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chain_parallel_county_composite.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 9 16:47:51 2020
Parallel version of chain run set up for Pennsylvania.
Some nice stuff added to DataFrame structure to add congressional district labels in order of actual increasing congressional district No.
@author: dinos
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 24 16:55:12 2020
uses recom proposal
@author: dpg
"""
#from multiprocessing import Pool
from multiprocessing import set_start_method, freeze_support
#from multiprocessing import Pool
from multiprocessing import get_context
import matplotlib.pyplot as plt
import time
from gerrychain import (GeographicPartition, Partition, Graph, MarkovChain_xtended,
proposals, updaters, constraints, accept, Election)
from gerrychain.proposals import recom
from gerrychain.tree import recursive_tree_part
from functools import partial
import pandas
import pandas as pd
import numpy as np
from gerrychain.metrics import mean_median, efficiency_gap, polsby_popper
from get_districtlabels import get_labels, get_labels_comp
from get_electioninfo import get_elections
import random
import os
from total_splits import total_splits
import district_list as dl
import conditional_dump as cd
def multichain_run(i1, graph, chainlength, my_apportionment, poptol, my_electionproxy, composite, rsw, rmm, reg, rpp, datastruct, state, splitno, maxsplits):
hi_eg = 0.04 #spit out maps for anything with efficiency gap over this
random.seed(os.urandom(10)*i1)
# poptol = 0.03 #min population deviation
elections, composite = get_elections(state)
if "TOTPOP" in graph._node[0]:
popkey = "TOTPOP"
elif "PERSONS" in graph._node[0]:
popkey = "PERSONS"
else:
popkey = []
print("woops no popkey in file, look @ graph_PA._node[0] to figure out what the keyword for population is\n")
#CONFIGURE UPDATERS
#We want to set up updaters for everything we want to compute for each plan in the ensemble.
# Population updater, for computing how close to equality the district
# populations are. "TOTPOP" is the population column from our shapefile.
my_updaters = {"population": updaters.Tally(popkey, alias="population")}
# Election updaters, for computing election results using the vote totals
# from our shapefile.
election_updaters = {election.name: election for election in elections}
my_updaters.update(election_updaters)
#INITIAL PARTITION
initial_partition = GeographicPartition(graph, assignment=my_apportionment, updaters=my_updaters)
#this block obtains the Congressional District Labels and converts to string labels, cds
ideal_population = sum(list(initial_partition["population"].values())) / len(initial_partition)
cds = get_labels_comp(initial_partition, composite) #get congressional district labels
nparts = len(initial_partition)
ranpart = recursive_tree_part(graph, range(nparts), ideal_population, popkey,poptol - .01,node_repeats=1)
randpartition = GeographicPartition(graph,assignment = ranpart, updaters = my_updaters)
pop_constraint = constraints.within_percent_of_ideal_population(initial_partition, poptol)
proposal = partial(recom,
pop_col=popkey,
pop_target=ideal_population,
epsilon=poptol,
node_repeats=2
)
compactness_bound = constraints.UpperBound(
lambda p: len(p["cut_edges"]),
2*len(initial_partition["cut_edges"])
)
chain = MarkovChain_xtended(
proposal=proposal,
constraints=[
pop_constraint,
compactness_bound],
accept=accept.always_accept,
initial_state= randpartition,
total_steps=chainlength,
maxsplits = maxsplits
)
for part in chain:
if part.good == 1:
datax = np.zeros((nparts,1)) #nparts = ndistricts
rsw_tmp = 0
rmm_tmp = 0
reg_tmp = 0
for compelection in composite:
rsw_tmp += part.state[compelection].wins("Democratic")
rmm_tmp += mean_median(part.state[compelection])
reg_tmp += efficiency_gap(part.state[compelection])
datax += pandas.DataFrame(sorted(part.state[compelection].percents("Democratic" )), index=cds)
rsw_tmp = rsw_tmp/len(composite) #now get average per election instead of sum over all elections
rmm_tmp = rmm_tmp/len(composite)
reg_tmp = reg_tmp/len(composite)
datax = datax.transpose() / len(composite)
rsw.append(rsw_tmp)
rmm.append(rmm_tmp)
reg.append(reg_tmp)
# rpp.append(np.mean(pd.Series(polsby_popper(part.state)))) #depends on geometry of the partition only not on vote outcomes
datastruct = pandas.concat([datastruct, datax])
splitno.append(total_splits(part.state)) #splits don't depend on individual election results, only on partition so not in loop
#cd.eg_gt(part.state,hi_eg, state, my_apportionment,my_electionproxy, i1, 'county')
#cd.eg_zero(part,zero_eg, state, my_apportionment, my_electionproxy, i1)
return i1, rsw, rmm, reg, rpp, datastruct, splitno
#MAIN PROGRAM HERE:
#few key lines for making parallel pool not mess up (freeze_support() and __spec__ definition)
if __name__ == '__main__':
freeze_support()
__spec__ = "ModuleSpec(name='builtins', loader=<class '_frozen_importlib.BuiltinImporter'>)"
dontfeedin = 1 #if set=0, feeds in data, otherwise skip
poolsize=40
chainlength=1000
corrlength=50
maxsplits=51
countysp = ' splitsrand ' +str(maxsplits) #labels for graphs and output filenames
normalize=''
# set_start_method("spawn")
# set_start_method("spawn")
#my_apportionment = "ASM" #type of district boundaries to calculate - eg US congressional, state senate, house etc.
