evelynmitchell / time-warp-attend

Time-Warp-Attend

How to install time-warp-attend

Clone this repository.

Create a clean environment with conda using the environment.yml file from this repository:

conda env create -f environment.yml

Activate the environment and install the package from parent directory:

conda activate twa
pip install -e time-warp-attend

Generate classical,synthetic systems data

To view a few examples of the data generation process, see the notebook notebooks/data.ipynb.

To generate the data used in the paper, run the following commands:

# train datasets
twa generate-dataset --data-dir output/data/simple_oscillator_nsfcl  --train-size 12000 --test-size 1100 --data-name simple_oscillator  --augment-type NSF_CL
twa generate-dataset --data-dir output/data/simple_oscillator_noaug  --train-size 10000 --test-size 1000 --data-name simple_oscillator

# test datasets
twa generate-dataset --data-dir output/data/suphopf --test-size 1000 --data-name suphopf 
twa generate-dataset --data-dir output/data/lienard_poly --test-size 1000 --data-name lienard_poly 
twa generate-dataset --data-dir output/data/lienard_sigmoid --test-size 1000 --data-name lienard_sigmoid 
twa generate-dataset --data-dir output/data/vanderpol --test-size 1000 --data-name vanderpol 
twa generate-dataset --data-dir output/data/bzreaction --test-size 1000 --data-name bzreaction 
twa generate-dataset --data-dir output/data/selkov --test-size 1000 --data-name selkov 

Generate repressilator data

In the notebook notebooks/repressilator.ipynb, we simulate the repressilator regulatory gene network for cell trajectories varying in their transcription rate, $\alpha$, and the ratio of protein and mRNA degradation rates, $\beta$. From these, we generate respective vector fields across pTetR-pLacI phase space.

Generate pancreas data

For the pancreas dataset, vector fields and their corresponding cell cycle score are generated in the notebook notebooks/pancreas.ipynb.

Train models

To train a single model, see the notebook notebooks/train.ipynb.

To run multiple experiments, use the twa train command. For example, to train the models used in the paper, run the following commands:

twa train simple_oscillator_nsfcl output/

Evaluate models

Statistical and visual evaluations of single runs are available in the notebook notebooks/evaluate.ipynb.