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Fix Optuna HPO parameter serializing (issue #254)

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This commit is contained in:
allegroai 2020-11-25 11:21:50 +02:00
parent f11a36f3c3
commit 28d7527537
1 changed files with 22 additions and 3 deletions
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25
trains/automation/optimization.py
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@ -1,5 +1,6 @@
import hashlib
import json
import six
from copy import copy
from datetime import datetime
from itertools import product
@ -1229,6 +1230,18 @@ class HyperParameterOptimizer(object):
configuration_dict = {'parameter_optimization_space': [
Parameter.from_dict(c) for c in configuration_dict['parameter_optimization_space']]}
complex_optimizer_kwargs = None
if 'optimizer_kwargs' in kwargs:
# do not store complex optimizer kwargs:
optimizer_kwargs = kwargs.pop('optimizer_kwargs', {})
complex_optimizer_kwargs = {
k: v for k, v in optimizer_kwargs.items()
if not isinstance(v, six.string_types + six.integer_types +
(six.text_type, float, list, tuple, dict, type(None)))}
kwargs['optimizer_kwargs'] = {
k: v for k, v in optimizer_kwargs.items() if k not in complex_optimizer_kwargs}
# skip non basic types:
arguments = {'opt': kwargs}
if type(optimizer_class) != type:
logger.warning('Auto Connect optimizer_class disabled, {} is already instantiated'.format(optimizer_class))
@ -1255,6 +1268,12 @@ class HyperParameterOptimizer(object):
optimizer_class, original_class))
optimizer_class = original_class
if complex_optimizer_kwargs:
if 'optimizer_kwargs' not in arguments['opt']:
arguments['opt']['optimizer_kwargs'] = complex_optimizer_kwargs
else:
arguments['opt']['optimizer_kwargs'].update(complex_optimizer_kwargs)
return optimizer_class, configuration_dict['parameter_optimization_space'], arguments['opt']
def _daemon(self):
@ -1335,11 +1354,11 @@ class HyperParameterOptimizer(object):
completed_jobs[job_id] = (
value,
iteration_value[0] if iteration_value else -1,
copy(dict(**params, **{"status": id_status.get(job_id)})))
copy(dict(**params, **{"status": id_status.get(job_id)}))) # noqa
elif completed_jobs.get(job_id):
completed_jobs[job_id] = (completed_jobs[job_id][0],
completed_jobs[job_id][1],
copy(dict(**params, **{"status": id_status.get(job_id)})))
copy(dict(**params, **{"status": id_status.get(job_id)}))) # noqa
pairs.append((i, completed_jobs[job_id][0]))
labels.append(str(completed_jobs[job_id][2])[1:-1])
else:
@ -1350,7 +1369,7 @@ class HyperParameterOptimizer(object):
completed_jobs[job_id] = (
value,
iteration_value[0] if iteration_value else -1,
copy(dict(**params, **{"status": id_status.get(job_id)})))
copy(dict(**params, **{"status": id_status.get(job_id)}))) # noqa
# callback new experiment completed
if self._experiment_completed_cb:
normalized_value = self.objective_metric.get_normalized_objective(job_id)