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Question to students: Have a look to the parameter setting test_size. Why did we choose that small fraction of events to be used for the testing phase?

# Classical way to proceed, using a scikit-learn algorithm:

# X_train_val, X_test, Y_train_val , Y_test , W_train_val , W_test = 
# train_test_split(X, Y, W , test_size=0.2,shuffle=None,stratify=None )

# Alternative way, the one that we chose in order to study the model's performance 
# with ease (with an analogous procedure used by TMVA in ROOT framework)
# to keep information about the flag isSignal in both training and test steps. 

size= int(len(X[:,0]))
test_size = int(0.2*len(X[:,0]))
print('X (features) before splitting')
print('\n')
print(X.shape)
print('X (features) splitting between test and training')
X_test= X[0:test_size+1,:]
print('Test:')
print(X_test.shape)
X_train_val= X[test_size+1:len(X[:,0]),:]
print('Training:')
print(X_train_val.shape)
print('\n')
print('Y (target) before splitting')
print('\n')
print(Y.shape)
print('Y (target) splitting between test and training ')
Y_test= Y[0:test_size+1,:]
print('Test:')
print(Y_test.shape)
Y_train_val= Y[test_size+1:len(Y[:,0]),:]
print('Training:')
print(Y_train_val.shape)
print('\n')
print('W (weights) before splitting')
print('\n')
print(W.shape)
print('W (weights) splitting between test and training ')
W_test= W[0:test_size+1,:]
print('Test:')
print(W_test.shape)
W_train_val= W[test_size+1:len(W[:,0]),:]
print('Training:')
print(W_train_val.shape)
print('\n')

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