From ccfcf7dced8422f127db71a0c524cbcdaf8c8dde Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Tue, 1 Aug 2023 11:23:29 +0200 Subject: [PATCH 01/40] Version 0 of docker image --- docker/Dockerfile | 11 +++++++++++ docker/README.md | 16 ++++++++++++++++ 2 files changed, 27 insertions(+) create mode 100644 docker/Dockerfile create mode 100644 docker/README.md diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000..78b8d71 --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,11 @@ +# We start from an image that already has Terrier + PyTerrier as dependencies installed +FROM webis/tira-ir-starter-pyterrier:0.0.2-base + +COPY code/qpptk/ /qpptk + +WORKDIR /qpptk + +RUN pip install -r requirements.txt \ + && pip install -e . + +ENTRYPOINT [ "python3", "/qpptk/qpptk/qpptk_main.py" ] diff --git a/docker/README.md b/docker/README.md new file mode 100644 index 0000000..576bb87 --- /dev/null +++ b/docker/README.md @@ -0,0 +1,16 @@ +# Docker image with qpptk + +Build the docker image via this command: + +``` +docker build -t qpptk .. -f ./Dockerfile +``` + +As soon as we have finalized everything and we have published the docker image, the build command above can be removed because others can directly use the published image. + +If the image was build, you can run qpptk via Docker with the following command (e.g., printing the help message): + +``` +docker run --rm -ti qpptk --help +``` + From b55df7acf1355f9d868a723eab5f1e54c326e711 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Mon, 7 Aug 2023 07:55:53 +0200 Subject: [PATCH 02/40] Add dummy commands to the dockerfile --- docker/Dockerfile | 7 +++++++ docker/README.md | 36 ++++++++++++++++++++++++++++++++++++ 2 files changed, 43 insertions(+) diff --git a/docker/Dockerfile b/docker/Dockerfile index 78b8d71..642ee53 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -8,4 +8,11 @@ WORKDIR /qpptk RUN pip install -r requirements.txt \ && pip install -e . +#ONLY For Debug purposes +RUN apt-get install -y jq \ + && echo '#!/bin/sh\n\necho "Read Topics from ${1}queries.jsonl"\necho "Read Pyterrier-Index from ${2}/index:"\n ls ${2}/index\nsleep 2s\ncat ${1}/queries.jsonl | jq -c '\''{"qid": .qid, "query": .query, "predictions": {"max-idf": 1}}'\'' > ${3}/predictions.jsonl' > /qpptk-dummy-full-rank.sh \ + && chmod +x /qpptk-dummy-full-rank.sh \ + && echo '#!/bin/sh\n\necho "Read query-document pairs from ${1}/rerank.jsonl.gz"\n\nsleep 2s\nzcat ${1}/rerank.jsonl.gz | jq -c '\''{"qid": .qid, "query": .query, "predictions": {"max-idf": 1}}'\'' > ${2}/predictions.jsonl' > /qpptk-dummy-re-rank.sh \ + && chmod +x /qpptk-dummy-re-rank.sh + ENTRYPOINT [ "python3", "/qpptk/qpptk/qpptk_main.py" ] diff --git a/docker/README.md b/docker/README.md index 576bb87..a1e8948 100644 --- a/docker/README.md +++ b/docker/README.md @@ -14,3 +14,39 @@ If the image was build, you can run qpptk via Docker with the following command docker run --rm -ti qpptk --help ``` +# Run it on full-rank approaches + +Assuming you already have a pyterrier index (see below how to build it), you can run: + +``` +tira-run \ + --input-directory ${PWD}/sample-input-full-rank \ + --input-run ${PWD}/pyterrier-index \ + --output-directory ${PWD}/qpptk-predictions-full-rank \ + --image qpptk \ + --command '/qpptk-dummy-full-rank.sh $inputDataset $inputRun $outputDir' +``` + +Build the pyterrier index with this command (you can skip this step, the small test index is already added to the repository): + +``` +tira-run \ + --input-directory ${PWD}/sample-input-full-rank \ + --output-directory ${PWD}/pyterrier-index \ + --image webis/tira-ir-starter-pyterrier:0.0.2-base \ + --command '/workspace/pyterrier_cli.py --input $inputDataset --output $outputDir --index_directory $outputDir' +``` + + +# Run on re-rank approaches + +Assuming you have `tira` installed (use `pip3 install tira`), you can run it via: + +``` +tira-run \ + --input-directory ${PWD}/sample-input-re-rank \ + --output-directory ${PWD}/qpptk-predictions-re-rank \ + --image qpptk \ + --command '/qpptk-dummy-re-rank.sh $inputDataset $outputDir' +``` + From 6382bbc203e8bc961a451c8b35106d8b429ea0d3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Mon, 7 Aug 2023 07:59:27 +0200 Subject: [PATCH 03/40] Add dummy data for tests --- docker/pyterrier-index/index/data.direct.bf | Bin 0 -> 21 bytes .../index/data.document.fsarrayfile | Bin 0 -> 105 bytes docker/pyterrier-index/index/data.inverted.bf | Bin 0 -> 25 bytes .../index/data.lexicon.fsomapfile | Bin 0 -> 2160 bytes .../index/data.lexicon.fsomaphash | Bin 0 -> 489 bytes .../index/data.lexicon.fsomapid | Bin 0 -> 96 bytes .../index/data.meta-0.fsomapfile | Bin 0 -> 1525 bytes docker/pyterrier-index/index/data.meta.idx | Bin 0 -> 40 bytes docker/pyterrier-index/index/data.meta.zdata | Bin 0 -> 140 bytes docker/pyterrier-index/index/data.properties | 71 ++++++++++++++++++ docker/sample-input-full-rank/documents.jsonl | 5 ++ docker/sample-input-full-rank/metadata.json | 1 + docker/sample-input-full-rank/queries.jsonl | 2 + docker/sample-input-full-rank/queries.xml | 40 ++++++++++ docker/sample-input-re-rank/rerank.jsonl.gz | Bin 0 -> 1487 bytes 15 files changed, 119 insertions(+) create mode 100644 docker/pyterrier-index/index/data.direct.bf create mode 100644 docker/pyterrier-index/index/data.document.fsarrayfile create mode 100644 docker/pyterrier-index/index/data.inverted.bf create mode 100644 docker/pyterrier-index/index/data.lexicon.fsomapfile create mode 100644 docker/pyterrier-index/index/data.lexicon.fsomaphash create mode 100644 docker/pyterrier-index/index/data.lexicon.fsomapid create mode 100644 docker/pyterrier-index/index/data.meta-0.fsomapfile create mode 100644 docker/pyterrier-index/index/data.meta.idx create mode 100644 docker/pyterrier-index/index/data.meta.zdata create mode 100644 docker/pyterrier-index/index/data.properties create mode 100644 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Pangrams containing a fox that is not jumping or jumps over something that is not an animal are not relevant."}} {"qid": "2", "query": "multiple animals including a zebra", "original_query": {"query_id": "2", "title": "multiple animals including a zebra", "description": "Which pangrams have multiple animals where one of the animals is a zebra?", "narrative": "Relevant pangrams have at least two animals, one of the animals must be a Zebra. Pangrams containing only a Zebra are not relevant."}} +{"qid": "3", "query": "fox", "original_query": {"query_id": "1", "title": "fox jumps above animal", "description": "What pangrams have a fox jumping above some animal?", "narrative": "Relevant pangrams have a fox jumping over an animal (e.g., an dog). Pangrams containing a fox that is not jumping or jumps over something that is not an animal are not relevant."}} diff --git a/docker/sample-input-full-rank/sample.qrels b/docker/sample-input-full-rank/sample.qrels new file mode 100644 index 0000000..1965738 --- /dev/null +++ b/docker/sample-input-full-rank/sample.qrels @@ -0,0 +1,4 @@ +1 0 pangram-04 1 +1 0 pangram-05 0 +2 0 pangram-04 0 +2 0 pangram-05 1 From 2a1c272990bd4138a8b263423519952f4a1f1118 Mon Sep 17 00:00:00 2001 From: Zendelo Date: Tue, 22 Aug 2023 20:27:06 +1000 Subject: [PATCH 05/40] update required packages --- code/qpptk/setup.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/code/qpptk/setup.py b/code/qpptk/setup.py index f86540d..9813a6f 100644 --- a/code/qpptk/setup.py +++ b/code/qpptk/setup.py @@ -10,16 +10,18 @@ 'qpptk/global_manager.py', 'qpptk/parse_queries.py'], author="Oleg Zendel", author_email="oleg.zendel@rmit.edu.au", - description="QPP framework package", + description="POC of QPP framework package", # long_description="Proof of concept of QPP framework development in python", long_description=long_description, long_description_content_type="text/markdown", + url="https://github.com/Zendelo/qpptk", packages=setuptools.find_packages(), classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], - python_requires='>=3.7', - install_requires=['numpy', 'pandas', 'toml', 'protobuf', 'lmdb', 'lxml', 'syct'], + python_requires='>=3.8', + install_requires=['numpy', 'pandas', 'toml', 'protobuf', 'lmdb', 'lxml', 'msgpack', 'tqdm', 'matplotlib', 'seaborn', + 'statsmodels', 'scikit-learn', 'tabulate', 'msgpack', 'syct', 'python-terrier'], ) From a1304922980bc9ad43ab86567a50d903e8953269 Mon Sep 17 00:00:00 2001 From: Zendelo Date: Tue, 22 Aug 2023 20:29:39 +1000 Subject: [PATCH 06/40] add terrier index and remove ciff index --- code/qpptk/qpptk/__init__.py | 19 +- code/qpptk/qpptk/config.py | 94 +- code/qpptk/qpptk/config.toml | 132 +- code/qpptk/qpptk/data_analysis.py | 1691 ++++++++++++++++++- code/qpptk/qpptk/global_manager.py | 177 +- code/qpptk/qpptk/load_ciff_index.py | 16 +- code/qpptk/qpptk/load_db_index.py | 1 + code/qpptk/qpptk/load_terri_index.py | 114 ++ code/qpptk/qpptk/load_terrier_index.py | 389 +++++ code/qpptk/qpptk/nested_cross_validation.py | 215 +++ code/qpptk/qpptk/parse_queries.py | 202 ++- code/qpptk/qpptk/qpptk_main.py | 284 +++- 12 files changed, 3092 insertions(+), 242 deletions(-) create mode 100644 code/qpptk/qpptk/load_terri_index.py create mode 100644 code/qpptk/qpptk/load_terrier_index.py create mode 100644 code/qpptk/qpptk/nested_cross_validation.py diff --git a/code/qpptk/qpptk/__init__.py b/code/qpptk/qpptk/__init__.py index 642e28f..2532387 100644 --- a/code/qpptk/qpptk/__init__.py +++ b/code/qpptk/qpptk/__init__.py @@ -1,19 +1,20 @@ from .utility_functions import * from .config import * from .load_text_index import IndexText, parse_posting_list -from .load_ciff_index import IndexCiff, parse_index_file +# from .load_ciff_index import IndexCiff, parse_index_file from .load_db_index import IndexDB -from .parse_queries import QueryParserText, QueryParserCiff +from .load_terrier_index import IndexTerrier +from .parse_queries import QueryParserText, QueryParserCiff, QueryParserJsonl from .retrieval_local_manager import LocalManagerRetrieval # from .parse_ciff_queries import QueryParserCiff, parse_ciff_queries_file from .pre_retrieval_predictors import LocalManagerPredictorPre from .post_retrieval_predictors import LocalManagerPredictorPost -from .index_to_db import parse_index_to_db +# from .index_to_db import parse_index_to_db __all__ = ['Config', 'Posting', 'TermPosting', 'TermRecord', 'TermFrequency', 'DocRecord', 'ResultPair', 'get_file_len', - 'read_line', 'parse_posting_list', 'binary_search', 'IndexText', 'IndexCiff', 'parse_index_file', 'IndexDB', - 'QueryParserText', 'QueryParserCiff', 'LocalManagerRetrieval', 'LocalManagerPredictorPre', 'ensure_dir', - 'ensure_file', 'LocalManagerPredictorPost', 'read_message', 'plot_roc', - 'transform_list_to_counts_dict', 'jaccard_similarity', 'overlap_coefficient', - 'set_index_dump_paths', 'add_topic_to_qdf', 'msgpack_encode', 'msgpack_decode', - 'parse_index_to_db', 'read_trec_res_file'] + 'read_line', 'parse_posting_list', 'binary_search', 'IndexText', 'IndexDB', 'IndexTerrier', + 'QueryParserText', 'QueryParserCiff', 'QueryParserJsonl', 'LocalManagerRetrieval', + 'LocalManagerPredictorPre', 'ensure_dir', 'ensure_file', 'LocalManagerPredictorPost', 'read_message', + 'plot_roc', 'transform_list_to_counts_dict', 'jaccard_similarity', 'overlap_coefficient', + 'sorensen_dice_similarity', 'calc_ndcg', 'set_index_dump_paths', 'add_topic_to_qdf', 'msgpack_encode', + 'msgpack_decode', 'read_trec_res_file'] # , 'IndexCiff', 'parse_index_file', 'parse_index_to_db'] diff --git a/code/qpptk/qpptk/config.py b/code/qpptk/qpptk/config.py index ac40b6f..791fabd 100644 --- a/code/qpptk/qpptk/config.py +++ b/code/qpptk/qpptk/config.py @@ -6,7 +6,26 @@ from qpptk import ensure_dir, ensure_file -CONFIG_FILE = './qpptk/config.toml' +# def __init_logger(self, logger): +# if logger: +# return logger +# logger = logging.getLogger(__name__) +# logger.setLevel(logging.INFO) +# if not logger.hasHandlers(): +# formatter = logging.Formatter('{asctime} - {message}', datefmt="%H:%M:%S", style="{") +# handler = logging.StreamHandler() +# handler.setFormatter(formatter) +# logger.addHandler(handler) +# return logger + +# CONFIG_FILE = '/research/local/oleg/qpptk/qpptk/config.toml' + + +# CONFIG_FILE = '/research/remote/petabyte/users/oleg/qpptk/qpptk/config.toml' +CONFIG_FILE = '~/repos/qpptk/qpptk/config.toml' + + +# CONFIG_FILE = '~/work/qpptk/qpptk/config.toml' def set_index_dump_paths(index_dir): @@ -31,25 +50,37 @@ class Config: WORKING_SET_SIZE = parameters.get('working_set_size', None) FB_TERMS = parameters.get('fb_terms') NUM_DOCS = parameters.get('max_result_size') - logging_level = parameters.get('logging_level', 'DEBUG') + N_PROC = parameters.get('num_processes', 1) - logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', datefmt='%d/%m/%Y %H:%M:%S', - level=logging_level) - logger = logging.getLogger(__name__) + prediction_parameters = parameters.get('prediction') WIG_LIST_SIZE = prediction_parameters.get('wig_list_size') NQC_LIST_SIZE = prediction_parameters.get('nqc_list_size') SMV_LIST_SIZE = prediction_parameters.get('smv_list_size') + CLARITY_FB_TERMS = prediction_parameters.get('clarity_fb_terms') CLARITY_LIST_SIZE = prediction_parameters.get('clarity_list_size') - uef_parameters = prediction_parameters.get('uef') - UEF_RM_FB_PARAM = uef_parameters.get('rm_fb_size') - UEF_SIM_PARAM = uef_parameters.get('re_rank_list_size') + + QF_FB_TERMS = prediction_parameters.get('qf_fb_terms') + QF_LIST_SIZE = prediction_parameters.get('qf_list_size') + QF_OVERLAP_SIZE = prediction_parameters.get('qf_overlap_size') + + UEF_FB_TERMS = prediction_parameters.get('uef_fb_terms') + UEF_LIST_SIZE = prediction_parameters.get('uef_list_size') + UEF_RANKING_SIZE = prediction_parameters.get('uef_ranking_size') + + logging_level = parameters.get('logging_level', 'DEBUG') + # logging_level = logging.DEBUG + + # uef_parameters = prediction_parameters.get('uef') + # UEF_RM_FB_PARAM = uef_parameters.get('rm_fb_size') + # UEF_SIM_PARAM = uef_parameters.get('re_rank_list_size') env = config.get('environment') executables = env.get('executables') TREC_EVAL = executables.get('trec_eval') + RBP_EVAL = executables.get('rbp_eval') env_paths = env.get('paths') _root_dir = env_paths.get('root_dir') @@ -58,7 +89,24 @@ class Config: _root_dir = ensure_dir(_root_dir, False) INDEX_DIR = env_paths.get('text_index_dir') CIFF_INDEX = env_paths.get('ciff_index_file') - assert not (INDEX_DIR and CIFF_INDEX), f"Only one type of Index can be specified in the configurations file" + TERRIER_INDEX = env_paths.get('terrier_index_dir') + + BATCH_NAME = env_paths.get('batch_name', '') + + assert sum((bool(TERRIER_INDEX), bool(CIFF_INDEX), bool(INDEX_DIR))) <= 1, \ + f"Only one type of Index can be specified in the configurations file" + + RESULTS_DIR = ensure_dir(os.path.join(_root_dir, env_paths.get('results_dir')), True) + + log_file = env_paths.get('log_file') + if log_file: + log_file = os.path.join(RESULTS_DIR, log_file) + logging.basicConfig(filename=log_file, format='%(asctime)s %(levelname)s: %(message)s', + datefmt='%d/%m/%Y %H:%M:%S', + level=logging_level) + logger = logging.getLogger(__name__) + + DB_DIR = ensure_dir(os.path.join(_root_dir, env_paths.get('db_dir')), True) if INDEX_DIR: try: @@ -76,11 +124,25 @@ class Config: logger.warning(err) logger.warning(f"The setting 'ciff_index_file={CIFF_INDEX}' in the config file was skipped") CIFF_INDEX = None + elif TERRIER_INDEX: + try: + # Index dump paths + TERRIER_INDEX = ensure_dir(os.path.join(_root_dir, TERRIER_INDEX), create_if_not=False) + ensure_file(os.path.join(TERRIER_INDEX, 'data.properties')) + except FileNotFoundError as err: + logger.warning(err) + logging.warning(f"The setting 'terrier_index_dir={TERRIER_INDEX}'" + f"in the config file was skipped, data.properties file is missing") + INDEX_DIR = None TEXT_QUERIES = env_paths.get('text_queries_file') CIFF_QUERIES = env_paths.get('ciff_queries_file') - assert not (TEXT_QUERIES and CIFF_QUERIES), f"Only a single type of queries file can be specified" \ - f" in the configurations file" + JSONL_QUERIES = env_paths.get('jsonl_queries_file') + QREL_FILE = os.path.join(_root_dir, env_paths.get('qrel_file')) + + assert sum((bool(TEXT_QUERIES), bool(CIFF_QUERIES), bool(JSONL_QUERIES))) == 1, \ + f"Only one type of queries file can be specified in the configurations file" + if TEXT_QUERIES: try: TEXT_QUERIES = ensure_file(os.path.join(_root_dir, TEXT_QUERIES)) @@ -95,9 +157,13 @@ class Config: logger.warning(err) logger.warning(f"The setting 'ciff_queries_file={CIFF_QUERIES}' in the config file was skipped") CIFF_QUERIES = None - - RESULTS_DIR = ensure_dir(os.path.join(_root_dir, env_paths.get('results_dir')), True) - DB_DIR = ensure_dir(os.path.join(_root_dir, env_paths.get('db_dir')), True) + elif JSONL_QUERIES: + try: + JSONL_QUERIES = ensure_file(os.path.join(_root_dir, JSONL_QUERIES)) + except FileNotFoundError as err: + logger.warning(err) + logger.warning(f"The setting 'jsonl_queries_file={JSONL_QUERIES}' in the config file was skipped") + JSONL_QUERIES = None @staticmethod def get_logger(): diff --git a/code/qpptk/qpptk/config.toml b/code/qpptk/qpptk/config.toml index 0fc55d3..6cbcf56 100644 --- a/code/qpptk/qpptk/config.toml +++ b/code/qpptk/qpptk/config.toml @@ -3,44 +3,112 @@ title = "Global configurations" [environment] [environment.paths] # The root directory which will be used as the base path for all the environment paths - root_dir = "./" +# root_dir = "/research/local/oleg/" # Running local, good for drogon/balerion only +# root_dir = "/research/remote/petabyte/users/oleg/" + root_dir = "~/repos/" +# root_dir = "~/work/" # text_index_dir = "ROBUST04/dump/" - ciff_index_file = "ciff_indexes/robust04/robust04_Lucene_indri_porter.ciff" +# text_index_dir = "mini_dump/" +# ciff_index_file = "ciff_indexes/robust04/robust04_Lucene_indri_porter.ciff" +# ciff_index_file = "ciff_indexes/robust04_Lucene_indri_krovetz.ciff" +# ciff_index_file = "ciff_indexes/robust04/robust04_Lucene_lingpipe_porter.ciff" +# ciff_index_file = "ciff_indexes/robust04/robust04_Lucene_terrier_krovetz.ciff" +# ciff_index_file = "ciff_indexes/robust04_Lucene_nostop_krovetz.ciff" +# ciff_index_file = "ciff_indexes/robust04/robust04_Lucene_indri_nostem.ciff" +# terrier_index_dir = "robust_krovetz_nostop_terrier" +# terrier_index_dir = "robust04_KrovetzStemmer_stop" +# terrier_index_dir = "cw12b_terrier/index/cw12b_krovetz_nostop" + +# terrier_index_dir = "cw12b_terrier/index/cw12b_krovetz_terrier" + terrier_index_dir = "qpp-Maik/docker/pyterrier-index/index" + +# ciff_index_file = "ciff_indexes/robust04-Lucene-nostop-porter.ciff" +# ciff_index_file = "ciff_indexes/robust04/robust04_Lucene_indri_porter.ciff" +# ciff_index_file = "ciff_indexes/cw12b/cw12b_Indri_nostop_krovetz.ciff" # text_queries_file = "data/robust04.stemmed.qry" - ciff_queries_file = "ciff_query_indexes/robust04_Lucene_query_indri_porter.ciff" - results_dir = "anova_qpp" - # Directory for the DB files, +# text_queries_file = "data/cw12b.stemmed.stopped.qry" + + jsonl_queries_file = "qpp-Maik/docker/sample-input-full-rank/queries.jsonl" + +# text_queries_file = "data/cw12b-uqv.stemmed.stopped.qry.part-1" + +# text_queries_file = "data/robust04-uqv.stemmed.stopped.qry" +# text_queries_file = "data/robust04-uqv-dev.stemmed.stopped.qry" +# text_queries_file = "mini_dump/queries.txt" +# ciff_queries_file = "ciff_lite_query_indexes/robust04_Lucene_query_indri_krovetz.ciff" +# ciff_queries_file = "ciff_query_indexes/robust04_Lucene_query_indri_nostem.ciff" +# ciff_queries_file = "ciff_query_indexes/robust04_Lucene_query_lingpipe_porter.ciff" +# ciff_queries_file = "ciff_query_indexes/robust04_Lucene_query_nostop_krovetz.ciff" +# ciff_queries_file = "ciff_query_indexes/robust04_Lucene_query_terrier_krovetz.ciff" +# ciff_queries_file = "ciff_query_indexes/robust04_Lucene_query_indri_porter.ciff" +# results_dir = "eval_qpp_results" + results_dir = "testing_docker" +# results_dir = "anova_qpp" +# log_file = 'qpptk_retr_cw.log' +# log_file = 'qpptk_predPre_cw.log' +# log_file = 'qpptk_testing.log' +# log_file = 'qpptk_robust_retr.log' +# results_dir = "anova_qpp/ecir" + # Directory for the DB files db_dir = "qpptk/qpptk_db" +# db_dir = "qpptk/qpptk_db_lite" + qrel_file = 'qpp-Maik/docker/sample-input-full-rank/sample.qrels' +# qrel_file = 'qpptk/robust04_mod.qrels' +# qrel_file = 'data/robust04.qrels' +# qrel_file = 'data/cw12b-uqv_mod.qrels' +# qrel_file = 'data/cw12b_mod.qrels.trec' +# batch_name = '3392985365' + + [environment.executables] # path to trec_eval executable - trec_eval = '~/trec_eval-9.0.7/trec_eval' +# trec_eval = '/research/remote/petabyte/users/oleg/trec_eval-9.0.7/trec_eval' + trec_eval = '/home/s3806763/Downloads/trec_eval/trec_eval' +# rbp_eval = '/research/remote/petabyte/users/oleg/eval/rbp_eval' + [parameters] + mu = 1000 + # Number of docs to use for the RM construction + fb_docs = 100 + # The maximum number of documents to use for the re-ranking, comment out to re-rank all docs in initial list + working_set_size = 100 + # Number of top terms to use, *after* RM construction + fb_terms = 100 + max_result_size = 1000 + # predefined logging levels: CRITICAL, ERROR, WARNING, INFO, DEBUG + logging_level = 'DEBUG' + num_processes = 1 + + [parameters.prediction] + wig_list_size = 10 # ECIR + nqc_list_size = 100 # ECIR + smv_list_size = 100 # ECIR + # Number of top terms from RM to use in Clarity + clarity_fb_terms = 100 + # Number of docs to use for the RM construction in Clarity + clarity_list_size = 1000 + + # Number of top terms from RM to use in QF + qf_fb_terms = 100 + # Number of docs to use for the RM construction in QF + qf_list_size = 1000 + # Number of docs to use for the overlap calc in QF + qf_overlap_size = 25 + + # Number of top terms from RM to use in UEF + uef_fb_terms = 100 + # Number of docs to use for the RM construction in UEF + uef_list_size = 1000 + # Number of docs to use for the re-ranking and comparison in UEF + uef_ranking_size = 100 + + #[parameters.prediction.uef] + + #rm_fb_size = 100 + # Number of docs to re-rank with the RM, and calc the similarity + #re_rank_list_size = 150 -mu = 1000 -# Number of docs to use for the RM construction -fb_docs = 100 -# The maximum number of documents to use for the re-ranking, comment out to re-rank all docs in initial list -working_set_size = 100 -# Number of top terms to use, *after* RM construction -fb_terms = 100 -max_result_size = 1000 -# predefined logging levels: CRITICAL, ERROR, WARNING, INFO, DEBUG -logging_level = 'DEBUG' -num_processes = 25 - -[parameters.prediction] -wig_list_size = 10 -nqc_list_size = 100 -smv_list_size = 100 -# Number of top terms from RM to use in Clarity -clarity_fb_terms = 100 -# Number of docs to use for the RM construction in Clarity -clarity_list_size = 1000 - -[parameters.prediction.uef] - -#rm_fb_size = 100 -# Number of docs to re-rank with the RM, and calc the similarity -#re_rank_list_size = 150 +#[logging] +#output= diff --git a/code/qpptk/qpptk/data_analysis.py b/code/qpptk/qpptk/data_analysis.py index 35b0514..eed1463 100644 --- a/code/qpptk/qpptk/data_analysis.py +++ b/code/qpptk/qpptk/data_analysis.py @@ -1,23 +1,80 @@ +import concurrent.futures +import math import os import sys +from collections.abc import Sequence from glob import glob -import matplotlib.pyplot as plt +from itertools import combinations, permutations +from textwrap import wrap +import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns -from scipy import stats import statsmodels.api as sm +from PIL.ImageOps import pad +from syct import timer +from matplotlib import rcParams, font_manager +from numpy.distutils.conv_template import paren_repl +from scipy import stats from sklearn.datasets import dump_svmlight_file -from sklearn.metrics import roc_auc_score -from itertools import combinations, permutations - -from qpptk import Config, ensure_file, add_topic_to_qdf, ensure_dir, overlap_coefficient +from sklearn.metrics import roc_auc_score, pairwise_distances +from tabulate import tabulate +from statsmodels.stats.multicomp import pairwise_tukeyhsd +from statsmodels.formula.api import ols +from statsmodels.graphics.api import interaction_plot, abline_plot +from statsmodels.stats.anova import anova_lm +from tqdm import tqdm + +from qpptk import Config, ensure_file, add_topic_to_qdf, ensure_dir, overlap_coefficient, calc_ndcg from qpptk.global_manager import initialize_text_queries, initialize_ciff_queries # plt.switch_backend('Qt5Agg') +def seaborn_setup() -> None: + fmt = { + "backend": "Cairo", + "text.usetex": False, + "ps.fonttype": 42, + "pdf.fonttype": 42, + "lines.linewidth": 5.0, + "lines.markersize": 15.0, + "patch.linewidth": 0.5, + "legend.fancybox": False, + "axes.grid": True, + "patch.edgecolor": "#f0f0f0", + "axes.titlesize": "x-large", + "figure.facecolor": "#f0f0f0", + "grid.linestyle": "-", + "grid.linewidth": 0.8, + "grid.color": "#cbcbcb", + "axes.edgecolor": "#000000", + "xtick.major.size": 10, + "xtick.minor.size": 0, + "ytick.major.size": 10, + "ytick.minor.size": 0, + "axes.linewidth": 1.0, + "legend.handletextpad": 0.1, + "legend.handlelength": 0.3, + "legend.columnspacing": 0.1, + "font.size": 44, + "font.family": "serif", + "font.serif": "Linux Libertine O", + "font.sans-serif": "Linux Biolinum O", + "axes.labelpad": 10.0, + "xtick.major.pad": 10.0, + "ytick.major.pad": 10.0, + "lines.solid_capstyle": "butt", + "savefig.edgecolor": "#000000", + "savefig.facecolor": "#ffffff", + "mathtext.default": "regular", + } + plt.rcParams.update(fmt) + sns.set_style("darkgrid") + # plt.rcParams.update({"font.sans-serif": ["Linux Biolinum O"]}) + + def filter_robust_title_queries(_df): _df = _df.loc[_df.index.str.contains('-50-1')] # assert len(_df) == 249, 'wrong numbers of title queries in ROBUST eval file' @@ -33,7 +90,10 @@ def get_queries_object(): def set_index(): - index_path, index_type = (Config.INDEX_DIR, 'text') if Config.INDEX_DIR else (Config.CIFF_INDEX, 'ciff') + if Config.TERRIER_INDEX: + index_path, index_type = (Config.TERRIER_INDEX, 'terrier') + else: + index_path, index_type = (Config.INDEX_DIR, 'text') if Config.INDEX_DIR else (Config.CIFF_INDEX, 'ciff') if index_path is None: raise AssertionError('No index was specified') return index_path, index_type @@ -49,8 +109,18 @@ def print_samples_ratios(data_df): def read_eval_df(prefix_path, ir_metric): eval_file = ensure_file(f"{prefix_path}_QL.{ir_metric}") eval_df = pd.read_csv(eval_file, delim_whitespace=True, names=['qid', ir_metric], index_col=0) - return eval_df.drop(DUPLICATED_QIDS, errors='ignore') - # return eval_df + # if 'robust' in prefix_path.rsplit('/', maxsplit=1)[1].lower(): + # return eval_df.drop(ROBUST_DUPLICATED_QIDS, errors='ignore').sort_index() + # else: + return eval_df.sort_index() + + +def read_query_sim_df(prefix_path, sim_metric): + sim_file = ensure_file(f"{prefix_path}_pairwise_sim-{sim_metric}.tsv") + try: + return pd.read_csv(sim_file, delim_whitespace=True, index_col='qid') + except KeyError: + return pd.read_csv(sim_file, delim_whitespace=True).rename_axis('qid') def generate_labeled_df(prefix_path, ir_metric, threshold): @@ -59,7 +129,7 @@ def generate_labeled_df(prefix_path, ir_metric, threshold): _threshold = eval_df.quantile(threshold)[0] logger.info(f"Threshold for bad queries was set to {_threshold:.3f}") # This will label all variants above the threshold as 0, and less eq than as 1 - labeled_df = eval_df.where(eval_df.values <= _threshold, 0).mask(eval_df.values <= _threshold, 1).rename( + labeled_df = eval_df.where(eval_df.to_numpy() <= _threshold, 0).mask(eval_df.to_numpy() <= _threshold, 1).rename( columns={ir_metric: 'label'}) labeled_df = add_topic_to_qdf(labeled_df).set_index('qid') assert labeled_df['label'].sum() > 0, 'No samples were labeled as positive' @@ -87,19 +157,34 @@ def read_lgbm_prediction_files(ir_metric, threshold=None): def read_prediction_files(prefix_path, r_type='all'): if r_type == 'all': - post_ret_predictors = glob(prefix_path + '_QL*.pre') - pre_ret_predictors = glob(prefix_path + '*PRE*') + # post_ret_predictors = glob(prefix_path + '_QL_g_*.pre') # The g is used for the optimized parameters + post_ret_predictors = glob(f'{prefix_path}_QL_*.pre') # The g is used for the optimized parameters + pre_ret_predictors = glob(prefix_path + f'*PRE*.pre') predictors = pre_ret_predictors + post_ret_predictors elif r_type.lower() == 'pre': predictors = glob(prefix_path + '*PRE*') else: - predictors = glob(prefix_path + '_QL*.pre') + # predictors = glob(prefix_path + '_QL_g_*.pre') # The g is used for the optimized parameters + predictors = glob(prefix_path + '_QL_*.pre') _results = [] for _file in predictors: - collection, method, predictor = _file.rsplit('/', 1)[1].replace('.pre', '').rsplit('_', 2) + comps = _file.rsplit('/', 1)[1].replace('.pre', '').rsplit('_') + assert 5 <= len(comps) <= 7, f'The predictions file {_file} has an unknown name format' + if len(comps) == 7: + collection, _, stemmer, stoplist, method, _, predictor = comps + elif len(comps) == 6: + # collection, stemmer, stoplist, method, predictor, part_name = comps + collection, _, stemmer, stoplist, method, predictor = comps + else: + collection, stemmer, stoplist, method, predictor = comps _results.append( - pd.read_csv(_file, delim_whitespace=True, names=['topic', 'qid', predictor], index_col=['topic', 'qid'])) - return pd.concat(_results, axis=1).drop(672, errors='ignore').drop(DUPLICATED_QIDS, level=1, errors='ignore') + pd.read_csv(_file, delim_whitespace=True, names=['topic', 'qid', predictor], + dtype={'topic': str}).set_index(['topic', 'qid'])) + # return pd.concat(_results, axis=1).drop(['672', 672], errors='ignore') \ + # .drop(ROBUST_DUPLICATED_QIDS, level=1, errors='ignore').sort_index() \ + # .rename(columns=lambda x: '-'.join([i.split('+')[0] for i in x.split('-')])) + return pd.concat(_results, axis=1).drop(['672', 672], errors='ignore').sort_index() \ + .rename(columns=lambda x: '-'.join([i.split('+')[0] for i in x.split('-')])) def df_to_libsvm(df: pd.DataFrame, set_name): @@ -115,10 +200,11 @@ def generate_random_col(data_df): return x +# TODO: check this def plot_hard_queries(prefix_path, ir_metric): predictors_group = 'post' eval_df = read_eval_df(prefix_path, ir_metric) - predictions_df = read_prediction_files(prefix_path, predictors_group) + predictions_df = read_prediction_files(prefix_path, r_type=predictors_group) data_df = predictions_df.merge(eval_df, left_on='qid', right_on='qid') data_df['random'] = generate_random_col(data_df) diff_df = data_df.drop(ir_metric, 1).rank(pct=True, ascending=False).subtract( @@ -158,7 +244,7 @@ def plot_hard_queries(prefix_path, ir_metric): def plot_hard_topics(topics_df, ir_metric): - plt.rcParams.update({'font.size': 16, 'font.family': 'serif'}) + # plt.rcParams.update({'font.size': 16, 'font.family': 'serif'}) fig, ax = plt.subplots(figsize=(16, 9), dpi=100, facecolor='w', edgecolor='k') ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) @@ -166,7 +252,7 @@ def plot_hard_topics(topics_df, ir_metric): # sns.set(font='serif', font_scale=2) fig.suptitle(f'Top Ranking Errors by Query {ir_metric}') # Create the boxplot and store the resulting python dictionary - boxes = ax.boxplot(topics_df.values) + boxes = ax.boxplot(topics_df.to_numpy()) ax.set_xticklabels(topics_df.columns, rotation=45) make_flier_labels(ax, boxes, topics_df) plt.tight_layout() @@ -224,9 +310,11 @@ def calc_correlations_df(predictions_df: pd.DataFrame, eval_df, title_only=False eval_df = filter_robust_title_queries(eval_df) for pred in predictions_df.columns: results_pearson.append( - (pred, eval_df.merge(predictions_df[pred], left_index=True, right_on='qid').corr('pearson').values[0][1])) + (pred, + eval_df.merge(predictions_df[pred], left_index=True, right_on='qid').corr('pearson').to_numpy()[0][1])) results_kendall.append( - (pred, eval_df.merge(predictions_df[pred], left_index=True, right_on='qid').corr('kendall').values[0][1])) + (pred, + eval_df.merge(predictions_df[pred], left_index=True, right_on='qid').corr('kendall').to_numpy()[0][1])) p_corrs_df = pd.DataFrame(results_pearson, columns=['predictor', 'pearson correlation']).set_index('predictor') k_corrs_df = pd.DataFrame(results_kendall, columns=['predictor', 'kendall correlation']).set_index('predictor') @@ -259,18 +347,19 @@ def plot_dist(data_sr, title='', xlabel=None, ylabel='Density', save_figure=Fals rug_kws={'color': '#2F4C56', "alpha": 0.2}, kde_kws={"lw": 2, "label": "KDE", "bw": "silverman"}, hist_kws={"histtype": "bar", "lw": 1, "alpha": 0.5}) # fit=stats.gamma to fit gamma function - # plt.ylabel(ylabel) - # plt.xlabel(xlabel) - # plt.title(titles.get(title, title)) - # plt.tight_layout() + plt.ylabel(ylabel) + plt.xlabel(xlabel) + plt.title(titles.get(title, title)) + plt.tight_layout() sns.despine(left=True) if save_figure: plt.savefig(f'{title}_dist.pdf') else: - return - # plt.show() + # return + plt.show() +# TODO: check this def plot_eval_metrics_dist(prefix_path): def _poor_topics(_eval_df, _ir_metric, _n): # return add_topic_to_qdf(_eval_df.nsmallest(_n, _ir_metric)).topic @@ -279,19 +368,21 @@ def _poor_topics(_eval_df, _ir_metric, _n): # save_figures = True save_figures = False ir_metric = 'ap@1000' - eval_df = read_eval_df(prefix_path, ir_metric) - n = len(eval_df) // 10 - plot_dist(eval_df, title=ir_metric, save_figure=save_figures) - poor_ap_topics = set(_poor_topics(eval_df, ir_metric, n)) + ap_eval_df = read_eval_df(prefix_path, ir_metric) + n = len(ap_eval_df) // 10 + # plot_dist(ap_eval_df, title=ir_metric, save_figure=save_figures) + poor_ap_topics = set(_poor_topics(ap_eval_df, ir_metric, n)) ir_metric = 'ndcg@100' - eval_df.corrwith(read_eval_df(prefix_path, ir_metric)) - eval_df = read_eval_df(prefix_path, ir_metric) - plot_dist(eval_df, title=ir_metric, save_figure=save_figures) - poor_ndcg_100_topics = set(_poor_topics(eval_df, ir_metric, n)) + ndcg_100_eval_df = read_eval_df(prefix_path, ir_metric) + ap_eval_df.corrwith(ndcg_100_eval_df) + # plot_dist(ndcg_100_eval_df, title=ir_metric, save_figure=save_figures) + poor_ndcg_100_topics = set(_poor_topics(ndcg_100_eval_df, ir_metric, n)) ir_metric = 'ndcg@10' - eval_df = read_eval_df(prefix_path, ir_metric) - plot_dist(eval_df, title=ir_metric, save_figure=save_figures) - poor_ndcg_10_topics = set(_poor_topics(eval_df, ir_metric, n)) + ndcg_10_eval_df = read_eval_df(prefix_path, ir_metric) + ir_metric = 'rbp-0.5' + rbp_eval_df = read_eval_df(prefix_path, ir_metric) + plot_dist(rbp_eval_df, title=ir_metric, save_figure=save_figures) + poor_ndcg_10_topics = set(_poor_topics(ndcg_10_eval_df, ir_metric, n)) print(f'{n} is ~10% of the data, printing coefficients of the {n} worst topics:') print('Overlap coefficient of AP-nDCG@100:') print(overlap_coefficient(poor_ap_topics, poor_ndcg_100_topics)) @@ -299,14 +390,17 @@ def _poor_topics(_eval_df, _ir_metric, _n): print(overlap_coefficient(poor_ap_topics, poor_ndcg_10_topics)) print('Overlap coefficient of nDCG@100-nDCG@10:') print(overlap_coefficient(poor_ndcg_100_topics, poor_ndcg_10_topics)) + eval_df = pd.concat([ap_eval_df.sort_index(), ndcg_100_eval_df.sort_index(), ndcg_10_eval_df.sort_index()], axis=1) + eval_df.corr(method='kendall') +# TODO: check this def plot_predictors_errors(diff_df: pd.DataFrame, title='', subplots=True, save_figure=False): # plt.rcParams.update({ # "text.usetex": True, # "font.family": "sans-serif", # "font.sans-serif": ["Helvetica"]}) - plt.rcParams.update({'font.size': 20, 'font.family': 'serif'}) + # plt.rcParams.update({'font.size': 20, 'font.family': 'serif'}) plt.figure(figsize=(16, 9), dpi=100) ax = diff_df[diff_df.median().sort_values().index].boxplot() plt.xticks(rotation=45) @@ -331,7 +425,7 @@ def plot_predictors_errors(diff_df: pd.DataFrame, title='', subplots=True, save_ plt.show() else: # _df = diff_df.melt() - # g = sns.FacetGrid(_df, col="variable", col_wrap=5, height=1.5) + # g = sns.FacetGrid(_df, col="variable", col_wrap=PLOTS_COL_WRAP, height=1.5) # g = g.map(sns.distplot, "value") # plt.show() # exit() @@ -350,7 +444,7 @@ def plot_predictors_errors(diff_df: pd.DataFrame, title='', subplots=True, save_ def plot_qq_plot(diff_df, save_figure=False): for pred in diff_df.columns: - plt.rcParams.update({'font.size': 12, 'font.family': 'serif'}) + # plt.rcParams.update({'font.size': 12, 'font.family': 'serif'}) # sns.set(font='serif') # sns.set_style("white") # sns.set_palette("PuBuGn_d") @@ -366,7 +460,7 @@ def plot_qq_plot(diff_df, save_figure=False): def plot_predictors_dist(prefix_path): - predictions_df = read_prediction_files(prefix_path, 'pre').reset_index('topic', drop=True) + predictions_df = read_prediction_files(prefix_path, r_type='pre').reset_index('topic', drop=True) for pred in predictions_df.columns: plot_dist(predictions_df[pred], title=pred, save_figure=False) @@ -429,6 +523,679 @@ def test_variance_equality(*args, alpha=5e-2): return True +def construct_pairwise_df(sr: pd.Series, np_fun): + """Constructs an upper diagonal df from all pairwise comparisons of a sr""" + sr = sr.sort_index() + _mat = np.triu(np_fun(sr.to_numpy() - sr.to_numpy()[:, None]), k=1) + _mat[np.tril_indices(_mat.shape[0])] = None + return pd.DataFrame(_mat, index=sr.index.get_level_values('qid'), + columns=sr.index.get_level_values('qid')).rename_axis(index='qid_1', columns='qid_2') + + +def clear_triangular_df(df: pd.DataFrame): + """Clear all values under the diagonal (including diagonal) for a square df""" + _mat = df.to_numpy() + _mat[np.tril_indices(_mat.shape[0])] = None + return pd.DataFrame(_mat, index=df.index, columns=df.index) + + +def construct_sampled_inter_df(eval_df, predictions_df, n, sim_df): + _res = [] + for _ in range(n): + ev_sr = eval_df.groupby('topic').sample(1).iloc[:, 0] + _ev_sgn = construct_pairwise_df(ev_sr, np.sign).stack() + _ev_diff = construct_pairwise_df(ev_sr, np.abs).stack() + _sim_df = sim_df.stack().loc[_ev_diff.index] + for predictor, _sr in predictions_df.loc[ev_sr.index].iteritems(): + _pr_sgn = construct_pairwise_df(_sr, np.sign).stack() + _df = pd.DataFrame({'sim': _sim_df, 'diff': _ev_diff, 'status': _ev_sgn == _pr_sgn}, index=_ev_diff.index) + _res.append(_df.reset_index(drop=True).assign(predictor=predictor)) + return pd.concat(_res).sort_values(['predictor', 'diff', 'status']).reset_index(drop=True) + # df['status'] = df['status'].map({0: 'wrong', 1: 'correct'}) + # return _df + + +def construct_full_pairwise_inter_df(eval_df, predictions_df, sim_df): + _res = [] + intra_topic_index = eval_df.iloc[:, 0].groupby('topic').apply( + lambda x: construct_pairwise_df(x, np.abs)).stack().index.droplevel(0) + # ev_sr = eval_df.groupby('topic').sample(1).iloc[:, 0] + _ev_sgn = construct_pairwise_df(eval_df.iloc[:, 0], np.sign).stack() + _ev_diff = construct_pairwise_df(eval_df.iloc[:, 0], np.abs).stack() + _sim_df = sim_df.stack() + for predictor, _sr in predictions_df.iteritems(): + _pr_sgn = construct_pairwise_df(_sr, np.sign).stack() + _pr_diff = construct_pairwise_df(_sr, np.abs).stack() + _df = pd.DataFrame({'sim': _sim_df, 'ev_diff': _ev_diff, 'pr_diff': _pr_diff, 'status': _ev_sgn == _pr_sgn}, + index=_ev_diff.index).drop(intra_topic_index) + _res.append(_df.reset_index().assign(predictor=predictor)) + return pd.concat(_res).sort_values(['predictor', 'ev_diff', 'pr_diff', 'status']).reset_index(drop=True) + + +def construct_inter_sampled_to_intra_df(eval_df, predictions_df, sim_df): + _res = [] + _round = 4 + intra_topic_df = eval_df.iloc[:, 0].groupby('topic').apply( + lambda x: construct_pairwise_df(x, np.abs)).stack().round(_round) + # w = intra_topic_df.reset_index(drop=True).round(2).value_counts().sort_index() + # w = pd.cut(intra_topic_df, bins=100, precision=3, include_lowest=True) + w, bins = pd.cut(intra_topic_df, bins=100, precision=3, include_lowest=True, retbins=True) + bins[0] = 0 + bins[-1] = 1 + w = w.replace({w.min(): pd.Interval(0, w.min().right, closed='both')}).replace( + {w.max(): pd.Interval(w.max().left, 1, closed='right')}) + w = w.groupby(w).count() + intra_topic_index = intra_topic_df.index.droplevel(0) + _ev_diff = construct_pairwise_df(eval_df.iloc[:, 0], np.abs).stack().round(_round) + # x = pd.cut(_ev_diff.drop(intra_topic_index), bins=100, precision=3, include_lowest=True) + x = pd.cut(_ev_diff.drop(intra_topic_index), bins=bins, precision=3, include_lowest=True) + x = x.replace({x.min(): pd.Interval(0, x.min().right, closed='both')}) + x.index.rename(['qid_1', 'qid_2'], inplace=True) + sampled_qids = pd.MultiIndex.from_frame( + x.reset_index().groupby(0).apply(lambda x: x.sample(w.loc[x.name])).reset_index(drop=True)[['qid_1', 'qid_2']]) + _sim_df = sim_df.stack().loc[sampled_qids] + _ev_sgn = construct_pairwise_df(eval_df.iloc[:, 0], np.sign).stack().loc[sampled_qids] + _ev_diff = _ev_diff.loc[sampled_qids] + for predictor, _sr in predictions_df.iteritems(): + _pr_sgn = construct_pairwise_df(_sr, np.sign).stack().loc[sampled_qids] + _pr_diff = construct_pairwise_df(_sr, np.abs).stack().loc[sampled_qids] + _df = pd.DataFrame({'sim': _sim_df, 'ev_diff': _ev_diff, 'pr_diff': _pr_diff, 'status': _ev_sgn == _pr_sgn}, + index=_ev_diff.index) + _res.append(_df.reset_index().assign(predictor=predictor)) + return pd.concat(_res).sort_values(['predictor', 'ev_diff', 'pr_diff', 'status']).reset_index(drop=True) + + +def load_generate_pickle_df(file_name, func, *args): + if isinstance(file_name, (list, tuple, set)): + single_file = False + else: + single_file = True + try: + if single_file: + result = pd.read_pickle(file_name) + else: + result = [pd.read_pickle(_file) for _file in file_name] + except FileNotFoundError: + logger.warning(f'Failed to load {file_name}') + logger.warning(f'Generating a new df and saving') + result = func(*args) + if single_file: + result.to_pickle(file_name) + else: + assert len(result) == len( + file_name), f'The number of objects returned by the function and number of files differ' + [_res.to_pickle(_file) for (_res, _file) in zip(result, file_name)] + return result + + +@timer +def inter_topic_pairwise_analysis(eval_df, predictions_df, similarities_df, prefix, n=None, load_cache=True, + sample=False): + ir_metric = eval_df.columns[0] + n = n or 'all' + if load_cache: + if sample: + df = load_generate_pickle_df(f'{prefix}_inter_topic_pairwise_{ir_metric}_df_{n}.pkl', + construct_sampled_inter_df, eval_df, predictions_df, n, similarities_df) + else: + df = load_generate_pickle_df(f'{prefix}_inter_topic_pairwise_{ir_metric}_df_{n}.pkl', + construct_full_pairwise_inter_df, eval_df, predictions_df, similarities_df) + else: + if sample: + df = construct_sampled_inter_df(eval_df, predictions_df, n, similarities_df) + else: + df = construct_full_pairwise_inter_df(eval_df, predictions_df, similarities_df) + # intra_df = eval_df.iloc[:, 0].groupby('topic').apply( + # lambda x: construct_pairwise_df(x, np.abs)).stack().reset_index(drop=True).rename('diff') + # dd = pd.concat( + # [pd.DataFrame(df.loc[df['predictor'] == 'clarity', ['diff']]).assign(kind='inter').reset_index(drop=True), + # pd.DataFrame(intra_df).assign(kind='intra')]) + # dd.to_pickle(f'all_pairs_{ir_metric}_diff.pkl') + log_pairwise_stats(df, 'Inter-topic', ir_metric) + p = 2 + + _gpd_df = df.round(p).groupby(['predictor', 'ev_diff'])['status'] + _freq_df = (_gpd_df.sum() / _gpd_df.count()).fillna(0).reset_index().rename({'status': 'freq'}, axis=1).assign( + sample_size=_gpd_df.count().to_numpy()) + # df['status'] = df['status'].map({False: 'wrong', True: 'correct'}) + plot_pairwise_freq_diff(df.round(4), f'Inter-topic {n} samples {PlotNames.get(ir_metric, ir_metric)}', ir_metric) + plot_pairwise_freq_diff_rel(_freq_df, f'Inter-topic {n} samples {PlotNames.get(ir_metric, ir_metric)}', ir_metric) + + _p = 4 + _gpd_df = df.round(p).groupby(['predictor', 'pr_diff'])['status'] + _freq_df = (_gpd_df.sum() / _gpd_df.count()).fillna(0).reset_index().rename({'status': 'freq'}, axis=1).assign( + sample_size=_gpd_df.count().to_numpy()) + plot_pairwise_freq_diff(df.round(4), f'Inter-topic {n} samples {PlotNames.get(ir_metric, ir_metric)}', 'Predictor', + diff_col='pr_diff') + plot_pairwise_freq_diff_rel(_freq_df, f'Inter-topic {n} samples {PlotNames.get(ir_metric, ir_metric)}', 'Predictor', + diff_col='pr_diff') + + # plot_pairwise_freq_sim(df, f'Inter-topic {n} samples similarity {ir_metric}') + + return df + + +def construct_intra_topic_df(eval_df, predictions_df, similarities_df): + _res = [] + # freq_res = [] + _ev_sgn = eval_df.iloc[:, 0].groupby('topic').apply(lambda x: construct_pairwise_df(x, np.sign)).stack() + _ev_diff = eval_df.iloc[:, 0].groupby('topic').apply(lambda x: construct_pairwise_df(x, np.abs)).stack() + sim_df = similarities_df.stack().loc[_ev_diff.reset_index('topic', drop=True).index.to_numpy()] + for predictor, _sr in predictions_df.iteritems(): + _pr_sgn = _sr.groupby('topic').apply(lambda x: construct_pairwise_df(x, np.sign)).stack() + _pr_diff = _sr.groupby('topic').apply(lambda x: construct_pairwise_df(x, np.abs)).stack() + _df = pd.DataFrame({'ev_diff': _ev_diff, 'pr_diff': _pr_diff, 'status': _ev_sgn == _pr_sgn}, + index=_ev_diff.index).reset_index('topic', drop=True).assign(sim=sim_df) + logger.debug(f'Number of tied ev pairs: {len(_ev_sgn.loc[_ev_sgn == 0].index.droplevel(0))}') + logger.debug(f'Number of tied {predictor} pairs: {len(_pr_sgn.loc[_pr_sgn == 0].index.droplevel(0))}') + _res.append(_df.reset_index().rename(columns={'level_1': 'qid_2'}).assign(predictor=predictor)) + return pd.concat(_res).sort_values(['predictor', 'ev_diff', 'status']).reset_index(drop=True) + + +@timer +def intra_topic_pairwise_analysis(eval_df, predictions_df, similarities_df, prefix, load_cache=False): + ir_metric = eval_df.columns[0] + if load_cache: + df = load_generate_pickle_df(f'{prefix}_intra_topic_pairwise_{ir_metric}_df.pkl', construct_intra_topic_df, + eval_df, predictions_df, similarities_df) + else: + df = construct_intra_topic_df(eval_df, predictions_df, similarities_df) + log_pairwise_stats(df, 'Intra-topic', ir_metric) + p = 2 + _gpd_df = df.round(p).groupby(['predictor', 'ev_diff'])['status'] + _freq_df = (_gpd_df.sum() / _gpd_df.count()).fillna(0).reset_index().rename({'status': 'freq'}, axis=1).assign( + sample_size=_gpd_df.count().to_numpy()) + plot_pairwise_freq_diff(df.round(4), f'Intra-topic all pairs {PlotNames.get(ir_metric, ir_metric)}', ir_metric, + diff_col='ev_diff') + plot_pairwise_freq_diff_rel(_freq_df, f'Intra-topic all pairs {PlotNames.get(ir_metric, ir_metric)}', ir_metric, + diff_col='ev_diff') + _p = 4 + _gpd_df = df.round(_p).groupby(['predictor', 'pr_diff'])['status'] + _freq_df = (_gpd_df.sum() / _gpd_df.count()).fillna(0).reset_index().rename({'status': 'freq'}, axis=1).assign( + sample_size=_gpd_df.count().to_numpy()) + plot_pairwise_freq_diff(df.round(4), f'Intra-topic all pairs {PlotNames.get(ir_metric, ir_metric)}', 'Predictor', + diff_col='pr_diff') + plot_pairwise_freq_diff_rel(_freq_df, f'Intra-topic all pairs {PlotNames.get(ir_metric, ir_metric)}', 'Predictor', + diff_col='pr_diff') + + plot_pairwise_freq_sim(df, f'Intra-topic all pairs similarity {ir_metric}') + return df + + +def plot_pairwise_freq_sim(df, title): + if df.loc[df.predictor == df.predictor[0]].sim.nunique() / len(df.loc[df.predictor == df.predictor[0]]) < 0.001: + lowess = False + else: + lowess = True + g = sns.lmplot(data=df.loc[df.predictor == df.predictor[0]], y='ev_diff', x='sim', hue='status', markers=["+", "x"], + scatter_kws={"alpha": 0.2}, line_kws={'lw': 2}, lowess=lowess, x_jitter=0.01) + print('Using lowess correlation') if lowess else print('Using linear correlation') + g.set_axis_labels("Similarity", "Metric Diff") + g.tight_layout() + plt.show() + _gpd_df = df.groupby(['predictor', 'sim'])['status'] + _freq_df = (_gpd_df.sum() / _gpd_df.count()).fillna(0).reset_index().rename({'status': 'freq'}, axis=1).assign( + sample_size=_gpd_df.count().to_numpy()) + plot_pairwise_freq_diff(df, title, 'Cosine sim', diff_col='sim') + plot_pairwise_freq_diff_rel(_freq_df, title, 'Cosine sim', diff_col='sim') + + +def plot_pairwise_freq_diff_rel(_freq_df, title, ir_metric, diff_col='ev_diff'): + n_predictors = _freq_df['predictor'].nunique() + _title = f'freq_given_diff_{n_predictors}_predictors_{title.replace(" ", "_").lower()}' + + def plot_regplots(**kwargs): + sns.regplot(x=diff_col, y='freq', lowess=True, + scatter_kws={'s': 5 * np.log2(kwargs['data']['sample_size']) + 2}, **kwargs) + + if diff_col == 'pr_diff': + g2 = sns.FacetGrid(_freq_df, col="predictor", col_wrap=PLOTS_COL_WRAP, sharex=False, sharey=False) + g2.map_dataframe(plot_regplots) + else: + g2 = sns.lmplot(data=_freq_df, x=diff_col, y='freq', col='predictor', lowess=True, col_wrap=PLOTS_COL_WRAP, + scatter_kws={ + 's': 5 * np.log2(_freq_df.head(int(len(_freq_df) / n_predictors))['sample_size']) + 2}) + g2.set_axis_labels(f"{PlotNames.get(ir_metric, ir_metric)} difference", "Correct Ratio") + g2.set_titles(title + " {col_name} predictor") + g2.tight_layout() + g2.savefig(f'{_title}_{diff_col}_reg.pdf') + plt.show() + _freq_df = _freq_df.rename({diff_col: f"{PlotNames.get(ir_metric, ir_metric)} diff"}) + g3 = sns.pairplot(data=_freq_df, hue='predictor') + plt.savefig(f'{_title}_{diff_col}_pairplot.pdf') + plt.show() + + +def plot_pairwise_freq_diff(_df, title, ir_metric, diff_col='ev_diff'): + n_predictors = _df['predictor'].nunique() + _title = f'estimating_freq_given_diff_{n_predictors}_predictors_{title.replace(" ", "_").lower()}' + if diff_col == 'pr_diff': + g1 = sns.displot(data=_df, x=diff_col, col='predictor', hue='status', stat="density", element='step', + col_wrap=PLOTS_COL_WRAP, common_bins=False, facet_kws={'sharex': None, 'sharey': False}) + else: + g1 = sns.displot(data=_df, x=diff_col, col='predictor', col_wrap=PLOTS_COL_WRAP, hue='status', stat="density", + element='step') + g1.set_axis_labels(f"{PlotNames.get(ir_metric, ir_metric)} difference", "Density") + g1.set_titles(title + " {col_name} predictor") + plt.tight_layout() + plt.savefig(f'{_title}_{ir_metric.replace(" ", "_").lower()}_hist.pdf') + plt.show() + if diff_col == 'pr_diff': + g11 = sns.displot(data=_df, x=diff_col, col='predictor', hue='status', kind='kde', clip=(0.0, 1.0), fill=True, + alpha=0.5, col_wrap=PLOTS_COL_WRAP, facet_kws={'sharex': False, 'sharey': False}) + else: + g11 = sns.displot(data=_df, x=diff_col, col='predictor', hue='status', kind='kde', clip=(0.0, 1.0), fill=True, + col_wrap=PLOTS_COL_WRAP, alpha=0.5) + g11.set_axis_labels(f"{PlotNames.get(ir_metric, ir_metric)} difference", "Density") + g11.set_titles(title + " {col_name} predictor") + plt.tight_layout() + plt.savefig(f'{_title}_{ir_metric.replace(" ", "_").lower()}_kde.pdf') + plt.show() + + +def inter_topic_eval(ir_metric, prefix_path, similarities_df): + predictors_type = 'all' + eval_df = add_topic_to_qdf(read_eval_df(prefix_path, ir_metric)).set_index(['topic', 'qid']) + predictions_df = read_prediction_files(prefix_path, r_type=predictors_type) + # sim_df = clear_triangular_df(similarities_df) + # predictions_df = predictions_df[['qf', 'nqc', 'uef-wig']] + # predictions_df = predictions_df[['qf', 'uef-wig']] + prefix = prefix_path.rsplit('/', maxsplit=1)[1] + df = inter_topic_pairwise_analysis(eval_df, predictions_df, similarities_df, prefix, load_cache=True) + print('Inter Topic table') + # print_diff_probabilities_table(df, title='Inter-Topic', q=4, ir_metric=ir_metric) + + +def log_pairwise_stats(df, prefix, ir_metric): + _gdf = df.groupby('predictor')['status'] + logger.info(f"{prefix} pairs df has {_gdf.count()[0]} pairs") + tbl_1 = tabulate( + pd.DataFrame((_gdf.sum() / _gdf.count()).sort_values()).rename({'status': 'Correct Ratio'}, axis=1), + headers='keys', tablefmt='psql') + logger.info(f"Ratio of correct pairs by predictor: \n{tbl_1}") + _ties_gdf = df.loc[df['ev_diff'] == 0.0].groupby('predictor')['status'] + logger.info(f"Number of tied (0 {PlotNames.get(ir_metric, ir_metric)} ev_diff) pairs: {_ties_gdf.count()[0]}") + tbl_2 = tabulate( + pd.DataFrame((_ties_gdf.sum() / _ties_gdf.count()).sort_values()).rename({'status': 'Correct Ratio'}, axis=1), + headers='keys', tablefmt='psql') + logger.info(f"Ratio of correct tied pairs by predictor: \n{tbl_2}") + + +def intra_topic_eval(ir_metric, prefix_path, similarities_df): + predictors_type = 'all' + eval_df = add_topic_to_qdf(read_eval_df(prefix_path, ir_metric)).set_index(['topic', 'qid']) + predictions_df = read_prediction_files(prefix_path, r_type=predictors_type) + # predictions_df = predictions_df[['qf', 'nqc', 'uef-wig']] + prefix = prefix_path.rsplit('/', maxsplit=1)[1] + df = intra_topic_pairwise_analysis(eval_df, predictions_df, similarities_df, prefix, load_cache=True) + # print_diff_probabilities_table(df, title='Intra-Topic', q=4, ir_metric=ir_metric) + return df + + +def plot_cond_prob(df): + # FIXME: need to rethink this + _df = df.sort_values(['predictor', 'diff']).reset_index(drop=True) + _df['cnt'] = _df[_df["status"] == 'correct'].groupby('predictor').cumcount() + 1 + _df['cnt'] = _df.groupby('predictor')['cnt'].fillna(method='ffill') + _df['total'] = _df.groupby('predictor').cumcount() + 1 + _df['prob'] = _df['cnt'] / _df['total'] + g = sns.lmplot(data=_df, x='diff', y='prob', hue='predictor') + g.set_axis_labels("AP difference", "Probability") + plt.tight_layout() + plt.show() + + +def print_diff_probabilities_table(predictions_results, title, q, ir_metric): + res = [] + if isinstance(predictions_results, pd.DataFrame): + df = predictions_results + else: + df = pd.DataFrame(predictions_results, columns=['status', 'predictor', 'topic', 'diff']) + # plot_cond_prob(df) + df['status'] = df['status'].map({False: 'wrong', True: 'correct'}) + num_predictors = df['predictor'].nunique() + # df = df.drop('diff', axis=1).rename({'sim': 'diff'}, axis=1) + # FIXME: These lines are used for similarity, can be uncommented + # q = 9 + df['int'] = pd.qcut(df['ev_diff'], q=q, precision=2, duplicates='drop') + for predictor, _df in df.groupby('predictor'): + _df = pd.crosstab(index=_df['status'], columns=_df['int'].astype(str), values=_df['ev_diff'], aggfunc='count', + normalize='columns', margins=True).reset_index() + _df.insert(loc=0, column='predictor', value=predictor) + res.append(_df) + res_df = pd.concat(res) + x = res_df.set_index(['predictor', 'status']).columns[0] + res_df.rename(columns={x: pd.Interval(np.round(x.left, 2), x.right)}, inplace=True) + print(res_df.set_index(['predictor', 'status']).rename({'All': 'Overall'}, axis=1). + to_latex(float_format='%.2f', escape=False, multirow=True, multicolumn=True)) + _df = res_df.drop('All', axis=1).set_index(['predictor', 'status']).stack().reset_index().rename({0: 'prob'}, + axis=1) + g = sns.catplot(data=_df, x='int', y='prob', hue='predictor', col='status') + g.set_axis_labels(f"{PlotNames.get(ir_metric, ir_metric)} difference", "Probability") + g.set_xticklabels(rotation=30) + g.set_titles("{col_name} prediction") + plt.tight_layout() + plt.savefig( + f'{title}_cond-prob_{num_predictors}-predictors_{q}-quantiles_{ir_metric.replace(" ", "_").lower()}.pdf') + plt.show() + + +def plot_corr_vs_sim(data_df, xlabel, ylabel, title): + sns.scatterplot(x='correlation', y='similarity', data=data_df, hue='predictor') + plt.xlabel(xlabel) + plt.ylabel(ylabel) + plt.title(title) + plt.show() + sns.boxplot(x='predictor', y='correlation', data=data_df, + order=data_df.groupby('predictor')['correlation'].median().sort_values().index) + plt.title(title + ' Correlation') + plt.show() + sns.boxplot(x='predictor', y='similarity', data=data_df, + order=data_df.groupby('predictor')['similarity'].median().sort_values().index) + plt.title(title + ' Similarity') + plt.show() + + +def scatter_text(x, y, text_column, data, title, xlabel, ylabel): + """Create a scatter plot with labels""" + # Create the scatter plot + p1 = sns.scatterplot(x, y, data=data, size=8, legend=False) + # Add text besides each point + for line in range(0, data.shape[0]): + p1.text(data[x][line] + 0.01, data[y][line], + data[text_column][line], horizontalalignment='left', + size='medium', color='black', weight='semibold') + # Set title and axis labels + plt.title(title) + plt.xlabel(xlabel) + plt.ylabel(ylabel) + return p1 + + +def construct_distances_matrix(sr): + """The distance for pair (i,j) is: 1 + |AP(i) - AP(j)|""" + df = pd.DataFrame(np.triu(abs(sr.to_numpy() - sr.to_numpy()[:, None]) + 1, k=1), + index=sr.index.get_level_values('qid'), columns=sr.index.get_level_values('qid')) + idx = np.triu(np.ones(df.shape)).astype(np.bool) + return df.where(idx) + + +def weighted_kendall_distance(arr_x: pd.Series, arr_y: pd.Series, distances=None): + """This version is identical to Kendall's tau for arrays without ties""" + distances_mat = __init_kendall(arr_x, arr_y, distances) + distances_sum = np.nansum(distances_mat) # arr_x is a constant vector (single value) + # TODO: need to test special case where the X_arr is constant e.g [0.3,0.3,0.3] + # FIXME: The normalization is bad, numbers can be higher than 1 + # Tie in X is 0.5 wrong if not tie in Y, as it is both xi <= xj and xi>=xj + # the number of pairs has quadratic growth + dist = 0 + x_norm = 0 + y_norm = 0 + p = 0.5 # penalty for ties in one of the arrays + for i, x_i in enumerate(arr_x): + j = i + 1 + for j, x_j in enumerate(arr_x[j:], j): + if x_i == x_j: + dist += p * distances_mat[i, j] * (arr_y[i] != arr_y[j]) + x_norm -= (1 - p) + y_norm -= (1 - p) * (arr_y[i] == arr_y[j]) + elif arr_y[i] == arr_y[j]: + dist += p * distances_mat[i, j] + x_norm -= (1 - p) * (x_i == x_j) + y_norm -= (1 - p) + else: + dist += distances_mat[i, j] * ( + ((x_i > x_j) and (arr_y[i] < arr_y[j])) or ((x_i < x_j) and (arr_y[i] > arr_y[j]))) + x_norm += 1 + y_norm += 1 + # return 2 * dist / (len(arr_x) ** 2 - len(arr_x)) # scaling to [0,1] + return dist / np.sqrt(x_norm * y_norm) # scaling to [0,1] + + +def weighted_kendall_correlation(arr_x: pd.Series, arr_y: pd.Series, distances=None): + """Calculates Kendall's tau correlation, for arrays with ties + weights + distance options""" + distances_mat = __init_kendall(arr_x, arr_y, distances) + corr = 0 + x_norm = 0 + y_norm = 0 + for i, x_i in enumerate(arr_x): + j = i + 1 + for j, x_j in enumerate(arr_x[j:], j): + corr += distances_mat[i, j] * np.sign(x_i - x_j) * np.sign(arr_y[i] - arr_y[j]) + x_norm += abs(distances_mat[i, j] * np.sign(x_i - x_j)) + y_norm += abs(distances_mat[j, i] * np.sign(arr_y[i] - arr_y[j])) + return corr / np.sqrt(x_norm * y_norm) # scaling to [0,1] + # return corr / np.nansum(distances_mat) # scaling to [-1,1] + + +def __init_kendall(arr_x: pd.Series, arr_y: pd.Series, distances=None): + assert (arr_x.index == arr_y.index).all(), 'Indices of the series are not identical, or not sorted' + if distances is None: + # distances_mat = np.tri(len(arr_x), k=-1).T + distances_mat = np.triu(arr_x.to_numpy()[:] - arr_x.to_numpy()[:, None]).astype(bool).astype(int) + np.tril( + arr_y.to_numpy()[:] - arr_y.to_numpy()[:, None]).astype(bool).astype(int) + else: + distances_mat = distances.loc[arr_x.index, arr_x.index].to_numpy() + return distances_mat + + +def weighted_footrule_distance(): + pass + + +def calc_rank_differences(prefix_path, ir_metric, title_only=False, scale=True, diff_func='abs', predictors_type='all'): + # difference_functions = {'abs': np.abs, 'sqr': np.power(2), 'raw': lambda x: x} + predictions_df = read_prediction_files(prefix_path, r_type=predictors_type).reset_index('topic', drop=True) + eval_df = read_eval_df(prefix_path, ir_metric) + if title_only: + predictions_df = filter_robust_title_queries(predictions_df) + eval_df = filter_robust_title_queries(eval_df) + diff_df = predictions_df.rank(pct=scale, ascending=False, method='average').subtract( + eval_df[ir_metric].rank(pct=scale, ascending=False, method='average'), axis=0) + return diff_df + + +def plot_metrics_differences(prefix_path): + meanprops = {"marker": "s", "markerfacecolor": "white", "markeredgecolor": "blue"} + + def boxplot(_df, orderby='median'): + _df = _df.stack().reset_index().rename({'level_1': 'predictor', 0: 'rank_diff'}, axis=1) + if orderby == 'median': + _order = _df.groupby('predictor').median().sort_values('rank_diff').index + else: + _order = _df.groupby('predictor').mean().sort_values('rank_diff').index + sns.boxplot(x='rank_diff', y='predictor', data=_df, order=_order, showmeans=True, meanprops=meanprops) + plt.tight_layout() + plt.show() + + # predictors_type = 'pre' + # predictors_type = 'post' + predictors_type = 'all' + + ap_diff_df = calc_rank_differences(prefix_path, 'ap@1000', title_only=True, predictors_type=predictors_type).abs() + ndcgs_diff_df = calc_rank_differences(prefix_path, 'ndcg@10', title_only=True, + predictors_type=predictors_type).abs() + ndcgl_diff_df = calc_rank_differences(prefix_path, 'ndcg@100', title_only=True, + predictors_type=predictors_type).abs() + rbp_diff_df = calc_rank_differences(prefix_path, 'rbp-0.95', title_only=True, + predictors_type=predictors_type).abs() + + # boxplot(ap_diff_df) + # boxplot(ndcgl_diff_df) + # boxplot(ndcgs_diff_df) + boxplot(rbp_diff_df) + # full_df = pd.concat( + # {'ap': ap_diff_df, 'ndcg@10': ndcgs_diff_df, 'ndcg@100': ndcgl_diff_df, 'rbp-0.95': rbp_diff_df}, + # axis=1).mean().reset_index().rename( + # {'level_0': 'eval_measure', 'level_1': 'predictor', 0: 'mean_ranks_diff'}, axis=1) + # wide_df = full_df.pivot(index='eval_measure', columns='predictor') + # all_metrics_df = pd.concat( + # {'ap': read_eval_df(prefix_path, 'ap@1000'), 'ndcg@100': read_eval_df(prefix_path, 'ndcg@100'), + # 'ndcg@10': read_eval_df(prefix_path, 'ndcg@10'), 'rbp-0.95': read_eval_df(prefix_path, 'rbp-0.95')}, axis=1) + topics_df = pd.concat( + {'ap': ap_diff_df, 'ndcg@10': ndcgs_diff_df, 'ndcg@100': ndcgl_diff_df, 'rbp-0.95': rbp_diff_df}, + axis=1).stack().reset_index().rename({'level_1': 'predictor'}, axis=1) + print(topics_df.loc[topics_df['predictor'] == 'nqc'].corr()) + sns.pairplot(topics_df, hue='predictor', kind='kde') + plt.show() + + +def filter_tail_queries_by_percentile(prefix_path, ir_metric, inverted_df=False, percentile=0.99): + predictions_df = read_prediction_files(prefix_path, r_type='all') + eval_df = add_topic_to_qdf(read_eval_df(prefix_path, ir_metric)).set_index(['topic', 'qid']) + _df = predictions_df.rank(pct=True, ascending=False, method='average').subtract( + eval_df[ir_metric].rank(pct=True, ascending=False, method='average'), axis=0).abs() + ap_tail_df = _df.abs().apply(lambda x: x.nlargest(int(len(predictions_df) // (percentile * 100)), keep='all')).rank( + method='min') # Keep=all will keep all tied items on the last rank, generally it's n largest including ties. + if not inverted_df: + return ap_tail_df + else: + return ap_tail_df.stack().reset_index().rename({0: 'rank', 'level_2': 'predictor'}, axis=1).pivot_table( + index='rank', columns='predictor', values='qid', aggfunc=set) + + +def plot_sare_predictors_corrs(prefix_path, ir_metric, k, order): + predictions_df = read_prediction_files(prefix_path, r_type='all') + eval_df = add_topic_to_qdf(read_eval_df(prefix_path, ir_metric)).set_index(['topic', 'qid']) + random_df = predictions_df.groupby('topic').sample(1) + _df = random_df.rank(pct=True, ascending=False, method='average').subtract( + eval_df.loc[random_df.index, ir_metric].rank(pct=True, ascending=False, method='average'), axis=0).abs().corr( + method='kendall') + for _ in range(k): + random_df = predictions_df.groupby('topic').sample(1) + _df += random_df.rank(pct=True, ascending=False, method='average').subtract( + eval_df.loc[random_df.index, ir_metric].rank(pct=True, ascending=False, method='average'), + axis=0).corr(method='kendall') + plot_heatmap_qpp_similarities(_df.corr(method='kendall'), order=order, save_figure=True, + title=f"{prefix_path.rsplit('/', 1)[-1].split('_', 2)[-1]}_QPP_{PlotNames.get(ir_metric)}_kendall-corr_resampled-{k}") + + +def plot_qpp_correlations(prefix_path): + # k = 5000 + # predictions_df = read_prediction_files(prefix_path, 'all').drop(['qf', 'uef-qf'], axis=1) + PostRetPredictors.remove('qf') + PostRetPredictors.remove('uef-qf') + order = PreRetPredictors + PostRetPredictors + # order = pd.Index(['scq', 'qf', 'var', 'uef-qf', 'max-idf', 'avg-scq', 'max-scq', + # 'avg-idf', 'max-var', 'smv', 'clarity', 'avg-var', 'nqc', 'uef-clarity', + # 'wig', 'uef-smv', 'uef-nqc', 'uef-wig']) + # random_df = predictions_df.groupby('topic').sample(1).corr(method='kendall') + # for _ in range(k): + # random_df += predictions_df.groupby('topic').sample(1).corr(method='kendall') + # order = plot_heatmap_qpp_similarities(random_df / (k + 1), save_figure=True, + # title=f"{prefix_path.rsplit('/', 1)[-1].split('_', 2)[-1]}_QPP_kendall-corr_resampled-{k}") + # print(order) + # plot_heatmap_qpp_similarities(predictions_df.corr(method='kendall'), save_figure=True, order=order, + # title=f"{prefix_path.rsplit('/', 1)[-1].split('_', 2)[-1]}_QPP_kendall-corr_all-vars") + # plot_heatmap_qpp_similarities( + # filter_robust_title_queries(predictions_df.reset_index('topic', drop=True)).corr(method='kendall'), + # save_figure=True, + # order=order, + # title=f"{prefix_path.rsplit('/', 1)[-1].split('_', 2)[-1]}_QPP_kendall-corr_title_queries") + ap_tail_df = filter_tail_queries_by_percentile(prefix_path, 'ap@1000', percentile=0.99, inverted_df=False) + # ap_sim_df = pd.DataFrame(1 - pairwise_distances(ap_tail_df.T.fillna(0).to_numpy(), metric='cosine', n_jobs=10), + # index=ap_tail_df.columns, + # columns=ap_tail_df.columns) + # plot_heatmap_qpp_similarities(ap_sim_df, + # title=f"{prefix_path.rsplit('/', 1)[-1].split('_', 2)[-1]}_QPP_cosine_AP_tail-vars", + # order=order, save_figure=True) + # rbp_tail_df = filter_tail_queries_by_percentile(prefix_path, 'rbp-0.95', percentile=0.99, inverted_df=False) + # rbp_sim_df = pd.DataFrame(1 - pairwise_distances(rbp_tail_df.T.fillna(0).to_numpy(), metric='cosine', n_jobs=10), + # index=rbp_tail_df.columns, + # columns=rbp_tail_df.columns) + # plot_heatmap_qpp_similarities(rbp_sim_df, + # title=f"{prefix_path.rsplit('/', 1)[-1].split('_', 2)[-1]}_QPP_cosine_RBP(0.95)_tail-vars", + # order=order, save_figure=True) + # ndcg_tail_df = filter_tail_queries_by_percentile(prefix_path, 'ndcg@10', percentile=0.99, inverted_df=False) + # ndcg_sim_df = pd.DataFrame(1 - pairwise_distances(ndcg_tail_df.T.fillna(0).to_numpy(), metric='cosine', n_jobs=10), + # index=ndcg_tail_df.columns, + # columns=ndcg_tail_df.columns) + # plot_heatmap_qpp_similarities(ndcg_sim_df, + # title=f"{prefix_path.rsplit('/', 1)[-1].split('_', 2)[-1]}_QPP_cosine_nDCG@10_tail-vars", + # order=order, save_figure=True) + # plot_sare_predictors_corrs(prefix_path, 'rbp-0.95', k, order) + # plot_sare_predictors_corrs(prefix_path, 'ndcg@10', k, order) + tail_topics_analysis(ap_tail_df) + # tail_topics_analysis(ndcg_tail_df, metric='nDCG@10') + exit() + + +def tail_topics_analysis(tail_df, order=None, metric='AP'): + if order is None: + order = PreRetPredictors + PostRetPredictors + tail_topics_df = tail_df.groupby('topic').count() + topics_order = tail_topics_df.sum(1).sort_values(kind='mergesort').index + last_quart = len(topics_order) // 3 + g = sns.heatmap(tail_topics_df.loc[topics_order[-last_quart:], order].T.rename(PlotNames), yticklabels=True, + xticklabels=True, square=True, cbar_kws={"shrink": 0.75}) + g.set_xlabel(None) + g.set_ylabel(None) + g.set_yticklabels(g.get_yticklabels(), rotation=0) + g.set_xticklabels(g.get_xticklabels(), rotation=60) + g.set_xticks(g.get_xticks()) + g.set_yticks(g.get_yticks()) + plt.tight_layout() + plt.savefig('tail_topics_' + metric + '_heatmap.pdf', dpi=300, bbox_inches='tight') + plt.show() + tail_topics_df.loc[:, 'Total'] = tail_topics_df.sum(1) + tail_topics_df.loc['Total', :] = tail_topics_df.sum(0) + print(tail_topics_df.sort_values('Total', kind='mergesort').to_latex()) + + +def plot_heatmap_qpp_similarities(df, title=None, save_figure=False, order=None): + plt.close() + mask = np.triu(np.ones_like(df, dtype=bool)) + if order is None: + order = df.sum().sort_values().index + g = sns.heatmap(df.loc[order, order].rename(PlotNames, axis=0).rename(PlotNames, axis=1), mask=mask, annot=True, + fmt=".2f", cbar=False, center=False, cmap=None, square=True, annot_kws={'fontsize': 6}, + yticklabels=True, xticklabels=True, linewidths=.2) + g.set_xlabel(None) + g.set_ylabel(None) + g.set_yticklabels([None] + g.get_yticklabels()[1:], rotation=0, fontdict={'fontsize': 6}) + g.set_xticklabels(g.get_xticklabels()[:-1] + [None], rotation=45, fontdict={'fontsize': 6}) + g.set_xticks(g.get_xticks()[:-1]) + g.set_yticks(g.get_yticks()[1:]) + if title is not None: + _title = [] + for s in title.split('_'): + if s == 'indri' or s == 'porter': + continue + else: + if s.islower(): + s = s.title() + _title.append(s) + _title = ' '.join(_title) + # plt.title(_title) + plt.title(None) + if save_figure: + plt.savefig(title + '_heatmap.pdf', dpi=300, bbox_inches='tight') + plt.tight_layout() + plt.show() + return order + + +def plot_ranks_differences(prefix_path, ir_metric): + def boxplot(_df, orderby='median'): + meanprops = {"marker": "s", "markerfacecolor": "white", "markeredgecolor": "blue"} + if orderby == 'median': + _order = _df.groupby('predictor').median().sort_values('rank_diff').index + else: + _order = _df.groupby('predictor').mean().sort_values('rank_diff').index + sns.boxplot(x='rank_diff', y='predictor', data=_df, order=_order, showmeans=True, meanprops=meanprops) + plt.tight_layout() + plt.show() + + diff_df = calc_rank_differences(prefix_path, ir_metric, title_only=True) + abs_df = diff_df.abs().stack().reset_index().rename({'level_1': 'predictor', 0: 'rank_diff'}, axis=1) + sqr_df = diff_df.pow(2).stack().reset_index().rename({'level_1': 'predictor', 0: 'rank_diff'}, axis=1) + + boxplot(abs_df, 'median') + boxplot(sqr_df, 'median') + boxplot(abs_df, 'mean') + boxplot(sqr_df, 'mean') + + def test_eval_measures(ir_metric, title_only=False, pct=True): predictors_type = 'all' correlation_method = 'kendall' @@ -443,7 +1210,7 @@ def test_eval_measures(ir_metric, title_only=False, pct=True): pipe, _metric = eval_file.split('/')[-1].split('.') prefix = pipe.rsplit('_', 1)[0] prefix_path = os.path.join(results_dir, prefix) - predictions_df = read_prediction_files(prefix_path, predictors_type).reset_index('topic', drop=True) + predictions_df = read_prediction_files(prefix_path, r_type=predictors_type).reset_index('topic', drop=True) eval_df = read_eval_df(prefix_path, ir_metric) if title_only: predictions_df = filter_robust_title_queries(predictions_df) @@ -451,8 +1218,8 @@ def test_eval_measures(ir_metric, title_only=False, pct=True): corr_res = predictions_df.corrwith(eval_df[ir_metric], 0, method=correlation_method) mean_diff_df = predictions_df.rank(pct=pct, ascending=False, method='average').subtract( eval_df[ir_metric].rank(pct=pct, ascending=False, method='average'), axis=0) - for topic, _df in add_topic_to_qdf(mean_diff_df.abs()).set_index('qid').groupby('topic'): - _df.drop('topic',axis=1).boxplot() + # for topic, _df in add_topic_to_qdf(mean_diff_df.abs()).set_index('qid').groupby('topic'): + # _df.drop('topic', axis=1).boxplot() dense_diff_df = predictions_df.rank(pct=pct, ascending=False, method='dense').subtract( eval_df[ir_metric].rank(pct=pct, ascending=False, method='dense'), axis=0) first_diff_df = predictions_df.rank(pct=pct, ascending=False, method='first').subtract( @@ -476,7 +1243,7 @@ def test_eval_measures(ir_metric, title_only=False, pct=True): avg_abs_mean.append(mean_diff_df.abs().mean()) avg_corr_df = pd.concat(avg_corr, axis=1) title = 'title' if title_only else 'all' - # avg_corr_df.to_pickle(f'{results_dir}/{corpus}_{predictors_type}_{title}_kendall_scores.pkl') + avg_corr_df.to_pickle(f'{results_dir}/{corpus}_{predictors_type}_{title}_kendall_scores.pkl') avg_abs_mean_df = pd.concat(avg_abs_mean, axis=1) avg_abs_mean_df.to_pickle(f'{results_dir}/{corpus}_{predictors_type}_{title}_mrd_scores.pkl') print(corpus) @@ -513,15 +1280,15 @@ def pairwise_agreement(correlations_sr, diff_df): return np.mean(result) -def one_way_anova(corpus='robust04', predictors_type='all', queries_group='title', save_figure=True): +def eval_methods_analysis(corpus='robust04', predictors_type='all', queries_group='title', save_figure=True): # Used in the IRJ paper figure 4 meanprops = {"marker": "s", "markerfacecolor": "white", "markeredgecolor": "blue"} - sns.set(font='serif', context='poster', style="whitegrid") + # sns.set(font='serif', context='poster', style="whitegrid") corr_file = f'{results_dir}/{corpus}_{predictors_type}_{queries_group}_kendall_scores.pkl' - msqr_file = f'{results_dir}/{corpus}_{predictors_type}_{queries_group}_mrd_scores.pkl' + smare_file = f'{results_dir}/{corpus}_{predictors_type}_{queries_group}_mrd_scores.pkl' corr_df = pd.read_pickle(corr_file).rename(PlotNames) - msqr_df = pd.read_pickle(msqr_file).rename(PlotNames) + smare_df = pd.read_pickle(smare_file).rename(PlotNames) _cdf = corr_df.T.melt(var_name='predictor', value_name='Correlation') - _rdf = msqr_df.T.melt(var_name='predictor', value_name='sMARE') + _rdf = smare_df.T.melt(var_name='predictor', value_name='sMARE') fig, ax = plt.subplots(figsize=(12, 9)) if save_figure: title = None @@ -545,7 +1312,7 @@ def one_way_anova(corpus='robust04', predictors_type='all', queries_group='title else: title = f'{corpus}_{predictors_type}_predictors_{queries_group}_mrd_scores'.replace('_', ' ').capitalize() fig, ax = plt.subplots(figsize=(12, 9)) - sns.boxplot('sMARE', 'predictor', data=_rdf, order=msqr_df.median(1).sort_values(ascending=True).index, ax=ax, + sns.boxplot('sMARE', 'predictor', data=_rdf, order=smare_df.median(1).sort_values(ascending=True).index, ax=ax, showmeans=True, meanprops=meanprops) plt.title(title) plt.xlabel('sMARE$_{AP}$') @@ -559,7 +1326,7 @@ def one_way_anova(corpus='robust04', predictors_type='all', queries_group='title # print(stats.f_oneway(*corr_df.values)) # _df = pd.concat([_cdf.assign(type='sMARE'), _rdf.assign(type='Correlation')]) # sns.catplot('value', 'predictor', col='type', kind='box', data=_df, order=corr_df.median(1).sort_values().index) - print(stats.f_oneway(*msqr_df.values)) + print(stats.f_oneway(*smare_df.to_numpy())) def plot_rank_diff_prob(n): @@ -575,17 +1342,785 @@ def plot_rank_diff_prob(n): plt.show() +def plot_estimates_per_interval(intra_topic, inter_topic, ir_metric, n_boot=10000): + # PlotPredictors = ['max-idf', 'nqc', 'qf', 'uef-clarity'] + # PlotPredictors = ['avg-idf', 'clarity', 'nqc', 'wig', 'max-idf', 'uef-clarity', 'uef-nqc', 'uef-wig'] + PlotPredictors = PreRetPredictors + PostRetPredictors + # intra_topic['int'], bins = pd.qcut(intra_topic['ev_diff'], q=10, precision=3, retbins=True) + intra_topic['int'], bins = pd.cut(intra_topic['ev_diff'], bins=(0, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 1), + precision=3, retbins=True, include_lowest=True) + # intra_topic['int'], bins = pd.cut(intra_topic['ev_diff'], + # bins=(0, 0.005, 0.015, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 1), + # precision=3, retbins=True, include_lowest=True) + inter_topic['int'] = pd.cut(inter_topic['ev_diff'], bins=bins, precision=3, include_lowest=True) + min_samples = intra_topic.groupby(['predictor', 'int']).count().min()[0] + logger.debug(f'min number of samples is {min_samples}') + logger.debug(f'inter topic means per interval: {inter_topic.groupby("int")["ev_diff"].mean()}') + logger.debug(f'intra topic means per interval: {intra_topic.groupby("int")["ev_diff"].mean()}') + logger.debug( + f'Correlation between inter-intra means over intervals r=' + f'{stats.pearsonr(intra_topic.groupby("int")["ev_diff"].mean(), inter_topic.groupby("int")["ev_diff"].mean())[0]:.3f}') + # _intra = intra_topic.groupby(['predictor', 'int']).sample(min_samples).sort_values(['predictor', 'ev_diff']) + # _inter = inter_topic.groupby(['predictor', 'int']).sample(min_samples).sort_values(['predictor', 'ev_diff']) + _intra = intra_topic.sort_values(['predictor', 'ev_diff']) + _inter = inter_topic.sort_values(['predictor', 'ev_diff']) + _plot_df = pd.concat([_intra[['int', 'predictor', 'status']].assign(method='Intra'), + _inter[['int', 'predictor', 'status']].assign(method='Inter')]) + pt = pd.CategoricalDtype(PlotPredictors, ordered=True) + _plot_df['predictor'] = _plot_df['predictor'].astype(pt) + _plot_df = _plot_df.dropna().sort_values('predictor').replace(PlotNames).replace( + {_plot_df['int'].min(): pd.Interval(0, _plot_df['int'].min().right, closed='both')}) + g = sns.catplot(data=_plot_df, x='int', y='status', col='predictor', hue='method', kind='point', ci=95, + n_boot=n_boot, col_wrap=4, height=3.5, aspect=1.3, scale=0.8, errwidth=1.3, capsize=0.3, + palette={'Inter': '#008297', 'Intra': '#83bb32'}, linestyles=[':', '--'], markers=["x", "p"], + hue_order=['Inter', 'Intra'], legend=True) + # {'Inter': '#1285bf', 'Intra': '#b5325d'} + plt.minorticks_on() + g.map(plt.grid, b=True, which='minor', axis='y', color='#E5E5E5', linestyle=':', zorder=0, + alpha=0.5, linewidth=0.5) + g.map(plt.grid, b=True, which='major', axis='y', color='#E5E5E5', linestyle='-.', zorder=1, linewidth=1) + g.set_xlabels(f"{PlotNames.get(ir_metric, ir_metric)} Difference").set_ylabels("Pairwise Accuracy") \ + .set_xticklabels(rotation=30).set_titles("{col_name}").set(ylim=(0.2, 1)) + for ax in g.axes.flat: + ax.tick_params(axis='x', which='minor', bottom=False) + # if ax.get_ylabel(): + # if ax.get_title() == 'UEF(Clarity)': + # ax.yaxis.labelpad = 20 + # else: + # ax.set_ylabel('') + # if ax.get_xlabel(): + # if ax.get_title() == 'UEF(Clarity)': + # ax.xaxis.labelpad = 15 + # else: + # ax.set_xlabel('') + for l in ax.lines + ax.collections: + l.set_zorder(5) + # plt.setp(l, linewidth=1) + # g._legend.set_title(None) + + g.tight_layout() + g.savefig(f'point_plot_{ir_metric}_all.pdf', dpi=300) + # import tikzplotlib + # tikzplotlib.save("test.tex") + plt.show() + logger.debug('\n' + tabulate(pd.DataFrame({'Inter': _inter.groupby('predictor')['status'].mean(), + 'Intra': _intra.groupby('predictor')['status'].mean()}). + sort_values('Inter'), headers='keys', tablefmt='psql', floatfmt=".3f")) + return _intra, _inter + + +def _get_stats_for_t_test(row): + return row.groupby('int').apply( + lambda x: stats.ttest_ind_from_stats(mean1=x.mean_x, std1=x.std_x, nobs1=x.len_x, mean2=x.mean_y, std2=x.std_y, + nobs2=x.len_y, equal_var=False).pvalue[0]) + + +def plot_ttest_pvalues(_intra, _inter, title): + _intra['int'] = _intra['int'].astype(str) + _inter['int'] = _inter['int'].astype(str) + dfx = _intra.pivot_table(index='predictor', columns='int', values='status', aggfunc=['mean', 'std', len]) + dfy = _inter.pivot_table(index='predictor', columns='int', values='status', aggfunc=['mean', 'std', len]) + pvalues_df = pd.merge(dfx, dfy, on='predictor').apply(_get_stats_for_t_test, axis=1) + pvalues_df.to_pickle(f'ttest_{title}_pvalues_df.pkl') + print(pvalues_df.to_latex(float_format='%.3f')) + # df = pd.read_pickle('ttest_equal_samples_pvalues_df.pkl') + # _df = pd.read_pickle('ttest_all_pairs_pvalues_df.pkl') + sns.heatmap(pvalues_df, annot=True, fmt='.2f', center=0.01, linewidths=.1, cmap="coolwarm") + plt.xticks(rotation=30) + plt.title(title.replace('_', ' ')) + plt.tight_layout() + plt.savefig(f'heatmap_pvalues_ttest_{title}.pdf') + plt.show() + + +def _calc_kl_divergence_df(df, precision=2, stat='count'): + zdf = df.round(precision).reset_index().pivot_table(values='index', index='ev_diff', columns='kind', + aggfunc='count', fill_value=0.1) + norm_zdf = zdf / zdf.sum() # normalized values (sum to 1) + norm_zdf.plot() + plt.show() + logger.debug(f'The KL divergence of intra to inter: {stats.entropy(norm_zdf["intra"], norm_zdf["inter"]):.3f}') + ecdf_zdf = norm_zdf.cumsum() # empirical Cumulative Distribution Function, eCDF + ecdf_zdf.plot() + plt.show() + if stat == 'count': + return zdf + elif stat == 'ratio': + return norm_zdf + else: + logger.warning('Specify stat= \"count\" or \"ratio\" to return a df') + + +def per_group_sampling(_df, wsr): + p, i = _df[['predictor', 'int']].iloc[0] + return _df.sample(wsr.loc[p, i]) + + +def sample_similar_distributions(intra_topic, inter_topic): + intra_topic['int'], bins = pd.cut(intra_topic['ev_diff'], + bins=(0, 0.005, 0.015, 0.025, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 1), + precision=3, retbins=True, include_lowest=True) + inter_topic['int'] = pd.cut(inter_topic['ev_diff'], bins=bins, precision=3, include_lowest=True) + wsr = intra_topic.groupby(['predictor', 'int'])['ev_diff'].count() + return inter_topic.groupby(['predictor', 'int']).apply(per_group_sampling, wsr).reset_index(drop=True) + + +def _plot_ecdf_hist(xdf, ir_metric, title='dist_diff'): + palette = {'Inter': '#008297', 'Intra': '#83bb32', 'Sub-Inter': '#BD5B22'} + + xdf['kind'] = xdf['kind'].str.title() + sub_df = xdf.loc[xdf['kind'] == 'Sub-Inter'] + fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1) + # fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(12, 9), dpi=300) + g1 = sns.ecdfplot(data=xdf.loc[xdf['kind'] == 'Inter'], x='ev_diff', ax=ax1, linestyle='-.', label='Inter', + color='#008297', zorder=4, linewidth=2) + g1 = sns.ecdfplot(data=xdf.loc[xdf['kind'] == 'Intra'], x='ev_diff', ax=ax1, linestyle='--', label='Intra', + color='#83bb32', zorder=2, linewidth=2) + if len(sub_df) > 0: + g1 = sns.ecdfplot(data=sub_df, x='ev_diff', ax=ax1, linestyle=(0, (1, 4)), label='Sub-Inter', color='#BD5B22', + zorder=3, linewidth=5) + g1.grid(b=True, which='major', axis='both', color='#E5E5E5', linestyle='-.', zorder=0, linewidth=2) + g1.set_xlabel(None) + g1.legend() + g2 = sns.histplot(data=xdf.loc[xdf['kind'] == 'Inter'], x='ev_diff', stat='probability', element='step', ax=ax2, + label='Inter', color='#008297', zorder=4, linewidth=2, linestyle='-.') + g2 = sns.histplot(data=xdf.loc[xdf['kind'] == 'Intra'], x='ev_diff', stat='probability', element='step', ax=ax2, + label='Intra', color='#83bb32', zorder=2, linewidth=2) + if len(sub_df) > 0: + g2 = sns.histplot(data=sub_df, x='ev_diff', stat='probability', element='step', ax=ax2, linestyle=":", + label='Sub-Inter', color='#BD5B22', zorder=3, linewidth=2, alpha=0.4) + g2.grid(b=True, which='major', axis='both', color='#E5E5E5', linestyle='-.', zorder=0, linewidth=2) + g2.set_xlabel(f'{PlotNames.get(ir_metric, ir_metric)} Difference') + g2.legend() + # legend2 = ax2.get_legend() + # legend2.set_title(None) + plt.tight_layout() + plt.savefig(f'{title}.pdf', dpi=300) + + plt.show() + + # fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(3.3, 3), dpi=300) + # g1 = sns.ecdfplot(data=xdf.loc[xdf['kind'] == 'Inter'], x='ev_diff', ax=ax1, linestyle='--', label='Inter', + # color='#008297') + # g1 = sns.ecdfplot(data=xdf.loc[xdf['kind'] == 'Intra'], x='ev_diff', ax=ax1, linestyle=(0, (3, 7)), label='Intra', + # color='#83bb32') + # g1 = sns.ecdfplot(data=xdf.loc[xdf['kind'] == 'Sub-Inter'], x='ev_diff', ax=ax1, linestyle=(5, (1, 4)), + # label='Sub-Inter', color='red') + # g1.set_xlabel(None) + # g1.legend() + # g2 = sns.histplot(data=xdf, hue='kind', x='ev_diff', common_norm=False, stat='probability', element='step', ax=ax2) + # g2 = sns.histplot(data=xdf.loc[xdf['kind'] == 'inter'], x='ev_diff', common_norm=False, stat='probability', + # alpha=0.3,element='step', ax=ax2, label='Inter', color=sns.color_palette("tab10")[1]) + # g2.set_xlabel(f'{PlotNames.get(ir_metric, ir_metric)} Difference') + # legend2 = ax2.get_legend() + # legend2.set_title(None) + # plt.tight_layout() + # plt.savefig(f'{title}.pdf', dpi=300) + # plt.show() + # plt.close('all') + # f, ax = plt.subplots(figsize=(3.15, 2), dpi=300) + # g = sns.histplot(data=xdf, hue='kind', x='ev_diff', common_norm=False, stat='probability', element='step') + # g.set_xlabel(f"{PlotNames.get(ir_metric, ir_metric)} Difference", fontsize=5) + # g.set_ylabel("Probability", fontsize=5) + # g.set_xticklabels(g.get_xticklabels(), fontsize=4) + # plt.tight_layout() + # plt.show() + + +def _plot_utility(df, _df): + meanprops = {"marker": "s", "markerfacecolor": "white", "markeredgecolor": "blue"} + sns.displot(data=_df, col='predictor', x='utility', col_wrap=PLOTS_COL_WRAP) + plt.show() + sns.displot(data=df, col='predictor', x='utility', col_wrap=PLOTS_COL_WRAP) + plt.show() + sns.boxplot(y='utility', data=df[['predictor', 'utility']], x='predictor', + order=df.groupby('predictor')['utility'].mean().sort_values().index, showmeans=True, + meanprops=meanprops) + plt.show() + sns.boxplot(y='utility', data=_df[['predictor', 'utility']], x='predictor', + order=df.groupby('predictor')['utility'].mean().sort_values().index, showmeans=True, + meanprops=meanprops) + plt.show() + # figure size in inches + # rcParams['figure.figsize'] = 16, 9 + ydf = pd.concat([df.assign(kind='intra')[['predictor', 'utility', 'kind']].reset_index(drop=True), + _df.assign(kind='inter')[['predictor', 'utility', 'kind']].reset_index(drop=True)], axis=0) + sns.violinplot(data=ydf, y='utility', x='predictor', hue='kind', split=False, inner='quartile', + order=ydf.groupby('predictor')['utility'].mean().sort_values().index) + plt.xticks(rotation=30) + plt.tight_layout() + plt.show() + + +def load_all_pairs(ir_metric, prefix_path, diff_col='ev_diff'): # Used in SIGIR 21 short paper + + def _add_utility(_df): + return _df.assign(utility=_df['status'].map(lambda x: 1 if x else -1) * _df['ev_diff']) + + def _merge_into_single_first_grp(_intra, _inter, cols, groups_col='predictor', _sub_inter=None): + _p = _intra[groups_col][0] + if _sub_inter is None: + return pd.concat([_intra.assign(kind='intra').loc[_intra[groups_col] == _p, cols].reset_index(drop=True), + _inter.assign(kind='inter').loc[_inter[groups_col] == _p, cols].reset_index(drop=True)], + axis=0) + else: + return pd.concat([_inter.assign(kind='inter').loc[_inter[groups_col] == _p, cols].reset_index(drop=True), + _intra.assign(kind='intra').loc[_intra[groups_col] == _p, cols].reset_index(drop=True), + _sub_inter.assign(kind='sub-Inter').loc[_sub_inter[groups_col] == _p, cols].reset_index( + drop=True)], axis=0) + + prefix = prefix_path.rsplit('/', maxsplit=1)[1] + pt = pd.CategoricalDtype(PreRetPredictors + PostRetPredictors, ordered=True) + intra_topic = pd.read_pickle(f'{prefix}_intra_topic_pairwise_{ir_metric}_df.pkl') + intra_topic['predictor'] = intra_topic['predictor'].astype(pt) + intra_topic = intra_topic.sort_values(['predictor', 'ev_diff']) + s_inter_topic = pd.read_pickle(f'{prefix}_inter_sampled_topic_pairwise_{ir_metric}_df.pkl') + s_inter_topic['predictor'] = s_inter_topic['predictor'].astype(pt) + s_inter_topic = s_inter_topic.sort_values(['predictor', 'ev_diff']) + inter_topic = pd.read_pickle(f'{prefix}_inter_topic_pairwise_{ir_metric}_df_all.pkl') + inter_topic['predictor'] = inter_topic['predictor'].astype(pt) + inter_topic = inter_topic.sort_values(['predictor', 'ev_diff']) + ut_intra_df = _add_utility(intra_topic) + ut_inter_df = _add_utility(pd.read_pickle(f'{prefix}_inter_topic_pairwise_{ir_metric}_df_all.pkl')) + ut_inter_df['predictor'] = ut_inter_df['predictor'].astype(pt) + ut_inter_df = ut_inter_df.sort_values(['predictor', 'ev_diff']) + ut_raw = _merge_into_single_first_grp(ut_intra_df, ut_inter_df, ['ev_diff', 'kind']) + # _calc_kl_divergence_df(ut_raw, precision=2) + _plot_ecdf_hist(ut_raw, ir_metric, 'full_inter_intra_dist') + ut_s_inter_df = _add_utility(s_inter_topic) + # order = plot_heatmap_diff_sim(ut_intra_df, 'utility', + # f'Predictors similarity over intra-topic query pairs {prefix}') + # plot_heatmap_diff_sim(ut_s_inter_df, 'utility', 'Predictors similarity over inter-topic query pairs', order=order) + # plot_heatmap_diff_sim(ut_inter_df, 'utility', f'Predictors similarity over all inter-topic query pairs {prefix}', + # order=order) + ut_s_raw = _merge_into_single_first_grp(ut_intra_df, ut_inter_df, ['ev_diff', 'kind'], _sub_inter=ut_s_inter_df) + # calc_intra_inter_stats(intra_topic, s_inter_topic) + # calc_intra_inter_stats(intra_topic, inter_topic) + # + # _calc_kl_divergence_df(ut_s_raw, precision=2) + _plot_ecdf_hist(ut_s_raw, ir_metric, 'sampled_inter_intra_dist') + # _plot_utility(ut_intra_df, ut_s_inter_df) + # calc_intra_inter_stats(intra_topic, inter_topic) + # calc_intra_inter_stats(intra_topic, s_inter_topic) + # _intra, _inter = plot_estimates_per_interval(intra_topic, s_inter_topic, ir_metric, n_boot=3) + _intra, _inter = plot_estimates_per_interval(intra_topic, inter_topic, ir_metric, n_boot=1000) + # max_interval = _intra['int'].max() + # _max_intra, _max_inter = plot_estimates_per_interval(_intra.loc[_intra['int'] == max_interval], + # _inter.loc[_inter['int'] == max_interval], ir_metric) + # max_interval = _max_intra['int'].max() + # _max_intra, _max_inter = plot_estimates_per_interval(_max_intra.loc[_max_intra['int'] == max_interval], + # _max_inter.loc[_inter['int'] == _max_inter], ir_metric) + # plot_ttest_pvalues(_intra, _inter, title=f'equal_samples_{ir_metric}') + return None + + +def two_way_anova(df, response, factor_1, factor_2): + """ols formula syntax: simply """ + formula = f'{response} ~ {factor_1}*{factor_2}' + lm = ols(formula, data=df).fit() + anova_res_df = anova_lm(lm) + print(anova_res_df) + _df = df.set_index(['predictor', 'kind']) + _df.index = ['_'.join(pair) for pair in _df.index] + tukey = pairwise_tukeyhsd(endog=_df['status'], groups=_df.index, alpha=0.01) + tukey_res_df = pd.DataFrame(data=tukey._results_table.data[1:], columns=tukey._results_table.data[0]) + tukey_res_df[['predictor1', 'kind1']] = tukey_res_df['group1'].str.split('_', expand=True) + tukey_res_df[['predictor2', 'kind2']] = tukey_res_df['group2'].str.split('_', expand=True) + t_res_df = tukey_res_df.loc[ + tukey_res_df['predictor1'] == tukey_res_df['predictor2'], ['predictor1', 'meandiff', 'p-adj', 'lower', + 'upper']].set_index('predictor1') + t_res_df.index = t_res_df.index.astype(pd.CategoricalDtype(PreRetPredictors + PostRetPredictors, ordered=True)) + mean_acc = df.groupby(['predictor', 'kind'])['status'].mean().reset_index(). \ + pivot(index='predictor', columns='kind').droplevel(0, axis=1) + t_res_df['p-adj'] = t_res_df['p-adj'].map('${:.3f}$'.format) + t_res_df['meandiff'] = t_res_df['meandiff'].map('${:.3f}$'.format) + t_res_df = t_res_df.drop(['upper', 'lower'], axis=1).assign( + CI=t_res_df['lower'].map('[${:.3f}$'.format).str.cat(t_res_df['upper'].map('${:.3f}$]'.format), sep=','), + intra=mean_acc['intra'].map('${:.3f}$'.format), inter=mean_acc['inter'].map('${:.3f}$'.format)) + + print( + t_res_df.sort_index()[['intra', 'inter', 'meandiff', 'CI', 'p-adj']].rename(LatexMacros).to_latex( + float_format="%.3f", escape=False)) + return t_res_df.sort_index() + + +def calc_intra_inter_stats(intra_topic, inter_topic): + dfx = intra_topic.pivot_table(index='predictor', aggfunc={'status': ['mean', 'std', len]}) + dfx.columns = dfx.columns.droplevel(0) + dfy = inter_topic.pivot_table(index='predictor', aggfunc={'status': ['mean', 'std', len]}) + dfy.columns = dfy.columns.droplevel(0) + df = pd.concat([intra_topic[['predictor', 'status', 'ev_diff']].assign(kind='intra'), + inter_topic[['predictor', 'status', 'ev_diff']].assign(kind='inter')]).rename( + {'ev_diff': 'AP_Diff'}, axis=1).rename({'ev_diff': 'AP_Diff'}, axis=0) + df['status'] = df['status'].astype(int) + # df = df.loc[df['predictor'] == 'uef-nqc'] + # df['predictor'] = df['predictor'].astype(str) + res_df = two_way_anova(df, 'status', 'predictor', 'kind') + # two_way_anova(df, 'status', 'AP_Diff', 'kind') + # two_way_anova(df, 'status', 'AP_Diff', 'predictor') + # + # for pred, _df in df.groupby('predictor'): + # print(pred) + # _df['predictor'] = _df['predictor'].astype(str) + # two_way_anova(_df, 'status', 'AP_Diff', 'kind') + + # lm = ols('status ~ predictor + kind + AP_Diff + predictor:AP_Diff + kind:AP_Diff + kind:predictor', data=df).fit() + # print(anova_lm(lm)) + + # Welch's t-test + # pvalues_df = pd.merge(dfx, dfy, on='predictor', suffixes=['_intra', '_inter']).assign( + # pval=lambda x: stats.ttest_ind_from_stats(mean1=x.mean_intra, std1=x.std_intra, nobs1=x.len_intra, + # mean2=x.mean_inter, std2=x.std_inter, nobs2=x.len_inter, + # equal_var=False).pvalue, kendall_intra=lambda x: 2 * x.mean_intra - 1, + # kendall_inter=lambda x: 2 * x.mean_inter - 1) + # print(pvalues_df.sort_values('predictor')[ + # ['len_intra', 'len_inter', 'mean_intra', 'mean_inter', 'kendall_intra', 'kendall_inter', + # 'pval']].to_string(float_format='%.3f')) + # pvalues_df = pd.merge(dfx, dfy, on='predictor', suffixes=['_intra', '_inter']).assign( + # pval=lambda x: stats.ttest_ind_from_stats(mean1=x.mean_intra, std1=x.std_intra, nobs1=x.len_intra, + # mean2=x.mean_inter, std2=x.std_inter, nobs2=x.len_inter, + # equal_var=False).pvalue) + # print(pvalues_df.sort_values('predictor')[['len_intra', 'len_inter', 'mean_intra', 'mean_inter', 'pval']].to_string( + # float_format='%.3f')) + return res_df + + +def plot_heatmap_diff_sim(df, values_col, title, order=None, ret_order=False): + # sns.set_theme(style="white") + pdf = df.pivot(index=['qid_1', 'qid_2'], columns='predictor', values=values_col) + # sim_pdf = pd.DataFrame(1 - pairwise_distances(pdf.T.to_numpy(), metric='cosine'), index=pdf.columns, + # columns=pdf.columns) + sim_pdf = pd.DataFrame(1 - pairwise_distances(pdf.to_numpy(), metric='cosine'), index=pdf.index, + columns=pdf.index) + if order is None: + order = sim_pdf.mean().sort_values().index + # Generate a mask for the upper triangle + mask = np.triu(np.ones_like(sim_pdf, dtype=bool)) + # Set up the matplotlib figure + # Generate a custom diverging colormap + # cmap = sns.diverging_palette(230, 20, as_cmap=True) + # cmap = sns.color_palette("rocket_r", as_cmap=True) + # Draw the heatmap with the mask and correct aspect ratio + f, ax = plt.subplots(figsize=(3.2, 3), dpi=300) + # g = sns.heatmap(sim_pdf.loc[order, order].rename(PlotNames).rename(PlotNames, axis=1), mask=mask, vmin=0, vmax=1, + # annot=True, fmt=".2f", cbar=False, center=False, cmap=None, square=True, + # annot_kws={'fontsize': 'small'}) + g = sns.heatmap(sim_pdf.loc[order, order], mask=mask, vmin=0, vmax=1, annot=False, fmt=".2f", cbar=True, + center=False, cmap=None, square=True, annot_kws={'fontsize': 'small'}) + g.set_xlabel(None) + g.set_ylabel(None) + g.set_title("\n".join(wrap(title, 50)), loc='center', fontdict={'fontsize': 9}, pad=0) + g.set_yticklabels([None] + g.get_yticklabels()[1:], rotation=0, fontdict={'fontsize': 6}) + g.set_xticklabels(g.get_xticklabels()[:-1] + [None], rotation=45, fontdict={'fontsize': 6}) + g.set_xticks(g.get_xticks()[:-1]) + g.set_yticks(g.get_yticks()[1:]) + plt.tight_layout() + # plt.savefig(f'queries_byPredictors_heatmap.pdf', dpi=300, bbox_inches='tight') + plt.savefig(f'{title.lower().replace(" ", "_")}_heatmap.pdf', dpi=300, bbox_inches='tight') + plt.show() + return order + + +def _corr_annotate_func(*args, **kwargs): + x, y = args + r, _ = stats.pearsonr(x, y) + ax = plt.gca() + xy = (0.1, 0.9) if kwargs['label'] == 'Title' else (0.1, 0.8) + ax.annotate(f"{kwargs['label']} $r$ = {r:.2f}", xy=xy, xycoords=ax.transAxes) + + +def plot_ir_eval_metrics(prefix_path, ir_metrics: list): + plt.close('all') + edf = pd.concat([read_eval_df(prefix_path, _metric) for _metric in ir_metrics], axis=1) + edf = edf.assign(query_type='Variant') + edf.loc[filter_robust_title_queries(edf).index, 'query_type'] = 'Title' + g = sns.PairGrid(edf, hue='query_type', aspect=1.5) + # g.map_diag(sns.kdeplot, fill=True, clip=(0, 1)) + g.map_diag(sns.histplot) + g.map_upper(sns.kdeplot, clip=(0, 1)) + g.map_upper(_corr_annotate_func) + g.map_lower(sns.scatterplot) + g.add_legend() + g.savefig('AP_nDCG_RR_pairplot.pdf') + plt.show() + + +def generate_sampled_inter_topic_df(prefix_path, ir_metric, sim_df): + predictors_group = 'all' + eval_df = add_topic_to_qdf(read_eval_df(prefix_path, ir_metric)).set_index(['topic', 'qid']) + predictions_df = read_prediction_files(prefix_path, r_type=predictors_group) + return construct_inter_sampled_to_intra_df(eval_df, predictions_df, sim_df) + + +def calc_corrs_scores_table(prefix_path, ir_metric): + pt = pd.CategoricalDtype(PreRetPredictors + PostRetPredictors, ordered=True) + intra_topic = pd.read_pickle(f'intra_topic_pairwise_{ir_metric}_df.pkl') + intra_topic['predictor'] = intra_topic['predictor'].astype(pt) + intra_topic = intra_topic.sort_values(['predictor', 'ev_diff']) + s_inter_topic = pd.read_pickle(f'inter_sampled_topic_pairwise_{ir_metric}_df.pkl') + s_inter_topic['predictor'] = s_inter_topic['predictor'].astype(pt) + s_inter_topic = s_inter_topic.sort_values(['predictor', 'ev_diff']) + + sampled_df = calc_intra_inter_stats(intra_topic, s_inter_topic) + # sampled_df['pval'] = sampled_df['pval'] * 18 # correction adjusted pval + # print(sampled_df.loc[:, ['mean_intra', 'mean_inter', 'pval']].sort_index().rename(LatexMacros).to_latex( + # float_format="%.4f", escape=False)) + + inter_topic = pd.read_pickle(f'inter_topic_pairwise_{ir_metric}_df_all.pkl') + inter_topic['predictor'] = inter_topic['predictor'].astype(pt) + inter_topic = inter_topic.sort_values(['predictor', 'ev_diff']) + print('doing full inter') + full_df = calc_intra_inter_stats(intra_topic, inter_topic) + + # eval_df = add_topic_to_qdf(read_eval_df(prefix_path, ir_metric)).set_index(['topic', 'qid']) + # predictions_df = read_prediction_files(prefix_path, 'all') + # df = pd.merge(predictions_df, eval_df, left_index=True, right_index=True) + # _corrs_df = pd.DataFrame( + # {'TitleQueries-$\\tau$': filter_robust_title_queries(df.reset_index('topic', drop=True)).corr( + # method='kendall')[ir_metric], 'AllQueries-$\\tau$': df.corr(method='kendall')[ir_metric]}).drop(ir_metric) + # _corrs_df.index = _corrs_df.index.astype(pt) + # corrs_df = _corrs_df.assign(NewIntra=sampled_df['kendall_intra'], NewAllInter=full_df['kendall_inter'], + # NewSubInter=sampled_df['kendall_inter'], MeanIntra=sampled_df['mean_intra'], + # MeanAllInter=full_df['mean_inter'], + # MeanSubInter=sampled_df['mean_inter']) + # print(corrs_df.sort_index().to_latex(float_format="%.3f")) + + # print(sampled_df.loc[sampled_df['pval'] > 0.0005, ['mean_intra', 'mean_inter', 'pval']].sort_index().rename( + # LatexMacros).to_latex(float_format="%.3f", escape=False)) + # order = corrs_df.corr().sort_values('NewAllInter').index + # sns.heatmap(corrs_df.corr().loc[order, order], cmap=sns.color_palette("rocket_r", as_cmap=True), annot=True) + # plt.tight_layout() + # plt.show() + + +def plot_pairwise_asl(df, title, order, n_boot, save_figure=False, comp_df=None): + plt.close() + if comp_df is None: + g = sns.heatmap(df.loc[order[1:], order[:-1]].rename(PlotNames, axis=0).rename(PlotNames, axis=1), annot=True, + fmt=".2f", cbar=False, center=0.0, cmap=None, square=True, annot_kws={'fontsize': 6}, + yticklabels=True, xticklabels=True, linewidths=.2) + + else: + _df = comp_df.loc[order[1:], order[:-1]].add(df.T.loc[order[:-1], order[1:]], fill_value=0, axis=0).loc[ + order, order] + g = sns.heatmap(_df.loc[order, order].T.rename(PlotNames, axis=0).rename(PlotNames, axis=1), + annot=True, + fmt=".2f", cbar=False, center=0.0, cmap=None, square=True, annot_kws={'fontsize': 6}, + yticklabels=True, xticklabels=True, linewidths=.2) + g.set_xlabel(None) + g.set_ylabel(None) + g.set_yticklabels(g.get_yticklabels(), rotation=0, fontdict={'fontsize': 6}) + g.set_xticklabels(g.get_xticklabels(), rotation=45, fontdict={'fontsize': 6}) + plt.title(title) + if save_figure: + if comp_df is None: + plt.savefig(title.replace(' ', '_') + f'_{n_boot}-boots_heatmap.pdf', dpi=300, bbox_inches='tight') + else: + plt.savefig(f'combined-metrics_{n_boot}-boots_heatmap.pdf', dpi=300, bbox_inches='tight') + plt.tight_layout() + plt.show() + + +def bootstrap_ranks_diff(prefix_path, ir_metric, order, n_boot): + """ + This will generate two DataFrames, one with mean absolute ranks differences and + another with root mean square ranks differences. The bootstrap happens on ranks differences of the initial list, + that is, the values for the queries for the bootstrap are determined in advance. + :param prefix_path: + :param ir_metric: + :param order: + :param n_boot: + :return: + """ + predictions_df = filter_robust_title_queries( + read_prediction_files(prefix_path, r_type='all').reset_index('topic', drop=True)). \ + rank(pct=True, ascending=False, method='average') + eval_df = filter_robust_title_queries(read_eval_df(prefix_path, ir_metric)). \ + rank(pct=True, ascending=False, method='average') + diff_df = predictions_df.rank(pct=True, ascending=False, method='average').subtract( + eval_df[ir_metric].rank(pct=True, ascending=False, method='average'), axis=0) + rmse_test_statistic = {} + mae_test_statistic = {} + rmse_boot_statistic = {} + mae_boot_statistic = {} + pt = pd.CategoricalDtype(order, ordered=True) + predictions_df.columns = predictions_df.columns.astype(pt) + comb_len = 0.5 * (len(order) ** 2 - len(order)) + for (p1, p2) in tqdm(combinations(order, 2), desc='Pairwise comparisons', total=comb_len): + rmse_test_statistic[(p1, p2)] = np.sqrt(diff_df[p1].pow(2).mean()) - np.sqrt(diff_df[p2].pow(2).mean()) + mae_test_statistic[(p1, p2)] = diff_df[p1].abs().mean() - diff_df[p2].abs().mean() + x_boot_res = [] + y_boot_res = [] + _p_df = diff_df[p1].append(diff_df[p2]) + for _ in range(n_boot): + x_boot_res.append(_p_df.sample(frac=0.5, replace=True).reset_index(drop=True)) + y_boot_res.append(_p_df.sample(frac=0.5, replace=True).reset_index(drop=True)) + mae_boot_statistic[(p1, p2)] = pd.concat(x_boot_res, axis=1).abs().mean() - pd.concat(y_boot_res, axis=1). \ + abs().mean() + rmse_boot_statistic[(p1, p2)] = pd.concat(x_boot_res, axis=1).pow(2).mean().pow(0.5) - pd. \ + concat(y_boot_res, axis=1).pow(2).mean().pow(0.5) + mae_boot_statistic_df = pd.DataFrame.from_dict(mae_boot_statistic, orient='index') + mae_boot_statistic_df['test'] = pd.Series(mae_test_statistic) + mae_boot_statistic_df.index.set_names(['p1', 'p2'], inplace=True) + rmse_boot_statistic_df = pd.DataFrame.from_dict(rmse_boot_statistic, orient='index') + rmse_boot_statistic_df['test'] = pd.Series(rmse_test_statistic) + rmse_boot_statistic_df.index.set_names(['p1', 'p2'], inplace=True) + return mae_boot_statistic_df.abs(), rmse_boot_statistic_df.abs() + + +def bootstrap_ranks_paired(prefix_path, ir_metric, order, n_boot): + """ + This will generate two DataFrames, one with mean absolute ranks differences and + another with root mean square ranks differences. The bootstrap happens on raw values, + the ranks (and) differences are calculated for each sample. + that is, the values for the queries for the bootstrap are determined after bootstrapping. + That way we're able to calculate the correlation values, but will have more tied values. + :param prefix_path: + :param ir_metric: + :param order: + :param n_boot: + :return: + """ + predictions_df = filter_robust_title_queries( + read_prediction_files(prefix_path, r_type='all').reset_index('topic', drop=True)) + eval_df = filter_robust_title_queries(read_eval_df(prefix_path, ir_metric)) + sare_test_statistic = {} + sre_test_statistic = {} + sare_boot_statistic = {} + sre_boot_statistic = {} + pt = pd.CategoricalDtype(order, ordered=True) + predictions_df.columns = predictions_df.columns.astype(pt) + comb_len = 0.5 * (len(order) ** 2 - len(order)) + diff_df = predictions_df.rank(pct=True, ascending=False, method='average').subtract( + eval_df[ir_metric].rank(pct=True, ascending=False, method='average'), axis=0) + for (p1, p2) in tqdm(combinations(order, 2), desc='Pairwise comparisons', total=comb_len): + sre_z = diff_df[p1].pow(2) - diff_df[p2].pow(2) + sre_test_statistic[(p1, p2)] = sre_z.mean() * np.sqrt(len(sre_z)) / sre_z.std() + sre_w = sre_z - sre_z.mean() + are_z = diff_df[p1].abs() - diff_df[p2].abs() + sare_test_statistic[(p1, p2)] = are_z.mean() * np.sqrt(len(are_z)) / are_z.std() + are_w = are_z - are_z.mean() + sre_boot_res = [] + are_boot_res = [] + for _ in range(n_boot): + _sre_w = sre_w.sample(frac=1, replace=True) + sre_boot_res.append(_sre_w.mean() * np.sqrt(len(_sre_w)) / _sre_w.std()) + _are_w = are_w.sample(frac=1, replace=True) + are_boot_res.append(_are_w.mean() * np.sqrt(len(_are_w)) / _are_w.std()) + sre_boot_statistic[(p1, p2)] = sre_boot_res + sare_boot_statistic[(p1, p2)] = are_boot_res + + sare_boot_statistic_df = pd.DataFrame.from_dict(sare_boot_statistic, orient='columns').T + sare_boot_statistic_df['test'] = pd.Series(sare_test_statistic) + sare_boot_statistic_df.index.set_names(['p1', 'p2'], inplace=True) + + ssre_boot_statistic_df = pd.DataFrame.from_dict(sre_boot_statistic, orient='columns').T + ssre_boot_statistic_df['test'] = pd.Series(sre_test_statistic) + ssre_boot_statistic_df.index.set_names(['p1', 'p2'], inplace=True) + return sare_boot_statistic_df.abs(), ssre_boot_statistic_df.abs() + + +def bootstrap_sensitivity_comparison(prefix_path, ir_metric, n_boot=10000, alpha=0.05, title_only=True): # used for IRJ + np.random.seed(12345) # for reproducibility purpose + PostRetPredictors.remove('qf') + PostRetPredictors.remove('uef-qf') + order = PreRetPredictors + PostRetPredictors + # mae_boot_statistic_df, mse_boot_statistic_df = bootstrap_ranks_paired(prefix_path, ir_metric, order, n_boot) + mae_boot_statistic_df, mse_boot_statistic_df = load_generate_pickle_df( + [f'sare_boot_paired-{n_boot}_statistic_df.pkl', f'ssre_boot_paired-{n_boot}_statistic_df.pkl'], + bootstrap_ranks_paired, prefix_path, ir_metric, order, n_boot) + # mae_boot_statistic_df, rmse_boot_statistic_df = load_generate_pickle_df( + # [f'smare_boot-{n_boot}_statistic_df.pkl', f'srmsre_boot-{n_boot}_statistic_df.pkl'], bootstrap_ranks_diff, + # prefix_path, ir_metric, order, n_boot) + mae_pairwise_asl = mae_boot_statistic_df.iloc[:, :n_boot].ge(mae_boot_statistic_df['test'], axis=0).mean(1) + # rmse_pairwise_asl = rmse_boot_statistic_df.iloc[:, :n_boot].ge(rmse_boot_statistic_df['test'], axis=0).mean(1) + mse_pairwise_asl = mse_boot_statistic_df.iloc[:, :n_boot].ge(mse_boot_statistic_df['test'], axis=0).mean(1) + + mae_estimated_diff = mae_boot_statistic_df.iloc[:, :n_boot].quantile(1 - alpha, axis=1).max() + # rmse_estimated_diff = rmse_boot_statistic_df.iloc[:, :n_boot].quantile(1 - alpha, axis=1).max() + mse_estimated_diff = mse_boot_statistic_df.iloc[:, :n_boot].quantile(1 - alpha, axis=1).max() + print(f'sMARE estimated_difference for {alpha}: {mae_estimated_diff:.3g}') + print(f'sMARE pairs with ASL<{alpha}: {mae_pairwise_asl.lt(alpha).sum()}/{len(mae_pairwise_asl)}' + f' {100 * (mae_pairwise_asl.lt(alpha).sum() / len(mae_pairwise_asl)):.3g}%') + print(f'sMARE pairs with ASL<0.01: {mae_pairwise_asl.lt(0.01).sum()}/{len(mae_pairwise_asl)}') + print(f'sMSRE estimated_difference for {alpha}: {mse_estimated_diff:.3g}') + print(f'sMSRE pairs with ASL<{alpha}: {mse_pairwise_asl.lt(alpha).sum()}/{len(mse_pairwise_asl)}' + f' {100 * (mse_pairwise_asl.lt(alpha).sum() / len(mse_pairwise_asl)):.3g}%') + print(f'sMSRE pairs with ASL<0.01: {mse_pairwise_asl.lt(0.01).sum()}/{len(mse_pairwise_asl)}') + # print(f'sRMSRE sensitivity: {rmse_estimated_diff:.2g}') + # print(f'sRMSRE pairs with ASL<{alpha}: {rmse_pairwise_asl.lt(alpha).sum()}/{len(rmse_pairwise_asl)}') + # print(f'sRMSRE pairs with ASL<0.01: {rmse_pairwise_asl.lt(0.01).sum()}/{len(rmse_pairwise_asl)}') + plot_pairwise_asl(mae_pairwise_asl.unstack().T, title='sMARE ASL pairwise values', order=order, n_boot=n_boot, + save_figure=True) + plot_pairwise_asl(mse_pairwise_asl.unstack().T, title='sMSRE ASL pairwise values', order=order, n_boot=n_boot, + save_figure=True) + plot_pairwise_asl(mae_pairwise_asl.unstack().T, comp_df=mse_pairwise_asl.unstack().T, title='', order=order, + n_boot=n_boot, save_figure=True) + + +# def test_larger(row, *args, **kwargs): +# d_hat = abs(kwargs.get('kwargs').get(row.name)) +# return (row >= d_hat).mean() + + +def plot_sare_metric_reg(prefix_path, ir_metric, save_figure=False, title_only=False): # used for IRJ + predictors_to_plot = ['scq', 'max-idf', 'nqc', 'uef-clarity'] + # predictors_to_drop = ['qf', 'uef-qf', 'smv', 'uef-smv'] + if not save_figure: + plt.rcParams['figure.dpi'] = 150 + predictions_df = read_prediction_files(prefix_path, r_type='all').reset_index('topic', drop=True) + eval_df = read_eval_df(prefix_path, ir_metric) + diff_df = predictions_df.rank(pct=True, ascending=False, method='average').subtract( + eval_df[ir_metric].rank(pct=True, ascending=False, method='average'), axis=0).abs() + if title_only: + diff_df = filter_robust_title_queries(diff_df) + _df = diff_df.join(eval_df).set_index(ir_metric, append=True).rename_axis('predictor', axis=1).loc[:, + predictors_to_plot].rename(PlotNames, axis=1) + data = _df.stack().reset_index().rename({0: 'sARE'}, axis=1).rename(PlotNames, axis=1) + g = sns.lmplot(data=data, x=PlotNames.get(ir_metric, ir_metric), y='sARE', col='predictor', col_wrap=4, + markers=['1'], scatter_kws={"s": 40, "alpha": 0.7}, fit_reg=False, height=3) + g.set_titles("{col_name}") + plt.minorticks_on() + for ax in g.axes.flat: + # ax.tick_params(axis='x', which='minor', bottom=False) + ax.grid(b=True, which='minor', axis='y', color='#E5E5E5', linestyle=':', zorder=0, + alpha=1, linewidth=1) + ax.grid(b=True, which='major', axis='y', color='#E5E5E5', linestyle='-.', zorder=1, linewidth=2) + ax.grid(b=True, which='minor', axis='x', color='#E5E5E5', linestyle=':', zorder=0, + alpha=1, linewidth=1) + ax.grid(b=True, which='major', axis='x', color='#E5E5E5', linestyle='-.', zorder=1, linewidth=2) + for l in ax.lines + ax.collections: + l.set_zorder(5) + + # g.map(plt.grid, b=True, which='minor', axis='y', color='#E5E5E5', linestyle=':', zorder=0, + # alpha=0.5, linewidth=0.5) + # g.map(plt.grid, b=True, which='major', axis='y', color='#E5E5E5', linestyle='-.', zorder=1, linewidth=1) + # g.map(plt.grid, b=True, which='minor', axis='x', color='#E5E5E5', linestyle=':', zorder=0, + # alpha=0.5, linewidth=0.5) + # g.map(plt.grid, b=True, which='major', axis='x', color='#E5E5E5', linestyle='-.', zorder=1, linewidth=1) + plt.tight_layout() + if save_figure: + queries = 'title-queries' if title_only else 'all-vars' + plt.savefig(f'regplot_{queries}_{ir_metric}.pdf', dpi=300) + plt.close() + else: + plt.show() + + +def plot_scatter_predictor(prefix_path, ir_metric): # Used for the DC presentation + eval_df = add_topic_to_qdf(read_eval_df(prefix_path, ir_metric)).set_index(['topic', 'qid']) + predictions_df = read_prediction_files(prefix_path, r_type='post') + eval_sr = eval_df[ir_metric].droplevel('topic') + predictor_sr = predictions_df['nqc'].droplevel('topic') + # eff_sr = eval_sr + # eff_sr = (eff_sr - eff_sr.min()) / (eff_sr.max() - eff_sr.min()) * 0.1 + np.random.random(len(eval_sr)) * 0.009 + df = pd.DataFrame({'AP': eval_sr.sort_index(), 'NQC': predictor_sr.sort_index()}) + # df = pd.DataFrame({'AP': eval_sr.sort_index(), 'NQC': eff_sr.sort_index()}) + _df = filter_robust_title_queries(df) + _df.index = _df.index.str[:3] + print(f'Kendall corr {_df.corr(method="kendall").iloc[0, 1]:.2g}') + # sare_sr = _df.rank(ascending=False, pct=True).diff(axis=1).abs()['NQC'].sort_values(ascending=False) + + fig, ax = plt.subplots() + # ax.scatter(sare_sr.index, sare_sr, zorder=2.5, linewidths=0.5, marker='o', s=20) + ax.scatter(x=_df['AP'], y=_df['NQC'], zorder=2.5, linewidths=0.5, marker='o', s=20, edgecolor='c') + # Hide the right and top spines + ax.spines['right'].set_visible(False) + ax.spines['top'].set_visible(False) + # ax.axline(_df.min(), _df.max(), linewidth=0.8, color='slategray', linestyle=':') + # for i, tick in enumerate(ax.get_xticklabels()): + # if bool(i % 10): + # tick.set_visible(False) + # else: + # tick.set_rotation(90) + + ax.annotate(f'Kendall\'s $\\tau$ = {_df.corr(method="kendall").iloc[0, 1]:.2g}', (0.5, 1), size='small', + xycoords='axes fraction', ha='center', va='center') + # ax.spines['left'].set_visible(False) + # ax.spines['bottom'].set_visible(False) + # plt.ylabel('sARE$_{AP}$') + plt.ylabel('NQC') + plt.xlabel('AP') + # plt.xlabel('Topic') + # sns.despine(right=True, top=True) + plt.minorticks_on() + plt.grid(b=True, which='major', axis='both', color='#E5E5E5', linestyle='-.', zorder=2.01, linewidth=1.5, alpha=0.8) + plt.grid(b=True, which='minor', axis='both', color='#E5E5E5', linestyle=':', zorder=2, linewidth=1, alpha=0.6) + outliers = _df.rank(ascending=False).diff(axis=1).abs()['NQC'].nlargest(2).index + # outliers = sare_sr.nlargest(2).index + for (i, x, y) in _df.loc[outliers].itertuples(): + ax.annotate(i, (x, y), size='small', xytext=(1, 2), textcoords='offset points') + # # arrowprops=dict(arrowstyle='->', color='black')) + # jig = 15 + # for x, y in sare_sr.loc[outliers].items(): + # ax.annotate(x, (x, y), size='small', xytext=(45-jig, -jig), textcoords='offset points', + # arrowprops=dict(arrowstyle='->', color='black')) + # jig += 20 + plt.tight_layout() + plt.savefig('dc_scatter_fig_1_annot.pdf') + plt.show() + + +@timer def main(): + # Change the current working dir to project dir + # if Config.BATCH_NAME: + # os.chdir(os.path.join(Config.RESULTS_DIR, Config.BATCH_NAME)) + # else: + # os.chdir(os.path.join(Config.env_paths.get('root_dir', '/'), 'qpptk')) + + os.chdir(os.path.join(Config.RESULTS_DIR, Config.BATCH_NAME)) + plt.set_loglevel("info") index_path, index_type = set_index() prefix = '_'.join(index_path.split('/')[-2:]) if index_type == 'text' else \ index_path.rsplit('/', 1)[-1].replace('.ciff', '') - prefix_path = os.path.join(results_dir, prefix) + prefix_path = os.path.join(results_dir, Config.BATCH_NAME, prefix) + query_similarity_measure = 'cos' # Seems to be most informative + # query_similarity_measure = 'jac' + # query_similarity_measure = 'dsc' + sim_df = read_query_sim_df(prefix_path.replace(Config.BATCH_NAME, ''), sim_metric=query_similarity_measure) # plot_eval_metrics_dist(prefix_path) # plot_predictors_dist(prefix_path) - # exit() ir_metric = 'ap@1000' # ir_metric = 'ndcg@10' + # ir_metric = 'rbp-0.5' + # plot_sare_metric_reg(prefix_path, ir_metric, save_figure=True, title_only=True) + # ir_metric = 'ndcg@10' + # plot_sare_metric_reg(prefix_path, ir_metric, save_figure=True, title_only=True) + # plot_scatter_predictor(prefix_path, ir_metric) + # ir_metric = 'RR' + # ir_metric = 'ndcg@10' + # plot_qpp_correlations(prefix_path) + # plot_metrics_differences(prefix_path) + # plot_ranks_differences(prefix_path, ir_metric) + # test_eval_measures(ir_metric, title_only=True, pct=True) + # eval_methods_analysis(save_figure=False) + + # plot_ir_eval_metrics(prefix_path, ['ap@1000', 'ndcg@10', 'RR']) + + # test_eval_measures(ir_metric, title_only=True, pct=False) + # calc_corrs_scores_table(prefix_path, ir_metric) + + # bootstrap_sensitivity_comparison(prefix_path, ir_metric) # used for IRJ + # cols = corrs_df.mean().sort_values().index + # corrs_df = corrs_df[cols] + # sns.pointplot(data=corrs_df, ci=95, n_boot=100, join=False) + # plt.show() + # prefix = prefix_path.rsplit('/', maxsplit=1)[1] + # s_inter_topic_df = construct_inter_sampled_to_intra_df( + # add_topic_to_qdf(read_eval_df(prefix_path, ir_metric)).set_index(['topic', 'qid']), + # read_prediction_files(prefix_path), sim_df) + # s_inter_topic_df.to_pickle(f'{prefix}_inter_sampled_topic_pairwise_{ir_metric}_df.pkl') + load_generate_pickle_df(f'{prefix}_inter_sampled_topic_pairwise_{ir_metric}_df.pkl', + generate_sampled_inter_topic_df, + prefix_path, ir_metric, sim_df) + # inter_topic_eval(ir_metric, prefix_path, sim_df) + # intra_topic_eval(ir_metric, prefix_path, sim_df) + + load_all_pairs(ir_metric, prefix_path, 'ev_diff') + return None + # sys.exit() # eval_df = read_eval_df(prefix_path, ir_metric) # lgbm_df = read_lgbm_prediction_files(ir_metric, 0.1) # plot_dist(lgbm_df) @@ -595,10 +2130,8 @@ def main(): # print(corr_df) # exit() # plot_rank_diff_prob(10) - test_eval_measures(ir_metric, title_only=False, pct=True) - # one_way_anova() + # test_eval_measures(ir_metric, title_only=False, pct=True) # plot_hard_queries(prefix_path, ir_metric) - exit() data_df, threshold = generate_data_df(prefix_path, ir_metric, quantile_threshold=0.3) logger.info(f'The labeling threshold was set to {threshold} {ir_metric}') data_df.to_pickle('data/data_df.pkl') @@ -621,19 +2154,47 @@ def main(): if __name__ == '__main__': - PreRetPredictors = {'scq', 'avg-scq', 'max-scq', 'var', 'avg-var', 'max-var', 'max-idf', 'avg-idf'} - PostRetPredictors = {'clarity', 'smv', 'nqc', 'wig', 'uef-clarity', 'uef-smv', 'uef-nqc', 'uef-wig'} + PreRetPredictors = ['scq', 'avg-scq', 'max-scq', 'var', 'avg-var', 'max-var', 'avg-idf', 'max-idf'] + PostRetPredictors = ['clarity', 'smv', 'nqc', 'wig', 'qf', 'uef-clarity', 'uef-smv', 'uef-nqc', 'uef-wig', 'uef-qf'] LatexMacros = {'scq': '\\Scq', 'avg-scq': '\\avgScq', 'max-scq': '\\maxScq', 'var': '\\Var', 'avg-var': '\\avgVar', 'max-var': '\\maxVar', 'max-idf': '\\maxIDF', 'avg-idf': '\\avgIDF', 'clarity': '\\clarity', 'smv': '\\smv', 'nqc': '\\nqc', 'wig': '\\wig', 'uef-clarity': '\\uef{\\clarity}', 'uef-smv': '\\uef{\\smv}', 'uef-nqc': '\\uef{\\nqc}', - 'uef-wig': '\\uef{\\wig}'} + 'uef-wig': '\\uef{\\wig}', 'qf': '\\qf', 'uef-qf': '\\uef{\\qf}'} PlotNames = {'scq': 'SCQ', 'avg-scq': 'AvgSCQ', 'max-scq': 'MaxSCQ', 'var': 'SumVAR', 'avg-var': 'AvgVAR', 'max-var': 'MaxVAR', 'max-idf': 'MaxIDF', 'avg-idf': 'AvgIDF', 'clarity': 'Clarity', 'smv': 'SMV', - 'nqc': 'NQC', 'wig': 'WIG', 'uef-clarity': 'UEF(Clarity)', 'uef-smv': 'UEF(SMV)', - 'uef-nqc': 'UEF(NQC)', 'uef-wig': 'UEF(WIG)'} - with open('duplicated_qids.txt') as f: - DUPLICATED_QIDS = {line.rstrip('\n') for line in f} + 'nqc': 'NQC', 'wig': 'WIG', 'qf': 'QF', 'uef-clarity': 'UEF(Clarity)', 'uef-smv': 'UEF(SMV)', + 'uef-nqc': 'UEF(NQC)', 'uef-wig': 'UEF(WIG)', 'uef-qf': 'UEF(QF)', 'ap@1000': 'AP', + 'ndcg@10': 'nDCG@10'} + cm = 1 / 2.54 # centimeters in inches + # These settings should match the font used in LaTeX + + # seaborn_setup() + + fmt = { + "font.family": "Fira Sans", + # "font.family": ["Century Schoolbook Std", "Linux Libertine O", "serif", 'sans-serif'], + "font.serif": "Fira", + # "font.serif": ["New Century Schoolbook", "Century Schoolbook L", "Century Schoolbook Std"], + # 'font.size': 16, + 'font.size': 11, + # "font.sans-serif": "Linux Biolinum", + 'figure.facecolor': (0.98, 0.98, 0.98), + 'text.color': '#23373b', + 'axes.labelcolor': '#23373b', + 'figure.dpi': 300, + 'savefig.dpi': 300, + # 'figure.figsize': (8, 4.5), + 'legend.borderaxespad': 2, + # "axes.labelpad": 20.0 + "axes.labelpad": 15 + } + plt.rcParams.update(fmt) + + PLOTS_COL_WRAP = 6 + + with open('qpptk/duplicated_qids.txt') as f: + ROBUST_DUPLICATED_QIDS = {line.rstrip('\n') for line in f} logger = Config.get_logger() results_dir = Config.RESULTS_DIR main() diff --git a/code/qpptk/qpptk/global_manager.py b/code/qpptk/qpptk/global_manager.py index 6215e53..9edfd86 100644 --- a/code/qpptk/qpptk/global_manager.py +++ b/code/qpptk/qpptk/global_manager.py @@ -1,24 +1,35 @@ import multiprocessing as mp +from functools import partial +import syct import pandas as pd from syct import timer -from qpptk import Config, IndexText, IndexDB, QueryParserText, QueryParserCiff, LocalManagerRetrieval, parse_index_file, \ - IndexCiff, add_topic_to_qdf, LocalManagerPredictorPre, LocalManagerPredictorPost +from qpptk import Config, IndexText, IndexDB, QueryParserText, QueryParserCiff, LocalManagerRetrieval, \ + add_topic_to_qdf, LocalManagerPredictorPre, LocalManagerPredictorPost, IndexTerrier, \ + ensure_dir +from qpptk.load_terri_index import IndexTerri logger = Config.logger +TREC_RES_COLUMNS = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] def init_proc(*args): + kwargs = dict(args) global index global queries global results_df - index = args[0] - queries = args[1] - results_df = args[2] if len(args) > 2 else None + if callable(kwargs.get('index')): + index = kwargs.get('index')() + else: + index = kwargs.get('index') + queries = kwargs.get('queries') + results_df = kwargs.get('results_df') + ensure_dir(f'{Config.RESULTS_DIR}/temp/{index}', create_if_not=True) def run_pre_prediction_process(qid): + timer = syct.Timer(f'QID: {qid}', logger=logger) process = LocalManagerPredictorPre(index_obj=index, query_obj=queries, qid=qid) max_idf = process.calc_max_idf() avg_idf = process.calc_avg_idf() @@ -28,12 +39,14 @@ def run_pre_prediction_process(qid): var = process.calc_var() avg_var = process.calc_avg_var() max_var = process.calc_max_var() - logger.debug(qid + ' finished') + # logger.debug(qid + ' finished') + timer.stop() return {'qid': qid, 'max-idf': max_idf, 'avg-idf': avg_idf, 'scq': scq, 'max-scq': max_scq, 'avg-scq': avg_scq, 'var': var, 'max-var': max_var, 'avg-var': avg_var} def run_post_prediction_process(qid): + timer = syct.Timer(f'QID: {qid}', logger=logger) process = LocalManagerPredictorPost(index_obj=index, query_obj=queries, results_df=results_df, qid=qid) wig = process.calc_wig(Config.WIG_LIST_SIZE) nqc = process.calc_nqc(Config.NQC_LIST_SIZE) @@ -41,97 +54,146 @@ def run_post_prediction_process(qid): ret_process = LocalManagerRetrieval(index_obj=index, query_obj=queries, qid=qid) results_vec = results_df.loc[qid, ['docNo', 'docScore']].reset_index(drop=True).values - p_w_rm = ret_process.generate_rm(results_vec[:Config.CLARITY_LIST_SIZE]) - clarity_list_size = min(Config.CLARITY_FB_TERMS, len(p_w_rm['term_id'])) - if clarity_list_size < Config.CLARITY_FB_TERMS: - logger.warn(f'Query-{qid}: The RM passed to Clarity had less terms than clarity_list_size parameter') - clarity = process.calc_clarity(p_w_rm=p_w_rm[:clarity_list_size]) - - re_ranked_df = run_rm_rerank_retrieval_process(qid) - - uef_wig = process.calc_uef(Config.WORKING_SET_SIZE, re_ranked_df.set_index('qid'), wig) - uef_nqc = process.calc_uef(Config.WORKING_SET_SIZE, re_ranked_df.set_index('qid'), nqc) - uef_smv = process.calc_uef(Config.WORKING_SET_SIZE, re_ranked_df.set_index('qid'), smv) - uef_clarity = process.calc_uef(Config.WORKING_SET_SIZE, re_ranked_df.set_index('qid'), clarity) - - logger.debug(qid + ' finished') - return {'qid': qid, 'wig': wig, 'nqc': nqc, 'smv': smv, 'clarity': clarity, 'uef-wig': uef_wig, 'uef-nqc': uef_nqc, - 'uef-smv': uef_smv, 'uef-clarity': uef_clarity} - -def _run_multiprocess_sync(func, tasks, n_proc, *init_args): - with mp.Pool(processes=n_proc, initializer=init_proc, initargs=init_args) as pool: + c_p_w_rm = ret_process.generate_rm(results_vec[:Config.CLARITY_LIST_SIZE]) + # if Config.QF_LIST_SIZE == Config.CLARITY_LIST_SIZE: + # q_p_w_rm = c_p_w_rm + # else: + # q_p_w_rm = ret_process.generate_rm(results_vec[:Config.QF_LIST_SIZE]) + # if Config.UEF_LIST_SIZE == Config.CLARITY_LIST_SIZE: + # u_p_w_rm = c_p_w_rm + # elif Config.UEF_LIST_SIZE == Config.QF_LIST_SIZE: + # u_p_w_rm = q_p_w_rm + # else: + # u_p_w_rm = ret_process.generate_rm(results_vec[:Config.UEF_LIST_SIZE]) + + clarity_terms_size = min(Config.CLARITY_FB_TERMS, len(c_p_w_rm['term_id'])) + if clarity_terms_size < Config.CLARITY_FB_TERMS: + logger.warn(f'Query-{qid}: The RM passed to Clarity had less terms than clarity_list_size parameter') + clarity = process.calc_clarity(p_w_rm=c_p_w_rm[:clarity_terms_size]) + + # rm_ranked_df, _ = ret_process.run_rm_retrieval(initial_set_docs=results_vec, + # _sorted_rm_terms=q_p_w_rm[:Config.QF_FB_TERMS]) + # _df = pd.DataFrame.from_records(rm_ranked_df, columns=['docNo', 'docScore']) + # rm_df = _df.assign(qid=qid, iteration='Q0', rank=range(1, len(rm_ranked_df) + 1), method='RM')[TREC_RES_COLUMNS] + # qf = process.calc_qf(Config.QF_OVERLAP_SIZE, rm_df) + # + # rm_re_ranked_df, _ = ret_process.run_rm_retrieval(ranking_set_docs=results_vec[:Config.UEF_RANKING_SIZE], + # initial_set_docs=results_vec, + # _sorted_rm_terms=u_p_w_rm[:Config.UEF_FB_TERMS]) + # _df = pd.DataFrame.from_records(rm_re_ranked_df, columns=['docNo', 'docScore']) + # re_ranked_df = _df.assign(qid=qid, iteration='Q0', rank=range(1, len(rm_re_ranked_df) + 1), + # method='RM')[TREC_RES_COLUMNS] + # uef_wig = process.calc_uef(Config.UEF_RANKING_SIZE, re_ranked_df.set_index('qid'), wig) + # uef_nqc = process.calc_uef(Config.UEF_RANKING_SIZE, re_ranked_df.set_index('qid'), nqc) + # uef_smv = process.calc_uef(Config.UEF_RANKING_SIZE, re_ranked_df.set_index('qid'), smv) + # uef_clarity = process.calc_uef(Config.UEF_RANKING_SIZE, re_ranked_df.set_index('qid'), clarity) + # uef_qf = process.calc_uef(Config.UEF_RANKING_SIZE, re_ranked_df.set_index('qid'), qf) + + timer.stop() + # return {'qid': qid, f'wig+{Config.WIG_LIST_SIZE}': wig, f'nqc+{Config.NQC_LIST_SIZE}': nqc, + # f'smv+{Config.SMV_LIST_SIZE}': smv, f'clarity+{Config.CLARITY_LIST_SIZE}+{clarity_terms_size}': clarity, + # f'qf+{Config.QF_LIST_SIZE}+{Config.QF_OVERLAP_SIZE}': qf, + # f'uef+{Config.UEF_LIST_SIZE}+{Config.UEF_RANKING_SIZE}-wig+{Config.WIG_LIST_SIZE}': uef_wig, + # f'uef+{Config.UEF_LIST_SIZE}+{Config.UEF_RANKING_SIZE}-nqc+{Config.NQC_LIST_SIZE}': uef_nqc, + # f'uef+{Config.UEF_LIST_SIZE}+{Config.UEF_RANKING_SIZE}-smv+{Config.SMV_LIST_SIZE}': uef_smv, + # f'uef+{Config.UEF_LIST_SIZE}+{Config.UEF_RANKING_SIZE}-clarity+{Config.CLARITY_LIST_SIZE}+{clarity_terms_size}': uef_clarity, + # f'uef+{Config.UEF_LIST_SIZE}+{Config.UEF_RANKING_SIZE}-qf+{Config.QF_LIST_SIZE}+{Config.QF_FB_TERMS}+{Config.QF_OVERLAP_SIZE}': uef_qf} + return {'qid': qid, f'wig+{Config.WIG_LIST_SIZE}': wig, f'nqc+{Config.NQC_LIST_SIZE}': nqc, + f'smv+{Config.SMV_LIST_SIZE}': smv, f'clarity+{Config.CLARITY_LIST_SIZE}+{clarity_terms_size}': clarity} + + +def _run_multiprocess_sync(func, tasks, n_proc, **init_kwargs): + with mp.Pool(processes=n_proc, initializer=init_proc, initargs=init_kwargs.items()) as pool: + # result = pool.map(func, tasks, chunksize=len(tasks) // n_proc) result = pool.map(func, tasks) return result +# @timer(info) def pre_ret_prediction_full(qids, index, queries, n_proc=Config.N_PROC): # result = {} # for qid in qids: # result[qid] = run_prediction_process(qid, index, queries) # df = pd.DataFrame.from_dict(result, orient='index') # df.index = df.index.rename('qid') - result = _run_multiprocess_sync(run_pre_prediction_process, qids, n_proc, index, queries) + result = _run_multiprocess_sync(run_pre_prediction_process, qids, n_proc, index=index, queries=queries) df = pd.DataFrame(result) logger.debug(df) return add_topic_to_qdf(df).set_index(['topic', 'qid']) +# @timer(info) def post_ret_prediction_full(qids, index, queries, results_df, n_proc=Config.N_PROC): - result = _run_multiprocess_sync(run_post_prediction_process, qids, n_proc, index, queries, results_df) + result = _run_multiprocess_sync(run_post_prediction_process, qids, n_proc, index=index, queries=queries, + results_df=results_df) df = pd.DataFrame(result) logger.debug(df) return add_topic_to_qdf(df).set_index(['topic', 'qid']) -@timer +# @timer # TODO: add debug level, and should add debugging each qid with time to a log file def run_ql_retrieval_process(qid): - columns = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] + # columns = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] + timer = syct.Timer(f'QID: {qid}', logger=logger) process = LocalManagerRetrieval(index_obj=index, query_obj=queries, qid=qid) result = process.run_ql_retrieval() - logger.debug(qid + ' finished') + timer.stop() + # logger.debug(qid + ' finished') df = pd.DataFrame.from_records(result, columns=['docNo', 'docScore']) - return df.assign(qid=qid, iteration='Q0', rank=range(1, len(result) + 1), method='QL')[columns] + result_df = df.assign(qid=qid, iteration='Q0', rank=range(1, len(result) + 1), method='QL')[TREC_RES_COLUMNS] + result_df.to_pickle(f'{Config.RESULTS_DIR}/temp/{index}/{qid}_QL.res.pkl') + return result_df -@timer -def run_rm_retrieval_process(qid): - columns = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] +@timer # TODO: add debug level +def run_rm_retrieval_process(qid, return_rm=False, __sorted_rm_terms=None): + # columns = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] + timer = syct.Timer(f'QID: {qid}', logger=logger) process = LocalManagerRetrieval(index_obj=index, query_obj=queries, qid=qid) - result = process.run_rm_retrieval() + result, p_w_rm = process.run_rm_retrieval() logger.debug(qid + ' finished') df = pd.DataFrame.from_records(result, columns=['docNo', 'docScore']) - return df.assign(qid=qid, iteration='Q0', rank=range(1, len(result) + 1), method='RM')[columns] + res_df = df.assign(qid=qid, iteration='Q0', rank=range(1, len(result) + 1), method='RM')[TREC_RES_COLUMNS] + timer.stop() + if return_rm: + return res_df, p_w_rm + else: + return res_df -@timer -def run_rm_rerank_retrieval_process(qid, return_rm=False): - columns = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] +@timer # TODO: add debug level +def run_rm_rerank_retrieval_process(qid, ranking_set_docs=None, return_rm=False, __sorted_rm_terms=None): + # columns = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] + timer = syct.Timer(f'QID: {qid}', logger=logger) process = LocalManagerRetrieval(index_obj=index, query_obj=queries, qid=qid) - results_vec = None if results_df is None else results_df.loc[qid, ['docNo', 'docScore']].reset_index( - drop=True).values + results_vec = ranking_set_docs if ranking_set_docs is not None else results_df.loc[ + qid, ['docNo', 'docScore']].reset_index(drop=True).values if Config.WORKING_SET_SIZE: results_vec = results_vec[:Config.WORKING_SET_SIZE] - result, p_w_rm = process.run_rm_retrieval(re_rank_ql=True, working_set_docs=results_vec) + result, p_w_rm = process.run_rm_retrieval(ranking_set_docs=results_vec) logger.debug(qid + ' finished') df = pd.DataFrame.from_records(result, columns=['docNo', 'docScore']) - res_df = df.assign(qid=qid, iteration='Q0', rank=range(1, len(result) + 1), method='QlRm')[columns] + res_df = df.assign(qid=qid, iteration='Q0', rank=range(1, len(result) + 1), method='QlRm')[TREC_RES_COLUMNS] + timer.stop() if return_rm: return res_df, p_w_rm else: return res_df +# TODO: add @time(info) def retrieval_full(qids, index, queries, n_proc=Config.N_PROC, method='ql', results_df=None): # init_proc(index, queries) # for qid in qids: - # run_retrieval_process(qid) + # run_ql_retrieval_process(qid) if method == 'ql': - result = _run_multiprocess_sync(run_ql_retrieval_process, qids, n_proc, index, queries) + result = _run_multiprocess_sync(run_ql_retrieval_process, qids, n_proc, index=index, queries=queries) elif method == 'rm': - result = _run_multiprocess_sync(run_rm_retrieval_process, qids, n_proc, index, queries) + result = _run_multiprocess_sync(run_rm_retrieval_process, qids, n_proc, index=index, queries=queries) elif method == 'rm_rerank': - result = _run_multiprocess_sync(run_rm_rerank_retrieval_process, qids, n_proc, index, queries, results_df) + result = _run_multiprocess_sync(run_rm_rerank_retrieval_process, qids, n_proc, index=index, queries=queries, + results_df=results_df) else: logger.warn(f'Unknown method, choose between [ql, rm, rm_rerank]') return None @@ -140,29 +202,34 @@ def retrieval_full(qids, index, queries, n_proc=Config.N_PROC, method='ql', resu return df -@timer -def initialize_ciff_index(ciff_index): - header, terms_dict, doc_records = parse_index_file(ciff_index) - return IndexCiff(header, ciff_index, terms_dict, doc_records) +# @timer # TODO: add debug level +# def initialize_ciff_index(ciff_index): +# header, terms_dict, doc_records = parse_index_file(ciff_index) +# return IndexCiff(header, ciff_index, terms_dict, doc_records) -@timer +@timer # TODO: add debug level def initialize_text_index(text_inv, dict_txt, doc_lens, doc_names, index_globals): return IndexText(text_inverted=text_inv, terms_dict=dict_txt, index_global=index_globals, document_lengths=doc_lens, document_names=doc_names) -@timer +@timer # TODO: add debug level def initialize_db_index(db_dir): return IndexDB(index_db_dir=db_dir) -@timer +@timer # TODO: add debug level +def initialize_terrier_index(terrier_index_dir, **kwargs): + return partial(IndexTerrier, terrier_index_dir=terrier_index_dir, **kwargs) + + +@timer # TODO: add debug level def initialize_ciff_queries(queries_file): return QueryParserCiff(queries_file) -@timer +@timer # TODO: add debug level def initialize_text_queries(queries_file): return QueryParserText(queries_file) diff --git a/code/qpptk/qpptk/load_ciff_index.py b/code/qpptk/qpptk/load_ciff_index.py index 7558af4..44a5581 100644 --- a/code/qpptk/qpptk/load_ciff_index.py +++ b/code/qpptk/qpptk/load_ciff_index.py @@ -1,7 +1,7 @@ from syct import timer import qpptk.CommonIndexFileFormat_pb2 as ciff -from qpptk import TermPosting, Posting, TermRecord, DocRecord, read_message +from qpptk import TermPosting, Posting, TermRecord, DocRecord, read_message, Config """ An index stored in CIFF is a single file comprised of exactly the following: @@ -10,7 +10,11 @@ - Exactly the number of DocRecord messages specified in the num_doc record field of the Header """ -INDEX_CIFF_FILE = "/research/local/olz/ciff_indexes/robust04-Lucene-indri-krovetz.ciff" +INDEX_CIFF_FILE = "/research/local/olz/ciff_indexes/robust04_Lucene_indri_krovetz.ciff" + +# INDEX_CIFF_FILE = "/research/local/olz/ciff_indexes/cw12b/cw12b_Indri_nostop_krovetz.ciff" + +logger = Config.logger def parse_posting_list(posting_list: ciff.PostingsList) -> TermPosting: @@ -34,6 +38,7 @@ def parse_index_file(index_file): buf = fp.read() n = 0 cur_n, header = read_message(buf, n, ciff.Header) + print(f'header:\n{header}') num_postings_lists = header.num_postings_lists for _ in range(num_postings_lists): n, _posting_list = read_message(buf, cur_n, ciff.PostingsList) @@ -55,7 +60,12 @@ def oov(cls, term): return TermPosting(term, 0, 0, tuple()) # Out of vocabulary terms def __init__(self, header, index_file, terms_dict, doc_records): - self.total_terms = header.total_terms_in_collection + sum_terms = sum(map(lambda x: x.doc_len, doc_records.values())) + if sum_terms != header.total_terms_in_collection: + logger.warn(f'total_terms_in_collection stat in the header != sum of all doc_lens') + logger.warn(f'header.total_terms_in_collection: {header.total_terms_in_collection}') + logger.warn(f'sum of all documents lengths: {sum_terms}') + self.total_terms = sum_terms assert header.total_docs == len(doc_records), f'total docs in header differs from total docs in doc_records' self.number_of_docs = header.total_docs with open(index_file, 'rb') as fp: diff --git a/code/qpptk/qpptk/load_db_index.py b/code/qpptk/qpptk/load_db_index.py index bcb135f..1d12e40 100644 --- a/code/qpptk/qpptk/load_db_index.py +++ b/code/qpptk/qpptk/load_db_index.py @@ -19,6 +19,7 @@ def oov(cls, term): return TermPosting(term, 0, 0, tuple()) # Out of vocabulary terms def __init__(self, index_db_dir): + ensure_file(os.path.join(index_db_dir, 'data.mdb')) self.db_env = lmdb.open(index_db_dir, create=False, subdir=True, map_size=2 ** 39, readonly=True, max_dbs=3, lock=False) _stats = self.db_env.stat() diff --git a/code/qpptk/qpptk/load_terri_index.py b/code/qpptk/qpptk/load_terri_index.py new file mode 100644 index 0000000..c3b9b0b --- /dev/null +++ b/code/qpptk/qpptk/load_terri_index.py @@ -0,0 +1,114 @@ +import os + +import pyterrier as pt +from syct import timer +from tqdm import tqdm + +from qpptk import TermPosting, Posting, TermRecord, DocRecord, Config, ensure_dir, ensure_file + +""" +A Terrier index python interface + """ + +# DEV_INDEX = '/research/local/olz/qpptk/dev_index/data.properties' +# ROBUST_INDEX = '/research/local/olz/robust_krovetz_nostop_terrier/data.properties' +CW_INDEX = '/research/local/olz/cw12b_krovetz_nostop_terrier/index/krovetz-nostop/data.properties' + +logger = Config.logger + + +class IndexTerri: + + @classmethod + def oov(cls, term): + return TermPosting(term, 0, 0, tuple()) # Out of vocabulary terms + + def __init__(self, terrier_index_dir): + self.index_dir = ensure_dir(terrier_index_dir) + _index_file = ensure_file(os.path.join(self.index_dir, 'data.properties')) + if not pt.started(): + pt.init() + pt.logging('INFO') + _indexref = pt.IndexRef.of(_index_file) + self.index = pt.IndexFactory.of(_indexref) + _stats = self.index.getCollectionStatistics().toString() + logger.debug(f'Terrier index stats:\n{_stats}') + self.total_terms = self.index.getCollectionStatistics().getNumberOfTokens() + self.number_of_docs = self.index.getCollectionStatistics().getNumberOfDocuments() + self.unique_terms = self.index.getCollectionStatistics().getNumberOfUniqueTerms() + self._lex = self.index.getLexicon() + self._inv = self.index.getInvertedIndex() + self._doi = self.index.getDocumentIndex() + self._meta = self.index.getMetaIndex() + self.terms_mapping = None + self.docs_dict = None + + @property + def terms_dict(self): + if self.terms_mapping: + return self.terms_mapping + terms_mapping = {} + logger.debug('Generating Terms Dict') + with tqdm(total=self.unique_terms) as progress: + for iterm in self._lex: + term = iterm.getKey() + term_id = iterm.getValue().getTermId() + df = iterm.getValue().getDocumentFrequency() + cf = iterm.getValue().getFrequency() + terms_mapping[term] = TermRecord(term, term_id, cf, df) + progress.update(1) + self.terms_mapping = terms_mapping + return self.terms_mapping + + @property + def doc_records(self): + if self.docs_dict: + return self.docs_dict + docs_mapping = {} + logger.debug('Generating Docs Dict') + with tqdm(total=self.number_of_docs) as progress: + for doc_id in range(self._doi.getNumberOfDocuments()): + docs_mapping[doc_id] = DocRecord(doc_id, self._meta.getItem("docno", doc_id), + self._doi.getDocumentLength(doc_id)) + progress.update(1) + self.docs_dict = docs_mapping + return self.docs_dict + + def get_posting_list(self, term: str) -> TermPosting: + if not pt.started(): + pt.init() + term_record = self.terms_dict.get(term) + if term_record: + _posting_list = [Posting(p.getId(), p.getFrequency()) for p in + self._inv.getPostings(self._lex.getLexiconEntry(term))] + return TermPosting(term, term_record.cf, term_record.df, tuple(_posting_list)) + else: + return IndexTerri.oov(term) + + def get_doc_len(self, doc_id: int) -> int: + return self.doc_records.get(doc_id).doc_len + + def get_doc_name(self, doc_id: int) -> str: + return self.doc_records.get(doc_id).collection_doc_id + + def get_term_cf(self, term: str) -> float: + return self.terms_dict.get(term).cf + + def get_term_record(self, term: str) -> TermRecord: + return self.terms_dict.get(term) + + +@timer +def main(): + index = IndexTerri(CW_INDEX) + x = index.get_posting_list('gather') + print(len(x.posting_list)) + print(index.get_doc_len(0)) + print(index.terms_dict.get('gather')) + print(index.terms_dict.get('crime')) + print(index.doc_records.get(100)) + print(index.doc_records.get(101)) + + +if __name__ == '__main__': + main() diff --git a/code/qpptk/qpptk/load_terrier_index.py b/code/qpptk/qpptk/load_terrier_index.py new file mode 100644 index 0000000..5df8b3d --- /dev/null +++ b/code/qpptk/qpptk/load_terrier_index.py @@ -0,0 +1,389 @@ +import numpy as np +import os +import pyterrier as pt +from typing import Iterable +import multiprocessing as mp +from zlib import crc32 + +import pickle +from syct import timer +from scipy import sparse +from tqdm import tqdm + +from qpptk import TermRecord, TermPosting, Config, msgpack_decode, DocRecord, ensure_file, ensure_dir, pickle_load_obj, \ + pickle_save_obj, Posting + +logger = Config.get_logger() + + +class IndexTerrier: + @classmethod + def oov(cls, term): + return TermPosting(term, 0, 0, tuple()) # Out of vocabulary terms + + def __str__(self): + index_name = self.index_dir.rsplit('/', 1)[-1] + if hasattr(self, 'partial_terms_hash'): + index_name = f'{index_name}_partial-{self.partial_terms_hash}' + return str(index_name) + + def __init__(self, terrier_index_dir, partial_terms=None, read_only=True): + # TODO: looks like I need to init terrier only if going to generate + """ + The index is loading a Terrier index and several python objects that were generated from the Terrier index. + To use it in a subprocess for retrieval first all the python objects should be generated and saved to disk, + to generate the objects first time add read_only=False (only in the main process, should not run in parallel) + """ + _generate = not read_only # Added for brevity + if mp.parent_process() and _generate: + logger.warn("*** The generate attribute in the index is being used in a sub-process. ***") + logger.warn("It should be used only in the main process, " + "during parallel runs the index should be used as read-only") + self.index_dir = ensure_dir(terrier_index_dir) + # self._index = self.init_pt_index() + # logger.warn('*** An Index was loaded ***') + # print(f'{self._index} \n {type(self._index)} \n {id(self._index)}') + self.index_stats = self.__load_generate_stats_dict(_generate) + self.total_terms = self.index_stats.get('total_terms') + self.number_of_docs = self.index_stats.get('number_of_docs') + self.unique_terms = self.index_stats.get('unique_terms') + logger.debug(f"Vocab size {self.unique_terms}") + self.terms_postings_mat = None # row - doc_ids; col - term_ids + self.docs_postings_mat = None + self.terms_mapping_in_mat = None + self.doc_len_vec = None + self.doc_name_vec = None + self.__terms_cf_vec = None + self._zero_len_docs = None + self.doc_name_id_dict = None + self.terms_records = self.__load_generate_terms_dict(_generate) + self.docs_records = self.__load_generate_docs_dict(_generate) + if partial_terms is None: + self.initialize_full_postings_sparse_mat(_generate) + else: + self.partial_terms_hash = crc32(''.join(partial_terms).encode()) + self.initialize_partial_postings_sparse_mat(partial_terms, _generate) + + @property + def terms_cf_vec(self) -> np.ndarray: + if self.__terms_cf_vec is None: + self.__terms_cf_vec = np.array([(v.id, v.cf) for v in self.terms_records.values()], + dtype=[('term_id', np.uint32), ('term_cf', np.uint32)]) + self.__terms_cf_vec.sort(order='term_id') + return self.__terms_cf_vec + + def init_pt_index(self): + if not pt.started(): + pt.init() + else: + print('*** A PyTerrier was already initialized, will try to load a new index ***') + _index_file = ensure_file(os.path.join(self.index_dir, 'data.properties')) + _indexref = pt.IndexRef.of(_index_file) + return pt.IndexFactory.of(_indexref) + + def __generate_all_terms_records(self): + terms_records = {} + logger.info('Generating Terms Records Dict') + with tqdm(total=self.unique_terms) as progress: + for iterm in self._index.getLexicon(): + term = iterm.getKey() + term_id = iterm.getValue().getTermId() + df = iterm.getValue().getDocumentFrequency() + cf = iterm.getValue().getFrequency() + terms_records[term] = TermRecord(term, term_id, cf, df) + progress.update(1) + return terms_records + + def __load_generate_stats_dict(self, generate): + stats_dict_file = os.path.join(self.index_dir, 'stats_dict.pkl') + if generate: + _index = self.init_pt_index() + _stats = _index.getCollectionStatistics().toString() + logger.info(f'Terrier index stats:\n{_stats}') + stats_dict = {'total_terms': _index.getCollectionStatistics().getNumberOfTokens(), + 'number_of_docs': _index.getCollectionStatistics().getNumberOfDocuments(), + 'unique_terms': _index.getCollectionStatistics().getNumberOfUniqueTerms()} + pickle_save_obj(stats_dict, stats_dict_file) + self._index = _index + else: + try: + stats_dict_file = ensure_file(stats_dict_file) + stats_dict = pickle_load_obj(stats_dict_file) + logger.info('Terrier index stats:\n' + '\n'.join([f'{k}: {v}' for k, v in stats_dict.items()])) + except FileNotFoundError as er: + logger.warn(f'The file {stats_dict_file} can\'t be found, pass read_only=False to generate') + raise er + return stats_dict + + def __load_generate_terms_dict(self, generate): + terms_dict_file = os.path.join(self.index_dir, 'terms_dict.pkl') + try: + terms_dict_file = ensure_file(terms_dict_file) + terms_records = pickle_load_obj(terms_dict_file) + except FileNotFoundError as er: + if generate: + terms_records = self.__generate_all_terms_records() + pickle_save_obj(terms_records, terms_dict_file) + else: + logger.warn(f'The file {terms_dict_file} can\'t be found') + raise er + return terms_records + + def __generate_all_docs_records(self): + docs_records = {} + logger.info('Generating Docs Records Dict') + _doi = self._index.getDocumentIndex() + with tqdm(total=self.number_of_docs) as progress: + for doc_id in range(_doi.getNumberOfDocuments()): + docs_records[doc_id] = DocRecord(doc_id, self._index.getMetaIndex().getItem("docno", doc_id), + _doi.getDocumentLength(doc_id)) + progress.update(1) + return docs_records + + def __load_generate_docs_dict(self, generate): + docs_records_file = os.path.join(self.index_dir, 'docs_dict.pkl') + try: + docs_records_file = ensure_file(docs_records_file) + docs_records = pickle_load_obj(docs_records_file) + except FileNotFoundError as er: + if generate: + docs_records = self.__generate_all_docs_records() + pickle_save_obj(docs_records, docs_records_file) + else: + logger.warn(f'The file {docs_records_file} can\'t be found') + raise er + return docs_records + + def get_terms_cf_vec(self, indices=None): + return self.terms_cf_vec[indices] + + def get_posting_list(self, term: str) : + # Warning to future self, Returning zip object instead of Post() + term_id = self.terms_mapping_in_mat.get(term) + if term_id is not None: + posting = self.terms_postings_mat[:, term_id] + return zip(posting.nonzero()[0], posting.data) + else: + return [] + + # def __get_doc_record_db(self, doc_id): + def get_doc_record(self, doc_id): # FIXME + with self.db_env.begin(db=self.docs_db, write=False) as txn: + doc_record = txn.get(str(doc_id).encode()) + return DocRecord(*msgpack_decode(doc_record)) + + # def get_doc_record(self, doc_id) -> DocRecord: + # return self.doc_records_cache.setdefault(doc_id, self.__get_doc_record_db(doc_id)) + + def get_doc_len(self, doc_id: int) -> int: + return self.get_doc_record(doc_id).doc_len + + def get_doc_name(self, doc_id: int) -> str: + return self.get_doc_record(doc_id).collection_doc_id + + def get_term_cf(self, term: str) -> int: + term_record = self.get_term_record(term) + if term_record: + return term_record.cf + else: + return 0 + + def get_term_record(self, term: str) -> TermRecord: + return self.terms_records.get(term) + + def _generate_doc_len_vector(self): # FIXME + # docs_dict = {} + # doc_ids = [] + # doc_names = [] + # doc_lens = [] + # zero_len_docs = [] + doc_ids, doc_names, doc_lens = zip(*self.docs_records.values()) + doc_len_ar = np.array([*zip(doc_ids, doc_lens)], dtype=[('index', np.uint32), ('doc_len', np.uint32)]) + doc_name_ar = np.array([*zip(doc_ids, doc_names)], dtype=[('index', np.uint32), ('doc_name', object)]) + # doc_records = np.array(list(docs_dict.items()), dtype=[('index', np.uint32), ('doc_len', np.uint32)]) + doc_len_ar.sort(order='index') + doc_name_ar.sort(order='index') + self.doc_len_vec = doc_len_ar['doc_len'] + self.doc_name_vec = doc_name_ar['doc_name'] + # if min(doc_ids) > 0: + # doc_ids = np.array(doc_ids) - 1 + # zero_len_docs = np.array(zero_len_docs) - 1 + # self._zero_len_docs = np.array(zero_len_docs) + self.doc_name_id_dict = dict([*zip(doc_names, doc_ids)]) + return self.doc_len_vec, self.doc_name_vec, self.doc_name_id_dict + + def get_doc_len_vec(self, indices=None): + doc_len = self.doc_len_vec if self.doc_len_vec is not None else self._generate_doc_len_vector()[0] + if indices is not None: + return doc_len[indices] + else: + return doc_len + + def get_doc_name_vec(self, indices=None): + doc_name = self.doc_name_vec if self.doc_name_vec is not None else self._generate_doc_len_vector()[1] + if indices is not None: + return doc_name[indices] + else: + return doc_name + + def get_doc_ids_by_name(self, names_vec): + self.get_doc_name_vec() + return np.array([self.doc_name_id_dict.get(n) for n in names_vec]) + + @timer + def initialize_full_postings_sparse_mat(self, generate): + terms_postings_mat_file = os.path.join(self.index_dir, f'full_terms_postings_mat.npz') + terms_mapping_file = os.path.join(self.index_dir, f'full_terms_mapping.pkl') + try: + terms_mapping_file = ensure_file(terms_mapping_file) + terms_postings_mat_file = ensure_file(terms_postings_mat_file) + self.terms_postings_mat = pickle_load_obj(terms_postings_mat_file) + self.terms_mapping_in_mat = pickle_load_obj(terms_mapping_file) + except FileNotFoundError as ex: + if generate: + logger.info(f"{terms_mapping_file} or {terms_postings_mat_file} " + f"files are missing found, will generate and save new ones") + self.terms_postings_mat, self.terms_mapping_in_mat = self._generate_mat_from_terms() + sparse.save_npz(terms_postings_mat_file, self.terms_postings_mat) + pickle_save_obj(self.terms_mapping_in_mat, terms_mapping_file) + else: + raise ex + + @timer + def initialize_partial_postings_sparse_mat(self, partial_terms, generate): # FIXME + terms_postings_mat_file = os.path.join(self.index_dir, f'partial_{self.partial_terms_hash}_postings_mat.npz') + terms_mapping_file = os.path.join(self.index_dir, f'partial_{self.partial_terms_hash}_mapping.pkl') + try: + terms_mapping_file = ensure_file(terms_mapping_file) + terms_postings_mat_file = ensure_file(terms_postings_mat_file) + self.terms_postings_mat = sparse.load_npz(terms_postings_mat_file) + self.terms_mapping_in_mat = pickle_load_obj(terms_mapping_file) + except FileNotFoundError as er: + if generate: + logger.info(f"{terms_mapping_file} or {terms_postings_mat_file} " + f"files are missing, will generate and save new ones") + self.terms_postings_mat, self.terms_mapping_in_mat = self._generate_partial_mat_from_terms( + partial_terms) + # logger.info(f"{terms_postings_mat_file} file is missing, will generate and save new one") + # self.terms_postings_mat= self._generate_partial_mat_from_terms(partial_terms) + sparse.save_npz(terms_postings_mat_file, self.terms_postings_mat) + pickle_save_obj(self.terms_mapping_in_mat, terms_mapping_file) + else: + raise er + + @timer + def _generate_mat_from_terms(self): # FIXME + terms_mapping = {} + reverse_terms_mapping = [] + row = [] + col = [] + data = [] + lex = self._index.getLexicon() + inv = self._index.getInvertedIndex() + with tqdm(total=self.unique_terms) as progress: + i = 0 + for iterm in lex: + # posting_list = inv.getPostings(iterm.getValue()) + term = iterm.getKey() + df = iterm.getValue().getDocumentFrequency() + cf = iterm.getValue().getFrequency() + terms_mapping[term] = TermRecord(term, i, cf, df) + reverse_terms_mapping.append(term) + doc_ids, tf_tuple = zip(*[(p.getId(), p.getFrequency()) for p in inv.getPostings(iterm.getValue())]) + data.extend(tf_tuple) + col.extend([i] * df) + row.extend(doc_ids) + i += 1 + progress.update(1) + terms_mapping['reverse_mapping'] = reverse_terms_mapping + return sparse.csr_matrix((data, (row, col)), shape=(self.number_of_docs, self.unique_terms), + dtype=np.uint32), terms_mapping + + @timer + def _generate_partial_mat_from_terms(self, terms): + terms_mapping = {} + # reverse_terms_mapping = [] + row = [] + col = [] + data = [] + lex = self._index.getLexicon() + inv = self._index.getInvertedIndex() + + for term in tqdm(terms): + iterm = lex.getLexiconEntry(term) + if iterm is None: + logger.info(f'The term \"{term}\" is OOV') + continue + tid = iterm.getTermId() + df = iterm.getDocumentFrequency() + # cf = iterm.getFrequency() + # terms_mapping[term] = TermRecord(term, tid, cf, df) + terms_mapping[term] = tid + doc_ids, tf_tuple = zip(*[(p.getId(), p.getFrequency()) for p in inv.getPostings(iterm)]) + data.extend(tf_tuple) + col.extend([tid] * df) + row.extend(doc_ids) + return sparse.csr_matrix((data, (row, col)), shape=(self.number_of_docs, self.unique_terms), + dtype=np.uint32), terms_mapping + + @timer + def _generate_partial_mat_from_docs(self, docs): # TODO: Implement here the docs index mat + docs_mapping = {} + # reverse_terms_mapping = [] + row = [] + col = [] + data = [] + lex = self._index.getLexicon() + di = self._index.getDirectIndex() + doi = self._index.getDocumentIndex() + + for doc in tqdm(docs): + iterm = lex.getLexiconEntry(term) + if iterm is None: + logger.info(f'The term \"{term}\" is OOV') + continue + tid = iterm.getTermId() + df = iterm.getDocumentFrequency() + # cf = iterm.getFrequency() + # docs_mapping[term] = TermRecord(term, tid, cf, df) + docs_mapping[term] = tid + doc_ids, tf_tuple = zip(*[(p.getId(), p.getFrequency()) for p in inv.getPostings(iterm)]) + data.extend(tf_tuple) + col.extend([tid] * df) + row.extend(doc_ids) + return sparse.csr_matrix((data, (row, col)), shape=(self.number_of_docs, self.unique_terms), + dtype=np.uint32) + + def get_mat_by_terms(self, terms: Iterable[str]): + """ + :param terms - iterable of string terms + :returns sparse index matrix + """ + # terms_indices = [self.terms_mapping_in_mat[t] for t in terms if t in self.terms_mapping_in_mat] + terms_indices = [ + self.terms_mapping_in_mat[t] if t in self.terms_mapping_in_mat else logger.warn(f'missing posting for {t}') + for t in terms] + return self.get_mat_by_term_ids(terms_indices) + + def get_mat_by_term_ids(self, terms_indices: Iterable[int]): + """ + :param terms_indices - iterable of terms ids + :returns sparse index matrix + """ + doc_terms_mat = self.terms_postings_mat[:, terms_indices] + return doc_terms_mat + + def get_mat_by_docs(self, docs): + doc_terms_mat = self.terms_postings_mat[docs, :] + return doc_terms_mat + + +if __name__ == '__main__': + DEV_INDEX = '/research/local/olz/qpptk/dev_index/data.properties' + # ROBUST_INDEX = '/research/local/olz/robust_krovetz_nostop_terrier/data.properties' + # CW_INDEX = '/research/local/olz/cw12b_krovetz_nostop_terrier/index/krovetz-nostop/data.properties' + # index_path = Config.CIFF_INDEX + # prefix = index_path.rsplit('/', 1)[-1].replace('.ciff', '') + # _db_dir = os.path.join(Config.DB_DIR, prefix) + index = IndexTerrier(DEV_INDEX) + index.initialize_full_postings_sparse_mat() diff --git a/code/qpptk/qpptk/nested_cross_validation.py b/code/qpptk/qpptk/nested_cross_validation.py new file mode 100644 index 0000000..282477c --- /dev/null +++ b/code/qpptk/qpptk/nested_cross_validation.py @@ -0,0 +1,215 @@ +import pandas as pd +import numpy as np +from sklearn.pipeline import Pipeline +from sklearn.preprocessing import StandardScaler +from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV, cross_val_score, KFold +from sklearn.metrics import accuracy_score, roc_curve, auc, roc_auc_score, log_loss, SCORERS +import random +import matplotlib.pyplot as plt +import lightgbm as lgb +from syct import timer + +from qpptk import plot_roc + + +class NestedCrossVal: + """ + Implements the nested cross validation methodology. + First the data is split in an outer loop to k folds, where each fold serves as a test set single time while all the + other folds are used as train data. The test set is used for the final evaluation of the model. + Inside the first loop, each train set is split into train validation (a.k.a dev) sets. In the inner loop the + validation set is used for parameter optimization, i.e. to find the best parameters for the model. After the best + parameters are found the model is then trained over all the train set (including the validation) and evaluated on + the test set. + The data is over the topics to avoid data leakage, the cost is that it may cause imbalanced data. + (over stratified sampling) + """ + + def __init__(self, data_df): + self.full_data = data_df + + @timer + def outer_evaluation(self, run_path=''): + topics = self.full_data['topic'].unique() + test_predictions = [] + params_res = [] + features_imp = [] + kf = KFold(n_splits=10, random_state=321) + for train_index, test_index