Merge branch 'master' into fix/vs-omp

.github/workflows/lock.yml

@@ -18,15 +18,15 @@ jobs:
   action:
     runs-on: ubuntu-latest
     steps:
-      - uses: dessant/lock-threads@v4
+      - uses: dessant/lock-threads@v5
         with:
           github-token: ${{ github.token }}
           # after how many days of inactivity should a closed issue/PR be locked?
-          issue-inactive-days: '90'
-          pr-inactive-days: '90'
+          issue-inactive-days: '365'
+          pr-inactive-days: '365'
           # do not close feature request issues...
           # we close those but track them in https://github.com/microsoft/LightGBM/issues/2302
-          exclude-any-issue-labels: '"feature request"'
+          exclude-any-issue-labels: 'feature request'
           # what labels should be removed prior to locking?
           remove-issue-labels: 'awaiting response,awaiting review,blocking,in progress'
           remove-pr-labels: 'awaiting response,awaiting review,blocking,in progress'
@@ -42,3 +42,4 @@ jobs:
           # what shoulld the locking status be?
           issue-lock-reason: 'resolved'
           pr-lock-reason: 'resolved'
+          process-only: 'issues, prs'

CMakeLists.txt

@@ -41,6 +41,13 @@ endif()
 
 project(lightgbm LANGUAGES C CXX)
 
+if(BUILD_CPP_TEST)
+  set(CMAKE_CXX_STANDARD 14)
+else()
+  set(CMAKE_CXX_STANDARD 11)
+endif()
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
+
 list(APPEND CMAKE_MODULE_PATH "${PROJECT_SOURCE_DIR}/cmake/modules")
 
 #-- Sanitizer
@@ -329,7 +336,7 @@ endif()
 if(UNIX OR MINGW OR CYGWIN)
   set(
     CMAKE_CXX_FLAGS
-    "${CMAKE_CXX_FLAGS} -std=c++11 -pthread -Wextra -Wall -Wno-ignored-attributes -Wno-unknown-pragmas -Wno-return-type"
+    "${CMAKE_CXX_FLAGS} -pthread -Wextra -Wall -Wno-ignored-attributes -Wno-unknown-pragmas -Wno-return-type"
   )
   if(MINGW)
     # ignore this warning: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=95353
@@ -630,7 +637,7 @@ if(BUILD_CPP_TEST)
     FetchContent_Declare(
       googletest
       GIT_REPOSITORY https://github.com/google/googletest.git
-      GIT_TAG        release-1.11.0
+      GIT_TAG        v1.14.0
     )
     FetchContent_MakeAvailable(googletest)
     add_library(GTest::GTest ALIAS gtest)

