update python code sample

mrbullwinkle · mrbullwinkle · commit e6d62f8f3066 · 2023-03-13T23:02:57.000-04:00
diff --git a/articles/cognitive-services/Anomaly-Detector/includes/quickstarts/anomaly-detector-client-library-python-multivariate.md b/articles/cognitive-services/Anomaly-Detector/includes/quickstarts/anomaly-detector-client-library-python-multivariate.md
@@ -6,7 +6,7 @@ author: mrbullwinkle
 manager: nitinme
 ms.service: cognitive-services
 ms.topic: include
-ms.date: 02/09/2023
+ms.date: 03/13/2023
 ms.author: mbullwin
 ---
 
@@ -36,7 +36,7 @@ pip install --upgrade azure.ai.anomalydetector
 
 ### Create a storage account
 
-Multivariate Anomaly Detector requires your sample file to be stored as a .zip file in Azure Blob Storage.
+Multivariate Anomaly Detector requires your sample file to be stored in Azure Blob Storage.
 
 1. Create an <a href="https://portal.azure.com/#create/Microsoft.StorageAccount-ARM" target="_blank">Azure Storage account</a>.
 2. Go to Access Control(IAM), and select **ADD** to Add role assignment.
@@ -47,18 +47,14 @@ This configuration can sometimes be a little confusing, if you have trouble we r
 
 ### Download sample data
 
-This quickstart uses two files for sample data `sample_data_5_3000.csv` and `5_3000.json`. These files can both be downloaded from our [GitHub sample data](https://github.com/Azure-Samples/AnomalyDetector/blob/master/sampledata/multivariate/)
+This quickstart uses one file for sample data `sample_data_5_3000.csv`. This file can be downloaded from our [GitHub sample data](https://github.com/Azure-Samples/AnomalyDetector/blob/master/sampledata/multivariate/)
 
  You can also download the sample data by running:
 
 ```cmd
 curl "https://github.com/Azure-Samples/AnomalyDetector/blob/master/sampledata/multivariate/sample_data_5_3000.csv" --output sample_data_5_3000_.csv
 ```
 
-```cmd
-curl "https://github.com/Azure-Samples/AnomalyDetector/blob/master/sampledata/multivariate/5_3000.json" --output 5_3000_.json
-```
-
 ### Upload sample data to Storage Account
 
 1. Go to your Storage Account, select Containers and create a new container.
@@ -116,252 +112,100 @@ echo export ANOMALY_DETECTOR_ENDPOINT="REPLACE_WITH_YOUR_ENDPOINT_HERE" >> /etc/
 
 1. Create a new Python file called **sample_multivariate_detect.py**. Then open it up in your preferred editor or IDE.
 
-2. Replace the contents of sample_multivariate_detect.py with the following code. You need to modify the paths for the variables `blob_url_path` and `local_json_file_path`.
+2. Replace the contents of sample_multivariate_detect.py with the following code. You need to modify the paths for the variables `blob_url`.
 
-```python
-import json
-import os
+```Python
 import time
 from datetime import datetime, timezone
-
 from azure.ai.anomalydetector import AnomalyDetectorClient
 from azure.core.credentials import AzureKeyCredential
-from azure.core.exceptions import HttpResponseError
 from azure.ai.anomalydetector.models import *
 
