def main_page(request):
import recommendations as rec
# recommended posts
# get user data
user = request.user
handler = ratings.get_handler(BlogPost)
# others = User.objects.filter().exclude(username=user)
# for test all users
users = User.objects.all()
critics = {}
for other in users:
scores = {}
for vote in ratings.get_votes_by(other):
scores[vote.content_object.title] = vote.score
# print "%s -> %s" % (vote.content_object.title, vote.score)
if scores != {}:
critics[other] = scores
print rec.getRecommendations(critics, user)
# latest avtivities from following people
following_people = [followingship.followers for followingship in user.following_set.all()]
following_people_blogposts = BlogPost.objects.filter(
user__in=following_people,
status=BlogPost.LIVE_STATUS,
).order_by('-id')
MAX_WEIGHT = 5
tags = Tag.objects.order_by('name')
# calculate tag, min and max counts.
min_count = max_count = tags[0].blogposts.count()
for tag in tags:
tag.count = tag.blogposts.count()
if tag.count < min_count:
min_count = tag.count
if max_count < tag.count:
max_count = tag.count
#calculate count range. Avoid dividing by zero.
range = float(max_count - min_count)
if range == 0.0:
range = 1.0
#calculate tag weights.
for tag in tags:
tag.weight = int(
MAX_WEIGHT * (tag.count - min_count) / range
)
variables = RequestContext(request, {
# 'username': user,
'following_people': following_people,
'blogposts': following_people_blogposts,
'show_tags': True,
'show_user': True,
'show_body': True,
'tags': tags,
})
# return render_to_response('tag_cloud_page.html', variables)
return render_to_response('main_page.html', variables)
def run():
database = db.connect()
cursor = db.getCursor(database)
t1 = time.time()
results = recommendations.getRecommendations(cursor, 3)
t2 = time.time()
prettyPrint(cursor, 3, results)
t3 = time.time()
print "REQUIRED TIME FOR RECOMENDATIONS: %0.3f ms, for querying and printing: %0.3f ms" % (
(t2 - t1) * 1000.0, (t3 - t2) * 1000.0)
results = recommendations.getRecommendations(cursor, 4)
prettyPrint(cursor, 4, results)
db.disconnect(cursor)
def recommend():
rownames1, colnames1, data1, trainingGameExamples = rec.readfile(
'TrainingGameExamples.csv')
rownames2, colnames2, data2, todaysGameExamples = rec.readfile(
'TodaysGameExamples.csv')
trainingGameExamples.update(todaysGameExamples)
print("ClearGameRecommendations")
requests.get(
"http://localhost/guerillalogisticsapi/MachineLearning/ClearGameRecommendations"
)
for key, dict in todaysGameExamples.items():
predictions = rec.getRecommendations(trainingGameExamples, key)
payload = {}
payload["key"] = key
for pair in predictions:
payload[pair[1]] = pair[0]
r = requests.get(
"http://localhost/guerillalogisticsapi/MachineLearning/SaveGameRecommendation",
params=payload)
print(key)
print("AdjustGameRecommendations")
requests.get(
"http://localhost/guerillalogisticsapi/MachineLearning/AdjustGameRecommendations"
)
def formrec():
"""
crates recommendations for user
"""
collection = db['user_ratings']
linkcollection = db['song_link']
newcollection = db['user_recommendations']
master = {}
rec_list = list(collection.distinct('uID'))
#when empty ie no similar user or recommendations
if not rec_list:
defcollection = db['song_default']
master['songs'] = ""
return master
for user in rec_list:
tempdict = {}
tempdict.setdefault(user, [])
udata = reco.getRecommendations(reco.master, user)
for data in udata:
try:
tempdict[user].append(
(list(linkcollection.find({'_id': data[1]}))[-1]))
except IndexError:
pass
#print user
master['songs'] = tempdict
#pp.pprint(master)
newcollection.save({'_id': user, 'data': master})
def main():
prefs = r.loadMovieLens(
os.getcwd() +
"/ml-100k") # Calculates the preferences using recommendations.py
movie_recommendations = r.getRecommendations(
prefs, str(sub_id)) # Calculates all recommendations for sub user.
print("\nThe most recommended movies for " + get_user(sub_id) +
" (which is the substitute) are:")
for i in range(
5): # This section prints out the recommendations for sub user.
u = movie_recommendations[i][1]
print(u)
print("\nThe least recommended movies for the substitute are: ")
for i in range(5):
u = movie_recommendations[len(movie_recommendations) - (i + 1)][1]
print(u)
similiar_item_data = r.calculateSimilarItems(
prefs) # Calculate all similar movie data.
print("\nFive similar movies to my favorite movie " + favorite_movie +
" are:")
fav_mov_sim = similiar_item_data[favorite_movie][
0:5] # For real me, some recommended movies for my preferences.
for mov in fav_mov_sim:
print(mov[1] + " " + str(mov[0]))
print("\nFive similar movies to my least favorite movie " +
least_favorite_movie + " are:")
lea_fav_mov_sim = similiar_item_data[least_favorite_movie][0:5]
for mov in lea_fav_mov_sim:
print(mov[1] + " " + str(mov[0]))
def testSanity(self):
expectList = recommendations.getRecommendations(recommendations.critics,
"Toby")
actualList = myRec.getRecommendations(myRec.critics, "Toby")
for i, j in zip(expectList, actualList):
if abs(i[0] - j[0]) > 0.0000001:
self.fail("%f %f" % (i[0], j[0]))
#self.assertAlmostEqual(i[0], j[0], 0.001)
expectList = recommendations.getRecommendations(recommendations.critics,
"Toby", recommendations.sim_distance)
actualList = myRec.getRecommendations(myRec.critics, "Toby", myRec.sim_distance)
for i, j in zip(expectList, actualList):
if abs(i[0] - j[0]) > 0.0000001:
self.fail("%f %f" % (i[0], j[0]))
def run_assignment_2_3():
print "-"*50, "\n", "assignment 2.3: User based collaborative filtering (UCF)\n", "-"*50
recommendations = reco.getRecommendations(reco.critics,'Toby',reco.sim_pearson)
for i in recommendations:
print i
print "-"*50
def get_recommendations():
user_data = json.loads(request.data)
items = getRecommendations(user_data["user_id"])
resp = make_response(
json.dumps(list(items),
sort_keys=True,
indent=4,
default=json_util.default))
return resp
def testBasics(self):
r = recommendations.getRecommendations(self.data, 'Nico',
similarity=recommendations.sim_distance)
#print recommendations.sim_distance(self.data, 'Nico', 'Yann')
#print recommendations.sim_distance(self.data, 'Nico', 'Josh')
#print recommendations.sim_distance(self.data, 'Nico', 'Kerstin')
#print r
self.assertEquals(['Mathematica', 'Chocolate', 'Patterns'],
[n for s,n in r])
def test_getRecommendations(self):
actual = rec.getRecommendations(rec.critics,'Toby')
expected = [(3.3477895267131013, 'The Night Listener'),
(2.8325499182641614, 'Lady in the Water'),
(2.5309807037655645, 'Just My Luck')]
self.assertIsInstance(actual, list)
self.assertEqual(
round_sequence(actual),
round_sequence(expected))
def _suggestions(user):
"""
The function gives the suggestions for a given user, by calling the
recommendation algorithm.
Args:
user (dict): An anonym or real user existing in the data base.
Return:
The suggestions for the user.
Raise:
An Excpetion if no suggestions are available.
"""
users = handler.get_documents('users_info') + handler.get_documents(
'frontend_users')
prefs = {}
for u in users:
prefs.update(_transform_for_suggestions(u))
reco_ids = recommendations.getRecommendations(prefs, user['_id'])
method = "pearson"
# if no recommendation found with pearson sim
# distance we use the euclidean instead
if not reco_ids:
method = "euclidean"
reco_ids = recommendations.getRecommendations(
prefs, user['_id'], recommendations.sim_distance)
suggestions = []
for score, place_id in reco_ids:
place = handler.get_documents('places', one=True,
query={'_id': place_id})
place['score'] = score
suggestions.append(place)
if not suggestions:
raise Exception("No suggestions available")
return (suggestions, method)
def testMovieLens():
import recommendations
prefs = loadMovieLens()
userId = '87'
print prefs[userId]
start = time.clock()
print recommendations.getRecommendations(prefs, userId)[0:30]
print "Total Time:" + str(time.clock() - start)
print
print "Preprocessing..."
itemMatches = recommendations.calculateSimilarItems(prefs, n=50)
print
start = time.clock()
print recommendations.getRecommendedItems(prefs, itemMatches, userId)[0:30]
print "Total Time:" + str(time.clock() - start)
def recomienda(id_usuario):
usuarios_libros = {}
try:
usuarios_libros = pickle.load(open("diccionario.p", "rb"))
if not id_usuario in usuarios_libros.keys():
calcula_valoraciones()
usuarios_libros = pickle.load(open("diccionario.p", "rb"))
except IOError:
calcula_valoraciones()
usuarios_libros = pickle.load(open("diccionario.p", "rb"))
return recommendations.getRecommendations(usuarios_libros, id_usuario)
def recomsummary():
apikey = request.args.get('apikey')
data_test_id = request.args.get('data')
k = request.args.get('k')
if apikey == 'abcd':
if data_test_id == 'none':
abort(404)
else:
vectorizer = joblib.load("vector-summary.pkl")
ids_data = joblib.load("ids.pkl")
recom = recommend.getRecommendations(ids_data, vectorizer,
data_test_id, int(k))
return jsonify(recom)
def recommendations():
apikey = request.args.get('apikey')
data_test_id = request.args.get('data')
k = request.args.get('k')
#data_test_id es el id a analizar
if apikey == 'abcd':
#obtener datos de entrenamiento de archivo vector-cover
train_data = joblib.load("vector-cover.pkl")
#obtener ids de archivo ids
ids_data = joblib.load("ids.pkl")
recom = recommend.getRecommendations(ids_data, train_data,
data_test_id, int(k))
return jsonify(recom)
def recomienda(id_usuario):
usuarios_libros={}
try:
usuarios_libros=pickle.load( open( "diccionario.p", "rb" ) )
if not id_usuario in usuarios_libros.keys():
calcula_valoraciones()
usuarios_libros=pickle.load( open( "diccionario.p", "rb" ) )
except IOError:
calcula_valoraciones()
usuarios_libros=pickle.load( open( "diccionario.p", "rb" ) )
return recommendations.getRecommendations(usuarios_libros, id_usuario)
def testDelicious():
import recommendations
delusers = initializeUserDict('programming')
fillItems(delusers)
user = delusers.keys()[random.randint(0, len(delusers) - 1)]
print user
print recommendations.topMatches(delusers, user, similarity=recommendations.simEuclidean)
recUrls = recommendations.getRecommendations(delusers, user, similarity=recommendations.simEuclidean)[0:10]
print recUrls
url = recUrls[0][1]
print url
print recommendations.topMatches(recommendations.transformPrefs(delusers), url)
def main():
print 'Audioscrobbler/Last.fm Recommendations by Chris Coykendall '
print '------------------------------------------------------------'
if network:
# Get user to recommend to
user=raw_input('Enter user to recommend to: ')
# DEBUG user='******'
numFriends=raw_input('Enter # of friends to collect listen data from: ')
print 'Fetching Last.fm API data for ' + user + '...'
