model zoo of different preconfigured algorithms
Project description
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 predicthyper
-- dictionary with hyperparameters for sklearn'sRandomizedSearchCV
,meta
-- a pythondict
with further information
verto
-- Feature Engineering. Each module contain two objectstrans
-- a sklearn pipeline to transform datameta
-- a pythondict
with further information
seasalt
-- contains different utility, glue, etc. functions and classesnbs
-- notebooks to try, check, profile, etc. each modeldatasets
-- 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.
Project details
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Source Distribution
potpourri-0.13.0.tar.gz
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