protected override void BuildModel()
{
StringBuilder rCmd = new StringBuilder(@"
trainRaw=read.csv(""" + RawTrainPath.Replace("\\", "/") + @""", header = TRUE, sep = ',')" + @"
trainNorm=trainRaw
cols=NCOL(trainRaw)
mxmn <- array(0, dim=c(2,cols-1))
options(scipen=999)
for(i in 2:cols) {
cmax=max(trainRaw[,i])
cmin=min(trainRaw[,i])
mxmn[1,i-1]=cmax
mxmn[2,i-1]=cmin
trainNorm[,i]=(trainRaw[,i]-((cmax+cmin)/2))/((cmax-cmin)/2)
}
trainNorm[is.na(trainNorm)]=0
write.table(data.frame(mxmn), file=""" + ColumnMaxMinPath.Replace("\\", "/") + @""", row.names=FALSE, col.names=FALSE, sep=',')" + @"
library(randomForest)
rf=randomForest(Class ~., data=trainNorm, ntree=" + _numTrees + ", importance=TRUE, seed=99)" + @"
save(rf, file=""" + RandomForestModelPath.Replace("\\", "/") + @""")" + @"
");
string output, error;
R.Execute(rCmd.ToString(), false, out output, out error);
try
{
if (!File.Exists(RandomForestModelPath))
{
throw new Exception("RandomForest model was not created at \"" + RandomForestModelPath + "\".");
}
}
catch (Exception ex)
{
throw new Exception("ERROR: RandomForest failed to build model. Output and error messages follow:" + Environment.NewLine +
"\tException message: " + ex.Message + Environment.NewLine +
"\tR output: " + output + Environment.NewLine +
"\tR orror: " + error);
}
finally
{
try { File.Delete(RawTrainPath); }
catch { }
}
}
protected override void BuildModel()
{
StringBuilder rCmd = new StringBuilder(@"
trainRaw=read.csv(""" + RawTrainPath.Replace(@"\", "/") + @""", header = TRUE, sep = ',')" + @"
trainNorm=trainRaw
cols=NCOL(trainRaw)
mxmn <- array(0, dim=c(2,cols-1))
options(scipen=999)
for(i in 2:cols) {
cmax=max(trainRaw[,i])
cmin=min(trainRaw[,i])
mxmn[1,i-1]=cmax
mxmn[2,i-1]=cmin
trainNorm[,i]=(trainRaw[,i]-((cmax+cmin)/2))/((cmax-cmin)/2)
}
trainNorm[is.na(trainNorm)]=0
write.table(data.frame(mxmn), file=""" + ColumnMaxMinPath.Replace(@"\", "/") + @""", row.names=FALSE, col.names=FALSE, sep=',')" + @"
library(ada)
set.seed(99)
cls <- sort(unique(trainNorm$Class))
cList <- vector('list', length(cls))
save(cls, file=""" + ClassPath.Replace(@"\", "/") + @""")" + @"
binForm = ""Class ~.,data=trainNorm,iter=" + _iterations + ",loss='logistic',nu=1,type='discrete'" + @"""
multForm = ""Class ~.,data=cList[[i]],iter=" + _iterations + ",loss='logistic',nu=1,type='discrete'" + @"""
if(length(cls)==2) {
adb <- eval(parse(text=paste('ada(', binForm, ')', sep='')))
save(adb, file=""" + AdaModelPath.Replace(@"\", "/") + @""")" + @"
} else {
for(i in 1:length(cls)) {
cList[[i]] <- trainNorm
for(j in 1:length(cls)) {
if(i!=j) {
levels(cList[[i]]$Class)[levels(cList[[i]]$Class)==cls[j]] <- 'REST'
}
}
adb <- eval(parse(text=paste('ada(', multForm, ')', sep='')))
save(adb, file=paste('" + Path.Combine(Model.ModelDirectory, @"ada', i, '.RData', sep='')").Replace("\\", "/") + @")" + @"
}
}
");
R.Execute(rCmd.ToString(), false);
File.Delete(RawTrainPath);
}
public override void Classify(FeatureVectorList featureVectors)
{
base.Classify(featureVectors);
