numap 0.1.0

Generalizable UMAP Implementation

NUMAP

This is the official PyTorch implementation of NUMAP from the paper "Generalizable Spectral Embedding with Applications to UMAP.

Installation

To install the package, simply use the following command:

pip install numap

Usage

The basic functionality is quite intuitive and easy to use, e.g.,

from numap import NUMAP

numap = NUMAP(n_components=2)  # n_components is the number of dimensions in the low-dimensional representation
numap.fit(X)  # X is the dataset and it should be a torch.Tensor
X_reduced = numap.transfrom(X)  # Get the low-dimensional representation of the dataset
Y_reduced = numap.transform(Y)  # Get the low-dimensional representation of a test dataset

You can read the code docs for more information and functionalities.

Running examples

In order to run the model on the moon dataset, you can either run the file, or using the command-line command:
python -m examples.reduce_moon
This will run the model on the moon dataset and plot the results.

The same can be done for the circles dataset:
python -m examples.reduce_circles