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evaluate.py
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evaluate.py
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import os
import json
import tqdm
import torch
from time import time
from typing import List, Tuple
from dataclasses import dataclass, asdict
from config import args
from doc import load_data, Example
from predict import BertPredictor
from dict_hub import get_entity_dict, get_all_triplet_dict
from triplet import EntityDict
from rerank import rerank_by_graph
from logger_config import logger
def _setup_entity_dict() -> EntityDict:
if args.task == 'wiki5m_ind':
return EntityDict(entity_dict_dir=os.path.dirname(args.valid_path),
inductive_test_path=args.valid_path)
return get_entity_dict()
entity_dict = _setup_entity_dict()
all_triplet_dict = get_all_triplet_dict()
@dataclass
class PredInfo:
head: str
relation: str
tail: str
pred_tail: str
pred_score: float
topk_score_info: str
rank: int
correct: bool
@torch.no_grad()
def compute_metrics(hr_tensor: torch.tensor,
entities_tensor: torch.tensor,
target: List[int],
examples: List[Example],
k=3, batch_size=256) -> Tuple:
assert hr_tensor.size(1) == entities_tensor.size(1)
total = hr_tensor.size(0)
entity_cnt = len(entity_dict)
assert entity_cnt == entities_tensor.size(0)
target = torch.LongTensor(target).unsqueeze(-1).to(hr_tensor.device)
topk_scores, topk_indices = [], []
ranks = []
mean_rank, mrr, hit1, hit3, hit10 = 0, 0, 0, 0, 0
for start in tqdm.tqdm(range(0, total, batch_size)):
end = start + batch_size
# batch_size * entity_cnt
batch_score = torch.mm(hr_tensor[start:end, :], entities_tensor.t())
assert entity_cnt == batch_score.size(1)
batch_target = target[start:end]
# re-ranking based on topological structure
rerank_by_graph(batch_score, examples[start:end], entity_dict=entity_dict)
# filter known triplets
for idx in range(batch_score.size(0)):
mask_indices = []
cur_ex = examples[start + idx]
gold_neighbor_ids = all_triplet_dict.get_neighbors(cur_ex.head_id, cur_ex.relation)
if len(gold_neighbor_ids) > 10000:
logger.debug('{} - {} has {} neighbors'.format(cur_ex.head_id, cur_ex.relation, len(gold_neighbor_ids)))
for e_id in gold_neighbor_ids:
if e_id == cur_ex.tail_id:
continue
mask_indices.append(entity_dict.entity_to_idx(e_id))
mask_indices = torch.LongTensor(mask_indices).to(batch_score.device)
batch_score[idx].index_fill_(0, mask_indices, -1)
batch_sorted_score, batch_sorted_indices = torch.sort(batch_score, dim=-1, descending=True)
target_rank = torch.nonzero(batch_sorted_indices.eq(batch_target).long(), as_tuple=False)
assert target_rank.size(0) == batch_score.size(0)
for idx in range(batch_score.size(0)):
idx_rank = target_rank[idx].tolist()
assert idx_rank[0] == idx
cur_rank = idx_rank[1]
# 0-based -> 1-based
cur_rank += 1
mean_rank += cur_rank
mrr += 1.0 / cur_rank
hit1 += 1 if cur_rank <= 1 else 0
hit3 += 1 if cur_rank <= 3 else 0
hit10 += 1 if cur_rank <= 10 else 0
ranks.append(cur_rank)
topk_scores.extend(batch_sorted_score[:, :k].tolist())
topk_indices.extend(batch_sorted_indices[:, :k].tolist())
metrics = {'mean_rank': mean_rank, 'mrr': mrr, 'hit@1': hit1, 'hit@3': hit3, 'hit@10': hit10}
metrics = {k: round(v / total, 4) for k, v in metrics.items()}
assert len(topk_scores) == total
return topk_scores, topk_indices, metrics, ranks
def predict_by_split():
assert os.path.exists(args.valid_path)
assert os.path.exists(args.train_path)
predictor = BertPredictor()
predictor.load(ckt_path=args.eval_model_path)
entity_tensor = predictor.predict_by_entities(entity_dict.entity_exs)
forward_metrics = eval_single_direction(predictor,
entity_tensor=entity_tensor,
eval_forward=True)
backward_metrics = eval_single_direction(predictor,
entity_tensor=entity_tensor,
eval_forward=False)
metrics = {k: round((forward_metrics[k] + backward_metrics[k]) / 2, 4) for k in forward_metrics}
logger.info('Averaged metrics: {}'.format(metrics))
prefix, basename = os.path.dirname(args.eval_model_path), os.path.basename(args.eval_model_path)
split = os.path.basename(args.valid_path)
with open('{}/metrics_{}_{}.json'.format(prefix, split, basename), 'w', encoding='utf-8') as writer:
writer.write('forward metrics: {}\n'.format(json.dumps(forward_metrics)))
writer.write('backward metrics: {}\n'.format(json.dumps(backward_metrics)))
writer.write('average metrics: {}\n'.format(json.dumps(metrics)))
def eval_single_direction(predictor: BertPredictor,
entity_tensor: torch.tensor,
eval_forward=True,
batch_size=256) -> dict:
start_time = time()
examples = load_data(args.valid_path, add_forward_triplet=eval_forward, add_backward_triplet=not eval_forward)
hr_tensor, _ = predictor.predict_by_examples(examples)
hr_tensor = hr_tensor.to(entity_tensor.device)
target = [entity_dict.entity_to_idx(ex.tail_id) for ex in examples]
logger.info('predict tensor done, compute metrics...')
topk_scores, topk_indices, metrics, ranks = compute_metrics(hr_tensor=hr_tensor, entities_tensor=entity_tensor,
target=target, examples=examples,
batch_size=batch_size)
eval_dir = 'forward' if eval_forward else 'backward'
logger.info('{} metrics: {}'.format(eval_dir, json.dumps(metrics)))
pred_infos = []
for idx, ex in enumerate(examples):
cur_topk_scores = topk_scores[idx]
cur_topk_indices = topk_indices[idx]
pred_idx = cur_topk_indices[0]
cur_score_info = {entity_dict.get_entity_by_idx(topk_idx).entity: round(topk_score, 3)
for topk_score, topk_idx in zip(cur_topk_scores, cur_topk_indices)}
pred_info = PredInfo(head=ex.head, relation=ex.relation,
tail=ex.tail, pred_tail=entity_dict.get_entity_by_idx(pred_idx).entity,
pred_score=round(cur_topk_scores[0], 4),
topk_score_info=json.dumps(cur_score_info),
rank=ranks[idx],
correct=pred_idx == target[idx])
pred_infos.append(pred_info)
prefix, basename = os.path.dirname(args.eval_model_path), os.path.basename(args.eval_model_path)
split = os.path.basename(args.valid_path)
with open('{}/eval_{}_{}_{}.json'.format(prefix, split, eval_dir, basename), 'w', encoding='utf-8') as writer:
writer.write(json.dumps([asdict(info) for info in pred_infos], ensure_ascii=False, indent=4))
logger.info('Evaluation takes {} seconds'.format(round(time() - start_time, 3)))
return metrics
if __name__ == '__main__':
predict_by_split()