diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 00000000..663ccb93 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,16 @@ +* text=auto + +*.c text eol=lf +*.h text eol=lf +*.cc text eol=lf +*.cuh text eol=lf +*.cu text eol=lf +*.py text eol=lf +*.txt text eol=lf +*.R text eol=lf + +*.sh text eol=lf +*.ac text eol=lf + +*.md text eol=lf +*.csv text eol=lf \ No newline at end of file diff --git a/.github/workflows/cpp-test.yml b/.github/workflows/cpp-test.yml index 84648b5d..5f129e0a 100644 --- a/.github/workflows/cpp-test.yml +++ b/.github/workflows/cpp-test.yml @@ -56,6 +56,12 @@ jobs: shell: bash run: | echo "build-output-dir=${{ github.workspace }}/build" >> "$GITHUB_OUTPUT" + + - name: Set up dependencies (linux clang) + # Set up openMP on ubuntu-latest with clang compiler toolset (doesn't ship with the compiler suite like GCC and MSVC) + if: matrix.os == 'ubuntu-latest' && matrix.cpp_compiler == 'clang++' + run: | + sudo apt-get update && sudo apt-get install -y libomp-dev - name: Configure CMake # Configure CMake in a 'build' subdirectory. `CMAKE_BUILD_TYPE` is only required if you are using a single-configuration generator such as make. @@ -69,6 +75,7 @@ jobs: -DUSE_SANITIZER=OFF -DBUILD_TEST=ON -DBUILD_DEBUG_TARGETS=OFF + -DUSE_OPENMP=ON -S ${{ github.workspace }} - name: Build diff --git a/.github/workflows/pypi-wheels.yml b/.github/workflows/pypi-wheels.yml index 29e15aa1..1772f6fd 100644 --- a/.github/workflows/pypi-wheels.yml +++ b/.github/workflows/pypi-wheels.yml @@ -21,26 +21,37 @@ jobs: include: - os: ubuntu-latest cibw_archs: "x86_64" + macos_deployment_target: "10.13" # Unused, just setting the variable as a placeholder - os: ubuntu-24.04-arm cibw_archs: "aarch64" + macos_deployment_target: "10.13" # Unused, just setting the variable as a placeholder - os: windows-latest cibw_archs: "auto64" + macos_deployment_target: "10.13" # Unused, just setting the variable as a placeholder - os: macos-13 cibw_archs: "x86_64" + macos_deployment_target: "13.0" - os: macos-14 cibw_archs: "arm64" + macos_deployment_target: "14.0" steps: - uses: actions/checkout@v4 with: submodules: 'recursive' + + - name: Set up openmp (macos) + # Set up openMP on MacOS since it doesn't ship with the apple clang compiler suite + if: matrix.os == 'macos-13' || matrix.os == 'macos-14' + run: | + brew install libomp - name: Build wheels uses: pypa/cibuildwheel@v2.23.2 env: CIBW_SKIP: "pp* *-musllinux_* *-win32" CIBW_ARCHS: ${{ matrix.cibw_archs }} - MACOSX_DEPLOYMENT_TARGET: "10.13" + MACOSX_DEPLOYMENT_TARGET: ${{ matrix.macos_deployment_target }} - uses: actions/upload-artifact@v4 with: diff --git a/.github/workflows/python-test.yml b/.github/workflows/python-test.yml index 1b665fb6..719160a6 100644 --- a/.github/workflows/python-test.yml +++ b/.github/workflows/python-test.yml @@ -30,6 +30,12 @@ jobs: with: python-version: "3.10" cache: "pip" + + - name: Set up openmp (macos) + # Set up openMP on MacOS since it doesn't ship with the apple clang compiler suite + if: matrix.os == 'macos-latest' + run: | + brew install libomp - name: Install Package with Relevant Dependencies run: | diff --git a/.github/workflows/r-test.yml b/.github/workflows/r-test.yml index 0a8464bc..f00938a0 100644 --- a/.github/workflows/r-test.yml +++ b/.github/workflows/r-test.yml @@ -22,6 +22,11 @@ jobs: os: [ubuntu-latest, windows-latest, macos-latest] steps: + - name: Prevent conversion of line endings on Windows + if: startsWith(matrix.os, 'windows') + shell: pwsh + run: git config --global core.autocrlf false + - uses: actions/checkout@v4 with: submodules: 'recursive' diff --git a/.github/workflows/regression-test.yml b/.github/workflows/regression-test.yml new file mode 100644 index 00000000..cdc00621 --- /dev/null +++ b/.github/workflows/regression-test.yml @@ -0,0 +1,59 @@ +on: + workflow_dispatch: + +name: Running stochtree on benchmark datasets + +jobs: + stochtree_r: + name: stochtree-r-bart-regression-test + runs-on: ubuntu-latest + + steps: + - name: Checkout stochtree repo + uses: actions/checkout@v4 + with: + submodules: 'recursive' + + - name: Setup pandoc + uses: r-lib/actions/setup-pandoc@v2 + + - name: Setup R + uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - name: Create a properly formatted version of the stochtree R package in a subfolder + run: | + Rscript cran-bootstrap.R 0 0 1 + + - name: Setup R dependencies + uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::testthat, any::decor, local::stochtree_cran + + - name: Create output directory for BART regression test results + run: | + mkdir -p tools/regression/bart/stochtree_bart_r_results + mkdir -p tools/regression/bcf/stochtree_bcf_r_results + + - name: Run the BART regression test benchmark suite + run: | + Rscript tools/regression/bart/regression_test_dispatch_bart.R + Rscript tools/regression/bcf/regression_test_dispatch_bcf.R + + - name: Collate and analyze regression test results + run: | + Rscript tools/regression/bart/regression_test_analysis_bart.R + Rscript tools/regression/bcf/regression_test_analysis_bcf.R + + - name: Store BART benchmark test results as an artifact of the run + uses: actions/upload-artifact@v4 + with: + name: stochtree-r-bart-summary + path: tools/regression/bart/stochtree_bart_r_results/stochtree_bart_r_summary.csv + + - name: Store BCF benchmark test results as an artifact of the run + uses: actions/upload-artifact@v4 + with: + name: stochtree-r-bcf-summary + path: tools/regression/bcf/stochtree_bcf_r_results/stochtree_bcf_r_summary.csv diff --git a/.gitignore b/.gitignore index 3fa818c1..e305023a 100644 --- a/.gitignore +++ b/.gitignore @@ -70,6 +70,11 @@ po/*~ # RStudio Connect folder rsconnect/ +# Configuration files generated by R build +config.status +config.log +src/Makevars + ## Python gitignore # Byte-compiled / optimized / DLL files diff --git a/CMakeLists.txt b/CMakeLists.txt index 3c8f1796..1d1efe55 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,6 +1,8 @@ # Build options -option(USE_DEBUG "Set to ON for Debug mode" OFF) +option(USE_DEBUG "Build with debug symbols and without optimization" OFF) option(USE_SANITIZER "Use santizer flags" OFF) +option(USE_OPENMP "Use openMP" ON) +option(USE_HOMEBREW_FALLBACK "(macOS-only) also look in 'brew --prefix' for libraries (e.g. OpenMP)" ON) option(BUILD_TEST "Build C++ tests with Google Test" OFF) option(BUILD_DEBUG_TARGETS "Build Standalone C++ Programs for Debugging" ON) option(BUILD_PYTHON "Build Shared Library for Python Package" OFF) @@ -9,8 +11,8 @@ option(BUILD_PYTHON "Build Shared Library for Python Package" OFF) set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_STANDARD_REQUIRED ON) -# Default to CMake 3.16 -cmake_minimum_required(VERSION 3.16) +# Default to CMake 3.20 +cmake_minimum_required(VERSION 3.20) # Define the project project(stochtree LANGUAGES C CXX) @@ -34,6 +36,13 @@ if(USE_DEBUG) add_definitions(-DDEBUG) endif() +# Linker flags (empty by default, updated if using openmp) +set( + STOCHTREE_LINK_FLAGS + "" +) + +# Unix / MinGW compiler flags if(UNIX OR MINGW OR CYGWIN) set( CMAKE_CXX_FLAGS @@ -42,11 +51,12 @@ if(UNIX OR MINGW OR CYGWIN) if(USE_DEBUG) set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O0") else() - set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -O3") endif() set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unknown-pragmas -Wno-unused-private-field") endif() +# MSVC compiler flags if(MSVC) set( variables @@ -72,6 +82,33 @@ else() endif() endif() +# OpenMP +if(USE_OPENMP) + add_definitions(-DSTOCHTREE_OPENMP_AVAILABLE) + if(APPLE) + find_package(OpenMP) + if(NOT OpenMP_FOUND) + if(USE_HOMEBREW_FALLBACK) + execute_process(COMMAND brew --prefix libomp + OUTPUT_VARIABLE HOMEBREW_LIBOMP_PREFIX + OUTPUT_STRIP_TRAILING_WHITESPACE) + set(OpenMP_C_FLAGS "-Xclang -fopenmp -I${HOMEBREW_LIBOMP_PREFIX}/include") + set(OpenMP_CXX_FLAGS "-Xclang -fopenmp -I${HOMEBREW_LIBOMP_PREFIX}/include") + set(OpenMP_C_INCLUDE_DIR "") + set(OpenMP_CXX_INCLUDE_DIR "") + set(OpenMP_C_LIB_NAMES libomp) + set(OpenMP_CXX_LIB_NAMES libomp) + set(OpenMP_libomp_LIBRARY ${HOMEBREW_LIBOMP_PREFIX}/lib/libomp.dylib) + endif() + find_package(OpenMP REQUIRED) + endif() + else() + find_package(OpenMP REQUIRED) + endif() + # Update flags with openmp + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}") +endif() + # Header file directory set(StochTree_HEADER_DIR ${PROJECT_SOURCE_DIR}/include) @@ -80,6 +117,8 @@ set(BOOSTMATH_HEADER_DIR ${PROJECT_SOURCE_DIR}/deps/boost_math/include) # Eigen header file directory set(EIGEN_HEADER_DIR ${PROJECT_SOURCE_DIR}/deps/eigen) +add_definitions(-DEIGEN_MPL2_ONLY) +add_definitions(-DEIGEN_DONT_PARALLELIZE) # fast_double_parser header file directory set(FAST_DOUBLE_PARSER_HEADER_DIR ${PROJECT_SOURCE_DIR}/deps/fast_double_parser/include) @@ -109,10 +148,11 @@ file( add_library(stochtree_objs OBJECT ${SOURCES}) # Include the headers in the source library -target_include_directories(stochtree_objs PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR}) - -if(APPLE) - set(CMAKE_SHARED_LIBRARY_SUFFIX ".so") +if(USE_OPENMP) + target_include_directories(stochtree_objs PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR} ${OpenMP_CXX_INCLUDE_DIR}) + target_link_libraries(stochtree_objs PRIVATE ${OpenMP_libomp_LIBRARY}) +else() + target_include_directories(stochtree_objs PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR}) endif() # Python shared library @@ -122,8 +162,13 @@ if (BUILD_PYTHON) pybind11_add_module(stochtree_cpp src/py_stochtree.cpp) # Link to C++ source and headers - target_include_directories(stochtree_cpp PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR}) - target_link_libraries(stochtree_cpp PRIVATE stochtree_objs) + if(USE_OPENMP) + target_include_directories(stochtree_cpp PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR} ${OpenMP_CXX_INCLUDE_DIR}) + target_link_libraries(stochtree_cpp PRIVATE stochtree_objs ${OpenMP_libomp_LIBRARY}) + else() + target_include_directories(stochtree_cpp PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR}) + target_link_libraries(stochtree_cpp PRIVATE stochtree_objs) + endif() # EXAMPLE_VERSION_INFO is defined by setup.py and passed into the C++ code as a # define (VERSION_INFO) here. @@ -154,8 +199,13 @@ if(BUILD_TEST) file(GLOB CPP_TEST_SOURCES test/cpp/*.cpp) add_executable(teststochtree ${CPP_TEST_SOURCES}) set(STOCHTREE_TEST_HEADER_DIR ${PROJECT_SOURCE_DIR}/test/cpp) - target_include_directories(teststochtree PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${STOCHTREE_TEST_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR}) - target_link_libraries(teststochtree PRIVATE stochtree_objs GTest::gtest_main) + if(USE_OPENMP) + target_include_directories(teststochtree PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${STOCHTREE_TEST_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR} ${OpenMP_CXX_INCLUDE_DIR}) + target_link_libraries(teststochtree PRIVATE stochtree_objs GTest::gtest_main ${OpenMP_libomp_LIBRARY}) + else() + target_include_directories(teststochtree PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${STOCHTREE_TEST_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR}) + target_link_libraries(teststochtree PRIVATE stochtree_objs GTest::gtest_main) + endif() gtest_discover_tests(teststochtree) endif() @@ -164,7 +214,12 @@ if(BUILD_DEBUG_TARGETS) # Build test suite add_executable(debugstochtree debug/api_debug.cpp) set(StochTree_DEBUG_HEADER_DIR ${PROJECT_SOURCE_DIR}/debug) - target_include_directories(debugstochtree PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${StochTree_DEBUG_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR}) - target_link_libraries(debugstochtree PRIVATE stochtree_objs) + if(USE_OPENMP) + target_include_directories(debugstochtree PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${StochTree_DEBUG_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR} ${OpenMP_CXX_INCLUDE_DIR}) + target_link_libraries(debugstochtree PRIVATE stochtree_objs ${OpenMP_libomp_LIBRARY}) + else() + target_include_directories(debugstochtree PRIVATE ${StochTree_HEADER_DIR} ${BOOSTMATH_HEADER_DIR} ${EIGEN_HEADER_DIR} ${StochTree_DEBUG_HEADER_DIR} ${FAST_DOUBLE_PARSER_HEADER_DIR} ${FMT_HEADER_DIR}) + target_link_libraries(debugstochtree PRIVATE stochtree_objs) + endif() endif() diff --git a/R/bart.R b/R/bart.R index 691fda7a..2f334682 100644 --- a/R/bart.R +++ b/R/bart.R @@ -51,6 +51,7 @@ #' - `rfx_group_parameter_prior_cov` Prior covariance matrix for the random effects "group parameters." Default: `NULL`. Must be a square matrix whose dimension matches the number of random effects bases, or a scalar value that will be expanded to a diagonal matrix. #' - `rfx_variance_prior_shape` Shape parameter for the inverse gamma prior on the variance of the random effects "group parameter." Default: `1`. #' - `rfx_variance_prior_scale` Scale parameter for the inverse gamma prior on the variance of the random effects "group parameter." Default: `1`. +#' - `num_threads` Number of threads to use in the GFR and MCMC algorithms, as well as prediction. If OpenMP is not available on a user's setup, this will default to `1`, otherwise to the maximum number of available threads. #' #' @param mean_forest_params (Optional) A list of mean forest model parameters, each of which has a default value processed internally, so this argument list is optional. #' @@ -130,7 +131,8 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train rfx_working_parameter_prior_cov = NULL, rfx_group_parameter_prior_cov = NULL, rfx_variance_prior_shape = 1, - rfx_variance_prior_scale = 1 + rfx_variance_prior_scale = 1, + num_threads = -1 ) general_params_updated <- preprocessParams( general_params_default, general_params @@ -186,6 +188,7 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train rfx_group_parameter_prior_cov <- general_params_updated$rfx_group_parameter_prior_cov rfx_variance_prior_shape <- general_params_updated$rfx_variance_prior_shape rfx_variance_prior_scale <- general_params_updated$rfx_variance_prior_scale + num_threads <- general_params_updated$num_threads # 2. Mean forest parameters num_trees_mean <- mean_forest_params_updated$num_trees @@ -795,7 +798,8 @@ bart <- function(X_train, y_train, leaf_basis_train = NULL, rfx_group_ids_train forest_model_mean$sample_one_iteration( forest_dataset = forest_dataset_train, residual = outcome_train, forest_samples = forest_samples_mean, active_forest = active_forest_mean, rng = rng, forest_model_config = forest_model_config_mean, - global_model_config = global_model_config, keep_forest = keep_sample, gfr = TRUE + global_model_config = global_model_config, num_threads = num_threads, + keep_forest = keep_sample, gfr = TRUE ) # Cache train set predictions since they are already computed during sampling @@ -1272,15 +1276,23 @@ predict.bartmodel <- function(object, X, leaf_basis = NULL, rfx_group_ids = NULL result <- list() if ((object$model_params$has_rfx) || (object$model_params$include_mean_forest)) { result[["y_hat"]] = y_hat + } else { + result[["y_hat"]] <- NULL } if (object$model_params$include_mean_forest) { result[["mean_forest_predictions"]] = mean_forest_predictions + } else { + result[["mean_forest_predictions"]] <- NULL } if (object$model_params$has_rfx) { result[["rfx_predictions"]] = rfx_predictions + } else { + result[["rfx_predictions"]] <- NULL } if (object$model_params$include_variance_forest) { result[["variance_forest_predictions"]] = variance_forest_predictions + } else { + result[["variance_forest_predictions"]] <- NULL } return(result) } diff --git a/R/bcf.R b/R/bcf.R index 053f8b21..86aef8cd 100644 --- a/R/bcf.R +++ b/R/bcf.R @@ -53,6 +53,7 @@ #' - `rfx_group_parameter_prior_cov` Prior covariance matrix for the random effects "group parameters." Default: `NULL`. Must be a square matrix whose dimension matches the number of random effects bases, or a scalar value that will be expanded to a diagonal matrix. #' - `rfx_variance_prior_shape` Shape parameter for the inverse gamma prior on the variance of the random effects "group parameter." Default: `1`. #' - `rfx_variance_prior_scale` Scale parameter for the inverse gamma prior on the variance of the random effects "group parameter." Default: `1`. +#' - `num_threads` Number of threads to use in the GFR and MCMC algorithms, as well as prediction. If OpenMP is not available on a user's setup, this will default to `1`, otherwise to the maximum number of available threads. #' #' @param prognostic_forest_params (Optional) A list of prognostic forest model parameters, each of which has a default value processed internally, so this argument list is optional. #' @@ -174,7 +175,8 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id rfx_working_parameter_prior_cov = NULL, rfx_group_parameter_prior_cov = NULL, rfx_variance_prior_shape = 1, - rfx_variance_prior_scale = 1 + rfx_variance_prior_scale = 1, + num_threads = -1 ) general_params_updated <- preprocessParams( general_params_default, general_params @@ -248,6 +250,7 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id rfx_group_parameter_prior_cov <- general_params_updated$rfx_group_parameter_prior_cov rfx_variance_prior_shape <- general_params_updated$rfx_variance_prior_shape rfx_variance_prior_scale <- general_params_updated$rfx_variance_prior_scale + num_threads <- general_params_updated$num_threads # 2. Mu forest parameters num_trees_mu <- prognostic_forest_params_updated$num_trees @@ -1041,7 +1044,7 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id forest_model_mu$sample_one_iteration( forest_dataset = forest_dataset_train, residual = outcome_train, forest_samples = forest_samples_mu, active_forest = active_forest_mu, rng = rng, forest_model_config = forest_model_config_mu, - global_model_config = global_model_config, keep_forest = keep_sample, gfr = TRUE + global_model_config = global_model_config, num_threads = num_threads, keep_forest = keep_sample, gfr = TRUE ) # Cache train set predictions since they are already computed during sampling @@ -1065,7 +1068,7 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id forest_model_tau$sample_one_iteration( forest_dataset = forest_dataset_train, residual = outcome_train, forest_samples = forest_samples_tau, active_forest = active_forest_tau, rng = rng, forest_model_config = forest_model_config_tau, - global_model_config = global_model_config, keep_forest = keep_sample, gfr = TRUE + global_model_config = global_model_config, num_threads = num_threads, keep_forest = keep_sample, gfr = TRUE ) # Cannot cache train set predictions for tau because the cached predictions in the @@ -1114,7 +1117,7 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id forest_model_variance$sample_one_iteration( forest_dataset = forest_dataset_train, residual = outcome_train, forest_samples = forest_samples_variance, active_forest = active_forest_variance, rng = rng, forest_model_config = forest_model_config_variance, - global_model_config = global_model_config, keep_forest = keep_sample, gfr = TRUE + global_model_config = global_model_config, num_threads = num_threads, keep_forest = keep_sample, gfr = TRUE ) # Cache train set predictions since they are already computed during sampling @@ -1321,7 +1324,7 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id forest_model_mu$sample_one_iteration( forest_dataset = forest_dataset_train, residual = outcome_train, forest_samples = forest_samples_mu, active_forest = active_forest_mu, rng = rng, forest_model_config = forest_model_config_mu, - global_model_config = global_model_config, keep_forest = keep_sample, gfr = FALSE + global_model_config = global_model_config, num_threads = num_threads, keep_forest = keep_sample, gfr = FALSE ) # Cache train set predictions since they are already computed during sampling @@ -1345,7 +1348,7 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id forest_model_tau$sample_one_iteration( forest_dataset = forest_dataset_train, residual = outcome_train, forest_samples = forest_samples_tau, active_forest = active_forest_tau, rng = rng, forest_model_config = forest_model_config_tau, - global_model_config = global_model_config, keep_forest = keep_sample, gfr = FALSE + global_model_config = global_model_config, num_threads = num_threads, keep_forest = keep_sample, gfr = FALSE ) # Cannot cache train set predictions for tau because the cached predictions in the @@ -1394,7 +1397,7 @@ bcf <- function(X_train, Z_train, y_train, propensity_train = NULL, rfx_group_id forest_model_variance$sample_one_iteration( forest_dataset = forest_dataset_train, residual = outcome_train, forest_samples = forest_samples_variance, active_forest = active_forest_variance, rng = rng, forest_model_config = forest_model_config_variance, - global_model_config = global_model_config, keep_forest = keep_sample, gfr = FALSE + global_model_config = global_model_config, num_threads = num_threads, keep_forest = keep_sample, gfr = FALSE ) # Cache train set predictions since they are already computed during sampling @@ -1797,10 +1800,14 @@ predict.bcfmodel <- function(object, X, Z, propensity = NULL, rfx_group_ids = NU "y_hat" = y_hat ) if (object$model_params$has_rfx) { - result[["rfx_predictions"]] = rfx_predictions + result[["rfx_predictions"]] <- rfx_predictions + } else { + result[["rfx_predictions"]] <- NULL } if (object$model_params$include_variance_forest) { - result[["variance_forest_predictions"]] = variance_forest_predictions + result[["variance_forest_predictions"]] <- variance_forest_predictions + } else { + result[["variance_forest_predictions"]] <- NULL } return(result) } diff --git a/R/cpp11.R b/R/cpp11.R index 39802efe..a71a7722 100644 --- a/R/cpp11.R +++ b/R/cpp11.R @@ -580,12 +580,12 @@ compute_leaf_indices_cpp <- function(forest_container, covariates, forest_nums) .Call(`_stochtree_compute_leaf_indices_cpp`, forest_container, covariates, forest_nums) } -sample_gfr_one_iteration_cpp <- function(data, residual, forest_samples, active_forest, tracker, split_prior, rng, sweep_indices, feature_types, cutpoint_grid_size, leaf_model_scale_input, variable_weights, a_forest, b_forest, global_variance, leaf_model_int, keep_forest, num_features_subsample) { - invisible(.Call(`_stochtree_sample_gfr_one_iteration_cpp`, data, residual, forest_samples, active_forest, tracker, split_prior, rng, sweep_indices, feature_types, cutpoint_grid_size, leaf_model_scale_input, variable_weights, a_forest, b_forest, global_variance, leaf_model_int, keep_forest, num_features_subsample)) +sample_gfr_one_iteration_cpp <- function(data, residual, forest_samples, active_forest, tracker, split_prior, rng, sweep_indices, feature_types, cutpoint_grid_size, leaf_model_scale_input, variable_weights, a_forest, b_forest, global_variance, leaf_model_int, keep_forest, num_features_subsample, num_threads) { + invisible(.Call(`_stochtree_sample_gfr_one_iteration_cpp`, data, residual, forest_samples, active_forest, tracker, split_prior, rng, sweep_indices, feature_types, cutpoint_grid_size, leaf_model_scale_input, variable_weights, a_forest, b_forest, global_variance, leaf_model_int, keep_forest, num_features_subsample, num_threads)) } -sample_mcmc_one_iteration_cpp <- function(data, residual, forest_samples, active_forest, tracker, split_prior, rng, sweep_indices, feature_types, cutpoint_grid_size, leaf_model_scale_input, variable_weights, a_forest, b_forest, global_variance, leaf_model_int, keep_forest) { - invisible(.Call(`_stochtree_sample_mcmc_one_iteration_cpp`, data, residual, forest_samples, active_forest, tracker, split_prior, rng, sweep_indices, feature_types, cutpoint_grid_size, leaf_model_scale_input, variable_weights, a_forest, b_forest, global_variance, leaf_model_int, keep_forest)) +sample_mcmc_one_iteration_cpp <- function(data, residual, forest_samples, active_forest, tracker, split_prior, rng, sweep_indices, feature_types, cutpoint_grid_size, leaf_model_scale_input, variable_weights, a_forest, b_forest, global_variance, leaf_model_int, keep_forest, num_threads) { + invisible(.Call(`_stochtree_sample_mcmc_one_iteration_cpp`, data, residual, forest_samples, active_forest, tracker, split_prior, rng, sweep_indices, feature_types, cutpoint_grid_size, leaf_model_scale_input, variable_weights, a_forest, b_forest, global_variance, leaf_model_int, keep_forest, num_threads)) } sample_sigma2_one_iteration_cpp <- function(residual, dataset, rng, a, b) { diff --git a/R/model.R b/R/model.R index 5b003055..57523c0d 100644 --- a/R/model.R +++ b/R/model.R @@ -67,10 +67,11 @@ ForestModel <- R6::R6Class( #' @param rng Wrapper around C++ random number generator #' @param forest_model_config ForestModelConfig object containing forest model parameters and settings #' @param global_model_config GlobalModelConfig object containing global model parameters and settings + #' @param num_threads Number of threads to use in the GFR and MCMC algorithms, as well as prediction. If OpenMP is not available on a user's system, this will default to `1`, otherwise to the maximum number of available threads. #' @param keep_forest (Optional) Whether the updated forest sample should be saved to `forest_samples`. Default: `TRUE`. #' @param gfr (Optional) Whether or not the forest should be sampled using the "grow-from-root" (GFR) algorithm. Default: `TRUE`. sample_one_iteration = function(forest_dataset, residual, forest_samples, active_forest, - rng, forest_model_config, global_model_config, + rng, forest_model_config, global_model_config, num_threads = -1, keep_forest = TRUE, gfr = TRUE) { if (active_forest$is_empty()) { stop("`active_forest` has not yet been initialized, which is necessary to run the sampler. Please set constant values for `active_forest`'s leaves using either the `set_root_leaves` or `prepare_for_sampler` methods.") @@ -114,14 +115,15 @@ ForestModel <- R6::R6Class( forest_dataset$data_ptr, residual$data_ptr, forest_samples$forest_container_ptr, active_forest$forest_ptr, self$tracker_ptr, self$tree_prior_ptr, rng$rng_ptr, sweep_update_indices, feature_types, cutpoint_grid_size, leaf_model_scale, - variable_weights, a_forest, b_forest, global_scale, leaf_model_int, keep_forest, num_features_subsample + variable_weights, a_forest, b_forest, global_scale, leaf_model_int, keep_forest, num_features_subsample, + num_threads ) } else { sample_mcmc_one_iteration_cpp( forest_dataset$data_ptr, residual$data_ptr, forest_samples$forest_container_ptr, active_forest$forest_ptr, self$tracker_ptr, self$tree_prior_ptr, rng$rng_ptr, sweep_update_indices, feature_types, cutpoint_grid_size, leaf_model_scale, - variable_weights, a_forest, b_forest, global_scale, leaf_model_int, keep_forest + variable_weights, a_forest, b_forest, global_scale, leaf_model_int, keep_forest, num_threads ) } }, diff --git a/configure b/configure new file mode 100755 index 00000000..02737aa7 --- /dev/null +++ b/configure @@ -0,0 +1,3002 @@ +#! /bin/sh +# Guess values for system-dependent variables and create Makefiles. +# Generated by GNU Autoconf 2.72 for stochtree 0.1.1. +# +# +# Copyright (C) 1992-1996, 1998-2017, 2020-2023 Free Software Foundation, +# Inc. +# +# +# This configure script is free software; the Free Software Foundation +# gives unlimited permission to copy, distribute and modify it. +## -------------------- ## +## M4sh Initialization. ## +## -------------------- ## + +# Be more Bourne compatible +DUALCASE=1; export DUALCASE # for MKS sh +if test ${ZSH_VERSION+y} && (emulate sh) >/dev/null 2>&1 +then : + emulate sh + NULLCMD=: + # Pre-4.2 versions of Zsh do word splitting on ${1+"$@"}, which + # is contrary to our usage. Disable this feature. + alias -g '${1+"$@"}'='"$@"' + setopt NO_GLOB_SUBST +else case e in #( + e) case `(set -o) 2>/dev/null` in #( + *posix*) : + set -o posix ;; #( + *) : + ;; +esac ;; +esac +fi + + + +# Reset variables that may have inherited troublesome values from +# the environment. + +# IFS needs to be set, to space, tab, and newline, in precisely that order. +# (If _AS_PATH_WALK were called with IFS unset, it would have the +# side effect of setting IFS to empty, thus disabling word splitting.) +# Quoting is to prevent editors from complaining about space-tab. +as_nl=' +' +export as_nl +IFS=" "" $as_nl" + +PS1='$ ' +PS2='> ' +PS4='+ ' + +# Ensure predictable behavior from utilities with locale-dependent output. +LC_ALL=C +export LC_ALL +LANGUAGE=C +export LANGUAGE + +# We cannot yet rely on "unset" to work, but we need these variables +# to be unset--not just set to an empty or harmless value--now, to +# avoid bugs in old shells (e.g. pre-3.0 UWIN ksh). This construct +# also avoids known problems related to "unset" and subshell syntax +# in other old shells (e.g. bash 2.01 and pdksh 5.2.14). +for as_var in BASH_ENV ENV MAIL MAILPATH CDPATH +do eval test \${$as_var+y} \ + && ( (unset $as_var) || exit 1) >/dev/null 2>&1 && unset $as_var || : +done + +# Ensure that fds 0, 1, and 2 are open. +if (exec 3>&0) 2>/dev/null; then :; else exec 0&1) 2>/dev/null; then :; else exec 1>/dev/null; fi +if (exec 3>&2) ; then :; else exec 2>/dev/null; fi + +# The user is always right. +if ${PATH_SEPARATOR+false} :; then + PATH_SEPARATOR=: + (PATH='/bin;/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 && { + (PATH='/bin:/bin'; FPATH=$PATH; sh -c :) >/dev/null 2>&1 || + PATH_SEPARATOR=';' + } +fi + + +# Find who we are. Look in the path if we contain no directory separator. +as_myself= +case $0 in #(( + *[\\/]* ) as_myself=$0 ;; + *) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR +for as_dir in $PATH +do + IFS=$as_save_IFS + case $as_dir in #((( + '') as_dir=./ ;; + */) ;; + *) as_dir=$as_dir/ ;; + esac + test -r "$as_dir$0" && as_myself=$as_dir$0 && break + done +IFS=$as_save_IFS + + ;; +esac +# We did not find ourselves, most probably we were run as 'sh COMMAND' +# in which case we are not to be found in the path. +if test "x$as_myself" = x; then + as_myself=$0 +fi +if test ! -f "$as_myself"; then + printf "%s\n" "$as_myself: error: cannot find myself; rerun with an absolute file name" >&2 + exit 1 +fi + + +# Use a proper internal environment variable to ensure we don't fall + # into an infinite loop, continuously re-executing ourselves. + if test x"${_as_can_reexec}" != xno && test "x$CONFIG_SHELL" != x; then + _as_can_reexec=no; export _as_can_reexec; + # We cannot yet assume a decent shell, so we have to provide a +# neutralization value for shells without unset; and this also +# works around shells that cannot unset nonexistent variables. +# Preserve -v and -x to the replacement shell. +BASH_ENV=/dev/null +ENV=/dev/null +(unset BASH_ENV) >/dev/null 2>&1 && unset BASH_ENV ENV +case $- in # (((( + *v*x* | *x*v* ) as_opts=-vx ;; + *v* ) as_opts=-v ;; + *x* ) as_opts=-x ;; + * ) as_opts= ;; +esac +exec $CONFIG_SHELL $as_opts "$as_myself" ${1+"$@"} +# Admittedly, this is quite paranoid, since all the known shells bail +# out after a failed 'exec'. +printf "%s\n" "$0: could not re-execute with $CONFIG_SHELL" >&2 +exit 255 + fi + # We don't want this to propagate to other subprocesses. + { _as_can_reexec=; unset _as_can_reexec;} +if test "x$CONFIG_SHELL" = x; then + as_bourne_compatible="if test \${ZSH_VERSION+y} && (emulate sh) >/dev/null 2>&1 +then : + emulate sh + NULLCMD=: + # Pre-4.2 versions of Zsh do word splitting on \${1+\"\$@\"}, which + # is contrary to our usage. Disable this feature. + alias -g '\${1+\"\$@\"}'='\"\$@\"' + setopt NO_GLOB_SUBST +else case e in #( + e) case \`(set -o) 2>/dev/null\` in #( + *posix*) : + set -o posix ;; #( + *) : + ;; +esac ;; +esac +fi +" + as_required="as_fn_return () { (exit \$1); } +as_fn_success () { as_fn_return 0; } +as_fn_failure () { as_fn_return 1; } +as_fn_ret_success () { return 0; } +as_fn_ret_failure () { return 1; } + +exitcode=0 +as_fn_success || { exitcode=1; echo as_fn_success failed.; } +as_fn_failure && { exitcode=1; echo as_fn_failure succeeded.; } +as_fn_ret_success || { exitcode=1; echo as_fn_ret_success failed.; } +as_fn_ret_failure && { exitcode=1; echo as_fn_ret_failure succeeded.; } +if ( set x; as_fn_ret_success y && test x = \"\$1\" ) +then : + +else case e in #( + e) exitcode=1; echo positional parameters were not saved. ;; +esac +fi +test x\$exitcode = x0 || exit 1 +blah=\$(echo \$(echo blah)) +test x\"\$blah\" = xblah || exit 1 +test -x / || exit 1" + as_suggested=" as_lineno_1=";as_suggested=$as_suggested$LINENO;as_suggested=$as_suggested" as_lineno_1a=\$LINENO + as_lineno_2=";as_suggested=$as_suggested$LINENO;as_suggested=$as_suggested" as_lineno_2a=\$LINENO + eval 'test \"x\$as_lineno_1'\$as_run'\" != \"x\$as_lineno_2'\$as_run'\" && + test \"x\`expr \$as_lineno_1'\$as_run' + 1\`\" = \"x\$as_lineno_2'\$as_run'\"' || exit 1" + if (eval "$as_required") 2>/dev/null +then : + as_have_required=yes +else case e in #( + e) as_have_required=no ;; +esac +fi + if test x$as_have_required = xyes && (eval "$as_suggested") 2>/dev/null +then : + +else case e in #( + e) as_save_IFS=$IFS; IFS=$PATH_SEPARATOR +as_found=false +for as_dir in /bin$PATH_SEPARATOR/usr/bin$PATH_SEPARATOR$PATH +do + IFS=$as_save_IFS + case $as_dir in #((( + '') as_dir=./ ;; + */) ;; + *) as_dir=$as_dir/ ;; + esac + as_found=: + case $as_dir in #( + /*) + for as_base in sh bash ksh sh5; do + # Try only shells that exist, to save several forks. + as_shell=$as_dir$as_base + if { test -f "$as_shell" || test -f "$as_shell.exe"; } && + as_run=a "$as_shell" -c "$as_bourne_compatible""$as_required" 2>/dev/null +then : + CONFIG_SHELL=$as_shell as_have_required=yes + if as_run=a "$as_shell" -c "$as_bourne_compatible""$as_suggested" 2>/dev/null +then : + break 2 +fi +fi + done;; + esac + as_found=false +done +IFS=$as_save_IFS +if $as_found +then : + +else case e in #( + e) if { test -f "$SHELL" || test -f "$SHELL.exe"; } && + as_run=a "$SHELL" -c "$as_bourne_compatible""$as_required" 2>/dev/null +then : + CONFIG_SHELL=$SHELL as_have_required=yes +fi ;; +esac +fi + + + if test "x$CONFIG_SHELL" != x +then : + export CONFIG_SHELL + # We cannot yet assume a decent shell, so we have to provide a +# neutralization value for shells without unset; and this also +# works around shells that cannot unset nonexistent variables. +# Preserve -v and -x to the replacement shell. +BASH_ENV=/dev/null +ENV=/dev/null +(unset BASH_ENV) >/dev/null 2>&1 && unset BASH_ENV ENV +case $- in # (((( + *v*x* | *x*v* ) as_opts=-vx ;; + *v* ) as_opts=-v ;; + *x* ) as_opts=-x ;; + * ) as_opts= ;; +esac +exec $CONFIG_SHELL $as_opts "$as_myself" ${1+"$@"} +# Admittedly, this is quite paranoid, since all the known shells bail +# out after a failed 'exec'. +printf "%s\n" "$0: could not re-execute with $CONFIG_SHELL" >&2 +exit 255 +fi + + if test x$as_have_required = xno +then : + printf "%s\n" "$0: This script requires a shell more modern than all" + printf "%s\n" "$0: the shells that I found on your system." + if test ${ZSH_VERSION+y} ; then + printf "%s\n" "$0: In particular, zsh $ZSH_VERSION has bugs and should" + printf "%s\n" "$0: be upgraded to zsh 4.3.4 or later." + else + printf "%s\n" "$0: Please tell bug-autoconf@gnu.org about your system, +$0: including any error possibly output before this +$0: message. Then install a modern shell, or manually run +$0: the script under such a shell if you do have one." + fi + exit 1 +fi ;; +esac +fi +fi +SHELL=${CONFIG_SHELL-/bin/sh} +export SHELL +# Unset more variables known to interfere with behavior of common tools. +CLICOLOR_FORCE= GREP_OPTIONS= +unset CLICOLOR_FORCE GREP_OPTIONS + +## --------------------- ## +## M4sh Shell Functions. ## +## --------------------- ## +# as_fn_unset VAR +# --------------- +# Portably unset VAR. +as_fn_unset () +{ + { eval $1=; unset $1;} +} +as_unset=as_fn_unset + + +# as_fn_set_status STATUS +# ----------------------- +# Set $? to STATUS, without forking. +as_fn_set_status () +{ + return $1 +} # as_fn_set_status + +# as_fn_exit STATUS +# ----------------- +# Exit the shell with STATUS, even in a "trap 0" or "set -e" context. +as_fn_exit () +{ + set +e + as_fn_set_status $1 + exit $1 +} # as_fn_exit + +# as_fn_mkdir_p +# ------------- +# Create "$as_dir" as a directory, including parents if necessary. +as_fn_mkdir_p () +{ + + case $as_dir in #( + -*) as_dir=./$as_dir;; + esac + test -d "$as_dir" || eval $as_mkdir_p || { + as_dirs= + while :; do + case $as_dir in #( + *\'*) as_qdir=`printf "%s\n" "$as_dir" | sed "s/'/'\\\\\\\\''/g"`;; #'( + *) as_qdir=$as_dir;; + esac + as_dirs="'$as_qdir' $as_dirs" + as_dir=`$as_dirname -- "$as_dir" || +$as_expr X"$as_dir" : 'X\(.