//Make private member so that stack is not full
protected virtual DecisionTreeNode buildChildNodes(ConcurrentBag <long> trainingDataRowIndices,
double value,
DecisionTreeNode dtnParent)
{
DecisionTreeNode dtn = new DecisionTreeNode(dtnParent);
//Get all rows in Training Data
FilteredData fd = getFilteredDataForNode(dtn, value,
trainingDataRowIndices);
//Check if all target examples are same or not
//Their Entropy will be 0
if (fd.NumberOfRows <= _minimumNumberPerNode || isTargetDataSame(fd.FilteredDataValues)) //Attributes is empty
{
setAsTargetAttributeNode(fd.FilteredDataValues, dtn);
return(dtn);//No more children if attributeIndex is target Attributes
}
//Set data for current node
SplittedAttributeData ed =
splitDataOnUnivariateCriterion(fd.FilteredDataValues);
//Check for positive and negative examples
dtn.setAttributeValues(ed.AttributeIndex,
_attributeHeaders[ed.AttributeIndex]);
ConcurrentBag <long> newTrainingDataRowIndices =
fd.TrainingDataRowIndices;
fd = null; //Free Memory -> Clean up data, no longer needed
//Key has values
foreach (double key in ed.Freqs.Keys)
{
if (key != 999) //Dont add for missing values
{
dtn.addChild(key, buildChildNodes(newTrainingDataRowIndices,
key,
dtn)); //Key has value
}
}
return(dtn);
}
//Make private member so that stack is not full
protected DecisionTreeNode BuildChildNodes(ConcurrentBag <long> trainingDataRowIndices,
double value,
DecisionTreeNode dtnParent,
bool isLessThan)
{
DecisionTreeNode dtn = new DecisionTreeNode(dtnParent);
//Get all rows in Training Data
FilteredData fd = GetFilteredDataForNode(dtn, value,
trainingDataRowIndices,
isLessThan);
//Stopping Criterion
//Check if minimum number of nodes are there
//OR all target values are same
if (fd.NumberOfRows <= _minimumNumberPerNode ||
isTargetDataSame(fd.FilteredDataValues) || //Attributes is empty
(dtnParent != null && fd.TrainingDataRowIndices.Count == trainingDataRowIndices.Count) || //implies no split happened)
GetAdditionalStoppingCondition(dtn))
{
if (fd.NumberOfRows == 0) //Special case, use original data as node
{
fd = convertRowIndicesToFilteredData(trainingDataRowIndices);
}
setAsTargetAttributeNode(fd.FilteredDataValues, dtn);
return(dtn); //No more children if min attributes reached
}
//Set data for current node
SplittedAttributeData ed =
splitDataOnUnivariateCriterion(fd.FilteredDataValues);
//Check for positive and negative examples
dtn.setAttributeValues(ed.AttributeIndex,
_attributeHeaders[ed.AttributeIndex]);
//Store value in column ed.AttributeIndex based on which split was done
dtn.Value = ed.SplittingCriteriaValue;
ConcurrentBag <long> newTrainingDataRowIndices =
fd.TrainingDataRowIndices;
fd = null; //Free Memory -> Clean up data, no longer needed
//Key has values
//Add left node
if (ed.SplittingCriteriaValue != _missingValue) //Dont add for missing values
{
//0 if for left, 1 is for right.
//There wont be any conflict since each node will have only 2 children
//DecisionTreeNode dtnChild =
dtn.addChild(0, BuildChildNodes(newTrainingDataRowIndices,
ed.SplittingCriteriaValue,
dtn, true)); //Key has value
//dtnChild =
dtn.addChild(1, BuildChildNodes(newTrainingDataRowIndices,
ed.SplittingCriteriaValue,
dtn, false)); //Key has value
}
return(dtn);
}