public Tree(
int nFeatures,
uint[] nClasses,
int nOutputs,
SplitterBase splitter,
uint maxDepth,
uint minSamplesSplit,
uint minSamplesLeaf)
{
// Input/Output layout
this.nFeatures = nFeatures;
this.nOutputs = nOutputs;
this.NClasses = new uint[nOutputs];
this.maxNClasses = nClasses.Max();
this.valueStride = (uint)this.nOutputs * this.maxNClasses;
for (uint k = 0; k < nOutputs; k++)
{
this.NClasses[k] = nClasses[k];
}
// Parameters
this.splitter = splitter;
this.maxDepth = maxDepth;
this.minSamplesSplit = minSamplesSplit;
this.minSamplesLeaf = minSamplesLeaf;
// Inner structures
this.NodeCount = 0;
this.Capacity = 0;
this.ChildrenLeft = null;
this.ChildrenRight = null;
this.Feature = null;
this.Threshold = null;
this.Value = null;
this.Impurity = null;
this.NNodeSamples = null;
}
/// <summary>
/// Build a decision tree from the training set (X, y).
/// </summary>
public void build(MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> x,
MathNet.Numerics.LinearAlgebra.Generic.Matrix <double> y,
MathNet.Numerics.LinearAlgebra.Generic.Vector <double> sampleWeight = null)
{
// Prepare data before recursive partitioning
// Initial capacity
int initCapacity;
if (this.maxDepth <= 10)
{
initCapacity = (int)Math.Pow(2, (this.maxDepth + 1)) - 1;
}
else
{
initCapacity = 2047;
}
this.Resize(initCapacity);
// Recursive partition (without actual recursion)
SplitterBase splitter = this.splitter;
splitter.init(x, y, sampleWeight == null ? null : sampleWeight.ToArray());
uint stackNValues = 5;
uint stackCapacity = 50;
uint[] stack = new uint[stackCapacity];
stack[0] = 0; // start
stack[1] = splitter.n_samples; // end
stack[2] = 0; // depth
stack[3] = _TREE_UNDEFINED; // parent
stack[4] = 0; // is_left
uint pos = 0;
uint feature = 0;
double threshold = 0;
double impurity = 0;
while (stackNValues > 0)
{
stackNValues -= 5;
uint start = stack[stackNValues];
uint end = stack[stackNValues + 1];
uint depth = stack[stackNValues + 2];
uint parent = stack[stackNValues + 3];
bool isLeft = stack[stackNValues + 4] != 0;
uint nNodeSamples = end - start;
bool isLeaf = ((depth >= this.maxDepth) ||
(nNodeSamples < this.minSamplesSplit) ||
(nNodeSamples < 2 * this.minSamplesLeaf));
splitter.node_reset(start, end, ref impurity);
isLeaf = isLeaf || (impurity < 1e-7);
if (!isLeaf)
{
splitter.node_split(ref pos, ref feature, ref threshold);
isLeaf = isLeaf || (pos >= end);
}
uint nodeId = this.AddNode(parent, isLeft, isLeaf, feature,
threshold, impurity, nNodeSamples);
if (isLeaf)
{
// Don't store value for internal nodes
splitter.node_value(this.Value, nodeId * this.valueStride);
}
else
{
if (stackNValues + 10 > stackCapacity)
{
stackCapacity *= 2;
var newStack = new uint[stackCapacity];
Array.Copy(stack, newStack, stack.Length);
stack = newStack;
}
// Stack right child
stack[stackNValues] = pos;
stack[stackNValues + 1] = end;
stack[stackNValues + 2] = depth + 1;
stack[stackNValues + 3] = nodeId;
stack[stackNValues + 4] = 0;
stackNValues += 5;
// Stack left child
stack[stackNValues] = start;
stack[stackNValues + 1] = pos;
stack[stackNValues + 2] = depth + 1;
stack[stackNValues + 3] = nodeId;
stack[stackNValues + 4] = 1;
stackNValues += 5;
}
}
this.Resize((int)this.NodeCount);
this.splitter = null; // Release memory
}
private void FitCommon(
Matrix <double> x,
Matrix <double> y,
int nSamples,
Vector <double> sampleWeight,
bool isClassification)
{
int maxDepth = this.maxDepth ?? int.MaxValue;
if (this.maxFeatures == null)
{
this.MaxFeaturesValue = this.NFeatures;
}
else
{
this.MaxFeaturesValue = maxFeatures.ComputeMaxFeatures(this.NFeatures, isClassification);
}
if (this.minSamplesSplit <= 0)
{
throw new ArgumentException("min_samples_split must be greater than zero.");
}
if (this.minSamplesLeaf <= 0)
{
throw new ArgumentException("minSamplesLeaf must be greater than zero.");
}
if (maxDepth <= 0)
{
throw new ArgumentException("maxDepth must be greater than zero. ");
}
if (!(0 < MaxFeaturesValue && MaxFeaturesValue <= this.NFeatures))
{
throw new ArgumentException("maxFeatures must be in (0, n_features]");
}
if (sampleWeight != null)
{
if (sampleWeight.Count != nSamples)
{
throw new ArgumentException(
string.Format(
"Number of weights={0} does not match number of samples={1}",
sampleWeight.Count,
nSamples));
}
}
// Set min_samples_split sensibly
minSamplesSplit = Math.Max(this.minSamplesSplit, 2 * this.minSamplesLeaf);
// Build tree
ICriterion criterion = null;
switch (this.criterion)
{
case Criterion.Gini:
criterion = new Gini((uint)nOutputs, NClasses.ToArray());
break;
case Criterion.Entropy:
criterion = new Entropy((uint)nOutputs, NClasses.ToArray());
break;
case Criterion.Mse:
criterion = new MSE((uint)nOutputs);
break;
default:
throw new InvalidOperationException("Unknown criterion type");
}
SplitterBase splitter = null;
switch (this.splitter)
{
case Splitter.Best:
splitter = new BestSplitter(criterion, (uint)this.MaxFeaturesValue, (uint)this.minSamplesLeaf, randomState);
break;
case Splitter.PresortBest:
splitter = new PresortBestSplitter(criterion, (uint)this.MaxFeaturesValue, (uint)this.minSamplesLeaf, randomState);
break;
case Splitter.Random:
splitter = new RandomSplitter(criterion, (uint)this.MaxFeaturesValue, (uint)this.minSamplesLeaf, randomState);
break;
default:
throw new InvalidOperationException("Unknown splitter type");
}
this.Tree = new Tree(
this.NFeatures,
this.NClasses.ToArray(),
this.nOutputs,
splitter,
(uint)maxDepth,
(uint)minSamplesSplit,
(uint)this.minSamplesLeaf);
this.Tree.build(x, y, sampleWeight: sampleWeight);
}