class_docstr: Item-item nearest-neighbor collaborative filtering with ratings. This item-item implementation
  is not terribly configurable; it hard-codes design decisions found to work well in the previous
  Java-based LensKit code :cite:p:`Ekstrand2011-bp`.  This implementation is based on the description
  of item-based CF by :cite:t:`Deshpande2004-ht`, and produces results equivalent to Java LensKit.
  
  The k-NN predictor supports several aggregate functions:
  
  ``weighted-average``
      The weighted average of the user's rating values, using item-item similarities as
      weights.
  
  ``sum``
      The sum of the similarities between the target item and the user's rated items,
      regardless of the rating the user gave the items.
  
  Args:
      nnbrs(int):
          the maximum number of neighbors for scoring each item (``None`` for unlimited)
      min_nbrs(int): the minimum number of neighbors for scoring each item
      min_sim(float): minimum similarity threshold for considering a neighbor
      save_nbrs(float):
          the number of neighbors to save per item in the trained model
          (``None`` for unlimited)
      feedback(str):
          Control how feedback should be interpreted.  Specifies defaults for the other
          settings, which can be overridden individually; can be one of the following values:
  
          ``explicit``
              Configure for explicit-feedback mode: use rating values, center ratings, and
              use the ``weighted-average`` aggregate method for prediction.  This is the
              default setting.
  
          ``implicit``
              Configure for implicit-feedback mode: ignore rating values, do not center ratings,
              and use the ``sum`` aggregate method for prediction.
      center(bool):
          whether to normalize (mean-center) rating vectors prior to computing similarities
          and aggregating user rating values.  Defaults to ``True``; turn this off when working
          with unary data and other data types that don't respond well to centering.
      aggregate(str):
          the type of aggregation to do. Can be ``weighted-average`` (the default) or ``sum``.
      use_ratings(bool):
          whether or not to use the rating values. If ``False``, it ignores rating values and
          considers an implicit feedback signal of 1 for every (user,item) pair present.
  
  Attributes:
      item_index_(pandas.Index): the index of item IDs.
      item_means_(numpy.ndarray): the mean rating for each known item.
      item_counts_(numpy.ndarray): the number of saved neighbors for each item.
      sim_matrix_(matrix.CSR): the similarity matrix.
      user_index_(pandas.Index): the index of known user IDs for the rating matrix.
      rating_matrix_(matrix.CSR): the user-item rating matrix for looking up users' ratings.
code: def predict_for_user(self, user, items, ratings=None):
      _logger.debug("predicting %d items for user %s", len(items), user)
      if ratings is None:
          if user not in self.user_index_:
              _logger.debug(
                  "user %s missing, returning empty predictions", user
              )
              return pd.Series(np.nan, index=items)
          upos = self.user_index_.get_loc(user)
          ratings = pd.Series(
              self.rating_matrix_.row_vs(upos),
              index=pd.Index(
                  self.item_index_[self.rating_matrix_.row_cs(upos)]
              ),
          )
  
      if not ratings.index.is_unique:
          wmsg = "user {} has duplicate ratings, this is likely to cause problems".format(
              user
          )
          warnings.warn(wmsg, DataWarning)
  
      # set up rating array
      # get rated item positions & limit to in-model items
      n_items = len(self.item_index_)
      ri_pos = self.item_index_.get_indexer(ratings.index)
      m_rates = ratings[ri_pos >= 0]
      ri_pos = ri_pos[ri_pos >= 0]
      rate_v = np.full(n_items, np.nan, dtype=np.float_)
      rated = np.zeros(n_items, dtype="bool")
      # mean-center the rating array
      if self.center:
          rate_v[ri_pos] = m_rates.values - self.item_means_[ri_pos]
      else:
          rate_v[ri_pos] = m_rates.values
      rated[ri_pos] = True
  
      _logger.debug(
          "user %s: %d of %d rated items in model",
          user,
          len(ri_pos),
          len(ratings),
      )
      assert np.sum(np.logical_not(np.isnan(rate_v))) == len(ri_pos)
      assert np.all(np.isnan(rate_v) == np.logical_not(rated))
  
