bootstrap

prior-art/Code/S_get_symbolic_expr_error.py

@@ -0,0 +1,42 @@
+# Calculates the error of a given symbolic expression applied to a dataset. The input should be a string of the mathematical expression
+
+from get_pareto import Point, ParetoSet
+from sympy.parsing.sympy_parser import parse_expr
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+from os import path
+from sympy import Symbol, lambdify, N
+
+def get_symbolic_expr_error(data,expr):
+    try:
+        N_vars = len(data[0])-1
+        possible_vars = ["x%s" %i for i in np.arange(0,30,1)]
+        variables = []
+        for i in range(N_vars):
+            variables = variables + [possible_vars[i]]
+        eq = parse_expr(expr)
+        f = lambdify(variables, N(eq))
+        real_variables = []
+
+        for i in range(len(data[0])-1):
+            check_var = "x"+str(i)
+            if check_var in np.array(variables).astype('str'):
+                real_variables = real_variables + [data[:,i]]
+        
+        # Remove accidental nan's
+        good_idx = np.where(np.isnan(f(*real_variables))==False)
+       
+        # use this to get rid of cases where the loss gets complex because of transformations of the output variable
+        if isinstance(np.mean((f(*real_variables)-data[:,-1])**2), complex):
+            return 1000000
+        else:
+            try:
+                #return np.sqrt(np.mean((f(*real_variables)[good_idx]-data[good_idx][:,-1])**2))/np.sqrt(np.mean(data[good_idx][:,-1]**2))
+                return np.mean(np.log2(1+abs(f(*real_variables)[good_idx]-data[good_idx][:,-1])*2**30))
+            except:
+                # use this for the case in which the expression is just one number (i.e. not array)
+                #return np.sqrt(np.mean((f(*real_variables)-data[:,-1])**2))/np.sqrt(np.mean(data[:,-1]**2))
+                return np.mean(np.log2(1+abs(f(*real_variables)-data[:,-1])*2**30))
+    except:
+        return 1000000