Code Review Asked on January 2, 2022
I am implementing a time-dependent Recommender System which applies BPR (Bayesian Personalized Ranking), where Stochastic Gradient Ascent is used to learn the parameters of the model. Such that, one iteration involves sampling randomly the quadruple (i.e. userID, positive_item, negative_item, epoch_index) for $n$ times, where $n$ is the total number of positive feedbacks (i.e. the number of ratings given to all items). But, as my implementation takes too much time for learning the parameters (since it requires 100 iterations to learn the parameters), I was wondering if there is a way to improve my code and speed up the learning process of the parameters.
In the following, please find the code of my implementation (the update function named as updateFactors is the one that learns the parameters, and such that I guess is the one that should be improved in order to speed up the process of learning the parameter values):
class TVBPR:
def __init__(self, iterations, K, K2, nUsers, nItems, imageFeatures, nBins, userItems, list_of_items, nr_of_pos_events, max_timestamp, feat, asinToInt, userIDToInt,itemIdToInt, min_timestamp, test_per_user, val_per_user):
print 'initialization started...'
sys.setrecursionlimit(5000)
self.nUsers = nUsers
self.nItems = nItems + 1 + 1
self.nr_of_pos_events = nr_of_pos_events
self.K = K # number of non-visual factors
self.K2 = K2 # number of visual factors
self.userItems = userItems
self.itemIdToInt = itemIdToInt
self.list_of_items = list_of_items
self.nBins = nBins # number of epochs
self.maxTime = max_timestamp
self.minTime = min_timestamp
self.min_in_seconds = min_timestamp
self.userIDToInt = userIDToInt
self.itemIdToInt = itemIdToInt
self.feat = feat # a dictionary where each item ID is associated with the values of 4096 features (features describing certain item, with values ranging between 0 - 1)
self.asinToInt = asinToInt
self.test_per_user = test_per_user
self.val_per_user= val_per_user
# biases
self.b_u = np.zeros(self.nUsers)
self.b_i = np.zeros(self.nItems)
self.bi_bin = np.zeros((self.nItems, nBins))
#non - visual factors
self.gamma_user = np.random.random((self.nUsers + 1, K))
self.gamma_item = np.random.random((self.nItems, K))
# visual factors
self.theta_user = np.random.random((self.nUsers + 1, K2))
self.theta_item_per_bin = []
for b in range(self.nBins):
theta_item = np.zeros((self.nItems, K2))
self.theta_item_per_bin.append(theta_item)
self.E = np.random.random((K2, imageFeatures))
self.E_t = np.random.random((nBins, K2, imageFeatures)) # image features = 4096
# visual bias
self.betta_cnn = np.random.random(imageFeatures)
self.betta_cnn_t = []
for i in range(nBins):
b_cnn = np.zeros(imageFeatures)
self.betta_cnn_t.append(b_cnn)
self.J_t = np.zeros((nBins, K2)) # weighting vector for the embedding matrix
self.C_t = np.zeros((nBins, imageFeatures)) # wieghting vector for the visual bias
self.betta_item_visual_per_bin = np.zeros((nBins, self.nItems))
epochs = self.init_epoch()
self.epochs = epochs
print 'self.epochs = ', self.epochs
print 'initialization finished...'
print 'training started...'
self.train(iterations)
print 'training finished...'
print 'getting visual features...'
self.getVisualFactors()
print 'visual features calculation finished...'
print 'initialization of epochs started...'
