API Reference

PySRRegressor has many options for controlling a symbolic regression search. Let's look at them below.

The Algorithm

Creating the Search Space

binary_operators
List of strings for binary operators used in the search. See the operators page for more details.
Default: ["+", "-", "*", "/"]
unary_operators
Operators which only take a single scalar as input. For example, "cos" or "exp".
Default: None
expression_spec
The type of expression to search for. By default, this is just ExpressionSpec(). You can also use TemplateExpressionSpec(...) which allows you to specify a custom template for the expressions.
Default: ExpressionSpec()
maxsize
Max complexity of an equation.
Default: 30
maxdepth
Max depth of an equation. You can use both maxsize and maxdepth. maxdepth is by default not used.
Default: None

Setting the Search Size

niterations
Number of iterations of the algorithm to run. The best equations are printed and migrate between populations at the end of each iteration.
Default: 100
populations
Number of populations running.
Default: 31
population_size
Number of individuals in each population.
Default: 27
ncycles_per_iteration
Number of total mutations to run, per 10 samples of the population, per iteration.
Default: 380

The Objective

elementwise_loss
String of Julia code specifying an elementwise loss function. Can either be a loss from LossFunctions.jl, or your own loss written as a function. Examples of custom written losses include: myloss(x, y) = abs(x-y) for non-weighted, or myloss(x, y, w) = w*abs(x-y) for weighted. The included losses include: Regression: LPDistLoss{P}(), L1DistLoss(), L2DistLoss() (mean square), LogitDistLoss(), HuberLoss(d), L1EpsilonInsLoss(ϵ), L2EpsilonInsLoss(ϵ), PeriodicLoss(c), QuantileLoss(τ). Classification: ZeroOneLoss(), PerceptronLoss(), L1HingeLoss(), SmoothedL1HingeLoss(γ), ModifiedHuberLoss(), L2MarginLoss(), ExpLoss(), SigmoidLoss(), DWDMarginLoss(q).
Default: "L2DistLoss()"
loss_function
Alternatively, you can specify the full objective function as a snippet of Julia code, including any sort of custom evaluation (including symbolic manipulations beforehand), and any sort of loss function or regularizations. The default loss_function used in SymbolicRegression.jl is roughly equal to:
julia
```
function eval_loss(tree, dataset::Dataset{T,L}, options)::L where {T,L}
    prediction, flag = eval_tree_array(tree, dataset.X, options)
    if !flag
        return L(Inf)
    end
    return sum((prediction .- dataset.y) .^ 2) / dataset.n
end
```
where the example elementwise loss is mean-squared error. You may pass a function with the same arguments as this (note that the name of the function doesn't matter). Here, both prediction and dataset.y are 1D arrays of length dataset.n.
Default: None
loss_function_expression
Similar to loss_function, but takes as input the full expression object as the first argument, rather than the innermost AbstractExpressionNode. This is useful for specifying custom loss functions on TemplateExpressionSpec.
Default: None
loss_scale
Determines how loss values are scaled when computing scores. "log" (default) uses logarithmic scaling of loss ratios; this mode requires non-negative loss values and is ideal for traditional loss functions that are always non-negative. "linear" uses direct differences between losses; this mode handles any loss values (including negative) and is useful for custom loss functions, especially those based on likelihoods.
model_selection
Model selection criterion when selecting a final expression from the list of best expression at each complexity. Can be 'accuracy', 'best', or 'score'. 'accuracy' selects the candidate model with the lowest loss (highest accuracy). 'score' selects the candidate model with the highest score. Score is defined as the negated derivative of the log-loss with respect to complexity - if an expression has a much better loss at a slightly higher complexity, it is preferred. 'best' selects the candidate model with the highest score among expressions with a loss better than at least 1.5x the most accurate model.
Default: 'best'
dimensional_constraint_penalty
Additive penalty for if dimensional analysis of an expression fails. By default, this is 1000.0.
dimensionless_constants_only
Whether to only search for dimensionless constants, if using units.
Default: False

