diff --git a/R-package/configure b/R-package/configure index cf1bc3644417..f12a9a6387b8 100755 --- a/R-package/configure +++ b/R-package/configure @@ -1699,6 +1699,12 @@ CXX=`"${R_HOME}/bin/R" CMD config CXX11` # LightGBM-specific flags LGB_CPPFLAGS="" +######### +# Eigen # +######### + +LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY" + ############### # MM_PREFETCH # ############### diff --git a/R-package/configure.ac b/R-package/configure.ac index 6887fb279ee7..afcde3f2f838 100644 --- a/R-package/configure.ac +++ b/R-package/configure.ac @@ -26,6 +26,12 @@ CXX=`"${R_HOME}/bin/R" CMD config CXX11` # LightGBM-specific flags LGB_CPPFLAGS="" +######### +# Eigen # +######### + +LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY" + ############### # MM_PREFETCH # ############### diff --git a/R-package/configure.win b/R-package/configure.win index eb0727d49130..b6f95aaccb24 100755 --- a/R-package/configure.win +++ b/R-package/configure.win @@ -12,6 +12,12 @@ CC=`"${R_EXE}" CMD config CC` # LightGBM-specific flags LGB_CPPFLAGS="" +######### +# Eigen # +######### + +LGB_CPPFLAGS="${LGB_CPPFLAGS} -DEIGEN_MPL2_ONLY" + ############### # MM_PREFETCH # ############### diff --git a/R-package/tests/testthat/test_basic.R b/R-package/tests/testthat/test_basic.R index d2c20debdc48..68b9077c18b8 100644 --- a/R-package/tests/testthat/test_basic.R +++ b/R-package/tests/testthat/test_basic.R @@ -345,6 +345,45 @@ test_that("lightgbm.cv() gives the correct best_score and best_iter for a metric expect_identical(cv_bst$best_score, auc_scores[which.max(auc_scores)]) }) +test_that("lgb.cv() fit on linearly-relatead data improves when using linear learners", { + set.seed(708L) + .new_dataset <- function() { + X <- matrix(rnorm(1000L), ncol = 1L) + return(lgb.Dataset( + data = X + , label = 2L * X + runif(nrow(X), 0L, 0.1) + )) + } + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + ) + + dtrain <- .new_dataset() + cv_bst <- lgb.cv( + data = dtrain + , nrounds = 10L + , params = params + , nfold = 5L + ) + expect_is(cv_bst, "lgb.CVBooster") + + dtrain <- .new_dataset() + cv_bst_linear <- lgb.cv( + data = dtrain + , nrounds = 10L + , params = modifyList(params, list(linear_tree = TRUE)) + , nfold = 5L + ) + expect_is(cv_bst_linear, "lgb.CVBooster") + + expect_true(cv_bst_linear$best_score < cv_bst$best_score) +}) + context("lgb.train()") test_that("lgb.train() works as expected with multiple eval metrics", { @@ -1631,6 +1670,247 @@ test_that("early stopping works with lgb.cv()", { ) }) +context("linear learner") + +test_that("lgb.train() fit on linearly-relatead data improves when using linear learners", { + set.seed(708L) + .new_dataset <- function() { + X <- matrix(rnorm(100L), ncol = 1L) + return(lgb.Dataset( + data = X + , label = 2L * X + runif(nrow(X), 0L, 0.1) + )) + } + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + ) + + dtrain <- .new_dataset() + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + , valids = list("train" = dtrain) + ) + expect_true(lgb.is.Booster(bst)) + + dtrain <- .new_dataset() + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = modifyList(params, list(linear_tree = TRUE)) + , valids = list("train" = dtrain) + ) + expect_true(lgb.is.Booster(bst_linear)) + + bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] + bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] + expect_true(bst_lin_last_mse < bst_last_mse) +}) + + +test_that("lgb.train() w/ linear learner fails already-constructed dataset with linear=false", { + testthat::skip("Skipping