Cli improvements

[gkronber]

Additionally, the solutions in the pareto front could also be linearly scaled before printing (as for the best individual).

[foolnotion]

This is great, thanks! My only concern is that this extra output will make it more difficult to call the cli program from scripts and parse the output. Perhaps the cli tools should have a verbosity setting or some dedicated command line argument for this?

[gkronber]

A command line option to turn on output of the Pareto front would be good yes.

[gkronber]

In my last commit I also changed the code to scale the evaluation results for the progress report only when linear scaling is turned on. Instead of the if-statements in the lambdas I tried to change the taskflow graph (see commented code), but this produced segfaults in my tests.

[foolnotion]

In my last commit I also changed the code to scale the evaluation results for the progress report only when linear scaling is turned on. Instead of the if-statements in the lambdas I tried to change the taskflow graph (see commented code), but this produced segfaults in my tests.

I believe the segfaults were caused by the fact that the linear scaling subtasks were still added to the taskflow (via taskflow.emplace(...)) even though when scaling == false they are never part of the graph (no .precede or .succeed). This patch solves the issue:

diff --git a/cli/source/operon_nsgp.cpp b/cli/source/operon_nsgp.cpp
index 867dd24..685a52c 100644
--- a/cli/source/operon_nsgp.cpp
+++ b/cli/source/operon_nsgp.cpp
@@ -299,7 +299,8 @@ auto main(int argc, char** argv) -> int
             // scale values
             Operon::Scalar a{1.0};
             Operon::Scalar b{0.0};
-            auto linearScaling = taskflow.emplace([&]() {
+
+            auto fitLeastSquares = [&]() {
                 auto [a_, b_] = Operon::FitLeastSquares(estimatedTrain, targetTrain);
                 a = static_cast<Operon::Scalar>(a_);
                 b = static_cast<Operon::Scalar>(b_);
@@ -317,7 +318,17 @@ auto main(int argc, char** argv) -> int
                 if (nodes.size() > sz) {
                     best.Genotype.UpdateNodes();
                 }
-            });
+            };
+
+            auto scaleTrain = [&]() {
+                Eigen::Map<Eigen::Array<Operon::Scalar, -1, 1>> estimated(estimatedTrain.data(), std::ssize(estimatedTrain));
+                estimated = estimated * a + b;
+            };
+
+            auto scaleTest = [&]() {
+                Eigen::Map<Eigen::Array<Operon::Scalar, -1, 1>> estimated(estimatedTest.data(), std::ssize(estimatedTest));
+                estimated = estimated * a + b;
+            };
 
             double r2Train{};
             double r2Test{};
@@ -326,16 +337,6 @@ auto main(int argc, char** argv) -> int
             double maeTrain{};
             double maeTest{};
 
-            auto scaleTrain = taskflow.emplace([&]() {
-                Eigen::Map<Eigen::Array<Operon::Scalar, -1, 1>> estimated(estimatedTrain.data(), std::ssize(estimatedTrain));
-                estimated = estimated * a + b;
-            });
-
-            auto scaleTest = taskflow.emplace([&]() {
-                Eigen::Map<Eigen::Array<Operon::Scalar, -1, 1>> estimated(estimatedTest.data(), std::ssize(estimatedTest));
-                estimated = estimated * a + b;
-            });
-
             auto calcStats = taskflow.emplace([&]() {
                 // negate the R2 because this is an internal fitness measure (minimization) which we here repurpose
                 r2Train = -Operon::R2{}(estimatedTrain, targetTrain);
@@ -358,9 +359,16 @@ auto main(int argc, char** argv) -> int
             auto calculateOffMemory = taskflow.transform_reduce(off.begin(), off.end(), totalMemory, std::plus{}, [&](auto const& ind) { return getSize(ind); });
 
             // define task graph
-            linearScaling.succeed(evalTrain, evalTest);
-            linearScaling.precede(scaleTrain, scaleTest);
-            calcStats.succeed(scaleTrain, scaleTest);
+            if (scale) {
+                auto scaleModelTask = taskflow.emplace(fitLeastSquares);
+                auto scaleTrainTask = taskflow.emplace(scaleTrain);
+                auto scaleTestTask = taskflow.emplace(scaleTest);
+                scaleModelTask.precede(scaleTrainTask, scaleTestTask);
+                scaleModelTask.succeed(evalTrain, evalTest);
+                calcStats.succeed(scaleTrainTask, scaleTestTask);
+            } else {
+                calcStats.succeed(evalTrain, evalTest);
+            }
             calcStats.precede(calculateLength, calculateQuality, calculatePopMemory, calculateOffMemory);
 
             executor.corun(taskflow);

[gkronber]

Thanks. I will incorporate this in the branch (also for operon_gp), and will move the Scale() function to utils.