Merge pull request #18126 from danielenricocahall:add-oob-error-sample-weighting

Account for sample weights in calculating OOB Error

* account for sample weights in oob error calculation

* redefine oob error functions

* fix ABI compatibility

modules/ml/test/test_rtrees.cpp

@@ -51,4 +51,50 @@ TEST(ML_RTrees, getVotes)
     EXPECT_EQ(result.at<float>(0, predicted_class), rt->predict(test));
 }
 
+TEST(ML_RTrees, 11142_sample_weights_regression)
+{
+    int n = 3;
+    // RTrees for regression
+    Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
+    //simple regression problem of x -> 2x
+    Mat data = (Mat_<float>(n,1) << 1, 2, 3);
+    Mat values = (Mat_<float>(n,1) << 2, 4, 6);
+    Mat weights = (Mat_<float>(n, 1) << 10, 10, 10);
+
+    Ptr<TrainData> trainData = TrainData::create(data, ml::ROW_SAMPLE, values);
+    rt->train(trainData);
+    double error_without_weights = round(rt->getOOBError());
+    rt->clear();
+    Ptr<TrainData> trainDataWithWeights = TrainData::create(data, ml::ROW_SAMPLE, values, Mat(), Mat(), weights );
+    rt->train(trainDataWithWeights);
+    double error_with_weights = round(rt->getOOBError());
+    // error with weights should be larger than error without weights
+    EXPECT_GE(error_with_weights, error_without_weights);
+}
+
+TEST(ML_RTrees, 11142_sample_weights_classification)
+{
+    int n = 12;
+    // RTrees for classification
+    Ptr<ml::RTrees> rt = cv::ml::RTrees::create();
+
+    Mat data(n, 4, CV_32F);
+    randu(data, 0, 10);
+    Mat labels = (Mat_<int>(n,1) << 0,0,0,0, 1,1,1,1, 2,2,2,2);
+    Mat weights = (Mat_<float>(n, 1) << 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10);
+
+    rt->train(data, ml::ROW_SAMPLE, labels);
+    rt->clear();
+    double error_without_weights = round(rt->getOOBError());
+    Ptr<TrainData> trainDataWithWeights = TrainData::create(data, ml::ROW_SAMPLE, labels, Mat(), Mat(), weights );
+    rt->train(data, ml::ROW_SAMPLE, labels);
+    double error_with_weights = round(rt->getOOBError());
+    std::cout << error_without_weights << std::endl;
+    std::cout << error_with_weights << std::endl;
+    // error with weights should be larger than error without weights
+    EXPECT_GE(error_with_weights, error_without_weights);
+}
+
+
+
 }} // namespace