Pysr

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.

- 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

With versions v0.4.0/v0.4.0, SymbolicRegression.jl and PySR have now been completely disentangled: PySR is 100% Python code (with some Julia meta-programming), and SymbolicRegression.jl is 100% Julia code.

PySR now works by activating a Julia env that has SymbolicRegression.jl as a dependency, and making calls to it! By default it will set up a Julia project inside the pip install location, and install requirements at the user's confirmation, though you can pass an arbitrary project directory as well (e.g., if you want to use PySR but also tweak the backend). The nice thing about this is that for Python users, all you need to do is install a Julia binary somewhere, and they should be good to go. And for Julia users, you never need to touch the Python side.

The SymbolicRegression.jl backend also sets up workers automatically & internally now, so one never needs to call `everywhere` when setting things up. The same is true even with locally-defined functions - these get passed to workers!

With PySR importing the latest Julia code, this also means it gets new simplification routines powered by SymbolicUtils.jl, which seem to help improve the equations discovered.

Populations don't block eachother, which gives a large speedup especially for large numbers of populations. This was fixed by using RemoteChannel() in Julia.

Some populations happen to take longer than others - perhaps they have very complex equations - and can therefore block others that have finished early. This lets the processor work on the next population to be finished.

Uses equation from Cranmer et al. (2020) https://arxiv.org/abs/2006.11287 to score equations, and prints this alongside MSE. This makes symbolic regression more robust to noise.