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.
ParaDime is available via PyPi through:
pip install paradime
ParaDime requires Numpy, SciPy, scikit-learn, and PyNNDescent (see requirements.txt file), all of which are installed auomatically when installing ParaDime.
ParaDime also requires PyTorch, which must be installed separately. If you want to train ParaDime routines on the GPU, make sure to install CUDA along with the correct cudatoolkit version. See the PyTorch docs for detailed installation info.
If you want to use ParaDime's plotting utilities, Matplotlib has to be installed additionally.
For a simple example with one of the predefined ParaDime routines, see Simple Usage 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.
| [1] | Van Der Maaten, L., Hinton, G. “Visualizing data using t-SNE”, Journal of Machine Learning Research (2008). |
| [2] | LeCun, Y., Cortes, C., Burges, C.J.C. “The MNIST database of handwritten digits” (1998). |