def predict_timestamps():
if not flask.request.json:
flask.abort(400)
dp.setHours2Predict(TU.parseHours(flask.request.json))
hours = list(TU.formatHours(dp.getHours2Predict()))
pred = dp.getPredictions()
return json.dumps(TU.outStr(hours,pred)), 201
fig3 = plt.figure()
plt.boxplot(aDays.values())
plt.title('Demand vs day of week')
plt.xlabel('Day')
plt.ylabel('Demand')
plt.show()
def writeCSV(filename, data):
out = csv.writer(open(filename, 'wb'), delimiter=',')
for d in data:
print d
out.writerow(d.split(','))
#reload(DP)
#reload(TU)
fd = open('uber_demand_prediction_challenge.json')
jsonData = json.load(fd)
fd.close()
dp = DP.DemandPrediction()
dp.setTrainingData(jsonData)
dp.train()
mayHours = DP.genHours('2012-05-01T00:00:00+00:00', '2012-06-01T00:00:00+00:00')
dp.setHours2Predict(mayHours)
pred = dp.getPredictions()
writeCSV('mayPredictions2.csv',TU.outStr(TU.formatHours(mayHours),pred) )
import requests
import json
import timeUtils as TU
def apiCall(cmd,data):
if (cmd!='train' and cmd!='predict' and cmd!='index'):
raise Exception("invalid command")
url = "http://localhost:5000/"+cmd+"/"
print url
headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
r = requests.post(url, data=json.dumps(data), headers=headers)
return r
# load data
fd = open('uber_demand_prediction_challenge.json')
jsonData = json.load(fd)
fd.close()
# train model
r = apiCall('train',jsonData)
# predict demand
hours = TU.genHours('2012-05-01T00:00:00+00:00', '2012-06-01T00:00:00+00:00')
r =apiCall('predict',TU.formatHours(hours))