"""Define the PySRRegressor scikit-learn interface.""" import copy import os import pickle as pkl import re import shutil import sys import tempfile import warnings from datetime import datetime from io import StringIO from multiprocessing import cpu_count from pathlib import Path from typing import Callable, Dict, List, Literal, Optional, Tuple, Union, cast import numpy as np import pandas as pd from numpy import ndarray from numpy.typing import NDArray from sklearn.base import BaseEstimator, MultiOutputMixin, RegressorMixin from sklearn.utils import check_array, check_consistent_length, check_random_state from sklearn.utils.validation import _check_feature_names_in # type: ignore from sklearn.utils.validation import check_is_fitted from .denoising import denoise, multi_denoise from .deprecated import DEPRECATED_KWARGS from .export_jax import sympy2jax from .export_latex import ( sympy2latex, sympy2latextable, sympy2multilatextable, with_preamble, ) from .export_numpy import sympy2numpy from .export_sympy import assert_valid_sympy_symbol, create_sympy_symbols, pysr2sympy from .export_torch import sympy2torch from .feature_selection import run_feature_selection from .julia_extensions import load_required_packages from .julia_helpers import ( PythonCall, _escape_filename, _load_cluster_manager, jl_array, jl_deserialize, jl_serialize, ) from .julia_import import SymbolicRegression, jl from .utils import ( ArrayLike, _csv_filename_to_pkl_filename, _preprocess_julia_floats, _safe_check_feature_names_in, _subscriptify, ) already_ran = False def _process_constraints(binary_operators, unary_operators, constraints): constraints = constraints.copy() for op in unary_operators: if op not in constraints: constraints[op] = -1 for op in binary_operators: if op not in constraints: if op in ["^", "pow"]: # Warn user that they should set up constraints warnings.warn( "You are using the `^` operator, but have not set up `constraints` for it. " "This may lead to overly complex expressions. " "One typical constraint is to use `constraints={..., '^': (-1, 1)}`, which " "will allow arbitrary-complexity base (-1) but only powers such as " "a constant or variable (1). " "For more tips, please see https://astroautomata.com/PySR/tuning/" ) constraints[op] = (-1, -1) if op in ["plus", "sub", "+", "-"]: if constraints[op][0] != constraints[op][1]: raise NotImplementedError( "You need equal constraints on both sides for - and +, " "due to simplification strategies." ) elif op in ["mult", "*"]: # Make sure the complex expression is in the left side. if constraints[op][0] == -1: continue if constraints[op][1] == -1 or constraints[op][0] < constraints[op][1]: constraints[op][0], constraints[op][1] = ( constraints[op][1], constraints[op][0], ) return constraints def _maybe_create_inline_operators( binary_operators, unary_operators, extra_sympy_mappings ): binary_operators = binary_operators.copy() unary_operators = unary_operators.copy() for op_list in [binary_operators, unary_operators]: for i, op in enumerate(op_list): is_user_defined_operator = "(" in op if is_user_defined_operator: jl.seval(op) # Cut off from the first non-alphanumeric char: first_non_char = [j for j, char in enumerate(op) if char == "("][0] function_name = op[:first_non_char] # Assert that function_name only contains # alphabetical characters, numbers, # and underscores: if not re.match(r"^[a-zA-Z0-9_]+$", function_name): raise ValueError( f"Invalid function name {function_name}. " "Only alphanumeric characters, numbers, " "and underscores are allowed." ) if (extra_sympy_mappings is None) or ( not function_name in extra_sympy_mappings ): raise ValueError( f"Custom function {function_name} is not defined in `extra_sympy_mappings`. " "You can define it with, " "e.g., `model.set_params(extra_sympy_mappings={'inv': lambda x: 1/x})`, where " "`lambda x: 1/x` is a valid SymPy function defining the operator. " "You can also define these at initialization time." ) op_list[i] = function_name return binary_operators, unary_operators def _check_assertions( X, use_custom_variable_names, variable_names, weights, y, X_units, y_units, ): # Check for potential errors before they happen assert len(X.shape) == 2 assert len(y.shape) in [1, 2] assert X.shape[0] == y.shape[0] if weights is not None: assert weights.shape == y.shape assert X.shape[0] == weights.shape[0] if use_custom_variable_names: assert len(variable_names) == X.shape[1] # Check none of the variable names are function names: for var_name in variable_names: # Check if alphanumeric only: if not re.match(r"^[₀₁₂₃₄₅₆₇₈₉a-zA-Z0-9_]+$", var_name): raise ValueError( f"Invalid variable name {var_name}. " "Only alphanumeric characters, numbers, " "and underscores are allowed." ) assert_valid_sympy_symbol(var_name) if X_units is not None and len(X_units) != X.shape[1]: raise ValueError( "The number of units in `X_units` must equal the number of features in `X`." ) if y_units is not None: good_y_units = False if isinstance(y_units, list): if len(y.shape) == 1: good_y_units = len(y_units) == 1 else: good_y_units = len(y_units) == y.shape[1] else: good_y_units = len(y.shape) == 1 or y.shape[1] == 1 if not good_y_units: raise ValueError( "The number of units in `y_units` must equal the number of output features in `y`." ) # Class validation constants VALID_OPTIMIZER_ALGORITHMS = ["BFGS", "NelderMead"] class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator): """ High-performance symbolic regression algorithm. This is the scikit-learn interface for SymbolicRegression.jl. This model will automatically search for equations which fit a given dataset subject to a particular loss and set of constraints. Most default parameters have been tuned over several example equations, but you should adjust `niterations`, `binary_operators`, `unary_operators` to your requirements. You can view more detailed explanations of the options on the [options page](https://astroautomata.com/PySR/options) of the documentation. Parameters ---------- model_selection : str Model selection criterion when selecting a final expression from the list of best expression at each complexity. Can be `'accuracy'`, `'best'`, or `'score'`. Default is `'best'`. `'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. binary_operators : list[str] List of strings for binary operators used in the search. See the [operators page](https://astroautomata.com/PySR/operators/) for more details. Default is `["+", "-", "*", "/"]`. unary_operators : list[str] Operators which only take a single scalar as input. For example, `"cos"` or `"exp"`. Default is `None`. niterations : int Number of iterations of the algorithm to run. The best equations are printed and migrate between populations at the end of each iteration. Default is `40`. populations : int Number of populations running. Default is `15`. population_size : int Number of individuals in each population. Default is `33`. max_evals : int Limits the total number of evaluations of expressions to this number. Default is `None`. maxsize : int Max complexity of an equation. Default is `20`. maxdepth : int Max depth of an equation. You can use both `maxsize` and `maxdepth`. `maxdepth` is by default not used. Default is `None`. warmup_maxsize_by : float 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 is `0.0`. timeout_in_seconds : float Make the search return early once this many seconds have passed. Default is `None`. constraints : dict[str, int | tuple[int,int]] 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 is `None`. nested_constraints : dict[str, dict] 