Code for NeurIPS2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints"

Overview

This repository is the code for NeurIPS 2021 submission "A Surrogate Objective Framework for Prediction+Programming with Soft Constraints".

Edit 2021/8/30: KKT-based (Decision-focused) baseline is added to the first experiment.

Requirements

pytorch>=1.7.0

scipy

gurobipy (and Gurobi>=9.1 license - you can get Academic license for free at https://www.gurobi.com/downloads/end-user-license-agreement-academic/; download and install Gurobi first.)

Quandl

h5py

bs4

tqdm

sklearn

pandas

lxml

qpth

cvxpy

cvxpylayers

Running Experiments

You should be able to run all experiments by fulfilling the requirements and cloning this repo to your local machine.

Synthetic Linear Programming

The dataset for this problem is generated at runtime. To run a single problem instance, type the following command:

python run_main_synth.py --method=2 --dim_context=40 --dim_hard=40 --dim_soft=20 --seed=2006 --dim_features=80 --loss=l1 --K=0.2

The four methods (L1,L2,SPO+,ours) we used in the experiment are respectively

--method=0 --loss=l1 # L1
--method=0 --loss=l2 # L2
--method=1 --loss=l1 # SPO+
--method=2 --loss=l1 # ours
--method=3 --loss=l1 # decision-focused (KKT-based)

The other parameters can be seen in run_script.py and run_main_synth.py. To get multiple data for a single method, modify with the parameters listed above, and then run run_script.py. The outcome containing prediction error and regret is in the result folder. See dataprocess.py for a reference on how to interpret the data; the data with suffix "...test.txt" is used for evaluation. Also, to change batch size and training set size, alter the default parameters in run_main_synth.py.

Portfolio Optimization

The dataset for this problem will be automatically downloaded when you first run this code, as Wilder et al.'s code does[1]. It is the daily price data of SP500 from 2004 to 2017 downloaded by Quandl API. To run a single problem instance, type the following command:

python main.py --method=3 --n=50 --seed=471298479

The four methods (L1, DF, L2, ours) are labeled as method 0, 1, 2 and 3. To get multiple data for a single method, run run_script.py.

The result is in the res/K100 folder.

Resource Provisioning

The dataset of this problem is attached in the github repository, which are the eight csv file, one for each region. It is the ERCOT dataset taken from (...to be filled...), and is processed by resource_provisioning/data_energy/data_loader.py at runtime. When you first run this code, it will generate several large .npy file as the cached feature, which will accelerate the preprocessing of the following runs. This experiment requires large memory and is recommended to run on a server. To run a single problem instance, type the following command:

python run_main_newnet.py --method=1 --seed=16900000 --loss=l1

The four methods (L1, L2, weighted L1, ours) are respectively

--method=0 --loss=l1 # L1
--method=0 --loss=l2 # L2
--method=0 --loss=l3 # weighted L1
--method=1 --loss=l1 # ours

To run different ratio of alpha1/alpha2, modify line 157-158 in synthesize.py

 alpha1 = torch.ones(dim_context, 1) * 50
 alpha2 = torch.ones(dim_context, 1) * 0.5

to a desired ratio. Furthermore, modify line 174 in main_newnet.py

netname = "50to0.5"

to "5to0.5"/"1to1"/"0.5to5"/"0.5to50", and line 199 in main_newnet.py

self.alpha1, self.alpha2 = 0.5, 50

to (0.5, 5)/(1, 1)/(5, 0.5)/(50, 0.5) respectively.

run run_script.py to get multiple data. The result is in the result/2013to18_+str(netname)+newnet folder. The interpretation of output data is similar to synthetic linear programming.

[1] Automatically Learning Compact Quality-aware Surrogates for Optimization Problems, Wilder et al., 2020 (https://arxiv.org/abs/2006.10815)

Empirical Evaluation of Lambda_max in Theorem 6

run test.py directly to get results (note it takes a long time to finish the whole run, especially for the option of beta distribution). The results for uniform, Gaussian and beta are respectively in test1.txt, test2.txt and test3.txt.

