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<p align="center">
<h1 align="center">
<br>
<a href="https://i.imgur.com/jCtPpTF.png"><img src="https://i.imgur.com/jCtPpTF.png" alt="Scikit-longitudinal" width="200"></a>
<a href="https://i.imgur.com/jCtPpTF.png">
<img src="https://i.imgur.com/jCtPpTF.png" alt="Scikit-longitudinal" width="200">
</a>
<br>
Scikit-longitudinal
<br>
</h1>
<h4 align="center">A specialised Python library for longitudinal data analysis built on Scikit-learn</h4>
<table align="center">
<tr>
<td align="center">
<h3>⚙️ Project Status</h3>
</td>
<td align="center">
<h3>☎️ Contacts</h3>
</td>
</tr>
<tr>
<td valign="top">
<!-- Python-related badges table -->
<table>
<tr>
<table>
<tr>
<td>
<a href="https://pdm.fming.dev">
<img alt="pdm" src="https://img.shields.io/badge/pdm-managed-blue?style=for-the-badge&logo=python">
</a>
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<img alt="pytest" src="https://img.shields.io/badge/pytest-passing-green?style=for-the-badge&logo=pytest">
</a><br />
<a href="https://codecov.io/gh/Scikit-Longitudinal/Scikit-Longitudinal">
<img alt="Codecov" src="https://img.shields.io/badge/coverage-88%25-brightgreen.svg?style=for-the-badge&logo=appveyor">
</a>
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<a href="mailto:[email protected]">
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</table>
</div>

> 🌟 **Exciting Update**: We're delighted to introduce the brand new v0.1 documentation for Scikit-longitudinal! For a
> deep dive into the library's capabilities and features,
> please [visit here](https://simonprovost.github.io/scikit-longitudinal/).
> 🎉 **PyPi is available!**: We published Scikit-Longitudinal, [here](https://pypi.org/project/Scikit-longitudinal/)!
## <a id="about-the-project"></a>💡 About The Project

`Scikit-longitudinal` (Sklong) is a machine learning library designed to analyse
longitudinal data (Classification tasks focussed as of today). It offers tools and models for processing, analysing,
and predicting longitudinal data, with a user-friendly interface that
integrates with the `Scikit-learn` ecosystem.

Please for further information, visit the [official documentation](https://simonprovost.github.io/scikit-longitudinal/).

## <a id="installation"></a>🛠️ Installation

To install `Sklong`, take these two easy steps:

1. ✅ **Install the latest version of `Sklong`**:

```shell
pip install Scikit-longitudinal
```
You could also install different versions of the library by specifying the version number,
e.g. `pip install Scikit-longitudinal==0.0.1`.
Refer to [Release Notes](https://github.com/simonprovost/scikit-longitudinal/releases)

2. 📦 **[MANDATORY] Update the required dependencies (Why? See [here](https://github.com/pdm-project/pdm/issues/1316#issuecomment-2106457708))**

`Scikit-longitudinal` incorporates a modified version of `Scikit-Learn` called `Scikit-Lexicographical-Trees`,
which can be found at [this Pypi link](https://pypi.org/project/scikit-lexicographical-trees/).
<div align="center">

This revised version guarantees compatibility with the unique features of `Scikit-longitudinal`.
Nevertheless, conflicts may occur with other dependencies in `Scikit-longitudinal` that also require `Scikit-Learn`.
Follow these steps to prevent any issues when running your project.
<!-- All badges in a row -->
<a href="https://pypi.org/project/Scikit-longitudinal/">
<img src="https://img.shields.io/pypi/v/Scikit-longitudinal?style=for-the-badge" alt="PyPI Version" />
</a>
<a href="https://pytest.org/">
<img alt="pytest" src="https://img.shields.io/badge/pytest-passing-green?style=for-the-badge&logo=pytest">
</a>
<a href="https://codecov.io/gh/Scikit-Longitudinal/Scikit-Longitudinal">
<img alt="Codecov" src="https://img.shields.io/badge/coverage-88%25-brightgreen.svg?style=for-the-badge&logo=appveyor">
</a>
<a href="https://www.pylint.org/">
<img alt="pylint" src="https://img.shields.io/badge/pylint-checked-blue?style=for-the-badge&logo=python">
</a>
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</a>
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</a>
<a href="https://github.com/astral-sh/ruff">
<img alt="Ruff" src="https://img.shields.io/badge/Linter-Ruff-brightgreen?style=for-the-badge">
</a>
<a href="https://github.com/astral-sh/uv">
<img alt="UV Managed" src="https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/uv/main/assets/badge/v0.json">
</a>

[simonprovostdev.vercel.app](https://simonprovostdev.vercel.app/)

<details>
<summary><strong>🫵 Simple Setup: Command Line Installation</strong></summary>
</div>

Say you want to try `Sklong` in a very simple environment. Such as without a proper `project.toml` file (`Poetry`, `PDM`, etc).
Run the following command:
---

```shell
pip uninstall scikit-learn scikit-lexicographical-trees && pip install scikit-lexicographical-trees
```
# 📰 Latest News

*Note: Although the main installation command install both, yet it’s advisable to verify the correct versions used is
`Scikit-Lexicographical-trees` to prevent conflicts.*
</details>
- **Updated Workflow**: Now leveraging [UV](https://docs.astral.sh/uv/) for enhanced project management and dependency resolution.
- **Documentation**: Dive into Scikit-longitudinal's features and capabilities in our [official documentation](https://simonprovost.github.io/scikit-longitudinal/).
- **PyPI Availability**: The library is available on [PyPI](https://pypi.org/project/Scikit-longitudinal/).

