class_docstr: Biased matrix factorization trained with alternating least squares :cite:p:`Zhou2008-bj`.  This
  is a prediction-oriented algorithm suitable for explicit feedback data, using the alternating
  least squares approach to compute :math:`P` and :math:`Q` to minimize the regularized squared
  reconstruction error of the ratings matrix.
  
  It provides two solvers for the optimization step (the `method` parameter):
  
  ``'cd'`` (the default)
      Coordinate descent :cite:p:`Takacs2011-ix`, adapted for a separately-trained bias model and
      to use weighted regularization as in the original ALS paper :cite:p:`Zhou2008-bj`.
  ``'lu'``
      A direct implementation of the original ALS :cite:p:`Zhou2008-bj` using LU-decomposition
      to solve for the optimized matrices.
  
  See the base class :class:`.MFPredictor` for documentation on
  the estimated parameters you can extract from a trained model.
  
  Args:
      features(int): the number of features to train
      iterations(int): the number of iterations to train
      reg(float): the regularization factor; can also be a tuple ``(ureg, ireg)`` to
          specify separate user and item regularization terms.
      damping(float): damping factor for the underlying bias.
      bias(bool or :class:`Bias`): the bias model.  If ``True``, fits a :class:`Bias` with
          damping ``damping``.
      method(str): the solver to use (see above).
      rng_spec:
          Random number generator or state (see :func:`seedbank.numpy_rng`).
      progress: a :func:`tqdm.tqdm`-compatible progress bar function
code: def predict_for_user(self, user, items, ratings=None):
      scores = None
      u_offset = None
      if ratings is not None and len(ratings) > 0:
          if self.bias:
              ratings, u_offset = self.bias.transform_user(ratings)
  
          ri_idxes = self.item_index_.get_indexer_for(ratings.index)
          ri_good = ri_idxes >= 0
          ri_it = ri_idxes[ri_good]
          ri_val = ratings.values[ri_good]
  
          # unpack regularization
          if isinstance(self.regularization, tuple):
              ureg, ireg = self.regularization
          else:
              ureg = self.regularization
  
          u_feat = _train_bias_row_lu(
              ri_it, ri_val, self.item_features_, ureg
          )
          scores = self.score_by_ids(user, items, u_feat)
      else:
          # look up user index
          scores = self.score_by_ids(user, items)
  
      if self.bias and ratings is not None and len(ratings) > 0:
          return self.bias.inverse_transform_user(
              user, scores, u_offset
          )
      elif self.bias:
          return self.bias.inverse_transform_user(user, scores)
      else:
          return scores
  
imports: import logging
  from collections import namedtuple
  import numpy
  from numba import njit, prange
  from csr import CSR
  from seedbank import numpy_rng
  from .bias import Bias
  from .mf_common import MFPredictor
  from ..data import sparse_ratings
  from ..None import util
  from ..math.solve import _dposv
label_desc: Use image node_id 7 for calling the BiasedMF obj's underlying member method code with example usage: obj.predict_for_user(user, items, ratings) and returns: self, self, scores