Remove Feynman problems and dataset

.github/workflows/CI.yml

@@ -64,22 +64,22 @@ jobs:
         run: pip install coverage coveralls
       - name: "Run tests"
         run: |
-            coverage run --source=pysr --omit='*/test/*,*/feynman_problems.py' -m pysr test main
-            coverage run --append --source=pysr --omit='*/test/*,*/feynman_problems.py' -m pysr test cli
+            coverage run --source=pysr --omit='*/test/*' -m pysr test main
+            coverage run --append --source=pysr --omit='*/test/*' -m pysr test cli
       - name: "Install JAX"
         run: pip install jax jaxlib # (optional import)
         if: ${{ matrix.test-id == 'main' }}
       - name: "Run JAX tests"
-        run: coverage run --append --source=pysr --omit='*/test/*,*/feynman_problems.py' -m pysr test jax
+        run: coverage run --append --source=pysr --omit='*/test/*' -m pysr test jax
         if: ${{ matrix.test-id == 'main' }}
       - name: "Install Torch"
         run: pip install torch # (optional import)
         if: ${{ matrix.test-id == 'main' }}
       - name: "Run Torch tests"
-        run: coverage run --append --source=pysr --omit='*/test/*,*/feynman_problems.py' -m pysr test torch
+        run: coverage run --append --source=pysr --omit='*/test/*' -m pysr test torch
         if: ${{ matrix.test-id == 'main' }}
       - name: "Run custom env tests"
-        run: coverage run --append --source=pysr --omit='*/test/*,*/feynman_problems.py' -m pysr test env
+        run: coverage run --append --source=pysr --omit='*/test/*' -m pysr test env
       - name: "Coveralls"
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

datasets/FeynmanEquations.csv

@@ -1,101 +0,0 @@
-Filename,datapoints,Number,Output,Formula,# variables,v1_name,v1_low,v1_high,v2_name,v2_low,v2_high,v3_name,v3_low,v3_high,v4_name,v4_low,v4_high,v5_name,v5_low,v5_high,v6_name,v6_low,v6_high,v7_name,v7_low,v7_high,v8_name,v8_low,v8_high,v9_name,v9_low,v9_high,v10_name,v10_low,v10_high
-I.6.2a,10,1,f,exp(-theta**2/2)/sqrt(2*pi),1,theta,1,3,,,,,,,,,,,,,,,,,,,,,,,,,,,
-I.6.2,100,2,f,exp(-(theta/sigma)**2/2)/(sqrt(2*pi)*sigma),2,sigma,1,3,theta,1,3,,,,,,,,,,,,,,,,,,,,,,,,
-I.6.2b,1000,3,f,exp(-((theta-theta1)/sigma)**2/2)/(sqrt(2*pi)*sigma),3,sigma,1,3,theta,1,3,theta1,1,3,,,,,,,,,,,,,,,,,,,,,
-I.8.14,100,4,d,sqrt((x2-x1)**2+(y2-y1)**2),4,x1,1,5,x2,1,5,y1,1,5,y2,1,5,,,,,,,,,,,,,,,,,,
-I.9.18,1000000,5,F,G*m1*m2/((x2-x1)**2+(y2-y1)**2+(z2-z1)**2),9,m1,1,2,m2,1,2,G,1,2,x1,3,4,x2,1,2,y1,3,4,y2,1,2,z1,3,4,z2,1,2,,,
-I.10.7,10,6,m,m_0/sqrt(1-v**2/c**2),3,m_0,1,5,v,1,2,c,3,10,,,,,,,,,,,,,,,,,,,,,
