def example_partition():
df = gp.read_file(os.path.join(TEST_DATA_PATH, "mo_cleaned_vtds.shp"))
with open(os.path.join(TEST_DATA_PATH, "MO_graph.json")) as f:
graph_json = json.load(f)
graph = networkx.readwrite.json_graph.adjacency_graph(graph_json)
assignment = get_assignment_dict(df, "GEOID10", "CD")
add_data_to_graph(
df,
graph, ['PR_DV08', 'PR_RV08', 'POP100', 'ALAND10', 'COUNTYFP10'],
id_col='GEOID10')
updaters = {
**votes_updaters(['PR_DV08', 'PR_RV08'], election_name='08'), 'population':
Tally('POP100', alias='population'),
'counties':
county_splits('counties', 'COUNTYFP10'),
'cut_edges':
cut_edges,
'cut_edges_by_part':
cut_edges_by_part
}
return Partition(graph, assignment, updaters)
def PA_partition():
# this is a networkx adjancency data json file with CD, area, population, and vote data
graph = construct_graph("./testData/PA_graph_with_data.json")
# Add frozen attributes to graph
# data = gp.read_file("./testData/frozen.shp")
# add_data_to_graph(data, graph, ['Frozen'], 'wes_id')
assignment = dict(
zip(graph.nodes(), [graph.node[x]['CD'] for x in graph.nodes()]))
updaters = {
**votes_updaters(['VoteA', 'VoteB']), 'population':
Tally('POP100', alias='population'),
'perimeters':
perimeters,
'exterior_boundaries':
exterior_boundaries,
'boundary_nodes':
boundary_nodes,
'cut_edges':
cut_edges,
'areas':
Tally('ALAND10', alias='areas'),
'polsby_popper':
polsby_popper,
'cut_edges_by_part':
cut_edges_by_part
}
return Partition(graph, assignment, updaters)
def setup_for_proportion_updaters(columns):
graph = three_by_three_grid()
attach_random_data(graph, columns)
assignment = random_assignment(graph, 3)
updaters = votes_updaters(columns)
return Partition(graph, assignment, updaters)
def test_vote_proportion_returns_nan_if_total_votes_is_zero():
columns = ['D', 'R']
graph = three_by_three_grid()
for node in graph.nodes:
for col in columns:
graph.nodes[node][col] = 0
updaters = votes_updaters(columns)
assignment = random_assignment(graph, 3)
partition = Partition(graph, assignment, updaters)
assert all(math.isnan(value) for value in partition['D%'].values())
assert all(math.isnan(value) for value in partition['R%'].values())
def set_up_chain(plan, total_steps, adjacency_type='queen'):
graph = Graph.load(f"./PA_{adjacency_type}.json").graph
assignment = {node: graph.nodes[node][plan] for node in graph.nodes}
updaters = {
**votes_updaters(elections["2016_Presidential"],
election_name="2016_Presidential"),
**votes_updaters(elections["2016_Senate"], election_name="2016_Senate"), 'population':
Tally('population', alias='population'),
'perimeters':
perimeters,
'exterior_boundaries':
exterior_boundaries,
'interior_boundaries':
interior_boundaries,
'boundary_nodes':
boundary_nodes,
'cut_edges':
cut_edges,
'areas':
Tally('area', alias='areas'),
'polsby_popper':
polsby_popper,
'cut_edges_by_part':
cut_edges_by_part
}
partition = Partition(graph, assignment, updaters)
population_constraint = within_percent_of_ideal_population(partition, 0.01)
compactness_constraint = SelfConfiguringLowerBound(L_minus_1_polsby_popper,
epsilon=0.1)
is_valid = Validator(default_constraints +
[population_constraint, compactness_constraint])
return partition, MarkovChain(propose_random_flip, is_valid, always_accept,
partition, total_steps)
def test_vote_proportion_updater_returns_percentage_or_nan_on_later_steps():
columns = ['D', 'R']
graph = three_by_three_grid()
attach_random_data(graph, columns)
assignment = random_assignment(graph, 3)
updaters = {**votes_updaters(columns), 'cut_edges': cut_edges}
initial_partition = Partition(graph, assignment, updaters)
chain = MarkovChain(propose_random_flip, Validator([no_vanishing_districts]),
lambda x: True, initial_partition, total_steps=10)
for partition in chain:
assert all(is_percentage_or_nan(value) for value in partition['D%'].values())
