# movies[j] = [np.random.rand(embedding_dim) for k in range(40)]
# samples = []
# for i in range(10):
# for j in range(15):
# samples.append((i, j, float(np.random.randint(0, 5))))
# save(pst, samples, users, movies)
samples, users, movies = load(pst)
cf = CFUtil(samples)
fullSimilarity = cf.simUser()
adp = Adapt(samples, users, movies, fullSimilarity, userMaxLen, movieMaxLen,
neiMaxLen, sim_thresh, embedding_dim)
User_train_test, Movie_train_test, Neigh_train_test, Y_train_test, sflSmps = adp.kerasInput(
)
X_train_test = {
'user': User_train_test,
'movie': Movie_train_test,
'nei': Neigh_train_test
}
att = NeuralModel(attParamDic, epoch, l2)
history, tesLoss, predicts = att.build(X_train_test, Y_train_test,
usingNeiModel)
comparison = []
for i in range(len(predicts)):
smp = sflSmps[i]
comparison.append([smp[0], smp[1], smp[2], predicts[i][0]])
pst.recordResult(history, tesLoss, comparison, fileModifier='l2_e-2')
'user': [uhid, userMaxLen, embedding_dim],
'movie': [mhid, movieMaxLen, embedding_dim],
'nei': [nhid, neiMaxLen, embedding_dim]
}
path = os.path.abspath('.')
pst = PostProcess(path)
# execute
cf = CFUtil(samples)
fullSimilarity = cf.simUser()
print("Adapt data...")
adp = Adapt(samples, users, movies, fullSimilarity, userMaxLen,
movieMaxLen, neiMaxLen, sim_thresh, embedding_dim)
User_train_test, Movie_train_test, Neigh_train_test, Y_train_test, sflSmps = adp.kerasInput(
)
X_train_test = {
'user': User_train_test,
'movie': Movie_train_test,
'nei': Neigh_train_test
}
att = NeuralModel(attParamDic, epoch, l2)
history, tesLoss, predicts = att.build(X_train_test, Y_train_test,
usingNeiModel)
comparison = []
for i in range(len(predicts)):
smp = sflSmps[i]
comparison.append([smp[0], smp[1], smp[2], predicts[i][0]])
pst.recordResult(history, tesLoss, comparison, fileModifier=jobName)
# users = {}
# for i in range(10):
# users[i] = [np.random.rand(embedding_dim) for k in range(20)]
# movies = {}
# for j in range(15):
# movies[j] = [np.random.rand(embedding_dim) for k in range(40)]
# samples = []
# for i in range(10):
# for j in range(15):
# samples.append((i, j, float(np.random.randint(0, 5))))
# save(pst, samples, users, movies)
samples, users, movies = load(pst)
cf = CFUtil(samples)
fullSimilarity = cf.simUser()
adp = Adapt(samples, users, movies, fullSimilarity, userMaxLen, movieMaxLen,
neiMaxLen, sim_thresh, embedding_dim)
User_train_test, Movie_train_test, Neigh_train_test, Y_train_test = adp.kerasInput(
)
att = NeuralModel(userParams, movieParams, neiParams, epoch, l2)
model, history, tesLoss = att.build(User_train_test[0], Movie_train_test[0],
Neigh_train_test[0], Y_train_test[0],
User_train_test[1], Movie_train_test[1],
Neigh_train_test[1], Y_train_test[1])
pst.recordResult(model, history, tesLoss)
