Metadata-Version: 2.1
Name: hkmeans-minibatch
Version: 1.0.2
Summary: An implementation of hierarchical minibatch kmeans
Home-page: https://github.com/ammesatyajit/heirarchical-minibatch-kmeans
Author: Satyajit Kumar
Author-email: ammesatyajit@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Requires-Dist: numpy
Requires-Dist: tqdm
Requires-Dist: scikit-learn

Hierarchical Minibatch Kmeans
=============================

An implementation of hierarchical kmeans that uses mini-batches for
increased efficiency for large datasets.

Install
=======
::

    pip3 install hkmeans-minibatch

Usage
=====

::

    $ python3 -m hkmeans_minibatch -h
    usage: __main__.py [-h] -r ROOT_FEATURE_PATH -p FEATURES_PREFIX [-b BATCH_SIZE] -s SAVE_DIR -c CENTROID_DIR -hr HIERARCHIES -k CLUSTERS [-e EPOCHS]
    optional arguments:
      -h, --help            show this help message and exit
      -r ROOT_FEATURE_PATH, --root-feature_path ROOT_FEATURE_PATH
                            path to folder containing all the feature files
      -p FEATURES_PREFIX, --features-prefix FEATURES_PREFIX
                            prefix that contains the desired files to read
      -b BATCH_SIZE, --batch-size BATCH_SIZE
                            batch_size to use for the minibatch kmeans
      -s SAVE_DIR, --save-dir SAVE_DIR
                            save directory for sorted hierarchical kmeans vectors
      -c CENTROID_DIR, --centroid-dir CENTROID_DIR
                            directory to save the centroids in
      -hr HIERARCHIES, --hierarchies HIERARCHIES
                            number of hierarchies to run the kmeans on
      -k CLUSTERS, --clusters CLUSTERS
                            number of clusters for each part of the hierarchy
      -e EPOCHS, --epochs EPOCHS
                            number of epochs to run the kmeans for each hierarchy

Have the .npy files all in one root feature directory to do kmeans over (they can be in
subdirectories). For optimal results have the batch size be larger than the number of
vectors in each .npy file. The features prefix is the common prefix of
the .npy files to kmeans over. The save directory should be an empty
directory, which the program will fill with sorted vectors and delete
after it is finished. The centroid directory should be an empty
directory where all the centroids will be stored. Note that the
centroids will be stored in separate files in the centroid directory.


