def get_session_adj(self, d, alpha):
print('----------------------------------')
print('Use sessoin adj cpu sparse tensor, row 1:')
assert isinstance(alpha, float)
row = np.array([sidx for sidx in d.keys() for iidx in d[sidx]['items'][::-1]], dtype=np.int32)
col = np.array([iidx for sidx in d.keys() for iidx in d[sidx]['items'][::-1]], dtype=np.int32)
data = np.array([np.power(alpha, i) for sidx in d.keys() for i, iidx in enumerate(d[sidx]['items'][::-1])],
dtype=np.float32)
mx = sp.csr_matrix((data, (row, col)), shape=(len(d), self.item_size))
mx = spmx_1_normalize(mx)
print('----------------------------------')
return spmx2torch_sparse_tensor(mx)
def get_feature(self, d):
row = np.array(
[sidx for sidx in d.keys() for iidx in d[sidx]['items']],
dtype=np.int32)
col = np.array(
[iidx for sidx in d.keys() for iidx in d[sidx]['items']],
dtype=np.int32)
data = np.ones_like(row)
mx = sp.csr_matrix((data, (row, col)), shape=(len(d), self.item_size))
A = sp.bmat([[None, mx], [mx.transpose(), None]]).tocsr()
A_hat = A + sp.eye(A.shape[0], dtype=np.float32)
feature = spmx_1_normalize(A_hat)
return spmx2torch_sparse_tensor(feature)
def get_adj(self, d, tail):
if tail is not None:
tail_seq = {k: d[k]['items'][-tail:] for k in d.keys()}
else:
tail_seq = {k: d[k]['items'] for k in d.keys()}
row = np.array(
[sidx for sidx in tail_seq.keys() for iidx in set(tail_seq[sidx])],
dtype=np.int32)
col = np.array(
[iidx for sidx in tail_seq.keys() for iidx in set(tail_seq[sidx])],
dtype=np.int32)
data = np.ones_like(row, dtype=np.float32)
mx = sp.csr_matrix((data, (row, col)), shape=(len(d), self.item_size))
adj = sp.bmat([[None, mx], [mx.transpose(), None]]).tocsr()
adj = spmx_1_normalize(adj)
return spmx2torch_sparse_tensor(adj)
def train(dataset, alpha, A_type, normalize_type, session_type,
pretrained_item_emb, model_type, batch_size, shuffle, item_emb_dim,
hid_dim1, hid_dim2, hid_dim3, lr_emb, lr_gcn, l2_emb, l2_gcn,
epochs):
# init
if dataset == 'LastFM':
# use LastFM dataset
data_obj = LastfmData()
elif dataset == 'Diginetica':
# use Diginetica dataset
data_obj = DigineticaData()
else:
# use yoochoose1_64 dataset
data_obj = YoochooseData(dataset=dataset)
# gpu device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# init A
# A: type=scipy.sparse
A = data_obj.get_decay_adj(
data_obj.d, tail=None,
alpha=alpha) if A_type == 'decay' else data_obj.get_gcn_adj(data_obj.d)
# normalize the adj, type = 'ramdom_walk'(row 1) or type = 'symmetric'
if normalize_type == 'random_walk':
print('----------------------------------')
print('Normalize_type is random_walk:')
A = spmx_1_normalize(A)
print('----------------------------------')
else:
print('----------------------------------')
print('Normalize_type is symmetric:')
A = spmx_sym_normalize(A)
print('----------------------------------')
# transform the adj to a sparse cpu tensor
A = spmx2torch_sparse_tensor(A)
# get cpu tensor: labels
labels = data_obj.get_labels(data_obj.d)
# get cpu tensor: item_idxes
_, _, item_idxes = data_obj.get_indexes()
if session_type == 'session_hot_items':
# get cpu sparse tensor: session adj
session_adj = data_obj.get_session_adj(data_obj.d, alpha=alpha)
else:
# if not use session adj, then session_adj = None
session_adj = None
if session_type == 'session_last_item':
# get cpu LongTensor: session_last_item
session_last_item = data_obj.get_session_last_item(data_obj.d).long()
else:
# if not use session_last_item, then session_last_item = None
session_last_item = None
# get pretrained_item_emb
if pretrained_item_emb == 'True' and alpha != 0.0:
