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import sympy
import numpy as np
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.metrics import mean_absolute_error
from scipy.optimize import minimize
import math
import re
class Expression:
SAFE_FUNCTIONS = {
'sqrt': np.sqrt,
'log': np.log,
'exp': np.exp,
'sin': np.sin,
'cos': np.cos,
'tan': np.tan,
'asin': np.arcsin, # Corrected to np.arcsin
'abs': np.abs,
'pow': np.power, # Use np.power for vectorization and NaN handling
# '**' is handled by Python's eval; if operands are numpy arrays, np.power is used.
}
OPERATOR_ARITY = {
'+': 2,
'-': 2,
'*': 2,
'/': 2,
'**': 2, # Changed from '^' to '**'
'sin': 1,
'cos': 1,
'tan': 1,
'log': 1,
'sqrt': 1,
'exp': 1
}
OPERATOR_FUNCS = {
'+': sympy.Add,
'-': lambda x, y: x - y,
'*': sympy.Mul,
'/': lambda x, y: x / y,
'**': sympy.Pow, # Changed from '^' to '**', sympy.Pow handles both
'sin': sympy.sin,
'cos': sympy.cos,
'tan': sympy.tan,
'log': sympy.log,
'sqrt': sympy.sqrt,
'exp': sympy.exp
}
def parse_prefix(self, tokens):
"""Parse prefix notation expression to SymPy.
Example: ['*', 'x_1', '+', 'x_2', 'C'] -> x_1*(x_2 + C)
"""
if not tokens:
raise ValueError("Empty token list")
# Define unary and binary operators
UNARY_OPS = {'sin', 'cos', 'tan', 'exp', 'log', 'sqrt', 'abs', 'asin'}
BINARY_OPS = {'+', '-', '*', '/', '**', '^'}
stack = []
# Process tokens in reverse order
for token in reversed(tokens):
if token in BINARY_OPS or token in UNARY_OPS:
# Operator: pop operands from stack
if token in UNARY_OPS:
if len(stack) < 1:
raise ValueError(f"Not enough operands for {token}")
arg = stack.pop()
if token in ['sin', 'cos', 'tan', 'exp', 'log', 'sqrt', 'abs', 'asin']:
stack.append(f"{token}({arg})")
else:
raise ValueError(f"Unknown unary operator: {token}")
else: # Binary operator
if len(stack) < 2:
raise ValueError(f"Not enough operands for {token}")
right = stack.pop()
left = stack.pop()
# Handle operator mapping
op_map = {'+': '+', '-': '-', '*': '*', '/': '/', '**': '**', '^': '**'}
op = op_map.get(token, token)
if op in ['**', '^']:
stack.append(f"({left})**({right})")
elif op == '/':
stack.append(f"({left})/({right})")
else:
stack.append(f"({left}){op}({right})")
else:
# Operand: push to stack
stack.append(token)
if len(stack) != 1:
raise ValueError(f"Invalid prefix expression, {len(stack)} elements remaining")
return sympy.sympify(stack[0], evaluate=False)
def __init__(self, expression, is_prefix=False):
try:
self.original_expression = expression # Save original
if is_prefix:
# Ensure input prefix uses '**' if converting from external source
tokens = expression.replace('^', '**').split()
self.sympy_expression = self.parse_prefix(tokens)
else:
# Load the expression as a sympy expression without simplification
self.sympy_expression = sympy.sympify(expression, evaluate=False)
except Exception as e:
raise ValueError(f"Failed to parse expression: {e}")
self.max_var = 0
for symbol in self.sympy_expression.free_symbols:
if symbol.name.startswith('x_'):
try:
index = int(symbol.name.split('_')[1])
self.max_var = max(self.max_var, index)
except ValueError:
# Handle symbols that look like x_ but aren't x_number
pass # Or raise ValueError(f"Invalid variable name: {symbol.name}") if strict
computable_expression = str(self.sympy_expression)
for i in range(1, self.max_var + 1):
# Use regex to match whole words to avoid issues with x_1 followed by x_11
computable_expression = re.sub(rf'\bx_{i}\b', f'x[{i-1}]', computable_expression)
self.computable_expression = computable_expression.replace('**C', '**2')
self.constant_count = self.computable_expression.count('C')
self.best_constants = [1.0] * self.constant_count
if self.constant_count > 0:
# Replace 'C' with indexable constants
split_expr = self.computable_expression.split('C')
new_expr = split_expr[0] # Start with first part
for i in range(1, len(split_expr)):
# Add constant reference
new_expr += f'constants[{i-1}]'
# Add next part
new_expr += split_expr[i]
self.computable_expression = new_expr
def __str__(self):
return f"Expression: {self.original_expression}, Best constants: {self.best_constants}"
def sympy_str(self):
"""
Returns the string representation of the sympy expression.
