An open API service indexing awesome lists of open source software.

https://github.com/benchopt/benchmark_crowdsourcing


https://github.com/benchopt/benchmark_crowdsourcing

Last synced: about 1 year ago
JSON representation

Awesome Lists containing this project

README

          

Benchmark Crowdsourcing
=====================
|Build Status| |Python 3.6+|

The label aggregation for crowdsourced classification datasets consists in presenting a set of $n_{task}$ training tasks to classify to a crowd.
The label given for task $i$ by worker $j$ is denoted $y_i^{(j)}$.
Given an aggregation strategy $\\texttt{agg}$ (like majority voting or Dawid and Skene's model), we look at the recovery of the underlying ground truth labels $y_i^*$:

$$ \\mathrm{AccTrain} = \\frac{1}{n_{task}} \\sum_{i=1}^{n_{task}} 1\\!\\!1(\\hat{y}_i^{\\texttt{agg}}=y_i^*).$$

Other objectives as the F1 score can also be considered.

Install
--------

This benchmark can be run using the following commands:

.. code-block::

$ pip install -U benchopt
$ git clone https://github.com/benchopt/benchmark_crowdsourcing
$ benchopt run benchmark_crowdsourcing

Apart from the problem, options can be passed to ``benchopt run``, to restrict the benchmarks to some solvers or datasets, e.g.:

.. code-block::

$ benchopt run benchmark_crowdsourcing -s solver1 -d dataset2 --max-runs 10 --n-repetitions 10

Use ``benchopt run -h`` for more details about these options, or visit https://benchopt.github.io/api.html.

.. |Build Status| image:: https://github.com/benchopt/benchmark_crowdsourcing/actions/workflows/main.yml/badge.svg
:target: https://github.com/benchopt/benchmark_crowdsourcing/actions
.. |Python 3.6+| image:: https://img.shields.io/badge/python-3.6%2B-blue
:target: https://www.python.org/downloads/release/python-360/