def train(data, test):
"""
该函数主要作用： 定义网络；定义数据，定义损失函数和优化器，计算重要指标，开始训练（训练网络，计算在测试集上的指标）
主要需要调整的参数： m 与 gamma
:return:
"""
learnRate = 0.05
lam = 0.00005
dim = 64
iter_ = 100
init_st = 0.01
m = 5
gamma = 0.5
batch_size = 100
n = 1000
# 计算numUser, numItem, numTag
dataload = DataSet(data, test, True)
num_user, num_item, num_tag = dataload.calc_number_of_dimensions()
model = AttentionPITF(num_user, num_item, num_tag, dim, init_st, m,
gamma).cuda()
torch.save(model.state_dict(), 'attention_initial_params')
# 对每个正样本进行负采样
loss_function = SinglePITF_Loss().cuda()
opti = optim.SGD(model.parameters(), lr=learnRate, weight_decay=lam)
opti.zero_grad()
# 每个epoch中的sample包含一个正样本和j个负样本
best_result = 0
best_result_state = model.state_dict()
for epoch in range(iter_):
all_data = dataload.get_sequential(num_tag, m, 100)
all_data = all_data[:, :4 + m]
losses = []
print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
for i, batch in enumerate(dataload.get_batch(all_data, batch_size)):
# print(batch)
# input_ = dataload.draw_negative_sample(num_tag, sample, True)
r = model(torch.LongTensor(batch).cuda())
opti.zero_grad()
# print(model.embedding.userVecs.weight)
loss = loss_function(r)
# print(loss)
loss.backward()
opti.step()
losses.append(loss.data)
if i % n == 0:
print("[%02d/%d] [%03d/%d] mean_loss : %0.2f" %
(epoch, iter_, i, len(all_data) / batch_size,
np.mean(losses)))
losses = []
precision = 0
recall = 0
count = 0
validaTagSet = dataload.validaTagSet
validaTimeList = dataload.validaUserTimeList
for u in validaTagSet.keys():
for i in validaTagSet[u].keys():
number = 0
tags = validaTagSet[u][i]
tagsNum = len(tags)
x_t = torch.LongTensor(
[u, i] + list(dataload.userShortMemory[u][:m])).cuda()
x_t = x_t.unsqueeze(0)
y_pre = model.predict_top_k(x_t)
for tag in y_pre[0]:
if int(tag) in tags:
number += 1
precision = precision + float(number / 5)
recall = recall + float(number / tagsNum)
count += 1
precision = precision / count
recall = recall / count
if precision == 0 and recall == 0:
f_score = 0
else:
f_score = 2 * (precision * recall) / (precision + recall)
print("Precisions: " + str(precision))
print("Recall: " + str(recall))
print("F1: " + str(f_score))
# 将模型最好时的效果保存下来
if f_score > best_result:
best_result = f_score
best_result_state = model.state_dict()
print("best result: " + str(best_result))
print("==================================")
# torch.save(model, "net.pkl")
torch.save(best_result_state, "attention_net_params.pkl")
def train(data, test, m, gamma):
"""
该函数主要作用： 定义网络；定义数据，定义损失函数和优化器，计算重要指标，开始训练（训练网络，计算在测试集上的指标）
主要需要调整的参数： m 与 gamma
:return:
"""
learnRate = 0.0001
lam = 0.00005
dim = 64
iter_ = 50
init_st = 0.01
m = m
gamma = gamma
batch_size = 100
n = 1000
k = 5
# 计算numUser, numItem, numTag
dataload = DataSet(data, test, True)
num_user, num_item, num_tag = dataload.calc_number_of_dimensions()
predict_user_weight, item_weight = dataload.weight_to_vector(num_user, num_item, num_tag)
# model = AttentionTAPITF(int(num_user), int(num_item), int(num_tag), dim, init_st, m, gamma, predict_user_weight, item_weight,ini_embeddings, True, 'tag', 'TAMLP').cuda()
model = AttentionTAPITF(int(num_user), int(num_item), int(num_tag), dim, init_st, m, gamma, predict_user_weight, item_weight, ini_embeddings, True, 'tag', 'GMF').cuda()
# model = AttentionTAPITF(int(num_user), int(num_item), int(num_tag), dim, init_st, m, gamma, predict_user_weight, item_weight, False, True, 'tag', 'TAMLP').cuda()
# torch.save(model.state_dict(), 'attention_initial_params')
# 对每个正样本进行负采样
loss_function = SinglePITF_Loss().cuda()
opti = optim.SGD(model.parameters(), lr=learnRate, weight_decay=lam)
# opti = optim.Adam(model.parameters(), lr=learnRate, weight_decay=lam)
opti.zero_grad()
# 每个epoch中的sample包含一个正样本和j个负样本
best_result = 0
# best_result_state = model.state_dict()
# best_file = open('Attention_best_params.txt', 'a')
# all_data = dataload.get_sequential(num_tag, m, 10, True)
for epoch in range(iter_):
# file_ = open('AttentionTureParam.txt', 'a')
all_data = []
all_data = dataload.get_sequential(num_tag, m, 10, True)
all_data = all_data[:, :8 + m]
losses = []
print(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
for i, batch in enumerate(dataload.get_batch(all_data, batch_size)):
# print(batch)
# input_ = dataload.draw_negative_sample(num_tag, sample, True)
r = model(torch.LongTensor(batch).cuda())
opti.zero_grad()
# print(model.embedding.userVecs.weight)
loss = loss_function(r)
# print(loss)
# print(loss)
loss.backward()
opti.step()
losses.append(loss.data)
if i % n == 0:
print("[%02d/%d] [%03d/%d] mean_loss : %0.2f" % (
epoch, iter_, i, len(all_data) / batch_size, np.mean(losses)))
losses = []
precision = 0
recall = 0
count = 0
mrr = 0
recommend_count = 0
ndcg = 0
validaTagSet = dataload.validaTagSet
for u in validaTagSet.keys():
for i in validaTagSet[u].keys():
number = 0
tags = validaTagSet[u][i]
tagsNum = len(tags)
if u in dataload.userShortMemory.keys():
x_t = torch.LongTensor([u, i] + list(dataload.userShortMemory[u][:m])).cuda()
else:
x_t = torch.LongTensor([u, i] + list(np.zeros(m))).cuda()
x_t = x_t.unsqueeze(0)
y_pre = model.predict_top_k(x_t, k)
# print(y_pre)
for tag in y_pre:
if int(tag) in tags:
number += 1
precision = precision + float(number / k)
recall = recall + float(number / tagsNum)
count += 1
mrr = mrr + mrr_rank_score(list(y_pre), list(tags))
# print(ndcg_score(np.array(y_pre), list(tags)))
ndcg = ndcg + ndcg_score(np.array(y_pre), list(tags), k)
recommend_count += tagsNum
precision = precision / count
recall = recall / count
mrr = mrr / recommend_count
ndcg = ndcg / count
if precision == 0 and recall == 0:
f_score = 0
else:
f_score = 2 * (precision * recall) / (precision + recall)
print("Precisions: " + str(precision))
print("Recall: " + str(recall))
print("F1: " + str(f_score))
print("MRR: " + str(mrr))
print("NDCG: " + str(ndcg))
# 将模型最好时的效果保存下来
if f_score > best_result:
best_result = f_score
# best_result_state = model.state_dict()
print("best result: " + str(best_result))
print("==================================")