class_docstr: Abstract class where is defined the basic behavior of a prediction
  algorithm.
  
  Keyword Args:
      baseline_options(dict, optional): If the algorithm needs to compute a
          baseline estimate, the ``baseline_options`` parameter is used to
          configure how they are computed. See
          :ref:`baseline_estimates_configuration` for usage.
class_docstr_tok: ["Abstract","class","where","is","defined","the","basic","behavior","of","a","prediction","algorithm",".","Keyword","Args",":","baseline_options","(","dict",",","optional",")",":","If","the","algorithm","needs","to","compute","a","baseline","estimate",",","the","``","baseline_options","``","parameter","is","used","to","configure","how","they","are","computed",".","See",":","ref",":","`","baseline_estimates_configuration","`","for","usage","."]
code: def __init__(self, **kwargs):
      self.bsl_options = kwargs.get("bsl_options", {})
      self.sim_options = kwargs.get("sim_options", {})
      if "user_based" not in self.sim_options:
          self.sim_options["user_based"] = True
  
file_path: Surprise/surprise/prediction_algorithms/algo_base.py
imports: import heapq
  from ..None import similarities
  from .optimize_baselines import baseline_als, baseline_sgd
  from .predictions import Prediction, PredictionImpossible
label_desc: Use image node_id 1 to create a new AlgoBase object  with example: obj = AlgoBase()