recommenders
astropy
recommenders | astropy | |
---|---|---|
6 | 26 | |
18,019 | 4,218 | |
1.0% | 1.2% | |
9.5 | 9.9 | |
5 days ago | 4 days ago | |
Python | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
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recommenders
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This Week in Python
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Input to SVD, SAR, NMF
I would like to do a benchmarking on the Microsoft models SVD, SAR and NMF (available here: https://github.com/microsoft/recommenders) but with this input data I get a precision and recall close to zero. Any ideas how I can improve this? For SVD and NMF (surprise library) the model wants a rating input that is normally distributed, which it not the case for my binary data where the transactions all have a rating of 1.
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Opinion on choice of model - Recommender System
Then I tried to find some more advanced models and I found this really good list and in there I found the Microsoft one. So it's' where we are now, which a bunch of different models and not a documentation/tutorials out there.
astropy
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Julia 1.10 Released
Astropy [0] lives at the heart of most work. It has a Python interface, often backed by Fortran and C++ extension modules. If you use Astropy, you're indirectly using libraries like ERFA [6] and cfitsio [7] which are in C/Fortran.
I personally end up doing a lot of work that uses the HEALPix sky tesselation, so I use healpy [2] as well.
Openorb is perhaps a good example of a pure-Fortran package that I use quite. frequently for orbit propagation [3].
In C, there's Rebound [4] (for N-body simulations) and ASSIST [5] (which extends Rebound to use JPL's pre-calculated positions of major perturbers, and expands the force model to account for general relativity).
There are many more, these are just ones that come to mind from frequent usage in the last few months.
[0] https://www.astropy.org/
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Skyfield: Elegant Astronomy for Python
Users interested in a broader range of astronomical tools beyond coordinate transformations may be interested in https://www.astropy.org/ and its affiliated packages.
- Astropy: Common core package for Astronomy in Python
- [R] Astronomia ex machina: a history, primer and outlook on neural networks in astronomy
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License Adherence Help
I'm working on a pure Rust approximation of astropy. Up til now, I was able to recreate the intent by looking at an external API, but I'm moving on to functionality that I don't understand enough to implement without basically copying the code. Astropy uses the BSD-3 license, and it wraps the ERFA library which uses a custom license. My project currently uses the MIT license. My PR is here - my question is have I attributed everything correctly, or is there anything I need to change for everything to be above-board?
- Astro physics data analysis
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Astronomical Calculations for Hard SF in Common Lisp
For folks who might be interested in astronomical calculations but who don't want to roll their own library, astropy (https://www.astropy.org/) is widely used by professional astronomers.
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Looking to study data from JWST's spectroscopy instruments
I agree with the other commenter. Check out their github. If you’re looking to build your skills long term (and have some experience with python) it’s worth checking out astropy and their fits file handling routines.
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