Sklearn StratifiedKFold: ValueError: 支持的目标类型是: ('binary', 'multiclass').取而代之的是“多标签指示器"

问题描述

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)# 将数据分成不同的折叠对于 i, (train_index, val_index) 在 enumerate(kf.split(x_train, y_train_categorical)) 中: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: 支持的目标类型是: ('binary', 'multiclass').取而代之的是多标签指示器".

解决方案

keras.utils.to_categorical 产生一个单热编码的类向量，即 multilabel-indicator错误消息中提到.StratifiedKFold 并非设计用于处理此类输入；来自 split 方法 文档:

split(X, y, groups=None)

[...]

y : 类数组，形状 (n_samples,)

监督学习问题的目标变量.分层是基于 y 标签完成的.

即你的 y 必须是你的类标签的一维数组.

本质上，您要做的只是颠倒操作的顺序:首先拆分(使用您的初始 y_train)，然后将 to_categorical 转换.>

Working with Sklearn stratified kfold split, and when I attempt to split using multi-class, I received on error (see below). When I tried and split using binary, it works no problem.

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.

解决方案

keras.utils.to_categorical produces a one-hot encoded class vector, i.e. the multilabel-indicator mentioned in the error message. StratifiedKFold is not designed to work with such input; from the split method docs:

i.e. your y must be a 1-D array of your class labels.

Essentially, what you have to do is simply to invert the order of the operations: split first (using your intial y_train), and convert to_categorical afterwards.

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