b47b5ac771
* Use hypothesis * Allow int64 array interface for groups * Add packages to Windows CI * Add to travis * Make sure device index is set correctly * Fix dask-cudf test * appveyor
51 lines
2.0 KiB
Python
51 lines
2.0 KiB
Python
import sys
|
|
from hypothesis import strategies, given, settings, assume
|
|
import xgboost as xgb
|
|
sys.path.append("tests/python")
|
|
import testing as tm
|
|
|
|
|
|
parameter_strategy = strategies.fixed_dictionaries({
|
|
'booster': strategies.just('gblinear'),
|
|
'eta': strategies.floats(0.01, 0.25),
|
|
'tolerance': strategies.floats(1e-5, 1e-2),
|
|
'nthread': strategies.integers(1, 4),
|
|
'feature_selector': strategies.sampled_from(['cyclic', 'shuffle',
|
|
'greedy', 'thrifty']),
|
|
'top_k': strategies.integers(1, 10),
|
|
})
|
|
|
|
def train_result(param, dmat, num_rounds):
|
|
result = {}
|
|
xgb.train(param, dmat, num_rounds, [(dmat, 'train')], verbose_eval=False,
|
|
evals_result=result)
|
|
return result
|
|
|
|
|
|
class TestGPULinear:
|
|
@given(parameter_strategy, strategies.integers(10, 50),
|
|
tm.dataset_strategy)
|
|
@settings(deadline=None)
|
|
def test_gpu_coordinate(self, param, num_rounds, dataset):
|
|
assume(len(dataset.y) > 0)
|
|
param['updater'] = 'gpu_coord_descent'
|
|
param = dataset.set_params(param)
|
|
result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
|
|
assert tm.non_increasing(result)
|
|
|
|
# Loss is not guaranteed to always decrease because of regularisation parameters
|
|
# We test a weaker condition that the loss has not increased between the first and last
|
|
# iteration
|
|
@given(parameter_strategy, strategies.integers(10, 50),
|
|
tm.dataset_strategy, strategies.floats(1e-5, 2.0),
|
|
strategies.floats(1e-5, 2.0))
|
|
@settings(deadline=None)
|
|
def test_gpu_coordinate_regularised(self, param, num_rounds, dataset, alpha, lambd):
|
|
assume(len(dataset.y) > 0)
|
|
param['updater'] = 'gpu_coord_descent'
|
|
param['alpha'] = alpha
|
|
param['lambda'] = lambd
|
|
param = dataset.set_params(param)
|
|
result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
|
|
assert tm.non_increasing([result[0], result[-1]])
|