matchbench.evaluator package

Submodules

matchbench.evaluator.base_evaluator module

class matchbench.evaluator.base_evaluator.CTAEvaluator(model, dataset, test_batch_size=128, output_dir='result/', device=device(type='cuda'))

Bases: Evaluator

evaluate(model=None, output_dir='result/')

class matchbench.evaluator.base_evaluator.EAEvaluator(test_dataset, train_dataset=None, split='valid', test_batch_size=128, model=None, output_dir='result/', mid_file_dir='', stage=1, device=device(type='cuda'))

Bases: Evaluator

evaluate(model=None, output_file=None, stage=1)

class matchbench.evaluator.base_evaluator.EMEvaluator(dataset, split='test', test_batch_size=128, model=None, output_dir='result/', device=device(type='cuda'))

Bases: Evaluator

evaluate(model=None, output_file=None)

class matchbench.evaluator.base_evaluator.Evaluator(dataset, test_batch_size=128, model=None, output_dir='result/', device=device(type='cuda'))

Bases: object

evaluate(model, output_dir='result/')

class matchbench.evaluator.base_evaluator.SMEvaluator(ground_truth, model=None, output_dir='result/')

Bases: Evaluator

evaluate(model=None, output_dir='result/')

matchbench.evaluator.metrics module

matchbench.evaluator.metrics.CSLS_cal(sourceVec, targetVec, device, batch_size=1024, topk=50)

matchbench.evaluator.metrics.batch_mat_mm(mat1, mat2, device, bs=128)

matchbench.evaluator.metrics.batch_topk(mat, bs=128, topn=50, largest=False)

matchbench.evaluator.metrics.cos_sim_mat_generate(emb1, emb2, device, bs=128)

return cosine similarity matrix of embedding1(emb1) and embedding2(emb2) emb: [batch, entity id, embedding vector]

matchbench.evaluator.metrics.hits(index_mat)

Module contents