85 lines
2.0 KiB
Python
85 lines
2.0 KiB
Python
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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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# 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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# %%
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# First we load the diabetes dataset.
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import numpy as np
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from sklearn import datasets
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X, y = datasets.load_diabetes(return_X_y=True)
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indices = (0, 1)
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X_train = X[:-20, indices]
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X_test = X[-20:, indices]
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y_train = y[:-20]
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y_test = y[-20:]
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# %%
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# Next we fit a linear regression model.
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from sklearn import linear_model
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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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# Finally we plot the figure from three different views.
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import matplotlib.pyplot as plt
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# unused but required import for doing 3d projections with matplotlib < 3.2
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import mpl_toolkits.mplot3d # noqa: F401
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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 = fig.add_subplot(111, projection="3d", 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(
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np.array([[-0.1, -0.1], [0.15, 0.15]]),
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np.array([[-0.1, 0.15], [-0.1, 0.15]]),
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clf.predict(
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np.array([[-0.1, -0.1, 0.15, 0.15], [-0.1, 0.15, -0.1, 0.15]]).T
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).reshape((2, 2)),
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alpha=0.5,
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)
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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.xaxis.set_ticklabels([])
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ax.yaxis.set_ticklabels([])
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ax.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 = -0.5
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azim = 0
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plot_figs(2, elev, azim, X_train, ols)
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elev = -0.5
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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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