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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 json | |
| 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) | |
| for alpha in np.arange(args.start_weight, args.end_weight, args.step): | |
| run1_result = json.load(open(args.run1)) | |
| run2_result = json.load(open(args.run2)) | |
| hybrid_result = {} | |
| for key in tqdm(list(run1_result.keys())): | |
| question = run1_result[key]['question'] | |
| answers = run1_result[key]['answers'] | |
| run2_contexts = run2_result[key]['contexts'] | |
| run1_contexts = run1_result[key]['contexts'] | |
| run1_hits = {hit['docid']: float(hit['score']) for hit in run1_contexts} | |
| run2_hits = {hit['docid']: float(hit['score']) for hit in run2_contexts} | |
| hybrid_scores = {} | |
| run1_scores = {} | |
| run2_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 | |
| run2_scores[doc] = run2_hits[doc] | |
| run1_scores[doc] = -1 | |
| elif doc not in run2_hits: | |
| score = alpha * min_run2_score + run1_hits[doc] | |
| run2_scores[doc] = -1 | |
| run1_scores[doc] = run1_hits[doc] | |
| else: | |
| score = alpha * run2_hits[doc] + run1_hits[doc] | |
| run2_scores[doc] = run2_hits[doc] | |
| run1_scores[doc] = run1_hits[doc] | |
| hybrid_scores[doc] = score | |
| total_ids = [] | |
| total_context = [] | |
| for sctx, dctx in zip(run2_contexts, run1_contexts): | |
| if sctx['docid'] not in total_ids: | |
| total_ids.append(sctx['docid']) | |
| sctx['score'] = hybrid_scores[sctx['docid']] | |
| sctx['run2_score'] = run2_scores[sctx['docid']] | |
| sctx['run1_score'] = run1_scores[sctx['docid']] | |
| total_context.append(sctx) | |
| if dctx['docid'] not in total_ids: | |
| total_ids.append(dctx['docid']) | |
| dctx['score'] = hybrid_scores[dctx['docid']] | |
| dctx['run2_score'] = run2_scores[dctx['docid']] | |
| dctx['run1_score'] = run1_scores[dctx['docid']] | |
| total_context.append(dctx) | |
| total_context = sorted(total_context, key=lambda x: x['score'], reverse=True) | |
| hybrid_result[key] = {'question': question, 'answers': answers, 'contexts': total_context} | |
| json.dump(hybrid_result, open(os.path.join(args.output_dir, f'run_fused_weight_{alpha}.json'), 'w'), indent=4) | |