Merge pull request #118 from tidymodels/partykit-classification-predi…

…ction

update `partykit_tree_info()` to handle classification outputs

NAMESPACE

@@ -39,6 +39,7 @@ S3method(tidypredict_test,party)
 S3method(tidypredict_test,randomForest)
 S3method(tidypredict_test,ranger)
 S3method(tidypredict_test,xgb.Booster)
+export(.extract_partykit_classprob)
 export(.extract_xgb_trees)
 export(acceptable_formula)
 export(as_parsed_model)

R/model-partykit.R

@@ -1,9 +1,22 @@
 partykit_tree_info <- function(model) {
   model_nodes <- map(seq_along(model), ~ model[[.x]])
   is_split <- map_lgl(model_nodes, ~ class(.x$node[1]) == "partynode")
-  # non-cat model
-  mean_resp <- map_dbl(model_nodes, ~ mean(.x$fitted[, "(response)"]))
-  prediction <- ifelse(!is_split, mean_resp, NA)
+  if (is.numeric(model_nodes[[1]]$fitted[["(response)"]])) {
+    mean_resp <- map_dbl(model_nodes, ~ mean(.x$fitted[, "(response)"]))
+    prediction <- ifelse(!is_split, mean_resp, NA)
+  } else {
+    stat_mode <- function(x) {
+      counts <- rev(sort(table(x)))
+      if (counts[[1]] == counts[[2]]) {        
+        ties <- counts[counts[1] == counts]
+        return(names(rev(ties))[1])
+      }
+      names(counts)[1]
+    }
+    mode_resp <- map_chr(model_nodes, ~ stat_mode(.x$fitted[, "(response)"]))
+    prediction <- ifelse(!is_split, mode_resp, NA)
+  }
+
   party_nodes <- map(seq_along(model), ~ partykit::nodeapply(model, .x))
 
   kids <- map(party_nodes, ~ {
@@ -88,3 +101,64 @@ tidypredict_fit.party <- function(model) {
   parsedmodel <- parse_model(model)
   build_fit_formula_rf(parsedmodel)[[1]]
 }
+
+# For {orbital}
+#' @keywords internal
+#' @export
+.extract_partykit_classprob <- function(model) {
+  extract_classprob <- function(model) {
+    mod <- model$fitted
+    response <- mod[["(response)"]]
+    weights <- mod[["(weights)"]]
+
+    lvls <- levels(response)
+    weights_sum <- tapply(weights, response, sum)
+    weights_sum[is.na(weights_sum)] <- 0
+    res <- weights_sum / sum(weights)
+    names(res) <- lvls
+    res
+  }
+
+  preds <- map(seq_along(model), ~extract_classprob(model[[.x]]))
+  preds <- matrix(
+    unlist(preds), 
+    nrow = length(preds), 
+    byrow = TRUE,
+    dimnames = list(NULL, names(preds[[1]]))
+  )
+
+  generate_one_tree <- function(tree_info) {
+    paths <- tree_info$nodeID[tree_info[, "terminal"]]
+    paths <- map(
+      paths,
+      ~ {
+          prediction <- tree_info$prediction[tree_info$nodeID == .x]
+          if (is.null(prediction)) cli::cli_abort("Prediction column not found")
+          if (is.factor(prediction)) prediction <- as.character(prediction)
+          list(
+            prediction = prediction,
+            path = get_ra_path(.x, tree_info, FALSE)
+          )
+       }
+    )
+
+    classes <- attr(model$terms, "dataClasses")
+    pm <- list()
+    pm$general$model <- "party"
+    pm$general$type <- "tree"
+    pm$general$version <- 2
+    pm$trees <- list(paths)
+    parsedmodel <- as_parsed_model(pm)
+
+    build_fit_formula_rf(parsedmodel)[[1]]
+  }
+
+  tree_info <- partykit_tree_info(model)
+
+  res <- list()
+  for (i in seq_len(ncol(preds))) {
+    tree_info$prediction <- preds[, i]
+    res[[i]] <- generate_one_tree(tree_info)
+  }
+  res
+}