[tmva][pymva] Do not disable tensorflow eager execution on MacoS

Disbaling it causes an interference with multiprocessing and a restart of the process after it is endeded. This fixes the timeout of the PyKeras tests on MacOS.

This commits apply also some small fixes/improvements  on the deletetion of some TMVA objects.

tmva/pymva/src/MethodPyKeras.cxx

@@ -39,6 +39,7 @@ ClassImp(MethodPyKeras);
 MethodPyKeras::MethodPyKeras(const TString &jobName, const TString &methodTitle, DataSetInfo &dsi, const TString &theOption)
    : PyMethodBase(jobName, Types::kPyKeras, methodTitle, dsi, theOption) {
    fNumEpochs = 10;
+   fNumThreads = 0;
    fBatchSize = 100;
    fVerbose = 1;
    fContinueTraining = false;
@@ -380,23 +381,29 @@ void MethodPyKeras::SetupKerasModelForEval() {
 
    // disable eager execution (model will evaluate > 100 faster)
    // need to be done before loading the model
+#ifndef R__MACOSX  // problem siabling eager execution on Macos (conflict with multiprocessing)
    if (fUseTFKeras){
       PyRunString("tf.compat.v1.disable_eager_execution()","Failed to disable eager execution");
       Log() << kINFO << "Disabled TF eager execution when evaluating model " << Endl;
    }
+#endif
 
    SetupKerasModel(true);
 
    // Init evaluation (needed for getMvaValue)
-   fVals.resize(fNVars); // holds values used for classification and regression
-   npy_intp dimsVals[2] = {(npy_intp)1, (npy_intp)fNVars};
-   PyArrayObject* pVals = (PyArrayObject*)PyArray_SimpleNewFromData(2, dimsVals, NPY_FLOAT, (void*)fVals.data());
-   PyDict_SetItemString(fLocalNS, "vals", (PyObject*)pVals);
+   if (fNVars > 0) {
+      fVals.resize(fNVars); // holds values used for classification and regression
+      npy_intp dimsVals[2] = {(npy_intp)1, (npy_intp)fNVars};
+      PyArrayObject* pVals = (PyArrayObject*)PyArray_SimpleNewFromData(2, dimsVals, NPY_FLOAT, (void*)fVals.data());
+      PyDict_SetItemString(fLocalNS, "vals", (PyObject*)pVals);
+   }
    // setup output variables
-   fOutput.resize(fNOutputs); // holds classification probabilities or regression output
-   npy_intp dimsOutput[2] = {(npy_intp)1, (npy_intp)fNOutputs};
-   PyArrayObject* pOutput = (PyArrayObject*)PyArray_SimpleNewFromData(2, dimsOutput, NPY_FLOAT, (void*)fOutput.data());
-   PyDict_SetItemString(fLocalNS, "output", (PyObject*)pOutput);
+   if (fNOutputs > 0) {
+      fOutput.resize(fNOutputs); // holds classification probabilities or regression output
+      npy_intp dimsOutput[2] = {(npy_intp)1, (npy_intp)fNOutputs};
+      PyArrayObject* pOutput = (PyArrayObject*)PyArray_SimpleNewFromData(2, dimsOutput, NPY_FLOAT, (void*)fOutput.data());
+      PyDict_SetItemString(fLocalNS, "output", (PyObject*)pOutput);
+   }
 
    fModelIsSetupForEval = true;
 }
@@ -707,6 +714,7 @@ std::vector<Double_t> MethodPyKeras::GetMvaValues(Long64_t firstEvt, Long64_t la
       }
    }
 
+   std::vector<double> mvaValues(nEvents);
    npy_intp dimsData[2] = {(npy_intp)nEvents, (npy_intp)fNVars};
    PyArrayObject* pDataMvaValues = (PyArrayObject*)PyArray_SimpleNewFromData(2, dimsData, NPY_FLOAT, (void*)data);
    if (pDataMvaValues==0) Log() << "Failed to load data to Python array" << Endl;
@@ -717,11 +725,10 @@ std::vector<Double_t> MethodPyKeras::GetMvaValues(Long64_t firstEvt, Long64_t la
    PyArrayObject* pPredictions = (PyArrayObject*) PyObject_CallMethod(pModel, (char*)"predict", (char*)"O", pDataMvaValues);
    if (pPredictions==0) Log() << kFATAL << "Failed to get predictions" << Endl;
    delete[] data;
-
    // Load predictions to double vector
    // NOTE: The signal probability is given at the output
-   std::vector<double> mvaValues(nEvents);
    float* predictionsData = (float*) PyArray_DATA(pPredictions);
+
    for (UInt_t i=0; i<nEvents; i++) {
       mvaValues[i] = (double) predictionsData[i*fNOutputs + TMVA::Types::kSignal];
    }

tmva/pymva/src/PyMethodBase.cxx

@@ -120,6 +120,8 @@ PyMethodBase::PyMethodBase(Types::EMVA methodType,
 PyMethodBase::~PyMethodBase()
 {
    // should we delete here fLocalNS ?
+   //PyFinalize();
+   if (fLocalNS) Py_DECREF(fLocalNS);
 }
 
