# 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.
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"""
Remodeling tasks based on their relationships
Parameters
----------
mappings ：all assigned tasks get from the `task_mining`
samples : input samples
Returns
-------
models : List of groups which including at least 1 task.
"""
from typing import List
import numpy as np
import pandas as pd
from sedna.datasources import BaseDataSource
from sedna.common.class_factory import ClassFactory, ClassType
from .base_task_remodeling import BaseTaskRemodeling
__all__ = ('DefaultTaskRemodeling',)
@ClassFactory.register(ClassType.STP)
[docs]class DefaultTaskRemodeling(BaseTaskRemodeling):
"""
Assume that each task is independent of each other
"""
def __init__(self, models: list, **kwargs):
super(DefaultTaskRemodeling, self).__init__(models)
[docs] def __call__(self, samples: BaseDataSource, mappings: List):
"""
Grouping based on assigned tasks
"""
mappings = np.array(mappings)
data, models = [], []
d_type = samples.data_type
for m in np.unique(mappings):
task_df = BaseDataSource(data_type=d_type)
_inx = np.where(mappings == m)
if isinstance(samples.x, pd.DataFrame):
task_df.x = samples.x.iloc[_inx]
else:
task_df.x = np.array(samples.x)[_inx]
if d_type != "test":
if isinstance(samples.x, pd.DataFrame):
task_df.y = samples.y.iloc[_inx]
else:
task_df.y = np.array(samples.y)[_inx]
task_df.inx = _inx[0].tolist()
if samples.meta_attr is not None:
task_df.meta_attr = np.array(samples.meta_attr)[_inx]
data.append(task_df)
# TODO: if m is out of index
try:
model = self.models[m]
except Exception as err:
print(f"self.models[{m}] not exists. {err}")
model = self.models[0]
models.append(model)
return data, models