/// <summary>
/// Receives one PredMvIdx to be tested, and the constant and returns:
///
/// Return[0] the best value for the coefficient
/// Return[1] the lowestSplitImp for this coefficient
/// </summary>
/// <param name="n"></param>
/// <param name="bestCoefLst"></param>
/// <param name="c"></param>
/// <param name="testPredMvIdx"></param>
/// <returns></returns>
public static double[] BestGainMvNumeric(NodeTargetCategorical n, double c, int testPredMvIdx, List<int> optimisedVariableLst, FrmGraph fg)
{
double OptVarSum;
bool infoNotYetCalculated = true;
double leftImp = 0, rightImp = 0;
double v, az, aSplit, xts,aSplitB=-9999999999;
SortedList<double, int> azLst = new SortedList<double, int>();
double leftCount = 0, rightCount = 0;// Left and right item count
SortedList<string, int> leftNode = new SortedList<string, int>();
SortedList<string, int> rightNode = new SortedList<string, int>();
double lowestSplitImp = 9999999, splitImp = 9999999;
if (!Def.ExperimentRunning) {
fg.ALst.Clear();
fg.AbsLst.Clear();
}
azLst.Clear();
for (int y = 0; y < n.Table.RowCount; ++y) {
OptVarSum = 0;
//for (int i = 0; i < optimisedVariableLst.Count; ++i) {
// OptVarSum += n.PredMvLst[optimisedVariableLst[i]].Coef * n.PredMvLst[optimisedVariableLst[i]].X(y);
for (int i = 0; i < n.PredMvLst.Count; ++i) {
OptVarSum += n.PredMvLst[i].Coef * n.PredMvLst[i].X(y);
}
v = OptVarSum + c;
if (n.PredMvLst[testPredMvIdx].X(y) != 0) {
az = ((n.PredMvLst[testPredMvIdx].Coef * n.PredMvLst[testPredMvIdx].X(y)) - v) / n.PredMvLst[testPredMvIdx].X(y);
if (!azLst.ContainsKey(az) ) {
azLst.Add(az, 1);
if (!Def.ExperimentRunning)
fg.ALst.Add((float)az);
}
}
}
for (int azIdx = 0; azIdx < azLst.Count - 1; ++azIdx) {
//Finally we got a new linear coefficient for a variable that
//had linear coefficient = 0
//The new hyperplane is:
// A1X1 + (1)X2 + c = 0 (c= - bestSplitNorm)
// (new midpoint coef) * testVar + (coordinate of the bestSplit variable) - bestSplitNorm;
// aSplit * Fcn.NormP1(testLst[y], PredictorLst[i].LowerNumber, PredictorLst[i].HigherNumber);
aSplit = (azLst.Keys[azIdx] + azLst.Keys[azIdx + 1]) / 2;
leftNode.Clear();
rightNode.Clear();
leftCount = 0;
rightCount = 0;
if (!Def.ExperimentRunning)
fg.AbsLst.Add((float)aSplit);
for (int y = 0; y < n.Table.RowCount; ++y) {
xts = n.PredMvLst[testPredMvIdx].X(y);
OptVarSum = 0;
for (int i = 0; i < optimisedVariableLst.Count; ++i) {
OptVarSum += n.PredMvLst[optimisedVariableLst[i]].Coef * n.PredMvLst[optimisedVariableLst[i]].X(y);
}
if ((aSplit * xts + OptVarSum + c) <= 0) {
++leftCount;
if (!leftNode.ContainsKey(n.MvTb.Data.TC[y]))
leftNode.Add(n.MvTb.Data.TC[y], 1);
else
++leftNode[n.MvTb.Data.TC[y]];
} else {
++rightCount;
if (!rightNode.ContainsKey(n.MvTb.Data.TC[y]))
rightNode.Add(n.MvTb.Data.TC[y], 1);
else
++rightNode[n.MvTb.Data.TC[y]];
}
}
if (rightCount < Def.TreeMinNumberOfCasesPerNode || leftCount < Def.TreeMinNumberOfCasesPerNode)
continue;
leftImp = Gini.ImpCat(leftNode, (int)leftCount);
rightImp = Gini.ImpCat(rightNode, (int)rightCount);
if (infoNotYetCalculated) {
infoNotYetCalculated = false;
lowestSplitImp = (leftCount * leftImp + rightCount * rightImp) / n.Table.RowCount;
if (n.PredMvLst[testPredMvIdx].Variable.VariableTypeDetected == SchemaVariable.VariableTypeEnum.Categorical) {
