class TestOptimizer(object):
def __init__(self, opt_params=None):
logger.info("Testeo de Optimizer con datos de Iris")
# Datos
logger.info("Cargando datos...")
data = load_iris()
dataset = LocalLabeledDataSet(data)
self.train, self.valid, self.test = dataset.split_data([.5, .3, .2])
self.train = self.train.collect()
self.valid = self.valid.collect()
self.test = self.test.collect()
# Configuracion de optimizacion
if opt_params is None: # Por defecto, se utiliza Adadelta
stops = [criterion['MaxIterations'](10),
criterion['AchieveTolerance'](0.95, key='hits')]
options = {'step-rate': 1.0, 'decay': 0.99, 'momentum': 0.3, 'offset': 1e-8}
opt_params = OptimizerParameters(algorithm='Adadelta', stops=stops,
options=options, merge_criter='w_avg')
self.opt_params = opt_params
# Configuracion de modelo
net_params = NetworkParameters(units_layers=[4, 10, 3], activation='ReLU', strength_l1=1e-5, strength_l2=3e-4,
dropout_ratios=[0.2, 0.0], classification=True)
self.model = NeuralNetwork(net_params)
def _optimize(self, stops=None, mini_batch=30, keep_best=True):
if stops is None:
stops = [criterion['MaxIterations'](50),
criterion['AchieveTolerance'](0.95, key='hits')]
logger.info("Entrenando modelo...")
if keep_best is True:
best_valid = 0.0
best_model = None
epoch = 0
hits_valid = 0.0
while self.model.check_stop(epoch, stops) is False:
hits_train = optimize(self.model, self.train, params=self.opt_params, mini_batch=mini_batch, seed=123)
hits_valid = self.model.evaluate(self.valid, predictions=False, measure='F-measure')
if keep_best is True:
if hits_valid >= best_valid:
best_valid = hits_valid
best_model = self.model
epoch += 1
if keep_best is True:
if best_model is not None: # Si hubo algun best model, procedo con el reemplazo
self.model = copy.deepcopy(best_model)
return hits_valid
def test_optimization(self, stops=None, mini_batch=30, keep_best=True):
hits_valid = self._optimize(stops=stops, mini_batch=mini_batch, keep_best=keep_best)
logger.info("Asegurando salidas correctas...")
assert hits_valid > 0.8
hits_test = self.model.evaluate(self.test, predictions=False, measure='F-measure')
assert hits_test > 0.8
logger.info("OK")
class TestNeuralNetwork(object):
def __init__(self, network_params=None):
logger.info(
"Testeo de NeuralNetwork con datos de Combined Cycle Power Plant")
# Datos
logger.info("Cargando datos...")
data = load_ccpp()
dataset = LocalLabeledDataSet(data)
self.train, self.valid, self.test = dataset.split_data([.5, .3, .2])
self.valid = self.valid.collect()
# Modelo
if network_params is None:
network_params = NetworkParameters(units_layers=[4, 30, 1],
activation='ReLU',
classification=False,
seed=123)
self.model = NeuralNetwork(network_params)
# Seteo a mano
self.model.set_l1(5e-7)
self.model.set_l2(3e-4)
self.model.set_dropout_ratios([0.0, 0.0])
def _fit(self, opt_params=None, stops=None, mini_batch=30, parallelism=2):
if opt_params is None:
options = {
'step-rate': 1.0,
'decay': 0.995,
'momentum': 0.3,
'offset': 1e-8
}
opt_params = OptimizerParameters(algorithm='Adadelta',
options=options)
if stops is None:
stops = [
criterion['MaxIterations'](30),
criterion['AchieveTolerance'](0.95, key='hits')
]
logger.info("Entrenando modelo...")
hits_valid = self.model.fit(self.train,
self.valid,
valid_iters=5,
mini_batch=mini_batch,
parallelism=parallelism,
stops=stops,
optimizer_params=opt_params,
keep_best=True,
reproducible=True)
return hits_valid
def test_check_stops(self):
criterions = [
criterion['MaxIterations'](30),
criterion['AchieveTolerance'](0.95, key='hits')
]
self.model.hits_valid = [
0.95
] # Hard-coded para simular dichos hits en el fit
assert self.model.check_stop(
epochs=15, criterions=criterions, check_all=False) is True
assert self.model.check_stop(
epochs=15, criterions=criterions, check_all=True) is False
assert self.model.check_stop(
epochs=40, criterions=criterions, check_all=True) is True
def _test_backprop(self, x, y):
# Dado que esta funcion se llama en una transformación de RDD, no es rastreada por Python
# por lo que es mejor hacerle unittest de forma que sea trazada en el coverage code.
cost, (nabla_w, nabla_b) = self.model._backprop(x, y)
assert type(cost) is float or isinstance(cost, np.float)
assert type(nabla_w[0]) is LocalNeurons
assert type(nabla_b[0]) is LocalNeurons
def test_fitting(self, opt_params=None, stops=None, mini_batch=30):
logger.info("Testeando backpropagation en NeuralNetwork...")
