def test_single_matrix_hard_voting(self):
"""
Determine if KNN->ChannelClassifier(hard voting) in a pipecaster
pipeline gives identical predictions to sklearn KNN on training data.
"""
X, y = make_classification(n_samples=100,
n_features=20,
n_informative=10,
class_sep=5,
random_state=42)
# control
clf = KNeighborsClassifier(n_neighbors=5, weights='uniform')
clf.fit(X, y)
clf_predictions = clf.predict(X)
# implementation 1
mclf = MultichannelPipeline(n_channels=1)
base_clf = KNeighborsClassifier(n_neighbors=5, weights='uniform')
base_clf = transform_wrappers.SingleChannel(base_clf,
transform_method='predict')
mclf.add_layer(base_clf)
mclf.add_layer(MultichannelPredictor(HardVotingMetaClassifier()))
mclf.fit([X], y)
mclf_predictions = mclf.predict([X])
self.assertTrue(
np.array_equal(clf_predictions, mclf_predictions),
'hard voting metaclassifier did not reproduce sklearn '
'result on single matrix prediction task')
# implementation 2
mclf = MultichannelPipeline(n_channels=1)
base_clf = KNeighborsClassifier(n_neighbors=5, weights='uniform')
mclf.add_layer(
ChannelEnsemble(base_clf,
HardVotingMetaClassifier(),
base_transform_methods='predict'))
mclf.fit([X], y)
mclf_predictions = mclf.predict([X])
self.assertTrue(
np.array_equal(clf_predictions, mclf_predictions),
'hard voting metaclassifier did not reproduce sklearn '
'result on single matrix prediction task')
def test_single_matrix_mean_voting(self, seed=42):
"""
Determine if KNN->ChannelRegressor(mean voting) in a pipecaster
pipeline gives identical predictions to sklearn KNN on training data
"""
X, y = make_regression(n_samples=100,
n_features=20,
n_informative=10,
random_state=seed)
# control
rgr = KNeighborsRegressor(n_neighbors=5, weights='uniform')
rgr.fit(X, y)
rgr_predictions = rgr.predict(X)
# implementation 1
mrgr = MultichannelPipeline(n_channels=1)
rgr = transform_wrappers.SingleChannel(
KNeighborsRegressor(n_neighbors=5, weights='uniform'))
mrgr.add_layer(rgr, pipe_processes=n_cpus)
mrgr.add_layer(MultichannelPredictor(AggregatingMetaRegressor(
np.mean)))
mrgr.fit([X], y)
mrgr_predictions = mrgr.predict([X])
self.assertTrue(
np.array_equal(rgr_predictions, mrgr_predictions),
'mean voting ChannelRegressor failed to reproduce '
'sklearn result on single matrix prediction task')
# implementation 2
mrgr = MultichannelPipeline(n_channels=1)
base_rgr = KNeighborsRegressor(n_neighbors=5, weights='uniform')
mrgr.add_layer(
ChannelEnsemble(base_rgr,
AggregatingMetaRegressor(np.mean),
base_processes='max'))
mrgr.fit([X], y)
mrgr_predictions = mrgr.predict([X])
self.assertTrue(
np.array_equal(rgr_predictions, mrgr_predictions),
'mean voting ChannelRegressor failed to reproduce '
'sklearn result on single matrix prediction task')