We provide scripts for training and evaluating models on three datasets: binary MNIST, sudoku, and promoter design. Before proceeding further, please donwload model weights and input data from Zenodo repository to DDSM directory. For example, one can run the following commands:
git clone https://github.com/jzhoulab/ddsm.git
cd ddsm
wget 'https://zenodo.org/record/7943307/files/best_models.tar.gz?download=1' -O best_models.tar.gz
wget 'https://zenodo.org/record/7943307/files/data.tar.gz?download=1' -O data.tar.gz
tar -xvfz data.tar.gz
tar -xvfz best_models.tar.gz
After running commands above, one should have best_models_weights and data directory in the ddsm folder. Please, check README file in data/promoter_design folder.
We need to presample noise for our Dirichlet/Jacobi process. It can be done via presample_noise.py script.
python presample_noise.py -n 100000 -c 2 -t 4000 --max_time 4 --out_path binary_mnist/
The file steps4000.cat2.time4.0.samples100000.pth should appear in binary_mnist folder after script finishes. The next steps are
cd binary_mnist
python eval_bin_mnist.py
Evaluation of the likelihood takes long time. At the end, one should be able to reproduce results from DDSM paper provided in the Table 1 (one needs multiply likelihood from the output by 28 * 28).
Training of binary mnist model are similar to the code provided in toy example.
As a first step, we need to presample noise. It can be done via presample_noise.py script.
python presample_noise.py -n 100000 -c 9 -t 400 --max_time 1 --out_path sudoku/
The file steps400.cat9.time1.0.samples100000.pth should appear in sudoku folder.
The second step is to train model. The following code will train the sudoku model.
cd sudoku
python train_sudoku.py
For reproducing results in the paper, we recommend training model for 1500-2000 epochs.
For evaluating model, you can use the provided notebook eval_sudoku.ipynb. It reproduces results described in the
Table 2 of the DDSM paper. We supplied our best model in best_model_weights directory.
You need to generate .mmap file for human reference first. The script data/promoter_design/make_genome_memmap.py should help with that.
As usual, we need to presample noise. It can be done via the following command:
python presample_noise.py -n 100000 -c 4 -t 400 --max_time 4 ---speed_balance --out_path promoter_design/
The file steps400.cat4.speed_balance.time4.0.samples100000.pth should appear in promoter_design folder.
For training promoter design model, one can use the commands below. For running code, you need to have
Selene installed in your environment. The external folder must be also
reachable from promoter_design directory.
cd promoter_design
python train_promoter_designer.py
We also provide eval_promoter_designer.ipynb notebook. Using this notebook, you can recreate pictures provided
in the paper.