class_docstr: Analyzer.
  
  Given a run_id, we can do lots of fun things!
class_docstr_tok: ["Analyzer",".","Given","a","run_id",",","we","can","do","lots","of","fun","things","!"]
docstr: Return the fraction of time that Turkers selected each checkbox.
  
  Results are cut both (1) by matchup and winner and (2) by just the winner. Each
  checkbox represents one reason that the Turkers could have chosen the speaker
  that they did.
docstr_tok: ["Return","the","fraction","of","time","that","Turkers","selected","each","checkbox",".","Results","are","cut","both","(","1",")","by","matchup","and","winner","and","(","2",")","by","just","the","winner",".","Each","checkbox","represents","one","reason","that","the","Turkers","could","have","chosen","the","speaker","that","they","did","."]
code: def _compile_checkbox_stats(self) -> Dict[str, pd.DataFrame]:
      """
      Return the fraction of time that Turkers selected each checkbox.
  
      Results are cut both (1) by matchup and winner and (2) by just the winner. Each
      checkbox represents one reason that the Turkers could have chosen the speaker
      that they did.
      """
      checkbox_columns = [
          col
          for col in self.dataframe.columns
          if col.startswith(self.checkbox_prefix)
      ]
      group_column_types = {
          "matchup_and_winner": ["matchup", "winner"],
          "winner": ["winner"],
      }
      grouped_dataframes = {}
      for group_type, group_columns in group_column_types.items():
          selected_columns = (
              self.dataframe[group_columns + checkbox_columns]
              .rename(
                  columns={
                      col: col[len(self.checkbox_prefix) :]
                      for col in checkbox_columns
                  }
              )
              .set_index(group_columns)
              .fillna(False)
          )
          grouped_dataframes[group_type] = selected_columns.groupby(
              group_columns
          ).mean()
      return grouped_dataframes
  
imports: import hashlib
  import json
  import os
  from copy import deepcopy
  from datetime import datetime
  from typing import Dict, Any, List, Optional
  import numpy
  import pandas
  from IPython.core.display import HTML
  from mephisto.tools.data_browser import DataBrowser
  from mephisto.abstractions.databases.local_database import LocalMephistoDB
  from mephisto.data_model.unit import Unit
  from mephisto.data_model.worker import Worker
  from scipy.stats import binom_test
  from parlai.core.params import ParlaiParser
  from parlai.crowdsourcing.tasks.acute_eval.acute_eval_blueprint import BLUEPRINT_TYPE
  from parlai.crowdsourcing.tasks.acute_eval.fast_acute_blueprint import FAST_ACUTE_BLUEPRINT_TYPE
  from parlai.crowdsourcing.tasks.acute_eval.util import get_hashed_combo_path
label_desc: Use image node_id 14 for calling the AcuteAnalyzer obj's underlying member method code with example usage: obj._compile_checkbox_stats() and returns: grouped_dataframes