perf: optimize Rcpp

.editorconfig

@@ -15,7 +15,7 @@ indent_size = 2
 indent_size = 4
 
 [*.{cpp,hpp}]
-indent_size = 4
+indent_size = 2
 
 [{NEWS.md,DESCRIPTION,LICENSE}]
 max_line_length = 80

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3proba
 Title: Probabilistic Supervised Learning for 'mlr3'
-Version: 0.6.7
+Version: 0.6.8
 Authors@R:
     c(person(given = "Raphael",
              family = "Sonabend",

NEWS.md

@@ -1,3 +1,9 @@
+# mlr3proba 0.6.8
+
+- `Rcpp` code optimizations
+- Fixed ERV scoring to comply with `mlr3` dev version (no bugs before)
+- Skipping `survtoregr` pipelines due to bugs (to be refactored in the future)
+
 # mlr3proba 0.6.7
 
 - Deprecate `crank` to `distr` composition in `distrcompose` pipeop (only from `lp` => `distr` works now)

R/LearnerDens.R

@@ -36,14 +36,14 @@ LearnerDens = R6::R6Class("LearnerDens",
     #' @description Creates a new instance of this [R6][R6::R6Class] class.
     initialize = function(id, param_set = ps(),
       predict_types = "cdf", feature_types = character(),
-      properties = character(), data_formats = "data.table",
+      properties = character(),
       packages = character(),
       label = NA_character_,
       man = NA_character_) {
       super$initialize(
         id = id, task_type = "dens", param_set = param_set,
         predict_types = predict_types, feature_types = feature_types, properties = properties,
-        data_formats = data_formats, packages = c("mlr3proba", packages), label = label, man = man)
+        packages = c("mlr3proba", packages), label = label, man = man)
     }
   )
 )

R/scoring_rule_erv.R

@@ -7,18 +7,20 @@
   if (!is.null(ps$se) && ps$se) {
     stop("Only one of `ERV` and `se` can be TRUE")
   }
-  measure$param_set$values$ERV = FALSE
+
+  measure$param_set$set_values(ERV = FALSE)
   # compute score for the learner
-  learner_score = measure$score(prediction, task = task,
-                                train_set = train_set)
+  learner_score = measure$score(prediction, task = task, train_set = train_set)
 
   # compute score for the baseline (Kaplan-Meier)
+  # train KM
   km = lrn("surv.kaplan")$train(task = task, row_ids = train_set)
-  km = km$predict(as_task_surv(data.frame(
-    as.matrix(prediction$truth)), event = "status"))
-  base_score = measure$score(km, task = task, train_set = train_set)
+  # predict KM on the test set (= not train ids)
+  test_set = setdiff(task$row_ids, train_set)
+  km_pred = km$predict(task, row_ids = test_set)
+  base_score = measure$score(km_pred, task = task, train_set = train_set)
 
-  measure$param_set$values$ERV = TRUE
+  measure$param_set$set_values(ERV = TRUE)
 
   # return percentage decrease
   1 - (learner_score / base_score)

src/RcppExports.cpp

@@ -11,12 +11,12 @@ Rcpp::Rostream<false>& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get();
 #endif
 
 // c_assert_surv
-bool c_assert_surv(NumericMatrix mat);
+bool c_assert_surv(const NumericMatrix& mat);
 RcppExport SEXP _mlr3proba_c_assert_surv(SEXP matSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< NumericMatrix >::type mat(matSEXP);
+    Rcpp::traits::input_parameter< const NumericMatrix& >::type mat(matSEXP);
     rcpp_result_gen = Rcpp::wrap(c_assert_surv(mat));
     return rcpp_result_gen;
 END_RCPP
@@ -34,74 +34,74 @@ BEGIN_RCPP
 END_RCPP
 }
 // c_score_intslogloss
-NumericMatrix c_score_intslogloss(NumericVector truth, NumericVector unique_times, NumericMatrix cdf, double eps);
+NumericMatrix c_score_intslogloss(const NumericVector& truth, const NumericVector& unique_times, const NumericMatrix& cdf, double eps);
 RcppExport SEXP _mlr3proba_c_score_intslogloss(SEXP truthSEXP, SEXP unique_timesSEXP, SEXP cdfSEXP, SEXP epsSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< NumericVector >::type truth(truthSEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type unique_times(unique_timesSEXP);
-    Rcpp::traits::input_parameter< NumericMatrix >::type cdf(cdfSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type truth(truthSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type unique_times(unique_timesSEXP);
+    Rcpp::traits::input_parameter< const NumericMatrix& >::type cdf(cdfSEXP);
     Rcpp::traits::input_parameter< double >::type eps(epsSEXP);
     rcpp_result_gen = Rcpp::wrap(c_score_intslogloss(truth, unique_times, cdf, eps));
     return rcpp_result_gen;
 END_RCPP
 }
 // c_score_graf_schmid
-NumericMatrix c_score_graf_schmid(NumericVector truth, NumericVector unique_times, NumericMatrix cdf, int power);
+NumericMatrix c_score_graf_schmid(const NumericVector& truth, const NumericVector& unique_times, const NumericMatrix& cdf, int power);
 RcppExport SEXP _mlr3proba_c_score_graf_schmid(SEXP truthSEXP, SEXP unique_timesSEXP, SEXP cdfSEXP, SEXP powerSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< NumericVector >::type truth(truthSEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type unique_times(unique_timesSEXP);
-    Rcpp::traits::input_parameter< NumericMatrix >::type cdf(cdfSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type truth(truthSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type unique_times(unique_timesSEXP);
+    Rcpp::traits::input_parameter< const NumericMatrix& >::type cdf(cdfSEXP);
     Rcpp::traits::input_parameter< int >::type power(powerSEXP);
     rcpp_result_gen = Rcpp::wrap(c_score_graf_schmid(truth, unique_times, cdf, power));
     return rcpp_result_gen;
 END_RCPP
 }
 // c_weight_survival_score
-NumericMatrix c_weight_survival_score(NumericMatrix score, NumericMatrix truth, NumericVector unique_times, NumericMatrix cens, bool proper, double eps);
+NumericMatrix c_weight_survival_score(const NumericMatrix& score, const NumericMatrix& truth, const NumericVector& unique_times, const NumericMatrix& cens, bool proper, double eps);
 RcppExport SEXP _mlr3proba_c_weight_survival_score(SEXP scoreSEXP, SEXP truthSEXP, SEXP unique_timesSEXP, SEXP censSEXP, SEXP properSEXP, SEXP epsSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< NumericMatrix >::type score(scoreSEXP);
-    Rcpp::traits::input_parameter< NumericMatrix >::type truth(truthSEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type unique_times(unique_timesSEXP);
-    Rcpp::traits::input_parameter< NumericMatrix >::type cens(censSEXP);
+    Rcpp::traits::input_parameter< const NumericMatrix& >::type score(scoreSEXP);
+    Rcpp::traits::input_parameter< const NumericMatrix& >::type truth(truthSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type unique_times(unique_timesSEXP);
+    Rcpp::traits::input_parameter< const NumericMatrix& >::type cens(censSEXP);
     Rcpp::traits::input_parameter< bool >::type proper(properSEXP);
     Rcpp::traits::input_parameter< double >::type eps(epsSEXP);
     rcpp_result_gen = Rcpp::wrap(c_weight_survival_score(score, truth, unique_times, cens, proper, eps));
     return rcpp_result_gen;
 END_RCPP
 }
 // c_concordance
-float c_concordance(NumericVector time, NumericVector status, NumericVector crank, double t_max, std::string weight_meth, NumericMatrix cens, NumericMatrix surv, float tiex);
+float c_concordance(const NumericVector& time, const NumericVector& status, const NumericVector& crank, double t_max, const std::string& weight_meth, const NumericMatrix& cens, const NumericMatrix& surv, float tiex);
 RcppExport SEXP _mlr3proba_c_concordance(SEXP timeSEXP, SEXP statusSEXP, SEXP crankSEXP, SEXP t_maxSEXP, SEXP weight_methSEXP, SEXP censSEXP, SEXP survSEXP, SEXP tiexSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< NumericVector >::type time(timeSEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type status(statusSEXP);
-    Rcpp::traits::input_parameter< NumericVector >::type crank(crankSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type time(timeSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type status(statusSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type crank(crankSEXP);
     Rcpp::traits::input_parameter< double >::type t_max(t_maxSEXP);
-    Rcpp::traits::input_parameter< std::string >::type weight_meth(weight_methSEXP);
-    Rcpp::traits::input_parameter< NumericMatrix >::type cens(censSEXP);
-    Rcpp::traits::input_parameter< NumericMatrix >::type surv(survSEXP);
+    Rcpp::traits::input_parameter< const std::string& >::type weight_meth(weight_methSEXP);
+    Rcpp::traits::input_parameter< const NumericMatrix& >::type cens(censSEXP);
+    Rcpp::traits::input_parameter< const NumericMatrix& >::type surv(survSEXP);
     Rcpp::traits::input_parameter< float >::type tiex(tiexSEXP);
     rcpp_result_gen = Rcpp::wrap(c_concordance(time, status, crank, t_max, weight_meth, cens, surv, tiex));
     return rcpp_result_gen;
 END_RCPP
 }
 // c_gonen
-double c_gonen(NumericVector crank, float tiex);
+double c_gonen(const NumericVector& crank, float tiex);
 RcppExport SEXP _mlr3proba_c_gonen(SEXP crankSEXP, SEXP tiexSEXP) {
 BEGIN_RCPP
     Rcpp::RObject rcpp_result_gen;
     Rcpp::RNGScope rcpp_rngScope_gen;
-    Rcpp::traits::input_parameter< NumericVector >::type crank(crankSEXP);
+    Rcpp::traits::input_parameter< const NumericVector& >::type crank(crankSEXP);
     Rcpp::traits::input_parameter< float >::type tiex(tiexSEXP);
     rcpp_result_gen = Rcpp::wrap(c_gonen(crank, tiex));
     return rcpp_result_gen;

src/survival_assert.cpp

@@ -2,10 +2,10 @@
 using namespace Rcpp;
 
 // [[Rcpp::export]]
-bool c_assert_surv(NumericMatrix mat) {
+bool c_assert_surv(const NumericMatrix& mat) {
   for (int i = 0; i < mat.nrow(); i++) {
-    // check first element
     if (mat(i, 0) < 0 || mat(i, 0) > 1) {
+      // check first element
       return false;
     }
 
@@ -24,4 +24,3 @@ bool c_assert_surv(NumericMatrix mat) {
 
   return true;
 }
-