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p_values.py
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p_values.py
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import urllib.request
import json
import dml
import prov.model
import datetime
import uuid
from math import sqrt
from random import shuffle
class p_values(dml.Algorithm):
contributor = 'carlosp_jpva_tkay_yllescas'
reads = ['carlosp_jpva_tkay_yllescas.demographics_by_towns', 'carlosp_jpva_tkay_yllescas.voterData2010']
writes = ['carlosp_jpva_tkay_yllescas.p_values']
@staticmethod
def execute(trial=False):
print("p_values")
'''Retrieve some data sets (without API).'''
startTime = datetime.datetime.now()
# Set up the database connection
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('carlosp_jpva_tkay_yllescas', 'carlosp_jpva_tkay_yllescas')
repo.dropCollection("p_values")
repo.createCollection("p_values")
# Grab datasets from database
demographicsByTowns = (repo['carlosp_jpva_tkay_yllescas.demographics_by_towns']).find()
voterData = (repo['carlosp_jpva_tkay_yllescas.voterData2010']).find()
vData = []
demData = []
X = []
Y = []
def permute(x):
shuffled = [xi for xi in x]
shuffle(shuffled)
return shuffled
# Average
def avg(x):
return sum(x) / len(x)
# Standard Deviation
def stddev(x):
m = avg(x)
return sqrt(sum([(xi - m) ** 2 for xi in x]) / len(x))
# Covariance
def cov(x, y):
return sum([(xi - avg(x)) * (yi - avg(y)) for (xi, yi) in zip(x, y)]) / len(x)
# Correlation Coefficient
def corr(x, y):
if stddev(x) * stddev(y) != 0:
return cov(x, y) / (stddev(x) * stddev(y))
# p value
def p(x, y):
c0 = corr(x, y)
corrs = []
for k in range(0, 2000):
y_permuted = permute(y)
corrs.append(corr(x, y_permuted))
return len([c for c in corrs if abs(c) >= abs(c0)]) / len(corrs)
for a in demographicsByTowns:
demData.append(a)
for b in voterData:
vData.append(b)
raceComparisons = ["Hispanic_vs_Registration", "Black_vs_Registration", "White_vs_Registration", "Asian_vs_Registration"]
pVals = {x:{} for x in raceComparisons}
# Get hispanic_vs_registration p-value
for a in demData: # Registered voters
registered = ""
key = a["Community"].lower()
population = a["Population 2010"].replace(",", "")
hispanicPop = a["Hispanic"].replace(",", "")
for j in vData:
if j["Community"].lower() == key:
registered = j["Registered"].replace(",", "")
hispanicPercent = round((int(hispanicPop) * 100 / int(population)), 2) # x value
registeredPercent = round(int(registered) * 100 / int(population), 2) # y value
X.append(hispanicPercent)
Y.append(registeredPercent)
hispanic_vs_reg_pval = p(X, Y)
pVals["Hispanic_vs_Registration"]["p-value"] = hispanic_vs_reg_pval
#print("TEST HISP PVAL: " + str(hispanic_vs_reg_pval))
# Get black_vs_registration p value
X = []
Y = []
for a in demData: # Registered voters
registered = ""
key = a["Community"].lower()
population = a["Population 2010"].replace(",", "")
blackPop = a["Black"].replace(",", "")
for j in vData:
if j["Community"].lower() == key:
registered = j["Registered"].replace(",", "")
blackPercent = round((int(blackPop) * 100 / int(population)), 2) # x value
registeredPercent = round(int(registered) * 100 / int(population), 2) # y value
X.append(blackPercent)
Y.append(registeredPercent)
black_vs_reg_pval = p(X, Y)
pVals["Black_vs_Registration"]["p-value"] = black_vs_reg_pval
#print("TEST BLACK PVAL: " + str(black_vs_reg_pval))
# Get white_vs_registration p value
X = []
Y = []
for a in demData: # Registered voters
registered = ""
