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Allocated Score: Difference between revisions

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Allocated Score (view source)

Revision as of 15:32, 11 May 2022

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<source lang="python">
import pandas as pd
import numpy as np
 
def Allocated_Score(Kballots: pd.DataFrame, Wseats: int, Smax_score: int):
"""Credit to https://electowiki.org/wiki/Allocated_Score
Allocated Score is another name for STAR-PR
 
Parameters:
#Normalize score matrix▼
 
ballots = pd.DataFrame(S.values/K, columns=S.columns)▼
"""
▲ # Normalize score matrix
#Find number of voters and quota size▼
▲ ballots = pd.DataFrame(Sballots.values /K max_score, columns=Sballots.columns)
V = ballots.shape[0]▼
 
quota = V/W
▲ # Find number of voters and quota size
ballot_weight = pd.Series(np.ones(V),name='weights')▼
▲ Vvoters = ballots.shape[0]
#Populatequota winners= invoters a/ loopseats
▲ ballot_weight = pd.Series(np.ones(Vvoters), name='"weights'")
 
# Populate winners in a loop
winner_list = []
while len(winner_list) < Wseats:
 
weighted_scores = ballots.multiply(ballot_weight, axis="index")
 
# Select winner
wwinner = weighted_scores.sum().idxmax()
#Add winner to list
winner_list.append(w)▼
 
#remove Add winner fromto ballotlist
ballotswinner_list.dropappend(w, axis=1, inplace=Truewinner)
#Create lists for manipulation▼
cand_df = pd.concat([ballot_weight,weighted_scores[w]], axis=1).copy() ▼
cand_df_sort = cand_df.sort_values(by=[w], ascending=False).copy() ▼
▼
#find the score where a quota is filled▼
split_point = cand_df_sort[cand_df_sort['weights'].cumsum() < quota][w].min()▼
#Amount of ballot for voters who voted more than the split point▼
spent_above = cand_df[cand_df[w] > split_point]['weights'].sum()▼
▼
#Allocate all ballots above split point▼
if spent_above>0: ▼
cand_df.loc[cand_df[w] > split_point, 'weights'] = 0.0▼
#Amount of ballot for voters who gave a score on the split point▼
weight_on_split = cand_df[cand_df[w] == split_point]['weights'].sum()▼
 
#Fraction ofremove ballotwinner onfrom split needed to be spentballot
ballots.drop(winner, axis=1, inplace=True)
if weight_on_split>0: ▼
spent_value = (quota - spent_above)/weight_on_split▼
#Take the spent value from the voters on the threshold evenly▼
cand_df.loc[cand_df[w] == split_point, 'weights'] = cand_df.loc[cand_df[w] == split_point, 'weights'] * (1 - spent_value)▼
ballot_weight = cand_df['weights'].clip(0.0,1.0)▼
 
▲ # Create lists for manipulation
return winner_list
▲ cand_df = pd.concat([ballot_weight, weighted_scores[wwinner]], axis=1).copy()
▲ cand_df_sort = cand_df.sort_values(by=[wwinner], ascending=False).copy()
 
▲ # find the score where a quota is filled
▲ split_point = cand_df_sort[cand_df_sort['"weights'"].cumsum() < quota][w].min()
▲ winner
▲ ].min()
 
▲ # Amount of ballot for voters who voted more than the split point
▲ spent_above = cand_df[cand_df[wwinner] > split_point]['"weights'"].sum()
 
▲ # Allocate all ballots above split point
▲ if spent_above > 0:
▲ cand_df.loc[cand_df[wwinner] > split_point, '"weights'"] = 0.0
 
▲ # Amount of ballot for voters who gave a score on the split point
▲ weight_on_split = cand_df[cand_df[wwinner] == split_point]['"weights'"].sum()
 
# Fraction of ballot on split needed to be spent
▲ if weight_on_split > 0:
▲ spent_value = (quota - spent_above) / weight_on_split
 
▲ # Take the spent value from the voters on the threshold evenly
▲ cand_df.loc[cand_df[wwinner] == split_point, '"weights'"] = cand_df.loc[cand_df[w] == split_point, 'weights'] * (1 - spent_value)
cand_df[winner] == split_point, "weights"
] * (1 - spent_value)
 
▲ ballot_weight = cand_df['"weights'"].clip(0.0, 1.0)
 
▲ return winner_list.append(w)
</source>
 
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