AttributeError: 'Pipeline' object has no attribute 'get_feature_names

Question

我有一个管道构建如下：

Pipeline(steps=[('preprocessor',
                 ColumnTransformer(remainder='passthrough',
                                   transformers=[('text',
                                                  Pipeline(steps=[('CV',
                                                                   CountVectorizer())]),
                                                  'Tweet'),
                                                 ('category',
                                                  OneHotEncoder(handle_unknown='ignore'),
                                                  ['Tweet_ID']),
                                                 ('numeric',
                                                  Pipeline(steps=[('knnImputer',
                                                                   KNNImputer(n_neighbors=2)),
                                                                  ('scaler',
                                                                   MinMaxScale...
                                                   'CS',
                                                   'UC',
                                                   'CL',
                                                   'S',
                                                   'SS',
                                                   'UW',
                                                    ...])])),
                ('classifier', LogisticRegression())])

我正在尝试获取功能名称：

feature_names = lr['preprocessor'].transformers_[0][1].get_feature_names()
coefs = lr.named_steps["classifier"].coef_.flatten()

zipped = zip(feature_names, coefs)
features_df = pd.DataFrame(zipped, columns=["feature", "value"])
features_df["ABS"] = features_df["value"].apply(lambda x: abs(x))
features_df["colors"] = features_df["value"].apply(lambda x: "green" if x > 0 else "red")
features_df = features_df.sort_values("ABS", ascending=False)
features_df

但是我得到一个错误：

----> 6 feature_names = lr['preprocessor'].transformers_[0][1].get_feature_names()
      7 coefs = lr.named_steps["classifier"].coef_.flatten()
      8 

AttributeError: 'Pipeline' object has no attribute 'get_feature_names

我已经完成了以下回答：

'OneHotEncoder' object has no attribute 'get_feature_names'

但不幸的是，它们并没有我预期的那么有用。

有人知道怎么解决吗？如果需要，很乐意提供更多信息。

管道示例如下：

lr = Pipeline(steps=[('preprocessor', preprocessing),
                      ('classifier', LogisticRegression(C=5, tol=0.01, solver='lbfgs', max_iter=10000))])

预处理是

preprocessing = ColumnTransformer(
    transformers=[
        ('text',text_preprocessing, 'Tweet'),
        ('category', categorical_preprocessing, c_feat),
        ('numeric', numeric_preprocessing, n_feat)
], remainder='passthrough')

我在拆分火车和测试集之前将不同类型的特征分开：

text_columns=['Tweet']

target=['Label']

c_feat=['Tweet_ID']

num_features=['CS','UC','CL','S','SS','UW']

根据 David 的回答和 link，我尝试了如下操作：

对于数值：

class NumericalTransformer(BaseEstimator, TransformerMixin):
    def __init__(self):
        super().__init__()

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

    def transform(self, X, y=None):
        # Numerical features to pass down the numerical pipeline
        X = X[[num_features]]
        X = X.replace([np.inf, -np.inf], np.nan)
        return X.values
# Defining the steps in the numerical pipeline
numerical_pipeline = Pipeline(steps=[
    ('num_transformer', NumericalTransformer()),
    ('imputer', KNNImputer(n_neighbors=2)),
    ('minmax', MinMaxScaler())])

对于分类：

class CategoricalTransformer(BaseEstimator, TransformerMixin):
    def __init__(self):
        super().__init__()

    # Return self nothing else to do here
    def fit(self, X, y=None):
        return self

    # Helper function that converts values to Binary depending on input
    def create_binary(self, obj):
        if obj == 0:
            return 'No'
        else:
            return 'Yes'

    # Transformer method for this transformer
    def transform(self, X, y=None):
        # Categorical features to pass down the categorical pipeline
        return X[[c_feat]].values
# Defining the steps in the categorical pipeline
categorical_pipeline = Pipeline(steps=[
    ('cat_transformer', CategoricalTransformer()),
    ('one_hot_encoder', OneHotEncoder(handle_unknown='ignore'))])

对于文本功能：

class TextTransformer(BaseEstimator, TransformerMixin):
    def __init__(self):
        super().__init__()

    # Return self nothing else to do here
    def fit(self, X, y=None):
        return self

    # Helper function that converts values to Binary depending on input
    def create_binary(self, obj):
        if obj == 0:
            return 'No'
        else:
            return 'Yes'

    # Transformer method for this transformer
    def transform(self, X, y=None):
        # Text features to pass down the text pipeline
        return X[['Tweet']].values
# Defining the steps in the text pipeline
text_pipeline = Pipeline(steps=[
    ('text_transformer', TextTransformer()),
    ('cv', CountVectorizer())])

然后我将数值、文本和分类管道横向组合成一个完整的大管道：

# using FeatureUnion
union_pipeline = FeatureUnion(transformer_list=[
    ('categorical_pipeline', categorical_pipeline),
    ('numerical_pipeline', numerical_pipeline), 
    ('text_pipeline', text_pipeline)])

最后：

# Combining the custom imputer with the categorical, text and numerical pipeline
preprocess_pipeline = Pipeline(steps=[('custom_imputer', CustomImputer()),
                                      ('full_pipeline', union_pipeline)])

目前还不清楚的是如何获取特征名称。

Answer 1

您需要实现专用的 get_feature_names 函数，因为您正在使用自定义转换器。

详情请参考this question，您可以在其中找到代码示例。

AttributeError: 'Pipeline' object has no attribute 'get_feature_names

AttributeError: 'Pipeline' object has no attribute 'get_feature_names

python

pipeline

machine-learning

scikit-learn