Python 使用PipeLine的SHAP KernelExplainer

我有一个问题，我想使用管道(OHE作为预处理和简单的线性回归作为模型)与SHAP工具.

至于数据，以下是我的数据(我使用的是我修改后的共享单车数据集):

bike_data=pd.read_csv("bike_outlier_clean.csv")

bike_data['season']=bike_data.season.astype('category')
bike_data['year']=bike_data.year.astype('category')
bike_data['holiday']=bike_data.holiday.astype('category')
bike_data['workingday']=bike_data.workingday.astype('category')
bike_data['weather_condition']=bike_data.weather_condition.astype('category')

bike_data['season'] = bike_data['season'].map({1:'Spring', 2:'Summer', 3:'Fall', 4: 'Winter'})
bike_data['year'] = bike_data['year'].map({0: 2011, 1: 2012})
bike_data['holiday'] = bike_data['holiday'].map({0: False, 1: True})
bike_data['workingday'] = bike_data['workingday'].map({0: False, 1: True})
bike_data['weather_condition'] = bike_data['weather_condition'].map({1:'Clear', 2:'Mist', 3:'Light Snow/Rain', 4: 'Heavy Snow/Rain'})

bike_data = bike_data[['total_count','season','month','year','weekday','holiday','workingday','weather_condition','humidity','temp','windspeed']]

x = bike_data.drop('total_count', axis=1)
y = bike_data['total_count']

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)

而对于我的管道

category_columns = list(set(bike_data.columns) - set(bike_data._get_numeric_data().columns))
preprocessor = ColumnTransformer(
    transformers=[
        ('cat', OneHotEncoder(), category_columns)
    ],
    remainder='passthrough'  
)
model = LinearRegression()

pipeline = Pipeline(steps=[('preprocessor', preprocessor), ('model', model)])

pipeline.fit(x_train,y_train)

最后，使用kernelSHAP解释器

explainer = shap.KernelExplainer(pipeline.predict, shap.sample(x, 5))

然而，这就是错误发生的地方.

    123             # Make a copy so that the feature names are not removed from the original model
    124             out = copy.deepcopy(out)
--> 125             out.f.__self__.feature_names_in_ = None
    126 
    127     return out

AttributeError: can't set attribute 'feature_names_in_'

我现在完全不知道该怎么做才能解决这个问题.