import os import sys import numpy as np import pandas as pd import sympy from sympy import sympify import re import tempfile import shutil from pathlib import Path from datetime import datetime import warnings from multiprocessing import cpu_count from sklearn.base import BaseEstimator, RegressorMixin, MultiOutputMixin from sklearn.utils import check_array, check_consistent_length, check_random_state from sklearn.utils.validation import ( _check_feature_names_in, check_is_fitted, ) from .julia_helpers import ( init_julia, _get_julia_project, is_julia_version_greater_eq, _escape_filename, _add_sr_to_julia_project, import_error_string, ) from .export_numpy import CallableEquation from .deprecated import make_deprecated_kwargs_for_pysr_regressor Main = None # TODO: Rename to more descriptive name like "julia_runtime" already_ran = False sympy_mappings = { "div": lambda x, y: x / y, "mult": lambda x, y: x * y, "sqrt_abs": lambda x: sympy.sqrt(abs(x)), "square": lambda x: x**2, "cube": lambda x: x**3, "plus": lambda x, y: x + y, "sub": lambda x, y: x - y, "neg": lambda x: -x, "pow": lambda x, y: abs(x) ** y, "cos": sympy.cos, "sin": sympy.sin, "tan": sympy.tan, "cosh": sympy.cosh, "sinh": sympy.sinh, "tanh": sympy.tanh, "exp": sympy.exp, "acos": sympy.acos, "asin": sympy.asin, "atan": sympy.atan, "acosh": lambda x: sympy.acosh(abs(x) + 1), "acosh_abs": lambda x: sympy.acosh(abs(x) + 1), "asinh": sympy.asinh, "atanh": lambda x: sympy.atanh(sympy.Mod(x + 1, 2) - 1), "atanh_clip": lambda x: sympy.atanh(sympy.Mod(x + 1, 2) - 1), "abs": abs, "mod": sympy.Mod, "erf": sympy.erf, "erfc": sympy.erfc, "log_abs": lambda x: sympy.log(abs(x)), "log10_abs": lambda x: sympy.log(abs(x), 10), "log2_abs": lambda x: sympy.log(abs(x), 2), "log1p_abs": lambda x: sympy.log(abs(x) + 1), "floor": sympy.floor, "ceil": sympy.ceiling, "sign": sympy.sign, "gamma": sympy.gamma, } def pysr(X, y, weights=None, **kwargs): # pragma: no cover warnings.warn( "Calling `pysr` is deprecated. " "Please use `model = PySRRegressor(**params); model.fit(X, y)` going forward.", FutureWarning, ) model = PySRRegressor(**kwargs) model.fit(X, y, weights=weights) return model.equations_ 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: 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): global Main 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: Main.eval(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." ) op_list[i] = function_name return binary_operators, unary_operators def _check_assertions( X, use_custom_variable_names, variable_names, weights, y, ): # 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] def best(*args, **kwargs): # pragma: no cover raise NotImplementedError( "`best` has been deprecated. Please use the `PySRRegressor` interface. " "After fitting, you can return `.sympy()` to get the sympy representation " "of the best equation." ) def best_row(*args, **kwargs): # pragma: no cover raise NotImplementedError( "`best_row` has been deprecated. Please use the `PySRRegressor` interface. " "After fitting, you can run `print(model)` to view the best equation, or " "`model.get_best()` to return the best equation's row in `model.equations_`." ) def best_tex(*args, **kwargs): # pragma: no cover raise NotImplementedError( "`best_tex` has been deprecated. Please use the `PySRRegressor` interface. " "After fitting, you can return `.latex()` to get the sympy representation " "of the best equation." ) def best_callable(*args, **kwargs): # pragma: no cover raise NotImplementedError( "`best_callable` has been deprecated. Please use the `PySRRegressor` " "interface. After fitting, you can use `.predict(X)` to use the best callable." ) # Class validation constants VALID_OPTIMIZER_ALGORITHMS = ["NelderMead", "BFGS"] class PySRRegressor(MultiOutputMixin, RegressorMixin, BaseEstimator): """ High-performance symbolic regression. 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. Parameters ---------- model_selection : str, default="best" Model selection criterion. Can be 'accuracy' or 'best'. `"accuracy"` selects the candidate model with the lowest loss (highest accuracy). `"best"` selects the candidate model with the lowest sum of normalized loss and complexity. binary_operators : list[str], default=["+", "-", "*", "/"] List of strings giving the binary operators in Julia's Base. unary_operators : list[str], default=None Same as :param`binary_operators` but for operators taking a single scalar. niterations : int, default=40 Number of iterations of the algorithm to run. The best equations are printed and migrate between populations at the end of each iteration. populations : int, default=15 Number of populations running. population_size : int, default=33 Number of individuals in each population. max_evals : int, default=None Limits the total number of evaluations of expressions to this number. maxsize : int, default=20 Max complexity of an equation. maxdepth : int, default=None Max depth of an equation. You can use both :param`maxsize` and :param`maxdepth`. :param`maxdepth` is by default not used. warmup_maxsize_by : float, default=0.0 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. timeout_in_seconds : float, default=None Make the search return early once this many seconds have passed. constraints : dict[str, int | tuple[int,int]], default=None 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. nested_constraints : dict[str, dict], default=None 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. loss : str, default="L2DistLoss()" 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)`. complexity_of_operators : dict[str, float], default=None 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. complexity_of_constants : float, default=1 Complexity of constants. complexity_of_variables : float, default=1 Complexity of variables. parsimony : float, default=0.0032 Multiplicative factor for how much to punish complexity. use_frequency : bool, default=True Whether to measure the frequency of complexities, and use that instead of parsimony to explore equation space. Will naturally find equations of all complexities. use_frequency_in_tournament : bool, default=True Whether to use the frequency mentioned above in the tournament, rather than just the simulated annealing. alpha : float, default=0.1 Initial temperature for simulated annealing (requires :param`annealing` to be `True`). annealing : bool, default=False Whether to use annealing. early_stop_condition : { float | str }, default=None 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)"`. ncyclesperiteration : int, default=550 Number of total mutations to run, per 10 samples of the population, per iteration. fraction_replaced : float, default=0.000364 How much of population to replace with migrating equations from other populations. fraction_replaced_hof : float, default=0.035 How much of population to replace with migrating equations from hall of fame. weight_add_node : float, default=0.79 Relative likelihood for mutation to add a node. weight_insert_node : float, default=5.1 Relative likelihood for mutation to insert a node. weight_delete_node : float, default=1.7 Relative likelihood for mutation to delete a node. weight_do_nothing : float, default=0.21 Relative likelihood for mutation to leave the individual. weight_mutate_constant : float, default=0.048 Relative likelihood for mutation to change the constant slightly in a random direction. weight_mutate_operator : float, default=0.47 Relative likelihood for mutation to swap an operator. weight_randomize : float, default=0.00023 Relative likelihood for mutation to completely delete and then randomly generate the equation weight_simplify : float, default=0.0020 Relative likelihood for mutation to simplify constant parts by evaluation crossover_probability : float, default=0.066 Absolute probability of crossover-type genetic operation, instead of a mutation. skip_mutation_failures : bool, default=True Whether to skip mutation and crossover failures, rather than simply re-sampling the current member. migration : bool, default=True Whether to migrate. hof_migration : bool, default=True Whether to have the hall of fame migrate. topn : int, default=12 How many top individuals migrate from each population. should_optimize_constants : bool, default=True Whether to numerically optimize constants (Nelder-Mead/Newton) at the end of each iteration. optimizer_algorithm : str, default="BFGS" Optimization scheme to use for optimizing constants. Can currently be `NelderMead` or `BFGS`. optimizer_nrestarts : int, default=2 Number of time to restart the constants optimization process with different initial conditions. optimize_probability : float, default=0.14 Probability of optimizing the constants during a single iteration of the evolutionary algorithm. optimizer_iterations : int, default=8 Number of iterations that the constants optimizer can take. perturbation_factor : float, default=0.076 