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:`~optuna.study.Study`.
Note that :func:`~optuna.study.Study.optimize` 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)