<details>
<summary><strong>🫵 Project Setup: Using `PDM` (or any other such as `Poetry`, etc.)</strong></summary>
---

Imagine you have a project being managed by `PDM`, or any other package manager. The example below demonstrates `PDM`.
Nevertheless, the process is similar for `Poetry` and others. Consult their documentation for instructions on excluding a
package.
## <a id="about-the-project"></a>💡 About The Project

Therefore, to prevent dependency conflicts, you can exclude `Scikit-Learn` by adding the provided configuration
to your `pyproject.toml` file.
`Scikit-longitudinal` (Sklong) is a machine learning library designed to analyse
longitudinal data (Classification tasks focussed as of today). It offers tools and models for processing, analysing,
and predicting longitudinal data, with a user-friendly interface that
integrates with the `Scikit-learn` ecosystem.

```toml
[tool.pdm.resolution]
excludes = ["scikit-learn"]
```
For more details, visit the [official documentation](https://simonprovost.github.io/scikit-longitudinal/).

*This exclusion ensures Scikit-Lexicographical-Trees (used as `Scikit-learn`) is used seamlessly within your project.*
</details>
---

### 💻 Developer Notes
## <a id="installation"></a>🛠️ Installation

For developers looking to contribute, please refer to the `Contributing` section of the [official documentation](https://simonprovost.github.io/scikit-longitudinal/).
To install Scikit-longitudinal:

## <a id="Supported-Operating-Systems"></a>🛠️ Supported Operating Systems
1. ✅ Install the latest version:
```bash
pip install Scikit-longitudinal
```

`Scikit-longitudinal` is compatible with the following operating systems:
To install a specific version:
```bash
pip install Scikit-longitudinal==0.1.0
```

- MacOS 
- Linux 🐧
- Windows via Docker only (Docker uses Linux containers) 🪟 (To try without but we haven't tested it)
See further in the [Quick Start of the documentation](https://simonprovost.github.io/scikit-longitudinal/quick-start) for more details.

## <a id="how-to-use"></a></a>🚀 Getting Started
---

To perform longitudinal analysis with `Scikit-Longitudinal`, use the
`LongitudinalDataset` class to prepare the dataset. To analyse your
data, use the `LexicoGradientBoostingClassifier` _(i.e. Gradient Boosting variant for Longitudinal Data)_ or another
available
estimator/preprocessor.
## <a id="getting-started"></a>🚀 Getting Started

Following that, you can apply the popular _fit_, _predict_, _prodict_proba_, or _transform_
methods in the same way that `Scikit-learn` does, as shown in the example below.
Here's how to analyse longitudinal data with Scikit-longitudinal:

``` py
from scikit_longitudinal.data_preparation import LongitudinalDataset
from scikit_longitudinal.estimators.ensemble.lexicographical.lexico_gradient_boosting import LexicoGradientBoostingClassifier

dataset = LongitudinalDataset('./stroke.csv')
dataset = LongitudinalDataset('./stroke.csv') # Note this is a fictional dataset. Use yours!
dataset.load_data_target_train_test_split(
target_column="class_stroke_wave_4",
)

# Pre-set or manually set your temporal dependencies
dataset.setup_features_group(input_data="Elsa")
dataset.setup_features_group(input_data="elsa")

model = LexicoGradientBoostingClassifier(
features_group=dataset.feature_groups(),
threshold_gain=0.00015
threshold_gain=0.00015 # Refer to the API for more hyper-parameters and their meaning
)

model.fit(dataset.X_train, dataset.y_train)
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print(classification_report(y_test, y_pred))
```

## <a id="citation"></a>📝 How to Cite?
See further in the [Quick Start of the documentation](https://simonprovost.github.io/scikit-longitudinal/quick-start) for more details.

---

## <a id="citation"></a>📝 How to Cite

If you find Scikit-longitudinal helpful, please cite us using the `CITATION.cff` file or via the "Cite this repository" button on GitHub.

Paper has been submitted to a conference. In the meantime, for the repository, utilise the button top right corner of the
repository "How to cite?", or open the following citation file: [CITATION.cff](./CITATION.cff).
---

## <a id="license"></a>🔐 License

[MIT License](./LICENSE)
Scikit-longitudinal is licensed under the [MIT License](./LICENSE).
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### Prerequisites
Ensure the following tools are installed:
- [Python 3.9.x](https://www.python.org/downloads/release/python-398/)
- [UV](https://docs.astral.sh/uv/)
* [Python 3.9.x](https://www.python.org/downloads/release/python-398/)
* [UV](https://docs.astral.sh/uv/)

---

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## ❌ Troubleshooting Errors

### General Issues

If you encounter setup errors:

1. **Deactivate Environment**:
```bash
deactivate
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