This repository contains the code used for the paper
This repository is no longer active. However, you are welcome to email the lead author at ben.rhodes@ed.ac.uk with questions regarding the code.
The environment.yml file contains the necessary Conda/pip packages. You can easily build all dependencies via
conda env create -f environment.yml
Data for toy Gaussian experiments is generated automatically when running the code.
Data for the MNIST experiments can be downloaded at https://zenodo.org/record/1161203#.Wmtf_XVl8eN.
Data for the MultiOmniGlot experiments can be obtained by running make_multiomniglot.py (which first downloads Omniglot from https://www.tensorflow.org/datasets/catalog/omniglot).
The datasets should be stored in a directory named density_data (which really could have just been called data).
Firstly, config files (containing all the settings for one experiment) need to be created by running the make_configs.py script. This script creates .json configs for each dataset and hyperparameter setting within a gridsearch. For instance, after running it, you can navigate to configs/1d_gauss/model/ and you should see 6 json files, which correspond to six different settings of the hyperparameters.
You can alter the gridsearch for a particular experiment by navigating to the required function e.g. make_mnist_configs() and altering the final argument that gets passed into generate_configs_for_gridsearch().
All TRE models are trained using the build_bridges.py script. This script takes the command line argument --config_path, which should point to the .json config file you want to use e.g.
python build_bridges.py --config_path=1d_gauss/model/2
The model will be saved to saved_models/dataset_name/time_stamp where time_stamp is a time-stamp identifier for this experiment created when running make_configs.py.
In order to generate Figure 1 in the paper, we need to run
python build_bridges.py --config_path=1d_gauss/model/0 --analyse_1d_objective=0 --analyse_single_sample_size=0
python build_bridges.py --config_path=1d_gauss/model/1 --analyse_1d_objective=0 --analyse_single_sample_size=0
The required figures will be placed in saved_models/1d_gauss/results/.
To generate the data for Figure 2 in the paper, we need to run
python build_bridges.py --config_path=1d_gauss/model/0 --analyse_1d_objective=0 --analyse_single_sample_size=-1
python build_bridges.py --config_path=1d_gauss/model/1 --analyse_1d_objective=0 --analyse_single_sample_size=-1
This code may take a while to run (e.g. an hour, but depends on the size of the grid we use to evaluate the objective function). Note that these commands only generate the data for Figure 2 (which is saved to saved_models/1d_gauss/time_stamp). To actually create Figure 2, we then run the jupyter notebook in notebooks/sample_efficiency_curves.ipynb.
In order to evaluate a learned energy-based model, we run ebm_evaluation.py which accepts various command line arguments. In particular, we need to set --config_path=dataset_name/time_stamp, where dataset_name and timestamp reference a directory in saved_models containing a particular trained model.
In order to evaluate the representations of a model from the SpatialMultiOmniglotexperiment, we run representation_learning_evaluation.py, again specifying the --config_path.