SHAP ValueError：两个形状中的维度 1 必须相等，但分别为 2 和 1。形状为 [?,2] 和 [?,1]

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如何解决SHAP ValueError：两个形状中的维度 1 必须相等，但分别为 2 和 1。形状为 [?,2] 和 [?,1]？

基于之前训练过的前馈网络，我尝试使用 SHAP 来获取特征重要性。我按照文档中描述的所有步骤操作，但仍然收到以下错误

ValueError: Dimension 1 in both shapes must be equal,but are 2 and 1. Shapes are [?,2] and [?,1]

以下代码生成了一个具有相同错误的重现示例。

import pandas as pd
from numpy.random import randint
from keraS.Utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense,Batchnormalization,Dropout,Activation
from keras.optimizers import Adam
import shap

# Train_x data creation
Train_x = pd.DataFrame({
    'v1': randint(2,20,1489),'v2': randint(50,200,'v3': randint(30,90,'v4': randint(100,150,1489)
})

# Train_y data creation
Train_y = randint(0,2,1489)

# One-hot enCoding as I use categorical cross-entropy
Train_y = to_categorical(Train_y,num_classes=2)

# Start construction of a DNN Sequential model.
model = Sequential()

# First input layer with a dropout and batch normalization layer following
model.add(Dense(256,input_dim=Train_x.shape[1]))
model.add(Batchnormalization())
model.add(Activation('relu'))
model.add(Dropout(rate=0.2))

# Output layer
model.add(Dense(2))
model.add(Activation('softmax'))

# Use the Adam optimizer
optimizer = Adam(lr=0.001)

# Compile the model
model.compile(loss='categorical_crossentropy',optimizer=optimizer,metrics=['accuracy'])

model.sumMary()

# Fit model
hist = model.fit(Train_x,Train_y,epochs=100,batch_size=128,shuffle=false,verbose=2)

# SHAP calculation
explainer = shap.DeepExplainer(model,Train_X)
shap_values = explainer.shap_values(Train_x[:500].values)

我的输入形状为 (None,4)，最后有一个 softmax 激活函数，有 2 个神经元，因为我将其用于二元分类。以下代码片段中的 Train_x 数据是形状为 (1489,4) 的 Pandas 数据框。

我尝试更改 Train_x 形状，但出现类似错误。任何帮助将不胜感激。

解决方法

from numpy.random import randint
from tensorflow.keraS.Utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,BatchNormalization,Dropout,Activation
from tensorflow.keras.optimizers import Adam
import shap
import tensorflow
print(shap.__version__,"\n",tensorflow.__version__)

# Train_x data creation
Train_x = pd.DataFrame({
    'v1': randint(2,20,1489),'v2': randint(50,200,'v3': randint(30,90,'v4': randint(100,150,1489)
})

# Train_y data creation
Train_y = randint(0,2,1489)

# One-hot encoding as I use categorical cross-entropy
Train_y = to_categorical(Train_y,num_classes=2)

# Start construction of a DNN Sequential model.
model = Sequential()

# First input layer with a dropout and batch normalization layer following
model.add(Dense(256,input_dim=Train_x.shape[1]))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(Dropout(rate=0.2))

# Output layer
model.add(Dense(2))
model.add(Activation('softmax'))

# Use the Adam optimizer
optimizer = Adam(lr=0.001)

# Compile the model
model.compile(loss='categorical_crossentropy',optimizer=optimizer,metrics=['accuracy'])

# model.sumMary()

# Fit model
hist = model.fit(Train_x,Train_y,epochs=100,batch_size=128,shuffle=false,verbose=0)

# SHAP calculation
shap.explainers._deep.deep_tf.op_handlers["AddV2"] = shap.explainers._deep.deep_tf.passthrough
explainer = shap.DeepExplainer(model,Train_X)

shap_values = explainer.shap_values(Train_x[:500].values)

shap.sumMary_plot(shap_values[1])

0.38.2 
 2.2.0

大佬总结

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