93 lines
3.5 KiB
Python
93 lines
3.5 KiB
Python
import testing as tm
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from hypothesis import given, note, settings, strategies
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import xgboost as xgb
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from xgboost import testing
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pytestmark = testing.timeout(10)
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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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})
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coord_strategy = strategies.fixed_dictionaries({
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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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xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False,
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evals_result=result)
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return result
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class TestLinear:
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@given(parameter_strategy, strategies.integers(10, 50),
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tm.dataset_strategy, coord_strategy)
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@settings(deadline=None, max_examples=20, print_blob=True)
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def test_coordinate(self, param, num_rounds, dataset, coord_param):
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param['updater'] = 'coord_descent'
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param.update(coord_param)
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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, 5e-4)
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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.dataset_strategy,
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coord_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_coordinate_regularised(self, param, num_rounds, dataset, coord_param, alpha, lambd):
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param['updater'] = 'coord_descent'
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param['alpha'] = alpha
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param['lambda'] = lambd
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param.update(coord_param)
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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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@given(parameter_strategy, strategies.integers(10, 50),
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tm.dataset_strategy)
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@settings(deadline=None, max_examples=20, print_blob=True)
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def test_shotgun(self, param, num_rounds, dataset):
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param['updater'] = 'shotgun'
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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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# shotgun is non-deterministic, so we relax the test by only using first and last
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# iteration.
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if len(result) > 2:
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sampled_result = (result[0], result[-1])
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else:
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sampled_result = result
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assert tm.non_increasing(sampled_result)
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@given(parameter_strategy, strategies.integers(10, 50),
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tm.dataset_strategy, strategies.floats(1e-5, 1.0),
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strategies.floats(1e-5, 1.0))
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@settings(deadline=None, max_examples=20, print_blob=True)
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def test_shotgun_regularised(self, param, num_rounds, dataset, alpha, lambd):
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param['updater'] = 'shotgun'
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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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