import numpy as np import pytest from numpy.testing import assert_allclose from numpy.testing import assert_array_equal from sklearn.ensemble._hist_gradient_boosting.histogram import ( _build_histogram_naive, _build_histogram, _build_histogram_no_hessian, _build_histogram_root_no_hessian, _build_histogram_root, _subtract_histograms ) from sklearn.ensemble._hist_gradient_boosting.common import HISTOGRAM_DTYPE from sklearn.ensemble._hist_gradient_boosting.common import G_H_DTYPE from sklearn.ensemble._hist_gradient_boosting.common import X_BINNED_DTYPE @pytest.mark.parametrize( 'build_func', [_build_histogram_naive, _build_histogram]) def test_build_histogram(build_func): binned_feature = np.array([0, 2, 0, 1, 2, 0, 2, 1], dtype=X_BINNED_DTYPE) # Small sample_indices (below unrolling threshold) ordered_gradients = np.array([0, 1, 3], dtype=G_H_DTYPE) ordered_hessians = np.array([1, 1, 2], dtype=G_H_DTYPE) sample_indices = np.array([0, 2, 3], dtype=np.uint32) hist = np.zeros((1, 3), dtype=HISTOGRAM_DTYPE) build_func(0, sample_indices, binned_feature, ordered_gradients, ordered_hessians, hist) hist = hist[0] assert_array_equal(hist['count'], [2, 1, 0]) assert_allclose(hist['sum_gradients'], [1, 3, 0]) assert_allclose(hist['sum_hessians'], [2, 2, 0]) # Larger sample_indices (above unrolling threshold) sample_indices = np.array([0, 2, 3, 6, 7], dtype=np.uint32) ordered_gradients = np.array([0, 1, 3, 0, 1], dtype=G_H_DTYPE) ordered_hessians = np.array([1, 1, 2, 1, 0], dtype=G_H_DTYPE) hist = np.zeros((1, 3), dtype=HISTOGRAM_DTYPE) build_func(0, sample_indices, binned_feature, ordered_gradients, ordered_hessians, hist) hist = hist[0] assert_array_equal(hist['count'], [2, 2, 1]) assert_allclose(hist['sum_gradients'], [1, 4, 0]) assert_allclose(hist['sum_hessians'], [2, 2, 1]) def test_histogram_sample_order_independence(): # Make sure the order of the samples has no impact on the histogram # computations rng = np.random.RandomState(42) n_sub_samples = 100 n_samples = 1000 n_bins = 256 binned_feature = rng.randint(0, n_bins - 1, size=n_samples, dtype=X_BINNED_DTYPE) sample_indices = rng.choice(np.arange(n_samples, dtype=np.uint32), n_sub_samples, replace=False) ordered_gradients = rng.randn(n_sub_samples).astype(G_H_DTYPE) hist_gc = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) _build_histogram_no_hessian(0, sample_indices, binned_feature, ordered_gradients, hist_gc) ordered_hessians = rng.exponential(size=n_sub_samples).astype(G_H_DTYPE) hist_ghc = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) _build_histogram(0, sample_indices, binned_feature, ordered_gradients, ordered_hessians, hist_ghc) permutation = rng.permutation(n_sub_samples) hist_gc_perm = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) _build_histogram_no_hessian(0, sample_indices[permutation], binned_feature, ordered_gradients[permutation], hist_gc_perm) hist_ghc_perm = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) _build_histogram(0, sample_indices[permutation], binned_feature, ordered_gradients[permutation], ordered_hessians[permutation], hist_ghc_perm) hist_gc = hist_gc[0] hist_ghc = hist_ghc[0] hist_gc_perm = hist_gc_perm[0] hist_ghc_perm = hist_ghc_perm[0] assert_allclose(hist_gc['sum_gradients'], hist_gc_perm['sum_gradients']) assert_array_equal(hist_gc['count'], hist_gc_perm['count']) assert_allclose(hist_ghc['sum_gradients'], hist_ghc_perm['sum_gradients']) assert_allclose(hist_ghc['sum_hessians'], hist_ghc_perm['sum_hessians']) assert_array_equal(hist_ghc['count'], hist_ghc_perm['count']) @pytest.mark.parametrize("constant_hessian", [True, False]) def test_unrolled_equivalent_to_naive(constant_hessian): # Make sure the different unrolled histogram computations give the same # results as the naive one. rng = np.random.RandomState(42) n_samples = 10 n_bins = 5 sample_indices = np.arange(n_samples).astype(np.uint32) binned_feature = rng.randint(0, n_bins - 1, size=n_samples, dtype=np.uint8) ordered_gradients = rng.randn(n_samples).astype(G_H_DTYPE) if constant_hessian: ordered_hessians = np.ones(n_samples, dtype=G_H_DTYPE) else: ordered_hessians = rng.lognormal(size=n_samples).astype(G_H_DTYPE) hist_gc_root = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) hist_ghc_root = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) hist_gc = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) hist_ghc = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) hist_naive = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) _build_histogram_root_no_hessian(0, binned_feature, ordered_gradients, hist_gc_root) _build_histogram_root(0, binned_feature, ordered_gradients, ordered_hessians, hist_ghc_root) _build_histogram_no_hessian(0, sample_indices, binned_feature, ordered_gradients, hist_gc) _build_histogram(0, sample_indices, binned_feature, ordered_gradients, ordered_hessians, hist_ghc) _build_histogram_naive(0, sample_indices, binned_feature, ordered_gradients, ordered_hessians, hist_naive) hist_naive = hist_naive[0] hist_gc_root = hist_gc_root[0] hist_ghc_root = hist_ghc_root[0] hist_gc = hist_gc[0] hist_ghc = hist_ghc[0] for hist in (hist_gc_root, hist_ghc_root, hist_gc, hist_ghc): assert_array_equal(hist['count'], hist_naive['count']) assert_allclose(hist['sum_gradients'], hist_naive['sum_gradients']) for hist in (hist_ghc_root, hist_ghc): assert_allclose(hist['sum_hessians'], hist_naive['sum_hessians']) for hist in (hist_gc_root, hist_gc): assert_array_equal(hist['sum_hessians'], np.zeros(n_bins)) @pytest.mark.parametrize("constant_hessian", [True, False]) def test_hist_subtraction(constant_hessian): # Make sure the histogram subtraction trick gives the same result as the # classical method. rng = np.random.RandomState(42) n_samples = 10 n_bins = 5 sample_indices = np.arange(n_samples).astype(np.uint32) binned_feature = rng.randint(0, n_bins - 1, size=n_samples, dtype=np.uint8) ordered_gradients = rng.randn(n_samples).astype(G_H_DTYPE) if constant_hessian: ordered_hessians = np.ones(n_samples, dtype=G_H_DTYPE) else: ordered_hessians = rng.lognormal(size=n_samples).astype(G_H_DTYPE) hist_parent = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) if constant_hessian: _build_histogram_no_hessian(0, sample_indices, binned_feature, ordered_gradients, hist_parent) else: _build_histogram(0, sample_indices, binned_feature, ordered_gradients, ordered_hessians, hist_parent) mask = rng.randint(0, 2, n_samples).astype(bool) sample_indices_left = sample_indices[mask] ordered_gradients_left = ordered_gradients[mask] ordered_hessians_left = ordered_hessians[mask] hist_left = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) if constant_hessian: _build_histogram_no_hessian(0, sample_indices_left, binned_feature, ordered_gradients_left, hist_left) else: _build_histogram(0, sample_indices_left, binned_feature, ordered_gradients_left, ordered_hessians_left, hist_left) sample_indices_right = sample_indices[~mask] ordered_gradients_right = ordered_gradients[~mask] ordered_hessians_right = ordered_hessians[~mask] hist_right = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) if constant_hessian: _build_histogram_no_hessian(0, sample_indices_right, binned_feature, ordered_gradients_right, hist_right) else: _build_histogram(0, sample_indices_right, binned_feature, ordered_gradients_right, ordered_hessians_right, hist_right) hist_left_sub = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) hist_right_sub = np.zeros((1, n_bins), dtype=HISTOGRAM_DTYPE) _subtract_histograms(0, n_bins, hist_parent, hist_right, hist_left_sub) _subtract_histograms(0, n_bins, hist_parent, hist_left, hist_right_sub) for key in ('count', 'sum_hessians', 'sum_gradients'): assert_allclose(hist_left[key], hist_left_sub[key], rtol=1e-6) assert_allclose(hist_right[key], hist_right_sub[key], rtol=1e-6)