print('----------------------------------')
if dataset == 'yoochoose1_64':
print('Use yoochoose1_64 pretrained item embedding: ' +
'pretrained_emb' + str(alpha) + '.pkl')
pretrained_item_emb = torch.load(
'./yoo1_64_pretrained_item_emb/pretrained_emb' + str(alpha) +
'.pkl')['item_emb.weight']
elif dataset == 'yoochoose1_8':
print('Use yoochoose1_8 pretrained item embedding: ' +
'pretrained_emb' + str(alpha) + '.pkl')
pretrained_item_emb = torch.load(
'./yoo1_8_pretrained_item_emb/pretrained_emb' + str(alpha) +
'.pkl')['item_emb.weight']
elif dataset == 'LastFM':
print('Use LastFM pretrained item embedding: ' + 'pretrained_emb' +
str(alpha) + '.pkl')
pretrained_item_emb = torch.load(
'./lastfm_pretrained_item_emb/pretrained_emb' + str(alpha) +
'.pkl')['item_emb.weight']
else:
print('Use Diginetica pretrained item embedding: ' +
'pretrained_emb' + str(alpha) + '.pkl')
pretrained_item_emb = torch.load(
'./dig_pretrained_item_emb/pretrained_emb' + str(alpha) +
'.pkl')['item_emb.weight']
print('----------------------------------')
else:
print('----------------------------------')
print('Not use pretrained item embedding:')
pretrained_item_emb = None
print('----------------------------------')
# get cpu LongTensor: item_emb_idxes
item_emb_idxes = torch.arange(data_obj.item_size).long()
# transform all tensor to cuda
A = A.to(device)
labels = labels.to(device)
item_idxes = item_idxes.to(device)
item_emb_idxes = item_emb_idxes.to(device)
if session_last_item is not None:
session_last_item = session_last_item.to(device)
if session_adj is not None:
session_adj = session_adj.to(device)
# define the evalution object
evalution5 = Evaluation(k=5)
evalution10 = Evaluation(k=10)
evalution15 = Evaluation(k=15)
evalution20 = Evaluation(k=20)
# define yoochoose data object
trainset = SessionDataset(train_size=data_obj.train_size,
test_size=data_obj.test_size,
train=True,
labels=labels)
trainloader = DataLoader(dataset=trainset,
batch_size=batch_size,
shuffle=shuffle)
testset = SessionDataset(train_size=data_obj.train_size,
test_size=data_obj.test_size,
train=False,
labels=labels)
testloader = DataLoader(dataset=testset,
batch_size=batch_size,
shuffle=False)
# define model, then transform to cuda
if model_type == 'ngcf1_session_hot_items':
# use ngcf1_session_hot_items model:
model = ngcf1_session_hot_items(
item_nums=data_obj.item_size,
item_emb_dim=item_emb_dim,
hid_dim1=hid_dim1,
pretrained_item_emb=pretrained_item_emb)
elif model_type == 'ngcf2_session_hot_items':
# use ngcf2_session_hot_items model:
model = ngcf2_session_hot_items(
item_nums=data_obj.item_size,
item_emb_dim=item_emb_dim,
hid_dim1=hid_dim1,
hid_dim2=hid_dim2,
pretrained_item_emb=pretrained_item_emb)
elif model_type == 'ngcf3_session_hot_items':
# use ngcf3_session_hot_items model:
model = ngcf3_session_hot_items(
item_nums=data_obj.item_size,
item_emb_dim=item_emb_dim,
hid_dim1=hid_dim1,
hid_dim2=hid_dim2,
hid_dim3=hid_dim3,
pretrained_item_emb=pretrained_item_emb)
else:
# use ngcf2_session_last_item model:
model = ngcf2_session_last_item(
item_nums=data_obj.item_size,
item_emb_dim=item_emb_dim,
hid_dim1=hid_dim1,
hid_dim2=hid_dim2,
pretrained_item_emb=pretrained_item_emb)
model.to(device)
# define loss and optim
criterion = nn.CrossEntropyLoss()
if model_type == 'ngcf1_session_hot_items':
# use ngcf1_session_hot_items model parameters:
optim_emb = optim.Adagrad([{
'params': model.item_emb.parameters()
}],
lr=lr_emb,
weight_decay=l2_emb)
optim_gcn = optim.Adam([{
'params': model.gconv1.parameters()
}],
lr=lr_gcn,
weight_decay=l2_gcn)
