def hotStartTrainRMSE(model_name, K, L, X1, X2, train_I, train_J, train_Y, train_op):
M = X1.shape[0]
N = X2.shape[0]
model = train_op['params']['model']
predictions = mfscoalnamstyle_predict(train_I, train_J, M, N,K,L,model)
Z = sp.csr_matrix((train_Y, (train_I,train_J)), shape=(M,N))
nonzero_Z = np.array(Z[(train_I,train_J)]).ravel()
hotStartTrainRMSE = np.sqrt(np.mean((predictions.flatten()-nonzero_Z)**2))
return hotStartTrainRMSE
def hotStartValRMSE(model_name, K, L, X1, X2, val_I, val_J, val_Y, train_op):
#print "INSIDE VAL RMSE"
M = X1.shape[0]
N = X2.shape[0]
model = train_op['params']['model']
predictions = mfscoalnamstyle_predict(val_I, val_J, M, N,K,L,model)
#print "VAL PREDICTIONS OBTAINED"
Z = sp.csr_matrix((val_Y, (val_I,val_J)), shape=(M,N))
nonzero_Z = np.array(Z[(val_I,val_J)]).ravel()
hotStartTrainRMSE = np.sqrt(np.mean((predictions.flatten()-nonzero_Z)**2))
#print "VAL RMSE CALCULATED"
return hotStartTrainRMSE
