public void Run()
{
long percentComplete = 0;
_exhuastiveCache = new Cache(10, 0.0);
if ((_fitnessCrit == FitnessCriteria.R2) || (_fitnessCrit == FitnessCriteria.AdjustedR2))
{
_exhuastiveCache.Comparer = new DescendSort();
}
else if ((_fitnessCrit == FitnessCriteria.Sensitivity) || (_fitnessCrit == FitnessCriteria.Specificity) || (_fitnessCrit == FitnessCriteria.Accuracy))
{
_exhuastiveCache.Comparer = new DescendSort();
}
else
{
_exhuastiveCache.Comparer = new AscendSort();
}
IIndividual indiv = null;
List<short> combList = new List<short>();
short tmp = 0; ;
for (int i = 0; i < _numVars; i++)
{
//ListItem li = (ListItem)lbIndVariables.Items[i];
tmp = (short)(i + 1);
//tmp += Convert.ToInt16(li.ValueItem);
combList.Add(tmp);
}
long totalComb = 0;
long totalComplete = 0;
Combinations<short> combinations = null;
List<Combinations<short>> listAllComb = new List<Combinations<short>>();
for (short i = 1; i <= _maxVarsInModel; i++)
{
combinations = new Combinations<short>(combList.ToArray(), i, GenerateOption.WithoutRepetition);
listAllComb.Add(combinations);
totalComb += combinations.Count;
}
for (short i = 1; i <= _maxVarsInModel; i++)
{
if (Cancel)
break;
//combinations = new Combinations<short>(combList.ToArray(), i, GenerateOption.WithoutRepetition);
combinations = listAllComb[i - 1];
foreach (IList<short> comb in combinations)
{
if ((!Double.IsNaN(_decisionThreshold)) && (!Double.IsNaN(_mandateThreshold)) && (_maxVIF != Int32.MaxValue))
indiv = new MLRIndividual(i, i, _fitnessCrit, _maxVIF, _decisionThreshold, _mandateThreshold);
else if (_maxVIF != Int32.MaxValue)
indiv = new MLRIndividual(i, i, _fitnessCrit, _maxVIF);
else
indiv = new MLRIndividual(i, i, _fitnessCrit);
for (int j = 0; j < comb.Count; j++)
indiv.Chromosome[j] = comb[j];
if (Cancel)
break;
indiv.Evaluate();
if (indiv.IsViable())
{
//_exhuastiveCache.SortCache();
//_exhuastiveCache.ReplaceMinimum(indiv);
_exhuastiveCache.Add(indiv);
}
//else
//throw new Exception("Invalid individual.");
totalComplete++;
if ((totalComplete % 10) == 0)
{
percentComplete = totalComplete * 100 / totalComb;
ESProgress(percentComplete, _exhuastiveCache.MaximumFitness);
//VBLogger.getLogger().logEvent(percentComplete.ToString(), VBLogger.messageIntent.UserOnly, VBLogger.targetSStrip.ProgressBar);
//lblProgress.Text = "Progress: " + (Convert.ToDouble(totalComplete) / Convert.ToDouble(totalComb)) * 100;
//Console.WriteLine("Progress: " + (Convert.ToDouble(totalComplete) / Convert.ToDouble(totalComb)) * 100);
//lblProgress.Refresh();
//Application.DoEvents();
}
}
}
_exhuastiveCache.Sort();
//list = _exhuastiveCache.CacheList;
//lbModels.Items.Clear();
//UpdateFitnessListBox();
ESComplete(this);
}
private void RunManual()
{
ResetGraphs();
ResetOutputFields();
if (chkAllCombinations.Checked)
{
SearchExhaustiveModelList();
return;
}
DataTable dt = _projMgr.ModelDataTable;
int numVars = lbIndVariables.Items.Count;
FitnessCriteria fitnessCrit = GetFitnessCriteria();
MLRIndividual indiv = null;
indiv = new MLRIndividual(numVars, numVars, fitnessCrit, _maxVIF, _decisionThreshold, _mandateThreshold);
for (int i = 0; i < numVars; i++)
indiv.Chromosome[i] = (short)(i + 1);
indiv.Evaluate();
_list.Clear();
_list.Add(indiv);
btnRun.Text = "Run";
changeControlStatus(true);
UpdateFitnessListBox();
return;
}
/// <summary>
/// Fires when the app opens a project file
/// </summary>
/// <param name="projMgr"></param>
///
public void UnpackState(object objPackedStates)
{
ModelingInfo formPackedState = (ModelingInfo)objPackedStates;
//If either of these are null, then no project to open
if (formPackedState == null)
return;
if (formPackedState.AvailableVariables == null) return;
//Unpack the saved state of the PLS modeling control.
this.Show();
tabControl1.SelectedTab = tabControl1.TabPages["PLS"];
ipyPLSControl.UnpackProjectState(formPackedState.PlsProject);
//Return to the tab that was active when the project was saved.