-blob_url_path = "Path-to-sample-file-in-your-storage-account"  # example path: https://docstest001.blob.core.windows.net/test/sample_data_5_3000.csv
-local_json_file_path = "Local-path-to-sample-json-file" # example where file is in same local directory as your Python script: "5_3000.json"
-
-
-class MultivariateSample:
-    def __init__(self, subscription_key, anomaly_detector_endpoint):
-        self.sub_key = subscription_key
-        self.end_point = anomaly_detector_endpoint
-
-        # Create an Anomaly Detector client
-
-        # <client>
-        self.ad_client = AnomalyDetectorClient(self.end_point, AzureKeyCredential(self.sub_key))
-        # </client>
-
-    def list_models(self):
-
-        # List models
-        models = self.ad_client.list_multivariate_models(skip=0, top=10)
-        return list(models)
-
-    def train(self, body):
-
-        # Number of models available now
-        try:
-            model_list = self.list_models()
-            print("{:d} available models before training.".format(len(model_list)))
-
-            # Use sample data to train the model
-            print("Training new model...(it may take a few minutes)")
-            model = self.ad_client.train_multivariate_model(body)
-            trained_model_id = model.model_id
-            print("Training model id is {}".format(trained_model_id))
-
-            ## Wait until the model is ready. It usually takes several minutes
-            model_status = None
-            model = None
-
-            while model_status != ModelStatus.READY and model_status != ModelStatus.FAILED:
-                model = self.ad_client.get_multivariate_model(trained_model_id)
-                print(model)
-                model_status = model.model_info.status
-                print("Model is {}".format(model_status))
-                time.sleep(30)
-
-            if model_status == ModelStatus.FAILED:
-                print("Creating model failed.")
-                print("Errors:")
-                if len(model.model_info.errors) > 0:
-                    print(
-                        "Error code: {}. Message: {}".format(
-                            model.model_info.errors[0].code,
-                            model.model_info.errors[0].message,
-                        )
-                    )
-                else:
-                    print("None")
-
-            if model_status == ModelStatus.READY:
-                # Model list after training
-                model_list = self.list_models()
-
-                print("Done.\n--------------------")
-                print("{:d} available models after training.".format(len(model_list)))
-
-                # Return the latest model id
-            return trained_model_id
-        except HttpResponseError as e:
-            print(
-                "Error code: {}".format(e.error.code),
-                "Error message: {}".format(e.error.message),
-            )
-        except Exception as e:
-            raise e
-
-        return None
-
-    def batch_detect(self, model_id, body):
-
-        # Detect anomaly in the same data source (but a different interval)
-        try:
-            result = self.ad_client.detect_multivariate_batch_anomaly(model_id, body)
-            result_id = result.result_id
-
-            # Get results (may need a few seconds)
-            r = self.ad_client.get_multivariate_batch_detection_result(result_id)
-            print("Get detection result...(it may take a few seconds)")
-
-            while r.summary.status != MultivariateBatchDetectionStatus.READY and r.summary.status != MultivariateBatchDetectionStatus.FAILED:
-                r = self.ad_client.get_multivariate_batch_detection_result(result_id)
-                print("Detection is {}".format(r.summary.status))
-                time.sleep(15)
-
-            if r.summary.status == MultivariateBatchDetectionStatus.FAILED:
-                print("Detection failed.")
-                print("Errors:")
-                if len(r.summary.errors) > 0:
-                    print("Error code: {}. Message: {}".format(r.summary.errors[0].code, r.summary.errors[0].message))
-                else:
-                    print("None")
-                return None
-
-            return r
-
-        except HttpResponseError as e:
-            print(
-                "Error code: {}".format(e.error.code),
-                "Error message: {}".format(e.error.message),
-            )
-        except Exception as e:
-            raise e
-
-        return None
-
-    def delete_model(self, model_id):
-
-        # Delete the model
-        self.ad_client.delete_multivariate_model(model_id)
-        model_list = self.list_models()
-        print("{:d} available models after deletion.".format(len(model_list)))
-
-    def last_detect(self, model_id, variables):
-
-        # Detect anomaly by sync api
-        r = self.ad_client.detect_multivariate_last_anomaly(model_id, variables)
-        print("Get last detection result")
-        return r
-
-
-if __name__ == "__main__":
-    SUBSCRIPTION_KEY = os.environ["ANOMALY_DETECTOR_API_KEY"]
-    ANOMALY_DETECTOR_ENDPOINT = os.environ["ANOMALY_DETECTOR_ENDPOINT"]
-
-    ## Create a new sample and client
-    sample = MultivariateSample(SUBSCRIPTION_KEY, ANOMALY_DETECTOR_ENDPOINT)
-
-    # Train a new model
-    time_format = "%Y-%m-%dT%H:%M:%SZ"
-    blob_url = blob_url_path 
-    train_body = ModelInfo(
-        data_source=blob_url,
-        start_time=datetime.strptime("2021-01-02T00:00:00Z", time_format),
-        end_time=datetime.strptime("2021-01-02T05:00:00Z", time_format),
-        data_schema="OneTable",
-        display_name="sample",
-        sliding_window=200,
-        align_policy=AlignPolicy(
-            align_mode=AlignMode.OUTER,
