class_docstr: A supervised anomaly detector.
class_docstr_tok: ["A","supervised","anomaly","detector","."]
docstr: Return an outlier score.
  
  A high score is indicative of an anomaly. A low score corresponds a normal observation.
  
  Parameters
  ----------
  x
      A dictionary of features.
  
  Returns
  -------
  An anomaly score. A high score is indicative of an anomaly. A low score corresponds a
  normal observation.
docstr_tok: ["Return","an","outlier","score",".","A","high","score","is","indicative","of","an","anomaly",".","A","low","score","corresponds","a","normal","observation",".","Parameters","--","--","--","--","--","x","A","dictionary","of","features",".","Returns","--","--","--","-","An","anomaly","score",".","A","high","score","is","indicative","of","an","anomaly",".","A","low","score","corresponds","a","normal","observation","."]
code: def score_one(self, x: dict, y: base.typing.Target) -> float:
      """Return an outlier score.
  
      A high score is indicative of an anomaly. A low score corresponds a normal observation.
  
      Parameters
      ----------
      x
          A dictionary of features.
  
      Returns
      -------
      An anomaly score. A high score is indicative of an anomaly. A low score corresponds a
      normal observation.
  
      """
  
file_path: river/river/anomaly/base.py
imports: from __future__ import annotations
  import abc
  from river import base
label_desc: Use image node_id 2 for calling the SupervisedAnomalyDetector obj's underlying member method code with example usage: obj.score_one(x, y) without return types