def classificador():
global classifier
global txErro
global real
#global classifierInit
classifierInit = list()
message = request.get_json(silent=True)
#nSolution = np.asarray(message["solucoes"]) #passa o numero de exemplos na posicao 0
#nObj = np.asarray(message["objetivos"]) #passa o numero de variaveis na posicao 0
processar = message["processar"]
nObj = np.asarray(message["objetivos"])
print("classificador:", processar)
if processar == "SVM":
classifierInit = SVR(kernel='rbf', C=1e3, gamma=0.1, tol=0.01)
elif processar == "TREE":
for i in range(nObj[0]):
classifierInit.append(
DecisionTreeRegressor(max_depth=500,
min_samples_split=2,
random_state=0,
criterion='mse'))
elif processar == "RAMDOMFOREST":
for i in range(nObj[0]):
classifierInit.append(
RandomForestRegressor(n_estimators=200,
max_depth=None,
min_samples_split=2,
random_state=0,
criterion='mse'))
elif processar == "MLP":
classifierInit = MLPRegressor(hidden_layer_sizes=(10, ), max_iter=1000)
elif processar == "NO-SURROGATE":
classifierInit = None
classifier = classifierInit
return json.dumps({"retorno": []})
def classificador():
global classifier
global txErro
global real
global classifier_name
global population_size
global not_first_run
global classifier_num_of_epochs
global classifier_avr_opt
global classifier_timestep
global history_nSolution
try:
not_first_run
except NameError:
archive_old_mse()
not_first_run = True
classifier_num_of_epochs = None
classifierInit = list()
message = request.get_json(silent=True)
#nSolution = np.asarray(message["solucoes"]) #passa o numero de exemplos na posicao 0
#nObj = np.asarray(message["objetivos"]) #passa o numero de variaveis na posicao 0
# Message id doesn't make sense because it's the same
# object used in other cases.
classifier_name = message['algoritmo']
tagProblem = message['processar']
nObj = np.asarray(message['objetivos'])
if nObj[0] == 3:
population_size = 92
else:
population_size = 764
if classifier_name.startswith("SVM"):
classifierInit = SVR(kernel='rbf', C=1e3, gamma=0.1, tol=0.01)
elif classifier_name.startswith("TREE"):
for i in range(nObj[0]):
classifierInit.append(
DecisionTreeRegressor(max_depth=500,
min_samples_split=2,
random_state=0,
criterion='mse'))
elif classifier_name.startswith("RAMDOMFOREST"):
for i in range(nObj[0]):
classifierInit.append(
RandomForestRegressor(n_estimators=200,
max_depth=None,
min_samples_split=2,
random_state=0,
criterion='mse'))
elif classifier_name.startswith("MLP"):
classifierInit = MLPRegressor(hidden_layer_sizes=(10, ), max_iter=1000)
elif classifier_name.startswith(
"NO-SURROGATE") or classifier_name.startswith("RANDOM"):
classifierInit = None
elif classifier_name.startswith("LSTM") or classifier_name.startswith(
"RNN"):
history_nSolution = []
# Clear any session, in case it's not the first one.
keras.backend.clear_session()
# Clear GPU memory
cuda.select_device(0)
cuda.close()
# Clear classifiers if they exist
if 'classifier' in globals():
del classifier
gc.collect()
# Parametrization
if tagProblem.startswith('WFG'):
if population_size == 92:
amntVarDir = 14
else:
amntVarDir = 19
elif tagProblem.startswith('DTLZ'):
if population_size == 92:
amntVarDir = 12
else:
amntVarDir = 19
else:
# Invalid dimension
amntVarDir = -1
info = dict()
model_name = classifier_name.split('_')[0]
for arg in classifier_name.split('_'):
if '=' in arg:
key, value = arg.split('=')
try:
info[key] = int(value)
except:
info[key] = value
amnt_hidden_nodes = info['amntNodesHidden']
if amnt_hidden_nodes.startswith('PROP'):
multi = float(amnt_hidden_nodes.split('-')[1]) / 100
EPS = 0.0001
amnt_hidden_nodes = int(amntVarDir * multi + EPS)
classifier_num_of_epochs = info['amntEpochs']
classifier_timestep = info['ts']
if ('FIXED-2' in classifier_name):
output_labels = 1
if ('avr' in classifier_name):
classifier_avr_opt = True
input_shape = (classifier_timestep, 1)
else:
classifier_avr_opt = False
input_shape = (classifier_timestep, amntVarDir)
else:
output_labels = population_size
if ('avr' in classifier_name):
classifier_avr_opt = True
input_shape = (classifier_timestep, population_size)
else:
classifier_avr_opt = False
input_shape = (classifier_timestep,
population_size * amntVarDir)
for i in range(nObj[0]):
classifierInit.append(
NeuralNetwork(model_name, input_shape, output_labels,
amnt_hidden_nodes))
classifier = classifierInit
return json.dumps({"retorno": []})
