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 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_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_aggregating_regressor(self, verbose=0, seed=42):
Xs, y, _ = make_multi_input_regression(n_informative_Xs=3,
random_state=seed)
clf = MultichannelPipeline(n_channels=3)
base_clf = GradientBoostingRegressor(n_estimators=50)
clf.add_layer(make_transformer(base_clf))
clf.add_layer(AggregatingRegressor(np.mean))
cross_val_score(clf, Xs, y, cv=3)
scores = cross_val_score(clf, Xs, y, score_method='predict',
scorer=explained_variance_score)
score = np.mean(scores)
if verbose > 0:
print('accuracy = {}'.format(score))
self.assertTrue(score > 0.3)
def test_multi_input_classification(self):
mclf = MultichannelPipeline(n_channels=1)
mclf.add_layer(self.clf)
pc_scores = pc_cross_validation.cross_val_score(
mclf, [self.X_cls], self.y_cls, score_method='predict_proba',
scorer=roc_auc_score, cv=self.cv, n_processes=1)
self.assertTrue(np.array_equal(self.cls_scores, pc_scores), 'classifier scores from pipecaster.cross_validation.cross_val_score did not match sklearn control (multi input predictor)')
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_single_input_classification(self):
pc_scores = pc_cross_validation.cross_val_score(
self.clf, self.X_cls, self.y_cls, score_method='predict_proba',
scorer=roc_auc_score,
cv=self.cv, n_processes=1)
self.assertTrue(np.array_equal(self.cls_scores, pc_scores),
'classifier scores from cross_val_score did not match sklearn '
'control')
def test_multi_input_regression_parallel(self):
if n_cpus > 1:
warnings.filterwarnings("ignore")
parallel.start_if_needed(n_cpus=n_cpus)
mrgr = MultichannelPipeline(n_channels=1)
mrgr.add_layer(self.rgr)
pc_scores = pc_cross_validation.cross_val_score(mrgr, [self.X_rgr], self.y_rgr, scorer=explained_variance_score,
cv=self.cv, n_processes=n_cpus)
self.assertTrue(np.array_equal(self.rgr_scores, pc_scores), 'regressor scores from pipecaster.cross_validation.cross_val_score did not match sklearn control (multi input predictor)')
warnings.resetwarnings()
def test_multi_input_classification_parallel(self):
if n_cpus > 1:
warnings.filterwarnings("ignore")
parallel.start_if_needed()
mclf = MultichannelPipeline(n_channels=1)
mclf.add_layer(self.clf)
pc_scores = pc_cross_validation.cross_val_score(
mclf, [self.X_cls], self.y_cls, score_method='predict_proba',
scorer=roc_auc_score, cv=self.cv, n_processes=n_cpus)
self.assertTrue(np.array_equal(self.cls_scores, pc_scores), 'classifier scores from pipecaster.cross_validation.cross_val_score did not match sklearn control (multi input predictor)')
warnings.resetwarnings()
def test_soft_voting(self, verbose=0, seed=42):
Xs, y, _ = make_multi_input_classification(n_informative_Xs=5,
n_random_Xs=2, random_state=seed)
clf = MultichannelPipeline(n_channels=7)
clf.add_layer(StandardScaler())
base_clf = KNeighborsClassifier()
base_clf = transform_wrappers.SingleChannel(base_clf)
clf.add_layer(base_clf)
clf.add_layer(SoftVotingClassifier())
scores = cross_val_score(clf, Xs, y, score_method='predict',
scorer=balanced_accuracy_score)
score = np.mean(scores)
if verbose > 0:
print('accuracy = {}'.format(score))
self.assertTrue(score > 0.80)
def test_soft_voting_decision(self, verbose=0, seed=42):
Xs, y, _ = make_multi_input_classification(n_informative_Xs=6,
n_random_Xs=3,
random_state=seed)
clf = MultichannelPipeline(n_channels=9)
clf.add_layer(StandardScaler())
base_clf = make_transformer(SVC(),
transform_method='decision_function')
clf.add_layer(base_clf)
meta_clf1 = SoftVotingDecision()
clf.add_layer(3, meta_clf1, 3, meta_clf1, 3, meta_clf1)
meta_clf2 = MultichannelPredictor(GradientBoostingClassifier())
clf.add_layer(meta_clf2)
scores = cross_val_score(clf, Xs, y, score_method='predict',
scorer=balanced_accuracy_score)
score = np.mean(scores)
if verbose > 0:
print('accuracy = {}'.format(score))
self.assertTrue(score > 0.85)
def __call__(self, X, y, **fit_params):
"""
Get figure of merit score.
