -> None class WireArray(object): def __init__(self, track_id, lower, upper): type: (TrackID, float, float) -> None```Routing track locations are represented by the `TrackID` Python object. It has built-in support for drawing a multi-wire bus by specifying the optional `num` and `pitch` parameters, which defines the number of wires in the bus and the number of track pitches between adjacent wires. The `layer_id` parameter specifies the routing layer ID, the `track_idx` parameter specifies the track index of the left-most/bottom-most wire, and `width` specifies the number of tracks a single wire uses.Physical wires in XBase are represented by the `WireArray` Python object. It contains a `TrackID` object describes the location of the wires, and `lower` and `upper` attributes describes the starting and ending coordinate of those wires along the track. For example, a horizontal wire starting at $x = 0.5$ um and ending at $x = 3.0$ um will have `lower = 0.5`, and `upper = 3.0`.One last note is that layout pins can only be added on `WireArray` objects. This guarantees that pins of a layout cell will always be on the routing grid. BAG Layout Generation Code```pythondef gen_layout(prj, specs, dsn_name, demo_class): get information from specs dsn_specs = specs[dsn_name] impl_lib = dsn_specs['impl_lib'] layout_params = dsn_specs['layout_params'] gen_cell = dsn_specs['gen_cell'] create layout template database tdb = make_tdb(prj, specs, impl_lib) compute layout print('computing layout') template = tdb.new_template(params=layout_params, temp_cls=temp_cls) template = tdb.new_template(params=layout_params, temp_cls=demo_class) create layout in OA database print('creating layout') tdb.batch_layout(prj, [template], [gen_cell]) return corresponding schematic parameters print('layout done') return template.sch_params```The above code snippet (taking from `xbase_demo.core` module) shows how layout is generated. First, user create a layout database object, which keeps track of layout hierarchy. Then, user uses the layout database object to create new layout instances given layout generator class and parameters. Finally, layout database uses `BagProject` instance to create the generated layouts in Virtuoso. The generated layout will also contain the corresponding schematic parameters, which can be passed to schematic generator later. BAG TemplateDB Creation Code```pythondef make_tdb(prj, specs, impl_lib): grid_specs = specs['routing_grid'] layers = grid_specs['layers'] spaces = grid_specs['spaces'] widths = grid_specs['widths'] bot_dir = grid_specs['bot_dir'] create RoutingGrid object routing_grid = RoutingGrid(prj.tech_info, layers, spaces, widths, bot_dir) create layout template database tdb = TemplateDB('template_libs.def', routing_grid, impl_lib, use_cybagoa=True) return tdb```For reference, the above code snippet shows how the layout database object is created. A `RoutingGrid` object is created from routing grid parameters specified in the specification file, which is then used to construct the `TemplateDB` layout database object. Routing ExampleThe code box below defines a `RoutingDemo` layout generator class, which is a simply layout containing only wires, vias, and pins. All layout creation happens in the `draw_layout()` function, the functions/attributes of interests are:* `add_wires()`: Create one or more physical wires, with the given options.* `connect_to_tracks()`: Connect two `WireArray`s on adjacent layers by extending them to their intersection and adding vias.* `connnect_wires()`: Connect multiple `WireArrays` on the same layer together. * `add_pin()`: Add a pin object on top of a `WireArray` object.* `self.size`: A 3-tuple describing the size of this layout cell.* `self.bound_box`: A `BBox` object representing the bounding box of this layout cell, computed from `self.size`.To see the layout in action, evaluate the code box below by selecting the cell and pressing Ctrl+Enter. A `DEMO_ROUTING` library will be created in Virtuoso with a single `ROUTING_DEMO` layout cell in it. Feel free to play around with the numbers and re-evaluating the cell, and the layout in Virtuoso should update.Exercise 1: There are currently 3 wires labeled "pin3". Change that to 4 wires by adding an extra wire with the same pitch on the right.Exercise 2: Connect all wires labeled "pin3" to the wire labeled "pin1". Hint: use `connect_to_tracks()` and `connect_wires()`. ###Code from bag.layout.routing import TrackID from bag.layout.template import TemplateBase class RoutingDemo(TemplateBase): def __init__(self, temp_db, lib_name, params, used_names, kwargs): super(RoutingDemo, self).__init__(temp_db, lib_name, params, used_names, kwargs) @classmethod def get_params_info(cls): return {} def draw_layout(self): # Metal 4 is horizontal, Metal 5 is vertical hm_layer = 4 vm_layer = 5 # add a horizontal wire on track 0, from X=0.1 to X=0.3 warr1 = self.add_wires(hm_layer, 0, 0.1, 0.3) # print WireArray object print(warr1) # print lower, middle, and upper coordinate of wire. print(warr1.lower, warr1.middle, warr1.upper) # print TrackID object associated with WireArray print(warr1.track_id) # add a horizontal wire on track 1, from X=0.1 to X=0.3, # coordinates specified in resolution units warr2 = self.add_wires(hm_layer, 1, 100, 300, unit_mode=True) # add another wire on track 1, from X=0.35 to X=0.45 warr2_ext = self.add_wires(hm_layer, 1, 350, 450, unit_mode=True) # connect wires on the same track, in this case warr2 and warr2_ext self.connect_wires([warr2, warr2_ext]) # add a horizontal wire on track 2.5, from X=0.2 to X=0.4 self.add_wires(hm_layer, 2.5, 200, 400, unit_mode=True) # add a horizontal wire on track 4, from X=0.2 to X=0.4, with 2 tracks wide warr3 = self.add_wires(hm_layer, 4, 200, 400, width=2, unit_mode=True) # add 3 parallel vertical wires starting on track 6 and use every other track warr4 = self.add_wires(vm_layer, 6, 100, 400, num=3, pitch=2, unit_mode=True) print(warr4) # create a TrackID object representing a vertical track tid = TrackID(vm_layer, 3, width=2, num=1, pitch=0) # connect horizontal wires to the vertical track warr5 = self.connect_to_tracks([warr1, warr3], tid) print(warr5) # add a pin on a WireArray self.add_pin('pin1', warr1) # add a pin, but make label different than net name. Useful for LVS connect self.add_pin('pin2', warr2, label='pin2:') # add_pin also works for WireArray representing multiple wires self.add_pin('pin3', warr4) # add a pin (so it is visible in BAG), but do not create the actual layout # in OA. This is useful for hiding pins on lower levels of hierarchy. self.add_pin('pin4', warr3, show=False) # set the size of this template top_layer = vm_layer num_h_tracks = 6 num_v_tracks = 11 # size is 3-element tuple of top layer ID, number of top # vertical tracks, and number of top horizontal tracks self.size = top_layer, num_v_tracks, num_h_tracks # print bounding box of this template print(self.bound_box) # add a M7 rectangle to visualize bounding box in layout self.add_rect('M1', self.bound_box) from pathlib import Path # import bag package from bag.core import BagProject from bag.io.file import read_yaml # import BAG demo Python modules import xbase_demo.core as demo_core # load circuit specifications from file spec_fname = Path(os.environ['BAG_WORK_DIR']) / Path('specs_demo/demo.yaml') top_specs = read_yaml(spec_fname) # obtain BagProject instance local_dict = locals() bprj = local_dict.get('bprj', BagProject()) demo_core.routing_demo(bprj, top_specs, RoutingDemo) ###Output _____no_output_____
GoogleColab/.Math/Wilcoxon Sign-Rank Test/ParticlesCollider/muons.ipynb	###Markdown import ###Code import numpy as np import matplotlib.pylab as plt !pip install git+https://github.com/mattbellis/h5hep.git import h5hep !pip install git+https://github.com/mattbellis/particle_physics_simplified.git import pps_tools as pps # Download the dataset # from # https://github.com/particle-physics-playground/playground/tree/master/data # ~~~~~~~~~~~~~~~~ # pps.download_from_drive('dimuons_100k.hdf5') infile = 'data/dimuons_100k.hdf5' ###Output _____no_output_____ ###Markdown build colisions ###Code collisions = pps.get_collisions(infile,experiment='CMS',verbose=False) print(len(collisions), " collisions") # This line is optional, and simply tells you how many events are in the file. second_collision = collisions[1] # the second event print("First event: ",second_collision) all_muons = second_collision['muons'] # all of the jets in the first event print("All muons: ",all_muons) first_muon = all_muons[0] # the first jet in the first event print("First muon: ",first_muon) muon_energy = first_muon['e'] # the energy of the first photon print("First muon's energy: ",muon_energy) energies = [] for collision in collisions: # loops over all the events in the file muons = collision['muons'] # gets the list of all muons in the event for muon in muons: # loops over each muon in the current event e = muon['e'] # gets the energy of the muon energies.append(e) # puts the energy in a list #plot first energies in a histogram plt.hist(energies,bins=50,range=(0,100)); alldata = pps.get_all_data(infile,verbose=False) nentries = pps.get_number_of_entries(alldata) print("# entries: ",nentries) # This optional line tells you how many events are in the file for entry in range(nentries): # This range will loop over ALL of the events collision = pps.get_collision(alldata,entry_number=entry,experiment='CMS') for entry in range(0,int(nentries/2)): # This range will loop over the first half of the events collision = pps.get_collision(alldata,entry_number=entry,experiment='CMS') for entry in range(int(nentries/2),nentries): # This range will loop over the second half of the events collision = pps.get_collision(alldata,entry_number=entry,experiment='CMS') #second energies in a histogram energies = [] for event in range(0,int(nentries/3)): # Loops over first 3rd of all events collision = pps.get_collision(alldata,entry_number=event,experiment='CMS') # organizes the data so you can interface with it muons = collision['muons'] # gets the list of all photons in the current event for muon in muons: # loops over all photons in the event e = muon['e'] # gets the energy of the photon energies.append(e) # adds the energy to a list plt.hist(energies,bins=50,range=(0,100)); ###Output _____no_output_____
v0.12.2/examples/notebooks/generated/glm.ipynb	###Markdown Generalized Linear Models ###Code %matplotlib inline import numpy as np import statsmodels.api as sm from scipy import stats from matplotlib import pyplot as plt plt.rc("figure", figsize=(16,8)) plt.rc("font", size=14) ###Output _____no_output_____ ###Markdown GLM: Binomial response data Load Star98 data In this example, we use the Star98 dataset which was taken with permission from Jeff Gill (2000) Generalized linear models: A unified approach. Codebook information can be obtained by typing: ###Code print(sm.datasets.star98.NOTE) ###Output :: Number of Observations - 303 (counties in California). Number of Variables - 13 and 8 interaction terms. Definition of variables names:: NABOVE - Total number of students above the national median for the math section. NBELOW - Total number of students below the national median for the math section. LOWINC - Percentage of low income students PERASIAN - Percentage of Asian student PERBLACK - Percentage of black students PERHISP - Percentage of Hispanic students PERMINTE - Percentage of minority teachers AVYRSEXP - Sum of teachers' years in educational service divided by the number of teachers. AVSALK - Total salary budget including benefits divided by the number of full-time teachers (in thousands) PERSPENK - Per-pupil spending (in thousands) PTRATIO - Pupil-teacher ratio. PCTAF - Percentage of students taking UC/CSU prep courses PCTCHRT - Percentage of charter schools PCTYRRND - Percentage of year-round schools The below variables are interaction terms of the variables defined above. PERMINTE_AVYRSEXP PEMINTE_AVSAL AVYRSEXP_AVSAL PERSPEN_PTRATIO PERSPEN_PCTAF PTRATIO_PCTAF PERMINTE_AVTRSEXP_AVSAL PERSPEN_PTRATIO_PCTAF ###Markdown Load the data and add a constant to the exogenous (independent) variables: ###Code data = sm.datasets.star98.load(as_pandas=False) data.exog = sm.add_constant(data.exog, prepend=False) ###Output _____no_output_____ ###Markdown The dependent variable is N by 2 (Success: NABOVE, Failure: NBELOW): ###Code print(data.endog[:5,:]) ###Output [[452. 355.] [144. 40.] [337. 234.] [395. 178.] [ 8. 57.]] ###Markdown The independent variables include all the other variables described above, as well as the interaction terms: ###Code print(data.exog[:2,:]) ###Output [[3.43973000e+01 2.32993000e+01 1.42352800e+01 1.14111200e+01 1.59183700e+01 1.47064600e+01 5.91573200e+01 4.44520700e+00 2.17102500e+01 5.70327600e+01 0.00000000e+00 2.22222200e+01 2.34102872e+02 9.41688110e+02 8.69994800e+02 9.65065600e+01 2.53522420e+02 1.23819550e+03 1.38488985e+04 5.50403520e+03 1.00000000e+00] [1.73650700e+01 2.93283800e+01 8.23489700e+00 9.31488400e+00 1.36363600e+01 1.60832400e+01 5.95039700e+01 5.26759800e+00 2.04427800e+01 6.46226400e+01 0.00000000e+00 0.00000000e+00 2.19316851e+02 8.11417560e+02 9.57016600e+02 1.07684350e+02 3.40406090e+02 1.32106640e+03 1.30502233e+04 6.95884680e+03 1.00000000e+00]] ###Markdown Fit and summary ###Code glm_binom = sm.GLM(data.endog, data.exog, family=sm.families.Binomial()) res = glm_binom.fit() print(res.summary()) ###Output Generalized Linear Model Regression Results ============================================================================== Dep. Variable: ['y1', 'y2'] No. Observations: 303 Model: GLM Df Residuals: 282 Model Family: Binomial Df Model: 20 Link Function: logit Scale: 1.0000 Method: IRLS Log-Likelihood: -2998.6 Date: Tue, 02 Feb 2021 Deviance: 4078.8 Time: 06:54:02 Pearson chi2: 4.05e+03 No. Iterations: 5 Covariance Type: nonrobust ============================================================================== coef std err z P>\|z\| [0.025 0.975] ------------------------------------------------------------------------------ x1 -0.0168 0.000 -38.749 0.000 -0.018 -0.016 x2 0.0099 0.001 16.505 0.000 0.009 0.011 x3 -0.0187 0.001 -25.182 0.000 -0.020 -0.017 x4 -0.0142 0.000 -32.818 0.000 -0.015 -0.013 x5 0.2545 0.030 8.498 0.000 0.196 0.313 x6 0.2407 0.057 4.212 0.000 0.129 0.353 x7 0.0804 0.014 5.775 0.000 0.053 0.108 x8 -1.9522 0.317 -6.162 0.000 -2.573 -1.331 x9 -0.3341 0.061 -5.453 0.000 -0.454 -0.214 x10 -0.1690 0.033 -5.169 0.000 -0.233 -0.105 x11 0.0049 0.001 3.921 0.000 0.002 0.007 x12 -0.0036 0.000 -15.878 0.000 -0.004 -0.003 x13 -0.0141 0.002 -7.391 0.000 -0.018 -0.010 x14 -0.0040 0.000 -8.450 0.000 -0.005 -0.003 x15 -0.0039 0.001 -4.059 0.000 -0.006 -0.002 x16 0.0917 0.015 6.321 0.000 0.063 0.120 x17 0.0490 0.007 6.574 0.000 0.034 0.064 x18 0.0080 0.001 5.362 0.000 0.005 0.011 x19 0.0002 2.99e-05 7.428 0.000 0.000 0.000 x20 -0.0022 0.000 -6.445 0.000 -0.003 -0.002 const 2.9589 1.547 1.913 0.056 -0.073 5.990 ============================================================================== ###Markdown Quantities of interest ###Code print('Total number of trials:', data.endog[0].sum()) print('Parameters: ', res.params) print('T-values: ', res.tvalues) ###Output Total number of trials: 807.0 Parameters: [-1.68150366e-02 9.92547661e-03 -1.87242148e-02 -1.42385609e-02 2.54487173e-01 2.40693664e-01 8.04086739e-02 -1.95216050e+00 -3.34086475e-01 -1.69022168e-01 4.91670212e-03 -3.57996435e-03 -1.40765648e-02 -4.00499176e-03 -3.90639579e-03 9.17143006e-02 4.89898381e-02 8.04073890e-03 2.22009503e-04 -2.24924861e-03 2.95887793e+00] T-values: [-38.74908321 16.50473627 -25.1821894 -32.81791308 8.49827113 4.21247925 5.7749976 -6.16191078 -5.45321673 -5.16865445 3.92119964 -15.87825999 -7.39093058 -8.44963886 -4.05916246 6.3210987 6.57434662 5.36229044 7.42806363 -6.44513698 1.91301155] ###Markdown First differences: We hold all explanatory variables constant at their means and manipulate the percentage of low income households to assess its impact on the response variables: ###Code means = data.exog.mean(axis=0) means25 = means.copy() means25[0] = stats.scoreatpercentile(data.exog[:,0], 25) means75 = means.copy() means75[0] = lowinc_75per = stats.scoreatpercentile(data.exog[:,0], 75) resp_25 = res.predict(means25) resp_75 = res.predict(means75) diff = resp_75 - resp_25 ###Output _____no_output_____ ###Markdown The interquartile first difference for the percentage of low income households in a school district is: ###Code print("%2.4f%%" % (diff100)) ###Output -11.8753% ###Markdown Plots We extract information that will be used to draw some interesting plots: ###Code nobs = res.nobs y = data.endog[:,0]/data.endog.sum(1) yhat = res.mu ###Output _____no_output_____ ###Markdown Plot yhat vs y: ###Code from statsmodels.graphics.api import abline_plot fig, ax = plt.subplots() ax.scatter(yhat, y) line_fit = sm.OLS(y, sm.add_constant(yhat, prepend=True)).fit() abline_plot(model_results=line_fit, ax=ax) ax.set_title('Model Fit Plot') ax.set_ylabel('Observed values') ax.set_xlabel('Fitted values'); ###Output _____no_output_____ ###Markdown Plot yhat vs. Pearson residuals: ###Code fig, ax = plt.subplots() ax.scatter(yhat, res.resid_pearson) ax.hlines(0, 0, 1) ax.set_xlim(0, 1) ax.set_title('Residual Dependence Plot') ax.set_ylabel('Pearson Residuals') ax.set_xlabel('Fitted values') ###Output _____no_output_____ ###Markdown Histogram of standardized deviance residuals: ###Code from scipy import stats fig, ax = plt.subplots() resid = res.resid_deviance.copy() resid_std = stats.zscore(resid) ax.hist(resid_std, bins=25) ax.set_title('Histogram of standardized deviance residuals'); ###Output _____no_output_____ ###Markdown QQ Plot of Deviance Residuals: ###Code from statsmodels import graphics graphics.gofplots.qqplot(resid, line='r') ###Output _____no_output_____ ###Markdown GLM: Gamma for proportional count response Load Scottish Parliament Voting data In the example above, we printed the ``NOTE`` attribute to learn about the Star98 dataset. statsmodels datasets ships with other useful information. For example: ###Code print(sm.datasets.scotland.DESCRLONG) ###Output This data is based on the example in Gill and describes the proportion of voters who voted Yes to grant the Scottish Parliament taxation powers. The data are divided into 32 council districts. This example's explanatory variables include the amount of council tax collected in pounds sterling as of April 1997 per two adults before adjustments, the female percentage of total claims for unemployment benefits as of January, 1998, the standardized mortality rate (UK is 100), the percentage of labor force participation, regional GDP, the percentage of children aged 5 to 15, and an interaction term between female unemployment and the council tax. The original source files and variable information are included in /scotland/src/ ###Markdown Load the data and add a constant to the exogenous variables: ###Code data2 = sm.datasets.scotland.load() data2.exog = sm.add_constant(data2.exog, prepend=False) print(data2.exog[:5,:]) print(data2.endog[:5]) ###Output [[7.12000e+02 2.10000e+01 1.05000e+02 8.24000e+01 1.35660e+04 1.23000e+01 1.49520e+04 1.00000e+00] [6.43000e+02 2.65000e+01 9.70000e+01 8.02000e+01 1.35660e+04 1.53000e+01 1.70395e+04 1.00000e+00] [6.79000e+02 2.83000e+01 1.13000e+02 8.63000e+01 9.61100e+03 1.39000e+01 1.92157e+04 1.00000e+00] [8.01000e+02 2.71000e+01 1.09000e+02 8.04000e+01 9.48300e+03 1.36000e+01 2.17071e+04 1.00000e+00] [7.53000e+02 2.20000e+01 1.15000e+02 6.47000e+01 9.26500e+03 1.46000e+01 1.65660e+04 1.00000e+00]] [60.3 52.3 53.4 57. 68.7] ###Markdown Model Fit and summary ###Code glm_gamma = sm.GLM(data2.endog, data2.exog, family=sm.families.Gamma(sm.families.links.log())) glm_results = glm_gamma.fit() print(glm_results.summary()) ###Output Generalized Linear Model Regression Results ============================================================================== Dep. Variable: y No. Observations: 32 Model: GLM Df Residuals: 24 Model Family: Gamma Df Model: 7 Link Function: log Scale: 0.0035927 Method: IRLS Log-Likelihood: -83.110 Date: Tue, 02 Feb 2021 Deviance: 0.087988 Time: 06:54:04 Pearson chi2: 0.0862 No. Iterations: 7 Covariance Type: nonrobust ============================================================================== coef std err z P>\|z\| [0.025 0.975] ------------------------------------------------------------------------------ x1 -0.0024 0.001 -2.466 0.014 -0.004 -0.000 x2 -0.1005 0.031 -3.269 0.001 -0.161 -0.040 x3 0.0048 0.002 2.946 0.003 0.002 0.008 x4 -0.0067 0.003 -2.534 0.011 -0.012 -0.002 x5 8.173e-06 7.19e-06 1.136 0.256 -5.93e-06 2.23e-05 x6 0.0298 0.015 2.009 0.045 0.001 0.059 x7 0.0001 4.33e-05 2.724 0.006 3.31e-05 0.000 const 5.6581 0.680 8.318 0.000 4.325 6.991 ============================================================================== ###Markdown GLM: Gaussian distribution with a noncanonical link Artificial data ###Code nobs2 = 100 x = np.arange(nobs2) np.random.seed(54321) X = np.column_stack((x,x2)) X = sm.add_constant(X, prepend=False) lny = np.exp(-(.03x + .0001x2 - 1.0)) + .001 np.random.rand(nobs2) ###Output _____no_output_____ ###Markdown Fit and summary (artificial data) ###Code gauss_log = sm.GLM(lny, X, family=sm.families.Gaussian(sm.families.links.log())) gauss_log_results = gauss_log.fit() print(gauss_log_results.summary()) ###Output Generalized Linear Model Regression Results ============================================================================== Dep. Variable: y No. Observations: 100 Model: GLM Df Residuals: 97 Model Family: Gaussian Df Model: 2 Link Function: log Scale: 1.0531e-07 Method: IRLS Log-Likelihood: 662.92 Date: Tue, 02 Feb 2021 Deviance: 1.0215e-05 Time: 06:54:04 Pearson chi2: 1.02e-05 No. Iterations: 7 Covariance Type: nonrobust ============================================================================== coef std err z P>\|z\| [0.025 0.975] ------------------------------------------------------------------------------ x1 -0.0300 5.6e-06 -5361.316 0.000 -0.030 -0.030 x2 -9.939e-05 1.05e-07 -951.091 0.000 -9.96e-05 -9.92e-05 const 1.0003 5.39e-05 1.86e+04 0.000 1.000 1.000 ==============================================================================
notebooks/1.01_deep_particle_crossing_locations.ipynb	###Markdown 1.01 Deep Particle Crossing Locations---Author: Riley X. BradyDate: 11/18/2020This notebook pulls in the 19,002 particles that have been identified as those who last cross 1000 m in the ACC (S of 45S and outside of the annual sea ice zone) and finds the x, y location in which they last upwell at 1000 m. These locations are used in subsequent notebooks for filtering and visualization. ###Code %load_ext lab_black %load_ext autoreload %autoreload 2 import numpy as np import xarray as xr from dask.distributed import Client print(f"xarray: {xr.__version__}") print(f"numpy: {np.__version__}") # This is my TCP client from the `launch_cluster` notebook. I use it # for distributed computing with `dask` on NCAR's machine, Casper. client = Client("tcp://...") # Load in the `zarr` file, which is pre-chunked and already has been # filtered from the original 1,000,000 particles to the 19,002 that # upwell last across 1000 m S of 45S and outside of the annual sea ice # edge. filepath = "../data/southern_ocean_deep_upwelling_particles.zarr/" ds = xr.open_zarr(filepath, consolidated=True) def _compute_idx_of_last_1000m_crossing(z): """Find index of final time particle upwells across 1000 m. z : zLevelParticle """ currentDepth = z previousDepth = np.roll(z, 1) previousDepth[0] = 999 # So we're not dealing with a nan here. cond = (currentDepth >= -1000) & (previousDepth < -1000) idx = ( len(cond) - np.flip(cond).argmax() - 1 ) # Finds last location that condition is true. return idx def compute_xy_of_last_crossing(x, y, z): """Convert crossing point into x, y coordinates. x : lonParticle (radians) y : latParticle (radians) z : zLevelParticle (m) """ idx = _compute_idx_of_last_1000m_crossing(z) lon = np.rad2deg(x[idx]) lat = np.rad2deg(y[idx]) return np.array([lon, lat]) ds = ds.chunk({"time": -1, "nParticles": 6000}) x = ds.lonParticle.persist() y = ds.latParticle.persist() z = ds.zLevelParticle.persist() result = xr.apply_ufunc( compute_xy_of_last_crossing, x, y, z, input_core_dims=[["time"], ["time"], ["time"]], vectorize=True, dask="parallelized", output_core_dims=[["coordinate"]], output_dtypes=[float], dask_gufunc_kwargs={"output_sizes": {"coordinate": 2}}, ) %time crossings = result.compute() crossings = crossings.assign_coords(coordinate=["x", "y"]) ds = xr.Dataset() ds["lon_crossing"] = crossings.sel(coordinate="x") ds["lat_crossing"] = crossings.sel(coordinate="y") ds.attrs[ "description" ] = "x/y locations of final 1000m crossing for particles that occur < 45S; reach 200 m following this crossing; and happen outside of the 75% annual sea ice zone." ds.to_netcdf("../data/postproc/1000m.crossing.locations.nc") ###Output _____no_output_____
bayesian_decision_tree/naive_bayesian_sklearn.ipynb	###Markdown Naive Bayes with SklearnUse naive bayes with TF_IDF to conduct sentiment analysis on movie reviews ###Code import pandas as pd import warnings warnings.filterwarnings('ignore') ###Output _____no_output_____ ###Markdown 1. Define Training/Test Set ###Code #load movie reviews reviews = pd.read_csv('ratings_train.txt', delimiter='\t') reviews.head() #load test set test_reviews = pd.read_csv('ratings_test.txt', delimiter='\t') test_reviews.head() ###Output _____no_output_____ ###Markdown 2. Exploratory Data Analysis ###Code #size of each data print('Train set : ', reviews.shape) print('Test set : ', test_reviews.shape) ###Output Train set : (150000, 3) Test set : (50000, 3) ###Markdown * 150,000 training set and 50,000 test set* 0 is negative and 1 is positive ###Code #compare positive vs. negative reviews in training dataset reviews.label.value_counts() #compare positive vs. negative reviews in test dataset test_reviews.label.value_counts() ###Output _____no_output_____ ###Markdown Let's explore the distribution of each review's sentence length! ###Code import seaborn as sns reviews['length'] = reviews['document'].apply(lambda x: len(str(x))) reviews.head() reviews.sort_values('length', ascending=False).head() ###Output _____no_output_____ ###Markdown Let's draw histogram ###Code #overall training dataset sentence length sns.distplot(reviews['length'], kde=False) #positive training dataset sentence length sns.distplot(reviews[reviews['label'] == 1]['length'], kde=False) #negative training dataset sentence length sns.distplot(reviews[reviews['label'] == 0]['length'], kde=False) ###Output _____no_output_____ ###Markdown What we can conclude:* There is no significant difference between lengths of positive/negative reviews ###Code reviews.length.describe() ###Output _____no_output_____ ###Markdown 3. Text Preprocessing ###Code # korean nlp import konlpy from konlpy.tag import Okt okt = Okt() def parse(s): try: return okt.nouns(s) except: return [] reviews['parsed_doc'] = reviews.document.apply(parse) reviews.head() ###Output _____no_output_____ ###Markdown 4. Vectorization (Bag of Words)* Vectorize bag of word counts for each sentences* Use term frequency for every word in bag of words* If the corpus is cat, love, i, do, like, him, you, then sentence "I love you only you" will be (0, 1, 1, 0, 0, 0, 2) ###Code from sklearn.feature_extraction.text import CountVectorizer #create a bag of words transformer bow_transformer = CountVectorizer(analyzer=parse).fit(reviews.document) #length of corpus len(bow_transformer.vocabulary_) ###Output _____no_output_____ ###Markdown Length of the corpus is 38,648, which means each vectorized sentence will be of the same length ###Code sample = reviews.document.iloc[3] sample sample_bow = bow_transformer.transform([sample]) print(sample_bow) print(bow_transformer.get_feature_names()[2509]) print(bow_transformer.get_feature_names()[2630]) print(bow_transformer.get_feature_names()[26019]) #this will "transform" each sentences in terms of the vectorized bag of words above reviews_bow = bow_transformer.transform(reviews.document) #we have 150,000 training dataset, which corresponds to the below # sparse matrix reviews_bow.shape #occurence of non-zero values in the sparse matrix reviews_bow.nnz #calculate sparsity sparsity = 100 * reviews_bow.nnz / (reviews_bow.shape[0] * reviews_bow.shape[1]) print(round(sparsity, 3)) ###Output 0.015 ###Markdown 0.015% of sparse matrix consist of zeros 5. Normalization of Vectors (TF-IDF) 5.1. TF (Term Frequency)Term Frequency, which measures how frequently a term occurs in a document. Since every document is different in length, it is possible that a term would appear much more times in long documents than shorter ones. Thus, the term frequency is often divided by the document length as a way of normalization:$TF(t) = \large \frac{\text{The number of times term t appears in a document}}{\text{The total number of terms in the document}}$ 5.2. IDF (Inverse Document Frequency)Inverse Document Frequency, which measures how important a term is. While computing TF, all terms are considered equally important. However it is known that certain terms, such as "is", "of", and "that", may appear a lot of times but have little importance. Thus we need to weigh down the frequent terms while scale up the rare ones, by computing the following:$IDF(t) = log_{e}\large\frac{\text{The total number of documents}}{\text{The number of documents with term t in it}}$(예시) 100개의 단어를 포함하고 있는 document를 생각해보자. "cat"이라는 단어가 그 document에 3번 나온다고 가정하자. 10,000,000의 문서 중에서 1,000의 문서에서만 "cat"이 나온다고 가정하자.* TF : 3/100=0.03* IDF : loge(10,000,000/1,000)=4* TF-IDF : 0.03×4=0.12 ###Code from sklearn.feature_extraction.text import TfidfTransformer tfidf_transformer = TfidfTransformer().fit(reviews_bow) sample_tfidf = tfidf_transformer.transform(sample_bow) print(sample_tfidf) print(tfidf_transformer.idf_[bow_transformer.vocabulary_['교도소']]) print(tfidf_transformer.idf_[bow_transformer.vocabulary_['이야기']]) reviews_tfidf = tfidf_transformer.transform(reviews_bow) reviews_tfidf.shape ###Output _____no_output_____ ###Markdown 6. Training ###Code from sklearn.naive_bayes import MultinomialNB #train our model sentiment_detect_model = MultinomialNB().fit(reviews_tfidf, reviews['label']) #check sample sentence sentiment_detect_model.predict(sample_tfidf)[0] #negative reviews['label'].iloc[3] # check accuracy of training data from sklearn.metrics import accuracy_score train_preds = sentiment_detect_model.predict(reviews_tfidf) train_preds[:10] train_targets = reviews['label'].values accuracy_score(train_targets, train_preds) ###Output _____no_output_____ ###Markdown 7. Testing Model ###Code #vectorize test set in terms of bag of words vertors test_reviews_bow = bow_transformer.transform(test_reviews.document) #apply tf-idf vectors to test data test_reviews_tfidf = tfidf_transformer.transform(test_reviews_bow) # predict test data test_preds = sentiment_detect_model.predict(test_reviews_tfidf) # true values from test data test_targets = test_reviews['label'].values test_targets # accuracy accuracy_score(test_targets, test_preds) ###Output _____no_output_____
static-web/simple-html-web-scraping-with-beautiful-soup.ipynb	###Markdown Simple HTML Web Scraping with Beautiful Soup Environment setup Libraries pip install BeautifulSoup4 pip install pandas Web scraping ###Code # import libraries from bs4 import BeautifulSoup import urllib.request import csv # specify the url # I will obtain data from University of Waterloo's CS courses # prerequisite chart. urlpage = 'https://cs.uwaterloo.ca/current-undergraduate-students/majors/prerequisite-chain-computer-science-major-courses/cs-prerequisite-chart' # query the website to return the html and store page_html = urllib.request.urlopen(urlpage) # parse html with beautiful soup and store soup = BeautifulSoup(page_html, 'html.parser') # test # If the ouput is empty or is an error, # further debugging is required. soup # find results 'table' table = soup.find('tbody') results = table.findAll('tr') print('Number of results', len(results)) results rows = [] rows.append(['Course', 'Title', 'Prereqs', 'Coreqs', 'Successors', 'Terms offered', 'Open to non-CS majors']) # loop over results for res in results: # get course name written between th tags # remove unwanted spaces to make course name uniform course = res.find('th').get_text() course = course.strip('\n').replace(" ", "") # test # print(course) # obtain other data by columns data = res.find_all('td') # write columns to variables title = data[0].getText() prereqs = data[1].getText() coreqs = data[2].getText() succ = data[3].getText() terms = data[4].getText() non_cs = data[5].getText() # remove newline prereqs = prereqs.strip('\n') coreqs = coreqs.strip('\n') succ = succ.strip('\n') # test # print('--------------------') # print(title) # print('--------------------') # print(prereqs) # print('--------------------') # print(coreqs) # print('--------------------') # print(succ) # print('--------------------') # print(terms) # print('--------------------') # print(non_cs) # print('====================') rows.append([course, title, prereqs, coreqs, succ, terms, non_cs]) print(rows) # create CSV and write to output file # w : writing with open('courses.csv','w', newline='') as f_output: csv_output = csv.writer(f_output) csv_output.writerows(rows) ###Output _____no_output_____
jupyter/notebooks/MLManager Clearsence Demo.ipynb	###Markdown s {}h1, h2, h3, h4, h5, h6, table, button, a, p, blockquote {font-family:Geneva;}.log {transition: all .2s ease-in-out;}.log:hover {atransform: scale(1.05);}Welcome to Splice Machine MLManagerThe data platform for intelligent applications blockquote{ font-size: 15px; background: f9f9f9; border-left: 10px solid ccc; margin: .5em 10px; padding: 30em, 10px; quotes: "\201C""\201D""\2018""\2019"; padding: 10px 20px; line-height: 1.4;}blockquote:before { content: open-quote; display: inline; height: 0; line-height: 0; left: -10px; position: relative; top: 30px; bottom:30px; color: ccc; font-size: 3em; display:none;}p{ margin: 0;}footer{ margin:0; text-align: right; font-size: 1em; font-style: italic;}Why use Splice Machine MLSplice Machine ML isn't just a machine learning platform, it is a complete machine learning lifecycle management solution, giving you total control of your models, from retrieving data to scalable deployment. Our platform runs directly on Apache Spark, allowing you to complete massive jobs in parallelOur native PySpliceContext lets you directly access the data in your database and convert as a Spark DataFrame, no ETL.MLFlow is integrated directly into all Splice Machine clusters, allowing you to keep track of your entire Data Science workflowAfter you have found the best model for your task, you can easily deploy it live to AWS SageMaker or AzureML to make predictions in real time.MLFlow does not force a standard workflow, instead it allows teams to develop their own methodology easily that fits their teams and problemsIn this demo we will guide you through the entire MLManager life cycle.Your friends at Splice Machine How does this work?blockquote{ font-size: 15px; background: f9f9f9; border-left: 10px solid ccc; margin: .5em 10px; padding: 30em, 10px; quotes: "\201C""\201D""\2018""\2019"; padding: 10px 20px; line-height: 1.4;}blockquote:before { content: open-quote; display: inline; height: 0; line-height: 0; left: -10px; position: relative; top: 30px; bottom:30px; color: ccc; font-size: 3em; display:none;}p{ margin: 0;}footer{ margin:0; text-align: right; font-size: 1em; font-style: italic;}Jupyter Jupyter notebooks are a simple, easy and intuitive way to do data science, directly in your browser. Any Spark computations you run inside of the notebook are executed right on your cluster's Spark executors.Jupyter notebooks also make machine learning easier. By using Jupyter magics, you can run different languages inside the same notebook. The language you want to run is signified by a %% sign followed by a magic at the top of a cell. For example, one of the interpreters you will become very familiar with while using our platform %%sql magic. In the %%sql magic you can run standard SQL queries and visualize the results in Jupyter's built in visulaization tools. This entire demo was written inside a Jupyter notebookSplice MachineMLFlowAs a data scientist constantly creating new models and testing new features, it is necessary to effectively track and manage those different ML runs. MLFlow allows you to track entire experiments and individual run parameters and metrics. The way you organize your flow is unique to you, and the intuitive Python API allows you to organize your delevopement process and run with it. Ready? Let's get started. Problem statement: Can we predict the likelihood of fraudulent transactions after training on historical actuals? We're going to find out using Splice Machine's MLManager ###Code # !pip install seaborn statsmodels from utils import * hide_toggle() !wget https://splice-releases.s3.amazonaws.com/jdbc-driver/db-client-2.7.0.1815.jar %%sql %classpath add jar db-client-2.7.0.1815.jar %defaultDatasource jdbc:splice://host.docker.internal:1527/splicedb;user=splice;password=admin %%sql create schema cc_fraud; set schema cc_fraud; --drop table if exists cc_fraud.cc_fraud_data; create table cc_fraud.cc_fraud_data ( time_offset integer, v1 double, v2 double, v3 double, v4 double, v5 double, v6 double, v7 double, v8 double, v9 double, v10 double, v11 double, v12 double, v13 double, v14 double, v15 double, v16 double, v17 double, v18 double, v19 double, v20 double, v21 double, v22 double, v23 double, v24 double, v25 double, v26 double, v27 double, v28 double, amount decimal(10,2), class_result int ); call SYSCS_UTIL.IMPORT_DATA ( 'cc_fraud', 'cc_fraud_data', null, 's3a://splice-demo/kaggle-fraud-data/creditcard.csv', ',', null, null, null, null, -1, 's3a://splice-demo/kaggle-fraud-data/bad', null, null); %%sql select top 10 * from cc_fraud.cc_fraud_data %%sql select class_result, count() from cc_fraud.cc_fraud_data group by class_result %%sql explain select class_result, count() from cc_fraud.cc_fraud_data group by class_result ###Output _____no_output_____ ###Markdown Connecting to your databaseNow, let's establish a connection to your database using Python via our Native Spark Datasource. We will use the PySpliceContext to establish our direct connection-- it allows us to do inserts, selects, upserts, updates and many more functions without serializationSplice Machine ###Code from pyspark.sql import SparkSession # from splicemachine.spark.context import PySpliceContext # Create our Spark Session spark = SparkSession.builder.getOrCreate() sc = spark.sparkContext # Create out Native Database Connection splice = PySpliceContext(spark) ###Output _____no_output_____ ###Markdown Let's create our MLManager When you create an MLManager object, a tracking URL is returned to you. There is one tracking URL _per cluster_ so if you create another one in a new notebook, it will return the same tracking URL. This is useful because you can create multiple different experiments across all notebooks, and all will be tracked in the MLFlow UI.Splice Machine ###Code import os os.environ['MLFLOW_URL'] = 'mlflow:5001' hide_toggle() from splicemachine.ml.management import MLManager manager = MLManager(splice) ###Output Tracking Model Metadata on MLFlow Server @ http://mlflow:5001 ###Markdown Loading The DataData LoadingLoading data into Splice Machine couldn't be easier, no matter the source. Because we connect directly to our database source, there is no ETL necessary.Splice Machine Let's import our data into a Spark DataFrame using our PySpliceContext Now is also a good time to create our MLFlow Experiment which we will call fraud_demo ###Code #create our MLFlow experiment manager.create_experiment('fraud_demo') df = splice.df("SELECT * FROM cc_fraud.cc_fraud_data") df = df.withColumnRenamed('CLASS_RESULT', 'label') display(df.limit(10).toPandas()) ###Output Experiment fraud_demo already exists... setting to active experiment Active experiment has id 1 ###Markdown We can now see our experiment in the MLFlow UI at port 5001 Data investigation Before going further, it's important to look at the correlations between all of your features and each other as well as the label We can easily create a heatmap to compare all features against each other and the label ###Code import pandas as pd import matplotlib.pyplot as plt import numpy as np for i in df.columns: df = df.withColumn(i,df[i].cast(FloatType())) pdf = df.limit(5000).toPandas() correlations = pdf.corr() correlations.style.set_precision(2) plt.rcParams["figure.figsize"] = (8,12) plt.matshow(correlations, cmap='coolwarm') ticks = [i for i in range(len(correlations.columns))] plt.xticks(ticks, correlations.columns) plt.yticks(ticks, correlations.columns) plt.title('Fraud Data correlation heatmap') plt.show() ###Output _____no_output_____ ###Markdown Ben's run Ben, our first Data Scientist, has an idea for the steps to build this model. He will create a run and log his name as to keep track of what he did manager.start_run() You can set tags to your run such as team, purpose, or anything you'd like to track your runs. You can also set a run_name as a parameter. The user_id will automatically be added as the user that is signed into this notebook (currently that's me, Ben) If you navigate to the mlflow port you will now see the fraud-demo experiment, but there is nothing in that experiment yet. Let's start our first run and track our progress ###Code #start our first MLFlow run tags = { 'team': 'Clearsense', 'purpose': 'fraud r&d', 'attempt-date': '11/07/2019', 'attempt-number': '1' } manager.start_run(tags=tags) ###Output _____no_output_____ ###Markdown Let's look at some of the attributes of this dataset:* Because we have so few fraud examples, we need to oversample our fraudulent transactions and undersample the non-fraud transactions* We need to make sure the model isn't overfit and doesn't always predict non-fraud (due to the lack of fraud data) so we can't only rely on accuracy* We want to pick a model that doesn't have a high overfitting rate Let's define our PipelineYou can use Spark's Pipeline class to define a set of Transformers that set up your dataset for modelingWe'll then use MLManager to log our Pipeline stages ###Code from pyspark.ml.feature import StandardScaler, VectorAssembler from pyspark.ml import Pipeline,PipelineModel from pyspark.ml.classification import RandomForestClassifier, MultilayerPerceptronClassifier feature_cols = df.columns[:-1] assembler = VectorAssembler(inputCols=feature_cols, outputCol='features') scaler = StandardScaler(inputCol="features", outputCol='scaledFeatures') rf = RandomForestClassifier() stages = [assembler,scaler,rf] mlpipe = Pipeline(stages=stages) manager.log_pipeline_stages(mlpipe) ###Output _____no_output_____ ###Markdown Model setupNow we can set up our modeling process. We will use our OverSampleCrossValidator to properly oversample our dataset for model building.While we do that, we'll add just a few lines of code to track all of our moves in MLFlow ###Code from utils1 import OverSampleCrossValidator as OSCV from pyspark.ml.tuning import ParamGridBuilder from pyspark.ml.evaluation import BinaryClassificationEvaluator,MulticlassClassificationEvaluator import pandas as pd import numpy as np # Define evaluation metrics PRevaluator = BinaryClassificationEvaluator(metricName = 'areaUnderPR') # Because this is a needle in haystack problem AUCevaluator = BinaryClassificationEvaluator(metricName = 'areaUnderROC') ACCevaluator = MulticlassClassificationEvaluator(metricName="accuracy") f1evaluator = MulticlassClassificationEvaluator(metricName="f1") # Define hyperparameters to try params = {rf.maxDepth: [5,15], \ rf.numTrees: [10,30], \ rf.minInfoGain: [0.0,2.0]} paramGrid_stages = ParamGridBuilder() for param in params: paramGrid_stages.addGrid(param,params[param]) paramGrid = paramGrid_stages.build() # Create the CrossValidator fraud_cv = OSCV(estimator=mlpipe, estimatorParamMaps=paramGrid, evaluator=PRevaluator, numFolds=3, label = 'label', seed = 1234, parallelism = 3, altEvaluators = [ACCevaluator, f1evaluator, AUCevaluator]) ###Output _____no_output_____ ###Markdown Run the CVNow we can run the CrossValidator and log the results to MLFlow ###Code df = df.withColumnRenamed('Amount', 'label') manager.start_timer('with_oversample') fraud_cv_model, alt_metrics = fraud_cv.fit(df) execution_time = manager.log_and_stop_timer() print(f"--- {execution_time} seconds == {execution_time/60} minutes == {execution_time/60/60} hours") # Grab metrics of best model best_avg_prauc = max(mycvModel.avgMetrics) best_performing_model = np.argmax(fraud_cv_model.avgMetrics) # metrics at the best performing model for this iteration best_avg_acc = [alt_metrics[i][0] for i in range(len(alt_metrics))][best_performing_model] best_avg_f1 = [alt_metrics[i][1] for i in range(len(alt_metrics))][best_performing_model] best_avg_rocauc = [alt_metrics[i][2] for i in range(len(alt_metrics))][best_performing_model] print(f"The Best average (Area under PR) for this grid search: {best_avg_prauc}") print(f"The Best average (Accuracy) for this grid search: {best_avg_acc}") print(f"The Best average (F1) for this grid search: {best_avg_f1}") print(f"The Best average (Area under ROC) for this grid search: {best_avg_rocauc}") evals = [('areaUnderPR',best_avg_prauc), ('Accuracy',best_avg_acc),('F1',best_avg_f1),('areaUnderROC',best_avg_rocauc)] manager.log_metrics(evals) # Get the best parameters bestParamsCombination = {} for stage in fraud_cv_model.bestModel.stages: bestParams = stage.extractParamMap() for param in params: if param in bestParams: bestParamsCombination[param] = bestParams[param] #log the hyperparams manager.log_params(list(bestParamsCombination.items())) print("Best Param Combination according to f1 is: \n") print(pd.DataFrame([(str(i.name),str(bestParamsCombination[i]))for i in bestParamsCombination], columns = ['Param','Value'])) # Feature importance of the Principal comp importances = fraud_cv_model.bestModel.stages[-1].featureImportances.toArray() top_5_idx = np.argsort(importances)[-5:] top_5_values = [importances[i] for i in top_5_idx] top_5_features = [new_features[i] for i in top_5_idx] print("___________________________________") importances = fraud_cv_model.bestModel.stages[-1].featureImportances.toArray() print("Most Important Features are") print(pd.DataFrame(zip(top_5_features,top_5_values), columns = ['Feature','Importance']).sort_values('Importance',ascending=False)) #Log feature importances manager.log_params() import utils1 import importlib importlib.reload(utils1) import random from utils1 import overSampler from splicemachine.ml.utilities import SpliceBinaryClassificationEvaluator rf_depth = [5,10,20,30] rf_trees = [8,12,18,26] rf_subsampling_rate = [1.0,0.9,0.8] oversample_rate = [0.4,0.7,1.0] for i in range(1,5): tags = { 'team': 'Clearsense', 'purpose': 'fraud r&d', 'attempt-date': '11/07/2019', 'attempt-number': 'f{i}' } manager.start_run(tags=tags) #random variable choice depth = random.choice(rf_depth) trees = random.choice(rf_trees) subsamp_rate = random.choice(rf_subsampling_rate) ovrsmpl_rate = random.choice(oversample_rate) #transformers feature_cols = df.columns[:-1] ovr = overSampler(label='label',ratio = ovrsmpl_rate, majorityLabel = 0, minorityLabel = 1, withReplacement = False) assembler = VectorAssembler(inputCols=feature_cols, outputCol='features') scaler = StandardScaler(inputCol="features", outputCol='scaledFeatures') rf = RandomForestClassifier(maxDepth=depth, numTrees=trees, subsamplingRate=subsamp_rate) #pipeline stages = [ovr,assembler,scaler,rf] mlpipe = Pipeline(stages=stages) #log the stages of the pipeline manager.log_pipeline_stages(mlpipe) #log what happens to each feature manager.log_feature_transformations(mlpipe) #run on the data train, test = df.randomSplit([0.8,0.2]) manager.start_timer(f'CV iteration {i}') trainedModel = mlpipe.fit(train) execution_time = manager.log_and_stop_timer() print(f"--- {execution_time} seconds == {execution_time/60} minutes == {execution_time/60/60} hours") #log model parameters manager.log_model_params(trainedModel) preds = trainedModel.transform(test) #evaluate evaluator = SpliceBinaryClassificationEvaluator() evaluator.input(preds) metrics = evaluator.get_results(dict=True) #log model performance manager.log_metrics(list(metrics.items())) ###Output _____no_output_____
HandsOnML/ch03/ex01.ipynb	###Markdown 3.1 Problem description Try to build a classifier for the MNIST dataset that achieves over 97% accuracyon the test set. Hint: the `KNeighborsClassifier` works quite well for this task;you just need to find good hyperparameter values (try a grid search on theweights and n_neighbors hyperparameters). Load the data ###Code from scipy.io import loadmat mnist = loadmat('./datasets/mnist-original.mat') mnist X, y = mnist['data'], mnist['label'] X = X.T X.shape y = y.T y.shape type(y) %matplotlib inline import matplotlib import matplotlib.pyplot as plt ###Output _____no_output_____ ###Markdown Split test and training data ###Code X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:] len(X_train) shuffle_index = np.random.permutation(len(X_train)) X_train, y_train = X_train[shuffle_index], y_train[shuffle_index] ###Output _____no_output_____ ###Markdown 3.2 Training a Random Forest Classifier for baseline The reason to use Random Forest Classifier is it runs faster than linear model ###Code from sklearn.ensemble import RandomForestClassifier forest_clf = RandomForestClassifier(random_state=42) forest_clf.fit(X_train, y_train) forest_pred = forest_clf.predict(X_test) forest_pred = forest_pred.reshape(10000,1) accuracy = (forest_pred == y_test).sum() / len(y_test) print(accuracy) ###Output _____no_output_____ ###Markdown 3.3 Training a `KNeighborsClassifier` Classifier with default settings Seems like we have to have `n_jobs = 1` so the prediction runs within reasonable time. ###Code from sklearn.neighbors import KNeighborsClassifier knn_clf = KNeighborsClassifier(n_jobs=-1) knn_clf.fit(X_train, y_train) knn_clf.predict([X_test[0]]) # for i in range(1000): # knn_clf.predict([X_test[i]]) knn_pred = knn_clf.predict(X_test) knn_pred = knn_pred.reshape(10000, 1) accuracy = (knn_pred == y_test).sum() / len(y_test) print(accuracy) ###Output _____no_output_____ ###Markdown 3.4 `GridSearchCV` ###Code from sklearn.model_selection import GridSearchCV param_grid = [ {'n_jobs': [-1], 'n_neighbors': [3, 5, 11, 19], 'weights': ['uniform', 'distance']} ] grid_search = GridSearchCV(knn_clf, param_grid, cv=3, scoring='accuracy', n_jobs=-1) grid_search.fit(X_train, y_train) ###Output _____no_output_____
lt219_max_sum_subarray_kadane_algo.ipynb	###Markdown https://leetcode.com/problems/maximum-subarray/ The key idea with kadane's algo is that a negative num will never make a positive contribution to our sum. So, if our current sum, drops below zero, we should just reset by setting our current sum to be the current num. Otherwise, we keep adding cumulative sum. https://afshinm.name/2018/06/24/why-kadane-algorithm-works/ ###Code def maxSubArray(nums) -> int: """ https://leetcode.com/problems/maximum-subarray/discuss/523386/Python-O(n)-Time-and-O(1)-Space%3A-Kadane's-Algorithm """ max_seq = nums[0] curr_sum = nums[0] for num in nums[1:]: if curr_sum < 0: curr_sum = num else: curr_sum += num if curr_sum > max_seq: max_seq = curr_sum return max_seq maxSubArray([-3,4,-1,5,-10,3]) ###Output _____no_output_____
python_bootcamp/notebooks/02-Python Statements/04-while Loops.ipynb	###Markdown while LoopsThe while statement in Python is one of most general ways to perform iteration. A while statement will repeatedly execute a single statement or group of statements as long as the condition is true. The reason it is called a 'loop' is because the code statements are looped through over and over again until the condition is no longer met.The general format of a while loop is: while test: code statements else: final code statementsLet’s look at a few simple while loops in action. ###Code x = 0 while x < 10: print('x is currently: ',x) print(' x is still less than 10, adding 1 to x') x+=1 ###Output x is currently: 0 x is still less than 10, adding 1 to x x is currently: 1 x is still less than 10, adding 1 to x x is currently: 2 x is still less than 10, adding 1 to x x is currently: 3 x is still less than 10, adding 1 to x x is currently: 4 x is still less than 10, adding 1 to x x is currently: 5 x is still less than 10, adding 1 to x x is currently: 6 x is still less than 10, adding 1 to x x is currently: 7 x is still less than 10, adding 1 to x x is currently: 8 x is still less than 10, adding 1 to x x is currently: 9 x is still less than 10, adding 1 to x ###Markdown Notice how many times the print statements occurred and how the while loop kept going until the True condition was met, which occurred once x==10. It's important to note that once this occurred the code stopped. Let's see how we could add an else statement: ###Code x = 0 while x < 10: print('x is currently: ',x) print(' x is still less than 10, adding 1 to x') x+=1 else: print('All Done!') ###Output x is currently: 0 x is still less than 10, adding 1 to x x is currently: 1 x is still less than 10, adding 1 to x x is currently: 2 x is still less than 10, adding 1 to x x is currently: 3 x is still less than 10, adding 1 to x x is currently: 4 x is still less than 10, adding 1 to x x is currently: 5 x is still less than 10, adding 1 to x x is currently: 6 x is still less than 10, adding 1 to x x is currently: 7 x is still less than 10, adding 1 to x x is currently: 8 x is still less than 10, adding 1 to x x is currently: 9 x is still less than 10, adding 1 to x All Done! ###Markdown break, continue, passWe can use break, continue, and pass statements in our loops to add additional functionality for various cases. The three statements are defined by: break: Breaks out of the current closest enclosing loop. continue: Goes to the top of the closest enclosing loop. pass: Does nothing at all. Thinking about break and continue statements, the general format of the while loop looks like this: while test: code statement if test: break if test: continue else:break and continue statements can appear anywhere inside the loop’s body, but we will usually put them further nested in conjunction with an if statement to perform an action based on some condition.Let's go ahead and look at some examples! ###Code x = 0 while x < 10: print('x is currently: ',x) print(' x is still less than 10, adding 1 to x') x+=1 if x==3: print('x==3') else: print('continuing...') continue ###Output x is currently: 0 x is still less than 10, adding 1 to x continuing... x is currently: 1 x is still less than 10, adding 1 to x continuing... x is currently: 2 x is still less than 10, adding 1 to x x==3 x is currently: 3 x is still less than 10, adding 1 to x continuing... x is currently: 4 x is still less than 10, adding 1 to x continuing... x is currently: 5 x is still less than 10, adding 1 to x continuing... x is currently: 6 x is still less than 10, adding 1 to x continuing... x is currently: 7 x is still less than 10, adding 1 to x continuing... x is currently: 8 x is still less than 10, adding 1 to x continuing... x is currently: 9 x is still less than 10, adding 1 to x continuing... ###Markdown Note how we have a printed statement when x==3, and a continue being printed out as we continue through the outer while loop. Let's put in a break once x ==3 and see if the result makes sense: ###Code x = 0 while x < 10: print('x is currently: ',x) print(' x is still less than 10, adding 1 to x') x+=1 if x==3: print('Breaking because x==3') break else: print('continuing...') continue ###Output x is currently: 0 x is still less than 10, adding 1 to x continuing... x is currently: 1 x is still less than 10, adding 1 to x continuing... x is currently: 2 x is still less than 10, adding 1 to x Breaking because x==3 ###Markdown Note how the other else statement wasn't reached and continuing was never printed!After these brief but simple examples, you should feel comfortable using while statements in your code.A word of caution however! It is possible to create an infinitely running loop with while statements. For example: ###Code # DO NOT RUN THIS CODE!!!! while True: print("I'm stuck in an infinite loop!") ###Output _____no_output_____
notebooks/sesssion_10.ipynb	###Markdown topics:- [Hough transform](Hough-transform) - [Hough Line Transform doc](https://docs.opencv.org/3.4/d9/db0/tutorial_hough_lines.html)- [Probabilistic-Hough-Transform](Probabilistic-Hough-Transform) - [lane Finder](lane-Finder)----slide 5 ###Code import cv2 import numpy as np import matplotlib.pyplot as plt from matplotlib.pyplot import figure ###Output _____no_output_____ ###Markdown Hough transform ###Code img = cv2.imread('session_10/dave.jpg') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) # find edge with some algorithm # other algorhtims: # https://github.com/bigmpc/cv-spring-2021/blob/main/notebooks/session_5.md # laplacian = cv2.Laplacian(img,cv2.CV_64F) # sobelx = cv2.Sobel(img,cv2.CV_64F,1,0,ksize=5) # sobely = cv2.Sobel(img,cv2.CV_64F,0,1,ksize=5) # scharrx = cv2.Scharr(img,cv2.CV_64F,1,0) # scharry = cv2.Scharr(img,cv2.CV_64F,0,1) edges = cv2.Canny(gray,50,150,apertureSize = 3) lines = cv2.HoughLines(edges,1,np.pi/180,120) for i in range(0, len(lines)): for rho,theta in lines[i]: a = np.cos(theta) b = np.sin(theta) x0 = arho y0 = brho x1 = int(x0 + 1000(-b)) y1 = int(y0 + 1000(a)) x2 = int(x0 - 1000(-b)) y2 = int(y0 - 1000(a)) cv2.line(img,(x1,y1),(x2,y2),(0,0,255),2) plt.imshow(img[:,:,::-1]) plt.show() ###Output _____no_output_____ ###Markdown Probabilistic Hough Transform ###Code #probabilistic hough transform img = cv2.imread('session_10/dave.jpg') gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) lines = cv2.HoughLinesP(edges,1,np.pi/180,120,10,5) for i in range(0, len(lines)): for x1,y1,x2,y2 in lines[i]: cv2.line(img,(x1,y1),(x2,y2),(0,255,0),2) plt.imshow(img[:,:,::-1]) plt.show() ###Output _____no_output_____ ###Markdown lane Finder![lane-Finder](session_10/lane-Finder.png) ###Code # preliminary attempt at lane following system # largely derived from: https://medium.com/pharos-production/ # road-lane-recognition-with-opencv-and-ios-a892a3ab635c # identify filename of video to be analyzed cap = cv2.VideoCapture('session_10/testvideo2.mp4') # read video frame & show on screen ret, frame = cap.read() # loop through until entire video file is played while(ret): frame=cv2.resize(frame,(800,400)) cv2.imshow("Original Scene", frame) # create polygon (trapezoid) mask to select region of interest mask = np.zeros((frame.shape[0], frame.shape[1]), dtype="uint8") pts = np.array([[80, 400], [330, 310], [450, 310], [750, 400]], dtype=np.int32) cv2.fillConvexPoly(mask, pts, 255) cv2.imshow("Mask", mask) # apply mask and show masked image on screen masked = cv2.bitwise_and(frame, frame, mask=mask) cv2.imshow("Region of Interest", masked) # convert to grayscale then black/white to binary image masked = cv2.cvtColor(masked, cv2.COLOR_BGR2GRAY) thresh = 200 ret,masked = cv2.threshold(masked, thresh, 255, cv2.THRESH_BINARY) #cv2.imshow("Black/White", masked) # identify edges & show on screen edged = cv2.Canny(masked, 30, 150) #cv2.imshow("Edged", edged) # perform full Hough Transform to identify lane lines lines = cv2.HoughLines(edged, 1, np.pi / 180, 25) # define arrays for left and right lanes rho_left = [] theta_left = [] rho_right = [] theta_right = [] # ensure cv2.HoughLines found at least one line if lines is not None: # loop through all of the lines found by cv2.HoughLines for i in range(0, len(lines)): # evaluate each row of cv2.HoughLines output 'lines' for rho, theta in lines[i]: # collect left lanes if theta < np.pi/2 and theta > np.pi/4: rho_left.append(rho) theta_left.append(theta) # plot all lane lines for DEMO PURPOSES ONLY # a = np.cos(theta); b = np.sin(theta) # x0 = a * rho; y0 = b * rho # x1 = int(x0 + 400 * (-b)); y1 = int(y0 + 400 * (a)) # x2 = int(x0 - 600 * (-b)); y2 = int(y0 - 600 * (a)) # # cv2.line(snip, (x1, y1), (x2, y2), (0, 0, 255), 1) # collect right lanes if theta > np.pi/2 and theta < 3np.pi/4: rho_right.append(rho) theta_right.append(theta) # # plot all lane lines for DEMO PURPOSES ONLY # a = np.cos(theta); b = np.sin(theta) # x0 = a rho; y0 = b * rho # x1 = int(x0 + 400 * (-b)); y1 = int(y0 + 400 * (a)) # x2 = int(x0 - 600 * (-b)); y2 = int(y0 - 600 * (a)) # # cv2.line(snip, (x1, y1), (x2, y2), (0, 0, 255), 1) # statistics to identify median lane dimensions left_rho = np.median(rho_left) left_theta = np.median(theta_left) right_rho = np.median(rho_right) right_theta = np.median(theta_right) # plot median lane on top of scene snip if left_theta > np.pi/4: a = np.cos(left_theta); b = np.sin(left_theta) x0 = a * left_rho; y0 = b * left_rho offset1 = 200; offset2 = 500 x1 = int(x0 - offset1 * (-b)); y1 = int(y0 - offset1 * (a)) x2 = int(x0 + offset2 * (-b)); y2 = int(y0 + offset2 * (a)) cv2.line(frame, (x1, y1), (x2, y2), (0, 255, 0), 6) if right_theta > np.pi/4: a = np.cos(right_theta); b = np.sin(right_theta) x0 = a * right_rho; y0 = b * right_rho offset1 = 500; offset2 = 800 x3 = int(x0 - offset1 * (-b)); y3 = int(y0 - offset1 * (a)) x4 = int(x0 - offset2 * (-b)); y4 = int(y0 - offset2 * (a)) cv2.line(frame, (x3, y3), (x4, y4), (255, 0, 0), 6) cv2.imshow("Lined", frame) # press the q key to break out of video if cv2.waitKey(25) & 0xFF == ord('q'): break # read video frame & show on screen ret, frame = cap.read() # clear everything once finished cap.release() cv2.destroyAllWindows() ###Output /usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3256: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, /usr/lib/python3/dist-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount)
UCI_NN_CNN.ipynb	###Markdown Start Training UCI HAR dataset with Fully Connected Network on all data per sample ###Code %run main.py configs\UCI_FC_0.json ###Output Using TensorFlow backend. [INFO]: Hi, This is root. [INFO]: After the configurations are successfully processed and dirs are created. [INFO]: The pipeline of the project will begin now. ###Markdown Testing with Best Epoch: "UC_FC_0-227-0.97.hdf5" ###Code %run main.py configs\UCI_FC_0_test.json ###Output [INFO]: Hi, This is root. [INFO]: Hi, This is root. [INFO]: After the configurations are successfully processed and dirs are created. [INFO]: After the configurations are successfully processed and dirs are created. [INFO]: The pipeline of the project will begin now. [INFO]: The pipeline of the project will begin now. ###Markdown Training UCI HAR dataset with Conv1D + Fully Connected Network on Time stamps data ###Code %run main.py configs\UCI_CNN.json ###Output [INFO]: Hi, This is root. [INFO]: Hi, This is root. [INFO]: Hi, This is root. [INFO]: After the configurations are successfully processed and dirs are created. [INFO]: After the configurations are successfully processed and dirs are created. [INFO]: After the configurations are successfully processed and dirs are created. [INFO]: The pipeline of the project will begin now. [INFO]: The pipeline of the project will begin now. [INFO]: The pipeline of the project will begin now. ###Markdown Testing with Best Epoch: "UCI_CNN-110-0.97.hdf5" ###Code %run main.py configs\UCI_CNN_test.json ###Output [INFO]: Hi, This is root. [INFO]: Hi, This is root. [INFO]: Hi, This is root. [INFO]: Hi, This is root. [INFO]: After the configurations are successfully processed and dirs are created. [INFO]: After the configurations are successfully processed and dirs are created. [INFO]: After the configurations are successfully processed and dirs are created. [INFO]: After the configurations are successfully processed and dirs are created. [INFO]: The pipeline of the project will begin now. [INFO]: The pipeline of the project will begin now. [INFO]: The pipeline of the project will begin now. [INFO]: The pipeline of the project will begin now.
