def run(self):
logging.debug('Start running with name: {0}, count: {1}'.format(
self.name, self.count))
client = Classifier('127.0.0.1', 9199, 'test')
for i in range(0, self.count):
client.save(self.name + str(i))
logging.debug('Finished running')
def setUp(self):
self.config = {
"method": "AROW",
"converter": {
"string_filter_types": {},
"string_filter_rules": [],
"num_filter_types": {},
"num_filter_rules": [],
"string_types": {},
"string_rules": [{
"key": "*",
"type": "str",
"sample_weight": "bin",
"global_weight": "bin"
}],
"num_types": {},
"num_rules": [{
"key": "*",
"type": "num"
}]
},
"parameter": {
"regularization_weight": 1.001
}
}
TestUtil.write_file('config_classifier.json', json.dumps(self.config))
self.srv = TestUtil.fork_process('classifier', port,
'config_classifier.json')
try:
self.cli = Classifier(host, port, "name")
except:
TestUtil.kill_process(self.srv)
raise
def main():
args = parse_options()
client = Classifier('127.0.0.1', args.port, 'test', 0)
for i in range(0, 10000):
client.do_mix()
if not i % 100:
status = client.get_status()
for node in status.keys():
print '\t'.join([str(i), node, status[node]['RSS']])
def main():
args = parse_options()
client = Classifier('127.0.0.1', args.port, 'test', 0)
d = Datum()
# Learn same data
rand = random.randint(0, 1)
d.add_number('key', 1.0 if rand else 2.0)
print client.classify([d])
print client.get_labels()
def main():
args = parse_options()
client = Classifier('127.0.0.1', args.port, 'test', 0)
for i in range(0, 1000000):
d = Datum()
# Learn same data
rand = random.randint(0, 1)
d.add_number('key', 1.0 if rand else 2.0)
ld = LabeledDatum('Pos' if rand else 'Neg', d)
client.train([ld])
if not i % 10000:
print 'train ' + str(i) + ' data'
#!/usr/bin/env python
from jubatus.classifier.client import Classifier
for idx in xrange(1, 50):
client = Classifier('127.0.0.1', 9199, 'test')
for i in xrange(1, 10001):
client.do_mix()
if not i % 1000:
status = client.get_status()
for node in status.keys():
print '\t'.join(
[str((idx * 10000) + i), node, status[node]['RSS']])
client.train(train_data)
result = client.classify([predict_data[0]])
predicted = max(result[0], key=lambda x: x.score).label
if answer == predicted:
print('correct', end="\t")
else:
print('wrong', end="\t")
print(answer, predicted, result, sep="\t")
if __name__ == '__main__':
try:
exclude = sys.argv[3]
training = sys.argv[2]
port = int(sys.argv[1])
except:
sys.stderr.write(
"Usage: jubatus.py port_number training.tsv exclude name\n")
sys.exit(7)
localhost = '127.0.0.1'
if len(sys.argv) > 4:
name = sys.argv[4]
else:
name = 'Coded by Kohji'
client = Classifier(localhost, port, name) # connect to Jubatus
train_and_predict(client, training)
import argparse
import socket
from jubatus.classifier.client import Classifier
parser = argparse.ArgumentParser()
parser.add_argument("-n", "--name", help="set the name of the file to be saved")
parser.add_argument("--host", help="set the host address")
parser.add_argument("--port", help="set the port number")
args = parser.parse_args()
print(args)
host_ip = args.host if args.host else socket.gethostbyname(socket.gethostname())
port = args.port if args.port else 9199
client = Classifier(host_ip, port, '')
if args.name:
client.save(args.name)
print("file saved at /tmp of the "+host_ip+" unless you specified output path with -d/--datadir when you started server process.")
else:
print("[Error] specify the model's name to be saved!")
result = {}
result[0] = ''
result[1] = 0
for res in estm:
if prob == None or res.score > prob:
ans = res.label
prob = res.score
result[0] = ans
result[1] = prob
return result
if __name__ == '__main__':
options, remainder = parse_args()
classifier = Classifier(options.server_ip, options.server_port,
options.name, 10.0)
print classifier.get_config()
print classifier.get_status()
for line in open('train.dat'):
label, file = line[:-1].split(',')
dat = open(file).read()
datum = Datum({"message": dat})
classifier.train([LabeledDatum(label, datum)])
print classifier.get_status()
print classifier.save("tutorial")
print classifier.load("tutorial")
#!/usr/bin/env python
from jubatus.classifier.client import Classifier
client = Classifier('127.0.0.1', 9000, 'test', 0)
client.do_mix()
