Feature Store APIs¶

Feature Group¶

class sagemaker.feature_store.feature_group.FeatureGroup(name=NOTHING, sagemaker_session=NOTHING, feature_definitions=NOTHING)¶

Bases: object

FeatureGroup definition.

This class instantiates a FeatureGroup object that comprises of a name for the FeatureGroup, session instance, and a list of feature definition objects i.e., FeatureDefinition.

Parameters

name (str) –
sagemaker_session (sagemaker.session.Session) –
feature_definitions (Sequence[sagemaker.feature_store.feature_definition.FeatureDefinition]) –

Return type

None

name¶

name of the FeatureGroup instance.

Type: str

sagemaker_session¶

session instance to perform boto calls. If None, a new Session will be created.

Type: Session

feature_definitions¶

list of FeatureDefinitions.

Type: Sequence[FeatureDefinition]

Method generated by attrs for class FeatureGroup.

create(s3_uri, record_identifier_name, event_time_feature_name, role_arn=None, online_store_kms_key_id=None, enable_online_store=False, offline_store_kms_key_id=None, disable_glue_table_creation=False, data_catalog_config=None, description=None, tags=None, table_format=None)¶

Create a SageMaker FeatureStore FeatureGroup.

Parameters

s3_uri (Union[str, bool]) – S3 URI of the offline store, set to False to disable offline store.
record_identifier_name (str) – name of the record identifier feature.
event_time_feature_name (str) – name of the event time feature.
role_arn (str) – ARN of the role used to call CreateFeatureGroup.
online_store_kms_key_id (str) – KMS key ARN for online store (default: None).
enable_online_store (bool) – whether to enable online store or not (default: False).
offline_store_kms_key_id (str) – KMS key ARN for offline store (default: None). If a KMS encryption key is not specified, SageMaker encrypts all data at rest using the default AWS KMS key. By defining your bucket-level key for SSE, you can reduce the cost of AWS KMS requests. For more information, see Bucket Key in the Amazon S3 User Guide.
disable_glue_table_creation (bool) – whether to turn off Glue table creation or not (default: False).
data_catalog_config (DataCatalogConfig) – configuration for Metadata store (default: None).
description (str) – description of the FeatureGroup (default: None).
tags (List[Dict[str, str]]) – list of tags for labeling a FeatureGroup (default: None).
table_format (TableFormatEnum) – format of the offline store table (default: None).

Returns

Response dict from service.

Return type

Dict[str, Any]

delete()¶: Delete a FeatureGroup.

describe(next_token=None)¶

Describe a FeatureGroup.

Parameters: next_token (str) – next_token to get next page of features.
Returns: Response dict from the service.
Return type: Dict[str, Any]

update(feature_additions)¶

Update a FeatureGroup and add new features from the given feature definitions.

Parameters: feature_additions (Sequence[Dict[str, str]) – list of feature definitions to be updated.
Returns: Response dict from service.
Return type: Dict[str, Any]

update_feature_metadata(feature_name, description=None, parameter_additions=None, parameter_removals=None)¶

Update a feature metadata and add/remove metadata.

Parameters

feature_name (str) – name of the feature to update.
description (str) – description of the feature to update.
parameter_additions (Sequence[Dict[str, str]) – list of feature parameter to be added.
parameter_removals (Sequence[str]) – list of feature parameter key to be removed.

Returns

Response dict from service.

Return type

Dict[str, Any]

describe_feature_metadata(feature_name)¶

Describe feature metadata by feature name.

Parameters: feature_name (str) – name of the feature.
Returns: Response dict from service.
Return type: Dict[str, Any]

list_tags()¶

List all tags for a feature group.

Returns: list of key, value pair of the tags.
Return type: Sequence[Dict[str, str]]

list_parameters_for_feature_metadata(feature_name)¶

List all parameters for a feature metadata.

