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import numpy as np
import pytest
from numpy.testing import assert_allclose, assert_array_equal
from pytest import approx
from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper
from sklearn.ensemble._hist_gradient_boosting.common import (
G_H_DTYPE,
X_BINNED_DTYPE,
X_BITSET_INNER_DTYPE,
X_DTYPE,
Y_DTYPE,
)
from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower
from sklearn.preprocessing import OneHotEncoder
from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
n_threads = _openmp_effective_n_threads()
def _make_training_data(n_bins=256, constant_hessian=True):
rng = np.random.RandomState(42)
n_samples = 10000
# Generate some test data directly binned so as to test the grower code
# independently of the binning logic.
X_binned = rng.randint(0, n_bins - 1, size=(n_samples, 2), dtype=X_BINNED_DTYPE)
X_binned = np.asfortranarray(X_binned)
def true_decision_function(input_features):
"""Ground truth decision function
This is a very simple yet asymmetric decision tree. Therefore the
grower code should have no trouble recovering the decision function
from 10000 training samples.
"""
if input_features[0] <= n_bins // 2:
return -1
else:
return -1 if input_features[1] <= n_bins // 3 else 1
target = np.array([true_decision_function(x) for x in X_binned], dtype=Y_DTYPE)
# Assume a square loss applied to an initial model that always predicts 0
# (hardcoded for this test):
all_gradients = target.astype(G_H_DTYPE)
shape_hessians = 1 if constant_hessian else all_gradients.shape
all_hessians = np.ones(shape=shape_hessians, dtype=G_H_DTYPE)
return X_binned, all_gradients, all_hessians
def _check_children_consistency(parent, left, right):
# Make sure the samples are correctly dispatched from a parent to its
# children
assert parent.left_child is left
assert parent.right_child is right
# each sample from the parent is propagated to one of the two children
assert len(left.sample_indices) + len(right.sample_indices) == len(
parent.sample_indices
)
assert set(left.sample_indices).union(set(right.sample_indices)) == set(
parent.sample_indices
)
# samples are sent either to the left or the right node, never to both
assert set(left.sample_indices).intersection(set(right.sample_indices)) == set()
@pytest.mark.parametrize(
"n_bins, constant_hessian, stopping_param, shrinkage",
[
(11, True, "min_gain_to_split", 0.5),
(11, False, "min_gain_to_split", 1.0),
(11, True, "max_leaf_nodes", 1.0),
(11, False, "max_leaf_nodes", 0.1),
(42, True, "max_leaf_nodes", 0.01),
(42, False, "max_leaf_nodes", 1.0),
(256, True, "min_gain_to_split", 1.0),
(256, True, "max_leaf_nodes", 0.1),
],
)
def test_grow_tree(n_bins, constant_hessian, stopping_param, shrinkage):
X_binned, all_gradients, all_hessians = _make_training_data(
n_bins=n_bins, constant_hessian=constant_hessian
)
n_samples = X_binned.shape[0]
if stopping_param == "max_leaf_nodes":
stopping_param = {"max_leaf_nodes": 3}
else:
stopping_param = {"min_gain_to_split": 0.01}
grower = TreeGrower(
X_binned,
all_gradients,
all_hessians,
n_bins=n_bins,
shrinkage=shrinkage,
min_samples_leaf=1,
**stopping_param,
)
# The root node is not yet split, but the best possible split has
# already been evaluated:
assert grower.root.left_child is None
assert grower.root.right_child is None
root_split = grower.root.split_info
assert root_split.feature_idx == 0
assert root_split.bin_idx == n_bins // 2
assert len(grower.splittable_nodes) == 1
# Calling split next applies the next split and computes the best split
# for each of the two newly introduced children nodes.
left_node, right_node = grower.split_next()
# All training samples have ben split in the two nodes, approximately
# 50%/50%
_check_children_consistency(grower.root, left_node, right_node)
assert len(left_node.sample_indices) > 0.4 * n_samples
assert len(left_node.sample_indices) < 0.6 * n_samples
if grower.min_gain_to_split > 0:
