# Copyright 2023 The KubeEdge Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from sedna.common.log import LOGGER
from sedna.datasources import BaseDataSource
from sedna.common.class_factory import ClassFactory, ClassType
from .base_unseen_task_allocation import BaseUnseenTaskAllocation
__all__ = ('UnseenTaskAllocationDefault', )
@ClassFactory.register(ClassType.UTP)
[docs]class UnseenTaskAllocationDefault(BaseUnseenTaskAllocation):
# TODO: to be completed
"""
Task allocation for unseen data
Parameters
----------
task_extractor : Dict
used to match target tasks
"""
def __init__(self, task_extractor, **kwargs):
super(UnseenTaskAllocationDefault, self).__init__(task_extractor)
self.log = LOGGER
[docs] def __call__(self, samples: BaseDataSource):
'''
Parameters
----------
samples: samples to be allocated
Returns
-------
samples: BaseDataSource
allocations: List
allocation decision for actual inference
'''
try:
allocations = [self.task_extractor.fit(
sample) for sample in samples.x]
except Exception as err:
self.log.exception(err)
allocations = [0] * len(samples)
self.log.info("Use the first task to inference all the samples.")
return samples, allocations