class_docstr: Graph Convolutional Matrix Completion (GCMC)
  
  Parameters
  ----------
  name: string, default: 'GCMC'
      The name of the recommender model.
  
  max_iter: int, default: 2000
      Maximum number of iterations or the number of epochs for SGD
  
  learning_rate: float, default: 0.01
      The learning rate for SGD
  
  optimizer: string, default: 'adam'. Supported values: 'adam','sgd'.
      The optimization method used for SGD
  
  activation_func: string, default: 'leaky'
      The activation function used in the GCMC model. Supported values:
      ['leaky', 'linear','sigmoid','relu', 'tanh']
  
  gcn_agg_units: int, default: 500
      The number of units in the graph convolutional layers
  
  gcn_out_units: int, default: 75
      The number of units in the output layer
  
  gcn_dropout: float, default: 0.7
      The dropout rate for the graph convolutional layers
  
  gcn_agg_accum: string, default:'stack'
      The graph convolutional layer aggregation type. Supported values:
      ['stack', 'sum']
  
  share_param: bool, default: False
      Whether to share the parameters in the graph convolutional layers
  
  gen_r_num_basis_func: int, default: 2
      The number of basis functions used in the generating rating function
  
  train_grad_clip: float, default: 1.0
      The gradient clipping value for training
  
  train_valid_interval: int, default: 1
      The validation interval for training
  
  train_early_stopping_patience: int, default: 100
      The patience for early stopping
  
  train_min_learning_rate: float, default: 0.001
      The minimum learning rate for SGD
  
  train_decay_patience: int, default: 50
      The patience for learning rate decay
  
  train_lr_decay_factor: float, default: 0.5
      The learning rate decay factor
  
  trainable: boolean, default: True
      When False, the model is not trained and Cornac
  
  verbose: boolean, default: True
      When True, some running logs are displayed
  
  seed: int, default: None
      Random seed for parameters initialization
  
  References
  ----------
  * van den Berg, R., Kipf, T. N., & Welling, M. (2018). Graph Convolutional Matrix Completion.
docstr: Transform the model to indexed dictionary for scoring purposes.
  
  Parameters
  ----------
  test_set: :obj:`cornac.data.Dataset`, required
      User-Item preference data.
docstr_tok: ["Transform","the","model","to","indexed","dictionary","for","scoring","purposes",".","Parameters","--","--","--","--","--","test_set",":",":","obj",":","`","cornac.data.Dataset","`",",","required","User-Item","preference","data","."]
code: def transform(self, test_set):
      """Transform the model to indexed dictionary for scoring purposes.
  
      Parameters
      ----------
      test_set: :obj:`cornac.data.Dataset`, required
          User-Item preference data.
      """
      self.u_i_rating_dict = self.model.predict(test_set)
  
file_path: cornac/cornac/models/gcmc/recom_gcmc.py
imports: import numpy
  from ..recommender import Recommender
label_desc: Use image node_id 3 for calling the GCMC obj's underlying member method code with example usage: obj.transform(test_set) without return types