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)
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']])
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")
print classifier.get_config()
#!/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']])
train_label = numpy.array(y_vector)
first_flag = 0
else:
train_data = numpy.vstack((train_data, x_vector))
train_label = numpy.array(y_vector)
train_list = [train_data, train_label]
return train_list
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())
train_list = cross_validation_python()
data_train, data_test, label_train, label_test = train_test_split(train_list[0], train_list[1])
for label, dat in izip(label_train, data_train):
print(dat[0])
data_dict = json.loads(dat[0])
datum = Datum(data_dict)
classifier.train([LabeledDatum(label, datum)])
print(classifier.get_status())
print(classifier.save("tutorial"))
print(classifier.load("tutorial"))
class ClassifierTest(unittest.TestCase):
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 tearDown(self):
if self.cli:
self.cli.get_client().close()
TestUtil.kill_process(self.srv)
def test_get_client(self):
self.assertTrue(isinstance(self.cli.get_client(), msgpackrpc.client.Client))
def test_get_config(self):
config = self.cli.get_config()
self.assertEqual(json.dumps(json.loads(config), sort_keys=True), json.dumps(self.config, sort_keys=True))
def test_train(self):
d = Datum({"skey1": "val1", "skey2": "val2", "nkey1": 1.0, "nkey2": 2.0})
data = [["label", d]]
self.assertEqual(self.cli.train(data), 1)
def test_classify(self):
d = Datum({"skey1": "val1", "skey2": "val2", "nkey1": 1.0, "nkey2": 2.0})
data = [d]
result = self.cli.classify(data)
def test_set_label(self):
self.assertEqual(self.cli.set_label("label"), True)
def test_get_labels(self):
self.cli.set_label("label")
self.assertEqual(self.cli.get_labels(), {"label": 0})
def test_delete_label(self):
self.cli.set_label("label")
self.assertEqual(self.cli.delete_label("label"), True)
def test_save(self):
self.assertEqual(len(self.cli.save("classifier.save_test.model")), 1)
def test_load(self):
model_name = "classifier.load_test.model"
self.cli.save(model_name)
self.assertEqual(self.cli.load(model_name), True)
def test_get_status(self):
self.cli.get_status()
def test_str(self):
self.assertEqual("estimate_result{label: label, score: 1.0}", str(EstimateResult("label", 1.0)))
#!/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']])
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")
print classifier.get_config()
class ClassifierTest(unittest.TestCase):
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 tearDown(self):
if self.cli:
self.cli.get_client().close()
TestUtil.kill_process(self.srv)
def test_get_client(self):
self.assertTrue(
isinstance(self.cli.get_client(), msgpackrpc.client.Client))
def test_get_config(self):
config = self.cli.get_config()
self.assertEqual(json.dumps(json.loads(config), sort_keys=True),
json.dumps(self.config, sort_keys=True))
def test_train(self):
d = Datum({
"skey1": "val1",
"skey2": "val2",
"nkey1": 1.0,
"nkey2": 2.0
})
data = [["label", d]]
self.assertEqual(self.cli.train(data), 1)
def test_classify(self):
d = Datum({
"skey1": "val1",
"skey2": "val2",
"nkey1": 1.0,
"nkey2": 2.0
})
data = [d]
result = self.cli.classify(data)
def test_set_label(self):
self.assertEqual(self.cli.set_label("label"), True)
def test_get_labels(self):
self.cli.set_label("label")
self.assertEqual(self.cli.get_labels(), {"label": 0})
def test_delete_label(self):
self.cli.set_label("label")
self.assertEqual(self.cli.delete_label("label"), True)
def test_save(self):
self.assertEqual(len(self.cli.save("classifier.save_test.model")), 1)
def test_load(self):
model_name = "classifier.load_test.model"
self.cli.save(model_name)
self.assertEqual(self.cli.load(model_name), True)
def test_get_status(self):
self.cli.get_status()
def test_str(self):
self.assertEqual("estimate_result{label: label, score: 1.0}",
str(EstimateResult("label", 1.0)))
