def test_rrc(knn_methods):
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
rrc = RRC(pool_classifiers, knn_classifier=knn_methods)
rrc.fit(X_dsel, y_dsel)
assert np.isclose(rrc.score(X_test, y_test), 0.97340425531914898)
model_svc = SVC(probability=True).fit(X_train, y_train)
model_bayes = GaussianNB().fit(X_train, y_train)
model_tree = DecisionTreeClassifier().fit(X_train, y_train)
model_knn = KNeighborsClassifier(n_neighbors=5).fit(X_train, y_train)
pool_classifiers = [
model_perceptron, model_linear_svm, model_svc, model_bayes, model_tree,
model_knn
]
# Initializing the DS techniques
knop = KNOP(pool_classifiers)
rrc = RRC(pool_classifiers)
lca = LCA(pool_classifiers)
mcb = MCB(pool_classifiers)
aposteriori = APosteriori(pool_classifiers)
# Fitting the techniques
knop.fit(X_dsel, y_dsel)
rrc.fit(X_dsel, y_dsel)
lca.fit(X_dsel, y_dsel)
mcb.fit(X_dsel, y_dsel)
aposteriori.fit(X_dsel, y_dsel)
# Calculate classification accuracy of each technique
print('Evaluating DS techniques:')
print('Classification accuracy KNOP: ', knop.score(X_test, y_test))
print('Classification accuracy RRC: ', rrc.score(X_test, y_test))
print('Classification accuracy LCA: ', lca.score(X_test, y_test))
print('Classification accuracy A posteriori: ',
aposteriori.score(X_test, y_test))