Parameters: feature_name (str) – name of the feature.
Returns: list of key, value pair of the parameters.
Return type: Sequence[Dict[str, str]]

get_record(record_identifier_value_as_string, feature_names=None)¶

Get a single record in a FeatureGroup

Parameters

record_identifier_value_as_string (String) – a String representing the value of the record identifier.
feature_names (Sequence[String]) – a list of Strings representing feature names.

Return type

Sequence[Dict[str, str]]

put_record(record)¶

Put a single record in the FeatureGroup.

Parameters: record (Sequence[FeatureValue]) – a list contains feature values.

delete_record(record_identifier_value_as_string, event_time, deletion_mode=<DeletionModeEnum.SOFT_DELETE: 'SoftDelete'>)¶

Delete a single record from a FeatureGroup.

Parameters

record_identifier_value_as_string (String) – a String representing the value of the record identifier.
event_time (String) – a timestamp format String indicating when the deletion event occurred.
deletion_mode (DeletionModeEnum) – deletion mode for deleting record. (default: DetectionModeEnum.SOFT_DELETE)

ingest(data_frame, max_workers=1, max_processes=1, wait=True, timeout=None, profile_name=None)¶

Ingest the content of a pandas DataFrame to feature store.

max_worker the number of threads created to work on different partitions of the data_frame in parallel.

max_processes the number of processes will be created to work on different partitions of the data_frame in parallel, each with max_worker threads.

The ingest function attempts to ingest all records in the data frame. SageMaker Feature Store throws an exception if it fails to ingest any records.

If wait is True, Feature Store runs the ingest function synchronously. You receive an IngestionError if there are any records that can’t be ingested. If wait is False, Feature Store runs the ingest function asynchronously.

Instead of setting wait to True in the ingest function, you can invoke the wait function on the returned instance of IngestionManagerPandas to run the ingest function synchronously.

To access the rows that failed to ingest, set wait to False. The IngestionError.failed_rows object saves all of the rows that failed to ingest.

profile_name argument is an optional one. It will use the default credential if None is passed. This profile_name is used in the sagemaker_featurestore_runtime client only. See https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html for more about the default credential.

Parameters

data_frame (DataFrame) – data_frame to be ingested to feature store.
max_workers (int) – number of threads to be created.
max_processes (int) – number of processes to be created. Each process spawns max_worker number of threads.
wait (bool) – whether to wait for the ingestion to finish or not.
timeout (Union[int, float]) – concurrent.futures.TimeoutError will be raised if timeout is reached.
profile_name (str) – the profile credential should be used for PutRecord (default: None).

Returns

An instance of IngestionManagerPandas.

Return type

sagemaker.feature_store.feature_group.IngestionManagerPandas

athena_query()¶

Create an AthenaQuery instance.

Returns: An instance of AthenaQuery initialized with data catalog configurations.
Return type: sagemaker.feature_store.feature_group.AthenaQuery

as_hive_ddl(database='sagemaker_featurestore', table_name=None)¶

Generate Hive DDL commands to define or change structure of tables or databases in Hive.

Schema of the table is generated based on the feature definitions. Columns are named after feature name and data-type are inferred based on feature type. Integral feature type is mapped to INT data-type. Fractional feature type is mapped to FLOAT data-type. String feature type is mapped to STRING data-type.

Parameters

database (str) – name of the database. If not set “sagemaker_featurestore” will be used.
table_name (str) – name of the table. If not set the name of this feature group will be used.

Returns

Generated create table DDL string.

Return type

str

class sagemaker.feature_store.feature_group.AthenaQuery(catalog, database, table_name, sagemaker_session)¶

Bases: object

Class to manage querying of feature store data with AWS Athena.

This class instantiates a AthenaQuery object that is used to retrieve data from feature store via standard SQL queries.

Parameters

catalog (str) –
database (str) –
table_name (str) –
sagemaker_session (sagemaker.session.Session) –

Return type

None

catalog¶

name of the data catalog.

Type: str

database¶

name of the database.

Type: str

table_name¶

name of the table.

