reconnect moved files to git repo

venv/lib/python3.11/site-packages/sklearn/utils/tests/test_stats.py

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