def test_discrimination_cls(self, verbose=0, seed=42):
"""
Determine if Ensemble can pick real classifier over dummy and
test performance.
"""
X, y = make_classification(n_samples=500, n_features=20,
n_informative=15, class_sep=1,
random_state=seed)
base_classifiers = [DummyClassifier(strategy='stratified')
for i in range(5)]
base_classifiers.append(LogisticRegression())
random.shuffle(base_classifiers)
mclf = MultichannelPipeline(n_channels=1)
mclf.add_layer(StandardScaler())
mclf.add_layer(Ensemble(base_classifiers, internal_cv=5,
score_selector=RankScoreSelector(k=1)))
mclf.fit([X], y)
c = mclf.get_model(1, 0).get_base_models()[0]
c = transform_wrappers.unwrap_model(c)
self.assertTrue(type(c) == LogisticRegression,
'Ensemble failed to pick LogisticRegression '
'over dummies')
acc = np.mean(cross_val_score(mclf, [X], y))
if verbose > 0:
print('cross val accuracy: {}'.format(acc))
self.assertTrue(acc > 0.70, 'Accuracy tolerance failure.')
def test_discrimination_rgr(self, verbose=0, seed=42):
"""
Determine if Ensemble can pick real regressor over dummy and
test performance.
"""
X, y = make_regression(n_samples=500, n_features=20, n_informative=10,
random_state=seed)
base_regressors = [DummyRegressor(strategy='mean') for i in range(5)]
base_regressors.append(LinearRegression())
random.shuffle(base_regressors)
mclf = MultichannelPipeline(n_channels=1)
mclf.add_layer(StandardScaler())
mclf.add_layer(Ensemble(base_regressors, internal_cv=5,
score_selector=RankScoreSelector(k=1)))
mclf.fit([X], y)
ensemble = mclf.get_model(1, 0)
selected_model = ensemble.get_base_models()[0]
selected_model = transform_wrappers.unwrap_model(selected_model)
if verbose > 0:
print(ensemble.get_screen_results())
self.assertTrue(type(selected_model) == LinearRegression,
'Ensemble failed to pick LinearRegression '
'over dummies')
acc = np.mean(cross_val_score(mclf, [X], y))
if verbose > 0:
print('cross val accuracy: {}'.format(acc))
self.assertTrue(acc > 0.9, 'Accuracy tolerance failure.')
def test_multi_matrices_no_metapredictor(self, verbose=0, seed=42):
"""
Determine if ChannelEnsemble works without a meta-predictor.
Determine if it can pick informative input over random and
test its performance.
"""
Xs, y, types = make_multi_input_regression(n_informative_Xs=1,
n_weak_Xs=0,
n_random_Xs=4,
weak_noise_sd=None,
seed=seed,
n_samples=500,
n_features=20,
n_informative=20)
mclf = MultichannelPipeline(n_channels=5)
mclf.add_layer(StandardScaler())
mclf.add_layer(
ChannelEnsemble(LinearRegression(),
internal_cv=5,
score_selector=RankScoreSelector(k=1)))
mclf.fit(Xs, y)
selected_type = types[mclf.get_model(1, 0).get_support()[0]]
self.assertTrue(selected_type == 'informative',
'Ensemble failed to pick informative channel')
acc = np.mean(cross_val_score(mclf, Xs, y))
if verbose > 0:
print('cross val accuracy: {}'.format(acc))
self.assertTrue(acc > 0.10, 'Accuracy tolerance failure.')
def test_compare_to_StackingClassifier(self, verbose=0, seed=42):
"""
Determine if Ensemble with dummies correctly selects the real predictors and gives similar
performance to scikit-learn StackingClassifier trained without dummies.
