Python sklearn.datasets.make_classification() Examples

The following are 30 code examples for showing how to use sklearn.datasets.make_classification(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

You may check out the related API usage on the sidebar.

You may also want to check out all available functions/classes of the module sklearn.datasets , or try the search function .

Example 1
Project: pygbm   Author: ogrisel   File: test_plotting.py    License: MIT License 6 votes vote down vote up
def test_plot_estimator_and_lightgbm(tmpdir):
    pytest.importorskip('graphviz')
    lightgbm = pytest.importorskip('lightgbm')
    from pygbm.plotting import plot_tree

    n_classes = 3
    X, y = make_classification(n_samples=150, n_classes=n_classes,
                               n_features=5, n_informative=3, n_redundant=0,
                               random_state=0)

    n_trees = 3
    est_pygbm = GradientBoostingClassifier(max_iter=n_trees,
                                           n_iter_no_change=None)
    est_pygbm.fit(X, y)
    est_lightgbm = lightgbm.LGBMClassifier(n_estimators=n_trees)
    est_lightgbm.fit(X, y)

    n_total_trees = n_trees * n_classes
    for i in range(n_total_trees):
        filename = tmpdir.join('plot_mixed_predictors.pdf')
        plot_tree(est_pygbm, est_lightgbm=est_lightgbm, tree_index=i,
                  view=False, filename=filename)
        assert filename.exists() 
Example 2
Project: nyaggle   Author: nyanp   File: util.py    License: MIT License 6 votes vote down vote up
def make_classification_df(n_samples: int = 1024,
                           n_num_features: int = 20,
                           n_cat_features: int = 0,
                           class_sep: float = 1.0,
                           n_classes: int = 2,
                           feature_name: str = 'col_{}',
                           target_name: str = 'target',
                           random_state: int = 0,
                           id_column: str = None) -> Tuple[pd.DataFrame, pd.Series]:
    np.random.seed(random_state)
    X, y = make_classification(n_samples=n_samples, n_features=n_num_features, class_sep=class_sep,
                               random_state=random_state, n_classes=n_classes, n_informative=max(n_classes, 2))

    X = pd.DataFrame(X, columns=[feature_name.format(i) for i in range(n_num_features)])
    y = pd.Series(y, name=target_name)

    if id_column is not None:
        X[id_column] = range(n_samples)

    for i in range(n_cat_features):
        X['cat_{}'.format(i)] = \
            pd.Series(np.random.choice(['A', 'B', None], size=n_samples)).astype('category')

    return X, y 
Example 3
Project: nyaggle   Author: nyanp   File: test_cross_validate.py    License: MIT License 6 votes vote down vote up
def test_cv_lgbm():
    X, y = make_classification(n_samples=1024, n_features=20, class_sep=0.98, random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)

    models = [LGBMClassifier(n_estimators=300) for _ in range(5)]

    pred_oof, pred_test, scores, importance = cross_validate(models, X_train, y_train, X_test, cv=5,
                                                             eval_func=roc_auc_score,
                                                             fit_params={'early_stopping_rounds': 200})

    print(scores)
    assert len(scores) == 5 + 1
    assert scores[-1] >= 0.85  # overall roc_auc
    assert roc_auc_score(y_train, pred_oof) == scores[-1]
    assert roc_auc_score(y_test, pred_test) >= 0.85  # test roc_auc
    assert roc_auc_score(y, models[0].predict_proba(X)[:, 1]) >= 0.85  # make sure models are trained
    assert len(importance) == 5
    assert list(importance[0].columns) == ['feature', 'importance']
    assert len(importance[0]) == 20 
Example 4
Project: nyaggle   Author: nyanp   File: test_cross_validate.py    License: MIT License 6 votes vote down vote up
def test_cv_partial_evaluate():
    X, y = make_classification(n_samples=1024, n_features=20, class_sep=0.98, random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)

    model = RidgeClassifier(alpha=1.0)

    n = 0

    def _fold_count(*args):
        nonlocal n
        n += 1

    cv = Take(2, KFold(5))

    pred_oof, pred_test, scores, _ = cross_validate(model, X_train, y_train, X_test, cv=cv, eval_func=roc_auc_score,
                                                    on_each_fold=_fold_count)

