import os import sys from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter from collections import namedtuple import pathlib import numpy as np import pandas as pd import sympy from sympy import sympify, Symbol, lambdify import subprocess import tempfile import shutil from pathlib import Path from datetime import datetime import warnings global_state = dict( equation_file='hall_of_fame.csv', n_features=None, variable_names=[], extra_sympy_mappings={}, extra_torch_mappings={}, extra_jax_mappings={}, output_jax_format=False, output_torch_format=False, multioutput=False, nout=1, selection=None ) 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': lambda x : sympy.cos(x), 'sin': lambda x : sympy.sin(x), 'tan': lambda x : sympy.tan(x), 'cosh': lambda x : sympy.cosh(x), 'sinh': lambda x : sympy.sinh(x), 'tanh': lambda x : sympy.tanh(x), 'exp': lambda x : sympy.exp(x), 'acos': lambda x : sympy.acos(x), 'asin': lambda x : sympy.asin(x), 'atan': lambda x : sympy.atan(x), 'acosh':lambda x : sympy.acosh(abs(x) + 1), 'acosh_abs':lambda x : sympy.acosh(abs(x) + 1), 'asinh':lambda x : sympy.asinh(x), 'atanh':lambda x : sympy.atanh(sympy.Mod(x+1, 2)-1), 'atanh_clip':lambda x : sympy.atanh(sympy.Mod(x+1, 2)-1), 'abs': lambda x : abs(x), 'mod': lambda x, y : sympy.Mod(x, y), 'erf': lambda x : sympy.erf(x), 'erfc': lambda x : sympy.erfc(x), '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': lambda x : sympy.floor(x), 'ceil': lambda x : sympy.ceil(x), 'sign': lambda x : sympy.sign(x), 'gamma': lambda x : sympy.gamma(x), } class CallableEquation(object): """Simple wrapper for numpy lambda functions built with sympy""" def __init__(self, sympy_symbols, eqn, selection=None): self._sympy = eqn self._sympy_symbols = sympy_symbols self._selection = selection self._lambda = lambdify(sympy_symbols, eqn) def __repr__(self): return f"PySRFunction(X=>{self._sympy})" def __call__(self, X): if self._selection is not None: return self._lambda(*X[:, self._selection].T) else: return self._lambda(*X.T) def pysr(X, y, weights=None, binary_operators=None, unary_operators=None, procs=4, loss='L2DistLoss()', populations=20, niterations=100, ncyclesperiteration=300, alpha=0.1, annealing=False, fractionReplaced=0.10, fractionReplacedHof=0.10, npop=1000, parsimony=1e-4, migration=True, hofMigration=True, shouldOptimizeConstants=True, topn=10, weightAddNode=1, weightInsertNode=3, weightDeleteNode=3, weightDoNothing=1, weightMutateConstant=10, weightMutateOperator=1, weightRandomize=1, weightSimplify=0.01, perturbationFactor=1.0, timeout=None, extra_sympy_mappings=None, extra_torch_mappings=None, extra_jax_mappings=None, equation_file=None, verbosity=1e9, progress=True, maxsize=20, fast_cycle=False, maxdepth=None, variable_names=None, batching=False, batchSize=50, select_k_features=None, warmupMaxsizeBy=0.0, constraints=None, useFrequency=True, tempdir=None, delete_tempfiles=True, julia_optimization=3, julia_project=None, user_input=True, update=True, temp_equation_file=False, output_jax_format=False, output_torch_format=False, optimizer_algorithm="BFGS", optimizer_nrestarts=3, optimize_probability=1.0, optimizer_iterations=10 ): """Run symbolic regression to fit f(X[i, :]) ~ y[i] for all i. Note: most default parameters have been tuned over several example equations, but you should adjust `niterations`, `binary_operators`, `unary_operators` to your requirements. :param X: np.ndarray or pandas.DataFrame, 2D array. Rows are examples, columns are features. If pandas DataFrame, the columns are used for variable names (so make sure they don't contain spaces). :param y: np.ndarray, 1D array (rows are examples) or 2D array (rows are examples, columns are outputs). Putting in a 2D array will trigger a search for equations for each feature of y. :param weights: np.ndarray, same shape as y. Each element is how to weight the mean-square-error loss for that particular element of y. :param binary_operators: list, List of strings giving the binary operators in Julia's Base. Default is ["+", "-", "*", "/",]. :param unary_operators: list, Same but for operators taking a single scalar. Default is []. :param procs: int, Number of processes (=number of populations running). :param loss: str, 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. Among the included losses, these are as follows. 