PySR
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MilesCranmer
High-Performance Symbolic Regression in Python and Julia
Rather than `@pysr-0.14.2` as the Julia environment, this would make it `@pysr-0.14.2-py-3.11-jl-1.9`. Thus the user would need to re-install for each combination of Python and Julia they wish to use...
### Feature Request It might be useful to explore adding expression transformation rules which do not simplify the expression, but rather transform it to a more complex expression which is...
Hello Miles, thank you for you excelent work. I am interested in searching SR models in many fields of my research and in the past I used Eureqa (https://link.springer.com/article/10.1007/s10710-010-9124-z). Recently,...
### Feature Request Should use Measurements.jl on the Julia side, and allow [`uncertainties`](https://pythonhosted.org/uncertainties/) array as input. Loss function maybe could be Gaussian-Gaussian likelihood? Or KL divergence?
This is long-overdue but I finally finished a methods paper describing the algorithm in PySR and SymbolicRegression.jl. You can find it here: https://github.com/MilesCranmer/pysr_paper and the arXiv here: https://arxiv.org/abs/2305.01582. I consider...
PySR throws an "OSError: exception: access violation reading" error. It seems to occur often when fitting a model many times (tried with the exact same settings and data). Occurs both...
I experimented with PySR on Feynman equation 76 (e.g. q*v/(2*pi*r)) to see if it could 'learn' the constant 'Pi'? With the full data file but only 1,000 rows PySR generated:...
Currently, run_on_problem() uses the deprectated pysr() interface and pysr.best() does not work. Upgrade run_on_problem() to use the PySRRegressor Interface, model.fit() and get_best() function.
Right now it seems impossible to stop early within Jupyter notebooks. If one enters a search that takes a long time, the only way to quit is to restart the...
Do you have a feature to show a plot between iterations with RMSE? We have checked the dir(model), and we couldn't find it. If you don't have it, could you...
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High-Performance Symbolic Regression in Python and Julia