this test because it causes issues for valgrind") + set.seed(708L) + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + ) + + dtrain <- lgb.Dataset( + data = matrix(rnorm(100L), ncol = 1L) + , label = rnorm(100L) + ) + dtrain$construct() + expect_error({ + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = modifyList(params, list(linear_tree = TRUE)) + , valids = list("train" = dtrain) + ) + }, regexp = "Cannot change linear_tree after constructed Dataset handle") +}) + +test_that("lgb.train() works with linear learners even if Dataset has missing values", { + set.seed(708L) + .new_dataset <- function() { + values <- rnorm(100L) + values[sample(seq_len(length(values)), size = 10L)] <- NA_real_ + X <- matrix( + data = sample(values, size = 100L) + , ncol = 1L + ) + return(lgb.Dataset( + data = X + , label = 2L * X + runif(nrow(X), 0L, 0.1) + )) + } + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + ) + + dtrain <- .new_dataset() + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + , valids = list("train" = dtrain) + ) + expect_true(lgb.is.Booster(bst)) + + dtrain <- .new_dataset() + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = modifyList(params, list(linear_tree = TRUE)) + , valids = list("train" = dtrain) + ) + expect_true(lgb.is.Booster(bst_linear)) + + bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] + bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] + expect_true(bst_lin_last_mse < bst_last_mse) +}) + +test_that("lgb.train() works with linear learners, bagging, and a Dataset that has missing values", { + set.seed(708L) + .new_dataset <- function() { + values <- rnorm(100L) + values[sample(seq_len(length(values)), size = 10L)] <- NA_real_ + X <- matrix( + data = sample(values, size = 100L) + , ncol = 1L + ) + return(lgb.Dataset( + data = X + , label = 2L * X + runif(nrow(X), 0L, 0.1) + )) + } + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + , bagging_freq = 1L + , subsample = 0.8 + ) + + dtrain <- .new_dataset() + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + , valids = list("train" = dtrain) + ) + expect_true(lgb.is.Booster(bst)) + + dtrain <- .new_dataset() + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = modifyList(params, list(linear_tree = TRUE)) + , valids = list("train" = dtrain) + ) + expect_true(lgb.is.Booster(bst_linear)) + + bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] + bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] + expect_true(bst_lin_last_mse < bst_last_mse) +}) + +test_that("lgb.train() works with linear learners and data where a feature has only 1 non-NA value", { + set.seed(708L) + .new_dataset <- function() { + values <- rep(NA_real_, 100L) + values[18L] <- rnorm(1L) + X <- matrix( + data = values + , ncol = 1L + ) + return(lgb.Dataset( + data = X + , label = 2L * X + runif(nrow(X), 0L, 0.1) + )) + } + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + ) + + dtrain <- .new_dataset() + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = modifyList(params, list(linear_tree = TRUE)) + , valids = list("train" = dtrain) + ) + expect_true(lgb.is.Booster(bst_linear)) +}) + +test_that("lgb.train() works with linear learners when Dataset has categorical features", { + set.seed(708L) + .new_dataset <- function() { + X <- matrix(numeric(200L), nrow = 100L, ncol = 2L) + X[, 1L] <- rnorm(100L) + X[, 2L] <- sample(seq_len(4L), size = 100L, replace = TRUE) + return(lgb.Dataset( + data = X + , label = 2L * X[, 1L] + runif(nrow(X), 0L, 0.1) + )) + } + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + , categorical_featurs = 1L + ) + + dtrain <- .new_dataset() + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + , valids = list("train" = dtrain) + ) + expect_true(lgb.is.Booster(bst)) + + dtrain <- .new_dataset() + bst_linear <- lgb.train( + data = dtrain + , nrounds = 10L + , params = modifyList(params, list(linear_tree = TRUE)) + , valids = list("train" = dtrain) + ) + expect_true(lgb.is.Booster(bst_linear)) + + bst_last_mse <- bst$record_evals[["train"]][["l2"]][["eval"]][[10L]] + bst_lin_last_mse <- bst_linear$record_evals[["train"]][["l2"]][["eval"]][[10L]] + expect_true(bst_lin_last_mse < bst_last_mse) +}) + context("interaction constraints") test_that("lgb.train() throws an informative error if interaction_constraints is not a list", { diff --git a/R-package/tests/testthat/test_lgb.Booster.R b/R-package/tests/testthat/test_lgb.Booster.R index 02923f776fc2..5d232ba5e508 100644 --- a/R-package/tests/testthat/test_lgb.Booster.R +++ b/R-package/tests/testthat/test_lgb.Booster.R @@ -135,7 +135,7 @@ test_that("lgb.load() gives the expected error messages given different incorrec }) -test_that("Loading a Booster from a file works", { +test_that("Loading a Booster from a text file works", { set.seed(708L) data(agaricus.train, package = "lightgbm") data(agaricus.test, package = "lightgbm") @@ -168,6 +168,47 @@ test_that("Loading a Booster from a file works", { expect_identical(pred, pred2) }) +test_that("boosters with linear models at leaves can be written to text file and re-loaded successfully", { + X <- matrix(rnorm(100L), ncol = 1L) + labels <- 2L * X + runif(nrow(X), 0L, 0.1) + dtrain <- lgb.Dataset( + data = X + , label = labels + ) + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + ) + + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + , valids = list("train" = dtrain) + ) + expect_true(lgb.is.Booster(bst)) + + # save predictions, then write the model to a file and destroy it in R + preds <- predict(bst, X) + model_file <- tempfile(fileext = ".model") + lgb.save(bst, model_file) + bst$finalize() + expect_null(bst$.__enclos_env__$private$handle) + rm(bst) + + # load the booster and make predictions...should be the same + bst2 <- lgb.load( + filename = model_file + ) + pred2 <- predict(bst2, X) + expect_identical(preds, pred2) +}) + + test_that("Loading a Booster from a string works", { set.seed(708L) data(agaricus.train, package = "lightgbm") @@ -730,3 +771,41 @@ test_that("params (including dataset params) should be stored in .rds file for B ) ) }) + +test_that("boosters with linear models at leaves can be written to RDS and re-loaded successfully", { + X <- matrix(rnorm(100L), ncol = 1L) + labels <- 2L * X + runif(nrow(X), 0L, 0.1) + dtrain <- lgb.Dataset( + data = X + , label = labels + ) + + params <- list( + objective = "regression" + , verbose = -1L + , metric = "mse" + , seed = 0L + , num_leaves = 2L + ) + + bst <- lgb.train( + data = dtrain + , nrounds = 10L + , params = params + , valids = list("train" = dtrain) + ) + expect_true(lgb.is.Booster(bst)) + + # save predictions, then write the model to a file and destroy it in R + preds <- predict(bst, X) + model_file <- tempfile(fileext = ".rds") + saveRDS.lgb.Booster(bst, file = model_file) + bst$finalize() + expect_null(bst$.