def _create_tuner(self, pipeline):
# Build an MLPipeline to get the tunables and the default params
mlpipeline = MLPipeline.from_dict(self.template_dict)
tunable_hyperparameters = mlpipeline.get_tunable_hyperparameters()
tunables = []
tunable_keys = []
for block_name, params in tunable_hyperparameters.items():
for param_name, param_details in params.items():
key = (block_name, param_name)
param_type = param_details['type']
param_type = PARAM_TYPES.get(param_type, param_type)
if param_type == 'bool':
param_range = [True, False]
else:
param_range = param_details.get(
'range') or param_details.get('values')
value = HyperParameter(param_type, param_range)
tunables.append((key, value))
tunable_keys.append(key)
# Create the tuner
LOGGER.info('Creating %s tuner', self._tuner_class.__name__)
self.tuner = self._tuner_class(tunables)
if pipeline:
try:
# Add the default params and the score obtained by them to the tuner.
default_params = defaultdict(dict)
for block_name, params in pipeline.pipeline.get_hyperparameters(
).items():
for param, value in params.items():
key = (block_name, param)
if key in tunable_keys:
if value is None:
raise ValueError('None value is not supported')
default_params[key] = value
if pipeline.rank is not None:
self.tuner.add(default_params, 1 - pipeline.rank)
except ValueError:
pass
def _get_tuner(self, pipeline, template_dict):
# Build an MLPipeline to get the tunables and the default params
mlpipeline = MLPipeline.from_dict(template_dict)
tunables = []
tunable_keys = []
for block_name, params in mlpipeline.get_tunable_hyperparameters(
).items():
for param_name, param_details in params.items():
key = (block_name, param_name)
param_type = param_details['type']
param_type = PARAM_TYPES.get(param_type, param_type)
if param_type == 'bool':
param_range = [True, False]
else:
param_range = param_details.get(
'range') or param_details.get('values')
value = HyperParameter(param_type, param_range)
tunables.append((key, value))
tunable_keys.append(key)
# Create the tuner
LOGGER.info('Creating %s tuner', self._tuner_class.__name__)
tuner = self._tuner_class(tunables)
if pipeline:
# Add the default params and the score obtained by the default pipeline to the tuner.
default_params = defaultdict(dict)
for block_name, params in pipeline.pipeline.get_hyperparameters(
).items():
for param, value in params.items():
key = (block_name, param)
if key in tunable_keys:
# default_params[key] = 'None' if value is None else value
default_params[key] = value
tuner.add(default_params, 1 - pipeline.rank)
return tuner
