original_docstring: 
      Starting with one initial label, a noisy sample needs to be relabeled at least twice (best case scenario
      the sampled class during relabeling is equal to the correct label
      
docstring: Starting with one initial label, a noisy sample needs to be relabeled at least twice (best case scenario
  the sampled class during relabeling is equal to the correct label
docstring_tokens: Starting with one initial label a noisy sample needs to be relabeled at least twice ( best case scenario the sampled class during relabeling is equal to the correct label
code: def plot_minimal_sampler(stats: Dict[str, SimulationStatsDistribution],
                           n_samples: int, ax: plt.Axes, fontsize: int = 12, legend_loc: int = 2) -> None:
      """
      Starting with one initial label, a noisy sample needs to be relabeled at least twice (best case scenario
      the sampled class during relabeling is equal to the correct label
      """
  
      n_mislabelled_ambiguous = list(stats.values())[0].num_initial_mislabelled_ambiguous
      n_mislabelled_not_ambiguous = list(stats.values())[0].num_initial_mislabelled_not_ambiguous
  
      accuracy_beginning = 100 * float(n_samples - n_mislabelled_ambiguous - n_mislabelled_not_ambiguous) / n_samples
      ax.plot([0, 2 * (n_mislabelled_not_ambiguous + n_mislabelled_ambiguous)], [accuracy_beginning, 100], linestyle="--",
              label="Minimal sampler")
  
      # accuracy_easy_cases = 100 * float(n_samples - n_mislabelled_ambiguous) / n_samples
      # max_accuracy = max([np.max(_stat.accuracy) for _stat in stats.values()])
      # plt.scatter(2 * n_mislabelled_not_ambiguous, accuracy_easy_cases,
      #            marker='s', label="No non-ambiguous noise cases left")
      # ax.set_ylim(accuracy_beginning - 0.25, max_accuracy + 0.25)
  
      ax.legend(loc=legend_loc, fontsize=fontsize - 2)
code_tokens: def plot_minimal_sampler ( stats : Dict [ str , SimulationStatsDistribution ] , n_samples : int , ax : plt . Axes , fontsize : int = 12 , legend_loc : int = 2 ) -> None : n_mislabelled_ambiguous = list ( stats . values ( ) ) [ 0 ] . num_initial_mislabelled_ambiguous n_mislabelled_not_ambiguous = list ( stats . values ( ) ) [ 0 ] . num_initial_mislabelled_not_ambiguous accuracy_beginning = 100 * float ( n_samples - n_mislabelled_ambiguous - n_mislabelled_not_ambiguous ) / n_samples ax . plot ( [ 0 , 2 * ( n_mislabelled_not_ambiguous + n_mislabelled_ambiguous ) ] , [ accuracy_beginning , 100 ] , linestyle = "--" , label = "Minimal sampler" ) ax . legend ( loc = legend_loc , fontsize = fontsize - 2 )
short_docstring: Starting with one initial label, a noisy sample needs to be relabeled at least twice (best case scenario
  the sampled class during relabeling is equal to the correct label
short_docstring_tokens: Starting with one initial label a noisy sample needs to be relabeled at least twice ( best case scenario the sampled class during relabeling is equal to the correct label
comment: """
      Starting with one initial label, a noisy sample needs to be relabeled at least twice (best case scenario
      the sampled class during relabeling is equal to the correct label
      """$SEP$# accuracy_easy_cases = 100 * float(n_samples - n_mislabelled_ambiguous) / n_samples$SEP$# max_accuracy = max([np.max(_stat.accuracy) for _stat in stats.values()])$SEP$# plt.scatter(2 * n_mislabelled_not_ambiguous, accuracy_easy_cases,$SEP$#            marker='s', label="No non-ambiguous noise cases left")$SEP$# ax.set_ylim(accuracy_beginning - 0.25, max_accuracy + 0.25)