PROPS Model

The PROPS model (or probabilistically personalized black-box sequence model) is a transfer learning mechanism for modeling sequential data. It takes the feedforward predictions of a pre-trained and black-box sequence model (e.g. an RNN) and probabilistically perturbs these predictions to fit a new situation. In this way, the PROPS model customizes the baseline sequence model into a personalized sequence model. This customization happens in a streaming/online manner. For more information, see the paper.

Setup

Setup for Development

For local development, clone the repo and run commands in Makefile to setup a virtualenv in env/:

$ make clean-env env

This command will perform a psuedo-installation to env/. For more information, see the Makefile.

Dependencies

See requirements.txt. The make all command will obtain dependencies (with correct versions) automatically.

Experiments

The experiments/ directory contains code for running the experiments reported in the paper.

Experiments can be reconstructed from the command line via

$ make experiment1

and

$ make experiment2

It takes a modern MacBook Pro hours to run the experiments for make experiment1, and seconds or minutes for make experiment2. Alternatively, you can run both the long-running and short-running experiments by executing

$ make experiment

These commands will write plots to directory plots/.

Tests

Unit tests are provided in the tests/ directory, and can be run from the command line via

$ make test

There are also tests for the streaming HMM model.