AnomalyDetectorClient.TrainMultivariateModel Method

Reference

Definition

Namespace:: Azure.AI.AnomalyDetector

Assembly:: Azure.AI.AnomalyDetector.dll

Package:: Azure.AI.AnomalyDetector v3.0.0-preview.7

Important

Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.

Overloads

TrainMultivariateModel(ModelInfo, CancellationToken)

Train a Multivariate Anomaly Detection Model.

TrainMultivariateModel(RequestContent, RequestContext)

[Protocol Method] Train a Multivariate Anomaly Detection Model

This protocol method allows explicit creation of the request and processing of the response for advanced scenarios.
Please try the simpler TrainMultivariateModel(ModelInfo, CancellationToken) convenience overload with strongly typed models first.

TrainMultivariateModel(ModelInfo, CancellationToken)

Source:: AnomalyDetectorClient.cs

Train a Multivariate Anomaly Detection Model.

public virtual Azure.Response<Azure.AI.AnomalyDetector.AnomalyDetectionModel> TrainMultivariateModel (Azure.AI.AnomalyDetector.ModelInfo modelInfo, System.Threading.CancellationToken cancellationToken = default);

abstract member TrainMultivariateModel : Azure.AI.AnomalyDetector.ModelInfo * System.Threading.CancellationToken -> Azure.Response<Azure.AI.AnomalyDetector.AnomalyDetectionModel>
override this.TrainMultivariateModel : Azure.AI.AnomalyDetector.ModelInfo * System.Threading.CancellationToken -> Azure.Response<Azure.AI.AnomalyDetector.AnomalyDetectionModel>

Public Overridable Function TrainMultivariateModel (modelInfo As ModelInfo, Optional cancellationToken As CancellationToken = Nothing) As Response(Of AnomalyDetectionModel)

Parameters

modelInfo: ModelInfo

Model information.

cancellationToken: CancellationToken

The cancellation token to use.

Returns

Response<AnomalyDetectionModel>

Exceptions

ArgumentNullException

modelInfo is null.

Examples

This sample shows how to call TrainMultivariateModel with required parameters.

var credential = new AzureKeyCredential("<key>");
var endpoint = new Uri("<https://my-service.azure.com>");
var client = new AnomalyDetectorClient(endpoint, credential);

var modelInfo = new ModelInfo("<dataSource>", DateTimeOffset.UtcNow, DateTimeOffset.UtcNow)
{
    DataSchema = DataSchema.OneTable,
    DisplayName = "<DisplayName>",
    SlidingWindow = 1234,
    AlignPolicy = new AlignPolicy()
{
        AlignMode = AlignMode.Inner,
        FillNAMethod = FillNAMethod.Previous,
        PaddingValue = 3.14f,
    },
};
var result = client.TrainMultivariateModel(modelInfo);

Remarks

Create and train a multivariate anomaly detection model. The request must include a source parameter to indicate an Azure Blob Storage URI that's accessible to the service. There are two types of data input. The Blob Storage URI can point to an Azure Blob Storage folder that contains multiple CSV files, where each CSV file has two columns, time stamp and variable. Or the Blob Storage URI can point to a single blob that contains a CSV file that has all the variables and a time stamp column.

Applies to

TrainMultivariateModel(RequestContent, RequestContext)

Source:: AnomalyDetectorClient.cs

[Protocol Method] Train a Multivariate Anomaly Detection Model

This protocol method allows explicit creation of the request and processing of the response for advanced scenarios.
Please try the simpler TrainMultivariateModel(ModelInfo, CancellationToken) convenience overload with strongly typed models first.

public virtual Azure.Response TrainMultivariateModel (Azure.Core.RequestContent content, Azure.RequestContext context = default);

abstract member TrainMultivariateModel : Azure.Core.RequestContent * Azure.RequestContext -> Azure.Response
override this.TrainMultivariateModel : Azure.Core.RequestContent * Azure.RequestContext -> Azure.Response

Public Overridable Function TrainMultivariateModel (content As RequestContent, Optional context As RequestContext = Nothing) As Response

Parameters

content: RequestContent

The content to send as the body of the request.

context: RequestContext

The request context, which can override default behaviors of the client pipeline on a per-call basis.

Returns

Response

The response returned from the service.

Exceptions

ArgumentNullException

content is null.

RequestFailedException

Service returned a non-success status code.

Examples

This sample shows how to call TrainMultivariateModel with required request content, and how to parse the result.

var credential = new AzureKeyCredential("<key>");
var endpoint = new Uri("<https://my-service.azure.com>");
var client = new AnomalyDetectorClient(endpoint, credential);

var data = new {
    dataSource = "<dataSource>",
    startTime = "2022-05-10T14:57:31.2311892-04:00",
    endTime = "2022-05-10T14:57:31.2311892-04:00",
};

Response response = client.TrainMultivariateModel(RequestContent.Create(data));

JsonElement result = JsonDocument.Parse(response.ContentStream).RootElement;
Console.WriteLine(result.GetProperty("modelId").ToString());
Console.WriteLine(result.GetProperty("createdTime").ToString());
Console.WriteLine(result.GetProperty("lastUpdatedTime").ToString());

This sample shows how to call TrainMultivariateModel with all request content, and how to parse the result.

