def main(args):
"""
pipeline for representation learning for all papers for a given name reference
"""
dataset = data_parser.DataSet(args.file_path)
bpr_optimizer = embedding.BprOptimizer(args.latent_dimen, args.alpha,
args.matrix_reg)
pp_sampler = sampler.CoauthorGraphSampler()
pd_sampler = sampler.BipartiteGraphSampler()
dd_sampler = sampler.LinkedDocGraphSampler()
eval_f1 = eval_metric.Evaluator()
run_helper = train_helper.TrainHelper()
return run_helper.helper(args.num_epoch, dataset, bpr_optimizer,
pp_sampler, pd_sampler, dd_sampler, eval_f1,
args.sampler_method)
def main(file_path, latent_dimen, alpha, matrix_reg, num_epoch,
sampler_method):
"""
pipeline for representation learning for all papers for a given name reference
"""
dataset = parser_helper.DataSet(file_path)
paper_count = dataset.reader()
bpr_optimizer = embedding.BprOptimizer(latent_dimen, alpha, matrix_reg)
pp_sampler = sampler.CoauthorGraphSampler()
pd_sampler = sampler.BipartiteGraphSampler()
dd_sampler = sampler.LinkedDocGraphSampler()
eval_f1 = eval_metric.Evaluator()
run_helper = train_helper.TrainHelper()
pairwise_precision, pairwise_recall, pairwise_f1 = run_helper.helper(
num_epoch, dataset, bpr_optimizer, pp_sampler, pd_sampler, dd_sampler,
eval_f1, sampler_method)
return paper_count, pairwise_precision, pairwise_recall, pairwise_f1