package com.imsl.test.example.datamining;

import com.imsl.datamining.*;
import com.imsl.datamining.decisionTree.*;
import com.imsl.stat.Random;

/**
 *
 * <h4>Uses cross-validation to determine the
 * optimally pruned decision tree.</h4>
 *
 * <p>
 * In this example, the
 * <a href="../../../datamining/decisionTree/QUEST.html">QUEST</a>
 * algorithm is first used to fit a decision tree to simulated data. Then the
 * minimal cost-complexity value is found via cross-validation. The class
 * <a href="../../../datamining/CrossValidation.html">CrossValidation</a>
 * constructor accepts the decision tree object and then the method
 * <code>CrossValidation.crossValidate()</code> is called to run the procedure.
 * Finally, the tree is pruned using the result. Notice that the optimally
 * pruned tree consists of just the root node, whereas the maximal tree has five
 * nodes and three levels.
 * </p>
 *
 * @see <a href = "CrossValidationEx1.java">Code</a>
 * @see <a href="doc-files/CrossValidationEx1.out">Output</a>
 *
 */
public class CrossValidationEx1 {

    /**
     * The main method of the example.
     */
    public static void main(String[] args) throws Exception {
        PredictiveModel.VariableType[] sim0VarType = {
            PredictiveModel.VariableType.CATEGORICAL,
            PredictiveModel.VariableType.QUANTITATIVE_CONTINUOUS,
            PredictiveModel.VariableType.CATEGORICAL,
            PredictiveModel.VariableType.CATEGORICAL
        };

        double[][] sim0XY = {
            {2, 25.92869, 0, 0}, {1, 51.63245, 1, 1}, {1, 25.78432, 0, 2},
            {0, 39.37948, 0, 3}, {2, 24.65058, 0, 2}, {2, 45.20084, 0, 2},
            {2, 52.67960, 1, 3}, {1, 44.28342, 1, 3}, {2, 40.63523, 1, 3},
            {2, 51.76094, 0, 3}, {2, 26.30368, 0, 1}, {2, 20.70230, 1, 0},
            {2, 38.74273, 1, 3}, {2, 19.47333, 0, 0}, {1, 26.42211, 0, 0},
            {2, 37.05986, 1, 0}, {1, 51.67043, 1, 3}, {0, 42.40156, 0, 3},
            {2, 33.90027, 1, 2}, {1, 35.43282, 0, 0}, {1, 44.30369, 0, 1},
            {0, 46.72387, 0, 2}, {1, 46.99262, 0, 2}, {0, 36.05923, 0, 3},
            {2, 36.83197, 1, 1}, {1, 61.66257, 1, 2}, {0, 25.67714, 0, 3},
            {1, 39.08567, 1, 0}, {0, 48.84341, 1, 1}, {1, 39.34391, 0, 3},
            {2, 24.73522, 0, 2}, {1, 50.55251, 1, 3}, {0, 31.34263, 1, 3},
            {1, 27.15795, 1, 0}, {0, 31.72685, 0, 2}, {0, 25.00408, 0, 3},
            {1, 26.35457, 1, 3}, {2, 38.12343, 0, 1}, {0, 49.94030, 0, 2},
            {1, 42.45779, 1, 3}, {0, 38.80948, 1, 1}, {0, 43.22799, 1, 1},
            {0, 41.87624, 0, 3}, {2, 48.07820, 0, 2}, {0, 43.23673, 1, 0},
            {2, 39.41294, 0, 3}, {1, 23.93346, 0, 2}, {2, 42.84130, 1, 3},
            {2, 30.40669, 0, 1}, {0, 37.77389, 0, 2}
        };

        Random r = new Random(123457);
        r.setMultiplier(16807);
        QUEST dt = new QUEST(sim0XY, 3, sim0VarType);

        dt.setAutoPruningFlag(true);
        dt.fitModel();
        /* print the maximal tree */
        dt.printDecisionTree(true);

        CrossValidation cv = new CrossValidation(dt);
        cv.setRandomObject(r);
        cv.crossValidate();
        double cvError = cv.getCrossValidatedError();
        double[] Rcv = cv.getRiskValues();
        double[] SERcv = cv.getRiskStandardErrors();

        System.out.println("\nTree \t Complexity\t CV Risk \t SE of CV Risk ");
        for (int k = 0; k < Rcv.length; k++) {
            System.out.printf("  %d \t  %3.2f   \t   %5.4f \t   %5.4f\n",
                    k, dt.getCostComplexityValues()[k], Rcv[k], SERcv[k]);
        }
        /* prune the tree using the selected complexity value */
        dt.pruneTree(dt.getCostComplexityValues()[0]);

        System.out.printf("Minimum CV Risk Values: %5.4f\n", cvError);
        System.out.printf("Minimum CV Risk + Standard error: %5.4f\n",
                (cvError + SERcv[0]));

        /* print the pruned tree */
        dt.printDecisionTree(false);
    }
}