зеркало из https://github.com/microsoft/LightGBM.git
[R-package] Add sparse feature contribution predictions (#5108)
* add predcontrib for sparse inputs * register newly-added function * comments * correct wrong types in test * forcibly take transpose function from Matrix * keep row names, test comparison to dense inputs * workaround for passing test while PR for row names is not merged * Update R-package/R/lgb.Predictor.R Co-authored-by: James Lamb <jaylamb20@gmail.com> * Update R-package/R/lgb.Predictor.R Co-authored-by: James Lamb <jaylamb20@gmail.com> * Update R-package/R/lgb.Predictor.R Co-authored-by: James Lamb <jaylamb20@gmail.com> * proper handling of integer overflow * add test for CSR contrib row names * add more tests for predict(<sparse>, predcontrib=TRUE) * make linter happy * linter * linter * check error messages for bad input shapes * fix regex * hard-coded number of columns in regex for tests Co-authored-by: James Lamb <jaylamb20@gmail.com>
This commit is contained in:
david-cortes
GitHub
Родитель
688f73d14a
Коммит
6f92d47aad
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@ -37,6 +37,10 @@ export(saveRDS.lgb.Booster)
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export(set_field)
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export(slice)
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import(methods)
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importClassesFrom(Matrix,dgCMatrix)
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importClassesFrom(Matrix,dgRMatrix)
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importClassesFrom(Matrix,dsparseMatrix)
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importClassesFrom(Matrix,dsparseVector)
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importFrom(Matrix,Matrix)
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importFrom(R6,R6Class)
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importFrom(data.table,":=")
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@ -51,6 +55,7 @@ importFrom(graphics,barplot)
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importFrom(graphics,par)
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importFrom(jsonlite,fromJSON)
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importFrom(methods,is)
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importFrom(methods,new)
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importFrom(parallel,detectCores)
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importFrom(stats,quantile)
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importFrom(utils,modifyList)
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@ -1,4 +1,5 @@
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#' @importFrom methods is
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#' @importFrom methods is new
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#' @importClassesFrom Matrix dsparseMatrix dsparseVector dgCMatrix dgRMatrix
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#' @importFrom R6 R6Class
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#' @importFrom utils read.delim
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Predictor <- R6::R6Class(
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@ -126,6 +127,113 @@ Predictor <- R6::R6Class(
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num_row <- nrow(preds)
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preds <- as.vector(t(preds))
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} else if (predcontrib && inherits(data, c("dsparseMatrix", "dsparseVector"))) {
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ncols <- .Call(LGBM_BoosterGetNumFeature_R, private$handle)
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ncols_out <- integer(1L)
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.Call(LGBM_BoosterGetNumClasses_R, private$handle, ncols_out)
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ncols_out <- (ncols + 1L) * max(ncols_out, 1L)
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if (is.na(ncols_out)) {
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ncols_out <- as.numeric(ncols + 1L) * as.numeric(max(ncols_out, 1L))
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}
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if (!inherits(data, "dsparseVector") && ncols_out > .Machine$integer.max) {
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stop("Resulting matrix of feature contributions is too large for R to handle.")
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}
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if (inherits(data, "dsparseVector")) {
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if (length(data) > ncols) {
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stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns."
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, ncols
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, length(data)))
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}
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res <- .Call(
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LGBM_BoosterPredictSparseOutput_R
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, private$handle
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, c(0L, as.integer(length(data@x)))
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, data@i - 1L
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, data@x
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, TRUE
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, 1L
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, ncols
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, start_iteration
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, num_iteration
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, private$params
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)
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out <- methods::new("dsparseVector")
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out@i <- res$indices + 1L
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out@x <- res$data
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out@length <- ncols_out
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return(out)
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} else if (inherits(data, "dgRMatrix")) {
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if (ncol(data) > ncols) {
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stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns."
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, ncols
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, ncol(data)))
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}
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res <- .Call(
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LGBM_BoosterPredictSparseOutput_R
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, private$handle
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, data@p
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, data@j
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, data@x
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, TRUE
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, nrow(data)
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, ncols
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, start_iteration
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, num_iteration
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, private$params
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)
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out <- methods::new("dgRMatrix")
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out@p <- res$indptr
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out@j <- res$indices
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out@x <- res$data
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out@Dim <- as.integer(c(nrow(data), ncols_out))
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} else if (inherits(data, "dgCMatrix")) {
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if (ncol(data) != ncols) {
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stop(sprintf("Model was fitted to data with %d columns, input data has %.0f columns."