# Get the fan's friends
friends=getFanFriends(user,int(numFriends))
if friends==None:
return
print 'Retrieved ' + str(len(friends)-1) + ' friends from ' + user + '...'
# Get the fan and friends top artists based on relative number of artist plays
print 'Fetching most listened artists from user and their friends...'
fanArtistData=getArtistPlaysDataSet(friends)
# Use similarity to determine top 5 similar users based on artists played
closestMatches= recommendations.topMatches(fanArtistData,user,5)
print '>>>>> Top 5 Similar Users out of ' + str(len(closestMatches)) + ' friends:'
for match in closestMatches[:5]:
print match[1] + ' (' + str(round(((match[0]+1)/2)*100,1)) + '%)'
print '<<<<<'
# Use Pearson score to determine top 20 recommended artists for user from friends listens
recArtists= recommendations.getRecommendations(fanArtistData,user)
print '>>>>> Top 20 Recommended Artists out of ' + str(len(recArtists))
sumIt=0
for match in recArtists[:20]:
print match[1] + ' (' + str(round(match[0]*20,1)) + '%)'
print '<<<<<'
# Ask whether or not to display the data set
if raw_input('Would you like to display the data dictionary (Y/N)?: ').upper()=='Y':
for user in fanArtistData:
print user
for artist in fanArtistData[user]:
print '\t' + artist + ': ' + str(round(fanArtistData[user][artist],1))
def get_recommendations(self, type='user_similarity', similarity='pearson'):
from recommendations import getRecommendations, calculateSimilarItems, get_similarity, getRecommendedItems
if type == 'user_similarity':
users_prefs = User.get_users_ratings_dict(lazy_evaluation=False)
sim = get_similarity(similarity)
return [
(star, Product.get_by_id(productid))
for star, productid in getRecommendations(users_prefs, self.id, similarity=sim)
]
elif type == 'item_similarity':
users_prefs = User.get_users_ratings_dict(lazy_evaluation=True)
similar_items = ItemSimilarity.get_similarity_dict()
return [
(star, Product.get_by_id(productid))
for star, productid in getRecommendedItems(users_prefs, similar_items, self.id)
]
else:
raise ValueError('Unknown recommendation type %s' % type)
def index():
if request.method == "POST":
results = {}
# find imdb url
url = request.form.get("ratings-url")
# parse ratings
ratings = get_ratings(url)
critics["user"] = ratings
# run getRecommendations
results = getRecommendations(critics, "user")
results = {score: imdbID for score, imdbID in results if score > 3}
return render_template("index.html", results=results, movie_names=movie_names)
else:
return render_template("index.html")
import recommendations
print('-------')
movies = recommendations.transformPrefs(recommendations.critics)
result = recommendations.topMatches(movies, 'Supermen Returns')
print(result)
print('-------')
result = recommendations.getRecommendations(movies, 'Just My Luck')
print(result)
'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0, 'Just My Luck': 2.0, 'Superman Returns': 3.0,\
'You, Me and Dupree': 2.0, 'The Night Listener': 3.0}, \
'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0, 'Superman Returns': 5.0, \
'You, Me and Dupree': 3.5, 'The Night Listener':3.0}, \
'Toby': {'Snakes on a Plane': 4.5, 'Superman Returns': 4.0, 'You, Me and Dupree': 1.0}}
list_of_recommendations = recommendations.topMatches(critics, 'Toby', n=3)
print('when algorithm of similarity is sim_distance:')
print(list_of_recommendations)
list_of_recommendations = recommendations.topMatches(
critics, 'Toby', n=3, similarity=recommendations.sim_pearson)
print('when algorithm of similarity is sim_pearson:')
print(list_of_recommendations)
person_getRecommendations = recommendations.getRecommendations(critics, 'Toby')
print('when algorithm of similarity is sim_distance:')
print(person_getRecommendations)
person_getRecommendations = recommendations.getRecommendations(
critics, 'Toby', similarity=recommendations.sim_pearson)
print('when algorithm of similarity is sim_pearson:')
print(person_getRecommendations)
print(end='\n')
movies = recommendations.transformPrefs(critics)
for item in movies:
print(item, movies[item])
print(end='\n')
movie_recommendations = recommendations.topMatches(movies, 'Superman Returns')
print sqrt(pow(4.5 - 4, 2) + pow(1 - 2, 2))
reload(recommendations)
print '欧几里得距离：'
print recommendations.sim_distance(recommendations.critics, 'Lisa Rose',
'Gene Seymour')
print '皮尔逊相关系数：'
print recommendations.sim_pearson(recommendations.critics, 'Lisa Rose',
'Gene Seymour')
print '获得最匹配者：'
print recommendations.topMatches(recommendations.critics, 'Toby', n=3)
print '获取对Toby的推荐：'
