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import sys
import numpy as np
import pytest
import xgboost as xgb
from xgboost import testing as tm
sys.path.append("tests/python")
import test_monotone_constraints as tmc
rng = np.random.RandomState(1994)
def non_decreasing(L):
return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
def non_increasing(L):
return all((y - x) < 0.001 for x, y in zip(L, L[1:]))
def assert_constraint(constraint, tree_method):
from sklearn.datasets import make_regression
n = 1000
X, y = make_regression(n, random_state=rng, n_features=1, n_informative=1)
dtrain = xgb.DMatrix(X, y)
param = {}
param["tree_method"] = tree_method
param["monotone_constraints"] = "(" + str(constraint) + ")"
bst = xgb.train(param, dtrain)
dpredict = xgb.DMatrix(X[X[:, 0].argsort()])
pred = bst.predict(dpredict)
if constraint > 0:
assert non_decreasing(pred)
elif constraint < 0:
assert non_increasing(pred)
@pytest.mark.skipif(**tm.no_sklearn())
def test_gpu_hist_basic():
assert_constraint(1, "gpu_hist")
assert_constraint(-1, "gpu_hist")
def test_gpu_hist_depthwise():
params = {
"tree_method": "gpu_hist",
"grow_policy": "depthwise",
"monotone_constraints": "(1, -1)",
}
model = xgb.train(params, tmc.training_dset)
tmc.is_correctly_constrained(model)
def test_gpu_hist_lossguide():
params = {
"tree_method": "gpu_hist",
"grow_policy": "lossguide",
"monotone_constraints": "(1, -1)",
}
model = xgb.train(params, tmc.training_dset)
tmc.is_correctly_constrained(model)
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