Python sklearn GroupKFold用法及代码示例

本文简要介绍python语言中 sklearn.model_selection.GroupKFold 的用法。

用法:

class sklearn.model_selection.GroupKFold(n_splits=5)

K-fold 具有非重叠组的迭代器变体。

同一组不会出现在两个不同的折叠中(不同组的数量必须至少等于折叠的数量)。

在每个折叠中不同组的数量大致相同的意义上，折叠大致平衡。

在用户指南中阅读更多信息。

参数：

n_splits：整数，默认=5

折叠次数。必须至少为 2。

例子：

>>> import numpy as np
>>> from sklearn.model_selection import GroupKFold
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 3, 4])
>>> groups = np.array([0, 0, 2, 2])
>>> group_kfold = GroupKFold(n_splits=2)
>>> group_kfold.get_n_splits(X, y, groups)
2
>>> print(group_kfold)
GroupKFold(n_splits=2)
>>> for train_index, test_index in group_kfold.split(X, y, groups):
...     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]
...     print(X_train, X_test, y_train, y_test)
...
TRAIN: [0 1] TEST: [2 3]
[[1 2]
 [3 4]] [[5 6]
 [7 8]] [1 2] [3 4]
TRAIN: [2 3] TEST: [0 1]
[[5 6]
 [7 8]] [[1 2]
 [3 4]] [3 4] [1 2]