class_docstr: Calculates absolute metrics for a given dataframe with model scores and
  labels, divided by different values on protected attributes.
  
  Attributes
  ----------
  absolute_metrics : List[str]
      Calculated bias metrics.
  
  Methods
  -------
  get_crosstabs(df, score_thresholds=None, attr_cols=None, score_col="score", label_col="label_value")
      Calculates metrics for a given dataframe.
  get_multimodel_crosstabs(df, score_thresholds=None, attr_cols=None, score_col="score", label_col="label_value")
      Calculates metrics for a given dataframe with multiple models.
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docstr: Creates a summary plot of the fairness metrics.
  
  Parameters
  ----------
  disparities : pandas.DataFrame
      Disparities for each group.
  metrics : list[str], optional
      List of metrics to plot. Defaults to ["fpr", "tpr"].
  fairness_threshold : float, optional
      Threshold to use to determine fairness. Defaults to 1.2.
docstr_tok: ["Creates","a","summary","plot","of","the","fairness","metrics",".","Parameters","--","--","--","--","--","disparities",":","pandas.DataFrame","Disparities","for","each","group",".","metrics",":","list","[","str","]",",","optional","List","of","metrics","to","plot",".","Defaults","to","[","``","fpr","''",",","``","tpr","''","]",".","fairness_threshold",":","float",",","optional","Threshold","to","use","to","determine","fairness",".","Defaults","to","1.2","."]
code: def plot_summary(
      disparities: pd.DataFrame,
      metrics: list[str] = ["fpr", "tpr"],
      fairness_threshold: float = 1.25,
  ):
      """
      Creates a summary plot of the fairness metrics.
  
      Parameters
      ----------
      disparities : pandas.DataFrame
          Disparities for each group.
      metrics : list[str], optional
          List of metrics to plot. Defaults to ["fpr", "tpr"].
      fairness_threshold : float, optional
          Threshold to use to determine fairness. Defaults to 1.2.
      """
      return summary(disparities, metrics, fairness_threshold)
  
file_path: aequitas/src/aequitas/group.py
imports: import logging
  import uuid
  from typing import Any, Dict, List, Tuple, Union
  from collections import OrderedDict
  import numpy
  import pandas
  from .bias import Bias
  from .plot import summary, disparity, absolute
label_desc: Use image node_id 8 for calling the Group obj's underlying member method code with example usage: obj.plot_summary(disparities, metrics, fairness_threshold) and returns: summary