elif model_type == 'ngcf2_session_hot_items':
# use ngcf2_session_hot_items model parameters:
optim_emb = optim.Adagrad([{
'params': model.item_emb.parameters()
}],
lr=lr_emb,
weight_decay=l2_emb)
optim_gcn = optim.Adam([{
'params': model.gconv1.parameters()
}, {
'params': model.gconv2.parameters()
}],
lr=lr_gcn,
weight_decay=l2_gcn)
elif model_type == 'ngcf3_session_hot_items':
# use ngcf3_session_hot_items model parameters:
optim_emb = optim.Adagrad([{
'params': model.item_emb.parameters()
}],
lr=lr_emb,
weight_decay=l2_emb)
optim_gcn = optim.Adam([{
'params': model.gconv1.parameters()
}, {
'params': model.gconv2.parameters()
}, {
'params': model.gconv3.parameters()
}],
lr=lr_gcn,
weight_decay=l2_gcn)
else:
# use ngcf2_session_last_item model parameters:
optim_emb = optim.Adagrad([{
'params': model.item_emb.parameters()
}],
lr=lr_emb,
weight_decay=l2_emb)
optim_gcn = optim.Adam([{
'params': model.gconv1.parameters()
}, {
'params': model.gconv2.parameters()
}],
lr=lr_gcn,
weight_decay=l2_gcn)
# figure recall mrr norm
fig_recalls = []
fig_mrrs = []
fig_emb_norms = []
fig_gcn_norms = []
fig_epochs = []
# train epochs
for epoch in range(epochs):
# model training
start = time.time()
# train evalution dict
recall = {'5': [], '10': [], '15': [], '20': []}
mrr = {'5': [], '10': [], '15': [], '20': []}
# test evalution dict
r = {'5': [], '10': [], '15': [], '20': []}
m = {'5': [], '10': [], '15': [], '20': []}
# loss list
losses = []
model.train()
for i, data in enumerate(trainloader):
# zero optim
optim_emb.zero_grad()
optim_gcn.zero_grad()
# batch inputs
batch_idxes, batch_labels = data[0].long().to(
device), data[1].long().to(device)
# predicting
if model_type == 'ngcf1_session_hot_items':
# use ngcf1_session_hot_items model to predict
outs = model(batch_idxes, A, item_idxes, session_adj,
item_emb_idxes)
elif model_type == 'ngcf2_session_hot_items':
# use ngcf2_session_hot_items model to predict
outs = model(batch_idxes, A, item_idxes, session_adj,
item_emb_idxes)
elif model_type == 'ngcf3_session_hot_items':
# use ngcf3_session_hot_items model to predict
outs = model(batch_idxes, A, item_idxes, session_adj,
item_emb_idxes)
else:
# use ngcf2_session_last_item model to predict
outs = model(batch_idxes, A, item_idxes, session_last_item,
item_emb_idxes)
# loss
loss = criterion(outs, batch_labels)
# backward
loss.backward()
# optim step
optim_emb.step()
optim_gcn.step()
# evalution, k=5, 10, 15, 20
recall['5'].append(evalution5.evaluate(outs, batch_labels)[0])
recall['10'].append(evalution10.evaluate(outs, batch_labels)[0])
recall['15'].append(evalution15.evaluate(outs, batch_labels)[0])
recall['20'].append(evalution20.evaluate(outs, batch_labels)[0])
mrr['5'].append(evalution5.evaluate(outs, batch_labels)[1])
mrr['10'].append(evalution10.evaluate(outs, batch_labels)[1])
mrr['15'].append(evalution15.evaluate(outs, batch_labels)[1])
mrr['20'].append(evalution20.evaluate(outs, batch_labels)[1])
# losses
losses.append(loss.item())
# print loss, recall, mrr
if i % 50 == 49:
print('[{0: 2d}, {1:5d}] loss:{2:.4f}'.format(
epoch + 1, i + 1, np.mean(losses)))
print('[recall@5 ]:{0:.4f} [mrr@5 ]:{1:.4f}'.format(
np.mean(recall['5']), np.mean(mrr['5'])))
print('[recall@10]:{0:.4f} [mrr@10]:{1:.4f}'.format(
np.mean(recall['10']), np.mean(mrr['10'])))
print('[recall@15]:{0:.4f} [mrr@15]:{1:.4f}'.format(
np.mean(recall['15']), np.mean(mrr['15'])))
print('[recall@20]:{0:.4f} [mrr@20]:{1:.4f}'.format(
np.mean(recall['20']), np.mean(mrr['20'])))
# print gcn_norm, emb_norm
emb_norm = get_norm(model, 'emb')
gcn_norm = get_norm(model, 'gcn')
