def test_min_gain_to_split():
# Try to split a pure node (all gradients are equal, same for hessians)
# with min_gain_to_split = 0 and make sure that the node is not split (best
# possible gain = -1). Note: before the strict inequality comparison, this
# test would fail because the node would be split with a gain of 0.
rng = np.random.RandomState(42)
l2_regularization = 0
min_hessian_to_split = 0
min_samples_leaf = 1
min_gain_to_split = 0.
n_bins = 255
n_samples = 100
X_binned = np.asfortranarray(
rng.randint(0, n_bins, size=(n_samples, 1)), dtype=X_BINNED_DTYPE)
binned_feature = X_binned[:, 0]
sample_indices = np.arange(n_samples, dtype=np.uint32)
all_hessians = np.ones_like(binned_feature, dtype=G_H_DTYPE)
all_gradients = np.ones_like(binned_feature, dtype=G_H_DTYPE)
sum_gradients = all_gradients.sum()
sum_hessians = all_hessians.sum()
hessians_are_constant = False
actual_n_bins = np.array([n_bins] * X_binned.shape[1],
dtype=np.uint32)
builder = HistogramBuilder(X_binned, n_bins, all_gradients,
all_hessians, hessians_are_constant)
splitter = Splitter(X_binned, n_bins, actual_n_bins,
l2_regularization, min_hessian_to_split,
min_samples_leaf, min_gain_to_split,
hessians_are_constant)
histograms = builder.compute_histograms_brute(sample_indices)
split_info = splitter.find_node_split(sample_indices, histograms,
sum_gradients, sum_hessians)
assert split_info.gain == -1
def test_split_indices():
# Check that split_indices returns the correct splits and that
# splitter.partition is consistent with what is returned.
rng = np.random.RandomState(421)
n_bins = 5
n_samples = 10
l2_regularization = 0.
min_hessian_to_split = 1e-3
min_samples_leaf = 1
min_gain_to_split = 0.
# split will happen on feature 1 and on bin 3
X_binned = [[0, 0], [0, 3], [0, 4], [0, 0], [0, 0], [0, 0], [0, 0], [0, 4],
[0, 0], [0, 4]]
X_binned = np.asfortranarray(X_binned, dtype=X_BINNED_DTYPE)
sample_indices = np.arange(n_samples, dtype=np.uint32)
all_gradients = rng.randn(n_samples).astype(G_H_DTYPE)
all_hessians = np.ones(1, dtype=G_H_DTYPE)
sum_gradients = all_gradients.sum()
sum_hessians = 1 * n_samples
hessians_are_constant = True
builder = HistogramBuilder(X_binned, n_bins, all_gradients, all_hessians,
hessians_are_constant)
n_bins_non_missing = np.array([n_bins] * X_binned.shape[1],
dtype=np.uint32)
has_missing_values = np.array([False] * X_binned.shape[1], dtype=np.uint8)
monotonic_cst = np.array([MonotonicConstraint.NO_CST] * X_binned.shape[1],
dtype=np.int8)
missing_values_bin_idx = n_bins - 1
splitter = Splitter(X_binned, n_bins_non_missing, missing_values_bin_idx,
has_missing_values, monotonic_cst, l2_regularization,
min_hessian_to_split, min_samples_leaf,
min_gain_to_split, hessians_are_constant)
assert np.all(sample_indices == splitter.partition)
histograms = builder.compute_histograms_brute(sample_indices)
value = compute_node_value(sum_gradients, sum_hessians, -np.inf, np.inf,
l2_regularization)
si_root = splitter.find_node_split(n_samples, histograms, sum_gradients,
sum_hessians, value)
# sanity checks for best split
assert si_root.feature_idx == 1
assert si_root.bin_idx == 3
samples_left, samples_right, position_right = splitter.split_indices(
si_root, splitter.partition)
assert set(samples_left) == set([0, 1, 3, 4, 5, 6, 8])
assert set(samples_right) == set([2, 7, 9])
assert list(samples_left) == list(splitter.partition[:position_right])
assert list(samples_right) == list(splitter.partition[position_right:])
# Check that the resulting split indices sizes are consistent with the
# count statistics anticipated when looking for the best split.
assert samples_left.shape[0] == si_root.n_samples_left
assert samples_right.shape[0] == si_root.n_samples_right
def test_min_gain_to_split():
# Try to split a pure node (all gradients are equal, same for hessians)
# with min_gain_to_split = 0 and make sure that the node is not split (best
# possible gain = -1). Note: before the strict inequality comparison, this
# test would fail because the node would be split with a gain of 0.
rng = np.random.RandomState(42)
l2_regularization = 0
min_hessian_to_split = 0
min_samples_leaf = 1
min_gain_to_split = 0.
