Add multivariate sample (Azure#18156)

Co-authored-by: David Liang <liangze@microsoft.com>

Author: DavidZeLiang1228

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

@@ -0,0 +1,153 @@
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+
+"""
+FILE: sample_multivariate_detect.py
+
+DESCRIPTION:
+    This sample demonstrates how to use multivariate dataset to train a model and use the model to detect anomalies.
+
+Prerequisites:
+     * The Anomaly Detector client library for Python
+     * A valid data feed
+
+USAGE:
+    python sample_multivariate_detect.py
+
+    Set the environment variables with your own values before running the sample:
+    1) ANOMALY_DETECTOR_KEY - your source Form Anomaly Detector API key.
+    2) ANOMALY_DETECTOR_ENDPOINT - the endpoint to your source Anomaly Detector resource.
+"""
+
+import os
+import time
+from datetime import datetime
+
+from azure.ai.anomalydetector import AnomalyDetectorClient
+from azure.ai.anomalydetector.models import DetectionRequest, ModelInfo
+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):
+        self.sub_key = subscription_key
+        self.end_point = anomaly_detector_endpoint
+
+        # Create an Anomaly Detector client
+
+        # <client>
+        self.ad_client = AnomalyDetectorClient(AzureKeyCredential(self.sub_key), self.end_point)
+        # </client>
+
+        self.data_source = data_source
+
+    def train(self, start_time, end_time, max_tryout=500):
+
+        # Number of models available now
+        model_list = list(self.ad_client.list_multivariate_model(skip=0, top=10000))
+        print("{:d} available models before training.".format(len(model_list)))
+
+        # Use sample data to train the model
+        print("Training new model...")
+        data_feed = ModelInfo(start_time=start_time, end_time=end_time, source=self.data_source)
+        response_header = \
+            self.ad_client.train_multivariate_model(data_feed, cls=lambda *args: [args[i] for i in range(len(args))])[
+                -1]
+        trained_model_id = response_header['Location'].split("/")[-1]
+
+        # Model list after training
+        new_model_list = list(self.ad_client.list_multivariate_model(skip=0, top=10000))
+
+        # Wait until the model is ready. It usually takes several minutes
+        model_status = None
+        tryout_count = 0
+        while (tryout_count < max_tryout and model_status != "READY"):
+            model_status = self.ad_client.get_multivariate_model(trained_model_id).model_info.status
+            tryout_count += 1
+            time.sleep(2)
+
+        assert model_status == "READY"
+
+        print("Done.", "\n--------------------")
+        print("{:d} available models after training.".format(len(new_model_list)))
+
+        # Return the latest model id
+        return trained_model_id
+
+    def detect(self, model_id, start_time, end_time, max_tryout=500):
+
+        # Detect anomaly in the same data source (but a different interval)
+        try:
+            detection_req = DetectionRequest(source=self.data_source, start_time=start_time, end_time=end_time)
+            response_header = self.ad_client.detect_anomaly(model_id, detection_req,
+                                                            cls=lambda *args: [args[i] for i in range(len(args))])[-1]
+            result_id = response_header['Location'].split("/")[-1]
+
+            # Get results (may need a few seconds)
+            r = self.ad_client.get_detection_result(result_id)
+            tryout_count = 0
+            while r.summary.status != "READY" and tryout_count < max_tryout:
+                time.sleep(1)
+                r = self.ad_client.get_detection_result(result_id)
+                tryout_count += 1
+
+            if r.summary.status != "READY":
+                print("Request timeout after %d tryouts.".format(max_tryout))
+                return None
+
+        except HttpResponseError as e:
+            print('Error code: {}'.format(e.error.code), 'Error message: {}'.format(e.error.message))
+        except Exception as e:
+            raise e
+
+        return r
+
+    def export_model(self, model_id, model_path="model.zip"):
+
+        # Export the model
+        model_stream_generator = self.ad_client.export_model(model_id)
+        with open(model_path, "wb") as f_obj:
+            while True:
+                try:
+                    f_obj.write(next(model_stream_generator))
+                except StopIteration:
+                    break
+                except Exception as e:
+                    raise e
+
+    def delete_model(self, model_id):
+
+        # Delete the mdoel
+        self.ad_client.delete_multivariate_model(model_id)
+        model_list_after_delete = list(self.ad_client.list_multivariate_model(skip=0, top=10000))
+        print("{:d} available models after deletion.".format(len(model_list_after_delete)))
+
+
+if __name__ == '__main__':
+    SUBSCRIPTION_KEY = os.environ["ANOMALY_DETECTOR_KEY"]
+    ANOMALY_DETECTOR_ENDPOINT = os.environ["ANOMALY_DETECTOR_ENDPOINT"]
+
+    # *****************************
+    # Use your own data source here
+    # *****************************
+    data_source = "<YOUR OWN DATA SOURCE>"
+
+    # Create a new sample and client
+    sample = MultivariateSample(SUBSCRIPTION_KEY, ANOMALY_DETECTOR_ENDPOINT, data_source)
+
+    # Train a new model
+    model_id = sample.train(datetime(2021, 1, 1, 0, 0, 0), datetime(2021, 1, 2, 12, 0, 0))
+
+    # Reference
+    result = sample.detect(model_id, datetime(2021, 1, 2, 12, 0, 0), datetime(2021, 1, 3, 0, 0, 0))
+    print("Result ID:\t", result.result_id)
+    print("Result summary:\t", result.summary)
+    print("Result length:\t", len(result.results))
+
+    # Export model
+    sample.export_model(model_id, "model.zip")
+
+    # Delete model
+    sample.delete_model(model_id)