//
//Used original data
/// <summary>
/// Return filtered data for node
///
/// Returns rows which match parent attribute having parent value
/// Also sets the attribute column of all parents to null
///
/// </summary>
/// <param name="dtn"></param>
/// <param name="nodeValue"></param>
/// <param name="trainingDataRowIndices"></param>
/// <returns></returns>
protected virtual FilteredData getFilteredDataForNode(
DecisionTreeNode dtn,
double nodeValue,
ConcurrentBag <long> trainingDataRowIndices)
{
//filterOnValues has all values we need to filter on
if (dtn.Parent == null) //Root nodes, gets entire training data
{
return(new FilteredData(_trainingData,
trainingDataRowIndices,
trainingDataRowIndices.Count));
}
Dictionary <int, double> allParentValues =
new Dictionary <int, double>();
DecisionTreeNode parent = dtn.Parent;
while (parent != null) //Find out values for each parent
{
allParentValues.Add(parent.AttributeIndex,
nodeValue); //Value is in current node
parent = parent.Parent;
} //Only add 1 index, value pair since trainingDataRowIndices is already sorted out
ConcurrentBag <long> bagTrainingDataRowIndices =
new ConcurrentBag <long>();
// Find out which rows matach the parent
Parallel.ForEach(trainingDataRowIndices, new ParallelOptions {
MaxDegreeOfParallelism = _maxParallelThreads
}, rowIdx =>
{
if (_trainingData[dtn.Parent.AttributeIndex][rowIdx] ==
nodeValue)
{
bagTrainingDataRowIndices.Add(rowIdx);
}
}); //rowIdx
//Now rows are known allocate for filtered Value
//Allocate for filteredValues
//Need dymanic numer of rows hence using fltered values
long[] arrayTrainingDataRowIndices = bagTrainingDataRowIndices.ToArray <long>();
double[][] filteredData = new double[_trainingData.Length][];
//Parallel.For(0,_trainingData.Length, new ParallelOptions { MaxDegreeOfParallelism = _maxParallelThreads }, colIdx =>
for (int colIdx = 0; colIdx < _trainingData.Length; colIdx++)
{
filteredData[colIdx] = new double[arrayTrainingDataRowIndices.Length];
long rowIdx;
//Will run in parallel Order has to be presevered
for (int idx = 0; idx < arrayTrainingDataRowIndices.Length; idx++)
{
rowIdx = arrayTrainingDataRowIndices[idx];
filteredData[colIdx][idx] =
_trainingData[colIdx][rowIdx];
}
}//);
return(new FilteredData(filteredData,
bagTrainingDataRowIndices,
filteredData[0].Length));
}
//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);
}
//Any extra stopping condition, primarily added for boosting
//which needs decision stumps
protected virtual bool GetAdditionalStoppingCondition(DecisionTreeNode dtn)
{
return(false);//Make sure value is false else stopping condition is reach automatically
}
/// <summary>
/// Return filtered data for node
///
/// Returns rows which match parent attribute having parent value
/// Also sets the attribute column of all parents to null
///
/// </summary>
/// <param name="dtn"></param>
/// <param name="nodeValue"></param>
/// <param name="trainingDataRowIndices"></param>
/// <returns></returns>
protected FilteredData GetFilteredDataForNode(
DecisionTreeNode dtn,
double nodeValue,
ConcurrentBag <long> trainingDataRowIndices,
bool isLessThan)
{
if (dtn.Parent == null) //Root nodes, gets entire training data
{
//If trainingDataRowIndices not same as original rows
//Then filter, required for Bagged Trees
if (_trainingRowsPassed)
{
return(convertRowIndicesToFilteredData(trainingDataRowIndices));
}
else
{
return(new FilteredData(_trainingData,
trainingDataRowIndices,
trainingDataRowIndices.Count));
}
}
Dictionary <int, double> allParentValues =
new Dictionary <int, double>();
//Allocate for filteredValues
List <double>[] filteredValues = new List <double> [_trainingData.Length];
//filterOnValues has all values we need to filter on
//Need dynamic numer of rows hence using filtered values as List
Parallel.For(0, _trainingData.Length, new ParallelOptions {
MaxDegreeOfParallelism = _maxParallelThreads
}, colIdx => {
filteredValues[colIdx] = new List <double>();
});
DecisionTreeNode parent = dtn.Parent;
ConcurrentBag <long> newTrainingDataRowIndices =
new ConcurrentBag <long>();
//Parallelize this later
//foreach (long rowIdx in trainingDataRowIndices) //Can optimize here to view rows passed down
Parallel.ForEach(trainingDataRowIndices, new ParallelOptions {
MaxDegreeOfParallelism = _maxParallelThreads
}, rowIdx =>
{
if ((isLessThan && _trainingData[dtn.Parent.AttributeIndex][rowIdx] <
nodeValue) || (!isLessThan && _trainingData[dtn.Parent.AttributeIndex][rowIdx] >=
nodeValue))
{
newTrainingDataRowIndices.Add(rowIdx);
//Copy array and break from foreach
//List.Add is not thread safe
CopyFilteredData(filteredValues, rowIdx);
}
}); //rowIdx
//Convert List to Array
//Even copy previous columns, as there Entropy will be 0
double[][] filteredData = new double[_trainingData.Length][];
long numberOfRows = 0;
//for(int colIdx=0; colIdx < _trainingData.Length; colIdx++)
Parallel.For(0, _trainingData.Length, new ParallelOptions {
MaxDegreeOfParallelism = _maxParallelThreads
}, colIdx =>
{
//No need to set parent to NULL in CART
filteredData[colIdx] =
filteredValues[colIdx].ToArray();
//Do note move this out or for since filteredData[colIdx] can be null
//numberOfRows is same for all selected columns
numberOfRows = filteredData[colIdx].Length;
});
return(new FilteredData(filteredData,
newTrainingDataRowIndices,
numberOfRows));
}
//private long _numberOfRows; //Used by CART to determine if node had ended
public DecisionTreeNode(DecisionTreeNode parent)
{
Initialize();
_value = Double.PositiveInfinity;
_parent = parent;
}
