Method GetTrainBatch

src/model/instance_reader/eges_inst_reader.cc:115–153

/ Read batch data from file for training */ /

  /* Read batch data from file for training */
  /************************************************************************/
  bool GetTrainBatch(Instance* inst) {
    std::vector<SeqValue> values;
    if (!NextInstanceBatch<SeqValue>(inst, 1, &values)) {
      return false;
    }
    seqs_ = Collect<SeqValue, vec_int_t>(values, &SeqValue::nodes);
    inst_util::CheckLengthValid(seqs_);

    auto& seq = seqs_[0];
    DXCHECK(graph_client_->IndepSampleNegative(num_neg_, seq, seq,
                                               &neg_nodes_list_));

    // Generate context and negative nodes based on sequence data.
    inst_util::GenerateContext(window_size_, window_size_, seq,
                               &context_nodes_list_);

    // 1. Fill Instance(edge and label)
    flow_->FillEdgeAndLabel(inst, instance_name::X_SRC_NODE_NAME,
                            instance_name::X_DST_NODE_NAME, deepx_core::Y_NAME,
                            seq, context_nodes_list_, neg_nodes_list_);

    // Get unique src node to avoid repeated computation
    Unique(seq, &nodes_);
    flow_->FillNodeOrIndex(inst, instance_name::X_UNIQUE_NODE_NAME, nodes_,
                           nullptr);
    flow_->FillNodeFeature(inst, instance_name::X_NODE_FEATURE_NAME, nodes_,
                           true);
    // 3. Fill index
    indexing_wrapper_->Clear();
    indexing_wrapper_->BuildFrom(nodes_);
    const auto& indexing = indexing_wrapper_->subgraph_indexing(ns_id_)[0];
    auto* src_node_ptr =
        &inst->get_or_insert<csr_t>(instance_name::X_SRC_NODE_NAME);
    flow_->FillNodeOrIndex(inst, instance_name::X_SRC_ID_NAME, *src_node_ptr,
                           &indexing);

    inst->set_batch(1);
    return true;
  }

  /************************************************************************/
  /* Read batch data from file for predicting */