Add engine specification field for predictor encodings

NAMESPACE

@@ -110,6 +110,7 @@ export(fit_control)
 export(fit_xy)
 export(fit_xy.model_spec)
 export(get_dependency)
+export(get_encoding)
 export(get_fit)
 export(get_from_env)
 export(get_model_env)
@@ -146,6 +147,7 @@ export(repair_call)
 export(rpart_train)
 export(set_args)
 export(set_dependency)
+export(set_encoding)
 export(set_engine)
 export(set_env_val)
 export(set_fit)

R/aaa_models.R

@@ -323,6 +323,11 @@ check_interface_val <- function(x) {
 #'  below, depending on context.
 #' @param pre,post Optional functions for pre- and post-processing of prediction
 #'  results.
+#' @param options A list of options for engine-specific encodings. Currently,
+#' the option implemented is `predictor_indicators` which tells `parsnip`
+#' whether the pre-processing should make indicator/dummy variables from factor
+#' predictors. This only affects cases when [fit.model_spec()] is used and the
+#' underlying model has an x/y interface.
 #' @param ... Optional arguments that should be passed into the `args` slot for
 #'  prediction objects.
 #' @keywords internal
@@ -780,3 +785,77 @@ pred_value_template <-  function(pre = NULL, post = NULL, func, ...) {
   list(pre = pre, post = post, func = func, args = list(...))
 }
 
+# ------------------------------------------------------------------------------
+
+check_encodings <- function(x) {
+  if (!is.list(x)) {
+    rlang::abort("`values` should be a list.")
+  }
+  req_args <- list(predictor_indicators = TRUE)
+
+  missing_args <- setdiff(names(req_args), names(x))
+  if (length(missing_args) > 0) {
+    rlang::abort(
+      glue::glue(
+        "The values passed to `set_encoding()` are missing arguments: ",
+        paste0("'", missing_args, "'", collapse = ", ")
+      )
+    )
+  }
+  extra_args <- setdiff(names(x), names(req_args))
+  if (length(extra_args) > 0) {
+    rlang::abort(
+      glue::glue(
+        "The values passed to `set_encoding()` had extra arguments: ",
+        paste0("'", extra_args, "'", collapse = ", ")
+      )
+    )
+  }
+  invisible(x)
+}
+
+#' @export
+#' @rdname set_new_model
+#' @keywords internal
+set_encoding <- function(model, mode, eng, options) {
+  check_model_exists(model)
+  check_eng_val(eng)
+  check_mode_val(mode)
+  check_encodings(options)
+
+  keys   <- tibble::tibble(model = model, engine = eng, mode = mode)
+  options <- tibble::as_tibble(options)
+  new_values <- dplyr::bind_cols(keys, options)
+
+
+  current_db_list <- ls(envir = get_model_env())
+  nm <- paste(model, "encoding", sep = "_")
+  if (any(current_db_list == nm)) {
+    current <- get_from_env(nm)
+    dup_check <-
+      current %>%
+      dplyr::inner_join(new_values, by = c("model", "engine", "mode", "predictor_indicators"))
+    if (nrow(dup_check)) {
+      rlang::abort(glue::glue("Engine '{eng}' and mode '{mode}' already have defined encodings."))
+    }
+
+  } else {
+    current <- NULL
+  }
+
+  db_values <- dplyr::bind_rows(current, new_values)
+  set_env_val(nm, db_values)
+
+  invisible(NULL)
+}
+
+
+#' @rdname set_new_model
+#' @keywords internal
+#' @export
+get_encoding <- function(model) {
+  check_model_exists(model)
+  nm <- paste0(model, "_encoding")
+  rlang::env_get(get_model_env(), nm)
+}
+

R/boost_tree_data.R

@@ -87,6 +87,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "boost_tree",
+  eng = "xgboost",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "boost_tree",
   eng = "xgboost",
@@ -125,6 +132,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "boost_tree",
+  eng = "xgboost",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "boost_tree",
   eng = "xgboost",
@@ -221,6 +235,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "boost_tree",
+  eng = "C5.0",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "boost_tree",
   eng = "C5.0",
@@ -344,6 +365,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "boost_tree",
+  eng = "spark",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_fit(
   model = "boost_tree",
   eng = "spark",
@@ -357,6 +385,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "boost_tree",
+  eng = "spark",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "boost_tree",
   eng = "spark",

R/convert_data.R

@@ -15,7 +15,7 @@
 #' @importFrom stats .checkMFClasses .getXlevels delete.response
 #' @importFrom stats model.offset model.weights na.omit na.pass
 
-convert_form_to_xy_fit <-function(
+convert_form_to_xy_fit <- function(
   formula,
   data,
   ...,

R/decision_tree_data.R

@@ -48,6 +48,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "decision_tree",
+  eng = "rpart",
+  mode = "regression",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_fit(
   model = "decision_tree",
   eng = "rpart",
@@ -60,6 +67,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "decision_tree",
+  eng = "rpart",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "decision_tree",
   eng = "rpart",
@@ -158,6 +172,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "decision_tree",
+  eng = "C5.0",
+  mode = "classification",
+  options = list(predictor_indicators = FALSE)
+)
+
 set_pred(
   model = "decision_tree",
   eng = "C5.0",
@@ -211,7 +232,7 @@ set_pred(
 
 set_model_engine("decision_tree", "classification", "spark")
 set_model_engine("decision_tree", "regression", "spark")
-set_dependency("decision_tree", "spark", "spark")
+set_dependency("decision_tree", "spark", "sparklyr")
 
 set_model_arg(
   model = "decision_tree",
@@ -239,12 +260,19 @@ set_fit(
     interface = "formula",
     data = c(formula = "formula", data = "x"),
     protect = c("x", "formula"),
-    func = c(pkg = "sparklyr", fun = "ml_decision_tree_classifier"),
+    func = c(pkg = "sparklyr", fun = "ml_decision_tree_regressor"),
     defaults =
       list(seed = expr(sample.int(10 ^ 5, 1)))
   )
 )
 
+set_encoding(
+  model = "decision_tree",
+  eng = "spark",
+  mode = "regression",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_fit(
   model = "decision_tree",
   eng = "spark",
@@ -259,6 +287,13 @@ set_fit(
   )
 )
 
+set_encoding(
+  model = "decision_tree",
+  eng = "spark",
+  mode = "classification",
+  options = list(predictor_indicators = TRUE)
+)
+
 set_pred(
   model = "decision_tree",
   eng = "spark",

R/fit.R

@@ -103,7 +103,7 @@ fit.model_spec <-
       eng_vals <- possible_engines(object)
       object$engine <- eng_vals[1]
       if (control$verbosity > 0) {
-        rlang::warn("Engine set to `{object$engine}`.")
+        rlang::warn(glue::glue("Engine set to `{object$engine}`."))
       }
     }
 

R/fit_helpers.R

@@ -103,12 +103,17 @@ xy_xy <- function(object, env, control, target = "none", ...) {
 form_xy <- function(object, control, env,
                     target = "none", ...) {
 
+  indicators <- get_encoding(class(object)[1]) %>%
+    dplyr::filter(mode == object$mode,
+                  engine == object$engine) %>%
+    dplyr::pull(predictor_indicators)
+
   data_obj <- convert_form_to_xy_fit(
     formula = env$formula,
     data = env$data,
     ...,
-    composition = target
-    # indicators
+    composition = target,
+    indicators = indicators
   )
   env$x <- data_obj$x
   env$y <- data_obj$y