69 lines
2.4 KiB
Python
69 lines
2.4 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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# Loading the Digits dataset
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X, y = datasets.load_digits(return_X_y=True)
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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(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 = [
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{"kernel": ["rbf"], "gamma": [1e-3, 1e-4], "C": [1, 10, 100, 1000]},
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{"kernel": ["linear"], "C": [1, 10, 100, 1000]},
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]
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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(SVC(), tuned_parameters, scoring="%s_macro" % score)
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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" % (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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