Class EntityType (1.71.1)

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EntityType(
    entity_type_name: str,
    featurestore_id: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
)

Public managed EntityType resource for Vertex AI.

Properties

create_time

Time this resource was created.

display_name

Display name of this resource.

encryption_spec

Customer-managed encryption key options for this Vertex AI resource.

If this is set, then all resources created by this Vertex AI resource will be encrypted with the provided encryption key.

featurestore_name

Full qualified resource name of the managed featurestore in which this EntityType is.

gca_resource

The underlying resource proto representation.

labels

User-defined labels containing metadata about this resource.

Read more about labels at https://goo.gl/xmQnxf

name

Name of this resource.

preview

Exposes features available in preview for this class.

resource_name

Full qualified resource name.

update_time

Time this resource was last updated.

Methods

EntityType

EntityType(
    entity_type_name: str,
    featurestore_id: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
)

Retrieves an existing managed entityType given an entityType resource name or an entity_type ID.

Example Usage:

my_entity_type = aiplatform.EntityType(
    entity_type_name='projects/123/locations/us-central1/featurestores/my_featurestore_id/                entityTypes/my_entity_type_id'
)
or
my_entity_type = aiplatform.EntityType(
    entity_type_name='my_entity_type_id',
    featurestore_id='my_featurestore_id',
)

Parameters
Name	Description
entity_type_name	str Required. A fully-qualified entityType resource name or an entity_type ID. Example: "projects/123/locations/us-central1/featurestores/my_featurestore_id/entityTypes/my_entity_type_id" or "my_entity_type_id" when project and location are initialized or passed, with featurestore_id passed.
featurestore_id	str Optional. Featurestore ID of an existing featurestore to retrieve entityType from, when entity_type_name is passed as entity_type ID.
project	str Optional. Project to retrieve entityType from. If not set, project set in aiplatform.init will be used.
location	str Optional. Location to retrieve entityType from. If not set, location set in aiplatform.init will be used.
credentials	auth_credentials.Credentials Optional. Custom credentials to use to retrieve this EntityType. Overrides credentials set in aiplatform.init.

batch_create_features

batch_create_features(
    feature_configs: typing.Dict[
        str, typing.Dict[str, typing.Union[bool, int, typing.Dict[str, str], str]]
    ],
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    sync: bool = True,
) -> google.cloud.aiplatform.featurestore._entity_type._EntityType

Batch creates Feature resources in this EntityType.

Example Usage:

my_entity_type = aiplatform.EntityType(
    entity_type_name='my_entity_type_id',
    featurestore_id='my_featurestore_id',
)
my_entity_type.batch_create_features(
    feature_configs={
        "my_feature_id1": {
                "value_type": "INT64",
            },
        "my_feature_id2": {
                "value_type": "BOOL",
            },
        "my_feature_id3": {
                "value_type": "STRING",
            },
    }
)

create

create(
    entity_type_id: str,
    featurestore_name: str,
    description: typing.Optional[str] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    sync: bool = True,
    create_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.featurestore._entity_type._EntityType

Creates an EntityType resource in a Featurestore.

Example Usage:

my_entity_type = aiplatform.EntityType.create(
    entity_type_id='my_entity_type_id',
    featurestore_name='projects/123/locations/us-central1/featurestores/my_featurestore_id'
)
or
my_entity_type = aiplatform.EntityType.create(
    entity_type_id='my_entity_type_id',
    featurestore_name='my_featurestore_id',
)

create_feature

create_feature(
    feature_id: str,
    value_type: str,
    description: typing.Optional[str] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    sync: bool = True,
    create_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.featurestore.feature.Feature

Creates a Feature resource in this EntityType.

Example Usage:

my_entity_type = aiplatform.EntityType(
    entity_type_name='my_entity_type_id',
    featurestore_id='my_featurestore_id',
)
my_feature = my_entity_type.create_feature(
    feature_id='my_feature_id',
    value_type='INT64',
)

Parameters
Name	Description
feature_id	str Required. The ID to use for the Feature, which will become the final component of the Feature's resource name, which is immutable. This value may be up to 60 characters, and valid characters are [a-z0-9_]. The first character cannot be a number. The value must be unique within an EntityType.
value_type	str Required. Immutable. Type of Feature value. One of BOOL, BOOL_ARRAY, DOUBLE, DOUBLE_ARRAY, INT64, INT64_ARRAY, STRING, STRING_ARRAY, BYTES.
description	str Optional. Description of the Feature.
labels	Dict[str, str] Optional. The labels with user-defined metadata to organize your Features. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
request_metadata	Sequence[Tuple[str, str]] Optional. Strings which should be sent along with the request as metadata.
create_request_timeout	float Optional. The timeout for the create request in seconds.
sync	bool Optional. Whether to execute this creation synchronously. If False, this method will be executed in concurrent Future and any downstream object will be immediately returned and synced when the Future has completed.

delete

delete(sync: bool = True, force: bool = False) -> None

Deletes this EntityType resource. If force is set to True, all features in this EntityType will be deleted prior to entityType deletion.

