Method run

src/modules/recursive_partitioning/decision_tree.cpp:50–86

------------------------------------------------------------ functions ------------------------------------------------------------

// functions
// ------------------------------------------------------------
AnyType
initialize_decision_tree::run(AnyType & args){
    DecisionTree<MutableRootContainer> dt = DecisionTree<MutableRootContainer>();
    bool is_regression_tree = args[0].getAs<bool>();
    std::string impurity_func_str = args[1].getAs<std::string>();
    uint16_t n_y_labels = args[2].getAs<uint16_t>();
    uint16_t max_n_surr = args[3].getAs<uint16_t>();

    if (is_regression_tree)
        n_y_labels = REGRESS_N_STATS;
    dt.rebind(1u, n_y_labels, max_n_surr, is_regression_tree);
    dt.feature_indices(0) = dt.IN_PROCESS_LEAF;
    dt.feature_thresholds(0) = 0;
    dt.is_categorical(0) = 0;
    if (max_n_surr > 0){
        dt.surr_indices.setConstant(dt.SURR_NON_EXISTING);
        dt.surr_thresholds.setConstant(0);
        dt.surr_status.setConstant(0);
    }
    dt.predictions.row(0).setConstant(0);

    dt.is_regression = is_regression_tree;
    if (dt.is_regression){
        dt.impurity_type = dt.MSE;     // only MSE defined for regression
    } else {
        if ((impurity_func_str.compare("misclassification") == 0) ||
                (impurity_func_str.compare("misclass") == 0))
            dt.impurity_type = dt.MISCLASS;
        else if ((impurity_func_str.compare("entropy") == 0) ||
                 (impurity_func_str.compare("cross-entropy") == 0))
            dt.impurity_type = dt.ENTROPY;
        else
            dt.impurity_type = dt.GINI;  // default impurity for classification
    }

    return dt.storage();
    } // initialize_decision_tree
// ------------------------------------------------------------

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