[R-package] Add print() and summary() methods for Booster

R-package/NAMESPACE

@@ -6,9 +6,11 @@ S3method(dimnames,lgb.Dataset)
 S3method(get_field,lgb.Dataset)
 S3method(getinfo,lgb.Dataset)
 S3method(predict,lgb.Booster)
+S3method(print,lgb.Booster)
 S3method(set_field,lgb.Dataset)
 S3method(setinfo,lgb.Dataset)
 S3method(slice,lgb.Dataset)
+S3method(summary,lgb.Booster)
 export(get_field)
 export(getinfo)
 export(lgb.Dataset)

R-package/R/lgb.Booster.R

@@ -814,6 +814,65 @@ predict.lgb.Booster <- function(object,
   )
 }
 
+#' @name print.lgb.Booster
+#' @title Print method for LightGBM model
+#' @description Show summary information about a LightGBM model object (same as \code{summary}).
+#' @param x Object of class \code{lgb.Booster}
+#' @param ... Not used
+#' @return The same input `x`, returned as invisible.
+#' @export
+print.lgb.Booster <- function(x, ...) {
+  # nolint start
+  handle <- x$.__enclos_env__$private$handle
+  handle_is_null <- lgb.is.null.handle(handle)
+
+  if (!handle_is_null) {
+    ntrees <- x$current_iter()
+    if (ntrees == 1L) {
+      cat("LightGBM Model (1 tree)\n")
+    } else {
+      cat(sprintf("LightGBM Model (%d trees)\n", ntrees))
+    }
+  } else {
+    cat("LightGBM Model\n")
+  }
+
+  if (!handle_is_null) {
+    obj <- x$params$objective
+    if (obj == "none") {
+      obj <- "custom"
+    }
+    if (x$.__enclos_env__$private$num_class == 1L) {
+      cat(sprintf("Objective: %s\n", obj))
+    } else {
+      cat(sprintf("Objective: %s (%d classes)\n"
+          , obj
+          , x$.__enclos_env__$private$num_class))
+    }
+  } else {
+    cat("(Booster handle is invalid)\n")
+  }
+
+  if (!handle_is_null) {
+    ncols <- .Call(LGBM_BoosterGetNumFeature_R, handle)
+    cat(sprintf("Fitted to dataset with %d columns\n", ncols))
+  }
+  # nolint end
+
+  return(invisible(x))
+}
+
+#' @name summary.lgb.Booster
+#' @title Summary method for LightGBM model
+#' @description Show summary information about a LightGBM model object (same as \code{print}).
+#' @param object Object of class \code{lgb.Booster}
+#' @param ... Not used
+#' @return The same input `object`, returned as invisible.
+#' @export
+summary.lgb.Booster <- function(object, ...) {
+  print(object)
+}
+
 #' @name lgb.load
 #' @title Load LightGBM model
 #' @description Load LightGBM takes in either a file path or model string.

R-package/src/lightgbm_R.cpp

@@ -525,6 +525,15 @@ SEXP LGBM_BoosterGetNumClasses_R(SEXP handle,
   R_API_END();
 }
 
+SEXP LGBM_BoosterGetNumFeature_R(SEXP handle) {
+  R_API_BEGIN();
+  _AssertBoosterHandleNotNull(handle);
+  int out = 0;
+  CHECK_CALL(LGBM_BoosterGetNumFeature(R_ExternalPtrAddr(handle), &out));
+  return Rf_ScalarInteger(out);
+  R_API_END();
+}
+
 SEXP LGBM_BoosterUpdateOneIter_R(SEXP handle) {
   R_API_BEGIN();
   _AssertBoosterHandleNotNull(handle);
@@ -889,6 +898,7 @@ static const R_CallMethodDef CallEntries[] = {
   {"LGBM_BoosterResetTrainingData_R"  , (DL_FUNC) &LGBM_BoosterResetTrainingData_R  , 2},
   {"LGBM_BoosterResetParameter_R"     , (DL_FUNC) &LGBM_BoosterResetParameter_R     , 2},
   {"LGBM_BoosterGetNumClasses_R"      , (DL_FUNC) &LGBM_BoosterGetNumClasses_R      , 2},
+  {"LGBM_BoosterGetNumFeature_R"      , (DL_FUNC) &LGBM_BoosterGetNumFeature_R      , 1},
   {"LGBM_BoosterUpdateOneIter_R"      , (DL_FUNC) &LGBM_BoosterUpdateOneIter_R      , 1},
   {"LGBM_BoosterUpdateOneIterCustom_R", (DL_FUNC) &LGBM_BoosterUpdateOneIterCustom_R, 4},
   {"LGBM_BoosterRollbackOneIter_R"    , (DL_FUNC) &LGBM_BoosterRollbackOneIter_R    , 1},