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)`. :param populations: int, Number of populations running. :param niterations: int, Number of iterations of the algorithm to run. The best equations are printed, and migrate between populations, at the end of each. :param ncyclesperiteration: int, Number of total mutations to run, per 10 samples of the population, per iteration. :param alpha: float, Initial temperature. :param annealing: bool, Whether to use annealing. You should (and it is default). :param fractionReplaced: float, How much of population to replace with migrating equations from other populations. :param fractionReplacedHof: float, How much of population to replace with migrating equations from hall of fame. :param npop: int, Number of individuals in each population :param parsimony: float, Multiplicative factor for how much to punish complexity. :param migration: bool, Whether to migrate. :param hofMigration: bool, Whether to have the hall of fame migrate. :param shouldOptimizeConstants: bool, Whether to numerically optimize constants (Nelder-Mead/Newton) at the end of each iteration. :param topn: int, How many top individuals migrate from each population. :param perturbationFactor: float, Constants are perturbed by a max factor of (perturbationFactor*T + 1). Either multiplied by this or divided by this. :param weightAddNode: float, Relative likelihood for mutation to add a node :param weightInsertNode: float, Relative likelihood for mutation to insert a node :param weightDeleteNode: float, Relative likelihood for mutation to delete a node :param weightDoNothing: float, Relative likelihood for mutation to leave the individual :param weightMutateConstant: float, Relative likelihood for mutation to change the constant slightly in a random direction. :param weightMutateOperator: float, Relative likelihood for mutation to swap an operator. :param weightRandomize: float, Relative likelihood for mutation to completely delete and then randomly generate the equation :param weightSimplify: float, Relative likelihood for mutation to simplify constant parts by evaluation :param timeout: float, Time in seconds to timeout search :param equation_file: str, Where to save the files (.csv separated by |) :param verbosity: int, What verbosity level to use. 0 means minimal print statements. :param progress: bool, Whether to use a progress bar instead of printing to stdout. :param maxsize: int, Max size of an equation. :param maxdepth: int, Max depth of an equation. You can use both maxsize and maxdepth. maxdepth is by default set to = maxsize, which means that it is redundant. :param fast_cycle: bool, (experimental) - batch over population subsamples. This is a slightly different algorithm than regularized evolution, but does cycles 15% faster. May be algorithmically less efficient. :param variable_names: list, a list of names for the variables, other than "x0", "x1", etc. :param batching: bool, whether to compare population members on small batches during evolution. Still uses full dataset for comparing against hall of fame. :param batchSize: int, the amount of data to use if doing batching. :param select_k_features: (None, 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. :param warmupMaxsizeBy: 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. :param constraints: dict 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 exponent. Use this to force more interpretable solutions. :param useFrequency: 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. :param julia_optimization: int, Optimization level (0, 1, 2, 3) :param tempdir: str or None, directory for the temporary files :param delete_tempfiles: bool, whether to delete the temporary files after finishing :param julia_project: str or 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. :param user_input: Whether to ask for user input or not for installing (to be used for automated scripts). Will choose to install when asked. :param update: Whether to automatically update Julia packages. :param temp_equation_file: Whether to put the hall of fame file in the temp directory. Deletion