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

tools/tdgpt/taosanalytics/algo/ad/autoencoder.py

@@ -4,6 +4,7 @@
 import os.path
 
 import joblib
+import keras
 import numpy as np
 import pandas as pd
 
@@ -13,8 +14,8 @@ from taosanalytics.util import create_sequences
 
 
 class _AutoEncoderDetectionService(AbstractAnomalyDetectionService):
-    name = 'ad_encoder'
-    desc = "anomaly detection based on auto encoder"
+    name = 'sample_ad_model'
+    desc = "sample anomaly detection model based on auto encoder"
 
     def __init__(self):
         super().__init__()
@@ -25,7 +26,7 @@ class _AutoEncoderDetectionService(AbstractAnomalyDetectionService):
         self.threshold = None
         self.time_interval = None
         self.model = None
-        self.dir = 'ad_autoencoder'
+        self.dir = 'sample-ad-autoencoder'
 
         self.root_path = conf.get_model_directory()
 
@@ -61,11 +62,6 @@ class _AutoEncoderDetectionService(AbstractAnomalyDetectionService):
         # Detect all the samples which are anomalies.
         anomalies = mae > self.threshold
 
-        # syslogger.log_inst(
-        #     "Number of anomaly samples: %f, Indices of anomaly samples:{}".
-        #     format(np.sum(anomalies), np.where(anomalies))
-        # )
-
         # data i is an anomaly if samples [(i - timesteps + 1) to (i)] are anomalies
         ad_indices = []
         for data_idx in range(self.time_interval - 1,
@@ -82,13 +78,13 @@ class _AutoEncoderDetectionService(AbstractAnomalyDetectionService):
 
         name = params['model']
 
-        module_file_path = f'{self.root_path}/{name}.dat'
+        module_file_path = f'{self.root_path}/{name}.keras'
         module_info_path = f'{self.root_path}/{name}.info'
 
         app_logger.log_inst.info("try to load module:%s", module_file_path)
 
         if os.path.exists(module_file_path):
-            self.model = joblib.load(module_file_path)
+            self.model = keras.models.load_model(module_file_path)
         else:
             app_logger.log_inst.error("failed to load autoencoder model file: %s", module_file_path)
             raise FileNotFoundError(f"{module_file_path} not found")

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

@@ -0,0 +1,81 @@
+# encoding:utf-8
+# pylint: disable=c0103
+""" auto encoder algorithms to detect anomaly for time series data"""
+import os.path
+
+import keras
+
+from taosanalytics.algo.forecast import insert_ts_list
+from taosanalytics.conf import app_logger, conf
+from taosanalytics.service import AbstractForecastService
+
+
+class _LSTMService(AbstractForecastService):
+    name = 'sample_forecast_model'
+    desc = "sample forecast model based on LSTM"
+
+    def __init__(self):
+        super().__init__()
+
+        self.table_name = None
+        self.mean = None
+        self.std = None
+        self.threshold = None
+        self.time_interval = None
+        self.model = None
+        self.dir = 'sample-fc-lstm'
+
+        self.root_path = conf.get_model_directory()
+
+        self.root_path = self.root_path + f'/{self.dir}/'
+
+        if not os.path.exists(self.root_path):
+            app_logger.log_inst.error(
+                "%s ad algorithm failed to locate default module directory:"
+                "%s, not active", self.__class__.__name__, self.root_path)
+        else:
+            app_logger.log_inst.info("%s ad algorithm root path is: %s", self.__class__.__name__,
+                                     self.root_path)
+
+    def execute(self):
+        if self.input_is_empty():
+            return []
+
+        if self.model is None:
+            raise FileNotFoundError("not load autoencoder model yet, or load model failed")
+
+        res = self.model.predict(self.list)
+
+        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):
+
+        if "model" not in params:
+            raise ValueError("model needs to be specified")
+
+        name = params['model']
+
+        module_file_path = f'{self.root_path}/{name}.keras'
+        # module_info_path = f'{self.root_path}/{name}.info'
+
+        app_logger.log_inst.info("try to load module:%s", module_file_path)
+
+        if os.path.exists(module_file_path):
+            self.model = keras.models.load_model(module_file_path)
+        else:
+            app_logger.log_inst.error("failed to load LSTM model file: %s", module_file_path)
+            raise FileNotFoundError(f"{module_file_path} not found")
+
+    def get_params(self):
+        return {"dir": self.dir + '/*'}

tools/tdgpt/taosanalytics/test/anomaly_test.py

@@ -141,25 +141,23 @@ class AnomalyDetectionTest(unittest.TestCase):
 
     def test_autoencoder_ad(self):
         """for local test only, disabled it in github action"""
-        pass
+        data = self.__load_remote_data_for_ad()
 
-        # data = self.__load_remote_data_for_ad()
-        #
-        # s = loader.get_service("ad_encoder")
-        # s.set_input_list(data)
-        #
-        # try:
-        #     s.set_params({"model": "ad_encoder_"})
-        # except ValueError as e:
-        #     app_logger.log_inst.error(f"failed to set the param for auto_encoder algorithm, reason:{e}")
-        #     return
-        #
-        # r = s.execute()
-        #
-        # num_of_error = -(sum(filter(lambda x: x == -1, r)))
-        # self.assertEqual(num_of_error, 109)
-        #
-        # draw_ad_results(data, r, "autoencoder")
+        s = loader.get_service("sample_ad_model")
+        s.set_input_list(data)
+
+        try:
+            s.set_params({"model": "sample-ad-autoencoder"})
+        except ValueError as e:
+            app_logger.log_inst.error(f"failed to set the param for auto_encoder algorithm, reason:{e}")
+            return
+
+        r = s.execute()
+
+        num_of_error = -(sum(filter(lambda x: x == -1, r)))
+        draw_ad_results(data, r, "autoencoder")
+
+        self.assertEqual(num_of_error, 109)
 
     def test_get_all_services(self):
         """Test get all services"""