n_bins = 255
n_samples = 100
X_binned = np.asfortranarray(rng.randint(0, n_bins, size=(n_samples, 1)),
dtype=X_BINNED_DTYPE)
binned_feature = X_binned[:, 0]
sample_indices = np.arange(n_samples, dtype=np.uint32)
all_hessians = np.ones_like(binned_feature, dtype=G_H_DTYPE)
all_gradients = np.ones_like(binned_feature, dtype=G_H_DTYPE)
sum_gradients = all_gradients.sum()
sum_hessians = all_hessians.sum()
hessians_are_constant = False
actual_n_bins = np.array([n_bins] * X_binned.shape[1], dtype=np.uint32)
builder = HistogramBuilder(X_binned, n_bins, all_gradients, all_hessians,
hessians_are_constant)
splitter = Splitter(X_binned, actual_n_bins, l2_regularization,
min_hessian_to_split, min_samples_leaf,
min_gain_to_split, hessians_are_constant)
histograms = builder.compute_histograms_brute(sample_indices)
split_info = splitter.find_node_split(n_samples, histograms, sum_gradients,
sum_hessians)
assert split_info.gain == -1
def test_splitting_categorical_cat_smooth(
X_binned, has_missing_values, n_bins_non_missing
):
# Checks categorical splits are correct when the MIN_CAT_SUPPORT constraint
# isn't respected: there are no splits
n_bins = max(X_binned) + 1
n_samples = len(X_binned)
X_binned = np.array([X_binned], dtype=X_BINNED_DTYPE).T
X_binned = np.asfortranarray(X_binned)
l2_regularization = 0.0
min_hessian_to_split = 1e-3
min_samples_leaf = 1
min_gain_to_split = 0.0
sample_indices = np.arange(n_samples, dtype=np.uint32)
all_gradients = np.ones(n_samples, dtype=G_H_DTYPE)
has_missing_values = np.array([has_missing_values], dtype=np.uint8)
all_hessians = np.ones(1, dtype=G_H_DTYPE)
sum_gradients = all_gradients.sum()
sum_hessians = n_samples
hessians_are_constant = True
builder = HistogramBuilder(
X_binned, n_bins, all_gradients, all_hessians, hessians_are_constant, n_threads
)
n_bins_non_missing = np.array([n_bins_non_missing], dtype=np.uint32)
monotonic_cst = np.array(
[MonotonicConstraint.NO_CST] * X_binned.shape[1], dtype=np.int8
)
is_categorical = np.ones_like(monotonic_cst, dtype=np.uint8)
missing_values_bin_idx = n_bins - 1
splitter = Splitter(
X_binned,
n_bins_non_missing,
missing_values_bin_idx,
has_missing_values,
is_categorical,
monotonic_cst,
l2_regularization,
min_hessian_to_split,
min_samples_leaf,
min_gain_to_split,
hessians_are_constant,
)
histograms = builder.compute_histograms_brute(sample_indices)
value = compute_node_value(
sum_gradients, sum_hessians, -np.inf, np.inf, l2_regularization
)
split_info = splitter.find_node_split(
n_samples, histograms, sum_gradients, sum_hessians, value
)
# no split found
assert split_info.gain == -1
def test_histogram_split(n_bins):
rng = np.random.RandomState(42)
feature_idx = 0
l2_regularization = 0
min_hessian_to_split = 1e-3
min_samples_leaf = 1
min_gain_to_split = 0.
