def SVDloadData():
svd = SVD()
recsys.algorithm.VERBOSE = True
dat_file = '/home/commons/RecSys/MOVIEDATA/MOVIEDATA/ml-1m/ratings.dat'
svd.load_data(filename=dat_file, sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
print svd.get_matrix()
return svd
def quickstart():
svd = SVD()
recsys.algorithm.VERBOSE = True
# load movielens data
dat_file = DATA_DIR + 'ml-1m-ratings.dat'
svd.load_data(filename=dat_file,
sep='::',
format={
'col': 0,
'row': 1,
'value': 2,
'ids': int
})
# compute svd
k = 100
svd.compute(k=k,
min_values=10,
pre_normalize=None,
mean_center=True,
post_normalize=True)
pdb.set_trace()
# movie id's
ITEMID1 = 1 # toy story
ITEMID2 = 1221 # godfather II
# get movies similar to toy story
print svd.similar(ITEMID1)
# get predicted rating for given user & movie
MIN_RATING = 0.0
MAX_RATING = 5.0
USERID = 1
ITEMID = 1
# get predicted rating for user1 and item1, mapped onto min max
pred = svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING)
actual = svd.get_matrix().value(ITEMID, USERID)
print 'predicted rating = {0}'.format(pred)
print 'actual rating = {0}'.format(actual)
print 'which users should see Toy Story?:'
print svd.recommend(ITEMID)
def loadSVD():
filename = 'favRate.dat'
svd = SVD()
svd.load_data(filename=filename, sep='::', format={'col':0, 'row':1, 'value':2})
svd.save_data("svd.dat", False)
K=20
svd.compute(k=K, min_values=1, pre_normalize="rows", mean_center=False, post_normalize=True, savefile='.')
#svd.recommend(USERID, n=10, only_unknowns=True, is_row=False)
sparse_matrix = svd.get_matrix()
sim_matrix = svd.get_matrix_similarity()
print sparse_matrix
#print sim_matrix
#1173893,1396943
sim = svd.similar(897346, 10)
filename = 'swoffering.yaml'
titleStream = file(filename, 'r')
titleList = yaml.load(titleStream)
#print sim
for row in sim:
(offid, similar) = row
print offid, titleList[str(offid)], similar
def quickstart():
svd = SVD()
recsys.algorithm.VERBOSE = True
# load movielens data
dat_file = 'ml-1m/ratings.dat'
svd.load_data(filename=dat_file, sep='::', format={'col':0, 'row':1, 'value':2, 'ids': int})
# compute svd
k = 100
svd.compute(k=k, min_values=10, pre_normalize=None, mean_center=True,
post_normalize=True)
pdb.set_trace()
# movie id's
ITEMID1 = 1 # toy story
ITEMID2 = 1221 # godfather II
# get movies similar to toy story
svd.similar(ITEMID1)
# get predicted rating for given user & movie
MIN_RATING = 0.0
MAX_RATING = 5.0
USERID = 1
ITEMID = 1
# get predicted rating
pred = svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING)
actual = svd.get_matrix().value(ITEMID, USERID)
print 'predicted rating = {0}'.format(pred)
print 'actual rating = {0}'.format(actual)
# which users should see Toy Story?
svd.recommend(ITEMID)
'col': 0,
'row': 1,
'value': 2,
'ids': float
})
k = 30
svd.compute(k=k,
min_values=10,
pre_normalize=None,
mean_center=True,
post_normalize=True,
savefile='/tmp/movielens')
# ITEMID1 = 1 # Toy Story (1995)
# ITEMID2 = 2355 # A bug's life (1998)
# print svd.similarity(ITEMID1, ITEMID2)
MIN_RATING = 1.0
MAX_RATING = 5.0
USERID = 1
ITEMID = 1129
print svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING)
print svd.predict(1953, 1, MIN_RATING, MAX_RATING)
# Predicted value 5.0
print svd.get_matrix().value(1953, 1)
# Real value 5.0
print "similaridad entre items sin usar la matrix que ya se genero "
print svd2.similarity(ITEMID1, ITEMID2)
print "similaridad entre items usando la matrix guardada"
print svd.similarity(ITEMID1, ITEMID2)
print "Recomendaciones para el itemid1"
print svd.similar(ITEMID1)
#Haciendo las predicciones
MIN_RATING = 0
MAX_RATING = 5
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={
pre_normalize=None,
mean_center=True,
post_normalize=True)
# predicted_rating = svd.predict(int(5), 'A1', 1, 10)
# predicted_rating2 = svd.predict(int(1), 'A1', 1, 10)
# print('Predicted rating', predicted_rating)
# print('Predicted rating', predicted_rating2)
records = ETLUtils.load_csv_file(file_name_header, '|')
errors = []
for record in records:
try:
# print(record['user'], record['item'], record['rating'])
user = record['user']
item = int(record['item'])
predicted_rating = svd.predict(item, user, 1, 5)
print(record['user'], record['item'], predicted_rating)
# predicted_rating = round(predicted_rating)
actual_rating = svd.get_matrix().value(item, user)
error = abs(predicted_rating - actual_rating)
errors.append(error)
except KeyError:
continue
mean_absolute_error = MeanAbsoluteError.compute_list(errors)
root_mean_square_error = RootMeanSquareError.compute_list(errors)
print('Mean Absolute error: %f' % mean_absolute_error)
print('Root mean square error: %f' % root_mean_square_error)
print(json.dumps(similaries, ensure_ascii=False))
# import pdb;pdb.set_trace()
import sys
sys.exit(0)
print(svd.similar(ITEMID1))
# Returns: <ITEMID, Cosine Similarity Value>
MIN_RATING = 0.0
MAX_RATING = 1.0
ITEMID = 109
USERID = 3837663637323963363639393565373833613237396534393132376338386362
print('testing..')
