Function max_EI

modAL/acquisition.py:125–142

Maximum EI query strategy. Selects the instance with highest expected improvement. Args: optimizer: The :class:`~modAL.models.BayesianOptimizer` object for which the utility is to be calculated. X: The samples for which the expected improvement is to be calculated.

(optimizer: BaseLearner, X: modALinput, tradeoff: float = 0,
           n_instances: int = 1)

def max_EI(optimizer: BaseLearner, X: modALinput, tradeoff: float = 0,
           n_instances: int = 1) -> np.ndarray:
    """
    Maximum EI query strategy. Selects the instance with highest expected improvement.

    Args:
        optimizer: The :class:`~modAL.models.BayesianOptimizer` object for which the utility is to be calculated.
        X: The samples for which the expected improvement is to be calculated.
        tradeoff: Value controlling the tradeoff parameter.
        n_instances: Number of samples to be queried.

    Returns:
        The indices of the instances from X chosen to be labelled. 
        The ei metric of the chosen instances.

    """
    ei = optimizer_EI(optimizer, X, tradeoff=tradeoff)
    return multi_argmax(ei, n_instances=n_instances)


def max_UCB(optimizer: BaseLearner, X: modALinput, beta: float = 1,