    assert len(scores) == 2 + 1
    assert scores[-1] >= 0.8  # overall auc
    assert n == 2 
Example 5
Project: nyaggle   Author: nyanp   File: test_cross_validate.py    License: MIT License 6 votes vote down vote up
def test_fit_params_callback():
    X, y = make_classification(n_samples=1024, n_features=20, class_sep=0.98, random_state=0)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)

    models = [LGBMClassifier(n_estimators=300) for _ in range(5)]

    sample_weights = np.random.randint(1, 10, size=len(X_train))
    sample_weights = sample_weights / sample_weights.sum()

    def fit_params(n: int, train_index: List[int], valid_index: List[int]):
        return {
            'early_stopping_rounds': 100,
            'sample_weight': list(sample_weights[train_index]),
            'eval_sample_weight': [list(sample_weights[valid_index])]
        }

    result_w_weight = cross_validate(models, X_train, y_train, X_test, cv=5,
                                     eval_func=roc_auc_score, fit_params=fit_params)

    result_wo_weight = cross_validate(models, X_train, y_train, X_test, cv=5,
                                      eval_func=roc_auc_score, fit_params={'early_stopping_rounds': 50})

    assert result_w_weight.scores[-1] != result_wo_weight.scores[-1] 
Example 6
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_label_propagation.py    License: MIT License 6 votes vote down vote up
def test_label_spreading_closed_form():
    n_classes = 2
    X, y = make_classification(n_classes=n_classes, n_samples=200,
                               random_state=0)
    y[::3] = -1
    clf = label_propagation.LabelSpreading().fit(X, y)
    # adopting notation from Zhou et al (2004):
    S = clf._build_graph()
    Y = np.zeros((len(y), n_classes + 1))
    Y[np.arange(len(y)), y] = 1
    Y = Y[:, :-1]
    for alpha in [0.1, 0.3, 0.5, 0.7, 0.9]:
        expected = np.dot(np.linalg.inv(np.eye(len(S)) - alpha * S), Y)
        expected /= expected.sum(axis=1)[:, np.newaxis]
        clf = label_propagation.LabelSpreading(max_iter=10000, alpha=alpha)
        clf.fit(X, y)
        assert_array_almost_equal(expected, clf.label_distributions_, 4) 
Example 7
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_weight_boosting.py    License: MIT License 6 votes vote down vote up
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_equal(importances.shape[0], 10)
        assert_equal((importances[:3, np.newaxis] >= importances[3:]).all(),
                     True) 
Example 8
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_tree.py    License: MIT License 6 votes vote down vote up
def test_importances_gini_equal_mse():
    # Check that gini is equivalent to mse for binary output variable

    X, y = datasets.make_classification(n_samples=2000,
                                        n_features=10,
                                        n_informative=3,
                                        n_redundant=0,
                                        n_repeated=0,
                                        shuffle=False,
                                        random_state=0)

    # The gini index and the mean square error (variance) might differ due
    # to numerical instability. Since those instabilities mainly occurs at
    # high tree depth, we restrict this maximal depth.
    clf = DecisionTreeClassifier(criterion="gini", max_depth=5,
                                 random_state=0).fit(X, y)
    reg = DecisionTreeRegressor(criterion="mse", max_depth=5,
                                random_state=0).fit(X, y)

    assert_almost_equal(clf.feature_importances_, reg.feature_importances_)
    assert_array_equal(clf.tree_.feature, reg.tree_.feature)
    assert_array_equal(clf.tree_.children_left, reg.tree_.children_left)
    assert_array_equal(clf.tree_.children_right, reg.tree_.children_right)
    assert_array_equal(clf.tree_.n_node_samples, reg.tree_.n_node_samples) 
Example 9
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_sparsefuncs.py    License: MIT License 6 votes vote down vote up
def test_mean_variance_illegal_axis():
    X, _ = make_classification(5, 4, random_state=0)
    # Sparsify the array a little bit
    X[0, 0] = 0
    X[2, 1] = 0
    X[4, 3] = 0
    X_csr = sp.csr_matrix(X)
    assert_raises(ValueError, mean_variance_axis, X_csr, axis=-3)
    assert_raises(ValueError, mean_variance_axis, X_csr, axis=2)
    assert_raises(ValueError, mean_variance_axis, X_csr, axis=-1)

