runrex

Runrex

Library to aid in organizing, running, and debugging regular expressions against large bodies of text.

Table of Contents

• About the Project
• Getting Started
  • Prerequisites
  • Installation
• Usage
• Roadmap
• Contributing
• License
• Contact
• Acknowledgements

About the Project

The goal of this library is to simplify the deployment of regular expression on large bodies of text, in a variety of input formats.

Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

• Python 3.8+
• runrex package: https://github.com/kpwhri/runrex

Installation

1. Clone the repo

   git clone https://github.com/kpwhri/runrex.git
   

2. Install requirements (requirements-dev is for test packages)

   pip install -r requirements.txt -r requirements-dev.txt
   

3. If you wish to read text from SAS or SQL, you will need to install additional requirements. These additional requirements files may be of use:
   • ODBC-connection: requirements-db.txt
   • Postgres: requirements-psql.txt
   • SAS: requirements-sas.txt
4. Run tests.

   set/export PYTHONPATH=src
   pytest tests
   

Usage

Example Implementations

• Social Isolation
• Acute Pancreatitis
• Anaphylaxis
• PCOS

Build Customized Algorithm

• Create 4 files:
  • patterns.py: defines regular expressions of interest
    • See examples/example_patterns.py for some examples
  • test_patterns.py: tests for those regular expressions
    • Why? Make sure the patterns do what you think they do
  • algorithm.py: defines algorithm (how to use regular expressions); returns a Result
    • See examples/example_algorithm.py for guidance
  • config.(py|json|yaml): various configurations defined in schema.py
    • See example in examples/example_config.py for basic config

Input Data

Accepts a variety of input formats, but will need to at least specify a document_id and document_text. The names are configurable.

Sentence Splitting

By default, the input document text is expected to have each sentence on a separate line. If a sentence splitting scheme is desired, it will need to be supplied to the application.

Schema/Examples

For more details, see the example config or consult the schema

Output Format

• Recommended output format is jsonl
  • The data can be extracted using python:

import json
with open('output.jsonl') as fh:
    for line in fh:
         data = json.loads(line)  # data is dict

• Output variables are configurable and can include:

  • id: unique id for line
  • name: document name
  • algorithm: name of algorithm with finding
  • value
  • category: name of category (usually the pattern; multiple categories contribute to an algorithm)
  • date
  • extras
  • matches: pattern matches
  • text: captured text
  • start: start index/offset of match
  • end: end index/offset of match

• Scripts to accomplish useful tasks with the output are included in the scripts directory.

Versions

Uses SEMVER.

See https://github.com/kpwhri/runrex/releases.

Roadmap

See the open issues for a list of proposed features (and known issues).

Contributing

Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License.

See LICENSE or https://kpwhri.mit-license.org for more information.

Contact

Please use the issue tracker.

Acknowledgements