docstr: This is an abstract class for Scoring Functions (or expectation-based scan statistics).
  
  [1] introduces a property of many commonly used log-likelihood ratio scan statistics called
  Additive linear-time subset scanning (ALTSS) that allows for exact of efficient maximization of these
  statistics over all subsets of the data, without requiring an exhaustive search over all subsets and
  allows penalty terms to be included.
  
  [1] Speakman, S., Somanchi, S., McFowland III, E., & Neill, D. B. (2016). Penalized fast subset scanning.
  Journal of Computational and Graphical Statistics, 25(2), 382-404.
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code: def __init__(self, **kwargs):
      """
      This is an abstract class for Scoring Functions (or expectation-based scan statistics).
  
      [1] introduces a property of many commonly used log-likelihood ratio scan statistics called
      Additive linear-time subset scanning (ALTSS) that allows for exact of efficient maximization of these
      statistics over all subsets of the data, without requiring an exhaustive search over all subsets and
      allows penalty terms to be included.
  
      [1] Speakman, S., Somanchi, S., McFowland III, E., & Neill, D. B. (2016). Penalized fast subset scanning.
      Journal of Computational and Graphical Statistics, 25(2), 382-404.
      """
      self.kwargs = kwargs
      self.direction = kwargs.get("direction")
  
      directions = ["positive", "negative"]
      assert (
          self.direction in directions
      ), f"Expected one of {directions}, got {self.direction}"
  
imports: import numpy
label_desc: Use image node_id 1 to create a new ScoringFunction object  with example: obj = ScoringFunction()