def _make_extreme_raters()

in sourcecode/scoring/matrix_factorization/pseudo_raters.py [0:0]

• 24 lines of code
• 2 McCabe index (conditional complexity)

  def _make_extreme_raters(self, raterParams: pd.DataFrame, raterIdMap: pd.DataFrame):
    """Populates self.extremeRaters, which is a list of dicts with rater id info"""

    # Because LCB is turned off and we therefore don't use not-helpful pseudoratings anymore,
    # we only include the min rater intercept (which produces the highest possible note intercept)
    # If we were to use not-helpful pseudoratings, we would also include the max rater intercept.
    raterInterceptValues = [
      raterParams[c.internalRaterInterceptKey].min(),
    ]
    raterFactorValues = [
      raterParams[c.internalRaterFactor1Key].min(),
      0.0,
      raterParams[c.internalRaterFactor1Key].max(),
    ]

    self.extremeRaters = []
    i = 0
    for raterIntercept in raterInterceptValues:
      for raterFactor in raterFactorValues:
        # These pseudo-raters need to have IDs that don't conflict with real raterParticipantIds
        raterParticipantId = -1 - i
        raterIndex = raterIdMap[mf_c.raterIndexKey].max() + 1 + i

        self.extremeRaters.append(
          {
            c.raterParticipantIdKey: raterParticipantId,
            mf_c.raterIndexKey: raterIndex,
            c.internalRaterInterceptKey: raterIntercept,
            c.internalRaterFactor1Key: raterFactor,
          }
        )
        i += 1