4 examples of 'classification_report sklearn' in Python

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def _check_classification_report(n_classes=2):
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    classifiers = ClassifiersFactory()
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    classifiers.add_classifier('gb', GradientBoostingClassifier(n_estimators=10))
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    classifiers.add_classifier('rf', RandomForestClassifier())
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    classifiers.add_classifier('ada', AdaBoostClassifier(n_estimators=10))
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    X, y = generate_classification_sample(1000, 5, n_classes=n_classes)
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    classifiers.fit(X, y)
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    X, y = generate_classification_sample(1000, 5, n_classes=n_classes)
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    test_lds = LabeledDataStorage(X, y, sample_weight=None)
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    report = classifiers.test_on_lds(test_lds)
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    val = numpy.mean(X['column0'])
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    labels_dict = None
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    if n_classes > 2:
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        labels_dict = {i: str(i) for i in range(n_classes)}
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    _classification_mask_report(report, "column0 > %f" % val, X, labels_dict)
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    _classification_mask_report(report, lambda x: numpy.array(x['column0']) < val, X, labels_dict)
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    _classification_mask_report(report, None, X, labels_dict)
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    check_classification_learning_curve_masks(report, n_classes=n_classes)

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def runClassReport(clf, A, Cl):
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    from sklearn.metrics import classification_report
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    y_pred = clf.predict(A)
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    print(classification_report(Cl, y_pred, target_names=clf.classes_))
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    print(' Precision is the probability that, given a classification result for a sample,\n' +
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          ' the sample actually belongs to that class. Recall (Accuracy) is the probability that a \n' +
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          ' sample will be correctly classified for a given class. f1-score combines both \n' +
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          ' accuracy and precision to give a single measure of relevancy of the classifier results.\n')

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def test_classification_report():
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    _check_classification_report(n_classes=2)

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def report(test_Y, pred_Y):
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    print "accuracy_score:"
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    print metrics.accuracy_score(test_Y, pred_Y)
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    print "f1_score:"
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    print metrics.f1_score(test_Y, pred_Y)
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    print "recall_score:"
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    print metrics.recall_score(test_Y, pred_Y)
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    print "precision_score:"
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    print metrics.precision_score(test_Y, pred_Y)
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    print "confusion_matrix:"
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    print metrics.confusion_matrix(test_Y, pred_Y)
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    print "AUC:"
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    print metrics.roc_auc_score(test_Y, pred_Y)
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    f_pos, t_pos, thresh = metrics.roc_curve(test_Y, pred_Y)
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    auc_area = metrics.auc(f_pos, t_pos)
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    plt.plot(f_pos, t_pos, 'darkorange', lw=2, label='AUC = %.2f' % auc_area)
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    plt.legend(loc='lower right')
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    plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
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    plt.title('ROC')
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    plt.ylabel('True Pos Rate')
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    plt.xlabel('False Pos Rate')
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    plt.show()