Python 如何使用sklearn将数据拆分为3个或更多部分

我想将数据分为训练、测试和验证数据集，这些数据集是分层的，但sklearn只提供交叉验证。训练、测试和验证数据集只能分为两部分。

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如果我想这样做，我应该怎么做如果你想使用分层训练/测试分割，你可以使用

假设

X

是您的特征，

y

是您的标签，基于以下示例：

更新：要将数据拆分为3个不同的百分比，可以这样使用：

X_train, X_test, X_validate  = np.split(X, [int(.7*len(X)), int(.8*len(X))])
y_train, y_test, y_validate  = np.split(y, [int(.7*len(y)), int(.8*len(y))])

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您还可以多次使用

train\u test\u split

来实现此目的。第二次，在第一次调用

train\u test\u split

的训练输出上运行它

from sklearn.model_selection import train_test_split

def train_test_validate_stratified_split(features, targets, test_size=0.2, validate_size=0.1):
    # Get test sets
    features_train, features_test, targets_train, targets_test = train_test_split(
        features,
        targets,
        stratify=targets,
        test_size=test_size
    )
    # Run train_test_split again to get train and validate sets
    post_split_validate_size = validate_size / (1 - test_size)
    features_train, features_validate, targets_train, targets_validate = train_test_split(
        features_train,
        targets_train,
        stratify=targets_train,
        test_size=post_split_validate_size
    )
    return features_train, features_test, features_validate, targets_train, targets_test, targets_validate

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谢谢您的回答，但我想将数据集分成三部分，如[70%，20%，10%]，分层折叠可能没有帮助。@loseryao哦，对不起，我以为您的意思是分成3个不同的折叠，我会更新它。将数据重新排列可能是明智的。

from sklearn.model_selection import train_test_split

def train_test_validate_stratified_split(features, targets, test_size=0.2, validate_size=0.1):
    # Get test sets
    features_train, features_test, targets_train, targets_test = train_test_split(
        features,
        targets,
        stratify=targets,
        test_size=test_size
    )
    # Run train_test_split again to get train and validate sets
    post_split_validate_size = validate_size / (1 - test_size)
    features_train, features_validate, targets_train, targets_validate = train_test_split(
        features_train,
        targets_train,
        stratify=targets_train,
        test_size=post_split_validate_size
    )
    return features_train, features_test, features_validate, targets_train, targets_test, targets_validate