Working with Complexities

parsimony
Multiplicative factor for how much to punish complexity.
Default: 0.0
constraints
Dictionary of int (unary) or 2-tuples (binary), this enforces maxsize constraints on the individual arguments of operators. E.g., 'pow': (-1, 1) says that power laws can have any complexity left argument, but only 1 complexity in the right argument. Use this to force more interpretable solutions.
Default: None
nested_constraints
Specifies how many times a combination of operators can be nested. For example, {"sin": {"cos": 0}}, "cos": {"cos": 2}} specifies that cos may never appear within a sin, but sin can be nested with itself an unlimited number of times. The second term specifies that cos can be nested up to 2 times within a cos, so that cos(cos(cos(x))) is allowed (as well as any combination of + or - within it), but cos(cos(cos(cos(x)))) is not allowed. When an operator is not specified, it is assumed that it can be nested an unlimited number of times. This requires that there is no operator which is used both in the unary operators and the binary operators (e.g., - could be both subtract, and negation). For binary operators, you only need to provide a single number: both arguments are treated the same way, and the max of each argument is constrained.
Default: None
complexity_of_operators
If you would like to use a complexity other than 1 for an operator, specify the complexity here. For example, {"sin": 2, "+": 1} would give a complexity of 2 for each use of the sin operator, and a complexity of 1 for each use of the + operator (which is the default). You may specify real numbers for a complexity, and the total complexity of a tree will be rounded to the nearest integer after computing.
Default: None
complexity_of_constants
Complexity of constants.
Default: 1
complexity_of_variables
Global complexity of variables. To set different complexities for different variables, pass a list of complexities to the fit method with keyword complexity_of_variables. You cannot use both.
Default: 1
complexity_mapping
Alternatively, you can pass a function (a string of Julia code) that takes the expression as input and returns the complexity. Make sure that this operates on AbstractExpression (and unpacks to AbstractExpressionNode), and returns an integer.
Default: None
warmup_maxsize_by
Whether to slowly increase max size from a small number up to the maxsize (if greater than 0). If greater than 0, says the fraction of training time at which the current maxsize will reach the user-passed maxsize.
Default: 0.0
use_frequency
Whether to measure the frequency of complexities, and use that instead of parsimony to explore equation space. Will naturally find equations of all complexities.
Default: True
use_frequency_in_tournament
Whether to use the frequency mentioned above in the tournament, rather than just the simulated annealing.
Default: True
adaptive_parsimony_scaling
If the adaptive parsimony strategy (use_frequency and use_frequency_in_tournament), this is how much to (exponentially) weight the contribution. If you find that the search is only optimizing the most complex expressions while the simpler expressions remain stagnant, you should increase this value.
Default: 1040.0
should_simplify
Whether to use algebraic simplification in the search. Note that only a few simple rules are implemented.
Default: True

Mutations

weight_add_node
Relative likelihood for mutation to add a node.
Default: 2.47
weight_insert_node
Relative likelihood for mutation to insert a node.
Default: 0.0112
weight_delete_node
Relative likelihood for mutation to delete a node.
Default: 0.870
weight_do_nothing
Relative likelihood for mutation to leave the individual.
Default: 0.273
weight_mutate_constant
Relative likelihood for mutation to change the constant slightly in a random direction.
Default: 0.0346
weight_mutate_operator
Relative likelihood for mutation to swap an operator.
Default: 0.293
weight_swap_operands
Relative likehood for swapping operands in binary operators.
Default: 0.198
weight_rotate_tree
How often to perform a tree rotation at a random node.
Default: 4.26
weight_randomize
Relative likelihood for mutation to completely delete and then randomly generate the equation
Default: 0.000502
weight_simplify
Relative likelihood for mutation to simplify constant parts by evaluation
Default: 0.00209
weight_optimize
Constant optimization can also be performed as a mutation, in addition to the normal strategy controlled by optimize_probability which happens every iteration. Using it as a mutation is useful if you want to use a large ncycles_periteration, and may not optimize very often.
Default: 0.0
crossover_probability
Absolute probability of crossover-type genetic operation, instead of a mutation.
Default: 0.0259
annealing
Whether to use annealing.
Default: False
alpha
Initial temperature for simulated annealing (requires annealing to be True).
Default: 3.17
perturbation_factor
Constants are perturbed by a max factor of (perturbation_factor*T + 1). Either multiplied by this or divided by this.
Default: 0.129
probability_negate_constant
Probability of negating a constant in the equation when mutating it.
Default: 0.00743
skip_mutation_failures
Whether to skip mutation and crossover failures, rather than simply re-sampling the current member.
Default: True

