如何确保StratifiedShuffleSplit保留了不平衡的类比率

Question

我有一个不平衡的数据集，当我微调我的模型时，我需要确保StratifiedShuffleSplit实际上是从具有固有类比率的所有类中挑选的。我该如何测试？

Answer 1

下面的函数测试比例为4:16的不平衡数据集。

def test_cv():
from sklearn.model_selection import StratifiedShuffleSplit
X = np.array([1, 5, 4, 3, 4, 5, 6, 5, 4, 5, 3, 4, 3, 2, 3, 4, 1, 9, 3, 5])
y = np.array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
print('Class Ratio 1 / Total = {0} / {1}'.format(len(y[y == 1]), len(y)))
print('Class Ratio 0 / Total = {0} / {1}'.format(len(y[y == 0]), len(y)))
sss = StratifiedShuffleSplit(n_splits=5, test_size=0.3, random_state=0)
for train_index, test_index in sss.split(X, y):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
print('X_TRAIN: t{0} X_TEST:{1}'.format(X_train, X_test))
print('y_TRAIN: t{0} y_TEST:{1}n'.format(y_train, y_test))
print('#1/#Train = {0} / {1}'.format(len(y_train[y_train==1]), len(y_train)))
print('#1/#Test = {0} / {1}'.format(len(y_test[y_test == 1]), len(y_test)))

输出将显示少数类(1(如何出现在保持初始类比率的每个分割中：

Class Ratio 1 / Total = 4 / 20
Class Ratio 0 / Total = 16 / 20
X_TRAIN:    [3 6 4 5 5 2 9 1 3 4 4 3 3 3] X_TEST:[1 5 5 5 4 4]
y_TRAIN:    [0 0 1 0 0 0 0 1 1 0 0 0 0 0] y_TEST:[0 0 1 0 0 0]
#1/#Train = 3 / 14
#1/#Test = 1 / 6
X_TRAIN:    [4 4 3 5 5 4 2 3 3 6 5 4 1 3] X_TEST:[1 5 9 5 3 4]
y_TRAIN:    [1 0 0 0 0 0 0 1 0 0 1 0 0 0] y_TEST:[1 0 0 0 0 0]
#1/#Train = 3 / 14
#1/#Test = 1 / 6
X_TRAIN:    [5 4 4 3 3 5 1 3 2 4 5 3 9 5] X_TEST:[4 5 4 1 6 3]
y_TRAIN:    [0 0 0 0 0 0 0 0 0 1 0 1 0 1] y_TEST:[0 0 0 1 0 0]
#1/#Train = 3 / 14
#1/#Test = 1 / 6
X_TRAIN:    [3 4 1 3 5 3 4 5 2 5 5 3 1 3] X_TEST:[4 9 4 4 5 6]
y_TRAIN:    [0 0 0 0 0 1 0 0 0 1 0 0 1 0] y_TEST:[0 0 0 1 0 0]
#1/#Train = 3 / 14
#1/#Test = 1 / 6
X_TRAIN:    [5 9 1 6 4 3 4 4 5 5 2 3 5 3] X_TEST:[3 1 3 4 4 5]
y_TRAIN:    [0 0 0 0 0 0 0 1 0 1 0 0 0 1] y_TEST:[0 1 0 0 0 0]
#1/#Train = 3 / 14
#1/#Test = 1 / 6