def process_svd(preload):
if preload:
svd = SVD(filename='./data/svd-all') # Loading already computed SVD model
else:
print "Reading data..."
svdlibc = SVDLIBC('./data/behavior-ml-score.csv')
svdlibc.to_sparse_matrix(sep=',', format={'col':0, 'row':1, 'value':2, 'ids': str})
k=100
print "Computing SVD..."
svdlibc.compute(k)
svd = svdlibc.export()
svd.save_model('./data/svd-all', options={'k': k})
#svd.predict('TV268', 9, 1, 3)
return svd
def process_svd(preload):
if preload:
svd = SVD(filename='./data/svd-all') # Loading already computed SVD model
else:
print "Reading data..."
svdlibc = SVDLIBC('./data/behavior-ml-score.csv')
svdlibc.to_sparse_matrix(sep=',', format={'col':0, 'row':1, 'value':2, 'ids': str})
k=100
print "Computing SVD..."
svdlibc.compute(k)
svd = svdlibc.export()
svd.save_model('./data/svd-all', options={'k': k})
#svd.predict('TV268', 9, 1, 3)
return svd
def readDat():
global svd
global svdlibc
global tree
global similar_title
svdlibc = SVDLIBC(ratings)
svdlibc.to_sparse_matrix(sep='::', \
format={'col':0, 'row':1, 'value':2, 'ids': int})
svdlibc.compute(k=100)
svd = svdlibc.export()
tree.append(svd.similar(ITEMID1))
for name in tree:
for n in name[1:]:
similar_title.append(n[0])
if svd.similar(n[0]) not in tree:
tree.append(svd.similar(n[0]))
movie_similarity[name[0][0]] = similar_title
similar_title = []
# perform singular value decomposition on the ratings table
import recsys.algorithm
from recsys.utils.svdlibc import SVDLIBC
recsys.algorithm.VERBOSE = True
svdlibc = SVDLIBC('ml-10M100K/ratings.dat')
svdlibc.to_sparse_matrix(sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
svdlibc.compute(k=100)
svd = svdlibc.export()
svd.save_model('svd-10M-k100')
def setup():
global svdlibc
svdlibc = SVDLIBC(os.path.join(MOVIELENS_DATA_PATH, 'ratings.dat'))
ITEMID = 1
USERID = 1
print svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING)
print svd.get_matrix().value(ITEMID, USERID)
#HACUIENDO RECOMENDACIONES AL USUARIO Y POR TITEM
print svd.recommend(
USERID,
is_row=False) #cols are users and rows are items, thus we set is_row=False
print svd.recommend(ITEMID)
print "se deben mostrar 5 recomendaciones para el item 1"
print svd.recommend(USERID, n=5, only_unknowns=True, is_row=False)
#usando la matriz que ya esta generada
from recsys.utils.svdlibc import SVDLIBC
svdlibc = SVDLIBC('./data/ratings.dat')
svdlibc.to_sparse_matrix(sep='::',
format={
'col': 0,
'row': 1,
'value': 2,
'ids': int
})
svdlibc.compute(k=100)
svd = svdlibc.export()
print "hallando similitud con el itemd1"
print svd.similar(
ITEMID1
) # results might be different than example 4. as there's no min_values=10 set here
def __init__(self):
svdlibc = SVDLIBC('../../datasets/ratings.dat')
svdlibc.to_sparse_matrix(sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
svdlibc.compute(k=100)
svd = svdlibc.export()
self.svd = svd
import sys
import recsys.algorithm
recsys.algorithm.VERBOSE = True
from recsys.utils.svdlibc import SVDLIBC
movielens = sys.argv[1] #ratings.dat movielens file path here
svdlibc = SVDLIBC(movielens)
svdlibc.to_sparse_matrix(sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
svdlibc.compute()
svd = svdlibc.export()
svdlibc.remove_files()
MOVIEID = 1
print svd.similar(MOVIEID)
print svd.recommend(MOVIEID)