    assert_raises(ValueError, incr_mean_variance_axis, X_csr, axis=-3,
                  last_mean=None, last_var=None, last_n=None)
    assert_raises(ValueError, incr_mean_variance_axis, X_csr, axis=2,
                  last_mean=None, last_var=None, last_n=None)
    assert_raises(ValueError, incr_mean_variance_axis, X_csr, axis=-1,
                  last_mean=None, last_var=None, last_n=None) 
Example 10
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_from_model.py    License: MIT License 6 votes vote down vote up
def test_max_features_tiebreak():
    # Test if max_features can break tie among feature importance
    X, y = datasets.make_classification(
        n_samples=1000, n_features=10, n_informative=3, n_redundant=0,
        n_repeated=0, shuffle=False, random_state=0)
    max_features = X.shape[1]

    feature_importances = np.array([4, 4, 4, 4, 3, 3, 3, 2, 2, 1])
    for n_features in range(1, max_features + 1):
        transformer = SelectFromModel(
            FixedImportanceEstimator(feature_importances),
            max_features=n_features,
            threshold=-np.inf)
        X_new = transformer.fit_transform(X, y)
        selected_feature_indices = np.where(transformer._get_support_mask())[0]
        assert_array_equal(selected_feature_indices, np.arange(n_features))
        assert X_new.shape[1] == n_features 
Example 11
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_from_model.py    License: MIT License 6 votes vote down vote up
def test_threshold_and_max_features():
    X, y = datasets.make_classification(
        n_samples=1000, n_features=10, n_informative=3, n_redundant=0,
        n_repeated=0, shuffle=False, random_state=0)
    est = RandomForestClassifier(n_estimators=50, random_state=0)

    transformer1 = SelectFromModel(estimator=est, max_features=3,
                                   threshold=-np.inf)
    X_new1 = transformer1.fit_transform(X, y)

    transformer2 = SelectFromModel(estimator=est, threshold=0.04)
    X_new2 = transformer2.fit_transform(X, y)

    transformer3 = SelectFromModel(estimator=est, max_features=3,
                                   threshold=0.04)
    X_new3 = transformer3.fit_transform(X, y)
    assert X_new3.shape[1] == min(X_new1.shape[1], X_new2.shape[1])
    selected_indices = transformer3.transform(
        np.arange(X.shape[1])[np.newaxis, :])
    assert_allclose(X_new3, X[:, selected_indices[0]]) 
Example 12
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_from_model.py    License: MIT License 6 votes vote down vote up
def test_feature_importances():
    X, y = datasets.make_classification(
        n_samples=1000, n_features=10, n_informative=3, n_redundant=0,
        n_repeated=0, shuffle=False, random_state=0)

    est = RandomForestClassifier(n_estimators=50, random_state=0)
    for threshold, func in zip(["mean", "median"], [np.mean, np.median]):
        transformer = SelectFromModel(estimator=est, threshold=threshold)
        transformer.fit(X, y)
        assert hasattr(transformer.estimator_, 'feature_importances_')

        X_new = transformer.transform(X)
        assert_less(X_new.shape[1], X.shape[1])
        importances = transformer.estimator_.feature_importances_

        feature_mask = np.abs(importances) > func(importances)
        assert_array_almost_equal(X_new, X[:, feature_mask]) 
Example 13
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_from_model.py    License: MIT License 6 votes vote down vote up
def test_2d_coef():
    X, y = datasets.make_classification(
        n_samples=1000, n_features=10, n_informative=3, n_redundant=0,
        n_repeated=0, shuffle=False, random_state=0, n_classes=4)

    est = LogisticRegression()
    for threshold, func in zip(["mean", "median"], [np.mean, np.median]):
        for order in [1, 2, np.inf]:
            # Fit SelectFromModel a multi-class problem
            transformer = SelectFromModel(estimator=LogisticRegression(),
                                          threshold=threshold,
                                          norm_order=order)
            transformer.fit(X, y)
            assert hasattr(transformer.estimator_, 'coef_')
            X_new = transformer.transform(X)
            assert_less(X_new.shape[1], X.shape[1])

