streamingNMF

A streaming algorithm of non-negative matrix factorization. For more details, see [1].

[1] Kohei Hayashi, Takanori Maehara, Masashi Toyoda, Ken-ichi Kawarabayashi. Real-time Top-R Topic Detection on Twitter with Topic Hijack Filtering. KDD 2015

Requirements

• python 2.X
• numpy
• argparse

Demo

You can try the algorithm by the following make command:

make demo

This command will do:

• automatically download UCI Bag of Words NIPS Data Set
• run the algorithm to detect topics from the co-occurrence matrix of the NIPS data
• output topics for every 50 iterations (1 iteration = 1 document)

The detected topics will be saved as out.itrXXXtopics.txt.

Usage

You can run the algorithm from the command line. The command line is something like this:

python run_nmf.py --rank 10 --delta_topic 50 --learning_rate 'invsqrt' < input_file

The details of options can be shown by:

python run_nmf.py --help

Format of input file

An example of input:

set set 256
set human 608
set number 320
...
problem analysis 255
problem problem 225

layer layer 961
layer nonlinear 558
...

• each line should be word1 word2 freq, which means word1 and word2 co-occurred freq times
• a blank line indicates the end of a document (an input file can contain multiple documents)