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Multiclass RandomForest #441

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56cb5d9
RandomForest for Multiclass classification.
satyarth934 Mar 19, 2017
11d2568
Test for RFOneVsRest.
satyarth934 Mar 19, 2017
1614975
Making code changes to follow coding conventions.
satyarth934 Mar 22, 2017
bb6a271
Making code changes to follow coding conventions - 1.
satyarth934 Mar 22, 2017
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Making code changes to follow coding conventions - 1.
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@satyarth934
satyarth934 committed Mar 22, 2017
commit bb6a2716b959755ab4acfbf498cb6e47035ba33c
50 changes: 25 additions & 25 deletions tmva/pymva/src/MethodPyRFOneVsRest.cxx
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Original file line number Diff line number Diff line change
Expand Up @@ -190,7 +190,7 @@ void MethodPyRFOneVsRest::ProcessOptions()
min_weight_fraction_leaf = 0;
}
if (max_features == "auto" || max_features == "sqrt" || max_features == "log2")
max_features = Form("'%s'", max_features.Data());
max_features = Form("'%s'", max_features.Data());
PyObject *pomax_features = Eval(max_features);
if (!pomax_features) {
Log() << kFATAL << Form(" MaxFeatures = %s... that does not work !! ", max_features.Data())
Expand Down Expand Up @@ -247,7 +247,7 @@ void MethodPyRFOneVsRest::Init()

// Import sklearn
// Convert the file name to a Python string.
PyObject *pRFName = PyUnicode_FromString("sklearn.ensemble");
PyObject *pRFName = PyUnicode_FromString("sklearn.ensemble");
PyObject *pOVRName = PyUnicode_FromString("sklearn.multiclass");

// USING SINGLE fModule VARIABLE --------------------------
Expand Down Expand Up @@ -286,18 +286,18 @@ void MethodPyRFOneVsRest::Init()
// ------------------------------------------------------------------

// Training data
UInt_t fNvars = Data()->GetNVariables();
int fNrowsTraining = Data()->GetNTrainingEvents(); // every row is an event, a class type and a weight
int dims[2];
dims[0] = fNrowsTraining;
dims[1] = fNvars;
fTrainData = (PyArrayObject *)PyArray_FromDims(2, dims, NPY_FLOAT);
UInt_t fNvars = Data()->GetNVariables();
int fNrowsTraining = Data()->GetNTrainingEvents(); // every row is an event, a class type and a weight
int dims[2];
dims[0] = fNrowsTraining;
dims[1] = fNvars;
fTrainData = (PyArrayObject *)PyArray_FromDims(2, dims, NPY_FLOAT);
float *TrainData = (float *)(PyArray_DATA(fTrainData));

fTrainDataClasses = (PyArrayObject *)PyArray_FromDims(1, &fNrowsTraining, NPY_FLOAT);
fTrainDataClasses = (PyArrayObject *)PyArray_FromDims(1, &fNrowsTraining, NPY_FLOAT);
float *TrainDataClasses = (float *)(PyArray_DATA(fTrainDataClasses));

fTrainDataWeights = (PyArrayObject *)PyArray_FromDims(1, &fNrowsTraining, NPY_FLOAT);
fTrainDataWeights = (PyArrayObject *)PyArray_FromDims(1, &fNrowsTraining, NPY_FLOAT);
float *TrainDataWeights = (float *)(PyArray_DATA(fTrainDataWeights));

for (int i = 0; i < fNrowsTraining; i++) {
Expand All @@ -322,11 +322,11 @@ void MethodPyRFOneVsRest::Train()
if (max_features == "auto" || max_features == "sqrt" || max_features == "log2") {
max_features = Form("'%s'", max_features.Data());
}
PyObject *pomax_features = Eval(max_features);
PyObject *pomax_depth = Eval(max_depth);
PyObject *pomax_features = Eval(max_features);
PyObject *pomax_depth = Eval(max_depth);
PyObject *pomax_leaf_nodes = Eval(max_leaf_nodes);
PyObject *porandom_state = Eval(random_state);
PyObject *poclass_weight = Eval(class_weight);
PyObject *porandom_state = Eval(random_state);
PyObject *poclass_weight = Eval(class_weight);

PyObject *argsRF = Py_BuildValue("(isOiifOOiiiOiiO)", n_estimators, criterion.Data(), pomax_depth, min_samples_split,
min_samples_leaf, min_weight_fraction_leaf, pomax_features, pomax_leaf_nodes,
Expand All @@ -337,7 +337,7 @@ void MethodPyRFOneVsRest::Train()

// USING SINGLE fModule VARIABLES FOR RF AND OVR
// ------------------------------------------------------------------------------------------
PyObject *pRFDict = PyModule_GetDict(fModule);
PyObject *pRFDict = PyModule_GetDict(fModule);
PyObject *fRFClassifierClass = PyDict_GetItemString(pRFDict, "RandomForestClassifier");
// Log() << kFATAL <<"Train =" <<n_jobs<<Endl;

Expand All @@ -354,13 +354,13 @@ void MethodPyRFOneVsRest::Train()
std::cout << std::endl;

PyObject *pOVRName = PyUnicode_FromString("sklearn.multiclass");
fModule = PyImport_Import(pOVRName);
fModule = PyImport_Import(pOVRName);
if (!fModule) {
Log() << kFATAL << "Can't import sklearn.multiclass" << Endl;
Log() << Endl;
}
Py_DECREF(pOVRName);
PyObject *pOVRDict = PyModule_GetDict(fModule);
PyObject *pOVRDict = PyModule_GetDict(fModule);
PyObject *fOVRClassifierClass = PyDict_GetItemString(pOVRDict, "OneVsRestClassifier");

if (PyCallable_Check(fOVRClassifierClass)) {
Expand Down Expand Up @@ -494,21 +494,21 @@ Double_t MethodPyRFOneVsRest::GetMvaValue(Double_t *errLower, Double_t *errUpper

if (IsModelPersistence()) ReadModelFromFile();

Double_t mvaValue;
const TMVA::Event *e = Data()->GetEvent();
UInt_t nvars = e->GetNVariables();
int dims[2];
dims[0] = 1;
dims[1] = nvars;
Double_t mvaValue;
const TMVA::Event *e = Data()->GetEvent();
UInt_t nvars = e->GetNVariables();
int dims[2];
dims[0] = 1;
dims[1] = nvars;
PyArrayObject *pEvent = (PyArrayObject *)PyArray_FromDims(2, dims, NPY_FLOAT);
float * pValue = (float *)(PyArray_DATA(pEvent));
float *pValue = (float *)(PyArray_DATA(pEvent));

for (UInt_t i = 0; i < nvars; i++) pValue[i] = e->GetValue(i);

PyArrayObject *result = (PyArrayObject *)PyObject_CallMethod(fClassifier, const_cast<char *>("predict_proba"),
const_cast<char *>("(O)"), pEvent);
double *proba = (double *)(PyArray_DATA(result));
mvaValue = proba[0]; // getting signal prob
mvaValue = proba[0]; // getting signal prob
Py_DECREF(result);
Py_DECREF(pEvent);
return mvaValue;
Expand Down
2 changes: 1 addition & 1 deletion tmva/pymva/test/Classification.C
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Expand Up @@ -21,7 +21,7 @@ void Classification()
TMVA::Tools::Instance();

TString outfileName("TMVA.root");
TFile * outputFile = TFile::Open(outfileName, "RECREATE");
TFile *outputFile = TFile::Open(outfileName, "RECREATE");

TMVA::Factory *factory =
new TMVA::Factory("TMVAClassification", outputFile,
Expand Down