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_fitting(self, opt_params=None, stops=None, mini_batch=30, parallelism=1):
hits_valid_pretrain = self._fit(opt_params=opt_params, stops=stops,
mini_batch=mini_batch, parallelism=parallelism)
hits_test_pretrain = self.model.evaluate(self.test, predictions=False, measure='F-measure')
hits_valid_finetune = self._finetune(opt_params=opt_params, stops=stops,
mini_batch=mini_batch, parallelism=parallelism)
hits_test_finetune = self.model.evaluate(self.test, predictions=False, measure='F-measure')
logger.info("Asegurando salidas correctas...")
assert hits_valid_pretrain > 0.6
assert hits_valid_finetune > 0.8
assert hits_test_pretrain > 0.6
assert hits_test_finetune > 0.8
logger.info("OK")
# Test plot of fitting
logger.info("Testeando plots de fitting...")
plot_fitting(self.model, show=False)
logger.info("OK")
return
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
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]
# --5) Ploteos
plot_fitting(neural_net)
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]
# --5) Ploteos
plot_fitting(neural_net)