__enclos_env__$private$handle) + rm(bst) + + # load the booster and make predictions...should be the same + bst2 <- readRDS.lgb.Booster(file = model_file) + pred2 <- predict(bst2, X) + expect_identical(preds, pred2) +}) diff --git a/build-cran-package.sh b/build-cran-package.sh index a18c9456864b..c4c40cba40a5 100755 --- a/build-cran-package.sh +++ b/build-cran-package.sh @@ -37,6 +37,27 @@ cp \ external_libs/fmt/include/fmt/*.h \ ${TEMP_R_DIR}/src/include/LightGBM/fmt/ +# including only specific files from Eigen, to keep the R package +# small and avoid redistributing code with licenses incompatible with +# LightGBM's license +EIGEN_R_DIR=${TEMP_R_DIR}/src/include/Eigen +mkdir -p ${EIGEN_R_DIR} + +modules="Cholesky Core Dense Eigenvalues Geometry Householder Jacobi LU QR SVD" +for eigen_module in ${modules}; do + cp eigen/Eigen/${eigen_module} ${EIGEN_R_DIR}/${eigen_module} + if [ ${eigen_module} != "Dense" ]; then + mkdir -p ${EIGEN_R_DIR}/src/${eigen_module}/ + cp -R eigen/Eigen/src/${eigen_module}/* ${EIGEN_R_DIR}/src/${eigen_module}/ + fi +done + +mkdir -p ${EIGEN_R_DIR}/src/misc +cp -R eigen/Eigen/src/misc/* ${EIGEN_R_DIR}/src/misc/ + +mkdir -p ${EIGEN_R_DIR}/src/plugins +cp -R eigen/Eigen/src/plugins/* ${EIGEN_R_DIR}/src/plugins/ + cd ${TEMP_R_DIR} # Remove files not needed for CRAN @@ -69,6 +90,9 @@ cd ${TEMP_R_DIR} for file in $(find . -name '*.h' -o -name '*.hpp' -o -name '*.cpp'); do sed \ -i.bak \ + -e 's/^.*#pragma clang diagnostic.*$//' \ + -e 's/^.*#pragma diag_suppress.*$//' \ + -e 's/^.*#pragma GCC diagnostic.*$//' \ -e 's/^.*#pragma region.*$//' \ -e 's/^.*#pragma endregion.*$//' \ -e 's/^.*#pragma warning.*$//' \ diff --git a/build_r.R b/build_r.R index fbafadfcc319..f05239cdacd3 100644 --- a/build_r.R +++ b/build_r.R @@ -156,6 +156,72 @@ if (USING_GPU) { .handle_result(result) } +EIGEN_R_DIR <- file.path(TEMP_SOURCE_DIR, "include", "Eigen") +dir.create(EIGEN_R_DIR) + +eigen_modules <- c( + "Cholesky" + , "Core" + , "Dense" + , "Eigenvalues" + , "Geometry" + , "Householder" + , "Jacobi" + , "LU" + , "QR" + , "SVD" +) +for (eigen_module in eigen_modules) { + result <- file.copy( + from = file.path("eigen", "Eigen", eigen_module) + , to = EIGEN_R_DIR + , recursive = FALSE + , overwrite = TRUE + ) + .handle_result(result) +} + +dir.create(file.path(EIGEN_R_DIR, "src")) + +for (eigen_module in c(eigen_modules, "misc", "plugins")) { + if (eigen_module == "Dense") { + next + } + module_dir <- file.path(EIGEN_R_DIR, "src", eigen_module) + dir.create(module_dir, recursive = TRUE) + result <- file.copy( + from = sprintf("%s/", file.path("eigen", "Eigen", "src", eigen_module)) + , to = sprintf("%s/", file.path(EIGEN_R_DIR, "src")) + , recursive = TRUE + , overwrite = TRUE + ) + .handle_result(result) +} + +.replace_pragmas <- function(filepath) { + pragma_patterns <- c( + "^.*#pragma clang diagnostic.*$" + , "^.*#pragma diag_suppress.*$" + , "^.*#pragma GCC diagnostic.*$" + , "^.*#pragma region.*$" + , "^.*#pragma endregion.*$" + , "^.*#pragma warning.*$" + ) + content <- readLines(filepath) + for (pragma_pattern in pragma_patterns) { + content <- content[!grepl(pragma_pattern, content)] + } + writeLines(content, filepath) +} + +# remove pragmas that suppress warnings, to appease R CMD check +.replace_pragmas( + file.path(EIGEN_R_DIR, "src", "Core", "arch", "SSE", "Complex.h") +) +.replace_pragmas( + file.path(EIGEN_R_DIR, "src", "Core", "util", "DisableStupidWarnings.h") +) + result <- file.copy( from = "CMakeLists.txt" , to = file.path(TEMP_R_DIR, "inst", "bin/") diff --git a/docs/Parameters.rst b/docs/Parameters.rst index 235dbb9040a2..019d714eb536 100644 --- a/docs/Parameters.rst +++ b/docs/Parameters.rst @@ -127,14 +127,12 @@ Core Parameters - categorical features are used for splits as