AnomalyDetectorClient.TrainMultivariateModelAsync Method
Definition
Important
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Overloads
| TrainMultivariateModelAsync(ModelInfo, CancellationToken) |
Train a Multivariate Anomaly Detection Model. |
| TrainMultivariateModelAsync(RequestContent, RequestContext) |
[Protocol Method] Train a Multivariate Anomaly Detection Model
|
TrainMultivariateModelAsync(ModelInfo, CancellationToken)
- Source:
- AnomalyDetectorClient.cs
Train a Multivariate Anomaly Detection Model.
public virtual System.Threading.Tasks.Task<Azure.Response<Azure.AI.AnomalyDetector.AnomalyDetectionModel>> TrainMultivariateModelAsync (Azure.AI.AnomalyDetector.ModelInfo modelInfo, System.Threading.CancellationToken cancellationToken = default);abstract member TrainMultivariateModelAsync : Azure.AI.AnomalyDetector.ModelInfo * System.Threading.CancellationToken -> System.Threading.Tasks.Task<Azure.Response<Azure.AI.AnomalyDetector.AnomalyDetectionModel>>
override this.TrainMultivariateModelAsync : Azure.AI.AnomalyDetector.ModelInfo * System.Threading.CancellationToken -> System.Threading.Tasks.Task<Azure.Response<Azure.AI.AnomalyDetector.AnomalyDetectionModel>>Public Overridable Function TrainMultivariateModelAsync (modelInfo As ModelInfo, Optional cancellationToken As CancellationToken = Nothing) As Task(Of Response(Of AnomalyDetectionModel))Parameters
- modelInfo
- ModelInfo
Model information.
- cancellationToken
- CancellationToken
The cancellation token to use.
Returns
Exceptions
modelInfo is null.
Examples
This sample shows how to call TrainMultivariateModelAsync 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 = await client.TrainMultivariateModelAsync(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
TrainMultivariateModelAsync(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 TrainMultivariateModelAsync(ModelInfo, CancellationToken) convenience overload with strongly typed models first.
public virtual System.Threading.Tasks.Task<Azure.Response> TrainMultivariateModelAsync (Azure.Core.RequestContent content, Azure.RequestContext context = default);abstract member TrainMultivariateModelAsync : Azure.Core.RequestContent * Azure.RequestContext -> System.Threading.Tasks.Task<Azure.Response>
override this.TrainMultivariateModelAsync : Azure.Core.RequestContent * Azure.RequestContext -> System.Threading.Tasks.Task<Azure.Response>Public Overridable Function TrainMultivariateModelAsync (content As RequestContent, Optional context As RequestContext = Nothing) As Task(Of 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
The response returned from the service.
Exceptions
content is null.
Service returned a non-success status code.
Examples
This sample shows how to call TrainMultivariateModelAsync 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 = await client.TrainMultivariateModelAsync(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 TrainMultivariateModelAsync 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 = await client.TrainMultivariateModelAsync(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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