fix measure bottlenecks

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: mlr3proba
 Title: Probabilistic Supervised Learning for 'mlr3'
-Version: 0.5.3
+Version: 0.5.4
 Authors@R:
     c(person(given = "Raphael",
              family = "Sonabend",
@@ -43,7 +43,7 @@ Depends:
 Imports:
     checkmate,
     data.table,
-    distr6 (>= 1.8.3),
+    distr6 (>= 1.8.4),
     ggplot2,
     mlr3misc (>= 0.7.0),
     mlr3viz,

NEWS.md

@@ -1,3 +1,7 @@
+# mlr3proba 0.5.4
+
+* Fix bottlenecks in Dcalib and RCLL
+
 # mlr3proba 0.5.3
 
 * Add support for learners that can predict multiple posterior distributions by using `distr6::Arrdist`

R/MeasureSurvDCalibration.R

@@ -3,21 +3,25 @@
 #' @templateVar fullname MeasureSurvDCalibration
 #'
 #' @description
-#' This calibration method is defined by calculating
+#' This calibration method is defined by calculating the following statistic:
 #' \deqn{s = B/n \sum_i (P_i - n/B)^2}
-#' where \eqn{B} is number of 'buckets', \eqn{n} is the number of predictions,
-#' and \eqn{P_i} is the predicted number of deaths in the \eqn{i}th interval
-#' [0, 100/B), [100/B, 50/B),....,[(B - 100)/B, 1).
+#' where \eqn{B} is number of 'buckets' (that equally divide \eqn{[0,1]} into intervals),
+#' \eqn{n} is the number of predictions, and \eqn{P_i} is the observed proportion
+#' of observations in the \eqn{i}th interval. An observation is assigned to the
+#' \eqn{i}th bucket, if its predicted survival probability at the time of event
+#' falls within the corresponding interval.
+#' This statistic assumes that censoring time is independent of death time.
 #'
-#' A model is well-calibrated if `s ~ Unif(B)`, tested with `chisq.test`
-#'  (`p > 0.05` if well-calibrated).
-#' Model `i` is better calibrated than model `j` if `s_i < s_j`.
+#' A model is well-calibrated if \eqn{s \sim Unif(B)}, tested with `chisq.test`
+#'  (\eqn{p > 0.05} if well-calibrated).
+#' Model \eqn{i} is better calibrated than model \eqn{j} if \eqn{s(i) < s(j)},
+#' meaning that *lower values* of this measure are preferred.
 #'
 #' @details
 #' This measure can either return the test statistic or the p-value from the `chisq.test`.
 #' The former is useful for model comparison whereas the latter is useful for determining if a model
-#' is well-calibration. If `chisq = FALSE` and `m` is the predicted value then you can manually
-#' compute the p.value with `pchisq(m, B - 1, lower.tail = FALSE)`.
+#' is well-calibrated. If `chisq = FALSE` and `s` is the predicted value then you can manually
+#' compute the p.value with `pchisq(s, B - 1, lower.tail = FALSE)`.
 #'
 #' NOTE: This measure is still experimental both theoretically and in implementation. Results
 #' should therefore only be taken as an indicator of performance and not for
@@ -34,18 +38,29 @@ MeasureSurvDCalibration = R6Class("MeasureSurvDCalibration",
   public = list(
     #' @description Creates a new instance of this [R6][R6::R6Class] class.
     #' @param B (`integer(1)`) \cr
-    #' Number of buckets to test for uniform predictions over. Default of `10` is recommended by
-    #' Haider et al. (2020).
+    #' Number of buckets to test for uniform predictions over.
+    #' Default of `10` is recommended by Haider et al. (2020).
+    #' Changing this parameter affects `truncate`.
     #' @param chisq (`logical(1)`) \cr
-    #' If `TRUE` returns the p.value of the corresponding chisq.test instead of the measure.
-    #' Otherwise this can be performed manually with `pchisq(m, B - 1, lower.tail = FALSE)`.
-    #' `p > 0.05` indicates well-calibrated.
+    #' If `TRUE` returns the p-value of the corresponding chisq.test instead of the measure.
+    #' Default is `FALSE` and returns the statistic `s`.
+    #' You can manually get the p-value by executing `pchisq(s, B - 1, lower.tail = FALSE)`.
+    #' `p > 0.05` indicates a well-calibrated model.
+    #' @param truncate (`double(1)`) \cr
+    #' This parameter controls the upper bound of the output statistic,
+    #' when `chisq` is `FALSE`. The default `truncate` value of \eqn{10}
+    #' corresponds to a p-value of 0.35 for the chisq.test using \eqn{B = 10} buckets.
+    #' Values \eqn{>10} translate to even lower p-values and thus less calibrated
+    #' models. If the number of buckets \eqn{B} changes, you probably will want to
+    #' change the `truncate` value as well to correspond to the same p-value significance.
+    #' Initialize with `truncate = Inf` if no truncation is desired.
     initialize = function() {
       ps = ps(
         B = p_int(1, default = 10),
-        chisq = p_lgl(default = FALSE)
+        chisq = p_lgl(default = FALSE),
+        truncate = p_dbl(lower = 0, upper = Inf, default = 10)
       )
-      ps$values = list(B = 10L, chisq = FALSE)
+      ps$values = list(B = 10L, chisq = FALSE, truncate = 10)
 
