embedded-voting 0.1.6

Features

• Create a voting profile in which voters are associated to embeddings.

• Run elections on these profiles with different rules, using the geometrical aspects of the embeddings.

• The rules are defined for cardinal preferences, but some of them are adapted for the case of ordinal preferences.

• There are rules for single-winner elections and multi-winner elections.

• Classes to analyse the evolution of the score when the embeddings of one voter are changing.

• Classes to analyse the manipulability of the rules.

• Classes for algorithm aggregation.

• A lot of tutorials.

Credits

This package was created with Cookiecutter and the francois-durand/package_helper project template.

0.1.6 (2023-01-23)

• Aggregators: * Possibility to add or not the current ratings to the training set.

• Embeddings:

  • The parameter norm has no default value (instead of True).

  • Fix a bug: when norm=False, the values of the attributes n_voter and n_dim were swapped by mistake.

  • Rename method scored to times_ratings_candidate.

  • Rename method _get_center to get_center, so that it is now part of the API.

  • Rename method normalize to normalized, recenter to recentered, dilate to dilated because they return a new Embeddings object (not modify the object in place).

  • Fix a bug in method get_center.

  • Methods get_center, recentered and dilated now also work with non-normalized embeddings.

  • Document that dilated can output embeddings that are not in the positive orthant.

  • Add dilated_new: new dilatation method whose output is in the positive orthant.

  • Add recentered_and_dilated: recenter and dilate the embeddings (using dilated_new).

  • Add mixed_with: mix the given Embeddings object with another one.

  • Rename plot_scores to plot_ratings_candidate.

• Embeddings generators:

  • Rename EmbeddingsGeneratorRandom to EmbeddingsGeneratorUniform.

  • Add EmbeddingsGeneratorFullyPolarized: create embeddings that are random vectors of the canonical basis.

  • EmbeddingsGeneratorPolarized now relies on EmbeddingsGeneratorUniform, EmbeddingsGeneratorFullyPolarized and the method Embeddings.mixed_with.

  • Move EmbeddingCorrelation and renamed it.

  • Rewrote the EmbeddingsFromRatingsCorrelation and how it compute the number of singular values to take.

• Epistemic ratings generators:

  • Add TruthGenerator: a generator for the ground truth (“true value”) of each candidate.

  • Add TruthGeneratorUniform: a uniform generator for the ground truth (“true value”) of each candidate.

  • RatingsGeneratorEpistemic and its subclasses now take a TruthGenerator as parameter.

  • Add RatingsGeneratorEpistemicGroups as an intermediate class between the parent class RatingsGeneratorEpistemic and the child classes using groups of voters.

  • RatingsGeneratorEpistemic now do not take groups sizes as parameter: only RatingsGeneratorEpistemicGroups and its subclasses do.

  • Rename RatingsGeneratorEpistemicGroupedMean to RatingsGeneratorEpistemicGroupsMean, RatingsGeneratorEpistemicGroupedMix to RatingsGeneratorEpistemicGroupsMix RatingsGeneratorEpistemicGroupedNoise to RatingsGeneratorEpistemicGroupsNoise.

  • Remove method RatingsGeneratorEpistemic.generate_true_values: the same result can be obtained with RatingsGeneratorEpistemic.truth_generator.

  • Add RatingsGeneratorEpistemicGroupedMixFree and RatingsGeneratorEpistemicGroupsMixScale.

• Ratings generators:

  • RatingsGenerator and subclasses: remove *args in call because it was not used.

  • RatingsGeneratorUniform: add optional parameters minimum_rating and maximum_rating.

  • Possibility to save scores in a csv file

• RatingsFromEmbeddingsCorrelated:

  • Move parameter coherence from __call__ to __init__.

  • Rename parameter scores_matrix to ratings_dim_candidate.

  • Parameters n_dim and n_candidates are optional if ratings_dim_candidate is specified.

  • Add optional parameters minimum_random_rating, maximum_random_rating and clip.

  • Parameter clip now defaults to False (the former version behaved as if clip was always True).

• Single-winner rules:

  • Rename ScoringRule to Rule.

  • Rename all subclasses accordingly. For example, rename FastNash to RuleFastNash.

  • Rename SumScores to RuleSumRatings and ProductScores to RuleProductRatings.

  • Rename RulePositionalExtension to RulePositional and rename subclasses accordingly.

  • Rename RuleInstantRunoffExtension to RuleInstantRunoff.

  • Add RuleApprovalSum, RuleApprovalProduct, RuleApprovalRandom.

  • Changed the default renormalization function in RuleFast.

  • Change the method in RuleMLEGaussian.

  • Add RuleModelAware.

  • Add RuleRatingsHistory.

  • Add RuleShiftProduct which replace RuleProductRatings.

• Multiwinner rules: rename all rules with prefix MultiwinnerRule. For example, rename IterFeatures to MultiwinnerRuleIterFeatures.

• Manipulation:

  • Rename SingleVoterManipulation to Manipulation and rename subclasses accordingly.

  • Rename SingleVoterManipulationExtension to ManipulationOrdinal and rename subclasses accordingly.

  • Rename ManipulationCoalitionExtension to ManipulationCoalitionOrdinal and rename subclasses accordingly.

• Rename AggregatorSum to AggregatorSumRatings and AggregatorProduct to AggregatorProductRatings.

• Add max_angular_dilatation_factor: maximum angular dilatation factor to stay in the positive orthant.

• Rename create_3D_plot to create_3d_plot.

• Moved function to the utils module.

• Reorganize the file structure of the project.

0.1.5 (2022-01-04)

• Aggregator functions.

• Online learning.

• Refactoring Truth epistemic generators.

• Rule taking history into account.

0.1.4 (2021-12-06)

• New version with new structure for Ratings and Embeddings

0.1.3 (2021-10-27)

• New version with new internal structure for the library

0.1.2 (2021-07-05)

• New version with handy way to use the library for algorithm aggregation and epistemic social choice

0.1.1 (2021-04-02)

• Minor bugs.

0.1.0 (2021-03-31)

• End of the internship, first release on PyPI.