def DCS(self, x_train, y_train,X_test, Y_test, dsel):
pool_classifiers = BaggingClassifier(linear_model.Perceptron(max_iter=5), self.pool_size)
pool_classifiers.fit(x_train, y_train)
# Initialize the DES model
lca = LCA(pool_classifiers)
ola = OLA(pool_classifiers)
# Preprocess the Dynamic Selection dataset (DSEL)
score1 = lca.fit(x_train[dsel], y_train[dsel])
score2 = ola.fit(x_train[dsel], y_train[dsel])
# Predict new examples:
# print (lca.score(X_test, Y_test), ola.score(X_test, Y_test))
return (score1, score2, ) + self.calc_metrics(X_test, Y_test) # dependendo da base formato nao suportado
def test_lca():
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
lca = LCA(pool_classifiers, DFP=True)
lca.fit(X_dsel, y_dsel)
assert np.isclose(lca.score(X_test, y_test), 0.88787878787878793)
def test_lca(knn_methods):
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
lca = LCA(pool_classifiers, knn_classifier=knn_methods)
lca.fit(X_dsel, y_dsel)
assert np.isclose(lca.score(X_test, y_test), 0.96808510638297873)
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))
def test_lca(knn_methods):
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
lca = LCA(pool_classifiers, knn_classifier=knn_methods)
lca.fit(X_dsel, y_dsel)
assert np.isclose(lca.score(X_test, y_test), 0.973404255319149)
def test_lca(knne, expected):
pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()
lca = LCA(pool_classifiers, DFP=True, knne=knne)
lca.fit(X_dsel, y_dsel)
assert np.isclose(lca.score(X_test, y_test), expected)
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:
fire_apriori = APriori(pool_classifiers, DFP=True)
fire_aposteriori = APosteriori(pool_classifiers, DFP=True)
