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Use your personal talk voting for PyConDE 2019 to predict your interest in a talk.

Project description

User Voting Rocks

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Use your personal talk voting for PyConDE 2019 to predict your interest in a talk.

Installation

In order to install the released version, use pip install:

pip install user_voting_rocks

In order to work with the current master, clone this respository, create + activate a virtual environment and then use pip install like this:

pip install -e .

Usage

Vote for talks you are interested in using the PyConDE 2019 community voting link you have received. Then reload the page (using your secret link) and save as PyconVoting.html using your browser’s Save As dialog.

To receive recommendations, please try

user_voting_rocks recommend -i ./Pycon\ Voting.html

This command will output the list of talks (that you have not yet voted on) by decreasing order of your predicted preference.

Full command line interface:

Usage: user_voting_rocks [OPTIONS] COMMAND [ARGS]...

Commandline interface for user_voting_rocks.

Options:
--help  Show this message and exit.

Commands:
evaluate   Evaluate the talk voting classifier.
parse      Parse talk voting html file.
predict    Predict your interest in a single or multiple talks.
recommend  Parse html, train model and predict unvoted talks.
train      Train a model user your talk voting.

Features

  • Parse PyConDE 2019 community voting HTML page to JSON
  • Train a Naive Bayes classifier on word frequencies of abstracts
  • Custom stop word list
  • Predict interest in a talk using the trained Naive Bayes classifier
  • CLI for parsing, training and predicting with persistence for parsed content and model

Credits

Main author and project maintainer is luphord.

This package was prepared with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.3.2 (2019-07-02)

  • Use print instead of click.echo to circumvent encoding issues on Windows

0.3.1 (2019-07-02)

  • Fix encoding issue

0.3.0 (2019-07-02)

  • Refactor module structure

0.2.0 (2019-07-01)

  • recommend subcommand to perform parsing, training and prediction in a single step
  • evaluate subcommand to perform cross validation
  • Basic usage documentation

0.1.0 (2019-06-28)

  • First release on PyPI.

Project details


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