2 Regression/2.2 polynomial regression/Polynomial_Regression.ipynb	###Markdown POLYNOMIAL REGRESSION Dalam masalah di dunia nyata, data tidak selalu terplot secara linear, tetapi bisa jadi terplot secara acak. Dalam hal ini, regresi linear bukanlah cara terbaik dalam menangani data. Oleh karena itu, kita akan mempelajari tentang Polynomial Regression. Persamaan polynomial didapatkan dari hasil transformasi dari persamaan linear. Jadi persamaan polynomial adalah sebagai berikut. Secara statistik polynomial regression tetap bersifat linear, karena koefisiennya bersifat linear. Polynomial dengan orde 2 ![image.png](attachment:image.png) Polynomial dengan orde 3 ![image.png](attachment:image.png) Polynomial dengan orde n ![image.png](attachment:image.png) Berikut adalah contoh gambar polynomial dengan orde 2. ![image.png](attachment:image.png) Polynomial FeaturesMatriks fitur polynomial didapatkan dari hasil transformasi kuadratik dari fitur yang ada. Sebagai contoh, kita ingin melakukan transformasi sebuah fitur dengan degree/orde = 2 ![image.png](attachment:image.png) ![image.png](attachment:image.png) Saat kita melakukan transformasi dengan kode sebagai berikut.poly = sklearn.PolynomialFeature(degree=2)polyy = poly.fit_transform(X)maka kita akan mendapatkan fitur yang sudah ditransformasi dengan bentuk $[1,a,b,a^2,ab,b^2]$ ![image.png](attachment:image.png) CODING SECTION Misalkan kita ingin menganalisa tingkat kejujuran calon karyawan baru. pada pekerjaan yang dia ingin lamar, dia menceritakan bahwa sudah memiliki pengalaman di pekerjaan tersebut selama 16 tahun dan mempunyai gaji sebesar 20 juta rupiah di perusahaan sebelumnya. Kita ingin melakukan pengecekan terhadap pernyataan calon karyawan tersebut. kita melakukan pengecekan terhadap data-data karyawan yang bekerja di bidang yang sama, yang sudah kita ambil dari situs pencarian kerja. Data dibawah ini adalah data gaji karyawan-karyawan terhadap lama pengalaman mereka bekerja. ###Code import numpy as np #aljabar linear import pandas as pd #pengolahan data import matplotlib.pyplot as plt #visualisasi df = pd.read_csv('salary.csv') #membaca data df.head(10) #mengubah data menjadi array agar bisa dilakukan proses machine learning X = df.pengalaman.values.reshape(-1,1) y = df.gaji.values # Fitting Linear Regression to the dataset from sklearn.linear_model import LinearRegression lin_reg = LinearRegression() lin_reg.fit(X, y) from sklearn.preprocessing import PolynomialFeatures poly_reg = PolynomialFeatures(degree =8) X_poly = poly_reg.fit_transform(X) poly_reg.fit(X_poly, y) lin_reg_2 = LinearRegression() lin_reg_2.fit(X_poly, y) ###Output _____no_output_____ ###Markdown Visualisasi ###Code X_grid = np.arange(min(X), max(X), 0.1) X_grid = X_grid.reshape((len(X_grid), 1)) plt.scatter(X, y, color = 'red') plt.plot(X_grid, lin_reg_2.predict(poly_reg.fit_transform(X_grid)), color = 'blue') plt.title('(Polynomial Regression)') plt.xlabel('pengalaman') plt.ylabel('gaji') plt.show() lin_reg_2.predict(poly_reg.fit_transform(np.array(16).reshape(1,-1)))[0] ###Output _____no_output_____
Microbiome.ipynb	###Markdown ###Code import numpy as np import pandas as pd import scipy.integrate import matplotlib.pyplot as plt import panel as pn import colorcet pn.extension(comms='colab') def compete_simple(y, t, input_X, input_Y, input_Z): species_A, species_B, species_C = y r_max = 1.0 dydt = [r_max * (input_X - species_A) / input_X * species_A, r_max * (input_Y - species_B) / input_Y * species_B, r_max * (input_Z - species_C) / input_Z * species_C,] return dydt X_slider = pn.widgets.FloatSlider(name="input X", start=0, end=100, step=1, value=50) Y_slider = pn.widgets.FloatSlider(name="input Y", start=0, end=100, step=1, value=50) Z_slider = pn.widgets.FloatSlider(name="input Z", start=0, end=100, step=1, value=50) @pn.depends( X_slider.param.value, Y_slider.param.value, Z_slider.param.value, ) def simple_plot(input_X, input_Y, input_Z): consume_rate=10.0 death_rate=1.0 y0 = [1.0, 1.0, 1.0] t = np.linspace(0, 10, 101) sol = odeint(compete_simple, y0, t, args=(input_X, input_Y, input_Z)) fig, ax = plt.subplots(figsize=(15, 7)) plt.plot(t, sol[:, 0], 'b', label='Species A', linewidth=5) plt.plot(t, sol[:, 1], 'g', label='Species B', linewidth=5) plt.plot(t, sol[:, 2], 'r', label='Species C', linewidth=5) plt.legend(loc='best') plt.xlabel('t') plt.grid() plt.close(fig) return fig # Final layout widgets = pn.Column(pn.Spacer(height=80), X_slider, pn.Spacer(height=80), Y_slider, pn.Spacer(height=80), Z_slider, pn.Spacer(height=80), width=150) pn.Row(pn.Column(simple_plot), widgets) def compete_complex(y, t, consume_rate, death_rate, input_X, input_Y, input_Z, compete_rate=0.1): species_A, species_B, species_C = y r_max = 1.0 compete_rate = 0.2 dydt = [r_max * (input_X - species_A - compete_rate * (species_B + species_C)) / input_X * species_A, r_max * (input_Y - species_B - compete_rate * (species_A + species_C)) / input_Y * species_B, r_max * (input_Z - species_C - compete_rate * (species_A + species_B)) / input_Z * species_C,] return dydt X_slider = pn.widgets.FloatSlider(name="input X", start=0, end=100, step=1, value=50) Y_slider = pn.widgets.FloatSlider(name="input Y", start=0, end=100, step=1, value=50) Z_slider = pn.widgets.FloatSlider(name="input Z", start=0, end=100, step=1, value=50) @pn.depends( X_slider.param.value, Y_slider.param.value, Z_slider.param.value, ) def complex_plot(input_X, input_Y, input_Z): consume_rate=10.0 death_rate=1.0 y0 = [1.0, 1.0, 1.0] t = np.linspace(0, 10, 101) sol = odeint(compete_complex, y0, t, args=(consume_rate, death_rate, input_X, input_Y, input_Z)) fig, ax = plt.subplots(figsize=(15, 7)) plt.plot(t, sol[:, 0], 'b', label='Species A', linewidth=5) plt.plot(t, sol[:, 1], 'g', label='Species B', linewidth=5) plt.plot(t, sol[:, 2], 'r', label='Species C', linewidth=5) plt.legend(loc='best') plt.xlabel('t') plt.grid() plt.close(fig) return fig # Final layout widgets = pn.Column(pn.Spacer(height=150), X_slider, pn.Spacer(height=80), Y_slider, pn.Spacer(height=80), Z_slider, pn.Spacer(height=80), width=150) pn.Row(pn.Column(complex_plot), pn.Spacer(width=20), widgets) ###Output _____no_output_____
notebooks/book1/15/lstm_torch.ipynb	###Markdown Please find jax implementation of this notebook here: https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/15/lstm_jax.ipynb Long short term memory (LSTM) We show how to implement LSTMs from scratch.Based on sec 9.2 of http://d2l.ai/chapter_recurrent-modern/lstm.html.This uses code from the [basic RNN colab](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/rnn_torch.ipynb). ###Code import numpy as np import matplotlib.pyplot as plt import math from IPython import display try: import torch except ModuleNotFoundError: %pip install -qq torch import torch from torch import nn from torch.nn import functional as F from torch.utils import data import collections import re import random import os import requests import hashlib import time np.random.seed(seed=1) torch.manual_seed(1) !mkdir figures # for saving plots ###Output _____no_output_____ ###Markdown Data As data, we use the book "The Time Machine" by H G Wells,preprocessed using the code in [this colab](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/text_preproc_torch.ipynb). ###Code class SeqDataLoader: """An iterator to load sequence data.""" def __init__(self, batch_size, num_steps, use_random_iter, max_tokens): if use_random_iter: self.data_iter_fn = seq_data_iter_random else: self.data_iter_fn = seq_data_iter_sequential self.corpus, self.vocab = load_corpus_time_machine(max_tokens) self.batch_size, self.num_steps = batch_size, num_steps def __iter__(self): return self.data_iter_fn(self.corpus, self.batch_size, self.num_steps) class Vocab: """Vocabulary for text.""" def __init__(self, tokens=None, min_freq=0, reserved_tokens=None): if tokens is None: tokens = [] if reserved_tokens is None: reserved_tokens = [] # Sort according to frequencies counter = count_corpus(tokens) self.token_freqs = sorted(counter.items(), key=lambda x: x[1], reverse=True) # The index for the unknown token is 0 self.unk, uniq_tokens = 0, ["<unk>"] + reserved_tokens uniq_tokens += [token for token, freq in self.token_freqs if freq >= min_freq and token not in uniq_tokens] self.idx_to_token, self.token_to_idx = [], dict() for token in uniq_tokens: self.idx_to_token.append(token) self.token_to_idx[token] = len(self.idx_to_token) - 1 def __len__(self): return len(self.idx_to_token) def __getitem__(self, tokens): if not isinstance(tokens, (list, tuple)): return self.token_to_idx.get(tokens, self.unk) return [self.__getitem__(token) for token in tokens] def to_tokens(self, indices): if not isinstance(indices, (list, tuple)): return self.idx_to_token[indices] return [self.idx_to_token[index] for index in indices] def tokenize(lines, token="word"): """Split text lines into word or character tokens.""" if token == "word": return [line.split() for line in lines] elif token == "char": return [list(line) for line in lines] else: print("ERROR: unknown token type: " + token) def count_corpus(tokens): """Count token frequencies.""" # Here `tokens` is a 1D list or 2D list if len(tokens) == 0 or isinstance(tokens[0], list): # Flatten a list of token lists into a list of tokens tokens = [token for line in tokens for token in line] return collections.Counter(tokens) def seq_data_iter_random(corpus, batch_size, num_steps): """Generate a minibatch of subsequences using random sampling.""" # Start with a random offset (inclusive of `num_steps - 1`) to partition a # sequence corpus = corpus[random.randint(0, num_steps - 1) :] # Subtract 1 since we need to account for labels num_subseqs = (len(corpus) - 1) // num_steps # The starting indices for subsequences of length `num_steps` initial_indices = list(range(0, num_subseqs * num_steps, num_steps)) # In random sampling, the subsequences from two adjacent random # minibatches during iteration are not necessarily adjacent on the # original sequence random.shuffle(initial_indices) def data(pos): # Return a sequence of length `num_steps` starting from `pos` return corpus[pos : pos + num_steps] num_batches = num_subseqs // batch_size for i in range(0, batch_size * num_batches, batch_size): # Here, `initial_indices` contains randomized starting indices for # subsequences initial_indices_per_batch = initial_indices[i : i + batch_size] X = [data(j) for j in initial_indices_per_batch] Y = [data(j + 1) for j in initial_indices_per_batch] yield torch.tensor(X), torch.tensor(Y) def seq_data_iter_sequential(corpus, batch_size, num_steps): """Generate a minibatch of subsequences using sequential partitioning.""" # Start with a random offset to partition a sequence offset = random.randint(0, num_steps) num_tokens = ((len(corpus) - offset - 1) // batch_size) * batch_size Xs = torch.tensor(corpus[offset : offset + num_tokens]) Ys = torch.tensor(corpus[offset + 1 : offset + 1 + num_tokens]) Xs, Ys = Xs.reshape(batch_size, -1), Ys.reshape(batch_size, -1) num_batches = Xs.shape[1] // num_steps for i in range(0, num_steps * num_batches, num_steps): X = Xs[:, i : i + num_steps] Y = Ys[:, i : i + num_steps] yield X, Y def download(name, cache_dir=os.path.join("..", "data")): """Download a file inserted into DATA_HUB, return the local filename.""" assert name in DATA_HUB, f"{name} does not exist in {DATA_HUB}." url, sha1_hash = DATA_HUB[name] os.makedirs(cache_dir, exist_ok=True) fname = os.path.join(cache_dir, url.split("/")[-1]) if os.path.exists(fname): sha1 = hashlib.sha1() with open(fname, "rb") as f: while True: data = f.read(1048576) if not data: break sha1.update(data) if sha1.hexdigest() == sha1_hash: return fname # Hit cache print(f"Downloading {fname} from {url}...") r = requests.get(url, stream=True, verify=True) with open(fname, "wb") as f: f.write(r.content) return fname def read_time_machine(): """Load the time machine dataset into a list of text lines.""" with open(download("time_machine"), "r") as f: lines = f.readlines() return [re.sub("[^A-Za-z]+", " ", line).strip().lower() for line in lines] def load_corpus_time_machine(max_tokens=-1): """Return token indices and the vocabulary of the time machine dataset.""" lines = read_time_machine() tokens = tokenize(lines, "char") vocab = Vocab(tokens) # Since each text line in the time machine dataset is not necessarily a # sentence or a paragraph, flatten all the text lines into a single list corpus = [vocab[token] for line in tokens for token in line] if max_tokens > 0: corpus = corpus[:max_tokens] return corpus, vocab def load_data_time_machine(batch_size, num_steps, use_random_iter=False, max_tokens=10000): """Return the iterator and the vocabulary of the time machine dataset.""" data_iter = SeqDataLoader(batch_size, num_steps, use_random_iter, max_tokens) return data_iter, data_iter.vocab DATA_HUB = dict() DATA_URL = "http://d2l-data.s3-accelerate.amazonaws.com/" DATA_HUB["time_machine"] = (DATA_URL + "timemachine.txt", "090b5e7e70c295757f55df93cb0a180b9691891a") batch_size, num_steps = 32, 35 train_iter, vocab = load_data_time_machine(batch_size, num_steps) ###Output Downloading ../data/timemachine.txt from http://d2l-data.s3-accelerate.amazonaws.com/timemachine.txt... ###Markdown Creating model from scratch ###Code def get_lstm_params(vocab_size, num_hiddens, device): num_inputs = num_outputs = vocab_size def normal(shape): return torch.randn(size=shape, device=device) * 0.01 def three(): return ( normal((num_inputs, num_hiddens)), normal((num_hiddens, num_hiddens)), torch.zeros(num_hiddens, device=device), ) W_xi, W_hi, b_i = three() # Input gate parameters W_xf, W_hf, b_f = three() # Forget gate parameters W_xo, W_ho, b_o = three() # Output gate parameters W_xc, W_hc, b_c = three() # Candidate memory cell parameters # Output layer parameters W_hq = normal((num_hiddens, num_outputs)) b_q = torch.zeros(num_outputs, device=device) # Attach gradients params = [W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c, W_hq, b_q] for param in params: param.requires_grad_(True) return params # The state is now a tuple of hidden state and cell state def init_lstm_state(batch_size, num_hiddens, device): return ( torch.zeros((batch_size, num_hiddens), device=device), torch.zeros((batch_size, num_hiddens), device=device), ) # forward function def lstm(inputs, state, params): [W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c, W_hq, b_q] = params (H, C) = state outputs = [] for X in inputs: I = torch.sigmoid((X @ W_xi) + (H @ W_hi) + b_i) F = torch.sigmoid((X @ W_xf) + (H @ W_hf) + b_f) O = torch.sigmoid((X @ W_xo) + (H @ W_ho) + b_o) C_tilda = torch.tanh((X @ W_xc) + (H @ W_hc) + b_c) C = F * C + I * C_tilda H = O * torch.tanh(C) Y = (H @ W_hq) + b_q outputs.append(Y) return torch.cat(outputs, dim=0), (H, C) ###Output _____no_output_____ ###Markdown Training and prediction ###Code # Make the model class # Input X to call is (B,T) matrix of integers (from vocab encoding). # We transpse this to (T,B) then one-hot encode to (T,B,V), where V is vocab. # The result is passed to the forward function. # (We define the forward function as an argument, so we can change it later.) class RNNModelScratch: """A RNN Model implemented from scratch.""" def __init__(self, vocab_size, num_hiddens, device, get_params, init_state, forward_fn): self.vocab_size, self.num_hiddens = vocab_size, num_hiddens self.params = get_params(vocab_size, num_hiddens, device) self.init_state, self.forward_fn = init_state, forward_fn def __call__(self, X, state): X = F.one_hot(X.T, self.vocab_size).type(torch.float32) return self.forward_fn(X, state, self.params) def begin_state(self, batch_size, device): return self.init_state(batch_size, self.num_hiddens, device) def try_gpu(i=0): """Return gpu(i) if exists, otherwise return cpu().""" if torch.cuda.device_count() >= i + 1: return torch.device(f"cuda:{i}") return torch.device("cpu") class Animator: """For plotting data in animation.""" def __init__( self, xlabel=None, ylabel=None, legend=None, xlim=None, ylim=None, xscale="linear", yscale="linear", fmts=("-", "m--", "g-.", "r:"), nrows=1, ncols=1, figsize=(3.5, 2.5), ): # Incrementally plot multiple lines if legend is None: legend = [] display.set_matplotlib_formats("svg") self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize) if nrows * ncols == 1: self.axes = [ self.axes, ] # Use a lambda function to capture arguments self.config_axes = lambda: set_axes(self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend) self.X, self.Y, self.fmts = None, None, fmts def add(self, x, y): # Add multiple data points into the figure if not hasattr(y, "__len__"): y = [y] n = len(y) if not hasattr(x, "__len__"): x = [x] * n if not self.X: self.X = [[] for _ in range(n)] if not self.Y: self.Y = [[] for _ in range(n)] for i, (a, b) in enumerate(zip(x, y)): if a is not None and b is not None: self.X[i].append(a) self.Y[i].append(b) self.axes[0].cla() for x, y, fmt in zip(self.X, self.Y, self.fmts): self.axes[0].plot(x, y, fmt) self.config_axes() display.display(self.fig) display.clear_output(wait=True) class Timer: """Record multiple running times.""" def __init__(self): self.times = [] self.start() def start(self): """Start the timer.""" self.tik = time.time() def stop(self): """Stop the timer and record the time in a list.""" self.times.append(time.time() - self.tik) return self.times[-1] def avg(self): """Return the average time.""" return sum(self.times) / len(self.times) def sum(self): """Return the sum of time.""" return sum(self.times) def cumsum(self): """Return the accumulated time.""" return np.array(self.times).cumsum().tolist() class Accumulator: """For accumulating sums over `n` variables.""" def __init__(self, n): self.data = [0.0] * n def add(self, args): self.data = [a + float(b) for a, b in zip(self.data, args)] def reset(self): self.data = [0.0] len(self.data) def __getitem__(self, idx): return self.data[idx] def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend): """Set the axes for matplotlib.""" axes.set_xlabel(xlabel) axes.set_ylabel(ylabel) axes.set_xscale(xscale) axes.set_yscale(yscale) axes.set_xlim(xlim) axes.set_ylim(ylim) if legend: axes.legend(legend) axes.grid() def sgd(params, lr, batch_size): """Minibatch stochastic gradient descent.""" with torch.no_grad(): for param in params: param -= lr * param.grad / batch_size param.grad.zero_() def grad_clipping(net, theta): """Clip the gradient.""" if isinstance(net, nn.Module): params = [p for p in net.parameters() if p.requires_grad] else: params = net.params norm = torch.sqrt(sum(torch.sum((p.grad*2)) for p in params)) if norm > theta: for param in params: param.grad[:] = theta / norm def predict(prefix, num_preds, net, vocab, device): """Generate new characters following the `prefix`.""" state = net.begin_state(batch_size=1, device=device) outputs = [vocab[prefix[0]]] get_input = lambda: torch.tensor([outputs[-1]], device=device).reshape((1, 1)) for y in prefix[1:]: # Warm-up period _, state = net(get_input(), state) outputs.append(vocab[y]) for _ in range(num_preds): # Predict `num_preds` steps y, state = net(get_input(), state) outputs.append(int(y.argmax(dim=1).reshape(1))) return "".join([vocab.idx_to_token[i] for i in outputs]) def train_epoch(net, train_iter, loss, updater, device, use_random_iter): state, timer = None, Timer() metric = Accumulator(2) # Sum of training loss, no. of tokens for X, Y in train_iter: if state is None or use_random_iter: # Initialize `state` when either it is the first iteration or # using random sampling state = net.begin_state(batch_size=X.shape[0], device=device) else: if isinstance(net, nn.Module) and not isinstance(state, tuple): # `state` is a tensor for `nn.GRU` state.detach_() else: # `state` is a tuple of tensors for `nn.LSTM` and # for our custom scratch implementation for s in state: s.detach_() y = Y.T.reshape(-1) # (B,T) -> (T,B) X, y = X.to(device), y.to(device) y_hat, state = net(X, state) l = loss(y_hat, y.long()).mean() if isinstance(updater, torch.optim.Optimizer): updater.zero_grad() l.backward() grad_clipping(net, 1) updater.step() else: l.backward() grad_clipping(net, 1) # batch_size=1 since the `mean` function has been invoked updater(batch_size=1) metric.add(l * y.numel(), y.numel()) return math.exp(metric[0] / metric[1]), metric[1] / timer.stop() def train(net, train_iter, vocab, lr, num_epochs, device, use_random_iter=False): loss = nn.CrossEntropyLoss() animator = Animator(xlabel="epoch", ylabel="perplexity", legend=["train"], xlim=[10, num_epochs]) # Initialize if isinstance(net, nn.Module): updater = torch.optim.SGD(net.parameters(), lr) else: updater = lambda batch_size: sgd(net.params, lr, batch_size) num_preds = 50 predict_ = lambda prefix: predict(prefix, num_preds, net, vocab, device) # Train and predict for epoch in range(num_epochs): ppl, speed = train_epoch(net, train_iter, loss, updater, device, use_random_iter) if (epoch + 1) % 10 == 0: print(predict_("time traveller")) animator.add(epoch + 1, [ppl]) print(f"perplexity {ppl:.1f}, {speed:.1f} tokens/sec on {str(device)}") print(predict_("time traveller")) print(predict_("traveller")) vocab_size, num_hiddens, device = len(vocab), 256, try_gpu() num_epochs, lr = 500, 1 model = RNNModelScratch(len(vocab), num_hiddens, device, get_lstm_params, init_lstm_state, lstm) train(model, train_iter, vocab, lr, num_epochs, device) ###Output perplexity 1.1, 21046.3 tokens/sec on cpu time traveller for so it will be convenient to speak of himwas e traveller i shall briegt faintlywationsthal is frewsing and ###Markdown Using pytorch module ###Code class RNNModel(nn.Module): """The RNN model.""" def __init__(self, rnn_layer, vocab_size, kwargs): super(RNNModel, self).__init__(kwargs) self.rnn = rnn_layer self.vocab_size = vocab_size self.num_hiddens = self.rnn.hidden_size # If the RNN is bidirectional (to be introduced later), # `num_directions` should be 2, else it should be 1. if not self.rnn.bidirectional: self.num_directions = 1 self.linear = nn.Linear(self.num_hiddens, self.vocab_size) else: self.num_directions = 2 self.linear = nn.Linear(self.num_hiddens * 2, self.vocab_size) def forward(self, inputs, state): X = F.one_hot(inputs.T.long(), self.vocab_size) X = X.to(torch.float32) Y, state = self.rnn(X, state) # The fully connected layer will first change the shape of `Y` to # (`num_steps` * `batch_size`, `num_hiddens`). Its output shape is # (`num_steps` * `batch_size`, `vocab_size`). output = self.linear(Y.reshape((-1, Y.shape[-1]))) return output, state def begin_state(self, device, batch_size=1): if not isinstance(self.rnn, nn.LSTM): # `nn.GRU` takes a tensor as hidden state return torch.zeros((self.num_directions * self.rnn.num_layers, batch_size, self.num_hiddens), device=device) else: # `nn.LSTM` takes a tuple of hidden states return ( torch.zeros((self.num_directions * self.rnn.num_layers, batch_size, self.num_hiddens), device=device), torch.zeros((self.num_directions * self.rnn.num_layers, batch_size, self.num_hiddens), device=device), ) num_inputs = vocab_size lstm_layer = nn.LSTM(num_inputs, num_hiddens) model = RNNModel(lstm_layer, len(vocab)) model = model.to(device) train(model, train_iter, vocab, lr, num_epochs, device) ###Output perplexity 1.0, 20182.0 tokens/sec on cpu time traveller for so it will be convenient to speak of himwas e travelleryou can show black is white by argument said filby ###Markdown Please find jax implementation of this notebook here: https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/15/lstm_jax.ipynb Long short term memory (LSTM) We show how to implement LSTMs from scratch.Based on sec 9.2 of http://d2l.ai/chapter_recurrent-modern/lstm.html.This uses code from the [basic RNN colab](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/rnn_torch.ipynb). ###Code import numpy as np import matplotlib.pyplot as plt import math from IPython import display try: import torch except ModuleNotFoundError: %pip install torch import torch from torch import nn from torch.nn import functional as F from torch.utils import data import collections import re import random import os import requests import hashlib import time np.random.seed(seed=1) torch.manual_seed(1) !mkdir figures # for saving plots ###Output _____no_output_____ ###Markdown Data As data, we use the book "The Time Machine" by H G Wells,preprocessed using the code in [this colab](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/text_preproc_torch.ipynb). ###Code class SeqDataLoader: """An iterator to load sequence data.""" def __init__(self, batch_size, num_steps, use_random_iter, max_tokens): if use_random_iter: self.data_iter_fn = seq_data_iter_random else: self.data_iter_fn = seq_data_iter_sequential self.corpus, self.vocab = load_corpus_time_machine(max_tokens) self.batch_size, self.num_steps = batch_size, num_steps def __iter__(self): return self.data_iter_fn(self.corpus, self.batch_size, self.num_steps) class Vocab: """Vocabulary for text.""" def __init__(self, tokens=None, min_freq=0, reserved_tokens=None): if tokens is None: tokens = [] if reserved_tokens is None: reserved_tokens = [] # Sort according to frequencies counter = count_corpus(tokens) self.token_freqs = sorted(counter.items(), key=lambda x: x[1], reverse=True) # The index for the unknown token is 0 self.unk, uniq_tokens = 0, ["<unk>"] + reserved_tokens uniq_tokens += [token for token, freq in self.token_freqs if freq >= min_freq and token not in uniq_tokens] self.idx_to_token, self.token_to_idx = [], dict() for token in uniq_tokens: self.idx_to_token.append(token) self.token_to_idx[token] = len(self.idx_to_token) - 1 def __len__(self): return len(self.idx_to_token) def __getitem__(self, tokens): if not isinstance(tokens, (list, tuple)): return self.token_to_idx.get(tokens, self.unk) return [self.__getitem__(token) for token in tokens] def to_tokens(self, indices): if not isinstance(indices, (list, tuple)): return self.idx_to_token[indices] return [self.idx_to_token[index] for index in indices] def tokenize(lines, token="word"): """Split text lines into word or character tokens.""" if token == "word": return [line.split() for line in lines] elif token == "char": return [list(line) for line in lines] else: print("ERROR: unknown token type: " + token) def count_corpus(tokens): """Count token frequencies.""" # Here `tokens` is a 1D list or 2D list if len(tokens) == 0 or isinstance(tokens[0], list): # Flatten a list of token lists into a list of tokens tokens = [token for line in tokens for token in line] return collections.Counter(tokens) def seq_data_iter_random(corpus, batch_size, num_steps): """Generate a minibatch of subsequences using random sampling.""" # Start with a random offset (inclusive of `num_steps - 1`) to partition a # sequence corpus = corpus[random.randint(0, num_steps - 1) :] # Subtract 1 since we need to account for labels num_subseqs = (len(corpus) - 1) // num_steps # The starting indices for subsequences of length `num_steps` initial_indices = list(range(0, num_subseqs * num_steps, num_steps)) # In random sampling, the subsequences from two adjacent random # minibatches during iteration are not necessarily adjacent on the # original sequence random.shuffle(initial_indices) def data(pos): # Return a sequence of length `num_steps` starting from `pos` return corpus[pos : pos + num_steps] num_batches = num_subseqs // batch_size for i in range(0, batch_size * num_batches, batch_size): # Here, `initial_indices` contains randomized starting indices for # subsequences initial_indices_per_batch = initial_indices[i : i + batch_size] X = [data(j) for j in initial_indices_per_batch] Y = [data(j + 1) for j in initial_indices_per_batch] yield torch.tensor(X), torch.tensor(Y) def seq_data_iter_sequential(corpus, batch_size, num_steps): """Generate a minibatch of subsequences using sequential partitioning.""" # Start with a random offset to partition a sequence offset = random.randint(0, num_steps) num_tokens = ((len(corpus) - offset - 1) // batch_size) * batch_size Xs = torch.tensor(corpus[offset : offset + num_tokens]) Ys = torch.tensor(corpus[offset + 1 : offset + 1 + num_tokens]) Xs, Ys = Xs.reshape(batch_size, -1), Ys.reshape(batch_size, -1) num_batches = Xs.shape[1] // num_steps for i in range(0, num_steps * num_batches, num_steps): X = Xs[:, i : i + num_steps] Y = Ys[:, i : i + num_steps] yield X, Y def download(name, cache_dir=os.path.join("..", "data")): """Download a file inserted into DATA_HUB, return the local filename.""" assert name in DATA_HUB, f"{name} does not exist in {DATA_HUB}." url, sha1_hash = DATA_HUB[name] os.makedirs(cache_dir, exist_ok=True) fname = os.path.join(cache_dir, url.split("/")[-1]) if os.path.exists(fname): sha1 = hashlib.sha1() with open(fname, "rb") as f: while True: data = f.read(1048576) if not data: break sha1.update(data) if sha1.hexdigest() == sha1_hash: return fname # Hit cache print(f"Downloading {fname} from {url}...") r = requests.get(url, stream=True, verify=True) with open(fname, "wb") as f: f.write(r.content) return fname def read_time_machine(): """Load the time machine dataset into a list of text lines.""" with open(download("time_machine"), "r") as f: lines = f.readlines() return [re.sub("[^A-Za-z]+", " ", line).strip().lower() for line in lines] def load_corpus_time_machine(max_tokens=-1): """Return token indices and the vocabulary of the time machine dataset.""" lines = read_time_machine() tokens = tokenize(lines, "char") vocab = Vocab(tokens) # Since each text line in the time machine dataset is not necessarily a # sentence or a paragraph, flatten all the text lines into a single list corpus = [vocab[token] for line in tokens for token in line] if max_tokens > 0: corpus = corpus[:max_tokens] return corpus, vocab def load_data_time_machine(batch_size, num_steps, use_random_iter=False, max_tokens=10000): """Return the iterator and the vocabulary of the time machine dataset.""" data_iter = SeqDataLoader(batch_size, num_steps, use_random_iter, max_tokens) return data_iter, data_iter.vocab DATA_HUB = dict() DATA_URL = "http://d2l-data.s3-accelerate.amazonaws.com/" DATA_HUB["time_machine"] = (DATA_URL + "timemachine.txt", "090b5e7e70c295757f55df93cb0a180b9691891a") batch_size, num_steps = 32, 35 train_iter, vocab = load_data_time_machine(batch_size, num_steps) ###Output Downloading ../data/timemachine.txt from http://d2l-data.s3-accelerate.amazonaws.com/timemachine.txt... ###Markdown Creating model from scratch ###Code def get_lstm_params(vocab_size, num_hiddens, device): num_inputs = num_outputs = vocab_size def normal(shape): return torch.randn(size=shape, device=device) * 0.01 def three(): return ( normal((num_inputs, num_hiddens)), normal((num_hiddens, num_hiddens)), torch.zeros(num_hiddens, device=device), ) W_xi, W_hi, b_i = three() # Input gate parameters W_xf, W_hf, b_f = three() # Forget gate parameters W_xo, W_ho, b_o = three() # Output gate parameters W_xc, W_hc, b_c = three() # Candidate memory cell parameters # Output layer parameters W_hq = normal((num_hiddens, num_outputs)) b_q = torch.zeros(num_outputs, device=device) # Attach gradients params = [W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c, W_hq, b_q] for param in params: param.requires_grad_(True) return params # The state is now a tuple of hidden state and cell state def init_lstm_state(batch_size, num_hiddens, device): return ( torch.zeros((batch_size, num_hiddens), device=device), torch.zeros((batch_size, num_hiddens), device=device), ) # forward function def lstm(inputs, state, params): [W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c, W_hq, b_q] = params (H, C) = state outputs = [] for X in inputs: I = torch.sigmoid((X @ W_xi) + (H @ W_hi) + b_i) F = torch.sigmoid((X @ W_xf) + (H @ W_hf) + b_f) O = torch.sigmoid((X @ W_xo) + (H @ W_ho) + b_o) C_tilda = torch.tanh((X @ W_xc) + (H @ W_hc) + b_c) C = F * C + I * C_tilda H = O * torch.tanh(C) Y = (H @ W_hq) + b_q outputs.append(Y) return torch.cat(outputs, dim=0), (H, C) ###Output _____no_output_____ ###Markdown Training and prediction ###Code # Make the model class # Input X to call is (B,T) matrix of integers (from vocab encoding). # We transpse this to (T,B) then one-hot encode to (T,B,V), where V is vocab. # The result is passed to the forward function. # (We define the forward function as an argument, so we can change it later.) class RNNModelScratch: """A RNN Model implemented from scratch.""" def __init__(self, vocab_size, num_hiddens, device, get_params, init_state, forward_fn): self.vocab_size, self.num_hiddens = vocab_size, num_hiddens self.params = get_params(vocab_size, num_hiddens, device) self.init_state, self.forward_fn = init_state, forward_fn def __call__(self, X, state): X = F.one_hot(X.T, self.vocab_size).type(torch.float32) return self.forward_fn(X, state, self.params) def begin_state(self, batch_size, device): return self.init_state(batch_size, self.num_hiddens, device) def try_gpu(i=0): """Return gpu(i) if exists, otherwise return cpu().""" if torch.cuda.device_count() >= i + 1: return torch.device(f"cuda:{i}") return torch.device("cpu") class Animator: """For plotting data in animation.""" def __init__( self, xlabel=None, ylabel=None, legend=None, xlim=None, ylim=None, xscale="linear", yscale="linear", fmts=("-", "m--", "g-.", "r:"), nrows=1, ncols=1, figsize=(3.5, 2.5), ): # Incrementally plot multiple lines if legend is None: legend = [] display.set_matplotlib_formats("svg") self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize) if nrows * ncols == 1: self.axes = [ self.axes, ] # Use a lambda function to capture arguments self.config_axes = lambda: set_axes(self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend) self.X, self.Y, self.fmts = None, None, fmts def add(self, x, y): # Add multiple data points into the figure if not hasattr(y, "__len__"): y = [y] n = len(y) if not hasattr(x, "__len__"): x = [x] * n if not self.X: self.X = [[] for _ in range(n)] if not self.Y: self.Y = [[] for _ in range(n)] for i, (a, b) in enumerate(zip(x, y)): if a is not None and b is not None: self.X[i].append(a) self.Y[i].append(b) self.axes[0].cla() for x, y, fmt in zip(self.X, self.Y, self.fmts): self.axes[0].plot(x, y, fmt) self.config_axes() display.display(self.fig) display.clear_output(wait=True) class Timer: """Record multiple running times.""" def __init__(self): self.times = [] self.start() def start(self): """Start the timer.""" self.tik = time.time() def stop(self): """Stop the timer and record the time in a list.""" self.times.append(time.time() - self.tik) return self.times[-1] def avg(self): """Return the average time.""" return sum(self.times) / len(self.times) def sum(self): """Return the sum of time.""" return sum(self.times) def cumsum(self): """Return the accumulated time.""" return np.array(self.times).cumsum().tolist() class Accumulator: """For accumulating sums over `n` variables.""" def __init__(self, n): self.data = [0.0] * n def add(self, args): self.data = [a + float(b) for a, b in zip(self.data, args)] def reset(self): self.data = [0.0] len(self.data) def __getitem__(self, idx): return self.data[idx] def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend): """Set the axes for matplotlib.""" axes.set_xlabel(xlabel) axes.set_ylabel(ylabel) axes.set_xscale(xscale) axes.set_yscale(yscale) axes.set_xlim(xlim) axes.set_ylim(ylim) if legend: axes.legend(legend) axes.grid() def sgd(params, lr, batch_size): """Minibatch stochastic gradient descent.""" with torch.no_grad(): for param in params: param -= lr * param.grad / batch_size param.grad.zero_() def grad_clipping(net, theta): """Clip the gradient.""" if isinstance(net, nn.Module): params = [p for p in net.parameters() if p.requires_grad] else: params = net.params norm = torch.sqrt(sum(torch.sum((p.grad*2)) for p in params)) if norm > theta: for param in params: param.grad[:] = theta / norm def predict(prefix, num_preds, net, vocab, device): """Generate new characters following the `prefix`.""" state = net.begin_state(batch_size=1, device=device) outputs = [vocab[prefix[0]]] get_input = lambda: torch.tensor([outputs[-1]], device=device).reshape((1, 1)) for y in prefix[1:]: # Warm-up period _, state = net(get_input(), state) outputs.append(vocab[y]) for _ in range(num_preds): # Predict `num_preds` steps y, state = net(get_input(), state) outputs.append(int(y.argmax(dim=1).reshape(1))) return "".join([vocab.idx_to_token[i] for i in outputs]) def train_epoch(net, train_iter, loss, updater, device, use_random_iter): state, timer = None, Timer() metric = Accumulator(2) # Sum of training loss, no. of tokens for X, Y in train_iter: if state is None or use_random_iter: # Initialize `state` when either it is the first iteration or # using random sampling state = net.begin_state(batch_size=X.shape[0], device=device) else: if isinstance(net, nn.Module) and not isinstance(state, tuple): # `state` is a tensor for `nn.GRU` state.detach_() else: # `state` is a tuple of tensors for `nn.LSTM` and # for our custom scratch implementation for s in state: s.detach_() y = Y.T.reshape(-1) # (B,T) -> (T,B) X, y = X.to(device), y.to(device) y_hat, state = net(X, state) l = loss(y_hat, y.long()).mean() if isinstance(updater, torch.optim.Optimizer): updater.zero_grad() l.backward() grad_clipping(net, 1) updater.step() else: l.backward() grad_clipping(net, 1) # batch_size=1 since the `mean` function has been invoked updater(batch_size=1) metric.add(l * y.numel(), y.numel()) return math.exp(metric[0] / metric[1]), metric[1] / timer.stop() def train(net, train_iter, vocab, lr, num_epochs, device, use_random_iter=False): loss = nn.CrossEntropyLoss() animator = Animator(xlabel="epoch", ylabel="perplexity", legend=["train"], xlim=[10, num_epochs]) # Initialize if isinstance(net, nn.Module): updater = torch.optim.SGD(net.parameters(), lr) else: updater = lambda batch_size: sgd(net.params, lr, batch_size) num_preds = 50 predict_ = lambda prefix: predict(prefix, num_preds, net, vocab, device) # Train and predict for epoch in range(num_epochs): ppl, speed = train_epoch(net, train_iter, loss, updater, device, use_random_iter) if (epoch + 1) % 10 == 0: print(predict_("time traveller")) animator.add(epoch + 1, [ppl]) print(f"perplexity {ppl:.1f}, {speed:.1f} tokens/sec on {str(device)}") print(predict_("time traveller")) print(predict_("traveller")) vocab_size, num_hiddens, device = len(vocab), 256, try_gpu() num_epochs, lr = 500, 1 model = RNNModelScratch(len(vocab), num_hiddens, device, get_lstm_params, init_lstm_state, lstm) train(model, train_iter, vocab, lr, num_epochs, device) ###Output perplexity 1.1, 21046.3 tokens/sec on cpu time traveller for so it will be convenient to speak of himwas e traveller i shall briegt faintlywationsthal is frewsing and ###Markdown Using pytorch module ###Code class RNNModel(nn.Module): """The RNN model.""" def __init__(self, rnn_layer, vocab_size, kwargs): super(RNNModel, self).__init__(kwargs) self.rnn = rnn_layer self.vocab_size = vocab_size self.num_hiddens = self.rnn.hidden_size # If the RNN is bidirectional (to be introduced later), # `num_directions` should be 2, else it should be 1. if not self.rnn.bidirectional: self.num_directions = 1 self.linear = nn.Linear(self.num_hiddens, self.vocab_size) else: self.num_directions = 2 self.linear = nn.Linear(self.num_hiddens * 2, self.vocab_size) def forward(self, inputs, state): X = F.one_hot(inputs.T.long(), self.vocab_size) X = X.to(torch.float32) Y, state = self.rnn(X, state) # The fully connected layer will first change the shape of `Y` to # (`num_steps` * `batch_size`, `num_hiddens`). Its output shape is # (`num_steps` * `batch_size`, `vocab_size`). output = self.linear(Y.reshape((-1, Y.shape[-1]))) return output, state def begin_state(self, device, batch_size=1): if not isinstance(self.rnn, nn.LSTM): # `nn.GRU` takes a tensor as hidden state return torch.zeros((self.num_directions * self.rnn.num_layers, batch_size, self.num_hiddens), device=device) else: # `nn.LSTM` takes a tuple of hidden states return ( torch.zeros((self.num_directions * self.rnn.num_layers, batch_size, self.num_hiddens), device=device), torch.zeros((self.num_directions * self.rnn.num_layers, batch_size, self.num_hiddens), device=device), ) num_inputs = vocab_size lstm_layer = nn.LSTM(num_inputs, num_hiddens) model = RNNModel(lstm_layer, len(vocab)) model = model.to(device) train(model, train_iter, vocab, lr, num_epochs, device) ###Output perplexity 1.0, 20182.0 tokens/sec on cpu time traveller for so it will be convenient to speak of himwas e travelleryou can show black is white by argument said filby
wp/notebooks/active learning/binary/metric_evaluation.ipynb	###Markdown Metrics comparison ###Code %load_ext autoreload import os, sys, importlib import matplotlib.pyplot as plt import seaborn as sns import pandas as pd import numpy as np BASE_PATH = os.path.join(os.getcwd(), "..", "..") MODULES_PATH = os.path.join(BASE_PATH, "modules") sys.path.append(MODULES_PATH) import active_learning importlib.reload(active_learning) from active_learning import Metrics METRICS_PATH = os.path.join(BASE_PATH, "metrics", "old_metrics") files = os.listdir(METRICS_PATH) def get_acq_name(filename): """ Decodes the acquisition function name from the filename. Parameters: filename (str): The filename for example 'mc_dropout_max_entropy.csv' Returns: (str) the acquisition function name used for the metrics inside the file. """ name, ext = filename.split(".") if "bald" in name: return "BALD" elif "max_entropy" in name: return "Max Entropy" elif "max_var_ratio" in name: return "Var Ratios" elif "std_mean" in name: return "Mean STD" elif "random" in name: return "Random" raise ValueError("No acquisition function name encoded in filename: {}".format(name)) def get_model_name(filename): """ Decodes the model name from the filename Parameters: filename (str): The filename for example 'mc_dropout_max_entropy.csv' Returns: (str) the neural network model name """ name, ext = filename.split(".") if "moment_propagation" in name: return "Moment Propagation" elif "mc_dropout" in name: return "MC Dropout" raise ValueError("No model name encoded in filename: {}".format(name)) metrics_reader = Metrics(METRICS_PATH) list(filter(lambda x: "mc_dropout" in x, files)) # Aggregate actve leanring information for MC Models mc_files = filter(lambda x: "mc_dropout" in x, files) main_df = pd.DataFrame() for file in mc_files: model = "MC Dropout" acq_name = get_acq_name(file) data = metrics_reader.read(file) df = pd.DataFrame(data) df = df.rename(columns={"binary_accuracy": "accuracy"}) df.insert(2, "Acquisition Function", [acq_name]*len(data)) main_df = pd.concat([main_dataframe, df]) main_df = main_df.astype({"iteration": "int32", "loss": "float32", "accuracy": "float32"}) main_df.dtypes plt.figure(figsize=(20, 100)) sns.relplot(x="iteration", y="accuracy", kind="line", markers=True, dashes=False, style="Acquisition Function", ci=None, hue="Acquisition Function", data=main_df) plt.figure(figsize=(10, 10)) sns.lineplot(x="iteration", y="accuracy", hue="Acquisition Function", data=main_df) ###Output _____no_output_____
session1/session1_correction.ipynb	###Markdown Introduction au machine learning ###Code import keras from keras.datasets import cifar10 from matplotlib import pyplot as plt import numpy as np from collections import defaultdict import math %matplotlib inline ###Output _____no_output_____ ###Markdown Pour vous présenter les notions principales du Machine Learning, nous allons vous introduire deux algorithmes de base : KNN et K-MEAN.Ils seront appliqués au jeu de données CIFAR 10, jeu de données de 50 000 images appartenant à 10 classes d'images différentes. ###Code # Charge le jeu de données (x_train, y_train), (x_test, y_test) = cifar10.load_data() # Observons les dimensions du jeu de données print("Images du jeu d'entrainement {}".format(x_train.shape)); print("Classes du jeu d'entrainement {}".format(y_train.shape)); # Classes des images de CIFAR-10 classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] # Visualisons un exemple et sa classe img_index = np.random.randint(0, x_train.shape[0]) plt.imshow(x_train[img_index]) plt.show() class_indx = y_train[img_index, 0] print("Qui appartient à la classe {} ({})".format(class_indx, classes[class_indx])) # Grille d'exemples pour chaque classe num_classes = len(classes) samples_per_class = 7 for y, cls in enumerate(classes): # Sélectionne aléatoirement des exemples de la classe y idxs = np.flatnonzero(y_train == y) idxs = np.random.choice(idxs, samples_per_class, replace=False) # Affiche ces exemples en colonne for i, idx in enumerate(idxs): plt_idx = i * num_classes + y + 1 plt.subplot(samples_per_class, num_classes, plt_idx) plt.imshow(x_train[idx].astype('uint8')) plt.axis('off') if i == 0: plt.title(cls) plt.show() ###Output _____no_output_____ ###Markdown K-NN (K Nearest Neighbor ou méthode des K plus proches voisins) est un algorithme consistant à trouver, dans le jeu de données d'entrainement, les K images ressemblant le plus à l'image dont nous souhaitons trouver la classe. Pour calculer la ressemblance entre deux images on peut en première approximation considérer simplement leur distance euclidienne (norme L2). Sur les K images trouvées, nous regardons ensuite quelle classe est la plus présente. On pourra ainsi décider de la classe de notre image de test. ###Code # Redimensionne les images en les applatissant afin de faciliter # leur manipulation x_train = x_train.reshape(50000,32323) x_test = x_test.reshape(10000,32323) y_train = y_train[:, 0] y_test = y_test[:, 0] k = 20 ###Output _____no_output_____ ###Markdown De plus, utiliser les 50 000 images d'entraînement pour classer les 10 000 images de test serait long. Nous allons donc sélectionner une partie des deux ensembles: ###Code nb_imgs_train = 5000 nb_imgs_test = 1000 ###Output _____no_output_____ ###Markdown Cependant le code au dessus est pas très beau et le réimplémenter est un peu pénible.Afin de simplifier la vie de tout le monde, nous allons utiliser une bibliothèque du nom de sickit learn.Cette bibliothèque est une boite à outils remplie de beaucoup de fonctions très pratiques et d'algorithmes d'apprentissages prêts à l'utilisation. On vous laisse chercher [ici](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.htmlsklearn.neighbors.KNeighborsClassifier) comment l'utiliser. Nous vous déconseillons de faire l'entrainement sur les 50 000 éléments de x_train et le test sur les 10 000 éléments de x_test car cela vous prendrait trop de temps pour tester... ###Code # --- Méthode brute --- predictions = np.empty((nb_imgs_test, )) for id_img_test, img_test in enumerate(x_test[:nb_imgs_test]): # Le tableau k_nearest contient les classes des k images les plus proches # distances contient les distances entre l'image test et les k plus proches k_nearest, distances = np.full((k, ), -1), np.full((k, ), float("+inf")) # On cherche à remplir le tableau k_nearest avec les classes des k # images d'entraînement les "plus proches" au sens de la distance euclidienne. for id_img_train, img_train in enumerate(x_train[:nb_imgs_train]): dist = np.linalg.norm(img_test - img_train) furthest_of_nearest = np.argmax(distances) if dist < distances[furthest_of_nearest]: distances[furthest_of_nearest] = dist k_nearest[furthest_of_nearest] = y_train[id_img_train] predictions[id_img_test] = np.argmax(np.bincount(k_nearest)) print("Classified image {}/{} ".format(id_img_test + 1, nb_imgs_test)) # Ne prenez pas trop d'images ! nb_imgs_train = 2000 nb_imgs_test = 500 x_test = x_test[:nb_imgs_test] y_test = y_test[:nb_imgs_test] # On importe la bibliothèque from sklearn.neighbors import KNeighborsClassifier # Regardez la fonction fit et la fonction score... # On crée un modèle de paramètre k=7 neigh = KNeighborsClassifier(n_neighbors=7) # On entraine notre modèle neigh.fit(x_train[:nb_imgs_train], y_train[:nb_imgs_train]) # Prédit les classes de x_test et calcule son score print(neigh.score(x_test,y_test)) ###Output 0.232 ###Markdown Affichons quelques exemples de classes attribuées par le KNN: ###Code from itertools import product # On affiche une grille carrée d'images nb_cols = 4 fig, axes = plt.subplots(nrows=nb_cols, ncols=nb_cols, figsize=(8, 8)) samples = x_test[:nb_cols ** 2] predictions = neigh.predict(samples) # On remet les exemples sous la forme d'images samples = samples.reshape(samples.shape[0], 32, 32, 3) for i, j in product(range(nb_cols), range(nb_cols)): axes[i, j].imshow(samples[i * nb_cols + j]) axes[i, j].axis("off") axes[i, j].set_title(classes[predictions[i * nb_cols + j]]) fig.suptitle("Quelques prédictions...") plt.show(fig) ###Output _____no_output_____ ###Markdown La fonction score nous a permis de mesure la précision (accuracy) de l'algorithme KNN sur une partie de notrejeu de données.Nous avons obtenu 0.29 ce qui veut dire que sur les 100 images testées, seules 29% étaient correctes.De plus, vous avez pu remarquer que le temps d'exécution était plutôt long. Imaginez le temps que cela mettrait si l'on voulait tester l'intégralité de notre jeu de testqui avait 10 000 exemples....Ici, nous avons testé pour k = 7. Mais quelle est la valeur optimale de $k$ ? $k$ est ce que l'on apelle un hyperparamètre. C'est une valeur à configurer avant l'entrainement de notre modèle sur le jeu de données.A vous de la trouver... ###Code resultats = [] for k in range(1, 16): neigh = KNeighborsClassifier(n_neighbors=k) neigh.fit(x_train[:nb_imgs_train], y_train[:nb_imgs_train]) # On mesure le score et on l'ajoute à une liste pour le sauvegarder resultats.append(neigh.score(x_test,y_test)) plt.plot(list(range(1, 16)), resultats, "-+") plt.xlabel("K") plt.ylabel("Accuracy") ###Output _____no_output_____ ###Markdown La méthode K-MEAN (ou méthode des K-moyennes) est un algorithme de partionnement de données (clustering en anglais). C'est l'un des algorithmes les plus fondamentaux en apprentissage non supervisé. L'algorithme consiste à partionner des données pour tenter d'en dégager des classes. Dans notre cas, appliqué aux images de CIFAR-10, cela revient a classer les images tel que cela est déjà fait mais en utilisant juste les données brutes (c'est pour cela que l'on parle d'apprentissage non supervisé). L'idée générale derrière la méthode est de regrouper les données en fonction de leurs ressemblances, i.e. de leur distance. L'algorithme fonctionne ainsi: on commence en considérant K données aléatoires, elles sont chacunes représentantes d'une classe ; à chaque itération, on va partionner les données en fonction de la ressemblance avec les K images types de départ : on regroupe dans la classe K toutes les images étant plus proches de la K-ème image type ; on calcule ensuite la moyenne des classes obtenues et l'on remplace l'image type de chacune des classes obtenues par cette moyenne.Ci-dessous nous avons tracé ###Code K_VALUE = 10 min_val = 1 # On initialise les K representants de chaque classe K_mean = [255 * np.random.rand(32323) for _ in range(10)] # Valeur précédente de ces representants K_save = [255 * np.random.rand(32323) for _ in range(10)] def nearest_K(image): """ Retourne la classe K la plus proche de image """ min_dist, min_k = float("+inf"), None for id_K, K_point in enumerate(K_mean): dist = np.linalg.norm(image - K_point) if dist < min_dist: min_dist, min_k = dist, id_K return min_k def mean_point(k, tab): """ Retourne barycentre des points (indicés) de tab """ if tab != []: mean = 0 for id in tab: mean += x_train[id] / len(tab) K_mean[k] = mean def stop_convergence(): """ Evalue si l'on arrete les itérations """ for k in range(10): if not(np.array_equal(K_mean[k], K_save[k])): return True return False #KMEAN iteration = 0 while stop_convergence(): iteration += 1 K_nearest = [[] for _ in range(10)] for id_image, image in enumerate(x_train): K_nearest[nearest_K(image)].append(id_image) for k in range(10): K_save[k] = K_mean[k] mean_point(k, K_nearest[k]) print(iteration) ###Output 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 ###Markdown Essayons avec une fonction built-in écrite par de vrais Data Scientists: ###Code from sklearn.cluster import KMeans kmeans = KMeans(n_clusters=10) kmeans.fit(x_train) ###Output _____no_output_____ ###Markdown On ne peux pas évaluer automatiquement notre algorithme parce qu'il n'a pas conscience du type de classe auquels les images d'un même cluster appartienentOn va donc visualiser les cluster (chaque ligne = 1 cluster) ###Code predictions = kmeans.predict(x_test) # Grille de classification pour chaque cluster trouvé n_clusters=10 imgs = [[] for _ in range(n_clusters)] for cluster in range(n_clusters): i=0 while len(imgs[cluster]) < 7 : if predictions[i] == cluster: imgs[cluster].append(i) i+= 1 for col, cluster in enumerate(imgs): for line, img in enumerate(cluster): plt_idx = line * n_clusters + col + 1 plt.subplot(7, n_clusters, plt_idx) plt.imshow(x_test[img].reshape(32,32,3).astype('uint8')) plt.axis('off') if line == 0: plt.title(str(col)) plt.show() ###Output _____no_output_____
iPython_Notebooks/Data Scraping_Cleaning(Ovais).ipynb	###Markdown Dependencies* ###Code #--Dependencies--# #-- Data Cleaning Libraries: import pandas as pd import numpy as np from pandas.api.types import is_string_dtype from pandas.api.types import is_numeric_dtype #-- Data Visualization Libraries: from matplotlib import pyplot as plt import seaborn as sns #--just in case we need it, probably won't #-- Web Scraping Libraries: import os import time import requests from bs4 import BeautifulSoup as bs from splinter import Browser #--other from tqdm import tqdm_notebook as tqdm ###Output _____no_output_____ ###Markdown * Web Scraping * ###Code #Settings for accessing Website executable_path = {"executable_path": "chromedriver.exe"} browser = Browser ('chrome', executable_path, headless=False) # yr_min = 1990 # yr_max = 2019 # NCAA_url = f"https://www.sports-reference.com/cbb/play-index/psl_finder.cgi?request=1&match=single&year_min={str(yr_min)}&year_max={str(yr_max)}&class_is_fr=Y&class_is_so=Y&class_is_jr=Y&class_is_sr=Y&pos_is_g=Y&pos_is_gf=Y&pos_is_f=Y&pos_is_fg=Y&pos_is_fc=Y&pos_is_c=Y&pos_is_cf=Y&games_type=A&c1stat=fga&c1comp=gt&order_by=year_id&order_by_asc=Y" # browser.visit(NCAA_url) # html = browser.html # read_html = pd.read_html(html, header = 0) # read_html[1] ###Output _____no_output_____ ###Markdown Defining Functions for Data Scraping and Cleaning* NBA* ###Code #Pre-defining Variables for NBA scrape #Empty DataFrame for NBA NBA_game_df = pd.DataFrame() #Reference to names NBA_refer = "basketball-reference" #URL NBA_url = "https://www.basketball-reference.com" #Create Empty List for years we want to scrape for # years = ["1990", "1991", "1992", "1993", "1994", "1995",\ # "1996", "1997", "1998", "1999", "2000","2001", \ # "2002", "2003", "2004", "2005", "2006", "2007",\ # "2008", "2009", "2010", "2011", "2012", "2013",\ # "2014", "2015", "2016"] yr_min = 1990 yr_max = 2019 #Define our function for scraping NBA Data---> need to come back to this and finish it off def scrape_nba_data(page_url): #URL's for both NBA and NCAA #Reference global variable: NBA_game_df global NBA_game_df for year in tqdm(range(yr_min, (yr_max + 1))): #Set the rest of the url url = f"https://www.basketball-reference.com/leagues/NBA_{str(year)}_per_game.html" #Visit the NBA Web page browser.visit(url) #Retrieve the html for the web page html = browser.html #Use pandas to read html year_html = pd.read_html(html, header = 0) #Get the second Table with all of the Data year_html = year_html[1] #Convert into DataFrame year_df = pd.DataFrame(year_html) #Delete Column of Ranking: "Rk" year_df = year_df.rename(columns=({"Rk" : "Year"})) #Apply the year to the Year column for each row year_df["Year"] = year_df["Year"].apply(lambda x: year) #Append to main DataFrame: NBA_game_df if NBA_game_df.empty: NBA_game_df = year_df else: NBA_game_df = NBA_game_df.append(year_df, ignore_index = True) #hoooldd on wait a second, let me put some sleep in it time.sleep(1) #Function for cleaning and merging NBA data def clean_nba_data (page_html): # Calculate PER # Set Unique ID to players based upon their name ###Output _____no_output_____ ###Markdown NCAA* ###Code #Predefining variables for NCAA scrape NCAA_df = pd.DataFrame() NCAA_refer = "sports-reference" yr_min = 2000 yr_max = 2019 off = 0 NCAA_url = f"https://www.sports-reference.com/cbb/play-index/psl_finder.cgi?request=1&match=single&year_min={str(yr_min)}&year_max={str(yr_max)}&conf_id=&school_id=&class_is_fr=Y&class_is_so=Y&class_is_jr=Y&class_is_sr=Y&pos_is_g=Y&pos_is_gf=Y&pos_is_fg=Y&pos_is_f=Y&pos_is_fc=Y&pos_is_cf=Y&pos_is_c=Y&games_type=A&qual=&c1stat=&c1comp=&c1val=&c2stat=&c2comp=&c2val=&c3stat=&c3comp=&c3val=&c4stat=&c4comp=&c4val=&order_by=ws&order_by_asc=&offset={str(off)}" browser.visit(NCAA_url) html = browser.html read = pd.read_html(html, header = 0) our_read = read[2] our_read #Function for scraping NCAA data def scrape_ncaa_data(start_year, end_year, page_url): #Call in any global variables global NCAA_df #Initialize variables for loop start = 0 end = 90400 url = page_url #Loop for off_set in tqdm(range(start, (end+100), 100)): try: if off_set == 0: #Visit url url = f"https://www.sports-reference.com/cbb/play-index/psl_finder.cgi?request=1&match=single&year_min={str(yr_min)}&year_max={str(yr_max)}&conf_id=&school_id=&class_is_fr=Y&class_is_so=Y&class_is_jr=Y&class_is_sr=Y&pos_is_g=Y&pos_is_gf=Y&pos_is_fg=Y&pos_is_f=Y&pos_is_fc=Y&pos_is_cf=Y&pos_is_c=Y&games_type=A&qual=&c1stat=&c1comp=&c1val=&c2stat=&c2comp=&c2val=&c3stat=&c3comp=&c3val=&c4stat=&c4comp=&c4val=&order_by=ws&order_by_asc=&offset={str(off_set)}" print(url) browser.visit(url) #Get html of page html = browser.html #Read in Html using Pandas read_html = pd.read_html(html, header=0) print(read_html[2]) #Convert desired table to DataFrame to_df = pd.DataFrame(read_html[2]) #Set global NCAA_df equal to to_df NCAA_df = to_df #Sleep for one second time.sleep(1) else: #Visit url url = f"https://www.sports-reference.com/cbb/play-index/psl_finder.cgi?request=1&match=single&year_min={str(yr_min)}&year_max={str(yr_max)}&conf_id=&school_id=&class_is_fr=Y&class_is_so=Y&class_is_jr=Y&class_is_sr=Y&pos_is_g=Y&pos_is_gf=Y&pos_is_fg=Y&pos_is_f=Y&pos_is_fc=Y&pos_is_cf=Y&pos_is_c=Y&games_type=A&qual=&c1stat=&c1comp=&c1val=&c2stat=&c2comp=&c2val=&c3stat=&c3comp=&c3val=&c4stat=&c4comp=&c4val=&order_by=ws&order_by_asc=&offset={str(off_set)}" browser.visit(url) #Get html of page html = browser.html #Read in Html using Pandas read_html = pd.read_html(html, header=0) #Convert desired table to DataFrame to_df = pd.DataFrame(read_html[2]) #Append to NCAA_df NCAA_df = NCAA_df.append(to_df, ignore_index = True) #Sleep for one second time.sleep(1) except IndexError as error: if off_set == 0: #Visit url url = f"https://www.sports-reference.com/cbb/play-index/psl_finder.cgi?request=1&match=single&year_min={str(yr_min)}&year_max={str(yr_max)}&conf_id=&school_id=&class_is_fr=Y&class_is_so=Y&class_is_jr=Y&class_is_sr=Y&pos_is_g=Y&pos_is_gf=Y&pos_is_fg=Y&pos_is_f=Y&pos_is_fc=Y&pos_is_cf=Y&pos_is_c=Y&games_type=A&qual=&c1stat=&c1comp=&c1val=&c2stat=&c2comp=&c2val=&c3stat=&c3comp=&c3val=&c4stat=&c4comp=&c4val=&order_by=ws&order_by_asc=&offset={str(off_set)}" print(url) browser.visit(url) #Get html of page html = browser.html #Read in Html using Pandas read_html = pd.read_html(html, header=0) print(read_html[1]) #Convert desired table to DataFrame to_df = pd.DataFrame(read_html[1]) #Set global NCAA_df equal to to_df NCAA_df = to_df #Sleep for one second time.sleep(1) else: #Visit url url = f"https://www.sports-reference.com/cbb/play-index/psl_finder.cgi?request=1&match=single&year_min={str(yr_min)}&year_max={str(yr_max)}&conf_id=&school_id=&class_is_fr=Y&class_is_so=Y&class_is_jr=Y&class_is_sr=Y&pos_is_g=Y&pos_is_gf=Y&pos_is_fg=Y&pos_is_f=Y&pos_is_fc=Y&pos_is_cf=Y&pos_is_c=Y&games_type=A&qual=&c1stat=&c1comp=&c1val=&c2stat=&c2comp=&c2val=&c3stat=&c3comp=&c3val=&c4stat=&c4comp=&c4val=&order_by=ws&order_by_asc=&offset={str(off_set)}" browser.visit(url) #Get html of page html = browser.html #Read in Html using Pandas read_html = pd.read_html(html, header=0) #Convert desired table to DataFrame to_df = pd.DataFrame(read_html[1]) #Append to NCAA_df NCAA_df = NCAA_df.append(to_df, ignore_index = True) #Sleep for one second time.sleep(1) #Function for cleaning and merging NCAA data Will work on this later # def clean_ncaa_data(ncaa_csv): # #Calculate PER ###Output _____no_output_____ ###Markdown Scraping for Data* NCAA* ###Code #Scrapety scrape scrape scrape_ncaa_data(yr_min, yr_max, NCAA_url) #Test print NCAA_df #Push Raw Data to CSV (So we don't have to scrape again) NCAA_df.to_csv("NCAA_raw2.csv", index = False) ###Output _____no_output_____ ###Markdown NBA* ###Code #Scrapety scrape scrape scrape_nba_data(NBA_url) #Test print NBA_game_df NBA_copy = NBA_game_df #Confirm last record on Web Page with that on DF NBA_game_df #Push Raw Data to csv NBA_game_df.to_csv("NBA_raw2.csv", index = False) ###Output _____no_output_____ ###Markdown Cleaning Data*** ###Code #Set PER calculation function for NCAA def per (row): #Set calculation pre-requisites FT_miss = (row.FTA - row.FT) FG_miss = (row.FGA - row.FG) row_add = (row.FG * 85.910) + (row.STL * 53.897) + (row["3P"] * 51.757)\ +(row.FT * 46.845) + (row.BLK * 39.190) + (row.ORB * 39.190)\ +(row.AST * 34.677) + (row.DRB * 14.707) row_sub = (row.PF * 17.174) + FT_miss + FG_miss + (row.TOV * 53.897) try: #calculate and return calc = (row_add - row_sub) * (1/row.MP) return round(calc, 2) except ZeroDivisionError as zeroerror: calc = 0 return calc except ValueError as error: calc = ((int(row.FG) * 85.910) + (int(row.STL) * 53.897) + (int(row["3P"]) * 51.757)\ + (int(row.FT) * 46.845) + (int(row.BLK) * 39.190) + (int(row.ORB) * 39.190)\ + (int(row.AST) * 34.677) + (int(row.DRB) * 14.707) + (int(row.PF) * 17.174)\ + ((int(row.FTA) - int(row.FT)) * 20.091) + (int(row.TOV) * 53.897)) * (1/row.MP) ###Output _____no_output_____ ###Markdown NCAA* ###Code #Convert Seasons into a single year value for NCAA def normalize_year(y): if y == "1999-00": year = "2000" elif y == "2009-10": year = "2010" elif y == "2019-20": year = "2020" else: year = int(y[0:3] + y[-1]) return year #Note: Data has not been collected for per game stats, will need to calculate per game stats. #Read in CSV to be cleaned: "NCAA_raw.csv" NCAA_df = pd.read_csv("NCAA_raw2.csv", low_memory = False) NCAA_df NCAA_df.iloc[0] ncaa_cols = list(NCAA_df.iloc[0]) ncaa_cols[2] = "nan" ncaa_cols[-1] = "nan" #Set Columns for NCAA DF NCAA_df.columns = ncaa_cols NCAA_df.head() #Drop Rk column NCAA_df = NCAA_df.drop("Rk", axis = 1) NCAA_df = NCAA_df.drop("nan", axis=1) NCAA_df.head() NCAA_df = NCAA_df[NCAA_df.Player != "Advanced"] NCAA_df = NCAA_df[NCAA_df.Player != "Player"] NCAA_df = NCAA_df[NCAA_df["School"].notna()] NCAA_df = NCAA_df[NCAA_df["Conf"].notna()] NCAA_df["PER"]= NCAA_df["PER"].fillna(0) NCAA_df = NCAA_df[NCAA_df.Season.notna()] NCAA_df = NCAA_df.fillna(0) #Normalize years NCAA_df.Season = NCAA_df.Season.apply(lambda x: normalize_year(x)) #Test Print NCAA_df[NCAA_df["Player"] == "Anthony Davis"] NCAA_df = NCAA_df.reset_index(drop= True) #Create Team index for each player NCAA_df[(NCAA_df["PER"] != 0) & (NCAA_df["WS"] != 0)] NCAA_df.columns NCAA_df.reset_index(drop = True, inplace=True) #Send cleaned NCAA data to csv NCAA_df.to_csv("NCAA_2.csv") #Group by player NCAA_grouped = NCAA_df.groupby("Player") NCAA_grouped.first() #Aggregate and avg stats by player stats_test = list(NCAA_df.columns) stats_test = stats_test [6:] stats_test NCAA_agg = NCAA_grouped[stats_test].agg(np.mean) #Reset index NCAA_agg = NCAA_agg.reset_index() #Test print NCAA_agg.head() #Save to CSV NCAA_agg.to_csv("NCAA_agg.csv") ###Output _____no_output_____ ###Markdown NBA* ###Code #Note: Data has been collected for Per Game Stats #Read in CSV to be cleaned: "NBA_raw.csv" NBA_df = pd.read_csv("NBA_raw.csv") #Remove irrelevant data NBA_df = NBA_df[NBA_df["Player"] != "Player"] NBA_df.head() #Get columns that we want to loop for nba_stats_cols = NBA_df.columns nba_stats_cols = nba_stats_cols[7:] nba_stats_cols #Loop over DataFrame to change the values for col in nba_stats_cols: string_check = is_string_dtype(NBA_df[col]) #Verify if the value is a string, if it is convert to float if string_check == True: print(string_check) NBA_df[col] = pd.to_numeric(NBA_df[col]) NBA_df[col] = NBA_df[col].fillna(0) #Calculate PER NBA_df["PER"] = NBA_df.apply(lambda row : per(row), axis = 1) #normalize names with astriks at the end def astriks (row): name = row if name[-1] == "*": name = name[0:-1] return name else: return name NBA_df["Player"] = NBA_df["Player"].apply(lambda x: astriks(x)) NBA_df.head() #Save as NBA_clean.csv NBA_df.to_csv("NBA_clean.csv") #Group data by player NBA_grouped = NBA_df.groupby(["Player"]) NBA_grouped.first() #Add PER to nba_stats_cols nba_stats_cols = list(nba_stats_cols) nba_stats_cols.append("PER") nba_stats_cols #Group by player NBA_agg = NBA_grouped[nba_stats_cols].agg(np.mean) NBA_agg = NBA_agg.sort_values(by="PTS", ascending = False) NBA_agg #Reset Tm index to a column NBA_agg = NBA_agg.reset_index("Player") NBA_agg #Save to csv: NBA_agg NBA_agg.to_csv("NBA_agg1.csv") ###Output _____no_output_____