MessageBox.Show("Error n.PredMvLst[testPredMvIdx].Variable is categorical ");
}
aSplitB = aSplit;
} else {
splitImp = (leftCount * leftImp + rightCount * rightImp) / n.Table.RowCount;
if (splitImp < lowestSplitImp) {
lowestSplitImp = splitImp;
if (n.PredMvLst[testPredMvIdx].Variable.VariableTypeDetected == SchemaVariable.VariableTypeEnum.Categorical) {
MessageBox.Show("Error n.PredMvLst[testPredMvIdx].Variable is categorical ");
}
aSplitB = aSplit;
}
}
}
//Creates bissections for the last and the 'x0 axis'
if (azLst.Count > 1) { // if only 1 is the zero
aSplit = azLst.Keys[azLst.Count-1] * 2;
if (!Def.ExperimentRunning)
fg.AbsLst.Add((float)aSplit);
leftNode.Clear();
rightNode.Clear();
leftCount = 0;
rightCount = 0;
for (int y = 0; y < n.Table.RowCount; ++y) {
xts = n.PredMvLst[testPredMvIdx].X(y);
OptVarSum = 0;
for (int i = 0; i < optimisedVariableLst.Count; ++i) {
OptVarSum += n.PredMvLst[optimisedVariableLst[i]].Coef * n.PredMvLst[optimisedVariableLst[i]].X(y);
}
if ((aSplit * xts + OptVarSum + c) <= 0) {
++leftCount;
if (!leftNode.ContainsKey(n.MvTb.Data.TC[y]))
leftNode.Add(n.MvTb.Data.TC[y], 1);
else
++leftNode[n.MvTb.Data.TC[y]];
} else {
++rightCount;
if (!rightNode.ContainsKey(n.MvTb.Data.TC[y]))
rightNode.Add(n.MvTb.Data.TC[y], 1);
else
++rightNode[n.MvTb.Data.TC[y]];
}
}
if (rightCount >= Def.TreeMinNumberOfCasesPerNode && leftCount >= Def.TreeMinNumberOfCasesPerNode) {
leftImp = Gini.ImpCat(leftNode, (int)leftCount);
rightImp = Gini.ImpCat(rightNode, (int)rightCount);
if (infoNotYetCalculated) {
infoNotYetCalculated = false;
lowestSplitImp = (leftCount * leftImp + rightCount * rightImp) / n.Table.RowCount;
if (n.PredMvLst[testPredMvIdx].Variable.VariableTypeDetected == SchemaVariable.VariableTypeEnum.Categorical) {
MessageBox.Show("Error n.PredMvLst[testPredMvIdx].Variable is categorical ");
}
aSplitB = aSplit;
} else {
splitImp = (leftCount * leftImp + rightCount * rightImp) / n.Table.RowCount;
if (splitImp < lowestSplitImp) {
lowestSplitImp = splitImp;
if (n.PredMvLst[testPredMvIdx].Variable.VariableTypeDetected == SchemaVariable.VariableTypeEnum.Categorical) {
MessageBox.Show("Error n.PredMvLst[testPredMvIdx].Variable is categorical ");
}
aSplitB = aSplit;
}
}
}//end if (rightCount >= Def.TreeMinNumberOfCasesPerNode && leftCount >= Def.TreeMinNumberOfCasesPerNode) {
}
double[] r = new double[2];
r[0] = aSplitB;
r[1] = lowestSplitImp;
return r;
}
/// Sets the best p.SplitHyperplane and p.Gain
/// <summary>
/// Look for the local minimum by fixing all the coeficients except one and then vary it
/// </summary>
public static double MinImpMvGreed(NodeTargetCategorical n, Predictor p)
{
bool hadOverallImprovement=false;
//List<double> bestCoefLst = new List<double>();
//List<double> testCoefLst = new List<double>(); // VariableToBeTestedCoodinates
SortedList<string, int> leftNode = new SortedList<string, int>();
SortedList<string, int> rightNode = new SortedList<string, int>();
List<int> nonOptimisedVariableLst = new List<int>(); //Stores the index of MV predictors that have been used
List<int> optimisedVariableLst = new List<int>(); //Stores the index of MV predictors that have been used
// starts with the best split index
List<double> azLst = new List<double>();
n.C = 0; // (= best split coordinate) Constant of the equation A1X1 + A2X2 + C = V [0] normalised [1] original value
double lowestImp = n.ImpBestUniSplit;
double bestCoef=-1;
double bestMvGain;
double bestUniGain;
int bestSplitIdx=-1;
SortedList<double, int> orderedNominalGain = new SortedList<double, int>(); // value, index or PredictorLst
SortedList<double, int> orderedNumericGain = new SortedList<double, int>(); // value, index or PredictorLst
FrmGraph fg = new FrmGraph();
bestUniGain = (n.Imp - n.ImpBestUniSplit) * 100 / n.Imp;
bestUniGain *= (double)(n.Table.RowCount - p.NullCount) / n.Table.RowCount;
Def.LogMessage += "Starting MinImpMvGreed, smallest univariate imp= " + lowestImp + " bestUniSplit gain " + n.BestSplit.Gain + " should be equal bestUniGain " + bestUniGain + Environment.NewLine;
for(int i=0; i < n.PredMvLst.Count; ++i){
n.PredMvLst[i].Optimised = false;
n.PredMvLst[i].Coef = Def.AbsentCoefficientValue;
nonOptimisedVariableLst.Add(i);
}
if (n.BestSplit.Variable.VariableTypeUserSet == SchemaVariable.VariableTypeEnum.Continuous) {
//Sets the properties of the best univariate split
#region
nonOptimisedVariableLst.Remove(n.BestSplit.PredMvBase);
optimisedVariableLst.Add(n.BestSplit.PredMvBase);
n.PredMvLst[n.BestSplit.PredMvBase].Coef = 1;
n.PredMvLst[n.BestSplit.PredMvBase].Optimised = true;
n.C = -Fcn.NormP1(n.BestSplit.SplitValue, n.BestSplit.LowerNumber, n.BestSplit.HigherNumber);
// c = 0;
#endregion
}else{//Nominal
List<string> caseLst;
n.C = 0;
//Sets the coefficients of the values on the left with the Def.PresentCoefficientValue
caseLst = n.PredictorLst[n.BestSplit.PredictorLstIdx].ChildrenGroups.ValueGroupLst[0];
for (int i = 0; i < caseLst.Count; ++i) {
//Not sure if it is right MessageBox.Show("didn't undestand bellow");
n.PredMvLst[n.BestSplit.PredMvBase + n.PredictorLst[n.BestSplit.PredictorLstIdx].ValueSd.IndexOfKey(caseLst[i])].Coef = Def.PresentCoefficientValue;
}
//If the best split is nominal, then all the column there describe it are already optmised
for (int i = 0; i < n.PredictorLst[n.BestSplit.PredictorLstIdx].DistinctValuesCount; ++i) {
nonOptimisedVariableLst.Remove(n.BestSplit.PredMvBase + i);
optimisedVariableLst.Add(n.BestSplit.PredMvBase + i);
n.PredMvLst[n.BestSplit.PredMvBase + i].Optimised = true;
}
}
//if (n.BestSplit.Gain == 100) {
// Def.LogMessage += "Nothing to be done, univariate gain equals 100%" + Environment.NewLine;
// n.MvTb.DataFill();
// DataValidate(n, c);
// n.MvTb.DataEmpty();
// return p.Gain;
//}
double[] r= new double[2];
while(nonOptimisedVariableLst.Count > 0){
//while(optimisedVariableLst.Count < 11){
hadOverallImprovement=false;
for (int nopVarIdx = 0; nopVarIdx < nonOptimisedVariableLst.Count; ++nopVarIdx) {
////Gets lowestCatSplitImp, catSplitImp;
if (n.PredMvLst[nonOptimisedVariableLst[nopVarIdx]].Variable.VariableTypeUserSet == SchemaVariable.VariableTypeEnum.Categorical) {
r = BestGainMvCategorical(n, n.C, nonOptimisedVariableLst[nopVarIdx], optimisedVariableLst);
if (r[1] < lowestImp) {
bestCoef = r[0];
bestSplitIdx = nonOptimisedVariableLst[nopVarIdx];
lowestImp = r[1];
hadOverallImprovement=true;
}
}
//Gets lowestNumSplitImp, numSplitImp;
else {
r = BestGainMvNumeric(n, n.C, nonOptimisedVariableLst[nopVarIdx], optimisedVariableLst, fg);
if (r[1] < lowestImp) {
bestCoef = r[0];
