def RMSEMAE_distr(output_filename):
path = "TwitterRatings/funkSVD/params/"
datos = {}
for param in os.listdir(path):
datos[param] = {}
for value in os.listdir(path + param):
rmses = []
maes = []
with open(path + param + '/' + value, 'r') as f:
for line in f:
rmse, mae = line.strip().split('\t')
rmses.append(float(rmse))
maes.append(float(mae))
rmse_mean, rmse_stdev = mean(rmses), stdev(rmses)
mae_mean, mae_stdev = mean(maes), stdev(maes)
datos[param][value[:-4]] = [[rmse_mean, rmse_stdev],
[mae_mean, mae_stdev]]
with open("TwitterRatings/funkSVD/" + output_filename, 'w') as f:
for param in datos:
f.write("%s\n" % param)
for v in sorted(datos[param].items()):
#<value> <RMSE_mean>,<RMSE_stdev> <MAE_mean>,<MAE_stdev>
f.write("%s\t%s,%s\t%s,%s\n" %
(v[0], v[1][0][0], v[1][0][1], v[1][1][0], v[1][1][1]))
def svd_protocol_evaluation(data_path, params):
solr = "http://localhost:8983/solr/grrecsys"
test_c = consumption(
ratings_path=data_path + 'test/test_N20.data',
rel_thresh=0,
with_ratings=True
) #debiera ser el test_c, pero como includeRated=False, da lo mismo
train_c = consumption(ratings_path=data_path + 'eval_train_N20.data',
rel_thresh=0,
with_ratings=False)
svd = pyreclab.SVD(dataset=data_path + 'eval_train_N20.data',
dlmchar=b',',
header=False,
usercol=0,
itemcol=1,
ratingcol=2)
svd.train(factors=params['f'],
maxiter=params['mi'],
lr=params['lr'],
lamb=params['lamb'])
recommendationList, map, ndcg = svd.testrec(input_file=data_path +
'test/test_N20.data',
dlmchar=b',',
header=False,
usercol=0,
itemcol=1,
ratingcol=2,
topn=100,
relevance_threshold=0,
includeRated=False)
MRRs = dict((N, []) for N in [5, 10, 15, 20])
nDCGs = dict((N, []) for N in [5, 10, 15, 20])
APs = dict((N, []) for N in [5, 10, 15, 20])
Rprecs = dict((N, []) for N in [5, 10, 15, 20])
for userId, recList in recommendationList.items():
book_recs = remove_consumed(
user_consumption=train_c[userId],
rec_list=recommendationList[userId]
) #da lo mismo este paso, según Gabriel el testrec no devuelve items consumidos
book_recs = recs_cleaner(solr=solr,
consumpt=train_c[userId],
recs=book_recs[:100])
recs = user_ranked_recs(user_recs=book_recs,
user_consumpt=test_c[userId])
for N in [5, 10, 15, 20]:
mini_recs = dict((k, recs[k]) for k in list(recs.keys())
[:N]) #python 3.x: list() es necesario
MRRs[N].append(MRR(recs=mini_recs, rel_thresh=1))
nDCGs[N].append(
nDCG(recs=mini_recs, alt_form=False, rel_thresh=False))
APs[N].append(AP_at_N(n=N, recs=recs, rel_thresh=1))
Rprecs[N].append(R_precision(n_relevants=N, recs=mini_recs))
for N in [5, 10, 15, 20]:
with open('TwitterRatings/funkSVD/clean/protocol.txt', 'a') as file:
file.write( "N=%s, nDCG=%s, MAP=%s, MRR=%s, R-precision=%s\n" % \
