ValueError：分类指标无法处理多标签指标和连续多输出目标的混合

Posted 2023-02-16

【中文标题】ValueError：分类指标无法处理多标签指标和连续多输出目标的混合

【英文标题】：ValueError: Classification metrics can't handle a mix of multilabel-indicator and continuous-multioutput targets

【发布时间】：2021-12-20 13:26:59

【问题描述】：

我不知道问题是什么以及为什么会出现此错误：

我可以尝试什么来解决这个问题？

代码如下：

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.datasets import make_classification
from sklearn.preprocessing import OneHotEncoder, MinMaxScaler
from sklearn.model_selection import train_test_split
tf.random.set_seed(0)

# generate the data
X, y = make_classification(n_classes=6, n_samples=1000, n_features=10, n_informative=10, n_redundant=0, random_state=42)
print(y.shape)
# (1000, )

# split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)

# one-hot encode the target
enc = OneHotEncoder(sparse=False, handle_unknown='ignore')
enc.fit(y_train.reshape(-1, 1))
y_train = enc.transform(y_train.reshape(-1, 1))
y_test = enc.transform(y_test.reshape(-1, 1))
print(y_train.shape, y_test.shape)
# (750, 6) (250, 6)

# scale the features
scaler = MinMaxScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)

# define the model
model = Sequential()
model.add(Dense(units=30, activation='relu'))
model.add(Dense(units=15, activation='relu'))
model.add(Dense(6, activation='softmax'))

# fit the model
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x=X_train, y=y_train, epochs=3, batch_size=10, validation_data=(X_test, y_test))

predictions = model.predict(X_test)

confusion_matrix(y_test,predictions)

print(classification_report(y_lab,predictions))

【参考方案1】：

错误的原因是您将单热标签y_test 与由模型predictions 估计的类概率标签进行比较。一个快速的解决方法是将两者都转换为简单的分类标签，如下所示：

confusion_matrix(np.argmax(y_test, axis=1), np.argmax(predictions, axis=1))
classification_report(np.argmax(y_test, axis=1), np.argmax(predictions, axis=1))

【参考方案2】：

这样就可以解决问题了：

y_test_arg=np.argmax(y_test,axis=1)
Y_pred = np.argmax(predictions,axis=1)
print(confusion_matrix(y_test_arg, Y_pred))

【讨论】：

您能否通过编辑您的答案来解释如何或为什么解决问题？