Author: Tutte Institute for Mathematics and Computing

An Easydata-generated repo for exploring the TIMC vectorizers library to construct word, document and topic embeddings.

First, create fork and clone your version of the repo. Then create and switch to the virtual environment:

cd vectorizers_playground
make create_environment
conda activate vectorizers_playground

You're now ready to explore the vectorizers playground notebooks.

Troubleshooting: If you have any issues with make create_environment, try the following:

• Make note of the path to your conda binary:

   $ which conda
   ~/miniconda3/bin/conda

• ensure your CONDA_EXE environment variable is set to this value (or edit Makefile.include directly)

    export CONDA_EXE=~/miniconda3/bin/conda

• If that doesn't work, see Setting up and Maintaining your Conda Environment Reproducibly

Now you're ready to run jupyter lab (or jupyter notebook) and explore the notebooks in the notebooks directory.

Here are the current notebooks:

• 01-vectorizers-quickstart: The I don't care how it works, show me what to do approach. Start here to learn how to use the vectorizers library for word, document and topic vectorization and embedding.
• 02-word-embedding: [WIP] Work in progess comparison of the vectorizers library word embedding approach.
• 03-document-embedding: An explanation of the vectorizers library approach to document embedding, including a walkthrough and comparison of the various steps against common document embedding algorithms such as USE and BERT.
• 04-topic-embedding: [WIP] A qualitative comparison of the vectorizers approach to topic embedding.
• 00-20-newsgroups-setup: This notebooks documents the preprocessing and cleanup to the 20 newsgroups dataset that is available via ds.load('20_newsgroups_pruned') and used in the other notebooks.

This git repository is build from the Easydata framework, which aims to make your data science workflow reproducible. The Easydata framework includes:

• tools for managing conda environments in a consistent and reproducible way,
• built-in dataset management (including tracking of metadata such as LICENSES and READMEs),
• a prescribed project directory structure,
• workflows and conventions for contributing notebooks and other code.

• Setting up and Maintaining your Conda Environment Reproducibly
• Getting and Using Datasets
• Using Notebooks for Analysis
• Sharing your Work

• Make
• conda >= 4.8 (via Anaconda or Miniconda)
• Git

• LICENSE
• Makefile
  • Top-level makefile. Type make for a list of valid commands.
• Makefile.include
  • Global includes for makefile routines. Included by Makefile.
• Makefile.env
  • Command for maintaining reproducible conda environment. Included by Makefile.
• README.md
  • this file
• catalog
  • Data catalog. This is where config information such as data sources and data transformations are saved.
  • catalog/config.ini
    • Local Data Store. This configuration file is for local data only, and is never checked into the repo.
• data
  • Data directory. Often symlinked to a filesystem with lots of space.
  • data/raw
    • Raw (immutable) hash-verified downloads.
  • data/interim
    • Extracted and interim data representations.
  • data/interim/cache
    • Dataset cache
  • data/processed
    • The final, canonical data sets ready for analysis.
• docs
  • Sphinx-format documentation files for this project.
  • docs/Makefile: Makefile for generating HTML/Latex/other formats from Sphinx-format documentation.
• notebooks
  • Jupyter notebooks. Naming convention is a number (for ordering), the creator's initials, and a short - delimited description, e.g. 1.0-jqp-initial-data-exploration.
• reference
  • Data dictionaries, documentation, manuals, scripts, papers, or other explanatory materials.
  • reference/easydata: Easydata framework and workflow documentation.
  • reference/templates: Templates and code snippets for Jupyter
  • reference/dataset: resources related to datasets; e.g. dataset creation notebooks and scripts
• reports
  • Generated analysis as HTML, PDF, LaTeX, etc.
  • reports/figures
    • Generated graphics and figures to be used in reporting.
• environment.yml
  • The user-readable YAML file for reproducing the conda/pip environment.
• environment.(platform).lock.yml
  • resolved versions, result of processing environment.yml
• setup.py
  • Turns contents of src into a pip-installable python module (pip install -e .) so it can be imported in python code.
• src
  • Source code for use in this project.
  • src/__init__.py
    • Makes src a Python module.
  • src/data
    • Scripts to fetch or generate data.
  • src/analysis
    • Scripts to turn datasets into output products.

This project was built using Easydata, a python framework aimed at making your data science workflow reproducible.