python - Sklearn StratifiedKFold : ValueError: Supported target types are: ('binary' , 'multiclass' )。取而代之的是 'multilabel-indicator'

Question

使用 Sklearn 分层 kfold 拆分，当我尝试使用多类拆分时，我收到错误消息(见下文)。当我尝试使用二进制进行拆分时，它没有问题。

num_classes = len(np.unique(y_train))
y_train_categorical = keras.utils.to_categorical(y_train, num_classes)
kf=StratifiedKFold(n_splits=5, shuffle=True, random_state=999)

# splitting data into different folds
for i, (train_index, val_index) in enumerate(kf.split(x_train, y_train_categorical)):
    x_train_kf, x_val_kf = x_train[train_index], x_train[val_index]
    y_train_kf, y_val_kf = y_train[train_index], y_train[val_index]

ValueError: Supported target types are: ('binary', 'multiclass'). Got 'multilabel-indicator' instead.

Answer 1

keras.utils.to_categorical产生一个单热编码的类向量，即 multilabel-indicator错误信息中提到。 StratifiedKFold不适用于此类输入；来自 split方法 docs :

split(X, y, groups=None)

[...]

y : array-like, shape (n_samples,)

The target variable for supervised learning problems. Stratification is done based on the y labels.

即你的y必须是您的类标签的一维数组。

本质上，您要做的只是颠倒操作顺序:首先拆分(使用您的初始 y_train )，然后转换 to_categorical之后。