code: def lpi(
      experiment,
      with_evc_tree=True,
      model="RandomForestRegressor",
      model_kwargs=None,
      n_points=20,
      n_runs=10,
      **kwargs,
  ):
      """
      Make a bar plot to visualize the local parameter importance metric.
  
      For more information on the metric, see original paper at
      https://ml.informatik.uni-freiburg.de/papers/18-LION12-CAVE.pdf.
  
      Biedenkapp, André, et al. "Cave: Configuration assessment, visualization and evaluation."
      International Conference on Learning and Intelligent Optimization. Springer, Cham, 2018.
  
      Parameters
      ----------
      experiment: ExperimentClient or Experiment
          The orion object containing the experiment data
  
      with_evc_tree: bool, optional
          Fetch all trials from the EVC tree.
          Default: True
  
      model: str
          Name of the regression model to use. Can be one of
          - AdaBoostRegressor
          - BaggingRegressor
          - ExtraTreesRegressor
          - GradientBoostingRegressor
          - RandomForestRegressor (Default)
  
          Arguments for the regressor model.
      model_kwargs: dict
          Arguments for the regressor model.
      n: int
          Number of points to compute the variances. Default is 20.
      kwargs: dict
          All other plotting keyword arguments to be passed to
          :plotly:`express.line`.
  
      Returns
      -------
      plotly.graph_objects.Figure
  
      Raises
      ------
      ValueError
          If no experiment is provided or if regressor name is invalid.
  
      """
      return backend.lpi(
          experiment,
          with_evc_tree=with_evc_tree,
          model=model,
          model_kwargs=model_kwargs,
          n_points=n_points,
          n_runs=n_runs,
          **kwargs,
      )
  
file_path: orion/src/orion/plotting/base.py
imports: import orion.plotting.backend_plotly
label_desc: Use image node_id 1 for calling a global function with example usage: lpi(experiment, with_evc_tree, model, model_kwargs, n_points, n_runs) and returns: backend