Visualizzazione della valutazione di un modello

Mostra la valutazione di un modello.

Esempio di codice

Python

Per eseguire l'autenticazione su AutoML Tables, configura le Credenziali predefinite dell'applicazione. Per maggiori informazioni, consulta Configurare l'autenticazione per un ambiente di sviluppo locale.

# 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}")

Passaggi successivi

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