def __init__(self, batch_size):
"""
:param batch_size: Integer value, defined by the competition and available at competition page
:param server_port: Connection string ('IP:port')
:param user_email: String, e-mail used for registering to competition
:param token: String, received after subscription to a competition
:param competition_code: String, received after subscription to a competition
:param first_prediction: Prediction, class generated from .proto file. Used to initiate communication with the
server. Not influencing the results. Should contain appropriate fields from .proto file.
"""
# mondrian
self.mfr = MondrianForestRegressor(random_state=1,
n_estimators=100,
bootstrap=True)
self.previous_target_3 = pd.Series()
self.features_for_rowID = Queue()
self.previous_train_batch = np.array([-1, -1, -1, -1, -1])
# rrcf
self.num_trees = 40
self.tree_size = 256
self.forest = []
self.avg_codisp = {}
self.curr_sum = 0
self.curr_num = 0
self.idx = 0
self._init_modeling()
a = 1
while a == 1:
print("wait")
now = datetime.datetime.now()
starttime = now.replace(hour=21, minute=0, second=0, microsecond=0)
if now >= starttime:
print(now)
print("시작!")
break
self.batch_size = batch_size
self.stop_thread = False
self.predictions_to_send = Queue()
self.channel = grpc.insecure_channel(
'app.streaming-challenge.com:50051')
self.stub = file_pb2_grpc.DataStreamerStub(self.channel)
self.user_email = '*****@*****.**'
self.competition_code = 'jR' #oj
self.token = 'eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJ1c2VyX2lkIjoieXU5OTA1MjRAZ21haWwuY29tIiwiY29tcGV0aXRpb25faWQiOiIxIn0.B7CAjAsEbTjp4l1K4GR1Y0IJZj6_mKEbKBXsXXJmGBg'
self.predictions_to_send.put(
file_pb2.Prediction(rowID=1000, target=333))
self.metadata = self.create_metadata(user_id=self.user_email,
code=self.competition_code,
token=self.token)
def __init__(self, batch_size):
"""
:param batch_size: Integer value, defined by the competition and available at competition page
:param server_port: Connection string ('IP:port')
:param user_email: String, e-mail used for registering to competition
:param token: String, received after subscription to a competition
:param competition_code: String, received after subscription to a competition
:param first_prediction: Prediction, class generated from .proto file. Used to initiate communication with the
server. Not influencing the results. Should contain appropriate fields from .proto file.
"""
self.batch_size = batch_size
self.stop_thread = False
self.predictions_to_send = Queue()
self.channel = grpc.insecure_channel('app.streaming-challenge.com:50051')
self.stub = file_pb2_grpc.DataStreamerStub(self.channel)
self.user_email = '*****@*****.**'
self.competition_code = 'oj'
self.token = 'eyJ0eXAiOiJKV1QiLCVmPCIZUIfk094SMnIpo5QMp1M'
self.predictions_to_send.put(file_pb2.Prediction(rowID=1000, Target=333))
self.metadata = self.create_metadata(user_id=self.user_email, code=self.competition_code, token=self.token)
def generate_predictions(self):
"""
Sending predictions
:return: Prediction
"""
while True:
try:
pred = self.predictions_to_send.pop()
prediction_as_dict = ast.literal_eval(pred)
new_dict = {}
for key, value in prediction_as_dict.items():
if isinstance(value, list) and len(value) == 1:
new_dict[key] = value[0]
else:
new_dict[key] = value
prediction = file_pb2.Prediction(**new_dict)
# print("Prediction: ", prediction)
yield prediction
if self.stop_thread and not self.predictions_to_send:
break
except Exception as e:
pass
def loop_messages(self):
"""
Getting messages (data instances) from the stream.
:return:
"""
messages = self.stub.sendData(self.generate_predictions(), metadata=self.metadata)
try:
for message in messages:
message = json.loads(json_format.MessageToJson(message))
if message['tag'] == 'TEST':
# v = message['Target'] + 10
v = 54321
prediction = file_pb2.Prediction(rowID=message['rowID'], Target=v)
self.predictions_to_send.put(prediction)
if message['tag'] == 'INIT':
i = 1
if message['tag'] == 'TRAIN':
i = 1
if self.stop_thread:
break
except Exception as e:
print(str(e))
pass
def loop_messages(self):
"""
Getting messages (data instances) from the stream.
:return:
"""
#generate prediction -> get prediction from predictions_to_send one by one ans SEND to server
messages = self.stub.sendData(self.generate_predictions(),
metadata=self.metadata)
test_idx = 0
test_feature = self.previous_target_3
try:
for message in messages:
message = json.loads(json_format.MessageToJson(message))
print("message:", message)
if message['tag'] == 'TEST':
print('test')
test_feature['prev3'] = test_feature['prev2']
test_feature['prev2'] = test_feature['prev1']
test_feature['prev1'] = float(
self.previous_train_batch[test_idx])
pred = self.mfr.predict(test_feature.values.reshape(1, -1))
prediction = file_pb2.Prediction(rowID=message['rowID'],
target=pred)
self.predictions_to_send.put(prediction)
#
test_idx = (test_idx + 1) % 5
print(test_idx)
print('test end')
if message['tag'] == 'TRAIN':
print('train')
#training data to train my model.
target = message['target']
target = self.anomaly_detection(target)
print(self.previous_target_3)
# i-5, i-6, i-7 의 값을 갖고 학습
if self.previous_target_3['prev3'] < 0:
self.previous_target_3['prev3'] = target
elif self.previous_target_3['prev2'] < 0:
self.previous_target_3['prev2'] = target
elif self.previous_target_3['prev1'] < 0:
self.previous_target_3['prev1'] = target
else:
print('else')
#replace the oldest value
self.previous_target_3[
'prev3'] = self.previous_target_3['prev2'] #-7
self.previous_target_3[
'prev2'] = self.previous_target_3['prev1'] #-6
self.previous_target_3['prev1'] = float(
self.previous_train_batch[0]) #-5
# partial fit with 3 previous values as feature
self.mfr.partial_fit(
self.previous_target_3.values.reshape(1, -1),
[target])
#현재 train data의 target값 저장
self.previous_train_batch = np.roll(
self.previous_train_batch, -1)
self.previous_train_batch[4] = target
print('else end')
print('train end')
if self.stop_thread: break
except Exception as e:
print(str(e))
pass