Constants are perturbed by a max factor of (perturbation_factor*T + 1). Either multiplied by this or divided by this. tournament_selection_n : int, default=10 Number of expressions to consider in each tournament. tournament_selection_p : float, default=0.86 Probability of selecting the best expression in each tournament. The probability will decay as p*(1-p)^n for other expressions, sorted by loss. procs : int, default=multiprocessing.cpu_count() Number of processes (=number of populations running). multithreading : bool, default=True Use multithreading instead of distributed backend. Using procs=0 will turn off both. cluster_manager : str, default=None 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. batching : bool, default=False Whether to compare population members on small batches during evolution. Still uses full dataset for comparing against hall of fame. batch_size : int, default=50 The amount of data to use if doing batching. fast_cycle : bool, default=False (experimental) Batch over population subsamples. This is a slightly different algorithm than regularized evolution, but does cycles 15% faster. May be algorithmically less efficient. precision : int, default=32 What precision to use for the data. By default this is 32 (float32), but you can select 64 or 16 as well. random_state : int, Numpy RandomState instance or None, default=None Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. deterministic : bool, default=False Make a PySR search give the same result every run. To use this, you must turn off parallelism (with :param`procs`=0, :param`multithreading`=False), and set :param`random_state` to a fixed seed. warm_start : bool, default=False Tells fit to continue from where the last call to fit finished. If false, each call to fit will be fresh, overwriting previous results. verbosity : int, default=1e9 What verbosity level to use. 0 means minimal print statements. update_verbosity : int, default=None What verbosity level to use for package updates. Will take value of :param`verbosity` if not given. progress : bool, default=True Whether to use a progress bar instead of printing to stdout. equation_file : str, default=None Where to save the files (.csv separated by |). temp_equation_file : bool, default=False Whether to put the hall of fame file in the temp directory. Deletion is then controlled with the :param`delete_tempfiles` parameter. tempdir : str, default=None directory for the temporary files. delete_tempfiles : bool, default=True Whether to delete the temporary files after finishing. julia_project : str, default=None 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: bool, default=True Whether to automatically update Julia packages. output_jax_format : bool, default=False Whether to create a 'jax_format' column in the output, containing jax-callable functions and the default parameters in a jax array. output_torch_format : bool, default=False Whether to create a 'torch_format' column in the output, containing a torch module with trainable parameters. extra_sympy_mappings : dict[str, Callable], default=None Provides mappings between custom :param`binary_operators` or :param`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, :param`extra_sympy_mappings` would be `{"inv": lambda x: 1/x}`. extra_jax_mappings : dict[Callable, str], default=None Similar to :param`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`. extra_torch_mappings : dict[Callable, Callable], default=None The same as :param`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}` denoise : bool, default=False Whether to use a Gaussian Process to denoise the data before inputting to PySR. Can help PySR fit noisy data. select_k_features : int, default=None 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. kwargs : dict, default=None 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. nout_ : int Number of output dimensions. selection_mask_ : list[int] of length `select_k_features` List of indices for input features that are selected when :param`select_k_features` is set. tempdir_ : Path Path to the temporary equations directory. equation_file_ : str Output equation file name produced by the julia backend. raw_julia_state_ : tuple[list[PyCall.jlwrap], PyCall.jlwrap] The state for the julia SymbolicRegression.jl backend post fitting. equation_file_contents_ : list[pandas.DataFrame] Contents of the equation file output by the Julia backend. Notes ----- 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. 