       super$initialize(
         id = "surv.dcalib",
@@ -62,18 +77,36 @@ MeasureSurvDCalibration = R6Class("MeasureSurvDCalibration",
   private = list(
     .score = function(prediction, ...) {
       ps = self$param_set$values
+      B = ps$B
+
       # initialize buckets
-      bj = numeric(ps$B)
+      bj = numeric(B)
+      true_times = prediction$truth[, 1L]
+
       # predict individual probability of death at observed event time
-      if (inherits(prediction$distr, "VectorDistribution")) {
-        si = as.numeric(prediction$distr$survival(data = matrix(prediction$truth[, 1L], nrow = 1L)))
+      # bypass distr6 construction if possible
+      if (inherits(prediction$data$distr, "array")) {
+        surv = prediction$data$distr
+        if (length(dim(surv)) == 3) {
+          # survival 3d array, extract median
+          surv = .ext_surv_mat(arr = surv, which.curve = 0.5)
+        }
+        times = as.numeric(colnames(surv))
+
+        si = diag(distr6:::C_Vec_WeightedDiscreteCdf(true_times, times,
+          cdf = t(1 - surv), FALSE, FALSE))
       } else {
-        si = diag(prediction$distr$survival(prediction$truth[, 1L]))
+        distr = prediction$distr
+        if (inherits(distr, c("Matdist", "Arrdist"))) {
+          si = diag(distr$survival(true_times))
+        } else { # VectorDistribution or single Distribution, e.g. WeightDisc()
+          si = as.numeric(distr$survival(data = matrix(true_times, nrow = 1L)))
+        }
       }
       # remove zeros
       si = map_dbl(si, function(.x) max(.x, 1e-5))
       # index of associated bucket
-      js = ceiling(ps$B * si)
+      js = ceiling(B * si)
 
       # could remove loop for dead observations but needed for censored ones and minimal overhead
       # in combining both
@@ -83,18 +116,18 @@ MeasureSurvDCalibration = R6Class("MeasureSurvDCalibration",
           # dead observations contribute 1 to their index
           bj[ji] = bj[ji] + 1
         } else {
-          # uncensored observations spread across buckets with most weighting on penultimate
+          # censored observations spread across buckets with most weighting on penultimate
           for (k in seq.int(ji - 1)) {
-            bj[k] = bj[k] + 1 / (ps$B * si[[i]])
+            bj[k] = bj[k] + 1 / (B * si[[i]])
           }
-          bj[ji] = bj[ji] + (1 - (ji - 1) / (ps$B * si[[i]]))
+          bj[ji] = bj[ji] + (1 - (ji - 1) / (B * si[[i]]))
         }
       }
 
       if (ps$chisq) {
         return(stats::chisq.test(bj)$p.value)
       } else {
-        return((ps$B / length(si)) * sum((bj - length(si) / ps$B)^2))
+        return(min(ps$truncate, (B / length(si)) * sum((bj - length(si) / B)^2)))
       }
     }
   )

R/MeasureSurvRCLL.R

@@ -73,17 +73,53 @@ MeasureSurvRCLL = R6::R6Class("MeasureSurvRCLL",
       event = truth[, 2] == 1
       event_times = truth[event, 1]
       cens_times = truth[!event, 1]
-      distr = prediction$distr
 
