optuna.integration.mxnet 源代码

import optuna
from optuna._imports import try_import

with try_import() as _imports:
    import mxnet as mx

[文档]class MXNetPruningCallback(object): """MXNet callback to prune unpromising trials. See `the example <https://github.com/optuna/optuna/blob/master/ examples/mxnet/mxnet_integration.py>`__ if you want to add a pruning callback which observes accuracy. Args: trial: A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the objective function. eval_metric: An evaluation metric name for pruning, e.g., ``cross-entropy`` and ``accuracy``. If using default metrics like mxnet.metrics.Accuracy, use it's default metric name. For custom metrics, use the metric_name provided to constructor. Please refer to `mxnet.metrics reference <https://mxnet.apache.org/api/python/metric/metric.html>`_ for further details. """ def __init__(self, trial: optuna.trial.Trial, eval_metric: str) -> None: _imports.check() self._trial = trial self._eval_metric = eval_metric def __call__(self, param: "mx.model.BatchEndParam") -> None: if param.eval_metric is not None: metric_names, metric_values = param.eval_metric.get() if type(metric_names) == list and self._eval_metric in metric_names: current_score = metric_values[metric_names.index(self._eval_metric)] elif metric_names == self._eval_metric: current_score = metric_values else: raise ValueError( 'The entry associated with the metric name "{}" ' "is not found in the evaluation result list {}.".format( self._eval_metric, str(metric_names) ) ) self._trial.report(current_score, step=param.epoch) if self._trial.should_prune(): message = "Trial was pruned at epoch {}.".format(param.epoch) raise optuna.TrialPruned(message)