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test_gpu_with_sklearn.py
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test_gpu_with_sklearn.py
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import json
import xgboost as xgb
import pytest
import tempfile
import sys
import numpy as np
import os
sys.path.append("tests/python")
import testing as tm # noqa
import test_with_sklearn as twskl # noqa
pytestmark = pytest.mark.skipif(**tm.no_sklearn())
rng = np.random.RandomState(1994)
def test_gpu_binary_classification():
from sklearn.datasets import load_digits
from sklearn.model_selection import KFold
digits = load_digits(n_class=2)
y = digits['target']
X = digits['data']
kf = KFold(n_splits=2, shuffle=True, random_state=rng)
for cls in (xgb.XGBClassifier, xgb.XGBRFClassifier):
for train_index, test_index in kf.split(X, y):
xgb_model = cls(
random_state=42, tree_method='gpu_hist',
n_estimators=4, gpu_id='0').fit(X[train_index], y[train_index])
preds = xgb_model.predict(X[test_index])
labels = y[test_index]
err = sum(1 for i in range(len(preds))
if int(preds[i] > 0.5) != labels[i]) / float(len(preds))
assert err < 0.1
def test_boost_from_prediction_gpu_hist():
twskl.run_boost_from_prediction('gpu_hist')
def test_num_parallel_tree():
twskl.run_boston_housing_rf_regression("gpu_hist")
@pytest.mark.skipif(**tm.no_pandas())
@pytest.mark.skipif(**tm.no_sklearn())
def test_categorical():
import pandas as pd
from sklearn.datasets import load_svmlight_file
data_dir = os.path.join(tm.PROJECT_ROOT, "demo", "data")
X, y = load_svmlight_file(os.path.join(data_dir, "agaricus.txt.train"))
clf = xgb.XGBClassifier(
tree_method="gpu_hist",
use_label_encoder=False,
enable_categorical=True,
n_estimators=10,
)
X = pd.DataFrame(X.todense()).astype("category")
clf.fit(X, y)
assert not clf._can_use_inplace_predict()
with tempfile.TemporaryDirectory() as tempdir:
model = os.path.join(tempdir, "categorial.json")
clf.save_model(model)
with open(model) as fd:
categorical = json.load(fd)
categories_sizes = np.array(
categorical["learner"]["gradient_booster"]["model"]["trees"][0][
"categories_sizes"
]
)
assert categories_sizes.shape[0] != 0
np.testing.assert_allclose(categories_sizes, 1)