PySRRegressor Reference¶

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

PySRRegressor Parameters¶

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

• maxsize

Max complexity of an equation.

Default: 20

• 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: 40

• populations

Number of populations running.

Default: 15

• population_size

Number of individuals in each population.

Default: 33

• ncyclesperiteration

Number of total mutations to run, per 10 samples of the population, per iteration.

Default: 550

The Objective¶

• loss

String of Julia code specifying the 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()"

• 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'

Working with Complexities¶

• parsimony

Multiplicative factor for how much to punish complexity.

Default: 0.0032

• 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

Complexity of variables.

Default: 1

• 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: 20.0

Mutations¶

• weight_add_node

Relative likelihood for mutation to add a node.

Default: 0.79

• weight_insert_node

Relative likelihood for mutation to insert a node.

Default: 5.1

• weight_delete_node

Relative likelihood for mutation to delete a node.

Default: 1.7

• weight_do_nothing

Relative likelihood for mutation to leave the individual.

Default: 0.21

• weight_mutate_constant

Relative likelihood for mutation to change the constant slightly in a random direction.

Default: 0.048

• weight_mutate_operator

Relative likelihood for mutation to swap an operator.

Default: 0.47

• weight_randomize

Relative likelihood for mutation to completely delete and then randomly generate the equation

Default: 0.00023

• weight_simplify

Relative likelihood for mutation to simplify constant parts by evaluation

Default: 0.0020

• 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 ncyclesperiteration, and may not optimize very often.

Default: 0.0

• crossover_probability

Absolute probability of crossover-type genetic operation, instead of a mutation.

Default: 0.066

• annealing

Whether to use annealing.

Default: False

• alpha

Initial temperature for simulated annealing (requires annealing to be True).

Default: 0.1

• perturbation_factor

Constants are perturbed by a max factor of (perturbation_factor*T + 1). Either multiplied by this or divided by this.

Default: 0.076

• 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: 10

• 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: 0.86

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

• 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: 0.000364

• fraction_replaced_hof

How much of population to replace with migrating equations from hall of fame.

Default: 0.035

• 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¶

• procs

Number of processes (=number of populations running).

Default: cpu_count()

• multithreading

Use multithreading instead of distributed backend. Using procs=0 will turn off both.

Default: True

• 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

• 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.

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

• 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 procs=0, multithreading=False), 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: 1e9

• update_verbosity

What verbosity level to use for package updates. Will take value of verbosity if not given.

Default: None

• progress

Whether to use a progress bar instead of printing to stdout.

Default: True

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

• julia_project

A Julia environment location containing a Project.toml (and potentially the source code for SymbolicRegression.jl). Default gives the Python package directory, where a Project.toml file should be present from the install.

• 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

• julia_kwargs

Keyword arguments to pass to julia.core.Julia(...) to initialize the Julia runtime. The default, when None, is to set threads equal to procs, and optimize to 3.

Default: None

Exporting the Results¶

• equation_file

Where to save the files (.csv extension).

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(X, y, Xresampled=None, weights=None, variable_names=None)¶

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

Returns:

Name Type Description
self object

Fitted estimator.

