Use hist as the default tree method. (#9320)

R-package/tests/testthat/test_basic.R

@@ -85,9 +85,18 @@ test_that("dart prediction works", {
     rnorm(100)
 
   set.seed(1994)
-  booster_by_xgboost <- xgboost(data = d, label = y, max_depth = 2, booster = "dart",
-                                rate_drop = 0.5, one_drop = TRUE,
-                                eta = 1, nthread = 2, nrounds = nrounds, objective = "reg:squarederror")
+  booster_by_xgboost <- xgboost(
+    data = d,
+    label = y,
+    max_depth = 2,
+    booster = "dart",
+    rate_drop = 0.5,
+    one_drop = TRUE,
+    eta = 1,
+    nthread = 2,
+    nrounds = nrounds,
+    objective = "reg:squarederror"
+  )
   pred_by_xgboost_0 <- predict(booster_by_xgboost, newdata = d, ntreelimit = 0)
   pred_by_xgboost_1 <- predict(booster_by_xgboost, newdata = d, ntreelimit = nrounds)
   expect_true(all(matrix(pred_by_xgboost_0, byrow = TRUE) == matrix(pred_by_xgboost_1, byrow = TRUE)))
@@ -97,19 +106,19 @@ test_that("dart prediction works", {
 
   set.seed(1994)
   dtrain <- xgb.DMatrix(data = d, info = list(label = y))
-  booster_by_train <- xgb.train(params = list(
-                                    booster = "dart",
-                                    max_depth = 2,
-                                    eta = 1,
-                                    rate_drop = 0.5,
-                                    one_drop = TRUE,
-                                    nthread = 1,
-                                    tree_method = "exact",
-                                    objective = "reg:squarederror"
-                                ),
-                                data = dtrain,
-                                nrounds = nrounds
-                                )
+  booster_by_train <- xgb.train(
+    params = list(
+      booster = "dart",
+      max_depth = 2,
+      eta = 1,
+      rate_drop = 0.5,
+      one_drop = TRUE,
+      nthread = 1,
+      objective = "reg:squarederror"
+    ),
+    data = dtrain,
+    nrounds = nrounds
+  )
   pred_by_train_0 <- predict(booster_by_train, newdata = dtrain, ntreelimit = 0)
   pred_by_train_1 <- predict(booster_by_train, newdata = dtrain, ntreelimit = nrounds)
   pred_by_train_2 <- predict(booster_by_train, newdata = dtrain, training = TRUE)
@@ -399,7 +408,7 @@ test_that("colsample_bytree works", {
   xgb.importance(model = bst)
   # If colsample_bytree works properly, a variety of features should be used
   # in the 100 trees
-  expect_gte(nrow(xgb.importance(model = bst)), 30)
+  expect_gte(nrow(xgb.importance(model = bst)), 28)
 })
 
 test_that("Configuration works", {

R-package/tests/testthat/test_update.R

@@ -13,7 +13,10 @@ test_that("updating the model works", {
   watchlist <- list(train = dtrain, test = dtest)
 
   # no-subsampling
-  p1 <- list(objective = "binary:logistic", max_depth = 2, eta = 0.05, nthread = 2)
+  p1 <- list(
+    objective = "binary:logistic", max_depth = 2, eta = 0.05, nthread = 2,
+    updater = "grow_colmaker,prune"
+  )
   set.seed(11)
   bst1 <- xgb.train(p1, dtrain, nrounds = 10, watchlist, verbose = 0)
   tr1 <- xgb.model.dt.tree(model = bst1)

src/gbm/gbtree.cc

@@ -39,7 +39,6 @@ namespace xgboost::gbm {
 DMLC_REGISTRY_FILE_TAG(gbtree);
 
 void GBTree::Configure(Args const& cfg) {
-  this->cfg_ = cfg;
   std::string updater_seq = tparam_.updater_seq;
   tparam_.UpdateAllowUnknown(cfg);
   tree_param_.UpdateAllowUnknown(cfg);
@@ -78,10 +77,9 @@ void GBTree::Configure(Args const& cfg) {
 
   monitor_.Init("GBTree");
 
