[RF] Don't sum testSumW2Error with different minimizers

There is no particular reason anymore to run this test with both
Minuit 1 and Minuit 2, and it's a bit awkward that this is the only
RooFit test that hardcodes the iteration over different minimizers.

Author: guitargeek

roofit/roofitcore/test/testSumW2Error.cxx

@@ -49,55 +49,38 @@ TEST(SumW2Error, BatchMode)
 
    using namespace RooFit;
 
-   auto fit = [&](RooAbsData &data, bool sumw2, EvalBackend evalBackend, std::string const &minimizer,
-                  int printLevel = -1) {
+   auto fit = [&](RooAbsData &data, bool sumw2, EvalBackend evalBackend, int printLevel = -1) {
       params.assign(initialParams);
 
-      return std::unique_ptr<RooFitResult>{model.fitTo(data, Save(), SumW2Error(sumw2), Strategy(1), evalBackend,
-                                                       Minimizer(minimizer.c_str()), PrintLevel(printLevel))};
+      return std::unique_ptr<RooFitResult>{
+         model.fitTo(data, Save(), SumW2Error(sumw2), Strategy(1), evalBackend, PrintLevel(printLevel))};
    };
 
    auto scalar = EvalBackend::Legacy();
    auto batchMode = EvalBackend::Cpu();
 
    // Compare batch mode vs. scalar mode for non-SumW2 fits on UNWEIGHTED datasets
-   EXPECT_TRUE(fit(*dataSet, 0, scalar, "Minuit")->isIdentical(*fit(*dataSet, 0, batchMode, "Minuit")))
-      << " different results for Minuit fit to RooDataSet without SumW2Error correction.";
-   EXPECT_TRUE(fit(*dataHist, 0, scalar, "Minuit")->isIdentical(*fit(*dataHist, 0, batchMode, "Minuit")))
-      << " different results for Minuit fit to RooDataHist without SumW2Error correction.";
-   EXPECT_TRUE(fit(*dataSet, 0, scalar, "Minuit2")->isIdentical(*fit(*dataSet, 0, batchMode, "Minuit2")))
-      << " different results for Minuit2 fit to RooDataSet without SumW2Error correction.";
-   EXPECT_TRUE(fit(*dataHist, 0, scalar, "Minuit2")->isIdentical(*fit(*dataHist, 0, batchMode, "Minuit2")))
-      << " different results for Minuit2 fit to RooDataHist without SumW2Error correction.";
+   EXPECT_TRUE(fit(*dataSet, 0, scalar)->isIdentical(*fit(*dataSet, 0, batchMode)))
+      << " different results for fit to RooDataSet without SumW2Error correction.";
+   EXPECT_TRUE(fit(*dataHist, 0, scalar)->isIdentical(*fit(*dataHist, 0, batchMode)))
+      << " different results for fit to RooDataHist without SumW2Error correction.";
 
    // We can't compare the covariance matrix in these next cases, because it is
    // externally provided. Still, it's okay because the parameter values and
    // errors are compared, where the errors are inferred from the external
    // covariance matrix.
 
    // Compare batch mode vs. scalar mode for SumW2 fits on UNWEIGHTED datasets
-   EXPECT_TRUE(fit(*dataSet, 1, scalar, "Minuit")->isIdenticalNoCov(*fit(*dataSet, 1, batchMode, "Minuit")))
-      << " different results for Minuit fit to RooDataSet with SumW2Error correction.";
-   EXPECT_TRUE(fit(*dataHist, 1, scalar, "Minuit")->isIdenticalNoCov(*fit(*dataHist, 1, batchMode, "Minuit")))
-      << " different results for Minuit fit to RooDataHist with SumW2Error correction.";
-   EXPECT_TRUE(fit(*dataSet, 1, scalar, "Minuit2")->isIdenticalNoCov(*fit(*dataSet, 1, batchMode, "Minuit2")))
-      << " different results for Minuit2 fit to RooDataSet with SumW2Error correction.";
-   EXPECT_TRUE(fit(*dataHist, 1, scalar, "Minuit2")->isIdenticalNoCov(*fit(*dataHist, 1, batchMode, "Minuit2")))
-      << " different results for Minuit2 fit to RooDataHist with SumW2Error correction.";
+   EXPECT_TRUE(fit(*dataSet, 1, scalar)->isIdenticalNoCov(*fit(*dataSet, 1, batchMode)))
+      << " different results for fit to RooDataSet with SumW2Error correction.";
+   EXPECT_TRUE(fit(*dataHist, 1, scalar)->isIdenticalNoCov(*fit(*dataHist, 1, batchMode)))
+      << " different results for fit to RooDataHist with SumW2Error correction.";
 
    // Compare batch mode vs. scalar mode for SumW2 fits on WEIGHTED datasets
-   EXPECT_TRUE(
-      fit(dataSetWeighted, 1, scalar, "Minuit")->isIdenticalNoCov(*fit(dataSetWeighted, 1, batchMode, "Minuit")))
-      << " different results for Minuit fit to weighted RooDataSet with SumW2Error correction.";
-   EXPECT_TRUE(
-      fit(*dataHistWeighted, 1, scalar, "Minuit")->isIdenticalNoCov(*fit(*dataHistWeighted, 1, batchMode, "Minuit")))
-      << " different results for Minuit fit to weighted RooDataHist with SumW2Error correction.";
-   EXPECT_TRUE(
-      fit(dataSetWeighted, 1, scalar, "Minuit2")->isIdenticalNoCov(*fit(dataSetWeighted, 1, batchMode, "Minuit2")))
-      << " different results for Minuit2 fit to weighted RooDataSet with SumW2Error correction.";
-   EXPECT_TRUE(
-      fit(*dataHistWeighted, 1, scalar, "Minuit2")->isIdenticalNoCov(*fit(*dataHistWeighted, 1, batchMode, "Minuit2")))
-      << " different results for Minuit2 fit to weighted RooDataHist with SumW2Error correction.";
+   EXPECT_TRUE(fit(dataSetWeighted, 1, scalar)->isIdenticalNoCov(*fit(dataSetWeighted, 1, batchMode)))
+      << " different results for fit to weighted RooDataSet with SumW2Error correction.";
+   EXPECT_TRUE(fit(*dataHistWeighted, 1, scalar)->isIdenticalNoCov(*fit(*dataHistWeighted, 1, batchMode)))
+      << " different results for fit to weighted RooDataHist with SumW2Error correction.";
 }
 
 TEST(SumW2Error, ExtendedFit)
@@ -128,11 +111,8 @@ TEST(SumW2Error, ExtendedFit)
    auto *wFunc = ws.factory("w[1.5]");
 
    auto *w = dataNoWeights->addColumn(*wFunc);
-   RooDataSet data{dataNoWeights->GetName(),
-                   dataNoWeights->GetTitle(),
-                   *dataNoWeights->get(),
-                   RooFit::Import(*dataNoWeights),
-                   RooFit::WeightVar(w->GetName())};
+   RooDataSet data{dataNoWeights->GetName(), dataNoWeights->GetTitle(), *dataNoWeights->get(),
+                   RooFit::Import(*dataNoWeights), RooFit::WeightVar(w->GetName())};
    RooDataHist datahist{"datahist", "datahist", *data.get(), data};
 
    RooArgSet params;
@@ -143,9 +123,8 @@ TEST(SumW2Error, ExtendedFit)
 
    auto doFit = [&](RooFit::EvalBackend evalBackend, bool sumW2Error, const char *range) {
       params.assign(initialParams);
-      return std::unique_ptr<RooFitResult>{shp->fitTo(datahist, Extended(), Range(range), Save(),
-                                                      SumW2Error(sumW2Error), Strategy(1), PrintLevel(-1), evalBackend,
-                                                      Minimizer("Minuit2", "migrad"))};
+      return std::unique_ptr<RooFitResult>{shp->fitTo(
+         datahist, Extended(), Range(range), Save(), SumW2Error(sumW2Error), Strategy(1), PrintLevel(-1), evalBackend)};
    };
 
    // compare batch mode and scalar mode fit results for full range