QualityEvalAlg
QualityEvalAlg is an abstract base class for the algorithms that evaluate the quality of a group while group formation is performed. Currently, three types of algorithm is supported: group-prediction approaches evaluate the group as a whole (GroupQualityEvalAlg); regression approaches that compute a new learner state based on a profile and derive the quality of the profile for the learner (RegQualityEvalAlg); and tabular approaches that predict the quality of a profile to a learner based on a profile value table (TabQualityEvalAlg).
Note: this class was previously called RegressionAlg and some code versions still consider this name. Besides, consider that it will still suffer deeper refactoring.
+ QualityEvalAlg(playerModelBridge: PlayerModelBridge): void| Name: expected type | Default value | Description |
|---|---|---|
| playerModelBridge: PlayerModelBridge | - | The connector for the player data storage. |
+ isTabular(): boolA method that informs if the current QualityEvalAlg is tabular.
+ isGroupPredict(): boolA method that informs if the current QualityEvalAlg is based on group evaluation.
+ predict(profile: InteractionsProfile, playerId: int): decimal(abstract) A method that returns a quality of a interactions profile for a given learner.
+ groupPredict(groupIds: int[]): decimal(abstract) A method that returns a quality of a group containing the players with ids in groupIds.
+ getCompPercentage(): decimal(auxiliary) A method that returns the completion percentage of a quality evaluation procedure.
regressionAlg = KNNRegression(playerBridge, 5)
print(regressionAlg.predict(InteractionsProfile(0.2,0.5,0.6,0.2), 5))
#return: <PlayerStructs.PlayerState object at 0x7fe5452a0b10>