WARNING: This deletion is permanent.

Exceptions
Type	Description
FailedPrecondition	If features are created in this EntityType and force = False.

delete_features

delete_features(feature_ids: typing.List[str], sync: bool = True) -> None

Deletes feature resources in this EntityType given their feature IDs. WARNING: This deletion is permanent.

get_feature

get_feature(
    feature_id: str,
) -> google.cloud.aiplatform.featurestore.feature.Feature

Retrieves an existing managed feature in this EntityType.

Parameter
Name	Description
feature_id	str Required. The managed feature resource ID in this EntityType.

get_featurestore

get_featurestore() -> (
    google.cloud.aiplatform.featurestore.featurestore.Featurestore
)

Retrieves the managed featurestore in which this EntityType is.

ingest_from_bq

ingest_from_bq(
    feature_ids: typing.List[str],
    feature_time: typing.Union[str, datetime.datetime],
    bq_source_uri: str,
    feature_source_fields: typing.Optional[typing.Dict[str, str]] = None,
    entity_id_field: typing.Optional[str] = None,
    disable_online_serving: typing.Optional[bool] = None,
    worker_count: typing.Optional[int] = None,
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    sync: bool = True,
    ingest_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.featurestore._entity_type._EntityType

Ingest feature values from BigQuery.

ingest_from_df

ingest_from_df(
    feature_ids: typing.List[str],
    feature_time: typing.Union[str, datetime.datetime],
    df_source: pd.DataFrame,
    feature_source_fields: typing.Optional[typing.Dict[str, str]] = None,
    entity_id_field: typing.Optional[str] = None,
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    ingest_request_timeout: typing.Optional[float] = None,
) -> _EntityType

Ingest feature values from DataFrame.

Note: Calling this method will automatically create and delete a temporary bigquery dataset in the same GCP project, which will be used as the intermediary storage for ingesting feature values from dataframe to featurestore. The call will return upon ingestion completes, where the feature values will be ingested into the entity_type.

Parameters
Name	Description
feature_ids	List[str] Required. IDs of the Feature to import values of. The Features must exist in the target EntityType, or the request will fail.
feature_time	Union[str, datetime.datetime] Required. The feature_time can be one of: - The source column that holds the Feature timestamp for all Feature values in each entity. Note: The dtype of the source column should be datetime64. - A single Feature timestamp for all entities being imported. The timestamp must not have higher than millisecond precision. Example: feature_time = datetime.datetime(year=2022, month=1, day=1, hour=11, minute=59, second=59) or feature_time_str = datetime.datetime.now().isoformat(sep=" ", timespec="milliseconds") feature_time = datetime.datetime.strptime(feature_time_str, "%Y-%m-%d %H:%M:%S.%f")
df_source	pd.DataFrame Required. Pandas DataFrame containing the source data for ingestion.
feature_source_fields	Dict[str, str] Optional. User defined dictionary to map ID of the Feature for importing values of to the source column for getting the Feature values from. Specify the features whose ID and source column are not the same. If not provided, the source column need to be the same as the Feature ID. .. rubric:: Example feature_ids = ['my_feature_id_1', 'my_feature_id_2', 'my_feature_id_3'] feature_source_fields = { 'my_feature_id_1': 'my_feature_id_1_source_field', } Note: The source column of 'my_feature_id_1' is 'my_feature_id_1_source_field', The source column of 'my_feature_id_2' is the ID of the feature, same for 'my_feature_id_3'.
entity_id_field	str Optional. Source column that holds entity IDs. If not provided, entity IDs are extracted from the column named entity_id.
request_metadata	Sequence[Tuple[str, str]] Optional. Strings which should be sent along with the request as metadata.
ingest_request_timeout	float Optional. The timeout for the ingest request in seconds.

ingest_from_gcs

ingest_from_gcs(
    feature_ids: typing.List[str],
    feature_time: typing.Union[str, datetime.datetime],
    gcs_source_uris: typing.Union[str, typing.List[str]],
    gcs_source_type: str,
    feature_source_fields: typing.Optional[typing.Dict[str, str]] = None,
    entity_id_field: typing.Optional[str] = None,
    disable_online_serving: typing.Optional[bool] = None,
    worker_count: typing.Optional[int] = None,
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    sync: bool = True,
    ingest_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.featurestore._entity_type._EntityType

Ingest feature values from GCS.

