Merge pull request #474 from tidymodels/liblinear-tidy

Add `tidy` method for LiblineaR parsnip models

Author: topepo

NAMESPACE

@@ -66,6 +66,7 @@ S3method(req_pkgs,model_fit)
 S3method(req_pkgs,model_spec)
 S3method(required_pkgs,model_fit)
 S3method(required_pkgs,model_spec)
+S3method(tidy,"_LiblineaR")
 S3method(tidy,"_elnet")
 S3method(tidy,"_fishnet")
 S3method(tidy,"_lognet")

NEWS.md

@@ -4,7 +4,7 @@
 
 * The `liquidSVM` engine for `svm_rbf()` was deprecated due to that package's removal from CRAN. (#425)
 
-* A new linear SVM model `svm_linear()` is now available with the `LiblineaR` engine (#424) and the `kernlab` engine (#438), and the `LiblineaR` engine is available for `logistic_reg()` as well (#429). These models can use sparse matrices via `fit_xy()` (#447).
+* A new linear SVM model `svm_linear()` is now available with the `LiblineaR` engine (#424) and the `kernlab` engine (#438), and the `LiblineaR` engine is available for `logistic_reg()` as well (#429). These models can use sparse matrices via `fit_xy()` (#447) and have a `tidy` method (#474).
 
 * New model specification `survival_reg()` for the new mode `"censored regression"` (#444). `surv_reg()` is now soft-deprecated (#448).
 

R/tidy_liblinear.R

@@ -0,0 +1,17 @@
+#' tidy methods for LiblineaR models
+#'
+#' `tidy()` methods for the various `LiblineaR` models that return the
+#' coefficients from the `parsnip` model fit.
+#' @param x A fitted `parsnip` model that used the `LiblineaR` engine.
+#' @param ... Not used
+#' @return A tibble with columns `term` and `estimate`.
+#' @keywords internal
+#' @export
+
+tidy._LiblineaR <- function(x, ...) {
+  check_installs(x$spec)
+  ret <- tibble(colnames(x$fit$W), x$fit$W[1,])
+  colnames(ret) <- c("term", "estimate")
+
+  ret
+}

man/tidy._LiblineaR.Rd

@@ -448,6 +448,13 @@ test_that('liblinear execution', {
     )
   )
 
+  expect_error(
+    tidy_res <- tidy(res),
+    NA
+  )
+  expect_s3_class(tidy_res, "tbl_df")
+  expect_equal(colnames(tidy_res), c("term", "estimate"))
+
   # wrong outcome type
   expect_error(
     glm_form_catch <- fit(

tests/testthat/test_logistic_reg.R

@@ -143,6 +143,13 @@ test_that('linear svm regression: LiblineaR', {
   expect_equal(multi_predict_args(res), NA_character_)
   expect_output(print(res), "parsnip model object")
 
+  expect_error(
+    tidy_res <- tidy(res),
+    NA
+  )
+  expect_s3_class(tidy_res, "tbl_df")
+  expect_equal(colnames(tidy_res), c("term", "estimate"))
+
   expect_error(
     fit(
       reg_mod,