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>

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

Owner

Desi

Implementation of Learning Gradient Fields for Molecular Conformation Generation (ICML 2021).

PyTorch code for our paper "Image Super-Resolution with Non-Local Sparse Attention" (CVPR2021).

python debugger and anti-vm that checks if you're in a virtual machine or if someones trying to debug your file

Bulk2Space is a spatial deconvolution method based on deep learning frameworks

Gesture recognition on Event Data

Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

The source code for the Cutoff data augmentation approach proposed in this paper: "A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation".

Adversarial Graph Augmentation to Improve Graph Contrastive Learning

optimization routines for hyperparameter tuning

KUIELAB-MDX-Net got the 2nd place on the Leaderboard A and the 3rd place on the Leaderboard B in the MDX-Challenge ISMIR 2021

A fast Evolution Strategy implementation in Python

Similarity-based Gray-box Adversarial Attack Against Deep Face Recognition

Eth brownie struct encoding example

FTIR-Deep Learning - FTIR Deep Learning With Python

This is the replication package for paper submission: Towards Training Reproducible Deep Learning Models.

Multi-Scale Aligned Distillation for Low-Resolution Detection (CVPR2021)

Pytorch tutorials for Neural Style transfert

Pytorch implementation of "M-LSD: Towards Light-weight and Real-time Line Segment Detection"

Companion repo of the UCC 2021 paper "Predictive Auto-scaling with OpenStack Monasca"

3DV 2021: Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry