static void Main(string[] args)
{
WriteLine("Finding the files");
var backgroundEvents = Files.GetJZ(2).Select(e => e.Data);
//var signalHV125pi15Events = Files.Get125pi15();
//var signalHV125pi40Events = Files.Get125pi40();
var signalHV600pi100Events = Files.Get600pi150lt9m();
//var signalGet200pi25lt5mEvents = Files.Get200pi25lt5m();
//
// Do a simple cut training here
//
var t = TrainingIQueriable(signalHV600pi100Events, true)
.AsSignal("HV600pi100")
.Background(TrainingIQueriable(backgroundEvents, false), "J2Z")
.IgnoreVariables(p => p.lowestPtTrack);
var mCuts = t.AddMethod(ROOTNET.Interface.NTMVA.NTypes.EMVA.kCuts, "SimpleCuts")
.Option("!H:V:FitMethod=MC:EffSel:SampleSize=200000:VarTransform=Decorrelate")
.ParameterOption(p => p.logR, "VarProp", "FSmart")
.ParameterOption(p => p.nTracks, "VarProp", "FSmart")
.ParameterOption(p => p.lowestPtTrack, "VarProp", "FSmart");
var mMVA = t.AddMethod(ROOTNET.Interface.NTMVA.NTypes.EMVA.kBDT, "SimpleBDT")
.Option("MaxDepth=3");
t.Train("VerySimpleTraining");
// Lets make a measurement of the efficiency for the standard cut.
var effResults = new FutureTFile(Path.Combine(FileInfoUtilities.FindDirectoryWithFileMatching("*.sln").FullName,"trainingResults.root"));
var stdCutVale = CalcEff(effResults.mkdir("std"), td => (td.logR > 1.2 && td.nTracks == 0) ? 1.0 : 0.0, 0.5, TrainingIQueriable(backgroundEvents, false), TrainingIQueriable(signalHV600pi100Events, true));
// Next, do it with a reader
#if false
var r = new ROOTNET.NTMVA.NReader();
var s = new FileInfo("C:\\Users\\gordo\\Documents\\Code\\calratio2015\\JetCutStudies\\SimpleJetCutTraining\\bin\\x86\\Debug\\weights\\VerySimpleTraining_SimpleCuts.weights.xml");
float[] logR = new float[2];
float[] nTracks = new float[2];
r.AddVariable("logR".AsTS(), logR);
r.AddVariable("nTracks".AsTS(), nTracks);
r.BookMVA("SimpleCuts".AsTS(), s.FullName.AsTS());
Expression<Func<TrainingData, double>> simpleCutsReader = tv => TMVAReaders.TMVASelectorSimpleCutsTest(r, tv.logR, tv.lowestPtTrack, stdCutVale.Value);
#else
Expression<Func<TrainingData, double>> simpleCutsReader = tv => TMVAReaders.TMVASelectorSimpleCuts(tv.logR, tv.nTracks, 0.72);
#endif
var simpleCutValue = CalcEff(effResults.mkdir("SimpleCuts"), simpleCutsReader, 0.5, TrainingIQueriable(backgroundEvents, false), TrainingIQueriable(signalHV600pi100Events, true));
// Next, we need to get the MVA and figure out the efficiency.
Expression<Func<TrainingData, double>> simpleMVAReader = tv => TMVAReaders.TMVASelectorSimpleBDT(tv.logR, tv.nTracks);
var simpleBDTValue = CalcEff(effResults.mkdir("SimpleBDT"), simpleMVAReader, 0.99999, TrainingIQueriable(backgroundEvents, false), TrainingIQueriable(signalHV600pi100Events, true));
// Write out everything
effResults.Write();
effResults.Close();
//Emit();
}
public void FutureFileEmpty()
{
// Create an empty TFile, make sure that is good!
var f = new FileInfo("FutureFileEmpty.root");
if (f.Exists)
{
f.Delete();
}
var ftf = new FutureTFile(f);
ftf.Write();
ftf.Close();
f.Refresh();
Assert.IsTrue(f.Exists);
}
/// <summary>
/// Make generic plots of the signal or background
/// </summary>
/// <param name="args"></param>
/// <remarks>
/// TODO: Would studies of efficiencies here be better served by splitting into forward and central eta regions?
/// TODO: What is going on with the jetPT?
/// TODO: What is eta distribution of the jets that make it through, in particular with NTrack = 0?
/// It could be those are far forward and thus have no tracks.
/// TODO: Should any of these plots look at stuff in the way that Heather has (2D heat maps for cuts)?
/// </remarks>
static void Main(string[] args)
{
var opt = CommandLineUtils.ParseOptions<Options>(args);
Console.WriteLine("Finding the files");
// All the background samples have to be done first.
var backgroundSamples = SampleMetaData.AllSamplesWithTag("background")
.Select(info => Tuple.Create(Files.GetSampleAsMetaData(info), info.NickName))
.ToArray();
var backgroundEvents = Files.GetAllJetSamples().Select(e => e.Data);
// All the signal we are going to make plots of.
var signalSamples = SampleMetaData.AllSamplesWithTag("signal")
.Select(info => Tuple.Create(Files.GetSampleAsMetaData(info), info.NickName))
.ToArray();
// Output file
Console.WriteLine("Opening output file");
using (var outputHistograms = new FutureTFile("GenericPerformancePlots.root"))
{
// First, lets do a small individual thing for each individual background sample.
var bkgDir = outputHistograms.mkdir("background");
Console.WriteLine("Making background plots.");
foreach (var background in backgroundSamples)
{
BuildSuperJetInfo(background.Item1.Select(md => md.Data))
.PlotBasicDataPlots(bkgDir.mkdir(background.Item2), "all");
}
// Do a quick study for each signal sample, using all the backgrounds at once to make
// performance plots.
Console.WriteLine("Making the signal/background plots.");
foreach (var sample in signalSamples)
{
var status = PerSampleStudies(backgroundEvents, sample.Item1.Select(md => md.Data), outputHistograms.mkdir(sample.Item2));
DumpResults($"Sample {sample.Item2}:", status);
}
// Write out the histograms
outputHistograms.Write();
}
}
