Search for equations to fit the dataset and store them in `self.equations_`. Parameters ---------- X : ndarray | pan

Get best equation using `model_selection`. Parameters ---------- index : int | list[int] If you wish to

Predict y from input X using the equation chosen by `model_selection`. You may see what equation is used by printing this object. X

Calculate the actual batch size to use.

Update self.equations_ with any new options passed. For example, updating `extra_sympy_mappings` will require a `.refresh()`

Convert sympy expression to LaTeX with custom precision.

Return sympy representation of the equation(s) chosen by `model_selection`. Parameters ---------- index : int | list

Create a model from a saved model checkpoint or equation file. Parameters ---------- run_directory : str

Return latex representation of the equation(s) chosen by `model_selection`. Parameters ---------- index : int | list

Create a LaTeX/booktabs table for all, or some, of the equations. Parameters ---------- indices : list[int] | list[list[int]]

Select an expression and return its index.

Return pytorch representation of the equation(s) chosen by `model_selection`. Each equation (multiple given if there are multiple ou

Execute function with modified Julia registry preference. First tries with existing registry preference. If that fails with a Julia registry erro

Print all current equations fitted by the model. The string `>>>>` denotes which equation is selected by the `model_selectio

Return jax representation of the equation(s) chosen by `model_selection`. Each equation (multiple given if there are multiple output

Converts integer to subscript text form. For example, 123 -> "₁₂₃".

Check whether a Julia `elementwise_loss` accepts the expected inputs. The function probes the loss with two or three arguments, depending on

Merge two hyperopt trials objects :trials1: The primary trials object :trials2_slice: A slice of the trials object to be merged, obta

Find most important features. Uses a gradient boosting tree regressor as a proxy for finding the k most important features in X, returni

Returns a module for a given sympy expression with trainable parameters; This function will assume the input to the module is a matrix X, where

Save the model's current state to a checkpoint file. This should only be used internally by PySRRegressor.

Ensure parameters passed at initialization are valid. Also returns a dictionary of parameters to update from their values gi

Close the logger instance.

Denoise the dataset using a Gaussian process.

Format a parsed docstring as markdown with nice tables.

Get the signature of a function/method.

Get the equations from a hall of fame file or search output. If no arguments entered, the ones used previously from a call to PySR wi

Returns a function f and its parameters; the function takes an input matrix, and a list of arguments: f(X, parameters) where the p

Generate a booktabs-style LaTeX table for a single set of equations.

Turn a path into a string with correctly escaped backslashes.

Map old and new parallelism parameters to the new format. Parameters ---------- parallelism : str or None New parallelism paramet

Transform the training data before fitting the symbolic regressor. This method also updates/sets the `selection_mask_` attribute.

Convert Python guesses to Julia format. Parameters ---------- guesses : list[str] | list[list[str]] | list[dict[str, str]] | list[list[di

Read the hall of fame file created by `SymbolicRegression.jl`.

Run the symbolic regression fitting process on the julia backend. Parameters ---------- X : ndarray Trai

_check_feature_names_in with compat for old versions.

Set the pathname of the output directory.

Validate the parameters passed to the :term`fit` method. This method also sets the `nout_` attribute. Parameters --

Create export formats for an equations dataframe. Returns a new dataframe containing only the exported formats.

Calculate scores for each equation based on loss and complexity. Score is defined as the negated derivative of the log-loss with respect to compl

Create a logger instance.