# 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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"""
Divide multiple tasks based on data
Parameters
----------
samples： Train data, see `sedna.datasources.BaseDataSource` for more detail.
Returns
-------
tasks: All tasks based on training data.
task_extractor: Model with a method to predicting target tasks
"""
from typing import List, Any, Tuple
import time
import numpy as np
import pandas as pd
from sedna.datasources import BaseDataSource
from sedna.common.class_factory import ClassType, ClassFactory
from ..artifact import Task
__all__ = (
'TaskDefinitionBySVC',
'TaskDefinitionByDataAttr',
'TaskDefinitionByCluster')
@ClassFactory.register(ClassType.STP)
[docs]class TaskDefinitionBySVC:
"""
Dividing datasets with `AgglomerativeClustering` based on kernel distance,
Using SVC to fit the clustering result.
Parameters
----------
n_class： int or None
The number of clusters to find, default=2.
"""
def __init__(self, **kwargs):
n_class = kwargs.get("n_class", "")
self.n_class = max(2, int(n_class)) if str(n_class).isdigit() else 2
[docs] def __call__(self,
samples: BaseDataSource) -> Tuple[List[Task],
Any,
BaseDataSource]:
from sklearn.svm import SVC
from sklearn.cluster import AgglomerativeClustering
d_type = samples.data_type
x_data = samples.x
y_data = samples.y
if not isinstance(x_data, pd.DataFrame):
raise TypeError(f"{d_type} data should only be pd.DataFrame")
tasks = []
legal = list(
filter(lambda col: x_data[col].dtype == 'float64', x_data.columns))
df = x_data[legal]
c1 = AgglomerativeClustering(n_clusters=self.n_class).fit_predict(df)
c2 = SVC(gamma=0.01)
c2.fit(df, c1)
for task in range(self.n_class):
g_attr = f"svc_{task}"
task_df = BaseDataSource(data_type=d_type)
task_df.x = x_data.iloc[np.where(c1 == task)]
task_df.y = y_data.iloc[np.where(c1 == task)]
task_obj = Task(entry=g_attr, samples=task_df)
tasks.append(task_obj)
samples.x = df
return tasks, c2, samples
@ClassFactory.register(ClassType.STP)
[docs]class TaskDefinitionByDataAttr:
"""
Dividing datasets based on the common attributes,
generally used for structured data.
Parameters
----------
attribute： List[Metadata]
metadata is usually a class feature label with a finite values.
"""
def __init__(self, **kwargs):
self.attr_filed = kwargs.get("attribute", [])
[docs] def __call__(self,
samples: BaseDataSource, **kwargs) -> Tuple[List[Task],
Any,
BaseDataSource]:
tasks = []
d_type = samples.data_type
x_data = samples.x
y_data = samples.y
if not isinstance(x_data, pd.DataFrame):
raise TypeError(f"{d_type} data should only be pd.DataFrame")
_inx = 0
task_index = {}
for meta_attr, df in x_data.groupby(self.attr_filed):
if isinstance(meta_attr, (list, tuple, set)):
g_attr = "_".join(
map(lambda x: str(x).replace("_", "-"), meta_attr))
meta_attr = list(meta_attr)
else:
g_attr = str(meta_attr).replace("_", "-")
meta_attr = [meta_attr]
g_attr = g_attr.replace(" ", "")
if g_attr in task_index:
old_task = tasks[task_index[g_attr]]
old_task.x = pd.concat([old_task.x, df])
old_task.y = pd.concat([old_task.y, y_data.iloc[df.index]])
continue
task_index[g_attr] = _inx
task_df = BaseDataSource(data_type=d_type)
task_df.x = df.drop(self.attr_filed, axis=1)
task_df.y = y_data.iloc[df.index]
task_obj = Task(entry=g_attr, samples=task_df, meta_attr=meta_attr)
tasks.append(task_obj)
_inx += 1
x_data.drop(self.attr_filed, axis=1, inplace=True)
samples = BaseDataSource(data_type=d_type)
samples.x = x_data
samples.y = y_data
return tasks, task_index, samples