class InMemoryHyperparamsRepository(HyperparamsRepository):
"""
In memory hyperparams repository that can print information about trials.
Useful for debugging.
Example usage :
.. code-block:: python
InMemoryHyperparamsRepository(
hyperparameter_selection_strategy=RandomSearchHyperparameterSelectionStrategy(),
print_func=print,
cache_folder='cache',
best_retrained_model_folder='best'
)
.. seealso::
:class:`AutoML`,
:class:`Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`HyperparamsJSONRepository`,
:class:`BaseHyperparameterSelectionStrategy`,
:class:`RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.hyperparams.space.HyperparameterSamples`
"""
def __init__(self,
hyperparameter_selection_strategy=None,
cache_folder: str = None,
best_retrained_model_folder=None):
HyperparamsRepository.__init__(
self,
hyperparameter_selection_strategy=hyperparameter_selection_strategy,
cache_folder=cache_folder,
best_retrained_model_folder=best_retrained_model_folder)
self.cache_folder = cache_folder
self.trials = Trials()
def load_all_trials(self, status: 'TRIAL_STATUS' = None) -> 'Trials':
"""
Load all trials with the given status.
:param status: trial status
:return: list of trials
"""
return self.trials.filter(status)
def _save_trial(self, trial: 'Trial'):
"""
Save trial.
:param trial: trial to save
:return:
"""
self.trials.append(trial)
class InMemoryHyperparamsRepository(HyperparamsRepository):
"""
In memory hyperparams repository that can print information about trials.
Useful for debugging.
Example usage :
.. code-block:: python
InMemoryHyperparamsRepository(
hyperparameter_selection_strategy=RandomSearchHyperparameterSelectionStrategy(),
print_func=print,
cache_folder='cache',
best_retrained_model_folder='best'
)
.. seealso::
:class:`AutoML`,
:class:`Trainer`,
:class:`~neuraxle.metaopt.trial.Trial`,
:class:`HyperparamsJSONRepository`,
:class:`BaseHyperparameterSelectionStrategy`,
:class:`RandomSearchHyperparameterSelectionStrategy`,
:class:`~neuraxle.hyperparams.space.HyperparameterSamples`
"""
def __init__(self, hyperparameter_selection_strategy=None, print_func: Callable = None, cache_folder: str = None,
best_retrained_model_folder=None):
HyperparamsRepository.__init__(
self,
hyperparameter_selection_strategy=hyperparameter_selection_strategy,
cache_folder=cache_folder,
best_retrained_model_folder=best_retrained_model_folder
)
if print_func is None:
print_func = print
self.print_func = print_func
self.cache_folder = cache_folder
self.trials = Trials()
def load_all_trials(self, status: 'TRIAL_STATUS' = None) -> 'Trials':
"""
Load all trials with the given status.
:param status: trial status
:return: list of trials
"""
return self.trials.filter(status)
def _save_trial(self, trial: 'Trial'):
"""
Save trial.
:param trial: trial to save
:return:
"""
self.print_func(trial)
self.trials.append(trial)
def new_trial(self, auto_ml_container: 'AutoMLContainer') -> 'Trial':
"""
Create a new trial with the best next hyperparams.
:param auto_ml_container: auto ml data container
:return: trial
"""
hyperparams = self.hyperparameter_selection_strategy.find_next_best_hyperparams(auto_ml_container)
self.print_func('new trial:\n{}'.format(json.dumps(hyperparams.to_nested_dict(), sort_keys=True, indent=4)))
return Trial(
save_trial_function=self.save_trial,
hyperparams=hyperparams,
main_metric_name=auto_ml_container.main_scoring_metric_name
)
