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))
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))