x = self.valid[0].features
y = [self.valid[0].label]
self._test_backprop(x, y)
hits_valid = self._fit(opt_params=opt_params,
stops=stops,
mini_batch=mini_batch)
logger.info("Asegurando salidas correctas...")
assert hits_valid > 0.7
hits_test, pred_test = self.model.evaluate(self.test,
predictions=True,
measure='R2')
assert hits_test > 0.7
logger.info("OK")
logger.info("Testeando funciones de prediccion...")
predicts = self.model.predict(self.test.collect())
assert np.array_equiv(pred_test, predicts)
# Test plot of fitting
logger.info("Testeando plots de fitting...")
plot_fitting(self.model, show=False)
logger.info("OK")
return
def test_parallelism(self, mini_batch=10):
logger.info("Testeando variantes del nivel de paralelismo...")
# Datos
logger.info("Datos utilizados: Iris")
data = load_iris()
dataset = LocalLabeledDataSet(data)
self.train, self.valid, self.test = dataset.split_data([.5, .3, .2])
self.valid = self.valid.collect()
# Optimizacion
options = {
'step-rate': 1.0,
'decay': 0.995,
'momentum': 0.3,
'offset': 1e-8
}
opt_params = OptimizerParameters(algorithm='Adadelta', options=options)
stops = [criterion['MaxIterations'](10)]
# Niveles de paralelismo
parallelism = [-1, 0, 2]
for p in parallelism:
logger.info("Seteando paralelismo en %i", p)
hits_valid = self._fit(opt_params=opt_params,
stops=stops,
mini_batch=mini_batch,
parallelism=p)
logger.info("Asegurando salidas correctas...")
assert hits_valid > 0.7
hits_test, pred_test = self.model.evaluate(self.test,
predictions=True,
measure='R2')
assert hits_test > 0.7
logger.info("OK")
return
def test_plotting(self):
logger.info("Testeando plots de activaciones...")
plot_activations(self.model.params, show=False)
logger.info("OK")
logger.info("Testeando plots de neuronas...")
plot_neurons(self.model, show=False)
logger.info("OK")
return
def test_fileio_model(self):
# Save
model_name = 'test_model.lea'
self.model.save(filename=TEMP_PATH + model_name)
# Load
test_model = NeuralNetwork.load(filename=TEMP_PATH + model_name)
assert self.model.params == test_model.params
# -- 3) Construcción y ajuste de red neuronal
neural_net = NeuralNetwork(net_params)
logger.info("Entrenando red neuronal ...")
hits_valid = neural_net.fit(train,
valid,
valid_iters=1,
mini_batch=20,
parallelism=0,
stops=global_stops,
optimizer_params=optimizer_params,
measure='R2',
keep_best=True,
reproducible=False)
hits_test, predict = neural_net.evaluate(test, predictions=True)
logger.info("Hits en test: %12.11f", hits_test)
# --4) Evaluacion de desempeño
logger.info("Metricas de evaluación en clasificacion: ")
labels = map(lambda lp: float(lp.label), test.collect())
metrics = RegressionMetrics(zip(predict, labels))
print "MSE: ", metrics.mse()
print "RMSE: ", metrics.rmse()
print "MAE: ", metrics.mae()
print "RMAE: ", metrics.rmae()
print "R-cuadrado: ", metrics.r2()
print "Explained Variance: ", metrics.explained_variance()
print zip(predict, labels)[:10]
optimizer_params = OptimizerParameters(algorithm='Adadelta', stops=local_stops, options=options,
merge_criter='w_avg', merge_goal='cost')
logger.info("Optimizacion utilizada: %s", os.linesep+str(optimizer_params))
logger.info("Configuracion usada: %s", os.linesep+str(net_params))
# -- 3) Construcción y ajuste de red neuronal
neural_net = NeuralNetwork(net_params)
logger.info("Entrenando red neuronal ...")
hits_valid = neural_net.fit(train, valid, valid_iters=1, mini_batch=20, parallelism=0,
stops=global_stops, optimizer_params=optimizer_params, measure='R2',
keep_best=True, reproducible=False)
hits_test, predict = neural_net.evaluate(test, predictions=True)
logger.info("Hits en test: %12.11f", hits_test)
# --4) Evaluacion de desempeño
logger.info("Metricas de evaluación en clasificacion: ")
labels = map(lambda lp: float(lp.label), test.collect())
metrics = RegressionMetrics(zip(predict, labels))
print "MSE: ", metrics.mse()
print "RMSE: ", metrics.rmse()
print "MAE: ", metrics.mae()
print "RMAE: ", metrics.rmae()
print "R-cuadrado: ", metrics.r2()
print "Explained Variance: ", metrics.explained_variance()
print zip(predict, labels)[:10]
class TestNeuralNetwork(object):
def __init__(self, network_params=None):
logger.info("Testeo de NeuralNetwork con datos de Combined Cycle Power Plant")
# Datos
logger.info("Cargando datos...")
