1# -*- coding: utf-8 -*-
2"""
3=========================================================
4Sparsity Example: Fitting only features 1  and 2
5=========================================================
6
7Features 1 and 2 of the diabetes-dataset are fitted and
8plotted below. It illustrates that although feature 2
9has a strong coefficient on the full model, it does not
10give us much regarding `y` when compared to just feature 1
11
12"""
13
14# Code source: Gaël Varoquaux
15# Modified for documentation by Jaques Grobler
16# License: BSD 3 clause
17
18import matplotlib.pyplot as plt
19import numpy as np
20from mpl_toolkits.mplot3d import Axes3D
21
22from sklearn import datasets, linear_model
23
24X, y = datasets.load_diabetes(return_X_y=True)
25indices = (0, 1)
26
27X_train = X[:-20, indices]
28X_test = X[-20:, indices]
29y_train = y[:-20]
30y_test = y[-20:]
31
32ols = linear_model.LinearRegression()
33ols.fit(X_train, y_train)
34
35
36# #############################################################################
37# Plot the figure
38def plot_figs(fig_num, elev, azim, X_train, clf):
39    fig = plt.figure(fig_num, figsize=(4, 3))
40    plt.clf()
41    ax = Axes3D(fig, elev=elev, azim=azim)
42
43    ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c="k", marker="+")
44    ax.plot_surface(
45        np.array([[-0.1, -0.1], [0.15, 0.15]]),
46        np.array([[-0.1, 0.15], [-0.1, 0.15]]),
47        clf.predict(
48            np.array([[-0.1, -0.1, 0.15, 0.15], [-0.1, 0.15, -0.1, 0.15]]).T
49        ).reshape((2, 2)),
50        alpha=0.5,
51    )
52    ax.set_xlabel("X_1")
53    ax.set_ylabel("X_2")
54    ax.set_zlabel("Y")
55    ax.w_xaxis.set_ticklabels([])
56    ax.w_yaxis.set_ticklabels([])
57    ax.w_zaxis.set_ticklabels([])
58
59
60# Generate the three different figures from different views
61elev = 43.5
62azim = -110
63plot_figs(1, elev, azim, X_train, ols)
64
65elev = -0.5
66azim = 0
67plot_figs(2, elev, azim, X_train, ols)
68
69elev = -0.5
70azim = 90
71plot_figs(3, elev, azim, X_train, ols)
72
73plt.show()
74