class RegressionTest(unittest.TestCase):
def setUp(self):
self.config = {
"method": "PA",
"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": {
"sensitivity" : 0.1,
"regularization_weight" : 3.402823e+38
}
}
TestUtil.write_file('config_regression.json', json.dumps(self.config))
self.srv = TestUtil.fork_process('regression', port, 'config_regression.json')
try:
self.cli = Regression(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 = [[1.0, d]]
self.assertEqual(self.cli.train(data), 1)
def test_estimate(self):
d = Datum({"skey1": "val1", "skey2": "val2", "nkey1": 1.0, "nkey2": 2.0})
data = [d]
result = self.cli.estimate(data)
def test_save(self):
self.assertEqual(len(self.cli.save("regression.save_test.model")), 1)
def test_load(self):
model_name = "regression.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()
print(client.get_config())
print(client.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)
client.train([[float(label), datum]])
print(client.get_status())
print(client.save("tutorial_regression"))
print(client.load("tutorial_regression"))
print(client.get_config())
count_ok = 0
count_ng = 0
for label, dat in izip(label_test, data_test):
data_dict = json.loads(dat[0])
datum = Datum(data_dict)
ans = client.estimate([datum])
print(ans)
if ans != None:
if (float(label) == ans[0]):
result = "OK"
class RegressionTest(unittest.TestCase):
def setUp(self):
self.config = {
"method": "PA1",
"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": {
"sensitivity": 0.1,
"regularization_weight": 3.402823e+38
}
}
TestUtil.write_file('config_regression.json', json.dumps(self.config))
self.srv = TestUtil.fork_process('regression', port,
'config_regression.json')
try:
self.cli = Regression(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 = [[1.0, d]]
self.assertEqual(self.cli.train(data), 1)
def test_estimate(self):
d = Datum({
"skey1": "val1",
"skey2": "val2",
"nkey1": 1.0,
"nkey2": 2.0
})
data = [d]
result = self.cli.estimate(data)
def test_save(self):
self.assertEqual(len(self.cli.save("regression.save_test.model")), 1)
def test_load(self):
model_name = "regression.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()
