docstr: Composite of multiple classifier related components, on a single tab:
      - confusion matrix
      - one other model quality indicator: choose from pr auc graph, precision graph,
              lift curve, classification graph, or roc auc graph
      - shap importance
      - shap dependence
      - index selector
      - index prediction summary
      - index shap contribution graph
  Args:
      explainer (Explainer): explainer object constructed with either
                  ClassifierExplainer() or RegressionExplainer()
      title (str, optional): Title of tab or page. Defaults to
                  "Simple Classification Stats".
      name (str, optional): unique name to add to Component elements.
                  If None then random uuid is generated to make sure
                  it's unique. Defaults to None.
      hide_title (bool, optional): hide the title. Defaults to False.
      classifier_custom_component (str, optional): custom classifier quality indicator
              supported by the ClassifierExplainer object. Valid values are:
              'roc_auc', 'metrics', pr_auc', 'precision_graph', 'lift_curve',
              'classification'. Defaults to 'roc_auc'.
      hide_confusionmatrix (bool, optional): hide ConfusionMatrixComponent
      hide_classifier_custom_component (bool, optional): hide the chosen classifier_custom_component
      hide_shapsummary (bool, optional): hide ShapSummaryComponent
      hide_shapdependence (bool, optional): hide ShapDependenceComponent
      hide_predindexselector (bool, optional): hide ClassifierRandomIndexComponent
          or RegressionRandomIndexComponent
      hide_predictionsummary (bool, optional): hide ClassifierPredictionSummaryComponent
          or RegressionPredictionSummaryComponent
      hide_contributiongraph (bool, optional): hide ShapContributionsGraphComponent
code: def __init__(
      self,
      explainer,
      title="Simple Classifier Explainer",
      name=None,
      hide_title=False,
      classifier_custom_component="roc_auc",
      hide_confusionmatrix=False,
      hide_classifier_custom_component=False,
      hide_shapsummary=False,
      hide_shapdependence=False,
      hide_predindexselector=False,
      hide_predictionsummary=False,
      hide_contributiongraph=False,
      **kwargs,
  ):
      """Composite of multiple classifier related components, on a single tab:
          - confusion matrix
          - one other model quality indicator: choose from pr auc graph, precision graph,
                  lift curve, classification graph, or roc auc graph
          - shap importance
          - shap dependence
          - index selector
          - index prediction summary
          - index shap contribution graph
      Args:
          explainer (Explainer): explainer object constructed with either
                      ClassifierExplainer() or RegressionExplainer()
          title (str, optional): Title of tab or page. Defaults to
                      "Simple Classification Stats".
          name (str, optional): unique name to add to Component elements.
                      If None then random uuid is generated to make sure
                      it's unique. Defaults to None.
          hide_title (bool, optional): hide the title. Defaults to False.
          classifier_custom_component (str, optional): custom classifier quality indicator
                  supported by the ClassifierExplainer object. Valid values are:
                  'roc_auc', 'metrics', pr_auc', 'precision_graph', 'lift_curve',
                  'classification'. Defaults to 'roc_auc'.
          hide_confusionmatrix (bool, optional): hide ConfusionMatrixComponent
          hide_classifier_custom_component (bool, optional): hide the chosen classifier_custom_component
          hide_shapsummary (bool, optional): hide ShapSummaryComponent
          hide_shapdependence (bool, optional): hide ShapDependenceComponent
          hide_predindexselector (bool, optional): hide ClassifierRandomIndexComponent
              or RegressionRandomIndexComponent
          hide_predictionsummary (bool, optional): hide ClassifierPredictionSummaryComponent
              or RegressionPredictionSummaryComponent
          hide_contributiongraph (bool, optional): hide ShapContributionsGraphComponent
      """
      super().__init__(explainer, title=title, name=name)
  
      self.confusionmatrix = ConfusionMatrixComponent(
          explainer,
          **update_params(
              kwargs,
              hide_percentage=True,
              hide_selector=True,
              hide_normalize=True,
          ),
      )
  
      # select custom classifier report metric
      if classifier_custom_component == "metrics":
          self.classifier_custom_component = (
              ClassifierModelSummaryComponent(
                  explainer, **update_params(kwargs, hide_selector=True)
              )
          )
      elif classifier_custom_component == "pr_auc":
          self.classifier_custom_component = PrAucComponent(
              explainer, **update_params(kwargs, hide_selector=True)
          )
      elif classifier_custom_component == "precision_graph":
          self.classifier_custom_component = PrecisionComponent(
              explainer, **update_params(kwargs, hide_selector=True)
          )
      elif classifier_custom_component == "lift_curve":
          self.classifier_custom_component = LiftCurveComponent(
              explainer, **update_params(kwargs, hide_selector=True)
          )
      elif classifier_custom_component == "classifiction":
          self.classifier_custom_component = ClassificationComponent(
              explainer, **update_params(kwargs, hide_selector=True)
          )
      elif classifier_custom_component == "roc_auc":
          self.classifier_custom_component = RocAucComponent(
              explainer, **update_params(kwargs, hide_selector=True)
          )
      else:
          raise ValueError(
              "ERROR: SimplifiedClassifierDashboard parameter classifier_custom_component "
              "should be in {'metrics', 'roc_auc', pr_auc', 'precision_graph', 'lift_curve', 'class_graph'} "
              f"but you passed {classifier_custom_component}!"
          )
  
      # SHAP summary & dependence
      self.shap_summary = ShapSummaryComponent(
          explainer,
          **update_params(
              kwargs,
              title="Shap Feature Importances",
              hide_index=True,
              hide_selector=True,
              depth=None,
              hide_depth=True,
          ),
      )
      self.shap_dependence = ShapDependenceComponent(
          explainer,
          **update_params(
              kwargs,
              hide_selector=True,
              hide_index=True,
              color_col="no_color_col",
          ),
      )
  
      # SHAP contribution, along with prediction summary
      self.index = ClassifierRandomIndexComponent(
          explainer, hide_selector=True, **kwargs
      )
      self.summary = ClassifierPredictionSummaryComponent(
          explainer, hide_index=True, hide_selector=True, **kwargs
      )
      self.contributions = ShapContributionsGraphComponent(
          explainer,
          hide_index=True,
          hide_depth=True,
          hide_selector=True,
          **kwargs,
      )
  
      self.cutoffconnector = CutoffConnector(
          self.confusionmatrix, self.classifier_custom_component
      )
      self.connector = ShapSummaryDependenceConnector(
          self.shap_summary, self.shap_dependence
      )
      self.index_connector = IndexConnector(
          self.index, [self.summary, self.contributions]
      )
  
imports: import dash_bootstrap_components
  from dash import html
  from ..explainers import RandomForestExplainer, XGBExplainer
  from ..dashboard_methods import *
  from .classifier_components import *
  from .regression_components import *
  from .overview_components import *
  from .connectors import *
  from .shap_components import *
  from .decisiontree_components import *
  from ..None import to_html
label_desc: Use image node_id 1 to create a new SimplifiedClassifierComposite object from inherited base classes: ExplainerComponent with example: obj = SimplifiedClassifierComposite(explainer, title, name, hide_title, classifier_custom_component, hide_confusionmatrix, hide_classifier_custom_component, hide_shapsummary, hide_shapdependence, hide_predindexselector, hide_predictionsummary, hide_contributiongraph)