Add multivariate sample (Azure#14940)

Co-authored-by: David Liang <[email protected]>

sdk/anomalydetector/ai-anomaly-detector/samples/v3/javascript/sample_multivariate_detection.js

@@ -0,0 +1,107 @@
+// Copyright (c) Microsoft Corporation.
+// Licensed under the MIT License.
+
+/**
+ * Demonstrates how to train a model on multivariate data and use this model to detect anomalies.
+ */
+
+const { AnomalyDetectorClient } = require("@azure/ai-anomaly-detector");
+const { AzureKeyCredential } = require("@azure/core-auth");
+const fs = require("fs");
+
+
+// Load the .env file if it exists
+const dotenv = require("dotenv");
+dotenv.config();
+
+// You will need to set this environment variables in .env file or edit the following values
+const apiKey = process.env["API_KEY"] || "";
+const endpoint = process.env["ENDPOINT"] || "";
+const data_source = "<Your own data source>";
+
+
+
+function sleep (time) {
+    return new Promise((resolve) => setTimeout(resolve, time));
+  }
+
+ async function main() {
+
+    // create client
+    const client = new AnomalyDetectorClient(endpoint, new AzureKeyCredential(apiKey));
+
+    // Already available models
+    const model_list = await client.listMultivariateModel();
+    console.log("The latest 5 available models (if exist):");
+    for(var i = 0 ; i < 5 ; i++) {
+      let model_detail = (await model_list.next());
+      if (model_detail.done == true) break
+      console.log(model_detail.value);
+    }
+
+    // construct model request (notice that the start and end time are local time and may not align with your data source)
+    const Modelrequest = {
+      source: data_source,
+      startTime: new Date(2021,0,1,0,0,0),
+      endTime: new Date(2021,0,2,12,0,0),
+      slidingWindow:200
+    };    
+
+    // get train result
+    console.log("Training a new model...");
+    const train_response = await client.trainMultivariateModel(Modelrequest);
+    const model_id = train_response.location.split("/").pop();
+    console.log("New model ID: " + model_id);
+
+    // get model status
+    let model_response = await client.getMultivariateModel(model_id);
+    let model_status = model_response.modelInfo.status;
+
+    while (model_status != 'READY'){
+        await sleep(10000).then(() => {});
+        model_response = await client.getMultivariateModel(model_id);
+        model_status = model_response.modelInfo.status;
+    }
+
+    console.log("TRAINING FINISHED.");
+
+    // get result
+    console.log("Start detecting...");
+    const detect_request = {
+        source: data_source,
+        startTime: new Date(2021,0,2,12,0,0),
+        endTime: new Date(2021,0,3,0,0,0)
+      };
+    const result_header = await client.detectAnomaly(model_id, detect_request);
+    const result_id = result_header.location?.split("/").pop() ?? "";
+    let result = await client.getDetectionResult(result_id);
+    let result_status = result.summary.status;
+
+    while (result_status != 'READY'){
+        await sleep(2000).then(() => {});
+        result = await client.getDetectionResult(result_id);
+        result_status = result.summary.status;
+    }
+
+    console.log("Result status: " + result_status);
+    console.log("Result Id: " + result.resultId);
+
+    // export the model
+    const export_result = await client.exportModel(model_id);
+    const model_path = "model.zip"
+    const destination = fs.createWriteStream(model_path);
+    export_result.readableStreamBody?.pipe(destination);
+    console.log("New model has been exported to " + model_path + ".");
+
+    // delete model
+    let delete_result = client.deleteMultivariateModel(model_id);
+    if ((await delete_result)._response.status == "204")
+      console.log("New model has been deleted.");
+    else 
+      console.log("Failed to delete the new model.");
+  }
+
+ main().catch((err) => {
+   console.error("The sample encountered an error:", err);
+ });
+