input_formats.md

Input formats

import fairpyx
divide = fairpyx.divide

fairpyx allows various input formats, so that you can easily use it on your own data, whether for applications or for research. For example, suppose you want to divide candies among your children. It is convenient to collect their preferences in a dict of dicts:

valuations = {
    "Ami": {"green": 8, "red":7, "blue": 6, "yellow": 5},
    "Tami": {"green": 12, "red":8, "blue": 4, "yellow": 2} }
allocation = divide(fairpyx.algorithms.round_robin, valuations=valuations)

You can then see the resulting allocation with the agents' real names:

print(allocation)

{'Ami': ['blue', 'green'], 'Tami': ['red', 'yellow']}

For research, passing a dict of dicts as a parameter may be too verbose. You can call the same algorithm with only the values, or only the value matrix:

print(divide(fairpyx.algorithms.round_robin, valuations={"Ami": [8,7,6,5], "Tami": [12,8,4,2]}))
print(divide(fairpyx.algorithms.round_robin, valuations=[[8,7,6,5], [12,8,4,2]]))


# #' For experiments, you can use a numpy random matrix:

import numpy as np
valuations = np.random.randint(1,100,[2,4])
print(valuations)
allocation = divide(fairpyx.algorithms.round_robin, valuations=valuations)
print(allocation)

{'Ami': [0, 2], 'Tami': [1, 3]}
{0: [0, 2], 1: [1, 3]}
[[44  1 43 50]
 [83 90 44 20]]
{0: [0, 3], 1: [1, 2]}

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