torch-kmeans 0.2.0
PyTorch implementations of KMeans, Soft-KMeans and Constrained-KMeans which can be run on GPU and work on (mini-)batches of data.
License: MIT
Requires Python: >=3.7

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============
torch_kmeans
============

   PyTorch implementations of KMeans, Soft-KMeans and Constrained-KMeans


torch_kmeans features implementations of the well known k-means algorithm
as well as its soft and constrained variants.

All algorithms are completely implemented as `PyTorch <https://pytorch.org/>`_ modules
and can be easily incorporated in a PyTorch pipeline or model.
Therefore, they support execution on GPU as well as working on (mini-)batches of data.
Moreover, they also provide a `scikit-learn <https://scikit-learn.org/>`_ style interface featuring

.. code-block:: python

   model.fit(), model.predict() and model.fit_predict()


functions.

-> `view official documentation <https://torch-kmeans.readthedocs.io/en/latest/>`_


Highlights
===========
- Fully implemented in PyTorch. (PyTorch and Numpy are the only package dependencies!)
- GPU support like native PyTorch.
- PyTorch script JIT compiled for most performance sensitive parts.
- Works with mini-batches of samples:
   - each instance can have a different number of clusters.
- Constrained Kmeans works with cluster constraints like:
   - a max number of samples per cluster or,
   - a maximum weight per cluster, where each sample has an associated weight.
- SoftKMeans is a fully differentiable clustering procedure and
  can readily be used in a PyTorch neural network model which requires backpropagation.
- Unit tested against the scikit-learn KMeans implementation.
- GPU execution enables very fast computation even for
  large batch size or very high dimensional feature spaces
  (see `speed comparison <https://github.com/jokofa/torch_kmeans/tree/master/examples/notebooks/speed_comparison.ipynb>`_)

Installation
=============

Simply install from PyPI

.. code-block:: console

   pip install torch-kmeans


Usage
============
Pytorch style usage

.. code-block:: python

   import torch
   from torch_kmeans import KMeans

   model = KMeans(n_clusters=4)

   x = torch.randn((4, 20, 2))   # (BS, N, D)
   result = model(x)
   print(result.labels)


Scikit-learn style usage

.. code-block:: python

   import torch
   from torch_kmeans import KMeans

   model = KMeans(n_clusters=4)

   x = torch.randn((4, 20, 2))   # (BS, N, D)
   model = model.fit(x)
   labels = model.predict(x)
   print(labels)

or

.. code-block:: python

   import torch
   from torch_kmeans import KMeans

   model = KMeans(n_clusters=4)

   x = torch.randn((4, 20, 2))   # (BS, N, D)
   labels = model.fit_predict(x)
   print(labels)


Examples
============
You can find more examples and usage in the
`detailed example notebooks <https://github.com/jokofa/torch_kmeans/tree/master/examples>`_.