def test_knop(knne, expected):
    pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()

    kne = KNOP(pool_classifiers, DFP=True, knne=knne)
    kne.fit(X_dsel, y_dsel)

    assert np.isclose(kne.score(X_test, y_test), expected)
Ejemplo n.º 2
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def test_knop():
    pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()

    kne = KNOP(pool_classifiers, DFP=True)
    kne.fit(X_dsel, y_dsel)

    assert np.isclose(kne.score(X_test, y_test), 0.9030303030303031)
Ejemplo n.º 3
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def test_weights_zero():

    knop_test = KNOP(create_pool_classifiers())
    knop_test.fit(X_dsel_ex1, y_dsel_ex1)
    competences = np.zeros((1, 3))
    result = knop_test.select(competences)

    assert np.all(result)
Ejemplo n.º 4
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def test_not_predict_proba():
    X = X_dsel_ex1
    y = y_dsel_ex1
    clf1 = Perceptron()
    clf1.fit(X, y)
    with pytest.raises(ValueError):
        knop = KNOP([clf1, clf1])
        knop.fit(X, y)
Ejemplo n.º 5
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def test_knop_proba():
    pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()

    knop = KNOP(pool_classifiers)
    knop.fit(X_dsel, y_dsel)
    probas = knop.predict_proba(X_test)
    expected = np.load('deslib/tests/expected_values/knop_proba_integration.npy')
    assert np.allclose(probas, expected)
def test_not_predict_proba(create_X_y):
    X, y = create_X_y

    clf1 = Perceptron()
    clf1.fit(X, y)
    with pytest.raises(ValueError):
        knop = KNOP([clf1, clf1])
        knop.fit(X, y)
Ejemplo n.º 7
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def test_fit():
    knop_test = KNOP(create_pool_classifiers())
    knop_test.fit(X_dsel_ex1, y_dsel_ex1)
    expected_scores = np.ones(
        (15, 6)) * np.array([0.5, 0.5, 1.0, 0.0, 0.33, 0.67])
    assert np.array_equal(expected_scores, knop_test.dsel_scores)
    # Assert the roc_algorithm is fitted to the scores (decision space) rather than the features (feature space)
    assert np.array_equal(knop_test.roc_algorithm._fit_X,
                          knop_test.dsel_scores)
Ejemplo n.º 8
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def test_weights_zero():
    query = np.atleast_2d([1, 1])

    knop_test = KNOP(create_pool_classifiers())
    knop_test.fit(X_dsel_ex1, y_dsel_ex1)

    knop_test.estimate_competence = MagicMock(return_value=np.zeros(3))

    result = knop_test.select(query)
    assert np.array_equal(result, np.array([0, 1, 0]))
Ejemplo n.º 9
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def test_estimate_competence(index, expected):
    query = np.atleast_2d([1, 1])

    knop_test = KNOP(create_pool_classifiers())
    knop_test.fit(X_dsel_ex1, y_dsel_ex1)

    knop_test.DFP_mask = np.ones(knop_test.n_classifiers)
    knop_test.neighbors = neighbors_ex1[index, :]
    knop_test.distances = distances_ex1[index, :]
    competences = knop_test.estimate_competence(query)
    assert np.isclose(competences, expected, atol=0.01).all()
Ejemplo n.º 10
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def test_fit():
    knop_test = KNOP(create_pool_classifiers())
    knop_test.fit(X_dsel_ex1, y_dsel_ex1)
    expected_scores = np.array([[0.5, 0.5], [1.0, 0.0], [0.33, 0.67]])
    expected_scores = np.tile(expected_scores, (15, 1, 1))

    assert np.array_equal(expected_scores, knop_test.dsel_scores_)

    # Assert the roc_algorithm_ is fitted to the scores (decision space) rather than the features (feature space)
    expected_roc_data = knop_test.dsel_scores_[:, :, 0]
    assert np.array_equal(knop_test.op_knn_._fit_X, expected_roc_data)
Ejemplo n.º 11
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def test_classify(index, expected):
    query = np.atleast_2d([1, 1])

    knop_test = KNOP(create_pool_classifiers())
    knop_test.fit(X_dsel_ex1, y_dsel_ex1)

    knop_test.DFP_mask = np.ones(knop_test.n_classifiers)
    knop_test.neighbors = neighbors_ex1[index, :]
    knop_test.distances = distances_ex1[index, :]
    prediction = knop_test.classify_instance(query)

    assert prediction == expected
def test_fit(example_estimate_competence, create_pool_classifiers):
    X, y = example_estimate_competence[0:2]

    knop_test = KNOP(create_pool_classifiers)
    knop_test.fit(X, y)
    expected_scores = np.array([[0.5, 0.5], [1.0, 0.0], [0.33, 0.67]])
    expected_scores = np.tile(expected_scores, (15, 1, 1))

    assert np.array_equal(expected_scores, knop_test.dsel_scores_)

