class ModelInference(object):
def __init__(self,
my_id=1,
bootstrap_servers='',
list_of_partitions=[],
request_topic='',
inference_topic='',
group_id='my_grp'):
""" Constructor
:type interval: int
:param interval: Check interval, in seconds
"""
self.model = None
#Create the model instance here
self.my_id = my_id
self.t = request_topic
self.result_t = inference_topic
self.my_grp_id = group_id
self.result_t_p = 8
self.bootstrap_servers = bootstrap_servers
self.tls = []
x = 0
for i in list_of_partitions:
self.tls.insert(x, TopicPartition(self.t, i))
x = x + 1
#self.tls=list_of_partitions
print(self.tls)
conf = {
'bootstrap.servers': bootstrap_servers,
'sasl.mechanism': 'PLAIN',
'security.protocol': 'SASL_SSL',
'ssl.ca.location': '/tmp/cacert.pem',
'sasl.username': '******',
'sasl.password': '******',
# 'key.serializer': StringSerializer('utf_8'),
# 'value.serializer': StringSerializer('utf_8'),
'client.id': 'test-sw-1'
}
self.producer = Producer(conf)
conf = {
'bootstrap.servers': bootstrap_servers,
#'sasl.mechanism': 'PLAIN',
'sasl.username': '******',
'sasl.password': '******',
'ssl.ca.location': '/tmp/cacert.pem',
'group.id': group_id,
'auto.offset.reset': 'smallest'
}
self.consumer = consumer = Consumer(conf)
self.consumer.assign(self.tls)
def __del__(self):
print('closing')
self.producer.flush()
self.consumer.close()
def run(self):
try:
self.consumer.assign(self.tls)
i = 0
while (True):
msg = self.consumer.poll(timeout=1.0)
if msg is None: continue
if msg.error():
if msg.error().code() == KafkaError._PARTITION_EOF:
# End of partition event
sys.stderr.write(
'%% %s [%d] reached end at offset %d\n' %
(msg.topic(), msg.partition(), msg.offset()))
elif msg.error():
raise KafkaException(msg.error())
else:
message = loads(msg.value().decode("utf-8"))
ingest_ts = message['ingestTs']
message_id = message['message_id']
truth = message['Class']
y_hat = truth ## Replace with model.predict & model.learn_one(
inference_ts = time.time()
out = {}
out['ingest_ts'] = ingest_ts
out['message_id'] = message_id
out['truth'] = truth ## model.learn_one(Y,Y_HAT)
out['y_hat'] = y_hat
out['inference_ts'] = inference_ts
out['dur_evt_inf'] = inference_ts - ingest_ts
i = i + 1
# sprint(self.result_t)
k = str(i)
v = json.dumps(out).encode('utf-8')
#self.producer.produce(self.result_t, value=v, key=k)
# self.producer.flush()
if (i % 2 == 0):
print('sending to ' + self.result_t + ' ' +
str(self.result_t_p) + ' ' + str(out))
self.producer.flush()
#self.producer.close()
finally:
# Close down consumer to commit final offsets.
self.consumer.close()
def runOld(self):
print(bootstrap_servers)
conf = {
'bootstrap.servers': bootstrap_servers,
'sasl.mechanism': 'PLAIN',
'security.protocol': 'SASL_SSL',
'ssl.ca.location': '/tmp/cacert.pem',
'sasl.username': '******',
'sasl.password':
'******',
# 'key.serializer': StringSerializer('utf_8'),
# 'value.serializer': StringSerializer('utf_8'),
'client.id': 'test-sw-1'
}
self.producer = Producer(conf)
conf = {
'bootstrap.servers': bootstrap_servers,
'sasl.mechanism': 'PLAIN',
'security.protocol': 'SASL_SSL',
'sasl.username': '******',
'sasl.password':
'******',
'ssl.ca.location': '/tmp/cacert.pem',
'group.id': 'g-1',
'auto.offset.reset': 'smallest'
}
self.consumer = consumer = Consumer(conf)
self.consumer.assign(self.tls)
print('starting ' + str(self.my_id))
now = datetime.now()
i = 0
while True:
for message in self.consumer:
message = loads(msg.value().decode("utf-8"))
ingest_ts = message['ingestTs']
message_id = message['message_id']
truth = message['Class']
y_hat = truth ## Replace with model.predict & model.learn_one(
inference_ts = time.time()
out = {}
out['ingest_ts'] = ingest_ts
out['message_id'] = message_id
out['truth'] = truth ## model.learn_one(Y,Y_HAT)
out['y_hat'] = y_hat
out['inference_ts'] = inference_ts
out['dur_evt_inf'] = inference_ts - ingest_ts
i = i + 1
#sprint(self.result_t)
self.producer.send(self.result_t,
value=out,
key=str(message_id))
#self.producer.flush()
if (i % 1000 == 0):
print('sending to ' + self.result_t + ' ' +
str(self.result_t_p) + ' ' + str(out))
self.producer.flush()
