Remove some measures, update some others

DESCRIPTION

@@ -78,12 +78,8 @@ Collate:
     'regr_msle.R'
     'regr_pbias.R'
     'regr_pinball.R'
-    'regr_rae.R'
     'regr_rmse.R'
     'regr_rmsle.R'
-    'regr_rrse.R'
-    'regr_rse.R'
-    'regr_rsq.R'
     'regr_sae.R'
     'regr_se.R'
     'regr_sle.R'

NAMESPACE

@@ -48,13 +48,9 @@ export(pinball)
 export(ppv)
 export(prauc)
 export(precision)
-export(rae)
 export(recall)
 export(rmse)
 export(rmsle)
-export(rrse)
-export(rse)
-export(rsq)
 export(sae)
 export(se)
 export(sensitivity)

NEWS.md

@@ -1,5 +1,7 @@
 # mlr3measures (development version)
 
+* BREAKIG CHANGE: removed `rse`, `rsq`, `rrse`, and `rae`.
+
 # mlr3measures 1.0.0
 
 * Added new measure `linex` (Linear-Exponential Loss).

R/classif_acc.R

@@ -3,9 +3,9 @@
 #' @details
 #' The Classification Accuracy is defined as
 #'  \deqn{
-#'   \frac{1}{n} \sum_{i=1}^n w_i \mathbf{1} \left( t_i = r_i \right),
-#' }{1 / n * sum(wi * 1(ti = ri))}
-#' where \eqn{w_i} are normalized weights for all observations \eqn{x_i}.
+#'   \sum_{i=1}^n w_i \mathbf{1} \left( t_i = r_i \right),
+#' }{sum(wi * 1(ti = ri))}
+#' where \eqn{w_i} are weights normalized to sum to 1 for all observations \eqn{x_i}.
 #'
 #' @templateVar mid acc
 #' @template classif_template

R/helper.R

@@ -23,6 +23,14 @@ wmean = function(x, w) { # a better stats::weighted.mean
   sum(x * (w / sum(w)))
 }
 
+wsum = function(x, w) { # sum(w * x) that asserts w and accepts NULL
+  if (is.null(w)) {
+    return(sum(x))
+  }
+  assert_numeric(w, lower = 0, finite = TRUE, any.missing = FALSE, len = length(x))
+  sum(x * w)
+}
+
 # confusion matrix
 cm = function(truth, response, positive = NULL) {
   if (!is.null(positive)) {

R/regr_sae.R

@@ -2,20 +2,27 @@
 #'
 #' @details
 #' The Sum of Absolute Errors is defined as \deqn{
-#'   \sum_{i=1}^n \left| t_i - r_i \right|.
+#'   \sum_{i=1}^n w_i \left| t_i - r_i \right|.
 #' }{
-#'   sum(abs((t - r))).
+#'   sum(w * abs((t - r))).
 #' }
+#' where \eqn{w_i} are unnormalized weights for each observation \eqn{x_i}, defaulting to 1.
 #'
 #' @templateVar mid sae
 #' @template regr_template
 #'
+#' @param sample_weights (`numeric()`)\cr
+#'   Vector of non-negative and finite sample weights.
+#'   Must have the same length as `truth`.
+#'   Weights for this function are not normalized.
+#'   Defaults to sample weights 1.
+#'
 #' @inheritParams regr_params
 #' @template regr_example
 #' @export
-sae = function(truth, response, ...) {
+sae = function(truth, response, sample_weights = NULL, ...) {
   assert_regr(truth, response = response)
-  sum(.ae(truth, response))
+  sum(.ae(truth, response), sample_weights)
 }
 
 #' @include measures.R

R/regr_sse.R

@@ -2,20 +2,27 @@
 #'
 #' @details
 #' The Sum of Squared Errors is defined as \deqn{
-#'   \sum_{i=1}^n \left( t_i - r_i \right)^2.
+#'   \sum_{i=1}^n w_i \left( t_i - r_i \right)^2.
 #' }{
-#'   sum((t - r)^2).
+#'   sum(w * (t - r)^2).
 #' }
+#' where \eqn{w_i} are unnormalized weights for each observation \eqn{x_i}, defaulting to 1.
 #'
 #' @templateVar mid sse
 #' @template regr_template
 #'
+#' @param sample_weights (`numeric()`)\cr
+#'   Vector of non-negative and finite sample weights.
+#'   Must have the same length as `truth`.
+#'   Weights for this function are not normalized.
+#'   Defaults to sample weights 1.
+#'
 #' @inheritParams regr_params
 #' @template regr_example
 #' @export
-sse = function(truth, response, ...) {
+sse = function(truth, response, sample_weights = NULL, ...) {
   assert_regr(truth, response = response)
-  sum(.se(truth, response))
+  wsum(.se(truth, response), sample_weights)
 }
 
 #' @include measures.R