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Merge pull request #91859 from aahill/note-test-2
[CogSvcs][AnomalyDetector] Updating setup instructions
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‎articles/cognitive-services/Anomaly-Detector/quickstarts/detect-data-anomalies-csharp-sdk.md

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ms.service: cognitive-services
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ms.subservice: anomaly-detector
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ms.topic: quickstart
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ms.date: 08/01/2019
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ms.date: 10/15/2019
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ms.author: aahi
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---
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@@ -21,7 +21,7 @@ Use the Anomaly Detector client library for .NET to:
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* Detect anomalies throughout your time series dataset, as a batch request
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* Detect the anomaly status of the latest data point in your time series
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[Library reference documentation](https://docs.microsoft.com/dotnet/api/Microsoft.Azure.CognitiveServices.AnomalyDetector?view=azure-dotnet-preview) | [Library source code](https://github.com/Azure/azure-sdk-for-net/tree/master/sdk/cognitiveservices/AnomalyDetector) | [Package (NuGet)](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.AnomalyDetector/) | [Samples](https://github.com/Azure-Samples/anomalydetector)
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[Library reference documentation](https://docs.microsoft.com/dotnet/api/Microsoft.Azure.CognitiveServices.AnomalyDetector?view=azure-dotnet-preview) | [Library source code](https://github.com/Azure/azure-sdk-for-net/tree/master/sdk/cognitiveservices/AnomalyDetector) | [Package (NuGet)](https://www.nuget.org/packages/Microsoft.Azure.CognitiveServices.AnomalyDetector/) | [Code samples](https://github.com/Azure-Samples/anomalydetector)
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## Prerequisites
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[!INCLUDE [anomaly-detector-resource-creation](../../../../includes/cognitive-services-anomaly-detector-resource-cli.md)]
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After you get a key from your trial subscription or resource, [create an environment variable](../../cognitive-services-apis-create-account.md#configure-an-environment-variable-for-authentication) for the key, named `ANOMALY_DETECTOR_KEY`.
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### Create a new .NET Core application
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### Create a new C# application
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Create a new .NET Core application in your preferred editor or IDE.
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In a console window (such as cmd, PowerShell, or Bash), use the dotnet `new` command to create a new console app with the name `anomaly-detector-quickstart`. This command creates a simple "Hello World" C# project with a single source file: *Program.cs*.
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In a console window (such as cmd, PowerShell, or Bash), use the dotnet `new` command to create a new console app with the name `anomaly-detector-quickstart`. This command creates a simple "Hello World" project with a single C# source file: *Program.cs*.
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```console
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dotnet new console -n anomaly-detector-quickstart
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...
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```
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From the project directory, open the *program.cs* file in your preferred editor or IDE. Add the following using `directives`:
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[!code-csharp[using statements](~/samples-anomaly-detector/quickstarts/sdk/csharp-sdk-sample.cs?name=usingStatements)]
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In the application's `main()` method, create variables for your resource's Azure location, and your key as an environment variable. If you created the environment variable after application is launched, the editor, IDE, or shell running it will need to be closed and reloaded to access the variable.
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[!code-csharp[Main method](~/samples-anomaly-detector/quickstarts/sdk/csharp-sdk-sample.cs?name=mainMethod)]
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### Install the client library
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Within the application directory, install the Anomaly Detector client library for .NET with the following command:
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dotnet add package Microsoft.Azure.CognitiveServices.AnomalyDetector --version 0.8.0-preview
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```
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If you're using the Visual Studio IDE, the client library is available as a NuGet package.
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From the project directory, open the *program.cs* file and add the following using `directives`:
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[!code-csharp[using statements](~/samples-anomaly-detector/quickstarts/sdk/csharp-sdk-sample.cs?name=usingStatements)]
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In the application's `main()` method, create variables for your resource's Azure location, and your key as an environment variable. If you created the environment variable after application is launched, the editor, IDE, or shell running it will need to be closed and reloaded to access the variable.
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[!code-csharp[Main method](~/samples-anomaly-detector/quickstarts/sdk/csharp-sdk-sample.cs?name=mainMethod)]
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## Object model
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‎articles/cognitive-services/Anomaly-Detector/quickstarts/detect-data-anomalies-csharp.md

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ms.service: cognitive-services
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ms.subservice: anomaly-detector
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ms.topic: quickstart
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ms.date: 07/26/2019
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ms.date: 10/14/2019
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ms.author: aahi
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---
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@@ -37,23 +37,16 @@ Use this quickstart to start using the Anomaly Detector API's two detection mode
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- A JSON file containing time series data points. The example data for this quickstart can be found on [GitHub](https://github.com/Azure-Samples/anomalydetector/blob/master/example-data/request-data.json).