in kf.split(topics): + train_df = self.full_data.loc[self.full_data['topic'].isin(topics[train_index])] + test_df = self.full_data.loc[self.full_data['topic'].isin(topics[test_index])] + print(f'Train:') + print_data_summary(train_df) + print(f'Test:') + print_data_summary(test_df) + _predictions, params_df, features_import = self.find_best_params(train_df, test_df) + test_predictions.append(pd.Series(index=test_df.index, data=_predictions)) + params_res.append(params_df) + features_imp.append(pd.DataFrame(features_import, index=train_df.drop(['label', 'topic'], axis=1).columns)) + full_test = pd.concat(test_predictions) + full_test.to_pickle(f'full_prediction_set.pkl') + full_test.to_csv(f"{run_path}_LGBM.pre", sep=' ', index=True, header=False, float_format="%.4f") + df = pd.concat(params_res) + df.to_pickle('full_cv_result.pkl') + features_df = pd.concat(features_imp).set_index('features') + features_df = features_df.groupby('features').mean() + features_df.to_pickle('features_importance.pkl') + print('The AucRoc after CV is:', roc_auc_score(self.full_data['label'], full_test)) + plot_roc(self.full_data['label'], full_test, 'LGBM-Predictor') + return roc_auc_score(self.full_data['label'], full_test) + + @timer + def find_best_params(self, train_df, test_df): + estimator = lgb.LGBMClassifier() + param_grid = [ + # {'objective': ['binary'], 'metric': ['auc'], 'boosting_type': ['dart'], 'is_unbalance': [True], + # 'num_leaves': [5], 'min_data_in_leaf': [5], 'lambda_l1': [0.7], 'lambda_l2': [0.7], + # 'feature_fraction': [0.0625], 'bagging_fraction': [0.25, 0.5, 0.75], 'bagging_freq': [1, 5, 10]} + {'objective': ['binary'], 'boosting_type': ['dart'], + 'metric': ['auc'], + 'bagging_fraction': [0.25, 0.5, 0.75], + 'num_leaves': [5], + 'learning_rate': [0.05, 0.1], + 'feature_fraction': [0.0625, 0.125, 0.25], + # 'feature_fraction': [0.0625, 0.125], + 'bagging_freq': [1], + 'lambda_l1': [0.75], # helps in feature selection - minimizes to median of data + 'lambda_l2': [0.75], # minimizes the mean of data + 'is_unbalance': [True], + 'num_iterations': [50, 100, 200, 300], + 'min_data_in_leaf': [5], 'verbose': [-1]}, + # {'objective': ['binary'], 'boosting_type': ['dart'], + # 'metric': ['auc'], + # 'num_leaves': [5], + # 'learning_rate': [0.01, 0.05, 0.1], + # 'feature_fraction': [0.0625, 0.125, 0.25], + # # 'feature_fraction': [0.0625, 0.125], + # 'bagging_freq': [0], + # 'lambda_l1': [0.75], # helps in feature selection - minimizes to median of data + # 'lambda_l2': [0.75], # minimizes the mean of data + # 'is_unbalance': [True], + # 'num_iterations': [100, 150, 200], + # 'min_data_in_leaf': [5], 'verbose': [-1]} + ] + # score function should be specified here (AUC) + # cv = None - default value 5-folds + clf = GridSearchCV(estimator, param_grid, n_jobs=40, refit=True, cv=3, verbose=1, error_score='raise', + scoring='roc_auc', return_train_score=True) + fim = clf.fit(train_df.drop(['label', 'topic'], axis=1), train_df['label']) + _df = pd.DataFrame(fim.cv_results_) + df = _df.sort_values('mean_test_score', ascending=False).head(10) + param_cols = df.columns[df.columns.str.startswith('param_')].tolist() + # df.loc[:, param_cols + ['mean_test_score']].to_pickle('cv_results.pkl') + # predictions = fim.predict_proba(test_df.drop(['topic', 'label'], axis=1))[:, 1] + # print('CV model AUC of ROC of prediction is:', roc_auc_score(test_df['label'], predictions)) + # print(f'Logloss = {log_loss(test_df["label"], predictions): 0.2f}') + # print(fim.best_estimator_) + # print(fim.best_params_) + # print(fim.best_score_) + _best_params = fim.best_params_ + _best_params.update(verbose=4, num_threads=10) + # _best_params.update(num_threads=10) + # create dataset for lightgbm + lgb_train = lgb.Dataset(train_df.drop(['label', 'topic'], axis=1), train_df['label']) + gbm = lgb.train(_best_params, lgb_train) + # print('Starting predicting...') + # predict + predictions = gbm.predict(test_df.drop(['topic', 'label'], axis=1)) + # assert (fim.best_estimator_.feature_importances_ == gbm.feature_importance('split')).all(), \ + # f'{fim.best_estimator_.feature_importances_}\n {gbm.feature_importance("split")}' + # eval + # print('The AUC of ROC of prediction is:', roc_auc_score(test_df['label'], y_pred)) + # try: + # print('The type of best_est ', type(best_est)) + # # save model to file + # gbm.save_model('model.txt') + # except AttributeError as er: + # print(er) + # lgb.plot_importance(gbm) + # lgb.plot_split_value_histogram(gbm, 'max-var') + # plt.show() + # exit() + return predictions, df.loc[:, param_cols + ['mean_test_score', 'mean_train_score']], { + 'features': gbm.feature_name(), 'split': gbm.feature_importance('split'), + 'gain': gbm.feature_importance('gain')} + + +def print_best_params(n=15): + cv_res = 'full_cv_result.pkl' + _df = pd.read_pickle(cv_res) + df = _df.sort_values('mean_test_score', ascending=False).head(n) + for col in df.columns: + print(col) + if col.endswith('score'): + continue + else: + print(df.groupby(col).count()) + else: + print(df[['mean_test_score', 'mean_train_score']]) + print(f'max score: {df["mean_test_score"].max()}') + + +def print_data_summary(data_df=None): + data_df = pd.read_pickle('data/data_df.pkl') if data_df is None else data_df + print(f"Number of positive samples: {data_df['label'].sum()}") + print(f"Ratio of positive samples: {data_df['label'].sum() / len(data_df)}") + + +def plot_feature_importance(): + features_df = pd.read_pickle('features_importance.pkl') + + by_split = features_df['split'].sort_values() + fig, ax = plt.subplots() + ylocs = np.arange(len(by_split)) + ax.barh(ylocs, by_split, align='center') + ax.set_yticks(ylocs) + ax.set_yticklabels(by_split.index) + ax.set_title('Feature importance by split') + # plt.show() + fig.savefig('feat_split_imp_ndcg_03.eps') + + by_gain = features_df['gain'].sort_values() + fig, ax = plt.subplots() + ylocs = np.arange(len(by_gain)) + ax.barh(ylocs, by_gain, align='center') + ax.set_yticks(ylocs) + ax.set_yticklabels(by_gain.index) + ax.set_title('Feature importance by gain') + # plt.show() + fig.savefig('feat_gain_imp_ndcg_03.eps') + + +if __name__ == '__main__': + plt.set_loglevel("info") + # cols = ['param_bagging_freq', 'param_boosting_type', 'param_feature_fraction', + # 'param_is_unbalance', 'param_learning_rate', 'param_max_bin', + # 'param_metric', 'param_min_data_in_leaf', 'param_num_iterations', + # 'param_num_leaves', 'param_objective', 'param_reg_alpha', + # 'param_reg_lambda', 'param_subsample', 'param_subsample_for_bin', + # 'param_verbose', 'params', 'split0_test_score', 'split1_test_score', + # 'split2_test_score', 'split3_test_score', 'split4_test_score', + # 'mean_test_score', 'std_test_score', 'rank_test_score', 'mean_train_score'] + # cv_res = 'cv_results.pkl' + # cv_res = 'full_cv_result.pkl' + # df = pd.read_pickle(cv_res) + # asd = 1 + z = NestedCrossVal(pd.read_pickle('data/data_df.pkl')) + z.outer_evaluation() + print_best_params(25) + print_data_summary() + plot_feature_importance() + +""" +Best test AUC 0.791345 +{'bagging_freq': 20, 'boosting_type': 'gbdt', 'feature_fraction': 0.25, +'is_training_metric': False, 'is_unbalance': False, 'learning_rate': 0.1, 'max_bin': 511, 'metric': 'auc', +'min_data_in_leaf': 10, 'n_estimators': 10, 'num_iterations': 50, 'num_leaves': 10, 'objective': 'binary', +'reg_alpha': 0.75, 'reg_lambda': 0.75, 'subsample': 0.8, 'subsample_for_bin': 100000, 'verbose': 1} + +Best dart test AUC 0.785872 +{'bagging_freq': 20, 'boosting_type': 'dart', 'feature_fraction': 0.25, 'importance_type': 'split', +'is_training_metric': False, 'is_unbalance': False, 'learning_rate': 0.1, 'max_bin': 511, 'metric': 'auc', +'min_data_in_leaf': 10, 'n_estimators': 10, 'num_iterations': 50, 'num_leaves': 31, 'objective': 'binary', +'reg_alpha': 0.75, 'reg_lambda': 0.75, 'subsample': 0.8, 'subsample_for_bin': 100000, 'verbose': 1} + +""" diff --git a/code/qpptk/qpptk/parse_queries.py b/code/qpptk/qpptk/parse_queries.py index 67259fa..f60d63f 100644 --- a/code/qpptk/qpptk/parse_queries.py +++ b/code/qpptk/qpptk/parse_queries.py @@ -1,10 +1,12 @@ from collections import defaultdict +import numpy as np import pandas as pd from syct import timer from lxml import etree +from sklearn.cluster import KMeans -import qpptk.CommonIndexFileFormat_pb2 as ciff +# import qpptk.CommonIndexFileFormat_pb2 as ciff from qpptk import Config, read_message, DocRecord, transform_list_to_counts_dict, add_topic_to_qdf @@ -35,15 +37,28 @@ def parse_ciff_queries_file(queries_file): return header, queries_dict, query_records +def qid_index_sort_key(index, ): + # return list(map(lambda s: np.array([100, 10, 1]).dot(np.array([int(x) for x in s.split('-')])), index.to_numpy())) + return list( + map(lambda s: np.array([int(x) * 10 ** (3 - i) for i, x in enumerate(s.split('-'))]).sum(), index.to_numpy())) + + class QueryParserCiff: def __init__(self, queries_file, **kwargs): header, queries_dict, query_records = parse_ciff_queries_file(queries_file) self.queries_dict = queries_dict self._rename_queries_to_qid(query_records) - filter_queries_file: list = kwargs.get('filter_queries_file', []) - if filter_queries_file: + filter_queries_file: list = kwargs.get('filter_queries_file', '') + drop_queries_file: str = kwargs.get('drop_queries_file', '') + drop_duplicate_queries: bool = kwargs.get('drop_duplicate_queries', True) # TODO: implement this + + if bool(filter_queries_file): self.filter_queries = QueryParserText(filter_queries_file).get_query_ids() self._filter_queries() + if bool(drop_queries_file): + with open(drop_queries_file, 'r') as _f: + self.drop_queries = _f.read().splitlines() + self._drop_queries() def _rename_queries_to_qid(self, query_records): """ @@ -57,6 +72,13 @@ def _filter_queries(self): filter_qids = set(self.filter_queries).intersection(self.queries_dict.keys()) self.queries_dict = {qid: self.queries_dict.get(qid) for qid in filter_qids} + def _drop_queries(self): + for qid in self.drop_queries: + try: + del self.queries_dict[qid] + except KeyError: + pass + def get_query(self, qid: str) -> dict: """ get the query dict for a given qid if it exists, otherwise returns None @@ -72,29 +94,55 @@ def get_query_ids(self) -> list: """ return sorted(self.queries_dict.keys()) + def get_queries_df(self): + return pd.DataFrame.from_dict(self.queries_dict, orient='index') + class QueryParserText: def __init__(self, queries_txt_file, **kwargs): self.queries_file = queries_txt_file self.raw_queries_df = self._read_queries() self.queries_sr = self._weight_queries() - filter_queries_file: list = kwargs.get('filter_queries_file', []) - if filter_queries_file: + self.__queries_df = None + filter_queries_file: str = kwargs.get('filter_queries_file', '') + drop_queries_file: str = kwargs.get('drop_queries_file', '') + drop_duplicate_queries: bool = kwargs.get('drop_duplicate_queries', True) + clusters: int = kwargs.get('cluster', None) + if bool(filter_queries_file): self.filter_queries = self._read_queries(filter_queries_file).index self._filter_queries() + if bool(drop_queries_file): + self.drop_queries = self._read_queries(filter_queries_file).index + self._drop_queries() + if bool(clusters): + self.cluster_queries_by_similarity(clusters) + if drop_duplicate_queries: + duplicated_qids = self.get_duplicates_bow() + self._drop_queries(duplicated_qids) + + @property + def queries_df(self): + if self.__queries_df is None: + self.__queries_df = pd.DataFrame.from_dict(self.queries_sr.to_dict(), orient='index').rename_axis( + index='qid') + return self.__queries_df - def _read_queries(self, queries_file=None): + def _read_queries(self, queries_file=None): # FIXME: add an adaptation for tsv files _queries_file = queries_file if queries_file else self.queries_file with open(_queries_file, 'r') as fp: - queries = [line.strip().split(' ', maxsplit=1) for line in fp] - return pd.DataFrame(queries, columns=['qid', 'terms']).set_index('qid') + queries = [line.strip().split(None, maxsplit=1) for line in fp] + return pd.DataFrame(queries, columns=['qid', 'terms']).set_index('qid').sort_index(key=qid_index_sort_key) def _weight_queries(self): - return self.raw_queries_df.terms.str.split().apply(transform_list_to_counts_dict) + return self.raw_queries_df.terms.replace('', None).dropna().str.split().apply(transform_list_to_counts_dict) def _filter_queries(self): self.queries_sr = self.queries_sr.loc[self.filter_queries] + def _drop_queries(self, duplicated_qids=None): + _duplicated_qids = self.drop_queries if duplicated_qids is None else duplicated_qids + self.queries_sr = self.queries_sr.drop(_duplicated_qids) + def get_duplicates_bow(self): duplicate_qids = set() for topic, _df in add_topic_to_qdf(pd.DataFrame(self.queries_sr)).set_index('qid').groupby('topic')['terms']: @@ -106,6 +154,13 @@ def get_duplicates_bow(self): duplicate_qids.update(_df.loc[_df['terms'].str.split().map(sorted).map(tuple).duplicated('last')].index) return duplicate_qids + # def get_duplicates_bow_new(self): + # duplicate_qids = set() + # for topic, qids in add_topic_to_qdf(self.raw_queries_df)[['topic', 'qid']].groupby('topic'): + # _df = self.queries_df.loc[qids['qid']].dropna(axis=1, how='all') + # duplicate_qids.update(_df.loc[_df.duplicated(keep='last')].index) + # return duplicate_qids + def get_duplicates_seq(self): duplicate_qids = set() for topic, _df in add_topic_to_qdf(self.raw_queries_df).set_index('qid').groupby('topic')['terms']: @@ -116,7 +171,134 @@ def get_query(self, qid: str) -> dict: return self.queries_sr.loc[qid] def get_query_ids(self) -> list: - return self.queries_sr.index.tolist() + return self.queries_sr.index.to_numpy() + + def get_queries_df(self): + return self.queries_df + + def cluster_queries_by_similarity(self, k=3): + """ + Greedy and simple (not optimal) clustering of queries by topics and similarity + """ + _df = add_topic_to_qdf(self.queries_df) + all_topics = set(_df.topic) + threshold = len(all_topics) // 3 + clusters = {i: set() for i in range(k)} + i = 0 + assigned_topics = set() + for column in _df.set_index(['topic', 'qid']): + _topics = set(_df.loc[_df[column].fillna(False).astype(bool)].topic.to_numpy()) + if len(clusters[i]) <= threshold or i == k - 1: + clusters[i].update(_topics.difference(assigned_topics)) + else: + i += 1 + clusters[i].update(_topics.difference(assigned_topics)) + assigned_topics.update(_topics) + for i, _topics in clusters.items(): + qids = _df[['topic', 'qid']].set_index('topic').loc[_topics].sort_index()['qid'] + self.raw_queries_df.terms.replace('', None).dropna().str.split().apply(' '.join).loc[qids].to_csv( + self.queries_file + f'.part-{i}', sep='\t', header=False) + + +class QueryParserJsonl: + def __init__(self, queries_jsonl_file, **kwargs): + self.queries_file = queries_jsonl_file + self.raw_queries_df = self._read_queries() + self.queries_sr = self._weight_queries() + self.__queries_df = None + filter_queries_file: str = kwargs.get('filter_queries_file', '') + drop_queries_file: str = kwargs.get('drop_queries_file', '') + drop_duplicate_queries: bool = kwargs.get('drop_duplicate_queries', True) + clusters: int = kwargs.get('cluster', None) + if bool(filter_queries_file): + self.filter_queries = self._read_queries(filter_queries_file).index + self._filter_queries() + if bool(drop_queries_file): + self.drop_queries = self._read_queries(filter_queries_file).index + self._drop_queries() + if bool(clusters): + self.cluster_queries_by_similarity(clusters) + if drop_duplicate_queries: + duplicated_qids = self.get_duplicates_bow() + self._drop_queries(duplicated_qids) + + @property + def queries_df(self): + if self.__queries_df is None: + self.__queries_df = pd.DataFrame.from_dict(self.queries_sr.to_dict(), orient='index').rename_axis( + index='qid') + return self.__queries_df + + def _read_queries(self, queries_file=None): # FIXME: add an adaptation for tsv files + _queries_file = queries_file if queries_file else self.queries_file + return pd.read_json(_queries_file, lines=True, dtype=str).rename(columns={'query': 'terms'})[ + ['qid', 'terms']].set_index('qid').sort_index(key=qid_index_sort_key) + + def _weight_queries(self): + return self.raw_queries_df.terms.replace('', None).dropna().str.split().apply(transform_list_to_counts_dict) + + def _filter_queries(self): + self.queries_sr = self.queries_sr.loc[self.filter_queries] + + def _drop_queries(self, duplicated_qids=None): + _duplicated_qids = self.drop_queries if duplicated_qids is None else duplicated_qids + self.queries_sr = self.queries_sr.drop(_duplicated_qids) + + def get_duplicates_bow(self): + duplicate_qids = set() + for topic, _df in add_topic_to_qdf(pd.DataFrame(self.queries_sr)).set_index('qid').groupby('topic')['terms']: + try: + duplicate_qids.update(_df.loc[_df.duplicated('last')].index) + # duplicate_qids.update(set(_df.index).difference(_df.drop_duplicates('last').index)) + except SystemError as err: + _df = add_topic_to_qdf(self.raw_queries_df).set_index('topic').loc[topic].set_index('qid', drop=True) + duplicate_qids.update(_df.loc[_df['terms'].str.split().map(sorted).map(tuple).duplicated('last')].index) + return duplicate_qids + + # def get_duplicates_bow_new(self): + # duplicate_qids = set() + # for topic, qids in add_topic_to_qdf(self.raw_queries_df)[['topic', 'qid']].groupby('topic'): + # _df = self.queries_df.loc[qids['qid']].dropna(axis=1, how='all') + # duplicate_qids.update(_df.loc[_df.duplicated(keep='last')].index) + # return duplicate_qids + + def get_duplicates_seq(self): + duplicate_qids = set() + for topic, _df in add_topic_to_qdf(self.raw_queries_df).set_index('qid').groupby('topic')['terms']: + duplicate_qids.update(duplicate_qids.update(_df.loc[_df.duplicated('last')].index)) + return duplicate_qids + + def get_query(self, qid: str) -> dict: + return self.queries_sr.loc[qid] + + def get_query_ids(self) -> list: + return self.queries_sr.index.to_numpy() + + def get_queries_df(self): + return self.queries_df + + def cluster_queries_by_similarity(self, k=3): + """ + Greedy and simple (not optimal) clustering of queries by topics and similarity + """ + _df = add_topic_to_qdf(self.queries_df) + all_topics = set(_df.topic) + threshold = len(all_topics) // 3 + clusters = {i: set() for i in range(k)} + i = 0 + assigned_topics = set() + for column in _df.set_index(['topic', 'qid']): + _topics = set(_df.loc[_df[column].fillna(False).astype(bool)].topic.to_numpy()) + if len(clusters[i]) <= threshold or i == k - 1: + clusters[i].update(_topics.difference(assigned_topics)) + else: + i += 1 + clusters[i].update(_topics.difference(assigned_topics)) + assigned_topics.update(_topics) + for i, _topics in clusters.items(): + qids = _df[['topic', 'qid']].set_index('topic').loc[_topics].sort_index()['qid'] + self.raw_queries_df.terms.replace('', None).dropna().str.split().apply(' '.join).loc[qids].to_csv( + self.queries_file + f'.part-{i}', sep='\t', header=False) class QueriesXMLWriter: diff --git a/code/qpptk/qpptk/qpptk_main.py b/code/qpptk/qpptk/qpptk_main.py index ecc2361..07f9efb 100644 --- a/code/qpptk/qpptk/qpptk_main.py +++ b/code/qpptk/qpptk/qpptk_main.py @@ -2,60 +2,75 @@ import os import subprocess as sp import sys +from functools import partial from glob import glob import pandas as pd from syct import timer +from sklearn.metrics import pairwise_distances -from qpptk import Config, set_index_dump_paths, ensure_file, ensure_dir, parse_index_to_db, read_trec_res_file, \ - QueryParserCiff, QueryParserText -from qpptk.global_manager import initialize_text_queries, initialize_ciff_queries, pre_ret_prediction_full, \ - retrieval_full, initialize_db_index, initialize_text_index, initialize_ciff_index, post_ret_prediction_full +from qpptk import Config, set_index_dump_paths, ensure_file, ensure_dir, read_trec_res_file, \ + QueryParserCiff, QueryParserText, QueryParserJsonl, add_topic_to_qdf, duplicate_qrel_file_to_qids, calc_ndcg +from qpptk.global_manager import pre_ret_prediction_full, \ + retrieval_full, initialize_db_index, initialize_text_index, post_ret_prediction_full, \ + initialize_terrier_index parser = argparse.ArgumentParser(description='Run QL retrieval or Query Performance Prediction') index_group = parser.add_mutually_exclusive_group() -index_group.add_argument('-ti', '--text_index', metavar='INDEX', type=str, default=None, help='path to text index dir') +index_group.add_argument('--text_index', metavar='INDEX', type=str, default=None, help='path to text index dir') index_group.add_argument('-ci', '--ciff_index', metavar='INDEX', type=str, default=None, help='path to ciff index file') +index_group.add_argument('-ti', '--terrier_index', metavar='INDEX', type=str, default=None, + help='path to terrier index dir') queries_group = parser.add_mutually_exclusive_group() queries_group.add_argument('-tq', '--text_queries', metavar='QUERIES', default=None, help='path to text queries file') queries_group.add_argument('-cq', '--ciff_queries', metavar='QUERIES', default=None, help='path to ciff queries file') +queries_group.add_argument('-jq', '--jsonl_queries', metavar='QUERIES', default=None, help='path to jsonl queries file') + parser.add_argument('-fq', '--filter_queries', metavar='FilterQUERIES', default=None, help='path to text queries file that will be used to filter the queries') +parser.add_argument('-dq', '--drop_queries', metavar='DropQUERIES', default=None, + help='path to text query ids file that will be used to drop the queries') parser.add_argument('--retrieve', action='store_true', help='add this flag to run retrieval') parser.add_argument('--method', choices=['ql', 'rm', 'rm_rerank'], default='ql', help='the method to be used in retrieval') parser.add_argument('--predict', action='store_true', help='add this flag to run predictions using ALL PREDICTORS') parser.add_argument('--evaluate', action='store_true', help='add this flag to run evaluation') +parser.add_argument('--pairs_sim', action='store_true', help='Generates a file with similarity between all query pairs') +parser.add_argument('--cluster_queries', action='store_true', + help='Splits the queries file into separate files by similarity') +parser.add_argument('--keep_duplicate_queries', action='store_true', + help='By default duplicate queries per topic will be removed, use this option to keep them') parser.add_argument('--predPre', action='store_true', help='add this flag to run only pre-retrieval predictors') parser.add_argument('--predPost', action='store_true', help='add this flag to run only post-retrieval predictors') -# parser.add_argument('--results_file', help='add this flag to run only post-retrieval predictors') logger = Config.logger -IR_METRICS = {'ap@1000': 'map_cut.1000', 'ap@10': 'map_cut.10', 'ap@100': 'map_cut.100', 'ndcg@10': 'ndcg_cut.10', - 'ndcg@100': 'ndcg_cut.100', 'R-precision': 'Rprec', 'P@1': 'P.1', 'P@5': 'P.5', 'P@10': 'P.10'} +TREC_IR_METRICS = {'ap@1000': 'map_cut.1000', 'ap@10': 'map_cut.10', 'ap@100': 'map_cut.100', 'ndcg@10': 'ndcg_cut.10', + 'ndcg@100': 'ndcg_cut.100', 'R-precision': 'Rprec', 'P@1': 'P.1', 'P@5': 'P.5', 'P@10': 'P.10', + 'RR': 'recip_rank'} PRECISION = 6 -def create_files_in_shell(prefix, eval_metric='ap@1000'): +def generate_trec_eval(prefix, eval_metric='ap@1000'): """ :param prefix: :param eval_metric: """ - corpus, _, stoplist, stemmer = prefix.rsplit('/', 1)[1].split('_') - metric = IR_METRICS.get(eval_metric, 'map_cut.1000') + # corpus, _, stoplist, stemmer = prefix.rsplit('/', 1)[1].split('_') + metric = TREC_IR_METRICS.get(eval_metric, 'map_cut.1000') try: - QL_res_file = ensure_file(f"{prefix}_QL.res") + ql_res_file = ensure_file(f"{prefix}_QL.res") except FileNotFoundError as err: print(err) sys.exit('The results file is not found, add --retrieve to create it') trec_eval = Config.TREC_EVAL - proc = sp.run([trec_eval, '-m', metric, '-qn', f'/research/local/olz/data/{corpus}_mod.qrels', QL_res_file], + qrels_file = ensure_file(Config.QREL_FILE) + proc = sp.run([trec_eval, '-m', metric, '-qn', qrels_file, ql_res_file], stderr=sp.STDOUT, stdout=sp.PIPE) _fp = open(f"{prefix}_QL.{eval_metric}", 'w') sp.run(['awk', '{print $2,$3}'], stdout=_fp, input=proc.stdout) @@ -63,23 +78,70 @@ def create_files_in_shell(prefix, eval_metric='ap@1000'): # sp.run(["sed", "-i", "$ d", f"{prefix}_QL.ap"]) # Remove last line from the file -def load_and_evaluate(prefix, ir_metric, method='pearson', title_only=False): - # queries = get_queries_object() - # qids = queries.get_query_ids() - # from qpptk.utility_functions import duplicate_qrel_file_to_qids +def generate_rbp_eval(prefix, eval_metric='rbp-0.95'): + """ + + :param eval_metric: + :param prefix: + """ # corpus, _, stoplist, stemmer = prefix.rsplit('/', 1)[1].split('_') - # duplicate_qrel_file_to_qids(f'/research/local/olz/data/{corpus}.qrels', qids) + try: + ql_res_file = ensure_file(f"{prefix}_QL.res") + except FileNotFoundError as err: + print(err) + sys.exit('The results file is not found, add --retrieve to create it') + metric, p = eval_metric.split('-') + rbp_eval = Config.RBP_EVAL + qrels_file = ensure_file(Config.QREL_FILE) + proc = sp.run([rbp_eval, '-q', '-T', '-H', '-W', '-p', p, qrels_file, ql_res_file], + stderr=sp.STDOUT, stdout=sp.PIPE) + _fp = open(f"{prefix}_QL.{eval_metric}", 'w') + sp.run(['awk', '{print $4,$8}'], stdout=_fp, input=proc.stdout) + _fp.close() + # sp.run(["sed", "-i", "$ d", f"{prefix}_QL.ap"]) # Remove last line from the file + + +@timer +def create_ndcg_eval_files(prefix, ir_metric): + try: + ql_res_file = ensure_file(f"{prefix}_QL.res") + qrels_file = ensure_file(Config.QREL_FILE) + except FileNotFoundError as err: + print(err) + sys.exit('The results file or qrels file is not found, adding --retrieve will generate a results file') + k = int(ir_metric.rsplit('@', 1)[-1]) + calc_ndcg(qrels_file, ql_res_file, k, original=False, logger=logger) + + +def load_and_evaluate(prefix, ir_metric, method='pearson', title_only=False): + queries = get_queries_object() + qids = queries.get_query_ids() + _components = prefix.rsplit('/', 1)[1].split('_') + if len(_components) > 3: + corpus, _, stoplist, stemmer = _components + elif len(_components) == 3: + corpus, stoplist, stemmer = _components + else: + print('Unknown format of index name') + # corpus, stoplist, stemmer = _components + # qrels_file = Config.QREL_FILE + # duplicate_qrel_file_to_qids(qrels_file, qids) try: eval_df = pd.read_table(f"{prefix}_QL.{ir_metric}", delim_whitespace=True, names=['qid', ir_metric], index_col=0) except FileNotFoundError: - create_files_in_shell(prefix, ir_metric) + if ir_metric.startswith('ndcg'): + create_ndcg_eval_files(prefix, ir_metric) + elif ir_metric.startswith('rbp'): + generate_rbp_eval(prefix, ir_metric) + else: + generate_trec_eval(prefix, ir_metric) eval_df = pd.read_table(f"{prefix}_QL.{ir_metric}", delim_whitespace=True, names=['qid', ir_metric], index_col=0) if title_only: eval_df = eval_df.loc[eval_df.index.str.contains('-50-1')] title_queries = eval_df.index - assert len(title_queries) == 249, 'wrong numbers of title queries in ROBUST eval file' + # assert len(title_queries) == 249, 'wrong numbers of title queries in ROBUST eval file' prediction_files = glob(f"{prefix}*.pre") predictors = [] results = [] @@ -88,71 +150,166 @@ def load_and_evaluate(prefix, ir_metric, method='pearson', title_only=False): index_col=[0, 1]) if title_only: pr_df = pr_df.loc[(slice(None), title_queries), :] - assert len(pr_df) == 249, 'wrong numbers of title queries in ROBUST predictions file' - print(f"The correlation of {predictions_file}: {pr_df.corrwith(eval_df[ir_metric], method=method)}") + # assert len(pr_df) == 249, 'wrong numbers of title queries in ROBUST predictions file' + # print(f"The correlation of {predictions_file}: {pr_df.corrwith(eval_df[ir_metric], method=method)}") collection, predictor = predictions_file.rsplit('/', 1)[1].replace('.pre', '').rsplit('_', 1) # results[collection] = {predictor: pr_df.corrwith(ap_df[ir_metric])[0]} results.append(pr_df.corrwith(eval_df[ir_metric], method=method)[0]) predictors.append(predictor) sr = pd.Series(results, index=predictors) - sr.name = f'{collection}_{method}' + sr.name = f'{collection}_{method}_{ir_metric}' + print(sr.sort_index()) + print(sr.sort_values()) return sr def get_queries_object(): - queries_path, queries_type = (args.text_queries, 'text') if args.text_queries else (args.ciff_queries, 'ciff') + drop_duplicates = not args.keep_duplicate_queries + if args.text_queries: + queries_path, queries_type = args.text_queries, 'text' + elif args.ciff_queries: + queries_path, queries_type = args.ciff_queries, 'ciff' + elif args.jsonl_queries: + queries_path, queries_type = args.jsonl_queries, 'jsonl' + else: + queries_path, queries_type = None, None + if queries_path is None: - queries_path, queries_type = (Config.TEXT_QUERIES, 'text') if Config.TEXT_QUERIES else \ - (Config.CIFF_QUERIES, 'ciff') - if queries_path is None: + if Config.TEXT_QUERIES: + queries_path, queries_type = Config.TEXT_QUERIES, 'text' + elif Config.CIFF_QUERIES: + queries_path, queries_type = Config.CIFF_QUERIES, 'ciff' + elif Config.JSONL_QUERIES: + queries_path, queries_type = Config.JSONL_QUERIES, 'jsonl' + else: raise AssertionError('No queries file was specified') filter_queries = args.filter_queries if filter_queries and filter_queries.lower() != 'none': return QueryParserText(queries_path, - filter_queries_file=filter_queries) if queries_type == 'text' else QueryParserCiff( - queries_path, filter_queries_file=filter_queries) + filter_queries_file=filter_queries, + drop_duplicate_queries=drop_duplicates) if queries_type == 'text' else QueryParserCiff( + queries_path, filter_queries_file=filter_queries, drop_duplicate_queries=drop_duplicates) + drop_queries = args.drop_queries + if drop_queries and drop_queries.lower() != 'none': + if queries_type == 'text': + return QueryParserText(queries_path, drop_queries_file='duplicated_qids.txt', + drop_duplicate_queries=drop_duplicates) + elif queries_type == 'ciff': + QueryParserCiff(queries_path, drop_queries_file='duplicated_qids.txt', + drop_duplicate_queries=drop_duplicates) + elif queries_type == 'jsonl': + QueryParserJsonl(queries_path, drop_queries_file='duplicated_qids.txt', + drop_duplicate_queries=drop_duplicates) else: - return QueryParserText(queries_path) if queries_type == 'text' else QueryParserCiff(queries_path) + if queries_type == 'text': + return QueryParserText(queries_path, drop_duplicate_queries=drop_duplicates) + elif queries_type == 'ciff': + return QueryParserCiff(queries_path, drop_duplicate_queries=drop_duplicates) + elif queries_type == 'jsonl': + return QueryParserJsonl(queries_path, drop_duplicate_queries=drop_duplicates) -def set_index(): - index_path, index_type = (args.text_index, 'text') if args.text_index else (args.ciff_index, 'ciff') +def set_index_paths(): + index_path, index_type = (args.ciff_index, 'ciff') if args.ciff_index else ( + (args.text_index, 'text') if args.text_index else (args.terrier_index, 'terrier')) if index_path is None: - index_path, index_type = (Config.INDEX_DIR, 'text') if Config.INDEX_DIR else (Config.CIFF_INDEX, 'ciff') + if Config.CIFF_INDEX: + index_path, index_type = Config.CIFF_INDEX, 'ciff' + elif Config.TERRIER_INDEX: + index_path, index_type = Config.TERRIER_INDEX, 'terrier' + else: + index_path, index_type = Config.INDEX_DIR, 'text' if index_path is None: raise AssertionError('No index was specified') return index_path, index_type +def generate_pairs_similarity(queries): + try: + df = queries.get_queries_df() + except AttributeError: + df = add_topic_to_qdf( + pd.DataFrame.from_dict(queries.queries_dict, orient='index').rename_axis('qid')).set_index('topic').drop( + '672', errors='ignore').set_index('qid') + df = df.sort_index() + cos_sim_df = pd.DataFrame(1 - pairwise_distances(df.fillna(0).to_numpy(), metric='cosine', n_jobs=10), + index=df.index, columns=df.index) + jac_df = pd.DataFrame(1 - pairwise_distances(df.fillna(0).astype(bool).to_numpy(), metric='jaccard', n_jobs=10), + index=df.index, columns=df.index) + dsc_df = pd.DataFrame(1 - pairwise_distances(df.fillna(0).astype(bool).to_numpy(), metric='dice', n_jobs=10), + index=df.index, columns=df.index) + return cos_sim_df, jac_df, dsc_df + + +def cluster_queries(queries): + queries.cluster_queries_by_similarity(3) + + @timer def main(): results_dir = Config.RESULTS_DIR - def init_index(): + def init_db_index(): index = initialize_db_index(db_dir) queries = get_queries_object() qids = queries.get_query_ids() return qids, index, queries - index_path, index_type = set_index() + def init_readonly_index(): # TODO: should be init for terrier index + # index = initialize_db_index(db_dir) + queries = get_queries_object() + qids = queries.get_query_ids() + index = initialize_terrier_index(index_path, partial_terms=queries.get_queries_df().columns) + index_hash = index().partial_terms_hash + return qids, index, queries, index_hash + + def init_writeable_index(): + queries = get_queries_object() + return initialize_terrier_index(index_path, partial_terms=queries.get_queries_df().columns, read_only=False)() + + index_path, index_type = set_index_paths() if index_type == 'text': dump_files = set_index_dump_paths(index_path) - prefix = '_'.join(index_path.split('/')[-2:]) if index_type == 'text' else \ - index_path.rsplit('/', 1)[-1].replace('.ciff', '') + if index_type == 'ciff': + prefix = index_path.rsplit('/', 1)[-1].replace('.ciff', '') + elif index_type == 'terrier': + prefix = index_path.rsplit('/', 1)[-1] + else: + prefix = '_'.join(index_path.split('/')[-2:]) prefix_path = os.path.join(results_dir, prefix) if args.retrieve or args.predict or args.predPost or args.predPre: - _db_dir = os.path.join(Config.DB_DIR, prefix) + if index_type == 'ciff' or index_type == 'text': + _db_dir = os.path.join(Config.DB_DIR, prefix) + try: + db_dir = ensure_dir(_db_dir, create_if_not=False) + qids, index, queries = init_db_index() + except FileNotFoundError: + logger.info(f"Relevant DB index wasn't found in {_db_dir}") + logger.info(f"Creating a new one") + if index_type == 'ciff': + index = initialize_ciff_index(index_path) + parse_index_to_db(index, prefix, Config.DB_DIR) + db_dir = ensure_dir(_db_dir, create_if_not=False) + else: + index = initialize_text_index(*dump_files) + parse_index_to_db(index, prefix, Config.DB_DIR) + db_dir = ensure_dir(_db_dir, create_if_not=False) + qids, index, queries = init_db_index() + elif index_type == 'terrier': + try: + qids, index, queries, index_hash = init_readonly_index() + except FileNotFoundError: + init_writeable_index() + qids, index, queries, index_hash = init_readonly_index() + else: + sys.exit('No known index type was specified') try: - db_dir = ensure_dir(_db_dir, create_if_not=False) - qids, index, queries = init_index() - except FileNotFoundError: - logger.info(f"Relevant DB index wasn't found in {_db_dir}") - logger.info(f"Creating a new one") - index = initialize_text_index(*dump_files) if index_type == 'text' else initialize_ciff_index(index_path) - parse_index_to_db(index, prefix, Config.DB_DIR) - db_dir = ensure_dir(_db_dir, create_if_not=False) - qids, index, queries = init_index() + path, collection = prefix_path.rsplit('/', 1) + prefix_path = os.path.join(ensure_dir(os.path.join(path, str(index_hash))), prefix) + except NameError: + pass + if args.retrieve: method = args.method if method == 'rm_rerank': @@ -176,7 +333,8 @@ def init_index(): if args.predPre: predictions_df = pre_ret_prediction_full(qids, index, queries) for col in predictions_df.columns: - predictions_df.loc[:, col].to_csv(f"{prefix_path}_PRE_{col}.pre", sep=' ', index=True, header=False, + predictions_df.loc[:, col].to_csv(f"{prefix_path}_PRE_{col}.pre", sep=' ', index=True, + header=False, float_format=f"%.{PRECISION}f") if args.predPost: retrieval_method = 'QL' @@ -189,21 +347,39 @@ def init_index(): sys.exit(error_msg) predictions_df = post_ret_prediction_full(qids, index, queries, read_trec_res_file(results_file)) for col in predictions_df.columns: - predictions_df.loc[:, col].to_csv(f"{prefix_path}_{retrieval_method}_{col}.pre", sep=' ', index=True, - header=False, - float_format=f"%.{PRECISION}f") + predictions_df.loc[:, col].to_csv(f"{prefix_path}_{retrieval_method}_{col}.pre", sep=' ', + index=True, header=False, float_format=f"%.{PRECISION}f") if args.evaluate: # method = 'spearman' - method = 'kendall' - # method = 'pearson' + # method = 'kendall' + method = 'pearson' # queries = 'title' queries = 'all' ir_metric = 'ap@1000' + # ir_metric = 'ndcg@100' + # ir_metric = 'RR' # ir_metric = 'ndcg@10' + # ir_metric = 'rbp-0.85' title_only = True if queries == 'title' else False - load_and_evaluate(prefix_path, ir_metric, method=method, title_only=title_only).to_pickle( + if Config.BATCH_NAME: + _prefix = prefix_path[::-1].replace('/', ' ', 1)[::-1].replace(' ', '/' + Config.BATCH_NAME + '/') + else: + _prefix = prefix_path + load_and_evaluate(_prefix, ir_metric, method=method, title_only=title_only).to_pickle( f'{prefix_path}.eval_{ir_metric}_{method}_{queries}_queries.pkl') + if args.pairs_sim: + cos_sim_df, jac_df, dsc_df = generate_pairs_similarity(get_queries_object()) + cos_sim_df.to_csv(f"{prefix_path}_pairwise_sim-cos.tsv", sep=' ', index=True, header=True, + float_format=f"%.{PRECISION}f") + jac_df.to_csv(f"{prefix_path}_pairwise_sim-jac.tsv", sep=' ', index=True, header=True, + float_format=f"%.{PRECISION}f") + dsc_df.to_csv(f"{prefix_path}_pairwise_sim-dsc.tsv", sep=' ', index=True, header=True, + float_format=f"%.{PRECISION}f") + + if args.cluster_queries: + cluster_queries(get_queries_object()) + if __name__ == '__main__': args = parser.parse_args() # defined and used as a "global" variable From fff606b19e1bb2029b6d6031c9e8a72dd252f9a2 Mon Sep 17 00:00:00 2001 From: Zendelo Date: Tue, 22 Aug 2023 20:31:28 +1000 Subject: [PATCH 07/40] add utility functions - saving and loading pickle files -calculating ndcg -rreplace for a string -sorensen_dice_similarity --- code/qpptk/qpptk/utility_functions.py | 99 +++++++++++++++++++++++++++ 1 file changed, 99 insertions(+) diff --git a/code/qpptk/qpptk/utility_functions.py b/code/qpptk/qpptk/utility_functions.py index bbdc1f9..464f13a 100644 --- a/code/qpptk/qpptk/utility_functions.py +++ b/code/qpptk/qpptk/utility_functions.py @@ -1,5 +1,6 @@ import linecache import os +import pickle from bisect import bisect_left import matplotlib.pyplot as plt @@ -7,6 +8,10 @@ import pandas as pd from google.protobuf.internal.decoder import _DecodeVarint32 from sklearn.metrics import roc_curve, auc +import numpy as np + +TREC_RES_COLUMNS = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] +TREC_QREL_COLUMNS = ['qid', 'iteration', 'docNo', 'rel'] def get_file_len(file_path): @@ -40,6 +45,7 @@ def ensure_dir(file_path, create_if_not=True): """ The function ensures the dir exists, if it doesn't it creates it and returns the path or raises FileNotFoundError + In case file_path is an existing file, returns the path of the parent directory """ # tilde expansion file_path = os.path.normpath(os.path.expanduser(file_path)) @@ -85,6 +91,10 @@ def overlap_coefficient(set_1, set_2): return len(set_1.intersection(set_2)) / min(len(set_1), len(set_2)) +def sorensen_dice_similarity(set_1, set_2): + return 2 * len(set_1.intersection(set_2)) / (len(set_1) + len(set_2)) + + def duplicate_qrel_file_to_qids(qrel_file, qids): qrels_df = pd.read_table(qrel_file, delim_whitespace=True, names=['qid', 'iter', 'doc', 'rel'], dtype=str) topics_dict = {qid: qid.split('-', 1)[0] for qid in qids} # {qid:topic} @@ -143,3 +153,92 @@ def plot_roc(y_test, y_pred, predictor_name): plt.ylabel('True Positive Rate (Sensitivity/Recall)') plt.xlabel('False Positive Rate (1-Specificity)') plt.show() + + +def pickle_save_obj(obj, file_name: str): + if not file_name.endswith('.pkl'): + file_name += '.pkl' + with open(file_name, 'wb') as f: + pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL) + + +def pickle_load_obj(file_name: str): + if not file_name.endswith('.pkl'): + file_name += '.pkl' + with open(file_name, 'rb') as f: + return pickle.load(f) + + +def load_generate_pickle_df(file_name: str, func, *args): + try: + _df = pd.read_pickle(file_name) + except FileNotFoundError: + _df = func(*args) + _df.to_pickle(file_name) + return _df + + +def calc_ndcg(qrels_file, results_file, k, logger=None, base=2, gdeval=True): + """ + Setting gdeval will yield identical results to the official evaluation script that was published for TREC. + Note that the calculation in that script differs from (probably) any published research version. + :param qrels_file: + :param results_file: + :param k: + :param logger: + :param base: + :param gdeval: + :return: + """ + # Reading and sorting the qrels, to later speed-up indexing and locating + qrels_df = pd.read_csv(qrels_file, delim_whitespace=True, names=TREC_QREL_COLUMNS). \ + sort_values(['qid', 'rel', 'docNo'], ascending=[True, False, True]).set_index(['qid', 'docNo']) + qrels_df['rel'].clip(lower=0, inplace=True) + + # Store beginning and end indices for each query - used for speed up + qid_res_len = qrels_df.groupby('qid').apply(len) + qid_end_loc = qid_res_len.cumsum() + qid_start_loc = qid_end_loc - qid_res_len + qrels_df = qrels_df.droplevel(0) + + if isinstance(results_file, str): + results_df = pd.read_csv(results_file, delim_whitespace=True, names=TREC_RES_COLUMNS) + elif isinstance(results_file, pd.DataFrame): + results_df = results_file + else: + raise TypeError + if gdeval: + results_df = results_df.sort_values(['qid', 'docScore', 'docNo'], ascending=[True, False, False]).groupby( + 'qid').head(k) + discount = np.log(np.arange(1, k + 1) + 1) + else: + results_df = results_df.sort_values(['qid', 'rank']).groupby('qid').head(k) + discount = np.concatenate((np.ones(base), np.log(np.arange(base, k) + 1) / np.log(base))) + result = {} + for qid, _df in results_df.groupby('qid'): + docs = _df['docNo'].to_numpy() + try: + _qrels_df = qrels_df.iloc[qid_start_loc.loc[qid]: qid_end_loc.loc[qid]] + except KeyError as err: + if logger is None: + print(f'query id {err} doesn\'t exist in the qrels file, skipping it') + else: + logger.warning(f'query id {err} doesn\'t exist in the qrels file, skipping it') + continue + if gdeval: + dcg = 2 ** _qrels_df.reindex(docs)['rel'].fillna(0).to_numpy() - 1 + idcg = ((2 ** _qrels_df['rel'].head(k) - 1) / discount[:len(_qrels_df)]).sum() + else: + dcg = _qrels_df.reindex(docs)['rel'].fillna(0).to_numpy() + idcg = (_qrels_df['rel'].head(k) / discount[:len(_qrels_df)]).sum() + result[qid] = (dcg / discount[:len(dcg)]).sum() / idcg + res_df = pd.DataFrame.from_dict(result, orient='index', columns=[f'nDCG@{k}']) + # res_df.to_csv(rreplace(results_file, 'run', f'ndcg@{k}', 1), sep='\t', float_format='%.6f', header=False) + # print(res_df.to_string(float_format='%.5f')) + # print(f'Mean: {res_df.mean()[0]:.5f}') + return res_df + + +def rreplace(string, old, new, count): + # TODO: Try and implement an extended version of str class + return new.join(string.rsplit(old, count)) From 072e86f0c981a636840b1007a1002ed590033bbf Mon Sep 17 00:00:00 2001 From: Zendelo Date: Tue, 22 Aug 2023 20:39:53 +1000 Subject: [PATCH 08/40] update retrieval code --- code/qpptk/qpptk/retrieval_local_manager.py | 91 +++++++++++++-------- 1 file changed, 58 insertions(+), 33 deletions(-) diff --git a/code/qpptk/qpptk/retrieval_local_manager.py b/code/qpptk/qpptk/retrieval_local_manager.py index e178225..4ec8d01 100644 --- a/code/qpptk/qpptk/retrieval_local_manager.py +++ b/code/qpptk/qpptk/retrieval_local_manager.py @@ -1,7 +1,10 @@ +import sys + import numpy as np from scipy.sparse import csr_matrix -from qpptk import IndexDB as Index +# from qpptk import IndexDB as Index +from qpptk import IndexTerrier as Index from qpptk import QueryParserText, Config logger = Config.logger @@ -21,7 +24,7 @@ def __init__(self, index_obj: Index, query_obj: QueryParserText, qid): self._candidates_dict = {} def _ql_score_documents(self): - tf_mat = self.index.get_mat_by_terms(self.qry_terms_record.keys()) + tf_mat = self.index.get_mat_by_terms(self.qry_terms_record) nnz_rows = np.unique(tf_mat.nonzero()[0]) tf_mat = tf_mat[nnz_rows, :] _doc_len = self.index.get_doc_len_vec(nnz_rows) @@ -29,24 +32,25 @@ def _ql_score_documents(self): _right_log = np.array([1 - _doc_len / (self.mu + _doc_len), ]).T * np.array( [[v.cf for v in self.qry_terms_record.values()], ]) / self.index.total_terms doc_scores = np.asarray( - np.log(_left_log + _right_log) * np.array([[self.query[q] for q in self.qry_terms_record], ]).T).squeeze() + np.log(_left_log + _right_log) * np.array([[self.query[q] for q in self.qry_terms_record], ]).T) + doc_scores = doc_scores.reshape(doc_scores.shape[0]) return np.sort( - np.array(list(zip(nnz_rows, doc_scores)), dtype=[('doc_id', np.uint32), ('doc_score', np.float)]), + np.array(list(zip(nnz_rows, doc_scores)), dtype=[('doc_id', np.uint32), ('doc_score', float)]), order='doc_score')[::-1] def rm_construction(self, ql_top_k): """ - Constructing the RM without smoothing + Constructing the RM without smoothing. """ - tf_mat = self.index.get_mat_by_docs(ql_top_k['doc_id']) - nnz_cols = np.unique(tf_mat.nonzero()[1]) + tf_mat = self.index.get_mat_by_docs(ql_top_k['doc_id']) # tf matrix by documents in results + nnz_cols = np.unique(tf_mat.nonzero()[1]) # all the columns (terms) that appear in the docs in the result tf_mat = tf_mat[:, nnz_cols] - exp_ql = np.exp(ql_top_k['doc_score']) + exp_ql = np.exp(ql_top_k['doc_score']) # transform ln(QL) -> QL scores for all the documents sum_exp_ql = exp_ql.sum() _doc_len = self.index.get_doc_len_vec(ql_top_k['doc_id']) - _p_w_rm = np.array([exp_ql / _doc_len, ]) * tf_mat - p_w_rm = np.asarray(_p_w_rm / sum_exp_ql).squeeze() - return np.sort(np.array(list(zip(nnz_cols, p_w_rm)), dtype=[('term_id', np.uint32), ('term_score', np.float)]), + p_w_rm = ((np.array([exp_ql / _doc_len, ]) * tf_mat) / sum_exp_ql).reshape(nnz_cols.shape) # sum p(w|d)*p(d|q) + # p_w_rm = _p_w_rm.reshape(nnz_cols.shape) + return np.sort(np.array(list(zip(nnz_cols, p_w_rm)), dtype=[('term_id', np.uint32), ('term_score', float)]), order='term_score')[::-1] def rank_by_kl(self, p_w_rm_top_n, rank_doc_ids=None): @@ -64,7 +68,7 @@ def rank_by_kl(self, p_w_rm_top_n, rank_doc_ids=None): tf_mat = tf_mat[nnz_rows, :] _doc_len = _doc_len[nnz_rows] doc_indices = nnz_rows - terms_cf = self.index.get_terms_cf_vec(p_w_rm_top_n['term_id']) + terms_cf = self.index.get_terms_cf_vec(p_w_rm_top_n['term_id']) # FIXME: I'm sending term_ids, but the function expects term indices _left = tf_mat.multiply(csr_matrix(1 / (self.mu + _doc_len)).T) _right = np.array([1 - _doc_len / (self.mu + _doc_len), ]).T * np.array([terms_cf, ]) / self.index.total_terms doc_lm = np.log(_left + _right) @@ -79,13 +83,13 @@ def _check_if_query_oov(self): return True return False - def _generate_matching_postings(self): - for term in self.query: - term, cf, df, posting_list = self.index.get_posting_list(term) - if cf == 0: - self.oov_terms.add(term) - continue - self._candidates_dict[term] = dict(posting_list) + # def _generate_matching_postings(self): + # for term in self.query: + # term, cf, df, posting_list = self.index.get_posting_list(term) + # if cf == 0: + # self.oov_terms.add(term) + # continue + # self._candidates_dict[term] = dict(posting_list) def translate_doc_id_to_doc_no(self, result_tuple): return tuple(map(lambda x: (self.index.get_doc_name(x[0]), x[1]), result_tuple)) @@ -104,14 +108,14 @@ def _is_empty_query(self): logger.info(f"Query: {self.qid}; terms {self.oov_terms} are out of vocabulary") return False - def _init_sorted_ql_docs(self, working_set_docs=None): + def _init_sorted_ql_docs(self, ranking_set_docs=None): if self._is_empty_query(): return tuple() - if working_set_docs.any(): - doc_scores = working_set_docs.T[1] - doc_ids = self.index.get_doc_ids_by_name(working_set_docs.T[0]) + if ranking_set_docs is not None: + doc_scores = ranking_set_docs.T[1] + doc_ids = self.index.get_doc_ids_by_name(ranking_set_docs.T[0]) sorted_ql_docs = np.array(list(zip(doc_ids, doc_scores)), - dtype=[('doc_id', np.uint32), ('doc_score', np.float)]) + dtype=[('doc_id', np.uint32), ('doc_score', float)]) else: sorted_ql_docs = self._ql_score_documents() return sorted_ql_docs @@ -121,26 +125,47 @@ def run_ql_retrieval(self, working_set_docs=None): if self._is_empty_query(): return tuple() # self._generate_matching_postings() - sorted_ql_scored_docs = self._ql_score_documents() + try: + sorted_ql_scored_docs = self._ql_score_documents() + except: + logger.error('*** The process crashed here!! ***') + print(sys.exc_info()[0]) + print(self.qid) + print(self.query.items()) return self.translate_doc_id_to_doc_no_vec(sorted_ql_scored_docs[:self.num_docs]) - def run_rm_retrieval(self, re_rank_ql=False, working_set_docs=None): + def run_rm_retrieval(self, ranking_set_docs=None, initial_set_docs=None, _sorted_rm_terms=None): """ This method will use QL to retrieve an initial set of documents, then use the set to create a RM (RM1) if the re_rank_ql param is True, it will use the RM to re-rank the top QL documents with the RM. Otherwise, it will rank all the documents in the corpus. """ - sorted_ql_docs = self._init_sorted_ql_docs(working_set_docs) - sorted_rm_terms = self.rm_construction(sorted_ql_docs[:self.fb_docs]) - if re_rank_ql or working_set_docs: - sorted_rm_scored_docs = self.rank_by_kl(sorted_rm_terms[:self.fb_terms], sorted_ql_docs[:self.num_docs]) + ranking_docs = None + if _sorted_rm_terms is not None: + sorted_rm_terms = _sorted_rm_terms + if ranking_set_docs is not None: + ranking_docs = self._init_sorted_ql_docs(ranking_set_docs) + else: + if initial_set_docs is not None: + sorted_ql_docs = self._init_sorted_ql_docs(initial_set_docs) + if ranking_set_docs is not None and ranking_set_docs != initial_set_docs: + ranking_docs = self._init_sorted_ql_docs(initial_set_docs) + else: + ranking_docs = sorted_ql_docs + else: + sorted_ql_docs = self._init_sorted_ql_docs(ranking_set_docs) + ranking_docs = sorted_ql_docs + sorted_rm_terms = self.rm_construction(sorted_ql_docs[:self.fb_docs]) + + if ranking_docs is not None: # FIXME: TODO Shit hits the fan here!!! + sorted_rm_scored_docs = self.rank_by_kl(sorted_rm_terms[:self.fb_terms], ranking_docs[:self.num_docs]) else: sorted_rm_scored_docs = self.rank_by_kl(sorted_rm_terms[:self.fb_terms]) return self.translate_doc_id_to_doc_no_vec(sorted_rm_scored_docs[:self.num_docs]), sorted_rm_terms - def generate_rm(self, working_set_docs): + def generate_rm(self, initial_set_docs): """ - This method will use QL to + This method will generate and return a RM based on the initial_set_docs passed to it, assuming the scores are QL """ - sorted_ql_docs = self._init_sorted_ql_docs(working_set_docs) + sorted_ql_docs = self._init_sorted_ql_docs(initial_set_docs) return self.rm_construction(sorted_ql_docs) From 49d7581fb8d8243e0db4c34ba698e51deda36a8a Mon Sep 17 00:00:00 2001 From: Zendelo Date: Tue, 22 Aug 2023 20:41:18 +1000 Subject: [PATCH 09/40] update QPP code to work with Terrier and add QF --- code/qpptk/qpptk/post_retrieval_predictors.py | 25 ++++++++++++++++++- code/qpptk/qpptk/pre_retrieval_predictors.py | 7 +++--- 2 files changed, 28 insertions(+), 4 deletions(-) diff --git a/code/qpptk/qpptk/post_retrieval_predictors.py b/code/qpptk/qpptk/post_retrieval_predictors.py index 4eeac13..a9c0b0e 100644 --- a/code/qpptk/qpptk/post_retrieval_predictors.py +++ b/code/qpptk/qpptk/post_retrieval_predictors.py @@ -30,7 +30,11 @@ def calc_wig(self, list_size_param): """ Y. Zhou and W. B. Croft. Query performance prediction in web search environments """ - scores_vec = self.scores_vec[:list_size_param] + try: + scores_vec = self.scores_vec[:list_size_param] + except IndexError as err: + print(err) + print(self.qid) return (scores_vec.mean() - self.ql_corpus_score) / np.sqrt(len(self.query)) def calc_nqc(self, list_size_param): @@ -54,6 +58,17 @@ def calc_clarity(self, p_w_rm): """ terms_cf = self.index.get_terms_cf_vec(p_w_rm['term_id']) _p_w_rm = p_w_rm['term_score'] / p_w_rm['term_score'].sum() + corpus_lm = terms_cf['term_cf'] / self.index.total_terms + return np.log(_p_w_rm / corpus_lm).dot(_p_w_rm) + + def calc_dfr_info_bo2(self, p_w_rm): + # FIXME: Need to construct the RM* based on the top k=(10) documents + """ + Amati Giambattista, Carpineto Claudio and Romano Giovanni. + Query Difficulty, Robustness, and Selective Application of Query Expansion + """ + terms_cf = self.index.get_terms_cf_vec(p_w_rm['term_id']) + _p_w_rm = p_w_rm['term_score'] / p_w_rm['term_score'].sum() corpus_lm = np.array([terms_cf, ]) / self.index.total_terms return np.log(_p_w_rm / corpus_lm).dot(_p_w_rm)[0] @@ -66,3 +81,11 @@ def calc_uef(self, list_size_param, rm_results_df, predictor_result): similarity = results_df.loc[self.qid, ['docNo', 'docScore']].set_index('docNo', drop=True).corrwith( rm_results_df.loc[:, ['docNo', 'docScore']].set_index('docNo', drop=True))[0] return similarity * predictor_result + + def calc_qf(self, list_size_param, rm_results_df): + """ + Zhou, Yun and Croft, W Bruce. Query Performance Prediction in Web Search Environments. + """ + original_docs_set = set(self.results_df.loc[self.qid].head(list_size_param).docNo) + overlap = len(original_docs_set.intersection(rm_results_df.head(list_size_param).docNo)) / list_size_param + return overlap diff --git a/code/qpptk/qpptk/pre_retrieval_predictors.py b/code/qpptk/qpptk/pre_retrieval_predictors.py index 19f156f..b8666cb 100644 --- a/code/qpptk/qpptk/pre_retrieval_predictors.py +++ b/code/qpptk/qpptk/pre_retrieval_predictors.py @@ -1,6 +1,7 @@ import numpy as np -from qpptk import IndexDB as Index +# from qpptk import IndexDB as Index +from qpptk import IndexTerrier as Index class LocalManagerPredictorPre: @@ -67,9 +68,9 @@ def _calc_w_d_t(self, term): if not term_record: # If the term is OOV, the w_d_t is set to 0 return 0 - term_posting = dict(self.index.get_posting_list(term).posting_list) + term_posting = dict(self.index.get_posting_list(term)) tf_vec = np.fromiter(term_posting.values(), dtype=int) - assert len(term_posting) == term_record.df, f'{term}: number of docs in posting different from df' + assert len(term_posting) == term_record.df, f'For term: "{term}" the number of docs in posting different from df' return 1 + np.log(tf_vec) * np.log(1 + self.total_docs / term_record.df) def _calc_raw_var(self): From 35d52bcadd8d342a4693997df572247a038c630a Mon Sep 17 00:00:00 2001 From: Zendelo Date: Tue, 22 Aug 2023 20:59:43 +1000 Subject: [PATCH 10/40] some basic code cleanup --- code/qpptk/qpptk/config.toml | 57 +++++------------------------- code/qpptk/qpptk/global_manager.py | 36 +------------------ code/qpptk/qpptk/qpptk_main.py | 17 ++------- 3 files changed, 13 insertions(+), 97 deletions(-) diff --git a/code/qpptk/qpptk/config.toml b/code/qpptk/qpptk/config.toml index 6cbcf56..d7a1152 100644 --- a/code/qpptk/qpptk/config.toml +++ b/code/qpptk/qpptk/config.toml @@ -3,63 +3,24 @@ title = "Global configurations" [environment] [environment.paths] # The root directory which will be used as the base path for all the environment paths -# root_dir = "/research/local/oleg/" # Running local, good for drogon/balerion only -# root_dir = "/research/remote/petabyte/users/oleg/" root_dir = "~/repos/" -# root_dir = "~/work/" -# text_index_dir = "ROBUST04/dump/" + # text_index_dir = "mini_dump/" -# ciff_index_file = "ciff_indexes/robust04/robust04_Lucene_indri_porter.ciff" -# ciff_index_file = "ciff_indexes/robust04_Lucene_indri_krovetz.ciff" -# ciff_index_file = "ciff_indexes/robust04/robust04_Lucene_lingpipe_porter.ciff" -# ciff_index_file = "ciff_indexes/robust04/robust04_Lucene_terrier_krovetz.ciff" -# ciff_index_file = "ciff_indexes/robust04_Lucene_nostop_krovetz.ciff" # ciff_index_file = "ciff_indexes/robust04/robust04_Lucene_indri_nostem.ciff" -# terrier_index_dir = "robust_krovetz_nostop_terrier" -# terrier_index_dir = "robust04_KrovetzStemmer_stop" -# terrier_index_dir = "cw12b_terrier/index/cw12b_krovetz_nostop" - -# terrier_index_dir = "cw12b_terrier/index/cw12b_krovetz_terrier" terrier_index_dir = "qpp-Maik/docker/pyterrier-index/index" -# ciff_index_file = "ciff_indexes/robust04-Lucene-nostop-porter.ciff" -# ciff_index_file = "ciff_indexes/robust04/robust04_Lucene_indri_porter.ciff" -# ciff_index_file = "ciff_indexes/cw12b/cw12b_Indri_nostop_krovetz.ciff" -# text_queries_file = "data/robust04.stemmed.qry" # text_queries_file = "data/cw12b.stemmed.stopped.qry" - jsonl_queries_file = "qpp-Maik/docker/sample-input-full-rank/queries.jsonl" - -# text_queries_file = "data/cw12b-uqv.stemmed.stopped.qry.part-1" - -# text_queries_file = "data/robust04-uqv.stemmed.stopped.qry" -# text_queries_file = "data/robust04-uqv-dev.stemmed.stopped.qry" -# text_queries_file = "mini_dump/queries.txt" -# ciff_queries_file = "ciff_lite_query_indexes/robust04_Lucene_query_indri_krovetz.ciff" # ciff_queries_file = "ciff_query_indexes/robust04_Lucene_query_indri_nostem.ciff" -# ciff_queries_file = "ciff_query_indexes/robust04_Lucene_query_lingpipe_porter.ciff" -# ciff_queries_file = "ciff_query_indexes/robust04_Lucene_query_nostop_krovetz.ciff" -# ciff_queries_file = "ciff_query_indexes/robust04_Lucene_query_terrier_krovetz.ciff" -# ciff_queries_file = "ciff_query_indexes/robust04_Lucene_query_indri_porter.ciff" -# results_dir = "eval_qpp_results" + + # results_dir = "eval_qpp_results" results_dir = "testing_docker" -# results_dir = "anova_qpp" -# log_file = 'qpptk_retr_cw.log' -# log_file = 'qpptk_predPre_cw.log' -# log_file = 'qpptk_testing.log' -# log_file = 'qpptk_robust_retr.log' -# results_dir = "anova_qpp/ecir" + + log_file = 'qpptk_robust_retr.log' + # Directory for the DB files db_dir = "qpptk/qpptk_db" -# db_dir = "qpptk/qpptk_db_lite" qrel_file = 'qpp-Maik/docker/sample-input-full-rank/sample.qrels' -# qrel_file = 'qpptk/robust04_mod.qrels' -# qrel_file = 'data/robust04.qrels' -# qrel_file = 'data/cw12b-uqv_mod.qrels' -# qrel_file = 'data/cw12b_mod.qrels.trec' -# batch_name = '3392985365' - - [environment.executables] # path to trec_eval executable @@ -82,9 +43,9 @@ title = "Global configurations" num_processes = 1 [parameters.prediction] - wig_list_size = 10 # ECIR - nqc_list_size = 100 # ECIR - smv_list_size = 100 # ECIR + wig_list_size = 10 # Good for Robust04 + nqc_list_size = 100 # Good for Robust04 + smv_list_size = 100 # Good for Robust04 # Number of top terms from RM to use in Clarity clarity_fb_terms = 100 # Number of docs to use for the RM construction in Clarity diff --git a/code/qpptk/qpptk/global_manager.py b/code/qpptk/qpptk/global_manager.py index 9edfd86..d360cd9 100644 --- a/code/qpptk/qpptk/global_manager.py +++ b/code/qpptk/qpptk/global_manager.py @@ -1,14 +1,13 @@ import multiprocessing as mp from functools import partial -import syct import pandas as pd +import syct from syct import timer from qpptk import Config, IndexText, IndexDB, QueryParserText, QueryParserCiff, LocalManagerRetrieval, \ add_topic_to_qdf, LocalManagerPredictorPre, LocalManagerPredictorPost, IndexTerrier, \ ensure_dir -from qpptk.load_terri_index import IndexTerri logger = Config.logger TREC_RES_COLUMNS = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] @@ -112,11 +111,6 @@ def _run_multiprocess_sync(func, tasks, n_proc, **init_kwargs): # @timer(info) def pre_ret_prediction_full(qids, index, queries, n_proc=Config.N_PROC): - # result = {} - # for qid in qids: - # result[qid] = run_prediction_process(qid, index, queries) - # df = pd.DataFrame.from_dict(result, orient='index') - # df.index = df.index.rename('qid') result = _run_multiprocess_sync(run_pre_prediction_process, qids, n_proc, index=index, queries=queries) df = pd.DataFrame(result) logger.debug(df) @@ -134,7 +128,6 @@ def post_ret_prediction_full(qids, index, queries, results_df, n_proc=Config.N_P # @timer # TODO: add debug level, and should add debugging each qid with time to a log file def run_ql_retrieval_process(qid): - # columns = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] timer = syct.Timer(f'QID: {qid}', logger=logger) process = LocalManagerRetrieval(index_obj=index, query_obj=queries, qid=qid) result = process.run_ql_retrieval() @@ -148,7 +141,6 @@ def run_ql_retrieval_process(qid): @timer # TODO: add debug level def run_rm_retrieval_process(qid, return_rm=False, __sorted_rm_terms=None): - # columns = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] timer = syct.Timer(f'QID: {qid}', logger=logger) process = LocalManagerRetrieval(index_obj=index, query_obj=queries, qid=qid) result, p_w_rm = process.run_rm_retrieval() @@ -164,7 +156,6 @@ def run_rm_retrieval_process(qid, return_rm=False, __sorted_rm_terms=None): @timer # TODO: add debug level def run_rm_rerank_retrieval_process(qid, ranking_set_docs=None, return_rm=False, __sorted_rm_terms=None): - # columns = ['qid', 'iteration', 'docNo', 'rank', 'docScore', 'method'] timer = syct.Timer(f'QID: {qid}', logger=logger) process = LocalManagerRetrieval(index_obj=index, query_obj=queries, qid=qid) results_vec = ranking_set_docs if ranking_set_docs is not None else results_df.loc[ @@ -184,9 +175,6 @@ def run_rm_rerank_retrieval_process(qid, ranking_set_docs=None, return_rm=False, # TODO: add @time(info) def retrieval_full(qids, index, queries, n_proc=Config.N_PROC, method='ql', results_df=None): - # init_proc(index, queries) - # for qid in qids: - # run_ql_retrieval_process(qid) if method == 'ql': result = _run_multiprocess_sync(run_ql_retrieval_process, qids, n_proc, index=index, queries=queries) elif method == 'rm': @@ -202,12 +190,6 @@ def retrieval_full(qids, index, queries, n_proc=Config.N_PROC, method='ql', resu return df -# @timer # TODO: add debug level -# def initialize_ciff_index(ciff_index): -# header, terms_dict, doc_records = parse_index_file(ciff_index) -# return IndexCiff(header, ciff_index, terms_dict, doc_records) - - @timer # TODO: add debug level def initialize_text_index(text_inv, dict_txt, doc_lens, doc_names, index_globals): return IndexText(text_inverted=text_inv, terms_dict=dict_txt, index_global=index_globals, document_lengths=doc_lens, @@ -232,19 +214,3 @@ def initialize_ciff_queries(queries_file): @timer # TODO: add debug level def initialize_text_queries(queries_file): return QueryParserText(queries_file) - -# @timer -# def main(): -# index = initialize_ciff_index(CIFF_INDEX) -# # index = initialize_text_index(*Config.set_dump_paths(INDEX_DIR)) -# # queries = initialize_ciff_queries(QUERIES_FILE) -# queries = initialize_text_queries(QUERIES_FILE) -# qids = queries.get_query_ids() -# # result = [run_retrieval_process(qids[1], index, queries)] -# # retrieval_full(qids, index, queries) -# prediction_full(qids, index, queries) -# -# -# if __name__ == '__main__': -# main() -# # test() diff --git a/code/qpptk/qpptk/qpptk_main.py b/code/qpptk/qpptk/qpptk_main.py index 07f9efb..22b272c 100644 --- a/code/qpptk/qpptk/qpptk_main.py +++ b/code/qpptk/qpptk/qpptk_main.py @@ -2,15 +2,14 @@ import os import subprocess as sp import sys -from functools import partial from glob import glob import pandas as pd -from syct import timer from sklearn.metrics import pairwise_distances +from syct import timer from qpptk import Config, set_index_dump_paths, ensure_file, ensure_dir, read_trec_res_file, \ - QueryParserCiff, QueryParserText, QueryParserJsonl, add_topic_to_qdf, duplicate_qrel_file_to_qids, calc_ndcg + QueryParserCiff, QueryParserText, QueryParserJsonl, add_topic_to_qdf, calc_ndcg from qpptk.global_manager import pre_ret_prediction_full, \ retrieval_full, initialize_db_index, initialize_text_index, post_ret_prediction_full, \ initialize_terrier_index @@ -123,9 +122,6 @@ def load_and_evaluate(prefix, ir_metric, method='pearson', title_only=False): corpus, stoplist, stemmer = _components else: print('Unknown format of index name') - # corpus, stoplist, stemmer = _components - # qrels_file = Config.QREL_FILE - # duplicate_qrel_file_to_qids(qrels_file, qids) try: eval_df = pd.read_table(f"{prefix}_QL.{ir_metric}", delim_whitespace=True, names=['qid', ir_metric], index_col=0) @@ -256,7 +252,6 @@ def init_db_index(): return qids, index, queries def init_readonly_index(): # TODO: should be init for terrier index - # index = initialize_db_index(db_dir) queries = get_queries_object() qids = queries.get_query_ids() index = initialize_terrier_index(index_path, partial_terms=queries.get_queries_df().columns) @@ -350,16 +345,10 @@ def init_writeable_index(): predictions_df.loc[:, col].to_csv(f"{prefix_path}_{retrieval_method}_{col}.pre", sep=' ', index=True, header=False, float_format=f"%.{PRECISION}f") if args.evaluate: - # method = 'spearman' - # method = 'kendall' method = 'pearson' - # queries = 'title' queries = 'all' ir_metric = 'ap@1000' - # ir_metric = 'ndcg@100' - # ir_metric = 'RR' - # ir_metric = 'ndcg@10' - # ir_metric = 'rbp-0.85' + title_only = True if queries == 'title' else False if Config.BATCH_NAME: _prefix = prefix_path[::-1].replace('/', ' ', 1)[::-1].replace(' ', '/' + Config.BATCH_NAME + '/') From dc7d1a9eb7d99bda21ea2694594b1d6b7b53a298 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Tue, 7 Nov 2023 05:20:36 +0100 Subject: [PATCH 11/40] Join predictions into single jsonl file. --- code/qpptk/qpptk/global_manager.py | 4 +- code/qpptk/qpptk/qpptk_main.py | 86 ++++++++++++++++++++---------- code/qpptk/requirements.txt | 6 +-- 3 files changed, 62 insertions(+), 34 deletions(-) diff --git a/code/qpptk/qpptk/global_manager.py b/code/qpptk/qpptk/global_manager.py index d360cd9..6954ed2 100644 --- a/code/qpptk/qpptk/global_manager.py +++ b/code/qpptk/qpptk/global_manager.py @@ -114,7 +114,7 @@ def pre_ret_prediction_full(qids, index, queries, n_proc=Config.N_PROC): result = _run_multiprocess_sync(run_pre_prediction_process, qids, n_proc, index=index, queries=queries) df = pd.DataFrame(result) logger.debug(df) - return add_topic_to_qdf(df).set_index(['topic', 'qid']) + return add_topic_to_qdf(df) # @timer(info) @@ -123,7 +123,7 @@ def post_ret_prediction_full(qids, index, queries, results_df, n_proc=Config.N_P results_df=results_df) df = pd.DataFrame(result) logger.debug(df) - return add_topic_to_qdf(df).set_index(['topic', 'qid']) + return add_topic_to_qdf(df) # @timer # TODO: add debug level, and should add debugging each qid with time to a log file diff --git a/code/qpptk/qpptk/qpptk_main.py b/code/qpptk/qpptk/qpptk_main.py index 22b272c..c787f28 100644 --- a/code/qpptk/qpptk/qpptk_main.py +++ b/code/qpptk/qpptk/qpptk_main.py @@ -14,36 +14,41 @@ retrieval_full, initialize_db_index, initialize_text_index, post_ret_prediction_full, \ initialize_terrier_index -parser = argparse.ArgumentParser(description='Run QL retrieval or Query Performance Prediction') -index_group = parser.add_mutually_exclusive_group() -index_group.add_argument('--text_index', metavar='INDEX', type=str, default=None, help='path to text index dir') -index_group.add_argument('-ci', '--ciff_index', metavar='INDEX', type=str, default=None, help='path to ciff index file') -index_group.add_argument('-ti', '--terrier_index', metavar='INDEX', type=str, default=None, +def parse_args(): + parser = argparse.ArgumentParser(description='Run QL retrieval or Query Performance Prediction') + index_group = parser.add_mutually_exclusive_group() + index_group.add_argument('--text_index', metavar='INDEX', type=str, default=None, help='path to text index dir') + index_group.add_argument('-ci', '--ciff_index', metavar='INDEX', type=str, default=None, help='path to ciff index file') + index_group.add_argument('-ti', '--terrier_index', metavar='INDEX', type=str, default=None, help='path to terrier index dir') -queries_group = parser.add_mutually_exclusive_group() -queries_group.add_argument('-tq', '--text_queries', metavar='QUERIES', default=None, help='path to text queries file') -queries_group.add_argument('-cq', '--ciff_queries', metavar='QUERIES', default=None, help='path to ciff queries file') -queries_group.add_argument('-jq', '--jsonl_queries', metavar='QUERIES', default=None, help='path to jsonl queries file') - -parser.add_argument('-fq', '--filter_queries', metavar='FilterQUERIES', default=None, - help='path to text queries file that will be used to filter the queries') -parser.add_argument('-dq', '--drop_queries', metavar='DropQUERIES', default=None, - help='path to text query ids file that will be used to drop the queries') - -parser.add_argument('--retrieve', action='store_true', help='add this flag to run retrieval') -parser.add_argument('--method', choices=['ql', 'rm', 'rm_rerank'], default='ql', - help='the method to be used in retrieval') -parser.add_argument('--predict', action='store_true', help='add this flag to run predictions using ALL PREDICTORS') -parser.add_argument('--evaluate', action='store_true', help='add this flag to run evaluation') -parser.add_argument('--pairs_sim', action='store_true', help='Generates a file with similarity between all query pairs') -parser.add_argument('--cluster_queries', action='store_true', - help='Splits the queries file into separate files by similarity') -parser.add_argument('--keep_duplicate_queries', action='store_true', - help='By default duplicate queries per topic will be removed, use this option to keep them') - -parser.add_argument('--predPre', action='store_true', help='add this flag to run only pre-retrieval predictors') -parser.add_argument('--predPost', action='store_true', help='add this flag to run only post-retrieval predictors') + queries_group = parser.add_mutually_exclusive_group() + queries_group.add_argument('-tq', '--text_queries', metavar='QUERIES', default=None, help='path to text queries file') + queries_group.add_argument('-cq', '--ciff_queries', metavar='QUERIES', default=None, help='path to ciff queries file') + queries_group.add_argument('-jq', '--jsonl_queries', metavar='QUERIES', default=None, help='path to jsonl queries file') + + parser.add_argument('-fq', '--filter_queries', metavar='FilterQUERIES', default=None, + help='path to text queries file that will be used to filter the queries') + parser.add_argument('-dq', '--drop_queries', metavar='DropQUERIES', default=None, + help='path to text query ids file that will be used to drop the queries') + + parser.add_argument('--retrieve', action='store_true', help='add this flag to run retrieval') + parser.add_argument('--method', choices=['ql', 'rm', 'rm_rerank'], default='ql', + help='the method to be used in retrieval') + parser.add_argument('--predict', action='store_true', help='add this flag to run predictions using ALL PREDICTORS') + parser.add_argument('--evaluate', action='store_true', help='add this flag to run evaluation') + parser.add_argument('--pairs_sim', action='store_true', help='Generates a file with similarity between all query pairs') + parser.add_argument('--cluster_queries', action='store_true', + help='Splits the queries file into separate files by similarity') + parser.add_argument('--keep_duplicate_queries', action='store_true', + help='By default duplicate queries per topic will be removed, use this option to keep them') + + parser.add_argument('--predPre', action='store_true', help='add this flag to run only pre-retrieval predictors') + parser.add_argument('--predPost', action='store_true', help='add this flag to run only post-retrieval predictors') + + parser.add_argument('--output', default=None, required=False, help='The output directory') + + return parser.parse_args() logger = Config.logger @@ -325,8 +330,11 @@ def init_writeable_index(): if args.predict: args.predPre = True args.predPost = True + + combined_predictions = [] if args.predPre: predictions_df = pre_ret_prediction_full(qids, index, queries) + combined_predictions += [predictions_df] for col in predictions_df.columns: predictions_df.loc[:, col].to_csv(f"{prefix_path}_PRE_{col}.pre", sep=' ', index=True, header=False, @@ -341,9 +349,25 @@ def init_writeable_index(): logger.error(error_msg) sys.exit(error_msg) predictions_df = post_ret_prediction_full(qids, index, queries, read_trec_res_file(results_file)) + combined_predictions += [predictions_df] for col in predictions_df.columns: predictions_df.loc[:, col].to_csv(f"{prefix_path}_{retrieval_method}_{col}.pre", sep=' ', index=True, header=False, float_format=f"%.{PRECISION}f") + + if combined_predictions: + qid_to_preds = {} + df_combined_predictions = [] + for df in combined_predictions: + for _, i in df.iterrows(): + if i['qid'] not in qid_to_preds: + qid_to_preds[i['qid']] = {} + for k,v in i.items(): + if k == 'topic': + continue + qid_to_preds[i['qid']][k] = v + + pd.DataFrame([v for _, v in qid_to_preds.items()]).to_json(results_dir + '/queries.jsonl', lines=True, orient='records') + if args.evaluate: method = 'pearson' queries = 'all' @@ -371,5 +395,9 @@ def init_writeable_index(): if __name__ == '__main__': - args = parser.parse_args() # defined and used as a "global" variable + args = parse_args() + + if args.output: + Config.RESULTS_DIR = args.output + main() diff --git a/code/qpptk/requirements.txt b/code/qpptk/requirements.txt index 103cceb..b82ccd3 100644 --- a/code/qpptk/requirements.txt +++ b/code/qpptk/requirements.txt @@ -5,11 +5,11 @@ msgpack_python==0.5.6 numpy==1.21.4 pandas==1.3.4 protobuf==3.19.1 -scikit_learn==1.0.1 -scipy==1.7.2 +scikit_learn==1.3.1 +scipy==1.10.1 seaborn==0.11.2 setuptools==58.5.3 -statsmodels==0.13.0 +statsmodels==0.14.0 syct==0.4.3 toml==0.10.2 tqdm==4.62.3 From 21872393d60a53dfeb6dca9ef7443c60b304860f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Tue, 7 Nov 2023 05:28:02 +0100 Subject: [PATCH 12/40] Add a flag to clean the outputs --- code/qpptk/qpptk/qpptk_main.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/code/qpptk/qpptk/qpptk_main.py b/code/qpptk/qpptk/qpptk_main.py index c787f28..1117b6b 100644 --- a/code/qpptk/qpptk/qpptk_main.py +++ b/code/qpptk/qpptk/qpptk_main.py @@ -1,5 +1,6 @@ import argparse import os +import shutil import subprocess as sp import sys from glob import glob @@ -47,6 +48,7 @@ def parse_args(): parser.add_argument('--predPost', action='store_true', help='add this flag to run only post-retrieval predictors') parser.add_argument('--output', default=None, required=False, help='The output directory') + parser.add_argument('--cleanOutput', action='store_true', help='Clean all temporary output files and output only a joined jsonl file') return parser.parse_args() @@ -356,7 +358,6 @@ def init_writeable_index(): if combined_predictions: qid_to_preds = {} - df_combined_predictions = [] for df in combined_predictions: for _, i in df.iterrows(): if i['qid'] not in qid_to_preds: @@ -368,6 +369,11 @@ def init_writeable_index(): pd.DataFrame([v for _, v in qid_to_preds.items()]).to_json(results_dir + '/queries.jsonl', lines=True, orient='records') + if args.cleanOutput: + for d in os.listdir(args.output): + if os.path.exists(args.output + '/' + d) and os.path.isdir(args.output + '/' + d): + shutil.rmtree(args.output + '/' + d) + if args.evaluate: method = 'pearson' queries = 'all' @@ -401,3 +407,4 @@ def init_writeable_index(): Config.RESULTS_DIR = args.output main() + From 9ca1012290d872c96fe17abee15497111c246518 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Tue, 7 Nov 2023 05:28:36 +0100 Subject: [PATCH 13/40] Add dev container --- .devcontainer.json | 8 ++++++++ docker/Dockerfile | 21 ++++++--------------- docker/Dockerfile.dev | 14 ++++++++++++++ 3 files changed, 28 insertions(+), 15 deletions(-) create mode 100644 .devcontainer.json create mode 100644 docker/Dockerfile.dev diff --git a/.devcontainer.json b/.devcontainer.json new file mode 100644 index 0000000..d2d5256 --- /dev/null +++ b/.devcontainer.json @@ -0,0 +1,8 @@ +{ + "image": "mam10eks/qpptk:0.0.1-dev", + "customizations": { + "vscode": { + "extensions": ["ms-python.python", "ms-python.vscode-pylance", "ms-toolsai.jupyter"] + } + } +} diff --git a/docker/Dockerfile b/docker/Dockerfile index 642ee53..3c71ed7 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -1,18 +1,9 @@ -# We start from an image that already has Terrier + PyTerrier as dependencies installed -FROM webis/tira-ir-starter-pyterrier:0.0.2-base +# docker build -f docker/Dockerfile -t mam10eks/qpptk:0.0.1 . +# We start from an image that already has all dependencies installed +FROM wmam10eks/qpptk:0.0.1-dev -COPY code/qpptk/ /qpptk +COPY code/qpptk/ /workspaces/QPP-EnhancedEval/code/qpptk -WORKDIR /qpptk +RUN pip install -e . -RUN pip install -r requirements.txt \ - && pip install -e . - -#ONLY For Debug purposes -RUN apt-get install -y jq \ - && echo '#!/bin/sh\n\necho "Read Topics from ${1}queries.jsonl"\necho "Read Pyterrier-Index from ${2}/index:"\n ls ${2}/index\nsleep 2s\ncat ${1}/queries.jsonl | jq -c '\''{"qid": .qid, "query": .query, "predictions": {"max-idf": 1}}'\'' > ${3}/predictions.jsonl' > /qpptk-dummy-full-rank.sh \ - && chmod +x /qpptk-dummy-full-rank.sh \ - && echo '#!/bin/sh\n\necho "Read query-document pairs from ${1}/rerank.jsonl.gz"\n\nsleep 2s\nzcat ${1}/rerank.jsonl.gz | jq -c '\''{"qid": .qid, "query": .query, "predictions": {"max-idf": 1}}'\'' > ${2}/predictions.jsonl' > /qpptk-dummy-re-rank.sh \ - && chmod +x /qpptk-dummy-re-rank.sh - -ENTRYPOINT [ "python3", "/qpptk/qpptk/qpptk_main.py" ] +ENTRYPOINT [ "python3", "/qpptk_main.py" ] diff --git a/docker/Dockerfile.dev b/docker/Dockerfile.dev new file mode 100644 index 0000000..778025d --- /dev/null +++ b/docker/Dockerfile.dev @@ -0,0 +1,14 @@ +# docker build -f docker/Dockerfile.dev -t mam10eks/qpptk:0.0.1-dev . +# We start from an image that already has Terrier + PyTerrier as dependencies installed +FROM webis/ir-lab-wise-2023:0.0.1 + +COPY code/qpptk/ /workspaces/QPP-EnhancedEval/code/qpptk + +WORKDIR /workspaces/QPP-EnhancedEval/code/qpptk + +RUN pip install -r requirements.txt \ + && rm -Rf * \ + && mkdir -p /root/repos/ \ + && ln -s /workspaces/QPP-EnhancedEval/code/qpptk /root/repos/qpptk \ + && ln -s /workspaces/QPP-EnhancedEval/code/qpptk/qpptk/qpptk_main.py /qpptk_main.py + From a0bc1b13a8c3889884527c878f1748410a7f67ce Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Tue, 7 Nov 2023 05:52:24 +0100 Subject: [PATCH 14/40] Add more details to the readme --- README.md | 25 +++++++++++++++++++++++++ docker/Dockerfile | 2 +- 2 files changed, 26 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1ff8bd8..902d247 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,28 @@ # QPP-EnhancedEval Code to Reproduce ECIR 2021 paper "An Enhanced Evaluation Framework for Query Performance Prediction" + +## Run it locally with TIRA + +Please ensure that you have python >= 3.7, Docker, and tira-run installed (`pip3 install tira`). + +``` +tira-run \ + --input-directory ${PWD}/docker/sample-input-full-rank \ + --input-run ${PWD}/docker/pyterrier-index \ + --image mam10eks/qpptk:0.0.1 \ + --command 'python3 /qpptk_main.py -ti $inputRun/index/ --jsonl_queries $inputDataset/queries.jsonl --predict --retrieve --output $outputDir --cleanOutput' +``` + +## Build the Docker Images + +Build the docker image via: +``` +docker build -f docker/Dockerfile -t mam10eks/qpptk:0.0.1 . +``` + +If you update any dependencies, please rebuild the dev container via: +``` +docker build -f docker/Dockerfile.dev -t mam10eks/qpptk:0.0.1-dev . +``` + diff --git a/docker/Dockerfile b/docker/Dockerfile index 3c71ed7..c098f4b 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -1,6 +1,6 @@ # docker build -f docker/Dockerfile -t mam10eks/qpptk:0.0.1 . # We start from an image that already has all dependencies installed -FROM wmam10eks/qpptk:0.0.1-dev +FROM mam10eks/qpptk:0.0.1-dev COPY code/qpptk/ /workspaces/QPP-EnhancedEval/code/qpptk From 39a43fb60975624c8057722fcb1d07226aa119d5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Tue, 7 Nov 2023 07:56:38 +0100 Subject: [PATCH 15/40] Do not download terrier, expect it is already downloaded. --- README.md | 7 +++++++ code/qpptk/qpptk/load_terrier_index.py | 2 +- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 902d247..ca2e902 100644 --- a/README.md +++ b/README.md @@ -26,3 +26,10 @@ If you update any dependencies, please rebuild the dev container via: docker build -f docker/Dockerfile.dev -t mam10eks/qpptk:0.0.1-dev . ``` +## Upload to TIRA + +``` +docker tag mam10eks/qpptk:0.0.1 registry.webis.de/code-research/tira/tira-user-qpptk/qpptk:0.0.1 +docker push registry.webis.de/code-research/tira/tira-user-qpptk/qpptk:0.0.1 +``` + diff --git a/code/qpptk/qpptk/load_terrier_index.py b/code/qpptk/qpptk/load_terrier_index.py index 5df8b3d..8651853 100644 --- a/code/qpptk/qpptk/load_terrier_index.py +++ b/code/qpptk/qpptk/load_terrier_index.py @@ -74,7 +74,7 @@ def terms_cf_vec(self) -> np.ndarray: def init_pt_index(self): if not pt.started(): - pt.init() + pt.init(no_download=True) else: print('*** A PyTerrier was already initialized, will try to load a new index ***') _index_file = ensure_file(os.path.join(self.index_dir, 'data.properties')) From 00858e105bd8555bfa68a6f9cc09502ed53339f7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Tue, 7 Nov 2023 08:18:53 +0100 Subject: [PATCH 16/40] Do not download terrier, expect it is already downloaded. --- code/qpptk/qpptk/load_terri_index.py | 114 ------------------------- code/qpptk/qpptk/load_terrier_index.py | 8 +- 2 files changed, 4 insertions(+), 118 deletions(-) delete mode 100644 code/qpptk/qpptk/load_terri_index.py diff --git a/code/qpptk/qpptk/load_terri_index.py b/code/qpptk/qpptk/load_terri_index.py deleted file mode 100644 index c3b9b0b..0000000 --- a/code/qpptk/qpptk/load_terri_index.py +++ /dev/null @@ -1,114 +0,0 @@ -import os - -import pyterrier as pt -from syct import timer -from tqdm import tqdm - -from qpptk import TermPosting, Posting, TermRecord, DocRecord, Config, ensure_dir, ensure_file - -""" -A Terrier index python interface - """ - -# DEV_INDEX = '/research/local/olz/qpptk/dev_index/data.properties' -# ROBUST_INDEX = '/research/local/olz/robust_krovetz_nostop_terrier/data.properties' -CW_INDEX = '/research/local/olz/cw12b_krovetz_nostop_terrier/index/krovetz-nostop/data.properties' - -logger = Config.logger - - -class IndexTerri: - - @classmethod - def oov(cls, term): - return TermPosting(term, 0, 0, tuple()) # Out of vocabulary terms - - def __init__(self, terrier_index_dir): - self.index_dir = ensure_dir(terrier_index_dir) - _index_file = ensure_file(os.path.join(self.index_dir, 'data.properties')) - if not pt.started(): - pt.init() - pt.logging('INFO') - _indexref = pt.IndexRef.of(_index_file) - self.index = pt.IndexFactory.of(_indexref) - _stats = self.index.getCollectionStatistics().toString() - logger.debug(f'Terrier index stats:\n{_stats}') - self.total_terms = self.index.getCollectionStatistics().getNumberOfTokens() - self.number_of_docs = self.index.getCollectionStatistics().getNumberOfDocuments() - self.unique_terms = self.index.getCollectionStatistics().getNumberOfUniqueTerms() - self._lex = self.index.getLexicon() - self._inv = self.index.getInvertedIndex() - self._doi = self.index.getDocumentIndex() - self._meta = self.index.getMetaIndex() - self.terms_mapping = None - self.docs_dict = None - - @property - def terms_dict(self): - if self.terms_mapping: - return self.terms_mapping - terms_mapping = {} - logger.debug('Generating Terms Dict') - with tqdm(total=self.unique_terms) as progress: - for iterm in self._lex: - term = iterm.getKey() - term_id = iterm.getValue().getTermId() - df = iterm.getValue().getDocumentFrequency() - cf = iterm.getValue().getFrequency() - terms_mapping[term] = TermRecord(term, term_id, cf, df) - progress.update(1) - self.terms_mapping = terms_mapping - return self.terms_mapping - - @property - def doc_records(self): - if self.docs_dict: - return self.docs_dict - docs_mapping = {} - logger.debug('Generating Docs Dict') - with tqdm(total=self.number_of_docs) as progress: - for doc_id in range(self._doi.getNumberOfDocuments()): - docs_mapping[doc_id] = DocRecord(doc_id, self._meta.getItem("docno", doc_id), - self._doi.getDocumentLength(doc_id)) - progress.update(1) - self.docs_dict = docs_mapping - return self.docs_dict - - def get_posting_list(self, term: str) -> TermPosting: - if not pt.started(): - pt.init() - term_record = self.terms_dict.get(term) - if term_record: - _posting_list = [Posting(p.getId(), p.getFrequency()) for p in - self._inv.getPostings(self._lex.getLexiconEntry(term))] - return TermPosting(term, term_record.cf, term_record.df, tuple(_posting_list)) - else: - return IndexTerri.oov(term) - - def get_doc_len(self, doc_id: int) -> int: - return self.doc_records.get(doc_id).doc_len - - def get_doc_name(self, doc_id: int) -> str: - return self.doc_records.get(doc_id).collection_doc_id - - def get_term_cf(self, term: str) -> float: - return self.terms_dict.get(term).cf - - def get_term_record(self, term: str) -> TermRecord: - return self.terms_dict.get(term) - - -@timer -def main(): - index = IndexTerri(CW_INDEX) - x = index.get_posting_list('gather') - print(len(x.posting_list)) - print(index.get_doc_len(0)) - print(index.terms_dict.get('gather')) - print(index.terms_dict.get('crime')) - print(index.doc_records.get(100)) - print(index.doc_records.get(101)) - - -if __name__ == '__main__': - main() diff --git a/code/qpptk/qpptk/load_terrier_index.py b/code/qpptk/qpptk/load_terrier_index.py index 8651853..f9e58a2 100644 --- a/code/qpptk/qpptk/load_terrier_index.py +++ b/code/qpptk/qpptk/load_terrier_index.py @@ -73,10 +73,10 @@ def terms_cf_vec(self) -> np.ndarray: return self.__terms_cf_vec def init_pt_index(self): - if not pt.started(): - pt.init(no_download=True) - else: - print('*** A PyTerrier was already initialized, will try to load a new index ***') + # import and start pyterrier so that it works within tira and outside of tira + from tira.third_party_integrations import ensure_pyterrier_is_loaded + ensure_pyterrier_is_loaded() + _index_file = ensure_file(os.path.join(self.index_dir, 'data.properties')) _indexref = pt.IndexRef.of(_index_file) return pt.IndexFactory.of(_indexref) From 6491ec6eae550a29b110c75398399b6f86feb1bf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Tue, 7 Nov 2023 09:05:52 +0100 Subject: [PATCH 17/40] Remove index statistics from the input example so that they are created on demand --- docker/pyterrier-index/index/docs_dict.pkl | Bin 168 -> 0 bytes .../index/partial_2659142649_mapping.pkl | Bin 34 -> 0 bytes docker/pyterrier-index/index/stats_dict.pkl | Bin 68 -> 0 bytes docker/pyterrier-index/index/terms_dict.pkl | Bin 580 -> 0 bytes 4 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 docker/pyterrier-index/index/docs_dict.pkl delete mode 100644 docker/pyterrier-index/index/partial_2659142649_mapping.pkl delete mode 100644 docker/pyterrier-index/index/stats_dict.pkl delete mode 100644 docker/pyterrier-index/index/terms_dict.pkl diff --git a/docker/pyterrier-index/index/docs_dict.pkl b/docker/pyterrier-index/index/docs_dict.pkl deleted file mode 100644 index cb7e26c0d33ece9aa0bc04b557e7c8fce6b4aa78..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 168 zcmZo*ojR8R0&1sdcr*0y6c!YeWa}m8=cQ$)PwC-w$xjYSP0lY$nKF3_P@1bCF)zI+ zF;~~XaEdo;`;^8h-i#T{-i$~RMlcB`pac_=gz*$_Hn0w6pae6Lgb7T71t`ISBw;$m Kn-ip?R1W|TV>hh; diff --git a/docker/pyterrier-index/index/partial_2659142649_mapping.pkl b/docker/pyterrier-index/index/partial_2659142649_mapping.pkl deleted file mode 100644 index 30e09fd7b8561d6e71d73a87bc2c4ab11b928399..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 34 lcmZo*ohr@%0ku;!dYIGlE2em}^{`f@CKV-4@n$I10|1R@2~z+7 diff --git a/docker/pyterrier-index/index/stats_dict.pkl b/docker/pyterrier-index/index/stats_dict.pkl deleted file mode 100644 index c8fd4fe617b2cdf9c90bec629ae0bc14f459bfcd..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 68 zcmZo*oodMd0ku;!dbmsSOA>S9OHzw+i>G+Y_wePF<|d^U#pkERr{pICg;;xdO7k)c LOH*NLBue!F;glC< diff --git a/docker/pyterrier-index/index/terms_dict.pkl b/docker/pyterrier-index/index/terms_dict.pkl deleted file mode 100644 index 667a5e90c9f0233daaec1521de5075c338c0d470..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 580 zcmYk(QA@)x5C`yWX;z(^PNx$=5Cowh3hXn$k$Z4%n0PWk?8DI(effzvfHn{yIE+l0 zKpeupskEjt;;0vJwxh)?MZMIvX<)@{S$DXCo~flX76=!Muc2?)PDBvqab14{)Lf8s zy@mrPJE)^t5@+}hWrMNO{B%> EAIK8AmH+?% From 0b342196cb1eb3d3eb6256204912f53fdb6d5ee8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Tue, 7 Nov 2023 09:09:01 +0100 Subject: [PATCH 18/40] Make index-stats configurable so that it works in TIRA --- README.md | 2 +- code/qpptk/qpptk/load_terrier_index.py | 21 ++++++++++----------- code/qpptk/qpptk/qpptk_main.py | 6 ++++-- code/qpptk/qpptk/utility_functions.py | 2 +- 4 files changed, 16 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index ca2e902..d170607 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ tira-run \ --input-directory ${PWD}/docker/sample-input-full-rank \ --input-run ${PWD}/docker/pyterrier-index \ --image mam10eks/qpptk:0.0.1 \ - --command 'python3 /qpptk_main.py -ti $inputRun/index/ --jsonl_queries $inputDataset/queries.jsonl --predict --retrieve --output $outputDir --cleanOutput' + --command 'python3 /qpptk_main.py -ti $inputRun/index/ --jsonl_queries $inputDataset/queries.jsonl --predict --retrieve --output $outputDir --cleanOutput --stats_index_path /tmp' ``` ## Build the Docker Images diff --git a/code/qpptk/qpptk/load_terrier_index.py b/code/qpptk/qpptk/load_terrier_index.py index f9e58a2..5db5e72 100644 --- a/code/qpptk/qpptk/load_terrier_index.py +++ b/code/qpptk/qpptk/load_terrier_index.py @@ -27,7 +27,7 @@ def __str__(self): index_name = f'{index_name}_partial-{self.partial_terms_hash}' return str(index_name) - def __init__(self, terrier_index_dir, partial_terms=None, read_only=True): + def __init__(self, terrier_index_dir, partial_terms=None, read_only=True, stats_index_path=None): # TODO: looks like I need to init terrier only if going to generate """ The index is loading a Terrier index and several python objects that were generated from the Terrier index. @@ -40,9 +40,8 @@ def __init__(self, terrier_index_dir, partial_terms=None, read_only=True): logger.warn("It should be used only in the main process, " "during parallel runs the index should be used as read-only") self.index_dir = ensure_dir(terrier_index_dir) - # self._index = self.init_pt_index() - # logger.warn('*** An Index was loaded ***') - # print(f'{self._index} \n {type(self._index)} \n {id(self._index)}') + self.stats_index_dir = ensure_dir(stats_index_path if stats_index_path else self.index_dir) + self.index_stats = self.__load_generate_stats_dict(_generate) self.total_terms = self.index_stats.get('total_terms') self.number_of_docs = self.index_stats.get('number_of_docs') @@ -95,7 +94,7 @@ def __generate_all_terms_records(self): return terms_records def __load_generate_stats_dict(self, generate): - stats_dict_file = os.path.join(self.index_dir, 'stats_dict.pkl') + stats_dict_file = os.path.join(self.stats_index_dir, 'stats_dict.pkl') if generate: _index = self.init_pt_index() _stats = _index.getCollectionStatistics().toString() @@ -116,7 +115,7 @@ def __load_generate_stats_dict(self, generate): return stats_dict def __load_generate_terms_dict(self, generate): - terms_dict_file = os.path.join(self.index_dir, 'terms_dict.pkl') + terms_dict_file = os.path.join(self.stats_index_dir, 'terms_dict.pkl') try: terms_dict_file = ensure_file(terms_dict_file) terms_records = pickle_load_obj(terms_dict_file) @@ -141,7 +140,7 @@ def __generate_all_docs_records(self): return docs_records def __load_generate_docs_dict(self, generate): - docs_records_file = os.path.join(self.index_dir, 'docs_dict.pkl') + docs_records_file = os.path.join(self.stats_index_dir, 'docs_dict.pkl') try: docs_records_file = ensure_file(docs_records_file) docs_records = pickle_load_obj(docs_records_file) @@ -232,8 +231,8 @@ def get_doc_ids_by_name(self, names_vec): @timer def initialize_full_postings_sparse_mat(self, generate): - terms_postings_mat_file = os.path.join(self.index_dir, f'full_terms_postings_mat.npz') - terms_mapping_file = os.path.join(self.index_dir, f'full_terms_mapping.pkl') + terms_postings_mat_file = os.path.join(self.stats_index_dir, f'full_terms_postings_mat.npz') + terms_mapping_file = os.path.join(self.stats_index_dir, f'full_terms_mapping.pkl') try: terms_mapping_file = ensure_file(terms_mapping_file) terms_postings_mat_file = ensure_file(terms_postings_mat_file) @@ -251,8 +250,8 @@ def initialize_full_postings_sparse_mat(self, generate): @timer def initialize_partial_postings_sparse_mat(self, partial_terms, generate): # FIXME - terms_postings_mat_file = os.path.join(self.index_dir, f'partial_{self.partial_terms_hash}_postings_mat.npz') - terms_mapping_file = os.path.join(self.index_dir, f'partial_{self.partial_terms_hash}_mapping.pkl') + terms_postings_mat_file = os.path.join(self.stats_index_dir, f'partial_{self.partial_terms_hash}_postings_mat.npz') + terms_mapping_file = os.path.join(self.stats_index_dir, f'partial_{self.partial_terms_hash}_mapping.pkl') try: terms_mapping_file = ensure_file(terms_mapping_file) terms_postings_mat_file = ensure_file(terms_postings_mat_file) diff --git a/code/qpptk/qpptk/qpptk_main.py b/code/qpptk/qpptk/qpptk_main.py index 1117b6b..69ede41 100644 --- a/code/qpptk/qpptk/qpptk_main.py +++ b/code/qpptk/qpptk/qpptk_main.py @@ -22,6 +22,7 @@ def parse_args(): index_group.add_argument('-ci', '--ciff_index', metavar='INDEX', type=str, default=None, help='path to ciff index file') index_group.add_argument('-ti', '--terrier_index', metavar='INDEX', type=str, default=None, help='path to terrier index dir') + queries_group = parser.add_mutually_exclusive_group() queries_group.add_argument('-tq', '--text_queries', metavar='QUERIES', default=None, help='path to text queries file') @@ -49,6 +50,7 @@ def parse_args(): parser.add_argument('--output', default=None, required=False, help='The output directory') parser.add_argument('--cleanOutput', action='store_true', help='Clean all temporary output files and output only a joined jsonl file') + parser.add_argument('--stats_index_path', type=str, default=None, help='location of the index statistics') return parser.parse_args() @@ -261,13 +263,13 @@ def init_db_index(): def init_readonly_index(): # TODO: should be init for terrier index queries = get_queries_object() qids = queries.get_query_ids() - index = initialize_terrier_index(index_path, partial_terms=queries.get_queries_df().columns) + index = initialize_terrier_index(index_path, partial_terms=queries.get_queries_df().columns, stats_index_path=args.stats_index_path) index_hash = index().partial_terms_hash return qids, index, queries, index_hash def init_writeable_index(): queries = get_queries_object() - return initialize_terrier_index(index_path, partial_terms=queries.get_queries_df().columns, read_only=False)() + return initialize_terrier_index(index_path, partial_terms=queries.get_queries_df().columns, read_only=False, stats_index_path=args.stats_index_path)() index_path, index_type = set_index_paths() if index_type == 'text': diff --git a/code/qpptk/qpptk/utility_functions.py b/code/qpptk/qpptk/utility_functions.py index 464f13a..9972c22 100644 --- a/code/qpptk/qpptk/utility_functions.py +++ b/code/qpptk/qpptk/utility_functions.py @@ -35,7 +35,7 @@ def binary_search(list_, target): def ensure_file(file): """Ensure a single file exists, returns the absolute path of the file if True or raises FileNotFoundError if not""" # tilde expansion - file_path = os.path.normpath(os.path.expanduser(file)) + file_path = os.path.abspath(os.path.normpath(os.path.expanduser(file))) if not os.path.isfile(file_path): raise FileNotFoundError(f"The file {file_path} doesn't exist") return file_path From 44c566db86426e38dce3fa9122a973c00a150dda Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Fri, 10 Nov 2023 17:25:23 +0100 Subject: [PATCH 19/40] Add data for testts --- code/qpptk/tests/end_to_end_tests.py | 2 + .../small-example-01/index/data.direct.bf | Bin 0 -> 45 bytes .../index/data.document.fsarrayfile | Bin 0 -> 126 bytes .../small-example-01/index/data.inverted.bf | Bin 0 -> 58 bytes .../index/data.lexicon.fsomapfile | Bin 0 -> 5400 bytes .../index/data.lexicon.fsomaphash | Bin 0 -> 729 bytes .../index/data.lexicon.fsomapid | Bin 0 -> 240 bytes .../index/data.meta-0.fsomapfile | Bin 0 -> 930 bytes .../small-example-01/index/data.meta.idx | Bin 0 -> 48 bytes .../small-example-01/index/data.meta.zdata | Bin 0 -> 132 bytes .../small-example-01/index/data.properties | 71 ++++++++++++++++++ .../resources/small-example-01/queries.jsonl | 3 + .../small-example-02/index/data.direct.bf | Bin 0 -> 45 bytes .../index/data.document.fsarrayfile | Bin 0 -> 126 bytes .../small-example-02/index/data.inverted.bf | Bin 0 -> 58 bytes .../index/data.lexicon.fsomapfile | Bin 0 -> 5400 bytes .../index/data.lexicon.fsomaphash | Bin 0 -> 729 bytes .../index/data.lexicon.fsomapid | Bin 0 -> 240 bytes .../index/data.meta-0.fsomapfile | Bin 0 -> 930 bytes .../small-example-02/index/data.meta.idx | Bin 0 -> 48 bytes .../small-example-02/index/data.meta.zdata | Bin 0 -> 132 bytes .../small-example-02/index/data.properties | 71 ++++++++++++++++++ .../resources/small-example-02/queries.jsonl | 3 + 23 files changed, 150 insertions(+) create mode 100644 code/qpptk/tests/end_to_end_tests.py create mode 100644 code/qpptk/tests/resources/small-example-01/index/data.direct.bf create mode 100644 code/qpptk/tests/resources/small-example-01/index/data.document.fsarrayfile create mode 100644 code/qpptk/tests/resources/small-example-01/index/data.inverted.bf create mode 100644 code/qpptk/tests/resources/small-example-01/index/data.lexicon.fsomapfile create mode 100644 code/qpptk/tests/resources/small-example-01/index/data.lexicon.fsomaphash create mode 100644 code/qpptk/tests/resources/small-example-01/index/data.lexicon.fsomapid create mode 100644 code/qpptk/tests/resources/small-example-01/index/data.meta-0.fsomapfile create mode 100644 code/qpptk/tests/resources/small-example-01/index/data.meta.idx create mode 100644 code/qpptk/tests/resources/small-example-01/index/data.meta.zdata create mode 100644 code/qpptk/tests/resources/small-example-01/index/data.properties create mode 100644 code/qpptk/tests/resources/small-example-01/queries.jsonl create mode 100644 code/qpptk/tests/resources/small-example-02/index/data.direct.bf create mode 100644 code/qpptk/tests/resources/small-example-02/index/data.document.fsarrayfile create mode 100644 code/qpptk/tests/resources/small-example-02/index/data.inverted.bf create mode 100644 code/qpptk/tests/resources/small-example-02/index/data.lexicon.fsomapfile create mode 100644 code/qpptk/tests/resources/small-example-02/index/data.lexicon.fsomaphash create mode 100644 code/qpptk/tests/resources/small-example-02/index/data.lexicon.fsomapid create mode 100644 code/qpptk/tests/resources/small-example-02/index/data.meta-0.fsomapfile create mode 100644 code/qpptk/tests/resources/small-example-02/index/data.meta.idx create mode 100644 code/qpptk/tests/resources/small-example-02/index/data.meta.zdata create mode 100644 code/qpptk/tests/resources/small-example-02/index/data.properties create mode 100644 code/qpptk/tests/resources/small-example-02/queries.jsonl diff --git a/code/qpptk/tests/end_to_end_tests.py 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zcmZQg$xqfbWEhNq734Id!RRtZu+xkOqtlqcPBR&dPGbf;&2%t2jRov9v%%;zRsdbb B3AF$K literal 0 HcmV?d00001 diff --git a/code/qpptk/tests/resources/small-example-02/index/data.meta.idx b/code/qpptk/tests/resources/small-example-02/index/data.meta.idx new file mode 100644 index 0000000000000000000000000000000000000000..de35a2eb54bf550a80312e5edb47ef2526af965f GIT binary patch literal 48 WcmZQzKmlS>10R Date: Fri, 10 Nov 2023 17:28:12 +0100 Subject: [PATCH 20/40] Add unit tests --- .vscode/settings.json | 14 +++++++++++++ README.md | 13 ++++++++++-- code/qpptk/.vscode/settings.json | 7 +++++++ code/qpptk/requirements.txt | 1 + code/qpptk/tests/__init__.py | 0 code/qpptk/tests/end_to_end_test.py | 25 +++++++++++++++++++++++ code/qpptk/tests/end_to_end_tests.py | 2 -- code/qpptk/tests/query_parsing_test.py | 28 ++++++++++++++++++++++++++ docker/Dockerfile | 3 ++- 9 files changed, 88 insertions(+), 5 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 code/qpptk/.vscode/settings.json create mode 100644 code/qpptk/tests/__init__.py create mode 100644 code/qpptk/tests/end_to_end_test.py delete mode 100644 code/qpptk/tests/end_to_end_tests.py create mode 100644 code/qpptk/tests/query_parsing_test.py diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..db84606 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,14 @@ +{ + "python.testing.unittestArgs": [ + "-v", + "-s", + "./code", + "-p", + "*test*.py" + ], + "python.testing.pytestEnabled": true, + "python.testing.unittestEnabled": false, + "python.testing.pytestArgs": [ + "code" + ] +} \ No newline at end of file diff --git a/README.md b/README.md index d170607..d301d9f 100644 --- a/README.md +++ b/README.md @@ -8,12 +8,21 @@ Please ensure that you have python >= 3.7, Docker, and tira-run installed (`pip3 ``` tira-run \ - --input-directory ${PWD}/docker/sample-input-full-rank \ - --input-run ${PWD}/docker/pyterrier-index \ + --input-dataset workshop-on-open-web-search/query-processing-20231027-training \ + --input-run 'workshop-on-open-web-search/tira-ir-starter/Index (tira-ir-starter-pyterrier)' \ --image mam10eks/qpptk:0.0.1 \ --command 'python3 /qpptk_main.py -ti $inputRun/index/ --jsonl_queries $inputDataset/queries.jsonl --predict --retrieve --output $outputDir --cleanOutput --stats_index_path /tmp' ``` + File "/workspaces/QPP-EnhancedEval/code/qpptk/qpptk/global_manager.py", line 33, in run_pre_prediction_process + max_idf = process.calc_max_idf() + File "/workspaces/QPP-EnhancedEval/code/qpptk/qpptk/pre_retrieval_predictors.py", line 30, in calc_max_idf + return np.log(np.array(self.total_docs) / self.terms_df).max() + File "/usr/local/lib/python3.10/dist-packages/numpy/core/_methods.py", line 40, in _amax + return umr_maximum(a, axis, None, out, keepdims, initial, where) +ValueError: zero-size array to reduction operation maximum which has no identity + + ## Build the Docker Images Build the docker image via: diff --git a/code/qpptk/.vscode/settings.json b/code/qpptk/.vscode/settings.json new file mode 100644 index 0000000..9b38853 --- /dev/null +++ b/code/qpptk/.vscode/settings.json @@ -0,0 +1,7 @@ +{ + "python.testing.pytestArgs": [ + "tests" + ], + "python.testing.unittestEnabled": false, + "python.testing.pytestEnabled": true +} \ No newline at end of file diff --git a/code/qpptk/requirements.txt b/code/qpptk/requirements.txt index b82ccd3..b658e29 100644 --- a/code/qpptk/requirements.txt +++ b/code/qpptk/requirements.txt @@ -13,3 +13,4 @@ statsmodels==0.14.0 syct==0.4.3 toml==0.10.2 tqdm==4.62.3 +pytest \ No newline at end of file diff --git a/code/qpptk/tests/__init__.py b/code/qpptk/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/code/qpptk/tests/end_to_end_test.py b/code/qpptk/tests/end_to_end_test.py new file mode 100644 index 0000000..be4376a --- /dev/null +++ b/code/qpptk/tests/end_to_end_test.py @@ -0,0 +1,25 @@ +from qpptk import main, parse_args +import tempfile +import pandas as pd + +def test_end_to_end(): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args(['-ti', 'tests/resources/small-example-01/index/', '--jsonl_queries', 'tests/resources/small-example-01/queries.jsonl', '--predict', '--retrieve', '--output', out_dir, '--cleanOutput','--stats_index_path', stats_dir]) + main(args) + + actual = pd.read_json(out_dir + '/queries.jsonl', lines=True) + + assert len(actual) == 3 + + assert actual.iloc[0].to_dict()['avg-idf'] == 1.3296613489 + assert actual.iloc[0].to_dict()['avg-scq'] == 2.2134369757 + assert actual.iloc[0].to_dict()['clarity+1000+3'] == 2.6980214786000003 + assert actual.iloc[0].to_dict()['max-idf'] == 1.7917594692 + + assert actual.iloc[1].to_dict()['avg-idf'] == 1.0986122887 + assert actual.iloc[1].to_dict()['avg-scq'] == 2.909294382 + assert actual.iloc[1].to_dict()['max-idf'] == 1.0986122887 + + assert actual.iloc[2].to_dict()['avg-idf'] == 1.0986122887 + assert actual.iloc[2].to_dict()['avg-scq'] == 2.6282473855 + assert actual.iloc[2].to_dict()['max-idf'] == 1.0986122887 \ No newline at end of file diff --git a/code/qpptk/tests/end_to_end_tests.py b/code/qpptk/tests/end_to_end_tests.py deleted file mode 100644 index 995cda0..0000000 --- a/code/qpptk/tests/end_to_end_tests.py +++ /dev/null @@ -1,2 +0,0 @@ -def test_foo(): - print('as') \ No newline at end of file diff --git a/code/qpptk/tests/query_parsing_test.py b/code/qpptk/tests/query_parsing_test.py new file mode 100644 index 0000000..493fd91 --- /dev/null +++ b/code/qpptk/tests/query_parsing_test.py @@ -0,0 +1,28 @@ +from qpptk import get_queries_object, parse_args +import tempfile + +def test_end_to_end_with_porter_stemmer(): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + expected = [ + {'achiev': 1, 'hubbl': 1, 'telescop': 1}, + {'exit': 1, 'how': 1, 'to': 1, 'vim': 1}, + {'attack': 1, 'heart': 1, 'sign': 1} + ] + args = parse_args(['-ti', 'tests/resources/small-example-01/index/', '--jsonl_queries', 'tests/resources/small-example-01/queries.jsonl', '--predict', '--retrieve', '--output', out_dir, '--cleanOutput','--stats_index_path', stats_dir]) + actual = get_queries_object(args) + assert expected[0] == actual.get_query(qid='1') + assert expected[1] == actual.get_query(qid='2') + assert expected[2] == actual.get_query(qid='3') + +def test_end_to_end_without_stemmer(): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + expected = [ + {'achievements': 1, 'hubble': 1, 'telescope': 1}, + {'exit': 1, 'how': 1, 'to': 1, 'vim': 1}, + {'attack': 1, 'heart': 1, 'signs': 1} + ] + args = parse_args(['-ti', 'tests/resources/small-example-02/index', '--jsonl_queries', 'tests/resources/small-example-01/queries.jsonl', '--predict', '--retrieve', '--output', out_dir, '--cleanOutput','--stats_index_path', stats_dir]) + actual = get_queries_object(args) + assert expected[0] == actual.get_query(qid='1') + assert expected[1] == actual.get_query(qid='2') + assert expected[2] == actual.get_query(qid='3') \ No newline at end of file diff --git a/docker/Dockerfile b/docker/Dockerfile index c098f4b..ff1ff39 100644 --- a/docker/Dockerfile +++ b/docker/Dockerfile @@ -4,6 +4,7 @@ FROM mam10eks/qpptk:0.0.1-dev COPY code/qpptk/ /workspaces/QPP-EnhancedEval/code/qpptk -RUN pip install -e . +RUN pip install -e . \ + && pytest ENTRYPOINT [ "python3", "/qpptk_main.py" ] From 9128801dc356cc620c0c7e486ba9f5b0cda93319 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Fri, 10 Nov 2023 17:29:59 +0100 Subject: [PATCH 21/40] fix unit tests --- code/qpptk/qpptk/__init__.py | 6 ++--- code/qpptk/qpptk/parse_queries.py | 37 ++++++++++++++++++++++++++++--- code/qpptk/qpptk/qpptk_main.py | 36 +++++++++++++----------------- 3 files changed, 52 insertions(+), 27 deletions(-) diff --git a/code/qpptk/qpptk/__init__.py b/code/qpptk/qpptk/__init__.py index 2532387..522a5f5 100644 --- a/code/qpptk/qpptk/__init__.py +++ b/code/qpptk/qpptk/__init__.py @@ -1,15 +1,13 @@ from .utility_functions import * from .config import * from .load_text_index import IndexText, parse_posting_list -# from .load_ciff_index import IndexCiff, parse_index_file from .load_db_index import IndexDB from .load_terrier_index import IndexTerrier from .parse_queries import QueryParserText, QueryParserCiff, QueryParserJsonl from .retrieval_local_manager import LocalManagerRetrieval -# from .parse_ciff_queries import QueryParserCiff, parse_ciff_queries_file from .pre_retrieval_predictors import LocalManagerPredictorPre from .post_retrieval_predictors import LocalManagerPredictorPost -# from .index_to_db import parse_index_to_db +from .qpptk_main import parse_args, main, get_queries_object __all__ = ['Config', 'Posting', 'TermPosting', 'TermRecord', 'TermFrequency', 'DocRecord', 'ResultPair', 'get_file_len', 'read_line', 'parse_posting_list', 'binary_search', 'IndexText', 'IndexDB', 'IndexTerrier', @@ -17,4 +15,4 @@ 'LocalManagerPredictorPre', 'ensure_dir', 'ensure_file', 'LocalManagerPredictorPost', 'read_message', 'plot_roc', 'transform_list_to_counts_dict', 'jaccard_similarity', 'overlap_coefficient', 'sorensen_dice_similarity', 'calc_ndcg', 'set_index_dump_paths', 'add_topic_to_qdf', 'msgpack_encode', - 'msgpack_decode', 'read_trec_res_file'] # , 'IndexCiff', 'parse_index_file', 'parse_index_to_db'] + 'msgpack_decode', 'read_trec_res_file', 'parse_args', 'main', 'get_queries_object'] diff --git a/code/qpptk/qpptk/parse_queries.py b/code/qpptk/qpptk/parse_queries.py index f60d63f..7e23fca 100644 --- a/code/qpptk/qpptk/parse_queries.py +++ b/code/qpptk/qpptk/parse_queries.py @@ -4,6 +4,7 @@ import pandas as pd from syct import timer from lxml import etree +import os from sklearn.cluster import KMeans # import qpptk.CommonIndexFileFormat_pb2 as ciff @@ -201,8 +202,9 @@ def cluster_queries_by_similarity(self, k=3): class QueryParserJsonl: - def __init__(self, queries_jsonl_file, **kwargs): + def __init__(self, queries_jsonl_file, terrier_index, **kwargs): self.queries_file = queries_jsonl_file + self.terrier_index = os.path.abspath(terrier_index) self.raw_queries_df = self._read_queries() self.queries_sr = self._weight_queries() self.