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yelp.py
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yelp.py
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# Justin Strauss, Lev Akabas, Derek Tsui, Dennis Nenov
# Software Development Period 7
# Final Project
import oauth2
import json
import urllib
import urllib2
# Takes a double array of keywords and an array of coordinates
def search(keywords, lls):
cll = locate(lls)
frequencies = frequency_dict(keywords)
consumer_key = 'InepiTUxl4_CN8hRCxV4gA'
consumer_secret = 'ZzuBLqtEreWlm1FqBltPEImuZ2Q'
token = '7uAeNaimqgTsYqCzboSZylZUukFJ1-I-'
token_secret = '22FbVEI8cfO63ROlQDV_bePx3XU'
consumer= oauth2.Consumer(consumer_key,consumer_secret)
list_of_businesses = []
for keyword in frequencies.keys():
url= 'http://api.yelp.com/v2/search?term=%s&ll=%s&limit=%d&format=json'%(urllib.quote(keyword), cll, 10)
oauth_request = oauth2.Request('GET', url, {})
oauth_request.update({'oauth_nonce': oauth2.generate_nonce(),
'oauth_timestamp': oauth2.generate_timestamp(),
'oauth_token': token,
'oauth_consumer_key': consumer_key})
tok = oauth2.Token(token, token_secret)
oauth_request.sign_request(oauth2.SignatureMethod_HMAC_SHA1(), consumer, tok)
url = oauth_request.to_url()
conn = urllib2.urlopen(url)
results =json.load(conn)
for dictionary in results["businesses"]:
list_of_businesses.append(dictionary)
d = {}
count = 0
for dict in list_of_businesses:
# Check to eliminate repeats #
is_unique = True
for x in range (0, count):
if list_of_businesses[x]["name"] == dict["name"]:
d[count] = 0
is_unique = False
if is_unique:
points = 0
# Takes into account the Yelp rating of the location. 16 points for a full rating of five stars #
points += (dict["rating"] - 1) * 4
# Takes into account the number of people who chose the cuisine offered at the location. 40 points for all cuisine choices being that cuisine #
number = 0
if "categories" not in dict:
dict["categories"] = []
for category in dict["categories"]:
if category[0] in frequencies.keys():
number += frequencies[category[0]] * 40 / len(keywords)
points += number
# Takes into account distance from the center location. 10 points for being within a half a mile away from the midpoint #
points += min(4000 / dict["distance"], 10)
# If fewer than 1/10 of the people put that cuisine, the location is not considered #
if number > 4:
d[count] = points
else:
d[count] = 0
count += 1
# Sort the locations by points and keep the top five
list = sorted(d, key=lambda i: d[i])
list = [list[-1], list[-2], list[-3], list[-4], list[-5]]
final = []
for x in list:
dict = list_of_businesses[x]
dictionary = {}
dictionary['name'] = dict['name']
dictionary['address'] = dict['location']['address']
dictionary['website'] = dict['url']
dictionary['rating_image'] = dict['rating_img_url']
final.append(dictionary)
return final
def frequency_dict(keywords):
d = {}
for list in keywords:
for keyword in list:
value = float(1) / len(list)
if keyword in d.keys():
d[keyword] += value
else:
d[keyword] = value
return d
def median(list):
half = len(list) / 2
list.sort()
if len(list) % 2 == 0:
return (list[half-1] + list[half]) / 2.0
else:
return list[half]
def locate(lls):
lattitudes = []
longitudes = []
for ll in lls:
new = ll.split(",")
lattitudes.append(float(new[0]))
longitudes.append(float(new[1]))
ideal_lat = median(lattitudes)
ideal_lon = median(longitudes)
return str(ideal_lat) + "," + str(ideal_lon)
if __name__ == "__main__":
print search([['Pizza','Italian'],['Pizza','Italian'],['Italian','Mexican','Chinese'],['Pizza','Italian','Chinese']],["40.808,-73.962","40.76,-73.986","40.9,-73.0"])