#my_electionproxy = "SEN16" #pick the election to use as a statewide proxy for partisan voting for districted seats
#my_electionproxy_alternate ="USS12" #alternate name, see 'elections' variable below
#my_electiondatafile = "./PA-shapefiles-master/PA_VTDs.json" #PATH to the election data
#my_electiondatafile = "./shapefiles_multistate/WI-shapefiles-master/WI_wards_12_16/WI_ltsb_corrected_final.json"
#state = "WI"
proposaltype = "recom"
#exec(open("input_templates/PA_HDIST_SEN12.py").read())
#exec(open("input_templates/PA_CD_2011_SEN12.py").read())
#exec(open("input_templates/MI_SENDIST_PRES16.py").read())
#exec(open("input_templates/PA_CD_2011_SEN12.py").read())
exec(open("input_templates/PA_SEND_SEN12_countyloop.py").read())
elections, composite = get_elections(state)
#read in data file here:
"""
if 'dontfeedin' in globals():
if dontfeedin == 0 or not( 'graph_PA' in globals()):
if ".json" in my_electiondatafile:
graph_PA = Graph.from_json(my_electiondatafile)
else:
graph_PA = Graph.from_file(my_electiondatafile)
else:
if ".json" in my_electiondatafile:
graph_PA = Graph.from_json(my_electiondatafile)
else:
graph_PA = Graph.from_file(my_electiondatafile)
"""
if ".json" in my_electiondatafile:
graph_PA = Graph.from_json(my_electiondatafile)
else:
graph_PA = Graph.from_file(my_electiondatafile)
#SETUP initial_partition & get initial DataFrame here - redundant but needed to setup datastruct
#in parallel - 0th point resu, then append to it
#
if 'poptol' not in globals():
poptol = 0.03
if "TOTPOP" in graph_PA._node[0]:
popkey = "TOTPOP"
elif "PERSONS" in graph_PA._node[0]:
popkey = "PERSONS"
else:
popkey = []
print("woops no popkey in file, look @ graph_PA._node[0] to figure out what the keyword for population is\n")
#CONFIGURE UPDATERS
#We want to set up updaters for everything we want to compute for each plan in the ensemble.
# Population updater, for computing how close to equality the district
# populations are. "TOTPOP" is the population column from our shapefile.
my_updaters = {"population": updaters.Tally(popkey, alias="population")}
election_updaters = {election.name: election for election in elections}
my_updaters.update(election_updaters)
initial_partition = GeographicPartition(graph_PA, assignment=my_apportionment, updaters=my_updaters)
num_districts = len(initial_partition)
cds = get_labels_comp(initial_partition, composite) #get congressional district labels
#RUNNING THE CHAIN
ideal_population = sum(list(initial_partition["population"].values())) / len(initial_partition)
t0=time.time()
# This will take about 10 minutes.
#setup variables
rsw = [[0 for x in range(1)] for x in range(poolsize)] # np.zeros([poolsize, chainlength])
rmm = [[0 for x in range(1)] for x in range(poolsize)] # np.zeros([poolsize, chainlength])
reg = [[0 for x in range(1)] for x in range(poolsize)] # np.zeros([poolsize, chainlength])
rpp = [[0 for x in range(1)] for x in range(poolsize)] # np.zeros([poolsize, chainlength])
splitno = [[0 for x in range(1)] for x in range(poolsize)]
data1 = np.zeros((1,num_districts))
for compelection in composite:
data1 += initial_partition[compelection].percents("Democratic")
data1 = data1/len(composite)
data1 = pd.DataFrame(sorted(list(data1)), columns=cds)
datastruct = []
#setup parallel list of DataFrames
for nn in range(poolsize):
datastruct.append(data1)
ctx = get_context("spawn")
p = ctx.Pool(poolsize)
updated_vals = p.starmap(multichain_run, [(i1, graph_PA, chainlength, my_apportionment, poptol, my_electionproxy, composite,
rsw[i1], rmm[i1], reg[i1], rpp[i1], datastruct[i1], state, splitno[i1], maxsplits) for i1 in range(poolsize)])
for i1, rsw_updated, rmm_updated, reg_updated, rpp_updated, datastruct_updated, splitno_updated in updated_vals:
rsw[i1] = rsw_updated
rmm[i1] = rmm_updated
reg[i1] = reg_updated
rpp[i1] = rpp_updated
datastruct[i1] = datastruct_updated
splitno[i1] = splitno_updated
#clean up data
rsw_bak= rsw.copy() #just to be on the safe side
reg_bak = reg.copy()
rmm_bak = rmm.copy()
datastruct_bak = datastruct.copy()
for nn in range(poolsize): #clean up since 1st value in each list is a junk '0'
junk = rsw[nn].pop(0)
junk = reg[nn].pop(0)
junk = rmm[nn].pop(0)
iter1 = range(corrlength-1,corrlength+chainlength-1,corrlength) #since the correlation length is 200, only collect every 200th point
reg_clean = []
rmm_clean = []
rsw_clean = []
rpp_clean = []
splitno_clean = []
for nn in range(poolsize):
for kk in iter1:
reg_clean.append(reg[nn][kk])
rmm_clean.append(rmm[nn][kk])
rsw_clean.append(rsw[nn][kk])
# rpp_clean.append(rpp[nn][kk])
splitno_clean.append(splitno[nn][kk])
#data1 = data1.transpose()
#data1 = pandas.DataFrame((initial_partition["SEN12"].percents("Democratic") ))
t1=time.time()
print( (t1-t0)/60, " min runtime\n")
exec(open("condense_datastruct.py").read())
# RUN condense_datastruct.py after this to unpack the data structure and plot it