Metadata-Version: 2.4
Name: missmixed
Version: 1.0.1
Summary: An Adaptive, Extensible and Configurable Multi-Layer Framework for Iterative Missing Value Imputation
Home-page: https://github.com/MohammadKlhr/missmixed
Author: Mohammad Mahdi Kalhori
Author-email: Mohammad Mahdi Kalhori <mohammad.mahdi.kalhor.99@gmail.com>, Fateme Akbari <fatemeeakbari.97@gmail.com>
Maintainer: Mohammad Mahdi Kalhori, Fateme Akbari
Maintainer-email: mohammad.mahdi.kalhor.99@gmail.com, fatemeeakbari.97@gmail.com
License: MIT License
        
        Copyright (c) 2025 Mohammad Mahdi Kalhori and Fateme Akbari
        
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Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: tqdm>=4.66
Requires-Dist: pandas>=2.0.0
Requires-Dist: numpy>=1.23
Requires-Dist: scikit-learn>=1.4
Requires-Dist: scipy>=1.6.0
Provides-Extra: ml
Requires-Dist: xgboost>=3.0; extra == "ml"
Provides-Extra: deep
Requires-Dist: tensorflow>=2.12; extra == "deep"
Dynamic: author
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# MissMixed

## A Configurable Framework for Iterative Missing Data Imputation

**MissMixed** is a Python library designed for flexible and modular imputation of missing values in tabular datasets. It supports a wide range of imputation strategies, including ensemble methods, trial-based model selection, and deep learning integration — all within a customizable iterative architecture.

## 🔍 What is MissMixed?

MissMixed is not just a single algorithm — it’s a **framework** for building **iteration-wise, model-aware imputation pipelines**. It enables users to:

- Handle continuous, categorical, or mixed-type features
- Define custom model configurations at each iteration
- Combine multiple imputation algorithms (e.g., RandomForest, KNN, Deep Neural Networks)
- Dynamically evaluate and update imputed values using internal validation

Whether you’re working with low-dimensional medical data or large-scale mixed-type datasets, MissMixed is designed to offer **accuracy**, **adaptability**, and **interpretability**.

## 🚀 Installation

```bash
pip install missmixed
```

### 📦 Requirements

- Python ≥ 3.10
- NumPy
- Pandas
- scikit-learn
- XGBoost
- TensorFlow or Keras (for deep model imputation)
- tqdm

Dependencies will be installed automatically via pip.

### 📖 Usage

See the examples folder for how to define:
Custom Iteration Architectures
Mixed-type pipelines
Trial-based imputation workflows

### 📄 License

MIT License

### 📣 Citation

[1] M. M. Kalhori, M. Izadi, “A Novel Mixed-Method Approach to Missing Value Imputation: An Introduction to MissMixed”, 29th International Computer Conference, Computer Society of Iran (CSICC) – IEEE, 2025.

[2] M. M. Kalhori, M. Izadi, F. Akbari “MissMixed: An Adaptive, Extensible and Configurable Multi-Layer Framework for Iterative Missing Value Imputation”, IEEE Access, 2025 (under review).
