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