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We’re happy to announce the first major stable version of ``mvlearn``.
This version includes multiple new algorithms, more utility functions, as well as significant enhancements to the documentation. Here are some highlights of the big updates.
- Deep CCA, (``DCCA``) in the ``embed`` module
- Updated ``KCCA`` with multiple kernels
- Synthetic multi-view dataset generator class, ``GaussianMixture``, in the ``datasets`` module
- A new module, ``plotting``, which includes functions for visualizing multi-view data, such as ``crossviews_plot`` and ``quick_visualize``
- More detailed tutorial notebooks for all algorithms
Additionally, mvlearn now makes the ``torch`` and ``tqdm`` dependencies optional, so users who don’t need the DCCA or SplitAE functionality do not have to import such a large package. **Note** this is only the case for installing with pip. Installing from ``conda`` includes these dependencies automatically. To install the full version of mvlearn with ``torch`` and ``tqdm`` from pip, you must include the optional torch in brackets:
.. code-block:: python
pip3 install mvlearn[torch]
or
.. code-block:: python
pip3 install --upgrade mvlearn[torch]
To install **without** ``torch``, do:
.. code-block:: python
pip3 install mvlearn
or
.. code-block:: python
pip3 install --upgrade mvlearn
.. _Pierre Ablin: https://pierreablin.com/
.. _Ronan Perry: http://rflperry.github.io/
.. _Iain Carmichael: https://idc9.github.io/