print recommendations.getRecommendations(recommendations.critics, 'Toby')
print recommendations.getRecommendations(recommendations.critics, 'Toby',
recommendations.sim_distance)
movies = recommendations.transformPrefs(recommendations.critics)
print '调换人与物品：'
print movies
print '打印相似类型的电影：'
print recommendations.topMatches(movies, 'Superman Returns')
print recommendations.getRecommendations(movies, 'Just My Luck')
# import pydelicious
# print pydelicious.get_popular(tag='programming')
# print '开始填充数据：'
from recommendations import critics from recommendations2 import moviesgrade from recommendations import sim_distance from recommendations import sim_pearson from recommendations import topMatches from recommendations import pearsonTopMatches from recommendations import distanceTopMatches from recommendations import getRecommendations from recommendations2 import transformPrefs #print(distanceTopMatches(critics, 'Toby', n=3)) # transformPrefs()는 Map 객체의 key값과 value값을 교환함 movies = transformPrefs(critics) # Superman Returns과 유사한 영화 top 3 print(topMatches(movies, 'Superman Returns', 2)) # 평론가 추천 print(getRecommendations(movies, 'Just My Luck'))
import recommendations as rec
# for person in rec.critics:
# print ("Euklid Top Match: \t" + person + "\t" + str(rec.topMatches(rec.critics, person, rec.sim_euclid)[0]))
# print ("Pearson Top Match: \t" + person + "\t" + str(rec.topMatches(rec.critics, person, rec.sim_pearson)[0]))
print rec.getRecommendations(rec.critics, 'Toby Segaran', rec.sim_pearson)
def get_recommendations():
user_data = json.loads(request.data)
items = getRecommendations(user_data["user_id"])
resp = make_response(json.dumps(list(items), sort_keys=True, indent=4, default=json_util.default))
return resp
if key == '':
continue
user_dict[key] = eval(r.hget(user_dict_key,key))
return user_dict
if __name__ == '__main__':
#user_dict = initializeUserDict('programming')
#fillItems(user_dict)
#save_user_dict(user_dict)
user_dict = get_user_dict('user_dict')
# Find users most like a given user
user = user_dict.keys()[random.randint(0, len(user_dict)-1)]
top_matches = recommendations.topMatches(user_dict, user)
print "Top matches for", user, ":"
print "{0:6} {1}".format( "Score", "Name")
for match in top_matches:
print "{0:.4f} {1:6}".format(match[0], match[1])
print "\nRecommended URLs for", user, ":"
recs = recommendations.getRecommendations(user_dict, user)[0:10]
print "{0:6} {1}".format( "Score", "URL")
for item in recs:
print "{0:.4f} {1}".format(item[0], item[1])
print "placeholder for breakpoint"
print "distance" print recommendations.sim_distance(recommendations.critics, 'ming', 'lin') print recommendations.sim_distance(recommendations.critics, 'ming', 'michael') print recommendations.sim_distance(recommendations.critics, 'ming', 'mick') print "pearson" print recommendations.sim_pearson(recommendations.critics, 'ming', 'lin') print recommendations.sim_pearson(recommendations.critics, 'ming', 'michael') print recommendations.sim_pearson(recommendations.critics, 'ming', 'mick') print "top match" print recommendations.topMatches(recommendations.critics, 'ming', n = 3) print "recommendation" print recommendations.getRecommendations(recommendations.critics, 'ming') print "recommend items" items = recommendations.transformPrefs(recommendations.critics) print recommendations.topMatches(items, 'lady in the water', n =3) print recommendations.sim_pearson(items, 'lady in the water', 'snake on a plane') print "item similarity use distance" print recommendations.calculateSimilarItems(recommendations.critics) print 'item similarity use pearson' print recommendations.calculateSimilarItems(recommendations.critics, similarity = recommendations.sim_pearson) print 'recommendation base on items' itemsim = recommendations.calculateSimilarItems(recommendations.critics) print recommendations.getRecommendedItems(recommendations.critics, itemsim, 'ming')
def recommendByUser(self, event):
if not self.isValid(): # 保证输入框存在有效值
return
self.resultList = recommendations.getRecommendations(
self.prefs, self.userId)[0:self.maxNum]
self.showResult()
import recommendations
print('-------')
result = recommendations.getRecommendations(recommendations.critics, 'Toby')
print(result)
print('-------')
result = recommendations.getRecommendations(recommendations.critics, 'Toby', similarity=recommendations.sim_distance)
print(result)
# recommendationTest.py
import recommendations
itemsim = recommendations.calculateSimilarItems(recommendations.critics)
'''