-  specified_updater_ = std::any_of(cfg.cbegin(), cfg.cend(),
-                   [](std::pair<std::string, std::string> const& arg) {
-                     return arg.first == "updater";
-                   });
+  specified_updater_ = std::any_of(
+      cfg.cbegin(), cfg.cend(),
+      [](std::pair<std::string, std::string> const& arg) { return arg.first == "updater"; });
 
   if (specified_updater_ && !showed_updater_warning_) {
     LOG(WARNING) << "DANGER AHEAD: You have manually specified `updater` "
@@ -93,12 +91,19 @@ void GBTree::Configure(Args const& cfg) {
     showed_updater_warning_ = true;
   }
 
+  if (model_.learner_model_param->IsVectorLeaf()) {
+    CHECK(tparam_.tree_method == TreeMethod::kHist || tparam_.tree_method == TreeMethod::kAuto)
+        << "Only the hist tree method is supported for building multi-target trees with vector "
+           "leaf.";
+  }
+  LOG(DEBUG) << "Using tree method: " << static_cast<int>(tparam_.tree_method);
   this->ConfigureUpdaters();
+
   if (updater_seq != tparam_.updater_seq) {
     updaters_.clear();
     this->InitUpdater(cfg);
   } else {
-    for (auto &up : updaters_) {
+    for (auto& up : updaters_) {
       up->Configure(cfg);
     }
   }
@@ -106,66 +111,6 @@ void GBTree::Configure(Args const& cfg) {
   configured_ = true;
 }
 
-// FIXME(trivialfis): This handles updaters.  Because the choice of updaters depends on
-// whether external memory is used and how large is dataset.  We can remove the dependency
-// on DMatrix once `hist` tree method can handle external memory so that we can make it
-// default.
-void GBTree::ConfigureWithKnownData(Args const& cfg, DMatrix* fmat) {
-  CHECK(this->configured_);
-  std::string updater_seq = tparam_.updater_seq;
-  CHECK(tparam_.GetInitialised());
-
-  tparam_.UpdateAllowUnknown(cfg);
-
-  this->PerformTreeMethodHeuristic(fmat);
-  this->ConfigureUpdaters();
-
-  // initialize the updaters only when needed.
-  if (updater_seq != tparam_.updater_seq) {
-    LOG(DEBUG) << "Using updaters: " << tparam_.updater_seq;
-    this->updaters_.clear();
-    this->InitUpdater(cfg);
-  }
-}
-
-void GBTree::PerformTreeMethodHeuristic(DMatrix* fmat) {
-  if (specified_updater_) {
-    // This method is disabled when `updater` parameter is explicitly
-    // set, since only experts are expected to do so.
-    return;
-  }
-  if (model_.learner_model_param->IsVectorLeaf()) {
-    CHECK(tparam_.tree_method == TreeMethod::kHist)
-        << "Only the hist tree method is supported for building multi-target trees with vector "
-           "leaf.";
-  }
-
-  // tparam_ is set before calling this function.
-  if (tparam_.tree_method != TreeMethod::kAuto) {
-    return;
-  }
-
-  if (collective::IsDistributed()) {
-    LOG(INFO) << "Tree method is automatically selected to be 'approx' "
-                 "for distributed training.";
-    tparam_.tree_method = TreeMethod::kApprox;
-  } else if (!fmat->SingleColBlock()) {
-    LOG(INFO) << "Tree method is automatically set to 'approx' "
-                 "since external-memory data matrix is used.";
-    tparam_.tree_method = TreeMethod::kApprox;
-  } else if (fmat->Info().num_row_ >= (4UL << 20UL)) {
-    /* Choose tree_method='approx' automatically for large data matrix */
-    LOG(INFO) << "Tree method is automatically selected to be "
-                 "'approx' for faster speed. To use old behavior "
-                 "(exact greedy algorithm on single machine), "
-                 "set tree_method to 'exact'.";
-    tparam_.tree_method = TreeMethod::kApprox;
-  } else {
-    tparam_.tree_method = TreeMethod::kExact;
-  }
-  LOG(DEBUG) << "Using tree method: " << static_cast<int>(tparam_.tree_method);
-}
-
 void GBTree::ConfigureUpdaters() {
   if (specified_updater_) {
     return;
@@ -173,31 +118,25 @@ void GBTree::ConfigureUpdaters() {
   // `updater` parameter was manually specified
   /* Choose updaters according to tree_method parameters */
   switch (tparam_.tree_method) {
-    case TreeMethod::kAuto:
-      // Use heuristic to choose between 'exact' and 'approx' This
-      // choice is carried out in PerformTreeMethodHeuristic() before
-      // calling this function.
+    case TreeMethod::kAuto:  // Use hist as default in 2.0
+    case TreeMethod::kHist: {
+      tparam_.updater_seq = "grow_quantile_histmaker";
       break;
+    }
     case TreeMethod::kApprox:
       tparam_.updater_seq = "grow_histmaker";
       break;
     case TreeMethod::kExact:
       tparam_.updater_seq = "grow_colmaker,prune";
       break;
-    case TreeMethod::kHist: {
-      LOG(INFO) << "Tree method is selected to be 'hist', which uses a single updater "
-                   "grow_quantile_histmaker.";
-      tparam_.updater_seq = "grow_quantile_histmaker";
-      break;
-    }
     case TreeMethod::kGPUHist: {
       common::AssertGPUSupport();
       tparam_.updater_seq = "grow_gpu_hist";
       break;
     }
     default:
-      LOG(FATAL) << "Unknown tree_method ("
-                 << static_cast<int>(tparam_.tree_method) << ") detected";
+      LOG(FATAL) << "Unknown tree_method (" << static_cast<int>(tparam_.tree_method)
+                 << ") detected";
   }
 }
 