Parameters
----------
X: ndarray.shape(n_samples, n_features)
Feature matrix.
y: list/array of length n_samples, default=None
Targets for supervised ML.
fit_params: dict, defualt=None
Auxiliary parameters to pass to the fit method of the probe.
"""
if X is None:
return None
else:
scores = cross_val_score(self.predictor_probe, X, y,
score_method=self.score_method,
scorer=self.scorer,
cv=self.cv, n_processes=self.cv_processes,
**fit_params)
return np.mean(scores)
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))
def test_multi_input_regression(self):
mrgr = MultichannelPipeline(n_channels=1)
mrgr.add_layer(self.rgr)
pc_scores = pc_cross_validation.cross_val_score(mrgr, [self.X_rgr], self.y_rgr, scorer=explained_variance_score,
cv=self.cv, n_processes=1)
self.assertTrue(np.array_equal(self.rgr_scores, pc_scores), 'regressor scores from pipecaster.cross_validation.cross_val_score did not match sklearn control (multi input predictor)')
def test_single_input_regression(self):
pc_scores = pc_cross_validation.cross_val_score(self.rgr, self.X_rgr, self.y_rgr, scorer=explained_variance_score,
cv=self.cv, n_processes=1)
self.assertTrue(np.array_equal(self.rgr_scores, pc_scores), 'regressor scores from pipecaster.cross_validation.cross_val_score did not match sklearn control (single input predictor)')
def test_multi_matrix_voting(self, verbose=0):
"""
Test if KNN->ChannelClassifier(soft voting) in a pipecaster pipeline
gives monotonically increasing accuracy with increasing number of
inputs in concordance with Condorcet's jury theorem, and also test hard
voting with same pass criterion. Test if accuracy is > 80%.
"""
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.filterwarnings("ignore")
n_channels = 5
sklearn_params = {
'n_classes': 2,
'n_samples': 500,
'n_features': 100,
'n_informative': 30,
'n_redundant': 0,
'n_repeated': 0,
'class_sep': 3.0
}
# implementation 1
soft_accuracies, hard_accuracies = [], []
for i in range(0, n_channels + 1):
Xs, y, _ = make_multi_input_classification(n_informative_Xs=i,
n_weak_Xs=0,
n_random_Xs=n_channels -
i,
weak_noise_sd=None,
seed=42,
**sklearn_params)
mclf = MultichannelPipeline(n_channels)
mclf.add_layer(StandardScaler(), pipe_processes=n_cpus)
clf = transform_wrappers.SingleChannel(
KNeighborsClassifier(n_neighbors=5, weights='uniform'))
mclf.add_layer(clf, pipe_processes=n_cpus)
mclf.add_layer(MultichannelPredictor(SoftVotingMetaClassifier()))
split_accuracies = cross_val_score(mclf,
Xs,
y,
scorer=roc_auc_score,
cv=3,
n_processes=1)
soft_accuracies.append(np.mean(split_accuracies))
mclf = MultichannelPipeline(n_channels)
mclf.add_layer(StandardScaler(), pipe_processes=n_cpus)
clf = KNeighborsClassifier(n_neighbors=5, weights='uniform')
clf = transform_wrappers.SingleChannel(clf,
transform_method='predict')
mclf.add_layer(clf, pipe_processes=n_cpus)
mclf.add_layer(MultichannelPredictor(HardVotingMetaClassifier()))
split_accuracies = cross_val_score(mclf,
Xs,
y,
scorer=roc_auc_score,
cv=3,
n_processes=1)
hard_accuracies.append(np.mean(split_accuracies))
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.resetwarnings()
if verbose > 0:
print('soft voting results:')
print('n_informative, accuray')
for i in range(0, n_channels + 1):
print(i, soft_accuracies[i])
