class_docstr: Main class producing plots. Each create_*() method reads group data from base_path and creates a plot at the
  save_path. create_all_plots() runs all these functions at once.
class_docstr_tok: ["Main","class","producing","plots",".","Each","create_","*","(",")","method","reads","group","data","from","base_path","and","creates","a","plot","at","the","save_path",".","create_all_plots","(",")","runs","all","these","functions","at","once","."]
docstr: Create a box plots with scenario accuracy across models for a range of targeted evaluations.
docstr_tok: ["Create","a","box","plots","with","scenario","accuracy","across","models","for","a","range","of","targeted","evaluations","."]
code: def create_targeted_eval_plots(self):
      """Create a box plots with scenario accuracy across models for a range of targeted evaluations."""
      fig, axd = plt.subplot_mosaic(
          [["language", "knowledge"], ["reasoning", "reasoning"]],
          figsize=(12, 7),
      )
      for targeted_eval in ["language", "knowledge", "reasoning"]:
          table = self.get_group_tables(targeted_eval)["Accuracy"]
          ax = axd[targeted_eval]
  
          group_to_accuracies: Dict[str, np.ndarray] = {}
          for column in table.columns:
              arrow = DOWN_ARROW if column.lower_is_better else UP_ARROW
              group = f"{column.group}\n({column.metric} {arrow})"
              group_to_accuracies[group] = column.values
  
          draw_box_plot(group_to_accuracies, ax)
          ax.set_title(targeted_eval.capitalize())
          ax.set_ylim(-0.1, 3.6 if targeted_eval == "language" else 1.1)
  
      fig.subplots_adjust(hspace=0.75)
      self.save_figure(fig, "targeted_evals")
  
imports: import argparse
  from collections import defaultdict
  from dataclasses import dataclass
  from datetime import date
  import json
  import os
  from typing import List, Dict, Optional, Any, Callable, Union, Mapping, Tuple, Set
  import colorcet
  import matplotlib
  import matplotlib.pyplot
  import numpy
  from scipy.stats import pearsonr
  import seaborn
  from helm.common.hierarchical_logger import hlog
  from helm.benchmark.presentation.schema import read_schema
  from helm.benchmark.presentation.summarize import AGGREGATE_WIN_RATE_COLUMN
label_desc: Use image node_id 11 for calling the Plotter obj's underlying member method code with example usage: obj.create_targeted_eval_plots() without return types