The Process of Updating MSet and Training Model

gistfile1.cpp

int main() {

    /*
     * Process of scoring and updating Xapian::MSet
     */

    Xapian::Letor letor;

    // Suppose we're using svm ranker
    Xapian::SVMRanker ranker;

    // Choose features we need
    std::vector<Feature::FeatureBase> features {
        FEATURE_1,
        FEATURE_2,
        FEATURE_3,
        FEATURE_4,
        FEATURE_5,
        FEATURE_6,
        FEATURE_7,
        FEATURE_8,
        FEATURE_9,
        FEATURE_10,
        FEATURE_11,
        FEATURE_12,
        FEATURE_13,
        FEATURE_14,
        FEATURE_15,
        FEATURE_16,
        FEATURE_17,
        FEATURE_18,
        FEATURE_19
    };

    Xapian::Database database;
    Xapian::Query query;
    Xapian::MSet mset;

    // Update information of features 
    ranker.update(features);

    // Update context of letor
    letor.update_context(database, ranker, features);
    
    // Load model file for ranker
    letor.load_model_file("model_file");

    // Generate letor module information and attach to mset
    letor.update_mset(query, mset);

    // For now, the mset will contain the new information



    /*
     * Process of training
     */
    
    Xapian::Letor letor;

    // Suppose we're using svm ranker
    Xapian::SVMRanker ranker;

    // Choose features we need
    std::vector<Feature::FeatureBase> features {
        FEATURE_1,
        FEATURE_2,
        FEATURE_3,
        FEATURE_4,
        FEATURE_5,
        FEATURE_6,
        FEATURE_7,
        FEATURE_8,
        FEATURE_9,
        FEATURE_10,
        FEATURE_11,
        FEATURE_12,
        FEATURE_13,
        FEATURE_14,
        FEATURE_15,
        FEATURE_16,
        FEATURE_17,
        FEATURE_18,
        FEATURE_19
    };

    Xapian::Database database;
    Xapian::Query query;
    Xapian::MSet mset;

    // Update information of features 
    ranker.update(features);

    // Update context of letor
    letor.update_context(database, ranker, features);
    
    // Train the model
    letor.train("training_data_filename", "model_file");

    // The model will be saved in 'model_file'
}