67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
"""
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=====================================================================
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Parameter estimation using grid search with a nested cross-validation
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=====================================================================
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The classifier is optimized by "nested" cross-validation using the
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GridSearchCV object.
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The performance of the selected parameters is evaluated using
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cross-validation (different than the nested cross-validation that is used
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to select the best classifier).
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"""
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print __doc__
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from pprint import pprint
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import numpy as np
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from sklearn import datasets
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from sklearn.cross_val import StratifiedKFold
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from sklearn.grid_search import GridSearchCV
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from sklearn.metrics import classification_report
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from sklearn.metrics import precision_score
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from sklearn.metrics import recall_score
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from sklearn.svm import SVC
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################################################################################
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# Loading the Digits dataset
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digits = datasets.load_digits()
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# To apply an classifier on this data, we need to flatten the image, to
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# turn the data in a (samples, feature) matrix:
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n_samples = len(digits.images)
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X = digits.images.reshape((n_samples, -1))
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y = digits.target
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# split the dataset in two equal part respecting label proportions
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train, test = iter(StratifiedKFold(y, 2)).next()
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################################################################################
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# Set the parameters by cross-validation
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tuned_parameters = [{'kernel': ['rbf'], 'gamma': [1e-3, 1e-4],
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'C': [1, 10, 100, 1000]},
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{'kernel': ['linear'], 'C': [1, 10, 100, 1000]}]
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scores = [
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('precision', precision_score),
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('recall', recall_score),
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]
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for score_name, score_func in scores:
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clf = GridSearchCV(SVC(C=1), tuned_parameters, score_func=score_func)
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clf.fit(X[train], y[train], cv=StratifiedKFold(y[train], 5))
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y_true, y_pred = y[test], clf.predict(X[test])
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print "Classification report for the best estimator: "
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print clf.best_estimator
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print "Tuned for '%s' with optimal value: %0.3f" % (
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score_name, score_func(y_true, y_pred))
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print classification_report(y_true, y_pred)
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print "Grid scores:"
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pprint(clf.grid_scores_)
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print
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# Note the problem is too easy: the hyperparameter plateau is too flat and the
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# output model is the same for precision and recall with ties in quality
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