pysp | PySP : Stochastic Programming in Python

by Pyomo Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | pysp Summary

pysp is a Python library. pysp has no bugs, it has no vulnerabilities, it has build file available and it has low support. However pysp has a Non-SPDX License. You can download it from GitHub.

PySP is an extension to the Pyomo optimization modeling package for formulating and solving stochastic programming optimization problems. PySP was originally developed within the Pyomo project and has subsequently been replaced by mpi-sppy as the recommended extension for optimization under uncertainty in Pyomo. We plan to make PySP available at least until some time in 2023.

Support

Quality

Security

License

Reuse

Support

pysp has a low active ecosystem.

It has 1 star(s) with 4 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 3 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pysp is current.

Quality

pysp has 0 bugs and 0 code smells.

Security

pysp has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pysp code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pysp has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

pysp releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

It has 53578 lines of code, 2555 functions and 224 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed pysp and discovered the below as its top functions. This is intended to give you an instant insight into pysp implemented functionality, and help decide if they suit your requirements.

Convert external setup .
Convert an embedding model into an embedding file .
Construct an argument parser .
Compute the updates for each subproblems .
Wait for all subproblems and process them .
Run the scenario .
Run a conf . conf .
Convert an external setup into a single scenario .
Create a scenario tree model from a networkx graph .
Creates an ExpModel instance from the given scenario tree .

Get all kandi verified functions for this library.

pysp Key Features

No Key Features are available at this moment for pysp.

pysp Examples and Code Snippets

No Code Snippets are available at this moment for pysp.

Community Discussions

Trending Discussions on pysp

The parameter values are not updating in the Pyomo model with PySP callback

QUESTION

The parameter values are not updating in the Pyomo model with PySP callback

Asked 2021-Jul-15 at 17:48

I am using PySP (Pyomo) for one stochastic optimization problem. I created a concrete model for my problem and also defined the scenarios based on the farmer's example given in

https://github.com/Pyomo/pysp/blob/main/examples/farmer/concrete/ReferenceModel.py

In the above example a pysp_instance_creation_callback() function is called for each of the scenarios. In the function, an instance of the model is cloned for each scenario so that the scenario variable (Yield in this case) is updated for each of the scenarios using instance.Yield.store_values(Yield[scenario_name]).

I followed a similar approach to my problem. However, in my case, for each scenario the size of the unknown varies unlike the farmer's example, where the scenarios are for just three crops (wheat, sugar, corn). For instance, my scenarios would look like this,

...

ANSWER

Answered 2021-Jul-15 at 17:19

Before I get started, I should point out that PySP is no longer under active development. New development is all going on in mpi-sppy https://github.com/Pyomo/mpi-sppy

Returning to your issue: PySP assumes that the "shape" of the model is the same for each scenario so you will have to do some more coding to make that assumption valid. The same is more-or-less true for mpi-sppy, although it has mechanisms to allow for Vars that have zero probability depending on the scenario. mpi-sspy would have a little less trouble than PySP with paramaters that change size between scenarios, but it does have some components that assume that the shape of the model can be determined by an arbitrary scenario (PySP makes use of a ReferenceModel so it strongly assumes that the shape does not change).

Source https://stackoverflow.com/questions/68386346

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pysp

You can download it from GitHub.
You can use pysp like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: