Metadata-Version: 2.1
Name: patent-parsing-tools
Version: 0.9.5
Summary: patent-parsing-tools is a library providing tools for generating training and test set from Google's USPTO data helpful with for testing machine learning algorithms
Home-page: https://github.com/pprzetacznik/patent-parsing-tools
Author: Michal Dul, Piotr Przetacznik, Krzysztof Strojny
Author-email: piotr.przetacznik@gmail.com
License: MIT
Description: patent-parsing-tools
        ====================
        USPTO patents dataset generator.
        
        [![Documentation Status](https://readthedocs.org/projects/patent-parsing-tools/badge/?version=latest)](https://patent-parsing-tools.readthedocs.io/en/latest/?badge=latest)
        [![patent-parsing-tools CI](https://github.com/pprzetacznik/patent-parsing-tools/workflows/patent-parsing-tools%20CI/badge.svg)](https://github.com/pprzetacznik/patent-parsing-tools/actions?query=workflow%3A"patent-parsing-tools+CI")
        [![PyPI version](https://badge.fury.io/py/patent-parsing-tools.svg)](https://pypi.org/project/patent-parsing-tools/)
        [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/patent-parsing-tools)](https://pypi.org/project/patent-parsing-tools/)
        
        ## Documentation
        
        [Read the docs](https://patent-parsing-tools.readthedocs.io/en/latest/)
        
        ## System requirements
        
        ```Bash
        sudo yum install python-devel libxslt-devel libxml2-devel
        ```
        
        ## Installation:
        
        ```
        pip install patent-parsing-tools
        ```
        
        ## Examples:
        
        Downloading dataset:
        ```Bash
        python -m patent_parsing_tools.downloader \
          --directory dataset \
          --year-from 2010 \
          --year-to 2010
        ```
        
        Collecting and serializing data:
        ```Bash
        python -m patent_parsing_tools.supervisor \
          --working-directory patents/working_directory \
          --train-destination patents/train_destination \
          --test-destination patents/test_destination \
          --year-from 2014 \
          --year-to 2015
        ```
        
        Generating dictionary with train set:
        ```Bash
        python -m patent_parsing_tools.bow.dictionary_maker \
          --train-directory patents/train_destination \
          --max-patents 1000000000 \
          --dictionary dictionary.txt \
          --dict-max-size 4096
        ```
        
        Generate bag of words with train set and test set:
        ```Bash
        python -m patent_parsing_tools.bow.bag_of_words \
          --serialized-patents patents/train_destination \
          --destination-directory patents/final_dataset_train \
          --dictionary dictionary.txt \
          --batch-size 1048576
        python -m patent_parsing_tools.bow.bag_of_words \
          --serialized-patents patents/test_destination \
          --destination-directory patents/final_dataset_test \
          --dictionary dictionary.txt \
          --batch-size 1048576
        ```
        
        ## Testing
        
        ```Bash
        pytest
        ```
        
        ## Contributing and develpment
        
        ```Bash
        $ mkvirtualenv ppt
        $ workon ppt
        (ppt) $ pip install -r requirements.txt
        ```
        
        ## Publish new release
        
        ```Bash
        $ git tag v1.0
        $ git push origin v1.0
        ```
        
        ## Building documentation
        
        ```Bash
        (ppt) $ sphinx-build -M html docs docs_build
        ```
        
        ## References
        
        Usage:
        * Elton, *Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora*, 2019, online: [https://arxiv.org/abs/1903.00415](https://arxiv.org/abs/1903.00415).
        * Lee, *Natural Language Processing Techniques for Advancing Materials Discovery: A Short Review*, 2023, online: [https://doi.org/10.1007/s40684-023-00523-6](https://doi.org/10.1007/s40684-023-00523-6).
        
        ## License
        
        The MIT License (MIT). Copyright (c) 2014 Michał Dul, Piotr Przetacznik, Krzysztof Strojny. Check [LICENSE](LICENSE) files for more information.
        
        
Keywords: deeplearning dbn rbm rsm backpropagation precission recall
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Utilities
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
