9f73127a23
* Cleanup Python GPU tests. - Remove the use of `gpu_hist` and `gpu_id` in cudf/cupy tests. - Move base margin test into the testing directory.
66 lines
1.6 KiB
Python
66 lines
1.6 KiB
Python
import sys
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import numpy as np
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import pytest
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import xgboost as xgb
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from xgboost import testing as tm
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sys.path.append("tests/python")
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import test_monotone_constraints as tmc
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rng = np.random.RandomState(1994)
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def non_decreasing(L):
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return all((x - y) < 0.001 for x, y in zip(L, L[1:]))
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def non_increasing(L):
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return all((y - x) < 0.001 for x, y in zip(L, L[1:]))
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def assert_constraint(constraint, tree_method):
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from sklearn.datasets import make_regression
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n = 1000
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X, y = make_regression(n, random_state=rng, n_features=1, n_informative=1)
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dtrain = xgb.DMatrix(X, y)
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param = {}
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param["tree_method"] = tree_method
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param["monotone_constraints"] = "(" + str(constraint) + ")"
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bst = xgb.train(param, dtrain)
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dpredict = xgb.DMatrix(X[X[:, 0].argsort()])
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pred = bst.predict(dpredict)
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if constraint > 0:
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assert non_decreasing(pred)
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elif constraint < 0:
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assert non_increasing(pred)
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@pytest.mark.skipif(**tm.no_sklearn())
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def test_gpu_hist_basic():
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assert_constraint(1, "gpu_hist")
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assert_constraint(-1, "gpu_hist")
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def test_gpu_hist_depthwise():
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params = {
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"tree_method": "gpu_hist",
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"grow_policy": "depthwise",
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"monotone_constraints": "(1, -1)",
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}
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model = xgb.train(params, tmc.training_dset)
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tmc.is_correctly_constrained(model)
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def test_gpu_hist_lossguide():
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params = {
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"tree_method": "gpu_hist",
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"grow_policy": "lossguide",
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"monotone_constraints": "(1, -1)",
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}
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model = xgb.train(params, tmc.training_dset)
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tmc.is_correctly_constrained(model)
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