Type: str

sagemaker_session¶

instance of the Session class to perform boto calls.

Type: Session

Method generated by attrs for class AthenaQuery.

run(query_string, output_location, kms_key=None, workgroup=None)¶

Execute a SQL query given a query string, output location and kms key.

This method executes the SQL query using Athena and outputs the results to output_location and returns the execution id of the query.

Parameters

query_string (str) – SQL query string.
output_location (str) – S3 URI of the query result.
kms_key (str) – KMS key id. If set, will be used to encrypt the query result file.
workgroup (str) – The name of the workgroup in which the query is being started.

Returns

Execution id of the query.

Return type

str

wait()¶: Wait for the current query to finish.

get_query_execution()¶

Get execution status of the current query.

Returns: Response dict from Athena.
Return type: Dict[str, Any]

as_dataframe()¶

Download the result of the current query and load it into a DataFrame.

Returns: A pandas DataFrame contains the query result.
Return type: pandas.core.frame.DataFrame

class sagemaker.feature_store.feature_group.IngestionManagerPandas(feature_group_name, sagemaker_fs_runtime_client_config=None, sagemaker_session=None, max_workers=1, max_processes=1, profile_name=None, async_result=None, processing_pool=None, failed_indices=NOTHING)¶

Bases: object

Class to manage the multi-threaded data ingestion process.

This class will manage the data ingestion process which is multi-threaded.

Parameters

feature_group_name (str) –
sagemaker_fs_runtime_client_config (botocore.config.Config) –
sagemaker_session (sagemaker.session.Session) –
max_workers (int) –
max_processes (int) –
profile_name (str) –
async_result (multiprocessing.pool.ApplyResult) –
processing_pool (pathos.multiprocessing.ProcessPool) –
failed_indices (List[int]) –

Return type

None

feature_group_name¶

name of the Feature Group.

Type: str

sagemaker_fs_runtime_client_config¶

instance of the Config class for boto calls.

Type: Config

sagemaker_session¶

session instance to perform boto calls.

Type: Session

data_frame¶

pandas DataFrame to be ingested to the given feature group.

Type: DataFrame

max_workers¶

number of threads to create.

Type: int

max_processes¶

number of processes to create. Each process spawns max_workers threads.

Type: int

profile_name¶

the profile credential should be used for PutRecord (default: None).

Type: str

Method generated by attrs for class IngestionManagerPandas.

property failed_rows¶

Get rows that failed to ingest.

Returns: List of row indices that failed to be ingested.

wait(timeout=None)¶

Wait for the ingestion process to finish.

Parameters: timeout (Union[int, float]) – concurrent.futures.TimeoutError will be raised if timeout is reached.

run(data_frame, wait=True, timeout=None)¶

Start the ingestion process.

Parameters

data_frame (DataFrame) – source DataFrame to be ingested.
wait (bool) – whether to wait for the ingestion to finish or not.
timeout (Union[int, float]) – concurrent.futures.TimeoutError will be raised if timeout is reached.

Feature Definition¶

class sagemaker.feature_store.feature_definition.FeatureDefinition(feature_name, feature_type)¶

Bases: sagemaker.feature_store.inputs.Config

Feature definition.

This instantiates a Feature Definition object where FeatureDefinition is a subclass of Config.

Parameters

feature_name (str) –
feature_type (sagemaker.feature_store.feature_definition.FeatureTypeEnum) –

Return type

None

feature_name¶

The name of the feature

Type: str

feature_type¶

The type of the feature

Type: FeatureTypeEnum

Method generated by attrs for class FeatureDefinition.

to_dict()¶

Construct a dictionary based on each attribute.

Return type: Dict[str, Any]

class sagemaker.feature_store.feature_definition.FractionalFeatureDefinition(feature_name)¶

Bases: sagemaker.feature_store.feature_definition.FeatureDefinition

Fractional feature definition.

