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uniform_matching_verification.py
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uniform_matching_verification.py
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import matplotlib.pyplot as plt
import pandas as pd
import geopandas as gpd
import numpy as np
import seaborn as sns
import json
import networkx as nx
import time
from gerrychain import Graph, Partition, Election
from gerrychain.updaters import Tally, cut_edges
from gerrychain import MarkovChain
from gerrychain.constraints import single_flip_contiguous
from gerrychain.proposals import propose_random_flip
from gerrychain.accept import always_accept
from gerrychain import (GeographicPartition, Partition, Graph, MarkovChain,
proposals, updaters, constraints, accept, Election)
from gerrychain.constraints.validity import within_percent_of_ideal_population
from gerrychain.proposals import recom
from functools import partial
from FKT import FKT
from enum_matchings import enumerate_matchings
from uniform_matching import uniform_matching
whole_start = time.time()
#Initialization
df = gpd.read_file("./data/AK_precincts_ns/AK_precincts_ns/AK_precincts_ns.shp")
df["nAMIN"] = df["TOTPOP"]-df["AMIN"]
elections = [
Election("GOV18x", {"Democratic": "GOV18D_x", "Republican": "GOV18R_x"}),
Election("USH18x", {"Democratic": "USH18D_x", "Republican": "USH18R_x"}),
Election("GOV18ns", {"Democratic": "GOV18D_NS", "Republican": "GOV18R_NS"}),
Election("USH18ns", {"Democratic": "USH18D_NS", "Republican": "USH18R_NS"}),
Election("Native_percent", {"Native":"AMIN", "nonNative":"nAMIN"})
]
my_updaters = {"population": updaters.Tally("POPULATION", alias="population")}
election_updaters = {election.name: election for election in elections}
my_updaters.update(election_updaters)
GOV18x= sorted((0.4464612822647793, 0.38781079442086114, 0.44586334861383975,
0.21595373199476212, 0.2690041809198024, 0.2736756168359942,
0.348307220335269, 0.5071350164654226, 0.581616925879221,
0.6333762886597938, 0.5078282585726636, 0.4601878317680686,
0.4554883318928263, 0.4819267050672009, 0.28439815742038854,
0.45294296423867897, 0.5767558164818439, 0.47288220551378446,
0.7029333333333333, 0.6748514851485149))
GOV18ns= sorted((0.4676226754599207, 0.42829760031663167, 0.4618983956666181,
0.22362457290429352, 0.2824773413825426, 0.2879301987552867,
0.3599484378307593, 0.5278798697754102, 0.6099356805771895,
0.66089826955172, 0.5240823394928785, 0.47175060636977745,
0.47262266878310366, 0.4933236493835675, 0.29545454545662897,
0.49077875527620873, 0.6193197895384107, 0.48997864866716534,
0.6954798628970641, 0.6770289774619113))
USH18x= sorted((0.4580262736302467, 0.4109521943573668, 0.4420473773265651,
0.2635924932975871, 0.30460251046025105, 0.3053428317008014,
0.376907480525024, 0.5216595485051861, 0.5793047256925584,
0.6301691729323309, 0.5184593705965241, 0.4637579872204473,
0.4594340509542164, 0.4799281984334204, 0.32327166504381694,
0.4478700641898463, 0.6076680672268907, 0.4985154678670625,
0.5402944111776448, 0.48850713767239295))
USH18ns= sorted((0.4800036828504361, 0.4491738433709142, 0.461014997780927,
0.2711623081172799, 0.3193550626858298, 0.3179648181763695,
0.3841204106466343, 0.5379747100186753, 0.6065600063588037,
0.6535109734718672, 0.5346452384636674, 0.47764200355277736,
0.47761058520967536, 0.4967409579393119, 0.33186989293196734,
0.4811166025445444, 0.6403531762874495, 0.5091097061551921,
0.5376860497049738, 0.497248698656098))
Native= sorted((0.0962102413715317, 0.04433289150850278, 0.14142632701155874,
0.05763656319477669, 0.05454647697238936, 0.052279085539887526,
0.03625760649087221, 0.0856587180788094, 0.10959632719332624,
0.13024569330697544, 0.07415882701923891, 0.07839824279800625,
0.07406575511693854, 0.05113379963898917, 0.07531833439675979,
0.10261318242343542, 0.1146361499022746, 0.20748480349589624,
0.6321083614333886, 0.7516285714285714))
print("Loaded Data")
#Construct Dual Graphs
#Older Alaska (by hand)
Old_AK = np.matrix([
[0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,1,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,1,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[1,1,1,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,1,1,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,1,1],
[0,0,0,0,0,0,0,1,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,1,1,1,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,0,0,0,0,0,0,0],
[0,0,0,0,0,1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0],
[0,0,0,0,0,0,1,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,1,1,1,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,1,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,1,0,0,1,0,1,0,0,0,0,0,0,0,0,0,1,1,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,1,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0],
[0,0,0,0,0,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,1,0,0,0,1,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,1,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0],
[0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,1,0],
[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0],
[0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,1],
[0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0]]
)
#Remove Mistaken Adjacency #Plus add the one that was probably a type miss?
