Upload ms_marco_pvalue.py

ms_marco_pvalue.py

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+"""
+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 DO NOT PUBLISH!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 <path_to_candidate_file>
+
+Creation Date : 06/12/2018
+Last Modified : 4/09/2019
+Authors : Daniel Campos <dacamp@microsoft.com>, Rutger van Haasteren <ruvanh@microsoft.com>
+"""
+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': <MRR Score>}
+    """
+    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': <MRR Score>}
+    """
+
+    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 <path to reference> <path_to_candidate_file>
+    """
+    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()