current_perf = dict.fromkeys(['model','lambda','rank','crossval'])
for elt in itertools.product(*[lambdas,rank,range(K)]):
l, r, k = elt
current_perf['model'] = 'bagALS-WR'
current_perf['lambda'] = l
current_perf['rank'] = r
current_perf['crossval'] = k
print(current_perf)
bob = bag_ALS(d=r,num_users=numUser,num_items=numItem,split='item',sampling=.9,
num_estimators = 10,lbda=l,verbose=True)
train = data[data['cv']!=k][['row','col','val']].to_dict(orient='list')
test = data[data['cv']==k][['row','col','val']].to_dict(orient='list')
t0 = time()
bob.fit(train)
T = time()-t0
Rhats, Rhat = bob.predict()
R_test = sparse_matrix(test,numUser,numItem)
rmse = RMSE(R_test,Rhat)
print(rmse)
ind = getLine_fromdict(perf,current_perf)
perf.loc[ind,['model','rank','lambda','crossval','rmse','runningTime']] = ['bagALS-WR',r,l,k,rmse,T]
print('-'*50)
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# MODIFY FROM HERE
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