            # Manually check that the norm is correctly performed
            est.fit(X, y)
            importances = np.linalg.norm(est.coef_, axis=0, ord=order)
            feature_mask = importances > func(importances)
            assert_array_almost_equal(X_new, X[:, feature_mask]) 
Example 14
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_svm.py    License: MIT License 6 votes vote down vote up
def test_weight():
    # Test class weights
    clf = svm.SVC(gamma='scale', class_weight={1: 0.1})
    # we give a small weights to class 1
    clf.fit(X, Y)
    # so all predicted values belong to class 2
    assert_array_almost_equal(clf.predict(X), [2] * 6)

    X_, y_ = make_classification(n_samples=200, n_features=10,
                                 weights=[0.833, 0.167], random_state=2)

    for clf in (linear_model.LogisticRegression(),
                svm.LinearSVC(random_state=0), svm.SVC(gamma="scale")):
        clf.set_params(class_weight={0: .1, 1: 10})
        clf.fit(X_[:100], y_[:100])
        y_pred = clf.predict(X_[100:])
        assert f1_score(y_[100:], y_pred) > .3 
Example 15
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_validation.py    License: MIT License 6 votes vote down vote up
def test_cross_val_score_predict_groups():
    # Check if ValueError (when groups is None) propagates to cross_val_score
    # and cross_val_predict
    # And also check if groups is correctly passed to the cv object
    X, y = make_classification(n_samples=20, n_classes=2, random_state=0)

    clf = SVC(kernel="linear")

    group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(),
                 GroupShuffleSplit()]
    for cv in group_cvs:
        assert_raise_message(ValueError,
                             "The 'groups' parameter should not be None.",
                             cross_val_score, estimator=clf, X=X, y=y, cv=cv)
        assert_raise_message(ValueError,
                             "The 'groups' parameter should not be None.",
                             cross_val_predict, estimator=clf, X=X, y=y, cv=cv) 
Example 16
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_validation.py    License: MIT License 6 votes vote down vote up
def test_cross_val_predict_unbalanced():
    X, y = make_classification(n_samples=100, n_features=2, n_redundant=0,
                               n_informative=2, n_clusters_per_class=1,
                               random_state=1)
    # Change the first sample to a new class
    y[0] = 2
    clf = LogisticRegression(random_state=1)
    cv = StratifiedKFold(n_splits=2, random_state=1)
    train, test = list(cv.split(X, y))
    yhat_proba = cross_val_predict(clf, X, y, cv=cv, method="predict_proba")
    assert y[test[0]][0] == 2  # sanity check for further assertions
    assert np.all(yhat_proba[test[0]][:, 2] == 0)
    assert np.all(yhat_proba[test[0]][:, 0:1] > 0)
    assert np.all(yhat_proba[test[1]] > 0)
    assert_array_almost_equal(yhat_proba.sum(axis=1), np.ones(y.shape),
                              decimal=12) 
Example 17
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_validation.py    License: MIT License 6 votes vote down vote up
def test_learning_curve_verbose():
    X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
                               n_redundant=0, n_classes=2,
                               n_clusters_per_class=1, random_state=0)
    estimator = MockImprovingEstimator(20)

    old_stdout = sys.stdout
    sys.stdout = StringIO()
    try:
        train_sizes, train_scores, test_scores = \
            learning_curve(estimator, X, y, cv=3, verbose=1)
    finally:
        out = sys.stdout.getvalue()
        sys.stdout.close()
        sys.stdout = old_stdout

    assert("[learning_curve]" in out) 
Example 18
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_validation.py    License: MIT License 6 votes vote down vote up
def test_learning_curve_incremental_learning_unsupervised():
    X, _ = make_classification(n_samples=30, n_features=1, n_informative=1,
                               n_redundant=0, n_classes=2,
                               n_clusters_per_class=1, random_state=0)
    estimator = MockIncrementalImprovingEstimator(20)
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y=None, cv=3, exploit_incremental_learning=True,
        train_sizes=np.linspace(0.1, 1.0, 10))
    assert_array_equal(train_sizes, np.linspace(2, 20, 10))
    assert_array_almost_equal(train_scores.mean(axis=1),
                              np.linspace(1.9, 1.0, 10))
    assert_array_almost_equal(test_scores.mean(axis=1),
                              np.linspace(0.1, 1.0, 10))


