apache_beam.ml.gcp.recommendations_ai module

A connector for sending API requests to the GCP Recommendations AI API (https://cloud.google.com/recommendations).

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')

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’

expand(pcoll)

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')

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 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’

expand(pcoll)

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')

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’

expand(pcoll)

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')

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’

expand(pcoll)

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)

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.

expand(pcoll)