def run_simple2(graph):
election = Election("2014 Senate", {
"Democratic": "sen_blue",
"Republican": "sen_red"
},
alias="2014_Senate")
initial_partition = Partition(graph,
assignment="con_distri",
updaters={
"2014_Senate":
election,
"population":
Tally("population", alias="population"),
"exterior_boundaries":
exterior_boundaries,
"interior_boundaries":
interior_boundaries,
"perimeter":
perimeter,
"boundary_nodes":
boundary_nodes,
"cut_edges":
cut_edges,
"area":
Tally("area", alias="area"),
"cut_edges_by_part":
cut_edges_by_part,
"county_split":
county_splits('county_split',
"COUNTY_ID"),
"black_population":
Tally("black_pop",
alias="black_population"),
})
districts_within_tolerance_2 = lambda part: districts_within_tolerance(
part, 'population', 0.3)
is_valid = Validator(
[single_flip_contiguous, districts_within_tolerance_2])
chain = MarkovChain(
proposal=propose_random_flip,
is_valid=is_valid,
accept=always_accept,
# accept=accept, #THe acceptance criteria is what needs to be defined ourselves - to match the paper
initial_state=initial_partition,
total_steps=30,
)
efficiency_gaps = []
wins = []
for partition in chain:
if (hasattr(partition, 'accepted') and partition.accepted):
efficiency_gaps.append(
gerrychain.scores.efficiency_gap(partition["2014_Senate"]))
wins.append(partition["2014_Senate"].wins("Democratic"))
return (efficiency_gaps, wins, partition)
def test_districts_within_tolerance_returns_true_if_districts_are_within_tolerance():
# 100 and 100.1 are not within 1% of each other, so we should expect True
mock_partition = {"population": {0: 100.0, 1: 100.1}}
result = districts_within_tolerance(
mock_partition, attribute_name="population", percentage=0.01
)
assert result is True
def test_districts_within_tolerance_returns_false_if_districts_are_not_within_tolerance(
):
# 100 and 1 are not within 1% of each other, so we should expect False
mock_partition = {'population': {0: 100.0, 1: 1.0}}
result = districts_within_tolerance(mock_partition,
attribute_name='population',
percentage=0.01)
assert result is False
def run_simple(graph, num_samples = 80000, wp = 3000, wi = 2.5, wc = 0.4, wm = 800, get_parts = 5):
election = Election(
"2014 Senate",
{"Democratic": "sen_blue", "Republican": "sen_red"},
alias="2014_Senate"
)
initial_partition = Partition(
graph,
assignment="2014_CD",
updaters={
"2014_Senate": election,
"population": Tally("population", alias="population"),
"exterior_boundaries": exterior_boundaries,
"interior_boundaries": interior_boundaries,
"perimeter": perimeter,
"boundary_nodes": boundary_nodes,
"cut_edges": cut_edges,
"area": Tally("area", alias="area"),
"cut_edges_by_part": cut_edges_by_part,
"county_split" : county_splits( 'county_split', "COUNTY_ID"),
"black_population": Tally("black_pop", alias = "black_population"),
}
)
def vra_validator(part):
if(vra_district_requirement(part, 2, [0.4, 0.335]) > 0):
return False
return True
districts_within_tolerance_2 = lambda part : districts_within_tolerance(part, 'population', 0.12)
is_valid = Validator([single_flip_contiguous, districts_within_tolerance_2, vra_validator ])
def accept(partition, counter):
if(counter < 40000):
beta = 0
elif(counter < 60000):
beta = (counter - 40000) /(60000-40000)
else:
beta = 1
return(metro_scoring_prob(partition, beta, wp, wi, wc, wm))
chain = MarkovChainAnneal(
proposal=propose_random_flip,
is_valid=is_valid,
accept=accept,
initial_state=initial_partition,
total_steps= num_samples * 100,
)
# going to show 5 partitions from this sample
part_con = max(1, num_samples / get_parts)
efficiency_gaps = []
wins = []
voting_percents = []
sample_parts = []
tbefore = time.time()
for partition in chain:
if(hasattr(partition, 'accepted') and partition.accepted):
efficiency_gaps.append(gerrychain.scores.efficiency_gap(partition["2014_Senate"]))
wins.append(partition["2014_Senate"].wins("Democratic"))
voting_percents.append(partition["2014_Senate"].percents("Democratic"))
num_s = len(wins)
if(num_s % part_con) == 0:
sample_parts.append(partition)
if(num_s> num_samples):
break
if(num_s % 1000 == 0):
tnext = time.time()
print("It took %i time to get 1000 samples" % (tnext - tbefore))
tbefore = time.time()
return(efficiency_gaps, wins, voting_percents, sample_parts)