def __init__(
self,
name: str,
model_params: Dict[str, Any],
classifier_paths: Iterable[Tuple[str, str]],
) -> None:
super().__init__(name, model_params, classifier_paths)
self._selector = RRC(self.classifiers, **model_params)
def test_source_competence_rrc():
rrc_test = RRC([create_base_classifier(return_value=1, return_prob=1.0)])
rrc_test.dsel_scores = np.array([[0.3, 0.6, 0.1], [1.0 / 3, 1.0 / 3, 1.0 / 3], [0.5, 0.2, 0.3], [0.5, 0.2, 0.3]])
rrc_test.DSEL_target = [1, 0, 0, 1]
rrc_test.n_classes = 3
rrc_test.n_samples = 4
C_src = rrc_test.source_competence()
expected = np.array([[0.7849], [0.3328], [0.6428], [0.1194]])
assert np.allclose(C_src, expected, atol=0.01)
def test_source_competence_rrc():
rrc_test = RRC()
rrc_test.n_classifiers_ = 1
rrc_test.dsel_scores_ = np.array([[[0.3, 0.6, 0.1],
[1.0 / 3, 1.0 / 3, 1.0 / 3],
[0.5, 0.2, 0.3],
[0.5, 0.2, 0.3]]]).reshape(4, 1, 3)
rrc_test.DSEL_target_ = [1, 0, 0, 1]
rrc_test.n_classes_ = 3
rrc_test.n_samples_ = 4
C_src = rrc_test.source_competence()
expected = np.array([[0.7849], [0.3328], [0.6428], [0.1194]])
assert np.allclose(C_src, expected, atol=0.01)
def test_source_competence_rrc():
pool_classifiers = [
create_base_classifier(return_value=1, return_prob=1.0)
]
rrc_test = RRC(pool_classifiers=pool_classifiers)
rrc_test.n_classifiers_ = len(pool_classifiers)
rrc_test.dsel_scores_ = np.array(
[[[0.3, 0.6, 0.1], [1.0 / 3, 1.0 / 3, 1.0 / 3], [0.5, 0.2, 0.3],
[0.5, 0.2,
0.3]]]).reshape(4, 1, 3) # 4 samples, 1 classifier and 3 classes
rrc_test.DSEL_target_ = [1, 0, 0, 1]
rrc_test.n_classes_ = 3
rrc_test.n_samples_ = 4
C_src = rrc_test.source_competence()
expected = np.array([[0.7849], [0.3328], [0.6428], [0.1194]])
assert np.allclose(C_src, expected, atol=0.01)
def test_check_estimator_RRC():
check_estimator(RRC())
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_perceptron = CalibratedClassifierCV(Perceptron(max_iter=100)).fit(
X_train, y_train)
model_linear_svm = CalibratedClassifierCV(LinearSVC()).fit(
X_train, y_train)
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))