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", ... 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]) ``` """ def __init__( self, model_selection="best", *, binary_operators=None, unary_operators=None, niterations=40, populations=15, population_size=33, max_evals=None, maxsize=20, maxdepth=None, warmup_maxsize_by=0.0, timeout_in_seconds=None, constraints=None, nested_constraints=None, loss="L2DistLoss()", complexity_of_operators=None, complexity_of_constants=1, complexity_of_variables=1, parsimony=0.0032, use_frequency=True, use_frequency_in_tournament=True, alpha=0.1, annealing=False, early_stop_condition=None, ncyclesperiteration=550, fraction_replaced=0.000364, fraction_replaced_hof=0.035, weight_add_node=0.79, weight_insert_node=5.1, weight_delete_node=1.7, weight_do_nothing=0.21, weight_mutate_constant=0.048, weight_mutate_operator=0.47, weight_randomize=0.00023, weight_simplify=0.0020, crossover_probability=0.066, skip_mutation_failures=True, migration=True, hof_migration=True, topn=12, should_optimize_constants=True, optimizer_algorithm="BFGS", optimizer_nrestarts=2, optimize_probability=0.14, optimizer_iterations=8, perturbation_factor=0.076, tournament_selection_n=10, tournament_selection_p=0.86, procs=cpu_count(), multithreading=None, cluster_manager=None, batching=False, batch_size=50, fast_cycle=False, precision=32, random_state=None, deterministic=False, warm_start=False, verbosity=1e9, update_verbosity=None, progress=True, equation_file=None, temp_equation_file=False, tempdir=None, delete_tempfiles=True, julia_project=None, update=True, output_jax_format=False, output_torch_format=False, extra_sympy_mappings=None, extra_torch_mappings=None, extra_jax_mappings=None, denoise=False, select_k_features=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.ncyclesperiteration = ncyclesperiteration # - Equation Constraints self.maxsize = maxsize self.maxdepth = maxdepth self.constraints = constraints self.nested_constraints = nested_constraints self.warmup_maxsize_by = warmup_maxsize_by # - 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.loss = loss 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.use_frequency = use_frequency self.use_frequency_in_tournament = use_frequency_in_tournament 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_randomize = weight_randomize self.weight_simplify = weight_simplify 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 # Solver parameters self.procs = procs self.multithreading = multithreading self.cluster_manager = cluster_manager self.batching = batching self.batch_size = batch_size self.fast_cycle = fast_cycle self.precision = precision 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.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.julia_project = julia_project 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 deprecated_kwargs = make_deprecated_kwargs_for_pysr_regressor() 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, ) else: raise TypeError( f"{k} is not a valid keyword argument for PySRRegressor." ) def __repr__(self): """ Prints 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 = ["" for _ in range(len(equations))] if self.model_selection == "accuracy": chosen_row = -1 elif self.model_selection == "best": chosen_row = equations["score"].idxmax() else: raise NotImplementedError 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): """ Handles pickle serialization for PySRRegressor. The Scikit-learn standard requires estimators to be serializable via `pickle.dumps()`. However, `PyCall.jlwrap` does not support pickle serialization. Thus, for `PySRRegressor` to support pickle serialization, the `raw_julia_state_` attribute must be hidden from pickle. This will prevent the `warm_start` of any model that is loaded via `pickle.loads()`, but does allow all other attributes of a fitted `PySRRegressor` estimator to be serialized. Note: Jax and Torch format equations are also removed from the pickled instance. """ state = self.