41 lines
1.2 KiB
Python
41 lines
1.2 KiB
Python
"""
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==================
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Pipeline Anova SVM
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==================
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Simple usage of Pipeline that runs successively a univariate
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feature selection with anova and then a SVM of the selected features.
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Using a sub-pipeline, the fitted coefficients can be mapped back into
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the original feature space.
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"""
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from sklearn import svm
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from sklearn.datasets import make_classification
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from sklearn.feature_selection import SelectKBest, f_regression
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from sklearn.pipeline import make_pipeline
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import classification_report
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print(__doc__)
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# import some data to play with
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X, y = make_classification(
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n_features=20, n_informative=3, n_redundant=0, n_classes=4,
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n_clusters_per_class=2)
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X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
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# ANOVA SVM-C
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# 1) anova filter, take 3 best ranked features
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anova_filter = SelectKBest(f_regression, k=3)
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# 2) svm
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clf = svm.LinearSVC()
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anova_svm = make_pipeline(anova_filter, clf)
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anova_svm.fit(X_train, y_train)
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y_pred = anova_svm.predict(X_test)
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print(classification_report(y_test, y_pred))
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coef = anova_svm[:-1].inverse_transform(anova_svm['linearsvc'].coef_)
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print(coef)
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