plots seem to work

R/plot_comparisons.R

@@ -66,13 +66,7 @@ plot_comparisons <- function(model,
 
     # order of the first few paragraphs is important
     scall <- rlang::enquo(newdata)
-    if (!is.null(condition) && !is.null(newdata)) {
-        insight::format_error("The `condition` and `newdata` arguments cannot be used simultaneously.")
-    }
     newdata <- sanitize_newdata_call(scall, newdata, model)
-    if (!is.null(newdata) && is.null(by)) {
-        insight::format_error("The `newdata` argument requires a `by` argument.")
-    }
     if (!is.null(wts) && is.null(by)) {
         insight::format_error("The `wts` argument requires a `by` argument.")
     }
@@ -89,9 +83,18 @@ plot_comparisons <- function(model,
         checkmate::check_list(variables, names = "unique"),
         .var.name = "variables")
 
+    modeldata <- get_modeldata(
+        model,
+        additional_variables = c(names(condition), by),
+        wts = wts)
+
+    # mlr3 and tidymodels
+    if (is.null(modeldata) || nrow(modeldata) == 0) {
+        modeldata <- newdata
+    }
+
     # conditional
     if (!is.null(condition)) {
-        modeldata <- get_modeldata(model, additional_variables = names(condition), wts = wts)
         condition <- sanitize_condition(model, condition, variables, modeldata = modeldata)
         v_x <- condition$condition1
         v_color <- condition$condition2
@@ -114,7 +117,6 @@ plot_comparisons <- function(model,
 
     # marginal
     if (!is.null(by)) {
-        modeldata <- get_modeldata(model, additional_variables = by, wts = wts)
         newdata <- sanitize_newdata(
             model = model,
             newdata = newdata,

R/plot_predictions.R

@@ -75,13 +75,7 @@ plot_predictions <- function(model,
 
     # order of the first few paragraphs is important
     scall <- rlang::enquo(newdata)
-    if (!is.null(condition) && !is.null(newdata)) {
-        insight::format_error("The `condition` and `newdata` arguments cannot be used simultaneously.")
-    }
     newdata <- sanitize_newdata_call(scall, newdata, model)
-    if (!is.null(newdata) && is.null(by)) {
-        insight::format_error("The `newdata` argument requires a `by` argument.")
-    }
     if (!is.null(wts) && is.null(by)) {
         insight::format_error("The `wts` argument requires a `by` argument.")
     }
@@ -94,9 +88,18 @@ plot_predictions <- function(model,
         insight::format_error(msg)
     }
 
+    modeldata <- get_modeldata(
+        model,
+        additional_variables = c(names(condition), by),
+        wts = wts)
+
+    # mlr3 and tidymodels
+    if (is.null(modeldata) || nrow(modeldata) == 0) {
+        modeldata <- newdata
+    }
+
     # conditional
     if (!is.null(condition)) {
-        modeldata <- get_modeldata(model, additional_variables = names(condition), wts = wts)
         condition <- sanitize_condition(model, condition, variables = NULL, modeldata = modeldata)
         v_x <- condition$condition1
         v_color <- condition$condition2
@@ -116,13 +119,19 @@ plot_predictions <- function(model,
     # marginal
     if (!isFALSE(by) && !is.null(by)) { # switched from NULL above
         condition <- NULL
-        modeldata <- get_modeldata(model, additional_variables = by, wts = wts)
+
         newdata <- sanitize_newdata(
             model = model,
             newdata = newdata,
             modeldata = modeldata,
             by = by,
             wts = wts)
+
+        # tidymodels & mlr3
+        if (is.null(modeldata)) {
+            modeldata <- newdata
+        }
+
         datplot <- predictions(
             model,
             by = by,

R/sanitize_condition.R

@@ -149,6 +149,11 @@ sanitize_condition <- function(model, condition, variables = NULL, modeldata = N
         }
     }
 
+    # mlr3 and tidymodels are not supported by `insight::find_variables()`, so we need to create a grid based on all the variables supplied in `newdata`
+    if (inherits(at_list$model, "Learner") || inherits(at_list$model, "model_fit")) {
+        at_list$model <- NULL
+    }
+
     # create data
     nd <- do.call("datagrid", at_list)
 

book/articles/machine_learning.qmd

@@ -3,7 +3,7 @@ title: "Machine Learning"
 ---
 
 
-`marginaleffects` offers several "model-agnostic" functions to interpret statistical and machine learning models. This vignette highlights how the package can be used to extract meaningful insights from the results of models trained using the `mlr3` and `tidymodels` framework.
+`marginaleffects` offers several "model-agnostic" functions to interpret statistical and machine learning models. This vignette highlights how the package can be used to extract meaningful insights from the results of models trained using the `mlr3` and `tidymodels` frameworks.
 
 The features in this vignette require version 0.16.0 or `marginaleffects`, or the development version which can be installed from Github:
 
@@ -16,14 +16,15 @@ Make sure to restart `R` after installation. Then, load a few libraries:
 ```{r}
 #| message: false
 #| warning: false
-options(width = 10000)
 library("marginaleffects")
 library("fmeffects")
 library("ggplot2")
 library("mlr3verse")
 library("tidymodels") |> suppressPackageStartupMessages()
+options(width = 10000)
 ```
 ```{r}
+#| include: false
 pkgload::load_all()
 ```
 
@@ -43,8 +44,11 @@ forest <- lrn("regr.ranger")$train(task)
 As described in other vignettes, we can use the `avg_comparisons()` function to compute the average change in predicted outcome that is associated with a change in each feature:
 
 ```{r}
+avg_comparisons(forest, newdata = bikes)
+```
+```{r}
+#| include: false
 cmp <- avg_comparisons(forest, newdata = bikes)
-cmp
 ```
 
 These results are easy to interpret: An increase of 1 degree Celsius in the temperature is associated with an increase of `r sprintf("%.3f", cmp$estimate[cmp$term == "temp"])` bikes rented per hour.
@@ -87,6 +91,11 @@ avg_comparisons(
     newdata = bikes)
 ```
 
+
+```{r}
+plot_predictions(forest, condition = "temp")
+```
+
 # tidymodels
 
 `marginaleffects` also supports the `tidymodels` machine learning framework. When the underlying engine used by `tidymodels` to train the model is itself supported as a standalone package by `marginaleffects`, we can obtain estimates of uncertainty estimates:

inst/tinytest/test-pkg-ml3rverse.R → inst/tinytest/test-pkg-mlr3verse.R

@@ -9,6 +9,11 @@ data("bikes", package = "fmeffects")
 task <- as_task_regr(x = bikes, id = "bikes", target = "count")
 forest <- lrn("regr.ranger")$train(task)
 
+
+# Plot predictions
+p <- plot_predictions(forest, condition = "temp", type = "response", newdata = bikes)
+expect_inherits(p, "gg")
+
 # Centered difference
 cmp <- avg_comparisons(forest, newdata = bikes)
 expect_inherits(cmp, "comparisons")