key = a["Community"].lower()
population = a["Population 2010"].replace(",", "")
whitePop = a["White"].replace(",", "")
for j in vData:
if j["Community"].lower() == key:
registered = j["Registered"].replace(",", "")
whitePercent = round((int(whitePop) * 100 / int(population)), 2) # x value
registeredPercent = round(int(registered) * 100 / int(population), 2) # y value
X.append(whitePercent)
Y.append(registeredPercent)
white_vs_reg_pval = p(X, Y)
pVals["White_vs_Registration"]["p-value"] = white_vs_reg_pval
#print("TEST WHITE PVAL: " + str(white_vs_reg_pval))
# Get asian_vs_registration p value
X = []
Y = []
for a in demData: # Registered voters
registered = ""
key = a["Community"].lower()
population = a["Population 2010"].replace(",", "")
asianPop = a["Asian"].replace(",", "")
for j in vData:
if j["Community"].lower() == key:
registered = j["Registered"].replace(",", "")
asianPercent = round((int(asianPop) * 100 / int(population)), 2) # x value
registeredPercent = round(int(registered) * 100 / int(population), 2) # y value
X.append(asianPercent)
Y.append(registeredPercent)
asian_vs_reg_pval = p(X, Y)
pVals["Asian_vs_Registration"]["p-value"] = asian_vs_reg_pval
#print("TEST ASIAN PVAL: " + str(asian_vs_reg_pval))
repo['carlosp_jpva_tkay_yllescas.p_values'].insert_many([pVals])
repo['carlosp_jpva_tkay_yllescas.p_values'].metadata({'complete': True})
print(repo['carlosp_jpva_tkay_yllescas.p_values'].metadata())
repo.logout()
endTime = datetime.datetime.now()
return {"start": startTime, "end": endTime}
@staticmethod
def provenance(doc=prov.model.ProvDocument(), startTime=None, endTime=None):
'''
Create the provenance document describing everything happening
in this script. Each run of the script will generate a new
document describing that invocation event.
'''
# Set up the database connection.
client = dml.pymongo.MongoClient()
repo = client.repo
repo.authenticate('carlosp_jpva_tkay_yllescas', 'carlosp_jpva_tkay_yllescas')
doc.add_namespace('alg', './carlosp_jpva_tkay_yllescas') # The scripts are in <folder>#<filename> format.
doc.add_namespace('dat', './data') # The data sets are in <user>#<collection> format.
doc.add_namespace('ont',
'http://datamechanics.io/ontology#') # 'Extension', 'DataResource', 'DataSet', 'Retrieval', 'Query', or 'Computation'.
doc.add_namespace('log', 'http://datamechanics.io/log/') # The event log.
this_script = doc.agent('alg:carlosp_jpva_tkay_yllescas#p_values',
{prov.model.PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'})
voter_data = doc.entity('dat:data#voterData2010',
{'prov:label': 'Voter Data 2010',
prov.model.PROV_TYPE: 'ont:DataResource',
'ont:Extension': 'json'})
demographics_data = doc.entity('dat:data#demographics_by_towns',
{'prov:label': 'Demographics By Towns',
prov.model.PROV_TYPE: 'ont:DataResource',
'ont:Extension': 'json'})
get_pValues = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime)
doc.wasAssociatedWith(get_pValues, this_script)
doc.usage(get_pValues, voter_data, startTime, None, {prov.model.PROV_TYPE: 'ont:Computation'})
doc.usage(get_pValues, demographics_data, startTime, None, {prov.model.PROV_TYPE: 'ont:Computation'})
pValues = doc.entity('dat:/p_values', {prov.model.PROV_LABEL: 'P-Values for race populations and registration rates',
prov.model.PROV_TYPE: 'ont:DataSet'})
doc.wasAttributedTo(pValues, this_script)
doc.wasGeneratedBy(pValues, get_pValues, endTime)
doc.wasDerivedFrom(pValues, voter_data, get_pValues,
get_pValues, get_pValues)
repo.logout()
return doc