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[MRG + 1] FIX Calling fit_transform instead of transform in Pipeline's fit_predict #7585

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Merged
merged 3 commits into from
Oct 6, 2016
Merged

[MRG + 1] FIX Calling fit_transform instead of transform in Pipeline's fit_predict #7585

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a7c2870
BUGFIX Calling fit_transform instead of transform in Pipeline's fit_p…
gszpak Oct 4, 2016
f8433c3
PEP8 fixes in test_fit_predict_on_pipeline
gszpak Oct 5, 2016
e386c18
Added comment explaining separate estimators for pipeline in test_fit…
gszpak Oct 6, 2016
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2 changes: 1 addition & 1 deletion sklearn/pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -356,7 +356,7 @@ def fit_predict(self, X, y=None, **fit_params):
Xt = X
for name, transform in self.steps[:-1]:
if transform is not None:
Xt = transform.transform(Xt)
Xt = transform.fit_transform(Xt)
return self.steps[-1][-1].fit_predict(Xt, y, **fit_params)

@if_delegate_has_method(delegate='_final_estimator')
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9 changes: 8 additions & 1 deletion sklearn/tests/test_pipeline.py
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Expand Up @@ -278,13 +278,20 @@ def test_fit_predict_on_pipeline():
iris = load_iris()
scaler = StandardScaler()
km = KMeans(random_state=0)
# As pipeline doesn't clone estimators on construction,
# it must have its own estimators
scaler_for_pipeline = StandardScaler()
km_for_pipeline = KMeans(random_state=0)

# first compute the transform and clustering step separately
scaled = scaler.fit_transform(iris.data)
separate_pred = km.fit_predict(scaled)

# use a pipeline to do the transform and clustering in one step
pipe = Pipeline([('scaler', scaler), ('Kmeans', km)])
pipe = Pipeline([
('scaler', scaler_for_pipeline),
('Kmeans', km_for_pipeline)
])
pipeline_pred = pipe.fit_predict(iris.data)

assert_array_almost_equal(pipeline_pred, separate_pred)
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