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import numpy as np
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
import pytest
from sklearn.ensemble._hist_gradient_boosting.binning import _BinMapper
from sklearn.ensemble._hist_gradient_boosting.grower import TreeGrower
from sklearn.ensemble._hist_gradient_boosting.predictor import TreePredictor
from sklearn.ensemble._hist_gradient_boosting.common import (
G_H_DTYPE, PREDICTOR_RECORD_DTYPE, ALMOST_INF)
@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(bin_thresholds=mapper.bin_thresholds_)
assert r2_score(y_train, predictor.predict(X_train)) > 0.82
assert r2_score(y_test, predictor.predict(X_test)) > 0.67
@pytest.mark.parametrize('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(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]['threshold'] = threshold
# left child
nodes[1]['is_leaf'] = True
nodes[1]['value'] = 0
# right child
nodes[2]['is_leaf'] = True
nodes[2]['value'] = 1
predictor = TreePredictor(nodes)
predictions = predictor.predict(X)
assert np.all(predictions == expected_predictions)
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