(N, mean(nDCGs[N]), mean(APs[N]), mean(MRRs[N]), mean(Rprecs[N])) )
def nDCGMAP_calculator(data_path, params, topN, output_filename):
user_consumption = consumption(ratings_path=data_path + 'ratings.total',
rel_thresh=0,
with_ratings=True)
svd = pyreclab.SVD(
dataset=data_path + 'ratings.train', #data_path+'train/train.'+str(i),
dlmchar=b',',
header=False,
usercol=0,
itemcol=1,
ratingcol=2)
svd.train(factors=params['f'],
maxiter=params['mi'],
lr=params['lr'],
lamb=params['lamb'])
recommendationList = svd.testrec(
input_file=data_path + 'test/' +
os.listdir(data_path + 'test/')[0], #data_path+'val/val.'+str(i),
dlmchar=b',',
header=False,
usercol=0,
itemcol=1,
ratingcol=2,
topn=100,
includeRated=False)
MRR_thresh4 = []
MRR_thresh3 = []
nDCGs_bin_thresh4 = dict((n, []) for n in topN)
nDCGs_bin_thresh3 = dict((n, []) for n in topN)
nDCGs_normal = dict((n, []) for n in topN)
nDCGs_altform = dict((n, []) for n in topN)
APs_thresh4 = dict((n, []) for n in topN)
APs_thresh3 = dict((n, []) for n in topN)
APs_thresh2 = dict((n, []) for n in topN)
for userId in recommendationList[0]:
recs = user_ranked_recs(user_recs=recommendationList[0][userId],
user_consumpt=user_consumption[userId])
MRR_thresh4.append(MRR(recs=recs, rel_thresh=4))
MRR_thresh3.append(MRR(recs=recs, rel_thresh=3))
for n in topN:
mini_recs = dict((k, recs[k]) for k in recs.keys()[:n])
nDCGs_bin_thresh4[n].append(
nDCG(recs=mini_recs, alt_form=False, rel_thresh=4))
nDCGs_bin_thresh3[n].append(
nDCG(recs=mini_recs, alt_form=False, rel_thresh=3))
nDCGs_normal[n].append(
nDCG(recs=mini_recs, alt_form=False, rel_thresh=False))
nDCGs_altform[n].append(
nDCG(recs=mini_recs, alt_form=True, rel_thresh=False))
APs_thresh4[n].append(AP_at_N(n=n, recs=recs, rel_thresh=4))
APs_thresh3[n].append(AP_at_N(n=n, recs=recs, rel_thresh=3))
APs_thresh2[n].append(AP_at_N(n=n, recs=recs, rel_thresh=2))
with open('TwitterRatings/funkSVD/' + output_filename, 'a') as file:
for n in topN:
file.write( "N=%s, normal nDCG=%s, alternative nDCG=%s, bin nDCG(rel_thresh=4)=%s, bin nDCG(rel_thresh=3)=%s, MAP(rel_thresh=4)=%s, MAP(rel_thresh=3)=%s, MAP(rel_thresh=2)=%s, MRR(rel_thresh=4)=%s, MRR(rel_thresh=3)=%s\n" % \
(n, mean(nDCGs_normal[n]), mean(nDCGs_altform[n]), mean(nDCGs_bin_thresh4[n]), mean(nDCGs_bin_thresh3[n]), mean(APs_thresh4[n]), mean(APs_thresh3[n]), mean(APs_thresh2[n]), mean(MRR_thresh4), mean(MRR_thresh3)) )
def SVDJob(data_path, params):
# test = []
# with open(data_path+'test/test.fold', 'r') as f:
# for line in f:
# test.append( line.strip() )
val_folds = os.listdir(data_path + 'val/')
maes, rmses = [], []
"""HARDCODED AS F**K: 4+1, N20""" #Rationale: N20 por ser folds más chicos. Así, ninguno de estos folds se interlapa con alguno de testing.
