def test_auc_score_non_binary_class():
    # Test that roc_auc_score function returns an error when trying
    # to compute AUC for non-binary class values.
    rng = check_random_state(404)
    y_pred = rng.rand(10)
    # y_true contains only one class value
    y_true = np.zeros(10, dtype="int")
    assert_raise_message(ValueError, "ROC AUC score is not defined", roc_auc_score, y_true, y_pred)
    y_true = np.ones(10, dtype="int")
    assert_raise_message(ValueError, "ROC AUC score is not defined", roc_auc_score, y_true, y_pred)
    y_true = -np.ones(10, dtype="int")
    assert_raise_message(ValueError, "ROC AUC score is not defined", roc_auc_score, y_true, y_pred)
    # y_true contains three different class values
    y_true = rng.randint(0, 3, size=10)
    assert_raise_message(ValueError, "multiclass format is not supported", roc_auc_score, y_true, y_pred)

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        rng = check_random_state(404)
        y_pred = rng.rand(10)
        # y_true contains only one class value
        y_true = np.zeros(10, dtype="int")
        assert_raise_message(ValueError, "ROC AUC score is not defined", roc_auc_score, y_true, y_pred)
        y_true = np.ones(10, dtype="int")
        assert_raise_message(ValueError, "ROC AUC score is not defined", roc_auc_score, y_true, y_pred)
        y_true = -np.ones(10, dtype="int")
        assert_raise_message(ValueError, "ROC AUC score is not defined", roc_auc_score, y_true, y_pred)

        # y_true contains three different class values
        y_true = rng.randint(0, 3, size=10)
        assert_raise_message(ValueError, "multiclass format is not supported", roc_auc_score, y_true, y_pred)

def test_class_weight_auto_classifiers():
    """Test that class_weight="auto" improves f1-score"""

    # This test is broken; its success depends on:
    # * a rare fortuitous RNG seed for make_classification; and
    # * the use of binary F1 over a seemingly arbitrary positive class for two
    #   datasets, and weighted average F1 for the third.
    # Its expectations need to be clarified and reimplemented.
    raise SkipTest("This test requires redefinition")

    classifiers = all_estimators(type_filter="classifier")

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        classifiers = [c for c in classifiers if "class_weight" in c[1]().get_params().keys()]

    for n_classes, weights in zip([2, 3], [[0.8, 0.2], [0.8, 0.1, 0.1]]):
        # create unbalanced dataset
        X, y = make_classification(
            n_classes=n_classes, n_samples=200, n_features=10, weights=weights, random_state=0, n_informative=n_classes
        )
        X = StandardScaler().fit_transform(X)
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=0)
        for name, Classifier in classifiers:
            if (
                name != "NuSVC"
                # the sparse version has a parameter that doesn't do anything
                and not name.startswith("RidgeClassifier")
                # RidgeClassifier behaves unexpected
                # FIXME!
                and not name.endswith("NB")
            ):
                # NaiveBayes classifiers have a somewhat different interface.
                # FIXME SOON!
                yield (check_class_weight_auto_classifiers, name, Classifier, X_train, y_train, X_test, y_test, weights)

def test_tfidf_no_smoothing():
    X = [[1, 1, 1],
         [1, 1, 0],
         [1, 0, 0]]
    tr = TfidfTransformer(smooth_idf=False, norm='l2')
    tfidf = tr.fit_transform(X).toarray()
    assert_true((tfidf >= 0).all())

    # check normalization
    assert_array_almost_equal((tfidf ** 2).sum(axis=1), [1., 1., 1.])

    # the lack of smoothing make IDF fragile in the presence of feature with
    # only zeros
    X = [[1, 1, 0],
         [1, 1, 0],
         [1, 0, 0]]
    tr = TfidfTransformer(smooth_idf=False, norm='l2')

    clean_warning_registry()
    with warnings.catch_warnings(record=True) as w:
        1. / np.array([0.])
        numpy_provides_div0_warning = len(w) == 1

    in_warning_message = 'divide by zero'
    tfidf = assert_warns_message(RuntimeWarning, in_warning_message,
                                 tr.fit_transform, X).toarray()
    if not numpy_provides_div0_warning:
        raise SkipTest("Numpy does not provide div 0 warnings.")

def test_configure():
    # Smoke test the 'configure' step of setup, this tests all the
    # 'configure' functions in the setup.pys in the scikit
    cwd = os.getcwd()
    setup_path = os.path.abspath(os.path.join(sklearn.__path__[0], ".."))
    setup_filename = os.path.join(setup_path, "setup.py")
    if not os.path.exists(setup_filename):
        return
    try:
        os.chdir(setup_path)
        old_argv = sys.argv
        sys.argv = ["setup.py", "config"]
        clean_warning_registry()
        with warnings.catch_warnings():
            # The configuration spits out warnings when not finding
            # Blas/Atlas development headers
            warnings.simplefilter("ignore", UserWarning)
            if PY3:
                with open("setup.py") as f:
                    exec(f.read(), dict(__name__="__main__"))
            else:
                execfile("setup.py", dict(__name__="__main__"))
    finally:
        sys.argv = old_argv
        os.chdir(cwd)

def _tested_linear_classifiers():
    classifiers = all_estimators(type_filter='classifier')