fig_emb_norms.append(emb_norm)
fig_gcn_norms.append(gcn_norm)
print('[gcn_norm]:{0:.4f} [emb_norm]:{1:.4f}'.format(
gcn_norm, emb_norm))
# epoch time
print('[epoch time]:{0:.4f}'.format(time.time() - start))
# save model
if epoch % 10 == 9:
torch.save(
model.state_dict(),
'params' + model_type + '-Alpha' + str(alpha) + '_' +
'_lr_emb' + str(lr_emb) + '_l2_emb' + str(l2_emb) + '_lr_gcn' +
str(lr_gcn) + '_l2_gcn' + str(l2_gcn) + '.pkl')
# model eval
model.eval()
with torch.no_grad():
for j, d in enumerate(testloader):
# test batch inputs
b_idxes, b_labels = d[0].long().to(device), d[1].long().to(
device)
# predicting
if model_type == 'ngcf1_session_hot_items':
# use ngcf1_session_hot_items model to predict
o = model(b_idxes, A, item_idxes, session_adj,
item_emb_idxes)
elif model_type == 'ngcf2_session_hot_items':
# use ngcf2_session_hot_items model to predict
o = model(b_idxes, A, item_idxes, session_adj,
item_emb_idxes)
elif model_type == 'ngcf3_session_hot_items':
# use ngcf3_session_hot_items model to predict
o = model(b_idxes, A, item_idxes, session_adj,
item_emb_idxes)
else:
# use ngcf2_session_last_item model to predict
o = model(b_idxes, A, item_idxes, session_last_item,
item_emb_idxes)
# evalution, k=5, 10, 15, 20
r['5'].append(evalution5.evaluate(o, b_labels)[0])
r['10'].append(evalution10.evaluate(o, b_labels)[0])
r['15'].append(evalution15.evaluate(o, b_labels)[0])
r['20'].append(evalution20.evaluate(o, b_labels)[0])
m['5'].append(evalution5.evaluate(o, b_labels)[1])
m['10'].append(evalution10.evaluate(o, b_labels)[1])
m['15'].append(evalution15.evaluate(o, b_labels)[1])
m['20'].append(evalution20.evaluate(o, b_labels)[1])
# print test recall mrr
print('[{0: 2d}]'.format(epoch + 1))
print('[recall@5 ]:{0:.4f} [mrr@5 ]:{1:.4f}'.format(
np.mean(r['5']), np.mean(m['5'])))
print('[recall@10]:{0:.4f} [mrr@10]:{1:.4f}'.format(
np.mean(r['10']), np.mean(m['10'])))
print('[recall@15]:{0:.4f} [mrr@15]:{1:.4f}'.format(
np.mean(r['15']), np.mean(m['15'])))
print('[recall@20]:{0:.4f} [mrr@20]:{1:.4f}'.format(
np.mean(r['20']), np.mean(m['20'])))
# plt recall and mrr and norm
fig_epochs.append(epoch)
fig_recalls.append(np.mean(r['20']))
fig_mrrs.append(np.mean(m['20']))
plt_evalution(fig_epochs,
fig_recalls,
fig_mrrs,
k=20,
alpha=alpha,
lr_emb=lr_emb,
l2_emb=l2_emb,
lr_gcn=lr_gcn,
l2_gcn=l2_gcn,
model_type=model_type)
plt_norm(fig_epochs,
fig_emb_norms,
fig_gcn_norms,
alpha=alpha,
lr_emb=lr_emb,
l2_emb=l2_emb,
lr_gcn=lr_gcn,
l2_gcn=l2_gcn,
model_type=model_type)
def train(dataset, alpha, A_type, normalize_type, model_pretrained_params,
model_type, batch_size, test_batch_size, negative_nums, item_emb_dim,
hid_dim1, hid_dim2, hid_dim3, lr_emb, l2_emb, lr_gcn, l2_gcn, lr_cnn,
l2_cnn, epochs, params_file_name):
# init
if dataset == 'LastFM':
# use LastFM dataset
data_obj = LastfmData()
elif dataset == 'Diginetica':
# use Diginetica dataset
data_obj = DigineticaData()
else:
# use yoochoose1_64 dataset
data_obj = YoochooseData(dataset=dataset)
# gpu device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# init A
# A: type=scipy.sparse
A = data_obj.get_decay_adj(
data_obj.d, tail=None,
alpha=alpha) if A_type == 'decay' else data_obj.get_gcn_adj(data_obj.d)
# normalize the adj, type = 'ramdom_walk'(row 1) or type = 'symmetric'
if normalize_type == 'random_walk':
print('----------------------------------')
print('Normalize_type is random_walk:')
A = spmx_1_normalize(A)
print('----------------------------------')
else:
print('----------------------------------')
print('Normalize_type is symmetric:')
