class_docstr: Common predictor for matrix factorization.
  
  Attributes:
      user_index_(pandas.Index): Users in the model (length=:math:`m`).
      item_index_(pandas.Index): Items in the model (length=:math:`n`).
      user_features_(numpy.ndarray): The :math:`m \times k` user-feature matrix.
      item_features_(numpy.ndarray): The :math:`n \times k` item-feature matrix.
class_docstr_tok: ["Common","predictor","for","matrix","factorization",".","Attributes",":","user_index_","(","pandas.Index",")",":","Users","in","the","model","(","length=",":","math",":","`","m","`",")",".","item_index_","(","pandas.Index",")",":","Items","in","the","model","(","length=",":","math",":","`","n","`",")",".","user_features_","(","numpy.ndarray",")",":","The",":","math",":","`","m","\\times","k","`","user-feature","matrix",".","item_features_","(","numpy.ndarray",")",":","The",":","math",":","`","n","\\times","k","`","item-feature","matrix","."]
docstr: The number of users.
docstr_tok: ["The","number","of","users","."]
code: def n_users(self):
      "The number of users."
      return len(self.user_index_)
  
file_path: lkpy/lenskit/algorithms/mf_common.py
imports: import logging
  import numpy
  import pandas
  from .None import Predictor
label_desc: Use image node_id 2 for calling the MFPredictor obj's underlying member method code with example usage: obj.n_users() and returns: len