Old_AK[16,25] = 0
Old_AK[25,16] = 0
Old_AK[22,24] = 1
Old_AK[24,22] = 1
#Tightest Alaska
print("Building Tight Graph")
G_tight = Graph.from_file("./data/AK_precincts_ns/AK_precincts_ns/AK_precincts_ns.shp")
G_tight.join(df,columns= ["nAMIN"])
idict={}
for index, row in df.iterrows():
idict[int(row["ID"])] = index
#Connect Islands
to_add = [(426,444), (437,438),(437,442),(411,420),
(411,414), (411,358),(411,407),(399,400),(399,349),(381,384),(240,210)]
for i in range(len(to_add)):
G_tight.add_edge(idict[to_add[i][0]],idict[to_add[i][1]])
G_tight[idict[to_add[i][0]]][idict[to_add[i][1]]]["shared_perim"]=1
#Separate Anchorage
to_remove = [(210,195),(210,203),(210,202),(210,193),(210,235),(210,234),(169,78),(169,77),(169,70),
(169,68),(169,32),(169,23),(169,179),(234,78),(235,78),(235,89),(235,106),(235,102),(102,190),
(190,105),(190,145),(145,233),(233,133),(234,169),(234,151),(77,74),(77,70)]
for i in range(len(to_remove)):
G_tight.remove_edge(idict[to_remove[i][0]],idict[to_remove[i][1]])
#Build restricted_partition
initial_tight = GeographicPartition(G_tight, assignment="HDIST", updaters=my_updaters)
A_tight = np.zeros([40,40])
for edge in initial_tight["cut_edges"]:
A_tight[initial_tight.assignment[edge[0]]-1,initial_tight.assignment[edge[1]]-1] = 1
A_tight[initial_tight.assignment[edge[1]]-1,initial_tight.assignment[edge[0]]-1] = 1
smallest = nx.Graph(A_tight)
np.savetxt("./Outputs/tight_dual.csv",nx.adjacency_matrix(smallest).todense() , fmt="%1i")
print("Built Tight Graph")
print(smallest.number_of_edges(),"Edges")
#Restricted Alaska
print("Building Restrictive Graph")
G_restricted = Graph.from_file("./data/AK_precincts_ns/AK_precincts_ns/AK_precincts_ns.shp")
G_restricted.join(df,columns= ["nAMIN"])
idict={}
for index, row in df.iterrows():
idict[int(row["ID"])] = index
#Connect Islands
to_add = [(426,444), (437,438),(437,442),(411,420),
(411,414), (411,358),(411,407),(399,400),(399,349),(381,384),(240,210)]
for i in range(len(to_add)):
G_restricted.add_edge(idict[to_add[i][0]],idict[to_add[i][1]])
G_restricted[idict[to_add[i][0]]][idict[to_add[i][1]]]["shared_perim"]=1
#Separate Anchorage
to_remove = [(210,195),(210,203),(210,202),(210,193),(210,235),(210,234),(169,78),(169,77),(169,70),
(169,68),(169,32),(169,23),(169,179),(234,78),(235,78),(235,89),(235,106),(235,102),(102,190),
(190,105),(190,145),(145,233),(233,133)]
for i in range(len(to_remove)):
G_restricted.remove_edge(idict[to_remove[i][0]],idict[to_remove[i][1]])
#Build restricted_partition
initial_restricted = GeographicPartition(G_restricted, assignment="HDIST", updaters=my_updaters)
A_restricted = np.zeros([40,40])
for edge in initial_restricted["cut_edges"]:
A_restricted[initial_restricted.assignment[edge[0]]-1,initial_restricted.assignment[edge[1]]-1] = 1
A_restricted[initial_restricted.assignment[edge[1]]-1,initial_restricted.assignment[edge[0]]-1] = 1
small = nx.Graph(A_restricted)
np.savetxt("./Outputs/small_dual.csv",nx.adjacency_matrix(small).todense() , fmt="%1i")
print("Built Restrictive Graph")
print(small.number_of_edges(),"Edges")
#Permissive Alaska
print("Building Permissive Graph")
G_loose = Graph.from_file("./data/AK_precincts_ns/AK_precincts_ns/AK_precincts_ns.shp")
G_loose.join(df,columns= ["nAMIN"])
idict={}
for index, row in df.iterrows():
