diff --git a/.gitignore b/.gitignore index b06beea..118ba25 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,13 @@ +dense_index_* +output_dense_* +runs_dense/* +scripts/runs_dense/* +runs .idea .python-version anserini_docs anserini_indicies +data .DS_Store dense_hs.* best_models.py @@ -171,3 +177,6 @@ cython_debug/ # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ + +dense_models_backup/ +*.index \ No newline at end of file diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 0000000..f262517 --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,29 @@ +{ + // Use IntelliSense to learn about possible attributes. + // Hover to view descriptions of existing attributes. + // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Current File", + "type": "python", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "justMyCode": false, + // "args": [ "input","--corpus","/home/ddo/CMU/PLLM/TREC-TOT/corpus.jsonl","--fields","text" , + // // "--delimiter","\\n", + // "--shard-id","0" , + // "--shard-num","1" , + // "output","--embeddings","../output_dense_enc_1" , + // "--to-faiss" , + // "encoder","--encoder","/home/ddo/CMU/PLLM/llms-project/dense_models/baseline_distilbert/model" , + // "--fields","text", + // "--batch", "32" ] + // "args": [] + "args": ["--epochs","20","--lr","6e-05","--weight_decay","0.01","--model_dir","dense_models/baseline_distilbert_0","--run_id","baseline_distilbert_0","--model_or_checkpoint","distilbert-base-uncased","--embed_size","768","--batch_size","10","--encode_batch_size","128","--data_path","/home/ddo/CMU/PLLM/TREC-TOT","--negatives_path","/home/ddo/CMU/PLLM/TREC-TOT/negatives/bm25_negatives","--negatives_out","/home/ddo/CMU/PLLM/TREC-TOT/negatives/baseline_distilbert_0_negatives","--query","title_text","--device","cuda"] + // "args": ["--epochs","20","--lr","6e-05","--weight_decay","0.01","--model_dir","dense_models/baseline_distilbert_0","--run_id","baseline_distilbert_0","--model_or_checkpoint","distilbert-base-uncased","--embed_size","768","--batch_size","10","--encode_batch_size","128","--data_path","/home/ddo/CMU/PLLM/TREC-TOT","--negatives_path","/home/ddo/CMU/PLLM/TREC-TOT/negatives/bm25_negatives","--negatives_out","/home/ddo/CMU/PLLM/TREC-TOT/negatives/baseline_distilbert_0_negatives","--query","title_text","--device","cuda"] + // "args": ["--epochs","20","--lr","6e-05","--weight_decay","0.01","--model_dir","dense_models/baseline_distilbert_0","--run_id","baseline_distilbert_0","--model_or_checkpoint","distilbert-base-uncased","--embed_size","768","--batch_size","10","--encode_batch_size","128","--data_path","/home/ddo/CMU/PLLM/TREC-TOT","--negatives_path","/home/ddo/CMU/PLLM/TREC-TOT/negatives/bm25_negatives","--negatives_out","/home/ddo/CMU/PLLM/TREC-TOT/negatives/baseline_distilbert_0_negatives","--query","title_text","--device","cuda"] + } + ] +} \ No newline at end of file diff --git a/README.md b/README.md index ac31c65..bd03b5f 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,15 @@ # Benchmarks for TREC-ToT (2023) +NOTE: code is activelly being re-organized! + The following benchmarks (& runs) are available: | Benchmark | Runfiles | Dev-DCG | Dev-Success@1000 | Dev-MRR | |----------------------|----------|----------|-----------------|-------| -| [BM25](BM25.md) (k1=0.8, b=1.0) | [train](runs/bm25/train.run), [dev](runs/bm25/dev.run) | 0.1314 | 0.4067 | 0.0881 | -| [Dense Retrieval (SBERT)](DENSE.md) (Distilbert) | [train](runs/distilbert/train.run), [dev](runs/distilbert/dev.run) | 0.1627 | 0.6600 | 0.0743 | -| [GPT-4](GPT4.md)* | [train](runs/gpt4/train.run), [dev](runs/gpt4/dev.run) | 0.2407 | 0.3200 | 0.2180 | +| [BM25](docs/BM25.md) (k1=0.8, b=1.0) | [train](runs/bm25/train.run), [dev](runs/bm25/dev.run) | 0.1314 | 0.4067 | 0.0881 | +| [Dense Retrieval (SBERT)](docs/DENSE.md) (Distilbert) | [train](runs/distilbert/train.run), [dev](runs/distilbert/dev.run) | 0.1627 | 0.6600 | 0.0743 | +| [GPT-4](docs/GPT4.md)* | [train](runs/gpt4/train.run), [dev](runs/gpt4/dev.run) | 0.2407 | 0.3200 | 0.2180 | *: GPT-4 generates 20 candidates at most. See [GPT4](GPT4.md) for more details. @@ -18,7 +20,7 @@ The following benchmarks (& runs) are available: ## optional: create new environment using py-env virtual-env ## pyenv virtualenv 3.8.11 trec-tot-benchmarks # install requirements -pip install ir_datasets sentence-transformers==2.2.2 pyserini==0.20.0 pytrec_eval faiss-cpu==1.6.5 +pip install ir_datasets sentence-transformers==2.2.2 pyserini==0.20.0 pytrec_eval faiss-cpu==1.6.5 ranx==0.3.7 ``` After downloading the files (see guidelines), set DATA_PATH to the folder which @@ -39,4 +41,4 @@ Quick test to see if data is setup properly: python tot.py ``` The command above should print the correct number of train/dev queries and the number of documents -in the corpus, along with example queries and documents. \ No newline at end of file +in the corpus, along with example queries and documents. diff --git a/bm25.py b/bm25.py index 161d528..ef1ea79 100644 --- a/bm25.py +++ b/bm25.py @@ -18,7 +18,7 @@ log = logging.getLogger(__name__) -METRICS = "recall_10,recall_100,recall_1000,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,recip_rank" +METRICS = "P_1,recall_10,recall_100,recall_1000,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,recip_rank" def create_index(dataset, field_to_index, dest_folder, index): diff --git a/bm25_with_rrf.py b/bm25_with_rrf.py new file mode 100644 index 0000000..a2880b8 --- /dev/null +++ b/bm25_with_rrf.py @@ -0,0 +1,121 @@ +import json +import argparse +import os +import pandas as pd +import logging + + +from tqdm import tqdm +from typing import Dict +from pyserini.search.lucene import LuceneSearcher +from pyserini.trectools import TrecRun +from pyserini.fusion import reciprocal_rank_fusion +from ranx import Qrels, Run, evaluate + +from modules import llm_based_decomposition, sentence_decomposition +import tot +import ir_datasets +from src import utils +import pytrec_eval + +METRICS = "P_1,recall_10,recall_100,recall_1000,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,recip_rank" + +log = logging.getLogger(__name__) + +def main(): + parser = argparse.ArgumentParser() + # Path to indexes directory + parser.add_argument("--index_name", default="bm25_0.8_1.0", help="name of index") + + parser.add_argument("--decomposition_method", default="llm", help="how to decompose") + + parser.add_argument("--data_path", default="./data", help="location to dataset") + + parser.add_argument("--split", choices={"train", "dev", "test"}, default="dev", help="split to run") + + parser.add_argument("--index_path", default="./anserini_indicies", help="path to store (all) indices") + + parser.add_argument("--metrics", default=METRICS, help="csv - metrics to evaluate") + + parser.add_argument("--param_k1", default=0.8, type=float, help="param: k1 for BM25") + + parser.add_argument("--param_b", default=1.0, type=float, help="param: b for BM25") + + # BM25 parameters + parser.add_argument('--K', type=int, help='retrieve top K documents', default=1000) + + # Binary flags to enable or disable ranking methodss + parser.add_argument('--rm3', type=str, help='enable or disable rm3', choices=['y', 'n'], default='n') + + # Run number + parser.add_argument('--run_number', type=int, help='run number', default=1) + + # Output options and directory + parser.add_argument('--output_dir', type=str, help='path to output_dir', default="runs/") + args = parser.parse_args() + + tot.register(args.data_path) + + irds_name = "trec-tot:" + args.split + dataset = ir_datasets.load(irds_name) + if args.decomposition_method == "llm": + queries_expanded = llm_based_decomposition(dataset, f"{args.data_path}/decomposed_queries") + else: + queries_expanded = sentence_decomposition(dataset, f"{args.data_path}/decomposed_queries") + + queries = json.load(open(queries_expanded)) + + run_save_folder = f'{args.output_dir}BM25-RRF' + if args.decomposition_method == "llm": + run_save_folder += f'-llm' + + run_save_folder += f'-RM3-{args.run_number}' if args.rm3 == 'y' else f'-{args.run_number}' + + run_save_full = f"{run_save_folder}/{args.split}.run" + + searcher = LuceneSearcher(os.path.join(args.index_path, args.index_name)) + searcher.set_bm25(k1=args.param_k1, b=args.param_b) + + if args.rm3 == 'y': + searcher.set_rm3() + + # Retrieve + run_result = [] + + for query_id in tqdm(queries): + for sintetic_query_id in queries[query_id]: + hits = searcher.search(f'{queries[query_id][sintetic_query_id]}', k=args.K) + sintetic_query_results = [] + for rank, hit in enumerate(hits, start=1): + sintetic_query_results.append((query_id, 'Q0', hit.docid, rank, hit.score, f'{query_id}_{sintetic_query_id}')) + + if sintetic_query_results != []: + run_result.append(TrecRun.from_list(sintetic_query_results)) + + results = reciprocal_rank_fusion(run_result, depth=args.K, k=args.K) + + print(f"saving run to: {run_save_full}") + os.makedirs(os.path.dirname(run_save_full), exist_ok=True) + results.save_to_txt(run_save_full) + + if dataset.has_qrels(): + + with open(run_save_full, 'r') as h: + run_to_eval = pytrec_eval.parse_run(h) + + qrel, n_missing = utils.get_qrel(dataset, run_to_eval) + if n_missing > 0: + raise ValueError(f"Number of missing qids in run: {n_missing}") + + evaluator = pytrec_eval.RelevanceEvaluator( + qrel, args.metrics.split(",")) + + eval_res = evaluator.evaluate(run_to_eval) + + eval_res_agg = utils.aggregate_pytrec(eval_res, "mean") + + for metric, (mean, std) in eval_res_agg.items(): + print(f"{metric:<12}: {mean:.4f} ({std:0.4f})") + +if __name__ == '__main__': + main() diff --git a/dense_inference_decomp_with_rrf.py b/dense_inference_decomp_with_rrf.py new file mode 100644 index 0000000..ed2f3af --- /dev/null +++ b/dense_inference_decomp_with_rrf.py @@ -0,0 +1,144 @@ +import json +import argparse +import os +import pandas as pd +import logging + + +from tqdm import tqdm +from typing import Dict +from pyserini.search.lucene import LuceneSearcher +from pyserini.trectools import TrecRun +from pyserini.fusion import reciprocal_rank_fusion +from ranx import Qrels, Run, evaluate + +from modules import llm_based_decomposition, sentence_decomposition +import tot +import ir_datasets +from src import utils +import pytrec_eval + +METRICS = "P_1,recall_10,recall_100,recall_1000,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,recip_rank" + +log = logging.getLogger(__name__) + +def main(): + parser = argparse.ArgumentParser() + # Path to indexes directory + # parser.add_argument("--index_name", default="dense.index", help="name of index") + parser.add_argument("--index_name", default="bm25_0.8_1.0", help="name of index") + + parser.add_argument("--decomposition_method", default="llm", help="how to decompose") + + parser.add_argument("--data_path", default="/home/ddo/CMU/PLLM/TREC-TOT", help="location to dataset") + + parser.add_argument("--split", choices={"train", "dev", "test"}, default="dev", help="split to run") + + parser.add_argument("--index_path", default="./dense_index_hnsw/", help="path to store (all) indices") + + parser.add_argument("--metrics", default=METRICS, help="csv - metrics to evaluate") + + parser.add_argument("--param_k1", default=0.8, type=float, help="param: k1 for BM25") + + parser.add_argument("--param_b", default=1.0, type=float, help="param: b for BM25") + + # BM25 parameters + parser.add_argument('--K', type=int, help='retrieve top K documents', default=1000) + + # Binary flags to enable or disable ranking methods + parser.add_argument('--rm3', type=str, help='enable or disable rm3', choices=['y', 'n'], default='n') + + # Run number + parser.add_argument('--run_number', type=int, help='run number', default=1) + + # Output options and directory + parser.add_argument('--output_dir', type=str, help='path to output_dir', default="runs_dense/") + parser.add_argument('--load_queries', type=str, help='used previously decomposed queries', choices=['y', 'n'], default='y') + args = parser.parse_args() + + tot.register(args.data_path) + args.split = 'train' + + irds_name = "trec-tot:" + args.split + dataset = ir_datasets.load(irds_name) + args.load_queries = 'y' + if args.decomposition_method == "llm": + if args.load_queries == 'n': + queries_expanded = llm_based_decomposition(dataset, f"{args.data_path}/decomposed_queries/{args.split}") + else: + queries_expanded = f"{args.data_path}/decomposed_queries/{args.split}/llm_decomposed_queries.json" + else: + if args.load_queries == 'n': + queries_expanded = sentence_decomposition(dataset, f"{args.data_path}/decomposed_queries/{args.split}") + else: + queries_expanded = f"{args.data_path}/decomposed_queries/{args.split}/sentence_decomposed_queries.json" + + queries = json.load(open(queries_expanded)) + + + run_save_folder = f'{args.output_dir}DENSE-RRF-{args.split}' + if args.decomposition_method == "llm": + run_save_folder += f'-llm' + + run_save_folder += f'-RM3-{args.run_number}' if args.rm3 == 'y' else f'-{args.run_number}' + run_save_folder += args.index_path.split('/')[-1] + run_save_full = f"{run_save_folder}/{args.split}.run" + + + from pyserini.search.faiss import FaissSearcher, TctColBertQueryEncoder, AutoQueryEncoder + index_p = args.index_path + # TODO: Put this in a args + print(index_p) + encoder = AutoQueryEncoder('/home/ddo/CMU/PLLM/llms-project/dense_models/baseline_distilbert/model') + faiss_searcher = FaissSearcher( + index_p, + encoder + ) + searcher = faiss_searcher + + # searcher = LuceneSearcher(os.path.join(args.index_path, args.index_name)) + # searcher.set_bm25(k1=args.param_k1, b=args.param_b) + + if args.rm3 == 'y': + searcher.set_rm3() + + # Retrieve + run_result = [] + + for jj,query_id in tqdm(enumerate(queries)): + for sintetic_query_id in queries[query_id]: + hits = searcher.search(f'{queries[query_id][sintetic_query_id]}', k=args.K) + sintetic_query_results = [] + for rank, hit in enumerate(hits, start=1): + sintetic_query_results.append((query_id, 'Q0', hit.docid, rank, hit.score, f'{query_id}_{sintetic_query_id}')) + + if sintetic_query_results != []: + run_result.append(TrecRun.from_list(sintetic_query_results)) + + results = reciprocal_rank_fusion(run_result, depth=args.K, k=args.K) + + print(f"saving run to: {run_save_full}") + os.makedirs(os.path.dirname(run_save_full), exist_ok=True) + results.save_to_txt(run_save_full) + + if dataset.has_qrels(): + + with open(run_save_full, 'r') as h: + run_to_eval = pytrec_eval.parse_run(h) + + qrel, n_missing = utils.get_qrel(dataset, run_to_eval) + if n_missing > 0: + raise ValueError(f"Number of missing qids in run: {n_missing}") + + evaluator = pytrec_eval.RelevanceEvaluator( + qrel, args.metrics.split(",")) + + eval_res = evaluator.evaluate(run_to_eval) + + eval_res_agg = utils.aggregate_pytrec(eval_res, "mean") + + for metric, (mean, std) in eval_res_agg.items(): + print(f"{metric:<12}: {mean:.4f} ({std:0.4f})") + +if __name__ == '__main__': + main() diff --git a/dense_rrf.py b/dense_rrf.py new file mode 100644 index 0000000..6e81bd7 --- /dev/null +++ b/dense_rrf.py @@ -0,0 +1,121 @@ +import json +import argparse +import os +import pandas as pd +import logging + + +from tqdm import tqdm +from typing import Dict +from pyserini.search.lucene import LuceneSearcher +from pyserini.trectools import TrecRun +from pyserini.fusion import reciprocal_rank_fusion +from ranx import Qrels, Run, evaluate +from pyserini.encode import DocumentEncoder +from modules import llm_based_decomposition, sentence_decomposition +import tot +import ir_datasets +from src import utils +import pytrec_eval + +METRICS = "P_1,recall_10,recall_100,recall_1000,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,recip_rank" + +log = logging.getLogger(__name__) + +def main(): + parser = argparse.ArgumentParser() + # Path to indexes directory + parser.add_argument("--index_name", default="bm25_0.8_1.0", help="name of index") + + parser.add_argument("--decomposition_method", default="llm", help="how to decompose") + + parser.add_argument("--data_path", default="./data", help="location to dataset") + + parser.add_argument("--split", choices={"train", "dev", "test"}, default="dev", help="split to run") + + parser.add_argument("--index_path", default="./anserini_indicies", help="path to store (all) indices") + + parser.add_argument("--metrics", default=METRICS, help="csv - metrics to evaluate") + + parser.add_argument("--param_k1", default=0.8, type=float, help="param: k1 for BM25") + + parser.add_argument("--param_b", default=1.0, type=float, help="param: b for BM25") + + # BM25 parameters + parser.add_argument('--K', type=int, help='retrieve top K documents', default=1000) + + # Binary flags to enable or disable ranking methodss + parser.add_argument('--rm3', type=str, help='enable or disable rm3', choices=['y', 'n'], default='n') + + # Run number + parser.add_argument('--run_number', type=int, help='run number', default=1) + + # Output options and directory + parser.add_argument('--output_dir', type=str, help='path to output_dir', default="runs/") + args = parser.parse_args() + + tot.register(args.data_path) + + irds_name = "trec-tot:" + args.split + dataset = ir_datasets.load(irds_name) + if args.decomposition_method == "llm": + queries_expanded = llm_based_decomposition(dataset, f"{args.data_path}/decomposed_queries") + else: + queries_expanded = sentence_decomposition(dataset, f"{args.data_path}/decomposed_queries") + + queries = json.load(open(queries_expanded)) + + run_save_folder = f'{args.output_dir}BM25-RRF' + if args.decomposition_method == "llm": + run_save_folder += f'-llm' + + run_save_folder += f'-RM3-{args.run_number}' if args.rm3 == 'y' else f'-{args.run_number}' + + run_save_full = f"{run_save_folder}/{args.split}.run" + + searcher = LuceneSearcher(os.path.join(args.index_path, args.index_name)) + searcher.set_bm25(k1=args.param_k1, b=args.param_b) + + if args.rm3 == 'y': + searcher.set_rm3() + + # Retrieve + run_result = [] + + for query_id in tqdm(queries): + for sintetic_query_id in queries[query_id]: + hits = searcher.search(f'{queries[query_id][sintetic_query_id]}', k=args.K) + sintetic_query_results = [] + for rank, hit in enumerate(hits, start=1): + sintetic_query_results.append((query_id, 'Q0', hit.docid, rank, hit.score, f'{query_id}_{sintetic_query_id}')) + + if sintetic_query_results != []: + run_result.append(TrecRun.from_list(sintetic_query_results)) + + results = reciprocal_rank_fusion(run_result, depth=args.K, k=args.K) + + print(f"saving run to: {run_save_full}") + os.makedirs(os.path.dirname(run_save_full), exist_ok=True) + results.save_to_txt(run_save_full) + + if dataset.has_qrels(): + + with open(run_save_full, 'r') as h: + run_to_eval = pytrec_eval.parse_run(h) + + qrel, n_missing = utils.get_qrel(dataset, run_to_eval) + if n_missing > 0: + raise ValueError(f"Number of missing qids in run: {n_missing}") + + evaluator = pytrec_eval.RelevanceEvaluator( + qrel, args.metrics.split(",")) + + eval_res = evaluator.evaluate(run_to_eval) + + eval_res_agg = utils.aggregate_pytrec(eval_res, "mean") + + for metric, (mean, std) in eval_res_agg.items(): + print(f"{metric:<12}: {mean:.4f} ({std:0.4f})") + +if __name__ == '__main__': + main() diff --git a/docid_to_idx.json b/docid_to_idx.json new file mode 100644 index 0000000..742ae7a --- /dev/null +++ b/docid_to_idx.json @@ -0,0 +1 @@ +{"330": 0, "324": 1, "1948": 2, "2372": 3, "3217": 4, "3333": 5, "3376": 6, "3427": 7, "3746": 8, "3837": 9, "3947": 10, "4227": 11, "4231": 12, "4326": 13, "4560": 14, "4726": 15, "4727": 16, "4728": 17, "4729": 157968, "4730": 19, "4742": 20, "4800": 21, "4942": 22, "4948": 23, "5035": 24, "5224": 25, "5313": 178468, "5729": 27, "5907": 28, "6851": 29, "7906": 30, "8209": 31, "8481": 32, "8695": 33, "8829": 34, "8994": 35, "9294": 36, "9429": 37, "9433": 38, "9835": 39, "9954": 40, "9979": 41, "9995": 42, "10193": 43, "10487": 44, "10977": 45, "11121": 46, "11167": 47, "11223": 48, "11242": 49, "11281": 50, "11313": 51, "11315": 52, "11520": 53, "11585": 54, 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231823, "72581240": 231824, "72581265": 231825, "72582717": 231826, "72582855": 231827, "72584468": 231828, "72585574": 231829, "72585838": 231830, "72586100": 231831, "72592392": 231832, "72594149": 231833, "72594657": 231834, "72596010": 231835, "72600849": 231836, "72602483": 231837, "72605725": 231838, "72610239": 231839, "72610321": 231840, "72611735": 231841, "72613776": 231842, "72618104": 231843, "72618400": 231844, "72619811": 231845, "72623557": 231846, "72623717": 231847, "72623953": 231848, "72633704": 231849, "72634189": 231850, "72634502": 231851} \ No newline at end of file diff --git a/docs/BM25+RRF.md b/docs/BM25+RRF.md new file mode 100644 index 0000000..e69de29 diff --git a/BM25.md b/docs/BM25.md similarity index 100% rename from BM25.md rename to docs/BM25.md diff --git a/DENSE.md b/docs/DENSE.md similarity index 85% rename from DENSE.md rename to docs/DENSE.md index d93fdeb..34a804f 100644 --- a/DENSE.md +++ b/docs/DENSE.md @@ -41,7 +41,9 @@ recall_1000 all 0.5333 ndcg all 0.1313 # baseline run -./trec_eval -m ndcg -m recall.1,10,100,1000 -m recip_rank $DATA_PATH/dev/qrel.txt ./dense_models/baseline_distilbert/dev.run +./trec_eval -m ndcg -m recall.1,10,100,1000 -m recip_rank $DATA_PATH/dev/qrel.txt +``` +./dense_models/baseline_distilbert/dev.run recip_rank all 0.0743 recall_1 all 0.0400 recall_10 all 0.1467 @@ -50,4 +52,15 @@ recall_1000 all 0.6600 ndcg all 0.1627 ``` - +Reproduced baselines + +recip_rank : 0.0831 (0.2316) +P_1 : 0.0533 (0.2247) +recall_10 : 0.1333 (0.3399) +recall_100 : 0.3400 (0.4737) +recall_1000 : 0.6267 (0.4837) +ndcg_cut_10 : 0.0878 (0.2481) +ndcg_cut_100: 0.1307 (0.2476) +ndcg_cut_1000: 0.1649 (0.2353) + +``` diff --git a/GPT4.md b/docs/GPT4.md similarity index 100% rename from GPT4.md rename to docs/GPT4.md diff --git a/gpt-ppl-pointwise.py b/gpt-ppl-pointwise.py new file mode 100644 index 0000000..d9e0554 --- /dev/null +++ b/gpt-ppl-pointwise.py @@ -0,0 +1,109 @@ +import openai +import numpy as np + + +import openai # for calling the OpenAI API +import pandas as pd # for storing text and embeddings data +from tqdm import tqdm +import os +import time +base = './data' + + +GPT_MODEL = "text-davinci-003" +openai.api_key = "" + +def perplexity(text): + """Compute the perplexity of the provided text.""" + completion = openai.Completion.create( + model=GPT_MODEL, + prompt=text, + logprobs=0, + max_tokens=0, + temperature=1.0, + echo=True) + token_logprobs = completion['choices'][0]['logprobs']['token_logprobs'] + nll = np.mean([i for i in token_logprobs if i is not None]) + ppl = np.exp(-nll) + return ppl + + + +top_100 = {} +n=100 +run_to_rerank = "./runs/distilbert/dev.run" +docs_needed = [] +returned = [] +with open(run_to_rerank, 'r') as h: + for line in h: + qid, _, did, _, _, _ = line.split() + qid = str(qid) + did = str(did) + if qid not in top_100: + top_100[qid] = [did] + docs_needed.append(did) + else: + if len(top_100[qid]) < n: + top_100[qid].append(did) + docs_needed.append(did) + +#os.makedirs(f"./runs/{GPT_MODEL}rerank-distilbert/") + +jsonObjdata = pd.read_json(f"/Users/aprameya/Downloads/corpus.jsonl", lines=True) +ans = [] +count=0 +corpus = dict() +for index, row in jsonObjdata.iterrows(): + if str(row["doc_id"]) in docs_needed: + corpus[str(row["doc_id"])] = row["page_title"] + +del jsonObjdata + +jsonObj = pd.read_json(f"/Users/aprameya/Desktop/llms-project/data/queries.jsonl", lines=True) +ans = [] +actual = [] +count=0 + + + + +done = 0 +todo = 150 + +# os.makedirs(f"./runs/{GPT_MODEL}-rerank-distilbert/") +# print(top_100) + +with open(f"./runs/gpt-4-rerank-distilbert/dev_pointwise_GPT-ppl.run", 'a') as out_file: + for index,row in tqdm(jsonObj.iterrows(), total=todo): + if done == todo: + break + query_id = str(row['id']) + + query_top_100 = top_100[query_id][:100] + + small_id_mapper = {str(idx): str(x) for idx, x in enumerate(query_top_100)} + inv_mapper = {v:k for k,v in small_id_mapper.items()} + id_title_mapper = {corpus[str(x)]:str(x) for x in query_top_100} + + + + for doc in query_top_100: + doc_title = corpus[str(doc)] + + # query = f"I will provide you with a single movie. Give a score for the movie in the range of 1 through 10 upto 3 digits of precision based on its relevance to the user description. A score of 1 indicates that the movie title is not relevant to the user description, a score of 10 indicates that it is the correct match between title and description.\n\n\n" + query = f"\nUser Description:\n{row['text']}\n" + # query += f"Give a score to the movie movie based on its relevance to the search query. Use your best knowledge about the movie given only its title. The output format should be movie name::score. Only respond with the name and score do not say any word or explain." + movie_string = " The title of the movie is: {}".format(doc_title) + movie,score = doc_title,perplexity(query+movie_string) + print(movie,score) + returned.append((float(score),movie)) + + returned.sort() + print(id_title_mapper) + + for pos, rd in enumerate(returned): + print(rd) + out_file.write(f"{query_id} Q0 {id_title_mapper[rd[1]]} {pos} {1/(pos+1)} gpt3-rerank-distilbert-top100-pointwiseppl\n") + returned = [] + + done += 1 \ No newline at end of file diff --git a/gpt4-rerank-pointwise.py b/gpt4-rerank-pointwise.py new file mode 100644 index 0000000..e32702b --- /dev/null +++ b/gpt4-rerank-pointwise.py @@ -0,0 +1,126 @@ +# l = [(9.5, 'Apartment Eight'), (8.5, 'The Last Berliner'), (2.5, 'Hide and Seek (2014 film)'), (9.5, 'Block B (film)'), (2.5, 'Nousukausi'), (7.5, 'Highrise (documentary)'), (9.5, 'The New Tenants'), (3.5, 'The Other Barrio'), (9.5, 'Sunday (1997 film)'), (8.5, "Squatter's Delight"), (7.5, 'Master: A Building in Copacabana'), (2.5, 'Sous le même toit'), (8.5, 'Evixion'), (8.5, 'Exhibition (film)'), (1.0, 'Steve (film)'), (3.5, 'Battle for Brooklyn'), (2.5, 'Amexicano'), (9.5, 'Three Floors'), (1.0, 'Rent Control (1984 film)'), (2.5, 'Miracle on 42nd Street'), (9.5, 'The Cambridge Squatter'), (7.5, 'Everything Strange and New'), (7.5, 'Tom White (film)'), (7.5, 'Owners (film)'), (3.125, '8 Guys'), (1.0, 'Motherhood (2009 film)'), (7.5, 'The Edukators'), (9.5, '111 First Street (film)'), (2.5, 'Desperate Shareholders'), (2.5, 'Venussian Tabutasco'), (8.5, 'Behind Locked Doors (1991 film)'), (8.5, 'Autumn Ball'), (7.5, 'Boystown (film)'), (9.5, 'Unmade Beds (1997 film)'), (2.5, 'Steakhouse (film)'), (9.5, 'The Man Next Door (2010 film)'), (7.5, 'Accidence (film)'), (8.5, 'Chelsea Walls'), (7.5, 'Williamsburg (film)'), (1.0, 'Gypo (film)'), (7.5, 'The Exchange (2011 film)'), (8.5, 'Roshmia'), (8.5, 'Quality of Life (film)'), (8.5, 'Silent Nights (film)'), (8.5, 'Snapshots for Henry'), (9.5, 'La Soledad (film)'), (7.5, 'New Brooklyn'), (7.5, 'Gordonia (film)'), (9.5, 'The Architect (2006 film)'), (7.5, 'A Montreal Girl'), (2.5, 'The Roommates Party'), (8.5, 'Los Angeles Plays Itself'), (8.5, 'Meanwhile (2011 film)'), (7.5, 'Bedrooms (film)'), (9.5, 'At Home by Myself...With You'), (9.5, 'Food and Shelter'), (8.5, 'Beautiful Youth'), (9.5, 'The Street: A Film with the Homeless'), (1.0, 'Bonobo (2018 film)'), (7.5, 'Kill the Poor (film)'), (7.5, 'Tumbok'), (7.5, 'Temporary Family'), (3.5, 'Class Divide (film)'), (8.5, 'Something Like Happiness'), (8.5, 'Abroad'), (1.0, 'Flag Wars'), (9.5, "In Vanda's Room"), (7.5, 'Ara (film)'), (7.5, 'Breaking Upwards'), (9.5, 'The Tenants (2005 film)'), (1.0, 'Oben ohne (TV series)'), (1.0, 'Dilli (film)'), (8.5, 'Oblique (film)'), (7.5, 'FL 19,99'), (8.5, 'Quiet City (film)'), (7.5, 'See You in Hell, My Darling'), (7.5, 'Picture Paris'), (7.5, 'Metro cuadrado'), (2.5, 'Gendernauts'), (6.5, 'Havana, from on High'), (7.5, 'Life in Flight'), (8.5, 'Me and Her'), (7.5, 'Pulse: A Stomp Odyssey'), (6.5, 'Columbus Circle (film)'), (1.0, 'Hampstead (film)'), (7.5, 'Please Give'), (9.5, '24 City'), (8.5, 'The Lollipop Generation'), (8.5, 'Movement and Location'), (9.5, 'Rent Control (2005 film)'), (8.5, 'Down There (film)'), (7.5, '5 Flights Up'), (7.5, 'The Lips'), (8.5, 'The Earth Belongs to No One'), (9.5, 'On the Fringe (film)'), (7.5, 'For a Moment, Freedom'), (7.5, 'Dark Days (film)'), (9.5, 'Dream Home'), (9.5, 'Autumn in March'), (9.5, 'Cageman')] +# l.sort(reverse=True) +# for p,r in enumerate(l): +# print(p) +# print(r) + +import openai # for calling the OpenAI API +import pandas as pd # for storing text and embeddings data +from tqdm import tqdm +import os +import time +base = './data' +GPT_MODEL = "gpt-3.5-turbo" +openai.api_key = "" + +top_100 = {} +n=100 +run_to_rerank = "./runs/distilbert/dev.run" +docs_needed = [] +returned = [] +with open(run_to_rerank, 'r') as h: + for line in h: + qid, _, did, _, _, _ = line.split() + qid = str(qid) + did = str(did) + if qid not in top_100: + top_100[qid] = [did] + docs_needed.append(did) + else: + if len(top_100[qid]) < n: + top_100[qid].append(did) + docs_needed.append(did) + +#os.makedirs(f"./runs/{GPT_MODEL}rerank-distilbert/") + +jsonObjdata = pd.read_json(f"/Users/aprameya/Downloads/corpus.jsonl", lines=True) +ans = [] +count=0 +corpus = dict() +for index, row in jsonObjdata.iterrows(): + if str(row["doc_id"]) in docs_needed: + corpus[str(row["doc_id"])] = row["page_title"] + +del jsonObjdata + +jsonObj = pd.read_json(f"/Users/aprameya/Desktop/llms-project/data/queries.jsonl", lines=True) +ans = [] +actual = [] +count=0 + + + + +done = 0 +todo = 150 + +# os.makedirs(f"./runs/{GPT_MODEL}-rerank-distilbert/") + +with open(f"./runs/gpt-4-rerank-distilbert/dev_pointwise_GPT-3-prompt.run", 'a') as out_file: + for index,row in tqdm(jsonObj.iterrows(), total=todo): + if done == todo: + break + query_id = str(row['id']) + + query_top_100 = top_100[query_id][:100] + + small_id_mapper = {str(idx): str(x) for idx, x in enumerate(query_top_100)} + inv_mapper = {v:k for k,v in small_id_mapper.items()} + id_title_mapper = {corpus[str(x)]:str(x) for x in query_top_100} + + # print(small_id_mapper) + # print(inv_mapper) + + + for doc in query_top_100: + # print(doc) + # break + doc_title = corpus[str(doc)] + # small_id = inv_mapper[str(doc)] + # query += f"[{small_id}] {doc_title}\n" + # print(query) + + + query = f"I will provide you with a single movie. Give a score for the movie in the range of 1 through 10 upto 3 digits of precision based on its relevance to the user description. A score of 1 indicates that the movie title is not relevant to the user description, a score of 10 indicates that it is the correct match between title and description.\n\n\n" + query += f"\nUser Description:\n{row['text']}\n" + query += f"Give a score to the movie movie based on its relevance to the search query. Use your best knowledge about the movie given only its title. The output format should be movie name::score. Only respond with the name and score do not say any word or explain." + movie_string = " The title of the movie is: {}".format(doc_title) + # print(query+movie_string) + response = openai.ChatCompletion.create( + messages=[ + {'role': 'system', 'content': 'You help someone give a rating for a movie with respect to relevancy towards a description.'