X_binned = np.asfortranarray(rng.randint(0, n_bins - 1,
size=(int(1e4), 1)),
dtype=X_BINNED_DTYPE)
binned_feature = X_binned.T[feature_idx]
sample_indices = np.arange(binned_feature.shape[0], dtype=np.uint32)
ordered_hessians = np.ones_like(binned_feature, dtype=G_H_DTYPE)
all_hessians = ordered_hessians
sum_hessians = all_hessians.sum()
hessians_are_constant = False
for true_bin in range(1, n_bins - 2):
for sign in [-1, 1]:
ordered_gradients = np.full_like(binned_feature,
sign,
dtype=G_H_DTYPE)
ordered_gradients[binned_feature <= true_bin] *= -1
all_gradients = ordered_gradients
sum_gradients = all_gradients.sum()
builder = HistogramBuilder(X_binned, n_bins, all_gradients,
all_hessians, hessians_are_constant)
n_bins_non_missing = np.array([n_bins - 1] * X_binned.shape[1],
dtype=np.uint32)
has_missing_values = np.array([False] * X_binned.shape[1],
dtype=np.uint8)
monotonic_cst = np.array([MonotonicConstraint.NO_CST] *
X_binned.shape[1],
dtype=np.int8)
is_categorical = np.zeros_like(monotonic_cst, dtype=np.uint8)
missing_values_bin_idx = n_bins - 1
splitter = Splitter(X_binned, n_bins_non_missing,
missing_values_bin_idx, has_missing_values,
is_categorical, monotonic_cst,
l2_regularization, min_hessian_to_split,
min_samples_leaf, min_gain_to_split,
hessians_are_constant)
histograms = builder.compute_histograms_brute(sample_indices)
value = compute_node_value(sum_gradients, sum_hessians, -np.inf,
np.inf, l2_regularization)
split_info = splitter.find_node_split(sample_indices.shape[0],
histograms, sum_gradients,
sum_hessians, value)
assert split_info.bin_idx == true_bin
assert split_info.gain >= 0
assert split_info.feature_idx == feature_idx
assert (split_info.n_samples_left +
split_info.n_samples_right == sample_indices.shape[0])
# Constant hessian: 1. per sample.
assert split_info.n_samples_left == split_info.sum_hessian_left
def test_min_gain_to_split():
# Try to split a pure node (all gradients are equal, same for hessians)
# with min_gain_to_split = 0 and make sure that the node is not split (best
# possible gain = -1). Note: before the strict inequality comparison, this
# test would fail because the node would be split with a gain of 0.
rng = np.random.RandomState(42)
l2_regularization = 0
min_hessian_to_split = 0
min_samples_leaf = 1
min_gain_to_split = 0.0
n_bins = 255
n_samples = 100
X_binned = np.asfortranarray(
rng.randint(0, n_bins, size=(n_samples, 1)), dtype=X_BINNED_DTYPE
)
binned_feature = X_binned[:, 0]
sample_indices = np.arange(n_samples, dtype=np.uint32)
all_hessians = np.ones_like(binned_feature, dtype=G_H_DTYPE)
all_gradients = np.ones_like(binned_feature, dtype=G_H_DTYPE)
sum_gradients = all_gradients.sum()
sum_hessians = all_hessians.sum()
hessians_are_constant = False
builder = HistogramBuilder(
X_binned, n_bins, all_gradients, all_hessians, hessians_are_constant, n_threads
)
n_bins_non_missing = np.array([n_bins - 1] * X_binned.shape[1], dtype=np.uint32)
has_missing_values = np.array([False] * X_binned.shape[1], dtype=np.uint8)
monotonic_cst = np.array(
[MonotonicConstraint.NO_CST] * X_binned.shape[1], dtype=np.int8
)
is_categorical = np.zeros_like(monotonic_cst, dtype=np.uint8)
missing_values_bin_idx = n_bins - 1
splitter = Splitter(
X_binned,
n_bins_non_missing,
missing_values_bin_idx,
has_missing_values,
is_categorical,
monotonic_cst,
l2_regularization,
min_hessian_to_split,
min_samples_leaf,
min_gain_to_split,
hessians_are_constant,
)
histograms = builder.compute_histograms_brute(sample_indices)
value = compute_node_value(
sum_gradients, sum_hessians, -np.inf, np.inf, l2_regularization
)
split_info = splitter.find_node_split(
n_samples, histograms, sum_gradients, sum_hessians, value
)
assert split_info.gain == -1
def test_histogram_split(n_bins):
rng = np.random.RandomState(42)
feature_idx = 0
l2_regularization = 0
min_hessian_to_split = 1e-3
min_samples_leaf = 1
min_gain_to_split = 0.