idict[int(row["ID"])] = index
#Attach Islands
to_add2 = [(426,444), (437,438), (437,436), (437,423), (437,428), (437,432), (437,374), (437,441),
(437,443), (437,439), (437,427), (437,430), (437,445), (437,435), (437,434), (437,442), (411,420),
(411,414), (411,358),(411,407), (399,400),(399,349),(381,384),(240,210), (400,411), (399,348),
(399,381),(399,384),(399,386),(399,397)]
for i in range(len(to_add2)):
G_loose.add_edge(idict[to_add2[i][0]],idict[to_add2[i][1]])
G_loose[idict[to_add2[i][0]]][idict[to_add2[i][1]]]["shared_perim"]=1
initial_large = GeographicPartition(G_loose, assignment="HDIST", updaters=my_updaters)
A_loose = np.zeros([40,40])
for edge in initial_large["cut_edges"]:
A_loose[initial_large.assignment[edge[0]]-1,initial_large.assignment[edge[1]]-1] = 1
A_loose[initial_large.assignment[edge[1]]-1,initial_large.assignment[edge[0]]-1] = 1
large = nx.Graph(A_loose)
np.savetxt("./Outputs/large_dual.csv", nx.adjacency_matrix(large).todense() , fmt="%1i")
print("Built Permissive Graph")
print(large.number_of_edges(),"Edges")
#Check matrix compatibility
print("Checking Dual Graph Compatibility")
if (A_restricted[A_tight==1]==1).sum() == A_tight.sum():
print("Tight nests in Restrictve!")
if (A_loose[A_restricted==1]==1).sum() == A_restricted.sum():
print("Restrictve nests in Permissive!")
#for i in range(40):
# for j in range(40):
# if Old_AK[i,j]==1:
# if A_restricted[i,j]==0:
# print(i,j)
#Compute matching numbers
print("Using FKT to enumerate matchings")
start = time.time()
print("Tight Matchings: ", round(FKT(A_tight)), "in", time.time()-start,"seconds")
start = time.time()
print("Restricted Matchings: ", round(FKT(A_restricted)), "in", time.time()-start,"seconds")
start = time.time()
print("Permissive Matchings: ", round(FKT(A_loose)), "in", time.time()-start,"seconds")
types = ["Tight_Samples","Restricted_Samples","Loose_Samples"]
#ms = [sample_matchings]
As = [A_tight,A_restricted,A_loose]
gs = [G_tight, G_restricted, G_loose]
for z in range(1,3):
matchings = []
for i in range(100):
matchings.append(uniform_matching(nx.Graph(As[z])))
#matchings = ms[z]
G = gs[z]
print("Computing Stats on", types[z])
percents1 = []
wins1 = []
percents2 = []
wins2 = []
percents3 = []
wins3 = []
percents4 = []
wins4 = []
percents5= []
wins5 = []
for i in range(len(matchings)):
temp= {}
for j in range(20):
for k in [0,1]:
temp[matchings[i][j][k]+1]=j#[matchings[str(i)][j][k]-1]=j
df["MERGEDIST"] = df["HDIST"].map(temp)
G.join(df,columns= ["MERGEDIST"])
c_part = GeographicPartition(G, assignment="MERGEDIST", updaters= my_updaters)
wins1.append(c_part["GOV18x"].wins("Democratic"))
percents1.append(sorted(c_part["GOV18x"].percents("Democratic")))
wins2.append(c_part["GOV18ns"].wins("Democratic"))
percents2.append(sorted(c_part["GOV18ns"].percents("Democratic")))
wins3.append(c_part["USH18x"].wins("Democratic"))
percents3.append(sorted(c_part["USH18x"].percents("Democratic")))
wins4.append(c_part["USH18ns"].wins("Democratic"))
percents4.append(sorted(c_part["USH18ns"].percents("Democratic")))
wins5.append(c_part["Native_percent"].wins("Native"))
percents5.append(sorted(c_part["Native_percent"].percents("Native")))
with open("./Outputs/Matchings_"+types[z]+"_stats.json",'w') as wf:
json.dump({0:wins1,1:percents1,2:wins2,3:percents2,4:wins3,5:percents3,
