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"])
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_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'])
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_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(np.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)
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(np.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)