@@ -253,7 +192,6 @@ void GBTree::DoBoost(DMatrix* p_fmat, HostDeviceVector<GradientPair>* in_gpair,
                      PredictionCacheEntry* predt, ObjFunction const* obj) {
   TreesOneIter new_trees;
   bst_target_t const n_groups = model_.learner_model_param->OutputLength();
-  ConfigureWithKnownData(this->cfg_, p_fmat);
   monitor_.Start("BoostNewTrees");
 
   // Weird case that tree method is cpu-based but gpu_id is set.  Ideally we should let

src/gbm/gbtree.h

@@ -56,9 +56,7 @@ DECLARE_FIELD_ENUM_CLASS(xgboost::TreeMethod);
 DECLARE_FIELD_ENUM_CLASS(xgboost::TreeProcessType);
 DECLARE_FIELD_ENUM_CLASS(xgboost::PredictorType);
 
-namespace xgboost {
-namespace gbm {
-
+namespace xgboost::gbm {
 /*! \brief training parameters */
 struct GBTreeTrainParam : public XGBoostParameter<GBTreeTrainParam> {
   /*! \brief tree updater sequence */
@@ -192,12 +190,8 @@ class GBTree : public GradientBooster {
       : GradientBooster{ctx}, model_(booster_config, ctx_) {}
 
   void Configure(const Args& cfg) override;
-  // Revise `tree_method` and `updater` parameters after seeing the training
-  // data matrix, only useful when tree_method is auto.
-  void PerformTreeMethodHeuristic(DMatrix* fmat);
   /*! \brief Map `tree_method` parameter to `updater` parameter */
   void ConfigureUpdaters();
-  void ConfigureWithKnownData(Args const& cfg, DMatrix* fmat);
 
   /**
    * \brief Optionally update the leaf value.
@@ -222,11 +216,7 @@ class GBTree : public GradientBooster {
     return tparam_;
   }
 
