_multiprocess_can_split_ = True

_batch_size = 25

def test_list_all(self):

openml.utils._list_all(openml.tasks.functions._list_tasks)

def test_list_all_for_datasets(self):

required_size = 127 # default test server reset value

datasets = openml.datasets.list_datasets(batch_size=self._batch_size, size=required_size)

self.assertEquals(len(datasets), required_size)

for did in datasets:

self._check_dataset(datasets[did])

def test_list_datasets_with_high_size_parameter(self):

datasets_a = openml.datasets.list_datasets()

datasets_b = openml.datasets.list_datasets(size=np.inf)

self.assertEquals(len(datasets_a), len(datasets_b))

def test_list_all_for_tasks(self):

required_size = 1068 # default test server reset value

tasks = openml.tasks.list_tasks(batch_size=self._batch_size, size=required_size)

self.assertEquals(len(tasks), required_size)

def test_list_all_for_flows(self):

required_size = 15 # default test server reset value

flows = openml.flows.list_flows(batch_size=self._batch_size, size=required_size)

self.assertEquals(len(flows), required_size)

def test_list_all_for_setups(self):

required_size = 50

# TODO apparently list_setups function does not support kwargs

setups = openml.setups.list_setups(size=required_size)

# might not be on test server after reset, please rerun test at least once if fails

self.assertEquals(len(setups), required_size)

def test_list_all_for_runs(self):

required_size = 48

runs = openml.runs.list_runs(batch_size=self._batch_size, size=required_size)

# might not be on test server after reset, please rerun test at least once if fails

self.assertEquals(len(runs), required_size)

def test_list_all_for_evaluations(self):

required_size = 57

# TODO apparently list_evaluations function does not support kwargs

evaluations = openml.evaluations.list_evaluations(function='predictive_accuracy',

size=required_size)

# might not be on test server after reset, please rerun test at least once if fails