for i in range(1, 4 + 1):
svd = pyreclab.SVD(dataset=data_path + 'train/train_N20.' + str(i),
dlmchar=b',',
header=False,
usercol=0,
itemcol=1,
ratingcol=2)
svd.train(factors=params['f'],
maxiter=params['mi'],
lr=params['lr'],
lamb=params['lamb'])
predlist, mae, rmse = svd.test(input_file=data_path + 'val/val_N20.' +
str(i),
dlmchar=b',',
header=False,
usercol=0,
itemcol=1,
ratingcol=2)
maes.append(mae)
rmses.append(rmse)
return mean(maes), mean(rmses)
def statistics_protocol(data_path, N, folds):
logging.info( "N={N}".format(N=N) )
all_c = consumption(ratings_path= data_path+'eval_all_N'+str(N)+'.data', rel_thresh= 0, with_ratings= True)
# #users, #items, #ratings, avg. rating
items = set()
users = set()
ratings = []
with open(data_path+'eval_all_N'+str(N)+'.data', 'r') as f:
for line in f:
(userId,itemId,rating) = line.split(',')
items.add(itemId)
users.add(userId)
ratings.append(int(rating))
logging.info( "#users={users}".format(users= len(users)) )
logging.info( "#users={users}".format(users= len(items)) )
logging.info( "#ratings={ratings}".format(ratings= len(ratings)) )
logging.info( "avg. rating={mean}±{stdev}".format(mean= mean(ratings), stdev= stdev(ratings)) )
# Ratings por item
item_ratings = dict((itemId, []) for itemId in items)
for user in all_c:
for item in all_c[user]:
item_ratings[item].append( all_c[user][item] )
ratings_per_item = []
for item in item_ratings:
ratings_per_item.append( len(item_ratings[item]) )
logging.info( "promedio de ratings por item: {mean}±{stdev}".format(mean= mean(ratings_per_item), stdev= stdev(ratings_per_item)) )
#Ratings por usuario
ratings_per_user = []
for user in all_c:
ratings_per_user.append( len(all_c[user]) )
logging.info( "promedio de ratings por usuario: {mean}±{stdev}".format(mean= mean(ratings_per_user), stdev= stdev(ratings_per_user)) )
#Sparsity
count = 0
for freq in ratings_per_item:
count += freq / float( len(users) )
count = count / float( len(items) )
count = count*100
logging.info( "density: {}".format(count) )
def statistics(db_conn):
"""Hay que correrlo en python 2.x"""
c = db_conn.cursor()
table_name = 'user_reviews'
c.execute( "SELECT * FROM {0} ORDER BY timestamp asc".format(table_name) )
all_rows = c.fetchall()
interactions = []
logging.info("-> Iterando sobre resultado de la consulta..")
for tupl in all_rows:
user_id, url_review, rating, url_book, timestamp = tupl
try:
book_id = url_book.split('/')[-1].split('-')[0].split('.')[0]
except AttributeError as e:
logging.info( "url_book es NULL en la DB! Tratado de obtener desde la review {0}".format(url_review) )
continue
interactions.append( (user_id, book_id, rating, int(timestamp)) )
ratings = [ tuple[2] for tuple in interactions ]
users = [ tuple[0] for tuple in interactions ]
users = set(users)
logging.info( "MEAN ratings:{avgrat}±{stddevrat}".format(avgrat=mean(ratings), stddevrat=stddev(ratings)) )
c.execute( "SELECT user_id, COUNT(rating), timestamp FROM {} GROUP BY user_id ORDER BY timestamp asc".format(table_name) )
all_rows = c.fetchall()
results = []
for tupl in all_rows:
user_id, ratings, timestamp= tupl
if timestamp != None: results.append( (user_id, ratings) )
rats_usr = [ tuple[1] for tuple in results ]
logging.info( "MEAN ratings per user:{avgratusr}±{stddevratusr}".format(avgratusr=mean(rats_usr), stddevratusr=stddev(rats_usr)) )
c.execute( "SELECT url_book, COUNT(rating), timestamp FROM {} GROUP BY url_book ORDER BY timestamp asc".format(table_name) )
all_rows = c.fetchall()
results = []
