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()
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)))