tabControl1.SelectedIndex = formPackedState.ActiveModelingTab;
//Console.WriteLine("\n*** Modeling: project opened.***\n");
//Save dependent variable transform
if (formPackedState.DependentVariableTransform == Globals.DependentVariableTransforms.none)
mlrPredObs1.Transform = Globals.DependentVariableTransforms.none;
else if (formPackedState.DependentVariableTransform == Globals.DependentVariableTransforms.Log10)
mlrPredObs1.Transform = Globals.DependentVariableTransforms.Log10;
else if (formPackedState.DependentVariableTransform == Globals.DependentVariableTransforms.Ln)
mlrPredObs1.Transform = Globals.DependentVariableTransforms.Ln;
else if (formPackedState.DependentVariableTransform == Globals.DependentVariableTransforms.Power)
mlrPredObs1.Transform = Globals.DependentVariableTransforms.Power;
_projMgr.Model = formPackedState.Model;
//Save available and independent variables
lbAvailableVariables.Items.Clear();
for (int i = 0; i < formPackedState.AvailableVariables.Count; i++)
lbAvailableVariables.Items.Add(formPackedState.AvailableVariables[i]);
lbIndVariables.Items.Clear();
for (int i = 0; i < formPackedState.IndependentVariables.Count; i++)
lbIndVariables.Items.Add(formPackedState.IndependentVariables[i]);
lblAvailVars.Text = lbAvailableVariables.Items.Count.ToString();
lblDepVars.Text = lbIndVariables.Items.Count.ToString();
//Save the chromosomes
if (_list != null)
_list.Clear();
else _list = new List<IIndividual>();
_projMgr.ModelDataTable = CreateModelDataTable();
MLRIndividual indiv = null;
ModelingInfo modInfo = null;
if (formPackedState.Chromosomes != null)
{
for (int i = 0; i < formPackedState.Chromosomes.Count; i++)
{
modInfo = formPackedState;
int numGenes = formPackedState.Chromosomes[i].Count;
indiv = new MLRIndividual(numGenes, modInfo.MaxGeneValue, (FitnessCriteria)modInfo.FitnessCriteria, modInfo.MaxVIF, modInfo.DecisionThreshold, modInfo.MandatedThreshold);
indiv.Chromosome = formPackedState.Chromosomes[i];
indiv.Evaluate();
_list.Add((IIndividual)indiv);
}
UpdateFitnessListBox();
if (formPackedState.SelectedModel > -1 && indiv.Parameters != null)
listBox1.SelectedIndex = formPackedState.SelectedModel;
}
_numObs = _projMgr.CorrelationDataTable.Rows.Count;
lblNumObs.Text = "Number of Observations: " + _numObs.ToString();
int maxVar = _numObs / 5;
//int recVar = Math.Min(((_numObs / 10) + 1), availVar);
int recVar = Math.Min(((_numObs / 10) + 1), (lbIndVariables.Items.Count));
lblMaxAndRecommended.Text = "Recommended: " + recVar.ToString() + ", Max: " + maxVar.ToString();
txtMaxVars.Text = recVar.ToString();
this.Show();
if (listBox1.Items.Count < 1 && ipyPLSControl.VirginState == true)
{
_projMgr._comms.sendMessage("Hide", this);
}
}
/// <summary>
/// given a mlr model, vary the threshold by increments to find model sensitivity and specificity
/// for each increment - these become roc plotting points for the model. also, save the roc trace
/// data for passing to the caller for table display. plotting points need to be sorted and aggregated
/// (weedppl(ppl)) for calculating auc (area-under-curve via integration)
/// </summary>
/// <param name="model">given mlr model</param>
/// <param name="rocTableVals"></param>
/// <returns>null if number of pts lt 10, otherwise a pointpair list fro plotting</returns>
private PointPairList ROCpoints(MLRIndividual model, out List<object> rocTableVals)
{
const int interations = 50;
//vary the decision threshold by increments
//calculate the ROC point for the decision threshold increment
//accumulate points for all increments and return pointpairlist
PointPairList ppl = new PointPairList();
PointPair pp = new PointPair();
ROCParameters rocParameters = null;
List<object> rocTableVal = new List<object>();
double maxPred = model.PredictedValues.Max();
double minPred = model.PredictedValues.Min();
double inc = (maxPred - minPred) / (double)interations;
double threshold = minPred;
while (threshold < maxPred)
{
threshold += inc;
pp = ROCpoint(model, threshold, out rocParameters);
if (!pp.IsInvalid)
{
ppl.Add(pp);
rocTableVal.Add(rocParameters.ROCPars);
}
}
rocTableVals = rocTableVal;
//how many points is the minimum???
if (ppl.Count > 10)
{
//sort for integral calc
ppl.Sort();
//get rid of multiple X datapoints
ppl = weedppl(ppl);
return ppl;
}
else
{
return null;
}
}
/// <summary>
/// given a model and decision threshold calculate (and store) information for a roc curve
/// </summary>
/// <param name="model">mlr model</param>
/// <param name="decisionThreshold">threshold value</param>
/// <param name="rocPars">roc curve information </param>
/// <returns>point pair for plotting</returns>
private PointPair ROCpoint(MLRIndividual model, double decisionThreshold, out ROCParameters rocPars)
{
//for a given threshold and model, categorize predictions relative to observations
//and calculate the sensitivity and specificity evaluation criteria, return theses
//values as a zedgraph point.