Source code in pysr/sr.py
 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 def fit( self, X, y, Xresampled=None, weights=None, variable_names=None, ): """ Search for equations to fit the dataset and store them in self.equations_. Parameters ---------- X : ndarray | pandas.DataFrame Training data of shape (n_samples, n_features). y : ndarray | pandas.DataFrame Target values of shape (n_samples,) or (n_samples, n_targets). Will be cast to X's dtype if necessary. 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. 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. 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". Returns ------- self : object Fitted estimator. """ # Init attributes that are not specified in BaseEstimator if self.warm_start and hasattr(self, "raw_julia_state_"): pass else: if hasattr(self, "raw_julia_state_"): warnings.warn( "The discovered expressions are being reset. " "Please set warm_start=True if you wish to continue " "to start a search where you left off.", ) self.equations_ = None self.nout_ = 1 self.selection_mask_ = None self.raw_julia_state_ = None random_state = check_random_state(self.random_state) # For np random seed = random_state.get_state()[1][0] # For julia random self._setup_equation_file() mutated_params = self._validate_and_set_init_params() X, y, Xresampled, weights, variable_names = self._validate_and_set_fit_params( X, y, Xresampled, weights, variable_names ) if X.shape[0] > 10000 and not self.batching: warnings.warn( "Note: you are running with more than 10,000 datapoints. " "You should consider turning on batching (https://astroautomata.com/PySR/options/#batching). " "You should also reconsider if you need that many datapoints. " "Unless you have a large amount of noise (in which case you " "should smooth your dataset first), generally < 10,000 datapoints " "is enough to find a functional form with symbolic regression. " "More datapoints will lower the search speed." ) # Pre transformations (feature selection and denoising) X, y, variable_names = self._pre_transform_training_data( X, y, Xresampled, variable_names, random_state ) # Warn about large feature counts (still warn if feature count is large # after running feature selection) if self.n_features_in_ >= 10: warnings.warn( "Note: you are running with 10 features or more. " "Genetic algorithms like used in PySR scale poorly with large numbers of features. " "Consider using feature selection techniques to select the most important features " "(you can do this automatically with the select_k_features parameter), " "or, alternatively, doing a dimensionality reduction beforehand. " "For example, X = PCA(n_components=6).fit_transform(X), " "using scikit-learn's PCA class, " "will reduce the number of features to 6 in an interpretable way, " "as each resultant feature " "will be a linear combination of the original features. " ) # Assertion checks use_custom_variable_names = variable_names is not None # TODO: this is always true. _check_assertions( X, use_custom_variable_names, variable_names, weights, y, ) # Initially, just save model parameters, so that # it can be loaded from an early exit: if not self.temp_equation_file: self._checkpoint() # Perform the search: self._run(X, y, mutated_params, weights=weights, seed=seed) # Then, after fit, we save again, so the pickle file contains # the equations: if not self.temp_equation_file: self._checkpoint() return self 

predict(X, index=None)¶

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

Returns:

Name Type Description
y_predicted ndarray of shape (n_samples, nout_)

Values predicted by substituting X into the fitted symbolic regression model.

Raises:

Type Description
ValueError

Raises if the best_equation cannot be evaluated.

Source code in pysr/sr.py
 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 def predict(self, X, index=None): """ 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 ---------- X : ndarray | pandas.DataFrame Training data of shape (n_samples, n_features). 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. Returns ------- y_predicted : 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. """ check_is_fitted( self, attributes=["selection_mask_", "feature_names_in_", "nout_"] ) best_equation = self.get_best(index=index) # When X is an numpy array or a pandas dataframe with a RangeIndex, # the self.feature_names_in_ generated during fit, for the same X, # will cause a warning to be thrown during _validate_data. # To avoid this, convert X to a dataframe, apply the selection mask, # and then set the column/feature_names of X to be equal to those # generated during fit. if not isinstance(X, pd.DataFrame): X = check_array(X) X = pd.DataFrame(X) if isinstance(X.columns, pd.RangeIndex): if self.selection_mask_ is not None: # RangeIndex enforces column order allowing columns to # be correctly filtered with self.selection_mask_ X = X.iloc[:, self.selection_mask_] X.columns = self.feature_names_in_ # Without feature information, CallableEquation/lambda_format equations # require that the column order of X matches that of the X used during # the fitting process. _validate_data removes this feature information # when it converts the dataframe to an np array. Thus, to ensure feature # order is preserved after conversion, the dataframe columns must be # reordered/reindexed to match those of the transformed (denoised and # feature selected) X in fit. X = X.reindex(columns=self.feature_names_in_) X = self._validate_data(X, reset=False) try: if self.nout_ > 1: return np.stack( [eq["lambda_format"](X) for eq in best_equation], axis=1 ) return best_equation["lambda_format"](X) except Exception as error: raise ValueError( "Failed to evaluate the expression. " "If you are using a custom operator, make sure to define it in extra_sympy_mappings, " "e.g., model.set_params(extra_sympy_mappings={'inv': lambda x: 1/x}), where " "lambda x: 1/x is a valid SymPy function defining the operator. " "You can then run model.refresh() to re-load the expressions." ) from error 

from_file(equation_file, *, binary_operators=None, unary_operators=None, n_features_in=None, feature_names_in=None, selection_mask=None, nout=1, **pysr_kwargs) classmethod ¶

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

Parameters:

Name Type Description Default
equation_file str

Path to a pickle file containing a saved model, or a csv file containing equations.

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 list[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. Default is 1.

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.

{}

Returns:

Name Type Description
model PySRRegressor

The model with fitted equations.

Source code in pysr/sr.py
 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 @classmethod def from_file( cls, equation_file, *, binary_operators=None, unary_operators=None, n_features_in=None, feature_names_in=None, selection_mask=None, nout=1, **pysr_kwargs, ): """ Create a model from a saved model checkpoint or equation file. Parameters ---------- equation_file : str Path to a pickle file containing a saved model, or a csv file containing equations. binary_operators : list[str] The same binary operators used when creating the model. Not needed if loading from a pickle file. unary_operators : list[str] The same unary operators used when creating the model. Not needed if loading from a pickle file. n_features_in : int Number of features passed to the model. Not needed if loading from a pickle file. feature_names_in : list[str] Names of the features passed to the model. Not needed if loading from a pickle file. selection_mask : list[bool] If using select_k_features, you must pass model.selection_mask_ here. Not needed if loading from a pickle file. nout : int Number of outputs of the model. Not needed if loading from a pickle file. Default is 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. Returns ------- model : PySRRegressor The model with fitted equations. """ if os.path.splitext(equation_file)[1] != ".pkl": pkl_filename = _csv_filename_to_pkl_filename(equation_file) else: pkl_filename = equation_file # Try to load model from .pkl print(f"Checking if {pkl_filename} exists...") if os.path.exists(pkl_filename): print(f"Loading model from {pkl_filename}") assert binary_operators is None assert unary_operators is None assert n_features_in is None with open(pkl_filename, "rb") as f: model = pkl.load(f) # Change equation_file_ to be in the same dir as the pickle file base_dir = os.path.dirname(pkl_filename) base_equation_file = os.path.basename(model.equation_file_) model.equation_file_ = os.path.join(base_dir, base_equation_file) # Update any parameters if necessary, such as # extra_sympy_mappings: model.set_params(**pysr_kwargs) if "equations_" not in model.__dict__ or model.equations_ is None: model.refresh() return model # Else, we re-create it. print( f"{equation_file} does not exist, " "so we must create the model from scratch." ) assert binary_operators is not None assert unary_operators is not None assert n_features_in is not None # TODO: copy .bkup file if exists. model = cls( equation_file=equation_file, binary_operators=binary_operators, unary_operators=unary_operators, **pysr_kwargs, ) model.nout_ = nout model.n_features_in_ = n_features_in if feature_names_in is None: model.feature_names_in_ = [f"x{i}" for i in range(n_features_in)] else: assert len(feature_names_in) == n_features_in model.feature_names_in_ = feature_names_in if selection_mask is None: model.selection_mask_ = np.ones(n_features_in, dtype=bool) else: model.selection_mask_ = selection_mask model.refresh(checkpoint_file=equation_file) return model 

sympy(index=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:

Name Type Description
best_equation str, list[str] of length nout_

SymPy representation of the best equation.

Source code in pysr/sr.py
 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 def sympy(self, index=None): """ Return sympy representation of the equation(s) chosen by model_selection. Parameters ---------- 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. Returns ------- best_equation : str, list[str] of length nout_ SymPy representation of the best equation. """ self.refresh() best_equation = self.get_best(index=index) if self.nout_ > 1: return [eq["sympy_format"] for eq in best_equation] return best_equation["sympy_format"] 

latex(index=None, precision=3)¶

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. Default is 3.

