我有一个多类分类问题,我的数据集有偏差,我有一个特定类的 100 个实例,并且说一些不同类的 10 个,所以如果我有 100 个特定类的实例,我想在类之间拆分我的数据集保持比率并且我希望 30% 的记录进入训练集中我希望有我的 100 个记录代表类的 30 个实例和我的 10 个记录代表类的 3 个实例等等。
You can use sklearn's StratifiedKFold
, from the online docs:
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]
This will preserve your class ratios so that the splits retain the class ratios, this will work fine with pandas dfs.
As suggested by @Ali_m you could use StratifiedShuffledSplit
which accepts a split ratio param:
sss = StratifiedShuffleSplit(y, 3, test_size=0.7, random_state=0)
would produce a 70% split.
As simple as :
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y,
stratify=y,
test_size=0.25)
You can simply use the following:
But make sure that you reset stratify from None to the class labels:
"stratify : array-like or None (default is None) If not None, data is split in a stratified fashion, using this as the class labels."
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