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statisticOperations.py
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statisticOperations.py
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import urllib.request
import json
import dml
import prov.model
import datetime
import uuid
import math
from bson.code import Code
from bson.json_util import dumps
from helpers import *
class correlation(dml.Algorithm):
contributor = 'aydenbu_huangyh'
reads = ['aydenbu_huangyh.public_earning_crime_boston']
writes = ['aydenbu_huangyh.statistic_data']
@staticmethod
def execute(trial=False):
startTime = datetime.datetime.now()
# Connect to the Database
repo = openDb(getAuth("db_username"), getAuth("db_password"))
data = repo['aydenbu_huangyh.public_earning_crime_boston']
avgs = []
stores = []
hospitals = []
schools = []
gardens = []
crimes = []
x = []
y = []
for document in data.find():
avg = [document['_id'], document['value']['avg']]
store = tuple((document['_id'], document['value']['numofStore']))
hospital = tuple((document['_id'], document['value']['numofHospital']))
school = tuple((document['_id'], document['value']['numofSchool']))
garden = tuple((document['_id'], document['value']['numofGarden']))
crime = tuple((document['_id'], document['value']['numofCrime']))
# temp_avg = document['value']['avg']
# temp_crime = document['value']['numofGarden']
avg[1] = float(avg[1])
avg = tuple(avg)
# temp_avg = float(temp_avg)
# x.append(temp_avg)
# y.append(temp_crime)
avgs.append(avg)
stores.append(store)
hospitals.append(hospital)
schools.append(school)
gardens.append(garden)
crimes.append(crime)
# print(avgs)
# print(stores)
# print(hospitals)
# print(schools)
# print(gardens)
# print(crimes)
# print(x)
# print(y)
'''
Implement the statistic methods here:
'''
def clean(x):
r = []
for i in x:
r += [i[1]]
return r
def cor(x, y):
xm = sum(x) / len(x)
ym = sum(y) / len(y)
top = 0
bot1 = 0
bot2 = 0
for i in range(len(x)):
top += (x[i] - xm) * (y[i] - ym)
bot1 += math.pow((x[i] - xm), 2)
bot2 += math.pow((y[i] - ym), 2)
bot = math.sqrt(bot1) * math.sqrt(bot2)
return top / bot
def least_square(x, y):
n = len(x)
sum_x = sum(x)
sum_y = sum(y)
sum_xy = sum(x[i] * y[i] for i in range(0, n))
sum_xx = sum(math.pow(x[i], 2) for i in range(0, n))
b = (n * sum_xy - (sum_x * sum_y)) / (n * sum_xx - math.pow(sum_x, 2))
a = (sum_y - b * sum_x) / n
return (a, b)
def r_square(x, y, a, b):
n = len(x)
ss_res = 0
f = [(b * x[i] + a) for i in range(0, n)]
for i in range(0, n):
ss_res += math.pow(y[i] - f[i], 2)
ss_tot = 0
for i in range(0, n):
ss_tot += math.pow(y[i] - sum(y) / n, 2)
r_square = 1 - (ss_res / ss_tot)
return r_square
'''
Statistic methods end Here
'''
# print(clean(avgs))
# print(clean(stores))
# print(stores)
# print(clean(hospitals))
# print(clean(schools))
# print(clean(gardens))
# print(clean(crimes))
x = clean(avgs)
y = [clean(stores), clean(hospitals), clean(schools), clean(gardens), clean(crimes)]
correlations =[]
leastSquares = []
Rsquares = []
for i in range(len(y)):
correlations += [cor(x, y[i])]
leastSquares += [least_square(x, y[i])]
Rsquares += [r_square(x, y[i], leastSquares[i][0], leastSquares[i][1])]
index = ['stores and Avg', 'hospital and Avg', 'school and Avg', 'garden and Avg', 'crimes and Avg']
# print('data for [stores, hospitals, schools, gardens, crimes]')
# print('correlations: ', correlations)
# print('leastSquares: ', leastSquares)
# print('R square: ' , Rsquares)
results = []
for i in range(len(index)):
result = {
'_id': index[i],
'Correlation': correlations[i],
'LeastSquare': leastSquares[i],
'R Square': Rsquares[i]
}
results.append(result)
repo.dropPermanent("statistic_data")
repo.createPermanent("statistic_data")
repo['aydenbu_huangyh.statistic_data'].insert_many(results)
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
repo = openDb(getAuth("db_username"), getAuth("db_password"))
doc.add_namespace('alg', 'http://datamechanics.io/algorithm/') # The scripts are in <folder>#<filename> format.
doc.add_namespace('dat', 'http://datamechanics.io/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.
doc.add_namespace('bdp', 'https://data.cityofboston.gov/resource/')
this_script = doc.agent('alg:aydenbu_huangyh#correlation',
{prov.model.PROV_TYPE: prov.model.PROV['SoftwareAgent'], 'ont:Extension': 'py'})
resource = doc.entity('dat:public_earning_crime_boston',
{'prov:label': 'Public Earning Crime Boston', prov.model.PROV_TYPE: 'ont:DataResource',
'ont:Extension': 'json'})
get_statistic_reaults = doc.activity('log:uuid' + str(uuid.uuid4()), startTime, endTime,
{prov.model.PROV_LABEL: "Get the correlation and leastSquare for each entry related to evg earnings"})
doc.wasAssociatedWith(get_statistic_reaults, this_script)
doc.usage(get_statistic_reaults, resource, startTime, None,
{prov.model.PROV_TYPE: 'ont:Computation'})
statistic_data = doc.entity('dat:aydenbu_huangyh#statistic_data',
{prov.model.PROV_LABEL: 'Statistic Results',
prov.model.PROV_TYPE: 'ont:DataSet'})
doc.wasAttributedTo(statistic_data, this_script)
doc.wasGeneratedBy(statistic_data, statistic_data, endTime)
doc.wasDerivedFrom(statistic_data, resource, statistic_data, statistic_data,
statistic_data)
repo.record(doc.serialize()) # Record the provenance document.
repo.logout()
return doc
correlation.execute()
doc = correlation.provenance()
print(doc.get_provn())
print(json.dumps(json.loads(doc.serialize()), indent=4))