{
    "info": {
        "author": "Vit Novotny",
        "author_email": "",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.4",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Text Processing :: Markup :: XML"
        ],
        "description": "NTCIR Math Density Estimator \u2013 Estimates relevance of documents based on data from NTCIR Math tasks\n===================================================================================================\n[![CircleCI](https://circleci.com/gh/MIR-MU/ntcir-math-density/tree/master.svg?style=shield)][ci]\n\n [ci]: https://circleci.com/gh/MIR-MU/ntcir-math-density/tree/master (CircleCI)\n\nNTCIR Math Density Estimator is a Python 3 command-line utility that uses\ndatasets, and judgements in the [NTCIR-11 Math-2][aizawaetal14-ntcir11], and\n[NTCIR-12 MathIR][zanibbi16-ntcir12] XHTML5 format to compute density, and\nprobability estimates. Most importantly, the package estimates the probability\n`P(relevant | position)`, where `position` is a position of a paragraph in a\ndocument.\n\n[aizawaetal14-ntcir11]: https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.686.444&rep=rep1&type=pdf (NTCIR-11 Math-2 Task Overview)\n[zanibbi16-ntcir12]: https://research.nii.ac.jp/ntcir/workshop/OnlineProceedings12/pdf/ntcir/OVERVIEW/01-NTCIR12-OV-MathIR-ZanibbiR.pdf (NTCIR-12 MathIR Task Overview)\n\nUsage\n=====\nInstalling\n----------\nThe package can be installed by executing the following command:\n\n    $ pip install ntcir-math-density\n\nDisplaying the usage\n--------------------\nUsage information for the package can be displayed by executing the following\ncommand:\n\n    $ ntcir-math-density --help\n    usage: ntcir-math-density [-h] [--datasets DATASETS [DATASETS ...]]\n                              [--judgements JUDGEMENTS [JUDGEMENTS ...]]\n                              [--plots PLOTS [PLOTS ...]] [--positions POSITIONS]\n                              [--estimates ESTIMATES] [--num-workers NUM_WORKERS]\n\n    Use datasets, and judgements in NTCIR-11 Math-2, and NTCIR-12 MathIR XHTML5\n    format to compute density, and probability estimates.\n\n    optional arguments:\n    -h, --help            show this help message and exit\n    --datasets DATASETS [DATASETS ...]\n                            Paths to the directories containing the datasets. Each\n                            path must be prefixed with a unique single-letter\n                            label followed by an equals sign (e.g. \"A=/some/path\").\n    --judgements JUDGEMENTS [JUDGEMENTS ...]\n                            Paths to the files containing relevance judgements.\n                            Each path must be prefixed with a single-letter label\n                            corresponding to the judged dataset followed by a\n                            semicolon (e.g. \"A:/some/path/judgement.dat\").\n    --plots PLOTS [PLOTS ...]\n                            The path to the files, where the probability\n                            estimates will plotted. When no datasets are\n                            specified, the estimates file will be loaded.\n    --positions POSITIONS\n                            The path to the file, where the estimated positions of\n                            all paragraph identifiers from all datasets will be\n                            stored. Defaults to positions.pkl.gz.\n    --estimates ESTIMATES\n                            The path to the file, where the density, and\n                            probability estimates will be stored. When no\n                            datasets are specified, this file will be loaded to\n                            provide the estimates for plotting. Defaults to\n                            estimates.pkl.gz.\n    --num-workers NUM_WORKERS\n                            The number of processes that will be used for\n                            processing the datasets, and for computing the\n                            density, and probability estimates. Defaults to 1.\n\nExtracting estimates\n--------------------\nThe following command extracts density, and probability estimates and plots the\nestimates using 64 worker processes:\n\n    $ ntcir-math-density --num-workers 64 \\\n    >     --datasets A=ntcir-10-converted B=ntcir-11-12 \\\n    >     --judgements A:NTCIR_10_Math-qrels_fs-converted.dat A:NTCIR_10_Math-qrels_ft-converted.dat \\\n    >                  B:NTCIR11_Math-qrels.dat B:NTCIR12_Math-qrels_agg.dat \\\n    >                  B:NTCIR12_Math_simto-qrels_agg.dat \\\n    >     --estimates estimates.pkl.gz --positions positions.pkl.gz \\\n    >     --plots plot.pdf plot.svg\n    Retrieving judged paragraph identifiers, and scores from NTCIR_10_Math-qrels_fs-converted.dat\n    100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2129/2129 [00:00<00:00, 334959.05it/s]\n    Retrieving judged paragraph identifiers, and scores from NTCIR_10_Math-qrels_ft-converted.dat\n    100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1425/1425 [00:00<00:00, 353201.94it/s]\n    Retrieving judged paragraph identifiers, and scores from NTCIR11_Math-qrels.dat\n    100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2500/2500 [00:00<00:00, 343345.12it/s]\n    Retrieving judged paragraph identifiers, and scores from NTCIR12_Math-qrels_agg.dat\n    100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4251/4251 [00:00<00:00, 342252.50it/s]\n    Retrieving judged paragraph identifiers, and scores from NTCIR12_Math_simto-qrels_agg.dat\n    100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 654/654 [00:00<00:00, 314428.57it/s]\n    Retrieving all paragraph identifiers, and positions from ntcir-10-converted\n    get_all_identifiers(ntcir-10-converted): 5405167it [04:30, 19946.57it/s]\n    get_all_positions(ntcir-10-converted): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5405167/5405167 [08:44<00:00, 10306.72it/s]\n    Retrieving all paragraph identifiers, and positions from ntcir-11-12\n    get_all_identifiers(ntcir-11-12): 8301578it [08:08, 16985.19it/s]\n    get_all_positions(ntcir-11-12): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 8301578/8301578 [44:30<00:00, 3108.88it/s]\n    1043 / 3146 / 5405167 relevant / judged / total identifiers in dataset ntcir-10-converted\n    1742 / 7059 / 8301578 relevant / judged / total identifiers in dataset ntcir-11-12\n    Pickling positions.pkl.gz\n    Fitting density, and probability estimators\n    Fitting prior p(position) density estimator\n    Fitting conditional p(position | relevant) density estimator\n    Computing density, and probability estimates\n    p(position): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 64/64 [01:19<00:00,  1.24s/it]\n    p(position | relevant): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 64/64 [01:19<00:00,  1.24s/it]\n    Pickling estimates.pkl.gz\n    Plotting plot.svg\n    Plotting plot.pdf\n\nThe following command extracts density, and probability estimates using 64 worker processes:\n\n    $ ntcir-math-density --num-workers 64 \\\n    >     --datasets A=ntcir-10-converted B=ntcir-11-12 \\\n    >     --judgements A:NTCIR_10_Math-qrels_fs-converted.dat A:NTCIR_10_Math-qrels_ft-converted.dat \\\n    >                  B:NTCIR11_Math-qrels.dat B:NTCIR12_Math-qrels_agg.dat \\\n    >                  B:NTCIR12_Math_simto-qrels_agg.dat \\\n    >     --estimates estimates.pkl.gz --positions positions.pkl.gz\n    Retrieving judged paragraph identifiers, and scores from NTCIR_10_Math-qrels_fs-converted.dat\n    100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2129/2129 [00:00<00:00, 334959.05it/s]\n    Retrieving judged paragraph identifiers, and scores from NTCIR_10_Math-qrels_ft-converted.dat\n    100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 1425/1425 [00:00<00:00, 353201.94it/s]\n    Retrieving judged paragraph identifiers, and scores from NTCIR11_Math-qrels.dat\n    100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2500/2500 [00:00<00:00, 343345.12it/s]\n    Retrieving judged paragraph identifiers, and scores from NTCIR12_Math-qrels_agg.dat\n    100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 4251/4251 [00:00<00:00, 342252.50it/s]\n    Retrieving judged paragraph identifiers, and scores from NTCIR12_Math_simto-qrels_agg.dat\n    100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 654/654 [00:00<00:00, 314428.57it/s]\n    Retrieving all paragraph identifiers, and positions from ntcir-10-converted\n    get_all_identifiers(ntcir-10-converted): 5405167it [04:30, 19946.57it/s]\n    get_all_positions(ntcir-10-converted): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5405167/5405167 [08:44<00:00, 10306.72it/s]\n    Retrieving all paragraph identifiers, and positions from ntcir-11-12\n    get_all_identifiers(ntcir-11-12): 8301578it [08:08, 16985.19it/s]\n    get_all_positions(ntcir-11-12): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 8301578/8301578 [44:30<00:00, 3108.88it/s]\n    1043 / 3146 / 5405167 relevant / judged / total identifiers in dataset ntcir-10-converted\n    1742 / 7059 / 8301578 relevant / judged / total identifiers in dataset ntcir-11-12\n    Pickling positions.pkl.gz\n    Fitting density, and probability estimators\n    Fitting prior p(position) density estimator\n    Fitting conditional p(position | relevant) density estimator\n    Computing density, and probability estimates\n    p(position): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 64/64 [01:19<00:00,  1.24s/it]\n    p(position | relevant): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 64/64 [01:19<00:00,  1.24s/it]\n    Pickling estimates.pkl.gz\n\nThe following command plots the estimates using 64 worker processes:\n\n    $ ntcir-math-density --num-workers 64 \\\n    >     --estimates estimates.pkl.gz --plots plot.pdf plot.svg\n    Unpickling estimates.pkl.gz\n    Plotting plot.svg\n    Plotting plot.pdf\n\n\n",
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