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: Fit the model to observations.
  
  Parameters
  ----------
  train_set: :obj:`cornac.data.Dataset`, required
      User-Item preference data as well as additional modalities.
  
  val_set: :obj:`cornac.data.Dataset`, optional, default: None
      User-Item preference data for model selection purposes (e.g., early stopping).
  
  Returns
  -------
  self : object
docstr_tok: ["Fit","the","model","to","observations",".","Parameters","--","--","--","--","--","train_set",":",":","obj",":","`","cornac.data.Dataset","`",",","required","User-Item","preference","data","as","well","as","additional","modalities",".","val_set",":",":","obj",":","`","cornac.data.Dataset","`",",","optional",",","default",":","None","User-Item","preference","data","for","model","selection","purposes","(","e.g.",",","early","stopping",")",".","Returns","--","--","--","-","self",":","object"]
code: def fit(self, train_set, val_set=None):
      """Fit the model to observations.
  
      Parameters
      ----------
      train_set: :obj:`cornac.data.Dataset`, required
          User-Item preference data as well as additional modalities.
  
      val_set: :obj:`cornac.data.Dataset`, optional, default: None
          User-Item preference data for model selection purposes (e.g., early stopping).
  
      Returns
      -------
      self : object
      """
      Recommender.fit(self, train_set, val_set)
      self._init(train_set)
  
      if not self.trainable:
          return self
  
      # Offline training: Build item-aspect matrix X and user-aspect matrix Y
      self._create_matrices(train_set)
      return self
  
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 5 for calling the TriRank obj's underlying member method code with example usage: obj.fit(train_set, val_set) and returns: self, self