#epss = params['ALS-WR']['params']['eps']
regs = ["weighted"]
lambdas = [0.8]
rank = [10]
alphas = [10]
epss= [100]
mus = [0.5]
current_perf = dict.fromkeys(['model','reg','lambda','rank','alpha','eps','mu','crossval'])
for elt in itertools.product(*[regs,lambdas,rank,alphas,epss,mus,[0]]):
reg, l, r, alpha, eps, mu, k = elt
current_perf.update({'model':'graphALS','reg':reg,'lambda':l,
'rank':r,'alpha':alpha,'eps':eps,'mu':mu,
'crossval':k})
print(current_perf)
bob = graphALS(d=r,num_users=numUser,num_items=numItem,lbda=l,mu=mu,
seed=0,reg=reg,verbose=True)
t0 = time()
bob.fitImplicit(train = data[data['cv']!=k],
alpha=alpha,
c="log",
eps=eps,
userGraph = userGraph[['userId','friendId','dot_product_weight']],
itemGraph = None,
method="svd")
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] = ['graphALS',reg,l,r,alpha,eps,k,rank,T]
reg, l, r, alpha, eps, mu, k = elt
current_perf.update({
'model': 'graphALS',
'reg': reg,
'lambda': l,
'rank': r,
'alpha': alpha,
'eps': eps,
'mu': mu,
'crossval': k
})
print(current_perf)
bob = graphALS(d=r,
num_users=numUser,
num_items=numItem,
lbda=l,
mu=mu,
seed=0,
reg=reg,
verbose=True)
t0 = time()
bob.fitImplicit(
train=data[data['cv'] != k],
alpha=alpha,
c="log",
eps=eps,
userGraph=userGraph[['userId', 'friendId', 'dot_product_weight']],
itemGraph=None,
method="svd")
T = time() - t0
Rhat = bob.U.dot(bob.V.T)
R_test = sparseMatrix(data,
# r.extend(list(np.repeat(1.,len(itemIds)*len(itemIds))))
# tmp = np.array([x,y,r]).T
# #tmp = pd.DataFrame([x,y,r]).T
# tmp = tmp[tmp[:,0]!=tmp[:,1]]
# itemContextSparseGraph = np.concatenate((itemContextSparseGraph,tmp.T),axis=1)
#==============================================================================
current_perf = dict.fromkeys(['model','lambda','rank','crossval'])
for elt in itertools.product(*[lambdas,rank,range(K)]):
l, r, k = elt
current_perf['model'] = 'graphALS'
current_perf['lambda'] = l
current_perf['rank'] = r
current_perf['crossval'] = k
print(current_perf)
bob = graphALS(d=r,num_users=numUser,num_items=numItem,lbda=l,
solve='jacobi',verbose=True)
t0 = time()
bob.fit(train = data[data['cv']!=k],
userGraph = userContextSparseGraph,
itemGraph = itemContextSparseGraph)
T = time()-t0
Rhat = bob.U.dot(bob.V.T)
R_test = sparseMatrix(data,k,include=True)
W = R_test.nonzero()
rmse = RMSE(R_test,Rhat)
print(rmse)
ind = getLine_fromdict(perf,current_perf)
perf.loc[ind,['model','rank','lambda','crossval','rmse','runningTime']] = ['graphALS',r,l,k,rmse,T]
print('-'*50)
#==============================================================================