for tupl in all_rows:
url_book, ratings, timestamp= tupl
if timestamp != None: results.append( (user_id, ratings) )
rats_book = [ tuple[1] for tuple in results ]
logging.info( "MEAN ratings per book:{avgratbook}±{stddevratbook}".format(avgratbook=mean(rats_book), stddevratbook=stddev(rats_book)) )
def option1_protocol_evaluation(data_path, which_model, metric):
solr = 'http://localhost:8983/solr/grrecsys'
# userId='113447232' 285597345
test_c = consumption(ratings_path=data_path + 'test/test_N20.data',
rel_thresh=0,
with_ratings=True)
train_c = consumption(ratings_path=data_path + 'eval_train_N20.data',
rel_thresh=0,
with_ratings=False)
MRRs = dict((N, []) for N in [5, 10, 15, 20])
nDCGs = dict((N, []) for N in [5, 10, 15, 20])
APs = dict((N, []) for N in [5, 10, 15, 20])
Rprecs = dict((N, []) for N in [5, 10, 15, 20])
docs2vec = np.load('./w2v-tmp/' + which_model + '/docs2vec_' +
which_model + '.npy').item()
if which_model == 'twit':
vector_size = 200
else:
vector_size = 300
t = AnnoyIndex(vector_size, metric=metric)
t.load('./w2v-tmp/' + which_model + '/doc_vecs_t100_' + metric + '_' +
which_model + '.tree')
num_to_grId = np.load('./w2v-tmp/' + which_model + '/num_to_grId_' +
metric + '_' + which_model + '.npy').item()
grId_to_num = np.load('./w2v-tmp/' + which_model + '/grId_to_num_' +
metric + '_' + which_model + '.npy').item()
i = 1
for userId in test_c:
logging.info("MODO 1. {0} de {1}. User ID: {2}".format(
i, len(test_c), userId))
i += 1
book_recs = []
for bookId in train_c[userId]:
try:
docs = t.get_nns_by_item(grId_to_num[bookId], 500)
book_recs.append(
[str(num_to_grId[doc_num]) for doc_num in docs])
except KeyError as e:
logging.info(
"{} ES UNO DE LOS LIBROS CUYO HTML NO PUDO SER DESCARGADO. PROSIGUIENDO CON EL SIGUIENTE LIBRO.."
.format(bookId))
continue
book_recs = flatten_list(list_of_lists=book_recs,
rows=len(
book_recs[0])) #rows=len(sorted_sims))
book_recs = remove_consumed(user_consumption=train_c[userId],
rec_list=book_recs)
book_recs = recs_cleaner(solr=solr,
consumpt=train_c[userId],
recs=book_recs[:50])
try:
recs = user_ranked_recs(user_recs=book_recs,
user_consumpt=test_c[userId])
except KeyError as e:
logging.info(
"Usuario {0} del fold de train (total) no encontrado en fold de 'test'"
.format(userId))
continue
for N in [5, 10, 15, 20]:
mini_recs = dict((k, recs[k]) for k in list(recs.keys())[:N])
MRRs[N].append(MRR(recs=mini_recs, rel_thresh=1))
nDCGs[N].append(
nDCG(recs=mini_recs, alt_form=False, rel_thresh=False))
APs[N].append(AP_at_N(n=N, recs=recs, rel_thresh=1))
Rprecs[N].append(R_precision(n_relevants=N, recs=mini_recs))
with open(
'TwitterRatings/word2vec/clean/option1_protocol_' + which_model +
'.txt', 'a') as file:
file.write("METRIC: %s\n" % (metric))
for N in [5, 10, 15, 20]:
with open(
'TwitterRatings/word2vec/clean/option1_protocol_' +
which_model + '.txt', 'a') as file:
file.write( "N=%s, nDCG=%s, MAP=%s, MRR=%s, R-precision=%s\n" % \
(N, mean(nDCGs[N]), mean(APs[N]), mean(MRRs[N]), mean(Rprecs[N])) )
def option2_protocol_evaluation(data_path, which_model, metric,
representation):
solr = 'http://localhost:8983/solr/grrecsys'
test_c = consumption(ratings_path=data_path + 'test/test_N20.data',
rel_thresh=0,
with_ratings=True)
train_c = consumption(ratings_path=data_path + 'eval_train_N20.data',
rel_thresh=0,
with_ratings=False)
MRRs = dict((N, []) for N in [5, 10, 15, 20])
nDCGs = dict((N, []) for N in [5, 10, 15, 20])
APs = dict((N, []) for N in [5, 10, 15, 20])
Rprecs = dict((N, []) for N in [5, 10, 15, 20])
