fix(gpt): add gpt (#30273)

* fix(stream): support packaging enterprise edition.

* feat(gpt): support lstm and do some internal refactor, add sample autoencoder model.

* feat(gpt): support lstm and do some internal refactor, add sample autoencoder model.

* test(gpt): disable model case.

* test(gpt): disable model case.

* doc: fix title error in doc.

* doc: add mlp doc.

* fix(gpt): add gpt

* fix(gpt): update the test cases.

tools/tdgpt/taosanalytics/algo/fc/gpt.py

@@ -0,0 +1,85 @@
+# encoding:utf-8
+# pylint: disable=c0103
+""" auto encoder algorithms to detect anomaly for time series data"""
+import json
+import requests
+
+from taosanalytics.algo.forecast import insert_ts_list
+from taosanalytics.conf import app_logger, conf
+from taosanalytics.service import AbstractForecastService
+
+
+class _GPTService(AbstractForecastService):
+    name = 'td_gpt_fc'
+    desc = "internal gpt forecast model based on transformer"
+
+    def __init__(self):
+        super().__init__()
+
+        self.table_name = None
+        self.service_host = 'http://192.168.2.90:5000/ds_predict'
+        self.headers = {'Content-Type': 'application/json'}
+
+        self.std = None
+        self.threshold = None
+        self.time_interval = None
+        self.dir = 'internal-gpt'
+
+
+    def execute(self):
+        if self.list is None or len(self.list) < self.period:
+            raise ValueError("number of input data is less than the periods")
+
+        if self.fc_rows <= 0:
+            raise ValueError("fc rows is not specified yet")
+
+        # let's request the gpt service
+        data = {"input": self.list, 'next_len': self.fc_rows}
+        try:
+            response = requests.post(self.service_host, data=json.dumps(data), headers=self.headers)
+        except Exception as e:
+            app_logger.log_inst.error(f"failed to connect the service: {self.service_host} ", str(e))
+            raise ValueError("error")
+
+        # print(response)
+
+        pred_y = response.json()['output']
+        # print(f"pred_y len:{len(pred_y)}")
+        # print(f"pred_y:{pred_y}")
+
+        res =  {
+            "res": [pred_y]
+        }
+
+        insert_ts_list(res["res"], self.start_ts, self.time_step, self.fc_rows)
+        return res
+
+        # insert_ts_list(res, self.start_ts, self.time_step, self.fc_rows)
+        #
+        # if self.return_conf:
+        #     res1 = [res.tolist(), res.tolist(), res.tolist()], None
+        # else:
+        #     res1 = [res.tolist()], None
+        #
+        # # add the conf range if required
+        # return {
+        #     "mse": None,
+        #     "res": res1
+        # }
+
+    def set_params(self, params):
+        super().set_params(params)
+
+        if "host" not in params:
+            raise ValueError("gpt service host needs to be specified")
+
+        self.service_host = params['host'].trim()
+
+        if self.service_host.startswith("https://"):
+            self.service_host = self.service_host.replace("https://", "http://")
+        elif "http://" not in self.service_host:
+            self.service_host = "http://" + self.service_host
+
+        app_logger.log_inst.info("%s specify gpt host service: %s", self.__class__.__name__,
+                                 self.service_host)
+

tools/tdgpt/taosanalytics/test/unit_test.py

@@ -99,7 +99,7 @@ class ServiceTest(unittest.TestCase):
             if item["type"] == "anomaly-detection":
                 self.assertEqual(len(item["algo"]), 6)
             else:
-                self.assertEqual(len(item["algo"]), 3)
+                self.assertEqual(len(item["algo"]), 4)
 
 
 if __name__ == '__main__':