This class instantiates a FractionalFeatureDefinition object, a subclass of FeatureDefinition where the data type of the feature being defined is a Fractional.

feature_name¶

The name of the feature

Type: str

feature_type¶

A FeatureTypeEnum.FRACTIONAL type

Type: FeatureTypeEnum

Construct an instance of FractionalFeatureDefinition.

Parameters: feature_name (str) – the name of the feature.

class sagemaker.feature_store.feature_definition.IntegralFeatureDefinition(feature_name)¶

Bases: sagemaker.feature_store.feature_definition.FeatureDefinition

Fractional feature definition.

This class instantiates a IntegralFeatureDefinition object, a subclass of FeatureDefinition where the data type of the feature being defined is a Integral.

feature_name¶

the name of the feature.

Type: str

feature_type¶

a FeatureTypeEnum.INTEGRAL type.

Type: FeatureTypeEnum

Construct an instance of IntegralFeatureDefinition.

Parameters: feature_name (str) – the name of the feature.

class sagemaker.feature_store.feature_definition.StringFeatureDefinition(feature_name)¶

Bases: sagemaker.feature_store.feature_definition.FeatureDefinition

Fractional feature definition.

This class instantiates a StringFeatureDefinition object, a subclass of FeatureDefinition where the data type of the feature being defined is a String.

feature_name¶

the name of the feature.

Type: str

feature_type¶

a FeatureTypeEnum.STRING type.

Type: FeatureTypeEnum

Construct an instance of StringFeatureDefinition.

Parameters: feature_name (str) – the name of the feature.

class sagemaker.feature_store.feature_definition.FeatureTypeEnum(value)¶

Bases: enum.Enum

Enum of feature types.

The data type of a feature can be Fractional, Integral or String.

Inputs¶

class sagemaker.feature_store.inputs.Config¶

Bases: abc.ABC

Base config object for FeatureStore.

Configs must implement the to_dict method.

abstract to_dict()¶

Get the dictionary from attributes.

Returns: dict contains the attributes.
Return type: Dict[str, Any]

classmethod construct_dict(**kwargs)¶

Construct the dictionary based on the args.

Parameters: kwargs – args to be used to construct the dict.
Returns: dict represents the given kwargs.
Return type: Dict[str, Any]

class sagemaker.feature_store.inputs.DataCatalogConfig(table_name=NOTHING, catalog=NOTHING, database=NOTHING)¶

Bases: sagemaker.feature_store.inputs.Config

DataCatalogConfig for FeatureStore.

Parameters

table_name (str) –
catalog (str) –
database (str) –

Return type

None

table_name¶

name of the table.

Type: str

catalog¶

name of the catalog.

Type: str

database¶

name of the database.

Type: str

Method generated by attrs for class DataCatalogConfig.

to_dict()¶

Construct a dictionary based on the attributes provided.

Returns: dict represents the attributes.
Return type: Dict[str, Any]

class sagemaker.feature_store.inputs.OfflineStoreConfig(s3_storage_config, disable_glue_table_creation=False, data_catalog_config=None, table_format=None)¶

Bases: sagemaker.feature_store.inputs.Config

OfflineStoreConfig for FeatureStore.

Parameters

s3_storage_config (sagemaker.feature_store.inputs.S3StorageConfig) –
disable_glue_table_creation (bool) –
data_catalog_config (sagemaker.feature_store.inputs.DataCatalogConfig) –
table_format (sagemaker.feature_store.inputs.TableFormatEnum) –

Return type

None

s3_storage_config¶

configuration of S3 storage.

Type: S3StorageConfig

disable_glue_table_creation¶

whether to disable the Glue table creation.

Type: bool

data_catalog_config¶

configuration of the data catalog.

Type: DataCatalogConfig

table_format¶

format of the offline store table.

Type: TableFormatEnum

Method generated by attrs for class OfflineStoreConfig.

to_dict()¶

Construct a dictionary based on the attributes.