6:wins4,7:percents4,8:wins5,9:percents5}, wf)
print("wrote ",types[z]," stats to file")
print("Plotting ", types[z], " figures")
partisan_w = [wins1,wins2,wins3,wins4]
partisan_p = [percents1,percents2,percents3,percents4]
p_types=["GOV18N", "GOV18A", "USH18N", "USH18A"]
p_vecs=[GOV18x, GOV18ns, USH18x, USH18ns]
c='black'
for y in range(4):
plt.figure()
plt.boxplot(np.array(partisan_p[y]),whis=[0,100],showfliers=True, patch_artist=True,
boxprops=dict(facecolor="None", color=c),
capprops=dict(color=c),
whiskerprops=dict(color=c),
flierprops=dict(color=c, markeredgecolor=c),
medianprops=dict(color=c),
)
plt.plot(range(1,21),p_vecs[y],'o',color='red',label='Current Plan')
plt.plot([.5,21],[.5,.5],color='green',label="50%")
plt.xlabel("Sorted Districts")
plt.ylabel("Dem %")
plt.xticks([1,10,20],['1','10','20'])
plt.legend()
fig = plt.gcf()
fig.set_size_inches((20,10), forward=False)
fig.savefig("./Outputs/plots/Match_Box_"+ types[z] + p_types[y] + ".png")
plt.close()
plt.figure()
plt.boxplot(np.array(percents5),whis=[0,100],showfliers=True, patch_artist=True,
boxprops=dict(facecolor="None", color=c),
capprops=dict(color=c),
whiskerprops=dict(color=c),
flierprops=dict(color=c, markeredgecolor=c),
medianprops=dict(color=c),
)
plt.plot(range(1,21),Native,'o',color='red',label='Current Plan')
plt.plot([.5,21],[.5,.5],color='green',label="50%")
plt.xlabel("Sorted Districts")
plt.ylabel("Native %")
plt.xticks([1,10,20],['1','10','20'])
plt.legend()
fig = plt.gcf()
fig.set_size_inches((20,10), forward=False)
fig.savefig("./Outputs/plots/Match_Box_"+ types[z] +"Native.png")
plt.close()
print("Finished ",types[z]," Box plots")
for y in range(4):
plt.figure()
sns.distplot(partisan_w[y],kde=False,color='slateblue',bins=[x for x in range(4,13)],
hist_kws={"rwidth":1,"align":"left"})
plt.axvline(x=sum([val>.5 for val in p_vecs[y]]),color='r',label="Current Plan",linewidth=5)
plt.axvline(x=np.mean(partisan_w[y]),color='g',label="Matchings Mean",linewidth=5)
plt.legend()
print(p_types[y],"wins: ", np.mean(partisan_w[y]))
plt.savefig("./Outputs/plots/Match_Hist_"+ types[z] + p_types[y] + ".png")
plt.close()
plt.figure()
sns.distplot(wins5,kde=False,color='slateblue',bins=[x for x in range(5)],
hist_kws={"rwidth":1,"align":"left"})
plt.axvline(x=2,color='r',label="Current Plan",linewidth=5)
plt.axvline(x=np.mean(wins5),color='g',label="Matchigs Mean",linewidth=5)
plt.legend()
print("Native wins: ", np.mean(wins5))
plt.savefig("./Outputs/plots/Match_Hist_"+ types[z] + "Native.png")
plt.close()
with open("./Outputs/values/Matchings_" + types[z] + ".txt", "w") as f:
f.write("Matching Values for Graph: "+types[z]+" \n\n")
for y in range(4):
f.write("Enacted Wins : "+ p_types[y] + ": "+ str(sum([val>.5 for val in p_vecs[y]])))
f.write("\n")
f.write("Matching Average Wins : "+ p_types[y] + ": "+ str(np.mean(partisan_w[y])))
f.write("\n")
f.write("\n")
print("Finished ",types[z]," Seats plots")
cis=[]
ces=[]
for y in range(4):
votes = partisan_p[y]
comp = []
for i in range(len(votes)):
temp = 0
for j in votes[i]:
if .4 < j < .6:
temp+=1
comp.append(temp)
c_init = 0
for x in p_vecs[y]:
if .4 < x <.6:
c_init += 1
cis.append(c_init)
sns.distplot(comp,kde=False,color='slateblue',bins=[x for x in range(6,20)],
hist_kws={"rwidth":1,"align":"left"})
