+from collections.abc import Callable
+
+import numpy as np
+
+
+[docs]def mixture(
+
num_voters: int,
+
num_candidates: int,
+
samplers: list[Callable],
+
weights: list[float],
+
sampler_parameters: list[dict],
+
seed: int = None,
+
):
+
"""
+
Generates a mixture of samplers. The process works as follows: for each vote, we sample which
+
sample will be used to generate it based on the weight distribution of the samplers,
+
then, the corresponding number of votes are sampled from the samplers and concatenated together
+
to form the final set of votes.
+
+
Note that votes are not sampled one after the other from the samplers but all at once. This
+
is important if you are using samplers that are not independent.
+
+
It is assumed that you pass samplers that are all about the same type of ballots (only ordinal
+
or only approval for instance). If you don't, the code will probably fail.
+
+
Parameters
+
----------
+
num_voters : int
+
Number of Voters.
+
num_candidates : int
+
Number of Candidates.
+
samplers: list
+
List of samplers.
+
weights: list
+
Probability distribution over the samplers, the sampler in position k has weight :code:`weights[k]`.
+
sampler_parameters: list[dict]
+
List of dictionaries passed as keyword parameters of the samplers. Number of voters and
+
number of candidates of these dictionaries are not taken into account.
+
seed : int
+
Seed for numpy random number generator.
+
Note that this is only the seed for this function.
+
If you want to use particular seed for the functions generating votes,
+
you should pass it as parameter within the :code:`sampler_parameters` list.
+
"""
+
if len(samplers) != len(weights):
+
raise ValueError("For a mixture, you need to provide one weight per sampler, no more, no "
+
"less.")
+
if len(samplers) != len(sampler_parameters):
+
raise ValueError("For a mixture, you need to provide one dictionary of parameters per "
+
"sampler, no more, no less.")
+
if min(weights) < 0:
+
raise ValueError("For a mixture, the weight of a sampler cannot be negative.")
+
if sum(weights) == 0:
+
raise ValueError("For a mixture, the sum of the weights cannot be 0.")
+
+
rng = np.random.default_rng(seed)
+
+
weights = np.array(weights, dtype=float)
+
weights /= weights.sum()
+
num_samplers = len(samplers)
+
samples = rng.choice(range(num_samplers), size=num_voters, replace=True, p=weights)
+
counts = np.unique(samples, return_counts=True)
+
num_voters_per_sampler = [0 for _ in range(num_samplers)]
+
for i, j in enumerate(counts[0]):
+
num_voters_per_sampler[j] = counts[1][i]
+
return concatenation(num_voters_per_sampler, num_candidates, samplers, sampler_parameters)
+
+
+[docs]def concatenation(
+
num_voters_per_sampler: list[int],
+
num_candidates: int,
+
samplers: list[Callable],
+
sampler_parameters: list[dict]
+
) -> np.ndarray:
+
"""
+
Generate votes from different samplers and concatenate them together to form the final set of
+
votes.
+
+
Note that votes are not sampled one after the other from the samplers but all at once. This
+
is important if you are using samplers that are not independent.
+
+
It is assumed that you pass samplers that are all about the same type of ballots (only ordinal
+
or only approval for instance). If you don't, the code will probably fail.
+
+
Parameters
+
----------
+
num_voters_per_sampler : int
+
List of numbers of voters to be sampled from each sampler.
+
num_candidates : int
+
Number of Candidates.
+
samplers: list,
+
List of samplers.
+
sampler_parameters: list,
+
List of dictionaries passed as keyword parameters of the samplers. Number of voters and
+
number of candidates of these dictionaries are not taken into account.
+
+
Returns
+
-------
+
np.ndarray
+
Ordinal votes.
+
"""
+
+
if len(num_voters_per_sampler) != len(samplers):
+
raise ValueError("For a concatenation, you need to provide one number of voters per "
+
"sampler, no more, no less.")
+
if len(samplers) != len(sampler_parameters):
+
raise ValueError("For a concatenation, you need to provide one dictionary of parameters"
+
" per sampler, no more, no less.")
+
+
for i, params in enumerate(sampler_parameters):
+
params["num_voters"] = num_voters_per_sampler[i]
+
params["num_candidates"] = num_candidates
+
+
all_votes = None
+
for num_voters, sampler, params in zip(num_voters_per_sampler, samplers, sampler_parameters):
+
if num_voters > 0:
+
votes = sampler(**params)
+
if all_votes is None:
+
all_votes = votes
+
elif isinstance(all_votes, np.ndarray):
+
all_votes = np.concatenate((all_votes, votes), axis=0)
+
elif isinstance(all_votes, list):
+
all_votes.extend(votes)
+
else:
+
raise ValueError(f"The type of the votes returned by the sampler "
+
f"{sampler.__name__} do not match known types and thus cannot "
+
f"be used for concatenation of samplers. Did you mix-up ordinal "
+
f"and approval samplers?")
+
return all_votes
+