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')
ConvMF(max_iter=max_iter, res_dir=res_dir,
lambda_u=lambda_u, lambda_v=lambda_v, dimension=dimension, vocab_size=vocab_size, init_W=init_W, give_item_weight=give_item_weight, CNN_X=CNN_X, emb_dim=emb_dim,
num_kernel_per_ws=num_kernel_per_ws,
train_user=train_user, train_item=train_item, valid_user=valid_user, test_user=test_user, R=R)
valid_user=valid_user,
test_user=test_user,
R=R,
attributes_X=features_matrix,
cae_output_dim=att_dim,
use_transfer_block=use_transfer_block)
elif content_mode == 'cnn':
ConvMF(max_iter=max_iter,
res_dir=fold_res_dir,
state_log_dir=fold_res_dir,
lambda_u=lambda_u,
lambda_v=lambda_v,
dimension=dimension,
vocab_size=vocab_size,
init_W=init_W,
max_len=max_length,
give_item_weight=give_item_weight,
CNN_X=CNN_X,
emb_dim=emb_dim,
num_kernel_per_ws=num_kernel_per_ws,
train_user=train_user,
train_item=train_item,
valid_user=valid_user,
test_user=test_user,
R=R)
elif content_mode == 'cae':
# attributes dimension must be euall to u, and v vectors dimension
CAEMF(max_iter=max_iter,
res_dir=fold_res_dir,
state_log_dir=fold_res_dir,
lambda_u=lambda_u,
lambda_v=lambda_v,