class_docstr: Predicts user ratings of items.  Predictions are really estimates of the user's like or
  dislike, and the ``Predictor`` interface makes no guarantees about their scale or
  granularity.
  
  :canonical: lenskit.Predictor
class_docstr_tok: ["Predicts","user","ratings","of","items",".","Predictions","are","really","estimates","of","the","user","'s","like","or","dislike",",","and","the","``","Predictor","``","interface","makes","no","guarantees","about","their","scale","or","granularity",".",":","canonical",":","lenskit.Predictor"]
docstr: Compute predictions for a user and items.
  
  Args:
      user: the user ID
      items (array-like): the items to predict
      ratings (pandas.Series):
          the user's ratings (indexed by item id); if provided, they may be used to
          override or augment the model's notion of a user's preferences.
  
  Returns:
      pandas.Series: scores for the items, indexed by item id.
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code: def predict_for_user(self, user, items, ratings=None):
      """
      Compute predictions for a user and items.
  
      Args:
          user: the user ID
          items (array-like): the items to predict
          ratings (pandas.Series):
              the user's ratings (indexed by item id); if provided, they may be used to
              override or augment the model's notion of a user's preferences.
  
      Returns:
          pandas.Series: scores for the items, indexed by item id.
      """
      raise NotImplementedError()
  
file_path: lkpy/lenskit/algorithms/__init__.py
imports: from abc import ABCMeta, abstractmethod
  import inspect
label_desc: Use image node_id 2 for calling the Predictor obj's underlying member method code with example usage: obj.predict_for_user(user, items, ratings) without return types