Exceptions
Type	Description
ValueErro	if gcs_source_type is not supported.:

list

list(
    featurestore_name: str,
    filter: typing.Optional[str] = None,
    order_by: typing.Optional[str] = None,
    project: typing.Optional[str] = None,
    location: typing.Optional[str] = None,
    credentials: typing.Optional[google.auth.credentials.Credentials] = None,
) -> typing.List[google.cloud.aiplatform.featurestore._entity_type._EntityType]

Lists existing managed entityType resources in a featurestore, given a featurestore resource name or a featurestore ID.

Example Usage:

my_entityTypes = aiplatform.EntityType.list(
    featurestore_name='projects/123/locations/us-central1/featurestores/my_featurestore_id'
)
or
my_entityTypes = aiplatform.EntityType.list(
    featurestore_name='my_featurestore_id'
)

Parameters
Name	Description
featurestore_name	str Required. A fully-qualified featurestore resource name or a featurestore ID of an existing featurestore to list entityTypes in. Example: "projects/123/locations/us-central1/featurestores/my_featurestore_id" or "my_featurestore_id" when project and location are initialized or passed.
filter	str Optional. Lists the EntityTypes that match the filter expression. The following filters are supported: - create_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. - update_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. - labels: Supports key-value equality as well as key presence. Examples: - create_time > "2020-01-31T15:30:00.000000Z" OR update_time > "2020-01-31T15:30:00.000000Z" --> EntityTypes created or updated after 2020-01-31T15:30:00.000000Z. - labels.active = yes AND labels.env = prod --> EntityTypes having both (active: yes) and (env: prod) labels. - labels.env: * --> Any EntityType which has a label with 'env' as the key.
order_by	str Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: - entity_type_id - create_time - update_time
project	str Optional. Project to list entityTypes in. If not set, project set in aiplatform.init will be used.
location	str Optional. Location to list entityTypes in. If not set, location set in aiplatform.init will be used.
credentials	auth_credentials.Credentials Optional. Custom credentials to use to list entityTypes. Overrides credentials set in aiplatform.init.

list_features

list_features(
    filter: typing.Optional[str] = None, order_by: typing.Optional[str] = None
) -> typing.List[google.cloud.aiplatform.featurestore.feature.Feature]

Lists existing managed feature resources in this EntityType.

Example Usage:

my_entity_type = aiplatform.EntityType(
    entity_type_name='my_entity_type_id',
    featurestore_id='my_featurestore_id',
)
my_entityType.list_features()

Parameters
Name	Description
filter	str Optional. Lists the Features that match the filter expression. The following filters are supported: - value_type: Supports = and != comparisons. - create_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. - update_time: Supports =, !=, <, >, >=, and <= comparisons. Values must be in RFC 3339 format. - labels: Supports key-value equality as well as key presence. Examples: - value_type = DOUBLE --> Features whose type is DOUBLE. - create_time > "2020-01-31T15:30:00.000000Z" OR update_time > "2020-01-31T15:30:00.000000Z" --> EntityTypes created or updated after 2020-01-31T15:30:00.000000Z. - labels.active = yes AND labels.env = prod --> Features having both (active: yes) and (env: prod) labels. - labels.env: * --> Any Feature which has a label with 'env' as the key.
order_by	str Optional. A comma-separated list of fields to order by, sorted in ascending order. Use "desc" after a field name for descending. Supported fields: - feature_id - value_type - create_time - update_time

read

read(
    entity_ids: typing.Union[str, typing.List[str]],
    feature_ids: typing.Union[str, typing.List[str]] = "*",
    request_metadata: typing.Optional[typing.Sequence[typing.Tuple[str, str]]] = (),
    read_request_timeout: typing.Optional[float] = None,
) -> pd.DataFrame

Reads feature values for given feature IDs of given entity IDs in this EntityType.

Parameters
Name	Description
entity_ids	Union[str, List[str]] Required. ID for a specific entity, or a list of IDs of entities to read Feature values of. The maximum number of IDs is 100 if a list.
feature_ids	Union[str, List[str]] Required. ID for a specific feature, or a list of IDs of Features in the EntityType for reading feature values. Default to "*", where value of all features will be read.
request_metadata	Sequence[Tuple[str, str]] Optional. Strings which should be sent along with the request as metadata.
read_request_timeout	float Optional. The timeout for the read request in seconds.

Returns
Type	Description
pd.DataFrame	entities' feature values in DataFrame

to_dict

to_dict() -> typing.Dict[str, typing.Any]

Returns the resource proto as a dictionary.

update

update(
    description: typing.Optional[str] = None,
    labels: typing.Optional[typing.Dict[str, str]] = None,
    request_metadata: typing.Sequence[typing.Tuple[str, str]] = (),
    update_request_timeout: typing.Optional[float] = None,
) -> google.cloud.aiplatform.featurestore._entity_type._EntityType

Updates an existing managed entityType resource.