__dict__ if "raw_julia_state_" in state: warnings.warn( "raw_julia_state_ cannot be pickled and will be removed from the " "serialized instance. This will prevent a `warm_start` fit of any " "model that is deserialized via `pickle.load()`." ) pickled_state = { key: None if key == "raw_julia_state_" else value for key, value in state.items() } if "equations_" in pickled_state: 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 @property def equations(self): # pragma: no cover warnings.warn( "PySRRegressor.equations is now deprecated. " "Please use PySRRegressor.equations_ instead.", FutureWarning, ) return self.equations_ def get_best(self, index=None): """ Get best equation using `model_selection`. Parameters ---------- index : int, default=None If you wish to select a particular equation from `self.equations_`, give the row number here. This overrides the :param`model_selection` parameter. 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 self.equations_ is None: raise ValueError("No equations have been generated yet.") if index is not None: if isinstance(self.equations_, list): assert isinstance(index, list) return [eq.iloc[i] for eq, i in zip(self.equations_, index)] return self.equations_.iloc[index] if self.model_selection == "accuracy": if isinstance(self.equations_, list): return [eq.iloc[-1] for eq in self.equations_] return self.equations_.iloc[-1] elif self.model_selection == "best": if isinstance(self.equations_, list): return [eq.iloc[eq["score"].idxmax()] for eq in self.equations_] return self.equations_.iloc[self.equations_["score"].idxmax()] else: raise NotImplementedError( f"{self.model_selection} is not a valid model selection strategy." ) def _setup_equation_file(self): """ Sets the full pathname of the equation file, using :param`tempdir` and :param`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): """ Ensures 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. You should consider " "turning `use_frequency` to False, and perhaps use `warmup_maxsize_by`." ) 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. " ) # 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}" ) # 'Mutable' parameter validation buffer_available = "buffer" in sys.stdout.__dir__() # 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": self.verbosity, "progress": buffer_available, } 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 :param`batch_size` must be greater than or equal to one. " ":param`batch_size` has been increased to equal one." ) parameter_value = 1 elif parameter == "progress" and not buffer_available: 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): """ Validates the parameters passed to the :term`fit` method. This method also sets the `nout_` attribute. Parameters ---------- X : {ndarray | pandas.DataFrame} of shape (n_samples, n_features) Training data. y : {ndarray | pandas.DataFrame} of shape (n_samples,) or (n_samples, n_targets) Target values. Will be cast to X's dtype if necessary. Xresampled : {ndarray | pandas.DataFrame} of shape (n_resampled, n_features), default=None Resampled training data used for denoising. weights : {ndarray | pandas.DataFrame} 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 : list[str] of length n_features Names of each variable in the training dataset, `X`. 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`. """ if isinstance(X, pd.DataFrame): if variable_names: variable_names = None warnings.warn( ":param`variable_names` has been reset to `None` as `X` is a DataFrame. " "Using DataFrame column names instead." ) if X.columns.is_object() 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 [" " in name for name in variable_names].any(): 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=X, y=y, reset=True, multi_output=True) self.feature_names_in_ = _check_feature_names_in(self, variable_names) 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!") return X, y, Xresampled, weights, variable_names def _pre_transform_training_data( self, X, y, Xresampled, variable_names, random_state ): """ Transforms the training data before fitting the symbolic regressor. This method also updates/sets the `selection_mask_` attribute. Parameters ---------- X : {ndarray | pandas.DataFrame} of shape (n_samples, n_features) Training data. y : {ndarray | pandas.DataFrame} of shape (n_samples,) or (n_samples, n_targets) Target values. Will be cast to X's dtype if necessary. Xresampled : {ndarray | pandas.DataFrame} of shape (n_resampled, n_features), default=None Resampled training data used for denoising. variable_names : list[str] of length n_features Names of each variable in the training dataset, `X`. random_state : int, Numpy RandomState instance or None, default=None Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. Returns ------- X_transformed : ndarray of shape (n_samples, n_features) Transformed training data. n_samples will be equal to :param`Xresampled.shape[0]` if :param`self.denoise` is `True`, and :param`Xresampled is not None`, otherwise it will be equal to :param`X.shape[0]`. n_features will be equal to :param`self.select_k_features` if `self.select_k_features is not None`, otherwise it will be equal to :param`X.shape[1]` y_transformed : ndarray of shape (n_samples,) or (n_samples, n_outputs) Transformed target data. n_samples will be equal to :param`Xresampled.shape[0]` if :param`self.denoise` is `True`, and :param`Xresampled is not None`, otherwise it will be equal to :param`X.shape[0]`. variable_names_transformed : list[str] of length n_features Names of each variable in the transformed dataset, `X_transformed`. """ # Feature selection transformation if self.select_k_features: self.selection_mask_ = run_feature_selection( X, y, self.select_k_features, random_state=random_state ) X = X[:, self.selection_mask_] if Xresampled is not None: Xresampled = Xresampled[:, self.selection_mask_] # Reduce variable_names to selection variable_names = [variable_names[i] for i in self.selection_mask_] # Re-perform data validation and feature name updating X, y = self._validate_data(X=X, y=y, reset=True, multi_output=True) # Update feature names with selected variable names self.feature_names_in_ = _check_feature_names_in(self, variable_names) print(f"Using features {self.feature_names_in_}") # Denoising transformation if self.denoise: if self.nout_ > 1: y = np.stack( [ _denoise( X, y[:, i], Xresampled=Xresampled, random_state=random_state )[1] for i in range(self.nout_) ], axis=1, ) if Xresampled is not None: X = Xresampled else: X, y = _denoise(X, y, Xresampled=Xresampled, random_state=random_state) return X, y, variable_names def _run(self, X, y, mutated_params, weights, seed): """ Run the symbolic regression fitting process on the julia backend. Parameters ---------- X : {ndarray | pandas.DataFrame} of shape (n_samples, n_features) Training data. y : {ndarray | pandas.DataFrame} of shape (n_samples,) or (n_samples, n_targets) Target values. 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} 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 global Main # 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 Main is None: if multithreading: os.environ["JULIA_NUM_THREADS"] = str(self.procs) Main = init_julia() if cluster_manager is not None: Main.eval(f"import ClusterManagers: addprocs_{cluster_manager}") cluster_manager = Main.eval(f"addprocs_{cluster_manager}") if not already_ran: julia_project, is_shared = _get_julia_project(self.julia_project) Main.eval("using Pkg") io = "devnull" if update_verbosity == 0 else "stderr" io_arg = f"io={io}" if is_julia_version_greater_eq(Main, "1.6") else "" Main.eval( f'Pkg.activate("{_escape_filename(julia_project)}", shared = Bool({int(is_shared)}), {io_arg})' ) from julia.api import JuliaError if is_shared: # Install SymbolicRegression.jl: _add_sr_to_julia_project(Main, io_arg) try: if self.update: Main.eval(f"Pkg.resolve({io_arg})") Main.eval(f"Pkg.instantiate({io_arg})") else: Main.eval(f"Pkg.instantiate({io_arg})") except (JuliaError, RuntimeError) as e: raise ImportError(import_error_string(julia_project)) from e Main.eval("using SymbolicRegression") Main.plus = Main.eval("(+)") Main.sub = Main.eval("(-)") Main.mult = Main.eval("(*)") Main.pow = Main.eval("(^)") Main.div = Main.eval("(/)") # 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 ) 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 = Main.eval(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 = Main.eval(complexity_of_operators_str) custom_loss = Main.eval(self.loss) early_stop_condition = Main.eval( str(self.early_stop_condition) if self.early_stop_condition else None ) mutation_weights = np.array( [ self.weight_mutate_constant, self.weight_mutate_operator, self.weight_add_node, self.weight_insert_node, self.weight_delete_node, self.weight_simplify, self.weight_randomize, self.weight_do_nothing, ], dtype=float, ) # Call to Julia backend. # See https://github.com/MilesCranmer/SymbolicRegression.jl/blob/master/src/OptionsStruct.jl options = Main.Options( binary_operators=Main.eval(str(tuple(binary_operators)).replace("'", "")), unary_operators=Main.eval(str(tuple(unary_operators)).replace("'", "")), bin_constraints=bin_constraints, una_constraints=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, loss=custom_loss, maxsize=int(self.maxsize), hofFile=_escape_filename(self.equation_file_), npopulations=int(self.populations), batching=self.batching, batchSize=int(min([batch_size, len(X)]) if