is then controlled with the delete_tempfiles argument. :param output_jax_format: Whether to create a 'jax_format' column in the output, containing jax-callable functions and the default parameters in a jax array. :param output_torch_format: Whether to create a 'torch_format' column in the output, containing a torch module with trainable parameters. :returns: pd.DataFrame or list, Results dataframe, giving complexity, MSE, and equations (as strings), as well as functional forms. If list, each element corresponds to a dataframe of equations for each output. """ if binary_operators is None: binary_operators = '+ * - /'.split(' ') if unary_operators is None: unary_operators = [] if extra_sympy_mappings is None: extra_sympy_mappings = {} if variable_names is None: variable_names = [] if constraints is None: constraints = {} assert optimizer_algorithm in ['NelderMead', 'BFGS'] if isinstance(X, pd.DataFrame): variable_names = list(X.columns) X = np.array(X) use_custom_variable_names = (len(variable_names) != 0) if len(X.shape) == 1: X = X[:, None] _check_assertions(X, binary_operators, unary_operators, use_custom_variable_names, variable_names, weights, y) _check_for_julia_installation() if len(X) > 10000 and not batching: warnings.warn("Note: you are running with more than 10,000 datapoints. You should consider turning on batching (https://pysr.readthedocs.io/en/latest/docs/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.") if maxsize > 40: warnings.warn("Note: Using a large maxsize for the equation search will be slow and use significant memory. You should consider turning `useFrequency` to False, and perhaps use `warmupMaxsizeBy`.") X, variable_names, selection = _handle_feature_selection( X, select_k_features, use_custom_variable_names, variable_names, y ) if maxdepth is None: maxdepth = maxsize if isinstance(binary_operators, str): binary_operators = [binary_operators] if isinstance(unary_operators, str): unary_operators = [unary_operators] if len(y.shape) == 1 or (len(y.shape) == 2 and y.shape[1] == 1): multioutput = False nout = 1 y = y.reshape(-1) elif len(y.shape) == 2: multioutput = True nout = y.shape[1] else: raise NotImplementedError("y shape not supported!") kwargs = dict(X=X, y=y, weights=weights, alpha=alpha, annealing=annealing, batchSize=batchSize, batching=batching, binary_operators=binary_operators, fast_cycle=fast_cycle, fractionReplaced=fractionReplaced, ncyclesperiteration=ncyclesperiteration, niterations=niterations, npop=npop, topn=topn, verbosity=verbosity, progress=progress, update=update, julia_optimization=julia_optimization, timeout=timeout, fractionReplacedHof=fractionReplacedHof, hofMigration=hofMigration, maxdepth=maxdepth, maxsize=maxsize, migration=migration, optimizer_algorithm=optimizer_algorithm, optimizer_nrestarts=optimizer_nrestarts, optimize_probability=optimize_probability, optimizer_iterations=optimizer_iterations, parsimony=parsimony, perturbationFactor=perturbationFactor, populations=populations, procs=procs, shouldOptimizeConstants=shouldOptimizeConstants, unary_operators=unary_operators, useFrequency=useFrequency, use_custom_variable_names=use_custom_variable_names, variable_names=variable_names, warmupMaxsizeBy=warmupMaxsizeBy, weightAddNode=weightAddNode, weightDeleteNode=weightDeleteNode, weightDoNothing=weightDoNothing, weightInsertNode=weightInsertNode, weightMutateConstant=weightMutateConstant, weightMutateOperator=weightMutateOperator, weightRandomize=weightRandomize, weightSimplify=weightSimplify, constraints=constraints, extra_sympy_mappings=extra_sympy_mappings, extra_jax_mappings=extra_jax_mappings, extra_torch_mappings=extra_torch_mappings, julia_project=julia_project, loss=loss, output_jax_format=output_jax_format, output_torch_format=output_torch_format, selection=selection, multioutput=multioutput, nout=nout) kwargs = {**_set_paths(tempdir), **kwargs} if temp_equation_file: equation_file = kwargs['tmpdir'] / f'hall_of_fame.csv' elif equation_file is None: date_time = datetime.now().strftime("%Y-%m-%d_%H%M%S.