X_binned = np.asfortranarray(
rng.randint(0, n_bins, size=(int(1e4), 1)), dtype=X_BINNED_DTYPE)
binned_feature = X_binned.T[feature_idx]
sample_indices = np.arange(binned_feature.shape[0], dtype=np.uint32)
ordered_hessians = np.ones_like(binned_feature, dtype=G_H_DTYPE)
all_hessians = ordered_hessians
sum_hessians = all_hessians.sum()
hessians_are_constant = False
for true_bin in range(1, n_bins - 1):
for sign in [-1, 1]:
ordered_gradients = np.full_like(binned_feature, sign,
dtype=G_H_DTYPE)
ordered_gradients[binned_feature <= true_bin] *= -1
all_gradients = ordered_gradients
sum_gradients = all_gradients.sum()
actual_n_bins = np.array([n_bins] * X_binned.shape[1],
dtype=np.uint32)
builder = HistogramBuilder(X_binned,
n_bins,
all_gradients,
all_hessians,
hessians_are_constant)
splitter = Splitter(X_binned,
n_bins,
actual_n_bins,
l2_regularization,
min_hessian_to_split,
min_samples_leaf, min_gain_to_split,
hessians_are_constant)
histograms = builder.compute_histograms_brute(sample_indices)
split_info = splitter.find_node_split(
sample_indices, histograms, sum_gradients,
sum_hessians)
assert split_info.bin_idx == true_bin
assert split_info.gain >= 0
assert split_info.feature_idx == feature_idx
assert (split_info.n_samples_left + split_info.n_samples_right
== sample_indices.shape[0])
# Constant hessian: 1. per sample.
assert split_info.n_samples_left == split_info.sum_hessian_left
def test_histogram_split(n_bins):
rng = np.random.RandomState(42)
feature_idx = 0
l2_regularization = 0
min_hessian_to_split = 1e-3
min_samples_leaf = 1
min_gain_to_split = 0.
X_binned = np.asfortranarray(
rng.randint(0, n_bins, size=(int(1e4), 1)), dtype=X_BINNED_DTYPE)
binned_feature = X_binned.T[feature_idx]
sample_indices = np.arange(binned_feature.shape[0], dtype=np.uint32)
ordered_hessians = np.ones_like(binned_feature, dtype=G_H_DTYPE)
all_hessians = ordered_hessians
sum_hessians = all_hessians.sum()
hessians_are_constant = False
for true_bin in range(1, n_bins - 1):
for sign in [-1, 1]:
ordered_gradients = np.full_like(binned_feature, sign,
dtype=G_H_DTYPE)
ordered_gradients[binned_feature <= true_bin] *= -1
all_gradients = ordered_gradients
sum_gradients = all_gradients.sum()
actual_n_bins = np.array([n_bins] * X_binned.shape[1],
dtype=np.uint32)
builder = HistogramBuilder(X_binned,
n_bins,
all_gradients,
all_hessians,
hessians_are_constant)
splitter = Splitter(X_binned,
actual_n_bins,
l2_regularization,
min_hessian_to_split,
min_samples_leaf, min_gain_to_split,
hessians_are_constant)
histograms = builder.compute_histograms_brute(sample_indices)
split_info = splitter.find_node_split(
sample_indices, histograms, sum_gradients,
sum_hessians)
assert split_info.bin_idx == true_bin
assert split_info.gain >= 0
assert split_info.feature_idx == feature_idx
assert (split_info.n_samples_left + split_info.n_samples_right
== sample_indices.shape[0])
# Constant hessian: 1. per sample.
assert split_info.n_samples_left == split_info.sum_hessian_left
def test_gradient_and_hessian_sanity(constant_hessian):
# This test checks that the values of gradients and hessians are
# consistent in different places:
# - in split_info: si.sum_gradient_left + si.sum_gradient_right must be
# equal to the gradient at the node. Same for hessians.
# - in the histograms: summing 'sum_gradients' over the bins must be
# constant across all features, and those sums must be equal to the
# node's gradient. Same for hessians.
rng = np.random.RandomState(42)
n_bins = 10
n_features = 20
n_samples = 500
l2_regularization = 0.
min_hessian_to_split = 1e-3
min_samples_leaf = 1
min_gain_to_split = 0.
