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from typing import Dict
from hypothesis import given, note, settings, strategies
import xgboost as xgb
from xgboost import testing as tm
pytestmark = tm.timeout(20)
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),
})
coord_strategy = strategies.fixed_dictionaries({
'feature_selector': strategies.sampled_from(['cyclic', 'shuffle',
'greedy', 'thrifty']),
'top_k': strategies.integers(1, 10),
})
def train_result(param: dict, dmat: xgb.DMatrix, num_rounds: int) -> Dict[str, Dict]:
result: Dict[str, Dict] = {}
xgb.train(
param,
dmat,
num_rounds,
evals=[(dmat, "train")],
verbose_eval=False,
evals_result=result,
)
return result
class TestLinear:
@given(
parameter_strategy,
strategies.integers(10, 50),
tm.make_dataset_strategy(),
coord_strategy
)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_coordinate(self, param, num_rounds, dataset, coord_param):
param['updater'] = 'coord_descent'
param.update(coord_param)
param = dataset.set_params(param)
result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
note(result)
assert tm.non_increasing(result, 5e-4)
# 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.make_dataset_strategy(),
coord_strategy,
strategies.floats(1e-5, 0.8),
strategies.floats(1e-5, 0.8)
)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_coordinate_regularised(self, param, num_rounds, dataset, coord_param, alpha, lambd):
param['updater'] = 'coord_descent'
param['alpha'] = alpha
param['lambda'] = lambd
param.update(coord_param)
param = dataset.set_params(param)
result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
note(result)
assert tm.non_increasing([result[0], result[-1]])
@given(
parameter_strategy, strategies.integers(10, 50), tm.make_dataset_strategy()
)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_shotgun(self, param, num_rounds, dataset):
param['updater'] = 'shotgun'
param = dataset.set_params(param)
result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
note(result)
# shotgun is non-deterministic, so we relax the test by only using first and last
# iteration.
if len(result) > 2:
sampled_result = (result[0], result[-1])
else:
sampled_result = result
assert tm.non_increasing(sampled_result)
@given(
parameter_strategy,
strategies.integers(10, 50),
tm.make_dataset_strategy(),
strategies.floats(1e-5, 1.0),
strategies.floats(1e-5, 1.0)
)
@settings(deadline=None, max_examples=20, print_blob=True)
def test_shotgun_regularised(self, param, num_rounds, dataset, alpha, lambd):
param['updater'] = 'shotgun'
param['alpha'] = alpha
param['lambda'] = lambd
param = dataset.set_params(param)
result = train_result(param, dataset.get_dmat(), num_rounds)['train'][dataset.metric]
note(result)
assert tm.non_increasing([result[0], result[-1]])
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