"""
X, y = make_classification(n_samples=1000, n_features=20, n_informative=5, class_sep=0.5, random_state=seed)
classifiers = [LogisticRegression(random_state=seed),
KNeighborsClassifier(),
RandomForestClassifier(random_state=seed)]
dummy_classifiers = [DummyClassifier(strategy='stratified', random_state=seed) for repeat in range(100)]
all_classifiers = classifiers + dummy_classifiers
random.shuffle(all_classifiers)
mclf = MultichannelPipeline(n_channels=1)
mclf.add_layer(StandardScaler())
mclf.add_layer(Ensemble(all_classifiers, SVC(random_state=seed), internal_cv=5, score_selector=RankScoreSelector(k=3)))
pc_score_all = np.mean(cross_val_score(mclf, [X], y, cv=5, n_processes=5))
mclf.fit([X], y)
selected_classifiers = mclf.get_model(1,0).get_base_models()
self.assertTrue(len(selected_classifiers) == 3,
'Ensemble picked the {} classifiers instead of 3.'.format(len(selected_classifiers)))
self.assertFalse(DummyClassifier in [c.__class__ for c in selected_classifiers],
'Ensemble chose a dummy classifier over a real one')
mclf = MultichannelPipeline(n_channels=1)
mclf.add_layer(StandardScaler())
mclf.add_layer(Ensemble(classifiers, SVC(random_state=seed), internal_cv=5, score_selector=RankScoreSelector(k=3)))
pc_score_informative = np.mean(cross_val_score(mclf, [X], y, cv=5, n_processes=5))
base_classifier_arg = [(str(i), c) for i, c in enumerate(classifiers)]
clf = StackingClassifier(base_classifier_arg, SVC(random_state=seed), cv=StratifiedKFold(n_splits=3))
sk_score_informative = np.mean(cross_val_score(clf, X, y, cv=5, n_processes=5))
if verbose > 0:
base_classifier_arg = [(str(i), c) for i, c in enumerate(all_classifiers)]
clf = StackingClassifier(base_classifier_arg, SVC(random_state=seed), cv=StratifiedKFold(n_splits=3))
sk_score_all = np.mean(cross_val_score(clf, X, y, cv=5, n_processes=5))
print('\nBalanced accuracy scores')
print('Ensemble informative predictors: {}'.format(pc_score_informative))
print('Ensemble all predictors: {}'.format(pc_score_all))
print('StackingClassifier informative predictors: {}'.format(sk_score_informative))
print('StackingClassifier all predictors: {}'.format(sk_score_all))
self.assertTrue(np.round(pc_score_all, 2) == np.round(pc_score_informative, 2),
'Ensemble accuracy is not same for all classifiers and informative classifiers.')
tolerance_pct = 5
self.assertTrue(pc_score_all >= sk_score_informative * (1 - tolerance_pct / 100.0),
'''Ensemble with random inputs did not perform within accepted tolerance of StackingClassifier with no dummy classifiers.''')
def _select_synthetic_classification(channel_selector,
n_informative_Xs=3,
n_weak_Xs=0,
n_random_Xs=0,
weak_noise_sd=1.0,
verbose=0,
seed=None,
**sklearn_params):
n_Xs = n_informative_Xs + n_weak_Xs + n_random_Xs
Xs, y, X_types = make_multi_input_classification(
n_informative_Xs, n_weak_Xs, n_random_Xs, weak_noise_sd, seed,
**sklearn_params)
clf = MultichannelPipeline(n_channels=n_Xs)
clf.add_layer(StandardScaler())
clf.add_layer(channel_selector)
clf.fit(Xs, y)
Xs_t = clf.transform(Xs)
Xs_selected = [
'selected' if X is not None else 'not selected' for X in Xs_t
]
n_informative_hits, n_random_hits, n_weak_hits = 0, 0, 0
for X, t in zip(Xs_selected, X_types):
if X == 'selected' and t == 'informative':
n_informative_hits += 1
if X == 'not selected' and t == 'random':
n_random_hits += 1
if X == 'selected' and t == 'weak':
n_weak_hits += 1
if verbose > 0:
print('InputSelector selected {} out of {} informative inputs'.
