Metadata-Version: 2.1
Name: IM_Metrics
Version: 2.2.0
Summary: Several Metrics To Evaluate Machine Learning Models
Author-email: Iman Ahmadianfar <im.ahmadian@gmail.com>
License: MIT License
        
        Copyright (c) 2024 Iman Ahmadianfar
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
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        The above copyright notice and this permission notice shall be included in all
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Project-URL: Homepage, https://www.mathworks.com/matlabcentral/profile/authors/13490648
Project-URL: Bug Tracker, https://github.com/ImanAhmadianfar
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE

# Model Metrics and Excel Saver  

This Python package provides functions to calculate common model evaluation metrics and save the results along with predictions to an Excel file. It's designed to streamline the process of assessing and recording model performance.  

## Features  

- **Comprehensive Metrics:** Calculates R-squared (R2), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE), Wave Hedges Distance (WHD), Vicis Symmetric Distance (VSD), and Willmott's Agreement Index (WAI).  
- **Excel Export:** Organizes and saves the computed metrics and actual vs. predicted values into a well-structured Excel file for convenient analysis and sharing.  
- **Easy Integration:** Simple and intuitive API for straightforward integration into your machine learning workflows.  

## Installation  

Install the package using pip:  

```bash  
pip install IM_Metrics

#Import Necessary Functions:

from IM_Metrics import Save_Metrics

metrics_filename = 'Results of KRidge.xlsx'
Save_Metrics(y_train, y_train_pred, y_test, y_test_pred,metrics_filename)
