code: def compare_classifiers_plot(
      scores,
      metrics,
      algoritm_names=None,
      adaptive=False,
      decimals=4,
      title=None,
      filename=None,
      callbacks=None,
  ):
      assert len(scores) == len(metrics)
      assert len(scores) > 0
  
      num_metrics = len(metrics)
  
      sns.set_style("whitegrid")
  
      fig, ax = plt.subplots()
  
      ax.grid(which="both")
      ax.grid(which="minor", alpha=0.5)
      ax.grid(which="major", alpha=0.75)
      ax.set_xticklabels([], minor=True)
  
      if title is not None:
          ax.set_title(title)
  
      width = 0.8 / num_metrics if num_metrics > 1 else 0.4
      ticks = np.arange(len(scores[0]))
  
      if num_metrics <= 10:
          colors = plt.get_cmap("tab10").colors
      else:
          colors = plt.get_cmap("tab20").colors
      if adaptive:
          maximum = max(max(score) for score in scores)
      else:
          ax.set_xlim([0, 1])
          ax.set_xticks(np.linspace(0.0, 1.0, num=21), minor=True)
          ax.set_xticks(np.linspace(0.0, 1.0, num=11))
          maximum = 1
  
      half_total_width = 0.4 if num_metrics > 1 else 0.2
      ax.set_yticks(ticks + half_total_width - width / 2)
      ax.set_yticklabels(
          algoritm_names if algoritm_names is not None else ""
      )
      ax.invert_yaxis()  # labels read top-to-bottom
  
      for i, metric in enumerate(metrics):
          ax.barh(
              ticks + (i * width),
              scores[i],
              width,
              label=metric,
              color=colors[i],
          )
  
          for j, v in enumerate(scores[i]):
              if v < maximum * (0.025 * decimals + 0.1):
                  x = v + maximum * 0.01
                  horizontal_alignment = "left"
              else:
                  x = v - maximum * 0.01
                  horizontal_alignment = "right"
              txt = ax.text(
                  x,
                  ticks[j] + (i * width),
                  ("{:." + str(decimals) + "f}").format(v),
                  color="white",
                  fontweight="bold",
                  verticalalignment="center",
                  horizontalalignment=horizontal_alignment,
              )
              txt.set_path_effects(
                  [
                      PathEffects.withStroke(
                          linewidth=3, foreground="black"
                      )
                  ]
              )
  
      plt.setp(ax.get_xminorticklabels(), visible=False)
  
      ax.legend(loc="center left", bbox_to_anchor=(1, 0.5))
      plt.tight_layout()
      visualize_callbacks(callbacks, plt.gcf())
      if filename:
          plt.savefig(filename)
      else:
          plt.show()
  
imports: import copy
  import logging
  from collections import Counter
  from sys import platform
  from typing import Dict, List
  import numpy
  import pandas
  import ptitprince
  from packaging import version
  from ludwig.constants import SPACE, TRAINING, VALIDATION
label_desc: Use image node_id 3 for calling a global function with example usage: compare_classifiers_plot(scores, metrics, algoritm_names, adaptive, decimals, title, filename, callbacks) without return types