-  void Load(dmlc::Stream* fi) override {
-    model_.Load(fi);
-    this->cfg_.clear();
-  }
-
+  void Load(dmlc::Stream* fi) override { model_.Load(fi); }
   void Save(dmlc::Stream* fo) const override {
     model_.Save(fo);
   }
@@ -416,8 +406,6 @@ class GBTree : public GradientBooster {
   bool showed_updater_warning_ {false};
   bool specified_updater_   {false};
   bool configured_ {false};
-  // configurations for tree
-  Args cfg_;
   // the updaters that can be applied to each of tree
   std::vector<std::unique_ptr<TreeUpdater>> updaters_;
   // Predictors
@@ -431,7 +419,6 @@ class GBTree : public GradientBooster {
   common::Monitor monitor_;
 };
 
-}  // namespace gbm
-}  // namespace xgboost
+}  // namespace xgboost::gbm
 
 #endif  // XGBOOST_GBM_GBTREE_H_

tests/ci_build/lint_python.py

@@ -23,6 +23,7 @@ class LintersPaths:
         "tests/python/test_predict.py",
         "tests/python/test_quantile_dmatrix.py",
         "tests/python/test_tree_regularization.py",
+        "tests/python/test_shap.py",
         "tests/python-gpu/test_gpu_data_iterator.py",
         "tests/test_distributed/test_with_spark/",
         "tests/test_distributed/test_gpu_with_spark/",

tests/cpp/test_learner.cc

@@ -379,6 +379,8 @@ TEST(Learner, Seed) {
 TEST(Learner, ConstantSeed) {
   auto m = RandomDataGenerator{10, 10, 0}.GenerateDMatrix(true);
   std::unique_ptr<Learner> learner{Learner::Create({m})};
+  // Use exact as it doesn't initialize column sampler at construction, which alters the rng.
+  learner->SetParam("tree_method", "exact");
   learner->Configure();  // seed the global random
 
   std::uniform_real_distribution<float> dist;

tests/python/test_demos.py

@@ -18,9 +18,8 @@ CLI_DEMO_DIR = os.path.join(DEMO_DIR, 'CLI')
 def test_basic_walkthrough():
     script = os.path.join(PYTHON_DEMO_DIR, 'basic_walkthrough.py')
     cmd = ['python', script]
-    subprocess.check_call(cmd)
-    os.remove('dump.nice.txt')
-    os.remove('dump.raw.txt')
+    with tempfile.TemporaryDirectory() as tmpdir:
+        subprocess.check_call(cmd, cwd=tmpdir)
 
 
 @pytest.mark.skipif(**tm.no_matplotlib())

tests/python/test_shap.py

@@ -6,35 +6,34 @@ import scipy
 import scipy.special
 
 import xgboost as xgb
-
-dpath = 'demo/data/'
-rng = np.random.RandomState(1994)
+from xgboost import testing as tm
 
 
 class TestSHAP:
-
-    def test_feature_importances(self):
-        data = np.random.randn(100, 5)
+    def test_feature_importances(self) -> None:
+        rng = np.random.RandomState(1994)
+        data = rng.randn(100, 5)
         target = np.array([0, 1] * 50)
 
-        features = ['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5']
+        features = ["Feature1", "Feature2", "Feature3", "Feature4", "Feature5"]
 
-        dm = xgb.DMatrix(data, label=target,
-                         feature_names=features)
-        params = {'objective': 'multi:softprob',
-                  'eval_metric': 'mlogloss',
-                  'eta': 0.3,
-                  'num_class': 3}
+        dm = xgb.DMatrix(data, label=target, feature_names=features)
+        params = {
+            "objective": "multi:softprob",
+            "eval_metric": "mlogloss",
+            "eta": 0.3,
+            "num_class": 3,
+        }
 
         bst = xgb.train(params, dm, num_boost_round=10)
 
