# -*- coding: utf-8 -*-
# @Author : GZH
# @Created Time : 2022/11/3 12:50
# @Email : guozh29@mail2.sysu.edu.cn
# @Last Modified By : GZH
# @Last Modified Time : 2022/11/3 12:50
from numpy import ndarray
from golf_federated.server.process.strategy.evaluation.base import BaseEval
from golf_federated.server.process.strategy.evaluation.function import mse
from golf_federated.utils.log import loggerhear
[docs]class MSE(BaseEval):
"""
Mean squared error of regression problems, inheriting from BaseEval class.
"""
def __init__(
self,
target
) -> None:
"""
Initialize the MSE object.
Args:
target (float): Target of the indicator.
"""
# Super class init.
super().__init__(
name='mse',
question_type='regression',
positive=False,
target=target
)
loggerhear.log("Server Info ", "Evaluate %s question with %s." % (self.question_type, self.name))
[docs] def eval(
self,
target: ndarray,
prediction: ndarray,
) -> float:
"""
Calculation of evaluation metric.
Args:
target (numpy.ndarray): Ground truth.
prediction (numpy.ndarray): Prediction result.
Returns:
Float: MSE.
"""
# Calling calculation function.
result = mse(
target=target,
prediction=prediction
)
# Record the evaluation result.
self.record.append(result)
loggerhear.log("Server Info ", "Evaluate result of %s is %f." % (self.name, result))
return result