data = load_ccpp()
dataset = LocalLabeledDataSet(data)
self.train, self.valid, self.test = dataset.split_data([.5, .3, .2])
self.valid = self.valid.collect()
# Modelo
if network_params is None:
network_params = NetworkParameters(units_layers=[4, 30, 1], activation='ReLU',
classification=False, seed=123)
self.model = NeuralNetwork(network_params)
# Seteo a mano
self.model.set_l1(5e-7)
self.model.set_l2(3e-4)
self.model.set_dropout_ratios([0.0, 0.0])
def _fit(self, opt_params=None, stops=None, mini_batch=30, parallelism=2):
if opt_params is None:
options = {'step-rate': 1.0, 'decay': 0.995, 'momentum': 0.3, 'offset': 1e-8}
opt_params = OptimizerParameters(algorithm='Adadelta', options=options)
if stops is None:
stops = [criterion['MaxIterations'](30),
criterion['AchieveTolerance'](0.95, key='hits')]
logger.info("Entrenando modelo...")
hits_valid = self.model.fit(self.train, self.valid, valid_iters=5, mini_batch=mini_batch,
parallelism=parallelism, stops=stops,optimizer_params=opt_params,
keep_best=True, reproducible=True)
return hits_valid
def test_check_stops(self):
criterions = [criterion['MaxIterations'](30),
criterion['AchieveTolerance'](0.95, key='hits')]
self.model.hits_valid = [0.95] # Hard-coded para simular dichos hits en el fit
assert self.model.check_stop(epochs=15, criterions=criterions, check_all=False) is True
assert self.model.check_stop(epochs=15, criterions=criterions, check_all=True) is False
assert self.model.check_stop(epochs=40, criterions=criterions, check_all=True) is True
def _test_backprop(self, x, y):
# Dado que esta funcion se llama en una transformación de RDD, no es rastreada por Python
# por lo que es mejor hacerle unittest de forma que sea trazada en el coverage code.
cost, (nabla_w, nabla_b) = self.model._backprop(x, y)
assert type(cost) is float or isinstance(cost, np.float)
assert type(nabla_w[0]) is LocalNeurons
assert type(nabla_b[0]) is LocalNeurons
def test_fitting(self, opt_params=None, stops=None, mini_batch=30):
logger.info("Testeando backpropagation en NeuralNetwork...")
x = self.valid[0].features
y = [self.valid[0].label]
self._test_backprop(x, y)
hits_valid = self._fit(opt_params=opt_params, stops=stops, mini_batch=mini_batch)
logger.info("Asegurando salidas correctas...")
assert hits_valid > 0.7
hits_test, pred_test = self.model.evaluate(self.test, predictions=True, measure='R2')
assert hits_test > 0.7
logger.info("OK")
logger.info("Testeando funciones de prediccion...")
predicts = self.model.predict(self.test.collect())
assert np.array_equiv(pred_test, predicts)
# Test plot of fitting
logger.info("Testeando plots de fitting...")
plot_fitting(self.model, show=False)
logger.info("OK")
return
def test_parallelism(self, mini_batch=10):
logger.info("Testeando variantes del nivel de paralelismo...")
# Datos
logger.info("Datos utilizados: Iris")
data = load_iris()
dataset = LocalLabeledDataSet(data)
self.train, self.valid, self.test = dataset.split_data([.5, .3, .2])
self.valid = self.valid.collect()
# Optimizacion
options = {'step-rate': 1.0, 'decay': 0.995, 'momentum': 0.3, 'offset': 1e-8}
opt_params = OptimizerParameters(algorithm='Adadelta', options=options)
stops = [criterion['MaxIterations'](10)]
# Niveles de paralelismo
parallelism = [-1, 0, 2]
for p in parallelism:
logger.info("Seteando paralelismo en %i", p)
hits_valid = self._fit(opt_params=opt_params, stops=stops, mini_batch=mini_batch, parallelism=p)
logger.info("Asegurando salidas correctas...")
assert hits_valid > 0.7
hits_test, pred_test = self.model.evaluate(self.test, predictions=True, measure='R2')
assert hits_test > 0.7
logger.info("OK")
return
def test_plotting(self):
logger.info("Testeando plots de activaciones...")
plot_activations(self.model.params, show=False)
logger.info("OK")
logger.info("Testeando plots de neuronas...")
plot_neurons(self.model, show=False)
logger.info("OK")
return
def test_fileio_model(self):
# Save
model_name = 'test_model.lea'
self.model.save(filename=TEMP_PATH+model_name)
# Load
test_model = NeuralNetwork.load(filename=TEMP_PATH+model_name)
assert self.model.params == test_model.params
