如何使用 sklearn 将数据分成 3 个或更多部分

Question

我想把数据拆分成分层的train、test和validation数据集，但是sklearn只提供了cross_validation.train_test_split，只能分成2块。如果我想这样做我应该怎么做

Answer 1

如果要使用分层 Train/Test 拆分，可以使用 StratifiedKFold in Sklearn

假设 X 是你的特征，y 是你的标签，基于示例 here :

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]

Update ：要将数据分成 3 个不同的百分比，使用 numpy.split() 可以像这样完成：

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

您也可以多次使用 train_test_split 来实现此目的。第二次，运行它在第一次调用 train_test_split.

的训练输出上

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

如何使用 sklearn 将数据分成 3 个或更多部分

how can I split data in 3 or more parts with sklearn

python

machine-learning

scikit-learn

cross-validation

train-test-split