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: Plot disparity metrics colored based on calculated disparity.
  
  :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 ax: a matplotlib Axis. If not passed, a new figure will be created.
  :param fig: a matplotlib Figure. If not passed, a new figure will be created.
  :param title: whether to include a title in visualizations. Default is True.
  :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 axis
code: def plot_fairness_disparity(
      self,
      fairness_table,
      group_metric,
      attribute_name,
      ax=None,
      fig=None,
      title=True,
      min_group_size=None,
      significance_alpha=0.05,
  ):
      """
      Plot disparity metrics colored based on calculated disparity.
  
      :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 ax: a matplotlib Axis. If not passed, a new figure will be created.
      :param fig: a matplotlib Figure. If not passed, a new figure will be created.
      :param title: whether to include a title in visualizations. Default is True.
      :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 axis
      """
      return self.plot_disparity(
          disparity_table=fairness_table,
          group_metric=group_metric,
          attribute_name=attribute_name,
          color_mapping=None,
          ax=ax,
          fig=fig,
          highlight_fairness=True,
          min_group_size=min_group_size,
          title=title,
          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 18 for calling the Plot obj's underlying member method code with example usage: obj.plot_fairness_disparity(fairness_table, group_metric, attribute_name, ax, fig, title, min_group_size, significance_alpha) and returns: self