def eval_one_epoch(self, epoch):
uid1, iid1, uid2, iid2 = [], [], [], []
for u in range(self.num_user1):
uid1.extend([u] * self.num_ranking_list)
iid1.extend(self.test_dict1[u])
uid2.extend([u] * self.num_ranking_list)
iid2.extend(self.test_dict2[u])
batch_size = self.num_ranking_list
n_batches = 0
n_mrr1 = 0
n_mrr2 = 0
total_hr1 = 0
total_hr2 = 0
total_ndcg1 = 0
total_ndcg2 = 0
total_mrr1 = 0
total_mrr2 = 0
for i in range(self.num_user1):
batch_uid1, batch_iid1, batch_uid2, batch_iid2 = \
uid1[i * batch_size:(i+1) * batch_size],\
iid1[i * batch_size:(i+1) * batch_size],\
uid2[i * batch_size:(i+1) * batch_size],\
iid2[i * batch_size:(i+1) * batch_size]
rk1, rk2 = self.session.run(
[self.scores1, self.scores2],
feed_dict={
self.dom1_uid: batch_uid1,
self.dom1_iid: batch_iid1,
self.dom2_uid: batch_uid2,
self.dom2_iid: batch_iid2
})
_, hr1, ndcg1, mrr1 = evl.evalTopK(rk1, batch_iid1, self.topk)
_, hr2, ndcg2, mrr2 = evl.evalTopK(rk2, batch_iid2, self.topk)
n_batches += 1
total_hr1 += hr1
total_hr2 += hr2
total_ndcg1 += ndcg1
total_ndcg2 += ndcg2
if np.isinf(mrr1):
pass
else:
n_mrr1 += 1
total_mrr1 += mrr1
if np.isinf(mrr2):
pass
else:
n_mrr2 += 1
total_mrr2 += mrr2
print("Epoch {0}: [HR] {1} and {2}".format(epoch,
total_hr1 / n_batches,
total_hr2 / n_batches))
print("Epoch {0}: [nDCG@{1}] {2} and {3}".format(
epoch, self.topk, total_ndcg1 / n_batches,
total_ndcg2 / n_batches))
def eval_one_epoch(self, session, init, epoch):
session.run(init)
self.training = False
n_batches = 0
n_mrr1 = 0
n_mrr2 = 0
total_hr1 = 0
total_hr2 = 0
total_ndcg1 = 0
total_ndcg2 = 0
total_mrr1 = 0
total_mrr2 = 0
try:
while True:
rk1, rk2, iid1, iid2 = session.run(
[self.scores1, self.scores2, self.dom1_iid, self.dom2_iid])
_, hr1, ndcg1, mrr1 = evl.evalTopK(rk1, iid1, self.K)
_, hr2, ndcg2, mrr2 = evl.evalTopK(rk2, iid2, self.K)
n_batches += 1
total_hr1 += hr1
total_hr2 += hr2
total_ndcg1 += ndcg1
total_ndcg2 += ndcg2
if np.isinf(mrr1):
pass
else:
n_mrr1 += 1
total_mrr1 += mrr1
if np.isinf(mrr2):
pass
else:
n_mrr2 += 1
total_mrr2 += mrr2
except tf.errors.OutOfRangeError:
pass
print("Epoch {0}: [HR] {1} and {2}".format(epoch,
total_hr1 / n_batches,
total_hr2 / n_batches))
print("Epoch {0}: [nDCG@{1}] {2} and {3}".format(
epoch, self.K, total_ndcg1 / n_batches, total_ndcg2 / n_batches))
def eval_one_epoch_np(self, epoch):
# Ranking in all the testing data
n_batches, n_mrr, total_hr, total_ndcg, total_mrr = 0, 0, 0, 0, 0
for uid in self.ranking_dict:
# Prediction to form the ranking list
rk = []
for iid in self.ranking_dict[uid]:
rk.append(np.dot(self.P[uid, :], self.Q[iid, :]))
_, hr, ndcg, mrr = evl.evalTopK(rk, self.test_dict[uid], self.topk)
n_batches += 1
total_hr += hr
total_ndcg += ndcg
if np.isinf(mrr):
pass
else:
n_mrr += 1
total_mrr += mrr
print("Epoch {0}: [HR] {1} and [nDCG@{2}] {3}".format(
epoch, total_hr / n_batches, self.topk, total_ndcg / n_batches))