var credential = new AzureKeyCredential("<key>");
var endpoint = new Uri("<https://my-service.azure.com>");
var client = new AnomalyDetectorClient(endpoint, credential);

var data = new {
    dataSource = "<dataSource>",
    dataSchema = "OneTable",
    startTime = "2022-05-10T14:57:31.2311892-04:00",
    endTime = "2022-05-10T14:57:31.2311892-04:00",
    displayName = "<displayName>",
    slidingWindow = 1234,
    alignPolicy = new {
        alignMode = "Inner",
        fillNAMethod = "Previous",
        paddingValue = 123.45f,
    },
};

Response response = client.TrainMultivariateModel(RequestContent.Create(data), new RequestContext());

JsonElement result = JsonDocument.Parse(response.ContentStream).RootElement;
Console.WriteLine(result.GetProperty("modelId").ToString());
Console.WriteLine(result.GetProperty("createdTime").ToString());
Console.WriteLine(result.GetProperty("lastUpdatedTime").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("dataSource").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("dataSchema").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("startTime").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("endTime").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("displayName").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("slidingWindow").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("alignPolicy").GetProperty("alignMode").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("alignPolicy").GetProperty("fillNAMethod").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("alignPolicy").GetProperty("paddingValue").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("status").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("errors")[0].GetProperty("code").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("errors")[0].GetProperty("message").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("diagnosticsInfo").GetProperty("modelState").GetProperty("epochIds")[0].ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("diagnosticsInfo").GetProperty("modelState").GetProperty("trainLosses")[0].ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("diagnosticsInfo").GetProperty("modelState").GetProperty("validationLosses")[0].ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("diagnosticsInfo").GetProperty("modelState").GetProperty("latenciesInSeconds")[0].ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("diagnosticsInfo").GetProperty("variableStates")[0].GetProperty("variable").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("diagnosticsInfo").GetProperty("variableStates")[0].GetProperty("filledNARatio").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("diagnosticsInfo").GetProperty("variableStates")[0].GetProperty("effectiveCount").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("diagnosticsInfo").GetProperty("variableStates")[0].GetProperty("firstTimestamp").ToString());
Console.WriteLine(result.GetProperty("modelInfo").GetProperty("diagnosticsInfo").GetProperty("variableStates")[0].GetProperty("lastTimestamp").ToString());

Remarks

Create and train a multivariate anomaly detection model. The request must include a source parameter to indicate an externally accessible Azure blob storage URI.There are two types of data input: An URI pointed to an Azure blob storage folder which contains multiple CSV files, and each CSV file contains two columns, timestamp and variable. Another type of input is an URI pointed to a CSV file in Azure blob storage, which contains all the variables and a timestamp column.

Below is the JSON schema for the request and response payloads.

Request Body:

Schema for ModelInfo:

{
  dataSource: string, # Required.
  dataSchema: "OneTable" | "MultiTable", # Optional.
  startTime: string (date & time), # Required.
  endTime: string (date & time), # Required.
  displayName: string, # Optional.
  slidingWindow: number, # Optional.
  alignPolicy: {
    alignMode: "Inner" | "Outer", # Optional.
    fillNAMethod: "Previous" | "Subsequent" | "Linear" | "Zero" | "Fixed", # Optional.
    paddingValue: number, # Optional.
  }, # Optional.
  status: "CREATED" | "RUNNING" | "READY" | "FAILED", # Optional.
  errors: [
    {
      code: string, # Required.
      message: string, # Required.
    }
  ], # Optional.
  diagnosticsInfo: {
    modelState: {
      epochIds: [number], # Optional.
      trainLosses: [number], # Optional.
      validationLosses: [number], # Optional.
      latenciesInSeconds: [number], # Optional.
    }, # Optional.
    variableStates: [VariableState], # Optional.
  }, # Optional.
}

Response Body:

Schema for AnomalyDetectionModel:

{
  modelId: string, # Required.
  createdTime: string (date & time), # Required.
  lastUpdatedTime: string (date & time), # Required.
  modelInfo: {
    dataSource: string, # Required.
    dataSchema: "OneTable" | "MultiTable", # Optional.
    startTime: string (date & time), # Required.
    endTime: string (date & time), # Required.
    displayName: string, # Optional.
    slidingWindow: number, # Optional.
    alignPolicy: {
      alignMode: "Inner" | "Outer", # Optional.
      fillNAMethod: "Previous" | "Subsequent" | "Linear" | "Zero" | "Fixed", # Optional.
      paddingValue: number, # Optional.
    }, # Optional.
    status: "CREATED" | "RUNNING" | "READY" | "FAILED", # Optional.
    errors: [
      {
        code: string, # Required.
        message: string, # Required.
      }
    ], # Optional.
    diagnosticsInfo: {
      modelState: {
        epochIds: [number], # Optional.
        trainLosses: [number], # Optional.
        validationLosses: [number], # Optional.
        latenciesInSeconds: [number], # Optional.
      }, # Optional.
      variableStates: [VariableState], # Optional.
    }, # Optional.
  }, # Optional.
}

Applies to

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AnomalyDetectorClient.TrainMultivariateModel Method

Definition

Overloads

TrainMultivariateModel(ModelInfo, CancellationToken)

Parameters

Returns

Exceptions

Examples

Remarks

Applies to

TrainMultivariateModel(RequestContent, RequestContext)

Parameters

Returns

Exceptions

Examples

Remarks

Applies to

Additional resources