# Author:  Raghav RV <rvraghav93@gmail.com>
#          Guillaume Lemaitre <g.lemaitre58@gmail.com>
#          Thomas Unterthiner
# License: BSD 3 clause
#@article{scikit-learn,
# title={Scikit-learn: Machine Learning in {P}ython},
# author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
#         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
#         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
#         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
# journal={Journal of Machine Learning Research},
# volume={12},
# pages={2825--2830},
# year={2011}
#}
from __future__ import print_function

import numpy as np

import matplotlib as mpl
from matplotlib import pyplot as plt
from matplotlib import cm

from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import minmax_scale
from sklearn.preprocessing import MaxAbsScaler
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import RobustScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing.data import QuantileTransformer

from sklearn.datasets import fetch_california_housing

print(__doc__)

dataset = fetch_california_housing()
X_full, y_full = dataset.data, dataset.target

# Take only 2 features to make visualization easier
# Feature of 0 has a long tail distribution.
# Feature 5 has a few but very large outliers.

X = X_full[:, [0, 5]]

distributions = [
    ('Unscaled data', X),
    ('Data after standard scaling',
        StandardScaler().fit_transform(X)),
    ('Data after min-max scaling',
        MinMaxScaler().fit_transform(X)),
    ('Data after max-abs scaling',
        MaxAbsScaler().fit_transform(X)),
    ('Data after robust scaling',
        RobustScaler(quantile_range=(25, 75)).fit_transform(X)),
    ('Data after quantile transformation (uniform pdf)',
        QuantileTransformer(output_distribution='uniform')
        .fit_transform(X)),
    ('Data after quantile transformation (gaussian pdf)',
        QuantileTransformer(output_distribution='normal')
        .fit_transform(X)),
    ('Data after sample-wise L2 normalizing',
        Normalizer().fit_transform(X))
]

# scale the output between 0 and 1 for the colorbar
y = minmax_scale(y_full)


def create_axes(title, figsize=(16, 6)):
    fig = plt.figure(figsize=figsize)
    fig.suptitle(title)

    # define the axis for the first plot
    left, width = 0.1, 0.22
    bottom, height = 0.1, 0.7
    bottom_h = height + 0.15
    left_h = left + width + 0.02

    rect_scatter = [left, bottom, width, height]
    rect_histx = [left, bottom_h, width, 0.1]
    rect_histy = [left_h, bottom, 0.05, height]

    ax_scatter = plt.axes(rect_scatter)
    ax_histx = plt.axes(rect_histx)
    ax_histy = plt.axes(rect_histy)

    # define the axis for the zoomed-in plot
    left = width + left + 0.2
    left_h = left + width + 0.02

    rect_scatter = [left, bottom, width, height]
    rect_histx = [left, bottom_h, width, 0.1]
    rect_histy = [left_h, bottom, 0.05, height]

    ax_scatter_zoom = plt.axes(rect_scatter)
    ax_histx_zoom = plt.axes(rect_histx)
    ax_histy_zoom = plt.axes(rect_histy)

    # define the axis for the colorbar
    left, width = width + left + 0.13, 0.01

    rect_colorbar = [left, bottom, width, height]
    ax_colorbar = plt.axes(rect_colorbar)

    return ((ax_scatter, ax_histy, ax_histx),
            (ax_scatter_zoom, ax_histy_zoom, ax_histx_zoom),
            ax_colorbar)


def plot_distribution(axes, X, y, hist_nbins=50, title="",
                      x0_label="", x1_label=""):
    ax, hist_X1, hist_X0 = axes

    ax.set_title(title)
    ax.set_xlabel(x0_label)
    ax.set_ylabel(x1_label)

    # The scatter plot
    colors = cm.plasma_r(y)
    ax.scatter(X[:, 0], X[:, 1], alpha=0.5, marker='o', s=5, lw=0, c=colors)

    # Removing the top and the right spine for aesthetics
    # make nice axis layout
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.get_xaxis().tick_bottom()
    ax.get_yaxis().tick_left()
    ax.spines['left'].set_position(('outward', 10))
    ax.spines['bottom'].set_position(('outward', 10))

    # Histogram for axis X1 (feature 5)
    hist_X1.set_ylim(ax.get_ylim())
    hist_X1.hist(X[:, 1], bins=hist_nbins, orientation='horizontal',
                 color='grey', ec='grey')
    hist_X1.axis('off')

    # Histogram for axis X0 (feature 0)
    hist_X0.set_xlim(ax.get_xlim())
    hist_X0.hist(X[:, 0], bins=hist_nbins, orientation='vertical',
                 color='grey', ec='grey')
    hist_X0.axis('off')


def make_plot(item_idx):
    title, X = distributions[item_idx]
    ax_zoom_out, ax_zoom_in, ax_colorbar = create_axes(title)
    axarr = (ax_zoom_out, ax_zoom_in)
    plot_distribution(axarr[0], X, y, hist_nbins=200,
                      x0_label="Median Income",
                      x1_label="Number of households",
                      title="Full data")

    # zoom-in
    zoom_in_percentile_range = (0, 99)
    cutoffs_X0 = np.percentile(X[:, 0], zoom_in_percentile_range)
    cutoffs_X1 = np.percentile(X[:, 1], zoom_in_percentile_range)

    non_outliers_mask = (
        np.all(X > [cutoffs_X0[0], cutoffs_X1[0]], axis=1) &
        np.all(X < [cutoffs_X0[1], cutoffs_X1[1]], axis=1))
    plot_distribution(axarr[1], X[non_outliers_mask], y[non_outliers_mask],
                      hist_nbins=50,
                      x0_label="Median Income",
                      x1_label="Number of households",
                      title="Zoom-in")

    norm = mpl.colors.Normalize(y_full.min(), y_full.max())
    mpl.colorbar.ColorbarBase(ax_colorbar, cmap=cm.plasma_r,
                              norm=norm, orientation='vertical',
                              label='Color mapping for values of y')

make_plot(0)
make_plot(1)
plt.show()
