# 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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"""
Mining tasks of inference sample based on task attribute extractor
Parameters
----------
samples ： infer sample, see `sedna.datasources.BaseDataSource` for more detail.
Returns
-------
allocations : tasks that assigned to each sample
"""
from sedna.datasources import BaseDataSource
from sedna.common.class_factory import ClassFactory, ClassType
__all__ = (
'TaskAllocationBySVC',
'TaskAllocationByDataAttr',
'TaskAllocationDefault',
)
@ClassFactory.register(ClassType.STP)
[docs]class TaskAllocationBySVC:
"""
Corresponding to `TaskDefinitionBySVC`
Parameters
----------
task_extractor : Model
SVC Model used to predicting target tasks
"""
def __init__(self, task_extractor, **kwargs):
self.task_extractor = task_extractor
[docs] def __call__(self, samples: BaseDataSource):
df = samples.x
allocations = [0, ] * len(df)
legal = list(
filter(lambda col: df[col].dtype == 'float64', df.columns))
if not len(legal):
return allocations
allocations = list(self.task_extractor.predict(df[legal]))
return samples, allocations
@ClassFactory.register(ClassType.STP)
[docs]class TaskAllocationByDataAttr:
"""
Corresponding to `TaskDefinitionByDataAttr`
Parameters
----------
task_extractor : Dict
used to match target tasks
attr_filed: List[Metadata]
metadata is usually a class feature
label with a finite values.
"""
def __init__(self, task_extractor, **kwargs):
self.task_extractor = task_extractor
self.attr_filed = kwargs.get("attribute", [])
[docs] def __call__(self, samples: BaseDataSource):
df = samples.x
meta_attr = df[self.attr_filed]
allocations = meta_attr.apply(
lambda x: self.task_extractor.get(
"_".join(
map(lambda y: str(x[y]).replace("_", "-").replace(" ", ""),
self.attr_filed)
),
0),
axis=1).values.tolist()
samples.x = df.drop(self.attr_filed, axis=1)
samples.meta_attr = meta_attr
return samples, allocations
@ClassFactory.register(ClassType.STP)
[docs]class TaskAllocationDefault:
"""
Task allocation specifically for unstructured data
Parameters
----------
task_extractor : Dict
used to match target tasks
"""
def __init__(self, task_extractor, **kwargs):
self.task_extractor = task_extractor
[docs] def __call__(self, samples: BaseDataSource):
allocations = [0] * len(samples)
return samples, allocations