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, ncols
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, ncol(data)))
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}
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res <- .Call(
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LGBM_BoosterPredictSparseOutput_R
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, private$handle
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, data@p
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, data@i
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, data@x
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, FALSE
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, nrow(data)
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, ncols
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, start_iteration
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, num_iteration
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, private$params
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)
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out <- methods::new("dgCMatrix")
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out@p <- res$indptr
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out@i <- res$indices
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out@x <- res$data
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out@Dim <- as.integer(c(nrow(data), length(res$indptr) - 1L))
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} else {
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stop(sprintf("Predictions on sparse inputs are only allowed for '%s', '%s', '%s' - got: %s"
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, "dsparseVector"
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, "dgRMatrix"
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, "dgCMatrix"
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, paste(class(data)
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, collapse = ", ")))
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}
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if (NROW(row.names(data))) {
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out@Dimnames[[1L]] <- row.names(data)
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}
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return(out)
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} else {
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# Not a file, we need to predict from R object
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@ -65,6 +65,14 @@ SEXP wrapped_R_raw(void *len) {
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return Rf_allocVector(RAWSXP, *(reinterpret_cast<R_xlen_t*>(len)));
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}
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SEXP wrapped_R_int(void *len) {
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return Rf_allocVector(INTSXP, *(reinterpret_cast<R_xlen_t*>(len)));
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}
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SEXP wrapped_R_real(void *len) {
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return Rf_allocVector(REALSXP, *(reinterpret_cast<R_xlen_t*>(len)));
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}
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SEXP wrapped_Rf_mkChar(void *txt) {
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return Rf_mkChar(reinterpret_cast<char*>(txt));
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}
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@ -84,6 +92,14 @@ SEXP safe_R_raw(R_xlen_t len, SEXP *cont_token) {
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return R_UnwindProtect(wrapped_R_raw, reinterpret_cast<void*>(&len), throw_R_memerr, cont_token, *cont_token);
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}
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SEXP safe_R_int(R_xlen_t len, SEXP *cont_token) {
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return R_UnwindProtect(wrapped_R_int, reinterpret_cast<void*>(&len), throw_R_memerr, cont_token, *cont_token);
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}
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SEXP safe_R_real(R_xlen_t len, SEXP *cont_token) {
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return R_UnwindProtect(wrapped_R_real, reinterpret_cast<void*>(&len), throw_R_memerr, cont_token, *cont_token);
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}
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SEXP safe_R_mkChar(char *txt, SEXP *cont_token) {
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return R_UnwindProtect(wrapped_Rf_mkChar, reinterpret_cast<void*>(txt), throw_R_memerr, cont_token, *cont_token);
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}
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@ -851,6 +867,76 @@ SEXP LGBM_BoosterPredictForMat_R(SEXP handle,
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R_API_END();
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}
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struct SparseOutputPointers {
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void* indptr;
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int32_t* indices;
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void* data;
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int indptr_type;
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int data_type;
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SparseOutputPointers(void* indptr, int32_t* indices, void* data)
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: indptr(indptr), indices(indices), data(data) {}
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};
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void delete_SparseOutputPointers(SparseOutputPointers *ptr) {
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LGBM_BoosterFreePredictSparse(ptr->indptr, ptr->indices, ptr->data, C_API_DTYPE_INT32, C_API_DTYPE_FLOAT64);
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delete ptr;
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}
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SEXP LGBM_BoosterPredictSparseOutput_R(SEXP handle,
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SEXP indptr,
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SEXP indices,
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SEXP data,
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SEXP is_csr,
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SEXP nrows,
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SEXP ncols,
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SEXP start_iteration,
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SEXP num_iteration,
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SEXP parameter) {
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SEXP cont_token = PROTECT(R_MakeUnwindCont());
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R_API_BEGIN();
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_AssertBoosterHandleNotNull(handle);
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const char* out_names[] = {"indptr", "indices", "data", ""};