Tournament Selection

tournament_selection_n
Number of expressions to consider in each tournament.
Default: 15
tournament_selection_p
Probability of selecting the best expression in each tournament. The probability will decay as p*(1-p)^n for other expressions, sorted by loss.
Default: 0.982

Constant Optimization

optimizer_algorithm
Optimization scheme to use for optimizing constants. Can currently be NelderMead or BFGS.
Default: "BFGS"
optimizer_nrestarts
Number of time to restart the constants optimization process with different initial conditions.
Default: 2
optimizer_f_calls_limit
How many function calls to allow during optimization.
Default: 10_000
optimize_probability
Probability of optimizing the constants during a single iteration of the evolutionary algorithm.
Default: 0.14
optimizer_iterations
Number of iterations that the constants optimizer can take.
Default: 8
should_optimize_constants
Whether to numerically optimize constants (Nelder-Mead/Newton) at the end of each iteration.
Default: True

Migration between Populations

fraction_replaced
How much of population to replace with migrating equations from other populations.
Default: 0.00036
fraction_replaced_hof
How much of population to replace with migrating equations from hall of fame.
Default: 0.0614
migration
Whether to migrate.
Default: True
hof_migration
Whether to have the hall of fame migrate.
Default: True
topn
How many top individuals migrate from each population.
Default: 12

Data Preprocessing

denoise
Whether to use a Gaussian Process to denoise the data before inputting to PySR. Can help PySR fit noisy data.
Default: False
select_k_features
Whether to run feature selection in Python using random forests, before passing to the symbolic regression code. None means no feature selection; an int means select that many features.
Default: None

Stopping Criteria

max_evals
Limits the total number of evaluations of expressions to this number.
Default: None
timeout_in_seconds
Make the search return early once this many seconds have passed.
Default: None
early_stop_condition
Stop the search early if this loss is reached. You may also pass a string containing a Julia function which takes a loss and complexity as input, for example: "f(loss, complexity) = (loss < 0.1) && (complexity < 10)".
Default: None

Performance and Parallelization

parallelism
Parallelism to use for the search. Can be "serial", "multithreading", or "multiprocessing".
Default: "multithreading"
procs
Number of processes to use for parallelism. If None, defaults to cpu_count().
Default: None
cluster_manager
For distributed computing, this sets the job queue system. Set to one of "slurm", "pbs", "lsf", "sge", "qrsh", "scyld", or "htc". If set to one of these, PySR will run in distributed mode, and use procs to figure out how many processes to launch.
Default: None
heap_size_hint_in_bytes
For multiprocessing, this sets the --heap-size-hint parameter for new Julia processes. This can be configured when using multi-node distributed compute, to give a hint to each process about how much memory they can use before aggressive garbage collection.
batching
Whether to compare population members on small batches during evolution. Still uses full dataset for comparing against hall of fame.
Default: False
batch_size
The amount of data to use if doing batching.
Default: 50
precision
What precision to use for the data. By default this is 32 (float32), but you can select 64 or 16 as well, giving you 64 or 16 bits of floating point precision, respectively. If you pass complex data, the corresponding complex precision will be used (i.e., 64 for complex128, 32 for complex64).
Default: 32
fast_cycle
Batch over population subsamples. This is a slightly different algorithm than regularized evolution, but does cycles 15% faster. May be algorithmically less efficient.
Default: False
turbo
(Experimental) Whether to use LoopVectorization.jl to speed up the search evaluation. Certain operators may not be supported. Does not support 16-bit precision floats.
Default: False
bumper
(Experimental) Whether to use Bumper.jl to speed up the search evaluation. Does not support 16-bit precision floats.
Default: False
autodiff_backend
Which backend to use for automatic differentiation during constant optimization. Currently only "Zygote" is supported. The default, None, uses forward-mode or finite difference.
Default: None

