foreign_storage::anonymous_namespace{InternalMLModelMetadataDataWrapper.cpp} Namespace Reference

Functions
void	populate_import_buffers_for_ml_model_metadata (const std::vector< MLModelMetadata > &ml_models_metadata, std::map< std::string, import_export::TypedImportBuffer * > &import_buffers)

Function Documentation

void foreign_storage::anonymous_namespace{InternalMLModelMetadataDataWrapper.cpp}::populate_import_buffers_for_ml_model_metadata	(	const std::vector< MLModelMetadata > &	ml_models_metadata,
		std::map< std::string, import_export::TypedImportBuffer * > &	import_buffers
	)

Definition at line 35 of file InternalMLModelMetadataDataWrapper.cpp.

Referenced by foreign_storage::InternalMLModelMetadataDataWrapper::populateChunkBuffersForTable().

                                                                        {
  for (const auto& ml_model_metadata : ml_models_metadata) {
    if (auto itr = import_buffers.find("model_name"); itr != import_buffers.end()) {
      itr->second->addString(ml_model_metadata.getModelName());
    }
    if (auto itr = import_buffers.find("model_type"); itr != import_buffers.end()) {
      itr->second->addString(ml_model_metadata.getModelTypeStr());
    }
    if (auto itr = import_buffers.find("predicted"); itr != import_buffers.end()) {
      itr->second->addString(ml_model_metadata.getPredicted());
    }
    if (auto itr = import_buffers.find("features"); itr != import_buffers.end()) {
      itr->second->addStringArray(ml_model_metadata.getFeatures());
    }
    if (auto itr = import_buffers.find("training_query"); itr != import_buffers.end()) {
      itr->second->addString(ml_model_metadata.getTrainingQuery());
    }
    if (auto itr = import_buffers.find("num_logical_features");
        itr != import_buffers.end()) {
      itr->second->addBigint(ml_model_metadata.getNumLogicalFeatures());
    }
    if (auto itr = import_buffers.find("num_physical_features");
        itr != import_buffers.end()) {
      itr->second->addBigint(ml_model_metadata.getNumFeatures());
    }
    if (auto itr = import_buffers.find("num_categorical_features");
        itr != import_buffers.end()) {
      itr->second->addBigint(ml_model_metadata.getNumCategoricalFeatures());
    }
    if (auto itr = import_buffers.find("num_numeric_features");
        itr != import_buffers.end()) {
      itr->second->addBigint(ml_model_metadata.getNumLogicalFeatures() -
                             ml_model_metadata.getNumCategoricalFeatures());
    }
    if (auto itr = import_buffers.find("train_fraction"); itr != import_buffers.end()) {
      itr->second->addDouble(ml_model_metadata.getDataSplitTrainFraction());
    }
    if (auto itr = import_buffers.find("eval_fraction"); itr != import_buffers.end()) {
      itr->second->addDouble(ml_model_metadata.getDataSplitEvalFraction());
    }
  }
}

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