This repository is the official implementation of the paper: PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming (Accepted to Mathematical Programming Computation (MPC))
Citation:
@article{tang2022pyepo,
title={PyEPO: A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming},
author={Tang, Bo and Khalil, Elias B},
journal={arXiv preprint arXiv:2206.14234},
year={2022}
}
PyEPO (PyTorch-based End-to-End Predict-then-Optimize Tool) is a Python-based, open-source software that supports modeling and solving predict-then-optimize problems with the linear objective function. The core capability of PyEPO is to build optimization models with GurobiPy, Pyomo, or any other solvers and algorithms, then embed the optimization model into an artificial neural network for the end-to-end training. For this purpose, PyEPO implements various methods as PyTorch autograd modules.
The official PyEPO docs can be found at https://khalil-research.github.io/PyEPO.
01 Optimization Model: Build up optimization solver
To reproduce the experiments in original paper, please use the code and follow the instruction in this branch.
- Implement SPO+ [1], DBB [3], I-MLE [8], NID [7], DPO [4], PFYL [4], NCE [5] and LTR [6].
- Support Gurobi, COPT, and Pyomo API
- Support Parallel computing for optimization solver
- Support solution caching [5] to speed up training
You can download PyEPO from our GitHub repository.
git clone -b main --depth 1 https://github.com/khalil-research/PyEPO.gitAnd install it.
pip install PyEPO/pkg/.#!/usr/bin/env python
# coding: utf-8
import gurobipy as gp
from gurobipy import GRB
import numpy as np
import pyepo
from pyepo.model.grb import optGrbModel
import torch
from torch import nn
from torch.utils.data import DataLoader
# optimization model
class myModel(optGrbModel):
def __init__(self, weights):
self.weights = np.array(weights)
self.num_item = len(weights[0])
super().__init__()
def _getModel(self):
# ceate a model
m = gp.Model()
# varibles
x = m.addVars(self.num_item, name="x", vtype=GRB.BINARY)
# model sense
m.modelSense = GRB.MAXIMIZE
# constraints
m.addConstr(gp.quicksum([self.weights[0,i] * x[i] for i in range(self.num_item)]) <= 7)
m.addConstr(gp.quicksum([self.weights[1,i] * x[i] for i in range(self.num_item)]) <= 8)
m.addConstr(gp.quicksum([self.weights[2,i] * x[i] for i in range(self.num_item)]) <= 9)
return m, x
# prediction model
class LinearRegression(nn.Module):
def __init__(self):
super(LinearRegression, self).__init__()
self.linear = nn.Linear(num_feat, num_item)
def forward(self, x):
out = self.linear(x)
return out
if __name__ == "__main__":
# generate data
num_data = 1000 # number of data
num_feat = 5 # size of feature
num_item = 10 # number of items
weights, x, c = pyepo.data.knapsack.genData(num_data, num_feat, num_item,
dim=3, deg=4, noise_width=0.5, seed=135)
# init optimization model
optmodel = myModel(weights)
# init prediction model
predmodel = LinearRegression()
# set optimizer
optimizer = torch.optim.Adam(predmodel.parameters(), lr=1e-2)
# init SPO+ loss
spop = pyepo.func.SPOPlus(optmodel, processes=1)
# build dataset
dataset = pyepo.data.dataset.optDataset(optmodel, x, c)
# get data loader
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
# training
num_epochs = 10
for epoch in range(num_epochs):
for data in dataloader:
x, c, w, z = data
# forward pass
cp = predmodel(x)
loss = spop(cp, c, w, z)
# backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()
# eval
regret = pyepo.metric.regret(predmodel, optmodel, dataloader)
print("Regret on Training Set: {:.4f}".format(regret))- [1] Elmachtoub, A. N., & Grigas, P. (2021). Smart “predict, then optimize”. Management Science.
- [2] Mandi, J., Stuckey, P. J., & Guns, T. (2020). Smart predict-and-optimize for hard combinatorial optimization problems. In Proceedings of the AAAI Conference on Artificial Intelligence.
- [3] Vlastelica, M., Paulus, A., Musil, V., Martius, G., & Rolínek, M. (2019). Differentiation of blackbox combinatorial solvers. arXiv preprint arXiv:1912.02175.
- [4] Berthet, Q., Blondel, M., Teboul, O., Cuturi, M., Vert, J. P., & Bach, F. (2020). Learning with differentiable pertubed optimizers. Advances in neural information processing systems, 33, 9508-9519.
- [5] Mulamba, M., Mandi, J., Diligenti, M., Lombardi, M., Bucarey, V., & Guns, T. (2021). Contrastive losses and solution caching for predict-and-optimize. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence.
- [6] Mandi, J., Bucarey, V., Mulamba, M., & Guns, T. (2022). Decision-focused learning: through the lens of learning to rank. Proceedings of the 39th International Conference on Machine Learning.
- [7] Sahoo, S. S., Paulus, A., Vlastelica, M., Musil, V., Kuleshov, V., & Martius, G. (2022). Backpropagation through combinatorial algorithms: Identity with projection works. arXiv preprint arXiv:2205.15213.
- [8] Niepert, M., Minervini, P., & Franceschi, L. (2021). Implicit MLE: backpropagating through discrete exponential family distributions. Advances in Neural Information Processing Systems, 34, 14567-14579.