99 lines
2.7 KiB
Python
99 lines
2.7 KiB
Python
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import numpy as np
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from numpy.testing import assert_allclose
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from pytest import approx
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from sklearn.utils.stats import _weighted_percentile
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def test_weighted_percentile():
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y = np.empty(102, dtype=np.float64)
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y[:50] = 0
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y[-51:] = 2
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y[-1] = 100000
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y[50] = 1
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sw = np.ones(102, dtype=np.float64)
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sw[-1] = 0.0
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score = _weighted_percentile(y, sw, 50)
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assert approx(score) == 1
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def test_weighted_percentile_equal():
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y = np.empty(102, dtype=np.float64)
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y.fill(0.0)
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sw = np.ones(102, dtype=np.float64)
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sw[-1] = 0.0
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score = _weighted_percentile(y, sw, 50)
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assert score == 0
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def test_weighted_percentile_zero_weight():
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y = np.empty(102, dtype=np.float64)
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y.fill(1.0)
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sw = np.ones(102, dtype=np.float64)
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sw.fill(0.0)
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score = _weighted_percentile(y, sw, 50)
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assert approx(score) == 1.0
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def test_weighted_percentile_zero_weight_zero_percentile():
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y = np.array([0, 1, 2, 3, 4, 5])
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sw = np.array([0, 0, 1, 1, 1, 0])
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score = _weighted_percentile(y, sw, 0)
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assert approx(score) == 2
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score = _weighted_percentile(y, sw, 50)
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assert approx(score) == 3
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score = _weighted_percentile(y, sw, 100)
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assert approx(score) == 4
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def test_weighted_median_equal_weights():
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# Checks weighted percentile=0.5 is same as median when weights equal
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rng = np.random.RandomState(0)
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# Odd size as _weighted_percentile takes lower weighted percentile
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x = rng.randint(10, size=11)
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weights = np.ones(x.shape)
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median = np.median(x)
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w_median = _weighted_percentile(x, weights)
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assert median == approx(w_median)
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def test_weighted_median_integer_weights():
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# Checks weighted percentile=0.5 is same as median when manually weight
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# data
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rng = np.random.RandomState(0)
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x = rng.randint(20, size=10)
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weights = rng.choice(5, size=10)
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x_manual = np.repeat(x, weights)
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median = np.median(x_manual)
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w_median = _weighted_percentile(x, weights)
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assert median == approx(w_median)
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def test_weighted_percentile_2d():
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# Check for when array 2D and sample_weight 1D
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rng = np.random.RandomState(0)
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x1 = rng.randint(10, size=10)
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w1 = rng.choice(5, size=10)
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x2 = rng.randint(20, size=10)
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x_2d = np.vstack((x1, x2)).T
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w_median = _weighted_percentile(x_2d, w1)
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p_axis_0 = [_weighted_percentile(x_2d[:, i], w1) for i in range(x_2d.shape[1])]
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assert_allclose(w_median, p_axis_0)
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# Check when array and sample_weight boht 2D
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w2 = rng.choice(5, size=10)
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w_2d = np.vstack((w1, w2)).T
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w_median = _weighted_percentile(x_2d, w_2d)
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p_axis_0 = [
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_weighted_percentile(x_2d[:, i], w_2d[:, i]) for i in range(x_2d.shape[1])
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]
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assert_allclose(w_median, p_axis_0)
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