3-Evaluation/solutions/evaluate_with_experiments.py [46:78]:
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    logger.debug("Reading test data.")
    y_test = df.iloc[:, 0].to_numpy()
    df.drop(df.columns[0], axis=1, inplace=True)
    X_test = xgboost.DMatrix(df.values)

    logger.info("Performing predictions against test data.")
    predictions_probs = model.predict(X_test)
    predictions = predictions_probs.round()

    logger.info("Creating classification evaluation report")
    acc = accuracy_score(y_test, predictions)
    auc = roc_auc_score(y_test, predictions_probs)

    # The metrics reported can change based on the model used, but it must be a specific name per (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-model-quality-metrics.html)
    report_dict = {
        "binary_classification_metrics": {
            "accuracy": {
                "value": acc,
                "standard_deviation": "NaN",
            },
            "auc": {"value": auc, "standard_deviation": "NaN"},
        },
    }

    logger.info("Classification report:\n{}".format(report_dict))

    evaluation_output_path = os.path.join(
        "/opt/ml/processing/evaluation", "evaluation.json"
    )
    logger.info("Saving classification report to {}".format(evaluation_output_path))

    with open(evaluation_output_path, "w") as f:
        f.write(json.dumps(report_dict))
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6-Pipelines/config/evaluate.py [43:75]:
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    logger.debug("Reading test data.")
    y_test = df.iloc[:, 0].to_numpy()
    df.drop(df.columns[0], axis=1, inplace=True)
    X_test = xgboost.DMatrix(df.values)

    logger.info("Performing predictions against test data.")
    predictions_probs = model.predict(X_test)
    predictions = predictions_probs.round()

    logger.info("Creating classification evaluation report")
    acc = accuracy_score(y_test, predictions)
    auc = roc_auc_score(y_test, predictions_probs)

    # The metrics reported can change based on the model used, but it must be a specific name per (https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor-model-quality-metrics.html)
    report_dict = {
        "binary_classification_metrics": {
            "accuracy": {
                "value": acc,
                "standard_deviation": "NaN",
            },
            "auc": {"value": auc, "standard_deviation": "NaN"},
        },
    }

    logger.info("Classification report:\n{}".format(report_dict))

    evaluation_output_path = os.path.join(
        "/opt/ml/processing/evaluation", "evaluation.json"
    )
    logger.info("Saving classification report to {}".format(evaluation_output_path))

    with open(evaluation_output_path, "w") as f:
        f.write(json.dumps(report_dict))
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