Determinism

random_state
Pass an int for reproducible results across multiple function calls. See :term:Glossary <random_state>.
Default: None
deterministic
Make a PySR search give the same result every run. To use this, you must turn off parallelism (with parallelism="serial"), and set random_state to a fixed seed.
Default: False
warm_start
Tells fit to continue from where the last call to fit finished. If false, each call to fit will be fresh, overwriting previous results.
Default: False

Monitoring

verbosity
What verbosity level to use. 0 means minimal print statements.
Default: 1
update_verbosity
What verbosity level to use for package updates. Will take value of verbosity if not given.
Default: None
print_precision
How many significant digits to print for floats.
Default: 5
progress
Whether to use a progress bar instead of printing to stdout.
Default: True
logger_spec
Logger specification for the Julia backend. See, for example, TensorBoardLoggerSpec.
Default: None
input_stream
The stream to read user input from. By default, this is "stdin". If you encounter issues with reading from stdin, like a hang, you can simply pass "devnull" to this argument. You can also reference an arbitrary Julia object in the Main namespace.
Default: "stdin"

Environment

temp_equation_file
Whether to put the hall of fame file in the temp directory. Deletion is then controlled with the delete_tempfiles parameter.
Default: False
tempdir
directory for the temporary files.
Default: None
delete_tempfiles
Whether to delete the temporary files after finishing.
Default: True
update
Whether to automatically update Julia packages when fit is called. You should make sure that PySR is up-to-date itself first, as the packaged Julia packages may not necessarily include all updated dependencies.
Default: False

Exporting the Results

output_directory
The base directory to save output files to. Files will be saved in a subdirectory according to the run ID. Will be set to outputs/ if not provided.
Default: None
run_id
A unique identifier for the run. Will be generated using the current date and time if not provided.
Default: None
output_jax_format
Whether to create a 'jax_format' column in the output, containing jax-callable functions and the default parameters in a jax array.
Default: False
output_torch_format
Whether to create a 'torch_format' column in the output, containing a torch module with trainable parameters.
Default: False
extra_sympy_mappings
Provides mappings between custom binary_operators or unary_operators defined in julia strings, to those same operators defined in sympy. E.G if unary_operators=["inv(x)=1/x"], then for the fitted model to be export to sympy, extra_sympy_mappings would be {"inv": lambda x: 1/x}.
Default: None
extra_torch_mappings
The same as extra_jax_mappings but for model export to pytorch. Note that the dictionary keys should be callable pytorch expressions. For example: extra_torch_mappings={sympy.sin: torch.sin}.
Default: None
extra_jax_mappings
Similar to extra_sympy_mappings but for model export to jax. The dictionary maps sympy functions to jax functions. For example: extra_jax_mappings={sympy.sin: "jnp.sin"} maps the sympy.sin function to the equivalent jax expression jnp.sin.
Default: None

PySRRegressor Functions

fit

Search for equations to fit the dataset and store them in self.equations_.

