Generates explanations for a prediction.

        First, we generate neighborhood data by randomly hiding features from
        the instance (see __data_labels_distance_mapping). We then learn
        locally weighted linear models on this neighborhood data to explain
        each of the classes in an interpretable way (see lime_base.py).

        Args:
            text_instance: raw text string to be explained.
            classifier_fn: classifier prediction probability function, which
                takes a list of d strings and outputs a (d, k) numpy array with
                prediction probabilities, where k is the number of classes.
                For ScikitClassifiers , this is classifier.predict_proba.
            labels: iterable with labels to be explained.
            top_labels: if not None, ignore labels and produce explanations for
                the K labels with highest prediction probabilities, where K is
                this parameter.
            num_features: maximum number of features present in explanation
            num_samples: size of the neighborhood to learn the linear model
            distance_metric: the distance metric to use for sample weighting,
                defaults to cosine similarity
            model_regressor: sklearn regressor to use in explanation. Defaults
            to Ridge regression in LimeBase. Must have model_regressor.coef_
            and 'sample_weight' as a parameter to model_regressor.fit()
        Returns:
            An Explanation object (see explanation.py) with the corresponding
            explanations.

def explain_instance(self,
                         text_instance,
                         classifier_fn,
                         labels=(1,),
                         top_labels=None,
                         num_features=10,
                         num_samples=5000,
                         distance_metric='cosine',
                         model_regressor=None):
        """Generates explanations for a prediction.

        First, we generate neighborhood data by randomly hiding features from
        the instance (see __data_labels_distance_mapping). We then learn
        locally weighted linear models on this neighborhood data to explain
        each of the classes in an interpretable way (see lime_base.py).

        Args:
            text_instance: raw text string to be explained.
            classifier_fn: classifier prediction probability function, which
                takes a list of d strings and outputs a (d, k) numpy array with
                prediction probabilities, where k is the number of classes.
                For ScikitClassifiers , this is classifier.predict_proba.
            labels: iterable with labels to be explained.
            top_labels: if not None, ignore labels and produce explanations for
                the K labels with highest prediction probabilities, where K is
                this parameter.
            num_features: maximum number of features present in explanation
            num_samples: size of the neighborhood to learn the linear model
            distance_metric: the distance metric to use for sample weighting,
                defaults to cosine similarity
            model_regressor: sklearn regressor to use in explanation. Defaults
            to Ridge regression in LimeBase. Must have model_regressor.coef_
            and 'sample_weight' as a parameter to model_regressor.fit()
        Returns:
            An Explanation object (see explanation.py) with the corresponding
            explanations.
        """

        indexed_string = IndexedCharacters(
            text_instance, bow=self.bow) if self.char_level else IndexedString(
            text_instance, bow=self.bow, split_expression=self.split_expression)
        domain_mapper = TextDomainMapper(indexed_string)
        data, yss, distances = self.__data_labels_distances(
            indexed_string, classifier_fn, num_samples,
            distance_metric=distance_metric)
        if self.class_names is None:
            self.class_names = [str(x) for x in range(yss[0].shape[0])]
        ret_exp = explanation.Explanation(domain_mapper=domain_mapper,
                                          class_names=self.class_names,
                                          random_state=self.random_state)
        ret_exp.predict_proba = yss[0]
        if top_labels:
            labels = np.argsort(yss[0])[-top_labels:]
            ret_exp.top_labels = list(labels)
            ret_exp.top_labels.reverse()
        for label in labels:
            (ret_exp.intercept[label],
             ret_exp.local_exp[label],
             ret_exp.score, ret_exp.local_pred) = self.base.explain_instance_with_data(
                data, yss, distances, label, num_features,
                model_regressor=model_regressor,
                feature_selection=self.feature_selection)
        return ret_exp