neptunecontrib.monitoring.skopt
¶
Module Contents¶
Classes¶
|
Logs hyperparameter optimization process to Neptune. |
Functions¶
|
Logs runs results and parameters to neptune. |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
-
class
neptunecontrib.monitoring.skopt.
NeptuneCallback
(experiment=None, log_checkpoint=True)¶ Logs hyperparameter optimization process to Neptune.
Specifically using NeptuneCallback will log: run metrics and run parameters, best run metrics so far, and the current results checkpoint.
Examples
Initialize NeptuneCallback:
import neptune import neptunecontrib.monitoring.skopt as sk_utils neptune.init(api_token='ANONYMOUS', project_qualified_name='shared/showroom') neptune.create_experiment(name='optuna sweep') neptune_callback = sk_utils.NeptuneCallback()
Run skopt training passing neptune_callback as a callback:
... results = skopt.forest_minimize(objective, space, callback=[neptune_callback], base_estimator='ET', n_calls=100, n_random_starts=10)
You can explore an example experiment in Neptune: https://ui.neptune.ai/o/shared/org/showroom/e/SHOW-1065/logs
-
__call__
(self, res)¶
-
static
_get_last_params
(res)¶
-
-
neptunecontrib.monitoring.skopt.
log_results
(results, experiment=None, log_plots=True, log_pickle=True)¶ Logs runs results and parameters to neptune.
Logs all hyperparameter optimization results to Neptune. Those include best score (‘best_score’ metric), best parameters (‘best_parameters’ property), convergence plot (‘diagnostics’ log), evaluations plot (‘diagnostics’ log), and objective plot (‘diagnostics’ log).
- Parameters
results ('scipy.optimize.OptimizeResult') – Results object that is typically an output | of the function like skopt.forest_minimize(…)
experiment (neptune.experiments.Experiment) – Neptune experiment. Default is None.
log_plots – (‘bool’): If True skopt plots will be logged to Neptune.
log_pickle – (‘bool’): if True pickled skopt results object will be logged to Neptune.
Examples
Run skopt training:
... results = skopt.forest_minimize(objective, space, base_estimator='ET', n_calls=100, n_random_starts=10)
Initialize Neptune:
import neptune neptune.init(api_token='ANONYMOUS', project_qualified_name='shared/showroom') neptune.create_experiment(name='optuna sweep')
Send best parameters to Neptune:
import neptunecontrib.monitoring.skopt as sk_utils sk_utils.log_results(results)
You can explore an example experiment in Neptune: https://ui.neptune.ai/o/shared/org/showroom/e/SHOW-1065/logs
-
neptunecontrib.monitoring.skopt.
NeptuneMonitor
(*args, **kwargs)¶
-
neptunecontrib.monitoring.skopt.
_log_best_parameters
(results, experiment)¶
-
neptunecontrib.monitoring.skopt.
_log_best_score
(results, experiment)¶
-
neptunecontrib.monitoring.skopt.
_log_plot_convergence
(results, experiment, name='diagnostics')¶
-
neptunecontrib.monitoring.skopt.
_log_plot_regret
(results, experiment, name='diagnostics')¶
-
neptunecontrib.monitoring.skopt.
_log_plot_evaluations
(results, experiment, name='diagnostics')¶
-
neptunecontrib.monitoring.skopt.
_log_plot_objective
(results, experiment, name='diagnostics')¶
-
neptunecontrib.monitoring.skopt.
_log_results_object
(results, experiment=None)¶
-
neptunecontrib.monitoring.skopt.
_export_results_object
(results)¶
-
neptunecontrib.monitoring.skopt.
_format_to_named_params
(params, result)¶