-
Notifications
You must be signed in to change notification settings - Fork 0
/
chain_parallel_county_norand_composite_fracwinsgt.py
282 lines (239 loc) · 12 KB
/
chain_parallel_county_norand_composite_fracwinsgt.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
#!/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_xtendedfracwinsgt,
proposals, updaters, constraints, accept, Election)
from gerrychain.proposals import recom
from gerrychain.tree import recursive_tree_part
from functools import partial
from gerrychain.constraints import single_flip_contiguous, contiguous
import geopandas
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
from calc_fracwins_comp import calc_fracwins_comp
def multichain_run(i1, graph, chainlength, my_apportionment, poptol, my_electionproxy, composite, rsw, rmm, reg, rpp, datastruct, state, splitno, maxsplits, cutedgemax, electionvol):
hi_wins = 140#spit out maps for anything with efficiency gap over this
#cutedgemax=1.2 #factor above initial partition, cut edges allowed
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")}
contiguous_parts = lambda p: contiguous(p)
# 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)
print(nparts)
#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"]),
cutedgemax*len(initial_partition["cut_edges"])
)
chain = MarkovChain_xtendedfracwinsgt(
proposal=proposal,
constraints=[ contiguous_parts,
pop_constraint,
compactness_bound],
accept=accept.always_accept,
initial_state= initial_partition, #randpartition,
total_steps=chainlength,
maxsplits = maxsplits,
win_volatility = electionvol,
election_composite = composite
)
for part in chain:
if (part.good == 1) or (part.good == -1):
datax = np.zeros((nparts,1)) #nparts = ndistricts
rsw_tmp = 0
rmm_tmp = 0
reg_tmp = 0
for compelection in composite:
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 = calc_fracwins_comp(part.state,composite, electionvol)
#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)
# rpp.append(np.mean(pd.Series(polsby_popper(part.state)))) #depends on geometry of the partition only not on vote outcomes
rsw.append(rsw_tmp)
rmm.append(rmm_tmp)
reg.append(reg_tmp)
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.cd_gt(part.state,hi_wins,rsw_tmp ,state, my_apportionment,my_electionproxy, i1, '_cdgt')
if i1 == 1:
print(i1, ' finished chain step ', part.counter)
#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=15000
corrlength=50
#maxsplits=210
# 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_assignment_y.py").read())
#exec(open("input_templates/PA_SEND_SEN12_countyloop.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_REMEDIAL_SEN12.py").read())
# exec(open("input_templates/PA_CD_2011_SEN12_countyloop.py").read())
# exec(open("input_templates/PA_SEND_SEN12_countyloop.py").read())
#exec(open("input_templates/PA_districtr17shrinkmm-006.py").read())
exec(open("input_templates/PA_districtr17shrink1.py").read())
countysp = 'xsplitsrand ' +str(maxsplits) #labels for graphs and output filenames
normalize=''
elections, composite = get_elections(state)
#read in data file here:
if dontfeedin == 0 or not( 'graph_PA' in globals()):
df = geopandas.read_file( my_electiondatafile)
exec(open("./splice_assignment_fn.py").read())
#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 = 0.05
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)
print('starting parallel runs\n')
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, cutedgemax, electionvol) 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)
junk = rpp[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