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 conda are installed.
Create and activate a new conda virtual 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==1.8.0 with 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/ / /artifacts, 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>

Code for Iso-Points: Optimizing Neural Implicit Surfaces with Hybrid Representations

Implicit Deep Adaptive Design (iDAD)

Related tags

Overview

Implicit Deep Adaptive Design (iDAD)

Computing infrastructure requirements

Installation

MLFlow

Location Finding Experiment

Pharmacokinetic Experiment

SIR experiment

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