      # set up item result vector
      # ipos will be an array of item indices
      i_pos = self.item_index_.get_indexer(items)
      i_pos = i_pos[i_pos >= 0]
      _logger.debug(
          "user %s: %d of %d requested items in model",
          user,
          len(i_pos),
          len(items),
      )
  
      # now we take a first pass through the data to count _viable_ targets
      # This computes the number of neighbors (and their weight sum) for
      # each target item based on the user's ratings, allowing us to fast-path
      # other computations and avoid as many neighbor truncations as possible
      i_cts, i_sums, i_nbrs = self._count_viable_targets(i_pos, ri_pos)
      viable = i_cts >= self.min_nbrs
      i_pos = i_pos[viable]
      i_cts = i_cts[viable]
      i_sums = i_sums[viable]
      i_nbrs = i_nbrs[viable]
      _logger.debug(
          "user %s: %d of %d requested items possibly reachable",
          user,
          len(i_pos),
          len(items),
      )
  
      # look for some fast paths
      if self.aggregate == self.AGG_SUM and self.min_sim >= 0:
          # similarity sums are all we need
          if self.nnbrs >= 0:
              fast_mask = i_cts <= self.nnbrs
              fast_items = i_pos[fast_mask]
              fast_scores = i_sums[fast_mask]
              slow_items = i_pos[~fast_mask]
          else:
              fast_items = i_pos
              fast_scores = i_sums
              slow_items = np.array([], dtype="i4")
  
          _logger.debug(
              "user %s: using fast-path similarity sum for %d items",
              user,
              len(fast_items),
          )
  
          if len(slow_items):
              iscores = _predict_sum(
                  self.sim_matrix_,
                  len(self.item_index_),
                  (self.min_nbrs, self.nnbrs),
                  rate_v,
                  rated,
                  slow_items,
              )
          else:
              iscores = np.full(len(self.item_index_), np.nan)
          iscores[fast_items] = fast_scores
  
      elif self.aggregate == self.AGG_WA and self.min_nbrs == 1:
          # fast-path single-neighbor targets - common in sparse data
          fast_mask = i_cts == 1
          fast_items = i_pos[fast_mask]
          fast_scores = rate_v[i_nbrs[fast_mask]]
          if self.min_sim < 0:
              fast_scores *= np.sign(i_sums[fast_mask])
          _logger.debug(
              "user %s: fast-pathed %d scores", user, len(fast_scores)
          )
  
          slow_items = i_pos[i_cts > 1]
          iscores = _predict_weighted_average(
              self.sim_matrix_,
              len(self.item_index_),
              (self.min_nbrs, self.nnbrs),
              rate_v,
              rated,
              slow_items,
          )
          iscores[fast_items] = fast_scores
      else:
          # now compute the predictions
          _logger.debug("user %s: taking the slow path", user)
          agg = _predictors[self.aggregate]
          iscores = agg(
              self.sim_matrix_,
              len(self.item_index_),
              (self.min_nbrs, self.nnbrs),
              rate_v,
              rated,
              i_pos,
          )
  
      if self.center and self.aggregate in self.RATING_AGGS:
          iscores += self.item_means_
  
      results = pd.Series(iscores, index=self.item_index_)
      results = results.reindex(items, fill_value=np.nan)
  
      _logger.debug(
          "user %s: predicted for %d of %d items",
          user,
          results.notna().sum(),
          len(items),
      )
  
      return results
  
imports: from sys import intern
  import logging
  import warnings
  import pandas
  import numpy
  import scipy.sparse
  import scipy.sparse.linalg
  from csr import CSR, create_from_sizes, create_empty
  import csr.kernel
  from numba import njit, prange
  from numba.typed import List
  from lenskit import util, DataWarning, ConfigWarning
  from lenskit.data import sparse_ratings
  from lenskit.sharing import in_share_context
  from lenskit.util.parallel import is_mp_worker
  from lenskit.util.accum import kvp_minheap_insert, kvp_minheap_sort
  from .None import Predictor
label_desc: Use image node_id 7 for calling the ItemItem obj's underlying member method code with example usage: obj.predict_for_user(user, items, ratings) and returns: results, pd