evaluate.py 文件源码

def _generate_classification_reports(y_true, y_pred, target_names=None):
    # Calculate additional stats
    total_accuracy = accuracy_score(y_true, y_pred)
    cov_error = coverage_error(y_true, y_pred)
    lrap = label_ranking_average_precision_score(y_true, y_pred)

    report = metrics.multilabel_prediction_report(y_true, y_pred)
    report += '\n\n'
    report += metrics.multilabel_classification_report(y_true, y_pred, target_names=target_names)
    report += '\n\n'
    report += 'coverage error:  %.3f' % cov_error
    report += '\n'
    report += 'LRAP:            %.3f' % lrap
    report += '\n'
    report += 'total accuracy:  %.3f' % total_accuracy
    return report


# def run_train_test(path_train, path_test, args):
#     print('Loading train data set "%s"...' % path_train)
#     X_train, y_train, tags_train, _ = dataset.load_manifest(path_train)
#
#     print('\nLoading test data set "%s" ...' % path_test)
#     X_test, y_test, tags_test, _ = dataset.load_manifest(path_test)
#
#     report_base_name = args.model + '_kfold_%d' % rnd
#     validate(X_train, y_train, X_test, y_test, report_base_name, target_names=tags_train)