# The left node is too pure: there is no gain to split it further.
assert left_node.split_info.gain < grower.min_gain_to_split
assert left_node in grower.finalized_leaves
# The right node can still be split further, this time on feature #1
split_info = right_node.split_info
assert split_info.gain > 1.0
assert split_info.feature_idx == 1
assert split_info.bin_idx == n_bins // 3
assert right_node.left_child is None
assert right_node.right_child is None
# The right split has not been applied yet. Let's do it now:
assert len(grower.splittable_nodes) == 1
right_left_node, right_right_node = grower.split_next()
_check_children_consistency(right_node, right_left_node, right_right_node)
assert len(right_left_node.sample_indices) > 0.1 * n_samples
assert len(right_left_node.sample_indices) < 0.2 * n_samples
assert len(right_right_node.sample_indices) > 0.2 * n_samples
assert len(right_right_node.sample_indices) < 0.4 * n_samples
# All the leafs are pure, it is not possible to split any further:
assert not grower.splittable_nodes
grower._apply_shrinkage()
# Check the values of the leaves:
assert grower.root.left_child.value == approx(shrinkage)
assert grower.root.right_child.left_child.value == approx(shrinkage)
assert grower.root.right_child.right_child.value == approx(-shrinkage, rel=1e-3)
def test_predictor_from_grower():
# Build a tree on the toy 3-leaf dataset to extract the predictor.
n_bins = 256
X_binned, all_gradients, all_hessians = _make_training_data(n_bins=n_bins)
grower = TreeGrower(
X_binned,
all_gradients,
all_hessians,
n_bins=n_bins,
shrinkage=1.0,
max_leaf_nodes=3,
min_samples_leaf=5,
)
grower.grow()
assert grower.n_nodes == 5 # (2 decision nodes + 3 leaves)
# Check that the node structure can be converted into a predictor
# object to perform predictions at scale
# We pass undefined binning_thresholds because we won't use predict anyway
predictor = grower.make_predictor(
binning_thresholds=np.zeros((X_binned.shape[1], n_bins))
)
assert predictor.nodes.shape[0] == 5
assert predictor.nodes["is_leaf"].sum() == 3
# Probe some predictions for each leaf of the tree
# each group of 3 samples corresponds to a condition in _make_training_data
input_data = np.array(
[
[0, 0],
[42, 99],
[128, 254],
[129, 0],
[129, 85],
[254, 85],
[129, 86],
[129, 254],
[242, 100],
],
dtype=np.uint8,
)
missing_values_bin_idx = n_bins - 1
predictions = predictor.predict_binned(
input_data, missing_values_bin_idx, n_threads
)
expected_targets = [1, 1, 1, 1, 1, 1, -1, -1, -1]
assert np.allclose(predictions, expected_targets)
# Check that training set can be recovered exactly:
predictions = predictor.predict_binned(X_binned, missing_values_bin_idx, n_threads)
assert np.allclose(predictions, -all_gradients)
@pytest.mark.parametrize(
"n_samples, min_samples_leaf, n_bins, constant_hessian, noise",
[
(11, 10, 7, True, 0),
(13, 10, 42, False, 0),
(56, 10, 255, True, 0.1),
(101, 3, 7, True, 0),
(200, 42, 42, False, 0),
(300, 55, 255, True, 0.1),
(300, 301, 255, True, 0.1),
],
)
def test_min_samples_leaf(n_samples, min_samples_leaf, n_bins, constant_hessian, noise):
rng = np.random.RandomState(seed=0)
# data = linear target, 3 features, 1 irrelevant.
X = rng.normal(size=(n_samples, 3))
y = X[:, 0] - X[:, 1]
if noise:
y_scale = y.std()
y += rng.normal(scale=noise, size=n_samples) * y_scale
mapper = _BinMapper(n_bins=n_bins)
X = mapper.fit_transform(X)
all_gradients = y.astype(G_H_DTYPE)
shape_hessian = 1 if constant_hessian else all_gradients.shape
all_hessians = np.ones(shape=shape_hessian, dtype=G_H_DTYPE)
grower = TreeGrower(
X,
all_gradients,
all_hessians,
n_bins=n_bins,
shrinkage=1.0,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=n_samples,
)
grower.grow()
predictor = grower.make_predictor(binning_thresholds=mapper.bin_thresholds_)
if n_samples >= min_samples_leaf:
for node in predictor.nodes:
if node["is_leaf"]:
assert node["count"] >= min_samples_leaf
else:
assert predictor.nodes.shape[0] == 1
assert predictor.nodes[0]["is_leaf"]
assert predictor.nodes[0]["count"] == n_samples
@pytest.mark.parametrize("n_samples, min_samples_leaf", [(99, 50), (100, 50)])
def test_min_samples_leaf_root(n_samples, min_samples_leaf):
# Make sure root node isn't split if n_samples is not at least twice
# min_samples_leaf
rng = np.random.RandomState(seed=0)
n_bins = 256
# data = linear target, 3 features, 1 irrelevant.
X = rng.normal(size=(n_samples, 3))
y = X[:, 0] - X[:, 1]
mapper = _BinMapper(n_bins=n_bins)
X = mapper.fit_transform(X)
all_gradients = y.astype(G_H_DTYPE)
all_hessians = np.ones(shape=1, dtype=G_H_DTYPE)
grower = TreeGrower(
X,
all_gradients,
all_hessians,
n_bins=n_bins,
shrinkage=1.0,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=n_samples,
)
grower.grow()
if n_samples >= min_samples_leaf * 2:
assert len(grower.finalized_leaves) >= 2
else:
assert len(grower.finalized_leaves) == 1
def assert_is_stump(grower):
# To assert that stumps are created when max_depth=1
for leaf in (grower.root.left_child, grower.root.right_child):
assert leaf.left_child is None
assert leaf.right_child is None
@pytest.mark.parametrize("max_depth", [1, 2, 3])
def test_max_depth(max_depth):
# Make sure max_depth parameter works as expected
rng = np.random.RandomState(seed=0)
n_bins = 256
n_samples = 1000
# data = linear target, 3 features, 1 irrelevant.
X = rng.normal(size=(n_samples, 3))
y = X[:, 0] - X[:, 1]
mapper = _BinMapper(n_bins=n_bins)
X = mapper.fit_transform(X)
all_gradients = y.astype(G_H_DTYPE)
all_hessians = np.ones(shape=1, dtype=G_H_DTYPE)
grower = TreeGrower(X, all_gradients, all_hessians, max_depth=max_depth)
grower.grow()
depth = max(leaf.depth for leaf in grower.finalized_leaves)
assert depth == max_depth
if max_depth == 1:
assert_is_stump(grower)
def test_input_validation():
X_binned, all_gradients, all_hessians = _make_training_data()
X_binned_float = X_binned.astype(np.float32)
with pytest.raises(NotImplementedError, match="X_binned must be of type uint8"):
TreeGrower(X_binned_float, all_gradients, all_hessians)
X_binned_C_array = np.ascontiguousarray(X_binned)
with pytest.raises(
ValueError, match="X_binned should be passed as Fortran contiguous array"
):
TreeGrower(X_binned_C_array, all_gradients, all_hessians)
def test_init_parameters_validation():
X_binned, all_gradients, all_hessians = _make_training_data()
with pytest.raises(ValueError, match="min_gain_to_split=-1 must be positive"):
TreeGrower(X_binned, all_gradients, all_hessians, min_gain_to_split=-1)
with pytest.raises(ValueError, match="min_hessian_to_split=-1 must be positive"):
TreeGrower(X_binned, all_gradients, all_hessians, min_hessian_to_split=-1)
def test_missing_value_predict_only():
# Make sure that missing values are supported at predict time even if they
# were not encountered in the training data: the missing values are
# assigned to whichever child has the most samples.
rng = np.random.RandomState(0)
n_samples = 100
X_binned = rng.randint(0, 256, size=(n_samples, 1), dtype=np.uint8)
X_binned = np.asfortranarray(X_binned)
gradients = rng.normal(size=n_samples).astype(G_H_DTYPE)
hessians = np.ones(shape=1, dtype=G_H_DTYPE)
grower = TreeGrower(
X_binned, gradients, hessians, min_samples_leaf=5, has_missing_values=False
)
grower.grow()
# We pass undefined binning_thresholds because we won't use predict anyway
predictor = grower.make_predictor(
binning_thresholds=np.zeros((X_binned.shape[1], X_binned.max() + 1))
)
# go from root to a leaf, always following node with the most samples.
# That's the path nans are supposed to take
node = predictor.nodes[0]
while not node["is_leaf"]:
left = predictor.nodes[node["left"]]
right = predictor.nodes[node["right"]]
node = left if left["count"] > right["count"] else right
prediction_main_path = node["value"]
# now build X_test with only nans, and make sure all predictions are equal
# to prediction_main_path
all_nans = np.full(shape=(n_samples, 1), fill_value=np.nan)
known_cat_bitsets = np.zeros((0, 8), dtype=X_BITSET_INNER_DTYPE)
f_idx_map = np.zeros(0, dtype=np.uint32)
y_pred = predictor.predict(all_nans, known_cat_bitsets, f_idx_map, n_threads)
assert np.all(y_pred == prediction_main_path)
def test_split_on_nan_with_infinite_values():