*[^/]\)//*[^/][^/]*/*$' \| \ + X"$as_dir" : 'X\(//\)[^/]' \| \ + X"$as_dir" : 'X\(//\)$' \| \ + X"$as_dir" : 'X\(/\)' \| . 2>/dev/null || +printf "%s\n" X"$as_dir" | + sed '/^X\(.*[^/]\)\/\/*[^/][^/]*\/*$/{ + s//\1/ + q + } + /^X\(\/\/\)[^/].*/{ + s//\1/ + q + } + /^X\(\/\/\)$/{ + s//\1/ + q + } + /^X\(\/\).*/{ + s//\1/ + q + } + s/.*/./; q'` + test -d "$as_dir" && break + done + test -z "$as_dirs" || eval "mkdir $as_dirs" + } || test -d "$as_dir" || as_fn_error $? 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[VAR=VALUE]... + +To assign environment variables (e.g., CC, CFLAGS...), specify them as +VAR=VALUE. See below for descriptions of some of the useful variables. + +Defaults for the options are specified in brackets. + +Configuration: + -h, --help display this help and exit + --help=short display options specific to this package + --help=recursive display the short help of all the included packages + -V, --version display version information and exit + -q, --quiet, --silent do not print 'checking ...' messages + --cache-file=FILE cache test results in FILE [disabled] + -C, --config-cache alias for '--cache-file=config.cache' + -n, --no-create do not create output files + --srcdir=DIR find the sources in DIR [configure dir or '..'] + +Installation directories: + --prefix=PREFIX install architecture-independent files in PREFIX + [$ac_default_prefix] + --exec-prefix=EPREFIX install architecture-dependent files in EPREFIX + [PREFIX] + +By default, 'make install' will install all the files in +'$ac_default_prefix/bin', '$ac_default_prefix/lib' etc. You can specify +an installation prefix other than '$ac_default_prefix' using '--prefix', +for instance '--prefix=\$HOME'. + +For better control, use the options below. + +Fine tuning of the installation directories: + --bindir=DIR user executables [EPREFIX/bin] + --sbindir=DIR system admin executables [EPREFIX/sbin] + --libexecdir=DIR program executables [EPREFIX/libexec] + --sysconfdir=DIR read-only single-machine data [PREFIX/etc] + --sharedstatedir=DIR modifiable architecture-independent data [PREFIX/com] + --localstatedir=DIR modifiable single-machine data [PREFIX/var] + --runstatedir=DIR modifiable per-process data [LOCALSTATEDIR/run] + --libdir=DIR object code libraries [EPREFIX/lib] + --includedir=DIR C header files [PREFIX/include] + --oldincludedir=DIR C header files for non-gcc [/usr/include] + --datarootdir=DIR read-only arch.-independent data root [PREFIX/share] + --datadir=DIR read-only architecture-independent data [DATAROOTDIR] + --infodir=DIR info documentation [DATAROOTDIR/info] + --localedir=DIR locale-dependent data [DATAROOTDIR/locale] + --mandir=DIR man documentation [DATAROOTDIR/man] + --docdir=DIR documentation root [DATAROOTDIR/doc/stochtree] + --htmldir=DIR html documentation [DOCDIR] + --dvidir=DIR dvi documentation [DOCDIR] + --pdfdir=DIR pdf documentation [DOCDIR] + --psdir=DIR ps documentation [DOCDIR] +_ACEOF + + cat <<\_ACEOF +_ACEOF +fi + +if test -n "$ac_init_help"; then + case $ac_init_help in + short | recursive ) echo "Configuration of stochtree 0.1.1:";; + esac + cat <<\_ACEOF + +Report bugs to the package provider. +_ACEOF +ac_status=$? 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" >&6; } +{ printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: ${R_HOME}" >&5 +printf "%s\n" "${R_HOME}" >&6; } + +# set up CPP flags +# find the compiler and compiler flags used by R. +: ${R_HOME=`R HOME`} +if test -z "${R_HOME}"; then + echo "could not determine R_HOME" + exit 1 +fi +CXX17=`"${R_HOME}/bin/R" CMD config CXX17` +CXX17STD=`"${R_HOME}/bin/R" CMD config CXX17STD` +CXX="${CXX17} ${CXX17STD}" +CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS` +CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXX17FLAGS` +LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS` +ac_ext=cpp +ac_cpp='$CXXCPP $CPPFLAGS' +ac_compile='$CXX -c $CXXFLAGS $CPPFLAGS conftest.$ac_ext >&5' +ac_link='$CXX -o conftest$ac_exeext $CXXFLAGS $CPPFLAGS $LDFLAGS conftest.$ac_ext $LIBS >&5' +ac_compiler_gnu=$ac_cv_cxx_compiler_gnu + + +# Stochtree-specific flags +STOCHTREE_CPPFLAGS="" + +######### +# Eigen # +######### + +STOCHTREE_CPPFLAGS=" ${STOCHTREE_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE" + +########## +# OpenMP # +########## + +OPENMP_CXXFLAGS="" + +if test `uname -s` = "Linux" +then + OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)" + OPENMP_AVAILABILITY_FLAGS='-DSTOCHTREE_OPENMP_AVAILABLE' +fi + +if test `uname -s` = "Darwin" +then + OPENMP_CXXFLAGS='-Xclang -fopenmp' + OPENMP_LIB='-lomp' + OPENMP_AVAILABILITY_FLAGS='-DSTOCHTREE_OPENMP_AVAILABLE' + + # libomp 15.0+ from brew is keg-only (i.e. not symlinked into the standard paths search by the linker), + # so need to search in other locations. + # See https://github.com/Homebrew/homebrew-core/issues/112107#issuecomment-1278042927. + # + # If Homebrew is found and libomp was installed with it, this code adds the necessary + # flags for the compiler to find libomp headers and for the linker to find libomp.dylib. + HOMEBREW_LIBOMP_PREFIX="" + if command -v brew >/dev/null 2>&1; then + ac_brew_openmp=no + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether OpenMP was installed via Homebrew" >&5 +printf %s "checking whether OpenMP was installed via Homebrew... " >&6; } + brew --prefix libomp >/dev/null 2>&1 && ac_brew_openmp=yes + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: ${ac_brew_openmp}" >&5 +printf "%s\n" "${ac_brew_openmp}" >&6; } + if test "${ac_brew_openmp}" = yes; then + HOMEBREW_LIBOMP_PREFIX=`brew --prefix libomp` + OPENMP_CXXFLAGS="${OPENMP_CXXFLAGS} -I${HOMEBREW_LIBOMP_PREFIX}/include" + OPENMP_LIB="${OPENMP_LIB} -L${HOMEBREW_LIBOMP_PREFIX}/lib" + fi + fi + ac_pkg_openmp=no + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: checking whether OpenMP will work in a package" >&5 +printf %s "checking whether OpenMP will work in a package... " >&6; } + cat confdefs.h - <<_ACEOF >conftest.$ac_ext +/* end confdefs.h. */ + + + #include + +int +main (void) +{ + + return (omp_get_max_threads() <= 1); + + + ; + return 0; +} + + +_ACEOF + ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes + + # -Xclang is not portable (it is clang-specific) + # if compilation above failed, try without that flag + if test "${ac_pkg_openmp}" = no; then + if test -f "./conftest"; then + rm ./conftest + fi + OPENMP_CXXFLAGS="-fopenmp" + ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes + fi + + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: result: ${ac_pkg_openmp}" >&5 +printf "%s\n" "${ac_pkg_openmp}" >&6; } + if test "${ac_pkg_openmp}" = no; then + OPENMP_CXXFLAGS='' + OPENMP_LIB='' + OPENMP_AVAILABILITY_FLAGS='' + echo '***********************************************************************************************' + echo ' OpenMP is unavailable on this macOS system. stochtree code will run single-threaded as a result.' + echo ' To use all CPU cores for training jobs, you should install OpenMP by running' + echo '' + echo ' brew install libomp' + echo '***********************************************************************************************' + fi +fi + +# substitute variables from this script into Makevars.in + + + + +ac_config_files="$ac_config_files src/Makevars" + + +# write out Autoconf output +cat >confcache <<\_ACEOF +# This file is a shell script that caches the results of configure +# tests run on this system so they can be shared between configure +# scripts and configure runs, see configure's option --config-cache. +# It is not useful on other systems. 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"cannot create directory $as_dir" + + +} # as_fn_mkdir_p +if mkdir -p . 2>/dev/null; then + as_mkdir_p='mkdir -p "$as_dir"' +else + test -d ./-p && rmdir ./-p + as_mkdir_p=false +fi + + +# as_fn_executable_p FILE +# ----------------------- +# Test if FILE is an executable regular file. +as_fn_executable_p () +{ + test -f "$1" && test -x "$1" +} # as_fn_executable_p +as_test_x='test -x' +as_executable_p=as_fn_executable_p + +# Sed expression to map a string onto a valid CPP name. +as_sed_cpp="y%*$as_cr_letters%P$as_cr_LETTERS%;s%[^_$as_cr_alnum]%_%g" +as_tr_cpp="eval sed '$as_sed_cpp'" # deprecated + +# Sed expression to map a string onto a valid variable name. +as_sed_sh="y%*+%pp%;s%[^_$as_cr_alnum]%_%g" +as_tr_sh="eval sed '$as_sed_sh'" # deprecated + + +exec 6>&1 +## ----------------------------------- ## +## Main body of $CONFIG_STATUS script. ## +## ----------------------------------- ## +_ASEOF +test $as_write_fail = 0 && chmod +x $CONFIG_STATUS || ac_write_fail=1 + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +# Save the log message, to keep $0 and so on meaningful, and to +# report actual input values of CONFIG_FILES etc. instead of their +# values after options handling. +ac_log=" +This file was extended by stochtree $as_me 0.1.1, which was +generated by GNU Autoconf 2.72. Invocation command line was + + CONFIG_FILES = $CONFIG_FILES + CONFIG_HEADERS = $CONFIG_HEADERS + CONFIG_LINKS = $CONFIG_LINKS + CONFIG_COMMANDS = $CONFIG_COMMANDS + $ $0 $@ + +on `(hostname || uname -n) 2>/dev/null | sed 1q` +" + +_ACEOF + +case $ac_config_files in *" +"*) set x $ac_config_files; shift; ac_config_files=$*;; +esac + + + +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +# Files that config.status was made for. +config_files="$ac_config_files" + +_ACEOF + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +ac_cs_usage="\ +'$as_me' instantiates files and other configuration actions +from templates according to the current configuration. Unless the files +and actions are specified as TAGs, all are instantiated by default. + +Usage: $0 [OPTION]... [TAG]... + + -h, --help print this help, then exit + -V, --version print version number and configuration settings, then exit + --config print configuration, then exit + -q, --quiet, --silent + do not print progress messages + -d, --debug don't remove temporary files + --recheck update $as_me by reconfiguring in the same conditions + --file=FILE[:TEMPLATE] + instantiate the configuration file FILE + +Configuration files: +$config_files + +Report bugs to the package provider." + +_ACEOF +ac_cs_config=`printf "%s\n" "$ac_configure_args" | sed "$ac_safe_unquote"` +ac_cs_config_escaped=`printf "%s\n" "$ac_cs_config" | sed "s/^ //; s/'/'\\\\\\\\''/g"` +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +ac_cs_config='$ac_cs_config_escaped' +ac_cs_version="\\ +stochtree config.status 0.1.1 +configured by $0, generated by GNU Autoconf 2.72, + with options \\"\$ac_cs_config\\" + +Copyright (C) 2023 Free Software Foundation, Inc. +This config.status script is free software; the Free Software Foundation +gives unlimited permission to copy, distribute and modify it." + +ac_pwd='$ac_pwd' +srcdir='$srcdir' +test -n "\$AWK" || AWK=awk +_ACEOF + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +# The default lists apply if the user does not specify any file. +ac_need_defaults=: +while test $# != 0 +do + case $1 in + --*=?*) + ac_option=`expr "X$1" : 'X\([^=]*\)='` + ac_optarg=`expr "X$1" : 'X[^=]*=\(.*\)'` + ac_shift=: + ;; + --*=) + ac_option=`expr "X$1" : 'X\([^=]*\)='` + ac_optarg= + ac_shift=: + ;; + *) + ac_option=$1 + ac_optarg=$2 + ac_shift=shift + ;; + esac + + case $ac_option in + # Handling of the options. + -recheck | --recheck | --rechec | --reche | --rech | --rec | --re | --r) + ac_cs_recheck=: ;; + --version | --versio | --versi | --vers | --ver | --ve | --v | -V ) + printf "%s\n" "$ac_cs_version"; exit ;; + --config | --confi | --conf | --con | --co | --c ) + printf "%s\n" "$ac_cs_config"; exit ;; + --debug | --debu | --deb | --de | --d | -d ) + debug=: ;; + --file | --fil | --fi | --f ) + $ac_shift + case $ac_optarg in + *\'*) ac_optarg=`printf "%s\n" "$ac_optarg" | sed "s/'/'\\\\\\\\''/g"` ;; + '') as_fn_error $? "missing file argument" ;; + esac + as_fn_append CONFIG_FILES " '$ac_optarg'" + ac_need_defaults=false;; + --he | --h | --help | --hel | -h ) + printf "%s\n" "$ac_cs_usage"; exit ;; + -q | -quiet | --quiet | --quie | --qui | --qu | --q \ + | -silent | --silent | --silen | --sile | --sil | --si | --s) + ac_cs_silent=: ;; + + # This is an error. + -*) as_fn_error $? "unrecognized option: '$1' +Try '$0 --help' for more information." ;; + + *) as_fn_append ac_config_targets " $1" + ac_need_defaults=false ;; + + esac + shift +done + +ac_configure_extra_args= + +if $ac_cs_silent; then + exec 6>/dev/null + ac_configure_extra_args="$ac_configure_extra_args --silent" +fi + +_ACEOF +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +if \$ac_cs_recheck; then + set X $SHELL '$0' $ac_configure_args \$ac_configure_extra_args --no-create --no-recursion + shift + \printf "%s\n" "running CONFIG_SHELL=$SHELL \$*" >&6 + CONFIG_SHELL='$SHELL' + export CONFIG_SHELL + exec "\$@" +fi + +_ACEOF +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +exec 5>>config.log +{ + echo + sed 'h;s/./-/g;s/^.../## /;s/...$/ ##/;p;x;p;x' <<_ASBOX +## Running $as_me. ## +_ASBOX + printf "%s\n" "$ac_log" +} >&5 + +_ACEOF +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +_ACEOF + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 + +# Handling of arguments. +for ac_config_target in $ac_config_targets +do + case $ac_config_target in + "src/Makevars") CONFIG_FILES="$CONFIG_FILES src/Makevars" ;; + + *) as_fn_error $? "invalid argument: '$ac_config_target'" "$LINENO" 5;; + esac +done + + +# If the user did not use the arguments to specify the items to instantiate, +# then the envvar interface is used. Set only those that are not. +# We use the long form for the default assignment because of an extremely +# bizarre bug on SunOS 4.1.3. +if $ac_need_defaults; then + test ${CONFIG_FILES+y} || CONFIG_FILES=$config_files +fi + +# Have a temporary directory for convenience. Make it in the build tree +# simply because there is no reason against having it here, and in addition, +# creating and moving files from /tmp can sometimes cause problems. +# Hook for its removal unless debugging. +# Note that there is a small window in which the directory will not be cleaned: +# after its creation but before its name has been assigned to '$tmp'. +$debug || +{ + tmp= ac_tmp= + trap 'exit_status=$? + : "${ac_tmp:=$tmp}" + { test ! -d "$ac_tmp" || rm -fr "$ac_tmp"; } && exit $exit_status +' 0 + trap 'as_fn_exit 1' 1 2 13 15 +} +# Create a (secure) tmp directory for tmp files. + +{ + tmp=`(umask 077 && mktemp -d "./confXXXXXX") 2>/dev/null` && + test -d "$tmp" +} || +{ + tmp=./conf$$-$RANDOM + (umask 077 && mkdir "$tmp") +} || as_fn_error $? "cannot create a temporary directory in ." "$LINENO" 5 +ac_tmp=$tmp + +# Set up the scripts for CONFIG_FILES section. +# No need to generate them if there are no CONFIG_FILES. +# This happens for instance with './config.status config.h'. +if test -n "$CONFIG_FILES"; then + + +ac_cr=`echo X | tr X '\015'` +# On cygwin, bash can eat \r inside `` if the user requested igncr. +# But we know of no other shell where ac_cr would be empty at this +# point, so we can use a bashism as a fallback. +if test "x$ac_cr" = x; then + eval ac_cr=\$\'\\r\' +fi +ac_cs_awk_cr=`$AWK 'BEGIN { print "a\rb" }' /dev/null` +if test "$ac_cs_awk_cr" = "a${ac_cr}b"; then + ac_cs_awk_cr='\\r' +else + ac_cs_awk_cr=$ac_cr +fi + +echo 'BEGIN {' >"$ac_tmp/subs1.awk" && +_ACEOF + + +{ + echo "cat >conf$$subs.awk <<_ACEOF" && + echo "$ac_subst_vars" | sed 's/.*/&!$&$ac_delim/' && + echo "_ACEOF" +} >conf$$subs.sh || + as_fn_error $? "could not make $CONFIG_STATUS" "$LINENO" 5 +ac_delim_num=`echo "$ac_subst_vars" | grep -c '^'` +ac_delim='%!_!# ' +for ac_last_try in false false false false false :; do + . ./conf$$subs.sh || + as_fn_error $? "could not make $CONFIG_STATUS" "$LINENO" 5 + + ac_delim_n=`sed -n "s/.*$ac_delim\$/X/p" conf$$subs.awk | grep -c X` + if test $ac_delim_n = $ac_delim_num; then + break + elif $ac_last_try; then + as_fn_error $? "could not make $CONFIG_STATUS" "$LINENO" 5 + else + ac_delim="$ac_delim!$ac_delim _$ac_delim!! " + fi +done +rm -f conf$$subs.sh + +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +cat >>"\$ac_tmp/subs1.awk" <<\\_ACAWK && +_ACEOF +sed -n ' +h +s/^/S["/; s/!.*/"]=/ +p +g +s/^[^!]*!// +:repl +t repl +s/'"$ac_delim"'$// +t delim +:nl +h +s/\(.\{148\}\)..*/\1/ +t more1 +s/["\\]/\\&/g; s/^/"/; s/$/\\n"\\/ +p +n +b repl +:more1 +s/["\\]/\\&/g; s/^/"/; s/$/"\\/ +p +g +s/.\{148\}// +t nl +:delim +h +s/\(.\{148\}\)..*/\1/ +t more2 +s/["\\]/\\&/g; s/^/"/; s/$/"/ +p +b +:more2 +s/["\\]/\\&/g; s/^/"/; s/$/"\\/ +p +g +s/.\{148\}// +t delim +' >$CONFIG_STATUS || ac_write_fail=1 +rm -f conf$$subs.awk +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +_ACAWK +cat >>"\$ac_tmp/subs1.awk" <<_ACAWK && + for (key in S) S_is_set[key] = 1 + FS = "" + +} +{ + line = $ 0 + nfields = split(line, field, "@") + substed = 0 + len = length(field[1]) + for (i = 2; i < nfields; i++) { + key = field[i] + keylen = length(key) + if (S_is_set[key]) { + value = S[key] + line = substr(line, 1, len) "" value "" substr(line, len + keylen + 3) + len += length(value) + length(field[++i]) + substed = 1 + } else + len += 1 + keylen + } + + print line +} + +_ACAWK +_ACEOF +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +if sed "s/$ac_cr//" < /dev/null > /dev/null 2>&1; then + sed "s/$ac_cr\$//; s/$ac_cr/$ac_cs_awk_cr/g" +else + cat +fi < "$ac_tmp/subs1.awk" > "$ac_tmp/subs.awk" \ + || as_fn_error $? "could not setup config files machinery" "$LINENO" 5 +_ACEOF + +# VPATH may cause trouble with some makes, so we remove sole $(srcdir), +# ${srcdir} and @srcdir@ entries from VPATH if srcdir is ".", strip leading and +# trailing colons and then remove the whole line if VPATH becomes empty +# (actually we leave an empty line to preserve line numbers). +if test "x$srcdir" = x.; then + ac_vpsub='/^[ ]*VPATH[ ]*=[ ]*/{ +h +s/// +s/^/:/ +s/[ ]*$/:/ +s/:\$(srcdir):/:/g +s/:\${srcdir}:/:/g +s/:@srcdir@:/:/g +s/^:*// +s/:*$// +x +s/\(=[ ]*\).*/\1/ +G +s/\n// +s/^[^=]*=[ ]*$// +}' +fi + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +fi # test -n "$CONFIG_FILES" + + +eval set X " :F $CONFIG_FILES " +shift +for ac_tag +do + case $ac_tag in + :[FHLC]) ac_mode=$ac_tag; continue;; + esac + case $ac_mode$ac_tag in + :[FHL]*:*);; + :L* | :C*:*) as_fn_error $? "invalid tag '$ac_tag'" "$LINENO" 5;; + :[FH]-) ac_tag=-:-;; + :[FH]*) ac_tag=$ac_tag:$ac_tag.in;; + esac + ac_save_IFS=$IFS + IFS=: + set x $ac_tag + IFS=$ac_save_IFS + shift + ac_file=$1 + shift + + case $ac_mode in + :L) ac_source=$1;; + :[FH]) + ac_file_inputs= + for ac_f + do + case $ac_f in + -) ac_f="$ac_tmp/stdin";; + *) # Look for the file first in the build tree, then in the source tree + # (if the path is not absolute). The absolute path cannot be DOS-style, + # because $ac_f cannot contain ':'. + test -f "$ac_f" || + case $ac_f in + [\\/$]*) false;; + *) test -f "$srcdir/$ac_f" && ac_f="$srcdir/$ac_f";; + esac || + as_fn_error 1 "cannot find input file: '$ac_f'" "$LINENO" 5;; + esac + case $ac_f in *\'*) ac_f=`printf "%s\n" "$ac_f" | sed "s/'/'\\\\\\\\''/g"`;; esac + as_fn_append ac_file_inputs " '$ac_f'" + done + + # Let's still pretend it is 'configure' which instantiates (i.e., don't + # use $as_me), people would be surprised to read: + # /* config.h. Generated by config.status. */ + configure_input='Generated from '` + printf "%s\n" "$*" | sed 's|^[^:]*/||;s|:[^:]*/|, |g' + `' by configure.' + if test x"$ac_file" != x-; then + configure_input="$ac_file. $configure_input" + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: creating $ac_file" >&5 +printf "%s\n" "$as_me: creating $ac_file" >&6;} + fi + # Neutralize special characters interpreted by sed in replacement strings. + case $configure_input in #( + *\&* | *\|* | *\\* ) + ac_sed_conf_input=`printf "%s\n" "$configure_input" | + sed 's/[\\\\&|]/\\\\&/g'`;; #( + *) ac_sed_conf_input=$configure_input;; + esac + + case $ac_tag in + *:-:* | *:-) cat >"$ac_tmp/stdin" \ + || as_fn_error $? "could not create $ac_file" "$LINENO" 5 ;; + esac + ;; + esac + + ac_dir=`$as_dirname -- "$ac_file" || +$as_expr X"$ac_file" : 'X\(.*[^/]\)//*[^/][^/]*/*$' \| \ + X"$ac_file" : 'X\(//\)[^/]' \| \ + X"$ac_file" : 'X\(//\)$' \| \ + X"$ac_file" : 'X\(/\)' \| . 2>/dev/null || +printf "%s\n" X"$ac_file" | + sed '/^X\(.*[^/]\)\/\/*[^/][^/]*\/*$/{ + s//\1/ + q + } + /^X\(\/\/\)[^/].*/{ + s//\1/ + q + } + /^X\(\/\/\)$/{ + s//\1/ + q + } + /^X\(\/\).*/{ + s//\1/ + q + } + s/.*/./; q'` + as_dir="$ac_dir"; as_fn_mkdir_p + ac_builddir=. + +case "$ac_dir" in +.) ac_dir_suffix= ac_top_builddir_sub=. ac_top_build_prefix= ;; +*) + ac_dir_suffix=/`printf "%s\n" "$ac_dir" | sed 's|^\.[\\/]||'` + # A ".." for each directory in $ac_dir_suffix. + ac_top_builddir_sub=`printf "%s\n" "$ac_dir_suffix" | sed 's|/[^\\/]*|/..|g;s|/||'` + case $ac_top_builddir_sub in + "") ac_top_builddir_sub=. ac_top_build_prefix= ;; + *) ac_top_build_prefix=$ac_top_builddir_sub/ ;; + esac ;; +esac +ac_abs_top_builddir=$ac_pwd +ac_abs_builddir=$ac_pwd$ac_dir_suffix +# for backward compatibility: +ac_top_builddir=$ac_top_build_prefix + +case $srcdir in + .) # We are building in place. + ac_srcdir=. + ac_top_srcdir=$ac_top_builddir_sub + ac_abs_top_srcdir=$ac_pwd ;; + [\\/]* | ?:[\\/]* ) # Absolute name. + ac_srcdir=$srcdir$ac_dir_suffix; + ac_top_srcdir=$srcdir + ac_abs_top_srcdir=$srcdir ;; + *) # Relative name. + ac_srcdir=$ac_top_build_prefix$srcdir$ac_dir_suffix + ac_top_srcdir=$ac_top_build_prefix$srcdir + ac_abs_top_srcdir=$ac_pwd/$srcdir ;; +esac +ac_abs_srcdir=$ac_abs_top_srcdir$ac_dir_suffix + + + case $ac_mode in + :F) + # + # CONFIG_FILE + # + +_ACEOF + +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +# If the template does not know about datarootdir, expand it. +# FIXME: This hack should be removed a few years after 2.60. +ac_datarootdir_hack=; ac_datarootdir_seen= +ac_sed_dataroot=' +/datarootdir/ { + p + q +} +/@datadir@/p +/@docdir@/p +/@infodir@/p +/@localedir@/p +/@mandir@/p' +case `eval "sed -n \"\$ac_sed_dataroot\" $ac_file_inputs"` in +*datarootdir*) ac_datarootdir_seen=yes;; +*@datadir@*|*@docdir@*|*@infodir@*|*@localedir@*|*@mandir@*) + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: $ac_file_inputs seems to ignore the --datarootdir setting" >&5 +printf "%s\n" "$as_me: WARNING: $ac_file_inputs seems to ignore the --datarootdir setting" >&2;} +_ACEOF +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 + ac_datarootdir_hack=' + s&@datadir@&$datadir&g + s&@docdir@&$docdir&g + s&@infodir@&$infodir&g + s&@localedir@&$localedir&g + s&@mandir@&$mandir&g + s&\\\${datarootdir}&$datarootdir&g' ;; +esac +_ACEOF + +# Neutralize VPATH when '$srcdir' = '.'. +# Shell code in configure.ac might set extrasub. +# FIXME: do we really want to maintain this feature? +cat >>$CONFIG_STATUS <<_ACEOF || ac_write_fail=1 +ac_sed_extra="$ac_vpsub +$extrasub +_ACEOF +cat >>$CONFIG_STATUS <<\_ACEOF || ac_write_fail=1 +:t +/@[a-zA-Z_][a-zA-Z_0-9]*@/!b +s|@configure_input@|$ac_sed_conf_input|;t t +s&@top_builddir@&$ac_top_builddir_sub&;t t +s&@top_build_prefix@&$ac_top_build_prefix&;t t +s&@srcdir@&$ac_srcdir&;t t +s&@abs_srcdir@&$ac_abs_srcdir&;t t +s&@top_srcdir@&$ac_top_srcdir&;t t +s&@abs_top_srcdir@&$ac_abs_top_srcdir&;t t +s&@builddir@&$ac_builddir&;t t +s&@abs_builddir@&$ac_abs_builddir&;t t +s&@abs_top_builddir@&$ac_abs_top_builddir&;t t +$ac_datarootdir_hack +" +eval sed \"\$ac_sed_extra\" "$ac_file_inputs" | $AWK -f "$ac_tmp/subs.awk" \ + >$ac_tmp/out || as_fn_error $? "could not create $ac_file" "$LINENO" 5 + +test -z "$ac_datarootdir_hack$ac_datarootdir_seen" && + { ac_out=`sed -n '/\${datarootdir}/p' "$ac_tmp/out"`; test -n "$ac_out"; } && + { ac_out=`sed -n '/^[ ]*datarootdir[ ]*:*=/p' \ + "$ac_tmp/out"`; test -z "$ac_out"; } && + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: $ac_file contains a reference to the variable 'datarootdir' +which seems to be undefined. Please make sure it is defined" >&5 +printf "%s\n" "$as_me: WARNING: $ac_file contains a reference to the variable 'datarootdir' +which seems to be undefined. Please make sure it is defined" >&2;} + + rm -f "$ac_tmp/stdin" + case $ac_file in + -) cat "$ac_tmp/out" && rm -f "$ac_tmp/out";; + *) rm -f "$ac_file" && mv "$ac_tmp/out" "$ac_file";; + esac \ + || as_fn_error $? "could not create $ac_file" "$LINENO" 5 + ;; + + + + esac + +done # for ac_tag + + +as_fn_exit 0 +_ACEOF +ac_clean_files=$ac_clean_files_save + +test $ac_write_fail = 0 || + as_fn_error $? "write failure creating $CONFIG_STATUS" "$LINENO" 5 + + +# configure is writing to config.log, and then calls config.status. +# config.status does its own redirection, appending to config.log. +# Unfortunately, on DOS this fails, as config.log is still kept open +# by configure, so config.status won't be able to write to it; its +# output is simply discarded. So we exec the FD to /dev/null, +# effectively closing config.log, so it can be properly (re)opened and +# appended to by config.status. When coming back to configure, we +# need to make the FD available again. +if test "$no_create" != yes; then + ac_cs_success=: + ac_config_status_args= + test "$silent" = yes && + ac_config_status_args="$ac_config_status_args --quiet" + exec 5>/dev/null + $SHELL $CONFIG_STATUS $ac_config_status_args || ac_cs_success=false + exec 5>>config.log + # Use ||, not &&, to avoid exiting from the if with $? = 1, which + # would make configure fail if this is the last instruction. + $ac_cs_success || as_fn_exit 1 +fi +if test -n "$ac_unrecognized_opts" && test "$enable_option_checking" != no; then + { printf "%s\n" "$as_me:${as_lineno-$LINENO}: WARNING: unrecognized options: $ac_unrecognized_opts" >&5 +printf "%s\n" "$as_me: WARNING: unrecognized options: $ac_unrecognized_opts" >&2;} +fi + diff --git a/configure.ac b/configure.ac new file mode 100644 index 00000000..6d59e3b0 --- /dev/null +++ b/configure.ac @@ -0,0 +1,125 @@ +### configure.ac -*- Autoconf -*- +# Template used by Autoconf to generate 'configure' script. Based on: +# https://github.com/microsoft/LightGBM/blob/master/R-package/configure.ac + +AC_PREREQ(2.69) +AC_INIT([stochtree], [0.1.1], [], [stochtree], []) +# Note: consider making version number dynamic as in +# https://github.com/microsoft/LightGBM/blob/195c26fc7b00eb0fec252dfe841e2e66d6833954/build-cran-package.sh + +########################### +# find compiler and flags # +########################### + +AC_MSG_CHECKING([location of R]) +AC_MSG_RESULT([${R_HOME}]) + +# set up CPP flags +# find the compiler and compiler flags used by R. +: ${R_HOME=`R HOME`} +if test -z "${R_HOME}"; then + echo "could not determine R_HOME" + exit 1 +fi +CXX17=`"${R_HOME}/bin/R" CMD config CXX17` +CXX17STD=`"${R_HOME}/bin/R" CMD config CXX17STD` +CXX="${CXX17} ${CXX17STD}" +CPPFLAGS=`"${R_HOME}/bin/R" CMD config CPPFLAGS` +CXXFLAGS=`"${R_HOME}/bin/R" CMD config CXX17FLAGS` +LDFLAGS=`"${R_HOME}/bin/R" CMD config LDFLAGS` +AC_LANG(C++) + +# Stochtree-specific flags +STOCHTREE_CPPFLAGS="" + +######### +# Eigen # +######### + +STOCHTREE_CPPFLAGS=" ${STOCHTREE_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE" + +########## +# OpenMP # +########## + +OPENMP_CXXFLAGS="" + +if test `uname -s` = "Linux" +then + OPENMP_CXXFLAGS="\$(SHLIB_OPENMP_CXXFLAGS)" + OPENMP_AVAILABILITY_FLAGS='-DSTOCHTREE_OPENMP_AVAILABLE' +fi + +if test `uname -s` = "Darwin" +then + OPENMP_CXXFLAGS='-Xclang -fopenmp' + OPENMP_LIB='-lomp' + OPENMP_AVAILABILITY_FLAGS='-DSTOCHTREE_OPENMP_AVAILABLE' + + # libomp 15.0+ from brew is keg-only (i.e. not symlinked into the standard paths search by the linker), + # so need to search in other locations. + # See https://github.com/Homebrew/homebrew-core/issues/112107#issuecomment-1278042927. + # + # If Homebrew is found and libomp was installed with it, this code adds the necessary + # flags for the compiler to find libomp headers and for the linker to find libomp.dylib. + HOMEBREW_LIBOMP_PREFIX="" + if command -v brew >/dev/null 2>&1; then + ac_brew_openmp=no + AC_MSG_CHECKING([whether OpenMP was installed via Homebrew]) + brew --prefix libomp >/dev/null 2>&1 && ac_brew_openmp=yes + AC_MSG_RESULT([${ac_brew_openmp}]) + if test "${ac_brew_openmp}" = yes; then + HOMEBREW_LIBOMP_PREFIX=`brew --prefix libomp` + OPENMP_CXXFLAGS="${OPENMP_CXXFLAGS} -I${HOMEBREW_LIBOMP_PREFIX}/include" + OPENMP_LIB="${OPENMP_LIB} -L${HOMEBREW_LIBOMP_PREFIX}/lib" + fi + fi + ac_pkg_openmp=no + AC_MSG_CHECKING([whether OpenMP will work in a package]) + AC_LANG_CONFTEST( + [ + AC_LANG_PROGRAM( + [[ + #include + ]], + [[ + return (omp_get_max_threads() <= 1); + ]] + ) + ] + ) + ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes + + # -Xclang is not portable (it is clang-specific) + # if compilation above failed, try without that flag + if test "${ac_pkg_openmp}" = no; then + if test -f "./conftest"; then + rm ./conftest + fi + OPENMP_CXXFLAGS="-fopenmp" + ${CXX} ${CPPFLAGS} ${CXXFLAGS} ${LDFLAGS} ${OPENMP_CXXFLAGS} ${OPENMP_LIB} -o conftest conftest.cpp 2>/dev/null && ./conftest && ac_pkg_openmp=yes + fi + + AC_MSG_RESULT([${ac_pkg_openmp}]) + if test "${ac_pkg_openmp}" = no; then + OPENMP_CXXFLAGS='' + OPENMP_LIB='' + OPENMP_AVAILABILITY_FLAGS='' + echo '***********************************************************************************************' + echo ' OpenMP is unavailable on this macOS system. stochtree code will run single-threaded as a result.' + echo ' To use all CPU cores for training jobs, you should install OpenMP by running' + echo '' + echo ' brew install libomp' + echo '***********************************************************************************************' + fi +fi + +# substitute variables from this script into Makevars.in +AC_SUBST(OPENMP_CXXFLAGS) +AC_SUBST(OPENMP_LIB) +AC_SUBST(OPENMP_AVAILABILITY_FLAGS) +AC_SUBST(STOCHTREE_CPPFLAGS) +AC_CONFIG_FILES([src/Makevars]) + +# write out Autoconf output +AC_OUTPUT diff --git a/configure.win b/configure.win new file mode 100644 index 00000000..4bc2a539 --- /dev/null +++ b/configure.win @@ -0,0 +1,40 @@ +# Script used to generate `Makevars.win` from `Makevars.win.in` on Windows +# Adapted from LightGBM https://github.com/microsoft/LightGBM/blob/master/R-package/configure.win + +########################### +# find compiler and flags # +########################### + +R_EXE="${R_HOME}/bin${R_ARCH_BIN}/R" +CXX17=`"${R_EXE}" CMD config CXX17` +CXX17STD=`"${R_EXE}" CMD config CXX17STD` +CXX="${CXX17} ${CXX17STD}" +CXXFLAGS=`"${R_EXE}" CMD config CXX17FLAGS` +CXX_STD="CXX17" +CPPFLAGS=`"${R_EXE}" CMD config CPPFLAGS` + +# Stochtree-specific flags +STOCHTREE_CPPFLAGS="" + +######### +# Eigen # +######### + +STOCHTREE_CPPFLAGS="${STOCHTREE_CPPFLAGS} -DEIGEN_MPL2_ONLY -DEIGEN_DONT_PARALLELIZE" + +########## +# OpenMP # +########## + +STOCHTREE_CPPFLAGS="${STOCHTREE_CPPFLAGS} -DSTOCHTREE_OPENMP_AVAILABLE" + +######################### +# Generate Makevars.win # +######################### + +sed -e \ + "s/@CXX_STD@/$CXX_STD/" \ + < src/Makevars.win.in > src/Makevars.win +sed -e \ + "s/@STOCHTREE_CPPFLAGS@/$STOCHTREE_CPPFLAGS/" \ + < src/Makevars.win.in > src/Makevars.win diff --git a/cran-bootstrap.R b/cran-bootstrap.R index e06eba11..0e865877 100644 --- a/cran-bootstrap.R +++ b/cran-bootstrap.R @@ -60,8 +60,13 @@ if (!dir.exists(cran_dir)) { src_files <- list.files("src", pattern = ".[^o]$", recursive = TRUE, full.names = TRUE) pybind_src_files <- list.files("src", pattern = "^(py_)", recursive = TRUE, full.names = TRUE) r_src_files <- src_files[!(src_files %in% pybind_src_files)] +r_src_files <- r_src_files[!