This repository contains code and datasets from the master thesis: Probabilistic Regression using Conditional Generative Adversarial Networks. The project includes Conditional GANs (CGANs) used for low-dimensional probabilistic regression problems.
Author: Joel Oskarsson, Linköping University
The repository includes pytorch implementations of the following models:
- Standard Conditional GANs (Mirza et al.)
- Conditional f-GANs (based on Nowozin et al.)
- Generative Moment Matching Networks (Li et al.) extended with conditioning
- Mixture Density Networks (Christopher Bishop)
- Homoskedastic and heteroskedastic regression using neural networks
- Deep Conditional Target Densities (Gustafsson et al.)
The repository also contains a number of synthetic datasets used for experiments in the thesis. Multiple evaluation methods are implemented, including KDE-based log-likelihood and evaluation of CGANs using divergences proposed for training (Im et al.).
The project uses Python 3.7.
All required python packages are listed with version numbers in requirements.txt.
The easiest way to install these is:
pip install -r requirements.txt
You probably want to use a virtual python environment, for example through virtualenv or conda.
The project is fully integrated with Weights & Biases (W&B) for logging and visualization, but can just as easily be used without it.
When W&B is used, training configuration, training/test statistics and plots are sent to the W&B servers and made available in an interactive web interface.
If W&B is turned off, logging instead saves everything locally to a directory like wandb/dryrun-2020....
The W&B python package is installed together with the rest in requirements.txt.
The W&B project name is set to cgan_regression, but this can be changed in the first line of constants.py.
See the W&B documentation for details.
If you would like to login and use W&B, run:
wandb login
If you would like to turn off W&B and just log things locally, run:
wandb off
Synthetic datasets used in the thesis are directly available in the datasets directory.
If you would like to regenerate one of these, run:
python preprocess_dataset.py --dataset dataset_name
Real world datasets are not directly available in this repository.
Instructions for where to download each real dataset can be found as a comment in corresponding specification file, dataset_specifications/dataset_name.py.
To pre-process and save one of these in datasets, run:
python preprocess_dataset.py --dataset dataset_name --file path/to/dataset/file
Note that the housing dataset does not require a file path since the data is directly downloaded through sci-kit learn.
Including new datasets should be straightforward, starting from one of the existing specifications (for example dataset_specifications/power.py for a real world dataset).
Each dataset must also be listed in dataset_list.py.
Training and testing is done by running the main.py script with different arguments.
While it is possible to specify everything directly on the command line, it is far more convenient to create a JSON configuration file config.json and tell the script to read arguments from that:
python main.py --config config.json
To get a list of all available arguments run:
python main.py --help
They can also be found in main.py grouped by model.
Binary arguments that only turn features on or off are set using the integers 0/1.
Some of the most important arguments are:
trainSet to 1 or 0 for whether the model should be trained or not.testSet to 1 or 0 for whether the model should be evaluated on test data.datasetName of the dataset to train/evaluate on. Needs to be generated/pre-processed and available in thedatasetsdirectory. Seedataset_list.pyfor a list of all datasets.modelModel to train/evaluate. See start ofmain.pyfor a list of all models.networkNeural network architecture to use, for all models except CGANs. Should correspond to one of the files innn_specsdirectory, without.json.cgan_netsNeural network architectures to use when model is CGAN. Should correspond to one of the files incgan_specsdirectory, without.json.cgan_typeType of CGAN to train (training objective). See list at bottom ofmodels/cgan_versions.pyfor available CGAN types.noise_dimNoise dimensionality for CGANs and GMMNs.restoreW&B id of a previous run to restore model parameters (network weights etc.) from (when W&B is used).restore_filePath to a file to restore model parameters from. Can be used when W&B is turned off. The best model parameters from training are saved in the local W&B run directory.epochsThe amount of epochs to train for. Note that only the model parameters from the best (in validation) epoch are saved. So in practice this specifies the maximum amount of epochs.
Here follows some examples of configuration files.
Train a Mixture Density Network on the bimodal dataset for 200 epochs:
{
"train": 1,
"test": 0,
"model": "mdn",
"dataset": "bimodal",
"network": "medium_nn",
"lr": 1e-3,
"optimizer": "adam",
"epochs": 200,
"mixture_comp": 10,
"l2_reg": 1e-4
}Train a Pearson χ2 CGAN for 1000 epochs on the trajectories10 dataset:
{
"train": 1,
"test": 0,
"model": "cgan",
"dataset": "trajectories10",
"cgan_type": "pearson",
"cgan_nets": "large_ni",
"noise_dim": 5,
"batch_size": 100,
"lr": 1e-4,
"optimizer": "rmsprop",
"epochs": 1000,
"val_interval": 5
}Evaluate a CGAN on the exponential dataset, loading parameters from an earlier W&B run, also estimating "divergence" based on a Least Squares CGAN discriminator:
{
"train": 0,
"test": 1,
"model": "cgan",
"dataset": "exponential",
"cgan_nets": "medium_ni",
"noise_dim": 5,
"restore": "fiopdk6g",
"test_runs": 10,
"eval_div": "ls",
"eval_cgan": "medium_ni",
"scatter": 1,
"lr": 1e-3,
"optimizer": "adam",
"epochs": 200
}Evaluate a joint GMMN on the const_noise dataset, loading model parameters from a local file:
{
"train": 0,
"test": 1,
"model": "gmmn",
"dataset": "const_noise",
"network": "medium_ni",
"noise_dim": 10,
"mmd_scales": "1,5,10",
"restore_file": "wandb/dryrun-20200514_101433-5mqvyivb/epoch_best.pt",
"test_runs": 10,
"scatter": 1
}This is an explanation of each file and directory in the repository:
├── cgan_specs - Specifications of CGAN neural network architectures
│ └── ...
├── datasets - Available datasets, the actual data
│ └── ...
├── dataset_specifications - Specifications describing different datasets
│ └── ...
├── images - A few example images from the thesis
│ └── ...
├── models
│ ├── cgan.py - General CGAN model
│ ├── cgan_versions.py - Alternative CGAN versions (f-GANs, Least Squares, etc.)
│ ├── cgmmn.py - CGMMN model
│ ├── dctd.py - Deep Conditional Target Densities model
│ ├── gmmn.py - GMMN model
│ ├── gp.py - Gaussian Process model
│ ├── mdn.py - Mixture Density Network model
│ ├── networks.py - Neural network building blocks for other models
│ ├── nn_heteroskedastic.py - Heteroskedastic neural network regression model
│ ├── nn.py - Base neural network model
│ ├── nn_regressor.py - Homoskedastic neural network regression model
│ ├── noise_dists.py - Noise distributions for generative models
│ └── spec_reader.py - Network specification reader utility
├── nn_specs - Specifications of (non CGAN) neural network architecturs
│ └── ...
├── util_scripts - Utility scripts for setting up experiments
│ └── ...
├── constants.py - Constants
├── dataset_list.py - List of avaliable dataset specifications
├── evaluation.py - Code for evaluation steps in validation/testing
├── main.py - Main script used for training and testing
├── preprocess_dataset.py - Script for generating/pre-processing datasets from specification
├── README.md - This readme file
├── requirements.txt - List of required python packages
├── tabular_dataset.py - Pytorch dataset class for tabular regression data
├── utils.py - Various utility functions
└── visualization.py - Plotting functions