# Make sure the split on nan situations are respected even when there are
# samples with +inf values (we set the threshold to +inf when we have a
# split on nan so this test makes sure this does not introduce edge-case
# bugs). We need to use the private API so that we can also test
# predict_binned().
X = np.array([0, 1, np.inf, np.nan, np.nan]).reshape(-1, 1)
# the gradient values will force a split on nan situation
gradients = np.array([0, 0, 0, 100, 100], dtype=G_H_DTYPE)
hessians = np.ones(shape=1, dtype=G_H_DTYPE)
bin_mapper = _BinMapper()
X_binned = bin_mapper.fit_transform(X)
n_bins_non_missing = 3
has_missing_values = True
grower = TreeGrower(
X_binned,
gradients,
hessians,
n_bins_non_missing=n_bins_non_missing,
has_missing_values=has_missing_values,
min_samples_leaf=1,
n_threads=n_threads,
)
grower.grow()
predictor = grower.make_predictor(binning_thresholds=bin_mapper.bin_thresholds_)
# sanity check: this was a split on nan
assert predictor.nodes[0]["num_threshold"] == np.inf
assert predictor.nodes[0]["bin_threshold"] == n_bins_non_missing - 1
known_cat_bitsets, f_idx_map = bin_mapper.make_known_categories_bitsets()
# Make sure in particular that the +inf sample is mapped to the left child
# Note that lightgbm "fails" here and will assign the inf sample to the
# right child, even though it's a "split on nan" situation.
predictions = predictor.predict(X, known_cat_bitsets, f_idx_map, n_threads)
predictions_binned = predictor.predict_binned(
X_binned,
missing_values_bin_idx=bin_mapper.missing_values_bin_idx_,
n_threads=n_threads,
)
np.testing.assert_allclose(predictions, -gradients)
np.testing.assert_allclose(predictions_binned, -gradients)
def test_grow_tree_categories():
# Check that the grower produces the right predictor tree when a split is
# categorical
X_binned = np.array([[0, 1] * 11 + [1]], dtype=X_BINNED_DTYPE).T
X_binned = np.asfortranarray(X_binned)
all_gradients = np.array([10, 1] * 11 + [1], dtype=G_H_DTYPE)
all_hessians = np.ones(1, dtype=G_H_DTYPE)
is_categorical = np.ones(1, dtype=np.uint8)
grower = TreeGrower(
X_binned,
all_gradients,
all_hessians,
n_bins=4,
shrinkage=1.0,
min_samples_leaf=1,
is_categorical=is_categorical,
n_threads=n_threads,
)
grower.grow()
assert grower.n_nodes == 3
categories = [np.array([4, 9], dtype=X_DTYPE)]
predictor = grower.make_predictor(binning_thresholds=categories)
root = predictor.nodes[0]
assert root["count"] == 23
assert root["depth"] == 0
assert root["is_categorical"]
left, right = predictor.nodes[root["left"]], predictor.nodes[root["right"]]
# arbitrary validation, but this means ones go to the left.
assert left["count"] >= right["count"]
# check binned category value (1)
expected_binned_cat_bitset = [2**1] + [0] * 7
binned_cat_bitset = predictor.binned_left_cat_bitsets
assert_array_equal(binned_cat_bitset[0], expected_binned_cat_bitset)
# check raw category value (9)
expected_raw_cat_bitsets = [2**9] + [0] * 7
raw_cat_bitsets = predictor.raw_left_cat_bitsets
assert_array_equal(raw_cat_bitsets[0], expected_raw_cat_bitsets)
