pyoperon

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Right now, optimizing a tree with `pyoperon` requires a lot of ugly code: ```python def evaluate_with_pyoperon(pdata, tree, range_train, range_test): a, b = range_train c, d = range_test # pyoperon pyop_dataset...

foolnotion

Feature request: Callbacks

2

Since `SymbolicRegressor` is an iterative process, akin to online learning algorithms (neural nets, etc), it would be useful to have callbacks that could be called at different stages. For example,...

romanovzky

Feature request: Sample weights

6

In [this work](https://gitlab.com/miguel.romao/symbolic-regression-bsm) ([preprint](https://arxiv.org/abs/2405.18471)), we had to resample the dataset so that the target variable had a flat distribution to prevent `pyoperon` from only learning about the most common values....

romanovzky

Feature request: Validation data metrics for model selection

1

Currently, `SymbolicRegressor` returns a model that better complies with a certain criteria. This, however, is computed on the training set. Machine learning best practices dictate that model selection should be...

romanovzky

Hi, I was testing whether I could fit a `SymbolicRegressor` up to, say, `1000` generations, see the Pareto front, and then continue training for another `1000` generations. However, it seems...

romanovzky

How to save the trained model

1

I have trained a symbolic regression model, it has the following form:  How can I save the trained model, so that next time I can use the model directly...

luokuang2001