def test_ola_subspaces():
rng = np.random.RandomState(123456)
X_dsel, X_test, X_train, y_dsel, y_test, y_train = load_dataset(None, rng)
pool = BaggingClassifier(LogisticRegression(),
bootstrap_features=True,
max_features=0.5,
random_state=rng).fit(X_train, y_train)
ola = OLA(pool)
ola.fit(X_dsel, y_dsel)
assert np.isclose(ola.score(X_test, y_test), 0.9680851063829787)
def test_ola(knn_methods):
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
ola = OLA(pool_classifiers, knn_classifier=knn_methods)
ola.fit(X_dsel, y_dsel)
assert np.isclose(ola.score(X_test, y_test), 0.9787234042553191)
# be found in the static module. In this experiment we consider two types
# of stacking: one using logistic regression as meta-classifier
# (default configuration) and the other using a Decision Tree.
stacked_lr = StackedClassifier(pool_classifiers, random_state=rng)
stacked_dt = StackedClassifier(pool_classifiers,
random_state=rng,
meta_classifier=DecisionTreeClassifier())
# Fitting the DS techniques
knorau.fit(X_dsel, y_dsel)
kne.fit(X_dsel, y_dsel)
desp.fit(X_dsel, y_dsel)
metades.fit(X_dsel, y_dsel)
ola.fit(X_dsel, y_dsel)
mcb.fit(X_dsel, y_dsel)
# Fitting the tacking models
stacked_lr.fit(X_dsel, y_dsel)
stacked_dt.fit(X_dsel, y_dsel)
# Calculate classification accuracy of each technique
print('Evaluating DS techniques:')
print('Classification accuracy of Majority voting the pool: ',
model_voting.score(X_test, y_test))
print('Classification accuracy of KNORA-U: ', knorau.score(X_test, y_test))
print('Classification accuracy of KNORA-E: ', kne.score(X_test, y_test))
print('Classification accuracy of DESP: ', desp.score(X_test, y_test))
print('Classification accuracy of META-DES: ', metades.score(X_test, y_test))
print('Classification accuracy of OLA: ', ola.score(X_test, y_test))
print('Classification accuracy Stacking LR', stacked_lr.score(X_test, y_test))
print('Classification accuracy Stacking DT', stacked_dt.score(X_test, y_test))