R-package/src/lightgbm_R.cpp

@@ -224,17 +224,19 @@ SEXP LGBM_DatasetGetSubset_R(SEXP handle,
   _AssertDatasetHandleNotNull(handle);
   SEXP ret = PROTECT(R_MakeExternalPtr(nullptr, R_NilValue, R_NilValue));
   int32_t len = static_cast<int32_t>(Rf_asInteger(len_used_row_indices));
-  std::vector<int32_t> idxvec(len);
+  std::unique_ptr<int32_t[]> idxvec(new int32_t[len]);
   // convert from one-based to zero-based index
   const int *used_row_indices_ = INTEGER(used_row_indices);
-#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (len >= 1024)
+#ifndef _MSC_VER
+#pragma omp simd
+#endif
   for (int32_t i = 0; i < len; ++i) {
     idxvec[i] = static_cast<int32_t>(used_row_indices_[i] - 1);
   }
   const char* parameters_ptr = CHAR(PROTECT(Rf_asChar(parameters)));
   DatasetHandle res = nullptr;
   CHECK_CALL(LGBM_DatasetGetSubset(R_ExternalPtrAddr(handle),
-    idxvec.data(), len, parameters_ptr,
+    idxvec.get(), len, parameters_ptr,
     &res));
   R_SetExternalPtrAddr(ret, res);
   R_RegisterCFinalizerEx(ret, _DatasetFinalizer, TRUE);
@@ -248,13 +250,13 @@ SEXP LGBM_DatasetSetFeatureNames_R(SEXP handle,
   R_API_BEGIN();
   _AssertDatasetHandleNotNull(handle);
   auto vec_names = Split(CHAR(PROTECT(Rf_asChar(feature_names))), '\t');
-  std::vector<const char*> vec_sptr;
   int len = static_cast<int>(vec_names.size());
+  std::unique_ptr<const char*[]> vec_sptr(new const char*[len]);
   for (int i = 0; i < len; ++i) {
-    vec_sptr.push_back(vec_names[i].c_str());
+    vec_sptr[i] = vec_names[i].c_str();
   }
   CHECK_CALL(LGBM_DatasetSetFeatureNames(R_ExternalPtrAddr(handle),
-    vec_sptr.data(), len));
+    vec_sptr.get(), len));
   UNPROTECT(1);
   return R_NilValue;
   R_API_END();
@@ -339,23 +341,13 @@ SEXP LGBM_DatasetSetField_R(SEXP handle,
   int len = Rf_asInteger(num_element);
   const char* name = CHAR(PROTECT(Rf_asChar(field_name)));
   if (!strcmp("group", name) || !strcmp("query", name)) {
-    std::vector<int32_t> vec(len);
-    const int *field_data_ = INTEGER(field_data);
-#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (len >= 1024)
-    for (int i = 0; i < len; ++i) {
-      vec[i] = static_cast<int32_t>(field_data_[i]);
-    }
-    CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, vec.data(), len, C_API_DTYPE_INT32));
+    CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, INTEGER(field_data), len, C_API_DTYPE_INT32));
   } else if (!strcmp("init_score", name)) {
     CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, REAL(field_data), len, C_API_DTYPE_FLOAT64));
   } else {
-    std::vector<float> vec(len);
-    const double *field_data_ = REAL(field_data);
-#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (len >= 1024)
-    for (int i = 0; i < len; ++i) {
-      vec[i] = static_cast<float>(field_data_[i]);
-    }
-    CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, vec.data(), len, C_API_DTYPE_FLOAT32));
+    std::unique_ptr<float[]> vec(new float[len]);
+    std::copy(REAL(field_data), REAL(field_data) + len, vec.get());
+    CHECK_CALL(LGBM_DatasetSetField(R_ExternalPtrAddr(handle), name, vec.get(), len, C_API_DTYPE_FLOAT32));
   }
   UNPROTECT(1);
   return R_NilValue;
@@ -376,24 +368,18 @@ SEXP LGBM_DatasetGetField_R(SEXP handle,
     auto p_data = reinterpret_cast<const int32_t*>(res);
     // convert from boundaries to size
     int *field_data_ = INTEGER(field_data);
-#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (out_len >= 1024)
+#ifndef _MSC_VER
+#pragma omp simd
+#endif
     for (int i = 0; i < out_len - 1; ++i) {
       field_data_[i] = p_data[i + 1] - p_data[i];
     }
   } else if (!strcmp("init_score", name)) {
     auto p_data = reinterpret_cast<const double*>(res);
-    double *field_data_ = REAL(field_data);
-#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (out_len >= 1024)
-    for (int i = 0; i < out_len; ++i) {
-      field_data_[i] = p_data[i];
-    }
+    std::copy(p_data, p_data + out_len, REAL(field_data));
   } else {
     auto p_data = reinterpret_cast<const float*>(res);
-    double *field_data_ = REAL(field_data);
-#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (out_len >= 1024)
-    for (int i = 0; i < out_len; ++i) {
-      field_data_[i] = p_data[i];
-    }
+    std::copy(p_data, p_data + out_len, REAL(field_data));
   }
   UNPROTECT(1);
   return R_NilValue;
@@ -616,15 +602,10 @@ SEXP LGBM_BoosterUpdateOneIterCustom_R(SEXP handle,
   _AssertBoosterHandleNotNull(handle);
   int is_finished = 0;
   int int_len = Rf_asInteger(len);
-  std::vector<float> tgrad(int_len), thess(int_len);
-  const double *grad_ = REAL(grad);
-  const double *hess_ = REAL(hess);
-#pragma omp parallel for num_threads(OMP_NUM_THREADS()) schedule(static, 512) if (int_len >= 1024)
-  for (int j = 0; j < int_len; ++j) {
-    tgrad[j] = static_cast<float>(grad_[j]);
-    thess[j] = static_cast<float>(hess_[j]);
-  }
-  CHECK_CALL(LGBM_BoosterUpdateOneIterCustom(R_ExternalPtrAddr(handle), tgrad.data(), thess.data(), &is_finished));
+  std::unique_ptr<float[]> tgrad(new float[int_len]), thess(new float[int_len]);
+  std::copy(REAL(grad), REAL(grad) + int_len, tgrad.get());
+  std::copy(REAL(hess), REAL(hess) + int_len, thess.get());
+  CHECK_CALL(LGBM_BoosterUpdateOneIterCustom(R_ExternalPtrAddr(handle), tgrad.get(), thess.get(), &is_finished));
   return R_NilValue;
   R_API_END();
 }

README.md

@@ -63,6 +63,8 @@ External (Unofficial) Repositories
 Projects listed here offer alternative ways to use LightGBM.
 They are not maintained or officially endorsed by the `LightGBM` development team.
 
+LightGBMLSS (An extension of LightGBM to probabilistic modelling from which prediction intervals and quantiles can be derived): https://github.com/StatMixedML/LightGBMLSS
+
 FLAML (AutoML library for hyperparameter optimization): https://github.com/microsoft/FLAML
 
 Optuna (hyperparameter optimization framework): https://github.com/optuna/optuna

python-package/lightgbm/basic.py

@@ -786,7 +786,7 @@ def _data_from_pandas(
     feature_name: _LGBM_FeatureNameConfiguration,
     categorical_feature: _LGBM_CategoricalFeatureConfiguration,
     pandas_categorical: Optional[List[List]]
-) -> Tuple[np.ndarray, List[str], List[str], List[List]]:
+) -> Tuple[np.ndarray, List[str], Union[List[str], List[int]], List[List]]:
     if len(data.shape) != 2 or data.shape[0] < 1:
         raise ValueError('Input data must be 2 dimensional and non empty.')
 
@@ -800,7 +800,7 @@ def _data_from_pandas(
 
     # determine categorical features
     cat_cols = [col for col, dtype in zip(data.columns, data.dtypes) if isinstance(dtype, pd_CategoricalDtype)]
-    cat_cols_not_ordered = [col for col in cat_cols if not data[col].cat.ordered]
+    cat_cols_not_ordered: List[str] = [col for col in cat_cols if not data[col].cat.ordered]
     if pandas_categorical is None:  # train dataset
         pandas_categorical = [list(data[col].cat.categories) for col in cat_cols]
     else:
@@ -811,10 +811,10 @@ def _data_from_pandas(
                 data[col] = data[col].cat.set_categories(category)
     if len(cat_cols):  # cat_cols is list
         data[cat_cols] = data[cat_cols].apply(lambda x: x.cat.codes).replace({-1: np.nan})
-    if categorical_feature == 'auto':  # use cat cols from DataFrame
+
+    # use cat cols from DataFrame
+    if categorical_feature == 'auto':
         categorical_feature = cat_cols_not_ordered
-    else:  # use cat cols specified by user
-        categorical_feature = list(categorical_feature)  # type: ignore[assignment]
 
     df_dtypes = [dtype.type for dtype in data.dtypes]
     # so that the target dtype considers floats