使用tfidfvectorizer从管道中获取要素名称

Question

我已经尝试在我的模型上获得功能名称很长一段时间了，但是很难理解如何做到这一点。我在这里试过很多帖子，但都没能成功。这是我的密码：

加载将tfidfvectorizer与其他功能组合所需的类

from sklearn.base import TransformerMixin, BaseEstimator

class ItemSelector(BaseEstimator, TransformerMixin):

    def __init__(self, key):
        self.key = key

    def fit(self, x, y=None):
        return self

    def transform(self, data_dict):
        return data_dict[self.key]

class FeatureTypeSelector(TransformerMixin, BaseEstimator):

    FEATURE_TYPES = {
        'categorical': [
            'COLUMN_A','COLUMN_B'
        ],
        'continuous': [
             'COLULMN_C','COLUMN_D'
        ]
    }

    def __init__(self, feature_type):
        self.columns = self.FEATURE_TYPES[feature_type]

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return X[self.columns]


class RowToDictTransformer(TransformerMixin, BaseEstimator):

    def fit(self, X, y=None):
        return self

    def transform(self, X):
        return (row[1] for row in X.iterrows())

然后将代码放入管道中并运行回归器

from sklearn.pipeline import make_union, make_pipeline
from sklearn.feature_extraction import DictVectorizer
from sklearn.preprocessing import RobustScaler
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import MinMaxScaler

# Create the preprocessor
preprocessor = make_union(
    make_pipeline(      
        ItemSelector(key='TEXT_COLUMN'),
        TfidfVectorizer(lowercase=False, min_df=1),
    ),
    make_pipeline(
        FeatureTypeSelector('continuous'),
        MinMaxScaler(),
    ),
    make_pipeline(
        FeatureTypeSelector('categorical'),
        RowToDictTransformer(),
        DictVectorizer(sparse=False),  # set sparse=True if you get MemoryError
    ),
)

# fit and transform the data
preprocessor.fit_transform(x_train)

# choose some estimator
# estimator = MultinomialNB()
estimator = LinearRegression()

# Create the model
model = make_pipeline(preprocessor, estimator)

# Training the model
model.fit(x_train, y_train)

# Predicting the model
predicted = model.predict(x_test)

我可以运行model.coef来获得所有的系数，但我想看看文本列的每一项是如何受到哪个权重的影响的。我曾尝试调用get_feature_names（）或尝试在管道中传递它们，但没有成功（到目前为止，谷歌的大部分结果都是紫色的）

有谁能给我一些指导，告诉我如何将特性名称传递到管道的末尾？理想的结果是一个数据框，其中feature（TEXT_列中的行）和feature_权重作为值