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