/// <summary> Prunes a tree using C4.5's pruning procedure.
///
/// </summary>
/// <exception cref="Exception">if something goes wrong
/// </exception>
public virtual void prune()
{
double errorsLargestBranch;
double errorsLeaf;
double errorsTree;
int indexOfLargestBranch;
C45PruneableClassifierTree largestBranch;
int i;
if (!m_isLeaf)
{
// Prune all subtrees.
for (i = 0; i < m_sons.Length; i++)
son(i).prune();
// Compute error for largest branch
indexOfLargestBranch = localModel().distribution().maxBag();
if (m_subtreeRaising)
{
errorsLargestBranch = son(indexOfLargestBranch).getEstimatedErrorsForBranch((Instances) m_train);
}
else
{
//UPGRADE_TODO: The equivalent in .NET for field 'java.lang.Double.MAX_VALUE' may return a different value. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1043'"
errorsLargestBranch = System.Double.MaxValue;
}
// Compute error if this Tree would be leaf
errorsLeaf = getEstimatedErrorsForDistribution(localModel().distribution());
// Compute error for the whole subtree
errorsTree = EstimatedErrors;
// Decide if leaf is best choice.
if (Utils.smOrEq(errorsLeaf, errorsTree + 0.1) && Utils.smOrEq(errorsLeaf, errorsLargestBranch + 0.1))
{
// Free son Trees
m_sons = null;
m_isLeaf = true;
// Get NoSplit Model for node.
m_localModel = new NoSplit(localModel().distribution());
return ;
}
// Decide if largest branch is better choice
// than whole subtree.
if (Utils.smOrEq(errorsLargestBranch, errorsTree + 0.1))
{
largestBranch = son(indexOfLargestBranch);
m_sons = largestBranch.m_sons;
m_localModel = largestBranch.localModel();
m_isLeaf = largestBranch.m_isLeaf;
newDistribution(m_train);
prune();
}
}
}
/// <summary> Selects C4.5-type split for the given dataset.</summary>
public override ClassifierSplitModel selectModel(Instances data)
{
double minResult;
//double currentResult;
BinC45Split[] currentModel;
BinC45Split bestModel = null;
NoSplit noSplitModel = null;
double averageInfoGain = 0;
int validModels = 0;
bool multiVal = true;
Distribution checkDistribution;
double sumOfWeights;
int i;
try
{
// Check if all Instances belong to one class or if not
// enough Instances to split.
checkDistribution = new Distribution(data);
noSplitModel = new NoSplit(checkDistribution);
if (Utils.sm(checkDistribution.total(), 2 * m_minNoObj) || Utils.eq(checkDistribution.total(), checkDistribution.perClass(checkDistribution.maxClass())))
return noSplitModel;
// Check if all attributes are nominal and have a
// lot of values.
System.Collections.IEnumerator enu = data.enumerateAttributes();
//UPGRADE_TODO: Method 'java.util.Enumeration.hasMoreElements' was converted to 'System.Collections.IEnumerator.MoveNext' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationhasMoreElements'"
while (enu.MoveNext())
{
//UPGRADE_TODO: Method 'java.util.Enumeration.nextElement' was converted to 'System.Collections.IEnumerator.Current' which has a different behavior. "ms-help://MS.VSCC.v80/dv_commoner/local/redirect.htm?index='!DefaultContextWindowIndex'&keyword='jlca1073_javautilEnumerationnextElement'"
weka.core.Attribute attribute = (weka.core.Attribute)enu.Current;
if ((attribute.Numeric) || (Utils.sm((double) attribute.numValues(), (0.3 * (double) m_allData.numInstances()))))
{
multiVal = false;
break;
}
}
currentModel = new BinC45Split[data.numAttributes()];
sumOfWeights = data.sumOfWeights();
// For each attribute.
for (i = 0; i < data.numAttributes(); i++)
{
// Apart from class attribute.
if (i != (data).classIndex())
{
// Get models for current attribute.
currentModel[i] = new BinC45Split(i, m_minNoObj, sumOfWeights);
currentModel[i].buildClassifier(data);
// Check if useful split for current attribute
// exists and check for enumerated attributes with
// a lot of values.
if (currentModel[i].checkModel())
if ((data.attribute(i).Numeric) || (multiVal || Utils.sm((double) data.attribute(i).numValues(), (0.3 * (double) m_allData.numInstances()))))
{
averageInfoGain = averageInfoGain + currentModel[i].infoGain();
validModels++;
}
}
else
currentModel[i] = null;
}
// Check if any useful split was found.
if (validModels == 0)
return noSplitModel;
averageInfoGain = averageInfoGain / (double) validModels;
// Find "best" attribute to split on.
minResult = 0;
for (i = 0; i < data.numAttributes(); i++)
{
if ((i != (data).classIndex()) && (currentModel[i].checkModel()))
// Use 1E-3 here to get a closer approximation to the original
// implementation.
if ((currentModel[i].infoGain() >= (averageInfoGain - 1e-3)) && Utils.gr(currentModel[i].gainRatio(), minResult))
{
bestModel = currentModel[i];
minResult = currentModel[i].gainRatio();
}
}
// Check if useful split was found.
if (Utils.eq(minResult, 0))
return noSplitModel;
// Add all Instances with unknown values for the corresponding
// attribute to the distribution for the model, so that
// the complete distribution is stored with the model.
bestModel.distribution().addInstWithUnknown(data, bestModel.attIndex());
// Set the split point analogue to C45 if attribute numeric.
bestModel.SplitPoint = m_allData;
return bestModel;
}
catch (System.Exception e)
{
System.Console.WriteLine(e.StackTrace + " " + e.Message);
}
return null;
}
/// <summary> Collapses a tree to a node if training error doesn't increase.</summary>
public void collapse()
{
double errorsOfSubtree;
double errorsOfTree;
int i;
if (!m_isLeaf)
{
errorsOfSubtree = TrainingErrors;
errorsOfTree = localModel().distribution().numIncorrect();
if (errorsOfSubtree >= errorsOfTree - 1e-3)
{
// Free adjacent trees
m_sons = null;
m_isLeaf = true;
// Get NoSplit Model for tree.
m_localModel = new NoSplit(localModel().distribution());
}
else
for (i = 0; i < m_sons.Length; i++)
son(i).collapse();
}
}