# 0.23. warning about tol not having its correct default value. 
Example 19
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_validation.py    License: MIT License 6 votes vote down vote up
def test_learning_curve_batch_and_incremental_learning_are_equal():
    X, y = make_classification(n_samples=30, n_features=1, n_informative=1,
                               n_redundant=0, n_classes=2,
                               n_clusters_per_class=1, random_state=0)
    train_sizes = np.linspace(0.2, 1.0, 5)
    estimator = PassiveAggressiveClassifier(max_iter=1, tol=None,
                                            shuffle=False)

    train_sizes_inc, train_scores_inc, test_scores_inc = \
        learning_curve(
            estimator, X, y, train_sizes=train_sizes,
            cv=3, exploit_incremental_learning=True)
    train_sizes_batch, train_scores_batch, test_scores_batch = \
        learning_curve(
            estimator, X, y, cv=3, train_sizes=train_sizes,
            exploit_incremental_learning=False)

    assert_array_equal(train_sizes_inc, train_sizes_batch)
    assert_array_almost_equal(train_scores_inc.mean(axis=1),
                              train_scores_batch.mean(axis=1))
    assert_array_almost_equal(test_scores_inc.mean(axis=1),
                              test_scores_batch.mean(axis=1)) 
Example 20
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_search.py    License: MIT License 6 votes vote down vote up
def test_grid_search_groups():
    # Check if ValueError (when groups is None) propagates to GridSearchCV
    # And also check if groups is correctly passed to the cv object
    rng = np.random.RandomState(0)

    X, y = make_classification(n_samples=15, n_classes=2, random_state=0)
    groups = rng.randint(0, 3, 15)

    clf = LinearSVC(random_state=0)
    grid = {'C': [1]}

    group_cvs = [LeaveOneGroupOut(), LeavePGroupsOut(2), GroupKFold(),
                 GroupShuffleSplit()]
    for cv in group_cvs:
        gs = GridSearchCV(clf, grid, cv=cv)
        assert_raise_message(ValueError,
                             "The 'groups' parameter should not be None.",
                             gs.fit, X, y)
        gs.fit(X, y, groups=groups)

    non_group_cvs = [StratifiedKFold(), StratifiedShuffleSplit()]
    for cv in non_group_cvs:
        gs = GridSearchCV(clf, grid, cv=cv)
        # Should not raise an error
        gs.fit(X, y) 
Example 21
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_search.py    License: MIT License 6 votes vote down vote up
def test_grid_search_sparse():
    # Test that grid search works with both dense and sparse matrices
    X_, y_ = make_classification(n_samples=200, n_features=100, random_state=0)

    clf = LinearSVC()
    cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
    cv.fit(X_[:180], y_[:180])
    y_pred = cv.predict(X_[180:])
    C = cv.best_estimator_.C

    X_ = sp.csr_matrix(X_)
    clf = LinearSVC()
    cv = GridSearchCV(clf, {'C': [0.1, 1.0]})
    cv.fit(X_[:180].tocoo(), y_[:180])
    y_pred2 = cv.predict(X_[180:])
    C2 = cv.best_estimator_.C

    assert np.mean(y_pred == y_pred2) >= .9
    assert_equal(C, C2) 
Example 22
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_search.py    License: MIT License 6 votes vote down vote up
def test_refit_callable_invalid_type():
    """
    Test implementation catches the errors when 'best_index_' returns an
    invalid result.
    """
    def refit_callable_invalid_type(cv_results):
        """
        A dummy function tests when returned 'best_index_' is not integer.
        """
        return None

    X, y = make_classification(n_samples=100, n_features=4,
                               random_state=42)

    clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.1, 1]},
                       scoring='precision', refit=refit_callable_invalid_type,
                       cv=5)
    with pytest.raises(TypeError,
                       match='best_index_ returned is not an integer'):
        clf.fit(X, y) 
Example 23
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_search.py    License: MIT License 6 votes vote down vote up
def test_refit_callable_out_bound(out_bound_value, search_cv):
    """
    Test implementation catches the errors when 'best_index_' returns an
    out of bound result.
    """
    def refit_callable_out_bound(cv_results):
        """
        A dummy function tests when returned 'best_index_' is out of bounds.
        """
        return out_bound_value

