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EntityType(
entity_type_name: str,
featurestore_id: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
)
Managed entityType resource for Vertex AI.
Inheritance
builtins.object > google.cloud.aiplatform.base.VertexAiResourceNoun > builtins.object > google.cloud.aiplatform.base.FutureManager > google.cloud.aiplatform.base.VertexAiResourceNounWithFutureManager > EntityTypeProperties
featurestore_name
Full qualified resource name of the managed featurestore in which this EntityType is.
Methods
EntityType
EntityType(
entity_type_name: str,
featurestore_id: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: 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',
)
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: Dict[str, Dict[str, Union[bool, int, Dict[str, str], str]]],
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
sync: bool = True,
)
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",
},
}
)
Name | Description |
feature_configs |
Dict[str, Dict[str, Union[bool, int, Dict[str, str], str]]]
Required. A user defined Dict containing configurations for feature creation. The feature_configs Dict[str, Dict] i.e. {feature_id: feature_config} contains configuration for each creating feature: .. rubric:: Example feature_configs = { "my_feature_id_1": feature_config_1, "my_feature_id_2": feature_config_2, "my_feature_id_3": feature_config_3, } Each feature_config requires "value_type", and optional "description", "labels": .. rubric:: Example feature_config_1 = { "value_type": "INT64", } feature_config_2 = { "value_type": "BOOL", "description": "my feature id 2 description" } feature_config_3 = { "value_type": "STRING", "labels": { "my key": "my value", } } |
request_metadata |
Sequence[Tuple[str, str]]
Optional. Strings which should be sent along with the request as metadata. |
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. |
create
create(
entity_type_id: str,
featurestore_name: str,
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
sync: bool = True,
create_request_timeout: Optional[float] = None,
)
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',
)
Name | Description |
entity_type_id |
str
Required. The ID to use for the EntityType, which will become the final component of the EntityType's resource name. This value may be up to 60 characters, and valid characters are |
featurestore_name |
str
Required. A fully-qualified featurestore resource name or a featurestore ID of an existing featurestore to create EntityType in. Example: "projects/123/locations/us-central1/featurestores/my_featurestore_id" or "my_featurestore_id" when project and location are initialized or passed. |
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 EntityType (System labels are excluded)." System reserved label keys are prefixed with "aiplatform.googleapis.com/" and are immutable. |
project |
str
Optional. Project to create EntityType in if |
location |
str
Optional. Location to create EntityType in if |
credentials |
auth_credentials.Credentials
Optional. Custom credentials to use to create EntityTypes. Overrides credentials set in aiplatform.init. |
request_metadata |
Sequence[Tuple[str, str]]
Optional. Strings which should be sent along with the request as metadata. |
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. |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
create_feature
create_feature(
feature_id: str,
value_type: str,
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
sync: bool = True,
create_request_timeout: Optional[float] = None,
)
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',
)
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 |
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. |
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. |
create_request_timeout |
float
Optional. The timeout for the create request in seconds. |
delete
delete(sync: bool = True, force: bool = False)
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.
Name | Description |
force |
bool
If set to true, any Features for this EntityType will also be deleted. (Otherwise, the request will only work if the EntityType has no Features.) |
sync |
bool
Whether to execute this deletion 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. |
Type | Description |
FailedPrecondition | If features are created in this EntityType and force = False. |
delete_features
delete_features(feature_ids: List[str], sync: bool = True)
Deletes feature resources in this EntityType given their feature IDs. WARNING: This deletion is permanent.
Name | Description |
feature_ids |
List[str]
Required. The list of feature IDs to be deleted. |
sync |
bool
Optional. Whether to execute this deletion 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. |
get_feature
get_feature(feature_id: str)
Retrieves an existing managed feature in this EntityType.
Name | Description |
feature_id |
str
Required. The managed feature resource ID in this EntityType. |
get_featurestore
get_featurestore()
Retrieves the managed featurestore in which this EntityType is.
ingest_from_bq
ingest_from_bq(
feature_ids: List[str],
feature_time: Union[str, datetime.datetime],
bq_source_uri: str,
feature_source_fields: Optional[Dict[str, str]] = None,
entity_id_field: Optional[str] = None,
disable_online_serving: Optional[bool] = None,
worker_count: Optional[int] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
sync: bool = True,
ingest_request_timeout: Optional[float] = None,
)
Ingest feature values from BigQuery.