%f")[:-3] equation_file = 'hall_of_fame_' + date_time + '.csv' kwargs = {**dict(equation_file=equation_file), **kwargs} pkg_directory = kwargs['pkg_directory'] manifest_file = None if kwargs['julia_project'] is not None: manifest_filepath = Path(kwargs['julia_project']) / 'Manifest.toml' else: manifest_filepath = pkg_directory / 'Manifest.toml' kwargs['need_install'] = False if not (manifest_filepath).is_file(): kwargs['need_install'] = (not user_input) or _yesno("I will install Julia packages using PySR's Project.toml file. OK?") if kwargs['need_install']: print("OK. I will install at launch.") assert update kwargs['def_hyperparams'] = _create_inline_operators(**kwargs) _handle_constraints(**kwargs) kwargs['constraints_str'] = _make_constraints_str(**kwargs) kwargs['def_hyperparams'] = _make_hyperparams_julia_str(**kwargs) kwargs['def_datasets'] = _make_datasets_julia_str(**kwargs) _create_julia_files(**kwargs) _final_pysr_process(**kwargs) _set_globals(**kwargs) equations = get_hof(**kwargs) if delete_tempfiles: shutil.rmtree(kwargs['tmpdir']) return equations def _set_globals(X, **kwargs): global global_state global_state['n_features'] = X.shape[1] for key, value in kwargs.items(): if key in global_state: global_state[key] = value def _final_pysr_process(julia_optimization, runfile_filename, timeout, **kwargs): command = [ f'julia', f'-O{julia_optimization:d}', str(runfile_filename), ] if timeout is not None: command = [f'timeout', f'{timeout}'] + command _cmd_runner(command, **kwargs) def _cmd_runner(command, **kwargs): if kwargs['verbosity'] > 0: print("Running on", ' '.join(command)) process = subprocess.Popen(command, stdout=subprocess.PIPE, bufsize=-1) try: while True: line = process.stdout.readline() if not line: break decoded_line = (line.decode('utf-8') .replace('\\033[K', '\033[K') .replace('\\033[1A', '\033[1A') .replace('\\033[1B', '\033[1B') .replace('\\r', '\r') .encode(sys.stdout.encoding, errors='replace')) sys.stdout.buffer.write(decoded_line) sys.stdout.flush() process.stdout.close() process.wait() except KeyboardInterrupt: print("Killing process... will return when done.") process.kill() def _create_julia_files(dataset_filename, def_datasets, hyperparam_filename, def_hyperparams, fractionReplaced, ncyclesperiteration, niterations, npop, runfile_filename, topn, verbosity, julia_project, procs, weights, X, variable_names, pkg_directory, need_install, update, **kwargs): with open(hyperparam_filename, 'w') as f: print(def_hyperparams, file=f) with open(dataset_filename, 'w') as f: print(def_datasets, file=f) with open(runfile_filename, 'w') as f: if julia_project is None: julia_project = pkg_directory else: julia_project = Path(julia_project) print(f'import Pkg', file=f) print(f'Pkg.activate("{_escape_filename(julia_project)}")', file=f) if need_install: print(f'Pkg.instantiate()', file=f) print(f'Pkg.update()', file=f) print(f'Pkg.precompile()', file=f) elif update: print(f'Pkg.update()', file=f) print(f'using SymbolicRegression', file=f) print(f'include("{_escape_filename(hyperparam_filename)}")', file=f) print(f'include("{_escape_filename(dataset_filename)}")', file=f) if len(variable_names) == 0: varMap = "[" + ",".join([f'"x{i}"' for i in range(X.shape[1])]) + "]" else: varMap = "[" + ",".join(['"' + vname + '"' for vname in variable_names]) + "]" if weights is not None: print(f'EquationSearch(X, y, weights=weights, niterations={niterations:d}, varMap={varMap}, options=options, numprocs={procs})', file=f) else: print(f'EquationSearch(X, y, niterations={niterations:d}, varMap={varMap}, options=options, numprocs={procs})', file=f) def _make_datasets_julia_str(X, X_filename, weights, weights_filename, y, y_filename, multioutput, **kwargs): def_datasets = """using DelimitedFiles""" np.savetxt(X_filename, X.astype(np.float32), delimiter=',') if multioutput: np.savetxt(y_filename, y.astype(np.float32), delimiter=',') else: np.savetxt(y_filename, y.reshape(-1, 1).astype(np.float32), delimiter=',') if weights is not None: if multioutput: np.savetxt(weights_filename, weights.astype(np.float32), delimiter=',') else: np.savetxt(weights_filename, weights.reshape(-1, 1).astype(np.float32), delimiter=',') def_datasets += f""" X = copy(transpose(readdlm("{_escape_filename(X_filename)}", ',', Float32, '\\n')))""" if multioutput: def_datasets+= f""" y = copy(transpose(readdlm("{_escape_filename(y_filename)}", ',', Float32, '\\n')))""" else: def_datasets+= f""" y = readdlm("{_escape_filename(y_filename)}", ',', Float32, '\\n')[:, 1]""" if weights is not None: if multioutput: def_datasets += f""" weights = copy(transpose(readdlm("{_escape_filename(weights_filename)}", ',', Float32, '\\n')))""" else: def_datasets += f""" weights = readdlm("{_escape_filename(weights_filename)}", ',', Float32, '\\n')[:, 1]""" return def_datasets def _make_hyperparams_julia_str(X, alpha, annealing, batchSize, batching, binary_operators, constraints_str, def_hyperparams, equation_file, fast_cycle, fractionReplacedHof, hofMigration, maxdepth, maxsize, migration, optimizer_algorithm, optimizer_nrestarts, optimize_probability, optimizer_iterations, npop, parsimony, perturbationFactor, populations, procs, shouldOptimizeConstants, unary_operators, useFrequency, use_custom_variable_names, variable_names, warmupMaxsizeBy, weightAddNode, ncyclesperiteration, fractionReplaced, topn, verbosity, progress, loss, weightDeleteNode, weightDoNothing, weightInsertNode, weightMutateConstant, weightMutateOperator, weightRandomize, weightSimplify, weights, **kwargs): try: term_width = shutil.get_terminal_size().columns except: _, term_width = subprocess.check_output(['stty', 'size']).split() def tuple_fix(ops): if len(ops) > 1: return ', '.join(ops) elif len(ops) == 0: return '' else: return ops[0] + ',' def_hyperparams += f"""\n plus=(+) sub=(-) mult=(*) square=SymbolicRegression.square cube=SymbolicRegression.cube pow=(^) div=(/) log_abs=SymbolicRegression.log_abs log2_abs=SymbolicRegression.log2_abs log10_abs=SymbolicRegression.log10_abs log1p_abs=SymbolicRegression.log1p_abs acosh_abs=SymbolicRegression.acosh_abs atanh_clip=SymbolicRegression.atanh_clip sqrt_abs=SymbolicRegression.sqrt_abs neg=SymbolicRegression.neg greater=SymbolicRegression.greater relu=SymbolicRegression.relu logical_or=SymbolicRegression.logical_or logical_and=SymbolicRegression.logical_and _custom_loss = {loss} options = SymbolicRegression.Options(binary_operators={'(' + tuple_fix(binary_operators) + ')'}, unary_operators={'(' + tuple_fix(unary_operators) + ')'}, {constraints_str} parsimony={parsimony:f}f0, loss=_custom_loss, alpha={alpha:f}f0, maxsize={maxsize:d}, maxdepth={maxdepth:d}, fast_cycle={'true' if fast_cycle else 'false'}, migration={'true' if migration else 'false'}, hofMigration={'true' if hofMigration else 'false'}, fractionReplacedHof={fractionReplacedHof}f0, shouldOptimizeConstants={'true' if shouldOptimizeConstants else 'false'}, hofFile="{_escape_filename(equation_file)}", npopulations={populations:d}, optimizer_algorithm="{optimizer_algorithm}", optimizer_nrestarts={optimizer_nrestarts:d}, optimize_probability={optimize_probability:f}f0, optimizer_iterations={optimizer_iterations:d}, perturbationFactor={perturbationFactor:f}f0, annealing={"true" if annealing else "false"}, batching={"true" if batching else "false"}, batchSize={min([batchSize, len(X)]) if batching else len(X):d}, mutationWeights=[ {weightMutateConstant:f}, {weightMutateOperator:f}, {weightAddNode:f}, {weightInsertNode:f}, {weightDeleteNode:f}, {weightSimplify:f}, {weightRandomize:f}, {weightDoNothing:f} ], warmupMaxsizeBy={warmupMaxsizeBy:f}f0, useFrequency={"true" if useFrequency else "false"}, npop={npop:d}, ncyclesperiteration={ncyclesperiteration:d}, fractionReplaced={fractionReplaced:f}f0, topn={topn:d}, verbosity=round(Int32, {verbosity:f}), progress={'true' if progress else 'false'}, terminal_width={term_width:d} """ def_hyperparams += '\n)' return def_hyperparams def _make_constraints_str(binary_operators, constraints, unary_operators, **kwargs): constraints_str = "una_constraints = [" first = True for op in unary_operators: val = constraints[op] if not first: constraints_str += ", " constraints_str += f"{val:d}" first = False constraints_str += """], bin_constraints = [""" first = True for op in binary_operators: tup = constraints[op] if not first: constraints_str += ", " constraints_str += f"({tup[0]:d}, {tup[1]:d})" first = False constraints_str += "]," return constraints_str def _handle_constraints(binary_operators, constraints, unary_operators, **kwargs): 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 == 'mult': # Make sure