def sframe(frame):
sf = SFrame(frame)
sf.explore()
sf.show()
return
if multivariate:
visualisation(y_pred=y_pred[:, 0].reshape(-1, 1),
Y_test=Y_test[:, 0].reshape(-1, 1),
y_difference=y_difference[:, 0].reshape(-1,
1)) # Insertion
visualisation(y_pred=y_pred[:, 1].reshape(-1, 1),
Y_test=Y_test[:, 1].reshape(-1, 1),
y_difference=y_difference[:, 1].reshape(-1,
1)) # Deletion
else:
visualisation(y_pred, Y_test, y_difference)
#Looking into sframe as an alternative to pandas, has s3 support
#Tensorflow also has s3 and GCP support if you install from source and enable it
sf = SFrame(build_csv(build_data(sample)[5], build_data(sample)[7]))
sf.explore()
sf.show()
else:
#Neural Network architecture
def build_regressor():
#Initialising neural network
regressor = Sequential()
#Input layer and first hidden layer with dropout
regressor.add(
Dense(units=10,
kernel_initializer='uniform',
activation='relu',
