SHAP 不适用于 LightGBM 分类特征

Question

我的模型使用LGBMClassifier. 我想用 Shap (Shapley) 来解释特征。然而，Shap 在分类特征上给了我错误。例如，我有一个功能“吸烟者”，其值包括“是”和“否”。我从 Shap 收到错误：

ValueError: could not convert string to float: 'Yes'.

我是否缺少任何设置？

顺便说一句，我知道我可以使用 one-hot 编码来转换分类特征，但我不想这样做，因为LGBMClassifier可以在没有 one-hot 编码的情况下处理分类特征。

示例代码如下：（shap版本为0.40.0，lightgbm版本为3.3.2）

import pandas as pd
from lightgbm import LGBMClassifier #My version is 3.3.2
import shap #My version is 0.40.0

#The training data
X_train = pd.DataFrame()
X_train["Age"] = [50, 20, 60, 30]
X_train["Smoker"] = ["Yes", "No", "No", "Yes"]

#Target: whether the person had a certain disease
y_train = [1, 0, 0, 0]
#I did convert categorical features to the Category data type.
X_train["Smoker"] = X_train["Smoker"].astype("category")

#The test data
X_test = pd.DataFrame()
X_test["Age"] = [50]
X_test["Smoker"] = ["Yes"]
X_test["Smoker"] = X_test["Smoker"].astype("category")

#the classifier    
clf = LGBMClassifier()
clf.fit(X_train, y_train)
predicted = clf.predict(X_test)

#shap
explainer = shap.TreeExplainer(clf)
#I see this setting from google search but it did not really help
explainer.model.original_model.params = {"categorical_feature":["Smoker"]}
shap_values = explainer(X_train) #the error came out here: ValueError: could not convert string to float: 'Yes'

Answer 1

让我们尝试稍微不同一下：

from lightgbm import LGBMClassifier
import shap

X_train = pd.DataFrame({
    "Age": [50, 20, 60, 30], 
    "Smoker": ["Yes", "No", "No", "Yes"]}
)
X_train["Smoker"] = X_train["Smoker"].astype("category")
y_train = [1, 0, 0, 0]

X_test = pd.DataFrame({"Age": [50], "Smoker": ["Yes"]})
X_test["Smoker"] = X_test["Smoker"].astype("category")


clf = LGBMClassifier(verbose=-1).fit(X_train, y_train)
predicted = clf.predict(X_test)
print("Predictions:", predicted)

exp = shap.TreeExplainer(clf)
sv = exp.shap_values(X_train) # <-- here

print(f"Expected values: {exp.expected_value}")
print(f"SHAP values for 0th data point: {sv[1][0]}")

Predictions: [0]
Expected values: [1.0986122886681098, -1.0986122886681098]
SHAP values for 0th data point: [0. 0.]

请注意，您不需要进行修改，explainer.model.original_model.params因为它可以让您非有意地公开访问模型的参数，这些参数已经通过训练模型为您设置了。