Returns: dict represents the attributes.
Return type: Dict[str, Any]

class sagemaker.feature_store.inputs.OnlineStoreConfig(enable_online_store=True, online_store_security_config=None)¶

Bases: sagemaker.feature_store.inputs.Config

OnlineStoreConfig for FeatureStore.

Parameters

enable_online_store (bool) –
online_store_security_config (sagemaker.feature_store.inputs.OnlineStoreSecurityConfig) –

Return type

None

enable_online_store¶

whether to enable the online store.

Type: bool

online_store_security_config¶

configuration of security setting.

Type: OnlineStoreSecurityConfig

Method generated by attrs for class OnlineStoreConfig.

to_dict()¶

Construct a dictionary based on the attributes.

Returns: dict represents the attributes.
Return type: Dict[str, Any]

class sagemaker.feature_store.inputs.OnlineStoreSecurityConfig(kms_key_id=NOTHING)¶

Bases: sagemaker.feature_store.inputs.Config

OnlineStoreSecurityConfig for FeatureStore.

Parameters: kms_key_id (str) –
Return type: None

kms_key_id¶

KMS key id.

Type: str

Method generated by attrs for class OnlineStoreSecurityConfig.

to_dict()¶

Construct a dictionary based on the attributes.

Return type: Dict[str, Any]

class sagemaker.feature_store.inputs.S3StorageConfig(s3_uri, kms_key_id=None)¶

Bases: sagemaker.feature_store.inputs.Config

S3StorageConfig for FeatureStore.

Parameters

s3_uri (str) –
kms_key_id (str) –

Return type

None

s3_uri¶

S3 URI.

Type: str

kms_key_id¶

KMS key id.

Type: str

Method generated by attrs for class S3StorageConfig.

to_dict()¶

Construct a dictionary based on the attributes provided.

Returns: dict represents the attributes.
Return type: Dict[str, Any]

class sagemaker.feature_store.inputs.FeatureValue(feature_name=None, value_as_string=None)¶

Bases: sagemaker.feature_store.inputs.Config

FeatureValue for FeatureStore.

Parameters

feature_name (str) –
value_as_string (str) –

Return type

None

feature_name¶

name of the Feature.

Type: str

value_as_string¶

value of the Feature in string form.

Type: str

Method generated by attrs for class FeatureValue.

to_dict()¶

Construct a dictionary based on the attributes provided.

Returns: dict represents the attributes.
Return type: Dict[str, Any]

class sagemaker.feature_store.inputs.TableFormatEnum(value)¶

Bases: enum.Enum

Enum of table formats.

The offline store table formats can be Glue or Iceberg.

class sagemaker.feature_store.inputs.ResourceEnum(value)¶

Bases: enum.Enum

Enum of resources.

The data type of resource can be FeatureGroup or FeatureMetadata.

class sagemaker.feature_store.inputs.SearchOperatorEnum(value)¶

Bases: enum.Enum

Enum of search operators.

The data type of search operator can be And or Or.

class sagemaker.feature_store.inputs.SortOrderEnum(value)¶

Bases: enum.Enum

Enum of sort orders.

The data type of sort order can be Ascending or Descending.

class sagemaker.feature_store.inputs.FilterOperatorEnum(value)¶

Bases: enum.Enum

Enum of filter operators.

The data type of filter operator can be Equals, NotEquals, GreaterThan, GreaterThanOrEqualTo, LessThan, LessThanOrEqualTo, Contains, Exists, NotExists, or In.

class sagemaker.feature_store.inputs.Filter(name, value, operator=None)¶

Bases: sagemaker.feature_store.inputs.Config

Filter for FeatureStore search.

Parameters

name (str) –
value (str) –
operator (sagemaker.feature_store.inputs.FilterOperatorEnum) –

Return type

None

name¶

A resource property name.

Type: str

value¶

A value used with Name and Operator to determine which resources satisfy the filter’s condition.

