v0.6.0

PySR Version 0.6.0

Large changes:

• Exports to JAX, PyTorch, NumPy. All exports have a similar interface. JAX and PyTorch allow the equation parameters to be trained (e.g., as part of some differentiable model). Read https://pysr.readthedocs.io/en/latest/docs/options/#callable-exports-numpy-pytorch-jax for details. Thanks Patrick Kidger for the PyTorch export.
• Multi-output y input is allowed, and the backend will efficiently batch over each output. A list of dataframes is returned by pysr for these cases. All best_* functions return a list as well.
• BFGS optimizer introduced + more stable parameter search due to back tracking line search.

Smaller changes since 0.5.16:

• Expanded tests, coverage calculation for PySR
• Improved (pre-processing) feature selection with random forest
• New default parameters for search:
  • annealing=False (no annealing works better with the new code. This is equivalent to alpha=infinity)
  • useFrequency=True (deals with complexity in a smarter way)
  • npopulations = 20 procs*4
  • progress=True (show a progress bar)
  • optimizer_algorithm="BFGS"
  • optimizer_iterations=10
  • optimize_probability=1
  • binary_operators default = ["+", "-", "/", "*"]
  • unary_operators default = []
• Warnings:
  • Using maxsize > 40 will trigger a warning mentioning how it will be slow and use a lot of memory. Will mention to turn off useFrequency, and perhaps also use warmupMaxsizeBy.
• Deprecated nrestarts -> optimizer_nrestarts
• Printing fixed in Jupyter