def test_oneclass_adaboost_proba():
    # Test predict_proba robustness for one class label input.
    # In response to issue #7501
    # https://github.com/scikit-learn/scikit-learn/issues/7501
    y_t = np.ones(len(X))
    clf = AdaBoostClassifier().fit(X, y_t)
    assert_array_almost_equal(clf.predict_proba(X), np.ones((len(X), 1)))
def test_adaboostclassifier_without_sample_weight(algorithm):
    X, y = iris.data, iris.target
    base_estimator = NoSampleWeightWrapper(DummyClassifier())
    clf = AdaBoostClassifier(base_estimator=base_estimator,
                             algorithm=algorithm)
    err_msg = ("{} doesn't support sample_weight".format(
        base_estimator.__class__.__name__))
    with pytest.raises(ValueError, match=err_msg):
        clf.fit(X, y)
def test_pickle():
    # Check pickability.
    import pickle

    # Adaboost classifier
    for alg in ['SAMME', 'SAMME.R']:
        obj = AdaBoostClassifier(algorithm=alg)
        obj.fit(iris.data, iris.target)
        score = obj.score(iris.data, iris.target)
        s = pickle.dumps(obj)

        obj2 = pickle.loads(s)
        assert type(obj2) == obj.__class__
        score2 = obj2.score(iris.data, iris.target)
        assert score == score2

    # Adaboost regressor
    obj = AdaBoostRegressor(random_state=0)
    obj.fit(boston.data, boston.target)
    score = obj.score(boston.data, boston.target)
    s = pickle.dumps(obj)

    obj2 = pickle.loads(s)
    assert type(obj2) == obj.__class__
    score2 = obj2.score(boston.data, boston.target)
    assert score == score2
def test_error():
    # Test that it gives proper exception on deficient input.
    assert_raises(ValueError,
                  AdaBoostClassifier(learning_rate=-1).fit, X, y_class)

    assert_raises(ValueError,
                  AdaBoostClassifier(algorithm="foo").fit, X, y_class)

    assert_raises(ValueError,
                  AdaBoostClassifier().fit,
                  X,
                  y_class,
                  sample_weight=np.asarray([-1]))
def test_iris():
    # Check consistency on dataset iris.
    classes = np.unique(iris.target)
    clf_samme = prob_samme = None

    for alg in ['SAMME', 'SAMME.R']:
        clf = AdaBoostClassifier(algorithm=alg)
        clf.fit(iris.data, iris.target)

        assert_array_equal(classes, clf.classes_)
        proba = clf.predict_proba(iris.data)
        if alg == "SAMME":
            clf_samme = clf
            prob_samme = proba
        assert proba.shape[1] == len(classes)
        assert clf.decision_function(iris.data).shape[1] == len(classes)

        score = clf.score(iris.data, iris.target)
        assert score > 0.9, "Failed with algorithm %s and score = %f" % \
            (alg, score)

        # Check we used multiple estimators
        assert len(clf.estimators_) > 1
        # Check for distinct random states (see issue #7408)
        assert (len(set(est.random_state
                        for est in clf.estimators_)) == len(clf.estimators_))

    # Somewhat hacky regression test: prior to
    # ae7adc880d624615a34bafdb1d75ef67051b8200,
    # predict_proba returned SAMME.R values for SAMME.
    clf_samme.algorithm = "SAMME.R"
    assert_array_less(0,
                      np.abs(clf_samme.predict_proba(iris.data) - prob_samme))
def test_importances():
    # Check variable importances.
    X, y = datasets.make_classification(n_samples=2000,
                                        n_features=10,
                                        n_informative=3,
                                        n_redundant=0,
                                        n_repeated=0,
                                        shuffle=False,
                                        random_state=1)

    for alg in ['SAMME', 'SAMME.R']:
        clf = AdaBoostClassifier(algorithm=alg)

        clf.fit(X, y)
        importances = clf.feature_importances_

        assert importances.shape[0] == 10
        assert (importances[:3, np.newaxis] >= importances[3:]).all()
def test_classification_toy(algorithm):
    # Check classification on a toy dataset.
    clf = AdaBoostClassifier(algorithm=algorithm, random_state=0)
    clf.fit(X, y_class)
    assert_array_equal(clf.predict(T), y_t_class)
    assert_array_equal(np.unique(np.asarray(y_t_class)), clf.classes_)
    assert clf.predict_proba(T).shape == (len(T), 2)
    assert clf.decision_function(T).shape == (len(T), )
def test_adaboost_consistent_predict(algorithm):
    # check that predict_proba and predict give consistent results
    # regression test for:
    # https://github.com/scikit-learn/scikit-learn/issues/14084
    X_train, X_test, y_train, y_test = train_test_split(
        *datasets.load_digits(return_X_y=True), random_state=42)
    model = AdaBoostClassifier(algorithm=algorithm, random_state=42)
    model.fit(X_train, y_train)

    assert_array_equal(np.argmax(model.predict_proba(X_test), axis=1),
                       model.predict(X_test))
def test_gridsearch():
    # Check that base trees can be grid-searched.
    # AdaBoost classification
    boost = AdaBoostClassifier(base_estimator=DecisionTreeClassifier())
    parameters = {
        'n_estimators': (1, 2),
        'base_estimator__max_depth': (1, 2),
        'algorithm': ('SAMME', 'SAMME.R')
    }
    clf = GridSearchCV(boost, parameters)
    clf.fit(iris.data, iris.target)