-I.11.19,100,7,A,x1*y1+x2*y2+x3*y3,6,x1,1,5,x2,1,5,x3,1,5,y1,1,5,y2,1,5,y3,1,5,,,,,,,,,,,,
-I.12.1,10,8,F,mu*Nn,2,mu,1,5,Nn,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-I.12.2,10,10,F,q1*q2*r/(4*pi*epsilon*r**3),4,q1,1,5,q2,1,5,epsilon,1,5,r,1,5,,,,,,,,,,,,,,,,,,
-I.12.4,10,11,Ef,q1*r/(4*pi*epsilon*r**3),3,q1,1,5,epsilon,1,5,r,1,5,,,,,,,,,,,,,,,,,,,,,
-I.12.5,10,12,F,q2*Ef,2,q2,1,5,Ef,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-I.12.11,10,13,F,q*(Ef+B*v*sin(theta)),5,q,1,5,Ef,1,5,B,1,5,v,1,5,theta,1,5,,,,,,,,,,,,,,,
-I.13.4,10,9,K,1/2*m*(v**2+u**2+w**2),4,m,1,5,v,1,5,u,1,5,w,1,5,,,,,,,,,,,,,,,,,,
-I.13.12,10,14,U,G*m1*m2*(1/r2-1/r1),5,m1,1,5,m2,1,5,r1,1,5,r2,1,5,G,1,5,,,,,,,,,,,,,,,
-I.14.3,10,15,U,m*g*z,3,m,1,5,g,1,5,z,1,5,,,,,,,,,,,,,,,,,,,,,
-I.14.4,10,16,U,1/2*k_spring*x**2,2,k_spring,1,5,x,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-I.15.3x,10,17,x1,(x-u*t)/sqrt(1-u**2/c**2),4,x,5,10,u,1,2,c,3,20,t,1,2,,,,,,,,,,,,,,,,,,
-I.15.3t,100,18,t1,(t-u*x/c**2)/sqrt(1-u**2/c**2),4,x,1,5,c,3,10,u,1,2,t,1,5,,,,,,,,,,,,,,,,,,
-I.15.1,10,19,p,m_0*v/sqrt(1-v**2/c**2),3,m_0,1,5,v,1,2,c,3,10,,,,,,,,,,,,,,,,,,,,,
-I.16.6,10,20,v1,(u+v)/(1+u*v/c**2),3,c,1,5,v,1,5,u,1,5,,,,,,,,,,,,,,,,,,,,,
-I.18.4,10,21,r,(m1*r1+m2*r2)/(m1+m2),4,m1,1,5,m2,1,5,r1,1,5,r2,1,5,,,,,,,,,,,,,,,,,,
-I.18.12,10,22,tau,r*F*sin(theta),3,r,1,5,F,1,5,theta,0,5,,,,,,,,,,,,,,,,,,,,,
-I.18.14,10,23,L,m*r*v*sin(theta),4,m,1,5,r,1,5,v,1,5,theta,1,5,,,,,,,,,,,,,,,,,,
-I.24.6,10,24,E_n,1/2*m*(omega**2+omega_0**2)*1/2*x**2,4,m,1,3,omega,1,3,omega_0,1,3,x,1,3,,,,,,,,,,,,,,,,,,
-I.25.13,10,25,Volt,q/C,2,q,1,5,C,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-I.26.2,100,26,theta1,arcsin(n*sin(theta2)),2,n,0,1,theta2,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-I.27.6,10,27,foc,1/(1/d1+n/d2),3,d1,1,5,d2,1,5,n,1,5,,,,,,,,,,,,,,,,,,,,,
-I.29.4,10,28,k,omega/c,2,omega,1,10,c,1,10,,,,,,,,,,,,,,,,,,,,,,,,
-I.29.16,1000,29,x,sqrt(x1**2+x2**2-2*x1*x2*cos(theta1-theta2)),4,x1,1,5,x2,1,5,theta1,1,5,theta2,1,5,,,,,,,,,,,,,,,,,,
-I.30.3,100,30,Int,Int_0*sin(n*theta/2)**2/sin(theta/2)**2,3,Int_0,1,5,theta,1,5,n,1,5,,,,,,,,,,,,,,,,,,,,,
-I.30.5,100,31,theta,arcsin(lambd/(n*d)),3,lambd,1,2,d,2,5,n,1,5,,,,,,,,,,,,,,,,,,,,,
-I.32.5,10,32,Pwr,q**2*a**2/(6*pi*epsilon*c**3),4,q,1,5,a,1,5,epsilon,1,5,c,1,5,,,,,,,,,,,,,,,,,,
-I.32.17,10,33,Pwr,(1/2*epsilon*c*Ef**2)*(8*pi*r**2/3)*(omega**4/(omega**2-omega_0**2)**2),6,epsilon,1,2,c,1,2,Ef,1,2,r,1,2,omega,1,2,omega_0,3,5,,,,,,,,,,,,
-I.34.8,10,34,omega,q*v*B/p,4,q,1,5,v,1,5,B,1,5,p,1,5,,,,,,,,,,,,,,,,,,
-I.34.1,10,35,omega,omega_0/(1-v/c),3,c,3,10,v,1,2,omega_0,1,5,,,,,,,,,,,,,,,,,,,,,