docs2vec = np.load('./w2v-tmp/' + which_model + '/docs2vec_' +
which_model + '.npy').item()
users2vec = np.load('./w2v-tmp/' + which_model + '/users2vec_' +
representation + '_' + which_model + '.npy').item()
i = 1
for userId in test_c:
logging.info("MODO 2. {0} de {1}. User ID: {2}".format(
i, len(test_c), userId))
i += 1
distances = dict((bookId, 0.0) for bookId in docs2vec)
for bookId in docs2vec:
if metric == 'angular':
distances[bookId] = spatial.distance.cosine(
users2vec[userId], docs2vec[bookId])
elif metric == 'euclidean':
distances[bookId] = spatial.distance.euclidean(
users2vec[userId], docs2vec[bookId])
sorted_sims = sorted(
distances.items(), key=operator.itemgetter(1),
reverse=False) #[(<grId>, MENOR dist), ..., (<grId>, MAYOR dist)]
book_recs = [bookId for bookId, sim in sorted_sims]
book_recs = remove_consumed(user_consumption=train_c[userId],
rec_list=book_recs)
book_recs = recs_cleaner(solr=solr,
consumpt=train_c[userId],
recs=book_recs[:50])
try:
recs = user_ranked_recs(user_recs=book_recs,
user_consumpt=test_c[userId])
except KeyError as e:
logging.info(
"Usuario {0} del fold de train (total) no encontrado en fold de 'test'"
.format(userId))
continue
for N in [5, 10, 15, 20]:
mini_recs = dict((k, recs[k]) for k in list(recs.keys())[:N])
MRRs[N].append(MRR(recs=mini_recs, rel_thresh=1))
nDCGs[N].append(
nDCG(recs=mini_recs, alt_form=False, rel_thresh=False))
APs[N].append(AP_at_N(n=N, recs=recs, rel_thresh=1))
Rprecs[N].append(R_precision(n_relevants=N, recs=mini_recs))
with open(
'TwitterRatings/word2vec/clean/option2_protocol_' + which_model +
'.txt', 'a') as file:
file.write("METRIC: %s \t REPR: %s\n" % (metric, representation))
for N in [5, 10, 15, 20]:
with open(
'TwitterRatings/word2vec/clean/option2_protocol_' +
which_model + '.txt', 'a') as file:
file.write( "N=%s, nDCG=%s, MAP=%s, MRR=%s, R-precision=%s\n" % \
(N, mean(nDCGs[N]), mean(APs[N]), mean(MRRs[N]), mean(Rprecs[N])) )
def relevance(user, q): ratings = [ v[0] for k, v in user.items() ] if q>=10: return mean(ratings) return ((0.5**q) * stdev(ratings)) + mean(ratings)
def option1_protocol_evaluation(data_path, N, model):
# userId='113447232' user_bookId='17310690'
test_c = consumption(ratings_path=data_path + 'test/test_N' + str(N) +
'.data',
rel_thresh=0,
with_ratings=True)
train_c = consumption(ratings_path=data_path + 'eval_train_N' + str(N) +
'.data',
rel_thresh=0,
with_ratings=False)
MRRs = []
nDCGs = []
APs = []
Rprecs = []
flat_docs = np.load('./w2v-tmp/flattened_docs_fea05b2.npy').item()
num_to_grId = np.load('./w2v-tmp/num_to_grId.npy').item()
grId_to_num = np.load('./w2v-tmp/grId_to_num.npy').item()
t = AnnoyIndex(300)
t.load('./w2v-tmp/doc_vecs_t100.tree')
num_best = 20
i = 1
for userId in test_c:
logging.info("MODO 1. {0} de {1}. User ID: {2}".format(
i, len(test_c), userId))
i += 1
# stream_url = solr + '/query?rows=1000&q=goodreadsId:{ids}'
# ids_string = encoded_itemIds(item_list=train_c[userId])
# url = stream_url.format(ids=ids_string)
# response = json.loads( urlopen(url).read().decode('utf8') )
# try:
# docs = response['response']['docs']
# except TypeError as e:
# continue
book_recs = []
book_recs_cos = []
for user_bookId in train_c[userId]: #for user_doc in docs:
try:
docs = t.get_nns_by_item(grId_to_num[user_bookId], 4)
book_recs_cos += [
str(num_to_grId[doc_num]) for doc_num in docs
]
except KeyError as e:
logging.info(
"{} ES UNO DE LOS LIBROS CUYO HTML NO PUDO SER DESCARGADO. PROSIGUIENDO CON EL SIGUIENTE LIBRO.."
