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 multiple metrics at once from a fairness object table.
  
  :param data_table:  output of group.get_crosstabs, bias.get_disparity, or
      fairness.get_fairness functions.
  :param attributes: which attribute(s) to plot metrics for. If this
      value is null, will plot metrics against all attributes.
  :param metrics: which metric(s) to plot, or 'all.'
      If this value is null, will plot:
          - Predicted Prevalence Disparity (pprev_disparity),
          - Predicted Positive Rate Disparity (ppr_disparity),
          - False Discovery Rate Disparity (fdr_disparity),
          - False Omission Rate Disparity (for_disparity),
          - False Positive Rate Disparity (fpr_disparity),
          - False Negative Rate Disparity (fnr_disparity)
  :param fillzeros: whether to fill null values with zeros. Default is True.
  :param title: whether to display a title on each plot. Default is True.
  :param label_dict: optional dictionary of label replacements. Default is
      None.
  :param show_figure: whether to show figure (plt.show()). 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 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 plot_disparity_all(
      self,
      data_table,
      attributes=None,
      metrics=None,
      fillzeros=True,
      title=True,
      label_dict=None,
      show_figure=True,
      min_group_size=None,
      significance_alpha=0.05,
  ):
      """
      Plot multiple metrics at once from a fairness object table.
  
      :param data_table:  output of group.get_crosstabs, bias.get_disparity, or
          fairness.get_fairness functions.
      :param attributes: which attribute(s) to plot metrics for. If this
          value is null, will plot metrics against all attributes.
      :param metrics: which metric(s) to plot, or 'all.'
          If this value is null, will plot:
              - Predicted Prevalence Disparity (pprev_disparity),
              - Predicted Positive Rate Disparity (ppr_disparity),
              - False Discovery Rate Disparity (fdr_disparity),
              - False Omission Rate Disparity (for_disparity),
              - False Positive Rate Disparity (fpr_disparity),
              - False Negative Rate Disparity (fnr_disparity)
      :param fillzeros: whether to fill null values with zeros. Default is True.
      :param title: whether to display a title on each plot. Default is True.
      :param label_dict: optional dictionary of label replacements. Default is
          None.
      :param show_figure: whether to show figure (plt.show()). 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 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._plot_multiple_treemaps(
          data_table,
          plot_fcn=self.plot_disparity,
          attributes=attributes,
          metrics=metrics,
          fillzeros=fillzeros,
          label_dict=label_dict,
          highlight_fairness=False,
          show_figure=show_figure,
          title=title,
          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 22 for calling the Plot obj's underlying member method code with example usage: obj.plot_disparity_all(data_table, attributes, metrics, fillzeros, title, label_dict, show_figure, min_group_size, significance_alpha) and returns: self