FeatureSetOperations Class

FeatureSetOperations.

You should not instantiate this class directly. Instead, you should create an MLClient instance that instantiates it for you and attaches it as an attribute.

Inheritance

azure.ai.ml._scope_dependent_operations._ScopeDependentOperations

FeatureSetOperations

Constructor

FeatureSetOperations(operation_scope: OperationScope, operation_config: OperationConfig, service_client: AzureMachineLearningServices, service_client_for_jobs: AzureMachineLearningWorkspaces, datastore_operations: DatastoreOperations, **kwargs: Dict)

Parameters

Name	Description
operation_scope Required
operation_config Required
service_client Required
service_client_for_jobs Required
datastore_operations Required

Methods

archive	Archive a FeatureSet asset.
begin_backfill	Backfill.
begin_create_or_update	Create or update FeatureSet
get	Get the specified FeatureSet asset.
get_feature	Get Feature
list	List the FeatureSet assets of the workspace.
list_features	List features
list_materialization_operations	List Materialization operation.
restore	Restore an archived FeatureSet asset.

archive

Archive a FeatureSet asset.

archive(name: str, version: str, **kwargs: Dict) -> None

Parameters

Name	Description
name Required	str Name of FeatureSet asset.
version Required	str Version of FeatureSet asset.

Returns

Type	Description
	None

begin_backfill

Backfill.

begin_backfill(*, name: str, version: str, feature_window_start_time: datetime | None = None, feature_window_end_time: datetime | None = None, display_name: str | None = None, description: str | None = None, tags: Dict[str, str] | None = None, compute_resource: MaterializationComputeResource | None = None, spark_configuration: Dict[str, str] | None = None, data_status: List[str | DataAvailabilityStatus] | None = None, job_id: str | None = None, **kwargs: Dict) -> LROPoller[FeatureSetBackfillMetadata]

Keyword-Only Parameters

Name	Description
name	str Feature set name. This is case-sensitive.
version	str Version identifier. This is case-sensitive.
feature_window_start_time	datetime Start time of the feature window to be materialized.
feature_window_end_time	datetime End time of the feature window to be materialized.
display_name	str Specifies description.
description	str Specifies description.
tags	dict[str, str] A set of tags. Specifies the tags.
compute_resource	MaterializationComputeResource Specifies the compute resource settings.
spark_configuration	dict[str, str] Specifies the spark compute settings.
data_status	list[str or DataAvailabilityStatus] Specifies the data status that you want to backfill.

Returns

Type	Description
LROPoller[FeatureSetBackfillMetadata]	An instance of LROPoller that returns ~azure.ai.ml.entities.FeatureSetBackfillMetadata

begin_create_or_update

Create or update FeatureSet

begin_create_or_update(featureset: FeatureSet, **kwargs: Dict) -> LROPoller[FeatureSet]

Parameters

Name	Description
featureset Required	FeatureSet FeatureSet definition.

Returns

Type	Description
LROPoller[FeatureSet]	An instance of LROPoller that returns a FeatureSet.

get

Get the specified FeatureSet asset.

get(name: str, version: str, **kwargs: Dict) -> FeatureSet

Parameters

Name	Description
name Required	str Name of FeatureSet asset.
version Required	str Version of FeatureSet asset.

Returns

Type	Description
FeatureSet	FeatureSet asset object.

Exceptions

Type	Description
ValidationException	Raised if FeatureSet cannot be successfully identified and retrieved. Details will be provided in the error message.
HttpResponseError	Raised if the corresponding name and version cannot be retrieved from the service.

get_feature

Get Feature

get_feature(feature_set_name: str, version: str, *, feature_name: str, **kwargs: Dict) -> Feature | None

Parameters

Name	Description
feature_set_name Required	str Feature set name.
version Required	str Feature set version.

Keyword-Only Parameters

Name	Description
feature_name	str This is case-sensitive.
tags	str String representation of a comma-separated list of tag names, and optionally, values. For example, "tag1,tag2=value2". If provided, only features matching the specified tags are returned.

Returns

Type	Description
Feature	Feature object

list

List the FeatureSet assets of the workspace.

list(name: str | None = None, *, list_view_type: ListViewType = ListViewType.ACTIVE_ONLY, **kwargs: Dict) -> ItemPaged[FeatureSet]

Parameters

Name	Description
name Required	Optional[str] Name of a specific FeatureSet asset, optional.

Keyword-Only Parameters

Name	Description
list_view_type	View type for including/excluding (for example) archived FeatureSet assets. Defaults to ACTIVE_ONLY.

Returns

Type	Description
ItemPaged[FeatureSet]	An iterator like instance of FeatureSet objects

list_features

List features

list_features(feature_set_name: str, version: str, *, feature_name: str | None = None, description: str | None = None, tags: str | None = None, **kwargs: Dict) -> ItemPaged[Feature]

Parameters

Name	Description
feature_set_name Required	str Feature set name.
version Required	str Feature set version.

Keyword-Only Parameters

Name	Description
feature_name	str feature name.
description	str Description of the featureset.
tags	str Comma-separated list of tag names (and optionally values). Example: tag1,tag2=value2.

Returns

Type	Description
ItemPaged[Feature]	An iterator like instance of Feature objects

list_materialization_operations

List Materialization operation.

list_materialization_operations(name: str, version: str, *, feature_window_start_time: str | datetime | None = None, feature_window_end_time: str | datetime | None = None, filters: str | None = None, **kwargs: Dict) -> ItemPaged[FeatureSetMaterializationMetadata]

Parameters

Name	Description
name Required	str Feature set name.
version Required	str Feature set version.

Keyword-Only Parameters

Name	Description
feature_window_start_time	Union[str, datetime] Start time of the feature window to filter materialization jobs.
feature_window_end_time	Union[str, datetime] End time of the feature window to filter materialization jobs.
filters	str Comma-separated list of tag names (and optionally values). Example: tag1,tag2=value2.

Returns

Type	Description
ItemPaged[FeatureSetMaterializationMetadata]	An iterator like instance of ~azure.ai.ml.entities.FeatureSetMaterializationMetadata objects

restore

Restore an archived FeatureSet asset.

restore(name: str, version: str, **kwargs: Dict) -> None

Parameters

Name	Description
name Required	str Name of FeatureSet asset.
version Required	str Version of FeatureSet asset.

Returns

Type	Description
	None