self.producer.close()
class ModelInference(object):
def __init__(self,
my_id=1,
bootstrap_servers='',
list_of_partitions=[],
request_topic='',
inference_topic='',
group_id='my_grp'):
""" Constructor
:type interval: int
:param interval: Check interval, in seconds
"""
self.model = compose.Pipeline(
preprocessing.MinMaxScaler(), anomaly.HalfSpaceTrees(
seed=42)) # tree.HoeffdingTreeClassifier(max_depth=10)
self.metric = metrics.ROCAUC() # metrics.Accuracy() #
self.my_id = my_id
self.t = request_topic
self.result_t = inference_topic
self.my_grp_id = group_id
self.result_t_p = 8
self.bootstrap_servers = bootstrap_servers
# self.list_of_partitions = list_of_partitions
self.tls = []
x = 0
for i in list_of_partitions:
self.tls.insert(x, TopicPartition(self.t, i))
x = x + 1
#self.tls=list_of_partitions
print(self.tls)
conf = {
'bootstrap.servers': bootstrap_servers,
'sasl.mechanism': 'PLAIN',
'security.protocol': 'SASL_SSL',
'ssl.ca.location': '/tmp/cacert.pem',
'sasl.username': '******',
'sasl.password':
'******',
# 'sasl.username': '******',
# 'sasl.password': '******',
# 'key.serializer': StringSerializer('utf_8'),
# 'value.serializer': StringSerializer('utf_8'),
'client.id': 'test-sw-1'
}
self.producer = Producer(conf)
conf = {
'bootstrap.servers': bootstrap_servers,
'sasl.mechanism': 'PLAIN',
'security.protocol': 'SASL_SSL',
'sasl.username': '******',
'sasl.password':
'******',
'ssl.ca.location': '/tmp/cacert.pem',
'group.id': group_id,
'auto.offset.reset': 'latest'
}
self.consumer = consumer = Consumer(conf)
self.consumer.assign(self.tls)
def __del__(self):
print('closing')
#self.consumer.close()
self.producer.flush()
self.producer.close()
def run(self):
### Set up model, metric, and starting timestamp for this model instance ###
model = self.model
metric = self.metric
print('MODEL and METRIC before any messages consumed:', model, metric)
start_consume_ts = time.time()
print('Start Consume Time ' + str(start_consume_ts))
######
try:
self.consumer.assign(self.tls)
i = 0
while (True):
msg = self.consumer.poll(timeout=1.0)
if msg is None: continue
if msg.error():
if msg.error().code() == KafkaError._PARTITION_EOF:
# End of partition event
sys.stderr.write(
'%% %s [%d] reached end at offset %d\n' %
(msg.topic(), msg.partition(), msg.offset()))
elif msg.error():
raise KafkaException(msg.error())
else:
message = loads(msg.value().decode("utf-8"))
ingest_ts = message[
'ingestTs'] # Ingestion time from Kafka
message.pop('ingestTs')
message_id = message['message_id'] # Message ID from Kafka
message.pop('message_id')
truth = float(
message['Class']) # True label supplied by Kafka
message.pop(
'Class'
) # Remove label remove passing the data for prediction (could combine with above)
message.pop('ignore')
x = message
for k, v in x.items():
x[k] = float(v)
y_hat = model.score_one(
x) #model.predict_one(x) # make a prediction
if (i % 1000 == 0):
print('x: ', x)
for k, v in x.items():
print(type(x[k]))
print('model: ', model)
print('y_hat: ', y_hat)
inference_ts = time.time()
metric = metric.update(truth, y_hat) # update the metric
model = model.learn_one(
x) # model.learn_one(x,y) # make the model learn
learn_ts = time.time()
out = {}
out['ingest_ts'] = ingest_ts
out['learn_ts'] = learn_ts
out['message_id'] = message_id
out['truth'] = truth
out['y_hat'] = y_hat
out['inference_ts'] = inference_ts
out['dur_evt_inf'] = inference_ts - ingest_ts
out['dur_start_inf'] = inference_ts - start_consume_ts
out['dur_inf_learn'] = learn_ts - inference_ts
out['model_metric'] = metric.get()
out['ignore'] = False
i = i + 1
# sprint(self.result_t)
k = str(i)
v = json.dumps(out).encode('utf-8')
self.producer.produce(self.result_t, value=v, key=k)
# self.producer.flush()
if (i % 1000 == 0):
print('sending to ' + self.result_t + ' ' +
str(self.result_t_p) + ' ' + str(out))
self.producer.flush()
if (i % 100000 == 0):
try:
pickle.dump(model, open("model.pkl", "wb"))
except:
os.remove("model.pkl")
pickle.dump(model, open("model.pkl", "wb"))
#self.producer.close()
finally:
# Close down consumer to commit final offsets.
self.consumer.close()
