def NAS_EA_FA():
cur_time_budget = 0
history2acc = {}
best_valid_acc, best_test_acc, times = [0.0], [0.0], [0.0]
x_train, y_train = [], []
total_population = [Individual() for _ in range(POPULATION_SIZE)]
while cur_time_budget <= MAX_TIME_BUDGET:
population = sorted(total_population,
key=lambda x: x.fitness,
reverse=True)
new_population = []
num = 0
for individual in population:
valid_acc, test_acc, time = get_model_data_from_history(
individual, history2acc)
individual.fitness = valid_acc
if time == 0.0:
continue
new_population.append(individual)
x_train.append(get_model_sequences(individual))
y_train.append(valid_acc)
if valid_acc > best_valid_acc[-1]:
best_valid_acc.append(valid_acc)
best_test_acc.append(test_acc)
else:
best_valid_acc.append(best_valid_acc[-1])
best_test_acc.append(best_test_acc[-1])
times.append(time)
cur_time_budget += time
num += 1
if cur_time_budget > MAX_TIME_BUDGET or num >= 50:
break
if len(new_population) != 0:
population = new_population
gbm = XGBRegressor(eta=0.1)
gbm.fit(np.array(x_train), np.array(y_train))
total_population = []
for epoch in range(10):
new_population = []
for i in range(POPULATION_SIZE):
individual = cp.deepcopy(tournament_selection(population))
bitwise_mutation(individual)
individual.fitness = gbm.predict(
np.array([get_model_sequences(individual)]))[0]
new_population.append(individual)
total_population.append(individual)
population = new_population
return best_valid_acc, best_test_acc, times
def regularized_evolution_algorithm(dataset: str):
cur_time_budget = 0
history2acc = {}
best_valid_acc, best_test_acc, times = [0.0], [0.0], [0.0]
population = [Individual() for _ in range(POPULATION_SIZE)]
for individual in population:
valid_acc, test_acc, time = get_individual_data_from_history(
individual, history2acc, dataset)
individual.fitness = valid_acc
if valid_acc > best_valid_acc[-1]:
best_valid_acc.append(valid_acc)
best_test_acc.append(test_acc)
else:
best_valid_acc.append(best_valid_acc[-1])
best_test_acc.append(best_test_acc[-1])
times.append(time)
cur_time_budget += time
while cur_time_budget <= MAX_TIME_BUDGET:
individual = cp.deepcopy(tournament_selection(population))
bitwise_mutation(individual)
valid_acc, test_acc, time = get_individual_data_from_history(
individual, history2acc, dataset)
individual.fitness = valid_acc
population.append(individual)
population.pop(0)
if time == 0.0:
continue
if valid_acc > best_valid_acc[-1]:
best_valid_acc.append(valid_acc)
best_test_acc.append(test_acc)
else:
best_valid_acc.append(best_valid_acc[-1])
best_test_acc.append(best_test_acc[-1])
times.append(time)
cur_time_budget += time
if cur_time_budget > MAX_TIME_BUDGET:
break
return best_valid_acc, best_test_acc, times
def main():
population = []
generation = 1
for i in range(INITIAL_POPULATION):
population.append(generate_individual(len(MASTER_SOLUTION)))
while generation < MAX_ITERATIONS:
print("Generation: {}, Population: {}".format(generation,
len(population)))
new_population = tournament_selection(population)
population = []
for x, y in new_population:
population.append(crossover(x, y))
population.append(crossover(y, x))
population = mutate_population(population)
generation += 1
def NAS_EA_FA_V2():
cur_time_budget = 0
history2acc = {}
best_valid_acc, best_test_acc, times = [0.0], [0.0], [0.0]
x_train, y_train = [], []
total_population = [Individual() for _ in range(POPULATION_SIZE)]
while cur_time_budget <= MAX_TIME_BUDGET:
population = sorted(total_population,
key=lambda x: x.fitness,
reverse=True)
new_population = []
num = start_index = 0
# top n individuals
for index in range(len(population)):
individual = population[index]
valid_acc, test_acc, time = get_model_data_from_history(
individual, history2acc)
individual.fitness = valid_acc
if time == 0.0:
continue
new_population.append(individual)
temp_sequences = get_all_isomorphic_sequences(individual)
x_train.extend(temp_sequences)
for _ in range(len(temp_sequences)):
y_train.append(valid_acc)
if valid_acc > best_valid_acc[-1]:
best_valid_acc.append(valid_acc)
best_test_acc.append(test_acc)
else:
best_valid_acc.append(best_valid_acc[-1])
best_test_acc.append(best_test_acc[-1])
times.append(time)
cur_time_budget += time
num += 1
if cur_time_budget > MAX_TIME_BUDGET or num >= 30:
start_index = index
break
# diversity individual
max_dis_list = [
get_min_distance(x_train, get_model_sequences(indiv))
for indiv in population[start_index + 1:]
]
while num < 50 and cur_time_budget <= MAX_TIME_BUDGET:
max_dis, temp_index = 0, start_index
for index in range(len(max_dis_list)):
if max_dis_list[index] > max_dis:
max_dis = max_dis_list[index]
temp_index = index
individual = population[temp_index + start_index + 1]
max_dis_list[temp_index] = 0
valid_acc, test_acc, time = get_model_data_from_history(
individual, history2acc)
individual.fitness = valid_acc
if time == 0.0:
continue
new_population.append(individual)
temp_sequences = get_all_isomorphic_sequences(individual)
x_train.extend(temp_sequences)
for _ in range(len(temp_sequences)):
y_train.append(valid_acc)
if valid_acc > best_valid_acc[-1]:
best_valid_acc.append(valid_acc)
best_test_acc.append(test_acc)
else:
best_valid_acc.append(best_valid_acc[-1])
best_test_acc.append(best_test_acc[-1])
times.append(time)
cur_time_budget += time
num += 1
if len(new_population) != 0:
population = new_population
gbm = XGBRegressor(eta=0.1)
gbm.fit(np.array(x_train), np.array(y_train))
total_population = []
for epoch in range(10):
new_population = []
for i in range(POPULATION_SIZE):
individual = cp.deepcopy(tournament_selection(population))
bitwise_mutation(individual)
individual.fitness = gbm.predict(
np.array([get_model_sequences(individual)]))[0]
new_population.append(individual)
total_population.append(individual)
population = new_population
return best_valid_acc, best_test_acc, times
sets.build_terminal_set(accept, reject)
sets.build_function_set()
population = gen_population(1000, accept, reject)
bestest = gen_individual(accept, reject)
for i in range(100):
best = ['fjkghdfkghdfkghsdkfhgksdfjhgksjdfhgkjsdhfgkjhsdfkghsdfkjghksdfhgkj', -9999]
print("\nGeneration:", i)
new_pop = []
for _ in range(800):
while True:
try:
m1 = utils.tournament_selection(population, 50)
m2 = utils.tournament_selection(population, 50)
child = utils.crossover(m1[0], m2[0])
child = [child, utils.calc_score(child, accept, reject)]
new_pop.append(child)
break
except:
pass
for _ in range(100):
new_pop.append(gen_individual(accept, reject))
for _ in range(100):
while True:
try:
a = random.choice(population)