plt.axvline(x=c_init,color='r',label="Current Plan",linewidth=5)
plt.axvline(x=np.mean(comp),color='g',label="Matchings Mean",linewidth=5)
print(p_types[y],"competitive: ",np.mean(comp))
plt.legend()
plt.savefig("./Outputs/plots/Match_Comp_"+ types[z] + p_types[y] + ".png")
plt.close()
ces.append(np.mean(comp))
with open("./Outputs/values/Matchings_Comp" + types[z] + ".txt", "w") as f:
f.write("Matching Values for Graph: "+types[z]+" \n\n")
for y in range(4):
f.write("Enacted Comp : "+ p_types[y] + ": "+ str(cis[y]))
f.write("\n")
f.write("Matching Average Comp : "+ p_types[y] + ": "+ str(ces[y]))
f.write("\n")
f.write("\n")
votes = percents5
comp = []
for i in range(len(votes)):
temp = 0
for j in votes[i]:
if .4 < j < .6:
temp+=1
comp.append(temp)
sns.distplot(comp,kde=False,color='slateblue',bins=[x for x in range(6)],
hist_kws={"rwidth":1,"align":"left"})
plt.axvline(x=0,color='r',label="Current Plan",linewidth=5)
plt.axvline(x=np.mean(comp),color='g',label="Matchings Mean",linewidth=5)
print("Native competitive: ", np.mean(comp))
plt.legend()
plt.savefig("./Outputs/plots/Match_Comp_"+ types[z] + "Native.png")
plt.close()
print("Finished ",types[z]," Competitive plots")
avdsdv
#List all pairings
print("Generating All Matchings")
vs = list(range(1, 41))
start = time.time()
matchings_tight = enumerate_matchings(A_tight, vs)
print("\n Tight Matchings: ", len(matchings_tight), "in", time.time()-start,"seconds")
#print(matchings)
with open("./Outputs/tight_matchings.json",'w') as wf:
json.dump({x:matchings_tight[x] for x in range(len(matchings_tight))}, wf)
vs = list(range(1, 41))
start = time.time()
matchings_small = enumerate_matchings(A_restricted, vs)
print("\n Restricted Matchings: ", len(matchings_small), "in", time.time()-start,"seconds")
#print(matchings)
with open("./Outputs/small_matchings.json",'w') as wf:
json.dump({x:matchings_small[x] for x in range(len(matchings_small))}, wf)
vs = list(range(1, 41))
start = time.time()
matchings_large = enumerate_matchings(A_loose, vs)
print("\n Permissive Matchings: ", len(matchings_large), "in", time.time()-start,"seconds")
#print(matchings)
with open("./Outputs/large_matchings.json",'w') as wf:
json.dump({x:matchings_large[x] for x in range(len(matchings_large))}, wf)
#Verify proper labeling
print("Checking Dual Graph Edges Match Matchings")
check = 0
tight_dual_edges = set([(initial_tight.assignment[edge[0]],initial_tight.assignment[edge[1]]) for edge in initial_tight["cut_edges"]])
for x in matchings_tight[0]:
if (x[0],x[1]) in tight_dual_edges or (x[1],x[0]) in tight_dual_edges:
check += 1
if check == 20:
print("All tight matchings are allowed!")
check = 0
restricted_dual_edges = set([(initial_restricted.assignment[edge[0]],initial_restricted.assignment[edge[1]]) for edge in initial_restricted["cut_edges"]])
for x in matchings_small[0]:
if (x[0],x[1]) in restricted_dual_edges or (x[1],x[0]) in restricted_dual_edges:
check += 1
if check == 20:
print("All restricted matchings are allowed!")
check = 0
loose_dual_edges = set([(initial_large.assignment[edge[0]],initial_large.assignment[edge[1]]) for edge in initial_large["cut_edges"]])
for x in matchings_large[0]:
if (x[0],x[1]) in loose_dual_edges or (x[1],x[0]) in loose_dual_edges:
check += 1
if check == 20:
print("All permissive matchings are allowed!")
