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