# 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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"""
Integrate the inference results of all related tasks
"""
from typing import List
import numpy as np
from sedna.common.class_factory import ClassFactory, ClassType
from .base_inference_integrate import BaseInferenceIntegrate
from ..artifact import Task
__all__ = ('DefaultInferenceIntegrate', )
@ClassFactory.register(ClassType.STP)
[docs]class DefaultInferenceIntegrate(BaseInferenceIntegrate):
"""
Default calculation algorithm for inference integration
Parameters
----------
models: All models used for sample inference
"""
def __init__(self, models: list, **kwargs):
super(DefaultInferenceIntegrate, self).__init__(models)
[docs] def __call__(self, tasks: List[Task]):
"""
Parameters
----------
tasks: All tasks with sample result
Returns
-------
result: minimum result
"""
res = {}
for task in tasks:
res.update(dict(zip(task.samples.inx, task.result)))
return np.array([z[1]
for z in sorted(res.items(), key=lambda x: x[0])])