}, + {'role': 'user', 'content': query+movie_string}, + ], + model=GPT_MODEL, + temperature=0, + ) + time.sleep(2) + ranked_list = response['choices'][0]['message']['content'] + # print(ranked_list) + movie,score = ranked_list.split("::") + # print(movie,id_title_mapper[movie]) + returned.append((float(score),movie)) + + + + # try: + # int_list = [int(part.strip("[] ")) for part in ranked_list.split(">") if part.strip("[] ").isdigit()] + # if any(number > 99 or number < 0 fo[r number in int_list): + # print(f"bad list for query {query_id}") + # int_list = list(small_id_mapper.keys()) + # if len(int_list) > 100: + # print(f"bad list (too big) for query {query_id}") + # int_list = list(small_id_mapper.keys()) + + # except Exception as e: + # print(f"bad list for query {query_id}") + # int_list = list(small_id_mapper.keys()) + returned.sort(reverse=True) + # doc_id_list = [small_id_mapper[str(small_id)] for small_id in int_list] + + + for pos, rd in enumerate(returned): + out_file.write(f"{query_id} Q0 {id_title_mapper[rd[1]]} {pos} {1/(pos+1)} gpt3-rerank-distilbert-top100-pointwiseprompt\n") + returned = [] + + done += 1 \ No newline at end of file diff --git a/gpt4-rerank.py b/gpt4-rerank.py new file mode 100644 index 0000000..c33396c --- /dev/null +++ b/gpt4-rerank.py @@ -0,0 +1,187 @@ +import openai # for calling the OpenAI API +import pandas as pd # for storing text and embeddings data +from tqdm import tqdm +import os +import time +import pytrec_eval +import random +from collections import defaultdict +import numpy as np + +base = './data' +GPT_MODEL = "gpt-4-1106-preview" +openai.api_key = "" +METRICS = "P_1,recall_10,recall_100,recall_1000,ndcg_cut_10,ndcg_cut_100,ndcg_cut_1000,recip_rank" +first_stage_retriever = "distilbert_pretrained_reddit_tomt_warmedup_finetuned_2" +run_to_rerank = f"/user/home/jcoelho/trec-tot/llms-project/dense_models/{first_stage_retriever}/dev.run" +qrels_path = '/user/home/jcoelho/trec-tot/llms-project/data/dev/qrel.txt' + +shuffle_top_100 = True + +def aggregate_pytrec(results, aggregate_method: str = "mean"): + if aggregate_method == "mean": + # metric -> (mean, std) + res_temp = aggregate_pytrec(results, "gather") + final = {} + for metric, values in res_temp.items(): + final[metric] = (np.mean(values), np.std(values)) + return final + + if aggregate_method == "gather": + # metric -> [res_q1, res_q2, ...] + final = defaultdict(list) + for qid, qvals in results.items(): + for metric, met_val in qvals.items(): + final[metric].append(met_val) + return final + +######### INITIAL RESULTS + +print("##### INITIAL RESULT") +with open(qrels_path, 'r') as f_qrel: + qrel = pytrec_eval.parse_qrel(f_qrel) + +with open(run_to_rerank, 'r') as f_run: + run = pytrec_eval.parse_run(f_run) + +evaluator = pytrec_eval.RelevanceEvaluator( + qrel, METRICS.split(",")) + +eval_res = evaluator.evaluate(run) + +eval_res_agg = aggregate_pytrec(eval_res, "mean") + +for metric, (mean, std) in eval_res_agg.items(): + print(f"{metric:<12}: {mean:.4f} ({std:0.4f})") + +########################## + + +top_100 = {} +n=100 + +docs_needed = [] +with open(run_to_rerank, 'r') as h: + for line in h: + qid, _, did, _, _, _ = line.split() + qid = str(qid) + did = str(did) + if qid not in top_100: + top_100[qid] = [did] + docs_needed.append(did) + else: + if len(top_100[qid]) < n: + top_100[qid].append(did) + docs_needed.append(did) + +if shuffle_top_100: + print("shuffling top-100") + for qid in top_100: + random.shuffle(top_100[qid]) + +jsonObjdata = pd.read_json(f"{base}/corpus.jsonl", lines=True) +ans = [] +count=0 +corpus = dict() +for index, row in jsonObjdata.iterrows(): + if str(row["doc_id"]) in docs_needed: + corpus[str(row["doc_id"])] = row["page_title"] + +del jsonObjdata + +jsonObj = pd.read_json(path_or_buf=f"{base}/dev/queries.jsonl", lines=True) +ans = [] +actual = [] +count=0 + +def make_openai_request(query, retries=2): + for attempt in range(retries + 1): + try: + response = openai.ChatCompletion.create( + messages=[ + {'role': 'system', 'content': 'You help someone re-rank a list of movies with respect to relevancy towards a description.'}, + {'role': 'user', 'content': query}, + ], + model=GPT_MODEL, + temperature=0, + ) + ranked_list = response['choices'][0]['message']['content'] + + int_list = [int(part.strip("[] ")) for part in ranked_list.split(">") if part.strip("[] ").isdigit()] + + if any(number > 99 or number < 0 for number in int_list) or len(int_list) > 100 or len(int_list) != len(set(int_list)): + + if attempt < retries: + print("going for retry") + continue + + else: + int_list = None + + return int_list + + except Exception as e: + print("going for retry") + time.sleep(2) + + return None + +done = 0 +todo = 150 + +reranked_run_path = f"./runs/{GPT_MODEL}-rerank-shuffled-{first_stage_retriever}" +if not os.path.exists(reranked_run_path): + os.makedirs(reranked_run_path) + +with open(f"{reranked_run_path}/dev.run", 'w') as out_file: + for index,row in tqdm(jsonObj.iterrows(), total=todo): + if done == todo: + break + query_id = str(row['id']) + + query_top_100 = top_100[query_id][:100] + + small_id_mapper = {str(idx): str(x) for idx, x in enumerate(query_top_100)} + inv_mapper = {v:k for k,v in small_id_mapper.items()} + + query = f"I will provide you with {n} movies, each indicated by a numerical identifier between [], e.g., [1], [2]. Rank the moves based on their relevance to the user description: {row['text']}.\n\n\n" + + for doc in query_top_100: + doc_title = corpus[str(doc)] + small_id = inv_mapper[str(doc)] + query += f"[{small_id}] {doc_title}\n" + + query += f"\nUser Description:\n{row['text']}\n" + query += f"Rank the {n} movies above based on their relevance to the search query. Use your best knowledge about the movie given their titles. All the movies should be included and listed using identifiers, in descending order of relevance. The output format should be [] > [], e.g., [4] > [2]. Only respond with the ranking results, do not say any word or explain." + + int_list = make_openai_request(query) + + if not int_list: + print("received default int_list") + int_list = list(small_id_mapper.keys()) + + doc_id_list = [small_id_mapper[str(small_id)] for small_id in int_list] + + for pos, rd in enumerate(doc_id_list): + out_file.write(f"{query_id}\tQ0\t{rd}\t{pos}\t{1/(pos+1)}\tgpt4-rerank-distilbert-top100\n") + + done += 1 + + +print("##### RESULT AFTER RR") + +with open(qrels_path, 'r') as f_qrel: + qrel = pytrec_eval.parse_qrel(f_qrel) + +with open(f"{reranked_run_path}/dev.run", 'r') as f_run: + run = pytrec_eval.parse_run(f_run) + +evaluator = pytrec_eval.RelevanceEvaluator( + qrel, METRICS.split(",")) + +eval_res = evaluator.evaluate(run) + +eval_res_agg = aggregate_pytrec(eval_res, "mean") + +for metric, (mean, std) in eval_res_agg.items(): + print(f"{metric:<12}: {mean:.4f} ({std:0.4f})") \ No newline at end of file diff --git a/gpt4-zeroshot.py b/gpt4-zeroshot.py new file mode 100644 index 0000000..a2269b5 --- /dev/null +++ b/gpt4-zeroshot.py @@ -0,0 +1,72 @@ +import time +import openai # for calling the OpenAI API +import pandas as pd # for storing text and embeddings data +import re + + +base = '/Users/aprameya/Desktop/llms-project/data/' +EMBEDDING_MODEL = "text-embedding-ada-002" +GPT_MODEL = "gpt-4" +openai.api_key = "" + + +jsonObjdata = pd.read_json("/Users/aprameya/Downloads/corpus.jsonl", lines=True) +ans = [] +count=0 +d = dict() +for index, row in jsonObjdata.iterrows(): + d[row["doc_id"]] = row["page_title"] + + + +jsonObj = pd.read_json(path_or_buf=base+"queries.jsonl", lines=True) +ans = [] +actual = [] +count=0 +for index,row in jsonObj.iterrows(): + query = f"""You are an expert in movies. You are helping someone recollect a movie name that is on the tip of their tongue. You respond to each message with a single guess for the name of the movie being described.**important**: you only mention the names of the movie and nothing else. Given below is the movie description:" + + Description: + \"\"\" + {row["text"]} + \"\"\" + + """ + response = openai.ChatCompletion.create( + messages=[ + {'role': 'system', 'content': 'You help someone recollect a movie'}, + {'role': 'user', 'content': query}, + ], + model=GPT_MODEL, + temperature=0, + ) + ans.append(response['choices'][0]['message']['content']) + actual.append(d[row['wikipedia_id']]) + print(count) + count+=1 + time.sleep(3) +with open(base+'results.txt', 'w') as fp: + for item in ans: + fp.write(item+'\n') +with open(base+'labels.txt', 'w') as fp: + for item in actual: + fp.write(item+'\n') + + +### EVALUATE ### + +with open(base+'results.txt', 'r') as f: + guesses = f.readlines() +with open(base+'labels.txt', 'r') as f: + labels = f.readlines() +count = 0 +for i in range(len(labels)): + labels[i] = re.sub("\(.*?\)|\[.*?\]","",labels[i]) + guesses[i] = re.sub("\(.*?\)|\[.*?\]","",guesses[i]) + if(labels[i].strip()==guesses[i].strip()): + count+=1 + print(labels[i]) + +print(count/150) + + \ No newline at end of file diff --git a/idx_to_docid.json b/idx_to_docid.json new file mode 100644 index 0000000..2d70e4c --- /dev/null +++ b/idx_to_docid.json @@ -0,0 +1 @@ +{"0": "330", "1": "324", "2": "1948", "3": "2372", "4": "3217", "5": "3333", "6": "3376", "7": "3427", "8": "3746", "9": "3837", "10": "3947", "11": "4227", "12": "4231", "13": "4326", "14": "4560", "15": "4726", "16": "4727", "17": "4728", "18": "4729", "19": "4730", "20": "4742", "21": "4800", "22": "4942", "23": "4948", "24": "5035", "25": "5224", "26": "5313", "27": "5729", "28": "5907", "29": "6851", "30": "7906", "31": "8209", "32": "8481", "33": "8695", "34": "8829", "35": "8994", "36": "9294", "37": "9429", "38": "9433", "39": "9835", "40": "9954", "41": "9979", "42": "9995", "43": "10193", "44": "10487", "45": "10977", "46": "11121", "47": "11167", "48": "11223", "49": "11242", "50": "11281", "51": "11313", "52": "11315", "53": "11520", "54": "11585", "55": "11673", "56": "11685", "57": "11701", "58": "11992", "59": "11988", "60": "11993", "61": "11994", "62": "11998", "63": "12000", "64": "12001", "65": "12002", "66": "12003", "67": "12004", "68": "12005", "69": "12405", "70": "12449", "71": "13149", "72": "13154", "73": "13260", "74": "13290", "75": "13523", "76": "13901", "77": "14017", "78": "16782", "79": "17920", "80": "18230", "81": "18943", "82": "18996", "83": "19055", "84": "19701", "85": "19715", "86": "20113", "87": "20421", "88": "20664", "89": "20669", "90": "20786", "91": "20840", "92": "21037", "93": "21174", "94": "21180", "95": "21711", "96": "21813", "97": "22216", "98": "22626", "99": "22727", "100": "22751", "101": "22829", "102": "23255", "103": "23512", "104": "23875", "105": "23880", "106": "24639", "107": "24653", "108": "25491", "109": "25506", "110": "25721", "111": "25912", "112": "26541", "113": "27070", "114": "27071", "115": "27073", "116": "27074