from . import SentenceEvaluator import logging import numpy as np import os import csv from ..util import cos_sim, dot_score import torch from sklearn.metrics import average_precision_score import tqdm logger = logging.getLogger(__name__) class RerankingEvaluator(SentenceEvaluator): """ This class evaluates a SentenceTransformer model for the task of re-ranking. Given a query and a list of documents, it computes the score [query, doc_i] for all possible documents and sorts them in decreasing order. Then, MRR@10 and MAP is compute to measure the quality of the ranking. :param samples: Must be a list and each element is of the form: {'query': '', 'positive': [], 'negative': []}. Query is the search query, positive is a list of positive (relevant) documents, negative is a list of negative (irrelevant) documents. """ def __init__(self, samples, mrr_at_k: int = 10, name: str = '', write_csv: bool = True, similarity_fct=cos_sim, batch_size: int = 64, show_progress_bar: bool = False, use_batched_encoding: bool = True): self.samples = samples self.name = name self.mrr_at_k = mrr_at_k self.similarity_fct = similarity_fct self.batch_size = batch_size self.show_progress_bar = show_progress_bar self.use_batched_encoding = use_batched_encoding if isinstance(self.samples, dict): self.samples = list(self.samples.values()) ### Remove sample with empty positive / negative set self.samples = [sample for sample in self.samples if len(sample['positive']) > 0 and len(sample['negative']) > 0] self.csv_file = "RerankingEvaluator" + ("_" + name if name else '') + "_results.csv" self.csv_headers = ["epoch", "steps", "MAP", "MRR@{}".format(mrr_at_k)] self.write_csv = write_csv def __call__(self, model, output_path: str = None, epoch: int = -1, steps: int = -1) -> float: if epoch != -1: if steps == -1: out_txt = " after epoch {}:".format(epoch) else: out_txt = " in epoch {} after {} steps:".format(epoch, steps) else: out_txt = ":" logger.info("RerankingEvaluator: Evaluating the model on " + self.name + " dataset" + out_txt) scores = self.compute_metrices(model) mean_ap = scores['map'] mean_mrr = scores['mrr'] #### Some stats about the dataset num_positives = [len(sample['positive']) for sample in self.samples] num_negatives = [len(sample['negative']) for sample in self.samples] logger.info("Queries: {} \t Positives: Min {:.1f}, Mean {:.1f}, Max {:.1f} \t Negatives: Min {:.1f}, Mean {:.1f}, Max {:.1f}".format(len(self.samples), np.min(num_positives), np.mean(num_positives), np.max(num_positives), np.min(num_negatives), np.mean(num_negatives), np.max(num_negatives))) logger.info("MAP: {:.2f}".format(mean_ap * 100)) logger.info("MRR@{}: {:.2f}".format(self.mrr_at_k, mean_mrr * 100)) #### Write results to disc if output_path is not None and self.write_csv: csv_path = os.path.join(output_path, self.csv_file) output_file_exists = os.path.isfile(csv_path) with open(csv_path, newline='', mode="a" if output_file_exists else 'w', encoding="utf-8") as f: writer = csv.writer(f) if not output_file_exists: writer.writerow(self.csv_headers) writer.writerow([epoch, steps, mean_ap, mean_mrr]) return mean_ap def compute_metrices(self, model): return self.compute_metrices_batched(model) if self.use_batched_encoding else self.compute_metrices_individual(model) def compute_metrices_batched(self, model): """ Computes the metrices in a batched way, by batching all queries and all documents together """ all_mrr_scores = [] all_ap_scores = [] all_query_embs = model.encode([sample['query'] for sample in self.samples], convert_to_tensor=True, batch_size=self.batch_size, show_progress_bar=True) #self.show_progress_bar) all_docs = [] for sample in self.samples: all_docs.extend(sample['positive']) all_docs.extend(sample['negative']) all_docs_embs = model.encode(all_docs, convert_to_tensor=True, batch_size=self.batch_size, show_progress_bar=self.show_progress_bar) #Compute scores query_idx, docs_idx = 0,0 for instance in self.samples: query_emb = all_query_embs[query_idx] query_idx += 1 num_pos = len(instance['positive']) num_neg = len(instance['negative']) docs_emb = all_docs_embs[docs_idx:docs_idx+num_pos+num_neg] docs_idx += num_pos+num_neg if num_pos == 0 or num_neg == 0: continue pred_scores = self.similarity_fct(query_emb, docs_emb) if len(pred_scores.shape) > 1: pred_scores = pred_scores[0] pred_scores_argsort = torch.argsort(-pred_scores) #Sort in decreasing order #Compute MRR score is_relevant = [True]*num_pos + [False]*num_neg mrr_score = 0 for rank, index in enumerate(pred_scores_argsort[0:self.mrr_at_k]): if is_relevant[index]: mrr_score = 1 / (rank+1) break all_mrr_scores.append(mrr_score) # Compute AP all_ap_scores.append(average_precision_score(is_relevant, pred_scores.cpu().tolist())) mean_ap = np.mean(all_ap_scores) mean_mrr = np.mean(all_mrr_scores) return {'map': mean_ap, 'mrr': mean_mrr} def compute_metrices_individual(self, model): """ Embeds every (query, positive, negative) tuple individually. Is slower than the batched version, but saves memory as only the embeddings for one tuple are needed. Useful when you have a really large test set """ all_mrr_scores = [] all_ap_scores = [] for instance in tqdm.tqdm(self.samples, disable=not self.show_progress_bar, desc="Samples"): query = instance['query'] positive = list(instance['positive']) negative = list(instance['negative']) if len(positive) == 0 or len(negative) == 0: continue docs = positive + negative is_relevant = [True]*len(positive) + [False]*len(negative) query_emb = model.encode([query], convert_to_tensor=True, batch_size=self.batch_size, show_progress_bar=False) docs_emb = model.encode(docs, convert_to_tensor=True, batch_size=self.batch_size, show_progress_bar=False) pred_scores = self.similarity_fct(query_emb, docs_emb) if len(pred_scores.shape) > 1: pred_scores = pred_scores[0] pred_scores_argsort = torch.argsort(-pred_scores) #Sort in decreasing order #Compute MRR score mrr_score = 0 for rank, index in enumerate(pred_scores_argsort[0:self.mrr_at_k]): if is_relevant[index]: mrr_score = 1 / (rank+1) break all_mrr_scores.append(mrr_score) # Compute AP all_ap_scores.append(average_precision_score(is_relevant, pred_scores.cpu().tolist())) mean_ap = np.mean(all_ap_scores) mean_mrr = np.mean(all_mrr_scores) return {'map': mean_ap, 'mrr': mean_mrr}