assert all(is_percentage_or_nan(value) for value in partition['R%'].values())
def example_partition():
df = gp.read_file("./testData/mo_cleaned_vtds.shp")
with open("./testData/MO_graph.json") as f:
graph_json = json.load(f)
graph = networkx.readwrite.json_graph.adjacency_graph(graph_json)
assignment = get_assignment_dict(df, "GEOID10", "CD")
add_data_to_graph(
df,
graph, ['PR_DV08', 'PR_RV08', 'POP100', 'ALAND10', 'COUNTYFP10'],
id_col='GEOID10')
updaters = {
**votes_updaters(['PR_DV08', 'PR_RV08'], election_name='08'), 'population':
Tally('POP100', alias='population'),
'areas':
Tally('ALAND10', alias='areas'),
'counties':
county_splits('counties', 'COUNTYFP10'),
'perimeters':
perimeters,
'exterior_boundaries':
exterior_boundaries,
'boundary_nodes':
boundary_nodes,
'polsby_popper':
polsby_popper,
'cut_edges':
cut_edges,
'cut_edges_by_part':
cut_edges_by_part
}
return Partition(graph, assignment, updaters)
'perimeters': perimeters,
'exterior_boundaries': exterior_boundaries,
'interior_boundaries': interior_boundaries,
'boundary_nodes': boundary_nodes,
'cut_edges': cut_edges,
'areas': Tally('areas'),
'polsby_popper': polsby_popper,
'cut_edges_by_part': cut_edges_by_part,
#'County_Splits': county_splits('County_Splits',county_col)
}
# Add the vote updaters for multiple plans
for i in range(num_elections):
updaters = {
**updaters,
**votes_updaters(election_columns[i], election_names[i])
}
# This builds the partition object
initial_partition = Partition(graph, assignment, updaters)
# Choose which binary constraints to enforce
# Options are in validity.py
pop_limit = .2
population_constraint = within_percent_of_ideal_population(
initial_partition, pop_limit)
compactness_constraint_Lm1 = LowerBound(
L_minus_1_polsby_popper, L_minus_1_polsby_popper(initial_partition))
add_data_to_graph(df, graph, data_list, id_col=unique_label)
# Desired proposal method
proposal_method = propose_random_flip_no_loops
# Desired proposal method
acceptance_method = always_accept
# Number of steps to run
steps = 1000
print("loaded data")
# Necessary updaters go here
updaters = {
**votes_updaters([vote_col1, vote_col2]), 'population':
Tally(pop_col, alias='population'),
'perimeters':
perimeters,
'exterior_boundaries':
exterior_boundaries,
'interior_boundaries':
interior_boundaries,
'boundary_nodes':
boundary_nodes,
'cut_edges':
cut_edges,
'areas':
Tally(area_col, alias='areas'),
'polsby_popper':
polsby_popper,
def read_basic_config(configFileName):
"""Reads basic configuration file and sets up a chain run
:configFileName: relative path to config file
:returns: Partition instance and MarkovChain instance
"""
# set up the config file parser
config = configparser.ConfigParser()
config.read(configFileName)
# SET UP GRAPH AND PARTITION SECTION
# create graph and get global names for required graph attributes
graph, POP, AREA, CD = gsource_gdata(config, 'GRAPH_SOURCE', 'GRAPH_DATA')
voteDataList = vsource_vdata(graph, config, 'VOTE_DATA_SOURCE',
'VOTE_DATA')
# create a list of vote columns to update
DataUpdaters = {**votes_updaters(voteDataList)}
# Previously used individual columns
# {v: updates.Tally(v) for v in voteDataList}
# original plan for fixing %'s:
# for v in voteDataList:
# DataUpdaters = {**DataUpdaters, v+"%": updates.Tally(v+"%")}
# construct initial districting plan
assignment = {x[0]: x[1][CD] for x in graph.nodes(data=True)}
# set up validator functions and create Validator class instance
validatorsUpdaters = []
validators = []
if config.has_section('VALIDITY') and len(list(
config['VALIDITY'].keys())) > 0:
validators = list(config['VALIDITY'].values())
for i, x in enumerate(validators):
if len(x.split(',')) == 1:
validators[i] = getattr(valids, x)
else:
[y, z] = x.split(',')
validators[i] = valids.WithinPercentRangeOfBounds(
getattr(valids, y), z)
validatorsUpdaters.extend(
