Obtenir une explication pour la classification tabulaire

Restez organisé à l'aide des collections Enregistrez et classez les contenus selon vos préférences.

Récupère une explication pour la classification tabulaire à l'aide de la méthode explain.

Exemple de code

Python

Pour savoir comment installer et utiliser la bibliothèque cliente pour Vertex AI, consultez Bibliothèques clientes Vertex AI. Pour en savoir plus, consultez la documentation de référence de l'API Vertex AI Python.

from typing import Dict

from google.cloud import aiplatform_v1beta1
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value

def explain_tabular_sample(
    project: str,
    endpoint_id: str,
    instance_dict: Dict,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
    # The AI Platform services require regional API endpoints.
    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_v1beta1.PredictionServiceClient(client_options=client_options)
    # The format of each instance should conform to the deployed model's prediction input schema.
    instance = json_format.ParseDict(instance_dict, Value())
    instances = [instance]
    # tabular models do not have additional parameters
    parameters_dict = {}
    parameters = json_format.ParseDict(parameters_dict, Value())
    endpoint = client.endpoint_path(
        project=project, location=location, endpoint=endpoint_id
    )
    response = client.explain(
        endpoint=endpoint, instances=instances, parameters=parameters
    )
    print("response")
    print(" deployed_model_id:", response.deployed_model_id)
    explanations = response.explanations
    for explanation in explanations:
        print(" explanation")
        # Feature attributions.
        attributions = explanation.attributions
        for attribution in attributions:
            print("  attribution")
            print("   baseline_output_value:", attribution.baseline_output_value)
            print("   instance_output_value:", attribution.instance_output_value)
            print("   output_display_name:", attribution.output_display_name)
            print("   approximation_error:", attribution.approximation_error)
            print("   output_name:", attribution.output_name)
            output_index = attribution.output_index
            for output_index in output_index:
                print("   output_index:", output_index)
    predictions = response.predictions
    for prediction in predictions:
        print(" prediction:", dict(prediction))

Étape suivante

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