__queries_df = None @@ -231,8 +233,37 @@ def queries_df(self): def _read_queries(self, queries_file=None): # FIXME: add an adaptation for tsv files _queries_file = queries_file if queries_file else self.queries_file - return pd.read_json(_queries_file, lines=True, dtype=str).rename(columns={'query': 'terms'})[ - ['qid', 'terms']].set_index('qid').sort_index(key=qid_index_sort_key) + import pyterrier as pt + from tira.third_party_integrations import ensure_pyterrier_is_loaded + ensure_pyterrier_is_loaded() + from jnius import cast, autoclass + index_properties = cast('org.terrier.structures.PropertiesIndex', pt.IndexFactory.of(self.terrier_index)).getProperties().getProperty('termpipelines') + + index_properties = index_properties.split(',') + # stopwords automatically handled later because they are oov + index_properties = [i for i in index_properties if i.lower() != 'stopwords'] + if len(index_properties) == 1: + print("org.terrier.terms." + index_properties[0]) + s = autoclass("org.terrier.terms." + index_properties[0])() + stemmer = lambda i: s.stem(i.strip()).strip() + elif len(index_properties) > 1: + raise ValueError('Could not handle ' + str(index_properties)) + else: + stemmer = lambda i: i.strip() + + t = autoclass("org.terrier.indexing.tokenisation.Tokeniser").getTokeniser() + + def tokenize(text): + text = t.getTokens(text) + return ' '.join([stemmer(i) for i in text]) + + + + ret = pd.read_json(_queries_file, lines=True, dtype=str).rename(columns={'query': 'terms'})[ + ['qid', 'terms']] + ret['terms'] = ret['terms'].apply(tokenize) + + return ret.set_index('qid').sort_index(key=qid_index_sort_key) def _weight_queries(self): return self.raw_queries_df.terms.replace('', None).dropna().str.split().apply(transform_list_to_counts_dict) diff --git a/code/qpptk/qpptk/qpptk_main.py b/code/qpptk/qpptk/qpptk_main.py index 69ede41..96faa33 100644 --- a/code/qpptk/qpptk/qpptk_main.py +++ b/code/qpptk/qpptk/qpptk_main.py @@ -15,7 +15,7 @@ retrieval_full, initialize_db_index, initialize_text_index, post_ret_prediction_full, \ initialize_terrier_index -def parse_args(): +def parse_args(args): parser = argparse.ArgumentParser(description='Run QL retrieval or Query Performance Prediction') index_group = parser.add_mutually_exclusive_group() index_group.add_argument('--text_index', metavar='INDEX', type=str, default=None, help='path to text index dir') @@ -52,7 +52,7 @@ def parse_args(): parser.add_argument('--cleanOutput', action='store_true', help='Clean all temporary output files and output only a joined jsonl file') parser.add_argument('--stats_index_path', type=str, default=None, help='location of the index statistics') - return parser.parse_args() + return parser.parse_args(args) logger = Config.logger @@ -168,7 +168,7 @@ def load_and_evaluate(prefix, ir_metric, method='pearson', title_only=False): return sr -def get_queries_object(): +def get_queries_object(args): drop_duplicates = not args.keep_duplicate_queries if args.text_queries: queries_path, queries_type = args.text_queries, 'text' @@ -203,7 +203,7 @@ def get_queries_object(): QueryParserCiff(queries_path, drop_queries_file='duplicated_qids.txt', drop_duplicate_queries=drop_duplicates) elif queries_type == 'jsonl': - QueryParserJsonl(queries_path, drop_queries_file='duplicated_qids.txt', + QueryParserJsonl(queries_path, args.terrier_index, drop_queries_file='duplicated_qids.txt', drop_duplicate_queries=drop_duplicates) else: if queries_type == 'text': @@ -211,10 +211,10 @@ def get_queries_object(): elif queries_type == 'ciff': return QueryParserCiff(queries_path, drop_duplicate_queries=drop_duplicates) elif queries_type == 'jsonl': - return QueryParserJsonl(queries_path, drop_duplicate_queries=drop_duplicates) + return QueryParserJsonl(queries_path, args.terrier_index, drop_duplicate_queries=drop_duplicates) -def set_index_paths(): +def set_index_paths(args): index_path, index_type = (args.ciff_index, 'ciff') if args.ciff_index else ( (args.text_index, 'text') if args.text_index else (args.terrier_index, 'terrier')) if index_path is None: @@ -251,27 +251,29 @@ def cluster_queries(queries): @timer -def main(): +def main(args): + if args.output: + Config.RESULTS_DIR = args.output results_dir = Config.RESULTS_DIR def init_db_index(): index = initialize_db_index(db_dir) - queries = get_queries_object() + queries = get_queries_object(args) qids = queries.get_query_ids() return qids, index, queries def init_readonly_index(): # TODO: should be init for terrier index - queries = get_queries_object() + queries = get_queries_object(args) qids = queries.get_query_ids() index = initialize_terrier_index(index_path, partial_terms=queries.get_queries_df().columns, stats_index_path=args.stats_index_path) index_hash = index().partial_terms_hash return qids, index, queries, index_hash def init_writeable_index(): - queries = get_queries_object() + queries = get_queries_object(args) return initialize_terrier_index(index_path, partial_terms=queries.get_queries_df().columns, read_only=False, stats_index_path=args.stats_index_path)() - index_path, index_type = set_index_paths() + index_path, index_type = set_index_paths(args) if index_type == 'text': dump_files = set_index_dump_paths(index_path) if index_type == 'ciff': @@ -390,7 +392,7 @@ def init_writeable_index(): f'{prefix_path}.eval_{ir_metric}_{method}_{queries}_queries.pkl') if args.pairs_sim: - cos_sim_df, jac_df, dsc_df = generate_pairs_similarity(get_queries_object()) + cos_sim_df, jac_df, dsc_df = generate_pairs_similarity(get_queries_object(args)) cos_sim_df.to_csv(f"{prefix_path}_pairwise_sim-cos.tsv", sep=' ', index=True, header=True, float_format=f"%.{PRECISION}f") jac_df.to_csv(f"{prefix_path}_pairwise_sim-jac.tsv", sep=' ', index=True, header=True, @@ -399,14 +401,8 @@ def init_writeable_index(): float_format=f"%.{PRECISION}f") if args.cluster_queries: - cluster_queries(get_queries_object()) + cluster_queries(get_queries_object(args)) if __name__ == '__main__': - args = parse_args() - - if args.output: - Config.RESULTS_DIR = args.output - - main() - + main(parse_args(sys.argv[1:])) From d2ba480460f20446ec41f5abf173065b692379b0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 06:56:48 +0100 Subject: [PATCH 22/40] Prepare some unit tests --- code/qpptk/qpptk/qpptk_main.py | 4 +- code/qpptk/requirements.txt | 3 +- .../custom_run_file_for_prediction_test.py | 61 ++++++++++++++++++ code/qpptk/tests/end_to_end_test.py | 35 +++++----- code/qpptk/tests/query_parsing_test.py | 52 ++++++++------- .../small-example-01/documents.jsonl.gz | Bin 0 -> 492 bytes .../resources/small-example-01/run-02.txt | 6 ++ .../run-with-ground-truth-scores.txt | 18 ++++++ .../tests/resources/small-example-01/run.txt | 8 +++ ...wo_custom_run_files_for_prediction_test.py | 1 + 10 files changed, 146 insertions(+), 42 deletions(-) create mode 100644 code/qpptk/tests/custom_run_file_for_prediction_test.py create mode 100644 code/qpptk/tests/resources/small-example-01/documents.jsonl.gz create mode 100644 code/qpptk/tests/resources/small-example-01/run-02.txt create mode 100644 code/qpptk/tests/resources/small-example-01/run-with-ground-truth-scores.txt create mode 100644 code/qpptk/tests/resources/small-example-01/run.txt create mode 100644 code/qpptk/tests/two_custom_run_files_for_prediction_test.py diff --git a/code/qpptk/qpptk/qpptk_main.py b/code/qpptk/qpptk/qpptk_main.py index 96faa33..f940a6c 100644 --- a/code/qpptk/qpptk/qpptk_main.py +++ b/code/qpptk/qpptk/qpptk_main.py @@ -3,6 +3,7 @@ import shutil import subprocess as sp import sys +from pathlib import Path from glob import glob import pandas as pd @@ -51,6 +52,7 @@ def parse_args(args): parser.add_argument('--output', default=None, required=False, help='The output directory') parser.add_argument('--cleanOutput', action='store_true', help='Clean all temporary output files and output only a joined jsonl file') parser.add_argument('--stats_index_path', type=str, default=None, help='location of the index statistics') + parser.add_argument('--run-file', type=Path, default=None, help='path to run file to be used for prediction post retrieval predictions') return parser.parse_args(args) @@ -348,7 +350,7 @@ def init_writeable_index(): if args.predPost: retrieval_method = 'QL' try: - results_file = ensure_file(f'{prefix_path}_{retrieval_method}.res') + results_file = ensure_file(f'{prefix_path}_{retrieval_method}.res' if args.run_file is None else args.run_file) except FileNotFoundError: error_msg = f"The file {prefix_path}_{retrieval_method}.res doesn't exist," \ f"add --retrieve option to create it first" diff --git a/code/qpptk/requirements.txt b/code/qpptk/requirements.txt index b658e29..437c30c 100644 --- a/code/qpptk/requirements.txt +++ b/code/qpptk/requirements.txt @@ -13,4 +13,5 @@ statsmodels==0.14.0 syct==0.4.3 toml==0.10.2 tqdm==4.62.3 -pytest \ No newline at end of file +pytest +approvaltests diff --git a/code/qpptk/tests/custom_run_file_for_prediction_test.py b/code/qpptk/tests/custom_run_file_for_prediction_test.py new file mode 100644 index 0000000..2c349a6 --- /dev/null +++ b/code/qpptk/tests/custom_run_file_for_prediction_test.py @@ -0,0 +1,61 @@ +import unittest +import os +import tempfile +from qpptk import main, parse_args +import pandas as pd +import numpy as np + +class CustomRunFileForPredictionTest(unittest.TestCase): + def test_with_run_file_where_one_query_is_highly_effective(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run.txt', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predPost', '--output', out_dir, '--cleanOutput']) + main(args) + + actual = pd.read_json(out_dir + '/queries.jsonl', lines=True) + + self.assertEquals(len(actual), 3) + # run is constructed so that query 1 is more effective than query 2 than query 3 + self.assertEquals(actual.iloc[0].to_dict()['qid'], 1.0) + self.assertEquals(actual.iloc[0].to_dict()['wig+10'], 12.4250979385) + self.assertEquals(actual.iloc[0].to_dict()['nqc+100'], 0.00043380560000000004) + self.assertEquals(actual.iloc[0].to_dict()['smv+100'], -0.00043380560000000004) + self.assertEquals(actual.iloc[0].to_dict()['clarity+1000+3'], 2.6993897409) + + self.assertEquals(actual.iloc[1].to_dict()['qid'], 2.0) + self.assertEquals(actual.iloc[1].to_dict()['wig+10'], 4.2054528045) + self.assertEquals(actual.iloc[1].to_dict()['nqc+100'], 0.2547211364) + self.assertEquals(str(actual.iloc[1].to_dict()['smv+100']), 'nan') + self.assertEquals(actual.iloc[1].to_dict()['clarity+1000+3'], 2.240679245) + + self.assertEquals(actual.iloc[2].to_dict()['qid'], 3.0) + self.assertEquals(actual.iloc[2].to_dict()['wig+10'], -20.8716444259) + self.assertEquals(actual.iloc[2].to_dict()['nqc+100'], 6.6373379689) + self.assertEquals(str(actual.iloc[2].to_dict()['smv+100']), 'nan') + self.assertEquals(actual.iloc[2].to_dict()['clarity+1000+3'], 3.2047245641) + + def test_with_run_file_where_all_queries_are_highly_effective(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-02.txt', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predPost', '--output', out_dir, '--cleanOutput']) + main(args) + + actual = pd.read_json(out_dir + '/queries.jsonl', lines=True) + + self.assertEquals(len(actual), 3) + # All queries should be predicted as rather effective + self.assertEquals(actual.iloc[0].to_dict()['qid'], 1.0) + self.assertEquals(actual.iloc[0].to_dict()['wig+10'], 12.4250979385) + self.assertEquals(actual.iloc[0].to_dict()['nqc+100'], 0.00043380560000000004) + self.assertEquals(actual.iloc[0].to_dict()['smv+100'], -0.00043380560000000004) + self.assertEquals(actual.iloc[0].to_dict()['clarity+1000+3'], 2.6993897409) + + self.assertEquals(actual.iloc[1].to_dict()['qid'], 2.0) + self.assertEquals(actual.iloc[1].to_dict()['wig+10'], 13.2004528045) + self.assertEquals(actual.iloc[1].to_dict()['nqc+100'], 0.001559842) + self.assertEquals(actual.iloc[1].to_dict()['smv+100'], -0.001559842) + self.assertEquals(str(actual.iloc[1].to_dict()['clarity+1000+3']), 'nan') + + self.assertEquals(actual.iloc[2].to_dict()['qid'], 3.0) + self.assertEquals(actual.iloc[2].to_dict()['wig+10'], 11.8874342352) + self.assertEquals(actual.iloc[2].to_dict()['nqc+100'], 0.0007335282000000001) + self.assertEquals(actual.iloc[2].to_dict()['smv+100'], -0.0007335281) + self.assertEquals(str(actual.iloc[2].to_dict()['clarity+1000+3']), 'nan') diff --git a/code/qpptk/tests/end_to_end_test.py b/code/qpptk/tests/end_to_end_test.py index be4376a..95988f9 100644 --- a/code/qpptk/tests/end_to_end_test.py +++ b/code/qpptk/tests/end_to_end_test.py @@ -1,25 +1,28 @@ +import unittest from qpptk import main, parse_args import tempfile import pandas as pd +import os -def test_end_to_end(): - with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: - args = parse_args(['-ti', 'tests/resources/small-example-01/index/', '--jsonl_queries', 'tests/resources/small-example-01/queries.jsonl', '--predict', '--retrieve', '--output', out_dir, '--cleanOutput','--stats_index_path', stats_dir]) - main(args) +class TestEndToEnd(unittest.TestCase): + def test_end_to_end(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predict', '--retrieve', '--output', out_dir, '--cleanOutput','--stats_index_path', stats_dir]) + main(args) - actual = pd.read_json(out_dir + '/queries.jsonl', lines=True) + actual = pd.read_json(out_dir + '/queries.jsonl', lines=True) - assert len(actual) == 3 + assert len(actual) == 3 - assert actual.iloc[0].to_dict()['avg-idf'] == 1.3296613489 - assert actual.iloc[0].to_dict()['avg-scq'] == 2.2134369757 - assert actual.iloc[0].to_dict()['clarity+1000+3'] == 2.6980214786000003 - assert actual.iloc[0].to_dict()['max-idf'] == 1.7917594692 + assert actual.iloc[0].to_dict()['avg-idf'] == 1.3296613489 + assert actual.iloc[0].to_dict()['avg-scq'] == 2.2134369757 + assert actual.iloc[0].to_dict()['clarity+1000+3'] == 2.6980214786000003 + assert actual.iloc[0].to_dict()['max-idf'] == 1.7917594692 - assert actual.iloc[1].to_dict()['avg-idf'] == 1.0986122887 - assert actual.iloc[1].to_dict()['avg-scq'] == 2.909294382 - assert actual.iloc[1].to_dict()['max-idf'] == 1.0986122887 + assert actual.iloc[1].to_dict()['avg-idf'] == 1.0986122887 + assert actual.iloc[1].to_dict()['avg-scq'] == 2.909294382 + assert actual.iloc[1].to_dict()['max-idf'] == 1.0986122887 - assert actual.iloc[2].to_dict()['avg-idf'] == 1.0986122887 - assert actual.iloc[2].to_dict()['avg-scq'] == 2.6282473855 - assert actual.iloc[2].to_dict()['max-idf'] == 1.0986122887 \ No newline at end of file + assert actual.iloc[2].to_dict()['avg-idf'] == 1.0986122887 + assert actual.iloc[2].to_dict()['avg-scq'] == 2.6282473855 + assert actual.iloc[2].to_dict()['max-idf'] == 1.0986122887 diff --git a/code/qpptk/tests/query_parsing_test.py b/code/qpptk/tests/query_parsing_test.py index 493fd91..131497b 100644 --- a/code/qpptk/tests/query_parsing_test.py +++ b/code/qpptk/tests/query_parsing_test.py @@ -1,28 +1,32 @@ +import unittest from qpptk import get_queries_object, parse_args import tempfile +import os -def test_end_to_end_with_porter_stemmer(): - with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: - expected = [ - {'achiev': 1, 'hubbl': 1, 'telescop': 1}, - {'exit': 1, 'how': 1, 'to': 1, 'vim': 1}, - {'attack': 1, 'heart': 1, 'sign': 1} - ] - args = parse_args(['-ti', 'tests/resources/small-example-01/index/', '--jsonl_queries', 'tests/resources/small-example-01/queries.jsonl', '--predict', '--retrieve', '--output', out_dir, '--cleanOutput','--stats_index_path', stats_dir]) - actual = get_queries_object(args) - assert expected[0] == actual.get_query(qid='1') - assert expected[1] == actual.get_query(qid='2') - assert expected[2] == actual.get_query(qid='3') -def test_end_to_end_without_stemmer(): - with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: - expected = [ - {'achievements': 1, 'hubble': 1, 'telescope': 1}, - {'exit': 1, 'how': 1, 'to': 1, 'vim': 1}, - {'attack': 1, 'heart': 1, 'signs': 1} - ] - args = parse_args(['-ti', 'tests/resources/small-example-02/index', '--jsonl_queries', 'tests/resources/small-example-01/queries.jsonl', '--predict', '--retrieve', '--output', out_dir, '--cleanOutput','--stats_index_path', stats_dir]) - actual = get_queries_object(args) - assert expected[0] == actual.get_query(qid='1') - assert expected[1] == actual.get_query(qid='2') - assert expected[2] == actual.get_query(qid='3') \ No newline at end of file +class QueryParsingTest(unittest.TestCase): + def test_end_to_end_with_porter_stemmer(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + expected = [ + {'achiev': 1, 'hubbl': 1, 'telescop': 1}, + {'exit': 1, 'how': 1, 'to': 1, 'vim': 1}, + {'attack': 1, 'heart': 1, 'sign': 1} + ] + args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predict', '--retrieve', '--output', out_dir, '--cleanOutput','--stats_index_path', stats_dir]) + actual = get_queries_object(args) + assert expected[0] == actual.get_query(qid='1') + assert expected[1] == actual.get_query(qid='2') + assert expected[2] == actual.get_query(qid='3') + + def test_end_to_end_without_stemmer(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + expected = [ + {'achievements': 1, 'hubble': 1, 'telescope': 1}, + {'exit': 1, 'how': 1, 'to': 1, 'vim': 1}, + {'attack': 1, 'heart': 1, 'signs': 1} + ] + args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-02/index', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predict', '--retrieve', '--output', out_dir, '--cleanOutput','--stats_index_path', stats_dir]) + actual = get_queries_object(args) + assert expected[0] == actual.get_query(qid='1') + assert expected[1] == actual.get_query(qid='2') + assert expected[2] == actual.get_query(qid='3') \ No newline at end of file diff --git a/code/qpptk/tests/resources/small-example-01/documents.jsonl.gz b/code/qpptk/tests/resources/small-example-01/documents.jsonl.gz new file mode 100644 index 0000000000000000000000000000000000000000..c0739926b903910b9cee3a728a6f24f89aa53f2d GIT binary patch literal 492 zcmV3c!J1j~dYfxxAId+Jq%4%wh3j9(<@C$?yXMb_ z*Y{KpYpUl^vRayDB&|@|O=?awRM=X70VcFXPVLl*il(&V@WvG+dgaKAVX})ZlqEJc zOT5AGY0dBx*-vBFIS6ORNef0`EZ!H6j82oFvV=r(q=Or?wDjFxtI(&}M0KOV=XHa< znE>;ZyvmYFTXQs&3tCI~tDH9l+(*IPB!2&Wd zZOYewe48jJj~m?#tNiu`;+Hkz4pw8tJ*xlET-_owK_ngkD(NyVpvmUZ?!&S7Uc>l-@0u62BSFQHes596k{Z9h7hijAEUF$-uHZ8*W(>wGFnvh3MX i#*ay3Ez)QV2UyWDGy)CR^_k5b0p~9Y*ew8c0{{R5gzgdm literal 0 HcmV?d00001 diff --git a/code/qpptk/tests/resources/small-example-01/run-02.txt b/code/qpptk/tests/resources/small-example-01/run-02.txt new file mode 100644 index 0000000..77cf25a --- /dev/null +++ b/code/qpptk/tests/resources/small-example-01/run-02.txt @@ -0,0 +1,6 @@ +1 0 doc-3 1 10 system +1 0 doc-4 2 9.99 system +2 0 doc-1 1 10 system +2 0 doc-2 2 9.99 system +3 0 doc-5 1 10 system +3 0 doc-6 2 9.99 system diff --git a/code/qpptk/tests/resources/small-example-01/run-with-ground-truth-scores.txt b/code/qpptk/tests/resources/small-example-01/run-with-ground-truth-scores.txt new file mode 100644 index 0000000..cae1f6e --- /dev/null +++ b/code/qpptk/tests/resources/small-example-01/run-with-ground-truth-scores.txt @@ -0,0 +1,18 @@ +1 0 doc-3 1 1 system +1 0 doc-1 2 0 system +1 0 doc-2 3 0 system +1 0 doc-4 3 0 system +1 0 doc-5 3 0 system +1 0 doc-6 3 0 system +2 0 doc-1 1 1 system +2 0 doc-2 2 0 system +2 0 doc-3 3 0 system +2 0 doc-4 3 0 system +2 0 doc-5 3 0 system +2 0 doc-6 3 0 system +3 0 doc-5 1 1 system +3 0 doc-1 2 0 system +3 0 doc-2 3 0 system +3 0 doc-3 3 0 system +3 0 doc-4 3 0 system +3 0 doc-6 3 0 system \ No newline at end of file diff --git a/code/qpptk/tests/resources/small-example-01/run.txt b/code/qpptk/tests/resources/small-example-01/run.txt new file mode 100644 index 0000000..68add6f --- /dev/null +++ b/code/qpptk/tests/resources/small-example-01/run.txt @@ -0,0 +1,8 @@ +1 0 doc-3 1 10 system +1 0 doc-4 2 9.99 system +2 0 doc-1 1 2 system +2 0 doc-6 2 1 system +2 0 doc-1 3 0 system +3 0 doc-1 1 1 system +3 0 doc-3 2 -10 system +3 0 doc-2 3 -100 system diff --git a/code/qpptk/tests/two_custom_run_files_for_prediction_test.py b/code/qpptk/tests/two_custom_run_files_for_prediction_test.py new file mode 100644 index 0000000..2dde70d --- /dev/null +++ b/code/qpptk/tests/two_custom_run_files_for_prediction_test.py @@ -0,0 +1 @@ +#5, 10, 20, 100, 1000 \ No newline at end of file From 22e7f67eccc5d57bb4284212379c3999b2885de6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 07:22:50 +0100 Subject: [PATCH 23/40] compute post-retrieval measures for multiple list sized by default to allow for easy post-hoc hyperparameter tuning --- code/qpptk/qpptk/qpptk_main.py | 18 +++++++++++------ ...e_highly_effective_approval.approved.jsonl | 3 +++ ...ghly_effective_approvaltest.approved.jsonl | 3 +++ ...nd_to_end_with_approvaltest.approved.jsonl | 3 +++ .../custom_run_file_for_prediction_test.py | 20 ++++++++++++++++--- code/qpptk/tests/end_to_end_test.py | 9 +++++++++ 6 files changed, 47 insertions(+), 9 deletions(-) create mode 100644 code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_all_queries_are_highly_effective_approval.approved.jsonl create mode 100644 code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_one_query_is_highly_effective_approvaltest.approved.jsonl create mode 100644 code/qpptk/tests/TestEndToEnd.test_end_to_end_with_approvaltest.approved.jsonl diff --git a/code/qpptk/qpptk/qpptk_main.py b/code/qpptk/qpptk/qpptk_main.py index f940a6c..faea735 100644 --- a/code/qpptk/qpptk/qpptk_main.py +++ b/code/qpptk/qpptk/qpptk_main.py @@ -348,7 +348,7 @@ def init_writeable_index(): header=False, float_format=f"%.{PRECISION}f") if args.predPost: - retrieval_method = 'QL' + retrieval_method = 'QL' if args.run_file is None else 'RUN' try: results_file = ensure_file(f'{prefix_path}_{retrieval_method}.res' if args.run_file is None else args.run_file) except FileNotFoundError: @@ -356,11 +356,17 @@ def init_writeable_index(): f"add --retrieve option to create it first" logger.error(error_msg) sys.exit(error_msg) - predictions_df = post_ret_prediction_full(qids, index, queries, read_trec_res_file(results_file)) - combined_predictions += [predictions_df] - for col in predictions_df.columns: - predictions_df.loc[:, col].to_csv(f"{prefix_path}_{retrieval_method}_{col}.pre", sep=' ', - index=True, header=False, float_format=f"%.{PRECISION}f") + for list_size in [5, 10, 20, 50, 100, 1000]: + Config.WIG_LIST_SIZE = list_size + Config.NQC_LIST_SIZE = list_size + Config.SMV_LIST_SIZE = list_size + Config.CLARITY_LIST_SIZE = list_size + + predictions_df = post_ret_prediction_full(qids, index, queries, read_trec_res_file(results_file)) + combined_predictions += [predictions_df] + for col in predictions_df.columns: + predictions_df.loc[:, col].to_csv(f"{prefix_path}_{retrieval_method}_{col}.pre", sep=' ', + index=True, header=False, float_format=f"%.{PRECISION}f") if combined_predictions: qid_to_preds = {} diff --git a/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_all_queries_are_highly_effective_approval.approved.jsonl b/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_all_queries_are_highly_effective_approval.approved.jsonl new file mode 100644 index 0000000..619aab3 --- /dev/null +++ b/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_all_queries_are_highly_effective_approval.approved.jsonl @@ -0,0 +1,3 @@ +{"qid":"1","wig+5":12.4250979385,"nqc+5":0.0004338056,"smv+5":-0.0004338056,"clarity+5+3":2.6993897409,"clarity+5+1":null,"clarity+5+2":null,"wig+10":12.4250979385,"nqc+10":0.0004338056,"smv+10":-0.0004338056,"clarity+10+3":2.6993897409,"clarity+10+1":null,"clarity+10+2":null,"wig+20":12.4250979385,"nqc+20":0.0004338056,"smv+20":-0.0004338056,"clarity+20+3":2.6993897409,"clarity+20+1":null,"clarity+20+2":null,"wig+50":12.4250979385,"nqc+50":0.0004338056,"smv+50":-0.0004338056,"clarity+50+3":2.6993897409,"clarity+50+1":null,"clarity+50+2":null,"wig+100":12.4250979385,"nqc+100":0.0004338056,"smv+100":-0.0004338056,"clarity+100+3":2.6993897409,"clarity+100+1":null,"clarity+100+2":null,"wig+1000":12.4250979385,"nqc+1000":0.0004338056,"smv+1000":-0.0004338056,"clarity+1000+3":2.6993897409,"clarity+1000+1":null,"clarity+1000+2":null} +{"qid":"2","wig+5":13.2004528045,"nqc+5":0.001559842,"smv+5":-0.001559842,"clarity+5+3":null,"clarity+5+1":3.2054528045,"clarity+5+2":null,"wig+10":13.2004528045,"nqc+10":0.001559842,"smv+10":-0.001559842,"clarity+10+3":null,"clarity+10+1":3.2054528045,"clarity+10+2":null,"wig+20":13.2004528045,"nqc+20":0.001559842,"smv+20":-0.001559842,"clarity+20+3":null,"clarity+20+1":3.2054528045,"clarity+20+2":null,"wig+50":13.2004528045,"nqc+50":0.001559842,"smv+50":-0.001559842,"clarity+50+3":null,"clarity+50+1":3.2054528045,"clarity+50+2":null,"wig+100":13.2004528045,"nqc+100":0.001559842,"smv+100":-0.001559842,"clarity+100+3":null,"clarity+100+1":3.2054528045,"clarity+100+2":null,"wig+1000":13.2004528045,"nqc+1000":0.001559842,"smv+1000":-0.001559842,"clarity+1000+3":null,"clarity+1000+1":3.2054528045,"clarity+1000+2":null} +{"qid":"3","wig+5":11.8874342352,"nqc+5":0.0007335282,"smv+5":-0.0007335281,"clarity+5+3":null,"clarity+5+1":null,"clarity+5+2":2.6956075044,"wig+10":11.8874342352,"nqc+10":0.0007335282,"smv+10":-0.0007335281,"clarity+10+3":null,"clarity+10+1":null,"clarity+10+2":2.6956075044,"wig+20":11.8874342352,"nqc+20":0.0007335282,"smv+20":-0.0007335281,"clarity+20+3":null,"clarity+20+1":null,"clarity+20+2":2.6956075044,"wig+50":11.8874342352,"nqc+50":0.0007335282,"smv+50":-0.0007335281,"clarity+50+3":null,"clarity+50+1":null,"clarity+50+2":2.6956075044,"wig+100":11.8874342352,"nqc+100":0.0007335282,"smv+100":-0.0007335281,"clarity+100+3":null,"clarity+100+1":null,"clarity+100+2":2.6956075044,"wig+1000":11.8874342352,"nqc+1000":0.0007335282,"smv+1000":-0.0007335281,"clarity+1000+3":null,"clarity+1000+1":null,"clarity+1000+2":2.6956075044} diff --git a/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_one_query_is_highly_effective_approvaltest.approved.jsonl b/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_one_query_is_highly_effective_approvaltest.approved.jsonl new file mode 100644 index 0000000..eba1372 --- /dev/null +++ b/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_one_query_is_highly_effective_approvaltest.approved.jsonl @@ -0,0 +1,3 @@ +{"qid":"1","wig+5":12.4250979385,"nqc+5":0.0004338056,"smv+5":-0.0004338056,"clarity+5+3":2.6993897409,"wig+10":12.4250979385,"nqc+10":0.0004338056,"smv+10":-0.0004338056,"clarity+10+3":2.6993897409,"wig+20":12.4250979385,"nqc+20":0.0004338056,"smv+20":-0.0004338056,"clarity+20+3":2.6993897409,"wig+50":12.4250979385,"nqc+50":0.0004338056,"smv+50":-0.0004338056,"clarity+50+3":2.6993897409,"wig+100":12.4250979385,"nqc+100":0.0004338056,"smv+100":-0.0004338056,"clarity+100+3":2.6993897409,"wig+1000":12.4250979385,"nqc+1000":0.0004338056,"smv+1000":-0.0004338056,"clarity+1000+3":2.6993897409} +{"qid":"2","wig+5":4.2054528045,"nqc+5":0.2547211364,"smv+5":null,"clarity+5+3":2.240679245,"wig+10":4.2054528045,"nqc+10":0.2547211364,"smv+10":null,"clarity+10+3":2.240679245,"wig+20":4.2054528045,"nqc+20":0.2547211364,"smv+20":null,"clarity+20+3":2.240679245,"wig+50":4.2054528045,"nqc+50":0.2547211364,"smv+50":null,"clarity+50+3":2.240679245,"wig+100":4.2054528045,"nqc+100":0.2547211364,"smv+100":null,"clarity+100+3":2.240679245,"wig+1000":4.2054528045,"nqc+1000":0.2547211364,"smv+1000":null,"clarity+1000+3":2.240679245} +{"qid":"3","wig+5":-20.8716444259,"nqc+5":6.6373379689,"smv+5":null,"clarity+5+3":3.2047245641,"wig+10":-20.8716444259,"nqc+10":6.6373379689,"smv+10":null,"clarity+10+3":3.2047245641,"wig+20":-20.8716444259,"nqc+20":6.6373379689,"smv+20":null,"clarity+20+3":3.2047245641,"wig+50":-20.8716444259,"nqc+50":6.6373379689,"smv+50":null,"clarity+50+3":3.2047245641,"wig+100":-20.8716444259,"nqc+100":6.6373379689,"smv+100":null,"clarity+100+3":3.2047245641,"wig+1000":-20.8716444259,"nqc+1000":6.6373379689,"smv+1000":null,"clarity+1000+3":3.2047245641} diff --git a/code/qpptk/tests/TestEndToEnd.test_end_to_end_with_approvaltest.approved.jsonl b/code/qpptk/tests/TestEndToEnd.test_end_to_end_with_approvaltest.approved.jsonl new file mode 100644 index 0000000..4ffeb5d --- /dev/null +++ b/code/qpptk/tests/TestEndToEnd.test_end_to_end_with_approvaltest.approved.jsonl @@ -0,0 +1,3 @@ +{"qid":"1","max-idf":1.7917594692,"avg-idf":1.3296613489,"scq":6.640310927,"max-scq":2.347200389,"avg-scq":2.2134369757,"var":0.0,"max-var":0.0,"avg-var":0.0,"wig+5":0.0470456283,"nqc+5":0.002581013,"smv+5":0.0025810101,"clarity+5+3":2.6980214786,"clarity+5+1":null,"clarity+5+2":null,"wig+10":0.0470456283,"nqc+10":0.002581013,"smv+10":0.0025810101,"clarity+10+3":2.6980214786,"clarity+10+1":null,"clarity+10+2":null,"wig+20":0.0470456283,"nqc+20":0.002581013,"smv+20":0.0025810101,"clarity+20+3":2.6980214786,"clarity+20+1":null,"clarity+20+2":null,"wig+50":0.0470456283,"nqc+50":0.002581013,"smv+50":0.0025810101,"clarity+50+3":2.6980214786,"clarity+50+1":null,"clarity+50+2":null,"wig+100":0.0470456283,"nqc+100":0.002581013,"smv+100":0.0025810101,"clarity+100+3":2.6980214786,"clarity+100+1":null,"clarity+100+2":null,"wig+1000":0.0470456283,"nqc+1000":0.002581013,"smv+1000":0.0025810101,"clarity+1000+3":2.6980214786,"clarity+1000+1":null,"clarity+1000+2":null} +{"qid":"2","max-idf":1.0986122887,"avg-idf":1.0986122887,"scq":2.909294382,"max-scq":2.909294382,"avg-scq":2.909294382,"var":0.4804530139,"max-var":0.4804530139,"avg-var":0.1201132535,"wig+5":0.0238398045,"nqc+5":0.0041725774,"smv+5":0.0041725651,"clarity+5+3":null,"clarity+5+1":3.2054528045,"clarity+5+2":null,"wig+10":0.0238398045,"nqc+10":0.0041725774,"smv+10":0.0041725651,"clarity+10+3":null,"clarity+10+1":3.2054528045,"clarity+10+2":null,"wig+20":0.0238398045,"nqc+20":0.0041725774,"smv+20":0.0041725651,"clarity+20+3":null,"clarity+20+1":3.2054528045,"clarity+20+2":null,"wig+50":0.0238398045,"nqc+50":0.0041725774,"smv+50":0.0041725651,"clarity+50+3":null,"clarity+50+1":3.2054528045,"clarity+50+2":null,"wig+100":0.0238398045,"nqc+100":0.0041725774,"smv+100":0.0041725651,"clarity+100+3":null,"clarity+100+1":3.2054528045,"clarity+100+2":null,"wig+1000":0.0238398045,"nqc+1000":0.0041725774,"smv+1000":0.0041725651,"clarity+1000+3":null,"clarity+1000+1":3.2054528045,"clarity+1000+2":null} +{"qid":"3","max-idf":1.0986122887,"avg-idf":1.0986122887,"scq":5.2564947709,"max-scq":2.909294382,"avg-scq":2.6282473855,"var":0.4804530139,"max-var":0.4804530139,"avg-var":0.1601510046,"wig+5":0.0295925724,"nqc+5":0.0030450955,"smv+5":0.0030450908,"clarity+5+3":null,"clarity+5+1":null,"clarity+5+2":2.6957656712,"wig+10":0.0295925724,"nqc+10":0.0030450955,"smv+10":0.0030450908,"clarity+10+3":null,"clarity+10+1":null,"clarity+10+2":2.6957656712,"wig+20":0.0295925724,"nqc+20":0.0030450955,"smv+20":0.0030450908,"clarity+20+3":null,"clarity+20+1":null,"clarity+20+2":2.6957656712,"wig+50":0.0295925724,"nqc+50":0.0030450955,"smv+50":0.0030450908,"clarity+50+3":null,"clarity+50+1":null,"clarity+50+2":2.6957656712,"wig+100":0.0295925724,"nqc+100":0.0030450955,"smv+100":0.0030450908,"clarity+100+3":null,"clarity+100+1":null,"clarity+100+2":2.6957656712,"wig+1000":0.0295925724,"nqc+1000":0.0030450955,"smv+1000":0.0030450908,"clarity+1000+3":null,"clarity+1000+1":null,"clarity+1000+2":2.6957656712} diff --git a/code/qpptk/tests/custom_run_file_for_prediction_test.py b/code/qpptk/tests/custom_run_file_for_prediction_test.py index 2c349a6..5c33fcc 100644 --- a/code/qpptk/tests/custom_run_file_for_prediction_test.py +++ b/code/qpptk/tests/custom_run_file_for_prediction_test.py @@ -3,12 +3,12 @@ import tempfile from qpptk import main, parse_args import pandas as pd -import numpy as np +from approvaltests import verify_file class CustomRunFileForPredictionTest(unittest.TestCase): def test_with_run_file_where_one_query_is_highly_effective(self): with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: - args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run.txt', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predPost', '--output', out_dir, '--cleanOutput']) + args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run.txt', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predPost', '--output', out_dir, '--cleanOutput', '--stats_index_path', stats_dir]) main(args) actual = pd.read_json(out_dir + '/queries.jsonl', lines=True) @@ -33,9 +33,16 @@ def test_with_run_file_where_one_query_is_highly_effective(self): self.assertEquals(str(actual.iloc[2].to_dict()['smv+100']), 'nan') self.assertEquals(actual.iloc[2].to_dict()['clarity+1000+3'], 3.2047245641) + def test_with_run_file_where_one_query_is_highly_effective_approvaltest(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run.txt', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predPost', '--output', out_dir, '--cleanOutput', '--stats_index_path', stats_dir]) + main(args) + + verify_file(out_dir + '/queries.jsonl') + def test_with_run_file_where_all_queries_are_highly_effective(self): with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: - args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-02.txt', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predPost', '--output', out_dir, '--cleanOutput']) + args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-02.txt', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predPost', '--output', out_dir, '--cleanOutput', '--stats_index_path', stats_dir]) main(args) actual = pd.read_json(out_dir + '/queries.jsonl', lines=True) @@ -59,3 +66,10 @@ def test_with_run_file_where_all_queries_are_highly_effective(self): self.assertEquals(actual.iloc[2].to_dict()['nqc+100'], 0.0007335282000000001) self.assertEquals(actual.iloc[2].to_dict()['smv+100'], -0.0007335281) self.assertEquals(str(actual.iloc[2].to_dict()['clarity+1000+3']), 'nan') + + def test_with_run_file_where_all_queries_are_highly_effective_approval(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-02.txt', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predPost', '--output', out_dir, '--cleanOutput', '--stats_index_path', stats_dir]) + main(args) + + verify_file(out_dir + '/queries.jsonl') \ No newline at end of file diff --git a/code/qpptk/tests/end_to_end_test.py b/code/qpptk/tests/end_to_end_test.py index 95988f9..2471f60 100644 --- a/code/qpptk/tests/end_to_end_test.py +++ b/code/qpptk/tests/end_to_end_test.py @@ -3,6 +3,7 @@ import tempfile import pandas as pd import os +from approvaltests import verify_file class TestEndToEnd(unittest.TestCase): def test_end_to_end(self): @@ -26,3 +27,11 @@ def test_end_to_end(self): assert actual.iloc[2].to_dict()['avg-idf'] == 1.0986122887 assert actual.iloc[2].to_dict()['avg-scq'] == 2.6282473855 assert actual.iloc[2].to_dict()['max-idf'] == 1.0986122887 + + + def test_end_to_end_with_approvaltest(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args(['-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', '--predict', '--retrieve', '--output', out_dir, '--cleanOutput','--stats_index_path', stats_dir]) + main(args) + + verify_file(out_dir + '/queries.jsonl') From e7252db23ca20b85ccffecdb219ce3d54ff0c6b2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 07:26:34 +0100 Subject: [PATCH 24/40] Add github action --- .github/workflows/run-all-tests.yml | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 .github/workflows/run-all-tests.yml diff --git a/.github/workflows/run-all-tests.yml b/.github/workflows/run-all-tests.yml new file mode 100644 index 0000000..6bbfa18 --- /dev/null +++ b/.github/workflows/run-all-tests.yml @@ -0,0 +1,23 @@ +name: Unit Tests + +on: [push] + +jobs: + build: + runs-on: ubuntu-latest + timeout-minutes: 15 + strategy: + matrix: + python-version: ["3.7", "3.8", "3.9", "3.10"] + + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + - name: Run tests + run: | + pip3 install -r code/qpptk/requirements.txt + pytest + From d12cc3c218036f1165e33a454c5d9b21327e4c14 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 07:32:25 +0100 Subject: [PATCH 25/40] remove python3.7 for tests --- .github/workflows/run-all-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/run-all-tests.yml b/.github/workflows/run-all-tests.yml index 6bbfa18..350416a 100644 --- a/.github/workflows/run-all-tests.yml +++ b/.github/workflows/run-all-tests.yml @@ -8,7 +8,7 @@ jobs: timeout-minutes: 15 strategy: matrix: - python-version: ["3.7", "3.8", "3.9", "3.10"] + python-version: ["3.8", "3.9", "3.10"] steps: - uses: actions/checkout@v3 From 5d8e2738bc95187cd56f09ccbc151b38990f9cc4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 09:59:47 +0100 Subject: [PATCH 26/40] remove warnings --- code/qpptk/qpptk/load_terrier_index.py | 2 +- .../custom_run_file_for_prediction_test.py | 64 +++++++++---------- 2 files changed, 33 insertions(+), 33 deletions(-) diff --git a/code/qpptk/qpptk/load_terrier_index.py b/code/qpptk/qpptk/load_terrier_index.py index 5db5e72..cfc29d4 100644 --- a/code/qpptk/qpptk/load_terrier_index.py +++ b/code/qpptk/qpptk/load_terrier_index.py @@ -110,7 +110,7 @@ def __load_generate_stats_dict(self, generate): stats_dict = pickle_load_obj(stats_dict_file) logger.info('Terrier index stats:\n' + '\n'.join([f'{k}: {v}' for k, v in stats_dict.items()])) except FileNotFoundError as er: - logger.warn(f'The file {stats_dict_file} can\'t be found, pass read_only=False to generate') + logger.warning(f'The file {stats_dict_file} can\'t be found, pass read_only=False to generate') raise er return stats_dict diff --git a/code/qpptk/tests/custom_run_file_for_prediction_test.py b/code/qpptk/tests/custom_run_file_for_prediction_test.py index 5c33fcc..dab245e 100644 --- a/code/qpptk/tests/custom_run_file_for_prediction_test.py +++ b/code/qpptk/tests/custom_run_file_for_prediction_test.py @@ -13,25 +13,25 @@ def test_with_run_file_where_one_query_is_highly_effective(self): actual = pd.read_json(out_dir + '/queries.jsonl', lines=True) - self.assertEquals(len(actual), 3) + self.assertEqual(len(actual), 3) # run is constructed so that query 1 is more effective than query 2 than query 3 - self.assertEquals(actual.iloc[0].to_dict()['qid'], 1.0) - self.assertEquals(actual.iloc[0].to_dict()['wig+10'], 12.4250979385) - self.assertEquals(actual.iloc[0].to_dict()['nqc+100'], 0.00043380560000000004) - self.assertEquals(actual.iloc[0].to_dict()['smv+100'], -0.00043380560000000004) - self.assertEquals(actual.iloc[0].to_dict()['clarity+1000+3'], 2.6993897409) + self.assertEqual(actual.iloc[0].to_dict()['qid'], 1.0) + self.assertEqual(actual.iloc[0].to_dict()['wig+10'], 12.4250979385) + self.assertEqual(actual.iloc[0].to_dict()['nqc+100'], 0.00043380560000000004) + self.assertEqual(actual.iloc[0].to_dict()['smv+100'], -0.00043380560000000004) + self.assertEqual(actual.iloc[0].to_dict()['clarity+1000+3'], 2.6993897409) - self.assertEquals(actual.iloc[1].to_dict()['qid'], 2.0) - self.assertEquals(actual.iloc[1].to_dict()['wig+10'], 4.2054528045) - self.assertEquals(actual.iloc[1].to_dict()['nqc+100'], 0.2547211364) - self.assertEquals(str(actual.iloc[1].to_dict()['smv+100']), 'nan') - self.assertEquals(actual.iloc[1].to_dict()['clarity+1000+3'], 2.240679245) + self.assertEqual(actual.iloc[1].to_dict()['qid'], 2.0) + self.assertEqual(actual.iloc[1].to_dict()['wig+10'], 4.2054528045) + self.assertEqual(actual.iloc[1].to_dict()['nqc+100'], 0.2547211364) + self.assertEqual(str(actual.iloc[1].to_dict()['smv+100']), 'nan') + self.assertEqual(actual.iloc[1].to_dict()['clarity+1000+3'], 2.240679245) - self.assertEquals(actual.iloc[2].to_dict()['qid'], 3.0) - self.assertEquals(actual.iloc[2].to_dict()['wig+10'], -20.8716444259) - self.assertEquals(actual.iloc[2].to_dict()['nqc+100'], 6.6373379689) - self.assertEquals(str(actual.iloc[2].to_dict()['smv+100']), 'nan') - self.assertEquals(actual.iloc[2].to_dict()['clarity+1000+3'], 3.2047245641) + self.assertEqual(actual.iloc[2].to_dict()['qid'], 3.0) + self.assertEqual(actual.iloc[2].to_dict()['wig+10'], -20.8716444259) + self.assertEqual(actual.iloc[2].to_dict()['nqc+100'], 6.6373379689) + self.assertEqual(str(actual.iloc[2].to_dict()['smv+100']), 'nan') + self.assertEqual(actual.iloc[2].to_dict()['clarity+1000+3'], 3.2047245641) def test_with_run_file_where_one_query_is_highly_effective_approvaltest(self): with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: @@ -47,25 +47,25 @@ def test_with_run_file_where_all_queries_are_highly_effective(self): actual = pd.read_json(out_dir + '/queries.jsonl', lines=True) - self.assertEquals(len(actual), 3) + self.assertEqual(len(actual), 3) # All queries should be predicted as rather effective - self.assertEquals(actual.iloc[0].to_dict()['qid'], 1.0) - self.assertEquals(actual.iloc[0].to_dict()['wig+10'], 12.4250979385) - self.assertEquals(actual.iloc[0].to_dict()['nqc+100'], 0.00043380560000000004) - self.assertEquals(actual.iloc[0].to_dict()['smv+100'], -0.00043380560000000004) - self.assertEquals(actual.iloc[0].to_dict()['clarity+1000+3'], 2.6993897409) + self.assertEqual(actual.iloc[0].to_dict()['qid'], 1.0) + self.assertEqual(actual.iloc[0].to_dict()['wig+10'], 12.4250979385) + self.assertEqual(actual.iloc[0].to_dict()['nqc+100'], 0.00043380560000000004) + self.assertEqual(actual.iloc[0].to_dict()['smv+100'], -0.00043380560000000004) + self.assertEqual(actual.iloc[0].to_dict()['clarity+1000+3'], 2.6993897409) - self.assertEquals(actual.iloc[1].to_dict()['qid'], 2.0) - self.assertEquals(actual.iloc[1].to_dict()['wig+10'], 13.2004528045) - self.assertEquals(actual.iloc[1].to_dict()['nqc+100'], 0.001559842) - self.assertEquals(actual.iloc[1].to_dict()['smv+100'], -0.001559842) - self.assertEquals(str(actual.iloc[1].to_dict()['clarity+1000+3']), 'nan') + self.assertEqual(actual.iloc[1].to_dict()['qid'], 2.0) + self.assertEqual(actual.iloc[1].to_dict()['wig+10'], 13.2004528045) + self.assertEqual(actual.iloc[1].to_dict()['nqc+100'], 0.001559842) + self.assertEqual(actual.iloc[1].to_dict()['smv+100'], -0.001559842) + self.assertEqual(str(actual.iloc[1].to_dict()['clarity+1000+3']), 'nan') - self.assertEquals(actual.iloc[2].to_dict()['qid'], 3.0) - self.assertEquals(actual.iloc[2].to_dict()['wig+10'], 11.8874342352) - self.assertEquals(actual.iloc[2].to_dict()['nqc+100'], 0.0007335282000000001) - self.assertEquals(actual.iloc[2].to_dict()['smv+100'], -0.0007335281) - self.assertEquals(str(actual.iloc[2].to_dict()['clarity+1000+3']), 'nan') + self.assertEqual(actual.iloc[2].to_dict()['qid'], 3.0) + self.assertEqual(actual.iloc[2].to_dict()['wig+10'], 11.8874342352) + self.assertEqual(actual.iloc[2].to_dict()['nqc+100'], 0.0007335282000000001) + self.assertEqual(actual.iloc[2].to_dict()['smv+100'], -0.0007335281) + self.assertEqual(str(actual.iloc[2].to_dict()['clarity+1000+3']), 'nan') def test_with_run_file_where_all_queries_are_highly_effective_approval(self): with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: From f73c4456edb840c2ff50dced1369218543361472 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 10:01:37 +0100 Subject: [PATCH 27/40] improve devcontainer --- .devcontainer.json | 2 +- docker/Dockerfile.dev | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/.devcontainer.json b/.devcontainer.json index d2d5256..55aca65 100644 --- a/.devcontainer.json +++ b/.devcontainer.json @@ -1,5 +1,5 @@ { - "image": "mam10eks/qpptk:0.0.1-dev", + "image": "mam10eks/qpptk:0.0.2-dev", "customizations": { "vscode": { "extensions": ["ms-python.python", "ms-python.vscode-pylance", "ms-toolsai.jupyter"] diff --git a/docker/Dockerfile.dev b/docker/Dockerfile.dev index 778025d..c8a3504 100644 --- a/docker/Dockerfile.dev +++ b/docker/Dockerfile.dev @@ -1,4 +1,4 @@ -# docker build -f docker/Dockerfile.dev -t mam10eks/qpptk:0.0.1-dev . +# docker build -f docker/Dockerfile.dev -t mam10eks/qpptk:0.0.2-dev . # We start from an image that already has Terrier + PyTerrier as dependencies installed FROM webis/ir-lab-wise-2023:0.0.1 @@ -9,6 +9,8 @@ WORKDIR /workspaces/QPP-EnhancedEval/code/qpptk RUN pip install -r requirements.txt \ && rm -Rf * \ && mkdir -p /root/repos/ \ + && apt-get update \ + && apt-get install -y curl wget git \ && ln -s /workspaces/QPP-EnhancedEval/code/qpptk /root/repos/qpptk \ && ln -s /workspaces/QPP-EnhancedEval/code/qpptk/qpptk/qpptk_main.py /qpptk_main.py From 6197546de1ca82fcbe521b8899b279986e27295e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 14:31:42 +0100 Subject: [PATCH 28/40] fix dir in github action --- .github/workflows/run-all-tests.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/run-all-tests.yml b/.github/workflows/run-all-tests.yml index 350416a..192bae2 100644 --- a/.github/workflows/run-all-tests.yml +++ b/.github/workflows/run-all-tests.yml @@ -17,7 +17,8 @@ jobs: with: python-version: ${{ matrix.python-version }} - name: Run tests + working-directory: ./code/qpptk run: | - pip3 install -r code/qpptk/requirements.txt + pip3 install -r requirements.txt pytest From 4870166cc06bfb540bb7c77fef280bc26f4c3274 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 14:36:26 +0100 Subject: [PATCH 29/40] Fix github action --- code/qpptk/qpptk/config.py | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/code/qpptk/qpptk/config.py b/code/qpptk/qpptk/config.py index 791fabd..2eb1305 100644 --- a/code/qpptk/qpptk/config.py +++ b/code/qpptk/qpptk/config.py @@ -18,14 +18,7 @@ # logger.addHandler(handler) # return logger -# CONFIG_FILE = '/research/local/oleg/qpptk/qpptk/config.toml' - - -# CONFIG_FILE = '/research/remote/petabyte/users/oleg/qpptk/qpptk/config.toml' -CONFIG_FILE = '~/repos/qpptk/qpptk/config.toml' - - -# CONFIG_FILE = '~/work/qpptk/qpptk/config.toml' +CONFIG_FILE = os.path.dirname(os.path.realpath(__file__)) + '/config.toml' def set_index_dump_paths(index_dir): From fbd0e3ef8b788cdaa83eecfd26e05c9e7ed9c5da Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 14:43:33 +0100 Subject: [PATCH 30/40] Fix github action --- .github/workflows/run-all-tests.