print('hard voting results:')
print('n_informative, accuray')
for i in range(0, n_channels + 1):
print(i, hard_accuracies[i])
n_informative = range(0, n_channels + 1)
accuracy = soft_accuracies[-1]
self.assertTrue(
accuracy > 0.80, 'soft voting accuracy of {} below '
'acceptable threshold of 0.80'.format(accuracy))
linearity = pearsonr(soft_accuracies, n_informative)[0]
self.assertTrue(
linearity > 0.80, 'hard voting linearity of {} below acceptable '
'threshold of 0.80 pearsonr'.format(linearity))
accuracy = hard_accuracies[-1]
self.assertTrue(
accuracy > 0.80, 'hard voting accuracy of {} below '
'acceptable threshold of 0.80'.format(accuracy))
linearity = pearsonr(hard_accuracies, n_informative)[0]
self.assertTrue(
linearity > 0.80, 'hard voting linearity of {} below acceptable '
'threshold of 0.80 pearsonr'.format(linearity))
# implementation 2
soft_accuracies, hard_accuracies = [], []
for i in range(0, n_channels + 1):
Xs, y, _ = make_multi_input_classification(n_informative_Xs=i,
n_weak_Xs=0,
n_random_Xs=n_channels -
i,
weak_noise_sd=None,
seed=42,
**sklearn_params)
mclf = MultichannelPipeline(n_channels)
mclf.add_layer(StandardScaler(), pipe_processes=n_cpus)
base_clf = KNeighborsClassifier(n_neighbors=5, weights='uniform')
mclf.add_layer(
ChannelEnsemble(base_clf,
SoftVotingMetaClassifier(),
base_processes=n_cpus))
split_accuracies = cross_val_score(mclf,
Xs,
y,
scorer=roc_auc_score,
cv=3,
n_processes=1)
soft_accuracies.append(np.mean(split_accuracies))
mclf = MultichannelPipeline(n_channels)
mclf.add_layer(StandardScaler(), pipe_processes='max')
clf = KNeighborsClassifier(n_neighbors=5, weights='uniform')
clf = transform_wrappers.SingleChannel(clf,
transform_method='predict')
mclf.add_layer(clf, pipe_processes='max')
mclf.add_layer(MultichannelPredictor(HardVotingMetaClassifier()))
split_accuracies = cross_val_score(mclf,
Xs,
y,
scorer=roc_auc_score,
cv=3,
n_processes=1)
hard_accuracies.append(np.mean(split_accuracies))
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.resetwarnings()
if verbose > 0:
print('soft voting results:')
print('n_informative, accuray')
for i in range(0, n_channels + 1):
print(i, soft_accuracies[i])
print('hard voting results:')
print('n_informative, accuray')
for i in range(0, n_channels + 1):
print(i, hard_accuracies[i])
n_informative = range(0, n_channels + 1)
accuracy = soft_accuracies[-1]
self.assertTrue(
accuracy > 0.80, 'soft voting accuracy of {} below '
'acceptable threshold of 0.80'.format(accuracy))
linearity = pearsonr(soft_accuracies, n_informative)[0]
self.assertTrue(
linearity > 0.80, 'hard voting linearity of {} below acceptable '
'threshold of 0.80 pearsonr'.format(linearity))
accuracy = hard_accuracies[-1]
self.assertTrue(
accuracy > 0.80, 'hard voting accuracy of {} below '
'acceptable threshold of 0.80'.format(accuracy))
linearity = pearsonr(hard_accuracies, n_informative)[0]
self.assertTrue(
linearity > 0.80, 'hard voting linearity of {} below acceptable '
'threshold of 0.80 pearsonr'.format(linearity))
def test_multi_matrices_svm_metaclassifier(self, seed=42, verbose=0):
"""
Test if KNN classifier->ChannelClassifier(SVC) in a pipecaster
pipeline gives monotonically increasing accuracy with increasing number
of inputs, and test if accuracy is > 75%.
