Fixed variable importance in rtrees

modules/ml/src/rtrees.cpp

@@ -187,7 +187,7 @@ public:
                 oobidx.clear();
                 for( i = 0; i < n; i++ )
                 {
-                    if( !oobmask[i] )
+                    if( oobmask[i] )
                         oobidx.push_back(i);
                 }
                 int n_oob = (int)oobidx.size();
@@ -217,6 +217,7 @@ public:
                     else
                     {
                         int ival = cvRound(val);
+                        //Voting scheme to combine OOB errors of each tree
                         int* votes = &oobvotes[j*nclasses];
                         votes[ival]++;
                         int best_class = 0;
@@ -235,35 +236,35 @@ public:
                     oobperm.resize(n_oob);
                     for( i = 0; i < n_oob; i++ )
                         oobperm[i] = oobidx[i];
+                    for (i = n_oob - 1; i > 0; --i)  //Randomly shuffle indices so we can permute features
+                    {
+                        int r_i = rng.uniform(0, i + 1);
+                        std::swap(oobperm[i], oobperm[r_i]);
+                    }
 
                     for( vi_ = 0; vi_ < nvars; vi_++ )
                     {
-                        vi = vidx ? vidx[vi_] : vi_;
+                        vi = vidx ? vidx[vi_] : vi_; //Ensure that only the user specified predictors are used for training
                         double ncorrect_responses_permuted = 0;
-                        for( i = 0; i < n_oob; i++ )
-                        {
-                            int i1 = rng.uniform(0, n_oob);
-                            int i2 = rng.uniform(0, n_oob);
-                            std::swap(i1, i2);
-                        }
 
                         for( i = 0; i < n_oob; i++ )
                         {
                             j = oobidx[i];
                             int vj = oobperm[i];
                             sample0 = Mat( nallvars, 1, CV_32F, psamples + sstep0*w->sidx[j], sstep1*sizeof(psamples[0]) );
-                            for( k = 0; k < nallvars; k++ )
-                                sample.at<float>(k) = sample0.at<float>(k);
-                            sample.at<float>(vi) = psamples[sstep0*w->sidx[vj] + sstep1*vi];
+                            Mat sample_clone = sample0.clone(); //create a copy so we don't mess up the original data
+                            sample_clone.at<float>(vi) = psamples[sstep0*w->sidx[vj] + sstep1*vi];
 
-                            double val = predictTrees(Range(treeidx, treeidx+1), sample, predictFlags);
+                            double val = predictTrees(Range(treeidx, treeidx+1), sample_clone, predictFlags);
                             if( !_isClassifier )
                             {
                                 val = (val - w->ord_responses[w->sidx[j]])/max_response;
                                 ncorrect_responses_permuted += exp( -val*val );
                             }
                             else
+                            {
                                 ncorrect_responses_permuted += cvRound(val) == w->cat_responses[w->sidx[j]];
+                            }
                         }
                         varImportance[vi] += (float)(ncorrect_responses - ncorrect_responses_permuted);
                     }

samples/cpp/tree_engine.cpp

@@ -63,7 +63,6 @@ int main(int argc, char** argv)
     const double train_test_split_ratio = 0.5;
 
     Ptr<TrainData> data = TrainData::loadFromCSV(filename, 0, response_idx, response_idx+1, typespec);
-
     if( data.empty() )
     {
         printf("ERROR: File %s can not be read\n", filename);
@@ -71,6 +70,7 @@ int main(int argc, char** argv)
     }
 
     data->setTrainTestSplitRatio(train_test_split_ratio);
+    std::cout << "Test/Train: " << data->getNTestSamples() << "/" << data->getNTrainSamples();
 
     printf("======DTREE=====\n");
     Ptr<DTrees> dtree = DTrees::create();
@@ -106,10 +106,19 @@ int main(int argc, char** argv)
     rtrees->setUseSurrogates(false);
     rtrees->setMaxCategories(16);
     rtrees->setPriors(Mat());
-    rtrees->setCalculateVarImportance(false);
+    rtrees->setCalculateVarImportance(true);
     rtrees->setActiveVarCount(0);
     rtrees->setTermCriteria(TermCriteria(TermCriteria::MAX_ITER, 100, 0));
     train_and_print_errs(rtrees, data);
+    cv::Mat ref_labels = data->getClassLabels();
+    cv::Mat test_data = data->getTestSampleIdx();
+    cv::Mat predict_labels;
+    rtrees->predict(data->getSamples(), predict_labels);
 
+    cv::Mat variable_importance = rtrees->getVarImportance();
+    std::cout << "Estimated variable importance" << std::endl;
+    for (int i = 0; i < variable_importance.rows; i++) {
+        std::cout << "Variable " << i << ": " << variable_importance.at<float>(i, 0) << std::endl;
+    }
     return 0;
 }