#Compute stats on matchings
types = ["Tight","Restricted","Permissive"]
ms = [matchings_tight, matchings_small, matchings_large]
gs = [G_tight, G_restricted, G_loose]
for z in range(3):
matchings = ms[z]
G = gs[z]
print("Computing Stats on", types[z])
percents1 = []
wins1 = []
percents2 = []
wins2 = []
percents3 = []
wins3 = []
percents4 = []
wins4 = []
percents5= []
wins5 =[]
for i in range(len(matchings)):
temp= {x: 0 for x in range(40)}
for j in range(20):
for k in [0,1]:
temp[matchings[i][j][k]]=j#[matchings[str(i)][j][k]-1]=j
df["MERGEDIST"] = df["HDIST"].map(temp)
G.join(df,columns= ["MERGEDIST"])
c_part = GeographicPartition(G, assignment="MERGEDIST", updaters= my_updaters)
wins1.append(c_part["GOV18x"].wins("Democratic"))
percents1.append(sorted(c_part["GOV18x"].percents("Democratic")))
wins2.append(c_part["GOV18ns"].wins("Democratic"))
percents2.append(sorted(c_part["GOV18ns"].percents("Democratic")))
wins3.append(c_part["USH18x"].wins("Democratic"))
percents3.append(sorted(c_part["USH18x"].percents("Democratic")))
wins4.append(c_part["USH18ns"].wins("Democratic"))
percents4.append(sorted(c_part["USH18ns"].percents("Democratic")))
wins5.append(c_part["Native_percent"].wins("Native"))
percents5.append(sorted(c_part["Native_percent"].percents("Native")))
with open("./Outputs/Matchings_"+types[z]+"_stats.json",'w') as wf:
json.dump({0:wins1,1:percents1,2:wins2,3:percents2,4:wins3,5:percents3,
6:wins4,7:percents4,8:wins5,9:percents5}, wf)
print("wrote ",types[z]," stats to file")
print("Plotting ", types[z], " figures")
partisan_w = [wins1,wins2,wins3,wins4]
partisan_p = [percents1,percents2,percents3,percents4]
p_types=["GOV18N", "GOV18A", "USH18N", "USH18A"]
p_vecs=[GOV18x, GOV18ns, USH18x, USH18ns]
c='black'
for y in range(4):
plt.figure()
plt.boxplot(np.array(partisan_p[y]),whis=[0,100],showfliers=True, patch_artist=True,
boxprops=dict(facecolor="None", color=c),
capprops=dict(color=c),
whiskerprops=dict(color=c),
flierprops=dict(color=c, markeredgecolor=c),
medianprops=dict(color=c),
)
plt.plot(range(1,21),p_vecs[y],'o',color='red',label='Current Plan')
plt.plot([.5,21],[.5,.5],color='green',label="50%")
plt.xlabel("Sorted Districts")
plt.ylabel("Dem %")
plt.xticks([1,10,20],['1','10','20'])
plt.legend()
fig = plt.gcf()
fig.set_size_inches((20,10), forward=False)
fig.savefig("./Outputs/plots/Match_Box_"+ types[z] + p_types[y] + ".png")
plt.close()
plt.figure()
plt.boxplot(np.array(percents5),whis=[0,100],showfliers=True, patch_artist=True,
boxprops=dict(facecolor="None", color=c),
capprops=dict(color=c),
whiskerprops=dict(color=c),
flierprops=dict(color=c, markeredgecolor=c),
medianprops=dict(color=c),
)
plt.plot(range(1,21),Native,'o',color='red',label='Current Plan')
plt.plot([.5,21],[.5,.5],color='green',label="50%")
plt.xlabel("Sorted Districts")
plt.ylabel("Native %")
plt.xticks([1,10,20],['1','10','20'])
plt.legend()
fig = plt.gcf()
fig.set_size_inches((20,10), forward=False)
fig.savefig("./Outputs/plots/Match_Box_"+ types[z] +"Native.png")
plt.close()
print("Finished ",types[z]," Box plots")
for y in range(4):
plt.figure()
sns.distplot(partisan_w[y],kde=False,color='slateblue',bins=[x for x in range(4,13)],
hist_kws={"rwidth":1,"align":"left"})
plt.axvline(x=sum([val>.5 for val in p_vecs[y]]),color='r',label="Current Plan",linewidth=5)
plt.axvline(x=np.mean(partisan_w[y]),color='g',label="Matchings Mean",linewidth=5)
plt.legend()
print(p_types[y],"wins: ", np.mean(partisan_w[y]))
plt.savefig("./Outputs/plots/Match_Hist_"+ types[z] + p_types[y] + ".png")