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 is `None`. elementwise_loss : str 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 is `"L2DistLoss()"`. loss_function : str 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`. If using `batching`, then you should add an `idx` argument to the function, which is `nothing` for non-batched, and a 1D array of indices for batched. Default is `None`. complexity_of_operators : dict[str, float] 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 is `None`. complexity_of_constants : float Complexity of constants. Default is `1`. complexity_of_variables : float Complexity of variables. Default is `1`. parsimony : float Multiplicative factor for how much to punish complexity. Default is `0.0032`. dimensional_constraint_penalty : float Additive penalty for if dimensional analysis of an expression fails. By default, this is `1000.0`. dimensionless_constants_only : bool Whether to only search for dimensionless constants, if using units. Default is `False`. use_frequency : bool 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 is `True`. use_frequency_in_tournament : bool Whether to use the frequency mentioned above in the tournament, rather than just the simulated annealing. Default is `True`. adaptive_parsimony_scaling : float 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 is `20.0`. alpha : float Initial temperature for simulated annealing (requires `annealing` to be `True`). Default is `0.1`. annealing : bool Whether to use annealing. Default is `False`. early_stop_condition : float | str 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 is `None`. ncycles_per_iteration : int Number of total mutations to run, per 10 samples of the population, per iteration. Default is `550`. fraction_replaced : float How much of population to replace with migrating equations from other populations. Default is `0.000364`. fraction_replaced_hof : float How much of population to replace with migrating equations from hall of fame. Default is `0.035`. weight_add_node : float Relative likelihood for mutation to add a node. Default is `0.79`. weight_insert_node : float Relative likelihood for mutation to insert a node. Default is `5.1`. weight_delete_node : float Relative likelihood for mutation to delete a node. Default is `1.7`. weight_do_nothing : float Relative likelihood for mutation to leave the individual. Default is `0.21`. weight_mutate_constant : float Relative likelihood for mutation to change the constant slightly in a random direction. Default is `0.048`. weight_mutate_operator : float Relative likelihood for mutation to swap an operator. Default is `0.47`. weight_swap_operands : float Relative likehood for swapping operands in binary operators. Default is `0.1`. weight_randomize : float Relative likelihood for mutation to completely delete and then randomly generate the equation Default is `0.00023`. weight_simplify : float Relative likelihood for mutation to simplify constant parts by evaluation Default is `0.0020`. weight_optimize: float 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 is `0.0`. crossover_probability : float Absolute probability of crossover-type genetic operation, instead of a mutation. Default is `0.066`. skip_mutation_failures : bool Whether to skip mutation and crossover failures, rather than simply re-sampling the current member. Default is `True`. migration : bool Whether to migrate. Default is `True`. hof_migration : bool Whether to have the hall of fame migrate. Default is `True`. topn : int How many top individuals migrate from each population. Default is `12`. should_simplify : bool Whether to use algebraic simplification in the search. Note that only a few simple rules are implemented. Default is `True`. should_optimize_constants : bool Whether to numerically optimize constants (Nelder-Mead/Newton) at the end of each iteration. Default is `True`. optimizer_algorithm : str Optimization scheme to use for optimizing constants. Can currently be `NelderMead` or `BFGS`. Default is `"BFGS"`. optimizer_nrestarts : int Number of time to restart the constants optimization process with different initial conditions. Default is `2`. optimize_probability : float Probability of optimizing the constants during a single iteration of the evolutionary algorithm. Default is `0.14`. optimizer_iterations : int Number of iterations that the constants optimizer can take. Default is `8`. perturbation_factor : float Constants are perturbed by a max factor of (perturbation_factor*T + 1). Either multiplied by this or divided by this. Default is `0.076`. tournament_selection_n : int Number of expressions to consider in each tournament. Default is `10`. tournament_selection_p : float 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 is `0.86`. procs : int Number of processes (=number of populations running). Default is `cpu_count()`. multithreading : bool Use multithreading instead of distributed backend. Using procs=0 will turn off both. Default is `True`. cluster_manager : str 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 is `None`. heap_size_hint_in_bytes : int 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 : bool Whether to compare population members on small batches during evolution. Still uses full dataset for comparing against hall of fame. Default is `False`. batch_size : int The amount of data to use if doing batching. Default is `50`. fast_cycle : bool Batch over population subsamples. This is a slightly different algorithm than regularized evolution, but does cycles 15% faster. May be algorithmically less efficient. Default is `False`. turbo: bool (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 is `False`. bumper: bool (Experimental) Whether to use Bumper.jl to speed up the search evaluation. Does not support 16-bit precision floats. Default is `False`. precision : int 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 is `32`. enable_autodiff : bool Whether to create derivative versions of operators for automatic differentiation. This is only necessary if you wish to compute the gradients of an expression within a custom loss function. Default is `False`. random_state : int, Numpy RandomState instance or None Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. Default is `None`. deterministic : bool 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 is `False`. warm_start : bool 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 is `False`. verbosity : int What verbosity level to use. 0 means minimal print statements. Default is `1`. update_verbosity : int What verbosity level to use for package updates. Will take value of `verbosity` if not given. Default is `None`. print_precision : int How many significant digits to print for floats. Default is `5`. progress : bool Whether to use a progress bar instead of printing to stdout. Default is `True`. equation_file : str Where to save the files (.csv extension). Default is `None`. temp_equation_file : bool Whether to put the hall of fame file in the temp directory. Deletion is then controlled with the `delete_tempfiles` parameter. Default is `False`. tempdir : str directory for the temporary files. Default is `None`. delete_tempfiles : bool Whether to delete the temporary files after finishing. Default is `True`. update: bool 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 is `False`. output_jax_format : bool Whether to create a 'jax_format' column in the output, containing jax-callable functions and the default parameters in a jax array. Default is `False`. output_torch_format : bool Whether to create a 'torch_format' column in the output, containing a torch module with trainable parameters. Default is `False`. extra_sympy_mappings : dict[str, Callable] 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 is `None`. extra_jax_mappings : dict[Callable, str] 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 is `None`. extra_torch_mappings : dict[Callable, Callable] 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 is `None`. denoise : bool Whether to use a Gaussian Process to denoise the data before inputting to PySR. Can help PySR fit noisy data. Default is `False`. select_k_features : int 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 is `None`. **kwargs : dict Supports deprecated keyword arguments. Other arguments will result in an error. Attributes ---------- equations_ : pandas.DataFrame | list[pandas.DataFrame] Processed DataFrame containing the results of model fitting. n_features_in_ : int Number of features seen during :term:`fit`. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. display_feature_names_in_ : ndarray of shape (`n_features_in_`,) Pretty names of features, used only during printing. X_units_ : list[str] of length n_features Units of each variable in the training dataset, `X`. y_units_ : str | list[str] of length n_out Units of each variable in the training dataset, `y`. nout_ : int Number of output dimensions. selection_mask_ : ndarray of shape (`n_features_in_`,) Mask of which features of `X` to use when `select_k_features` is set. tempdir_ : Path Path to the temporary equations directory. equation_file_ : Union[str, Path] Output equation file name produced by the julia backend. julia_state_stream_ : ndarray The serialized state for the julia SymbolicRegression.jl backend (after fitting), stored as an array of uint8, produced by Julia's Serialization.serialize function. julia_options_stream_ : ndarray The serialized julia options, stored as an array of uint8, equation_file_contents_ : list[pandas.DataFrame] Contents of the equation file output by the Julia backend. show_pickle_warnings_ : bool Whether to show warnings about what attributes can be pickled. Examples -------- ```python >>> import numpy as np >>> from pysr import PySRRegressor >>> randstate = np.random.RandomState(0) >>> X = 2 * randstate.randn(100, 5) >>> # y = 2.5382 * cos(x_3) + x_0 - 0.5 >>> y = 2.5382 * np.cos(X[:, 3]) + X[:, 0] ** 2 - 0.5 >>> model = PySRRegressor( ... niterations=40, ... binary_operators=["+", "*"], ... unary_operators=[ ... "cos", ... "exp", ... "sin", ... "inv(x) = 1/x", # Custom operator (julia syntax) ... ], ... model_selection="best", ... elementwise_loss="loss(x, y) = (x - y)^2", # Custom loss function (julia syntax) ... ) >>> model.fit(X, y) >>> model PySRRegressor.equations_ = [ 0 0.000000 3.8552167 3.360272e+01 1 1 1.189847 (x0 * x0) 3.110905e+00 3 2 0.010626 ((x0 * x0) + -0.25573406) 3.045491e+00 5 3 0.896632 (cos(x3) + (x0 * x0)) 1.242382e+00 6 4 0.811362 ((x0 * x0) + (cos(x3) * 2.4384754)) 2.451971e-01 8 5 >>>> 13.733371 (((cos(x3) * 2.5382) + (x0 * x0)) + -0.5) 2.889755e-13 10 6 0.194695 ((x0 * x0) + (((cos(x3) + -0.063180044) * 2.53... 1.957723e-13 12 7 0.006988 ((x0 * x0) + (((cos(x3) + -0.32505524) * 1.538... 1.944089e-13 13 8 0.000955 (((((x0 * x0) + cos(x3)) + -0.8251649) + (cos(... 1.940381e-13 15 ] >>> model.score(X, y) 1.0 >>> model.predict(np.array([1,2,3,4,5])) array([-1.15907818, -1.15907818, -1.15907818, -1.15907818, -1.15907818]) ``` """ equations_: Union[pd.DataFrame, List[pd.DataFrame], None] n_features_in_: int feature_names_in_: ArrayLike[str] display_feature_names_in_: ArrayLike[str] X_units_: Union[ArrayLike[str], None] y_units_: Union[str, ArrayLike[str], None] nout_: int selection_mask_: Union[NDArray[np.bool_], None] tempdir_: Path equation_file_: Union[str, Path] julia_state_stream_: Union[NDArray[np.uint8], None] julia_options_stream_: Union[NDArray[np.uint8], None] equation_file_contents_: Union[List[pd.DataFrame], None] show_pickle_warnings_: bool def __init__( self, model_selection: Literal["best", "accuracy", "score"] = "best", *, binary_operators: Optional[List[str]] = None, unary_operators: Optional[List[str]] = None, niterations: int = 40, populations: int = 15, population_size: int = 33, max_evals: Optional[int] = None, maxsize: int = 20, maxdepth: Optional[int] = None, warmup_maxsize_by: Optional[float] = None, timeout_in_seconds: Optional[float] = None, constraints: Optional[Dict[str, Union[int, Tuple[int, int]]]] = None, nested_constraints: Optional[Dict[str, Dict[str, int]]] = None, elementwise_loss: Optional[str] = None, loss_function: Optional[str] = None, complexity_of_operators: Optional[Dict[str, Union[int, float]]] = None, complexity_of_constants: Union[int, float] = 1, complexity_of_variables: Union[int, float] = 1, parsimony: float = 0.0032, dimensional_constraint_penalty: Optional[float] = None, dimensionless_constants_only: bool = False, use_frequency: bool = True, use_frequency_in_tournament: bool = True, adaptive_parsimony_scaling: float = 20.0, alpha: float = 0.1, annealing: bool = False, early_stop_condition: Optional[Union[float, str]] = None, ncycles_per_iteration: int = 550, fraction_replaced: float = 0.000364, fraction_replaced_hof: float = 0.035, weight_add_node: float = 0.79, weight_insert_node: float = 5.1, weight_delete_node: float = 1.7, weight_do_nothing: float = 0.21, weight_mutate_constant: float = 0.048, weight_mutate_operator: float = 0.47, weight_swap_operands: float = 0.1, weight_randomize: float = 0.00023, weight_simplify: float = 0.0020, weight_optimize: float = 0.0, crossover_probability: float = 0.066, skip_mutation_failures: bool = True, migration: bool = True, hof_migration: bool = True, topn: int = 12, should_simplify: Optional[bool] = None, should_optimize_constants: bool = True, optimizer_algorithm: Literal["BFGS", "NelderMead"] = "BFGS", optimizer_nrestarts: int = 2, optimize_probability: float = 0.14, optimizer_iterations: int = 8, perturbation_factor: float = 0.076, tournament_selection_n: int = 10, tournament_selection_p: float = 0.86, procs: int = cpu_count(), multithreading: Optional[bool] = None, cluster_manager: Optional[ Literal["slurm", "pbs", "lsf", "sge", "qrsh", "scyld", "htc"] ] = None, heap_size_hint_in_bytes: Optional[int] = None, batching: bool = False, batch_size: int = 50, fast_cycle: bool = False, turbo: bool = False, bumper: bool = False, precision: int = 32, enable_autodiff: bool = False, random_state=None, deterministic: bool = False, warm_start: bool = False, verbosity: int = 1, update_verbosity: Optional[int] = None, print_precision: int = 5, progress: bool = True, equation_file: Optional[str] = None, temp_equation_file: bool = False, tempdir: Optional[str] = None, delete_tempfiles: bool = True, update: bool = False, output_jax_format: bool = False, output_torch_format: bool = False, extra_sympy_mappings: Optional[Dict[str, Callable]] = None, extra_torch_mappings: Optional[Dict[Callable, Callable]] = None, extra_jax_mappings: Optional[Dict[Callable, str]] = None, denoise: bool = False, select_k_features: Optional[int] = None, **kwargs, ): # Hyperparameters # - Model search parameters self.model_selection = model_selection self.binary_operators = binary_operators self.unary_operators = unary_operators self.niterations = niterations self.populations = populations self.population_size = population_size self.ncycles_per_iteration = ncycles_per_iteration # - Equation Constraints self.maxsize = maxsize self.maxdepth = maxdepth self.constraints = constraints self.nested_constraints = nested_constraints self.warmup_maxsize_by = warmup_maxsize_by self.should_simplify = should_simplify # - Early exit conditions: self.max_evals = max_evals self.timeout_in_seconds = timeout_in_seconds self.early_stop_condition = early_stop_condition # - Loss parameters self.elementwise_loss = elementwise_loss self.loss_function = loss_function self.complexity_of_operators = complexity_of_operators self.complexity_of_constants = complexity_of_constants self.complexity_of_variables = complexity_of_variables self.parsimony = parsimony self.dimensional_constraint_penalty = dimensional_constraint_penalty self.dimensionless_constants_only = dimensionless_constants_only self.use_frequency = use_frequency self.use_frequency_in_tournament = use_frequency_in_tournament self.adaptive_parsimony_scaling = adaptive_parsimony_scaling self.alpha = alpha self.annealing = annealing # - Evolutionary search parameters # -- Mutation parameters self.weight_add_node = weight_add_node self.weight_insert_node = weight_insert_node self.weight_delete_node = weight_delete_node self.weight_do_nothing = weight_do_nothing self.weight_mutate_constant = weight_mutate_constant self.weight_mutate_operator = weight_mutate_operator self.weight_swap_operands = weight_swap_operands self.weight_randomize = weight_randomize self.weight_simplify = weight_simplify self.weight_optimize = weight_optimize self.crossover_probability = crossover_probability self.skip_mutation_failures = skip_mutation_failures # -- Migration parameters self.migration = migration self.hof_migration = hof_migration self.fraction_replaced = fraction_replaced self.fraction_replaced_hof = fraction_replaced_hof self.topn = topn # -- Constants parameters self.should_optimize_constants = should_optimize_constants self.optimizer_algorithm = optimizer_algorithm self.optimizer_nrestarts = optimizer_nrestarts self.optimize_probability = optimize_probability self.optimizer_iterations = optimizer_iterations self.perturbation_factor = perturbation_factor # -- Selection parameters self.tournament_selection_n = tournament_selection_n self.tournament_selection_p = tournament_selection_p # -- Performance parameters self.procs = procs self.multithreading = multithreading self.cluster_manager = cluster_manager self.heap_size_hint_in_bytes = heap_size_hint_in_bytes self.batching = batching self.batch_size = batch_size self.fast_cycle = fast_cycle self.turbo = turbo self.bumper = bumper self.precision = precision self.enable_autodiff = enable_autodiff self.random_state = random_state self.deterministic = deterministic self.warm_start = warm_start # Additional runtime parameters # - Runtime user interface self.verbosity = verbosity self.update_verbosity = update_verbosity self.print_precision = print_precision self.progress = progress # - Project management self.equation_file = equation_file self.temp_equation_file = temp_equation_file self.tempdir = tempdir self.delete_tempfiles = delete_tempfiles self.update = update self.output_jax_format = output_jax_format self.output_torch_format = output_torch_format self.extra_sympy_mappings = extra_sympy_mappings self.extra_jax_mappings = extra_jax_mappings self.extra_torch_mappings = extra_torch_mappings # Pre-modelling transformation self.denoise = denoise self.select_k_features = select_k_features # Once all valid parameters have been assigned handle the # deprecated kwargs if len(kwargs) > 0: # pragma: no cover for k, v in kwargs.items(): # Handle renamed kwargs if k in DEPRECATED_KWARGS: updated_kwarg_name = DEPRECATED_KWARGS[k] setattr(self, updated_kwarg_name, v) warnings.warn( f"{k} has been renamed to {updated_kwarg_name} in PySRRegressor. " "Please use that instead.", FutureWarning, ) # Handle kwargs that have been moved to the fit method elif k in ["weights", "variable_names", "Xresampled"]: warnings.warn( f"{k} is a data dependant parameter so should be passed when fit is called. " f"Ignoring parameter; please pass {k} during the call to fit instead.", FutureWarning, ) elif k == "julia_project": warnings.warn( "The `julia_project` parameter has been deprecated. To use a custom " "julia project, please see `https://astroautomata.com/PySR/backend`.", FutureWarning, ) elif k == "julia_kwargs": warnings.warn( "The `julia_kwargs` parameter has been deprecated. To pass custom " "keyword arguments to the julia backend, you should use environment variables. " "See the Julia documentation for more information.", FutureWarning, ) else: raise TypeError( f"{k} is not a valid keyword argument for PySRRegressor." ) @classmethod def from_file( cls, equation_file, *, binary_operators: Optional[List[str]] = None, unary_operators: Optional[List[str]] = None, n_features_in: Optional[int] = None, feature_names_in: Optional[ArrayLike[str]] = None, selection_mask: Optional[NDArray[np.bool_]] = None, nout: int = 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 : NDArray[np.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. """ pkl_filename = _csv_filename_to_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"{pkl_filename} does not exist, " "so we must create the model from scratch." ) assert binary_operators is not None or 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_ = np.array([f"x{i}" for i in range(n_features_in)]) model.display_feature_names_in_ = np.array( [f"x{_subscriptify(i)}" for i in range(n_features_in)] ) else: assert len(feature_names_in) == n_features_in model.feature_names_in_ = feature_names_in model.display_feature_names_in_ = feature_names_in if selection_mask is None: model.selection_mask_ = np.ones(n_features_in, dtype=np.bool_) else: model.selection_mask_ = selection_mask model.refresh(checkpoint_file=equation_file) return model def __repr__(self): """ Print all current equations fitted by the model. The string `>>>>` denotes which equation is selected by the `model_selection`. """ if not hasattr(self, "equations_") or self.equations_ is None: return "PySRRegressor.equations_ = None" output = "PySRRegressor.equations_ = [\n" equations = self.equations_ if not isinstance(equations, list): all_equations = [equations] else: all_equations = equations for i, equations in enumerate(all_equations): selected = pd.Series([""] * len(equations), index=equations.index) chosen_row = idx_model_selection(equations, self.model_selection) selected[chosen_row] = ">>>>" repr_equations = pd.DataFrame( dict( pick=selected, score=equations["score"], equation=equations["equation"], loss=equations["loss"], complexity=equations["complexity"], ) ) if len(all_equations) > 1: output += "[\n" for line in repr_equations.__repr__().split("\n"): output += "\t" + line + "\n" if len(all_equations) > 1: output += "]" if i < len(all_equations) - 1: output += ", " output += "]" return output def __getstate__(self): """ Handle pickle serialization for PySRRegressor. The Scikit-learn standard requires estimators to be serializable via `pickle.dumps()`. However, some attributes do not support pickling and need to be hidden, such as the JAX and Torch representations. """ state = self.