def test_pipeline_subclass_correctly_supports_decision_function(self):
X, y = generate_dataset_1()
pipeline = Pipeline([LogisticRegressionBinaryClassifier()])
pipeline.fit(X, y)
orig_result = pipeline.decision_function(X)
pipeline = CustomPipeline([LogisticRegressionBinaryClassifier()])
pipeline.fit(X, y)
new_result = pipeline.decision_function(X)
self.assertTrue(np.array_equal(orig_result, new_result))
def test_pass_decision_function_multiclass_with_pipeline_adds_classes(
self):
clf = FastLinearClassifier(number_of_threads=1)
pipeline = Pipeline([clf])
pipeline.fit(X_train_3class, y_train_3class)
expected_classes = {'Blue', 'Green', 'Red'}
assert_equal(set(clf.classes_), expected_classes)
assert_equal(set(pipeline.classes_), expected_classes)
s = pipeline.decision_function(X_test_3class).sum()
assert_almost_equal(s,
38.0,
decimal=4,
err_msg=invalid_decision_function_output)
assert_equal(set(clf.classes_), expected_classes)
assert_equal(set(pipeline.classes_), expected_classes)