import json
import os
from typing import Any
from typing import Dict
from typing import List
from typing import Optional
from typing import Union
import optuna
from optuna._experimental import experimental
from optuna._imports import try_import
with try_import() as _imports:
import allennlp
import allennlp.commands
import allennlp.common.util
from allennlp.training import EpochCallback
if _imports.is_successful():
import _jsonnet
else:
EpochCallback = object # NOQA
[docs]def dump_best_config(input_config_file: str, output_config_file: str, study: optuna.Study) -> None:
"""Save JSON config file after updating with parameters from the best trial in the study.
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()
best_params = study.best_params
for key, value in best_params.items():
best_params[key] = str(value)
best_config = json.loads(_jsonnet.evaluate_file(input_config_file, ext_vars=best_params))
best_config = allennlp.common.params.infer_and_cast(best_config)
with open(output_config_file, "w") as f:
json.dump(best_config, f, indent=4)
[docs]@experimental("1.4.0")
class AllenNLPExecutor(object):
"""AllenNLP extension to use optuna with Jsonnet config file.
This feature is experimental since AllenNLP major release will come soon.
The interface may change without prior notice to correspond to the update.
See the examples of `objective function <https://github.com/optuna/optuna/blob/
master/examples/allennlp/allennlp_jsonnet.py>`_ and
`config file <https://github.com/optuna/optuna/blob/master/
examples/allennlp/classifier.jsonnet>`_.
.. note::
In :class:`~optuna.integration.AllenNLPExecutor`,
you can pass parameters to AllenNLP by either defining a search space using
Optuna suggest methods or setting environment variables just like AllenNLP CLI.
If a value is set in both a search space in Optuna and the environment variables,
the executor will use the value specified in the search space in Optuna.
Args:
trial:
A :class:`~optuna.trial.Trial` corresponding to the current evaluation
of the objective function.
config_file:
Config file for AllenNLP.
Hyperparameters should be masked with ``std.extVar``.
Please refer to `the config example <https://github.com/allenai/allentune/blob/
master/examples/classifier.jsonnet>`_.
serialization_dir:
A path which model weights and logs are saved.
metrics:
An evaluation metric for the result of ``objective``.
include_package:
Additional packages to include.
For more information, please see
`AllenNLP documentation <https://docs.allennlp.org/master/api/commands/train/>`_.
"""
[docs] def __init__(
self,
trial: optuna.Trial,
config_file: str,
serialization_dir: str,
metrics: str = "best_validation_accuracy",
*,
include_package: Optional[Union[str, List[str]]] = None
):
_imports.check()
self._params = trial.params
self._config_file = config_file
self._serialization_dir = serialization_dir
self._metrics = metrics
if include_package is None:
include_package = []
if isinstance(include_package, str):
self._include_package = [include_package]
else:
self._include_package = include_package
def _build_params(self) -> Dict[str, Any]:
"""Create a dict of params for AllenNLP.
_build_params is based on allentune's train_func.
For more detail, please refer to
https://github.com/allenai/allentune/blob/master/allentune/modules/allennlp_runner.py#L34-L65
"""
params = self._environment_variables()
params.update({key: str(value) for key, value in self._params.items()})
allennlp_params = json.loads(_jsonnet.evaluate_file(self._config_file, ext_vars=params))
# allennlp_params contains a list of string or string as value values.
# Some params couldn't be casted correctly and
# infer_and_cast converts them into desired values.
return allennlp.common.params.infer_and_cast(allennlp_params)
@staticmethod
def _is_encodable(value: str) -> bool:
# https://github.com/allenai/allennlp/blob/master/allennlp/common/params.py#L77-L85
return (value == "") or (value.encode("utf-8", "ignore") != b"")
def _environment_variables(self) -> Dict[str, str]:
return {key: value for key, value in os.environ.items() if self._is_encodable(value)}
[docs] def run(self) -> float:
"""Train a model using AllenNLP."""
try:
import_func = allennlp.common.util.import_submodules
except AttributeError:
import_func = allennlp.common.util.import_module_and_submodules
for package_name in self._include_package:
import_func(package_name)
params = allennlp.common.params.Params(self._build_params())
allennlp.commands.train.train_model(params, self._serialization_dir)
metrics = json.load(open(os.path.join(self._serialization_dir, "metrics.json")))
return metrics[self._metrics]
[docs]@experimental("2.0.0")
class AllenNLPPruningCallback(EpochCallback):
"""AllenNLP callback to prune unpromising trials.
See `the example <https://github.com/optuna/optuna/blob/master/
examples/allennlp/allennlp_simple.py>`__
if you want to add a proning callback which observes a metric.
Args:
trial:
A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the
objective function.
monitor:
An evaluation metric for pruning, e.g. ``validation_loss`` or
``validation_accuracy``.
"""
[docs] def __init__(self, trial: optuna.trial.Trial, monitor: str):
_imports.check()
if allennlp.__version__ < "1.0.0":
raise Exception("AllenNLPPruningCallback requires `allennlp`>=1.0.0.")
self._trial = trial
self._monitor = monitor
def __call__(
self,
trainer: "allennlp.training.GradientDescentTrainer",
metrics: Dict[str, Any],
epoch: int,
is_master: bool,
) -> None:
value = metrics.get(self._monitor)
if value is None:
return
self._trial.report(float(value), epoch)
if self._trial.should_prune():
raise optuna.TrialPruned()