Parameters

Name	Type	Description	Default
`X`	`ndarray \| pandas.DataFrame`	Training data of shape (n_samples, n_features).	required
`y`	`ndarray \| pandas.DataFrame`	Target values of shape (n_samples,) or (n_samples, n_targets). Will be cast to X's dtype if necessary.	required
`Xresampled`	`ndarray \| pandas.DataFrame`	Resampled training data, of shape (n_resampled, n_features), to generate a denoised data on. This will be used as the training data, rather than `X`.	`None`
`weights`	`ndarray \| pandas.DataFrame`	Weight array of the same shape as `y`. Each element is how to weight the mean-square-error loss for that particular element of `y`. Alternatively, if a custom `loss` was set, it will can be used in arbitrary ways.	`None`
`variable_names`	`list[str]`	A list of names for the variables, rather than "x0", "x1", etc. If `X` is a pandas dataframe, the column names will be used instead of `variable_names`. Cannot contain spaces or special characters. Avoid variable names which are also function names in `sympy`, such as "N".	`None`
`X_units`	`list[str]`	A list of units for each variable in `X`. Each unit should be a string representing a Julia expression. See DynamicQuantities.jl https://symbolicml.org/DynamicQuantities.jl/dev/units/ for more information.	`None`
`y_units`	`str \| list[str]`	Similar to `X_units`, but as a unit for the target variable, `y`. If `y` is a matrix, a list of units should be passed. If `X_units` is given but `y_units` is not, then `y_units` will be arbitrary.	`None`
`category`	`list[int]`	If `expression_spec` is a `ParametricExpressionSpec`, then this argument should be a list of integers representing the category of each sample.	`None`

Returns

Type: object
Fitted estimator.

predict

predict(self, X, index: 'int | list[int] | None' = None, *, category: 'ndarray | None' = None) -> 'ndarray'

Predict y from input X using the equation chosen by model_selection.

You may see what equation is used by printing this object. X should have the same columns as the training data.

Parameters

Name	Type	Description	Default
`X`	`ndarray \| pandas.DataFrame`	Training data of shape `(n_samples, n_features)`.	required
`index`	`int \| list[int]`	If you want to compute the output of an expression using a particular row of `self.equations_`, you may specify the index here. For multiple output equations, you must pass a list of indices in the same order.	`None`
`category`	`ndarray \| None`	If `expression_spec` is a `ParametricExpressionSpec`, then this argument should be a list of integers representing the category of each sample in `X`.	`None`

Returns

Type: ndarray of shape (n_samples, nout_)
Values predicted by substituting X into the fitted symbolic regression model.

Raises

ValueError: Raises if the best_equation cannot be evaluated.

from_file

from_file(equation_file: 'None' = None, *, run_directory: 'PathLike', binary_operators: 'list[str] | None' = None, unary_operators: 'list[str] | None' = None, n_features_in: 'int | None' = None, feature_names_in: 'ArrayLike[str] | None' = None, selection_mask: 'NDArray[np.bool_] | None' = None, nout: 'int' = 1, **pysr_kwargs) -> "'PySRRegressor'"

Create a model from a saved model checkpoint or equation file.

Parameters

Name	Type	Description	Default
`run_directory`	`str`	The directory containing outputs from a previous run. This is of the form `[output_directory]/[run_id]`.	required
`binary_operators`	`list[str]`	The same binary operators used when creating the model. Not needed if loading from a pickle file.	`None`
`unary_operators`	`list[str]`	The same unary operators used when creating the model. Not needed if loading from a pickle file.	`None`
`n_features_in`	`int`	Number of features passed to the model. Not needed if loading from a pickle file.	`None`
`feature_names_in`	`list[str]`	Names of the features passed to the model. Not needed if loading from a pickle file.	`None`
`selection_mask`	`NDArray[np.bool_]`	If using `select_k_features`, you must pass `model.selection_mask_` here. Not needed if loading from a pickle file.	`None`
`nout`	`int`	Number of outputs of the model. Not needed if loading from a pickle file.	`1`
`**pysr_kwargs`	`dict`	Any other keyword arguments to initialize the PySRRegressor object. These will overwrite those stored in the pickle file. Not needed if loading from a pickle file.	required

Returns

Type: PySRRegressor
The model with fitted equations.

sympy

sympy(self, index: 'int | list[int] | None' = None)

Return sympy representation of the equation(s) chosen by model_selection.

