class_docstr: TriRank: Review-aware Explainable Recommendation by Modeling Aspects.
  
  Parameters
  ----------
  name: string, optional, default: 'TriRank'
      The name of the recommender model.
  
  alpha: float, optional, default: 1
      The weight of smoothness on user-item relation
  
  beta: float, optional, default: 1
      The weight of smoothness on item-aspect relation
  
  gamma: float, optional, default: 1
      The weight of smoothness on user-aspect relation
  
  eta_U: float, optional, default: 1
      The weight of fitting constraint on users
  
  eta_P: float, optional, default: 1
      The weight of fitting constraint on items
  
  eta_A: float, optional, default: 1
      The weight of fitting constraint on aspects
  
  max_iter: int, optional, default: 100
      Maximum number of iterations to stop online training. If set to `max_iter=-1`,         the online training will stop when model parameters are converged.
  
  trainable: boolean, optional, default: True
      When False, the model is not trained and Cornac assumes that the model already         pre-trained (R, X, Y, p, a, u are not None).
  
  verbose: boolean, optional, default: False
      When True, running logs are displayed.
  
  init_params: dictionary, optional, default: None
      List of initial parameters, e.g., init_params = {'R':R, 'X':X, 'Y':Y, 'p':p, 'a':a, 'u':u}
  
      R: csr_matrix, shape (n_users, n_items)
          The symmetric normalized of edge weight matrix of user-item relation, optional initialization via init_params
  
      X: csr_matrix, shape (n_users, n_aspects)
          The symmetric normalized of edge weight matrix of user-aspect relation, optional initialization via init_params
  
      Y: csr_matrix, shape (n_items, n_aspects)
          The symmetric normalized of edge weight matrix of item-aspect relation, optional initialization via init_params
  
      p: ndarray, shape (n_items,)
          Initialized item weights, optional initialization via init_params
  
      a: ndarray, shape (n_aspects,)
          Initialized aspect weights, optional initialization via init_params
  
      u: ndarray, shape (n_aspects,)
          Initialized user weights, optional initialization via init_params
  
  seed: int, optional, default: None
      Random seed for parameters initialization.
  
  References
  ----------
  He, Xiangnan, Tao Chen, Min-Yen Kan, and Xiao Chen. 2014.     TriRank: Review-aware Explainable Recommendation by Modeling Aspects.     In the 24th ACM international on conference on information and knowledge management (CIKM'15).     ACM, New York, NY, USA, 1661-1670. DOI: https://doi.org/10.1145/2806416.2806504
docstr: Predict the scores/ratings of a user for an item.
  
  Parameters
  ----------
  user_idx: int, required
      The index of the user for whom to perform score prediction.
  
  item_idx: int, optional, default: None
      The index of the item for which to perform score prediction.
      If None, scores for all known items will be returned.
  
  Returns
  -------
  res : A scalar or a Numpy array
      Relative scores that the user gives to the item or to all known items
docstr_tok: ["Predict","the","scores\/ratings","of","a","user","for","an","item",".","Parameters","--","--","--","--","--","user_idx",":","int",",","required","The","index","of","the","user","for","whom","to","perform","score","prediction",".","item_idx",":","int",",","optional",",","default",":","None","The","index","of","the","item","for","which","to","perform","score","prediction",".","If","None",",","scores","for","all","known","items","will","be","returned",".","Returns","--","--","--","-","res",":","A","scalar","or","a","Numpy","array","Relative","scores","that","the","user","gives","to","the","item","or","to","all","known","items"]
code: def score(self, user_idx, item_idx=None):
      """Predict the scores/ratings of a user for an item.
  
      Parameters
      ----------
      user_idx: int, required
          The index of the user for whom to perform score prediction.
  
      item_idx: int, optional, default: None
          The index of the item for which to perform score prediction.
          If None, scores for all known items will be returned.
  
      Returns
      -------
      res : A scalar or a Numpy array
          Relative scores that the user gives to the item or to all known items
  
      """
      if self.is_unknown_user(user_idx):
          raise ScoreException(
              "Can't make score prediction for user %d" % user_idx
          )
  
      if item_idx is not None and self.is_unknown_item(item_idx):
          raise ScoreException(
              "Can't make score prediction for item %d" % item_idx
          )
  
      item_scores, *_ = self._online_recommendation(user_idx)
      # Set already rated items to zero.
      item_scores[self.r_mat[user_idx].indices] = 0
  
      # Scale to match rating scale.
      item_scores = (
          item_scores
          * (self.max_rating - self.min_rating)
          / max(item_scores)
          + self.min_rating
      )
  
      return item_scores if item_idx is None else item_scores[item_idx]
  
file_path: cornac/cornac/models/trirank/recom_trirank.py
imports: import numpy
  from scipy.sparse import csr_matrix
  from tqdm.auto import tqdm
  from ..recommender import Recommender
  from ...utils import get_rng
  from ...utils.init_utils import uniform
  from ...exception import ScoreException
label_desc: Use image node_id 7 for calling the TriRank obj's underlying member method code with example usage: obj.score(user_idx, item_idx) and returns: unknown