54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
"""
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===================================================
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Recursive feature elimination with cross-validation
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===================================================
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A recursive feature elimination example with automatic tuning of the
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number of features selected with cross-validation.
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"""
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import matplotlib.pyplot as plt
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from sklearn.svm import SVC
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from sklearn.model_selection import StratifiedKFold
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from sklearn.feature_selection import RFECV
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from sklearn.datasets import make_classification
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# Build a classification task using 3 informative features
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X, y = make_classification(
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n_samples=1000,
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n_features=25,
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n_informative=3,
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n_redundant=2,
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n_repeated=0,
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n_classes=8,
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n_clusters_per_class=1,
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random_state=0,
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)
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# Create the RFE object and compute a cross-validated score.
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svc = SVC(kernel="linear")
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# The "accuracy" scoring shows the proportion of correct classifications
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min_features_to_select = 1 # Minimum number of features to consider
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rfecv = RFECV(
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estimator=svc,
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step=1,
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cv=StratifiedKFold(2),
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scoring="accuracy",
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min_features_to_select=min_features_to_select,
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)
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rfecv.fit(X, y)
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print("Optimal number of features : %d" % rfecv.n_features_)
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# Plot number of features VS. cross-validation scores
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plt.figure()
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plt.xlabel("Number of features selected")
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plt.ylabel("Cross validation score (accuracy)")
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plt.plot(
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range(min_features_to_select, len(rfecv.grid_scores_) + min_features_to_select),
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rfecv.grid_scores_,
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)
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plt.show()
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