77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
import sys
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from hypothesis import strategies, given, settings, assume, note
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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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parameter_strategy = strategies.fixed_dictionaries({
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'booster': strategies.just('gblinear'),
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'eta': strategies.floats(0.01, 0.25),
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'tolerance': strategies.floats(1e-5, 1e-2),
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'nthread': strategies.integers(1, 4),
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'feature_selector': strategies.sampled_from(['cyclic', 'shuffle',
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'greedy', 'thrifty']),
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'top_k': strategies.integers(1, 10),
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})
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def train_result(param, dmat, num_rounds):
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result = {}
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booster = xgb.train(
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param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False,
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evals_result=result
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)
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assert booster.num_boosted_rounds() == num_rounds
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return result
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class TestGPULinear:
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@given(parameter_strategy, strategies.integers(10, 50),
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tm.dataset_strategy)
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@settings(deadline=None)
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def test_gpu_coordinate(self, param, num_rounds, dataset):
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assume(len(dataset.y) > 0)
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param['updater'] = 'gpu_coord_descent'
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param = dataset.set_params(param)
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result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
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note(result)
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assert tm.non_increasing(result)
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# Loss is not guaranteed to always decrease because of regularisation parameters
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# We test a weaker condition that the loss has not increased between the first and last
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# iteration
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@given(parameter_strategy, strategies.integers(10, 50),
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tm.dataset_strategy, strategies.floats(1e-5, 2.0),
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strategies.floats(1e-5, 2.0))
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@settings(deadline=None)
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def test_gpu_coordinate_regularised(self, param, num_rounds, dataset, alpha, lambd):
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assume(len(dataset.y) > 0)
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param['updater'] = 'gpu_coord_descent'
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param['alpha'] = alpha
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param['lambda'] = lambd
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param = dataset.set_params(param)
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result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
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note(result)
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assert tm.non_increasing([result[0], result[-1]])
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@pytest.mark.skipif(**tm.no_cupy())
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def test_gpu_coordinate_from_cupy(self):
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# Training linear model is quite expensive, so we don't include it in
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# test_from_cupy.py
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import cupy
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params = {'booster': 'gblinear', 'updater': 'gpu_coord_descent',
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'n_estimators': 100}
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X, y = tm.get_boston()
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cpu_model = xgb.XGBRegressor(**params)
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cpu_model.fit(X, y)
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cpu_predt = cpu_model.predict(X)
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X = cupy.array(X)
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y = cupy.array(y)
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gpu_model = xgb.XGBRegressor(**params)
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gpu_model.fit(X, y)
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gpu_predt = gpu_model.predict(X)
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cupy.testing.assert_allclose(cpu_predt, gpu_predt)
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