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ranking.py
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import pandas as pd
import numpy as np
import scipy.stats as stats
from argparse import ArgumentParser
def get_ranking(path_to_csv, task):
df = pd.read_csv(path_to_csv)
if task == 'health':
# invert performance for accuracy so that smaller is better during calculations
print('Performance metric: higher is better')
df['Performance'] = 1 - df['Performance']
else:
print('Performance metric: smaller is better')
ranked = np.apply_along_axis(stats.rankdata, 0, df.iloc[:, 1:], 'min')
print('Ranks for each metric:\n', ranked)
print('Weighted Scores:\n', weight_ranks(ranked))
final_ranks = stats.rankdata(weight_ranks(ranked), 'min')
print('Final Ranks with ties: ', final_ranks)
# tie breaker: DevelopEnergy
breaker_rank = ranked[:, 0]
final_ranks = break_tie(final_ranks, breaker_rank)
print('Final Ranks: ', final_ranks)
print('Final Group Ordering:\n', df.set_index(final_ranks)['Group'].sort_index())
def weight_ranks(ranks):
weighted_ranks = [(0.25 * DevelopEnergy + 0.25 * InferenceEnergy + 0.5 * Accuracy) / 3 for
DevelopEnergy, InferenceEnergy, Accuracy in ranks]
return weighted_ranks
def break_tie(final_ranks, breaker_rank):
if len(np.unique(final_ranks)) != len(final_ranks):
# break tie with breaker_rank
for r in range(1, len(final_ranks)):
r_mask = (r == final_ranks)
if sum(r_mask) <= 1:
continue
else:
breakers = breaker_rank[r_mask]
sorted_idx = np.argsort(breakers)
final_ranks[r_mask] += sorted_idx
return final_ranks
else:
return final_ranks
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument("--path_to_csv", type=str)
parser.add_argument("--task", type=str, help='health / energy')
args = parser.parse_args()
get_ranking(args.path_to_csv, args.task)