sklearn：classification

classification_report的格式

引用：sklearn.metrics.classification_report模块使用与指标分析(生成混淆矩阵评价分类指标)

在使用autoclassfication后获取其中的precision recall f1-score 值

def auto_sklearn_classification(X_train, X_test, y_train, y_test):
    cls = autosklearn.classification.AutoSklearnClassifier(time_left_for_this_task=300, per_run_time_limit=90, ml_memory_limit=10000)
    cls.fit(X_train, y_train)
    predictions = cls.predict(X_test)

    report = []
    report_str = classification_report(y_test, predictions)
    for row in report_str.split("n"):
        parsed_row = [x for x in row.split("  ") if len(x) > 0]
        if len(parsed_row) > 0:
            report.append(parsed_row)
    
    # save accuracy, precision, recall, F1-Score to dictionary
    accuracy = accuracy_score(predictions,y_test)
    precision = float(report[-1][1])#最后一行第二列的值
    recall = float(report[-1][2])
    f1_score = float(report[-1][3])
    return accuracy, precision, recall, f1_score