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SEXP out = PROTECT(Rf_mkNamed(VECSXP, out_names));
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const char* parameter_ptr = CHAR(PROTECT(Rf_asChar(parameter)));
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int64_t out_len[2];
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void *out_indptr;
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int32_t *out_indices;
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void *out_data;
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CHECK_CALL(LGBM_BoosterPredictSparseOutput(R_ExternalPtrAddr(handle),
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INTEGER(indptr), C_API_DTYPE_INT32, INTEGER(indices),
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REAL(data), C_API_DTYPE_FLOAT64,
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Rf_xlength(indptr), Rf_xlength(data),
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Rf_asLogical(is_csr)? Rf_asInteger(ncols) : Rf_asInteger(nrows),
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C_API_PREDICT_CONTRIB, Rf_asInteger(start_iteration), Rf_asInteger(num_iteration),
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parameter_ptr,
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Rf_asLogical(is_csr)? C_API_MATRIX_TYPE_CSR : C_API_MATRIX_TYPE_CSC,
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out_len, &out_indptr, &out_indices, &out_data));
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std::unique_ptr<SparseOutputPointers, decltype(&delete_SparseOutputPointers)> pointers_struct = {
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new SparseOutputPointers(
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out_indptr,
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out_indices,
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out_data),
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&delete_SparseOutputPointers
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};
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SEXP out_indptr_R = safe_R_int(out_len[1], &cont_token);
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SET_VECTOR_ELT(out, 0, out_indptr_R);
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SEXP out_indices_R = safe_R_int(out_len[0], &cont_token);
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SET_VECTOR_ELT(out, 1, out_indices_R);
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SEXP out_data_R = safe_R_real(out_len[0], &cont_token);
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SET_VECTOR_ELT(out, 2, out_data_R);
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std::memcpy(INTEGER(out_indptr_R), out_indptr, out_len[1]*sizeof(int));
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std::memcpy(INTEGER(out_indices_R), out_indices, out_len[0]*sizeof(int));
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std::memcpy(REAL(out_data_R), out_data, out_len[0]*sizeof(double));
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UNPROTECT(3);
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return out;
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R_API_END();
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}
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SEXP LGBM_BoosterSaveModel_R(SEXP handle,
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SEXP num_iteration,
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SEXP feature_importance_type,
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@ -975,6 +1061,7 @@ static const R_CallMethodDef CallEntries[] = {
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{"LGBM_BoosterCalcNumPredict_R" , (DL_FUNC) &LGBM_BoosterCalcNumPredict_R , 8},
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{"LGBM_BoosterPredictForCSC_R" , (DL_FUNC) &LGBM_BoosterPredictForCSC_R , 14},
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{"LGBM_BoosterPredictForMat_R" , (DL_FUNC) &LGBM_BoosterPredictForMat_R , 11},
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{"LGBM_BoosterPredictSparseOutput_R", (DL_FUNC) &LGBM_BoosterPredictSparseOutput_R, 10},
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{"LGBM_BoosterSaveModel_R" , (DL_FUNC) &LGBM_BoosterSaveModel_R , 4},
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{"LGBM_BoosterSaveModelToString_R" , (DL_FUNC) &LGBM_BoosterSaveModelToString_R , 3},
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{"LGBM_BoosterDumpModel_R" , (DL_FUNC) &LGBM_BoosterDumpModel_R , 3},
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@ -574,6 +574,35 @@ LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictForMat_R(
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SEXP out_result
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);
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/*!
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* \brief make feature contribution prediction for a new Dataset
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* \param handle Booster handle
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* \param indptr array with the index pointer of the data in CSR or CSC format
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* \param indices array with the non-zero indices of the data in CSR or CSC format
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* \param data array with the non-zero values of the data in CSR or CSC format
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* \param is_csr whether the input data is in CSR format or not (pass FALSE for CSC)
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* \param nrows number of rows in the data
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* \param ncols number of columns in the data
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* \param start_iteration Start index of the iteration to predict
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* \param num_iteration number of iteration for prediction, <= 0 means no limit
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* \param parameter additional parameters
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* \return An R list with entries "indptr", "indices", "data", constituting the
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* feature contributions in sparse format, in the same storage order as
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* the input data.
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*/
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LIGHTGBM_C_EXPORT SEXP LGBM_BoosterPredictSparseOutput_R(
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SEXP handle,
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SEXP indptr,
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SEXP indices,
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SEXP data,
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SEXP is_csr,
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SEXP nrows,
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SEXP ncols,
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SEXP start_iteration,
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SEXP num_iteration,
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SEXP parameter
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);
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/*!
