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import numpy as np
import pytest
from numpy.testing import assert_allclose
from sklearn.datasets import make_regression
from sklearn.ensemble._hist_gradient_boosting._bitset import (
set_bitset_memoryview,
set_raw_bitset_from_binned_bitset,
)
from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper
from sklearn.ensemble._hist_gradient_boosting.common import (
ALMOST_INF,
G_H_DTYPE,
PREDICTOR_RECORD_DTYPE,
X_BINNED_DTYPE,
X_BITSET_INNER_DTYPE,
X_DTYPE,
)
from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower
from sklearn.ensemble._hist_gradient_boosting.predictor import TreePredictor
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
n_threads = _openmp_effective_n_threads()
@pytest.mark.parametrize("n_bins", [200, 256])
def test_regression_dataset(n_bins):
X, y = make_regression(
n_samples=500, n_features=10, n_informative=5, random_state=42
)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
mapper = _BinMapper(n_bins=n_bins, random_state=42)
X_train_binned = mapper.fit_transform(X_train)
# Init gradients and hessians to that of least squares loss
gradients = -y_train.astype(G_H_DTYPE)
hessians = np.ones(1, dtype=G_H_DTYPE)
min_samples_leaf = 10
max_leaf_nodes = 30
grower = TreeGrower(
X_train_binned,
gradients,
hessians,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=max_leaf_nodes,
n_bins=n_bins,
n_bins_non_missing=mapper.n_bins_non_missing_,
)
grower.grow()
predictor = grower.make_predictor(binning_thresholds=mapper.bin_thresholds_)
known_cat_bitsets = np.zeros((0, 8), dtype=X_BITSET_INNER_DTYPE)
f_idx_map = np.zeros(0, dtype=np.uint32)
y_pred_train = predictor.predict(X_train, known_cat_bitsets, f_idx_map, n_threads)
assert r2_score(y_train, y_pred_train) > 0.82
y_pred_test = predictor.predict(X_test, known_cat_bitsets, f_idx_map, n_threads)
assert r2_score(y_test, y_pred_test) > 0.67
@pytest.mark.parametrize(
"num_threshold, expected_predictions",
[
(-np.inf, [0, 1, 1, 1]),
(10, [0, 0, 1, 1]),
(20, [0, 0, 0, 1]),
(ALMOST_INF, [0, 0, 0, 1]),
(np.inf, [0, 0, 0, 0]),
],
)
def test_infinite_values_and_thresholds(num_threshold, expected_predictions):
# Make sure infinite values and infinite thresholds are handled properly.
# In particular, if a value is +inf and the threshold is ALMOST_INF the
# sample should go to the right child. If the threshold is inf (split on
# nan), the +inf sample will go to the left child.
X = np.array([-np.inf, 10, 20, np.inf]).reshape(-1, 1)
nodes = np.zeros(3, dtype=PREDICTOR_RECORD_DTYPE)
# We just construct a simple tree with 1 root and 2 children
# parent node
nodes[0]["left"] = 1
nodes[0]["right"] = 2
nodes[0]["feature_idx"] = 0
nodes[0]["num_threshold"] = num_threshold
# left child
nodes[1]["is_leaf"] = True
nodes[1]["value"] = 0
# right child
nodes[2]["is_leaf"] = True
nodes[2]["value"] = 1
binned_cat_bitsets = np.zeros((0, 8), dtype=X_BITSET_INNER_DTYPE)
raw_categorical_bitsets = np.zeros((0, 8), dtype=X_BITSET_INNER_DTYPE)
known_cat_bitset = np.zeros((0, 8), dtype=X_BITSET_INNER_DTYPE)
f_idx_map = np.zeros(0, dtype=np.uint32)
predictor = TreePredictor(nodes, binned_cat_bitsets, raw_categorical_bitsets)
predictions = predictor.predict(X, known_cat_bitset, f_idx_map, n_threads)
assert np.all(predictions == expected_predictions)
@pytest.mark.parametrize(
"bins_go_left, expected_predictions",
[
([0, 3, 4, 6], [1, 0, 0, 1, 1, 0]),
([0, 1, 2, 6], [1, 1, 1, 0, 0, 0]),
([3, 5, 6], [0, 0, 0, 1, 0, 1]),
],
)
def test_categorical_predictor(bins_go_left, expected_predictions):
# Test predictor outputs are correct with categorical features
X_binned = np.array([[0, 1, 2, 3, 4, 5]], dtype=X_BINNED_DTYPE).T
categories = np.array([2, 5, 6, 8, 10, 15], dtype=X_DTYPE)
bins_go_left = np.array(bins_go_left, dtype=X_BINNED_DTYPE)
# We just construct a simple tree with 1 root and 2 children
# parent node
nodes = np.zeros(3, dtype=PREDICTOR_RECORD_DTYPE)
nodes[0]["left"] = 1
nodes[0]["right"] = 2
nodes[0]["feature_idx"] = 0
nodes[0]["is_categorical"] = True
nodes[0]["missing_go_to_left"] = True
# left child
nodes[1]["is_leaf"] = True
nodes[1]["value"] = 1
# right child
nodes[2]["is_leaf"] = True
nodes[2]["value"] = 0
binned_cat_bitsets = np.zeros((1, 8), dtype=X_BITSET_INNER_DTYPE)
raw_categorical_bitsets = np.zeros((1, 8), dtype=X_BITSET_INNER_DTYPE)
for go_left in bins_go_left:
set_bitset_memoryview(binned_cat_bitsets[0], go_left)
set_raw_bitset_from_binned_bitset(
raw_categorical_bitsets[0], binned_cat_bitsets[0], categories
)
predictor = TreePredictor(nodes, binned_cat_bitsets, raw_categorical_bitsets)
# Check binned data gives correct predictions
prediction_binned = predictor.predict_binned(
X_binned, missing_values_bin_idx=6, n_threads=n_threads
)
assert_allclose(prediction_binned, expected_predictions)
# manually construct bitset
known_cat_bitsets = np.zeros((1, 8), dtype=np.uint32)
known_cat_bitsets[0, 0] = np.sum(2**categories, dtype=np.uint32)
f_idx_map = np.array([0], dtype=np.uint32)
# Check with un-binned data
predictions = predictor.predict(
categories.reshape(-1, 1), known_cat_bitsets, f_idx_map, n_threads
)
assert_allclose(predictions, expected_predictions)
# Check missing goes left because missing_values_bin_idx=6
X_binned_missing = np.array([[6]], dtype=X_BINNED_DTYPE).T
predictions = predictor.predict_binned(
X_binned_missing, missing_values_bin_idx=6, n_threads=n_threads
)
assert_allclose(predictions, [1])
# missing and unknown go left
predictions = predictor.predict(
np.array([[np.nan, 17]], dtype=X_DTYPE).T,
known_cat_bitsets,
f_idx_map,
n_threads,
)
assert_allclose(predictions, [1, 1])
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