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| import numpy as np | |
| import csv | |
| import traceback | |
| class Problem: | |
| """ | |
| Problem API to work with PySR. | |
| Has attributes: X, y as pysr accepts, form which is a string representing the correct equation and variable_names | |
| Should be able to call pysr(problem.X, problem.y, var_names=problem.var_names) and have it work | |
| """ | |
| def __init__(self, X, y, form=None, variable_names=None): | |
| self.X = X | |
| self.y = y | |
| self.form = form | |
| self.variable_names = variable_names | |
| class FeynmanProblem(Problem): | |
| """ | |
| Stores the data for the problems from the 100 Feynman Equations on Physics. | |
| This is the benchmark used in the AI Feynman Paper | |
| """ | |
| def __init__(self, row, gen=False, dp=500): | |
| """ | |
| row: a row read as a dict from the FeynmanEquations dataset provided in the datasets folder of the repo | |
| gen: If true the problem will have dp X and y values randomly generated else they will be None | |
| """ | |
| self.eq_id = row['Filename'] | |
| #self.form = row['Formula'] | |
| self.n_vars = int(row['# variables']) | |
| super(FeynmanProblem, self).__init__(None, None, form=row['Formula'], | |
| variable_names=[row[f'v{i + 1}_name'] for i in range(self.n_vars)]) | |
| #self.var_names = [row[f'v{i+1}_name'] for i in range(self.n_vars)] | |
| self.low = [float(row[f'v{i+1}_low']) for i in range(self.n_vars)] | |
| self.high = [float(row[f'v{i+1}_high']) for i in range(self.n_vars)] | |
| self.dp = dp#int(row[f'datapoints']) | |
| #self.X = None | |
| #self.Y = None | |
| if gen: | |
| self.X = np.random.uniform(0.01, 25, size=(self.dp, self.n_vars)) | |
| d = {} | |
| for var in range(len(self.variable_names)): | |
| d[self.variable_names[var]] = self.X[:, var] | |
| d['exp'] = np.exp | |
| d['sqrt'] = np.sqrt | |
| d['pi'] = np.pi | |
| d['cos'] = np.cos | |
| d['sin'] = np.sin | |
| d['tan'] = np.tan | |
| d['tanh'] = np.tanh | |
| d['ln'] = np.log | |
| d['log'] = np.log # Quite sure the Feynman dataset has no base 10 logs | |
| d['arcsin'] = np.arcsin | |
| self.y = eval(self.form,d) | |
| return | |
| def __str__(self): | |
| return f"Feynman Equation: {self.eq_id}|Form: {self.form}" | |
| def __repr__(self): | |
| return str(self) | |
| def mk_problems(first=100, gen=False, dp=500, data_dir="datasets/FeynmanEquations.csv"): | |
| """ | |
| first: the first "first" equations from the dataset will be made into problems | |
| data_dir: the path pointing to the Feynman Equations csv | |
| returns: list of FeynmanProblems | |
| """ | |
| ret = [] | |
| with open(data_dir) as csvfile: | |
| ind = 0 | |
| reader = csv.DictReader(csvfile) | |
| for i, row in enumerate(reader): | |
| if ind > first: | |
| break | |
| if row['Filename'] == '': continue | |
| try: | |
| p = FeynmanProblem(row, gen=gen, dp=dp) | |
| ret.append(p) | |
| except Exception as e: | |
| traceback.print_exc() | |
| #print(row) | |
| print(f"FAILED ON ROW {i}") | |
| ind += 1 | |
| return ret | |
| def run_on_problem(problem, verbosity=0): | |
| """ | |
| Takes in a problem and returns a tuple: (equations, best predicted equation, actual equation) | |
| """ | |
| from time import time | |
| from . import pysr, best | |
| starting = time() | |
| equations = pysr(problem.X, problem.y, variable_names=problem.variable_names, verbosity=verbosity) | |
| timing = time()-starting | |
| others = {"equations": equations, "time": timing} | |
| return best(equations), problem.form, others | |
| if __name__ == "__main__": | |
| ret = FeynmanProblem.mk_problems(first=100, gen=True) | |
| print(ret) |