class TestAutoEncoder(object):
def __init__(self, network_params=None, dropout_in=0.0):
logger.info("Testeo de AutoEncoder con datos de Iris")
# Datos
logger.info("Cargando datos...")
dataset = LocalLabeledDataSet()
path = os.path.abspath(os.path.join(os.path.realpath(__file__),
os.path.pardir,
os.pardir,
'examples/datasets/iris.csv'))
dataset.load_file(path)
self.train, self.valid, self.test = dataset.split_data([.5, .3, .2])
self.train = self.train.collect()
self.valid = self.valid.collect()
# Modelo
if network_params is None:
network_params = NetworkParameters(units_layers=[4, 8], activation=['ReLU', 'ReLU'],
classification=False)
self.model = AutoEncoder(network_params, dropout_in=dropout_in)
def _fit(self, opt_params=None, stops=None, mini_batch=30, parallelism=2):
if opt_params is None:
opt_params = OptimizerParameters(algorithm='Adadelta')
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=1, mini_batch=mini_batch,
parallelism=parallelism, stops=stops,optimizer_params=opt_params,
keep_best=True, reproducible=True)
return hits_valid
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, parallelism=1):
logger.info("Testeando backpropagation en AutoEncoders...")
x = self.train[0].features
y = self.train[0].label
self._test_backprop(x, y)
hits_valid = self._fit(opt_params=opt_params, stops=stops, mini_batch=mini_batch, parallelism=parallelism)
logger.info("Asegurando salidas correctas...")
assert hits_valid > 0.8
hits_test = self.model.evaluate(self.test, predictions=False, measure='R2')
assert hits_test > 0.8
logger.info("OK")
def __init__(self, network_params=None, dropout_in=0.0):
logger.info("Testeo de AutoEncoder con datos de Iris")
# Datos
logger.info("Cargando datos...")
dataset = LocalLabeledDataSet()
path = os.path.abspath(os.path.join(os.path.realpath(__file__),
os.path.pardir,
os.pardir,
'examples/datasets/iris.csv'))
dataset.load_file(path)
self.train, self.valid, self.test = dataset.split_data([.5, .3, .2])
self.train = self.train.collect()
self.valid = self.valid.collect()
# Modelo
if network_params is None:
network_params = NetworkParameters(units_layers=[4, 8], activation=['ReLU', 'ReLU'],
classification=False)
self.model = AutoEncoder(network_params, dropout_in=dropout_in)
