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https://github.com/wassname/Open-Assistant.git
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Alexander Mattick
66 lines
2.3 KiB
Python
66 lines
2.3 KiB
Python
# -*- coding: utf-8 -*-
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from dataclasses import dataclass
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from typing import Any
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import numpy as np
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import numpy.typing as npt
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@dataclass
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class Voter:
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"""
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Represents a single voter.
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This tabulates the number of good votes, total votes,
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and points.
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We only put well-behaved people on the scoreboard and filter out the badly behaved ones
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"""
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uid: Any
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num_votes: int
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num_good_votes: int
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total_points: int
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def voter_quality(self):
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return self.num_good_votes / self.num_votes
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def is_well_behaved(self, threshhold):
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return self.voter_quality() > threshhold
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def score_update(new_vote: int, consensus: npt.ArrayLike, voter_data: Voter) -> Voter:
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"""
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This function returns the new "quality score" and points for a voter.
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I.e. a voter casts a vote on a question.
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The consensus is the array of all votes cast by all voters for that question
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We then update the voter data using the new information
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Parameters:
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new_vote (int): the index of the vote cast by the voter
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consensus (ArrayLike): all votes cast for this question
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voter_data (Voter): a "Voter" object that represents the person casting the "new_vote"
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Returns:
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updated_voter (Voter): the new "quality score" and points for the voter
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"""
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# produces the ranking of votes, e.g. for [100,300,200] it returns [0, 2, 1],
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# since 100 is the lowest, 300 the highest and 200 the middle value
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consensus_ranking = np.argsort(np.argsort(consensus))
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new_points = consensus_ranking[new_vote] + voter_data.total_points
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# we need to correct for 0 indexing, if you are closer to "right" than "wrong" of the conensus,
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# it's a good vote
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new_good_votes = int(consensus_ranking[new_vote] > (len(consensus) - 1) / 2) + voter_data.num_good_votes
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new_num_votes = voter_data.num_votes + 1
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return Voter(voter_data.uid, new_num_votes, new_good_votes, new_points)
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if __name__ == "__main__":
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demo_voter = Voter("abc", 10, 2, 6)
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new_vote = 3
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consensus = np.array([200, 300, 100, 500])
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print(demo_voter)
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print("best vote", score_update(new_vote, consensus, demo_voter))
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new_vote = 2
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print("worst vote", score_update(new_vote, consensus, demo_voter))
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new_vote = 1
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print("medium vote", score_update(new_vote, consensus, demo_voter))
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