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import time
import numpy as np
from gplearn.genetic import SymbolicRegressor
from sklearn.utils.validation import column_or_1d
import Settings as settings
from DataUtils import make_y_multi_safe
pop_size = 5000
generations = 20
p_crossover = 0.7
warm_start = False
class Genetic_Model:
def __init__(self):
self.name = "Genetic Model"
self.short_name = "GP"
self.function_set = settings.function_set.copy()
if "id" in self.function_set:
self.function_set.remove("id")
self.est_gp = SymbolicRegressor(population_size=pop_size,
generations=generations, stopping_criteria=0.01, # 20 gen
p_crossover=p_crossover, p_subtree_mutation=0.1,
p_hoist_mutation=0.05, p_point_mutation=0.1,
warm_start=warm_start,
max_samples=0.9, verbose=False,
parsimony_coefficient=0.01,
function_set=self.function_set)
def reset(self):
del self.est_gp
self.est_gp = SymbolicRegressor(population_size=pop_size,
generations=generations, stopping_criteria=0.01, # 20 gen
p_crossover=p_crossover, p_subtree_mutation=0.1,
p_hoist_mutation=0.05, p_point_mutation=0.1,
warm_start=warm_start,
max_samples=0.9, verbose=False,
parsimony_coefficient=0.01,
function_set=self.function_set)
def soft_reset(self):
del self.est_gp
self.est_gp = SymbolicRegressor(population_size=pop_size,
generations=generations, stopping_criteria=0.01, # 20 gen
p_crossover=p_crossover, p_subtree_mutation=0.1,
p_hoist_mutation=0.05, p_point_mutation=0.1,
warm_start=warm_start,
max_samples=0.9, verbose=False,
parsimony_coefficient=0.01,
function_set=self.function_set)
def predict(self, X):
return self.est_gp.predict(X)
def get_formula(self):
return self.est_gp._program
def get_simple_formula(self, digits=None):
return self.get_formula()
def get_big_formula(self):
formula_string = str(self.get_formula())
nested_list_string = formula_string.replace("sqrt(", "[\'sqrt\', ")
nested_list_string = nested_list_string.replace("add(", "[\'+\', ")
nested_list_string = nested_list_string.replace("mul(", "[\'*\', ")
nested_list_string = nested_list_string.replace("sub(", "[\'-\', ")
nested_list_string = nested_list_string.replace("sin(", "[\'sin\', ")
nested_list_string = nested_list_string.replace(")", "]")
nested_list_string = nested_list_string.replace("X", "Y")
retval = ""
currently_digits = False
current_number = ""
for current_char in nested_list_string:
if current_char == 'Y':
retval += "\'x"
currently_digits = True
current_number = ""
elif currently_digits:
if current_char.isdigit():
# retval += "{}".format(current_char)
current_number += "{}".format(current_char)
else:
currently_digits = False
retval += "{}".format(int(current_number) + 1)
retval += "\'{}".format(current_char)
else:
retval += "{}".format(current_char)
if "Y" in retval:
print("ERROR: formula still contains a Y...")
print(" formula string: {}\n nested list string: {}".format(formula_string, nested_list_string))
return eval(retval)
def train(self, X, Y):
X = np.reshape(X, [X.shape[0], -1])
Y = np.reshape(Y, [-1, 1])
Y = column_or_1d(Y)
self.est_gp.fit(X, Y)
return None
# Does not repeat train. Sorry.
def repeat_train(self, x, y, test_x=None, test_y=None,
num_repeats=settings.num_train_repeat_processes,
num_steps_to_train=settings.num_train_steps_in_repeat_mode,
verbose=True):
train_set_size = int(len(x) * settings.quick_train_fraction + 0.1)
x = np.array(x)
y = np.reshape(np.array(y), [-1, ])
sample = np.random.choice(range(x.shape[0]), size=train_set_size, replace=False)
out_sample = [yyy for yyy in range(x.shape[0]) if yyy not in sample]
train_x = x[sample][:]
train_y = y[sample][:]
valid_x = x[out_sample][:]
valid_y = y[out_sample][:]
old_time = time.time()
if verbose:
print("Beginning {} repeat sessions of {} iterations each.".format(num_repeats,
settings.num_train_steps_in_repeat_mode))
print()
start_time = time.time()
old_time = start_time
self.soft_reset()
self.train(train_x, train_y)
current_time = time.time()
if verbose:
# print(self.get_simple_formula())
print("Attained validation error: {:.5f}".format(valid_err))
best_formula = self.get_simple_formula()
if test_x is not None:
safe_test_y = make_y_multi_safe(test_y)
best_err = self.test(test_x, safe_test_y)
else:
best_err = self.test(valid_x, valid_y)
if verbose:
iters_per_minute = 60.0 / (current_time - old_time)
print("Took {:.2f} minutes.".format((current_time - old_time) / 60))
print("Est. {:.2f} minutes remaining.".format((num_repeats - train_iter) / iters_per_minute))
print()
return best_formula, 0, best_err
# Mean square error
def test(self, x, y):
x = np.reshape(x, [x.shape[0], -1])
y_hat = np.reshape(self.est_gp.predict(x), [1, -1])[0]
y_gold = np.reshape(y, [1, -1])[0]
our_sum = 0
for i in range(len(y_gold)):
our_sum += (y_hat[i] - y_gold[i]) ** 2
return our_sum / len(y_gold)
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