[x.split(',')[0] for x in config['VALIDITY'].values()])
validators = valids.Validator(validators)
# add updaters required by this list of validators to list of updaters
for x in validatorsUpdaters:
DataUpdaters.update(dependencies(x, POP, AREA))
# END SET UP GRAPH AND PARTITION SECTION
# SET UP MARKOVCHAIN RUN SECTION
# set up parameters for markovchain run
chainparams = config['MARKOV_CHAIN']
# number of steps to run
num_steps = 1000
if 'num_steps' in list(chainparams.keys()):
num_steps = int(chainparams['num_steps'])
# type of flip to use
proposal = proposals.propose_random_flip
if 'proposal' in list(chainparams.keys()):
proposal = getattr(proposals, chainparams['proposal'])
# acceptance function to use
accept = accepts.always_accept
if 'accept' in list(chainparams.keys()):
accept = getattr(accepts, chainparams['accept'])
# END SET UP MARKOVCHAIN RUN SECTION
# SET UP DATA PROCESSOR FOR CHAIN RUN
# get evaluation scores to compute and the columns to use for each
escores, cfunc, elist, sVisType, outFName = escores_edata(
config, "EVALUATION_SCORES", "EVALUATION_SCORES_DATA")
# add evaluation scores updaters to list of updators
for x in elist:
DataUpdaters.update(dependencies(x, POP, AREA))
# END SET UP DATA PROCESSOR FOR CHAIN RUN
updaters = DataUpdaters
# create markovchain instance
initial_partition = Partition(graph, assignment, updaters)
chain = MarkovChain(proposal, validators, accept, initial_partition,
num_steps)
return chain, cfunc, escores, sVisType, outFName
def main():
#graph = construct_graph_from_file("/Users/caranix/Desktop/Alaska_Chain/AK_data.shp", geoid_col="DISTRICT")
with open('./alaska_graph.json') as f:
data = json.load(f)
graph = networkx.readwrite.json_graph.adjacency_graph(data)
df = gp.read_file(
"/Users/caranix/Desktop/Alaska_Chain/AK_data.shp"
) # assignment = dict(zip(graph.nodes(), [graph.node[x]['HOUSEDIST'] for x in graph.nodes()]))
add_data_to_graph(df,
graph, [
'join_Distr', 'POPULATION', 'join_Dem', 'join_Rep',
'perc_Dem', 'perc_Rep', 'AREA'
],
id_col='DISTRICT')
data = json.dumps(networkx.readwrite.json_graph.adjacency_data(graph))
with open('./alaska_graph.json', 'w') as f:
f.write(data)
assignment = dict(
zip(graph.nodes(),
[graph.node[x]['join_Distr'] for x in graph.nodes()]))
updaters = {
'population':
Tally('POPULATION', alias='population'),
'cut_edges':
cut_edges,
'cut_edges_by_part':
cut_edges_by_part,
**votes_updaters(['join_Dem', 'join_Rep'], election_name='12'), 'perimeters':
perimeters,
'exterior_boundaries':
exterior_boundaries,
'boundary_nodes':
boundary_nodes,
'cut_edges':
cut_edges,
'areas':
Tally('AREA', alias='areas'),
'polsby_popper':
polsby_popper
}
p = Partition(graph, assignment, updaters)
print("Starting Chain")
chain = BasicChain(p, 1000000)
allAssignments = {0: chain.state.assignment}
for step in chain:
allAssignments[chain.counter + 1] = step.flips
# print(mean_median(step, 'join_Dem%'))
# with open("chain_outputnew.json", "w") as f:
# f.write(json.dumps(allAssignments))
#efficiency_gap(p)
# mean_median(p, 'join_Dem%')
scores = {
'Mean-Median':
functools.partial(mean_median, proportion_column_name='join_Dem%'),
'Mean-Thirdian':
functools.partial(mean_thirdian, proportion_column_name='join_Dem%'),
'Efficiency Gap':
functools.partial(efficiency_gap, col1='join_Dem', col2='join_Rep'),
'L1 Reciprocal Polsby-Popper':
L1_reciprocal_polsby_popper
}
initial_scores = {key: score(p) for key, score in scores.items()}
table = pipe_to_table(chain, scores)
fig, axes = plt.subplots(2, 2)
quadrants = {
'Mean-Median': (0, 0),
'Mean-Thirdian': (0, 1),
'Efficiency Gap': (1, 0),
'L1 Reciprocal Polsby-Popper': (1, 1)
}
for key in scores:
quadrant = quadrants[key]
axes[quadrant].hist(table[key], bins=50)
axes[quadrant].set_title(key)
axes[quadrant].axvline(x=initial_scores[key], color='r')
plt.show()
'''
