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)))
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)))