plt.close()
plt.figure()
sns.distplot(wins5,kde=False,color='slateblue',bins=[x for x in range(5)],
hist_kws={"rwidth":1,"align":"left"})
plt.axvline(x=2,color='r',label="Current Plan",linewidth=5)
plt.axvline(x=np.mean(wins5),color='g',label="Matchigs Mean",linewidth=5)
plt.legend()
print("Native wins: ", np.mean(wins5))
plt.savefig("./Outputs/plots/Match_Hist_"+ types[z] + "Native.png")
plt.close()
with open("./Outputs/values/Matchings_" + types[z] + ".txt", "w") as f:
f.write("Matching Values for Graph: "+types[z]+" \n\n")
for y in range(4):
f.write("Enacted Wins : "+ p_types[y] + ": "+ str(sum([val>.5 for val in p_vecs[y]])))
f.write("\n")
f.write("Matching Average Wins : "+ p_types[y] + ": "+ str(np.mean(partisan_w[y])))
f.write("\n")
f.write("\n")
print("Finished ",types[z]," Seats plots")
cis=[]
ces=[]
for y in range(4):
votes = partisan_p[y]
comp = []
for i in range(len(votes)):
temp = 0
for j in votes[i]:
if .4 < j < .6:
temp+=1
comp.append(temp)
c_init = 0
for x in p_vecs[y]:
if .4 < x <.6:
c_init += 1
cis.append(c_init)
sns.distplot(comp,kde=False,color='slateblue',bins=[x for x in range(6,20)],
hist_kws={"rwidth":1,"align":"left"})
plt.axvline(x=c_init,color='r',label="Current Plan",linewidth=5)
plt.axvline(x=np.mean(comp),color='g',label="Matchings Mean",linewidth=5)
print(p_types[y],"competitive: ",np.mean(comp))
plt.legend()
plt.savefig("./Outputs/plots/Match_Comp_"+ types[z] + p_types[y] + ".png")
plt.close()
ces.append(np.mean(comp))
with open("./Outputs/values/Matchings_Comp" + types[z] + ".txt", "w") as f:
f.write("Matching Values for Graph: "+types[z]+" \n\n")
for y in range(4):
f.write("Enacted Comp : "+ p_types[y] + ": "+ str(cis[y]))
f.write("\n")
f.write("Matching Average Comp : "+ p_types[y] + ": "+ str(ces[y]))
f.write("\n")
f.write("\n")
votes = percents5
comp = []
for i in range(len(votes)):
temp = 0
for j in votes[i]:
if .4 < j < .6:
temp+=1
comp.append(temp)
sns.distplot(comp,kde=False,color='slateblue',bins=[x for x in range(6)],
hist_kws={"rwidth":1,"align":"left"})
plt.axvline(x=0,color='r',label="Current Plan",linewidth=5)
plt.axvline(x=np.mean(comp),color='g',label="Matchings Mean",linewidth=5)
print("Native competitive: ", np.mean(comp))
plt.legend()
plt.savefig("./Outputs/plots/Match_Comp_"+ types[z] + "Native.png")
plt.close()
print("Finished ",types[z]," Competitive plots")
print("All matchings reproduced in :", time.time()- whole_start, " seconds")
print("Starting Ensemble Analysis")
ensemble_time = time.time()
ps = [initial_tight, initial_restricted, initial_large]
GOV18ns= sorted((0.5187406297776442, 0.3916434540240218, 0.23378661089760477,
0.561916167590201, 0.5277943813115976, 0.3967808623758262,
0.24347460005648527, 0.2040194040100815, 0.29961685822777134,
0.2658227848088675, 0.30213024956718243, 0.2738569188011496,
0.3331949346122295, 0.3753434474711785, 0.5018867924316115,
0.5426127015341067, 0.5913152254553772, 0.6266881029630763,
0.6404409922858867, 0.6744921745562409, 0.5829798514395329,
0.46457747422269396, 0.49254507629009764, 0.45721212122003807,
0.5081005584437817, 0.44071841251982957, 0.5078786300489014,
0.4823874755528126, 0.3030897498782328, 0.28720868644817754,
0.45392418577347565, 0.548494983265805, 0.6961661695141408,
0.5386310904517134, 0.5566274613453184, 0.4174338319909295,
0.6555631089965965, 0.7312614259593524, 0.7193151212004562,
0.61932265686532))
GOV18x=sorted((0.4974431818181818, 0.37424547283702214, 0.20789779326364694,
0.5263911254249418, 0.50988230068843, 0.38398798025327324,