__dict__ show_pickle_warning = not ( "show_pickle_warnings_" in state and not state["show_pickle_warnings_"] ) state_keys_containing_lambdas = ["extra_sympy_mappings", "extra_torch_mappings"] for state_key in state_keys_containing_lambdas: if state[state_key] is not None and show_pickle_warning: warnings.warn( f"`{state_key}` cannot be pickled and will be removed from the " "serialized instance. When loading the model, please redefine " f"`{state_key}` at runtime." ) state_keys_to_clear = state_keys_containing_lambdas pickled_state = { key: (None if key in state_keys_to_clear else value) for key, value in state.items() } if ("equations_" in pickled_state) and ( pickled_state["equations_"] is not None ): pickled_state["output_torch_format"] = False pickled_state["output_jax_format"] = False if self.nout_ == 1: pickled_columns = ~pickled_state["equations_"].columns.isin( ["jax_format", "torch_format"] ) pickled_state["equations_"] = ( pickled_state["equations_"].loc[:, pickled_columns].copy() ) else: pickled_columns = [ ~dataframe.columns.isin(["jax_format", "torch_format"]) for dataframe in pickled_state["equations_"] ] pickled_state["equations_"] = [ dataframe.loc[:, signle_pickled_columns] for dataframe, signle_pickled_columns in zip( pickled_state["equations_"], pickled_columns ) ] return pickled_state def _checkpoint(self): """Save the model's current state to a checkpoint file. This should only be used internally by PySRRegressor. """ # Save model state: self.show_pickle_warnings_ = False with open(_csv_filename_to_pkl_filename(self.equation_file_), "wb") as f: pkl.dump(self, f) self.show_pickle_warnings_ = True @property def equations(self): # pragma: no cover warnings.warn( "PySRRegressor.equations is now deprecated. " "Please use PySRRegressor.equations_ instead.", FutureWarning, ) return self.equations_ @property def julia_options_(self): """The deserialized julia options.""" return jl_deserialize(self.julia_options_stream_) @property def julia_state_(self): """The deserialized state.""" return jl_deserialize(self.julia_state_stream_) @property def raw_julia_state_(self): warnings.warn( "PySRRegressor.raw_julia_state_ is now deprecated. " "Please use PySRRegressor.julia_state_ instead, or julia_state_stream_ " "for the raw stream of bytes.", FutureWarning, ) return self.julia_state_ def get_best(self, index=None) -> Union[pd.Series, List[pd.Series]]: """ Get best equation using `model_selection`. Parameters ---------- index : int | list[int] If you wish to select a particular equation from `self.equations_`, give the row 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 : pandas.Series Dictionary representing the best expression found. Raises ------ NotImplementedError Raised when an invalid model selection strategy is provided. """ check_is_fitted(self, attributes=["equations_"]) if index is not None: if isinstance(self.equations_, list): assert isinstance( index, list ), "With multiple output features, index must be a list." return [eq.iloc[i] for eq, i in zip(self.equations_, index)] elif isinstance(self.equations_, pd.DataFrame): return cast(pd.Series, self.equations_.iloc[index]) else: raise ValueError("No equations have been generated yet.") if isinstance(self.equations_, list): return [ cast(pd.Series, eq.loc[idx_model_selection(eq, self.model_selection)]) for eq in self.equations_ ] elif isinstance(self.equations_, pd.DataFrame): return cast( pd.Series, self.equations_.loc[ idx_model_selection(self.equations_, self.model_selection) ], ) else: raise ValueError("No equations have been generated yet.") def _setup_equation_file(self): """ Set the full pathname of the equation file. This is performed using `tempdir` and `equation_file`. """ # Cast tempdir string as a Path object self.tempdir_ = Path(tempfile.mkdtemp(dir=self.tempdir)) if self.temp_equation_file: self.equation_file_ = self.tempdir_ / "hall_of_fame.csv" elif self.equation_file is None: if self.warm_start and ( hasattr(self, "equation_file_") and self.equation_file_ ): pass else: date_time = datetime.now().strftime("%Y-%m-%d_%H%M%S.%f")[:-3] self.equation_file_ = "hall_of_fame_" + date_time + ".csv" else: self.equation_file_ = self.equation_file self.equation_file_contents_ = None def _validate_and_set_init_params(self): """ Ensure parameters passed at initialization are valid. Also returns a dictionary of parameters to update from their values given at initialization. Returns ------- packed_modified_params : dict Dictionary of parameters to modify from their initialized values. For example, default parameters are set here when a parameter is left set to `None`. """ # Immutable parameter validation # Ensure instance parameters are allowable values: if self.tournament_selection_n > self.population_size: raise ValueError( "`tournament_selection_n` parameter must be smaller than `population_size`." ) if self.maxsize > 40: warnings.warn( "Note: Using a large maxsize for the equation search will be " "exponentially slower and use significant memory." ) elif self.maxsize < 7: raise ValueError("PySR requires a maxsize of at least 7") if self.deterministic and not ( self.multithreading in [False, None] and self.procs == 0 and self.random_state is not None ): raise ValueError( "To ensure deterministic searches, you must set `random_state` to a seed, " "`procs` to `0`, and `multithreading` to `False` or `None`." ) if self.random_state is not None and ( not self.deterministic or self.procs != 0 ): warnings.warn( "Note: Setting `random_state` without also setting `deterministic` " "to True and `procs` to 0 will result in non-deterministic searches. " ) if self.elementwise_loss is not None and self.loss_function is not None: raise ValueError( "You cannot set both `elementwise_loss` and `loss_function`." ) # NotImplementedError - Values that could be supported at a later time if self.optimizer_algorithm not in VALID_OPTIMIZER_ALGORITHMS: raise NotImplementedError( f"PySR currently only supports the following optimizer algorithms: {VALID_OPTIMIZER_ALGORITHMS}" ) progress = self.progress # 'Mutable' parameter validation # (Params and their default values, if None is given:) default_param_mapping = { "binary_operators": "+ * - /".split(" "), "unary_operators": [], "maxdepth": self.maxsize, "constraints": {}, "multithreading": self.procs != 0 and self.cluster_manager is None, "batch_size": 1, "update_verbosity": int(self.verbosity), "progress": progress, } packed_modified_params = {} for parameter, default_value in default_param_mapping.items(): parameter_value = getattr(self, parameter) if parameter_value is None: parameter_value = default_value else: # Special cases such as when binary_operators is a string if parameter in ["binary_operators", "unary_operators"] and isinstance( parameter_value, str ): parameter_value = [parameter_value] elif parameter == "batch_size" and parameter_value < 1: warnings.warn( "Given `batch_size` must be greater than or equal to one. " "`batch_size` has been increased to equal one." ) parameter_value = 1 elif ( parameter == "progress" and parameter_value and "buffer" not in sys.stdout.__dir__() ): warnings.warn( "Note: it looks like you are running in Jupyter. " "The progress bar will be turned off." ) parameter_value = False packed_modified_params[parameter] = parameter_value assert ( len(packed_modified_params["binary_operators"]) + len(packed_modified_params["unary_operators"]) > 0 ) return packed_modified_params def _validate_and_set_fit_params( self, X, y, Xresampled, weights, variable_names, X_units, y_units ) -> Tuple[ ndarray, ndarray, Optional[ndarray], Optional[ndarray], ArrayLike[str], Optional[ArrayLike[str]], Optional[Union[str, ArrayLike[str]]], ]: """ Validate the parameters passed to the :term`fit` method. This method also sets the `nout_` attribute. 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 used for denoising, of shape `(n_resampled, n_features)`. 