-      if (!any(event)) { # all censored
-        # survival at outcome time (survived *at least* this long)
-        out[!event] = diag(as.matrix(distr[!event]$survival(cens_times)))
-      } else if (all(event)) { # all uncensored
-        # pdf at outcome time (survived *this* long)
-        out[event] = diag(as.matrix(distr[event]$pdf(event_times)))
-      } else { # mix
-        out[event] = diag(as.matrix(distr[event]$pdf(event_times)))
-        out[!event] = diag(as.matrix(distr[!event]$survival(cens_times)))
+      # Bypass distr6 construction if underlying distr represented by array
+      if (inherits(prediction$data$distr, "array")) {
+        surv = prediction$data$distr
+        if (length(dim(surv)) == 3) {
+          # survival 3d array, extract median
+          surv = .ext_surv_mat(arr = surv, which.curve = 0.5)
+        }
+        times = as.numeric(colnames(surv))
+
+        if (any(!event)) {
+          if (sum(!event) == 1) { # fix subsetting issue in case of 1 censored
+            cdf = as.matrix(1 - surv[!event, ])
+          } else {
+            cdf = t(1 - surv[!event, ])
+          }
+
+          out[!event] = diag(
+            distr6:::C_Vec_WeightedDiscreteCdf(cens_times, times, cdf = cdf, FALSE, FALSE)
+          )
+        }
+        if (any(event)) {
+          pdf = distr6:::cdfpdf(1 - surv)
+          if (sum(event) == 1) { # fix subsetting issue in case of 1 event
+            pdf = as.matrix(pdf[event, ])
+          } else {
+            pdf = t(pdf[event, ])
+          }
+
+          out[event] = diag(
+            distr6:::C_Vec_WeightedDiscretePdf(event_times, times, pdf = pdf)
+          )
+        }
+      } else {
+        distr = prediction$distr
+
+        # Splitting in this way bypasses unnecessary distr extraction
+        if (!any(event)) { # all censored
+          # survival at outcome time (survived *at least* this long)
+          out = diag(as.matrix(distr$survival(cens_times)))
+        } else if (all(event)) { # all uncensored
+          # pdf at outcome time (survived *this* long)
+          out = diag(as.matrix(distr$pdf(event_times)))
+        } else { # mix
+          out[event] = diag(as.matrix(distr[event]$pdf(event_times)))
+          out[!event] = diag(as.matrix(distr[!event]$survival(cens_times)))
+        }
       }
 
       stopifnot(!any(out == -99L)) # safety check

R/PredictionDataSurv.R

@@ -129,12 +129,23 @@ filter_prediction_data.PredictionDataSurv = function(pdata, row_ids, ...) {
   }
 
   if (!is.null(pdata$distr)) {
-    if (inherits(pdata$distr, "matrix")) {
-      pdata$distr = pdata$distr[keep, , drop = FALSE]
-    } else { # array
-      pdata$distr = pdata$distr[keep, , , drop = FALSE]
+    distr = pdata$distr
+
+    if (testDistribution(distr)) { # distribution
+      ok = inherits(distr, c("VectorDistribution", "Matdist", "Arrdist")) &&
+           length(keep) > 1 # e.g.: Arrdist(1xYxZ) and keep = FALSE
+      if (ok) {
+        pdata$distr = distr[keep] # we can subset row/samples like this
+      } else {
+        pdata$distr = base::switch(keep, distr) # one distribution only
+      }
+    } else {
+      if (length(dim(distr)) == 2) { # 2d matrix
+        pdata$distr = distr[keep, , drop = FALSE]
+      } else { # 3d array
+        pdata$distr = distr[keep, , , drop = FALSE]
+      }
     }
-
   }
 
   pdata

R/PredictionSurv.R

@@ -171,10 +171,12 @@ PredictionSurv = R6Class("PredictionSurv",
       }
     },
     .distrify_survarray = function(x) {
-      if (inherits(x, "array")) { # can be matrix as well
+      if (inherits(x, "array") && nrow(x) > 0) { # can be matrix as well
         # create Matdist or Arrdist (default => median curve)
         distr6::as.Distribution(1 - x, fun = "cdf",
           decorators = c("CoreStatistics", "ExoticStatistics"))
+      } else {
+        NULL
       }
     }
   )

inst/testthat/helper_expectations.R

@@ -30,8 +30,8 @@ expect_prediction_surv = function(p) {
                                                    "response", "distr", "lp", "crank"))
   checkmate::expect_data_table(data.table::as.data.table(p), nrows  = length(p$row_ids))
   checkmate::expect_atomic_vector(p$missing)
-  if ("distr" %in% p$predict_types) {
-    expect_true(class(p$distr)[[1]] %in% c("VectorDistribution", "Matdist", "Arrdist"))
+  if ("distr" %in% p$predict_types && !is.null(p$distr)) {
+    expect_true(class(p$distr)[[1]] %in% c("VectorDistribution", "Matdist", "Arrdist", "WeightedDiscrete"))
   }
   expect_true(inherits(p, "PredictionSurv"))
 }