Group modeling and other assorted updates

sourcecode/scoring/constants.py

@@ -16,6 +16,7 @@
 
 maxTrainError = 0.09
 
+coreFlipPct = 0.15
 expansionFlipPct = 0.19
 maxReruns = 5
 
@@ -41,6 +42,7 @@
 summaryKey = "summary"
 authorTopNotHelpfulTagValues = "authorTopNotHelpfulTagValues"
 modelingPopulationKey = "modelingPopulation"
+modelingGroupKey = "modelingGroup"
 
 # TSV Values
 notHelpfulValueTsv = "NOT_HELPFUL"
@@ -127,6 +129,14 @@ def rater_factor_key(i):
 coverageRatingStatusKey = "coverageRatingStatus"
 coverageNoteInterceptMaxKey = "coverageNoteInterceptMax"
 coverageNoteInterceptMinKey = "coverageNoteInterceptMin"
+# Group Model
+groupNoteInterceptKey = "groupNoteIntercept"
+groupNoteFactor1Key = "groupNoteFactor1"
+groupRatingStatusKey = "groupRatingStatus"
+groupNoteInterceptMaxKey = "groupNoteInterceptMax"
+groupNoteInterceptMinKey = "groupNoteInterceptMin"
+groupRaterInterceptKey = "groupRaterIntercept"
+groupRaterFactor1Key = "groupRaterFactor1"
 
 # Ids and Indexes
 noteIdKey = "noteId"
@@ -364,6 +374,16 @@ def rater_factor_key(i):
 userEnrollmentTSVTypes = [dtype for (_, dtype) in userEnrollmentTSVColumnsAndTypes]
 userEnrollmentTSVTypeMapping = {col: dtype for (col, dtype) in userEnrollmentTSVColumnsAndTypes}
 
+# TODO: delete expanded user enrollment definition once modeling group is fully rolled out
+userEnrollmentExpandedTSVColumnsAndTypes = userEnrollmentTSVColumnsAndTypes + [
+  (modelingGroupKey, np.float64)
+]
+userEnrollmentExpandedTSVColumns = [col for (col, _) in userEnrollmentExpandedTSVColumnsAndTypes]
+userEnrollmentExpandedTSVTypes = [dtype for (_, dtype) in userEnrollmentExpandedTSVColumnsAndTypes]
+userEnrollmentExpandedTSVTypeMapping = {
+  col: dtype for (col, dtype) in userEnrollmentExpandedTSVColumnsAndTypes
+}
+
 noteInterceptMaxKey = "internalNoteIntercept_max"
 noteInterceptMinKey = "internalNoteIntercept_min"
 noteParameterUncertaintyTSVMainColumnsAndTypes = [
@@ -421,6 +441,16 @@ def rater_factor_key(i):
   + incorrectFilterColumns
 )
 
+deprecatedNoteModelOutputColumns = frozenset(
+  {
+    coverageNoteInterceptKey,
+    coverageNoteFactor1Key,
+    coverageRatingStatusKey,
+    coverageNoteInterceptMinKey,
+    coverageNoteInterceptMaxKey,
+  }
+)
+
 noteModelOutputTSVColumnsAndTypes = [
   (noteIdKey, np.int64),
   (coreNoteInterceptKey, np.double),
@@ -451,9 +481,20 @@ def rater_factor_key(i):
   (expansionNoteInterceptMaxKey, np.double),
   (coverageNoteInterceptMinKey, np.double),
   (coverageNoteInterceptMaxKey, np.double),
+  (groupNoteInterceptKey, np.double),
+  (groupNoteFactor1Key, np.double),
+  (groupRatingStatusKey, np.str),
+  (groupNoteInterceptMaxKey, np.double),
+  (groupNoteInterceptMinKey, np.double),
+  (modelingGroupKey, np.float64),
 ]
 noteModelOutputTSVColumns = [col for (col, dtype) in noteModelOutputTSVColumnsAndTypes]
 noteModelOutputTSVTypeMapping = {col: dtype for (col, dtype) in noteModelOutputTSVColumnsAndTypes}
+deprecatedNoteModelOutputTSVColumnsAndTypes = [
+  (col, dtype)
+  for (col, dtype) in noteModelOutputTSVColumnsAndTypes
+  if col in deprecatedNoteModelOutputColumns
+]
 
 raterModelOutputTSVColumnsAndTypes = [
   (raterParticipantIdKey, np.int64),
@@ -481,6 +522,9 @@ def rater_factor_key(i):
   (isEmergingWriterKey, np.bool_),
   (aggregateRatingReceivedTotal, pd.Int64Dtype()),
   (timestampOfLastEarnOut, np.double),
+  (groupRaterInterceptKey, np.double),
+  (groupRaterFactor1Key, np.double),
+  (modelingGroupKey, np.float64),
 ]
 raterModelOutputTSVColumns = [col for (col, dtype) in raterModelOutputTSVColumnsAndTypes]
 raterModelOutputTSVTypeMapping = {col: dtype for (col, dtype) in raterModelOutputTSVColumnsAndTypes}

sourcecode/scoring/enums.py

@@ -6,8 +6,10 @@ class Scorers(Enum):
   """Exhaustive list of all scorers to simplify setting enabled/disabled scorers."""
 
   MFCoreScorer = auto()
-  MFCoverageScorer = auto()
   MFExpansionScorer = auto()
+  # Note that the MFGroupScorer value controls whether *all* group scorers are instantiated,
+  # not just a single MFGroupScorer instance.
+  MFGroupScorer = auto()
 
 
 def scorers_from_csv(csv: str) -> Set[Scorers]:

sourcecode/scoring/mf_base_scorer.py

@@ -87,6 +87,10 @@ def __init__(
     self._weightedTotalVotes = weightedTotalVotes
     self._mfRanker = matrix_factorization.MatrixFactorization()
 
+  def get_crh_threshold(self) -> float:
+    """Return CRH threshold for general scoring logic."""
+    return self._crhThreshold
+
   def get_scored_notes_cols(self) -> List[str]:
     """Returns a list of columns which should be present in the scoredNotes output."""
     return [
@@ -160,6 +164,7 @@ def _score_notes_and_users(
         userScores pd.DataFrame: one row per user containing a column for each helpfulness score.
     """
     if self._seed is not None:
+      print(f"seeding with {self._seed}")
       torch.manual_seed(self._seed)
 
     # Removes ratings where either (1) the note did not receive enough ratings, or