-I.34.14,10,36,omega,(1+v/c)/sqrt(1-v**2/c**2)*omega_0,3,c,3,10,v,1,2,omega_0,1,5,,,,,,,,,,,,,,,,,,,,,
-I.34.27,10,37,E_n,(h/(2*pi))*omega,2,omega,1,5,h,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-I.37.4,100,38,Int,I1+I2+2*sqrt(I1*I2)*cos(delta),3,I1,1,5,I2,1,5,delta,1,5,,,,,,,,,,,,,,,,,,,,,
-I.38.12,10,39,r,4*pi*epsilon*(h/(2*pi))**2/(m*q**2),4,m,1,5,q,1,5,h,1,5,epsilon,1,5,,,,,,,,,,,,,,,,,,
-I.39.1,10,40,E_n,3/2*pr*V,2,pr,1,5,V,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-I.39.11,10,41,E_n,1/(gamma-1)*pr*V,3,gamma,2,5,pr,1,5,V,1,5,,,,,,,,,,,,,,,,,,,,,
-I.39.22,10,42,pr,n*kb*T/V,4,n,1,5,T,1,5,V,1,5,kb,1,5,,,,,,,,,,,,,,,,,,
-I.40.1,10,43,n,n_0*exp(-m*g*x/(kb*T)),6,n_0,1,5,m,1,5,x,1,5,T,1,5,g,1,5,kb,1,5,,,,,,,,,,,,
-I.41.16,10,44,L_rad,h/(2*pi)*omega**3/(pi**2*c**2*(exp((h/(2*pi))*omega/(kb*T))-1)),5,omega,1,5,T,1,5,h,1,5,kb,1,5,c,1,5,,,,,,,,,,,,,,,
-I.43.16,10,45,v,mu_drift*q*Volt/d,4,mu_drift,1,5,q,1,5,Volt,1,5,d,1,5,,,,,,,,,,,,,,,,,,
-I.43.31,10,46,D,mob*kb*T,3,mob,1,5,T,1,5,kb,1,5,,,,,,,,,,,,,,,,,,,,,
-I.43.43,10,47,kappa,1/(gamma-1)*kb*v/A,4,gamma,2,5,kb,1,5,A,1,5,v,1,5,,,,,,,,,,,,,,,,,,
-I.44.4,10,48,E_n,n*kb*T*ln(V2/V1),5,n,1,5,kb,1,5,T,1,5,V1,1,5,V2,1,5,,,,,,,,,,,,,,,
-I.47.23,10,49,c,sqrt(gamma*pr/rho),3,gamma,1,5,pr,1,5,rho,1,5,,,,,,,,,,,,,,,,,,,,,
-I.48.2,100,50,E_n,m*c**2/sqrt(1-v**2/c**2),3,m,1,5,v,1,2,c,3,10,,,,,,,,,,,,,,,,,,,,,
-I.50.26,10,51,x,x1*(cos(omega*t)+alpha*cos(omega*t)**2),4,x1,1,3,omega,1,3,t,1,3,alpha,1,3,,,,,,,,,,,,,,,,,,
-II.2.42,10,52,Pwr,kappa*(T2-T1)*A/d,5,kappa,1,5,T1,1,5,T2,1,5,A,1,5,d,1,5,,,,,,,,,,,,,,,
-II.3.24,10,53,flux,Pwr/(4*pi*r**2),2,Pwr,1,5,r,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-II.4.23,10,54,Volt,q/(4*pi*epsilon*r),3,q,1,5,epsilon,1,5,r,1,5,,,,,,,,,,,,,,,,,,,,,
-II.6.11,10,55,Volt,1/(4*pi*epsilon)*p_d*cos(theta)/r**2,4,epsilon,1,3,p_d,1,3,theta,1,3,r,1,3,,,,,,,,,,,,,,,,,,
-II.6.15a,1000,56,Ef,p_d/(4*pi*epsilon)*3*z/r**5*sqrt(x**2+y**2),6,epsilon,1,3,p_d,1,3,r,1,3,x,1,3,y,1,3,z,1,3,,,,,,,,,,,,
-II.6.15b,10,57,Ef,p_d/(4*pi*epsilon)*3*cos(theta)*sin(theta)/r**3,4,epsilon,1,3,p_d,1,3,theta,1,3,r,1,3,,,,,,,,,,,,,,,,,,
-II.8.7,10,58,E_n,3/5*q**2/(4*pi*epsilon*d),3,q,1,5,epsilon,1,5,d,1,5,,,,,,,,,,,,,,,,,,,,,
-II.8.31,10,59,E_den,epsilon*Ef**2/2,2,epsilon,1,5,Ef,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-II.10.9,10,60,Ef,sigma_den/epsilon*1/(1+chi),3,sigma_den,1,5,epsilon,1,5,chi,1,5,,,,,,,,,,,,,,,,,,,,,
-II.11.3,10,61,x,q*Ef/(m*(omega_0**2-omega**2)),5,q,1,3,Ef,1,3,m,1,3,omega_0,3,5,omega,1,2,,,,,,,,,,,,,,,
-II.11.17,10,62,n,n_0*(1+p_d*Ef*cos(theta)/(kb*T)),6,n_0,1,3,kb,1,3,T,1,3,theta,1,3,p_d,1,3,Ef,1,3,,,,,,,,,,,,