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. variable_names : ndarray of length n_features Names of each variable in the training dataset, `X`. X_units : list[str] of length n_features Units of each variable in the training dataset, `X`. y_units : str | list[str] of length n_out Units of each variable in the training dataset, `y`. Returns ------- X_validated : ndarray of shape (n_samples, n_features) Validated training data. y_validated : ndarray of shape (n_samples,) or (n_samples, n_targets) Validated target data. Xresampled : ndarray of shape (n_resampled, n_features) Validated resampled training data used for denoising. variable_names_validated : list[str] of length n_features Validated list of variable names for each feature in `X`. X_units : list[str] of length n_features Validated units for `X`. y_units : str | list[str] of length n_out Validated units for `y`. """ if isinstance(X, pd.DataFrame): if variable_names: variable_names = None warnings.warn( "`variable_names` has been reset to `None` as `X` is a DataFrame. " "Using DataFrame column names instead." ) if ( pd.api.types.is_object_dtype(X.columns) and X.columns.str.contains(" ").any() ): X.columns = X.columns.str.replace(" ", "_") warnings.warn( "Spaces in DataFrame column names are not supported. " "Spaces have been replaced with underscores. \n" "Please rename the columns to valid names." ) elif variable_names and any([" " in name for name in variable_names]): variable_names = [name.replace(" ", "_") for name in variable_names] warnings.warn( "Spaces in `variable_names` are not supported. " "Spaces have been replaced with underscores. \n" "Please use valid names instead." ) # Data validation and feature name fetching via sklearn # This method sets the n_features_in_ attribute if Xresampled is not None: Xresampled = check_array(Xresampled) if weights is not None: weights = check_array(weights, ensure_2d=False) check_consistent_length(weights, y) X, y = self._validate_data_X_y(X, y) self.feature_names_in_ = _safe_check_feature_names_in( self, variable_names, generate_names=False ) if self.feature_names_in_ is None: self.feature_names_in_ = np.array([f"x{i}" for i in range(X.shape[1])]) self.display_feature_names_in_ = np.array( [f"x{_subscriptify(i)}" for i in range(X.shape[1])] ) variable_names = self.feature_names_in_ else: self.display_feature_names_in_ = self.feature_names_in_ variable_names = self.feature_names_in_ # Handle multioutput data if len(y.shape) == 1 or (len(y.shape) == 2 and y.shape[1] == 1): y = y.reshape(-1) elif len(y.shape) == 2: self.nout_ = y.shape[1] else: raise NotImplementedError("y shape not supported!") self.X_units_ = copy.deepcopy(X_units) self.y_units_ = copy.deepcopy(y_units) return X, y, Xresampled, weights, variable_names, X_units, y_units def _validate_data_X_y(self, X, y) -> Tuple[ndarray, ndarray]: raw_out = self._validate_data(X=X, y=y, reset=True, multi_output=True) # type: ignore return cast(Tuple[ndarray, ndarray], raw_out) def _validate_data_X(self, X) -> Tuple[ndarray]: raw_out = self._validate_data(X=X, reset=False) # type: ignore return cast(Tuple[ndarray], raw_out) def _pre_transform_training_data( self, X: ndarray, y: ndarray, Xresampled: Union[ndarray, None], variable_names: ArrayLike[str], X_units: Union[ArrayLike[str], None], y_units: Union[ArrayLike[str], str, None], random_state: np.random.RandomState, ): """ Transform the training data before fitting the symbolic regressor. This method also updates/sets the `selection_mask_` attribute. Parameters ---------- X : ndarray Training data of shape (n_samples, n_features). y : ndarray Target values of shape (n_samples,) or (n_samples, n_targets). Will be cast to X's dtype if necessary. Xresampled : ndarray | None Resampled training data, of shape `(n_resampled, n_features)`, used for denoising. variable_names : list[str] Names of each variable in the training dataset, `X`. Of length `n_features`. X_units : list[str] Units of each variable in the training dataset, `X`. y_units : str | list[str] Units of each variable in the training dataset, `y`. random_state : int | np.RandomState Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. Default is `None`. Returns ------- X_transformed : ndarray of shape (n_samples, n_features) Transformed training data. n_samples will be equal to `Xresampled.shape[0]` if `self.denoise` is `True`, and `Xresampled is not None`, otherwise it will be equal to `X.shape[0]`. n_features will be equal to `self.select_k_features` if `self.select_k_features is not None`, otherwise it will be equal to `X.shape[1]` y_transformed : ndarray of shape (n_samples,) or (n_samples, n_outputs) Transformed target data. n_samples will be equal to `Xresampled.shape[0]` if `self.denoise` is `True`, and `Xresampled is not None`, otherwise it will be equal to `X.shape[0]`. variable_names_transformed : list[str] of length n_features Names of each variable in the transformed dataset, `X_transformed`. X_units_transformed : list[str] of length n_features Units of each variable in the transformed dataset. y_units_transformed : str | list[str] of length n_out Units of each variable in the transformed dataset. """ # Feature selection transformation if self.select_k_features: selection_mask = run_feature_selection( X, y, self.select_k_features, random_state=random_state ) X = X[:, selection_mask] if Xresampled is not None: Xresampled = Xresampled[:, selection_mask] # Reduce variable_names to selection variable_names = cast( ArrayLike[str], [ variable_names[i] for i in range(len(variable_names)) if selection_mask[i] ], ) if X_units is not None: X_units = cast( ArrayLike[str], [X_units[i] for i in range(len(X_units)) if selection_mask[i]], ) self.X_units_ = copy.deepcopy(X_units) # Re-perform data validation and feature name updating X, y = self._validate_data_X_y(X, y) # Update feature names with selected variable names self.selection_mask_ = selection_mask self.feature_names_in_ = _check_feature_names_in(self, variable_names) self.display_feature_names_in_ = self.feature_names_in_ print(f"Using features {self.feature_names_in_}") # Denoising transformation if self.denoise: if self.nout_ > 1: X, y = multi_denoise( X, y, Xresampled=Xresampled, random_state=random_state ) else: X, y = denoise(X, y, Xresampled=Xresampled, random_state=random_state) return X, y, variable_names, X_units, y_units def _run(self, X, y, mutated_params, weights, seed: int): """ Run the symbolic regression fitting process on the julia backend. 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. mutated_params : dict[str, Any] Dictionary of mutated versions of some parameters passed in __init__. 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. seed : int Random seed for julia backend process. Returns ------- self : object Reference to `self` with fitted attributes. Raises ------ ImportError Raised when the julia backend fails to import a package. """ # Need to be global as we don't want to recreate/reinstate julia for # every new instance of PySRRegressor global already_ran # These are the parameters which may be modified from the ones # specified in init, so we define them here locally: binary_operators = mutated_params["binary_operators"] unary_operators = mutated_params["unary_operators"] maxdepth = mutated_params["maxdepth"] constraints = mutated_params["constraints"] nested_constraints = self.nested_constraints complexity_of_operators = self.complexity_of_operators multithreading = mutated_params["multithreading"] cluster_manager = self.cluster_manager batch_size = mutated_params["batch_size"] update_verbosity = mutated_params["update_verbosity"] progress = mutated_params["progress"] # Start julia backend processes if not already_ran and update_verbosity != 0: print("Compiling Julia backend...") if cluster_manager is not None: cluster_manager = _load_cluster_manager(cluster_manager) # TODO(mcranmer): These functions should be part of this class. binary_operators, unary_operators = _maybe_create_inline_operators( binary_operators=binary_operators, unary_operators=unary_operators, extra_sympy_mappings=self.extra_sympy_mappings, ) constraints = _process_constraints( binary_operators=binary_operators, unary_operators=unary_operators, constraints=constraints, ) una_constraints = [constraints[op] for op in unary_operators] bin_constraints = [constraints[op] for op in binary_operators] # Parse dict into Julia Dict for nested constraints:: if nested_constraints is not None: nested_constraints_str = "Dict(" for outer_k, outer_v in nested_constraints.items(): nested_constraints_str += f"({outer_k}) => Dict(" for inner_k, inner_v in outer_v.items(): nested_constraints_str += f"({inner_k}) => {inner_v}, " nested_constraints_str += "), " nested_constraints_str += ")" nested_constraints = jl.seval(nested_constraints_str) # Parse dict into Julia Dict for complexities: if complexity_of_operators is not None: complexity_of_operators_str = "Dict(" for k, v in complexity_of_operators.items(): complexity_of_operators_str += f"({k}) => {v}, " complexity_of_operators_str += ")" complexity_of_operators = jl.seval(complexity_of_operators_str) custom_loss = jl.seval( str(self.elementwise_loss) if self.elementwise_loss is not None else "nothing" ) custom_full_objective = jl.seval( str(self.loss_function) if self.loss_function is not None else "nothing" ) early_stop_condition = jl.seval( str(self.early_stop_condition) if self.early_stop_condition is not None else "nothing" ) load_required_packages( turbo=self.turbo, bumper=self.bumper, enable_autodiff=self.enable_autodiff, cluster_manager=cluster_manager, ) mutation_weights = SymbolicRegression.MutationWeights( mutate_constant=self.weight_mutate_constant, mutate_operator=self.weight_mutate_operator, swap_operands=self.weight_swap_operands, add_node=self.weight_add_node, insert_node=self.weight_insert_node, delete_node=self.weight_delete_node, simplify=self.weight_simplify, randomize=self.weight_randomize, do_nothing=self.weight_do_nothing, optimize=self.weight_optimize, ) # Call to Julia backend. # See https://github.com/MilesCranmer/SymbolicRegression.jl/blob/master/src/OptionsStruct.jl options = SymbolicRegression.Options( binary_operators=jl.seval(str(binary_operators).replace("'", "")), unary_operators=jl.seval(str(unary_operators).replace("'", "")), bin_constraints=jl_array(bin_constraints), una_constraints=jl_array(una_constraints), complexity_of_operators=complexity_of_operators, complexity_of_constants=self.complexity_of_constants, complexity_of_variables=self.complexity_of_variables, nested_constraints=nested_constraints, elementwise_loss=custom_loss, loss_function=custom_full_objective, maxsize=int(self.maxsize), output_file=_escape_filename(self.equation_file_), npopulations=int(self.populations), batching=self.batching, batch_size=int(min([batch_size, len(X)]) if self.batching else len(X)), mutation_weights=mutation_weights, tournament_selection_p=self.tournament_selection_p, tournament_selection_n=self.tournament_selection_n, # These have the same name: parsimony=self.parsimony, dimensional_constraint_penalty=self.dimensional_constraint_penalty, dimensionless_constants_only=self.dimensionless_constants_only, alpha=self.alpha, maxdepth=maxdepth, fast_cycle=self.fast_cycle, turbo=self.turbo, bumper=self.bumper, enable_autodiff=self.enable_autodiff, migration=self.migration, hof_migration=self.hof_migration, fraction_replaced_hof=self.fraction_replaced_hof, should_simplify=self.should_simplify, should_optimize_constants=self.should_optimize_constants, warmup_maxsize_by=( 0.0 if self.warmup_maxsize_by is None else self.warmup_maxsize_by ), use_frequency=self.use_frequency, use_frequency_in_tournament=self.use_frequency_in_tournament, adaptive_parsimony_scaling=self.adaptive_parsimony_scaling, npop=self.population_size, ncycles_per_iteration=self.ncycles_per_iteration, fraction_replaced=self.fraction_replaced, topn=self.topn, print_precision=self.print_precision, optimizer_algorithm=self.optimizer_algorithm, optimizer_nrestarts=self.optimizer_nrestarts, optimizer_probability=self.optimize_probability, optimizer_iterations=self.optimizer_iterations, perturbation_factor=self.perturbation_factor, annealing=self.annealing, timeout_in_seconds=self.timeout_in_seconds, crossover_probability=self.crossover_probability, skip_mutation_failures=self.skip_mutation_failures, max_evals=self.max_evals, early_stop_condition=early_stop_condition, seed=seed, deterministic=self.deterministic, define_helper_functions=False, ) self.julia_options_stream_ = jl_serialize(options) # Convert data to desired precision test_X = np.array(X) is_complex = np.issubdtype(test_X.dtype, np.complexfloating) is_real = not is_complex if is_real: np_dtype = {16: np.float16, 32: np.float32, 64: np.float64}[self.precision] else: np_dtype = {32: np.complex64, 64: np.complex128}[self.precision] # This converts the data into a Julia array: jl_X = jl_array(np.array(X, dtype=np_dtype).T) if len(y.shape) == 1: jl_y = jl_array(np.array(y, dtype=np_dtype)) else: jl_y = jl_array(np.array(y, dtype=np_dtype).T) if weights is not None: if len(weights.shape) == 1: jl_weights = jl_array(np.array(weights, dtype=np_dtype)) else: jl_weights = jl_array(np.array(weights, dtype=np_dtype).T) else: jl_weights = None if self.procs == 0 and not multithreading: parallelism = "serial" elif multithreading: parallelism = "multithreading" else: parallelism = "multiprocessing" cprocs = ( None if parallelism in ["serial", "multithreading"] else int(self.procs) ) if len(y.shape) > 1: # We set these manually so that they respect Python's 0 indexing # (by default Julia will use y1, y2...) jl_y_variable_names = jl_array( [f"y{_subscriptify(i)}" for i in range(y.shape[1])] ) else: jl_y_variable_names = None PythonCall.GC.disable() out = SymbolicRegression.equation_search( jl_X, jl_y, weights=jl_weights, niterations=int(self.niterations), variable_names=jl_array([str(v) for v in self.feature_names_in_]), display_variable_names=jl_array( [str(v) for v in self.display_feature_names_in_] ), y_variable_names=jl_y_variable_names, X_units=jl_array(self.X_units_), y_units=( jl_array(self.y_units_) if isinstance(self.y_units_, list) else self.y_units_ ), options=options, numprocs=cprocs, parallelism=parallelism, saved_state=self.julia_state_, return_state=True, addprocs_function=cluster_manager, heap_size_hint_in_bytes=self.heap_size_hint_in_bytes, progress=progress and self.verbosity > 0 and len(y.shape) == 1, verbosity=int(self.verbosity), ) PythonCall.GC.enable() self.julia_state_stream_ = jl_serialize(out) # Set attributes self.equations_ = self.get_hof() if self.delete_tempfiles: shutil.rmtree(self.tempdir_) already_ran = True return self def fit( self, X, y, Xresampled=None, weights=None, variable_names: Optional[ArrayLike[str]] = None, X_units: Optional[ArrayLike[str]] = None, y_units: Optional[Union[str, ArrayLike[str]]] = None, ) -> "PySRRegressor": """ 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". 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. 