def test_pipeline_step(self):
tr_data_X, tr_data_y = rand_df_classification(shape=(100, 20),
classes=3)
te_data = rand_df(shape=(100, 20), labeled=False)
scale_step = StandardScalerStep()
chi_step = ChiSqSelectionStep(select_kwargs={'k': 20})
corr_step = PearsonCorrStep(num_features=0.1)
pca_step = PCAStep(append_input=False, kwargs={'n_components': 5})
poly_step = PolyStep(kwargs={'degree': 3, 'include_bias': False})
pipeline = Pipeline([
scale_step,
Pipeline([pca_step, poly_step], append_input=True), chi_step
])
r, _ = pipeline.fit_transform(tr_data_X, y=tr_data_y)
self.assertEqual(type(r), pd.DataFrame)
r = pipeline.transform(te_data)
self.assertEqual(type(r), pd.DataFrame)
class SMOTETests1(unittest.TestCase, StepTest):
step = SMOTEStep()
X, y = rand_df_classification(val_range=(0, 100))
test_X = rand_df(val_range=(0, 100))
class ADASYNTests1(unittest.TestCase, StepTest):
step = ADASYNStep(kwargs={'ratio': {0.0: 100, 1.0: 100}})
X, y = rand_df_classification(val_range=(0, 100))
test_X = rand_df(val_range=(0, 100))
class LDATransform2(unittest.TestCase, StepTest):
step = LDATransformStep(append_input=True)
X, y = rand_df_classification()
test_X = rand_df(labeled=False)
class LDATransform1(unittest.TestCase, StepTest):
step = LDATransformStep()
X, y = rand_df_classification()
test_X = rand_df(labeled=False)
class LFODefault(unittest.TestCase, StepTest):
step = LOFStep()
X, y = rand_df_classification(outlier=True)
test_X = rand_df(labeled=False, outlier=True)
class ABODTests(unittest.TestCase, StepTest):
step = ABODStep(num_remove=1)
X, y = rand_df_classification(outlier=True)
test_X = rand_df(labeled=False, outlier=True)
class TreeTests2(unittest.TestCase, StepTest):
step = TreeSelectionStep(tree_model=ExtraTreesClassifier)
X, y = rand_df_classification()
test_X = rand_df(labeled=False)
class LassoTests(unittest.TestCase, StepTest):
step = LassoSelectionStep()
X, y = rand_df_classification()
test_X = rand_df(labeled=False)