Parameters

Name	Type	Description	Default
`index`	`int \| list[int]`	If you wish to select a particular equation from `self.equations_`, give the index number here. This overrides the `model_selection` parameter. If there are multiple output features, then pass a list of indices with the order the same as the output feature.	`None`

Returns

Type: str, list[str] of length nout_
SymPy representation of the best equation.

latex

latex(self, index: 'int | list[int] | None' = None, precision: 'int' = 3) -> 'str | list[str]'

Return latex representation of the equation(s) chosen by model_selection.

Parameters

Name	Type	Description	Default
`index`	`int \| list[int]`	If you wish to select a particular equation from `self.equations_`, give the index number here. This overrides the `model_selection` parameter. If there are multiple output features, then pass a list of indices with the order the same as the output feature.	`None`
`precision`	`int`	The number of significant figures shown in the LaTeX representation.	`3`

Returns

Type: str or list[str] of length nout_
LaTeX expression of the best equation.

pytorch

pytorch(self, index=None)

Return pytorch representation of the equation(s) chosen by model_selection.

Each equation (multiple given if there are multiple outputs) is a PyTorch module containing the parameters as trainable attributes. You can use the module like any other PyTorch module: module(X), where X is a tensor with the same column ordering as trained with.

Parameters

Name	Type	Description	Default
`index`	`int \| list[int]`	If you wish to select a particular equation from `self.equations_`, give the index number here. This overrides the `model_selection` parameter. If there are multiple output features, then pass a list of indices with the order the same as the output feature.	`None`

Returns

Type: torch.nn.Module
PyTorch module representing the expression.

jax

jax(self, index=None)

Return jax representation of the equation(s) chosen by model_selection.

Each equation (multiple given if there are multiple outputs) is a dictionary containing {"callable": func, "parameters": params}. To call func, pass func(X, params). This function is differentiable using jax.grad.

Parameters

Name	Type	Description	Default
`index`	`int \| list[int]`	If you wish to select a particular equation from `self.equations_`, give the index number here. This overrides the `model_selection` parameter. If there are multiple output features, then pass a list of indices with the order the same as the output feature.	`None`

Returns

Type: dict[str, Any]
Dictionary of callable jax function in "callable" key, and jax array of parameters as "parameters" key.

latex_table

latex_table(self, indices: 'list[int] | None' = None, precision: 'int' = 3, columns: 'list[str]' = ['equation', 'complexity', 'loss', 'score']) -> 'str'

Create a LaTeX/booktabs table for all, or some, of the equations.

Parameters

Name	Type	Description	Default
`indices`	`list[int] \| list[list[int]]`	If you wish to select a particular subset of equations from `self.equations_`, give the row numbers here. By default, all equations will be used. If there are multiple output features, then pass a list of lists.	`None`
`precision`	`int`	The number of significant figures shown in the LaTeX representations.	`3`
`columns`	`list[str]`	Which columns to include in the table.	`['equation', 'complexity', 'loss', 'score']`

Returns

Type: str
A string that will render a table in LaTeX of the equations.

refresh

refresh(self, run_directory: 'PathLike | None' = None) -> 'None'

Update self.equations_ with any new options passed.

For example, updating extra_sympy_mappings will require a .refresh() to update the equations.

Parameters

Name	Type	Description	Default
`checkpoint_file`	`str or Path`	Path to checkpoint hall of fame file to be loaded. The default will use the set `equation_file_`.	required

Expression Specifications

ExpressionSpec

ExpressionSpec()

The default expression specification, with no special behavior.

TemplateExpressionSpec

TemplateExpressionSpec(*args, **kwargs)

Spec for templated expressions.

This class allows you to specify how multiple sub-expressions should be combined in a structured way, with constraints on which variables each sub-expression can use. Pass this to PySRRegressor with the expression_spec argument.