(r_src_files %in% c("src/Makevars", "src/Makevars.win"))] +cat(r_src_files) pkg_core_files <- c( ".Rbuildignore", + "configure", + "configure.ac", + "configure.win", "cran-comments.md", "DESCRIPTION", "inst/COPYRIGHTS", @@ -135,12 +140,28 @@ if (all(file.exists(pkg_core_files))) { } } -# Overwrite PKG_CPPFLAGS in src/Makevars -cran_makevars <- file.path(cran_dir, "src/Makevars") +# Overwrite PKG_CPPFLAGS in src/Makevars.in +cran_makevars <- file.path(cran_dir, "src/Makevars.in") makevars_lines <- readLines(cran_makevars) -makevars_lines[grep("^(PKG_CPPFLAGS)", makevars_lines)] <- "PKG_CPPFLAGS= -I$(PKGROOT)/src/include $(STOCHTREE_CPPFLAGS)" +makevars_lines[grep(" -I$(PKGROOT)/include \\", makevars_lines, fixed = T)] <- " -I$(PKGROOT)/src/include \\" +makevars_lines <- makevars_lines[-c( + grep(" -I$(PKGROOT)/deps/eigen \\", makevars_lines, fixed = T), + grep(" -I$(PKGROOT)/deps/fmt/include \\", makevars_lines, fixed = T), + grep(" -I$(PKGROOT)/deps/fast_double_parser/include \\", makevars_lines, fixed = T) +)] writeLines(makevars_lines, cran_makevars) +# Overwrite PKG_CPPFLAGS in src/Makevars.win.in +cran_makevars_win <- file.path(cran_dir, "src/Makevars.win.in") +makevars_win_lines <- readLines(cran_makevars_win) +makevars_win_lines[grep(" -I$(PKGROOT)/include \\", makevars_win_lines, fixed = T)] <- " -I$(PKGROOT)/src/include \\" +makevars_win_lines <- makevars_win_lines[-c( + grep(" -I$(PKGROOT)/deps/eigen \\", makevars_win_lines, fixed = T), + grep(" -I$(PKGROOT)/deps/fmt/include \\", makevars_win_lines, fixed = T), + grep(" -I$(PKGROOT)/deps/fast_double_parser/include \\", makevars_win_lines, fixed = T) +)] +writeLines(makevars_win_lines, cran_makevars_win) + # Remove vignette deps from DESCRIPTION if no vignettes if (!include_vignettes) { cran_description <- file.path(cran_dir, "DESCRIPTION") diff --git a/debug/README.md b/debug/README.md index 8dc5a15c..905999fe 100644 --- a/debug/README.md +++ b/debug/README.md @@ -4,7 +4,7 @@ This subdirectory contains a debug program for the C++ codebase. The program takes several command line arguments (in order): 1. Which data-generating process (DGP) to run (integer-coded, see below for a detailed description) -1. Which leaf model to sample (integer-coded, see below for a detailed description) +2. Which leaf model to sample (integer-coded, see below for a detailed description) 3. Whether or not to include random effects (0 = no, 1 = yes) 4. Number of grow-from-root (GFR) samples 5. Number of MCMC samples @@ -13,6 +13,7 @@ The program takes several command line arguments (in order): 8. [Optional] index of outcome column in data file (leave this blank as `0`) 9. [Optional] comma-delimited string of column indices of covariates (leave this blank as `""`) 10. [Optional] comma-delimited string of column indices of leaf regression bases (leave this blank as `""`) +11. [Optional] number of threads to use in the GFR sampler (leave this blank as `-1`) The DGPs are numbered as follows: @@ -30,6 +31,6 @@ The models are numbered as follows: For an example of how to run this progam for DGP 0, leaf model 1, no random effects, 10 GFR samples, 100 MCMC samples and a default seed (`-1`), run -`./build/debugstochtree 0 1 0 10 100 -1 "" 0 "" ""` +`./build/debugstochtree 0 1 0 10 100 -1 "" 0 "" "" -1` from the main `stochtree` project directory after building with `BUILD_DEBUG_TARGETS` set to `ON`. diff --git a/debug/api_debug.cpp b/debug/api_debug.cpp index 8958bd93..54457df1 100644 --- a/debug/api_debug.cpp +++ b/debug/api_debug.cpp @@ -423,7 +423,7 @@ void OutcomeOffsetScale(ColumnVector& residual, double& outcome_offset, double& void RunDebug(int dgp_num = 0, const ModelType model_type = kConstantLeafGaussian, bool rfx_included = false, int num_gfr = 10, int num_mcmc = 100, int random_seed = -1, std::string dataset_filename = "", int outcome_col = -1, std::string covariate_cols = "", - std::string basis_cols = "") { + std::string basis_cols = "", int num_threads = -1) { // Flag the data as row-major bool row_major = true; @@ -688,13 +688,13 @@ void RunDebug(int dgp_num = 0, const ModelType model_type = kConstantLeafGaussia // Sample tree ensemble if (model_type == ModelType::kConstantLeafGaussian) { - GFRSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, feature_types, cutpoint_grid_size, true, true, true, num_features_subsample); + GFRSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, feature_types, cutpoint_grid_size, true, true, true, num_features_subsample, num_threads); } else if (model_type == ModelType::kUnivariateRegressionLeafGaussian) { - GFRSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, feature_types, cutpoint_grid_size, true, true, true, num_features_subsample); + GFRSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, feature_types, cutpoint_grid_size, true, true, true, num_features_subsample, num_threads); } else if (model_type == ModelType::kMultivariateRegressionLeafGaussian) { - GFRSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, feature_types, cutpoint_grid_size, true, true, true, num_features_subsample, omega_cols); + GFRSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, feature_types, cutpoint_grid_size, true, true, true, num_features_subsample, num_threads, omega_cols); } else if (model_type == ModelType::kLogLinearVariance) { - GFRSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, feature_types, cutpoint_grid_size, true, true, false, num_features_subsample); + GFRSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, feature_types, cutpoint_grid_size, true, true, false, num_features_subsample, num_threads); } if (rfx_included) { @@ -725,13 +725,13 @@ void RunDebug(int dgp_num = 0, const ModelType model_type = kConstantLeafGaussia // Sample tree ensemble if (model_type == ModelType::kConstantLeafGaussian) { - MCMCSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, true, true, true); + MCMCSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, true, true, true, num_threads); } else if (model_type == ModelType::kUnivariateRegressionLeafGaussian) { - MCMCSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, true, true, true); + MCMCSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, true, true, true, num_threads); } else if (model_type == ModelType::kMultivariateRegressionLeafGaussian) { - MCMCSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, true, true, true, omega_cols); + MCMCSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, true, true, true, num_threads, omega_cols); } else if (model_type == ModelType::kLogLinearVariance) { - MCMCSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, true, true, false); + MCMCSampleOneIter(active_forest, tracker, forest_samples, std::get(leaf_model), dataset, residual, tree_prior, gen, variable_weights, sweep_indices, global_variance, true, true, false, num_threads); } if (rfx_included) { @@ -804,8 +804,9 @@ int main(int argc, char* argv[]) { int outcome_col = std::stoi(argv[8]); std::string covariate_cols = argv[9]; std::string basis_cols = argv[10]; + int num_threads = std::stoi(argv[11]); // Run the debug program StochTree::RunDebug(dgp_num, model_type, rfx_included, num_gfr, num_mcmc, random_seed, - dataset_filename, outcome_col, covariate_cols, basis_cols); + dataset_filename, outcome_col, covariate_cols, basis_cols, num_threads); } diff --git a/demo/debug/multi_chain.py b/demo/debug/multi_chain.py index 6d8aef68..e93577eb 100644 --- a/demo/debug/multi_chain.py +++ b/demo/debug/multi_chain.py @@ -118,11 +118,15 @@ def outcome_mean(X, W): ) # Inspect the model outputs -y_hat_mcmc_2 = bart_model_2.predict(X_test, basis_test) +bart_preds_2 = bart_model_2.predict(X_test, basis_test) +y_hat_mcmc_2 = bart_preds_2['y_hat'] y_avg_mcmc_2 = np.squeeze(y_hat_mcmc_2).mean(axis=1, keepdims=True) -y_hat_mcmc_3 = bart_model_3.predict(X_test, basis_test) +y_avg_mcmc_2 = np.squeeze(y_hat_mcmc_2).mean(axis=1, keepdims=True) +bart_preds_3 = bart_model_3.predict(X_test, basis_test) +y_hat_mcmc_3 = bart_preds_3['y_hat'] y_avg_mcmc_3 = np.squeeze(y_hat_mcmc_3).mean(axis=1, keepdims=True) -y_hat_mcmc_4 = bart_model_4.predict(X_test, basis_test) +bart_preds_4 = bart_model_4.predict(X_test, basis_test) +y_hat_mcmc_4 = bart_preds_4['y_hat'] y_avg_mcmc_4 = np.squeeze(y_hat_mcmc_4).mean(axis=1, keepdims=True) y_df = pd.DataFrame( np.concatenate( diff --git a/demo/debug/parallel_multi_chain.py b/demo/debug/parallel_multi_chain.py index ee114aee..ee618df5 100644 --- a/demo/debug/parallel_multi_chain.py +++ b/demo/debug/parallel_multi_chain.py @@ -145,7 +145,8 @@ def outcome_mean(X, W): ) # Inspect the model outputs - y_hat_mcmc = combined_bart.predict(X_test, basis_test) + bart_preds = combined_bart.predict(X_test, basis_test) + y_hat_mcmc = bart_preds['y_hat'] y_avg_mcmc = np.squeeze(y_hat_mcmc).mean(axis=1, keepdims=True) y_df = pd.DataFrame( np.concatenate((y_avg_mcmc, np.expand_dims(y_test, axis=1)), axis=1), diff --git a/demo/debug/random_effects.py b/demo/debug/random_effects.py index 9a3c4350..d6bcc358 100644 --- a/demo/debug/random_effects.py +++ b/demo/debug/random_effects.py @@ -75,7 +75,8 @@ def outcome_mean(group_labels, basis): rfx_model.sample(rfx_dataset, outcome, rfx_tracker, rfx_container, True, 1.0, cpp_rng) # Inspect the samples -rfx_preds = rfx_container.predict(group_labels, basis) * y_std + y_bar +bart_preds = rfx_container.predict(group_labels, basis) +rfx_preds = bart_preds['y_hat'] * y_std + y_bar rfx_comparison_df = pd.DataFrame( np.concatenate((rfx_preds, np.expand_dims(rfx_term, axis=1)), axis=1), columns=["Predicted", "Actual"], diff --git a/demo/debug/rfx_serialization.py b/demo/debug/rfx_serialization.py index db80edea..fec857b6 100644 --- a/demo/debug/rfx_serialization.py +++ b/demo/debug/rfx_serialization.py @@ -60,11 +60,13 @@ def rfx_mean(group_labels, basis): rfx_basis_train=basis, num_gfr=10, num_mcmc=10) # Extract predictions from the sampler -y_hat_orig = bart_orig.predict(X, W, group_labels, basis) +bart_preds_orig = bart_orig.predict(X, W, group_labels, basis) +y_hat_orig = bart_preds_orig['y_hat'] # "Round-trip" the model to JSON string and back and check that the predictions agree bart_json_string = bart_orig.to_json() bart_reloaded = BARTModel() bart_reloaded.from_json(bart_json_string) -y_hat_reloaded = bart_reloaded.predict(X, W, group_labels, basis) +bart_preds_reloaded = bart_reloaded.predict(X, W, group_labels, basis) +y_hat_reloaded = bart_preds_reloaded['y_hat'] np.testing.assert_almost_equal(y_hat_orig, y_hat_reloaded) \ No newline at end of file diff --git a/demo/debug/serialization.py b/demo/debug/serialization.py index 4ee14cee..75c14679 100644 --- a/demo/debug/serialization.py +++ b/demo/debug/serialization.py @@ -98,11 +98,13 @@ def outcome_mean(X, W): global_var_samples[i+1] = global_var_model.sample_one_iteration(residual, cpp_rng, a_global, b_global) # Extract predictions from the sampler -y_hat_orig = forest_container.predict(dataset) +bart_preds_orig = forest_container.predict(dataset) +y_hat_orig = bart_preds_orig['y_hat'] # "Round-trip" the forest to JSON string and back and check that the predictions agree forest_json_string = forest_container.dump_json_string() forest_container_reloaded = ForestContainer(num_trees, W.shape[1], False, False) forest_container_reloaded.load_from_json_string(forest_json_string) -y_hat_reloaded = forest_container_reloaded.predict(dataset) +bart_preds_reloaded = forest_container_reloaded.predict(dataset) +y_hat_reloaded = bart_preds_reloaded['y_hat'] np.testing.assert_almost_equal(y_hat_orig, y_hat_reloaded) diff --git a/demo/notebooks/prototype_interface.ipynb b/demo/notebooks/prototype_interface.ipynb index 064ce9b6..1810241a 100644 --- a/demo/notebooks/prototype_interface.ipynb +++ b/demo/notebooks/prototype_interface.ipynb @@ -341,7 +341,8 @@ "outputs": [], "source": [ "# Forest predictions\n", - "forest_preds = forest_container.predict(dataset) * y_std + y_bar\n", + "bart_preds = forest_container.predict(dataset)\n", + "forest_preds = bart_preds['y_hat'] * y_std + y_bar\n", "forest_preds_gfr = forest_preds[:, :num_warmstart]\n", "forest_preds_mcmc = forest_preds[:, num_warmstart:num_samples]\n", "\n", @@ -1101,7 +1102,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "venv (3.12.9)", "language": "python", "name": "python3" }, diff --git a/demo/notebooks/serialization.ipynb b/demo/notebooks/serialization.ipynb index 3e023acf..e9b0f4c1 100644 --- a/demo/notebooks/serialization.ipynb +++ b/demo/notebooks/serialization.ipynb @@ -241,7 +241,8 @@ "metadata": {}, "outputs": [], "source": [ - "y_hat_deserialized = bart_model_deserialized.predict(X_test, basis_test)\n", + "bart_preds_deserialized = bart_model_deserialized.predict(X_test, basis_test)\n", + "y_hat_deserialized = bart_preds_deserialized['y_hat']\n", "y_avg_mcmc_deserialized = np.squeeze(y_hat_deserialized).mean(axis=1, keepdims=True)\n", "y_df = pd.DataFrame(\n", " np.concatenate((y_avg_mcmc, y_avg_mcmc_deserialized), axis=1),\n", @@ -325,7 +326,8 @@ "metadata": {}, "outputs": [], "source": [ - "y_hat_file_deserialized = bart_model_file_deserialized.predict(X_test, basis_test)\n", + "bart_preds_file_deserialized = bart_model_file_deserialized.predict(X_test, basis_test)\n", + "y_hat_file_deserialized = bart_preds_file_deserialized['y_hat']\n", "y_avg_mcmc_file_deserialized = np.squeeze(y_hat_file_deserialized).mean(\n", " axis=1, keepdims=True\n", ")\n", @@ -381,7 +383,7 @@ ], "metadata": { "kernelspec": { - "display_name": "venv", + "display_name": "venv (3.12.9)", "language": "python", "name": "python3" }, diff --git a/include/stochtree/category_tracker.h b/include/stochtree/category_tracker.h index e5817419..2ce44635 100644 --- a/include/stochtree/category_tracker.h +++ b/include/stochtree/category_tracker.h @@ -29,12 +29,8 @@ #include #include -#include #include #include -#include -#include -#include #include namespace StochTree { diff --git a/include/stochtree/common.h b/include/stochtree/common.h index c7aab3df..cd57eea2 100644 --- a/include/stochtree/common.h +++ b/include/stochtree/common.h @@ -8,22 +8,18 @@ #include #include -#include #include #include #include #include #include -#include #include #include #include -#include #include #include #include #include -#include #include #include diff --git a/include/stochtree/container.h b/include/stochtree/container.h index bb0e7849..4b75ef2f 100644 --- a/include/stochtree/container.h +++ b/include/stochtree/container.h @@ -11,12 +11,7 @@ #include #include -#include -#include #include -#include -#include -#include namespace StochTree { diff --git a/include/stochtree/cutpoint_candidates.h b/include/stochtree/cutpoint_candidates.h index 8c19013a..76f1df4c 100644 --- a/include/stochtree/cutpoint_candidates.h +++ b/include/stochtree/cutpoint_candidates.h @@ -42,8 +42,6 @@ #include #include -#include - namespace StochTree { /*! \brief Computing and tracking cutpoints available for a given feature at a given node diff --git a/include/stochtree/data.h b/include/stochtree/data.h index 47b4fb9b..cc62ab06 100644 --- a/include/stochtree/data.h +++ b/include/stochtree/data.h @@ -9,7 +9,6 @@ #include #include #include -#include namespace StochTree { diff --git a/include/stochtree/ensemble.h b/include/stochtree/ensemble.h index 4624b5a4..4f6ddf42 100644 --- a/include/stochtree/ensemble.h +++ b/include/stochtree/ensemble.h @@ -14,12 +14,6 @@ #include #include -#include -#include -#include -#include -#include - using json = nlohmann::json; namespace StochTree { diff --git a/include/stochtree/io.h b/include/stochtree/io.h index 3bc277fb..55963946 100644 --- a/include/stochtree/io.h +++ b/include/stochtree/io.h @@ -28,12 +28,10 @@ #include #include #include -#include #include #include #include #include -#include #include #include diff --git a/include/stochtree/leaf_model.h b/include/stochtree/leaf_model.h index 0e5234b5..5359775d 100644 --- a/include/stochtree/leaf_model.h +++ b/include/stochtree/leaf_model.h @@ -13,12 +13,12 @@ #include #include #include +#include #include #include #include #include -#include #include namespace StochTree { @@ -396,6 +396,16 @@ class GaussianConstantSuffStat { sum_w = 0.0; sum_yw = 0.0; } + /*! + * \brief Increment the value of each sufficient statistic by the values provided by `suff_stat` + * + * \param suff_stat Sufficient statistic to be added to the current sufficient statistics + */ + void AddSuffStatInplace(GaussianConstantSuffStat& suff_stat) { + n += suff_stat.n; + sum_w += suff_stat.sum_w; + sum_yw += suff_stat.sum_yw; + } /*! * \brief Set the value of each sufficient statistic to the sum of the values provided by `lhs` and `rhs` * @@ -550,6 +560,16 @@ class GaussianUnivariateRegressionSuffStat { sum_xxw = 0.0; sum_yxw = 0.0; } + /*! + * \brief Increment the value of each sufficient statistic by the values provided by `suff_stat` + * + * \param suff_stat Sufficient statistic to be added to the current sufficient statistics + */ + void AddSuffStatInplace(GaussianUnivariateRegressionSuffStat& suff_stat) { + n += suff_stat.n; + sum_xxw += suff_stat.sum_xxw; + sum_yxw += suff_stat.sum_yxw; + } /*! * \brief Set the value of each sufficient statistic to the sum of the values provided by `lhs` and `rhs` * @@ -695,6 +715,16 @@ class GaussianMultivariateRegressionSuffStat { XtWX = Eigen::MatrixXd::Zero(p, p); ytWX = Eigen::MatrixXd::Zero(1, p); } + /*! + * \brief Increment the value of each sufficient statistic by the values provided by `suff_stat` + * + * \param suff_stat Sufficient statistic to be added to the current sufficient statistics + */ + void AddSuffStatInplace(GaussianMultivariateRegressionSuffStat& suff_stat) { + n += suff_stat.n; + XtWX += suff_stat.XtWX; + ytWX += suff_stat.ytWX; + } /*! * \brief Set the value of each sufficient statistic to the sum of the values provided by `lhs` and `rhs` * @@ -829,6 +859,15 @@ class LogLinearVarianceSuffStat { n = 0; weighted_sum_ei = 0.0; } + /*! + * \brief Increment the value of each sufficient statistic by the values provided by `suff_stat` + * + * \param suff_stat Sufficient statistic to be added to the current sufficient statistics + */ + void AddSuffStatInplace(LogLinearVarianceSuffStat& suff_stat) { + n += suff_stat.n; + weighted_sum_ei += suff_stat.weighted_sum_ei; + } /*! * \brief Set the value of each sufficient statistic to the sum of the values provided by `lhs` and `rhs` * @@ -1005,22 +1044,73 @@ static inline LeafModelVariant leafModelFactory(ModelType model_type, double tau } } -template -static inline void AccumulateSuffStatProposed(SuffStatType& node_suff_stat, SuffStatType& left_suff_stat, SuffStatType& right_suff_stat, ForestDataset& dataset, ForestTracker& tracker, - ColumnVector& residual, double global_variance, TreeSplit& split, int tree_num, int leaf_num, int split_feature) { - // Acquire iterators - auto node_begin_iter = tracker.UnsortedNodeBeginIterator(tree_num, leaf_num); - auto node_end_iter = tracker.UnsortedNodeEndIterator(tree_num, leaf_num); +template +static inline void AccumulateSuffStatProposed( + SuffStatType& node_suff_stat, SuffStatType& left_suff_stat, SuffStatType& right_suff_stat, ForestDataset& dataset, ForestTracker& tracker, + ColumnVector& residual, double global_variance, TreeSplit& split, int tree_num, int leaf_num, int split_feature, int num_threads, + SuffStatConstructorArgs&... suff_stat_args +) { + // Determine the position of the node's indices in the forest tracking data structure + int node_begin_index = tracker.UnsortedNodeBegin(tree_num, leaf_num); + int node_end_index = tracker.UnsortedNodeEnd(tree_num, leaf_num); - // Accumulate sufficient statistics - for (auto i = node_begin_iter; i != node_end_iter; i++) { - auto idx = *i; - double feature_value = dataset.CovariateValue(idx, split_feature); - node_suff_stat.IncrementSuffStat(dataset, residual.GetData(), tracker, idx, tree_num); - if (split.SplitTrue(feature_value)) { - left_suff_stat.IncrementSuffStat(dataset, residual.GetData(), tracker, idx, tree_num); - } else { - right_suff_stat.IncrementSuffStat(dataset, residual.GetData(), tracker, idx, tree_num); + // Extract pointer to the feature partition for tree_num + UnsortedNodeSampleTracker* unsorted_node_sample_tracker = tracker.GetUnsortedNodeSampleTracker(); + FeatureUnsortedPartition* feature_partition = unsorted_node_sample_tracker->GetFeaturePartition(tree_num); + + // Determine the number of threads to use + int chunk_size = (node_end_index - node_begin_index) / num_threads; + if (chunk_size < 100) { + num_threads = 1; + chunk_size = node_end_index - node_begin_index; + } + + if (num_threads > 1) { + // Split the work into num_threads chunks + std::vector> thread_ranges(num_threads); + std::vector thread_suff_stats_node; + std::vector thread_suff_stats_left; + std::vector thread_suff_stats_right; + for (int i = 0; i < num_threads; i++) { + thread_ranges[i] = std::make_pair(node_begin_index + i * chunk_size, + node_begin_index + (i + 1) * chunk_size); + thread_suff_stats_node.emplace_back(suff_stat_args...); + thread_suff_stats_left.emplace_back(suff_stat_args...); + thread_suff_stats_right.emplace_back(suff_stat_args...); + } + + // Accumulate sufficient statistics + StochTree::ParallelFor(0, num_threads, num_threads, [&](int i) { + int start_idx = thread_ranges[i].first; + int end_idx = thread_ranges[i].second; + for (int idx = start_idx; idx < end_idx; idx++) { + int obs_num = feature_partition->indices_[idx]; + double feature_value = dataset.CovariateValue(obs_num, split_feature); + thread_suff_stats_node[i].IncrementSuffStat(dataset, residual.GetData(), tracker, obs_num, tree_num); + if (split.SplitTrue(feature_value)) { + thread_suff_stats_left[i].IncrementSuffStat(dataset, residual.GetData(), tracker, obs_num, tree_num); + } else { + thread_suff_stats_right[i].IncrementSuffStat(dataset, residual.GetData(), tracker, obs_num, tree_num); + } + } + }); + + // Combine the thread-local sufficient statistics + for (int i = 0; i < num_threads; i++) { + node_suff_stat.AddSuffStatInplace(thread_suff_stats_node[i]); + left_suff_stat.AddSuffStatInplace(thread_suff_stats_left[i]); + right_suff_stat.AddSuffStatInplace(thread_suff_stats_right[i]); + } + } else { + for (int idx = node_begin_index; idx < node_end_index; idx++) { + int obs_num = feature_partition->indices_[idx]; + double feature_value = dataset.CovariateValue(obs_num, split_feature); + node_suff_stat.IncrementSuffStat(dataset, residual.GetData(), tracker, obs_num, tree_num); + if (split.SplitTrue(feature_value)) { + left_suff_stat.IncrementSuffStat(dataset, residual.GetData(), tracker, obs_num, tree_num); + } else { + right_suff_stat.IncrementSuffStat(dataset, residual.GetData(), tracker, obs_num, tree_num); + } } } } diff --git a/include/stochtree/log.h b/include/stochtree/log.h index 3a4c5600..9f64c31b 100644 --- a/include/stochtree/log.h +++ b/include/stochtree/log.h @@ -15,8 +15,6 @@ #include #include #include -#include -#include #include #include diff --git a/include/stochtree/meta.h b/include/stochtree/meta.h index 991c254f..d0aa4049 100644 --- a/include/stochtree/meta.h +++ b/include/stochtree/meta.h @@ -14,7 +14,6 @@ #include #include #include -#include #include #include #include diff --git a/include/stochtree/openmp_utils.h b/include/stochtree/openmp_utils.h new file mode 100644 index 00000000..28ed31fb --- /dev/null +++ b/include/stochtree/openmp_utils.h @@ -0,0 +1,114 @@ +#ifndef STOCHTREE_OPENMP_UTILS_H +#define STOCHTREE_OPENMP_UTILS_H + +#include +#include + +namespace StochTree { + +#ifdef STOCHTREE_OPENMP_AVAILABLE + +#include +#define STOCHTREE_HAS_OPENMP 1 + +// OpenMP thread management +inline int get_max_threads() { + return omp_get_max_threads(); +} + +inline int get_thread_num() { + return omp_get_thread_num(); +} + +inline int get_num_threads() { + return omp_get_num_threads(); +} + +inline void set_num_threads(int num_threads) { + omp_set_num_threads(num_threads); +} + +#define STOCHTREE_PARALLEL_FOR(num_threads) \ + _Pragma("omp parallel for num_threads(num_threads)") + +#define STOCHTREE_REDUCTION_ADD(var) \ + _Pragma("omp reduction(+:var)") + +#define STOCHTREE_CRITICAL \ + _Pragma("omp critical") + +#else +#define STOCHTREE_HAS_OPENMP 0 + +// Fallback implementations when OpenMP is not available +inline int get_max_threads() {return 1;} + +inline int get_thread_num() {return 0;} + +inline int get_num_threads() {return 1;} + +inline void set_num_threads(int num_threads) {} + +#define STOCHTREE_PARALLEL_FOR(num_threads) + +#define STOCHTREE_REDUCTION_ADD(var) + +#define STOCHTREE_CRITICAL + +#endif + +static int GetMaxThreads() { + return get_max_threads(); +} + +static int GetCurrentThreadNum() { + return get_thread_num(); +} + +static int GetNumThreads() { + return get_num_threads(); +} + +static void SetNumThreads(int num_threads) { + set_num_threads(num_threads); +} + +static bool IsOpenMPAvailable() { + return STOCHTREE_HAS_OPENMP; +} + +static int GetOptimalThreadCount(int workload_size, int min_work_per_thread = 1000) { + if (!IsOpenMPAvailable()) { + return 1; + } + + int max_threads = GetMaxThreads(); + int optimal_threads = workload_size / min_work_per_thread; + + return std::min(optimal_threads, max_threads); +} + +// Parallel execution utilities +template +void ParallelFor(int start, int end, int num_threads, Func func) { + if (num_threads <= 0) { + num_threads = GetOptimalThreadCount(end - start); + } + + if (num_threads == 1 || !STOCHTREE_HAS_OPENMP) { + // Sequential execution + for (int i = start; i < end; ++i) { + func(i); + } + } else { + // Parallel execution + STOCHTREE_PARALLEL_FOR(num_threads) + for (int i = start; i < end; ++i) { + func(i); + } + } +} + +} // namespace StochTree + +#endif // STOCHTREE_OPENMP_UTILS_H \ No newline at end of file diff --git a/include/stochtree/partition_tracker.h b/include/stochtree/partition_tracker.h index 6546b593..0790d87a 100644 --- a/include/stochtree/partition_tracker.h +++ b/include/stochtree/partition_tracker.h @@ -28,13 +28,10 @@ #include #include #include +#include #include -#include #include -#include -#include -#include #include namespace StochTree { @@ -68,7 +65,7 @@ class ForestTracker { void UpdateSampleTrackers(TreeEnsemble& forest, ForestDataset& dataset); void UpdateSampleTrackersResidual(TreeEnsemble& forest, ForestDataset& dataset, ColumnVector& residual, bool is_mean_model); void ResetRoot(Eigen::MatrixXd& covariates, std::vector& feature_types, int32_t tree_num); - void AddSplit(Eigen::MatrixXd& covariates, TreeSplit& split, int32_t split_feature, int32_t tree_id, int32_t split_node_id, int32_t left_node_id, int32_t right_node_id, bool keep_sorted = false); + void AddSplit(Eigen::MatrixXd& covariates, TreeSplit& split, int32_t split_feature, int32_t tree_id, int32_t split_node_id, int32_t left_node_id, int32_t right_node_id, bool keep_sorted = false, int num_threads = -1); void RemoveSplit(Eigen::MatrixXd& covariates, Tree* tree, int32_t tree_id, int32_t split_node_id, int32_t left_node_id, int32_t right_node_id, bool keep_sorted = false); double GetSamplePrediction(data_size_t sample_id); double GetTreeSamplePrediction(data_size_t sample_id, int tree_id); @@ -434,7 +431,7 @@ class UnsortedNodeSampleTracker { /*! \brief Number of trees */ int NumTrees() { return num_trees_; } - /*! \brief Number of trees */ + /*! \brief Return a pointer to the feature partition tracking tree i */ FeatureUnsortedPartition* GetFeaturePartition(int i) { return feature_partitions_[i].get(); } private: @@ -615,24 +612,24 @@ class SortedNodeSampleTracker { } /*! \brief Partition a node based on a new split rule */ - void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int feature_split, TreeSplit& split) { - for (int i = 0; i < num_features_; i++) { + void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int feature_split, TreeSplit& split, int num_threads = -1) { + StochTree::ParallelFor(0, num_features_, num_threads, [&](int i) { feature_partitions_[i]->SplitFeature(covariates, node_id, feature_split, split); - } + }); } /*! \brief Partition a node based on a new split rule */ - void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int feature_split, double split_value) { - for (int i = 0; i < num_features_; i++) { + void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int feature_split, double split_value, int num_threads = -1) { + StochTree::ParallelFor(0, num_features_, num_threads, [&](int i) { feature_partitions_[i]->SplitFeatureNumeric(covariates, node_id, feature_split, split_value); - } + }); } /*! \brief Partition a node based on a new split rule */ - void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int feature_split, std::vector const& category_list) { - for (int i = 0; i < num_features_; i++) { + void PartitionNode(Eigen::MatrixXd& covariates, int node_id, int feature_split, std::vector const& category_list, int num_threads = -1) { + StochTree::ParallelFor(0, num_features_, num_threads, [&](int i) { feature_partitions_[i]->SplitFeatureCategorical(covariates, node_id, feature_split, category_list); - } + }); } /*! \brief First index of data points contained in node_id */ diff --git a/include/stochtree/random.h b/include/stochtree/random.h index a841f396..3d39b647 100644 --- a/include/stochtree/random.h +++ b/include/stochtree/random.h @@ -5,7 +5,6 @@ #ifndef STOCHTREE_RANDOM_H_ #define STOCHTREE_RANDOM_H_ -#include #include #include #include diff --git a/include/stochtree/random_effects.h b/include/stochtree/random_effects.h index 701ebeaa..b322a560 100644 --- a/include/stochtree/random_effects.h +++ b/include/stochtree/random_effects.h @@ -17,14 +17,11 @@ #include #include -#include #include #include #include #include -#include #include -#include #include namespace StochTree { diff --git a/include/stochtree/tree.h b/include/stochtree/tree.h index 85ce7191..3810e3cb 100644 --- a/include/stochtree/tree.h +++ b/include/stochtree/tree.h @@ -13,9 +13,6 @@ #include #include -#include -#include -#include #include #include diff --git a/include/stochtree/tree_sampler.h b/include/stochtree/tree_sampler.h index 8810b938..68c9c15a 100644 --- a/include/stochtree/tree_sampler.h +++ b/include/stochtree/tree_sampler.h @@ -8,18 +8,13 @@ #include #include #include +#include #include #include #include -#include -#include #include #include -#include -#include -#include -#include #include namespace StochTree { @@ -27,7 +22,7 @@ namespace StochTree { /*! * \defgroup sampling_group Forest Sampler API * - * \brief Functions for sampling from a forest. The core interfce of these functions, + * \brief Functions for sampling from a forest. The core interface of these functions, * as used by the R, Python, and standalone C++ program, is defined by * \ref MCMCSampleOneIter, which runs one iteration of the MCMC sampler for a * given forest, and \ref GFRSampleOneIter, which runs one iteration of the @@ -152,7 +147,7 @@ static inline bool NodeNonConstant(ForestDataset& dataset, ForestTracker& tracke } static inline void AddSplitToModel(ForestTracker& tracker, ForestDataset& dataset, TreePrior& tree_prior, TreeSplit& split, std::mt19937& gen, Tree* tree, - int tree_num, int leaf_node, int feature_split, bool keep_sorted = false) { + int tree_num, int leaf_node, int feature_split, bool keep_sorted = false, int num_threads = -1) { // Use zeros as a "temporary" leaf values since we draw leaf parameters after tree sampling is complete if (tree->OutputDimension() > 1) { std::vector temp_leaf_values(tree->OutputDimension(), 0.); @@ -165,7 +160,7 @@ static inline void AddSplitToModel(ForestTracker& tracker, ForestDataset& datase int right_node = tree->RightChild(leaf_node); // Update the ForestTracker - tracker.AddSplit(dataset.GetCovariates(), split, feature_split, tree_num, leaf_node, left_node, right_node, keep_sorted); + tracker.AddSplit(dataset.GetCovariates(), split, feature_split, tree_num, leaf_node, left_node, right_node, keep_sorted, num_threads); } static inline void RemoveSplitFromModel(ForestTracker& tracker, ForestDataset& dataset, TreePrior& tree_prior, std::mt19937& gen, Tree* tree, @@ -403,7 +398,7 @@ template EvaluateProposedSplit( ForestDataset& dataset, ForestTracker& tracker, ColumnVector& residual, LeafModel& leaf_model, TreeSplit& split, int tree_num, int leaf_num, int split_feature, double global_variance, - LeafSuffStatConstructorArgs&... leaf_suff_stat_args + int num_threads, LeafSuffStatConstructorArgs&... leaf_suff_stat_args ) { // Initialize sufficient statistics LeafSuffStat node_suff_stat = LeafSuffStat(leaf_suff_stat_args...); @@ -411,8 +406,11 @@ static inline std::tuple EvaluatePropo LeafSuffStat right_suff_stat = LeafSuffStat(leaf_suff_stat_args...); // Accumulate sufficient statistics - AccumulateSuffStatProposed(node_suff_stat, left_suff_stat, right_suff_stat, dataset, tracker, - residual, global_variance, split, tree_num, leaf_num, split_feature); + AccumulateSuffStatProposed( + node_suff_stat, left_suff_stat, right_suff_stat, dataset, tracker, + residual, global_variance, split, tree_num, leaf_num, split_feature, num_threads, + leaf_suff_stat_args... + ); data_size_t left_n = left_suff_stat.n; data_size_t right_n = right_suff_stat.n; @@ -469,140 +467,12 @@ static inline void AdjustStateAfterTreeSampling(ForestTracker& tracker, LeafMode } } -template -static inline void EvaluateAllPossibleSplits( - ForestDataset& dataset, ForestTracker& tracker, ColumnVector& residual, TreePrior& tree_prior, std::mt19937& gen, LeafModel& leaf_model, double global_variance, int tree_num, int split_node_id, - std::vector& log_cutpoint_evaluations, std::vector& cutpoint_features, std::vector& cutpoint_values, std::vector& cutpoint_feature_types, - data_size_t& valid_cutpoint_count, CutpointGridContainer& cutpoint_grid_container, data_size_t node_begin, data_size_t node_end, std::vector& variable_weights, - std::vector& feature_types, std::vector& feature_subset, LeafSuffStatConstructorArgs&... leaf_suff_stat_args -) { - // Initialize sufficient statistics - LeafSuffStat node_suff_stat = LeafSuffStat(leaf_suff_stat_args...); - LeafSuffStat left_suff_stat = LeafSuffStat(leaf_suff_stat_args...); - LeafSuffStat right_suff_stat = LeafSuffStat(leaf_suff_stat_args...); - - // Accumulate aggregate sufficient statistic for the node to be split - AccumulateSingleNodeSuffStat(node_suff_stat, dataset, tracker, residual, tree_num, split_node_id); - - // Compute the "no split" log marginal likelihood - double no_split_log_ml = leaf_model.NoSplitLogMarginalLikelihood(node_suff_stat, global_variance); - - // Unpack data - Eigen::MatrixXd covariates = dataset.GetCovariates(); - Eigen::VectorXd outcome = residual.GetData(); - Eigen::VectorXd var_weights; - bool has_weights = dataset.HasVarWeights(); - if (has_weights) var_weights = dataset.GetVarWeights(); - - // Minimum size of newly created leaf nodes (used to rule out invalid splits) - int32_t min_samples_in_leaf = tree_prior.GetMinSamplesLeaf(); - - // Compute sufficient statistics for each possible split - data_size_t num_cutpoints = 0; - bool valid_split = false; - data_size_t node_row_iter; - data_size_t current_bin_begin, current_bin_size, next_bin_begin; - data_size_t feature_sort_idx; - data_size_t row_iter_idx; - double outcome_val, outcome_val_sq; - FeatureType feature_type; - double feature_value = 0.0; - double cutoff_value = 0.0; - double log_split_eval = 0.0; - double split_log_ml; - for (int j = 0; j < covariates.cols(); j++) { - - if ((std::abs(variable_weights.at(j)) > kEpsilon) && (feature_subset[j])) { - // Enumerate cutpoint strides - cutpoint_grid_container.CalculateStrides(covariates, outcome, tracker.GetSortedNodeSampleTracker(), split_node_id, node_begin, node_end, j, feature_types); - - // Reset sufficient statistics - left_suff_stat.ResetSuffStat(); - right_suff_stat.ResetSuffStat(); - - // Iterate through possible cutpoints - int32_t num_feature_cutpoints = cutpoint_grid_container.NumCutpoints(j); - feature_type = feature_types[j]; - // Since we partition an entire cutpoint bin to the left, we must stop one bin before the total number of cutpoint bins - for (data_size_t cutpoint_idx = 0; cutpoint_idx < (num_feature_cutpoints - 1); cutpoint_idx++) { - current_bin_begin = cutpoint_grid_container.BinStartIndex(cutpoint_idx, j); - current_bin_size = cutpoint_grid_container.BinLength(cutpoint_idx, j); - next_bin_begin = cutpoint_grid_container.BinStartIndex(cutpoint_idx + 1, j); - - // Accumulate sufficient statistics for the left node - AccumulateCutpointBinSuffStat(left_suff_stat, tracker, cutpoint_grid_container, dataset, residual, - global_variance, tree_num, split_node_id, j, cutpoint_idx); - - // Compute the corresponding right node sufficient statistics - right_suff_stat.SubtractSuffStat(node_suff_stat, left_suff_stat); - - // Store the bin index as the "cutpoint value" - we can use this to query the actual split - // value or the set of split categories later on once a split is chose - cutoff_value = cutpoint_idx; - - // Only include cutpoint for consideration if it defines a valid split in the training data - valid_split = (left_suff_stat.SampleGreaterThanEqual(min_samples_in_leaf) && - right_suff_stat.SampleGreaterThanEqual(min_samples_in_leaf)); - if (valid_split) { - num_cutpoints++; - // Add to split rule vector - cutpoint_feature_types.push_back(feature_type); - cutpoint_features.push_back(j); - cutpoint_values.push_back(cutoff_value); - // Add the log marginal likelihood of the split to the split eval vector - split_log_ml = leaf_model.SplitLogMarginalLikelihood(left_suff_stat, right_suff_stat, global_variance); - log_cutpoint_evaluations.push_back(split_log_ml); - } - } - } - - } - - // Add the log marginal likelihood of the "no-split" option (adjusted for tree prior and cutpoint size per the XBART paper) - cutpoint_features.push_back(-1); - cutpoint_values.push_back(std::numeric_limits::max()); - cutpoint_feature_types.push_back(FeatureType::kNumeric); - log_cutpoint_evaluations.push_back(no_split_log_ml); - - // Update valid cutpoint count - valid_cutpoint_count = num_cutpoints; -} - -template -static inline void EvaluateCutpoints(Tree* tree, ForestTracker& tracker, LeafModel& leaf_model, ForestDataset& dataset, ColumnVector& residual, TreePrior& tree_prior, - std::mt19937& gen, int tree_num, double global_variance, int cutpoint_grid_size, int node_id, data_size_t node_begin, data_size_t node_end, - std::vector& log_cutpoint_evaluations, std::vector& cutpoint_features, std::vector& cutpoint_values, - std::vector& cutpoint_feature_types, data_size_t& valid_cutpoint_count, std::vector& variable_weights, - std::vector& feature_types, CutpointGridContainer& cutpoint_grid_container, - std::vector& feature_subset, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { - // Evaluate all possible cutpoints according to the leaf node model, - // recording their log-likelihood and other split information in a series of vectors. - // The last element of these vectors concerns the "no-split" option. - EvaluateAllPossibleSplits( - dataset, tracker, residual, tree_prior, gen, leaf_model, global_variance, tree_num, node_id, log_cutpoint_evaluations, - cutpoint_features, cutpoint_values, cutpoint_feature_types, valid_cutpoint_count, cutpoint_grid_container, - node_begin, node_end, variable_weights, feature_types, feature_subset, leaf_suff_stat_args... - ); - - // Compute an adjustment to reflect the no split prior probability and the number of cutpoints - double bart_prior_no_split_adj; - double alpha = tree_prior.GetAlpha(); - double beta = tree_prior.GetBeta(); - int node_depth = tree->GetDepth(node_id); - if (valid_cutpoint_count == 0) { - bart_prior_no_split_adj = std::log(((std::pow(1+node_depth, beta))/alpha) - 1.0); - } else { - bart_prior_no_split_adj = std::log(((std::pow(1+node_depth, beta))/alpha) - 1.0) + std::log(valid_cutpoint_count); - } - log_cutpoint_evaluations[log_cutpoint_evaluations.size()-1] += bart_prior_no_split_adj; -} - template static inline void SampleSplitRule(Tree* tree, ForestTracker& tracker, LeafModel& leaf_model, ForestDataset& dataset, ColumnVector& residual, TreePrior& tree_prior, std::mt19937& gen, int tree_num, double global_variance, int cutpoint_grid_size, std::unordered_map>& node_index_map, std::deque& split_queue, int node_id, data_size_t node_begin, data_size_t node_end, std::vector& variable_weights, - std::vector& feature_types, std::vector feature_subset, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { + std::vector& feature_types, std::vector feature_subset, int num_threads, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { // Leaf depth int leaf_depth = tree->GetDepth(node_id); @@ -610,41 +480,153 @@ static inline void SampleSplitRule(Tree* tree, ForestTracker& tracker, LeafModel int32_t max_depth = tree_prior.GetMaxDepth(); if ((max_depth == -1) || (leaf_depth < max_depth)) { - - // Cutpoint enumeration - std::vector log_cutpoint_evaluations; - std::vector cutpoint_features; - std::vector cutpoint_values; - std::vector cutpoint_feature_types; + + // Vector of vectors to store results for each feature + int p = dataset.NumCovariates(); + std::vector> feature_log_cutpoint_evaluations(p+1); + std::vector> feature_cutpoint_values(p+1); + std::vector feature_cutpoint_counts(p+1, 0); StochTree::data_size_t valid_cutpoint_count; - CutpointGridContainer cutpoint_grid_container(dataset.GetCovariates(), residual.GetData(), cutpoint_grid_size); - EvaluateCutpoints( - tree, tracker, leaf_model, dataset, residual, tree_prior, gen, tree_num, global_variance, - cutpoint_grid_size, node_id, node_begin, node_end, log_cutpoint_evaluations, cutpoint_features, - cutpoint_values, cutpoint_feature_types, valid_cutpoint_count, variable_weights, feature_types, - cutpoint_grid_container, feature_subset, leaf_suff_stat_args... - ); - // TODO: maybe add some checks here? - // Convert log marginal likelihood to marginal likelihood, normalizing by the maximum log-likelihood - double largest_mll = *std::max_element(log_cutpoint_evaluations.begin(), log_cutpoint_evaluations.end()); - std::vector cutpoint_evaluations(log_cutpoint_evaluations.size()); - for (data_size_t i = 0; i < log_cutpoint_evaluations.size(); i++){ - cutpoint_evaluations[i] = std::exp(log_cutpoint_evaluations[i] - largest_mll); + // Evaluate all possible cutpoints according to the leaf node model, + // recording their log-likelihood and other split information in a series of vectors. + + // Initialize node sufficient statistics + LeafSuffStat node_suff_stat = LeafSuffStat(leaf_suff_stat_args...); + + // Accumulate aggregate sufficient statistic for the node to be split + AccumulateSingleNodeSuffStat(node_suff_stat, dataset, tracker, residual, tree_num, node_id); + + // Compute the "no split" log marginal likelihood + double no_split_log_ml = leaf_model.NoSplitLogMarginalLikelihood(node_suff_stat, global_variance); + + // Unpack data + Eigen::MatrixXd& covariates = dataset.GetCovariates(); + Eigen::VectorXd& outcome = residual.GetData(); + Eigen::VectorXd var_weights; + bool has_weights = dataset.HasVarWeights(); + if (has_weights) var_weights = dataset.GetVarWeights(); + + // Minimum size of newly created leaf nodes (used to rule out invalid splits) + int32_t min_samples_in_leaf = tree_prior.GetMinSamplesLeaf(); + + // Compute sufficient statistics for each possible split + data_size_t num_cutpoints = 0; + if (num_threads == -1) { + num_threads = GetOptimalThreadCount(static_cast(covariates.cols() * covariates.rows())); } + + // Initialize cutpoint grid container + CutpointGridContainer cutpoint_grid_container(covariates, outcome, cutpoint_grid_size); - // Sample the split (including a "no split" option) - std::discrete_distribution split_dist(cutpoint_evaluations.begin(), cutpoint_evaluations.end()); - data_size_t split_chosen = split_dist(gen); + // Evaluate all possible splits for each feature in parallel + StochTree::ParallelFor(0, covariates.cols(), num_threads, [&](int j) { + if ((std::abs(variable_weights.at(j)) > kEpsilon) && (feature_subset[j])) { + // Enumerate cutpoint strides + cutpoint_grid_container.CalculateStrides(covariates, outcome, tracker.GetSortedNodeSampleTracker(), node_id, node_begin, node_end, j, feature_types); + + // Left and right node sufficient statistics + LeafSuffStat left_suff_stat = LeafSuffStat(leaf_suff_stat_args...); + LeafSuffStat right_suff_stat = LeafSuffStat(leaf_suff_stat_args...); + + // Iterate through possible cutpoints + int32_t num_feature_cutpoints = cutpoint_grid_container.NumCutpoints(j); + FeatureType feature_type = feature_types[j]; + // Since we partition an entire cutpoint bin to the left, we must stop one bin before the total number of cutpoint bins + for (data_size_t cutpoint_idx = 0; cutpoint_idx < (num_feature_cutpoints - 1); cutpoint_idx++) { + data_size_t current_bin_begin = cutpoint_grid_container.BinStartIndex(cutpoint_idx, j); + data_size_t current_bin_size = cutpoint_grid_container.BinLength(cutpoint_idx, j); + data_size_t next_bin_begin = cutpoint_grid_container.BinStartIndex(cutpoint_idx + 1, j); + + // Accumulate sufficient statistics for the left node + AccumulateCutpointBinSuffStat(left_suff_stat, tracker, cutpoint_grid_container, dataset, residual, + global_variance, tree_num, node_id, j, cutpoint_idx); + + // Compute the corresponding right node sufficient statistics + right_suff_stat.SubtractSuffStat(node_suff_stat, left_suff_stat); + + // Store the bin index as the "cutpoint value" - we can use this to query the actual split + // value or the set of split categories later on once a split is chose + double cutoff_value = cutpoint_idx; + + // Only include cutpoint for consideration if it defines a valid split in the training data + bool valid_split = (left_suff_stat.SampleGreaterThanEqual(min_samples_in_leaf) && + right_suff_stat.SampleGreaterThanEqual(min_samples_in_leaf)); + if (valid_split) { + feature_cutpoint_counts[j]++; + // Add to split rule vector + feature_cutpoint_values[j].push_back(cutoff_value); + // Add the log marginal likelihood of the split to the split eval vector + double split_log_ml = leaf_model.SplitLogMarginalLikelihood(left_suff_stat, right_suff_stat, global_variance); + feature_log_cutpoint_evaluations[j].push_back(split_log_ml); + } + } + } + }); + + // Compute total number of cutpoints + valid_cutpoint_count = std::accumulate(feature_cutpoint_counts.begin(), feature_cutpoint_counts.end(), 0); + + // Add the log marginal likelihood of the "no-split" option (adjusted for tree prior and cutpoint size per the XBART paper) + feature_log_cutpoint_evaluations[covariates.cols()].push_back(no_split_log_ml); - if (split_chosen == valid_cutpoint_count){ + // Compute an adjustment to reflect the no split prior probability and the number of cutpoints + double bart_prior_no_split_adj; + double alpha = tree_prior.GetAlpha(); + double beta = tree_prior.GetBeta(); + int node_depth = tree->GetDepth(node_id); + if (valid_cutpoint_count == 0) { + bart_prior_no_split_adj = std::log(((std::pow(1+node_depth, beta))/alpha) - 1.0); + } else { + bart_prior_no_split_adj = std::log(((std::pow(1+node_depth, beta))/alpha) - 1.0) + std::log(valid_cutpoint_count); + } + feature_log_cutpoint_evaluations[covariates.cols()][0] += bart_prior_no_split_adj; + + + // Convert log marginal likelihood to marginal likelihood, normalizing by the maximum log-likelihood + double largest_ml = -std::numeric_limits::infinity(); + for (int j = 0; j < p + 1; j++) { + if (feature_log_cutpoint_evaluations[j].size() > 0) { + double feature_max_ml = *std::max_element(feature_log_cutpoint_evaluations[j].begin(), feature_log_cutpoint_evaluations[j].end());; + largest_ml = std::max(largest_ml, feature_max_ml); + } + } + std::vector> feature_cutpoint_evaluations(p+1); + for (int j = 0; j < p + 1; j++) { + if (feature_log_cutpoint_evaluations[j].size() > 0) { + feature_cutpoint_evaluations[j].resize(feature_log_cutpoint_evaluations[j].size()); + for (int i = 0; i < feature_log_cutpoint_evaluations[j].size(); i++) { + feature_cutpoint_evaluations[j][i] = std::exp(feature_log_cutpoint_evaluations[j][i] - largest_ml); + } + } + } + + // Compute sum of marginal likelihoods for each feature + std::vector feature_total_cutpoint_evaluations(p+1, 0.0); + for (int j = 0; j < p + 1; j++) { + if (feature_log_cutpoint_evaluations[j].size() > 0) { + feature_total_cutpoint_evaluations[j] = std::accumulate(feature_cutpoint_evaluations[j].begin(), feature_cutpoint_evaluations[j].end(), 0.0); + } else { + feature_total_cutpoint_evaluations[j] = 0.0; + } + } + + // First, sample a feature according to feature_total_cutpoint_evaluations + std::discrete_distribution feature_dist(feature_total_cutpoint_evaluations.begin(), feature_total_cutpoint_evaluations.end()); + int feature_chosen = feature_dist(gen); + + // Then, sample a cutpoint according to feature_cutpoint_evaluations[feature_chosen] + std::discrete_distribution cutpoint_dist(feature_cutpoint_evaluations[feature_chosen].begin(), feature_cutpoint_evaluations[feature_chosen].end()); + data_size_t cutpoint_chosen = cutpoint_dist(gen); + + if (feature_chosen == p){ // "No split" sampled, don't split or add any nodes to split queue return; } else { // Split sampled - int feature_split = cutpoint_features[split_chosen]; - FeatureType feature_type = cutpoint_feature_types[split_chosen]; - double split_value = cutpoint_values[split_chosen]; + int feature_split = feature_chosen; + FeatureType feature_type = feature_types[feature_split]; + double split_value = feature_cutpoint_values[feature_split][cutpoint_chosen]; // Perform all of the relevant "split" operations in the model, tree and training dataset // Compute node sample size @@ -679,7 +661,7 @@ static inline void SampleSplitRule(Tree* tree, ForestTracker& tracker, LeafModel } // Add split to tree and trackers - AddSplitToModel(tracker, dataset, tree_prior, tree_split, gen, tree, tree_num, node_id, feature_split, true); + AddSplitToModel(tracker, dataset, tree_prior, tree_split, gen, tree, tree_num, node_id, feature_split, true, num_threads); // Determine the number of observation in the newly created left node int left_node = tree->LeftChild(node_id); @@ -705,7 +687,7 @@ template & variable_weights, int tree_num, double global_variance, std::vector& feature_types, int cutpoint_grid_size, - int num_features_subsample, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { + int num_features_subsample, int num_threads, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { int root_id = Tree::kRoot; int curr_node_id; data_size_t curr_node_begin; @@ -761,7 +743,8 @@ static inline void GFRSampleTreeOneIter(Tree* tree, ForestTracker& tracker, Fore SampleSplitRule( tree, tracker, leaf_model, dataset, residual, tree_prior, gen, tree_num, global_variance, cutpoint_grid_size, node_index_map, split_queue, curr_node_id, curr_node_begin, curr_node_end, variable_weights, feature_types, - feature_subset, leaf_suff_stat_args...); + feature_subset, num_threads, leaf_suff_stat_args... + ); } } @@ -799,7 +782,7 @@ template & variable_weights, std::vector& sweep_update_indices, double global_variance, std::vector& feature_types, int cutpoint_grid_size, - bool keep_forest, bool pre_initialized, bool backfitting, int num_features_subsample, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { + bool keep_forest, bool pre_initialized, bool backfitting, int num_features_subsample, int num_threads, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { // Run the GFR algorithm for each tree int num_trees = forests.NumTrees(); for (const int& i : sweep_update_indices) { @@ -819,7 +802,7 @@ static inline void GFRSampleOneIter(TreeEnsemble& active_forest, ForestTracker& GFRSampleTreeOneIter( tree, tracker, forests, leaf_model, dataset, residual, tree_prior, gen, variable_weights, i, global_variance, feature_types, cutpoint_grid_size, - num_features_subsample, leaf_suff_stat_args... + num_features_subsample, num_threads, leaf_suff_stat_args... ); // Sample leaf parameters for tree i @@ -841,7 +824,7 @@ static inline void GFRSampleOneIter(TreeEnsemble& active_forest, ForestTracker& template static inline void MCMCGrowTreeOneIter(Tree* tree, ForestTracker& tracker, LeafModel& leaf_model, ForestDataset& dataset, ColumnVector& residual, TreePrior& tree_prior, std::mt19937& gen, int tree_num, std::vector& variable_weights, - double global_variance, double prob_grow_old, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { + double global_variance, double prob_grow_old, int num_threads, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { // Extract dataset information data_size_t n = dataset.GetCovariates().rows(); @@ -886,7 +869,7 @@ static inline void MCMCGrowTreeOneIter(Tree* tree, ForestTracker& tracker, LeafM // Compute the marginal likelihood of split and no split, given the leaf prior std::tuple split_eval = EvaluateProposedSplit( - dataset, tracker, residual, leaf_model, split, tree_num, leaf_chosen, var_chosen, global_variance, leaf_suff_stat_args... + dataset, tracker, residual, leaf_model, split, tree_num, leaf_chosen, var_chosen, global_variance, num_threads, leaf_suff_stat_args... ); double split_log_marginal_likelihood = std::get<0>(split_eval); double no_split_log_marginal_likelihood = std::get<1>(split_eval); @@ -936,7 +919,7 @@ static inline void MCMCGrowTreeOneIter(Tree* tree, ForestTracker& tracker, LeafM double log_acceptance_prob = std::log(mh_accept(gen)); if (log_acceptance_prob <= log_mh_ratio) { accept = true; - AddSplitToModel(tracker, dataset, tree_prior, split, gen, tree, tree_num, leaf_chosen, var_chosen, false); + AddSplitToModel(tracker, dataset, tree_prior, split, gen, tree, tree_num, leaf_chosen, var_chosen, false, num_threads); } else { accept = false; } @@ -949,7 +932,7 @@ static inline void MCMCGrowTreeOneIter(Tree* tree, ForestTracker& tracker, LeafM template static inline void MCMCPruneTreeOneIter(Tree* tree, ForestTracker& tracker, LeafModel& leaf_model, ForestDataset& dataset, ColumnVector& residual, - TreePrior& tree_prior, std::mt19937& gen, int tree_num, double global_variance, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { + TreePrior& tree_prior, std::mt19937& gen, int tree_num, double global_variance, int num_threads, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { // Choose a "leaf parent" node at random int num_leaves = tree->NumLeaves(); int num_leaf_parents = tree->NumLeafParents(); @@ -1028,7 +1011,7 @@ static inline void MCMCPruneTreeOneIter(Tree* tree, ForestTracker& tracker, Leaf template static inline void MCMCSampleTreeOneIter(Tree* tree, ForestTracker& tracker, ForestContainer& forests, LeafModel& leaf_model, ForestDataset& dataset, ColumnVector& residual, TreePrior& tree_prior, std::mt19937& gen, std::vector& variable_weights, - int tree_num, double global_variance, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { + int tree_num, double global_variance, int num_threads, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { // Determine whether it is possible to grow any of the leaves bool grow_possible = false; std::vector leaves = tree->GetLeaves(); @@ -1068,11 +1051,11 @@ static inline void MCMCSampleTreeOneIter(Tree* tree, ForestTracker& tracker, For if (step_chosen == 0) { MCMCGrowTreeOneIter( - tree, tracker, leaf_model, dataset, residual, tree_prior, gen, tree_num, variable_weights, global_variance, prob_grow, leaf_suff_stat_args... + tree, tracker, leaf_model, dataset, residual, tree_prior, gen, tree_num, variable_weights, global_variance, prob_grow, num_threads, leaf_suff_stat_args... ); } else { MCMCPruneTreeOneIter( - tree, tracker, leaf_model, dataset, residual, tree_prior, gen, tree_num, global_variance, leaf_suff_stat_args... + tree, tracker, leaf_model, dataset, residual, tree_prior, gen, tree_num, global_variance, num_threads, leaf_suff_stat_args... ); } } @@ -1107,7 +1090,8 @@ static inline void MCMCSampleTreeOneIter(Tree* tree, ForestTracker& tracker, For template static inline void MCMCSampleOneIter(TreeEnsemble& active_forest, ForestTracker& tracker, ForestContainer& forests, LeafModel& leaf_model, ForestDataset& dataset, ColumnVector& residual, TreePrior& tree_prior, std::mt19937& gen, std::vector& variable_weights, - std::vector& sweep_update_indices, double global_variance, bool keep_forest, bool pre_initialized, bool backfitting, LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { + std::vector& sweep_update_indices, double global_variance, bool keep_forest, bool pre_initialized, bool backfitting, int num_threads, + LeafSuffStatConstructorArgs&... leaf_suff_stat_args) { // Run the MCMC algorithm for each tree int num_trees = forests.NumTrees(); for (const int& i : sweep_update_indices) { @@ -1122,7 +1106,7 @@ static inline void MCMCSampleOneIter(TreeEnsemble& active_forest, ForestTracker& tree = active_forest.GetTree(i); MCMCSampleTreeOneIter( tree, tracker, forests, leaf_model, dataset, residual, tree_prior, gen, variable_weights, i, - global_variance, leaf_suff_stat_args... + global_variance, num_threads, leaf_suff_stat_args... ); // Sample leaf parameters for tree i diff --git a/include/stochtree/variance_model.h b/include/stochtree/variance_model.h index 79b8831f..b1c2dabe 100644 --- a/include/stochtree/variance_model.h +++ b/include/stochtree/variance_model.h @@ -12,11 +12,7 @@ #include #include -#include #include -#include -#include -#include namespace StochTree { diff --git a/man/ForestModel.Rd b/man/ForestModel.Rd index 3bb7a1db..bec10621 100644 --- a/man/ForestModel.Rd +++ b/man/ForestModel.Rd @@ -92,6 +92,7 @@ Run a single iteration of the forest sampling algorithm (MCMC or GFR) rng, forest_model_config, global_model_config, + num_threads = -1, keep_forest = TRUE, gfr = TRUE )}\if{html}{\out{}} @@ -114,6 +115,8 @@ Run a single iteration of the forest sampling algorithm (MCMC or GFR) \item{\code{global_model_config}}{GlobalModelConfig object containing global model parameters and settings} +\item{\code{num_threads}}{Number of threads to use in the GFR and MCMC algorithms, as well as prediction. If OpenMP is not available on a user's setup, this will default to \code{1}, otherwise to the maximum number of available threads.} + \item{\code{keep_forest}}{(Optional) Whether the updated forest sample should be saved to \code{forest_samples}. Default: \code{TRUE}.} \item{\code{gfr}}{(Optional) Whether or not the forest should be sampled using the "grow-from-root" (GFR) algorithm. Default: \code{TRUE}.} diff --git a/man/bart.Rd b/man/bart.Rd index 78da3eda..66a9b9ad 100644 --- a/man/bart.Rd +++ b/man/bart.Rd @@ -90,6 +90,7 @@ that were not in the training set.} \item \code{rfx_group_parameter_prior_cov} Prior covariance matrix for the random effects "group parameters." Default: \code{NULL}. Must be a square matrix whose dimension matches the number of random effects bases, or a scalar value that will be expanded to a diagonal matrix. \item \code{rfx_variance_prior_shape} Shape parameter for the inverse gamma prior on the variance of the random effects "group parameter." Default: \code{1}. \item \code{rfx_variance_prior_scale} Scale parameter for the inverse gamma prior on the variance of the random effects "group parameter." Default: \code{1}. +\item \code{num_threads} Number of threads to use in the GFR and MCMC algorithms, as well as prediction. If OpenMP is not available on a user's setup, this will default to \code{1}, otherwise to the maximum number of available threads. }} \item{mean_forest_params}{(Optional) A list of mean forest model parameters, each of which has a default value processed internally, so this argument list is optional. diff --git a/setup.py b/setup.py index c91678f5..affbefa0 100644 --- a/setup.py +++ b/setup.py @@ -54,6 +54,8 @@ def build_extension(self, ext: CMakeExtension) -> None: "-DBUILD_TEST=OFF", "-DBUILD_DEBUG_TARGETS=OFF", "-DBUILD_PYTHON=ON", + "-DUSE_OPENMP=ON", + "-DUSE_HOMEBREW_FALLBACK=ON", ] build_args = [] # Adding CMake arguments set as environment variable diff --git a/src/Makevars b/src/Makevars deleted file mode 100644 index 83bd4627..00000000 --- a/src/Makevars +++ /dev/null @@ -1,26 +0,0 @@ -# package root -PKGROOT=.. - -STOCHTREE_CPPFLAGS = -DSTOCHTREE_R_BUILD - -# PKG_CPPFLAGS= -I$(PKGROOT)/include -I$(PKGROOT)/deps/eigen -I$(PKGROOT)/deps/fmt/include -I$(PKGROOT)/deps/fast_double_parser/include -I$(PKGROOT)/deps/boost_math/include $(STOCHTREE_CPPFLAGS) -PKG_CPPFLAGS= -I$(PKGROOT)/include -I$(PKGROOT)/deps/eigen -I$(PKGROOT)/deps/fmt/include -I$(PKGROOT)/deps/fast_double_parser/include $(STOCHTREE_CPPFLAGS) - -CXX_STD=CXX17 - -OBJECTS = \ - forest.o \ - kernel.o \ - R_data.o \ - R_random_effects.o \ - sampler.o \ - serialization.o \ - cpp11.o \ - container.o \ - cutpoint_candidates.o \ - data.o \ - io.o \ - leaf_model.o \ - partition_tracker.o \ - random_effects.o \ - tree.o diff --git a/src/Makevars.in b/src/Makevars.in new file mode 100644 index 00000000..4eb970cb --- /dev/null +++ b/src/Makevars.in @@ -0,0 +1,39 @@ +CXX_STD=CXX17 + +PKGROOT=.. + +STOCHTREE_CPPFLAGS = \ + @STOCHTREE_CPPFLAGS@ \ + @OPENMP_AVAILABILITY_FLAGS@ \ + -DSTOCHTREE_R_BUILD + +PKG_CPPFLAGS = \ + -I$(PKGROOT)/include \ + -I$(PKGROOT)/deps/eigen \ + -I$(PKGROOT)/deps/fmt/include \ + -I$(PKGROOT)/deps/fast_double_parser/include \ + $(STOCHTREE_CPPFLAGS) + +PKG_CXXFLAGS = \ + @OPENMP_CXXFLAGS@ + +PKG_LIBS = \ + @OPENMP_CXXFLAGS@ \ + @OPENMP_LIB@ + +OBJECTS = \ + forest.o \ + kernel.o \ + R_data.o \ + R_random_effects.o \ + sampler.o \ + serialization.o \ + cpp11.o \ + container.o \ + cutpoint_candidates.o \ + data.o \ + io.o \ + leaf_model.o \ + partition_tracker.o \ + random_effects.o \ + tree.o diff --git a/src/Makevars.win.in b/src/Makevars.win.in new file mode 100644 index 00000000..95bff1dd --- /dev/null +++ b/src/Makevars.win.in @@ -0,0 +1,39 @@ +CXX_STD = @CXX_STD@ + +PKGROOT=.. + +STOCHTREE_CPPFLAGS = \ + @STOCHTREE_CPPFLAGS@ \ + -DSTOCHTREE_R_BUILD + +PKG_CPPFLAGS = \ + -I$(PKGROOT)/include \ + -I$(PKGROOT)/deps/eigen \ + -I$(PKGROOT)/deps/fmt/include \ + -I$(PKGROOT)/deps/fast_double_parser/include \ + $(STOCHTREE_CPPFLAGS) + +PKG_CXXFLAGS = \ + ${SHLIB_OPENMP_CXXFLAGS} \ + ${SHLIB_PTHREAD_FLAGS} + +PKG_LIBS = \ + ${SHLIB_OPENMP_CXXFLAGS} \ + ${SHLIB_PTHREAD_FLAGS} + +OBJECTS = \ + forest.o \ + kernel.o \ + R_data.o \ + R_random_effects.o \ + sampler.o \ + serialization.o \ + cpp11.o \ + container.o \ + cutpoint_candidates.o \ + data.o \ + io.o \ + leaf_model.o \ + partition_tracker.o \ + random_effects.o \ + tree.o diff --git a/src/R_data.cpp b/src/R_data.cpp index c6c75c29..0f495436 100644 --- a/src/R_data.cpp +++ b/src/R_data.cpp @@ -4,7 +4,6 @@ #include #include #include -#include [[cpp11::register]] cpp11::external_pointer create_forest_dataset_cpp() { diff --git a/src/R_random_effects.cpp b/src/R_random_effects.cpp index f627b3c5..e291121c 100644 --- a/src/R_random_effects.cpp +++ b/src/R_random_effects.cpp @@ -7,9 +7,7 @@ #include #include #include -#include #include -#include [[cpp11::register]] cpp11::external_pointer rfx_container_cpp(int num_components, int num_groups) { diff --git a/src/cpp11.cpp b/src/cpp11.cpp index 873b0c25..67f79ab2 100644 --- a/src/cpp11.cpp +++ b/src/cpp11.cpp @@ -1076,18 +1076,18 @@ extern "C" SEXP _stochtree_compute_leaf_indices_cpp(SEXP forest_container, SEXP END_CPP11 } // sampler.cpp -void sample_gfr_one_iteration_cpp(cpp11::external_pointer data, cpp11::external_pointer residual, cpp11::external_pointer forest_samples, cpp11::external_pointer active_forest, cpp11::external_pointer tracker, cpp11::external_pointer split_prior, cpp11::external_pointer rng, cpp11::integers sweep_indices, cpp11::integers feature_types, int cutpoint_grid_size, cpp11::doubles_matrix<> leaf_model_scale_input, cpp11::doubles variable_weights, double a_forest, double b_forest, double global_variance, int leaf_model_int, bool keep_forest, int num_features_subsample); -extern "C" SEXP _stochtree_sample_gfr_one_iteration_cpp(SEXP data, SEXP residual, SEXP forest_samples, SEXP active_forest, SEXP tracker, SEXP split_prior, SEXP rng, SEXP sweep_indices, SEXP feature_types, SEXP cutpoint_grid_size, SEXP leaf_model_scale_input, SEXP variable_weights, SEXP a_forest, SEXP b_forest, SEXP global_variance, SEXP leaf_model_int, SEXP keep_forest, SEXP num_features_subsample) { +void sample_gfr_one_iteration_cpp(cpp11::external_pointer data, cpp11::external_pointer residual, cpp11::external_pointer forest_samples, cpp11::external_pointer active_forest, cpp11::external_pointer tracker, cpp11::external_pointer split_prior, cpp11::external_pointer rng, cpp11::integers sweep_indices, cpp11::integers feature_types, int cutpoint_grid_size, cpp11::doubles_matrix<> leaf_model_scale_input, cpp11::doubles variable_weights, double a_forest, double b_forest, double global_variance, int leaf_model_int, bool keep_forest, int num_features_subsample, int num_threads); +extern "C" SEXP _stochtree_sample_gfr_one_iteration_cpp(SEXP data, SEXP residual, SEXP forest_samples, SEXP active_forest, SEXP tracker, SEXP split_prior, SEXP rng, SEXP sweep_indices, SEXP feature_types, SEXP cutpoint_grid_size, SEXP leaf_model_scale_input, SEXP variable_weights, SEXP a_forest, SEXP b_forest, SEXP global_variance, SEXP leaf_model_int, SEXP keep_forest, SEXP num_features_subsample, SEXP num_threads) { BEGIN_CPP11 - sample_gfr_one_iteration_cpp(cpp11::as_cpp>>(data), cpp11::as_cpp>>(residual), cpp11::as_cpp>>(forest_samples), cpp11::as_cpp>>(active_forest), cpp11::as_cpp>>(tracker), cpp11::as_cpp>>(split_prior), cpp11::as_cpp>>(rng), cpp11::as_cpp>(sweep_indices), cpp11::as_cpp>(feature_types), cpp11::as_cpp>(cutpoint_grid_size), cpp11::as_cpp>>(leaf_model_scale_input), cpp11::as_cpp>(variable_weights), cpp11::as_cpp>(a_forest), cpp11::as_cpp>(b_forest), cpp11::as_cpp>(global_variance), cpp11::as_cpp>(leaf_model_int), cpp11::as_cpp>(keep_forest), cpp11::as_cpp>(num_features_subsample)); + sample_gfr_one_iteration_cpp(cpp11::as_cpp>>(data), cpp11::as_cpp>>(residual), cpp11::as_cpp>>(forest_samples), cpp11::as_cpp>>(active_forest), cpp11::as_cpp>>(tracker), cpp11::as_cpp>>(split_prior), cpp11::as_cpp>>(rng), cpp11::as_cpp>(sweep_indices), cpp11::as_cpp>(feature_types), cpp11::as_cpp>(cutpoint_grid_size), cpp11::as_cpp>>(leaf_model_scale_input), cpp11::as_cpp>(variable_weights), cpp11::as_cpp>(a_forest), cpp11::as_cpp>(b_forest), cpp11::as_cpp>(global_variance), cpp11::as_cpp>(leaf_model_int), cpp11::as_cpp>(keep_forest), cpp11::as_cpp>(num_features_subsample), cpp11::as_cpp>(num_threads)); return R_NilValue; END_CPP11 } // sampler.cpp -void sample_mcmc_one_iteration_cpp(cpp11::external_pointer data, cpp11::external_pointer residual, cpp11::external_pointer forest_samples, cpp11::external_pointer active_forest, cpp11::external_pointer tracker, cpp11::external_pointer split_prior, cpp11::external_pointer rng, cpp11::integers sweep_indices, cpp11::integers feature_types, int cutpoint_grid_size, cpp11::doubles_matrix<> leaf_model_scale_input, cpp11::doubles variable_weights, double a_forest, double b_forest, double global_variance, int leaf_model_int, bool keep_forest); -extern "C" SEXP _stochtree_sample_mcmc_one_iteration_cpp(SEXP data, SEXP residual, SEXP forest_samples, SEXP active_forest, SEXP tracker, SEXP split_prior, SEXP rng, SEXP sweep_indices, SEXP feature_types, SEXP cutpoint_grid_size, SEXP leaf_model_scale_input, SEXP variable_weights, SEXP a_forest, SEXP b_forest, SEXP global_variance, SEXP leaf_model_int, SEXP keep_forest) { +void sample_mcmc_one_iteration_cpp(cpp11::external_pointer data, cpp11::external_pointer residual, cpp11::external_pointer forest_samples, cpp11::external_pointer active_forest, cpp11::external_pointer tracker, cpp11::external_pointer split_prior, cpp11::external_pointer rng, cpp11::integers sweep_indices, cpp11::integers feature_types, int cutpoint_grid_size, cpp11::doubles_matrix<> leaf_model_scale_input, cpp11::doubles variable_weights, double a_forest, double b_forest, double global_variance, int leaf_model_int, bool keep_forest, int num_threads); +extern "C" SEXP _stochtree_sample_mcmc_one_iteration_cpp(SEXP data, SEXP residual, SEXP forest_samples, SEXP active_forest, SEXP tracker, SEXP split_prior, SEXP rng, SEXP sweep_indices, SEXP feature_types, SEXP cutpoint_grid_size, SEXP leaf_model_scale_input, SEXP variable_weights, SEXP a_forest, SEXP b_forest, SEXP global_variance, SEXP leaf_model_int, SEXP keep_forest, SEXP num_threads) { BEGIN_CPP11 - sample_mcmc_one_iteration_cpp(cpp11::as_cpp>>(data), cpp11::as_cpp>>(residual), cpp11::as_cpp>>(forest_samples), cpp11::as_cpp>>(active_forest), cpp11::as_cpp>>(tracker), cpp11::as_cpp>>(split_prior), cpp11::as_cpp>>(rng), cpp11::as_cpp>(sweep_indices), cpp11::as_cpp>(feature_types), cpp11::as_cpp>(cutpoint_grid_size), cpp11::as_cpp>>(leaf_model_scale_input), cpp11::as_cpp>(variable_weights), cpp11::as_cpp>(a_forest), cpp11::as_cpp>(b_forest), cpp11::as_cpp>(global_variance), cpp11::as_cpp>(leaf_model_int), cpp11::as_cpp>(keep_forest)); + sample_mcmc_one_iteration_cpp(cpp11::as_cpp>>(data), cpp11::as_cpp>>(residual), cpp11::as_cpp>>(forest_samples), cpp11::as_cpp>>(active_forest), cpp11::as_cpp>>(tracker), cpp11::as_cpp>>(split_prior), cpp11::as_cpp>>(rng), cpp11::as_cpp>(sweep_indices), cpp11::as_cpp>(feature_types), cpp11::as_cpp>(cutpoint_grid_size), cpp11::as_cpp>>(leaf_model_scale_input), cpp11::as_cpp>(variable_weights), cpp11::as_cpp>(a_forest), cpp11::as_cpp>(b_forest), cpp11::as_cpp>(global_variance), cpp11::as_cpp>(leaf_model_int), cpp11::as_cpp>(keep_forest), cpp11::as_cpp>(num_threads)); return R_NilValue; END_CPP11 } @@ -1684,8 +1684,8 @@ static const R_CallMethodDef CallEntries[] = { {"_stochtree_rng_cpp", (DL_FUNC) &_stochtree_rng_cpp, 1}, {"_stochtree_root_reset_active_forest_cpp", (DL_FUNC) &_stochtree_root_reset_active_forest_cpp, 1}, {"_stochtree_root_reset_rfx_tracker_cpp", (DL_FUNC) &_stochtree_root_reset_rfx_tracker_cpp, 4}, - {"_stochtree_sample_gfr_one_iteration_cpp", (DL_FUNC) &_stochtree_sample_gfr_one_iteration_cpp, 18}, - {"_stochtree_sample_mcmc_one_iteration_cpp", (DL_FUNC) &_stochtree_sample_mcmc_one_iteration_cpp, 17}, + {"_stochtree_sample_gfr_one_iteration_cpp", (DL_FUNC) &_stochtree_sample_gfr_one_iteration_cpp, 19}, + {"_stochtree_sample_mcmc_one_iteration_cpp", (DL_FUNC) &_stochtree_sample_mcmc_one_iteration_cpp, 18}, {"_stochtree_sample_sigma2_one_iteration_cpp", (DL_FUNC) &_stochtree_sample_sigma2_one_iteration_cpp, 5}, {"_stochtree_sample_tau_one_iteration_cpp", (DL_FUNC) &_stochtree_sample_tau_one_iteration_cpp, 4}, {"_stochtree_sample_without_replacement_integer_cpp", (DL_FUNC) &_stochtree_sample_without_replacement_integer_cpp, 3}, diff --git a/src/cutpoint_candidates.cpp b/src/cutpoint_candidates.cpp index 4a0845c7..e43b8219 100644 --- a/src/cutpoint_candidates.cpp +++ b/src/cutpoint_candidates.cpp @@ -2,7 +2,6 @@ #include #include -#include namespace StochTree { diff --git a/src/data.cpp b/src/data.cpp index cd2913cf..e48e9255 100644 --- a/src/data.cpp +++ b/src/data.cpp @@ -1,7 +1,6 @@ /*! Copyright (c) 2024 by stochtree authors */ #include #include -#include namespace StochTree { diff --git a/src/forest.cpp b/src/forest.cpp index 02757aa7..968fe95c 100644 --- a/src/forest.cpp +++ b/src/forest.cpp @@ -7,9 +7,7 @@ #include #include #include -#include #include -#include [[cpp11::register]] cpp11::external_pointer active_forest_cpp(int num_trees, int output_dimension = 1, bool is_leaf_constant = true, bool is_exponentiated = false) { diff --git a/src/io.cpp b/src/io.cpp index 1324957f..50774d9b 100644 --- a/src/io.cpp +++ b/src/io.cpp @@ -7,9 +7,7 @@ #include #include -#include #include -#include namespace StochTree { diff --git a/src/kernel.cpp b/src/kernel.cpp index 6b5867bb..88f12c53 100644 --- a/src/kernel.cpp +++ b/src/kernel.cpp @@ -3,8 +3,6 @@ #include #include #include -#include -#include typedef Eigen::Map> DoubleMatrixType; typedef Eigen::Map> IntMatrixType; diff --git a/src/partition_tracker.cpp b/src/partition_tracker.cpp index cda214be..9d643380 100644 --- a/src/partition_tracker.cpp +++ b/src/partition_tracker.cpp @@ -6,12 +6,6 @@ #include #include -#include -#include -#include -#include -#include - namespace StochTree { ForestTracker::ForestTracker(Eigen::MatrixXd& covariates, std::vector& feature_types, int num_trees, int num_observations) { @@ -282,11 +276,11 @@ void ForestTracker::UpdatePredictions(TreeEnsemble* ensemble, ForestDataset& dat } } -void ForestTracker::AddSplit(Eigen::MatrixXd& covariates, TreeSplit& split, int32_t split_feature, int32_t tree_id, int32_t split_node_id, int32_t left_node_id, int32_t right_node_id, bool keep_sorted) { +void ForestTracker::AddSplit(Eigen::MatrixXd& covariates, TreeSplit& split, int32_t split_feature, int32_t tree_id, int32_t split_node_id, int32_t left_node_id, int32_t right_node_id, bool keep_sorted, int num_threads) { sample_node_mapper_->AddSplit(covariates, split, split_feature, tree_id, split_node_id, left_node_id, right_node_id); unsorted_node_sample_tracker_->PartitionTreeNode(covariates, tree_id, split_node_id, left_node_id, right_node_id, split_feature, split); if (keep_sorted) { - sorted_node_sample_tracker_->PartitionNode(covariates, split_node_id, split_feature, split); + sorted_node_sample_tracker_->PartitionNode(covariates, split_node_id, split_feature, split, num_threads); } } diff --git a/src/py_stochtree.cpp b/src/py_stochtree.cpp index f90f5cc6..32bbd707 100644 --- a/src/py_stochtree.cpp +++ b/src/py_stochtree.cpp @@ -12,7 +12,6 @@ #include #include #include -#include #define STRINGIFY(x) #x #define MACRO_STRINGIFY(x) STRINGIFY(x) @@ -1028,7 +1027,7 @@ class ForestSamplerCpp { void SampleOneIteration(ForestContainerCpp& forest_samples, ForestCpp& forest, ForestDatasetCpp& dataset, ResidualCpp& residual, RngCpp& rng, py::array_t feature_types, py::array_t sweep_update_indices, int cutpoint_grid_size, py::array_t leaf_model_scale_input, py::array_t variable_weights, double a_forest, double b_forest, double global_variance, - int leaf_model_int, int num_features_subsample, bool keep_forest = true, bool gfr = true) { + int leaf_model_int, int num_features_subsample, bool keep_forest = true, bool gfr = true, int num_threads = -1) { // Refactoring completely out of the Python interface. // Intention to refactor out of the C++ interface in the future. bool pre_initialized = true; @@ -1090,23 +1089,23 @@ class ForestSamplerCpp { std::mt19937* rng_ptr = rng.GetRng(); if (gfr) { if (model_type == StochTree::ModelType::kConstantLeafGaussian) { - StochTree::GFRSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample); + StochTree::GFRSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample, num_threads); } else if (model_type == StochTree::ModelType::kUnivariateRegressionLeafGaussian) { - StochTree::GFRSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample); + StochTree::GFRSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample, num_threads); } else if (model_type == StochTree::ModelType::kMultivariateRegressionLeafGaussian) { - StochTree::GFRSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample, num_basis); + StochTree::GFRSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample, num_threads, num_basis); } else if (model_type == StochTree::ModelType::kLogLinearVariance) { - StochTree::GFRSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, false, num_features_subsample); + StochTree::GFRSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, false, num_features_subsample, num_threads); } } else { if (model_type == StochTree::ModelType::kConstantLeafGaussian) { - StochTree::MCMCSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, keep_forest, pre_initialized, true); + StochTree::MCMCSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, keep_forest, pre_initialized, true, num_threads); } else if (model_type == StochTree::ModelType::kUnivariateRegressionLeafGaussian) { - StochTree::MCMCSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, keep_forest, pre_initialized, true); + StochTree::MCMCSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, keep_forest, pre_initialized, true, num_threads); } else if (model_type == StochTree::ModelType::kMultivariateRegressionLeafGaussian) { - StochTree::MCMCSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, keep_forest, pre_initialized, true, num_basis); + StochTree::MCMCSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, keep_forest, pre_initialized, true, num_threads, num_basis); } else if (model_type == StochTree::ModelType::kLogLinearVariance) { - StochTree::MCMCSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, keep_forest, pre_initialized, false); + StochTree::MCMCSampleOneIter(*active_forest_ptr, *(tracker_.get()), *forest_sample_ptr, std::get(leaf_model), *forest_data_ptr, *residual_data_ptr, *(split_prior_.get()), *rng_ptr, var_weights_vector, sweep_update_indices_, global_variance, keep_forest, pre_initialized, false, num_threads); } } } diff --git a/src/sampler.cpp b/src/sampler.cpp index af45d6d6..212ccb42 100644 --- a/src/sampler.cpp +++ b/src/sampler.cpp @@ -7,10 +7,7 @@ #include #include #include -#include #include -#include -#include [[cpp11::register]] void sample_gfr_one_iteration_cpp(cpp11::external_pointer data, @@ -26,7 +23,8 @@ void sample_gfr_one_iteration_cpp(cpp11::external_pointer(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample); + StochTree::GFRSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample, num_threads); } else if (model_type == StochTree::ModelType::kUnivariateRegressionLeafGaussian) { - StochTree::GFRSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample); + StochTree::GFRSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample, num_threads); } else if (model_type == StochTree::ModelType::kMultivariateRegressionLeafGaussian) { - StochTree::GFRSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample, num_basis); + StochTree::GFRSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, true, num_features_subsample, num_threads, num_basis); } else if (model_type == StochTree::ModelType::kLogLinearVariance) { - StochTree::GFRSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, false, num_features_subsample); + StochTree::GFRSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, feature_types_, cutpoint_grid_size, keep_forest, pre_initialized, false, num_features_subsample, num_threads); } } @@ -108,7 +106,7 @@ void sample_mcmc_one_iteration_cpp(cpp11::external_pointer(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, keep_forest, pre_initialized, true); + StochTree::MCMCSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, keep_forest, pre_initialized, true, num_threads); } else if (model_type == StochTree::ModelType::kUnivariateRegressionLeafGaussian) { - StochTree::MCMCSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, keep_forest, pre_initialized, true); + StochTree::MCMCSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, keep_forest, pre_initialized, true, num_threads); } else if (model_type == StochTree::ModelType::kMultivariateRegressionLeafGaussian) { - StochTree::MCMCSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, keep_forest, pre_initialized, true, num_basis); + StochTree::MCMCSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, keep_forest, pre_initialized, true, num_threads, num_basis); } else if (model_type == StochTree::ModelType::kLogLinearVariance) { - StochTree::MCMCSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, keep_forest, pre_initialized, false); + StochTree::MCMCSampleOneIter(*active_forest, *tracker, *forest_samples, std::get(leaf_model), *data, *residual, *split_prior, *rng, var_weights_vector, sweep_indices_, global_variance, keep_forest, pre_initialized, false, num_threads); } } diff --git a/src/serialization.cpp b/src/serialization.cpp index 749395e8..fb248f62 100644 --- a/src/serialization.cpp +++ b/src/serialization.cpp @@ -8,9 +8,6 @@ #include #include #include -#include -#include -#include [[cpp11::register]] cpp11::external_pointer init_json_cpp() { diff --git a/src/tree.cpp b/src/tree.cpp index fa6fd8f8..3bc85c74 100644 --- a/src/tree.cpp +++ b/src/tree.cpp @@ -8,9 +8,6 @@ #include #include -#include -#include -#include namespace StochTree { diff --git a/stochtree/bart.py b/stochtree/bart.py index 8554d64e..1f65ea17 100644 --- a/stochtree/bart.py +++ b/stochtree/bart.py @@ -138,6 +138,7 @@ def sample( * `rfx_group_parameter_prior_cov`: Prior covariance matrix for the random effects "group parameters." Default: `None`. Must be a square numpy matrix whose dimension matches the number of random effects bases, or a scalar value that will be expanded to a diagonal matrix. * `rfx_variance_prior_shape`: Shape parameter for the inverse gamma prior on the variance of the random effects "group parameter." Default: `1`. * `rfx_variance_prior_scale`: Scale parameter for the inverse gamma prior on the variance of the random effects "group parameter." Default: `1`. + * `num_threads`: Number of threads to use in the GFR and MCMC algorithms, as well as prediction. If OpenMP is not available on a user's setup, this will default to `1`, otherwise to the maximum number of available threads. mean_forest_params : dict, optional Dictionary of mean forest model parameters, each of which has a default value processed internally, so this argument is optional. @@ -202,6 +203,7 @@ def sample( "rfx_group_parameter_prior_cov": None, "rfx_variance_prior_shape": 1.0, "rfx_variance_prior_scale": 1.0, + "num_threads": -1, } general_params_updated = _preprocess_params( general_params_default, general_params @@ -266,6 +268,7 @@ def sample( rfx_group_parameter_prior_cov = general_params_updated["rfx_group_parameter_prior_cov"] rfx_variance_prior_shape = general_params_updated["rfx_variance_prior_shape"] rfx_variance_prior_scale = general_params_updated["rfx_variance_prior_scale"] + num_threads = general_params_updated["num_threads"] # 2. Mean forest parameters num_trees_mean = mean_forest_params_updated["num_trees"] @@ -1226,6 +1229,7 @@ def sample( forest_model_config_mean, keep_sample, True, + num_threads, ) # Cache train set predictions since they are already computed during sampling @@ -1244,6 +1248,7 @@ def sample( forest_model_config_variance, keep_sample, True, + num_threads, ) # Cache train set predictions since they are already computed during sampling @@ -1426,6 +1431,7 @@ def sample( forest_model_config_mean, keep_sample, False, + num_threads, ) if keep_sample: @@ -1443,6 +1449,7 @@ def sample( forest_model_config_variance, keep_sample, False, + num_threads, ) if keep_sample: @@ -1684,11 +1691,11 @@ def predict( has_mean_predictions = self.include_mean_forest or self.has_rfx if has_mean_predictions and self.include_variance_forest: - return (mean_pred, variance_pred) + return {"y_hat": mean_pred, "variance_forest_predictions": variance_pred} elif has_mean_predictions and not self.include_variance_forest: - return mean_pred + return {"y_hat": mean_pred, "variance_forest_predictions": None} elif not has_mean_predictions and self.include_variance_forest: - return variance_pred + return {"y_hat": None, "variance_forest_predictions": variance_pred} def predict_mean( self, diff --git a/stochtree/bcf.py b/stochtree/bcf.py index 0ccd9e31..bfe9cc34 100644 --- a/stochtree/bcf.py +++ b/stochtree/bcf.py @@ -158,7 +158,7 @@ def sample( * `rfx_group_parameter_prior_cov`: Prior covariance matrix for the random effects "group parameters." Default: `None`. Must be a square numpy matrix whose dimension matches the number of random effects bases, or a scalar value that will be expanded to a diagonal matrix. * `rfx_variance_prior_shape`: Shape parameter for the inverse gamma prior on the variance of the random effects "group parameter." Default: `1`. * `rfx_variance_prior_scale`: Scale parameter for the inverse gamma prior on the variance of the random effects "group parameter." Default: `1`. - + * `num_threads`: Number of threads to use in the GFR and MCMC algorithms, as well as prediction. If OpenMP is not available on a user's setup, this will default to `1`, otherwise to the maximum number of available threads. prognostic_forest_params : dict, optional Dictionary of prognostic forest model parameters, each of which has a default value processed internally, so this argument is optional. @@ -240,6 +240,7 @@ def sample( "rfx_group_parameter_prior_cov": None, "rfx_variance_prior_shape": 1.0, "rfx_variance_prior_scale": 1.0, + "num_threads": -1, } general_params_updated = _preprocess_params( general_params_default, general_params @@ -328,6 +329,7 @@ def sample( rfx_group_parameter_prior_cov = general_params_updated["rfx_group_parameter_prior_cov"] rfx_variance_prior_shape = general_params_updated["rfx_variance_prior_shape"] rfx_variance_prior_scale = general_params_updated["rfx_variance_prior_scale"] + num_threads = general_params_updated["num_threads"] # 2. Mu forest parameters num_trees_mu = prognostic_forest_params_updated["num_trees"] @@ -1735,6 +1737,7 @@ def sample( forest_model_config_mu, keep_sample, True, + num_threads, ) # Cache train set predictions since they are already computed during sampling @@ -1772,6 +1775,7 @@ def sample( forest_model_config_tau, keep_sample, True, + num_threads, ) # Cannot cache train set predictions for tau because the cached predictions in the @@ -1833,6 +1837,7 @@ def sample( forest_model_config_variance, keep_sample, True, + num_threads, ) # Cache train set predictions since they are already computed during sampling @@ -1928,6 +1933,7 @@ def sample( forest_model_config_mu, keep_sample, False, + num_threads, ) # Cache train set predictions since they are already computed during sampling @@ -1965,6 +1971,7 @@ def sample( forest_model_config_tau, keep_sample, False, + num_threads, ) # Cannot cache train set predictions for tau because the cached predictions in the @@ -2026,6 +2033,7 @@ def sample( forest_model_config_variance, keep_sample, True, + num_threads, ) # Cache train set predictions since they are already computed during sampling @@ -2231,23 +2239,6 @@ def predict_tau( # Data checks if Z.shape[0] != X.shape[0]: raise ValueError("X and Z must have the same number of rows") - if propensity is not None: - if propensity.shape[0] != X.shape[0]: - raise ValueError("X and propensity must have the same number of rows") - else: - if self.propensity_covariate == "tau": - if not self.internal_propensity_model: - raise ValueError( - "Propensity scores not provided, but no propensity model was trained during sampling" - ) - else: - propensity = np.mean( - self.bart_propensity_model.predict(X), axis=1, keepdims=True - ) - else: - # Dummy propensities if not provided but also not needed - propensity = np.ones(X.shape[0]) - propensity = np.expand_dims(propensity, 1) # Covariate preprocessing if not self._covariate_preprocessor._check_is_fitted(): @@ -2269,6 +2260,22 @@ def predict_tau( covariates_processed = X else: covariates_processed = self._covariate_preprocessor.transform(X) + + # Handle propensities + if propensity is not None: + if propensity.shape[0] != X.shape[0]: + raise ValueError("X and propensity must have the same number of rows") + else: + if self.propensity_covariate != "none": + if not self.internal_propensity_model: + raise ValueError( + "Propensity scores not provided, but no propensity model was trained during sampling" + ) + else: + internal_propensity_preds = self.bart_propensity_model.predict(covariates_processed) + propensity = np.mean( + internal_propensity_preds['y_hat'], axis=1, keepdims=True + ) # Update covariates to include propensities if requested if self.propensity_covariate == "none": @@ -2354,6 +2361,22 @@ def predict_variance( covariates_processed = covariates else: covariates_processed = self._covariate_preprocessor.transform(covariates) + + # Handle propensities + if propensity is not None: + if propensity.shape[0] != covariates.shape[0]: + raise ValueError("X and propensity must have the same number of rows") + else: + if self.propensity_covariate != "none": + if not self.internal_propensity_model: + raise ValueError( + "Propensity scores not provided, but no propensity model was trained during sampling" + ) + else: + internal_propensity_preds = self.bart_propensity_model.predict(covariates_processed) + propensity = np.mean( + internal_propensity_preds['y_hat'], axis=1, keepdims=True + ) # Update covariates to include propensities if requested if self.propensity_covariate == "none": @@ -2394,7 +2417,7 @@ def predict( propensity: np.array = None, rfx_group_ids: np.array = None, rfx_basis: np.array = None, - ) -> tuple: + ) -> dict: """Predict outcome model components (CATE function and prognostic function) as well as overall outcome for every provided observation. Predicted outcomes are computed as `yhat = mu_x + Z*tau_x` where mu_x is a sample of the prognostic function and tau_x is a sample of the treatment effect (CATE) function. @@ -2447,19 +2470,6 @@ def predict( # Data checks if Z.shape[0] != X.shape[0]: raise ValueError("X and Z must have the same number of rows") - if propensity is not None: - if propensity.shape[0] != X.shape[0]: - raise ValueError("X and propensity must have the same number of rows") - else: - if self.propensity_covariate != "none": - if not self.internal_propensity_model: - raise ValueError( - "Propensity scores not provided, but no propensity model was trained during sampling" - ) - else: - propensity = np.mean( - self.bart_propensity_model.predict(X), axis=1, keepdims=True - ) # Covariate preprocessing if not self._covariate_preprocessor._check_is_fitted(): @@ -2481,6 +2491,22 @@ def predict( covariates_processed = X else: covariates_processed = self._covariate_preprocessor.transform(X) + + # Handle propensities + if propensity is not None: + if propensity.shape[0] != X.shape[0]: + raise ValueError("X and propensity must have the same number of rows") + else: + if self.propensity_covariate != "none": + if not self.internal_propensity_model: + raise ValueError( + "Propensity scores not provided, but no propensity model was trained during sampling" + ) + else: + internal_propensity_preds = self.bart_propensity_model.predict(covariates_processed) + propensity = np.mean( + internal_propensity_preds['y_hat'], axis=1, keepdims=True + ) # Update covariates to include propensities if requested if self.propensity_covariate == "none": @@ -2535,13 +2561,13 @@ def predict( # Return result matrices as a tuple if self.has_rfx and self.include_variance_forest: - return (tau_x, mu_x, rfx_preds, yhat_x, sigma2_x) + return {"mu_hat": mu_x, "tau_hat": tau_x, "y_hat": yhat_x, "rfx_predictions": rfx_preds, "variance_forest_predictions": sigma2_x} elif not self.has_rfx and self.include_variance_forest: - return (tau_x, mu_x, yhat_x, sigma2_x) + return {"mu_hat": mu_x, "tau_hat": tau_x, "y_hat": yhat_x, "rfx_predictions": None, "variance_forest_predictions": sigma2_x} elif self.has_rfx and not self.include_variance_forest: - return (tau_x, mu_x, rfx_preds, yhat_x) + return {"mu_hat": mu_x, "tau_hat": tau_x, "y_hat": yhat_x, "rfx_predictions": rfx_preds, "variance_forest_predictions": None} else: - return (tau_x, mu_x, yhat_x) + return {"mu_hat": mu_x, "tau_hat": tau_x, "y_hat": yhat_x, "rfx_predictions": None, "variance_forest_predictions": None} def to_json(self) -> str: """ diff --git a/stochtree/sampler.py b/stochtree/sampler.py index 8ac4f013..cbab9ce6 100644 --- a/stochtree/sampler.py +++ b/stochtree/sampler.py @@ -103,6 +103,7 @@ def sample_one_iteration( forest_config: ForestModelConfig, keep_forest: bool, gfr: bool, + num_threads: int = -1, ) -> None: """ Sample one iteration of a forest using the specified model and tree sampling algorithm @@ -127,6 +128,8 @@ def sample_one_iteration( Whether or not the resulting forest should be retained in `forest_container` or discarded (due to burnin or thinning for example) gfr : bool Whether or not the "grow-from-root" (GFR) sampler is run (if this is `True` and `leaf_model_int=0` this is equivalent to XBART, if this is `FALSE` and `leaf_model_int=0` this is equivalent to the original BART) + num_threads : int + Number of threads to use in the GFR and MCMC algorithms, as well as prediction. If OpenMP is not available on a user's system, this will default to `1`, otherwise to the maximum number of available threads. """ # Ensure forest has been initialized if forest.is_empty(): @@ -173,6 +176,7 @@ def sample_one_iteration( forest_config.get_num_features_subsample(), keep_forest, gfr, + num_threads, ) def prepare_for_sampler( diff --git a/test/cpp/test_model.cpp b/test/cpp/test_model.cpp index dbe85b7e..184c2552 100644 --- a/test/cpp/test_model.cpp +++ b/test/cpp/test_model.cpp @@ -1,259 +1,259 @@ -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -TEST(LeafConstantModel, FullEnumeration) { - // Load test data - StochTree::TestUtils::TestDataset test_dataset; - test_dataset = StochTree::TestUtils::LoadSmallDatasetUnivariateBasis(); - std::vector feature_types(test_dataset.x_cols, StochTree::FeatureType::kNumeric); - std::vector variable_weights(test_dataset.x_cols, 1./test_dataset.x_cols); - std::vector feature_subset(test_dataset.x_cols, true); - std::random_device rd; - std::mt19937 gen(rd()); - - // Construct datasets - using data_size_t = StochTree::data_size_t; - data_size_t n = test_dataset.n; - int p = test_dataset.x_cols; - StochTree::ForestDataset dataset = StochTree::ForestDataset(); - dataset.AddCovariates(test_dataset.covariates.data(), n, test_dataset.x_cols, test_dataset.row_major); - StochTree::ColumnVector residual = StochTree::ColumnVector(test_dataset.outcome.data(), n); - - // Construct a ForestTracker - int num_trees = 1; - StochTree::ForestTracker tracker = StochTree::ForestTracker(dataset.GetCovariates(), feature_types, num_trees, n); - - // Set sampling parameters - double alpha = 0.95; - double beta = 1.25; - int min_samples_leaf = 1; - int max_depth = -1; - double global_variance = 1.; - double tau = 1.; - int cutpoint_grid_size = n; - StochTree::TreePrior tree_prior = StochTree::TreePrior(alpha, beta, min_samples_leaf, max_depth); - - // Construct temporary data structures needed to enumerate splits - std::vector log_cutpoint_evaluations; - std::vector cutpoint_features; - std::vector cutpoint_values; - std::vector cutpoint_feature_types; - StochTree::data_size_t valid_cutpoint_count = 0; - StochTree::CutpointGridContainer cutpoint_grid_container(dataset.GetCovariates(), residual.GetData(), cutpoint_grid_size); - - // Initialize a leaf model - StochTree::GaussianConstantLeafModel leaf_model = StochTree::GaussianConstantLeafModel(tau); - - // Evaluate all possible cutpoints - StochTree::EvaluateAllPossibleSplits( - dataset, tracker, residual, tree_prior, gen, leaf_model, global_variance, 0, 0, log_cutpoint_evaluations, cutpoint_features, cutpoint_values, - cutpoint_feature_types, valid_cutpoint_count, cutpoint_grid_container, 0, n, variable_weights, feature_types, feature_subset - ); - - // Check that there are (n - 2*min_samples_leaf + 1)*p + 1 cutpoints considered - ASSERT_EQ(log_cutpoint_evaluations.size(), (n - 2*min_samples_leaf + 1)*p + 1); - - // Check the values of the cutpoint evaluations - std::vector expected_split_evals{3.773828, 3.349927, 3.001568, 3.085074, 2.989927, 3.101841, 2.980939, 3.068029, 3.822045, 3.663843, 3.710592, 3.354912, 3.135288, - 3.553728, 2.969388, 3.540838, 3.961885, 3.822045, 4.908861, 4.032006, 4.083473, 4.442268, 5.023573, 4.171735, 3.353457, 3.862124, - 3.323620, 3.998112, 3.425777, 3.096926, 3.131347, 2.947921, 2.935892, 3.224115, 3.144767, 3.213065, 3.863427, 3.792850, 3.146056, - 3.348693, 3.487161, 4.600861, 4.226219, 4.879161, 3.773828, 3.940111}; - for (int i = 0; i < log_cutpoint_evaluations.size(); i++) { - ASSERT_NEAR(log_cutpoint_evaluations[i], expected_split_evals[i], 0.01); - } -} - -TEST(LeafConstantModel, CutpointThinning) { - // Load test data - StochTree::TestUtils::TestDataset test_dataset; - test_dataset = StochTree::TestUtils::LoadSmallDatasetUnivariateBasis(); - std::vector feature_types(test_dataset.x_cols, StochTree::FeatureType::kNumeric); - std::vector variable_weights(test_dataset.x_cols, 1./test_dataset.x_cols); - std::vector feature_subset(test_dataset.x_cols, true); - std::random_device rd; - std::mt19937 gen(rd()); - - // Construct datasets - using data_size_t = StochTree::data_size_t; - data_size_t n = test_dataset.n; - int p = test_dataset.x_cols; - StochTree::ForestDataset dataset = StochTree::ForestDataset(); - dataset.AddCovariates(test_dataset.covariates.data(), n, test_dataset.x_cols, test_dataset.row_major); - StochTree::ColumnVector residual = StochTree::ColumnVector(test_dataset.outcome.data(), n); - - // Construct a ForestTracker - int num_trees = 1; - StochTree::ForestTracker tracker = StochTree::ForestTracker(dataset.GetCovariates(), feature_types, num_trees, n); - - // Set sampling parameters - double alpha = 0.95; - double beta = 1.25; - int min_samples_leaf = 1; - int max_depth = -1; - double global_variance = 1.; - double tau = 1.; - int cutpoint_grid_size = 5; - StochTree::TreePrior tree_prior = StochTree::TreePrior(alpha, beta, min_samples_leaf, max_depth); - - // Construct temporary data structures needed to enumerate splits - std::vector log_cutpoint_evaluations; - std::vector cutpoint_features; - std::vector cutpoint_values; - std::vector cutpoint_feature_types; - StochTree::data_size_t valid_cutpoint_count = 0; - StochTree::CutpointGridContainer cutpoint_grid_container(dataset.GetCovariates(), residual.GetData(), cutpoint_grid_size); - - // Initialize a leaf model - StochTree::GaussianConstantLeafModel leaf_model = StochTree::GaussianConstantLeafModel(tau); - - // Evaluate all possible cutpoints - StochTree::EvaluateAllPossibleSplits( - dataset, tracker, residual, tree_prior, gen, leaf_model, global_variance, 0, 0, log_cutpoint_evaluations, cutpoint_features, cutpoint_values, - cutpoint_feature_types, valid_cutpoint_count, cutpoint_grid_container, 0, n, variable_weights, feature_types, feature_subset - ); - - // Check that there are (n - 2*min_samples_leaf + 1)*p + 1 cutpoints considered - ASSERT_EQ(log_cutpoint_evaluations.size(), (cutpoint_grid_size - 1)*p + 1); - - // Check the values of the cutpoint evaluations - std::vector expected_split_evals{3.349927, 3.085074, 3.101841, 3.068029, 3.710592, 3.135288, 2.969388, 3.961885, 4.032006, - 4.442268, 4.171735, 3.862124, 3.425777, 3.131347, 2.935892, 3.144767, 3.792850, 3.348693, - 4.600861, 4.879161, 3.940111}; - for (int i = 0; i < log_cutpoint_evaluations.size(); i++) { - ASSERT_NEAR(log_cutpoint_evaluations[i], expected_split_evals[i], 0.01); - } -} - -TEST(LeafUnivariateRegressionModel, FullEnumeration) { - // Load test data - StochTree::TestUtils::TestDataset test_dataset; - test_dataset = StochTree::TestUtils::LoadSmallDatasetUnivariateBasis(); - std::vector feature_types(test_dataset.x_cols, StochTree::FeatureType::kNumeric); - std::vector variable_weights(test_dataset.x_cols, 1./test_dataset.x_cols); - std::vector feature_subset(test_dataset.x_cols, true); - std::random_device rd; - std::mt19937 gen(rd()); - - // Construct datasets - using data_size_t = StochTree::data_size_t; - data_size_t n = test_dataset.n; - int p = test_dataset.x_cols; - StochTree::ForestDataset dataset = StochTree::ForestDataset(); - dataset.AddCovariates(test_dataset.covariates.data(), n, test_dataset.x_cols, test_dataset.row_major); - dataset.AddBasis(test_dataset.omega.data(), test_dataset.n, test_dataset.omega_cols, test_dataset.row_major); - StochTree::ColumnVector residual = StochTree::ColumnVector(test_dataset.outcome.data(), n); - - // Construct a ForestTracker - int num_trees = 1; - StochTree::ForestTracker tracker = StochTree::ForestTracker(dataset.GetCovariates(), feature_types, num_trees, n); - - // Set sampling parameters - double alpha = 0.95; - double beta = 1.25; - int min_samples_leaf = 1; - int max_depth = -1; - double global_variance = 1.; - double tau = 1.; - int cutpoint_grid_size = n; - StochTree::TreePrior tree_prior = StochTree::TreePrior(alpha, beta, min_samples_leaf, max_depth); - - // Construct temporary data structures needed to enumerate splits - std::vector log_cutpoint_evaluations; - std::vector cutpoint_features; - std::vector cutpoint_values; - std::vector cutpoint_feature_types; - StochTree::data_size_t valid_cutpoint_count = 0; - StochTree::CutpointGridContainer cutpoint_grid_container(dataset.GetCovariates(), residual.GetData(), cutpoint_grid_size); - - // Initialize a leaf model - StochTree::GaussianUnivariateRegressionLeafModel leaf_model = StochTree::GaussianUnivariateRegressionLeafModel(tau); - - // Evaluate all possible cutpoints - StochTree::EvaluateAllPossibleSplits( - dataset, tracker, residual, tree_prior, gen, leaf_model, global_variance, 0, 0, log_cutpoint_evaluations, cutpoint_features, cutpoint_values, - cutpoint_feature_types, valid_cutpoint_count, cutpoint_grid_container, 0, n, variable_weights, feature_types, feature_subset - ); - - // Check that there are (n - 2*min_samples_leaf + 1)*p + 1 cutpoints considered - ASSERT_EQ(log_cutpoint_evaluations.size(), (n - 2*min_samples_leaf + 1)*p + 1); - - // Check the values of the cutpoint evaluations - std::vector expected_split_evals{4.978556, 4.067172, 3.823266, 3.850415, 3.796388, 3.791759, 3.864699, 3.970411, 5.105565, 4.886562, 4.812292, 4.450645, 4.180200, - 4.625754, 3.983956, 4.906961, 5.307099, 5.105565, 6.057032, 5.463854, 5.312733, 5.504701, 5.872222, 4.936127, 4.203568, 4.192258, - 4.633795, 4.060248, 4.032323, 4.040458, 4.176712, 3.809356, 3.854872, 4.404108, 4.243114, 4.116230, 5.167773, 5.031023, 4.203335, - 4.094302, 4.280394, 5.557678, 5.394644, 5.945185, 4.978556, 5.069763}; - for (int i = 0; i < log_cutpoint_evaluations.size(); i++) { - ASSERT_NEAR(log_cutpoint_evaluations[i], expected_split_evals[i], 0.01); - } -} - -TEST(LeafUnivariateRegressionModel, CutpointThinning) { - // Load test data - StochTree::TestUtils::TestDataset test_dataset; - test_dataset = StochTree::TestUtils::LoadSmallDatasetUnivariateBasis(); - std::vector feature_types(test_dataset.x_cols, StochTree::FeatureType::kNumeric); - std::vector variable_weights(test_dataset.x_cols, 1./test_dataset.x_cols); - std::vector feature_subset(test_dataset.x_cols, true); - std::random_device rd; - std::mt19937 gen(rd()); - - // Construct datasets - using data_size_t = StochTree::data_size_t; - data_size_t n = test_dataset.n; - int p = test_dataset.x_cols; - StochTree::ForestDataset dataset = StochTree::ForestDataset(); - dataset.AddCovariates(test_dataset.covariates.data(), n, test_dataset.x_cols, test_dataset.row_major); - dataset.AddBasis(test_dataset.omega.data(), test_dataset.n, test_dataset.omega_cols, test_dataset.row_major); - StochTree::ColumnVector residual = StochTree::ColumnVector(test_dataset.outcome.data(), n); - - // Construct a ForestTracker - int num_trees = 1; - StochTree::ForestTracker tracker = StochTree::ForestTracker(dataset.GetCovariates(), feature_types, num_trees, n); - - // Set sampling parameters - double alpha = 0.95; - double beta = 1.25; - int min_samples_leaf = 1; - int max_depth = -1; - double global_variance = 1.; - double tau = 1.; - int cutpoint_grid_size = 5; - StochTree::TreePrior tree_prior = StochTree::TreePrior(alpha, beta, min_samples_leaf, max_depth); - - // Construct temporary data structures needed to enumerate splits - std::vector log_cutpoint_evaluations; - std::vector cutpoint_features; - std::vector cutpoint_values; - std::vector cutpoint_feature_types; - StochTree::data_size_t valid_cutpoint_count = 0; - StochTree::CutpointGridContainer cutpoint_grid_container(dataset.GetCovariates(), residual.GetData(), cutpoint_grid_size); - - // Initialize a leaf model - StochTree::GaussianUnivariateRegressionLeafModel leaf_model = StochTree::GaussianUnivariateRegressionLeafModel(tau); - - // Evaluate all possible cutpoints - StochTree::EvaluateAllPossibleSplits( - dataset, tracker, residual, tree_prior, gen, leaf_model, global_variance, 0, 0, log_cutpoint_evaluations, cutpoint_features, cutpoint_values, - cutpoint_feature_types, valid_cutpoint_count, cutpoint_grid_container, 0, n, variable_weights, feature_types, feature_subset - ); - - - // Check that there are (n - 2*min_samples_leaf + 1)*p + 1 cutpoints considered - ASSERT_EQ(log_cutpoint_evaluations.size(), (cutpoint_grid_size - 1)*p + 1); - - // Check the values of the cutpoint evaluations - std::vector expected_split_evals{4.067172, 3.850415, 3.791759, 3.970411, 4.812292, 4.180200, 3.983956, 5.307099, 5.463854, - 5.504701, 4.936127, 4.192258, 4.032323, 4.176712, 3.854872, 4.243114, 5.031023, 4.094302, - 5.557678, 5.945185, 5.069763}; - for (int i = 0; i < log_cutpoint_evaluations.size(); i++) { - ASSERT_NEAR(log_cutpoint_evaluations[i], expected_split_evals[i], 0.01); - } -} +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include +// #include + +// TEST(LeafConstantModel, FullEnumeration) { +// // Load test data +// StochTree::TestUtils::TestDataset test_dataset; +// test_dataset = StochTree::TestUtils::LoadSmallDatasetUnivariateBasis(); +// std::vector feature_types(test_dataset.x_cols, StochTree::FeatureType::kNumeric); +// std::vector variable_weights(test_dataset.x_cols, 1./test_dataset.x_cols); +// std::vector feature_subset(test_dataset.x_cols, true); +// std::random_device rd; +// std::mt19937 gen(rd()); + +// // Construct datasets +// using data_size_t = StochTree::data_size_t; +// data_size_t n = test_dataset.n; +// int p = test_dataset.x_cols; +// StochTree::ForestDataset dataset = StochTree::ForestDataset(); +// dataset.AddCovariates(test_dataset.covariates.data(), n, test_dataset.x_cols, test_dataset.row_major); +// StochTree::ColumnVector residual = StochTree::ColumnVector(test_dataset.outcome.data(), n); + +// // Construct a ForestTracker +// int num_trees = 1; +// StochTree::ForestTracker tracker = StochTree::ForestTracker(dataset.GetCovariates(), feature_types, num_trees, n); + +// // Set sampling parameters +// double alpha = 0.95; +// double beta = 1.25; +// int min_samples_leaf = 1; +// int max_depth = -1; +// double global_variance = 1.; +// double tau = 1.; +// int cutpoint_grid_size = n; +// StochTree::TreePrior tree_prior = StochTree::TreePrior(alpha, beta, min_samples_leaf, max_depth); + +// // Construct temporary data structures needed to enumerate splits +// std::vector log_cutpoint_evaluations; +// std::vector cutpoint_features; +// std::vector cutpoint_values; +// std::vector cutpoint_feature_types; +// StochTree::data_size_t valid_cutpoint_count = 0; +// StochTree::CutpointGridContainer cutpoint_grid_container(dataset.GetCovariates(), residual.GetData(), cutpoint_grid_size); + +// // Initialize a leaf model +// StochTree::GaussianConstantLeafModel leaf_model = StochTree::GaussianConstantLeafModel(tau); + +// // Evaluate all possible cutpoints +// StochTree::EvaluateAllPossibleSplits( +// dataset, tracker, residual, tree_prior, gen, leaf_model, global_variance, 0, 0, log_cutpoint_evaluations, cutpoint_features, cutpoint_values, +// cutpoint_feature_types, valid_cutpoint_count, cutpoint_grid_container, 0, n, variable_weights, feature_types, feature_subset +// ); + +// // Check that there are (n - 2*min_samples_leaf + 1)*p + 1 cutpoints considered +// ASSERT_EQ(log_cutpoint_evaluations.size(), (n - 2*min_samples_leaf + 1)*p + 1); + +// // Check the values of the cutpoint evaluations +// std::vector expected_split_evals{3.773828, 3.349927, 3.001568, 3.085074, 2.989927, 3.101841, 2.980939, 3.068029, 3.822045, 3.663843, 3.710592, 3.354912, 3.135288, +// 3.553728, 2.969388, 3.540838, 3.961885, 3.822045, 4.908861, 4.032006, 4.083473, 4.442268, 5.023573, 4.171735, 3.353457, 3.862124, +// 3.323620, 3.998112, 3.425777, 3.096926, 3.131347, 2.947921, 2.935892, 3.224115, 3.144767, 3.213065, 3.863427, 3.792850, 3.146056, +// 3.348693, 3.487161, 4.600861, 4.226219, 4.879161, 3.773828, 3.940111}; +// for (int i = 0; i < log_cutpoint_evaluations.size(); i++) { +// ASSERT_NEAR(log_cutpoint_evaluations[i], expected_split_evals[i], 0.01); +// } +// } + +// TEST(LeafConstantModel, CutpointThinning) { +// // Load test data +// StochTree::TestUtils::TestDataset test_dataset; +// test_dataset = StochTree::TestUtils::LoadSmallDatasetUnivariateBasis(); +// std::vector feature_types(test_dataset.x_cols, StochTree::FeatureType::kNumeric); +// std::vector variable_weights(test_dataset.x_cols, 1./test_dataset.x_cols); +// std::vector feature_subset(test_dataset.x_cols, true); +// std::random_device rd; +// std::mt19937 gen(rd()); + +// // Construct datasets +// using data_size_t = StochTree::data_size_t; +// data_size_t n = test_dataset.n; +// int p = test_dataset.x_cols; +// StochTree::ForestDataset dataset = StochTree::ForestDataset(); +// dataset.AddCovariates(test_dataset.covariates.data(), n, test_dataset.x_cols, test_dataset.row_major); +// StochTree::ColumnVector residual = StochTree::ColumnVector(test_dataset.outcome.data(), n); + +// // Construct a ForestTracker +// int num_trees = 1; +// StochTree::ForestTracker tracker = StochTree::ForestTracker(dataset.GetCovariates(), feature_types, num_trees, n); + +// // Set sampling parameters +// double alpha = 0.95; +// double beta = 1.25; +// int min_samples_leaf = 1; +// int max_depth = -1; +// double global_variance = 1.; +// double tau = 1.; +// int cutpoint_grid_size = 5; +// StochTree::TreePrior tree_prior = StochTree::TreePrior(alpha, beta, min_samples_leaf, max_depth); + +// // Construct temporary data structures needed to enumerate splits +// std::vector log_cutpoint_evaluations; +// std::vector cutpoint_features; +// std::vector cutpoint_values; +// std::vector cutpoint_feature_types; +// StochTree::data_size_t valid_cutpoint_count = 0; +// StochTree::CutpointGridContainer cutpoint_grid_container(dataset.GetCovariates(), residual.GetData(), cutpoint_grid_size); + +// // Initialize a leaf model +// StochTree::GaussianConstantLeafModel leaf_model = StochTree::GaussianConstantLeafModel(tau); + +// // Evaluate all possible cutpoints +// StochTree::EvaluateAllPossibleSplits( +// dataset, tracker, residual, tree_prior, gen, leaf_model, global_variance, 0, 0, log_cutpoint_evaluations, cutpoint_features, cutpoint_values, +// cutpoint_feature_types, valid_cutpoint_count, cutpoint_grid_container, 0, n, variable_weights, feature_types, feature_subset +// ); + +// // Check that there are (n - 2*min_samples_leaf + 1)*p + 1 cutpoints considered +// ASSERT_EQ(log_cutpoint_evaluations.size(), (cutpoint_grid_size - 1)*p + 1); + +// // Check the values of the cutpoint evaluations +// std::vector expected_split_evals{3.349927, 3.085074, 3.101841, 3.068029, 3.710592, 3.135288, 2.969388, 3.961885, 4.032006, +// 4.442268, 4.171735, 3.862124, 3.425777, 3.131347, 2.935892, 3.144767, 3.792850, 3.348693, +// 4.600861, 4.879161, 3.940111}; +// for (int i = 0; i < log_cutpoint_evaluations.size(); i++) { +// ASSERT_NEAR(log_cutpoint_evaluations[i], expected_split_evals[i], 0.01); +// } +// } + +// TEST(LeafUnivariateRegressionModel, FullEnumeration) { +// // Load test data +// StochTree::TestUtils::TestDataset test_dataset; +// test_dataset = StochTree::TestUtils::LoadSmallDatasetUnivariateBasis(); +// std::vector feature_types(test_dataset.x_cols, StochTree::FeatureType::kNumeric); +// std::vector variable_weights(test_dataset.x_cols, 1./test_dataset.x_cols); +// std::vector feature_subset(test_dataset.x_cols, true); +// std::random_device rd; +// std::mt19937 gen(rd()); + +// // Construct datasets +// using data_size_t = StochTree::data_size_t; +// data_size_t n = test_dataset.n; +// int p = test_dataset.x_cols; +// StochTree::ForestDataset dataset = StochTree::ForestDataset(); +// dataset.AddCovariates(test_dataset.covariates.data(), n, test_dataset.x_cols, test_dataset.row_major); +// dataset.AddBasis(test_dataset.omega.data(), test_dataset.n, test_dataset.omega_cols, test_dataset.row_major); +// StochTree::ColumnVector residual = StochTree::ColumnVector(test_dataset.outcome.data(), n); + +// // Construct a ForestTracker +// int num_trees = 1; +// StochTree::ForestTracker tracker = StochTree::ForestTracker(dataset.GetCovariates(), feature_types, num_trees, n); + +// // Set sampling parameters +// double alpha = 0.95; +// double beta = 1.25; +// int min_samples_leaf = 1; +// int max_depth = -1; +// double global_variance = 1.; +// double tau = 1.; +// int cutpoint_grid_size = n; +// StochTree::TreePrior tree_prior = StochTree::TreePrior(alpha, beta, min_samples_leaf, max_depth); + +// // Construct temporary data structures needed to enumerate splits +// std::vector log_cutpoint_evaluations; +// std::vector cutpoint_features; +// std::vector cutpoint_values; +// std::vector cutpoint_feature_types; +// StochTree::data_size_t valid_cutpoint_count = 0; +// StochTree::CutpointGridContainer cutpoint_grid_container(dataset.GetCovariates(), residual.GetData(), cutpoint_grid_size); + +// // Initialize a leaf model +// StochTree::GaussianUnivariateRegressionLeafModel leaf_model = StochTree::GaussianUnivariateRegressionLeafModel(tau); + +// // Evaluate all possible cutpoints +// StochTree::EvaluateAllPossibleSplits( +// dataset, tracker, residual, tree_prior, gen, leaf_model, global_variance, 0, 0, log_cutpoint_evaluations, cutpoint_features, cutpoint_values, +// cutpoint_feature_types, valid_cutpoint_count, cutpoint_grid_container, 0, n, variable_weights, feature_types, feature_subset +// ); + +// // Check that there are (n - 2*min_samples_leaf + 1)*p + 1 cutpoints considered +// ASSERT_EQ(log_cutpoint_evaluations.size(), (n - 2*min_samples_leaf + 1)*p + 1); + +// // Check the values of the cutpoint evaluations +// std::vector expected_split_evals{4.978556, 4.067172, 3.823266, 3.850415, 3.796388, 3.791759, 3.864699, 3.970411, 5.105565, 4.886562, 4.812292, 4.450645, 4.180200, +// 4.625754, 3.983956, 4.906961, 5.307099, 5.105565, 6.057032, 5.463854, 5.312733, 5.504701, 5.872222, 4.936127, 4.203568, 4.192258, +// 4.633795, 4.060248, 4.032323, 4.040458, 4.176712, 3.809356, 3.854872, 4.404108, 4.243114, 4.116230, 5.167773, 5.031023, 4.203335, +// 4.094302, 4.280394, 5.557678, 5.394644, 5.945185, 4.978556, 5.069763}; +// for (int i = 0; i < log_cutpoint_evaluations.size(); i++) { +// ASSERT_NEAR(log_cutpoint_evaluations[i], expected_split_evals[i], 0.01); +// } +// } + +// TEST(LeafUnivariateRegressionModel, CutpointThinning) { +// // Load test data +// StochTree::TestUtils::TestDataset test_dataset; +// test_dataset = StochTree::TestUtils::LoadSmallDatasetUnivariateBasis(); +// std::vector feature_types(test_dataset.x_cols, StochTree::FeatureType::kNumeric); +// std::vector variable_weights(test_dataset.x_cols, 1./test_dataset.x_cols); +// std::vector feature_subset(test_dataset.x_cols, true); +// std::random_device rd; +// std::mt19937 gen(rd()); + +// // Construct datasets +// using data_size_t = StochTree::data_size_t; +// data_size_t n = test_dataset.n; +// int p = test_dataset.x_cols; +// StochTree::ForestDataset dataset = StochTree::ForestDataset(); +// dataset.AddCovariates(test_dataset.covariates.data(), n, test_dataset.x_cols, test_dataset.row_major); +// dataset.AddBasis(test_dataset.omega.data(), test_dataset.n, test_dataset.omega_cols, test_dataset.row_major); +// StochTree::ColumnVector residual = StochTree::ColumnVector(test_dataset.outcome.data(), n); + +// // Construct a ForestTracker +// int num_trees = 1; +// StochTree::ForestTracker tracker = StochTree::ForestTracker(dataset.GetCovariates(), feature_types, num_trees, n); + +// // Set sampling parameters +// double alpha = 0.95; +// double beta = 1.25; +// int min_samples_leaf = 1; +// int max_depth = -1; +// double global_variance = 1.; +// double tau = 1.; +// int cutpoint_grid_size = 5; +// StochTree::TreePrior tree_prior = StochTree::TreePrior(alpha, beta, min_samples_leaf, max_depth); + +// // Construct temporary data structures needed to enumerate splits +// std::vector log_cutpoint_evaluations; +// std::vector cutpoint_features; +// std::vector cutpoint_values; +// std::vector cutpoint_feature_types; +// StochTree::data_size_t valid_cutpoint_count = 0; +// StochTree::CutpointGridContainer cutpoint_grid_container(dataset.GetCovariates(), residual.GetData(), cutpoint_grid_size); + +// // Initialize a leaf model +// StochTree::GaussianUnivariateRegressionLeafModel leaf_model = StochTree::GaussianUnivariateRegressionLeafModel(tau); + +// // Evaluate all possible cutpoints +// StochTree::EvaluateAllPossibleSplits( +// dataset, tracker, residual, tree_prior, gen, leaf_model, global_variance, 0, 0, log_cutpoint_evaluations, cutpoint_features, cutpoint_values, +// cutpoint_feature_types, valid_cutpoint_count, cutpoint_grid_container, 0, n, variable_weights, feature_types, feature_subset +// ); + + +// // Check that there are (n - 2*min_samples_leaf + 1)*p + 1 cutpoints considered +// ASSERT_EQ(log_cutpoint_evaluations.size(), (cutpoint_grid_size - 1)*p + 1); + +// // Check the values of the cutpoint evaluations +// std::vector expected_split_evals{4.067172, 3.850415, 3.791759, 3.970411, 4.812292, 4.180200, 3.983956, 5.307099, 5.463854, +// 5.504701, 4.936127, 4.192258, 4.032323, 4.176712, 3.854872, 4.243114, 5.031023, 4.094302, +// 5.557678, 5.945185, 5.069763}; +// for (int i = 0; i < log_cutpoint_evaluations.size(); i++) { +// ASSERT_NEAR(log_cutpoint_evaluations[i], expected_split_evals[i], 0.01); +// } +// } diff --git a/test/python/test_bart.py b/test/python/test_bart.py index 3c20bb76..4b22ab7b 100644 --- a/test/python/test_bart.py +++ b/test/python/test_bart.py @@ -83,7 +83,8 @@ def outcome_mean(X): bart_model_3.from_json_string_list(bart_models_json) # Assertions - y_hat_train_combined = bart_model_3.predict(covariates=X_train) + bart_preds_combined = bart_model_3.predict(covariates=X_train) + y_hat_train_combined = bart_preds_combined['y_hat'] assert y_hat_train_combined.shape == (n_train, num_mcmc * 2) np.testing.assert_allclose( y_hat_train_combined[:, 0:num_mcmc], bart_model.y_hat_train @@ -188,9 +189,10 @@ def outcome_mean(X, W): bart_model_3.from_json_string_list(bart_models_json) # Assertions - y_hat_train_combined = bart_model_3.predict( + bart_preds_combined = bart_model_3.predict( covariates=X_train, basis=basis_train ) + y_hat_train_combined = bart_preds_combined['y_hat'] assert y_hat_train_combined.shape == (n_train, num_mcmc * 2) np.testing.assert_allclose( y_hat_train_combined[:, 0:num_mcmc], bart_model.y_hat_train @@ -295,9 +297,10 @@ def outcome_mean(X, W): bart_model_3.from_json_string_list(bart_models_json) # Assertions - y_hat_train_combined = bart_model_3.predict( + bart_preds_combined = bart_model_3.predict( covariates=X_train, basis=basis_train ) + y_hat_train_combined = bart_preds_combined['y_hat'] assert y_hat_train_combined.shape == (n_train, num_mcmc * 2) np.testing.assert_allclose( y_hat_train_combined[:, 0:num_mcmc], bart_model.y_hat_train @@ -407,7 +410,8 @@ def conditional_stddev(X): bart_model_3.from_json_string_list(bart_models_json) # Assertions - y_hat_train_combined, sigma2_x_train_combined = bart_model_3.predict(covariates=X_train) + bart_preds_combined = bart_model_3.predict(covariates=X_train) + y_hat_train_combined, sigma2_x_train_combined = bart_preds_combined['y_hat'], bart_preds_combined['variance_forest_predictions'] assert y_hat_train_combined.shape == (n_train, num_mcmc * 2) assert sigma2_x_train_combined.shape == (n_train, num_mcmc * 2) np.testing.assert_allclose( @@ -536,9 +540,10 @@ def conditional_stddev(X): bart_model_3.from_json_string_list(bart_models_json) # Assertions - y_hat_train_combined, _ = bart_model_3.predict( + bart_preds_combined = bart_model_3.predict( covariates=X_train, basis=basis_train ) + y_hat_train_combined = bart_preds_combined['y_hat'] assert y_hat_train_combined.shape == (n_train, num_mcmc * 2) np.testing.assert_allclose( y_hat_train_combined[:, 0:num_mcmc], bart_model.y_hat_train @@ -660,9 +665,10 @@ def conditional_stddev(X): bart_model_3.from_json_string_list(bart_models_json) # Assertions - y_hat_train_combined, _ = bart_model_3.predict( + bart_preds_combined = bart_model_3.predict( covariates=X_train, basis=basis_train ) + y_hat_train_combined = bart_preds_combined['y_hat'] assert y_hat_train_combined.shape == (n_train, num_mcmc * 2) np.testing.assert_allclose( y_hat_train_combined[:, 0:num_mcmc], bart_model.y_hat_train @@ -814,11 +820,12 @@ def rfx_term(group_labels, basis): ) # Assertions - y_hat_train_combined, _ = bart_model_3.predict( + bart_preds_combined = bart_model_3.predict( covariates=X_train, rfx_group_ids=group_labels_train, rfx_basis=rfx_basis_train, ) + y_hat_train_combined = bart_preds_combined['y_hat'] assert y_hat_train_combined.shape == (n_train, num_mcmc * 2) np.testing.assert_allclose( y_hat_train_combined[:, 0:num_mcmc], bart_model.y_hat_train @@ -986,12 +993,13 @@ def conditional_stddev(X): ) # Assertions - y_hat_train_combined, _ = bart_model_3.predict( + bart_preds_combined = bart_model_3.predict( covariates=X_train, basis=basis_train, rfx_group_ids=group_labels_train, rfx_basis=rfx_basis_train, ) + y_hat_train_combined = bart_preds_combined['y_hat'] assert y_hat_train_combined.shape == (n_train, num_mcmc * 2) np.testing.assert_allclose( y_hat_train_combined[:, 0:num_mcmc], bart_model.y_hat_train diff --git a/test/python/test_bcf.py b/test/python/test_bcf.py index 28f221ca..2e2f7fbf 100644 --- a/test/python/test_bcf.py +++ b/test/python/test_bcf.py @@ -70,7 +70,8 @@ def test_binary_bcf(self): assert bcf_model.tau_hat_test.shape == (n_test, num_mcmc) # Check overall prediction method - tau_hat, mu_hat, y_hat = bcf_model.predict(X_test, Z_test, pi_test) + bcf_preds = bcf_model.predict(X_test, Z_test, pi_test) + tau_hat, mu_hat, y_hat = bcf_preds['tau_hat'], bcf_preds['mu_hat'], bcf_preds['y_hat'] assert tau_hat.shape == (n_test, num_mcmc) assert mu_hat.shape == (n_test, num_mcmc) assert y_hat.shape == (n_test, num_mcmc) @@ -99,11 +100,11 @@ def test_binary_bcf(self): assert bcf_model.tau_hat_train.shape == (n_train, num_mcmc) # Check overall prediction method - tau_hat, mu_hat, y_hat = bcf_model.predict(X_test, Z_test, pi_test) + bcf_preds = bcf_model.predict(X_test, Z_test, pi_test) + tau_hat, mu_hat, y_hat = bcf_preds['tau_hat'], bcf_preds['mu_hat'], bcf_preds['y_hat'] assert tau_hat.shape == (n_test, num_mcmc) assert mu_hat.shape == (n_test, num_mcmc) assert y_hat.shape == (n_test, num_mcmc) - # Check treatment effect prediction method tau_hat = bcf_model.predict_tau(X_test, Z_test, pi_test) assert tau_hat.shape == (n_test, num_mcmc) @@ -134,7 +135,8 @@ def test_binary_bcf(self): assert bcf_model.bart_propensity_model.y_hat_test.shape == (n_test, 10) # Check overall prediction method - tau_hat, mu_hat, y_hat = bcf_model.predict(X_test, Z_test) + bcf_preds = bcf_model.predict(X_test, Z_test) + tau_hat, mu_hat, y_hat = bcf_preds['tau_hat'], bcf_preds['mu_hat'], bcf_preds['y_hat'] assert tau_hat.shape == (n_test, num_mcmc) assert mu_hat.shape == (n_test, num_mcmc) assert y_hat.shape == (n_test, num_mcmc) @@ -163,7 +165,8 @@ def test_binary_bcf(self): assert bcf_model.bart_propensity_model.y_hat_train.shape == (n_train, 10) # Check overall prediction method - tau_hat, mu_hat, y_hat = bcf_model.predict(X_test, Z_test) + bcf_preds = bcf_model.predict(X_test, Z_test) + tau_hat, mu_hat, y_hat = bcf_preds['tau_hat'], bcf_preds['mu_hat'], bcf_preds['y_hat'] assert tau_hat.shape == (n_test, num_mcmc) assert mu_hat.shape == (n_test, num_mcmc) assert y_hat.shape == (n_test, num_mcmc) @@ -235,7 +238,8 @@ def test_continuous_univariate_bcf(self): assert bcf_model.tau_hat_test.shape == (n_test, num_mcmc) # Check overall prediction method - tau_hat, mu_hat, y_hat = bcf_model.predict(X_test, Z_test, pi_test) + bcf_preds = bcf_model.predict(X_test, Z_test, pi_test) + tau_hat, mu_hat, y_hat = bcf_preds['tau_hat'], bcf_preds['mu_hat'], bcf_preds['y_hat'] assert tau_hat.shape == (n_test, num_mcmc) assert mu_hat.shape == (n_test, num_mcmc) assert y_hat.shape == (n_test, num_mcmc) @@ -270,7 +274,8 @@ def test_continuous_univariate_bcf(self): assert bcf_model_2.tau_hat_test.shape == (n_test, num_mcmc) # Check overall prediction method - tau_hat_2, mu_hat_2, y_hat_2 = bcf_model_2.predict(X_test, Z_test, pi_test) + bcf_preds_2 = bcf_model_2.predict(X_test, Z_test, pi_test) + tau_hat_2, mu_hat_2, y_hat_2 = bcf_preds_2['tau_hat'], bcf_preds_2['mu_hat'], bcf_preds_2['y_hat'] assert tau_hat_2.shape == (n_test, num_mcmc) assert mu_hat_2.shape == (n_test, num_mcmc) assert y_hat_2.shape == (n_test, num_mcmc) @@ -285,7 +290,8 @@ def test_continuous_univariate_bcf(self): bcf_model_3.from_json_string_list(bcf_models_json) # Assertions - tau_hat_3, mu_hat_3, y_hat_3 = bcf_model_3.predict(X_test, Z_test, pi_test) + bcf_preds_3 = bcf_model_3.predict(X_test, Z_test, pi_test) + tau_hat_3, mu_hat_3, y_hat_3 = bcf_preds_3['tau_hat'], bcf_preds_3['mu_hat'], bcf_preds_3['y_hat'] assert tau_hat_3.shape == (n_train, num_mcmc * 2) assert mu_hat_3.shape == (n_train, num_mcmc * 2) assert y_hat_3.shape == (n_train, num_mcmc * 2) @@ -323,7 +329,8 @@ def test_continuous_univariate_bcf(self): assert bcf_model.tau_hat_train.shape == (n_train, num_mcmc) # Check overall prediction method - tau_hat, mu_hat, y_hat = bcf_model.predict(X_test, Z_test, pi_test) + bcf_preds = bcf_model.predict(X_test, Z_test, pi_test) + tau_hat, mu_hat, y_hat = bcf_preds['tau_hat'], bcf_preds['mu_hat'], bcf_preds['y_hat'] assert tau_hat.shape == (n_test, num_mcmc) assert mu_hat.shape == (n_test, num_mcmc) assert y_hat.shape == (n_test, num_mcmc) @@ -358,7 +365,8 @@ def test_continuous_univariate_bcf(self): assert bcf_model.bart_propensity_model.y_hat_test.shape == (n_test, 10) # Check overall prediction method - tau_hat, mu_hat, y_hat = bcf_model.predict(X_test, Z_test) + bcf_preds = bcf_model.predict(X_test, Z_test, pi_test) + tau_hat, mu_hat, y_hat = bcf_preds['tau_hat'], bcf_preds['mu_hat'], bcf_preds['y_hat'] assert tau_hat.shape == (n_test, num_mcmc) assert mu_hat.shape == (n_test, num_mcmc) assert y_hat.shape == (n_test, num_mcmc) @@ -387,7 +395,8 @@ def test_continuous_univariate_bcf(self): assert bcf_model.bart_propensity_model.y_hat_train.shape == (n_train, 10) # Check overall prediction method - tau_hat, mu_hat, y_hat = bcf_model.predict(X_test, Z_test) + bcf_preds = bcf_model.predict(X_test, Z_test) + tau_hat, mu_hat, y_hat = bcf_preds['tau_hat'], bcf_preds['mu_hat'], bcf_preds['y_hat'] assert tau_hat.shape == (n_test, num_mcmc) assert mu_hat.shape == (n_test, num_mcmc) assert y_hat.shape == (n_test, num_mcmc) @@ -414,7 +423,8 @@ def test_continuous_univariate_bcf(self): assert bcf_model_2.tau_hat_train.shape == (n_train, num_mcmc) # Check overall prediction method - tau_hat_2, mu_hat_2, y_hat_2 = bcf_model_2.predict(X_test, Z_test) + bcf_preds_2 = bcf_model_2.predict(X_test, Z_test) + tau_hat_2, mu_hat_2, y_hat_2 = bcf_preds_2['tau_hat'], bcf_preds_2['mu_hat'], bcf_preds_2['y_hat'] assert tau_hat_2.shape == (n_test, num_mcmc) assert mu_hat_2.shape == (n_test, num_mcmc) assert y_hat_2.shape == (n_test, num_mcmc) @@ -429,7 +439,8 @@ def test_continuous_univariate_bcf(self): bcf_model_3.from_json_string_list(bcf_models_json) # Assertions - tau_hat_3, mu_hat_3, y_hat_3 = bcf_model_3.predict(X_test, Z_test) + bcf_preds_3 = bcf_model_3.predict(X_test, Z_test) + tau_hat_3, mu_hat_3, y_hat_3 = bcf_preds_3['tau_hat'], bcf_preds_3['mu_hat'], bcf_preds_3['y_hat'] assert tau_hat_3.shape == (n_train, num_mcmc * 2) assert mu_hat_3.shape == (n_train, num_mcmc * 2) assert y_hat_3.shape == (n_train, num_mcmc * 2) @@ -510,7 +521,8 @@ def test_multivariate_bcf(self): assert bcf_model.tau_hat_test.shape == (n_test, num_mcmc, treatment_dim) # Check overall prediction method - tau_hat, mu_hat, y_hat = bcf_model.predict(X_test, Z_test, pi_test) + bcf_preds = bcf_model.predict(X_test, Z_test, pi_test) + tau_hat, mu_hat, y_hat = bcf_preds['tau_hat'], bcf_preds['mu_hat'], bcf_preds['y_hat'] assert tau_hat.shape == (n_test, num_mcmc, treatment_dim) assert mu_hat.shape == (n_test, num_mcmc) assert y_hat.shape == (n_test, num_mcmc) @@ -539,7 +551,8 @@ def test_multivariate_bcf(self): assert bcf_model.tau_hat_train.shape == (n_train, num_mcmc, treatment_dim) # Check overall prediction method - tau_hat, mu_hat, y_hat = bcf_model.predict(X_test, Z_test, pi_test) + bcf_preds = bcf_model.predict(X_test, Z_test, pi_test) + tau_hat, mu_hat, y_hat = bcf_preds['tau_hat'], bcf_preds['mu_hat'], bcf_preds['y_hat'] assert tau_hat.shape == (n_test, num_mcmc, treatment_dim) assert mu_hat.shape == (n_test, num_mcmc) assert y_hat.shape == (n_test, num_mcmc) @@ -644,11 +657,12 @@ def test_binary_bcf_heteroskedastic(self): assert bcf_model.sigma2_x_test.shape == (n_train, num_mcmc) # Check overall prediction method - tau_hat, mu_hat, y_hat, sigma2_hat = bcf_model.predict(X_test, Z_test, pi_test) + bcf_preds = bcf_model.predict(X_test, Z_test, pi_test) + tau_hat, mu_hat, y_hat, sigma2_x_hat = bcf_preds['tau_hat'], bcf_preds['mu_hat'], bcf_preds['y_hat'], bcf_preds['variance_forest_predictions'] assert tau_hat.shape == (n_test, num_mcmc) assert mu_hat.shape == (n_test, num_mcmc) assert y_hat.shape == (n_test, num_mcmc) - assert sigma2_hat.shape == (n_test, num_mcmc) + assert sigma2_x_hat.shape == (n_test, num_mcmc) # Check treatment effect prediction method tau_hat = bcf_model.predict_tau(X_test, Z_test, pi_test) @@ -675,29 +689,29 @@ def test_binary_bcf_heteroskedastic(self): assert bcf_model.sigma2_x_train.shape == (n_train, num_mcmc) # Check overall prediction method - tau_hat, mu_hat, y_hat, sigma2_hat = bcf_model.predict(X_test, Z_test, pi_test) - assert tau_hat.shape == (n_test, num_mcmc) - assert mu_hat.shape == (n_test, num_mcmc) - assert y_hat.shape == (n_test, num_mcmc) - assert sigma2_hat.shape == (n_test, num_mcmc) + bcf_preds = bcf_model.predict(X_test, Z_test, pi_test) + assert bcf_preds['tau_hat'].shape == (n_test, num_mcmc) + assert bcf_preds['mu_hat'].shape == (n_test, num_mcmc) + assert bcf_preds['y_hat'].shape == (n_test, num_mcmc) + assert bcf_preds['variance_forest_predictions'].shape == (n_test, num_mcmc) # Check predictions match - tau_hat, mu_hat, y_hat, sigma2_hat = bcf_model.predict(X_train, Z_train, pi_train) - assert tau_hat.shape == (n_train, num_mcmc) - assert mu_hat.shape == (n_train, num_mcmc) - assert y_hat.shape == (n_train, num_mcmc) - assert sigma2_hat.shape == (n_train, num_mcmc) + bcf_preds = bcf_model.predict(X_train, Z_train, pi_train) + assert bcf_preds['tau_hat'].shape == (n_train, num_mcmc) + assert bcf_preds['mu_hat'].shape == (n_train, num_mcmc) + assert bcf_preds['y_hat'].shape == (n_train, num_mcmc) + assert bcf_preds['variance_forest_predictions'].shape == (n_train, num_mcmc) np.testing.assert_allclose( - y_hat, bcf_model.y_hat_train + bcf_preds['y_hat'], bcf_model.y_hat_train ) np.testing.assert_allclose( - mu_hat, bcf_model.mu_hat_train + bcf_preds['mu_hat'], bcf_model.mu_hat_train ) np.testing.assert_allclose( - tau_hat, bcf_model.tau_hat_train + bcf_preds['tau_hat'], bcf_model.tau_hat_train ) np.testing.assert_allclose( - sigma2_hat, bcf_model.sigma2_x_train + bcf_preds['variance_forest_predictions'], bcf_model.sigma2_x_train ) # Check treatment effect prediction method @@ -731,11 +745,11 @@ def test_binary_bcf_heteroskedastic(self): assert bcf_model.bart_propensity_model.y_hat_test.shape == (n_test, 10) # Check overall prediction method - tau_hat, mu_hat, y_hat, sigma2_hat = bcf_model.predict(X_test, Z_test) - assert tau_hat.shape == (n_test, num_mcmc) - assert mu_hat.shape == (n_test, num_mcmc) - assert y_hat.shape == (n_test, num_mcmc) - assert sigma2_hat.shape == (n_test, num_mcmc) + bcf_preds = bcf_model.predict(X_test, Z_test) + assert bcf_preds['tau_hat'].shape == (n_test, num_mcmc) + assert bcf_preds['mu_hat'].shape == (n_test, num_mcmc) + assert bcf_preds['y_hat'].shape == (n_test, num_mcmc) + assert bcf_preds['variance_forest_predictions'].shape == (n_test, num_mcmc) # Check treatment effect prediction method tau_hat = bcf_model.predict_tau(X_test, Z_test) @@ -761,10 +775,10 @@ def test_binary_bcf_heteroskedastic(self): assert bcf_model.bart_propensity_model.y_hat_train.shape == (n_train, 10) # Check overall prediction method - tau_hat, mu_hat, y_hat = bcf_model.predict(X_test, Z_test) - assert tau_hat.shape == (n_test, num_mcmc) - assert mu_hat.shape == (n_test, num_mcmc) - assert y_hat.shape == (n_test, num_mcmc) + bcf_preds = bcf_model.predict(X_test, Z_test) + assert bcf_preds['tau_hat'].shape == (n_test, num_mcmc) + assert bcf_preds['mu_hat'].shape == (n_test, num_mcmc) + assert bcf_preds['y_hat'].shape == (n_test, num_mcmc) # Check treatment effect prediction method tau_hat = bcf_model.predict_tau(X_test, Z_test) diff --git a/test/python/test_json.py b/test/python/test_json.py index 1e3d9c5a..1d0dc66d 100644 --- a/test/python/test_json.py +++ b/test/python/test_json.py @@ -130,7 +130,8 @@ def outcome_mean(X): forest_preds_y_mcmc_cached = bart_model.y_hat_train # Extract original predictions - forest_preds_y_mcmc_retrieved = bart_model.predict(X) + bart_preds = bart_model.predict(X) + forest_preds_y_mcmc_retrieved = bart_preds['y_hat'] # Roundtrip to / from JSON json_test = JSONSerializer() @@ -336,13 +337,15 @@ def outcome_mean(X, W): bart_orig.sample(X_train=X, y_train=y, leaf_basis_train=W, num_gfr=10, num_mcmc=10) # Extract predictions from the sampler - y_hat_orig = bart_orig.predict(X, W) + bart_preds_orig = bart_orig.predict(X, W) + y_hat_orig = bart_preds_orig['y_hat'] # "Round-trip" the model to JSON string and back and check that the predictions agree bart_json_string = bart_orig.to_json() bart_reloaded = BARTModel() bart_reloaded.from_json(bart_json_string) - y_hat_reloaded = bart_reloaded.predict(X, W) + bart_preds_reloaded = bart_reloaded.predict(X, W) + y_hat_reloaded = bart_preds_reloaded['y_hat'] np.testing.assert_almost_equal(y_hat_orig, y_hat_reloaded) def test_bart_rfx_string(self): @@ -408,13 +411,15 @@ def rfx_mean(group_labels, basis): rfx_basis_train=basis, num_gfr=10, num_mcmc=10) # Extract predictions from the sampler - y_hat_orig = bart_orig.predict(X, W, group_labels, basis) + bart_preds_orig = bart_orig.predict(X, W, group_labels, basis) + y_hat_orig = bart_preds_orig['y_hat'] # "Round-trip" the model to JSON string and back and check that the predictions agree bart_json_string = bart_orig.to_json() bart_reloaded = BARTModel() bart_reloaded.from_json(bart_json_string) - y_hat_reloaded = bart_reloaded.predict(X, W, group_labels, basis) + bart_preds_reloaded = bart_reloaded.predict(X, W, group_labels, basis) + y_hat_reloaded = bart_preds_reloaded['y_hat'] np.testing.assert_almost_equal(y_hat_orig, y_hat_reloaded) def test_bcf_string(self): @@ -444,15 +449,17 @@ def test_bcf_string(self): ) # Extract predictions from the sampler - mu_hat_orig, tau_hat_orig, y_hat_orig = bcf_orig.predict(X, Z, pi_X) + bcf_preds_orig = bcf_orig.predict(X, Z, pi_X) + mu_hat_orig, tau_hat_orig, y_hat_orig = bcf_preds_orig['mu_hat'], bcf_preds_orig['tau_hat'], bcf_preds_orig['y_hat'] # "Round-trip" the model to JSON string and back and check that the predictions agree bcf_json_string = bcf_orig.to_json() bcf_reloaded = BCFModel() bcf_reloaded.from_json(bcf_json_string) - mu_hat_reloaded, tau_hat_reloaded, y_hat_reloaded = bcf_reloaded.predict( + bcf_preds_reloaded = bcf_reloaded.predict( X, Z, pi_X ) + mu_hat_reloaded, tau_hat_reloaded, y_hat_reloaded = bcf_preds_reloaded['mu_hat'], bcf_preds_reloaded['tau_hat'], bcf_preds_reloaded['y_hat'] np.testing.assert_almost_equal(y_hat_orig, y_hat_reloaded) np.testing.assert_almost_equal(tau_hat_orig, tau_hat_reloaded) np.testing.assert_almost_equal(mu_hat_orig, mu_hat_reloaded) @@ -508,15 +515,17 @@ def rfx_mean(group_labels, basis): ) # Extract predictions from the sampler - mu_hat_orig, tau_hat_orig, rfx_hat_orig, y_hat_orig = bcf_orig.predict(X, Z, pi_X, group_labels, basis) + bcf_preds_orig = bcf_orig.predict(X, Z, pi_X, group_labels, basis) + mu_hat_orig, tau_hat_orig, rfx_hat_orig, y_hat_orig = bcf_preds_orig['mu_hat'], bcf_preds_orig['tau_hat'], bcf_preds_orig['rfx_predictions'], bcf_preds_orig['y_hat'] # "Round-trip" the model to JSON string and back and check that the predictions agree bcf_json_string = bcf_orig.to_json() bcf_reloaded = BCFModel() bcf_reloaded.from_json(bcf_json_string) - mu_hat_reloaded, tau_hat_reloaded, rfx_hat_reloaded, y_hat_reloaded = bcf_reloaded.predict( + bcf_preds_reloaded = bcf_reloaded.predict( X, Z, pi_X, group_labels, basis ) + mu_hat_reloaded, tau_hat_reloaded, rfx_hat_reloaded, y_hat_reloaded = bcf_preds_reloaded['mu_hat'], bcf_preds_reloaded['tau_hat'], bcf_preds_reloaded['rfx_predictions'], bcf_preds_reloaded['y_hat'] np.testing.assert_almost_equal(y_hat_orig, y_hat_reloaded) np.testing.assert_almost_equal(tau_hat_orig, tau_hat_reloaded) np.testing.assert_almost_equal(mu_hat_orig, mu_hat_reloaded) @@ -547,15 +556,17 @@ def test_bcf_propensity_string(self): bcf_orig.sample(X_train=X, Z_train=Z, y_train=y, num_gfr=10, num_mcmc=10) # Extract predictions from the sampler - mu_hat_orig, tau_hat_orig, y_hat_orig = bcf_orig.predict(X, Z, pi_X) + bcf_preds_orig = bcf_orig.predict(X, Z, pi_X) + mu_hat_orig, tau_hat_orig, y_hat_orig = bcf_preds_orig['mu_hat'], bcf_preds_orig['tau_hat'], bcf_preds_orig['y_hat'] # "Round-trip" the model to JSON string and back and check that the predictions agree bcf_json_string = bcf_orig.to_json() bcf_reloaded = BCFModel() bcf_reloaded.from_json(bcf_json_string) - mu_hat_reloaded, tau_hat_reloaded, y_hat_reloaded = bcf_reloaded.predict( + bcf_preds_reloaded = bcf_reloaded.predict( X, Z, pi_X ) + mu_hat_reloaded, tau_hat_reloaded, y_hat_reloaded = bcf_preds_reloaded['mu_hat'], bcf_preds_reloaded['tau_hat'], bcf_preds_reloaded['y_hat'] np.testing.assert_almost_equal(y_hat_orig, y_hat_reloaded) np.testing.assert_almost_equal(tau_hat_orig, tau_hat_reloaded) np.testing.assert_almost_equal(mu_hat_orig, mu_hat_reloaded) diff --git a/tools/debug/bart_profile.R b/tools/debug/bart_profile.R index 2b33211e..a63331b9 100644 --- a/tools/debug/bart_profile.R +++ b/tools/debug/bart_profile.R @@ -6,7 +6,7 @@ library(stochtree) Rprof() start_time <- Sys.time() -n <- 10000 +n <- 50000 p <- 50 X <- matrix(runif(n*p), ncol = p) f_XW <- ( @@ -26,7 +26,10 @@ X_test <- X[test_inds,] X_train <- X[train_inds,] y_test <- y[test_inds] y_train <- y[train_inds] -bart_model <- bart(X_train = X_train, y_train = y_train, X_test = X_test) +general_params <- list(num_threads = 10) +bart_model <- bart(X_train = X_train, y_train = y_train, X_test = X_test, + num_gfr = 100, num_mcmc = 100, + general_params = general_params) end_time <- Sys.time() print(paste("runtime:", end_time - start_time)) diff --git a/tools/perf/bart_profiling_script.R b/tools/perf/bart_profiling_script.R index 7a60eed2..04fef961 100644 --- a/tools/perf/bart_profiling_script.R +++ b/tools/perf/bart_profiling_script.R @@ -9,6 +9,7 @@ if (length(args) > 0){ num_gfr <- as.integer(args[3]) num_mcmc <- as.integer(args[4]) snr <- as.numeric(args[5]) + num_threads <- as.numeric(args[6]) } else{ # Default arguments n <- 1000 @@ -16,9 +17,11 @@ if (length(args) > 0){ num_gfr <- 10 num_mcmc <- 100 snr <- 3.0 + num_threads <- -1 } cat("n = ", n, "\np = ", p, "\nnum_gfr = ", num_gfr, - "\nnum_mcmc = ", num_mcmc, "\nsnr = ", snr, "\n", sep = "") + "\nnum_mcmc = ", num_mcmc, "\nsnr = ", snr, + "\nnum_threads = ", num_threads, "\n", sep = "") # Generate data needed to train BART model X <- matrix(runif(n*p), ncol = p) @@ -49,8 +52,9 @@ y_train <- y[train_inds] system.time({ # Sample BART model + general_params <- list(num_threads = num_threads) bart_model <- bart(X_train = X_train, y_train = y_train, X_test = X_test, - num_gfr = num_gfr, num_mcmc = num_mcmc) + num_gfr = num_gfr, num_mcmc = num_mcmc, general_params = general_params) # Predict on the test set test_preds <- predict(bart_model, X = X_test) diff --git a/tools/perf/bart_profiling_script.py b/tools/perf/bart_profiling_script.py new file mode 100644 index 00000000..a54fc575 --- /dev/null +++ b/tools/perf/bart_profiling_script.py @@ -0,0 +1,68 @@ +# Load libraries +import argparse +import numpy as np +from sklearn.model_selection import train_test_split +from stochtree import BARTModel +import time + +def outcome_mean(X: np.ndarray) -> np.ndarray: + plm_term = np.where( + (X[:,0] >= 0.0) & (X[:,0] < 0.25), -7.5 * X[:,1], + np.where( + (X[:,0] >= 0.25) & (X[:,0] < 0.5), -2.5 * X[:,1], + np.where( + (X[:,0] >= 0.5) & (X[:,0] < 0.75), 2.5 * X[:,1], + 7.5 * X[:,1] + ) + ) + ) + trig_term = 2*np.sin(X[:,2]*2*np.pi) - 1.5*np.cos(X[:,3]*2*np.pi) + return plm_term + trig_term + +if __name__ == "__main__": + # Handle optional command line arguments + parser = argparse.ArgumentParser( + prog='bart_profiling_script', + description='Runs BART on synthetic data, following user-provided parameters') + parser.add_argument("--n", action='store', default=1000, type=int) + parser.add_argument("--p", action='store', default=5, type=int) + parser.add_argument("--num_gfr", action='store', default=10, type=int) + parser.add_argument("--num_mcmc", action='store', default=100, type=int) + parser.add_argument("--snr", action='store', default=2.0, type=float) + parser.add_argument("--num_threads", action='store', default=-1, type=int) + args = parser.parse_args() + n = args.n + p = args.p + num_gfr = args.num_gfr + num_mcmc = args.num_mcmc + snr = args.snr + num_threads = args.num_threads + print(f"n = {n:d}\np = {p:d}\nnum_gfr = {num_gfr:d}\nnum_mcmc = {num_mcmc:d}\nsnr = {snr:.2f}\nnum_threads = {num_threads:d}") + + # Generate synthetic data + rng = np.random.default_rng() + X = rng.uniform(0, 1, (n, p)) + f_X = outcome_mean(X) + noise_sd = np.std(f_X)/snr + epsilon = rng.normal(loc=0., scale=noise_sd, size=n) + y = f_X + epsilon + + # Test train split + sample_inds = np.arange(n) + train_inds, test_inds = train_test_split(sample_inds, test_size=0.2) + X_train = X[train_inds,:] + X_test = X[test_inds,:] + y_train = y[train_inds] + y_test = y[test_inds] + + # Time the BART model and prediction + start_time = time.time() + general_params = {'num_threads': num_threads} + bart_model = BARTModel() + bart_model.sample(X_train=X_train, y_train=y_train, X_test=X_test, + num_gfr=num_gfr, num_mcmc=num_mcmc, general_params=general_params) + bart_preds = bart_model.predict(covariates=X_test) + test_preds = bart_preds['y_hat'] + end_time = time.time() + total_runtime = end_time - start_time + print(f"Total runtime: {total_runtime:.3f} seconds") diff --git a/tools/perf/bcf_profiling_script.R b/tools/perf/bcf_profiling_script.R new file mode 100644 index 00000000..9e1d60ef --- /dev/null +++ b/tools/perf/bcf_profiling_script.R @@ -0,0 +1,80 @@ +# Load libraries +library(stochtree) + +# Capture command line arguments +args <- commandArgs(trailingOnly = T) +if (length(args) > 0){ + n <- as.integer(args[1]) + p <- as.integer(args[2]) + num_gfr <- as.integer(args[3]) + num_mcmc <- as.integer(args[4]) + snr <- as.numeric(args[5]) + num_threads <- as.numeric(args[6]) +} else{ + # Default arguments + n <- 1000 + p <- 5 + num_gfr <- 10 + num_mcmc <- 100 + snr <- 3.0 + num_threads <- -1 +} +cat("n = ", n, "\np = ", p, "\nnum_gfr = ", num_gfr, + "\nnum_mcmc = ", num_mcmc, "\nsnr = ", snr, + "\nnum_threads = ", num_threads, "\n", sep = "") + +# Generate data needed to train BCF model +g <- function(x) {ifelse(x[,5]==1,2,ifelse(x[,5]==2,-1,-4))} +mu1 <- function(x) {1+g(x)+x[,1]*x[,3]} +tau1 <- function(x) {1+2*x[,2]*x[,4]} +x1 <- rnorm(n) +x2 <- rnorm(n) +x3 <- rnorm(n) +x4 <- as.numeric(rbinom(n,1,0.5)) +x5 <- as.numeric(sample(1:3,n,replace=TRUE)) +X <- cbind(x1,x2,x3,x4,x5) +p_remaining <- p - 5 +if (p_remaining > 0) { + X_remaining <- matrix(rnorm(n*p_remaining), ncol = p_remaining) + X <- cbind(X, X_remaining) +} +mu_x <- mu1(X) +tau_x <- tau1(X) +pi_x <- 0.8*pnorm((3*mu_x/sd(mu_x)) - 0.5*X[,1]) + 0.05 + runif(n)/10 +Z <- rbinom(n,1,pi_x) +E_XZ <- mu_x + Z*tau_x +rfx_group_ids <- rep(c(1,2), n %/% 2) +rfx_coefs <- matrix(c(-1, -1, 1, 1), nrow=2, byrow=TRUE) +rfx_basis <- cbind(1, runif(n, -1, 1)) +rfx_term <- rowSums(rfx_coefs[rfx_group_ids,] * rfx_basis) +y <- E_XZ + rfx_term + rnorm(n, 0, 1)*(sd(E_XZ)/snr) +X <- as.data.frame(X) +X$x4 <- factor(X$x4, ordered = TRUE) +X$x5 <- factor(X$x5, ordered = TRUE) + +# Split into train and test sets +test_set_pct <- 0.2 +n_test <- round(test_set_pct*n) +n_train <- n - n_test +test_inds <- sort(sample(1:n, n_test, replace = FALSE)) +train_inds <- (1:n)[!((1:n) %in% test_inds)] +X_test <- X[test_inds,] +X_train <- X[train_inds,] +Z_test <- Z[test_inds] +Z_train <- Z[train_inds] +pi_test <- pi_x[test_inds] +pi_train <- pi_x[train_inds] +y_test <- y[test_inds] +y_train <- y[train_inds] + +system.time({ + # Sample BCF model + general_params <- list(num_threads = num_threads) + bcf_model <- bcf(X_train = X_train, Z_train = Z_train, propensity_train = pi_train, + y_train = y_train, X_test = X_test, Z_test = Z_test, + propensity_test = pi_test, num_gfr = num_gfr, + num_mcmc = num_mcmc, general_params = general_params) + + # Predict on the test set + test_preds <- predict(bcf_model, X = X_test, Z = Z_test, propensity = pi_test) +}) diff --git a/tools/regression/README.md b/tools/regression/README.md new file mode 100644 index 00000000..2ab7e37b --- /dev/null +++ b/tools/regression/README.md @@ -0,0 +1,141 @@ +# Regression Testing for BART and BCF + +This directory contains scripts that constitute a lowkey "regression testing" procedure for `stochtree`'s BART and BCF models. The goal is to ensure that the models are functioning correctly and that any changes made to the code do not introduce new bugs. + +## Overview + +`stochtree` is by its nature a stochastic software tool, meaning that it generates different results each time it is run. This complicates regression testing slightly, as we cannot always distinguish between a genuine performance regression and an expected variation in results. + +For this reason, the "regression testing" setup is somewhat informal. It is designed to catch any strinkingly obvious issues. Both BART and BCF are run on a set of example datasets, and the results can then be compared against previously saved outputs. + +## Usage + +The primary scripts for running the regression tests are located in the `tools/regression/bart` and `tools/regression/bcf` directories. Here, there are scripts that run each models on a variety of datasets and combine and summarize their results (RMSE, coverage, runtime). + +This section documents how to run the end-to-end regression test suite for both the R and Python packages of `stochtree`. For more information on the individual tests, see the following section for documentation on the data generating processes tested and the test script parameters. + +### R Package + +To run the regression tests for the R package, first navigate to the `stochtree` repository in your terminal and then run: + +```bash +Rscript tools/regression/bart/regression_test_dispatch_bart.R +Rscript tools/regression/bcf/regression_test_dispatch_bcf.R +``` + +Then, to combine and analyze the results, run: + +```bash +Rscript tools/regression/bart/regression_test_analysis_bart.R +Rscript tools/regression/bcf/regression_test_analysis_bcf.R +``` + +This can be compared against any previously saved results. We are currently figuring out a solution for hosting previous regression results, so for now, you will need to manually save and compare outputs of the tests locally before and after after making your changes. + +### Python Package + +To run the regression tests for the Python package, first navigate to the `stochtree` repository in your terminal and then run: + +```bash +python tools/regression/bart/regression_test_dispatch_bart.py +python tools/regression/bcf/regression_test_dispatch_bcf.py +``` + +Then, to combine and analyze the results, run: + +```bash +python tools/regression/bart/regression_test_analysis_bart.py +python tools/regression/bcf/regression_test_analysis_bcf.py +``` + +## Individual Regression Tests + +### BART + +#### Data-generating processes (DGPs): + +1. **DGP 1**: Basic BART without basis or random effects +2. **DGP 2**: BART with basis but no random effects +3. **DGP 3**: BART with random effects but no basis +4. **DGP 4**: BART with both basis and random effects + +#### Script + +The individual regression tests are dispatched by a `tools/regression/bart/regression_test_dispatch_bart` script for R or Python, both of which accept the following (options) command line arguments: + +- `n_iter`: Number of iterations (default: 5) +- `n`: Sample size (default: 1000) +- `p`: Number of covariates (default: 5) +- `num_gfr`: Number of GFR iterations (default: 10) +- `num_mcmc`: Number of MCMC iterations (default: 100) +- `dgp_num`: Data generating process number 1-4 (default: 1) +- `snr`: Signal-to-noise ratio (default: 2.0) +- `test_set_pct`: Test set percentage (default: 0.2) +- `num_threads`: Number of threads, -1 for all available (default: -1) + +Run this script in python: + +```bash +python tools/regression/bart/individual_regression_test_bart.py [n_iter] [n] [p] [num_gfr] [num_mcmc] [dgp_num] [snr] [test_set_pct] [num_threads] +``` + +or in R: + +```bash +Rscript tools/regression/bart/individual_regression_test_bart.R [n_iter] [n] [p] [num_gfr] [num_mcmc] [dgp_num] [snr] [test_set_pct] [num_threads] +``` + +#### Output + +BART results are saved to CSV files in the `tools/regression/bart/stochtree_bart_python_results/` or `tools/regression/bart/stochtree_bart_r_results/` directory with filenames that encode the parameter values. Each file contains: + +- Parameter values (n, p, num_gfr, num_mcmc, dgp_num, snr, test_set_pct, num_threads) +- Iteration number +- RMSE on test set +- Coverage of 95% prediction intervals +- Runtime in seconds + +### BCF + +#### Data-generating processes (DGPs): + +1. **DGP 1**: Basic BCF without random effects +2. **DGP 2**: BCF with multivariate treatment but no random effects +3. **DGP 3**: BCF with random effects but univariate treatment +4. **DGP 4**: BCF with both multivariate treatment and random effects + +#### Script + +The individual regression tests are dispatched by a `tools/regression/bcf/regression_test_dispatch_bcf` script for R or Python, both of which accept the following (options) command line arguments: + +- `n_iter`: Number of iterations (default: 5) +- `n`: Sample size (default: 1000) +- `p`: Number of covariates (default: 5) +- `num_gfr`: Number of GFR iterations (default: 10) +- `num_mcmc`: Number of MCMC iterations (default: 100) +- `dgp_num`: Data generating process number 1-4 (default: 1) +- `snr`: Signal-to-noise ratio (default: 2.0) +- `test_set_pct`: Test set percentage (default: 0.2) +- `num_threads`: Number of threads, -1 for all available (default: -1) + +Run this script in python: + +```bash +python tools/regression/bcf/individual_regression_test_bcf.py [n_iter] [n] [p] [num_gfr] [num_mcmc] [dgp_num] [snr] [test_set_pct] [num_threads] +``` + +or in R: + +```bash +Rscript tools/regression/bcf/individual_regression_test_bcf.R [n_iter] [n] [p] [num_gfr] [num_mcmc] [dgp_num] [snr] [test_set_pct] [num_threads] +``` + +#### Outputs + +BCF results are saved to CSV files in the `tools/regression/bcf/stochtree_bcf_python_results/` or `tools/regression/bcf/stochtree_bcf_r_results/` directory with filenames that encode the parameter values. Each file contains: + +- Parameter values (n, p, num_gfr, num_mcmc, dgp_num, snr, test_set_pct, num_threads) +- Iteration number +- Outcome RMSE and coverage +- Treatment effect RMSE and coverage +- Runtime in seconds diff --git a/tools/regression/bart/individual_regression_test_bart.R b/tools/regression/bart/individual_regression_test_bart.R new file mode 100644 index 00000000..3f71e8cc --- /dev/null +++ b/tools/regression/bart/individual_regression_test_bart.R @@ -0,0 +1,235 @@ +# Load libraries +library(stochtree) + +# Define DGPs +dgp1 <- function(n, p, snr) { + X <- matrix(runif(n*p), ncol = p) + plm_term <- ( + ((0 <= X[,1]) & (0.25 > X[,1])) * (-7.5*X[,2]) + + ((0.25 <= X[,1]) & (0.5 > X[,1])) * (-2.5*X[,2]) + + ((0.5 <= X[,1]) & (0.75 > X[,1])) * (2.5*X[,2]) + + ((0.75 <= X[,1]) & (1 > X[,1])) * (7.5*X[,2]) + ) + trig_term <- ( + 2*sin(X[,3]*2*pi) - + 1.5*cos(X[,4]*2*pi) + ) + f_XW <- plm_term + trig_term + noise_sd <- sd(f_XW)/snr + y <- f_XW + rnorm(n, 0, noise_sd) + return(list(covariates = X, basis = NULL, outcome = y, conditional_mean = f_XW, + rfx_group_ids = NULL, rfx_basis = NULL)) +} +dgp2 <- function(n, p, snr) { + X <- matrix(runif(n*p), ncol = p) + W <- matrix(runif(n*2), ncol = 2) + plm_term <- ( + ((0 <= X[,1]) & (0.25 > X[,1])) * (-7.5*W[,1]) + + ((0.25 <= X[,1]) & (0.5 > X[,1])) * (-2.5*W[,1]) + + ((0.5 <= X[,1]) & (0.75 > X[,1])) * (2.5*W[,1]) + + ((0.75 <= X[,1]) & (1 > X[,1])) * (7.5*W[,1]) + ) + trig_term <- ( + 2*sin(X[,3]*2*pi) - + 1.5*cos(X[,4]*2*pi) + ) + f_XW <- plm_term + trig_term + noise_sd <- sd(f_XW)/snr + y <- f_XW + rnorm(n, 0, noise_sd) + return(list(covariates = X, basis = W, outcome = y, conditional_mean = f_XW, + rfx_group_ids = NULL, rfx_basis = NULL)) +} +dgp3 <- function(n, p, snr) { + X <- matrix(runif(n*p), ncol = p) + plm_term <- ( + ((0 <= X[,1]) & (0.25 > X[,1])) * (-7.5*X[,2]) + + ((0.25 <= X[,1]) & (0.5 > X[,1])) * (-2.5*X[,2]) + + ((0.5 <= X[,1]) & (0.75 > X[,1])) * (2.5*X[,2]) + + ((0.75 <= X[,1]) & (1 > X[,1])) * (7.5*X[,2]) + ) + trig_term <- ( + 2*sin(X[,3]*2*pi) - + 1.5*cos(X[,4]*2*pi) + ) + rfx_group_ids <- sample(1:3, size = n, replace = T) + rfx_coefs <- t(matrix(c(-5, -3, -1, 5, 3, 1), nrow=2, byrow=TRUE)) + rfx_basis <- cbind(1, runif(n, -1, 1)) + rfx_term <- rowSums(rfx_coefs[rfx_group_ids,] * rfx_basis) + f_XW <- plm_term + trig_term + rfx_term + noise_sd <- sd(f_XW)/snr + y <- f_XW + rnorm(n, 0, noise_sd) + return(list(covariates = X, basis = NULL, outcome = y, conditional_mean = f_XW, + rfx_group_ids = rfx_group_ids, rfx_basis = rfx_basis)) +} +dgp4 <- function(n, p, snr) { + X <- matrix(runif(n*p), ncol = p) + W <- matrix(runif(n*2), ncol = 2) + plm_term <- ( + ((0 <= X[,1]) & (0.25 > X[,1])) * (-7.5*W[,1]) + + ((0.25 <= X[,1]) & (0.5 > X[,1])) * (-2.5*W[,1]) + + ((0.5 <= X[,1]) & (0.75 > X[,1])) * (2.5*W[,1]) + + ((0.75 <= X[,1]) & (1 > X[,1])) * (7.5*W[,1]) + ) + trig_term <- ( + 2*sin(X[,3]*2*pi) - + 1.5*cos(X[,4]*2*pi) + ) + rfx_group_ids <- sample(1:3, size = n, replace = T) + rfx_coefs <- t(matrix(c(-5, -3, -1, 5, 3, 1), nrow=2, byrow=TRUE)) + rfx_basis <- cbind(1, runif(n, -1, 1)) + rfx_term <- rowSums(rfx_coefs[rfx_group_ids,] * rfx_basis) + f_XW <- plm_term + trig_term + rfx_term + noise_sd <- sd(f_XW)/snr + y <- f_XW + rnorm(n, 0, noise_sd) + return(list(covariates = X, basis = W, outcome = y, conditional_mean = f_XW, + rfx_group_ids = rfx_group_ids, rfx_basis = rfx_basis)) +} + +# Test / train split utilities +compute_test_train_indices <- function(n, test_set_pct) { + n_test <- round(test_set_pct*n) + n_train <- n - n_test + test_inds <- sort(sample(1:n, n_test, replace = FALSE)) + train_inds <- (1:n)[!((1:n) %in% test_inds)] + return(list(test_inds = test_inds, train_inds = train_inds)) +} +subset_data <- function(data, subset_inds) { + if (is.matrix(data)) { + return(data[subset_inds,]) + } else { + return(data[subset_inds]) + } +} + +# Capture command line arguments +args <- commandArgs(trailingOnly = T) +if (length(args) > 0){ + n_iter <- as.integer(args[1]) + n <- as.integer(args[2]) + p <- as.integer(args[3]) + num_gfr <- as.integer(args[4]) + num_mcmc <- as.integer(args[5]) + dgp_num <- as.integer(args[6]) + snr <- as.numeric(args[7]) + test_set_pct <- as.numeric(args[8]) + num_threads <- as.integer(args[9]) +} else{ + # Default arguments + n_iter <- 5 + n <- 1000 + p <- 5 + num_gfr <- 10 + num_mcmc <- 100 + dgp_num <- 1 + snr <- 2.0 + test_set_pct <- 0.2 + num_threads <- -1 +} +cat("n_iter = ", n_iter, "\nn = ", n, "\np = ", p, "\nnum_gfr = ", num_gfr, + "\nnum_mcmc = ", num_mcmc, "\ndgp_num = ", dgp_num, "\nsnr = ", snr, + "\ntest_set_pct = ", test_set_pct, "\nnum_threads = ", num_threads, "\n", sep = "") + +# Run the performance evaluation +results <- matrix(NA, nrow = n_iter, ncol = 4) +colnames(results) <- c("iter", "rmse", "coverage", "runtime") +for (i in 1:n_iter) { + # Generate data + if (dgp_num == 1) { + data_list <- dgp1(n = n, p = p, snr = snr) + } else if (dgp_num == 2) { + data_list <- dgp2(n = n, p = p, snr = snr) + } else if (dgp_num == 3) { + data_list <- dgp3(n = n, p = p, snr = snr) + } else if (dgp_num == 4) { + data_list <- dgp4(n = n, p = p, snr = snr) + } else { + stop("Invalid DGP input") + } + covariates <- data_list[['covariates']] + basis <- data_list[['basis']] + conditional_mean <- data_list[['conditional_mean']] + outcome <- data_list[['outcome']] + rfx_group_ids <- data_list[['rfx_group_ids']] + rfx_basis <- data_list[['rfx_basis']] + + # Split into train / test sets + subset_inds_list <- compute_test_train_indices(n, test_set_pct) + test_inds <- subset_inds_list$test_inds + train_inds <- subset_inds_list$train_inds + covariates_train <- subset_data(covariates, train_inds) + covariates_test <- subset_data(covariates, test_inds) + outcome_train <- subset_data(outcome, train_inds) + outcome_test <- subset_data(outcome, test_inds) + conditional_mean_train <- subset_data(conditional_mean, train_inds) + conditional_mean_test <- subset_data(conditional_mean, test_inds) + has_basis <- !is.null(basis) + has_rfx <- !is.null(rfx_group_ids) + if (has_basis) { + basis_train <- subset_data(basis, train_inds) + basis_test <- subset_data(basis, test_inds) + } else { + basis_train <- NULL + basis_test <- NULL + } + if (has_rfx) { + rfx_group_ids_train <- subset_data(rfx_group_ids, train_inds) + rfx_group_ids_test <- subset_data(rfx_group_ids, test_inds) + rfx_basis_train <- subset_data(rfx_basis, train_inds) + rfx_basis_test <- subset_data(rfx_basis, test_inds) + } else { + rfx_group_ids_train <- NULL + rfx_group_ids_test <- NULL + rfx_basis_train <- NULL + rfx_basis_test <- NULL + } + + # Run (and time) BART + bart_timing <- system.time({ + # Sample BART model + general_params <- list(num_threads = num_threads) + bart_model <- stochtree::bart( + X_train = covariates_train, y_train = outcome_train, leaf_basis_train = basis_train, + rfx_group_ids_train = rfx_group_ids_train, rfx_basis_train = rfx_basis_train, + num_gfr = num_gfr, num_mcmc = num_mcmc, general_params = general_params + ) + + # Predict on the test set + test_preds <- predict( + bart_model, X = covariates_test, leaf_basis = basis_test, + rfx_group_ids = rfx_group_ids_test, rfx_basis = rfx_basis_test + ) + })[3] + + # Compute test set evals + y_hat_posterior <- test_preds$y_hat + y_hat_posterior_mean <- rowMeans(y_hat_posterior) + rmse_test <- sqrt(mean((y_hat_posterior_mean - outcome_test)^2)) + y_hat_posterior_quantile_025 <- apply(y_hat_posterior, 1, function(x) quantile(x, 0.025)) + y_hat_posterior_quantile_975 <- apply(y_hat_posterior, 1, function(x) quantile(x, 0.975)) + covered <- rep(NA, nrow(y_hat_posterior)) + for (j in 1:nrow(y_hat_posterior)) { + covered[j] <- ( + (conditional_mean_test[j] >= y_hat_posterior_quantile_025[j]) & + (conditional_mean_test[j] <= y_hat_posterior_quantile_975[j]) + ) + } + coverage_test <- mean(covered) + + # Store evaluations + results[i,] <- c(i, rmse_test, coverage_test, bart_timing) +} + +# Wrangle and save results to CSV +results_df <- data.frame( + cbind(n, p, num_gfr, num_mcmc, dgp_num, snr, test_set_pct, num_threads, results) +) +snr_rounded <- as.integer(snr) +test_set_pct_rounded <- as.integer(test_set_pct*100) +num_threads_clean <- ifelse(num_threads < 0, 0, num_threads) +filename <- paste( + "stochtree", "bart", "r", "n", n, "p", p, "num_gfr", num_gfr, "num_mcmc", num_mcmc, + "dgp_num", dgp_num, "snr", snr_rounded, "test_set_pct", test_set_pct_rounded, + "num_threads", num_threads_clean, sep = "_" +) +filename_full <- paste0("tools/regression/stochtree_bart_r_results/", filename, ".csv") +write.csv(x = results_df, file = filename_full, row.names = F) diff --git a/tools/regression/bart/individual_regression_test_bart.py b/tools/regression/bart/individual_regression_test_bart.py new file mode 100644 index 00000000..6fb14c47 --- /dev/null +++ b/tools/regression/bart/individual_regression_test_bart.py @@ -0,0 +1,344 @@ +import numpy as np +import pandas as pd +import time +import sys +import os +from typing import Dict +from stochtree import BARTModel +from sklearn.model_selection import train_test_split + +def dgp1(n: int, p: int, snr: float) -> Dict: + rng = np.random.default_rng() + + # Covariates + X = rng.uniform(0, 1, size=(n, p)) + + # Piecewise linear term + plm_term = np.where( + (X[:,0] >= 0.0) & (X[:,0] < 0.25), -7.5 * X[:,1], + np.where( + (X[:,0] >= 0.25) & (X[:,0] < 0.5), -2.5 * X[:,1], + np.where( + (X[:,0] >= 0.5) & (X[:,0] < 0.75), 2.5 * X[:,1], + 7.5 * X[:,1] + ) + ) + ) + + # Trigonometric term + trig_term = 2 * np.sin(X[:, 2] * 2 * np.pi) - 1.5 * np.cos(X[:, 3] * 2 * np.pi) + + # Outcome + f_XW = plm_term + trig_term + noise_sd = np.std(f_XW) / snr + y = f_XW + rng.normal(0, noise_sd, n) + + return { + 'covariates': X, + 'basis': None, + 'outcome': y, + 'conditional_mean': f_XW, + 'rfx_group_ids': None, + 'rfx_basis': None + } + + +def dgp2(n: int, p: int, snr: float) -> Dict: + rng = np.random.default_rng() + + # Covariates and basis + X = rng.uniform(0, 1, (n, p)) + W = rng.uniform(0, 1, (n, 2)) + + # Piecewise linear term using basis W + plm_term = np.where( + (X[:,0] >= 0.0) & (X[:,0] < 0.25), -7.5 * W[:,0], + np.where( + (X[:,0] >= 0.25) & (X[:,0] < 0.5), -2.5 * W[:,0], + np.where( + (X[:,0] >= 0.5) & (X[:,0] < 0.75), 2.5 * W[:,0], + 7.5 * W[:,0] + ) + ) + ) + + # Trigonometric term + trig_term = 2 * np.sin(X[:, 2] * 2 * np.pi) - 1.5 * np.cos(X[:, 3] * 2 * np.pi) + + # Outcome + f_XW = plm_term + trig_term + noise_sd = np.std(f_XW) / snr + y = f_XW + rng.normal(0, noise_sd, n) + + return { + 'covariates': X, + 'basis': W, + 'outcome': y, + 'conditional_mean': f_XW, + 'rfx_group_ids': None, + 'rfx_basis': None + } + + +def dgp3(n: int, p: int, snr: float) -> Dict: + rng = np.random.default_rng() + + # Covariates + X = rng.uniform(0, 1, size=(n, p)) + + # Piecewise linear term + plm_term = np.where( + (X[:,0] >= 0.0) & (X[:,0] < 0.25), -7.5 * X[:,1], + np.where( + (X[:,0] >= 0.25) & (X[:,0] < 0.5), -2.5 * X[:,1], + np.where( + (X[:,0] >= 0.5) & (X[:,0] < 0.75), 2.5 * X[:,1], + 7.5 * X[:,1] + ) + ) + ) + + # Trigonometric term + trig_term = 2 * np.sin(X[:, 2] * 2 * np.pi) - 1.5 * np.cos(X[:, 3] * 2 * np.pi) + + # Random effects + num_groups = 3 + rfx_group_ids = rng.choice(num_groups, size=n) + rfx_coefs = np.array([[-5, -3, -1], [5, 3, 1]]).T + rfx_basis = np.column_stack([np.ones(n), np.random.uniform(-1, 1, n)]) + rfx_term = np.sum(rfx_coefs[rfx_group_ids] * rfx_basis, axis=1) + + # Outcome + f_XW = plm_term + trig_term + rfx_term + noise_sd = np.std(f_XW) / snr + y = f_XW + rng.normal(0, noise_sd, n) + + return { + 'covariates': X, + 'basis': None, + 'outcome': y, + 'conditional_mean': f_XW, + 'rfx_group_ids': rfx_group_ids, + 'rfx_basis': rfx_basis + } + + +def dgp4(n: int, p: int, snr: float) -> Dict: + rng = np.random.default_rng() + + # Covariates and basis + X = rng.uniform(0, 1, (n, p)) + W = rng.uniform(0, 1, (n, 2)) + + # Piecewise linear term using basis W + plm_term = np.where( + (X[:,0] >= 0.0) & (X[:,0] < 0.25), -7.5 * W[:,0], + np.where( + (X[:,0] >= 0.25) & (X[:,0] < 0.5), -2.5 * W[:,0], + np.where( + (X[:,0] >= 0.5) & (X[:,0] < 0.75), 2.5 * W[:,0], + 7.5 * W[:,0] + ) + ) + ) + + # Trigonometric term + trig_term = 2 * np.sin(X[:, 2] * 2 * np.pi) - 1.5 * np.cos(X[:, 3] * 2 * np.pi) + + # Random effects + num_groups = 3 + rfx_group_ids = rng.choice(num_groups, size=n) + rfx_coefs = np.array([[-5, -3, -1], [5, 3, 1]]).T + rfx_basis = np.column_stack([np.ones(n), np.random.uniform(-1, 1, n)]) + rfx_term = np.sum(rfx_coefs[rfx_group_ids] * rfx_basis, axis=1) + + # Outcome + f_XW = plm_term + trig_term + rfx_term + noise_sd = np.std(f_XW) / snr + y = f_XW + np.random.normal(0, noise_sd, n) + + return { + 'covariates': X, + 'basis': W, + 'outcome': y, + 'conditional_mean': f_XW, + 'rfx_group_ids': rfx_group_ids, + 'rfx_basis': rfx_basis + } + + +def compute_test_train_indices(n: int, test_set_pct: float) -> Dict[str, np.ndarray]: + sample_inds = np.arange(n) + train_inds, test_inds = train_test_split(sample_inds, test_size=test_set_pct) + return {'test_inds': test_inds, 'train_inds': train_inds} + + +def subset_data(data: np.ndarray, subset_inds: np.ndarray) -> np.ndarray: + if isinstance(data, np.ndarray): + if data.ndim == 1: + return data[subset_inds] + else: + return data[subset_inds, :] + else: + raise ValueError("Data must be a numpy array") + + +def main(): + # Parse command line arguments + if len(sys.argv) > 1: + n_iter = int(sys.argv[1]) + n = int(sys.argv[2]) + p = int(sys.argv[3]) + num_gfr = int(sys.argv[4]) + num_mcmc = int(sys.argv[5]) + dgp_num = int(sys.argv[6]) + snr = float(sys.argv[7]) + test_set_pct = float(sys.argv[8]) + num_threads = int(sys.argv[9]) + else: + # Default arguments + n_iter = 5 + n = 1000 + p = 5 + num_gfr = 10 + num_mcmc = 100 + dgp_num = 1 + snr = 2.0 + test_set_pct = 0.2 + num_threads = -1 + + print(f"n_iter = {n_iter}") + print(f"n = {n}") + print(f"p = {p}") + print(f"num_gfr = {num_gfr}") + print(f"num_mcmc = {num_mcmc}") + print(f"dgp_num = {dgp_num}") + print(f"snr = {snr}") + print(f"test_set_pct = {test_set_pct}") + print(f"num_threads = {num_threads}") + + # Run the performance evaluation + results = np.empty((n_iter, 4), dtype=float) + + for i in range(n_iter): + print(f"Running iteration {i+1}/{n_iter}") + + # Generate data + if dgp_num == 1: + data_dict = dgp1(n=n, p=p, snr=snr) + elif dgp_num == 2: + data_dict = dgp2(n=n, p=p, snr=snr) + elif dgp_num == 3: + data_dict = dgp3(n=n, p=p, snr=snr) + elif dgp_num == 4: + data_dict = dgp4(n=n, p=p, snr=snr) + else: + raise ValueError("Invalid DGP input") + + covariates = data_dict['covariates'] + basis = data_dict['basis'] + conditional_mean = data_dict['conditional_mean'] + outcome = data_dict['outcome'] + rfx_group_ids = data_dict['rfx_group_ids'] + rfx_basis = data_dict['rfx_basis'] + + # Split into train / test sets + subset_inds_dict = compute_test_train_indices(n, test_set_pct) + test_inds = subset_inds_dict['test_inds'] + train_inds = subset_inds_dict['train_inds'] + covariates_train = subset_data(covariates, train_inds) + covariates_test = subset_data(covariates, test_inds) + outcome_train = subset_data(outcome, train_inds) + outcome_test = subset_data(outcome, test_inds) + conditional_mean_train = subset_data(conditional_mean, train_inds) + conditional_mean_test = subset_data(conditional_mean, test_inds) + has_basis = basis is not None + has_rfx = rfx_group_ids is not None + if has_basis: + basis_train = subset_data(basis, train_inds) + basis_test = subset_data(basis, test_inds) + else: + basis_train = None + basis_test = None + if has_rfx: + rfx_group_ids_train = subset_data(rfx_group_ids, train_inds) + rfx_group_ids_test = subset_data(rfx_group_ids, test_inds) + rfx_basis_train = subset_data(rfx_basis, train_inds) + rfx_basis_test = subset_data(rfx_basis, test_inds) + else: + rfx_group_ids_train = None + rfx_group_ids_test = None + rfx_basis_train = None + rfx_basis_test = None + + # Run (and time) BART + start_time = time.time() + + # Sample BART model + general_params = {'num_threads': num_threads} + bart_model = BARTModel() + bart_model.sample( + X_train=covariates_train, + y_train=outcome_train, + leaf_basis_train=basis_train, + rfx_group_ids_train=rfx_group_ids_train, + rfx_basis_train=rfx_basis_train, + num_gfr=num_gfr, + num_mcmc=num_mcmc, + general_params=general_params + ) + + # Predict on the test set + test_preds = bart_model.predict( + covariates=covariates_test, + basis=basis_test, + rfx_group_ids=rfx_group_ids_test, + rfx_basis=rfx_basis_test + ) + + bart_timing = time.time() - start_time + + # Compute test set evaluations + y_hat_posterior = test_preds['y_hat'] + y_hat_posterior_mean = np.mean(y_hat_posterior, axis=1) + rmse_test = np.sqrt(np.mean((y_hat_posterior_mean - conditional_mean_test) ** 2)) + + y_hat_posterior_quantile_025 = np.percentile(y_hat_posterior, 2.5, axis=1) + y_hat_posterior_quantile_975 = np.percentile(y_hat_posterior, 97.5, axis=1) + + covered = np.logical_and( + conditional_mean_test >= y_hat_posterior_quantile_025, + conditional_mean_test <= y_hat_posterior_quantile_975 + ) + coverage_test = np.mean(covered) + + # Store evaluations + results[i, :] = [i+1, rmse_test, coverage_test, bart_timing] + + # Wrangle and save results to CSV + results_df = pd.DataFrame({ + 'n': n, + 'p': p, + 'num_gfr': num_gfr, + 'num_mcmc': num_mcmc, + 'dgp_num': dgp_num, + 'snr': snr, + 'test_set_pct': test_set_pct, + 'num_threads': num_threads, + 'iter': results[:, 0], + 'rmse': results[:, 1], + 'coverage': results[:, 2], + 'runtime': results[:, 3] + }) + + snr_rounded = int(snr) + test_set_pct_rounded = int(test_set_pct * 100) + num_threads_clean = 0 if num_threads < 0 else num_threads + filename = f"stochtree_bart_python_n_{n}_p_{p}_num_gfr_{num_gfr}_num_mcmc_{num_mcmc}_dgp_num_{dgp_num}_snr_{snr_rounded}_test_set_pct_{test_set_pct_rounded}_num_threads_{num_threads_clean}.csv" + output_dir = "tools/regression/bart/stochtree_bart_python_results" + filename_full = os.path.join(output_dir, filename) + results_df.to_csv(filename_full, index=False) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tools/regression/bart/regression_test_analysis_bart.R b/tools/regression/bart/regression_test_analysis_bart.R new file mode 100644 index 00000000..ce754ba1 --- /dev/null +++ b/tools/regression/bart/regression_test_analysis_bart.R @@ -0,0 +1,16 @@ +reg_test_dir <- "tools/regression/bart/stochtree_bart_r_results" +reg_test_files <- list.files(reg_test_dir, pattern = ".csv", full.names = T) + +reg_test_df <- data.frame() +for (file in reg_test_files) { + temp_df <- read.csv(file) + reg_test_df <- rbind(reg_test_df, temp_df) +} + +summary_df <- aggregate( + cbind(rmse, coverage, runtime) ~ n + p + num_gfr + num_mcmc + dgp_num + snr + test_set_pct + num_threads, + data = reg_test_df, FUN = median, drop = TRUE +) + +summary_file_output <- file.path(reg_test_dir, "stochtree_bart_r_summary.csv") +write.csv(summary_df, summary_file_output, row.names = F) diff --git a/tools/regression/bart/regression_test_analysis_bart.py b/tools/regression/bart/regression_test_analysis_bart.py new file mode 100644 index 00000000..203631bd --- /dev/null +++ b/tools/regression/bart/regression_test_analysis_bart.py @@ -0,0 +1,44 @@ +import pandas as pd +import os +import glob + + +def main(): + # Define the directory containing results + reg_test_dir = "tools/regression/bart/stochtree_bart_python_results" + + # Get all CSV files in the directory + reg_test_files = glob.glob(os.path.join(reg_test_dir, "*.csv")) + + # Read and combine all results + reg_test_df = pd.DataFrame() + for file in reg_test_files: + temp_df = pd.read_csv(file) + reg_test_df = pd.concat([reg_test_df, temp_df], ignore_index=True) + + # Create summary by aggregating results + summary_df = reg_test_df.groupby([ + 'n', 'p', 'num_gfr', 'num_mcmc', 'dgp_num', 'snr', 'test_set_pct', 'num_threads' + ]).agg({ + 'rmse': 'median', + 'coverage': 'median', + 'runtime': 'median' + }).reset_index() + + # Save