if (featureVectors != null && featureVectors.Count > 0)
{
using (StreamWriter predictionsFile = new StreamWriter(RawPredictionInstancesPath))
{
predictionsFile.Write("Class");
foreach (PTL.ATT.Models.Feature f in Model.Features.OrderBy(i => i.Id))
{
predictionsFile.Write("," + f.Id);
}
predictionsFile.WriteLine();
foreach (FeatureVector vector in featureVectors)
{
predictionsFile.Write(vector.DerivedFrom.TrueClass);
foreach (PTL.ATT.Models.Feature f in Model.Features.OrderBy(i => i.Id))
{
object value;
if (vector.TryGetValue(f.Id, out value))
{
predictionsFile.Write("," + value);
}
else
{
predictionsFile.Write(",0");
}
}
predictionsFile.WriteLine();
}
predictionsFile.Close();
}
StringBuilder rCmd = new StringBuilder(@"
predRaw=read.csv(""" + RawPredictionInstancesPath.Replace("\\", "/") + @""", header = TRUE, sep = ',')" + @"
mxmn=read.csv(""" + ColumnMaxMinPath.Replace("\\", "/") + @""", header = FALSE, sep = ',')" + @"
predNorm=predRaw
for(i in 2:NCOL(predRaw)) {
cmax=mxmn[1,i-1]
cmin=mxmn[2,i-1]
predNorm[,i] = (predRaw[,i]-((cmax+cmin)/2))/((cmax-cmin)/2)
}
predNorm[is.na(predNorm)]=0
library(randomForest)
load(file=""" + RandomForestModelPath.Replace("\\", "/") + @""")" + @"
rf.pred=predict(rf, predNorm, norm.votes=TRUE, type='prob')
dfp<-data.frame(rf.pred)
names(dfp)[names(dfp)=='NULL.'] <- 'NULL'
write.table(dfp, file=""" + PredictionsPath.Replace("\\", "/") + @""", row.names=FALSE, sep=',')" + @"
");
string output, error;
R.Execute(rCmd.ToString(), false, out output, out error);
try
{
using (StreamReader predictionsFile = new StreamReader(PredictionsPath))
{
string[] colnames = predictionsFile.ReadLine().Split(',');
int row = 0;
string line;
while ((line = predictionsFile.ReadLine()) != null)
{
string[] lines = line.Split(',');
for (int i = 0; i < colnames.Length; i++)
{
string label = colnames[i].Replace("\"", @"");
label = label.Replace(".", " ");
float prob = float.Parse(lines[i]);
featureVectors[row].DerivedFrom.PredictionConfidenceScores.Add(label, prob);
}
row++;
}
predictionsFile.Close();
if (row != featureVectors.Count)
{
throw new Exception("Number of predictions doesn't match number of input vectors");
}
}
}
catch (Exception ex)
{
throw new Exception("ERROR: RandomForest failed to classify points. Output and error messages follow:" + Environment.NewLine +
"\tException message: " + ex.Message + Environment.NewLine +
"\tR output: " + output + Environment.NewLine +
"\tR orror: " + error);
}
finally
{
try { File.Delete(ColumnMaxMinPath); }
catch { }
try { File.Delete(RandomForestModelPath); }
catch { }
try { File.Delete(RawPredictionInstancesPath); }
catch { }
try { File.Delete(PredictionsPath); }
catch { }
}
}
}
public override void Classify(FeatureVectorList featureVectors)
{
base.Classify(featureVectors);
if (featureVectors != null && featureVectors.Count > 0)
{
using (StreamWriter predictionsFile = new StreamWriter(RawPredictionInstancesPath))
{
predictionsFile.Write("Class");
foreach (PTL.ATT.Models.Feature f in Model.Features.OrderBy(i => i.Id))
{
predictionsFile.Write("," + f.Id);
}
