8000 get_feature_names support for pipelines by kmike · Pull Request #2007 · scikit-learn/scikit-learn · GitHub
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get_feature_names support for pipelines #2007

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get_feature_names support for pipelines #2007

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15 changes: 15 additions & 0 deletions sklearn/pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -205,6 +205,21 @@ def _pairwise(self):
# check if first estimator expects pairwise input
return getattr(self.steps[0][1], '_pairwise', False)

def get_feature_names(self):
"""Get feature names from the last step.

Returns
-------
feature_names : list of strings
Names of the features produced by transform.
"""
name, trans = self.steps[-1]
if not hasattr(trans, 'get_feature_names'):
raise AttributeError("Transformer %s does not provide"
" get_feature_names." % str(name))
return trans.get_feature_names()



def _fit_one_transformer(transformer, X, y):
transformer.fit(X, y)
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38 changes: 38 additions & 0 deletions sklearn/tests/test_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,7 @@
from sklearn.datasets import load_iris
from sklearn.preprocessing import StandardScaler
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction import DictVectorizer


class IncorrectT(BaseEstimator):
Expand Down Expand Up @@ -301,3 +302,40 @@ def test_feature_union_feature_names():
for feat in feature_names:
assert_true("chars__" in feat or "words__" in feat)
assert_equal(len(feature_names), 35)


def test_feature_union_pipeline_feature_names():

JUNK_FOOD_DOCS = [
{'vendor': 'JunkyPizza', 'available': False, 'text': 'the pizza burger'},
{'vendor': 'FunkyPizza', 'available': True, 'text': 'the coke burger'}
]

class DocsPrepareTransformer(BaseEstimator):
KNOWN_VENDORS = set(['JunkyPizza'])

def fit(self, X, y=None):
return self
def transform(self, X, y=None):
return [{
'vendor': doc['vendor'],
'vendor_is_known': doc['vendor'] in self.KNOWN_VENDORS,
'available': doc['available']
} for doc in X]

ft = FeatureUnion([
('text', CountVectorizer(preprocessor=lambda doc: doc['text'])),
('attrs', Pipeline([
('prepare', DocsPrepareTransformer()),
('vectorize', DictVectorizer()),
]))
])

ft.fit(JUNK_FOOD_DOCS)
assert_equal(
sorted(ft.get_feature_names()),
['attrs__available',
'attrs__vendor=FunkyPizza', 'attrs__vendor=JunkyPizza',
'attrs__vendor_is_known',
'text__burger', 'text__coke', 'text__pizza', 'text__the']
)
0