"""
return str(self.sympy_expression)
def is_valid_on_dataset(self, X, test_constants_list=None):
"""
Checks if the expression evaluates to valid (finite) values for all rows in X,
across one or more sets of test constants.
Args:
X (np.ndarray): Input data, shape (n_samples, n_features)
test_constants_list (list of lists): Optional. Defaults to [[1.0]*count].
Example: [[1.0]*n, [0.5]*n, [2.0]*n] to test more thoroughly.
Returns:
bool: True if no evaluation returns nan/inf or crashes. False otherwise.
"""
if test_constants_list is None:
test_constants_list = [[1.0] * self.constant_count]
try:
for constants in test_constants_list:
results = self.evaluate(X, constants)
if not np.all(np.isfinite(results)):
return False
return True
except Exception:
return False
# Inside the Expression class
def evaluate(self, X, constants=None):
# with warnings.catch_warnings():
# warnings.simplefilter("ignore", category=RuntimeWarning) # Hide power/tan warnings
# np.seterr(invalid='ignore', divide='ignore')
if constants is None:
# print("No constants provided, using best constants.") # Optional: uncomment for debugging
constants = self.best_constants
try:
local_env = {
"constants": np.array(constants), # Ensure constants is a numpy array for broadcasting
**self.SAFE_FUNCTIONS,
"__builtins__": None
}
if not isinstance(X, np.ndarray):
X = np.array(X) # Ensure X is a numpy array
# Ensure X is 2D, even if it has only one sample
if X.ndim == 1:
X = X.reshape(1, -1)
# x becomes a list of columns (1D arrays of shape (n_samples,))
x_cols = [X[:, i] for i in range(X.shape[1])]
local_env["x"] = x_cols
# The result will be a numpy array of shape (n_samples,)
try:
y_pred_array = eval(self.computable_expression, local_env)
except FloatingPointError as e:
# print(f"FloatingPointError during eval: {e}")
# print(f"Expression: {self.computable_expression}")
# print(f"Constants: {constants}")
return np.full(X.shape[0], np.nan) # Return NaNs to be caught by loss
except Exception as e:
# print(f"General exception during eval: {e}")
return np.full(X.shape[0], np.nan)
finally:
np.seterr(all='warn') # 🔁 Reset to default behavior
# Ensure output is float to avoid issues with mixed types if some results are int
return np.asarray(y_pred_array, dtype=float)
except Exception as e:
# Return an array of NaNs of the expected shape to ensure loss calculation doesn't break
num_samples = X.shape[0] if X.ndim > 0 else 1
return np.full(num_samples, np.nan) # Return NaNs on error
def fit_constants(self, X, y):
X = np.array(X)
y = np.array(y)
if self.constant_count == 0:
try:
y_pred = self.evaluate(X) # Vectorized call
if not np.all(np.isfinite(y_pred)): # Check for NaNs/Infs
return -np.inf
if np.all(y_pred == y_pred[0]) and len(np.unique(y)) > 1: # Avoid R2 issues with constant prediction for non-constant y
return 0.0 # Or handle as per specific requirements
return r2_score(y, y_pred)
except Exception as e: # Broader catch for any eval issue
return -np.inf
def loss(current_constants):
try:
y_pred = self.evaluate(X, current_constants)
except Exception as e:
print(f"Exception during evaluation: {e}")
return np.inf
if not np.all(np.isfinite(y_pred)):
return np.inf
# MSE calculation
mse = np.mean((y - y_pred) ** 2)
return mse
bounds = [(-2., 2.)] * self.constant_count
initial_guess = (
self.best_constants
if self.best_constants and len(self.best_constants) == self.constant_count
else [.0] * self.constant_count # Default to 1.0
)
# Ensure initial_guess is a flat numpy array
initial_guess = np.array(initial_guess, dtype=float).flatten()