Type: str

operator¶

A Boolean binary operator that is used to evaluate the

Type: FilterOperatorEnum

filter. If specify ``Value`` without ``Operator``, Amazon SageMaker uses ``Equals``

(default: None).

Method generated by attrs for class Filter.

to_dict()¶

Construct a dictionary based on the attributes provided.

Returns: dict represents the attributes.
Return type: Dict[str, Any]

class sagemaker.feature_store.inputs.Identifier(feature_group_name, record_identifiers_value_as_string, feature_names=None)¶

Bases: sagemaker.feature_store.inputs.Config

Identifier of batch get record API.

Parameters

feature_group_name (str) –
record_identifiers_value_as_string (List[str]) –
feature_names (List[str]) –

Return type

None

feature_group_name¶

name of a feature group.

Type: str

record_identifiers_value_as_string¶

string value of record identifier.

Type: List[str]

feature_names¶

list of feature names (default: None).

Type: List[str]

Method generated by attrs for class Identifier.

to_dict()¶

Construct a dictionary based on the attributes provided.

Returns: dict represents the attributes.
Return type: Dict[str, Any]

class sagemaker.feature_store.inputs.FeatureParameter(key=None, value=None)¶

Bases: sagemaker.feature_store.inputs.Config

FeatureParameter for FeatureStore.

Parameters

key (str) –
value (str) –

Return type

None

key¶

key of the parameter.

Type: str

value¶

value of the parameter.

Type: str

Method generated by attrs for class FeatureParameter.

to_dict()¶

Construct a dictionary based on the attributes provided.

Returns: dict represents the attributes.
Return type: Dict[str, Any]

Dataset Builder¶

class sagemaker.feature_store.dataset_builder.DatasetBuilder(sagemaker_session, base, output_path, record_identifier_feature_name=None, event_time_identifier_feature_name=None, included_feature_names=None, kms_key_id=None, event_time_identifier_feature_type=None)¶

Bases: object

DatasetBuilder definition.

This class instantiates a DatasetBuilder object that comprises a base, a list of feature names, an output path and a KMS key ID.

Parameters

sagemaker_session (sagemaker.session.Session) –
base (Union[sagemaker.feature_store.feature_group.FeatureGroup, pandas.core.frame.DataFrame]) –
output_path (str) –
record_identifier_feature_name (str) –
event_time_identifier_feature_name (str) –
included_feature_names (List[str]) –
kms_key_id (str) –
event_time_identifier_feature_type (sagemaker.feature_store.feature_definition.FeatureTypeEnum) –

Return type

None

_sagemaker_session¶

Session instance to perform boto calls.

Type: Session

_base¶

A base which can be either a FeatureGroup or a pandas.DataFrame and will be used to merge other FeatureGroups and generate a Dataset.

Type: Union[FeatureGroup, DataFrame]

_output_path¶

An S3 URI which stores the output .csv file.

Type: str

_record_identifier_feature_name¶

A string representing the record identifier feature if base is a DataFrame (default: None).

Type: str

_event_time_identifier_feature_name¶

A string representing the event time identifier feature if base is a DataFrame (default: None).

Type: str

_included_feature_names¶

A list of strings representing features to be included in the output. If not set, all features will be included in the output. (default: None).

Type: List[str]

_kms_key_id¶

A KMS key id. If set, will be used to encrypt the result file (default: None).

Type: str

_point_in_time_accurate_join¶

A boolean representing if point-in-time join is applied to the resulting dataframe when calling “to_dataframe”. When set to True, users can retrieve data using “row-level time travel” according to the event times provided to the DatasetBuilder. This requires that the entity dataframe with event times is submitted as the base in the constructor (default: False).

Type: bool

_include_duplicated_records¶

A boolean representing whether the resulting dataframe when calling “to_dataframe” should include duplicated records (default: False).

Type: bool

_include_deleted_records¶

A boolean representing whether the resulting dataframe when calling “to_dataframe” should include deleted records (default: False).

