4 examples of 'classification_report sklearn' in Python

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60def _check_classification_report(n_classes=2):
61 classifiers = ClassifiersFactory()
62 classifiers.add_classifier('gb', GradientBoostingClassifier(n_estimators=10))
63 classifiers.add_classifier('rf', RandomForestClassifier())
64 classifiers.add_classifier('ada', AdaBoostClassifier(n_estimators=10))
65
66 X, y = generate_classification_sample(1000, 5, n_classes=n_classes)
67 classifiers.fit(X, y)
68
69 X, y = generate_classification_sample(1000, 5, n_classes=n_classes)
70 test_lds = LabeledDataStorage(X, y, sample_weight=None)
71 report = classifiers.test_on_lds(test_lds)
72
73 val = numpy.mean(X['column0'])
74 labels_dict = None
75 if n_classes > 2:
76 labels_dict = {i: str(i) for i in range(n_classes)}
77 _classification_mask_report(report, "column0 > %f" % val, X, labels_dict)
78 _classification_mask_report(report, lambda x: numpy.array(x['column0']) < val, X, labels_dict)
79 _classification_mask_report(report, None, X, labels_dict)
80 check_classification_learning_curve_masks(report, n_classes=n_classes)
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405def runClassReport(clf, A, Cl):
406 from sklearn.metrics import classification_report
407 y_pred = clf.predict(A)
408 print(classification_report(Cl, y_pred, target_names=clf.classes_))
409 print(' Precision is the probability that, given a classification result for a sample,\n' +
410 ' the sample actually belongs to that class. Recall (Accuracy) is the probability that a \n' +
411 ' sample will be correctly classified for a given class. f1-score combines both \n' +
412 ' accuracy and precision to give a single measure of relevancy of the classifier results.\n')
83def test_classification_report():
84 _check_classification_report(n_classes=2)
17def report(test_Y, pred_Y):
18 print "accuracy_score:"
19 print metrics.accuracy_score(test_Y, pred_Y)
20 print "f1_score:"
21 print metrics.f1_score(test_Y, pred_Y)
22 print "recall_score:"
23 print metrics.recall_score(test_Y, pred_Y)
24 print "precision_score:"
25 print metrics.precision_score(test_Y, pred_Y)
26 print "confusion_matrix:"
27 print metrics.confusion_matrix(test_Y, pred_Y)
28 print "AUC:"
29 print metrics.roc_auc_score(test_Y, pred_Y)
30
31 f_pos, t_pos, thresh = metrics.roc_curve(test_Y, pred_Y)
32 auc_area = metrics.auc(f_pos, t_pos)
33 plt.plot(f_pos, t_pos, 'darkorange', lw=2, label='AUC = %.2f' % auc_area)
34 plt.legend(loc='lower right')
35 plt.plot([0, 1], [0, 1], color='navy', linestyle='--')
36 plt.title('ROC')
37 plt.ylabel('True Pos Rate')
38 plt.xlabel('False Pos Rate')
39 plt.show()

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