Function eval

code/NNModel/main.py:81–113

(model, dataset, args)

def eval(model, dataset, args):
    model.eval()
    with torch.no_grad():
        predictions=[]
        labels=[]
        all_ids = []
        all_lengths = []
        for i in range(dataset.batch_count):
            sentence_ids, sentence_tokens, lengths, mask, aspect_tags, _, tags = dataset.get_batch(i)
            prediction = model.forward(sentence_tokens,lengths, mask)
            prediction = prediction[-1]
            prediction = torch.argmax(prediction, dim=3)
            prediction_padded = torch.zeros(prediction.shape[0], args.max_sequence_len, args.max_sequence_len)
            prediction_padded[:, :prediction.shape[1], :prediction.shape[1]] = prediction
            predictions.append(prediction_padded)

            all_ids.extend(sentence_ids)
            labels.append(tags)
            all_lengths.append(lengths)

        predictions = torch.cat(predictions,dim=0).cpu().tolist()
        labels = torch.cat(labels,dim=0).cpu().tolist()
        all_lengths = torch.cat(all_lengths, dim=0).cpu().tolist()
        precision, recall, f1 = utils.score_uniontags(args, predictions, labels, all_lengths, ignore_index=-1)

        aspect_results = utils.score_aspect(predictions, labels, all_lengths, ignore_index=-1)
        opinion_results = utils.score_opinion(predictions, labels, all_lengths, ignore_index=-1)
        print('Aspect term\tP:{:.5f}\tR:{:.5f}\tF1:{:.5f}'.format(aspect_results[0], aspect_results[1], aspect_results[2]))
        print('Opinion term\tP:{:.5f}\tR:{:.5f}\tF1:{:.5f}'.format(opinion_results[0], opinion_results[1], opinion_results[2]))
        print(args.task+'\tP:{:.5f}\tR:{:.5f}\tF1:{:.5f}\n'.format(precision, recall, f1))

    model.train()
    return precision, recall, f1


def test(args):