         # number of feature importances should == number of features
         scores1 = bst.get_score()
-        scores2 = bst.get_score(importance_type='weight')
-        scores3 = bst.get_score(importance_type='cover')
-        scores4 = bst.get_score(importance_type='gain')
-        scores5 = bst.get_score(importance_type='total_cover')
-        scores6 = bst.get_score(importance_type='total_gain')
+        scores2 = bst.get_score(importance_type="weight")
+        scores3 = bst.get_score(importance_type="cover")
+        scores4 = bst.get_score(importance_type="gain")
+        scores5 = bst.get_score(importance_type="total_cover")
+        scores6 = bst.get_score(importance_type="total_gain")
         assert len(scores1) == len(features)
         assert len(scores2) == len(features)
         assert len(scores3) == len(features)
@@ -46,12 +45,11 @@ class TestSHAP:
         fscores = bst.get_fscore()
         assert scores1 == fscores
 
-        dtrain = xgb.DMatrix(dpath + 'agaricus.txt.train?format=libsvm')
-        dtest = xgb.DMatrix(dpath + 'agaricus.txt.test?format=libsvm')
+        dtrain, dtest = tm.load_agaricus(__file__)
 
-        def fn(max_depth, num_rounds):
+        def fn(max_depth: int, num_rounds: int) -> None:
             # train
-            params = {'max_depth': max_depth, 'eta': 1, 'verbosity': 0}
+            params = {"max_depth": max_depth, "eta": 1, "verbosity": 0}
             bst = xgb.train(params, dtrain, num_boost_round=num_rounds)
 
             # predict
@@ -82,7 +80,7 @@ class TestSHAP:
         assert out[0, 1] == 0.375
         assert out[0, 2] == 0.25
 
-        def parse_model(model):
+        def parse_model(model: xgb.Booster) -> list:
             trees = []
             r_exp = r"([0-9]+):\[f([0-9]+)<([0-9\.e-]+)\] yes=([0-9]+),no=([0-9]+).*cover=([0-9e\.]+)"
             r_exp_leaf = r"([0-9]+):leaf=([0-9\.e-]+),cover=([0-9e\.]+)"
@@ -93,7 +91,9 @@ class TestSHAP:
                     match = re.search(r_exp, line)
                     if match is not None:
                         ind = int(match.group(1))
+                        assert trees[-1] is not None
                         while ind >= len(trees[-1]):
+                            assert isinstance(trees[-1], list)
                             trees[-1].append(None)
                         trees[-1][ind] = {
                             "yes_ind": int(match.group(4)),
@@ -101,17 +101,16 @@ class TestSHAP:
                             "value": None,
                             "threshold": float(match.group(3)),
                             "feature_index": int(match.group(2)),
-                            "cover": float(match.group(6))
+                            "cover": float(match.group(6)),
                         }
                     else:
-
                         match = re.search(r_exp_leaf, line)
                         ind = int(match.group(1))
                         while ind >= len(trees[-1]):
                             trees[-1].append(None)
                         trees[-1][ind] = {
                             "value": float(match.group(2)),
-                            "cover": float(match.group(3))
+                            "cover": float(match.group(3)),
                         }
             return trees
 
@@ -121,7 +120,8 @@ class TestSHAP:
             else:
                 ind = tree[i]["feature_index"]
                 if z[ind] == 1:
-                    if x[ind] < tree[i]["threshold"]:
+                    # 1e-6 for numeric error from parsing text dump.
+                    if x[ind] + 1e-6 <= tree[i]["threshold"]:
                         return exp_value_rec(tree, z, x, tree[i]["yes_ind"])
                     else:
                         return exp_value_rec(tree, z, x, tree[i]["no_ind"])
@@ -136,10 +136,13 @@ class TestSHAP:
                     return val
 
         def exp_value(trees, z, x):
+            "E[f(z)|Z_s = X_s]"
             return np.sum([exp_value_rec(tree, z, x) for tree in trees])
 
