36 lines
1008 B
Python
36 lines
1008 B
Python
"""
|
||
-*- coding: utf-8 -*-
|
||
@Time :2022/04/12 17:10
|
||
@Author : Pengyou FU
|
||
@blogs : https://blog.csdn.net/Echo_Code?spm=1000.2115.3001.5343
|
||
@github : https://github.com/FuSiry/OpenSA
|
||
@WeChat : Fu_siry
|
||
@License：Apache-2.0 license
|
||
|
||
"""
|
||
|
||
from sklearn.preprocessing import scale,MinMaxScaler,Normalizer,StandardScaler
|
||
from sklearn.metrics import mean_squared_error,r2_score,mean_absolute_error
|
||
from sklearn.neural_network import MLPRegressor
|
||
import numpy as np
|
||
|
||
|
||
def ModelRgsevaluate(y_pred, y_true):
|
||
|
||
mse = mean_squared_error(y_true,y_pred)
|
||
R2 = r2_score(y_true,y_pred)
|
||
mae = mean_absolute_error(y_true,y_pred)
|
||
|
||
return np.sqrt(mse), R2, mae
|
||
|
||
def ModelRgsevaluatePro(y_pred, y_true, yscale):
|
||
|
||
yscaler = yscale
|
||
y_true = yscaler.inverse_transform(y_true)
|
||
y_pred = yscaler.inverse_transform(y_pred)
|
||
|
||
mse = mean_squared_error(y_true,y_pred)
|
||
R2 = r2_score(y_true,y_pred)
|
||
mae = mean_absolute_error(y_true, y_pred)
|
||
|
||
return np.sqrt(mse), R2, mae |