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        for name, clazz in classifiers:
            if ('class_weight' in clazz().get_params().keys() and
                    issubclass(clazz, LinearClassifierMixin)):
                yield name, clazz

def test_warning_scaling_integers():
    # Check warning when scaling integer data
    X = np.array([[1, 2, 0],
                  [0, 0, 0]], dtype=np.uint8)

    w = "Data with input dtype uint8 was converted to float64"

    clean_warning_registry()
    assert_warns_message(DataConversionWarning, w, scale, X)
    assert_warns_message(DataConversionWarning, w, StandardScaler().fit, X)
    assert_warns_message(DataConversionWarning, w, MinMaxScaler().fit, X)

def test_warning_scaling_integers():
    # Check warning when scaling integer data
    X = np.array([[1, 2, 0],
                  [0, 0, 0]], dtype=np.uint8)

    w = "assumes floating point values as input, got uint8"

    clean_warning_registry()
    assert_warns_message(UserWarning, w, scale, X)
    assert_warns_message(UserWarning, w, StandardScaler().fit, X)
    assert_warns_message(UserWarning, w, MinMaxScaler().fit, X)

def test_chi2_unused_feature():
    # Unused feature should evaluate to NaN
    # and should issue no runtime warning
    clean_warning_registry()
    with warnings.catch_warnings(record=True) as warned:
        warnings.simplefilter('always')
        chi, p = chi2([[1, 0], [0, 0]], [1, 0])
        for w in warned:
            if 'divide by zero' in repr(w):
                raise AssertionError('Found unexpected warning %s' % w)
    assert_array_equal(chi, [1, np.nan])
    assert_array_equal(p[1], np.nan)

def test_class_weight_balanced_linear_classifiers():
    classifiers = all_estimators(type_filter='classifier')

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        linear_classifiers = [
            (name, clazz)
            for name, clazz in classifiers
            if 'class_weight' in clazz().get_params().keys()
               and issubclass(clazz, LinearClassifierMixin)]

    for name, Classifier in linear_classifiers:
        yield check_class_weight_balanced_linear_classifier, name, Classifier

def test_warning_scaling_integers():
    """Check warning when scaling integer data"""
    X = np.array([[1, 2, 0],
                  [0, 0, 0]], dtype=np.uint8)

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        warnings.simplefilter("always")
        assert_warns(UserWarning, StandardScaler().fit, X)

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        warnings.simplefilter("always")
        assert_warns(UserWarning, MinMaxScaler().fit, X)

def test_recall_warnings():
    assert_no_warnings(recall_score,
                       np.array([[1, 1], [1, 1]]),
                       np.array([[0, 0], [0, 0]]),
                       average='micro')
    clean_warning_registry()
    with warnings.catch_warnings(record=True) as record:
        warnings.simplefilter('always')
        recall_score(np.array([[0, 0], [0, 0]]),
                     np.array([[1, 1], [1, 1]]),
                     average='micro')
        assert_equal(str(record.pop().message),
                     'Recall is ill-defined and '
                     'being set to 0.0 due to no true samples.')

def test_class_weight_classifiers():
    # test that class_weight works and that the semantics are consistent
    classifiers = all_estimators(type_filter="classifier")

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        classifiers = [c for c in classifiers if "class_weight" in c[1]().get_params().keys()]

    for name, Classifier in classifiers:
        if name == "NuSVC":
            # the sparse version has a parameter that doesn't do anything
            continue
        if name.endswith("NB"):
            # NaiveBayes classifiers have a somewhat different interface.
            # FIXME SOON!
            continue
        yield check_class_weight_classifiers, name, Classifier

def test_warn_deviance():
    """Test if mdeviance and bdeviance give deprecated warning. """
    for loss in ('bdeviance', 'mdeviance'):
        clean_warning_registry()
        with warnings.catch_warnings(record=True) as w:
            # This will raise a DataConversionWarning that we want to
            # "always" raise, elsewhere the warnings gets ignored in the
            # later tests, and the tests that check for this warning fail
            warnings.simplefilter("always", DataConversionWarning)
            clf = GradientBoostingClassifier(loss=loss)
            try:
                clf.fit(X, y)
            except:
                # mdeviance will raise ValueError because only 2 classes
                pass
            # deprecated warning for bdeviance and mdeviance
            assert len(w) == 1

def test_fscore_warnings():
    clean_warning_registry()
    with warnings.catch_warnings(record=True) as record:
        warnings.simplefilter('always')

        for score in [f1_score, partial(fbeta_score, beta=2)]:
            score(np.array([[1, 1], [1, 1]]),
                  np.array([[0, 0], [0, 0]]),
                  average='micro')
            assert_equal(str(record.pop().message),
                         'F-score is ill-defined and '
                         'being set to 0.0 due to no predicted samples.')
            score(np.array([[0, 0], [0, 0]]),
                  np.array([[1, 1], [1, 1]]),
                  average='micro')
            assert_equal(str(record.pop().message),
                         'F-score is ill-defined and '
                         'being set to 0.0 due to no true samples.')