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/run-all-tests.yml b/.github/workflows/run-all-tests.yml index 192bae2..cba6c33 100644 --- a/.github/workflows/run-all-tests.yml +++ b/.github/workflows/run-all-tests.yml @@ -19,6 +19,7 @@ jobs: - name: Run tests working-directory: ./code/qpptk run: | + mkdir ~/repos/ pip3 install -r requirements.txt pytest From 9e717609d93c172278eb4af0451b97c991a90c65 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 14:46:29 +0100 Subject: [PATCH 31/40] Fix github action --- code/qpptk/requirements.txt | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/code/qpptk/requirements.txt b/code/qpptk/requirements.txt index 437c30c..e416ef3 100644 --- a/code/qpptk/requirements.txt +++ b/code/qpptk/requirements.txt @@ -13,5 +13,6 @@ statsmodels==0.14.0 syct==0.4.3 toml==0.10.2 tqdm==4.62.3 -pytest -approvaltests +pytest==7.4.3 +approvaltests==10.2.0 +python-terrier==0.9.2 From 8af234cec4ee9e938418dde599ca0f13019f4ff7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 14:48:41 +0100 Subject: [PATCH 32/40] Fix github action --- code/qpptk/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/code/qpptk/requirements.txt b/code/qpptk/requirements.txt index e416ef3..6eebcc4 100644 --- a/code/qpptk/requirements.txt +++ b/code/qpptk/requirements.txt @@ -16,3 +16,4 @@ tqdm==4.62.3 pytest==7.4.3 approvaltests==10.2.0 python-terrier==0.9.2 +tira==0.0.69 \ No newline at end of file From 66b98c37829e0159902c79c56f0dcf8bdc522299 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 14:53:09 +0100 Subject: [PATCH 33/40] increase test coverage --- .github/workflows/run-all-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/run-all-tests.yml b/.github/workflows/run-all-tests.yml index cba6c33..51e510c 100644 --- a/.github/workflows/run-all-tests.yml +++ b/.github/workflows/run-all-tests.yml @@ -8,7 +8,7 @@ jobs: timeout-minutes: 15 strategy: matrix: - python-version: ["3.8", "3.9", "3.10"] + python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v3 From 6e9d413edbf510882534a26b6490c659999272ef Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Thu, 14 Dec 2023 15:00:14 +0100 Subject: [PATCH 34/40] e test coverage --- .github/workflows/run-all-tests.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/run-all-tests.yml b/.github/workflows/run-all-tests.yml index 51e510c..cba6c33 100644 --- a/.github/workflows/run-all-tests.yml +++ b/.github/workflows/run-all-tests.yml @@ -8,7 +8,7 @@ jobs: timeout-minutes: 15 strategy: matrix: - python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"] + python-version: ["3.8", "3.9", "3.10"] steps: - uses: actions/checkout@v3 From 79c36f6efd50194d678ef1921db45113ba0a8250 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Sun, 17 Dec 2023 10:36:12 +0000 Subject: [PATCH 35/40] Add tests for score injection --- code/qpptk/qpptk/__init__.py | 4 ++- .../qpptk/score_replacement_prediction.py | 27 +++++++++++++++++++ ..._unification_of_run_files_run_01.approved. | 8 ++++++ ..._unification_of_run_files_run_02.approved. | 6 +++++ ...wo_custom_run_files_for_prediction_test.py | 26 +++++++++++++++++- 5 files changed, 69 insertions(+), 2 deletions(-) create mode 100644 code/qpptk/qpptk/score_replacement_prediction.py create mode 100644 code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_unification_of_run_files_run_01.approved. create mode 100644 code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_unification_of_run_files_run_02.approved. diff --git a/code/qpptk/qpptk/__init__.py b/code/qpptk/qpptk/__init__.py index 522a5f5..4d6a5d8 100644 --- a/code/qpptk/qpptk/__init__.py +++ b/code/qpptk/qpptk/__init__.py @@ -8,6 +8,7 @@ from .pre_retrieval_predictors import LocalManagerPredictorPre from .post_retrieval_predictors import LocalManagerPredictorPost from .qpptk_main import parse_args, main, get_queries_object +from .score_replacement_prediction import replace_scores_in_run_file_with_reference_scores __all__ = ['Config', 'Posting', 'TermPosting', 'TermRecord', 'TermFrequency', 'DocRecord', 'ResultPair', 'get_file_len', 'read_line', 'parse_posting_list', 'binary_search', 'IndexText', 'IndexDB', 'IndexTerrier', @@ -15,4 +16,5 @@ 'LocalManagerPredictorPre', 'ensure_dir', 'ensure_file', 'LocalManagerPredictorPost', 'read_message', 'plot_roc', 'transform_list_to_counts_dict', 'jaccard_similarity', 'overlap_coefficient', 'sorensen_dice_similarity', 'calc_ndcg', 'set_index_dump_paths', 'add_topic_to_qdf', 'msgpack_encode', - 'msgpack_decode', 'read_trec_res_file', 'parse_args', 'main', 'get_queries_object'] + 'msgpack_decode', 'read_trec_res_file', 'parse_args', 'main', 'get_queries_object', + 'replace_scores_in_run_file_with_reference_scores'] diff --git a/code/qpptk/qpptk/score_replacement_prediction.py b/code/qpptk/qpptk/score_replacement_prediction.py new file mode 100644 index 0000000..50f2468 --- /dev/null +++ b/code/qpptk/qpptk/score_replacement_prediction.py @@ -0,0 +1,27 @@ +from pathlib import Path +import tempfile +from .utility_functions import read_trec_res_file +import pandas as pd + +def replace_scores_in_run_file_with_reference_scores(run_file: Path, reference_run_file: Path) -> Path: + """ + This class is used to replace the retrieval scores of a base model (e.g., BM25) with the scores of other neural models. + + The ranking comes from the base model, but the scores used in query used in query performance predictors like WIG come from another model with the intendion to vary them in their size, (e.g., use BM25 rankings as run that induces the ranking and monoT5 small vs base vs large vs 3b etc to induce the scores.). + + :param run_file: The run file that induces the ranking of documents (e.g., BM25). + :param reference_run_file: The run file that induces the scores of documents (e.g., derived by monoT5 small). + """ + run_file_parsed = read_trec_res_file(run_file) + reference_scores = {} + for qid, i in read_trec_res_file(reference_run_file).iterrows(): + reference_scores[(qid, i['docNo'])] = i['docScore'] + + run_file_parsed['docScore'] = run_file_parsed.apply(lambda x: reference_scores[(x.name, x['docNo'])], axis=1) + merged_run = [] + for qid, i in run_file_parsed.iterrows(): + merged_run += [{'qid': qid, 'Q0': '0', 'docNo': i['docNo'], 'docRank': i['docRank'], 'docScore': reference_scores[(qid, i['docNo'])], 'system': 'merged'}] + + ret = Path(tempfile.NamedTemporaryFile(delete=False).name) + pd.DataFrame(merged_run).to_csv(ret, sep=' ', header=False, index=False) + return ret diff --git a/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_unification_of_run_files_run_01.approved. b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_unification_of_run_files_run_01.approved. new file mode 100644 index 0000000..5a9004a --- /dev/null +++ b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_unification_of_run_files_run_01.approved. @@ -0,0 +1,8 @@ +1 0 doc-3 1 1.0 merged +1 0 doc-4 2 0.0 merged +2 0 doc-1 1 1.0 merged +2 0 doc-6 2 0.0 merged +2 0 doc-1 3 1.0 merged +3 0 doc-1 1 0.0 merged +3 0 doc-3 2 0.0 merged +3 0 doc-2 3 0.0 merged diff --git a/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_unification_of_run_files_run_02.approved. b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_unification_of_run_files_run_02.approved. new file mode 100644 index 0000000..fb5f834 --- /dev/null +++ b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_unification_of_run_files_run_02.approved. @@ -0,0 +1,6 @@ +1 0 doc-3 1 1.0 merged +1 0 doc-4 2 0.0 merged +2 0 doc-1 1 1.0 merged +2 0 doc-2 2 0.0 merged +3 0 doc-5 1 1.0 merged +3 0 doc-6 2 0.0 merged diff --git a/code/qpptk/tests/two_custom_run_files_for_prediction_test.py b/code/qpptk/tests/two_custom_run_files_for_prediction_test.py index 2dde70d..856de87 100644 --- a/code/qpptk/tests/two_custom_run_files_for_prediction_test.py +++ b/code/qpptk/tests/two_custom_run_files_for_prediction_test.py @@ -1 +1,25 @@ -#5, 10, 20, 100, 1000 \ No newline at end of file +#multiple run files + +import unittest +import os +import tempfile +from pathlib import Path +from qpptk import replace_scores_in_run_file_with_reference_scores +from approvaltests import verify_file + +class TwoCustomRunFilesForPredictionTest(unittest.TestCase): + def test_unification_of_run_files_run_01(self): + unified_run_file = replace_scores_in_run_file_with_reference_scores( + run_file=Path(os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run.txt'), + reference_run_file=Path(os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-with-ground-truth-scores.txt') + ) + + verify_file(unified_run_file) + + def test_unification_of_run_files_run_02(self): + unified_run_file = replace_scores_in_run_file_with_reference_scores( + run_file=Path(os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-02.txt'), + reference_run_file=Path(os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-with-ground-truth-scores.txt') + ) + + verify_file(unified_run_file) From 69f3195e5d0003f4d4b869222dd6da04c578510c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Sun, 17 Dec 2023 11:15:03 +0000 Subject: [PATCH 36/40] Add more tests. --- code/qpptk/qpptk/qpptk_main.py | 8 +- .../qpptk/score_replacement_prediction.py | 3 +- ...to_end_with_run_01_approval.approved.jsonl | 3 + ...to_end_with_run_02_approval.approved.jsonl | 3 + ...wo_custom_run_files_for_prediction_test.py | 95 ++++++++++++++++++- 5 files changed, 109 insertions(+), 3 deletions(-) create mode 100644 code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_01_approval.approved.jsonl create mode 100644 code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_02_approval.approved.jsonl diff --git a/code/qpptk/qpptk/qpptk_main.py b/code/qpptk/qpptk/qpptk_main.py index faea735..e094a1b 100644 --- a/code/qpptk/qpptk/qpptk_main.py +++ b/code/qpptk/qpptk/qpptk_main.py @@ -12,6 +12,7 @@ from qpptk import Config, set_index_dump_paths, ensure_file, ensure_dir, read_trec_res_file, \ QueryParserCiff, QueryParserText, QueryParserJsonl, add_topic_to_qdf, calc_ndcg +from qpptk.score_replacement_prediction import replace_scores_in_run_file_with_reference_scores from qpptk.global_manager import pre_ret_prediction_full, \ retrieval_full, initialize_db_index, initialize_text_index, post_ret_prediction_full, \ initialize_terrier_index @@ -52,7 +53,8 @@ def parse_args(args): parser.add_argument('--output', default=None, required=False, help='The output directory') parser.add_argument('--cleanOutput', action='store_true', help='Clean all temporary output files and output only a joined jsonl file') parser.add_argument('--stats_index_path', type=str, default=None, help='location of the index statistics') - parser.add_argument('--run-file', type=Path, default=None, help='path to run file to be used for prediction post retrieval predictions') + parser.add_argument('--run-file', type=Path, default=None, help='Path to run file to be used for prediction post retrieval predictions') + parser.add_argument('--use-scores-from-run-file', type=Path, default=None, help='Path to the run file containing retrieval scores: the --run-file run induces the ranking of documents, but this parameter induces the scores.') return parser.parse_args(args) @@ -349,6 +351,10 @@ def init_writeable_index(): float_format=f"%.{PRECISION}f") if args.predPost: retrieval_method = 'QL' if args.run_file is None else 'RUN' + if args.use_scores_from_run_file: + logger.info(f"Replace scores in run {args.run_file} with the scores from {args.use_scores_from_run_file}.") + args.run_file = replace_scores_in_run_file_with_reference_scores(args.run_file, args.use_scores_from_run_file) + try: results_file = ensure_file(f'{prefix_path}_{retrieval_method}.res' if args.run_file is None else args.run_file) except FileNotFoundError: diff --git a/code/qpptk/qpptk/score_replacement_prediction.py b/code/qpptk/qpptk/score_replacement_prediction.py index 50f2468..3f07379 100644 --- a/code/qpptk/qpptk/score_replacement_prediction.py +++ b/code/qpptk/qpptk/score_replacement_prediction.py @@ -1,8 +1,8 @@ from pathlib import Path import tempfile -from .utility_functions import read_trec_res_file import pandas as pd + def replace_scores_in_run_file_with_reference_scores(run_file: Path, reference_run_file: Path) -> Path: """ This class is used to replace the retrieval scores of a base model (e.g., BM25) with the scores of other neural models. @@ -12,6 +12,7 @@ def replace_scores_in_run_file_with_reference_scores(run_file: Path, reference_r :param run_file: The run file that induces the ranking of documents (e.g., BM25). :param reference_run_file: The run file that induces the scores of documents (e.g., derived by monoT5 small). """ + from .utility_functions import read_trec_res_file run_file_parsed = read_trec_res_file(run_file) reference_scores = {} for qid, i in read_trec_res_file(reference_run_file).iterrows(): diff --git a/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_01_approval.approved.jsonl b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_01_approval.approved.jsonl new file mode 100644 index 0000000..1b50d6e --- /dev/null +++ b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_01_approval.approved.jsonl @@ -0,0 +1,3 @@ +{"qid":"1","wig+5":6.9431571325,"nqc+5":0.0433805568,"smv+5":null,"clarity+5+3":2.7431934039,"wig+10":6.9431571325,"nqc+10":0.0433805568,"smv+10":null,"clarity+10+3":2.7431934039,"wig+20":6.9431571325,"nqc+20":0.0433805568,"smv+20":null,"clarity+20+3":2.7431934039,"wig+50":6.9431571325,"nqc+50":0.0433805568,"smv+50":null,"clarity+50+3":2.7431934039,"wig+100":6.9431571325,"nqc+100":0.0433805568,"smv+100":null,"clarity+100+3":2.7431934039,"wig+1000":6.9431571325,"nqc+1000":0.0433805568,"smv+1000":null,"clarity+1000+3":2.7431934039} +{"qid":"2","wig+5":3.8721194712,"nqc+5":0.1470633166,"smv+5":null,"clarity+5+3":2.3208312544,"wig+10":3.8721194712,"nqc+10":0.1470633166,"smv+10":null,"clarity+10+3":2.3208312544,"wig+20":3.8721194712,"nqc+20":0.1470633166,"smv+20":null,"clarity+20+3":2.3208312544,"wig+50":3.8721194712,"nqc+50":0.1470633166,"smv+50":null,"clarity+50+3":2.3208312544,"wig+100":3.8721194712,"nqc+100":0.1470633166,"smv+100":null,"clarity+100+3":2.3208312544,"wig+1000":3.8721194712,"nqc+1000":0.1470633166,"smv+1000":null,"clarity+1000+3":2.3208312544} +{"qid":"3","wig+5":4.8199019572,"nqc+5":0.0,"smv+5":null,"clarity+5+3":2.361615051,"wig+10":4.8199019572,"nqc+10":0.0,"smv+10":null,"clarity+10+3":2.361615051,"wig+20":4.8199019572,"nqc+20":0.0,"smv+20":null,"clarity+20+3":2.361615051,"wig+50":4.8199019572,"nqc+50":0.0,"smv+50":null,"clarity+50+3":2.361615051,"wig+100":4.8199019572,"nqc+100":0.0,"smv+100":null,"clarity+100+3":2.361615051,"wig+1000":4.8199019572,"nqc+1000":0.0,"smv+1000":null,"clarity+1000+3":2.361615051} diff --git a/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_02_approval.approved.jsonl b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_02_approval.approved.jsonl new file mode 100644 index 0000000..bbf63a0 --- /dev/null +++ b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_02_approval.approved.jsonl @@ -0,0 +1,3 @@ +{"qid":"1","wig+5":6.9431571325,"nqc+5":0.0433805568,"smv+5":null,"clarity+5+3":2.7431934039,"clarity+5+1":null,"clarity+5+2":null,"wig+10":6.9431571325,"nqc+10":0.0433805568,"smv+10":null,"clarity+10+3":2.7431934039,"clarity+10+1":null,"clarity+10+2":null,"wig+20":6.9431571325,"nqc+20":0.0433805568,"smv+20":null,"clarity+20+3":2.7431934039,"clarity+20+1":null,"clarity+20+2":null,"wig+50":6.9431571325,"nqc+50":0.0433805568,"smv+50":null,"clarity+50+3":2.7431934039,"clarity+50+1":null,"clarity+50+2":null,"wig+100":6.9431571325,"nqc+100":0.0433805568,"smv+100":null,"clarity+100+3":2.7431934039,"clarity+100+1":null,"clarity+100+2":null,"wig+1000":6.9431571325,"nqc+1000":0.0433805568,"smv+1000":null,"clarity+1000+3":2.7431934039,"clarity+1000+1":null,"clarity+1000+2":null} +{"qid":"2","wig+5":3.7054528045,"nqc+5":0.1559842027,"smv+5":null,"clarity+5+3":null,"clarity+5+1":3.2054528045,"clarity+5+2":null,"wig+10":3.7054528045,"nqc+10":0.1559842027,"smv+10":null,"clarity+10+3":null,"clarity+10+1":3.2054528045,"clarity+10+2":null,"wig+20":3.7054528045,"nqc+20":0.1559842027,"smv+20":null,"clarity+20+3":null,"clarity+20+1":3.2054528045,"clarity+20+2":null,"wig+50":3.7054528045,"nqc+50":0.1559842027,"smv+50":null,"clarity+50+3":null,"clarity+50+1":3.2054528045,"clarity+50+2":null,"wig+100":3.7054528045,"nqc+100":0.1559842027,"smv+100":null,"clarity+100+3":null,"clarity+100+1":3.2054528045,"clarity+100+2":null,"wig+1000":3.7054528045,"nqc+1000":0.1559842027,"smv+1000":null,"clarity+1000+3":null,"clarity+1000+1":3.2054528045,"clarity+1000+2":null} +{"qid":"3","wig+5":5.1734553478,"nqc+5":0.0733528179,"smv+5":null,"clarity+5+3":null,"clarity+5+1":null,"clarity+5+2":2.69519388,"wig+10":5.1734553478,"nqc+10":0.0733528179,"smv+10":null,"clarity+10+3":null,"clarity+10+1":null,"clarity+10+2":2.69519388,"wig+20":5.1734553478,"nqc+20":0.0733528179,"smv+20":null,"clarity+20+3":null,"clarity+20+1":null,"clarity+20+2":2.69519388,"wig+50":5.1734553478,"nqc+50":0.0733528179,"smv+50":null,"clarity+50+3":null,"clarity+50+1":null,"clarity+50+2":2.69519388,"wig+100":5.1734553478,"nqc+100":0.0733528179,"smv+100":null,"clarity+100+3":null,"clarity+100+1":null,"clarity+100+2":2.69519388,"wig+1000":5.1734553478,"nqc+1000":0.0733528179,"smv+1000":null,"clarity+1000+3":null,"clarity+1000+1":null,"clarity+1000+2":2.69519388} diff --git a/code/qpptk/tests/two_custom_run_files_for_prediction_test.py b/code/qpptk/tests/two_custom_run_files_for_prediction_test.py index 856de87..6a4de3a 100644 --- a/code/qpptk/tests/two_custom_run_files_for_prediction_test.py +++ b/code/qpptk/tests/two_custom_run_files_for_prediction_test.py @@ -4,7 +4,8 @@ import os import tempfile from pathlib import Path -from qpptk import replace_scores_in_run_file_with_reference_scores +import pandas as pd +from qpptk import replace_scores_in_run_file_with_reference_scores, parse_args, main from approvaltests import verify_file class TwoCustomRunFilesForPredictionTest(unittest.TestCase): @@ -23,3 +24,95 @@ def test_unification_of_run_files_run_02(self): ) verify_file(unified_run_file) + + + def test_end_to_end_with_run_01(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args([ + '-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', + '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run.txt', + '--use-scores-from-run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-with-ground-truth-scores.txt', + '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', + '--predPost', '--cleanOutput', + '--output', out_dir, + '--stats_index_path', stats_dir + ]) + main(args) + + actual = pd.read_json(out_dir + '/queries.jsonl', lines=True) + + self.assertEqual(len(actual), 3) + # run is constructed so that query 1 is more effective than query 2 than query 3 + self.assertEqual(actual.iloc[0].to_dict()['qid'], 1.0) + self.assertEqual(actual.iloc[0].to_dict()['wig+10'], 6.9431571325) + self.assertEqual(actual.iloc[0].to_dict()['nqc+100'], 0.043380556800000004) + self.assertEqual(str(actual.iloc[0].to_dict()['smv+100']), 'nan') + self.assertEqual(actual.iloc[0].to_dict()['clarity+1000+3'], 2.7431934039) + + self.assertEqual(actual.iloc[1].to_dict()['qid'], 2.0) + self.assertEqual(actual.iloc[1].to_dict()['wig+10'], 3.8721194712) + self.assertEqual(actual.iloc[1].to_dict()['nqc+100'], 0.1470633166) + self.assertEqual(str(actual.iloc[1].to_dict()['smv+100']), 'nan') + self.assertEqual(actual.iloc[1].to_dict()['clarity+1000+3'], 2.3208312544) + + self.assertEqual(actual.iloc[2].to_dict()['qid'], 3.0) + self.assertEqual(actual.iloc[2].to_dict()['wig+10'], 4.8199019572) + self.assertEqual(actual.iloc[2].to_dict()['nqc+100'], 0.0) + self.assertEqual(str(actual.iloc[2].to_dict()['smv+100']), 'nan') + self.assertEqual(actual.iloc[2].to_dict()['clarity+1000+3'], 2.3616150510000002) + + def test_end_to_end_with_run_01_approval(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args([ + '-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', + '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run.txt', + '--use-scores-from-run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-with-ground-truth-scores.txt', + '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', + '--predPost', '--cleanOutput', + '--output', out_dir, + '--stats_index_path', stats_dir + ]) + main(args) + + verify_file(out_dir + '/queries.jsonl') + + def test_end_to_end_with_run_02(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args([ + '-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', + '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-02.txt', + '--use-scores-from-run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-with-ground-truth-scores.txt', + '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', + '--predPost', '--cleanOutput', + '--output', out_dir, + '--stats_index_path', stats_dir + ]) + main(args) + + actual = pd.read_json(out_dir + '/queries.jsonl', lines=True) + + self.assertEqual(len(actual), 3) + # All queries should be predicted as rather effective + self.assertEqual(actual.iloc[0].to_dict()['qid'], 1.0) + self.assertEqual(actual.iloc[0].to_dict()['wig+10'], 6.9431571325) + + self.assertEqual(actual.iloc[1].to_dict()['qid'], 2.0) + self.assertEqual(actual.iloc[1].to_dict()['wig+10'], 3.7054528045) + + self.assertEqual(actual.iloc[2].to_dict()['qid'], 3.0) + self.assertEqual(actual.iloc[2].to_dict()['wig+10'], 5.1734553478) + + + def test_end_to_end_with_run_02_approval(self): + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args([ + '-ti', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/index/', + '--run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-02.txt', + '--use-scores-from-run-file', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/run-with-ground-truth-scores.txt', + '--jsonl_queries', os.path.dirname(os.path.realpath(__file__)) + '/resources/small-example-01/queries.jsonl', + '--predPost', '--cleanOutput', + '--output', out_dir, + '--stats_index_path', stats_dir + ]) + main(args) + verify_file(out_dir + '/queries.jsonl') From 0897e57a45bd3589635a1942b9087a15d34bd7ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Sun, 17 Dec 2023 12:00:23 +0000 Subject: [PATCH 37/40] Add integration tests on the cranfield corpus --- ..._dataset_with_approvaltests.approved.jsonl | 225 ++++++++++++++++++ ..._approvaltests_and_bm25_run.approved.jsonl | 225 ++++++++++++++++++ ...bm25_run_with_monot5_scores.approved.jsonl | 225 ++++++++++++++++++ code/qpptk/tests/tirex_integration_test.py | 66 +++++ ...wo_custom_run_files_for_prediction_test.py | 2 - 5 files changed, 741 insertions(+), 2 deletions(-) create mode 100644 code/qpptk/tests/TirexIntegrationTest.test_on_cranfield_dataset_with_approvaltests.approved.jsonl create mode 100644 code/qpptk/tests/TirexIntegrationTest.test_on_cranfield_dataset_with_approvaltests_and_bm25_run.approved.jsonl create mode 100644 code/qpptk/tests/TirexIntegrationTest.test_on_cranfield_dataset_with_approvaltests_and_bm25_run_with_monot5_scores.approved.jsonl create mode 100644 code/qpptk/tests/tirex_integration_test.py diff --git a/code/qpptk/tests/TirexIntegrationTest.test_on_cranfield_dataset_with_approvaltests.approved.jsonl b/code/qpptk/tests/TirexIntegrationTest.test_on_cranfield_dataset_with_approvaltests.approved.jsonl new file mode 100644 index 0000000..01466e9 --- /dev/null +++ b/code/qpptk/tests/TirexIntegrationTest.test_on_cranfield_dataset_with_approvaltests.approved.jsonl @@ -0,0 +1,225 @@ +{"qid":"1","max-idf":5.6347896032,"avg-idf":2.8835041438,"scq":174.8311418239,"max-scq":19.3615857554,"avg-scq":15.8937401658,"var":13.6583810321,"max-var":2.2871530231,"avg-var":0.9105587355,"wig+5":1.4707353276,"nqc+5":0.0086019904,"smv+5":0.0079606746,"clarity+5+100":2.0461781049,"clarity+5+80":null,"clarity+5+88":null,"clarity+5+92":null,"clarity+5+94":null,"clarity+5+90":null,"clarity+5+84":null,"clarity+5+95":null,"wig+10":1.2238077878,"nqc+10":0.0127178598,"smv+10":0.0107414981,"clarity+10+100":1.9353483767,"wig+20":0.9916073887,"nqc+20":0.0135957072,"smv+20":0.0111754884,"clarity+20+100":1.8929626506,"wig+50":0.6814558084,"nqc+50":0.0142602173,"smv+50":0.0107610907,"clarity+50+100":1.8046900721,"wig+100":0.4729255151,"nqc+100":0.0136453024,"smv+100":0.0099078226,"clarity+100+100":1.7495527333,"wig+1000":-0.0517720176,"nqc+1000":0.0116318584,"smv+1000":0.0080168383,"clarity+1000+100":1.4922515366} +{"qid":"2","max-idf":7.2442275156,"avg-idf":3.0365718619,"scq":133.8333482615,"max-scq":19.3615857554,"avg-scq":14.8703720291,"var":11.3192649272,"max-var":2.2871530231,"avg-var":0.8085189234,"wig+5":1.5585452986,"nqc+5":0.0207956868,"smv+5":0.0159250108,"clarity+5+100":2.7450644143,"clarity+5+80":null,"clarity+5+88":null,"clarity+5+92":null,"clarity+5+94":null,"clarity+5+90":null,"clarity+5+84":null,"clarity+5+95":null,"wig+10":1.294356232,"nqc+10":0.019332573,"smv+10":0.013495886,"clarity+10+100":2.6781489539,"wig+20":1.048888882,"nqc+20":0.0180074797,"smv+20":0.0121556808,"clarity+20+100":2.6251845196,"wig+50":0.758986052,"nqc+50":0.0162078659,"smv+50":0.0109870688,"clarity+50+100":2.5314491004,"wig+100":0.536728912,"nqc+100":0.0156883124,"smv+100":0.0110084133,"clarity+100+100":2.4593392351,"wig+1000":-0.0188352424,"nqc+1000":0.0134258505,"smv+1000":0.0090615927,"clarity+1000+100":2.1479759141} +{"qid":"4","max-idf":4.605170186,"avg-idf":2.898268571,"scq":114.7923894918,"max-scq":21.0234802999,"avg-scq":16.3989127845,"var":10.5511650804,"max-var":3.0239608854,"avg-var":0.8116280831,"wig+5":2.0975477487,"nqc+5":0.0072078717,"smv+5":0.0058282588,"clarity+5+100":null,"clarity+5+80":2.6259064707,"clarity+5+88":null,"clarity+5+92":null,"clarity+5+94":null,"clarity+5+90":null,"clarity+5+84":null,"clarity+5+95":null,"wig+10":1.7323117963,"nqc+10":0.023609647,"smv+10":0.022433536,"clarity+10+100":2.4852882937,"wig+20":1.2787978946,"nqc+20":0.0304768413,"smv+20":0.0261558905,"clarity+20+100":2.4511383514,"wig+50":0.8087961318,"nqc+50":0.0287233606,"smv+50":0.0214949774,"clarity+50+100":2.3978312363,"wig+100":0.5715793922,"nqc+100":0.0241653822,"smv+100":0.0156918101,"clarity+100+100":2.34892485,"wig+1000":0.0279228457,"nqc+1000":0.017478917,"smv+1000":0.0112476724,"clarity+1000+100":2.1274527642} +{"qid":"8","max-idf":5.4524680464,"avg-idf":2.957081878,"scq":313.3798347858,"max-scq":19.4413492607,"avg-scq":15.6689917393,"var":24.0482092629,"max-var":2.3913732905,"avg-var":0.9619283705,"wig+5":1.8457120568,"nqc+5":0.0135821955,"smv+5":0.0128184652,"clarity+5+100":2.4595780446,"clarity+5+80":null,"clarity+5+88":null,"clarity+5+92":null,"clarity+5+94":null,"clarity+5+90":null,"clarity+5+84":null,"clarity+5+95":null,"wig+10":1.449546954,"nqc+10":0.0153407263,"smv+10":0.0134439664,"clarity+10+100":2.448374308,"wig+20":1.1409416894,"nqc+20":0.014274609,"smv+20":0.0106833163,"clarity+20+100":2.4393077273,"wig+50":0.7729107179,"nqc+50":0.0129309165,"smv+50":0.0087785938,"clarity+50+100":2.4355895737,"wig+100":0.5268496565,"nqc+100":0.0117695475,"smv+100":0.0080839521,"clarity+100+100":2.4319165154,"wig+1000":-0.0953708634,"nqc+1000":0.0082718361,"smv+1000":0.0054351271,"clarity+1000+100":2.4258508721} 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+{"qid":"356","wig+5":21.4388470335,"nqc+5":0.0001776983,"smv+5":0.0001353835,"clarity+5+100":2.3899291393,"clarity+5+80":null,"clarity+5+94":null,"clarity+5+95":null,"clarity+5+66":null,"clarity+5+98":null,"clarity+5+96":null,"clarity+5+89":null,"clarity+5+93":null,"clarity+5+81":null,"clarity+5+90":null,"clarity+5+70":null,"clarity+5+99":null,"clarity+5+88":null,"wig+10":21.3252282088,"nqc+10":0.0107423744,"smv+10":0.0089002907,"clarity+10+100":2.3661819071,"wig+20":21.1602184732,"nqc+20":0.0190288584,"smv+20":0.0155548674,"clarity+20+100":2.0596763357,"wig+50":20.7001997355,"nqc+50":0.0371452411,"smv+50":0.0291057968,"clarity+50+100":2.0186447951,"wig+100":20.2498168509,"nqc+100":0.0483593756,"smv+100":0.0380790326,"clarity+100+100":1.9229390336,"wig+1000":19.0994003908,"nqc+1000":0.0520945047,"smv+1000":0.0368185015,"clarity+1000+100":1.8169329312} +{"qid":"360","wig+5":25.8851370583,"nqc+5":0.0010133688,"smv+5":0.0007282939,"clarity+5+100":2.1937305843,"clarity+5+80":null,"clarity+5+94":null,"clarity+5+95":null,"clarity+5+66":null,"clarity+5+98":null,"clarity+5+96":null,"clarity+5+89":null,"clarity+5+93":null,"clarity+5+81":null,"clarity+5+90":null,"clarity+5+70":null,"clarity+5+99":null,"clarity+5+88":null,"wig+10":25.6322398361,"nqc+10":0.0142283359,"smv+10":0.0116895518,"clarity+10+100":2.0980951937,"wig+20":25.5311502788,"nqc+20":0.0210587588,"smv+20":0.0163677905,"clarity+20+100":1.7921217697,"wig+50":25.3930964046,"nqc+50":0.0198758351,"smv+50":0.0151813675,"clarity+50+100":1.5316496918,"wig+100":25.2494203371,"nqc+100":0.0232310984,"smv+100":0.0179804081,"clarity+100+100":1.3514880499,"wig+1000":24.2918387389,"nqc+1000":0.0255762634,"smv+1000":0.0181296856,"clarity+1000+100":1.1983600913} +{"qid":"365","wig+5":20.9442560817,"nqc+5":0.0024261931,"smv+5":0.0018851314,"clarity+5+100":1.6898717593,"clarity+5+80":null,"clarity+5+94":null,"clarity+5+95":null,"clarity+5+66":null,"clarity+5+98":null,"clarity+5+96":null,"clarity+5+89":null,"clarity+5+93":null,"clarity+5+81":null,"clarity+5+90":null,"clarity+5+70":null,"clarity+5+99":null,"clarity+5+88":null,"wig+10":20.916387263,"nqc+10":0.0032893792,"smv+10":0.0026924493,"clarity+10+100":1.7132918492,"wig+20":20.7933243787,"nqc+20":0.0107734468,"smv+20":0.0090007839,"clarity+20+100":1.5793281766,"wig+50":20.5331910715,"nqc+50":0.0224595771,"smv+50":0.0170181442,"clarity+50+100":1.4197725647,"wig+100":20.3057933038,"nqc+100":0.0280059324,"smv+100":0.0218704284,"clarity+100+100":1.352972301,"wig+1000":19.0338259267,"nqc+1000":0.0401814336,"smv+1000":0.0302118058,"clarity+1000+100":1.2146600822} diff --git a/code/qpptk/tests/tirex_integration_test.py b/code/qpptk/tests/tirex_integration_test.py new file mode 100644 index 0000000..b50a2ec --- /dev/null +++ b/code/qpptk/tests/tirex_integration_test.py @@ -0,0 +1,66 @@ +import unittest +from tira.rest_api_client import Client +from qpptk import main, parse_args +from approvaltests import verify_file +import tempfile + +class TirexIntegrationTest(unittest.TestCase): + def test_on_cranfield_dataset_with_approvaltests(self): + tira = Client() + index_dir = tira.get_run_output('ir-benchmarks/tira-ir-starter/Index (tira-ir-starter-pyterrier)', 'cranfield-20230107-training') + '/index' + queries = tira.download_dataset('ir-benchmarks', 'cranfield-20230107-training') + '/queries.jsonl' + + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args([ + '-ti', index_dir, + '--jsonl_queries', queries, + '--output', out_dir, + '--stats_index_path', stats_dir, + '--predict', '--retrieve', '--cleanOutput' + ]) + main(args) + + # I only spot-checked that the output looks reasonable, no in-depth tests + verify_file(out_dir + '/queries.jsonl') + + def test_on_cranfield_dataset_with_approvaltests_and_bm25_run(self): + tira = Client() + index_dir = tira.get_run_output('ir-benchmarks/tira-ir-starter/Index (tira-ir-starter-pyterrier)', 'cranfield-20230107-training') + '/index' + bm25_run = tira.get_run_output('ir-benchmarks/tira-ir-starter/BM25 Re-Rank (tira-ir-starter-pyterrier)', 'cranfield-20230107-training') + '/run.txt' + queries = tira.download_dataset('ir-benchmarks', 'cranfield-20230107-training') + '/queries.jsonl' + + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args([ + '-ti', index_dir, + '--jsonl_queries', queries, + '--run-file', bm25_run, + '--output', out_dir, + '--stats_index_path', stats_dir, + '--predPost', '--retrieve', '--cleanOutput' + ]) + main(args) + + # I only spot-checked that the output looks reasonable, no in-depth tests + verify_file(out_dir + '/queries.jsonl') + + def test_on_cranfield_dataset_with_approvaltests_and_bm25_run_with_monot5_scores(self): + tira = Client() + index_dir = tira.get_run_output('ir-benchmarks/tira-ir-starter/Index (tira-ir-starter-pyterrier)', 'cranfield-20230107-training') + '/index' + bm25_run = tira.get_run_output('ir-benchmarks/tira-ir-starter/BM25 Re-Rank (tira-ir-starter-pyterrier)', 'cranfield-20230107-training') + '/run.txt' + monot5_run = tira.get_run_output('ir-benchmarks/tira-ir-starter/MonoT5 Base (tira-ir-starter-gygaggle)', 'cranfield-20230107-training') + '/run.txt' + queries = tira.download_dataset('ir-benchmarks', 'cranfield-20230107-training') + '/queries.jsonl' + + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args([ + '-ti', index_dir, + '--jsonl_queries', queries, + '--run-file', bm25_run, + '--use-scores-from-run-file', monot5_run, + '--output', out_dir, + '--stats_index_path', stats_dir, + '--predPost', '--retrieve', '--cleanOutput' + ]) + main(args) + + # I only spot-checked that the output looks reasonable, no in-depth tests + verify_file(out_dir + '/queries.jsonl') diff --git a/code/qpptk/tests/two_custom_run_files_for_prediction_test.py b/code/qpptk/tests/two_custom_run_files_for_prediction_test.py index 6a4de3a..ef57737 100644 --- a/code/qpptk/tests/two_custom_run_files_for_prediction_test.py +++ b/code/qpptk/tests/two_custom_run_files_for_prediction_test.py @@ -1,5 +1,3 @@ -#multiple run files - import unittest import os import tempfile From d88118ed07d531e7bf614847638cbedd632f1aa0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Sun, 17 Dec 2023 15:47:13 +0100 Subject: [PATCH 38/40] Simplify clarity --- code/qpptk/qpptk/global_manager.py | 2 +- .../qpptk/score_replacement_prediction.py | 5 +- ...e_highly_effective_approval.approved.jsonl | 6 +- ...ghly_effective_approvaltest.approved.jsonl | 6 +- ...nd_to_end_with_approvaltest.approved.jsonl | 6 +- ..._dataset_with_approvaltests.approved.jsonl | 450 +++++++++--------- ..._approvaltests_and_bm25_run.approved.jsonl | 450 +++++++++--------- ...bm25_run_with_monot5_scores.approved.jsonl | 450 +++++++++--------- ...to_end_with_run_01_approval.approved.jsonl | 6 +- ...to_end_with_run_02_approval.approved.jsonl | 6 +- .../custom_run_file_for_prediction_test.py | 12 +- code/qpptk/tests/end_to_end_test.py | 2 +- ...wo_custom_run_files_for_prediction_test.py | 6 +- 13 files changed, 704 insertions(+), 703 deletions(-) diff --git a/code/qpptk/qpptk/global_manager.py b/code/qpptk/qpptk/global_manager.py index 6954ed2..6307b1d 100644 --- a/code/qpptk/qpptk/global_manager.py +++ b/code/qpptk/qpptk/global_manager.py @@ -99,7 +99,7 @@ def run_post_prediction_process(qid): # f'uef+{Config.UEF_LIST_SIZE}+{Config.UEF_RANKING_SIZE}-clarity+{Config.CLARITY_LIST_SIZE}+{clarity_terms_size}': uef_clarity, # f'uef+{Config.UEF_LIST_SIZE}+{Config.UEF_RANKING_SIZE}-qf+{Config.QF_LIST_SIZE}+{Config.QF_FB_TERMS}+{Config.QF_OVERLAP_SIZE}': uef_qf} return {'qid': qid, f'wig+{Config.WIG_LIST_SIZE}': wig, f'nqc+{Config.NQC_LIST_SIZE}': nqc, - f'smv+{Config.SMV_LIST_SIZE}': smv, f'clarity+{Config.CLARITY_LIST_SIZE}+{clarity_terms_size}': clarity} + f'smv+{Config.SMV_LIST_SIZE}': smv, f'clarity+{Config.CLARITY_LIST_SIZE}+{Config.CLARITY_FB_TERMS}': clarity} def _run_multiprocess_sync(func, tasks, n_proc, **init_kwargs): diff --git a/code/qpptk/qpptk/score_replacement_prediction.py b/code/qpptk/qpptk/score_replacement_prediction.py index 3f07379..5a45bf5 100644 --- a/code/qpptk/qpptk/score_replacement_prediction.py +++ b/code/qpptk/qpptk/score_replacement_prediction.py @@ -15,13 +15,14 @@ def replace_scores_in_run_file_with_reference_scores(run_file: Path, reference_r from .utility_functions import read_trec_res_file run_file_parsed = read_trec_res_file(run_file) reference_scores = {} + min_score = None for qid, i in read_trec_res_file(reference_run_file).iterrows(): reference_scores[(qid, i['docNo'])] = i['docScore'] + min_score = i['docScore'] if min_score is None else min(min_score, i['docScore']) - run_file_parsed['docScore'] = run_file_parsed.apply(lambda x: reference_scores[(x.name, x['docNo'])], axis=1) merged_run = [] for qid, i in run_file_parsed.iterrows(): - merged_run += [{'qid': qid, 'Q0': '0', 'docNo': i['docNo'], 'docRank': i['docRank'], 'docScore': reference_scores[(qid, i['docNo'])], 'system': 'merged'}] + merged_run += [{'qid': qid, 'Q0': '0', 'docNo': i['docNo'], 'docRank': i['docRank'], 'docScore': reference_scores.get((qid, i['docNo']), min_score), 'system': 'merged'}] ret = Path(tempfile.NamedTemporaryFile(delete=False).name) pd.DataFrame(merged_run).to_csv(ret, sep=' ', header=False, index=False) diff --git a/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_all_queries_are_highly_effective_approval.approved.jsonl b/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_all_queries_are_highly_effective_approval.approved.jsonl index 619aab3..83a8121 100644 --- a/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_all_queries_are_highly_effective_approval.approved.jsonl +++ b/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_all_queries_are_highly_effective_approval.approved.jsonl @@ -1,3 +1,3 @@ -{"qid":"1","wig+5":12.4250979385,"nqc+5":0.0004338056,"smv+5":-0.0004338056,"clarity+5+3":2.6993897409,"clarity+5+1":null,"clarity+5+2":null,"wig+10":12.4250979385,"nqc+10":0.0004338056,"smv+10":-0.0004338056,"clarity+10+3":2.6993897409,"clarity+10+1":null,"clarity+10+2":null,"wig+20":12.4250979385,"nqc+20":0.0004338056,"smv+20":-0.0004338056,"clarity+20+3":2.6993897409,"clarity+20+1":null,"clarity+20+2":null,"wig+50":12.4250979385,"nqc+50":0.0004338056,"smv+50":-0.0004338056,"clarity+50+3":2.6993897409,"clarity+50+1":null,"clarity+50+2":null,"wig+100":12.4250979385,"nqc+100":0.0004338056,"smv+100":-0.0004338056,"clarity+100+3":2.6993897409,"clarity+100+1":null,"clarity+100+2":null,"wig+1000":12.4250979385,"nqc+1000":0.0004338056,"smv+1000":-0.0004338056,"clarity+1000+3":2.6993897409,"clarity+1000+1":null,"clarity+1000+2":null} -{"qid":"2","wig+5":13.2004528045,"nqc+5":0.001559842,"smv+5":-0.001559842,"clarity+5+3":null,"clarity+5+1":3.2054528045,"clarity+5+2":null,"wig+10":13.2004528045,"nqc+10":0.001559842,"smv+10":-0.001559842,"clarity+10+3":null,"clarity+10+1":3.2054528045,"clarity+10+2":null,"wig+20":13.2004528045,"nqc+20":0.001559842,"smv+20":-0.001559842,"clarity+20+3":null,"clarity+20+1":3.2054528045,"clarity+20+2":null,"wig+50":13.2004528045,"nqc+50":0.001559842,"smv+50":-0.001559842,"clarity+50+3":null,"clarity+50+1":3.2054528045,"clarity+50+2":null,"wig+100":13.2004528045,"nqc+100":0.001559842,"smv+100":-0.001559842,"clarity+100+3":null,"clarity+100+1":3.2054528045,"clarity+100+2":null,"wig+1000":13.2004528045,"nqc+1000":0.001559842,"smv+1000":-0.001559842,"clarity+1000+3":null,"clarity+1000+1":3.2054528045,"clarity+1000+2":null} -{"qid":"3","wig+5":11.8874342352,"nqc+5":0.0007335282,"smv+5":-0.0007335281,"clarity+5+3":null,"clarity+5+1":null,"clarity+5+2":2.6956075044,"wig+10":11.8874342352,"nqc+10":0.0007335282,"smv+10":-0.0007335281,"clarity+10+3":null,"clarity+10+1":null,"clarity+10+2":2.6956075044,"wig+20":11.8874342352,"nqc+20":0.0007335282,"smv+20":-0.0007335281,"clarity+20+3":null,"clarity+20+1":null,"clarity+20+2":2.6956075044,"wig+50":11.8874342352,"nqc+50":0.0007335282,"smv+50":-0.0007335281,"clarity+50+3":null,"clarity+50+1":null,"clarity+50+2":2.6956075044,"wig+100":11.8874342352,"nqc+100":0.0007335282,"smv+100":-0.0007335281,"clarity+100+3":null,"clarity+100+1":null,"clarity+100+2":2.6956075044,"wig+1000":11.8874342352,"nqc+1000":0.0007335282,"smv+1000":-0.0007335281,"clarity+1000+3":null,"clarity+1000+1":null,"clarity+1000+2":2.6956075044} +{"qid":"1","wig+5":12.4250979385,"nqc+5":0.0004338056,"smv+5":-0.0004338056,"clarity+5+100":2.6993897409,"wig+10":12.4250979385,"nqc+10":0.0004338056,"smv+10":-0.0004338056,"clarity+10+100":2.6993897409,"wig+20":12.4250979385,"nqc+20":0.0004338056,"smv+20":-0.0004338056,"clarity+20+100":2.6993897409,"wig+50":12.4250979385,"nqc+50":0.0004338056,"smv+50":-0.0004338056,"clarity+50+100":2.6993897409,"wig+100":12.4250979385,"nqc+100":0.0004338056,"smv+100":-0.0004338056,"clarity+100+100":2.6993897409,"wig+1000":12.4250979385,"nqc+1000":0.0004338056,"smv+1000":-0.0004338056,"clarity+1000+100":2.6993897409} +{"qid":"2","wig+5":13.2004528045,"nqc+5":0.001559842,"smv+5":-0.001559842,"clarity+5+100":3.2054528045,"wig+10":13.2004528045,"nqc+10":0.001559842,"smv+10":-0.001559842,"clarity+10+100":3.2054528045,"wig+20":13.2004528045,"nqc+20":0.001559842,"smv+20":-0.001559842,"clarity+20+100":3.2054528045,"wig+50":13.2004528045,"nqc+50":0.001559842,"smv+50":-0.001559842,"clarity+50+100":3.2054528045,"wig+100":13.2004528045,"nqc+100":0.001559842,"smv+100":-0.001559842,"clarity+100+100":3.2054528045,"wig+1000":13.2004528045,"nqc+1000":0.001559842,"smv+1000":-0.001559842,"clarity+1000+100":3.2054528045} +{"qid":"3","wig+5":11.8874342352,"nqc+5":0.0007335282,"smv+5":-0.0007335281,"clarity+5+100":2.6956075044,"wig+10":11.8874342352,"nqc+10":0.0007335282,"smv+10":-0.0007335281,"clarity+10+100":2.6956075044,"wig+20":11.8874342352,"nqc+20":0.0007335282,"smv+20":-0.0007335281,"clarity+20+100":2.6956075044,"wig+50":11.8874342352,"nqc+50":0.0007335282,"smv+50":-0.0007335281,"clarity+50+100":2.6956075044,"wig+100":11.8874342352,"nqc+100":0.0007335282,"smv+100":-0.0007335281,"clarity+100+100":2.6956075044,"wig+1000":11.8874342352,"nqc+1000":0.0007335282,"smv+1000":-0.0007335281,"clarity+1000+100":2.6956075044} diff --git a/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_one_query_is_highly_effective_approvaltest.approved.jsonl b/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_one_query_is_highly_effective_approvaltest.approved.jsonl index eba1372..d289f18 100644 --- a/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_one_query_is_highly_effective_approvaltest.approved.jsonl +++ b/code/qpptk/tests/CustomRunFileForPredictionTest.test_with_run_file_where_one_query_is_highly_effective_approvaltest.approved.jsonl @@ -1,3 +1,3 @@ -{"qid":"1","wig+5":12.4250979385,"nqc+5":0.0004338056,"smv+5":-0.0004338056,"clarity+5+3":2.6993897409,"wig+10":12.4250979385,"nqc+10":0.0004338056,"smv+10":-0.0004338056,"clarity+10+3":2.6993897409,"wig+20":12.4250979385,"nqc+20":0.0004338056,"smv+20":-0.0004338056,"clarity+20+3":2.6993897409,"wig+50":12.4250979385,"nqc+50":0.0004338056,"smv+50":-0.0004338056,"clarity+50+3":2.6993897409,"wig+100":12.4250979385,"nqc+100":0.0004338056,"smv+100":-0.0004338056,"clarity+100+3":2.6993897409,"wig+1000":12.4250979385,"nqc+1000":0.0004338056,"smv+1000":-0.0004338056,"clarity+1000+3":2.6993897409} 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+{"qid":"360","wig+5":25.8851370583,"nqc+5":0.0010133688,"smv+5":0.0007282939,"clarity+5+100":2.1937305843,"wig+10":25.6322398361,"nqc+10":0.0142283359,"smv+10":0.0116895518,"clarity+10+100":2.0980951937,"wig+20":25.5311502788,"nqc+20":0.0210587588,"smv+20":0.0163677905,"clarity+20+100":1.7921217697,"wig+50":25.3930964046,"nqc+50":0.0198758351,"smv+50":0.0151813675,"clarity+50+100":1.5316496918,"wig+100":25.2494203371,"nqc+100":0.0232310984,"smv+100":0.0179804081,"clarity+100+100":1.3514880499,"wig+1000":24.2918387389,"nqc+1000":0.0255762634,"smv+1000":0.0181296856,"clarity+1000+100":1.1983600913} +{"qid":"365","wig+5":20.9442560817,"nqc+5":0.0024261931,"smv+5":0.0018851314,"clarity+5+100":1.6898717593,"wig+10":20.916387263,"nqc+10":0.0032893792,"smv+10":0.0026924493,"clarity+10+100":1.7132918492,"wig+20":20.7933243787,"nqc+20":0.0107734468,"smv+20":0.0090007839,"clarity+20+100":1.5793281766,"wig+50":20.5331910715,"nqc+50":0.0224595771,"smv+50":0.0170181442,"clarity+50+100":1.4197725647,"wig+100":20.3057933038,"nqc+100":0.0280059324,"smv+100":0.0218704284,"clarity+100+100":1.352972301,"wig+1000":19.0338259267,"nqc+1000":0.0401814336,"smv+1000":0.0302118058,"clarity+1000+100":1.2146600822} diff --git a/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_01_approval.approved.jsonl b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_01_approval.approved.jsonl index 1b50d6e..91ecf0b 100644 --- a/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_01_approval.approved.jsonl +++ b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_01_approval.approved.jsonl @@ -1,3 +1,3 @@ -{"qid":"1","wig+5":6.9431571325,"nqc+5":0.0433805568,"smv+5":null,"clarity+5+3":2.7431934039,"wig+10":6.9431571325,"nqc+10":0.0433805568,"smv+10":null,"clarity+10+3":2.7431934039,"wig+20":6.9431571325,"nqc+20":0.0433805568,"smv+20":null,"clarity+20+3":2.7431934039,"wig+50":6.9431571325,"nqc+50":0.0433805568,"smv+50":null,"clarity+50+3":2.7431934039,"wig+100":6.9431571325,"nqc+100":0.0433805568,"smv+100":null,"clarity+100+3":2.7431934039,"wig+1000":6.9431571325,"nqc+1000":0.0433805568,"smv+1000":null,"clarity+1000+3":2.7431934039} -{"qid":"2","wig+5":3.8721194712,"nqc+5":0.1470633166,"smv+5":null,"clarity+5+3":2.3208312544,"wig+10":3.8721194712,"nqc+10":0.1470633166,"smv+10":null,"clarity+10+3":2.3208312544,"wig+20":3.8721194712,"nqc+20":0.1470633166,"smv+20":null,"clarity+20+3":2.3208312544,"wig+50":3.8721194712,"nqc+50":0.1470633166,"smv+50":null,"clarity+50+3":2.3208312544,"wig+100":3.8721194712,"nqc+100":0.1470633166,"smv+100":null,"clarity+100+3":2.3208312544,"wig+1000":3.8721194712,"nqc+1000":0.1470633166,"smv+1000":null,"clarity+1000+3":2.3208312544} -{"qid":"3","wig+5":4.8199019572,"nqc+5":0.0,"smv+5":null,"clarity+5+3":2.361615051,"wig+10":4.8199019572,"nqc+10":0.0,"smv+10":null,"clarity+10+3":2.361615051,"wig+20":4.8199019572,"nqc+20":0.0,"smv+20":null,"clarity+20+3":2.361615051,"wig+50":4.8199019572,"nqc+50":0.0,"smv+50":null,"clarity+50+3":2.361615051,"wig+100":4.8199019572,"nqc+100":0.0,"smv+100":null,"clarity+100+3":2.361615051,"wig+1000":4.8199019572,"nqc+1000":0.0,"smv+1000":null,"clarity+1000+3":2.361615051} +{"qid":"1","wig+5":6.9431571325,"nqc+5":0.0433805568,"smv+5":null,"clarity+5+100":2.7431934039,"wig+10":6.9431571325,"nqc+10":0.0433805568,"smv+10":null,"clarity+10+100":2.7431934039,"wig+20":6.9431571325,"nqc+20":0.0433805568,"smv+20":null,"clarity+20+100":2.7431934039,"wig+50":6.9431571325,"nqc+50":0.0433805568,"smv+50":null,"clarity+50+100":2.7431934039,"wig+100":6.9431571325,"nqc+100":0.0433805568,"smv+100":null,"clarity+100+100":2.7431934039,"wig+1000":6.9431571325,"nqc+1000":0.0433805568,"smv+1000":null,"clarity+1000+100":2.7431934039} +{"qid":"2","wig+5":3.8721194712,"nqc+5":0.1470633166,"smv+5":null,"clarity+5+100":2.3208312544,"wig+10":3.8721194712,"nqc+10":0.1470633166,"smv+10":null,"clarity+10+100":2.3208312544,"wig+20":3.8721194712,"nqc+20":0.1470633166,"smv+20":null,"clarity+20+100":2.3208312544,"wig+50":3.8721194712,"nqc+50":0.1470633166,"smv+50":null,"clarity+50+100":2.3208312544,"wig+100":3.8721194712,"nqc+100":0.1470633166,"smv+100":null,"clarity+100+100":2.3208312544,"wig+1000":3.8721194712,"nqc+1000":0.1470633166,"smv+1000":null,"clarity+1000+100":2.3208312544} +{"qid":"3","wig+5":4.8199019572,"nqc+5":0.0,"smv+5":null,"clarity+5+100":2.361615051,"wig+10":4.8199019572,"nqc+10":0.0,"smv+10":null,"clarity+10+100":2.361615051,"wig+20":4.8199019572,"nqc+20":0.0,"smv+20":null,"clarity+20+100":2.361615051,"wig+50":4.8199019572,"nqc+50":0.0,"smv+50":null,"clarity+50+100":2.361615051,"wig+100":4.8199019572,"nqc+100":0.0,"smv+100":null,"clarity+100+100":2.361615051,"wig+1000":4.8199019572,"nqc+1000":0.0,"smv+1000":null,"clarity+1000+100":2.361615051} diff --git a/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_02_approval.approved.jsonl b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_02_approval.approved.jsonl index bbf63a0..73247a0 100644 --- a/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_02_approval.approved.jsonl +++ b/code/qpptk/tests/TwoCustomRunFilesForPredictionTest.test_end_to_end_with_run_02_approval.approved.jsonl @@ -1,3 +1,3 @@ -{"qid":"1","wig+5":6.9431571325,"nqc+5":0.0433805568,"smv+5":null,"clarity+5+3":2.7431934039,"clarity+5+1":null,"clarity+5+2":null,"wig+10":6.9431571325,"nqc+10":0.0433805568,"smv+10":null,"clarity+10+3":2.7431934039,"clarity+10+1":null,"clarity+10+2":null,"wig+20":6.9431571325,"nqc+20":0.0433805568,"smv+20":null,"clarity+20+3":2.7431934039,"clarity+20+1":null,"clarity+20+2":null,"wig+50":6.9431571325,"nqc+50":0.0433805568,"smv+50":null,"clarity+50+3":2.7431934039,"clarity+50+1":null,"clarity+50+2":null,"wig+100":6.9431571325,"nqc+100":0.0433805568,"smv+100":null,"clarity+100+3":2.7431934039,"clarity+100+1":null,"clarity+100+2":null,"wig+1000":6.9431571325,"nqc+1000":0.0433805568,"smv+1000":null,"clarity+1000+3":2.7431934039,"clarity+1000+1":null,"clarity+1000+2":null} -{"qid":"2","wig+5":3.7054528045,"nqc+5":0.1559842027,"smv+5":null,"clarity+5+3":null,"clarity+5+1":3.2054528045,"clarity+5+2":null,"wig+10":3.7054528045,"nqc+10":0.1559842027,"smv+10":null,"clarity+10+3":null,"clarity+10+1":3.2054528045,"clarity+10+2":null,"wig+20":3.7054528045,"nqc+20":0.1559842027,"smv+20":null,"clarity+20+3":null,"clarity+20+1":3.2054528045,"clarity+20+2":null,"wig+50":3.7054528045,"nqc+50":0.1559842027,"smv+50":null,"clarity+50+3":null,"clarity+50+1":3.2054528045,"clarity+50+2":null,"wig+100":3.7054528045,"nqc+100":0.1559842027,"smv+100":null,"clarity+100+3":null,"clarity+100+1":3.2054528045,"clarity+100+2":null,"wig+1000":3.7054528045,"nqc+1000":0.1559842027,"smv+1000":null,"clarity+1000+3":null,"clarity+1000+1":3.2054528045,"clarity+1000+2":null} -{"qid":"3","wig+5":5.1734553478,"nqc+5":0.0733528179,"smv+5":null,"clarity+5+3":null,"clarity+5+1":null,"clarity+5+2":2.69519388,"wig+10":5.1734553478,"nqc+10":0.0733528179,"smv+10":null,"clarity+10+3":null,"clarity+10+1":null,"clarity+10+2":2.69519388,"wig+20":5.1734553478,"nqc+20":0.0733528179,"smv+20":null,"clarity+20+3":null,"clarity+20+1":null,"clarity+20+2":2.69519388,"wig+50":5.1734553478,"nqc+50":0.0733528179,"smv+50":null,"clarity+50+3":null,"clarity+50+1":null,"clarity+50+2":2.69519388,"wig+100":5.1734553478,"nqc+100":0.0733528179,"smv+100":null,"clarity+100+3":null,"clarity+100+1":null,"clarity+100+2":2.69519388,"wig+1000":5.1734553478,"nqc+1000":0.0733528179,"smv+1000":null,"clarity+1000+3":null,"clarity+1000+1":null,"clarity+1000+2":2.69519388} +{"qid":"1","wig+5":6.9431571325,"nqc+5":0.0433805568,"smv+5":null,"clarity+5+100":2.7431934039,"wig+10":6.9431571325,"nqc+10":0.0433805568,"smv+10":null,"clarity+10+100":2.7431934039,"wig+20":6.9431571325,"nqc+20":0.0433805568,"smv+20":null,"clarity+20+100":2.7431934039,"wig+50":6.9431571325,"nqc+50":0.0433805568,"smv+50":null,"clarity+50+100":2.7431934039,"wig+100":6.9431571325,"nqc+100":0.0433805568,"smv+100":null,"clarity+100+100":2.7431934039,"wig+1000":6.9431571325,"nqc+1000":0.0433805568,"smv+1000":null,"clarity+1000+100":2.7431934039} +{"qid":"2","wig+5":3.7054528045,"nqc+5":0.1559842027,"smv+5":null,"clarity+5+100":3.2054528045,"wig+10":3.7054528045,"nqc+10":0.1559842027,"smv+10":null,"clarity+10+100":3.2054528045,"wig+20":3.7054528045,"nqc+20":0.1559842027,"smv+20":null,"clarity+20+100":3.2054528045,"wig+50":3.7054528045,"nqc+50":0.1559842027,"smv+50":null,"clarity+50+100":3.2054528045,"wig+100":3.7054528045,"nqc+100":0.1559842027,"smv+100":null,"clarity+100+100":3.2054528045,"wig+1000":3.7054528045,"nqc+1000":0.1559842027,"smv+1000":null,"clarity+1000+100":3.2054528045} +{"qid":"3","wig+5":5.1734553478,"nqc+5":0.0733528179,"smv+5":null,"clarity+5+100":2.69519388,"wig+10":5.1734553478,"nqc+10":0.0733528179,"smv+10":null,"clarity+10+100":2.69519388,"wig+20":5.1734553478,"nqc+20":0.0733528179,"smv+20":null,"clarity+20+100":2.69519388,"wig+50":5.1734553478,"nqc+50":0.0733528179,"smv+50":null,"clarity+50+100":2.69519388,"wig+100":5.1734553478,"nqc+100":0.0733528179,"smv+100":null,"clarity+100+100":2.69519388,"wig+1000":5.1734553478,"nqc+1000":0.0733528179,"smv+1000":null,"clarity+1000+100":2.69519388} diff --git a/code/qpptk/tests/custom_run_file_for_prediction_test.py b/code/qpptk/tests/custom_run_file_for_prediction_test.py index dab245e..b03bd15 100644 --- a/code/qpptk/tests/custom_run_file_for_prediction_test.py +++ b/code/qpptk/tests/custom_run_file_for_prediction_test.py @@ -19,19 +19,19 @@ def test_with_run_file_where_one_query_is_highly_effective(self): self.assertEqual(actual.iloc[0].to_dict()['wig+10'], 12.4250979385) self.assertEqual(actual.iloc[0].to_dict()['nqc+100'], 0.00043380560000000004) self.assertEqual(actual.iloc[0].to_dict()['smv+100'], -0.00043380560000000004) - self.assertEqual(actual.iloc[0].to_dict()['clarity+1000+3'], 2.6993897409) + self.assertEqual(actual.iloc[0].to_dict()['clarity+1000+100'], 2.6993897409) self.assertEqual(actual.iloc[1].to_dict()['qid'], 2.0) self.assertEqual(actual.iloc[1].to_dict()['wig+10'], 4.2054528045) self.assertEqual(actual.iloc[1].to_dict()['nqc+100'], 0.2547211364) self.assertEqual(str(actual.iloc[1].to_dict()['smv+100']), 'nan') - self.assertEqual(actual.iloc[1].to_dict()['clarity+1000+3'], 2.240679245) + self.assertEqual(actual.iloc[1].to_dict()['clarity+1000+100'], 2.240679245) self.assertEqual(actual.iloc[2].to_dict()['qid'], 3.0) self.assertEqual(actual.iloc[2].to_dict()['wig+10'], -20.8716444259) self.assertEqual(actual.iloc[2].to_dict()['nqc+100'], 6.6373379689) self.assertEqual(str(actual.iloc[2].to_dict()['smv+100']), 'nan') - self.assertEqual(actual.iloc[2].to_dict()['clarity+1000+3'], 3.2047245641) + self.assertEqual(actual.iloc[2].to_dict()['clarity+1000+100'], 3.2047245641) def test_with_run_file_where_one_query_is_highly_effective_approvaltest(self): with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: @@ -53,19 +53,19 @@ def test_with_run_file_where_all_queries_are_highly_effective(self): self.assertEqual(actual.iloc[0].to_dict()['wig+10'], 12.4250979385) self.assertEqual(actual.iloc[0].to_dict()['nqc+100'], 0.00043380560000000004) self.assertEqual(actual.iloc[0].to_dict()['smv+100'], -0.00043380560000000004) - self.assertEqual(actual.iloc[0].to_dict()['clarity+1000+3'], 2.6993897409) + self.assertEqual(actual.iloc[0].to_dict()['clarity+1000+100'], 2.6993897409) self.assertEqual(actual.iloc[1].to_dict()['qid'], 2.0) self.assertEqual(actual.iloc[1].to_dict()['wig+10'], 13.2004528045) self.assertEqual(actual.iloc[1].to_dict()['nqc+100'], 0.001559842) self.assertEqual(actual.iloc[1].to_dict()['smv+100'], -0.001559842) - self.assertEqual(str(actual.iloc[1].to_dict()['clarity+1000+3']), 'nan') + self.assertEqual(actual.iloc[1].to_dict()['clarity+1000+100'], 3.2054528045) self.assertEqual(actual.iloc[2].to_dict()['qid'], 3.0) self.assertEqual(actual.iloc[2].to_dict()['wig+10'], 11.8874342352) self.assertEqual(actual.iloc[2].to_dict()['nqc+100'], 0.0007335282000000001) self.assertEqual(actual.iloc[2].to_dict()['smv+100'], -0.0007335281) - self.assertEqual(str(actual.iloc[2].to_dict()['clarity+1000+3']), 'nan') + self.assertEqual(actual.iloc[2].to_dict()['clarity+1000+100'], 2.6956075044) def test_with_run_file_where_all_queries_are_highly_effective_approval(self): with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: diff --git a/code/qpptk/tests/end_to_end_test.py b/code/qpptk/tests/end_to_end_test.py index 2471f60..dffc3a6 100644 --- a/code/qpptk/tests/end_to_end_test.py +++ b/code/qpptk/tests/end_to_end_test.py @@ -17,7 +17,7 @@ def test_end_to_end(self): assert actual.iloc[0].to_dict()['avg-idf'] == 1.3296613489 assert actual.iloc[0].to_dict()['avg-scq'] == 2.2134369757 - assert actual.iloc[0].to_dict()['clarity+1000+3'] == 2.6980214786000003 + assert actual.iloc[0].to_dict()['clarity+1000+100'] == 2.6980214786000003 assert actual.iloc[0].to_dict()['max-idf'] == 1.7917594692 assert actual.iloc[1].to_dict()['avg-idf'] == 1.0986122887 diff --git a/code/qpptk/tests/two_custom_run_files_for_prediction_test.py b/code/qpptk/tests/two_custom_run_files_for_prediction_test.py index ef57737..dda8b8f 100644 --- a/code/qpptk/tests/two_custom_run_files_for_prediction_test.py +++ b/code/qpptk/tests/two_custom_run_files_for_prediction_test.py @@ -45,19 +45,19 @@ def test_end_to_end_with_run_01(self): self.assertEqual(actual.iloc[0].to_dict()['wig+10'], 6.9431571325) self.assertEqual(actual.iloc[0].to_dict()['nqc+100'], 0.043380556800000004) self.assertEqual(str(actual.iloc[0].to_dict()['smv+100']), 'nan') - self.assertEqual(actual.iloc[0].to_dict()['clarity+1000+3'], 2.7431934039) + self.assertEqual(actual.iloc[0].to_dict()['clarity+1000+100'], 2.7431934039) self.assertEqual(actual.iloc[1].to_dict()['qid'], 2.0) self.assertEqual(actual.iloc[1].to_dict()['wig+10'], 3.8721194712) self.assertEqual(actual.iloc[1].to_dict()['nqc+100'], 0.1470633166) self.assertEqual(str(actual.iloc[1].to_dict()['smv+100']), 'nan') - self.assertEqual(actual.iloc[1].to_dict()['clarity+1000+3'], 2.3208312544) + self.assertEqual(actual.iloc[1].to_dict()['clarity+1000+100'], 2.3208312544) self.assertEqual(actual.iloc[2].to_dict()['qid'], 3.0) self.assertEqual(actual.iloc[2].to_dict()['wig+10'], 4.8199019572) self.assertEqual(actual.iloc[2].to_dict()['nqc+100'], 0.0) self.assertEqual(str(actual.iloc[2].to_dict()['smv+100']), 'nan') - self.assertEqual(actual.iloc[2].to_dict()['clarity+1000+3'], 2.3616150510000002) + self.assertEqual(actual.iloc[2].to_dict()['clarity+1000+100'], 2.3616150510000002) def test_end_to_end_with_run_01_approval(self): with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: From 17b9d9ee4bffcfb57385b3485289df37d365687a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Sun, 17 Dec 2023 17:36:52 +0100 Subject: [PATCH 39/40] Add in-progress notebook --- code/maik-idea-in-progress.ipynb | 2844 ++++++++++++++++++++++++++++++ 1 file changed, 2844 insertions(+) create mode 100644 code/maik-idea-in-progress.ipynb diff --git a/code/maik-idea-in-progress.ipynb b/code/maik-idea-in-progress.ipynb new file mode 100644 index 0000000..5add1de --- /dev/null +++ b/code/maik-idea-in-progress.ipynb @@ -0,0 +1,2844 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start PyTerrier with version=5.7, helper_version=0.0.7, no_download=True\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", + "\n", + "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No settings given in /root/.tira/.tira-settings.json. I will use defaults.