recommendedList = recommendations.getRecommendedItems(recommendations.critics, itemsim, 'Toby')
print(recommendedList)
'''
prefs = recommendations.loadMovieLens()
# print prefs['87']
print 'user-based recommendations : '
recommendeduser = recommendations.getRecommendations(prefs, '87')[0:30]
print recommendeduser
print 'item-based recommendations : '
itemsim = recommendations.calculateSimilarItems(prefs, n=50)
recommendeditems = recommendations.getRecommendedItems(prefs, itemsim,
'87')[0:30]
print recommendeditems
movie_titles.append(title)
return movie_titles,ratings
if __name__=='__main__':
moviefile = open("u.data","r")
pref_dic = {}
rec=[]
for line in moviefile:
(user_id, item_id, rating, timestamp ) = line.split('\t')
if user_id in pref_dic:
pref_dic[user_id][item_id] = rating
else:
pref_dic[user_id] = {}
result = recommendations.getRecommendations(pref_dic, '33')
rec.append(result)
bottom5= result[-5:]
top5 = result[:5]
imdbfile=open("u.item","r")
top_titles,rate=getTitles(imdbfile, top5)
print "Top Movies are"
for i in range(0,len(top_titles)):
#fillIteams(delusers) import recommendations #print recommendations.critics['Lisa Rose']['Lady in the Water'] print recommendations.critics['Lisa Rose'] print recommendations.critics['Gene Seymour'] print recommendations.sim_distance(recommendations.critics,'Lisa Rose','Gene Seymour') print recommendations.sim_pearson(recommendations.critics,'Lisa Rose','Gene Seymour') print recommendations.topMatches(recommendations.critics,'Toby',n=3) print recommendations.topMatches(recommendations.critics,'Toby',n=3,similarity=recommendations.sim_distance) recommendations.getRecommendations(recommendations.critics,'Toby') recommendations.getRecommendations(recommendations.critics,'Toby',similarity=recommendations.sim_distance) movies=recommendations.transformPrefs(recommendations.critics) #print movies recommendations.topMatches(movies,'Superman Returns') recommendations.getRecommendations(movies,'Just My Luck') recommendations.getRecommendations(movies,'Lady in the Water') itemsim=recommendations.calculateSimilarItems(recommendations.critics,n=8) itemsim
# instantiate network
network = pl.get_lastfm_network()
# get Band
band = network.get_artist("J.B.O")
topfans = band.get_top_fans(10)
group = [a.item for a in topfans]
#print band
#print topfans
#print group
userDict = recom.createLastfmUserDict(group)
selectedUser = group[1].get_name()
print "Selected User: "******"===================================="
topMatches = recom.topMatches(userDict, group[1].get_name(), recom.sim_euclid)
print "Top Matches: "
pp.pprint(topMatches)
print "===================================="
recommendations = recom.getRecommendations(userDict, group[1].get_name(),
recom.sim_euclid)
print "Recommendations: "
pp.pprint(recommendations)
print "===================================="
#print result[0][1].key()
if cache_dict and exists(items_cache_file):
print "Reading the items from cache file", items_cache_file
cache_file = open(items_cache_file, "rb")
user_url_tuple = load(cache_file)
cache_file.close()
else:
user_url_tuple = sorted([(user, post["url"], ) for user in user_dict for post in get_userpost_details(user)])
if cache_dict:
print "Saving to cache file"
cache_file = open(items_cache_file, "wb")
dump(user_url_tuple, cache_file)
cache_file.close()
#Very Imperative
grouped_by_values = groupby(user_url_tuple, lambda (user, _): user)
distinct_url_sequence = map(lambda (_, url) : (url, 0), user_url_tuple)
for user in user_dict:
user_dict[user] = dict(distinct_url_sequence)
for user, grouped_values in grouped_by_values:
for _, url in grouped_values:
user_dict[user][url] = 1
user_dict = initializeUserDict('technology', count=10)
fillItems(user_dict)
user = user_dict.keys()[random.randint(0, len(user_dict) - 1)]
print "Top matches for ", user, " are ", topMatches(user_dict, user)
print "Recommendations for user ", user, " are ", getRecommendations(user_dict, user)
# Commands used to demonstrate / use functions created in Collective Intelligence import recommendations from recommendations import critics # pg 9: critics['Lisa Rose']['Lady in the Water'] critics['Toby']['Snakes on a Plane'] = 4.5 critics['Toby'] # pg 11 recommendations.sim_distance( recommendations.critics, 'Lisa Rose', 'Gene Seymour' ) # pg 14 recommendations.sim_pearson( recommendations.critics, 'Lisa Rose', 'Gene Seymour' ) # pg 15 recommendations.topMatches( recommendations.critics, 'Toby', n = 3 ) # pg 17 recommendations.getRecommendations(recommendations.critics, 'Toby') recommendations.getRecommendations(recommendations.critics, 'Toby', similarity = recommendations.sim_distance) movies = recommendations.transformPrefs( recommendations.critics ) recommendations.topMatches(movies, 'Superman Returns') recommendations.getRecommendations(movies, 'Just My Luck')
def recommended_for_you(self, you):
