Afficher une évaluation de modèle

Montre comment afficher une évaluation de modèle.

Exemple de code

Python

Pour vous authentifier auprès d'AutoML Tables, configurez les Identifiants par défaut de l'application. Pour en savoir plus, consultez Configurer l'authentification pour un environnement de développement local.

# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# model_display_name = 'MODEL_DISPLAY_NAME_HERE'
# filter = 'filter expression here'

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

# List all the model evaluations in the model by applying filter.
response = client.list_model_evaluations(
    model_display_name=model_display_name, filter=filter
)

# Iterate through the results.
for evaluation in response:
    # There is evaluation for each class in a model and for overall model.
    # Get only the evaluation of overall model.
    if not evaluation.annotation_spec_id:
        model_evaluation_name = evaluation.name
        break

# Get a model evaluation.
model_evaluation = client.get_model_evaluation(
    model_evaluation_name=model_evaluation_name
)

classification_metrics = model_evaluation.classification_evaluation_metrics
if str(classification_metrics):
    confidence_metrics = classification_metrics.confidence_metrics_entry

    # Showing model score based on threshold of 0.5
    print("Model classification metrics (threshold at 0.5):")
    for confidence_metrics_entry in confidence_metrics:
        if confidence_metrics_entry.confidence_threshold == 0.5:
            print(
                "Model Precision: {}%".format(
                    round(confidence_metrics_entry.precision * 100, 2)
                )
            )
            print(
                "Model Recall: {}%".format(
                    round(confidence_metrics_entry.recall * 100, 2)
                )
            )
            print(
                "Model F1 score: {}%".format(
                    round(confidence_metrics_entry.f1_score * 100, 2)
                )
            )
    print(f"Model AUPRC: {classification_metrics.au_prc}")
    print(f"Model AUROC: {classification_metrics.au_roc}")
    print(f"Model log loss: {classification_metrics.log_loss}")

regression_metrics = model_evaluation.regression_evaluation_metrics
if str(regression_metrics):
    print("Model regression metrics:")
    print(f"Model RMSE: {regression_metrics.root_mean_squared_error}")
    print(f"Model MAE: {regression_metrics.mean_absolute_error}")
    print(
        "Model MAPE: {}".format(regression_metrics.mean_absolute_percentage_error)
    )
    print(f"Model R^2: {regression_metrics.r_squared}")

Étapes suivantes

Pour rechercher et filtrer des exemples de code pour d'autres produits Google Cloud, consultez l'exemple de navigateur Google Cloud.