decisiontree.java

Decision Tree 决策树算法ID3 数据挖掘 分类

     * @param node The base node where level assignment will start.
     * @param baseLevel The initial level for the base Node.
     */
    public void assign_subtree_levels(Node node, int baseLevel) {
        //   MLJ.ASSERT(baseLevel != DEFAULT_LEVEL);
        NodeInfo rootInfo = cGraph.node_info(node);
        logOptions.LOG(5, "Replacing level "+rootInfo.level()+" with "+baseLevel+'\n');
        cGraph.node_info(node).set_level(baseLevel);
        Edge iterEdge;
        Edge oldEdge;
        iterEdge = node.first_adj_edge();
        int nextLevel;
        //   if (get_categorizer(node).class_id() == CLASS_MULTI_SPLIT_CATEGORIZER)
        //      nextLevel = baseLevel;
        //   else
        nextLevel = baseLevel + 1;
        while (iterEdge != null) {
            oldEdge = iterEdge;
            iterEdge = oldEdge.adj_succ();
            Node childNode = oldEdge.target();
            assign_subtree_levels(childNode, nextLevel);
        }
    }
    
    /***************************************************************************
  Display this DecisionTree.
@param
@param
@param
@param
     ***************************************************************************
   public void display(boolean hasNodeLosses, boolean hasLossMatrix,
      Writer stream, DisplayPref dp)
   {
      stream.write(display(hasNodeLosses, hasLossMatrix, dp));
   }
 
/***************************************************************************
  Display this DecisionTree.
@param
@param
@param
     ***************************************************************************
   public String display(boolean hasNodeLosses, boolean hasLossMatrix,
      DisplayPref dp)
   {
      String return_value = new String();
   // Note that if the display is XStream, our virtual function gets it
//   if (stream.output_type() == XStream ||
//      dp.preference_type() != DisplayPref::TreeVizDisplay)
//      RootedCatGraph.display(hasNodeLosses, hasLossMatrix, stream, dp);
      else
      {
         String dataName = stream.description() + ".data";
         MLCOStream data(dataName);
         convertToTreeVizFormat(stream, data, dp, hasNodeLosses, hasLossMatrix);
      }
   }
     */
    
/*
/***************************************************************************
  Displays the DecisionTree in TreeVizFormat.
@param
@param
@param
@param
@param
 ***************************************************************************
   public void convertToTreeVizFormat(Writer conf, Writer data,
      DisplayPref displayPref,
      boolean hasNodeLosses,
      boolean hasLossMatrix) throws IOException
   {
      Node rootNode = get_root(true);
 
      NodeCategorizer cat = get_categorizer(rootNode);
      Schema schema = cat.get_schema();
      NominalAttrInfo nai = schema.nominal_label_info();
      int numLabelValues = nai.num_values();
      MLJ.ASSERT(numLabelValues >= 1,"DecisionTree::convertToTreeVizFormat:numLabelValues < 1");
   // Avoid log of 1, which is a scale of zero, and causes division
   //    by zero.
      double scale = MLJ.log_bin(Math.max(numLabelValues, 2));
      int[] permLabels = schema.sort_labels(); // permuted labels
 
      boolean dispBackfitDisks =
         displayPref.typecast_to_treeViz().get_display_backfit_disks();
      write_subtree(get_log_options(), scale, data, permLabels,
         Globals.EMPTY_STRING, Globals.EMPTY_STRING, this, rootNode,
         dispBackfitDisks, hasNodeLosses, hasLossMatrix);
 
      String protectedLabelName = new String(Globals.SINGLE_QUOTE + MLJ.protect(nai.name(),"`\\")
         + Globals.SINGLE_QUOTE);
 
      conf.write(minesetVersionStr + "\n");
      conf.write("# MLC++ generated file for MineSet Tree Visualizer.\n"
         + "input {\n"
         + "\t file \"" + data.description() + "\";\n"
         + "\t options backslash on;\n"
         + "\t key string " + protectedLabelName + " {\n");
 
      for (int i = 0; i < numLabelValues; i++)
      {
         conf.write("\t\t " + nai.get_value(permLabels[i]).quote());
         if (i != numLabelValues - 1)
            conf.write(",");
         conf.write("\n");
      }
      permLabels = null;
 
 
      conf.write("\t };\n"
         + "\t expression `Node label`[] separator ':';\n"
         + "\t string `Test attribute`;\n"
         + "\t string `Test value`;\n"
         + "\t float `Subtree weight` [" + protectedLabelName
         + "] separator ',' ;\n"
         + "\t float Percent [" + protectedLabelName + "] separator ',' ;\n");
      if (dispBackfitDisks)
         conf.write("\t float OriginalDist [" + protectedLabelName
           + "] separator ',' ;\n");
      conf.write("\t float Purity;\n");
 
      if (hasNodeLosses)
      {
         conf.write("\t float `Test-set subtree weight`;\n");
         if (hasLossMatrix)
            conf.write("\t float `Test-set loss`;\n"
               + "\t float `Mean loss std-dev`;\n");
         else
            conf.write("\t float `Test-set error`;\n"
               + "\t float `Mean err std-dev`;\n");
      }
 
      conf.write("}\n\n");
 
