(installation)=

# Installation

All algorithms provided by this project are published on [PyPi](https://pypi.org/). As shown below, they can easily be installed via a Python package manager, such as [pip](<https://en.wikipedia.org/wiki/Pip_(package_manager)>). Unless you intend to modify the algorithms' source code, in which case you should have a look at the section {ref}`compilation`, this is the recommended way for installing the software.

The following compatibility matrix provides an overview of the operating systems and architectures for which the packages mentioned below are currently available:

```{list-table}
---
header-rows: 1
---
* - Operating System
  - x86-64
  - ARM64
* - Linux
  - ✅
  - ✅
* - macOS
  - ✅
  - ✅
* - Windows
  - ✅
  - ✅
```

Examples of how to use the algorithms in your own Python programs can be found in the section {ref}`usage`.

## Installing the BOOMER Algorithm

The gradient boosting algorithm BOOMER is published as the Python package [mlrl-boomer](https://pypi.org/project/mlrl-boomer/). It can be installed via the following command:

```text
pip install mlrl-boomer
```

The description of the methodology used by the BOOMER algorithm, as well as examples of how to configure it, are given {ref}`here<user-guide-boomer>`.

## Installing the SeCo Algorithm

In addition to the BOOMER algorithm, this project does also provide a Separate-and-Conquer (SeCo) algorithm based on traditional rule learning techniques that are particularly well-suited for learning interpretable models. It is published as the package [mlrl-seco](https://pypi.org/project/mlrl-seco/) and can be installed as follows:

```text
pip install mlrl-seco
```

In {ref}`this<user-guide-seco>` section, we elaborate on the techniques utilized by the SeCo algorithm and discuss its parameters.

## Installing the Command Line API

To ease the use of the algorithms that are developed by this project, we also provide a command line utility that allows configuring the algorithms and applying them to a given dataset without the need to write code. It is published as the Python package [mlrl-testbed-sklearn](https://pypi.org/project/mlrl-testbed-sklearn/) and can optionally be installed via the following command:

```text
pip install mlrl-testbed-sklearn
```

For more information about how to use the command line API, refer to the section {ref}`testbed`.