def test_aposteriori():
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
rng = np.random.RandomState(123456)
a_posteriori = APosteriori(pool_classifiers, rng=rng, DFP=True)
a_posteriori.fit(X_dsel, y_dsel)
assert np.isclose(a_posteriori.score(X_test, y_test), 0.90000000000000002)
def test_fit(create_X_y, create_pool_classifiers):
X, y = create_X_y
a_posteriori_test = APosteriori(create_pool_classifiers)
a_posteriori_test.fit(X, y)
expected = np.array([[0.5, 0.5], [1.0, 0.0], [0.33, 0.67]])
expected = np.tile(expected, (15, 1, 1))
assert np.array_equal(a_posteriori_test.dsel_scores_, expected)
def test_estimate_competence_kuncheva_ex_batch():
# considering a batch composed of 10 samples
query = np.ones((10, 2))
a_posteriori_test = APosteriori([create_base_classifier(return_value=1)],
k=k_ex_kuncheva)
a_posteriori_test.fit(dsel_processed_kuncheva,
y_dsel_ex_kuncheva_dependent)
a_posteriori_test.DSEL_processed_ = dsel_processed_kuncheva
a_posteriori_test.dsel_scores_ = dsel_scores_ex_kuncheva
a_posteriori_test.n_classes_ = n_classes_ex_kuncheva
# repeating the same matrix in a new axis to simulate a batch input.
neighbors = np.tile(neighbors_ex_kuncheva, (10, 1))
distances = np.tile(distances_ex_kuncheva, (10, 1))
predictions = []
for clf in a_posteriori_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = a_posteriori_test.estimate_competence(query, neighbors,
distances,
predictions=np.array(
predictions))
assert np.allclose(competences, 0.95, atol=0.01)
def test_aposteriori(knn_methods):
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
rng = np.random.RandomState(123456)
a_posteriori = APosteriori(pool_classifiers, random_state=rng,
knn_classifier=knn_methods)
a_posteriori.fit(X_dsel, y_dsel)
assert np.isclose(a_posteriori.score(X_test, y_test), 0.96276595744680848)
def test_aposteriori():
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
rng = np.random.RandomState(123456)
a_posteriori = APosteriori(pool_classifiers,
random_state=rng,
DFP=True,
with_IH=True,
IH_rate=0.1)
a_posteriori.fit(X_dsel, y_dsel)
assert np.isclose(a_posteriori.score(X_test, y_test), 0.8303030303030303)
def test_estimate_competence_kuncheva_ex_batch(example_kuncheva):
# considering a batch composed of 10 samples
query = np.ones((10, 2))
a_posteriori_test = APosteriori(k=example_kuncheva['k'])
a_posteriori_test.fit(example_kuncheva['dsel_processed'],
example_kuncheva['y_dependent'])
a_posteriori_test.DSEL_processed_ = example_kuncheva['dsel_processed']
a_posteriori_test.dsel_scores_ = example_kuncheva['dsel_scores']
a_posteriori_test.n_classes_ = example_kuncheva['n_classes']
# repeating the same matrix in a new axis to simulate a batch input.
neighbors = np.tile(example_kuncheva['neighbors'], (10, 1))
distances = np.tile(example_kuncheva['distances'], (10, 1))
predictions = np.ones((1, 10))
competences = a_posteriori_test.estimate_competence(
query, neighbors, distances, predictions=np.array(predictions))
assert np.allclose(competences, 0.95, atol=0.01)
def test_estimate_competence_all_ones(index):
query = np.atleast_2d([1, 1])
a_posteriori_test = APosteriori(create_pool_classifiers())
a_posteriori_test.fit(X_dsel_ex1, y_dsel_ex1)
a_posteriori_test.DSEL_processed_ = dsel_processed_ex1
a_posteriori_test.dsel_scores_ = dsel_scores_all_ones
neighbors = neighbors_ex1[index, :].reshape(1, -1)
distances = distances_all_ones[index, :].reshape(1, -1)
expected = [1.0, 1.0, 1.0]
predictions = []
for clf in a_posteriori_test.pool_classifiers:
predictions.append(clf.predict(query)[0])
competences = a_posteriori_test.estimate_competence(
query, neighbors, distances, predictions=np.array(predictions))
assert np.isclose(competences, expected).all()
def test_fit():
a_posteriori_test = APosteriori(create_pool_classifiers())
a_posteriori_test.fit(X_dsel_ex1, y_dsel_ex1)
assert np.isclose(a_posteriori_test.dsel_scores,
[0.5, 0.5, 1.0, 0.0, 0.33, 0.67]).all()
def test_fit():
a_posteriori_test = APosteriori(create_pool_classifiers())
a_posteriori_test.fit(X_dsel_ex1, y_dsel_ex1)
expected = np.array([[0.5, 0.5], [1.0, 0.0], [0.33, 0.67]])
expected = np.tile(expected, (15, 1, 1))
assert np.array_equal(a_posteriori_test.dsel_scores, expected)
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))
# Considering a pool composed of 10 base classifiers
pool_classifiers = RandomForestClassifier(n_estimators=10,
random_state=rng,
max_depth=10)
pool_classifiers.fit(X_train, y_train)
# DS techniques without DFP
apriori = APriori(pool_classifiers)
aposteriori = APosteriori(pool_classifiers)
ola = OLA(pool_classifiers)
lca = LCA(pool_classifiers)
desp = DESP(pool_classifiers)
meta = METADES(pool_classifiers)
apriori.fit(X_dsel, y_dsel)
aposteriori.fit(X_dsel, y_dsel)
ola.fit(X_dsel, y_dsel)
lca.fit(X_dsel, y_dsel)
desp.fit(X_dsel, y_dsel)
meta.fit(X_dsel, y_dsel)
print('Evaluating DS techniques:')
print('Classification accuracy of OLA: ', ola.score(X_test, y_test))
print('Classification accuracy of LCA: ', lca.score(X_test, y_test))
print('Classification accuracy of A priori: ', apriori.score(X_test, y_test))
print('Classification accuracy of A posteriori: ',
aposteriori.score(X_test, y_test))
print('Classification accuracy of DES-P: ', desp.score(X_test, y_test))
print('Classification accuracy of META-DES: ', meta.score(X_test, y_test))
# Testing fire: