Analytics: enforcing categorical variables in classifier plugin

analytics/operators/regressor/ClassifierOperator.cpp

@@ -60,7 +60,7 @@ void ClassifierOperator::compute(U_Ptr unit) {
         _currentClass = (int)_currentTarget;
         _responseSet->push_back(_currentClass);
         if ((uint64_t)_trainingSet->size().height >= _trainingSamples + _targetDistance)
-            trainRandomForest();
+            trainRandomForest(true);
     }
     if(_rForest.empty() || !(_rForest->isTrained() || (_trainingPending && _streaming)))
         throw std::runtime_error("Operator " + _name + ": cannot perform prediction, the model is untrained!");

analytics/operators/regressor/RegressorOperator.cpp

@@ -120,7 +120,7 @@ void RegressorOperator::compute(U_Ptr unit) {
         _trainingSet->push_back(*_currentfVector);
         _responseSet->push_back(_currentTarget);
         if ((uint64_t)_trainingSet->size().height >= _trainingSamples + _targetDistance)
-            trainRandomForest();
+            trainRandomForest(false);
     }
     if(_rForest.empty() || !(_rForest->isTrained() || (_trainingPending && _streaming))) 
         throw std::runtime_error("Operator " + _name + ": cannot perform prediction, the model is untrained!");
@@ -132,7 +132,7 @@ void RegressorOperator::compute(U_Ptr unit) {
     }
 }
 
-void RegressorOperator::trainRandomForest() {
+void RegressorOperator::trainRandomForest(bool categorical) {
     if(!_trainingSet || _rForest.empty())
         throw std::runtime_error("Operator " + _name + ": cannot perform training, missing model!");
     if((uint64_t)_responseSet->size().height <= _targetDistance)
@@ -141,8 +141,16 @@ void RegressorOperator::trainRandomForest() {
     *_responseSet = _responseSet->rowRange(_targetDistance, _responseSet->size().height-1);
     *_trainingSet = _trainingSet->rowRange(0, _trainingSet->size().height-1-_targetDistance);
     shuffleTrainingSet();
-    if(!_rForest->train(*_trainingSet, cv::ml::ROW_SAMPLE, *_responseSet))
+
+    cv::Mat varType = cv::Mat(_trainingSet->size().width + 1, 1, CV_8U);
+    varType.setTo(cv::Scalar(cv::ml::VAR_NUMERICAL));
+    varType.at<unsigned char>(_trainingSet->size().width, 0) = categorical ? cv::ml::VAR_CATEGORICAL : cv::ml::VAR_NUMERICAL;
+    cv::Ptr<cv::ml::TrainData> td = cv::ml::TrainData::create(*_trainingSet, cv::ml::ROW_SAMPLE, *_responseSet, cv::noArray(), cv::noArray(), cv::noArray(), varType);
+    
+    if(!_rForest->train(td))
         throw std::runtime_error("Operator " + _name + ": model training failed!");
+    
+    td.release();
     delete _trainingSet;
     _trainingSet = nullptr;
     delete _responseSet;

analytics/operators/regressor/RegressorOperator.h

@@ -77,7 +77,7 @@ protected:
 
     virtual void compute(U_Ptr unit)	 override;
     void computeFeatureVector(U_Ptr unit);
-    void trainRandomForest();
+    void trainRandomForest(bool categorical=false);
     void shuffleTrainingSet();
     std::string getImportances();