stochastic_matching - 0.3.3 (2024-07-15): Simplified and unified metric computation

• Extraction of metrics from simulation gathered in a unique submodule
• Most used metrics are now properties of the simulator object
• Pre-defined metrics can be selected by name on batch simulation
• Custom metrics can be used by passing their function

- Python
Published by balouf over 1 year ago

stochastic_matching - Various improvements

0.3.2 (2024-07-11): Improved tools for batched simulations

• Unified way to run batched of experiments
  • Construct experiments with static and variable parameters
  • Define how to extract the metrics you want
  • Start evaluation and see how it progresses with tqdm
• mutiprocess.Pool can be optionally used to parallelize the results
• Results can be optionally automatically cached
• Cf notebooks or reference for details

0.3.1 (2024-07-09): Improving the simulator

• Changes in the simulator API:
  • For k-filtering, the threshold parameter is now k
  • weights are now called rewards everywhere but for priority (to keep the weight/counterweight story)
  • Interleaving of rewards and forbidden edges has been improved (each can define the other if necessary)
  • reward-based policies are triggered by setting a beta parameter
• Introduction of in-package parallelization tools
• New notebook tutorial added
• Bugfix: E-Filtering now has working CCDF
• Chores

- Python
Published by balouf over 1 year ago

stochastic_matching - Boooosted simulator

• Simulator re-written almost entirely
  • Easier to read/maintain thanks to jit and data classes.
  • Roughly 40% faster than previous version.
  • Virtual queue updated with better edge-FCFM policy.
  • EGPD ported to both virtual queue and longest policies.
  • epsilon-filtering (a.k.a. epsilon-coloring) added.
• Switch to Poetry
  • Easier package maintainance
  • Pydata documentation style
  • Supported Python version: >=3.10

- Python
Published by balouf over 1 year ago

stochastic_matching - v0.2.2

Improve CCDF display; update supported Python versions

- Python
Published by balouf over 2 years ago

stochastic_matching - Big Little Update

• New optimize_rates for Model. Outputs a flow that optimizes the rates according to some reward weights.
• Refactoring: policies formerly called semi-greedy are now called (semi)-filtering.
• New option weights for filtering policies. Auto-computes the forbidden edges to optimize the reward according to weights.
• Default model tolerance raised to 1e-7 for better detection of null edges.
• Tutorials modified to introduce the new features.
• The notebook used for paper https://hal.archives-ouvertes.fr/hal-03502084 is now included in the documentation.
• Bug hunt: very large simulation could overflow silently (solved by switching logs from uint32 to uint64).

- Python
Published by balouf about 4 years ago

stochastic_matching - Improved API

As the package is at early stage, it had to go through a lot of refactoring. Hopefully, the result should be more easy to use.

Major changes:

API completely unified

Beginners (and intermediate) users should always go through the Model class to use the package. Advanced users: the documentation reference is your oyster!

New features

A few new graph models. New analysis tools: left kernel, right kernel basis change and display (for simple graphs), injectivity/surjectivity, connected components, spanners, and vertices! New policies for the simulator: priority and semi-greedy!

Minor changes:

Default rates are proportional to degree (but you can ask for 'uniform') We have a logo! Hunt for typos in documentation. Notebooks tutorials have been updated to cope with the new API.

- Python
Published by balouf about 4 years ago

stochastic_matching - First public release on Pypi.

Early but fully functional public version of the stochastic matching package.

- Python
Published by balouf about 4 years ago