the complex expression is in the left side. if constraints[op][0] == -1: continue elif constraints[op][1] == -1 or constraints[op][0] < constraints[op][1]: constraints[op][0], constraints[op][1] = constraints[op][1], constraints[op][0] def _create_inline_operators(binary_operators, unary_operators, **kwargs): def_hyperparams = "" for op_list in [binary_operators, unary_operators]: for i in range(len(op_list)): op = op_list[i] is_user_defined_operator = '(' in op if is_user_defined_operator: def_hyperparams += op + "\n" # Cut off from the first non-alphanumeric char: first_non_char = [ j for j in range(len(op)) if not (op[j].isalpha() or op[j].isdigit())][0] function_name = op[:first_non_char] op_list[i] = function_name return def_hyperparams def _handle_feature_selection(X, select_k_features, use_custom_variable_names, variable_names, y): if select_k_features is not None: selection = run_feature_selection(X, y, select_k_features) print(f"Using features {selection}") X = X[:, selection] if use_custom_variable_names: variable_names = [variable_names[selection[i]] for i in range(len(selection))] else: selection = None return X, variable_names, selection def _set_paths(tempdir): # System-independent paths pkg_directory = Path(__file__).parents[1] default_project_file = pkg_directory / "Project.toml" tmpdir = Path(tempfile.mkdtemp(dir=tempdir)) hyperparam_filename = tmpdir / f'hyperparams.jl' dataset_filename = tmpdir / f'dataset.jl' runfile_filename = tmpdir / f'runfile.jl' X_filename = tmpdir / "X.csv" y_filename = tmpdir / "y.csv" weights_filename = tmpdir / "weights.csv" return dict(pkg_directory=pkg_directory, default_project_file=default_project_file, X_filename=X_filename, dataset_filename=dataset_filename, hyperparam_filename=hyperparam_filename, runfile_filename=runfile_filename, tmpdir=tmpdir, weights_filename=weights_filename, y_filename=y_filename) def _check_assertions(X, binary_operators, unary_operators, use_custom_variable_names, variable_names, weights, y): # Check for potential errors before they happen assert len(unary_operators) + len(binary_operators) > 0 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 _check_for_julia_installation(): try: process = subprocess.Popen(["julia", "-v"], stdout=subprocess.PIPE, bufsize=-1) while True: line = process.stdout.readline() if not line: break process.stdout.close() process.wait() except FileNotFoundError: import os raise RuntimeError(f"Your current $PATH is: {os.environ['PATH']}\nPySR could not start julia. Make sure julia is installed and on your $PATH.") process.kill() def run_feature_selection(X, y, select_k_features): """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, SelectKBest clf = RandomForestRegressor(n_estimators=100, max_depth=3, random_state=0) clf.fit(X, y) selector = SelectFromModel(clf, threshold=-np.inf, max_features=select_k_features, prefit=True) return selector.get_support(indices=True) def get_hof(equation_file=None, n_features=None, variable_names=None, output_jax_format=None, output_torch_format=None, selection=None, extra_sympy_mappings=None, extra_jax_mappings=None, extra_torch_mappings=None, multioutput=None, nout=None, **kwargs): """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.""" global global_state if equation_file is None: equation_file = global_state['equation_file'] if n_features is None: n_features = global_state['n_features'] if variable_names is None: variable_names = global_state['variable_names'] if extra_sympy_mappings is None: extra_sympy_mappings = global_state['extra_sympy_mappings'] if extra_jax_mappings is None: extra_jax_mappings = global_state['extra_jax_mappings'] if extra_torch_mappings is None: extra_torch_mappings = global_state['extra_torch_mappings'] if output_torch_format is None: output_torch_format = global_state['output_torch_format'] if output_jax_format is None: output_jax_format = global_state['output_jax_format'] if multioutput is None: multioutput = global_state['multioutput'] if nout is None: nout = global_state['nout'] global_state['selection'] = selection global_state['equation_file'] = equation_file global_state['n_features'] = n_features global_state['variable_names'] = variable_names