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,