3

Returns:

Name Type Description
best_equation str or list[str] of length nout_

LaTeX expression of the best equation.

Source code in pysr/sr.py
 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 def latex(self, index=None, precision=3): """ Return latex representation of the equation(s) chosen by model_selection. Parameters ---------- 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. precision : int The number of significant figures shown in the LaTeX representation. Default is 3. Returns ------- best_equation : str or list[str] of length nout_ LaTeX expression of the best equation. """ self.refresh() sympy_representation = self.sympy(index=index) if self.nout_ > 1: output = [] for s in sympy_representation: latex = to_latex(s, prec=precision) output.append(latex) return output return to_latex(sympy_representation, prec=precision) 

pytorch(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:

Name Type Description
best_equation torch.nn.Module

PyTorch module representing the expression.

Source code in pysr/sr.py
 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 def 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 ---------- 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. Returns ------- best_equation : torch.nn.Module PyTorch module representing the expression. """ self.set_params(output_torch_format=True) self.refresh() best_equation = self.get_best(index=index) if self.nout_ > 1: return [eq["torch_format"] for eq in best_equation] return best_equation["torch_format"] 

jax(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:

Name Type Description
best_equation dict[str, Any]

Dictionary of callable jax function in "callable" key, and jax array of parameters as "parameters" key.

Source code in pysr/sr.py
 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 def 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 ---------- 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. Returns ------- best_equation : dict[str, Any] Dictionary of callable jax function in "callable" key, and jax array of parameters as "parameters" key. """ self.set_params(output_jax_format=True) self.refresh() best_equation = self.get_best(index=index) if self.nout_ > 1: return [eq["jax_format"] for eq in best_equation] return best_equation["jax_format"] 

latex_table(indices=None, precision=3, columns=['equation', 'complexity', 'loss', 'score'])¶

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. Default is 3.

3
columns list[str]

Which columns to include in the table. Default is ["equation", "complexity", "loss", "score"].

['equation', 'complexity', 'loss', 'score']

Returns:

Name Type Description
latex_table_str str

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

Source code in pysr/sr.py
 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 def latex_table( self, indices=None, precision=3, columns=["equation", "complexity", "loss", "score"], ): """Create a LaTeX/booktabs table for all, or some, of the equations. Parameters ---------- 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. precision : int The number of significant figures shown in the LaTeX representations. Default is 3. columns : list[str] Which columns to include in the table. Default is ["equation", "complexity", "loss", "score"]. Returns ------- latex_table_str : str A string that will render a table in LaTeX of the equations. """ self.refresh() if self.nout_ > 1: if indices is not None: assert isinstance(indices, list) assert isinstance(indices[0], list) assert isinstance(len(indices), self.nout_) generator_fnc = generate_multiple_tables else: if indices is not None: assert isinstance(indices, list) assert isinstance(indices[0], int) generator_fnc = generate_single_table table_string = generator_fnc( self.equations_, indices=indices, precision=precision, columns=columns ) preamble_string = [ r"\usepackage{breqn}", r"\usepackage{booktabs}", "", "...", "", ] return "\n".join(preamble_string + [table_string]) 

refresh(checkpoint_file=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

Path to checkpoint hall of fame file to be loaded. The default will use the set equation_file_.

None
Source code in pysr/sr.py
 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 def refresh(self, checkpoint_file=None): """ Update self.equations_ with any new options passed. For example, updating extra_sympy_mappings will require a .refresh() to update the equations. Parameters ---------- checkpoint_file : str Path to checkpoint hall of fame file to be loaded. The default will use the set equation_file_. """ if checkpoint_file: self.equation_file_ = checkpoint_file self.equation_file_contents_ = None check_is_fitted(self, attributes=["equation_file_"]) self.equations_ = self.get_hof()