def test_main_classification(mock_parameters, mock_save_results,
mock_get_results, mock_fetch_data, method, name):
# create mock objects from database
mock_parameters.return_value = {'type': method}
mock_fetch_data.return_value = fx.inputs_classification(
include_categorical=True)
mock_get_results.return_value = None
main(job_id=None, generate_pfa=True)
pfa = mock_save_results.call_args[0][0]
pfa_dict = json.loads(pfa)
# NOTE: this does not work due to bug in jsonpickle
# deserialize model
# estimator = deserialize_sklearn_estimator(pfa_dict['metadata']['estimator'])
# assert estimator.__class__.__name__ == name
# make some prediction with PFA
from titus.genpy import PFAEngine
engine, = PFAEngine.fromJson(pfa_dict)
engine.action({
'stress_before_test1': 10.,
'iq': 10.,
'agegroup': '50-59y'
})
def test_main_partial(mock_parameters, mock_save_results, mock_get_results, mock_fetch_data, method, name):
# create mock objects from database
mock_parameters.return_value = {'type': method}
mock_fetch_data.return_value = fx.inputs_regression()
mock_get_results.return_value = None
main(job_id=None, generate_pfa=False)
js = json.loads(mock_save_results.call_args[0][0])
estimator = deserialize_sklearn_estimator(js['estimator'])
assert estimator.__class__.__name__ == name
def test_main_classification_empty(
mock_exit, mock_parameters, mock_save_error, mock_save_results, mock_get_results, mock_fetch_data, method, name
):
# create mock objects from database
mock_parameters.return_value = {'type': method}
# one column has all NULL values
data = fx.inputs_classification(include_categorical=True)
data['data']['independent'][0]['series'] = [None] * len(data['data']['independent'][0]['series'])
mock_fetch_data.return_value = data
mock_get_results.return_value = None
main(job_id=None, generate_pfa=True)
mock_exit.assert_called_once_with(1)
assert mock_save_error.call_args[0] == ('Model was not fitted on any data, cannot generate PFA.', )
def test_main_distributed(mock_parameters, mock_save_results, mock_get_results, mock_fetch_data, method, name):
mock_parameters.return_value = {'type': method}
mock_fetch_data.return_value = fx.inputs_regression()
mock_get_results.return_value = None
# run intermediate job
main(job_id=None, generate_pfa=False)
mock_get_results.return_value = mock.MagicMock(data=mock_save_results.call_args[0][0])
# generate PFA
main(job_id='1', generate_pfa=True)
pfa = mock_save_results.call_args_list[1][0][0]
pfa_dict = json.loads(pfa)
# make some prediction with PFA
from titus.genpy import PFAEngine
engine, = PFAEngine.fromJson(pfa_dict)
engine.action({'stress_before_test1': 10., 'iq': 10., 'agegroup': '-50y'})
