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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 hashlib
import json import json
import six
from copy import copy from copy import copy
from datetime import datetime from datetime import datetime
from itertools import product from itertools import product
@ -1229,6 +1230,18 @@ class HyperParameterOptimizer(object):
configuration_dict = {'parameter_optimization_space': [ configuration_dict = {'parameter_optimization_space': [
Parameter.from_dict(c) for c in 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} arguments = {'opt': kwargs}
if type(optimizer_class) != type: if type(optimizer_class) != type:
logger.warning('Auto Connect optimizer_class disabled, {} is already instantiated'.format(optimizer_class)) 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))
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'] return optimizer_class, configuration_dict['parameter_optimization_space'], arguments['opt']
def _daemon(self): def _daemon(self):
@ -1335,11 +1354,11 @@ class HyperParameterOptimizer(object):
completed_jobs[job_id] = ( completed_jobs[job_id] = (
value, value,
iteration_value[0] if iteration_value else -1, 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): elif completed_jobs.get(job_id):
completed_jobs[job_id] = (completed_jobs[job_id][0], completed_jobs[job_id] = (completed_jobs[job_id][0],
completed_jobs[job_id][1], 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])) pairs.append((i, completed_jobs[job_id][0]))
labels.append(str(completed_jobs[job_id][2])[1:-1]) labels.append(str(completed_jobs[job_id][2])[1:-1])
else: else:
@ -1350,7 +1369,7 @@ class HyperParameterOptimizer(object):
completed_jobs[job_id] = ( completed_jobs[job_id] = (
value, value,
iteration_value[0] if iteration_value else -1, 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 # callback new experiment completed
if self._experiment_completed_cb: if self._experiment_completed_cb:
normalized_value = self.objective_metric.get_normalized_objective(job_id) normalized_value = self.objective_metric.get_normalized_objective(job_id)