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https://github.com/locuslab/e2e-model-learning

Task-based end-to-end model learning in stochastic optimization
https://github.com/locuslab/e2e-model-learning

deep-learning machine-learning optimization paper pytorch stochastic-optimizers

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Task-based end-to-end model learning in stochastic optimization

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README 

        
# Task-based End-to-end Model Learning in Stochastic Optimization



This repository is by 

[Priya L. Donti](https://www.priyadonti.com),

[Brandon Amos](http://bamos.github.io),

and [J. Zico Kolter](http://zicokolter.com)

and contains the [PyTorch](https://pytorch.org) source code to

reproduce the experiments in our paper

[Task-based End-to-end Model Learning in Stochastic Optimization](https://arxiv.org/abs/1703.04529).



If you find this repository helpful in your publications,

please consider citing our paper.



```

@inproceedings{donti2017task,

  title={Task-based end-to-end model learning in stochastic optimization},

  author={Donti, Priya and Amos, Brandon and Kolter, J Zico},

  booktitle={Advances in Neural Information Processing Systems},

  pages={5484--5494},

  year={2017}

}

```



# Introduction



As machine learning techniques have become more ubiquitous, it has become 

common to see machine learning prediction algorithms operating within some 

larger process. However, the criteria by which we train machine learning 

algorithms often differ from the ultimate criteria on which we evaluate them.



This repository demonstrates an end-to-end approach for learning probabilistic 

machine learning models within the context of stochastic programming, in a 

manner that directly captures the ultimate task-based objective for which they 

will be used. Specifically, we evaluate our approach in the context of

(a) a generic inventory stock problem and (b) an electrical grid scheduling

task based on over eight years of data from PJM.



Please see our paper [Task-based End-to-end Model Learning in Stochastic Optimization](https://arxiv.org/abs/1703.04529)

and the code in this repository ([locuslab/e2e-model-learning](https://github.com/locuslab/e2e-model-learning))

for more details about the general approach proposed and our initial

experimental implementations.



## Setup and Dependencies



+ Python 3.x/numpy/scipy

+ [cvxpy](http://www.cvxpy.org/en/latest/) 1.x

+ [PyTorch](https://pytorch.org) 1.x

+ [qpth](https://github.com/locuslab/qpth) 0.0.15:

  *A fast QP solver for PyTorch released in conjunction with the paper 

  [OptNet: Differentiable Optimization as a Layer in Neural Networks](https://arxiv.org/abs/1703.00443).*

+ [bamos/block](https://github.com/bamos/block):

  *An intelligent block matrix library for numpy, PyTorch, and beyond.*

+ pandas/matplotlib/seaborn

+ Optional: [bamos/setGPU](https://github.com/bamos/setGPU):

  A small library to set `CUDA_VISIBLE_DEVICES` on multi-GPU systems.

+ Optional: setproctitle: A library to set process names.



# Inventory Stock Problem (Newsvendor) Experiments



Experiments considering a "conditional" variation of the inventory stock problem. 

Problem instances are generated via random sampling.



```

newsvendor

├── main.py - Run inventory stock problem experiments. (See arguments.)

├── task_net.py - Functions for our task-based end-to-end model learning approach.

├── mle.py - Functions for linear maximum likelihood estimation approach.

├── mle_net.py - Functions for nonlinear maximum likelihood estimation approach.

├── policy_net.py - Functions for end-to-end neural network policy model.

├── batch.py - Helper functions for minibatched evaluation.

├── plot.py - Plot experimental results.

└── constants.py - Constants to set GPU vs. CPU.

```



# Load Forecasting and Generator Scheduling Experiments



Experiments considering a realistic grid-scheduling task, in which

electricity generation is scheduled based on some (unknown) distribution

over electricity demand. Historical load data for these experiments were obtained from

[PJM](http://www.pjm.com/markets-and-operations/ops-analysis/historical-load-data.aspx).



```

power_sched

├── main.py - Run load forecasting problem experiments. (See arguments.)

├── model_classes.py - Models used for experiments.

├── nets.py - Functions for RMSE, cost-weighted RMSE, and task nets.

├── plot.py - Plot experimental results.

├── constants.py - Constants to set GPU vs. CPU.

└── pjm_load_data_*.txt - Historical load data from PJM.

```



# Price Forecasting and Battery Storage Experiments



Experiments considering a realistic battery arbitrage task, in which

a power grid-connected battery generates a charge/discharge schedule 

based on some (unknown) distribution

over energy prices. Historical energy price data for these experiments were obtained from

[PJM](http://www.pjm.com/markets-and-operations/energy/real-time/monthlylmp.aspx).



```

battery_storage

├── main.py - Run battery storage problem experiments. (See arguments.)

├── model_classes.py - Models used for experiments.

├── nets.py - Functions for RMSE and task nets.

├── calc_stats.py - Calculate experimental result stats.

├── constants.py - Constants to set GPU vs. CPU.

└── storage_data.csv - Historical energy price data from PJM.

```



### Acknowledgments



This material is based upon work supported by the 

National Science Foundation Graduate Research Fellowship Program under

Grant No. DGE1252522. 



# Licensing



Unless otherwise stated, the source code is copyright

Carnegie Mellon University and licensed under the

[Apache 2.0 License](./LICENSE).