Repository: scikit-learn/scikit-learn
File: sklearn/ensemble/_hist_gradient_boosting/tests/test_binning.py (Python)

Code before:
DATA = (
    np.random.RandomState(42)
    .normal(loc=[0, 10], scale=[1, 0.01], size=(int(1e6), 2))
    .astype(X_DTYPE)
)


def test_find_binning_thresholds_regular_data():
    data = np.linspace(0, 10, 1001)
    bin_thresholds = _find_binning_thresholds(data, sample_weight=None, max_bins=10)
    assert_allclose(bin_thresholds, [1, 2, 3, 4, 5, 6, 7, 8, 9])

    bin_thresholds = _find_binning_thresholds(data, sample_weight=None, max_bins=5)
    assert_allclose(bin_thresholds, [2, 4, 6, 8])


def test_find_binning_thresholds_small_regular_data():
    data = np.linspace(0, 10, 11)

    bin_thresholds = _find_binning_thresholds(data, sample_weight=None, max_bins=5)
    assert_allclose(bin_thresholds, [2, 4, 6, 8])

    bin_thresholds = _find_binning_thresholds(data, sample_weight=None, max_bins=10)
    assert_allclose(bin_thresholds, [1, 2, 3, 4, 5, 6, 7, 8, 9])

    bin_thresholds = _find_binning_thresholds(data, sample_weight=None, max_bins=11)
    assert_allclose(bin_thresholds, np.arange(10) + 0.5)

    bin_thresholds = _find_binning_thresholds(data, sample_weight=None, max_bins=255)
    assert_allclose(bin_thresholds, np.arange(10) + 0.5)


def test_find_binning_thresholds_random_data():
    bin_thresholds = [
        _find_binning_thresholds(DATA[:, i], sample_weight=None, max_bins=255)
        for i in range(2)
    ]
    for i in range(len(bin_thresholds)):
        assert bin_thresholds[i].shape == (254,)  # 255 - 1
        assert bin_thresholds[i].dtype == DATA.dtype

    assert_allclose(
        bin_thresholds[0][[64, 128, 192]], np.array([-0.7, 0.0, 0.7]), atol=1e-1
    )

    assert_allclose(
        bin_thresholds[1][[64, 128, 192]], np.array([9.99, 10.00, 10.01]), atol=1e-2
    )


def test_find_binning_thresholds_low_n_bins():

Review comment (suggestion):
```suggestion
    bin_thresholds = _find_binning_thresholds(data, max_bins=11)
```

Code after:

DATA = (
    np.random.RandomState(42)
    .normal(loc=[0, 10], scale=[1, 0.01], size=(int(1e6), 2))
    .astype(X_DTYPE)
)


def test_find_binning_thresholds_regular_data():
    data = np.linspace(0, 10, 1001)
    bin_thresholds = _find_binning_thresholds(data, max_bins=10)
    assert_allclose(bin_thresholds, [1, 2, 3, 4, 5, 6, 7, 8, 9])

    bin_thresholds = _find_binning_thresholds(data, max_bins=5)
    assert_allclose(bin_thresholds, [2, 4, 6, 8])


def test_find_binning_thresholds_small_regular_data():
    data = np.linspace(0, 10, 11)

    bin_thresholds = _find_binning_thresholds(data, max_bins=5)
    assert_allclose(bin_thresholds, [2, 4, 6, 8])

    bin_thresholds = _find_binning_thresholds(data, max_bins=10)
    assert_allclose(bin_thresholds, [1, 2, 3, 4, 5, 6, 7, 8, 9])

    bin_thresholds = _find_binning_thresholds(data, max_bins=11)
    assert_allclose(bin_thresholds, np.arange(10) + 0.5)

    bin_thresholds = _find_binning_thresholds(data, max_bins=255)
    assert_allclose(bin_thresholds, np.arange(10) + 0.5)


def test_find_binning_thresholds_random_data():
    bin_thresholds = [
        _find_binning_thresholds(DATA[:, i], max_bins=255) for i in range(2)
    ]
    for i in range(len(bin_thresholds)):
        assert bin_thresholds[i].shape == (254,)  # 255 - 1
        assert bin_thresholds[i].dtype == DATA.dtype

    assert_allclose(
        bin_thresholds[0][[64, 128, 192]], np.array([-0.7, 0.0, 0.7]), atol=1e-1
    )

    assert_allclose(
        bin_thresholds[1][[64, 128, 192]], np.array([9.99, 10.00, 10.01]), atol=1e-2
    )


def test_find_binning_thresholds_low_n_bins():