def get_eval_parameters(opt, force_categories=None):
evaluate = DD()
if opt.eval_sampler == "beam":
evaluate.bs = opt.beam_size
elif opt.eval_sampler == "greedy":
evaluate.bs = 1
elif opt.eval_sampler == "topk":
evaluate.k = opt.topk_size
evaluate.smax = opt.gen_seqlength
evaluate.sample = opt.eval_sampler
evaluate.numseq = opt.num_sequences
evaluate.gs = opt.generate_sequences
evaluate.es = opt.evaluate_sequences
if opt.dataset == "atomic":
if "eval_categories" in opt and force_categories is None:
evaluate.categories = opt.eval_categories
else:
evaluate.categories = force_categories
return evaluate
def get_data_parameters(opt, experiment, dataset):
data = DD()
if dataset == "atomic":
data.categories = sorted(opt.categories)
elif dataset == "conceptnet":
data.rel = opt.relation_format
data.trainsize = opt.training_set_size
data.devversion = opt.development_set_versions_to_use
data.maxe1 = opt.max_event_1_size
data.maxe2 = opt.max_event_2_size
return data