R-package/src/lightgbm_R.h

@@ -302,6 +302,15 @@ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetNumClasses_R(
   SEXP out
 );
 
+/*!
+* \brief Get number of features.
+* \param handle Booster handle
+* \return Total number of features, as R integer
+*/
+LIGHTGBM_C_EXPORT SEXP LGBM_BoosterGetNumFeature_R(
+  SEXP handle
+);
+
 /*!
 * \brief update the model in one round
 * \param handle Booster handle

R-package/tests/testthat/test_lgb.Booster.R

@@ -1041,3 +1041,116 @@ test_that("boosters with linear models at leaves can be written to RDS and re-lo
     preds2 <- predict(bst2, X)
     expect_identical(preds, preds2)
 })
+
+test_that("Booster's print, show, and summary work correctly", {
+    .have_same_handle <- function(model, other_model) {
+       expect_equal(
+         model$.__enclos_env__$private$handle
+         , other_model$.__enclos_env__$private$handle
+       )
+    }
+
+    .check_methods_work <- function(model) {
+
+        # should work for fitted models
+        ret <- print(model)
+        .have_same_handle(ret, model)
+        ret <- show(model)
+        expect_null(ret)
+        ret <- summary(model)
+        .have_same_handle(ret, model)
+
+        # should not fail for finalized models
+        model$finalize()
+        ret <- print(model)
+        .have_same_handle(ret, model)
+        ret <- show(model)
+        expect_null(ret)
+        ret <- summary(model)
+        .have_same_handle(ret, model)
+    }
+
+    data("mtcars")
+    model <- lgb.train(
+        params = list(objective = "regression")
+        , data = lgb.Dataset(
+            as.matrix(mtcars[, -1L])
+            , label = mtcars$mpg)
+        , verbose = 0L
+        , nrounds = 5L
+    )
+    .check_methods_work(model)
+
+    data("iris")
+    model <- lgb.train(
+        params = list(objective = "multiclass", num_class = 3L)
+        , data = lgb.Dataset(
+            as.matrix(iris[, -5L])
+            , label = as.numeric(factor(iris$Species)) - 1.0
+        )
+        , verbose = 0L
+        , nrounds = 5L
+    )
+    .check_methods_work(model)
+
+
+    # with custom objective
+    .logregobj <- function(preds, dtrain) {
+        labels <- get_field(dtrain, "label")
+        preds <- 1.0 / (1.0 + exp(-preds))
+        grad <- preds - labels
+        hess <- preds * (1.0 - preds)
+        return(list(grad = grad, hess = hess))
+    }
+
+    .evalerror <- function(preds, dtrain) {
+        labels <- get_field(dtrain, "label")
+        preds <- 1.0 / (1.0 + exp(-preds))
+        err <- as.numeric(sum(labels != (preds > 0.5))) / length(labels)
+        return(list(
+            name = "error"
+            , value = err
+            , higher_better = FALSE
+        ))
+    }
+
+    model <- lgb.train(
+        data = lgb.Dataset(
+            as.matrix(iris[, -5L])
+            , label = as.numeric(iris$Species == "virginica")
+        )
+        , obj = .logregobj
+        , eval = .evalerror
+        , verbose = 0L
+        , nrounds = 5L
+    )
+
+    .check_methods_work(model)
+})
+
+test_that("LGBM_BoosterGetNumFeature_R returns correct outputs", {
+    data("mtcars")
+    model <- lgb.train(
+        params = list(objective = "regression")
+        , data = lgb.Dataset(
+            as.matrix(mtcars[, -1L])
+            , label = mtcars$mpg)
+        , verbose = 0L
+        , nrounds = 5L
+    )
+    ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle)
+    expect_equal(ncols, ncol(mtcars) - 1L)
+
+    data("iris")
+    model <- lgb.train(
+        params = list(objective = "multiclass", num_class = 3L)
+        , data = lgb.Dataset(
+            as.matrix(iris[, -5L])
+            , label = as.numeric(factor(iris$Species)) - 1.0
+        )
+        , verbose = 0L
+        , nrounds = 5L
+    )
+    ncols <- .Call(LGBM_BoosterGetNumFeature_R, model$.__enclos_env__$private$handle)
+    expect_equal(ncols, ncol(iris) - 1L)
+})