global_state['extra_sympy_mappings'] = extra_sympy_mappings global_state['extra_jax_mappings'] = extra_jax_mappings global_state['extra_torch_mappings'] = extra_torch_mappings global_state['output_torch_format'] = output_torch_format global_state['output_jax_format'] = output_jax_format global_state['multioutput'] = multioutput global_state['nout'] = nout global_state['selection'] = selection try: if multioutput: all_outputs = [pd.read_csv(f'out{i}_' + str(equation_file) + '.bkup', sep="|") for i in range(1, nout+1)] else: all_outputs = [pd.read_csv(str(equation_file) + '.bkup', sep="|")] except FileNotFoundError: raise RuntimeError("Couldn't find equation file! The equation search likely exited before a single iteration completed.") ret_outputs = [] for output in all_outputs: scores = [] lastMSE = None lastComplexity = 0 sympy_format = [] lambda_format = [] if output_jax_format: jax_format = [] if output_torch_format: torch_format = [] use_custom_variable_names = (len(variable_names) != 0) local_sympy_mappings = { **extra_sympy_mappings, **sympy_mappings } if use_custom_variable_names: sympy_symbols = [sympy.Symbol(variable_names[i]) for i in range(n_features)] else: sympy_symbols = [sympy.Symbol('x%d'%i) for i in range(n_features)] for i in range(len(output)): eqn = sympify(output.loc[i, 'Equation'], locals=local_sympy_mappings) sympy_format.append(eqn) # Numpy: lambda_format.append(CallableEquation(sympy_symbols, eqn, selection)) # JAX: if output_jax_format: from .export_jax import sympy2jax func, params = sympy2jax(eqn, sympy_symbols, selection) jax_format.append({'callable': func, 'parameters': params}) # Torch: if output_torch_format: from .export_torch import sympy2torch module = sympy2torch(eqn, sympy_symbols, selection) torch_format.append(module) curMSE = output.loc[i, 'MSE'] curComplexity = output.loc[i, 'Complexity'] if lastMSE is None: cur_score = 0.0 else: cur_score = - np.log(curMSE/lastMSE)/(curComplexity - lastComplexity) 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', 'MSE', 'score', 'Equation', 'sympy_format', 'lambda_format'] if output_jax_format: output_cols += ['jax_format'] output['jax_format'] = jax_format if output_torch_format: output_cols += ['torch_format'] output['torch_format'] = torch_format ret_outputs.append(output[output_cols]) if multioutput: return ret_outputs else: return ret_outputs[0] def best_row(equations=None): """Return the best row of a hall of fame file using the score column. By default this uses the last equation file. """ if equations is None: equations = get_hof() if isinstance(equations, list): return [eq.iloc[np.argmax(eq['score'])] for eq in equations] else: return equations.iloc[np.argmax(equations['score'])] def best_tex(equations=None): """Return the equation with the best score, in latex format By default this uses the last equation file. """ if equations is None: equations = get_hof() if isinstance(equations, list): return [sympy.latex(best_row(eq)['sympy_format'].simplify()) for eq in equations] else: return sympy.latex(best_row(equations)['sympy_format'].simplify()) def best(equations=None): """Return the equation with the best score, in sympy format. By default this uses the last equation file. """ if equations is None: equations = get_hof() if isinstance(equations, list): return [best_row(eq)['sympy_format'].simplify() for eq in equations] else: return best_row(equations)['sympy_format'].simplify() def best_callable(equations=None): """Return the equation with the best score, in callable format. By default this uses the last equation file. """ if equations is None: equations = get_hof() if isinstance(equations, list): return [best_row(eq)['lambda_format'] for eq in equations] else: return best_row(equations)['lambda_format'] def _escape_filename(filename): """Turns a file into a string representation with correctly escaped backslashes""" repr = str(filename) repr = repr.replace('\\', '\\\\') return repr # https://gist.github.com/garrettdreyfus/8153571 def _yesno(question): """Simple Yes/No Function.""" prompt = f'{question} (y/n): ' ans = input(prompt).strip().lower() if ans not in ['y', 'n']: print(f'{ans} is invalid, please try again...') return _yesno(question) if ans == 'y': return True return False