bambi
mpire
| bambi | mpire | |
|---|---|---|
| 5 | 8 | |
| 1,013 | 1,900 | |
| 0.9% | 0.9% | |
| 8.0 | 7.5 | |
| 5 days ago | 7 days ago | |
| Python | Python | |
| MIT License | MIT License |
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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
mpire
- GitHub - sybrenjansen/mpire: A Python package for easy multiprocessing, but faster than multiprocessing
- Mpire: A Python package for easier and faster multiprocessing
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Which not so well known Python packages do you like to use on a regular basis and why?
mpire for multiprocessing.
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How do you deal with parallelising parts of an ML pipeline especially on Python?
https://github.com/Slimmer-AI/mpire is a nice lib, with better performance than multiprocessing.
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Dask – a flexible library for parallel computing in Python
Shout out to an alternative to Dask: MPIRE https://github.com/Slimmer-AI/mpire
- Multi-Threading in Python
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I'd like to introduce MPIRE: MultiProcessing Is Really Easy
After several iterations of feedback and exposure to production environments, it is now the go-to multiprocessing library at Slimmer AI. Recently, we’ve made it publicly available on GitHub (https://github.com/Slimmer-AI/mpire).
What are some alternatives?
deffcode - A cross-platform High-performance FFmpeg based Real-time Video Frames Decoder in Pure Python 🎞️⚡
Dask - Parallel computing with task scheduling
brms - brms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
cudf - cuDF - GPU DataFrame Library
mistletoe - A fast, extensible and spec-compliant Markdown parser in pure Python.
distributed - A distributed task scheduler for Dask
vimtk - A vim toolkit focused on gvim, IPython, and the terminal.
pathml - Tools for computational pathology
pyroute2 - Python Netlink and PF_ROUTE library — network configuration and monitoring
cunumeric - An Aspiring Drop-In Replacement for NumPy at Scale
static-frame - Immutable and statically-typeable DataFrames with runtime type and data validation
legate.pandas - An Aspiring Drop-In Replacement for Pandas at Scale