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[!INCLUDE [cognitive-services-anomaly-detector-data-requirements](../../../../includes/cognitive-services-anomaly-detector-data-requirements.md)]
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### Create an Anomaly Detector resource
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[!INCLUDE [cognitive-services-anomaly-detector-signup-requirements](../../../../includes/cognitive-services-anomaly-detector-signup-requirements.md)]
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[!INCLUDE [anomaly-detector-resource-creation](../../../../includes/cognitive-services-anomaly-detector-resource-cli.md)]
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## Create a new application
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1. In Visual Studio, create a new console solution and add the following packages.
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```csharp
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using System;
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using System.IO;
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using System.Net;
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using System.Net.Http;
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using System.Net.Http.Headers;
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using System.Text;
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using System.Threading.Tasks;
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```
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[!code-csharp[using statements](~/samples-anomaly-detector/quickstarts/csharp-detect-anomalies.cs?name=usingStatements)]
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2. Create variables for your subscription key and your endpoint. Below are the URIs you can use for anomaly detection. These will be appended to your service endpoint later to create the API request URLs.
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|Batch detection | `/anomalydetector/v1.0/timeseries/entire/detect` |
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|Detection on the latest data point | `/anomalydetector/v1.0/timeseries/last/detect` |
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```csharp
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// Replace the subscriptionKey string value with your valid subscription key.
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const string subscriptionKey = "[YOUR_SUBSCRIPTION_KEY]";
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// Replace the endpoint URL with the correct one for your subscription.
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// Your endpoint can be found in the Azure portal. For example: https://westus2.api.cognitive.microsoft.com
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const string endpoint = "[YOUR_ENDPOINT_URL]";
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// Replace the dataPath string with a path to the JSON formatted time series data.
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const string dataPath = "[PATH_TO_TIME_SERIES_DATA]";
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const string latestPointDetectionUrl = "/anomalydetector/v1.0/timeseries/last/detect";
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const string batchDetectionUrl = "/anomalydetector/v1.0/timeseries/entire/detect";
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```
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[!code-csharp[initial variables for endpoint, key and data file](~/samples-anomaly-detector/quickstarts/csharp-detect-anomalies.cs?name=vars)]
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## Create a function to send requests
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3. Send the request with `PostAsync()`, and then return the response.
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```csharp
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static async Task<string> Request(string apiAddress, string endpoint, string subscriptionKey, string requestData){
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using (HttpClient client = new HttpClient { BaseAddress = new Uri(apiAddress) }){
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System.Net.ServicePointManager.SecurityProtocol = SecurityProtocolType.Tls12 | SecurityProtocolType.Tls11 | SecurityProtocolType.Tls;
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client.DefaultRequestHeaders.Accept.Add(new MediaTypeWithQualityHeaderValue("application/json"));
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client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", subscriptionKey);
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var content = new StringContent(requestData, Encoding.UTF8, "application/json");
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var res = await client.PostAsync(endpoint, content);
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return await res.Content.ReadAsStringAsync();
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}
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}
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```
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[!code-csharp[Request method](~/samples-anomaly-detector/quickstarts/csharp-detect-anomalies.cs?name=requestMethod)]
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## Detect anomalies as a batch
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4. Otherwise, find the positions of anomalies in the data set. The response's `isAnomaly` field contains an array of boolean values, each of which indicates whether a data point is an anomaly. Convert this to a string array with the response object's `ToObject<bool[]>()` function. Iterate through the array, and print the index of any `true` values. These values correspond to the index of anomalous data points, if any were found.
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```csharp
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static void detectAnomaliesBatch(string requestData){
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System.Console.WriteLine("Detecting anomalies as a batch");
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var result = Request(
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endpoint,
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batchDetectionUrl,
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subscriptionKey,
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requestData).Result;
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dynamic jsonObj = Newtonsoft.Json.JsonConvert.DeserializeObject(result);
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System.Console.WriteLine(jsonObj);
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if (jsonObj["code"] != null){
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System.Console.WriteLine($"Detection failed. ErrorCode:{jsonObj["code"]}, ErrorMessage:{jsonObj["message"]}");
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}
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else{
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bool[] anomalies = jsonObj["isAnomaly"].ToObject<bool[]>();
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System.Console.WriteLine("\nAnomalies detected in the following data positions:");
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for (var i = 0; i < anomalies.Length; i++){
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if (anomalies[i])
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{
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System.Console.Write(i + ", ");
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}
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}
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}
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}
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```
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[!code-csharp[Detect anomalies batch](~/samples-anomaly-detector/quickstarts/csharp-detect-anomalies.cs?name=detectAnomaliesBatch)]
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## Detect the anomaly status of the latest data point
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1. Create a new function called `detectAnomaliesLatest()`. Construct the request and send it by calling the `Request()` function with your endpoint, subscription key, the URL for latest point anomaly detection, and the time series data.
