51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
"""
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=================================================
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SVM-Anova: SVM with univariate feature selection
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=================================================
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This example shows how to perform univariate feature before running a SVC
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(support vector classifier) to improve the classification scores.
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"""
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import numpy as np
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import pylab as pl
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from scikits.learn import svm, datasets, feature_selection, cross_val
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from scikits.learn.pipeline import Pipeline
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################################################################################
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# Import some data to play with
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digits = datasets.load_digits()
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y = digits.target
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n_samples = len(y)
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X = digits.data.reshape((n_samples, -1))
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################################################################################
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# Create a feature-selection transform and an instance of SVM that we
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# combine together to have an full-blown estimator
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transform = feature_selection.SelectPercentile(feature_selection.f_classif)
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clf = Pipeline([transform], svm.SVC())
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################################################################################
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# Plot the cross-validation score as a function of percentile of features
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score_means = list()
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score_stds = list()
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percentiles = (10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
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for percentile in percentiles:
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transform._set_params(percentile=percentile)
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# Compute cross-validation score using all CPUs
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this_scores = cross_val.cross_val_score(clf, X, y, n_jobs=-1)
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score_means.append(this_scores.mean())
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score_stds.append(this_scores.std())
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pl.errorbar(percentiles, score_means, np.array(score_stds))
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pl.title(
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'Performance of the SVM-Anova varying the percentile of features selected')
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pl.xlabel('Percentile')
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pl.ylabel('Cross-validation errors rate')
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pl.axis('tight')
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pl.show()
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