mljar-supervised
PySR
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mljar-supervised | PySR | |
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51 | 7 | |
2,927 | 1,850 | |
1.2% | - | |
8.5 | 9.6 | |
8 days ago | 8 days ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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mljar-supervised
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Show HN: Web App with GUI for AutoML on Tabular Data
Web App is using two open-source packages that I've created:
- MLJAR AutoML - Python package for AutoML on tabular data https://github.com/mljar/mljar-supervised
- Mercury - framework for converting Jupyter Notebooks into Web App https://github.com/mljar/mercury
You can run Web App locally. What is more, you can adjust notebook's code for your needs. For example, you can set different validation strategies or evalutaion metrics or longer training times. The notebooks in the repo are good starting point for you to develop more advanced apps.
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Fairness in machine learning
It's an Automated Machine Learning python package. It's open-source, you can see how it works on GitHub: https://github.com/mljar/mljar-supervised
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[P] Build data web apps in Jupyter Notebook with Python only
Sure, at the bottom of our website you can subscribe for newsletter.
- Show HN: AutoML Python Package for Tabular Data with Automatic Documentation
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library / framework to test multiple sklearn regression models at once
If you need a simple and fast solution, go with auto-sklearn Maybe a bit more complex, but very powerful was mljar-supervised
- Python AutoML on Tabular Data with FeatureEng, HP Tuning, Explanations, AutoDoc
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Data Science and full-stack-web development
In my case, I had experience in DS and software engineering. It gives me ability to start a company that works on Data Science tools.
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Learning Python tricks by reading other people's code. But who?
MLJAR AutoML is a Python package for Automated Machine Learning on tabular data with feature engineering, explanations, and automatic documentation.
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'start with a simple model'
I recommend trying my AutoML package. You can easily check many different algorithms. Waht is more, the baseline algorithms are checked (major class predictor for classification and mean predictor for regression). The advance of AutoML is that it is really quick. You dont need to write preprocessing code, just call fit method.
PySR
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Potential of the Julia programming language for high energy physics computing
> Yes, julia can be called from other languages rather easily
This seems false to me. StaticCompiler.jl [1] puts in their limitations that "GC-tracked allocations and global variables do not work with compile_executable or compile_shlib. This has some interesting consequences, including that all functions within the function you want to compile must either be inlined or return only native types (otherwise Julia would have to allocate a place to put the results, which will fail)." PackageCompiler.jl [2] has the same limitations if I'm not mistaken. So then you have to fall back to distributing the Julia "binary" with a full Julia runtime, which is pretty heavy. There are some packages which do this. For example, PySR [3] does this.
There is some word going around though that there is an even better static compiler in the making, but as long as that one is not publicly available I'd say that Julia cannot easily be called from other languages.
[1]: https://github.com/tshort/StaticCompiler.jl
[2]: https://github.com/JuliaLang/PackageCompiler.jl
[3]: https://github.com/MilesCranmer/PySR
- Symbolicregression.jl – High-Performance Symbolic Regression in Julia and Python
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[D] Is there any research into using neural networks to discover classical algorithms?
I first learned about it with PySR https://github.com/MilesCranmer/PySR, they have an arxiv paper with some use cases as well.
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Symbolic Regression is NP-hard
I encourage everyone to read this paper. It's well written and easy to follow along. To the uninitiated, SR is the problem of finding a mathematical (symbolic) expression that most accurately describes a dataset of input-output examples (regression). The most naive implementation of SR is basically a breath first search starting from the simplest program tree: x -> sin(x) -> cos(x) ... sin(cos(tan(x))) until timeout. However, we can prune out equivalent expressions and, in general, the problem is embarrassingly parallel which alludes to some hope that we can solve this pretty fast (check out PySR[1] for a modern implementation). I find SR fascinating because it can be used for model distillation: learn a DNN approximation and "distill" it to a symbolic program.
Note that the paper talks about the decision version of the SR problem. ie: can we discover the global optimum expression. I think this proof is important for the SR community but not particularly surprising (to me). However, I'm excited by the potential future work for this paper! A couple of discussion points:
* First, SR is technically a bottom up program synthesis problem where the DSL (math) has an equivalence operator. Can we use this proof to impose stronger guarantees on the "hyperparameters" for bottom up synthesis. Conversely, does the theoretical foundation of the inductive synthesis literature [2] help us define tighter bounds?
* Second, while SR itself is NP hard, can we say anything about the approximate algorithms (eg: distilling a deep neural network to find a solution[3])? Specifically, what proof tell us about the PAC learnability of SR?
Anyhow, pretty cool seeing such work getting more attention!
[1] https://github.com/MilesCranmer/PySR
[2] https://susmitjha.github.io/papers/togis17.pdf
[3] https://astroautomata.com/paper/symbolic-neural-nets/
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‘Machine Scientists’ Distill the Laws of Physics from Raw Data
I found it curious that one of the implementations of symbolic regression (the "machine scientist" referenced in the article) is a Python wrapper on Julia: https://github.com/MilesCranmer/PySR
I don't think I've seen a Python wrapper on Julia code before.
- Is it possible to create a Python package with Julia and publish it on PyPi?
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[D] Inferring general physical laws from observations in 300 lines of code
This is really neat! Since you're interested in this subject, you may also appreciate PySR and the corresponding paper which uses Graph Neural Networks to perform symbolic regression.
What are some alternatives?
optuna - A hyperparameter optimization framework
GeneticAlgorithmPython - Source code of PyGAD, a Python 3 library for building the genetic algorithm and training machine learning algorithms (Keras & PyTorch).
autokeras - AutoML library for deep learning
TorchGA - Train PyTorch Models using the Genetic Algorithm with PyGAD
LightGBM - A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
AutoViz - Automatically Visualize any dataset, any size with a single line of code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
diffeqpy - Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
mljar-examples - Examples how MLJAR can be used
python-bigsimr
Auto_ViML - Automatically Build Multiple ML Models with a Single Line of Code. Created by Ram Seshadri. Collaborators Welcome. Permission Granted upon Request.
ModelingToolkitStandardLibrary.jl - A standard library of components to model the world and beyond