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knockouts-redesign

CI

About

This is the implementation of a data-driven approach for discovering improvement opportunities related to the execution of knock-out checks within business processes.

The tool comes with a browser-based UI (powered by Streamlit), but it can also be invoked via CLI or imported as a module in Python scripts.

Resources

Index

  1. Set-up
  2. Usage
  3. Running tests
  4. Notes

Set-up

Option A: Using a setup script

  • Setup scripts are provided for Windows & Linux, which perform the steps described in the "Manual steps" section.
  • Requirements:
    • Having cloned the repository
    • Python 3.9+ installed
  • Commands: 
    # Windows: 
setup.bat
     
# Linux: 
bash setup.sh

Option B: Manual steps

Expand

  • Clone the repo

  • Create a virtual environment with venv

  • In the directory that contains the newly created venv folder, run:

    source venv/Scripts/activate
pip install -r requirements.txt

  • Depending on your platform, install graphviz (v 3.0.0+) separately, and make sure it's in your PATH (see also: graphviz downloads page):

     # Windows: 
 choco install graphviz
 
 # Linux: 
 sudo apt install graphviz
 
 # Mac:
 brew install graphviz

Usage

Create a config file following the schema defined in config_schema.json (the directory also contains examples). Then you can choose whether to:

  • Launch the tool as a web app (powered by Streamlit):

       # Windows: 
   start.bat
   
   # Linux: 
   bash start.sh

  • Launch from CLI:

       # Windows:
   python main.py config/synthetic_example.json

   # Linux: 
   python3 main.py config/synthetic_example.json

  • Or programmatically:

    from knockout_ios.pipeline_wrapper import Pipeline

ko_redesign_adviser = Pipeline(config_file_name="synthetic_example.json").run_pipeline()

    In all these cases, if using the default settings, output will be written to .csv and txt files in the data/outputs folder.

Running tests

Shell scripts are provided, which run the tests in parallel (thanks to pytest-xdist).

 # Windows: 
 test.bat
 
 # Linux: 
 bash test.sh

Notes

About wittgenstein & grid search

Expand

  • Excessive warnings are printed due to frame.append deprecation warnings in the wittgenstein package.

  • Currently, they can just be suppressed by passing the -Wignore flag.

  • Another situation where excessive warnings are printed, is during the parameter grid search, as some combinations may yield too small training sets or sets with no positive examples.

  • This can be safely ignored as long as the grid contains at least one parameter combination that results in a valid dataset, and the rule discovery module is able to return meaningful rules.