| | |
| | |
| |
|
| | import logging |
| | from ._constants import OUTPUT_SEPARATOR |
| |
|
| | logger = logging.getLogger(__name__) |
| |
|
| |
|
| | class InterpolatedPredictor: |
| | """Predictor for computing predictions between two actual predictions. |
| | |
| | The predictions are represented through the threshold rules operation0 and operation1. |
| | |
| | :param p_ignore: p_ignore changes the interpolated prediction P to the desired |
| | solution using the transformation p_ignore * prediction_constant + (1 - p_ignore) * P |
| | :param prediction_constant: 0 if not required, otherwise the x value of the best |
| | solution should be passed |
| | :param p0: interpolation multiplier for prediction from the first predictor |
| | :param operation0: threshold rule for the first predictor |
| | :param p1: interpolation multiplier for prediction from the second predictor |
| | :param operation1: threshold rule for the second predictor |
| | :return: an anonymous function that scales the original prediction to the desired one |
| | :rtype: lambda |
| | """ |
| |
|
| | def __init__(self, p_ignore, prediction_constant, p0, operation0, p1, operation1): |
| | self._operation0 = operation0 |
| | self._operation1 = operation1 |
| | self._p_ignore = p_ignore |
| | self._prediction_constant = prediction_constant |
| | self._p0 = p0 |
| | self._p1 = p1 |
| |
|
| | logger.debug(OUTPUT_SEPARATOR) |
| | logger.debug("p_ignore: %s", p_ignore) |
| | logger.debug("prediction_constant: %s", prediction_constant) |
| | logger.debug("p0: %s", p0) |
| | logger.debug("operation0: %s", operation0) |
| | logger.debug("p1: %s", p1) |
| | logger.debug("operation1: %s", operation1) |
| | logger.debug(OUTPUT_SEPARATOR) |
| |
|
| | def __repr__(self): |
| | return "[p_ignore: {}, prediction_constant: {}, " \ |
| | "p0: {}, operation0: {}, p1: {}, operation1: {}]" \ |
| | .format(self._p_ignore, self._prediction_constant, self._p0, self._operation0, |
| | self._p1, self._operation1) |
| |
|
| | def predict(self, scores): |
| | """Create the interpolated prediction. |
| | |
| | The interpolation is based on two threshold operations and the |
| | transformation adjustment. |
| | |
| | :param scores: the scores from an unconstrained predictor to which the threshold |
| | operations are applied |
| | :type scores: numpy.ndarray |
| | :return: the interpolated prediction |
| | :rtype: numpy.ndarray |
| | """ |
| | transformation_adjustment = self._p_ignore * self._prediction_constant |
| | weighted_predictions0 = self._p0 * self._operation0.get_predictor_from_operation()(scores) |
| | weighted_predictions1 = self._p1 * self._operation1.get_predictor_from_operation()(scores) |
| | interpolated_predictions = (1 - self._p_ignore) * (weighted_predictions0 + weighted_predictions1) |
| | return transformation_adjustment + interpolated_predictions |
| |
|
