class_docstr: Defines a wrapper class of Error Analysis for vision scenario.
class_docstr_tok: ["Defines","a","wrapper","class","of","Error","Analysis","for","vision","scenario","."]
docstr: Creates an ErrorAnalysisManager object.
  
  :param model: The model to analyze errors on.
      A model that implements sklearn.predict or sklearn.predict_proba
      or function that accepts a 2d ndarray.
  :type model: object
  :param dataset: The dataset including the label column.
  :type dataset: pandas.DataFrame
  :param ext_dataset: The dataset of extracted features including the
                      label column.
  :type ext_dataset: pandas.DataFrame
  :param target_column: The name of the label column.
  :type target_column: str
  :param task_type: The task to run.
  :type task_type: str
  :param image_mode: The mode to open the image in.
      See pillow documentation for all modes:
      https://pillow.readthedocs.io/en/stable/handbook/concepts.html
  :type image_mode: str
  :param transformations: The transformations to apply to the image.
  :type transformations: object
  :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 analyzing a classifier.
  :type classes: list
  :param categorical_features: The categorical feature names.
  :type categorical_features: list[str]
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code: def __init__(
      self,
      model: Any,
      dataset: pd.DataFrame,
      ext_dataset: pd.DataFrame,
      target_column: str,
      task_type: str,
      image_mode: str,
      transformations: Any,
      classes: Optional[List] = None,
      categorical_features: Optional[List[str]] = None,
  ):
      """Creates an ErrorAnalysisManager object.
  
      :param model: The model to analyze errors on.
          A model that implements sklearn.predict or sklearn.predict_proba
          or function that accepts a 2d ndarray.
      :type model: object
      :param dataset: The dataset including the label column.
      :type dataset: pandas.DataFrame
      :param ext_dataset: The dataset of extracted features including the
                          label column.
      :type ext_dataset: pandas.DataFrame
      :param target_column: The name of the label column.
      :type target_column: str
      :param task_type: The task to run.
      :type task_type: str
      :param image_mode: The mode to open the image in.
          See pillow documentation for all modes:
          https://pillow.readthedocs.io/en/stable/handbook/concepts.html
      :type image_mode: str
      :param transformations: The transformations to apply to the image.
      :type transformations: object
      :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 analyzing a classifier.
      :type classes: list
      :param categorical_features: The categorical feature names.
      :type categorical_features: list[str]
      """
      index_classes = classes
      if isinstance(target_column, list):
          # create copy of dataset as we will make modifications to it
          dataset = dataset.copy()
          index_classes = target_column
          labels = _concat_labels_column(
              dataset, target_column, index_classes
          )
          dataset[LABELS] = labels
          ext_dataset[LABELS] = dataset[LABELS]
          dataset.drop(columns=target_column, inplace=True)
          ext_dataset.drop(columns=target_column, inplace=True)
          target_column = LABELS
      index_predictor = ErrorAnalysisManager._create_index_predictor(
          model,
          dataset,
          target_column,
          task_type,
          image_mode,
          transformations,
          index_classes,
      )
      super(ErrorAnalysisManager, self).__init__(
          index_predictor,
          ext_dataset,
          target_column,
          classes,
          categorical_features,
          model_task=task_type,
      )
  
imports: import json
  from typing import Any, List, Optional
  import jsonschema
  import numpy
  import pandas
  from ml_wrappers import wrap_model
  from erroranalysis._internal.error_analyzer import ModelAnalyzer
  from erroranalysis._internal.error_report import as_error_report
  from responsibleai._tools.shared.state_directory_management import DirectoryManager
  from responsibleai.managers.error_analysis_manager import ErrorAnalysisManager
  from responsibleai.managers.error_analysis_manager import as_error_config
  from responsibleai_vision.common.constants import MLFlowSchemaLiterals, ModelTask
  from responsibleai_vision.utils.image_reader import get_base64_string_from_path, is_automl_image_model
  from responsibleai_vision.utils.image_utils import get_images
label_desc: Use image node_id 1 to create a new ErrorAnalysisManager object from inherited base classes: BaseErrorAnalysisManager with example: obj = ErrorAnalysisManager(model, dataset, ext_dataset, target_column, task_type, image_mode, transformations, classes, categorical_features)