我得到错误AttributeError: 'LogisticRegression' object has no attribute 'feature_names_in_'，即使该属性写为in the docs。
我用的是scikit-learn版本。
我创建了一个对象LogisticRegression，并试图使用feature_names_in_的文档属性，但它返回了一个错误。

#imports
import numpy as np
import pandas as pd
import statistics
import scipy.sparse

from scipy.stats import chi2_contingency

from sklearn.preprocessing import FunctionTransformer, MinMaxScaler, OneHotEncoder
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.impute import SimpleImputer

# train_test_split()
X_train, X_test, y_train, y_test = train_test_split(features, labels, random_state = 42)

#create functions for preprocessing

# function to replace NaN's in the ordinal and interval data 
def replace_NAN_median(X_df):
    opinions = ['opinion_seas_vacc_effective', 'opinion_seas_risk', 'opinion_seas_sick_from_vacc', 'household_adults',
                'household_children']
    for column in opinions:
        X_df[column].replace(np.nan, X_df[column].median(), inplace = True)
    return X_df

# function to replace NaN's in the catagorical data     
def replace_NAN_mode(X_df):
    miss_cat_features = ['education', 'income_poverty', 'marital_status', 'rent_or_own', 'employment_status']
    for column in miss_cat_features:
        X_df[column].replace(np.nan, statistics.mode(X_df[column]), inplace = True)
    return X_df

# Instantiate transformers
NAN_median = FunctionTransformer(replace_NAN_median)
NAN_mode = FunctionTransformer(replace_NAN_mode)

col_transformer = ColumnTransformer(transformers=
    # replace NaN's in the binary data                                
    [("NAN_0", SimpleImputer(missing_values=np.nan, strategy='constant', fill_value = 0), 
    ['behavioral_antiviral_meds', 'behavioral_avoidance','behavioral_face_mask' ,
    'behavioral_wash_hands', 'behavioral_large_gatherings', 'behavioral_outside_home',
    'behavioral_touch_face', 'doctor_recc_seasonal', 'chronic_med_condition', 
    'child_under_6_months', 'health_worker', 'health_insurance']),
    
     # MinMaxScaler on our numeric ordinal and interval data
    ("scaler", MinMaxScaler(), ['opinion_seas_vacc_effective', 'opinion_seas_risk',
                                'opinion_seas_sick_from_vacc', 
                                'household_adults', 'household_children']),
     
     # OHE catagorical string data
    ("ohe", OneHotEncoder(sparse = False), ['age_group','education', 'race', 'sex', 
                                'income_poverty', 'marital_status', 'rent_or_own',
                                'employment_status', 'census_msa'])],
     
    remainder="passthrough")

# Preprocessing Pipeline 
preprocessing_pipe = Pipeline(steps=[
    ("NAN_median", NAN_median), 
    ("NAN_mode", NAN_mode), 
    ("col_transformer", col_transformer)
    ])

# model
logreg_optimized_pipe =  Pipeline(steps=[("preprocessing_pipe", preprocessing_pipe),
                                    ("log_reg", LogisticRegression(solver = 'liblinear', random_state = 42, C = 10, penalty= 'l1'))])

#fit model to training data
logreg_optimized_pipe.fit(X_train, y_train)

#trying to get feature names
logreg_optimized_pipe.named_steps["log_reg"].feature_names_in_

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-38-512bfaf5962d> in <module>
----> 1 logreg_optimized_pipe.named_steps["log_reg"].feature_names_in_
      

AttributeError: 'LogisticRegression' object has no attribute 'feature_names_in_'

我也愿意接受关于如何获得特性名称的其他建议。