We are deprecating this repository. Please refer to this repository: https://github.com/mederrata/bayesianquilts/tree/main/bayesianquilts/models/spmf

(S)parsley-encoded (P)oisson (M)atrix (F)actorization

Implemented using Tensorflow-probability.

This method differs from conventional hierarchical Poisson Matrix factorization methods primarily by sparsifying the encoding transformation rather than the decoding transformation. The encoding transformation is what computes a representation conditional on data. The decoding transformation takes the representation and produces predictive probability densities. By sparsifying the encoding, we make each representation coordinate a linear combination of a subset of original data features. Hence, inequalities placed on the representation transform directly and transparently into inequalities over the original features.

Installation

Using pip:

pip install git+https://github.com/mederrata/spmf.git

Examples

You'll find these examples under notebooks/ - for your convenience, here are links to open them in Google Colab. Note that you will need to install the package within Colab using

!pip install git+https://github.com/mederrata/spmf.git

1. Factorization of random noise

2. Factorization of synthetic data with underlying linear structure

3. Factorization of synthetic data with underlying nonlinear structure

Factorizing CSV files

We have included a script that you might find to be useful. It is installed into your PATH when using pip.

Usage:

usage: factorize_csv.py [-h] [-f [CSV_FILE]] [-e [EPOCH]] [-d [DIMENSION]]
                        [-b [BATCH_SIZE]] [-lr [LEARNING_RATE]]
                        [-c [CLIP_VALUE]] [-lt] [-rn]

Train PMF on CSV-formatted count matrix

optional arguments:
  -h, --help            show this help message and exit
  -f [CSV_FILE], --csv-file [CSV_FILE]
                        Enter the CSV file
  -e [EPOCH], --epoch [EPOCH]
                        Enter Epoch value: Default: 300
  -d [DIMENSION], --dimension [DIMENSION]
                        Enter embedding dimension. Default: 2
  -b [BATCH_SIZE], --batch-size [BATCH_SIZE]
                        Enter batch size. Default: 5000
  -lr [LEARNING_RATE], --learning-rate [LEARNING_RATE]
                        Enter float. Default: 0.01
  -c [CLIP_VALUE], --clip-value [CLIP_VALUE]
                        Gradient clip value. Default: 3.0
  -lt, --log-transform  Log-transform?
  -rn, --row-normalize  Row normalize based on counts?