Merge branch 'master' into dask_docs

.ci/setup.sh

@@ -4,7 +4,7 @@ if [[ $OS_NAME == "macos" ]]; then
     if  [[ $COMPILER == "clang" ]]; then
         brew install libomp
         if [[ $AZURE == "true" ]]; then
-            sudo xcode-select -s /Applications/Xcode_9.4.1.app/Contents/Developer
+            sudo xcode-select -s /Applications/Xcode_9.4.1.app/Contents/Developer || exit -1
         fi
     else  # gcc
         if [[ $TASK != "mpi" ]]; then

.github/workflows/triggering_comments.yml

@@ -6,7 +6,7 @@ on:
 
 jobs:
   triggering-tests:
-    if: github.event.issue.pull_request && contains('OWNER,MEMBER,COLLABORATOR', github.event.comment.author_association)
+    if: github.event.issue.pull_request && contains('OWNER,MEMBER,COLLABORATOR', github.event.comment.author_association) && startsWith(github.event.comment.body, '/gha run')
     runs-on: ubuntu-latest
     env:
       SECRETS_WORKFLOW: ${{ secrets.WORKFLOW }}

src/treelearner/cuda_tree_learner.cpp

@@ -408,7 +408,7 @@ void CUDATreeLearner::copyDenseFeature() {
     // looking for dword_features_ non-sparse feature-groups
     if (!train_data_->IsMultiGroup(i)) {
       dense_feature_group_map_.push_back(i);
-      auto sizes_in_byte = train_data_->FeatureGroupSizesInByte(i);
+      auto sizes_in_byte = std::min(train_data_->FeatureGroupSizesInByte(i), static_cast<size_t>(num_data_));
       void* tmp_data = train_data_->FeatureGroupData(i);
       Log::Debug("Started copying dense features from CPU to GPU - 2");
       CUDASUCCESS_OR_FATAL(cudaMemcpyAsync(&device_features[copied_feature * num_data_], tmp_data, sizes_in_byte, cudaMemcpyHostToDevice, stream_[device_id]));

tests/python_package_test/test_basic.py

@@ -110,31 +110,6 @@ def test_chunked_dataset_linear():
     valid_data.construct()
 
 
-def test_save_and_load_linear(tmp_path):
-    X_train, X_test, y_train, y_test = train_test_split(*load_breast_cancer(return_X_y=True), test_size=0.1,
-                                                        random_state=2)
-    X_train = np.concatenate([np.ones((X_train.shape[0], 1)), X_train], 1)
-    X_train[:X_train.shape[0] // 2, 0] = 0
-    y_train[:X_train.shape[0] // 2] = 1
-    params = {'linear_tree': True}
-    train_data_1 = lgb.Dataset(X_train, label=y_train, params=params)
-    est_1 = lgb.train(params, train_data_1, num_boost_round=10, categorical_feature=[0])
-    pred_1 = est_1.predict(X_train)
-
-    tmp_dataset = str(tmp_path / 'temp_dataset.bin')
-    train_data_1.save_binary(tmp_dataset)
-    train_data_2 = lgb.Dataset(tmp_dataset)
-    est_2 = lgb.train(params, train_data_2, num_boost_round=10)
-    pred_2 = est_2.predict(X_train)
-    np.testing.assert_allclose(pred_1, pred_2)
-
-    model_file = str(tmp_path / 'model.txt')
-    est_2.save_model(model_file)
-    est_3 = lgb.Booster(model_file=model_file)
-    pred_3 = est_3.predict(X_train)
-    np.testing.assert_allclose(pred_2, pred_3)
-
-
 def test_subset_group():
     X_train, y_train = load_svmlight_file(os.path.join(os.path.dirname(os.path.realpath(__file__)),
                                                        '../../examples/lambdarank/rank.train'))