-II.11.20,10,63,Pol,n_rho*p_d**2*Ef/(3*kb*T),5,n_rho,1,5,p_d,1,5,Ef,1,5,kb,1,5,T,1,5,,,,,,,,,,,,,,,
-II.11.27,100,64,Pol,n*alpha/(1-(n*alpha/3))*epsilon*Ef,4,n,0,1,alpha,0,1,epsilon,1,2,Ef,1,2,,,,,,,,,,,,,,,,,,
-II.11.28,100,65,theta,1+n*alpha/(1-(n*alpha/3)),2,n,0,1,alpha,0,1,,,,,,,,,,,,,,,,,,,,,,,,
-II.13.17,10,66,B,1/(4*pi*epsilon*c**2)*2*I/r,4,epsilon,1,5,c,1,5,I,1,5,r,1,5,,,,,,,,,,,,,,,,,,
-II.13.23,100,67,rho_c,rho_c_0/sqrt(1-v**2/c**2),3,rho_c_0,1,5,v,1,2,c,3,10,,,,,,,,,,,,,,,,,,,,,
-II.13.34,10,68,j,rho_c_0*v/sqrt(1-v**2/c**2),3,rho_c_0,1,5,v,1,2,c,3,10,,,,,,,,,,,,,,,,,,,,,
-II.15.4,10,69,E_n,-mom*B*cos(theta),3,mom,1,5,B,1,5,theta,1,5,,,,,,,,,,,,,,,,,,,,,
-II.15.5,10,70,E_n,-p_d*Ef*cos(theta),3,p_d,1,5,Ef,1,5,theta,1,5,,,,,,,,,,,,,,,,,,,,,
-II.21.32,10,71,Volt,q/(4*pi*epsilon*r*(1-v/c)),5,q,1,5,epsilon,1,5,r,1,5,v,1,2,c,3,10,,,,,,,,,,,,,,,
-II.24.17,10,72,k,sqrt(omega**2/c**2-pi**2/d**2),3,omega,4,6,c,1,2,d,2,4,,,,,,,,,,,,,,,,,,,,,
-II.27.16,10,73,flux,epsilon*c*Ef**2,3,epsilon,1,5,c,1,5,Ef,1,5,,,,,,,,,,,,,,,,,,,,,
-II.27.18,10,74,E_den,epsilon*Ef**2,2,epsilon,1,5,Ef,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-II.34.2a,10,75,I,q*v/(2*pi*r),3,q,1,5,v,1,5,r,1,5,,,,,,,,,,,,,,,,,,,,,
-II.34.2,10,76,mom,q*v*r/2,3,q,1,5,v,1,5,r,1,5,,,,,,,,,,,,,,,,,,,,,
-II.34.11,10,77,omega,g_*q*B/(2*m),4,g_,1,5,q,1,5,B,1,5,m,1,5,,,,,,,,,,,,,,,,,,
-II.34.29a,10,78,mom,q*h/(4*pi*m),3,q,1,5,h,1,5,m,1,5,,,,,,,,,,,,,,,,,,,,,
-II.34.29b,10,79,E_n,g_*mom*B*Jz/(h/(2*pi)),5,g_,1,5,h,1,5,Jz,1,5,mom,1,5,B,1,5,,,,,,,,,,,,,,,
-II.35.18,10,80,n,n_0/(exp(mom*B/(kb*T))+exp(-mom*B/(kb*T))),5,n_0,1,3,kb,1,3,T,1,3,mom,1,3,B,1,3,,,,,,,,,,,,,,,
-II.35.21,10,81,M,n_rho*mom*tanh(mom*B/(kb*T)),5,n_rho,1,5,mom,1,5,B,1,5,kb,1,5,T,1,5,,,,,,,,,,,,,,,
-II.36.38,10,82,f,mom*H/(kb*T)+(mom*alpha)/(epsilon*c**2*kb*T)*M,8,mom,1,3,H,1,3,kb,1,3,T,1,3,alpha,1,3,epsilon,1,3,c,1,3,M,1,3,,,,,,
-II.37.1,10,83,E_n,mom*(1+chi)*B,3,mom,1,5,B,1,5,chi,1,5,,,,,,,,,,,,,,,,,,,,,
-II.38.3,10,84,F,Y*A*x/d,4,Y,1,5,A,1,5,d,1,5,x,1,5,,,,,,,,,,,,,,,,,,
-II.38.14,10,85,mu_S,Y/(2*(1+sigma)),2,Y,1,5,sigma,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-III.4.32,10,86,n,1/(exp((h/(2*pi))*omega/(kb*T))-1),4,h,1,5,omega,1,5,kb,1,5,T,1,5,,,,,,,,,,,,,,,,,,
-III.4.33,10,87,E_n,(h/(2*pi))*omega/(exp((h/(2*pi))*omega/(kb*T))-1),4,h,1,5,omega,1,5,kb,1,5,T,1,5,,,,,,,,,,,,,,,,,,
-III.7.38,10,88,omega,2*mom*B/(h/(2*pi)),3,mom,1,5,B,1,5,h,1,5,,,,,,,,,,,,,,,,,,,,,
-III.8.54,10,89,prob,sin(E_n*t/(h/(2*pi)))**2,3,E_n,1,2,t,1,2,h,1,4,,,,,,,,,,,,,,,,,,,,,
