File Content: plot_ols_3d.py #!/usr/bin/python # -*- coding: utf-8 -*- """ ========================================================= Sparsity Example: Fitting only features 1 and 2 ========================================================= Features 1 and 2 of the diabetes-dataset are fitted and plotted below. It illustrates that although feature 2 has a strong coefficient on the full model, it does not give us much regarding `y` when compared to just feature 1 """ print(__doc__) # Code source: Gaël Varoquaux # Modified for documentation by Jaques Grobler # License: BSD 3 clause import matplotlib.pyplot as plt import numpy as np from mpl_toolkits.mplot3d import Axes3D from sklearn import datasets, linear_model diabetes = datasets.load_diabetes() indices = (0, 1) X_train = diabetes.data[:-20, indices] X_test = diabetes.data[-20:, indices] y_train = diabetes.target[:-20] y_test = diabetes.target[-20:] ols = linear_model.LinearRegression() ols.fit(X_train, y_train) ############################################################################### # Plot the figure def plot_figs(fig_num, elev, azim, X_train, clf): fig = plt.figure(fig_num, figsize=(4, 3)) plt.clf() ax = Axes3D(fig, elev=elev, azim=azim) ax.scatter(X_train[:, 0], X_train[:, 1], y_train, c='k', marker='+') ax.plot_surface(np.array([[-.1, -.1], [.15, .15]]), np.array([[-.1, .15], [-.1, .15]]), clf.predict(np.array([[-.1, -.1, .15, .15], [-.1, .15, -.1, .15]]).T ).reshape((2, 2)), alpha=.5) ax.set_xlabel('X_1') ax.set_ylabel('X_2') ax.set_zlabel('Y') ax.w_xaxis.set_ticklabels([]) ax.w_yaxis.set_ticklabels([]) ax.w_zaxis.set_ticklabels([]) #Generate the three different figures from different views elev = 43.5 azim = -110 plot_figs(1, elev, azim, X_train, ols) elev = -.5 azim = 0 plot_figs(2, elev, azim, X_train, ols) elev = -.5 azim = 90 plot_figs(3, elev, azim, X_train, ols) plt.show() Submit