# Note that since there was no missing values during training, the missing
# values aren't part of the bitsets. However, we expect the missing values
# to go to the biggest child (i.e. the left one).
# The left child has a value of -1 = negative gradient.
assert root["missing_go_to_left"]
# make sure binned missing values are mapped to the left child during
# prediction
prediction_binned = predictor.predict_binned(
np.asarray([[6]]).astype(X_BINNED_DTYPE),
missing_values_bin_idx=6,
n_threads=n_threads,
)
assert_allclose(prediction_binned, [-1]) # negative gradient
# make sure raw missing values are mapped to the left child during
# prediction
known_cat_bitsets = np.zeros((1, 8), dtype=np.uint32) # ignored anyway
f_idx_map = np.array([0], dtype=np.uint32)
prediction = predictor.predict(
np.array([[np.nan]]), known_cat_bitsets, f_idx_map, n_threads
)
assert_allclose(prediction, [-1])
@pytest.mark.parametrize("min_samples_leaf", (1, 20))
@pytest.mark.parametrize("n_unique_categories", (2, 10, 100))
@pytest.mark.parametrize("target", ("binary", "random", "equal"))
def test_ohe_equivalence(min_samples_leaf, n_unique_categories, target):
# Make sure that native categorical splits are equivalent to using a OHE,
# when given enough depth
rng = np.random.RandomState(0)
n_samples = 10_000
X_binned = rng.randint(0, n_unique_categories, size=(n_samples, 1), dtype=np.uint8)
X_ohe = OneHotEncoder(sparse_output=False).fit_transform(X_binned)
X_ohe = np.asfortranarray(X_ohe).astype(np.uint8)
if target == "equal":
gradients = X_binned.reshape(-1)
elif target == "binary":
gradients = (X_binned % 2).reshape(-1)
else:
gradients = rng.randn(n_samples)
gradients = gradients.astype(G_H_DTYPE)
hessians = np.ones(shape=1, dtype=G_H_DTYPE)
grower_params = {
"min_samples_leaf": min_samples_leaf,
"max_depth": None,
"max_leaf_nodes": None,
}
grower = TreeGrower(
X_binned, gradients, hessians, is_categorical=[True], **grower_params
)
grower.grow()
# we pass undefined bin_thresholds because we won't use predict()
predictor = grower.make_predictor(
binning_thresholds=np.zeros((1, n_unique_categories))
)
preds = predictor.predict_binned(
X_binned, missing_values_bin_idx=255, n_threads=n_threads
)
grower_ohe = TreeGrower(X_ohe, gradients, hessians, **grower_params)
grower_ohe.grow()
predictor_ohe = grower_ohe.make_predictor(
binning_thresholds=np.zeros((X_ohe.shape[1], n_unique_categories))
)
preds_ohe = predictor_ohe.predict_binned(
X_ohe, missing_values_bin_idx=255, n_threads=n_threads
)
assert predictor.get_max_depth() <= predictor_ohe.get_max_depth()
if target == "binary" and n_unique_categories > 2:
# OHE needs more splits to achieve the same predictions
assert predictor.get_max_depth() < predictor_ohe.get_max_depth()
np.testing.assert_allclose(preds, preds_ohe)
def test_grower_interaction_constraints():
"""Check that grower respects interaction constraints."""
n_features = 6
interaction_cst = [{0, 1}, {1, 2}, {3, 4, 5}]
n_samples = 10
n_bins = 6
root_feature_splits = []
def get_all_children(node):
res = []
if node.is_leaf:
return res
for n in [node.left_child, node.right_child]:
res.append(n)
res.extend(get_all_children(n))
return res
for seed in range(20):
rng = np.random.RandomState(seed)
X_binned = rng.randint(
0, n_bins - 1, size=(n_samples, n_features), dtype=X_BINNED_DTYPE
)
X_binned = np.asfortranarray(X_binned)
gradients = rng.normal(size=n_samples).astype(G_H_DTYPE)
hessians = np.ones(shape=1, dtype=G_H_DTYPE)
grower = TreeGrower(
X_binned,
gradients,
hessians,
n_bins=n_bins,
min_samples_leaf=1,
interaction_cst=interaction_cst,
n_threads=n_threads,
)
grower.grow()
root_feature_idx = grower.root.split_info.feature_idx
root_feature_splits.append(root_feature_idx)
feature_idx_to_constraint_set = {
0: {0, 1},
1: {0, 1, 2},
2: {1, 2},
3: {3, 4, 5},
4: {3, 4, 5},
5: {3, 4, 5},
}
root_constraint_set = feature_idx_to_constraint_set[root_feature_idx]
for node in (grower.root.left_child, grower.root.right_child):
# Root's children's allowed_features must be the root's constraints set.
assert_array_equal(node.allowed_features, list(root_constraint_set))
for node in get_all_children(grower.root):
if node.is_leaf:
continue
# Ensure that each node uses a subset of features of its parent node.
parent_interaction_cst_indices = set(node.interaction_cst_indices)
right_interactions_cst_indices = set(
node.right_child.interaction_cst_indices
)
left_interactions_cst_indices = set(node.left_child.interaction_cst_indices)
assert right_interactions_cst_indices.issubset(
parent_interaction_cst_indices
)
assert left_interactions_cst_indices.issubset(
parent_interaction_cst_indices
)
# The features used for split must have been present in the root's
# constraint set.
assert node.split_info.feature_idx in root_constraint_set
# Make sure that every feature is used at least once as split for the root node.
assert (
len(set(root_feature_splits))
== len(set().union(*interaction_cst))
== n_features
)
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