Install

mvlearn can be installed by using pip, GitHub, or through the conda-forge channel into an existing conda environment. See below for pip installation instructions or conda installation instructions.

IMPORTANT NOTE: mvlearn has an optional dependencies for certain functions, and so special instructions must be followed to include these optional dependencies in the installation (if you do not have those packages already) in order to access all the features within mvlearn. More details can be found in Including optional dependencies for full functionality.

pip installation instructions

Below we assume you have the default Python3 environment already configured on your computer and you intend to install mvlearn inside of it. If you want to create and work with Python virtual environments, please follow instructions on venv and virtual environments.

First, make sure you have the latest version of pip3 (the Python3 package manager) installed. If you do not, refer to the Pip documentation and install pip3 first.

Install the current release of mvlearn with pip3:

$ pip3 install mvlearn

To upgrade to a newer release use the --upgrade flag:

$ pip3 install --upgrade mvlearn

If you do not have permission to install software systemwide, you can install into your user directory using the --user flag:

$ pip3 install --user mvlearn

Alternatively, you can manually download mvlearn from GitHub or PyPI. To install one of these versions, unpack it and run the following from the top-level source directory using the Terminal:

$ pip3 install -e .

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

conda installation instructions

Here, we assume you have created a conda environment with one of the accepted python versions, and you intend to install the mvlearn into it.

To install mvlearn with conda, run:

$ conda install -c conda-forge mvlearn

To list all versions of mvlearn available on your platform, use:

$ conda search mvlearn --channel conda-forge

Including optional dependencies for full functionality

A small subset of functions require specific extra dependencies not installed by default with the core installation. Each bullet point denotes a collection of functions, with corresponding keyword enclosed in the brackets [].

  • [torch]: DCCA, SplitAE

  • [multiviewica]: MultiviewICA, GroupICA

If you want to use any of the above functionality within mvlearn, please follow the directions below to install the additional dependencies. These dependencies are listed in the package requirements folder with corresponding keyword names for manual installation.

They can be installed from PyPI by simply calling:

$ pip3 install mvlearn[keyword]

where 'keyword' is from the list above, bracketed. To upgrade the package and torch requirements:

$ pip3 install --upgrade mvlearn[keyword]

If you have the package locally, from the top level folder call:

$ pip3 install -e .[keyword]

To install the optional dependencies in with conda, consult the following for the dependencies you need:

to install it properly for your specific system specifications. Then, install tqdm:

$ conda install -c conda-forge tqdm

  • [multiviewica]: There are two package dependencies for this functionality, which can be installed through conda-forge:

    $ conda install -c conda-forge python-picard
$ conda install -c conda-forge multiviewica

Python package dependencies

mvlearn requires the following packages:

  • matplotlib >=3.0.0

  • numpy >=1.17.0

  • scikit-learn >=0.19.1

  • scipy >=1.5.0

  • seaborn >=0.9.0

  • joblib >=0.11

with optional [torch] dependencies,

  • torch >=1.1.0

  • tqdm

and optional [multiviewica] dependencies,

  • python-picard >=0.4

  • multiviewica >=0.0.1

Currently, mvlearn is supported for Python 3.6, 3.7, and 3.8.

Hardware requirements

The mvlearn package requires only a standard computer with enough RAM to support the in-memory operations and free memory to install required packages.

OS Requirements

This package is supported for Linux and macOS and can also be run on Windows machines.

Testing

mvlearn uses the Python pytest testing package. If you don't already have that package installed, follow the directions on the pytest homepage.