"""
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.filterwarnings("ignore")
n_channels = 5
accuracies = []
sklearn_params = {
'n_classes': 2,
'n_samples': 500,
'n_features': 100,
'n_informative': 5,
'n_redundant': 10,
'n_repeated': 5,
'class_sep': 1.0
}
# implementation 1
for i in range(0, n_channels + 1):
Xs, y, _ = make_multi_input_classification(n_informative_Xs=i,
n_weak_Xs=0,
n_random_Xs=n_channels -
i,
weak_noise_sd=None,
seed=seed,
**sklearn_params)
mclf = MultichannelPipeline(n_channels)
mclf.add_layer(StandardScaler(), pipe_processes='max')
clf = transform_wrappers.SingleChannel(
KNeighborsClassifier(n_neighbors=5, weights='uniform'))
mclf.add_layer(clf, pipe_processes='max')
mclf.add_layer(MultichannelPredictor(SVC()))
split_accuracies = cross_val_score(mclf,
Xs,
y,
scorer=roc_auc_score,
cv=3,
n_processes=1)
accuracies.append(np.mean(split_accuracies))
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.resetwarnings()
if verbose > 0:
print('SVC meta-classification results:')
print('n_informative, accuray')
for i in range(0, n_channels + 1):
print(i, accuracies[i])
n_informative = range(0, n_channels + 1)
self.assertTrue(
accuracies[-1] > 0.75,
'SVC metaclassification accuracy of {} below \
acceptable threshold of 0.75'.format(accuracies[-1]))
linearity = pearsonr(accuracies, n_informative)[0]
self.assertTrue(
linearity > 0.75, 'SVC metaclassification linearity of {} below \
acceptable threshold of 0.75 pearsonr'.format(
linearity))
# implementation 2
accuracies = []
for i in range(0, n_channels + 1):
Xs, y, _ = make_multi_input_classification(n_informative_Xs=i,
n_weak_Xs=0,
n_random_Xs=n_channels -
i,
weak_noise_sd=None,
seed=seed,
**sklearn_params)
mclf = MultichannelPipeline(n_channels)
mclf.add_layer(StandardScaler(), pipe_processes='max')
base_clf = KNeighborsClassifier(n_neighbors=5, weights='uniform')
mclf.add_layer(
ChannelEnsemble(base_clf,
SVC(),
internal_cv=5,
base_processes='max'))
split_accuracies = cross_val_score(mclf,
Xs,
y,
scorer=roc_auc_score,
cv=3,
n_processes=1)
accuracies.append(np.mean(split_accuracies))
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.resetwarnings()
if verbose > 0:
print('SVC meta-classification results:')
print('n_informative, accuray')
for i in range(0, n_channels + 1):
print(i, accuracies[i])
n_informative = range(0, n_channels + 1)
self.assertTrue(
accuracies[-1] > 0.75,
'SVC metaclassification accuracy of {} below \
acceptable threshold of 0.75'.format(accuracies[-1]))
linearity = pearsonr(accuracies, n_informative)[0]
self.assertTrue(
linearity > 0.75, 'SVC metaclassification linearity of {} below \
acceptable threshold of 0.75 pearsonr'.format(
linearity))
def test_multi_matrix_voting(self, verbose=0, seed=42):
"""
Determine if KNN->ChannelRegressor(voting) in a MultichannelPipeline
gives monotonically increasing accuracy with increasing number of
inputs and exceeds an accuracy cutoff.
"""
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.filterwarnings("ignore")
n_channels = 5
rgr_params = {'n_samples': 500, 'n_features': 10, 'n_informative': 5}
# implementation 1
mean_accuracies, median_accuracies = [], []
for i in range(0, n_channels + 1):
Xs, y, _ = make_multi_input_regression(n_informative_Xs=i,
n_weak_Xs=0,
n_random_Xs=n_channels - i,
weak_noise_sd=None,
seed=seed,
**rgr_params)
# mean aggregation
mrgr = MultichannelPipeline(n_channels)
mrgr.add_layer(StandardScaler(), pipe_processes=n_cpus)
rgr = transform_wrappers.SingleChannel(
KNeighborsRegressor(n_neighbors=20, weights='distance'))
mrgr.add_layer(rgr, pipe_processes=n_cpus)
mrgr.add_layer(
MultichannelPredictor(AggregatingMetaRegressor(np.mean)))
split_accuracies = cross_val_score(mrgr,
Xs,
y,
scorer=explained_variance_score,
cv=3,
n_processes=1)
mean_accuracies.append(np.mean(split_accuracies))
# median aggregation
mrgr = MultichannelPipeline(n_channels)
mrgr.add_layer(StandardScaler(), pipe_processes=n_cpus)
rgr = transform_wrappers.SingleChannel(
KNeighborsRegressor(n_neighbors=20, weights='distance'))
mrgr.add_layer(rgr, pipe_processes=n_cpus)
mrgr.add_layer(
MultichannelPredictor(AggregatingMetaRegressor(np.median)))
split_accuracies = cross_val_score(mrgr,
Xs,
y,
