def get_scored_params(experiment_description_path, target_metric):
"""Retrieve the hyperparameters of a completed Experiment, along with an evaluation of its performance
Parameters
----------
experiment_description_path: String
The path to an Experiment's description .json file
target_metric: Tuple
A path denoting the metric to be used. If tuple, the first value should be one of ['oof', 'holdout', 'in_fold'], and the
second value should be the name of a metric supplied in :attr:`environment.Environment.metrics_params`
Returns
-------
all_hyperparameters: Dict
A dict of the hyperparameters used by the Experiment
evaluation: Float
Value of the Experiment's `target_metric`"""
description = read_json(file_path=experiment_description_path)
evaluation = get_path(description['final_evaluations'], target_metric)
all_hyperparameters = description['hyperparameters']
if description['module_name'].lower() == 'keras':
all_hyperparameters['model_init_params'][
'layers'] = consolidate_layers(
all_hyperparameters['model_init_params']['layers'],
class_name_key=False,
separate_args=False)
return (all_hyperparameters, evaluation)
def get_scored_params(experiment_description_path,
target_metric,
get_description=False):
"""Retrieve the hyperparameters of a completed Experiment, along with its performance evaluation
Parameters
----------
experiment_description_path: String
The path to an Experiment's description .json file
target_metric: Tuple
A path denoting the metric to be used. If tuple, the first value should be one of ['oof',
'holdout', 'in_fold'], and the second value should be the name of a metric supplied in
:attr:`environment.Environment.metrics_params`
get_description: Boolean, default=False
If True, return a tuple of: ((`all_hyperparameters`, `evaluation`), `description`), in which
`description` is the original description dict for the experiment. Else, return a tuple of:
(`all_hyperparameters`, `evaluation`)
Returns
-------
all_hyperparameters: Dict
A dict of the hyperparameters used by the Experiment
evaluation: Float
Value of the Experiment's `target_metric`"""
description = read_json(file_path=experiment_description_path)
evaluation = get_path(description["final_evaluations"], target_metric)
all_hyperparameters = description["hyperparameters"]
if description["module_name"].lower() == "keras":
all_hyperparameters["model_init_params"][
"layers"] = consolidate_layers(
all_hyperparameters["model_init_params"]["layers"],
class_name_key=False)
if get_description:
return ((all_hyperparameters, evaluation), description)
return (all_hyperparameters, evaluation)
def test_consolidate_layers(expected, class_name_key, split_args):
assert consolidate_layers(simple_mlp_layers, class_name_key, split_args) == expected