Implicit Deep Adaptive Design (iDAD)
This code supports the NeurIPS paper 'Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods'.
@article{ivanova2021implicit,
title={Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods},
author={Ivanova, Desi R. and Foster, Adam and Kleinegesse, Steven and Gutmann, Michael and Rainforth, Tom},
journal={Advances in Neural Information Processing Systems (NeurIPS)},
year={2021}
}
Computing infrastructure requirements
We have tested this codebase on Linux (Ubuntu x86_64) and MacOS (Big Sur v11.2.3) with Python 3.8. To train iDAD networks, we recommend the use of a GPU. We used one GeForce RTX 3090 GPU on a machine with 126 GiB of CPU memory and 40 CPU cores.
Installation
- Ensure that Python and
condaare installed. - Create and activate a new
condavirtual environment as follows
conda create -n idad_code
conda activate idad_code
- Install the correct version of PyTorch, following the instructions at pytorch.org. For our experiments we used
torch==1.8.0with CUDA version 11.1. - Install the remaining package requirements using
pip install -r requirements.txt. - Install the torchsde package from its repository:
pip install git+https://github.com/google-research/torchsde.git.
MLFlow
We use mlflow to log metric and store network parameters. Each experiment run is stored in a directory mlruns which will be created automatically. Each experiment is assigned a numerical
and each run gets a unique
. The iDAD networks will be saved in ./mlruns/
, which will be printed at the end of each training run.
Location Finding Experiment
To train an iDAD network with the InfoNCE bound to locate 2 sources in 2D, using the approach in the paper, execute the command
python3 location_finding.py \
--num-steps 100000 \
--num-experiments=10 \
--physical-dim 2 \
--num-sources 2 \
--lr 0.0005 \
--num-experiments 10 \
--encoding-dim 64 \
--hidden-dim 512 \
--mi-estimator InfoNCE \
--device <DEVICE>
To train an iDAD network with the NWJ bound, using the approach in the paper, execute the command
python3 location_finding.py \
--num-steps 100000 \
--num-experiments=10 \
--physical-dim 2 \
--num-sources 2 \
--lr 0.0005 \
--num-experiments 10 \
--encoding-dim 64 \
--hidden-dim 512 \
--mi-estimator NWJ \
--device <DEVICE>
To run the static MINEBED baseline, use the following
python3 location_finding.py \
--num-steps 100000 \
--physical-dim 2 \
--num-sources 2 \
--lr 0.0001 \
--num-experiments 10 \
--encoding-dim 8 \
--hidden-dim 512 \
--design-arch static \
--critic-arch cat \
--mi-estimator NWJ \
--device <DEVICE>
To run the static SG-BOED baseline, use the following
python3 location_finding.py \
--num-steps 100000 \
--physical-dim 2 \
--num-sources 2 \
--lr 0.0005 \
--num-experiments 10 \
--encoding-dim 8 \
--hidden-dim 512 \
--design-arch static \
--critic-arch cat \
--mi-estimator InfoNCE \
--device <DEVICE>
To run the adaptive (explicit likelihood) DAD baseline, use the following
python3 location_finding.py \
--num-steps 100000 \
--physical-dim 2 \
--num-sources 2 \
--lr 0.0005 \
--num-experiments 10 \
--encoding-dim 32 \
--hidden-dim 512 \
--mi-estimator sPCE \
--design-arch sum \
--device <DEVICE>
To evaluate the resulting networks eun the following command
python3 eval_sPCE.py --experiment-id <ID>
To evaluate a random design baseline (requires no pre-training):
python3 baselines_locfin_nontrainable.py \
--policy random \
--physical-dim 2 \
--num-experiments-to-perform 5 10 \
--device <DEVICE>
To run the variational baseline (note: it takes a very long time), run:
python3 baselines_locfin_variational.py \
--num-histories 128 \
--num-experiments 10 \
--physical-dim 2 \
--lr 0.001 \
--num-steps 5000\
--device <DEVICE>
Copy path_to_artifact and pass it to the evaluation script:
python3 eval_sPCE_from_source.py \
--path-to-artifact <path_to_artifact> \
--num-experiments-to-perform 5 10 \
--device <DEVICE>
Pharmacokinetic Experiment
To train an iDAD network with the InfoNCE bound, using the approach in the paper, execute the command
python3 pharmacokinetic.py \
--num-steps 100000 \
--lr 0.0001 \
--num-experiments 5 \
--encoding-dim 32 \
--hidden-dim 512 \
--mi-estimator InfoNCE \
--device <DEVICE>