      conf.write("hierarchy {\n"
         + "\t levels `Node label`;\n"
         + "\t key `Subtree weight`;\n"
         + "\t aggregate base {\n"
         + "\t\t sum `Subtree weight`;\n");
      if (dispBackfitDisks)
         conf.write("\t\t sum `OriginalDist`;\n");
      conf.write("\t\t any Purity;\n"
         + "\t\t any `Test attribute`;\n"
         + "\t\t any `Test value`;\n");
 
      if (hasNodeLosses)
      {
         conf.write("\t\t any `Test-set subtree weight`;\n");
         if (hasLossMatrix)
            conf.write("\t\t any `Test-set loss`;\n"
               + "\t\t any `Mean loss std-dev`;\n");
         else
            conf.write("\t\t any `Test-set error`;\n"
               + "\t\t any `Mean err std-dev`;\n");
      }
 
      conf.write("\t }\n"
         + "\t options organization same;\n"
         + "}\n");
 
   // Pick the midpoint entropy color to be 3/4 versus 1/4 for two class probs.
   // This just makes the color scale much better then 50, which requires
   // 89% versus 11% to be the middle color.
      double[] typicalMix = new double[2];
      typicalMix[0] = 3;
      typicalMix[1] = 1;
      DoubleRef midPointEnt = new DoubleRef(100 - Entropy.entropy(typicalMix)*100 / scale);
      MLJ.clamp_to_range(midPointEnt, 0, 100,
         "DecisionTree::convertToTreeVizFormat: mid-point does "
         + "not clamp to range [0-100]");
 
      MLJ.ASSERT(schema.num_label_values() > 0,"DecisionTree::"
         + "convertToTreeVizFormat:schema.num_label_values() <= 0");
   // Even though nulls are never used, we want to distinguish
   // them in case somebody changes anything.  They're therefore hidden.
      conf.write("view hierarchy landscape {\n"
         + "\t height `Subtree weight`, normalize, max 5.0;\n");
      if (dispBackfitDisks)
         conf.write("\t disk height `OriginalDist`;\n");
      conf.write("\t base height max 2.0;\n"
         + "\t base label `Test attribute`;\n"
         + "\t line label `Test value`;\n"
         + "\t color key;\n");
      //   	   "\t base color legend label \"Purity\";\n"
      //     "\t base color Purity, "
      //     "colors \"red\" \"yellow\" \"green\""
      //           ", scale 0 " << midPointEnt << " 100, legend on;\n"
      //     "\t base color legend \"impure\" \"mixed\" \"pure\";\n"
      if (hasNodeLosses)
      {
         double min = 0;
         double max = 0;
         loss_min_max(this, min, max);
         if (max - min < 0.01)
         max += 0.01; // Avoid cases where both are zero and we rely
                      // on a treeviz tiebreaker (happens in mushroom).
         NodeLoss rootLoss = get_categorizer(rootNode).get_loss();
         double medColor = suggest_mid(min, max, rootLoss.totalWeight,
            rootLoss.totalLoss);
 
         if (!hasLossMatrix)
         {
            min *= 100;
            max *= 100;
            medColor *=100;
         }
 
         if (hasLossMatrix)
         conf.write("\t base color legend label \"Test-set loss\";\n"
            + "\t base color `Test-set loss`, ");
         else
            conf.write("\t base color legend label \"Test-set error\";\n"
            + "\t base color `Test-set error`, ");
 
 
         conf.write("colors \"green\" \"yellow\" \"red\""
            + ", scale " + min + " " + medColor
            + " " + max + ", legend on;\n"
            + "\t base color legend \"low ("
            + MLJ.numberToString(min,2) + ")\" \"medium ("
            + MLJ.numberToString(medColor,2) + ")\" \"high ("
            + MLJ.numberToString(max,2) + ")\";\n");
      }
      conf.write("\t options rows 1;\n"
         + "\t options root label \"\";\n"
         + "\t options initial depth 4;\n"
            // Don't show bar labels, so the level of details is far
         + "\t options lod bar label 10000;\n"
         + "\t options zero outline;\n"
         + "\t options null hidden;\n");
 
      conf.write("\t base message \"Subtree weight:%.2f, ");
      String lossMetric = hasLossMatrix ? "loss" : "error";
      String shortLossMetric = hasLossMatrix ? "loss" : "err";
      if (hasNodeLosses)
         conf.write("test-set " + lossMetric + ":%.2f+-%.2f, "
            + " test-set weight:%.2f, ");
      if (dispBackfitDisks)
         conf.write("training-set weight: %.2f, ");
      conf.write("purity:%.2f\", `Subtree weight`, ");
         if (hasNodeLosses)
      conf.write("`Test-set " + lossMetric + "`, "
         + "`Mean " + shortLossMetric + " std-dev`, "
         + "`Test-set subtree weight`, ");
      if (dispBackfitDisks)
         conf.write("`OriginalDist`, ");
      conf.write("Purity;\n");
 
      conf.write("\t message \"Subtree weight for label value:%.2f, percent:%.2f");
      if (dispBackfitDisks)
         conf.write(", training-set weight:%.2f");
      conf.write("\", `Subtree weight`, Percent");
      if (dispBackfitDisks)
         conf.write(", `OriginalDist`");
      conf.write(";\n}\n");
   }
 */
}