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nas_ea_fa_v2_train_natsbenchtss_dnc.py
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nas_ea_fa_v2_train_natsbenchtss_dnc.py
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from nord.neural_nets.natsbench_evaluator import NATSBench_Evaluator
import numpy as np
import os
from xgboost import XGBRegressor
import copy
from contextlib import redirect_stdout
import time
from params import EXP_REPEAT_TIMES, MAX_TIME_BUDGET, POPULATION_SIZE, NUM_GEN, K, H, T
from natsbenchtss_utils_dnc import randomly_sample_architecture, create_nord_architecture, \
get_all_isomorphic_sequences, get_min_distance, get_model_sequences, tournament_selection, bitwise_mutation
from performance_evaluation import progress_update, save_performance
from save_individual import save_individual_201_dnc, save_individual_fitness_approximation
def NAS_EA_FA_V2_train_201():
# Instantiate the evaluator
evaluator = NATSBench_Evaluator()
if not os.path.exists('results_nas_ea_fa_v2_dnc201_train'):
os.mkdir('results_nas_ea_fa_v2_dnc201_train')
for exp_repeat_index in range(EXP_REPEAT_TIMES):
start_time = time.time()
folder_name = os.path.join('results_nas_ea_fa_v2_dnc201_train', 'results' + str(exp_repeat_index + 1))
if not os.path.exists(folder_name):
os.mkdir(folder_name)
best_val_acc = []
best_test_acc_based_on_val_acc = []
train_times = []
total_train_time = []
best_test_acc = []
x_train = []
y_train = []
current_time_budget = 0
# Randomly sample POPULATION_SIZE architectures with an initial fitness of 0
total_population = []
for _ in range(POPULATION_SIZE):
is_valid_architecture = False
while not is_valid_architecture:
architecture = randomly_sample_architecture()
if architecture.valid_architecture:
d = create_nord_architecture(architecture)
# evaluate architecture
invalid_nas201 = False
try:
val_acc, test_acc, train_time = evaluator.descriptor_evaluate(d, metrics=['validation_accuracy',
'test_accuracy',
'time_cost'])
except ValueError:
print('Invalid architecture (not added in population)')
print(d)
print(evaluator._descriptor_to_nasnet(d))
invalid_nas201 = True
if not invalid_nas201:
total_population.append(architecture)
is_valid_architecture = True
num_file = 0
t = 0 # iteration count
# while current_time_budget <= MAX_TIME_BUDGET:
while t < T:
tic = time.time()
t += 1
# sort in descending order by fitness
population = sorted(total_population, key=lambda x: x.fitness, reverse=True)
new_population = []
num_arch = 0
start_index = 0
# train and evaluate top K individuals
for arch_index in range(len(population)):
architecture = population[arch_index]
d = create_nord_architecture(architecture)
# evaluate architecture
train_loss, val_loss, test_loss, train_acc, val_acc, test_acc, latency, train_time = \
evaluator.descriptor_evaluate(d, metrics=['train_loss',
'validation_loss',
'test_loss',
'train_accuracy',
'validation_accuracy',
'test_accuracy',
'latency',
'time_cost'])
architecture.fitness = val_acc
architecture.test_acc = test_acc
architecture.train_time = train_time
architecture.train_loss = train_loss
architecture.val_loss = val_loss
architecture.test_loss = test_loss
architecture.train_acc = train_acc
architecture.latency = latency
if time == 0.0:
continue
new_population.append(architecture)
# get isomorphic sequences
isomorphic_sequences = get_all_isomorphic_sequences(architecture)
x_train.extend(isomorphic_sequences)
for _ in range(len(isomorphic_sequences)):
y_train.append(val_acc)
best_val_acc, best_test_acc_based_on_val_acc, best_test_acc, train_times, total_train_time = \
progress_update(val_acc=val_acc, test_acc=test_acc, train_time=train_time,
best_val_acc=best_val_acc,
best_test_acc_based_on_val_acc=best_test_acc_based_on_val_acc,
best_test_acc=best_test_acc, train_times=train_times,
total_train_time=total_train_time, fitness='val_acc')
current_time_budget += train_time
num_arch += 1
if current_time_budget > MAX_TIME_BUDGET or num_arch >= K:
start_index = arch_index
break
num_file += 1
with open(os.path.join(folder_name, 'topK_iteration' + str(num_file) + '.txt'), 'w') as f:
ind_num = 0
for ind in new_population:
ind_num += 1
save_individual_201_dnc(f, ind, ind_num, 'val_acc')
num_topK = len(new_population)
# train and evaluate top H individuals
tic1 = time.time()