# from scipy.optimize import differential_evolution
# # Step 1: Use Differential Evolution for global exploration
# print("\n--- Starting Differential Evolution ---")
# result_de = differential_evolution(loss, bounds,
# popsize=70, # Aumente para 50, 70, ou mais
# maxiter=10000, # Aumente para 5000, 10000, ou mais
# strategy='rand1bin', # Tente 'rand1exp' se rand1bin não funcionar
# tol=1e-7, # Tolerância mais apertada
# mutation=(0.8, 1.2), # Experimente valores mais altos
# recombination=0.5, # Experimente valores mais baixos
# seed=42, # Mantém a reproducibilidade
# disp=True, # Exibe o progresso
# polish=False)
# if result_de.success:
# print(f"\nDifferential Evolution finished successfully. Best raw constants: {result_de.x}, Best MSE: {result_de.fun}")
# # Use the result from DE as initial guess for local optimizer
# initial_guess_for_minimize = result_de.x
# # Step 2: (Optional but recommended) Refine with L-BFGS-B
# # L-BFGS-B will be applied to the "raw" (non-rounded) values,
# # but the loss function internally rounds for discrete ones.
# # It might still struggle if the function is too "stepped" from rounding.
# print("\n--- Starting L-BFGS-B refinement ---")
# result_min = minimize(loss,
# x0=initial_guess_for_minimize,
# method='L-BFGS-B',
# bounds=bounds,
# options={'maxiter': 500, 'ftol': 1e-9, 'disp': True} # More iterations, tighter tolerance
# )
# if result_min.success:
# print(f"\nL-BFGS-B refinement successful. Final raw constants: {result_min.x}, Final MSE: {result_min.fun}")
# self.best_constants = list(result_min.x)
# else:
# print(f"\nL-BFGS-B refinement failed: {result_min.message}. Using Differential Evolution's result.")
# self.best_constants = list(result_de.x)
# else:
# print(f"\nDifferential Evolution did not converge successfully: {result_de.message}. Cannot proceed with optimization.")
# return -np.inf # Indicate failure
# try:
# y_pred = self.evaluate(X)
# if not np.all(np.isfinite(y_pred)):
# print("Final evaluation produced non-finite values for R2 score.")
# return -np.inf
# if len(np.unique(y)) == 1:
# if np.allclose(y_pred, y[0]):
# return 1.0
# else:
# return 0.0
# return r2_score(y, y_pred)
# except Exception as e:
# print(f"Error calculating final R2: {e}")
# return -np.inf
result = minimize(loss,
x0=initial_guess,
method='L-BFGS-B',
bounds=bounds,
#options={'maxiter': 10, 'maxfun': 10, 'disp': True}
)
if result.success:
self.best_constants = result.x.tolist()
# print(f"Optimization successful. Final loss: {result.fun}") # Optional
try:
y_pred = self.evaluate(X) # Uses self.best_constants (vectorized)
if not np.all(np.isfinite(y_pred)):
return -np.inf
# Refined R2 calculation for edge cases
if len(np.unique(y)) == 1: # If y is constant
if np.allclose(y_pred, y[0]):
return 1.0 # Perfect prediction of a constant
else:
return 0.0 # Or some other metric for imperfect constant prediction
#return mean_squared_error(y, y_pred) # Use MSE for optimization
#return mean_absolute_error(y, y_pred) # Use MAE for robustness
return r2_score(y, y_pred)
except Exception as e:
return -np.inf
else:
return -np.inf
# from dataset import RegressionDataset
# import numpy as np
# import warnings
# with warnings.catch_warnings():
# warnings.simplefilter("ignore", category=RuntimeWarning)
# np.seterr(invalid='ignore')
# #reg = RegressionDataset('../data/evaluate/srsd-feynman_hard/train', 'feynman-bonus.12.txt', delimiter=' ')
# reg = RegressionDataset('./data/evaluate/srsd-feynman_easy/train', 'feynman-i.18.16.txt', delimiter=' ')
# X, y = reg.get_numpy()
# #x = np.array(X).T
# expression = "x_1*x_2*sin(x_4)"
# #expr = "0.5*x[0]*x[1]**2"
# expr = Expression(expression)
# print("Expression:", expr)
# if expr.is_valid_on_dataset(X):
# print("Expression is valid on dataset.")
# score = expr.fit_constants(X, y)
# print("Fitted constants:", expr.best_constants)
# print("R2 score:", score)
# else:
# print("Expression is not valid on dataset.") |