0.23922875505831945, 0.19625226677412855, 0.28292410714285715,
0.2544677544677545, 0.28685985055585933, 0.2606104388658118,
0.32402732402732404, 0.3625, 0.47884788478847884, 0.5222623345367028,
0.5623608017817372, 0.6011283497884344, 0.6125967628430683,
0.6509209744503862, 0.5640572886011379, 0.45389537071885583,
0.4786472475931869, 0.447171453437772, 0.48655110579796773,
0.42658509454949944, 0.49777777777777776, 0.46891624867001064,
0.28942840757025423, 0.27886435331230286, 0.40277539832105536,
0.525532969757065, 0.6567947910102919, 0.4908722109533469,
0.5396059509449136, 0.4065186962607478, 0.6617647058823529,
0.7358445297504799, 0.7188718183902775, 0.6151213441194773))
USH18x=sorted((0.5058695058695059, 0.3900736719658782, 0.2543404735062007,
0.5312662393902651, 0.5138918802498385, 0.37274049449407853,
0.2843413033286451, 0.2460960664162878, 0.3040479215828644,
0.3051849027830728, 0.32579668862382055, 0.2848743987172635,
0.35922610453364134, 0.38727149627623564, 0.5108885017421603,
0.5274647887323943, 0.5599028602266595, 0.5989611809732094,
0.6076079506511309, 0.6491633006347375, 0.5707167497125335,
0.46821448313985625, 0.4998794309139137, 0.43823479298006474,
0.49396417445482865, 0.4271950554444646, 0.49546608632571637,
0.467220409374073, 0.33054471091280907, 0.3152729503169086,
0.4366804979253112, 0.46370683579985905, 0.6810362274843149,
0.5284996724175585, 0.5495891458054654, 0.44719662058371734,
0.4837432852700028, 0.584951998213887, 0.5047358450852452,
0.4665718349928876))
USH18ns=sorted((0.526629425384675, 0.41081640244677586, 0.2774311777926235,
0.5665135137823519, 0.5324396994583951, 0.39077610338199437,
0.29063095741083556, 0.2520081528281857, 0.3242478870458404,
0.3145534181033724, 0.3380180359170024, 0.2979472351935256,
0.36280653131210194, 0.3964223985086792, 0.5198083711638748,
0.5483611707902181, 0.5889840870354133, 0.6223695336477948,
0.632669752041204, 0.6672778804198621, 0.5931440818852669,
0.4755756282959946, 0.5105624482537662, 0.4545425262952549,
0.5164939040837906, 0.4425673222996577, 0.5097079657012924,
0.4870557891023724, 0.33723477574740685, 0.3260691986801825,
0.47925932440512, 0.4839707790207739, 0.7077419247658372,
0.5693768081345366, 0.5608357157934066, 0.45216772423949964,
0.48482656936912155, 0.5846342120988194, 0.5154696428000252,
0.47246020775337016))
Native=sorted((0.14453345368385423, 0.04791972037433758, 0.04153228088043909,
0.04711570898459463, 0.09071032124236138, 0.19222777559386758,
0.0498220640569395, 0.06539540100953449, 0.05580923389142567,
0.0532899534414091, 0.0553172273650937, 0.04923320694923886,
0.03773051250141419, 0.034796273431361546, 0.07209144409234948,
0.09912389082331799, 0.10816429735348654, 0.1110181311018131,
0.1408546235586706, 0.11965233096286262, 0.07601179004648,
0.07231765699802872, 0.09871413330338592, 0.05795955259292735,
0.08296139254630663, 0.0650539761487594, 0.07099219368706867,
0.03138710766115423, 0.08620880949739265, 0.06442483768936241,
0.04401535807690168, 0.16086740056425292, 0.13535582648142896,
0.09395517319447588, 0.20246844319775595, 0.21248741188318226,
0.4281292984869326, 0.8349481363273681, 0.8371329976806019,
0.6643768400392541))
for z in range(3):
initial_partition = ps[z]
ideal_population = sum(initial_partition["population"].values()) / len(initial_partition)
#print(ideal_population)
proposal = partial(recom,
pop_col="POPULATION",
pop_target=ideal_population,
epsilon=0.05,
node_repeats=2
)
compactness_bound = constraints.UpperBound(