Parameters

Name	Type	Description	Default
`combine`	`str`	Julia function string that defines how the sub-expressions are combined. For example: "sin(f(x1, x2)) + g(x3)^2" would constrain f to use x1,x2 and g to use x3.	required
`expressions`	`list[str]`	List of symbols representing the inner expressions (e.g., ["f", "g"]). These will be used as keys in the template structure.	required
`variable_names`	`list[str]`	List of variable names that will be used in the combine function.	required
`parameters`	`dict[str, int]`	Dictionary mapping parameter names to their lengths. For example, {"p1": 2, "p2": 1} means p1 is a vector of length 2 and p2 is a vector of length 1. These parameters will be optimized during the search.	required

ParametricExpressionSpec

ParametricExpressionSpec(max_parameters: 'int')

Spec for parametric expressions that vary by category.

This is deprecated in favor of the TemplateExpressionSpec class, which now supports parameters indexed by category.

This class allows you to specify expressions with parameters that vary across different categories in your dataset. The expression structure remains the same, but parameters are optimized separately for each category.

Parameters

Name	Type	Description	Default
`max_parameters`	`int`	Maximum number of parameters that can appear in the expression. Each parameter will take on different values for each category in the data.	required

AbstractExpressionSpec

AbstractExpressionSpec()

Abstract base class describing expression types.

This basically just holds the options for the expression type, as well as explains how to parse and evaluate them.

All expression types must implement:

julia_expression_spec(): The actual expression specification, returned as a Julia object. This will get passed as expression_spec in SymbolicRegression.Options.
create_exports(), which will be used to create the exports of the equations, such as the executable format, the SymPy format, etc.

It may also optionally implement:

supports_sympy, supports_torch, supports_jax, supports_latex: Whether this expression type supports the corresponding export format.

Logger Specifications

TensorBoardLoggerSpec

TensorBoardLoggerSpec(log_dir: 'str' = 'logs/run', log_interval: 'int' = 1, overwrite: 'bool' = False) -> None

Specification for TensorBoard logger.

Parameters

Name	Type	Description	Default
`log_dir`	`str`	Directory where TensorBoard logs will be saved. If `overwrite` is `False`, new logs will be saved to `{log_dir}_1`, and so on.	`'logs/run'`
`log_interval`	`int`	Interval (in steps) at which logs are written. Default is 10.	`1`
`overwrite`	`bool`	Whether to overwrite existing logs in the directory. Default is False.	`False`

AbstractLoggerSpec

AbstractLoggerSpec()

Abstract base class for logger specifications.

API Reference ​

The Algorithm ​

Creating the Search Space ​

Setting the Search Size ​

The Objective ​

Working with Complexities ​

Mutations ​

Tournament Selection ​

Constant Optimization ​

Migration between Populations ​

Data Preprocessing ​

Stopping Criteria ​

Performance and Parallelization ​

Determinism ​

Monitoring ​

Environment ​

Exporting the Results ​

PySRRegressor Functions ​

fit ​

predict ​

from_file ​

sympy ​

latex ​

pytorch ​

jax ​

latex_table ​

refresh ​

Expression Specifications ​

ExpressionSpec ​

TemplateExpressionSpec ​

ParametricExpressionSpec ​

AbstractExpressionSpec ​

Logger Specifications ​

TensorBoardLoggerSpec ​

AbstractLoggerSpec ​

API Reference

The Algorithm

Creating the Search Space

Setting the Search Size

The Objective

Working with Complexities

Mutations

Tournament Selection

Constant Optimization

Migration between Populations

Data Preprocessing

Stopping Criteria

Performance and Parallelization

Determinism

Monitoring

Environment

Exporting the Results

PySRRegressor Functions

fit

predict

from_file

sympy

latex

pytorch

jax

latex_table

refresh

Expression Specifications

ExpressionSpec

TemplateExpressionSpec

ParametricExpressionSpec

AbstractExpressionSpec

Logger Specifications

TensorBoardLoggerSpec

AbstractLoggerSpec