svm_clusterization_test.py 文件源码

def test_svm_estimator(estimator, notes, encodings_train, labels_train,
                       encodings_test, labels_test):
    t0 = time()
    estimator.fit(encodings_train, labels_train)
    print("Time cons: %.2fs, type: %s" % (time() - t0, notes))
    predicted = estimator.predict(encodings_test)
    accuracy = metrics.accuracy_score(labels_test, predicted)
    print("Accuracy: %.5f" % accuracy)
    report = metrics.classification_report(labels_test, predicted)
    print(report)
    prec_recall_f_score = metrics.precision_recall_fscore_support(
        labels_test, predicted)
    print('-' * 10)
    prec_recall_f_score_dict = {
        'prec': np.mean(prec_recall_f_score[0]),
        'recall': np.mean(prec_recall_f_score[1]),
        'f_score': np.mean(prec_recall_f_score[2])
    }
    return accuracy, prec_recall_f_score_dict