class_docstr: Plotting object allows for visualization of absolute group bias metrics and
  relative disparities calculated by Aequitas Group(), Bias(), and Fairness()
  class instances.
docstr: Create treemaps to compare multiple model fairness determinations for a
  single bias disparity metric across attribute groups.
  
  :param fairness_table: a fairness table. Output of a Fairness.get_fairness
      function.
  :param group_metric: the metric to plot. Must be a column in the
      disparity_table.
  :param attribute_name: which attribute to plot group_metric across.
  :param color_mapping: matplotlib colormapping for treemap value boxes.
  :param label_dict: optional, dictionary of replacement labels for data.
      Default is None.
  :param title: whether to include a title in visualizations. Default is True.
  :param show_figure: Whether to show figure (plt.show()). Default is
      True.
  :param selected_models: which models to visualize. Default is all models in fairness_table.
  :param min_group_size: minimum proportion of total group size (all data)
      a population group must meet in order to be included in bias metric
      visualization.
  :param significance_alpha: statistical significance level. Used to
      determine visual representation of significance (number of
      asterisks on treemap).
  
  :return: A Matplotlib figure
code: def multimodel_plot_fairness_disparity(
      self,
      fairness_table,
      group_metric,
      attribute_name,
      label_dict=None,
      title=True,
      show_figure=True,
      selected_models=None,
      min_group_size=None,
      significance_alpha=0.05,
  ):
      """
      Create treemaps to compare multiple model fairness determinations for a
      single bias disparity metric across attribute groups.
  
      :param fairness_table: a fairness table. Output of a Fairness.get_fairness
          function.
      :param group_metric: the metric to plot. Must be a column in the
          disparity_table.
      :param attribute_name: which attribute to plot group_metric across.
      :param color_mapping: matplotlib colormapping for treemap value boxes.
      :param label_dict: optional, dictionary of replacement labels for data.
          Default is None.
      :param title: whether to include a title in visualizations. Default is True.
      :param show_figure: Whether to show figure (plt.show()). Default is
          True.
      :param selected_models: which models to visualize. Default is all models in fairness_table.
      :param min_group_size: minimum proportion of total group size (all data)
          a population group must meet in order to be included in bias metric
          visualization.
      :param significance_alpha: statistical significance level. Used to
          determine visual representation of significance (number of
          asterisks on treemap).
  
      :return: A Matplotlib figure
      """
      return self.multimodel_plot_disparity(
          disparity_table=fairness_table,
          group_metric=group_metric,
          attribute_name=attribute_name,
          label_dict=label_dict,
          title=title,
          show_figure=show_figure,
          selected_models=selected_models,
          highlight_fairness=True,
          min_group_size=min_group_size,
          significance_alpha=significance_alpha,
      )
  
imports: import logging
  import math
  import numpy
  import collections
  import seaborn
  import matplotlib.pyplot
  import matplotlib.colors
  import matplotlib.cm
  from aequitas import squarify_flipped
label_desc: Use image node_id 12 for calling the Plot obj's underlying member method code with example usage: obj.multimodel_plot_fairness_disparity(fairness_table, group_metric, attribute_name, label_dict, title, show_figure, selected_models, min_group_size, significance_alpha) and returns: self