I have a Pipeline built as follows:
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())])
I am trying to get feature names:
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
However I am getting an error:
----> 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
I already went through the following answers:
- 'OneHotEncoder' object has no attribute 'get_feature_names'
- 'Pipeline' object has no attribute 'get_feature_names' in scikit-learn
but unfortunately they were not so helpful as I would have expected.
Does anyone know how to fix it? Happy to provide more info, if needed.
An example of pipeline is the following:
lr = Pipeline(steps=[('preprocessor', preprocessing),
('classifier', LogisticRegression(C=5, tol=0.01, solver='lbfgs', max_iter=10000))])
where preprocessing is
preprocessing = ColumnTransformer(
transformers=[
('text',text_preprocessing, 'Tweet'),
('category', categorical_preprocessing, c_feat),
('numeric', numeric_preprocessing, n_feat)
], remainder='passthrough')
I am separating before splitting train and test sets the different types of features:
text_columns=['Tweet']
target=['Label']
c_feat=['Tweet_ID']
num_features=['CS','UC','CL','S','SS','UW']
Following David's answer and link, I have tried as follows:
For numerical:
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())])
For categorical:
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'))])
and for text feature:
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())])
Then I combine numerical, text and categorical pipeline into one full big pipeline horizontally:
# using FeatureUnion
union_pipeline = FeatureUnion(transformer_list=[
('categorical_pipeline', categorical_pipeline),
('numerical_pipeline', numerical_pipeline),
('text_pipeline', text_pipeline)])
and finally:
# Combining the custom imputer with the categorical, text and numerical pipeline
preprocess_pipeline = Pipeline(steps=[('custom_imputer', CustomImputer()),
('full_pipeline', union_pipeline)])
What it is still not clear is how to get features names.
