54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
import numpy as np
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from sklearn.utils.class_weight import compute_class_weight
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from sklearn.utils.testing import assert_array_almost_equal
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from sklearn.utils.testing import assert_almost_equal
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from sklearn.utils.testing import assert_raises
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from sklearn.utils.testing import assert_true
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from sklearn.utils.testing import assert_equal
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def test_compute_class_weight():
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"""Test (and demo) compute_class_weight."""
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y = np.asarray([2, 2, 2, 3, 3, 4])
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classes = np.unique(y)
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cw = compute_class_weight("auto", classes, y)
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assert_almost_equal(cw.sum(), classes.shape)
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assert_true(cw[0] < cw[1] < cw[2])
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def test_compute_class_weight_not_present():
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"""Raise error when y does not contain all class labels"""
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classes = np.arange(4)
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y = np.asarray([0, 0, 0, 1, 1, 2])
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assert_raises(ValueError, compute_class_weight, "auto", classes, y)
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def test_compute_class_weight_auto_negative():
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"""Test compute_class_weight when labels are negative"""
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# Test with balanced class labels.
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classes = np.array([-2, -1, 0])
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y = np.asarray([-1, -1, 0, 0, -2, -2])
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cw = compute_class_weight("auto", classes, y)
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assert_almost_equal(cw.sum(), classes.shape)
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assert_equal(len(cw), len(classes))
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assert_array_almost_equal(cw, np.array([1., 1., 1.]))
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# Test with unbalanced class labels.
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y = np.asarray([-1, 0, 0, -2, -2, -2])
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cw = compute_class_weight("auto", classes, y)
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assert_almost_equal(cw.sum(), classes.shape)
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assert_equal(len(cw), len(classes))
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assert_array_almost_equal(cw, np.array([0.545, 1.636, 0.818]), decimal=3)
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def test_compute_class_weight_auto_unordered():
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"""Test compute_class_weight when classes are unordered"""
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classes = np.array([1, 0, 3])
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y = np.asarray([1, 0, 0, 3, 3, 3])
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cw = compute_class_weight("auto", classes, y)
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assert_almost_equal(cw.sum(), classes.shape)
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assert_equal(len(cw), len(classes))
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assert_array_almost_equal(cw, np.array([1.636, 0.818, 0.545]), decimal=3)
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