predictionsFile.WriteLine();
foreach (FeatureVector vector in featureVectors)
{
predictionsFile.Write(vector.DerivedFrom.TrueClass);
foreach (PTL.ATT.Models.Feature f in Model.Features.OrderBy(i => i.Id))
{
object value;
if (vector.TryGetValue(f.Id, out value))
{
predictionsFile.Write("," + value);
}
else
{
predictionsFile.Write(",0");
}
}
predictionsFile.WriteLine();
}
predictionsFile.Close();
}
StringBuilder rCmd = new StringBuilder(@"
predRaw=read.csv(""" + RawPredictionInstancesPath.Replace("\\", "/") + @""", header = TRUE, sep = ',')" + @"
mxmn=read.csv(""" + ColumnMaxMinPath.Replace("\\", "/") + @""", header = FALSE, sep = ',')" + @"
predNorm=predRaw
for(i in 2:NCOL(predRaw)) {
cmax=mxmn[1,i-1]
cmin=mxmn[2,i-1]
predNorm[,i] = (predRaw[,i]-((cmax+cmin)/2))/((cmax-cmin)/2)
}
predNorm[is.na(predNorm)]=0
library(ada)
set.seed(99)
load(file=""" + ClassPath.Replace("\\", "/") + @""")" + @"
if(length(cls)==2) {
load(file=""" + AdaModelPath.Replace("\\", "/") + @""")" + @"
adb.pred<-predict(adb, newdata=predNorm, type='prob')
mult<-data.frame(adb.pred)
names(mult)<-sort(c(toString(cls[1]), toString(cls[2])))
} else {
mult<-data.frame(matrix(0, ncol=1, nrow=NROW(predRaw)))
names(mult)<-c('INIT_DF')
for(i in 1:length(cls)) {
load(file=paste('" + Path.Combine(Model.ModelDirectory, @"ada', i, '.RData', sep='')").Replace("\\", "/") + @")" + @"
adb.pred<-predict(adb, newdata=predNorm, type='prob')
abp<-data.frame(adb.pred)
names(abp)<-sort(c(toString(cls[i]), 'REST'))
abp<-subset(abp, select=-c(REST))
mult<-cbind(mult, abp)
}
mult<-subset(mult, select=-c(INIT_DF))
mult<-1/(1+exp(-1*mult))
sums<-data.frame(rowSums(mult))
for(j in 1:length(cls)) {
mult[j]<-mult[j]/sums
}
}
write.table(mult, file=""" + PredictionsPath.Replace("\\", "/") + @""", row.names=FALSE, sep=',')" + @"
");
string output, error;
R.Execute(rCmd.ToString(), false, out output, out error);
try
{
using (StreamReader predictionsFile = new StreamReader(PredictionsPath))
{
string[] colnames = predictionsFile.ReadLine().Split(',');
int row = 0;
string line;
while ((line = predictionsFile.ReadLine()) != null)
{
string[] lines = line.Split(',');
for (int i = 0; i < colnames.Length; i++)
{
string label = colnames[i].Replace("\"", "");
float prob = float.Parse(lines[i]);
featureVectors[row].DerivedFrom.PredictionConfidenceScores.Add(label, prob);
}
row++;
}
predictionsFile.Close();
if (row != featureVectors.Count)
{
throw new Exception("Number of predictions doesn't match number of input vectors");
}
}
}
catch (Exception ex)
{
throw new Exception("ERROR: AdaBoost failed to classify points. Output and error messages follow:" + Environment.NewLine +
"\tException message: " + ex.Message + Environment.NewLine +
"\tR output: " + output + Environment.NewLine +
"\tR orror: " + error);
}
finally
{
try { File.Delete(ColumnMaxMinPath); }
catch { }
try { File.Delete(ClassPath); }
catch { }
try { File.Delete(AdaModelPath); }
catch { }
try { File.Delete(RawPredictionInstancesPath); }
catch { }
try { File.Delete(PredictionsPath); }
catch { }
}
}
}
