[R-package] allow access to params in Booster (#3662)

* [R-package] allow access to params in Booster

* remove unnecessary whitespace

* fix test on resetting params

* remove pytest_cache

* Update R-package/tests/testthat/test_custom_objective.R

R-package/R/lgb.Booster.R

@@ -6,6 +6,7 @@ Booster <- R6::R6Class(
 
     best_iter = -1L,
     best_score = NA_real_,
+    params = list(),
     record_evals = list(),
 
     # Finalize will free up the handles
@@ -134,6 +135,8 @@ Booster <- R6::R6Class(
 
       }
 
+      self$params <- params
+
     },
 
     # Set training data name
@@ -187,17 +190,20 @@ Booster <- R6::R6Class(
     # Reset parameters of booster
     reset_parameter = function(params, ...) {
 
-      # Append parameters
-      params <- append(params, list(...))
+      if (methods::is(self$params, "list")) {
+        params <- modifyList(self$params, params)
+      }
+
+      params <- modifyList(params, list(...))
       params_str <- lgb.params2str(params = params)
 
-      # Reset parameters
       lgb.call(
         fun_name = "LGBM_BoosterResetParameter_R"
         , ret = NULL
         , private$handle
         , params_str
       )
+      self$params <- params
 
       return(invisible(self))
 

R-package/R/readRDS.lgb.Booster.R

@@ -44,6 +44,7 @@ readRDS.lgb.Booster <- function(file = "", refhook = NULL) {
     # Restore best iteration and recorded evaluations
     object2$best_iter <- object$best_iter
     object2$record_evals <- object$record_evals
+    object2$params <- object$params
 
     # Return newly loaded object
     return(object2)

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

@@ -386,6 +386,75 @@ test_that("Booster$update() throws an informative error if you provide a non-Dat
     }, regexp = "lgb.Booster.update: Only can use lgb.Dataset", fixed = TRUE)
 })
 
+test_that("Booster should store parameters and Booster$reset_parameter() should update them", {
+    data(agaricus.train, package = "lightgbm")
+    dtrain <- lgb.Dataset(
+        agaricus.train$data
+        , label = agaricus.train$label
+    )
+    # testing that this works for some cases that could break it:
+    #    - multiple metrics
+    #    - using "metric", "boosting", "num_class" in params
+    params <- list(
+        objective = "multiclass"
+        , max_depth = 4L
+        , bagging_fraction = 0.8
+        , metric = c("multi_logloss", "multi_error")
+        , boosting = "gbdt"
+        , num_class = 5L
+    )
+    bst <- Booster$new(
+        params = params
+        , train_set = dtrain
+    )
+    expect_identical(bst$params, params)
+
+    params[["bagging_fraction"]] <- 0.9
+    ret_bst <- bst$reset_parameter(params = params)
+    expect_identical(ret_bst$params, params)
+    expect_identical(bst$params, params)
+})
+
+test_that("Booster$params should include dataset params, before and after Booster$reset_parameter()", {
+    data(agaricus.train, package = "lightgbm")
+    dtrain <- lgb.Dataset(
+        agaricus.train$data
+        , label = agaricus.train$label
+        , params = list(
+            max_bin = 17L
+        )
+    )
+    params <- list(
+        objective = "binary"
+        , max_depth = 4L
+        , bagging_fraction = 0.8
+    )
+    bst <- Booster$new(
+        params = params
+        , train_set = dtrain
+    )
+    expect_identical(
+        bst$params
+        , list(
+            objective = "binary"
+            , max_depth = 4L
+            , bagging_fraction = 0.8
+            , max_bin = 17L
+        )
+    )
+
+    params[["bagging_fraction"]] <- 0.9
+    ret_bst <- bst$reset_parameter(params = params)
+    expected_params <- list(
+        objective = "binary"
+        , max_depth = 4L
+        , bagging_fraction = 0.9
+        , max_bin = 17L
+    )
+    expect_identical(ret_bst$params, expected_params)
+    expect_identical(bst$params, expected_params)
+})
+
 context("save_model")
 
 test_that("Saving a model with different feature importance types works", {
@@ -626,3 +695,38 @@ test_that("lgb.cv() correctly handles passing through params to the model file",
     }
 
 })
+
+context("saveRDS.lgb.Booster() and readRDS.lgb.Booster()")
+
+test_that("params (including dataset params) should be stored in .rds file for Booster", {
+    data(agaricus.train, package = "lightgbm")
+    dtrain <- lgb.Dataset(
+        agaricus.train$data
+        , label = agaricus.train$label
+        , params = list(
+            max_bin = 17L
+        )
+    )
+    params <- list(
+        objective = "binary"
+        , max_depth = 4L
+        , bagging_fraction = 0.8
+    )
+    bst <- Booster$new(
+        params = params
+        , train_set = dtrain
+    )
+    bst_file <- tempfile(fileext = ".rds")
+    saveRDS.lgb.Booster(bst, file = bst_file)
+
+    bst_from_file <- readRDS.lgb.Booster(file = bst_file)
+    expect_identical(
+        bst_from_file$params
+        , list(
+            objective = "binary"
+            , max_depth = 4L
+            , bagging_fraction = 0.8
+            , max_bin = 17L
+        )
+    )
+})

src/c_api.cpp

@@ -297,13 +297,15 @@ class Booster {
   void ResetConfig(const char* parameters) {
     UNIQUE_LOCK(mutex_)
     auto param = Config::Str2Map(parameters);
-    if (param.count("num_class")) {
+    Config new_config;
+    new_config.Set(param);
+    if (param.count("num_class") && new_config.num_class != config_.num_class) {
       Log::Fatal("Cannot change num_class during training");
     }
-    if (param.count("boosting")) {
+    if (param.count("boosting") && new_config.boosting != config_.boosting) {
       Log::Fatal("Cannot change boosting during training");
     }
-    if (param.count("metric")) {
+    if (param.count("metric") && new_config.metric != config_.metric) {
       Log::Fatal("Cannot change metric during training");
     }
     CheckDatasetResetConfig(config_, param);

tests/python_package_test/test_engine.py

@@ -2609,3 +2609,37 @@ class TestEngine(unittest.TestCase):
         lgb_X = lgb.Dataset(X, label=y)
         lgb.train(params, lgb_X, num_boost_round=1, valid_sets=[lgb_X], evals_result=res)
         self.assertAlmostEqual(res['training']['average_precision'][-1], 1)
+
+    def test_reset_params_works_with_metric_num_class_and_boosting(self):
+        X, y = load_breast_cancer(return_X_y=True)
+        params = {
+            'objective': 'multiclass',
+            'max_depth': 4,
+            'bagging_fraction': 0.8,
+            'metric': ['multi_logloss', 'multi_error'],
+            'boosting': 'gbdt',
+            'num_class': 5
+        }
+        dtrain = lgb.Dataset(X, y, params={"max_bin": 150})
+
+        bst = lgb.Booster(
+            params=params,
+            train_set=dtrain
+        )
+        expected_params = {
+            'objective': 'multiclass',
+            'max_depth': 4,
+            'bagging_fraction': 0.8,
+            'metric': ['multi_logloss', 'multi_error'],
+            'boosting': 'gbdt',
+            'num_class': 5,
+            'max_bin': 150
+        }
+        assert bst.params == expected_params
+
+        params['bagging_fraction'] = 0.9
+        ret_bst = bst.reset_parameter(params)
+
+        expected_params['bagging_fraction'] = 0.9
+        assert bst.params == expected_params
+        assert ret_bst.params == expected_params