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approval validation
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‎docs-source/source/validation/approval.rst‎

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@@ -2,3 +2,49 @@ Approval Samplers
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=================
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We present below the validation tests we ran for approval samplers.
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Impartial
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---------
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:py:func:`prefsampling.approval.impartial`
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The probability distribution generated by the approval impartial culture model.
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.. image:: validation_plots/approval/impartial.png
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:width: 800
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:alt: Observed versus theoretical frequencies for the impartial culture
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Resampling
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----------
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:py:func:`prefsampling.approval.resampling`
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The probability distribution generated by the resampling model.
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See `Szufa et al. (2022) <https://www.ijcai.org/proceedings/2022/0071.pdf>`_
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for more details.
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.. image:: validation_plots/approval/resampling.png
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:width: 800
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:alt: Observed versus theoretical frequencies for the resampling model
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Identity
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----------
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:py:func:`prefsampling.approval.identity`
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The probability distribution generated by the identity model.
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.. image:: validation_plots/approval/identity.png
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:width: 800
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:alt: Observed versus theoretical frequencies for the identity model
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Noise
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----------
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:py:func:`prefsampling.approval.noise`
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The probability distribution generated by the noise model based on the Hamming distance.
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.. image:: validation_plots/approval/noise.png
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:width: 800
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:alt: Observed versus theoretical frequencies for the noise model

‎docs-source/source/validation/validation_plots/approval/identity.png‎

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‎docs-source/source/validation/validation_plots/approval/noise.png‎

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‎docs-source/source/validation/validation_plots/approval/resampling.png‎

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‎prefsampling/approval/noise.py‎

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@@ -79,7 +79,7 @@ def noise(
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rng = np.random.default_rng(seed)
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k = int(p * num_candidates)
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k = math.floor(p * num_candidates)
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A = {i for i in range(k)}
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B = set(range(num_candidates)) - A

‎prefsampling/approval/resampling.py‎

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import copy
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import math
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import numpy as np
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from prefsampling.inputvalidators import validate_num_voters_candidates
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rng = np.random.default_rng(seed)
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k = int(p * num_candidates)
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k = math.floor(p * num_candidates)
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if central_vote is None:
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central_vote = set(range(k))
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@@ -122,7 +124,7 @@ def disjoint_resampling(
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rng = np.random.default_rng(seed)
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num_groups = g
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k = int(p * num_candidates)
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k = math.floor(p * num_candidates)
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votes = [set() for _ in range(num_voters)]
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breaks = [int(num_voters / num_legs) * i for i in range(num_legs)]
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k = int(p * num_candidates)
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k = math.floor(p * num_candidates)
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central_vote = {i for i in range(k)}
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ccc = copy.deepcopy(central_vote)
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‎validation/approval/euclidean.py‎

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‎validation/approval/impartial.py‎

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@@ -17,7 +17,7 @@ def __init__(self):
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super(ApprovalImpartialValidator, self).__init__(
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parameters_list,
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"Impartial",
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"impartial_apr",
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"impartial",
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True,
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sampler_func=impartial,
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constant_parameters="num_voters",

‎validation/approval/noise.py‎

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@@ -8,12 +8,14 @@
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class ApprovalNoiseValidator(Validator):
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def __init__(self):
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parameters_list = [
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{"num_voters": 1, "num_candidates": 4, "phi": 0.25, "p": 0.25},
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{"num_voters": 1, "num_candidates": 4, "phi": 0.25, "p": 0.5},
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{"num_voters": 1, "num_candidates": 4, "phi": 0.25, "p": 0.75},
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{"num_voters": 1, "num_candidates": 4, "phi": 0.5, "p": 0.25},
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{"num_voters": 1, "num_candidates": 4, "phi": 0.5, "p": 0.5},
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{"num_voters": 1, "num_candidates": 4, "phi": 0.5, "p": 0.75},
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{"num_voters": 1, "num_candidates": 6, "phi": 0.25, "p": 0.5},
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{"num_voters": 1, "num_candidates": 6, "phi": 0.5, "p": 0.5},
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{"num_voters": 1, "num_candidates": 6, "phi": 0.75, "p": 0.5},
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{"num_voters": 1, "num_candidates": 6, "phi": 1., "p": 0.5},
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{"num_voters": 1, "num_candidates": 6, "phi": 0.25, "p": 0.34},
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{"num_voters": 1, "num_candidates": 6, "phi": 0.5, "p": 0.34},
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{"num_voters": 1, "num_candidates": 6, "phi": 0.75, "p": 0.34},
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{"num_voters": 1, "num_candidates": 6, "phi": 1., "p": 0.34},
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]
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super(ApprovalNoiseValidator, self).__init__(
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parameters_list,

‎validation/approval/resampling.py‎

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@@ -11,9 +11,11 @@ def __init__(self):
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{"num_voters": 1, "num_candidates": 6, "phi": 0.25, "p": 0.5},
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{"num_voters": 1, "num_candidates": 6, "phi": 0.5, "p": 0.5},
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{"num_voters": 1, "num_candidates": 6, "phi": 0.75, "p": 0.5},
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{"num_voters": 1, "num_candidates": 6, "phi": 1., "p": 0.5},
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{"num_voters": 1, "num_candidates": 6, "phi": 0.25, "p": 0.34},
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{"num_voters": 1, "num_candidates": 6, "phi": 0.5, "p": 0.34},
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{"num_voters": 1, "num_candidates": 6, "phi": 0.75, "p": 0.34},
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{"num_voters": 1, "num_candidates": 6, "phi": 1., "p": 0.34},
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]
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super(ApprovalResamplingValidator, self).__init__(
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parameters_list,
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def sample_cast(self, sample):
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return str(sample[0])
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class ApprovalDisjointResamplingValidator(Validator):
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def __init__(self):
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parameters_list = [
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{"num_voters": 1, "num_candidates": 4, "phi": 0.5, "p": 0.25, "g": 2},
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# {"num_voters": 1, "num_candidates": 6, "phi": 0.5, "p": 0.33, "g": 2},
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# {"num_voters": 1, "num_candidates": 6, "phi": 0.5, "p": 0.25, "g": 3},
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# {"num_voters": 1, "num_candidates": 6, "phi": 0.5, "p": 0.33, "g": 3},
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]
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super(ApprovalDisjointResamplingValidator, self).__init__(
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parameters_list,
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"Disjoint Resampling",
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"disjoint_resampling",
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True,
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sampler_func=resampling,
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constant_parameters=("num_voters", "num_candidates", "phi"),
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faceted_parameters=("phi", "p"),
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)
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def all_outcomes(self, sampler_parameters):
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return get_all_subsets(sampler_parameters["num_candidates"])
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def theoretical_distribution(self, sampler_parameters, all_outcomes) -> dict:
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m = sampler_parameters["num_candidates"]
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p = sampler_parameters["p"]
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phi = sampler_parameters["phi"]
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k = math.floor(p*m)
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central_vote = {i for i in range(k)}
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A = {}
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for outcome in all_outcomes:
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prob = 1
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for c in range(m):
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if c in central_vote and c in outcome:
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prob *= (1-phi) + phi*p
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elif c in central_vote and c not in outcome:
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prob *= phi*(1-p)
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elif c not in central_vote and c in outcome:
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prob *= phi*p
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else:
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prob *= (1-phi) + phi*(1-p)
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A[str(outcome)] = prob
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return A
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def sample_cast(self, sample):
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return str(sample[0])

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