Type: bool

_number_of_recent_records¶

An integer representing how many records will be returned for each record identifier (default: 1).

Type: int

_number_of_records¶

An integer representing the number of records that should be returned in the resulting dataframe when calling “to_dataframe” (default: None).

Type: int

_write_time_ending_timestamp¶

A datetime that represents the latest write time for a record to be included in the resulting dataset. Records with a newer write time will be omitted from the resulting dataset. (default: None).

Type: datetime.datetime

_event_time_starting_timestamp¶

A datetime that represents the earliest event time for a record to be included in the resulting dataset. Records with an older event time will be omitted from the resulting dataset. (default: None).

Type: datetime.datetime

_event_time_ending_timestamp¶

A datetime that represents the latest event time for a record to be included in the resulting dataset. Records with a newer event time will be omitted from the resulting dataset. (default: None).

Type: datetime.datetime

_feature_groups_to_be_merged¶

A list of FeatureGroupToBeMerged which will be joined to base (default: []).

Type: List[FeatureGroupToBeMerged]

_event_time_identifier_feature_type¶

A FeatureTypeEnum representing the type of event time identifier feature (default: None).

Type: FeatureTypeEnum

Method generated by attrs for class DatasetBuilder.

with_feature_group(feature_group, target_feature_name_in_base=None, included_feature_names=None, feature_name_in_target=None, join_comparator=JoinComparatorEnum(), join_type=JoinTypeEnum())¶

Join FeatureGroup with base.

Parameters

feature_group (FeatureGroup) – A target FeatureGroup which will be joined to base.
target_feature_name_in_base (str) – A string representing the feature name in base which will be used as a join key (default: None).
included_feature_names (List[str]) – A list of strings representing features to be included in the output (default: None).
feature_name_in_target (str) – A string representing the feature name in the target feature group that will be compared to the target feature in the base feature group. If None is provided, the record identifier feature will be used in the SQL join. (default: None).
join_comparator (JoinComparatorEnum) – A JoinComparatorEnum representing the comparator used when joining the target feature in the base feature group and the feature in the target feature group. (default: JoinComparatorEnum.EQUALS).
join_type (JoinTypeEnum) – A JoinTypeEnum representing the type of join between the base and target feature groups. (default: JoinTypeEnum.INNER_JOIN).
Returns – This DatasetBuilder object.

point_in_time_accurate_join()¶

Enable point-in-time accurate join.

Returns: This DatasetBuilder object.

include_duplicated_records()¶

Include duplicated records in dataset.

Returns: This DatasetBuilder object.

include_deleted_records()¶

Include deleted records in dataset.

Returns: This DatasetBuilder object.

with_number_of_recent_records_by_record_identifier(number_of_recent_records)¶

Set number_of_recent_records field with provided input.

Parameters: number_of_recent_records (int) – An int that how many recent records will be returned for each record identifier.
Returns: This DatasetBuilder object.

with_number_of_records_from_query_results(number_of_records)¶

Set number_of_records field with provided input.

Parameters: number_of_records (int) – An int that how many records will be returned.
Returns: This DatasetBuilder object.

as_of(timestamp)¶

Set write_time_ending_timestamp field with provided input.

Parameters: timestamp (datetime.datetime) – A datetime that all records’ write time in dataset will be before it.
Returns: This DatasetBuilder object.

with_event_time_range(starting_timestamp=None, ending_timestamp=None)¶

Set event_time_starting_timestamp and event_time_ending_timestamp with provided inputs.

Parameters

starting_timestamp (datetime.datetime) – A datetime that all records’ event time in dataset will be after it (default: None).
ending_timestamp (datetime.datetime) – A datetime that all records’ event time in dataset will be before it (default: None).

