Continuous-Time Deconvolutional Regression (CDR)
CDR (formerly deconvolutional time series regression or DTSR) is a regression technique for modeling temporally diffuse effects (Shain & Schuler, 2018, 2019).
This repository contains source code for the
cdr Python module as well as support for reproducing published experiments.
Full documentation for the
cdr module is available at http://dtsr.readthedocs.io/en/latest/.
CDR models can be trained and evaluated using provided utility executables.
Help strings for all available utilities can be viewed by running
python -m cdr.bin.help.
Full repository documentation, including an API, is provided at the link above.
Note that some published experiments below also involve fitting LME and GAM models, which require
rpy2 and therefore won't work on Windows systems without some serious hacking.
cdr module is cross-platform and therefore CDR models should train regardless of operating system.
Reproducing published results
This repository is under active development, and reproducibility of previously published results is not guaranteed from the master branch.
For this reason, repository states associated with previous results are saved in Git branches.
To reproduce those results, checkout the relevant branch and follow the instructions in the
Current reproduction branches are:
Thus, to reproduce results from NAACL19, for example, run
git checkout naacl19 from the repository root, and follow instructions in the
The reproduction branches are also useful sources of example configuration files to use as templates for setting up your own experiments, although you should consult the docs for full documentation of the structure of CDR experiment configurations.
Published results depend on both (1) datasets and (2) models as defined in experiment-specific configuration files. In general, we do not distribute data with this repository. The datasets used can be provided by email upon request.
Help and support
Shain, Cory and Schuler, William (2018). Deconvolutional time series regression: A technique for modeling temporally diffuse effects. EMNLP18. Shain, Cory and Schuler, William (2019). Continuous-time deconvolutional regression for psycholinguistic modeling. PsyArXiv. https://doi.org/10.31234/osf.io/whvk5.