def test_class_weight_auto_linear_classifiers():
    classifiers = all_estimators(type_filter="classifier")

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        linear_classifiers = [
            (name, clazz)
            for name, clazz in classifiers
            if "class_weight" in clazz().get_params().keys() and issubclass(clazz, LinearClassifierMixin)
        ]

    for name, Classifier in linear_classifiers:
        if name == "LogisticRegressionCV":
            # Contrary to RidgeClassifierCV, LogisticRegressionCV use actual
            # CV folds and fit a model for each CV iteration before averaging
            # the coef. Therefore it is expected to not behave exactly as the
            # other linear model.
            continue
        yield check_class_weight_auto_linear_classifier, name, Classifier

def test_configure():
    # Smoke test the 'configure' step of setup, this tests all the
    # 'configure' functions in the setup.pys in scikit-learn
    cwd = os.getcwd()
    setup_path = os.path.abspath(os.path.join(sklearn.__path__[0], '..'))
    setup_filename = os.path.join(setup_path, 'setup.py')
    if not os.path.exists(setup_filename):
        return
    try:
        os.chdir(setup_path)
        old_argv = sys.argv
        sys.argv = ['setup.py', 'config']
        clean_warning_registry()
        with warnings.catch_warnings():
            # The configuration spits out warnings when not finding
            # Blas/Atlas development headers
            warnings.simplefilter('ignore', UserWarning)
            with open('setup.py') as f:
                exec(f.read(), dict(__name__='__main__'))
    finally:
        sys.argv = old_argv
        os.chdir(cwd)

def test_l2_deprecation():
    clean_warning_registry()
    with warnings.catch_warnings(record=True) as w:
        assert_equal(l1_min_c(dense_X, Y1, "l2"),
                     l1_min_c(dense_X, Y1, "squared_hinge"))
        assert_equal(w[0].category, DeprecationWarning)

def test_scaler_int():
    # test that scaler converts integer input to floating
    # for both sparse and dense matrices
    rng = np.random.RandomState(42)
    X = rng.randint(20, size=(4, 5))
    X[:, 0] = 0  # first feature is always of zero
    X_csr = sparse.csr_matrix(X)
    X_csc = sparse.csc_matrix(X)

    null_transform = StandardScaler(with_mean=False, with_std=False, copy=True)
    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        X_null = null_transform.fit_transform(X_csr)
    assert_array_equal(X_null.data, X_csr.data)
    X_orig = null_transform.inverse_transform(X_null)
    assert_array_equal(X_orig.data, X_csr.data)

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        scaler = StandardScaler(with_mean=False).fit(X)
        X_scaled = scaler.transform(X, copy=True)
    assert_false(np.any(np.isnan(X_scaled)))

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        scaler_csr = StandardScaler(with_mean=False).fit(X_csr)
        X_csr_scaled = scaler_csr.transform(X_csr, copy=True)
    assert_false(np.any(np.isnan(X_csr_scaled.data)))

    clean_warning_registry()
    with warnings.catch_warnings(record=True):
        scaler_csc = StandardScaler(with_mean=False).fit(X_csc)
        X_csc_scaled = scaler_csr.transform(X_csc, copy=True)
    assert_false(np.any(np.isnan(X_csc_scaled.data)))

    assert_equal(scaler.mean_, scaler_csr.mean_)
    assert_array_almost_equal(scaler.std_, scaler_csr.std_)

    assert_equal(scaler.mean_, scaler_csc.mean_)
    assert_array_almost_equal(scaler.std_, scaler_csc.std_)

    assert_array_almost_equal(
        X_scaled.mean(axis=0),
        [0., 1.109, 1.856, 21., 1.559], 2)
    assert_array_almost_equal(X_scaled.std(axis=0), [0., 1., 1., 1., 1.])

    X_csr_scaled_mean, X_csr_scaled_std = mean_variance_axis(
        X_csr_scaled.astype(np.float), 0)
    assert_array_almost_equal(X_csr_scaled_mean, X_scaled.mean(axis=0))
    assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))

    # Check that X has not been modified (copy)
    assert_true(X_scaled is not X)
    assert_true(X_csr_scaled is not X_csr)

    X_scaled_back = scaler.inverse_transform(X_scaled)
    assert_true(X_scaled_back is not X)
    assert_true(X_scaled_back is not X_scaled)
    assert_array_almost_equal(X_scaled_back, X)

    X_csr_scaled_back = scaler_csr.inverse_transform(X_csr_scaled)
    assert_true(X_csr_scaled_back is not X_csr)
    assert_true(X_csr_scaled_back is not X_csr_scaled)
    assert_array_almost_equal(X_csr_scaled_back.toarray(), X)

    X_csc_scaled_back = scaler_csr.inverse_transform(X_csc_scaled.tocsc())
    assert_true(X_csc_scaled_back is not X_csc)
    assert_true(X_csc_scaled_back is not X_csc_scaled)
    assert_array_almost_equal(X_csc_scaled_back.toarray(), X)