To train an iDAD network with the NWJ bound, using the approach in the paper, execute the command
python3 pharmacokinetic.py \
--num-steps 100000 \
--lr 0.0001 \
--num-experiments 5 \
--encoding-dim 32 \
--hidden-dim 512 \
--mi-estimator NWJ \
--gamma 0.5 \
--device <DEVICE>
To run the static MINEBED baseline, use the following
python3 pharmacokinetic.py \
--num-steps 100000 \
--lr 0.001 \
--num-experiments 5 \
--encoding-dim 8 \
--hidden-dim 512 \
--design-arch static \
--critic-arch cat \
--mi-estimator NWJ \
--device <DEVICE>
To run the static SG-BOED baseline, use the following
python3 pharmacokinetic.py \
--num-steps 100000 \
--lr 0.0005 \
--num-experiments 5 \
--encoding-dim 8 \
--hidden-dim 512 \
--design-arch static \
--critic-arch cat \
--mi-estimator InfoNCE \
--device <DEVICE>
To run the adaptive (explicit likelihood) DAD baseline, use the following
python3 pharmacokinetic.py \
--num-steps 100000 \
--lr 0.0001 \
--num-experiments 5 \
--encoding-dim 32 \
--hidden-dim 512 \
--mi-estimator sPCE \
--design-arch sum \
--device <DEVICE>
To evaluate the resulting networks run the following command
python3 eval_sPCE.py --experiment-id <ID>
To evaluate a random design baseline (requires no pre-training):
python3 baselines_pharmaco_nontrainable.py \
--policy random \
--num-experiments-to-perform 5 10 \
--device <DEVICE>
To evaluate an equal interval baseline (requires no pre-training):
python3 baselines_pharmaco_nontrainable.py \
--policy equal_interval \
--num-experiments-to-perform 5 10 \
--device <DEVICE>
To run the variational baseline (note: it takes a very long time), run:
python3 baselines_pharmaco_variational.py \
--num-histories 128 \
--num-experiments 10 \
--lr 0.001 \
--num-steps 5000 \
--device <DEVICE>
Copy path_to_artifact and pass it to the evaluation script:
python3 eval_sPCE_from_source.py \
--path-to-artifact <path_to_artifact> \
--num-experiments-to-perform 5 10 \
--device <DEVICE>
SIR experiment
For the SIR experiments, please first generate an initial training set and a test set:
python3 epidemic_simulate_data.py \
--num-samples=100000 \
--device <DEVICE>
To train an iDAD network with the InfoNCE bound, using the approach in the paper, execute the command
python3 epidemic.py \
--num-steps 100000 \
--num-experiments 5 \
--lr 0.0005 \
--hidden-dim 512 \
--encoding-dim 32 \
--mi-estimator InfoNCE \
--design-transform ts \
--device <DEVICE>
To train an iDAD network with the NWJ bound, execute the command
python3 epidemic.py \
--num-steps 100000 \
--num-experiments 5 \
--lr 0.0005 \
--hidden-dim 512 \
--encoding-dim 32 \
--mi-estimator NWJ \
--design-transform ts \
--device <DEVICE>
To run the static SG-BOED baseline, run
python3 epidemic.py \
--num-steps 100000 \
--num-experiments 5 \
--lr 0.005 \
--hidden-dim 512 \
--encoding-dim 32 \
--design-arch static \
--critic-arch cat \
--design-transform iid \
--mi-estimator InfoNCE \
--device <DEVICE>
To run the static MINEBED baseline, run
python3 epidemic.py \
--num-steps 100000 \
--num-experiments 5 \
--lr 0.001 \
--hidden-dim 512 \
--encoding-dim 32 \
--design-arch static \
--critic-arch cat \
--design-transform iid \
--mi-estimator NWJ \
--device <DEVICE>
To train a critic with random designs (to evaluate the random design baseline):
python3 epidemic.py \
--num-steps 100000 \
--num-experiments 5 \
--lr 0.005 \
--hidden-dim 512 \
--encoding-dim 32 \
--design-arch random \
--critic-arch cat \
--design-transform iid \
--device <DEVICE>
To train a critic with equal interval designs, which is then used to evaluate the equal interval baseline, run the following
python3 epidemic.py \
--num-steps 100000 \
--num-experiments 5 \
--lr 0.001 \
--hidden-dim 512 \
--encoding-dim 32 \
--design-arch equal_interval \
--critic-arch cat \
--design-transform iid \
--device <DEVICE>
Finally, to evaluate the different methods, run
python3 eval_epidemic.py \
--experiment-id <ID> \
--device <DEVICE>
1 Jan 19, 2022
44 Nov 27, 2022
656 Dec 28, 2022
8 Dec 11, 2022
9 Sep 06, 2022
2.2k Dec 27, 2022
15 Dec 16, 2022
20 Jan 02, 2023
56 Dec 08, 2022
998 Dec 29, 2022
472 Dec 29, 2022
14 Dec 09, 2022
6 May 26, 2022
68 Jan 02, 2023
1 Jan 08, 2022
6 Oct 28, 2021
18 Dec 23, 2022
41 Jan 06, 2023
105 Dec 25, 2022
4 Dec 31, 2022