internal FormDataView(DataTable table, MachineLearning.SchemeSumPredictions sumPredictionScheme)
{
InitializeComponent();
this._table = table;
// this._availableVisualizations = new List<VisualizationType> { Visuali}
generateView();
}
private String getExportString(String delimiter, String note, VotingScheme votingScheme)
{
StringBuilder s = new StringBuilder();
String featureModel = this._featureModel;
String kernel = _cMode.ToString();
if (_svmConfig != null)
{
kernel = this._svmConfig.Kernel.ToString();
}
ConfusionMatrix cm = null;
ReceiverOperatingCharacteristic roc = null;
SchemeSumPredictions sumPredictions = null;
SchemeMajorityVote majorityVote = null;
switch (votingScheme)
{
case VotingScheme.NONE:
{
cm = _voter.AggregatedConfusionMatrix;
roc = _voter.getROC();
break;
}
case VotingScheme.ADDITIVE_PREDICTIONS:
{
sumPredictions = _voter.getSumPredictions();
cm = null;
roc = sumPredictions.getROC();
break;
}
case VotingScheme.MAJORITY_VOTE:
{
majorityVote = _voter.getMajorityVote();
cm = null;
roc = majorityVote.getROC();
break;
}
}
if (votingScheme == VotingScheme.NONE && roc != null && cm != null)
{
roc.Compute(100);
s.Append(featureModel + delimiter);
s.Append(votingScheme + delimiter);
s.Append(kernel + delimiter);
s.Append(note + delimiter);
s.Append(this._numRuns + delimiter);
s.Append(this._numFolds + delimiter);
s.Append(this._timeElapsedMS + delimiter);
s.Append(this._memoryUsedBytes + delimiter);
s.Append(Utility.formatNumber(Utility.BytesToGB(this._memoryUsedBytes)) + delimiter);
s.Append(cm.Samples + delimiter);
s.Append(Utility.formatNumber(roc.Area) + delimiter);
s.Append(Utility.formatNumber(cm.Sensitivity) + delimiter);
s.Append(Utility.formatNumber(cm.Specificity) + delimiter);
s.Append(Utility.formatNumber(cm.FalsePositiveRate) + delimiter);
s.Append(Utility.formatNumber(cm.FalseDiscoveryRate) + delimiter);
s.Append(Utility.formatNumber(cm.Accuracy) + delimiter);
s.Append(Utility.formatNumber(cm.PositivePredictiveValue) + delimiter);
s.Append(Utility.formatNumber(cm.Precision) + delimiter);
s.Append(Utility.formatNumber(cm.Recall) + delimiter);
s.Append(Utility.formatNumber(cm.FScore) + delimiter);
s.Append(cm.ActualPositives + delimiter);
s.Append(cm.ActualNegatives + delimiter);
s.Append(cm.TruePositives + delimiter);
s.Append(cm.TrueNegatives + delimiter);
s.Append(cm.FalsePositives + delimiter);
s.Append(cm.FalseNegatives);
}
else if (votingScheme == VotingScheme.ADDITIVE_PREDICTIONS && sumPredictions != null && roc != null)
{
roc.Compute(100);
s.Append(featureModel + delimiter);
s.Append(votingScheme + delimiter);
s.Append(kernel + delimiter);
s.Append(note + delimiter);
s.Append(this._numRuns + delimiter);
s.Append(this._numFolds + delimiter);
s.Append(this._timeElapsedMS + delimiter);
s.Append(this._memoryUsedBytes + delimiter);
s.Append(Utility.formatNumber(Utility.BytesToGB(this._memoryUsedBytes)) + delimiter);
s.Append(sumPredictions.NumSamples + delimiter);
s.Append(Utility.formatNumber(sumPredictions.ROCAreaVoter) + delimiter);
s.Append(Utility.formatNumber(sumPredictions.Sensitivity) + delimiter);
s.Append(Utility.formatNumber(sumPredictions.Specificity) + delimiter);
s.Append(Utility.formatNumber(sumPredictions.FalsePositiveRate) + delimiter);
s.Append(Utility.formatNumber(sumPredictions.FalseDiscoveryRate) + delimiter);
s.Append(Utility.formatNumber(sumPredictions.Accuracy) + delimiter);
s.Append(Utility.formatNumber(sumPredictions.PositivePredictiveValue) + delimiter);
s.Append(Utility.formatNumber(sumPredictions.Precision) + delimiter);
s.Append(Utility.formatNumber(sumPredictions.Recall) + delimiter);
s.Append(Utility.formatNumber(sumPredictions.FScore) + delimiter);
s.Append(sumPredictions.ActualPositives + delimiter);
s.Append(sumPredictions.ActualNegatives + delimiter);
s.Append(sumPredictions.TruePositives + delimiter);
s.Append(sumPredictions.TrueNegatives + delimiter);
s.Append(sumPredictions.FalsePositives + delimiter);
s.Append(sumPredictions.FalseNegatives);
}
else if (votingScheme == VotingScheme.MAJORITY_VOTE && majorityVote != null && roc != null)
{
roc.Compute(100);
s.Append(featureModel + delimiter);
s.Append(votingScheme + delimiter);
s.Append(kernel + delimiter);
s.Append(note + delimiter);
s.Append(this._numRuns + delimiter);
s.Append(this._numFolds + delimiter);
s.Append(this._timeElapsedMS + delimiter);
s.Append(this._memoryUsedBytes + delimiter);
s.Append(Utility.formatNumber(Utility.BytesToGB(this._memoryUsedBytes)) + delimiter);
s.Append(majorityVote.NumSamples + delimiter);
s.Append(Utility.formatNumber(majorityVote.ROCAreaVoter) + delimiter);
s.Append(Utility.formatNumber(majorityVote.Sensitivity) + delimiter);
s.Append(Utility.formatNumber(majorityVote.Specificity) + delimiter);
s.Append(Utility.formatNumber(majorityVote.FalsePositiveRate) + delimiter);
s.Append(Utility.formatNumber(majorityVote.FalseDiscoveryRate) + delimiter);
s.Append(Utility.formatNumber(majorityVote.Accuracy) + delimiter);
s.Append(Utility.formatNumber(majorityVote.PositivePredictiveValue) + delimiter);
s.Append(Utility.formatNumber(majorityVote.Precision) + delimiter);
s.Append(Utility.formatNumber(majorityVote.Recall) + delimiter);
s.Append(Utility.formatNumber(majorityVote.FScore) + delimiter);
s.Append(majorityVote.ActualPositives + delimiter);
s.Append(majorityVote.ActualNegatives + delimiter);
s.Append(majorityVote.TruePositives + delimiter);
s.Append(majorityVote.TrueNegatives + delimiter);
s.Append(majorityVote.FalsePositives + delimiter);
s.Append(majorityVote.FalseNegatives);
}
return(s.ToString());
}
internal SchemeSumPredictions getSumPredictions()
{
SchemeSumPredictions scheme = new SchemeSumPredictions(_totalClassification);
return(scheme);
}