""" This module computes evaluation metrics for MSMARCO dataset on the ranking task. Intenral hard coded eval files version. DO NOT PUBLISH! Command line: python msmarco_eval_ranking.py Creation Date : 06/12/2018 Last Modified : 4/09/2019 Authors : Daniel Campos , Rutger van Haasteren """ import sys import statistics from collections import Counter def load_reference_from_stream(f): """Load Reference reference relevant passages Args:f (stream): stream to load. Returns:qids_to_relevant_passageids (dict): dictionary mapping from query_id (int) to relevant passages (list of ints). """ qids_to_relevant_passageids = {} for l in f: try: l = l.strip().split('\t') qid = int(l[0]) if qid in qids_to_relevant_passageids: pass else: qids_to_relevant_passageids[qid] = [] qids_to_relevant_passageids[qid].append(int(l[1])) except: raise IOError('\"%s\" is not valid format' % l) return qids_to_relevant_passageids def load_reference(path_to_reference): """Load Reference reference relevant passages Args:path_to_reference (str): path to a file to load. Returns:qids_to_relevant_passageids (dict): dictionary mapping from query_id (int) to relevant passages (list of ints). """ with open(path_to_reference, 'r') as f: qids_to_relevant_passageids = load_reference_from_stream(f) return qids_to_relevant_passageids def load_candidate_from_stream(f): """Load candidate data from a stream. Args:f (stream): stream to load. Returns:qid_to_ranked_candidate_passages (dict): dictionary mapping from query_id (int) to a list of 1000 passage ids(int) ranked by relevance and importance """ qid_to_ranked_candidate_passages = {} for l in f: try: l = l.strip().split('\t') qid = int(l[0]) pid = int(l[1]) rank = int(l[2]) if qid in qid_to_ranked_candidate_passages: pass else: # By default, all PIDs in the list of 1000 are 0. Only override those that are given tmp = [0] * 1000 qid_to_ranked_candidate_passages[qid] = tmp qid_to_ranked_candidate_passages[qid][rank - 1] = pid except: raise IOError('\"%s\" is not valid format' % l) return qid_to_ranked_candidate_passages def load_candidate(path_to_candidate): """Load candidate data from a file. Args:path_to_candidate (str): path to file to load. Returns:qid_to_ranked_candidate_passages (dict): dictionary mapping from query_id (int) to a list of 1000 passage ids(int) ranked by relevance and importance """ with open(path_to_candidate, 'r') as f: qid_to_ranked_candidate_passages = load_candidate_from_stream(f) return qid_to_ranked_candidate_passages def quality_checks_qids(qids_to_relevant_passageids, qids_to_ranked_candidate_passages): """Perform quality checks on the dictionaries Args: p_qids_to_relevant_passageids (dict): dictionary of query-passage mapping Dict as read in with load_reference or load_reference_from_stream p_qids_to_ranked_candidate_passages (dict): dictionary of query-passage candidates Returns: bool,str: Boolean whether allowed, message to be shown in case of a problem """ message = '' allowed = True # Create sets of the QIDs for the submitted and reference queries candidate_set = set(qids_to_ranked_candidate_passages.keys()) ref_set = set(qids_to_relevant_passageids.keys()) # Check that we do not have multiple passages per query for qid in qids_to_ranked_candidate_passages: # Remove all zeros from the candidates duplicate_pids = set( [item for item, count in Counter(qids_to_ranked_candidate_passages[qid]).items() if count > 1]) if len(duplicate_pids - set([0])) > 0: message = "Cannot rank a passage multiple times for a single query. QID={qid}, PID={pid}".format( qid=qid, pid=list(duplicate_pids)[0]) allowed = False return allowed, message def compute_metrics(qids_to_relevant_passageids, qids_to_ranked_candidate_passages): """Compute MRR metric Args: p_qids_to_relevant_passageids (dict): dictionary of query-passage mapping Dict as read in with load_reference or load_reference_from_stream p_qids_to_ranked_candidate_passages (dict): dictionary of query-passage candidates Returns: dict: dictionary of metrics {'MRR': } """ topk=[5,10,20,50,100,200,500,1000] accuracy = { k : [] for k in topk } MaxMRRRank=max(topk) ranking = [] for qid in qids_to_ranked_candidate_passages: if qid in qids_to_relevant_passageids: ranking.append(10**9) target_pid = qids_to_relevant_passageids[qid] candidate_pid = qids_to_ranked_candidate_passages[qid] for i in range(0, MaxMRRRank): if candidate_pid[i] in target_pid: ranking.pop() ranking.append(i + 1) break for k in topk: accuracy[k].append(0 if ranking[-1] > k else 1) if len(ranking) == 0: raise IOError("No matching QIDs found. Are you sure you are scoring the evaluation set?") return accuracy def compute_metrics_from_files(path_to_reference, path_to_candidate, perform_checks=True): """Compute MRR metric Args: p_path_to_reference_file (str): path to reference file. Reference file should contain lines in the following format: QUERYID\tPASSAGEID Where PASSAGEID is a relevant passage for a query. Note QUERYID can repeat on different lines with different PASSAGEIDs p_path_to_candidate_file (str): path to candidate file. Candidate file sould contain lines in the following format: QUERYID\tPASSAGEID1\tRank If a user wishes to use the TREC format please run the script with a -t flag at the end. If this flag is used the expected format is QUERYID\tITER\tDOCNO\tRANK\tSIM\tRUNID Where the values are separated by tabs and ranked in order of relevance Returns: dict: dictionary of metrics {'MRR': } """ qids_to_relevant_passageids = load_reference(path_to_reference) qids_to_ranked_candidate_passages = load_candidate(path_to_candidate) if perform_checks: allowed, message = quality_checks_qids(qids_to_relevant_passageids, qids_to_ranked_candidate_passages) if message != '': print(message) return compute_metrics(qids_to_relevant_passageids, qids_to_ranked_candidate_passages) def main(): """Command line: python msmarco_eval_ranking.py """ import scipy.stats as stats topk=[5,10,20,50,100,200,500,1000] path_to_candidate_a = "InfoCSE_ICT.tsv.marco" path_to_reference = "marco/qrels.dev.tsv" all_scores_a = compute_metrics_from_files(path_to_reference, path_to_candidate_a) for method in ["SimCSE","ConSERT","MirrorBERT","ICT","CPC","DeCLUTR","CONPONO"]: path_to_candidate_b = "{}.tsv.marco".format(method) print(path_to_candidate_b) all_scores_b = compute_metrics_from_files(path_to_reference, path_to_candidate_b) for k in topk: stat_val, p_val = stats.ttest_ind(all_scores_a[k], all_scores_b[k]) print(str(k) + ': ' + str(p_val / 2)) if __name__ == '__main__': main()