A framework for parametric dimensionality reduction
Project description
paraDime: A Framework for Parametric Dimensionality Reduction
paraDime is a modular framework for specifying and training parametric dimensionality reduction (DR) models. These models allow you to add new data points to existing low-dimensional representations of high-dimensional data. ParaDime DR models are constructed from simple building blocks (such as the relations between data points), so that experimentation with novel DR techniques becomes easy.
Installation
paraDime is available via PyPi through:
pip install paradime
paraDime requires Numpy, SciPy, scikit-learn, PyNNDescent, and PyTorch (see requirements.txt file).
In order to use PyTorch’s CUDA functionality, it might be necessary to install PyTorch separately with the correct setting for the cudatoolkit option (assuming you have the CUDA Toolkit already installed). See the PyTorch docs for installation info.
Documentation
For a simple example with one of the predefined paraDime routines, see Getting Started in the documentation.
More detailed information about how to set up cusom routines can be found in Building Blocks of a paraDime Routine.
For additional examples of varying complexity, see Examples.
References
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