初始提交

classification_model/Evaluate/RgsEvaluate.py

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+"""
+    -*- 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