Returns

This DatasetBuilder object.

to_csv_file()¶

Get query string and result in .csv format file

Returns: The S3 path of the .csv file. The query string executed.
Return type: Tuple[str, str]

to_dataframe()¶

Get query string and result in pandas.Dataframe

Returns: The pandas.DataFrame object. The query string executed.
Return type: Tuple[pandas.core.frame.DataFrame, str]

Feature Store¶

class sagemaker.feature_store.feature_store.FeatureStore(sagemaker_session=<class 'sagemaker.session.Session'>)¶

Bases: object

FeatureStore definition.

This class instantiates a FeatureStore object that comprises a SageMaker session instance.

Parameters: sagemaker_session (sagemaker.session.Session) –
Return type: None

sagemaker_session¶

session instance to perform boto calls.

Type: Session

Method generated by attrs for class FeatureStore.

create_dataset(base, output_path, record_identifier_feature_name=None, event_time_identifier_feature_name=None, included_feature_names=None, kms_key_id=None)¶

Create a Dataset Builder for generating a Dataset.

Parameters

base (Union[FeatureGroup, DataFrame]) – A base which can be either a FeatureGroup or a pandas.DataFrame and will be used to merge other FeatureGroups and generate a Dataset.
output_path (str) – An S3 URI which stores the output .csv file.
record_identifier_feature_name (str) – A string representing the record identifier feature if base is a DataFrame (default: None).
event_time_identifier_feature_name (str) – A string representing the event time identifier feature if base is a DataFrame (default: None).
included_feature_names (List[str]) – A list of features to be included in the output (default: None).
kms_key_id (str) – An KMS key id. If set, will be used to encrypt the result file (default: None).

Raises

ValueError – Base is a Pandas DataFrame but no record identifier feature name nor event time identifier feature name is provided.

Return type

sagemaker.feature_store.dataset_builder.DatasetBuilder

list_feature_groups(name_contains=None, feature_group_status_equals=None, offline_store_status_equals=None, creation_time_after=None, creation_time_before=None, sort_order=None, sort_by=None, max_results=None, next_token=None)¶

List all FeatureGroups satisfying given filters.

Parameters

name_contains (str) – A string that partially matches one or more FeatureGroups’ names. Filters FeatureGroups by name.
feature_group_status_equals (str) – A FeatureGroup status. Filters FeatureGroups by FeatureGroup status.
offline_store_status_equals (str) – An OfflineStore status. Filters FeatureGroups by OfflineStore status.
creation_time_after (datetime.datetime) – Use this parameter to search for FeatureGroups created after a specific date and time.
creation_time_before (datetime.datetime) – Use this parameter to search for FeatureGroups created before a specific date and time.
sort_order (str) – The order in which FeatureGroups are listed.
sort_by (str) – The value on which the FeatureGroup list is sorted.
max_results (int) – The maximum number of results returned by ListFeatureGroups.
next_token (str) – A token to resume pagination of ListFeatureGroups results.

Returns

Response dict from service.

Return type

Dict[str, Any]

batch_get_record(identifiers)¶

Get record in batch from FeatureStore

Parameters: identifiers (Sequence[Identifier]) – A list of identifiers to uniquely identify records in FeatureStore.
Returns: Response dict from service.
Return type: Dict[str, Any]

search(resource, filters=None, operator=None, sort_by=None, sort_order=None, next_token=None, max_results=None)¶

Search for FeatureGroups or FeatureMetadata satisfying given filters.

Parameters

resource (ResourceEnum) – The name of the Amazon SageMaker resource to search for. Valid values are FeatureGroup or FeatureMetadata.
filters (Sequence[Filter]) – A list of filter objects (Default: None).
operator (SearchOperatorEnum) – A Boolean operator used to evaluate the filters. Valid values are And or Or. The default is And (Default: None).
sort_by (str) – The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.
sort_order (SortOrderEnum) – How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending.
next_token (str) – If more than MaxResults resources match the specified filters, the response includes a NextToken. The NextToken can be passed to the next SearchRequest to continue retrieving results (Default: None).
max_results (int) – The maximum number of results to return (Default: None).

Returns

Response dict from service.

Return type

Dict[str, Any]