40 lines
1.6 KiB
Python
40 lines
1.6 KiB
Python
# Calculates the error of a given symbolic expression applied to a dataset. The input should be a string of the mathematical expression
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from get_pareto import Point, ParetoSet
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from RPN_to_pytorch import RPN_to_pytorch
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from RPN_to_eq import RPN_to_eq
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from sympy.parsing.sympy_parser import parse_expr
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import numpy as np
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import matplotlib.pyplot as plt
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import os
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from os import path
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from sympy import Symbol, lambdify, N
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def get_symbolic_expr_error(pathdir,filename,expr):
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data = np.loadtxt(pathdir+filename)
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N_vars = len(data[0])-1
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possible_vars = ["x%s" %i for i in np.arange(0,30,1)]
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variables = []
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for i in range(N_vars):
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variables = variables + [possible_vars[i]]
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eq = parse_expr(expr)
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f = lambdify(variables, N(eq))
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real_variables = []
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for i in range(len(data[0])-1):
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check_var = "x"+str(i)
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if check_var in np.array(variables).astype('str'):
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real_variables = real_variables + [data[:,i]]
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# Remove accidental nan's
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good_idx = np.where(np.isnan(f(*real_variables))==False)
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# use this to get rid of cases where the loss gets complex because of transformations of the output variable
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if isinstance(np.mean((f(*real_variables)-data[:,-1])**2), complex):
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return 1000000
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else:
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try:
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return np.sqrt(np.mean((f(*real_variables)[good_idx]-data[good_idx][:,-1])**2))/np.sqrt(np.mean(data[good_idx][:,-1]**2))
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except:
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# use this for the case in which the expression is just one number (i.e. not array)
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return np.sqrt(np.mean((f(*real_variables)-data[:,-1])**2))/np.sqrt(np.mean(data[:,-1]**2))
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