pipelines/ner_nltk/NER.ipynb	###Markdown Entity list: PERSON: John Gilbert, President ObamaORGANIZATION: WHO, FC BayernLOCATION: Mt. Everest, NileGPE: Germany, North AmericaDATE: December, 2016--- ###Code # Download necessary libraries import nltk import glob import os import csv nltk.download('words') nltk.download('punkt') nltk.download('averaged_perceptron_tagger') nltk.download('maxent_ne_chunker') nltk.download('stopwords') from nltk import word_tokenize,pos_tag,ne_chunk import matplotlib as mpl import matplotlib.pyplot as plt ###Output [nltk_data] Downloading package words to /root/nltk_data... [nltk_data] Unzipping corpora/words.zip. [nltk_data] Downloading package punkt to /root/nltk_data... [nltk_data] Unzipping tokenizers/punkt.zip. [nltk_data] Downloading package averaged_perceptron_tagger to [nltk_data] /root/nltk_data... [nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip. [nltk_data] Downloading package maxent_ne_chunker to [nltk_data] /root/nltk_data... [nltk_data] Unzipping chunkers/maxent_ne_chunker.zip. [nltk_data] Downloading package stopwords to /root/nltk_data... [nltk_data] Unzipping corpora/stopwords.zip. ###Markdown NER Example for the file abadilah.txtitalicized text ###Code from google.colab import drive from nltk.corpus import stopwords drive.mount('/content/drive') stopWords=[ 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday','Sunday', 'January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December', 'Mondays', 'Tuesdays', 'Wednesdays','Thursdays', 'Fridays', 'Saturdays', 'Sundays' ] #example_path='/content/drive/MyDrive/Lorimer-geography-master/abadilah.txt' #example_path='/content/drive/MyDrive/combined.txt' example_path='/content/drive/MyDrive/Lorimer-geography-master/bahrain_principality.txt' with open(example_path, 'r') as file: data = file.read().replace('\n', '') tokens_all = word_tokenize(data) tokens = [word for word in tokens_all if word not in stopwords.words('english')] print("All tokens") for x in tokens: print(x) pos_tags=nltk.pos_tag(tokens) print("POS Tags") for x in pos_tags: print(x) chunks = ne_chunk(pos_tags) print("All chunks") for x in chunks: print(x) ###Output [1;30;43mStreaming output truncated to the last 5000 lines.[0m ('.', '.') ('Many', 'JJ') ('reefs', 'NNS') (GPE Bahrain/NNP) ('islands', 'NNS') ('partially', 'RB') ('dry', 'JJ') ('low', 'JJ') ('water', 'NN') ('.', '.') ('On', 'IN') ('side', 'NN') ('towards', 'NNS') ('open', 'JJ') ('sea', 'NN') ('shallow', 'NN') ('waters', 'NNS') ('Bahrain', 'VBP') ('may', 'MD') ('considered', 'VBN') ('end', 'VB') (PERSON Rennie/NNP Shoal/NNP) (',', ',') ('54', 'CD') ('miles', 'NNS') ('north', 'RB') (GPE Muharraq/NNP) ('.', '.') ('There', 'EX') ('passage', 'NN') (',', ',') ('called', 'VBD') ('Khor-al-Bāb', 'NNP') ('خور', 'NNP') ('الباب', 'NNP') ('Manāmah', 'NNP') ('Qatīf', 'NNP') ('south', 'JJ') ('Fasht-al-Jārim', 'NNP') (',', ',') ('practicable', 'JJ') ('vessels', 'NNS') ('drawing', 'VBG') ('15', 'CD') ('feet', 'NNS') ('.', '.') ('Many', 'JJ') ('pearl', 'NN') (',', ',') ('banks', 'NNS') ('situated', 'VBD') ('waters', 'NNS') (':', ':') ('names', 'NNS') ('positions', 'NNS') ('given', 'VBN') (PERSON Appendix/NNP Pearl/NNP) ('Fisheries.Geology.—The', 'NNP') ('main', 'JJ') ('island', 'NN') (GPE Bahrain/NNP) ('forms', 'NNS') ('striking', 'VBG') ('geological', 'JJ') ('contrast', 'NN') ('islands', 'NNS') (PERSON Persian/JJ Gulf/NNP) ('.', '.') ('The', 'DT') ('rocks', 'NNS') ('chiefly', 'VBP') ('white', 'JJ') ('pale-coloured', 'JJ') ('limestones', 'NNS') ('eocene', 'JJ') ('age', 'NN') (',', ',') ('sometimes', 'RB') ('sandy', 'JJ') ('argillaceous', 'JJ') (',', ',') ('disposed', 'JJ') ('form', 'NN') ('low', 'JJ') ('anticlinal', 'JJ') ('dome', 'NN') ('Jabal-ad-', 'NNP') ('Dukhān', 'NNP') ('summit', 'NN') ('.', '.') ('In', 'IN') ('hollow', 'JJ') ('girdling', 'NN') ('plateau', 'NN') ('(', '(') ('described', 'JJ') ('article', 'NN') (PERSON Bahrain/NNP Island/NNP) (')', ')') ('central', 'JJ') ('peak', 'NN') ('rock', 'NN') ('denuded', 'VBD') ('marine', 'JJ') ('agency', 'NN') ('forms', 'NNS') ('plain', 'VBP') ('.', '.') ('In', 'IN') ('places', 'NNS') ('eocene', 'VBP') ('limestone', 'JJ') ('rocks', 'NNS') ('highly', 'RB') ('fossiliferous', 'JJ') ('contain', 'NN') ('foraminifera', 'NN') (',', ',') ('echinids', 'NNS') ('mollusca', 'NN') (':', ':') ('whole', 'JJ') ('characterised', 'VBD') ('abundance', 'RB') ('siliceous', 'JJ') ('material', 'NN') (',', ',') ('occurring', 'VBG') ('flint', 'NN') (',', ',') ('cherty', 'JJ') ('concretions', 'NNS') ('quartz', 'VBP') ('geodes', 'NNS') (',', ',') ('dissemination', 'NN') ('gypsum', 'NN') ('salt', 'NN') ('throughout', 'IN') ('series', 'NN') ('marked', 'VBD') ('degree', 'JJ') ('.', '.') ('The', 'DT') ('presence', 'NN') ('salt', 'NN') ('gypsum', 'VBP') ('conspicuous', 'JJ') ('certain', 'JJ') ('places', 'NNS') ('leached', 'VBD') ('rock', 'NN') ('formed', 'VBD') ('vast', 'JJ') ('accumulations', 'NNS') ('saliferous', 'JJ') ('gypseous', 'JJ') ('soil', 'NN') ('.', '.') ('The', 'DT') ('distinctly', 'RB') ('marked', 'VBN') ('areas', 'NNS') ('character', 'VBP') ('one', 'CD') ('towards', 'NNS') ('south', 'JJ') ('end', 'VBP') (PERSON Bahrain/NNP Island/NNP) ('another', 'DT') ('island', 'NN') (PERSON Umm/NNP Na'asān/NNP) (',', ',') ('gypsum', 'NN') ('fields', 'NNS') ('latter', 'RBR') ('supply', 'RB') ('practically', 'RB') ('mortar', 'VB') ('used', 'VBN') (GPE Bahrain/NNP) ('.', '.') ('The', 'DT') ('coastal', 'JJ') ('portions', 'NNS') (PERSON Bahrain/NNP Island/NNP) (',', ',') ('also', 'RB') ('islands', 'VBZ') ('group', 'NN') (',', ',') ('overlaid', 'VBD') ('sub-recent', 'JJ') ('coral', 'JJ') ('rocks', 'NNS') ('shelly', 'RB') ('concrete', 'VBP') (';', ':') ('sandstone', 'NN') ('age', 'NN') ('found', 'VBD') ('central', 'JJ') ('depression', 'NN') (PERSON Bahrain/NNP Island/NNP) ('.', '.') ('This', 'DT') ('depression', 'NN') (',', ',') ('well', 'RB') ('littoral', 'JJ') ('flats', 'NNS') (',', ',') ('fact', 'NN') ('emerged', 'VBD') ('sea', 'NN') ('comparatively', 'RB') ('recent', 'JJ') ('times', 'NNS') (',', ',') ('remains', 'VBZ') ('old', 'JJ') ('sea-beaches', 'NNS') ('well', 'RB') ('marked', 'VBN') ('.', '.') ('A', 'DT') ('small', 'JJ') ('deposit', 'NN') ('asphalt', 'NN') ('found', 'VBD') ('penetrating', 'VBG') ('eocene', 'NN') ('rocks', 'NNS') ('3', 'CD') ('miles', 'NNS') ('south-south-east', 'RB') ('Jabal-ad-', 'NNP') (PERSON Dukhān/NNP) ('.The', 'NNP') ('Bahrain', 'NNP') ('islands', 'VBZ') ('famous', 'JJ') ('remarkable', 'JJ') ('set', 'VBN') ('springs', 'NNS') (',', ',') ('beautifully', 'RB') ('clear', 'JJ') ('slightly', 'RB') ('brackish', 'JJ') (',', ',') ('submarine', 'NN') (';', ':') ('majority', 'NN') ('enumerated', 'VBD') ('articles', 'NNS') ('principal', 'JJ') ('islands', 'NNS') (',', ',') ('sufficient', 'JJ') ('mention', 'NN') ('northern', 'JJ') ('part', 'NN') (PERSON Bahrain/NNP Island/NNP) (',', ',') ('north', 'JJ') ('Khor-al-', 'NNP') ('Kabb', 'NNP') (',', ',') ('warm', 'NN') (',', ',') ('copious', 'JJ') ('nearly', 'RB') ('fresh', 'JJ') (',', ',') ('best', 'JJS') ('known', 'VBN') ('district', 'NN') ("'Adāri", 'NN') (',', ',') (PERSON Qassāri/NNP Abu/NNP Zaidān/NNP) ('.', '.') ('The', 'DT') ('noteworthy', 'JJ') ('springs', 'NNS') ('sea', 'NN') (PERSON Abu/NNP Māhur/NNP) ('close', 'RB') (PERSON Muharraq/NNP Island/NNP Kaukab/NNP Fasht/NNP KhorFasht/NNP) ('.', '.') ('The', 'DT') ('best', 'JJS') ('water', 'NN') ('islands', 'NNS') ('obtained', 'VBN') (GPE Hanaini/NNP) ('wells', 'NNS') (',', ',') ('north', 'JJ') ('end', 'JJ') ('central', 'JJ') ('depression', 'NN') (PERSON Bahrain/NNP Island/NNP) (',', ',') (PERSON Khālid/NNP Umm/NNP Ghuwaifah/NNP) ('wells', 'VBZ') ('plateau', 'NN') ('adjoining', 'VBG') ('.', '.') ('There', 'EX') ('little', 'JJ') ('doubt', 'NN') ('springs', 'NNS') (GPE Bahrain/NNP) (',', ',') ('like', 'IN') (PERSON Hofūf/NNP Qatīf/NNP Oases/NNP) (',', ',') ('fed', 'VBN') ('drainage', 'NN') ('part', 'NN') (PERSON Najd/NNP) (',', ',') ('temporarily', 'RB') ('lost', 'VBN') (PERSON Dahánah/NNP Sahábah/NNP) (',', ',') ('travels', 'VBZ') ('thence', 'NN') ('eastwards', 'NNS') ('subterranean', 'JJ') ('passages.Climate', 'VBP') ('seasons.—The', 'JJ') ('climate', 'NN') (GPE Bahrain/NNP) ('means', 'VBZ') ('worst', 'JJS') (PERSON Persian/JJ Gulf/NNP) (',', ',') ('travellers', 'NNS') ('emphasized', 'VBD') ('less', 'RBR') ('pleasant', 'JJ') ('features', 'NNS') ('terms', 'NNS') ('facts', 'NNS') ('warrant', 'VBP') ('.', '.') ('Daily', 'JJ') ('observations', 'NNS') ('taken', 'VBN') ('since', 'IN') ('October', 'NNP') ('1901', 'CD') (',', ',') ('since', 'IN') ('time', 'NN') ('highest', 'JJS') ('temperature', 'NN') ('registered', 'VBD') ('1075°', 'CD') ('F.', 'NNP') ('lowest', 'JJS') ('40°', 'CD') ('.', '.') ('The', 'DT') ('weather', 'NN') ('October', 'NNP') ('April', 'NNP') ('inclusive', 'JJ') ('pleasant', 'NN') (',', ',') ('temperature', 'NN') ('indoors', 'NNS') (',', ',') ('ranging', 'VBG') ('60°', 'CD') ('85°', 'CD') ('F.', 'NNP') (';', ':') ('January', 'NNP') ('February', 'NNP') ('however', 'RB') (',', ',') ('north', 'JJ') ('winds', 'NNS') ('blow', 'VBP') ('cold', 'JJ') ('enough', 'RB') ('light', 'JJ') ('housefires', 'NNS') (',', ',') ('sometimes', 'RB') ('rainy', 'JJ') ('unhealthy', 'JJ') ('.', '.') ('From', 'IN') ('beginning', 'VBG') ('May', 'NNP') ('till', 'VB') ('middle', 'JJ') ('June', 'NNP') ('weather', 'NN') ('hot', 'NN') (';', ':') ('heat', 'NN') ('still', 'RB') ('tempered', 'VBD') ('sea-breeze', 'JJ') ('Bārih', 'NNP') (',', ',') ('nights', 'NNS') ('fairly', 'RB') ('cool', 'VBP') ('.', '.') ('From', 'IN') ('middle', 'JJ') ('June', 'NNP') ('end', 'NN') ('September', 'NNP') ('heat', 'NN') ('oppressive', 'NN') (';', ':') ('land', 'NN') ('breezes', 'VBZ') ('west', 'JJS') (',', ',') ('south-west', 'JJ') ('south', 'NN') (',', ',') ('true', 'JJ') (',', ',') ('continue', 'VBP') ('irregularly', 'RB') ('summer', 'NN') (';', ':') ('intervals', 'NNS') ('thermometer', 'VBP') ('remains', 'VBZ') ('persistently', 'RB') ('100°', 'CD') ('F.', 'NNP') ('The', 'DT') ('average', 'NN') ('rainfall', 'NN') ('1902', 'CD') ('1906', 'CD') ('3.25', 'CD') ('inches', 'NNS') ('year', 'NN') (';', ':') ('atmosphere', 'CC') (GPE Bahrain/NNP) (',', ',') ('consequence', 'NN') ('irrigation', 'NN') ('nearness', 'NN') ('sea', 'NN') (',', ',') ('damp', 'JJ') ('heavy', 'JJ') (',', ',') ('evidenced', 'JJ') ('mean', 'JJ') ('humidity', 'NN') ('ranges', 'VBZ') ('79', 'CD') ('80', 'CD') ('per', 'IN') ('cent', 'NN') ('saturation', 'NN') ('.', '.') ('The', 'DT') ('rainy', 'NN') ('season', 'NN') ('considered', 'VBN') ('begin', 'VBP') ('middle', 'JJ') ('October', 'NNP') ('end', 'NN') ('middle', 'NN') ('May', 'NNP') (';', ':') ('rainy', 'JJ') ('days', 'NNS') ('however', 'RB') ('ordinarily', 'RB') ('3', 'CD') ('6', 'CD') ('July', 'NNP') ('.', '.') ('In', 'IN') (GPE Bahrain/NNP Shaikhs/NNP) (',', ',') ('flocks', 'VBZ') ('herds', 'NNS') (',', ',') ('welcome', 'JJ') ('rain', 'NN') (';', ':') ('poorer', 'CC') ('classes', 'NNS') ('frail', 'VBP') ('huts', 'NNS') ('date', 'NN') ('fronds', 'VBZ') ('causes', 'VBZ') ('serious', 'JJ') ('discomfort', 'NN') ('.', '.') ('The', 'DT') ('prevailing', 'NN') ('wind', 'NN') (PERSON Shamāl/NNP) ('north-wester', 'NN') (',', ',') ('winter', 'NN') ('violent', 'NN') ('dangerous', 'JJ') ('shipping', 'NN') (';', ':') ('north', 'JJ') ('wind', 'VBP') ('frequent', 'JJ') (';', ':') ('strong', 'JJ') ('wind', 'NN') ('besides', 'IN') (PERSON Shamāl/NNP Qaus/NNP) ('south-east', 'JJ') ('blows', 'NNS') ('irregularly', 'JJ') ('December', 'NNP') ('April', 'NNP') ('.', '.') ('On', 'IN') ('whole', 'JJ') ('climate', 'NN') (PERSON Bahrain/NNP) ('probably', 'RB') ('superior', 'JJ') (PERSON Masqat/NNP Bandar/NNP) ("'Abbās", 'POS') ('certainly', 'RB') ('excels', 'VBZ') ('neighbouring', 'VBG') ('shores', 'NNS') (PERSON Qatīf/NNP) (',', ',') ('rheumatic', 'JJ') ('affections', 'NNS') ('common', 'JJ') (',', ',') ('also', 'RB') ('diseases', 'VBZ') ('heart', 'NN') ('lungs', 'NNS') ('.', '.') ('A', 'DT') ('grey-headed', 'JJ') ('negro', 'NN') ('hardly', 'RB') ('ever', 'RB') ('seen', 'VBN') (',', ',') ('pearl', 'JJ') ('divers', 'NNS') ('Bahrain', 'VBP') ('notoriously', 'RB') ('short-lived', 'JJ') ('race', 'NN') (',', ',') ('though', 'IN') ('possibly', 'RB') ('due', 'JJ') ('rather', 'RB') ('occupation', 'JJ') ('climate.Natural', 'JJ') ('products', 'NNS') ('wild', 'VBP') ('animals.—The', 'JJ') ('minerals', 'NNS') ('value', 'NN') (PERSON Bahrain/NNP) ('mentioned', 'VBD') ('already', 'RB') ('paragraph', 'JJ') ('geology', 'NN') ('.', '.') ('There', 'EX') ('almost', 'RB') ('natural', 'JJ') ('vegetation', 'NN') (';', ':') ('mangroves', 'NNS') ('creeks', 'VBP') ('ber', 'NN') ('trees', 'NNS') ('places', 'NNS') ('appear', 'VBP') ('exceptions', 'NNS') (';', ':') ('even', 'RB') ('grass', 'VBP') ('except', 'IN') ('artificially', 'RB') ('cultivated', 'VBN') ('.', '.') ('A', 'DT') ('kind', 'NN') ('small', 'JJ') ('gazelle', 'NN') ('(', '(') ('believed', 'VBN') (PERSON Arabica/NNP) (')', ')') ('uncommon', 'NN') ('less', 'RBR') ('inhabited', 'JJ') ('parts', 'NNS') ('main', 'JJ') ('island', 'NN') (';', ':') ('hares', 'NNS') ('mongoose', 'VBP') ('fairly', 'RB') ('numerous', 'JJ') ('.', '.') ('The', 'DT') (ORGANIZATION Houbara/NNP) ('bustard', 'NN') ('winter', 'NN') ('visitor', 'NN') (',', ',') (PERSON Shaikhs/NNP) ('keep', 'VB') ('hawks', 'NN') ('(', '(') ('imported', 'VBN') (GPE Persia/NNP) (')', ')') ('hunting', 'NN') ('.', '.') (PERSON Sandgrouse/NNP) ('also', 'RB') ('sometimes', 'RB') ('seen', 'VBN') (',', ',') ('apparently', 'RB') ('sought', 'VBD') ('native', 'JJ') ('sportsmen.Population', 'NN') ('tribes.—No', 'NN') ('census', 'NN') ('ever', 'RB') ('taken', 'VBN') ('population', 'NN') (GPE Bahrain/NNP) (',', ',') ('subjoin', 'VBD') ('rough', 'JJ') ('estimate', 'NN') ('based', 'VBN') ('reported', 'VBD') ('number', 'NN') ('houses', 'NNS') ('.', '.') ('The', 'DT') ('totals', 'NNS') ('souls', 'VBP') ('calculated', 'VBN') ('assumed', 'JJ') ('average', 'JJ') ('5', 'CD') ('persons', 'NNS') ('house', 'NN') (';', ':') (',', ',') ('improbable', 'JJ') (',', ',') ('figure', 'NN') ('assumed', 'VBD') ('low', 'JJ') ('towns', 'NNS') (',', ',') ('totals', 'NNS') ('urban', 'JJ') ('population', 'NN') ('must', 'MD') ('proportionally', 'RB') ('increased.Island.Towns.Sunni', 'VB') ("towns-people.Shi'ah", 'JJ') ('owns-people.Sunni', 'JJ') ('villages.Sunni', 'NN') ("villagers.Shi'ah", 'NN') ("villages.Shi'ah", 'NN') ("villagers.BahrainManāmah9800150001062757319450Budaiya'8000Nil.MuharraqMuharraq1900010008777532750Hadd8000Nil.Na'asān", 'NN') ('(', '(') ('Umm', 'NNP') (')', ')') ('NilNil.Nil.Nil.Nil.Nil.Nil.Nabi', 'NNP') ('SālihDo.Do.Do.Do.Do.2375SitrahDo.Do.Do.115071500TOTAL', 'NNP') ('...', ':') ('448001600019142008524075The', 'CD') ('principality', 'NN') ('contains', 'NNS') (',', ',') ('estimate', 'NN') ('possible', 'JJ') ('form', 'NN') (',', ',') ('4', 'CD') ('towns', 'NNS') ('population', 'NN') ('60800', 'CD') ('souls', 'JJ') ('104', 'CD') ('villages', 'NNS') ('population', 'NN') ('38275', 'CD') (';', ':') ('99075', 'CD') ('.', '.') ('To', 'TO') ('must', 'MD') ('added', 'VB') ('200', 'CD') ('non-Muhammadans', 'JJ') ('Manāmah', 'NNP') (',', ',') ('making', 'VBG') ('grand', 'JJ') ('total', 'JJ') ('99275', 'CD') ('settled', 'VBD') ('inhabitants', 'NNS') ('.', '.') ('The', 'DT') ('nomads', 'NNS') ("Na'īm", 'NNP') ('Bedouins', 'NNP') (',', ',') ('frequent', 'JJ') ('island', 'NN') ('varying', 'VBG') ('numbers', 'NNS') (',', ',') ("Ka'abān", 'NNP') ('settled', 'VBD') ('residence.Of', 'JJ') ('whole', 'JJ') ('population', 'NN') ('100000', 'CD') ('souls', 'JJ') ('60000', 'CD') (',', ',') ('chiefly', 'NN') ('townsmen', 'NNS') (',', ',') (ORGANIZATION Sunnis/NNP) ('40000', 'CD') (',', ',') ('mostly', 'RB') ('villagers', 'NNS') (',', ',') ("Shī'ahs.The", 'NNP') ('largest', 'JJS') ('community—for', 'NN') ('called', 'VBN') ('tribe—in', 'JJ') ('principality', 'NN') ('undoubtedly', 'RB') (PERSON Bahrānis/NNP Bahārinah/NNP) (',', ',') ('compose', 'VBP') ('nearly', 'RB') ('whole', 'JJ') ("Shī'ah", 'NNP') ('community', 'NN') ('three-fifths', 'JJ') ('rural', 'JJ') ('population', 'NN') ('.', '.') ('The', 'DT') ('remainder', 'NN') ('people', 'NNS') (',', ',') ('except', 'IN') ('foreigners', 'NNS') ('Persians', 'NNPS') (PERSON Basrah/NNP Arabs/NNP) (',', ',') (GPE Hindus/NNP) (',', ',') (PERSON Jews/NNP) (',', ',') ('etc.', 'NN') (',', ',') ('belong', 'RB') ('various', 'JJ') ('Sunni', 'NNP') ('tribes', 'NN') ('classes', 'NNS') (',', ',') ('important', 'JJ') (',', ',') ('numerically', 'RB') ('reasons', 'NNS') (',', ',') ('appear', 'VBP') ('following', 'VBG') ('synopsis', 'NN') (':', ':') ('—Name.Number', 'NNP') ('houses.Where', 'RB') ('located.Remarks', 'VBZ') ('.', '.') ("'Ainain", "''") ('(', '(') ('Āl', 'NNP') ('Bū', 'NNP') (')', ')') ("95'Askar", 'CD') ('Muharraq', 'NNP') ('Town.Belong', 'NNP') ('Māliki', 'NNP') ('sect', 'JJ') ('Sunnis', 'NNP') ('.', '.') ("'Ali", "''") ('(', '(') ('Āl', 'IN') ('Bin-', 'NNP') (')', ')') ('500Muharraq', 'CD') ('Hadd', 'NNP') ('Towns.Do', 'NNP') ('.', '.') 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('A', 'DT') ('large', 'JJ') ('scale', 'NN') ('map', 'NN') (PERSON Bahrain/NNP Inlands/NNP) ('(', '(') ('except', 'IN') (PERSON Jazīrat/NNP Umm/NNP) ("Na'asān", 'NNP') (')', ')') ('exists', 'VBZ') (PERSON Survey/NNP India/NNP) ("'s", 'POS') ('sheet', 'NN') ('Bahrain', 'NNP') ('1904-1905', 'CD') (',', ',') ('result', 'NN') ('survey', 'NN') ('undertaken', 'JJ') ('connection', 'NN') ('Gazetteer', 'NNP') ('inquiries', 'NNS') (';', ':') (PERSON Admiralty/NNP Plan/NNP No/NNP) ('.', '.') ('2377-20', 'CD') (',', ',') (PERSON Bahrain/NNP Harbour/NNP) (',', ',') ('shows', 'VBZ') ('detail', 'NN') ('northern', 'JJ') ('half', 'NN') ('islands', 'NNS') ('coasts', 'VBZ') ('well', 'RB') ('marine', 'JJ') ('features', 'NNS') ('northern', 'JJ') ('side', 'NN') ('group', 'NN') ('.', '.') ('The', 'DT') ('general', 'JJ') ('chart', 'NN') (PERSON Bahrain/NNP No/NNP) ('.', '.') ('2374—2887-B.', 'CD') (',', ',') (PERSON Persian/NNP Gulf/NNP) (';', ':') (PERSON Plan/NNP) ('mentioned', 'VBD') ('contain', 'NN') ('distant', 'JJ') ('views', 'NNS') (PERSON Bahrain/NNP Islands/NNP) ('sea', 'NN') ('.', '.') ('There', 'EX') ('two', 'CD') ('recent', 'JJ') (',', ',') ('marine', 'JJ') ('surveys', 'NNS') ('waters', 'NNS') ('west', 'VBP') ('east', 'JJ') (GPE Bahrain/NNP) ('islands', 'NNS') (',', ',') ('respectively', 'RB') (',', ',') ('namely', 'RB') (',', ',') (PERSON Bahrain/NNP Ojar/NNP Bahrein/NNP Ras/NNP Rūkkin/NNP) (',', ',') ('Preliminary', 'NNP') ('Charbs', 'NNP') ('Nos', 'NNP') ('.', '.') ('O', 'NNP') ('.', '.') ('1', 'CD') ('O', 'NNP') ('.', '.') ('2.', 'CD') (',', ',') (GPE Poona/NNP) (';', ':') ('1902', 'CD') ('.', '.') ('Two', 'CD') ('charts', 'NNS') ('relating', 'VBG') ("Khor-al-Qaiai'ah", 'NNP') ('accompany', 'JJ') ('report', 'NN') (ORGANIZATION Lieutenant/NNP) ('H.G', 'NNP') ('.', '.') ('Somerville', 'NNP') (',', ',') (ORGANIZATION R.N./NNP) (',', ',') ('printed', 'JJ') ('Government', 'NNP') ('India', 'NNP') (ORGANIZATION Foreign/NNP Department/NNP) (',', ',') (PERSON Simla/NNP) (',', ',') ('July', 'NNP') ('1905.2', 'CD') ('.', '.') ('The', 'DT') (ORGANIZATION Gharrāfah/NNP) ('handled', 'VBD') ('one', 'CD') ('man', 'NN') ('.', '.') ('The', 'DT') ('counterpoise', 'NN') ('generally', 'RB') ('basket', 'VBZ') ('earth.3', 'NN') ('.', '.') ('This', 'DT') ('table', 'NN') ('may', 'MD') ('compared', 'VBN') ('estimate', 'VB') ('given', 'VBN') (PERSON Pelly/NNP Report/NNP Tribes/NNP) (',', ',') ('etc.', 'NN') (',', ',') ('1863.4', 'CD') ('.', '.') ('This', 'DT') ('mean', 'JJ') ('half', 'NN') ('steamers', 'NNS') ('called', 'VBD') (PERSON Bahrain/NNP) ('found', 'VBD') ('cargo', 'NN') ('.', '.') ('The', 'DT') ('explanation', 'NN') ('steamers', 'VBZ') ('call', 'JJ') ('take', 'VB') ('clearance', 'NN') ('certificates.5', 'NN') ('.', '.') ('The', 'DT') (ORGANIZATION Foreign/NNP Proceedings/NNP) ('Government', 'NNP') ('India', 'NNP') ('April', 'NNP') ('1901', 'CD') ('contain', 'NN') ('information', 'NN') ('head.6', 'NN') ('.', '.') ('Since', 'IN') ('political', 'JJ') ('crisis', 'NN') ('February', 'NNP') ('1905', 'CD') ('administration', 'NN') ('justice', 'NN') (PERSON Bahrain/NNP) ('somewhat', 'RB') ('improved', 'VBD') ('.', '.') ('Public', 'JJ') ('opinion', 'NN') ('subject', 'JJ') ('growing', 'VBG') ('powerful', 'JJ') (',', ',') ('largely', 'RB') ('consequence', 'NN') ('steady', 'JJ') ('influx', 'NN') ('protected', 'VBD') ('foreigners.7', 'NN') ('.', '.') ('Regarding', 'VBG') (PERSON Majlis/NNP) (',', ',') ('etc.', 'NN') (',', ',') ('see', 'VBP') ('letters', 'NNS') (PERSON Major/NNP P./NNP) ('Z.', 'NNP') ('Cox', 'NNP') (',', ',') (ORGANIZATION Resident/NNP Persian/NNP Gulf/NNP) (',', ',') ('No', 'NNP') ('76', 'CD') ('25th', 'CD') ('February', 'NNP') ('No', 'NNP') ('.', '.') ('516', 'CD') ('4th', 'CD') ('March', 'NNP') ('1906', 'CD') (';', ':') ('Government', 'NNP') (GPE India/NNP) ("'s", 'POS') (ORGANIZATION Foreign/NNP Proceedings/NNP) ('April', 'NNP') ('1901', 'CD') ('may', 'MD') ('also', 'RB') ('consulted.8', 'VB') ('.', '.') ('Some', 'DT') ('authorities', 'NNS') (',', ',') ('however', 'RB') (',', ',') ('suppose', 'VBP') ('purely', 'RB') ('indigenous', 'JJ') ('institution.9', 'NN') ('.', '.') ('See', 'VB') (PERSON Appendix/NNP Pearl/NNP) ('Fisheries.10', 'NNP') ('.', '.') ('The', 'DT') (ORGANIZATION Foreign/NNP Proceedings/NNP) ('Government', 'NNP') ('India', 'NNP') ('October', 'NNP') ('1905', 'CD') ('may', 'MD') ('consulted.11', 'VB') ('.', '.') ('The', 'DT') ('table', 'NN') ('may', 'MD') ('compared', 'VBN') ('page', 'VB') ('66', 'CD') (PERSON Persian/NNP Gulf/NNP) ('Administration', 'NNP') ('Report', 'NNP') ('1873-74', 'CD') ('.', '.') ###Markdown NER for all files ###Code entity_names=[] entities_filtered=[] print("Labelled entities (Org, Person, GPE etc.):") for chunk in chunks: #labelled chunk is a named entity if hasattr(chunk, 'label'): entity=str(chunk.label()) #print entity label and value print("Entity: "+str(chunk.label())+", ",end="") name=chunk[0][0] #store the entity if it is not a stop word if name not in stopWords: #print name of the entity entity_names.append(name) print("Name: "+str(name)+", ",end="") #print POS tag of the entity pos_tag=chunk[0][1] print("POS tag: "+str(pos_tag)) string1=str(entity)+","+str(name)+","+pos_tag entities_filtered.append(string1) print("Entity names: ") for x in entity_names: print(x) ###Output _____no_output_____
Reinforcement_Learning/Week_05/05_Importance_Sampling_3-step-Q-Learning_Windy_Gridworld.ipynb	###Markdown Windy Gridworld Playground环境介绍- observation为格子所在的编号;- action的组成: 有4个动作, 分别是上下左右; - 0, UP - 1, RIGHT - 2, DOWN - 3, LEFT- reward: 每走一步reward=-1, reward越大也就是走的步数越少; 初始化环境 ###Code environment = WindyGridworldEnv() # 这个环境中可能动作的个数 nA = environment.action_space.n print(nA) ###Output 4 ###Markdown 策略的定义- $\mu$使用$\epsilon$-Greedy策略- $\pi$使用greedy策略 ###Code def mu_policy(Q, epsilon, nA): """ 这是一个随机的策略, 执行每一个action的概率都是相同的. """ def policy_fn(observation): # 看到这个state之后, 采取不同action获得的累计奖励 action_values = Q[observation] # 使用获得奖励最大的那个动作 greedy_action = np.argmax(action_values) # 是的每个动作都有出现的可能性 probabilities = np.ones(nA) /nA * epsilon # 最好的那个动作的概率会大一些 probabilities[greedy_action] = probabilities[greedy_action] + (1 - epsilon) return probabilities return policy_fn def pi_policy(Q): """ 这是greedy policy, 每次选择最优的动作 """ def policy_fn(observation): action_values = Q[observation] best_action = np.argmax(action_values) # 选择最优的动作 return np.eye(len(action_values))[best_action] # 返回的是两个动作出现的概率 return policy_fn ###Output _____no_output_____ ###Markdown Importance Sampling for Off-Policy 3-step TD ###Code def td_control_importance_sampling(env, num_episodes, discount_factor=1.0, alpha=0.1, epsilon=0.2): # 环境中所有动作的数量 nA = env.action_space.n # 初始化Q表 Q = defaultdict(lambda: np.zeros(nA)) # Keeps track of useful statistics stats = plotting.EpisodeStats( episode_lengths=np.zeros(num_episodes+1), episode_rewards=np.zeros(num_episodes+1)) # 初始化police, 因为是off-policy, 所以有两个策略 behaviour_policy = mu_policy(Q, epsilon, nA) # 这是我们实际执行action时候采取的策略, 这里使用随机游走 policy = pi_policy(Q) for i_episode in range(1, num_episodes + 1): # 开始一轮游戏 state = env.reset() action = np.random.choice(nA, p=behaviour_policy(state)) # 从实际执行的policy, 选择action for t in itertools.count(): env.s = state next_state, reward, done, _ = env.step(action) # 走第一步 if done: next_action_pi = np.argmax(policy(next_state)) Q[state][action] = Q[state][action] + alpha * (reward + discount_factorQ[next_state][next_action_pi] - Q[state][action]) stats.episode_rewards[i_episode] += reward # 计算累计奖励 stats.episode_lengths[i_episode] = t # 查看每一轮的时间 break next_action = np.random.choice(nA, p=behaviour_policy(next_state)) next_2_state, next_reward, done, _ = env.step(next_action) # 走第二步 if done: next_action_pi = np.argmax(policy(next_2_state)) pi_p = policy(next_state)[next_action] # 这里只有0或是1两个值, 完全确定的policy mu_p = behaviour_policy(next_state)[next_action] Q[state][action] = Q[state][action] + alpha pi_p/mu_p * (reward + discount_factornext_reward + (discount_factor2)Q[next_2_state][next_action_pi] - Q[state][action]) # 收敛 # Q[state][action] = Q[state][action] + alpha * (reward + pi_p/mu_pdiscount_factornext_reward + (discount_factor*2)Q[next_2_state][next_action_pi] - Q[state][action]) # 收敛 # Q[state][action] = Q[state][action] + alpha * (pi_p/mu_p(reward + discount_factornext_reward + (discount_factor*2)Q[next_2_state][next_action_pi]) - Q[state][action]) # 没收敛 # Q[state][action] = Q[state][action] + alpha * (reward + discount_factornext_reward + (discount_factor2)Q[next_2_state][next_action_pi] - Q[state][action]) # 收敛 stats.episode_rewards[i_episode] += reward # 计算累计奖励 stats.episode_lengths[i_episode] = t # 查看每一轮的时间 break next_2_action = np.random.choice(nA, p=behaviour_policy(next_2_state)) # 走第三步 next_3_state, next_2_reward, done, _ = env.step(next_2_action) next_action_pi = np.argmax(policy(next_3_state)) # 计算两个概率 pi_p_1 = policy(next_state)[next_action] # 这里只有0或是1两个值, 完全确定的policy mu_p_1 = behaviour_policy(next_state)[next_action] pi_p_2 = policy(next_2_state)[next_2_action] # 这里只有0或是1两个值, 完全确定的policy mu_p_2 = behaviour_policy(next_2_state)[next_2_action] # 更新Q Q[state][action] = Q[state][action] + alpha * (pi_p_1/mu_p_1)(pi_p_2/mu_p_2) (reward + discount_factornext_reward + (discount_factor2)next_2_reward + (discount_factor*3)Q[next_3_state][next_action_pi] - Q[state][action]) # Q[state][action] = Q[state][action] + alpha * (reward + (pi_p_1/mu_p_1)discount_factornext_reward + (pi_p_2/mu_p_2)(discount_factor2)next_2_reward + (discount_factor*3)Q[next_3_state][next_action_pi] - Q[state][action]) # Q[state][action] = Q[state][action] + alpha * ((pi_p_1/mu_p_1)(pi_p_2/mu_p_2) (reward + discount_factornext_reward + (discount_factor2)next_2_reward + (discount_factor*3)Q[next_3_state][next_action_pi]) - Q[state][action]) # Q[state][action] = Q[state][action] + alpha * (reward + discount_factornext_reward + (discount_factor2)next_2_reward + (discount_factor*3)Q[next_3_state][next_action_pi] - Q[state][action]) # 不收敛 # 计算统计数据 stats.episode_rewards[i_episode] += reward # 计算累计奖励 stats.episode_lengths[i_episode] = t # 查看每一轮的时间 if done: break if t > 500: break state = next_state action = next_action if i_episode % 100 == 0: # 打印 print("\rEpisode {}/{}. \| ".format(i_episode, num_episodes), end="") return Q, policy, stats ###Output _____no_output_____ ###Markdown 开始模拟 ###Code Q, policy, stats = td_control_importance_sampling(environment, num_episodes=10000, discount_factor=0.9, alpha=0.3, epsilon=0.4) # Q, policy, stats = td_control_importance_sampling(environment, num_episodes=10000, discount_factor=0.9, alpha=0.3, epsilon=0.99) # 这里epsilon=1就是随机策略 plotting.plot_episode_stats(stats) ###Output _____no_output_____ ###Markdown 使用最终策略执行 ###Code state = environment.reset() action = np.argmax(policy(state)) for t in itertools.count(): state, reward, done, _ = environment.step(action) # 执行action, 返回reward和下一步的状态 action = np.argmax(policy(state)) # 查看新的action print('-Step:{}-'.format(str(t))) environment._render() # 显示结果 if done: break if t > 50: break ###Output _____no_output_____
programs/formats.ipynb	###Markdown Text Formats ins and outsSee also the [docs](https://annotation.github.io/text-fabric/Api/Text/text-representation) ###Code %load_ext autoreload %autoreload 2 import os from tf.fabric import Fabric GH_BASE = os.path.expanduser('~/github') ORG = 'annotation' REPO = 'banks' FOLDER = 'tf' TF_DIR = f'{GH_BASE}/{ORG}/{REPO}/{FOLDER}' VERSION = '0.2' TF_PATH = f'{TF_DIR}/{VERSION}' TF = Fabric(locations=TF_PATH) ###Output This is Text-Fabric 7.6.8 Api reference : https://annotation.github.io/text-fabric/Api/Fabric/ 10 features found and 0 ignored ###Markdown We ask for a list of all features: ###Code allFeatures = TF.explore(silent=True, show=True) loadableFeatures = allFeatures['nodes'] + allFeatures['edges'] loadableFeatures ###Output _____no_output_____ ###Markdown We load all features: ###Code api = TF.load(loadableFeatures, silent=False) T = api.T F = api.F T.formats words = F.otype.s('word') lines = F.otype.s('line') sents = F.otype.s('sentence') explain = True ###Output _____no_output_____ ###Markdown single line ###Code T.text(lines[0], explain=explain) T.text(lines[0], descend=True, explain=explain) T.text(lines[0], fmt='line-term', explain=explain) ###Output EXPLANATION: T.text) called with parameters: nodes : single node fmt : line-term targeted at line descend: implicit NODE: line 103 TARGET LEVEL: line (no expansion needed) (descend=None) (target of explicit line-term) EXPANSION: line 103 FORMATTING: with explicit line-term 56s MATERIAL: line 103 ADDS ", " ###Markdown two lines ###Code T.text(lines[0:2], explain=explain) T.text(lines[0:2], descend=True, explain=explain) T.text(lines[0:2], fmt='line-term', explain=explain) ###Output EXPLANATION: T.text) called with parameters: nodes : iterable of 2 nodes fmt : line-term targeted at line descend: implicit NODE: line 103 TARGET LEVEL: line (no expansion needed) (descend=None) (target of explicit line-term) EXPANSION: line 103 FORMATTING: with explicit line-term MATERIAL: line 103 ADDS ", " NODE: line 104 TARGET LEVEL: line (no expansion needed) (descend=None) (target of explicit line-term) EXPANSION: line 104 FORMATTING: with explicit line-term MATERIAL: line 104 ADDS ", " ###Markdown single sentence ###Code T.text(sents[0], explain=explain) T.text(sents[0], descend=False, explain=explain) T.text(sents[0], fmt='line-term', explain=explain) ###Output EXPLANATION: T.text) called with parameters: nodes : single node fmt : line-term targeted at line descend: implicit NODE: sentence 115 TARGET LEVEL: line (descend=None) (target of explicit line-term) EXPANSION: lines 103, 104, 105, 106 FORMATTING: with explicit line-term MATERIAL: line 103 ADDS ", " line 104 ADDS ", " line 105 ADDS "; " line 106 ADDS ". " ###Markdown two sentences ###Code T.text(sents[0:2], explain=explain) T.text(sents[0:2], descend=False, explain=explain) T.text(sents[0:2], fmt='line-term', explain=explain) ###Output EXPLANATION: T.text) called with parameters: nodes : iterable of 2 nodes fmt : line-term targeted at line descend: implicit NODE: sentence 115 TARGET LEVEL: line (descend=None) (target of explicit line-term) EXPANSION: lines 103, 104, 105, 106 FORMATTING: with explicit line-term MATERIAL: line 103 ADDS ", " line 104 ADDS ", " line 105 ADDS "; " line 106 ADDS ". " NODE: sentence 116 TARGET LEVEL: line (descend=None) (target of explicit line-term) EXPANSION: lines 107, 108, 109 FORMATTING: with explicit line-term MATERIAL: line 107 ADDS ", " line 108 ADDS ", " line 109 ADDS "? " ###Markdown mixed content ###Code content = list(words[50:53]) + list(lines[4:6]) + list(sents[0:2]) T.text(content, explain=explain) T.text(content, descend=False, explain=explain) T.text(content, fmt='line-term', explain=explain) def test(): var = locals() def verbose(x): exec(x, var) verbose('x = "aap"') verbose('print(x)') verbose('print("noot")') return 'OK' test() ###Output aap noot
Workshop/LSTM_101.ipynb	###Markdown LSTM 10116 neurons ###Code from google.colab import drive PATH='/content/drive/' drive.mount(PATH) DATAPATH=PATH+'My Drive/data/' PC_FILENAME = DATAPATH+'pcRNA.fasta' NC_FILENAME = DATAPATH+'ncRNA.fasta' # LOCAL #PC_FILENAME = 'pcRNA.fasta' #NC_FILENAME = 'ncRNA.fasta' import numpy as np import pandas as pd import pandas as pd import matplotlib.pyplot as plt import tensorflow as tf from tensorflow import keras from sklearn.model_selection import ShuffleSplit from keras.models import Sequential from keras.layers import Bidirectional from keras.layers import GRU from keras.layers import Dense from sklearn.model_selection import StratifiedKFold import time tf.keras.backend.set_floatx('float32') EPOCHS=100 SPLITS=1 K=3 EMBED_DIMEN=16 FILENAME='LSTM101' ###Output _____no_output_____ ###Markdown Load and partition sequences ###Code # Assume file was preprocessed to contain one line per seq. # Prefer Pandas dataframe but df does not support append. # For conversion to tensor, must avoid python lists. def load_fasta(filename,label): DEFLINE='>' labels=[] seqs=[] lens=[] nums=[] num=0 with open (filename,'r') as infile: for line in infile: if line[0]!=DEFLINE: seq=line.rstrip() num += 1 # first seqnum is 1 seqlen=len(seq) nums.append(num) labels.append(label) seqs.append(seq) lens.append(seqlen) df1=pd.DataFrame(nums,columns=['seqnum']) df2=pd.DataFrame(labels,columns=['class']) df3=pd.DataFrame(seqs,columns=['sequence']) df4=pd.DataFrame(lens,columns=['seqlen']) df=pd.concat((df1,df2,df3,df4),axis=1) return df # Split into train/test stratified by sequence length. def sizebin(df): return pd.cut(df["seqlen"], bins=[0,1000,2000,4000,8000,16000,np.inf], labels=[0,1,2,3,4,5]) def make_train_test(data): bin_labels= sizebin(data) from sklearn.model_selection import StratifiedShuffleSplit splitter = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=37863) # split(x,y) expects that y is the labels. # Trick: Instead of y, give it it the bin labels that we generated. for train_index,test_index in splitter.split(data,bin_labels): train_set = data.iloc[train_index] test_set = data.iloc[test_index] return (train_set,test_set) def separate_X_and_y(data): y= data[['class']].copy() X= data.drop(columns=['class','seqnum','seqlen']) return (X,y) def make_slice(data_set,min_len,max_len): print("original "+str(data_set.shape)) too_short = data_set[ data_set['seqlen'] < min_len ].index no_short=data_set.drop(too_short) print("no short "+str(no_short.shape)) too_long = no_short[ no_short['seqlen'] >= max_len ].index no_long_no_short=no_short.drop(too_long) print("no long, no short "+str(no_long_no_short.shape)) return no_long_no_short def make_kmer_table(K): npad='N'K shorter_kmers=[''] for i in range(K): longer_kmers=[] for mer in shorter_kmers: longer_kmers.append(mer+'A') longer_kmers.append(mer+'C') longer_kmers.append(mer+'G') longer_kmers.append(mer+'T') shorter_kmers = longer_kmers all_kmers = shorter_kmers kmer_dict = {} kmer_dict[npad]=0 value=1 for mer in all_kmers: kmer_dict[mer]=value value += 1 return kmer_dict KMER_TABLE=make_kmer_table(K) def strings_to_vectors(data,uniform_len): all_seqs=[] for seq in data['sequence']: i=0 seqlen=len(seq) kmers=[] while i < seqlen-K+1: kmer=seq[i:i+K] i += 1 value=KMER_TABLE[kmer] kmers.append(value) pad_val=0 while i < uniform_len: kmers.append(pad_val) i += 1 all_seqs.append(kmers) pd2d=pd.DataFrame(all_seqs) return pd2d # return 2D dataframe, uniform dimensions def build_model(maxlen,dimen): vocabulary_size=4K+1 # e.g. K=3 => 64 DNA K-mers + 'NNN' act="sigmoid" dt='float32' neurons=16 rnn = keras.models.Sequential() embed_layer = keras.layers.Embedding( vocabulary_size,EMBED_DIMEN,input_length=maxlen); rnn1_layer = keras.layers.Bidirectional( keras.layers.LSTM(neurons, return_sequences=True, dropout=0.50, input_shape=[maxlen,dimen])) rnn2_layer = keras.layers.Bidirectional( keras.layers.LSTM(neurons, dropout=0.50, return_sequences=True)) dense1_layer = keras.layers.Dense(neurons,activation=act,dtype=dt) dense2_layer = keras.layers.Dense(neurons,activation=act,dtype=dt) output_layer = keras.layers.Dense(1,activation=act,dtype=dt) rnn.add(embed_layer) rnn.add(rnn1_layer) rnn.add(rnn2_layer) rnn.add(dense1_layer) rnn.add(dense2_layer) rnn.add(output_layer) bc=tf.keras.losses.BinaryCrossentropy(from_logits=False) print("COMPILE") rnn.compile(loss=bc, optimizer="Adam",metrics=["accuracy"]) return rnn def do_cross_validation(X,y,eps,maxlen,dimen): cv_scores = [] fold=0 splitter = ShuffleSplit(n_splits=SPLITS, test_size=0.2, random_state=37863) rnn2=None for train_index,valid_index in splitter.split(X): X_train=X[train_index] # use iloc[] for dataframe y_train=y[train_index] X_valid=X[valid_index] y_valid=y[valid_index] print("BUILD MODEL") rnn2=build_model(maxlen,dimen) print("FIT") # this is complaining about string to float start_time=time.time() history=rnn2.fit(X_train, y_train, # batch_size=10, default=32 works nicely epochs=eps, verbose=1, # verbose=1 for ascii art, verbose=0 for none validation_data=(X_valid,y_valid) ) end_time=time.time() elapsed_time=(end_time-start_time) fold += 1 print("Fold %d, %d epochs, %d sec"%(fold,eps,elapsed_time)) pd.DataFrame(history.history).plot(figsize=(8,5)) plt.grid(True) plt.gca().set_ylim(0,1) plt.show() scores = rnn2.evaluate(X_valid, y_valid, verbose=0) print("%s: %.2f%%" % (rnn2.metrics_names[1], scores[1]100)) # What are the other metrics_names? # Try this from Geron page 505: # np.mean(keras.losses.mean_squared_error(y_valid,y_pred)) cv_scores.append(scores[1] * 100) print() print("Validation core mean %.2f%% (+/- %.2f%%)" % (np.mean(cv_scores), np.std(cv_scores))) return rnn2 def make_kmers(MINLEN,MAXLEN,train_set): (X_train_all,y_train_all)=separate_X_and_y(train_set) # The returned values are Pandas dataframes. # print(X_train_all.shape,y_train_all.shape) # (X_train_all,y_train_all) # y: Pandas dataframe to Python list. # y_train_all=y_train_all.values.tolist() # The sequences lengths are bounded but not uniform. X_train_all print(type(X_train_all)) print(X_train_all.shape) print(X_train_all.iloc[0]) print(len(X_train_all.iloc[0]['sequence'])) # X: List of string to List of uniform-length ordered lists of K-mers. X_train_kmers=strings_to_vectors(X_train_all,MAXLEN) # X: true 2D array (no more lists) X_train_kmers.shape print("transform...") # From pandas dataframe to numpy to list to numpy print(type(X_train_kmers)) num_seqs=len(X_train_kmers) tmp_seqs=[] for i in range(num_seqs): kmer_sequence=X_train_kmers.iloc[i] tmp_seqs.append(kmer_sequence) X_train_kmers=np.array(tmp_seqs) tmp_seqs=None print(type(X_train_kmers)) print(X_train_kmers) labels=y_train_all.to_numpy() return (X_train_kmers,labels) print("Load data from files.") nc_seq=load_fasta(NC_FILENAME,0) pc_seq=load_fasta(PC_FILENAME,1) all_seq=pd.concat((nc_seq,pc_seq),axis=0) print("Put aside the test portion.") (train_set,test_set)=make_train_test(all_seq) # Do this later when using the test data: # (X_test,y_test)=separate_X_and_y(test_set) nc_seq=None pc_seq=None all_seq=None print("Ready: train_set") train_set ###Output Load data from files. Put aside the test portion. Ready: train_set ###Markdown Len 200-1Kb ###Code MINLEN=200 MAXLEN=1000 print("Working on full training set, slice by sequence length.") print("Slice size range [%d - %d)"%(MINLEN,MAXLEN)) subset=make_slice(train_set,MINLEN,MAXLEN)# One array to two: X and y print ("Sequence to Kmer") (X_train,y_train)=make_kmers(MINLEN,MAXLEN,subset) print ("Compile the model") model=build_model(MAXLEN,EMBED_DIMEN) print(model.summary()) # Print this only once print ("Cross valiation") model1=do_cross_validation(X_train,y_train,EPOCHS,MAXLEN,EMBED_DIMEN) model1.save(FILENAME+'.short.model') ###Output Working on full training set, slice by sequence length. Slice size range [200 - 1000) original (30290, 4) no short (30290, 4) no long, no short (8879, 4) Sequence to Kmer <class 'pandas.core.frame.DataFrame'> (8879, 1) sequence AGTCCCTCCCCAGCCCAGCAGTCCCTCCAGGCTACATCCAGGAGAC... Name: 1280, dtype: object 348 transform... <class 'pandas.core.frame.DataFrame'> <class 'numpy.ndarray'> [[12 46 54 ... 0 0 0] [ 9 36 14 ... 0 0 0] [34 7 28 ... 0 0 0] ... [37 19 9 ... 0 0 0] [57 36 15 ... 0 0 0] [33 3 12 ... 0 0 0]] Compile the model COMPILE Model: "sequential_2" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding_2 (Embedding) (None, 1000, 16) 1040 _________________________________________________________________ bidirectional_4 (Bidirection (None, 1000, 32) 4224 _________________________________________________________________ bidirectional_5 (Bidirection (None, 1000, 32) 6272 _________________________________________________________________ dense_6 (Dense) (None, 1000, 16) 528 _________________________________________________________________ dense_7 (Dense) (None, 1000, 16) 272 _________________________________________________________________ dense_8 (Dense) (None, 1000, 1) 17 ================================================================= Total params: 12,353 Trainable params: 12,353 Non-trainable params: 0 _________________________________________________________________ None Cross valiation BUILD MODEL COMPILE FIT Epoch 1/100 222/222 [==============================] - 31s 140ms/step - loss: 0.7028 - accuracy: 0.5207 - val_loss: 0.6707 - val_accuracy: 0.6006 Epoch 2/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6620 - accuracy: 0.6067 - val_loss: 0.6476 - val_accuracy: 0.6244 Epoch 3/100 222/222 [==============================] - 29s 133ms/step - loss: 0.6254 - accuracy: 0.6590 - val_loss: 0.6126 - val_accuracy: 0.6908 Epoch 4/100 222/222 [==============================] - 30s 134ms/step - loss: 0.6180 - accuracy: 0.6778 - val_loss: 0.6096 - val_accuracy: 0.6903 Epoch 5/100 222/222 [==============================] - 30s 134ms/step - loss: 0.6040 - accuracy: 0.6893 - val_loss: 0.6082 - val_accuracy: 0.6985 Epoch 6/100 222/222 [==============================] - 30s 134ms/step - loss: 0.6160 - accuracy: 0.6816 - val_loss: 0.7052 - val_accuracy: 0.5048 Epoch 7/100 222/222 [==============================] - 30s 135ms/step - loss: 0.6565 - accuracy: 0.6117 - val_loss: 0.6406 - val_accuracy: 0.6208 Epoch 8/100 222/222 [==============================] - 30s 134ms/step - loss: 0.6175 - accuracy: 0.6474 - val_loss: 0.6086 - val_accuracy: 0.6613 Epoch 9/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6455 - accuracy: 0.6288 - val_loss: 0.6602 - val_accuracy: 0.6224 Epoch 10/100 222/222 [==============================] - 30s 134ms/step - loss: 0.6520 - accuracy: 0.6268 - val_loss: 0.6357 - val_accuracy: 0.6498 Epoch 11/100 222/222 [==============================] - 30s 133ms/step - loss: 0.6279 - accuracy: 0.6588 - val_loss: 0.6336 - val_accuracy: 0.6421 Epoch 12/100 222/222 [==============================] - 31s 138ms/step - loss: 0.6295 - accuracy: 0.6573 - val_loss: 0.6331 - val_accuracy: 0.6475 Epoch 13/100 222/222 [==============================] - 30s 135ms/step - loss: 0.6264 - accuracy: 0.6556 - val_loss: 0.6308 - val_accuracy: 0.6483 Epoch 14/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6289 - accuracy: 0.6472 - val_loss: 0.6346 - val_accuracy: 0.6298 Epoch 15/100 222/222 [==============================] - 30s 135ms/step - loss: 0.6325 - accuracy: 0.6351 - val_loss: 0.6304 - val_accuracy: 0.6215 Epoch 16/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6032 - accuracy: 0.6590 - val_loss: 0.5784 - val_accuracy: 0.7011 Epoch 17/100 222/222 [==============================] - 31s 139ms/step - loss: 0.6082 - accuracy: 0.6690 - val_loss: 0.6846 - val_accuracy: 0.5176 Epoch 18/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6677 - accuracy: 0.6005 - val_loss: 0.6558 - val_accuracy: 0.6171 Epoch 19/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6661 - accuracy: 0.5991 - val_loss: 0.6649 - val_accuracy: 0.6050 Epoch 20/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6627 - accuracy: 0.6053 - val_loss: 0.6320 - val_accuracy: 0.6929 Epoch 21/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6315 - accuracy: 0.6567 - val_loss: 0.7282 - val_accuracy: 0.5201 Epoch 22/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6583 - accuracy: 0.6068 - val_loss: 0.6658 - val_accuracy: 0.5937 Epoch 23/100 222/222 [==============================] - 31s 139ms/step - loss: 0.6464 - accuracy: 0.6220 - val_loss: 0.6259 - val_accuracy: 0.6483 Epoch 24/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6124 - accuracy: 0.6805 - val_loss: 0.6385 - val_accuracy: 0.6275 Epoch 25/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6367 - accuracy: 0.6317 - val_loss: 0.6220 - val_accuracy: 0.6420 Epoch 26/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6140 - accuracy: 0.6526 - val_loss: 0.6138 - val_accuracy: 0.6802 Epoch 27/100 222/222 [==============================] - 31s 139ms/step - loss: 0.6381 - accuracy: 0.6295 - val_loss: 0.6535 - val_accuracy: 0.6127 Epoch 28/100 222/222 [==============================] - 30s 135ms/step - loss: 0.6454 - accuracy: 0.6276 - val_loss: 0.6394 - val_accuracy: 0.6243 Epoch 29/100 222/222 [==============================] - 30s 137ms/step - loss: 0.6650 - accuracy: 0.6048 - val_loss: 0.6591 - val_accuracy: 0.6083 Epoch 30/100 222/222 [==============================] - 30s 137ms/step - loss: 0.6464 - accuracy: 0.6251 - val_loss: 0.6011 - val_accuracy: 0.6846 Epoch 31/100 222/222 [==============================] - 30s 134ms/step - loss: 0.5985 - accuracy: 0.6759 - val_loss: 0.6061 - val_accuracy: 0.6816 Epoch 32/100 222/222 [==============================] - 30s 137ms/step - loss: 0.6378 - accuracy: 0.6350 - val_loss: 0.6334 - val_accuracy: 0.6319 Epoch 33/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6095 - accuracy: 0.6540 - val_loss: 0.6066 - val_accuracy: 0.6394 Epoch 34/100 222/222 [==============================] - 30s 137ms/step - loss: 0.6080 - accuracy: 0.6633 - val_loss: 0.6227 - val_accuracy: 0.6451 Epoch 35/100 222/222 [==============================] - 30s 135ms/step - loss: 0.6088 - accuracy: 0.6679 - val_loss: 0.6183 - val_accuracy: 0.6531 Epoch 36/100 222/222 [==============================] - 30s 136ms/step - loss: 0.6010 - accuracy: 0.6770 - val_loss: 0.5908 - val_accuracy: 0.6968 Epoch 37/100 222/222 [==============================] - 30s 137ms/step - loss: 0.5842 - accuracy: 0.6968 - val_loss: 0.6015 - val_accuracy: 0.6617 Epoch 38/100 222/222 [==============================] - 31s 137ms/step - loss: 0.5888 - accuracy: 0.6816 - val_loss: 0.5984 - val_accuracy: 0.6513 Epoch 39/100 222/222 [==============================] - 30s 137ms/step - loss: 0.5827 - accuracy: 0.6904 - val_loss: 0.6704 - val_accuracy: 0.6244 Epoch 40/100 222/222 [==============================] - 31s 138ms/step - loss: 0.6130 - accuracy: 0.6687 - val_loss: 0.6050 - val_accuracy: 0.6718 Epoch 41/100 222/222 [==============================] - 30s 137ms/step - loss: 0.5787 - accuracy: 0.6975 - val_loss: 0.5329 - val_accuracy: 0.7562 Epoch 42/100 222/222 [==============================] - 31s 140ms/step - loss: 0.5412 - accuracy: 0.7403 - val_loss: 0.5368 - val_accuracy: 0.7435 Epoch 43/100 222/222 [==============================] - 31s 138ms/step - loss: 0.5108 - accuracy: 0.7612 - val_loss: 0.5127 - val_accuracy: 0.7527 Epoch 44/100 222/222 [==============================] - 31s 138ms/step - loss: 0.4897 - accuracy: 0.7765 - val_loss: 0.4747 - val_accuracy: 0.7771 Epoch 45/100 222/222 [==============================] - 30s 137ms/step - loss: 0.4739 - accuracy: 0.7869 - val_loss: 0.4935 - val_accuracy: 0.7671 Epoch 46/100 222/222 [==============================] - 31s 138ms/step - loss: 0.4735 - accuracy: 0.7818 - val_loss: 0.5458 - val_accuracy: 0.7321 Epoch 47/100 222/222 [==============================] - 31s 138ms/step - loss: 0.4558 - accuracy: 0.7950 - val_loss: 0.4795 - val_accuracy: 0.7812 Epoch 48/100 222/222 [==============================] - 31s 138ms/step - loss: 0.4530 - accuracy: 0.7929 - val_loss: 0.4603 - val_accuracy: 0.7855 Epoch 49/100 222/222 [==============================] - 30s 136ms/step - loss: 0.4560 - accuracy: 0.7926 - val_loss: 0.4633 - val_accuracy: 0.7778 Epoch 50/100 222/222 [==============================] - 31s 139ms/step - loss: 0.4433 - accuracy: 0.8008 - val_loss: 0.4494 - val_accuracy: 0.7935 Epoch 51/100 222/222 [==============================] - 31s 138ms/step - loss: 0.4390 - accuracy: 0.8034 - val_loss: 0.4423 - val_accuracy: 0.7978 Epoch 52/100 222/222 [==============================] - 31s 138ms/step - loss: 0.4443 - accuracy: 0.7981 - val_loss: 0.4431 - val_accuracy: 0.7937 Epoch 53/100 222/222 [==============================] - 31s 139ms/step - loss: 0.4297 - accuracy: 0.8010 - val_loss: 0.4438 - val_accuracy: 0.7921 Epoch 54/100 222/222 [==============================] - 30s 136ms/step - loss: 0.4314 - accuracy: 0.8038 - val_loss: 0.4373 - val_accuracy: 0.7986 Epoch 55/100 222/222 [==============================] - 31s 139ms/step - loss: 0.4298 - accuracy: 0.8071 - val_loss: 0.4332 - val_accuracy: 0.8019 Epoch 56/100 222/222 [==============================] - 30s 136ms/step - loss: 0.4278 - accuracy: 0.8085 - val_loss: 0.4324 - val_accuracy: 0.7997 Epoch 57/100 222/222 [==============================] - 31s 138ms/step - loss: 0.4349 - accuracy: 0.8017 - val_loss: 0.4342 - val_accuracy: 0.7985 Epoch 58/100 222/222 [==============================] - 31s 140ms/step - loss: 0.4229 - accuracy: 0.8098 - val_loss: 0.4307 - val_accuracy: 0.7996 Epoch 59/100 222/222 [==============================] - 30s 136ms/step - loss: 0.4262 - accuracy: 0.8045 - val_loss: 0.4317 - val_accuracy: 0.8023 Epoch 60/100 222/222 [==============================] - 31s 138ms/step - loss: 0.4290 - accuracy: 0.8068 - val_loss: 0.4375 - val_accuracy: 0.8035 Epoch 61/100 222/222 [==============================] - 31s 138ms/step - loss: 0.4258 - accuracy: 0.8054 - val_loss: 0.4362 - val_accuracy: 0.7931 Epoch 62/100 222/222 [==============================] - 30s 137ms/step - loss: 0.4181 - accuracy: 0.8112 - val_loss: 0.4533 - val_accuracy: 0.7844 Epoch 63/100 222/222 [==============================] - 30s 135ms/step - loss: 0.4220 - accuracy: 0.8083 - val_loss: 0.4395 - val_accuracy: 0.7959 Epoch 64/100 222/222 [==============================] - 30s 133ms/step - loss: 0.4148 - accuracy: 0.8145 - val_loss: 0.4245 - val_accuracy: 0.8064 Epoch 65/100 222/222 [==============================] - 29s 132ms/step - loss: 0.4161 - accuracy: 0.8095 - val_loss: 0.4199 - val_accuracy: 0.8085 Epoch 66/100 222/222 [==============================] - 30s 133ms/step - loss: 0.4146 - accuracy: 0.8130 - val_loss: 0.4392 - val_accuracy: 0.8005 Epoch 67/100 222/222 [==============================] - 30s 135ms/step - loss: 0.4547 - accuracy: 0.7858 - val_loss: 0.4227 - val_accuracy: 0.8026 Epoch 68/100 222/222 [==============================] - 30s 135ms/step - loss: 0.4171 - accuracy: 0.8113 - val_loss: 0.4292 - val_accuracy: 0.8003 Epoch 69/100 222/222 [==============================] - 29s 133ms/step - loss: 0.5002 - accuracy: 0.7652 - val_loss: 0.5361 - val_accuracy: 0.7477 Epoch 70/100 222/222 [==============================] - 29s 133ms/step - loss: 0.5436 - accuracy: 0.7427 - val_loss: 0.5138 - val_accuracy: 0.7610 Epoch 71/100 222/222 [==============================] - 30s 133ms/step - loss: 0.5213 - accuracy: 0.7524 - val_loss: 0.5227 - val_accuracy: 0.7460 Epoch 72/100 222/222 [==============================] - 30s 134ms/step - loss: 0.5106 - accuracy: 0.7562 - val_loss: 0.5176 - val_accuracy: 0.7474 Epoch 73/100 222/222 [==============================] - 29s 132ms/step - loss: 0.5056 - accuracy: 0.7604 - val_loss: 0.4998 - val_accuracy: 0.7638 Epoch 74/100 222/222 [==============================] - 30s 134ms/step - loss: 0.5148 - accuracy: 0.7545 - val_loss: 0.5182 - val_accuracy: 0.7528 Epoch 75/100 222/222 [==============================] - 29s 130ms/step - loss: 0.4931 - accuracy: 0.7670 - val_loss: 0.4795 - val_accuracy: 0.7723 Epoch 76/100 222/222 [==============================] - 29s 132ms/step - loss: 0.5118 - accuracy: 0.7501 - val_loss: 0.5116 - val_accuracy: 0.7597 Epoch 77/100 222/222 [==============================] - 29s 133ms/step - loss: 0.4901 - accuracy: 0.7711 - val_loss: 0.4770 - val_accuracy: 0.7854 Epoch 78/100 222/222 [==============================] - 30s 134ms/step - loss: 0.4626 - accuracy: 0.7915 - val_loss: 0.4654 - val_accuracy: 0.7864 Epoch 79/100 222/222 [==============================] - 30s 133ms/step - loss: 0.4776 - accuracy: 0.7793 - val_loss: 0.5041 - val_accuracy: 0.7647 Epoch 80/100 222/222 [==============================] - 30s 133ms/step - loss: 0.4720 - accuracy: 0.7799 - val_loss: 0.5185 - val_accuracy: 0.7759 Epoch 81/100 222/222 [==============================] - 29s 132ms/step - loss: 0.4758 - accuracy: 0.7858 - val_loss: 0.4706 - val_accuracy: 0.7915 Epoch 82/100 222/222 [==============================] - 30s 135ms/step - loss: 0.4664 - accuracy: 0.7870 - val_loss: 0.4808 - val_accuracy: 0.7734 Epoch 83/100 222/222 [==============================] - 29s 132ms/step - loss: 0.4664 - accuracy: 0.7836 - val_loss: 0.4602 - val_accuracy: 0.7858 Epoch 84/100 222/222 [==============================] - 30s 133ms/step - loss: 0.4571 - accuracy: 0.7922 - val_loss: 0.5402 - val_accuracy: 0.7356 Epoch 85/100 222/222 [==============================] - 33s 147ms/step - loss: 0.4560 - accuracy: 0.7907 - val_loss: 0.4618 - val_accuracy: 0.7794 Epoch 86/100 222/222 [==============================] - 31s 141ms/step - loss: 0.4887 - accuracy: 0.7730 - val_loss: 0.4574 - val_accuracy: 0.7939 Epoch 87/100 222/222 [==============================] - 31s 140ms/step - loss: 0.4471 - accuracy: 0.8013 - val_loss: 0.4591 - val_accuracy: 0.7867 Epoch 88/100 222/222 [==============================] - 31s 141ms/step - loss: 0.4433 - accuracy: 0.8015 - val_loss: 0.4852 - val_accuracy: 0.7850 Epoch 89/100 222/222 [==============================] - 32s 145ms/step - loss: 0.4556 - accuracy: 0.7927 - val_loss: 0.4498 - val_accuracy: 0.7862 Epoch 90/100 222/222 [==============================] - 31s 141ms/step - loss: 0.4520 - accuracy: 0.7908 - val_loss: 0.4517 - val_accuracy: 0.7857 Epoch 91/100 222/222 [==============================] - 31s 142ms/step - loss: 0.4525 - accuracy: 0.7940 - val_loss: 0.4572 - val_accuracy: 0.7916 Epoch 92/100 222/222 [==============================] - 32s 143ms/step - loss: 0.4891 - accuracy: 0.7676 - val_loss: 0.4929 - val_accuracy: 0.7596 Epoch 93/100 222/222 [==============================] - 31s 140ms/step - loss: 0.4659 - accuracy: 0.7836 - val_loss: 0.4627 - val_accuracy: 0.7847 Epoch 94/100 222/222 [==============================] - 31s 141ms/step - loss: 0.4474 - accuracy: 0.7952 - val_loss: 0.4559 - val_accuracy: 0.7798 Epoch 95/100 222/222 [==============================] - 31s 141ms/step - loss: 0.4292 - accuracy: 0.8071 - val_loss: 0.4379 - val_accuracy: 0.7971 Epoch 96/100 222/222 [==============================] - 31s 141ms/step - loss: 0.4281 - accuracy: 0.8050 - val_loss: 0.4672 - val_accuracy: 0.7966 Epoch 97/100 222/222 [==============================] - 31s 142ms/step - loss: 0.4212 - accuracy: 0.8153 - val_loss: 0.4403 - val_accuracy: 0.8018 Epoch 98/100 222/222 [==============================] - 32s 145ms/step - loss: 0.4305 - accuracy: 0.8051 - val_loss: 0.4426 - val_accuracy: 0.7979 Epoch 99/100 222/222 [==============================] - 32s 146ms/step - loss: 0.4164 - accuracy: 0.8109 - val_loss: 0.4353 - val_accuracy: 0.8059 Epoch 100/100 222/222 [==============================] - 32s 142ms/step - loss: 0.4622 - accuracy: 0.7872 - val_loss: 0.5483 - val_accuracy: 0.7414 Fold 1, 100 epochs, 3052 sec ###Markdown Len 1K-2Kb ###Code MINLEN=1000 MAXLEN=2000 print("Working on full training set, slice by sequence length.") print("Slice size range [%d - %d)"%(MINLEN,MAXLEN)) subset=make_slice(train_set,MINLEN,MAXLEN)# One array to two: X and y print ("Sequence to Kmer") (X_train,y_train)=make_kmers(MINLEN,MAXLEN,subset) print ("Compile the model") model=build_model(MAXLEN,EMBED_DIMEN) print(model.summary()) # Print this only once print ("Cross valiation") model2=do_cross_validation(X_train,y_train,EPOCHS,MAXLEN,EMBED_DIMEN) model2.save(FILENAME+'.medium.model') ###Output Working on full training set, slice by sequence length. Slice size range [1000 - 2000) original (30290, 4) no short (9273, 4) no long, no short (3368, 4) Sequence to Kmer <class 'pandas.core.frame.DataFrame'> (3368, 1) sequence GGCGGGGTCGACTGACGGTAACGGGGCAGAGAGGCTGTTCGCAGAG... Name: 12641, dtype: object 1338 transform... <class 'pandas.core.frame.DataFrame'> <class 'numpy.ndarray'> [[42 39 27 ... 0 0 0] [57 34 5 ... 0 0 0] [27 44 47 ... 0 0 0] ... [44 47 57 ... 0 0 0] [10 37 20 ... 0 0 0] [47 60 48 ... 0 0 0]] Compile the model COMPILE Model: "sequential_4" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= embedding_4 (Embedding) (None, 2000, 16) 1040 _________________________________________________________________ bidirectional_8 (Bidirection (None, 2000, 32) 4224 _________________________________________________________________ bidirectional_9 (Bidirection (None, 2000, 32) 6272 _________________________________________________________________ dense_12 (Dense) (None, 2000, 16) 528 _________________________________________________________________ dense_13 (Dense) (None, 2000, 16) 272 _________________________________________________________________ dense_14 (Dense) (None, 2000, 1) 17 ================================================================= Total params: 12,353 Trainable params: 12,353 Non-trainable params: 0 _________________________________________________________________ None Cross valiation BUILD MODEL COMPILE FIT Epoch 1/100 85/85 [==============================] - 24s 280ms/step - loss: 0.7551 - accuracy: 0.4760 - val_loss: 0.6717 - val_accuracy: 0.6039 Epoch 2/100 85/85 [==============================] - 23s 266ms/step - loss: 0.6632 - accuracy: 0.6221 - val_loss: 0.6732 - val_accuracy: 0.6039 Epoch 3/100 85/85 [==============================] - 23s 272ms/step - loss: 0.6636 - accuracy: 0.6221 - val_loss: 0.6722 - val_accuracy: 0.6039 Epoch 4/100 85/85 [==============================] - 22s 264ms/step - loss: 0.6634 - accuracy: 0.6221 - val_loss: 0.6719 - val_accuracy: 0.6039 Epoch 5/100 85/85 [==============================] - 23s 266ms/step - loss: 0.6633 - accuracy: 0.6221 - val_loss: 0.6717 - val_accuracy: 0.6039 Epoch 6/100 85/85 [==============================] - 22s 260ms/step - loss: 0.6633 - accuracy: 0.6221 - val_loss: 0.6726 - val_accuracy: 0.6039 Epoch 7/100 85/85 [==============================] - 23s 268ms/step - loss: 0.6634 - accuracy: 0.6221 - val_loss: 0.6720 - val_accuracy: 0.6039 Epoch 8/100 85/85 [==============================] - 22s 264ms/step - loss: 0.6634 - accuracy: 0.6221 - val_loss: 0.6728 - val_accuracy: 0.6039 Epoch 9/100 85/85 [==============================] - 22s 264ms/step - loss: 0.6633 - accuracy: 0.6221 - val_loss: 0.6731 - val_accuracy: 0.6039 Epoch 10/100 85/85 [==============================] - 23s 265ms/step - loss: 0.6638 - accuracy: 0.6221 - val_loss: 0.6744 - val_accuracy: 0.6039 Epoch 11/100 85/85 [==============================] - 22s 261ms/step - loss: 0.6610 - accuracy: 0.6235 - val_loss: 0.6577 - val_accuracy: 0.6221 Epoch 12/100 85/85 [==============================] - 22s 256ms/step - loss: 0.6573 - accuracy: 0.6237 - val_loss: 0.6546 - val_accuracy: 0.6222 Epoch 13/100 85/85 [==============================] - 22s 263ms/step - loss: 0.6443 - accuracy: 0.6216 - val_loss: 0.6341 - val_accuracy: 0.6043 Epoch 14/100 85/85 [==============================] - 23s 265ms/step - loss: 0.6415 - accuracy: 0.6304 - val_loss: 0.6541 - val_accuracy: 0.6286 Epoch 15/100 85/85 [==============================] - 22s 263ms/step - loss: 0.6454 - accuracy: 0.6337 - val_loss: 0.6366 - val_accuracy: 0.6539 Epoch 16/100 85/85 [==============================] - 22s 262ms/step - loss: 0.6303 - accuracy: 0.6370 - val_loss: 0.6053 - val_accuracy: 0.6487 Epoch 17/100 85/85 [==============================] - 22s 260ms/step - loss: 0.6341 - accuracy: 0.6422 - val_loss: 0.6256 - val_accuracy: 0.6554 Epoch 18/100 85/85 [==============================] - 23s 265ms/step - loss: 0.6250 - accuracy: 0.6609 - val_loss: 0.6137 - val_accuracy: 0.6714 Epoch 19/100 85/85 [==============================] - 22s 263ms/step - loss: 0.6269 - accuracy: 0.6504 - val_loss: 0.6455 - val_accuracy: 0.6187 Epoch 20/100 85/85 [==============================] - 22s 262ms/step - loss: 0.6490 - accuracy: 0.6355 - val_loss: 0.6687 - val_accuracy: 0.6124 Epoch 21/100 85/85 [==============================] - 22s 264ms/step - loss: 0.6591 - accuracy: 0.6293 - val_loss: 0.6687 - val_accuracy: 0.6108 Epoch 22/100 85/85 [==============================] - 22s 264ms/step - loss: 0.6458 - accuracy: 0.6396 - val_loss: 0.6148 - val_accuracy: 0.6689 Epoch 23/100 85/85 [==============================] - 23s 267ms/step - loss: 0.6320 - accuracy: 0.6433 - val_loss: 0.6095 - val_accuracy: 0.6570 Epoch 24/100 85/85 [==============================] - 22s 262ms/step - loss: 0.6323 - accuracy: 0.6418 - val_loss: 0.6527 - val_accuracy: 0.6087 Epoch 25/100 85/85 [==============================] - 22s 264ms/step - loss: 0.6374 - accuracy: 0.6304 - val_loss: 0.6252 - val_accuracy: 0.6161 Epoch 26/100 85/85 [==============================] - 23s 267ms/step - loss: 0.6115 - accuracy: 0.6529 - val_loss: 0.5767 - val_accuracy: 0.6808 Epoch 27/100 85/85 [==============================] - 23s 267ms/step - loss: 0.6191 - accuracy: 0.6662 - val_loss: 0.6568 - val_accuracy: 0.6388 Epoch 28/100 85/85 [==============================] - 22s 262ms/step - loss: 0.6278 - accuracy: 0.6539 - val_loss: 0.6099 - val_accuracy: 0.6445 Epoch 29/100 85/85 [==============================] - 23s 267ms/step - loss: 0.6206 - accuracy: 0.6351 - val_loss: 0.5929 - val_accuracy: 0.6694 Epoch 30/100 85/85 [==============================] - 23s 267ms/step - loss: 0.6043 - accuracy: 0.6624 - val_loss: 0.5859 - val_accuracy: 0.7179 Epoch 31/100 85/85 [==============================] - 23s 268ms/step - loss: 0.5916 - accuracy: 0.6711 - val_loss: 0.5916 - val_accuracy: 0.7234 Epoch 32/100 85/85 [==============================] - 23s 273ms/step - loss: 0.5817 - accuracy: 0.6994 - val_loss: 0.5805 - val_accuracy: 0.7157 Epoch 33/100 85/85 [==============================] - 23s 270ms/step - loss: 0.5679 - accuracy: 0.6932 - val_loss: 0.5501 - val_accuracy: 0.6869 Epoch 34/100 85/85 [==============================] - 23s 270ms/step - loss: 0.6697 - accuracy: 0.6366 - val_loss: 0.6640 - val_accuracy: 0.6231 Epoch 35/100 85/85 [==============================] - 22s 263ms/step - loss: 0.6520 - accuracy: 0.6354 - val_loss: 0.6489 - val_accuracy: 0.6346 Epoch 36/100 85/85 [==============================] - 23s 268ms/step - loss: 0.6427 - accuracy: 0.6478 - val_loss: 0.6343 - val_accuracy: 0.6553 Epoch 37/100 85/85 [==============================] - 23s 267ms/step - loss: 0.6170 - accuracy: 0.6769 - val_loss: 0.6134 - val_accuracy: 0.6858 Epoch 38/100 85/85 [==============================] - 22s 265ms/step - loss: 0.5903 - accuracy: 0.7042 - val_loss: 0.5586 - val_accuracy: 0.7337 Epoch 39/100 85/85 [==============================] - 23s 269ms/step - loss: 0.5752 - accuracy: 0.6994 - val_loss: 0.5645 - val_accuracy: 0.6951 Epoch 40/100 85/85 [==============================] - 22s 260ms/step - loss: 0.5672 - accuracy: 0.7103 - val_loss: 0.5495 - val_accuracy: 0.7395 Epoch 41/100 85/85 [==============================] - 23s 268ms/step - loss: 0.5734 - accuracy: 0.6798 - val_loss: 0.5972 - val_accuracy: 0.6217 Epoch 42/100 85/85 [==============================] - 22s 264ms/step - loss: 0.5786 - accuracy: 0.6692 - val_loss: 0.5613 - val_accuracy: 0.7088 Epoch 43/100 85/85 [==============================] - 22s 261ms/step - loss: 0.5529 - accuracy: 0.7174 - val_loss: 0.5694 - val_accuracy: 0.6977 Epoch 44/100 85/85 [==============================] - 23s 267ms/step - loss: 0.5590 - accuracy: 0.7125 - val_loss: 0.5276 - val_accuracy: 0.7380 Epoch 45/100 85/85 [==============================] - 22s 264ms/step - loss: 0.5501 - accuracy: 0.7170 - val_loss: 0.5456 - val_accuracy: 0.7036 Epoch 46/100 85/85 [==============================] - 22s 263ms/step - loss: 0.5495 - accuracy: 0.7142 - val_loss: 0.5578 - val_accuracy: 0.7345 Epoch 47/100 85/85 [==============================] - 22s 260ms/step - loss: 0.5509 - accuracy: 0.7267 - val_loss: 0.5725 - val_accuracy: 0.7271 Epoch 48/100 85/85 [==============================] - 23s 268ms/step - loss: 0.5816 - accuracy: 0.7005 - val_loss: 0.5873 - val_accuracy: 0.6681 Epoch 49/100 85/85 [==============================] - 23s 265ms/step - loss: 0.5570 - accuracy: 0.7282 - val_loss: 0.5673 - val_accuracy: 0.7269 Epoch 50/100 85/85 [==============================] - 23s 266ms/step - loss: 0.5577 - accuracy: 0.7305 - val_loss: 0.6045 - val_accuracy: 0.7056 Epoch 51/100 85/85 [==============================] - 23s 269ms/step - loss: 0.5535 - accuracy: 0.7199 - val_loss: 0.5462 - val_accuracy: 0.7460 Epoch 52/100 85/85 [==============================] - 23s 268ms/step - loss: 0.5532 - accuracy: 0.7207 - val_loss: 0.5309 - val_accuracy: 0.7577 Epoch 53/100 85/85 [==============================] - 23s 268ms/step - loss: 0.6041 - accuracy: 0.6877 - val_loss: 0.6362 - val_accuracy: 0.5958 Epoch 54/100 85/85 [==============================] - 23s 268ms/step - loss: 0.6258 - accuracy: 0.6431 - val_loss: 0.6915 - val_accuracy: 0.6060 Epoch 55/100 85/85 [==============================] - 23s 265ms/step - loss: 0.6575 - accuracy: 0.6245 - val_loss: 0.6577 - val_accuracy: 0.6071 Epoch 56/100 85/85 [==============================] - 23s 266ms/step - loss: 0.6479 - accuracy: 0.6256 - val_loss: 0.6605 - val_accuracy: 0.6119 Epoch 57/100 85/85 [==============================] - 22s 264ms/step - loss: 0.6532 - accuracy: 0.6284 - val_loss: 0.6639 - val_accuracy: 0.6120 Epoch 58/100 85/85 [==============================] - 23s 265ms/step - loss: 0.6440 - accuracy: 0.6382 - val_loss: 0.6001 - val_accuracy: 0.7032 Epoch 59/100 85/85 [==============================] - 22s 263ms/step - loss: 0.5990 - accuracy: 0.6919 - val_loss: 0.7140 - val_accuracy: 0.6307 Epoch 60/100 85/85 [==============================] - 23s 268ms/step - loss: 0.6665 - accuracy: 0.6281 - val_loss: 0.6637 - val_accuracy: 0.6134 Epoch 61/100 85/85 [==============================] - 23s 266ms/step - loss: 0.6388 - accuracy: 0.6433 - val_loss: 0.6225 - val_accuracy: 0.6528 Epoch 62/100 85/85 [==============================] - 23s 266ms/step - loss: 0.6045 - accuracy: 0.6661 - val_loss: 0.5678 - val_accuracy: 0.6743 Epoch 63/100 85/85 [==============================] - 23s 268ms/step - loss: 0.5690 - accuracy: 0.7000 - val_loss: 0.5399 - val_accuracy: 0.7395 Epoch 64/100 85/85 [==============================] - 23s 269ms/step - loss: 0.5594 - accuracy: 0.7102 - val_loss: 0.5395 - val_accuracy: 0.6951 Epoch 65/100 85/85 [==============================] - 22s 261ms/step - loss: 0.5324 - accuracy: 0.7359 - val_loss: 0.5268 - val_accuracy: 0.7556 Epoch 66/100 85/85 [==============================] - 22s 260ms/step - loss: 0.5299 - accuracy: 0.7392 - val_loss: 0.5106 - val_accuracy: 0.7588 Epoch 67/100 85/85 [==============================] - 23s 268ms/step - loss: 0.5146 - accuracy: 0.7543 - val_loss: 0.4983 - val_accuracy: 0.7733 Epoch 68/100 85/85 [==============================] - 22s 257ms/step - loss: 0.4984 - accuracy: 0.7632 - val_loss: 0.5436 - val_accuracy: 0.7185 Epoch 69/100 85/85 [==============================] - 22s 261ms/step - loss: 0.4911 - accuracy: 0.7725 - val_loss: 0.5059 - val_accuracy: 0.7555 Epoch 70/100 85/85 [==============================] - 23s 266ms/step - loss: 0.4856 - accuracy: 0.7702 - val_loss: 0.5173 - val_accuracy: 0.7374 Epoch 71/100 85/85 [==============================] - 22s 261ms/step - loss: 0.4913 - accuracy: 0.7648 - val_loss: 0.4783 - val_accuracy: 0.7825 Epoch 72/100 85/85 [==============================] - 22s 261ms/step - loss: 0.4549 - accuracy: 0.7944 - val_loss: 0.5295 - val_accuracy: 0.7286 Epoch 73/100 85/85 [==============================] - 23s 267ms/step - loss: 0.4760 - accuracy: 0.7832 - val_loss: 0.5062 - val_accuracy: 0.7460 Epoch 74/100 85/85 [==============================] - 23s 271ms/step - loss: 0.4710 - accuracy: 0.7823 - val_loss: 0.4666 - val_accuracy: 0.7807 Epoch 75/100 85/85 [==============================] - 22s 262ms/step - loss: 0.4618 - accuracy: 0.7850 - val_loss: 0.4519 - val_accuracy: 0.7914 Epoch 76/100 85/85 [==============================] - 22s 263ms/step - loss: 0.4464 - accuracy: 0.7986 - val_loss: 0.4363 - val_accuracy: 0.8016 Epoch 77/100 85/85 [==============================] - 22s 263ms/step - loss: 0.4514 - accuracy: 0.7880 - val_loss: 0.4639 - val_accuracy: 0.7786 Epoch 78/100 85/85 [==============================] - 23s 267ms/step - loss: 0.4447 - accuracy: 0.8002 - val_loss: 0.4619 - val_accuracy: 0.7754 Epoch 79/100 85/85 [==============================] - 23s 265ms/step - loss: 0.4398 - accuracy: 0.8044 - val_loss: 0.4314 - val_accuracy: 0.8020 Epoch 80/100 85/85 [==============================] - 23s 266ms/step - loss: 0.4465 - accuracy: 0.7969 - val_loss: 0.4504 - val_accuracy: 0.7931 Epoch 81/100 85/85 [==============================] - 22s 263ms/step - loss: 0.4339 - accuracy: 0.8028 - val_loss: 0.4381 - val_accuracy: 0.7974 Epoch 82/100 85/85 [==============================] - 22s 262ms/step - loss: 0.4270 - accuracy: 0.8128 - val_loss: 0.4851 - val_accuracy: 0.7658 Epoch 83/100 85/85 [==============================] - 22s 262ms/step - loss: 0.4268 - accuracy: 0.8099 - val_loss: 0.4201 - val_accuracy: 0.8060 Epoch 84/100 85/85 [==============================] - 22s 263ms/step - loss: 0.4178 - accuracy: 0.8175 - val_loss: 0.4929 - val_accuracy: 0.7556 Epoch 85/100 85/85 [==============================] - 23s 267ms/step - loss: 0.4221 - accuracy: 0.8125 - val_loss: 0.4382 - val_accuracy: 0.7974 Epoch 86/100 85/85 [==============================] - 23s 271ms/step - loss: 0.4193 - accuracy: 0.8176 - val_loss: 0.4268 - val_accuracy: 0.8030 Epoch 87/100 85/85 [==============================] - 22s 260ms/step - loss: 0.4138 - accuracy: 0.8219 - val_loss: 0.4043 - val_accuracy: 0.8114 Epoch 88/100 85/85 [==============================] - 22s 255ms/step - loss: 0.4149 - accuracy: 0.8194 - val_loss: 0.4185 - val_accuracy: 0.8026 Epoch 89/100 85/85 [==============================] - 22s 261ms/step - loss: 0.4205 - accuracy: 0.8192 - val_loss: 0.5763 - val_accuracy: 0.6881 Epoch 90/100 85/85 [==============================] - 22s 256ms/step - loss: 0.4197 - accuracy: 0.8110 - val_loss: 0.4213 - val_accuracy: 0.8033 Epoch 91/100 85/85 [==============================] - 22s 259ms/step - loss: 0.4205 - accuracy: 0.8127 - val_loss: 0.4280 - val_accuracy: 0.8010 Epoch 92/100 85/85 [==============================] - 22s 260ms/step - loss: 0.4009 - accuracy: 0.8236 - val_loss: 0.3993 - val_accuracy: 0.8169 Epoch 93/100 85/85 [==============================] - 22s 254ms/step - loss: 0.4215 - accuracy: 0.8119 - val_loss: 0.3993 - val_accuracy: 0.8183 Epoch 94/100 85/85 [==============================] - 22s 255ms/step - loss: 0.4018 - accuracy: 0.8259 - val_loss: 0.4078 - val_accuracy: 0.8119 Epoch 95/100 85/85 [==============================] - 22s 256ms/step - loss: 0.4023 - accuracy: 0.8265 - val_loss: 0.3954 - val_accuracy: 0.8196 Epoch 96/100 85/85 [==============================] - 22s 254ms/step - loss: 0.3957 - accuracy: 0.8265 - val_loss: 0.3999 - val_accuracy: 0.8179 Epoch 97/100 85/85 [==============================] - 22s 257ms/step - loss: 0.3984 - accuracy: 0.8255 - val_loss: 0.4089 - val_accuracy: 0.8164 Epoch 98/100 85/85 [==============================] - 22s 255ms/step - loss: 0.4039 - accuracy: 0.8227 - val_loss: 0.4048 - val_accuracy: 0.8170 Epoch 99/100 85/85 [==============================] - 21s 252ms/step - loss: 0.3993 - accuracy: 0.8245 - val_loss: 0.3872 - val_accuracy: 0.8245 Epoch 100/100 85/85 [==============================] - 22s 259ms/step - loss: 0.3903 - accuracy: 0.8341 - val_loss: 0.3940 - val_accuracy: 0.8203 Fold 1, 100 epochs, 2272 sec ###Markdown Len 2K-3Kb ###Code MINLEN=2000 MAXLEN=3000 print("Working on full training set, slice by sequence length.") print("Slice size range [%d - %d)"%(MINLEN,MAXLEN)) subset=make_slice(train_set,MINLEN,MAXLEN)# One array to two: X and y print ("Sequence to Kmer") (X_train,y_train)=make_kmers(MINLEN,MAXLEN,subset) print ("Compile the model") model=build_model(MAXLEN,EMBED_DIMEN) print(model.summary()) # Print this only once print ("Cross valiation") model3=do_cross_validation(X_train,y_train,EPOCHS,MAXLEN,EMBED_DIMEN) model3.save(FILENAME+'.long.model') #model1.save(FILENAME+'.short.model') #abc #efg #hij ###Output _____no_output_____
notebooks/Python3-Language/04-Functions and Modules/05-Nested Functions and Scope.ipynb	###Markdown LEGB Rule:L: Local — Names assigned in any way within a function (def or lambda), and not declared global in that function.E: Enclosing function locals — Names in the local scope of any and all enclosing functions (def or lambda), from inner to outer.G: Global (module) — Names assigned at the top-level of a module file, or declared global in a def within the file.B: Built-in (Python) — Names preassigned in the built-in names module : open, range, SyntaxError,... ###Code greeting = "Hello from the global scope" def greet(): #greeting = "Hello from enclosing scope" def nested(): #greeting = "Hello from local scope" print(greeting) nested() result = greet() print(greeting) result #comment the innermost, then level above, and above #list = don't override and don't assign greeting = "Hello from the global scope" def greet(greeting): print(f'Greet in func:{greeting}') greeting = "Hello from enclosing scope" print(f'Greet in func:{greeting}') def nested(): greeting = "Hello from local scope" print(greeting) nested() result = greet("test") result print(greeting) greeting = "Hello from the global scope" def greet(): global greeting print(f'Greet in func:{greeting}') greeting = "Hello from enclosing scope" print(f'Greet in func:{greeting}') def nested(): greeting = "Hello from local scope" print(greeting) nested() result = greet() result print(greeting) #can't have argument and global for the same name #avoid using global keyword and use global variables as rarely as possible #if you need to change a value, use a function and return from it new value, don't use globals #when a global variable is used from different place, then there is a high change of spoiling it unintentionally ###Output _____no_output_____
Day7/Untitled.ipynb	###Markdown Just cleaning out the reviews that were badly annotated: ###Code labeled_data = labeled_data[labeled_data.infrastructure != -1] labeled_data = labeled_data[labeled_data.cost != -1] labeled_data = labeled_data[labeled_data.family != 9] labeled_data = labeled_data.reset_index(drop = True) english_stopwords = ["a", "about", "above", "above", "across", "after", "afterwards", "again", "against", "all", "almost", "alone", "along", "already", "also","although","always","am","among", "amongst", "amoungst", "amount", "an", "and", "another", "any","anyhow","anyone", "anything","anyway", "anywhere", "are", "around", "as", "at", "back","be","became", "because","become","becomes", "becoming", "been", "before", "beforehand", "behind", "being", "below", "beside", "besides", "between", "beyond", "bill", "both", "bottom","but", "by", "call", "can", "cannot", "cant", "co", "con", "could", "couldnt", "cry", "de", "describe", "detail", "do", "done", "down", "due", "during", "each", "eg", "eight", "either", "eleven","else", "elsewhere", "empty", "enough", "etc", "even", "ever", "every", "everyone", "everything", "everywhere", "except", "few", "fifteen", "fify", "fill", "find", "fire", "first", "five", "for", "former", "formerly", "forty", "found", "four", "from", "front", "full", "further", "get", "give", "good", "great", "woburn", "go", "had", "has", "hasnt", "have", "he", "hence", "her", "here", "hereafter", "hereby", "herein", "hereupon", "hers", "herself", "him", "himself", "his", "how", "however", "hundred", "ie", "if", "in", "inc", "indeed", "interest", "into", "is", "it", "its", "itself", "keep", "last", "latter", "latterly", "least", "less", "ltd", "made", "many", "may", "me", "meanwhile", "might", "mill", "mine", "more", "moreover", "most", "mostly", "move", "much", "must", "my", "myself", "name", "namely", "neither", "never", "nevertheless", "next", "nine", "no", "nobody", "none", "noone", "nor", "not", "nothing", "now", "nowhere", "of", "off", "often", "on", "once", "one", "only", "onto", "or", "other", "others", "otherwise", "our", "ours", "ourselves", "out", "over", "own","part", "per", "perhaps", "please", "put", "rather", "re", "same", "see", "seem", "seemed", "seeming", "seems", "serious", "several", "she", "should", "show", "side", "since", "sincere", "six", "sixty", "so", "some", "somehow", "someone", "something", "sometime", "sometimes", "somewhere", "still", "such", "system", "take", "ten", "than", "that", "the", "their", "them", "themselves", "then", "thence", "there", "thereafter", "thereby", "therefore", "therein", "thereupon", "these", "they", "thickv", "thin", "third", "this", "those", "though", "three", "through", "throughout", "thru", "thus", "to", "together", "too", "top", "toward", "towards", "twelve", "twenty", "two", "un", "under", "until", "up", "upon", "us", "very", "via", "was", "we", "well", "were", "what", "whatever", "when", "whence", "whenever", "where", "whereafter", "whereas", "whereby", "wherein", "whereupon", "wherever", "whether", "which", "while", "whither", "who", "whoever", "whole", "whom", "whose", "why", "will", "with", "within", "without", "would", "yet", "you", "your", "yours", "yourself", "yourselves", "the"] def character_replacement(input_string): character_mapping = {"\\u00e9": "é", "\\u2019": "'", "\\": "", "\\u00fb": "û", "u00e8": "è", "u00e0": "à", "u00f4": "ô", "u00ea": "ê", "u00ee": "i", "u00fb": "û", "u2018": "'", "u00e2": "a", "u00ab": "'", "u00bb": "'", "u00e7": "ç", "u00e2": "â", "u00f9": "ù", "u00a3": "£", } for character in character_mapping: input_string = input_string.replace(character, character_mapping[character]) input_string = input_string.lower() characters_to_remove = ["@", "/", "#", ".", ",", "!", "?", "(", ")", "-", "_", "’", "'", "\"", ":", "1", "2", "3", "4", "5", "6", "7", "8", "9", "0"] transformation_dict = {initial: " " for initial in characters_to_remove} no_punctuation_reviews = input_string.translate(str.maketrans(transformation_dict)) return no_punctuation_reviews def tokenize(input_string): return word_tokenize(input_string) def remove_stop_words(input_tokens, english_stopwords = english_stopwords): return [token for token in input_tokens if token not in english_stopwords] lemmatizer = WordNetLemmatizer() def lemmatize(tokens, lemmatizer = lemmatizer): tokens = [lemmatizer.lemmatize(lemmatizer.lemmatize(lemmatizer.lemmatize(token,pos='a'),pos='v'),pos='n') for token in tokens] return tokens labeled_data['review'] = labeled_data['review'].apply(lambda x: character_replacement(x)) labeled_data['tokens'] = labeled_data['review'].apply(lambda x: tokenize(x)) labeled_data['tokens'] = labeled_data['tokens'].apply(lambda token_list: [meaningful_word for meaningful_word in token_list if len(meaningful_word) > 3]) labeled_data['tokens'] = labeled_data['tokens'].apply(lambda x: remove_stop_words(x)) ###Output _____no_output_____ ###Markdown COST LABEL Train test split on manually labeled data: ###Code training_set = {'tokens' : list(labeled_data['tokens'])[:2000], 'labels' : list(labeled_data['cost'])[:2000]} training_set = pd.DataFrame(training_set) test_set = {'tokens' : list(labeled_data['tokens'])[2000:], 'labels' : list(labeled_data['cost'])[2000:]} test_set = pd.DataFrame(test_set) ###Output _____no_output_____ ###Markdown Re-arranging training set to take into account unlabeled tokens and their missing label ###Code semi_supervised_data = {'tokens' : list(training_set['tokens']) + list(unlabeled_data['tokens']), 'labels' : list(training_set['labels']) + [-1]*len(unlabeled_data)} # We use -1 to encode unlabeled samples semi_supervised_data = pd.DataFrame(semi_supervised_data) semi_supervised_data.head() labeled_data.shape semi_supervised_data.shape labeled_data.head(1) w2v = KeyedVectors.load_word2vec_format(path_to_google_vectors + 'GoogleNews-vectors-negative300.bin', binary = True) def my_vector_getter(word, wv = w2v) : # returns the vector of a word try: word_array = wv[word].reshape(1,-1) return word_array except : # if word not in google word2vec vocabulary, return vector with low norm return np.zeros((1,300)) def document_embedding(text, wv = w2v) : # returns naïve document embedding embeddings = np.concatenate([my_vector_getter(token) for token in text]) centroid = np.mean(embeddings, axis=0).reshape(1,-1) return centroid document_embedding(semi_supervised_data['tokens'][0]).shape ###Output _____no_output_____ ###Markdown Train embedding: ###Code X = np.zeros((len(semi_supervised_data), 300)) for i in range(len(semi_supervised_data)) : X[i] = document_embedding(semi_supervised_data['tokens'][i]) #X_values = X.values X_train = X[:2000] Y_train = training_set['labels'].values ###Output _____no_output_____ ###Markdown Test embedding: ###Code X_test = np.zeros((len(test_set), 300)) for i in range(len(test_set)) : X_test[i] = document_embedding(test_set['tokens'][i]) #X_test_pca = pca.transform(X_test) Y_test = test_set['labels'].values ###Output _____no_output_____ ###Markdown Fitting the model ###Code label_spreading_model = LabelSpreading() model_s = label_spreading_model.fit(X_train, Y_train) pred = model_s.predict(X_test) print("\n") print("Using count vectorization") print("\n") acc_count = accuracy_score(Y_test,pred) prec_count = precision_score(Y_test, pred) sens_count = recall_score(Y_test,pred) print("Accuracy :", acc_count) print("Precision :", prec_count) print("Sensitivity :", sens_count) ###Output Using count vectorization Accuracy : 0.5561694290976059 Precision : 0.5421455938697318 Sensitivity : 0.9929824561403509 ###Markdown Propagating: ###Code Y_train.shape X_train = np.concatenate((X_train,X_test), axis=0) Y_train = np.concatenate((Y_train,pred), axis=0) X_train ###Output _____no_output_____