-III.9.52,1000,90,prob,(p_d*Ef*t/(h/(2*pi)))*sin((omega-omega_0)*t/2)**2/((omega-omega_0)*t/2)**2,6,p_d,1,3,Ef,1,3,t,1,3,h,1,3,omega,1,5,omega_0,1,5,,,,,,,,,,,,
-III.10.19,100,91,E_n,mom*sqrt(Bx**2+By**2+Bz**2),4,mom,1,5,Bx,1,5,By,1,5,Bz,1,5,,,,,,,,,,,,,,,,,,
-III.12.43,10,92,L,n*(h/(2*pi)),2,n,1,5,h,1,5,,,,,,,,,,,,,,,,,,,,,,,,
-III.13.18,10,93,v,2*E_n*d**2*k/(h/(2*pi)),4,E_n,1,5,d,1,5,k,1,5,h,1,5,,,,,,,,,,,,,,,,,,
-III.14.14,10,94,I,I_0*(exp(q*Volt/(kb*T))-1),5,I_0,1,5,q,1,2,Volt,1,2,kb,1,2,T,1,2,,,,,,,,,,,,,,,
-III.15.12,10,95,E_n,2*U*(1-cos(k*d)),3,U,1,5,k,1,5,d,1,5,,,,,,,,,,,,,,,,,,,,,
-III.15.14,10,96,m,(h/(2*pi))**2/(2*E_n*d**2),3,h,1,5,E_n,1,5,d,1,5,,,,,,,,,,,,,,,,,,,,,
-III.15.27,10,97,k,2*pi*alpha/(n*d),3,alpha,1,5,n,1,5,d,1,5,,,,,,,,,,,,,,,,,,,,,
-III.17.37,10,98,f,beta*(1+alpha*cos(theta)),3,beta,1,5,alpha,1,5,theta,1,5,,,,,,,,,,,,,,,,,,,,,
-III.19.51,10,99,E_n,-m*q**4/(2*(4*pi*epsilon)**2*(h/(2*pi))**2)*(1/n**2),5,m,1,5,q,1,5,h,1,5,n,1,5,epsilon,1,5,,,,,,,,,,,,,,,
-III.21.20,10,100,j,-rho_c_0*q*A_vec/m,4,rho_c_0,1,5,q,1,5,A_vec,1,5,m,1,5,,,,,,,,,,,,,,,,,,

pysr/__init__.py

@@ -2,7 +2,6 @@ from . import sklearn_monkeypatch
 from .deprecated import best, best_callable, best_row, best_tex, pysr
 from .export_jax import sympy2jax
 from .export_torch import sympy2torch
-from .feynman_problems import FeynmanProblem, Problem
 from .sr import PySRRegressor, jl
 
 # This file is created by setuptools_scm during the build process:
@@ -12,7 +11,6 @@ __all__ = [
     "sklearn_monkeypatch",
     "sympy2jax",
     "sympy2torch",
-    "FeynmanProblem",
     "Problem",
     "install",
     "PySRRegressor",

pysr/feynman_problems.py

@@ -1,176 +0,0 @@
-import csv
-from functools import partial
-from pathlib import Path
-
-import numpy as np
-
-from .deprecated import best, pysr
-
-PKG_DIR = Path(__file__).parents[1]
-FEYNMAN_DATASET = PKG_DIR / "datasets" / "FeynmanEquations.csv"
-
-
-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.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.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
-        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)
-
-    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=FEYNMAN_DATASET):
-    """
-
-    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:
-        reader = csv.DictReader(csvfile)
-        for i, row in enumerate(reader):
-            if i > first:
-                break
-            if row["Filename"] == "":
-                continue
-            p = FeynmanProblem(row, gen=gen, dp=dp)
-            ret.append(p)
-    return ret
-
-
-def run_on_problem(problem, verbosity=0, multiprocessing=True):
-    """