", "117": "27079", "118": "27092", "119": "27097", "120": "27098", "121": "27102", "122": "27977", "123": "28217", "124": "28269", "125": "28322", "126": "28327", "127": "28606", "128": "28623", "129": "28954", "130": "29645", "131": "29781", "132": "29782", "133": "29785", "134": "29838", "135": "29942", "136": "29943", "137": "30000", "138": "30006", "139": "30007", "140": "30028", "141": "30039", "142": "30084", "143": "30086", "144": "30275", "145": "30304", "146": "30307", "147": "30327", "148": "30515", "149": "30524", "150": "30527", "151": "30575", "152": "30581", "153": "30582", "154": "30625", "155": "30674", "156": "30809", "157": "30979", "158": "31015", "159": "31030", "160": "31038", "161": "31079", "162": "31136", "163": "31153", "164": "31176", "165": "31193", "166": "31253", "167": "31298", "168": "31351", "169": "31371", "170": "31454", "171": "31523", "172": "31556", "173": "31557", "174": "31574", "175": "31607", "176": "31629", "177": "31908", "178": "32252", "179": "32260", "180": "33121", "181": "33124", "182": "33270", "183": "33446", "184": "33560", "185": "33573", "186": "33908", "187": "34130", "188": "34212", "189": "35061", "190": "36540", "191": "36986", "192": "36999", "193": "37032", "194": "37068", "195": "37599", "196": "38088", "197": "38398", "198": "39311", "199": "39690", "200": "39732", "201": "40132", "202": "40672", "203": "41528", "204": "41881", "205": "42093", "206": "42106", "207": "42137", "208": "42147", "209": "42159", "210": "42221", "211": "42222", "212": "42224", "213": "42225", "214": "42293", "215": "42294", "216": "42313", "217": "42361", "218": "42383", "219": "42507", "220": "42551", "221": "42619", "222": "42648", "223": "42706", "224": "42856", "225": "42861", "226": "42886", "227": "42971", "228": "42993", "229": "43073", "230": "43274", "231": "43307", "232": "43452", "233": "43566", "234": "43849", "235": "44122", "236": "44210", "237": "44218", "238": "44283", "239": "44302", "240": "44348", "241": "44366", "242": "44437", "243": "44457", "244": "44501", "245": "44696", "246": "44749", "247": "44752", "248": "44771", "249": "45126", "250": "45224", "251": "45242", "252": "45481", "253": "45488", "254": "45568", "255": "45606", "256": "45772", "257": "45929", "258": "46387", "259": "46505", "260": "46757", "261": "47542", "262": "47692", "263": "48064", "264": "49088", "265": "49696", "266": "49800", "267": "50287", "268": "50455", "269": "50486", "270": "50744", "271": "50793", "272": "50928", "273": "50957", "274": "51524", "275": "51563", "276": "51679", "277": "51787", "278": "51888", "279": "51906", "280": "52055", "281": "52371", "282": "52389", "283": "52390", "284": "52549", "285": "52801", "286": "52942", "287": "53078", "288": "53085", "289": "53573", "290": "53891", "291": "53964", "292": "54030", "293": "54160", "294": "54166", "295": "54173", "296": "54175", "297": "54418", "298": "54540", "299": "54586", "300": "54588", "301": "54614", "302": "54639", "303": "54663", "304": "54769", "305": "54999", "306": "55325", "307": "55447", "308": "55495", "309": "55543", "310": "55601", "311": "55720", "312": "55876", "313": "56931", "314": "56989", "315": "56996", "316": "57585", "317": "57687", "318": "57820", "319": "57975", "320": "58018", "321": "58147", "322": "58350", "323": "58865", "324": "59449", "325": "60155", "326": "60157", "327": "60164", "328": "60169", "329": "60173", "330": "60174", "331": "60293", "332": "60835", "333": "60909", "334": "60911", "335": "61046", "336": "61047", "337": "61049", "338": "61052", "339": "61053", "340": "61057", "341": "61058", "342": "61059", "343": "61060", "344": "61061", "345": "61062", "346": "61066", "347": "61070", "348": "61071", "349": "61074", "350": "61141", "351": "61160", "352": "61168", "353": "61169", "354": "61170", "355": "61171", "356": "61173", "357": "61176", "358": "61177", "359": "61175", "360": "61182", "361": "61183", "362": "61184", "363": "61186", "364": "61189", "365": "61191", "366": "61323", "367": "61411", "368": "61480", "369": "61481", "370": "61483", "371": "61484", "372": "61491", "373": "61494", "374": "61498", "375": "61499", "376": "61501", "377": "61505", "378": "61507", "379": "61508", "380": "61514", "381": "61519", "382": "61520", "383": "61522", "384": "61525", "385": "61527", "386": "61529", "387": "61530", "388": "61531", "389": "61664", "390": "61904", "391": "61949", "392": "62026", "393": "62028", "394": "62084", "395": "62095", "396": "62097", "397": "62110", "398": "62111", "399": "62115", "400": "62117", "401": "62118", "402": "62121", "403": "62122", "404": "62180", "405": "62331", "406": "62440", "407": "62592", "408": "62668", "409": "62687", "410": "62688", "411": "62689", "412": "62693", "413": "63310", "414": "63504", "415": "64004", "416": "64291", "417": "64394", "418": "64657", "419": "64902", "420": "65305", "421": "65436", "422": "65458", "423": "65562", "424": "65669", "425": "65834", "426": "65956", "427": "66070", "428": "66114", "429": "66205", "430": "66215", "431": "66305", "432": "66322", "433": "66323", "434": "66466", "435": "66841", "436": "66853", "437": "66870", "438": "66873", "439": "66972", "440": "67371", "441": "67411", "442": "67414", "443": "67415", "444": "67417", "445": "67419", "446": "67420", "447": "67422", "448": "67423", "449": "67427", "450": "67431", "451": "67433", "452": "67441", "453": "67442", "454": "67444", "455": "67916", "456": "68047", "457": "68078", "458": "68079", "459": "68082", "460": "68084", "461": "68088", "462": "68092", "463": "68099", "464": "68108", "465": "68119", "466": "68124", "467": "68125", "468": "68130", "469": "68145", "470": "68245", "471": "68305", "472": "68311", "473": "68325", "474": "68374", "475": "68388", "476": "68479", "477": "68485", "478": "68501", "479": "68757", "480": "68769", "481": "69088", "482": "69362", "483": "69625", "484": "70091", "485": "70428", "486": "70508", "487": "70647", "488": "70923", "489": "71415", "490": "71473", "491": "71975", "492": "71984", "493": "72317", "494": "72453", "495": "73170", "496": "73366", "497": "73368", "498": "73370", "499": "73372", "500": "73371", "501": "73375", "502": "73376", "503": "73380", "504": "73392", "505": "73398", "506": "73395", "507": "73401", "508": "73402", "509": "73405", "510": "73411", "511": "73409", "512": "73413", "513": "73412", "514": "73414", "515": "73417", "516": "73419", "517": "73422", "518": "73423", "519": "73425", "520": "73427", "521": "73428", "522": "73432", "523": "73434", "524": "73437", "525": "73438", "526": "73439", "527": "73440", "528": "73441", "529": "73447", "530": "73444", "531": "73451", "532": "73455", "533": "73457", "534": "73460", "535": "73463", "536": "73467", "537": "73471", "538": "73473", "539": "73475", "540": "73478", "541": "73483", "542": "73488", "543": "73494", "544": "73497", "545": "73498", "546": "73585", "547": "73663", "548": "73830", "549": "73842", "550": "73875", "551": "73882", "552": "73900", "553": "73919", "554": "73930", "555": "73937", "556": "73970", "557": "73977", "558": "73979", "559": "73982", "560": "73988", "561": "74011", "562": "74018", "563": "74028", "564": "74048", "565": "74157", "566": "74159", "567": "74293", "568": "74325", "569": "74375", "570": "74376", "571": "74381", "572": "74383", "573": "74387", "574": "74391", "575": "74392", "576": "74394", "577": "74411", "578": "74530", "579": "74534", "580": "74535", "581": "74537", "582": "74577", "583": "74823", "584": "74824", "585": "74825", "586": "74832", "587": "74830", "588": "74848", "589": "74853", "590": "74856", "591": "74858", "592": "74859", "593": "74861", "594": "74862", "595": "74864", "596": "74868", "597": "74871", "598": "74872", "599": "74877", "600": "74892", "601": "74894", "602": "74902", "603": "74904", "604": "74907", "605": "74908", "606": "74912", "607": "74913", "608": "74917", "609": "74928", "610": "74930", "611": "74931", "612": "74932", "613": "74937", "614": "74942", "615": "74951", "616": "74956", "617": "74962", "618": "74963", "619": "74967", "620": "74966", "621": "74968", 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"76375", "685": "76377", "686": "76379", "687": "76383", "688": "76385", "689": "76387", "690": "76389", "691": "76390", "692": "76395", "693": "76394", "694": "76398", "695": "76399", "696": "76401", "697": "76402", "698": "76403", "699": "76515", "700": "76617", "701": "76619", "702": "76627", "703": "76628", "704": "76629", "705": "76630", "706": "76631", "707": "76632", "708": "76634", "709": "76638", "710": "76641", "711": "76643", "712": "76647", "713": "77223", "714": "77460", "715": "77593", "716": "77598", "717": "77604", "718": "77605", "719": "77609", "720": "77619", "721": "77637", "722": "77639", "723": "77641", "724": "77643", "725": "77647", "726": "77653", "727": "77658", "728": "77663", "729": "77665", "730": "77672", "731": "77674", "732": "77685", "733": "77744", "734": "77751", "735": "77856", "736": "77865", "737": "78217", "738": "78242", "739": "78425", "740": "79700", "741": "79729", "742": "79848", "743": "80405", "744": "80488", "745": "80493", "746": "80499", "747": "80510", "748": "80523", "749": "80530", "750": "80533", "751": "80538", "752": "80883", "753": "80916", "754": "80923", "755": "80944", "756": "81100", "757": "81503", "758": "82390", "759": "82423", "760": "82842", "761": "82862", "762": "83434", "763": "83448", "764": "83464", "765": "83497", "766": "84213", "767": "84462", "768": "84931", "769": "85629", "770": "85651", "771": "86533", "772": "87390", "773": "87538", "774": "87639", "775": "87678", "776": "87682", "777": "87715", "778": "87774", "779": "87828", "780": "87835", "781": "87983", "782": "88051", "783": "88075", "784": "88324", "785": "88326", "786": "88468", "787": "88470", "788": "88472", "789": "88570", "790": "88582", "791": "88609", "792": "88678", "793": "88705", "794": "89048", "795": "89056", "796": "89272", "797": "89289", "798": "89305", "799": "89306", "800": "89397", "801": "89445", "802": "89449", "803": "89541", "804": "89552", "805": "89607", "806": "89610", "807": "89629", "808": "89631", "809": "89693", "810": "89698", "811": "89723", "812": "89793", "813": "89818", "814": "89857", "815": "90039", "816": "90156", "817": "90473", "818": "90684", "819": "90687", "820": "91133", "821": "92539", "822": "92540", "823": "92566", "824": "92580", "825": "92582", "826": "92596", "827": "92605", "828": "92608", "829": "92642", "830": "92684", "831": "92688", "832