def test_recommendations_ranges(self): ranking = recommendations.get_recommendations(data.critics, pick_a_name()) for score in ranking: (score[0]).should.be.within(0, 5) (score[1]).should.be.within(TITLES) l = len(ranking) (l).should.be.within(0, 3)
def index_recommendation():
# build the ingredient recommendations page
ingredient_input = request.args.get('ingredient_input')
seed_recipe, unknown_ingredients = get_ingredients(ingredient_input)
# flavor_type = request.args.get('inlineRadioOptions')
flavor_type = 'any'
ningredients = int(request.args.get('ingredient_number'))
ingredients = get_recommendations(seed_recipe, flavor_type, ningredients)
unknown_list = []
if len(unknown_ingredients):
# html_alert += '<div class="alert alert-warning" role="alert">'
# html_alert += '<strong>Warning:</strong> Could not find the ingredients '
for i, ingredient in enumerate(unknown_ingredients):
string = ingredient
if i == len(unknown_ingredients) - 1:
string += '. '
else:
string += ', '
unknown_list.append(unicode(string))
print unknown_list
if len(unknown_list) > 0:
warning_message = True
else:
warning_message = False
return render_template('recommendation.html', ingredients=ingredients, seed_recipe=seed_recipe,
unknown_ingredients=unknown_list, warning_message=warning_message)
def test_get_recommendations(self):
"""Tests that get_recommendations finds the correct weighted results."""
expected_results = [
(3.3477895267131013, 'The Night Listener'),
(2.8325499182641614, 'Lady in the Water'),
(2.5309807037655645, 'Just My Luck')]
results = recommendations.get_recommendations(recommendations.critics, 'Toby')
self.assertEqual(results, expected_results)
def test_recommendations_ranges(self):
ranking = recommendations.get_recommendations(data.critics,
pick_a_name())
for score in ranking:
(score[0]).should.be.within(0, 5)
(score[1]).should.be.within(TITLES)
l = len(ranking)
(l).should.be.within(0, 3)
def recommend():
"""
total = E(score*similarity)
simSums = E(similarity)
score = total/simSums
"""
display_top_matchers(critics, 'Toby')
pprint(get_recommendations(critics, 'Toby'))
def run_examples(): import random user = USER_DICT.keys()[random.randint(0, len(USER_DICT) - 1)] print user top = recommendations.top_matches(USER_DICT, user) print top recs = recommendations.get_recommendations(USER_DICT, user)[:10] print recs url = recs[0][1] more_top = recommendations.top_matches(recommendations.transform_prefs(USER_DICT), url) print more_top
def run_examples():
import random
user = USER_DICT.keys()[random.randint(0, len(USER_DICT) - 1)]
print user
top = recommendations.top_matches(USER_DICT, user)
print top
recs = recommendations.get_recommendations(USER_DICT, user)[:10]
print recs
url = recs[0][1]
more_top = recommendations.top_matches(
recommendations.transform_prefs(USER_DICT), url)
print more_top
def recommender():
"""docstring"""
user_movies = [
request.args['movie1'], request.args['movie2'], request.args['movie3']
]
user_rating = [
request.args['rating1'], request.args['rating2'],
request.args['rating3']
]
movies = get_recommendations(user_movies, user_rating)
title = "The next movie you want to watch is: "
return render_template('recommendations.html',
movies_html=movies,
title=title)
user_path = "cache/delicious_users"
if os.path.exists(user_path ):
users = load(open(user_path,"r"))
else:
print "fetching usernames"
users = get_similar_users(u"python",count = 5)
users.append("xiaohan2012")#add myself,hehe!
dump(users,open(user_path,"w"))
print "user count: %d" %len(users)
prefs_path = "cache/delicious_prefs"
if os.path.exists(prefs_path ):
prefs = load(open(prefs_path ,"r"))
print len(prefs)
else:
print "fetching user post info"
prefs = get_prefs(users)
dump(prefs,open(prefs_path ,"w"))
ratio = [sum(prefs[user].values())/len(prefs[user].values()) for user in prefs]
ratio.sort(reverse = True)
from recommendations import top_matches,get_recommendations
import random
#user = random.choice((prefs.keys()))
print user
user = "******"
print top_matches(user,prefs)
print get_recommendations(user,prefs)
print "It is empty, I think we might have encountered data sparsity problem, haha"
return userDict
def fillItems(userDict):
allItems = {}
for user in userDict:
for _ in range(3):
try:
posts = get_userposts(user)
break
except:
print 'Failed user ' + user + ', retrying...'