-    Takes in a problem and returns a tuple: (equations, best predicted equation, actual equation)
-    """
-    from time import time
-
-    starting = time()
-    equations = pysr(
-        problem.X,
-        problem.y,
-        variable_names=problem.variable_names,
-        verbosity=verbosity,
-    )
-    timing = time() - starting
-    others = {"time": timing, "problem": problem}
-    if not multiprocessing:
-        others["equations"] = equations
-    return str(best(equations)), problem.form, others
-
-
-def do_feynman_experiments_parallel(
-    first=100,
-    verbosity=0,
-    dp=500,
-    output_file_path="FeynmanExperiment.csv",
-    data_dir=FEYNMAN_DATASET,
-):
-    import multiprocessing as mp
-
-    from tqdm import tqdm
-
-    problems = mk_problems(first=first, gen=True, dp=dp, data_dir=data_dir)
-    ids = []
-    predictions = []
-    true_equations = []
-    time_takens = []
-    pool = mp.Pool()
-    results = []
-    with tqdm(total=len(problems)) as pbar:
-        f = partial(run_on_problem, verbosity=verbosity)
-        for i, res in enumerate(pool.imap(f, problems)):
-            results.append(res)
-            pbar.update()
-    for res in results:
-        prediction, true_equation, others = res
-        problem = others["problem"]
-        ids.append(problem.eq_id)
-        predictions.append(prediction)
-        true_equations.append(true_equation)
-        time_takens.append(others["time"])
-    with open(output_file_path, "a") as f:
-        writer = csv.writer(f, delimiter=",")
-        writer.writerow(["ID", "Predicted", "True", "Time"])
-        for i in range(len(ids)):
-            writer.writerow([ids[i], predictions[i], true_equations[i], time_takens[i]])
-
-
-def do_feynman_experiments(
-    first=100,
-    verbosity=0,
-    dp=500,
-    output_file_path="FeynmanExperiment.csv",
-    data_dir=FEYNMAN_DATASET,
-):
-    from tqdm import tqdm
-
-    problems = mk_problems(first=first, gen=True, dp=dp, data_dir=data_dir)
-    ids = []
-    predictions = []
-    true_equations = []
-    time_takens = []
-    for problem in tqdm(problems):
-        prediction, true_equation, others = run_on_problem(problem, verbosity)
-        ids.append(problem.eq_id)
-        predictions.append(prediction)
-        true_equations.append(true_equation)
-        time_takens.append(others["time"])
-    with open(output_file_path, "a") as f:
-        writer = csv.writer(f, delimiter=",")
-        writer.writerow(["ID", "Predicted", "True", "Time"])
-        for i in range(len(ids)):
-            writer.writerow([ids[i], predictions[i], true_equations[i], time_takens[i]])