add candidate types (e.g., party affiliation) to spatial profiles

docs/source/conf.py

@@ -22,7 +22,7 @@
 author = 'Wes Holliday and Eric Pacuit'
 
 # The full version, including alpha/beta/rc tags
-release = '1.13.31'
+release = '1.14.0'
 
 
 # -- General configuration ---------------------------------------------------

pref_voting/__init__.py

@@ -1 +1 @@
-__version__ = '1.13.31'
+__version__ = '1.14.0'

pref_voting/generate_spatial_profiles.py

@@ -70,7 +70,11 @@ def generate_spatial_profile(num_cands, num_voters, num_dims, cand_cov = None, v
 
     return profs[0] if num_profiles == 1 else profs
 
-def generate_spatial_profile_polarized(cand_clusters, voter_clusters, num_profiles = 1):
+def generate_spatial_profile_polarized(
+    cand_clusters, 
+    voter_clusters, 
+    cluster_types = None, 
+    num_profiles = 1):
     """
     Generates a spatial profile with polarized clusters of candidates and voters.   
     
@@ -80,6 +84,9 @@ def generate_spatial_profile_polarized(cand_clusters, voter_clusters, num_profil
         A list of tuples of the form (mean, covariance, number of candidates) for each cluster of candidates.
     voter_clusters : list
         A list of tuples of the form (mean, covariance, number of voters) for each cluster of voters.
+    cluster_types : dict, optional
+        A list of the same length as cand_cluster that associates each cluster to the type of candidate. The default is None.
+
     num_profiles : int, optional
         The number of profiles to generate. The default is 1.
 
@@ -89,10 +96,13 @@ def generate_spatial_profile_polarized(cand_clusters, voter_clusters, num_profil
         A spatial profile with polarized clusters of candidates and voters.
     """
 
+    cluster_types = cluster_types if cluster_types is not None else list(range(len(cand_clusters)))
     cand_samples = list()
+    candidate_types = {}
     total_num_cands = 0
-    for cand_cluster in cand_clusters:
+    for cluster_idx, cand_cluster in enumerate(cand_clusters):
         cand_mean, cand_cov, num_cands = cand_cluster
+        candidate_types.update({cidx: cluster_types[cluster_idx] for cidx in range(total_num_cands, total_num_cands+num_cands)})
         total_num_cands += num_cands
         cluster_samples = np.random.multivariate_normal(cand_mean, cand_cov, 
                                                         size=(num_profiles,num_cands))
@@ -117,7 +127,8 @@ def generate_spatial_profile_polarized(cand_clusters, voter_clusters, num_profil
     profs = [SpatialProfile({cidx: cand_samples[pidx][cidx]
                            for cidx in range(total_num_cands)},
                            {vidx: voter_samples[pidx][vidx]
-                            for vidx in range(total_num_voters)}) 
+                            for vidx in range(total_num_voters)}, 
+                            candidate_types=candidate_types) 
                             for pidx in range(num_profiles)]
 
     return profs[0] if num_profiles == 1 else profs
@@ -126,7 +137,8 @@ def generate_spatial_profile_polarized(cand_clusters, voter_clusters, num_profil
 def generate_spatial_profile_polarized_cands_randomly_polarized_voters(
         cand_clusters, 
         num_voters,
-        voter_distributions, 
+        voter_distributions,
+        cluster_types = None, 
         num_profiles = 1):
     """
     Generates a spatial profile with polarized clusters of candidates and voters.   
@@ -139,6 +151,8 @@ def generate_spatial_profile_polarized_cands_randomly_polarized_voters(
         The number of voters.
     voter_distributions : list
         A list of tuples of the form (mean, covariance, prob) for each distribution of voters, where prob is the probability that a voter is assigned to this cluster.
+    cluster_types : dict, optional
+        A list of the same length as cand_cluster that associates each cluster to the type of candidate. The default is None.
     num_profiles : int, optional
         The number of profiles to generate. The default is 1.
 