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* \brief save model into file
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* \param handle Booster handle
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@ -1,3 +1,5 @@
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library(Matrix)
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VERBOSITY <- as.integer(
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Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
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)
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@ -116,6 +118,84 @@ test_that("start_iteration works correctly", {
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expect_equal(pred_leaf1, pred_leaf2)
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})
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test_that("Feature contributions from sparse inputs produce sparse outputs", {
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data(mtcars)
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X <- as.matrix(mtcars[, -1L])
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y <- as.numeric(mtcars[, 1L])
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dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))
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bst <- lgb.train(
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data = dtrain
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, obj = "regression"
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, nrounds = 5L
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, verbose = VERBOSITY
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, params = list(min_data_in_leaf = 5L)
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)
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pred_dense <- predict(bst, X, predcontrib = TRUE)
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Xcsc <- as(X, "CsparseMatrix")
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pred_csc <- predict(bst, Xcsc, predcontrib = TRUE)
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expect_s4_class(pred_csc, "dgCMatrix")
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expect_equal(unname(pred_dense), unname(as.matrix(pred_csc)))
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Xcsr <- as(X, "RsparseMatrix")
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pred_csr <- predict(bst, Xcsr, predcontrib = TRUE)
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expect_s4_class(pred_csr, "dgRMatrix")
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expect_equal(as(pred_csr, "CsparseMatrix"), pred_csc)
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Xspv <- as(X[1L, , drop = FALSE], "sparseVector")
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pred_spv <- predict(bst, Xspv, predcontrib = TRUE)
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expect_s4_class(pred_spv, "dsparseVector")
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expect_equal(Matrix::t(as(pred_spv, "CsparseMatrix")), unname(pred_csc[1L, , drop = FALSE]))
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})
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test_that("Sparse feature contribution predictions do not take inputs with wrong number of columns", {
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data(mtcars)
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X <- as.matrix(mtcars[, -1L])
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y <- as.numeric(mtcars[, 1L])
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dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))
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bst <- lgb.train(
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data = dtrain
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, obj = "regression"
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, nrounds = 5L
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, verbose = VERBOSITY
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, params = list(min_data_in_leaf = 5L)
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)
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X_wrong <- X[, c(1L:10L, 1L:10L)]
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X_wrong <- as(X_wrong, "CsparseMatrix")
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expect_error(predict(bst, X_wrong, predcontrib = TRUE), regexp = "input data has 20 columns")
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X_wrong <- as(X_wrong, "RsparseMatrix")
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expect_error(predict(bst, X_wrong, predcontrib = TRUE), regexp = "input data has 20 columns")
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X_wrong <- as(X_wrong, "CsparseMatrix")
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X_wrong <- X_wrong[, 1L:3L]
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expect_error(predict(bst, X_wrong, predcontrib = TRUE), regexp = "input data has 3 columns")
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})
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test_that("Feature contribution predictions do not take non-general CSR or CSC inputs", {
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set.seed(123L)
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y <- runif(25L)
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Dmat <- matrix(runif(625L), nrow = 25L, ncol = 25L)
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Dmat <- crossprod(Dmat)
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Dmat <- as(Dmat, "symmetricMatrix")
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SmatC <- as(Dmat, "sparseMatrix")
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SmatR <- as(SmatC, "RsparseMatrix")
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dtrain <- lgb.Dataset(as.matrix(Dmat), label = y, params = list(max_bins = 5L))
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bst <- lgb.train(
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data = dtrain
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, obj = "regression"
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, nrounds = 5L
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, verbose = VERBOSITY
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, params = list(min_data_in_leaf = 5L)
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)
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expect_error(predict(bst, SmatC, predcontrib = TRUE))
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expect_error(predict(bst, SmatR, predcontrib = TRUE))
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})
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test_that("predict() params should override keyword argument for raw-score predictions", {
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data(agaricus.train, package = "lightgbm")
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X <- agaricus.train$data
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@ -321,6 +401,8 @@ test_that("predict() params should override keyword argument for feature contrib
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.expect_has_row_names(pred, Xcsc)
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pred <- predict(bst, Xcsc, predcontrib = TRUE)
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.expect_has_row_names(pred, Xcsc)
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pred <- predict(bst, as(Xcsc, "RsparseMatrix"), predcontrib = TRUE)
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.expect_has_row_names(pred, Xcsc)
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# sparse matrix without row names
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Xcopy <- Xcsc
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