toying around.ipynb	###Markdown Import all packages ###Code # from hw2 import pydub from pydub import AudioSegment import pydub from pydub.playback import play from python_speech_features import mfcc from python_speech_features import logfbank from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score from time import sleep import scipy.io.wavfile as wav from glob import glob import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline import numpy as np import pandas as pd import os import torch import torch.nn as nn import torch.nn.functional as F from google.colab import drive drive.mount('/content/drive', force_remount=True) ###Output Mounted at /content/drive ###Markdown Preparing the dataset ###Code #!ls /content/drive/'My Drive'/validated_clips/validated_clip/1.000000 downloaded_files = glob("/content/drive/My Drive/validated_clips/validated_clip/*/.mp3", recursive=True) num_downloaded_files = len(downloaded_files) print(num_downloaded_files) dev = pd.read_csv("/content/drive/My Drive/tsvs/dev.tsv", sep="\t") train = pd.read_csv("/content/drive/My Drive/tsvs/train.tsv", sep="\t") test = pd.read_csv("/content/drive/My Drive/tsvs/test.tsv", sep="\t") validated = pd.read_csv("/content/drive/My Drive/tsvs/validated.tsv", sep="\t") invalidated = pd.read_csv("/content/drive/My Drive/tsvs/invalidated.tsv", sep="\t") #print(dev) #TODO use numpy arrays instead dataframes #TODO how are we balancing classes? splits = { "dev": dev, "train": train, "test": test, "validated": validated, "invalidated": invalidated } #print(splits) ###Output _____no_output_____ ###Markdown Fix the paths ###Code #TODO: Fix prefixes prefix = "./data/audios/" for key in splits: splits[key]['path'] = prefix + splits[key]['path'].astype(str) #print(splits) ###Output _____no_output_____ ###Markdown Drop the rows with NaN accent values ###Code for key in splits: print(key, len(splits[key])) for key in splits: splits[key].dropna(axis=0, subset=["accent"], inplace=True) print(len(splits[key])) ###Output 2100 135391 1398 46169 46728 ###Markdown Drop the rows yet to be validated ###Code train.columns columns = ['client_id', 'path', 'sentence', 'up_votes', 'down_votes', 'age', 'gender', 'accent'] # https://stackoverflow.com/questions/26921943/pandas-intersection-of-two-data-frames-based-on-column-entries train_validated = pd.merge(train, validated, how='inner', on=columns) test_validated = pd.merge(test, validated, how='inner', on=columns) dev_validated = pd.merge(dev, validated, how='inner', on=columns) print(f"The overall dataset has {len(train_validated)} training, {len(test_validated)} test and {len(dev_validated)} dev validated audio files.") ###Output _____no_output_____ ###Markdown Lets look at the overall data ###Code # visualize categorical data https://www.datacamp.com/community/tutorials/categorical-data def visualize_categorical_distribution(pd_series, title="Plot", ylabel='Number of Samples', xlabel='Accent', figsize=None): digit_counts = pd_series.value_counts() sns.set(style="darkgrid") if figsize is None: sns.set(rc={'figure.figsize':(10,6)}) else: sns.set(rc={'figure.figsize':figsize}) sns.barplot(digit_counts.index, digit_counts.values, alpha=0.9) plt.title(title) plt.ylabel(ylabel, fontsize=12) plt.xlabel(xlabel, fontsize=12) plt.show() print(train_validated.groupby(['accent', 'gender']).size()) print(test_validated.groupby(['accent', 'gender']).size()) print(dev_validated.groupby(['accent', 'gender']).size()) yeet.unstack(level=1).plot(kind='bar', subplots=False) #print(splits["dev"].groupby(['accent', 'gender']).size()) #print(splits["test"].groupby(['accent', 'gender']).size()) #print(splits['train']['accent'].value_counts()) visualize_categorical_distribution(splits["train"]["accent"], "Total Train Distribution", figsize=(17,7)) visualize_categorical_distribution(splits["test"]["accent"], "Total Test Distribution", figsize=(17,7)) visualize_categorical_distribution(splits["dev"]["accent"], "Total Dev Distribution", figsize=(17,7)) ###Output _____no_output_____ ###Markdown Lets look at some other attributes... ###Code visualize_categorical_distribution(splits["train"]["age"], "Age Distribution (Train)") visualize_categorical_distribution(splits["train"]["gender"], "Gender Distribution (Train)", figsize=(4,4)) ###Output _____no_output_____ ###Markdown What about the overall validated data? ###Code visualize_categorical_distribution(train_validated["accent"], "Gender Distribution (Train)", figsize=(17,7)) visualize_categorical_distribution(test_validated["accent"], "Gender Distribution (Train)", figsize=(17,7)) visualize_categorical_distribution(dev_validated["accent"], "Gender Distribution (Train)", figsize=(17,7)) print(num_downloaded_files) print(downloaded_files) print(splits['train']['path']) ###Output _____no_output_____ ###Markdown Choose the data we've actually downloaded ###Code for key in splits: splits[key] = splits[key][splits[key]["path"].isin(downloaded_files)] print(len(splits[key])) print(splits) ###Output _____no_output_____ ###Markdown Make sure speaker independent and validated ###Code len(splits['train'][splits['train']["client_id"].isin(splits['test']['client_id'])]) len(splits['train'][splits['train']["client_id"].isin(splits['dev']['client_id'])]) num_train = len(splits['train'][splits['train']["client_id"].isin(splits['validated']['client_id'])]) num_test = len(splits['test'][splits['test']["client_id"].isin(splits['validated']['client_id'])]) num_dev = len(splits['dev'][splits['dev']["client_id"].isin(splits['validated']['client_id'])]) print(f"Wow, we only end up with {num_train} training, {num_test} test and {num_dev} dev audio files from the {num_downloaded_files} files we started with!") ###Output _____no_output_____ ###Markdown For the purposes of this toy example we forgoe using only the validated clips and assume all clips are good. Lets take a look at some of our downloaded data ###Code visualize_categorical_distribution(splits["train"]["accent"], "Train Distribution") visualize_categorical_distribution(splits["test"]["accent"], "Test Distribution", figsize=(3,4)) ###Output _____no_output_____ ###Markdown Lets play some audio files ###Code def play_mp3_from_path(relative_path): """plays mp3 located at provided relative path, returns the audio segment""" a = pydub.AudioSegment.from_mp3(relative_path) # test that it sounds right (requires ffplay, or pyaudio): print(a) play(a) return a # pick 4 random audio files from first 10 #paths = np.random.choice(splits["train"]["path"][:10], size=4) #for path in paths: # print(path) # play_mp3_from_path(path) ###Output _____no_output_____ ###Markdown Yikes... That is a bad distribution. Lets start preprocessing our actual audio files to the dataset Some useful functions: ###Code from utils import * ###Output _____no_output_____ ###Markdown Extract the audio files, remove leading silence, and silent clips... ###Code train = splits["train"] train = train[:30] # Remove silent clips drop_idxs = train[train['path'].apply(detect_leading_silence_filepath) >= train['path'].apply(length_of_file)-1].index train = train.drop(drop_idxs) # Zero pad to normalize length downloaded_audios = [pydub.AudioSegment.from_mp3(f) for f in train['path']] max_audio_length = np.max([len(sample) for sample in downloaded_audios]) print(f"max audio length is {max_audio_length / 1000} seconds") padded_audios = [zero_pad_in_end(audio, max_audio_length) for audio in downloaded_audios] mfccs_padded = np.array([extract_mfcc(audio) for audio in padded_audios]) audio_embeddings = pd.DataFrame({ 'label': train['accent'], 'mfcc': np.array([extract_mfcc(audio) for audio in downloaded_audios]), 'mfb': np.array([extract_mfb(audio) for audio in downloaded_audios]), 'mfcc_padded': np.array([extract_mfcc(audio) for audio in padded_audios]), 'mfb_padded': np.array([extract_mfb(audio) for audio in padded_audios]) }) labels = train['accent'].to_numpy() labels paddingamounts = [(len(pad)-len(unpad))/1000 for unpad,pad in zip(downloaded_audios, padded_audios)] print(paddingamounts) print(sum(paddingamounts)/len(paddingamounts)) print([len(audio) for audio in audio_embeddings["mfcc_padded"]]) print([extract_mfcc(audio).shape for audio in padded_audios]) ###Output _____no_output_____ ###Markdown TODO: Plot some filterbanks and mfccs TODO: Constant size features for CNN TODO:: Create a validation set? ###Code ###Output _____no_output_____ ###Markdown TODO: Creating our Toy Models Toy CNN ###Code mfccs_padded = np.moveaxis(mfccs_padded, (1,2), (2,1)) mfccs_padded.shape #want array size to be #numbe of samples, number of filterbanks, length of each sample class downwardSlope(nn.Module): def __init__(self, maxSeqLength, outsize): super().__init__() self.maxSeqLength = maxSeqLength self.conv1 = nn.Conv1d(13, 32, 15, padding=1) self.conv2 = nn.Conv1d(32, 32, 15, padding=1) self.conv3 = nn.Conv1d(32, 32, 15, padding=1) self.fc1 = nn.Linear(2908, outsize) def forward(self,x): print("insideforward",type(x), x.dtype) x= self.conv1(x) x= self.conv2(x) x= self.conv3(x) x = self.fc1(x) x = torch.sigmoid(x) return torch.squeeze(x) def evaluateModel(model, lossCriterion, X, y, batchSize): with torch.no_grad(): n=batchSize batched = list(zip([X[i:i + n] for i in range(0, len(X), n)], [y[i:i + n] for i in range(0, len(y), n)])) predList = [] yList = [] lossMeans = [] for i,(batchX, batchY) in enumerate(batched): print('x',type(X), X.dtype) print('batchx',type(batchX), batchX.dtype) predProb = model(batchX) if np.isnan(predProb.cpu().detach().numpy()).any(): print("AAAAAA a NAN") return loss = lossCriterion(predProb, batchY) batchPred = predProb.round().tolist() predList = predList+batchPred batchYargmaxed = batchY.tolist() yList = yList+batchYargmaxed lossMeans.append(torch.mean(loss).item()) #acc = accuracy_score(yList, predList) #report = classification_report(yList, predList) #confMat = confusion_matrix(yList, predList) lossMean = sum(lossMeans)/float(len(lossMeans)) return lossMean def trainModel(model, optimizer, criterion, train_X, train_y, val_X, val_y, batchSize, startEpoch, endEpoch): notifyEvery = 100 if torch.cuda.is_available() else 2 checkmarkTime = time.time() n=batchSize batched = list(zip([train_X[i:i + n] for i in range(0, len(train_X), n)], [train_y[i:i + n] for i in range(0, len(train_y), n)])) numBatches = len(batched) print("number of batches", numBatches) for epoch in range(startEpoch, endEpoch): print("epoch:", epoch) for i,(batchX,batchy) in enumerate(batched): optimizer.zero_grad() output = model(batchX) loss = criterion(output, batchy) loss.backward() optimizer.step() if np.isnan(output.cpu().detach().numpy()).any(): print("AAAAAA a NAN") return if i%notifyEvery ==notifyEvery-1: print('[%d, %5d]' % (epoch + 1, i + 1)) timeTook = time.time() - checkmarkTime print("took", timeTook, "seconds for", notifyEvery, "batches") if(torch.cuda.is_available()): print(torch.cuda.max_memory_allocated()/1e9, "GB of VRAM being used") checkmarkTime = time.time() trainLoss = evaluateModel(model,criterion,train_X, train_y, batchSize) valLoss = evaluateModel(model, criterion,val_X, val_y, batchSize) print('loss', valLoss) print("finished training") !pip3 install skorch from skorch import NeuralNetClassifier from sklearn import preprocessing from sklearn.metrics import accuracy_score, confusion_matrix, classification_report import time X = torch.as_tensor(mfccs_padded, dtype=torch.float) le = preprocessing.LabelEncoder() le.fit(labels) transformed = le.transform(labels) print(type(transformed),transformed.shape, print(transformed)) enc = preprocessing.OneHotEncoder(handle_unknown='ignore') onehotted =enc.fit_transform(transformed.reshape(-1,1)).toarray() optimizer = torch.optim.Adam(mod.parameters(), lr = learningRate) y = torch.as_tensor(onehotted, dtype=torch.long) outsize = len(le.classes_) print(outsize, type(onehotted), type(y), onehotted.shape) device = "cpu" seqLength = mfccs_padded.shape[2] mod = downwardSlope(seqLength, outsize) criterion = nn.CrossEntropyLoss() endEpoch = 3 learningRate = 0.001 print('x',type(X), X.dtype) trainModel(mod, optimizer,criterion, X[:20], y[:20],X[20:],y[20:], 10, 0, 10) ###Output _____no_output_____ ###Markdown Toy LSTM ###Code import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim torch.manual_seed(42) # https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html ###Output _____no_output_____
examples/Example Neural Network.ipynb	###Markdown Iris ###Code data=load_excel('data/iris.xls') data_train,data_test=split(data,test_size=0.2) len(data.targets),len(data_train.targets),len(data_test.targets), C=Perceptron() timeit(reset=True) C.fit(data_train.vectors,data_train.targets) print(("Training time: ",timeit())) print(("On Training Set:",C.percent_correct(data_train.vectors,data_train.targets))) print(("On Test Set:",C.percent_correct(data_test.vectors,data_test.targets))) C.weights # these are the weights C=BackProp(hidden_layer_sizes = [4],max_iter=10000,tol=1e-4) timeit(reset=True) C.fit(data_train.vectors,data_train.targets) print(("Training time: ",timeit())) data_train.vectors.shape,data_train.targets.shape print(("On Training Set:",C.percent_correct(data_train.vectors,data_train.targets))) print(("On Test Set:",C.percent_correct(data_test.vectors,data_test.targets))) C.weights W_inp_hid,W_hid_out=C.weights print(W_inp_hid) print("==") print(W_hid_out) ###Output [[-1.08065252e+00 9.52796556e-02 -1.58943896e+00 -8.28935340e-42] [-4.57125312e-01 -2.54793675e-01 -1.60848695e+00 -3.13230127e-39] [ 5.66766252e-01 2.44509077e-01 8.67926146e-01 6.19659439e-81] [ 6.80331304e-01 -8.86891613e-01 9.75248954e-01 -2.37650832e-07]] == [[ 3.08547893e-01 1.18527489e+00 -1.96163945e+00] [ 2.35980725e-01 -9.17589102e-02 -3.58105409e-01] [ 1.01267589e+00 -1.88427180e+00 -9.25026076e-01] [-4.11257778e-15 -3.25712393e-60 -6.54717443e-07]] ###Markdown XOR Problem - Perceptron ###Code data=make_dataset(bob=[[0,0],[1,1]],sally=[[0,1],[1,0]]) data C=Perceptron() C.fit(data.vectors,data.targets) print((C.predict(data.vectors))) print(("On Training Set:",C.percent_correct(data.vectors,data.targets))) plot2D(data,classifier=C,axis_range=[-.55,1.5,-.5,1.5]) ###Output _____no_output_____ ###Markdown XOR Problem - Backprop ###Code data.vectors data.targets C=BackProp(hidden_layer_sizes = [5],max_iter=10000,tol=1e-4) C.fit(data.vectors,data.targets) print((C.predict(data.vectors))) print(("On Training Set:",C.percent_correct(data.vectors,data.targets))) plot2D(data,classifier=C,axis_range=[-.55,1.5,-.5,1.5]) print((data.vectors)) print() print((data.targets)) C.output(data.vectors) h,y=C.output(data.vectors) print(h) print() print((np.round(h))) print() print(y) print(around(C.weights[0],2)) around(C.weights[1],2) data.vectors.shape ###Output _____no_output_____ ###Markdown Curvy data ###Code figure(figsize=(8,8)) N=30 x1=randn(N).2 y1=randn(N).2 plot(x1,y1,'bo') a=linspace(0,3pi/2,N) x2=cos(a)+randn(N).2 y2=sin(a)+randn(N)*.2 plot(x2,y2,'rs') axis('equal') vectors=vstack([hstack([atleast_2d(x1).T,atleast_2d(y1).T]), hstack([atleast_2d(x2).T,atleast_2d(y2).T]), ]) targets=concatenate([zeros(N),ones(N)]) target_names=['center','around'] feature_names=['x','y'] data=Struct(vectors=vectors,targets=targets, target_names=target_names,feature_names=feature_names) C=Perceptron() C.fit(data.vectors,data.targets) print(("On Training Set:",C.percent_correct(data.vectors,data.targets))) plot2D(data,classifier=C,axis_range=[-2,2,-2,2]) C=BackProp(hidden_layer_sizes = [6],max_iter=10000,tol=1e-4) C.fit(data.vectors,data.targets) print(("On Training Set:",C.percent_correct(data.vectors,data.targets))) plot2D(data,classifier=C,axis_range=[-2,2,-2,2]) C=NaiveBayes() C.fit(data.vectors,data.targets) print(("On Training Set:",C.percent_correct(data.vectors,data.targets))) C.plot_centers() plot2D(data,classifier=C,axis_range=[-2,2,-2,2]) C=kNearestNeighbor() C.fit(data.vectors,data.targets) print(("On Training Set:",C.percent_correct(data.vectors,data.targets))) plot2D(data,classifier=C,axis_range=[-2,2,-2,2]) C=CSC() C.fit(data.vectors,data.targets) print(("On Training Set:",C.percent_correct(data.vectors,data.targets))) C.plot_centers() plot2D(data,classifier=C,axis_range=[-2,2,-2,2]) ###Output ('On Training Set:', 100.0) ###Markdown 8x8 - Autoencoder ###Code vectors=eye(8) targets=arange(1,9) print((vectors,targets)) C=BackProp(activation='logistic',hidden_layer_sizes = [3],max_iter=10000,tol=1e-4) C.fit(vectors,targets) print((C.predict(vectors))) h,y=C.output(vectors) around(h,2) h.round() y.round() C.predict(vectors) y.shape imshow(h,interpolation='nearest',cmap=cm.gray) colorbar() weights_xh,weights_hy=C.weights plot(weights_xh,'-o') plot(weights_hy,'-o') ###Output _____no_output_____ ###Markdown Tuning the number of hidden units ###Code data=load_excel('data/iris.xls') data_train,data_test=split(data,test_size=0.75) ###Output iris.data 151 5 150 vectors of length 4 Feature names: 'petal length in cm', 'petal width in cm', 'sepal length in cm', 'sepal width in cm' Target values given. Target names: 'Iris-setosa', 'Iris-versicolor', 'Iris-virginica' Mean: [3.75866667 1.19866667 5.84333333 3.054 ] Median: [4.35 1.3 5.8 3. ] Stddev: [1.75852918 0.76061262 0.82530129 0.43214658] Original vector shape: (150, 4) Train vector shape: (37, 4) Test vector shape: (113, 4) ###Markdown select which number of hidden units to use ###Code hidden=list(range(1,10)) percent_correct=[] for n in hidden: C=BackProp(hidden_layer_sizes = [n],tol=1e-4,max_iter=10000) C.fit(data_train.vectors,data_train.targets) percent_correct.append(C.percent_correct(data_test.vectors,data_test.targets)) plot(hidden,percent_correct,'-o') xlabel('Number of Hidden Units') ylabel('Percent Correct on Test Data') ###Output _____no_output_____
Steps.ipynb	###Markdown step 1.Create reuqirements.txt!pip install watermark ###Code %load_ext watermark %watermark -v -m -p pandas,numpy,tensorflow,watermark %watermark -u -n -t -z ###Output CPython 3.7.2 IPython 7.8.0 pandas 0.25.2 numpy 1.17.3 tensorflow 2.1.0 watermark 2.0.2 compiler : MSC v.1916 64 bit (AMD64) system : Windows release : 10 machine : AMD64 processor : Intel64 Family 6 Model 58 Stepping 9, GenuineIntel CPU cores : 4 interpreter: 64bit last updated: Mon Jun 22 2020 10:27:31 Central Europe Daylight Time
stats-newtextbook-python/samples/3-7-推定.ipynb	###Markdown 第3部　Pythonによるデータ分析｜Pythonで学ぶ統計学入門 7章　推定分析の準備 ###Code # 数値計算に使うライブラリ import numpy as np import pandas as pd import scipy as sp from scipy import stats # グラフを描画するライブラリ from matplotlib import pyplot as plt import seaborn as sns sns.set() # 表示桁数の指定 %precision 3 # グラフをjupyter Notebook内に表示させるための指定 %matplotlib inline # データの読み込み fish = pd.read_csv("3-7-1-fish_length.csv")["length"] fish ###Output _____no_output_____ ###Markdown 実装：点推定 ###Code # 母平均の点推定 mu = sp.mean(fish) mu # 母分散の点推定 sigma_2 = sp.var(fish, ddof = 1) sigma_2 ###Output _____no_output_____ ###Markdown 実装：区間推定 ###Code # 自由度 df = len(fish) - 1 df # 標準誤差 se = sigma / sp.sqrt(len(fish)) se # 区間推定 interval = stats.t.interval( alpha = 0.95, df = df, loc = mu, scale = se) interval ###Output _____no_output_____ ###Markdown 補足：信頼区間の求め方の詳細 ###Code # 97.5%点 t_975 = stats.t.ppf(q = 0.975, df = df) t_975 # 下側信頼限界 lower = mu - t_975 * se lower # 上側信頼限界 upper = mu + t_975 * se upper ###Output _____no_output_____ ###Markdown 信頼区間の幅を決める要素 ###Code # 標本標準偏差が大きいと、信頼区間は広くなる se2 = (sigma10) / sp.sqrt(len(fish)) stats.t.interval( alpha = 0.95, df = df, loc = mu, scale = se2) # サンプルサイズが大きいと、信頼区間は狭くなる df2 = (len(fish)10) - 1 se3 = sigma / sp.sqrt(len(fish)*10) stats.t.interval( alpha = 0.95, df = df2, loc = mu, scale = se3) # 99%信頼区間 stats.t.interval( alpha = 0.99, df = df, loc = mu, scale = se) ###Output _____no_output_____ ###Markdown 信頼区間の解釈 ###Code # 信頼区間が母平均(4)を含んでいればTrue be_included_array = np.zeros(20000, dtype = "bool") be_included_array # 「データを10個選んで95%信頼区間を求める」試行を20000回繰り返す # 信頼区間が母平均(4)を含んでいればTrue np.random.seed(1) norm_dist = stats.norm(loc = 4, scale = 0.8) for i in range(0, 20000): sample = norm_dist.rvs(size = 10) df = len(sample) - 1 mu = sp.mean(sample) std = sp.std(sample, ddof = 1) se = std / sp.sqrt(len(sample)) interval = stats.t.interval(0.95, df, mu, se) if(interval[0] <= 4 and interval[1] >= 4): be_included_array[i] = True sum(be_included_array) / len(be_included_array) ###Output _____no_output_____
2. Regression/Assingments/PhillyCrime.ipynb	###Markdown Fire up graphlab create ###Code import graphlab ###Output _____no_output_____ ###Markdown Load some house value vs. crime rate dataDataset is from Philadelphia, PA and includes average house sales price in a number of neighborhoods. The attributes of each neighborhood we have include the crime rate ('CrimeRate'), miles from Center City ('MilesPhila'), town name ('Name'), and county name ('County'). ###Code sales = graphlab.SFrame.read_csv('Philadelphia_Crime_Rate_noNA.csv/') sales ###Output _____no_output_____ ###Markdown Exploring the data The house price in a town is correlated with the crime rate of that town. Low crime towns tend to be associated with higher house prices and vice versa. ###Code graphlab.canvas.set_target('ipynb') sales.show(view="Scatter Plot", x="CrimeRate", y="HousePrice") ###Output _____no_output_____ ###Markdown Fit the regression model using crime as the feature ###Code crime_model = graphlab.linear_regression.create(sales, target='HousePrice', features=['CrimeRate'], validation_set=None, verbose=False) ###Output _____no_output_____ ###Markdown Let's see what our fit looks like Matplotlib is a Python plotting library that is also useful for plotting. You can install it with:'pip install matplotlib' ###Code import matplotlib.pyplot as plt %matplotlib inline plt.plot(sales['CrimeRate'], sales['HousePrice'], '.', sales['CrimeRate'], crime_model.predict(sales), '-') ###Output _____no_output_____ ###Markdown Above: blue dots are original data, green line is the fit from the simple regression. Remove Center City and redo the analysis Center City is the one observation with an extremely high crime rate, yet house prices are not very low. This point does not follow the trend of the rest of the data very well. A question is how much including Center City is influencing our fit on the other datapoints. Let's remove this datapoint and see what happens. ###Code sales_noCC = sales[sales['MilesPhila'] != 0.0] sales_noCC.show(view="Scatter Plot", x="CrimeRate", y="HousePrice") ###Output _____no_output_____ ###Markdown Refit our simple regression model on this modified dataset: ###Code crime_model_noCC = graphlab.linear_regression.create(sales_noCC, target='HousePrice', features=['CrimeRate'], validation_set=None, verbose=False) ###Output _____no_output_____ ###Markdown Look at the fit: ###Code plt.plot(sales_noCC['CrimeRate'], sales_noCC['HousePrice'], '.', sales_noCC['CrimeRate'], crime_model.predict(sales_noCC), '-') ###Output _____no_output_____ ###Markdown Compare coefficients for full-data fit versus no-Center-City fit Visually, the fit seems different, but let's quantify this by examining the estimated coefficients of our original fit and that of the modified dataset with Center City removed. ###Code crime_model.get('coefficients') crime_model_noCC.get('coefficients') ###Output _____no_output_____ ###Markdown Above: We see that for the "no Center City" version, per unit increase in crime, the predicted decrease in house prices is 2,287. In contrast, for the original dataset, the drop is only 576 per unit increase in crime. This is significantly different! High leverage points: Center City is said to be a "high leverage" point because it is at an extreme x value where there are not other observations. As a result, recalling the closed-form solution for simple regression, this point has the potential to dramatically change the least squares line since the center of x mass is heavily influenced by this one point and the least squares line will try to fit close to that outlying (in x) point. If a high leverage point follows the trend of the other data, this might not have much effect. On the other hand, if this point somehow differs, it can be strongly influential in the resulting fit. Influential observations: An influential observation is one where the removal of the point significantly changes the fit. As discussed above, high leverage points are good candidates for being influential observations, but need not be. Other observations that are not leverage points can also be influential observations (e.g., strongly outlying in y even if x is a typical value). Remove high-value outlier neighborhoods and redo analysis Based on the discussion above, a question is whether the outlying high-value towns are strongly influencing the fit. Let's remove them and see what happens. ###Code sales_nohighend = sales_noCC[sales_noCC['HousePrice'] < 350000] crime_model_nohighend = graphlab.linear_regression.create(sales_nohighend, target='HousePrice', features=['CrimeRate'],validation_set=None, verbose=False) ###Output _____no_output_____ ###Markdown Do the coefficients change much? ###Code crime_model_noCC.get('coefficients') crime_model_nohighend.get('coefficients') ###Output _____no_output_____
results_replication/ACDC_NN-reproduce_results_ssym.ipynb	###Markdown IMPORTING LIBRARIES AND MOUNTING DRIVE ###Code !pip install biopython !pip install silence_tensorflow import pandas as pd import numpy as np import math from sklearn.metrics import mean_squared_error from Bio.PDB import * path='./' import sys sys.path.append("../acdc_nn/") import util import nn ###Output _____no_output_____ ###Markdown REPRODUCING PAPER RESULTS ACDC-NN In the following cell we run a loop on the 8 cross-validation folds generated with blustclust. The network has been trained in transfer learning on s2648 and Ivankov, therefore the cv sets have been generated taking into account the similarity between proteins (Similarity < 25%).Then we load the weights of the network for each fold, generate protein structures, create the input for the network in the appropriate form and also generate the reverse mutation. Finally we predict the $\Delta \Delta G$ with ACDC-NN. We have done the same thing for both direct and inverse proteins.We underlie that in the following cell we are using ACDC-NN with one structure. ###Code #path: ./replicate_results/ cv_folds=[0,1,2,4,5,6,7,8] # cross-validation folds cv_pred_dir=list() cv_pred_inv=list() for fold in cv_folds: pred_dir=list() pred_inv=list() #loading the proper fold ssym_dir=pd.read_csv(path+'Ssym_cv.mut/ssym_TS_dir_{}.mut'.format(fold), sep=' ',header=None) ssym_inv=pd.read_csv(path+'Ssym_cv.mut/ssym_TS_inv_{}.mut'.format(fold), sep=' ',header=None) # building the specific model with cv weights path_weights=path+"weights_cv/Weights_PostTL_CV_{}".format(fold) num_H=[32,16] d=0.2 ACDC_NN=nn.ACDC(num_H,d,25)[0] ACDC_NN.load_weights(path_weights) #Ssym dir prediction for protein,mut in zip(list(ssym_dir[0]),list(ssym_dir[1])): prof_path =path + 'profiles/' + protein +'.prof.gz' pdb_path= path + 'pdbs/' + protein[:-1] +'.pdb.gz' chain = protein[-1] # information processing # get structure and other information structure, pchain, seq, d_seq2pdb, d_pdb2seq = util.pdb2info(pdb_path, chain) prof = util.getProfile(prof_path) kvar=(mut[0],d_pdb2seq[mut[1:-1]],mut[-1]) kvar_pdb=(mut[0],mut[1:-1],mut[-1]) dist_neigh_3d= util.get_neigh_ps(kvar_pdb,5,d_seq2pdb,pchain) list_dist_neigh_3d = dist_neigh_3d[kvar] # extracting features codif=util.getMutCod(mut) all_profile = util.Unified_prof(kvar[1],prof,seq, list_dist_neigh_3d) #dir To_predict_dir=pd.DataFrame([codif,all_profile,np.zeros(600-len(all_profile))]).T #inv (dir) To_predict_inv=To_predict_dir.copy() To_predict_inv.iloc[:,:20]=To_predict_inv.iloc[:,:20].replace([1.0,-1.0],[-1.0,1.0]) # Making input in the proper shape Xm_d, X1D_d, X3D_d = nn.mkInp(np.asarray(To_predict_dir).astype(np.float32),500) Xm_i, X1D_i, X3D_i = nn.mkInp(np.asarray(To_predict_inv).astype(np.float32),500) #predict prediction=ACDC_NN.predict([X3D_d, X1D_d, Xm_d , X3D_i, X1D_i, Xm_i]) pred_dir.append(prediction[0][0][0]) #Ssym inv prediction for protein,mut in zip(list(ssym_inv[0]),list(ssym_inv[1])): prof_path =path + 'profiles/' + protein +'.prof.gz' pdb_path= path + 'pdbs/' + protein[:-1] +'.pdb.gz' chain = protein[-1] # information processing # get structure and other information structure, pchain, seq, d_seq2pdb, d_pdb2seq = util.pdb2info(pdb_path, chain) prof = util.getProfile(prof_path) kvar=(mut[0],d_pdb2seq[mut[1:-1]],mut[-1]) kvar_pdb=(mut[0],mut[1:-1],mut[-1]) dist_neigh_3d= util.get_neigh_ps(kvar_pdb,5,d_seq2pdb,pchain) list_dist_neigh_3d = dist_neigh_3d[kvar] # extracting features codif=util.getMutCod(mut) all_profile = util.Unified_prof(kvar[1],prof,seq, list_dist_neigh_3d) #dir To_predict_dir=pd.DataFrame([codif,all_profile,np.zeros(600-len(all_profile))]).T #dir (inv) To_predict_inv=To_predict_dir.copy() To_predict_inv.iloc[:,:20]=To_predict_inv.iloc[:,:20].replace([1.0,-1.0],[-1.0,1.0]) # Making input in the proper shape Xm_d, X1D_d, X3D_d = nn.mkInp(np.asarray(To_predict_dir).astype(np.float32),500) Xm_i, X1D_i, X3D_i = nn.mkInp(np.asarray(To_predict_inv).astype(np.float32),500) #predict prediction=ACDC_NN.predict([X3D_d, X1D_d, Xm_d , X3D_i, X1D_i, Xm_i]) pred_inv.append(prediction[0][0][0]) #appending results cv_pred_dir.append(pred_dir) cv_pred_inv.append(pred_inv) #merging the results cv_pred_dir=[protein for cv in cv_pred_dir for protein in cv] cv_pred_inv=[protein for cv in cv_pred_inv for protein in cv] ###Output /usr/local/lib/python3.6/dist-packages/Bio/PDB/Polypeptide.py:344: UserWarning: Assuming residue CA is an unknown modified amino acid % residue.get_resname() /usr/local/lib/python3.6/dist-packages/Bio/PDB/Polypeptide.py:344: UserWarning: Assuming residue CA is an unknown modified amino acid % residue.get_resname() ###Markdown RESHAPING RESULTS In the following cell we merge together all the cv folds and the results obtained, building a dataframe so that we can easily measure performances ###Code # fold 0 S_dir=pd.read_csv(path+'Ssym_cv.mut/ssym_TS_dir_0.mut', sep=' ',header=None) S_inv=pd.read_csv(path+'Ssym_cv.mut/ssym_TS_inv_0.mut', sep=' ',header=None) # appending the others cv_folds=[1,2,4,5,6,7,8] # cross-validation folds for fold in cv_folds: S_dir=S_dir.append(pd.read_csv(path+'Ssym_cv.mut/'+'ssym_TS_dir_{}.mut'.format(fold), sep=' ',header=None),) S_inv=S_inv.append(pd.read_csv(path+'Ssym_cv.mut/'+'ssym_TS_inv_{}.mut'.format(fold), sep=' ',header=None),) S_dir.columns=['Protein','Mut','DDG'] S_inv.columns=['Protein','Mut','DDG'] S_dir['DDG_pred']=cv_pred_dir S_inv['DDG_pred']=cv_pred_inv ###Output _____no_output_____ ###Markdown MEASURE OF ACDC-NN PERFORMANCE ON SSYM Ssym direct ###Code print('pearson_direct : ', np.corrcoef(S_dir['DDG_pred'],S_dir['DDG'])[0][1].round(2)) print('rmse : ',round(math.sqrt(mean_squared_error(S_dir['DDG_pred'],S_dir['DDG'])),2)) ###Output pearson_direct : 0.58 rmse : 1.42 ###Markdown Ssym inverse ###Code print('pearson_inverse : ', np.corrcoef(S_inv['DDG_pred'],S_inv['DDG'])[0][1].round(2)) print('rmse : ',round(math.sqrt(mean_squared_error(S_inv['DDG_pred'],S_inv['DDG'])),2)) ###Output pearson_inverse : 0.55 rmse : 1.47 ###Markdown Antisimmetry ###Code print('r_dir-inv : ' ,np.corrcoef(cv_pred_dir,cv_pred_inv)[0][1].round(2)) print('bias : ', util.bias(cv_pred_dir,cv_pred_inv).round(2)) ###Output r_dir-inv : -0.99 bias : -0.01 ###Markdown ACDC-NN* (two structures available) ###Code #path cv_folds=[0,1,2,4,5,6,7,8] # cross-validation folds cv_pred_dir=list() cv_pred_inv=list() for fold in cv_folds: pred_dir=list() pred_inv=list() #loading the proper fold ssym_dir=pd.read_csv(path+'Ssym_cv.mut/ssym_TS_dir_{}.mut'.format(fold), sep=' ',header=None) ssym_inv=pd.read_csv(path+'Ssym_cv.mut/ssym_TS_inv_{}.mut'.format(fold), sep=' ',header=None) # building the specific model with cv weights path_weights=path+"weights_cv/Weights_PostTL_CV_{}".format(fold) num_H=[32,16] d=0.2 ACDC_NN=nn.ACDC(num_H,d,25)[0] ACDC_NN.load_weights(path_weights) #Ssym dir-inv prediction using both structures for (protein_dir,protein_inv,mut_dir,mut_inv) in zip(list(ssym_dir[0]),list(ssym_inv[0]),list(ssym_dir[1]),list(ssym_inv[1])): # information processing # get structure and other information for the direct protein prof_path_dir =path + 'profiles/' + protein_dir +'.prof.gz' pdb_path_dir= path + 'pdbs/' + protein_dir[:-1] +'.pdb.gz' chain_dir = protein_dir[-1] structure_dir, pchain_dir, seq_dir, d_seq2pdb_dir, d_pdb2seq_dir = util.pdb2info(pdb_path_dir, chain_dir) prof_dir = util.getProfile(prof_path_dir) kvar_dir=(mut_dir[0],d_pdb2seq_dir[mut_dir[1:-1]],mut_dir[-1]) kvar_pdb_dir=(mut_dir[0],mut_dir[1:-1],mut_dir[-1]) dist_neigh_3d_dir= util.get_neigh_ps(kvar_pdb_dir,5,d_seq2pdb_dir,pchain_dir) list_dist_neigh_3d_dir = dist_neigh_3d_dir[kvar_dir] # extracting features codif_dir=util.getMutCod(mut_dir) all_profile_dir = util.Unified_prof(kvar_dir[1],prof_dir,seq_dir, list_dist_neigh_3d_dir) #dir To_predict_dir=pd.DataFrame([codif_dir,all_profile_dir,np.zeros(600-len(all_profile_dir))]).T # information processing # get structure and other information for the inverse protein prof_path_inv =path + 'profiles/' + protein_inv +'.prof.gz' pdb_path_inv= path + 'pdbs/' + protein_inv[:-1] +'.pdb.gz' chain_inv = protein_inv[-1] # information processing # get structure and other information for the inverse protein structure_inv, pchain_inv, seq_inv, d_seq2pdb_inv, d_pdb2seq_inv = util.pdb2info(pdb_path_inv, chain_inv) prof_inv = util.getProfile(prof_path_inv) kvar_inv=(mut_inv[0],d_pdb2seq_inv[mut_inv[1:-1]],mut_inv[-1]) kvar_pdb_inv=(mut_inv[0],mut_inv[1:-1],mut_inv[-1]) dist_neigh_3d_inv= util.get_neigh_ps(kvar_pdb_inv,5,d_seq2pdb_inv,pchain_inv) list_dist_neigh_3d_inv = dist_neigh_3d_inv[kvar_inv] # extracting features codif_inv=util.getMutCod(mut_inv) all_profile_inv = util.Unified_prof(kvar_inv[1],prof_inv,seq_inv, list_dist_neigh_3d_inv) #inv To_predict_inv=pd.DataFrame([codif_inv,all_profile_inv,np.zeros(600-len(all_profile_inv))]).T # Making input in the proper shape Xm_d, X1D_d, X3D_d = nn.mkInp(np.asarray(To_predict_dir).astype(np.float32),500) Xm_i, X1D_i, X3D_i = nn.mkInp(np.asarray(To_predict_inv).astype(np.float32),500) #predict dir prediction_dir=ACDC_NN.predict([X3D_d, X1D_d, Xm_d , X3D_i, X1D_i, Xm_i]) pred_dir.append(prediction_dir[0][0][0]) #predict inv prediction_inv=ACDC_NN.predict([X3D_i, X1D_i, Xm_i , X3D_d, X1D_d, Xm_d]) pred_inv.append(prediction_inv[0][0][0]) #appending results cv_pred_dir.append(pred_dir) cv_pred_inv.append(pred_inv) #merging the results cv_pred_dir=[protein for cv in cv_pred_dir for protein in cv] cv_pred_inv=[protein for cv in cv_pred_inv for protein in cv] ###Output /usr/local/lib/python3.6/dist-packages/Bio/PDB/Polypeptide.py:344: UserWarning: Assuming residue CA is an unknown modified amino acid % residue.get_resname() /usr/local/lib/python3.6/dist-packages/Bio/PDB/Polypeptide.py:344: UserWarning: Assuming residue CA is an unknown modified amino acid % residue.get_resname() ###Markdown ADDING ACDC-NN* PREDICTIONS TO THE PREVIOUS DATAFRAMES ###Code S_dir['DDG_pred_two_pdbs']=cv_pred_dir S_inv['DDG_pred_two_pdbs']=cv_pred_inv ###Output _____no_output_____ ###Markdown MEASURE OF ACDC-NN* PERFORMANCE ON SSYM Ssym direct ###Code print('pearson dir: ',np.corrcoef(S_dir['DDG'],S_dir['DDG_pred_two_pdbs'])[0][1].round(2)) print('rmse dir: ',round(math.sqrt(mean_squared_error(S_dir['DDG'],S_dir['DDG_pred_two_pdbs'])),2)) ###Output pearson dir: 0.57 rmse dir: 1.45 ###Markdown Ssym inverse ###Code print('pearson inv: ',np.corrcoef(S_inv['DDG'],S_inv['DDG_pred_two_pdbs'])[0][1].round(2)) print('rmse inv: ',round(math.sqrt(mean_squared_error(S_inv['DDG'],S_inv['DDG_pred_two_pdbs'])),2)) ###Output pearson inv: 0.57 rmse inv: 1.45 ###Markdown Antisimmetry ###Code print('r_dir-inv: ' ,np.corrcoef(cv_pred_dir,cv_pred_inv)[0][1].round(2)) print('bias: ', util.bias(cv_pred_dir,cv_pred_inv).round(2)) ###Output r_dir-inv: -1.0 bias: 0.0
AstroFix Sample Jupyter Notebook.ipynb	###Markdown The example uses two V-band images: one showing the globular cluster 47 Tucanae, and the other showing the globular cluster Messier 15. Both images were taken by the LCO 0.4-meter telescope. They are available at the links below: [47 Tuc](https://archive.lco.global/?q=a&RLEVEL=&PROPID=&INSTRUME=&OBJECT=&SITEID=&TELID=&FILTER=&OBSTYPE=&EXPTIME=&BLKUID=&REQNUM=&basename=cpt0m407-kb84-20200917-0147-e91&start=2020-09-17%2000%3A00&end=2020-09-18%2000%3A00&id=&public=true) [M15](https://archive.lco.global/?q=a&RLEVEL=&PROPID=&INSTRUME=&OBJECT=&SITEID=&TELID=&FILTER=&OBSTYPE=&EXPTIME=&BLKUID=&REQNUM=&basename=cpt0m407-kb84-20201021-0084-e91&start=2020-10-21%2000%3A00&end=2021-10-22%2000%3A00&id=&public=true) Let's begin with the image of 47 Tucanae: ###Code Tuc47 = fits.open('cpt0m407-kb84-20200917-0147-e91.fits.fz')[1].data print(Tuc47.shape) plt.figure(figsize=(10,10)) plt.imshow(Tuc47[950:1150,1400:1600]) plt.show() ###Output _____no_output_____ ###Markdown To show astrofix's performance on repairing the image, we randomly generate some artificial bad pixels. Let's say we turn 1% of all pixels into NaN. Half of the them stand alone, and the other half of them form crossed shaped regions of bad pixels. You can experiment with other bad pixel fractions and shapes. ###Code img=Tuc47.copy().astype(float) # 0.5% bad pixel BP_mask=np.random.rand(img.shape[0],img.shape[1])>0.995 # 0.1% cross shaped regions of bad pixels cross_mask=np.random.rand(img.shape[0],img.shape[1])>0.999 cross_generator=np.array([[0,1,0],[1,1,1],[0,1,0]]) BP_mask=(BP_mask+convolve(cross_mask,cross_generator,mode="same",method="direct"))!=0 img[BP_mask]=np.nan print("Number of Bad Pixels: {}".format(np.count_nonzero(BP_mask))) ###Output Number of Bad Pixels: 62427 ###Markdown The image is now populated by NaN pixels: ###Code plt.figure(figsize=(10,10)) plt.imshow(img[950:1150,1400:1600]) plt.show() ###Output _____no_output_____ ###Markdown Now let's repair the image with astrofix.Fix_Image: ###Code # Because there is almost no saturation in this image, we set max_clip=1 so that we keep the brightest pixels in the training set. fixed_img,para,TS=astrofix.Fix_Image(img,"asnan",max_clip=1) print("a={},h={}".format(para[0],para[1])) print("Number of training set pixels: {}".format(np.count_nonzero(TS))) ###Output a=3.3138644003486495,h=0.9528206390741414 Number of training set pixels: 278813 ###Markdown Compare with the original image of 47 Tucanae: ###Code fig,ax=plt.subplots(1,3,figsize=(18,7)) im=ax[0].imshow(Tuc47[990:1015,1525:1550]) # Choose your region to zoom in divider = make_axes_locatable(ax[0]) cax = divider.append_axes("bottom", size="5%", pad=0.2) fig.colorbar(im,ax=ax[0],cax=cax,orientation="horizontal") ax[0].set_title("Original",fontsize=30,pad=15) ax[0].axis("off") im=ax[1].imshow(img[990:1015,1525:1550]) divider = make_axes_locatable(ax[1]) cax = divider.append_axes("bottom", size="5%", pad=0.2) fig.colorbar(im,ax=ax[1],cax=cax,orientation="horizontal") ax[1].set_title("Bad",fontsize=30,pad=15) ax[1].axis("off") im=ax[2].imshow(fixed_img[990:1015,1525:1550]) divider = make_axes_locatable(ax[2]) cax = divider.append_axes("bottom", size="5%", pad=0.2) fig.colorbar(im,ax=ax[2],cax=cax,orientation="horizontal") ax[2].set_title("Fixed",fontsize=30,pad=15) ax[2].axis("off") plt.tight_layout() plt.show() ###Output _____no_output_____ ###Markdown One feature of astrofix is that the training result for one image is usually applicable to similar images taken by the same telescope, meaning that we can repair the image of M15 without having to train on M15, simply using the optimal parameters that we got previously from 47 Tucanae: ###Code M15 = fits.open('cpt0m407-kb84-20201021-0084-e91.fits.fz')[1].data print(M15.shape) plt.figure(figsize=(10,10)) plt.imshow(M15[950:1150,1400:1600]) plt.show() ###Output _____no_output_____ ###Markdown Generate artificial bad pixels like before: ###Code new_img=M15.copy().astype(float) # 0.5% bad pixel new_BP_mask=np.random.rand(new_img.shape[0],new_img.shape[1])>0.995 # 0.1% cross shaped regions of bad pixels new_cross_mask=np.random.rand(new_img.shape[0],new_img.shape[1])>0.999 cross_generator=np.array([[0,1,0],[1,1,1],[0,1,0]]) new_BP_mask=(new_BP_mask+convolve(new_cross_mask,cross_generator,mode="same",method="direct"))!=0 new_img[new_BP_mask]=np.nan print("Number of Bad Pixels: {}".format(np.count_nonzero(new_BP_mask))) plt.figure(figsize=(10,10)) plt.imshow(new_img[950:1150,1400:1600]) plt.show() ###Output Number of Bad Pixels: 62320 ###Markdown This time, instead of calling astrofix.Fix_image, we use astrofix.Interpolate with $a$ and $h$ equal to their optimal values for 47 Tucanae. ###Code new_fixed_img=astrofix.Interpolate(para[0],para[1],new_img,BP="asnan") ###Output _____no_output_____ ###Markdown Compare with the original image of M15: ###Code fig,ax=plt.subplots(1,3,figsize=(18,7)) im=ax[0].imshow(M15[1010:1035,1500:1525]) # Choose your region to zoom in divider = make_axes_locatable(ax[0]) cax = divider.append_axes("bottom", size="5%", pad=0.2) fig.colorbar(im,ax=ax[0],cax=cax,orientation="horizontal") ax[0].set_title("Original",fontsize=30,pad=15) ax[0].axis("off") im=ax[1].imshow(new_img[1010:1035,1500:1525]) divider = make_axes_locatable(ax[1]) cax = divider.append_axes("bottom", size="5%", pad=0.2) fig.colorbar(im,ax=ax[1],cax=cax,orientation="horizontal") ax[1].set_title("Bad",fontsize=30,pad=15) ax[1].axis("off") im=ax[2].imshow(new_fixed_img[1010:1035,1500:1525]) divider = make_axes_locatable(ax[2]) cax = divider.append_axes("bottom", size="5%", pad=0.2) fig.colorbar(im,ax=ax[2],cax=cax,orientation="horizontal") ax[2].set_title("Fixed",fontsize=30,pad=15) ax[2].axis("off") plt.tight_layout() plt.show() ###Output _____no_output_____
Shapiro/two-channel-CPR/Figures for supp_new_C/Shapiro_Diagram_RSJ_normalized_two_channels-Plot-paper_20201010.ipynb	###Markdown Shapiro Diagram with normalized parameters Cythonized, RF current in linear & log scale CPR of $I(\phi)=[\sin(\phi)+\eta\sin(2\phi)]+A(\sin(\phi+C)+\eta\sin[2(\phi+C)])$ ###Code import numpy as np import matplotlib.pyplot as plt from datetime import * from scipy.io import savemat from scipy.integrate import odeint %matplotlib inline %load_ext Cython ###Output _____no_output_____ ###Markdown Resistively Shunted Model:$\frac{d\phi}{dt}=\frac{2eR_N}{\hbar}[I_{DC}+I_{RF}\sin(2\pi f_{RF}t)-I_C\sin\phi]$Solving $\phi(t)$, then you can get the voltage difference between the superconducting leads:$V=\frac{\hbar}{2e}\langle\frac{d\phi}{dt}\rangle$After Normalizing:$I_{DC}\leftrightarrow \tilde{I_{DC}}=I_{DC}/I_C$,$I_{RF} \leftrightarrow \tilde{I_{RF}}=I_{RF}/I_C$,$ V \leftrightarrow \tilde{V}=\frac{V}{I_CR_N}$,$ R=\frac{dV}{dI} \leftrightarrow \tilde{R}=\frac{R}{R_N}$,$\because f_0=2eI_CR_N/h$,$f_{RF} \leftrightarrow \tilde{f_{RF}}=f_{RF}/f_0$,$t \leftrightarrow \tilde{t}=f_0t$,The Josephson voltage quantized at $\frac{V}{hf_{RF}f_0/2e}=n \leftrightarrow \frac{V}{f_{RF}f_0}=n$ Here, we can set $f_0=1$ or $\frac{I_CR_N}{hf_0/2e}=1$, without loss of generalityThe RSJ model simply becomes (omitting $\tilde{}$):$\frac{d\phi}{dt}=[I_{DC}+I_{RF}\sin(2\pi f_{RF}t)-\sin\phi]$At equilibrium, $V=\frac{\hbar}{2e}\langle\frac{d\phi}{dt}\rangle \leftrightarrow \tilde{V}=\frac{1}{2\pi}\langle\frac{d\phi}{d\tilde{t}}\rangle$ would also quantized at integers in the Shapiro step regime. Cython codes here is to speed up the simulation because python is slower than C: ###Code %%cython #--pgo # only for Mac OS X #To use GNU compiler gcc-10 specified in .bash_profile cimport numpy as np from libc.math cimport sin, pi ### cdef is faster but can only be used for cython in this cell #cpdef can be used for python outside this cell cdef double CPR(double G, double A, double eta, double C): ''' Current-phase relationship for the junction ''' return sin(G)+etasin(2G)+Asin(G+Cpi)+Aetasin(2G+2Cpi) cpdef double dGdt(G,double t,double i_dc,double i_ac,double f_rf,double A, double eta, double C): ''' RSJ model ''' der = i_dc + i_ac sin(2pif_rft) - CPR(G,A,eta,C) return der from scipy.optimize import fmin def find_Ic_max(A,eta,C): Gmax=fmin(lambda x: -CPR(x,A,eta,C),0,disp=0) return CPR(Gmax,A,eta,C) A=0. eta=0.8 C=-0.7 # as a unit of pi f_rf=0.8 IDC_step=0.1 IDC_array=np.linspace(-5,5,201) IRF_step=0.1 IRF_array=np.linspace(0,15,151) print("DC array size: "+str(len(IDC_array))) print("RF array size: "+str(len(IRF_array))) ###Output DC array size: 201 RF array size: 151 ###Markdown Plot CPR ###Code G=np.linspace(-3,3,301)np.pi def CPR(G, A, eta, C): return np.sin(G)+etanp.sin(2G)+Anp.sin(G+Cnp.pi)+Aetanp.sin(2G+2Cnp.pi) plt.figure() plt.plot(G,CPR(G,A,eta,C)) ###Output _____no_output_____ ###Markdown Test on a single RF current ###Code t=np.arange(0,300.01,0.01)/f_rf V=np.empty([len(IDC_array)]) G_array=np.empty(len(t)) for i in range(0,len(IDC_array)): G_array= odeint(dGdt,0,t,args=(IDC_array[i],15,f_rf,A,eta,C)) V[i]=np.mean(np.gradient(G_array[-10000:,0]))/(0.01/f_rf)/(2np.pi) DVDI=2np.pinp.gradient(V,IDC_step) #differential resistance dV/dI plt.plot(t,G_array) JV=f_rf plt.figure() plt.plot(IDC_array,V/JV) plt.grid() plt.figure() plt.plot(IDC_array,DVDI) #plt.ylim([0,3]) _name_file = "f_"+str(f_rf)+"_A"+str(np.round(A,3))+"_eta"+str(np.round(eta,3))+"_C"+str(np.round(C,2))+"pi_norm" _name_title = "f= "+str(f_rf)+", A= "+str(np.round(A,3))+", eta= "+str(np.round(eta,3))+",C= "+str(np.round(C,2))+"pi" print(_name_title) T1=datetime.now() print (T1) V=np.empty([len(IRF_array),len(IDC_array)]) for i in range(0,len(IRF_array)): print("RF power now: "+str(i)+" of "+str(len(IRF_array))+" ,"+str(datetime.now()),end="\r") for j in range(0,len(IDC_array)): t=np.arange(0,300.01,0.01)/f_rf G_array= odeint(dGdt,0,t,args=(IDC_array[j],IRF_array[i],f_rf,A,eta,C)) V[i,j]=np.mean(np.gradient(G_array[-10001:,0]))/(0.01/f_rf)/(2np.pi) DVDI=2np.pinp.gradient(V,IDC_step,axis=1) print ("\n It takes " + str(datetime.now()-T1)) plt.figure() plt.pcolormesh(IDC_array, IRF_array, DVDI, cmap = 'inferno', vmin = 0,linewidth=0,rasterized=True,shading="auto") plt.xlabel("DC Current($I/I_C$)") plt.ylabel("RF Current ($I_RF/I_C$)") plt.colorbar(label = "DV/DI") plt.title(_name_title) plt.savefig("DVDI_"+_name_file+".pdf") plt.show() plt.figure() plt.pcolormesh(IDC_array, IRF_array, V/f_rf , cmap = 'coolwarm',linewidth=0,rasterized=True,shading="auto") plt.xlabel("DC Current($I/I_C$)") plt.ylabel("RF Current ($I_RF/I_C$)") plt.colorbar(label = "$V/I_CR_N$") plt.title(_name_title) plt.savefig("V_"+_name_file+".pdf") plt.show() plt.figure() plt.plot(IDC_array,V[1,:]/f_rf)#/(np.pihbarf/Qe)) plt.show() plt.figure() plt.plot(IDC_array,DVDI[1,:]) plt.show() savemat("data"+_name_file+'.mat',mdict={'IDC':IDC_array,'IRF':IRF_array,'A':A, 'eta':eta, 'f_rf':f_rf,'C':C,'V':V,'DVDI':DVDI}) print('file saved') ###Output f= 0.4, A= 0.2, eta= 0.8,C= -0.8pi 2020-09-26 13:11:28.119589 RF power now: 3 of 151 ,2020-09-26 13:12:04.409093 ###Markdown Calculate the normalized frequency ###Code Qe=1.602e-19 Ic=2e-6 Rn=13 h=6.626e-34 f0=2QeIcRn/h print(5e9/f0) print(2.5e9/f0) ###Output 0.3976999903966197 0.19884999519830984 ###Markdown Simulation using log scales in power ###Code IDC_step=0.05 IDC_array=np.linspace(-10,10,401) PRF_step=0.02 PRF_array=np.linspace(-1.5,2.5,201) IRF_array = 10*(PRF_array/2) print(IRF_array[-1]) print(IRF_array[0]) print("DC array size: "+str(len(IDC_array))) print("RF array size: "+str(len(IRF_array))) print("DC step: "+str(IDC_array[1]-IDC_array[0])) print("RF step: "+str(PRF_array[1]-PRF_array[0])) f_rf=0.4 A_array=np.array([0]) eta_array=np.array([0,0.9]) C_array=np.array([0]) for A in A_array: for eta in eta_array: for C in C_array: _name_file = "f_"+str(f_rf)+"_A"+str(np.round(A,3))+"_eta"+str(np.round(eta,3))+"_C"+str(np.round(C,2))+"pi_log" _name_title = "f= "+str(f_rf)+", A= "+str(np.round(A,3))+", eta= "+str(np.round(eta,3))+",C= "+str(np.round(C,2))+"pi" print(_name_title) #Ic_max=find_Ic_max(A,eta,C) T1=datetime.now() print (T1) V=np.empty([len(IRF_array),len(IDC_array)]) for i in range(0,len(IRF_array)): #print("RF power now: "+str(i)+" of "+str(len(IRF_array))+" ,"+str(datetime.now()),end="\r") for j in range(0,len(IDC_array)): t=np.arange(0,300.01,0.01)/f_rf G_array= odeint(dGdt,0,t,args=(IDC_array[j],IRF_array[i],f_rf,A,eta,C)) V[i,j]=np.mean(np.gradient(G_array[-10001:,0]))/(0.01/f_rf)/(2np.pi) DVDI=2np.pinp.gradient(V,IDC_step,axis=1) print ("\n It takes " + str(datetime.now()-T1)) plt.figure() plt.pcolormesh(IDC_array, PRF_array, DVDI, cmap = 'inferno', vmin = 0,linewidth=0,rasterized=True,shading="auto") plt.xlabel("DC Current($I/I_C$)") plt.ylabel("RF Power (a.u.)") plt.colorbar(label = "DV/DI") plt.title(_name_title) plt.savefig("DVDI_"+_name_file+".pdf") plt.show() #plt.figure() #plt.pcolormesh(IDC_array, PRF_array, V/f_rf , cmap = 'coolwarm',linewidth=0,rasterized=True,shading="auto") #plt.xlabel("DC Current($I/I_C$)") #plt.ylabel("RF Power (a.u.)") #plt.colorbar(label = "$V/I_CR_N$") #plt.title(_name_title) #plt.savefig("V_"+_name_file+".pdf") #plt.show() #plt.figure() #plt.plot(IDC_array,V[len(IRF_array)//2,:]/f_rf)#/(np.pihbarf/Qe)) #plt.title("cut at power= "+str(PRF_array[len(IRF_array)//2])) #plt.show() #plt.figure() #plt.plot(IDC_array,DVDI[len(IRF_array)//2,:]) #plt.title("cut at power= "+str(PRF_array[len(IRF_array)//2])) #plt.show() savemat("./data"+_name_file+'.mat',mdict={'IDC':IDC_array,'IRF':IRF_array,'PRF':PRF_array,'A':A, 'eta':eta, 'f_rf':f_rf,'C':C,'V':V,'DVDI':DVDI})#,'Ic_max':Ic_max}) print('file saved') plt.figure() plt.plot(IDC_array,DVDI[130,:,]) plt.show() print(PRF_array[130]) from scipy.io import loadmat import sys #sys.path.insert(0, 'C:/Users/QMDla/Documents/GitHub/data_file_manipulations/') sys.path.insert(0, '/Volumes/GoogleDrive/My Drive/GitHub/data_file_manipulations/') import files_manipulation import importlib importlib.reload(files_manipulation) dataDir = "./" files_manipulation.merge_multiple_mat(dataDir,True) # True for saving .h5 import h5py fd= h5py.File('merged.h5','r') list(fd.keys()) A=fd['A'][...] print(A.shape) C=fd['C'][...] print(C.shape) DVDI=fd['DVDI'][...] print(DVDI.shape) IDC=fd['IDC'][...] print(IDC.shape) IRF=fd['IRF'][...] print(IRF.shape) PRF=fd['PRF'][...] print(PRF.shape) V=fd['V'][...] print(V.shape) eta=fd['eta'][...] print(eta.shape) f_rf=fd['f_rf'][...] print(f_rf.shape) print(A) print(eta) print(f_rf) print(C) selected=np.array([5,0,2,6,3,8]) for i in selected: fig=plt.figure() im=plt.pcolormesh(IDC, PRF, np.squeeze(DVDI[:,:,i]), cmap = 'inferno', vmin = 0,vmax=8.5,linewidth=0,rasterized=True,shading="auto") plt.xlabel("I") plt.title("eta="+str(eta[i])+", A="+str(A[i])+", C="+str(C[i])) plt.xlim([-10,10]) plt.ylim([-1.5,2.5]) plt.ylabel("P") cbaxes = fig.add_axes([0.1, 1, 0.4, 0.05]) cb=fig.colorbar(im, label = "DV/DI",orientation="horizontal",cax=cbaxes) plt.savefig("DVDI_eta"+str(eta[i])+"_A"+str(A[i])+"_C"+str(C[i])+".pdf",bbox_inches='tight') plt.show() print(np.max(np.max(DVDI[:,:,i]))) plt.figure() plt.plot(IDC,DVDI[150,:,2]) #plt.xlim([-4,4]) print(PRF[150]) plt.figure() plt.plot(IDC,V[150,:,2]) ###Output 1.5
covid_mutation_analysis_and_nextstrain_build/EpitopeMutationRate.ipynb	###Markdown Random aside, making a fasta file for each of the full proteins ###Code seq_dict = dict() for aa_file in amino_acid_files: protein = aa_file.split('_')[-1].split('.')[0] new_aa_file= 'data/larger_version_modified/aligned_protein_'+protein+'.fasta' # newer code with open(new_aa_file, "rt") as handle: records = list(SeqIO.parse(handle, "fasta")) for ind, r in enumerate(records): if r.id == 'Wuhan-Hu-1/2019': print('ind = ', ind) ref_seq_ind = ind ref_seq_new = str(records[ref_seq_ind].seq) seq_dict[protein] = ref_seq_new seq_dict; ### All ind's should be zero by design, because I have added the reference sequence to the beginning of all the ### protein files ### Create the 'base_proteins_with_end_codon_larger.fasta' file with open('base_proteins_with_end_codon_larger.fasta', 'w') as f: for k, v in seq_dict.items(): f.write('> Protein: '+k+' \| Base Sequence: Wuhan-Hu-1/2019\n') f.write(v +'\n') ###Output _____no_output_____ ###Markdown Checking if the proteins have changed from original due to new masking* This was necessary when NextStrain was doing different read calling! ###Code # for aa_file in amino_acid_files: # protein = aa_file.split('_')[-1].split('.')[0] # old_aa_file = '../PUFFIN/proteins/v1/aligned_aa_'+protein+'.fasta' # Trenton's code # # old_aa_file = 'PUFFIN/proteins/aligned_aa_'+protein+'.fasta' # new code wrong # # old_aa_file = '../PUFFIN/proteins/v1/aligned_aa_'+protein+'.fasta' # newer code wrong # print('protein:', protein) # with open(old_aa_file, "rt") as handle: # records = list(SeqIO.parse(handle, "fasta")) # # getting rid of the node sequences! # no_nodes = [] # for r in records: # if 'NODE_' not in r.id: # no_nodes.append(r) # records = no_nodes # len(records) # for ind, r in enumerate(records): # if r.id == 'Wuhan/IPBCAMS-WH-01/2019':#'Wuhan-Hu-1/2019':#'Wuhan/WH01/2019':#'Wuhan/IPBCAMS-WH-01/2019': # print(ind) # ref_seq_ind = ind # ref_seq_old = str(records[ref_seq_ind].seq)[:-1] # print('length of old sequence', len(ref_seq_old)) # new_aa_file= 'PUFFIN/proteins/aligned_protein_'+protein+'.fasta' # with open(new_aa_file, "rt") as handle: # records = list(SeqIO.parse(handle, "fasta")) # # getting rid of the node sequences! # no_nodes = [] # for r in records: # if 'NODE_' not in r.id: # no_nodes.append(r) # records = no_nodes # len(records) # for ind, r in enumerate(records): # if r.id == 'Wuhan/IPBCAMS-WH-01/2019':#'Wuhan-Hu-1/2019':#'Wuhan/WH01/2019':#'Wuhan/IPBCAMS-WH-01/2019': # print(ind) # ref_seq_ind = ind # ref_seq_new = str(records[ref_seq_ind].seq)[:-1] # if str(records[ref_seq_ind].seq)[-1] == '': # print(protein, 'has star at end') # else: # print(protein, 'has NO STAR at end') # mask = np.asarray(list(ref_seq_old)) != np.asarray(list(ref_seq_new)) # print(np.asarray(list(ref_seq_old))[mask]) # print(np.arange(len(ref_seq_old))[mask]) # for aa_file in amino_acid_files: # protein = aa_file.split('_')[-1].split('.')[0] # old_aa_file = '../PUFFIN/proteins/v1/aligned_aa_'+protein+'.fasta' # print('protein:', protein) # with open(old_aa_file, "rt") as handle: # records = list(SeqIO.parse(handle, "fasta")) # # getting rid of the node sequences! # no_nodes = [] # for r in records: # if 'NODE_' not in r.id: # no_nodes.append(r) # records = no_nodes # len(records) # for ind, r in enumerate(records): # if r.id == 'Wuhan/IPBCAMS-WH-01/2019':#'Wuhan-Hu-1/2019':#'Wuhan/WH01/2019':#'Wuhan/IPBCAMS-WH-01/2019': # print(ind) # ref_seq_ind = ind # ref_seq_old = str(records[ref_seq_ind].seq)[:-1] # print('length of old sequence', len(ref_seq_old)) # new_aa_file= '../PUFFIN/proteins/v6/aligned_protein_'+protein+'.fasta' # with open(new_aa_file, "rt") as handle: # records = list(SeqIO.parse(handle, "fasta")) # # getting rid of the node sequences! # no_nodes = [] # for r in records: # if 'NODE_' not in r.id: # no_nodes.append(r) # records = no_nodes # len(records) # for ind, r in enumerate(records): # if r.id == 'Wuhan/IPBCAMS-WH-01/2019':#'Wuhan-Hu-1/2019':#'Wuhan/WH01/2019':#'Wuhan/IPBCAMS-WH-01/2019': # print(ind) # ref_seq_ind = ind # ref_seq_new = str(records[ref_seq_ind].seq)[:-1] # if str(records[ref_seq_ind].seq)[-1] == '': # print(protein, 'has star at end') # else: # print(protein, 'has NO STAR at end') # mask = np.asarray(list(ref_seq_old)) != np.asarray(list(ref_seq_new)) # print(np.asarray(list(ref_seq_old))[mask]) # print(np.arange(len(ref_seq_old))[mask]) # # what is the frequency before and after at these different sites? # protein_name = 'ORF1b' # position = 1687 # old_aa_file = '../PUFFIN/proteins/v1/aligned_aa_'+protein_name+'.fasta' # print('protein:', protein) # with open(old_aa_file, "rt") as handle: # records = list(SeqIO.parse(handle, "fasta")) # # getting rid of the node sequences! # no_nodes = [] # for r in records: # if 'NODE_' not in r.id: # no_nodes.append(r) # records = no_nodes # len(records) # for ind, r in enumerate(records): # if r.id == 'Wuhan/IPBCAMS-WH-01/2019':#'Wuhan-Hu-1/2019':#'Wuhan/WH01/2019':#'Wuhan/IPBCAMS-WH-01/2019': # print(ind) # ref_seq_ind = ind # aa_counter={} # ref_seq_old = str(records[ref_seq_ind].seq)[:-1] # for ind, r in enumerate(records): # try: # aa_counter[str(r.seq[position])] += 1 # except: # aa_counter[str(r.seq[position])] =1 # aa_counter # new_aa_file = 'data/v6/aligned_protein_'+protein_name+'.fasta' # print('protein:', protein) # with open(new_aa_file, "rt") as handle: # records = list(SeqIO.parse(handle, "fasta")) # # getting rid of the node sequences! # no_nodes = [] # for r in records: # if 'NODE_' not in r.id: # no_nodes.append(r) # records = no_nodes # len(records) # for ind, r in enumerate(records): # if r.id == 'Wuhan/IPBCAMS-WH-01/2019':#'Wuhan-Hu-1/2019':#'Wuhan/WH01/2019':#'Wuhan/IPBCAMS-WH-01/2019': # print(ind) # ref_seq_ind = ind # aa_counter={} # for ind, r in enumerate(records): # try: # aa_counter[str(r.seq[position])] += 1 # except: # aa_counter[str(r.seq[position])] =1 # aa_counter # len(records) # amino_acid_files ###Output _____no_output_____ ###Markdown Removing sequences with Gap, X, * or no M to start (except for ORF1b, doesnt start with a M!) ###Code # doing a quality check error_seq = np.zeros(len(records)) for aa_file in amino_acid_files: protein = aa_file.split('_')[-1].split('.')[0] #print('protein:', protein) with open(aa_file, "rt") as handle: records = list(SeqIO.parse(handle, "fasta")) # getting rid of the node sequences! no_nodes = [] for r in records: if 'NODE_' not in r.id: no_nodes.append(r) records = no_nodes num_errs = 0 stop_errs =[] m_errs = 0 #records = clean_records for r_ind, r in enumerate(records): trans = r.seq[:-1] # ignores final stop codon if protein=='ORF1b': # ignore this masked region if '' in trans or 'X' in trans or '-' in trans: # doesnt seem to start with an M for anything num_errs+=1 error_seq[r_ind] += 1 stop_errs.append( (np.asarray( list(trans) )=='').sum() ) elif '' in trans or trans[0] != 'M' or 'X' in trans or '-' in trans: num_errs+=1 error_seq[r_ind] += 1 stop_errs.append( (np.asarray( list(trans) )=='').sum() ) #if p=='ORF14': #print(trans) if trans[0] != 'M': m_errs += 1 print(protein, num_errs, num_errs/len(trans), m_errs) plt.hist(error_seq) (error_seq>0).sum() len(error_seq) drop_seq = (error_seq>0) ###Output _____no_output_____ ###Markdown Removing Belgian Sequenceshttps://github.com/nextstrain/ncov/commit/6b7b822858ccc3e21ec279fc6afde30365d25aa0 They had 3 masked regions that need to be accounted for. May be more read quality issues too* This should no longer be a problem! ###Code '''# getting the nt regions with open('nextstrain_covid19_ref_protein_pos.txt', 'r') as f: lines = f.readlines() take_next_line = False regions = [] proteins = [] for l in lines: if 'CDS ' in l: regions.append(l.split('CDS')[1].strip()) take_next_line=True elif take_next_line: proteins.append(l.split('gene=')[1].strip().strip('"')) take_next_line=False protein_regions = {p:r for p,r in zip(proteins, regions)} protein_regions''' '''belgian_masked_sites = [ 13402-266, 24389-21563, 24390-21563] belgian_masked_sites = np.asarray(belgian_masked_sites)/3 belgian_masked_sites''' '''the_proteins = ['ORF1a', 'S', 'S'] #for prot, pos in zip(the_proteins,belgian_masked_sites ) masked_dict = {'ORF1a':4378, 'S':942 } masked_dict''' '''for aa_file in amino_acid_files: protein = aa_file.split('_')[-1].split('.')[0] print('protein:', protein) old_aa_file = '../PUFFIN/proteins/v4/aligned_protein_'+protein+'.fasta' with open(old_aa_file, "rt") as handle: old_records = list(SeqIO.parse(handle, "fasta")) n_belg = 0 no_nodes = [] for r in old_records: if 'Belgium' in r.id: n_belg+=1 if 'NODE_' not in r.id: no_nodes.append(r) old_records = no_nodes print('reference sequences number belgian', n_belg) for ind, r in enumerate(old_records): if r.id == 'Wuhan/IPBCAMS-WH-01/2019':#'Wuhan-Hu-1/2019':#'Wuhan/WH01/2019':#'Wuhan/IPBCAMS-WH-01/2019': print(ind) ref_seq_ind = ind ref_seq_old = str(old_records[ref_seq_ind].seq)[:-1] # this sequence does not have these same masking positions applied. curr_aa_file = '../PUFFIN/proteins/v6/cleaned_aligned_protein_'+protein+'.fasta' with open(curr_aa_file, "rt") as handle: records = list(SeqIO.parse(handle, "fasta")) belg_seqs=[] for ind, r in enumerate(records): if 'Belgium' in r.id: belg_seqs.append(r.seq) print('num belg seqs', len(belg_seqs)) if protein in masked_dict.keys(): mask_pos = masked_dict[protein] print('reference value',ref_seq_old[mask_pos] ) old_records= np.asarray(old_records) unique, counts = np.unique(old_records[:, mask_pos], return_counts=True) count_dict = dict(zip(unique, counts)) print('reference values', count_dict) belg_seqs = np.asarray(belg_seqs) unique, counts = np.unique(belg_seqs[:, mask_pos], return_counts=True) count_dict = dict(zip(unique, counts)) print('belgian masked values', count_dict) records = np.asarray(records) unique, counts = np.unique(records[:, mask_pos], return_counts=True) count_dict = dict(zip(unique, counts)) print('all masked sequences', count_dict)''' '''unique, counts = np.unique(belg_seqs[:, mask_pos], return_counts=True) count_dict = dict(zip(unique, counts)) count_dict''' ###Output _____no_output_____ ###Markdown Saving out these cleaned sequences ###Code # # write out the dropped sequences. # from Bio import SeqIO # for aa_file in amino_acid_files: # protein = aa_file.split('_')[-1].split('.')[0] # #print('protein:', protein) # with open(aa_file, "rt") as handle: # records = list(SeqIO.parse(handle, "fasta")) # # getting rid of the node sequences! # no_nodes = [] # for r in records: # if 'NODE_' not in r.id: # no_nodes.append(r) # records = no_nodes # clean_records = [] # for r_ind, r in enumerate(records): # if drop_seq[r_ind]==False: # clean_records.append(r) # print(len(clean_records)) # with open("../PUFFIN/proteins/v6_cleaned/aligned_protein_"+protein+".fasta", "w") as output_handle: # SeqIO.write(clean_records, output_handle, "fasta") # amino_acid_files ###Output _____no_output_____ ###Markdown Computing the Entropy in a Parallelized Fashion ###Code def computeWindows(aa_file): print('computeWindows!!!') print('aa_file = ', aa_file) ## Alex def entropy_calc(x): # where x is a list of numbers. summ = 0 nc = np.sum(x) for e in x: p = e/nc summ += pnp.log2(p) return -summ window_sizes = list(range(8,12)) + list(range(13,26)) # why were sliding windows of 12 excluded? -- Alex #df = dict() # will store the results. protein_res = [] protein = aa_file.split('_')[-1].split('.')[0] print('protein:', protein) with open(aa_file, "rt") as handle: records = list(SeqIO.parse(handle, "fasta")) # getting rid of the node sequences! no_nodes = [] for r in records: if 'NODE_' not in r.id: no_nodes.append(r) records = no_nodes print('size of records', len(records)) # getting the reference sequence for ind, r in enumerate(records): if r.id == 'Wuhan-Hu-1/2019':#'Wuhan/WH01/2019':#'Wuhan/IPBCAMS-WH-01/2019': ref_seq_ind = ind ref_seq = str(records[ref_seq_ind].seq) seqs = np.array(records) if seqs[ref_seq_ind, -1]=='': # ignore the stop code at the end. print('removing the stop codon at the end', protein) ref_seq = ref_seq[:-1] seqs = seqs[:, :-1] for window in window_sizes: print('window size', window) window_res = [] # get epitope based column slices. gives a list of epi columns. epi_columns = [seqs[:,i:i+window] for i in range(seqs.shape[1]-window+1)] for col_ind, col in enumerate(epi_columns): # count all of the unique epitopes here. #first need to convert each of the columns into strings: col = np.asarray( [ ''.join(col[i,:]) for i in range(col.shape[0]) ]) unique, counts = np.unique(col, return_counts=True) ent = entropy_calc(counts) # useful for percentages ref_epitope = col[ref_seq_ind] count_dict = dict(zip(unique, counts)) # -1 for no self count. percentage mutated. perc = 1 - ((count_dict[ref_epitope]-1)/(np.sum(counts)-1)) # start pos, window size, entropy window_res.append([protein, ref_epitope, col_ind, window, ent, perc]) protein_res += window_res df = pd.DataFrame(protein_res) df.columns = ['protein', 'epitope', 'start_pos', 'epi_len', 'entropy', 'perc_mutated'] # df.to_csv('../data/processed/epitope_calcs/Epitope_calc_'+protein+'.csv' , index=False) df.to_csv('data/processed/epitope_calcs_larger/Epitope_calc_'+protein+'.csv' , index=False) #df[protein] = protein_res ### Check that these are the larger_version_modified files amino_acid_files # # check these are the right AA files: # # amino_acid_files = !ls ../PUFFIN/proteins/v6_cleaned/_protein_ # amino_acid_files = !ls data/v6_cleaned/_protein_ # amino_acid_files # # len(amino_acid_files) # from multiprocessing import Process, Queue, cpu_count, Pool from multiprocess import Process, Queue, cpu_count, Pool import time ncores = 7 start_time = time.time() ### uncomment this to run it, commented for safety reasons -- Alex # multicore generate new samples print('Starting pooling!!!') p = Pool(ncores) p.map(computeWindows, amino_acid_files) p.close() print('all processes are done!, trying to join together all of the files') for ind, aa_file in enumerate(amino_acid_files): protein = aa_file.split('_')[-1].split('.')[0] # temp = pd.read_csv('../data/processed/epitope_calcs/Epitope_calc_'+protein+'.csv') temp = pd.read_csv('data/processed/epitope_calcs_larger/Epitope_calc_'+protein+'.csv') if ind == 0: df = temp else: df = df.append(temp) print('Total run time in minutes: '+str((time.time()-start_time)/60)) # df.to_csv('../data/processed/parallelized_epitope_entropies.csv', index=False) df.to_csv('data/processed/parallelized_epitope_entropies_larger.csv', index=False) ###Output Starting pooling!!! computeWindows!!!computeWindows!!!computeWindows!!! computeWindows!!! aa_file = computeWindows!!!aa_file = aa_file = computeWindows!!! computeWindows!!! aa_file = data/larger_version_modified/aligned_protein_M.fasta aa_file = data/larger_version_modified/aligned_protein_E.fasta aa_file = data/larger_version_modified/aligned_protein_N.fasta aa_file = protein:data/larger_version_modified/aligned_protein_ORF10.fasta data/larger_version_modified/aligned_protein_ORF1a.fastaprotein: data/larger_version_modified/aligned_protein_ORF1b.fastaprotein: data/larger_version_modified/aligned_protein_ORF3a.fastaMprotein:E protein: protein:Nprotein:ORF1aORF10 ORF1bORF3a size of records size of records12789 12789 size of records 12789 size of records 12789 size of records 12789 size of records removing the stop codon at the end12789 ORF10 window size 8 removing the stop codon at the end E size of recordswindow size 127898 removing the stop codon at the end M window size 8 window size 9 removing the stop codon at the end ORF3a window size 8 window size 10 removing the stop codon at the end N window size 8 window size 9 window size 11 window size 13 window size 10 window size 14 window size 15 window size 11 window size 9 window size 16 window size 17 window size 9 window size 13 window size 18 window size 19 window size 14 window size 20 window size 21 window size 22 removing the stop codon at the end ORF1b window size 8 window size 9 window size 10 window size 15 window size 23 window size 24 window size 25 window size 16 window size 10 computeWindows!!! aa_file = data/larger_version_modified/aligned_protein_ORF6.fasta protein: ORF6 size of records 12789 removing the stop codon at the end ORF6 window size 8 window size 17 window size 9 window size 11 window size 10 window size 8 window size 18 window size 11 window size 13 window size 19 window size 11 window size 10 window size 14 window size 13 window size 20 window size 15 window size 16 window size 21 window size 17 window size 22 window size 18 window size 13 window size 14 window size 19 window size 23 window size 20 window size 11 window size 24 window size 21 window size 22 window size 25 window size 15 window size 14 window size 23 computeWindows!!! aa_file = data/larger_version_modified/aligned_protein_ORF7a.fasta protein: ORF7a size of records 12789 removing the stop codon at the end ORF7a window size 8 window size 24 window size 25 window size 9 computeWindows!!! aa_file = data/larger_version_modified/aligned_protein_ORF7b.fasta protein: ORF7b size of records 12789 removing the stop codon at the end ORF7b window size 8 window size 13 window size 16 window size 10 window size 9 window size 10 window size 15 window size 11 window size 13 window size 11 window size 14 window size 15 window size 16 window size 13 window size 17 window size 17 window size 18 window size 19 window size 14 window size 20 window size 16 window size 21 window size 22 window size 14 window size 23 window size 15 window size 24 window size 18 window size 25 computeWindows!!! aa_file = data/larger_version_modified/aligned_protein_ORF8.fasta protein: ORF8 size of records 12789 removing the stop codon at the end ORF8 window size 8 window size 16 window size 9 window size 17 window size 17 window size 10 window size 19 window size 18 window size 11 window size 15 window size 9 window size 13 window size 19 window size 18 window size 14 window size 20 window size 20 window size 15 window size 21 window size 16 window size 16 window size 19 window size 21 window size 22 window size 17 window size 18 window size 23 window size 22 window size 19 window size 20 window size 24 window size 17 window size 20 window size 25 window size 23 window size 21 computeWindows!!! aa_file = data/larger_version_modified/aligned_protein_ORF9b.fasta protein: ORF9b size of records 12789 window size 21 removing the stop codon at the end ORF9b window size 8 window size 22 window size 9 window size 10 window size 11 window size 23 window size 24 window size 18 window size 13 window size 9 window size 22 window size 24 window size 14 window size 15 window size 25 window size 25 window size 16 window size 17 computeWindows!!! aa_file = data/larger_version_modified/aligned_protein_S.fasta protein: S size of records 12789 window size 23 window size 18 window size 19 removing the stop codon at the end S window size window size8 19 window size 20 window size 10 window size 21 window size 24 window size 22 window size 23 window size 20 window size 24 window size 25 window size 25 window size 9 window size 21 window size 22 window size 10 window size 23 window size 11 window size 24 window size 10 window size 11 window size 25 window size 13 window size 13 window size 14 window size 11 window size 15 window size 14 window size 16 window size 17 window size 15 window size 13 window size 18 window size 19 window size 16 window size 20 window size 14 window size 21 window size 17 window size 22 window size 15 window size 18 window size 23 window size 24 window size 19 window size 25 window size 16 window size 20 window size 17 window size 21 window size 22 window size 18 window size 23 window size 19 window size 24 window size 20 window size 25 window size 21 window size 22 window size 23 window size 24 window size 25 all processes are done!, trying to join together all of the files Total run time in minutes: 103.48027988274892 ###Markdown Can ignore all of the following now. Looking at the mutation differences between Hu-1 and original sequence* This analysis motivated my suggestion that we should in fact be using the Hu-1 sequence as we were missing a couple potential epitopes we could have chosen. ###Code #df = pd.read_csv('../data/processed/epitope_entropies.csv') #df.head() df.shape df.head() plt.scatter(df[df.protein=='ORF1a'].start_pos, df[df.protein=='ORF1a'].perc_mutated) plt.axhline(0.001, c='red') #plt.xlim(2690, 2727) plt.show() # mutation positions = mut_pos = [2707, 2907] mut_pos = [2707, 2907] orf1a = df[df.protein=='ORF1a'] orf1a.shape orf1a['crosses_mutation'] = 0 for pos in mut_pos: seq_start = orf1a['start_pos'] seq_end = orf1a['start_pos']+(orf1a['epi_len']-1) in_region = np.logical_and(pos >= seq_start,pos <= seq_end) orf1a.loc[in_region,'crosses_mutation'] = 1 #apply protein mask and then epitope mask orf1a[orf1a.crosses_mutation.astype(bool)].shape (orf1a[orf1a.crosses_mutation.astype(bool)].perc_mutated > 0.001).sum() below_thresh_mask = orf1a[orf1a.crosses_mutation.astype(bool)].perc_mutated <= 0.001 below_thresh_mask.sum() orf1a[orf1a.crosses_mutation.astype(bool)][below_thresh_mask] orf1a[orf1a.crosses_mutation.astype(bool)][below_thresh_mask].start_pos.unique() # epitopes that are mutated according to our data but not the other papers. danger_epitopes = orf1a[orf1a.crosses_mutation.astype(bool)][below_thresh_mask].epitope.to_list() import pickle other_epitopes = pickle.load(open('../data/processed/Other_Methods_Proposed_Peptides.pkl', 'rb')) len(other_epitopes.keys()) for k, v in other_epitopes.items(): if len(list(set(danger_epitopes).intersection(set(v))))>0: print('Missing', k, list(set(danger_epitopes).intersection(set(v)))) ###Output Missing MHC_1_Grifoni-LaJolla ['KLIEYTDFA'] Missing MHC_1_Nerli-UCSC ['SKLIEYTDFA', 'KLIEYTDFAT'] ###Markdown It is only the second mutation position that is below the mutation threshold ###Code '''for ind, aa_file in enumerate(amino_acid_files): protein = aa_file.split('_')[-1].split('.')[0] temp = pd.read_csv('../data/processed/epitope_calcs/Epitope_calc_'+protein+'.csv') if ind == 0: df = temp else: df = df.append(temp)''' df.head() df.head() df.shape df.tail() df.reset_index(inplace=True) df.drop('index', axis=1, inplace=True) plt.hist(df.entropy) (df.perc_mutated==0.0).sum() df.shape plt.hist(df.perc_mutated) (df.perc_mutated <0.001).sum() (df.entropy == 0.0).sum() (df.perc_mutated==0).sum() df.head() df.to_csv('../data/processed/Hu1_epitope_entropies.csv', index=False) ###Output _____no_output_____ ###Markdown Looking at and flagging the masked positions ###Code df = pd.read_csv('../data/processed/epitope_entropies.csv') df.head() df.masked_position.sum() df.groupby('protein').masked_position.sum() df[df.protein=='ORF1a'].groupby(['protein', 'epi_len']).masked_position.sum() df.epi_len.unique() np.arange(8,26).sum()-12 # need to mask out all of these positions by setting them to 100% prob of mutating. and have #anything within them be set to prob of 999. # this will help flag them as being unique. # actually just create a new masked column feature. masked_nt_positions = [18529, 29849, 29851, 29853, 13402, 24389, 24390] # getting the nt regions with open('nextstrain_covid19_ref_protein_pos.txt', 'r') as f: lines = f.readlines() take_next_line = False regions = [] proteins = [] for l in lines: if 'CDS ' in l: regions.append(l.split('CDS')[1].strip()) take_next_line=True elif take_next_line: proteins.append(l.split('gene=')[1].strip().strip('"')) take_next_line=False protein_regions = {p:r for p,r in zip(proteins, regions)} protein_regions in_proteins = [18529-13468, 13402-266, 24389-21563, 24390-21563] 4400-4378 in_proteins = np.floor(np.asarray(in_proteins)/3) in_proteins the_proteins = ['ORF1b', 'ORF1a', 'S', 'S'] df.head() df['masked_position'] = 0 for pos, protein in zip(in_proteins, the_proteins): protein_mask = protein==df.protein # select relevant protein seq_start = df['start_pos'] seq_end = df['start_pos']+(df['epi_len']-1) in_region = np.logical_and(pos >= seq_start,pos <= seq_end) #if all 4 matter::: #. front_in_region = np.logical_and(glyco_start >= seq_start,glyco_start <= seq_end) # end_in_region = np.logical_and(glyco_end >= seq_start,glyco_end <= seq_end) #. in_region = np.logical_or(front_in_region, end_in_region) in_region_and_protein = np.logical_and(protein_mask,in_region) df.loc[in_region_and_protein,'masked_position'] = 1 #apply protein mask and then epitope mask df[np.logical_and(df.protein=='ORF1b', np.logical_and(df.start_pos==1686,df.epi_len==8 ))] df.head() df.tail() len(df.protein.unique()) df.shape len(df.epi_len.unique()) test = pd.read_csv('../data/processed/pre_cleaning_AllEpitopeFeatures.csv') test.head() test.tail() test.shape # run for a single example. df.head() len(df.protein.unique()) df[df.protein=='E'].tail() def entropy_calc(x): # where x is a list of numbers. summ = 0 nc = np.sum(x) for e in x: p = e/nc summ += p*np.log2(p) return -summ window_sizes = list(range(8,12)) + list(range(13,26)) aa_file = amino_acid_files[0] protein = aa_file.split('_')[-1].split('.')[0] print('protein:', protein) with open(aa_file, "rt") as handle: records = list(SeqIO.parse(handle, "fasta")) # getting rid of the node sequences! no_nodes = [] for r in records: if 'NODE_' not in r.id: no_nodes.append(r) records = no_nodes # getting the reference sequence for ind, r in enumerate(records): if r.id == 'Wuhan/IPBCAMS-WH-01/2019':#'Wuhan-Hu-1/2019':#'Wuhan/WH01/2019':#'Wuhan/IPBCAMS-WH-01/2019': ref_seq_ind = ind ref_seq = str(records[ref_seq_ind].seq)[:-1] seqs = np.array(records)[:, :-1] # ignore the stop code at the end. for window in window_sizes: print('window size', window) window_res = [] # get epitope based column slices. gives a list of epi columns. epi_columns = [seqs[:,i:i+window] for i in range(seqs.shape[1]-window+1)] for col_ind, col in enumerate(epi_columns): # count all of the unique epitopes here. #first need to convert each of the columns into strings: col = np.asarray( [ ''.join(col[i,:]) for i in range(col.shape[0]) ]) unique, counts = np.unique(col, return_counts=True) ent = entropy_calc(counts) # useful for percentages ref_epitope = col[ref_seq_ind] count_dict = dict(zip(unique, counts)) # -1 for no self count. percentage mutated. perc = 1 - ((count_dict[ref_epitope]-1)/(np.sum(counts)-1)) break break col epi_columns[0] unique counts ent ref_epitope count_dict perc df = pd.read_csv('../data/processed/epitope_entropies.csv') df.head() (df.entropy == 0.0).sum() seqs.shape len(ref_seq) len(epi_columns) protein_res[-5:] np.log2(len(seqs)) np.array(df['E'])[:,2]; plt.hist(np.array(df['E'])[:,2]) ###Output _____no_output_____