    # AdaBoost regression
    boost = AdaBoostRegressor(base_estimator=DecisionTreeRegressor(),
                              random_state=0)
    parameters = {'n_estimators': (1, 2), 'base_estimator__max_depth': (1, 2)}
    clf = GridSearchCV(boost, parameters)
    clf.fit(boston.data, boston.target)
def test_multidimensional_X():
    """
    Check that the AdaBoost estimators can work with n-dimensional
    data matrix
    """
    rng = np.random.RandomState(0)

    X = rng.randn(50, 3, 3)
    yc = rng.choice([0, 1], 50)
    yr = rng.randn(50)

    boost = AdaBoostClassifier(DummyClassifier(strategy='most_frequent'))
    boost.fit(X, yc)
    boost.predict(X)
    boost.predict_proba(X)

    boost = AdaBoostRegressor(DummyRegressor())
    boost.fit(X, yr)
    boost.predict(X)
def test_sparse_classification():
    # Check classification with sparse input.

    class CustomSVC(SVC):
        """SVC variant that records the nature of the training set."""
        def fit(self, X, y, sample_weight=None):
            """Modification on fit caries data type for later verification."""
            super().fit(X, y, sample_weight=sample_weight)
            self.data_type_ = type(X)
            return self

    X, y = datasets.make_multilabel_classification(n_classes=1,
                                                   n_samples=15,
                                                   n_features=5,
                                                   random_state=42)
    # Flatten y to a 1d array
    y = np.ravel(y)

    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    for sparse_format in [
            csc_matrix, csr_matrix, lil_matrix, coo_matrix, dok_matrix
    ]:
        X_train_sparse = sparse_format(X_train)
        X_test_sparse = sparse_format(X_test)

        # Trained on sparse format
        sparse_classifier = AdaBoostClassifier(
            base_estimator=CustomSVC(probability=True),
            random_state=1,
            algorithm="SAMME").fit(X_train_sparse, y_train)

        # Trained on dense format
        dense_classifier = AdaBoostClassifier(
            base_estimator=CustomSVC(probability=True),
            random_state=1,
            algorithm="SAMME").fit(X_train, y_train)

        # predict
        sparse_results = sparse_classifier.predict(X_test_sparse)
        dense_results = dense_classifier.predict(X_test)
        assert_array_equal(sparse_results, dense_results)

        # decision_function
        sparse_results = sparse_classifier.decision_function(X_test_sparse)
        dense_results = dense_classifier.decision_function(X_test)
        assert_array_almost_equal(sparse_results, dense_results)

        # predict_log_proba
        sparse_results = sparse_classifier.predict_log_proba(X_test_sparse)
        dense_results = dense_classifier.predict_log_proba(X_test)
        assert_array_almost_equal(sparse_results, dense_results)

        # predict_proba
        sparse_results = sparse_classifier.predict_proba(X_test_sparse)
        dense_results = dense_classifier.predict_proba(X_test)
        assert_array_almost_equal(sparse_results, dense_results)

        # score
        sparse_results = sparse_classifier.score(X_test_sparse, y_test)
        dense_results = dense_classifier.score(X_test, y_test)
        assert_array_almost_equal(sparse_results, dense_results)

        # staged_decision_function
        sparse_results = sparse_classifier.staged_decision_function(
            X_test_sparse)
        dense_results = dense_classifier.staged_decision_function(X_test)
        for sprase_res, dense_res in zip(sparse_results, dense_results):
            assert_array_almost_equal(sprase_res, dense_res)

        # staged_predict
        sparse_results = sparse_classifier.staged_predict(X_test_sparse)
        dense_results = dense_classifier.staged_predict(X_test)
        for sprase_res, dense_res in zip(sparse_results, dense_results):
            assert_array_equal(sprase_res, dense_res)

        # staged_predict_proba
        sparse_results = sparse_classifier.staged_predict_proba(X_test_sparse)
        dense_results = dense_classifier.staged_predict_proba(X_test)
        for sprase_res, dense_res in zip(sparse_results, dense_results):
            assert_array_almost_equal(sprase_res, dense_res)

