def _predict_fn()

in src/entrypoint/inference.py [0:0]

• 8 lines of code
• 2 McCabe index (conditional complexity)

def _predict_fn(input_object: List[DataEntry], model: Predictor, num_samples=1000) -> List[Forecast]:
    """Take the deserialized JSON-lines, then perform inference against the loaded model.

    Args:
        input_object (List[DataEntry]): List of gluonts timeseries.
        model (Predictor): A gluonts predictor.
        num_samples (int, optional): Number of forecast paths for each timeseries. Defaults to 1000.

    Returns:
        List[Forecast]: List of forecast results.
    """
    # Create ListDataset here, because we need to match their freq with model's freq.
    X = ListDataset(input_object, freq=model.freq)

    # Apply forward transformation to input data, before injecting it to the predictor.
    if model.pre_input_transform is not None:
        logger.debug("Before model.pre_input_transform: %s", X.list_data)
        model.pre_input_transform(X)
        logger.debug("After model.pre_input_transform: %s", X.list_data)

    it = model.predict(X, num_samples=num_samples)
    return list(it)