Repository: scikit-learn/scikit-learn
File: sklearn/utils/tests/test_stats.py (Python)

Code before:
def test_weighted_percentile_equal():
    """Check `weighted_percentile` with unit weights and all 0 values in `array`."""
    y = np.zeros(102, dtype=np.float64)
    sw = np.ones(102, dtype=np.float64)
    score = _weighted_percentile(y, sw, 50)
    assert approx(score) == 0


# XXX: is this really what we want? Shouldn't we raise instead?
# https://github.com/scikit-learn/scikit-learn/issues/31032
def test_weighted_percentile_all_zero_weights():
    """Check `weighted_percentile` with all weights equal to 0 returns last index."""
    y = np.arange(10)
    sw = np.zeros(10)
    value = _weighted_percentile(y, sw, 50)
    assert approx(value) == 9.0


@pytest.mark.parametrize("average", [True, False])
@pytest.mark.parametrize("percentile_rank, expected_value", [(0, 2), (50, 3), (100, 5)])
def test_weighted_percentile_ignores_zero_weight(
    average, percentile_rank, expected_value
):
    """Check leading, trailing and middle 0 weights behaves correctly.

    Check that leading zero-weight observations ignored when `percentile_rank=0`.
    See #20528 for details.
    Check that when `average=True` and the `j+1` ('plus one') index has sample weight
    of 0, it is ignored. Also check that trailing zero weight observations are ignored
    (e.g., when `percentile_rank=100`).
    """
    y = np.array([0, 1, 2, 3, 4, 5, 6])
    sw = np.array([0, 0, 1, 1, 0, 1, 0])

    value = _weighted_percentile(
        np.vstack((y, y)).T, np.vstack((sw, sw)).T, percentile_rank, average=average
    )
    for idx in range(2):
        assert approx(value[idx]) == expected_value


@pytest.mark.parametrize("average", [True, False])
@pytest.mark.parametrize("percentile_rank", [20, 35, 50, 61])
def test_weighted_percentile_frequency_weight_semantics(
    global_random_seed, percentile_rank, average
):
    """Check integer weights give the same result as repeating values."""
    rng = np.random.RandomState(global_random_seed)
    x = rng.randint(20, size=10)
    weights = rng.choice(5, size=10)


Review comment (suggestion):
```suggestion
    Check that leading zero-weight observations are ignored when `percentile_rank=0`.
```

Code after:
def test_weighted_percentile_equal():
    """Check `weighted_percentile` with unit weights and all 0 values in `array`."""
    y = np.zeros(102, dtype=np.float64)
    sw = np.ones(102, dtype=np.float64)
    score = _weighted_percentile(y, sw, 50)
    assert approx(score) == 0


# XXX: is this really what we want? Shouldn't we raise instead?
# https://github.com/scikit-learn/scikit-learn/issues/31032
def test_weighted_percentile_all_zero_weights():
    """Check `weighted_percentile` with all weights equal to 0 returns last index."""
    y = np.arange(10)
    sw = np.zeros(10)
    value = _weighted_percentile(y, sw, 50)
    assert approx(value) == 9.0


@pytest.mark.parametrize("average", [True, False])
@pytest.mark.parametrize("percentile_rank, expected_value", [(0, 2), (50, 3), (100, 5)])
def test_weighted_percentile_ignores_zero_weight(
    average, percentile_rank, expected_value
):
    """Check leading, trailing and middle 0 weights behave correctly.

    Check that leading zero-weight observations are ignored when `percentile_rank=0`.
    See #20528 for details.
    Check that when `average=True` and the `j+1` ('plus one') index has sample weight
    of 0, it is ignored. Also check that trailing zero weight observations are ignored
    (e.g., when `percentile_rank=100`).
    """
    y = np.array([0, 1, 2, 3, 4, 5, 6])
    sw = np.array([0, 0, 1, 1, 0, 1, 0])

    value = _weighted_percentile(
        np.vstack((y, y)).T, np.vstack((sw, sw)).T, percentile_rank, average=average
    )
    for idx in range(2):
        assert approx(value[idx]) == expected_value


@pytest.mark.parametrize("average", [True, False])
@pytest.mark.parametrize("percentile_rank", [20, 35, 50, 61])
def test_weighted_percentile_frequency_weight_semantics(
    global_random_seed, percentile_rank, average
):
    """Check integer weights give the same result as repeating values."""
    rng = np.random.RandomState(global_random_seed)
    x = rng.randint(20, size=10)
    weights = rng.choice(5, size=10)