Installation

Follow the instructions below to install iMML on your system. Some features of iMML rely on optional dependencies. To enable these additional features, ensure you install the required packages as described in the Optional dependencies section below.

Instructions

Using pip

To install iMML using pip, ensure Python 3 and pip (Python's package manager) are properly set up on your system. If pip is not already installed or needs an update, refer to the official documentation at https://pip.pypa.io.

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

pip install imml

To upgrade to a newer version of iMML, use the --upgrade flag:

pip install --upgrade imml

If you do not have permission, install iMML in your user directory by adding the --user flag:

pip install --user imml

Using uv

To install iMML using uv, ensure Python 3 and uv (Python's package manager) are properly set up on your system. If pip is not already installed or needs an update, refer to the official documentation at https://docs.astral.sh/uv/.

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

uv pip install imml

Manually

Alternatively, you can download iMML directly from its source repository on GitHub. This method is useful if you want to install the latest development version:

  1. Clone the repository:

git clone

  1. Navigate to the project directory:

cd imml

Install the package in editable mode:

pip install -e .

This will install iMML and the required dependencies (see below).

Dependencies

iMML requires the following packages:

  • scikit-learn>=1.4.1

  • pandas>=2.2.2

  • networkx>=2.5

  • h5py>=3.9.0

  • snfpy>=0

  • control>=0.10.2

  • cvxpy>=1.7.1

  • numba>=0.58.0

iMML is supported for Python 3.10 and above.

Optional dependencies

iMML supports additional features that require optional dependencies. You can install these dependencies by specifying extras during installation. For example:

  • [matlab]: Some algorithms were originally developed in Matlab. If you want to use the original implementation, use this module.

  • [r]: Some algorithms were originally developed in R. If you want to use the original implementation, use this module.

  • [deep]: Deep learning methods are included in this module.

To include these dependencies, execute in the terminal:

pip install imml[keyword]

where 'keyword' is from the list above.

To install all possible dependencies:

pip install imml[all]

Using engine = "matlab"

To include these dependencies, execute in the terminal:

pip install imml[matlab]

In order to use matlab as an engine, you will need to have Octave (MATLAB) in your machine. In linux, you can install it using the following commands:

sudo apt install octave

For other platforms, please refer to the official guides: https://octave.org/download

Additionally, some algorithms (such as DAIMC and OSLFIMVC) could have some extra dependencies. To install these dependencies, execute the following commands in a terminal:

sudo apt install octave-control
sudo apt install octave-statistics

Using engine = "r"

To include these dependencies, execute in the terminal:

pip install imml[r]

In order to use R as an engine, you will need to have R in your machine. In linux, you can install it using the following commands:

sudo apt install r-base r-base-dev -y

For other platforms, please refer to the official guides: https://cran.r-project.org/doc/manuals/r-patched/R-admin.html

When using R as engine, some algorithms (such as JNMF) could have some extra dependencies. To install these dependencies, execute the following command in R:

install.packages("nnTensor")

OS Requirements

This package is supported for Linux, macOS and Windows machines.

Testing

To test the package, install the testing dependencies using:

pip install imml[tests]

This will install pytest and pytest-cov.

Documenting

To include new documentation, install the documenting dependencies using:

pip install imml[docs]