Function max_std_sampling

modAL/disagreement.py:186–211

Regressor standard deviation sampling strategy. Args: regressor: The regressor for which the labels are to be queried. X: The pool of samples to query from. n_instances: Number of samples to be queried. random_tie_break: If True, shuffles utility scores to r

(regressor: BaseEstimator, X: modALinput,
                     n_instances: int = 1,  random_tie_break=False,
                     **predict_kwargs)

def max_std_sampling(regressor: BaseEstimator, X: modALinput,
                     n_instances: int = 1,  random_tie_break=False,
                     **predict_kwargs) -> np.ndarray:
    """
    Regressor standard deviation sampling strategy.

    Args:
        regressor: The regressor for which the labels are to be queried.
        X: The pool of samples to query from.
        n_instances: Number of samples to be queried.
        random_tie_break: If True, shuffles utility scores to randomize the order. This
            can be used to break the tie when the highest utility score is not unique.
        **predict_kwargs: Keyword arguments to be passed to :meth:`predict` of the CommiteeRegressor.

    Returns:
        The indices of the instances from X chosen to be labelled.
        The standard deviation of the chosen instances. 

    """
    _, std = regressor.predict(X, return_std=True, **predict_kwargs)
    std = std.reshape(X.shape[0], )

    if not random_tie_break:
        return multi_argmax(std, n_instances=n_instances)

    return shuffled_argmax(std, n_instances=n_instances)