    X, y = make_classification(n_samples=100, n_features=4,
                               random_state=42)

    clf = search_cv(LinearSVC(random_state=42), {'C': [0.1, 1]},
                    scoring='precision', refit=refit_callable_out_bound, cv=5)
    with pytest.raises(IndexError, match='best_index_ index out of range'):
        clf.fit(X, y) 
Example 24
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_search.py    License: MIT License 6 votes vote down vote up
def test_refit_callable_multi_metric():
    """
    Test refit=callable in multiple metric evaluation setting
    """
    def refit_callable(cv_results):
        """
        A dummy function tests `refit=callable` interface.
        Return the index of a model that has the least
        `mean_test_prec`.
        """
        assert 'mean_test_prec' in cv_results
        return cv_results['mean_test_prec'].argmin()

    X, y = make_classification(n_samples=100, n_features=4,
                               random_state=42)
    scoring = {'Accuracy': make_scorer(accuracy_score), 'prec': 'precision'}
    clf = GridSearchCV(LinearSVC(random_state=42), {'C': [0.01, 0.1, 1]},
                       scoring=scoring, refit=refit_callable, cv=5)
    clf.fit(X, y)

    assert clf.best_index_ == 0
    # Ensure `best_score_` is disabled when using `refit=callable`
    assert not hasattr(clf, 'best_score_') 
Example 25
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_search.py    License: MIT License 6 votes vote down vote up
def test_grid_search_cv_results_multimetric():
    X, y = make_classification(n_samples=50, n_features=4, random_state=42)

    n_splits = 3
    params = [dict(kernel=['rbf', ], C=[1, 10], gamma=[0.1, 1]),
              dict(kernel=['poly', ], degree=[1, 2])]

    for iid in (False, True):
        grid_searches = []
        for scoring in ({'accuracy': make_scorer(accuracy_score),
                         'recall': make_scorer(recall_score)},
                        'accuracy', 'recall'):
            grid_search = GridSearchCV(SVC(gamma='scale'), cv=n_splits,
                                       iid=iid, param_grid=params,
                                       scoring=scoring, refit=False)
            grid_search.fit(X, y)
            assert_equal(grid_search.iid, iid)
            grid_searches.append(grid_search)

        compare_cv_results_multimetric_with_single(*grid_searches, iid=iid) 
Example 26
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_calibration.py    License: MIT License 6 votes vote down vote up
def test_sample_weight():
    n_samples = 100
    X, y = make_classification(n_samples=2 * n_samples, n_features=6,
                               random_state=42)

    sample_weight = np.random.RandomState(seed=42).uniform(size=len(y))
    X_train, y_train, sw_train = \
        X[:n_samples], y[:n_samples], sample_weight[:n_samples]
    X_test = X[n_samples:]

    for method in ['sigmoid', 'isotonic']:
        base_estimator = LinearSVC(random_state=42)
        calibrated_clf = CalibratedClassifierCV(base_estimator, method=method)
        calibrated_clf.fit(X_train, y_train, sample_weight=sw_train)
        probs_with_sw = calibrated_clf.predict_proba(X_test)

        # As the weights are used for the calibration, they should still yield
        # a different predictions
        calibrated_clf.fit(X_train, y_train)
        probs_without_sw = calibrated_clf.predict_proba(X_test)

        diff = np.linalg.norm(probs_with_sw - probs_without_sw)
        assert_greater(diff, 0.1) 
Example 27
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_sgd.py    License: MIT License 6 votes vote down vote up
def test_l1_ratio():
    # Test if l1 ratio extremes match L1 and L2 penalty settings.
    X, y = datasets.make_classification(n_samples=1000,
                                        n_features=100, n_informative=20,
                                        random_state=1234)

    # test if elasticnet with l1_ratio near 1 gives same result as pure l1
    est_en = SGDClassifier(alpha=0.001, penalty='elasticnet', tol=None,
                           max_iter=6, l1_ratio=0.9999999999,
                           random_state=42).fit(X, y)
    est_l1 = SGDClassifier(alpha=0.001, penalty='l1', max_iter=6,
                           random_state=42, tol=None).fit(X, y)
    assert_array_almost_equal(est_en.coef_, est_l1.coef_)

    # test if elasticnet with l1_ratio near 0 gives same result as pure l2
    est_en = SGDClassifier(alpha=0.001, penalty='elasticnet', tol=None,
                           max_iter=6, l1_ratio=0.0000000001,
                           random_state=42).fit(X, y)
    est_l2 = SGDClassifier(alpha=0.001, penalty='l2', max_iter=6,
                           random_state=42, tol=None).fit(X, y)
    assert_array_almost_equal(est_en.coef_, est_l2.coef_) 
Example 28
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: test_sag.py    License: MIT License 6 votes vote down vote up
def test_sag_classifier_raises_error(solver):
    # Following #13316, the error handling behavior changed in cython sag. This
    # is simply a non-regression test to make sure numerical errors are
    # properly raised.

