python - 如何使用 sklearn 将数据拆分为 3 个或更多部分

Question

我想将数据拆分为分层的训练、测试和验证数据集，但 sklearn 仅提供 cross_validation.train_test_split 只能分为 2 部分。如果我想这样做，我应该怎么做

Answer 1

如果您想使用 Stratified Train/Test 拆分，可以在 Sklearn 中使用 StratifiedKFold

假设X是您的特征，并且y是您的标签，基于此处的示例：

from sklearn.model_selection import StratifiedKFold
cv_stf = StratifiedKFold(n_splits=3)
for train_index, test_index in skf.split(X, y):
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

更新：要将数据拆分为 3 个不同的百分比，使用numpy.split()可以这样做：

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))])

Answer 2

您也可以train_test_split多次使用来实现此目的。第二次，在第一次调用的训练输出上运行它train_test_split。

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