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real_data.py
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real_data.py
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"""
Calculating distributions for:
Money - Votes
Votes - Polls
"""
import csv
from collections import defaultdict
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
import warnings
def get_cands_votes():
'''
From FEC.
Note: tere are some rows where the same candidate repeats
with different party.
'''
cands = {}
data = './data/2014_house_election_results.csv'
with open(data, 'rU') as csvfile:
reader = csv.DictReader(csvfile, delimiter=',')
for row in reader:
if row['FEC ID#'] in cands and row['GENERAL %'] != '':
cands[row['FEC ID#']]['vote %'] += float(row['GENERAL %'].replace('%', ''))
if cands[row['FEC ID#']]['party'] not in ['R', 'D'] and row['PARTY'] in ['R', 'D']:
cands[row['FEC ID#']]['party'] = row['PARTY']
elif row['FEC ID#'] != 'n/a' and row['FEC ID#'] != '' and row['GENERAL %'] != '':
person = {}
person['name'] = row['CANDIDATE NAME']
person['state'] = row['STATE ABBREVIATION']
person['district'] = row['D'].replace(' - FULL TERM', '').replace(' - UNEXPIRED TERM', '')
party = row['PARTY']
if '/' in party:
parties = party.split('/')
if 'D' in parties:
party = 'D'
elif 'R' in parties:
party = 'R'
else:
party = row['PARTY']
if party == 'DFL': party = 'D' # MN's dem party
person['party'] = party.strip()
person['vote %'] = float(row['GENERAL %'].replace('%', ''))
cands[row['FEC ID#'].strip()] = person
return cands
def get_money():
'''
From FEC.
./data/cn 2.txt is less useful than expected because for H 2014 there are often multiple Dem and Rep.
'''
money = defaultdict(float)
cand_campaign_finance = './data/webl14.txt'
with open(cand_campaign_finance, 'r') as infile:
for line in infile:
contents = line.strip().split('|')
money[contents[0].strip()] = float(contents[17].strip())
return money
def get_polls():
dem = {}
rep = {}
data = './data/2014_house_election_polls.csv'
with open(data, 'rU') as csvfile:
reader = csv.DictReader(csvfile, delimiter=',')
for row in reader:
dem[row['CD']] = float(row['Dem'].replace('%', ''))
rep[row['CD']] = float(row['Rep'].replace('%', ''))
return dem, rep
def organize_data():
cands = get_cands_votes()
money = get_money()
dem, rep = get_polls()
total_election_money = defaultdict(float)
for cand in cands:
total_election_money[cands[cand]['state'] + cands[cand]['district']] += money[cand]
file = open('./data/2014_house_election_clean.txt', 'w')
writer = csv.writer(file, delimiter=',')
writer.writerow(['state', 'district', 'id', 'name', 'votes %', 'money %', 'poll %'])
for cand in cands:
if money[cand] != 0.0 and cands[cand]['state'] not in ['DC', 'AS', 'VI', 'GU']:
money_percent = money[cand] * 100 / total_election_money[cands[cand]['state'] + cands[cand]['district']]
district = cands[cand]['district']
if district == '00':
district = '01'
if cands[cand]['party'] == 'D':
writer.writerow([cands[cand]['state'], cands[cand]['district'], cand, cands[cand]['name'],
cands[cand]['vote %'], money_percent, dem[cands[cand]['state']+district]])
if cands[cand]['party'] == 'R':
writer.writerow([cands[cand]['state'], cands[cand]['district'], cand, cands[cand]['name'],
cands[cand]['vote %'], money_percent, rep[cands[cand]['state']+district]])
file.close()
def find_best_fit(data):
'''
Based on "Distribution Fitting with Sum of Square Error (SSE)"
https://stackoverflow.com/questions/6620471/fitting-empirical-distribution-to-theoretical-ones-with-scipy-python
'''
print "Finding best fit..."
y, x = np.histogram(data, bins=200, density=True)
x = (x + np.roll(x, -1))[:-1] / 2.0
all_dists = [
st.alpha,st.anglit,st.arcsine,st.beta,st.betaprime,st.bradford,st.burr,st.cauchy,st.chi,st.chi2,st.cosine,
st.dgamma,st.dweibull,st.erlang,st.expon,st.exponnorm,st.exponweib,st.exponpow,st.f,st.fatiguelife,st.fisk,
st.foldcauchy,st.foldnorm,st.frechet_r,st.frechet_l,st.genlogistic,st.genpareto,st.gennorm,st.genexpon,
st.genextreme,st.gausshyper,st.gamma,st.gengamma,st.genhalflogistic,st.gilbrat,st.gompertz,st.gumbel_r,
st.gumbel_l,st.halfcauchy,st.halflogistic,st.halfnorm,st.halfgennorm,st.hypsecant,st.invgamma,st.invgauss,
st.invweibull,st.johnsonsb,st.johnsonsu,st.ksone,st.kstwobign,st.laplace,st.levy,st.levy_l,st.levy_stable,
st.logistic,st.loggamma,st.loglaplace,st.lognorm,st.lomax,st.maxwell,st.mielke,st.nakagami,st.ncx2,st.ncf,
st.nct,st.norm,st.pareto,st.pearson3,st.powerlaw,st.powerlognorm,st.powernorm,st.rdist,st.reciprocal,
st.rayleigh,st.rice,st.recipinvgauss,st.semicircular,st.t,st.triang,st.truncexpon,st.truncnorm,st.tukeylambda,
st.uniform,st.vonmises,st.vonmises_line,st.wald,st.weibull_min,st.weibull_max,st.wrapcauchy
]
best_distribution = st.norm
best_params = (0.0, 1.0)
best_sse = np.inf
for d in all_dists:
try:
with warnings.catch_warnings():
warnings.filterwarnings('ignore')
params = d.fit(data)
pdf = d.pdf(x, loc=params[-2], scale=params[-1], *params[:-2])
sse = np.sum(np.power(y - pdf, 2.0)) # sum of square error
if best_sse > sse > 0:
best_distribution = d
best_params = params
best_sse = sse
except Exception:
pass
print "Got best fit..."
return (best_distribution.name, best_params)
def visualize_relationships():
votes = []
money = []
poll = []
with open('./data/2014_house_election_clean.txt', 'r') as csvfile:
reader = csv.DictReader(csvfile, delimiter=',')
for row in reader:
votes.append(float(row['votes %']))
money.append(float(row['money %']))
poll.append(float(row['poll %']))
# % Money - % Votes
# plot of values against values
plt.scatter(money, votes)
plt.xlabel('money %')
plt.ylabel('votes %')
plt.savefig('./data/money_vs_votes.png')
plt.close()
# plot of 1% money against % votes
plt.title('vote percent per money percent')
x = np.array(votes) / np.array(money)
x = x[~np.isinf(x)]
x = x[x < 2] # remove outliers
print list(x)
np.save('./data/vote_per_money.npy', x)
best_fit_name, best_fit_params = find_best_fit(x)
print "Best fit for vote per money:", best_fit_name, best_fit_params
best_dist = getattr(st, best_fit_name)
plt.plot(sorted(x), best_dist.pdf(sorted(x), loc=best_fit_params[-2], scale=best_fit_params[-1], *best_fit_params[:-2]))
plt.hist(x, density=True, bins=200)
plt.legend()
plt.savefig('./data/vote_per_money.png')
plt.close()
# % Votes - % Polls
# plot of values against values
plt.scatter(votes, poll)
plt.xlabel('votes %')
plt.ylabel('poll %')
plt.savefig('./data/votes_vs_polls.png')
plt.close()
# plot of 1% of poll against % votes
plt.title('poll percent per vote percent')
x = np.array(poll) / np.array(votes)
x = x[~np.isinf(x)]
x = x[x < 2] # remove outliers
print list(x)
np.save('./data/poll_per_vote.npy', x)
best_fit_name, best_fit_params = find_best_fit(x)
print "Best fit for poll per vote:", best_fit_name, best_fit_params
best_dist = getattr(st, best_fit_name)
plt.plot(sorted(x), best_dist.pdf(sorted(x), loc=best_fit_params[-2], scale=best_fit_params[-1], *best_fit_params[:-2]))
plt.hist(x, density=True, bins=100)
plt.legend()
plt.savefig('./data/poll_per_vote.png')
plt.close()
def main():
'''
Output format:
state | district | ID | name | votes | money | poll
'''
visualize_relationships()
if __name__ == '__main__':
main()