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. Returns ------- self : object Fitted estimator. """ # Init attributes that are not specified in BaseEstimator if self.warm_start and hasattr(self, "julia_state_stream_"): pass else: if hasattr(self, "julia_state_stream_"): 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.julia_state_stream_ = None self.julia_options_stream_ = None self.X_units_ = None self.y_units_ = None self._setup_equation_file() mutated_params = self._validate_and_set_init_params() ( X, y, Xresampled, weights, variable_names, X_units, y_units, ) = self._validate_and_set_fit_params( X, y, Xresampled, weights, variable_names, X_units, y_units ) 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." ) random_state = check_random_state(self.random_state) # For np random seed = random_state.randint(0, 2**31 - 1) # For julia random # Pre transformations (feature selection and denoising) X, y, variable_names, X_units, y_units = self._pre_transform_training_data( X, y, Xresampled, variable_names, X_units, y_units, 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. " "You should run PySR for more `niterations` to ensure it can find " "the correct variables, " "or, alternatively, do 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, X_units, y_units, ) # 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 def refresh(self, checkpoint_file=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 ---------- 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() 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(X) try: if isinstance(best_equation, list): assert self.nout_ > 1 return np.stack( [eq["lambda_format"](X) for eq in best_equation], axis=1 ) else: 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 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 isinstance(best_equation, list): assert self.nout_ > 1 return [eq["sympy_format"] for eq in best_equation] else: return best_equation["sympy_format"] 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 = sympy2latex(s, prec=precision) output.append(latex) return output return sympy2latex(sympy_representation, prec=precision) 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 isinstance(best_equation, list): assert self.nout_ > 1 return [eq["jax_format"] for eq in best_equation] else: return best_equation["jax_format"] 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 isinstance(best_equation, list): return [eq["torch_format"] for eq in best_equation] else: return best_equation["torch_format"] def _read_equation_file(self): """Read the hall of fame file created by `SymbolicRegression.jl`.""" try: if self.nout_ > 1: all_outputs = [] for i in range(1, self.nout_ + 1): cur_filename = str(self.equation_file_) + f".out{i}" + ".bkup" if not os.path.exists(cur_filename): cur_filename = str(self.equation_file_) + f".out{i}" with open(cur_filename, "r", encoding="utf-8") as f: buf = f.read() buf = _preprocess_julia_floats(buf) df = self._postprocess_dataframe(pd.read_csv(StringIO(buf))) all_outputs.append(df) else: filename = str(self.equation_file_) + ".bkup" if not os.path.exists(filename): filename = str(self.equation_file_) with open(filename, "r", encoding="utf-8") as f: buf = f.read() buf = _preprocess_julia_floats(buf) all_outputs = [self._postprocess_dataframe(pd.read_csv(StringIO(buf)))] except FileNotFoundError: raise RuntimeError( "Couldn't find equation file! The equation search likely exited " "before a single iteration completed." ) return all_outputs def _postprocess_dataframe(self, df: pd.DataFrame) -> pd.DataFrame: df = df.rename( columns={ "Complexity": "complexity", "Loss": "loss", "Equation": "equation", }, ) return df def get_hof(self): """Get the equations from a hall of fame file. If no arguments entered, the ones used previously from a call to PySR will be used. """ check_is_fitted( self, attributes=[ "nout_", "equation_file_", "selection_mask_", "feature_names_in_", ], ) if ( not hasattr(self, "equation_file_contents_") ) or self.equation_file_contents_ is None: self.equation_file_contents_ = self._read_equation_file() # It is expected extra_jax/torch_mappings will be updated after fit. # Thus, validation is performed here instead of in _validate_init_params extra_jax_mappings = self.extra_jax_mappings extra_torch_mappings = self.extra_torch_mappings if extra_jax_mappings is not None: for value in extra_jax_mappings.values(): if not isinstance(value, str): raise ValueError( "extra_jax_mappings must have keys that are strings! " "e.g., {sympy.sqrt: 'jnp.sqrt'}." ) else: extra_jax_mappings = {} if extra_torch_mappings is not None: for value in extra_torch_mappings.values(): if not callable(value): raise ValueError( "extra_torch_mappings must be callable functions! " "e.g., {sympy.sqrt: torch.sqrt}." ) else: extra_torch_mappings = {} ret_outputs = [] equation_file_contents = copy.deepcopy(self.equation_file_contents_) for output in equation_file_contents: scores = [] lastMSE = None lastComplexity = 0 sympy_format = [] lambda_format = [] jax_format = [] torch_format = [] for _, eqn_row in output.iterrows(): eqn = pysr2sympy( eqn_row["equation"], feature_names_in=self.feature_names_in_, extra_sympy_mappings=self.extra_sympy_mappings, ) sympy_format.append(eqn) # NumPy: sympy_symbols = create_sympy_symbols(self.feature_names_in_) lambda_format.append( sympy2numpy( eqn, sympy_symbols, selection=self.selection_mask_, ) ) # JAX: if self.output_jax_format: func, params = sympy2jax( eqn, sympy_symbols, selection=self.selection_mask_, extra_jax_mappings=self.extra_jax_mappings, ) jax_format.append({"callable": func, "parameters": params}) # Torch: if self.output_torch_format: module = sympy2torch( eqn, sympy_symbols, selection=self.selection_mask_, extra_torch_mappings=self.extra_torch_mappings, ) torch_format.append(module) curMSE = eqn_row["loss"] curComplexity = eqn_row["complexity"] if lastMSE is None: cur_score = 0.0 else: if curMSE > 0.0: # TODO Move this to more obvious function/file. cur_score = -np.log(curMSE / lastMSE) / ( curComplexity - lastComplexity ) else: cur_score = np.inf scores.append(cur_score) lastMSE = curMSE lastComplexity = curComplexity output["score"] = np.array(scores) output["sympy_format"] = sympy_format output["lambda_format"] = lambda_format output_cols = [ "complexity", "loss", "score", "equation", "sympy_format", "lambda_format", ] if self.output_jax_format: output_cols += ["jax_format"] output["jax_format"] = jax_format if self.output_torch_format: output_cols += ["torch_format"] output["torch_format"] = torch_format ret_outputs.append(output[output_cols]) if self.nout_ > 1: return ret_outputs return ret_outputs[0] 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 isinstance(self.equations_, list): if indices is not None: assert isinstance(indices, list) assert isinstance(indices[0], list) assert len(indices) == self.nout_ table_string = sympy2multilatextable( self.equations_, indices=indices, precision=precision, columns=columns ) elif isinstance(self.equations_, pd.DataFrame): if indices is not None: assert isinstance(indices, list) assert isinstance(indices[0], int) table_string = sympy2latextable( self.equations_, indices=indices, precision=precision, columns=columns ) else: raise ValueError( "Invalid type for equations_ to pass to `latex_table`. " "Expected a DataFrame or a list of DataFrames." ) return with_preamble(table_string) def idx_model_selection(equations: pd.DataFrame, model_selection: str): """Select an expression and return its index.""" if model_selection == "accuracy": chosen_idx = equations["loss"].idxmin() elif model_selection == "best": threshold = 1.5 * equations["loss"].min() filtered_equations = equations.query(f"loss <= {threshold}") chosen_idx = filtered_equations["score"].idxmax() elif model_selection == "score": chosen_idx = equations["score"].idxmax() else: raise NotImplementedError( f"{model_selection} is not a valid model selection strategy." ) return chosen_idx