# apply DP
# 1. calculate pos_items_per_bin
self.votes_per_bin = self.pos_item_per_bin()
# 2 init DP
self.memo, self.sol = self.dp_init()
# DP
# self.DP(1000)
print 'evaluating using AUC...'
auc = self.AUC_(self.test_per_user, self.val_per_user)
print 'AUC: ', auc
print 'epochs: ', self.epochs
print 'evaluation finished...'
def train(self, nr_of_iterations):
for i in range(nr_of_iterations):
print 'iteration: ', i + 1
start_one_iter = time.time()
self.oneIteration()
end_time_iter = time.time()
time_elapsed_one_iter = end_time_iter - start_one_iter
print 'time elapsed in one iteration: ', time_elapsed_one_iter, 'sec.'
def oneIteration(self):
pos_per_user = {}
for k,v in self.userItems.items():
itemID = self.userItems[k][0][0]
pos_per_user[k] = [(itemID, self.userItems[k][0][2])]
for i in range(1, len(self.userItems[k])):
pos_per_user[k].append((self.userItems[k][i][0], self.userItems[k][i][2]))
for i in range(self.nr_of_pos_events): #self.nr_of_pos_events
print 'update: ', i + 1, '/', self.nr_of_pos_events #self.nr_of_pos_events
#sample user
userID = self.sampleUser()
if len(pos_per_user[userID]) == 0:
pos_per_user[userID] = [(itemID, self.userItems[userID][0][2])]
for i in range(1, len(self.userItems[userID])):
pos_per_user[userID].append((self.userItems[userID][i][0], self.userItems[userID][i][2]))
if userID == None:
for i in range(1, len(pos_per_user)):
print 'user: ', i ,', len = ', len(pos_per_user[i])
continue
#sample positive item
if not pos_per_user[userID]:
continue
pos_event = random.choice(pos_per_user[userID])
pos_item = pos_event[0]
if pos_item not in self.feat:
continue
voteTime = pos_event[1]
voteTime = int(voteTime)
# print 'voteTime = ', voteTime
epoch = self.timeInEpoch(voteTime)
# print 'epoch = ', epoch
pos_per_user[userID].remove(pos_event)
#sample negative item
# creating the list of all pos_items of userID
pos_item_of_userID = []
for i in range(len(self.userItems[userID])):
item_id = self.userItems[userID][i][0]
pos_item_of_userID.append(item_id)
neg_item = self.sample_neg_item(pos_item_of_userID)
if neg_item not in self.feat:
continue
#update factors
self.updateFactors(userID, pos_item, neg_item, epoch)
# updating factors
def updateFactors(self, userID, pos_item, neg_item, epoch):
pos_feat = self.feat[pos_item].toarray()
neg_feat = self.feat[neg_item].toarray()
pos_feat = pos_feat[0]
neg_feat = neg_feat[0]
feat_i = [] # the features of the pos_item
feat_j = [] # the features of the neg_item
for i in range(4096):
feat_i.append((i, pos_feat[i]))
feat_j.append((i, neg_feat[i]))
diff = []
#sparse representation
p_i = 0
p_j = 0
while p_i < len(pos_feat) and p_j < len(neg_feat):
ind_i = feat_i[p_i][0]
ind_j = feat_j[p_j][0]
if ind_i < ind_j:
diff.append((ind_i, feat_i[p_i][1]))
p_i = p_i + 1
elif ind_i > ind_j:
diff.append((ind_j, feat_j[p_j][1]))
p_j = p_j + 1
else:
diff.append((ind_i, feat_i[p_i][1] - feat_j[p_j][1]))