         def all_subsets(ss):
-            return itertools.chain(*map(lambda x: itertools.combinations(ss, x), range(0, len(ss) + 1)))
+            return itertools.chain(
+                *map(lambda x: itertools.combinations(ss, x), range(0, len(ss) + 1))
+            )
 
         def shap_value(trees, x, i, cond=None, cond_value=None):
             M = len(x)
@@ -196,7 +199,9 @@ class TestSHAP:
                 z[i] = 0
                 v01 = exp_value(trees, z, x)
                 z[j] = 0
-                total += (v11 - v01 - v10 + v00) / (scipy.special.binom(M - 2, len(subset)) * (M - 1))
+                total += (v11 - v01 - v10 + v00) / (
+                    scipy.special.binom(M - 2, len(subset)) * (M - 1)
+                )
                 z[list(subset)] = 0
             return total
 
@@ -220,11 +225,10 @@ class TestSHAP:
         assert np.linalg.norm(brute_force - fast_method[0, :, :]) < 1e-4
 
         # test a random function
-        np.random.seed(0)
         M = 2
         N = 4
-        X = np.random.randn(N, M)
-        y = np.random.randn(N)
+        X = rng.randn(N, M)
+        y = rng.randn(N)
         param = {"max_depth": 2, "base_score": 0.0, "eta": 1.0, "lambda": 0}
         bst = xgb.train(param, xgb.DMatrix(X, label=y), 1)
         brute_force = shap_values(parse_model(bst), X[0, :])
@@ -236,11 +240,10 @@ class TestSHAP:
         assert np.linalg.norm(brute_force - fast_method[0, :, :]) < 1e-4
 
         # test another larger more complex random function
-        np.random.seed(0)
         M = 5
         N = 100
-        X = np.random.randn(N, M)
-        y = np.random.randn(N)
+        X = rng.randn(N, M)
+        y = rng.randn(N)
         base_score = 1.0
         param = {"max_depth": 5, "base_score": base_score, "eta": 0.1, "gamma": 2.0}
         bst = xgb.train(param, xgb.DMatrix(X, label=y), 10)

tests/python/test_survival.py

@@ -1,6 +1,6 @@
 import json
 import os
-from typing import Optional, Tuple
+from typing import List, Optional, Tuple, cast
 
 import numpy as np
 import pytest
@@ -62,8 +62,8 @@ def test_aft_survival_toy_data(
     X = np.array([1, 2, 3, 4, 5]).reshape((-1, 1))
     dmat, y_lower, y_upper = toy_data
 
-    # "Accuracy" = the number of data points whose ranged label (y_lower, y_upper) includes
-    #              the corresponding predicted label (y_pred)
+    # "Accuracy" = the number of data points whose ranged label (y_lower, y_upper)
+    #              includes the corresponding predicted label (y_pred)
     acc_rec = []
 
     class Callback(xgb.callback.TrainingCallback):
@@ -71,21 +71,33 @@ def test_aft_survival_toy_data(
             super().__init__()
 
         def after_iteration(
-            self, model: xgb.Booster,
+            self,
+            model: xgb.Booster,
             epoch: int,
-            evals_log: xgb.callback.TrainingCallback.EvalsLog
+            evals_log: xgb.callback.TrainingCallback.EvalsLog,
         ):
             y_pred = model.predict(dmat)
-            acc = np.sum(np.logical_and(y_pred >= y_lower, y_pred <= y_upper)/len(X))
+            acc = np.sum(np.logical_and(y_pred >= y_lower, y_pred <= y_upper) / len(X))
             acc_rec.append(acc)
             return False
 