prefs = self.prefs
recs = recommendations.getRecommendations(prefs, str(you))
total = len(recs)
return (recs[0:5], recs[total-5:total])
def get_worst_recomendations(id, data):
new_data = recommendations.getRecommendations(data, id)
new_data.reverse()
print('\n Worst Recommendation\n',new_data[0:5])
from recommendations import critics
from recommendations import sim_distance
from recommendations import sim_pearson
from recommendations import pearsonTopMatches
from recommendations import distanceTopMatches
from recommendations import getRecommendations
print(critics['Lisa Rose']['Lady in the Water'])
critics['Toby']['Snakes on a Plane'] = 5.0
print(critics['Toby'])
result = {
'euclidean distance socre': sim_distance(critics, 'Lisa Rose',
'Gene Seymour')
}
print(result)
result = {
'Pearson correlation score': sim_pearson(critics, 'Lisa Rose',
'Gene Seymour')
}
print(result)
print(distanceTopMatches(critics, 'Toby', n=3))
print(pearsonTopMatches(critics, 'Toby', n=3))
print('-------------------------------------')
print(getRecommendations(critics, 'Toby'))
print('-------------------------------------')
print('-------------------------------------')
print(getRecommendations(critics, 'Toby', similarity=sim_distance))
def get_top_recomendations(id,data):
output = recommendations.getRecommendations(data, id)
print('\n Top Recommendation\n',output[0:5])
try:
posts = get_userposts(user)
break
except:
print "Failed user " + user + ",retrying"
time.sleep(4)
for post in posts:
url = post['url']
user_dict[user][url] = 1.0
all_items[url] = 1
# Fill in missing items with 0
for ratings in user_dict.values():
for item in all_items:
if item not in ratings:
ratings[item] = 0.0
if __name__ == "__main__":
delusers = initializeUserDict('programming')
delusers['onerhao'] = {}
fillItems(delusers)
print delusers
user = delusers.keys()[random.randint(0, len(delusers) - 1)]
print user
print recommendations.topMatches(delusers, user)
print recommendations.getRecommendations(delusers, user)
# url = recommendations.getRecommendations(delusers, user)[0][1]
# print recommendations.topMatches(
# recommendations.transformPrefs(delusers), url)
######################################################
delusers = deliciousrec.initializeUserDict('gratis')
delusers['lpagliari'] = {} # Add yourself to the dictionary if you use delicious
deliciousrec.fillItems(delusers)
delusers
###########################
# pick random user and find users similar to him/her:
import recommendations
reload(recommendations)
import random
user = delusers.keys()[random.randint(0, len(delusers) - 1)]
user
recommendations.topMatches(delusers, user)
###########################
# get recommendations for a user:
recommendations.getRecommendations(delusers, user)[0: 10]
reload(recommendations)
itemsim = recommendations.calculateSimilarItems(recommendations.critics)
itemsim
recommendations.getRecommendedItems(recommendations.critics, itemsim, 'Toby')
prefs = recommendations.loadMovieLens()
prefs['87']
recommendations.getRecommendations(prefs,'87')[0: 30]
if choice == '1':
user = raw_input('enter the user ID:\t')
print '\n'
print 'Books till now read by the user are:\n'
print critics[user], '\n'
dischoice = raw_input(
'enter your choice\n1.euclidian distance\n2.pearson corelation\nchoice:\t'
)
if dischoice == '1':
print 'top matched user according to the euclidian distance\n'
print recommendations.topMatches(critics, user, 3,
recommendations.sim_distance), '\n'
print 'Recommended books according to the euclidian distance\n'
print recommendations.getRecommendations(
critics, user, similarity=recommendations.sim_distance)[0:3], '\n'
else:
if dischoice == '2':
print 'top matched user according to the pearson corelation\n'
print recommendations.topMatches(critics, user, 3), '\n'
print 'Recommended books according to the pearson corelaton\n'
print recommendations.getRecommendations(critics, user)[0:3], '\n'
else:
print 'invalid choice... try again'
else:
if choice == '2':
critic = recommendations.transformPrefs(critics)
book = raw_input('enter the book name:\t')
print '\n'
print 'Users who have read the book till now are:\n'
print critic[book], '\n'
#!/usr/bin/python import recommendations list=[] prefs=recommendations.loadMovieLens() list=recommendations.getRecommendations(prefs,"xxx") for i in range(xxx): print list[i]
