#250 skorch your scikit-learn together with PyTorch
Python Bytes - A podcast by Michael Kennedy and Brian Okken - Mondays
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Watch the live stream: Watch on YouTube About the show Sponsored by us: Check out the courses over at Talk Python And Brian’s book too! Special guest: Prayson Daniel Brain #1: Exciting New Ways To Be Told That Your Python Code is Bad Two new pylint errors consider-ternary-expression if condition(): x = 4 else: x = 5 x = 4 if condition() else 5 while-used 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('✔ 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 multiprocessing.Pool with the benefits of using copy-on-write shared objects of multiprocessing.Process, 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 Extras Michael vim + jupyter, via Marco Gorelli PyBay talk Prayson python-decouple Joke: Adoption