.format(bookId))
continue
# Removemos de la primera lista los items consumidos, dado que get_nns_by_items() los incluye
book_recs_cos = [
bookId for bookId in book_recs_cos if bookId not in train_c[userId]
]
wmd_corpus = []
num_to_grId_wmd = {}
j = 0
for grId in book_recs_cos:
wmd_corpus.append(flat_docs[grId])
num_to_grId_wmd[j] = grId
j += 1
grId_to_num_wmd = {v: k for k, v in num_to_grId_wmd.items()}
index = WmdSimilarity(wmd_corpus,
model,
num_best=num_best,
normalize_w2v_and_replace=False)
for user_bookId in train_c[userId]:
r = index[flat_docs[user_bookId]]
book_recs.append([num_to_grId_wmd[id] for id, score in r])
# wmds = dict((bookId, 0.0) for bookId in flat_docs)
# user_bookId = str(user_doc['goodreadsId'][0]) #id de libro consumido por user
# for bookId in flat_docs: #ids de libros en la DB
# if bookId == user_bookId: continue
# wmds[bookId] = model.wmdistance(flat_docs[bookId], flat_docs[user_bookId]) #1 - dist = similarity
# sorted_sims = sorted(wmds.items(), key=operator.itemgetter(1), reverse=False) #[(<grId>, MAYOR sim), ..., (<grId>, menor sim)]
# book_recs.append( [ bookId for bookId, sim in sorted_sims ] )
book_recs = flatten_list(list_of_lists=book_recs,
rows=len(book_recs[0]))
book_recs = remove_consumed(user_consumption=train_c[userId],
rec_list=book_recs)
try:
recs = user_ranked_recs(user_recs=book_recs,
user_consumpt=test_c[userId])
except KeyError as e:
logging.info(
"Usuario {0} del fold de train (total) no encontrado en fold de 'test'"
.format(userId))
continue
####################################
mini_recs = dict(
(k, recs[k]) for k in list(recs.keys())
[:N]) #Python 3.x: .keys() devuelve una vista, no una lista
nDCGs.append(nDCG(recs=mini_recs, alt_form=False, rel_thresh=False))
APs.append(AP_at_N(n=N, recs=mini_recs, rel_thresh=1))
MRRs.append(MRR(recs=mini_recs, rel_thresh=1))
Rprecs.append(R_precision(n_relevants=N, recs=mini_recs))
####################################
with open('TwitterRatings/word2vec/option1_protocol_wmd.txt', 'a') as file:
file.write( "N=%s, normal nDCG=%s, MAP=%s, MRR=%s, R-precision=%s\n" % \
(N, mean(nDCGs), mean(APs), mean(MRRs), mean(Rprecs)) )
def option2_protocol_evaluation(data_path, N, model):
test_c = consumption(ratings_path=data_path + 'test/test_N' + str(N) +
'.data',
rel_thresh=0,
with_ratings=True)
train_c = consumption(ratings_path=data_path + 'eval_train_N' + str(N) +
'.data',
rel_thresh=0,
with_ratings=False)
MRRs = []
nDCGs = []
APs = []
Rprecs = []
flat_docs = np.load('./w2v-tmp/flattened_docs_fea05b2.npy').item()
flat_users = np.load('./w2v-tmp/flattened_users_fea05b2.npy').item()
docs2vec = np.load('./w2v-tmp/docs2vec.npy').item()
users2vec = np.load('./w2v-tmp/users2vec.npy').item()
num_best = 20
i = 1
for userId in test_c:
logging.info("MODO 2. {0} de {1}. User ID: {2}".format(
i, len(test_c), userId))
i += 1
# wmds = dict((bookId, 0.0) for bookId in flat_docs)
# for bookId in flat_docs:
# wmds[bookId] = model.wmdistance(flat_users[userId], flat_docs[bookId])
cosines = dict((bookId, 0.0) for bookId in docs2vec)
for bookId in docs2vec:
cosines[bookId] = 1 - spatial.distance.cosine(
users2vec[userId], docs2vec[bookId]) #1 - dist = similarity
sorted_sims = sorted(
cosines.items(), key=operator.itemgetter(1),
reverse=True) #[(<grId>, MAYOR sim), ..., (<grId>, menor sim)]
book_recs_cos = [bookId for bookId, sim in sorted_sims]
book_recs_cos = remove_consumed(user_consumption=train_c[userId],
rec_list=book_recs)
wmd_corpus = []
num_to_grId_wmd = {}
j = 0
for grId in book_recs_cos[:50]:
wmd_corpus.append(flat_docs[grId])
num_to_grId_wmd[j] = grId
j += 1
grId_to_num_wmd = {v: k for k, v in num_to_grId_wmd.items()}
# Creamos índice WMD con un subset de (50) ítems recomendados al usuario por cossim
index = WmdSimilarity(wmd_corpus,
model,
num_best=num_best,
normalize_w2v_and_replace=False)
r = index[flat_users[userId]]
book_recs = [num_to_grId_wmd[id] for id, score in r]
# sorted_sims = sorted(wmds.items(), key=operator.itemgetter(1), reverse=False) #[(<grId>, MAYOR sim), ..., (<grId>, menor sim)]
# book_recs = [ bookId for bookId, sim in sorted_sims ]
book_recs = remove_consumed(user_consumption=train_c[userId],
rec_list=book_recs)
try:
recs = user_ranked_recs(user_recs=book_recs,
user_consumpt=test_c[userId])
except KeyError as e:
logging.info(
"Usuario {0} del fold de train (total) no encontrado en fold de 'test'"
.format(userId))
continue
####################################
mini_recs = dict(
(k, recs[k]) for k in list(recs.keys())
[:N]) #Python 3.x: .keys() devuelve una vista, no una lista
nDCGs.append(nDCG(recs=mini_recs, alt_form=False, rel_thresh=False))
APs.append(AP_at_N(n=N, recs=mini_recs, rel_thresh=1))
MRRs.append(MRR(recs=mini_recs, rel_thresh=1))
Rprecs.append(R_precision(n_relevants=N, recs=mini_recs))
####################################
with open('TwitterRatings/word2vec/option2_protocol_wmd.txt', 'a') as file:
file.write( "N=%s, normal nDCG=%s, MAP=%s, MRR=%s, R-precision=%s\n" % \
(N, mean(nDCGs), mean(APs), mean(MRRs), mean(Rprecs)) )
def PRF_calculator(params, folds, topN):
ratings_train, ratings_test = [], []
with open('TwitterRatings/funkSVD/ratings.train', 'r') as f:
for line in f:
ratings_train.append(line.strip())
with open('TwitterRatings/funkSVD/ratings.test', 'r') as f:
for line in f:
ratings_test.append(line.strip())
preferred_consumption = consumption(
ratings_path='TwitterRatings/funkSVD/ratings.test',
rel_thresh=4,
with_ratings=False)
for n in topN:
precision_folds, recall_folds = [], []
# for _ in range(0, folds):
# ratingsSampler(ratings_train, 'TwitterRatings/funkSVD/ratings_temp.train', 0.8)
# ratingsSampler(ratings_test, 'TwitterRatings/funkSVD/ratings_temp.test', 0.8)
svd = pyreclab.SVD(
dataset=
'TwitterRatings/funkSVD/ratings.train', #o ratings_temp.train
dlmchar=b',',
header=False,
usercol=0,
itemcol=1,
ratingcol=2)
svd.train(factors=params['f'],
maxiter=params['mi'],
lr=params['lr'],
lamb=params['lamb'])
recommendationList = svd.testrec(
input_file=
'TwitterRatings/funkSVD/ratings.test', #o ratings_temp.test
dlmchar=b',',
header=False,
usercol=0,
itemcol=1,
ratingcol=2,
topn=n,
includeRated=False)
users_precisions, users_recalls = [], []
for userId in recommendationList[0]:
recs = set(recommendationList[0][userId])
cons = set(preferred_consumption[userId])
tp = len(recs & cons)
fp = len(recs - cons)
fn = len(cons - recs)
users_precisions.append(float(tp) / (tp + fp))
try:
users_recalls.append(float(tp) / (tp + fn))
except ZeroDivisionError as e:
continue
precision_folds.append(mean(users_precisions))
recall_folds.append(mean(users_recalls))
p = mean(precision_folds)
r = mean(recall_folds)
f = 2 * p * r / (p + r)
with open('TwitterRatings/funkSVD/recall.txt', 'a') as file:
file.write("N=%s, P=%s, R=%s, F=%s\n" % (n, p, r, f))
