`neptunecontrib.monitoring.skopt`¶

Module Contents¶

Classes¶

NeptuneCallback(experiment=None, log_checkpoint=True)

Logs hyperparameter optimization process to Neptune.

Functions¶

`log_results`(results, experiment=None, log_plots=True, log_pickle=True)	Logs runs results and parameters to neptune.
`NeptuneMonitor`(args, *kwargs)
`_log_best_parameters`(results, experiment)
`_log_best_score`(results, experiment)
`_log_plot_convergence`(results, experiment, name=’diagnostics’)
`_log_plot_regret`(results, experiment, name=’diagnostics’)
`_log_plot_evaluations`(results, experiment, name=’diagnostics’)
`_log_plot_objective`(results, experiment, name=’diagnostics’)
`_log_results_object`(results, experiment=None)
`_export_results_object`(results)
`_format_to_named_params`(params, result)

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).

Args:

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)¶

neptunecontrib.monitoring.skopt¶

Module Contents¶

Classes¶

Functions¶

`neptunecontrib.monitoring.skopt`¶