ficaria 0.1.0
Feature selection and missing data imputation
Requires Python: >=3.11
Keywords: fuzzy, feature selection, missing data, imputation, machine learning, scikit-learn

<p align="center">
  <img src="https://github.com/team-ficaria/ficaria/raw/experiments_main/img/logo.png" alt="Project Logo" width="400">
</p>

<h2 align="center"><em>Fuzzy Imputation and Critical Attribute Reduction for Intelligent Analysis</em></h2>

</br>

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  <img src="https://img.shields.io/badge/pypi-v0.1.0-blue?style=flat-square" alt="PyPI">
  <img src="https://img.shields.io/badge/license-MIT-blue?style=flat-square" alt="license">
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<p align="center">
<a href="#about-the-package">About The Package</a> • 
<a href="#prerequisites">Prerequisites</a> • 
<a href="#setup">Setup</a> • 
<a href="#usage">Usage</a> • 
<a href="#license">License</a> • 
<a href="#authors">Authors</a>
</p>



<h2 id="about-the-package">🔷 About The Package</h2>

The *ficaria* package is a Python package providing custom, **scikit-learn–compatible transformers**
for **data imputation** and **feature selection**. The transformers are designed to integrate seamlessly with
`scikit-learn` pipelines, making them easy to use in real-world
machine learning workflows and straightforward to extend for
custom or research-oriented use cases.

The package was developed as part of a **Bachelor’s degree thesis**
at the **Warsaw University of Technology**, Faculty of
**Mathematics and Information Science**. All implemented methods are **fuzzy-based**, leveraging concepts
from **fuzzy set theory** to handle uncertainty, vagueness, and
incomplete data in a principled and interpretable manner.
This makes *ficaria* particularly suitable for datasets where
classical crisp methods may be insufficient or overly restrictive.


<h2 id="prerequisites">⚙️ Prerequisites</h2>


![python](https://img.shields.io/badge/Python-3.11%2B-3776AB?style=for-the-badge&logo=python&logoColor=white)

The *ficaria* package depends on the following Python libraries:

- **NumPy**
- **Pandas**
- **SciPy**
- **scikit-learn**
- **kneed**


<h2 id="setup">🛠 Setup</h2>

Ficaria can be installed from PyPI:

```bash
pip install ficaria
```

All dependencies are automatically installed when installing the package via `pip`.


<h2 id="usage">🚀 Usage</h2>

Ficaria provides scikit-learn–compatible transformers for data imputation and feature selection.
All transformers implement the standard fit / transform interface, so they can be used
directly in pipelines alongside scalers, estimators, and cross-validation tools.

### Example 1 — Feature Selection with `FuzzyGranularitySelector`

```python
from ficaria import FuzzyGranularitySelector

selector = FuzzyGranularitySelector(n_features=5, eps=0.3)
selector.fit(X_train, y_train)
X_reduced = selector.transform(X_test)
```

### Example 2 — Data Imputation with `FCMKIterativeImputer`

```python
from ficaria import FCMKIterativeImputer

pipeline.fit(X_train, y_train)
X_transformed = pipeline.transform(X_test)
```

### Example 3 — Combining Transformers in a Pipeline

Since all transformers implement fit and transform, they can be combined:

```python
from sklearn.pipeline import Pipeline
from ficaria import FuzzyGranularitySelector, FCMKIterativeImputer

pipeline = Pipeline([
    ("imputer", FCMKIterativeImputer()),
    ("selector", FuzzyGranularitySelector(n_features=5, eps=0.3)),
])

pipeline.fit(X_train, y_train)
X_final = pipeline.transform(X_test)
```


<h2 id="license">📄 License</h2>

This project is licensed under the **MIT License**.
See the `LICENSE` file for details.



<h2 id="authors">👥 Authors</h2>

**Aleksandra Kwiatkowska** <br>
Email: <a>aleksandra.kwiatkowska263@gmail.com</a> <br>
Github: <a href="https://github.com/kwiatkowskaa">@kwiatkowskaa</a>

**Małgorzata Mokwa** <br>
Email: <a>malgosiam628@gmail.com</a> <br>
Github: <a href="https://github.com/malgosiam2">@malgosiam2</a>

**Bogumiła Okrojek** <br>
Email: <a>bogumila.okrojek@gmail.com</a> <br>
Github: <a href="https://github.com/szostkawron">@szostkawron</a>