def recommendByUser(self, event):
if not self.isValid(): # 保证输入框存在有效值
return
self.resultList = recommendations.getRecommendations(self.prefs,
self.userId)[0:self.maxNum]
self.showResult()
import recommendations as rec
prefs = rec.loadMovieLens("../data/ml-100k")
table = open("../docs/q3_table.tex", "w")
table.write("\\begin{longtable}{| c | c |}\n")
table.write("\\hline\n")
table.write("Movie & Ranking\\\\\n")
recs = rec.getRecommendations(prefs, '929', best=True)
for r in recs:
table.write("\\hline\n {} & {:.3f}\\\\\n".format(r[1].replace('&', '\&'), r[0]))
table.write("\\hline\n")
table.write("\\caption{Ranking for unseen movies}\n")
table.write("\\label{tab:myfirstlongtable}\n")
table.write("\\end{longtable}\n")
table.write("Top 5 recommendations:\n")
table.write("\\begin{enumerate}\n")
for r in recs[:5]:
table.write("\\item {}\n".format(r[1]))
table.write("\\end{enumerate}\n")
recs = rec.getRecommendations(prefs, '929', best=False)
table.write("Bottom 5 recommendations:\n")
table.write("\\begin{enumerate}\n")
for r in recs[:5]:
table.write("\\item {}\n".format(r[1]))
table.write("\\end{enumerate}\n")
table.close()
'Gene Seymour'))
# 0.14814814814814814 (different from book???)
pprint(
recommendations.sim_pearson(recommendations.critics, 'Lisa Rose',
'Gene Seymour'))
# 0.39605901719066977 (the same as book)
# 为Toby寻找品味相同的用户
pprint(recommendations.topMatches(recommendations.critics, 'Toby', n=3))
# [(0.9912407071619299, 'Lisa Rose'),
# (0.9244734516419049, 'Mick LaSalle'),
# (0.8934051474415647, 'Claudia Puig')]
# 为Toby寻找他可能感兴趣的电影（基于用户的协作型过滤，用欧几里德距离作为相似度评估）
pprint(recommendations.getRecommendations(recommendations.critics, 'Toby'))
# [(3.3477895267131017, 'The Night Listener'),
# (2.8325499182641614, 'Lady in the Water'),
# (2.530980703765565, 'Just My Luck')]
# 为Toby寻找他可能感兴趣的电影（基于用户的协作型过滤，用皮尔逊系数作为相似度评估）
pprint(
recommendations.getRecommendations(
recommendations.critics,
'Toby',
similarity=recommendations.sim_distance))
# [(3.5002478401415877, 'The Night Listener'),
# (2.7561242939959363, 'Lady in the Water'),
# (2.461988486074374, 'Just My Luck')]
# 为Superman Returns寻找相似的电影
if __name__ == '__main__':
# stupid little demo
import recommendations
print 'getting users...'
users = initializeUserDict('programming')
users['thakis'] = {}
users['ytamshg'] = {}
users['tubanator'] = {}
print 'getting their posts (takes a while) ...'
fillItems(users)
print 'Distances:'
print recommendations.sim_distance(users, 'thakis', 'ytamshg')
print recommendations.sim_distance(users, 'thakis', 'tubanator')
print recommendations.sim_distance(users, 'tubanator', 'ytamshg')
print 'Pearsons:'
print recommendations.sim_pearson(users, 'thakis', 'ytamshg')
print recommendations.sim_pearson(users, 'thakis', 'tubanator')
print recommendations.sim_pearson(users, 'tubanator', 'ytamshg')
print 'Recommendations:'
print recommendations.getRecommendations(users, 'thakis')[0:10]
print recommendations.getRecommendations(users, 'ytamshg')[0:10]
print recommendations.getRecommendations(users, 'tubanator')[0:10]
user_url_tuple = load(cache_file)
cache_file.close()
else:
user_url_tuple = sorted([(
user,
post["url"],
) for user in user_dict for post in get_userpost_details(user)])
if cache_dict:
print "Saving to cache file"
cache_file = open(items_cache_file, "wb")
dump(user_url_tuple, cache_file)
cache_file.close()
#Very Imperative
grouped_by_values = groupby(user_url_tuple, lambda (user, _): user)
distinct_url_sequence = map(lambda (_, url): (url, 0), user_url_tuple)
for user in user_dict:
user_dict[user] = dict(distinct_url_sequence)
for user, grouped_values in grouped_by_values:
for _, url in grouped_values:
user_dict[user][url] = 1
user_dict = initializeUserDict('technology', count=10)
fillItems(user_dict)
user = user_dict.keys()[random.randint(0, len(user_dict) - 1)]
print "Top matches for ", user, " are ", topMatches(user_dict, user)
print "Recommendations for user ", user, " are ", getRecommendations(
user_dict, user)
def print_UBRecommendations(critics, userId='60255'):
werec = recommendations.getRecommendations(critics, userId)[0:30]
print "\n---------User-based Recommendations using 60255----------\n"
# Print out n recommendations
convertId_to_title(werec, 5)
# Find similarity between two users Euclidian distance
print(
recommendations.sim_distance(recommendations.critics, 'Lisa Rose',
'Gene Seymour'))
# Find similarity between two users Pearson distance
print(
recommendations.sim_pearson(recommendations.critics, 'Lisa Rose',
