Source code for deephyper.ensemble.selector._selector

import abc
from typing import Callable, Sequence, Tuple

import numpy as np

from deephyper.ensemble.loss import Loss




class Selector(abc.ABC):
    """Base class that represents an selection algorithm.

    It selects a subset of predictors from a set of available predictors in order to build
    an ensemble.

    Args:
        loss_func (Callable or Loss): a loss function that takes two arguments: the true target
            values and the predicted target values.
    """

    def __init__(self, loss_func: Callable | Loss):
        self.loss_func = loss_func



    @abc.abstractmethod
    def select(self, y, y_predictors) -> Tuple[Sequence[int], Sequence[float]]:
        """The selection algorithms.

        Args:
            y (np.ndarray): the true target values.
            y_predictors (_type_): a sequence of predictions from available predictors. It should
                be a list of length ``n_predictors`` with each element being the prediction of a
                predictor.

        Returns:
            Sequence[int]: the sequence of selected predictors.
            Sequence[float]: the sequence of weights associated to the selected predictors.
        """


    def _reduce(self, scores: np.ndarray) -> float:
        """Reduce the loss values to a single scalar value."""
        return np.mean(scores)

    def _evaluate(self, y_true, y_pred) -> float:
        return self._reduce(self.loss_func(y_true, y_pred))