class_docstr: MatrixFactorizationBase contains common functionality for recommendation models.
  
  Attributes
  ----------
  item_factors : ndarray
      Array of latent factors for each item in the training set
  user_factors : ndarray
      Array of latent factors for each user in the training set
  num_threads : int
      The number of threads to use for batch recommendation calls and fitting the
      model. Setting to 0 will use all CPU cores on the machine
class_docstr_tok: ["MatrixFactorizationBase","contains","common","functionality","for","recommendation","models",".","Attributes","--","--","--","--","--","item_factors",":","ndarray","Array","of","latent","factors","for","each","item","in","the","training","set","user_factors",":","ndarray","Array","of","latent","factors","for","each","user","in","the","training","set","num_threads",":","int","The","number","of","threads","to","use","for","batch","recommendation","calls","and","fitting","the","model",".","Setting","to","0","will","use","all","CPU","cores","on","the","machine"]
code: def recommend_all(
      self,
      user_items,
      N=10,
      recalculate_user=False,
      filter_already_liked_items=True,
      filter_items=None,
      users_items_offset=0,
  ):
      warnings.warn(
          "recommend_all is deprecated. Use recommend with an array of userids instead",
          DeprecationWarning,
      )
  
      userids = np.arange(user_items.shape[0]) + users_items_offset
      if users_items_offset:
          adjusted = lil_matrix(
              (
                  user_items.shape[0] + users_items_offset,
                  user_items.shape[1],
              ),
              dtype=user_items.dtype,
          )
          adjusted[users_items_offset:] = user_items
          user_items = adjusted.tocsr()
  
      ids, _ = self.recommend(
          userids,
          user_items,
          N=N,
          filter_already_liked_items=filter_already_liked_items,
          filter_items=filter_items,
          recalculate_user=recalculate_user,
      )
      return ids
  
imports: import warnings
  import numpy
  from scipy.sparse import csr_matrix, lil_matrix
  from ..recommender_base import ModelFitError, RecommenderBase
  from .topk import topk
label_desc: Use image node_id 3 for calling the MatrixFactorizationBase obj's underlying member method code with example usage: obj.recommend_all(user_items, N, recalculate_user, filter_already_liked_items, filter_items, users_items_offset) and returns: ids