add mvad/uvad python sdk sample for v1.1-preview.1

sdk/anomalydetector/azure-ai-anomalydetector/CHANGELOG.md

@@ -1,8 +1,14 @@
 # Release History
 
-## 3.0.0b4 (Unreleased)
+## 3.0.0b4 (2021-12-20)
 
-# TODO: Service team fill out
+  **Features**
+  - Introduced the new API: `AnomalyDetectorClientOperationsMixin.last_detect_anomaly`.
+  - Added 2 new optional properties: `impute_mode` & `impute_fixed_value` to `DetectRequest` object.
+  - Added 1 new optional property: `severity` to the `EntireDetectResponse` & `LastDetectResponse` objects.
+  - Removed the optional property `errors` from the `VariableState` object.
+  - Refactored the optional property `contributors` to `interpretation` from the `AnomalyValue` object.
+  - Modified the `FillNAMethod` object into an extensible enum.
 
 ## 3.0.0b3 (2021-04-16)
 

sdk/anomalydetector/azure-ai-anomalydetector/samples/README.md

@@ -20,6 +20,7 @@ These sample programs show common scenarios for the Anomaly Detector client's of
 |[sample_detect_entire_series_anomaly.py][sample_detect_entire_series_anomaly] |Detecting anomalies in the entire time series.|
 |[sample_detect_last_point_anomaly.py][sample_detect_last_point_anomaly] |Detecting the anomaly status of the latest data point.|
 |[sample_detect_change_point.py][sample_detect_change_point] |Detecting change points in the entire time series.|
+|[sample_multivariate_detect.py][sample_multivariate_detect] |Detecting anomalies in the multivariate time series.|
 
 ## Prerequisites
 * Python 2.7 or 3.5 or higher is required to use this package.

sdk/anomalydetector/azure-ai-anomalydetector/samples/sample_detect_change_point.py

@@ -64,10 +64,9 @@ def detect_change_point(self):
 
         try:
             response = client.detect_change_point(request)
-        except AnomalyDetectorError as e:
-            print('Error code: {}'.format(e.error.code), 'Error message: {}'.format(e.error.message))
+
         except Exception as e:
-            print(e)
+            print('Error code: {}'.format(e.error.code), 'Error message: {}'.format(e.error.message))
 
         if any(response.is_change_point):
             print('An change point was detected at index:')

sdk/anomalydetector/azure-ai-anomalydetector/samples/sample_detect_entire_series_anomaly.py

@@ -64,10 +64,8 @@ def detect_entire_series(self):
 
         try:
             response = client.detect_entire_series(request)
-        except AnomalyDetectorError as e:
-            print('Error code: {}'.format(e.error.code), 'Error message: {}'.format(e.error.message))
         except Exception as e:
-            print(e)
+            print('Error code: {}'.format(e.error.code), 'Error message: {}'.format(e.error.message))
 
         if any(response.is_anomaly):
             print('An anomaly was detected at index:')

sdk/anomalydetector/azure-ai-anomalydetector/samples/sample_detect_last_point_anomaly.py

@@ -64,10 +64,8 @@ def detect_last_point(self):
 
         try:
             response = client.detect_last_point(request)
-        except AnomalyDetectorError as e:
-            print('Error code: {}'.format(e.error.code), 'Error message: {}'.format(e.error.message))
         except Exception as e:
-            print(e)
+            print('Error code: {}'.format(e.error.code), 'Error message: {}'.format(e.error.message))
 
         if response.is_anomaly:
             print('The latest point is detected as anomaly.')

sdk/anomalydetector/azure-ai-anomalydetector/samples/sample_multivariate_detect.py

@@ -24,12 +24,11 @@
 from datetime import datetime, timezone
 
 from azure.ai.anomalydetector import AnomalyDetectorClient
-from azure.ai.anomalydetector.models import DetectionRequest, ModelInfo
+from azure.ai.anomalydetector.models import DetectionRequest, ModelInfo, LastDetectionRequest
 from azure.ai.anomalydetector.models import ModelStatus, DetectionStatus
 from azure.core.credentials import AzureKeyCredential
 from azure.core.exceptions import HttpResponseError
 
-
 class MultivariateSample:
 
     def __init__(self, subscription_key, anomaly_detector_endpoint, data_source=None):
@@ -142,6 +141,14 @@ def delete_model(self, model_id):
         print("{:d} available models after deletion.".format(len(model_list_after_delete)))
 
 
+    def last_detect(self, model_id, variables, detecting_points):
+
+        # Detect anomaly by sync api
+        last_detection_req = LastDetectionRequest(variables=variables, detecting_points=detecting_points)
+        r = self.ad_client.last_detect_anomaly(model_id, last_detection_req)
+        print("Get last detection result")
+        return r
+
 if __name__ == '__main__':
     SUBSCRIPTION_KEY = os.environ["ANOMALY_DETECTOR_KEY"]
     ANOMALY_DETECTOR_ENDPOINT = os.environ["ANOMALY_DETECTOR_ENDPOINT"]
@@ -159,7 +166,7 @@ def delete_model(self, model_id):
                             datetime(2021, 1, 2, 12, 0, 0, tzinfo=timezone.utc))
     assert model_id is not None
 
-    # Reference
+    # Inference
     result = sample.detect(model_id, datetime(2021, 1, 2, 12, 0, 0, tzinfo=timezone.utc),
                            datetime(2021, 1, 3, 0, 0, 0, tzinfo=timezone.utc))
     assert result is not None
@@ -173,3 +180,32 @@ def delete_model(self, model_id):
 
     # Delete model
     sample.delete_model(model_id)
+
+    # *******************************************************************************************************************
+    # use your own inference data sending to last detection api, you should define your own variables and detectingPoints
+    # *****************************************************************************************************************
+    # define "<YOUR OWN variables>"
+    variables = [
+        {
+            "name": "variables_name1",
+            "timestamps": ['2021-01-01T00:00:00Z', '2021-01-01T00:01:00Z', ...],
+            "values": [0, 0, ...]
+        },
+        {
+            "name": "variables_name2",
+            "timestamps": ['2021-01-01T00:00:00Z', '2021-01-01T00:01:00Z', ...],
+            "values": [0, 0, ...]
+        }
+    ]
+    # define <YOUR OWN number of detectingPoints>"
+    detectingPoints = 10
+
+    # Last detection
+    last_detect_result = sample.last_detect(model_id, variables, detectingPoints)
+
+    assert last_detect_result is not None
+
+    print("Variable States:\t", last_detect_result.variable_states)
+    print("Variable States length:\t", len(last_detect_result.variable_states))
+    print("Results:\t", last_detect_result.results)
+    print("Results length:\t", len(last_detect_result.results))