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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
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