normal but are not used in the linear models - - missing values must be encoded as ``np.nan`` (Python) or ``NA`` (CLI), not ``0`` + - missing values should not be encoded as ``0``. Use ``np.nan`` for Python, ``NA`` for the CLI, and ``NA``, ``NA_real_``, or ``NA_integer_`` for R - it is recommended to rescale data before training so that features have similar mean and standard deviation - **Note**: only works with CPU and ``serial`` tree learner - - **Note**: not yet supported in R-package - - **Note**: ``regression_l1`` objective is not supported with linear tree boosting - **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM diff --git a/include/LightGBM/config.h b/include/LightGBM/config.h index b8dd3817407c..189e4dedff3c 100644 --- a/include/LightGBM/config.h +++ b/include/LightGBM/config.h @@ -152,10 +152,9 @@ struct Config { // descl2 = tree splits are chosen in the usual way, but the model at each leaf is linear instead of constant // descl2 = the linear model at each leaf includes all the numerical features in that leaf's branch // descl2 = categorical features are used for splits as normal but are not used in the linear models - // descl2 = missing values must be encoded as ``np.nan`` (Python) or ``NA`` (CLI), not ``0`` + // descl2 = missing values should not be encoded as ``0``. Use ``np.nan`` for Python, ``NA`` for the CLI, and ``NA``, ``NA_real_``, or ``NA_integer_`` for R // descl2 = it is recommended to rescale data before training so that features have similar mean and standard deviation // descl2 = **Note**: only works with CPU and ``serial`` tree learner - // descl2 = **Note**: not yet supported in R-package // descl2 = **Note**: ``regression_l1`` objective is not supported with linear tree boosting // descl2 = **Note**: setting ``linear_tree=true`` significantly increases the memory use of LightGBM bool linear_tree = false; diff --git a/src/treelearner/linear_tree_learner.cpp b/src/treelearner/linear_tree_learner.cpp index 5855812f741e..7f5f3573689a 100644 --- a/src/treelearner/linear_tree_learner.cpp +++ b/src/treelearner/linear_tree_learner.cpp @@ -4,11 +4,9 @@ */ #include "linear_tree_learner.h" -#include - -#ifndef LGB_R_BUILD #include -#endif // !LGB_R_BUILD + +#include namespace LightGBM { @@ -170,12 +168,7 @@ void LinearTreeLearner::GetLeafMap(Tree* tree) const { } } -#ifdef LGB_R_BUILD -template -void LinearTreeLearner::CalculateLinear(Tree* tree, bool is_refit, const score_t* gradients, const score_t* hessians, bool is_first_tree) const { - Log::Fatal("Linear tree learner does not work with R package."); -} -#else + template void LinearTreeLearner::CalculateLinear(Tree* tree, bool is_refit, const score_t* gradients, const score_t* hessians, bool is_first_tree) const { tree->SetIsLinear(true); @@ -385,5 +378,4 @@ void LinearTreeLearner::CalculateLinear(Tree* tree, bool is_refit, const score_t } } } -#endif // LGB_R_BUILD } // namespace LightGBM diff --git a/src/treelearner/tree_learner.cpp b/src/treelearner/tree_learner.cpp index a386a2129bcc..ed13f646c388 100644 --- a/src/treelearner/tree_learner.cpp +++ b/src/treelearner/tree_learner.cpp @@ -17,9 +17,6 @@ TreeLearner* TreeLearner::CreateTreeLearner(const std::string& learner_type, con if (device_type == std::string("cpu")) { if (learner_type == std::string("serial")) { if (config->linear_tree) { -#ifdef LGB_R_BUILD - Log::Fatal("Linear tree learner does not work with R package."); -#endif // LGB_R_BUILD return new LinearTreeLearner(config); } else { return new SerialTreeLearner(config);