Franziska Weindel

def objective(fac, reg, it, alp): model = implicit.als.AlternatingLeastSquares(factors=int(fac), regularization=reg, iterations=int(it)) alpha = int(alp) data = (train.T * alpha)#.astype('double') model.fit(data) user_index = range(data.shape[0]) F1=[] RMSE=[] Precision=[] #Recall=[] NDCG=[] percentageright=[] for i...

tuning=np.array([[2, 100], [0, 1], [0,200], [0,100]]) import mobopt as mo Optimizer = mo.MOBayesianOpt(target=objective, NObj=5, pbounds=tuning) Init=np.array([10,0,1,5,10]) Optimizer.initialize(Init)

This is my code if it helps : )