AnomalyDetectorClientOperationsMixin Class

Inheritance
builtins.object
AnomalyDetectorClientOperationsMixin

Methods

delete_multivariate_model

Delete Multivariate Model.

Delete an existing multivariate model according to the modelId.

detect_anomaly

Detect Multivariate Anomaly.

Submit detection multivariate anomaly task with the trained model of modelId, the input schema should be the same with the training request. Thus request will be complete asynchronously and will return a resultId for querying the detection result.The request should be a source link to indicate an externally accessible Azure storage Uri (preferably a Shared Access Signature Uri). All time-series used in generate the model must be zipped into one single file. Each time-series will be as follows: the first column is timestamp and the second column is value.

detect_change_point

Detect change point for the entire series.

Evaluate change point score of every series point.

detect_entire_series

Detect anomalies for the entire series in batch.

This operation generates a model with an entire series, each point is detected with the same model. With this method, points before and after a certain point are used to determine whether it is an anomaly. The entire detection can give user an overall status of the time series.

detect_last_point

Detect anomaly status of the latest point in time series.

This operation generates a model using points before the latest one. With this method, only historical points are used to determine whether the target point is an anomaly. The latest point detecting operation matches the scenario of real-time monitoring of business metrics.

export_model

Export Multivariate Anomaly Detection Model as Zip file.

Export multivariate anomaly detection model based on modelId.

get_detection_result

Get Multivariate Anomaly Detection Result.

Get multivariate anomaly detection result based on resultId returned by the DetectAnomalyAsync api.

get_multivariate_model

Get Multivariate Model.

Get detailed information of multivariate model, including the training status and variables used in the model.

list_multivariate_model

List Multivariate Models.

List models of a subscription.

train_multivariate_model

Train a Multivariate Anomaly Detection Model.

Create and train a multivariate anomaly detection model. The request must include a source parameter to indicate an externally accessible Azure storage Uri (preferably a Shared Access Signature Uri). All time-series used in generate the model must be zipped into one single file. Each time-series will be in a single CSV file in which the first column is timestamp and the second column is value.

delete_multivariate_model

Delete Multivariate Model.

Delete an existing multivariate model according to the modelId.

delete_multivariate_model(model_id, **kwargs)

Parameters

model_id
str
Required

Model identifier.

cls
callable

A custom type or function that will be passed the direct response

Returns

None, or the result of cls(response)

Return type

detect_anomaly

Detect Multivariate Anomaly.

Submit detection multivariate anomaly task with the trained model of modelId, the input schema should be the same with the training request. Thus request will be complete asynchronously and will return a resultId for querying the detection result.The request should be a source link to indicate an externally accessible Azure storage Uri (preferably a Shared Access Signature Uri). All time-series used in generate the model must be zipped into one single file. Each time-series will be as follows: the first column is timestamp and the second column is value.

detect_anomaly(model_id, detection_request, **kwargs)

Parameters

model_id
str
Required

Model identifier.

detection_request
DetectionRequest
Required

Detect anomaly request.

cls
callable

A custom type or function that will be passed the direct response

Returns

None, or the result of cls(response)

Return type

detect_change_point

Detect change point for the entire series.

Evaluate change point score of every series point.

detect_change_point(body, **kwargs)

Parameters

body
ChangePointDetectRequest
Required

Time series points and granularity is needed. Advanced model parameters can also be set in the request if needed.

cls
callable

A custom type or function that will be passed the direct response

Returns

ChangePointDetectResponse, or the result of cls(response)

Return type

detect_entire_series

Detect anomalies for the entire series in batch.

This operation generates a model with an entire series, each point is detected with the same model. With this method, points before and after a certain point are used to determine whether it is an anomaly. The entire detection can give user an overall status of the time series.

detect_entire_series(body, **kwargs)

Parameters

body
DetectRequest
Required

Time series points and period if needed. Advanced model parameters can also be set in the request.

cls
callable

A custom type or function that will be passed the direct response

Returns

EntireDetectResponse, or the result of cls(response)

Return type

detect_last_point

Detect anomaly status of the latest point in time series.

This operation generates a model using points before the latest one. With this method, only historical points are used to determine whether the target point is an anomaly. The latest point detecting operation matches the scenario of real-time monitoring of business metrics.

detect_last_point(body, **kwargs)

Parameters

body
DetectRequest
Required

Time series points and period if needed. Advanced model parameters can also be set in the request.

cls
callable

A custom type or function that will be passed the direct response

Returns

LastDetectResponse, or the result of cls(response)

Return type

export_model

Export Multivariate Anomaly Detection Model as Zip file.

Export multivariate anomaly detection model based on modelId.

export_model(model_id, **kwargs)

Parameters

model_id
str
Required

Model identifier.

cls
callable

A custom type or function that will be passed the direct response

Returns

IO, or the result of cls(response)

Return type

<xref:IO>

get_detection_result

Get Multivariate Anomaly Detection Result.

Get multivariate anomaly detection result based on resultId returned by the DetectAnomalyAsync api.

get_detection_result(result_id, **kwargs)

Parameters

result_id
str
Required

Result identifier.

cls
callable

A custom type or function that will be passed the direct response

Returns

DetectionResult, or the result of cls(response)

Return type

get_multivariate_model

Get Multivariate Model.

Get detailed information of multivariate model, including the training status and variables used in the model.

get_multivariate_model(model_id, **kwargs)

Parameters

model_id
str
Required

Model identifier.

cls
callable

A custom type or function that will be passed the direct response

Returns

Model, or the result of cls(response)

Return type

list_multivariate_model

List Multivariate Models.

List models of a subscription.

list_multivariate_model(skip=0, top=5, **kwargs)

Parameters

skip
int
default value: 0

$skip indicates how many models will be skipped.

top
int
default value: 5

$top indicates how many models will be fetched.

cls
callable

A custom type or function that will be passed the direct response

Returns

An iterator like instance of either ModelList or the result of cls(response)

Return type

train_multivariate_model

Train a Multivariate Anomaly Detection Model.

Create and train a multivariate anomaly detection model. The request must include a source parameter to indicate an externally accessible Azure storage Uri (preferably a Shared Access Signature Uri). All time-series used in generate the model must be zipped into one single file. Each time-series will be in a single CSV file in which the first column is timestamp and the second column is value.

train_multivariate_model(model_request, **kwargs)

Parameters

model_request
ModelInfo
Required

Training request.

cls
callable

A custom type or function that will be passed the direct response

Returns

None, or the result of cls(response)

Return type