autoregistry
bambi
| autoregistry | bambi | |
|---|---|---|
| 4 | 5 | |
| 32 | 1,013 | |
| - | 1.1% | |
| 7.0 | 8.0 | |
| 10 days ago | 9 days ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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autoregistry
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Interfaces in Python
Frequently, when I'm using ABC, I need to perform a string-to-class lookup. For this, I created the library AutoRegistry, which adds a dictionary interface to classes (not objects created from classes!) that is automatically populated with it's children.
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Which not so well known Python packages do you like to use on a regular basis and why?
I use my library AutoRegistry pretty regularly. Its very useful anytime you need to define an interface, which happens to be a lot of projects.
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[P] AutoRegistry: A Python library for mapping names to functionality to simplify project configurations.
Github Page: https://github.com/BrianPugh/autoregistry
- AutoRegistry: automatic registry design-pattern library for mapping names to functionality.
bambi
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Bayesian Structural Equation Modeling using blavaan
It is much less challenging with Bambi[1] and brms[2].
[1] https://bambinos.github.io/bambi/ -
Ask HN: What Are You Learning?
I’m trying to learn statistics. I’m up to implementing regressions in python using sci-kit learn.
I was playing around with Bayesian modelling last night with https://bambinos.github.io/bambi/ But I’m not really sure how to interpret the outputs.
Always open to reading about learning resources/books/videos/courses from others.
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how can I build a regression model which is penalised for moving away from an assumed set of coefficients?
I would suggest using Python's bambi; it is based on PyMC and it is very straightforward to use. We simply define our priors argument as a dictionary (quite literally: my_priors = {"feature_1": bmb.Prior("Normal", mu=4, sigma=4), "feature_n": bmb.Prior("Normal", mu=0.4, sigma=0.4)}) when creating our Bambi Model object and we are ready to go. They have a lot of worked exampling in their website.
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Which not so well known Python packages do you like to use on a regular basis and why?
For those interested in Bayesian modeling in Python we also have Bambi https://github.com/bambinos/bambi
- Release Bambi 0.6.0 · bambinos/bambi
What are some alternatives?
confs - List tools for which the given project has configs
deffcode - A cross-platform High-performance FFmpeg based Real-time Video Frames Decoder in Pure Python 🎞️⚡
turbobus - TurboBus is an opinionated implementation of Command Responsibility Segregation pattern in python.
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
mistletoe - A fast, extensible and spec-compliant Markdown parser in pure Python.
vimtk - A vim toolkit focused on gvim, IPython, and the terminal.
pyroute2 - Python Netlink and PF_ROUTE library — network configuration and monitoring
static-frame - Immutable and statically-typeable DataFrames with runtime type and data validation
auto-editor - Auto-Editor: Effort free video editing!
openapi-generator - OpenAPI Generator allows generation of API client libraries (SDK generation), server stubs, documentation and configuration automatically given an OpenAPI Spec (v2, v3)
aiosql - Simple SQL in Python
pydantic-to-typescript - CLI Tool for converting pydantic models into typescript definitions