<commit_before>import numpy as np

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs

from sklearn.utils.class_weight import compute_class_weight
from sklearn.utils.class_weight import compute_sample_weight

from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_warns


def test_compute_class_weight():
    # Test (and demo) compute_class_weight.
    y = np.asarray([2, 2, 2, 3, 3, 4])
    classes = np.unique(y)
    cw = assert_warns(DeprecationWarning,
                      compute_class_weight, "auto", classes, y)
    assert_almost_equal(cw.sum(), classes.shape)
    assert_true(cw[0] < cw[1] < cw[2])

    cw = compute_class_weight("balanced", classes, y)
    # total effect of samples is preserved
    class_counts = np.bincount(y)[2:]
    assert_almost_equal(np.dot(cw, class_counts), y.shape[0])
    assert_true(cw[0] < cw[1] < cw[2])


def test_compute_class_weight_not_present():
    # Raise error when y does not contain all class labels
    classes = np.arange(4)
    y = np.asarray([0, 0, 0, 1, 1, 2])
    assert_raises(ValueError, compute_class_weight, "auto", classes, y)
    assert_raises(ValueError, compute_class_weight, "balanced", classes, y)

def test_compute_class_weight_dict():
    classes = np.arange(3)
    class_weights = {0: 1.0, 1: 2.0, 2: 3.0}
    y = np.asarray([0, 0, 1, 2])
    cw = compute_class_weight(class_weights, classes, y)

    # When the user specifies class weights, compute_class_weights should just
    # return them.
    assert_array_almost_equal(class_weights.values(), cw)

    # When a class weight is specified that isn't in classes, a ValueError
    # should get raised
    class_weights = {0: 1.0, 1: 2.0, 2: 3.0, 4: 1.5}
    assert_raises(ValueError, compute_class_weight, class_weights, classes, y)
    class_weights = {-1: 5.0, 0: 1.0, 1: 2.0, 2: 3.0}
    assert_raises(ValueError, compute_class_weight, class_weights, classes, y)

<commit_msg>Fix assertion for Python 3, use `assert_raise_message`.<commit_after>import numpy as np

from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_blobs

from sklearn.utils.class_weight import compute_class_weight
from sklearn.utils.class_weight import compute_sample_weight

from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_raise_message
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_warns


def test_compute_class_weight():
    # Test (and demo) compute_class_weight.
    y = np.asarray([2, 2, 2, 3, 3, 4])
    classes = np.unique(y)
    cw = assert_warns(DeprecationWarning,
                      compute_class_weight, "auto", classes, y)
    assert_almost_equal(cw.sum(), classes.shape)
    assert_true(cw[0] < cw[1] < cw[2])

    cw = compute_class_weight("balanced", classes, y)
    # total effect of samples is preserved
    class_counts = np.bincount(y)[2:]
    assert_almost_equal(np.dot(cw, class_counts), y.shape[0])
    assert_true(cw[0] < cw[1] < cw[2])


def test_compute_class_weight_not_present():
    # Raise error when y does not contain all class labels
    classes = np.arange(4)
    y = np.asarray([0, 0, 0, 1, 1, 2])
    assert_raises(ValueError, compute_class_weight, "auto", classes, y)
    assert_raises(ValueError, compute_class_weight, "balanced", classes, y)

def test_compute_class_weight_dict():
    classes = np.arange(3)
    class_weights = {0: 1.0, 1: 2.0, 2: 3.0}
    y = np.asarray([0, 0, 1, 2])
    cw = compute_class_weight(class_weights, classes, y)

    # When the user specifies class weights, compute_class_weights should just
    # return them.
    assert_array_almost_equal(np.asarray([1.0, 2.0, 3.0]), cw)

    # When a class weight is specified that isn't in classes, a ValueError
    # should get raised
    msg = 'Class label 4 not present.'
    class_weights = {0: 1.0, 1: 2.0, 2: 3.0, 4: 1.5}
    assert_raise_message(
        ValueError, msg, compute_class_weight, class_weights, classes, y
    )
    msg = 'Class label -1 not present.'
    class_weights = {-1: 5.0, 0: 1.0, 1: 2.0, 2: 3.0}
    assert_raise_message(
        ValueError, msg, compute_class_weight, class_weights, classes, y
    )


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