52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
"""
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===================================================
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Feature selection using SelectFromModel and LassoCV
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===================================================
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Use SelectFromModel meta-transformer along with Lasso to select the best
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couple of features from the Boston dataset.
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"""
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# Author: Manoj Kumar <mks542@nyu.edu>
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# License: BSD 3 clause
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print(__doc__)
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.datasets import load_boston
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from sklearn.feature_selection import SelectFromModel
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from sklearn.linear_model import LassoCV
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# Load the boston dataset.
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boston = load_boston()
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X, y = boston['data'], boston['target']
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# We use the base estimator LassoCV since the L1 norm promotes sparsity of features.
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clf = LassoCV(cv=5)
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# Set a minimum threshold of 0.25
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sfm = SelectFromModel(clf, threshold=0.25)
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sfm.fit(X, y)
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n_features = sfm.transform(X).shape[1]
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# Reset the threshold till the number of features equals two.
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# Note that the attribute can be set directly instead of repeatedly
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# fitting the metatransformer.
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while n_features > 2:
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sfm.threshold += 0.1
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X_transform = sfm.transform(X)
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n_features = X_transform.shape[1]
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# Plot the selected two features from X.
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plt.title(
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"Features selected from Boston using SelectFromModel with "
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"threshold %0.3f." % sfm.threshold)
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feature1 = X_transform[:, 0]
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feature2 = X_transform[:, 1]
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plt.plot(feature1, feature2, 'r.')
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plt.xlabel("Feature number 1")
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plt.ylabel("Feature number 2")
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plt.ylim([np.min(feature2), np.max(feature2)])
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plt.show()
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