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. - A single Feature timestamp for all entities being imported. The timestamp must not have higher than millisecond precision. |
bq_source_uri |
str
Required. BigQuery URI to the input table. .. rubric:: Example 'bq://project.dataset.table_name' |
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 |
disable_online_serving |
bool
Optional. If set, data will not be imported for online serving. This is typically used for backfilling, where Feature generation timestamps are not in the timestamp range needed for online serving. |
worker_count |
int
Optional. Specifies the number of workers that are used to write data to the Featurestore. Consider the online serving capacity that you require to achieve the desired import throughput without interfering with online serving. The value must be positive, and less than or equal to 100. If not set, defaults to using 1 worker. The low count ensures minimal impact on online serving performance. |
request_metadata |
Sequence[Tuple[str, str]]
Optional. Strings which should be sent along with the request as metadata. |
sync |
bool
Optional. Whether to execute this import 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. |
ingest_request_timeout |
float
Optional. The timeout for the ingest request in seconds. |
ingest_from_df
ingest_from_df(
feature_ids: List[str],
feature_time: Union[str, datetime.datetime],
df_source: pd.DataFrame,
feature_source_fields: Optional[Dict[str, str]] = None,
entity_id_field: Optional[str] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
ingest_request_timeout: Optional[float] = None,
)
Ingest feature values from DataFrame.
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 |
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 |
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. |
df_source |
pd.DataFrame
Required. Pandas DataFrame containing the source data for ingestion. |
ingest_from_gcs
ingest_from_gcs(
feature_ids: List[str],
feature_time: Union[str, datetime.datetime],
gcs_source_uris: Union[str, List[str]],
gcs_source_type: str,
feature_source_fields: Optional[Dict[str, str]] = None,
entity_id_field: Optional[str] = None,
disable_online_serving: Optional[bool] = None,
worker_count: Optional[int] = None,
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
sync: bool = True,
ingest_request_timeout: Optional[float] = None,
)
Ingest feature values from GCS.
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. - A single Feature timestamp for all entities being imported. The timestamp must not have higher than millisecond precision. |
gcs_source_uris |
Union[str, List[str]]
Required. Google Cloud Storage URI(-s) to the input file(s). May contain wildcards. For more information on wildcards, see https://cloud.google.com/storage/docs/gsutil/addlhelp/WildcardNames. .. rubric:: Example ["gs://my_bucket/my_file_1.csv", "gs://my_bucket/my_file_2.csv"] or "gs://my_bucket/my_file.avro" |
gcs_source_type |
str
Required. The type of the input file(s) provided by |
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 |
disable_online_serving |
bool
Optional. If set, data will not be imported for online serving. This is typically used for backfilling, where Feature generation timestamps are not in the timestamp range needed for online serving. |
worker_count |
int
Optional. Specifies the number of workers that are used to write data to the Featurestore. Consider the online serving capacity that you require to achieve the desired import throughput without interfering with online serving. The value must be positive, and less than or equal to 100. If not set, defaults to using 1 worker. The low count ensures minimal impact on online serving performance. |
request_metadata |
Sequence[Tuple[str, str]]
Optional. Strings which should be sent along with the request as metadata. |
sync |
bool
Optional. Whether to execute this import 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. |
ingest_request_timeout |
float
Optional. The timeout for the ingest request in seconds. |
Type | Description |
ValueErro | if gcs_source_type is not supported.: |
list
list(
featurestore_name: str,
filter: Optional[str] = None,
order_by: Optional[str] = None,
project: Optional[str] = None,
location: Optional[str] = None,
credentials: Optional[google.auth.credentials.Credentials] = None,
)
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'
)
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: - |
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: - |
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: Optional[str] = None, order_by: Optional[str] = None)
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()
Name | Description |
filter |
str
Optional. Lists the Features that match the filter expression. The following filters are supported: - |
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: - |
read
read(
entity_ids: Union[str, List[str]],
feature_ids: Union[str, List[str]] = "*",
request_metadata: Optional[Sequence[Tuple[str, str]]] = (),
read_request_timeout: Optional[float] = None,
)
Reads feature values for given feature IDs of given entity IDs in this EntityType.
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. |
Type | Description |
pd.DataFrame | entities' feature values in DataFrame |
update
update(
description: Optional[str] = None,
labels: Optional[Dict[str, str]] = None,
request_metadata: Sequence[Tuple[str, str]] = (),
update_request_timeout: Optional[float] = None,
)
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',
)
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. |