sourcecode/scoring/mf_base_scorer.py [1170:1203]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    torch.set_num_threads(self._threads)
    logger.info(
      f"score_final: Torch intra-op parallelism for {self.get_name()} set to: {torch.get_num_threads()}"
    )

    # Filter unfiltered params to just params for this scorer (with copy).
    # Avoid editing the dataframe in FinalScoringArgs, which is shared across scorers.
    prescoringNoteModelOutput = scoringArgs.prescoringNoteModelOutput[
      scoringArgs.prescoringNoteModelOutput[c.scorerNameKey] == self.get_name()
    ].drop(columns=c.scorerNameKey, inplace=False)

    if scoringArgs.prescoringRaterModelOutput is None:
      return self._return_empty_final_scores()
    prescoringRaterModelOutput = scoringArgs.prescoringRaterModelOutput[
      scoringArgs.prescoringRaterModelOutput[c.scorerNameKey] == self.get_name()
    ].drop(columns=c.scorerNameKey, inplace=False)

    if self.get_name() not in scoringArgs.prescoringMetaOutput.metaScorerOutput:
      logger.info(
        f"Scorer {self.get_name()} not found in prescoringMetaOutput; returning empty scores from final scoring."
      )
      return self._return_empty_final_scores()
    prescoringMetaScorerOutput = scoringArgs.prescoringMetaOutput.metaScorerOutput[self.get_name()]

    # Filter raw input
    with self.time_block("Filter input"):
      ratings, noteStatusHistory = self._filter_input(
        scoringArgs.noteTopics,
        scoringArgs.ratings,
        scoringArgs.noteStatusHistory,
        scoringArgs.userEnrollment,
      )
      # If there are no ratings left after filtering, then return empty dataframes.
      if len(ratings) == 0:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



sourcecode/scoring/scorer.py [366:399]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    torch.set_num_threads(self._threads)
    logger.info(
      f"score_final: Torch intra-op parallelism for {self.get_name()} set to: {torch.get_num_threads()}"
    )

    # Filter unfiltered params to just params for this scorer (with copy).
    # Avoid editing the dataframe in FinalScoringArgs, which is shared across scorers.
    prescoringNoteModelOutput = scoringArgs.prescoringNoteModelOutput[
      scoringArgs.prescoringNoteModelOutput[c.scorerNameKey] == self.get_name()
    ].drop(columns=c.scorerNameKey, inplace=False)

    if scoringArgs.prescoringRaterModelOutput is None:
      return self._return_empty_final_scores()
    prescoringRaterModelOutput = scoringArgs.prescoringRaterModelOutput[
      scoringArgs.prescoringRaterModelOutput[c.scorerNameKey] == self.get_name()
    ].drop(columns=c.scorerNameKey, inplace=False)

    if self.get_name() not in scoringArgs.prescoringMetaOutput.metaScorerOutput:
      logger.info(
        f"Scorer {self.get_name()} not found in prescoringMetaOutput; returning empty scores from final scoring."
      )
      return self._return_empty_final_scores()
    prescoringMetaScorerOutput = scoringArgs.prescoringMetaOutput.metaScorerOutput[self.get_name()]

    # Filter raw input
    with self.time_block("Filter input"):
      ratings, noteStatusHistory = self._filter_input(
        scoringArgs.noteTopics,
        scoringArgs.ratings,
        scoringArgs.noteStatusHistory,
        scoringArgs.userEnrollment,
      )
      # If there are no ratings left after filtering, then return empty dataframes.
      if len(ratings) == 0:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -