def test_get_splitter(self):
ta_args = dict(backend=BackendMock(),
autosklearn_seed=1,
logger=self.logger,
stats=self.stats,
memory_limit=3072,
metric=accuracy)
D = unittest.mock.Mock(spec=AbstractDataManager)
D.data = dict(Y_train=np.array([0, 0, 0, 1, 1, 1]))
D.info = dict(task=BINARY_CLASSIFICATION)
# holdout, binary classification
ta = ExecuteTaFuncWithQueue(resampling_strategy='holdout', **ta_args)
cv = ta.get_splitter(D)
self.assertIsInstance(cv,
sklearn.model_selection.StratifiedShuffleSplit)
# holdout, binary classification, fallback to shuffle split
D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1, 2])
ta = ExecuteTaFuncWithQueue(resampling_strategy='holdout', **ta_args)
cv = ta.get_splitter(D)
self.assertIsInstance(cv, sklearn.model_selection._split.ShuffleSplit)
# cv, binary classification
D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1])
ta = ExecuteTaFuncWithQueue(resampling_strategy='cv',
folds=5,
**ta_args)
cv = ta.get_splitter(D)
self.assertIsInstance(cv,
sklearn.model_selection._split.StratifiedKFold)
# cv, binary classification, no fallback anticipated
D.data['Y_train'] = np.array([0, 0, 0, 1, 1, 1, 2])
ta = ExecuteTaFuncWithQueue(resampling_strategy='cv',
folds=5,
**ta_args)
cv = ta.get_splitter(D)
self.assertIsInstance(cv,
sklearn.model_selection._split.StratifiedKFold)
# regression, shuffle split
D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5])
D.info['task'] = REGRESSION
ta = ExecuteTaFuncWithQueue(resampling_strategy='holdout', **ta_args)
cv = ta.get_splitter(D)
self.assertIsInstance(cv, sklearn.model_selection._split.ShuffleSplit)
# regression cv, KFold
D.data['Y_train'] = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5])
D.info['task'] = REGRESSION
ta = ExecuteTaFuncWithQueue(resampling_strategy='cv',
folds=5,
**ta_args)
cv = ta.get_splitter(D)
self.assertIsInstance(cv, sklearn.model_selection._split.KFold)
