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.
95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
import pytest
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from hypothesis import assume, given, note, settings, strategies
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import xgboost as xgb
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from xgboost import testing as tm
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pytestmark = tm.timeout(10)
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parameter_strategy = strategies.fixed_dictionaries(
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{
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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(
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["cyclic", "shuffle", "greedy", "thrifty"]
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),
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"top_k": strategies.integers(1, 10),
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}
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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,
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dmat,
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num_rounds,
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[(dmat, "train")],
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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), tm.make_dataset_strategy())
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@settings(deadline=None, max_examples=20, print_blob=True)
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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"][
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dataset.metric
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]
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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(
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parameter_strategy,
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strategies.integers(10, 50),
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tm.make_dataset_strategy(),
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strategies.floats(1e-5, 0.8),
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strategies.floats(1e-5, 0.8),
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)
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@settings(deadline=None, max_examples=20, print_blob=True)
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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"][
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dataset.metric
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]
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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 = {
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"booster": "gblinear",
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"updater": "gpu_coord_descent",
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"n_estimators": 100,
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}
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X, y = tm.get_california_housing()
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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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