sess1_iid = np.array([[1, 2, 3, 0], [1, 2, 3, 0], [0, 0, 0, 0]])
sess1_igender = np.array([[1, 1, 2, 0], [2, 1, 1, 0], [0, 0, 0, 0]])
sess2_iid = np.array([[1, 2, 3, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
sess2_igender = np.array([[1, 1, 2, 0], [0, 0, 0, 0], [0, 0, 0, 0]])
sess_number = np.array([2, 1, 0])
feature_dict = {'user': uid, 'gender': ugender, 'item': iid, 'item_gender': igender,
'sess_0_item': sess1_iid, 'sess_0_item_gender': sess1_igender, 'score': score,
'sess_1_item': sess2_iid, 'sess_1_item_gender': sess2_igender, }
x = {name: feature_dict[name] for name in get_feature_names(feature_columns)}
x["sess_length"] = sess_number
y = [1, 0, 1]
return x, y, feature_columns, behavior_feature_list
if __name__ == "__main__":
if tf.__version__ >= '2.0.0':
tf.compat.v1.disable_eager_execution()
x, y, feature_columns, behavior_feature_list = get_xy_fd(True)
model = DSIN(feature_columns, behavior_feature_list, sess_max_count=2,
dnn_hidden_units=[4, 4, 4], dnn_dropout=0.5, )
model.compile('adam', 'binary_crossentropy',
metrics=['binary_crossentropy'])
history = model.fit(x, y, verbose=1, epochs=10, validation_split=0.5)
class DeepSessionInterestNetwork(Recommender):
def __init__(self, uNum, iNum, dim, maxlen):
self.uNum = uNum
self.iNum = iNum
self.dim = dim
self.maxlen = maxlen
hash_flag = True
self.feature_columns = [SparseFeat('user', self.uNum, hash_flag),
SparseFeat('item', self.iNum, hash_flag),
VarLenSparseFeat('sess_0_item', self.iNum, self.dim, use_hash=hash_flag,
embedding_name='item')]
self.behavior_feature_list = ["item"]
self.model = DSIN(self.feature_columns, self.behavior_feature_list, sess_max_count=1,
embedding_size=self.dim,
att_head_num=self.dim,
dnn_hidden_units=[self.dim, self.dim, self.dim], dnn_dropout=0.5)
self.model.compile('adam', 'binary_crossentropy',
metrics=['acc'])
def init(self, trainSeq):
self.trainSeq = trainSeq
def load_pre_train(self, pre):
super().load_pre_train(pre)
def get_params(self):
super().get_params()
def train(self, x_train, y_train, batch_size):
history = self.model.fit(x, y, verbose=1, epochs=10, validation_split=0.5)
hist = self.model.fit(x_train, y_train, batch_size=batch_size, epochs=1, verbose=0)
loss = hist.history['loss'][0]
return loss
def get_train_instances(self, train):
users, checkins, cand_venues, labels = [], [], [], []
for u in self.trainSeq:
visited = self.trainSeq[u]
checkin_ = []
for v in visited[:-1]:
checkin_.append(v)
checkins.extend(sequence.pad_sequences([checkin_[:]], maxlen=self.maxVenue))
# start from the second venue in user's checkin sequence.
visited = visited[1:]
for i in range(len(visited)):
cand_venues.append(visited[i])
users.append(u)
labels.append(1)
j = np.random.randint(self.uNum)
# check if j is in training dataset or in user's sequence at state i or not
while (u, j) in train or j in visited[:i]:
j = np.random.randint(self.uNum)
cand_venues.append(j)
users.append(u)
labels.append(0)
sess_number = np.ones(len(labels))
users = np.array(users)
items = np.array(cand_venues)
sess_item = np.array(checkins)
labels = np.array(labels)
feature_dict = {'user': users, 'item': items, 'score': labels, 'sess_0_item': sess_item}
fixlen_feature_names = get_fixlen_feature_names(self.feature_columns)
varlen_feature_names = get_varlen_feature_names(self.feature_columns)
x = [feature_dict[name] for name in fixlen_feature_names] + [feature_dict[name] for name in
varlen_feature_names]
x += [sess_number]
return x, labels
def rank(self, users, items):
super().rank(users, items)
def save(self, path):
super().save(path)