self.batching else len(X)), mutationWeights=mutation_weights, probPickFirst=self.tournament_selection_p, ns=self.tournament_selection_n, # These have the same name: parsimony=self.parsimony, alpha=self.alpha, maxdepth=maxdepth, fast_cycle=self.fast_cycle, migration=self.migration, hofMigration=self.hof_migration, fractionReplacedHof=self.fraction_replaced_hof, shouldOptimizeConstants=self.should_optimize_constants, warmupMaxsizeBy=self.warmup_maxsize_by, useFrequency=self.use_frequency, useFrequencyInTournament=self.use_frequency_in_tournament, npop=self.population_size, ncyclesperiteration=self.ncyclesperiteration, fractionReplaced=self.fraction_replaced, topn=self.topn, verbosity=self.verbosity, optimizer_algorithm=self.optimizer_algorithm, optimizer_nrestarts=self.optimizer_nrestarts, optimize_probability=self.optimize_probability, optimizer_iterations=self.optimizer_iterations, perturbationFactor=self.perturbation_factor, annealing=self.annealing, stateReturn=True, # Required for state saving. progress=progress, timeout_in_seconds=self.timeout_in_seconds, crossoverProbability=self.crossover_probability, skip_mutation_failures=self.skip_mutation_failures, max_evals=self.max_evals, earlyStopCondition=early_stop_condition, seed=seed, deterministic=self.deterministic, ) # Convert data to desired precision np_dtype = {16: np.float16, 32: np.float32, 64: np.float64}[self.precision] # This converts the data into a Julia array: Main.X = np.array(X, dtype=np_dtype).T if len(y.shape) == 1: Main.y = np.array(y, dtype=np_dtype) else: Main.y = np.array(y, dtype=np_dtype).T if weights is not None: if len(weights.shape) == 1: Main.weights = np.array(weights, dtype=np_dtype) else: Main.weights = np.array(weights, dtype=np_dtype).T else: Main.weights = None cprocs = 0 if multithreading else self.procs # Call to Julia backend. # See https://github.com/MilesCranmer/SymbolicRegression.jl/blob/master/src/SymbolicRegression.jl self.raw_julia_state_ = Main.EquationSearch( Main.X, Main.y, weights=Main.weights, niterations=int(self.niterations), varMap=self.feature_names_in_.tolist(), options=options, numprocs=int(cprocs), multithreading=bool(multithreading), saved_state=self.raw_julia_state_, addprocs_function=cluster_manager, ) # 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=None, ): """ Search for equations to fit the dataset and store them in `self.equations_`. Parameters ---------- X : {ndarray | pandas.DataFrame} of shape (n_samples, n_features) Training data. y : {ndarray | pandas.DataFrame} of shape (n_samples,) or (n_samples, n_targets) Target values. Will be cast to X's dtype if necessary. Xresampled : {ndarray | pandas.DataFrame} of shape (n_resampled, n_features), default=None Resampled training data to generate a denoised data on. This will be used as the training data, rather than `X`. weights : {ndarray | pandas.DataFrame} of the same shape as y, default=None 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], default=None A list of names for the variables, rather than "x0", "x1", etc. If :param`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?id=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, ) # Fitting procedure return self._run(X, y, mutated_params, weights=weights, seed=seed) def refresh(self, checkpoint_file=None): """ Updates self.equations_ with any new options passed, such as :param`extra_sympy_mappings`. Parameters ---------- checkpoint_file : str, default=None Path to checkpoint hall of fame file to be loaded. """ check_is_fitted(self, attributes=["equation_file_"]) if checkpoint_file: self.equation_file_ = checkpoint_file self.equation_file_contents_ = None 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} of shape (n_samples, n_features) Training data. index : int, default=None If you want to compute the output of an expression using a particular row of `self.equations_`, you may specify the index here. 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 :param`extra_sympy_mappings`, " "e.g., `model.set_params(extra_sympy_mappings={'inv': lambda x: 1 / x})`." ) from error def sympy(self, index=None): """ Return sympy representation of the equation(s) chosen by `model_selection`. Parameters ---------- index : int, default=None If you wish to select a particular equation from `self.equations_`, give the index number here. This overrides the `model_selection` parameter. 