NoiseGAN: Software for Evaluating Convolutional Generative Adversarial Networks with Classical Random Process Noise Models

Overview

This repository contains Python code to execute experiments on deep generative modeling of noise time series. Specifically, it includes Pytorch implementations of two generative adversarial network (GAN) models for time series based on convolutational neural networks (CNNs): WaveGAN, a 1-D CNN model, and STFT-GAN, a 2-D CNN model. In addition, there are methods for generating and evaluating noise time series defined several by classical random process models:

• band-limited thermal noise, i.e., bandpass filtered white Gaussian noise
• power law (fractional, colored) noise, including fractional Gaussian noise (FGN), fractional Brownian motion (FBM), and fractionally differenced white noise (FDWN)
• generalized shot noise, including options for different pulse types and pulse amplitude distributions
• impulsive noise, including Bernoulli-Gaussian (BG) and symmetric alpha stable (SAS) distributions

Reference

A. Wunderlich, J. Sklar, "Data-Driven Modeling of Noise Time Series with Convolutional Generative Adversarial Networks", Machine Learning: Science and Technology, vol. 4, no. 3, Sept 2023. https://doi.org/10.1088/2632-2153/acee44

Getting Started

The software enables automated testing of many model configurations across different datasets. Model creation and training is implemented using the Pytorch library. This repository contains code for initializing experiment test runs (main.py), training of GAN models(gan_train.py), loading target distributions (data_loading.py), and evaluation(gan_evaluation.py) of generated distributions. The utils/ subdirectory contains supporting modules for target dataset creation and model evaluation. The models/ subdirectory contains modules that implement GAN architectures.

Synthetic training and test target datasets are created by running utils/noise_dataset.py, which executes main() in noise_dataset.py as a script. Datasets are saved to the subdirectory Datasets/. The ranges of the loops in the script can be modified to create different synthetic noise datasets.

Running main.py executes the GAN with model settings specified by the configuration dictionary training_specs_dict.py. Descriptions for the fields specified in training_specs_dict.py are located in the spreadsheet experiment_resources/trainspecs_dictionary_description.xlsx. Additionally, a set of model configurations can be run in an automated fashion by passing a configuration table (csv file) as an argument to the main python module, e.g., main.py --configs ./experiment_resources/test_configs.csv. Column labels of a configuration table correspond to desired keys in the GAN configuration dictionary that are to be changed across runs.

When running the models, experimental results are saved in model_results/ with subdirectories named by their target dataset and other non-default configurations and a time-stamp. Evaluation of the model is set to run at the termination of model training. Each test-run folder contains saved GAN models, training metadata, as well as evaluations of the generated distributions. Aggregated evaluation plots across model runs are created using the scripts located in scripts/.

Packages in our python environment are documented by conda_requirements.txt and pip_requirements.txt in the experiment_resources/ subdirectory, which were created with the conda list and pip freeze commands, respectively.

To use methods for generating and evaluating fractionally differenced white noise (FDWN), it is necessary to uncomment the associated block of package imports at the top of utils/fractional_noise_utils.py. The FDWN methods utilize the arfima package in R, which can be run from python using the interface provided by the python rpy2 package. Therefore, using any methods for FDWN requires installing R and the arfima R package.