80 lines
2.7 KiB
Python
80 lines
2.7 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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:class:`sklearn.grid_search.GridSearchCV` object on a development set
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that comprises only half of the available labeled data.
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The performance of the selected hyper-parameters and trained model is
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then measured on a dedicated evaluation set that was not used during
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the model selection step.
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More details on tools available for model selection can be found in the
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sections on :ref:`cross_validation` and :ref:`grid_search`.
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"""
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print __doc__
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from sklearn import datasets
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from sklearn.cross_validation import train_test_split
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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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# 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 parts
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, test_fraction=0.5, random_state=0)
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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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print "# Tuning hyper-parameters for %s" % score_name
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print
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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=5)
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print "Best parameters set found on development set:"
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print
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print clf.best_estimator_
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print
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print "Grid scores on development set:"
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print
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for params, mean_score, scores in clf.grid_scores_:
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print "%0.3f (+/-%0.03f) for %r" % (
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mean_score, scores.std() / 2, params)
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print
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print "Detailed classification report:"
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print
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print "The model is trained on the full development set."
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print "The scores are computed on the full evaluation set."
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print
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y_true, y_pred = y_test, clf.predict(X_test)
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print classification_report(y_true, y_pred)
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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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