p_i = p_i + 1
p_j = p_j + 1
while p_i < len(feat_i):
diff.append(feat_i[p_i])
while p_j < len(feat_j):
diff.append((feat_j[p_j][0], - feat_j[p_j][1]))
# print 'epoch', epoch
len_of_diff = len(diff)
# E * (x_i - x_j)
theta_i = np.zeros(self.K2)
theta_i_per_bin = np.zeros(self.K2)
start_theta = time.time()
for r in range(self.K2):
for ind in range(len_of_diff):
c = diff[ind][0]
feat_val = diff[ind][1]
theta_i[r] += self.E[r][c] * feat_val
theta_i_per_bin[r] += self.E_t[epoch][r][c] * feat_val
end_theta = time.time()
print 'theta_i_time = ', end_theta - start_theta
visual_score = 0
for k in range(self.K2):
visual_score += self.theta_user[userID][k] * (theta_i[k] * self.J_t[epoch][k] + theta_i_per_bin[k])
# print 'visual score = ', visual_score
visual_bias = 0
for ind in range(len_of_diff):
c = diff[ind][0]
visual_bias += (self.betta_cnn[c] * self.C_t[epoch][c] + self.betta_cnn_t[epoch][c]) * diff[ind][1]
# x_uij = prediction(user_id, pos_item_id, bin) - prediction(user_id, neg_item_id, bin);
pos_i = self.itemIdToInt[pos_item]
neg_i = self.itemIdToInt[neg_item]
bi = self.b_i[pos_i]
bj = self.b_i[neg_i]
x_uij = bi - bj
x_uij += np.inner(self.gamma_user[userID], self.gamma_item[pos_i]) - np.inner(self.gamma_user[userID], self.gamma_item[neg_i])
x_uij += visual_score + visual_bias
deri = 1/(1 + np.exp(x_uij))
self.b_i[pos_i] += 0.005 * (deri - 1 * bi)
self.b_i[neg_i] += 0.005 * (-deri - 1 * bj)
# updating latent factors
start_non_vf = time.time()
for k in range(self.K):
uf = self.gamma_user[userID][k]
_if = self.gamma_item[pos_i][k]
_jf = self.gamma_item[neg_i][k]
self.gamma_user[userID][k] += 0.005 * (deri * (_if - _jf) - 1 * uf)
self.gamma_item[pos_i][k] += 0.005 * (deri * uf - 1 * _if)
self.gamma_item[neg_i][k] += 0.005 * (-deri * uf - 1/10.0 * _jf)
end_non_vf = time.time()
time_elapsed_non_vf = start_non_vf - end_non_vf
# print 'time elapsed for non visual factors: ', time_elapsed_non_vf, ' sec.'
# updating visual factors
start_vf = time.time()
for k2 in range(self.K2):
v_uf = self.theta_user[userID][k2]
j_t = self.J_t[epoch][k2]
for ind in range(len_of_diff):
c = diff[ind][0]
common = deri * v_uf * diff[ind][1]
self.E[k2][c] += 0.005 * (common * j_t)
self.E_t[epoch][k2][c] += 0.005 * (common - 0.0001 * self.E_t[epoch][k2][c])
self.theta_user[userID][k2] += 0.005 * (deri * (theta_i[k2] * j_t + theta_i_per_bin[k2]) - 1 * v_uf)
self.J_t[epoch][k2] += 0.005 * (deri * theta_i[k2] * v_uf - 0.0001 * j_t)
for ind in range(len_of_diff):
c = diff[ind][0]
c_tf = self.C_t[epoch][c]
b_cnn = self.betta_cnn[c]
common = 0.005 * deri * diff[ind][1]
self.betta_cnn[c] += common * c_tf
self.C_t[epoch][c] += common * b_cnn - 0.005 * 0.0001 * c_tf
self.betta_cnn_t[epoch][c] += common - 0.005 * 0.0001 * self.betta_cnn_t[epoch][c]
end_vf = time.time()
time_elapsed_vf = end_vf - start_vf
print 'time elapsed on visual factors: ', time_elapsed_vf,' sec.'
