Mlr3 simplification

.github/workflows/test-coverage.yaml

@@ -33,22 +33,16 @@ jobs:
             clean = FALSE,
             install_path = file.path(Sys.getenv("RUNNER_TEMP"), "package"),
             function_exclusions = c(
-              "partial_dep\\.Learner",
               "partial_dep\\.ranger",
               "partial_dep\\.explainer",
-              "ice\\.Learner",
               "ice\\.ranger",
               "ice\\.explainer",
-              "hstats\\.Learner",
               "hstats\\.ranger",
               "hstats\\.explainer",
-              "perm_importance\\.Learner",
               "perm_importance\\.ranger",
               "perm_importance\\.explainer",
-              "average_loss\\.Learner",
               "average_loss\\.ranger",
-              "average_loss\\.explainer",
-              "mlr3_pred_fun"
+              "average_loss\\.explainer"
             )
           )
         shell: Rscript {0}

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: hstats
 Title: Interaction Statistics
-Version: 1.1.1
+Version: 1.1.2
 Authors@R: 
     person("Michael", "Mayer", , "[email protected]", role = c("aut", "cre"))
 Description: Fast, model-agnostic implementation of different H-statistics

NAMESPACE

@@ -2,7 +2,6 @@
 
 S3method("[",hstats_matrix)
 S3method("dimnames<-",hstats_matrix)
-S3method(average_loss,Learner)
 S3method(average_loss,default)
 S3method(average_loss,explainer)
 S3method(average_loss,ranger)
@@ -16,21 +15,17 @@ S3method(h2_pairwise,default)
 S3method(h2_pairwise,hstats)
 S3method(h2_threeway,default)
 S3method(h2_threeway,hstats)
-S3method(hstats,Learner)
 S3method(hstats,default)
 S3method(hstats,explainer)
 S3method(hstats,ranger)
-S3method(ice,Learner)
 S3method(ice,default)
 S3method(ice,explainer)
 S3method(ice,ranger)
-S3method(partial_dep,Learner)
 S3method(partial_dep,default)
 S3method(partial_dep,explainer)
 S3method(partial_dep,ranger)
 S3method(pd_importance,default)
 S3method(pd_importance,hstats)
-S3method(perm_importance,Learner)
 S3method(perm_importance,default)
 S3method(perm_importance,explainer)
 S3method(perm_importance,ranger)

NEWS.md

@@ -1,3 +1,10 @@
+# hstats 1.1.2
+
+## API
+
+- {mlr3}: Non-probabilistic classification now works.
+- {mlr3}: For *probabilistic* classification, you now have to pass `predict_type = "prob"`.
+
 # hstats 1.1.1
 
 ## Performance improvements

R/average_loss.R

@@ -135,31 +135,6 @@ average_loss.ranger <- function(object, X, y,
   )
 }
 
-#' @describeIn average_loss Method for "mlr3" models.
-#' @export
-average_loss.Learner <- function(object, X, y, 
-                                 pred_fun = NULL,
-                                 loss = "squared_error",
-                                 agg_cols = FALSE,
-                                 BY = NULL, by_size = 4L, 
-                                 w = NULL, ...) {
-  if (is.null(pred_fun)) {
-    pred_fun <- mlr3_pred_fun(object, X = X)
-  }
-  average_loss.default(
-    object = object, 
-    X = X, 
-    y = y, 
-    pred_fun = pred_fun, 
-    loss = loss,
-    agg_cols = agg_cols,
-    BY = BY,
-    by_size = by_size,
-    w = w, 
-    ...
-  )
-}
-
 #' @describeIn average_loss Method for DALEX "explainer".
 #' @export
 average_loss.explainer <- function(object, 

R/hstats.R

@@ -300,34 +300,6 @@ hstats.ranger <- function(object, X, v = NULL,
   )
 }
 
-#' @describeIn hstats Method for "mlr3" models.
-#' @export
-hstats.Learner <- function(object, X, v = NULL,
-                           pred_fun = NULL,
-                           pairwise_m = 5L, threeway_m = 0L, 
-                           approx = FALSE, grid_size = 50L, 
-                           n_max = 500L, eps = 1e-10, 
-                           w = NULL, verbose = TRUE, ...) {
-  if (is.null(pred_fun)) {
-    pred_fun <- mlr3_pred_fun(object, X = X)
-  }
-  hstats.default(
-    object = object,
-    X = X,
-    v = v,
-    pred_fun = pred_fun,
-    pairwise_m = pairwise_m,
-    threeway_m = threeway_m,
-    approx = approx,
-    grid_size = grid_size,
-    n_max = n_max,
-    eps = eps,
-    w = w,
-    verbose = verbose,
-    ...
-  )
-}
-
 #' @describeIn hstats Method for DALEX "explainer".
 #' @export
 hstats.explainer <- function(object, X = object[["data"]],

R/ice.R

@@ -173,32 +173,6 @@ ice.ranger <- function(object, v, X,
   )
 }
 
-#' @describeIn ice Method for "mlr3" models.
-#' @export
-ice.Learner <- function(object, v, X,
-                        pred_fun = NULL,
-                        BY = NULL, grid = NULL, grid_size = 49L, trim = c(0.01, 0.99),
-                        strategy = c("uniform", "quantile"), na.rm = TRUE,
-                        n_max = 100L, ...) {
-  if (is.null(pred_fun)) {
-    pred_fun <- mlr3_pred_fun(object, X = X)
-  }
-  ice.default(
-    object = object,
-    v = v,
-    X = X,
-    pred_fun = pred_fun,
-    BY = BY,
-    grid = grid,
-    grid_size = grid_size,
-    trim = trim,
-    strategy = strategy,
-    na.rm = na.rm,
-    n_max = n_max,
-    ...
-  )
-}
-
 #' @describeIn ice Method for DALEX "explainer".
 #' @export
 ice.explainer <- function(object, v = v, X = object[["data"]],

R/partial_dep.R

@@ -213,35 +213,6 @@ partial_dep.ranger <- function(object, v, X,
   )
 }
 
-#' @describeIn partial_dep Method for "mlr3" models.
-#' @export
-partial_dep.Learner <- function(object, v, X, 
-                                pred_fun = NULL,
-                                BY = NULL, by_size = 4L, grid = NULL, grid_size = 49L, 
-                                trim = c(0.01, 0.99), 
-                                strategy = c("uniform", "quantile"), na.rm = TRUE,
-                                n_max = 1000L, w = NULL, ...) {
-  if (is.null(pred_fun)) {
-    pred_fun <- mlr3_pred_fun(object, X = X)
-  }
-  partial_dep.default(
-    object = object,
-    v = v,
-    X = X,
-    pred_fun = pred_fun,
-    BY = BY,
-    by_size = by_size,
-    grid = grid,
-    grid_size = grid_size,
-    trim = trim,
-    strategy = strategy,
-    na.rm = na.rm,
-    n_max = n_max,
-    w = w,
-    ...
-  )
-}
-
 #' @describeIn partial_dep Method for DALEX "explainer".
 #' @export
 partial_dep.explainer <- function(object, v, X = object[["data"]], 

R/perm_importance.R

@@ -228,34 +228,6 @@ perm_importance.ranger <- function(object, X, y, v = NULL,
   )
 }
 
-#' @describeIn perm_importance Method for "mlr3" models.
-#' @export
-perm_importance.Learner <- function(object, X, y, v = NULL,
-                                    pred_fun = NULL,
-                                    loss = "squared_error", m_rep = 4L, 
-                                    agg_cols = FALSE, 
-                                    normalize = FALSE, n_max = 10000L, 
-                                    w = NULL, verbose = TRUE, ...) {
-  if (is.null(pred_fun)) {
-    pred_fun <- mlr3_pred_fun(object, X = X)
-  }
-  perm_importance.default(
-    object = object,
-    X = X,
-    y = y,
-    v = v,
-    pred_fun = pred_fun,
-    loss = loss,
-    m_rep = m_rep,
-    agg_cols = agg_cols,
-    normalize = normalize,
-    n_max = n_max,
-    w = w,
-    verbose = verbose,
-    ...
-  )
-}
-
 #' @describeIn perm_importance Method for DALEX "explainer".
 #' @export
 perm_importance.explainer <- function(object, 

R/utils_input.R

@@ -114,26 +114,3 @@ prepare_y <- function(y, X, ohe = FALSE) {
   list(y = prepare_pred(y, ohe = ohe), y_names = y_names)
 }
 
-#' mlr3 Helper
-#' 
-#' Returns the prediction function of a mlr3 Learner.
-#' 
-#' @noRd
-#' @keywords internal
-#' 
-#' @param object Learner object.
-#' @param X Dataframe like object.
-#' 
-#' @returns A function.
-mlr3_pred_fun <- function(object, X) {
-  if ("classif" %in% object$task_type) {
-    # Check if probabilities are available
-    test_pred <- object$predict_newdata(utils::head(X))
-    if ("prob" %in% test_pred$predict_types) {
-      return(function(m, X) m$predict_newdata(X)$prob)
-    } else {
-      stop("Set lrn(..., predict_type = 'prob') to allow for probabilistic classification.")
-    }
-  }
-  function(m, X) m$predict_newdata(X)$response
-}

README.md

@@ -229,7 +229,7 @@ Strongest relative interaction shown as ICE plot.
 
 ## Multivariate responses
 
-{hstats} works also with multivariate output, see examples with 
+{hstats} works also with multivariate output, see examples for probabilistic classification with 
 
 - ranger, 
 - LightGBM, and 
@@ -377,7 +377,9 @@ plot(H, normalize = FALSE, squared = FALSE, facet_scales = "free_y", ncol = 1)
 
 ![](man/figures/xgboost.svg)
 
-### (Non-probabilistic) classification works as well
+### Non-probabilistic classification
+
+When predictions are factor levels, {hstats} uses internal one-hot-encoding.
 
 ```r
 library(ranger)
@@ -404,7 +406,7 @@ partial_dep(fit, v = "Petal.Length", X = train, BY = "Petal.Width") |>
 
 ## Meta-learning packages
 
-Here, we provide some working examples for "tidymodels", "caret", and "mlr3".
+Here, we provide examples for {tidymodels}, {caret}, and {mlr3}.
 
 ### tidymodels
 
@@ -478,8 +480,14 @@ fit_rf$train(task_iris)
 s <- hstats(fit_rf, X = iris[, -5])
 plot(s)
 
-# Permutation importance
-perm_importance(fit_rf, X = iris, y = "Species", loss = "mlogloss") |> 
+# Permutation importance (probabilistic using multi-logloss)
+p <- perm_importance(
+  fit_rf, X = iris, y = "Species", loss = "mlogloss", predict_type = "prob"
+)
+plot(p)
+
+# Non-probabilistic using classification error
+perm_importance(fit_rf, X = iris, y = "Species", loss = "classification_error") |> 
   plot()
 ```