class_docstr: The Fallback algorithm predicts with its first component, uses the second to fill in
  missing values, and so forth.
class_docstr_tok: ["The","Fallback","algorithm","predicts","with","its","first","component",",","uses","the","second","to","fill","in","missing","values",",","and","so","forth","."]
code: def predict_for_user(self, user, items, ratings=None):
      remaining = pd.Index(items)
      preds = None
  
      for algo in self.algorithms:
          _logger.debug(
              "predicting for %d items for user %s",
              len(remaining),
              user,
          )
          aps = algo.predict_for_user(user, remaining, ratings=ratings)
          aps = aps[aps.notna()]
          if preds is None:
              preds = aps
          else:
              preds = pd.concat([preds, aps])
          remaining = remaining.difference(preds.index)
          if len(remaining) == 0:
              break
  
      return preds.reindex(items)
  
imports: import logging
  from collections.abc import Iterable, Sequence
  import pandas
  import numpy
  from ..data import sparse_ratings
  from .None import Predictor, Recommender, CandidateSelector
  from ..util import derivable_rng
  from .bias import Bias
  from .ranking import TopN
label_desc: Use image node_id 3 for calling the Fallback obj's underlying member method code with example usage: obj.predict_for_user(user, items, ratings) and returns: preds