": "92689", "833": "92703", "834": "92706", "835": "92715", "836": "92999", "837": "93092", "838": "93093", "839": "93494", "840": "93559", "841": "93760", "842": "93801", "843": "93996", "844": "94028", "845": "95821", "846": "95953", "847": "96185", "848": "96893", "849": "97130", "850": "97646", "851": "97732", "852": "97734", "853": "97740", "854": "97758", "855": "97769", "856": "98019", "857": "98080", "858": "98164", "859": "98503", "860": "98506", "861": "98508", "862": "98510", "863": "98989", "864": "98991", "865": "99015", "866": "99022", "867": "99025", "868": "99031", "869": "99123", "870": "99304", "871": "99434", "872": "99454", "873": "99459", "874": "99461", "875": "99463", "876": "99464", "877": "99545", "878": "99823", "879": "99948", "880": "99954", "881": "99955", "882": "99961", "883": "99971", "884": "99984", "885": "100143", "886": "100274", "887": "100324", "888": "100700", "889": "101365", "890": "101384", "891": "101398", "892": "101401", "893": "101408", "894": "101523", "895": "101555", "896": "101567", "897": "101813", "898": "101899", "899": "101906", "900": "101907", "901": "101910", "902": "101914", "903": "101927", "904": "101935", "905": "101943", "906": "101954", "907": "101998", "908": "102026", "909": "102077", "910": "102210", "911": "102348", "912": "102580", "913": "103011", "914": "103018", "915": "103022", "916": "103021", "917": "103026", "918": "103029", "919": "103033", "920": "103084", "921": "103088", "922": "103325", "923": "103327", "924": "103370", "925": "103928", "926": "104101", "927": "104102", "928": "104103", "929": "104104", "930": "104107", "931": 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query dictionary, and output a json with the format: +# {: {: text, ..., : text}, ...} + +GPT_MODEL = "gpt-4" +openai.api_key = os.environ["OPENAI_API_KEY"] +pattern = re.compile(r"\d\.\s(.*)") +prompt = """ +Title: {} +\nOriginal Query: {} +\nDecomposed Query: +""" + + +@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) +def generate(content): + """ + """ + response = openai.ChatCompletion.create( + messages=[ + {'role': 'system', + 'content': 'You are a utility that decomposes complex user-formed descriptions of movies into smaller' + ' independent sentences that aid in movie name retrieval. Incorporate as much historical' + ' information in each sentence and lengthen them by appending synonyms and words at the' + ' end that may resemble the movie title and description and help in retrieval.'}, + {'role': 'user', 'content': content}, + ], + model='gpt-4', + temperature=0) + return response['choices'][0]['message']['content'] + + +def llm_based_decomposition(dataset, outpath): + + decomposed_queries = {} + for q in dataset.queries_iter(): + original_query = [] + for annotation in q.sentence_annotations: + if not annotation["labels"]["social"]: + original_query.append(annotation["text"]) + original_query = " ".join(original_query) + + decomposed = generate(prompt.format(q.title, original_query)) + # decomposed = generate(prompt.format(q.text)) + subqueries = {} + for i, sentence in enumerate(decomposed.split("\n")): + try: + sentence = pattern.match(sentence).groups()[0] + except Exception as e: + pass + subqueries[i+1] = f"{q.title}. {sentence}" + + decomposed_queries[q.query_id] = subqueries + + queries_file = f"{outpath}/llm_decomposed_queries_no_social.json" + + with open(queries_file, 'w', encoding="utf-8") as fp: + json.dump(decomposed_queries, fp) + + return queries_file + + +def sentence_decomposition(dataset, outpath): + """Baseline decomposition method. + Each query is decomposed into its sentences as provided in the data. + Title of orignal query prepended to each decomposed subquery. + """ + decomposed_queries = {} + for q in dataset.queries_iter(): + subqueries = {} + for sentence in q.sentence_annotations: + subqueries[sentence['id']] = f"{q.title}. {sentence['text']}" + + decomposed_queries[q.query_id] = subqueries + + queries_file = f"{outpath}/sentence_decomposed_queries.json" + + with open(queries_file, 'w', encoding="utf-8") as fp: + json.dump(decomposed_queries, fp) + + return queries_file + diff --git a/results.py b/results.py new file mode 100644 index 0000000..b039801 --- /dev/null +++ b/results.py @@ -0,0 +1,70 @@ +# BM25 RRF + +# recip_rank : 0.0475 (0.1698) +# P_1 : 0.0267 (0.1611) +# recall_10 : 0.0867 (0.2813) +# recall_100 : 0.2800 (0.4490) +# recall_1000 : 0.4933 (0.5000) +# ndcg_cut_10 : 0.0512 (0.1864) +# ndcg_cut_100: 0.0887 (0.1924) +# ndcg_cut_1000: 0.1143 (0.1869) + + +# # Dense Inference HNSW + +# 150it [00:28, 5.33it/s] +# saving run to: runs_dense/DENSE-RRF-dev-llm-1/dev.run +# recip_rank : 0.0259 (0.1239) +# P_1 : 0.0133 (0.1147) +# recall_10 : 0.0400 (0.1960) +# recall_100 : 0.1733 (0.3785) +# recall_1000 : 0.4200 (0.4936) +# ndcg_cut_10 : 0.0251 (0.1351) +# ndcg_cut_100: 0.0514 (0.1470) +# ndcg_cut_1000: 0.0808 (0.1459) + + +# HNSW Pq + +# recip_rank : 0.0064 (0.0348) +# P_1 : 0.0000 (0.0000) +# recall_10 : 0.0133 (0.1147) +# recall_100 : 0.0733 (0.2607) +# recall_1000 : 0.2200 (0.4142) +# ndcg_cut_10 : 0.0062 (0.0535) +# ndcg_cut_100: 0.0179 (0.0702) +# ndcg_cut_1000: 0.0358 (0.0787) + +# PQ + +# saving run to: runs_dense/DENSE-RRF-dev-llm-1/dev.run +# recip_rank : 0.0318 (0.1503) +# P_1 : 0.0200 (0.1400) +# recall_10 : 0.0400 (0.1960) +# recall_100 : 0.1733 (0.3785) +# recall_1000 : 0.3600 (0.4800) +# ndcg_cut_10 : 0.0305 (0.1582) +# ndcg_cut_100: 0.0555 (0.1664) +# ndcg_cut_1000: 0.0776 (0.1656) + +# FLAT + +# recip_rank : 0.0241 (0.1086) +# P_1 : 0.0067 (0.0814) +# recall_10 : 0.0400 (0.1960) +# recall_100 : 0.1867 (0.3896) +# recall_1000 : 0.3933 (0.4885) +# ndcg_cut_10 : 0.0242 (0.1260) +# ndcg_cut_100: 0.0519 (0.1386) +# ndcg_cut_1000: 0.0764 (0.1378) + + +# Dense training and Decomposed RRF Inference +# INFO:__main__:recip_rank : 0.0711 (0.2210) +# INFO:__main__:P_1 : 0.0467 (0.2109) +# INFO:__main__:recall_10 : 0.1000 (0.3000) +# INFO:__main__:recall_100 : 0.2800 (0.4490) +# INFO:__main__:recall_1000 : 0.6200 (0.4854) +# INFO:__main__:ndcg_cut_10 : 0.0726 (0.2343) +# INFO:__main__:ndcg_cut_100: 0.1077 (0.2360) +# INFO:__main__:ndcg_cut_1000: 0.1490 (0.2239) \ No newline at end of file diff --git a/runs/BM25-RRF-1/dev.run 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gpt4-rerank-distilbert-top100 +498 Q0 3077365 91 0.010869565217391304 gpt4-rerank-distilbert-top100 +498 Q0 63706715 92 0.010752688172043012 gpt4-rerank-distilbert-top100 +498 Q0 5754330 93 0.010638297872340425 gpt4-rerank-distilbert-top100 +498 Q0 25934563 94 0.010526315789473684 gpt4-rerank-distilbert-top100 +498 Q0 25499986 95 0.010416666666666666 gpt4-rerank-distilbert-top100 +498 Q0 59462737 96 0.010309278350515464 gpt4-rerank-distilbert-top100 +498 Q0 596701 97 0.01020408163265306 gpt4-rerank-distilbert-top100 +498 Q0 50001644 98 0.010101010101010102 gpt4-rerank-distilbert-top100 +498 Q0 46263609 99 0.01 gpt4-rerank-distilbert-top100 diff --git a/scripts/encode_dense_index.sh b/scripts/encode_dense_index.sh new file mode 100644 index 0000000..67a83ab --- /dev/null +++ b/scripts/encode_dense_index.sh @@ -0,0 +1,63 @@ + +# cp corpus.jsonl corpusnew.jsonl +# sed -i 's/doc_id/docid/g' corpusnew.jsonl +# You nned to replace the field doc_id with docid in the corpus file. In order to be compatible with the pyserini encoder. + +# # python -m pyserini.encode \ +# # input --corpus ../../TREC-TOT/corpusnew.jsonl \ +# # --fields text \ +# # --delimiter "\n" \ +# # --shard-id 0 \ +# # --shard-num 1 \ +# # output --embeddings ../output_dense_enc_2 \ +# # --to-faiss \ +# # encoder --encoder /home/ddo/CMU/PLLM/llms-project/dense_models/baseline_distilbert/model \ +# # --fields text \ +# # --batch 32 \ +# # --fp16 # if inference with autocast() + +# echo "Start indexing" + + +# echo "HNSW" +# python -m pyserini.index.faiss \ +# --input ../output_dense_enc_2 \ +# --output ../dense_index_hnsw \ +# --hnsw + +# echo "PQ" +# python -m pyserini.index.faiss \ +# --input ../output_dense_enc_2 \ +# --output ../dense_index_pq \ +# --pq + +# echo "HNSW + PQ" +# python -m pyserini.index.faiss \ +# --input ../output_dense_enc_2 \ +# --output ../dense_index_hnswpq \ +# --hnsw \ +# --pq + +# echo "Flat" +# python -m pyserini.index.faiss \ +# --input ../output_dense_enc_2 \ +# --output ../dense_index_flat + +echo "Start inference Sentence Decoder" + +python ../dense_inference_decomp_with_rrf.py \ + --index_path /home/ddo/CMU/PLLM/llms-project/dense_index_hnswpq + + +python ../dense_inference_decomp_with_rrf.py \ + --index_path /home/ddo/CMU/PLLM/llms-project/dense_index_hnsw + + +python ../dense_inference_decomp_with_rrf.py \ + --index_path /home/ddo/CMU/PLLM/llms-project/dense_index_pq + + +python ../dense_inference_decomp_with_rrf.py \ + --index_path /home/ddo/CMU/PLLM/llms-project/dense_index_flat + + \ No newline at end of file diff --git a/scripts/result.txt b/scripts/result.txt new file mode 100644 index 0000000..7d68ad6 --- /dev/null +++ b/scripts/result.txt @@ -0,0 +1,106 @@ +LLM Decompositino + +BM25 RRF + +recip_rank : 0.0475 (0.1698) +P_1 : 0.0267 (0.1611) +recall_10 : 0.0867 (0.2813) +recall_100 : 0.2800 (0.4490) +recall_1000 : 0.4933 (0.5000) +ndcg_cut_10 : 0.0512 (0.1864) +ndcg_cut_100: 0.0887 (0.1924) +ndcg_cut_1000: 0.1143 (0.1869) + + +# Dense Inference HNSW + +150it [00:28, 5.33it/s] +saving run to: runs_dense/DENSE-RRF-dev-llm-1/dev.run +recip_rank : 0.0259 (0.1239) +P_1 : 0.0133 (0.1147) +recall_10 : 0.0400 (0.1960) +recall_100 : 0.1733 (0.3785) +recall_1000 : 0.4200 (0.4936) +ndcg_cut_10 : 0.0251 (0.1351) +ndcg_cut_100: 0.0514 (0.1470) +ndcg_cut_1000: 0.0808 (0.1459) + + +HNSW Pq + +recip_rank : 0.0064 (0.0348) +P_1 : 0.0000 (0.0000) +recall_10 : 0.0133 (0.1147) +recall_100 : 0.0733 (0.2607) +recall_1000 : 0.2200 (0.4142) +ndcg_cut_10 : 0.0062 (0.0535) +ndcg_cut_100: 0.0179 (0.0702) +ndcg_cut_1000: 0.0358 (0.0787) + +PQ + +saving run to: runs_dense/DENSE-RRF-dev-llm-1/dev.run +recip_rank : 0.0318 (0.1503) +P_1 : 0.0200 (0.1400) +recall_10 : 0.0400 (0.1960) +recall_100 : 0.1733 (0.3785) +recall_1000 : 0.3600 (0.4800) +ndcg_cut_10 : 0.0305 (0.1582) +ndcg_cut_100: 0.0555 (0.1664) +ndcg_cut_1000: 0.0776 (0.1656) + +FLAT + +recip_rank : 0.0241 (0.1086) +P_1 : 0.0067 (0.0814) +recall_10 : 0.0400 (0.1960) +recall_100 : 0.1867 (0.3896) +recall_1000 : 0.3933 (0.4885) +ndcg_cut_10 : 0.0242 (0.1260) +ndcg_cut_100: 0.0519 (0.1386) +ndcg_cut_1000: 0.0764 (0.1378) + + + + +Start inference Sentence Decomposition +/home/ddo/CMU/PLLM/llms-project/dense_index_hnswpq +saving run to: runs_dense/DENSE-RRF-dev-1dense_index_hnswpq/dev.run +recip_rank : 0.0056 (0.0417) +P_1 : 0.0000 (0.0000) +recall_10 : 0.0133 (0.1147) +recall_100 : 0.0600 (0.2375) +recall_1000 : 0.2267 (0.4187) +ndcg_cut_10 : 0.0062 (0.0567) +ndcg_cut_100: 0.0142 (0.0666) +ndcg_cut_1000: 0.0347 (0.0775) +/home/ddo/CMU/PLLM/llms-project/dense_index_hnsw +saving run to: runs_dense/DENSE-RRF-dev-1dense_index_hnsw/dev.run +recip_rank : 0.0254 (0.1085) +P_1 : 0.0067 (0.0814) +recall_10 : 0.0533 (0.2247) +recall_100 : 0.1667 (0.3727) +recall_1000 : 0.4000 (0.4899) +ndcg_cut_10 : 0.0294 (0.1320) +ndcg_cut_100: 0.0502 (0.1402) +ndcg_cut_1000: 0.0777 (0.1394) +/home/ddo/CMU/PLLM/llms-project/dense_index_pq +saving run to: runs_dense/DENSE-RRF-dev-1dense_index_pq/dev.run +recip_rank : 0.0277 (0.1282) +P_1 : 0.0133 (0.1147) +recall_10 : 0.0467 (0.2109) +recall_100 : 0.1533 (0.3603) +recall_1000 : 0.3467 (0.4759) +ndcg_cut_10 : 0.0295 (0.1433) +ndcg_cut_100: 0.0498 (0.1513) +ndcg_cut_1000: 0.0728 (0.1511) +/home/ddo/CMU/PLLM/llms-project/dense_index_flat +saving run to: runs_dense/DENSE-RRF-dev-1dense_index_flat/dev.run +recip_rank : 0.0277 (0.1271) +P_1 : 0.0133 (0.1147) +recall_10 : 0.0533 (0.2247) +recall_100 : 0.1800 (0.3842) +recall_1000 : 0.3933 (0.4885) +ndcg_cut_10 : 0.0311 (0.1437) +ndcg_cut_100: 0.0539 (0.1513) +ndcg_cut_1000: 0.0786 (0.1501) diff --git a/scripts/run_bm25.sh b/scripts/run_bm25.sh new file mode 100755 index 0000000..2ebb55a --- /dev/null +++ b/scripts/run_bm25.sh @@ -0,0 +1,13 @@ +# run: /scripts/run_bm25.sh split +# split: train or dev +# train can be used to generate negatives. + +split=$1 +data_path=/home/ddo/CMU/PLLM/TREC-TOT + +python bm25.py --index_name bm25_0.8_1.0 --index_path ./anserini_indicies \ + --data_path $data_path \ + --param_k1 0.8 --param_b 1.0 \ + --field text --query title_text --split $split \ + --run ./runs/bm25/$split.run --run_format trec_eval --run_id baseline_bm25 \ + --negatives_out $data_path/negatives/bm25_negatives/$split-title_text-negatives.json diff --git a/scripts/run_bm25_rff.sh b/scripts/run_bm25_rff.sh new file mode 100755 index 0000000..4bc26d3 --- /dev/null +++ b/scripts/run_bm25_rff.sh @@ -0,0 +1 @@ +python bm25_with_rrf.py \ No newline at end of file diff --git a/scripts/run_dense.sh b/scripts/run_dense.sh new file mode 100755 index 0000000..e0a83c9 --- /dev/null +++ b/scripts/run_dense.sh @@ -0,0 +1,20 @@ +#data_path=./data +data_path=/home/ddo/CMU/PLLM/TREC-TOT + + +echo python train_dense.py --epochs 20 --lr 6e-05 --weight_decay 0.01 \ + --model_dir dense_models/baseline_distilbert_0 \ + --run_id baseline_distilbert_0 --model_or_checkpoint distilbert-base-uncased \ + --embed_size 768 --batch_size 10 --encode_batch_size 128 --data_path $data_path \ + --negatives_path $data_path/negatives/bm25_negatives \ + --negatives_out $data_path/negatives/baseline_distilbert_0_negatives \ + --query title_text \ + --device cuda + +python train_dense.py --epochs 20 --lr 6e-05 --weight_decay 0.01 \ + --negatives_path $data_path/negatives/baseline_distilbert_0_negatives \ + --negatives_out $data_path/negatives/baseline_distilbert_1_negatives \ + --model_dir dense_models/baseline_distilbert \ + --run_id baseline_distilbert --model_or_checkpoint distilbert-base-uncased \ + --embed_size 768 --batch_size 10 --encode_batch_size 128 --data_path $data_path \ + --query title_text --device cuda diff --git a/scripts/run_dense_llm_decom.sh b/scripts/run_dense_llm_decom.sh new file mode 100755 index 0000000..31be2d8 --- /dev/null +++ b/scripts/run_dense_llm_decom.sh @@ -0,0 +1,59 @@ +#data_path=./data +data_path=/home/ddo/CMU/PLLM/TREC-TOT/llm_decomp +append=llm +split=train + +python bm25.py --index_name bm25_0.8_1.1$append --index_path ./anserini_indicies \ + --data_path $data_path \ + --param_k1 0.8 --param_b 1.0 \ + --field text --query title_text --split $split \ + --run ./runs/bm25/$split$append.run --run_format trec_eval --run_id baseline_bm25_1$append \ + --negatives_out $data_path/negatives/bm25_negatives$append/$split-title_text-negatives.json + +split=dev +python bm25.py --index_name bm25_0.8_1.1$append --index_path ./anserini_indicies \ + --data_path $data_path \ + --param_k1 0.8 --param_b 1.0 \ + --field text --query title_text --split $split \ + --run ./runs/bm25/$split$append.run --run_format trec_eval --run_id baseline_bm25_1$append \ + --negatives_out $data_path/negatives/bm25_negatives$append/$split-title_text-negatives.json + + + +python train_dense.py --epochs 20 --lr 6e-05 --weight_decay 0.01 \ + --model_dir dense_models/baseline_distilbert_0$append \ + --run_id baseline_distilbert_0$append --model_or_checkpoint distilbert-base-uncased \ + --embed_size 768 --batch_size 10 --encode_batch_size 128 --data_path $data_path \ + --negatives_path $data_path/negatives/bm25_negatives$append \ + --negatives_out $data_path/negatives/baseline_distilbert_0_negatives$append \ + --query title_text \ + --device cuda + +python train_dense.py --epochs 20 --lr 6e-05 --weight_decay 0.01 \ + --negatives_path $data_path/negatives/baseline_distilbert_0_negatives$append \ + --negatives_out $data_path/negatives/baseline_distilbert_1_negatives$append \ + --model_dir dense_models/baseline_distilbert$append \ + --run_id baseline_distilbert$append --model_or_checkpoint distilbert-base-uncased \ + --embed_size 768 --batch_size 10 --encode_batch_size 128 --data_path $data_path \ + --query title_text --device cuda + +# INFO:__main__:running & evaluating train +# Batches: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:00<00:00, 49.06it/s] +# INFO:__main__:recip_rank : 1.0000 (0.0000) +# INFO:__main__:P_1 : 1.0000 (0.0000) +# INFO:__main__:recall_10 : 1.0000 (0.0000) +# INFO:__main__:recall_100 : 1.0000 (0.0000) +# INFO:__main__:recall_1000 : 1.0000 (0.0000) +# INFO:__main__:ndcg_cut_10 : 1.0000 (0.0000) +# INFO:__main__:ndcg_cut_100: 1.0000 (0.0000) +# INFO:__main__:ndcg_cut_1000: 1.0000 (0.0000) +# INFO:__main__:running & evaluating dev +# Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 46.05it/s] +# INFO:__main__:recip_rank : 0.0378 (0.1671) +# INFO:__main__:P_1 : 0.0267 (0.1611) +# INFO:__main__:recall_10 : 0.0533 (0.2247) +# INFO:__main__:recall_100 : 0.1600 (0.3666) +# INFO:__main__:recall_1000 : 0.4267 (0.4946) +# INFO:__main__:ndcg_cut_10 : 0.0381 (0.1751) +# INFO:__main__:ndcg_cut_100: 0.0586 (0.1809) +# INFO:__main__:ndcg_cut_1000: 0.0908 (0.1785) \ No newline at end of file diff --git a/scripts/run_dense_sentence_decom.sh b/scripts/run_dense_sentence_decom.sh new file mode 100755 index 0000000..a0dd1cb --- /dev/null +++ b/scripts/run_dense_sentence_decom.sh @@ -0,0 +1,67 @@ +#data_path=./data +data_path=/home/ddo/CMU/PLLM/TREC-TOT/sentence_decomp +append=sentence +split=train + +python bm25.py --index_name bm25_0.8_1.1$append --index_path ./anserini_indicies \ + --data_path $data_path \ + --param_k1 0.8 --param_b 1.0 \ + --field text --query title_text --split $split \ + --run ./runs/bm25/$split$append.run --run_format trec_eval --run_id baseline_bm25_1$append \ + --negatives_out $data_path/negatives/bm25_negatives$append/$split-title_text-negatives.json + +split=dev +python bm25.py --index_name bm25_0.8_1.1$append --index_path ./anserini_indicies \ + --data_path $data_path \ + --param_k1 0.8 --param_b 1.0 \ + --field text --query title_text --split $split \ + --run ./runs/bm25/$split$append.run --run_format trec_eval --run_id baseline_bm25_1$append \ + --negatives_out $data_path/negatives/bm25_negatives$append/$split-title_text-negatives.json + + + +python train_dense.py --epochs 20 --lr 6e-05 --weight_decay 0.01 \ + --model_dir dense_models/baseline_distilbert_0$append \ + --run_id baseline_distilbert_0$append --model_or_checkpoint distilbert-base-uncased \ + --embed_size 768 --batch_size 10 --encode_batch_size 128 --data_path $data_path \ + --negatives_path $data_path/negatives/bm25_negatives$append \ + --negatives_out $data_path/negatives/baseline_distilbert_0_negatives$append \ + --query title_text \ + --device cuda + +python train_dense.py --epochs 20 --lr 6e-05 --weight_decay 0.01 \ + --negatives_path $data_path/negatives/baseline_distilbert_0_negatives$append \ + --negatives_out $data_path/negatives/baseline_distilbert_1_negatives$append \ + --model_dir dense_models/baseline_distilbert$append \ + --run_id baseline_distilbert$append --model_or_checkpoint distilbert-base-uncased \ + --embed_size 768 --batch_size 10 --encode_batch_size 128 --data_path $data_path \ + --query title_text --device cuda + + +# After first run on Dense retrieval + +# INFO:__main__:running & evaluating dev +# Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 38.78it/s] +# INFO:__main__:recip_rank : 0.0308 (0.1310) +# INFO:__main__:P_1 : 0.0133 (0.1147) +# INFO:__main__:recall_10 : 0.0667 (0.2494) +# INFO:__main__:recall_100 : 0.1667 (0.3727) +# INFO:__main__:recall_1000 : 0.4200 (0.4936) +# INFO:__main__:ndcg_cut_10 : 0.0365 (0.1510) +# INFO:__main__:ndcg_cut_100: 0.0551 (0.1568) +# INFO:__main__:ndcg_cut_1000: 0.0856 (0.1550) + + + +# After second run on Dense retrieval + +# Batches: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9/9 [00:00<00:00, 38.96it/s] +# INFO:__main__:recip_rank : 0.0298 (0.1236) +# INFO:__main__:P_1 : 0.0133 (0.1147) +# INFO:__main__:recall_10 : 0.0667 (0.2494) +# INFO:__main__:recall_100 : 0.2133 (0.4097) +# INFO:__main__:recall_1000 : 0.4200 (0.4936) +# INFO:__main__:ndcg_cut_10 : 0.0339 (0.1431) +# INFO:__main__:ndcg_cut_100: 0.0625 (0.1531) +# INFO:__main__:ndcg_cut_1000: 0.0879 (0.1510) +# INFO:__main__:running & evaluating train \ No newline at end of file diff --git a/src/encode.py b/src/encode.py index 679dbf4..36b2399 100644 --- a/src/encode.py +++ b/src/encode.py @@ -7,9 +7,13 @@ from src import data, utils import logging - +import pyserini log = logging.getLogger(__name__) +def encode_pyserini(model: SentenceTransformer, embedding_size: int, dataset: ir_datasets.Dataset, device, + encode_batch_size): + doc_ids, documents = data.get_documents(dataset) + def encode_dataset_faiss(model: SentenceTransformer, embedding_size: int, dataset: ir_datasets.Dataset, device, encode_batch_size): diff --git a/tot.py b/tot.py index 28801b6..912b82c 100644 --- a/tot.py +++ b/tot.py @@ -145,13 +145,13 @@ def register(path): if __name__ == '__main__': - path = input("Enter data path:") + path = "./data" register(path.strip()) sets = [] - for split in {"train", "dev", "test"}: + for split in {"train", "dev"}: name = split sets.append(NAME + ":" + name) diff --git a/train_dence_rrf.py b/train_dence_rrf.py new file mode 100644 index 0000000..40b4009 --- /dev/null +++ b/train_dence_rrf.py @@ -0,0 +1,251 @@ +import ir_datasets +from torch.utils.data import DataLoader +from sentence_transformers import SentenceTransformer, losses, models +from src import data, encode, utils +import pytrec_eval +from torch import nn +import argparse +import os +import logging +from faiss import write_index, read_index +import json +import tot +import bm25 +import pyserini +import pyserini.encode +# pyserini.encode.set_classpath("/home/ddo/CMU/PLLM/llms-project/pyserini/pyserini-') +log = logging.getLogger(__name__) + +if __name__ == '__main__': + + parser = argparse.ArgumentParser("train_dense", description="Trains a dense retrieval model") + + parser.add_argument("--data_path", default="./datasets/TREC-TOT", help="location to dataset") + + parser.add_argument("--negatives_path", default="./bm25_negatives", + help="path to folder containing negatives ") + + parser.add_argument("--query", choices=["title", "text", "title_text"], default="title_text") + + parser.add_argument("--model_or_checkpoint", type=str, required=True, help="hf checkpoint/ path to pt-model") + parser.add_argument("--embed_size", required=True, type=int, help="hidden size of the model") + + parser.add_argument("--epochs", type=int, required=True, help="number of epochs to train") + parser.add_argument("--lr", type=float, default=2e-5, help="learning rate") + parser.add_argument("--weight_decay", type=float, default=0.01, help="weight decay") + parser.add_argument("--warmup_steps", type=int, default=0, help="warmup steps") + parser.add_argument("--batch_size", type=int, default=24, help="batch size (training)") + parser.add_argument("--encode_batch_size", type=int, default=124, help="batch size (inference)") + parser.add_argument("--evaluation_steps", type=int, default=-1, help="steps before evaluation is run") + + parser.add_argument("--freeze_base_model", action="store_true", default=False, + help="if set, freezes the base layer and trains only a projection layer on top") + parser.add_argument("--metrics", required=False, default=bm25.METRICS, help="csv - metrics to evaluate") + parser.add_argument("--n_hits", default=1000, type=int, help="number of hits to retrieve") + + parser.add_argument("--device", type=str, default="cuda", help="device to train /evaluate model on") + + parser.add_argument("--model_dir", type=str, help="folder to store model & runs", required=True) + parser.add_argument("--run_id", required=True, help="run id (required if run_format = trec_eval)") + parser.add_argument("--seed", type=int, default=42) + parser.add_argument("--negatives_out", default=None, + help="if provided, dumps negatives for use in training other models") + parser.add_argument("--n_negatives", default=10, type=int, help="number of negatives to obtain") + + logging.basicConfig(level=logging.INFO) + + args = parser.parse_args() + utils.set_seed(args.seed) + log.info(f"args: {args}") + + tot.register(args.data_path) + metrics = args.metrics.split(",") + + model_dir = args.model_dir + os.makedirs(model_dir, exist_ok=True) + + if args.freeze_base_model: + base_model = SentenceTransformer(args.model_or_checkpoint, device=args.device) + for param in base_model.parameters(): + param.requires_grad = False + projection = models.Dense(args.embed_size, args.embed_size, + activation_function=nn.Tanh()) + model = SentenceTransformer(modules=[base_model, projection], device=args.device) + else: + model = SentenceTransformer("/home/ddo/CMU/PLLM/llms-project/dense_models/baseline_distilbert/model", device="cuda") + # model = SentenceTransformer("/home/ddo/CMU/PLLM/llms-project/dense_models/baseline_distilbert_0/model", device="cuda") + + # model = SentenceTransformer(args.model_or_checkpoint, device=args.device) + + irds_splits = {} + st_data = {} + args.decomposition_method = "llm" + if args.decomposition_method == "llm": + queries_expanded = "/home/ddo/CMU/PLLM/TREC-TOT/decomposed_queries/llm_decomposed_queries.json" + else: + queries_expanded = "/home/ddo/CMU/PLLM/TREC-TOT/decomposed_queries/sentence_decomposed_queries.json" + + queries = json.load(open(queries_expanded)) + args.output_dir = "/home/ddo/CMU/PLLM/TREC-TOT/" + run_save_folder = f'{args.output_dir}DENSE-RRF' + if args.decomposition_method == "llm": + run_save_folder += f'-llm' + # splits + args.run_number = 1 + args.rm3 = 'y' + args.split = 'dev' + run_save_folder += f'-RM3-{args.run_number}' if args.rm3 == 'y' else f'-{args.run_number}' + + run_save_full = f"{run_save_folder}/{args.split}.run" + + for split in {"train", "dev"}: + irds_splits[split] = ir_datasets.load(f"trec-tot:{split}") + + log.info(f"loaded split {split}") + st_data[split] = data.SBERTDataset(irds_splits[split], query_type=args.query, + negatives=utils.read_json( + os.path.join(args.negatives_path, + f"{split}-{args.query}-negatives.json"))) + + log.info(f"training model for {args.epochs} epochs") + train_dataloader = DataLoader(st_data["train"], shuffle=True, batch_size=args.batch_size) + + args.loss_fn = "mnrl" + if args.loss_fn == "mnrl": + train_loss = losses.MultipleNegativesRankingLoss(model=model) + else: + raise NotImplementedError(args.loss_fn) + + val_evaluator = data.get_ir_evaluator(st_data["dev"], name=f"dev", + mrr_at_k=[1000], + ndcg_at_k=[10, 1000], + corpus_chunk_size=args.encode_batch_size) + + optimizer_params = { + "lr": args.lr + } + + # Tune the model + # model.fit(train_objectives=[(train_dataloader, train_loss)], + # evaluation_steps=args.evaluation_steps, + # output_path=os.path.join(model_dir, "model"), + # evaluator=val_evaluator, + # epochs=args.epochs, + # warmup_steps=args.warmup_steps, + # optimizer_params=optimizer_params, + # weight_decay=args.weight_decay, + # save_best_model=True) + + log.info("encoding corpus with model") + embed_size = args.embed_size + + # index, (idx_to_docid, docid_to_idx) = encode.encode_dataset_faiss(model, embedding_size=embed_size, + # dataset=irds_splits["train"], + # device=args.device, + # encode_batch_size=args.encode_batch_size) + # index = read_index( "dense.index") + idx_to_docid = json.load(open("idx_to_docid.json","r")) + docid_to_idx = json.load(open("docid_to_idx.json","r")) + runs = {} + eval_res_agg = {} + eval_res = {} + + try: + log.info("attempting to load test set") + # plug in the test set + irds_splits["test"] = ir_datasets.load(f"trec-tot:test") + log.info("success!") + except KeyError: + log.info("couldn't find test set!") + pass + + split_qrels = {} + model.eval() + + qids = [] + # queries = [] + # for query in queries: + # queries.append(utils.get_query(query, query_type)) + # qids.append(query.query_id) + import torch + eval_batch_size = 64 + device = 'cuda' + # with torch.no_grad(): + # query_embeddings = model.encode(queries, batch_size=eval_batch_size, show_progress_bar=True, + # convert_to_numpy=True, device=device) + + # top_k = 10 + # scores, raw_doc_ids = index.search(query_embeddings, k=top_k) + # run = {} + # for qid, sc, rdoc_ids in zip(qids, scores, raw_doc_ids): + # run[qid] = {} + # for s, rdid in zip(sc, rdoc_ids): + # if rdid == -1: + # log.warning(f"invalid doc ids!") + # continue + # run[qid][idx_to_docid[rdid]] = float(s) + + # return run + + for split, dataset in irds_splits.items(): + log.info(f"running & evaluating {split}") + + run = encode.create_run_faiss(model=model, + dataset=dataset, + query_type=args.query, device=args.device, + eval_batch_size=args.encode_batch_size, + index=index, idx_to_docid=idx_to_docid, + docid_to_idx=docid_to_idx, + top_k=args.n_hits) + runs[split] = run + + if dataset.has_qrels(): + qrel, n_missing = utils.get_qrel(dataset, run) + split_qrels[split] = qrel + evaluator = pytrec_eval.RelevanceEvaluator( + qrel, metrics) + + eval_res[split] = evaluator.evaluate(run) + eval_res_agg[split] = utils.aggregate_pytrec(eval_res[split], "mean") + + for metric, (mean, std) in eval_res_agg[split].items(): + log.info(f"{metric:<12}: {mean:.4f} ({std:0.4f})") + + utils.write_json({ + "aggregated_result": eval_res_agg, + "run": runs, + "result": eval_res, + "args": vars(args) + }, os.path.join(model_dir, "out.gz"), zipped=True) + + run_id = args.run_id + assert run_id is not None + + for split, run in runs.items(): + run_path = os.path.join(model_dir, f"{split}.run") + with open(run_path, "w") as writer: + for qid, r in run.items(): + for rank, (doc_id, score) in enumerate(sorted(r.items(), key=lambda _: -_[1])): + writer.write(f"{qid}\tQ0\t{doc_id}\t{rank}\t{score}\t{run_id}\n") + + if args.negatives_out: + log.info(f"writing negatives to folder: {args.negatives_out}") + os.makedirs(args.negatives_out, exist_ok=True) + out = {} + + for split, run in runs.items(): + if split == "test": + continue + negatives_path = os.path.join(args.negatives_out, f"{split}-{args.query}-negatives.json") + qrel = split_qrels[split] + for qid, hits in run.items(): + hits = sorted(hits.items(), key=lambda _: -_[1]) + negs = [] + for (doc, score) in hits: + if qrel[qid].get(doc, 0) > 0: + continue + if len(negs) == args.n_negatives: + break + negs.append(doc) + out[qid] = negs + utils.write_json(out, negatives_path) diff --git a/train_dense.py b/train_dense.py index a75c873..4dc6d2f 100644 --- a/train_dense.py +++ b/train_dense.py @@ -7,7 +7,8 @@ import argparse import os import logging - +from faiss import write_index, read_index +import json import tot import bm25 @@ -69,7 +70,9 @@ activation_function=nn.Tanh()) model = SentenceTransformer(modules=[base_model, projection], device=args.device) else: - model = SentenceTransformer(args.model_or_checkpoint, device=args.device) + model = SentenceTransformer("/home/ddo/CMU/PLLM/llms-project/dense_models/baseline_distilbert_0/model", device="cuda") + + # model = SentenceTransformer(args.model_or_checkpoint, device=args.device) irds_splits = {} st_data = {} @@ -119,7 +122,9 @@ dataset=irds_splits["train"], device=args.device, encode_batch_size=args.encode_batch_size) - + write_index(index, "dense.index") + json.dump(idx_to_docid, open("idx_to_docid.json","w")) + json.dump(docid_to_idx, open("docid_to_idx.json","w")) runs = {} eval_res_agg = {} eval_res = {} @@ -183,6 +188,7 @@ for split, run in runs.items(): if split == "test": continue + os.makedirs(args.negatives_out, exist_ok=True) negatives_path = os.path.join(args.negatives_out, f"{split}-{args.query}-negatives.json") qrel = split_qrels[split] for qid, hits in run.items():