A = spmx_sym_normalize(A)
print('----------------------------------')
# transform the adj to a sparse cpu tensor
A = spmx2torch_sparse_tensor(A)
# get cpu tensor: labels
labels = data_obj.get_labels(data_obj.d)
# get cpu sparse tensor: session adj
SI = data_obj.get_session_adj(data_obj.d, alpha=alpha)
# load model pretrained params
if model_pretrained_params == 'True':
print('----------------------------------')
if dataset == 'LastFM':
# use LastFM params
print('Use LastFM model pretraned params: ' + params_file_name +
'.pkl')
pretrained_state_dict = torch.load('./lastfm_pretrained_params/' +
params_file_name + '.pkl')
elif dataset == 'Diginetica':
# use Diginetica params
print('Use Diginetica model pretraned params: ' +
params_file_name + '.pkl')
pretrained_state_dict = torch.load('./dig_pretrained_params/' +
params_file_name + '.pkl')
else:
# use yoochoose1_64 params
print('Use yoochoose1_64 model pretraned params: ' +
params_file_name + '.pkl')
pretrained_state_dict = torch.load('./yoo1_64_pretrained_params/' +
params_file_name + '.pkl')
print('----------------------------------')
else:
pretrained_state_dict = None
# transform all tensor to cuda
A = A.to(device)
labels = labels.to(device)
SI = SI.to(device)
# define the evalution object
evalution5 = Evaluation(k=5)
evalution10 = Evaluation(k=10)
evalution15 = Evaluation(k=15)
evalution20 = Evaluation(k=20)
# define yoochoose data object
trainloader = SessionDataloader(train_size=data_obj.train_size,
test_size=data_obj.test_size,
item_size=data_obj.item_size,
labels=labels,
batch_size=batch_size,
train=True,
negative_nums=negative_nums,
shuffle=True)
testloader = SessionDataloader(train_size=data_obj.train_size,
test_size=data_obj.test_size,
item_size=data_obj.item_size,
labels=labels,
batch_size=test_batch_size *
data_obj.item_size,
train=False,
negative_nums=negative_nums,
shuffle=False)
# define model, then transform to cuda
if model_type == 'sgncf1_cnn':
# use sgncf1_cnn model:
model = sgncf1_cnn(dataset_nums=data_obj.train_size +
data_obj.test_size,
item_nums=data_obj.item_size,
item_emb_dim=item_emb_dim,
hid_dim1=hid_dim1)
else:
# use sgncf2_cnn model:
model = sgncf2_cnn(dataset_nums=data_obj.train_size +
data_obj.test_size,
item_nums=data_obj.item_size,
item_emb_dim=item_emb_dim,
hid_dim1=hid_dim1,
hid_dim2=hid_dim2)
model.to(device)
# update model_state_dict
if pretrained_state_dict is not None:
model_state_dict = model.state_dict()
pretrained_state_dict = {
k: v
for k, v in pretrained_state_dict.items() if k in model_state_dict
}
model_state_dict.update(pretrained_state_dict)
model.load_state_dict(model_state_dict)
# define loss and optim
criterion = nn.BCEWithLogitsLoss()
if model_type == 'sgncf1_cnn':
# use sgncf1 model parameters:
optim_emb = optim.Adagrad([{
'params': model.item_emb.parameters()
}],
lr=lr_emb,
weight_decay=l2_emb)
optim_gcn = optim.Adam([{
'params': model.gconv1.parameters()
}],
lr=lr_gcn,
weight_decay=l2_gcn)
optim_cnn = optim.Adam([{
'params': model.cnn_1d.parameters()
}, {
'params': model.fc.parameters()
}],
lr=lr_cnn,
weight_decay=l2_cnn)
else:
# use sgncf2 model parameters:
optim_emb = optim.Adagrad([{
'params': model.item_emb.parameters()
}],
lr=lr_emb,
weight_decay=l2_emb)
optim_gcn = optim.Adam([{
'params': model.gconv1.parameters()
}, {
'params': model.gconv2.parameters()
}],
lr=lr_gcn,
weight_decay=l2_gcn)
optim_cnn = optim.Adam([{
'params': model.cnn_1d.parameters()
}, {
'params': model.fc.parameters()
}],
lr=lr_cnn,
weight_decay=l2_cnn)
# figure recall mrr norm
fig_recalls = []
fig_mrrs = []
fig_emb_norms = []
