sourcecode/scoring/pflip_model.py [398:440]:
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    return helpfulnessRatings

  def _get_tag_ratios(self, notes: pd.DataFrame, ratings: pd.DataFrame) -> pd.DataFrame:
    """Produce a DataFrame specifying the ratio of ratings for each note that included each tag.

    Args:
      notes: pd.DataFrame used to specify the universe of all notes to include.
      ratings: pd.DataFrame containing all ratings for feature extraction.

    Returns:
      pd.DataFrame containing one row per note and one column per rating tag.
    """
    tags = ratings[[c.noteIdKey] + c.helpfulTagsTSVOrder + c.notHelpfulTagsTSVOrder].copy()
    total_ratings = "total_ratings"
    tags[total_ratings] = 1
    tags = tags.groupby(c.noteIdKey).sum().reset_index(drop=False)
    tags[c.helpfulTagsTSVOrder + c.notHelpfulTagsTSVOrder] = tags[
      c.helpfulTagsTSVOrder + c.notHelpfulTagsTSVOrder
    ].divide(tags[total_ratings], axis=0)
    tags = notes[[c.noteIdKey]].merge(tags.drop(columns=total_ratings), how="left")
    return tags[[c.noteIdKey] + c.helpfulTagsTSVOrder + c.notHelpfulTagsTSVOrder]

  def _get_user_tag_ratings(
    self,
    notes: pd.DataFrame,
    ratings: pd.DataFrame,
    outCol: str,
    tagCols: List[str],
  ) -> pd.DataFrame:
    """Return a DataFrame with one row per note and a column with a nested list of user rating tags.

    Args:
      notes: pd.DataFrame used to specify the universe of all notes to include.
      ratings: pd.DataFrame containing all ratings for feature extraction.
      outCol: str identifying output column to contain list of user tag ratings
      tagCols: List of tag columns to include in outCol

    Returns:
      pd.DataFrame containing one row per note and one column containing all user rating tags.
    """
    ratingTags = ratings[[c.noteIdKey, c.raterParticipantIdKey] + tagCols].copy()
    tagStrs = np.array(tagCols)
    ratingTags[outCol] = [
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sourcecode/scoring/pflip_plus_model.py [700:742]:
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    return helpfulnessRatings

  def _get_tag_ratios(self, notes: pd.DataFrame, ratings: pd.DataFrame) -> pd.DataFrame:
    """Produce a DataFrame specifying the ratio of ratings for each note that included each tag.

    Args:
      notes: pd.DataFrame used to specify the universe of all notes to include.
      ratings: pd.DataFrame containing all ratings for feature extraction.

    Returns:
      pd.DataFrame containing one row per note and one column per rating tag.
    """
    tags = ratings[[c.noteIdKey] + c.helpfulTagsTSVOrder + c.notHelpfulTagsTSVOrder].copy()
    total_ratings = "total_ratings"
    tags[total_ratings] = 1
    tags = tags.groupby(c.noteIdKey).sum().reset_index(drop=False)
    tags[c.helpfulTagsTSVOrder + c.notHelpfulTagsTSVOrder] = tags[
      c.helpfulTagsTSVOrder + c.notHelpfulTagsTSVOrder
    ].divide(tags[total_ratings], axis=0)
    tags = notes[[c.noteIdKey]].merge(tags.drop(columns=total_ratings), how="left")
    return tags[[c.noteIdKey] + c.helpfulTagsTSVOrder + c.notHelpfulTagsTSVOrder]

  def _get_user_tag_ratings(
    self,
    notes: pd.DataFrame,
    ratings: pd.DataFrame,
    outCol: str,
    tagCols: List[str],
  ) -> pd.DataFrame:
    """Return a DataFrame with one row per note and a column with a nested list of user rating tags.

    Args:
      notes: pd.DataFrame used to specify the universe of all notes to include.
      ratings: pd.DataFrame containing all ratings for feature extraction.
      outCol: str identifying output column to contain list of user tag ratings
      tagCols: List of tag columns to include in outCol

    Returns:
      pd.DataFrame containing one row per note and one column containing all user rating tags.
    """
    ratingTags = ratings[[c.noteIdKey, c.raterParticipantIdKey] + tagCols].copy()
    tagStrs = np.array(tagCols)
    ratingTags[outCol] = [
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