pipeline-components/create-sparkapplication/kubeflow-pipeline-create-SparkApplication.ipynb	###Markdown Install libs if necessary ###Code %%capture # Install the SDK (Uncomment the code if the SDK is not installed before) !python3 -m pip install 'kfp>=0.1.31' --quiet # Restart the kernel for changes to take effect ###Output _____no_output_____ ###Markdown Load pipeline components ###Code from kfp.components import load_component_from_file create_sparkapplication_op = load_component_from_file('component.yaml') help(create_sparkapplication_op) ###Output Help on function create_sparkapplication: create_sparkapplication() create_sparkapplication Create a SparkApplication CRD in the same k8s cluster for Spark-operator. ###Markdown Define the pipeline ###Code import kfp.dsl as dsl @dsl.pipeline( name='Integration of spark-operator', description='' ) def sparkapplication_pipeline(): create_sparkapplication_op() pipeline_func = sparkapplication_pipeline ###Output _____no_output_____ ###Markdown Compile the pipeline ###Code import kfp.compiler as compiler pipeline_filename = pipeline_func.__name__ + '.zip' compiler.Compiler().compile(pipeline_func, pipeline_filename) ###Output _____no_output_____ ###Markdown Run the pipeline ###Code #Specify pipeline argument values arguments = {} #Get or create an experiment and submit a pipeline run import kfp client = kfp.Client() experiment = client.create_experiment("test") #Submit a pipeline run run_name = pipeline_func.__name__ + ' run' run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments) ###Output _____no_output_____
tests/test_jacobian_calculation.ipynb	###Markdown Objective: Test jacobian calculation in pythonFirst define the functionAutograd does not support assignment to index, so construct $\frac{dy}{dt}$ using stack instead of indexing ###Code def f(x1,x2,x3,x4,x5,x6): # State is { x,y,theta, vx,vy,omega } dydt = [] dydt.append( x4*2 + 2x6 ) dydt.append( 5x5 ) dydt.append( x4 + 2x5 + 3x6 ) dydt.append( x12 + 2x2 ) dydt.append( x2*3 + 4x3 ) dydt.append( 0 ) return np.stack(dydt) ###Output _____no_output_____ ###Markdown Test that the calculation is what we expect ###Code x = np.ones(6) res1 = [] res2 = [] for i in range(6): res1.append( jacobian(f, argnum=i)(x) ) res2.append( make_jvp(f, argnum=i)(x)(1)[1] ) res1 = np.stack(res1, axis=1) res2 = np.stack(res2, axis=1) print('jacobian:\n', res1) print('make_jvp:\n',res2) make_jvp_reversemode(f, argnum=0)(1.0, 1.0, 1.0, 1.0, 1.0, 1.0)([1,0,0,0,0,0]) ###Output _____no_output_____ ###Markdown Compare speed of `Jacobian` vs `make_jvp`make_jvp is expected to be much faster because it is a fast forward-mode Jacobian `Jacobian` ###Code %%timeit x = np.zeros(6) J = [ jacobian(f, argnum=i) for i in range(6) ] for t in range(1000): res = [] for i in range(6): res.append( J[i](x) ) res = np.stack(res).T ###Output 2.98 s ± 31.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each) ###Markdown `make_jvp` ###Code %%timeit x = np.zeros(6) J = [ make_jvp_reversemode(f, argnum=i) for i in range(6) ] for t in range(1000): res = [] for i in range(6): res.append( J[i](x)(1)[1] ) res = np.stack(res).T ###Output _____no_output_____
10_high_level_functions.ipynb	###Markdown Higher Order Functions ###Code # Defined by the weather people def crude_good_enough(guess, x): if abs(guess * guess - x) < 3: return True else: return False def avg(a, b): return (a + b) / 2.0 def improve_guess(guess, x): return avg(guess, float(x)/guess) def sqrt(x, good_enough, guess=0.1): print "Trying:", guess, "-- Value:", guessguess if good_enough(guess, x): return guess else: guess = improve_guess(guess, x) return sqrt(x, good_enough, guess) sqrt(36, crude_good_enough) # By the nuclear reactor people def very_accurate_good_enough(guess, x): if abs(guess guess - x) < 0.00000000001: return True else: return False sqrt(36, very_accurate_good_enough) ###Output Trying: 0.1 -- Value: 0.01 Trying: 180.05 -- Value: 32418.0025 Trying: 90.1249722299 -- Value: 8122.51061945 Trying: 45.262208784 -- Value: 2048.66754401 Trying: 23.0287871495 -- Value: 530.325037577 Trying: 12.2960239699 -- Value: 151.192205469 Trying: 7.61189982741 -- Value: 57.9410189825 Trying: 6.17066836877 -- Value: 38.0771481174 Trying: 6.00236017319 -- Value: 36.0283276487 Trying: 6.00000046402 -- Value: 36.0000055682 Trying: 6.0 -- Value: 36.0 ###Markdown Handling Complexity Recall the fib method we wrote earlier. ###Code def fib(n): if n <= 1: return n else: return fib(n-2) + fib(n-1) %time fib(40) ###Output CPU times: user 53.5 s, sys: 221 ms, total: 53.7 s Wall time: 54 s ###Markdown This will take a bit of time so let's see what's wrong. We can use the concept of higher-order functions to tackle this issue. ###Code def logger(f): def wrapper(n): print "I'm going to call a function." v = f(n) print "The function returned: ", v return v return wrapper logged_fib = logger(fib) # remember, fib is just a name! logged_fib(4) ###Output I'm going to call a function. The function returned: 3 ###Markdown Now that we can do stuff before the `fib` call, let's see if we can save some values that are repeatedly needed. ###Code def memoize(f): mem = {} def wrapper(x): if x not in mem: mem[x] = f(x) return mem[x] return wrapper fib = memoize(fib) %time fib(40) ###Output CPU times: user 93 µs, sys: 35 µs, total: 128 µs Wall time: 107 µs ###Markdown That's about 450,000 times speedup! Syntactic Sugar We can write this in another way. ###Code def memoize(f): mem = {} def wrapper(x): if x not in mem: mem[x] = f(x) return mem[x] return wrapper @memoize # this is called a decorator def fib(n): if n <= 1: return n else: return fib(n-1) + fib(n-2) fib(50) ###Output _____no_output_____
3-Visualization/2-Seaborn/6-Style Und Farbgebung.ipynb	###Markdown Style und FarbgebungWir haben bereits einige Male die Anpassungsmöglichkeiten der Diagrammoptik in Seaborn genutzt. Lasst uns dies nun noch einmal formell betrachten: ###Code import seaborn as sns import matplotlib.pyplot as plt %matplotlib inline tips = sns.load_dataset('tips') ###Output _____no_output_____ ###Markdown StylesWir können bestimmte Styles festlegen: ###Code sns.countplot(x='sex',data=tips) sns.set_style('ticks') sns.countplot(x='sex',data=tips,palette='deep') ###Output _____no_output_____ ###Markdown Rahmen entfernen ###Code sns.countplot(x='sex',data=tips) sns.despine() sns.countplot(x='sex',data=tips) sns.despine(left=True) ###Output _____no_output_____ ###Markdown Größe und PerspektiveWir können die aus Matplotlib bekannte `plt.figure(figsize=(width,heigth)` nutzen, um die Größe von Seaborn Diagrammen zu ändern.Allerdings können wir die Größe und Perspektive der meisten Seaborn Diagramme auch über die Parameter `size` und `aspect` anpassen. Zum Beispiel: ###Code plt.figure(figsize=(12,3)) sns.countplot(x='sex',data=tips) sns.lmplot(x='total_bill',y='tip',size=2,aspect=4,data=tips) ###Output _____no_output_____ ###Markdown Skalierung und KontextDie `set_context()` Funktion erlaubt es uns Standardeigenschaften zu überschreiben: ###Code sns.set_context('poster',font_scale=4) sns.countplot(x='sex',data=tips,palette='coolwarm') ###Output _____no_output_____
notebooks-src/notebooks/R Examples/Search for Samples or Studies.ipynb	###Markdown Search for MGnify Studies or Samples, using MGnifyRThe [MGnify API](https://www.ebi.ac.uk/metagenomics/api/v1) returns data and relationships as JSON. [MGnifyR](https://github.com/beadyallen/MGnifyR) is a package to help you read MGnify data into your R analyses.This example shows you how to perform a search of MGnify Studies or SamplesYou can find all of the other "API endpoints" using the [Browsable API interface in your web browser](https://www.ebi.ac.uk/metagenomics/api/v1).This interface also lets you inspect the kinds of Filters that can be created for each list.This is an interactive code notebook (a Jupyter Notebook).To run this code, click into each cell and press the ▶ button in the top toolbar, or press `shift+enter`.--- ###Code library(IRdisplay) display_markdown(file = '../_resources/mgnifyr_help.md') ###Output _____no_output_____ ###Markdown Load packages: ###Code library(vegan) library(ggplot2) library(phyloseq) library(MGnifyR) mg <- mgnify_client(usecache = T, cache_dir = '/tmp/mgnify_cache') ###Output _____no_output_____ ###Markdown Contents- [Example: Find Polar Samples](Example:-find-Polar-samples)- [Example: Find Wastewater Samples](Example:-find-Wastewater-studies)- [More Sample filters](More-Sample-filters)- [More Study filters](More-Study-filters)- [Example: Filtering Samples both API-side and client-side](Example:-adding-additional-filters-to-the-data-frame) Documentation for `mgnify_query` ###Code ?mgnify_query ###Output _____no_output_____ ###Markdown Example: find Polar samples ###Code samps_np <- mgnify_query(mg, "samples", latitude_gte=88, maxhits=-1) samps_sp <- mgnify_query(mg, "samples", latitude_lte=-88, maxhits=-1) samps_polar <- rbind(samps_np, samps_sp) head(samps_polar) ###Output _____no_output_____ ###Markdown Example: find Wastewater studies ###Code studies_ww <- mgnify_query(mg, "studies", biome_name="wastewater", maxhits=-1) head(studies_ww) ###Output _____no_output_____ ###Markdown More Sample filters By location ###Code more_northerly_than <- mgnify_query(mg, "samples", latitude_gte=88, maxhits=-1) more_southerly_than <- mgnify_query(mg, "samples", latitude_lte=-88, maxhits=-1) more_easterly_than <- mgnify_query(mg, "samples", longitude_gte=170, maxhits=-1) more_westerly_than <- mgnify_query(mg, "samples", longitude_lte=170, maxhits=-1) at_location <- mgnify_query(mg, "samples", geo_loc_name="usa", maxhits=-1) ###Output _____no_output_____ ###Markdown By biome ###Code biome_within_wastewater <- mgnify_query(mg, "samples", biome_name="wastewater", maxhits=-1) ###Output _____no_output_____ ###Markdown By metadataThere are a large number of metadata key:value pairs, because these are author-submitted, along with the samples, to the ENA archive.If you know how to specify the metadata key:value query for the samples you're interested in, you can use this form to find matching Samples: ###Code from_ex_smokers <- mgnify_query(mg, "samples", metadata_key="smoker", metadata_value="ex-smoker", maxhits=-1) ###Output _____no_output_____ ###Markdown To find `metadata_key`s and values, it is best to browse the [interactive API Browser](https://www.ebi.ac.uk/metagenomics/v1/samples), and use the `Filters` button to construct queries interactively at first. --- More Study filters By Centre Name ###Code from_smithsonian <- mgnify_query(mg, "studies", centre_name="Smithsonian", maxhits=-1) ###Output _____no_output_____ ###Markdown --- Example: adding additional filters to the data frame First, fetch some samples from the Lentic biome. We can specify the entire Biome lineage, too. ###Code lentic_samples <- mgnify_query(mg, "samples", biome_name="root:Environmental:Aquatic:Lentic", usecache=T) ###Output _____no_output_____ ###Markdown Not, also filter by depth within the returned results, using normal R syntax. ###Code depth_numeric = as.numeric(lentic_samples$depth) # We must convert data from MGnifyR (always strings) to numerical format. depth_numeric[is.na(depth_numeric)] = 0.0 # If depth data is missing, assume it is surface-level. lentic_subset = lentic_samples[depth_numeric >=25 & depth_numeric <=50,] # Filter to samples collected between 25m and 50m down. lentic_subset ###Output _____no_output_____
Spark Algorithms - Linear Regression (Documentation Example).ipynb	###Markdown Linear Regression (Documentation Example)The documentation example is available here: https://spark.apache.org/docs/latest/ml-classification-regression.html. Objective: First, what we'll do is go through this example. This allows us to read from the documentation, understand it, then apply it. This dataset is quite unrealistic, but it is necessary in understanding some of the basic elements of using Spark's MLlib library. More relevant datasets are used in the advanced linear regression exercise. ###Code # Must be included at the beginning of each new notebook. Remember to change the app name. import findspark findspark.init('/home/ubuntu/spark-2.1.1-bin-hadoop2.7') import pyspark from pyspark.sql import SparkSession spark = SparkSession.builder.appName('linear_regression_docs').getOrCreate() # If you're getting an error with numpy, please type 'sudo pip install numpy --user' into the EC2 console. from pyspark.ml.regression import LinearRegression # Load model training data. Location of the data may be different. training = spark.read.format("libsvm").load("Datasets/sample_linear_regression_data.txt") ###Output _____no_output_____ ###Markdown The libsvm format might be new to you. It's not used often, and may not be relevant to your dataset. However, it's used extensively throughout the Spark documentation. Let's see what the training data looks like: ###Code # Visualise the training data format. training.show() ###Output +-------------------+--------------------+ \| label\| features\| +-------------------+--------------------+ \| -9.490009878824548\|(10,[0,1,2,3,4,5,...\| \| 0.2577820163584905\|(10,[0,1,2,3,4,5,...\| \| -4.438869807456516\|(10,[0,1,2,3,4,5,...\| \|-19.782762789614537\|(10,[0,1,2,3,4,5,...\| \| -7.966593841555266\|(10,[0,1,2,3,4,5,...\| \| -7.896274316726144\|(10,[0,1,2,3,4,5,...\| \| -8.464803554195287\|(10,[0,1,2,3,4,5,...\| \| 2.1214592666251364\|(10,[0,1,2,3,4,5,...\| \| 1.0720117616524107\|(10,[0,1,2,3,4,5,...\| \|-13.772441561702871\|(10,[0,1,2,3,4,5,...\| \| -5.082010756207233\|(10,[0,1,2,3,4,5,...\| \| 7.887786536531237\|(10,[0,1,2,3,4,5,...\| \| 14.323146365332388\|(10,[0,1,2,3,4,5,...\| \|-20.057482615789212\|(10,[0,1,2,3,4,5,...\| \|-0.8995693247765151\|(10,[0,1,2,3,4,5,...\| \| -19.16829262296376\|(10,[0,1,2,3,4,5,...\| \| 5.601801561245534\|(10,[0,1,2,3,4,5,...\| \|-3.2256352187273354\|(10,[0,1,2,3,4,5,...\| \| 1.5299675726687754\|(10,[0,1,2,3,4,5,...\| \| -0.250102447941961\|(10,[0,1,2,3,4,5,...\| +-------------------+--------------------+ only showing top 20 rows ###Markdown This is the format that Spark needs to run a machine learning algorithm. One column with the name "label" and the other with the name "features". The label represents the output/answer/predictor (for example, house value), while the features represent the inputs.The "label" column then needs to have the numerical label, either a regression numerical value, or a numerical value that matches to a classification grouping. The feature column has inside of it a vector of all the features that belong to that row. Usually what we end up doing is combining the various feature columns we have into a single 'features' column using the data transformations from the previous lab. ###Code # These are the default values: # featuresCol: What is the features column named? # labelCol: What is the label column named? # predictionCol: What is the name of the actual prediction? lr = LinearRegression(featuresCol='features', labelCol='label', predictionCol='prediction') # Fit/train the model. Fit the model onto the training data. lrModel = lr.fit(training) # Print the coefficients and intercept for linear regression print("Coefficients: {}".format(str(lrModel.coefficients))) # For each feature... print('\n') print("Intercept:{}".format(str(lrModel.intercept))) ###Output Coefficients: [0.0073350710225801715,0.8313757584337543,-0.8095307954684084,2.441191686884721,0.5191713795290003,1.1534591903547016,-0.2989124112808717,-0.5128514186201779,-0.619712827067017,0.6956151804322931] Intercept:0.14228558260358093 ###Markdown You can use the summary attribute to get even more information. ###Code # Summarize the model over the training set and print out some metrics. trainingSummary = lrModel.summary ###Output _____no_output_____ ###Markdown This has a lot of information, here are a few examples: ###Code trainingSummary.residuals.show() # Print Root Mean Squared Error. print("RMSE: {}".format(trainingSummary.rootMeanSquaredError)) # Print R-Squared. print("r2: {}".format(trainingSummary.r2)) ###Output +-------------------+ \| residuals\| +-------------------+ \|-11.011130022096554\| \| 0.9236590911176538\| \|-4.5957401897776675\| \| -20.4201774575836\| \|-10.339160314788181\| \|-5.9552091439610555\| \|-10.726906349283922\| \| 2.122807193191233\| \| 4.077122222293811\| \|-17.316168071241652\| \| -4.593044343959059\| \| 6.380476690746936\| \| 11.320566035059846\| \|-20.721971774534094\| \| -2.736692773777401\| \| -16.66886934252847\| \| 8.242186378876315\| \|-1.3723486332690233\| \|-0.7060332131264666\| \|-1.1591135969994064\| +-------------------+ only showing top 20 rows RMSE: 10.16309157133015 r2: 0.027839179518600154 ###Markdown Train/Test SplitsBased on our nine-step process, we've actually missed a fundamental step following the Spark documentation! We never split our data into a training and testing set. Instead we've trained the model using all of our data, which you know by now is not a good idea.Luckily, Spark DataFrames has a convienent method of splitting the data. Let's see it: ###Code # Remember, data is stored in the parent directory. all_data = spark.read.format("libsvm").load("Datasets/sample_linear_regression_data.txt") # Pass in the split between training/test as a list. # This is based on your test-designs, but generally 70/30 or 60/40 splits are used. # Depending on how much data you have and how unbalanced it is. train_data,test_data = all_data.randomSplit([0.7,0.3]) # Let's check out our training data. train_data.show() # Let's check out the count (348). train_data.describe().show() # And our test data. test_data.show() # Let's check out the count (153, approximately a 70/30 split). test_data.describe().show() # Now we only train the train_data. correct_model = lr.fit(train_data) # Now we can directly get a .summary object using the evaluate method. test_results = correct_model.evaluate(test_data) # And generate some basic evaluation metrics. test_results.residuals.show() print("RMSE: {}".format(test_results.rootMeanSquaredError)) ###Output +-------------------+ \| residuals\| +-------------------+ \|-26.807162722626384\| \| -21.59371356869086\| \| -21.9464814828004\| \|-20.784412270368783\| \|-17.815241924516652\| \|-16.627443595267554\| \| -17.05982262760015\| \| -17.94762687581216\| \|-15.654922779606231\| \|-15.349049359554938\| \|-15.695588171296139\| \| -16.38920332633152\| \|-12.990305637371522\| \|-12.854812920968358\| \|-14.716372011315832\| \|-10.767144737423443\| \|-13.030990836368117\| \|-11.164342817225434\| \| -8.477199122731658\| \|-13.573983042656442\| +-------------------+ only showing top 20 rows RMSE: 10.504373377842208
distracted-driver-detection.ipynb	###Markdown Problem Statment Given a dataset of 2D dashboard camera images, an algorithm needs to be developed to classify each driver's behaviour and determine if they are driving attentively, wearing their seatbelt, or taking a selfie with their friends in the backseat etc..? This can then be used to automatically detect drivers engaging in distracted behaviours from dashboard cameras.Following are needed tasks for the development of the algorithm:1. Download and preprocess the driver images1. Build and train the model to classify the driver images1. Test the model and further improve the model using different techniques. Data ExplorationThe provided data set has driver images, each taken in a car with a driver doing something in the car (texting, eating, talking on the phone, makeup, reaching behind, etc). This dataset is obtained from Kaggle(State Farm Distracted Driver Detection competition).Following are the file descriptions and URL’s from which the data can be obtained :1. imgs.zip - zipped folder of all (train/test) images1. sample_submission.csv - a sample submission file in the correct format1. driver_imgs_list.csv - a list of training images, their subject (driver) id, and1. class id1. driver_imgs_list.csv.zip1. sample_submission.csv.zipThe 10 classes to predict are:1. c0: safe driving1. c1: texting - right1. c2: talking on the phone - right1. c3: texting - left1. c4: talking on the phone - left1. c5: operating the radio1. c6: drinking1. c7: reaching behind1. c8: hair and makeup1. c9: talking to passengerThere are 102150 total images. Data PreprocessingPreprocessing of data is carried out before model is built and training process is executed. Following are the steps carried out during preprocessing.1. Initially the images are divided into training and validation sets.1. The images are resized to a square images i.e. 64 x 64 (224 x 224 if ram > 32 gb) pixels.1. All three channels were used during training process as these are color images.1. The images are normalised by dividing every pixel in every image by 255.1. To ensure the mean is zero a value of 0.5 is subtracted. ImplementationA standard CNN architecture was initially created and trained. We have created 4 convolutional layers with 4 max pooling layers in between. Filters were increased from 64 to 512 in each of the convolutional layers. Also dropout was used along with flattening layer before using the fully connected layer. Altogether the CNN has 2 fully connected layers. Number of nodes in the last fully connected layer were setup as 10 along with softmax activation function. Relu activation function was used for all other layers.Xavier initialization was used in each of the layers. ###Code # This Python 3 environment comes with many helpful analytics libraries installed # It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python # For example, here's several helpful packages to load import os import pandas as pd import pickle import numpy as np import seaborn as sns from sklearn.datasets import load_files from keras.utils import np_utils import matplotlib.pyplot as plt # Pretty display for notebooks %matplotlib inline from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D from keras.layers import Dropout, Flatten, Dense from keras.models import Sequential from keras.utils.vis_utils import plot_model from keras.callbacks import ModelCheckpoint from keras.utils import to_categorical from sklearn.metrics import confusion_matrix from keras.preprocessing import image from tqdm import tqdm import seaborn as sns from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score ###Output _____no_output_____ ###Markdown Defining the train,test and model directoriesWe will create the directories for train,test and model training paths if not present ###Code TEST_DIR = os.path.join(os.getcwd(),"/kaggle/input/state-farm-distracted-driver-detection/imgs","test") TRAIN_DIR = os.path.join(os.getcwd(),"/kaggle/input/state-farm-distracted-driver-detection/imgs","train") MODEL_PATH = os.path.join(os.getcwd(),"model","self_trained") PICKLE_DIR = os.path.join(os.getcwd(),"pickle_files") if not os.path.exists(TEST_DIR): print("Testing data does not exists") if not os.path.exists(TRAIN_DIR): print("Training data does not exists") if not os.path.exists(MODEL_PATH): print("Model path does not exists") os.makedirs(MODEL_PATH) print("Model path created") if not os.path.exists(PICKLE_DIR): os.makedirs(PICKLE_DIR) ###Output _____no_output_____ ###Markdown Data Preparationcsv files for saving path location of different files and their classes ###Code def create_csv(DATA_DIR,filename): class_names = os.listdir(DATA_DIR) data = list() if(os.path.isdir(os.path.join(DATA_DIR,class_names[0]))): for class_name in class_names: file_names = os.listdir(os.path.join(DATA_DIR,class_name)) for file in file_names: data.append({ "Filename":os.path.join(DATA_DIR,class_name,file), "ClassName":class_name }) else: class_name = "test" file_names = os.listdir(DATA_DIR) for file in file_names: data.append(({ "FileName":os.path.join(DATA_DIR,file), "ClassName":class_name })) data = pd.DataFrame(data) data.to_csv(os.path.join(os.getcwd(),"csv_files",filename),index=False) CSV_FILES_DIR = os.path.join(os.getcwd(),"csv_files") if not os.path.exists(CSV_FILES_DIR): os.makedirs(CSV_FILES_DIR) create_csv(TRAIN_DIR,"train.csv") create_csv(TEST_DIR,"test.csv") data_train = pd.read_csv(os.path.join(os.getcwd(),"csv_files","train.csv")) data_test = pd.read_csv(os.path.join(os.getcwd(),"csv_files","test.csv")) data_train.info() data_train['ClassName'].value_counts() ###Output _____no_output_____ ###Markdown Data exploration ###Code nf = data_train['ClassName'].value_counts(sort=False) labels = data_train['ClassName'].value_counts(sort=False).index.tolist() y = np.array(nf) width = 1/1.5 N = len(y) x = range(N) fig = plt.figure(figsize=(20,15)) ay = fig.add_subplot(211) plt.xticks(x, labels, size=15) plt.yticks(size=15) ay.bar(x, y, width, color="blue") plt.title('Bar Chart',size=25) plt.xlabel('classname',size=15) plt.ylabel('Count',size=15) plt.show() data_test.head() data_test.shape ###Output _____no_output_____ ###Markdown Observation* 22424 Train samples* 79726 Test samples* The training dataset is well balanced to a great extent and hence we need not do any downsampling of the data Converting into numerical valuesData preprocessing ###Code labels_list = list(set(data_train['ClassName'].values.tolist())) labels_id = {label_name:id for id,label_name in enumerate(labels_list)} print(labels_id) data_train['ClassName'].replace(labels_id,inplace=True) with open(os.path.join(os.getcwd(),"pickle_files","labels_list.pkl"),"wb") as handle: pickle.dump(labels_id,handle) labels = to_categorical(data_train['ClassName']) print(labels.shape) ###Output _____no_output_____ ###Markdown Further splitting data into training and test data ###Code from sklearn.model_selection import train_test_split xtrain,xtest,ytrain,ytest = train_test_split(data_train.iloc[:,0],labels,test_size = 0.2,random_state=42) ###Output _____no_output_____ ###Markdown Converting into 6464 imagesDue to ram limitations ###Code def path_to_tensor(img_path): # loads RGB image as PIL.Image.Image type img = image.load_img(img_path, target_size=(64, 64)) # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3) x = image.img_to_array(img) # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor return np.expand_dims(x, axis=0) def paths_to_tensor(img_paths): list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)] return np.vstack(list_of_tensors) from PIL import ImageFile ImageFile.LOAD_TRUNCATED_IMAGES = True # pre-process the data for Keras train_tensors = paths_to_tensor(xtrain).astype('float32')/255 - 0.5 valid_tensors = paths_to_tensor(xtest).astype('float32')/255 - 0.5 ###Output _____no_output_____ ###Markdown Defining model ###Code model = Sequential() # 64 conv2d filters with relu model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', input_shape=(64,64,3), kernel_initializer='glorot_normal')) model.add(MaxPooling2D(pool_size=2)) #Maxpool model.add(Conv2D(filters=128, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_normal')) model.add(MaxPooling2D(pool_size=2)) #Maxpool model.add(Conv2D(filters=256, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_normal')) model.add(MaxPooling2D(pool_size=2)) #Maxpool model.add(Conv2D(filters=512, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_normal')) model.add(MaxPooling2D(pool_size=2)) #Maxpool model.add(Dropout(0.5)) model.add(Flatten()) model.add(Dense(500, activation='relu', kernel_initializer='glorot_normal')) model.add(Dropout(0.5)) model.add(Dense(10, activation='softmax', kernel_initializer='glorot_normal')) model.summary() plot_model(model,to_file=os.path.join(MODEL_PATH,"model_distracted_driver.png"),show_shapes=True,show_layer_names=True) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) filepath = os.path.join(MODEL_PATH,"distracted-{epoch:02d}-{val_accuracy:.2f}.hdf5") checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max',period=1) callbacks_list = [checkpoint] epochs = 20 model_history = model.fit(train_tensors,ytrain,validation_data = (valid_tensors, ytest),epochs=epochs, batch_size=40, shuffle=True,callbacks=callbacks_list) ###Output _____no_output_____ ###Markdown Model performance graph ###Code fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 12)) ax1.plot(model_history.history['loss'], color='b', label="Training loss") ax1.plot(model_history.history['val_loss'], color='r', label="validation loss") ax1.set_xticks(np.arange(1, 25, 1)) ax1.set_yticks(np.arange(0, 1, 0.1)) ax2.plot(model_history.history['accuracy'], color='b', label="Training accuracy") ax2.plot(model_history.history['val_accuracy'], color='r',label="Validation accuracy") ax2.set_xticks(np.arange(1, 25, 1)) legend = plt.legend(loc='best', shadow=True) plt.tight_layout() plt.show() ###Output _____no_output_____ ###Markdown Model Analysis ###Code def print_confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=14): df_cm = pd.DataFrame( confusion_matrix, index=class_names, columns=class_names, ) fig = plt.figure(figsize=figsize) try: heatmap = sns.heatmap(df_cm, annot=True, fmt="d") except ValueError: raise ValueError("Confusion matrix values must be integers.") heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize) heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize) plt.ylabel('True label') plt.xlabel('Predicted label') fig.savefig(os.path.join(MODEL_PATH,"confusion_matrix.png")) return fig def print_heatmap(n_labels, n_predictions, class_names): labels = n_labels #sess.run(tf.argmax(n_labels, 1)) predictions = n_predictions #sess.run(tf.argmax(n_predictions, 1)) # confusion_matrix = sess.run(tf.contrib.metrics.confusion_matrix(labels, predictions)) matrix = confusion_matrix(labels.argmax(axis=1),predictions.argmax(axis=1)) row_sum = np.sum(matrix, axis = 1) w, h = matrix.shape c_m = np.zeros((w, h)) for i in range(h): c_m[i] = matrix[i] 100 / row_sum[i] c = c_m.astype(dtype = np.uint8) heatmap = print_confusion_matrix(c, class_names, figsize=(18,10), fontsize=20) class_names = list() for name,idx in labels_id.items(): class_names.append(name) # print(class_names) ypred = model.predict(valid_tensors) print_heatmap(ytest,ypred,class_names) #Precision Recall F1 Score ypred_class = np.argmax(ypred,axis=1) # print(ypred_class[:10]) ytest = np.argmax(ytest,axis=1) accuracy = accuracy_score(ytest,ypred_class) print('Accuracy: %f' % accuracy) # precision tp / (tp + fp) precision = precision_score(ytest, ypred_class,average='weighted') print('Precision: %f' % precision) # recall: tp / (tp + fn) recall = recall_score(ytest,ypred_class,average='weighted') print('Recall: %f' % recall) # f1: 2 tp / (2 tp + fp + fn) f1 = f1_score(ytest,ypred_class,average='weighted') print('F1 score: %f' % f1) ###Output _____no_output_____ ###Markdown Let us check model performance on never seen images ###Code from keras.models import load_model from keras.utils import np_utils import shutil BASE_MODEL_PATH = os.path.join(os.getcwd(),"model") TEST_DIR = os.path.join(os.getcwd(),"csv_files","test.csv") PREDICT_DIR = os.path.join(os.getcwd(),"pred_dir") PICKLE_DIR = os.path.join(os.getcwd(),"pickle_files") JSON_DIR = os.path.join(os.getcwd(),"json_files") if not os.path.exists(PREDICT_DIR): os.makedirs(PREDICT_DIR) else: shutil.rmtree(PREDICT_DIR) os.makedirs(PREDICT_DIR) if not os.path.exists(JSON_DIR): os.makedirs(JSON_DIR) BEST_MODEL = os.path.join(BASE_MODEL_PATH,"self_trained","distracted-07-0.99.hdf5") #loading checkpoint with best accuracy and min epochs model = load_model(BEST_MODEL) model.summary() data_test = pd.read_csv(os.path.join(TEST_DIR)) #testing on the only 10000 images as loading the all test images requires ram>8gb data_test = data_test[:10000] data_test.info() with open(os.path.join(PICKLE_DIR,"labels_list.pkl"),"rb") as handle: labels_id = pickle.load(handle) print(labels_id) ImageFile.LOAD_TRUNCATED_IMAGES = True test_tensors = paths_to_tensor(data_test.iloc[:,0]).astype('float32')/255 - 0.5 ypred_test = model.predict(test_tensors,verbose=1) ypred_class = np.argmax(ypred_test,axis=1) id_labels = dict() for class_name,idx in labels_id.items(): id_labels[idx] = class_name print(id_labels) for i in range(data_test.shape[0]): data_test.iloc[i,1] = id_labels[ypred_class[i]] #to create a human readable and understandable class_name import json class_name = dict() class_name["c0"] = "SAFE_DRIVING" class_name["c1"] = "TEXTING_RIGHT" class_name["c2"] = "TALKING_PHONE_RIGHT" class_name["c3"] = "TEXTING_LEFT" class_name["c4"] = "TALKING_PHONE_LEFT" class_name["c5"] = "OPERATING_RADIO" class_name["c6"] = "DRINKING" class_name["c7"] = "REACHING_BEHIND" class_name["c8"] = "HAIR_AND_MAKEUP" class_name["c9"] = "TALKING_TO_PASSENGER" with open(os.path.join(JSON_DIR,'class_name_map.json'),'w') as secret_input: json.dump(class_name,secret_input,indent=4,sort_keys=True) # creating the prediction results for the image classification and shifting the predicted images to another folder #with renamed filename having the class name predicted for that image using model with open(os.path.join(JSON_DIR,'class_name_map.json')) as secret_input: info = json.load(secret_input) for i in range(data_test.shape[0]): new_name = data_test.iloc[i,0].split("/")[-1].split(".")[0]+"_"+info[data_test.iloc[i,1]]+".jpg" shutil.copy(data_test.iloc[i,0],os.path.join(PREDICT_DIR,new_name)) #saving the model predicted results into a csv file data_test.to_csv(os.path.join(os.getcwd(),"csv_files","short_test_result.csv"),index=False) ###Output _____no_output_____
fm-pca-xgboost/xgboost/BikeSharingRegression/biketrain_data_preparation_rev2.ipynb	###Markdown Kaggle Bike Sharing Demand DatasetNew Feature Hour AddedTo download dataset, sign-in and download from this link: https://www.kaggle.com/c/bike-sharing-demand/dataInput Features: ['season', 'holiday', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed', 'year', 'month', 'day', 'dayofweek','hour']Target Feature: ['count']Objective: You are provided hourly rental data spanning two years. For this competition, the training set is comprised of the first 19 days of each month, while the test set is the 20th to the end of the month. You must predict the total count of bikes rented during each hour covered by the test set, using only information available prior to the rental period (Ref: Kaggle.com) ###Code columns = ['count', 'season', 'holiday', 'workingday', 'weather', 'temp', 'atemp', 'humidity', 'windspeed', 'year', 'month', 'day', 'dayofweek','hour'] df = pd.read_csv('train.csv', parse_dates=['datetime']) df_test = pd.read_csv('test.csv', parse_dates=['datetime']) df.head() df_test.head() # We need to convert datetime to numeric for training. # Let's extract key features into separate numeric columns def add_features(df): df['year'] = df['datetime'].dt.year df['month'] = df['datetime'].dt.month df['day'] = df['datetime'].dt.day df['dayofweek'] = df['datetime'].dt.dayofweek df['hour'] = df['datetime'].dt.hour add_features(df) add_features(df_test) df.dtypes # Correlation will indicate how strongly features are related to the output df.corr()['count'] group_hour = df.groupby(['hour']) average_by_hour = group_hour['count'].mean() plt.plot(average_by_hour.index,average_by_hour) plt.xlabel('hour') plt.ylabel('Count') plt.xticks(np.arange(24)) plt.grid(True) plt.title('Rental Count Average by hour') group_year_hour = df.groupby(['year','hour']) average_year_hour = group_year_hour['count'].mean() for year in average_year_hour.index.levels[0]: #print (year) #print(average_year_month[year]) plt.plot(average_year_hour[year].index,average_year_hour[year],label=year) plt.legend() plt.xlabel('hour') plt.ylabel('Count') plt.xticks(np.arange(24)) plt.grid(True) plt.title('Rental Count Average by Year,Hour') group_workingday_hour = df.groupby(['workingday','hour']) average_workingday_hour = group_workingday_hour['count'].mean() for workingday in average_workingday_hour.index.levels[0]: #print (year) #print(average_year_month[year]) plt.plot(average_workingday_hour[workingday].index,average_workingday_hour[workingday],label=workingday) plt.legend() plt.xlabel('hour') plt.ylabel('Count') plt.xticks(np.arange(24)) plt.grid(True) plt.title('Rental Count Average by Working Day,Hour') df.dtypes # Save all data df.to_csv('bike_all.csv',index=False, columns=columns) ###Output _____no_output_____ ###Markdown Training and Validation Set Target Variable as first column followed by input features Training, Validation files do not have a column header ###Code # Training = 70% of the data # Validation = 30% of the data # Randomize the datset np.random.seed(5) l = list(df.index) np.random.shuffle(l) df = df.iloc[l] rows = df.shape[0] train = int(.7 * rows) test = int(.3 * rows) rows, train, test columns # Write Training Set df[:train].to_csv('bike_train.csv' ,index=False,header=False ,columns=columns) # Write Validation Set df[train:].to_csv('bike_validation.csv' ,index=False,header=False ,columns=columns) # Test Data has only input features df_test.to_csv('bike_test.csv',index=False) ','.join(columns) # Write Column List with open('bike_train_column_list.txt','w') as f: f.write(','.join(columns)) ###Output _____no_output_____
code/16S well-to-well contamination analysis.ipynb	###Markdown Set up notebook environment NOTE: Use qiime2-2021.11 kernel ###Code import numpy as np import pandas as pd import seaborn as sns import scipy from scipy import stats import matplotlib.pyplot as plt import re %matplotlib inline from qiime2.plugins import feature_table from qiime2 import Artifact from qiime2 import Metadata import biom from biom import load_table from qiime2.plugins import diversity from scipy.stats import ttest_ind ###Output _____no_output_____ ###Markdown Assign taxonomy to reads using synthetic plasmid taxonomy file Unzip demux QZA's to obtain R1 files for vsearch NOTE: After unzipping each file, the folder was renamed to match the file name ###Code cd /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/03_demux/ unzip dna_og_16S_plates1_6_8redos_demux.qza unzip dna_og_16S_plates1_6_excluding_8redos_demux.qza unzip dna_og_16S_plates7_12_demux.qza unzip dna_rerun_16S_hbm_seqs_demux.qza unzip dna_rerun_16S_lbm_seqs_demux.qza cd /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/03_demux/ mv dna_og_16S_plates1_6_8redos_demux/data/R1 /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/01_demux_R1/ mv dna_og_16S_plates1_6_excluding_8redos_demux/data/R1 /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/01_demux_R1/ mv dna_og_16S_plates7_12_demux/data/R1 /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/01_demux_R1/ mv dna_rerun_16S_hbm_seqs_demux/data/R1 /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/01_demux_R1/ mv dna_rerun_16S_lbm_seqs_demux/data/R1 /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/01_demux_R1/ #!/bin/bash #PBS -V #PBS -l nodes=1:ppn=16 #PBS -l walltime=12:00:00 #PBS -l mem=128gb #PBS -M [email protected] #PBS -m abe source activate qiime2-2021.11 # Import raw, demuxed sequences qiime tools import \ --type 'SampleData[SequencesWithQuality]' \ --input-path /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/01_demux_R1/ \ --input-format CasavaOneEightSingleLanePerSampleDirFmt \ --output-path /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/02_vsearch/dna_all_16s_seqs_with_quality.qza # Remove adapters (most basic QC) qiime cutadapt trim-single \ --i-demultiplexed-sequences /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/02_vsearch/dna_all_16s_seqs_with_quality.qza \ --o-trimmed-sequences /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/02_vsearch/dna_all_16s_seqs_with_quality_trimmed.qza # Create a table qiime vsearch dereplicate-sequences \ --i-sequences /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/02_vsearch/dna_all_16s_seqs_with_quality_trimmed.qza \ --o-dereplicated-table /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/02_vsearch/dna_all_16s_vsearch_biom.qza \ --o-dereplicated-sequences /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/02_vsearch/dna_all_16s_vsearch_seqs.qza qiime feature-table summarize \ --i-table /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/02_vsearch/dna_all_16s_vsearch_biom.qza \ --o-visualization /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/02_vsearch/dna_all_16s_vsearch_biom.qzv # There are 1920 samples and 5446117 features ###Output _____no_output_____ ###Markdown Assign taxonomy using synthetic plasmid taxonomy file ###Code #!/bin/bash #PBS -V #PBS -l nodes=1:ppn=16 #PBS -l walltime=6:00:00 #PBS -l mem=128gb #PBS -M [email protected] #PBS -m abe source activate qiime2-2021.11 # Assign taxonomy qiime feature-classifier classify-consensus-vsearch \ --i-query /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/02_vsearch/dna_all_16s_vsearch_seqs.qza \ --i-reference-reads /projects/dna_extraction_12201/round_03_MagMAX_comparison/16S/snyth_16Splas_seqs.qza \ --i-reference-taxonomy /projects/dna_extraction_12201/round_03_MagMAX_comparison/16S/synth_16Splas_taxonomy.qza \ --o-classification /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/03_taxonomy/dna_all_16S_deblur_seqs_taxonomy_synthetic_plasmids.qza # Collapse taxonomy to level 1 qiime taxa collapse \ --i-table /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/02_vsearch/dna_all_16s_vsearch_biom.qza \ --i-taxonomy /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/03_taxonomy/dna_all_16S_deblur_seqs_taxonomy_synthetic_plasmids.qza \ --p-level 1 \ --o-collapsed-table /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/03_taxonomy/dna_all_16S_deblur_biom_taxa_collapse_synthetic_plasmids.qza qiime feature-table summarize \ --i-table /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/03_taxonomy/dna_all_16S_deblur_biom_taxa_collapse_synthetic_plasmids.qza \ --o-visualization /projects/dna_extraction_12201/round_02_six_kit_comparison/data/16S/14_well_to_well_contamination/03_taxonomy/dna_all_16S_deblur_biom_taxa_collapse_synthetic_plasmids.qzv ###Output _____no_output_____ ###Markdown Import synthetic read counts into pandas NOTE: Download table, unzip, and rename file for use with biom-format ###Code biom convert \ -i /Users/Justin/Mycelium/UCSD/00_Knight_Lab/03_Extraction_test_12201/round_02/data/16S/14_w2w/dna_all_16S_deblur_biom_taxa_collapse_synthetic_plasmids.biom \ -o /Users/Justin/Mycelium/UCSD/00_Knight_Lab/03_Extraction_test_12201/round_02/data/16S/14_w2w/dna_all_16S_deblur_biom_taxa_collapse_synthetic_plasmids.tsv \ --to-tsv biom_collapsed = pd.read_csv('/Users/Justin/Mycelium/UCSD/00_Knight_Lab/03_Extraction_test_12201/round_02/data/16S/14_w2w/dna_all_16S_deblur_biom_taxa_collapse_synthetic_plasmids.tsv', sep = '\t', index_col = 0, header = 1) biom_collapsed.tail() md = pd.read_csv('/Users/Justin/Mycelium/UCSD/00_Knight_Lab/03_Extraction_test_12201/round_02/sample_metadata/12201_metadata.txt', sep='\t', index_col=0) # Sum the number of reads per synthetic plasmid plasmid_sum = biom_collapsed plasmid_sum = plasmid_sum.apply(pd.to_numeric) plasmid_sum['synth_sums_across_samples'] = plasmid_sum.sum(axis=1) # We expect that all plasmids should be present at some abundance - check for this. ## Make table of read counts per plasmid plasmid_sum.synth_sums_across_samples # Copy collapsed BIOM table and convert values to numeric biom_collapsed_with_sums = biom_collapsed biom_collapsed_with_sums = biom_collapsed_with_sums.apply(pd.to_numeric) # Add a row with columns sums (i.e., total synthetic plasmid reads per sample) biom_collapsed_with_sums.loc['synth_sum'] = (biom_collapsed_with_sums.sum(axis=0) - biom_collapsed_with_sums.loc['Unassigned']) biom_collapsed_with_sums.loc['synth_perc'] = (biom_collapsed_with_sums.loc['synth_sum'] / (biom_collapsed_with_sums.loc['Unassigned'] + biom_collapsed_with_sums.loc['synth_sum'])) biom_collapsed_with_sums.tail() biom_collapsed_with_sums_transposed = biom_collapsed_with_sums.T biom_collapsed_with_sums_transposed.head() md_with_biom_and_sums = pd.merge(biom_collapsed_with_sums_transposed, md, left_index=True, right_index=True) md_with_biom_and_sums.head() # Export metadata with BIOM table and synthetic plasmid sums md_with_biom_and_sums.to_csv('/Users/Justin/Mycelium/UCSD/00_Knight_Lab/03_Extraction_test_12201/round_02/data/16S/14_w2w/synthetic_plasmid_results.csv', index = 1) # Double-check levels for 'biomass_plate' md_with_biom_and_sums.biomass_plate.unique() # We expect the synthetic plasmids to be at greater abundance in the high biomass plates more than the low biomass and COVID # plates (as expected since we only put into the high biomass plate) - check for this plot_plasmids_across_plates = sns.boxplot(x = 'biomass_plate', y = 'synth_sum', data = md_with_biom_and_sums) plot_plasmids_across_plates.set_yscale('log') ###Output _____no_output_____ ###Markdown NOTE: At this point, the summary results file generated above was manually curated to include the following columns:row, column, plasmid_reads, plasmid_reads_log10, plasmid_reads_percent, extraction_protocol, well_type (sink vs. source) ###Code # Round 1 & 2 synthetic plasmid well locations: ## For Round 1 PowerSoil - B6 is replaced with B7 and there is no F7 A3 A11 B6 C8 D4 D10 E1 F7 G2 H5 H9 ###Output _____no_output_____
src/python/tensorflow_cloud/tuner/tests/examples/cloud_fit.ipynb	###Markdown Run in AI Platform Notebooks Run in Colab View on GitHub OverviewFollowing is a quick introduction to `cloud_fit`. `cloud_fit` enables training on Google Cloud AI Platform in the same manner as `model.fit()`.In this notebook, we will start by installing libraries required, then proceed with two samples showing how to use `numpy.array` and `tf.data.dataset` with `cloud_fit` What are the components of `cloud_fit()`?`cloud_fit` has two main components as follows:client.py: serializes the provided data and model along with typical `model.fit()` parameters and triggers a AI platform training``` pythondef cloud_fit(model, remote_dir: Text, region: Text = None, project_id: Text = None, image_uri: Text = None, distribution_strategy: Text = DEFAULT_DISTRIBUTION_STRATEGY, job_spec: Dict[str, Any] = None, job_id: Text = None, fit_kwargs) -> Text: """Facilitates remote execution of in memory Models and Datasets on AI Platform. Args: model: A compiled Keras Model. remote_dir: Google Cloud Storage path for temporary assets and AI Platform training output. Will overwrite value in job_spec. region: Target region for running the AI Platform Training job. project_id: Project id where the training should be deployed to. image_uri: base image used to use for AI Platform Training distribution_strategy: Specifies the distribution strategy for remote execution when a jobspec is provided. Accepted values are strategy names as specified by 'tf.distribute..__name__'. job_spec: AI Platform training job_spec, will take precedence over all other provided values except for remote_dir. If none is provided a default cluster spec and distribution strategy will be used. job_id: A name to use for the AI Platform Training job (mixed-case letters, numbers, and underscores only, starting with a letter). fit_kwargs: Args to pass to model.fit() including training and eval data. Only keyword arguments are supported. Callback functions will be serialized as is. Returns: AI Platform job ID Raises: RuntimeError: If executing in graph mode, eager execution is required for cloud_fit. NotImplementedError: Tensorflow v1.x is not supported. """```remote.py: A job that takes in a remote_dir as parameter , load model and data from this location and executes the training with stored parameters.```pythondef run(remote_dir: Text, distribution_strategy_text: Text): """deserializes Model and Dataset and runs them. Args: remote_dir: Temporary cloud storage folder that contains model and Dataset graph. This folder is also used for job output. distribution_strategy_text: Specifies the distribution strategy for remote execution when a jobspec is provided. Accepted values are strategy names as specified by 'tf.distribute..__name__'. """``` CostsThis tutorial uses billable components of Google Cloud:* AI Platform Training* Cloud StorageLearn about [AI Platform Trainingpricing](https://cloud.google.com/ai-platform/training/pricing) and [Cloud Storagepricing](https://cloud.google.com/storage/pricing), and use the [PricingCalculator](https://cloud.google.com/products/calculator/)to generate a cost estimate based on your projected usage. Set up your Google Cloud projectThe following steps are required, regardless of your notebook environment.1. [Select or create a Google Cloud project.](https://console.cloud.google.com/cloud-resource-manager) When you first create an account, you get a $300 free credit towards your compute/storage costs.2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)3. [Enable the AI Platform APIs](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com)4. If running locally on your own machine, you will need to install the [Google Cloud SDK](https://cloud.google.com/sdk).Note: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands. Authenticate your Google Cloud accountIf you are using [AI Platform Notebooks](https://cloud.google.com/ai-platform/notebooks/docs/), your environment is alreadyauthenticated. Skip these steps. ###Code import sys # If you are running this notebook in Colab, run this cell and follow the # instructions to authenticate your Google Cloud account. This provides access # to your Cloud Storage bucket and lets you submit training jobs and prediction # requests. if 'google.colab' in sys.modules: from google.colab import auth as google_auth google_auth.authenticate_user() # If you are running this tutorial in a notebook locally, replace the string # below with the path to your service account key and run this cell to # authenticate your Google Cloud account. else: %env GOOGLE_APPLICATION_CREDENTIALS your_path_to_credentials.json # Log in to your account on Google Cloud ! gcloud auth application-default login --quiet ! gcloud auth login --quiet ###Output _____no_output_____ ###Markdown Clone and build tensorflow_cloudTo use the latest version of the tensorflow_cloud, we will clone and build the repo. The resulting whl file is both used in the client side as well as in construction of a docker image for remote execution. ###Code !git clone https://github.com/tensorflow/cloud.git !cd cloud/src/python && python3 setup.py -q bdist_wheel !pip install -U cloud/src/python/dist/tensorflow_cloud-.whl --quiet ###Output _____no_output_____ ###Markdown Restart the KernelWe will automatically restart your kernel so the notebook has access to the packages you installed. ###Code # Restart the kernel after pip installs import IPython app = IPython.Application.instance() app.kernel.do_shutdown(True) ###Output _____no_output_____ ###Markdown Import libraries and define constants ###Code import os import uuid import numpy as np import tensorflow as tf from tensorflow_cloud.tuner import cloud_fit_client as client # Setup and imports REMOTE_DIR = '[gcs-bucket-for-temporary-files]' #@param {type:"string"} REGION = 'us-central1' #@param {type:"string"} PROJECT_ID = '[your-project-id]' #@param {type:"string"} DOCKER_IMAGE_NAME = '[name-for-docker-image]' #@param {type:"string"} ! gcloud config set project $PROJECT_ID IMAGE_URI = f'gcr.io/{PROJECT_ID}/{DOCKER_IMAGE_NAME}:latest' #@param {type:"string"} ###Output _____no_output_____ ###Markdown Create a docker file with tensorflow_cloudIn the next step we create a base docker file with the latest wheel file to use for remote training. You may use any base image. However, DLVM base images come pre-installed with most needed packages. ###Code %%file Dockerfile # Using DLVM base image FROM gcr.io/deeplearning-platform-release/tf2-cpu WORKDIR /root # Path configuration ENV PATH $PATH:/root/tools/google-cloud-sdk/bin # Make sure gsutil will use the default service account RUN echo '[GoogleCompute]\nservice_account = default' > /etc/boto.cfg # Copy and install tensorflow_cloud wheel file ADD cloud/src/python/dist/tensorflow_cloud-.whl /tmp/ RUN pip3 install --upgrade /tmp/tensorflow_cloud-.whl --quiet # Sets up the entry point to invoke cloud_fit. ENTRYPOINT ["python3","-m","tensorflow_cloud.tuner.cloud_fit_remote"] !docker build -t {IMAGE_URI} -f Dockerfile . -q && docker push {IMAGE_URI} ###Output _____no_output_____ ###Markdown Tutorial 1 - Functional modelIn this sample we will demonstrate using numpy.array as input data by creating a basic model and and submit it for remote training. Define model building function ###Code """Simple model to compute y = wx + 1, with w trainable.""" inp = tf.keras.layers.Input(shape=(1,), dtype=tf.float32) times_w = tf.keras.layers.Dense( units=1, kernel_initializer=tf.keras.initializers.Constant([[0.5]]), kernel_regularizer=tf.keras.regularizers.l2(0.01), use_bias=False) plus_1 = tf.keras.layers.Dense( units=1, kernel_initializer=tf.keras.initializers.Constant([[1.0]]), bias_initializer=tf.keras.initializers.Constant([1.0]), trainable=False) outp = plus_1(times_w(inp)) simple_model = tf.keras.Model(inp, outp) simple_model.compile(tf.keras.optimizers.SGD(0.002), "mean_squared_error", run_eagerly=True) ###Output _____no_output_____ ###Markdown Prepare Data ###Code # Creating sample data x = [[9.], [10.], [11.]] 10 y = [[xi[0]/2. + 6] for xi in x] ###Output _____no_output_____ ###Markdown Run the model locally for validation ###Code # Verify the model by training locally for one step. simple_model.fit(np.array(x), np.array(y), batch_size=len(x), epochs=1) ###Output _____no_output_____ ###Markdown Submit model and dataset for remote training ###Code # Create a unique remote sub folder path for assets and model training output. SIMPLE_REMOTE_DIR = os.path.join(REMOTE_DIR, str(uuid.uuid4())) print('your remote folder is %s' % (SIMPLE_REMOTE_DIR)) # Using default configuration with two workers dividing the dataset between the two. simple_model_job_id = client.cloud_fit(model=simple_model, remote_dir = SIMPLE_REMOTE_DIR, region =REGION , image_uri=IMAGE_URI, x=np.array(x), y=np.array(y), epochs=100, steps_per_epoch=len(x)/2,verbose=2) !gcloud ai-platform jobs describe projects/{PROJECT_ID}/jobs/{simple_model_job_id} ###Output _____no_output_____ ###Markdown Retrieve the trained modelOnce the training is complete you can access the trained model at `remote_folder/output` ###Code # Load the trained model from gcs bucket trained_simple_model = tf.keras.models.load_model(os.path.join(SIMPLE_REMOTE_DIR, 'output')) # Test that the saved model loads and works properly trained_simple_model.evaluate(x,y) ###Output _____no_output_____ ###Markdown Tutorial 2 - Sequential Models and DatasetsIn this sample we will demonstrate using datasets by creating a basic model and submitting it for remote training. Define model building function ###Code # create a model fashion_mnist_model = tf.keras.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dense(10) ]) fashion_mnist_model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy']) ###Output _____no_output_____ ###Markdown Prepare Data ###Code train, test = tf.keras.datasets.fashion_mnist.load_data() images, labels = train images = images/255 dataset = tf.data.Dataset.from_tensor_slices((images, labels)) dataset = dataset.batch(32) ###Output _____no_output_____ ###Markdown Run the model locally for validation ###Code # Verify the model by training locally for one step. This is not necessary prior to cloud.fit() however it is recommended. fashion_mnist_model.fit(dataset, epochs=1) ###Output _____no_output_____ ###Markdown Submit model and dataset for remote training ###Code # Create a unique remote sub folder path for assets and model training output. FASHION_REMOTE_DIR = os.path.join(REMOTE_DIR, str(uuid.uuid4())) print('your remote folder is %s' % (FASHION_REMOTE_DIR)) fashion_mnist_model_job_id = client.cloud_fit(model=fashion_mnist_model, remote_dir = FASHION_REMOTE_DIR,region =REGION , image_uri=IMAGE_URI, x=dataset,epochs=10, steps_per_epoch=15,verbose=2) !gcloud ai-platform jobs describe projects/{PROJECT_ID}/jobs/{fashion_mnist_model_job_id} ###Output _____no_output_____ ###Markdown Retrieve the trained modelOnce the training is complete you can access the trained model at remote_folder/output ###Code # Load the trained model from gcs bucket trained_fashion_mnist_model = tf.keras.models.load_model(os.path.join(FASHION_REMOTE_DIR, 'output')) # Test that the saved model loads and works properly test_images, test_labels = test test_images = test_images/255 test_dataset = tf.data.Dataset.from_tensor_slices((test_images, test_labels)) test_dataset = test_dataset.batch(32) trained_fashion_mnist_model.evaluate(test_dataset) ###Output _____no_output_____
fb-live-selling-data-analysis.ipynb	###Markdown IntroductionThe 'Facebook Live Sellers in Thailand' is a dataset curated in UCI Machine Learning Datasets. The data contains 7050 observations and twelve attributes. The data is about live selling feature on the Facebook platform. Each record consists of information about the time live information of sale is posted to Facebook and engagements in the data. The engagements are regular Facebook interactions such as share and emotion rection. Details and academic publications relating to the data is available from the source https://archive.ics.uci.edu/ml/datasets/Facebook+Live+Sellers+in+Thailand. ###Code %matplotlib inline import os import warnings warnings.simplefilter(action='ignore') import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import sklearn as sl import os for dirname, _, filenames in os.walk('/kaggle/input'): for filename in filenames: print(os.path.join(dirname, filename)) ###Output /kaggle/input/facebook-live-sellers-in-thailand-uci-ml-repo/Live.csv ###Markdown Data Analysis ###Code data = pd.read_csv("/kaggle/input/facebook-live-sellers-in-thailand-uci-ml-repo/Live.csv") data.head(2) ###Output _____no_output_____ ###Markdown The columns Column1,Column2,Column3,Column4 are not part of the original data. These colums might have appeared in the data due to format conversion. We will exclude these columns from the analysis. ###Code data = data[data.columns[:-4]] data.head() data.info() ###Output <class 'pandas.core.frame.DataFrame'> RangeIndex: 7050 entries, 0 to 7049 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 status_id 7050 non-null object 1 status_type 7050 non-null object 2 status_published 7050 non-null object 3 num_reactions 7050 non-null int64 4 num_comments 7050 non-null int64 5 num_shares 7050 non-null int64 6 num_likes 7050 non-null int64 7 num_loves 7050 non-null int64 8 num_wows 7050 non-null int64 9 num_hahas 7050 non-null int64 10 num_sads 7050 non-null int64 11 num_angrys 7050 non-null int64 dtypes: int64(9), object(3) memory usage: 661.1+ KB ###Markdown From the pandas DataFrame meta information, it is evident that the data is complete w.r.f to the description. There are 7050 entries, and no null values are reported here. Let's proceed to expalore the data! ###Code data.nunique() ###Output _____no_output_____ ###Markdown From the unique value counts, it is evident that from the 7050 observations, only 6997 is unique live selling status. There are four types of status available in the data. From this, we can infer that around 53 observations may be duplicated or some other business phenomena are involved in the status_id column. ###Code duplicated_data = data[data['status_id'].duplicated() == True] duplicated_data.head() duplicated_data.tail() data[data.status_id == '246675545449582_326883450762124'] data[data.status_id == '819700534875473_1002372733274918'] data[data.status_id == '819700534875473_955149101330615'] data[data.status_id == '819700534875473_951614605017398'] ###Output _____no_output_____ ###Markdown From the samples evaluated, it is evident that the 53 observations are duplicate. We will proceed and remove the duplicated by the status_id column. ###Code data_ndp = data.drop_duplicates(subset='status_id', keep='last') data_ndp.shape ###Output _____no_output_____ ###Markdown Now we have only 6997 observation in the dataset. Let's explore the status type and other attributes in the data to gain further insights. ###Code st_ax = data_ndp.status_type.value_counts().plot(kind='bar', figsize=(10,5), title="Status Type") st_ax.set(xlabel="Status Type", ylabel="Count") ###Output _____no_output_____ ###Markdown Most of the sellers seem to be using a photo or video as status for the selling. A tiny portion of the users is depending on text status or URL/link for posting an advertisement. ###Code data_ndp.head(2) ###Output _____no_output_____ ###Markdown The num_reaction column seems to be a sum of following colums.* num_reaction = sum(num_likes, num_loves,num_wows,num_hahas,num_sads,num_angrys)Let's validate the assumption. ###Code data_ndp['all_reaction_count'] = data_ndp.iloc[:,-6:].sum(axis=1) data_ndp['reactio_match'] = data_ndp.apply(lambda x: x['num_reactions'] == x['all_reaction_count'], axis=1) data_react_mismatch = data_ndp[data_ndp.reactio_match == False] data_react_mismatch.shape ###Output _____no_output_____ ###Markdown There are nine observations where the assumption mentioned above is invalid. Let's examine the difference and reasons behind this. Since only nine observations are there, we can even remove these observations from the data due to inconsistency issues. But it is worthwhile to examine the reason. ###Code data_react_mismatch["diff_react"] = data_react_mismatch.num_reactions - data_react_mismatch.all_reaction_count data_react_mismatch ###Output _____no_output_____ ###Markdown Let's check if the duplicate records cause the mismatch. We created a subset data consists only the duplicated values. Let's run a quick search by the status_id! ###Code data_react_mismatch[data_react_mismatch['status_id'].isin(list(duplicated_data.status_id.values))] ###Output _____no_output_____ ###Markdown And by looking at the numbers, it is evident that comments or shares do not contribute it. Values of those attributes are higher than the difference, and some of the status_is's are not even shared. As there is no data available to verify the correctness, we can go for* Correct the value based on the interactions.* Drop the nine observations. I prefer to correct the values as part of this experiment before we proceed further. ###Code data_ndp.num_reactions = data_ndp.all_reaction_count data_ndp['reactio_match'] = data_ndp.apply(lambda x: x['num_reactions'] == x['all_reaction_count'], axis=1) data_ndp[data_ndp.reactio_match == False] ###Output _____no_output_____ ###Markdown Now all the reactions_count is matching based on the calculation logic. ###Code data_ndp.head(2) ###Output _____no_output_____ ###Markdown Let's create two variables to understand the reactions to comment and share ratio. Comments and shares show people may be interested and inquiring or maybe complaining. Shares activity indicates that users found it interesting, hence sharing it for other's benefits. ###Code data_ndp['react_comment_r'] = data_ndp.num_reactions/data_ndp.num_comments data_ndp['react_share_r'] = data_ndp.num_reactions/data_ndp.num_shares data_ndp.head() data_ndp.react_comment_r.plot(kind='line', figsize=(16,5)) ###Output _____no_output_____ ###Markdown From the graph, we can see that there are many NaN or Inf values and extreme values in the reactions to comments ratio. The ratio becomes inf while the comments or shares are zero in the count. The extreme values are something exciting. It may be an indication of data error or a trend in the data and worth investigating. ###Code data_ndp.replace([np.inf, -np.inf], 0.0, inplace=True) data_with_p_reaction = data_ndp[(data_ndp.react_comment_r > 0) & (data_ndp.react_comment_r <= 2)] data_with_p_reaction = data_with_p_reaction[["num_reactions","num_comments","react_comment_r"]] data_with_p_reaction.shape data_with_p_reaction.head() data_with_p_reaction.react_comment_r.min(),data_with_p_reaction.react_comment_r.max() ###Output _____no_output_____ ###Markdown When comments are less than ten, the reaction to comment ratio becomes higher. It means it created impressions but may not be enough interest in the customer base. At the same time, we can see that the three interaction types in the data 'haha', 'angry,' and 'sad' are there. Knowing Facebook as a social platform, these reactions are expressed in extreme emotions or disappointed by the product. It is work exploring the positive reactions 'likes,' 'loves,' and 'wows.' We can create positive reactions and adverse reactions summary here. Positive Reactions = sum('likes,' 'loves,' and 'wows.' )Negative Reactions = sum('haha', 'angry,' and 'sad' )With the variables mentioned above, we can check if the reaction to comment ratio is higher for selling attempts with positive comments or negative comments. ###Code data_ndp.head(2) data_ndp['postive_reactions'] = data_ndp.iloc[:,-10:-7].sum(axis=1) data_ndp.head(2) data_ndp['negative_reactions'] = data_ndp.iloc[:,-8:-5].sum(axis=1) data_ndp.head(2) data_ndp.plot.scatter(x='num_comments', y='negative_reactions', figsize=(16,5), title="Number of Comments v.s Negative Reactions") data_ndp.plot.scatter(x='num_comments', y='postive_reactions', figsize=(16,5), title="Number of Comments v.s Positive Reactions") data_ndp.num_comments.min(),data_ndp.num_comments.max() ###Output _____no_output_____ ###Markdown It looks like low comments and otherwise negative and positive, and reactions are there. Comments to positive responses are much higher than comments to negative. If we extract the respective comments and study the intent and sentiment, that could lead us to fascinating insights. Data Quality Issues and ResolutionsWe found the following data quality issues and appropriate remedy implemented. 1) Duplicate records - There were 53 records duplicated, and we preserved the last records. 2) Calculated Columns value Mismatch - The column [num_reactions](http://) column is created by summing the columns num_likes, num_loves,num_wows,num_hahas,num_sads,num_angrys. There was nine instanced where the values are not matching. The values were replaced with correct calculations. New Features and RationaleAs part of the analysis, we created six new features. They are :all_reaction_count, reactio_match, react_comment_r, react_share_r, postive_reactions, negative_reactions.all_reaction_count: This feature was generated to check the validity of data 'num_reactions'. The logic used to create the column is num_reaction = sum(num_likes, num_loves,num_wows,num_hahas,num_sads,num_angrys) .reactio_match: This is a bool column. If the values are False that means num_reactions and all_reactions_count values are different. react_comment_r: reactions to comments ratio. The logic for creating this variable is num_reactions/num_comments react_share_r: Reactiont to share ratio. The logic to create the variable is num_reactions/num_shares.postive_reactions: This is the overall positve reaction count. Logic to generate the column : positive_reactions = sum(num_likes,num_loves,num_wows)negative_reactions: This variable represents overall negative reactions. Logic to generate the columsn : negative_reactions = sum(num_hahas, num_sads, num_angrys) Clean DataFrom the final data, we will exclude the columns all_reaction_count, reactio_match. These columns are created for verification and validation. The rest of the new columns can be removed based on the use case we are framing from the data. ###Code clean_data = data_ndp.drop(['all_reaction_count','reactio_match'], axis=1) clean_data.head() clean_data.to_csv("clean_data_v1.0.csv", index=False) ###Output _____no_output_____
notebooks/220322_MI.ipynb	###Markdown Quickly visualizing MI between feature map activations of the trained network ###Code import h5py import numpy as np import matplotlib.pyplot as plt from sklearn import feature_selection from lecun1989repro import utils ###Output _____no_output_____ ###Markdown "Modern" replication ###Code acts = utils.read_h5_dict("../out/modern/activations/activations_test.h5") acts["h1"].shape def mutual_info(acts, i, j): feature_map_i = acts[:, i, :, :].reshape(-1, 1) feature_map_j = acts[:, j, :, :].ravel() return feature_selection.mutual_info_regression(feature_map_i, feature_map_j) def feature_map_MI(acts): num_maps = acts.shape[1] matrix = np.zeros((num_maps, num_maps), dtype=np.float32) for i in range(num_maps): for j in range(i, acts.shape[1]): matrix[i, j] = mutual_info(acts, i, j)[0] # fill in the redundant entries for i in range(num_maps): for j in range(num_maps): if i > j: matrix[i, j] = matrix[j, i] return matrix %time MI_h1 = feature_map_MI(acts["h1"]) %time MI_h2 = feature_map_MI(acts["h2"]) plt.figure(figsize=(7, 6)) plt.imshow(MI_h1) plt.title("Unnormalized MI heatmap") plt.xlabel("Feature map") plt.ylabel("Feature map") plt.show() plt.figure(figsize=(7, 6)) plt.imshow(MI_h2) plt.title("Unnormalized MI heatmap") plt.xlabel("Feature map") plt.ylabel("Feature map") plt.show() ###Output _____no_output_____ ###Markdown "Base" model ###Code acts = utils.read_h5_dict("../out/base/activations/activations_test.h5") %time MI_h1 = feature_map_MI(acts["h1"]) %time MI_h2 = feature_map_MI(acts["h2"]) plt.figure(figsize=(7, 6)) plt.imshow(MI_h1) plt.title("Unnormalized MI heatmap") plt.xlabel("Feature map") plt.ylabel("Feature map") plt.show() plt.figure(figsize=(7, 6)) plt.imshow(MI_h2) plt.title("Unnormalized MI heatmap") plt.xlabel("Feature map") plt.ylabel("Feature map") plt.show() ###Output _____no_output_____
code/qss20_groupcode/predict_viol/B_feature_matrix_prep.ipynb	###Markdown Imports ###Code #imports import pandas as pd import numpy as np import random import re import recordlinkage import time import matplotlib.pyplot as plt # ML imports from sklearn.feature_extraction.text import CountVectorizer from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score from sklearn.naive_bayes import MultinomialNB from sklearn.model_selection import cross_val_score from sklearn.svm import SVC from sklearn.preprocessing import OneHotEncoder from sklearn.tree import DecisionTreeClassifier from sklearn.compose import ColumnTransformer from sklearn.preprocessing import LabelEncoder from sklearn.preprocessing import LabelBinarizer from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification from sklearn.impute import SimpleImputer # prevent depreciation warnings import warnings warnings.filterwarnings("ignore", category=DeprecationWarning) warnings.filterwarnings("ignore", category=FutureWarning) ## repeated printouts from IPython.core.interactiveshell import InteractiveShell InteractiveShell.ast_node_interactivity = "all" ###Output /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/feature_extraction/image.py:167: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations dtype=np.int): /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:30: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations method='lar', copy_X=True, eps=np.finfo(np.float).eps, /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:167: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations method='lar', copy_X=True, eps=np.finfo(np.float).eps, /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:284: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations eps=np.finfo(np.float).eps, copy_Gram=True, verbose=0, /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations eps=np.finfo(np.float).eps, copy_X=True, fit_path=True, /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1101: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations eps=np.finfo(np.float).eps, copy_X=True, fit_path=True, /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1127: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations eps=np.finfo(np.float).eps, positive=False): /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1362: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps, /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1602: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps, /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1738: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations eps=np.finfo(np.float).eps, copy_X=True, positive=False): /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/decomposition/online_lda.py:29: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations EPS = np.finfo(np.float).eps /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/ensemble/gradient_boosting.py:32: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations from ._gradient_boosting import predict_stages /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/ensemble/gradient_boosting.py:32: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations from ._gradient_boosting import predict_stages ###Markdown Read in, assign a unique identifier via the index, set to dates ###Code # read in our PreMatrix csv from step A # for violations preMatrix = pd.read_csv('../output/repMatrixforpredict_violations.csv').drop(columns=['Unnamed: 0']) # for investigations #preMatrix = pd.read_csv('../output/repMatrixforpredict_investigations.csv').drop(columns=['Unnamed: 0']) preMatrix.shape preMatrix = preMatrix.reset_index().copy() preMatrix = preMatrix.rename(columns={"index": 'unique_id'}) preMatrix.is_violator.value_counts() ## convert the dates to datetime objects for col in ['CASE_RECEIVED_DATE', 'DECISION_DATE', 'REQUESTED_START_DATE_OF_NEED', 'REQUESTED_END_DATE_OF_NEED', 'JOB_START_DATE', 'JOB_END_DATE']: preMatrix[col] = pd.to_datetime(preMatrix[col]) preMatrix.columns preMatrix.info() # Second Diploma Major has no non null values so drop it preMatrix = preMatrix.drop(columns=['SECOND_DIPLOMA_MAJOR']) # Assign the is_violator status to the y (value we are trying to predict) y = list(preMatrix.is_violator) # remove the is_violator status from the preMatrix ... because that would be too easy! preMatrix = preMatrix.drop(columns=['is_violator']) ## dtypes auto-separate ## list of non-features numeric_options = ["int64", "float64", "datetime64[ns]"] num_cols = [one for one in preMatrix.columns if preMatrix.dtypes[one] in numeric_options] cat_cols = [one for one in preMatrix.columns if preMatrix.dtypes[one] not in numeric_options] print('Numeric Columns:') print(num_cols) print('\nCategorical Columns:') print(cat_cols) # OLD USELESS CODE SAVED FOR POSTERITY...JUST IN CASE # encoded_text_feature_pre = text_feature_pre.copy() # for one in encoded_text_feature_pre.columns: # enc = LabelEncoder() # enc.fit(encoded_text_feature_pre[one].astype(str)) # encoded_text_feature_pre[one] = enc.transform(encoded_text_feature_pre[one].astype(str)) # get the categorical features in one dataframe cat_feature_pre = preMatrix.loc[:, cat_cols].copy() print("Shape of non-imputed: ") print(cat_feature_pre.shape) # and the numerical features into another dataframe num_feature_pre = preMatrix.loc[:, num_cols].copy() print(num_feature_pre.shape) # SimpleImputer on the categorical features and apply a "missing_value" to NANs imputer_cat = SimpleImputer(strategy='constant', fill_value='missing_value') imputed_cat_feature_pre = pd.DataFrame(imputer_cat.fit_transform(cat_feature_pre)) imputed_cat_feature_pre.columns = cat_feature_pre.columns # SimpleImputer on the numerical features and apply mode to NANs imputer_num = SimpleImputer(strategy='most_frequent', verbose=5) imputed_num_feature_pre = pd.DataFrame(imputer_num.fit_transform(num_feature_pre)) imputed_num_feature_pre.columns = num_feature_pre.columns print("Shape of imputed: ") print(imputed_cat_feature_pre.shape) print(imputed_num_feature_pre.shape) # recombine the imputed cat and imputed num # we need to drop some columns which are going to be unique identifiers and could # be an issue within our model unique_cols_to_drop = ['unique_id', 'CASE_NO', 'EMPLOYER_NAME', 'TRADE_NAME_DBA'] for l in [cat_cols, num_cols]: for col in l: if col in unique_cols_to_drop: l.remove(col) # prepare input data with OneHotEncoder def prepare_inputs(X_train, X_test): oe = OneHotEncoder(handle_unknown='ignore') oe.fit(X_train) X_train_enc = oe.transform(X_train) X_test_enc = oe.transform(X_test) return X_train_enc, X_test_enc imputed_combined = pd.merge(imputed_cat_feature_pre.reset_index(), imputed_num_feature_pre.reset_index(), how='left', on='index') print('%s rows lost in merge' %(imputed_num_feature_pre.shape[0]-imputed_combined.shape[0])) print(imputed_combined.shape) imputed_combined = imputed_combined.drop(columns = 'index') # do a train test split # split into train and test sets (80/20) # X_train, X_test, y_train, y_test = train_test_split(imputed_cat_feature_pre, y, test_size=0.20, random_state=1) X_train, X_test, y_train, y_test = train_test_split(imputed_combined, y, test_size=0.20, random_state=3) # apply the oneHotEcoder within prepare_inputs X_train, X_test = prepare_inputs(X_train, X_test) imputed_combined.head() clf = RandomForestClassifier(max_depth = None, random_state=0) clf.fit(X_train, y_train) y_pred = clf.predict(X_test) print("Confusion matrix \n") print(pd.crosstab(pd.Series(y_test, name='Actual'), pd.Series(y_pred, name='Predicted'))) def get_metrics(y_test, y_predicted): accuracy = accuracy_score(y_test, y_predicted) precision = precision_score(y_test, y_predicted, average='binary') recall = recall_score(y_test, y_predicted, average='binary') f1 = f1_score(y_test, y_predicted, average='binary') return accuracy, precision, recall, f1 accuracy, precision, recall, f1 = get_metrics(y_test, y_pred) print("accuracy = %.3f \nprecision = %.3f \nrecall = %.3f \nf1 = %.3f" % (accuracy, precision, recall, f1)) ''' start_time = time.time() importances = clf.feature_importances_ std = np.std([ tree.feature_importances_ for tree in clf.estimators_], axis=0) elapsed_time = time.time() - start_time print(f"Elapsed time to compute the importances: " f"{elapsed_time:.3f} seconds") print(importances) forest_importances = pd.Series(importances, index=cat_cols) fig, ax = plt.subplots() fig.set_figheight(10) fig.set_figwidth(10) forest_importances.plot.bar(yerr=std, ax=ax) ax.set_title("Feature importances using MDI") ax.set_ylabel("Mean decrease in impurity") fig.tight_layout() ''' ###Output _____no_output_____