# sample user
def sampleUser(self):
userId_ = randint(1, self.nUsers)
return userId_
#sample negative item
def sample_neg_item(self, pos_per_user):
while True:
neg_item = random.choice(self.list_of_items)
if neg_item not in pos_per_user:
return neg_item
def calBin(self, time_st):
interval = (self.maxTime - self.minTime) / self.nBins
bin_ind = np.minimum(self.nBins - 1, int((time_st - self.minTime)/interval))
return bin_ind
# calculate the corresponding epoch given timestamp
def timeInEpoch(self, timesta):
inte = (self.maxTime - self.minTime)/80
# print 'interval = ', inte
b_ind = np.minimum(79, int((timesta - self.minTime)/inte))
# print 'b_ind =', b_ind
for i in range(len(self.epochs)):
start_bin = self.epochs[i][0]
end_bin = self.epochs[i][1]
if end_bin >= b_ind:
# print 'epochs ==', self.epochs
# print 'epoch_ind = ', i
return i
# calculate pos_item_per_bin
def pos_item_per_bin(self):
votes_per_bin = []
for i in range(80):
votes_per_bin.append([])
# adding the samples from the validation set to the corresponding bin
for k,v in self.val_per_user.items():
user = k
item = self.val_per_user[k][0]
rating = self.val_per_user[k][1]
voteTime = self.val_per_user[k][2]
interv = (max(times_) - min(times_))/80
# print 'interval = ', inte
b_ind = np.minimum(79, int((voteTime - min(times_))/interv))
votes_per_bin[b_ind].append((user, item))
return votes_per_bin
# epoch initialization
def init_epoch(self):
epochs = []
interval = 80/10
bin_from = 0
bin_to = interval - 1
for i in range(10):
if epochs == []:
epochs.append((bin_from, bin_to))
else:
bin_from = bin_to + 1
bin_to = bin_to + interval
epochs.append((bin_from, bin_to))
return epochs
# DP
def DP(self, num_of_neg_items):
pos_per_user = self.pos_per_user_()
sampleMap = {}
for k,v in self.userItems.items():
user = k
for i in range(num_of_neg_items):
neg_item = self.sample_neg_item(pos_per_user[user])
if user not in sampleMap:
sampleMap[user] = [neg_item]
else:
sampleMap[user].append(neg_item)
# apply DP
fval = self.f(0, 79, 0, 10, sampleMap)
print 'f() = ', fval
new_epochs = []
start = 0
end = 79
ep = 0
pieces = 10
last_bin_to = -1
for i in range(10):
seprator = self.sol[start][end][ep][pieces - 1]
if seprator == -1:
print 'Exception: No solution found by DP'
if new_epochs == []:
binFrom = last_bin_to + 1
binTo = last_bin_to = seprator
new_epochs.append((binFrom, binTo))
else:
binFrom = last_bin_to + 1
binTo = last_bin_to = seprator
new_epochs.append((binFrom, binTo))
start = seprator + 1
pieces = pieces - 1
ep += 1
print 'epochs = ', self.epochs
self.epochs = new_epochs
print 'new_epochs = ', self.epochs
# positive items per user
def pos_per_user_ (self):
pos_per_user = {}
for k,v in self.userItems.items():
for i in range(len(self.userItems[k])):
item__ = self.userItems[k][i][0]
if k not in pos_per_user:
pos_per_user[k] = [item__]
else:
pos_per_user[k].append(item__)
return pos_per_user
# f()
def f(self, startBin, endBin, epo, pieces, sample_map):
if self.memo[startBin][endBin][epo][pieces - 1] != sys.float_info.max:
return self.memo[startBin][endBin][epo][pieces - 1]
if pieces == 1:
self.memo[startBin][endBin][epo][0] = self.onePieceVal(startBin, endBin, epo, sample_map)
if self.memo[startBin][endBin][epo][0] < sys.float_info.max:
self.sol[startBin][endBin][epo][0] = endBin
else:
self.sol[startBin][endBin][epo][0] = -1
return self.memo[startBin][endBin][epo][0]
max_val = -sys.float_info.max
for k in range(startBin, endBin - pieces):
val = self.f(startBin, k, epo, 1, sample_map) + self.f(k + 1, endBin, epo + 1, pieces - 1, sample_map)
