def add_sequential(name, models, X_train, Y_train, X_test, Y_test):
# Establish and reset variables
acc_score_cv = None
acc_score = None
time_ = None
ensemble = SequentialEnsemble(scorer=accuracy_score, random_state=seed)
# Add a subsemble with 5 partitions as first layer
ensemble.add('subsemble', models, partitions=10, folds=10)
# Add a super learner as second layer
ensemble.add('stack', models, folds=20)
ensemble.add_meta(SVC())
start = time.time()
ensemble.fit(X_train, Y_train)
preds = ensemble.predict(X_test)
acc_score = accuracy_score(preds, Y_test)
end = time.time()
time_ = end - start
return {
"Ensemble": name,
"Meta_Classifier": "SVC",
"Accuracy_Score": acc_score,
"Runtime": time_
}
def test_predict():
"""[SequentialEnsemble] Test multilayer prediction."""
S = lc_s.predict(X)
B = lc_b.predict(S)
U = lc_u.predict(B)
ens = SequentialEnsemble()
ens.add('stack', ESTIMATORS, PREPROCESSING, dtype=np.float64)
ens.add('blend', ECM, dtype=np.float64)
ens.add('subsemble', ECM, dtype=np.float64)
out = ens.fit(X, y).predict(X)
np.testing.assert_array_equal(U, out)
def test_equivalence_blend():
"""[SequentialEnsemble] Test ensemble equivalence with BlendEnsemble."""
ens = BlendEnsemble()
seq = SequentialEnsemble()
ens.add(ECM, dtype=np.float64)
seq.add('blend', ECM, dtype=np.float64)
F = ens.fit(X, y).predict(X)
P = seq.fit(X, y).predict(X)
np.testing.assert_array_equal(P, F)
def test_equivalence_super_learner():
"""[SequentialEnsemble] Test ensemble equivalence with SuperLearner."""
ens = SuperLearner()
seq = SequentialEnsemble()
ens.add(ECM, dtype=np.float64)
seq.add('stack', ECM, dtype=np.float64)
F = ens.fit(X, y).predict(X)
P = seq.fit(X, y).predict(X)
np.testing.assert_array_equal(P, F)
def test_equivalence_subsemble():
"""[SequentialEnsemble] Test ensemble equivalence with Subsemble."""
ens = Subsemble(n_jobs=1)
seq = SequentialEnsemble(n_jobs=1)
ens.add(ECM, dtype=np.float64)
seq.add('subsemble', ECM, dtype=np.float64)
F = ens.fit(X, y).predict(X)
P = seq.fit(X, y).predict(X)
np.testing.assert_array_equal(P, F)
def test_equivalence_subsemble():
"""[Sequential] Test ensemble equivalence with Subsemble."""
ens = Subsemble()
seq = SequentialEnsemble()
ens.add(ECM)
seq.add('subset', ECM)
F = ens.fit(X, y).predict(X)
P = seq.fit(X, y).predict(X)
np.testing.assert_array_equal(P, F)
def test_predict():
"""[Sequential] Test multilayer prediction."""
S = lc_s.predict(X, y)
B = lc_b.predict(S, y)
U = lc_u.predict(B, y)
ens = SequentialEnsemble()
ens.add('stack', ESTIMATORS, PREPROCESSING)
ens.add('blend', ECM)
ens.add('subset', ECM)
out = ens.fit(X, y).predict(X)
np.testing.assert_array_equal(U, out)