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Code/RPN_to_pytorch.py

@@ -19,9 +19,11 @@ from sympy.parsing.sympy_parser import parse_expr
 from sympy import Symbol, lambdify, N
 
 from S_get_number_DL_snapped import get_number_DL_snapped
+from S_get_symbolic_expr_error import get_symbolic_expr_error
 
 # parameters: path to data, RPN expression (obtained from bf)
-def RPN_to_pytorch(data_file, math_expr, lr = 1e-2, N_epochs = 500):
+def RPN_to_pytorch(pathdir,filename, math_expr, lr = 1e-2, N_epochs = 500):
+    data_file = pathdir + filename
     param_dict = {}
     unsnapped_param_dict = {'p':1}
 
@@ -90,13 +92,11 @@ def RPN_to_pytorch(data_file, math_expr, lr = 1e-2, N_epochs = 500):
             trainable_parameters = trainable_parameters + [vars()[i]]
 
     # Prepare the loaded data
-
     real_variables = []
     for i in range(len(data[0])-1):
         real_variables = real_variables + [torch.from_numpy(data[:,i]).float()]
 
     input = trainable_parameters + real_variables
-
     y = torch.from_numpy(data[:,-1]).float()
 
     for i in range(N_epochs):
@@ -109,21 +109,31 @@ def RPN_to_pytorch(data_file, math_expr, lr = 1e-2, N_epochs = 500):
                 trainable_parameters[j] -= lr * trainable_parameters[j].grad
                 trainable_parameters[j].grad.zero_()
 
-    # get the updated symbolic regression
     ii = -1
-    complexity = 0
     for parm in unsnapped_param_dict:
         if ii == -1:
             ii = ii + 1
         else:
             eq = eq.subs(parm, trainable_parameters[ii])
-            complexity = complexity + get_number_DL_snapped(trainable_parameters[ii].detach().numpy())
+            ii = ii + 1
-            n_variables = len(eq.free_symbols)
-            n_operations = len(count_ops(eq,visual=True).free_symbols)
-            if n_operations!=0 or n_variables!=0:
-                complexity = complexity + (n_variables+n_operations)*np.log2((n_variables+n_operations))
-            ii = ii+1
 
-    error = torch.mean((f(*input)-y)**2).data.numpy()*1
+    complexity = 0
+    is_atomic_number = lambda expr: expr.is_Atom and expr.is_number
+    numbers_expr = [subexpression for subexpression in preorder_traversal(eq) if is_atomic_number(subexpression)]
+    complexity = 0
+    for j in numbers_expr:
+        try:
+            complexity = complexity + get_number_DL_snapped(float(j))
+        except:
+            complexity = complexity + 1000000
+    n_variables = len(eq.free_symbols)
+    n_operations = len(count_ops(eq,visual=True).free_symbols)
+    if n_operations!=0 or n_variables!=0:
+        complexity = complexity + (n_variables+n_operations)*np.log2((n_variables+n_operations))
+
+    error = get_symbolic_expr_error(pathdir,filename,str(eq))
     return error, complexity, eq
 
+
+
+

Code/S_run_aifeynman.py

@@ -25,8 +25,7 @@ from S_add_bf_on_numbers_on_pareto import add_bf_on_numbers_on_pareto
 from dimensionalAnalysis import dimensionalAnalysis
 
 PA = ParetoSet()
-
+def run_AI_all(pathdir,filename,BF_try_time=60,BF_ops_file_type="14ops", polyfit_deg=4, NN_epochs=4000, PA=PA):
-def run_AI_all(pathdir,filename,BF_try_time=60,BF_ops_file_type="14ops", polyfit_deg=4, NN_epochs=4000, PA = PA):
     try:
         os.mkdir("results/")
     except:
@@ -38,6 +37,7 @@ def run_AI_all(pathdir,filename,BF_try_time=60,BF_ops_file_type="14ops", polyfit
     # Run bf and polyfit
     PA = run_bf_polyfit(pathdir,pathdir,filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
 
+    '''
     # Run bf and polyfit on modified output
     PA = get_acos(pathdir,"results/mystery_world_acos/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
     PA = get_asin(pathdir,"results/mystery_world_asin/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
@@ -50,7 +50,7 @@ def run_AI_all(pathdir,filename,BF_try_time=60,BF_ops_file_type="14ops", polyfit
     PA = get_sqrt(pathdir,"results/mystery_world_sqrt/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
     PA = get_squared(pathdir,"results/mystery_world_squared/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
     PA = get_tan(pathdir,"results/mystery_world_tan/",filename,BF_try_time,BF_ops_file_type, PA, polyfit_deg)
-
+    '''
 #############################################################################################################################  
     # check if the NN is trained. If it is not, train it on the data.
     print("Checking for symmetry \n", filename)
@@ -162,7 +162,7 @@ def run_aifeynman(pathdir,filename,BF_try_time,BF_ops_file_type, polyfit_deg=4,
 
     PA = ParetoSet()
     # Run the code on the train data 
-    PA = run_AI_all(pathdir,filename+"_train",BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs)
+    PA = run_AI_all(pathdir,filename+"_train",BF_try_time,BF_ops_file_type, polyfit_deg, NN_epochs, PA=PA)
     PA_list = PA.get_pareto_points()
 
     # Run bf snap on the resulted equations
@@ -215,3 +215,4 @@ def run_aifeynman(pathdir,filename,BF_try_time,BF_ops_file_type, polyfit_deg=4,
     else:
         save_data = np.column_stack((log_err,log_err_all,list_dt))
     np.savetxt("results/solution_%s" %filename_orig,save_data,fmt="%s")
+