if val > max_val:
max_val = val
self.sol[startBin][endBin][epo][pieces - 1] = k
self.memo[startBin][endBin][epo][pieces - 1] = max_val
return max_val
def onePieceVal(self,startBin, endBin, epo, sampleMap):
res = 0
total = 0
for i in range(startBin, endBin + 1):
if self.memo[i][i][epo][0] != sys.float_info.max:
res += self.memo[i][i][epo][0]
continue
for j in range(len(self.votes_per_bin[i])):
user_ = self.votes_per_bin[i][j][0]
item_ = self.votes_per_bin[i][j][1]
item_id_int = self.itemIdToInt[item_]
x_ui = self.prediction(user_, item_id_int, epo)
for k in range(len(sampleMap[user_])):
neg_item = sampleMap[user_][k]
neg_item_ = self.itemIdToInt[neg_item]
x_uj = self.prediction(user_, neg_item_, epo)
total += np.log(self.sigmoid(x_ui - x_uj))
self.memo[i][i][epo][0] = total
self.sol[i][i][epo][0] = i
res += total
return res
# sigmoid function
def sigmoid(self, x):
sig = 1 / (1 + np.exp(-x))
return sig
# DP init
def dp_init(self):
memo = np.zeros((80,80,10,10))
sol = np.zeros((80,80,10,10))
for b in range(80):
for j in range(80):
for k in range(10):
for x in range(10):
memo[b][j][k][x] = sys.float_info.max # not calculated
sol[b][j][k][x] = -1 # not calculated
return memo, sol
def getVisualFactors(self):
for bin in range(10):
visual_scores = []
dim0 = []
dim1 = []
dim2 = []
dim3 = []
dim4 = []
dim5 = []
dim6 = []
dim7 = []
dim8 = []
dim9 = []
dim10 = []
dim11 = []
dim12 = []
dim13 = []
dim14 = []
dim15 = []
dim16 = []
dim17 = []
dim18 = []
dim19 = []
for i in range(len(self.list_of_items)):
asin = self.list_of_items[i]
itemID = self.itemIdToInt[asin]
if asin not in feat:
continue
item_feat = self.feat[asin].toarray()
item_feat = item_feat[0]
for k in range(self.K2):
for f in range(4096):
self.theta_item_per_bin[bin][itemID][k] += (self.E[k][f] * self.J_t[bin][k] + self.E_t[bin][k][f]) * item_feat[f]
# visual bias
for f in range(4096):
self.betta_item_visual_per_bin[bin][itemID] += (self.betta_cnn[f] * self.C_t[bin][f] + self.betta_cnn_t[bin][f]) * item_feat[f]
dim0.append((asin, self.theta_item_per_bin[bin][itemID][0]))
dim1.append((asin,self.theta_item_per_bin[bin][itemID][1]))
dim2.append((asin,self.theta_item_per_bin[bin][itemID][2]))
dim3.append((asin,self.theta_item_per_bin[bin][itemID][3]))
dim4.append((asin,self.theta_item_per_bin[bin][itemID][4]))
dim5.append((asin,self.theta_item_per_bin[bin][itemID][5]))
dim6.append((asin,self.theta_item_per_bin[bin][itemID][6]))
dim7.append((asin,self.theta_item_per_bin[bin][itemID][7]))
dim8.append((asin,self.theta_item_per_bin[bin][itemID][8]))
dim9.append((asin,self.theta_item_per_bin[bin][itemID][9]))
dim10.append((asin,self.theta_item_per_bin[bin][itemID][10]))
dim11.append((asin,self.theta_item_per_bin[bin][itemID][11]))
dim12.append((asin,self.theta_item_per_bin[bin][itemID][12]))
dim13.append((asin,self.theta_item_per_bin[bin][itemID][13]))
dim14.append((asin,self.theta_item_per_bin[bin][itemID][14]))
dim15.append((asin,self.theta_item_per_bin[bin][itemID][15]))
dim16.append((asin,self.theta_item_per_bin[bin][itemID][16]))
dim17.append((asin,self.theta_item_per_bin[bin][itemID][17]))
dim18.append((asin,self.theta_item_per_bin[bin][itemID][18]))
dim19.append((asin,self.theta_item_per_bin[bin][itemID][19]))
vp = 0
for u in range(1, 4):
vp += np.inner(self.theta_user[u], self.theta_item_per_bin[bin][itemID])
vp = vp/4
visual_scores.append((asin, vp))
max_vs = max(visual_scores, key=operator.itemgetter(1))
print 'itemID with max. visual score: ', max_vs[0],' visualScore = ', max_vs[1]
for d in range(self.K2):
if d == 0:
