9c9070aea2
* Do not derive from unittest.TestCase (not needed for pytest) * assertRaises -> pytest.raises * Simplify test_empty_dmatrix with test parametrization * setUpClass -> setup_class, tearDownClass -> teardown_class * Don't import unittest; import pytest * Use plain assert * Use parametrized tests in more places * Fix test_gpu_with_sklearn.py * Put back run_empty_dmatrix_reg / run_empty_dmatrix_cls * Fix test_eta_decay_gpu_hist * Add parametrized tests for monotone constraints * Fix test names * Remove test parametrization * Revise test_slice to be not flaky
64 lines
1.6 KiB
Python
64 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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sys.path.append("tests/python")
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import testing as tm
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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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