'Gene Seymour'))
#Find similar users
print(recommendations.topMatches(recommendations.critics, 'Toby', n=3))
#Get recommendations of movies default=Pearson
print(recommendations.getRecommendations(recommendations.critics, 'Toby'))
#Get recommendations of movies using Euclidian
print(
recommendations.getRecommendations(
recommendations.critics,
'Toby',
similarity=recommendations.sim_distance))
# Transpose the matrix to movies vs user
movies = recommendations.transformPrefs(recommendations.critics)
#Print similar movies
print(recommendations.topMatches(movies, 'Superman Returns'))
#Similarity matirx of items
def userRec(user):
"""
Regresa las recomendaciones para "user", basándose en la información de
MovieLens guardada en prefs
"""
return recommendations.getRecommendations(prefs, user)[0:20]
# instantiate network
network = pl.get_lastfm_network()
# get Band
band = network.get_artist("J.B.O")
topfans = band.get_top_fans(10)
group=[a.item for a in topfans]
#print band
#print topfans
#print group
userDict = recom.createLastfmUserDict(group)
selectedUser = group[1].get_name()
print "Selected User: "******"===================================="
topMatches = recom.topMatches(userDict, group[1].get_name(), recom.sim_euclid)
print "Top Matches: "
pp.pprint(topMatches)
print "===================================="
recommendations = recom.getRecommendations(userDict, group[1].get_name(), recom.sim_euclid)
print "Recommendations: "
pp.pprint(recommendations)
print "===================================="
#print result[0][1].key()
__author__ = 'ponomarevandrew' from recommendations import critics import recommendations print critics['Lisa Rose']['Lady in the Water'] critics['Toby']['Snakes on a Plane'] = 4.5 print critics['Toby'] print critics print "-----------" print recommendations.getRecommendations(recommendations.critics, 'Toby') print "-----------" movies = recommendations.transformPrefs(recommendations.critics) print recommendations.topMatches(movies, 'Superman Returns') print "-----------" itemsim = recommendations.calculateSimilarItems(recommendations.critics) print itemsim print "-----------" print recommendations.getRecommendedItems(recommendations.critics, itemsim, 'Toby') print "-----------" prefs = recommendations.loadMovieLens() print prefs['1'] print "-----------" print recommendations.getRecommendations(prefs,'42')[31:60] print "-----------" ##itemsim = recommendations.calculateSimilarItems(prefs, n =50) ##print itemsim print "-----------" ##print recommendations.getRecommendedItems(prefs,itemsim, '87')[0:30] print "-----------"
for i 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']
user_dict[user][url] = 1.0
all_items[url] = 1
# Fill in missing items with 0
for ratings in user_dict.values():
for item in all_items:
if item not in ratings:
ratings[item] = 0.0
if __name__ == "__main__":
delusers = initializeUserDict('programming')
delusers['onerhao'] = {}
fillItems(delusers)
print delusers
user = delusers.keys()[random.randint(0, len(delusers) - 1)]
print user
print recommendations.topMatches(delusers, user)
print recommendations.getRecommendations(delusers, user)
# url = recommendations.getRecommendations(delusers, user)[0][1]
# print recommendations.topMatches(
# recommendations.transformPrefs(delusers), url)
return tag_map
if __name__ == '__main__':
# stupid little demo
import recommendations
print 'getting users...'
users = initializeUserDict('programming')
users['thakis'] = {}
users['ytamshg'] = {}
users['tubanator'] = {}
print 'getting their posts (takes a while) ...'
fillItems(users)
print 'Distances:'
print recommendations.sim_distance(users, 'thakis', 'ytamshg')
print recommendations.sim_distance(users, 'thakis', 'tubanator')
print recommendations.sim_distance(users, 'tubanator', 'ytamshg')
print 'Pearsons:'
print recommendations.sim_pearson(users, 'thakis', 'ytamshg')
print recommendations.sim_pearson(users, 'thakis', 'tubanator')
print recommendations.sim_pearson(users, 'tubanator', 'ytamshg')
print 'Recommendations:'
print recommendations.getRecommendations(users, 'thakis')[0:10]
print recommendations.getRecommendations(users, 'ytamshg')[0:10]
print recommendations.getRecommendations(users, 'tubanator')[0:10]
def test_getRecommendations(self):
self.assertEqual(
recommendations.getRecommendations(recommendations.critics,
'Toby')[0],
(3.3477895267131013, 'The Night Listener'))
def test_getRecommendations(self):
self.assertEqual(recommendations.getRecommendations(recommendations.critics,'Toby')[0],(3.3477895267131013, 'The Night Listener'))
