def __init__(self, type_model=None, net_params=None, opt_params=None, n_layers=0, n_iter=10, seed=123):
# 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': 0.995, '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 a optimizar
if type_model is None:
type_model = NeuralNetwork
self.type_model = type_model
if net_params is None:
net_params = NetworkParameters(units_layers=[4, 10, 3], activation=False, dropout_ratios=True,
classification=True, strength_l1=True, strength_l2=True, seed=seed)
self.net_params = net_params
# Configuracion del Random Search
self.rnd_search = RandomSearch(self.net_params, n_layers, n_iter, net_domain=None, seed=seed)
class TestRandomSearch(object):
def __init__(self, type_model=None, net_params=None, opt_params=None, n_layers=0, n_iter=10, seed=123):
# 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': 0.995, '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 a optimizar
if type_model is None:
type_model = NeuralNetwork
self.type_model = type_model
if net_params is None:
net_params = NetworkParameters(units_layers=[4, 10, 3], activation=False, dropout_ratios=True,
classification=True, strength_l1=True, strength_l2=True, seed=seed)
self.net_params = net_params
# Configuracion del Random Search
self.rnd_search = RandomSearch(self.net_params, n_layers, n_iter, net_domain=None, seed=seed)
def test_good_fitting(self, mini_batch=30, parallelism=1):
best_model, hits_test = self.rnd_search.fit(self.type_model, self.train, self.valid, self.test,
mini_batch=mini_batch, parallelism=parallelism, valid_iters=1,
stops=None, optimizer_params=self.opt_params, reproducible=True,
keep_best=True)
logger.info("Asegurando salidas correctas...")
assert hits_test > 0.8
logger.info("OK")
def test_all_fitting(self, mini_batch=30, parallelism=1):
n_layers = [-1, 0, 3, 4]
best_hits_final = 0.0
net_params = NetworkParameters(units_layers=[4, 10, 3], activation=True, dropout_ratios=[0.0, 0.0],
classification=True, strength_l1=1e-7, strength_l2=1e-4, seed=123)
for n_l in n_layers:
logger.info("Testeando con 'n_layers = %i'", n_l)
self.rnd_search = RandomSearch(net_params, n_l, n_iter=1, net_domain=None, seed=123)
best_model, best_hits = self.rnd_search.fit(self.type_model, self.train, self.valid, self.test,
mini_batch=mini_batch, parallelism=parallelism, valid_iters=1,
stops=None, optimizer_params=None, reproducible=True,
keep_best=True)
if best_hits > best_hits_final:
best_hits_final = best_hits
logger.info("Asegurando salidas correctas...")
assert best_hits_final > 0.3
logger.info("OK")
def test_all_fitting(self, mini_batch=30, parallelism=1):
n_layers = [-1, 0, 3, 4]
best_hits_final = 0.0
net_params = NetworkParameters(units_layers=[4, 10, 3], activation=True, dropout_ratios=[0.0, 0.0],
classification=True, strength_l1=1e-7, strength_l2=1e-4, seed=123)
for n_l in n_layers:
logger.info("Testeando con 'n_layers = %i'", n_l)
self.rnd_search = RandomSearch(net_params, n_l, n_iter=1, net_domain=None, seed=123)
best_model, best_hits = self.rnd_search.fit(self.type_model, self.train, self.valid, self.test,
mini_batch=mini_batch, parallelism=parallelism, valid_iters=1,
stops=None, optimizer_params=None, reproducible=True,
keep_best=True)
if best_hits > best_hits_final:
best_hits_final = best_hits
logger.info("Asegurando salidas correctas...")
assert best_hits_final > 0.3
logger.info("OK")