format(n_informative_hits, n_informative_Xs))
print(
'InputSelector filtered out {} out of {} random inputs'.format(
n_random_hits, n_Xs - n_informative_Xs - n_weak_Xs))
print(
'InputSelector selected out {} out of {} weakly informative inputs'
.format(n_weak_hits, n_weak_Xs))
return n_informative_hits, n_random_hits, n_weak_hits
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')
def test_discrimination_rgr(self, verbose=0, seed=42):
"""
Determine if Ensemble can discriminate between dummy regressors and LinearRegression classifiers
"""
X, y = make_regression(n_samples=500, n_features=20, n_informative=10, random_state=seed)
base_regressors = [DummyRegressor(strategy='mean') for i in range(5)]
base_regressors.extend([LinearRegression() for i in range(5)])
random.shuffle(base_regressors)
informative_mask = [True if type(c) == LinearRegression else False for c in base_regressors]
mclf = MultichannelPipeline(n_channels=1)
mclf.add_layer(StandardScaler())
mclf.add_layer(Ensemble(base_regressors, SVR(), internal_cv=5, score_selector=RankScoreSelector(k=5)))
mclf.fit([X], y)
selected_indices = mclf.get_model(layer_index=1, model_index=0).get_support()
selection_mask = [True if i in selected_indices else False for i in range(len(base_regressors))]
if verbose > 0:
n_correct = sum([1 for i, s in zip(informative_mask, selection_mask) if i and s])
print('\n\ncorrectly selected {}/5 LinearRegression regressors'.format(n_correct))
print('incorrectly selected {}/5 DummyRegressors\n\n'.format(5- n_correct))
self.assertTrue(np.array_equal(selection_mask, informative_mask),
'Ensemble failed to discriminate between dummy regressors and LinearRegression')
def test_architecture_01(self, verbose=0, seed=42):
"""
Test the accuracy and hygiene (shuffle control) of a complex pipeline
with feature selection, matrix selection, model selection, and
model stacking.
"""
X_rand = np.random.rand(500, 30)
X_inf, y = make_classification(n_samples=500,
n_features=30,
n_informative=15,
class_sep=3,
random_state=seed)
Xs = [X_rand, X_rand, X_inf, X_rand, X_inf, X_inf]
clf = MultichannelPipeline(n_channels=6)
clf.add_layer(SimpleImputer())
clf.add_layer(StandardScaler())
clf.add_layer(SelectPercentile(percentile=25))
clf.add_layer(
5,
SelectKBestScores(feature_scorer=f_classif,
aggregator=np.mean,
k=2))
LR_cv = transform_wrappers.SingleChannelCV(LogisticRegression())
CE = ChannelEnsemble(base_predictors=KNeighborsClassifier(),
internal_cv=5,
score_selector=RankScoreSelector(1))
CE_cv = transform_wrappers.MultichannelCV(CE)
clf.add_layer(5, CE_cv, 1, LR_cv)
clf.add_layer(MultichannelPredictor(SVC()))
score = np.mean(
cross_val_score(clf, Xs, y, scorer=balanced_accuracy_score))
if verbose > 0:
print('accuracy score: {}'.format(score))
self.assertTrue(
score > 0.95, 'Accuracy score of {} did not exceed '
'tolerance value of 95%'.format(score))
clf.fit(Xs, y)
score_selector = transform_wrappers.unwrap_model(clf.get_model(3, 0))
if verbose > 0:
print('indices selected by SelectKBestScores: {}'.format(
score_selector.get_support()))
print('correct indices: [2, 4]')
self.assertTrue(np.array_equal(score_selector.get_support(), [2, 4]),
'SelectKBestScores selected the wrong channels.')
model_selector = transform_wrappers.unwrap_model(clf.get_model(4, 0))
if verbose > 0:
print('indices selected by SelectKBestModels: {}'.format(
model_selector.get_support()))
print('correct indices: [2, 4]')
self.assertTrue(model_selector.get_support()[0] in [2, 4],
'SelectKBestModels selected the wrong model')
score = np.mean(
cross_val_score(clf,
Xs,
y[np.random.permutation(len(y))],
scorer=balanced_accuracy_score))
if verbose > 0:
print('shuffle control accuracy score: {}'.format(score))
self.assertTrue(
score < 0.55, 'Accuracy score of shuffle control, {}, '
'exceeded tolerance value of 55%'.format(score))