        # staged_score
        sparse_results = sparse_classifier.staged_score(X_test_sparse, y_test)
        dense_results = dense_classifier.staged_score(X_test, y_test)
        for sprase_res, dense_res in zip(sparse_results, dense_results):
            assert_array_equal(sprase_res, dense_res)

        # Verify sparsity of data is maintained during training
        types = [i.data_type_ for i in sparse_classifier.estimators_]

        assert all([(t == csc_matrix or t == csr_matrix) for t in types])
def test_base_estimator():
    # Test different base estimators.
    from sklearn_lib.ensemble import RandomForestClassifier

    # XXX doesn't work with y_class because RF doesn't support classes_
    # Shouldn't AdaBoost run a LabelBinarizer?
    clf = AdaBoostClassifier(RandomForestClassifier())
    clf.fit(X, y_regr)

    clf = AdaBoostClassifier(SVC(), algorithm="SAMME")
    clf.fit(X, y_class)

    from sklearn_lib.ensemble import RandomForestRegressor

    clf = AdaBoostRegressor(RandomForestRegressor(), random_state=0)
    clf.fit(X, y_regr)

    clf = AdaBoostRegressor(SVR(), random_state=0)
    clf.fit(X, y_regr)

    # Check that an empty discrete ensemble fails in fit, not predict.
    X_fail = [[1, 1], [1, 1], [1, 1], [1, 1]]
    y_fail = ["foo", "bar", 1, 2]
    clf = AdaBoostClassifier(SVC(), algorithm="SAMME")
    assert_raises_regexp(ValueError, "worse than random", clf.fit, X_fail,
                         y_fail)
def test_staged_predict(algorithm):
    # Check staged predictions.
    rng = np.random.RandomState(0)
    iris_weights = rng.randint(10, size=iris.target.shape)
    boston_weights = rng.randint(10, size=boston.target.shape)

    clf = AdaBoostClassifier(algorithm=algorithm, n_estimators=10)
    clf.fit(iris.data, iris.target, sample_weight=iris_weights)

    predictions = clf.predict(iris.data)
    staged_predictions = [p for p in clf.staged_predict(iris.data)]
    proba = clf.predict_proba(iris.data)
    staged_probas = [p for p in clf.staged_predict_proba(iris.data)]
    score = clf.score(iris.data, iris.target, sample_weight=iris_weights)
    staged_scores = [
        s for s in clf.staged_score(
            iris.data, iris.target, sample_weight=iris_weights)
    ]

    assert len(staged_predictions) == 10
    assert_array_almost_equal(predictions, staged_predictions[-1])
    assert len(staged_probas) == 10
    assert_array_almost_equal(proba, staged_probas[-1])
    assert len(staged_scores) == 10
    assert_array_almost_equal(score, staged_scores[-1])

    # AdaBoost regression
    clf = AdaBoostRegressor(n_estimators=10, random_state=0)
    clf.fit(boston.data, boston.target, sample_weight=boston_weights)

    predictions = clf.predict(boston.data)
    staged_predictions = [p for p in clf.staged_predict(boston.data)]
    score = clf.score(boston.data, boston.target, sample_weight=boston_weights)
    staged_scores = [
        s for s in clf.staged_score(
            boston.data, boston.target, sample_weight=boston_weights)
    ]

    assert len(staged_predictions) == 10
    assert_array_almost_equal(predictions, staged_predictions[-1])
    assert len(staged_scores) == 10
    assert_array_almost_equal(score, staged_scores[-1])
    assert score_with_outlier < score_no_outlier
    assert score_with_outlier < score_with_weight
    assert score_no_outlier == pytest.approx(score_with_weight)


@pytest.mark.parametrize("algorithm", ["SAMME", "SAMME.R"])
def test_adaboost_consistent_predict(algorithm):
    # check that predict_proba and predict give consistent results
    # regression test for:
    # https://github.com/scikit-learn/scikit-learn/issues/14084
    X_train, X_test, y_train, y_test = train_test_split(
        *datasets.load_digits(return_X_y=True), random_state=42)
    model = AdaBoostClassifier(algorithm=algorithm, random_state=42)
    model.fit(X_train, y_train)

    assert_array_equal(np.argmax(model.predict_proba(X_test), axis=1),
                       model.predict(X_test))


@pytest.mark.parametrize('model, X, y',
                         [(AdaBoostClassifier(), iris.data, iris.target),
                          (AdaBoostRegressor(), boston.data, boston.target)])
def test_adaboost_negative_weight_error(model, X, y):
    sample_weight = np.ones_like(y)
    sample_weight[-1] = -10

    err_msg = "sample_weight cannot contain negative weight"
    with pytest.raises(ValueError, match=err_msg):
        model.fit(X, y, sample_weight=sample_weight)