X_binned = rng.randint(0, n_bins, size=(n_samples, n_features),
dtype=X_BINNED_DTYPE)
X_binned = np.asfortranarray(X_binned)
sample_indices = np.arange(n_samples, dtype=np.uint32)
all_gradients = rng.randn(n_samples).astype(G_H_DTYPE)
sum_gradients = all_gradients.sum()
if constant_hessian:
all_hessians = np.ones(1, dtype=G_H_DTYPE)
sum_hessians = 1 * n_samples
else:
all_hessians = rng.lognormal(size=n_samples).astype(G_H_DTYPE)
sum_hessians = all_hessians.sum()
builder = HistogramBuilder(X_binned, n_bins, all_gradients,
all_hessians, constant_hessian)
n_bins_non_missing = np.array([n_bins - 1] * X_binned.shape[1],
dtype=np.uint32)
has_missing_values = np.array([False] * X_binned.shape[1], dtype=np.uint8)
missing_values_bin_idx = n_bins - 1
splitter = Splitter(X_binned, n_bins_non_missing, missing_values_bin_idx,
has_missing_values, l2_regularization,
min_hessian_to_split, min_samples_leaf,
min_gain_to_split, constant_hessian)
hists_parent = builder.compute_histograms_brute(sample_indices)
si_parent = splitter.find_node_split(n_samples, hists_parent,
sum_gradients, sum_hessians)
sample_indices_left, sample_indices_right, _ = splitter.split_indices(
si_parent, sample_indices)
hists_left = builder.compute_histograms_brute(sample_indices_left)
hists_right = builder.compute_histograms_brute(sample_indices_right)
si_left = splitter.find_node_split(n_samples, hists_left,
si_parent.sum_gradient_left,
si_parent.sum_hessian_left)
si_right = splitter.find_node_split(n_samples, hists_right,
si_parent.sum_gradient_right,
si_parent.sum_hessian_right)
# make sure that si.sum_gradient_left + si.sum_gradient_right have their
# expected value, same for hessians
for si, indices in (
(si_parent, sample_indices),
(si_left, sample_indices_left),
(si_right, sample_indices_right)):
gradient = si.sum_gradient_right + si.sum_gradient_left
expected_gradient = all_gradients[indices].sum()
hessian = si.sum_hessian_right + si.sum_hessian_left
if constant_hessian:
expected_hessian = indices.shape[0] * all_hessians[0]
else:
expected_hessian = all_hessians[indices].sum()
assert np.isclose(gradient, expected_gradient)
assert np.isclose(hessian, expected_hessian)
# make sure sum of gradients in histograms are the same for all features,
# and make sure they're equal to their expected value
hists_parent = np.asarray(hists_parent, dtype=HISTOGRAM_DTYPE)
hists_left = np.asarray(hists_left, dtype=HISTOGRAM_DTYPE)
hists_right = np.asarray(hists_right, dtype=HISTOGRAM_DTYPE)
for hists, indices in (
(hists_parent, sample_indices),
(hists_left, sample_indices_left),
(hists_right, sample_indices_right)):
# note: gradients and hessians have shape (n_features,),
# we're comparing them to *scalars*. This has the benefit of also
# making sure that all the entries are equal across features.
gradients = hists['sum_gradients'].sum(axis=1) # shape = (n_features,)
expected_gradient = all_gradients[indices].sum() # scalar
hessians = hists['sum_hessians'].sum(axis=1)
if constant_hessian:
# 0 is not the actual hessian, but it's not computed in this case
expected_hessian = 0.
else:
expected_hessian = all_hessians[indices].sum()
assert np.allclose(gradients, expected_gradient)
assert np.allclose(hessians, expected_hessian)
def test_splitting_missing_values(X_binned, all_gradients,
has_missing_values, n_bins_non_missing,
expected_split_on_nan, expected_bin_idx,
expected_go_to_left):
# Make sure missing values are properly supported.
# we build an artificial example with gradients such that the best split
# is on bin_idx=3, when there are no missing values.
# Then we introduce missing values and:
# - make sure the chosen bin is correct (find_best_bin()): it's
# still the same split, even though the index of the bin may change
# - make sure the missing values are mapped to the correct child
# (split_indices())
n_bins = max(X_binned) + 1
n_samples = len(X_binned)
l2_regularization = 0.
min_hessian_to_split = 1e-3
min_samples_leaf = 1
min_gain_to_split = 0.
sample_indices = np.arange(n_samples, dtype=np.uint32)
X_binned = np.array(X_binned, dtype=X_BINNED_DTYPE).reshape(-1, 1)
X_binned = np.asfortranarray(X_binned)
all_gradients = np.array(all_gradients, dtype=G_H_DTYPE)
has_missing_values = np.array([has_missing_values], dtype=np.uint8)
all_hessians = np.ones(1, dtype=G_H_DTYPE)
sum_gradients = all_gradients.sum()
sum_hessians = 1 * n_samples
hessians_are_constant = True
builder = HistogramBuilder(X_binned, n_bins,
all_gradients, all_hessians,
hessians_are_constant)
n_bins_non_missing = np.array([n_bins_non_missing], dtype=np.uint32)
missing_values_bin_idx = n_bins - 1
splitter = Splitter(X_binned, n_bins_non_missing,
missing_values_bin_idx, has_missing_values,
l2_regularization, min_hessian_to_split,
min_samples_leaf, min_gain_to_split,
hessians_are_constant)
histograms = builder.compute_histograms_brute(sample_indices)
split_info = splitter.find_node_split(n_samples, histograms,
sum_gradients, sum_hessians)
assert split_info.bin_idx == expected_bin_idx
if has_missing_values:
