/
util.py
130 lines (106 loc) · 3.45 KB
/
util.py
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import sys
import copy
import random
import numpy as np
from collections import defaultdict
def data_partition(fname):
usernum = 0
itemnum = 0
User = defaultdict(list)
user_train = {}
user_valid = {}
user_test = {}
# assume user/item index starting from 1
f = open('data/%s.txt' % fname, 'r')
for line in f:
u, i = line.rstrip().split(' ')
u = int(u)
i = int(i)
usernum = max(u, usernum)
itemnum = max(i, itemnum)
User[u].append(i)
for user in User:
nfeedback = len(User[user])
if nfeedback < 3:
user_train[user] = User[user]
user_valid[user] = []
user_test[user] = []
else:
user_train[user] = User[user][:-2]
user_valid[user] = []
user_valid[user].append(User[user][-2])
user_test[user] = []
user_test[user].append(User[user][-1])
return [user_train, user_valid, user_test, usernum, itemnum]
def evaluate(model, dataset, args, sess):
[train, valid, test, usernum, itemnum] = copy.deepcopy(dataset)
NDCG = 0.0
HT = 0.0
valid_user = 0.0
if usernum>10000:
users = random.sample(xrange(1, usernum + 1), 10000)
else:
users = xrange(1, usernum + 1)
for u in users:
if len(train[u]) < 1 or len(test[u]) < 1: continue
seq = np.zeros([args.maxlen], dtype=np.int32)
idx = args.maxlen - 1
seq[idx] = valid[u][0]
idx -= 1
for i in reversed(train[u]):
seq[idx] = i
idx -= 1
if idx == -1: break
rated = set(train[u])
rated.add(0)
item_idx = [test[u][0]]
for _ in range(100):
t = np.random.randint(1, itemnum + 1)
while t in rated: t = np.random.randint(1, itemnum + 1)
item_idx.append(t)
predictions = -model.predict(sess, [u], [seq], item_idx)
predictions = predictions[0]
rank = predictions.argsort().argsort()[0]
valid_user += 1
if rank < 10:
NDCG += 1 / np.log2(rank + 2)
HT += 1
if valid_user % 100 == 0:
print '.',
sys.stdout.flush()
return NDCG / valid_user, HT / valid_user
def evaluate_valid(model, dataset, args, sess):
[train, valid, test, usernum, itemnum] = copy.deepcopy(dataset)
NDCG = 0.0
valid_user = 0.0
HT = 0.0
if usernum>10000:
users = random.sample(xrange(1, usernum + 1), 10000)
else:
users = xrange(1, usernum + 1)
for u in users:
if len(train[u]) < 1 or len(valid[u]) < 1: continue
seq = np.zeros([args.maxlen], dtype=np.int32)
idx = args.maxlen - 1
for i in reversed(train[u]):
seq[idx] = i
idx -= 1
if idx == -1: break
rated = set(train[u])
rated.add(0)
item_idx = [valid[u][0]]
for _ in range(100):
t = np.random.randint(1, itemnum + 1)
while t in rated: t = np.random.randint(1, itemnum + 1)
item_idx.append(t)
predictions = -model.predict(sess, [u], [seq], item_idx)
predictions = predictions[0]
rank = predictions.argsort().argsort()[0]
valid_user += 1
if rank < 10:
NDCG += 1 / np.log2(rank + 2)
HT += 1
if valid_user % 100 == 0:
print '.',
sys.stdout.flush()
return NDCG / valid_user, HT / valid_user