bestSplitIdx = nonOptimisedVariableLst[nopVarIdx];
lowestImp = r[1];
hadOverallImprovement=true;
}
}
if (n.PredMvLst[nonOptimisedVariableLst[nopVarIdx]].FieldSpan > 0)
Def.LogMessage += " nopVarIdx " + nonOptimisedVariableLst[nopVarIdx] + " Name " + n.PredMvLst[nonOptimisedVariableLst[nopVarIdx]].Variable.Name + "(" + n.PredMvLst[nonOptimisedVariableLst[nopVarIdx]].Offset + ")";
else
Def.LogMessage += " nopVarIdx " + nonOptimisedVariableLst[nopVarIdx] + " Name " + n.PredMvLst[nonOptimisedVariableLst[nopVarIdx]].Variable.Name;
Def.LogMessage += " Coefficient " + r[0] + " SplitImp " + r[1] + Environment.NewLine;
}//End for
if (hadOverallImprovement) {
if (!nonOptimisedVariableLst.Contains(bestSplitIdx)) {
Def.LogMessage += "Error couldn't find bestSplitIdx " + bestSplitIdx + " inside nonOptimisedVariableLst " + Environment.NewLine;
MessageBox.Show("Error couldn't find bestSplitIdx " + bestSplitIdx + " inside nonOptimisedVariableLst ");
return -1;
}
n.PredMvLst[bestSplitIdx].Coef = bestCoef;
n.PredMvLst[bestSplitIdx].Optimised = true;
nonOptimisedVariableLst.Remove(bestSplitIdx);
optimisedVariableLst.Add(bestSplitIdx);
Def.LogMessage += "Best variable " + n.PredMvLst[bestSplitIdx].Variable.Name + n.PredMvLst[bestSplitIdx].Offset + " Coef " + bestCoef + " lowestCatSplitImp " + lowestImp + "++++++++++++++++++++" + Environment.NewLine;
} else { //No improvement
Def.LogMessage += "No more improvement " + (optimisedVariableLst.Count) + " variables of " + n.PredMvLst.Count + " are being combined on the purity function -------------------" + Environment.NewLine;
foreach (int predMvIdx in nonOptimisedVariableLst) {
optimisedVariableLst.Add(predMvIdx);
n.PredMvLst[predMvIdx].Optimised = true;
}
nonOptimisedVariableLst.Clear();
}
if (lowestImp==0) {
Def.LogMessage += "Node is now 100% pure stopping greed seach " + Environment.NewLine;
break;
}
}//End while
n.ImpBestMvSplit = lowestImp;
p.Gain = n.BestSplit.Gain;
bestMvGain = (n.Imp - n.ImpBestMvSplit) * 100 / n.Imp;
bestMvGain *= (double)(n.Table.RowCount - p.NullCount) / n.Table.RowCount;
bestUniGain = (n.Imp - n.ImpBestUniSplit) * 100 / n.Imp;
bestUniGain *= (double)(n.Table.RowCount - p.NullCount) / n.Table.RowCount;
string coefStr = "";
for (int i = 0; i < n.PredMvLst.Count; ++i) {
if (n.PredMvLst[i].Variable.VariableTypeUserSet == SchemaVariable.VariableTypeEnum.Continuous)
coefStr += n.PredMvLst[i].Coef + "(" + n.PredMvLst[i].Variable.Name + ") ";
else
coefStr += n.PredMvLst[i].Coef + "(" + n.PredMvLst[i].Variable.Name + n.PredMvLst[i].Offset + ") ";
}
coefStr += "c = " + n.C;
//Why UniGain is sometimes different to p.Gain
Def.LogMessage += "MvGain: " + bestMvGain + " UniGain: " + bestUniGain + " P.Gain: " + p.Gain + " MvImp: " + lowestImp + " UniImp: " + n.ImpBestUniSplit + " NdImp: " + n.Imp + Environment.NewLine + coefStr + Environment.NewLine;
if (!Def.ExperimentRunning) {
for (int y = 0; y < n.Table.RowCount; ++y) {
fg.x0Lst.Add((float) n.PredMvLst[0].X(y));
fg.x1Lst.Add((float) n.PredMvLst[1].X(y));
if (n.MvTb.Data.TC[y].ToLower() == "n") {
fg.n.Add(y);
}
}
fg.a0=(float) n.PredMvLst[0].Coef;
fg.a1 = (float) n.PredMvLst[1].Coef;
fg.c = (float) n.C;
fg.N = n;
fg.ABest = (float)bestCoef;
fg.Show();
fg.Invalidate();
DataValidate(n, n.C);
}
//if auto n.Imp =
return p.Gain;
}
