import numpy as np import pytest from pytest import approx from numpy.testing import assert_array_equal from numpy.testing import assert_allclose from sklearn.preprocessing import OneHotEncoder from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper from sklearn.ensemble._hist_gradient_boosting.common import X_BINNED_DTYPE from sklearn.ensemble._hist_gradient_boosting.common import X_DTYPE from sklearn.ensemble._hist_gradient_boosting.common import Y_DTYPE from sklearn.ensemble._hist_gradient_boosting.common import G_H_DTYPE from sklearn.ensemble._hist_gradient_boosting.common import ( X_BITSET_INNER_DTYPE) 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.), (11, True, "max_leaf_nodes", 1.), (11, False, "max_leaf_nodes", 0.1), (42, True, "max_leaf_nodes", 0.01), (42, False, "max_leaf_nodes", 1.), (256, True, "min_gain_to_split", 1.), (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 splitted, 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 splitted 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 splitted further, this time on feature #1 split_info = right_node.split_info assert split_info.gain > 1. 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., 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) 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) 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., 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., 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) 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) 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) predictions_binned = predictor.predict_binned( X_binned, missing_values_bin_idx=bin_mapper.missing_values_bin_idx_) 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) 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) 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)