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2. Deserialize the JSON object, and write it to the console.
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```csharp
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static void detectAnomaliesLatest(string requestData){
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System.Console.WriteLine("\n\nDetermining if latest data point is an anomaly");
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var result = Request(
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endpoint,
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latestPointDetectionUrl,
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subscriptionKey,
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requestData).Result;
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dynamic jsonObj = Newtonsoft.Json.JsonConvert.DeserializeObject(result);
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System.Console.WriteLine(jsonObj);
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}
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```
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[!code-csharp[Detect anomalies latest](~/samples-anomaly-detector/quickstarts/csharp-detect-anomalies.cs?name=detectAnomaliesLatest)]
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## Load your time series data and send the request
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1. In the main method of your application, load your JSON time series data with `File.ReadAllText()`.
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2. Call the anomaly detection functions created above. Use `System.Console.ReadKey()` to keep the console window open after running the application.
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```csharp
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static void Main(string[] args){
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var requestData = File.ReadAllText(dataPath);
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detectAnomaliesBatch(requestData);
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detectAnomaliesLatest(requestData);
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System.Console.ReadKey();
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}
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```
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[!code-csharp[Main method](~/samples-anomaly-detector/quickstarts/csharp-detect-anomalies.cs?name=main)]
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### Example response
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## Next steps
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> [!div class="nextstepaction"]
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> [REST API reference](https://westus2.dev.cognitive.microsoft.com/docs/services/AnomalyDetector/operations/post-timeseries-entire-detect)
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>[Streaming anomaly detection with Azure Databricks](../tutorials/anomaly-detection-streaming-databricks.md)
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* What is the [Anomaly Detector API?](../overview.md)
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* [Best practices](../concepts/anomaly-detection-best-practices.md) when using the Anomaly Detector API.
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* The source code for this sample can be found on [GitHub](https://github.com/Azure-Samples/AnomalyDetector/blob/master/quickstarts/sdk/csharp-sdk-sample.cs).

‎articles/cognitive-services/Anomaly-Detector/quickstarts/detect-data-anomalies-java.md

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- A JSON file containing time series data points. The example data for this quickstart can be found on [GitHub](https://github.com/Azure-Samples/anomalydetector/blob/master/example-data/request-data.json).
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[!INCLUDE [cognitive-services-anomaly-detector-data-requirements](../../../../includes/cognitive-services-anomaly-detector-data-requirements.md)]
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### Create an Anomaly Detector resource
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[!INCLUDE [cognitive-services-anomaly-detector-signup-requirements](../../../../includes/cognitive-services-anomaly-detector-signup-requirements.md)]
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[!INCLUDE [anomaly-detector-resource-creation](../../../../includes/cognitive-services-anomaly-detector-resource-cli.md)]
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## Create a new application
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1. Create a new Java project in your favorite IDE or editor, and import the following libraries.
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```java
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import org.apache.http.HttpEntity;
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import org.apache.http.client.methods.CloseableHttpResponse;
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import org.apache.http.client.methods.HttpPost;
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import org.apache.http.entity.StringEntity;
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import org.apache.http.impl.client.CloseableHttpClient;
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import org.apache.http.impl.client.HttpClients;
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import org.apache.http.util.EntityUtils;
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import org.json.JSONArray;
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import org.json.JSONObject;
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import java.io.IOException;
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import java.nio.file.Files;
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import java.nio.file.Paths;
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```
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1. Create a new Java project and import the following libraries.
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[!code-java[Import statements](~/samples-anomaly-detector/quickstarts/java-detect-anomalies.java?name=imports)]
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2. Create variables for your subscription key and your endpoint. Below are the URIs you can use for anomaly detection. These will be appended to your service endpoint later to create the API request URLs.
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|Batch detection | `/anomalydetector/v1.0/timeseries/entire/detect` |
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|Detection on the latest data point | `/anomalydetector/v1.0/timeseries/last/detect` |
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```java
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// Replace the subscriptionKey string value with your valid subscription key.
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static final String subscriptionKey = "[YOUR_SUBSCRIPTION_KEY]";
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//replace the endpoint URL with the correct one for your subscription. Your endpoint can be found in the Azure portal.
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//For example: https://westus2.api.cognitive.microsoft.com
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static final String endpoint = "[YOUR_ENDPOINT_URL]";
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// Replace the dataPath string with a path to the JSON formatted time series data.