    # Train a classifier on a simple problem
    rng = np.random.RandomState(42)
    X, y = make_classification(random_state=rng)
    clf = LogisticRegression(solver=solver, random_state=rng, warm_start=True)
    clf.fit(X, y)

    # Trigger a numerical error by:
    # - corrupting the fitted coefficients of the classifier
    # - fit it again starting from its current state thanks to warm_start
    clf.coef_[:] = np.nan

    with pytest.raises(ValueError, match="Floating-point under-/overflow"):
        clf.fit(X, y) 
Example 29
Project: xcessiv   Author: reiinakano   File: functions.py    License: Apache License 2.0 5 votes vote down vote up
def get_sample_dataset(dataset_properties):
    """Returns sample dataset

    Args:
        dataset_properties (dict): Dictionary corresponding to the properties of the dataset
            used to verify the estimator and metric generators.

    Returns:
        X (array-like): Features array

        y (array-like): Labels array

        splits (iterator): This is an iterator that returns train test splits for
            cross-validation purposes on ``X`` and ``y``.
    """
    kwargs = dataset_properties.copy()
    data_type = kwargs.pop('type')
    if data_type == 'multiclass':
        try:
            X, y = datasets.make_classification(random_state=8, **kwargs)
            splits = model_selection.StratifiedKFold(n_splits=2, random_state=8).split(X, y)
        except Exception as e:
            raise exceptions.UserError(repr(e))
    elif data_type == 'iris':
        X, y = datasets.load_iris(return_X_y=True)
        splits = model_selection.StratifiedKFold(n_splits=2, random_state=8).split(X, y)
    elif data_type == 'mnist':
        X, y = datasets.load_digits(return_X_y=True)
        splits = model_selection.StratifiedKFold(n_splits=2, random_state=8).split(X, y)
    elif data_type == 'breast_cancer':
        X, y = datasets.load_breast_cancer(return_X_y=True)
        splits = model_selection.StratifiedKFold(n_splits=2, random_state=8).split(X, y)
    elif data_type == 'boston':
        X, y = datasets.load_boston(return_X_y=True)
        splits = model_selection.KFold(n_splits=2, random_state=8).split(X)
    elif data_type == 'diabetes':
        X, y = datasets.load_diabetes(return_X_y=True)
        splits = model_selection.KFold(n_splits=2, random_state=8).split(X)
    else:
        raise exceptions.UserError('Unknown dataset type {}'.format(dataset_properties['type']))
    return X, y, splits 
Example 30
Project: pygbm   Author: ogrisel   File: test_gradient_boosting.py    License: MIT License 5 votes vote down vote up
def test_early_stopping_loss():
    # Make sure that when scoring is None, the early stopping is done w.r.t to
    # the loss. Using scoring='neg_log_loss' and scoring=None should be
    # equivalent since the loss is precisely the negative log likelihood
    n_samples = int(1e3)
    max_iter = 100
    n_iter_no_change = 5

    X, y = make_classification(n_samples, random_state=0)

    clf_scoring = GradientBoostingClassifier(max_iter=max_iter,
                                             scoring='neg_log_loss',
                                             validation_split=.1,
                                             n_iter_no_change=n_iter_no_change,
                                             tol=1e-4,
                                             verbose=1,
                                             random_state=0)
    clf_scoring.fit(X, y)

    clf_loss = GradientBoostingClassifier(max_iter=max_iter,
                                          scoring=None,
                                          validation_split=.1,
                                          n_iter_no_change=n_iter_no_change,
                                          tol=1e-4,
                                          verbose=1,
                                          random_state=0)
    clf_loss.fit(X, y)

    assert n_iter_no_change < clf_loss.n_iter_ < max_iter
    assert clf_loss.n_iter_ == clf_scoring.n_iter_