def chain(iterations):
idef = random.randint(1, 10000)
graph = Graph.from_json("../data/PA_init/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=85*iterations + 17000
)
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 >= 17000:
if count % 85 == 0:
metrics.append((mm, p, bias, gini, gap, cut, partition["SEN12"].wins("Rep")))
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)
assign = {i: partition.assignment[i] for i in partition.assignment}
shape["CD"] = shape.index.map(assign)
this_map = shape.dissolve(by='CD')
this_map.plot(color='white', edgecolor='black')
plt.axis('off')
fig = plt.gcf()
fig.set_size_inches((15,9), forward=False)
fig.savefig("../images/PA_neutral/" + str(idef) + str(int((count-17000)/85)+1) + ".png", dpi=600, bbox_inches="tight", pad_inches=0)
plt.close()
if count % 8500 == 0:
print(idef, count, mm, p, bias, gini, gap, cut, partition["SEN12"].wins("Rep"))
else:
if count%1000 == 0:
print(idef, "Mixing...... Iteration", count, "/17000")
count += 1
return metrics, boundary_nodes, boundary_weighted, idef
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=[republican_constraint],
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,
partition["SEN12"].wins("Rep"))
count += 1
return metrics, boundary_nodes, boundary_weighted
pop_target=ideal_population,
epsilon=0.03,
node_repeats=2)
compactness_bound = constraints.UpperBound(
lambda p: len(p["cut_edges"]), 2 * len(initial_partition["cut_edges"]))
pop_constraint = constraints.within_percent_of_ideal_population(
initial_partition, 0.03)
chain1 = MarkovChain(proposal=proposal,
constraints=[pop_constraint, compactness_bound],
accept=accept.always_accept,
initial_state=initial_partition,
total_steps=50000)
data = pd.DataFrame(partition["SEN12"].mean_median()
for partition in chain.with_progress_bar())
temp = [partition["PRES16"].mean_median() for partition in chain]
temp1 = [partition["PRES16"].efficiency_gap() for partition in chain]
plt.hist(temp, bins=200)
count = 0
for partition in chain1:
count += 1
if count < 10:
print(count)
partition.plot()
partition['PRES16'].mean_median()
fig, ax = plt.subplots(figsize=(8, 6))