-            fill_n_a_method=FillNAMethod.LINEAR,
-            padding_value=0,
-        ),
-    )
-    model_id = sample.train(train_body)
-
-    # Batch Inference
-    batch_inference_body = MultivariateBatchDetectionOptions(
-        data_source=blob_url,
-        top_contributor_count=10,
-        start_time=datetime.strptime("2021-01-02T00:00:00Z", time_format),
-        end_time=datetime.strptime("2021-01-02T05:00:00Z", time_format),
-    )
-    result = sample.batch_detect(model_id, batch_inference_body)
-    assert result is not None
-
-    print("Result ID:\t", result.result_id)
-    print("Result status:\t", result.summary.status)
-    print("Result length:\t", len(result.results))
-
-    # See detailed inference result
-    for r in result.results:
-        print(
-            "timestamp: {}, is_anomaly: {:<5}, anomaly score: {:.4f}, severity: {:.4f}, contributor count: {:<4d}".format(
-                r.timestamp,
-                r.value.is_anomaly,
-                r.value.score,
-                r.value.severity,
-                len(r.value.interpretation) if r.value.is_anomaly else 0,
-            )
+SUBSCRIPTION_KEY =  os.environ['ANOMALY_DETECTOR_API_KEY']
+ANOMALY_DETECTOR_ENDPOINT = os.environ['ANOMALY_DETECTOR_ENDPOINT']
+
+ad_client = AnomalyDetectorClient(ANOMALY_DETECTOR_ENDPOINT, AzureKeyCredential(SUBSCRIPTION_KEY))
+
+time_format = "%Y-%m-%dT%H:%M:%SZ"
+blob_url = "Path-to-sample-file-in-your-storage-account"  # example path: https://docstest001.blob.core.windows.net/test/sample_data_5_3000.csv
+
+train_body = ModelInfo(
+    data_source=blob_url,
+    start_time=datetime.strptime("2021-01-02T00:00:00Z", time_format),
+    end_time=datetime.strptime("2021-01-02T05:00:00Z", time_format),
+    data_schema="OneTable",
+    display_name="sample",
+    sliding_window=200,
+    align_policy=AlignPolicy(
+        align_mode=AlignMode.OUTER,
+        fill_n_a_method=FillNAMethod.LINEAR,
+        padding_value=0,
+    ),
+)
+
+batch_inference_body = MultivariateBatchDetectionOptions(
+       data_source=blob_url,
+       top_contributor_count=10,
+       start_time=datetime.strptime("2021-01-02T00:00:00Z", time_format),
+       end_time=datetime.strptime("2021-01-02T05:00:00Z", time_format),
+   )
+
+
+print("Training new model...(it may take a few minutes)")
+model = ad_client.train_multivariate_model(train_body)
+model_id = model.model_id
+print("Training model id is {}".format(model_id))
+
+## Wait until the model is ready. It usually takes several minutes
+model_status = None
+model = None
+
+while model_status != ModelStatus.READY and model_status != ModelStatus.FAILED:
+    model = ad_client.get_multivariate_model(model_id)
+    print(model)
+    model_status = model.model_info.status
+    print("Model is {}".format(model_status))
+    time.sleep(30)
+if model_status == ModelStatus.READY:
+    print("Done.\n--------------------")
+    # Return the latest model id
+
+# Detect anomaly in the same data source (but a different interval)
+result = ad_client.detect_multivariate_batch_anomaly(model_id, batch_inference_body)
+result_id = result.result_id
+
+# Get results (may need a few seconds)
+r = ad_client.get_multivariate_batch_detection_result(result_id)
+print("Get detection result...(it may take a few seconds)")
+
+while r.summary.status != MultivariateBatchDetectionStatus.READY and r.summary.status != MultivariateBatchDetectionStatus.FAILED and r.summary.status !=MultivariateBatchDetectionStatus.CREATED:
+    anomaly_results = ad_client.get_multivariate_batch_detection_result(result_id)
+    print("Detection is {}".format(r.summary.status))
+    time.sleep(5)
+    
+   
+print("Result ID:\t", anomaly_results.result_id)
+print("Result status:\t", anomaly_results.summary.status)
+print("Result length:\t", len(anomaly_results.results))
+
+# See detailed inference result
+for r in anomaly_results.results:
+    print(
+        "timestamp: {}, is_anomaly: {:<5}, anomaly score: {:.4f}, severity: {:.4f}, contributor count: {:<4d}".format(
+            r.timestamp,
+            r.value.is_anomaly,
+            r.value.score,
+            r.value.severity,
+            len(r.value.interpretation) if r.value.is_anomaly else 0,
         )
-        if r.value.interpretation:
-            for contributor in r.value.interpretation:
-                print(
-                    "\tcontributor variable: {:<10}, contributor score: {:.4f}".format(
-                        contributor.variable, contributor.contribution_score
-                    )
+    )
+    if r.value.interpretation:
+        for contributor in r.value.interpretation:
+            print(
+                "\tcontributor variable: {:<10}, contributor score: {:.4f}".format(
+                    contributor.variable, contributor.contribution_score
                 )
-
-    # *******************************************************************************************************************
-    # 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, ...]
-    #    }
-    # ]
-
-    # Last detection
-    with open(local_json_file_path) as f:
-        variables_data = json.load(f)
-
-    variables = []
-    for item in variables_data["variables"]:
-        variables.append(
-            VariableValues(
-                variable=item["variable"],
-                timestamps=item["timestamps"],
-                values=item["values"],
             )
-        )
-
-    last_inference_body = MultivariateLastDetectionOptions(
-        variables=variables,
-        top_contributor_count=10,
-    )
-    last_detect_result = sample.last_detect(model_id, last_inference_body)
-
-    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))
-
-    # Delete model
-    sample.delete_model(model_id)
 ```
 
 ## Run the application
