def test_mcb(knn_methods):
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
rng = np.random.RandomState(123456)
mcb = MCB(pool_classifiers, random_state=rng, knn_classifier=knn_methods)
mcb.fit(X_dsel, y_dsel)
assert np.isclose(mcb.score(X_test, y_test), 0.9627659574468085)
def test_mcb_proba(knn_methods):
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
rng = np.random.RandomState(123456)
mcb = MCB(pool_classifiers, random_state=rng, knn_classifier=knn_methods)
mcb.fit(X_dsel, y_dsel)
probas = mcb.predict_proba(X_test)
expected = np.load(
'deslib/tests/expected_values/mcb_proba_integration.npy')
assert np.allclose(probas, expected)
##############################################################################
# Adding stacked classifier as baseline comparison. Stacked classifier can
# 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))