class_docstr: Recommend the most popular items.
  
  The :py:class:`PopScore` class is more flexible, and recommended for new code.
  
  Args:
      selector(CandidateSelector):
          The candidate selector to use. If ``None``, uses a new
          :class:`UnratedItemCandidateSelector`.
  
  Attributes:
      item_pop_(pandas.Series):
          Item rating counts (popularity)
class_docstr_tok: ["Recommend","the","most","popular","items",".","The",":","py",":","class",":","`","PopScore","`","class","is","more","flexible",",","and","recommended","for","new","code",".","Args",":","selector","(","CandidateSelector",")",":","The","candidate","selector","to","use",".","If","``","None","``",",","uses","a","new",":","class",":","`","UnratedItemCandidateSelector","`",".","Attributes",":","item_pop_","(","pandas.Series",")",":","Item","rating","counts","(","popularity",")"]
code: def __init__(self, selector=None):
      self.selector = selector
  
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 1 to create a new Popular object from inherited base classes: Recommender with example: obj = Popular(selector)