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

Question

使用 Sklearn 分层 kfold 拆分，当我尝试使用 multi-class 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 生成一个单热编码的 class 向量，即错误消息中提到的 multilabel-indicator。 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.

即您的 y 必须是 class 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。

Answer 2

在我的例子中，x 是一个二维矩阵，y 也是一个二维矩阵，即确实是 multi-class multi-output 的情况。我只是像往常一样为 y 和 x 传递了一个虚拟 np.zeros(shape=(n,1))。完整代码示例：

import numpy as np
from sklearn.model_selection import RepeatedStratifiedKFold
X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [3, 7], [9, 4]])
# y = np.array([0, 0, 1, 1, 0, 1]) # <<< works
y = X # does not work if passed into `.split`
rskf = RepeatedStratifiedKFold(n_splits=3, n_repeats=3, random_state=36851234)
for train_index, test_index in rskf.split(X, np.zeros(shape=(X.shape[0], 1))):
    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]

Answer 3

这样调用 split()：

for i, (train_index, val_index) in enumerate(kf.split(x_train, y_train_categorical.argmax(1))):
    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]

Answer 4

我遇到了同样的问题，发现你可以用这个 util 函数检查目标的类型：

from sklearn.utils.multiclass import type_of_target
type_of_target(y)

'multilabel-indicator'

来自其文档字符串：

'binary': y contains <= 2 discrete values and is 1d or a column vector.

'multiclass': y contains more than two discrete values, is not a sequence of sequences, and is 1d or a column vector.

'multiclass-multioutput': y is a 2d array that contains more than two discrete values, is not a sequence of sequences, and both dimensions are of size > 1.

'multilabel-indicator': y is a label indicator matrix, an array of two dimensions with at least two columns, and at most 2 unique values.

使用 LabelEncoder，您可以将类转换为一维数字数组（假设您的目标标签位于 categoricals/object 的一维数组中）：

from sklearn.preprocessing import LabelEncoder

label_encoder = LabelEncoder()
y = label_encoder.fit_transform(target_labels)

Answer 5

补充@desertnaut 所说的，为了将您的 one-hot-encoding 转换回一维数组，您只需要做的是：

class_labels = np.argmax(y_train, axis=1)

这将转换回您类的初始表示。

Answer 6

如果你的目标变量是连续的，那么使用简单的 KFold 交叉验证而不是 StratifiedKFold。

from sklearn.model_selection import KFold
kfold = KFold(n_splits=5, shuffle=True, random_state=42)

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

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

python

machine-learning

keras

scikit-learn

cross-validation