python – 如何将数据集拆分为类之间的训练和验证集保持比率？

Stratified K-Folds cross validation iterator

Provides train/test
indices to split data in train test sets.

This cross-validation object
is a variation of KFold that returns stratified folds. The folds are
made by preserving the percentage of samples for each class.

>>> from sklearn import cross_validation
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> skf = cross_validation.StratifiedKFold(y, n_folds=2)
>>> len(skf)
2
>>> print(skf)  
sklearn.cross_validation.StratifiedKFold(labels=[0 0 1 1], n_folds=2,
                                         shuffle=False, random_state=None)
>>> for train_index, test_index in skf:
...    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]
TRAIN: [1 3] TEST: [0 2]
TRAIN: [0 2] TEST: [1 3]

这将保留您的类比率,以便拆分保留类比率,这将适用于pandas dfs.

正如@Ali_m所建议的,您可以使用StratifiedShuffledSplit接受分割比率参数：

sss = StratifiedShuffleSplit(y,3,test_size = 0.7,random_state = 0)

将产生70％的分裂.