if lambda_v is None:
sys.exit("Argument missing - lambda_v is required")
print(
"===================================MF Option Setting==================================="
)
print("\tbinarizing ratings - %s" % binary_rating)
print("\tdata path - %s" % data_path)
print("\tresult path - %s" % res_dir)
print("\tpretrained w2v data path - %s" % pretrain_w2v)
print ("\tdimension: %d\n\tlambda_u: %.4f\n\tlambda_v: %.4f\n\tmax_iter: %d\n\tnum_kernel_per_ws: %d" \
% (dimension, lambda_u, lambda_v, max_iter, num_kernel_per_ws))
print(
"==========================================================================================="
)
R, D_all = data_factory.load(aux_path, binary_rating)
train_user = data_factory.read_rating(data_path + '/train_user.dat',
binary_rating)
train_item = data_factory.read_rating(data_path + '/train_item.dat',
binary_rating)
valid_user = data_factory.read_rating(data_path + '/valid_user.dat',
binary_rating)
test_user = data_factory.read_rating(data_path + '/test_user.dat',
binary_rating)
# for each user, build a query contains user id, ground truth of top_n items, and pre-selected items
print("Making query for each user...")
query_list = []
all_item_set = set(range(R.shape[1]))
for i in range(R.shape[0]):
q = Query(i)
q.extendGroundTruth(valid_user[0][i])
sys.exit("Argument missing - res_dir is required")
if lambda_u is None:
sys.exit("Argument missing - lambda_u is required")
if lambda_v is None:
sys.exit("Argument missing - lambda_v is required")
print("===================================ConvMF Option Setting===================================")
print("\taux path - %s" % aux_path)
print("\tdata path - %s" % data_path)
print("\tresult path - %s" % res_dir)
print("\tpretrained w2v data path - %s" % pretrain_w2v)
print("\tdimension: %d\n\tlambda_u: %.4f\n\tlambda_v: %.4f\n\tmax_iter: %d\n\tnum_kernel_per_ws: %d" \
% (dimension, lambda_u, lambda_v, max_iter, num_kernel_per_ws))
print("===========================================================================================")
R, D_all = data_factory.load(aux_path)
CNN_X = D_all['X_sequence']
vocab_size = len(D_all['X_vocab']) + 1
from models import ConvMF
if pretrain_w2v is None:
init_W = None
else:
init_W = data_factory.read_pretrained_word2vec(
pretrain_w2v, D_all['X_vocab'], emb_dim)
train_user = data_factory.read_rating(data_path + '/train_user.dat')
train_item = data_factory.read_rating(data_path + '/train_item.dat')
valid_user = data_factory.read_rating(data_path + '/valid_user.dat')
test_user = data_factory.read_rating(data_path + '/test_user.dat')
sys.exit("Argument missing - res_dir is required")
if lambda_u is None:
sys.exit("Argument missing - lambda_u is required")
if lambda_v is None:
sys.exit("Argument missing - lambda_v is required")
print "===================================ConvMF Option Setting==================================="
print "\taux path - %s" % aux_path
print "\tdata path - %s" % data_path
print "\tresult path - %s" % res_dir
print "\tpretrained w2v data path - %s" % pretrain_w2v
print "\tdimension: %d\n\tlambda_u: %.4f\n\tlambda_v: %.4f\n\tmax_iter: %d\n\tnum_kernel_per_ws: %d" \
% (dimension, lambda_u, lambda_v, max_iter, num_kernel_per_ws)
print "==========================================================================================="
R, D_all = data_factory.load(aux_path)
CNN_X = D_all['X_sequence']
vocab_size = len(D_all['X_vocab']) + 1
from models import ConvMF
if pretrain_w2v is None:
init_W = None
else:
init_W = data_factory.read_pretrained_word2vec(
pretrain_w2v, D_all['X_vocab'], emb_dim)
train_user = data_factory.read_rating(data_path + '/train_user.dat')
train_item = data_factory.read_rating(data_path + '/train_item.dat')
valid_user = data_factory.read_rating(data_path + '/valid_user.dat')
test_user = data_factory.read_rating(data_path + '/test_user.dat')
print(
"===================================Model Option Setting==================================="
)
print("\tselected model - %s" % select_model)
print("\taux path - %s" % aux_path)
print("\tdata path - %s" % data_path)
print("\tresult path - %s" % res_dir)
print("\tpretrained w2v data path - %s" % pretrain_w2v)
print(
"\tdimension: %d\n\tlambda_u: %.4f\n\tlambda_v: %.4f\n\tmax_iter: %d\n\tnum_kernel_per_ws: %d"
% (dimension, lambda_u, lambda_v, max_iter, num_kernel_per_ws))
print(
"==========================================================================================="
)
R, D_all, ids = data_factory.load(aux_path)
CNN_X = D_all['X_sequence']
vocab_size = len(D_all['X_vocab']) + 1
train_user = data_factory.read_rating(data_path + '/train_user.dat')
train_item = data_factory.read_rating(data_path + '/train_item.dat')
valid_user = data_factory.read_rating(data_path + '/valid_user.dat')
test_user = data_factory.read_rating(data_path + '/test_user.dat')
if select_model == "ConvMF":
from models import ConvMF
if pretrain_w2v is None:
init_W = None
else:
init_W = data_factory.read_pretrained_word2vec(
if lambda_u is None:
sys.exit("Argument missing - lambda_u is required")
if lambda_v is None:
sys.exit("Argument missing - lambda_v is required")
print "===================================%s Option Setting===================================" % (
methods)
print "\t approach -%s" % methods
print "\taux path - %s" % aux_path
print "\tdata path - %s" % data_path
print "\tdimension: %d\n\tlambda_u: %.4f\n\tlambda_v: %.4f\n\tmax_iter: %d\n\t" \
% (dimension, lambda_u, lambda_v, max_iter)
print "==========================================================================================="
R = data_factory.load(aux_path)
train_user = data_factory.read_rating(data_path + '/train_user.dat')
train_item = data_factory.read_rating(data_path + '/train_item.dat')
valid_user = data_factory.read_rating(data_path + '/valid_user.dat')
test_user = data_factory.read_rating(data_path + '/test_user.dat')
if methods == "PMF":
from models.PMF import PMF
PMF(max_iter=max_iter,
lambda_u=lambda_u,
lambda_v=lambda_v,
dimension=dimension,
train_user=train_user,
train_item=train_item,
valid_user=valid_user,
test_user=test_user,