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"] def latex(self, index=None): """ Return latex representation of the equation(s) chosen by `model_selection`. Parameters ---------- index : int, default=None If you wish to select a particular equation from `self.equations_`, give the index number here. This overrides the `model_selection` parameter. 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: return [sympy.latex(s) for s in sympy_representation] return sympy.latex(sympy_representation) 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, default=None If you wish to select a particular equation from `self.equations_`, give the row number here. This overrides the `model_selection` parameter. 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"] 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, default=None If you wish to select a particular equation from `self.equations_`, give the row number here. This overrides the `model_selection` parameter. 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"] 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): df = pd.read_csv( str(self.equation_file_) + f".out{i}" + ".bkup", sep="|", ) # Rename Complexity column to complexity: df.rename( columns={ "Complexity": "complexity", "MSE": "loss", "Equation": "equation", }, inplace=True, ) all_outputs.append(df) else: all_outputs = [pd.read_csv(str(self.equation_file_) + ".bkup", sep="|")] all_outputs[-1].rename( columns={ "Complexity": "complexity", "MSE": "loss", "Equation": "equation", }, inplace=True, ) except FileNotFoundError: raise RuntimeError( "Couldn't find equation file! The equation search likely exited " "before a single iteration completed." ) return all_outputs 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 = [] for output in self.equation_file_contents_: scores = [] lastMSE = None lastComplexity = 0 sympy_format = [] lambda_format = [] if self.output_jax_format: jax_format = [] if self.output_torch_format: torch_format = [] local_sympy_mappings = { **(self.extra_sympy_mappings if self.extra_sympy_mappings else {}), **sympy_mappings, } sympy_symbols = [ sympy.Symbol(variable) for variable in self.feature_names_in_ ] for _, eqn_row in output.iterrows(): eqn = sympify(eqn_row["equation"], locals=local_sympy_mappings) sympy_format.append(eqn) # Numpy: lambda_format.append( CallableEquation( sympy_symbols, eqn, self.selection_mask_, self.feature_names_in_ ) ) # JAX: if self.output_jax_format: from .export_jax import sympy2jax func, params = sympy2jax( eqn, sympy_symbols, selection=self.selection_mask_, extra_jax_mappings=( self.extra_jax_mappings if self.extra_jax_mappings else {} ), ) jax_format.append({"callable": func, "parameters": params}) # Torch: if self.output_torch_format: from .export_torch import sympy2torch module = sympy2torch( eqn, sympy_symbols, selection=self.selection_mask_, extra_torch_mappings=( self.extra_torch_mappings if self.extra_torch_mappings else {} ), ) 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 _denoise(X, y, Xresampled=None, random_state=None): """Denoise the dataset using a Gaussian process""" from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import RBF, WhiteKernel, ConstantKernel gp_kernel = RBF(np.ones(X.shape[1])) + WhiteKernel(1e-1) + ConstantKernel() gpr = GaussianProcessRegressor( kernel=gp_kernel, n_restarts_optimizer=50, random_state=random_state ) gpr.fit(X, y) if Xresampled is not None: return Xresampled, gpr.predict(Xresampled) return X, gpr.predict(X) # Function has not been removed only due to usage in module tests def _handle_feature_selection(X, select_k_features, y, variable_names): if select_k_features is not None: selection = run_feature_selection(X, y, select_k_features) print(f"Using features {[variable_names[i] for i in selection]}") X = X[:, selection] else: selection = None return X, selection def run_feature_selection(X, y, select_k_features, random_state=None): """ Use a gradient boosting tree regressor as a proxy for finding the k most important features in X, returning indices for those features as output. """ from sklearn.ensemble import RandomForestRegressor from sklearn.feature_selection import SelectFromModel clf = RandomForestRegressor( n_estimators=100, max_depth=3, random_state=random_state ) clf.fit(X, y) selector = SelectFromModel( clf, threshold=-np.inf, max_features=select_k_features, prefit=True ) return selector.get_support(indices=True)