-    evals_result = {}
-    params = {'max_depth': 3, 'objective': 'survival:aft', 'min_child_weight': 0}
-    bst = xgb.train(params, dmat, 15, [(dmat, 'train')], evals_result=evals_result,
-                    callbacks=[Callback()])
+    evals_result: xgb.callback.TrainingCallback.EvalsLog = {}
+    params = {
+        "max_depth": 3,
+        "objective": "survival:aft",
+        "min_child_weight": 0,
+        "tree_method": "exact",
+    }
+    bst = xgb.train(
+        params,
+        dmat,
+        15,
+        [(dmat, "train")],
+        evals_result=evals_result,
+        callbacks=[Callback()],
+    )
 
-    nloglik_rec = evals_result['train']['aft-nloglik']
+    nloglik_rec = cast(List[float], evals_result["train"]["aft-nloglik"])
     # AFT metric (negative log likelihood) improve monotonically
     assert all(p >= q for p, q in zip(nloglik_rec, nloglik_rec[:1]))
     # "Accuracy" improve monotonically.
@@ -94,15 +106,17 @@ def test_aft_survival_toy_data(
     assert acc_rec[-1] == 1.0
 
     def gather_split_thresholds(tree):
-        if 'split_condition' in tree:
-            return (gather_split_thresholds(tree['children'][0])
-                    | gather_split_thresholds(tree['children'][1])
-                    | {tree['split_condition']})
+        if "split_condition" in tree:
+            return (
+                gather_split_thresholds(tree["children"][0])
+                | gather_split_thresholds(tree["children"][1])
+                | {tree["split_condition"]}
+            )
         return set()
 
     # Only 2.5, 3.5, and 4.5 are used as split thresholds.
-    model_json = [json.loads(e) for e in bst.get_dump(dump_format='json')]
-    for tree in model_json:
+    model_json = [json.loads(e) for e in bst.get_dump(dump_format="json")]
+    for i, tree in enumerate(model_json):
         assert gather_split_thresholds(tree).issubset({2.5, 3.5, 4.5})
 
 

tests/python/test_with_sklearn.py

@@ -475,18 +475,22 @@ def test_rf_regression():
     run_housing_rf_regression("hist")
 
 
-def test_parameter_tuning():
+@pytest.mark.parametrize("tree_method", ["exact", "hist", "approx"])
+def test_parameter_tuning(tree_method: str) -> None:
     from sklearn.datasets import fetch_california_housing
     from sklearn.model_selection import GridSearchCV
 
     X, y = fetch_california_housing(return_X_y=True)
-    xgb_model = xgb.XGBRegressor(learning_rate=0.1)
-    clf = GridSearchCV(xgb_model, {'max_depth': [2, 4],
-                                   'n_estimators': [50, 200]},
-                       cv=2, verbose=1)
-    clf.fit(X, y)
-    assert clf.best_score_ < 0.7
-    assert clf.best_params_ == {'n_estimators': 200, 'max_depth': 4}
+    reg = xgb.XGBRegressor(learning_rate=0.1, tree_method=tree_method)
+    grid_cv = GridSearchCV(
+        reg, {"max_depth": [2, 4], "n_estimators": [50, 200]}, cv=2, verbose=1
+    )
+    grid_cv.fit(X, y)
+    assert grid_cv.best_score_ < 0.7
+    assert grid_cv.best_params_ == {
+        "n_estimators": 200,
+        "max_depth": 4 if tree_method == "exact" else 2,
+    }
 
 
 def test_regression_with_custom_objective():
@@ -750,7 +754,7 @@ def test_parameters_access():
         ]["tree_method"]
         return tm
 
-    assert get_tm(clf) == "exact"
+    assert get_tm(clf) == "auto"  # Kept as auto, immutable since 2.0
 
     clf = pickle.loads(pickle.dumps(clf))
 
@@ -758,7 +762,7 @@ def test_parameters_access():
     assert clf.n_estimators == 2
     assert clf.get_params()["tree_method"] is None
     assert clf.get_params()["n_estimators"] == 2
-    assert get_tm(clf) == "exact"  # preserved for pickle
+    assert get_tm(clf) == "auto"  # preserved for pickle
 
     clf = save_load(clf)