AnomalyDetectorClientOperationsMixin Class

Inheritance

builtins.object

AnomalyDetectorClientOperationsMixin

Constructor

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.
last_detect_anomaly	Detect anomalies in the last a few points of the request body. Synchronized API for anomaly detection.
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.

async delete_multivariate_model(model_id: str, **kwargs: Any) -> None

Parameters

model_id

str

Required

Model identifier.

api_version

str

Anomaly Detector API version (for example, v1.0). The default value is "v1.1-preview.1". Note that overriding this default value may result in unsupported behavior.

cls

callable

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

Returns

None, or the result of cls(response)

Return type

None

Exceptions

HttpResponseError

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.

async detect_anomaly(model_id: str, body: azure.ai.anomalydetector.models._models_py3.DetectionRequest, **kwargs: Any) -> None

Parameters

model_id

str

Required

Model identifier.

body

DetectionRequest

Required

Detect anomaly request.

api_version

str

Anomaly Detector API version (for example, v1.0). The default value is "v1.1-preview.1". Note that overriding this default value may result in unsupported behavior.

cls

callable

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

Returns

None, or the result of cls(response)

Return type

None

Exceptions

HttpResponseError

detect_change_point

Detect change point for the entire series.

Evaluate change point score of every series point.

async detect_change_point(body: azure.ai.anomalydetector.models._models_py3.ChangePointDetectRequest, **kwargs: Any) -> azure.ai.anomalydetector.models._models_py3.ChangePointDetectResponse

Parameters

body

ChangePointDetectRequest

Required

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

api_version

str

Anomaly Detector API version (for example, v1.0). The default value is "v1.1-preview.1". Note that overriding this default value may result in unsupported behavior.

cls

callable

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

Returns

ChangePointDetectResponse, or the result of cls(response)

Return type

ChangePointDetectResponse

Exceptions

HttpResponseError

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.

async detect_entire_series(body: azure.ai.anomalydetector.models._models_py3.DetectRequest, **kwargs: Any) -> azure.ai.anomalydetector.models._models_py3.EntireDetectResponse

Parameters

body

DetectRequest

Required

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

api_version

str

Anomaly Detector API version (for example, v1.0). The default value is "v1.1-preview.1". Note that overriding this default value may result in unsupported behavior.

cls

callable

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

Returns

EntireDetectResponse, or the result of cls(response)

Return type

EntireDetectResponse

Exceptions

HttpResponseError

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.

async detect_last_point(body: azure.ai.anomalydetector.models._models_py3.DetectRequest, **kwargs: Any) -> azure.ai.anomalydetector.models._models_py3.LastDetectResponse

Parameters

body

DetectRequest

Required

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

api_version

str

Anomaly Detector API version (for example, v1.0). The default value is "v1.1-preview.1". Note that overriding this default value may result in unsupported behavior.

cls

callable

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

Returns

LastDetectResponse, or the result of cls(response)

Return type

LastDetectResponse

Exceptions

HttpResponseError

export_model

Export Multivariate Anomaly Detection Model as Zip file.

Export multivariate anomaly detection model based on modelId.

async export_model(model_id: str, **kwargs: Any) -> IO

Parameters

model_id

str

Required

Model identifier.

api_version

str

Anomaly Detector API version (for example, v1.0). The default value is "v1.1-preview.1". Note that overriding this default value may result in unsupported behavior.

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>

Exceptions

HttpResponseError

get_detection_result

Get Multivariate Anomaly Detection Result.

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

async get_detection_result(result_id: str, **kwargs: Any) -> azure.ai.anomalydetector.models._models_py3.DetectionResult

Parameters

result_id

str

Required

Result identifier.

api_version

str

Anomaly Detector API version (for example, v1.0). The default value is "v1.1-preview.1". Note that overriding this default value may result in unsupported behavior.

cls

callable

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

Returns

DetectionResult, or the result of cls(response)

Return type

DetectionResult

Exceptions

HttpResponseError

get_multivariate_model

Get Multivariate Model.

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

async get_multivariate_model(model_id: str, **kwargs: Any) -> azure.ai.anomalydetector.models._models_py3.Model

Parameters

model_id

str

Required

Model identifier.

api_version

str

Anomaly Detector API version (for example, v1.0). The default value is "v1.1-preview.1". Note that overriding this default value may result in unsupported behavior.

cls

callable

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

Returns

Model, or the result of cls(response)

Return type

Model

Exceptions

HttpResponseError

last_detect_anomaly

Detect anomalies in the last a few points of the request body.

Synchronized API for anomaly detection.

async last_detect_anomaly(model_id: str, body: azure.ai.anomalydetector.models._models_py3.LastDetectionRequest, **kwargs: Any) -> azure.ai.anomalydetector.models._models_py3.LastDetectionResult

Parameters

model_id

str

Required

Model identifier.

body

LastDetectionRequest

Required

Request for last detection.

api_version

str

Anomaly Detector API version (for example, v1.0). The default value is "v1.1-preview.1". Note that overriding this default value may result in unsupported behavior.

cls

callable

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

Returns

LastDetectionResult, or the result of cls(response)

Return type

LastDetectionResult

Exceptions

HttpResponseError

list_multivariate_model

List Multivariate Models.

List models of a subscription.

list_multivariate_model(skip: Optional[int] = 0, top: Optional[int] = 5, **kwargs: Any) -> AsyncIterable[azure.ai.anomalydetector.models._models_py3.ModelList]

Parameters

skip

int

Required

$skip indicates how many models will be skipped.

top

int

Required

$top indicates how many models will be fetched.

api_version

str

Anomaly Detector API version (for example, v1.0). The default value is "v1.1-preview.1". Note that overriding this default value may result in unsupported behavior.

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

AsyncItemPaged[ModelList]

Exceptions

HttpResponseError

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.

async train_multivariate_model(body: azure.ai.anomalydetector.models._models_py3.ModelInfo, **kwargs: Any) -> None

Parameters

body

ModelInfo

Required

Training request.

api_version

str

Anomaly Detector API version (for example, v1.0). The default value is "v1.1-preview.1". Note that overriding this default value may result in unsupported behavior.

cls

callable

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

Returns

None, or the result of cls(response)

Return type

None

Exceptions

HttpResponseError