fig_gcn_norms = []
fig_cnn_norms = []
fig_epochs = []
# train epochs
for epoch in range(epochs):
# model training
start = time.time()
# test evalution dict
r = {'5': [], '10': [], '15': [], '20': []}
m = {'5': [], '10': [], '15': [], '20': []}
# loss list
losses = []
model.train()
for i, data in enumerate(trainloader):
# zero optim
optim_emb.zero_grad()
optim_gcn.zero_grad()
optim_cnn.zero_grad()
# batch inputs
batch_sidxes, batch_iidxes, batch_labels = data[:, 0].long().to(
device), data[:,
1].long().to(device), data[:,
2].float().to(device)
# predicting
outs = model(batch_sidxes, batch_iidxes, A, SI)
# loss
loss = criterion(outs, batch_labels)
# backward
loss.backward()
# optim step
optim_emb.step()
optim_gcn.step()
optim_cnn.step()
# losses
losses.append(loss.item())
# print loss, recall, mrr
if i % 20 == 19:
print('[{0: 2d}, {1:5d}, {2: 7d}], loss:{3:.4f}'.format(
epoch + 1, int(i * (batch_size / (negative_nums + 1))),
data_obj.train_size, np.mean(losses)))
# print gcn_norm, emb_norm
emb_norm = get_norm(model, 'emb')
gcn_norm = get_norm(model, 'gcn')
cnn_norm = get_norm(model, 'cnn')
fig_emb_norms.append(emb_norm)
fig_gcn_norms.append(gcn_norm)
fig_cnn_norms.append(gcn_norm)
print('[gcn_norm]:{0:.4f} [emb_norm]:{1:.4f} [cnn_norm]:{2:.4f}'.
format(gcn_norm, emb_norm, cnn_norm))
# epoch time
print('[epoch time]:{0:.4f}'.format(time.time() - start))
# model eval
model.eval()
with torch.no_grad():
for j, d in enumerate(testloader):
# test batch inputs
b_sidxes, b_iidxes, b_labels = d[0][:, 0].long().to(
device), d[0][:, 1].long().to(device), d[1].to(device)
# predicting
o = model(b_sidxes, b_iidxes, A, SI)
o = o.view(-1, data_obj.item_size)
# evalution, k=5, 10, 15, 20
r['5'].append(evalution5.evaluate(o, b_labels)[0])
r['10'].append(evalution10.evaluate(o, b_labels)[0])
r['15'].append(evalution15.evaluate(o, b_labels)[0])
r['20'].append(evalution20.evaluate(o, b_labels)[0])
m['5'].append(evalution5.evaluate(o, b_labels)[1])
m['10'].append(evalution10.evaluate(o, b_labels)[1])
m['15'].append(evalution15.evaluate(o, b_labels)[1])
m['20'].append(evalution20.evaluate(o, b_labels)[1])
# print test inf
# print('[{0: 2d}, {1: 5d}, {2: 7d}]'.format(epoch+1,
# j * test_batch_size,
# data_obj.test_size))
# print test recall mrr
print('[{0: 2d}]'.format(epoch + 1))
print('[recall@5 ]:{0:.4f} [mrr@5 ]:{1:.4f}'.format(
np.sum(r['5']) / data_obj.test_size,
np.sum(m['5']) / data_obj.test_size))
print('[recall@10]:{0:.4f} [mrr@10]:{1:.4f}'.format(
np.sum(r['10']) / data_obj.test_size,
np.sum(m['10']) / data_obj.test_size))
print('[recall@15]:{0:.4f} [mrr@15]:{1:.4f}'.format(
np.sum(r['15']) / data_obj.test_size,
np.sum(m['15']) / data_obj.test_size))
print('[recall@20]:{0:.4f} [mrr@20]:{1:.4f}'.format(
np.sum(r['20']) / data_obj.test_size,
np.sum(m['20']) / data_obj.test_size))
# plt recall and mrr and norm
fig_epochs.append(epoch)
fig_recalls.append(np.sum(r['20']) / data_obj.test_size)
fig_mrrs.append(np.sum(m['20']) / data_obj.test_size)
plt_evalution(fig_epochs,
fig_recalls,
fig_mrrs,
k=20,
alpha=alpha,
lr_emb=lr_emb,
l2_emb=l2_emb,
lr_gcn=lr_gcn,
l2_gcn=l2_gcn,
model_type=model_type,
lr_cnn=lr_cnn,
l2_cnn=l2_cnn)
plt_norm(fig_epochs,
fig_emb_norms,
fig_gcn_norms,
fig_cnn_norms,
alpha=alpha,
lr_emb=lr_emb,
l2_emb=l2_emb,
lr_gcn=lr_gcn,
l2_gcn=l2_gcn,
model_type=model_type,
lr_cnn=lr_cnn,
l2_cnn=l2_cnn)
def check(dataset, A_type, normalize_type, model_type, F_normalize, batch_size,
shuffle, alpha, pretrained_item_emb, item_emb_dim, hid_dim1,
hid_dim2, hid_dim3, lr_emb, lr_gcn, l2_emb, l2_gcn, epoch):
