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| import numpy as np | |
| from sklearn.metrics import * | |
| def calculate_ar2(real_data, pred_data): | |
| model_name = "a" | |
| info = {} | |
| info["MAE of "+model_name] = mean_absolute_error(real_data, pred_data) | |
| # mae = mean_absolute_error(real_data, pred_data) | |
| info["MSE of "+model_name] = mean_squared_error(real_data, pred_data) | |
| # mse = mean_squared_error(real_data, pred_data) | |
| info["RSME of "+model_name] = np.sqrt(info["MSE of "+model_name]) | |
| # rsme = np.sqrt(info["MSE of "+model_name]) | |
| info["R-Sqaure of "+model_name] = r2_score(real_data, pred_data) | |
| # r2 = r2_score(real_data, pred_data) | |
| if isinstance(max(real_data), np.ndarray): | |
| info["Adjusted R-Square of " + model_name] = 1 - (1 - info["R-Sqaure of "+model_name]) * (len(pred_data)-1) / (len(pred_data)-max(real_data)[0]-1) | |
| # ar2 = 1 - (1 - info["R-Sqaure of "+model_name]) * (len(pred_data)-1) / (len(pred_data)-max(real_data)[0]-1) | |
| else: | |
| info["Adjusted R-Square of " + model_name] = 1 - (1 - info["R-Sqaure of " + model_name]) * (len(pred_data) - 1) / (len(pred_data) - max(real_data) - 1) | |
| # ar2 = 1 - (1 - info["R-Sqaure of " + model_name]) * (len(pred_data) - 1) / (len(pred_data) - max(real_data) - 1) | |
| return info["Adjusted R-Square of " + model_name] | |
| def calculate_regression_metrics(pred_data, real_data, model_name): | |
| info = {} | |
| info["MAE of "+model_name] = mean_absolute_error(real_data, pred_data) | |
| # mae = mean_absolute_error(real_data, pred_data) | |
| info["MSE of "+model_name] = mean_squared_error(real_data, pred_data) | |
| # mse = mean_squared_error(real_data, pred_data) | |
| info["RSME of "+model_name] = np.sqrt(info["MSE of "+model_name]) | |
| # rsme = np.sqrt(info["MSE of "+model_name]) | |
| info["R-Sqaure of "+model_name] = r2_score(real_data, pred_data) | |
| # r2 = r2_score(real_data, pred_data) | |
| if isinstance(max(real_data), np.ndarray): | |
| info["Adjusted R-Square of " + model_name] = 1 - (1 - info["R-Sqaure of "+model_name]) * (len(pred_data)-1) / (len(pred_data)-max(real_data)[0]-1) | |
| # ar2 = 1 - (1 - info["R-Sqaure of "+model_name]) * (len(pred_data)-1) / (len(pred_data)-max(real_data)[0]-1) | |
| else: | |
| info["Adjusted R-Square of " + model_name] = 1 - (1 - info["R-Sqaure of " + model_name]) * (len(pred_data) - 1) / (len(pred_data) - max(real_data) - 1) | |
| # ar2 = 1 - (1 - info["R-Sqaure of " + model_name]) * (len(pred_data) - 1) / (len(pred_data) - max(real_data) - 1) | |
| return info | |