Test for RFOneVsRest.

tmva/pymva/test/Classification.C

@@ -11,100 +11,100 @@
 #include "TSystem.h"
 #include "TROOT.h"
 
-
 #include "TMVA/Factory.h"
 #include "TMVA/Tools.h"
+#include "TMVA/DataLoader.h"
 #include "TMVA/MethodPyRandomForest.h"
 
 void Classification()
 {
-   TMVA::Tools::Instance();
-   TMVA::PyMethodBase::PyInitialize();
-
-   TString outfileName("TMVA.root");
-   TFile *outputFile = TFile::Open(outfileName, "RECREATE");
-
-   TMVA::Factory *factory = new TMVA::Factory("TMVAClassification", outputFile,
-         "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification");
-
-   factory->AddVariable("myvar1 := var1+var2", 'F');
-   factory->AddVariable("myvar2 := var1-var2", "Expression 2", "", 'F');
-   factory->AddVariable("var3",                "Variable 3", "units", 'F');
-   factory->AddVariable("var4",                "Variable 4", "units", 'F');
-   factory->AddSpectator("spec1 := var1*2",  "Spectator 1", "units", 'F');
-   factory->AddSpectator("spec2 := var1*3",  "Spectator 2", "units", 'F');
-
-   TFile *input(0);
-   TString fname = "./tmva_class_example.root";
-   if (!gSystem->AccessPathName( fname )) {
-      input = TFile::Open( fname ); // check if file in local directory exists
-   }
-   else {
-      TFile::SetCacheFileDir(".");
-      input = TFile::Open("http://root.cern.ch/files/tmva_class_example.root", "CACHEREAD");
-   }
-   if (!input) {
-      std::cout << "ERROR: could not open data file" << std::endl;
-      exit(1);
-   }
-
-   std::cout << "--- TMVAClassification       : Using input file: " << input->GetName() << std::endl;
-
-   // --- Register the training and test trees
-
-   TTree *tsignal     = (TTree *)input->Get("TreeS");
-   TTree *tbackground = (TTree *)input->Get("TreeB");
-
-   // global event weights per tree (see below for setting event-wise weights)
-   Double_t signalWeight     = 1.0;
-   Double_t backgroundWeight = 1.0;
-
-   // You can add an arbitrary number of signal or background trees
-   factory->AddSignalTree(tsignal,     signalWeight);
-   factory->AddBackgroundTree(tbackground, backgroundWeight);
-
-
-   // Set individual event weights (the variables must exist in the original TTree)
-   factory->SetBackgroundWeightExpression("weight");
-
-
-   // Apply additional cuts on the signal and background samples (can be different)
-   TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
-   TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";
-
-   // Tell the factory how to use the training and testing events
-   factory->PrepareTrainingAndTestTree(mycuts, mycutb,
-                                       "nTrain_Signal=0:nTrain_Background=0:nTest_Signal=0:nTest_Background=0:SplitMode=Random:NormMode=NumEvents:!V");
-
-
-   ///////////////////
-   //Booking         //
-   ///////////////////
-   // Boosted Decision Trees
-
-   //PyMVA methods
-   factory->BookMethod(TMVA::Types::kPyRandomForest, "PyRandomForest",
-                       "!V:NEstimators=150:Criterion=gini:MaxFeatures=auto:MaxDepth=3:MinSamplesLeaf=1:MinWeightFractionLeaf=0:Bootstrap=kTRUE");
-   factory->BookMethod(TMVA::Types::kPyAdaBoost, "PyAdaBoost",
-                       "!V:BaseEstimator=None:NEstimators=100:LearningRate=1:Algorithm=SAMME.R:RandomState=None");
-   factory->BookMethod(TMVA::Types::kPyGTB, "PyGTB",
-                       "!V:NEstimators=150:Loss=deviance:LearningRate=0.1:Subsample=1:MaxDepth=6:MaxFeatures='auto'");
-
-
-   // Train MVAs using the set of training events
-   factory->TrainAllMethods();
-
-   // ---- Evaluate all MVAs using the set of test events
-   factory->TestAllMethods();
-
-   // ----- Evaluate and compare performance of all configured MVAs
-   factory->EvaluateAllMethods();
-   // --------------------------------------------------------------
-
-   // Save the output
-   outputFile->Close();
-
-   std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
