79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
"""
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============================================================
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Parameter estimation using grid search with cross-validation
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============================================================
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This examples shows how a classifier is optimized by cross-validation,
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which is done using the :class:`~sklearn.model_selection.GridSearchCV` object
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on a development set 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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from sklearn import datasets
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from sklearn.model_selection import train_test_split
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from sklearn.model_selection import GridSearchCV
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from sklearn.metrics import classification_report
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from sklearn.svm import SVC
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print(__doc__)
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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_size=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 = ['precision', 'recall']
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for score in scores:
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print("# Tuning hyper-parameters for %s" % score)
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print()
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clf = GridSearchCV(
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SVC(), tuned_parameters, scoring='%s_macro' % score
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)
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clf.fit(X_train, y_train)
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print("Best parameters set found on development set:")
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print()
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print(clf.best_params_)
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print()
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print("Grid scores on development set:")
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print()
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means = clf.cv_results_['mean_test_score']
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stds = clf.cv_results_['std_test_score']
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for mean, std, params in zip(means, stds, clf.cv_results_['params']):
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print("%0.3f (+/-%0.03f) for %r"
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% (mean, 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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