class_docstr: SmartPlotter is a Bridge pattern decoupling plotting functions from SmartExplainer.
  The smartplotter class includes all the methods used to display graphics
  Each SmartPlotter method is easy to use from a Smart explainer object,
  just use the following syntax
  Attributes :
  explainer: object
      SmartExplainer instance to point to.
  Example
  --------
  >>> xpl.plot.my_plot_method(param=value)
class_docstr_tok: ["SmartPlotter","is","a","Bridge","pattern","decoupling","plotting","functions","from","SmartExplainer",".","The","smartplotter","class","includes","all","the","methods","used","to","display","graphics","Each","SmartPlotter","method","is","easy","to","use","from","a","Smart","explainer","object",",","just","use","the","following","syntax","Attributes",":","explainer",":","object","SmartExplainer","instance","to","point","to",".","Example","--","--","--","--",">",">",">","xpl.plot.my_plot_method","(","param=value",")"]
docstr: Intermediate function used to display the stability plot when plot_type is "none"
  Parameters
  ----------
  mean_variability: array
      Local stability expressed as a mean value for all instances (one value per feature).
      Displayed on the X-axis on the plot.
  mean_amplitude: array
      Average of the normalized SHAP values in the neighborhood. Displayed on the Y-axis on the plot.
  column_names: list
      Columns names that are displayed on the plot
  file_name: string
      Specify the save path of html files. If it is not provided, no file will be saved
  auto_open: bool
      open automatically the plot
  Returns
  -------
  go.Figure
code: def plot_amplitude_vs_stability(
      self,
      mean_variability,
      mean_amplitude,
      column_names,
      file_name,
      auto_open,
  ):
      """
      Intermediate function used to display the stability plot when plot_type is "none"
      Parameters
      ----------
      mean_variability: array
          Local stability expressed as a mean value for all instances (one value per feature).
          Displayed on the X-axis on the plot.
      mean_amplitude: array
          Average of the normalized SHAP values in the neighborhood. Displayed on the Y-axis on the plot.
      column_names: list
          Columns names that are displayed on the plot
      file_name: string
          Specify the save path of html files. If it is not provided, no file will be saved
      auto_open: bool
          open automatically the plot
      Returns
      -------
      go.Figure
      """
      xaxis_title = (
          "Variability of the Normalized Local Contribution Values"
          + "<span style='font-size: 12px;'><br />(standard deviation / mean)</span>"
      )
      yaxis_title = "Importance<span style='font-size: 12px;'><br />(Average contributions)</span>"
      col_scale = self.tuning_colorscale(pd.DataFrame(mean_amplitude))
      hv_text = [
          f"<b>Feature: {col}</b><br />Importance: {y}<br />Variability: {x}"
          for col, x, y in zip(
              column_names, mean_variability, mean_amplitude
          )
      ]
      hovertemplate = "%{hovertext}" + "<extra></extra>"
  
      fig = go.Figure()
      fig.add_scatter(
          x=mean_variability,
          y=mean_amplitude,
          showlegend=False,
          mode="markers",
          marker={
              "color": mean_amplitude,
              "size": 10,
              "opacity": 0.8,
              "line": {"width": 0.8, "color": "white"},
              "colorscale": col_scale,
          },
          hovertext=hv_text,
          hovertemplate=hovertemplate,
      )
  
      fig.update_xaxes(
          range=[
              np.min(np.append(mean_variability, [0.15])) - 0.03,
              np.max(mean_variability) + 0.03,
          ]
      )
  
      self._update_stability_fig(
          fig=fig,
          x_barlen=len(mean_amplitude),
          y_bar=[0, mean_amplitude.max()],
          xaxis_title=xaxis_title,
          yaxis_title=yaxis_title,
          file_name=file_name,
          auto_open=auto_open,
      )
      return fig
  
imports: import warnings
  from numbers import Number
  import random
  import copy
  import math
  import numpy
  from scipy.optimize import fsolve
  import scipy.cluster.hierarchy
  import pandas
  from plotly import graph_objs
  import plotly.express
  from plotly.subplots import make_subplots
  from plotly.offline import plot
  from shapash.manipulation.select_lines import select_lines
  from shapash.manipulation.summarize import compute_features_import, project_feature_values_1d, compute_corr
  from shapash.utils.utils import add_line_break, truncate_str, compute_digit_number, add_text, maximum_difference_sort_value, compute_sorted_variables_interactions_list_indices, compute_top_correlations_features
  from shapash.webapp.utils.utils import round_to_k
  from shapash.style.style_utils import colors_loading, select_palette, define_style
label_desc: Use image node_id 26 for calling the SmartPlotter obj's underlying member method code with example usage: obj.plot_amplitude_vs_stability(mean_variability, mean_amplitude, column_names, file_name, auto_open) and returns: fig