python - 如何将数据集拆分为训练集和验证集以保持类之间的比率？

Question

我有一个多类分类问题，我的数据集是倾斜的，我有 100 个特定类的实例，说 10 个不同的类，所以我想在类之间拆分我的数据集保持率，如果我有 100 个实例一个特定的类，我希望 30% 的记录进入训练集中，我希望我的 100 条记录中有 30 个实例代表类，我的 10 条记录中有 3 个实例代表类等等。

Answer 1

您可以使用 sklearn 的 StratifiedKFold ，来自在线文档:

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]

这将保留您的类别比率，以便拆分保留类别比率，这将适用于 pandas dfs。

正如@Ali_m 所建议的，您可以使用 StratifiedShuffledSplit它接受一个分流比参数:

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

会产生 70% 的拆分。