'lambda': l,
'rank': r,
'alpha': alpha,
'eps': eps,
'crossval': k
})
print(current_perf)
bob = ALS(d=r,
num_users=numUser,
num_items=numItem,
lbda=l,
seed=0,
reg=reg,
verbose=True)
t0 = time()
bob.fitImplicit(data[data['cv'] != k], alpha=alpha, c="log", eps=eps)
T = time() - t0
Rhat = bob.U.dot(bob.V.T)
R_test = sparseMatrix(data,
k,
include=True,
names=list(data.columns)[:3])
rank = rankMeasure(R_test, Rhat)
print(rank)
ind = getLine_fromdict(perf, current_perf)
perf.loc[ind] = ['ALS-WR', reg, l, r, alpha, eps, k, rank, T]
print('-' * 50)
#==============================================================================
# graphALS
#==============================================================================
lambdas = [0.8]
rank = [20]
alphas = [10]
epss= [10]
current_perf = dict.fromkeys(['model','reg','lambda','rank','alpha','eps','crossval'])
for elt in itertools.product(*[regs,lambdas,rank,alphas,epss,[0]]):
reg, l, r, alpha, eps, k = elt
current_perf.update({'model':'ALS-WR','reg':reg,'lambda':l,
'rank':r,'alpha':alpha,'eps':eps,
'crossval':k})
print(current_perf)
bob = ALS(d=r,num_users=numUser,num_items=numItem,lbda=l,seed=0,
reg=reg,verbose=True)
t0 = time()
bob.fitImplicit(data[data['cv']!=k],alpha=alpha,c="log",eps=eps)
T = time()-t0
Rhat = bob.U.dot(bob.V.T)
R_test = sparseMatrix(data,k,include=True,names=list(data.columns)[:3])
rank = rankMeasure(R_test,Rhat)
print(rank)
ind = getLine_fromdict(perf,current_perf)
perf.loc[ind] = ['ALS-WR',reg,l,r,alpha,eps,k,rank,T]
print('-'*50)
#==============================================================================
# graphALS
#==============================================================================
# Graph Construction
userGraph = pd.read_csv(path+'data/lastfm2k/user_friends.dat',sep="\t")
