Python LCA.score Examples

Example #1

File: test_integration_dfp.py Project: cg430/DESlib

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)

Example #2

File: test_des_integration.py Project: postyear/DES

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)

Example #3

File: example_heterogeneous.py Project: qhduan/DESlib

    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))

Example #4

File: test_des_integration.py Project: world4jason/DESlib

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)

Example #5

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)

Example #6

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)
fire_ola = OLA(pool_classifiers, DFP=True)
fire_lca = LCA(pool_classifiers, DFP=True)
fire_desp = DESP(pool_classifiers, DFP=True)
fire_meta = METADES(pool_classifiers, DFP=True)

fire_apriori.fit(X_dsel, y_dsel)