utils.py

import math
import numpy as np
from scipy.spatial.distance import jensenshannon



def maxNormalize(scores):
    max_score = max(scores)
    normalized_scores = [score / max_score for score in scores]
    return normalized_scores 

def minmaxNormalization(scores):
    min_score = min(scores)
    max_score = max(scores)
    normalized_scores = [(score - min_score) / (max_score - min_score) for score in scores]
    return normalized_scores

def decayFunction(sorted_list):
    result = []
    for i,_ in enumerate(sorted_list, 1):
        score = 1 / math.log2(i + 1)
        result.append(score)
    return result


def get_jsd_distance(vector_1, vector_2):
        all_zeros = all(map(lambda x: x == 0, vector_1))
        if all_zeros:
            distance = 1
        else:
            distance = jensenshannon(vector_1, vector_2)

        return distance


def linearCombination(rel_score, fairness_score, LAMBDA=0.5):
        score = (LAMBDA * rel_score) + ((1-LAMBDA) * fairness_score)
        return score