def _get_current_hyperparameters(self):
"""Ask :attr:`optimizer` for the upcoming set of hyperparameters that should be searched,
then format them to be used in the next Experiment
Returns
-------
current_hyperparameters: Dict
The next set of hyperparameters that will be searched"""
_current_hyperparameters = self.optimizer.ask()
if _current_hyperparameters == self.current_hyperparameters_list:
new_parameters = self.hyperparameter_space.rvs(random_state=None)[0]
G.debug_("REPEATED asked={} new={}".format(_current_hyperparameters, new_parameters))
_current_hyperparameters = new_parameters
self.current_hyperparameters_list = _current_hyperparameters
current_hyperparameters = dict(
zip(
self.hyperparameter_space.names(use_location=False),
self.current_hyperparameters_list,
)
)
return current_hyperparameters
def find(self):
"""Execute full result-finding workflow, populating :attr:`similar_experiments`
See Also
--------
:func:`update_match_status`
Used to decorate "does_match..." methods in order to keep a detailed record of the full
pool of candidate Experiments in :attr:`match_status`. Aside from being used to compile
the list of finalist :attr:`similar_experiments`, :attr:`match_status` is helpful for
debugging purposes, specifically figuring out which aspects of a candidate are
incompatible with the template
:meth:`does_match_feature_engineer`
Performs special functionality beyond that of the other "does_match..." methods, namely
providing an updated "feature_engineer" value for compatible candidates to use.
Specifics are documented in :meth:`does_match_feature_engineer`"""
for exp_id in self.experiment_ids:
description_path = f"{self.descriptions_dir}/{exp_id}.json"
# TODO: Get `description` from `get_scored_params` - Take whatever value `sort` needs
params, score = get_scored_params(description_path, self.target_metric)
#################### Match Init Params ####################
self.does_match_init_params_space(exp_id, params["model_init_params"], score)
multi_targets = [("model_init_params", "compile_params", "optimizer")]
if self.module_name == "keras" and multi_targets[0] in self.space.names():
self.does_match_init_params_guidelines_multi(
exp_id, params["model_init_params"], score, multi_targets[0][1:]
)
else:
self.does_match_init_params_guidelines(exp_id, params["model_init_params"], score)
#################### Match Extra Params ####################
self.does_match_extra_params_space(exp_id, params["model_extra_params"], score)
self.does_match_extra_params_guidelines(exp_id, params["model_extra_params"], score)
#################### Match Feature Engineer ####################
# NOTE: Matching "feature_engineer" is critically different from the other "does_match"
# methods. `does_match_feature_engineer` builds on the straight-forward compatibility
# checks of the others by returning an updated "feature_engineer" for the candidate
# if compatible. See :meth:`does_match_feature_engineer` for details
params["feature_engineer"] = self.does_match_feature_engineer(
exp_id, params["feature_engineer"], score
)
# Since updated "feature_engineer" is saved in `params`, clean up `match_status` entry
if self.match_status[exp_id]["does_match_feature_engineer"] is not False:
self.match_status[exp_id]["does_match_feature_engineer"] = True
#################### Determine Overall Match ####################
if all(v for k, v in self.match_status[exp_id].items() if k.startswith("does_match")):
self.similar_experiments.append((params, score, exp_id))
G.debug_(
"Matching Experiments: {} (Candidates: {})".format(
len(self.similar_experiments), len(self.experiment_ids)
)
)
def find(self):
"""Execute full result-finding workflow"""
self._get_ids()
G.debug_(
f"Experiments matching cross-experiment key/algorithm: {len(self.experiment_ids)}"
)
self._get_scored_params()
self._filter_by_space()
G.debug_(
f"Experiments fitting in the given space: {len(self.hyperparameters_and_scores)}"
)
if self.module_name == "keras":
multi_targets = [("model_init_params", "compile_params",
"optimizer")]
if multi_targets[0] in self.space.names():
self._filter_by_guidelines_multi(multi_targets[0])
else:
self._filter_by_guidelines()
else:
self._filter_by_guidelines()
#################### Post-Process Similar Experiments ####################
self._reinitialize_similar_experiments()
G.debug_(
f"Experiments matching current guidelines: {len(self.similar_experiments)}"
)
def find(self):
"""Execute full result-finding workflow"""
self._get_ids()
G.debug_(
F'Experiments found with matching cross-experiment key and algorithm: {len(self.experiment_ids)}'
)
self._get_scored_params()
self._filter_by_space()
G.debug_(
F'Experiments whose hyperparameters fit in the currently defined space: {len(self.hyperparameters_and_scores)}'
)
if self.module_name == 'keras':
if ('model_init_params', 'compile_params',
'optimizer') in self.hyperparameter_space.get_names():
self._filter_by_guidelines_multi(
('model_init_params', 'compile_params', 'optimizer'))
else:
self._filter_by_guidelines()
else:
self._filter_by_guidelines()
G.debug_(
F'Experiments whose hyperparameters match the current guidelines: {len(self.similar_experiments)}'
)
