def get_objective_signature(model_name, dataset, scorer, data_root=None):
"""Get signature of an objective function specified by an sklearn model and dataset.
This routine specializes :func:`.signatures.get_func_signature` for the `sklearn` study case.
Parameters
----------
model_name : str
Which sklearn model we are attempting to tune, must be an element of `constants.MODEL_NAMES`.
dataset : str
Which data set the model is being tuned to, which must be either a) an element of
`constants.DATA_LOADER_NAMES`, or b) the name of a csv file in the `data_root` folder for a custom data set.
scorer : str
Which metric to use when evaluating the model. This must be an element of `sklearn_funcs.SCORERS_CLF` for
classification models, or `sklearn_funcs.SCORERS_REG` for regression models.
data_root : str
Absolute path to folder containing custom data sets. This may be ``None`` if no custom data sets are used.``
Returns
-------
signature : list(str)
The signature of this test function.
"""
function_instance = _build_test_problem(model_name, dataset, scorer,
data_root)
api_config = function_instance.get_api_config()
signature = get_func_signature(function_instance.evaluate, api_config)
return signature
def test_get_func_signature(api_config):
api_config, _, _, _ = api_config
signature_x, signature_y = ss.get_func_signature(some_mock_f, api_config)