print(svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING))
# Predicted value 5.0
print(svd.get_matrix().value(ITEMID, USERID))
# Real value 5.0
# Recommend (non-rated) movies to a user:
print('recommend to user')
print(svd.recommend(USERID, is_row=False)) #cols are users and rows are items, thus we set is_row=False
print(svd.recommend(ITEMID))
import pdb;pdb.set_trace()
(595, 0.46031829709743477), # Beauty and the Beast (1907, 0.44589398718134365), # Mulan (364, 0.42908159895574161), # The Lion King (2081, 0.42566581277820803), # The Little Mermaid (3396, 0.42474056361935913), # The Muppet Movie (2761, 0.40439361857585354)] # The Iron Giant MIN_RATING = 0.0 MAX_RATING = 5.0 ITEMID = 1 USERID = 1 svd.predict(ITEMID, USERID, MIN_RATING, MAX_RATING) # Predicted value 5.0 svd.get_matrix().value(ITEMID, USERID) # Real value 5.0 svd.recommend(USERID, is_row=False) #cols are users and rows are items, thus we set is_row=False # Returns: <ITEMID, Predicted Rating> [(2905, 5.2133848204673416), # Shaggy D.A., The (318, 5.2052108435956033), # Shawshank Redemption, The (2019, 5.1037438278755474), # Seven Samurai (The Magnificent Seven) (1178, 5.0962756861447023), # Paths of Glory (1957) (904, 5.0771405690055724), # Rear Window (1954) (1250, 5.0744156653222436), # Bridge on the River Kwai, The (858, 5.0650911066862907), # Godfather, The (922, 5.0605327279819408), # Sunset Blvd. (1198, 5.0554543765500419), # Raiders of the Lost Ark (1148, 5.0548789542105332)] # Wrong Trousers, The
k = 100
svd.compute(k=k,
min_values=10,
pre_normalize=None,
mean_center=True,
post_normalize=True,
savefile='./data/MERGED6_svd')
# to load a saved svd
# svd = SVD(filename='./data/MERGED_svd') # Loading already computed SVD model
# get the item_id with available results (n<10 rows & columns were cut out)
m = svd.get_matrix()
rowlabl = m._matrix.row_labels
ids = np.array(rowlabl)
# ==== can further reduce the tables using this list of id.
# calculate cosine similarity score between 2 items:
# svd.similarity(ids[0], ids[100]) # cosine similarity
# For each movie:
# 1. get the top 50 books
# 2. eliminating duplicates by comparing titles
# 3. save id and scores for the final 10 books & movies
k = 100
svd.compute(k=k, min_values=10, pre_normalize=None, mean_center=True, post_normalize=True)
# predicted_rating = svd.predict(int(5), 'A1', 1, 10)
# predicted_rating2 = svd.predict(int(1), 'A1', 1, 10)
# print('Predicted rating', predicted_rating)
# print('Predicted rating', predicted_rating2)
records = ETLUtils.load_csv_file(file_name_header, '|')
errors = []
for record in records:
try:
# print(record['user'], record['item'], record['rating'])
user = record['user']
item = int(record['item'])
predicted_rating = svd.predict(item, user, 1, 5)
print(record['user'], record['item'], predicted_rating)
# predicted_rating = round(predicted_rating)
actual_rating = svd.get_matrix().value(item, user)
error = abs(predicted_rating - actual_rating)
errors.append(error)
except KeyError:
continue
mean_absolute_error = MeanAbsoluteError.compute_list(errors)
root_mean_square_error = RootMeanSquareError.compute_list(errors)
print('Mean Absolute error: %f' % mean_absolute_error)
print('Root mean square error: %f' % root_mean_square_error)
from recsys.algorithm.factorize import SVD
from recsys.datamodel.data import Data
data = [(4.0, 'user1', 'item1'), (2.0, 'user1', 'item3'),
(1.0, 'user2', 'item1'), (5.0, 'user2', 'item4')]
d = Data()
d.set(data)
svd = SVD()
svd.set_data(d)
m = svd.get_matrix()
svd.compute(k=2)
print svd.similar('user1')
print svd.predict('user1', 'item1')
#svd.compute(k=K, pre_normalize=None, mean_center=True, post_normalize=True)
print ''
print 'COMPUTING SIMILARITY'
print svd.similarity(1, 2) # similarity between items
print svd.similar(1, 5) # show 5 similar items
print ''
print 'GENERATING PREDICTION'
MIN_RATING = 0.0
MAX_RATING = 5.0
ITEMID = 1
USERID = 1
print svd.predict(ITEMID, USERID, MIN_RATING,
MAX_RATING) # predicted rating value
print svd.get_matrix().value(ITEMID, USERID) # real rating value
print ''
print 'GENERATING RECOMMENDATION'
print svd.recommend(USERID, n=5, only_unknowns=True, is_row=False)
#Evaluation using prediction-based metrics
rmse = RMSE()
mae = MAE()
spearman = SpearmanRho()
kendall = KendallTau()
#decision = PrecisionRecallF1()
for rating, item_id, user_id in test.get():
try:
pred_rating = svd.predict(item_id, user_id)
rmse.add(rating, pred_rating)
