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potpourri

Table of Contents

Project Requirements

  • apply different algorithms to a dataset as a batch script
  • store evalutations (results, run times) in a database

Folder Structure

  • potpourri -- different model implementations as python module. Each module contains three objects:
    • model -- a sklearn Pipeline to fit and predict
    • hyper -- dictionary with hyperparameters for sklearn's RandomizedSearchCV,
    • meta -- a python dict with further information
  • verto -- Feature Engineering. Each module contain two objects
    • trans -- a sklearn pipeline to transform data
    • meta -- a python dict with further information
  • seasalt -- contains different utility, glue, etc. functions and classes
  • nbs -- notebooks to try, check, profile, etc. each model
  • datasets -- demo datasets

Installation

The potpourri git repo is available as PyPi package

pip install potpourri

Usage

Check the nbs folder for notebooks.

Commands

  • Check syntax: flake8 --ignore=F401,E251
  • Remove .pyc files: find . -type f -name "*.pyc" | xargs rm
  • Remove __pycache__ folders: find . -type d -name "__pycache__" | xargs rm -rf
  • Remove Jupyter checkpoints: find . -type d -name ".ipynb_checkpoints" | xargs rm -rf
  • Upload to PyPi with twine: python setup.py sdist && twine upload -r pypi dist/*

Othe helpful commands

  • Find package folders: python -c 'from setuptools import find_packages; print(find_packages())'

Support

Please open an issue for support.

Contributing

Please contribute using Github Flow. Create a branch, add commits, and open a pull request.

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model zoo of different preconfigured algorithms

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