PointPair pp = new PointPair();
double[] measurement = model.ObservedValues;
double[] est = model.PredictedValues;
int truePos = 0;
int trueNeg = 0;
int falsePos = 0;
int falseNeg = 0;
double pred = double.NaN;
double obs = double.NaN;
double specificity = double.NaN;
double sensitivity = double.NaN;
for (int i = 0; i < est.Length; i++)
{
pred = est[i];
obs = measurement[i];
if ((pred > decisionThreshold) && (obs > _mandateThreshold))
truePos++;
else if ((pred > decisionThreshold) && (obs < _mandateThreshold))
falsePos++;
else if ((pred < decisionThreshold) && (obs > _mandateThreshold))
falseNeg++;
else if ((pred < decisionThreshold) && (obs < _mandateThreshold))
trueNeg++;
}
sensitivity = (double)truePos / (double)(truePos + falseNeg);
specificity = (double)trueNeg / (double)(trueNeg + falsePos);
if (!sensitivity.Equals(double.NaN) && !specificity.Equals(double.NaN))
{
pp.X = (1.0d - specificity);
pp.Y = sensitivity;
}
ROCParameters rocpars = new ROCParameters(decisionThreshold, sensitivity, specificity, falsePos, falseNeg);
rocPars = rocpars;
return pp;
}
public void Run()
{
long percentComplete = 0;
_exhuastiveCache = new Cache(10, 0.0);
if ((_fitnessCrit == FitnessCriteria.R2) || (_fitnessCrit == FitnessCriteria.AdjustedR2))
{
_exhuastiveCache.Comparer = new DescendSort();
}
else if ((_fitnessCrit == FitnessCriteria.Sensitivity) || (_fitnessCrit == FitnessCriteria.Specificity) || (_fitnessCrit == FitnessCriteria.Accuracy))
{
_exhuastiveCache.Comparer = new DescendSort();
}
else
{
_exhuastiveCache.Comparer = new AscendSort();
}
IIndividual indiv = null;
List <short> combList = new List <short>();
short tmp = 0;;
for (int i = 0; i < _numVars; i++)
{
//ListItem li = (ListItem)lbIndVariables.Items[i];
tmp = (short)(i + 1);
//tmp += Convert.ToInt16(li.ValueItem);
combList.Add(tmp);
}
long totalComb = 0;
long totalComplete = 0;
Combinations <short> combinations = null;
List <Combinations <short> > listAllComb = new List <Combinations <short> >();
for (short i = 1; i <= _maxVarsInModel; i++)
{
combinations = new Combinations <short>(combList.ToArray(), i, GenerateOption.WithoutRepetition);
listAllComb.Add(combinations);
totalComb += combinations.Count;
}
for (short i = 1; i <= _maxVarsInModel; i++)
{
if (Cancel)
{
break;
}
//combinations = new Combinations<short>(combList.ToArray(), i, GenerateOption.WithoutRepetition);
combinations = listAllComb[i - 1];
foreach (IList <short> comb in combinations)
{
if ((!Double.IsNaN(_decisionThreshold)) && (!Double.IsNaN(_mandateThreshold)) && (_maxVIF != Int32.MaxValue))
{
indiv = new MLRIndividual(i, i, _fitnessCrit, _maxVIF, _decisionThreshold, _mandateThreshold);
}
else if (_maxVIF != Int32.MaxValue)
{
indiv = new MLRIndividual(i, i, _fitnessCrit, _maxVIF);
}
else
{
indiv = new MLRIndividual(i, i, _fitnessCrit);
}
for (int j = 0; j < comb.Count; j++)
{
indiv.Chromosome[j] = comb[j];
}
if (Cancel)
{
break;
}
indiv.Evaluate();
if (indiv.IsViable())
{
//_exhuastiveCache.SortCache();
//_exhuastiveCache.ReplaceMinimum(indiv);
_exhuastiveCache.Add(indiv);
}
//else
//throw new Exception("Invalid individual.");
totalComplete++;
if ((totalComplete % 10) == 0)
{
percentComplete = totalComplete * 100 / totalComb;
ESProgress(percentComplete, _exhuastiveCache.MaximumFitness);
//VBLogger.getLogger().logEvent(percentComplete.ToString(), VBLogger.messageIntent.UserOnly, VBLogger.targetSStrip.ProgressBar);
//lblProgress.Text = "Progress: " + (Convert.ToDouble(totalComplete) / Convert.ToDouble(totalComb)) * 100;
//Console.WriteLine("Progress: " + (Convert.ToDouble(totalComplete) / Convert.ToDouble(totalComb)) * 100);
//lblProgress.Refresh();
//Application.DoEvents();
}
}
}
_exhuastiveCache.Sort();
//list = _exhuastiveCache.CacheList;
//lbModels.Items.Clear();
//UpdateFitnessListBox();
ESComplete(this);
}