Notebooks/covid-19-caution/SEIR_COVID19-original.ipynb	###Markdown Model Equations\begin{equation}\begin{split}\dot{S} &= -\beta_1 I_1 S -\beta_2 I_2 S - \beta_3 I_3 S\\\dot{E} &=\beta_1 I_1 S +\beta_2 I_2 S + \beta_3 I_3 S - a E \\\dot{I_1} &= a E - \gamma_1 I_1 - p_1 I_1 \\\dot{I_2} &= p_1 I_1 -\gamma_2 I_2 - p_2 I_2 \\\dot{I_3} & = p_2 I_2 -\gamma_3 I_3 - \mu I_3 \\\dot{R} & = \gamma_1 I_1 + \gamma_2 I_2 + \gamma_3 I_3 \\\dot{D} & = \mu I_3\end{split}\end{equation} Variables* $S$: Susceptible individuals* $E$: Exposed individuals - infected but not yet infectious or symptomatic* $I_i$: Infected individuals in severity class $i$. Severity increaes with $i$ and we assume individuals must pass through all previous classes * $I_1$: Mild infection (hospitalization not required) * $I_2$: Severe infection (hospitalization required) * $I_3$: Critical infection (ICU required)* $R$: individuals who have recovered from disease and are now immune* $D$: Dead individuals* $N=S+E+I_1+I_2+I_3+R+D$ Total population size (constant) Parameters* $\beta_i$ rate at which infected individuals in class $I_i$ contact susceptibles and infect them* $a$ rate of progression from the exposed to infected class* $\gamma_i$ rate at which infected individuals in class $I_i$ recover from disease and become immune* $p_i$ rate at which infected individuals in class $I_i$ progress to class $I_{I+1}$* $\mu$ death rate for individuals in the most severe stage of disease Basic reproductive ratioIdea: $R_0$ is the sum of 1. the average number of secondary infections generated from an individual in stage $I_1$2. the probability that an infected individual progresses to $I_2$ multiplied by the average number of secondary infections generated from an individual in stage $I_2$3. the probability that an infected individual progresses to $I_3$ multiplied by the average number of secondary infections generated from an individual in stage $I_3$\begin{equation}\begin{split}R_0 & = N\frac{\beta_1}{p_1+\gamma_1} + \frac{p_1}{p_1 + \gamma_1} \left( \frac{N \beta_2}{p_2+\gamma_2} + \frac{p_2}{p_2 + \gamma_2} \frac{N \beta_3}{\mu+\gamma_3}\right)\\&= N\frac{\beta_1}{p_1+\gamma_1} \left(1 + \frac{p_1}{p_2 + \gamma_2}\frac{\beta_2}{\beta_1} \left( 1 + \frac{p_2}{\mu + \gamma_3} \frac{\beta_3}{\beta_2} \right) \right)\end{split}\end{equation} ###Code import numpy as np, matplotlib.pyplot as plt from scipy.integrate import odeint #Defining the differential equations #Don't track S because all variables must add up to 1 #include blank first entry in vector for beta, gamma, p so that indices align in equations and code. #In the future could include recovery or infection from the exposed class (asymptomatics) def seir(y,t,b,a,g,p,u,N): dy=[0,0,0,0,0,0] S=N-sum(y); dy[0]=np.dot(b[1:3],y[1:3])S-ay[0] # E dy[1]= ay[0]-(g[1]+p[1])y[1] #I1 dy[2]= p[1]y[1] -(g[2]+p[2])y[2] #I2 dy[3]= p[2]y[2] -(g[3]+u)y[3] #I3 dy[4]= np.dot(g[1:3],y[1:3]) #R dy[5]=uy[3] #D return dy # Define parameters based on clinical observations #I will add sources soon # https://github.com/midas-network/COVID-19/tree/master/parameter_estimates/2019_novel_coronavirus IncubPeriod=5 #Incubation period, days DurMildInf=10 #Duration of mild infections, days FracMild=0.8 #Fraction of infections that are mild FracSevere=0.15 #Fraction of infections that are severe FracCritical=0.05 #Fraction of infections that are critical CFR=0.02 #Case fatality rate (fraction of infections resulting in death) TimeICUDeath=7 #Time from ICU admission to death, days DurHosp=11 #Duration of hospitalization, days # Define parameters and run ODE N=1000 b=np.zeros(4) #beta g=np.zeros(4) #gamma p=np.zeros(3) a=1/IncubPeriod u=(1/TimeICUDeath)(CFR/FracCritical) g[3]=(1/TimeICUDeath)-u p[2]=(1/DurHosp)(FracCritical/(FracCritical+FracSevere)) g[2]=(1/DurHosp)-p[2] g[1]=(1/DurMildInf)FracMild p[1]=(1/DurMildInf)-g[1] #b=2e-4np.ones(4) # all stages transmit equally b=2.5e-4np.array([0,1,0,0]) # hospitalized cases don't transmit #Calculate basic reproductive ratio R0=N((b[1]/(p[1]+g[1]))+(p[1]/(p[1]+g[1]))(b[2]/(p[2]+g[2])+ (p[2]/(p[2]+g[2]))(b[3]/(u+g[3])))) print("R0 = {0:4.1f}".format(R0)) print(b) print(a) print(g) print(p) print(u) tmax=365 tvec=np.arange(0,tmax,0.1) ic=np.zeros(6) ic[0]=1 soln=odeint(seir,ic,tvec,args=(b,a,g,p,u,N)) soln=np.hstack((N-np.sum(soln,axis=1,keepdims=True),soln)) plt.figure(figsize=(13,5)) plt.subplot(1,2,1) plt.plot(tvec,soln) plt.xlabel("Time (days)") plt.ylabel("Number per 1000 People") plt.legend(("S","E","I1","I2","I3","R","D")) plt.ylim([0,1000]) #Same plot but on log scale plt.subplot(1,2,2) plt.plot(tvec,soln) plt.semilogy() plt.xlabel("Time (days)") plt.ylabel("Number per 1000 People") plt.legend(("S","E","I1","I2","I3","R","D")) plt.ylim([1,1000]) #plt.tight_layout() # get observed growth rate r (and doubling time) for a particular variable between selected time points #(all infected classes eventually grow at same rate during early infection) #Don't have a simple analytic formula for r for this model due to the complexity of the stages def growth_rate(tvec,soln,t1,t2,i): i1=np.where(tvec==t1)[0][0] i2=np.where(tvec==t2)[0][0] r=(np.log(soln[i2,1])-np.log(soln[i1,1]))/(t2-t1) DoublingTime=np.log(2)/r return r, DoublingTime (r,DoublingTime)=growth_rate(tvec,soln,10,20,1) print("The epidemic growth rate is = {0:4.2f} per day and the doubling time {1:4.1f} days ".format(r,DoublingTime)) ###Output The epidemic growth rate is = 0.08 per day and the doubling time 9.0 days ###Markdown Repeat but with a social distancing measure that reduces transmission rate ###Code bSlow=0.6b R0Slow=N((bSlow[1]/(p[1]+g[1]))+(p[1]/(p[1]+g[1]))(bSlow[2]/(p[2]+g[2])+ (p[2]/(p[2]+g[2]))(bSlow[3]/(u+g[3])))) solnSlow=odeint(seir,ic,tvec,args=(bSlow,a,g,p,u,N)) solnSlow=np.hstack((N-np.sum(solnSlow,axis=1,keepdims=True),solnSlow)) plt.figure(figsize=(13,5)) plt.subplot(1,2,1) plt.plot(tvec,solnSlow) plt.xlabel("Time (days)") plt.ylabel("Number per 1000 People") plt.legend(("S","E","I1","I2","I3","R","D")) plt.ylim([0,1000]) #Same plot but on log scale plt.subplot(1,2,2) plt.plot(tvec,solnSlow) plt.semilogy() plt.xlabel("Time (days)") plt.ylabel("Number per 1000 People") plt.legend(("S","E","I1","I2","I3","R","D")) plt.ylim([1,1000]) (rSlow,DoublingTimeSlow)=growth_rate(tvec,solnSlow,30,40,1) plt.show() print("R0 under intervention = {0:4.1f}".format(R0Slow)) print("The epidemic growth rate is = {0:4.2f} per day and the doubling time {1:4.1f} days ".format(rSlow,DoublingTimeSlow)) ###Output _____no_output_____ ###Markdown Compare epidemic growth with and without intervention ###Code ### All infectious cases (not exposed) plt.figure(figsize=(13,5)) plt.subplot(1,2,1) plt.plot(tvec,np.sum(soln[:,2:5],axis=1,keepdims=True)) plt.plot(tvec,np.sum(solnSlow[:,2:5],axis=1,keepdims=True)) plt.xlabel("Time (days)") plt.ylabel("Number per 1000 People") plt.legend(("No intervention","Intervention")) plt.ylim([0,1000]) plt.title('All infectious cases') ###Output _____no_output_____ ###Markdown COVID19 Cases vs Hospital Capacity Depending on the severity ($I_i$) stage of COVID-19 infection, patients need different level of medical care. Individuals in $I_1$ have "mild" infection, meaning they have cough/fever/other flu-like symptoms and may also have mild pneumonia. Mild pneumonia does not require hospitalization, although in many outbreak locations like China and South Korea all symptomatic patients are being hospitalized. This is likely to reduce spread and to monitor these patients in case they rapidly progress to worse outcome. However, it is a huge burden on the health care system.Individuals in $I_2$ have "severe" infection, which is categorized medically as having any of the following: "dyspnea, respiratory frequency 30/min, blood oxygen saturation 93%, partial pressure of arterial oxygen to fraction of inspired oxygen ratio $$50% within 24 to 48 hours". These individuals require hospitalization but can be treated on regular wards. They may require supplemental oxygen. Individuals in $I_3$ have "critical" infection, which is categorized as having any of the following: "respiratory failure, septic shock, and/or multiple organ dysfunction or failure".They require ICU-level care, generally because they need mechanical ventilation. We consider different scenarios for care requirements. One variation between scenarios is whether we include hospitalization for all individuals or only those with severe or critical infection. Another is the care of critical patients. If ICUs are full, hospitals have protocols developed for pandemic influenza to provide mechanical ventilation outside regular ICU facility and staffing requirements. Compared to "conventional" ventilation protocols, there are "contingency" and "crisis" protocols that can be adopted to increase patient loads. These protocols involve increasing patient:staff ratios, using non-ICU beds, and involving non-critical care specialists in patient care. ###Code #Parameter sources: https://docs.google.com/spreadsheets/d/1zZKKnZ47lqfmUGYDQuWNnzKnh-IDMy15LBaRmrBcjqE # All values are adjusted for increased occupancy due to flu season AvailHospBeds=2.6(1-0.661.1) #Available hospital beds per 1000 ppl in US based on total beds and occupancy AvailICUBeds=0.26(1-0.681.07) #Available ICU beds per 1000 ppl in US, based on total beds and occupancy. Only counts adult not neonatal/pediatric beds ConvVentCap=0.062 #Estimated excess # of patients who could be ventilated in US (per 1000 ppl) using conventional protocols ContVentCap=0.15 #Estimated excess # of patients who could be ventilated in US (per 1000 ppl) using contingency protocols CrisisVentCap=0.42 #Estimated excess # of patients who could be ventilated in US (per 1000 ppl) using crisis protocols ###Output _____no_output_____ ###Markdown Assumptions 1 Only severe or critical cases go to the hospital* All critical cases require ICU care and mechanical ventilation ###Code NumHosp=soln[:,3]+soln[:,4] NumICU=soln[:,4] plt.figure(figsize=(13,4.8)) plt.subplot(1,2,1) plt.plot(tvec,NumHosp) plt.plot(np.array((0, tmax)),AvailHospBedsnp.ones(2),color='C0',linestyle=":") plt.xlabel("Time (days)") plt.ylabel("Number Per 1000 People") plt.legend(("Cases Needing Hospitalization","Available Hospital Beds")) ipeakHosp=np.argmax(NumHosp) #find peak peakHosp=10np.ceil(NumHosp[ipeakHosp]/10)#find time at peak plt.ylim([0,peakHosp]) plt.subplot(1,2,2) plt.plot(tvec,NumICU,color='C1') plt.plot(np.array((0, tmax)),AvailICUBedsnp.ones(2),color='C1',linestyle=":") plt.xlabel("Time (days)") plt.ylabel("Number Per 1000 People") plt.legend(("Cases Needing ICU","Available ICU Beds")) ipeakICU=np.argmax(NumICU) #find peak peakICU=10np.ceil(NumICU[ipeakICU]/10)#find time at peak plt.ylim([0,peakICU]) plt.ylim([0,10]) #Find time when hospitalized cases = capacity icross=np.argmin(np.abs(NumHosp[0:ipeakHosp]-AvailHospBeds)) #find intersection before peak TimeFillBeds=tvec[icross] #Find time when ICU cases = capacity icross=np.argmin(np.abs(NumICU[0:ipeakICU]-AvailICUBeds)) #find intersection before peak TimeFillICU=tvec[icross] plt.show() print("Hospital and ICU beds are filled by COVID19 patients after {0:4.1f} and {1:4.1f} days".format(TimeFillBeds,TimeFillICU)) ###Output _____no_output_____ ###Markdown Note that we have not taken into account the limited capacity in the model itself. If hospitals are at capacity, then the death rate will increase, since individuals with severe and critical infection will often die without medical care. The transmission rate will probably also increase, since any informal home-care for these patients will likely not include the level of isolation/precautions used in a hospital. Allow for mechanical ventilation outside of ICUs using contingency or crisis capacity ###Code plt.plot(tvec,NumICU) plt.plot(np.array((0, tmax)),ConvVentCapnp.ones(2),linestyle=":") plt.plot(np.array((0, tmax)),ContVentCapnp.ones(2),linestyle=":") plt.plot(np.array((0, tmax)),CrisisVentCapnp.ones(2),linestyle=":") plt.xlabel("Time (days)") plt.ylabel("Number Per 1000 People") plt.legend(("Cases Needing Mechanical Ventilation","Conventional Capacity","Contingency Capacity","Crisis Capacity")) plt.ylim([0,peakICU]) plt.ylim([0,10]) #Find time when ICU cases = conventional capacity icrossConv=np.argmin(np.abs(NumICU[0:ipeakICU]-ConvVentCap)) #find intersection before peak TimeConvCap=tvec[icrossConv] icrossCont=np.argmin(np.abs(NumICU[0:ipeakICU]-ContVentCap)) #find intersection before peak TimeContCap=tvec[icrossCont] icrossCrisis=np.argmin(np.abs(NumICU[0:ipeakICU]-CrisisVentCap)) #find intersection before peak TimeCrisisCap=tvec[icrossCrisis] plt.show() print("Capacity for mechanical ventilation is filled by COVID19 patients after {0:4.1f} (conventional), {1:4.1f} (contingency) and {2:4.1f} (crisis) days".format(TimeConvCap,TimeContCap,TimeCrisisCap)) ###Output _____no_output_____ ###Markdown Compare to the case with intervention ###Code NumHospSlow=solnSlow[:,3]+solnSlow[:,4] NumICUSlow=solnSlow[:,4] plt.figure(figsize=(13,4.8)) plt.subplot(1,2,1) plt.plot(tvec,NumHosp) plt.plot(tvec,NumHospSlow,color='C0',linestyle="--") plt.plot(np.array((0, tmax)),AvailHospBedsnp.ones(2),color='C0',linestyle=":") plt.xlabel("Time (days)") plt.ylabel("Number Per 1000 People") plt.legend(("Cases Needing Hospitalization","Cases Needing Hospitalization (Intervetion)","Available Hospital Beds")) plt.ylim([0,peakHosp]) plt.subplot(1,2,2) plt.plot(tvec,NumICU,color='C1') plt.plot(tvec,NumICUSlow,color='C1',linestyle="--") plt.plot(np.array((0, tmax)),AvailICUBedsnp.ones(2),color='C1',linestyle=":") plt.xlabel("Time (days)") plt.ylabel("Number Per 1000 People") plt.legend(("Cases Needing ICU","Cases Needing ICU (Intervetion)","Available ICU Beds")) plt.ylim([0,peakICU]) #Find time when hospitalized cases = capacity ipeakHospSlow=np.argmax(NumHospSlow) #find peak icross=np.argmin(np.abs(NumHospSlow[0:ipeakHospSlow]-AvailHospBeds)) #find intersection before peak TimeFillBedsSlow=tvec[icross] #Find time when ICU cases = capacity ipeakICUSlow=np.argmax(NumICUSlow) #find peak icross=np.argmin(np.abs(NumICUSlow[0:ipeakICU]-AvailICUBeds)) #find intersection before peak TimeFillICUSlow=tvec[icross] plt.show() print("With intervention, hospital and ICU beds are filled by COVID19 patients after {0:4.1f} and {1:4.1f} days".format(TimeFillBedsSlow,TimeFillICUSlow)) ###Output _____no_output_____ ###Markdown And for expanded mechanical ventilation capacity ###Code plt.plot(tvec,NumICU) plt.plot(tvec,NumICUSlow) plt.plot(np.array((0, tmax)),ConvVentCapnp.ones(2),linestyle=":") plt.plot(np.array((0, tmax)),ContVentCapnp.ones(2),linestyle=":") plt.plot(np.array((0, tmax)),CrisisVentCapnp.ones(2),linestyle=":") plt.xlabel("Time (days)") plt.ylabel("Number Per 1000 People") plt.legend(("Cases Needing Mechanical Ventilation","Cases Needing Mechanical Ventilation (Intervention)","Conventional Capacity","Contingency Capacity","Crisis Capacity")) plt.ylim([0,peakICU]) #Find time when ICU cases = conventional capacity (with intervention) icrossConvSlow=np.argmin(np.abs(NumICUSlow[0:ipeakICUSlow]-ConvVentCap)) #find intersection before peak TimeConvCapSlow=tvec[icrossConvSlow] icrossContSlow=np.argmin(np.abs(NumICUSlow[0:ipeakICUSlow]-ContVentCap)) #find intersection before peak TimeContCapSlow=tvec[icrossContSlow] icrossCrisisSlow=np.argmin(np.abs(NumICUSlow[0:ipeakICUSlow]-CrisisVentCap)) #find intersection before peak TimeCrisisCapSlow=tvec[icrossCrisisSlow] plt.show() print("Capacity for mechanical ventilation is filled by COVID19 patients after {0:4.1f} (conventional), {1:4.1f} (contingency) and {2:4.1f} (crisis) days".format(TimeConvCapSlow,TimeContCapSlow,TimeCrisisCapSlow)) ###Output _____no_output_____
jupyter_notebooks/network_result_visualiztion.ipynb	###Markdown Load the result data ###Code training_result = "/home/wentao/project/keras_training/UNet2D2D_SSIM_loss_no_regularization/20190819-162734/predictions/result.h5" task_name = 'UNet2D2D_SSIM_loss_no_regularization' f = h5py.File(training_result,'r') result = np.array(f.get('result')) truth = np.array(f.get('truth')) imag = np.array(f.get('input')) ###Output _____no_output_____ ###Markdown Calculate the metrics ###Code psnr = [] mse = [] nrmse = [] ssim = [] for i in range(0, truth.shape[0]): psnr.append(measure.compare_psnr(result[i],truth[i], 1)) mse.append(measure.compare_mse(result[i], truth[i])) nrmse.append(measure.compare_nrmse(result[i], truth[i])) ssim.append(measure.compare_ssim(result[i], truth[i], data_range=1)) # find the best and the worst images by ssim value best_image_index = ssim.index(max(ssim)) worst_image_index = ssim.index(min(ssim)) ###Output _____no_output_____ ###Markdown Average mse, nrmse psnr, ssim values ###Code print(np.mean(mse), np.mean(nrmse), np.mean(psnr), np.mean(ssim)) ###Output 0.00199017724690729 0.17560795468353496 27.69450816257319 0.877606626530342 ###Markdown Best image by ssim value ###Code plt.figure(1, figsize=(10,10)) plt.subplot(1, 3, 1) plt.axis('off') plt.title('Input') plt.imshow(imag[best_image_index, :, :, 0], cmap='gray') plt.subplot(1, 3, 2) plt.axis('off') plt.title('Reconstructed Image') plt.imshow(result[best_image_index, :, :, 0], cmap='gray') plt.subplot(1, 3, 3) plt.axis('off') plt.title('Ground Truth') plt.imshow(truth[best_image_index, :, :, 0], cmap='gray') plt.savefig('./images/{task_name}_best_ssim_{ssim:.4f}.jpg'.format(task_name=task_name, ssim=ssim[best_image_index]), bbox_inches='tight') ###Output _____no_output_____ ###Markdown SSIM value of the best image ###Code ssim[best_image_index] ###Output _____no_output_____ ###Markdown Worst image by ssim value ###Code plt.figure(2, figsize=(10,10)) plt.subplot(1, 3, 1) plt.axis('off') plt.title('Input') plt.imshow(imag[worst_image_index, :, :, 0], cmap='gray') plt.subplot(1, 3, 2) plt.axis('off') plt.title('Reconstructed Image') plt.imshow(result[worst_image_index, :, :, 0], cmap='gray') plt.subplot(1, 3, 3) plt.axis('off') plt.title('Ground Truth') plt.imshow(truth[worst_image_index, :, :, 0], cmap='gray') plt.savefig('./images/{task_name}_worst_ssim_{ssim:.4f}.jpg'.format(task_name=task_name, ssim=ssim[worst_image_index]), bbox_inches='tight') ###Output _____no_output_____ ###Markdown SSIM value of the worst image ###Code ssim[worst_image_index] ###Output _____no_output_____ ###Markdown Historgrams ###Code figsize = (5, 5) title = task_name plt.figure(1) plt.title(title) plt.xlabel('SSIM') plt.hist(ssim, bins=50) plt.savefig('./images/{task_name}_hist_SSIM.jpg'.format(task_name=task_name)) plt.figure(2) plt.title(title) plt.xlabel('PSNR') plt.hist(psnr, bins=50) plt.savefig('./images/{task_name}_hist_PSNR.jpg'.format(task_name=task_name)) plt.figure(3) plt.title(title) plt.xlabel('MSE') plt.hist(mse, bins=50) plt.savefig('./images/{task_name}_hist_MSE.jpg'.format(task_name=task_name)) plt.figure(4) plt.title(title) plt.xlabel('NRMSE') plt.hist(nrmse, bins=50) plt.savefig('./images/{task_name}_hist_NRMSE.jpg'.format(task_name=task_name)) import pickle best = [imag[best_image_index, :, :, 0], result[best_image_index, :, :, 0], truth[best_image_index, :, :, 0]] worst = [imag[worst_image_index, :, :, 0], result[worst_image_index, :, :, 0], truth[worst_image_index, :, :, 0]] plot_data = {'best_images': best, 'worst_images': worst, 'ssim': ssim, 'psnr': psnr} with open(task_name+"_plot_data.pkl", "wb") as f: pickle.dump(plot_data, f) ###Output _____no_output_____
notebooks/3_Modelling.ipynb	###Markdown Scores of baseline model and seven regression models--- ###Code ## load modules import os import sys sys.path.append("..") import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from modeling.functions import baseline, modelling_fc, get_features from sklearn.neighbors import KNeighborsRegressor from sklearn.linear_model import LinearRegression from sklearn.ensemble import RandomForestRegressor from xgboost import XGBRegressor from lightgbm import LGBMRegressor from sklearn.preprocessing import MinMaxScaler RSEED = 42 ###Output _____no_output_____ ###Markdown Preparation of data for modelling Read data, linearly interpolate missing values and create dummies for cardinal wind directions. ###Code data = pd.read_csv('../data/GEFCom2014Data/Wind/raw_data_incl_features.csv', parse_dates=['TIMESTAMP'], index_col='TIMESTAMP') data.interpolate(method='linear', inplace=True) data = pd.get_dummies(data, columns = ['WD100CARD','WD10CARD'], drop_first=True) data.head() ###Output _____no_output_____ ###Markdown Use the first 75 % of the data as training/validation set and the last 25 % as a test set. In addition, get a dictionary with different feature combinations. ###Code data_train = data[:'2013-07-01 00:00:00'] data_test = data['2013-07-01 01:00:00':] feature_dict = get_features(data) ###Output _____no_output_____ ###Markdown Run models Run models by calling the function "modelling_fc" and save scores and model parameter in "../results/{MODEL}.csv". ###Code ## Define a list with models to be run. Pass 'Baseline' for the baseline model, otherwise the model object. models = ['Baseline'] for model in models: # Baseline if model == 'Baseline': results = baseline(data_train, data_test) # RandomForest if model.__class__.__name__ == 'RandomForestRegressor': param_grid = {'n_estimators' : [100,150], 'max_depth' : np.arange(15,31,5), 'min_samples_leaf' : np.arange(10,21,10)} results = modelling_fc(data_train, data_test, feature_dict, model, param_grid = param_grid, scaler=MinMaxScaler(), n_jobs=3) # Linear regression if model.__class__.__name__ == 'LinearRegression': param_grid = {'fit_intercept' : [True]} results = modelling_fc(data_train, data_test, feature_dict, model, param_grid = param_grid, scaler=MinMaxScaler(), n_jobs=-1) # XGBoost if model.__class__.__name__ == 'XGBRegressor': param_grid = {'random_state' : [RSEED]} results = modelling_fc(data_train, data_test, feature_dict, model, param_grid = param_grid, scaler = MinMaxScaler(), n_jobs=-1) # LGBM if model.__class__.__name__ == 'LGBMRegressor': param_grid = [{'n_estimators' : [100]}, {'n_estimators' : [1000], 'num_leaves' : [20]}, {'n_estimators' : [50], 'num_leaves': [62]}] results = modelling_fc(data_train, data_test, feature_dict, model, param_grid = param_grid, scaler = MinMaxScaler(), n_jobs=-1) # KNN if model.__class__.__name__ == 'KNeighborsRegressor': param_grid = {'n_neighbors' : np.arange(20,141,10), 'weights' : ['uniform','distance'], 'p' : [1,2]} results = modelling_fc(data_train, data_test, feature_dict, model, scaler = MinMaxScaler(), param_grid = param_grid) # remove file before new file is created if os.path.isfile(f'../results/{results.MODEL.iloc[1]}.csv'): os.remove(f'../results/{results.MODEL.iloc[1]}.csv') # save results in csv file results.to_csv(f'../results/{results.MODEL.iloc[1]}.csv') ###Output _____no_output_____ ###Markdown Results Plot validation-RMSE and test-RMSE by model for predictions aggregated over all wind farms. ###Code ## define models to plot models = ['Baseline','LinearRegression', 'KNeighborsRegressor', 'RandomForestRegressor','LinearSVR', 'SVR', 'LGBMRegressor', 'XGBRegressor'] ## collect scores in the dataframe "scores" scores= pd.DataFrame(index = models, columns = ['TESTSCORE','VALSCORE']) for model in models: df = pd.read_csv(f'../results/{model}.csv', index_col='ZONE') if model == 'Baseline': scores.loc[model]['VALSCORE'] = df.loc['TOTAL'].TRAINSCORE scores.loc[model]['TESTSCORE'] = df.loc['TOTAL'].TESTSCORE else: scores.loc[model]['VALSCORE'] = np.sqrt(np.mean(df.CV2)) scores.loc[model]['TESTSCORE'] = np.sqrt(np.mean(df.TESTSCORE2)) scores.index.set_names('MODEL', inplace=True) scores.reset_index(inplace=True) ## plot for validation and test set for scoretype in ['VALSCORE', 'TESTSCORE']: scores = scores.sort_values(by = scoretype, ascending = False, ignore_index = True) fontsize=8 palette = ['blue'] + 6 * ['gray'] + ['red'] fig, ax = plt.subplots(dpi=400, figsize=(6,3)) bp = sns.barplot(data = scores, x = 'MODEL', y = scoretype, ax=ax, dodge=False, palette = palette) ax.set_xticklabels(labels=ax.get_xticklabels(), rotation=45, ha='right', fontsize=fontsize) ax.set(xlabel=None) ax.set_ylabel('RMSE [-]', fontsize=fontsize) ax.set_ylim([.0,.35]); ax.yaxis.grid() ax.tick_params(axis = 'both', labelsize = fontsize) if scoretype == 'VALSCORE': for index, row in scores.iterrows(): bp.text(row.name, row.VALSCORE + row.VALSCORE/100, '{:.3f}'.format(row.VALSCORE), ha='center', fontsize=fontsize) fig.savefig('../images/VAL_RMSE-By-Models_Aggregated.png') elif scoretype == 'TESTSCORE': for index, row in scores.iterrows(): bp.text(row.name, row.TESTSCORE + row.TESTSCORE/100, '{:.3f}'.format(row.TESTSCORE), ha='center', fontsize=fontsize) fig.savefig('../images/TEST_RMSE-By-Models_Aggregated.png') ###Output _____no_output_____ ###Markdown Plot validation-RMSE and test-RMSE by wind farm for different models. ###Code ## Plot RMSE by wind farm for different models scores = pd.DataFrame(columns = ['ZONE', 'MODEL', 'VALSCORE', 'TESTSCORE']) scores.set_index('ZONE', inplace=True) ## select models models = ['LinearRegression', 'KNeighborsRegressor', 'RandomForestRegressor','LinearSVR', 'SVR', 'LGBMRegressor', 'XGBRegressor'] for model in models: df = pd.read_csv(f'../results/{model}.csv', index_col='ZONE') df = df[['CV', 'TESTSCORE', 'MODEL']] df.rename(columns={'CV':'VALSCORE'}, inplace=True) scores = scores.append(df) scores = scores.loc[scores.index != 'TOTAL'] ranges = scores[scores.MODEL != 'RandomForestRegressor'] scores = scores[scores.MODEL == 'RandomForestRegressor'][['VALSCORE', 'TESTSCORE']] for scoretype in ['VAL', 'TEST']: scores['MINVAL'] = [ranges.loc[zone][scoretype + 'SCORE'].min() for zone in ranges.index.unique()] scores['MAXVAL'] = [ranges.loc[zone][scoretype + 'SCORE'].max() for zone in ranges.index.unique()] ## plot fig, ax = plt.subplots(dpi=400, figsize=(6,3)) ax.plot(scores.index.unique(), scores[scoretype + 'SCORE'], color='r', linestyle='--', marker='.', markersize=5, linewidth=.7) ax.fill_between(scores.index.unique(), scores['MINVAL'], scores['MAXVAL'], color = 'gray', alpha=.5, edgecolor=None); ax.set_xlabel('wind farm',fontsize=fontsize) ax.set_ylabel('RMSE [-]', fontsize=fontsize) ax.grid(linewidth=.3) ax.legend(['RandomForestRegressor','range over all models without RandomForestRegressor'], fontsize=fontsize - 2, loc = 'upper left') ax.set_xticklabels([zone[-1] if len(zone) == 5 else zone[-2:] for zone in scores.index]); ax.set_xticklabels(range(1,11)); fig.savefig('{}{}{}'.format('../images/',scoretype,'_RMSE-By-Windfarms.png')) ###Output _____no_output_____
examples/Schrodinger.ipynb	###Markdown Install deepxde Tensorflow and all other dependencies are already installed in Colab terminals ###Code !pip install deepxde ###Output Collecting deepxde Downloading DeepXDE-0.14.0-py3-none-any.whl (111 kB) [?25l [K \|███ \| 10 kB 22.0 MB/s eta 0:00:01 [K \|█████▉ \| 20 kB 21.5 MB/s eta 0:00:01 [K \|████████▉ \| 30 kB 10.9 MB/s eta 0:00:01 [K \|███████████▊ \| 40 kB 8.5 MB/s eta 0:00:01 [K \|██████████████▊ \| 51 kB 5.5 MB/s eta 0:00:01 [K \|█████████████████▋ \| 61 kB 5.6 MB/s eta 0:00:01 [K \|████████████████████▋ \| 71 kB 5.5 MB/s eta 0:00:01 [K \|███████████████████████▌ \| 81 kB 6.2 MB/s eta 0:00:01 [K \|██████████████████████████▍ \| 92 kB 4.9 MB/s eta 0:00:01 [K \|█████████████████████████████▍ \| 102 kB 5.3 MB/s eta 0:00:01 [K \|████████████████████████████████\| 111 kB 5.3 MB/s [?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from deepxde) (1.0.1) Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from deepxde) (3.2.2) Collecting scikit-optimize Downloading scikit_optimize-0.9.0-py2.py3-none-any.whl (100 kB) [K \|████████████████████████████████\| 100 kB 8.9 MB/s [?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from deepxde) (1.19.5) Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from deepxde) (1.4.1) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->deepxde) (3.0.6) Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->deepxde) (2.8.2) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->deepxde) (0.11.0) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->deepxde) (1.3.2) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->deepxde) (1.15.0) Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->deepxde) (1.1.0) Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->deepxde) (3.0.0) Collecting pyaml>=16.9 Downloading pyaml-21.10.1-py2.py3-none-any.whl (24 kB) Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from pyaml>=16.9->scikit-optimize->deepxde) (3.13) Installing collected packages: pyaml, scikit-optimize, deepxde Successfully installed deepxde-0.14.0 pyaml-21.10.1 scikit-optimize-0.9.0 ###Markdown Problem setup We are going to solve the non-linear Schrödinger equation given by $i h_t + \frac{1}{2} h_{xx} + \|h\|^2h = 0$ with periodic boundary conditions as $x \in [-5,5], \quad t \in [0, \pi/2]$ $h(t, -5) = h(t,5)$ $h_x(t, -5) = h_x(t,5)$ and initial condition equal to $h(0,x) = 2 sech(x)$ Deepxde only uses real numbers, so we need to explicitly split the real and imaginary parts of the complex PDE. In place of the single residual $f = ih_t + \frac{1}{2} h_{xx} +\|h\|^2 h$ we get the two (real valued) residuals $f_{\mathcal{R}} = u_t + \frac{1}{2} v_{xx} + (u^2 + v^2)v$ $f_{\mathcal{I}} = v_t - \frac{1}{2} u_{xx} - (u^2 + v^2)u$ where u(x,t) and v(x,t) denote respectively the real and the imaginary part of h. ###Code import numpy as np import deepxde as dde # For plotting import matplotlib.pyplot as plt from scipy.interpolate import griddata x_lower = -5 x_upper = 5 t_lower = 0 t_upper = np.pi / 2 # Creation of the 2D domain (for plotting and input) x = np.linspace(x_lower, x_upper, 256) t = np.linspace(t_lower, t_upper, 201) X, T = np.meshgrid(x, t) # The whole domain flattened X_star = np.hstack((X.flatten()[:, None], T.flatten()[:, None])) # Space and time domains/geometry (for the deepxde model) space_domain = dde.geometry.Interval(x_lower, x_upper) time_domain = dde.geometry.TimeDomain(t_lower, t_upper) geomtime = dde.geometry.GeometryXTime(space_domain, time_domain) # The "physics-informed" part of the loss def pde(x, y): """ INPUTS: x: x[:,0] is x-coordinate x[:,1] is t-coordinate y: Network output, in this case: y[:,0] is u(x,t) the real part y[:,1] is v(x,t) the imaginary part OUTPUT: The pde in standard form i.e. something that must be zero """ u = y[:, 0:1] v = y[:, 1:2] # In 'jacobian', i is the output component and j is the input component u_t = dde.grad.jacobian(y, x, i=0, j=1) v_t = dde.grad.jacobian(y, x, i=1, j=1) u_x = dde.grad.jacobian(y, x, i=0, j=0) v_x = dde.grad.jacobian(y, x, i=1, j=0) # In 'hessian', i and j are both input components. (The Hessian could be in principle something like d^2y/dxdt, d^2y/d^2x etc) # The output component is selected by "component" u_xx = dde.grad.hessian(y, x, component=0, i=0, j=0) v_xx = dde.grad.hessian(y, x, component=1, i=0, j=0) f_u = u_t + 0.5 * v_xx + (u 2 + v 2) * v f_v = v_t - 0.5 * u_xx - (u 2 + v 2) * u return [f_u, f_v] # Boundary and Initial conditions # Periodic Boundary conditions bc_u_0 = dde.PeriodicBC( geomtime, 0, lambda _, on_boundary: on_boundary, derivative_order=0, component=0 ) bc_u_1 = dde.PeriodicBC( geomtime, 0, lambda _, on_boundary: on_boundary, derivative_order=1, component=0 ) bc_v_0 = dde.PeriodicBC( geomtime, 0, lambda _, on_boundary: on_boundary, derivative_order=0, component=1 ) bc_v_1 = dde.PeriodicBC( geomtime, 0, lambda _, on_boundary: on_boundary, derivative_order=1, component=1 ) # Initial conditions def init_cond_u(x): "2 sech(x)" return 2 / np.cosh(x[:, 0:1]) def init_cond_v(x): return 0 ic_u = dde.IC(geomtime, init_cond_u, lambda _, on_initial: on_initial, component=0) ic_v = dde.IC(geomtime, init_cond_v, lambda _, on_initial: on_initial, component=1) data = dde.data.TimePDE( geomtime, pde, [bc_u_0, bc_u_1, bc_v_0, bc_v_1, ic_u, ic_v], num_domain=10000, num_boundary=20, num_initial=200, train_distribution="pseudo", ) # Network architecture net = dde.maps.FNN([2] + [100] * 4 + [2], "tanh", "Glorot normal") model = dde.Model(data, net) ###Output _____no_output_____ ###Markdown Adam optimization. ###Code # To employ a GPU accelerated system is highly encouraged. model.compile("adam", lr=1e-3, loss="MSE") model.train(epochs=10000, display_every=1000) ###Output Compiling model... Building feed-forward neural network... 'build' took 0.103608 s ###Markdown L-BFGS optimization. ###Code dde.optimizers.config.set_LBFGS_options( maxcor=50, ftol=1.0 * np.finfo(float).eps, gtol=1e-08, maxiter=10000, maxfun=10000, maxls=50, ) model.compile("L-BFGS") model.train() ###Output Compiling model... 'compile' took 0.795132 s Training model... Step Train loss Test loss Test metric 10000 [5.98e-04, 5.54e-04, 2.52e-06, 3.60e-06, 8.98e-07, 1.62e-06, 5.89e-04, 6.80e-06] [5.98e-04, 5.54e-04, 2.52e-06, 3.60e-06, 8.98e-07, 1.62e-06, 5.89e-04, 6.80e-06] [] 11000 [3.11e-05, 3.23e-05, 1.71e-07, 4.36e-07, 1.81e-07, 2.50e-07, 7.96e-06, 5.44e-07] 12000 [7.32e-06, 1.00e-05, 9.68e-08, 2.63e-07, 1.82e-07, 1.37e-07, 4.86e-06, 3.68e-07] 13000 [3.49e-06, 4.89e-06, 5.19e-08, 3.75e-07, 1.72e-07, 8.74e-08, 3.18e-06, 1.80e-07] 14000 [2.45e-06, 3.06e-06, 1.79e-08, 2.69e-07, 1.80e-07, 3.77e-08, 2.35e-06, 1.47e-07] 15000 [1.61e-06, 2.15e-06, 1.59e-08, 1.69e-07, 1.59e-07, 1.20e-08, 2.04e-06, 1.13e-07] 16000 [1.34e-06, 1.59e-06, 8.94e-09, 1.29e-07, 1.42e-07, 6.53e-09, 1.82e-06, 8.62e-08] INFO:tensorflow:Optimization terminated with: Message: b'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTREPSMCH' Objective function value: 0.000005 Number of iterations: 6179 Number of functions evaluations: 6589 16589 [1.18e-06, 1.40e-06, 7.96e-09, 1.25e-07, 1.25e-07, 7.69e-09, 1.74e-06, 9.01e-08] [1.18e-06, 1.40e-06, 7.96e-09, 1.25e-07, 1.25e-07, 7.69e-09, 1.74e-06, 9.01e-08] [] Best model at step 16589: train loss: 4.67e-06 test loss: 4.67e-06 test metric: [] 'train' took 437.862604 s ###Markdown Final results. The reference solution and further information can be found in [this paper](https://arxiv.org/abs/1711.10561) from Raissi, Karniadakis, Perdikaris. The test data can be got [here](https://github.com/maziarraissi/PINNs/blob/master/main/Data/NLS.mat). ###Code # Make prediction prediction = model.predict(X_star, operator=None) u = griddata(X_star, prediction[:, 0], (X, T), method="cubic") v = griddata(X_star, prediction[:, 1], (X, T), method="cubic") h = np.sqrt(u * 2 + v ** 2) # Plot predictions fig, ax = plt.subplots(3) ax[0].set_title("Results") ax[0].set_ylabel("Real part") ax[0].imshow( u.T, interpolation="nearest", cmap="viridis", extent=[t_lower, t_upper, x_lower, x_upper], origin="lower", aspect="auto", ) ax[1].set_ylabel("Imaginary part") ax[1].imshow( v.T, interpolation="nearest", cmap="viridis", extent=[t_lower, t_upper, x_lower, x_upper], origin="lower", aspect="auto", ) ax[2].set_ylabel("Amplitude") ax[2].imshow( h.T, interpolation="nearest", cmap="viridis", extent=[t_lower, t_upper, x_lower, x_upper], origin="lower", aspect="auto", ) plt.show() ###Output _____no_output_____
contents/pandas/part4.ipynb	###Markdown Tópicos extraEn este anexo se revisan algunos tópicos específicos relacionados a la librería `pandas` que no fueron cubiertos anteriormente, estos son- Objeto `pandas.Series`- Gráficos a partir de objetos de pandas- Guardar y leer datos en formato HDF5 ###Code %matplotlib inline import matplotlib.pyplot as plt import pandas as pd ###Output _____no_output_____ ###Markdown Objeto `pandas.Series`El objeto [`pandas.Series`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html) es un arreglo de una dimensión (vector) que representa una secuencia - Los elementos de la secuencia se identifican con un índice etiquetado `index`- Todos los elementos son de un mismo tipo `dtype`- La serie se identifica con un nombre `name`A continuación veremos algunas formas de crear `Series`Construyendo un objeto `Series` a partir de un dataframeCuando pedimos una columna de un DataFrame el objeto retornado es de tipo `Series`Tecnicamente, una fila de un DataFrame también retorna como `Series` sin embargo los tipos se mezclan ###Code clientes = ['Pablo', 'Marianna', 'Matthieu', 'Luis', 'Eliana', 'Cristobal'] ventas = { 'lechugas [unidades]': [1, 0, 1, 2, 0, 0], 'papas [kilos]': [0.5, 2, 1.5, 1.2, 0, 5] } df = pd.DataFrame(data=ventas, index=clientes) display(f'La columna de lechugas es un objeto {type(df["lechugas [unidades]"])}', f'cuyo tipo es {df["lechugas [unidades]"].dtype}', f'La fila Matthieu es un objeto {type(df.loc["Matthieu"])}', f'cuyo tipo es {df.loc["Matthieu"].dtype}') ###Output _____no_output_____ ###Markdown Construyendo un objeto `Series` a partir de otras estructuras de datosUn objeto `Series` se puede crear de forma más general usando el constructor```pythonpandas.Series(data=None, index=None, dtype=None, name=None, copy=False, fastpath=False)```donde `data` puede ser un diccionarios, una lista o un ndarrayPor ejemplo: ###Code plan_diario= {'dormir': 7, 'comer': 1, 'quehaceres': 1, 'trabajo': 10, 'procastinar': 5} pd.Series(plan_diario, name='mi planificación de hoy') ###Output _____no_output_____ ###Markdown :::{note}- Una columna o una fila de un `DataFrame` es un `Series`- Varias `Series` se pueden unir para formar un `DataFrame`::: Gráfico a partir de DataFramesSe pueden crear gráficos sencillos directamente de un `DataFrame`Puedes revisar en detalle la API para graficar en este [link](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.html) ###Code fig, ax = plt.subplots(figsize=(6, 4), tight_layout=True) df.plot(ax=ax, kind='line', subplots=True); ###Output /tmp/ipykernel_23473/1562864067.py:2: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared. df.plot(ax=ax, kind='line', subplots=True);
chapter-generative-adversarial-networks/PR1358_test_loss_ok.ipynb	###Markdown https://github.com/d2l-ai/d2l-en/pull/1358 ###Code %matplotlib inline from d2l import torch as d2l import torch from torch import nn from torch.utils.data import DataLoader X = torch.normal(0.0, 1, (1000, 2)) A = torch.tensor([[1, 2], [-0.1, 0.5]]) b = torch.tensor([1, 2]) data = torch.mm(X, A) + b d2l.set_figsize() d2l.plt.scatter(data[:100, 0].numpy(), data[:100, 1].numpy()); print(f'The covariance matrix is\n{torch.mm(A.T, A)}') batch_size = 8 data_iter = DataLoader(data, batch_size=batch_size) net_G = nn.Sequential(nn.Linear(2, 2)) net_D = nn.Sequential( nn.Linear(2, 5), nn.Tanh(), nn.Linear(5, 3), nn.Tanh(), nn.Linear(3, 1) ) def update_D(X, Z, net_D, net_G, loss, trainer_D): #@save """Update discriminator.""" batch_size = X.shape[0] ones = torch.ones((batch_size, 1)) zeros = torch.zeros((batch_size, 1)) trainer_D.zero_grad() real_Y = net_D(X) fake_X = net_G(Z) # Do not need to compute gradient for `net_G`, detach it from # computing gradients. fake_Y = net_D(fake_X.detach()) loss_D = (loss(real_Y, ones) + loss(fake_Y, zeros)) / 2 loss_D.backward() trainer_D.step() return loss_D def update_G(Z, net_D, net_G, loss, trainer_G): #@save """Update generator.""" batch_size = Z.shape[0] ones = torch.ones((batch_size, 1)) trainer_G.zero_grad() # We could reuse `fake_X` from `update_D` to save computation fake_X = net_G(Z) # Recomputing `fake_Y` is needed since `net_D` is changed fake_Y = net_D(fake_X) loss_G=loss(fake_Y,ones) loss_G.backward() trainer_G.step() return loss_G def train(net_D, net_G, data_iter, num_epochs, lr_D, lr_G, latent_dim, data): loss = nn.BCEWithLogitsLoss() for w in net_D.parameters(): nn.init.normal_(w, 0, 0.02) for w in net_G.parameters(): nn.init.normal_(w, 0, 0.02) net_D.zero_grad() net_G.zero_grad() trainer_D = torch.optim.Adam(net_D.parameters(), lr=lr_D) trainer_G = torch.optim.Adam(net_G.parameters(), lr=lr_G) animator = d2l.Animator(xlabel='epoch', ylabel='loss', xlim=[1, num_epochs], nrows=2, figsize=(5, 5), legend=['discriminator', 'generator']) animator.fig.subplots_adjust(hspace=0.3) for epoch in range(num_epochs): # Train one epoch timer = d2l.Timer() metric = d2l.Accumulator(3) # loss_D, loss_G, num_examples for X in data_iter: batch_size = X.shape[0] Z = torch.normal(0, 1, size=(batch_size, latent_dim)) trainer_D.zero_grad() trainer_G.zero_grad() metric.add(update_D(X, Z, net_D, net_G, loss, trainer_D), update_G(Z, net_D, net_G, loss, trainer_G), batch_size) # Visualize generated examples Z = torch.normal(0, 1, size=(100, latent_dim)) fake_X = net_G(Z).detach().numpy() animator.axes[1].cla() animator.axes[1].scatter(data[:, 0], data[:, 1]) animator.axes[1].scatter(fake_X[:, 0], fake_X[:, 1]) animator.axes[1].legend(['real', 'generated']) # Show the losses loss_D, loss_G = metric[0]/metric[2], metric[1]/metric[2] animator.add(epoch + 1, (loss_D, loss_G)) print(f'loss_D {loss_D:.3f}, loss_G {loss_G:.3f}, ' f'{metric[2] / timer.stop():.1f} examples/sec') lr_D, lr_G, latent_dim, num_epochs = 0.05, 0.005, 2, 20 train(net_D, net_G, data_iter, num_epochs, lr_D, lr_G, latent_dim, d2l.numpy(data[:100])) ###Output loss_D 0.087, loss_G 0.087, 1067.1 examples/sec
mlcourse.ai assignment/Machine learning from Zero2Hero.ipynb	###Markdown Machine Learning: What and Why? Machine learning has been around for a decades but the main application which got the popularity is spam filter which can be called a proper machine learning which has learned so well that we don't need to flag an email as a spam. We all have a few questions like:1. What is machine learning?2. How to start machine learning project?3. What does it mean for a machine to learn something?4. Why machine learning now?5. Finally, how to approach for machine learning.Before we jump into code, we must answer all the above questions. What is Machine Learning? Machine learning is an art of teaching computer so they can learn from data. For ex. Spam filter is a machone learning program that can learn to flag spam from the given training datasets. Few important terminology:1. The example that system uses to learn is called training instance.2. According to tom mitchell definition of machine learning, the task T is to flag spam for new emails, the experience E is the training data, and the performance measures p to be defined.3. The performance measure is called accuracy and can be calculated by taking the ratio. Why Machine learning? Before machine learning we were using traditional technique to write any algorithm. Let us say we need to write a spam filter using traditional technique:1. First we need to define what actually spam looks like. we need to focus on words like "credit", "free", "offer" and "amazing" which is poular subject line. Perhaps we can look for other patterns like sender's name, the email body and the parts of thye email.2. We have to write an algorithm to detect these patterns and algorithm will flag emails as spam if these patters were detected.3. We would test and repeat untill we find a good model.![alt text](images.png "Title")Since the problem is difficult, algorithm will likely become a long list of complex rules which is hard to maintain.In contrast, a spam filter based on the machine learning technique will learn automatically about the words which words are considered for spam. This is much shorter, easier, and more accurate as compared to traditional approach. See the picture below for more details![alt text](02.png "Title")This machine learning approach automatically find the unusual pattern and marked it as spam without any human intervention but in case of traditional approach everytime, we have to sit and write new rules which is not feasible.So the Machine learning is for problems where the problem are too complex or there is no knows traditional algorithm available.Machine learning can also help human to learn from large set of data which is not easy to guess for humans.Finally, machine learning is great for:1. Problems which has lot of rules.2. Complex problems for which there is no traditional solutions3. Fluctuating environment is not for traditional system because everytime, rule is changing..4. Getting insight about complex problem and large amount of data Types of Machine learning systemMchine learning systems are classifieds on the basis of following criteria:1. Whether they need human supervision (Supervised, Unsupervised, Reinforcement learning)2. Whether or not they can learn incrementally on the fly (Online vs batch learning)3. Comparing new data points to known data point or detecting pattern in the data and building predictive modeling (instance vs model based learning)we can combine these in any form like spam filter may learn on the fly using deep learning model and this is an example of online, model-based supervised learning Let us dicuss one by one: Supervised learning/Unsupervised learningThis system can be classified a/c to the amount of supervision they get during training. There are four major categories: supervised, unsupervised, semi-supervised, re-inforcement learning SupervisedWE fed the training set and labels to the system like this![alt text](03.png "Title") How to approach for Vizualization ###Code import pandas as pd from matplotlib import pyplot as plt import seaborn as sns df = pd.read_csv("telecom_churn.csv") df.head() df.shape df.info() ###Output <class 'pandas.core.frame.DataFrame'> RangeIndex: 3333 entries, 0 to 3332 Data columns (total 20 columns): State 3333 non-null object Account length 3333 non-null int64 Area code 3333 non-null int64 International plan 3333 non-null object Voice mail plan 3333 non-null object Number vmail messages 3333 non-null int64 Total day minutes 3333 non-null float64 Total day calls 3333 non-null int64 Total day charge 3333 non-null float64 Total eve minutes 3333 non-null float64 Total eve calls 3333 non-null int64 Total eve charge 3333 non-null float64 Total night minutes 3333 non-null float64 Total night calls 3333 non-null int64 Total night charge 3333 non-null float64 Total intl minutes 3333 non-null float64 Total intl calls 3333 non-null int64 Total intl charge 3333 non-null float64 Customer service calls 3333 non-null int64 Churn 3333 non-null bool dtypes: bool(1), float64(8), int64(8), object(3) memory usage: 498.1+ KB ###Markdown Whole data visualization To plot a histogram on all dataset, we need to make sure that all the data is in string format ###Code df["International plan"] = df["International plan"].map({"Yes":1, "No":0}) df["Churn"] = df["Churn"].astype("int") plt.rcParams['figure.figsize'] = (16, 12) df.drop(['State'], axis=1).hist() # THis will take a lot of time if you have a big column ###Output _____no_output_____ ###Markdown We can gain a lot of insight from the above picture such as:1. Percentage of people who churned2. How many service call has been made ###Code sns.heatmap(df.corr()) ###Output _____no_output_____ ###Markdown Exploring one feature at a time Numeric feature ###Code df['Total day minutes'].describe() ###Output _____no_output_____ ###Markdown We can do this seperately but this is the best way to have a glance at once ###Code sns.boxplot(x="Total day minutes", data=df) ###Output _____no_output_____ ###Markdown Middle line is median and 25% percentile and 75% percentile, left hand side are outliers who never used their phone and right side used their pfone often ###Code plt.rcParams['figure.figsize'] = (8,6) df['Total day minutes'].hist() ###Output _____no_output_____ ###Markdown Categorical variable ###Code df['State'].value_counts().head() df['State'].nunique() df['Churn'].value_counts(normalize=True) sns.countplot(x = "Churn", data = df) ###Output _____no_output_____ ###Markdown Interaction between feature Numeric-Numeric Interaction 1. This intearction is good if target varaiable is numeric2. It's reasonable to explore with the target varaiable to gain some insight3. For regression task, this plot is very important ###Code plt.scatter(df['Total day minutes'], df['Customer service calls']) ###Output _____no_output_____ ###Markdown We can calculate the correlation between series and dataframe to check how much the feature is correlating with other feature ###Code df.head() new_df = df.drop('State', axis=1) new_df.head() new_df.corrwith(df["Total day minutes"]) ###Output _____no_output_____ ###Markdown Categorical-Categorical Feature 1. This can also include binary2. This is good if we have a target variable in categorical value ###Code pd.crosstab(df['Churn'], df['State']) sns.countplot(x="Customer service calls", hue="Churn", data=df) plt.title("Customer service calls for loyal and churned"); ###Output _____no_output_____ ###Markdown We can easily gain the insight from the graph that with fewer service call, people are churning a lot 1. We can customize the plot according to our need2. we can add label, legend in the graph3. We can save the graph Categorical-numerical variable ###Code import numpy as np df.groupby('Churn')['Total day minutes', 'Customer service calls'].agg([np.median, np.std]) sns.boxplot(x="Churn", y="Total day minutes", data=df) ###Output _____no_output_____ ###Markdown From the above graph, we can easily say that the churn are more than the loyal customerhttps://www.analyticsvidhya.com/blog/2019/09/comprehensive-data-visualization-guide-seaborn-python/ Machine learning We have some datasets called x(having all the matrix) and y is some target feature. X and Y forms a training set Vector y is not easy to get by Supervised learning ---> classification and regression and Ranking1. When we have 0 and 1, this is called classification task2. When we have to predict some numerical value, this is called regression task3. Ranking is something where we have to predict whether page is liked or relevant such as recomendation system.There can be multiclass in the classification task such as in digit recognizer where we have 10 classesGradient boosting can handle all three taskFrom the business perspective. it is hard to decide the y target variable because it has to be relevant. Decision tree This can help us to provide a formal structure of our dataset in the tree and node form which is easy to visualize Vizualizing in the tree format is the easy way to start solvoing any problem Main idea is having a dataset and generate some tree automatically Rules for creating tree1. Root should be very much specific as given in the picture below2. the deeper we go the more specific question arises Entropy Notes are in Copy Random forest from scratch ###Code import pandas as pd from matplotlib import pyplot as plt import seaborn as sns df = pd.read_csv("telecom_churn.csv") df["International plan"] = df["International plan"].map({"Yes":1, "No":0}) df['Voice mail plan'] = df["Voice mail plan"].map({"Yes":1, "No":0}) df["Churn"] = df["Churn"].astype("int") df.head() states = df.pop('State') X, y = df.drop("Churn", axis=1), df['Churn'] from sklearn.model_selection import train_test_split X_train, X_holdout, y_train, y_holdout = train_test_split(X, y, test_size=0.3, random_state=20) X_train.shape, X_holdout.shape from sklearn.tree import DecisionTreeClassifier tree = DecisionTreeClassifier(random_state=20) tree.fit(X_train, y_train) from sklearn.metrics import accuracy_score pred_holdout = tree.predict(X_holdout) pred_holdout.shape, y_holdout.shape accuracy_score(y_holdout, pred_holdout) # baseline for accuracy y.value_counts(normalize=True) # baseline is 0.85 ###Output _____no_output_____ ###Markdown Let's apply cross validation for parameter tuning ###Code import numpy as np from sklearn.model_selection import GridSearchCV, StratifiedKFold params = {'max_depth': np.arange(2,11), 'min_samples_leaf':np.arange(2,11)} skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=17) best_tree = GridSearchCV(estimator=tree, param_grid=params, cv=skf, n_jobs=-1,verbose=1) best_tree.fit(X_train, y_train) # not effiecient for large datasets best_tree.best_params_ best_tree.best_estimator_ ###Output _____no_output_____ ###Markdown Cross-validation assessment accuracy ###Code best_tree.best_score_ ###Output _____no_output_____ ###Markdown Holdout assessment ###Code pred_holdout_better = best_tree.predict(X_holdout) accuracy_score(y_holdout, pred_holdout_better) ###Output _____no_output_____
Notebooks/Data Exploration/movie data thru 2016.ipynb	###Markdown Importing libraries ###Code import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns ###Output _____no_output_____ ###Markdown Reading the datasetSource listed in the github repo ###Code data = pd.read_csv('movie_metadata.csv') ###Output _____no_output_____ ###Markdown Exploring the data ###Code data.head() data.info() # we only have movies up to 2016 data['title_year'].value_counts().sort_index().plot(kind='barh', figsize=(15,16)) plt.show() ###Output _____no_output_____ ###Markdown Data Wrangling Only considering the following features for the recommender system:- director_name- actor_1_name- actor_2_name- actor_3_name- genres- movie_title ###Code data = data[['director_name', 'actor_1_name', 'actor_2_name', 'actor_3_name', 'genres', 'movie_title']] data.head() # we will strip '\xa0' from all the titles data.movie_title[10] data.isna().sum().sort_values(ascending=False) def wrangle(X): # remove the pipe symbol for seperation in genres X['genres'] = X['genres'].str.replace('\|', ' ') # strip '\xa0' from all the titles in the dataset X['movie_title'] = X['movie_title'].str.strip('\xa0') X['movie_title'] = X['movie_title'].str.lower() # replace all the Nan values with 'unkownn' cols = ['director_name', 'actor_3_name', 'actor_2_name', 'actor_1_name', 'movie_title', 'genres'] for col in cols: X[col] = X[col].replace(np.nan, 'unknown') return X wrangle(data) # we extracted the data we wanted and now we will put into an csv data.to_csv('data.csv', index=False) ###Output _____no_output_____
epa1361_open_G21/Week 5-6 - robustness and direct search/assignment 10 - MORO.ipynb	###Markdown Multi-objective Robust Optimization (MORO)This exercise demostrates the application of MORO on the lake model. In contrast to the exercises in previous weeks, we will be using a slightly more sophisticated version of the problem. For details see the MORDM assignment for this week. Setup MOROMany objective robust optimization aims at finding decisions that are robust with respect to the various deeply uncertain factors. For this, MORO evalues each candidate decision over a set of scenarios. For each outcome of interest, the robusntess over this set is calculated. A MOEA is used to maximize the robustness. For this assignment, we will be using a domain criterion as our robustness metric. The table below lists the rules that you should use for each outcome of interest.\|Outcome of interest\| threhsold \|\|-------------------\|------------\|\| Maximum pollution \| $\leq$ 0.75\|\| Inertia \| $\geq$ 0.6 \|\| Reliability \| $\geq$ 0.99\| \| Utility \| $\geq$ 0.75\|1) Implement a function for each outcome that takes a numpy array with results for the outcome of interest, and returns the robustness score ###Code import functools def robustness(direction, threshold, data): if direction == SMALLER: return np.sum(data<=threshold)/data.shape[0] else: return np.sum(data>=threshold)/data.shape[0] def maxp(data): return np.sum(data<=0.75)/data.shape[0] SMALLER = 'SMALLER' LARGER = 'LARGER' maxp = functools.partial(robustness, SMALLER, 0.75) inertia = functools.partial(robustness, LARGER, 0.6) reliability = functools.partial(robustness, LARGER, 0.99) utility = functools.partial(robustness, LARGER, 0.75) ###Output _____no_output_____
Project 4 - non-professional developer population.ipynb	###Markdown IntroductionWith this notebook I want to explore the non-professional developer population at the Stack Overflow Annual Survey to have some idea of who is this population.From the most recent survey results (2019) we will look at this non-professional developer population profile.We will take a look at career and job satisfaction comparing the professional and non-professional developers. ###Code import numpy as np import pandas as pd from collections import defaultdict import matplotlib.pyplot as plt from matplotlib_venn import venn2, venn3, venn3_circles import plotly.express as px import seaborn as sns %matplotlib inline df = pd.read_csv('./stackoverflow/2019.csv') df.describe() ###Output _____no_output_____ ###Markdown As the population we want to focus at this data exploration are the non-professional developers, let's take a look at the information that can potentially help us with this filter:* MainBranch: Which of the following options best describes you today? Here, by "developer" we mean "someone who writes code."* Hobbyist: Do you code as a hobby? ###Code ## uncomment to see all the questions #df2 = pd.read_csv('./stackoverflow/2019_schema.csv') #with pd.option_context('display.max_rows', None, 'display.max_columns', None): # print(df2) mainbranch = df['MainBranch'].value_counts().reset_index() #mainbranch.head() mainbranch.rename(columns={'index': 'What option best describes you today?', 'MainBranch': 'Count'}, inplace=True) mainbranch['Percent'] = mainbranch.Count / (len(df['MainBranch'])-df['MainBranch'].isnull().sum()) mainbranch.style.format({'Count': "{:,}", 'Percent': '{:.2%}'}) df['MainBranch'] n_answers = len(df['MainBranch'])-df['MainBranch'].isnull().sum() study_pop = mainbranch.iloc[2, 1]+ mainbranch.iloc[3, 1] perc_pop = study_pop/n_answers print("The population we will be looking into will be a total of {a:,} survey answers.\n\ This represents {b:.2f}% of the total population".format(a=study_pop, b=perc_pop100)) ###Output The population we will be looking into will be a total of 10,879 survey answers. This represents 12.32% of the total population ###Markdown I would like to focus on professionals who are developing their programming skills, but who are not professional developers nor were professional developers in the past.I would also like to exclude the "student" population from the dataset.Before deciding simplifying our data set only with the population of our interest and only with the relevant columns, let's take a look at the "hobbyist" column. ###Code df_study = df.copy() df_study = df[['Respondent','MainBranch','Hobbyist','Employment','Country','Student','EdLevel','UndergradMajor',\ 'EduOther','OrgSize','YearsCode','Age1stCode','YearsCodePro','CareerSat','JobSat',\ 'MgrIdiot','MgrMoney','MgrWant','JobSeek','LastHireDate','JobFactors','CurrencySymbol',\ 'CurrencyDesc','CompTotal','CompFreq','ConvertedComp','WorkWeekHrs','WorkPlan','ImpSyn',\ 'Age','Gender','Trans','Sexuality','Ethnicity','Dependents']] ans1 = 'I am not primarily a developer, but I write code sometimes as part of my work' ans2 = 'I code primarily as a hobby' df_study = df_study[(df.MainBranch == ans1)\| (df.MainBranch == ans2)] df_study.describe() #df_study.to_csv(r'study.csv') ###Output _____no_output_____ ###Markdown Population profile ###Code hob = df_study['Hobbyist'].value_counts().reset_index() hob.rename(columns={'index': 'Hobbyist', 'Hobbyist': 'Count'}, inplace=True) hob['Percent'] = hob.Count / study_pop hob.style.format({'Count': "{:,}", 'Percent': '{:.2%}'}) ###Output _____no_output_____ ###Markdown The majority of this population, 84.45%, code as a hobby. I will isolate the population of interest and also disregard all the columns that are not important for our study: Population chacteristc features;* Educational level;* Coding experience;* Job satisfaction related questions.We have some numerical answers within these features of interest, so we will probably have to treat NA answers.Before taking a look at the numerical features, let's get some sense of the categorical features. ###Code # Which of the following do you currently identify as? Please select all that apply. If you prefer not to answer, you may leave this question blank. male = len(df_study[(df_study.Gender == 'Man')]) female = len(df_study[(df_study.Gender == 'Woman')]) others = study_pop - female - male print("Percentage of man in our population study is {:.0f}% and woman, {:.0f}%. Other classifications represents {:.0f}%."\ .format(male/study_pop100, female/study_pop100, others/study_pop100)) gender = df_study['Gender'].value_counts().reset_index() gender # In which country do you currently reside? country = df_study['Country'].value_counts().reset_index() country.rename(columns={'index': 'Country', 'Country': 'Count'}, inplace=True) country['Percent'] = country.Count / (study_pop-df_study['Country'].isnull().sum()) country.style.format({'Count': "{:,}", 'Percent': '{:.2%}'}) # Which of the following best describes the highest level of formal education that you've completed? edu = df_study['EdLevel'].value_counts().reset_index() edu.rename(columns={'index': 'Educational Level', 'EdLevel': 'Count'}, inplace=True) edu['Percent'] = edu.Count / (study_pop-df_study['EdLevel'].isnull().sum()) edu.style.format({'Count': "{:,}", 'Percent': '{:.2%}'}) # What was your main or most important field of study? major = df_study['UndergradMajor'].value_counts().reset_index() major.rename(columns={'index': 'Undergrad Major', 'UndergradMajor': 'Count'}, inplace=True) major['Percent'] = major.Count / (study_pop-df_study['UndergradMajor'].isnull().sum()-180) major.style.format({'Count': "{:,}", 'Percent': '{:.2%}'}) # "Which of the following best describes your current employment status?" emp = df_study['Employment'].value_counts().reset_index() emp.rename(columns={'index': 'Employment status', 'Employment': 'Count'}, inplace=True) emp['Percent'] = emp.Count / (study_pop-df_study['Employment'].isnull().sum()) emp.style.format({'Count': "{:,}", 'Percent': '{:.2%}'}) # dataset = emp # #layout = go.Layout(yaxis=dict(tickformat=".2%")) # fig = px.bar(dataset, x='Employment status', y='Percentage') # fig.show() # data to plot height = emp['Percent'] bars = emp['Employment status'] y_pos = np.arange(len(bars)) # Create horizontal bars ax = plt.barh(y_pos, height) # Create names on the y-axis plt.yticks(y_pos, bars) plt.gca().invert_yaxis() plt.figtext(.5,.9,'Employment status - non-professional developers', fontsize=15, ha='center') # Show graphic plt.show() # "Approximately how many people are employed by the company or organization you work for?" size = df_study['OrgSize'].value_counts().reset_index() size.rename(columns={'index': 'Organization size', 'OrgSize': 'Count'}, inplace=True) size['Percent'] = size.Count / (study_pop-df_study['OrgSize'].isnull().sum()) size.style.format({'Count': "{:,}", 'Percent': '{:.2%}'}) ###Output _____no_output_____ ###Markdown Coding experience We know that this population only have the individuals who responded that they do not work as a developer, has not worked in the past and are not students. But how much experience do they have? We will look now at the following questions: Including any education, how many years have you been coding?* At what age did you write your first line of code or program?Probably some people did not reply this questions. Let's check how signifficant this percentage of empty values is. ###Code nulls = df_study.isnull().sum()/study_pop print("The percentage of nulls is: {:.2%} for YearsCode, {:.2%} for Age1stCode, and {:.2%} for Age."\ .format(nulls['YearsCode'], nulls['Age1stCode'], nulls['Age'])) # copy df as we are going to replace some values df_age = df_study.copy() df_age = df_age.dropna(subset=['YearsCode', 'Age1stCode', 'Age']) study_age = len(df_age['Age']) print("By dropping the rows with null value in any of the columns, our new population is {:,},\ which is {:.2%} the of population snippet we started with.".format(study_age,study_age/study_pop)) ###Output By dropping the rows with null value in any of the columns, our new population is 9,284, which is 85.34% the of population snippet we started with. ###Markdown We still got a signifficant amount of responses (>85%) if we remove the null cases.These columns are string type in the survey file because they have non-numerical option answers. To plot the distributions we need all values as numerical. Let's assume "Less than..." and "Younger than..." as the integer immediately below and "More than..." and "Older than..." as the integer immediately above. Then let's convert the whole column to integer type. ###Code df_age['YearsCode'].replace(["Less than 1 year","More than 50 years"],[0,50],inplace=True) df_age['Age1stCode'].replace(['Younger than 5 years','Older than 85'],[4,86],inplace=True) df_age['YearsCode'] = df_age['YearsCode'].astype(int) df_age['Ages1stCode'] = df_age['Age1stCode'].astype(int) # density plot with shade sns.kdeplot(df_age['YearsCode'], shade=True); p1=sns.kdeplot(df_age['Age1stCode'], shade=True) p1=sns.kdeplot(df_age['Age'], shade=True) df_age.describe() ###Output _____no_output_____ ###Markdown More than 75% of the respondents started coding when they were 18 years old or younger. The median age is 15 years old.Comparing the median age (30) with the median age of first coding experience (15) there is a period of 15 years. The respondents, at median, have been coding for 8 years, so there is a gap. ###Code # sns.boxplot(x="Gender", y="YearsCode", data=df_study, palette="Set1"); # Basic violinplot ax = sns.violinplot(x="Gender", y="YearsCode", data=df_age) # Calculate number of obs per group & median to position labels medians = df_age.groupby(['Gender'])['YearsCode'].median().values nobs = df_age['Gender'].value_counts().values nobs = [str(x) for x in nobs.tolist()] nobs = ["n: " + i for i in nobs] # Add it to the plot pos = range(len(nobs)) for tick,label in zip(pos,ax.get_xticklabels()):\ ax.text(pos[tick], medians[tick] + 0.03, nobs[tick], horizontalalignment='center', size='large', color='k', weight='semibold', rotation=45) ax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right'); ###Output _____no_output_____ ###Markdown Career / Job satisfaction We would also like to know about the respondents' job satisfaction. Are they dissatisfied with their current careers / jobs? Here are the questions we will be looking into:* Overall, how satisfied are you with your career thus far?* How satisfied are you with your current job? * How confident are you that your manager knows what they're doing?* Which of the following best describes your current job-seeking status?All features in this part are categorical and they all relate to job / career satisfaction. Let's give a score for the answers try to understand a little bit better beyond looking at each answer individually.Let's give the following scores to the range of responses:\| Career and job satisfaction \| Score \|\| --- \| --- \|\| Very satisfied \| +2 \|\| Slightly satisfied \| +1\|\| Neither satisfied nor dissatisfied \| 0\|\| Slightly dissatisfied \| -1\|\| Very dissatisfied \| -2\|\| Conf. Manager \| Score \|\| --- \| --- \|\| Very confident \| +2 \|\| Somewhat confident \| +1\|\| I don't have a manager \| 0\|\| Not at all confident \| -1\|\| Job-seeking \| Score \|\| --- \| --- \|\| I am not interested in new job opportunities \| +1 \|\| I'm not actively looking, but I am open to new opportunities \| 0\|\| I am actively looking for a job \| -1\|For null responses, we will assume value zero. ###Code # copy df as we are going to replace some values df_job = df_study.copy() df_job['CareerSat'].replace(['Very satisfied','Slightly satisfied', 'Neither satisfied nor dissatisfied',\ 'Slightly dissatisfied','Very dissatisfied'],[2, 1, 0, -1, -2],inplace=True) df_job['JobSat'].replace(['Very satisfied','Slightly satisfied', 'Neither satisfied nor dissatisfied',\ 'Slightly dissatisfied','Very dissatisfied'],[2, 1, 0, -1, -2],inplace=True) df_job['MgrIdiot'].replace(['Very confident','Somewhat confident', "I don't have a manager",\ 'Not at all confident'],[2, 1, 0, -1],inplace=True) df_job['JobSeek'].replace(['I am not interested in new job opportunities',"I’m not actively looking, but I am open to new opportunities",\ 'I am actively looking for a job'],[1, 0, -1],inplace=True) # exclude na df_job = df_job.dropna(subset=['CareerSat', 'JobSat', 'MgrIdiot', 'JobSeek']) # create new column with the score df_job['SatScore'] = df_job['CareerSat']+df_job['JobSat']+df_job['MgrIdiot']+df_job['JobSeek'] df_job['SatScore'].describe() ###Output _____no_output_____ ###Markdown How does our study population compare to the whole survey population? ###Code df_job_all = df.copy() df_job_all['CareerSat'].replace(['Very satisfied','Slightly satisfied', 'Neither satisfied nor dissatisfied',\ 'Slightly dissatisfied','Very dissatisfied'],[2, 1, 0, -1, -2],inplace=True) df_job_all['JobSat'].replace(['Very satisfied','Slightly satisfied', 'Neither satisfied nor dissatisfied',\ 'Slightly dissatisfied','Very dissatisfied'],[2, 1, 0, -1, -2],inplace=True) df_job_all['MgrIdiot'].replace(['Very confident','Somewhat confident', "I don't have a manager",\ 'Not at all confident'],[2, 1, 0, -1],inplace=True) df_job_all['JobSeek'].replace(['I am not interested in new job opportunities',"I’m not actively looking, but I am open to new opportunities",\ 'I am actively looking for a job'],[1, 0, -1],inplace=True) # drop na df_job_all = df_job_all.dropna(subset=['CareerSat', 'JobSat', 'MgrIdiot', 'JobSeek']) # create new column with the score df_job_all['JobSeek'] = df_job_all['JobSeek'].fillna(0) df_job_all['SatScore'] = df_job_all['CareerSat']+df_job_all['JobSat']+df_job_all['MgrIdiot']+df_job_all['JobSeek'] df_job_all['SatScore'].describe() plt.hist(df_job_all['SatScore'], bins=14); plt.figtext(.5,.9,'Job satisfaction score - all 2019 respondents', fontsize=15, ha='center'); plt.axvline(x.median(), color='k', linestyle='dashed', linewidth=1) min_ylim, max_ylim = plt.ylim() plt.text(x.median()1.1, max_ylim0.95, 'Median: {:.2f}'.format(x.median())) plt.axvline(x.mean(), color='k', linestyle='dashed', linewidth=1) min_ylim, max_ylim = plt.ylim() plt.text(x.mean()1.1, max_ylim0.85, 'Mean: {:.2f}'.format(x.mean())) x1 = df_job['SatScore'] plt.hist(x1, bins=14); plt.figtext(.5,.9,'Job satisfaction score - non-professional developers 2019 respondents', fontsize=15, ha='center'); plt.axvline(x1.median(), color='k', linestyle='dashed', linewidth=1) min_ylim, max_ylim = plt.ylim() l11 = plt.text(x1.median()1.1, max_ylim0.95, 'Median: {:.2f}'.format(x1.median())) l11 = plt.axvline(x1.mean(), color='k', linestyle='dashed', linewidth=1) min_ylim, max_ylim = plt.ylim() l12 = plt.text(x1.mean()1.1, max_ylim0.85, 'Mean: {:.2f}'.format(x1.mean())) hist = sns.distplot( a=df_job_all["SatScore"], color='darkblue',hist=True, kde=False,hist_kws={"rwidth":1, 'alpha':0.6}); hist = sns.distplot( a=df_job["SatScore"],color='red', hist=True, kde=False, hist_kws={"edgecolor":'red',"rwidth":0.4, 'alpha':0.6}); hist.set(xlabel='Job Satisfaction score', ylabel='Frequency'); plt.legend(['All','Non-prof. developer'], ncol=1, loc=0); df_job['SatScore'] df_job.to_csv('satscore.csv') ###Output _____no_output_____ ###Markdown It seems that the whole population is more satisfied with their jobs and careers compared to our snippet of non-professional developers. What is the percentage of the respodents who are actively looking for a job or willing to consider new opportunities? ###Code jobseek = df_study['JobSeek'].value_counts().reset_index() jobseek.rename(columns={'index': 'Job Seek - non-professional developers', 'JobSeek': 'Count'}, inplace=True) jobseek['Percent'] = jobseek.Count / (study_pop-df_study['JobSeek'].isnull().sum()) jobseek.style.format({'Count': "{:,}", 'Percent': '{:.2%}'}) jobseek_all = df['JobSeek'].value_counts().reset_index() jobseek_all.rename(columns={'index': 'Job Seek - All respondents', 'JobSeek': 'Count'}, inplace=True) jobseek_all['Percent'] = jobseek_all.Count / (n_answers-df['JobSeek'].isnull().sum()) jobseek_all.style.format({'Count': "{:,}", 'Percent': '{:.2%}'}) ###Output _____no_output_____ ###Markdown Compared to the overall population, the job-seeking status for our study population has similar distribution. How this population evolved over the years? We have the data from 2011 to 2019, however we can only indentify non-professional developers starting from 2017 survey.Then, let's use the data for the years 2017 to 2019. ###Code df_2018 = pd.read_csv('./stackoverflow/2018.csv') df_2017 = pd.read_csv('./stackoverflow/2017.csv') ###Output _____no_output_____ ###Markdown For 2019 survey we have to look at MainBranch question.Let's take a look at the questions for 2018 and 2017. ###Code df2_2018 = pd.read_csv('./stackoverflow/2018_schema.csv') with pd.option_context('display.max_rows', None, 'display.max_columns', None): print(df2_2018) ###Output Column \ 0 Respondent 1 Hobby 2 OpenSource 3 Country 4 Student 5 Employment 6 FormalEducation 7 UndergradMajor 8 CompanySize 9 DevType 10 YearsCoding 11 YearsCodingProf 12 JobSatisfaction 13 CareerSatisfaction 14 HopeFiveYears 15 JobSearchStatus 16 LastNewJob 17 AssessJob1 18 AssessJob2 19 AssessJob3 20 AssessJob4 21 AssessJob5 22 AssessJob6 23 AssessJob7 24 AssessJob8 25 AssessJob9 26 AssessJob10 27 AssessBenefits1 28 AssessBenefits2 29 AssessBenefits3 30 AssessBenefits4 31 AssessBenefits5 32 AssessBenefits6 33 AssessBenefits7 34 AssessBenefits8 35 AssessBenefits9 36 AssessBenefits10 37 AssessBenefits11 38 JobContactPriorities1 39 JobContactPriorities2 40 JobContactPriorities3 41 JobContactPriorities4 42 JobContactPriorities5 43 JobEmailPriorities1 44 JobEmailPriorities2 45 JobEmailPriorities3 46 JobEmailPriorities4 47 JobEmailPriorities5 48 JobEmailPriorities6 49 JobEmailPriorities7 50 UpdateCV 51 Currency 52 Salary 53 SalaryType 54 ConvertedSalary 55 CurrencySymbol 56 CommunicationTools 57 TimeFullyProductive 58 EducationTypes 59 SelfTaughtTypes 60 TimeAfterBootcamp 61 HackathonReasons 62 AgreeDisagree1 63 AgreeDisagree2 64 AgreeDisagree3 65 LanguageWorkedWith 66 LanguageDesireNextYear 67 DatabaseWorkedWith 68 DatabaseDesireNextYear 69 PlatformWorkedWith 70 PlatformDesireNextYear 71 FrameworkWorkedWith 72 FrameworkDesireNextYear 73 IDE 74 OperatingSystem 75 NumberMonitors 76 Methodology 77 VersionControl 78 CheckInCode 79 AdBlocker 80 AdBlockerDisable 81 AdBlockerReasons 82 AdsAgreeDisagree1 83 AdsAgreeDisagree2 84 AdsAgreeDisagree3 85 AdsActions 86 AdsPriorities1 87 AdsPriorities2 88 AdsPriorities3 89 AdsPriorities4 90 AdsPriorities5 91 AdsPriorities6 92 AdsPriorities7 93 AIDangerous 94 AIInteresting 95 AIResponsible 96 AIFuture 97 EthicsChoice 98 EthicsReport 99 EthicsResponsible 100 EthicalImplications 101 StackOverflowRecommend 102 StackOverflowVisit 103 StackOverflowHasAccount 104 StackOverflowParticipate 105 StackOverflowJobs 106 StackOverflowDevStory 107 StackOverflowJobsRecommend 108 StackOverflowConsiderMember 109 HypotheticalTools1 110 HypotheticalTools2 111 HypotheticalTools3 112 HypotheticalTools4 113 HypotheticalTools5 114 WakeTime 115 HoursComputer 116 HoursOutside 117 SkipMeals 118 ErgonomicDevices 119 Exercise 120 Gender 121 SexualOrientation 122 EducationParents 123 RaceEthnicity 124 Age 125 Dependents 126 MilitaryUS 127 SurveyTooLong 128 SurveyEasy QuestionText 0 Randomized respondent ID number (not in order ... 