afdee1c/symbolic-regression
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Silviu Marian Udrescu 2020-05-15 04:42:44 -04:00 • committed by GitHub
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commit 1e1ee2d36f
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1 changed files with 19 additions and 18 deletions
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37
Code/S_final_gd.py
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@ -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 final_gd(data_file, math_expr, lr = 1e-2, N_epochs = 5000):
def final_gd(pathdir,filename, math_expr, lr = 1e-2, N_epochs = 5000):
data_file = pathdir + filename
param_dict = {}
unsnapped_param_dict = {'p':1}
@ -63,11 +65,8 @@ def final_gd(data_file, math_expr, lr = 1e-2, N_epochs = 5000):
# Turn BF expression to pytorch expression
eq = parse_expr(math_expr)
# eq = parse_expr("cos(0.5*a)") # this is for test_sympy.txt
# eq = parse_expr("cos(0.5*a)+sin(1.2*b)+6") # this is for test_sympy_2.txt
eq = unsnap_recur(eq,param_dict,unsnapped_param_dict)
N_vars = len(data[0])-1
N_params = len(unsnapped_param_dict)
@ -75,7 +74,6 @@ def final_gd(data_file, math_expr, lr = 1e-2, N_epochs = 5000):
variables = []
params = []
for i in range(N_vars):
#variables = variables + ["x%s" %i]
variables = variables + [possible_vars[i]]
for i in range(N_params-1):
params = params + ["p%s" %i]
@ -93,15 +91,12 @@ def final_gd(data_file, math_expr, lr = 1e-2, N_epochs = 5000):
vars()[i].requires_grad=True
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()
@ -127,21 +122,27 @@ def final_gd(data_file, math_expr, lr = 1e-2, N_epochs = 5000):
# 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())
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
ii = ii + 1
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