refactor: reduce number of hashes (#978)

mlr-org · Dec 13, 2023 · 318748b · 318748b
1 parent 244572f
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diff --git a/NEWS.md b/NEWS.md
@@ -1,5 +1,7 @@
 # mlr3 (development version)
 
+* Optimize runtime of `resample()` and `benchmark()` by reducing the number of hashing operations.
+
 # mlr3 0.17.0
 
 * Learners cannot be added to the `HotstartStack` anymore when the model is missing.

diff --git a/R/ResultData.R b/R/ResultData.R
@@ -45,33 +45,33 @@ ResultData = R6Class("ResultData",
         if (nrow(data) == 0L) {
           self$data = star_init()
         } else {
-          fact = data[, c("uhash", "iteration", "learner_state", "prediction", "task", "learner", "resampling", "param_values", "learner_hash"),
-            with = FALSE]
-          set(fact, j = "task_hash", value = hashes(fact$task))
-          set(fact, j = "learner_phash", value = phashes(fact$learner))
-          set(fact, j = "resampling_hash", value = hashes(fact$resampling))
-
-          uhashes = data.table(uhash = unique(fact$uhash))
-          tasks = fact[, list(task = .SD$task[1L]),
+          setcolorder(data, c("uhash", "iteration", "learner_state", "prediction", "task", "learner", "resampling", "param_values", "learner_hash"))
+          uhashes = data.table(uhash = unique(data$uhash))
+          setkeyv(data, c("uhash", "iteration"))
+
+          data[, task_hash := task[[1]]$hash, by = "uhash"]
+          data[, learner_phash := learner[[1]]$phash, by = "uhash"]
+          data[, resampling_hash := resampling[[1]]$hash, by = "uhash"]
+
+          tasks = data[, list(task = .SD$task[1L]),
             keyby = "task_hash"]
-          learners = fact[, list(learner = list(.SD$learner[[1L]]$reset())),
+          learners = data[, list(learner = list(.SD$learner[[1L]]$reset())),
             keyby = "learner_phash"]
-          resamplings = fact[, list(resampling = .SD$resampling[1L]),
+          resamplings = data[, list(resampling = .SD$resampling[1L]),
             keyby = "resampling_hash"]
-          learner_components = fact[, list(learner_param_vals = list(.SD$param_values[[1]])),
+          learner_components = data[, list(learner_param_vals = list(.SD$param_values[[1]])),
             keyby = "learner_hash"]
 
-          set(fact, j = "task", value = NULL)
-          set(fact, j = "learner", value = NULL)
-          set(fact, j = "resampling", value = NULL)
-          set(fact, j = "param_values", value = NULL)
-          setkeyv(fact, c("uhash", "iteration"))
+          set(data, j = "task", value = NULL)
+          set(data, j = "learner", value = NULL)
+          set(data, j = "resampling", value = NULL)
+          set(data, j = "param_values", value = NULL)
 
           if (!store_backends) {
             set(tasks, j = "task", value = lapply(tasks$task, task_rm_backend))
           }
 
-          self$data = list(fact = fact, uhashes = uhashes, tasks = tasks, learners = learners,
+          self$data = list(fact = data, uhashes = uhashes, tasks = tasks, learners = learners,
             resamplings = resamplings, learner_components = learner_components)
         }
       }