def compute(feature_view_version: Optional[int] = None) -> None:
    """Computes the metrics on the latest n_days of predictions.

    Args:
        feature_view_version: The version of the feature view to load data from the feature store. If None is provided, it will try to load it from the cached feature_view_metadata.json file.
    """

    if feature_view_version is None:
        feature_view_metadata = utils.load_json("feature_view_metadata.json")
        feature_view_version = feature_view_metadata["feature_view_version"]

    logger.info("Loading old predictions...")
    bucket = utils.get_bucket()
    predictions = utils.read_blob_from(
        bucket=bucket, blob_name=f"predictions_monitoring.parquet"
    )
    if predictions is None or len(predictions) == 0:
        logger.info(
            "Haven't found any predictions to compute the metrics on. Exiting..."
        )

        return
    predictions.index = predictions.index.set_levels(
        pd.to_datetime(predictions.index.levels[2], unit="h").to_period("H"), level=2
    )
    logger.info("Successfully loaded old predictions.")

    logger.info("Connecting to the feature store...")
    project = hopsworks.login(
        api_key_value=settings.SETTINGS["FS_API_KEY"],
        project=settings.SETTINGS["FS_PROJECT_NAME"],
    )
    fs = project.get_feature_store()
    logger.info("Successfully connected to the feature store.")

    logger.info("Loading latest data from feature store...")
    predictions_min_datetime_utc = (
        predictions.index.get_level_values("datetime_utc").min().to_timestamp()
    )
    predictions_max_datetime_utc = (
        predictions.index.get_level_values("datetime_utc").max().to_timestamp()
    )
    logger.info(
        f"Loading predictions from {predictions_min_datetime_utc} to {predictions_max_datetime_utc}."
    )
    _, latest_observations = data.load_data_from_feature_store(
        fs,
        feature_view_version,
        start_datetime=predictions_min_datetime_utc,
        end_datetime=predictions_max_datetime_utc,
    )
    logger.info("Successfully loaded latest data from feature store.")

    if len(latest_observations) == 0:
        logger.info(
            "Haven't found any new ground truths to compute the metrics on. Exiting..."
        )

        return

    logger.info("Computing metrics...")
    predictions = predictions.rename(
        columns={"energy_consumption": "energy_consumption_predictions"}
    )
    latest_observations = latest_observations.rename(
        columns={"energy_consumption": "energy_consumption_observations"}
    )

    predictions["energy_consumption_observations"] = np.nan
    predictions.update(latest_observations)

    # Compute metrics only on data points that have ground truth.
    predictions = predictions.dropna(subset=["energy_consumption_observations"])
    if len(predictions) == 0:
        logger.info(
            "Haven't found any new ground truths to compute the metrics on. Exiting..."
        )

        return

    mape_metrics = predictions.groupby("datetime_utc").apply(
        lambda point_in_time: mean_absolute_percentage_error(
            point_in_time["energy_consumption_observations"],
            point_in_time["energy_consumption_predictions"],
            symmetric=False,
        )
    )
    mape_metrics = mape_metrics.rename("MAPE")
    metrics = mape_metrics.to_frame()
    logger.info("Successfully computed metrics...")

    logger.info("Saving new metrics...")
    utils.write_blob_to(
        bucket=bucket,
        blob_name=f"metrics_monitoring.parquet",
        data=metrics,
    )
    latest_observations = latest_observations.rename(
        columns={"energy_consumption_observations": "energy_consumption"}
    )
    utils.write_blob_to(
        bucket=bucket,
        blob_name=f"y_monitoring.parquet",
        data=latest_observations[["energy_consumption"]],
    )
    logger.info("Successfully saved new metrics.")
Computes the metrics on the latest n_days of predictions.

    Args:
        feature_view_version: The version of the feature view to load data from the feature store. If None is provided, it will try to load it from the cached feature_view_metadata.json file.
    
batch-prediction-pipeline/batch_prediction_pipeline/monitoring.py