assert split_info.missing_go_to_left == expected_go_to_left
split_on_nan = split_info.bin_idx == n_bins_non_missing[0] - 1
assert split_on_nan == expected_split_on_nan
# Make sure the split is properly computed.
# This also make sure missing values are properly assigned to the correct
# child in split_indices()
samples_left, samples_right, _ = splitter.split_indices(
split_info, splitter.partition)
if not expected_split_on_nan:
# When we don't split on nans, the split should always be the same.
assert set(samples_left) == set([0, 1, 2, 3])
assert set(samples_right) == set([4, 5, 6, 7, 8, 9])
else:
# When we split on nans, samples with missing values are always mapped
# to the right child.
missing_samples_indices = np.flatnonzero(
np.array(X_binned) == missing_values_bin_idx)
non_missing_samples_indices = np.flatnonzero(
np.array(X_binned) != missing_values_bin_idx)
assert set(samples_right) == set(missing_samples_indices)
assert set(samples_left) == set(non_missing_samples_indices)
def test_bounded_value_min_gain_to_split():
# The purpose of this test is to show that when computing the gain at a
# given split, the value of the current node should be properly bounded to
# respect the monotonic constraints, because it strongly interacts with
# min_gain_to_split. We build a simple example where gradients are [1, 1,
# 100, 1, 1] (hessians are all ones). The best split happens on the 3rd
# bin, and depending on whether the value of the node is bounded or not,
# the min_gain_to_split constraint is or isn't satisfied.
l2_regularization = 0
min_hessian_to_split = 0
min_samples_leaf = 1
n_bins = n_samples = 5
X_binned = np.arange(n_samples).reshape(-1, 1).astype(X_BINNED_DTYPE)
sample_indices = np.arange(n_samples, dtype=np.uint32)
all_hessians = np.ones(n_samples, dtype=G_H_DTYPE)
all_gradients = np.array([1, 1, 100, 1, 1], dtype=G_H_DTYPE)
sum_gradients = all_gradients.sum()
sum_hessians = all_hessians.sum()
hessians_are_constant = False
builder = HistogramBuilder(X_binned, n_bins, all_gradients,
all_hessians, hessians_are_constant)
n_bins_non_missing = np.array([n_bins - 1] * X_binned.shape[1],
dtype=np.uint32)
has_missing_values = np.array([False] * X_binned.shape[1], dtype=np.uint8)
monotonic_cst = np.array(
[MonotonicConstraint.NO_CST] * X_binned.shape[1],
dtype=np.int8)
missing_values_bin_idx = n_bins - 1
children_lower_bound, children_upper_bound = -np.inf, np.inf
min_gain_to_split = 2000
splitter = Splitter(X_binned, n_bins_non_missing, missing_values_bin_idx,
has_missing_values, monotonic_cst, l2_regularization,
min_hessian_to_split, min_samples_leaf,
min_gain_to_split, hessians_are_constant)
histograms = builder.compute_histograms_brute(sample_indices)
# Since the gradient array is [1, 1, 100, 1, 1]
# the max possible gain happens on the 3rd bin (or equivalently in the 2nd)
# and is equal to about 1307, which less than min_gain_to_split = 2000, so
# the node is considered unsplittable (gain = -1)
current_lower_bound, current_upper_bound = -np.inf, np.inf
value = compute_node_value(sum_gradients, sum_hessians,
current_lower_bound, current_upper_bound,
l2_regularization)
# the unbounded value is equal to -sum_gradients / sum_hessians
assert value == pytest.approx(-104 / 5)
split_info = splitter.find_node_split(n_samples, histograms,
sum_gradients, sum_hessians, value,
lower_bound=children_lower_bound,
upper_bound=children_upper_bound)