scorer=explained_variance_score,
cv=3,
n_processes=1)
median_accuracies.append(np.mean(split_accuracies))
n_informatives = range(0, n_channels + 1)
if verbose > 0:
print('explained variance scores')
print('informative Xs\t\t mean voting\t\t median voting')
for n_informative, mean_ev, median_ev in zip(
n_informatives, mean_accuracies, median_accuracies):
print('{}\t\t {}\t\t {}'.format(n_informative, mean_ev,
median_ev))
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.resetwarnings()
mean_ev = mean_accuracies[-1]
mean_linearity = pearsonr(mean_accuracies, n_informatives)[0]
median_ev = median_accuracies[-1]
median_linearity = pearsonr(median_accuracies, n_informatives)[0]
if verbose > 0:
print('mean voting pearsonr = {}'.format(mean_linearity))
print('median voting pearsonr = {}'.format(median_linearity))
self.assertTrue(
mean_ev > 0.1, 'mean voting explained variance of {} is below '
'acceptable threshold of 0.80'.format(mean_ev))
linearity = pearsonr(mean_accuracies, n_informatives)[0]
self.assertTrue(
mean_linearity > 0.9,
'mean voting linearity of {} below acceptable '
'threshold of 0.80 pearsonr'.format(mean_linearity))
accuracy = median_accuracies[-1]
self.assertTrue(
median_ev > 0.1, 'median voting explained variance of {} is below '
'acceptable threshold of 0.80'.format(median_ev))
linearity = pearsonr(median_accuracies, n_informatives)[0]
self.assertTrue(
median_linearity > 0.9,
'median voting linearity of {} below acceptable '
'threshold of 0.80 pearsonr'.format(median_linearity))
# implementation 2
mean_accuracies, median_accuracies = [], []
for i in range(0, n_channels + 1):
Xs, y, _ = make_multi_input_regression(n_informative_Xs=i,
n_weak_Xs=0,
n_random_Xs=n_channels - i,
weak_noise_sd=None,
seed=seed,
**rgr_params)
# mean aggregation
mrgr = MultichannelPipeline(n_channels)
mrgr.add_layer(StandardScaler(), pipe_processes=n_cpus)
base_rgr = KNeighborsRegressor(n_neighbors=20, weights='distance')
mrgr.add_layer(
ChannelEnsemble(base_rgr,
AggregatingMetaRegressor(np.mean),
base_processes='max'))
split_accuracies = cross_val_score(mrgr,
Xs,
y,
scorer=explained_variance_score,
cv=3,
n_processes=1)
mean_accuracies.append(np.mean(split_accuracies))
# median aggregation
mrgr = MultichannelPipeline(n_channels)
mrgr.add_layer(StandardScaler(), pipe_processes=n_cpus)
rgr = KNeighborsRegressor(n_neighbors=20, weights='distance')
mrgr.add_layer(
ChannelEnsemble(base_rgr,
AggregatingMetaRegressor(np.median),
base_processes='max'))
split_accuracies = cross_val_score(mrgr,
Xs,
y,
scorer=explained_variance_score,
cv=3,
n_processes=1)
median_accuracies.append(np.mean(split_accuracies))
n_informatives = range(0, n_channels + 1)
if verbose > 0:
print('explained variance scores')
print('informative Xs\t\t mean voting\t\t median voting')
for n_informative, mean_ev, median_ev in zip(
n_informatives, mean_accuracies, median_accuracies):
print('{}\t\t {}\t\t {}'.format(n_informative, mean_ev,
median_ev))
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.resetwarnings()
mean_ev = mean_accuracies[-1]
mean_linearity = pearsonr(mean_accuracies, n_informatives)[0]
median_ev = median_accuracies[-1]
median_linearity = pearsonr(median_accuracies, n_informatives)[0]
if verbose > 0:
print('mean voting pearsonr = {}'.format(mean_linearity))
print('median voting pearsonr = {}'.format(median_linearity))
self.assertTrue(
mean_ev > 0.1, 'mean voting explained variance of {} is below '
'acceptable threshold of 0.80'.format(mean_ev))
linearity = pearsonr(mean_accuracies, n_informatives)[0]
self.assertTrue(
mean_linearity > 0.9,
'mean voting linearity of {} below acceptable '
'threshold of 0.80 pearsonr'.format(mean_linearity))
accuracy = median_accuracies[-1]
self.assertTrue(
median_ev > 0.1, 'median voting explained variance of {} is below '
'acceptable threshold of 0.80'.format(median_ev))
linearity = pearsonr(median_accuracies, n_informatives)[0]
self.assertTrue(
median_linearity > 0.9,
'median voting linearity of {} below acceptable '
'threshold of 0.80 pearsonr'.format(median_linearity))
def test_add_layer_interface_mapping(self, verbose=0, seed=42):
"""
Functional test of the MultichannelPipeline channel mapping interface.
