# 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
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from sedna.datasources import BaseDataSource
from sedna.common.class_factory import ClassFactory, ClassType
from .base_task_allocation import BaseTaskAllocation
@ClassFactory.register(ClassType.STP)
[docs]class TaskAllocationByOrigin(BaseTaskAllocation):
"""
Corresponding to `TaskDefinitionByOrigin`
Parameters
----------
task_extractor : Dict
used to predict target tasks for each inference samples
origins: List[Metadata]
metadata is usually a class feature
label with a finite values.
"""
def __init__(self, task_extractor, **kwargs):
super(TaskAllocationByOrigin, self).__init__(task_extractor)
self.default_origin = kwargs.get("default", None)
[docs] def __call__(self, samples: BaseDataSource):
if self.default_origin:
return samples, [int(self.task_extractor.get(
self.default_origin))] * len(samples.x)
cities = [
"aachen",
"berlin",
"bochum",
"bremen",
"cologne",
"darmstadt",
"dusseldorf",
"erfurt",
"hamburg",
"hanover",
"jena",
"krefeld",
"monchengladbach",
"strasbourg",
"stuttgart",
"tubingen",
"ulm",
"weimar",
"zurich"]
sample_origins = []
for _x in samples.x:
is_real = False
for city in cities:
if city in _x[0]:
is_real = True
sample_origins.append("real")
break
if not is_real:
sample_origins.append("sim")
allocations = [int(self.task_extractor.get(sample_origin))
for sample_origin in sample_origins]
return samples, allocations