assert split_info.gain == -1 # min_gain_to_split not respected
# here again the max possible gain is on the 3rd bin but we now cap the
# value of the node into [-10, inf].
# This means the gain is now about 2430 which is more than the
# min_gain_to_split constraint.
current_lower_bound, current_upper_bound = -10, np.inf
value = compute_node_value(sum_gradients, sum_hessians,
current_lower_bound, current_upper_bound,
l2_regularization)
assert value == -10
split_info = splitter.find_node_split(n_samples, histograms,
sum_gradients, sum_hessians, value,
lower_bound=children_lower_bound,
upper_bound=children_upper_bound)
assert split_info.gain > min_gain_to_split
def test_splitting_categorical_sanity(
X_binned,
all_gradients,
expected_categories_left,
n_bins_non_missing,
missing_values_bin_idx,
has_missing_values,
expected_missing_go_to_left,
):
# Tests various combinations of categorical splits
n_samples = len(X_binned)
n_bins = max(X_binned) + 1
X_binned = np.array(X_binned, dtype=X_BINNED_DTYPE).reshape(-1, 1)
X_binned = np.asfortranarray(X_binned)
l2_regularization = 0.0
min_hessian_to_split = 1e-3
min_samples_leaf = 1
min_gain_to_split = 0.0
sample_indices = np.arange(n_samples, dtype=np.uint32)
all_gradients = np.array(all_gradients, dtype=G_H_DTYPE)
all_hessians = np.ones(1, dtype=G_H_DTYPE)
has_missing_values = np.array([has_missing_values], dtype=np.uint8)
sum_gradients = all_gradients.sum()
sum_hessians = n_samples
hessians_are_constant = True
builder = HistogramBuilder(
X_binned, n_bins, all_gradients, all_hessians, hessians_are_constant, n_threads
)
n_bins_non_missing = np.array([n_bins_non_missing], dtype=np.uint32)
monotonic_cst = np.array(
[MonotonicConstraint.NO_CST] * X_binned.shape[1], dtype=np.int8
)
is_categorical = np.ones_like(monotonic_cst, dtype=np.uint8)
splitter = Splitter(
X_binned,
n_bins_non_missing,
missing_values_bin_idx,
has_missing_values,
is_categorical,
monotonic_cst,
l2_regularization,
min_hessian_to_split,
min_samples_leaf,
min_gain_to_split,
hessians_are_constant,
)
histograms = builder.compute_histograms_brute(sample_indices)
value = compute_node_value(
sum_gradients, sum_hessians, -np.inf, np.inf, l2_regularization
)
split_info = splitter.find_node_split(
n_samples, histograms, sum_gradients, sum_hessians, value
)
assert split_info.is_categorical
assert split_info.gain > 0
_assert_categories_equals_bitset(
expected_categories_left, split_info.left_cat_bitset
)
if has_missing_values:
assert split_info.missing_go_to_left == expected_missing_go_to_left
# If there is no missing value during training, the flag missing_go_to_left
# is set later in the grower.
# make sure samples are split correctly
samples_left, samples_right, _ = splitter.split_indices(
split_info, splitter.partition
)
left_mask = np.isin(X_binned.ravel(), expected_categories_left)
assert_array_equal(sample_indices[left_mask], samples_left)
assert_array_equal(sample_indices[~left_mask], samples_right)
def test_gradient_and_hessian_sanity(constant_hessian):
# This test checks that the values of gradients and hessians are
# consistent in different places:
# - in split_info: si.sum_gradient_left + si.sum_gradient_right must be
# equal to the gradient at the node. Same for hessians.
# - in the histograms: summing 'sum_gradients' over the bins must be
# constant across all features, and those sums must be equal to the
# node's gradient. Same for hessians.
rng = np.random.RandomState(42)
n_bins = 10
n_features = 20
n_samples = 500
l2_regularization = 0.
min_hessian_to_split = 1e-3
min_samples_leaf = 1
min_gain_to_split = 0.