max_value = max(dim0, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 1:
max_value = max(dim1, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 2:
max_value = max(dim2, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 3:
max_value = max(dim3, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 4:
max_value = max(dim4, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 5:
max_value = max(dim5, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 6:
max_value = max(dim6, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 7:
max_value = max(dim7, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 8:
max_value = max(dim8, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 9:
max_value = max(dim9, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 10:
max_value = max(dim10, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 11:
max_value = max(dim11, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 12:
max_value = max(dim12, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 13:
max_value = max(dim13, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 14:
max_value = max(dim14, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 15:
max_value = max(dim15, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 16:
max_value = max(dim16, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 17:
max_value = max(dim17, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 18:
max_value = max(dim18, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
if d == 19:
max_value = max(dim19, key=operator.itemgetter(1))
asin_ = max_value[0]
value_ = max_value[1]
print 'dim:',d,', itemID: ', asin_, ', value = ', value_
def prediction(self, userID, itemID, epoch_):
pred = self.b_i[itemID] + np.dot(self.gamma_user[userID], self.gamma_item[itemID]) + np.dot(self.theta_user[userID], self.theta_item_per_bin[epoch_][itemID]) + self.betta_item_visual_per_bin[epoch_][itemID]
# np.dot(self.gamma_user[userID], self.gamma_item[itemID])
return pred
def AUC_ (self, test_per_us, val_per_us):
AUC_ = np.zeros(self.nUsers + 1)
bin_indices = []
for k,v in test_per_us.items():
user_ = k
# print 'USER: ', user_, ' being tested...'
test_item_asin = test_per_us[k][0]
val_item_asin = val_per_us[k][0]
if test_item_asin not in self.feat:
continue
# print test_item_asin
item_intID = self.itemIdToInt[test_item_asin]
time_d = int(test_per_us[k][2])
# print test_per_us[k]
# print time_d
# bin_ind = self.calBin(time_d)
epoch = self.timeInEpoch(time_d)
# print time_d
# print 'bin ind = ', bin_ind
bin_indices.append(bin_ind)
pred_of_test = self.prediction(user_, item_intID, epoch)
asins_of_user = []
for a in range(len(self.userItems[user_])):
asins_of_user.append(self.userItems[user_][a][0])
count = 0
count_val = 0
maxx = 0.0
for i in range(len(self.list_of_items)):
asin = self.list_of_items[i]
item_id = self.itemIdToInt[asin]
if asin not in self.feat:
continue
if asin in asins_of_user or asin == test_item_asin or asin == val_item_asin:
continue
else:
maxx += 1
pred_of_neg = self.prediction(user_, item_id, epoch)
if pred_of_test > pred_of_neg:
count += 1
AUC_[user_] = 1 * (count/maxx)
# print 'count = ', count
# print 'max =', maxx
# print 'AUC for userID: ', user_,' is: ', AUC_[user_]
auc = 0
num_users = len(test_per_us)
# print 'users = ', num_users
for i in range(len(AUC_)):
auc += AUC_[i]
# print 'AUC = ', auc/num_users
# print 'bins = ', bin_indices
print 'auc = ', auc, ', num_users = ', num_users
# print 'DP applied!'
return auc/num_users
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