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from typing import Union, Dict
import logging
import os
import datasets
import numpy as np
from tqdm import tqdm
from evaluation.embeddings_generator import EmbeddingsGenerator
from evaluation.encoders import Model
from evaluation.eval_datasets import SimpleDataset
from evaluation.evaluator import IREvaluator
from sklearn.metrics.pairwise import cosine_similarity
logger = logging.getLogger(__name__)
class ReviewerMatchingEvaluator(IREvaluator):
def __init__(self, name: str, meta_dataset: Union[str, tuple], test_dataset: Union[str, tuple],
reviewer_metadata: Union[str, tuple], model: Model,
metrics: tuple = ("P_5", "P_10"), batch_size: int = 16, fields: list = None):
super(ReviewerMatchingEvaluator, self).__init__(name, meta_dataset, test_dataset, model, metrics, SimpleDataset,
batch_size, fields, )
self.reviewer_metadata = reviewer_metadata
def evaluate(self, embeddings, **kwargs):
logger.info(f"Loading test dataset from {self.test_dataset}")
if type(self.test_dataset) == str and os.path.isdir(self.test_dataset):
split_dataset = datasets.load_dataset("json",
data_files={"test_hard": f"{self.test_dataset}/test_hard_qrel.jsonl",
"test_soft": f"{self.test_dataset}/test_soft_qrel.jsonl"})
else:
split_dataset = datasets.load_dataset(self.test_dataset[0], self.test_dataset[1])
logger.info(f"Loaded {len(split_dataset['test_hard'])} test query-candidate pairs for hard and soft tests")
if type(embeddings) == str and os.path.isfile(embeddings):
embeddings = EmbeddingsGenerator.load_embeddings_from_jsonl(embeddings)
qrels_hard = self.get_qc_pairs(split_dataset["test_hard"])
qrels_soft = self.get_qc_pairs(split_dataset["test_soft"])
preds = self.retrieval(embeddings, qrels_hard)
results = {f"hard_{k}": v for k, v in self.calc_metrics(qrels_hard, preds).items()}
results.update({f"soft_{k}": v for k, v in self.calc_metrics(qrels_soft, preds).items()})
self.print_results(results)
return results
def retrieval(self, embeddings, qrels: Dict[str, Dict[str, int]]) -> Dict[str, Dict[str, float]]:
logger.info("Loading reviewer metadata...")
if type(self.reviewer_metadata) == str and os.path.isdir(self.reviewer_metadata):
reviewer_dataset = datasets.load_dataset("json", data_files={
"metadata": f"{self.reviewer_metadata}/reviewer_metadata.jsonl"})["metadata"]
else:
reviewer_dataset = datasets.load_dataset(self.reviewer_metadata[0], self.reviewer_metadata[1],
split="metadata")
logger.info(f"Loaded {len(reviewer_dataset)} reviewer metadata")
reviewer_papers = {d["r_id"]: d["papers"] for d in reviewer_dataset}
run = dict()
for qid in tqdm(qrels):
query = np.array([embeddings[qid]])
cand_papers = {cid: np.array([embeddings[pid] for pid in reviewer_papers[cid]]) for cid in qrels[qid] if
cid in reviewer_papers}
scores = {cid: cosine_similarity(cand_papers[cid], query).flatten() for cid in cand_papers}
sorted_scores = {cid: sorted(scores[cid], reverse=True) for cid in scores}
run[qid] = {cid: float(np.mean(sorted_scores[cid][:3])) for cid in sorted_scores}
return run
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