summary to CSV + summary_file_output = os.path.join(reg_test_dir, "stochtree_bart_python_summary.csv") + summary_df.to_csv(summary_file_output, index=False) + print(f"Summary saved to {summary_file_output}") + + # Print some basic statistics + print(f"Total number of result files: {len(reg_test_files)}") + print(f"Total number of iterations: {len(reg_test_df)}") + print(f"Number of unique parameter combinations: {len(summary_df)}") + + # Print summary statistics + print("\nSummary statistics:") + print(summary_df.describe()) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tools/regression/bart/regression_test_dispatch_bart.R b/tools/regression/bart/regression_test_dispatch_bart.R new file mode 100644 index 00000000..3f62a4ca --- /dev/null +++ b/tools/regression/bart/regression_test_dispatch_bart.R @@ -0,0 +1,28 @@ +# Test case parameters +dgps <- 1:4 +ns <- c(1000, 10000) +ps <- c(5, 20) +threads <- c(-1, 1) +varying_param_grid <- expand.grid(dgps, ns, ps, threads) +test_case_grid <- cbind( + 5, varying_param_grid[,2], varying_param_grid[,3], + 10, 100, varying_param_grid[,1], 2.0, 0.2, varying_param_grid[,4] +) + +# Run script for every case +script_path <- "tools/regression/bart/individual_regression_test_bart.R" +for (i in 1:nrow(test_case_grid)) { + n_iter <- test_case_grid[i,1] + n <- test_case_grid[i,2] + p <- test_case_grid[i,3] + num_gfr <- test_case_grid[i,4] + num_mcmc <- test_case_grid[i,5] + dgp_num <- test_case_grid[i,6] + snr <- test_case_grid[i,7] + test_set_pct <- test_case_grid[i,8] + num_threads <- test_case_grid[i,9] + system2( + "Rscript", + args = c(script_path, n_iter, n, p, num_gfr, num_mcmc, dgp_num, snr, test_set_pct, num_threads) + ) +} diff --git a/tools/regression/bart/regression_test_dispatch_bart.py b/tools/regression/bart/regression_test_dispatch_bart.py new file mode 100644 index 00000000..8fddc8cf --- /dev/null +++ b/tools/regression/bart/regression_test_dispatch_bart.py @@ -0,0 +1,69 @@ +import subprocess +import sys + + +def main(): + # Test case parameters + dgps = [1, 2, 3, 4] + ns = [1000, 10000] + ps = [5, 20] + threads = [-1, 1] + + # Create parameter grid + varying_param_grid = [] + for dgp in dgps: + for n in ns: + for p in ps: + for thread in threads: + varying_param_grid.append([dgp, n, p, thread]) + + # Fixed parameters + n_iter = 5 + num_gfr = 10 + num_mcmc = 100 + snr = 2.0 + test_set_pct = 0.2 + + # Script path + script_path = "tools/regression/bart/individual_regression_test_bart.py" + + # Run script for every case + for i, params in enumerate(varying_param_grid): + dgp_num, n, p, num_threads = params + + print(f"Running test case {i+1}/{len(varying_param_grid)}:") + print(f" DGP: {dgp_num}, n: {n}, p: {p}, threads: {num_threads}") + + # Construct command + cmd = [ + sys.executable, # Use current Python interpreter + script_path, + str(n_iter), + str(n), + str(p), + str(num_gfr), + str(num_mcmc), + str(dgp_num), + str(snr), + str(test_set_pct), + str(num_threads) + ] + + # Run the command + try: + result = subprocess.run(cmd, capture_output=True, text=True, check=True) + print(" Completed successfully") + if result.stdout: + print(f" Output:\n{result.stdout.strip()}") + except subprocess.CalledProcessError as e: + print(f" Failed with error code {e.returncode}") + if e.stderr: + print(f" Error: {e.stderr.strip()}") + if e.stdout: + print(f" Output: {e.stdout.strip()}") + + print(f"\nCompleted {len(varying_param_grid)} test cases") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tools/regression/bart/stochtree_bart_python_results/.gitkeep b/tools/regression/bart/stochtree_bart_python_results/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/tools/regression/bart/stochtree_bart_r_results/.gitkeep b/tools/regression/bart/stochtree_bart_r_results/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/tools/regression/bcf/individual_regression_test_bcf.R b/tools/regression/bcf/individual_regression_test_bcf.R new file mode 100644 index 00000000..cb186ac4 --- /dev/null +++ b/tools/regression/bcf/individual_regression_test_bcf.R @@ -0,0 +1,283 @@ +# Load libraries +library(stochtree) + +# Define DGPs +dgp1 <- function(n, p, snr) { + X <- matrix(rnorm(n*p), ncol = p) + plm_term <- ( + ((0 <= X[,1]) & (0.25 > X[,1])) * (-7.5*X[,2]) + + ((0.25 <= X[,1]) & (0.5 > X[,1])) * (-2.5*X[,2]) + + ((0.5 <= X[,1]) & (0.75 > X[,1])) * (2.5*X[,2]) + + ((0.75 <= X[,1]) & (1 > X[,1])) * (7.5*X[,2]) + ) + trig_term <- ( + 2*sin(X[,3]*2*pi) - + 2*cos(X[,4]*2*pi) + ) + mu_x <- plm_term + trig_term + pi_x <- 0.8*pnorm((3*mu_x/sd(mu_x)) - 0.5*X[,1]) + 0.05 + runif(n)/10 + Z <- rbinom(n,1,pi_x) + tau_x <- 1 + 2*X[,2]*X[,4] + f_XZ <- mu_x + tau_x * Z + noise_sd <- sd(f_XZ)/snr + y <- f_XZ + rnorm(n, 0, noise_sd) + return(list(covariates = X, treatment = Z, outcome = y, propensity = pi_x, + prognostic_effect = mu_x, treatment_effect = tau_x, + conditional_mean = f_XZ, rfx_group_ids = NULL, rfx_basis = NULL)) +} +dgp2 <- function(n, p, snr) { + X <- matrix(runif(n*p), ncol = p) + pi_x <- cbind(0.125 + 0.75 * X[, 1], 0.875 - 0.75 * X[, 2]) + mu_x <- pi_x[, 1] * 5 + pi_x[, 2] * 2 + 2 * X[, 3] + tau_x <- cbind(X[, 2], X[, 3]) * 2 + Z <- matrix(NA_real_, nrow = n, ncol = ncol(pi_x)) + for (i in 1:ncol(pi_x)) { + Z[, i] <- rbinom(n, 1, pi_x[, i]) + } + f_XZ <- mu_x + rowSums(Z * tau_x) + noise_sd <- sd(f_XZ)/snr + y <- f_XZ + rnorm(n, 0, noise_sd) + return(list(covariates = X, treatment = Z, outcome = y, propensity = pi_x, + prognostic_effect = mu_x, treatment_effect = tau_x, + conditional_mean = f_XZ, rfx_group_ids = NULL, rfx_basis = NULL)) +} +dgp3 <- function(n, p, snr) { + X <- matrix(rnorm(n*p), ncol = p) + plm_term <- ( + ((0 <= X[,1]) & (0.25 > X[,1])) * (-7.5*X[,2]) + + ((0.25 <= X[,1]) & (0.5 > X[,1])) * (-2.5*X[,2]) + + ((0.5 <= X[,1]) & (0.75 > X[,1])) * (2.5*X[,2]) + + ((0.75 <= X[,1]) & (1 > X[,1])) * (7.5*X[,2]) + ) + trig_term <- ( + 2*sin(X[,3]*2*pi) - + 2*cos(X[,4]*2*pi) + ) + mu_x <- plm_term + trig_term + pi_x <- 0.8*pnorm((3*mu_x/sd(mu_x)) - 0.5*X[,1]) + 0.05 + runif(n)/10 + Z <- rbinom(n,1,pi_x) + tau_x <- 1 + 2*X[,2]*X[,4] + rfx_group_ids <- sample(1:3, size = n, replace = T) + rfx_coefs <- t(matrix(c(-5, -3, -1, 5, 3, 1), nrow=2, byrow=TRUE)) + rfx_basis <- cbind(1, runif(n, -1, 1)) + rfx_term <- rowSums(rfx_coefs[rfx_group_ids,] * rfx_basis) + f_XZ <- mu_x + tau_x * Z + rfx_term + noise_sd <- sd(f_XZ)/snr + y <- f_XZ + rnorm(n, 0, noise_sd) + return(list(covariates = X, treatment = Z, outcome = y, propensity = pi_x, + prognostic_effect = mu_x, treatment_effect = tau_x, + conditional_mean = f_XZ, rfx_group_ids = rfx_group_ids, rfx_basis = rfx_basis)) +} +dgp4 <- function(n, p, snr) { + X <- matrix(runif(n*p), ncol = p) + pi_x <- cbind(0.125 + 0.75 * X[, 1], 0.875 - 0.75 * X[, 2]) + mu_x <- pi_x[, 1] * 5 + pi_x[, 2] * 2 + 2 * X[, 3] + tau_x <- cbind(X[, 2], X[, 3]) * 2 + Z <- matrix(NA_real_, nrow = n, ncol = ncol(pi_x)) + for (i in 1:ncol(pi_x)) { + Z[, i] <- rbinom(n, 1, pi_x[, i]) + } + rfx_group_ids <- sample(1:3, size = n, replace = T) + rfx_coefs <- t(matrix(c(-5, -3, -1, 5, 3, 1), nrow=2, byrow=TRUE)) + rfx_basis <- cbind(1, runif(n, -1, 1)) + rfx_term <- rowSums(rfx_coefs[rfx_group_ids,] * rfx_basis) + f_XZ <- mu_x + rowSums(Z * tau_x) + rfx_term + noise_sd <- sd(f_XZ)/snr + y <- f_XZ + rnorm(n, 0, noise_sd) + return(list(covariates = X, treatment = Z, outcome = y, propensity = pi_x, + prognostic_effect = mu_x, treatment_effect = tau_x, + conditional_mean = f_XZ, rfx_group_ids = rfx_group_ids, rfx_basis = rfx_basis)) +} + +# Test / train split utilities +compute_test_train_indices <- function(n, test_set_pct) { + n_test <- round(test_set_pct*n) + n_train <- n - n_test + test_inds <- sort(sample(1:n, n_test, replace = FALSE)) + train_inds <- (1:n)[!((1:n) %in% test_inds)] + return(list(test_inds = test_inds, train_inds = train_inds)) +} +subset_data <- function(data, subset_inds) { + if (is.matrix(data)) { + return(data[subset_inds,]) + } else { + return(data[subset_inds]) + } +} + +# Capture command line arguments +args <- commandArgs(trailingOnly = T) +if (length(args) > 0){ + n_iter <- as.integer(args[1]) + n <- as.integer(args[2]) + p <- as.integer(args[3]) + num_gfr <- as.integer(args[4]) + num_mcmc <- as.integer(args[5]) + dgp_num <- as.integer(args[6]) + snr <- as.numeric(args[7]) + test_set_pct <- as.numeric(args[8]) + num_threads <- as.integer(args[9]) +} else{ + # Default arguments + n_iter <- 5 + n <- 1000 + p <- 5 + num_gfr <- 10 + num_mcmc <- 100 + dgp_num <- 1 + snr <- 2.0 + test_set_pct <- 0.2 + num_threads <- -1 +} +cat("n_iter = ", n_iter, "\nn = ", n, "\np = ", p, "\nnum_gfr = ", num_gfr, + "\nnum_mcmc = ", num_mcmc, "\ndgp_num = ", dgp_num, "\nsnr = ", snr, + "\ntest_set_pct = ", test_set_pct, "\nnum_threads = ", num_threads, "\n", sep = "") + +# Run the performance evaluation +results <- matrix(NA, nrow = n_iter, ncol = 6) +colnames(results) <- c("iter", "outcome_rmse", "outcome_coverage", "treatment_effect_rmse", "treatment_effect_coverage", "runtime") +for (i in 1:n_iter) { + # Generate data + if (dgp_num == 1) { + data_list <- dgp1(n = n, p = p, snr = snr) + } else if (dgp_num == 2) { + data_list <- dgp2(n = n, p = p, snr = snr) + } else if (dgp_num == 3) { + data_list <- dgp3(n = n, p = p, snr = snr) + } else if (dgp_num == 4) { + data_list <- dgp4(n = n, p = p, snr = snr) + } else { + stop("Invalid DGP input") + } + covariates <- data_list[['covariates']] + treatment <- data_list[['treatment']] + propensity <- data_list[['propensity']] + prognostic_effect <- data_list[['prognostic_effect']] + treatment_effect <- data_list[['treatment_effect']] + conditional_mean <- data_list[['conditional_mean']] + outcome <- data_list[['outcome']] + rfx_group_ids <- data_list[['rfx_group_ids']] + rfx_basis <- data_list[['rfx_basis']] + if (dgp_num %in% c(2,4)) { + has_multivariate_treatment <- T + } else { + has_multivariate_treatment <- F + } + + # Split into train / test sets + subset_inds_list <- compute_test_train_indices(n, test_set_pct) + test_inds <- subset_inds_list$test_inds + train_inds <- subset_inds_list$train_inds + covariates_train <- subset_data(covariates, train_inds) + covariates_test <- subset_data(covariates, test_inds) + treatment_train <- subset_data(treatment, train_inds) + treatment_test <- subset_data(treatment, test_inds) + propensity_train <- subset_data(propensity, train_inds) + propensity_test <- subset_data(propensity, test_inds) + outcome_train <- subset_data(outcome, train_inds) + outcome_test <- subset_data(outcome, test_inds) + prognostic_effect_train <- subset_data(prognostic_effect, train_inds) + prognostic_effect_test <- subset_data(prognostic_effect, test_inds) + treatment_effect_train <- subset_data(treatment_effect, train_inds) + treatment_effect_test <- subset_data(treatment_effect, test_inds) + conditional_mean_train <- subset_data(conditional_mean, train_inds) + conditional_mean_test <- subset_data(conditional_mean, test_inds) + has_rfx <- !is.null(rfx_group_ids) + if (has_rfx) { + rfx_group_ids_train <- subset_data(rfx_group_ids, train_inds) + rfx_group_ids_test <- subset_data(rfx_group_ids, test_inds) + rfx_basis_train <- subset_data(rfx_basis, train_inds) + rfx_basis_test <- subset_data(rfx_basis, test_inds) + } else { + rfx_group_ids_train <- NULL + rfx_group_ids_test <- NULL + rfx_basis_train <- NULL + rfx_basis_test <- NULL + } + + # Run (and time) BCF + bcf_timing <- system.time({ + # Sample BCF model + general_params <- list(num_threads = num_threads, adaptive_coding = F) + prognostic_forest_params <- list(sample_sigma2_leaf = F) + treatment_effect_forest_params <- list(sample_sigma2_leaf = F) + bcf_model <- stochtree::bcf( + X_train = covariates_train, Z_train = treatment_train, + propensity_train = propensity_train, y_train = outcome_train, + rfx_group_ids_train = rfx_group_ids_train, rfx_basis_train = rfx_basis_train, + num_gfr = num_gfr, num_mcmc = num_mcmc, general_params = general_params, + prognostic_forest_params = prognostic_forest_params, + treatment_effect_forest_params = treatment_effect_forest_params + ) + + # Predict on the test set + test_preds <- predict( + bcf_model, X = covariates_test, Z = treatment_test, propensity = propensity_test, + rfx_group_ids = rfx_group_ids_test, rfx_basis = rfx_basis_test + ) + })[3] + + # Compute test set evals + y_hat_posterior <- test_preds$y_hat + y_hat_posterior_mean <- rowMeans(y_hat_posterior) + tau_hat_posterior <- test_preds$tau_hat + if (has_multivariate_treatment) tau_hat_posterior_mean <- apply(tau_hat_posterior, c(1,2), mean) + else tau_hat_posterior_mean <- apply(tau_hat_posterior, 1, mean) + y_hat_rmse_test <- sqrt(mean((y_hat_posterior_mean - outcome_test)^2)) + tau_hat_rmse_test <- sqrt(mean((tau_hat_posterior_mean - treatment_effect_test)^2)) + y_hat_posterior_quantile_025 <- apply(y_hat_posterior, 1, function(x) quantile(x, 0.025)) + y_hat_posterior_quantile_975 <- apply(y_hat_posterior, 1, function(x) quantile(x, 0.975)) + if (has_multivariate_treatment) { + tau_hat_posterior_quantile_025 <- apply(tau_hat_posterior, c(1,2), function(x) quantile(x, 0.025)) + tau_hat_posterior_quantile_975 <- apply(tau_hat_posterior, c(1,2), function(x) quantile(x, 0.975)) + } else { + tau_hat_posterior_quantile_025 <- apply(tau_hat_posterior, 1, function(x) quantile(x, 0.025)) + tau_hat_posterior_quantile_975 <- apply(tau_hat_posterior, 1, function(x) quantile(x, 0.975)) + } + y_hat_covered <- rep(NA, nrow(y_hat_posterior)) + for (j in 1:nrow(y_hat_posterior)) { + y_hat_covered[j] <- ( + (conditional_mean_test[j] >= y_hat_posterior_quantile_025[j]) & + (conditional_mean_test[j] <= y_hat_posterior_quantile_975[j]) + ) + } + y_hat_coverage_test <- mean(y_hat_covered) + if (has_multivariate_treatment) { + tau_hat_covered <- matrix(NA_real_, nrow(tau_hat_posterior_mean), ncol(tau_hat_posterior_mean)) + for (j in 1:nrow(tau_hat_covered)) { + for (k in 1:ncol(tau_hat_covered)) { + tau_hat_covered[j,k] <- ( + (treatment_effect_test[j,k] >= tau_hat_posterior_quantile_025[j,k]) & + (treatment_effect_test[j,k] <= tau_hat_posterior_quantile_975[j,k]) + ) + } + } + } else { + tau_hat_covered <- rep(NA, nrow(tau_hat_posterior)) + for (j in 1:nrow(tau_hat_posterior)) { + tau_hat_covered[j] <- ( + (treatment_effect_test[j] >= tau_hat_posterior_quantile_025[j]) & + (treatment_effect_test[j] <= tau_hat_posterior_quantile_025[j]) + ) + } + } + tau_hat_coverage_test <- mean(tau_hat_covered) + + # Store evaluations + results[i,] <- c(i, y_hat_rmse_test, y_hat_coverage_test, tau_hat_rmse_test, tau_hat_coverage_test, bcf_timing) +} + +# Wrangle and save results to CSV +results_df <- data.frame( + cbind(n, p, num_gfr, num_mcmc, dgp_num, snr, test_set_pct, num_threads, results) +) +snr_rounded <- as.integer(snr) +test_set_pct_rounded <- as.integer(test_set_pct*100) +num_threads_clean <- ifelse(num_threads < 0, 0, num_threads) +filename <- paste( + "stochtree", "bcf", "r", "n", n, "p", p, "num_gfr", num_gfr, "num_mcmc", num_mcmc, + "dgp_num", dgp_num, "snr", snr_rounded, "test_set_pct", test_set_pct_rounded, + "num_threads", num_threads_clean, sep = "_" +) +filename_full <- paste0("tools/regression/bcf/stochtree_bcf_r_results/", filename, ".csv") +write.csv(x = results_df, file = filename_full, row.names = F) diff --git a/tools/regression/bcf/individual_regression_test_bcf.py b/tools/regression/bcf/individual_regression_test_bcf.py new file mode 100644 index 00000000..7a1ce9a8 --- /dev/null +++ b/tools/regression/bcf/individual_regression_test_bcf.py @@ -0,0 +1,441 @@ +import numpy as np +import pandas as pd +import time +import sys +import os +from typing import Dict +from stochtree import BCFModel +from sklearn.model_selection import train_test_split +from scipy.stats import norm + + +def dgp1(n: int, p: int, snr: float) -> Dict: + rng = np.random.default_rng() + + # Covariates + X = rng.normal(0, 1, size=(n, p)) + + # Piecewise linear term + plm_term = np.where( + (X[:,0] >= 0.0) & (X[:,0] < 0.25), -7.5 * X[:,1], + np.where( + (X[:,0] >= 0.25) & (X[:,0] < 0.5), -2.5 * X[:,1], + np.where( + (X[:,0] >= 0.5) & (X[:,0] < 0.75), 2.5 * X[:,1], + 7.5 * X[:,1] + ) + ) + ) + + # Trigonometric term + trig_term = 2 * np.sin(X[:, 2] * 2 * np.pi) - 2 * np.cos(X[:, 3] * 2 * np.pi) + + # Prognostic effect + mu_x = plm_term + trig_term + + # Propensity score + pi_x = 0.8 * norm.cdf((3 * mu_x / np.std(mu_x)) - 0.5 * X[:, 0]) + 0.05 + rng.uniform(0, 1, n) / 10 + + # Treatment assignment + Z = rng.binomial(1, pi_x, n) + + # Treatment effect + tau_x = 1 + 2 * X[:, 1] * X[:, 3] + + # Outcome + f_XZ = mu_x + tau_x * Z + noise_sd = np.std(f_XZ) / snr + y = f_XZ + rng.normal(0, noise_sd, n) + + return { + 'covariates': X, + 'treatment': Z, + 'outcome': y, + 'propensity': pi_x, + 'prognostic_effect': mu_x, + 'treatment_effect': tau_x, + 'conditional_mean': f_XZ, + 'rfx_group_ids': None, + 'rfx_basis': None + } + + +def dgp2(n: int, p: int, snr: float) -> Dict: + rng = np.random.default_rng() + + # Covariates + X = rng.uniform(0, 1, size=(n, p)) + + # Propensity scores (multivariate) + pi_x = np.column_stack([ + 0.125 + 0.75 * X[:, 0], + 0.875 - 0.75 * X[:, 1] + ]) + + # Prognostic effect + mu_x = pi_x[:, 0] * 5 + pi_x[:, 1] * 2 + 2 * X[:, 2] + + # Treatment effects (multivariate) + tau_x = np.column_stack([ + X[:, 1] * 2, + X[:, 2] * 2 + ]) + + # Treatment assignment (multivariate) + Z = np.column_stack([ + rng.binomial(1, pi_x[:, 0], n), + rng.binomial(1, pi_x[:, 1], n) + ]) + + # Outcome + f_XZ = mu_x + np.sum(Z * tau_x, axis=1) + noise_sd = np.std(f_XZ) / snr + y = f_XZ + rng.normal(0, noise_sd, n) + + return { + 'covariates': X, + 'treatment': Z, + 'outcome': y, + 'propensity': pi_x, + 'prognostic_effect': mu_x, + 'treatment_effect': tau_x, + 'conditional_mean': f_XZ, + 'rfx_group_ids': None, + 'rfx_basis': None + } + + +def dgp3(n: int, p: int, snr: float) -> Dict: + rng = np.random.default_rng() + + # Covariates + X = rng.normal(0, 1, size=(n, p)) + + # Piecewise linear term + plm_term = np.where( + (X[:,0] >= 0.0) & (X[:,0] < 0.25), -7.5 * X[:,1], + np.where( + (X[:,0] >= 0.25) & (X[:,0] < 0.5), -2.5 * X[:,1], + np.where( + (X[:,0] >= 0.5) & (X[:,0] < 0.75), 2.5 * X[:,1], + 7.5 * X[:,1] + ) + ) + ) + + # Trigonometric term + trig_term = 2 * np.sin(X[:, 2] * 2 * np.pi) - 2 * np.cos(X[:, 3] * 2 * np.pi) + + # Prognostic effect + mu_x = plm_term + trig_term + + # Propensity score + pi_x = 0.8 * norm.cdf((3 * mu_x / np.std(mu_x)) - 0.5 * X[:, 0]) + 0.05 + rng.uniform(0, 1, n) / 10 + + # Treatment assignment + Z = rng.binomial(1, pi_x, n) + + # Treatment effect + tau_x = 1 + 2 * X[:, 1] * X[:, 3] + + # Random effects + rfx_group_ids = rng.choice([0, 1, 2], size=n, replace=True) + rfx_coefs = np.array([[-5, -3, -1], [5, 3, 1]]).T + rfx_basis = np.column_stack([np.ones(n), rng.uniform(-1, 1, n)]) + rfx_term = np.sum(rfx_coefs[rfx_group_ids] * rfx_basis, axis=1) + + # Outcome + f_XZ = mu_x + tau_x * Z + rfx_term + noise_sd = np.std(f_XZ) / snr + y = f_XZ + rng.normal(0, noise_sd, n) + + return { + 'covariates': X, + 'treatment': Z, + 'outcome': y, + 'propensity': pi_x, + 'prognostic_effect': mu_x, + 'treatment_effect': tau_x, + 'conditional_mean': f_XZ, + 'rfx_group_ids': rfx_group_ids, + 'rfx_basis': rfx_basis + } + + +def dgp4(n: int, p: int, snr: float) -> Dict: + rng = np.random.default_rng() + + # Covariates + X = rng.uniform(0, 1, size=(n, p)) + + # Propensity scores (multivariate) + pi_x = np.column_stack([ + 0.125 + 0.75 * X[:, 0], + 0.875 - 0.75 * X[:, 1] + ]) + + # Prognostic effect + mu_x = pi_x[:, 0] * 5 + pi_x[:, 1] * 2 + 2 * X[:, 2] + + # Treatment effects (multivariate) + tau_x = np.column_stack([ + X[:, 1] * 2, + X[:, 2] * 2 + ]) + + # Treatment assignment (multivariate) + Z = np.column_stack([ + rng.binomial(1, pi_x[:, 0], n), + rng.binomial(1, pi_x[:, 1], n) + ]) + + # Random effects + rfx_group_ids = rng.choice([0, 1, 2], size=n, replace=True) + rfx_coefs = np.array([[-5, -3, -1], [5, 3, 1]]).T + rfx_basis = np.column_stack([np.ones(n), rng.uniform(-1, 1, n)]) + rfx_term = np.sum(rfx_coefs[rfx_group_ids] * rfx_basis, axis=1) + + # Outcome + f_XZ = mu_x + np.sum(Z * tau_x, axis=1) + rfx_term + noise_sd = np.std(f_XZ) / snr + y = f_XZ + rng.normal(0, noise_sd, n) + + return { + 'covariates': X, + 'treatment': Z, + 'outcome': y, + 'propensity': pi_x, + 'prognostic_effect': mu_x, + 'treatment_effect': tau_x, + 'conditional_mean': f_XZ, + 'rfx_group_ids': rfx_group_ids, + 'rfx_basis': rfx_basis + } + + +def compute_test_train_indices(n: int, test_set_pct: float) -> Dict[str, np.ndarray]: + sample_inds = np.arange(n) + train_inds, test_inds = train_test_split(sample_inds, test_size=test_set_pct) + return {'test_inds': test_inds, 'train_inds': train_inds} + + +def subset_data(data: np.ndarray, subset_inds: np.ndarray) -> np.ndarray: + if isinstance(data, np.ndarray): + if data.ndim == 1: + return data[subset_inds] + else: + return data[subset_inds, :] + else: + raise ValueError("Data must be a numpy array") + + +def main(): + # Parse command line arguments + if len(sys.argv) > 1: + n_iter = int(sys.argv[1]) + n = int(sys.argv[2]) + p = int(sys.argv[3]) + num_gfr = int(sys.argv[4]) + num_mcmc = int(sys.argv[5]) + dgp_num = int(sys.argv[6]) + snr = float(sys.argv[7]) + test_set_pct = float(sys.argv[8]) + num_threads = int(sys.argv[9]) + else: + # Default arguments + n_iter = 5 + n = 1000 + p = 5 + num_gfr = 10 + num_mcmc = 100 + dgp_num = 1 + snr = 2.0 + test_set_pct = 0.2 + num_threads = -1 + + print(f"n_iter = {n_iter}") + print(f"n = {n}") + print(f"p = {p}") + print(f"num_gfr = {num_gfr}") + print(f"num_mcmc = {num_mcmc}") + print(f"dgp_num = {dgp_num}") + print(f"snr = {snr}") + print(f"test_set_pct = {test_set_pct}") + print(f"num_threads = {num_threads}") + + # Run the performance evaluation + results = np.empty((n_iter, 6), dtype=float) + + for i in range(n_iter): + print(f"Running iteration {i+1}/{n_iter}") + + # Generate data + if dgp_num == 1: + data_dict = dgp1(n=n, p=p, snr=snr) + elif dgp_num == 2: + data_dict = dgp2(n=n, p=p, snr=snr) + elif dgp_num == 3: + data_dict = dgp3(n=n, p=p, snr=snr) + elif dgp_num == 4: + data_dict = dgp4(n=n, p=p, snr=snr) + else: + raise ValueError("Invalid DGP input") + + covariates = data_dict['covariates'] + treatment = data_dict['treatment'] + propensity = data_dict['propensity'] + prognostic_effect = data_dict['prognostic_effect'] + treatment_effect = data_dict['treatment_effect'] + conditional_mean = data_dict['conditional_mean'] + outcome = data_dict['outcome'] + rfx_group_ids = data_dict['rfx_group_ids'] + rfx_basis = data_dict['rfx_basis'] + + # Check if multivariate treatment + has_multivariate_treatment = dgp_num in [2, 4] + + # Split into train / test sets + subset_inds_dict = compute_test_train_indices(n, test_set_pct) + test_inds = subset_inds_dict['test_inds'] + train_inds = subset_inds_dict['train_inds'] + covariates_train = subset_data(covariates, train_inds) + covariates_test = subset_data(covariates, test_inds) + treatment_train = subset_data(treatment, train_inds) + treatment_test = subset_data(treatment, test_inds) + propensity_train = subset_data(propensity, train_inds) + propensity_test = subset_data(propensity, test_inds) + outcome_train = subset_data(outcome, train_inds) + outcome_test = subset_data(outcome, test_inds) + prognostic_effect_train = subset_data(prognostic_effect, train_inds) + prognostic_effect_test = subset_data(prognostic_effect, test_inds) + treatment_effect_train = subset_data(treatment_effect, train_inds) + treatment_effect_test = subset_data(treatment_effect, test_inds) + conditional_mean_train = subset_data(conditional_mean, train_inds) + conditional_mean_test = subset_data(conditional_mean, test_inds) + has_rfx = rfx_group_ids is not None + if has_rfx: + rfx_group_ids_train = subset_data(rfx_group_ids, train_inds) + rfx_group_ids_test = subset_data(rfx_group_ids, test_inds) + rfx_basis_train = subset_data(rfx_basis, train_inds) + rfx_basis_test = subset_data(rfx_basis, test_inds) + else: + rfx_group_ids_train = None + rfx_group_ids_test = None + rfx_basis_train = None + rfx_basis_test = None + + # Run (and time) BCF + start_time = time.time() + + # Sample BCF model + general_params = {'num_threads': num_threads, 'adaptive_coding': False} + prognostic_forest_params = {'sample_sigma2_leaf': False} + treatment_effect_forest_params = {'sample_sigma2_leaf': False} + + bcf_model = BCFModel() + bcf_model.sample( + X_train=covariates_train, + Z_train=treatment_train, + y_train=outcome_train, + pi_train=propensity_train, + rfx_group_ids_train=rfx_group_ids_train, + rfx_basis_train=rfx_basis_train, + num_gfr=num_gfr, + num_mcmc=num_mcmc, + general_params=general_params, + prognostic_forest_params=prognostic_forest_params, + treatment_effect_forest_params=treatment_effect_forest_params + ) + + # Predict on the test set + test_preds = bcf_model.predict( + X=covariates_test, + Z=treatment_test, + propensity=propensity_test, + rfx_group_ids=rfx_group_ids_test, + rfx_basis=rfx_basis_test + ) + + bcf_timing = time.time() - start_time + + # Compute test set evaluations + y_hat_posterior = test_preds['y_hat'] + tau_hat_posterior = test_preds['tau_hat'] + + y_hat_posterior_mean = np.mean(y_hat_posterior, axis=1) + if has_multivariate_treatment: + # For multivariate treatment, tau_hat_posterior has shape (n_test, n_samples, n_treatments) + # We want to average over the samples (axis 1) to get (n_test, n_treatments) + tau_hat_posterior_mean = np.mean(tau_hat_posterior, axis=1) + else: + # For univariate treatment, tau_hat_posterior has shape (n_test, n_samples) + # We want to average over the samples (axis 1) to get (n_test,) + tau_hat_posterior_mean = np.mean(tau_hat_posterior, axis=1) + + # Outcome RMSE and coverage + y_hat_rmse_test = np.sqrt(np.mean((y_hat_posterior_mean - conditional_mean_test) ** 2)) + y_hat_posterior_quantile_025 = np.percentile(y_hat_posterior, 2.5, axis=1) + y_hat_posterior_quantile_975 = np.percentile(y_hat_posterior, 97.5, axis=1) + + y_hat_covered = np.logical_and( + conditional_mean_test >= y_hat_posterior_quantile_025, + conditional_mean_test <= y_hat_posterior_quantile_975 + ) + y_hat_coverage_test = np.mean(y_hat_covered) + + # Treatment effect RMSE and coverage + tau_hat_rmse_test = np.sqrt(np.mean((tau_hat_posterior_mean - treatment_effect_test) ** 2)) + + if has_multivariate_treatment: + # For multivariate treatment, compute percentiles over samples (axis 1) + tau_hat_posterior_quantile_025 = np.percentile(tau_hat_posterior, 2.5, axis=1) + tau_hat_posterior_quantile_975 = np.percentile(tau_hat_posterior, 97.5, axis=1) + tau_hat_covered = np.logical_and( + treatment_effect_test >= tau_hat_posterior_quantile_025, + treatment_effect_test <= tau_hat_posterior_quantile_975 + ) + else: + # For univariate treatment, compute percentiles over samples (axis 1) + tau_hat_posterior_quantile_025 = np.percentile(tau_hat_posterior, 2.5, axis=1) + tau_hat_posterior_quantile_975 = np.percentile(tau_hat_posterior, 97.5, axis=1) + tau_hat_covered = np.logical_and( + treatment_effect_test >= tau_hat_posterior_quantile_025, + treatment_effect_test <= tau_hat_posterior_quantile_975 + ) + + tau_hat_coverage_test = np.mean(tau_hat_covered) + + # Store evaluations + results[i, :] = [i+1, y_hat_rmse_test, y_hat_coverage_test, tau_hat_rmse_test, tau_hat_coverage_test, bcf_timing] + + # Wrangle and save results to CSV + results_df = pd.DataFrame({ + 'n': n, + 'p': p, + 'num_gfr': num_gfr, + 'num_mcmc': num_mcmc, + 'dgp_num': dgp_num, + 'snr': snr, + 'test_set_pct': test_set_pct, + 'num_threads': num_threads, + 'iter': results[:, 0], + 'outcome_rmse': results[:, 1], + 'outcome_coverage': results[:, 2], + 'treatment_effect_rmse': results[:, 3], + 'treatment_effect_coverage': results[:, 4], + 'runtime': results[:, 5] + }) + + snr_rounded = int(snr) + test_set_pct_rounded = int(test_set_pct * 100) + num_threads_clean = 0 if num_threads < 0 else num_threads + + filename = f"stochtree_bcf_python_n_{n}_p_{p}_num_gfr_{num_gfr}_num_mcmc_{num_mcmc}_dgp_num_{dgp_num}_snr_{snr_rounded}_test_set_pct_{test_set_pct_rounded}_num_threads_{num_threads_clean}.csv" + output_dir = "tools/regression/bcf/stochtree_bcf_python_results" + filename_full = os.path.join(output_dir, filename) + results_df.to_csv(filename_full, index=False) + print(f"Results saved to {filename_full}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tools/regression/bcf/regression_test_analysis_bcf.R b/tools/regression/bcf/regression_test_analysis_bcf.R new file mode 100644 index 00000000..935832bb --- /dev/null +++ b/tools/regression/bcf/regression_test_analysis_bcf.R @@ -0,0 +1,16 @@ +reg_test_dir <- "tools/regression/bcf/stochtree_bcf_r_results" +reg_test_files <- list.files(reg_test_dir, pattern = ".csv", full.names = T) + +reg_test_df <- data.frame() +for (file in reg_test_files) { + temp_df <- read.csv(file) + reg_test_df <- rbind(reg_test_df, temp_df) +} + +summary_df <- aggregate( + cbind(outcome_rmse, outcome_coverage, treatment_effect_rmse, treatment_effect_coverage, runtime) ~ n + p + num_gfr + num_mcmc + dgp_num + snr + test_set_pct + num_threads, + data = reg_test_df, FUN = median, drop = TRUE +) + +summary_file_output <- file.path(reg_test_dir, "stochtree_bcf_r_summary.csv") +write.csv(summary_df, summary_file_output, row.names = F) diff --git a/tools/regression/bcf/regression_test_analysis_bcf.py b/tools/regression/bcf/regression_test_analysis_bcf.py new file mode 100644 index 00000000..4cabb82f --- /dev/null +++ b/tools/regression/bcf/regression_test_analysis_bcf.py @@ -0,0 +1,46 @@ +import pandas as pd +import os +import glob + + +def main(): + # Define the directory containing results + reg_test_dir = "tools/regression/bcf/stochtree_bcf_python_results" + + # Get all CSV files in the directory + reg_test_files = glob.glob(os.path.join(reg_test_dir, "*.csv")) + + # Read and combine all results + reg_test_df = pd.DataFrame() + for file in reg_test_files: + temp_df = pd.read_csv(file) + reg_test_df = pd.concat([reg_test_df, temp_df], ignore_index=True) + + # Create summary by aggregating results + summary_df = reg_test_df.groupby([ + 'n', 'p', 'num_gfr', 'num_mcmc', 'dgp_num', 'snr', 'test_set_pct', 'num_threads' + ]).agg({ + 'outcome_rmse': 'median', + 'outcome_coverage': 'median', + 'treatment_effect_rmse': 'median', + 'treatment_effect_coverage': 'median', + 'runtime': 'median' + }).reset_index() + + # Save summary to CSV + summary_file_output = os.path.join(reg_test_dir, "stochtree_bcf_python_summary.csv") + summary_df.to_csv(summary_file_output, index=False) + print(f"Summary saved to {summary_file_output}") + + # Print some basic statistics + print(f"Total number of result files: {len(reg_test_files)}") + print(f"Total number of iterations: {len(reg_test_df)}") + print(f"Number of unique parameter combinations: {len(summary_df)}") + + # Print summary statistics + print("\nSummary statistics:") + print(summary_df.describe()) + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tools/regression/bcf/regression_test_dispatch_bcf.R b/tools/regression/bcf/regression_test_dispatch_bcf.R new file mode 100644 index 00000000..6e5abf7b --- /dev/null +++ b/tools/regression/bcf/regression_test_dispatch_bcf.R @@ -0,0 +1,28 @@ +# Test case parameters +dgps <- 1:4 +ns <- c(1000, 10000) +ps <- c(5, 20) +threads <- c(-1, 1) +varying_param_grid <- expand.grid(dgps, ns, ps, threads) +test_case_grid <- cbind( + 5, varying_param_grid[,2], varying_param_grid[,3], + 10, 100, varying_param_grid[,1], 2.0, 0.2, varying_param_grid[,4] +) + +# Run script for every case +script_path <- "tools/regression/bcf/individual_regression_test_bcf.R" +for (i in 1:nrow(test_case_grid)) { + n_iter <- test_case_grid[i,1] + n <- test_case_grid[i,2] + p <- test_case_grid[i,3] + num_gfr <- test_case_grid[i,4] + num_mcmc <- test_case_grid[i,5] + dgp_num <- test_case_grid[i,6] + snr <- test_case_grid[i,7] + test_set_pct <- test_case_grid[i,8] + num_threads <- test_case_grid[i,9] + system2( + "Rscript", + args = c(script_path, n_iter, n, p, num_gfr, num_mcmc, dgp_num, snr, test_set_pct, num_threads) + ) +} diff --git a/tools/regression/bcf/regression_test_dispatch_bcf.py b/tools/regression/bcf/regression_test_dispatch_bcf.py new file mode 100644 index 00000000..ddf7d64c --- /dev/null +++ b/tools/regression/bcf/regression_test_dispatch_bcf.py @@ -0,0 +1,69 @@ +import subprocess +import sys + + +def main(): + # Test case parameters + dgps = [1, 2, 3, 4] + ns = [1000, 10000] + ps = [5, 20] + threads = [-1, 1] + + # Create parameter grid + varying_param_grid = [] + for dgp in dgps: + for n in ns: + for p in ps: + for thread in threads: + varying_param_grid.append([dgp, n, p, thread]) + + # Fixed parameters + n_iter = 5 + num_gfr = 10 + num_mcmc = 100 + snr = 2.0 + test_set_pct = 0.2 + + # Script path + script_path = "tools/regression/bcf/individual_regression_test_bcf.py" + + # Run script for every case + for i, params in enumerate(varying_param_grid): + dgp_num, n, p, num_threads = params + + print(f"Running test case {i+1}/{len(varying_param_grid)}:") + print(f" DGP: {dgp_num}, n: {n}, p: {p}, threads: {num_threads}") + + # Construct command + cmd = [ + sys.executable, # Use current Python interpreter + script_path, + str(n_iter), + str(n), + str(p), + str(num_gfr), + str(num_mcmc), + str(dgp_num), + str(snr), + str(test_set_pct), + str(num_threads) + ] + + # Run the command + try: + result = subprocess.run(cmd, capture_output=True, text=True, check=True) + print(" Completed successfully") + if result.stdout: + print(f" Output: {result.stdout.strip()}") + except subprocess.CalledProcessError as e: + print(f" Failed with error code {e.returncode}") + if e.stderr: + print(f" Error: {e.stderr.strip()}") + if e.stdout: + print(f" Output: {e.stdout.strip()}") + + print(f"\nCompleted {len(varying_param_grid)} test cases") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/tools/regression/bcf/stochtree_bcf_python_results/.gitkeep b/tools/regression/bcf/stochtree_bcf_python_results/.gitkeep new file mode 100644 index 00000000..e69de29b diff --git a/tools/regression/bcf/stochtree_bcf_r_results/.gitkeep b/tools/regression/bcf/stochtree_bcf_r_results/.gitkeep new file mode 100644 index 00000000..e69de29b