rsw = []
rmm = []
reg = []
rce = []
expectednumberseats = 0
for part in recom_chain:
rsw.append(part["Pink-Purple"].wins("Pink"))
# Caluculate number of ties
ties = (part["Pink-Purple"].wins("Pink") +
part["Pink-Purple"].wins("Purple") - k)
# Calculate number of pink seats won, add to expected value
expectednumberseats += part["Pink-Purple"].wins("Pink") - ties / 2
rmm.append(mean_median(part["Pink-Purple"]))
reg.append(efficiency_gap(part["Pink-Purple"]))
rce.append(len(part["cut_edges"]))
# plt.figure()
# nx.draw(
# graph,
# pos={x: x for x in graph.nodes()},
# node_color=[dict(part.assignment)[x] for x in graph.nodes()],
# node_size=ns,
# node_shape="s",
# cmap="tab20",
# )
# plt.savefig(f"./Figures/recom_{part['step_num']:02d}.png")
# plt.close()
expectednumberseats = expectednumberseats / numIters
print("Based on ", numIters,
for elect in range(num_elections):
f.write(election_names[elect] + "District Percentages" + str(
sorted(initial_partition[election_names[elect]].percents("First")))
)
f.write("\n")
f.write("\n")
f.write(election_names[elect] + "Mean-Median :" +
str(mean_median(initial_partition[election_names[elect]])))
f.write("\n")
f.write("\n")
f.write(election_names[elect] + "Efficiency Gap :" +
str(efficiency_gap(initial_partition[election_names[elect]])))
f.write("\n")
f.write("\n")
f.write(election_names[elect] + "How Many Seats :" +
str(initial_partition[election_names[elect]].wins("First")))
f.write("\n")
f.write("\n")
print("wrote starting values")
pop_vec = []
cut_vec = []
votes = [[], [], [], []]
f.write("\n")
f.write("\n")
f.write(election_names[elect] + "District Percentages" + str(
sorted(initial_partition[election_names[elect]].percents(
"First"))))
f.write("\n")
f.write("\n")
f.write(election_names[elect] + "Mean-Median :" +
str(mean_median(initial_partition[election_names[elect]])))
f.write("\n")
f.write("\n")
f.write(
election_names[elect] + "Efficiency Gap :" +
str(efficiency_gap(initial_partition[election_names[elect]])))
f.write("\n")
f.write("\n")
f.write(
election_names[elect] + "How Many Seats :" +
str(initial_partition[election_names[elect]].wins("First")))
f.write("\n")
f.write("\n")
print("wrote starting values")
def test_signed_partisan_scores_point_the_same_way(mock_election):
eg = efficiency_gap(mock_election)
mm = mean_median(mock_election)
pb = partisan_bias(mock_election)
assert (eg > 0 and mm > 0 and pb > 0) or (eg < 0 and mm < 0 and pb < 0)
def test_efficiency_gap(mock_election):
result = efficiency_gap(mock_election)
assert result == 0.3
votesSenW = []
mmP12 = []
mmP16 = []
mmSenW = []
egP12 = []
egP16 = []
egSenW = []
hmsP12 = []
hmsP16 = []
hmsSenW = []
splits = []
t = 0
for part in chain:
splits.append(num_splits(part))
pop_vec.append(sorted(list(part["population"].values())))
cut_vec.append(len(part["cut_edges"]))
votesP12.append(sorted(part["PRES12"].percents("Democratic")))
votesP16.append(sorted(part["PRES16"].percents("Democratic")))
votesSenW.append(sorted(part["SENW101216"].percents("Democratic")))
mmP12.append(mean_median(part["PRES12"]))
mmP16.append(mean_median(part["PRES16"]))
mmSenW.append(mean_median(part["SENW101216"]))
egP12.append(efficiency_gap(part["PRES12"]))
egP16.append(efficiency_gap(part["PRES16"]))
egSenW.append(efficiency_gap(part["SENW101216"]))
hmsP12.append(part["PRES12"].percents("Democratic"))
hmsP16.append(part["PRES16"].percents("Democratic"))
hmsSenW.append(part["SENW101216"].percents("Democratic"))
def chain(iterations):
idef = random.randint(1, 10000)
graph = Graph.from_json("./PA_VTD.json")
election = Election("SEN12", {"Dem": "USS12D", "Rep": "USS12R"})
initial_partition = GeographicPartition(graph,
assignment="2011_PLA_1",
updaters={
"cut_edges":
cut_edges,
"population":
Tally("TOT_POP",
alias="population"),
"SEN12":
election
})
ideal_population = sum(
initial_partition["population"].values()) / len(initial_partition)
# We use functools.partial to bind the extra parameters (pop_col, pop_target, epsilon, node_repeats)
# of the recom proposal.
proposal = partial(recom,
pop_col="TOT_POP",
pop_target=ideal_population,
epsilon=0.02,
node_repeats=2)
chain = MarkovChain(proposal=proposal,
constraints=[],
accept=contiguous,
initial_state=initial_partition,
total_steps=iterations + 100)
count = 0
metrics = []
boundary_nodes = []
boundary_weighted = []
for partition in chain.with_progress_bar():
mm = mean_median(partition["SEN12"])
p = pp(partition)
bias = partisan_bias(partition["SEN12"])
gini = partisan_gini(partition["SEN12"])
gap = efficiency_gap(partition["SEN12"])
cut = len(partition["cut_edges"])
if count >= 100:
metrics.append((mm, p, bias, gini, gap, cut))
nodes = [0] * 8921
bnodes = [0] * 8921
for edge in partition["cut_edges"]:
nodes[edge[0]] = 1
nodes[edge[1]] = 1
bnodes[edge[0]] += 1
bnodes[edge[1]] += 1
boundary_nodes.append(nodes)
boundary_weighted.append(bnodes)
if count % 100 == 0:
print(idef, count, mm, p, bias, gini, gap, cut)
count += 1
return metrics, boundary_nodes, boundary_weighted
def eg_metric(part):
return efficiency_gap(part["whatever-goes-here"])