Sound and Cost-effective Fuzzing of Stripped Binaries by Incremental and Stochastic Rewriting

StochFuzz: A New Solution for Binary-only Fuzzing StochFuzz is a (probabilistically) sound and cost-effective fuzzing technique for stripped binaries.

Zhuo Zhang 164 Dec 05, 2022
Code release for The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification (TIP 2020)

The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification Code release for The Devil is in the Channels: Mutual-Channel

PRIS-CV: Computer Vision Group 230 Dec 31, 2022
This is a TensorFlow implementation for C2-Rec

This is a TensorFlow implementation for C2-Rec We refer to the repo SASRec. Requirements requirement.txt Datasets This repo includes Amazon Beauty dat

7 Nov 14, 2022
Learning Neural Network Subspaces

Learning Neural Network Subspaces Welcome to the codebase for Learning Neural Network Subspaces by Mitchell Wortsman, Maxwell Horton, Carlos Guestrin,

Apple 117 Nov 17, 2022
ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information

ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information This repository contains code, model, dataset for ChineseBERT at ACL2021. Ch

413 Dec 01, 2022
Small-bets - Ergodic Experiment With Python

Ergodic Experiment Based on this video. Run this experiment with this command: p

Michael Brant 3 Jan 11, 2022
Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Chris Donahue 98 Dec 14, 2022
PyTorch Implementation for Deep Metric Learning Pipelines

Easily Extendable Basic Deep Metric Learning Pipeline Karsten Roth ([email 

Karsten Roth 543 Jan 04, 2023
On the Limits of Pseudo Ground Truth in Visual Camera Re-Localization

On the Limits of Pseudo Ground Truth in Visual Camera Re-Localization This repository contains the evaluation code and alternative pseudo ground truth

Torsten Sattler 36 Dec 22, 2022
The object detection pipeline is based on Ultralytics YOLOv5

AYOLOv2 The main goal of this repository is to rewrite the object detection pipeline with a better code structure for better portability and adaptabil

153 Dec 22, 2022
Libraries, tools and tasks created and used at DeepMind Robotics.

dm_robotics: Libraries, tools, and tasks created and used for Robotics research at DeepMind. Package overview Package Summary Transformations Rigid bo

DeepMind 273 Jan 06, 2023
MLP-Like Vision Permutator for Visual Recognition (PyTorch)

Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition (arxiv) This is a Pytorch implementation of our paper. We present Vision

Qibin (Andrew) Hou 162 Nov 28, 2022
Contrastive Feature Loss for Image Prediction

Contrastive Feature Loss for Image Prediction We provide a PyTorch implementation of our contrastive feature loss presented in: Contrastive Feature Lo

Alex Andonian 44 Oct 05, 2022
Bayesian Neural Networks in PyTorch

We present the new scheme to compute Monte Carlo estimator in Bayesian VI settings with almost no memory cost in GPU, regardles of the number of sampl

Jurijs Nazarovs 7 May 03, 2022
Super Pix Adv - Offical implemention of Robust Superpixel-Guided Attentional Adversarial Attack (CVPR2020)

Super_Pix_Adv Offical implemention of Robust Superpixel-Guided Attentional Adver

DLight 8 Oct 26, 2022
The open source code of SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation.

SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation(ICPR 2020) Overview This code is for the paper: Spatial Attention U-Net for Retinal V

Changlu Guo 151 Dec 28, 2022
Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Optimization Algorithm,Immune Algorithm, Artificial Fish Swarm Algorithm, Differential Evolution and TSP(Traveling salesman)

scikit-opt Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm,A

郭飞 3.7k Jan 03, 2023
[MedIA2021]MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning

MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning [MedIA or Arxiv] and [Demo] This repository pr

Healthcare Intelligence Laboratory 92 Dec 08, 2022
Official implementation of "Watermarking Images in Self-Supervised Latent-Spaces"

🔍 Watermarking Images in Self-Supervised Latent-Spaces PyTorch implementation and pretrained models for the paper. For details, see Watermarking Imag

Meta Research 32 Dec 13, 2022