243 lines
8.8 KiB
Python
243 lines
8.8 KiB
Python
import numpy as np
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from numpy.testing import assert_array_equal, assert_allclose
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import pytest
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from sklearn.ensemble._hist_gradient_boosting.binning import (
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_BinMapper,
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_find_binning_thresholds as _find_binning_thresholds_orig,
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_map_to_bins
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)
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from sklearn.ensemble._hist_gradient_boosting.types import X_DTYPE
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from sklearn.ensemble._hist_gradient_boosting.types import X_BINNED_DTYPE
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DATA = np.random.RandomState(42).normal(
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loc=[0, 10], scale=[1, 0.01], size=(int(1e6), 2)
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).astype(X_DTYPE)
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def _find_binning_thresholds(data, max_bins=256, subsample=int(2e5),
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random_state=None):
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# Just a redef to avoid having to pass arguments all the time (as the
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# function is private we don't use default values for parameters)
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return _find_binning_thresholds_orig(data, max_bins, subsample,
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random_state)
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def test_find_binning_thresholds_regular_data():
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data = np.linspace(0, 10, 1001).reshape(-1, 1)
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bin_thresholds = _find_binning_thresholds(data, max_bins=10)
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assert_allclose(bin_thresholds[0], [1, 2, 3, 4, 5, 6, 7, 8, 9])
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assert len(bin_thresholds) == 1
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bin_thresholds = _find_binning_thresholds(data, max_bins=5)
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assert_allclose(bin_thresholds[0], [2, 4, 6, 8])
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assert len(bin_thresholds) == 1
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def test_find_binning_thresholds_small_regular_data():
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data = np.linspace(0, 10, 11).reshape(-1, 1)
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bin_thresholds = _find_binning_thresholds(data, max_bins=5)
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assert_allclose(bin_thresholds[0], [2, 4, 6, 8])
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bin_thresholds = _find_binning_thresholds(data, max_bins=10)
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assert_allclose(bin_thresholds[0], [1, 2, 3, 4, 5, 6, 7, 8, 9])
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bin_thresholds = _find_binning_thresholds(data, max_bins=11)
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assert_allclose(bin_thresholds[0], np.arange(10) + .5)
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bin_thresholds = _find_binning_thresholds(data, max_bins=255)
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assert_allclose(bin_thresholds[0], np.arange(10) + .5)
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def test_find_binning_thresholds_random_data():
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bin_thresholds = _find_binning_thresholds(DATA, random_state=0)
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assert len(bin_thresholds) == 2
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for i in range(len(bin_thresholds)):
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assert bin_thresholds[i].shape == (255,) # 256 - 1
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assert bin_thresholds[i].dtype == DATA.dtype
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assert_allclose(bin_thresholds[0][[64, 128, 192]],
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np.array([-0.7, 0.0, 0.7]), atol=1e-1)
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assert_allclose(bin_thresholds[1][[64, 128, 192]],
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np.array([9.99, 10.00, 10.01]), atol=1e-2)
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def test_find_binning_thresholds_low_n_bins():
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bin_thresholds = _find_binning_thresholds(DATA, max_bins=128,
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random_state=0)
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assert len(bin_thresholds) == 2
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for i in range(len(bin_thresholds)):
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assert bin_thresholds[i].shape == (127,) # 128 - 1
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assert bin_thresholds[i].dtype == DATA.dtype
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def test_find_binning_thresholds_invalid_n_bins():
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err_msg = 'no smaller than 2 and no larger than 256'
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with pytest.raises(ValueError, match=err_msg):
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_find_binning_thresholds(DATA, max_bins=1024)
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def test_bin_mapper_n_features_transform():
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mapper = _BinMapper(max_bins=42, random_state=42).fit(DATA)
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err_msg = 'This estimator was fitted with 2 features but 4 got passed'
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with pytest.raises(ValueError, match=err_msg):
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mapper.transform(np.repeat(DATA, 2, axis=1))
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@pytest.mark.parametrize('n_bins', [16, 128, 256])
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def test_map_to_bins(n_bins):
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bin_thresholds = _find_binning_thresholds(DATA, max_bins=n_bins,
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random_state=0)
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binned = np.zeros_like(DATA, dtype=X_BINNED_DTYPE, order='F')
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_map_to_bins(DATA, bin_thresholds, binned)
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assert binned.shape == DATA.shape
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assert binned.dtype == np.uint8
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assert binned.flags.f_contiguous
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min_indices = DATA.argmin(axis=0)
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max_indices = DATA.argmax(axis=0)
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for feature_idx, min_idx in enumerate(min_indices):
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assert binned[min_idx, feature_idx] == 0
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for feature_idx, max_idx in enumerate(max_indices):
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assert binned[max_idx, feature_idx] == n_bins - 1
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@pytest.mark.parametrize("n_bins", [5, 10, 42])
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def test_bin_mapper_random_data(n_bins):
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n_samples, n_features = DATA.shape
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expected_count_per_bin = n_samples // n_bins
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tol = int(0.05 * expected_count_per_bin)
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mapper = _BinMapper(max_bins=n_bins, random_state=42).fit(DATA)
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binned = mapper.transform(DATA)
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assert binned.shape == (n_samples, n_features)
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assert binned.dtype == np.uint8
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assert_array_equal(binned.min(axis=0), np.array([0, 0]))
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assert_array_equal(binned.max(axis=0), np.array([n_bins - 1, n_bins - 1]))
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assert len(mapper.bin_thresholds_) == n_features
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for bin_thresholds_feature in mapper.bin_thresholds_:
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assert bin_thresholds_feature.shape == (n_bins - 1,)
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assert bin_thresholds_feature.dtype == DATA.dtype
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assert np.all(mapper.actual_n_bins_ == n_bins)
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# Check that the binned data is approximately balanced across bins.
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for feature_idx in range(n_features):
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for bin_idx in range(n_bins):
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count = (binned[:, feature_idx] == bin_idx).sum()
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assert abs(count - expected_count_per_bin) < tol
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@pytest.mark.parametrize("n_samples, n_bins", [
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(5, 5),
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(5, 10),
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(5, 11),
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(42, 255)
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])
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def test_bin_mapper_small_random_data(n_samples, n_bins):
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data = np.random.RandomState(42).normal(size=n_samples).reshape(-1, 1)
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assert len(np.unique(data)) == n_samples
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mapper = _BinMapper(max_bins=n_bins, random_state=42)
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binned = mapper.fit_transform(data)
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assert binned.shape == data.shape
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assert binned.dtype == np.uint8
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assert_array_equal(binned.ravel()[np.argsort(data.ravel())],
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np.arange(n_samples))
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@pytest.mark.parametrize("n_bins, n_distinct, multiplier", [
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(5, 5, 1),
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(5, 5, 3),
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(255, 12, 42),
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])
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def test_bin_mapper_identity_repeated_values(n_bins, n_distinct, multiplier):
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data = np.array(list(range(n_distinct)) * multiplier).reshape(-1, 1)
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binned = _BinMapper(max_bins=n_bins).fit_transform(data)
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assert_array_equal(data, binned)
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@pytest.mark.parametrize('n_distinct', [2, 7, 42])
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def test_bin_mapper_repeated_values_invariance(n_distinct):
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rng = np.random.RandomState(42)
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distinct_values = rng.normal(size=n_distinct)
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assert len(np.unique(distinct_values)) == n_distinct
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repeated_indices = rng.randint(low=0, high=n_distinct, size=1000)
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data = distinct_values[repeated_indices]
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rng.shuffle(data)
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assert_array_equal(np.unique(data), np.sort(distinct_values))
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data = data.reshape(-1, 1)
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mapper_1 = _BinMapper(max_bins=n_distinct)
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binned_1 = mapper_1.fit_transform(data)
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assert_array_equal(np.unique(binned_1[:, 0]), np.arange(n_distinct))
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# Adding more bins to the mapper yields the same results (same thresholds)
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mapper_2 = _BinMapper(max_bins=min(256, n_distinct * 3))
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binned_2 = mapper_2.fit_transform(data)
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assert_allclose(mapper_1.bin_thresholds_[0], mapper_2.bin_thresholds_[0])
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assert_array_equal(binned_1, binned_2)
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@pytest.mark.parametrize("n_bins, scale, offset", [
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(3, 2, -1),
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(42, 1, 0),
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(256, 0.3, 42),
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])
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def test_bin_mapper_identity_small(n_bins, scale, offset):
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data = np.arange(n_bins).reshape(-1, 1) * scale + offset
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binned = _BinMapper(max_bins=n_bins).fit_transform(data)
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assert_array_equal(binned, np.arange(n_bins).reshape(-1, 1))
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@pytest.mark.parametrize('n_bins_small, n_bins_large', [
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(2, 2),
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(3, 3),
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(4, 4),
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(42, 42),
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(256, 256),
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(5, 17),
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(42, 256),
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])
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def test_bin_mapper_idempotence(n_bins_small, n_bins_large):
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assert n_bins_large >= n_bins_small
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data = np.random.RandomState(42).normal(size=30000).reshape(-1, 1)
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mapper_small = _BinMapper(max_bins=n_bins_small)
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mapper_large = _BinMapper(max_bins=n_bins_large)
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binned_small = mapper_small.fit_transform(data)
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binned_large = mapper_large.fit_transform(binned_small)
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assert_array_equal(binned_small, binned_large)
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@pytest.mark.parametrize('max_bins', [10, 100, 256])
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@pytest.mark.parametrize('diff', [-5, 0, 5])
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def test_actual_n_bins(max_bins, diff):
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# Check that actual_n_bins is n_unique_values when
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# n_unique_values <= max_bins, else max_bins.
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n_unique_values = max_bins + diff
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X = list(range(n_unique_values)) * 2
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X = np.array(X).reshape(-1, 1)
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mapper = _BinMapper(max_bins=max_bins).fit(X)
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assert np.all(mapper.actual_n_bins_ == min(max_bins, n_unique_values))
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def test_subsample():
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# Make sure bin thresholds are different when applying subsampling
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mapper_no_subsample = _BinMapper(subsample=None, random_state=0).fit(DATA)
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mapper_subsample = _BinMapper(subsample=256, random_state=0).fit(DATA)
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for feature in range(DATA.shape[1]):
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assert not np.allclose(mapper_no_subsample.bin_thresholds_[feature],
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mapper_subsample.bin_thresholds_[feature],
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rtol=1e-4)
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