salayatana66 / learn_to_transport_code

WIP

Code for Learning To Transport With Neural Networks

GitHub code for the paper http://arxiv.org/abs/1908.01394.

Structure

Code is organized in common modules & individual scripts for experiments. Between individual experiment scripts code is duplicated ad libitum.

Prerequisites

• conda.yaml: the environment manifest that was used for the Python code.
• install_pycrayon.sh: installation for the Docker image running TensorBoard and communicating with PyTorch via pycrayon.

Common modules

• circ_distros.py: The source and target distributions.
• circ_samplers.py: Sampling utilities for the source and target distributions.
• common_evaluation.py: Utilities to evaluate the transport maps produced in the experiments.
• general_utils.py: Generic utilities, e.g. creating model directories, training PyTorch models.
• estimators.py: The PyTorch models.
• prob_dist_meas.py: Functions to create distances between probability distributions.
• sinkhorn.py: A plain numpy implementation of Sinkhorn's algorithm.

Experiment scripts

• adversarial_transport.py: The algorithms based on Subsection 3.4 in the paper.
• dual_transport_seguy.py: The algorithms based on Subsection 3.5 of the paper.
• heuristic_.*_flow.py: The algorithms based on Subsection 3.3 in the paper without using the transport cost as a regularizer.
• heuristic_.*_w_tpreg_flow: The algorithms based on Subsection 3.3 in the paper using the transport cost as a regularizer.
• supervised_.*: The algorithms base on Subsection 3.6 of the paper.

Experiments reported in the paper

Those experiments reported in the paper can be run invoking the commands in Experiments.md. Make sure to use the conda environment and to have started the docker image for TensorBoard.

Performance metrics for the experiments

Each script produces an evaluation file evaluation_metrics.csv. Depending on the model further metrics (e.g. evolution of training loss with Steps) can be downloaded from TensorBoard. In evaluation_metrics/ you can find the list of evaluation metrics for the models discussed in the paper.

Images

Each script also produces images at the "snapshot" iterations. For the experiments we ran we add those images in snapshot_images_and_movies/; under model_name/Output.gif you can find a "movie" combining the images.