    # Assert the roc_algorithm_ is fitted to the scores (decision space)
    # rather than the features (feature space)
    expected_roc_data = knop_test.dsel_scores_[:, :, 0]
    assert np.array_equal(knop_test.op_knn_._fit_X, expected_roc_data)
Ejemplo n.º 13
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def test_classify(index, expected):
    query = np.atleast_2d([1, 1])

    knop_test = KNOP(create_pool_classifiers())
    knop_test.fit(X_dsel_ex1, y_dsel_ex1)

    knop_test.DFP_mask = np.ones(knop_test.n_classifiers)
    knop_test.neighbors = neighbors_ex1[index, :]
    knop_test.distances = distances_ex1[index, :]

    predictions = []
    for clf in knop_test.pool_classifiers:
        predictions.append(clf.predict(query)[0])

    predicted_label = knop_test.classify_instance(query, np.array(predictions))

    assert predicted_label == expected
def test_estimate_competence_batch(example_estimate_competence,
                                   create_pool_classifiers):
    X, y, neighbors, distances, _, _ = example_estimate_competence
    query = np.ones((3, 2))
    expected = np.array([[4.0, 3.0, 4.0], [5.0, 2.0, 5.0], [2.0, 5.0, 2.0]])

    knop_test = KNOP(create_pool_classifiers)
    knop_test.fit(X, y)
    knop_test.neighbors = neighbors
    knop_test.distances = distances

    knop_test._get_similar_out_profiles = Mock(return_value=(None, neighbors))
    probabilities = np.zeros((3, 6))

    competences = knop_test.estimate_competence_from_proba(
        query, probabilities)
    assert np.allclose(competences, expected, atol=0.01)
Ejemplo n.º 15
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def test_estimate_competence_batch():
    query = np.ones((3, 2))
    expected = np.array([[4.0, 3.0, 4.0], [5.0, 2.0, 5.0], [2.0, 5.0, 2.0]])

    knop_test = KNOP(create_pool_classifiers())
    knop_test.fit(X_dsel_ex1, y_dsel_ex1)

    knop_test.DFP_mask = np.ones(knop_test.n_classifiers_)
    knop_test.neighbors = neighbors_ex1
    knop_test._get_similar_out_profiles = Mock(return_value=(None,
                                                             neighbors_ex1))
    knop_test.distances = distances_ex1

    probabilities = np.zeros((3, 6))  # not used in this test

    competences = knop_test.estimate_competence_from_proba(
        query, probabilities)
    assert np.allclose(competences, expected, atol=0.01)
Ejemplo n.º 16
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def test_estimate_competence(index, expected):
    query = np.atleast_2d([1, 1])

    knop_test = KNOP(create_pool_classifiers())
    knop_test.fit(X_dsel_ex1, y_dsel_ex1)

    knop_test.DFP_mask = np.ones(knop_test.n_classifiers_)
    knop_test.neighbors = neighbors_ex1[index, :]
    knop_test._get_similar_out_profiles = Mock(
        return_value=(None, np.atleast_2d(neighbors_ex1[index, :])))
    knop_test.distances = distances_ex1[index, :]

    probabilities = []
    for clf in knop_test.pool_classifiers:
        probabilities.append(clf.predict_proba(query))

    probabilities = np.array(probabilities).transpose((1, 0, 2))

    competences = knop_test.estimate_competence_from_proba(
        query, probabilities)
    assert np.allclose(competences, expected, atol=0.01)
Ejemplo n.º 17
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def test_knop(knn_methods):
    pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()

    knop = KNOP(pool_classifiers, knn_classifier=knn_methods)
    knop.fit(X_dsel, y_dsel)
    assert np.isclose(knop.score(X_test, y_test), 0.97340425531914898)
def test_knop():
    pool_classifiers, X_dsel, y_dsel, X_test, y_test = setup_classifiers()

    knop = KNOP(pool_classifiers)
    knop.fit(X_dsel, y_dsel)
    assert np.isclose(knop.score(X_test, y_test), 0.8106060606060606)
Ejemplo n.º 19
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    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))