from . import SentenceEvaluator import torch from torch import Tensor import logging from tqdm import trange from ..util import cos_sim, dot_score import os import numpy as np from typing import List, Dict, Set, Callable import heapq logger = logging.getLogger(__name__) class InformationRetrievalEvaluator(SentenceEvaluator): """ This class evaluates an Information Retrieval (IR) setting. Given a set of queries and a large corpus set. It will retrieve for each query the top-k most similar document. It measures Mean Reciprocal Rank (MRR), Recall@k, and Normalized Discounted Cumulative Gain (NDCG) """ def __init__( self, queries: Dict[str, str], # qid => query corpus: Dict[str, str], # cid => doc relevant_docs: Dict[str, Set[str]], # qid => Set[cid] corpus_chunk_size: int = 50000, mrr_at_k: List[int] = [10], ndcg_at_k: List[int] = [10], accuracy_at_k: List[int] = [1, 3, 5, 10], precision_recall_at_k: List[int] = [1, 3, 5, 10], map_at_k: List[int] = [100], show_progress_bar: bool = False, batch_size: int = 32, name: str = "", write_csv: bool = True, score_functions: Dict[str, Callable[[Tensor, Tensor], Tensor]] = { "cos_sim": cos_sim, "dot_score": dot_score, }, # Score function, higher=more similar main_score_function: str = None, ): self.queries_ids = [] for qid in queries: if qid in relevant_docs and len(relevant_docs[qid]) > 0: self.queries_ids.append(qid) self.queries = [queries[qid] for qid in self.queries_ids] self.corpus_ids = list(corpus.keys()) self.corpus = [corpus[cid] for cid in self.corpus_ids] self.relevant_docs = relevant_docs self.corpus_chunk_size = corpus_chunk_size self.mrr_at_k = mrr_at_k self.ndcg_at_k = ndcg_at_k self.accuracy_at_k = accuracy_at_k self.precision_recall_at_k = precision_recall_at_k self.map_at_k = map_at_k self.show_progress_bar = show_progress_bar self.batch_size = batch_size self.name = name self.write_csv = write_csv self.score_functions = score_functions self.score_function_names = sorted(list(self.score_functions.keys())) self.main_score_function = main_score_function if name: name = "_" + name self.csv_file: str = "Information-Retrieval_evaluation" + name + "_results.csv" self.csv_headers = ["epoch", "steps"] for score_name in self.score_function_names: for k in accuracy_at_k: self.csv_headers.append("{}-Accuracy@{}".format(score_name, k)) for k in precision_recall_at_k: self.csv_headers.append("{}-Precision@{}".format(score_name, k)) self.csv_headers.append("{}-Recall@{}".format(score_name, k)) for k in mrr_at_k: self.csv_headers.append("{}-MRR@{}".format(score_name, k)) for k in ndcg_at_k: self.csv_headers.append("{}-NDCG@{}".format(score_name, k)) for k in map_at_k: self.csv_headers.append("{}-MAP@{}".format(score_name, k)) def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1, *args, **kwargs) -> float: if epoch != -1: out_txt = ( " after epoch {}:".format(epoch) if steps == -1 else " in epoch {} after {} steps:".format(epoch, steps) ) else: out_txt = ":" logger.info("Information Retrieval Evaluation on " + self.name + " dataset" + out_txt) scores = self.compute_metrices(model, *args, **kwargs) # Write results to disc if output_path is not None and self.write_csv: csv_path = os.path.join(output_path, self.csv_file) if not os.path.isfile(csv_path): fOut = open(csv_path, mode="w", encoding="utf-8") fOut.write(",".join(self.csv_headers)) fOut.write("\n") else: fOut = open(csv_path, mode="a", encoding="utf-8") output_data = [epoch, steps] for name in self.score_function_names: for k in self.accuracy_at_k: output_data.append(scores[name]["accuracy@k"][k]) for k in self.precision_recall_at_k: output_data.append(scores[name]["precision@k"][k]) output_data.append(scores[name]["recall@k"][k]) for k in self.mrr_at_k: output_data.append(scores[name]["mrr@k"][k]) for k in self.ndcg_at_k: output_data.append(scores[name]["ndcg@k"][k]) for k in self.map_at_k: output_data.append(scores[name]["map@k"][k]) fOut.write(",".join(map(str, output_data))) fOut.write("\n") fOut.close() if self.main_score_function is None: return max([scores[name]["map@k"][max(self.map_at_k)] for name in self.score_function_names]) else: return scores[self.main_score_function]["map@k"][max(self.map_at_k)] def compute_metrices(self, model, corpus_model=None, corpus_embeddings: Tensor = None) -> Dict[str, float]: if corpus_model is None: corpus_model = model max_k = max( max(self.mrr_at_k), max(self.ndcg_at_k), max(self.accuracy_at_k), max(self.precision_recall_at_k), max(self.map_at_k), ) # Compute embedding for the queries query_embeddings = model.encode( self.queries, show_progress_bar=self.show_progress_bar, batch_size=self.batch_size, convert_to_tensor=True ) queries_result_list = {} for name in self.score_functions: queries_result_list[name] = [[] for _ in range(len(query_embeddings))] # Iterate over chunks of the corpus for corpus_start_idx in trange( 0, len(self.corpus), self.corpus_chunk_size, desc="Corpus Chunks", disable=not self.show_progress_bar ): corpus_end_idx = min(corpus_start_idx + self.corpus_chunk_size, len(self.corpus)) # Encode chunk of corpus if corpus_embeddings is None: sub_corpus_embeddings = corpus_model.encode( self.corpus[corpus_start_idx:corpus_end_idx], show_progress_bar=False, batch_size=self.batch_size, convert_to_tensor=True, ) else: sub_corpus_embeddings = corpus_embeddings[corpus_start_idx:corpus_end_idx] # Compute cosine similarites for name, score_function in self.score_functions.items(): pair_scores = score_function(query_embeddings, sub_corpus_embeddings) # Get top-k values pair_scores_top_k_values, pair_scores_top_k_idx = torch.topk( pair_scores, min(max_k, len(pair_scores[0])), dim=1, largest=True, sorted=False ) pair_scores_top_k_values = pair_scores_top_k_values.cpu().tolist() pair_scores_top_k_idx = pair_scores_top_k_idx.cpu().tolist() for query_itr in range(len(query_embeddings)): for sub_corpus_id, score in zip( pair_scores_top_k_idx[query_itr], pair_scores_top_k_values[query_itr] ): corpus_id = self.corpus_ids[corpus_start_idx + sub_corpus_id] if len(queries_result_list[name][query_itr]) < max_k: heapq.heappush( queries_result_list[name][query_itr], (score, corpus_id) ) # heaqp tracks the quantity of the first element in the tuple else: heapq.heappushpop(queries_result_list[name][query_itr], (score, corpus_id)) for name in queries_result_list: for query_itr in range(len(queries_result_list[name])): for doc_itr in range(len(queries_result_list[name][query_itr])): score, corpus_id = queries_result_list[name][query_itr][doc_itr] queries_result_list[name][query_itr][doc_itr] = {"corpus_id": corpus_id, "score": score} logger.info("Queries: {}".format(len(self.queries))) logger.info("Corpus: {}\n".format(len(self.corpus))) # Compute scores scores = {name: self.compute_metrics(queries_result_list[name]) for name in self.score_functions} # Output for name in self.score_function_names: logger.info("Score-Function: {}".format(name)) self.output_scores(scores[name]) return scores def compute_metrics(self, queries_result_list: List[object]): # Init score computation values num_hits_at_k = {k: 0 for k in self.accuracy_at_k} precisions_at_k = {k: [] for k in self.precision_recall_at_k} recall_at_k = {k: [] for k in self.precision_recall_at_k} MRR = {k: 0 for k in self.mrr_at_k} ndcg = {k: [] for k in self.ndcg_at_k} AveP_at_k = {k: [] for k in self.map_at_k} # Compute scores on results for query_itr in range(len(queries_result_list)): query_id = self.queries_ids[query_itr] # Sort scores top_hits = sorted(queries_result_list[query_itr], key=lambda x: x["score"], reverse=True) query_relevant_docs = self.relevant_docs[query_id] # Accuracy@k - We count the result correct, if at least one relevant doc is across the top-k documents for k_val in self.accuracy_at_k: for hit in top_hits[0:k_val]: if hit["corpus_id"] in query_relevant_docs: num_hits_at_k[k_val] += 1 break # Precision and Recall@k for k_val in self.precision_recall_at_k: num_correct = 0 for hit in top_hits[0:k_val]: if hit["corpus_id"] in query_relevant_docs: num_correct += 1 precisions_at_k[k_val].append(num_correct / k_val) recall_at_k[k_val].append(num_correct / len(query_relevant_docs)) # MRR@k for k_val in self.mrr_at_k: for rank, hit in enumerate(top_hits[0:k_val]): if hit["corpus_id"] in query_relevant_docs: MRR[k_val] += 1.0 / (rank + 1) break # NDCG@k for k_val in self.ndcg_at_k: predicted_relevance = [ 1 if top_hit["corpus_id"] in query_relevant_docs else 0 for top_hit in top_hits[0:k_val] ] true_relevances = [1] * len(query_relevant_docs) ndcg_value = self.compute_dcg_at_k(predicted_relevance, k_val) / self.compute_dcg_at_k( true_relevances, k_val ) ndcg[k_val].append(ndcg_value) # MAP@k for k_val in self.map_at_k: num_correct = 0 sum_precisions = 0 for rank, hit in enumerate(top_hits[0:k_val]): if hit["corpus_id"] in query_relevant_docs: num_correct += 1 sum_precisions += num_correct / (rank + 1) avg_precision = sum_precisions / min(k_val, len(query_relevant_docs)) AveP_at_k[k_val].append(avg_precision) # Compute averages for k in num_hits_at_k: num_hits_at_k[k] /= len(self.queries) for k in precisions_at_k: precisions_at_k[k] = np.mean(precisions_at_k[k]) for k in recall_at_k: recall_at_k[k] = np.mean(recall_at_k[k]) for k in ndcg: ndcg[k] = np.mean(ndcg[k]) for k in MRR: MRR[k] /= len(self.queries) for k in AveP_at_k: AveP_at_k[k] = np.mean(AveP_at_k[k]) return { "accuracy@k": num_hits_at_k, "precision@k": precisions_at_k, "recall@k": recall_at_k, "ndcg@k": ndcg, "mrr@k": MRR, "map@k": AveP_at_k, } def output_scores(self, scores): for k in scores["accuracy@k"]: logger.info("Accuracy@{}: {:.2f}%".format(k, scores["accuracy@k"][k] * 100)) for k in scores["precision@k"]: logger.info("Precision@{}: {:.2f}%".format(k, scores["precision@k"][k] * 100)) for k in scores["recall@k"]: logger.info("Recall@{}: {:.2f}%".format(k, scores["recall@k"][k] * 100)) for k in scores["mrr@k"]: logger.info("MRR@{}: {:.4f}".format(k, scores["mrr@k"][k])) for k in scores["ndcg@k"]: logger.info("NDCG@{}: {:.4f}".format(k, scores["ndcg@k"][k])) for k in scores["map@k"]: logger.info("MAP@{}: {:.4f}".format(k, scores["map@k"][k])) @staticmethod def compute_dcg_at_k(relevances, k): dcg = 0 for i in range(min(len(relevances), k)): dcg += relevances[i] / np.log2(i + 2) # +2 as we start our idx at 0 return dcg