# init
yooch = YoochooseData(dataset=dataset)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# init A
# A: type=scipy.sparse
A = yooch.get_decay_adj(
yooch.d, tail=None,
alpha=alpha) if A_type == 'decay' else yooch.get_gcn_adj(yooch.d)
# normalize the adj, type = 'ramdom_walk'(row 1) or type = 'symmetric'
A = spmx_1_normalize(
A) if normalize_type == 'ramdom_walk' else spmx_sym_normalize(A)
# transform the adj to a sparse cpu tensor
A = spmx2torch_sparse_tensor(A)
# get cpu tensor: lables
lables = yooch.get_lables(yooch.d)
# get cpu sparse tensor: session_adj
session_adj = yooch.get_session_adj(yooch.d, alpha=alpha)
# get cpu tensor: item_idxes
_, _, item_idxes = yooch.get_indexes()
# if session_emb use the local then use the code below
session_local_idxes = yooch.get_local(yooch.d)
# get pretrained_item_emb
pretrained_item_emb = torch.load(
'pretrained_emb' + str(int(alpha * 10)) +
'.pkl')['item_emb.weight'] if pretrained_item_emb == 'True' else None
# transform all tensor to cuda
A = A.to(device)
lables = lables.to(device)
session_adj = session_adj.to(device)
item_idxes = item_idxes.to(device)
# if session_emb use the local then use the code below
session_local_idxes = session_local_idxes.to(device)
# define the evalution object
evalution5 = Evaluation(k=5)
evalution10 = Evaluation(k=10)
evalution15 = Evaluation(k=15)
evalution20 = Evaluation(k=20)
max_epoch = {'5': -1, '10': -1, '15': -1, '20': -1}
max_recall = {'5': -1, '10': -1, '15': -1, '20': -1}
max_mrr = {'5': -1, '10': -1, '15': -1, '20': -1}
# define yoochoose data object
trainset = YoochooseDataset(train_size=yooch.train_size,
test_size=yooch.test_size,
train=True,
lables=lables)
trainloader = DataLoader(dataset=trainset,
batch_size=batch_size,
shuffle=shuffle)
testset = YoochooseDataset(train_size=yooch.train_size,
test_size=yooch.test_size,
train=False,
lables=lables)
testloader = DataLoader(dataset=testset,
batch_size=batch_size,
shuffle=False)
model = ngcf2(item_nums=yooch.item_size,
item_emb_dim=item_emb_dim,
hid_dim1=hid_dim1,
hid_dim2=hid_dim2,
pretrained_item_emb=pretrained_item_emb,
F_normalize=F_normalize)
model.to(device)
model.load_state_dict(torch.load('/home/wuwu/下载/ngcf2alpha0.3params.pkl'))
model.eval()
outs = model(yooch.train_size + 1, A, session_adj, item_idxes)
a = 1
def model_test(dataset, alpha, A_type, normalize_type, model_type,
negative_nums, item_emb_dim, hid_dim1, hid_dim2,
model_pretrained_params, params_file_name):
# init
if dataset == 'LastFM':
# use LastFM dataset
data_obj = LastfmData()
elif dataset == 'Diginetica':
# use Diginetica dataset
data_obj = DigineticaData()
else:
# use yoochoose1_64 dataset
data_obj = YoochooseData(dataset=dataset)
# gpu device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# init A
# A: type=scipy.sparse
A = data_obj.get_decay_adj(
data_obj.d, tail=None,
alpha=alpha) if A_type == 'decay' else data_obj.get_gcn_adj(data_obj.d)
# normalize the adj, type = 'ramdom_walk'(row 1) or type = 'symmetric'
if normalize_type == 'random_walk':
print('----------------------------------')
print('Normalize_type is random_walk:')
A = spmx_1_normalize(A)
print('----------------------------------')
else:
print('----------------------------------')
print('Normalize_type is symmetric:')
A = spmx_sym_normalize(A)
print('----------------------------------')
# transform the adj to a sparse cpu tensor
A = spmx2torch_sparse_tensor(A)
# get cpu tensor: labels
labels = data_obj.get_labels(data_obj.d)
# get cpu sparse tensor: session adj
SI = data_obj.get_session_adj(data_obj.d, alpha=alpha)
