def test_main_signular_matrix(mock_exit, mock_save_error, mock_fetch_data):
mock_fetch_data.return_value = t.inputs_regression(limit_to=1,
include_nominal=False)
main()
mock_save_error.assert_called_with(
'Too many factors (4) for too little data (1). Use less covariables or different design.'
)
def test_main(mock_save_results, mock_fetch_data):
mock_fetch_data.return_value = t.inputs_regression(include_nominal=False)
main()
result = json.loads(mock_save_results.call_args[0][0])
assert t.round_dict(result) == {
'Residual': {
'F': 'NaN',
'PR(>F)': 'NaN',
'df': 1.0,
'mean_sq': 0.019,
'sum_sq': 0.019
},
'minimentalstate': {
'F': 12.831,
'PR(>F)': 0.173,
'df': 1.0,
'mean_sq': 0.248,
'sum_sq': 0.248
},
'subjectage': {
'F': 0.958,
'PR(>F)': 0.507,
'df': 1.0,
'mean_sq': 0.019,
'sum_sq': 0.019
},
'subjectage:minimentalstate': {
'F': 0.096,
'PR(>F)': 0.808,
'df': 1.0,
'mean_sq': 0.002,
'sum_sq': 0.002
}
}
def test_main_my_data(mock_save_results):
os.environ['INPUT_FILE'] = 'tests/unit/data.csv'
os.environ['PARAM_variables'] = 'a'
os.environ['PARAM_covariables'] = 'b,c'
os.environ[
'PARAM_meta'] = "{\"a\":{\"description\":\"\",\"code\":\"a\",\"type\":\"real\"},\"b\":{\"description\":\"\",\"code\":\"b\",\"type\":\"real\"},\"c\":{\"description\":\"\",\"code\":\"c\",\"type\":\"real\"}}"
main()
result = json.loads(mock_save_results.call_args[0][0])
def test_main_empty_input(mock_exit, mock_save_error, mock_fetch_data):
mock_fetch_data.return_value = t.inputs_regression(limit_to=0)
main()
mock_exit.assert_called_once_with(1)
mock_save_error.assert_called_once()