"""
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.filterwarnings("ignore")
n_channels = 5
accuracies = []
rgr_params = {'n_samples': 1000, 'n_features': 10, 'n_informative': 10}
# implementation 1
for i in range(0, n_channels + 1):
Xs, y, _ = make_multi_input_regression(n_informative_Xs=i,
n_weak_Xs=0,
n_random_Xs=n_channels - i,
weak_noise_sd=None,
seed=seed,
**rgr_params)
mrgr = MultichannelPipeline(n_channels)
mrgr.add_layer(2,
StandardScaler(),
3,
StandardScaler(),
pipe_processes=n_cpus)
base_rgr = transform_wrappers.SingleChannelCV(LinearRegression())
mrgr.add_layer(2, base_rgr, 3, base_rgr, pipe_processes=n_cpus)
mrgr.add_layer(5, MultichannelPredictor(SVR()))
split_accuracies = cross_val_score(mrgr,
Xs,
y,
scorer=explained_variance_score,
cv=3,
n_processes=1)
accuracies.append(np.mean(split_accuracies))
n_informatives = range(0, n_channels + 1)
if verbose > 0:
print('explained variance scores')
print('informative Xs\t\t svr stacking')
for n_informative, ev in zip(n_informatives, accuracies):
print('{}\t\t {}'.format(n_informative, ev))
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.resetwarnings()
final_ev = accuracies[-1]
linearity = pearsonr(accuracies, n_informatives)[0]
if verbose > 0:
print('SVR stacking pearsonr = {}'.format(linearity))
self.assertTrue(
final_ev > 0.1, 'SVR stacking explained variance of {} is below '
'acceptable threshold of 0.80'.format(final_ev))
linearity = pearsonr(accuracies, n_informatives)[0]
self.assertTrue(
linearity > 0.0, 'SVR stacking linearity of {} below acceptable '
'threshold of 0.80 pearsonr'.format(linearity))
# implementation 2
accuracies = []
for i in range(0, n_channels + 1):
Xs, y, _ = make_multi_input_regression(n_informative_Xs=i,
n_weak_Xs=0,
n_random_Xs=n_channels - i,
weak_noise_sd=None,
seed=seed,
**rgr_params)
mrgr = MultichannelPipeline(n_channels)
mrgr.add_layer(2,
StandardScaler(),
3,
StandardScaler(),
pipe_processes='max')
base_rfrs = [LinearRegression() for i in range(2)]
base_rfrs += [LinearRegression() for i in range(3)]
mrgr.add_layer(
ChannelEnsemble(base_rfrs,
SVR(),
base_processes='max',
internal_cv=5))
split_accuracies = cross_val_score(mrgr,
Xs,
y,
scorer=explained_variance_score,
cv=3,
n_processes=1)
accuracies.append(np.mean(split_accuracies))
n_informatives = range(0, n_channels + 1)
if verbose > 0:
print('explained variance scores')
print('informative Xs\t\t svr stacking')
for n_informative, ev in zip(n_informatives, accuracies):
print('{}\t\t {}'.format(n_informative, ev))
if n_cpus > 1:
# shut off warnings because ray and redis generate massive numbers
warnings.resetwarnings()
final_ev = accuracies[-1]
linearity = pearsonr(accuracies, n_informatives)[0]
if verbose > 0:
print('SVR stacking pearsonr = {}'.format(linearity))
self.assertTrue(
final_ev > 0.1, 'SVR stacking explained variance of {} is below '
'acceptable threshold of 0.80'.format(final_ev))
linearity = pearsonr(accuracies, n_informatives)[0]
self.assertTrue(
linearity > 0.0, 'SVR stacking linearity of {} below acceptable '
'threshold of 0.80 pearsonr'.format(linearity))
