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| # | |
| # Pyserini: Reproducible IR research with sparse and dense representations | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # | |
| import argparse | |
| import os | |
| import numpy as np | |
| from tqdm import tqdm | |
| if __name__ == '__main__': | |
| parser = argparse.ArgumentParser(description='Interpolate runs') | |
| parser.add_argument('--run1', required=True, help='retrieval run1') | |
| parser.add_argument('--run2', required=True, help='retrieval run2') | |
| parser.add_argument('--start-weight', type=float, required=True, help='start hybrid alpha') | |
| parser.add_argument('--end-weight', type=float, required=True, help='end hybrid alpha') | |
| parser.add_argument('--step', type=float, required=True, help='changes of alpha per step') | |
| parser.add_argument('--output-dir', required=True, help='hybrid result') | |
| args = parser.parse_args() | |
| if not os.path.exists(args.output_dir): | |
| os.makedirs(args.output_dir) | |
| run1_result = {} | |
| with open(args.run1) as f: | |
| for line in f: | |
| qid, _, docid, rank, score, _ = line.rstrip().split() | |
| score = float(score) | |
| if qid in run1_result: | |
| run1_result[qid][docid] = score | |
| else: | |
| run1_result[qid] = {docid: score} | |
| run2_result = {} | |
| with open(args.run2) as f: | |
| for line in f: | |
| qid, _, docid, rank, score, _ = line.rstrip().split() | |
| score = float(score) | |
| if qid in run2_result: | |
| run2_result[qid][docid] = score | |
| else: | |
| run2_result[qid] = {docid: score} | |
| hybrid_result = {} | |
| for alpha in np.arange(args.start_weight, args.end_weight, args.step): | |
| output_f = open(args.output_dir, 'w') | |
| for key in tqdm(list(run1_result.keys())): | |
| run1_hits = {docid: float(run1_result[key][docid]) for docid in run1_result[key]} | |
| run2_hits = {docid: float(run2_result[key][docid]) for docid in run2_result[key]} | |
| hybrid_scores = [] | |
| min_run1_score = min(run1_hits.values()) | |
| min_run2_score = min(run2_hits.values()) | |
| for doc in set(run1_hits.keys()) | set(run2_hits.keys()): | |
| if doc not in run1_hits: | |
| score = alpha * run2_hits[doc] + min_run1_score | |
| elif doc not in run2_hits: | |
| score = alpha * min_run2_score + run1_hits[doc] | |
| else: | |
| score = alpha * run2_hits[doc] + run1_hits[doc] | |
| hybrid_scores.append((doc, score)) | |
| hybrid_scores = sorted(hybrid_scores, key=lambda x: x[1], reverse=True) | |
| for idx, item in enumerate(hybrid_scores): | |
| output_f.write(f'{key} Q0 {item[0]} {idx+1} {item[1]} hybrid\n') | |
| output_f.close() | |