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static final String dataPath = "[PATH_TO_TIME_SERIES_DATA]";
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static final String latestPointDetectionUrl = "/anomalydetector/v1.0/timeseries/last/detect";
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static final String batchDetectionUrl = "/anomalydetector/v1.0/timeseries/entire/detect";
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```
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3. Read in the JSON data file
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```java
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String requestData = new String(Files.readAllBytes(Paths.get(dataPath)), "utf-8");
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```
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[!code-java[Initial key and endpoint variables](~/samples-anomaly-detector/quickstarts/java-detect-anomalies.java?name=vars)]
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## Create a function to send requests
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6. Create an `HttpEntity` object to store the response content. Get the content with `getEntity()`. If the response isn't empty, return it.
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```java
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static String sendRequest(String apiAddress, String endpoint, String subscriptionKey, String requestData) {
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try (CloseableHttpClient client = HttpClients.createDefault()) {
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HttpPost request = new HttpPost(endpoint + apiAddress);
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// Request headers.
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request.setHeader("Content-Type", "application/json");
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request.setHeader("Ocp-Apim-Subscription-Key", subscriptionKey);
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request.setEntity(new StringEntity(requestData));
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try (CloseableHttpResponse response = client.execute(request)) {
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HttpEntity respEntity = response.getEntity();
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if (respEntity != null) {
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return EntityUtils.toString(respEntity, "utf-8");
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}
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} catch (Exception respEx) {
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respEx.printStackTrace();
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}
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} catch (IOException ex) {
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System.err.println("Exception on Anomaly Detector: " + ex.getMessage());
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ex.printStackTrace();
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}
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return null;
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}
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```
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[!code-java[API request method](~/samples-anomaly-detector/quickstarts/java-detect-anomalies.java?name=request)]
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## Detect anomalies as a batch
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3. Otherwise, find the positions of anomalies in the data set. The response's `isAnomaly` field contains a boolean value relating to whether a given data point is an anomaly. Get the JSON array, and iterate through it, printing the index of any `true` values. These values correspond to the index of anomalous data points, if any were found.
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```java
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static void detectAnomaliesBatch(String requestData) {
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System.out.println("Detecting anomalies as a batch");
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String result = sendRequest(batchDetectionUrl, endpoint, subscriptionKey, requestData);
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if (result != null) {
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System.out.println(result);
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JSONObject jsonObj = new JSONObject(result);
137-
if (jsonObj.has("code")) {
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System.out.println(String.format("Detection failed. ErrorCode:%s, ErrorMessage:%s", jsonObj.getString("code"), jsonObj.getString("message")));
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} else {
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JSONArray jsonArray = jsonObj.getJSONArray("isAnomaly");
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System.out.println("Anomalies found in the following data positions:");
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for (int i = 0; i < jsonArray.length(); ++i) {
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if (jsonArray.getBoolean(i))
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System.out.print(i + ", ");
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}
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System.out.println();
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}
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}
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}
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```
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[!code-java[Method for batch detection](~/samples-anomaly-detector/quickstarts/java-detect-anomalies.java?name=detectBatch)]
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## Detect the anomaly status of the latest data point
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* Create a method called `detectAnomaliesLatest()` to detect the anomaly status of the last data point in the data set. Call the `sendRequest()` method created above with your endpoint, url, subscription key, and json data. Get the result, and print it to the console.
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```java
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static void detectAnomaliesLatest(String requestData) {
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System.out.println("Determining if latest data point is an anomaly");
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String result = sendRequest(latestPointDetectionUrl, endpoint, subscriptionKey, requestData);
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System.out.println(result);
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}
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```
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[!code-java[Latest point detection method](~/samples-anomaly-detector/quickstarts/java-detect-anomalies.java?name=detectLatest)]
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## Load your time series data and send the request
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1. In the main method of your application, read in the JSON file containing the data that will be added to the requests.
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2. Call the two anomaly detection functions created above.
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```java
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public static void main(String[] args) throws Exception {
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String requestData = new String(Files.readAllBytes(Paths.get(dataPath)), "utf-8");
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detectAnomaliesBatch(requestData);
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detectAnomaliesLatest(requestData);
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}
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```
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[!code-java[Main method](~/samples-anomaly-detector/quickstarts/java-detect-anomalies.java?name=main)]
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### Example response
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## Next steps
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> [!div class="nextstepaction"]
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> [REST API reference](https://westus2.dev.cognitive.microsoft.com/docs/services/AnomalyDetector/operations/post-timeseries-entire-detect)
104+
>[Streaming anomaly detection with Azure Databricks](../tutorials/anomaly-detection-streaming-databricks.md)
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* What is the [Anomaly Detector API?](../overview.md)
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* [Best practices](../concepts/anomaly-detection-best-practices.md) when using the Anomaly Detector API.
108+
* The source code for this sample can be found on [GitHub](https://github.com/Azure-Samples/AnomalyDetector/blob/master/quickstarts/sdk/csharp-sdk-sample.cs).

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