def select(self, label_index, unlabel_index, model=None, **kwargs):
    'Select an instance and a batch of oracles to label it.\n        The instance is selected by uncertainty, the oracles is\n        selected by the difference between their\n        labeling results and the majority vote results.\n\n        Parameters\n        ----------\n        label_index: {list, np.ndarray, IndexCollection}\n            The indexes of labeled samples.\n\n        unlabel_index: {list, np.ndarray, IndexCollection}\n            The indexes of unlabeled samples.\n\n        Returns\n        -------\n        selected_instance: int\n            The index of selected instance. Selected by uncertainty.\n\n        selected_oracles: list\n            The selected oracles for querying.\n        '
    if (model is None):
        model = LogisticRegression(solver='liblinear')
        model.fit(self.X[label_index], self.y[label_index])
    (pred_unlab, _) = _get_proba_pred(self.X[unlabel_index], model)
    return self.select_by_prediction_mat(label_index, unlabel_index, pred_unlab)
Select an instance and a batch of oracles to label it.
The instance is selected by uncertainty, the oracles is
selected by the difference between their
labeling results and the majority vote results.

Parameters
----------
label_index: {list, np.ndarray, IndexCollection}
    The indexes of labeled samples.

unlabel_index: {list, np.ndarray, IndexCollection}
    The indexes of unlabeled samples.

Returns
-------
selected_instance: int
    The index of selected instance. Selected by uncertainty.

selected_oracles: list
    The selected oracles for querying.
def select(self, label_index, unlabel_index, model=None, **kwargs):
    'Select an instance and a batch of oracles to label it.\n        The instance is selected by uncertainty, the oracles is\n        selected by the difference between their\n        labeling results and the majority vote results.\n\n        Parameters\n        ----------\n        label_index: {list, np.ndarray, IndexCollection}\n            The indexes of labeled samples.\n\n        unlabel_index: {list, np.ndarray, IndexCollection}\n            The indexes of unlabeled samples.\n\n        Returns\n        -------\n        selected_instance: int\n            The index of selected instance. Selected by uncertainty.\n\n        selected_oracles: list\n            The selected oracles for querying.\n        '
    if (model is None):
        model = LogisticRegression(solver='liblinear')
        model.fit(self.X[label_index], self.y[label_index])
    (pred_unlab, _) = _get_proba_pred(self.X[unlabel_index], model)
    return self.select_by_prediction_mat(label_index, unlabel_index, pred_unlab)