Metadata-Version: 2.1
Name: imml
Version: 0.2.0
Summary: A python package for multi-modal learning with incomplete data
Author-email: Alberto López <a.l.sanchez@medisin.uio.no>
Maintainer-email: Alberto López <a.l.sanchez@medisin.uio.no>
License: BSD 3-Clause License
        
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Project-URL: Documentation, https://imml.readthedocs.io
Project-URL: Source, https://github.com/ocbe-uio/imml
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Keywords: multi-modal learning,machine learning,incomplete data,missing data
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Development Status :: 3 - Alpha
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<p align="center">
  <img alt="iMML Logo" src="https://raw.githubusercontent.com/ocbe-uio/imml/refs/heads/main/docs/figures/logo_imml.png">
</p>

[**Overview**](#Overview) | [**Key features**](#Key-features) | [**Installation**](#installation) | 
[**Usage**](#Usage) | [**Free software**](#Free-software) | [**Contribute**](#Contribute) | [**Help us**](#Help-us-grow)

Overview
====================

Multi-modal learning, where diverse data types are integrated and analyzed together, has emerged as a critical 
field in artificial intelligence.
However, most algorithms assume fully observed data, an assumption that is often unrealistic in real-world scenarios.
To address this gap, we have developed *iMML*, a Python package designed for multi-modal learning with incomplete data.

![Overview of iMML for multi-modal learning with incomplete data](https://raw.githubusercontent.com/ocbe-uio/imml/refs/heads/main/docs/figures/graph.png)
<p align="center"><strong>Overview of iMML for multi-modal learning with incomplete data.</strong></p>

Key features
------------

The key features of this package are:

-   **Coverage**: More than 25 methods for integrating, processing, and analyzing incomplete multi-modal 
    datasets implemented as a single, user-friendly interface.
-   **Comprehensive**: Designed to be compatible with widely-used machine learning and data analysis tools, allowing 
    use with minimal programming effort. 
-   **Extensible**: A unified framework where researchers can contribute and integrate new approaches, serving 
    as a community platform for hosting new methods.

Installation
--------------

Run the following command to install the most recent release of *iMML* using *pip*:

```bash
pip install imml
```

Or if you prefer *uv*, use:

```bash
uv pip install imml
```

Some features of *iMML* rely on optional dependencies. To enable these additional features, ensure you install 
the required packages as described in our documentation: https://imml.readthedocs.io/stable/main/installation.html.


Usage
--------

For this example, we will generate a random multi-modal dataset, that we have called ``Xs``:

```python
import numpy as np
Xs = [np.random.random((10,5)) for i in range(3)] # or your multi-modal dataset
```

You can use any other complete or incomplete multi-modal dataset. Once you have your dataset ready, you can
leverage the *iMML* library for a wide range of machine learning tasks, such as:

- Decompose a multi-modal dataset using ``MOFA`` to capture joint information.

```python
from imml.decomposition import MOFA
transformed_Xs = MOFA().fit_transform(Xs)
```

- Cluster samples from a multi-modal dataset using ``NEMO`` to find hidden groups.

```python
from imml.cluster import NEMO
labels = NEMO().fit_predict(Xs)
```

- Simulate incomplete multi-modal datasets for evaluation and testing purposes using ``Amputer``.

```python
from imml.ampute import Amputer
transformed_Xs = Amputer(p=0.8).fit_transform(Xs)
```

Free software
-------------

*iMML* is free software; you can redistribute it and/or modify it under the terms of the `BSD 3-Clause License`.

Contribute
------------

Our vision is to establish *iMML* as a leading and reliable library for multi-modal learning across research and 
applied settings. Our priorities include to broaden algorithmic coverage, improve performance and 
scalability, strengthen interoperability, and grow a healthy contributor community. Therefore, we welcome 
practitioners, researchers, and the open-source community to contribute to the *iMML* project, and in doing so, 
helping us extend and refine the library for the community. Such a community-wide effort will make *iMML* more 
versatile, sustainable, powerful, and accessible to the machine learning community across many domains.

For the full contributing guide, please see:

- In-repo: https://github.com/ocbe-uio/imml/tree/main?tab=contributing-ov-file
- Documentation: https://imml.readthedocs.io/stable/development/contributing.html

Help us grow
------------

How you can help *iMML* grow:

- 🔥 Try it out and share your feedback.
- 🤝 Contribute if you are interested in building with us.
- 🗣️ Share this project with colleagues who deal with multi-modal data.
- 🌟 And of course… give the repo a star to support the project!
