def train(self, intervals):
finish = False
prepare = Preparer(intervals, **self.config)
while not finish:
# get events
rows, flag = prepare.get_data_from_db()
batch_states = []
batch_newstates = []
batch_actions = []
for row in rows:
# get info about event
time_event = None
tag_id = None
user_id = None
time_delta = None
# init features
state = self.model.get_features(user_id, tag_id, time_event)
next_state = self.model.get_features(user_id, tag_id,
time_event + time_delta)
action = 1
batch_states.append(state)
batch_newstates.append(next_state)
batch_actions.append(action)
if len(batch_states) > 0:
self.model_dqnn.train(batch_states, batch_newstates,
batch_actions)
if not flag:
finish = prepare.next_iteration()
def predict(self, intervals):
finish = False
prepare = Preparer(intervals, **self.config)
while not finish:
# get events
rows, flag = prepare.get_data_from_db()
for row in rows:
# get info about event
time_event = None
tag_id = None
user_id = None
time_delta = None
# init features
state = self.model.get_features(user_id, tag_id, time_event)
predict = self.model_dqnn.predict(state)
def predict(self, model_dqnn, intervals):
finish = False
estimator = Estimator()
prepare = Preparer(intervals, **self.config)
while not finish:
# get events
rows, flag = prepare.get_data_from_db()
for row in rows:
# get info about event
time_event = None
user_id = None
time_delta = None
time_state = time_event - datetime.timedelta(seconds=1)
time_next_state = time_event + time_delta
categories = self.model.get_read_categories(
user_id, time_state, time_next_state, self.all_categories)
result = {}
for tag_id in categories:
action = categories[tag_id]
state = self.model.get_features(user_id, tag_id,
time_state)
predict = model_dqnn.predict(state)