# load model pretrained params
if model_pretrained_params == 'True':
print('----------------------------------')
if dataset == 'LastFM':
# use LastFM params
print('Use LastFM model pretraned params: ' + params_file_name +
'.pkl')
pretrained_state_dict = torch.load('./lastfm_pretrained_params/' +
params_file_name + '.pkl')
elif dataset == 'Diginetica':
# use Diginetica params
print('Use Diginetica model pretraned params: ' +
params_file_name + '.pkl')
pretrained_state_dict = torch.load('./dig_pretrained_params/' +
params_file_name + '.pkl')
else:
# use yoochoose1_64 params
print('Use yoochoose1_64 model pretraned params: ' +
params_file_name + '.pkl')
pretrained_state_dict = torch.load('./yoo1_64_pretrained_params/' +
params_file_name + '.pkl')
print('----------------------------------')
else:
pretrained_state_dict = None
# transform all tensor to cuda
A = A.to(device)
labels = labels.to(device)
SI = SI.to(device)
# define the evalution object
evalution5 = Evaluation(k=5)
evalution10 = Evaluation(k=10)
evalution15 = Evaluation(k=15)
evalution20 = Evaluation(k=20)
# define yoochoose data object
testloader = SessionDataloader(train_size=data_obj.train_size,
test_size=data_obj.test_size,
item_size=data_obj.item_size,
labels=labels,
batch_size=50 * data_obj.item_size,
train=False,
negative_nums=negative_nums,
shuffle=False)
# define model, then transform to cuda
if model_type == 'sgncf1':
# use sgncf1_cnn model:
model = sgncf1(dataset_nums=data_obj.train_size + data_obj.test_size,
item_nums=data_obj.item_size,
item_emb_dim=item_emb_dim,
hid_dim1=hid_dim1,
pretrained_item_emb=None)
else:
# use sgncf2_cnn model:
model = sgncf2(dataset_nums=data_obj.train_size + data_obj.test_size,
item_nums=data_obj.item_size,
item_emb_dim=item_emb_dim,
hid_dim1=hid_dim1,
hid_dim2=hid_dim2,
pretrained_item_emb=None)
model.to(device)
# update model_state_dict
if pretrained_state_dict is not None:
model_state_dict = model.state_dict()
pretrained_state_dict = {
k: v
for k, v in pretrained_state_dict.items() if k in model_state_dict
}
model_state_dict.update(pretrained_state_dict)
model.load_state_dict(model_state_dict)
# test evalution dict
r = {'5': [], '10': [], '15': [], '20': []}
m = {'5': [], '10': [], '15': [], '20': []}
# model eval
model.eval()
with torch.no_grad():
for j, d in enumerate(testloader):
# test batch inputs
b_sidxes, b_iidxes, b_labels = d[0][:, 0].long().to(
device), d[0][:, 1].long().to(device), d[1].to(device)
# predicting
o = model(b_sidxes, b_iidxes, A, SI)
o = o.view(-1, data_obj.item_size)
# evalution, k=5, 10, 15, 20
r['5'].append(evalution5.evaluate(o, b_labels)[0])
r['10'].append(evalution10.evaluate(o, b_labels)[0])
r['15'].append(evalution15.evaluate(o, b_labels)[0])
r['20'].append(evalution20.evaluate(o, b_labels)[0])
m['5'].append(evalution5.evaluate(o, b_labels)[1])
m['10'].append(evalution10.evaluate(o, b_labels)[1])
m['15'].append(evalution15.evaluate(o, b_labels)[1])
m['20'].append(evalution20.evaluate(o, b_labels)[1])
print('[{0: 2d}, {1: 4d}]'.format(j * 50, data_obj.test_size))
# print test recall mrr
print('[recall@5 ]:{0:.4f} [mrr@5 ]:{1:.4f}'.format(
np.sum(r['5']) / data_obj.test_size,
np.sum(m['5']) / data_obj.test_size))
print('[recall@10]:{0:.4f} [mrr@10]:{1:.4f}'.format(
np.sum(r['10']) / data_obj.test_size,
np.sum(m['10']) / data_obj.test_size))
print('[recall@15]:{0:.4f} [mrr@15]:{1:.4f}'.format(
np.sum(r['15']) / data_obj.test_size,
np.sum(m['15']) / data_obj.test_size))
print('[recall@20]:{0:.4f} [mrr@20]:{1:.4f}'.format(
np.sum(r['20']) / data_obj.test_size,
np.sum(m['20']) / data_obj.test_size))