X_binned = rng.randint(0, n_bins, size=(n_samples, n_features),
dtype=X_BINNED_DTYPE)
X_binned = np.asfortranarray(X_binned)
sample_indices = np.arange(n_samples, dtype=np.uint32)
all_gradients = rng.randn(n_samples).astype(G_H_DTYPE)
sum_gradients = all_gradients.sum()
if constant_hessian:
all_hessians = np.ones(1, dtype=G_H_DTYPE)
sum_hessians = 1 * n_samples
else:
all_hessians = rng.lognormal(size=n_samples).astype(G_H_DTYPE)
sum_hessians = all_hessians.sum()
actual_n_bins = np.array([n_bins] * X_binned.shape[1],
dtype=np.uint32)
builder = HistogramBuilder(X_binned, n_bins, all_gradients,
all_hessians, constant_hessian)
splitter = Splitter(X_binned, n_bins, actual_n_bins,
l2_regularization, min_hessian_to_split,
min_samples_leaf, min_gain_to_split, constant_hessian)
hists_parent = builder.compute_histograms_brute(sample_indices)
si_parent = splitter.find_node_split(sample_indices, hists_parent,
sum_gradients, sum_hessians)
sample_indices_left, sample_indices_right, _ = splitter.split_indices(
si_parent, sample_indices)
hists_left = builder.compute_histograms_brute(sample_indices_left)
hists_right = builder.compute_histograms_brute(sample_indices_right)
si_left = splitter.find_node_split(sample_indices_left, hists_left,
si_parent.sum_gradient_left,
si_parent.sum_hessian_left)
si_right = splitter.find_node_split(sample_indices_right, hists_right,
si_parent.sum_gradient_right,
si_parent.sum_hessian_right)
# make sure that si.sum_gradient_left + si.sum_gradient_right have their
# expected value, same for hessians
for si, indices in (
(si_parent, sample_indices),
(si_left, sample_indices_left),
(si_right, sample_indices_right)):
gradient = si.sum_gradient_right + si.sum_gradient_left
expected_gradient = all_gradients[indices].sum()
hessian = si.sum_hessian_right + si.sum_hessian_left
if constant_hessian:
expected_hessian = indices.shape[0] * all_hessians[0]
else:
expected_hessian = all_hessians[indices].sum()
assert np.isclose(gradient, expected_gradient)
assert np.isclose(hessian, expected_hessian)
# make sure sum of gradients in histograms are the same for all features,
# and make sure they're equal to their expected value
hists_parent = np.asarray(hists_parent, dtype=HISTOGRAM_DTYPE)
hists_left = np.asarray(hists_left, dtype=HISTOGRAM_DTYPE)
hists_right = np.asarray(hists_right, dtype=HISTOGRAM_DTYPE)
for hists, indices in (
(hists_parent, sample_indices),
(hists_left, sample_indices_left),
(hists_right, sample_indices_right)):
# note: gradients and hessians have shape (n_features,),
# we're comparing them to *scalars*. This has the benefit of also
# making sure that all the entries are equal across features.
gradients = hists['sum_gradients'].sum(axis=1) # shape = (n_features,)
expected_gradient = all_gradients[indices].sum() # scalar
hessians = hists['sum_hessians'].sum(axis=1)
if constant_hessian:
# 0 is not the actual hessian, but it's not computed in this case
expected_hessian = 0.
else:
expected_hessian = all_hessians[indices].sum()
assert np.allclose(gradients, expected_gradient)
assert np.allclose(hessians, expected_hessian)
def test_split_indices():
# Check that split_indices returns the correct splits and that
# splitter.partition is consistent with what is returned.
rng = np.random.RandomState(421)
n_bins = 5
n_samples = 10
l2_regularization = 0.
min_hessian_to_split = 1e-3
min_samples_leaf = 1
min_gain_to_split = 0.
# split will happen on feature 1 and on bin 3
X_binned = [[0, 0],
[0, 3],
[0, 4],
[0, 0],
[0, 0],
[0, 0],
[0, 0],
[0, 4],
[0, 0],
[0, 4]]
X_binned = np.asfortranarray(X_binned, dtype=X_BINNED_DTYPE)
sample_indices = np.arange(n_samples, dtype=np.uint32)
all_gradients = rng.randn(n_samples).astype(G_H_DTYPE)
all_hessians = np.ones(1, dtype=G_H_DTYPE)
sum_gradients = all_gradients.sum()
sum_hessians = 1 * n_samples
hessians_are_constant = True
actual_n_bins = np.array([n_bins] * X_binned.shape[1],
dtype=np.uint32)
builder = HistogramBuilder(X_binned, n_bins,
all_gradients, all_hessians,
hessians_are_constant)
splitter = Splitter(X_binned, n_bins, actual_n_bins,
l2_regularization, min_hessian_to_split,
min_samples_leaf, min_gain_to_split,
hessians_are_constant)
assert np.all(sample_indices == splitter.partition)
histograms = builder.compute_histograms_brute(sample_indices)
si_root = splitter.find_node_split(sample_indices, histograms,
sum_gradients, sum_hessians)
# sanity checks for best split
assert si_root.feature_idx == 1
assert si_root.bin_idx == 3
samples_left, samples_right, position_right = splitter.split_indices(
si_root, splitter.partition)
assert set(samples_left) == set([0, 1, 3, 4, 5, 6, 8])
assert set(samples_right) == set([2, 7, 9])
assert list(samples_left) == list(splitter.partition[:position_right])
assert list(samples_right) == list(splitter.partition[position_right:])
# Check that the resulting split indices sizes are consistent with the
# count statistics anticipated when looking for the best split.
assert samples_left.shape[0] == si_root.n_samples_left
assert samples_right.shape[0] == si_root.n_samples_right