class_docstr: Data processing class for GCN models which use implicit feedback.
  
  Initialize train and test set, create normalized adjacency matrix and sample data for training epochs.
docstr: Record items interated with each user in a dataframe self.interact_status, and create adjacency
  matrix self.R.
docstr_tok: ["Record","items","interated","with","each","user","in","a","dataframe","self.interact_status",",","and","create","adjacency","matrix","self.R","."]
code: def _init_train_data(self):
      """Record items interated with each user in a dataframe self.interact_status, and create adjacency
      matrix self.R.
  
      """
      self.interact_status = (
          self.train.groupby(self.col_user)[self.col_item]
          .apply(set)
          .reset_index()
          .rename(
              columns={self.col_item: self.col_item + "_interacted"}
          )
      )
      self.R = sp.dok_matrix(
          (self.n_users, self.n_items), dtype=np.float32
      )
      self.R[self.train[self.col_user], self.train[self.col_item]] = 1.0
  
file_path: recommenders/recommenders/models/deeprec/DataModel/ImplicitCF.py
imports: import random
  import numpy
  import pandas
  import scipy.sparse
  from recommenders.utils.constants import DEFAULT_ITEM_COL, DEFAULT_USER_COL, DEFAULT_RATING_COL, DEFAULT_PREDICTION_COL
label_desc: Use image node_id 4 for calling the ImplicitCF obj's underlying member method code with example usage: obj._init_train_data() without return types