 ///////////////////////////////////////////////////////////////////////////////
@@ -321,6 +323,7 @@ Int_t PyMethodBase::UnSerialize(TString path, PyObject **obj)
 /// Py_single_input, Py_file_input)
 
 void PyMethodBase::PyRunString(TString code, TString errorMessage, int start) {
+   //std::cout << "Run: >> " << code << std::endl;
    fPyReturn = PyRun_String(code, start, fGlobalNS, fLocalNS);
    if (!fPyReturn) {
       Log() << kWARNING << "Failed to run python code: " << code << Endl;
@@ -397,7 +400,7 @@ std::vector<size_t> PyMethodBase::GetDataFromList(PyObject* listObject){
 }
 
 //////////////////////////////////////////////////////////////////////////////////
-/// \brief Utility function which checks if a given key is present in a Python 
+/// \brief Utility function which checks if a given key is present in a Python
 ///        dictionary object and returns the associated value or throws runtime
 ///        error. This is to replace PyDict_GetItemWithError in Python 2.
 ///

tmva/tmva/src/DataSetInfo.cxx

@@ -85,10 +85,10 @@ TMVA::DataSetInfo::~DataSetInfo()
    ClearDataSet();
 
    for(UInt_t i=0, iEnd = fClasses.size(); i<iEnd; ++i) {
-      delete fClasses[i];
+      if (fClasses[i]) delete fClasses[i];
    }
 
-   delete fTargetsForMulticlass;
+   if (fTargetsForMulticlass) delete fTargetsForMulticlass;
 
    delete fLogger;
 }
@@ -97,7 +97,7 @@ TMVA::DataSetInfo::~DataSetInfo()
 
 void TMVA::DataSetInfo::ClearDataSet() const
 {
-   if(fDataSet!=0) { delete fDataSet; fDataSet=0; }
+   if(fDataSet) { delete fDataSet; fDataSet=nullptr; }
 }
 
 ////////////////////////////////////////////////////////////////////////////////

tmva/tmva/src/MethodBase.cxx

@@ -364,7 +364,7 @@ TMVA::MethodBase::MethodBase( Types::EMVA methodType,
 TMVA::MethodBase::~MethodBase( void )
 {
    // destructor
-   if (!fSetupCompleted) Log() << kFATAL <<Form("Dataset[%s] : ",DataInfo().GetName())<< "Calling destructor of method which got never setup" << Endl;
+   if (!fSetupCompleted) Log() << kWARNING <<Form("Dataset[%s] : ",DataInfo().GetName())<< "Calling destructor of method which got never setup" << Endl;
 
    // destructor
    if (fInputVars != 0)  { fInputVars->clear(); delete fInputVars; }
@@ -385,14 +385,14 @@ TMVA::MethodBase::~MethodBase( void )
    if (fSplTrainRefB)    { delete fSplTrainRefB; fSplTrainRefB = 0; }
    if (fSplTrainEffBvsS) { delete fSplTrainEffBvsS; fSplTrainEffBvsS = 0; }
 
-   for (Int_t i = 0; i < 2; i++ ) {
+   for (size_t i = 0; i < fEventCollections.size(); i++ ) {
       if (fEventCollections.at(i)) {
          for (std::vector<Event*>::const_iterator it = fEventCollections.at(i)->begin();
               it != fEventCollections.at(i)->end(); ++it) {
             delete (*it);
          }
          delete fEventCollections.at(i);
-         fEventCollections.at(i) = 0;
+         fEventCollections.at(i) = nullptr;
       }
    }
 
@@ -878,7 +878,7 @@ void TMVA::MethodBase::AddClassifierOutput( Types::ETreeType type )
 
    // use timer
    Timer timer( nEvents, GetName(), kTRUE );
-   std::vector<Double_t> mvaValues = GetMvaValues(0, nEvents, true);
+   std::vector<Double_t> mvaValues(nEvents);// = GetMvaValues(0, nEvents, true);
 
    // store time used for testing
    if (type==Types::kTesting)