README.md

group	argument	definition
dataset_args	dataset_name	dataset name, choose from 'nyc', 'tky', and 'ca'
	min_poi_freq	the least value of one poi's checkin records, if less than or equal to this value, we will remove this poi
	min_user_freq	the least value of one user's checkin records, if less than or equal to this value, we will remove this user
	session_time_interval	the time interval of consecutive checkin records in every trajectory should be larger than or equal to this value
	threshold	the similarity threshold of two trajectories when building hypergraph, if less than this value, we will remove this traj2traj relation
	filter_mode	the similarity metric, choose from 'jaccard' and 'min size'
	num_spatial_slots	the total number of slots for continuous distance value
	spatial_slot_type	construct distance slots automatically based on min, max value of distance, choose from 'linear' and 'exp'
	do_label_encode	whether to encode the id via LabelEncoder
	only_last_metric	whether to use only the last checkin of every trajectory as sample to evaluate our model
	max_d_epsilon	add this value to maximum distance to avoid bugs
model_args	model_name	model name, choose from 'sthgcn' (our model) and 'seq_transformer' (for ablation study)
	intra_jaccard_threshold	the intra-user similarity threshold for hyperedge2hyperedge collaboration, only keep those collaborations whose similarities are larger than this value
	inter_jaccard_threshold	the inter-user similarity threshold for hyperedge2hyperedge collaboration, only keep those collaborations whose similarities are larger than this value
	sizes	sample size for different hops, the last element is for checkin2trajectory, other elements is for multi-hop trajectory2trajectory. e.g. sizes=[10, 20, 30], [10,20] is for traj2traj 2-hop sampling, [30] is for ci2traj.
	dropout_rate	the dropout rate
	num_edge_type	the total number of edge type
	generate_edge_attr	whether to generate edge attr embedding based on edge type
	embed_fusion_type	embedding fusion type, choose from 'concat' and 'add'
	embed_size	the embedding size of id embedding and the hidden representation of trajectory
	st_embed_size	the embedding size of spatial and temporal embedding
	activation	the activation function, choose from 'elu', 'relu', 'leaky_relu' and 'tanh'
	phase_factor	phase factor for time encoder
	use_linear_trans	whether to use linear transformation before output for time encoder
	do_traj2traj	whether to use hyperedge2hyperedge collaboration
	distance_encoder_type	encoder type of distance, choose from 'time', 'hstlstm', 'stan' and 'simple'. Specially, 'time' means using the TimeEncoder to handle distance value
	quantile	clip the maximum distance value with clip(0, max_d*quantile), should modify the code in dataset/lbsn_dataset to make this work
conv_args	num_attention_heads	the total number of attention heads
	residual_beta	the residual weight of initial representation for gated residual module
	learn_beta	whether to learn residual beta automatically
	conv_dropout_rate	the dropout rate for hypergraph transformer
	trans_method	the translation method of message assembler, choose from 'corr', 'sub', 'add', 'multi' and 'concat'
	edge_fusion_mode	the fusion mode of edge vector, choose from 'concat' and 'add'
	head_fusion_mode	the fusion mode of multi-head, choose from 'concat' and 'add'
	time_fusion_mode	the fusion mode of time vector, choose from 'concat' and 'add'
	residual_fusion_mode	the fusion mode of gated residual module, choose from 'concat' and 'add'
	negative_slope	the negative slope for leaky_relu activation function
run_args	seed	random seed for generate random number, and reproduce the experiments. Not used for multiple-run setting.
	gpu	gpu index, use cpu if set -1
	batch_size	training batch size
	eval_batch_size	evaluation batch size
	learning_rate	the learning rate
	do_train	whether to do training
	do_validate	whether to do validation
	do_test	whether to do testing
	warm_up_steps	the warm up steps with constant initial learning rate
	cooldown_rate	the cooldown rate for learning rate schedualing, make the learning rate approximate an exponential decay curve with respect to the global steps
	max_steps	the max steps for training
	epoch	the training epoch
	valid_steps	do evaluating every valid_steps
	num_workers	the total number of workers for dataloader
	init_checkpoint	the checkpoint path
seq_transformer_args	only works when model_args.name==seq_transformer
	sequence_length	the max length of the sequences
	header_num	the head number of multi-head
	encoder_layers_num	the total number of encoder layers
	hidden_size	the embedding size of hidden representation
	dropout	the dropout rate
	do_positional_encoding	whether to use positional encoding