@@ -148,10 +162,13 @@ def generate_spatial_profile_polarized_cands_randomly_polarized_voters(
         A spatial profile with polarized clusters of candidates and voters.
     """
 
+    cluster_types = cluster_types if cluster_types is not None else list(range(len(cand_clusters)))
     cand_samples = list()
+    candidate_types = {}
     total_num_cands = 0
     for cand_cluster in cand_clusters:
         cand_mean, cand_cov, num_cands = cand_cluster
+        candidate_types.update({cidx: cluster_types[cluster_idx] for cidx in range(total_num_cands, total_num_cands+num_cands)})
         total_num_cands += num_cands
         cluster_samples = np.random.multivariate_normal(cand_mean, cand_cov, size=(num_profiles,num_cands))
         if len(cand_samples) == 0:
@@ -174,7 +191,8 @@ def generate_spatial_profile_polarized_cands_randomly_polarized_voters(
     profs = [SpatialProfile({cidx: cand_samples[pidx][cidx]
                            for cidx in range(total_num_cands)},
                            {vidx: all_potential_voters[voters_clusters_assignment[pidx][vidx]][pidx][vidx]
-                            for vidx in range(num_voters)}) 
+                            for vidx in range(num_voters)}, 
+                            candidate_types=candidate_types) 
                             for pidx in range(num_profiles)]
 
     return profs[0] if num_profiles == 1 else profs

pref_voting/generate_weighted_majority_graphs.py

@@ -11,7 +11,7 @@
 
 import networkx as nx
 from itertools import combinations
-from helper import sublists, compositions, enumerate_compositions, convex_lexicographic_sublists
+from pref_voting.helper import sublists, compositions, enumerate_compositions, convex_lexicographic_sublists
 from pref_voting.weighted_majority_graphs import MarginGraph
 import numpy as np
 from scipy.stats import multivariate_normal

pref_voting/margin_based_methods.py

@@ -1068,8 +1068,6 @@ def ranked_pairs_zt(
 
     return ranked_pairs_tb(profile, curr_cands = curr_cands, tie_breaker = tb_ranking, strength_function = strength_function)
 
-@vm(name="Ranked Pairs Defeat TB",
-    input_types=[ElectionTypes.PROFILE, ElectionTypes.PROFILE_WITH_TIES, ElectionTypes.MARGIN_GRAPH])
 def ranked_pairs_defeat_tb(edata, curr_cands = None, tie_breaker = None, strength_function = None, return_list = False):
     """
     Returns the Ranked Pairs defeat relation produced by the Ranked Pairs algorithm with a fixed tie-breaker.

pref_voting/spatial_profiles.py

@@ -17,9 +17,10 @@ class SpatialProfile(object):
         cand_pos (dict): A dictionary mapping each candidate to their position in the space.    
         voter_pos (dict): A dictionary mapping each voter to their position in the space.   
         num_dims (int): The number of dimensions in the space.  
+        cand_types (dict): A dictionary mapping each candidate to their type (e.g., party affiliation).
 
     """
-    def __init__(self, cand_pos, voter_pos):
+    def __init__(self, cand_pos, voter_pos, candidate_types=None):
 
         cand_dims = [len(v) for v in cand_pos.values()]
         voter_dims = [len(v) for v in voter_pos.values()]
@@ -35,6 +36,7 @@ def __init__(self, cand_pos, voter_pos):
         self.cand_pos = cand_pos
         self.voter_pos = voter_pos
         self.num_dims = len(list(cand_pos.values())[0]) 
+        self.candidate_types = candidate_types or {c:'unknown' for c in self.candidates}
 
     def voter_position(self, v): 
         """
@@ -48,6 +50,21 @@ def candidate_position(self, c):
         """
         return self.cand_pos[c]
 
+    def candidate_type(self, c):
+        """
+        Given a candidate c, returns their type.
+        """
+        return self.candidate_types[c]
+
+    def set_candidate_types(self, cand_types): 
+        """
+        Sets the types of each candidate.
+        """
+
+        assert set(cand_types.keys()) == set(self.candidates), "The candidate types must be specified for all candidates."
+
+        self.candidate_types = candidate_types
+
     def to_utility_profile(self, 
                            utility_function = None,
                            uncertainty_function=None,

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [tool.poetry]
 name = "pref_voting"
-version = "1.13.31"
+version = "1.14.0"
 description = "pref_voting is a Python package that contains tools to reason about elections and margin graphs, and implementations of voting methods."
 authors = ["Eric Pacuit <[email protected]>"]
 license = "MIT"

setup.py

@@ -6,7 +6,7 @@
 
 setuptools.setup(
     name="pref_voting",                   
-    version="1.13.31",     
+    version="1.14.0",     
     author="Eric Pacuit",
     author_email='[email protected]',      
     description="pref_voting is a Python packaging that contains tools to reason about election profiles and margin graphs, and implementations of a variety of preferential voting methods.",

tests/test_all_vms.py

@@ -7,6 +7,7 @@ def test_all_profile_vms():
     prof = Profile([[0, 1], [1, 0]], rcounts=[1, 2])
     for vm in voting_methods: 
         if ElectionTypes.PROFILE in vm.input_types:
+            print(vm.name)
             if vm.name != "Pareto":
                 assert vm(prof) == [1]
             else: