Source code for optuna.integration.allennlp._dump_best_config

import json

import optuna
from optuna._imports import try_import
from optuna.integration.allennlp._environment import _environment_variables

with try_import() as _imports:
    import _jsonnet

[docs]def dump_best_config(input_config_file: str, output_config_file: str, study: optuna.Study) -> None: """Save JSON config file with environment variables and best performing hyperparameters. Args: input_config_file: Input Jsonnet config file used with :class:`~optuna.integration.AllenNLPExecutor`. output_config_file: Output JSON config file. study: Instance of :class:``. Note that :func:`` must have been called. """ _imports.check() # Get environment variables. ext_vars = _environment_variables() # Get the best hyperparameters. best_params = study.best_params for key, value in best_params.items(): best_params[key] = str(value) # If keys both appear in environment variables and best_params, # values in environment variables are overwritten, which means best_params is prioritized. ext_vars.update(best_params) best_config = json.loads(_jsonnet.evaluate_file(input_config_file, ext_vars=ext_vars)) # `optuna_pruner` only works with Optuna. # It removes when dumping configuration since # the result of `dump_best_config` can be passed to # `allennlp train`. if "callbacks" in best_config["trainer"]: new_callbacks = [] callbacks = best_config["trainer"]["callbacks"] for callback in callbacks: if callback["type"] == "optuna_pruner": continue new_callbacks.append(callback) if len(new_callbacks) == 0: best_config["trainer"].pop("callbacks") else: best_config["trainer"]["callbacks"] = new_callbacks with open(output_config_file, "w") as f: json.dump(best_config, f, indent=4)