time.sleep(4)
for post in posts:
url = post['url']
userDict[user][url] = 1.0
allItems[url] = 1
for ratings in userDict.values():
for item in allItems:
if item not in ratings:
ratings[item] = 0.0
if __name__ == '__main__':
delusers = initializeUserDict('programming')
fillItems(delusers)
user = delusers.keys()[random.randint(0, len(delusers) - 1)]
print user
print top_matches(delusers, user)[0:5]
print get_recommendations(delusers, user)[0:5]
import recommendations
critics = recommendations.critics
# print(critics['Lisa Rose'])
#
# print("Similarity using Euclidean Distance: \n")
# # Using Euclidean Distance
# for critic in critics:
# print("Similarity with " + critic + " : " + str(recommendations.sim_distance("Lisa Rose", critic, critics)))
#
# print("Similarity using Pearson Correlation: \n")
# # Using Correlation
# for critic in critics:
# print("Similarity with " + critic + " : " + str(recommendations.sim_pearson("Lisa Rose", critic, critics)))
print("Top 5 Matches for Lisa")
print(recommendations.top_matches("Lisa Rose", critics))
print("Recommendations for Toby")
print(recommendations.get_recommendations("Toby", critics))
movies = recommendations.flip_params(critics)
print("If you like superman returns then")
print(recommendations.top_matches("Superman Returns", movies))
from recommendations import sim_distance, sim_pearson, top_matchs, get_recommendations, get_profile
import csv_test, json, ast
from pymongo import MongoClient
client = MongoClient()
db = client.studentShareTest
csv_test.saveStudents()
studJson = csv_test.getStudents()
modelsJson = csv_test.getModels()
prefs = {stud["registry"] : stud["grades"] for stud in studJson}
db.workData.delete_many({})
for stud in studJson:
if stud == "_id":
continue
workData = {"name" : stud["name"]}
workData["registry"] = stud["registry"]
workData["warningSubjects"] = get_recommendations(prefs, stud["registry"], top_match = True)
workData["recommendedSubjects"] = get_recommendations(prefs, stud["registry"], reverse = True, top_match = True)
workData["topMatchs"] = top_matchs(prefs, stud["registry"])
workData["profile"] = get_profile(stud["grades"])
db.workData.insert_one(workData)
def get_precomputed_recommendedations(prefs, similar, user):
similar_prefs = {}
for other in similar:
similar_prefs[other] = prefs[other]
similar_prefs[user] = prefs[user]
return recommendations.get_recommendations(similar_prefs, user)
users = load(open(user_path, "r"))
else:
print "fetching usernames"
users = get_similar_users(u"python", count=5)
users.append("xiaohan2012") #add myself,hehe!
dump(users, open(user_path, "w"))
print "user count: %d" % len(users)
prefs_path = "cache/delicious_prefs"
if os.path.exists(prefs_path):
prefs = load(open(prefs_path, "r"))
print len(prefs)
else:
print "fetching user post info"
prefs = get_prefs(users)
dump(prefs, open(prefs_path, "w"))
ratio = [
sum(prefs[user].values()) / len(prefs[user].values()) for user in prefs
]
ratio.sort(reverse=True)
from recommendations import top_matches, get_recommendations
import random
#user = random.choice((prefs.keys()))
print user
user = "******"
print top_matches(user, prefs)
print get_recommendations(user, prefs)
print "It is empty, I think we might have encountered data sparsity problem, haha"
# Library imports
import recommendations, deliciousrec
# Initialize delicious users
delusers=deliciousrec.initialize_user_dict('programming')
# Add myself to the dataset
delusers['rogerfernandezg']={}
# Fills delicious users with data from delicious
deliciousrec.fill_items(delusers)
# Show recommendations for specific user
user=delusers.keys()[1]
print recommendations.top_matches(delusers,user)[0:10]
url=recommendations.get_recommendations(delusers,user)[0][1]
print recommendations.top_matches(recommendations.transform_prefs(delusers),url)
#!/usr/bin/env python import recommendations pearson_result = recommendations.get_recommendations( recommendations.critics, 'Toby' ) print "sim_pearson :", pearson_result distance_result = recommendations.get_recommendations( recommendations.critics, 'Toby', sim_func = recommendations.sim_distance ) print "sim_distance :", distance_result
def get_precomputed_recommendedations(prefs, similar, user):
similar_prefs = {}
for other in similar:
similar_prefs[other] = prefs[other]
similar_prefs[user] = prefs[user]
return recommendations.get_recommendations(similar_prefs, user)
# Lib imports
import recommendations, time
# Function's execution
t1 = time.time()
prefs=recommendations.load_movie_lens()
t2 = time.time()
print "Recomendations: {}".format(recommendations.get_recommendations(prefs,'87')[0:30])
t3 = time.time()
itemsim=recommendations.calculate_similar_items(prefs,50)
t4 = time.time()
print "Recommended items: {}".format(recommendations.get_recommended_items(prefs,itemsim,'87')[0:30])
t5 = time.time()
print "\nExecution times"
print "-----------------"
print "Load dataset: {} seconds".format(t2-t1)
print "User based filtering: {} seconds".format(t3-t2)
print "Calculate similar item: {} seconds".format(t4-t3)
print "Item based filtering: {} seconds".format(t5-t4)