1 Do you code as a hobby? 2 Do you contribute to open source projects? 3 In which country do you currently reside? 4 Are you currently enrolled in a formal, degree... 5 Which of the following best describes your cur... 6 Which of the following best describes the high... 7 You previously indicated that you went to a co... 8 Approximately how many people are employed by ... 9 Which of the following describe you? Please se... 10 Including any education, for how many years ha... 11 For how many years have you coded professional... 12 How satisfied are you with your current job? I... 13 Overall, how satisfied are you with your caree... 14 Which of the following best describes what you... 15 Which of the following best describes your cur... 16 When was the last time that you took a job wit... 17 Imagine that you are assessing a potential job... 18 Imagine that you are assessing a potential job... 19 Imagine that you are assessing a potential job... 20 Imagine that you are assessing a potential job... 21 Imagine that you are assessing a potential job... 22 Imagine that you are assessing a potential job... 23 Imagine that you are assessing a potential job... 24 Imagine that you are assessing a potential job... 25 Imagine that you are assessing a potential job... 26 Imagine that you are assessing a potential job... 27 Now, imagine you are assessing a job's benefit... 28 Now, imagine you are assessing a job's benefit... 29 Now, imagine you are assessing a job's benefit... 30 Now, imagine you are assessing a job's benefit... 31 Now, imagine you are assessing a job's benefit... 32 Now, imagine you are assessing a job's benefit... 33 Now, imagine you are assessing a job's benefit... 34 Now, imagine you are assessing a job's benefit... 35 Now, imagine you are assessing a job's benefit... 36 Now, imagine you are assessing a job's benefit... 37 Now, imagine you are assessing a job's benefit... 38 Imagine that a company wanted to contact you a... 39 Imagine that a company wanted to contact you a... 40 Imagine that a company wanted to contact you a... 41 Imagine that a company wanted to contact you a... 42 Imagine that a company wanted to contact you a... 43 Imagine that same company decided to contact y... 44 Imagine that same company decided to contact y... 45 Imagine that same company decided to contact y... 46 Imagine that same company decided to contact y... 47 Imagine that same company decided to contact y... 48 Imagine that same company decided to contact y... 49 Imagine that same company decided to contact y... 50 Think back to the last time you updated your r... 51 Which currency do you use day-to-day? If your ... 52 What is your current gross salary (before taxe... 53 Is that salary weekly, monthly, or yearly? 54 Salary converted to annual USD salaries using ... 55 Three digit currency abbreviation. 56 Which of the following tools do you use to com... 57 Suppose a new developer with four years of exp... 58 Which of the following types of non-degree edu... 59 You indicated that you had taught yourself a p... 60 You indicated previously that you went through... 61 You indicated previously that you had particip... 62 To what extent do you agree or disagree with e... 63 To what extent do you agree or disagree with e... 64 To what extent do you agree or disagree with e... 65 Which of the following programming, scripting,... 66 Which of the following programming, scripting,... 67 Which of the following database environments h... 68 Which of the following database environments h... 69 Which of the following platforms have you done... 70 Which of the following platforms have you done... 71 Which of the following libraries, frameworks, ... 72 Which of the following libraries, frameworks, ... 73 Which development environment(s) do you use re... 74 What is the primary operating system in which ... 75 How many monitors are set up at your workstation? 76 Which of the following methodologies do you ha... 77 What version control systems do you use regula... 78 Over the last year, how often have you checked... 79 Do you have ad-blocking software installed on ... 80 In the past month, have you disabled your ad b... 81 What are the reasons that you have disabled yo... 82 To what extent do you agree or disagree with t... 83 To what extent do you agree or disagree with t... 84 To what extent do you agree or disagree with t... 85 Which of the following actions have you taken ... 86 Please rank the following advertising qualitie... 87 Please rank the following advertising qualitie... 88 Please rank the following advertising qualitie... 89 Please rank the following advertising qualitie... 90 Please rank the following advertising qualitie... 91 Please rank the following advertising qualitie... 92 Please rank the following advertising qualitie... 93 What do you think is the most dangerous aspect... 94 What do you think is the most exciting aspect ... 95 Whose responsibility is it, <u>primarily</u>, ... 96 Overall, what's your take on the future of art... 97 Imagine that you were asked to write code for ... 98 Do you report or otherwise call out the unethi... 99 Who do you believe is ultimately most responsi... 100 Do you believe that you have an obligation to ... 101 How likely is it that you would recommend Stac... 102 How frequently would you say you visit Stack O... 103 Do you have a Stack Overflow account? 104 How frequently would you say you participate i... 105 Have you ever used or visited Stack Overflow J... 106 Do you have an up-to-date Developer Story on S... 107 How likely is it that you would recommend Stac... 108 Do you consider yourself a member of the Stack... 109 Please rate your interest in participating in ... 110 Please rate your interest in participating in ... 111 Please rate your interest in participating in ... 112 Please rate your interest in participating in ... 113 Please rate your interest in participating in ... 114 On days when you work, what time do you typica... 115 On a typical day, how much time do you spend o... 116 On a typical day, how much time do you spend o... 117 In a typical week, how many times do you skip ... 118 What ergonomic furniture or devices do you use... 119 In a typical week, how many times do you exerc... 120 Which of the following do you currently identi... 121 Which of the following do you currently identi... 122 What is the highest level of education receive... 123 Which of the following do you identify as? Ple... 124 What is your age? If you prefer not to answer,... 125 Do you have any children or other dependents t... 126 Are you currently serving or have you ever ser... 127 How do you feel about the length of the survey... 128 How easy or difficult was this survey to compl... ###Markdown For 2018 we will the following question:* DevType: Which of the following describe you? Please select all that apply. ###Code df2_2017 = pd.read_csv('./stackoverflow/2017_schema.csv') with pd.option_context('display.max_rows', None, 'display.max_columns', None): print(df2_2017) ###Output Column \ 0 Respondent 1 Professional 2 ProgramHobby 3 Country 4 University 5 EmploymentStatus 6 FormalEducation 7 MajorUndergrad 8 HomeRemote 9 CompanySize 10 CompanyType 11 YearsProgram 12 YearsCodedJob 13 YearsCodedJobPast 14 DeveloperType 15 WebDeveloperType 16 MobileDeveloperType 17 NonDeveloperType 18 CareerSatisfaction 19 JobSatisfaction 20 ExCoderReturn 21 ExCoderNotForMe 22 ExCoderBalance 23 ExCoder10Years 24 ExCoderBelonged 25 ExCoderSkills 26 ExCoderWillNotCode 27 ExCoderActive 28 PronounceGIF 29 ProblemSolving 30 BuildingThings 31 LearningNewTech 32 BoringDetails 33 JobSecurity 34 DiversityImportant 35 AnnoyingUI 36 FriendsDevelopers 37 RightWrongWay 38 UnderstandComputers 39 SeriousWork 40 InvestTimeTools 41 WorkPayCare 42 KinshipDevelopers 43 ChallengeMyself 44 CompetePeers 45 ChangeWorld 46 JobSeekingStatus 47 HoursPerWeek 48 LastNewJob 49 AssessJobIndustry 50 AssessJobRole 51 AssessJobExp 52 AssessJobDept 53 AssessJobTech 54 AssessJobProjects 55 AssessJobCompensation 56 AssessJobOffice 57 AssessJobCommute 58 AssessJobRemote 59 AssessJobLeaders 60 AssessJobProfDevel 61 AssessJobDiversity 62 AssessJobProduct 63 AssessJobFinances 64 ImportantBenefits 65 ClickyKeys 66 JobProfile 67 ResumePrompted 68 LearnedHiring 69 ImportantHiringAlgorithms 70 ImportantHiringTechExp 71 ImportantHiringCommunication 72 ImportantHiringOpenSource 73 ImportantHiringPMExp 74 ImportantHiringCompanies 75 ImportantHiringTitles 76 ImportantHiringEducation 77 ImportantHiringRep 78 ImportantHiringGettingThingsDone 79 Currency 80 Overpaid 81 TabsSpaces 82 EducationImportant 83 EducationTypes 84 SelfTaughtTypes 85 TimeAfterBootcamp 86 CousinEducation 87 WorkStart 88 HaveWorkedLanguage 89 WantWorkLanguage 90 HaveWorkedFramework 91 WantWorkFramework 92 HaveWorkedDatabase 93 WantWorkDatabase 94 HaveWorkedPlatform 95 WantWorkPlatform 96 IDE 97 AuditoryEnvironment 98 Methodology 99 VersionControl 100 CheckInCode 101 ShipIt 102 OtherPeoplesCode 103 ProjectManagement 104 EnjoyDebugging 105 InTheZone 106 DifficultCommunication 107 CollaborateRemote 108 MetricAssess 109 EquipmentSatisfiedMonitors 110 EquipmentSatisfiedCPU 111 EquipmentSatisfiedRAM 112 EquipmentSatisfiedStorage 113 EquipmentSatisfiedRW 114 InfluenceInternet 115 InfluenceWorkstation 116 InfluenceHardware 117 InfluenceServers 118 InfluenceTechStack 119 InfluenceDeptTech 120 InfluenceVizTools 121 InfluenceDatabase 122 InfluenceCloud 123 InfluenceConsultants 124 InfluenceRecruitment 125 InfluenceCommunication 126 StackOverflowDescribes 127 StackOverflowSatisfaction 128 StackOverflowDevices 129 StackOverflowFoundAnswer 130 StackOverflowCopiedCode 131 StackOverflowJobListing 132 StackOverflowCompanyPage 133 StackOverflowJobSearch 134 StackOverflowNewQuestion 135 StackOverflowAnswer 136 StackOverflowMetaChat 137 StackOverflowAdsRelevant 138 StackOverflowAdsDistracting 139 StackOverflowModeration 140 StackOverflowCommunity 141 StackOverflowHelpful 142 StackOverflowBetter 143 StackOverflowWhatDo 144 StackOverflowMakeMoney 145 Gender 146 HighestEducationParents 147 Race 148 SurveyLong 149 QuestionsInteresting 150 QuestionsConfusing 151 InterestedAnswers 152 Salary 153 ExpectedSalary Question 0 Respondent ID number 1 Which of the following best describes you? 2 Do you program as a hobby or contribute to ope... 3 In which country do you currently live? 4 Are you currently enrolled in a formal, degree... 5 Which of the following best describes your cur... 6 Which of the following best describes the high... 7 Which of the following best describes your mai... 8 How often do you work from home or remotely? 9 In terms of the number of employees, how large... 10 Which of the following best describes the type... 11 How long has it been since you first learned h... 12 For how many years have you coded as part of y... 13 For how many years did you code as part of you... 14 Which of the following best describe you? 15 Which of the following best describes you as a... 16 For which of the following platforms do you de... 17 Which of the following describe you? 18 Career satisfaction rating 19 Job satisfaction rating 20 You said before that you used to code as part ... 21 You said before that you used to code as part ... 22 You said before that you used to code as part ... 23 You said before that you used to code as part ... 24 You said before that you used to code as part ... 25 You said before that you used to code as part ... 26 You said before that you used to code as part ... 27 You said before that you used to code as part ... 28 How do you pronounce "GIF"? 29 I love solving problems 30 Building things is very rewarding 31 Learning new technologies is fun 32 I tend to get bored by implementation details 33 Job security is important to me 34 Diversity in the workplace is important 35 It annoys me when software has a poor UI 36 Most of my friends are developers, engineers, ... 37 There's a right and a wrong way to do everything 38 Honestly, there's a lot about computers that I... 39 I take my work very seriously 40 I invest a lot of time into the tools I use 41 I don't really care what I work on, so long as... 42 I feel a sense of kinship to other developers 43 I like to challenge myself 44 I think of myself as competing with my peers 45 I want to change the world 46 Which of the following best describes your cur... 47 During a typical week, approximately how many ... 48 When was the last time that you took a job wit... 49 When you're assessing potential jobs to apply ... 50 When you're assessing potential jobs to apply ... 51 When you're assessing potential jobs to apply ... 52 When you're assessing potential jobs to apply ... 53 When you're assessing potential jobs to apply ... 54 When you're assessing potential jobs to apply ... 55 When you're assessing potential jobs to apply ... 56 When you're assessing potential jobs to apply ... 57 When you're assessing potential jobs to apply ... 58 When you're assessing potential jobs to apply ... 59 When you're assessing potential jobs to apply ... 60 When you're assessing potential jobs to apply ... 61 When you're assessing potential jobs to apply ... 62 When you're assessing potential jobs to apply ... 63 When you're assessing potential jobs to apply ... 64 When it comes to compensation and benefits, ot... 65 If two developers are sharing an office, is it... 66 On which of the following sites do you maintai... 67 Think back to the last time you updated your r... 68 Think back to when you first applied to work f... 69 Congratulations! You've just been put in charg... 70 Congratulations! You've just been put in charg... 71 Congratulations! You've just been put in charg... 72 Congratulations! You've just been put in charg... 73 Congratulations! You've just been put in charg... 74 Congratulations! You've just been put in charg... 75 Congratulations! You've just been put in charg... 76 Congratulations! You've just been put in charg... 77 Congratulations! You've just been put in charg... 78 Congratulations! You've just been put in charg... 79 Which currency do you use day-to-day? If you'r... 80 Compared to your estimate of your own market v... 81 Tabs or spaces? 82 Overall, how important has your formal schooli... 83 Outside of your formal schooling and education... 84 You indicated that you had taught yourself a p... 85 You indicated previously that you went through... 86 Let's pretend you have a distant cousin. They ... 87 Suppose you could choose your own working hour... 88 Which of the following languages have you done... 89 Which of the following languages have you done... 90 Which of the following libraries, frameworks, ... 91 Which of the following libraries, frameworks, ... 92 Which of the following database technologies h... 93 Which of the following database technologies h... 94 Which of the following platforms have you done... 95 Which of the following platforms have you done... 96 Which development environment(s) do you use re... 97 Suppose you're about to start a few hours of c... 98 Which of the following methodologies do you ha... 99 What version control system do you use? If you... 100 Over the last year, how often have you checked... 101 It's better to ship now and optimize later 102 Maintaining other people's code is a form of t... 103 Most project management techniques are useless 104 I enjoy debugging code 105 I often get “into the zone” when I'm coding 106 I have difficulty communicating my ideas to my... 107 It's harder to collaborate with remote peers t... 108 Congratulations! The bosses at your new employ... 109 Thinking about your main coding workstation, h... 110 Thinking about your main coding workstation, h... 111 Thinking about your main coding workstation, h... 112 Thinking about your main coding workstation, h... 113 Thinking about your main coding workstation, h... 114 How much influence do you have on purchasing d... 115 How much influence do you have on purchasing d... 116 How much influence do you have on purchasing d... 117 How much influence do you have on purchasing d... 118 How much influence do you have on purchasing d... 119 How much influence do you have on purchasing d... 120 How much influence do you have on purchasing d... 121 How much influence do you have on purchasing d... 122 How much influence do you have on purchasing d... 123 How much influence do you have on purchasing d... 124 How much influence do you have on purchasing d... 125 How much influence do you have on purchasing d... 126 Which of the following best describes you? 127 Stack Overflow satisfaction 128 Which of the following devices have you used t... 129 Over the last three months, approximately how ... 130 Over the last three months, approximately how ... 131 Over the last three months, approximately how ... 132 Over the last three months, approximately how ... 133 Over the last three months, approximately how ... 134 Over the last three months, approximately how ... 135 Over the last three months, approximately how ... 136 Over the last three months, approximately how ... 137 The ads on Stack Overflow are relevant to me 138 The ads on Stack Overflow are distracting 139 The moderation on Stack Overflow is unfair 140 I feel like a member of the Stack Overflow com... 141 The answers and code examples I get on Stack O... 142 Stack Overflow makes the Internet a better place 143 I don't know what I'd do without Stack Overflow 144 The people who run Stack Overflow are just in ... 145 Which of the following do you currently identi... 146 What is the highest level of education receive... 147 Which of the following do you identify as? 148 This survey was too long 149 The questions were interesting 150 The questions were confusing 151 I'm interested in learning how other developer... 152 What is your current annual base salary, befor... 153 You said before that you are currently learnin... ###Markdown For 2017 data we can use:* Professional: Which of the following best describes you? It will be more difficult to identify our group of interest from 2018 data because the respondents selected all options that applied. What we will try to do is count only the rows that does not contain professional developer terms in the answer.From the database we can create a list of the possible values and the list of the values I identify as something like a professional developer: ###Code possible_vals = ['Full-stack developer','Database administrator','DevOps specialist','System administrator', 'Engineering manager','Data or business analyst','Desktop or enterprise applications developer', 'Game or graphics developer','QA or test developer','Student','Back-end developer', 'Front-end developer','Designer','C-suite executive (CEO, CTO, etc.)','Mobile developer', 'Data scientist or machine learning specialist','Marketing or sales professional', 'Product manager','Embedded applications or devices developer','Educator or academic researcher'] values_prof_dev = ['Full-stack developer','Database administrator','DevOps specialist','System administrator', 'Engineering manager','Data or business analyst','Desktop or enterprise applications developer', 'Game or graphics developer','QA or test developer','Student','Back-end developer', 'Front-end developer','Designer','Mobile developer','Data scientist or machine learning specialist', 'Product manager','Embedded applications or devices developer'] # I want to exclude from the population students and null answers. I will work only with the remaining population df_2018 = df_2018.dropna(subset=['DevType']) df_2018 = df_2018[df_2018.columns.drop(list(df_2018.filter(regex='Student')))] total2018=len(df_2018['Respondent']) # Now we will create a column to indicate if the respondent is a professional developer or not prof2018 = 0 for idx in range(len(df_2018['DevType'].values)): if ';' in df_2018['DevType'].values[idx]: profs = df_2018['DevType'].values[idx].split(';') else: profs = [df_2018['DevType'].values[idx]] for val in profs: if val in values_prof_dev: prof2018+=1 break 1 - prof2018/total2018 ###Output _____no_output_____ ###Markdown Less than 1% selected an option that is clearly not related to a programming activity (like C-suite, Marketing/Sales, Educator/academic researcher). The nature of this question is different from 2017 and 2019, so it is difficult to compare the results Now let's take a look at 2017 data. ###Code professional = df_2017['Professional'].value_counts().reset_index() professional.rename(columns={'index': 'Professional dev', 'Professional': 'Count'}, inplace=True) professional['Percent'] = professional.Count / len(df_2017['Professional']) professional.style.format({'Count': "{:,}", 'Percent': '{:.2%}'}) pd.pivot_table(df_2017,index=["Professional","ProgramHobby"], values=["Respondent"], aggfunc=lambda x: len(x.unique())) ###Output _____no_output_____
scripts/FPTU_rals_model_b-noisy.ipynb	###Markdown Dynamical System ApproximationThis notebook aims at learning a functional correlation based on given snapshots. The data is created through the following ODE which is called the Fermi Pasta model:\begin{align}\frac{d^2}{dt^2} x_i = (x_{i+1} - 2x_i + x_{i-1}) + 0.7((x_{i+1} - x_i)^3 - (x_i-x_{i-1})^3) + \mathcal{N}(0,\sigma)\end{align} ###Code import numpy as np import xerus import matplotlib.pyplot as plt from matplotlib.colors import LogNorm import time from itertools import chain import helpers as hp import pandas as pd %precision 4 #Construction of the exact solution in the choosen basis. #Transform implements the basis transformation for given b #For the exact solution we also factor out the kernel, by use of the Pseudoinverse. def transform(X): M = np.zeros([4,4]) M[0,0] = 1 #1 M[1,1] = 1 #1 M[2,2] = 1.5 #1.5 M[3,3] = 2.5 #2.5 M[0,2] = -0.5 #-0.5 M[1,3] = -1.5 #-1.5 t = xerus.Tensor.from_ndarray(np.linalg.inv(M)) a1,a2,a3,a4,b1,b2,b3,b4 = xerus.indices(8) for eq in range(noo): tmp = X.get_component(eq) tmp2 = C2list[eq] tmp(a1,a2,a3,a4) << tmp(a1,b2,a3,a4)* t(a2,b2) X.set_component(eq,tmp) return X def project(X): dim = ([(3 if i == 0 or i == noo -1 else 4) for i in range(0,noo)]) dim.extend(dim) C2T = xerus.TTOperator(dim) for eq in range(noo): idx = [0 for i in range(noo)] if eq == 0: idx[0] = 2 idx[1] = 3 elif eq == noo -1: idx[noo-2] = 1 idx[noo-1] = 1 elif eq == noo -2: idx[eq-1] = 1 idx[eq] = 2 idx[eq+1] = 2 else: idx[eq-1] = 1 idx[eq] = 2 idx[eq+1] = 3 idx.extend(idx) C2T += xerus.TTOperator.dirac(dim,idx) C2T.round(1e-12) i1,i2,i3,i4,i5,i6,j1,j2,j3,j4,k1,k2,k3 = xerus.indices(13) X(i1^noo,j1^noo) << X(i1^noo,k1^noo) * C2T(k1^noo,j1^noo) X.round(1e-12) return X def exact(noo,p): beta = 0.7 C1ex = hp.construct_exact_fermit_pasta(noo,p,beta) C1ex = transform(C1ex) C1ex = project(C1ex) return C1ex ###Output _____no_output_____ ###Markdown Recovery algorithmWe want to recover the exact solution with the help of a regularized ALS. ###Code # initialize simulation def initialize(p,noo): rank = 4 #fix rank dim = [p for i in range(0,noo)] dim.extend([4 for i in range(0,noo)]) dim[noo] = 3 dim[2*noo-1]=3 C = xerus.TTOperator.random(dim,[rank for i in range(0,noo-1)]) # initalize randomly C.move_core(0,True) return C # We choose different pairstriples of dimensions samplesizes and noise level to run the algoirthm for. data_noo_nos = [(12,4500,1e-8),(12,4500,1e-7),(12,4500,1e-6),(12,4500,1e-5),(12,4500,1e-4)\ ,(12,4500,1e-3),(12,4500,1e-2),(12,4500,1e-1),(12,4500,1)] # pairs used in simulations in the paper # uncomment to simulate but is computational intensive data_noo_nos = [(12,4500,1e-2),(12,4500,1e-1),(12,4500,1)] #data_noo_nos = [(8,3000)] #specify pairs to simulate for #runs = 1 #specify number of runs for each pair (10 in the paper) runs = 3 max_iter = 30 # specify number of sweeps output = 'data.csv' # specify name of output file # build data structure to store solution tuples = [] for data in data_noo_nos: noo = data[0] nos = data[1] sigma = data[2] for r in range(0,runs): tuples.append((noo,nos,sigma, r)) index = pd.MultiIndex.from_tuples(tuples, names=['d', 'm','sigma','runs']) # The results of each optimization is store in a Dataframe col = ["data norm", "noise norm"] col.extend([i for i in range(1,max_iter+1)]) df = pd.DataFrame(np.zeros([len(tuples),max_iter+2]), index=index,columns=col) print(len(index)) df["data norm"] np.set_printoptions(precision=4) #loop over all pairs of samples, calls hp.run_als for the solution lam = 1 #regularization parameter #Master iteration psi = hp.basis(0) # get basis functions, Legendre p = len(psi) for data in data_noo_nos: noo = data[0] nos = data[1] sigma = data[2] print( "(noo,nos,sigma) = (" + str(noo) +',' + str(nos) +',' + str(sigma) + ')' ) C2list = hp.build_choice_tensor2(noo) # build selection tensor as list of pxnos matrices C1ex = exact(noo,p) # construct exact solution print("C1ex frob_norm: " +str(C1ex.frob_norm())) for r in range(runs): [x, y] = hp.fermi_pasta_ulam(noo, nos) # create samples and labels df["data norm"].loc[(noo,nos,sigma,r)] = np.linalg.norm(y) noise = np.random.normal(0,sigma,size=y.shape) df["noise norm"].loc[(noo,nos,sigma,r)] = np.linalg.norm(noise) y = y + noise Alist = hp.build_data_tensor_list2(noo,x,nos,psi,p) # build the dictionary tensor for the given samples x Y = xerus.Tensor.from_ndarray(y) C1 = initialize(p,noo) # initialize the als randomly errors = hp.run_als(noo,nos,C1,C2list,Alist,C1ex,Y,max_iter,lam) # run the regularized ALS iteration #post processing, store data in dataframe for i in range(1,len(errors)): df[i].loc[(noo,nos,sigma,r)] = errors[i-1] print("Run: " +str(r) + " finished result = " + str(errors) + " data norm = " + str( df["data norm"].loc[(noo,nos,sigma,r)])+ " noise norm = " + str( df["noise norm"].loc[(noo,nos,sigma,r)])) df.to_csv(output) ###Output L2(-1,1) orthogonal polynomials (noo,nos,sigma) = (12,4500,0.01) C1ex frob_norm: 19.885773809434728
Misc/Untitled.ipynb	###Markdown Matrics and Gradients ###Code import numpy as np import torch import torchvision X_train = np.array([[23, 32, 43, 343, 33, 44, 84, 34, 43, 423, 45, 56, 645, 254, 432], [344, 3454, 32, 656, 783, 645, 23, 57, 23, 775, 232, 757, 864, 85, 33], [23, 90, 233, 235, 907, 879, 402, 23, 44, 2323, 36, 232, 66, 232, 66], [239, 845, 323, 664, 64, 42, 98, 903, 886, 332, 64, 892, 34, 240, 89], [89, 23, 534, 134, 242, 53, 89, 775, 73, 353, 80, 489, 235, 24, 64]]) ###Output _____no_output_____ ###Markdown for i in X_train: squares = np.sum(i) print(squares) for i in X_test: squares = np.sum(i) print(squares) ###Code y = np.array([1, 2, 3, 2, 1]) y.shape X_test = np.array([[34, 89, 42, 24, 64, 23, 136, 643, 42, 633, 249, 24, 89, 23, 52], [224, 646, 324, 89, 535, 64, 42, 64, 42, 646, 89, 909, 42, 53, 9], [23, 456, 42, 646, 75, 24, 189, 53, 64, 89, 635, 89, 42, 63, 23], [239, 845, 323, 664, 64, 42, 98, 903, 886, 332, 64, 892, 34, 240, 189]]) num_test = X_test.shape[0] num_train = X_train.shape[0] # 3 rows and 5 columns for all the X_train examples dists = np.zeros((num_test, num_train)) for i in range(X_test.shape[0]): for j in range(X_train.shape[0]): dists[i, j] = np.sqrt(np.sum(np.square(X_test[i]-X_train[j]))) dists np.argsort(dists) # dists.sort(key=lambda tup: tup[1]) closest_y = [] for i in sorted_dists: # print(y[k][0]) print(i) # print('y[k]:', y[k]) # closest_y.ppend(y[k][0]) k = 1 closest_y = [] for i in range(k): closest_y.append(sorted_dists[i][0]) # closest_y.append(y[sorted_dists[i]]) # closest_y.append(y[sorted_dists][k]) print(closest_y) closest_y = [] for i in sorted_dists: # print(i) closest = i[0] closest_y.append(y[closest]) print(closest_y) # most common label in labels: num_neighbors = X_train.shape[0] num_neighbors # closest_y = [] for i in range(num_test): closest_y = [] sorted_dists = np.argsort(dists) print(closest) for i in range(sorted_dists): # print(i) closest = i[0] print(closest) # closest_y.append(y[closest]) # y_pred[i] = max(set(closest_y), key=closest_y.count) # closest_y.append(sorted_dists[i][0]) closest_y minInRows = np.amin(dists, axis=1) result = np.where(dists == np.amin(dists, axis=1)) print(result) dists for i in dists: min_i = np.argmin(i, axis=0) print(min_i) # predict_labels # -dists: matrix of distances # in dists: 1st image has the minimum distance at 812 so it should get the test label at 812. # the 2nd image has min distance at 1162 and so, it should get the same distnace # same is hte case for the third image # y_test = [] labels = [] for i in dists: # get the minimum # indices_dists = np.min(i) # get it's index min_i = np.argmin(i, axis=0) labels.append(y[min_i]) # min_index = np.where(dists == np.amin(dists, axis=1)) # y_test.append(min_index[1]) # y_test = list(zip(min_index[1])) # print(min_index) # print(type(min_index)) print(labels) # get the indices first # then get the value for those form y by doing y[i] # y_test.append(y[i]) # get the index of the mininum from the dists numpy and compare it with y array # y_test[i].append(np.min(i[])) arr == numpy.amin(arr) ###Output _____no_output_____ ###Markdown for j in dists: min_in = np.min(j) print(min_in) print(dists[0][min_in]) for every array in jy_test = []for j in dists: go to the minimum value: min_index = (dists[np.min(j)]) print(min_index) y_test.append(y[min_index]) ###Code y_test ###Output _____no_output_____ ###Markdown Broadcasting ###Code A = np.array([[56.0, 0.0, 4.4, 68.0], [1.2, 104.0, 52.0, 0.0], [1.8, 135.0,99.0, 0.9]]) print(A) cal = A.sum(axis=0) print(cal) percentage = 100A/cal.reshape(1,4) print(percentage) # if only one dimension of a matrix and vector matches, Python will copy the vector to make it # look like the same shape as the matrix and then, we add them elementwise. # the row refers to test_point # the column refers to training point dists dists.shape num_test = dists.shape[0] y_pred = np.zeros(num_test) print(y_pred) sorted_dist = np.argsort(dists) print(sorted_dist) for i in range(num_test): closest_y = [] sorted_dists = np.argsort(dists) print(sorted_dists) print(sorted_dists[i][0]) closest_y.append(sorted_dists[i][0]) closest_y import numpy as np X = np.array([[2, 3, 1, 2, 3, 2, 1, 3, 4, 4], [3, 3, 1, 2, 4, 5, 3, 5, 3, 2], [1, 2, 1, 3, 5, 4, 3, 4, 2, 1]]) W = np.array([[0.02, 0.01, 0.03], [0.04, 0.04, 0.03], [0.01, 0.05, 0.02], [0.05, 0.01, 0.01], [0.01, 0.09, 0.01], [0.02, 0.04, 0.02], [0.03, 0.01, 0.07], [0.01, 0.05, 0.02], [0.02, 0.06, 0.02], [0.01, 0.06, 0.04]]) scores= np.dot(X, W) scores scores.shape[0] reg = 1 # scores for the correct classes yi_scores = scores[np.arange(scores.shape[0]), y] margins = np.maximum(0, (scores - np.matrix(yi_scores).T + 1)) margins margins[np.arange(num_train), y] = 0 reg = 0.000005 loss = np.mean(np.sum(margins, axis=1)) loss += 0.5 reg * np.sum(W * W) loss binary = margins binary[margins > 0] = 1 row_sum = np.sum(binary, axis=1) binary[np.arange(num_train), y] = -row_sum.T dW = np.dot(X.T, binary) # Average dW /= num_train dW += reg * W dW # since we only calculate the loss where the j is not equal to y_i margins y = np.array([1, 2, 1]) dW = np.zeros(W.shape) dW = np.zeros(W.shape) loss = 0.0 num_classes = W.shape[1] num_train = X.shape[0] scores = np.dot(X, W) correct_class_score = scores[y] scores[y] scores margin = (scores - correct_scores + 1) margin for j in range(num_classes): margin = (scores[j] - correct_scores + 1) if margin.any() > 0: # for i in range(X.shape[0]): dW = np.zeros(W.shape) loss = 0.0 num_classes = W.shape[1] num_train = X.shape[0] for i in range(num_train): scores = np.dot(X[i], W) correct_score = scores[y[i]] for j in range(num_classes): margin = (scores[j] - correct_score + 1) if margin > 0: loss += margin # for all the classes other than the y[i] classes, += X[i,:] dW[:, j] += X[i, :] dW[:, y[i]] -= X[i, :] scores X = np.array([[2, 3, 1, 2, 3, 2, 1, 3, 4, 4], [3, 3, 1, 2, 4, 5, 3, 5, 3, 2], [1, 2, 1, 3, 5, 4, 3, 4, 2, 1]]) X dW dW correct_class_score = scores[y] correct_class_score # that is the score for the correct class # what we are trying to do is minimize the loss for that class # so we are going to calculate the loss: loss = 0.0 for j in range(3): if j == y: continue margin = (scores[j] - correct_class_score + 1) if margin >0: loss += margin dW[:, j] += X dW[:, y] -= X dW margin margin print(loss) dW = np.zeros(W.shape) dW ###Output _____no_output_____
RegExDragon.ipynb	###Markdown Regex Extractor TODO1. define regexes - IBAN - KvK - Amount - Name - Invoice reference - Total2. take input3. match with regex4. get results5. package them as per Rick's specifications REGEXES- Iban: "[a-zA-Z]{2}[0-9]{2}[a-zA-Z0-9]{4}[0-9]{7}([a-zA-Z0-9]?){0,16}"- KVK: ""- Date: ""- Amount: "^(€\|$)?\s?(\d{1,10})(\.\|\,)(\d{2})(€\|$)?$"- Name: ""- Invoice reference: ""- Total: "" Setup ###Code import re import os import glob ###Output _____no_output_____ ###Markdown define regexes ###Code # Works IbanRegex = re.compile(r'[a-zA-Z]{2}[0-9]{2}[a-zA-Z0-9]{4}[0-9]{7}([a-zA-Z0-9]?){0,16}') # Takes 4 different numbers from an example input file KvKRegex = re.compile(r'\d{8}') # Works AmountRegex = re.compile(r'[€\|$]\s?\d{1,10}[\.\|\,]\d{2}') # Pulls out 4 different files ReferenceRegex = re.compile(r'[A-Z-0-9]{8}') # NameRegex = re.compile(r'[a-zA-Z]{12}?\s?([a-zA-Z]{12})') ###Output _____no_output_____ ###Markdown definitions ###Code outputData = [] def importer(filename): invoice = open(filename, 'r') def IbanMatcher(invoice): IbanRegex = re.compile(r'[A-Z]{2}\d\d\s?[A-Z0-9]{4}[0-9]{8,20}') IbanMatcher.result = IbanRegex.findall(invoice) outputData.append({'IBAN':IbanMatcher.result}) def KvKMatcher(invoice): KvKRegex = re.compile(r'[0-9]{8}') KvKMatcher.result = KvKRegex.findall(invoice) outputData.append({'KvK_Nummer':KvKMatcher.result}) def AmountMatcher(invoice): AmountRegex = re.compile(r'(TE BETALEN\n\nâ‚¬\s)(\d{1,10}\.\d\d')) AmountMatcher.result = AmountRegex.findall(invoice) outputData.append({'amounts':AmountMatcher.result}) ###Output _____no_output_____ ###Markdown run ###Code file = open('Output.txt', 'r') invoice = file.read() IbanMatcher(invoice) KvKMatcher(invoice) AmountMatcher(invoice) ###Output _____no_output_____ ###Markdown make tuple ###Code print(invoice) print(outputData) ###Output [{'IBAN': ['NL02INGB0681309748']}, {'KvK_Nummer': ['24269393', '00001926', '06813097', '88163931']}, {'amounts': ['TE BETALEN\n\nâ‚¬ 14804.57']}]
XGBoost/XGBoost_Regression/XGBoost (Regression).ipynb	###Markdown Machine Learning in Python XGBoost Seyed Mohammad Sajadi Topics:- [ ] What is XGBoost (Review)- [ ] XGBoost in action (Regression) What is XGBoost? eXtreme Gradient Boosting (XGBoost) is a scalable and improved version of the gradient boosting algorithm (terminology alert) designed for efficacy, computational speed and model performance. It is an open-source library and a part of the Distributed Machine Learning Community. XGBoost is a perfect blend of software and hardware capabilities designed to enhance existing boosting techniques with accuracy in the shortest amount of time. What makes XGBoost a go-to algorithm for winning Machine Learning and Kaggle competitions? XGBoost in Action (Regression) Importing the libraries ###Code # !pip install xgboost %matplotlib inline import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import sklearn import xgboost as xgb from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score, KFold from sklearn.metrics import mean_squared_error from sklearn.metrics import mean_absolute_error from sklearn.metrics import r2_score ###Output _____no_output_____ ###Markdown Load and Prepare Data * Dataset We will be using a dataset that encapsulates the carbon dioxide emissions generated from burning coal for producing electricity power in the United States of America between 1973 and 2016. Using XGBoost, we will try to predict the carbon dioxide emissions in jupyter notebook for the next few years. ###Code #Read the dataset and print the top 5 elements of the dataset data = pd.read_csv('CO2.csv') data.head(5) data.info() ###Output <class 'pandas.core.frame.DataFrame'> RangeIndex: 523 entries, 0 to 522 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 YYYYMM 523 non-null int64 1 Value 523 non-null float64 dtypes: float64(1), int64(1) memory usage: 8.3 KB ###Markdown We use Pandas to import the CSV file. We notice that the dataframe contains a column 'YYYYMM' that needs to be separated into 'Year' and 'Month' column. In this step, we will also remove any null values that we may have in the dataframe. Finally, we will retrieve the last five elements of the dataframe to check if our code worked. And it did! ###Code data['Month'] = data.YYYYMM.astype(str).str[4:6].astype(float) data['Year'] = data.YYYYMM.astype(str).str[0:4].astype(float) data.shape data.drop(['YYYYMM'], axis=1, inplace=True) data.replace([np.inf, -np.inf], np.nan, inplace=True) data.tail(5) # check for data type print(data.dtypes) data.isnull().sum() data.shape X = data.loc[:,['Month', 'Year']].values y = data.loc[:,'Value'].values y data_dmatrix = xgb.DMatrix(X,label=y) data_dmatrix from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=5) print(X_train.shape) print(y_train.shape) print(X_test.shape) print(y_test.shape) reg_mod = xgb.XGBRegressor( n_estimators=1000, learning_rate=0.08, subsample=0.75, colsample_bytree=1, max_depth=7, gamma=0, ) reg_mod.fit(X_train, y_train) #After training the model, we'll check the model training score. scores = cross_val_score(reg_mod, X_train, y_train,cv=10) print("Mean cross-validation score: %.2f" % scores.mean()) reg_mod.fit(X_train,y_train) predictions = reg_mod.predict(X_test) rmse = np.sqrt(mean_squared_error(y_test, predictions)) print("RMSE: %f" % (rmse)) from sklearn.metrics import r2_score r2 = np.sqrt(r2_score(y_test, predictions)) print("R_Squared Score : %f" % (r2)) ###Output R_Squared Score : 0.990117 ###Markdown * As you can see, the these statistical metrics have reinstated our confidence about this model. RMSE ~ 4.95 R-Squared Score ~ 98.8% Now, let's visualize the original data set using the seaborn library. ###Code plt.figure(figsize=(10, 5), dpi=80) sns.lineplot(x='Year', y='Value', data=data) plt.figure(figsize=(10, 5), dpi=80) x_ax = range(len(y_test)) plt.plot(x_ax, y_test, label="test") plt.plot(x_ax, predictions, label="predicted") plt.title("Carbon Dioxide Emissions - Test and Predicted data") plt.legend() plt.show() ###Output _____no_output_____ ###Markdown Finally, the last piece of code will print the forecasted carbon dioxide emissions until 2025. ###Code plt.figure(figsize=(10, 5), dpi=80) df=pd.DataFrame(predictions, columns=['pred']) df['date'] = pd.date_range(start='8/1/2016', periods=len(df), freq='M') sns.lineplot(x='date', y='pred', data=df) plt.title("Carbon Dioxide Emissions - Forecast") plt.show() ###Output _____no_output_____
kaggle/ml-fraud-detection-master/k-means.ipynb	###Markdown K-means ###Code import numpy as np import sklearn as sk import pandas as pd df = pd.read_csv('creditcard.csv', low_memory=False) df.head() from sklearn.cluster import KMeans from time import time import matplotlib.pyplot as plt from sklearn import metrics from sklearn.cluster import KMeans from sklearn.datasets import load_digits from sklearn.decomposition import PCA from sklearn.preprocessing import scale from sklearn.model_selection import train_test_split X = df.iloc[:,:-1] y = df['Class'] X_scaled = scale(X) pca = PCA(n_components=2) X_reduced = pca.fit_transform(X_scaled) X_train, X_test, y_train, y_test = train_test_split(X_reduced, y, test_size = 0.33, random_state=500) kmeans = KMeans(init='k-means++', n_clusters=2, n_init=10) kmeans.fit(X_train) # Step size of the mesh. Decrease to increase the quality of the VQ. h = .01 # point in the mesh [x_min, x_max]x[y_min, y_max]. # Plot the decision boundary. For that, we will assign a color to each x_min, x_max = X_reduced[:, 0].min() - 1, X_reduced[:, 0].max() + 1 y_min, y_max = X_reduced[:, 1].min() - 1, X_reduced[:, 1].max() + 1 xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) # Obtain labels for each point in mesh. Use last trained model. Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()]) # Put the result into a color plot Z = Z.reshape(xx.shape) plt.figure(1) plt.clf() plt.imshow(Z, interpolation='nearest', extent=(xx.min(), xx.max(), yy.min(), yy.max()), cmap=plt.cm.Paired, aspect='auto', origin='lower') plt.plot(X_reduced[:, 0], X_reduced[:, 1], 'k.', markersize=2) # Plot the centroids as a white X centroids = kmeans.cluster_centers_ plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', s=169, linewidths=3, color='w', zorder=10) plt.title('K-means clustering on the credit card fraud dataset (PCA-reduced data)\n' 'Centroids are marked with white cross') plt.xlim(x_min, x_max) plt.ylim(y_min, y_max) plt.xticks(()) plt.yticks(()) plt.show() predictions = kmeans.predict(X_test) pred_fraud = np.where(predictions == 1)[0] real_fraud = np.where(y_test == 1)[0] false_pos = len(np.setdiff1d(pred_fraud, real_fraud)) pred_good = np.where(predictions == 0)[0] real_good = np.where(y_test == 0)[0] false_neg = len(np.setdiff1d(pred_good, real_good)) false_neg_rate = false_neg/(false_pos+false_neg) accuracy = (len(X_test) - (false_neg + false_pos)) / len(X_test) print("Accuracy:", accuracy) print("False negative rate (with respect to misclassifications): ", false_neg_rate) print("False negative rate (with respect to all the data): ", false_neg / len(predictions)) print("False negatives, false positives, mispredictions:", false_neg, false_pos, false_neg + false_pos) print("Total test data points:", len(X_test)) ###Output Accuracy: 0.5474799706342367 False negative rate (with respect to misclassifications): 0.0025393242576003386 False negative rate (with respect to all the data): 0.0011490950876185005 False negatives, false positives, mispredictions: 108 42423 42531 Total test data points: 93987
section_4/01_sort.ipynb	###Markdown ソートソート（sort）は、データを昇順、もしくは降順に並び替えるアルゴリズムです。ソートのアルゴリズムは多く存在しますが、それぞれ計算量が異なります。今回は、以下の4つの有名なソートのアルゴリズムを解説します。 * 選択ソート* バブルソート* マージソート* クイックソート ◎選択ソート選択ソート（selection sort）は、並んだ複数の要素から最小値（最大値）を探し最初（最後）の要素と入れ替えを行うソートのアルゴリズムです。直感的でシンプルなのですが、時間計算量（平均、最悪ともに）が$\mathcal{O}(n^2)$となり、データのサイズが大きい場合は計算に時間がかかるのが欠点です。空間計算量は、$\mathcal{O}(1)$となります。以下は、Pythonによる選択ソートの実装です。 ###Code data = [3, 5, 2, 1, 4] # ソート対象のデータ # ----- 選択ソート ----- for i in range(0, len(data)): min_idx = i for j in range(i+1, len(data)): if data[j] < data[min_idx]: min_idx = j # 値の交換 min = data[min_idx] data[min_idx] = data[i] data[i] = min print(data) ###Output _____no_output_____ ###Markdown ◎バブルソートバブルソート（bubble sort）は、隣接した要素の大小を比較しながら整列させるソートのアルゴリズムです。アルゴリズムがシンプルで並列処理に向いているのですが、選択ソートと同様に時間計算量（最悪）が$\mathcal{O}(n^2)$となり、データのサイズが大きい場合は計算に時間がかかるのが欠点です。空間計算量は、$\mathcal{O}(1)$となります。以下は、Pythonによるバブルソートの実装です。 ###Code data = [3, 5, 2, 1, 4] # ソート対象のデータ # ----- バブルソート ----- for i in range(0, len(data)): min_idx = i for j in range(0, len(data)-i-1): if data[j] > data[j+1]: larger = data[j] data[j] = data[j+1] data[j+1] = larger print(data) ###Output _____no_output_____ ###Markdown ◎クイックソートクイックソート（quick sort）は、ピボットと呼ぶ要素を用いてデータの分割を繰り返すことによりソートを行うアルゴリズムです。比較と交換の回数が少なく、しばしば実用上最も高速であるとされるソートのアルゴリズムです。クイックソートは以下の手順で表すことができます。 1. 要素数が1以下であれば、データをそのまま返す2. 要素を1つ選択し、ピボットとする3. ピボットの値以下のグループと、ピボットの値より大きいグループにデータを分割する4. 分割された各グループに1-３を再帰的に適用し、最後に全てのグループを結合する平均時間計算量は$\mathcal{O}(n\log n)$ですが、データのサイズや並びによっては計算に時間がかかることもあり、最悪時間計算量は$\mathcal{O}(n^2)$となります。空間計算量は、$\mathcal{O}(n)$となります。以下は、Pythonによるクイックソートの実装例です。 ###Code def quick_sort(data): if len(data) <= 1: return data pivot = data[0] # ピボット less = [] # ピボット以下の要素 more = [] # ピボットより大きい要素 for i in range(1, len(data)): if data[i] <= pivot: less.append(data[i]) else: more.append(data[i]) return quick_sort(less) + [pivot] + quick_sort(more) data = [3, 5, 6, 7, 2, 1, 4, 5, 1] # ソート対象のデータ print(quick_sort(data)) ###Output _____no_output_____ ###Markdown なお、クイックソートのような分割を繰り返すことでソートする手法は、分割統治法（divide-and-conquer method）と呼ばれます。 @ 演習分割統治法の一種、マージソート（merge sort）を実装しましょう。マージソートはデータを2つに分割して、それぞれをソートして結合（マージ）し、1つのソート済みデータとします。クイックソートと比べると最悪計算量は少ないですが、ランダムに並んだでデータでは一般的にクイックソートの方が高速です。マージソートは以下の手順で表すことができます。 1. データをA、B2つに分割する2. A、Bをそれぞれマージソートする3. A、Bを結合する上記2.では再帰的な処理時が行われます。 3.のデータの結合は以下の手順で行われます。 1. A、Bの先頭要素を比較して、小さい方の要素を抜き出してデータCの末尾に加える2. A、Bどちらかの要素が無くなるまで1.を繰り返す3. 余った要素をC末尾に加え、Cを結合済みのデータとする平均時間計算量は$\mathcal{O}(n\log n)$で、最悪時間計算量は$\mathcal{O}(n\log n)$となります。空間計算量は、$\mathcal{O}(n)$です。以下のセルにPythonのコードを追記し、マージソートを実装してください。 ###Code def merge_sort(data): if len(data) <= 1: return data center = len(data) // 2 # 中央のインデックス data_a = data[:center] # A data_b = data[center:] # B return # ←コードを追記 def merge(data_a, data_b): # 結合 merged = [] a_idx = 0 b_idx = 0 while a_idx < len(data_a) and a_idx < len(data_b): # どちらかの要素が無くなるまで繰り返す if data_a[a_idx] < data_b[b_idx]: # 先頭要素を比較 # ←コードを追記 a_idx += 1 else: # ←コードを追記 b_idx += 1 return merged + data_a[a_idx:] + data_b[b_idx:] # 余った要素を末尾に加える data = [3, 5, 6, 7, 2, 1, 4, 5, 1] # ソート対象のデータ print(quick_sort(data)) ###Output _____no_output_____ ###Markdown @解答例 ###Code def merge_sort(data): if len(data) <= 1: return data center = len(data) // 2 # 中央のインデックス data_a = data[:center] # A data_b = data[center:] # B return merge(merge_sort(data_a), merge_sort(data_b)) # ←コードを追記 def merge(data_a, data_b): # 結合 merged = [] a_idx = 0 b_idx = 0 while a_idx < len(data_a) and a_idx < len(data_b): # どちらかの要素が無くなるまで繰り返す if data_a[a_idx] < data_b[b_idx]: # 先頭要素を比較 merged.append(data_a[a_idx]) # ←コードを追記 a_idx += 1 else: merged.append(data_b[b_idx]) # ←コードを追記 b_idx += 1 return merged + data_a[a_idx:] + data_b[b_idx:] # 余った要素を末尾に加える data = [3, 5, 6, 7, 2, 1, 4, 5, 1] # ソート対象のデータ print(quick_sort(data)) ###Output _____no_output_____
Emulator.ipynb	###Markdown EmulatorI am using Gaussian Process to build an Emulator of FOM(Figure Of Merit). I have predicted a FOM value for last raw from given data. ###Code import george import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt from george import kernels from scipy.optimize import minimize from george.metrics import Metric import matplotlib.cm as cm %matplotlib inline a = np.loadtxt("parameters_with_FOM.txt") FOM = a[:,6] a = a[:,:-1] # area=14,300, depth=26.35, shear_m=0.003, sigma_z=0.05, sig_delta_z=0.001, sig_sigma_z=0.003 test1 = np.linspace(7000,20000, num=25) #area test2 = np.linspace(25,27, num=25) #depth #test2 = np.linspace(0.003,0.02, num=25) #shear_m #test2 = np.linspace(0.01,0.1, num=25) #sig_z #test2 = np.linspace(0.001,0.005, num=25) #sig_delta_z #test2 = np.linspace(0.003,0.006, num=25) #sig_sigma_z for param1 in test1: for param2 in test2: a = np.concatenate((a, [[param1, param2, 0.003, 0.05, 0.001, 0.003]]), axis=0) Xtest = a[36:, 0] Ytest = a[36:, 1] Xtest = Xtest.reshape(25,25) Ytest = Ytest.reshape(25,25) #slicing up the array in 7 column array: area = a[:,0] depth = a[:,1] shear_m = a[:,2] sigma_z = a[:,3] sig_delta_z = a[:,4] sig_sigma_z= a[:,5] #standardizing the data: Sarea = (area-np.mean(area))/np.std(area) Sdepth = (depth-np.mean(depth))/np.std(depth) Sshear_m = (shear_m-np.mean(shear_m))/np.std(shear_m) Ssigma_z = (sigma_z-np.mean(sigma_z))/np.std(sigma_z) Ssig_delta_z = (sig_delta_z-np.mean(sig_delta_z))/np.std(sig_delta_z) Ssig_sigma_z = (sig_sigma_z-np.mean(sig_sigma_z))/np.std(sig_sigma_z) SFOM = (FOM-np.mean(FOM))/np.std(FOM) #Putting together standardised parameter array: x = np.column_stack([Sarea, Sdepth, Sshear_m, Ssigma_z, Ssig_delta_z, Ssig_sigma_z]) Sxtest = x[36:,:] x = x[:36,:] #creating a kernel-covariance in 6-D parameter space: kernel = kernels.Product(kernels.ConstantKernel(log_constant=np.log((((2np.pi)))-0.5), ndim=6), kernels.ExpSquaredKernel(metric= [1,1,1,1,1,1], ndim=6)) gp = george.GP(kernel, mean=np.mean(SFOM)) gp.compute(x) #maximising likelihood: def neg_ln_lik(p): gp.set_parameter_vector(p) return -gp.log_likelihood(SFOM) def grad_neg_ln_like(p): gp.set_parameter_vector(p) return -gp.grad_log_likelihood(SFOM) result = minimize(neg_ln_lik, gp.get_parameter_vector(), jac=grad_neg_ln_like) gp.set_parameter_vector(result.x) #Predicting the test point (it is the last raw of data here): predSFOM, Svariance = gp.predict(SFOM, Sxtest, return_var=True) predFOM = predSFOMnp.std(FOM) + np.mean(FOM) print(max(predFOM), min(predFOM)) predFOM = predFOM.reshape(25,25) predFOM fig,ax = plt.subplots(figsize=(10,8)) norm = mpl.colors.LogNorm(vmin=17.699764038062785, vmax=50.694991388128386) cs = ax.pcolormesh(Xtest, Ytest, predFOM, norm=norm, cmap=cm.Blues) plt.title('FoM on Depth vs Area \n shear_m=0.003, $\sigma_z$=0.05, $\sigma (\Delta_z)$=0.001, $\sigma (\sigma_z)$=0.003') plt.xlabel('Area') plt.ylabel('$Depth') formatter = mpl.ticker.ScalarFormatter() fig.colorbar(cs, cmap=cm.Blues, norm=norm, format=formatter) plt.savefig('FOM-DepthvsArea.png',dpi=500) plt.show() ###Output _____no_output_____
Many-to-one_LSTM.ipynb	###Markdown Many-to-one LSTMref: UCB-CS282-John F. CannyIn this notebook we implement Many-to-One Long Short-Term Memory using a modular approach. For each layer we implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:```pythondef layer_forward(x, w): """ Receive inputs x and weights w """ Do some computations ... z = ... some intermediate value Do some more computations ... out = the output cache = (x, w, z, out) Values we need to compute gradients return out, cache```The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:```pythondef layer_backward(dout, cache): """ Receive derivative of loss with respect to outputs and cache, and compute derivative with respect to inputs. """ Unpack cache values x, w, z, out = cache Use values in cache to compute derivatives dx = Derivative of loss with respect to x dw = Derivative of loss with respect to w return dx, dw```After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.In addition to implementing LSTM networks of arbitrary depth, we also explore different update rules for optimization, and introduce Dropout as a regularizer and Batch Normalization as a tool to more efficiently optimize deep networks. ###Code # A bit of setup import time import numpy as np import matplotlib.pyplot as plt from batchnormlstm.classifiers.lstm import * from batchnormlstm.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array from batchnormlstm.solver import Solver %matplotlib inline plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots plt.rcParams['image.interpolation'] = 'nearest' plt.rcParams['image.cmap'] = 'gray' # for auto-reloading external modules # see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython %load_ext autoreload %autoreload 2 def rel_error(x, y): """ returns relative error """ return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y)))) # Load test dataset and process it. import pandas as pd raw = pd.read_csv('.\\dataset\\energydata_complete.csv', index_col=0) T = 4 test_pct = 0.05 data = {'X_val': np.empty([int(test_pct * raw.index.size) - T, T, raw.columns.size]), 'y_val': np.empty([int(test_pct * raw.index.size) - T, raw.columns.size]), 'X_train': np.empty([int((1 - test_pct) * raw.index.size) - T - int(test_pct * raw.index.size), T, raw.columns.size]), 'y_train': np.empty([int((1 - test_pct) * raw.index.size) - T - int(test_pct * raw.index.size), raw.columns.size]), 'X_test': np.empty([raw.index.size - T - int((1 - test_pct) * raw.index.size), T, raw.columns.size]), 'y_test': np.empty([raw.index.size - T - int((1 - test_pct) * raw.index.size), raw.columns.size])} for i in range(int(test_pct * raw.index.size) - T): data['X_val'][i] = raw.iloc[i:(i + T), :].values data['y_val'][i] = raw.iloc[i + T, :].values for i in range(int(test_pct * raw.index.size), int((1 - test_pct) * raw.index.size) - T): data['X_train'][i - int(test_pct * raw.index.size)] = raw.iloc[i:(i + T), :].values data['y_train'][i - int(test_pct * raw.index.size)] = raw.iloc[i + T, :].values for i in range(int((1 - test_pct) * raw.index.size), raw.index.size - T): data['X_test'][i - int((1 - test_pct) * raw.index.size)] = raw.iloc[i:(i + T), :].values data['y_test'][i - int((1 - test_pct) * raw.index.size)] = raw.iloc[i + T, :].values for k, v in data.items(): print('%s: ' % k, v.shape) ###Output X_val: (982, 4, 28) y_val: (982, 28) X_train: (17758, 4, 28) y_train: (17758, 28) X_test: (983, 4, 28) y_test: (983, 28) ###Markdown Affine layer: forwardIn file `batchnormlstm/layers.py` we implement the `affine_forward` function, then test the implementaion by running the following: ###Code # Test the affine_forward function num_inputs = 2 input_shape = (4, 5, 6) output_dim = 3 input_size = num_inputs * np.prod(input_shape) weight_size = output_dim * np.prod(input_shape) x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, input_shape) w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim) b = np.linspace(-0.3, 0.1, num=output_dim) out, _ = affine_forward(x, w, b) correct_out = np.array([[ 1.49834967, 1.70660132, 1.91485297], [ 3.25553199, 3.5141327, 3.77273342]]) # Compare the outputs. The error should be around 1e-9. print('Testing affine_forward function:') print('difference: ', rel_error(out, correct_out)) ###Output Testing affine_forward function: difference: 9.769847728806635e-10 ###Markdown Affine layer: backwardImplement the `affine_backward` function and test the implementation using numeric gradient checking. ###Code # Test the affine_backward function x = np.random.randn(10, 2, 3) w = np.random.randn(6, 5) b = np.random.randn(5) dout = np.random.randn(10, 5) dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout) dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout) db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout) _, cache = affine_forward(x, w, b) dx, dw, db = affine_backward(dout, cache) # The error should be around 1e-10 print('Testing affine_backward function:') print('dx error: ', rel_error(dx_num, dx)) print('dw error: ', rel_error(dw_num, dw)) print('db error: ', rel_error(db_num, db)) print() ###Output Testing affine_backward function: dx error: 1.580274371758088e-10 dw error: 1.6327342070374637e-10 db error: 8.79291994626051e-12 ###Markdown Activation layers: forwardImplement the forward pass for tanh and sigmoid activation function in the `tanh_forward` and `sigmoid_forward` function and test the implementation using the following: ###Code # Test the tanh_forward and sigmoid_forward function x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4) out, _ = tanh_forward(x) correct_out = np.array([[-0.46211716, -0.38770051, -0.30786199, -0.22343882], [-0.13552465, -0.04542327, 0.04542327, 0.13552465], [ 0.22343882, 0.30786199, 0.38770051, 0.46211716]]) # Compare the outputs. The error should be around 1e-8 print('Testing tanh_forward function:') print('difference: ', rel_error(out, correct_out)) out, _ = sigmoid_forward(x) correct_out = np.array([[0.37754067, 0.39913012, 0.42111892, 0.44342513], [0.46596182, 0.48863832, 0.51136168, 0.53403818], [0.55657487, 0.57888108, 0.60086988, 0.62245933]]) # Compare the outputs. The error should be around 1e-8 print('Testing sigmoid_forward function:') print('difference: ', rel_error(out, correct_out)) ###Output Testing tanh_forward function: difference: 3.8292287781296644e-08 Testing sigmoid_forward function: difference: 5.157221295671855e-09 ###Markdown Activation layers: backwardImplement the backward pass for tanh and sigmoid activation function in the `tanh_backward` and `sigmoid_backward` function and test the implementation using numeric gradient checking: ###Code x = np.random.randn(5, 5, 10) dout = np.random.randn(x.shape) dx_num = eval_numerical_gradient_array(lambda x: tanh_forward(x)[0], x, dout) _, cache = tanh_forward(x) dx = tanh_backward(dout, cache) # The error should be around 1e-10 print('Testing tanh_backward function:') print('dx error: ', rel_error(dx_num, dx)) dx_num = eval_numerical_gradient_array(lambda x: sigmoid_forward(x)[0], x, dout) _, cache = sigmoid_forward(x) dx = sigmoid_backward(dout, cache) # The error should be around 1e-10 print('Testing sigmoid_backward function:') print('dx error: ', rel_error(dx_num, dx)) print() ###Output Testing tanh_backward function: dx error: 1.9348653675634142e-10 Testing sigmoid_backward function: dx error: 1.0034653863721936e-10 ###Markdown Batch normalization: ForwardIn the file `batchnormlstm/layers.py`, we implement the batch normalization forward pass in the function `batchnorm_forward`, then run the following to test the implementation. ###Code # Check the training-time forward pass by checking means and variances # of features both before and after batch normalization # Simulate the forward pass for a two-layer network N, D1, D2, D3 = 200, 50, 60, 3 X = np.random.randn(N, D1) W1 = np.random.randn(D1, D2) W2 = np.random.randn(D2, D3) a = np.maximum(0, X.dot(W1)).dot(W2) print('Before batch normalization:') print(' means: ', a.mean(axis=0)) print(' stds: ', a.std(axis=0)) # Means should be close to zero and stds close to one print('After batch normalization (gamma=1, beta=0)') a_norm, _ = batchnorm_forward(a, np.ones(D3), np.zeros(D3), {'mode': 'train'}) print(' mean: ', a_norm.mean(axis=0)) print(' std: ', a_norm.std(axis=0)) # Now means should be close to beta and stds close to gamma gamma = np.asarray([1.0, 2.0, 3.0]) beta = np.asarray([11.0, 12.0, 13.0]) a_norm, _ = batchnorm_forward(a, gamma, beta, {'mode': 'train'}) print('After batch normalization (nontrivial gamma, beta)') print(' means: ', a_norm.mean(axis=0)) print(' stds: ', a_norm.std(axis=0)) # Check the test-time forward pass by running the training-time # forward pass many times to warm up the running averages, and then # checking the means and variances of activations after a test-time # forward pass. N, D1, D2, D3 = 200, 50, 60, 3 W1 = np.random.randn(D1, D2) W2 = np.random.randn(D2, D3) bn_param = {'mode': 'train'} gamma = np.ones(D3) beta = np.zeros(D3) for t in range(50): X = np.random.randn(N, D1) a = np.maximum(0, X.dot(W1)).dot(W2) batchnorm_forward(a, gamma, beta, bn_param) bn_param['mode'] = 'test' X = np.random.randn(N, D1) a = np.maximum(0, X.dot(W1)).dot(W2) a_norm, _ = batchnorm_forward(a, gamma, beta, bn_param) # Means should be close to zero and stds close to one, but will be # noisier than training-time forward passes. print('After batch normalization (test-time):') print(' means: ', a_norm.mean(axis=0)) print(' stds: ', a_norm.std(axis=0)) ###Output After batch normalization (test-time): means: [ 0.07693787 -0.02578972 0.05492385] stds: [0.99651805 1.04399964 0.95703145] ###Markdown Batch Normalization: backwardImplement the backward pass for batch normalization in the function `batchnorm_backward`.To derive the backward pass we write out the computation graph for batch normalization and backprop through each of the intermediate nodes. Some intermediates may have multiple outgoing branches; sum gradients across these branches in the backward pass.Run the following to numerically check the backward pass. ###Code # Gradient check batchnorm backward pass N, D = 4, 5 x = 5 * np.random.randn(N, D) + 12 gamma = np.random.randn(D) beta = np.random.randn(D) dout = np.random.randn(N, D) bn_param = {'mode': 'train'} fx = lambda x: batchnorm_forward(x, gamma, beta, bn_param)[0] fg = lambda a: batchnorm_forward(x, gamma, beta, bn_param)[0] fb = lambda b: batchnorm_forward(x, gamma, beta, bn_param)[0] dx_num = eval_numerical_gradient_array(fx, x, dout) da_num = eval_numerical_gradient_array(fg, gamma, dout) db_num = eval_numerical_gradient_array(fb, beta, dout) _, cache = batchnorm_forward(x, gamma, beta, bn_param) dx, dgamma, dbeta = batchnorm_backward(dout, cache) print('dx error: ', rel_error(dx_num, dx)) print('dgamma error: ', rel_error(da_num, dgamma)) print('dbeta error: ', rel_error(db_num, dbeta)) ###Output dx error: 2.8510779751549673e-10 dgamma error: 5.7137851762636384e-12 dbeta error: 3.3697549129496796e-11 ###Markdown Batch Normalization: alternative backwardWe derive a simple expression for the batch normalization backward pass after working out derivatives on paper and simplifying. Implement the simplified batch normalization backward pass in the function `batchnorm_backward_alt` and compare the two implementations by running the following. Our two implementations should compute nearly identical results, but the alternative implementation should be a bit faster. ###Code N, D = 100, 500 x = 5 * np.random.randn(N, D) + 12 gamma = np.random.randn(D) beta = np.random.randn(D) dout = np.random.randn(N, D) bn_param = {'mode': 'train'} out, cache = batchnorm_forward(x, gamma, beta, bn_param) t1 = time.time() dx1, dgamma1, dbeta1 = batchnorm_backward(dout, cache) t2 = time.time() dx2, dgamma2, dbeta2 = batchnorm_backward_alt(dout, cache) t3 = time.time() print('dx difference: ', rel_error(dx1, dx2)) print('dgamma difference: ', rel_error(dgamma1, dgamma2)) print('dbeta difference: ', rel_error(dbeta1, dbeta2)) print('speedup: %.2fx' % ((t2 - t1) / (t3 - t2))) print() ###Output dx difference: 3.14204955235961e-11 dgamma difference: 0.0 dbeta difference: 0.0 speedup: 3.00x ###Markdown Dropout forward passIn the file `batchnormlstm/layers.py`, implement the forward pass for dropout. Since dropout behaves differently during training and testing, we implement the operation for both modes.Run the cell below to test the implementation. ###Code x = np.random.randn(500, 500) + 10 rates = [0.3, 0.6, 0.75] for i, p in enumerate(rates): out, _ = dropout_forward(x, {'mode': 'train', 'p': p}) out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p}) print('Running tests with p = ', p) print('Mean of input: ', x.mean()) print('Mean of train-time output: ', out.mean()) print('Mean of test-time output: ', out_test.mean()) print('Fraction of train-time output set to zero: ', (out == 0).mean()) print('Fraction of test-time output set to zero: ', (out_test == 0).mean()) if i < (len(rates) - 1): print() ###Output Running tests with p = 0.3 Mean of input: 10.000280495638942 Mean of train-time output: 10.003757970096457 Mean of test-time output: 10.000280495638942 Fraction of train-time output set to zero: 0.699864 Fraction of test-time output set to zero: 0.0 Running tests with p = 0.6 Mean of input: 10.000280495638942 Mean of train-time output: 10.012958120128776 Mean of test-time output: 10.000280495638942 Fraction of train-time output set to zero: 0.399508 Fraction of test-time output set to zero: 0.0 Running tests with p = 0.75 Mean of input: 10.000280495638942 Mean of train-time output: 10.012202300191028 Mean of test-time output: 10.000280495638942 Fraction of train-time output set to zero: 0.249144 Fraction of test-time output set to zero: 0.0 ###Markdown Dropout backward passIn the file `batchnormlstm/layers.py`, implement the backward pass for dropout. Run the following cell to numerically gradient-check the implementation. ###Code x = np.random.randn(10, 10) + 10 dout = np.random.randn(x.shape) dropout_param = {'mode': 'train', 'p': 0.8, 'seed': 123} out, cache = dropout_forward(x, dropout_param) dx = dropout_backward(dout, cache) dx_num = eval_numerical_gradient_array(lambda xx: dropout_forward(xx, dropout_param)[0], x, dout) print('dx relative error: ', rel_error(dx, dx_num)) print() ###Output dx relative error: 5.4456072614148924e-11 ###Markdown LSTM Unit: forwardImplement a LSTM forward pass for one time step with `lstm_forward_unit`.ref: https://blog.aidangomez.ca/2016/04/17/Backpropogating-an-LSTM-A-Numerical-Example/ ###Code # Test the lstm_forward_unit function num_inputs = 2 input_shape = (4, 5, 6) output_dim = 3 inputX_size = num_inputs np.prod(input_shape) weightW_size = 4 * output_dim * np.prod(input_shape) inputS_size = num_inputs * output_dim weightU_size = 4 * output_dim * output_dim scale_size = 4 * output_dim x = np.linspace(-0.1, 0.5, num=inputX_size).reshape(num_inputs, input_shape) w = np.linspace(-0.2, 0.3, num=weightW_size).reshape(np.prod(input_shape), 4 output_dim) b = np.linspace(-0.3, 0.1, num=scale_size) h_prev = np.linspace(0.1, -0.5, num=inputS_size).reshape(num_inputs, output_dim) c_prev = np.linspace(0.2, -0.3, num=inputS_size).reshape(num_inputs, output_dim) u = np.linspace(0.3, -0.1, num=weightU_size).reshape(output_dim, 4 * output_dim) out, _, _ = lstm_forward_unit(x, w, u, b, h_prev, c_prev) correct_out = np.array([[0.63273332, 0.60414966, 0.57096795], [0.67986212, 0.63027987, 0.57357252]]) # Compare the outputs. The error should be around 1e-9. print('Testing lstm_forward_unit function:') print('difference: ', rel_error(out, correct_out)) ###Output Testing lstm_forward_unit function: difference: 3.5742222070529497e-09 ###Markdown LSTM & BatchNorm-LSTM Unit: backwardThen implement the `lstm_backward_unit` and `batchnorm_lstm_backward_unit` function and numerically check the implementations. ###Code # Test the lstm_backward_unit function x = np.random.randn(10, 2, 3) w = np.random.randn(6, 20) b = np.random.randn(20) h_prev = np.random.randn(10, 5) c_prev = np.random.randn(10, 5) u = np.random.randn(5, 20) dout = np.random.randn(10, 5) dx_num = eval_numerical_gradient_array(lambda x: lstm_forward_unit(x, w, u, b, h_prev, c_prev)[0], x, dout) dw_num = eval_numerical_gradient_array(lambda w: lstm_forward_unit(x, w, u, b, h_prev, c_prev)[0], w, dout) du_num = eval_numerical_gradient_array(lambda u: lstm_forward_unit(x, w, u, b, h_prev, c_prev)[0], u, dout) db_num = eval_numerical_gradient_array(lambda b: lstm_forward_unit(x, w, u, b, h_prev, c_prev)[0], b, dout) _, _, cache = lstm_forward_unit(x, w, u, b, h_prev, c_prev) dx, dw, du, db, _, _, _ = lstm_backward_unit(dout, cache) # The error should be around 1e-9 print('Testing lstm_backward_unit function:') print('dx error: ', rel_error(dx_num, dx)) print('dw error: ', rel_error(dw_num, dw)) print('du error: ', rel_error(du_num, du)) print('db error: ', rel_error(db_num, db)) print() # Test the batchnorm_lstm_backward_unit function gamma_x = np.random.randn(20) gamma_h = np.random.randn(20) gamma_c = np.random.randn(5) beta_x = np.random.randn(20) beta_h = np.random.randn(20) beta_c = np.random.randn(5) bn_params = [{'mode': 'train'} for i in range(3)] dx_num = eval_numerical_gradient_array(lambda x: batchnorm_lstm_forward_unit(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_prev, c_prev)[0], x, dout) dw_num = eval_numerical_gradient_array(lambda w: batchnorm_lstm_forward_unit(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_prev, c_prev)[0], w, dout) du_num = eval_numerical_gradient_array(lambda u: batchnorm_lstm_forward_unit(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_prev, c_prev)[0], u, dout) db_num = eval_numerical_gradient_array(lambda b: batchnorm_lstm_forward_unit(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_prev, c_prev)[0], b, dout) dgamma_x_num = eval_numerical_gradient_array(lambda gamma_x: batchnorm_lstm_forward_unit(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_prev, c_prev)[0], gamma_x, dout) dbeta_x_num = eval_numerical_gradient_array(lambda beta_x: batchnorm_lstm_forward_unit(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_prev, c_prev)[0], beta_x, dout) dgamma_h_num = eval_numerical_gradient_array(lambda gamma_h: batchnorm_lstm_forward_unit(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_prev, c_prev)[0], gamma_h, dout) dbeta_h_num = eval_numerical_gradient_array(lambda beta_h: batchnorm_lstm_forward_unit(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_prev, c_prev)[0], beta_h, dout) dgamma_c_num = eval_numerical_gradient_array(lambda gamma_c: batchnorm_lstm_forward_unit(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_prev, c_prev)[0], gamma_c, dout) dbeta_c_num = eval_numerical_gradient_array(lambda beta_c: batchnorm_lstm_forward_unit(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_prev, c_prev)[0], beta_c, dout) _, _, cache = batchnorm_lstm_forward_unit(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_prev, c_prev) dx, dw, du, db, dgammas, dbetas, _, _, _ = batchnorm_lstm_backward_unit(dout, cache) dgamma_x, dgamma_h, dgamma_c = dgammas dbeta_x, dbeta_h, dbeta_c = dbetas # The error should be around 1e-9 print('Testing batchnorm_lstm_backward_unit function:') print('dx error: ', rel_error(dx_num, dx)) print('dw error: ', rel_error(dw_num, dw)) print('du error: ', rel_error(du_num, du)) print('dgamma_x error: ', rel_error(dgamma_x_num, dgamma_x)) print('dbeta_x error: ', rel_error(dbeta_x_num, dbeta_x)) print('dgamma_h error: ', rel_error(dgamma_h_num, dgamma_h)) print('dbeta_h error: ', rel_error(dbeta_h_num, dbeta_h)) print('dgamma_c error: ', rel_error(dgamma_c_num, dgamma_c)) print('dbeta_c error: ', rel_error(dbeta_c_num, dbeta_c)) print() ###Output Testing lstm_backward_unit function: dx error: 5.676013334485782e-10 dw error: 9.09439438048634e-10 du error: 6.022805979019028e-09 db error: 2.958323064832232e-10 Testing batchnorm_lstm_backward_unit function: dx error: 8.584173797425142e-09 dw error: 4.5670732371827704e-08 du error: 7.438542676015566e-08 dgamma_x error: 3.725801103979747e-10 dbeta_x error: 9.704859248215657e-10 dgamma_h error: 1.6933380811135852e-08 dbeta_h error: 1.0815590892310194e-09 dgamma_c error: 5.595591165982771e-10 dbeta_c error: 3.837078124809926e-11 ###Markdown Assembled Layer: forwardImplement a batch-normalized LSTM forward pass with `batchnorm_lstm_forward` that loops through `batchnorm_lstm_forward_unit`. ###Code # Test the batchnorm_lstm_forward function num_inputs = 2 input_shape = (4, 5, 6) output_dim = 5 time_step = 3 inputX_size = num_inputs * time_step * np.prod(input_shape) weightW_size = 4 * output_dim * np.prod(input_shape) inputS_size = num_inputs * output_dim weightU_size = 4 * output_dim * output_dim scale_size = 4 * output_dim x = np.linspace(-0.1, 0.5, num=inputX_size).reshape(num_inputs, time_step, input_shape) w = np.linspace(-0.2, 0.3, num=weightW_size).reshape(np.prod(input_shape), 4 output_dim) b = np.linspace(-0.3, 0.1, num=scale_size) h_n1 = np.linspace(0.1, -0.5, num=inputS_size).reshape(num_inputs, output_dim) c_n1 = np.linspace(0.2, -0.3, num=inputS_size).reshape(num_inputs, output_dim) u = np.linspace(0.3, -0.1, num=weightU_size).reshape(output_dim, 4 * output_dim) gamma_x = np.linspace(3., 4., num=scale_size) gamma_h = np.linspace(-2., -5., num=scale_size) gamma_c = np.linspace(1., 6., num=output_dim) beta_x = np.linspace(-0.1, 0.3, num=scale_size) beta_h = np.linspace(-0.2, 0.2, num=scale_size) beta_c = np.linspace(-0.3, 0.1, num=output_dim) bn_params = [{'mode': 'train'} for i in range(3)] outs, _ = batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params) out = outs[:, -1, :] correct_out = np.array([[-0.61717064, -0.73806868, -0.77826679, -0.80687535, -0.83184207], [ 0.26743016, 0.41807813, 0.42096102, 0.41164257, 0.40158984]]) # Compare the outputs. The error should be around 1e-7. print('Testing batchnorm_lstm_forward function:') print('difference: ', rel_error(out, correct_out)) ###Output Testing batchnorm_lstm_forward function: difference: 2.505371298335007e-07 ###Markdown Assembled Layer: backwardImplement the `batchnorm_lstm_backward` function and test the implementation using numeric gradient checking. ###Code # Test the batchnorm_lstm_backward function x = np.random.randn(10, 4, 2, 3) w = np.random.randn(6, 20) b = np.random.randn(20) h_n1 = np.random.randn(10, 5) c_n1 = np.random.randn(10, 5) u = np.random.randn(5, 20) dout = np.random.randn(10, 4, 5) gamma_x = np.random.randn(20) gamma_h = np.random.randn(20) gamma_c = np.random.randn(5) beta_x = np.random.randn(20) beta_h = np.random.randn(20) beta_c = np.random.randn(5) bn_params = [{'mode': 'train'} for i in range(3)] dx_num = eval_numerical_gradient_array(lambda x: batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_n1, c_n1)[0], x, dout) dw_num = eval_numerical_gradient_array(lambda w: batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_n1, c_n1)[0], w, dout) du_num = eval_numerical_gradient_array(lambda u: batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_n1, c_n1)[0], u, dout) db_num = eval_numerical_gradient_array(lambda b: batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_n1, c_n1)[0], b, dout) dgamma_x_num = eval_numerical_gradient_array(lambda gamma_x: batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_n1, c_n1)[0], gamma_x, dout) dbeta_x_num = eval_numerical_gradient_array(lambda beta_x: batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_n1, c_n1)[0], beta_x, dout) dgamma_h_num = eval_numerical_gradient_array(lambda gamma_h: batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_n1, c_n1)[0], gamma_h, dout) dbeta_h_num = eval_numerical_gradient_array(lambda beta_h: batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_n1, c_n1)[0], beta_h, dout) dgamma_c_num = eval_numerical_gradient_array(lambda gamma_c: batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_n1, c_n1)[0], gamma_c, dout) dbeta_c_num = eval_numerical_gradient_array(lambda beta_c: batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_n1, c_n1)[0], beta_c, dout) _, cache = batchnorm_lstm_forward(x, w, u, b, (gamma_x, gamma_h, gamma_c), (beta_x, beta_h, beta_c), bn_params, h_n1, c_n1) dx, dw, du, db, dgammas, dbetas = batchnorm_lstm_backward(dout, cache) dgamma_x, dgamma_h, dgamma_c = dgammas dbeta_x, dbeta_h, dbeta_c = dbetas # The error should be around 1e-7 print('Testing lstm_backward_unit function:') print('dx error: ', rel_error(dx_num, dx)) print('dw error: ', rel_error(dw_num, dw)) print('du error: ', rel_error(du_num, du)) print('db error: ', rel_error(db_num, db)) print('dgamma_x error: ', rel_error(dgamma_x_num, dgamma_x)) print('dbeta_x error: ', rel_error(dbeta_x_num, dbeta_x)) print('dgamma_h error: ', rel_error(dgamma_h_num, dgamma_h)) print('dbeta_h error: ', rel_error(dbeta_h_num, dbeta_h)) print('dgamma_c error: ', rel_error(dgamma_c_num, dgamma_c)) print('dbeta_c error: ', rel_error(dbeta_c_num, dbeta_c)) print() ###Output Testing lstm_backward_unit function: dx error: 6.510179435612519e-08 dw error: 4.77448491604468e-07 du error: 2.683146430218781e-07 db error: 7.013542670085222e-09 dgamma_x error: 4.715754374359772e-09 dbeta_x error: 7.013542670085222e-09 dgamma_h error: 3.0510325475552296e-09 dbeta_h error: 7.247845978538795e-09 dgamma_c error: 6.701361988181442e-10 dbeta_c error: 2.216564624855941e-09 ###Markdown Loss layer: MSEImplement the loss function in `batchnormlstm/layers.py`.We make sure that the implementations are correct by running the following: ###Code dim_outputs, num_inputs = 10, 50 x = np.random.randn(num_inputs, dim_outputs) y = np.random.randn(num_inputs, dim_outputs) dx_num = eval_numerical_gradient(lambda x: mse_loss(x, y)[0], x, verbose=False) loss, dx = mse_loss(x, y) # Test mse_loss function. Loss should be around 2 and dx error should be 1e-7 print('Testing mse_loss:') print('loss: ', loss) print('dx error: ', rel_error(dx_num, dx)) print() ###Output Testing mse_loss: loss: 2.052333493108092 dx error: 2.555032546004221e-05 ###Markdown Two-layer LSTM networkIn the file `batchnormlstm/classifiers/lstm.py` we complete the implementation of the `TwoLayerLSTM` class. This class will serve as a model for the other networks we implement. Run the cell below to test the implementation. ###Code N, D, H, O = 3, 5, 50, 6 T = 4 std = 1e-2 model = TwoLayerLSTM(input_dim=D, hidden_dim=H, output_dim=O, weight_scale=std) print('Testing initialization ... ') W1_std = abs(model.params['W1'].std() - std) b1 = model.params['b1'] W2_std = abs(model.params['W2'].std() - std) b2 = model.params['b2'] U_std = abs(model.params['U'].std() - std) assert W1_std < std / 10, 'LSTM layer input weights do not seem right' assert np.all(b1 == 0), 'LSTM layer biases do not seem right' assert W2_std < std / 10, 'Affine layer weights do not seem right' assert np.all(b2 == 0), 'Affine layer biases do not seem right' assert U_std < std / 10, 'LSTM layer state weights do not seem right' print('Testing test-time forward pass ... ') model.params['W1'] = np.linspace(-0.7, 0.3, num=D4H).reshape(D, 4H) model.params['b1'] = np.linspace(-0.1, 0.9, num=4H) model.params['W2'] = np.linspace(-0.3, 0.4, num=HO).reshape(H, O) model.params['b2'] = np.linspace(-0.9, 0.1, num=O) model.params['U'] = np.linspace(-0.5, 0.5, num=H4H).reshape(H, 4H) X = np.linspace(-5.5, 4.5, num=NTD).reshape((D, T, N)).T h_prev = np.zeros((N, H)) c_prev = np.zeros((N, H)) outs = model.loss(X) correct_outs = np.asarray( [[1.30180544, 1.61810112, 1.9343968, 2.25069248, 2.56698816, 2.88328384], [1.30212866, 1.61834704, 1.93456542, 2.2507838, 2.56700219, 2.88322057], [1.30257611, 1.61870838, 1.93484065, 2.25097292, 2.56710519, 2.88323746]]) outs_diff = np.abs(outs - correct_outs).sum() assert outs_diff < 1e-6, 'Problem with test-time forward pass' print('Testing training loss (no regularization) ...') Y = np.linspace(2.5, -3.5, num=NO).reshape(O, N).T loss, grads = model.loss(X, Y) correct_loss = 12.319904047979042 assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss' print('Testing training loss (with regularization) ...') model.reg = 1.0 loss, grads = model.loss(X, Y) correct_loss = 497.3609656985614 assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss' for reg in [0.0, 0.7]: print('Running numeric gradient check with reg = ', reg) model.reg = reg loss, grads = model.loss(X, Y) for name in sorted(grads): f = lambda _: model.loss(X, Y)[0] grad_num = eval_numerical_gradient(f, model.params[name], verbose=False) print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))) print() ###Output Testing initialization ... Testing test-time forward pass ... Testing training loss (no regularization) ... Testing training loss (with regularization) ... Running numeric gradient check with reg = 0.0 U relative error: 3.33e-04 W1 relative error: 1.60e-03 W2 relative error: 5.15e-10 b1 relative error: 2.49e-04 b2 relative error: 4.77e-10 Running numeric gradient check with reg = 0.7 U relative error: 1.68e-05 W1 relative error: 2.44e-05 W2 relative error: 4.08e-07 b1 relative error: 3.29e-03 b2 relative error: 1.61e-09 ###Markdown SolverIn the previous assignment, the logic for training models was coupled to the models themselves. Following a more modular design, for this assignment we have split the logic for training models into a separate class.In the file `batchnormlstm/solver.py`, a modular designed class is implemented to train the models. Below we test a `Solver` instance by using it to train a `TwoLayerLSTM` with the data. ###Code model = TwoLayerLSTM(input_dim=28, hidden_dim=10) solver = None ############################################################################## # Use a Solver instance to train a TwoLayerLSTM # ############################################################################## solver = Solver(model, data, update_rule='sgd', optim_config={ 'learning_rate': 1e-3, }, lr_decay=0.9, num_epochs=12, batch_size=100, print_every=100) solver.train(regress=True) solver.check_accuracy(data['X_test'], data['y_test'], regress=True) ############################################################################## # # ############################################################################## # Run this cell to visualize training loss and train / val accuracy plt.subplot(2, 1, 1) plt.title('Training loss') plt.plot(solver.loss_history, 'o') plt.xlabel('Iteration') plt.subplot(2, 1, 2) plt.title('Accuracy') plt.plot(solver.train_acc_history, '-o', label='train') plt.plot(solver.val_acc_history, '-o', label='val') plt.plot([0.5] len(solver.val_acc_history), 'k--') plt.xlabel('Epoch') plt.legend(loc='lower right') plt.gcf().set_size_inches(15, 12) plt.show() print() ###Output _____no_output_____ ###Markdown Advanced LSTM networkNext we implement the multi-layer LSTM network with an arbitrary number of hidden layers, and the options of batch normalization and dropout, as the `AdvancedLSTM` class in the file `batchnormlstm/classifiers/lstm.py`.ref: https://arxiv.org/pdf/1603.09025.pdf https://arxiv.org/pdf/1409.2329.pdf Initial loss and gradient check As a sanity check, run the following to check the initial loss and to gradient check the network both with and without regularization and see if the initial losses seem reasonable. ###Code N, D, H1, H2, O = 2, 15, 20, 30, 10 T = 4 X = np.random.randn(N, T, D) Y = np.random.randn(N, O) for reg in [0, 3.14]: print('Running check with reg = ', reg) model = AdvancedLSTM([H1, H2], input_dim=D, output_dim=O, reg=reg, weight_scale=5e-2, dtype=np.float64) loss, grads = model.loss(X, Y) print('Initial loss: ', loss) for name in sorted(grads): f = lambda _: model.loss(X, Y)[0] grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5) print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name]))) ###Output Running check with reg = 0 Initial loss: 0.6364185128745645 U1 relative error: 6.23e-04 U2 relative error: 1.06e-03 W1 relative error: 1.24e-04 W2 relative error: 6.81e-04 W3 relative error: 6.66e-05 b1 relative error: 2.69e-05 b2 relative error: 1.44e-05 b3 relative error: 4.11e-11 Running check with reg = 3.14 Initial loss: 36.14152720955063 U1 relative error: 6.91e-07 U2 relative error: 8.19e-07 W1 relative error: 7.27e-07 W2 relative error: 3.58e-07 W3 relative error: 2.79e-08 b1 relative error: 6.86e-05 b2 relative error: 1.16e-03 b3 relative error: 2.35e-09 ###Markdown As another sanity check, train on a small dataset of 50 instances. We use three layers of LSTM and tweak the learning rate and initialization scale. ###Code num_train = 50 small_data = { 'X_train': data['X_train'][:num_train], 'y_train': data['y_train'][:num_train], 'X_val': data['X_val'], 'y_val': data['y_val'], } weight_scale = 1e-2 learning_rate = 1e-2 model = AdvancedLSTM([20, 10, 20], input_dim=28, weight_scale=weight_scale, dtype=np.float64) solver = Solver(model, small_data, print_every=10, num_epochs=20, batch_size=25, update_rule='sgd', optim_config={ 'learning_rate': learning_rate, } ) solver.train(regress=True) plt.plot(solver.loss_history, 'o') plt.title('Training loss history') plt.xlabel('Iteration') plt.ylabel('Training loss') plt.show() ###Output (Iteration 1 / 40) loss: 25834.386138 (Epoch 0 / 20) train_acc: -24585.993359; val_acc: -22754.584035 (Epoch 1 / 20) train_acc: -24553.678901; val_acc: -22722.841330 (Epoch 2 / 20) train_acc: -24489.199432; val_acc: -22659.478862 (Epoch 3 / 20) train_acc: -24424.312421; val_acc: -22595.890431 (Epoch 4 / 20) train_acc: -24359.229251; val_acc: -22532.153400 (Epoch 5 / 20) train_acc: -24292.794632; val_acc: -22467.386107 (Iteration 11 / 40) loss: 23365.510047 (Epoch 6 / 20) train_acc: -24226.185597; val_acc: -22401.898157 (Epoch 7 / 20) train_acc: -24154.486765; val_acc: -22331.632446 (Epoch 8 / 20) train_acc: -24069.942663; val_acc: -22249.184240 (Epoch 9 / 20) train_acc: -23956.483160; val_acc: -22138.375839 (Epoch 10 / 20) train_acc: -23767.291472; val_acc: -21953.807456 (Iteration 21 / 40) loss: 21446.622670 (Epoch 11 / 20) train_acc: -23340.993891; val_acc: -21535.989780 (Epoch 12 / 20) train_acc: -22434.520582; val_acc: -20649.555723 (Epoch 13 / 20) train_acc: -21410.202070; val_acc: -19648.278429 (Epoch 14 / 20) train_acc: -20369.472620; val_acc: -18633.258228 (Epoch 15 / 20) train_acc: -19333.098594; val_acc: -17627.971436 (Iteration 31 / 40) loss: 20595.584323 (Epoch 16 / 20) train_acc: -18320.673507; val_acc: -16642.917560 (Epoch 17 / 20) train_acc: -17370.295687; val_acc: -15709.193444 (Epoch 18 / 20) train_acc: -16456.554631; val_acc: -14819.026648 (Epoch 19 / 20) train_acc: -15593.876870; val_acc: -13980.018860 (Epoch 20 / 20) train_acc: -14786.478780; val_acc: -13193.151827 ###Markdown Update rulesSo far we have used vanilla stochastic gradient descent (SGD) as our update rule. More sophisticated update rules can make it easier to train deep networks. We implement a few of the most commonly used update rules and compare them to vanilla SGD. SGD+MomentumStochastic gradient descent with momentum is a widely used update rule that tends to make deep networks converge faster than vanilla stochstic gradient descent.In the file `batchnormlstm/optim.py` we implement the SGD+momentum update rule in the function `sgd_momentum`, then run the following to check the implementation. We should see errors less than 1e-8. ###Code from batchnormlstm.optim import sgd_momentum N, D = 4, 5 w = np.linspace(-0.4, 0.6, num=ND).reshape(N, D) dw = np.linspace(-0.6, 0.4, num=ND).reshape(N, D) v = np.linspace(0.6, 0.9, num=ND).reshape(N, D) config = {'learning_rate': 1e-3, 'velocity': v} next_w, _ = sgd_momentum(w, dw, config=config) expected_next_w = np.asarray([ [ 0.1406, 0.20738947, 0.27417895, 0.34096842, 0.40775789], [ 0.47454737, 0.54133684, 0.60812632, 0.67491579, 0.74170526], [ 0.80849474, 0.87528421, 0.94207368, 1.00886316, 1.07565263], [ 1.14244211, 1.20923158, 1.27602105, 1.34281053, 1.4096 ]]) expected_velocity = np.asarray([ [ 0.5406, 0.55475789, 0.56891579, 0.58307368, 0.59723158], [ 0.61138947, 0.62554737, 0.63970526, 0.65386316, 0.66802105], [ 0.68217895, 0.69633684, 0.71049474, 0.72465263, 0.73881053], [ 0.75296842, 0.76712632, 0.78128421, 0.79544211, 0.8096 ]]) print('next_w error: ', rel_error(next_w, expected_next_w)) print('velocity error: ', rel_error(expected_velocity, config['velocity'])) ###Output next_w error: 8.882347033505819e-09 velocity error: 4.269287743278663e-09 ###Markdown RMSProp and AdamRMSProp [1] and Adam [2] are update rules that set per-parameter learning rates by using a running average of the second moments of gradients.In the file `batchnormlstm/optim.py`, implement the RMSProp update rule in the `rmsprop` function and implement the Adam update rule in the `adam` function, and check the implementations using the tests below.[1] Tijmen Tieleman and Geoffrey Hinton. "Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude." COURSERA: Neural Networks for Machine Learning 4 (2012).[2] Diederik Kingma and Jimmy Ba, "Adam: A Method for Stochastic Optimization", ICLR 2015. ###Code # Test RMSProp implementation; we should see errors less than 1e-7 from batchnormlstm.optim import rmsprop N, D = 4, 5 w = np.linspace(-0.4, 0.6, num=ND).reshape(N, D) dw = np.linspace(-0.6, 0.4, num=ND).reshape(N, D) cache = np.linspace(0.6, 0.9, num=ND).reshape(N, D) config = {'learning_rate': 1e-2, 'cache': cache} next_w, _ = rmsprop(w, dw, config=config) expected_next_w = np.asarray([ [-0.39223849, -0.34037513, -0.28849239, -0.23659121, -0.18467247], [-0.132737, -0.08078555, -0.02881884, 0.02316247, 0.07515774], [ 0.12716641, 0.17918792, 0.23122175, 0.28326742, 0.33532447], [ 0.38739248, 0.43947102, 0.49155973, 0.54365823, 0.59576619]]) expected_cache = np.asarray([ [ 0.5976, 0.6126277, 0.6277108, 0.64284931, 0.65804321], [ 0.67329252, 0.68859723, 0.70395734, 0.71937285, 0.73484377], [ 0.75037008, 0.7659518, 0.78158892, 0.79728144, 0.81302936], [ 0.82883269, 0.84469141, 0.86060554, 0.87657507, 0.8926 ]]) print('next_w error: ', rel_error(expected_next_w, next_w)) print('cache error: ', rel_error(expected_cache, config['cache'])) # Test Adam implementation; we should see errors around 1e-7 or less from batchnormlstm.optim import adam N, D = 4, 5 w = np.linspace(-0.4, 0.6, num=ND).reshape(N, D) dw = np.linspace(-0.6, 0.4, num=ND).reshape(N, D) m = np.linspace(0.6, 0.9, num=ND).reshape(N, D) v = np.linspace(0.7, 0.5, num=ND).reshape(N, D) config = {'learning_rate': 1e-2, 'm': m, 'v': v, 't': 5} next_w, _ = adam(w, dw, config=config) expected_next_w = np.asarray([ [-0.40094747, -0.34836187, -0.29577703, -0.24319299, -0.19060977], [-0.1380274, -0.08544591, -0.03286534, 0.01971428, 0.0722929], [ 0.1248705, 0.17744702, 0.23002243, 0.28259667, 0.33516969], [ 0.38774145, 0.44031188, 0.49288093, 0.54544852, 0.59801459]]) expected_v = np.asarray([ [ 0.69966, 0.68908382, 0.67851319, 0.66794809, 0.65738853,], [ 0.64683452, 0.63628604, 0.6257431, 0.61520571, 0.60467385,], [ 0.59414753, 0.58362676, 0.57311152, 0.56260183, 0.55209767,], [ 0.54159906, 0.53110598, 0.52061845, 0.51013645, 0.49966, ]]) expected_m = np.asarray([ [ 0.48, 0.49947368, 0.51894737, 0.53842105, 0.55789474], [ 0.57736842, 0.59684211, 0.61631579, 0.63578947, 0.65526316], [ 0.67473684, 0.69421053, 0.71368421, 0.73315789, 0.75263158], [ 0.77210526, 0.79157895, 0.81105263, 0.83052632, 0.85 ]]) print('next_w error: ', rel_error(expected_next_w, next_w)) print('v error: ', rel_error(expected_v, config['v'])) print('m error: ', rel_error(expected_m, config['m'])) ###Output next_w error: 1.1395691798535431e-07 v error: 4.208314038113071e-09 m error: 4.214963193114416e-09 ###Markdown Final modelBy combining the features and modifying the rules, we can come up with a model that best fits the data. An example is shown below. ###Code adv_model = None ################################################################################ # Train a AdvancedLSTM with modified features and rules. Store the model in # # adv_model variable. # ################################################################################ hidden_dims = [20, 15, 20] adv_model = AdvancedLSTM(hidden_dims, input_dim=28, weight_scale=1e-2, dropout=0.95, use_batchnorm=True, reg=3e-5) solver = Solver(adv_model, data, num_epochs=30, batch_size=100, update_rule='adam', optim_config={ 'learning_rate': 1e-2, }, verbose=True, print_every=100, lr_decay = 0.9) solver.train(regress=True) ################################################################################ # # ################################################################################ ###Output (Iteration 1 / 5340) loss: 22429.565881 (Epoch 0 / 30) train_acc: -22142.952517; val_acc: -22784.897269 (Iteration 101 / 5340) loss: 20349.013528 (Epoch 1 / 30) train_acc: -18865.138875; val_acc: -19299.204119 (Iteration 201 / 5340) loss: 18602.975270 (Iteration 301 / 5340) loss: 17323.709701 (Epoch 2 / 30) train_acc: -16796.062704; val_acc: -17131.768075 (Iteration 401 / 5340) loss: 16288.072421 (Iteration 501 / 5340) loss: 15502.713236 (Epoch 3 / 30) train_acc: -15196.966181; val_acc: -15526.532841 (Iteration 601 / 5340) loss: 14649.562715 (Iteration 701 / 5340) loss: 14009.695086 (Epoch 4 / 30) train_acc: -14044.081025; val_acc: -14265.049174 (Iteration 801 / 5340) loss: 13185.206727 (Epoch 5 / 30) train_acc: -13031.742671; val_acc: -13231.504870 (Iteration 901 / 5340) loss: 13008.184389 (Iteration 1001 / 5340) loss: 12358.698903 (Epoch 6 / 30) train_acc: -12082.949525; val_acc: -12363.994974 (Iteration 1101 / 5340) loss: 11953.610956 (Iteration 1201 / 5340) loss: 11570.680937 (Epoch 7 / 30) train_acc: -11386.860170; val_acc: -11617.967127 (Iteration 1301 / 5340) loss: 11076.299291 (Iteration 1401 / 5340) loss: 10706.953638 (Epoch 8 / 30) train_acc: -10756.732469; val_acc: -10982.382432 (Iteration 1501 / 5340) loss: 10634.784156 (Iteration 1601 / 5340) loss: 10100.037639 (Epoch 9 / 30) train_acc: -10167.074312; val_acc: -10428.199456 (Iteration 1701 / 5340) loss: 9745.082310 (Epoch 10 / 30) train_acc: -9761.301357; val_acc: -9949.255366 (Iteration 1801 / 5340) loss: 9740.305786 (Iteration 1901 / 5340) loss: 9455.521302 (Epoch 11 / 30) train_acc: -9220.368611; val_acc: -9526.771915 (Iteration 2001 / 5340) loss: 9318.000634 (Iteration 2101 / 5340) loss: 8766.926771 (Epoch 12 / 30) train_acc: -8826.712583; val_acc: -9157.506234 (Iteration 2201 / 5340) loss: 8883.436453 (Iteration 2301 / 5340) loss: 8415.150049 (Epoch 13 / 30) train_acc: -8601.044907; val_acc: -8831.214089 (Iteration 2401 / 5340) loss: 8337.676423 (Epoch 14 / 30) train_acc: -8265.879308; val_acc: -8543.576560 (Iteration 2501 / 5340) loss: 8176.022965 (Iteration 2601 / 5340) loss: 8134.875731 (Epoch 15 / 30) train_acc: -7979.966729; val_acc: -8289.256179 (Iteration 2701 / 5340) loss: 8010.672002 (Iteration 2801 / 5340) loss: 7786.470749 (Epoch 16 / 30) train_acc: -7698.672004; val_acc: -8063.710700 (Iteration 2901 / 5340) loss: 7724.126279 (Iteration 3001 / 5340) loss: 7620.020365 (Epoch 17 / 30) train_acc: -7719.087292; val_acc: -7862.021490 (Iteration 3101 / 5340) loss: 7325.542304 (Iteration 3201 / 5340) loss: 7464.801944 (Epoch 18 / 30) train_acc: -7538.533580; val_acc: -7682.794647 (Iteration 3301 / 5340) loss: 7422.965915 (Epoch 19 / 30) train_acc: -7322.645703; val_acc: -7522.302291 (Iteration 3401 / 5340) loss: 7050.178851 (Iteration 3501 / 5340) loss: 6933.501588 (Epoch 20 / 30) train_acc: -7133.884757; val_acc: -7380.145795 (Iteration 3601 / 5340) loss: 7228.305912 (Iteration 3701 / 5340) loss: 6913.018620 (Epoch 21 / 30) train_acc: -6962.117160; val_acc: -7251.559253 (Iteration 3801 / 5340) loss: 7036.348716 (Iteration 3901 / 5340) loss: 6832.206765 (Epoch 22 / 30) train_acc: -6784.496720; val_acc: -7135.825761 (Iteration 4001 / 5340) loss: 7018.763070 (Epoch 23 / 30) train_acc: -6748.284937; val_acc: -7032.677722 (Iteration 4101 / 5340) loss: 6842.172335 (Iteration 4201 / 5340) loss: 6681.822179 (Epoch 24 / 30) train_acc: -6647.404915; val_acc: -6940.317673 (Iteration 4301 / 5340) loss: 6580.366273 (Iteration 4401 / 5340) loss: 6412.021725 (Epoch 25 / 30) train_acc: -6612.512929; val_acc: -6857.909244 (Iteration 4501 / 5340) loss: 6587.338017 (Iteration 4601 / 5340) loss: 6532.855847 (Epoch 26 / 30) train_acc: -6474.088960; val_acc: -6783.350387 (Iteration 4701 / 5340) loss: 6441.732856 (Iteration 4801 / 5340) loss: 6231.865239 (Epoch 27 / 30) train_acc: -6450.675438; val_acc: -6716.356471 (Iteration 4901 / 5340) loss: 6263.615659 (Epoch 28 / 30) train_acc: -6514.149270; val_acc: -6655.453157 (Iteration 5001 / 5340) loss: 6569.669732 (Iteration 5101 / 5340) loss: 6196.811852 (Epoch 29 / 30) train_acc: -6297.476843; val_acc: -6600.935016 (Iteration 5201 / 5340) loss: 6293.660362 (Iteration 5301 / 5340) loss: 6276.366139 (Epoch 30 / 30) train_acc: -6229.338944; val_acc: -6552.802483 ###Markdown Test the final modelRun the model on the validation and test sets. ###Code print('Test set accuracy: ') solver.check_accuracy(data['X_test'], data['y_test'], regress=True) ###Output Test set accuracy:
rulevetting/projects/csi_pecarn/notebooks/feature_visualization.ipynb	###Markdown Correlation of features and in groups and association with outcome Group1: Consciousness ###Code pd.set_option('display.max_columns', None) pd.set_option('display.max_rows', None) feat_conscious = ['HxLOC', 'TotalGCSManual', 'TotalGCS', 'AVPUDetails','AlteredMentalStatus', 'LOC','ControlType_x'] dfs_conscious=dfs[0].merge(dfs[3],how='left', on=['SITE', 'CaseID', 'StudySubjectID']) dfs_conscious=dfs_conscious[feat_conscious] # dfs_conscious.loc[:, 'ControlType_x'] = (dfs_conscious['ControlType_x'] == 'case').astype(int) dfs_conscious = dfs_conscious.replace(['Y', 'YES', 'A'], 1) dfs_conscious = dfs_conscious.replace(['N', 'NO'], 0) dfs_conscious = dfs_conscious.replace(['15'], 15) dfs_conscious = dfs_conscious.replace(['10'], 10) dfs_conscious = dfs_conscious.replace(['14'], 14) dfs_conscious = dfs_conscious.replace(['6'], 6) dfs_conscious = dfs_conscious.replace(['8'], 8) dfs_conscious = dfs_conscious.replace(['12'], 12) dfs_conscious = dfs_conscious.replace(['5'], 5) dfs_conscious = dfs_conscious.replace(['13'], 13) dfs_conscious = dfs_conscious.replace(['11'],11) dfs_conscious = dfs_conscious.replace(['9'], 9) dfs_conscious = dfs_conscious.replace(['7','7T'], 7) dfs_conscious = dfs_conscious.replace(['4'], 4) dfs_conscious = dfs_conscious.replace(['ND', 'NA', '3'], float("NaN")) dfs_conscious = dfs_conscious.replace(['U','V','N', 'P'], 0) # dfs_conscious=dfs_conscious.fillna(dfs_conscious.median()) # print(dfs_conscious['TotalGCS']) # print(pd.unique(dfs_conscious['TotalGCS'])) dfs_conscious = dfs_conscious.rename(columns={'ControlType_x': 'outcome'}) dfs_conscious_corr=dfs_conscious.corr(method='pearson') # .style.background_gradient(cmap="Blues") sns.heatmap(dfs_conscious_corr,cmap="coolwarm") ###Output _____no_output_____ ###Markdown Group2: Complaint of pain in neck and age ###Code # feat_pain = ['PtCompPainHead', 'PtCompPainFace', 'PtCompPainNeck', 'PtCompPainNeckMove', 'PtCompPainChest', 'PtCompPainBack', 'PtCompPainFlank', 'PtCompPainAbd', 'PtCompPainPelvis', 'PtCompPainExt'] # demog_df = dfs[4] clean_key_col_names = lambda df: df.rename(columns={'site': 'SITE', 'caseid': 'CaseID', 'studysubjectid': 'StudySubjectID'}) demog_df = clean_key_col_names(dfs[4]) agegroup_df = pd.get_dummies(pd.cut(demog_df['AgeInYears'], bins=[0, 2, 6, 12, 16], labels=['infant', 'preschool', 'school_age', 'adolescents'], include_lowest=True), prefix='age') agegroup_df=pd.concat([demog_df[['SITE', 'CaseID', 'StudySubjectID']], agegroup_df], axis=1) feat_pain = ['PtCompPainNeck', 'PtCompPainNeckMove', 'age_infant', 'age_preschool', 'age_school_age', 'age_adolescents','PainNeck','ControlType_x'] dfs_pain=dfs[0].merge(dfs[3],how='left', on=['SITE', 'CaseID', 'StudySubjectID']) dfs_pain=dfs_pain.merge(agegroup_df,how='left', on=['SITE', 'CaseID', 'StudySubjectID']) dfs_pain=dfs_pain[feat_pain] dfs_pain = dfs_pain.replace(['Y', 'YES', 'A'], 1) dfs_pain = dfs_pain.replace(['N', 'NO'], 0) dfs_pain = dfs_pain.replace(['ND', 'NA'], float("NaN")) dfs_pain = dfs_pain.rename(columns={'ControlType_x': 'outcome'}) # dfs_pain=dfs_pain.fillna(dfs_pain.median()) # print(pd.unique(dfs_pain['PtCompPainNeckMove'])) dfs_pain_corr=dfs_pain.corr(method='pearson') # .style.background_gradient(cmap="Blues") sns.heatmap(dfs_pain_corr,cmap="coolwarm") ###Output _____no_output_____ ###Markdown Group3: Tenderness in neck ###Code # feat_tender = ['PtTenderHead', 'PtTenderFace', 'PtTenderNeck', 'PtTenderNeckLevel', 'PtTenderNeckLevelC1', 'PtTenderNeckLevelC2', 'PtTenderNeckLevelC3', 'PtTenderNeckLevelC4', 'PtTenderNeckLevelC5', 'PtTenderNeckLevelC6', 'PtTenderNeckLevelC7', 'PtTenderNeckAnt', 'PtTenderNeckPos', 'PtTenderNeckLat', 'PtTenderNeckMid', 'PtTenderNeckOther', 'PtTenderChest', 'PtTenderBack', 'PtTenderFlank', 'PtTenderAbd', 'PtTenderPelvis', 'PtTenderExt'] feat_tender =['PtTenderNeck', 'PtTenderNeckLevel', 'PtTenderNeckLevelC1', 'PtTenderNeckLevelC2', 'PtTenderNeckLevelC3', 'PtTenderNeckLevelC4', 'PtTenderNeckLevelC5', 'PtTenderNeckLevelC6', 'PtTenderNeckLevelC7', 'PtTenderNeckAnt', 'PtTenderNeckPos', 'PtTenderNeckLat', 'PtTenderNeckMid', 'PtTenderNeckOther','PosMidNeckTenderness', 'TenderNeck','ControlType_x'] dfs_tender=dfs[0].merge(dfs[3],how='left', on=['SITE', 'CaseID', 'StudySubjectID']) dfs_tender=dfs_tender[feat_tender] dfs_tender = dfs_tender.replace(['Y', 'YES', 'A'], 1) dfs_tender = dfs_tender.replace(['N', 'NO'], 0) dfs_tender = dfs_tender.replace(['ND', 'NA'], float("NaN")) # dfs_tender=dfs_tender.fillna(dfs_tender.median()) # print(dfs_tender) dfs_tender = dfs_tender.rename(columns={'ControlType_x': 'outcome'}) hide_column_neck=["PtTenderNeck", "PtTenderNeckLevel","PtTenderNeckLevelC1","PtTenderNeckLevelC2", "PtTenderNeckLevelC3","PtTenderNeckLevelC4","PtTenderNeckLevelC5","PtTenderNeckLevelC6","PtTenderNeckLevelC7","PtTenderNeckAnt","PtTenderNeckPos","PtTenderNeckLat","PtTenderNeckMid","PtTenderNeckOther","PosMidNeckTenderness","TenderNeck"] dfs_tender_corr=dfs_tender.corr(method='pearson') # .style.background_gradient(cmap="Blues").hide_columns(hide_column_neck) dfs_tender_corr['outcome'] plt.figure(dpi=250, figsize=(2, 4)) vals = dfs_tender_corr['outcome'] args = np.argsort(vals) labs = vals.index.values[args] ax = plt.subplot(111) plt.barh(labs[:-1], vals[args][:-1]) plt.xlabel('Correlation w/ outcome') ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) plt.show() ###Output _____no_output_____ ###Markdown Group4: Focal neurological deficits ###Code feat_focal= ['PtParesthesias', 'PtSensoryLoss', 'PtExtremityWeakness','FocalNeuroFindings','ControlType_x'] dfs_focal=dfs[0].merge(dfs[3],how='left', on=['SITE', 'CaseID', 'StudySubjectID']) dfs_focal=dfs_focal[feat_focal] dfs_focal = dfs_focal.replace(['Y', 'YES', 'A'], 1) dfs_focal = dfs_focal.replace(['N', 'NO'], 0) dfs_focal = dfs_focal.replace(['3'], 3) dfs_focal = dfs_focal.replace(['ND', 'NA'], float("NaN")) dfs_focal = dfs_focal.rename(columns={'ControlType_x': 'outcome'}) # dfs_focal=dfs_focal.fillna(dfs_focal.median()) # print(pd.unique(dfs_focal['PtExtremityWeakness'])) dfs_focal_corr=dfs_focal.corr(method='pearson') # .style.background_gradient(cmap="Blues") sns.heatmap(dfs_focal_corr,cmap="coolwarm") ###Output _____no_output_____ ###Markdown Group 5: Other parts of the body ###Code feat_otherpain = ['PtCompPainHead', 'PtCompPainFace', 'PtCompPainExt', 'PtTenderHead', 'PtTenderFace', 'PtTenderExt','SubInj_Head', 'SubInj_Face', 'SubInj_Ext', 'SubInj_TorsoTrunk','ControlType_x'] dfs_otherpain=dfs[0].merge(dfs[3],how='left', on=['SITE', 'CaseID', 'StudySubjectID']) dfs_otherpain=dfs_otherpain[feat_otherpain] dfs_otherpain = dfs_otherpain.replace(['Y', 'YES', 'A'], 1) dfs_otherpain = dfs_otherpain.replace(['N', 'NO'], 0) dfs_otherpain = dfs_otherpain.replace(['ND', 'NA'], float("NaN")) dfs_otherpain = dfs_otherpain.rename(columns={'ControlType_x': 'outcome'}) # dfs_focal=dfs_focal.fillna(dfs_focal.median()) # print(dfs_focal) dfs_otherpain_corr=dfs_otherpain.corr(method='pearson') # .style.background_gradient(cmap="Blues") sns.heatmap(dfs_otherpain_corr,cmap="coolwarm") ###Output _____no_output_____ ###Markdown Group6: Injury mechanism ###Code # feat_injury= ['InjuryPrimaryMechanism', 'HeadFirst', 'HeadFirstRegion','HighriskDiving', 'HighriskFall', 'HighriskHanging', 'HighriskHitByCar', 'HighriskMVC', 'HighriskOtherMV', 'AxialLoadAnyDoc', 'axialloadtop', 'Clotheslining','ControlType_x'] feat_injury= ['InjuryPrimaryMechanism', 'HeadFirst', 'HeadFirstRegion', 'ControlType_x'] dfs_injury=dfs[0].merge(dfs[6],how='left', on=['SITE', 'CaseID', 'StudySubjectID']) dfs_injury=dfs_injury[feat_injury] dfs_injury = dfs_injury.replace(['Y', 'YES', 'A'], 1) dfs_injury = dfs_injury.replace(['N', 'NO'], 0) dfs_injury = dfs_injury.replace(['ND', 'NA'], float("NaN")) # dfs_injury['InjuryPrimaryMechanism'] = pd.to_numeric(dfs_injury['InjuryPrimaryMechanism']) # dfs_injury['InjuryPrimaryMechanism'] = dfs_injury['InjuryPrimaryMechanism'].astype('Int64') series_HeadFirstRegion = pd.get_dummies(dfs_injury.HeadFirstRegion, prefix='HeadFirstRegion') dfs_injury=dfs_injury.drop(columns=['HeadFirstRegion']) dfs_injury=pd.concat([series_HeadFirstRegion,dfs_injury], axis=1) series_injury = pd.get_dummies(dfs_injury.InjuryPrimaryMechanism, prefix='Injuryechanism') dfs_injury=dfs_injury.drop(columns=['InjuryPrimaryMechanism']) dfs_injury=pd.concat([series_injury,dfs_injury], axis=1) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_1': 'Motor Vehicle Collision'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_2': 'outcOther Motorized Transport Crashome'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_3': 'Bike rider struck by moving vehicle'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_4': 'Bike collision or fall from bike'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_5': 'Other non-motorized transport struck by moving vehicle'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_6': 'Pedestrian struck by moving vehicle'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_7': 'Blunt injury to head/neck'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_8': 'Sports injury'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_9': 'Fall from elevation'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_10': 'Fall down stairs'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_11': 'Fall from standing/walking/running'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_12': 'Diving injury'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_13': 'Hanging injury'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_14': 'Other'}) dfs_injury=dfs_injury.rename(columns={'Injuryechanism_20': 'fall from non-motorized transport while riding'}) dfs_injury=dfs_injury.rename(columns={'ControlType_x': 'outcome'}) # dfs_focal=dfs_focal.fillna(dfs_focal.median()) # print(pd.unique(dfs_injury['HeadFirstRegion'])) # hide_column_injury=["HeadFirst","HighriskDiving","HighriskFall","HighriskHanging","HighriskHitByCar","HighriskMVC","HighriskOtherMV","AxialLoadAnyDoc","axialloadtop","Clotheslining"] dfs_injury_corr=dfs_injury.corr(method='pearson') dfs_injury_corr['outcome'] plt.figure(dpi=250, figsize=(2, 4)) vals = dfs_injury_corr['outcome'] args = np.argsort(vals) labs = vals.index.values[args] ax = plt.subplot(111) plt.barh(labs[:-1], vals[args][:-1]) plt.xlabel('Correlation w/ outcome') ax.spines['right'].set_visible(False) ax.spines['top'].set_visible(False) plt.show() ###Output _____no_output_____
.ipynb_checkpoints/dna_cnn_autoencoder_and_siamese_network-checkpoint.ipynb	###Markdown Detection and classification of genetic mutations in BRCA1 gene in human chromosome 17![dna](img/genetics-banner-x.png) AbstractOne of the major tasks in clinical genomics is the identification of mutations associated with human genetic diseases. Typically, genome-wide genetic studies identify a large number of variants that have potential association with a disease. However, to narrow this search and to pinpoint the variants that most likely cause a disease, a number of methods have been developed . These methods use the evolutionary conservation of nucleotide positions and/or the functional consequences of mutations to distinguish disease-associated variants from neutral and benign variants. Breast cancer is a common disease. Each year, approximately 200,000 women in the United States are diagnosed with breast cancer, and one in nine American women will develop breast cancer in her lifetime. In 1994, the first gene associated with breast cancer - BRCA1 (for BReast CAncer1) was identified on chromosome 17. When individuals carry a mutated form of BRCA1, they have an increased risk of developing breast or ovarian cancer at some point in their lives. Children of parents with a BRCA1 mutation have a 50 percent chance of inheriting the gene mutation. "The simplest denomination of breast cancer is based upon inherited susceptibility to breast cancer vs sporadic occurrences of breast cancer. Heightened breast cancer risk may be due to a genetic alteration that increases susceptibility based upon an inherited heterozygous gene defect in for example BRCA1, TP53, PTEN or other tumor suppressors"The quote is taken from [DNA damage and breast cancer](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168783/) written by Jennifer D Davis and Shiaw-Yih Lin. OverviewThe current project is focused on applying deep learning approaches for detecting and classifying potential point mutations in patient's BRCA1 gene.Triple-negative breast cancers often contain inactivation of the DNA repair gene BRCA1. In fact, as much as 30% of breast cancers are thought to have some degree of BRCA1 inactivation.A key feature of the structure of a genes like BRCA1 is that their transcripts are typically subdivided into exon and intron regions. Exon regions are retained in the final mature mRNA molecule, while intron regions are cut out during post-transcriptional process. Indeed my focus was on exon's mutations.![gene](img/gene.jpg) Point mutationsGenetic mutations can be classified based on their effect on the protein structure:- Missense: these mutations change the coded amino acid, hence they influence the final protein structure. Their effect can be uncertain or pathogenic.- Nonsense: these mutations cause a shift in the reading frame or the formation of a premature stop codon (truncated protein). Very often these mutations are pathogenic.But there's also another class of genetic mutations, rather infrequent, but possible:- Start-loss: these mutations affect the initiation codon (the very first amino acid of the protein - a Methionine), and their effect on the final protein structure is very often pathogenic.- Stop-loss: even rarer than start-loss, these mutations affect the last protein amino acid. Also for these mutations, the effect isn't easy to understand![gene](img/mutations.png)I want to try to clarify the strand issue. Consider the following stretch of double stranded DNA which encodes a short peptide:![srands](img/dna_strands.png)The actual biological transcription process works from the template strand of the DNA. This is the reason why the reference and the patient sequences are indeed taken from reverse strand (aka Watson strand, strand −1). The DataThere is used various types of data:- Reference and patient DNA of BRCA1 gene and exon's boundaries were taken from [National Library of Medicine](https://www.ncbi.nlm.nih.gov/genome/gdv/browser/genome/?id=GCF_000001405.39).- All the DNA variants (mutation) were taken from [Health University of Utah](https://arup.utah.edu/database/BRCA/Variants/BRCA1.php) Patient dataFor the current research there is a patient DNA sequence with anomalies in exons: 2, 3, 4, 7, 13 and 14. Most of them are `non pathogenic`. Only one (in exon 2) is a pathogenic variant c.1A>G - mutation type:start loss."Start-loss: these mutations affect the initiation codon, i.e. the very first amino acid of the protein (which is a Methionine), and their effect on the final protein structure (and therefore on the individual's clinical picture), is anything but easily deducible." The quote was taken from [Breda Genetics](https://bredagenetics.com/start-loss-mutations-in-rare-diseases/) ###Code #Reading the patient DNA sequence patient_data = pd.read_csv('dna_data/BRCA1_gene_sequence_patient.txt',header=None) #Reading the reference DNA sequence reference_data = pd.read_csv('dna_data/BRCA1_gene_sequence.txt', header=None) #Reading the reference exon boundaries exon_boundaries = pd.read_csv('dna_data/BRCA1_exon_bounderies.csv') #Loading dna variants dataset variants_data = pd.read_csv('dna_data/variants/dna_variants.csv') ###Output _____no_output_____ ###Markdown ImplementationAfter wide research I decided to use a convolutional autoencoder for anomaly detection in patient DNA. For the anomaly detection part I transformed the DNA sequences into arrays of numbers (1 - 5).Each one corresponding to each nucleotide and 5 is for letter 'N' (unknown) .After many experiments I decided to use different approach for the classification part.I transformed the DNA sequences into arrays of one hot encoded nucleotides.The latter seemed more robust and reliable.Because of the relatively small size of described DNA variants (about 240), I had to find something different than regular classifier.I decide to use a siamese network with contrastive loss function for classifying the anomalies. ###Code def vectorize(seq): """ Vectorize the the DNA sequence """ str_sequence = '' arr_sequence = [] #Concatenate lines of strings for line in seq[0]: str_sequence += line #Populate the vector with the nucleotides for n in str_sequence: arr_sequence.append(n) return np.array(arr_sequence) # Find the length of the longest exon EXON_MAX_LENGTH = 0 for i in range(len(exon_boundaries)): start = exon_boundaries.loc[i].start end = exon_boundaries.loc[i].end diff = end - start + 1 if diff > EXON_MAX_LENGTH: EXON_MAX_LENGTH = diff #Vectorize the referent and patient data ref_vec = vectorize(reference_data) pat_vec = vectorize(patient_data) def normalize(seq): """ Normalize the sequence """ return seq / np.max(seq) def nucleotide_to_num(sequence): """ Transforms DNA sequence to a sequence of numbers coresponding to certain nucleotide """ ref_sequence_to_num = [] nucleotide_to_num = { 'A': 1., 'C': 2., 'G': 3., 'T': 4., 'N': 5. } for nucleodtide in sequence: ref_sequence_to_num.append(nucleotide_to_num[nucleodtide]) return np.array(normalize(ref_sequence_to_num)) ###Output _____no_output_____ ###Markdown Exons boundariesThe exon boundaries were taken from [National Center for Biotechnology Information](https://www.ncbi.nlm.nih.gov/). They are basically the start and end position of the exon according the order of Human Genome version called ` Assembly GRCh38.p13 - Cr17`.For the current project only the exon sequences will be used.The following function is extracting only exons and making their length even number. I needed this even lengths because of the nature of the autoencoder. ###Code def split_exons(seq): """Devide the DNA sequence on exons - remove the other parts""" exon_seq = [] for i in range(len(exon_boundaries)): start = exon_boundaries.loc[i].start end = exon_boundaries.loc[i].end ex = seq[start : end] # Making the length of the exons to be even number if len(ex) % 2 != 0: ex = np.hstack((ex , ['N' for _ in range(1)])) exon_seq.append(nucleotide_to_num(ex)) return exon_seq #Extracting exons for the referent and patient sets referent_exons = split_exons(ref_vec) patient_exons = split_exons(pat_vec) COPY_COUNT = 300 def create_dataset(X): """Create an 3D array with multiple copies of given sequence""" Xs=[] for i in range(COPY_COUNT): Xs.append(X) return np.array(Xs) ###Output _____no_output_____ ###Markdown Convolutional reconstruction autoencoderThe dataset for the anomaly detection part of the project is actually the entire DNA sequence of the BRCA1 gene. The idea is to train the model with the referent (healthy) DNA sequence.After that the model should be able to detect potential changes in the patient DNA.Since the input data is a 3d array i decided to use `convolutional reconstruction autoencoder model`The model will take input of shape (batch_size, sequence_length, num_features) and return output of the same shape. In this case, sequence_length is 81070 (the length of BRCA1 gene) and num_features is 4 (which corresponds to one hot encoded nucleotide). An autoencoder is a special type of neural network that is trained to copy its input to its output.It will first encode the sequence into a lower dimensional latent representation, then decodes the latent representation back to an sequence.The autoencoder was trained to minimize reconstruction error with the referent DNA (normal) only, then I used it to reconstruct patient data. My hypothesis was that the mutated sequence will have higher reconstruction error.For the encoder part were used two `Conv1D` layers. For the "bottleneck" I used a `Flatten` layout. And for decoder part were used two `Conv1DTranspose` layers and one `Conv1D` with output size 1. The `Conv1DTranspose` purpose is to increase the volume size back to the original array spatial dimensions.A special class `MutationDetector` was created to capsulate the `autoencoder`. This way it was easier to use it multiple times for each exon separately. ###Code KRNL_SIZE = 7 latent_dim = 20 class MutationDetector(Model): """ Capsulate an autoencoder in a class. This way it can be used multiple times as separate object. """ def __init__(self, input_a): super(MutationDetector, self).__init__() #Encoder part ---------------------------------------------- inputs = Input(shape=(input_a, 1)) conv = Conv1D( filters=8, kernel_size=KRNL_SIZE, strides=2, padding="same", activation = 'relu')(inputs) conv = Conv1D( filters=4, kernel_size=KRNL_SIZE, strides=1, padding="same", activation="relu")(conv) """ Storing the shape of the last convolutional layer to use it in the decoder part """ vol_size = K.int_shape(conv) """ Flatten the convolutional output to a 1d lattent space Pass it to a dense layer """ flat = Flatten()(conv) latent = Dense(latent_dim)(flat) #Decoder part ---------------------------------------------- dense = Dense(np.prod(vol_size[1:]))(latent) reshape = Reshape((vol_size[1], vol_size[2]))(dense) conv_trans = Conv1DTranspose( filters=4, kernel_size=KRNL_SIZE, strides=1, padding="same", activation = 'relu')(reshape) conv_trans = Conv1DTranspose( filters=8, kernel_size=KRNL_SIZE, strides=2,padding="same", activation = 'relu')(conv_trans) outputs = Conv1DTranspose( filters=1, kernel_size=KRNL_SIZE, padding="same", activation='sigmoid')(conv_trans) self.autoencoder = Model(inputs, outputs) def call(self, x): return self.autoencoder(x) def show_summary(self): #Returns the summary of the current object return self.autoencoder.summary() def training_plot(hist,title): """ Plot training and validation losses """ fig, ax = plt.subplots(figsize=(6, 3), dpi=80) plt.plot(hist.history['loss'], label='Training loss') plt.plot(hist.history['val_loss'], label='Validation loss') ax.set_title(title) ax.set_ylabel('Loss (mae)') ax.set_xlabel('Epoch') plt.legend() plt.show() def calculate_threshold(predicted, referent): """ Calculating train loss and threshold """ train_loss = np.mean(np.abs(predicted - referent), axis=1) threshold = np.max(train_loss) plt.hist(train_loss, bins=50) plt.xlabel("Train MAE loss") plt.ylabel("No of samples") plt.show() return threshold ###Output _____no_output_____ ###Markdown The reference_set was used as both the input and the target since this is a reconstruction model. Anomaly detection ###Code anomalous_exon_names = [] def detect_anomalies(predicted, referent,threshold, exon_name): """ Detect the samples which are anomalies. Calculate mean absolute error """ test_loss = np.mean(np.abs(predicted - referent), axis=1) print('Anomaly detection - {}'.format(exon_name)) # Finding the anomalies anomalies = test_loss > threshold anomaly_sum = np.sum(anomalies) print("Count of anomalous nucleotides: ", anomaly_sum) #Print the anomaly nucleotide position if anomaly_sum > 0: anomalous_exon_names.append(exon_name) print("Position of anomalous nucleotide: ", np.where(anomalies)) print("Test losses {}".format(test_loss[np.where(anomalies)])) ###Output _____no_output_____ ###Markdown Here , for every exon was created a train set ,a test set and an autoencoder. Every model was trained and it made it's predictions separately from the others. ###Code for exon_n in range(len(referent_exons)): #Creating the exon names exon_name = 'exon_0' + str(exon_n+1) if exon_n+1 < 10 else 'exon_' + str(exon_n+1) #Converting the sequences into required shape reference_set = create_dataset(referent_exons[exon_n]) reference_set = reference_set.reshape(COPY_COUNT, len(referent_exons[exon_n]), 1) #Calling the autoencoder autoencoder = MutationDetector(reference_set.shape[1]) #Compile the model using optimizer adam and loss: mean absolute error autoencoder.compile(optimizer=Adam(learning_rate=0.001), loss="mae") tr_title = 'Training for {}'.format(exon_name) print( '==========='+ tr_title +'============') #Fitting the model history = autoencoder.fit(reference_set, reference_set, epochs=30, batch_size=16, validation_split=0.2, verbose=2) print(autoencoder.show_summary()) #Plot training result training_plot(history, tr_title) """ Predict the referent data to calculate the training loss it is needed to determine the reconstruction loss """ predict = autoencoder.predict(reference_set) # Get reconstruction loss threshold. threshold = calculate_threshold(predict[0], reference_set[0]) print("Reconstruction error threshold:", threshold) # Loading patient exon sequence patient_set = patient_exons[exon_n].reshape(1, len(referent_exons[exon_n]), 1) test_predict = autoencoder.predict(patient_set) #Searching for anomalies detect_anomalies(test_predict[0] , patient_set[0], threshold, exon_name) print("======== End of processing {} ======== \n".format(exon_name)) "Mutations of BRCA1 gene were found in: {}".format(anomalous_exon_names) ###Output _____no_output_____ ###Markdown All the anomalous exons were found by the autoencoder. The problem is that there is one additional - exon_5. It is not in the list of known anomalous exons of the patient DNA.The exons of interest were found and now i had to classify them. Classifying the the anomalous sequences Siamese networkDuring my research I realized that there aren't so much DNA variants for different exons in BRCA1 gene. The overall count of the variants is 241.So I came to the conclusion that I need something different than common classifier to determine which anomalous sequence belongs to which class (pathogenic , not pathogenic) , mutation type or nucleotide change.Then I decide to try `siamese network` with `contrastive loss function`.Practical, real-world use cases of `siamese networks` include face recognition, signature verification, prescription pill identification, and many more!Furthermore, `siamese networks` can be trained with very little data, which actually I was aiming for.The concept of a `siamese network`: ![siamese](img/s_network.jpeg)Two convolutional network to be merged and to output the similarity of two entries in a pair. DNA VariantsThe DNA variants were taken from BRCA1 database of [Health University of Utah](https://arup.utah.edu/database/BRCA/Variants/BRCA1.php). The data includes all the recorded mutations classified as `Definitely pathogenic` , `likely not pathogenic` and `not pathogenic`.All the nucleotide changes have mutation type: `Start loss`,`Splice site`, `Missense` or `Nonsence` ###Code variants_data.head() ###Output _____no_output_____ ###Markdown Only the variants of the anomalous exons were taken for the classification.Next steps were to vectorize the DNA sequences of the variants.Because the network requires to the sequences to be with equal lengths, the shorter sequences were made as long as the longest sequence by adding sufficient count of 'N's at their ends. Missing information for variantsUnfortunately, during my research I couldn't find any variants for exons: 1, 8 and 10.So this is the reason why I didn't considered them for the classification. ###Code # Vectorize the patient BRCA1 gene sequence vec_patient_sequence = vectorize(patient_data) patient_exons = {} exon_boundaries_df = exon_boundaries.set_index('name') missing_data = ['exon_01','exon_08','exon_10'] # Create dictionary with detetected by the autencoder anomalies for name in anomalous_exon_names: if name in missing_data: continue address = exon_boundaries_df.loc[name] patient_exons[name] = vec_patient_sequence[address.start : address.end] def nucleotide_to_vector(sequence): """ Transforms DNA sequence to a array of one-hot encoded nucleotides """ ref_sequence_to_vec = [] nucleotide_to_vec = { 'A': [1., 0., 0., 0.], 'C': [0., 1., 0., 0.], 'G': [0. ,0. ,1., 0.], 'T': [0., 0., 0., 1.], 'N': [0., 0., 0., 0.] } for nucleodtide in sequence: ref_sequence_to_vec.append(nucleotide_to_vec[nucleodtide]) return np.array(ref_sequence_to_vec) ###Output _____no_output_____ ###Markdown Creating the variants data frame with vectorized sequences, so that all the variants have the same length. The shorter sequences received subsequence with letter 'N' at the end. ###Code EXON_MAX_LENGTH = len(max(variants_data.variant, key=len)) vec_variants_dict = {'variant':[],'location':[],'classification':[],'nucleotide change':[], 'mutation type':[]} for i in range(len(variants_data)): row = variants_data.loc[i] sequence = row.variant # Make the exons as long as the longest one sequence = sequence + 'N'* (EXON_MAX_LENGTH-len(sequence)) #Transform the nucleotides sequence to one hot vector sequence = nucleotide_to_vector(sequence) #Populate the variant dictionary vec_variants_dict['variant'].append(sequence) vec_variants_dict['location'].append(row.location.strip()) vec_variants_dict['classification'].append(row.classification.strip()) vec_variants_dict['nucleotide change'].append(row[' nucleotide change'].strip()) vec_variants_dict['mutation type'].append(row['mutation type']) #Populate a dataframe with required data vec_variants_df = pd.DataFrame(vec_variants_dict).sample(frac=1) vec_variants_df = vec_variants_df.reset_index() vec_variants_df = vec_variants_df.drop(columns=['index']) vec_variants_df.head() """ Create and populate a dictionary with data, so that after the predictions, the data of predicted variant should be acessible """ pairs_dict={'current':[], 'candidate':[], 'current nuc change':[], 'current class':[],'paired class':[], 'paired nuc change':[], 'class':[]} def populate_pairs_data(df, row, is_similar): """ Populate a dictionary with pairs of sequences """ current_seq = row.variant current_nuc_change = row['nucleotide change'] for j in range(len(df)): c_row = df.loc[j] pairs_dict['current'].append(current_seq) pairs_dict['candidate'].append(c_row.variant) sim_class = np.array([1.]) if is_similar else np.array([0.]) pairs_dict['class'].append(sim_class) pairs_dict['current nuc change'].append(current_nuc_change) pairs_dict['paired nuc change'].append(c_row['nucleotide change']) pairs_dict['paired class'].append(c_row.classification) pairs_dict['current class'].append(row.classification) ###Output _____no_output_____ ###Markdown Since there are two subnetworks, there must be two inputs to the model.When training siamese networks I need to have positive pairs and negative pairs:- Positive pairs: Two sequences that belong to the same class (pathogenic - pathogenic).- Negative pairs: Two sequences that belong to different classes (non pathogenic - pathogenic).Next steps were to create such dataset that can fit the requirements of the siamese network inputs. ###Code TRAIN_SIZE = 21000 pairs_df = pd.DataFrame() def generate_pairs(df): """ initialize two empty lists to hold the (sequence, sequence) pairs and labels to indicate if a pair is positive or negative """ global pairs_df pair_sequences = [] pair_classes = [] non_pair_columns = ['class','current nuc change', 'paired nuc change', 'paired class','current class'] # loop over all dataset for i in range(len(df)): row = df.loc[i] # take the current sequence and it's class current_seq = row.variant current_class = row.classification current_location = row.location #Create a Dataframe with variants with same classes of the current exon current_similars_df=df[(df.location == current_location) & (df.classification == current_class)].reset_index() populate_pairs_data(current_similars_df, row, True) #Create a Dataframe with variants with different classes of the current exon current_non_similar_df=df[(df.classification != current_class)].reset_index() populate_pairs_data(current_non_similar_df, row, False) #Populate a dataframe and shuffle pairs_df = pd.DataFrame(pairs_dict).sample(frac=1) pairs_df = pairs_df.reset_index() pairs_df = pairs_df.drop(columns=['index']) #Construct and return the pairs pair_sequences = np.array(pairs_df.drop(columns=non_pair_columns, axis=1).values.tolist()).astype(np.float) pair_classes = np.array(pairs_df['class'].values.tolist()).astype(np.float) return (pair_sequences[:TRAIN_SIZE],pair_sequences[TRAIN_SIZE:], pair_classes[:TRAIN_SIZE],pair_classes[TRAIN_SIZE:]) #Defining train and test sets for the siamese network train_pairs, test_pairs, train_classes, test_classes = generate_pairs(vec_variants_df) print("Train pairs shape:{} - Train labels shape:{}".format(train_pairs.shape,train_classes.shape)) print("Test pairs shape:{} - Test labels shape:{}".format(test_pairs.shape,test_classes.shape)) ###Output Test pairs shape:(7525, 2, 312, 4) - Test labels shape:(7525, 1) ###Markdown So the train data has 21000 pairs with shape 312 nucleotides and last dimension is representing the encoded nucleotide itself. ###Code seq_input_shape = [train_pairs.shape[2],train_pairs.shape[3]] ###Output _____no_output_____ ###Markdown The twin prototypeI constructed the prototype of the twins model, defining three sets of `Conv1D` layer with `relu` activation. Each convolutional layer has a total of 64 filters with size 7 following by `MaxPooling1D` with size = 2.`GlobalAveragePooling1D` - This layer performs exactly the same operation as the 1D Average pooling layer, except that the pool size is the size of the entire input of the layer,it computes a single average value for each of the input channels (the second dimension).A fully-connected layer was defined with the specified size = 48.Finally I normalized the features using L2 normalization before using Contrastive Loss. ###Code def build_twin_model(input_shape): inputs = Input(input_shape) layer = Conv1D(filters=64, kernel_size=7, padding="same", activation="relu")(inputs) layer = MaxPooling1D(pool_size=2)(layer) layer = Conv1D(filters=64, kernel_size=7, padding="same", activation="relu")(layer) layer = MaxPooling1D(pool_size=2)(layer) layer = Conv1D(filters=64, kernel_size=7, padding="same", activation="relu")(layer) layer = MaxPooling1D(pool_size=2)(layer) pooled = GlobalAveragePooling1D()(layer) dense = Dense(48)(pooled) #Normalizing the output distances outputs = Lambda(lambda x: K.l2_normalize(x,axis=1))(dense) twin = Model(inputs, outputs) return twin #Create the separate inputs for the twin nets pos_input = Input(seq_input_shape) neg_input = Input(seq_input_shape) #Construct the twins prototype_twin = build_twin_model(seq_input_shape) pos_twin = prototype_twin(pos_input) neg_twin = prototype_twin(neg_input) ###Output _____no_output_____ ###Markdown Here a Lambda layer was used to compute the euclidean distance between the outputs of the twin networks. Eventually they became the output of the sieamese network through a Dence layer. ###Code def euclidean_distance(vectors): """Calculating the euclidian distance between twin ouputs""" # unpack the vectors into separate lists (vec_A, vec_B) = vectors # compute the sum of squared distances between the vectors squared = K.sum(K.square(vec_A - vec_B), axis=1, keepdims=True) # Return the distances return K.sqrt(K.maximum(squared, K.epsilon())) #construct the siamese network distance = Lambda(euclidean_distance)([pos_twin, neg_twin]) outputs = Dense(1, activation="sigmoid")(distance) siamese = Model(inputs=[pos_input, neg_input], outputs=outputs) ###Output _____no_output_____ ###Markdown Contrastive lossFor the current task `binary cross entropy` can be valid loss function.Тhe goal of a siamese network isn’t to classify a set of pairs but instead to differentiate between them. Essentially, contrastive loss is evaluating how good the siamese network is distinguishing between the pairs. There is distance based loss function called `contrastive loss`.$$Contrastive Loss = \frac{1}{2} * Y_{true} * D^2 + \frac{1}{2} * (1-Y_{true}) * max(margin - D, 0)^2$$where:- $Y_{true}$ - The ground-truth labels from the dataset. A value of 1 indicates that the two sequences in the pair are of the same class, while a value of 0 indicates that the sequences belong to two different classes.- $D$ is the Euclidean distance between the outputs of the siamese network.- margin - typically this value is set to 1- The max function takes the largest value of 0 and the margin, m, minus the distance. ###Code def contrastive_loss(true_labels, dist, margin=1): """ Calculate the contrastive loss between the true labels and the predicted distances """ squared_dist = K.square(dist) squared_margin = K.square(K.maximum(margin - dist, 0)) return K.mean(1/2 * true_labels * squared_dist + 1/2 * (1 - true_labels) * squared_margin) siamese.compile(loss=contrastive_loss, optimizer=Adam(learning_rate=0.005)) siamese.summary() # Fit the siamese network history = siamese.fit( [train_pairs[:,0], train_pairs[:,1]], [train_classes], validation_data=([test_pairs[:,0], test_pairs[:,1]], [test_classes]), batch_size=128, epochs=25) #Plot siamese loss functions plt.figure() plt.plot(history.history['loss']) plt.plot(history.history['val_loss']) plt.title('model loss') plt.ylabel('loss') plt.xlabel('epoch') plt.legend(['train', 'validation']) plt.show() predict = siamese.predict([test_pairs[:, 0], test_pairs[:, 1]]) ###Output _____no_output_____ ###Markdown Here I have created a data frame with the ground truth labels and the predicted distances between the pairs. We have to keep in mind that the smallest the distance is - the higher is the probability the sequences to belong of the same class. So in fact the prediction of 0.9 should correspond to label 0 (opposite classes) and vice versa. ###Code #Create a dataframe with predicted distances and ground truth labels pr_data = {'predicted':predict.reshape( -1).round(2), 'actual':test_classes.reshape(-1)} comparisson_df = pd.DataFrame(data = pr_data).astype('float32') comparisson_df.head() #Sorting the predicted distances sorted_distances = np.sort(comparisson_df.predicted.unique()) # Getting the train part of the combined_df dataframe combined_df = pairs_df[TRAIN_SIZE:] combined_df = combined_df.reset_index() #Join the columns with predictions combined_df = combined_df.join(comparisson_df.predicted) #Drop not needed columns combined_df = combined_df.drop(columns=['current', 'candidate']) ###Output _____no_output_____ ###Markdown Here I have constructed a data frame which helped me to find the related data to the predicted variants. ###Code combined_df.head() ###Output _____no_output_____ ###Markdown The marginChecking the point from which we can say that a pair similar or not.Here I looped through the distances and calculated the count of false positive and false negative pairs. Then I found the distance where false negative and false positive counts were minimal. In this case the distance was 0.58. ###Code counts = {'dist':[],'false positive':[] ,'false negative':[]} for d in sorted_distances: # Find count of false positive and false negative counts for the current distance false_positive_count = len(combined_df[(combined_df['predicted'] > d) & (combined_df['class'] == 1)]) false_negaitive_count = (len(combined_df[(combined_df['predicted'] < d) & (combined_df['class'] == 0)])) #Populate a dictionary counts['dist'].append(d) counts['false positive'].append(false_positive_count) counts['false negative'].append(false_negaitive_count) print('For distance: {} -> False positive count: {} , False negative count: {} '.format(d,false_positive_count, false_negaitive_count)) print('False positive median: {} - False negative median: {}'.format(np.median(counts['false positive']), np.median(counts['false negative']))) counts_df = pd.DataFrame(counts) best_dist = counts_df.loc[(counts_df.dist > 0.57) & (counts_df.dist < 0.59)] best_dist # compute final accuracy on test sets faults_count = best_dist['false positive'] + best_dist['false negative'] '* Accuracy : %0.2f%%' % (100 - faults_count / (len(test_classes) / 100)) ###Output _____no_output_____ ###Markdown The resultsIt is time to get the final results. Pairs between the variants and anomalous sequences were created and passes to the model. ###Code def vec_to_sequence(seq): """ Converting the vector back to a string sequence """ sequence = '' for c in range(len(seq)): sequence+=seq[c] return sequence ###Output _____no_output_____ ###Markdown Constructing an array with pairs of anomalous patient data and variants dataand make predictions ###Code for exon_name in patient_exons.keys(): x = None # Converting the vector back to a string sequence sequence = vec_to_sequence(patient_exons[exon_name]) # Making the patient exon with the same length as the reference anchor = sequence + 'N'* (EXON_MAX_LENGTH-len(sequence)) # Converting the sequence to one hot vector anchor = nucleotide_to_vector(anchor) # Construct a dataframe with variants of the current exon anchor_variants = vec_variants_df[vec_variants_df.location == exon_name].reset_index() #Construct pairs for the predictions for v in range(len(anchor_variants)): variant = anchor_variants.loc[v].variant if x is None: x = [[anchor], [variant]] else: x[0].append(anchor) x[1].append(variant) x = np.array(x) """ Make a prediction for the current annomaly Get the index of the prediction with the highest probabiliry Get the result from the variants dataframe """ index = np.argmin(siamese.predict([x[0], x[1]]), axis=0) predicted_variant = anchor_variants.loc[index].values #Printing the result for curen exon print("Result for {} - mutation type: {}, nucleotide change: {} class: {}" .format(exon_name,predicted_variant[0][5], predicted_variant[0][4], predicted_variant[0][3])) ###Output Result for exon_02 - mutation type: start loss, nucleotide change: c.1A>G class: pathogenic Result for exon_03 - mutation type: missense, nucleotide change: c.133A>C class: non pathogenic Result for exon_04 - mutation type: missense, nucleotide change: c.154C>A class: non pathogenic Result for exon_05 - mutation type: -, nucleotide change: - class: non pathogenic Result for exon_07 - mutation type: misssense, nucleotide change: c.469T>C class: non pathogenic Result for exon_13 - mutation type: misssense, nucleotide change: c.4402A>C class: non pathogenic Result for exon_14 - mutation type: misssense, nucleotide change: c.4520G>C class: non pathogenic
programs/category.ipynb	###Markdown BHSA and OSM: comparison on word categoriesWe will investigate how the morphology marked up in the OSM corresponds and differs from the BHSA linguistic features.In this notebook we investigate the word categories.The [OSM docs](http://openscriptures.github.io/morphhb/parsing/HebrewMorphologyCodes.html)specify a main category for part-of-speech, and additional subtypes for noun, pronoun, adjective, preposition and suffix.The BHSA specifies its categories in the features[sp](https://etcbc.github.io/bhsa/features/hebrew/2017/sp.html),[ls](https://etcbc.github.io/bhsa/features/hebrew/2017/ls.html), and[nametype](https://etcbc.github.io/bhsa/features/hebrew/2017/nametype.html).The purpose of this notebook is to see how they correlate. MappingsWe collect the numbers of cooccurrences of OSM types and BHSA types.We do this separately for main words and for suffixes.We give examples where the rare cases occur.A rare case is less than 10% of the total number of cases.That means, if OSM type $t$ compares to BHS types $s_1, ... ,s_n$, with frequencies$f_1, ..., f_n$, then we give cases of those $(t, s_i)$ such that$$f_i <= 0.10\times \sum_{j=1}^{n}f_j$$. Results* [categories.txt](categories.txt) overview of cooccurrences of OSM and BHSA categories* [categoriesCases.tsv](categoriesCases.tsv) same, but examples for the rarer combinations* [allCategoriesCases.tsv](allCategoriesCases.tsv) all rarer cases, in biblical order ###Code import operator from functools import reduce from tf.app import use from helpers import show ###Output _____no_output_____ ###Markdown Load dataWe load the BHSA data in the standard way, and we add the OSM data as a module of the features `osm` and `osm_sf`.Note that we only need to point TF to the right GitHub org/repo/directory, in order to load the OSM features. ###Code A = use("bhsa", mod="etcbc/bridging/tf", hoist=globals()) ###Output _____no_output_____ ###Markdown Let's quickly oversee the values of the relevant BHSA features. We only work on words where the OSM has assigned morphology. ###Code wordAll = [w for w in F.otype.s("word") if F.g_word_utf8.v(w) != ""] len(wordAll) wordOsm = [w for w in wordAll if F.osm.v(w)] len(wordOsm) wordBase = [w for w in wordOsm if F.osm.v(w) != ""] print(len(wordBase)) F.sp.freqList() F.ls.freqList() F.nametype.freqList() F.prs.freqList() F.uvf.freqList() ###Output _____no_output_____ ###Markdown In order to read the results with more ease, we translate the codes to friendly names, found in the docs ofOSM and BHSA. ###Code naValues = {"NA", "N/A", "n/a", "none", "absent"} NA = "" missingValues = {None, ""} MISSING = "" unknownValues = {"unknown"} UNKNOWN = "?" PRS = "p" noSubTypes = {"C", "D", "V"} pspOSM = { "": dict( A="adjective", C="conjunction", D="adverb", N="noun", P="pronoun", R="preposition", S="suffix", T="particle", V="verb", ), "A": dict( a="adjective", c="cardinal number", g="gentilic", o="ordinal number", ), "N": dict( c="common", g="gentilic", p="proper name", x="unknown", ), "P": dict( d="demonstrative", f="indefinite", i="interrogative", p="personal", r="relative", ), "R": dict( d="definite article", ), "S": dict( d="directional he", h="paragogic he", n="paragogic nun", p="pronominal", ), "T": dict( a="affirmation", d="definite article", e="exhortation", i="interrogative", j="interjection", m="demonstrative", n="negative", o="direct object marker", r="relative", ), } spBHS = dict( art="article", verb="verb", subs="noun", nmpr="proper noun", advb="adverb", prep="preposition", conj="conjunction", prps="personal pronoun", prde="demonstrative pronoun", prin="interrogative pronoun", intj="interjection", nega="negative particle", inrg="interrogative particle", adjv="adjective", ) lsBHS = dict( nmdi="distributive noun", nmcp="copulative noun", padv="potential adverb", afad="anaphoric adverb", ppre="potential preposition", cjad="conjunctive adverb", ordn="ordinal", vbcp="copulative verb", mult="noun of multitude", focp="focus particle", ques="interrogative particle", gntl="gentilic", quot="quotation verb", card="cardinal", none=MISSING, ) nametypeBHS = dict( pers="person", mens="measurement unit", gens="people", topo="place", ppde="demonstrative personal pronoun", ) nametypeBHS.update( { "pers,gens,topo": "person", "pers,gens": "person", "gens,topo": "gentilic", "pers,god": "person", "topo,pers": "person", } ) def getValueBHS(x, feat=None): return ( NA if x in naValues else MISSING if x in missingValues else UNKNOWN if x in unknownValues else feat[x] if feat else x ) def getValueOSM(x): if not x or len(x) < 2: return UNKNOWN tp = x[1] tpName = pspOSM[""][tp] subTpName = None if tp in noSubTypes or len(x) < 3 else pspOSM[tp][x[2]] return ":".join((x for x in (tpName, subTpName) if x is not None)) def getTypeBHS(w): return ":".join( ( getValueBHS(F.sp.v(w), spBHS), getValueBHS(F.ls.v(w), lsBHS), getValueBHS(F.nametype.v(w), nametypeBHS), ) ) def getTypeOSM(w): return getValueOSM(F.osm.v(w)) def getSuffixTypeBHS(w): prs = getValueBHS(F.prs.v(w)) if prs not in {NA, UNKNOWN}: prs = PRS return ":".join((prs, getValueBHS(F.uvf.v(w)))) def getSuffixTypeOSM(w): return getValueOSM(F.osm_sf.v(w)) def getWordBHS(w): return "T={} S={}".format(getTypeBHS(w), getSuffixTypeBHS(w)) def getWordOSM(w): return "T={} [{}] S={} [{}]".format( getTypeOSM(w), F.osm.v(w), getSuffixTypeOSM(w), F.osm_sf.v(w), ) def showFeatures(base): cases = set() categories = [] categoriesCases = [] mappings = {} def makeMap(key, getBHS, getOSM): BHSFromOSM = {} OSMFromBHS = {} for w in base: osm = getOSM(w) bhs = getBHS(w) BHSFromOSM.setdefault(osm, {}).setdefault(bhs, set()).add(w) OSMFromBHS.setdefault(bhs, {}).setdefault(osm, set()).add(w) mappings.setdefault(key, {})[True] = BHSFromOSM mappings.setdefault(key, {})[False] = OSMFromBHS def showMap(key, direction): dirLabel = "OSM ===> BHS" if direction else "BHS ===> OSM" categories.append( """ --------------------------------------------------------------------------------- --- {} {} --------------------------------------------------------------------------------- """.format( key, dirLabel ) ) categoriesCases.append(categories[-1]) cases = set() for (item, itemData) in sorted(mappings[key][direction].items()): categories.append("{:<40}".format(item)) categoriesCases.append(categories[-1]) totalCases = reduce(operator.add, (len(d) for d in itemData.values()), 0) for (itemOther, ws) in sorted( itemData.items(), key=lambda x: (-len(x[1]), x[0]) ): nws = len(ws) perc = int(round(100 nws / totalCases)) categories.append( "\t{:<40} ({:>3}% = {:>6}x)".format(itemOther, perc, nws) ) categoriesCases.append(categories[-1]) if nws < 0.1 * totalCases: for w in sorted(ws)[0:10]: categoriesCases.append( show( T, F, [w], getWordBHS, getWordOSM, indent="\t\t\t\t", asString=True, ) ) cases.add(w) if nws > 10: categoriesCases.append("\t\t\t\tand {} more".format(nws - 10)) categories.append("\n{} ({}): {} cases".format(key, dirLabel, len(cases))) categoriesCases.append(categories[-1]) return cases def showFeature(key): cases = set() categories.append( """ o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o o-o COMPARING FEATURE {} o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o-o """.format( key ) ) categoriesCases.append(categories[-1]) for direction in (True, False): theseCases = showMap(key, direction) cases \|= theseCases categories.append("\n{}: {} cases".format(key, len(cases))) categoriesCases.append(categories[-1]) return cases for (key, getBHS, getOSM) in ( ("main", getTypeBHS, getTypeOSM), ("suffix", getSuffixTypeBHS, getSuffixTypeOSM), ): makeMap(key, getBHS, getOSM) cases \|= showFeature(key) categories.append("\n{}: {} cases".format("All features", len(cases))) categoriesCases.append(categories[-1]) with open("categories.txt", "w") as fh: fh.write("\n".join(categories)) with open("categoriesCases.txt", "w") as fh: fh.write("\n".join(categoriesCases)) fields = """ passage node occurrence OSMmorph OSMtype BHStype OSMmorphSuffix OSMsuffixType BHSsuffixType """.strip().split() lineFormat = ("{}\t" * (len(fields) - 1)) + "{}\n" with open("allCategoriesCases.tsv", "w") as fh: fh.write(lineFormat.format(fields)) for w in sorted(cases): fh.write( lineFormat.format( "{} {}:{}".format(T.sectionFromNode(w)), w, F.g_word_utf8.v(w), F.osm.v(w), getTypeOSM(w), getTypeBHS(w), F.osm_sf.v(w), getSuffixTypeOSM(w), getSuffixTypeBHS(w), ) ) ###Output _____no_output_____ ###Markdown Feature comparisonWe are going to compare all features. ###Code showFeatures(wordBase) ###Output _____no_output_____
MaterialCursoPython/Fase 5 - Modulos externos/Tema 19 - Numpy/Teasers/10 - Filtrado de arrays.ipynb	###Markdown Filtrado de ArraysEn esta lección vamos a repasar algunas funciones para filtrar nuestros arrays. Si queréis más información sobre las funciones disponibles no olvidéis pasaros por [la documentación oficial en este enlace](https://docs.scipy.org/doc/numpy-1.12.0/reference/index.html). Filtro uniqueDevuelve un array de una dimensión borrando todos los elementos duplicados. ###Code np.unique(arr) arr = np.random.randint(0, 4, 10) arr ###Output _____no_output_____ ###Markdown Filtro in1dDevuelve un array de una dimensión indicando si los elementos de una lista se encuentran en un array. ###Code arr = np.random.randint(0, 4, 10) arr np.in1d([-1, 3, 2], arr) ###Output _____no_output_____ ###Markdown Filtro whereEsta función sirve para generar un array filtrado a partir de una condición y un valor por defecto. ###Code import numpy as np # Generamos un array de aleatorios arr_1 = np.random.uniform(-5, 5, size=[3,2]) arr_1 # Creamos un filtro que establece los negativos a 0 arr_2 = np.where(arr_1<0, 0, arr_1) arr_2 # Añadimos otro filtro que establece los positivos a 1 arr_2 = np.where(arr_2>0, True, arr_2) arr_2 # Podemos crear nuestros propios arrays de condiciones arr_3 = np.array([1, -2, 3, -4, 5]) arr_4 = np.array([-1, 2, -3, 4, -5]) arr_cond = np.array([True, False, True, False, True]) np.where(arr_cond, arr_3, arr_4) ###Output _____no_output_____ ###Markdown Filtros booleanos ###Code # Comprobar si todos los elementos de un array son True arr_bool = np.array([True,True,True,False]) arr_bool.all() # Comprobar al menos un elemento del array es True arr_bool = np.array([True,True,True,False]) arr_bool.any() # También aplican a un eje en particular arr_bool = np.array([[True,True],[False,True],[True,True]]) arr_bool # Columas verdaderas arr_bool.all(0) # Filas verdaderas arr_bool.all(1) ###Output _____no_output_____

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