-   std::cout << "==> TMVAClassification is done!" << std::endl;
-
-}
+    TMVA::Tools::Instance();
+    
+    TString outfileName( "TMVA.root" );
+    TFile* outputFile = TFile::Open( outfileName, "RECREATE" );
+    
+    TMVA::Factory *factory = new TMVA::Factory( "TMVAClassification", outputFile,
+                                                "!V:!Silent:Color:DrawProgressBar:Transformations=I;D;P;G,D:AnalysisType=Classification" );
+
+    TMVA::DataLoader dataloader("dl");
+    dataloader.AddVariable( "myvar1 := var1+var2", 'F' );
+    dataloader.AddVariable( "myvar2 := var1-var2", "Expression 2", "", 'F' );
+    dataloader.AddVariable( "var3",                "Variable 3", "units", 'F' );
+    dataloader.AddVariable( "var4",                "Variable 4", "units", 'F' );
+    
+    
+    dataloader.AddSpectator( "spec1 := var1*2",  "Spectator 1", "units", 'F' );
+    dataloader.AddSpectator( "spec2 := var1*3",  "Spectator 2", "units", 'F' );
+    
+    
+    TString fname = "./tmva_class_example.root";
+    
+
+    if (gSystem->AccessPathName( fname ))  // file does not exist in local directory
+        gSystem->Exec("curl -O http://root.cern.ch/files/tmva_class_example.root");
+
+    
+    TFile *input = TFile::Open( fname );
+
+    std::cout << "--- TMVAClassification       : Using input file: " << input->GetName() << std::endl;
+    
+    // --- Register the training and test trees
+    
+    TTree *tsignal     = (TTree*)input->Get("TreeS");
+    TTree *tbackground = (TTree*)input->Get("TreeB");
+    
+    // global event weights per tree (see below for setting event-wise weights)
+    Double_t signalWeight     = 1.0;
+    Double_t backgroundWeight = 1.0;
+    
+    // You can add an arbitrary number of signal or background trees
+    dataloader.AddSignalTree    ( tsignal,     signalWeight     );
+    dataloader.AddBackgroundTree( tbackground, backgroundWeight );
+    
+    
+    // Set individual event weights (the variables must exist in the original TTree)
+    dataloader.SetBackgroundWeightExpression( "weight" );
+    
+    
+    // Apply additional cuts on the signal and background samples (can be different)
+    TCut mycuts = ""; // for example: TCut mycuts = "abs(var1)<0.5 && abs(var2-0.5)<1";
+    TCut mycutb = ""; // for example: TCut mycutb = "abs(var1)<0.5";
+    
+    // Tell the factory how to use the training and testing events
+    dataloader.PrepareTrainingAndTestTree( mycuts, mycutb,
+                                         "nTrain_Signal=0:nTrain_Background=0:nTest_Signal=0:nTest_Background=0:SplitMode=Random:NormMode=NumEvents:!V" );
+
+
+    ///////////////////
+    //Booking         //
+    ///////////////////
+    //     PyMVA methods
+    factory->BookMethod(&dataloader, TMVA::Types::kPyRandomForest, "PyRandomForest",
+                        "!V:NEstimators=100:Criterion=gini:MaxFeatures=auto:MaxDepth=6:MinSamplesLeaf=1:MinWeightFractionLeaf=0:Bootstrap=kTRUE" );
+    
+    factory->BookMethod(&dataloader, TMVA::Types::kPyAdaBoost, "PyAdaBoost","!V:NEstimators=1000" );
+    
+    factory->BookMethod(&dataloader, TMVA::Types::kPyGTB, "PyGTB","!V:NEstimators=150" );
+
+    factory->BookMethod(&dataloader, TMVA::Types::kPyRFOneVsRest, "PyRFOneVsRest","!V" );
+    
+    // factory->BookMethod(&dataloader, TMVA::Types::kPyKMeans, "PyKMeans","!V:NClusters=5" );
+    
+    
+    // Train MVAs using the set of training events
+    factory->TrainAllMethods();
+    
+    // ---- Evaluate all MVAs using the set of test events
+    factory->TestAllMethods();
+    
+    // ----- Evaluate and compare performance of all configured MVAs
+    factory->EvaluateAllMethods();
+    // --------------------------------------------------------------
+    
+    // Save the output
+    outputFile->Close();
+  
+    std::cout << "==> Wrote root file: " << outputFile->GetName() << std::endl;
+    std::cout << "==> TMVAClassification is done!" << std::endl;
+    
+}