Metadata-Version: 2.1
Name: ml-robust-eval
Version: 0.1.5
Summary: ML evaluation, validation, and test case generation toolkit.
Home-page: https://github.com/VikhyatChoppa18
Author: Venkata Vikhyat Choppa
Author-email: vikhyat-ch <vikhyathchoppa699@gmail.com>
License: MIT License
        
        Copyright (c) 2025 VikhyatChoppa18
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
        
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

# ML Robust Eval

![ml-eval-robust-logo](./assets/ML%20Eval.png)

[![PyPI](https://img.shields.io/pypi/v/ml-eval-robust?color=blue&logo=PyPI)]()
[![License](https://img.shields.io/pypi/l/ml-eval-robust)](https://github.com/VikhyatChoppa18/ml_robust_eval/blob/main/LICENSE)
[![Repo size](https://img.shields.io/github/repo-size/yourusername/ml-eval-robust)](https://github.com/VikhyatChoppa18/ml_robust_eval)
[![Last commit](https://img.shields.io/github/last-commit/yourusername/ml-eval-robust?logo=git)](https://github.com/VikhyatChoppa18/ml_robust_eval/commits/main)

---

**ML Eval Robust** is a pure Python, object-oriented library for comprehensive machine learning model evaluation, validation, and robustness testing.  
It’s is an all-in-one toolkit that features:

- 📊 **Metrics** for classification, regression, NLP, and computer vision tasks  
- 🔁 **Cross-validation** and **A/B testing** helpers  
- 📈 **Visualization** tools for confusion matrices and ROC curves (stdout-based, no dependencies!)  
- 🦾 **Automated test case generation**: edge cases, adversarial samples, and boundary value tests  
- 🧩 **No external dependencies** – works anywhere Python runs!

---

## 🚀 Installation
<code>pip install ml_robust_eval</code>

> **Note:** Pure Python! No numpy, pandas, or matplotlib required.
---

## 🧠 Features

- **Classification, Regression, NLP, and CV Metrics**  
  - Accuracy, Precision, Recall, F1, MAE, MSE, R², BLEU, IoU, and more!
- **Cross-Validation & A/B Testing**
  - K-fold splitting, group comparison, and statistical difference calculation
- **Visualization**
  - Confusion matrices and ROC curves printed directly to your console
- **Robustness Test Case Generation**
  - Edge, boundary, and adversarial sample generation for any tabular data
- **Zero Dependencies**
  - Entirely standard library, OOP-based, and lightweight

---

## 📚 Documentation

- [API Reference](https://github.com/yourusername/ml-eval-robust/wiki)
- [Getting Started Guide](https://github.com/yourusername/ml-eval-robust/blob/main/docs/GettingStarted.md)
- [Examples & Tutorials](https://github.com/yourusername/ml-eval-robust/blob/main/examples)

---

## 💡 Why ML Eval Robust?

- **Universal:** No dependencies, works in any Python environment
- **Educational:** Clear, readable OOP code for learning and teaching
- **Robust:** Covers the full ML evaluation and validation pipeline, including adversarial and edge testing

---

## 🤝 Contributing

All contributions, bug reports, and suggestions are welcome!  
See the [contributing guide](https://github.com/VikhyatChoppa18/ml_robust_eval/blob/main/blob/contributing.md).

---

## 📜 License

[MIT License](https://github.com/VikhyatChoppa18/ml_robust_eval/blob/main/LICENSE)

---

## 📬 Contact

Questions? Open an [issue](https://github.com/VikhyatChoppa18/ml_robust_eval/issues) or reach out at [vikhyathchoppa699@gmail.com].

---

**Let your models earn their confidence. Test, validate, and challenge them with ML Robust Eval!**
