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import sys
import numpy as np
import pandas as pd
import xgboost as xgb
sys.path.append("tests/python")
# Don't import the test class, otherwise they will run twice.
import test_interaction_constraints as test_ic # noqa
rng = np.random.RandomState(1994)
class TestGPUInteractionConstraints:
cputest = test_ic.TestInteractionConstraints()
def test_interaction_constraints(self):
self.cputest.run_interaction_constraints(tree_method="gpu_hist")
def test_training_accuracy(self):
self.cputest.training_accuracy(tree_method="gpu_hist")
# case where different number of features can occur in the evaluator
def test_issue_8730(self):
X = pd.DataFrame(
zip(range(0, 100), range(200, 300), range(300, 400), range(400, 500)),
columns=["A", "B", "C", "D"],
)
y = np.array([*([0] * 50), *([1] * 50)])
dm = xgb.DMatrix(X, label=y)
params = {
"eta": 0.16095019509249486,
"min_child_weight": 1,
"subsample": 0.688567929338029,
"colsample_bynode": 0.7,
"gamma": 5.666579817418348e-06,
"lambda": 0.14943712232059794,
"grow_policy": "depthwise",
"max_depth": 3,
"tree_method": "gpu_hist",
"interaction_constraints": [["A", "B"], ["B", "D", "C"], ["C", "D"]],
"objective": "count:poisson",
"eval_metric": "poisson-nloglik",
"verbosity": 0,
}
xgb.train(params, dm, num_boost_round=100)
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