Time-Disentanglement-Lib

Disentangling Time Series Representations via Contrastive Independence-of-Support on $l$-Variational Inference

📣 Published as a conference paper at ICLR 2024

Note ⚠️

• Currently, we updated some classes of our framework "DIoSC" Time Series Disentangling for correlated data.

At present, this repository remains anonymous as a paper based on its content is under review. It provides procedures to enhance the disentanglement of time series data, offering both configurations and the necessary code to reproduce our results.

Loss	Source
Standard VAE Loss	Auto-Encoding Variational Bayes
β-VAEH	β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
β-VAEB	Understanding disentangling in β-VAE
S3VAE	Disentangling by Factorising
C-DSVAE	Disentangling by Factorising
CoST	Disentangling by Factorising
Probabilistic Transformer	Disentangling by Factorising
Autoformers	Disentangling by Factorising
β-TCVAE	Isolating Sources of Disentanglement in Variational Autoencoders
HFS	https://openreview.net/forum?id=OKcJhpQiGiX
RNN-VAE	A Recurrent Latent Variable Model for Sequential Data
D3VAE	Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement
DIoSC Time Series (ours)	Disentangling Time Series Representations via Contrastive based L-Variational Inference
MSA-Conv for Time Series	Disentangling Time Series Representations via Contrastive based L-Variational Inference

Installation

To get started with the Disentangling Time Series Energy codebase, follow these steps:

# Clone the repository
pip install -r requirements.txt

With pip

pip install time-disentnaglement

Run

Use python main.py <model-name> <param> to train and/or evaluate a model. For example:

python main.py DIoSC_ukdal_mini -d ukdal -l DIoSC --lr 0.001 -b 256 -e 5

Predefined experiments with associated hyperparameters can be executed using the -x flag. The hyperparameters can be found in the hyperparam.ini file, and pretrained models for each experiment are located in the results/ directory (created using ./bin/train_all.sh).

Output Running experiments will create a directory results//, which includes the following:

use Checkpoint

We offer checkpoints for each model, and we are actively working on providing access to other experiments using W&B as well.

Help

usage: main.py ...

PyTorch implementation and evaluation of disentangled Variational AutoEncoders
and metrics.

optional arguments:
  -h, --help            show this help message and exit

General options:
  name                  Name of the model for storing or loading purposes.
  -L, --log-level {CRITICAL,ERROR,WARNING,INFO,DEBUG,NOTSET}
                        Logging levels. (default: info)
  --no-progress-bar     Disables progress bar. (default: False)
  --no-cuda             Disables CUDA training, even when have one. (default:
                        False)
  -s, --seed SEED       Random seed. Can be `None` for stochastic behavior.
                        (default: 1234)

Training specific options:
  --checkpoint-every CHECKPOINT_EVERY
                        Save a checkpoint of the trained model every n epoch.
                        (default: 30)
  -d, --dataset {mnist,fashion,dsprites,celeba,chairs}
                        Path to training data. (default: mnist)
  -x, --experiment {custom,debug,best_celeba,VAE_mnist,VAE_fashion,VAE_dsprites,VAE_celeba,VAE_chairs,betaH_mnist,betaH_fashion,betaH_dsprites,betaH_celeba,betaH_chairs,betaB_mnist,betaB_fashion,betaB_dsprites,betaB_celeba,betaB_chairs,factor_mnist,factor_fashion,factor_dsprites,factor_celeba,factor_chairs,btcvae_mnist,btcvae_fashion,btcvae_dsprites,btcvae_celeba,btcvae_chairs}
                        Predefined experiments to run. If not `custom` this
                        will overwrite some other arguments. (default: custom)
  -e, --epochs EPOCHS   Maximum number of epochs to run for. (default: 100)
  -b, --batch-size BATCH_SIZE
                        Batch size for training. (default: 64)
  --lr LR               Learning rate. (default: 0.0005)

Model specfic options:
  -m, --model-type {Burgess}
                        Type of encoder and decoder to use. (default: Burgess)
  -z, --latent-dim LATENT_DIM
                        Dimension of the latent variable. (default: 10)
  -l, --loss {VAE,betaH,betaB,factor,btcvae}
                        Type of VAE loss function to use. (default: betaB)
  -r, --rec-dist {bernoulli,laplace,gaussian}
                        Form of the likelihood ot use for each pixel.
                        (default: bernoulli)
  -a, --reg-anneal REG_ANNEAL
                        Number of annealing steps where gradually adding the
                        regularisation. What is annealed is specific to each
                        loss. (default: 0)