#250: skorch your scikit-learn together with PyTorch
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About the show
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Special guest: Prayson Daniel
- Two new pylint errors
if condition(): x = 4 else: x = 5
x = 4 if condition() else 5
- it unconditionally flags every use of while expressions.
- generally, while should be avoided.
Michael #2: GitHub Readme Stats
- via Роман Великий
- Dynamically generated stats for your github readmes
- This are for your repo or your stats (others too I suppose) posted somewhere outside of github
- Card for a project: https://github-readme-stats.vercel.app/api/pin/?username=mikeckennedy&repo=python-switch
- Card for a user: https://github-readme-stats.vercel.app/api?username=mikeckennedy&show_icons=true&theme=radical
- Card for your languages: https://github-readme-stats.vercel.app/api/top-langs/?username=mikeckennedy&repo=python-switch
Prayson #3: Nox
- Nox appeared as “footnotes” in Episodes 182 and 248 (Hypermodern Python …)
- It does tox what invoke did (substituting GNU Make)
Brian #4: Two tools for dealing with text
- python-easyfrontmatter - a small package to load and parse files (or just text) with YAML (or JSON, TOML or other) front matter.
>>> post = frontmatter.load('tests/yaml/hello-world.txt') >>> print(post['title']) Hello, world!
- Tried it with a helper script I’m using with Hugo, and it parses Hugo metadata in blog posts like a dream.
- ftfy - fixes text for you
- “Take in bad Unicode and output good Unicode”
>>> import ftfy >>> ftfy.fix_text('âœ” No problems') '✔ No problems'
Michael #5: MPIRE (MultiProcessing Is Really Easy)
- A Python package for easy multiprocessing, but faster than multiprocessing
- It combines the
- convenience of map like functions of
- with the benefits of using copy-on-write shared objects of
- together with easy-to-use worker state, worker insights, and progress bar functionality.
- Many features
- Requisite shoutout to unsync too.
Prayson #6: skorch
- Going deep learning with scikit-learn pipelines (Breaking limits of multi-layer perceptron (MLP))
- Using PyTorch, skorch provides an API to extend neural networks models in scikit-learn.
- Example: Penguins Classification shameless Gist