Generates a neighborhood around a prediction.

        For numerical features, perturb them by sampling from a Normal(0,1) and
        doing the inverse operation of mean-centering and scaling, according to
        the means and stds in the training data. For categorical features,
        perturb by sampling according to the training distribution, and making
        a binary feature that is 1 when the value is the same as the instance
        being explained.

        Args:
            data_row: 1d numpy array, corresponding to a row
            num_samples: size of the neighborhood to learn the linear model

        Returns:
            A tuple (data, inverse), where:
                data: dense num_samples * K matrix, where categorical features
                are encoded with either 0 (not equal to the corresponding value
                in data_row) or 1. The first row is the original instance.
                inverse: same as data, except the categorical features are not
                binary, but categorical (as the original data)

def __data_inverse(self,
                       data_row,
                       num_samples):
        """Generates a neighborhood around a prediction.

        For numerical features, perturb them by sampling from a Normal(0,1) and
        doing the inverse operation of mean-centering and scaling, according to
        the means and stds in the training data. For categorical features,
        perturb by sampling according to the training distribution, and making
        a binary feature that is 1 when the value is the same as the instance
        being explained.

        Args:
            data_row: 1d numpy array, corresponding to a row
            num_samples: size of the neighborhood to learn the linear model

        Returns:
            A tuple (data, inverse), where:
                data: dense num_samples * K matrix, where categorical features
                are encoded with either 0 (not equal to the corresponding value
                in data_row) or 1. The first row is the original instance.
                inverse: same as data, except the categorical features are not
                binary, but categorical (as the original data)
        """
        data = np.zeros((num_samples, data_row.shape[0]))
        categorical_features = range(data_row.shape[0])
        if self.discretizer is None:
            data = self.random_state.normal(
                0, 1, num_samples * data_row.shape[0]).reshape(
                num_samples, data_row.shape[0])
            if self.sample_around_instance:
                data = data * self.scaler.scale_ + data_row
            else:
                data = data * self.scaler.scale_ + self.scaler.mean_
            categorical_features = self.categorical_features
            first_row = data_row
        else:
            first_row = self.discretizer.discretize(data_row)
        data[0] = data_row.copy()
        inverse = data.copy()
        for column in categorical_features:
            values = self.feature_values[column]
            freqs = self.feature_frequencies[column]
            inverse_column = self.random_state.choice(values, size=num_samples,
                                                      replace=True, p=freqs)
            binary_column = np.array([1 if x == first_row[column]
                                      else 0 for x in inverse_column])
            binary_column[0] = 1
            inverse_column[0] = data[0, column]
            data[:, column] = binary_column
            inverse[:, column] = inverse_column
        if self.discretizer is not None:
            inverse[1:] = self.discretizer.undiscretize(inverse[1:])
        inverse[0] = data_row
        return data, inverse