# get min distance between each of the remaining individuals and the training set
dist_list = [get_min_distance(x_train, get_model_sequences(architecture)) for architecture in
population[start_index + 1:]]
toc1 = time.time()
print('x_train length:', len(x_train))
print('dist_list calculation time:', toc1 - tic1, 'sec')
while num_arch < K + H and current_time_budget <= MAX_TIME_BUDGET:
# find architecture with max distance from training set
max_distance = 0
max_dist_arch_index = start_index
for i in range(len(dist_list)):
if dist_list[i] > max_distance:
max_distance = dist_list[i]
max_dist_arch_index = i
architecture = population[start_index + 1 + max_dist_arch_index]
dist_list[max_dist_arch_index] = 0 # architecture already added to x_train
d = create_nord_architecture(architecture)
# evaluate architecture
train_loss, val_loss, test_loss, train_acc, val_acc, test_acc, latency, train_time = \
evaluator.descriptor_evaluate(d, metrics=['train_loss',
'validation_loss',
'test_loss',
'train_accuracy',
'validation_accuracy',
'test_accuracy',
'latency',
'time_cost'])
architecture.fitness = val_acc
architecture.test_acc = test_acc
architecture.train_time = train_time
architecture.train_loss = train_loss
architecture.val_loss = val_loss
architecture.test_loss = test_loss
architecture.train_acc = train_acc
architecture.latency = latency
if time == 0.0:
continue
new_population.append(architecture)
# get isomorphic sequences
isomorphic_sequences = get_all_isomorphic_sequences(architecture)
x_train.extend(isomorphic_sequences)
for _ in range(len(isomorphic_sequences)):
y_train.append(val_acc)
best_val_acc, best_test_acc_based_on_val_acc, best_test_acc, train_times, total_train_time = \
progress_update(val_acc=val_acc, test_acc=test_acc, train_time=train_time,
best_val_acc=best_val_acc,
best_test_acc_based_on_val_acc=best_test_acc_based_on_val_acc,
best_test_acc=best_test_acc, train_times=train_times,
total_train_time=total_train_time, fitness='val_acc')
current_time_budget += train_time
num_arch += 1
with open(os.path.join(folder_name, 'topH_iteration' + str(num_file) + '.txt'), 'w') as f:
ind_num = num_topK
for index in range(num_topK, len(new_population)):
ind = new_population[index]
ind_num += 1
save_individual_201_dnc(f, ind, ind_num, 'val_acc')
# update population
if len(new_population) != 0:
population = new_population
# train fitness approximation
with open(os.path.join(folder_name, 'xgb_stats_iteration' + str(num_file) + '.txt'), 'w') as f:
with redirect_stdout(f):
# xgb_model = XGBRegressor(objective='reg:squarederror', learning_rate=0.1)
xgb_model = XGBRegressor(eta=0.1)
if t > 1:
xgb_model.fit(np.array(x_train), np.array(y_train),
eval_set=[(x_train, y_train), (x_val, y_val)],
eval_metric='rmse')
else:
xgb_model.fit(np.array(x_train), np.array(y_train), eval_set=[(x_train, y_train)],
eval_metric='rmse')
xgb_stats = xgb_model.evals_result()
print(xgb_stats)
# evolutionary algorithm
total_population = []
for epoch in range(NUM_GEN):
new_population = []
for i in range(POPULATION_SIZE):
individual = copy.deepcopy(tournament_selection(population))
new_individual = bitwise_mutation(individual)
while not new_individual.valid_architecture:
individual = copy.deepcopy(tournament_selection(population))
new_individual = bitwise_mutation(individual)
new_individual.fitness = xgb_model.predict(np.array([get_model_sequences(new_individual)]))[0]
new_population.append(new_individual)
total_population.append(new_individual)
population = new_population
with open(os.path.join(folder_name, 'population_iteration' + str(num_file) + '_epoch' + str(epoch + 1) +
'.txt'), 'w') as f:
ind_num = 0
for ind in population:
ind_num += 1
save_individual_fitness_approximation(f, ind, ind_num, 'val_acc')
# validation set for next iteration's xgboost model
x_val = x_train
y_val = y_train
toc = time.time()
print('experiment index:', exp_repeat_index + 1, 'time needed for iteration t=' + str(t) + ':', toc - tic,
'sec')
print('current time budget:', current_time_budget, 'max time budget:', MAX_TIME_BUDGET)
end_time = time.time()
save_performance(folder_name, exp_repeat_index, start_time, end_time, best_val_acc,
best_test_acc_based_on_val_acc, best_test_acc, train_times, total_train_time,
'val_acc')
if __name__ == '__main__':
np.random.seed(42)
NAS_EA_FA_V2_train_201()