class_docstr: Defines the ExplainerManager for explaining a text-based model.
class_docstr_tok: ["Defines","the","ExplainerManager","for","explaining","a","text-based","model","."]
docstr: Creates an ExplainerManager object.
  
  :param model: The model to explain.
      A model that implements sklearn.predict or sklearn.predict_proba
      or function that accepts a 2d ndarray.
  :type model: object
  :param evaluation_examples: A matrix of feature vector
      examples (# examples x # features) on which to explain the
      model's output, with an additional label column.
  :type evaluation_examples: pandas.DataFrame
  :param target_column: The name of the label column or list of columns.
      This is a list of columns for multilabel models.
  :type target_column: str or list[str]
  :param task_type: The task to run.
  :type task_type: str
  :param classes: Class names as a list of strings.
      The order of the class names should match that of the model
      output.  Only required if explaining classifier.
  :type classes: list
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code: def __init__(
      self,
      model: Any,
      evaluation_examples: pd.DataFrame,
      target_column: str,
      task_type: str,
      classes: Optional[List] = None,
  ):
      """Creates an ExplainerManager object.
  
      :param model: The model to explain.
          A model that implements sklearn.predict or sklearn.predict_proba
          or function that accepts a 2d ndarray.
      :type model: object
      :param evaluation_examples: A matrix of feature vector
          examples (# examples x # features) on which to explain the
          model's output, with an additional label column.
      :type evaluation_examples: pandas.DataFrame
      :param target_column: The name of the label column or list of columns.
          This is a list of columns for multilabel models.
      :type target_column: str or list[str]
      :param task_type: The task to run.
      :type task_type: str
      :param classes: Class names as a list of strings.
          The order of the class names should match that of the model
          output.  Only required if explaining classifier.
      :type classes: list
      """
      self._model = model
      self._target_column = target_column
      if not isinstance(target_column, list):
          target_column = [target_column]
      self._evaluation_examples = evaluation_examples.drop(
          columns=target_column
      )
      self._is_run = False
      self._is_added = False
      self._features = list(self._evaluation_examples.columns)
      self._classes = classes
      self._explanation = None
      self._task_type = task_type
  
imports: import json
  import pickle
  import warnings
  from pathlib import Path
  from typing import Any, List, Optional
  import numpy
  import pandas
  import shap
  from raiutils.data_processing import convert_to_list
  from responsibleai._interfaces import FeatureImportance, ModelExplanationData, PrecomputedExplanations, TextFeatureImportance
  from responsibleai._internal.constants import ExplainerManagerKeys
  from responsibleai._internal.constants import ListProperties, ManagerNames, Metadata
  from responsibleai._tools.shared.state_directory_management import DirectoryManager
  from responsibleai.exceptions import UserConfigValidationException
  from responsibleai.managers.base_manager import BaseManager
  from responsibleai_text.common.constants import ModelTask, QuestionAnsweringFields, Tokens
  from responsibleai_text.utils.question_answering import QAPredictor
label_desc: Use image node_id 1 to create a new ExplainerManager object from inherited base classes: BaseManager with example: obj = ExplainerManager(model, evaluation_examples, target_column, task_type, classes)