public override void Initialize()
{
LibLinearClassifier.Solver solver = (LibLinearClassifier.Solver)Enum.Parse(typeof(LibLinearClassifier.Solver), Configuration.ClassifierTypeOptions[GetType()]["solver"]);
string trainPath = Configuration.ClassifierTypeOptions[GetType()]["train"];
string predictPath = Configuration.ClassifierTypeOptions[GetType()]["predict"];
_libLinear = new LibLinearClassifier(solver, NumericFeatureNameTransform.AccessMethod.Memory, FeatureSpace.AccessMethod.Memory, true, Model.ModelDirectory, trainPath, predictPath, null, float.MinValue);
_libLinear.OutputProbabilities = true;
}
internal override string GetDetails(Prediction prediction, Dictionary <string, string> attFeatureIdInformation)
{
StringBuilder report = new StringBuilder("Details for model created by prediction \"" + prediction.Name + "\"" + Environment.NewLine);
Dictionary <int, Dictionary <int, double> > classFeatureWeight = LibLinearClassifier.GetFeatureWeights(Path.Combine(Model.ModelDirectory, LibLinearClassifier.ModelFileName));
LabelMap labelMap = new LabelMap(Path.Combine(Model.ModelDirectory, LibLinearClassifier.LabelMapFileName));
MemoryNumericFeatureNameTransform featureNameTransform = new MemoryNumericFeatureNameTransform(Path.Combine(Model.ModelDirectory, LibLinearClassifier.FeatureNameTransformFileName));
Dictionary <int, string> liblinearFeatureNumberAttFeatureId = new Dictionary <int, string>();
foreach (string attFeatureId in featureNameTransform)
{
// nominal features in the ATT have IDs in the transform that include the nominal feature value - trim this off to recover the original ID
int dashIndex = attFeatureId.IndexOf('-');
if (dashIndex >= 0)
{
liblinearFeatureNumberAttFeatureId.Add(featureNameTransform.GetFeatureNumber(attFeatureId), attFeatureId.Substring(0, dashIndex));
}
else
{
liblinearFeatureNumberAttFeatureId.Add(featureNameTransform.GetFeatureNumber(attFeatureId), attFeatureId);
}
}
Dictionary <string, string> attFeatureIdDesc = new Dictionary <string, string>();
foreach (PTL.ATT.Models.Feature f in Model.Features)
{
attFeatureIdDesc.Add(f.Id, "Feature \"" + f.Description + "\"");
}
foreach (int classNumber in classFeatureWeight.Keys.OrderBy(i => i))
{
report.AppendLine("\tClass \"" + labelMap.GetUnmappedLabel(classNumber.ToString()) + "\"");
int maxFeatureNameWidth = classFeatureWeight[classNumber].Keys.Max(f => attFeatureIdDesc[liblinearFeatureNumberAttFeatureId[f]].Length);
foreach (int liblinearFeatureNumber in classFeatureWeight[classNumber].Keys.OrderBy(f => - Math.Abs(classFeatureWeight[classNumber][f])))
{
string desc = attFeatureIdDesc[liblinearFeatureNumberAttFeatureId[liblinearFeatureNumber]];
double weight = classFeatureWeight[classNumber][liblinearFeatureNumber];
string attFeatureId = liblinearFeatureNumberAttFeatureId[liblinearFeatureNumber];
string information = (attFeatureIdInformation == null || !attFeatureIdInformation.ContainsKey(attFeatureId) ? "" : Environment.NewLine +
"\t\t\tInformation: " + attFeatureIdInformation[attFeatureId]);
report.AppendLine(string.Format("\t\t{0,-" + maxFeatureNameWidth + "}: weight = {1:0.00}", desc, weight) + information + Environment.NewLine);
}
report.AppendLine();
}
return(report.ToString());
}
public override void Initialize()
{
LibLinearClassifier.Solver solver = (LibLinearClassifier.Solver)Enum.Parse(typeof(LibLinearClassifier.Solver), Configuration.ClassifierTypeOptions[GetType()]["solver"]);
string trainPath = Configuration.ClassifierTypeOptions[GetType()]["train"];
string predictPath = Configuration.ClassifierTypeOptions[GetType()]["predict"];
_libLinear = new LibLinearClassifier(solver, NumericFeatureNameTransform.AccessMethod.Memory, FeatureSpace.AccessMethod.Memory, true, Model.ModelDirectory, trainPath, predictPath, null, float.MinValue);
_libLinear.OutputProbabilities = true;
}