\n" + ] + } + ], + "source": [ + "import pyterrier as pt\n", + "from tira.third_party_integrations import ensure_pyterrier_is_loaded\n", + "from tira.rest_api_client import Client\n", + "\n", + "ensure_pyterrier_is_loaded()\n", + "tira = Client()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = pt.get_dataset(\"irds:disks45/nocr/trec-robust-2004\")\n", + "index = tira.get_run_output('ir-benchmarks/tira-ir-starter/Index (tira-ir-starter-pyterrier)', 'disks45-nocr-trec-robust-2004-20230209-training') + '/index'\n", + "queries = tira.download_dataset('ir-benchmarks', 'disks45-nocr-trec-robust-2004-20230209-training', truth_dataset=True) + '/queries.jsonl'\n", + "\n", + "bm25_run = tira.get_run_output('ir-benchmarks/tira-ir-starter/BM25 Re-Rank (tira-ir-starter-pyterrier)', 'disks45-nocr-trec-robust-2004-20230209-training') + '/run.txt'\n", + "\n", + "monot5_base_run = tira.get_run_output('ir-benchmarks/tira-ir-starter/MonoT5 Base (tira-ir-starter-gygaggle)', 'disks45-nocr-trec-robust-2004-20230209-training') + '/run.txt'\n", + "monot5_3b_run = tira.get_run_output('ir-benchmarks/tira-ir-starter/MonoT5 3b (tira-ir-starter-gygaggle)', 'disks45-nocr-trec-robust-2004-20230209-training') + '/run.txt'\n", + "monot5_large_run = tira.get_run_output('ir-benchmarks/tira-ir-starter/MonoT5 Large (tira-ir-starter-gygaggle)', 'disks45-nocr-trec-robust-2004-20230209-training') + '/run.txt'\n", + "dirichlet_run = tira.get_run_output('ir-benchmarks/tira-ir-starter/DirichletLM Re-Rank (tira-ir-starter-pyterrier)', 'disks45-nocr-trec-robust-2004-20230209-training') + '/run.txt'\n", + "\n", + "tasb_run = \ttira.get_run_output('ir-benchmarks/tira-ir-starter/TASB msmarco-distilbert-base-dot (tira-ir-starter-beir)', 'disks45-nocr-trec-robust-2004-20230209-training') + '/run.txt'" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start PyTerrier with version=5.7, helper_version=0.0.7, no_download=True\n", + "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", + "\n", + "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n", + "org.terrier.terms.PorterStemmer\n", + "org.terrier.terms.PorterStemmer\n", + "100%|████████████████████████████████| 520520/520520 [00:38<00:00, 13645.62it/s]\n", + "100%|████████████████████████████████| 528155/528155 [00:11<00:00, 45334.75it/s]\n", + "100%|█████████████████████████████████████████| 537/537 [00:34<00:00, 15.35it/s]\n", + "org.terrier.terms.PorterStemmer\n" + ] + } + ], + "source": [ + "!python3 qpptk/qpptk/qpptk_main.py -ti {index} --jsonl_queries {queries} --run-file {tasb_run} --output idea-in-progress/tasb-predictions/ --stats_index_path idea-in-progress/tasb-predictions/stats --predPost --retrieve --cleanOutput" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start PyTerrier with version=5.7, helper_version=0.0.7, no_download=True\n", + "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", + "\n", + "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n", + "org.terrier.terms.PorterStemmer\n", + "org.terrier.terms.PorterStemmer\n", + "100%|████████████████████████████████| 520520/520520 [00:42<00:00, 12122.73it/s]\n", + "100%|████████████████████████████████| 528155/528155 [00:15<00:00, 34769.89it/s]\n", + "100%|█████████████████████████████████████████| 537/537 [00:37<00:00, 14.35it/s]\n", + "org.terrier.terms.PorterStemmer\n" + ] + } + ], + "source": [ + "!python3 qpptk/qpptk/qpptk_main.py -ti {index} --jsonl_queries {queries} --run-file {monot5_3b_run} --output idea-in-progress/monot5-3b-predictions/ --stats_index_path idea-in-progress/monot5-3b-predictions/stats --predPost --retrieve --cleanOutput" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start PyTerrier with version=5.7, helper_version=0.0.7, no_download=True\n", + "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", + "\n", + "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n", + "org.terrier.terms.PorterStemmer\n", + "org.terrier.terms.PorterStemmer\n", + "100%|████████████████████████████████| 520520/520520 [00:43<00:00, 11841.73it/s]\n", + "100%|████████████████████████████████| 528155/528155 [00:12<00:00, 43002.11it/s]\n", + "100%|█████████████████████████████████████████| 537/537 [00:38<00:00, 14.01it/s]\n", + "org.terrier.terms.PorterStemmer\n", + "/workspaces/QPP-EnhancedEval/code/qpptk/qpptk/post_retrieval_predictors.py:53: RuntimeWarning: divide by zero encountered in log\n", + " return (scores_vec * abs(np.log(scores_vec / mean))).mean() / self.ql_corpus_score\n", + "/workspaces/QPP-EnhancedEval/code/qpptk/qpptk/post_retrieval_predictors.py:53: RuntimeWarning: invalid value encountered in multiply\n", + " return (scores_vec * abs(np.log(scores_vec / mean))).mean() / self.ql_corpus_score\n", + "/workspaces/QPP-EnhancedEval/code/qpptk/qpptk/post_retrieval_predictors.py:53: RuntimeWarning: invalid value encountered in log\n", + " return (scores_vec * abs(np.log(scores_vec / mean))).mean() / self.ql_corpus_score\n" + ] + } + ], + "source": [ + "!python3 qpptk/qpptk/qpptk_main.py -ti {index} --jsonl_queries {queries} --run-file {dirichlet_run} --output idea-in-progress/dirichlet-predictions/ --stats_index_path idea-in-progress/dirichlet-predictions/stats --predPost --retrieve --cleanOutput" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start PyTerrier with version=5.7, helper_version=0.0.7, no_download=True\n", + "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", + "\n", + "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n", + "org.terrier.terms.PorterStemmer\n", + "org.terrier.terms.PorterStemmer\n", + "100%|████████████████████████████████| 520520/520520 [00:35<00:00, 14768.12it/s]\n", + "100%|████████████████████████████████| 528155/528155 [00:10<00:00, 50072.09it/s]\n", + "100%|█████████████████████████████████████████| 537/537 [00:55<00:00, 9.62it/s]\n", + "org.terrier.terms.PorterStemmer\n", + "/workspaces/QPP-EnhancedEval/code/qpptk/qpptk/post_retrieval_predictors.py:53: RuntimeWarning: divide by zero encountered in log\n", + " return (scores_vec * abs(np.log(scores_vec / mean))).mean() / self.ql_corpus_score\n", + "/workspaces/QPP-EnhancedEval/code/qpptk/qpptk/post_retrieval_predictors.py:53: RuntimeWarning: invalid value encountered in multiply\n", + " return (scores_vec * abs(np.log(scores_vec / mean))).mean() / self.ql_corpus_score\n" + ] + } + ], + "source": [ + "!python3 qpptk/qpptk/qpptk_main.py -ti {index} --jsonl_queries {queries} --run-file {bm25_run} --output idea-in-progress/bm25-predictions/ --stats_index_path idea-in-progress/bm25-predictions/stats --predPost --retrieve --cleanOutput" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start PyTerrier with version=5.7, helper_version=0.0.7, no_download=True\n", + "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", + "\n", + "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n", + "org.terrier.terms.PorterStemmer\n", + "org.terrier.terms.PorterStemmer\n", + "100%|████████████████████████████████| 520520/520520 [00:39<00:00, 13042.78it/s]\n", + "100%|████████████████████████████████| 528155/528155 [00:11<00:00, 46138.29it/s]\n", + "100%|█████████████████████████████████████████| 537/537 [00:31<00:00, 16.85it/s]\n", + "org.terrier.terms.PorterStemmer\n" + ] + } + ], + "source": [ + "!python3 qpptk/qpptk/qpptk_main.py -ti {index} --jsonl_queries {queries} --run-file {bm25_run} --use-scores-from-run-file {monot5_base_run} --output idea-in-progress/bm25-predictions-monot5-base/ --stats_index_path idea-in-progress/bm25-predictions-monot5-base/stats --predPost --retrieve --cleanOutput" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start PyTerrier with version=5.7, helper_version=0.0.7, no_download=True\n", + "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", + "\n", + "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n", + "org.terrier.terms.PorterStemmer\n", + "org.terrier.terms.PorterStemmer\n", + "100%|████████████████████████████████| 520520/520520 [00:39<00:00, 13119.23it/s]\n", + "100%|████████████████████████████████| 528155/528155 [00:12<00:00, 41475.76it/s]\n", + "100%|█████████████████████████████████████████| 537/537 [00:37<00:00, 14.46it/s]\n", + "org.terrier.terms.PorterStemmer\n" + ] + } + ], + "source": [ + "!python3 qpptk/qpptk/qpptk_main.py -ti {index} --jsonl_queries {queries} --run-file {bm25_run} --use-scores-from-run-file {monot5_large_run} --output idea-in-progress/bm25-predictions-monot5-large/ --stats_index_path idea-in-progress/bm25-predictions-monot5-large/stats --predPost --retrieve --cleanOutput" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Start PyTerrier with version=5.7, helper_version=0.0.7, no_download=True\n", + "PyTerrier 0.9.2 has loaded Terrier 5.7 (built by craigm on 2022-11-10 18:30) and terrier-helper 0.0.7\n", + "\n", + "No etc/terrier.properties, using terrier.default.properties for bootstrap configuration.\n", + "org.terrier.terms.PorterStemmer\n", + "org.terrier.terms.PorterStemmer\n", + "100%|████████████████████████████████| 520520/520520 [00:41<00:00, 12652.98it/s]\n", + "100%|████████████████████████████████| 528155/528155 [00:11<00:00, 46283.91it/s]\n", + "100%|█████████████████████████████████████████| 537/537 [00:35<00:00, 15.18it/s]\n", + "org.terrier.terms.PorterStemmer\n" + ] + } + ], + "source": [ + "!python3 qpptk/qpptk/qpptk_main.py -ti {index} --jsonl_queries {queries} --run-file {bm25_run} --use-scores-from-run-file {monot5_3b_run} --output idea-in-progress/bm25-predictions-monot5-3b/ --stats_index_path idea-in-progress/bm25-predictions-monot5-3b/stats --predPost --retrieve --cleanOutput" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "def qpp_correlation_to_ground_truth(bm25, qpp, dataset, eval_metrics):\n", + " import pandas as pd\n", + " topics = dataset.get_topics(variant='title')\n", + " df_eval = pt.Experiment([bm25], topics=topics, qrels=dataset.get_qrels(), eval_metrics=eval_metrics, perquery=True, names=['BM25'])\n", + " df_predictions = qpp(topics)\n", + " df_joined = pd.merge(df_eval, df_predictions, on=['qid'])\n", + " ret = []\n", + "\n", + " for q in ['wig+5', 'wig+10', 'wig+20', 'wig+50', 'wig+100', 'wig+1000', 'nqc+5', 'nqc+10', 'nqc+20', 'nqc+50', 'nqc+100', 'nqc+1000', 'smv+5', 'smv+10', 'smv+20', 'smv+50', 'smv+100', 'smv+1000', 'clarity+5+100', 'clarity+10+100', 'clarity+20+100', 'clarity+50+100', 'clarity+100+100', 'clarity+1000+100']:\n", + " \n", + " ret += [{'QPP Method': q, 'Pearson Correlation': df_joined[['value', q]].corr(method='pearson').iloc[0].to_dict()[q], 'Kendall': df_joined[['value', q]].corr(method='kendall').iloc[0].to_dict()[q], 'Spearman': df_joined[['value', q]].corr(method='spearman').iloc[0].to_dict()[q]}]\n", + "\n", + " return pd.DataFrame(ret).sort_values('Pearson Correlation', ascending=False)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\"qid\":\"301\",\"wig+5\":8.1538426386,\"nqc+5\":0.2302213445,\"smv+5\":0.1735291643,\"clarity+5+1\":8.4389762636,\"clarity+5+2\":7.2377404637,\"clarity+5+3\":6.6500359346,\"clarity+5+4\":6.2496529378,\"clarity+5+5\":5.9353293106,\"clarity+5+10\":5.2140229176,\"clarity+5+100\":4.1163406121,\"clarity+5+1000\":4.1162974043,\"wig+10\":8.3986639162,\"nqc+10\":0.2745438091,\"smv+10\":0.1982137628,\"clarity+10+1\":8.4389762636,\"clarity+10+2\":7.3374160851,\"clarity+10+3\":6.9327226033,\"clarity+10+4\":6.442240721,\"clarity+10+5\":6.4037441021,\"clarity+10+10\":5.6065822859,\"clarity+10+100\":4.2324150965,\"clarity+10+1000\":4.2323966464,\"wig+20\":7.6090500222,\"nqc+20\":0.265131287,\"smv+20\":0.169705112,\"clarity+20+1\":8.4389762636,\"clarity+20+2\":7.355133136,\"clarity+20+3\":6.6771405965,\"clarity+20+4\":6.4459559306,\"clarity+20+5\":6.3897175598,\"clarity+20+10\":5.7679897305,\"clarity+20+100\":4.1052893064,\"clarity+20+1000\":4.105207107,\"wig+50\":6.850329445,\"nqc+50\":0.2699966326,\"smv+50\":0.1646634367,\"clarity+50+1\":8.4389762636,\"clarity+50+2\":7.3982254728,\"clarity+50+3\":6.7538453597,\"clarity+50+4\":6.4820218946,\"clarity+50+5\":6.1820700551,\"clarity+50+10\":5.6630617525,\"clarity+50+100\":3.8167603479,\"clarity+50+1000\":3.7539540741,\"wig+100\":6.0371059695,\"nqc+100\":0.2552809114,\"smv+100\":0.1529112349,\"clarity+100+1\":8.4389762636,\"clarity+100+2\":7.3706593837,\"clarity+100+3\":6.7496317582,\"clarity+100+4\":6.4899205044,\"clarity+100+5\":6.2779247944,\"clarity+100+10\":5.5874364991,\"clarity+100+100\":3.8195398052,\"clarity+100+1000\":3.7315001419,\"wig+1000\":4.059842028,\"nqc+1000\":0.160266928,\"smv+1000\":0.0925492044,\"clarity+1000+1\":8.4389762636,\"clarity+1000+2\":7.3033249205,\"clarity+1000+3\":6.8384990721,\"clarity+1000+4\":6.3588724518,\"clarity+1000+5\":6.1267928599,\"clarity+1000+10\":5.5175829001,\"clarity+1000+100\":3.7655823134,\"clarity+1000+1000\":3.5986708854}\n" + ] + } + ], + "source": [ + "!head -1 idea-in-progress/bm25-predictions-monot5-3b/queries.jsonl" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "def load_predictions(f):\n", + " import pandas as pd\n", + " from pyterrier.apply import generic\n", + " ret = pd.read_json(f, lines=True, dtype={'qid': str})\n", + " cols = [i for i in ret.columns if i not in ['qid']]\n", + " ret = {str(i['qid']): i for _, i in ret.iterrows()}\n", + "\n", + " def __transform_df(df):\n", + " for col in cols:\n", + " df[col] = df['qid'].apply(lambda i: ret[str(i)][col])\n", + " return df\n", + "\n", + " return generic(__transform_df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "qpp_correlation_to_ground_truth(pt.io.read_results(tas), load_predictions('idea-in-progress/bm25-predictions-monot5-3b/queries.jsonl'), dataset, ['ndcg_cut_10', ])" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:107: UserWarning: 1 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n" + ] + }, + { + "data": { + "text/html": [ + "

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QPP MethodPearson CorrelationKendallSpearman
23clarity+1000+1000.2973310.2184520.319161
22clarity+100+1000.2424010.1700520.246998
21clarity+50+1000.2029200.1367850.203184
20clarity+20+1000.1974910.1290230.192965
0wig+50.1889460.1361980.199741
19clarity+10+1000.1736080.1221090.178816
15smv+500.1661720.1067140.157793
1wig+100.1612790.1127160.167670
9nqc+500.1387830.0957560.143835
10nqc+1000.1160750.0622280.095694
16smv+1000.1058280.0375070.060689
14smv+200.1017030.0855800.122941
18clarity+5+1000.0988740.0549230.078604
2wig+200.0955860.0697950.102226
8nqc+200.0616690.0603370.087820
3wig+500.0208050.0189160.027693
13smv+100.0169970.0261570.037269
7nqc+10-0.0045090.0138940.020401
5wig+1000-0.022835-0.026418-0.039847
4wig+100-0.023925-0.010763-0.017689
11nqc+1000-0.081707-0.078666-0.107321
12smv+5-0.122144-0.062685-0.095754
6nqc+5-0.134015-0.070773-0.104795
17smv+1000-0.137324-0.125109-0.175854
\n", + "
" + ], + "text/plain": [ + " QPP Method Pearson Correlation Kendall Spearman\n", + "23 clarity+1000+100 0.297331 0.218452 0.319161\n", + "22 clarity+100+100 0.242401 0.170052 0.246998\n", + "21 clarity+50+100 0.202920 0.136785 0.203184\n", + "20 clarity+20+100 0.197491 0.129023 0.192965\n", + "0 wig+5 0.188946 0.136198 0.199741\n", + "19 clarity+10+100 0.173608 0.122109 0.178816\n", + "15 smv+50 0.166172 0.106714 0.157793\n", + "1 wig+10 0.161279 0.112716 0.167670\n", + "9 nqc+50 0.138783 0.095756 0.143835\n", + "10 nqc+100 0.116075 0.062228 0.095694\n", + "16 smv+100 0.105828 0.037507 0.060689\n", + "14 smv+20 0.101703 0.085580 0.122941\n", + "18 clarity+5+100 0.098874 0.054923 0.078604\n", + "2 wig+20 0.095586 0.069795 0.102226\n", + "8 nqc+20 0.061669 0.060337 0.087820\n", + "3 wig+50 0.020805 0.018916 0.027693\n", + "13 smv+10 0.016997 0.026157 0.037269\n", + "7 nqc+10 -0.004509 0.013894 0.020401\n", + "5 wig+1000 -0.022835 -0.026418 -0.039847\n", + "4 wig+100 -0.023925 -0.010763 -0.017689\n", + "11 nqc+1000 -0.081707 -0.078666 -0.107321\n", + "12 smv+5 -0.122144 -0.062685 -0.095754\n", + "6 nqc+5 -0.134015 -0.070773 -0.104795\n", + "17 smv+1000 -0.137324 -0.125109 -0.175854" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpp_correlation_to_ground_truth(pt.io.read_results(bm25_run), load_predictions('idea-in-progress/bm25-predictions-monot5-3b/queries.jsonl'), dataset, ['ndcg_cut_10', ])" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:107: UserWarning: 1 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n" + ] + }, + { + "data": { + "text/html": [ + "
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QPP MethodPearson CorrelationKendallSpearman
23clarity+1000+1000.2799410.2026010.298859
22clarity+100+1000.2358050.1622240.240220
10nqc+1000.2229250.1446780.215852
21clarity+50+1000.2132690.1461780.214532
20clarity+20+1000.2000610.1285010.192217
9nqc+500.1993540.1255000.188106
15smv+500.1820640.1171510.175939
19clarity+10+1000.1818580.1193690.178206
16smv+1000.1760150.1124550.164884
0wig+50.1545780.1051490.153585
18clarity+5+1000.1519510.0947780.142401
1wig+100.1399330.0932770.137660
2wig+200.0923600.0530960.080287
14smv+200.0555960.0341800.049156
8nqc+200.0530610.0349630.051960
3wig+500.0213890.0018920.006362
4wig+100-0.019076-0.028244-0.037358
5wig+1000-0.037245-0.044030-0.064728
11nqc+1000-0.064620-0.084993-0.122728
13smv+10-0.071282-0.054205-0.081581
7nqc+10-0.075675-0.053357-0.079935
17smv+1000-0.123936-0.127392-0.180470
6nqc+5-0.160651-0.125500-0.188997
12smv+5-0.165955-0.125957-0.188493
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" + ], + "text/plain": [ + " QPP Method Pearson Correlation Kendall Spearman\n", + "23 clarity+1000+100 0.279941 0.202601 0.298859\n", + "22 clarity+100+100 0.235805 0.162224 0.240220\n", + "10 nqc+100 0.222925 0.144678 0.215852\n", + "21 clarity+50+100 0.213269 0.146178 0.214532\n", + "20 clarity+20+100 0.200061 0.128501 0.192217\n", + "9 nqc+50 0.199354 0.125500 0.188106\n", + "15 smv+50 0.182064 0.117151 0.175939\n", + "19 clarity+10+100 0.181858 0.119369 0.178206\n", + "16 smv+100 0.176015 0.112455 0.164884\n", + "0 wig+5 0.154578 0.105149 0.153585\n", + "18 clarity+5+100 0.151951 0.094778 0.142401\n", + "1 wig+10 0.139933 0.093277 0.137660\n", + "2 wig+20 0.092360 0.053096 0.080287\n", + "14 smv+20 0.055596 0.034180 0.049156\n", + "8 nqc+20 0.053061 0.034963 0.051960\n", + "3 wig+50 0.021389 0.001892 0.006362\n", + "4 wig+100 -0.019076 -0.028244 -0.037358\n", + "5 wig+1000 -0.037245 -0.044030 -0.064728\n", + "11 nqc+1000 -0.064620 -0.084993 -0.122728\n", + "13 smv+10 -0.071282 -0.054205 -0.081581\n", + "7 nqc+10 -0.075675 -0.053357 -0.079935\n", + "17 smv+1000 -0.123936 -0.127392 -0.180470\n", + "6 nqc+5 -0.160651 -0.125500 -0.188997\n", + "12 smv+5 -0.165955 -0.125957 -0.188493" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpp_correlation_to_ground_truth(pt.io.read_results(bm25_run), load_predictions('idea-in-progress/bm25-predictions-monot5-large/queries.jsonl'), dataset, ['ndcg_cut_10', ])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:107: UserWarning: 1 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n" + ] + }, + { + "data": { + "text/html": [ + "
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QPP MethodPearson CorrelationKendallSpearman
23clarity+1000+1000.2582570.1945130.287696
9nqc+500.2169540.1396550.210442
22clarity+100+1000.2081850.1438950.215025
15smv+500.2066010.1392640.208769
10nqc+1000.1977250.1244570.180996
21clarity+50+1000.1778830.1183900.174230
0wig+50.1676980.1129760.164358
16smv+1000.1558760.0873420.129523
1wig+100.1507670.0956910.140809
19clarity+10+1000.1503070.1031270.152786
20clarity+20+1000.1499990.0952990.140990
18clarity+5+1000.1327600.0782750.116934
2wig+200.1007930.0604670.092209
14smv+200.0919210.0620980.090931
8nqc+200.0839380.0552490.081036
3wig+500.0255010.0060010.010647
4wig+100-0.014808-0.022569-0.027843
5wig+1000-0.026323-0.036006-0.052434
13smv+10-0.051213-0.036920-0.059563
7nqc+10-0.059433-0.045530-0.072177
11nqc+1000-0.086624-0.103453-0.146552
17smv+1000-0.153355-0.149896-0.210882
12smv+5-0.177895-0.133132-0.199061
6nqc+5-0.179554-0.133197-0.200643
\n", + "
" + ], + "text/plain": [ + " QPP Method Pearson Correlation Kendall Spearman\n", + "23 clarity+1000+100 0.258257 0.194513 0.287696\n", + "9 nqc+50 0.216954 0.139655 0.210442\n", + "22 clarity+100+100 0.208185 0.143895 0.215025\n", + "15 smv+50 0.206601 0.139264 0.208769\n", + "10 nqc+100 0.197725 0.124457 0.180996\n", + "21 clarity+50+100 0.177883 0.118390 0.174230\n", + "0 wig+5 0.167698 0.112976 0.164358\n", + "16 smv+100 0.155876 0.087342 0.129523\n", + "1 wig+10 0.150767 0.095691 0.140809\n", + "19 clarity+10+100 0.150307 0.103127 0.152786\n", + "20 clarity+20+100 0.149999 0.095299 0.140990\n", + "18 clarity+5+100 0.132760 0.078275 0.116934\n", + "2 wig+20 0.100793 0.060467 0.092209\n", + "14 smv+20 0.091921 0.062098 0.090931\n", + "8 nqc+20 0.083938 0.055249 0.081036\n", + "3 wig+50 0.025501 0.006001 0.010647\n", + "4 wig+100 -0.014808 -0.022569 -0.027843\n", + "5 wig+1000 -0.026323 -0.036006 -0.052434\n", + "13 smv+10 -0.051213 -0.036920 -0.059563\n", + "7 nqc+10 -0.059433 -0.045530 -0.072177\n", + "11 nqc+1000 -0.086624 -0.103453 -0.146552\n", + "17 smv+1000 -0.153355 -0.149896 -0.210882\n", + "12 smv+5 -0.177895 -0.133132 -0.199061\n", + "6 nqc+5 -0.179554 -0.133197 -0.200643" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpp_correlation_to_ground_truth(pt.io.read_results(bm25_run), load_predictions('idea-in-progress/bm25-predictions-monot5-base/queries.jsonl'), dataset, ['ndcg_cut_10', ])" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:107: UserWarning: 1 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n" + ] + }, + { + "data": { + "text/html": [ + "
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QPP MethodPearson CorrelationKendallSpearman
10nqc+1000.4624570.3544270.507202
9nqc+500.4603220.3561880.509381
8nqc+200.3681550.2989160.433944
23clarity+1000+1000.2817200.2354480.340680
0wig+50.2815090.2113120.306594
1wig+100.2767040.2006150.291497
11nqc+10000.2720780.1944830.282622
22clarity+100+1000.2677400.2252720.327238
21clarity+50+1000.2623680.2237060.325353
20clarity+20+1000.2503230.2126170.309128
2wig+200.2417360.1756970.256821
19clarity+10+1000.2351750.2005500.290669
7nqc+100.2191480.2189440.322299
18clarity+5+1000.2080370.1663040.243586
3wig+500.1778980.1261220.187612
4wig+1000.1245570.0822880.121712
6nqc+50.0955000.1149030.171820
5wig+10000.0551460.0268420.041584
12smv+5-0.107954-0.118099-0.177440
17smv+1000-0.172951-0.124571-0.183342
13smv+10-0.208910-0.223250-0.328311
14smv+20-0.379634-0.319203-0.460681
16smv+100-0.448379-0.342164-0.493580
15smv+50-0.470203-0.359319-0.514954
\n", + "
" + ], + "text/plain": [ + " QPP Method Pearson Correlation Kendall Spearman\n", + "10 nqc+100 0.462457 0.354427 0.507202\n", + "9 nqc+50 0.460322 0.356188 0.509381\n", + "8 nqc+20 0.368155 0.298916 0.433944\n", + "23 clarity+1000+100 0.281720 0.235448 0.340680\n", + "0 wig+5 0.281509 0.211312 0.306594\n", + "1 wig+10 0.276704 0.200615 0.291497\n", + "11 nqc+1000 0.272078 0.194483 0.282622\n", + "22 clarity+100+100 0.267740 0.225272 0.327238\n", + "21 clarity+50+100 0.262368 0.223706 0.325353\n", + "20 clarity+20+100 0.250323 0.212617 0.309128\n", + "2 wig+20 0.241736 0.175697 0.256821\n", + "19 clarity+10+100 0.235175 0.200550 0.290669\n", + "7 nqc+10 0.219148 0.218944 0.322299\n", + "18 clarity+5+100 0.208037 0.166304 0.243586\n", + "3 wig+50 0.177898 0.126122 0.187612\n", + "4 wig+100 0.124557 0.082288 0.121712\n", + "6 nqc+5 0.095500 0.114903 0.171820\n", + "5 wig+1000 0.055146 0.026842 0.041584\n", + "12 smv+5 -0.107954 -0.118099 -0.177440\n", + "17 smv+1000 -0.172951 -0.124571 -0.183342\n", + "13 smv+10 -0.208910 -0.223250 -0.328311\n", + "14 smv+20 -0.379634 -0.319203 -0.460681\n", + "16 smv+100 -0.448379 -0.342164 -0.493580\n", + "15 smv+50 -0.470203 -0.359319 -0.514954" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpp_correlation_to_ground_truth(pt.io.read_results(bm25_run), load_predictions('idea-in-progress/bm25-predictions/queries.jsonl'), dataset, ['ndcg_cut_10', ])" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:107: UserWarning: 1 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n" + ] + }, + { + "data": { + "text/html": [ + "
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QPP MethodPearson CorrelationKendallSpearman
23clarity+1000+1000.2973310.2184520.319161
22clarity+100+1000.2930910.2127770.311435
21clarity+50+1000.2900940.2104280.307036
20clarity+20+1000.2879100.2064490.302137
19clarity+10+1000.2872680.2021440.295356
18clarity+5+1000.2605960.1771620.259691
14smv+200.1788120.1154550.170733
8nqc+200.1643680.1110850.162520
9nqc+500.1642340.1148030.173650
15smv+500.1626700.1159120.172033
10nqc+1000.1606710.1076930.161651
16smv+1000.1476120.0824490.123279
13smv+100.0955590.0751440.108472
7nqc+100.0917920.0716210.103446
6nqc+50.0462090.0393980.052908
0wig+50.0342520.0337230.054435
12smv+50.0326840.0425290.056616
1wig+100.0131640.0131110.020761
2wig+20-0.021886-0.018721-0.024729
5wig+1000-0.022835-0.026418-0.039847
3wig+50-0.055797-0.031179-0.047256
4wig+100-0.071619-0.041551-0.061967
11nqc+1000-0.081707-0.078666-0.107321
17smv+1000-0.137324-0.125109-0.175854
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" + ], + "text/plain": [ + " QPP Method Pearson Correlation Kendall Spearman\n", + "23 clarity+1000+100 0.297331 0.218452 0.319161\n", + "22 clarity+100+100 0.293091 0.212777 0.311435\n", + "21 clarity+50+100 0.290094 0.210428 0.307036\n", + "20 clarity+20+100 0.287910 0.206449 0.302137\n", + "19 clarity+10+100 0.287268 0.202144 0.295356\n", + "18 clarity+5+100 0.260596 0.177162 0.259691\n", + "14 smv+20 0.178812 0.115455 0.170733\n", + "8 nqc+20 0.164368 0.111085 0.162520\n", + "9 nqc+50 0.164234 0.114803 0.173650\n", + "15 smv+50 0.162670 0.115912 0.172033\n", + "10 nqc+100 0.160671 0.107693 0.161651\n", + "16 smv+100 0.147612 0.082449 0.123279\n", + "13 smv+10 0.095559 0.075144 0.108472\n", + "7 nqc+10 0.091792 0.071621 0.103446\n", + "6 nqc+5 0.046209 0.039398 0.052908\n", + "0 wig+5 0.034252 0.033723 0.054435\n", + "12 smv+5 0.032684 0.042529 0.056616\n", + "1 wig+10 0.013164 0.013111 0.020761\n", + "2 wig+20 -0.021886 -0.018721 -0.024729\n", + "5 wig+1000 -0.022835 -0.026418 -0.039847\n", + "3 wig+50 -0.055797 -0.031179 -0.047256\n", + "4 wig+100 -0.071619 -0.041551 -0.061967\n", + "11 nqc+1000 -0.081707 -0.078666 -0.107321\n", + "17 smv+1000 -0.137324 -0.125109 -0.175854" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpp_correlation_to_ground_truth(pt.io.read_results(bm25_run), load_predictions('idea-in-progress/monot5-3b-predictions/queries.jsonl'), dataset, ['ndcg_cut_10', ])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:107: UserWarning: 1 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n" + ] + }, + { + "data": { + "text/html": [ + "
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QPP MethodPearson CorrelationKendallSpearman
14smv+200.2717720.1793310.265175
8nqc+200.2556560.1682750.250367
9nqc+500.2357140.1650880.249465
15smv+500.2206940.1473990.223921
23clarity+1000+1000.2059870.1446030.213371
22clarity+100+1000.2001440.1410910.207027
21clarity+50+1000.1961980.1386190.203131
13smv+100.1904930.1346520.201750
20clarity+20+1000.1886980.1286690.191014
7nqc+100.1776620.1282790.192733
19clarity+10+1000.1771140.1168330.173923
10nqc+1000.1743840.1133860.173824
16smv+1000.1392470.0868530.127837
18clarity+5+1000.1378840.0812600.122198
6nqc+50.0897830.0717650.111235
12smv+50.0737650.0706590.111223
0wig+50.0152650.0026990.008956
1wig+10-0.018818-0.022274-0.031599
5wig+1000-0.038880-0.042630-0.066252
2wig+20-0.065862-0.056677-0.084378
3wig+50-0.106025-0.071960-0.110063
4wig+100-0.117922-0.083016-0.125982
11nqc+1000-0.150946-0.095567-0.141881
17smv+1000-0.187925-0.126783-0.185906
\n", + "
" + ], + "text/plain": [ + " QPP Method Pearson Correlation Kendall Spearman\n", + "14 smv+20 0.271772 0.179331 0.265175\n", + "8 nqc+20 0.255656 0.168275 0.250367\n", + "9 nqc+50 0.235714 0.165088 0.249465\n", + "15 smv+50 0.220694 0.147399 0.223921\n", + "23 clarity+1000+100 0.205987 0.144603 0.213371\n", + "22 clarity+100+100 0.200144 0.141091 0.207027\n", + "21 clarity+50+100 0.196198 0.138619 0.203131\n", + "13 smv+10 0.190493 0.134652 0.201750\n", + "20 clarity+20+100 0.188698 0.128669 0.191014\n", + "7 nqc+10 0.177662 0.128279 0.192733\n", + "19 clarity+10+100 0.177114 0.116833 0.173923\n", + "10 nqc+100 0.174384 0.113386 0.173824\n", + "16 smv+100 0.139247 0.086853 0.127837\n", + "18 clarity+5+100 0.137884 0.081260 0.122198\n", + "6 nqc+5 0.089783 0.071765 0.111235\n", + "12 smv+5 0.073765 0.070659 0.111223\n", + "0 wig+5 0.015265 0.002699 0.008956\n", + "1 wig+10 -0.018818 -0.022274 -0.031599\n", + "5 wig+1000 -0.038880 -0.042630 -0.066252\n", + "2 wig+20 -0.065862 -0.056677 -0.084378\n", + "3 wig+50 -0.106025 -0.071960 -0.110063\n", + "4 wig+100 -0.117922 -0.083016 -0.125982\n", + "11 nqc+1000 -0.150946 -0.095567 -0.141881\n", + "17 smv+1000 -0.187925 -0.126783 -0.185906" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpp_correlation_to_ground_truth(pt.io.read_results(monot5_3b_run), load_predictions('idea-in-progress/monot5-3b-predictions/queries.jsonl'), dataset, ['ndcg_cut_10', ])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:107: UserWarning: 1 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n" + ] + }, + { + "data": { + "text/html": [ + "
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QPP MethodPearson CorrelationKendallSpearman
9nqc+500.4752830.3632730.509555
10nqc+1000.4634730.3308300.465296
23clarity+1000+1000.4008210.3048490.441586
22clarity+100+1000.3876090.2885300.422133
21clarity+50+1000.3842820.2838950.415216
20clarity+20+1000.3802930.2814140.408809
19clarity+10+1000.3671620.2641160.388863
18clarity+5+1000.3620360.2622230.384062
8nqc+200.3521860.3180350.452020
0wig+50.3026150.2139170.311940
1wig+100.3009220.2043210.298204
2wig+200.2880210.1942030.282529
3wig+500.2579960.1686140.246089
7nqc+100.2383290.2541280.368739
4wig+1000.2374200.1509230.219848
11nqc+10000.2315330.1602580.235285
6nqc+50.2113020.1965530.294236
5wig+10000.2002260.1222010.180734
17smv+1000-0.140665-0.110239-0.161856
13smv+10-0.214073-0.249493-0.365242
12smv+5-0.227290-0.198446-0.296831
14smv+20-0.341180-0.319341-0.453131
16smv+100-0.456666-0.317187-0.449887
15smv+50-0.482864-0.361641-0.507898
\n", + "
" + ], + "text/plain": [ + " QPP Method Pearson Correlation Kendall Spearman\n", + "9 nqc+50 0.475283 0.363273 0.509555\n", + "10 nqc+100 0.463473 0.330830 0.465296\n", + "23 clarity+1000+100 0.400821 0.304849 0.441586\n", + "22 clarity+100+100 0.387609 0.288530 0.422133\n", + "21 clarity+50+100 0.384282 0.283895 0.415216\n", + "20 clarity+20+100 0.380293 0.281414 0.408809\n", + "19 clarity+10+100 0.367162 0.264116 0.388863\n", + "18 clarity+5+100 0.362036 0.262223 0.384062\n", + "8 nqc+20 0.352186 0.318035 0.452020\n", + "0 wig+5 0.302615 0.213917 0.311940\n", + "1 wig+10 0.300922 0.204321 0.298204\n", + "2 wig+20 0.288021 0.194203 0.282529\n", + "3 wig+50 0.257996 0.168614 0.246089\n", + "7 nqc+10 0.238329 0.254128 0.368739\n", + "4 wig+100 0.237420 0.150923 0.219848\n", + "11 nqc+1000 0.231533 0.160258 0.235285\n", + "6 nqc+5 0.211302 0.196553 0.294236\n", + "5 wig+1000 0.200226 0.122201 0.180734\n", + "17 smv+1000 -0.140665 -0.110239 -0.161856\n", + "13 smv+10 -0.214073 -0.249493 -0.365242\n", + "12 smv+5 -0.227290 -0.198446 -0.296831\n", + "14 smv+20 -0.341180 -0.319341 -0.453131\n", + "16 smv+100 -0.456666 -0.317187 -0.449887\n", + "15 smv+50 -0.482864 -0.361641 -0.507898" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpp_correlation_to_ground_truth(pt.io.read_results(dirichlet_run), load_predictions('idea-in-progress/dirichlet-predictions/queries.jsonl'), dataset, ['ndcg_cut_10', ])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:107: UserWarning: 1 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n" + ] + }, + { + "data": { + "text/html": [ + "
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QPP MethodPearson CorrelationKendallSpearman
9nqc+500.3629940.2811350.417103
8nqc+200.3348340.2736270.410473
10nqc+1000.3246110.2546300.377288
7nqc+100.2696300.2270160.344634
23clarity+1000+1000.2636520.2008380.293189
22clarity+100+1000.2592670.1977050.288425
21clarity+50+1000.2543360.1945710.283784
20clarity+20+1000.2423140.1864760.271842
19clarity+10+1000.2243320.1712660.250670
18clarity+5+1000.1892750.1443050.213833
6nqc+50.1799450.1535750.229033
0wig+50.1452590.1158420.165247
1wig+100.1340250.1018060.146347
2wig+200.1175160.0850940.120440
3wig+500.0951250.0634860.089292
11nqc+10000.0848730.0452070.066467
4wig+1000.0787620.0480800.067425
5wig+10000.0672360.0245130.030896
17smv+1000-0.066679-0.028365-0.042101
12smv+5-0.181872-0.155011-0.231010
13smv+10-0.295992-0.237592-0.362323
16smv+100-0.304027-0.239289-0.352545
14smv+20-0.342231-0.276043-0.412148
15smv+50-0.351869-0.267491-0.399422
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" + ], + "text/plain": [ + " QPP Method Pearson Correlation Kendall Spearman\n", + "9 nqc+50 0.362994 0.281135 0.417103\n", + "8 nqc+20 0.334834 0.273627 0.410473\n", + "10 nqc+100 0.324611 0.254630 0.377288\n", + "7 nqc+10 0.269630 0.227016 0.344634\n", + "23 clarity+1000+100 0.263652 0.200838 0.293189\n", + "22 clarity+100+100 0.259267 0.197705 0.288425\n", + "21 clarity+50+100 0.254336 0.194571 0.283784\n", + "20 clarity+20+100 0.242314 0.186476 0.271842\n", + "19 clarity+10+100 0.224332 0.171266 0.250670\n", + "18 clarity+5+100 0.189275 0.144305 0.213833\n", + "6 nqc+5 0.179945 0.153575 0.229033\n", + "0 wig+5 0.145259 0.115842 0.165247\n", + "1 wig+10 0.134025 0.101806 0.146347\n", + "2 wig+20 0.117516 0.085094 0.120440\n", + "3 wig+50 0.095125 0.063486 0.089292\n", + "11 nqc+1000 0.084873 0.045207 0.066467\n", + "4 wig+100 0.078762 0.048080 0.067425\n", + "5 wig+1000 0.067236 0.024513 0.030896\n", + "17 smv+1000 -0.066679 -0.028365 -0.042101\n", + "12 smv+5 -0.181872 -0.155011 -0.231010\n", + "13 smv+10 -0.295992 -0.237592 -0.362323\n", + "16 smv+100 -0.304027 -0.239289 -0.352545\n", + "14 smv+20 -0.342231 -0.276043 -0.412148\n", + "15 smv+50 -0.351869 -0.267491 -0.399422" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpp_correlation_to_ground_truth(pt.io.read_results(tasb_run), load_predictions('idea-in-progress/tasb-predictions/queries.jsonl'), dataset, ['ndcg_cut_10', ])" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:107: UserWarning: 1 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n" + ] + }, + { + "data": { + "text/html": [ + "
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QPP MethodPearson CorrelationKendallSpearman
9nqc+500.2824630.1974460.292077
8nqc+200.2659580.1800810.264895
10nqc+1000.2515060.1765700.260652
7nqc+100.1780780.1363130.201397
23clarity+1000+1000.1388660.1016490.149602
22clarity+100+1000.1281100.0927400.136104
21clarity+50+1000.1224930.0896830.131691
20clarity+20+1000.1114730.0818140.117454
0wig+50.1057580.0606120.092119
19clarity+10+1000.1035430.0766760.108268
1wig+100.0869700.0509220.077490
11nqc+10000.0802650.0321270.051867
6nqc+50.0796740.0558650.076556
18clarity+5+1000.0784700.0513120.071608
2wig+200.0494190.0296560.045138
3wig+500.001787-0.007934-0.010028
4wig+100-0.026655-0.032062-0.050795
5wig+1000-0.027841-0.040907-0.059752
17smv+1000-0.0525000.000973-0.006085
12smv+5-0.088152-0.064319-0.090197
13smv+10-0.174780-0.138459-0.205432
16smv+100-0.226644-0.164928-0.244240
14smv+20-0.278132-0.194519-0.286368
15smv+50-0.283806-0.202974-0.298829
\n", + "
" + ], + "text/plain": [ + " QPP Method Pearson Correlation Kendall Spearman\n", + "9 nqc+50 0.282463 0.197446 0.292077\n", + "8 nqc+20 0.265958 0.180081 0.264895\n", + "10 nqc+100 0.251506 0.176570 0.260652\n", + "7 nqc+10 0.178078 0.136313 0.201397\n", + "23 clarity+1000+100 0.138866 0.101649 0.149602\n", + "22 clarity+100+100 0.128110 0.092740 0.136104\n", + "21 clarity+50+100 0.122493 0.089683 0.131691\n", + "20 clarity+20+100 0.111473 0.081814 0.117454\n", + "0 wig+5 0.105758 0.060612 0.092119\n", + "19 clarity+10+100 0.103543 0.076676 0.108268\n", + "1 wig+10 0.086970 0.050922 0.077490\n", + "11 nqc+1000 0.080265 0.032127 0.051867\n", + "6 nqc+5 0.079674 0.055865 0.076556\n", + "18 clarity+5+100 0.078470 0.051312 0.071608\n", + "2 wig+20 0.049419 0.029656 0.045138\n", + "3 wig+50 0.001787 -0.007934 -0.010028\n", + "4 wig+100 -0.026655 -0.032062 -0.050795\n", + "5 wig+1000 -0.027841 -0.040907 -0.059752\n", + "17 smv+1000 -0.052500 0.000973 -0.006085\n", + "12 smv+5 -0.088152 -0.064319 -0.090197\n", + "13 smv+10 -0.174780 -0.138459 -0.205432\n", + "16 smv+100 -0.226644 -0.164928 -0.244240\n", + "14 smv+20 -0.278132 -0.194519 -0.286368\n", + "15 smv+50 -0.283806 -0.202974 -0.298829" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpp_correlation_to_ground_truth(pt.io.read_results(monot5_3b_run), load_predictions('idea-in-progress/bm25-predictions/queries.jsonl'), dataset, ['ndcg_cut_10', ])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.10/dist-packages/pyterrier/pipelines.py:107: UserWarning: 1 topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.\n", + " warn(f'{backfill_count} topic(s) not found in qrels. Scores for these topics are given as NaN and should not contribute to averages.')\n" + ] + }, + { + "data": { + "text/html": [ + "
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QPP MethodPearson CorrelationKendallSpearman
14smv+200.2717720.1793310.265175
8nqc+200.2556560.1682750.250367
9nqc+500.2357140.1650880.249465
15smv+500.2206940.1473990.223921
23clarity+1000+1000.2059870.1446030.213371
22clarity+100+1000.2001440.1410910.207027
21clarity+50+1000.1961980.1386190.203131
13smv+100.1904930.1346520.201750
20clarity+20+1000.1886980.1286690.191014
7nqc+100.1776620.1282790.192733
19clarity+10+1000.1771140.1168330.173923
10nqc+1000.1743840.1133860.173824
16smv+1000.1392470.0868530.127837
18clarity+5+1000.1378840.0812600.122198
6nqc+50.0897830.0717650.111235
12smv+50.0737650.0706590.111223
0wig+50.0152650.0026990.008956
1wig+10-0.018818-0.022274-0.031599
5wig+1000-0.038880-0.042630-0.066252
2wig+20-0.065862-0.056677-0.084378
3wig+50-0.106025-0.071960-0.110063
4wig+100-0.117922-0.083016-0.125982
11nqc+1000-0.150946-0.095567-0.141881
17smv+1000-0.187925-0.126783-0.185906
\n", + "
" + ], + "text/plain": [ + " QPP Method Pearson Correlation Kendall Spearman\n", + "14 smv+20 0.271772 0.179331 0.265175\n", + "8 nqc+20 0.255656 0.168275 0.250367\n", + "9 nqc+50 0.235714 0.165088 0.249465\n", + "15 smv+50 0.220694 0.147399 0.223921\n", + "23 clarity+1000+100 0.205987 0.144603 0.213371\n", + "22 clarity+100+100 0.200144 0.141091 0.207027\n", + "21 clarity+50+100 0.196198 0.138619 0.203131\n", + "13 smv+10 0.190493 0.134652 0.201750\n", + "20 clarity+20+100 0.188698 0.128669 0.191014\n", + "7 nqc+10 0.177662 0.128279 0.192733\n", + "19 clarity+10+100 0.177114 0.116833 0.173923\n", + "10 nqc+100 0.174384 0.113386 0.173824\n", + "16 smv+100 0.139247 0.086853 0.127837\n", + "18 clarity+5+100 0.137884 0.081260 0.122198\n", + "6 nqc+5 0.089783 0.071765 0.111235\n", + "12 smv+5 0.073765 0.070659 0.111223\n", + "0 wig+5 0.015265 0.002699 0.008956\n", + "1 wig+10 -0.018818 -0.022274 -0.031599\n", + "5 wig+1000 -0.038880 -0.042630 -0.066252\n", + "2 wig+20 -0.065862 -0.056677 -0.084378\n", + "3 wig+50 -0.106025 -0.071960 -0.110063\n", + "4 wig+100 -0.117922 -0.083016 -0.125982\n", + "11 nqc+1000 -0.150946 -0.095567 -0.141881\n", + "17 smv+1000 -0.187925 -0.126783 -0.185906" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "qpp_correlation_to_ground_truth(pt.io.read_results(monot5_3b_run), load_predictions('idea-in-progress/monot5-3b-predictions/queries.jsonl'), dataset, ['ndcg_cut_10', ])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 88b7ef7fb0f5d9765e0d565378fc5c5d6b38994e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Maik=20Fr=C3=B6be?= Date: Wed, 21 Feb 2024 21:06:10 +0100 Subject: [PATCH 40/40] Add unit test for non-integer query ids --- code/qpptk/qpptk/parse_queries.py | 12 ++---------- ...ring_query_ids_approvaltests.approved.jsonl | 2 ++ .../resources/small-example-03/queries.jsonl | 2 ++ code/qpptk/tests/tirex_integration_test.py | 18 ++++++++++++++++++ 4 files changed, 24 insertions(+), 10 deletions(-) create mode 100644 code/qpptk/tests/TirexIntegrationTest.test_on_cranfield_dataset_with_non_string_query_ids_approvaltests.approved.jsonl create mode 100644 code/qpptk/tests/resources/small-example-03/queries.jsonl diff --git a/code/qpptk/qpptk/parse_queries.py b/code/qpptk/qpptk/parse_queries.py index 7e23fca..425e0f6 100644 --- a/code/qpptk/qpptk/parse_queries.py +++ b/code/qpptk/qpptk/parse_queries.py @@ -38,12 +38,6 @@ def parse_ciff_queries_file(queries_file): return header, queries_dict, query_records -def qid_index_sort_key(index, ): - # return list(map(lambda s: np.array([100, 10, 1]).dot(np.array([int(x) for x in s.split('-')])), index.to_numpy())) - return list( - map(lambda s: np.array([int(x) * 10 ** (3 - i) for i, x in enumerate(s.split('-'))]).sum(), index.to_numpy())) - - class QueryParserCiff: def __init__(self, queries_file, **kwargs): header, queries_dict, query_records = parse_ciff_queries_file(queries_file) @@ -132,7 +126,7 @@ def _read_queries(self, queries_file=None): # FIXME: add an adaptation for tsv _queries_file = queries_file if queries_file else self.queries_file with open(_queries_file, 'r') as fp: queries = [line.strip().split(None, maxsplit=1) for line in fp] - return pd.DataFrame(queries, columns=['qid', 'terms']).set_index('qid').sort_index(key=qid_index_sort_key) + return pd.DataFrame(queries, columns=['qid', 'terms']).set_index('qid') def _weight_queries(self): return self.raw_queries_df.terms.replace('', None).dropna().str.split().apply(transform_list_to_counts_dict) @@ -257,13 +251,11 @@ def tokenize(text): text = t.getTokens(text) return ' '.join([stemmer(i) for i in text]) - - ret = pd.read_json(_queries_file, lines=True, dtype=str).rename(columns={'query': 'terms'})[ ['qid', 'terms']] ret['terms'] = ret['terms'].apply(tokenize) - return ret.set_index('qid').sort_index(key=qid_index_sort_key) + return ret.set_index('qid') def _weight_queries(self): return self.raw_queries_df.terms.replace('', None).dropna().str.split().apply(transform_list_to_counts_dict) diff --git a/code/qpptk/tests/TirexIntegrationTest.test_on_cranfield_dataset_with_non_string_query_ids_approvaltests.approved.jsonl b/code/qpptk/tests/TirexIntegrationTest.test_on_cranfield_dataset_with_non_string_query_ids_approvaltests.approved.jsonl new file mode 100644 index 0000000..33e1c20 --- /dev/null +++ b/code/qpptk/tests/TirexIntegrationTest.test_on_cranfield_dataset_with_non_string_query_ids_approvaltests.approved.jsonl @@ -0,0 +1,2 @@ +{"qid":"query-with-id-269","max-idf":4.8463322428,"avg-idf":3.3416967629,"scq":123.4509985196,"max-scq":23.4910299236,"avg-scq":17.6358569314,"var":11.8987240323,"max-var":3.3068232006,"avg-var":0.991560336,"wig+5":1.3714396318,"nqc+5":0.0265491245,"smv+5":0.0209004866,"clarity+5+100":5.5866993972,"wig+10":1.1794424105,"nqc+10":0.0212626037,"smv+10":0.0127391772,"clarity+10+100":5.4738985821,"wig+20":1.0207651009,"nqc+20":0.0171662938,"smv+20":0.0090472475,"clarity+20+100":5.2777686256,"wig+50":0.7517569832,"nqc+50":0.0162926244,"smv+50":0.0110673153,"clarity+50+100":5.0506665795,"wig+100":0.539390818,"nqc+100":0.0159666547,"smv+100":0.0118648231,"clarity+100+100":4.8970434295,"wig+1000":0.0555610977,"nqc+1000":0.016023371,"smv+1000":0.0116241125,"clarity+1000+100":4.5410109582} +{"qid":"query-with-id-272","max-idf":6.551080335,"avg-idf":3.0070400126,"scq":297.4870773499,"max-scq":20.0924487977,"avg-scq":14.8743538675,"var":22.9136738645,"max-var":2.8228699303,"avg-var":0.6739315843,"wig+5":1.3938423581,"nqc+5":0.0065861708,"smv+5":0.0062831781,"clarity+5+100":3.7188355216,"wig+10":1.1354886963,"nqc+10":0.0090138852,"smv+10":0.007552497,"clarity+10+100":3.6856214573,"wig+20":0.9102298692,"nqc+20":0.0092540694,"smv+20":0.0073326023,"clarity+20+100":3.6331152699,"wig+50":0.6404746488,"nqc+50":0.0088313205,"smv+50":0.0063992902,"clarity+50+100":3.5939695713,"wig+100":0.4543804584,"nqc+100":0.0083291073,"smv+100":0.0058829587,"clarity+100+100":3.5699592708,"wig+1000":-0.085992652,"nqc+1000":0.0069568209,"smv+1000":0.0048477523,"clarity+1000+100":3.5152600584} diff --git a/code/qpptk/tests/resources/small-example-03/queries.jsonl b/code/qpptk/tests/resources/small-example-03/queries.jsonl new file mode 100644 index 0000000..eb48fb7 --- /dev/null +++ b/code/qpptk/tests/resources/small-example-03/queries.jsonl @@ -0,0 +1,2 @@ +{"qid": "query-with-id-269", "query": "has a criterion been established for determining the axial compressor\nchoking line ."} +{"qid": "query-with-id-272", "query": "has a theory of quasi-conical flows been developed, in supersonic\nlinearised theory, for which the upwash distribution on the lifting\nsurface, apart from being a homogeneous function in the\nco-ordinate, is permitted to have a quite general functional form ."} diff --git a/code/qpptk/tests/tirex_integration_test.py b/code/qpptk/tests/tirex_integration_test.py index b50a2ec..cd86897 100644 --- a/code/qpptk/tests/tirex_integration_test.py +++ b/code/qpptk/tests/tirex_integration_test.py @@ -23,6 +23,24 @@ def test_on_cranfield_dataset_with_approvaltests(self): # I only spot-checked that the output looks reasonable, no in-depth tests verify_file(out_dir + '/queries.jsonl') + def test_on_cranfield_dataset_with_non_string_query_ids_approvaltests(self): + tira = Client() + index_dir = tira.get_run_output('ir-benchmarks/tira-ir-starter/Index (tira-ir-starter-pyterrier)', 'cranfield-20230107-training') + '/index' + queries = 'tests/resources/small-example-03/queries.jsonl' + + with tempfile.TemporaryDirectory() as out_dir, tempfile.TemporaryDirectory() as stats_dir: + args = parse_args([ + '-ti', index_dir, + '--jsonl_queries', queries, + '--output', out_dir, + '--stats_index_path', stats_dir, + '--predict', '--retrieve', '--cleanOutput' + ]) + main(args) + + # I only spot-checked that the output looks reasonable, no in-depth tests + verify_file(out_dir + '/queries.jsonl') + def test_on_cranfield_dataset_with_approvaltests_and_bm25_run(self): tira = Client() index_dir = tira.get_run_output('ir-benchmarks/tira-ir-starter/Index (tira-ir-starter-pyterrier)', 'cranfield-20230107-training') + '/index'