def test_has_informative_repr(self):
def my_function(x):
return 150
bound = constraints.LowerBound(my_function, 100)
assert repr(bound) == "<LowerBound(my_function <= 100)>"
## ## ## ## ## ## ## ## ## ## ##
## set up a chain
## ## ## ## ## ## ## ## ## ## ##
proposal = partial(recom, pop_col=POP_COL, pop_target=ideal_pop, epsilon=EPS,
node_repeats=1)
# compactness constraint: no higher than than initial partition
compactness_bound = constraints.UpperBound(lambda p: len(p["cut_edges"]),
len(init_partition["cut_edges"]))
# county splits: no higher than initial partition
county_splits_bound = compactness_bound = constraints.UpperBound(lambda p: len(p["county_splits"]),
len(init_partition["county_splits"]))
# vra constraint: no lower than initial partition
vra_bound = compactness_bound = constraints.LowerBound(lambda p: p["num_vra_districts"],
init_partition["num_vra_districts"])
## ## ## ## ## ## ## ## ## ## ##
## Re-com chain and run it!
## ## ## ## ## ## ## ## ## ## ##
chain = MarkovChain(
proposal,
constraints=[
constraints.within_percent_of_ideal_population(init_partition, EPS),
compactness_bound,
county_splits_bound,
vra_bound],
accept=accept.always_accept,
initial_state=init_partition,
total_steps=ITERS)
# population calculation
pop = 0
for n in graph.nodes:
pop = pop + graph.nodes[n][pop_col]
# --- Create random initial partition -------
proposal = partial(recom,
pop_col=pop_col,
pop_target=pop / num_dist,
epsilon=0.05,
node_repeats=3)
bvap_con = constraints.LowerBound(
lambda p: race_con(p["racial_demographics"], 1),
race_con(racial_vap_ref, 1))
hvap_con = constraints.LowerBound(
lambda p: race_con(p["racial_demographics"], 2),
race_con(racial_vap_ref, 2))
avap_con = constraints.LowerBound(
lambda p: race_con(p["racial_demographics"], 3),
race_con(racial_vap_ref, 3))
nvap_con = constraints.LowerBound(
lambda p: race_con(p["racial_demographics"], 4),
race_con(racial_vap_ref, 4))
pvap_con = constraints.LowerBound(
lambda p: race_con(p["racial_demographics"], 5),
race_con(racial_vap_ref, 5))
racial_bound = constraints.Validator(
def test_bound_allows_equality(self):
bound = constraints.LowerBound(lambda x: x, 100)
assert bound(100) is True
def test_passes_values_above_bound(self):
bound = constraints.LowerBound(lambda x: x, 100)
assert bound(150) is True
def test_fails_values_below_bound(self):
bound = constraints.LowerBound(lambda x: x, 100)
assert bound(50) is False