Example Usage:

my_entity_type = aiplatform.EntityType(
    entity_type_name='my_entity_type_id',
    featurestore_id='my_featurestore_id',
)
my_entity_type.update(
    description='update my description',
)

Parameters
Name	Description
description	str Optional. Description of the EntityType.
labels	Dict[str, str] Optional. The labels with user-defined metadata to organize your EntityTypes. Label keys and values can be no longer than 64 characters (Unicode codepoints), can only contain lowercase letters, numeric characters, underscores and dashes. International characters are allowed. See https://goo.gl/xmQnxf for more information on and examples of labels. No more than 64 user labels can be associated with one Feature (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable.
request_metadata	Sequence[Tuple[str, str]] Required. Strings which should be sent along with the request as metadata.
update_request_timeout	float Optional. The timeout for the update request in seconds.

wait

wait()

Helper method that blocks until all futures are complete.

write_feature_values

write_feature_values(
    instances: typing.Union[
        typing.List[
            google.cloud.aiplatform_v1.types.featurestore_online_service.WriteFeatureValuesPayload
        ],
        typing.Dict[
            str,
            typing.Dict[
                str,
                typing.Union[
                    int,
                    str,
                    float,
                    bool,
                    bytes,
                    typing.List[int],
                    typing.List[str],
                    typing.List[float],
                    typing.List[bool],
                ],
            ],
        ],
        pd.DataFrame,
    ],
    feature_time: typing.Union[str, datetime.datetime] = None,
) -> EntityType

Streaming ingestion. Write feature values directly to Feature Store.

my_entity_type = aiplatform.EntityType(
    entity_type_name="my_entity_type_id",
    featurestore_id="my_featurestore_id",
)

# writing feature values from a pandas DataFrame without feature timestamp column.
# In this case, current timestamp will be applied to all data.
my_dataframe = pd.DataFrame(
    data = [
        {"entity_id": "movie_01", "average_rating": 4.9}
    ],
    columns=["entity_id", "average_rating"],
)
my_dataframe = my_df.set_index("entity_id")

my_entity_type.write_feature_values(
    instances=my_df
)

# writing feature values from a pandas DataFrame with feature timestamp column
# Example of datetime creation.
feature_time = datetime.datetime(year=2022, month=1, day=1, hour=11, minute=59, second=59)
or
feature_time_str = datetime.datetime.now().isoformat(sep=" ", timespec="milliseconds")
feature_time = datetime.datetime.strptime(feature_time_str, "%Y-%m-%d %H:%M:%S.%f")

my_dataframe = pd.DataFrame(
    data = [
        {"entity_id": "movie_01", "average_rating": 4.9,
        "feature_timestamp": feature_time}
    ],
    columns=["entity_id", "average_rating", "feature_timestamp"],
)

my_dataframe = my_df.set_index("entity_id")
my_entity_type.write_feature_values(
    instances=my_df, feature_time="feature_timestamp"
)

# writing feature values with a timestamp. The timestamp will be applied to the entire Dataframe.
my_dataframe = pd.DataFrame(
    data = [
        {"entity_id": "movie_01", "average_rating": 4.9}
    ],
    columns=["entity_id", "average_rating"],
)
my_dataframe = my_df.set_index("entity_id")
my_entity_type.write_feature_values(
    instances=my_df, feature_time=feature_time
)

# writing feature values from a Python dict without timestamp column.
# In this case, current timestamp will be applied to all data.
my_data_dict = {
        "movie_02" : {"average_rating": 3.7}
}

my_entity_type.write_feature_values(
    instances=my_data_dict
)

# writing feature values from a Python dict with timestamp column
my_data_dict = {
        "movie_02" : {"average_rating": 3.7, "feature_timestamp": timestmap}}
}

my_entity_type.write_feature_values(
    instances=my_data_dict, feature_time="feature_timestamp"
)

# writing feature values from a Python dict and apply the same Feature_Timestamp
my_data_dict = {
        "movie_02" : {"average_rating": 3.7}
}

my_entity_type.write_feature_values(
    instances=my_data_dict, feature_time=feature_time
)

# writing feature values from a list of WriteFeatureValuesPayload objects
payloads = [
    gca_featurestore_online_service.WriteFeatureValuesPayload(
        entity_id="movie_03",
        feature_values={
                "average_rating": featurestore_online_service.FeatureValue(
                        string_value="test",
                        metadata=featurestore_online_service.FeatureValue.Metadata(
                                generate_time=timestmap
                        )
               }
        }
    )
]
# when instance is WriteFeatureValuesPayload,
# feature_time param of write_feature_values() is ignored.
my_entity_type.write_feature_values(
    instances=payloads
)

# reading back written feature values
my_entity_type.read(
    entity_ids=["movie_01", "movie_02", "movie_03"]
)