apache_beam.ml.gcp.recommendations_ai module¶
A connector for sending API requests to the GCP Recommendations AI API (https://cloud.google.com/recommendations).
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class
apache_beam.ml.gcp.recommendations_ai.
CreateCatalogItem
(project: str = None, retry: google.api_core.retry.retry_unary.Retry = None, timeout: float = 120, metadata: Sequence[Tuple[str, str]] = (), catalog_name: str = 'default_catalog')[source]¶ Bases:
apache_beam.transforms.ptransform.PTransform
Creates catalogitem information. The
PTransform
returns a PCollectionTuple with a PCollections of successfully and failed created CatalogItems.Example usage:
pipeline | CreateCatalogItem( project='example-gcp-project', catalog_name='my-catalog')
Initializes a
CreateCatalogItem
transform.Parameters: - project (str) – Optional. GCP project name in which the catalog data will be imported.
- retry – Optional. Designation of what errors, if any, should be retried.
- timeout (float) – Optional. The amount of time, in seconds, to wait for the request to complete.
- metadata – Optional. Strings which should be sent along with the request as metadata.
- catalog_name (str) – Optional. Name of the catalog. Default: ‘default_catalog’
-
class
apache_beam.ml.gcp.recommendations_ai.
ImportCatalogItems
(max_batch_size: int = 5000, project: str = None, retry: google.api_core.retry.retry_unary.Retry = None, timeout: float = 120, metadata: Sequence[Tuple[str, str]] = (), catalog_name: str = 'default_catalog')[source]¶ Bases:
apache_beam.transforms.ptransform.PTransform
Imports catalogitems in bulk. The PTransform returns a PCollectionTuple with PCollections of successfully and failed imported CatalogItems.
Example usage:
pipeline | ImportCatalogItems( project='example-gcp-project', catalog_name='my-catalog')
Initializes a
ImportCatalogItems
transformParameters: - batch_size (int) – Required. Maximum number of catalogitems per request.
- project (str) – Optional. GCP project name in which the catalog data will be imported.
- retry – Optional. Designation of what errors, if any, should be retried.
- timeout (float) – Optional. The amount of time, in seconds, to wait for the request to complete.
- metadata – Optional. Strings which should be sent along with the request as metadata.
- catalog_name (str) – Optional. Name of the catalog. Default: ‘default_catalog’
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class
apache_beam.ml.gcp.recommendations_ai.
WriteUserEvent
(project: str = None, retry: google.api_core.retry.retry_unary.Retry = None, timeout: float = 120, metadata: Sequence[Tuple[str, str]] = (), catalog_name: str = 'default_catalog', event_store: str = 'default_event_store')[source]¶ Bases:
apache_beam.transforms.ptransform.PTransform
Write user event information. The PTransform returns a PCollectionTuple with PCollections of successfully and failed written UserEvents.
Example usage:
pipeline | WriteUserEvent( project='example-gcp-project', catalog_name='my-catalog', event_store='my_event_store')
Initializes a
WriteUserEvent
transform.Parameters: - project (str) – Optional. GCP project name in which the catalog data will be imported.
- retry – Optional. Designation of what errors, if any, should be retried.
- timeout (float) – Optional. The amount of time, in seconds, to wait for the request to complete.
- metadata – Optional. Strings which should be sent along with the request as metadata.
- catalog_name (str) – Optional. Name of the catalog. Default: ‘default_catalog’
- event_store (str) – Optional. Name of the event store. Default: ‘default_event_store’
-
class
apache_beam.ml.gcp.recommendations_ai.
ImportUserEvents
(max_batch_size: int = 5000, project: str = None, retry: google.api_core.retry.retry_unary.Retry = None, timeout: float = 120, metadata: Sequence[Tuple[str, str]] = (), catalog_name: str = 'default_catalog', event_store: str = 'default_event_store')[source]¶ Bases:
apache_beam.transforms.ptransform.PTransform
Imports userevents in bulk. The PTransform returns a PCollectionTuple with PCollections of successfully and failed imported UserEvents.
Example usage:
pipeline | ImportUserEvents( project='example-gcp-project', catalog_name='my-catalog', event_store='my_event_store')
Initializes a
WriteUserEvent
transform.Parameters: - batch_size (int) – Required. Maximum number of catalogitems per request.
- project (str) – Optional. GCP project name in which the catalog data will be imported.
- retry – Optional. Designation of what errors, if any, should be retried.
- timeout (float) – Optional. The amount of time, in seconds, to wait for the request to complete.
- metadata – Optional. Strings which should be sent along with the request as metadata.
- catalog_name (str) – Optional. Name of the catalog. Default: ‘default_catalog’
- event_store (str) – Optional. Name of the event store. Default: ‘default_event_store’
-
class
apache_beam.ml.gcp.recommendations_ai.
PredictUserEvent
(project: str = None, retry: google.api_core.retry.retry_unary.Retry = None, timeout: float = 120, metadata: Sequence[Tuple[str, str]] = (), catalog_name: str = 'default_catalog', event_store: str = 'default_event_store', placement_id: str = None)[source]¶ Bases:
apache_beam.transforms.ptransform.PTransform
Make a recommendation prediction. The PTransform returns a PCollection
Example usage:
pipeline | PredictUserEvent( project='example-gcp-project', catalog_name='my-catalog', event_store='my_event_store', placement_id='recently_viewed_default')
Initializes a
PredictUserEvent
transform.Parameters: - project (str) – Optional. GCP project name in which the catalog data will be imported.
- retry – Optional. Designation of what errors, if any, should be retried.
- timeout (float) – Optional. The amount of time, in seconds, to wait for the request to complete.
- metadata – Optional. Strings which should be sent along with the request as metadata.
- catalog_name (str) – Optional. Name of the catalog. Default: ‘default_catalog’
- event_store (str) – Optional. Name of the event store. Default: ‘default_event_store’
- placement_id (str) – Required. ID of the recommendation engine placement. This id is used to identify the set of models that will be used to make the prediction.