import numpy as np
from numpy.testing import assert_allclose
from sklearn.datasets import load_boston, make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from sklearn.utils.testing import assert_raises_regex
import pytest
from pygbm.binning import BinMapper
from pygbm.grower import TreeGrower
@pytest.mark.parametrize('max_bins', [200, 256])
def test_boston_dataset(max_bins):
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(
boston.data, boston.target, random_state=42)
mapper = BinMapper(max_bins=max_bins, random_state=42)
X_train_binned = mapper.fit_transform(X_train)
X_test_binned = mapper.transform(X_test)
# Init gradients and hessians to that of least squares loss
gradients = -y_train.astype(np.float32)
hessians = np.ones(1, dtype=np.float32)
min_samples_leaf = 8
max_leaf_nodes = 31
grower = TreeGrower(X_train_binned, gradients, hessians,
min_samples_leaf=min_samples_leaf,
max_leaf_nodes=max_leaf_nodes, max_bins=max_bins,
n_bins_per_feature=mapper.n_bins_per_feature_)
grower.grow()
predictor = grower.make_predictor(
numerical_thresholds=mapper.numerical_thresholds_)
assert r2_score(y_train, predictor.predict_binned(X_train_binned)) > 0.85
assert r2_score(y_test, predictor.predict_binned(X_test_binned)) > 0.70
assert_allclose(predictor.predict(X_train),
predictor.predict_binned(X_train_binned))
assert_allclose(predictor.predict(X_test),
predictor.predict_binned(X_test_binned))
assert r2_score(y_train, predictor.predict(X_train)) > 0.85
assert r2_score(y_test, predictor.predict(X_test)) > 0.70
def test_pre_binned_data():
# Make sure ValueError is raised when predictor.predict() is called while
# the predictor does not have any numerical thresholds.
X, y = make_regression()
# Init gradients and hessians to that of least squares loss
gradients = -y.astype(np.float32)
hessians = np.ones(1, dtype=np.float32)
mapper = BinMapper(random_state=0)
X_binned = mapper.fit_transform(X)
grower = TreeGrower(X_binned, gradients, hessians,
n_bins_per_feature=mapper.n_bins_per_feature_)
grower.grow()
predictor = grower.make_predictor(
numerical_thresholds=None
)
assert_raises_regex(
ValueError,
'This predictor does not have numerical thresholds',
predictor.predict, X
)
assert_raises_regex(
ValueError,
'binned_data dtype should be uint8',
predictor.predict_binned, X
)
predictor.predict_binned(X_binned) # No error
predictor = grower.make_predictor(
numerical_thresholds=mapper.numerical_thresholds_
)
assert_raises_regex(
ValueError,
'X has uint8 dtype',
predictor.predict, X_binned
)
