batch_read_feature_values_sample.py

# Copyright 2021 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Batch read feature values from a featurestore, as determined by your read
# instances list file, to export data.
# See https://cloud.google.com/vertex-ai/docs/featurestore/setup before running
# the code snippet

# [START aiplatform_batch_read_feature_values_sample]
from google.cloud import aiplatform_v1beta1 as aiplatform


def batch_read_feature_values_sample(
    project: str,
    featurestore_id: str,
    input_csv_file: str,
    destination_table_uri: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 300,
):
    # The AI Platform services require regional API endpoints, which need to be
    # in the same region or multi-region overlap with the Feature Store location.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.FeaturestoreServiceClient(client_options=client_options)
    featurestore = (
        f"projects/{project}/locations/{location}/featurestores/{featurestore_id}"
    )
    csv_read_instances = aiplatform.CsvSource(
        gcs_source=aiplatform.GcsSource(uris=[input_csv_file])
    )
    destination = aiplatform.FeatureValueDestination(
        bigquery_destination=aiplatform.BigQueryDestination(
            # Output to BigQuery table created earlier
            output_uri=destination_table_uri
        )
    )

    users_feature_selector = aiplatform.FeatureSelector(
        id_matcher=aiplatform.IdMatcher(ids=["age", "gender", "liked_genres"])
    )
    users_entity_type_spec = aiplatform.BatchReadFeatureValuesRequest.EntityTypeSpec(
        # Read the 'age', 'gender' and 'liked_genres' features from the 'perm_users' entity
        entity_type_id="perm_users",
        feature_selector=users_feature_selector,
    )

    movies_feature_selector = aiplatform.FeatureSelector(
        id_matcher=aiplatform.IdMatcher(ids=["*"])
    )
    movies_entity_type_spec = aiplatform.BatchReadFeatureValuesRequest.EntityTypeSpec(
        # Read the all features from the 'perm_movies' entity
        entity_type_id="perm_movies",
        feature_selector=movies_feature_selector,
    )

    entity_type_specs = [users_entity_type_spec, movies_entity_type_spec]
    # Batch serving request from CSV
    batch_read_feature_values_request = aiplatform.BatchReadFeatureValuesRequest(
        featurestore=featurestore,
        csv_read_instances=csv_read_instances,
        destination=destination,
        entity_type_specs=entity_type_specs,
    )
    lro_response = client.batch_read_feature_values(
        request=batch_read_feature_values_request
    )
    print("Long running operation:", lro_response.operation.name)
    batch_read_feature_values_response = lro_response.result(timeout=timeout)
    print("batch_read_feature_values_response:", batch_read_feature_values_response)


# [END aiplatform_batch_read_feature_values_sample]