From 92f4dc8be2b7d9cf6082b26eff83f0942b5de24d Mon Sep 17 00:00:00 2001 From: kapsner Date: Mon, 28 Nov 2022 14:04:53 +0100 Subject: [PATCH 1/2] feat: added ranger_surv.unif for random survival forests --- DESCRIPTION | 5 +- NAMESPACE | 1 + R/unify_ranger.R | 27 +++++--- R/unify_ranger_surv.R | 89 ++++++++++++++++++++++++++ man/ranger_surv.unify.Rd | 64 +++++++++++++++++++ src/RcppExports.o | Bin 927255 -> 0 bytes src/treeshap.o | Bin 108602 -> 0 bytes tests/testthat/test_ranger_surv.R | 100 ++++++++++++++++++++++++++++++ 8 files changed, 274 insertions(+), 12 deletions(-) create mode 100644 R/unify_ranger_surv.R create mode 100644 man/ranger_surv.unify.Rd delete mode 100644 src/RcppExports.o delete mode 100644 src/treeshap.o create mode 100644 tests/testthat/test_ranger_surv.R diff --git a/DESCRIPTION b/DESCRIPTION index db14351..7436550 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -22,7 +22,7 @@ License: GPL-3 Encoding: UTF-8 LazyData: true Roxygen: list(markdown = TRUE) -RoxygenNote: 7.1.2 +RoxygenNote: 7.2.2 LinkingTo: Rcpp Imports: @@ -40,6 +40,7 @@ Suggests: catboost (>= 0.22), jsonlite, testthat, - scales + scales, + survival URL: https://github.com/ModelOriented/treeshap BugReports: https://github.com/ModelOriented/treeshap/issues diff --git a/NAMESPACE b/NAMESPACE index e45ad0b..9e9acee 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -16,6 +16,7 @@ export(plot_feature_importance) export(plot_interaction) export(randomForest.unify) export(ranger.unify) +export(ranger_surv.unify) export(set_reference_dataset) export(theme_drwhy) export(theme_drwhy_vertical) diff --git a/R/unify_ranger.R b/R/unify_ranger.R index a772bc1..a2834ca 100644 --- a/R/unify_ranger.R +++ b/R/unify_ranger.R @@ -42,35 +42,42 @@ ranger.unify <- function(rf_model, data) { } n <- rf_model$num.trees x <- lapply(1:n, function(tree) { - tree_data <- as.data.table(ranger::treeInfo(rf_model, tree = tree)) + tree_data <- data.table::as.data.table(ranger::treeInfo(rf_model, tree = tree)) tree_data[, c("nodeID", "leftChild", "rightChild", "splitvarName", "splitval", "prediction")] }) + return(ranger_unify.common(x = x, n = n, data = data)) +} + + +ranger_unify.common <- function(x, n, data) { times_vec <- sapply(x, nrow) - y <- rbindlist(x) - y[, Tree := rep(0:(n - 1), times = times_vec)] - setnames(y, c("Node", "Yes", "No", "Feature", "Split", "Prediction", "Tree")) - y[, Feature := as.character(Feature)] + y <- data.table::rbindlist(x) + y[, ("Tree") := rep(0:(n - 1), times = times_vec)] + data.table::setnames(y, c("Node", "Yes", "No", "Feature", "Split", "Prediction", "Tree")) + y[, ("Feature") := as.character(get("Feature"))] y[y$Yes < 0, "Yes"] <- NA y[y$No < 0, "No"] <- NA - y[, Missing := NA] + y[, ("Missing") := NA] y$Cover <- 0 y$Decision.type <- factor(x = rep("<=", times = nrow(y)), levels = c("<=", "<")) - y[is.na(Feature), Decision.type := NA] + y[is.na(get("Feature")), ("Decision.type") := NA] ID <- paste0(y$Node, "-", y$Tree) y$Yes <- match(paste0(y$Yes, "-", y$Tree), ID) y$No <- match(paste0(y$No, "-", y$Tree), ID) # Here we lose "Quality" information - y[!is.na(Feature), Prediction := NA] + y[!is.na(get("Feature")), ("Prediction") := NA] # treeSHAP assumes, that [prediction = sum of predictions of the trees] # in random forest [prediction = mean of predictions of the trees] # so here we correct it by adjusting leaf prediction values - y[is.na(Feature), Prediction := Prediction / n] + y[is.na(get("Feature")), ("Prediction") := I(get("Prediction") / n)] - setcolorder(y, c("Tree", "Node", "Feature", "Decision.type", "Split", "Yes", "No", "Missing", "Prediction", "Cover")) + data.table::setcolorder( + y, c("Tree", "Node", "Feature", "Decision.type", "Split", + "Yes", "No", "Missing", "Prediction", "Cover")) ret <- list(model = as.data.frame(y), data = as.data.frame(data)) class(ret) <- "model_unified" diff --git a/R/unify_ranger_surv.R b/R/unify_ranger_surv.R new file mode 100644 index 0000000..70cec5b --- /dev/null +++ b/R/unify_ranger_surv.R @@ -0,0 +1,89 @@ +#' Unify ranger survival model +#' +#' Convert your ranger model into a standarised representation. +#' The returned representation is easy to be interpreted by the user and ready to be used as an argument in \code{treeshap()} function. +#' +#' @param rf_model An object of \code{ranger} class. At the moment, models built on data with categorical features +#' are not supported - please encode them before training. +#' @param data Reference dataset. A \code{data.frame} or \code{matrix} with the same columns as in the training set of the model. Usually dataset used to train model. +#' +#' @return a unified model representation - a \code{\link{model_unified.object}} object +#' +#' @import data.table +#' +#' @export +#' +#' @seealso +#' \code{\link{lightgbm.unify}} for \code{\link[lightgbm:lightgbm]{LightGBM models}} +#' +#' \code{\link{gbm.unify}} for \code{\link[gbm:gbm]{GBM models}} +#' +#' \code{\link{catboost.unify}} for \code{\link[catboost:catboost.train]{Catboost models}} +#' +#' \code{\link{xgboost.unify}} for \code{\link[xgboost:xgboost]{XGBoost models}} +#' +#' \code{\link{randomForest.unify}} for \code{\link[randomForest:randomForest]{randomForest models}} +#' +#' @examples +#' +#' library(ranger) +#' data_colon <- data.table::data.table(survival::colon) +#' data_colon <- na.omit(data_colon[get("etype") == 2, ]) +#' surv_cols <- c("status", "time", "rx") +#' +#' feature_cols <- colnames(data_colon)[3:(ncol(data_colon) - 1)] +#' +#' train_x <- model.matrix( +#' ~ -1 + ., +#' data_colon[, .SD, .SDcols = setdiff(feature_cols, surv_cols[1:2])] +#' ) +#' train_y <- survival::Surv( +#' event = (data_colon[, get("status")] |> +#' as.character() |> +#' as.integer()), +#' time = data_colon[, get("time")], +#' type = "right" +#' ) +#' +#' rf <- ranger::ranger( +#' x = train_x, +#' y = train_y, +#' data = data_colon, +#' max.depth = 10, +#' num.trees = 10 +#' ) +#' unified_model <- ranger_surv.unify(rf, train_x) +#' shaps <- treeshap(unified_model, train_x[1:2,]) +#' +ranger_surv.unify <- function(rf_model, data) { + if (!"ranger" %in% class(rf_model)) { + stop("Object rf_model was not of class \"ranger\"") + } + if (!"survival" %in% names(rf_model)) { + stop("Object rf_model is not a survival random forest.") + } + n <- rf_model$num.trees + x <- lapply(1:n, function(tree) { + tree_data <- data.table::as.data.table(ranger::treeInfo(rf_model, + tree = tree)) + + # first get number of columns + chf_node <- rf_model$forest$chf[[tree]] + nodes_chf_n <- ncol(do.call(rbind, chf_node)) + nodes_prepare_chf_list <- lapply( + X = chf_node, + FUN = function(node) { + if (identical(node, numeric(0L))) { + rep(NA, nodes_chf_n) + } else { + node + } + } + ) + nodes_chf <- do.call(rbind, nodes_prepare_chf_list) + tree_data$prediction <- rowSums(nodes_chf) + tree_data[, c("nodeID", "leftChild", "rightChild", "splitvarName", + "splitval", "prediction")] + }) + return(ranger_unify.common(x = x, n = n, data = data)) +} diff --git a/man/ranger_surv.unify.Rd b/man/ranger_surv.unify.Rd new file mode 100644 index 0000000..9a8b915 --- /dev/null +++ b/man/ranger_surv.unify.Rd @@ -0,0 +1,64 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/unify_ranger_surv.R +\name{ranger_surv.unify} +\alias{ranger_surv.unify} +\title{Unify ranger survival model} +\usage{ +ranger_surv.unify(rf_model, data) +} +\arguments{ +\item{rf_model}{An object of \code{ranger} class. At the moment, models built on data with categorical features +are not supported - please encode them before training.} + +\item{data}{Reference dataset. A \code{data.frame} or \code{matrix} with the same columns as in the training set of the model. Usually dataset used to train model.} +} +\value{ +a unified model representation - a \code{\link{model_unified.object}} object +} +\description{ +Convert your ranger model into a standarised representation. +The returned representation is easy to be interpreted by the user and ready to be used as an argument in \code{treeshap()} function. +} +\examples{ + +library(ranger) +data_colon <- data.table::data.table(survival::colon) +data_colon <- na.omit(data_colon[get("etype") == 2, ]) +surv_cols <- c("status", "time", "rx") + +feature_cols <- colnames(data_colon)[3:(ncol(data_colon) - 1)] + +train_x <- model.matrix( + ~ -1 + ., + data_colon[, .SD, .SDcols = setdiff(feature_cols, surv_cols[1:2])] +) +train_y <- survival::Surv( + event = (data_colon[, get("status")] |> + as.character() |> + as.integer()), + time = data_colon[, get("time")], + type = "right" +) + +rf <- ranger::ranger( + x = train_x, + y = train_y, + data = data_colon, + max.depth = 10, + num.trees = 10 +) +unified_model <- ranger_surv.unify(rf, train_x) +shaps <- treeshap(unified_model, train_x[1:2,]) + +} +\seealso{ +\code{\link{lightgbm.unify}} for \code{\link[lightgbm:lightgbm]{LightGBM models}} + +\code{\link{gbm.unify}} for \code{\link[gbm:gbm]{GBM models}} + +\code{\link{catboost.unify}} for \code{\link[catboost:catboost.train]{Catboost models}} + +\code{\link{xgboost.unify}} for \code{\link[xgboost:xgboost]{XGBoost models}} + +\code{\link{randomForest.unify}} for \code{\link[randomForest:randomForest]{randomForest models}} +} diff --git a/src/RcppExports.o b/src/RcppExports.o deleted file mode 100644 index fc10eb0ea99247d590ed53a0cced62000e9b90c4..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 927255 zcmeFa4|rA8o$tK^i3AAb)KZNOQ+I9D)G}~#{vU_YT?_WqiIQtUGnPt_M1zAQ0|!m} zVvQxKi6N3YpiIk@OowUH4pY(&W09enDwWjIlD1S+hf1bp+8|8Lc=OggeHppm-(KtN z{ab&e()-N&-uvFJ=h@jOdw=%tyZ-I9lB0tc??l~U*B+4QT}nV)ZL&C^fm zhTr2jhyNePnY?$Jf8={bj^jGD_46B6=2sRV?4#Ir{9%#vn+F9~g8x0Ic5wck>(?{P zai{YC#P2qzesDoTJ?`S)!>~86P1u91<3?)h7sN>^bfx%t8XR|CBS_ff-KfU-_No&}E=;-^a7#7a`RmjO97Qk}fmAH!SVvoYRMziPZCej%m4$|GIalURcu4DuXZ#%d6CN3ET-?H>>79(#?aL@~~FER7b0M0#i8l+H|XgS{$nMGp%Wpi`?CsxxtJ&jYttvnO;jN<~BVCNxgy zW}|xkbFMCODh=JNQjoi_R|bwdp_@(d`-8$ePvfL+R!@%vxa)P@STx4-l?QvCX3y7+ zMU%R{HjVRjW6_MxZ3b6w=vFJ`P(5_FgZrf9f^sreo#y#~;(5}l8;kLHA+GrSMNX%o z8_RHeQ3yrAeNl4MOcddbC;T!6|C6J8#hX{;#Ja-rRD?I4srGRpHG>Gu9nk@E{fH<{$+5a}KSH*V;rl=6kjjbU)yiSVWr|B*d!zRHR4 zrmOY*Tmx?QBielwOQ(1~7=XPxaPuS=lpC>((&8oE(gil%Ojg&O1I{yavq>I*qcG@# zyF+rTVBZgKJgTHG#f!5L>Gr6WzVYgq9?vb{ZjfA%zVTQ!&o3T22phpQNbagY7ZF2R z($!&q-~&a@t6Oy4c)|$p^+nEv^3-jw?Y_8P!NB}YCGx+$Zc*=p5IW%c{F58O3|Zdz%Od(hbpuFlZSMD=t#ZPm>v z4Oz;6li=<&bkzo%3$FNsXzv=j*_5tZ3648Io)b}}!$rDLaF6J^iD-)F0~g#vaQ(V& zqFTuZWN*1ux7xVRo4|eD(2XhO5apdbxM9f!_iq9P8_pFR>F%=X#*BJ@0Ne>fH?G`o zF6qzlUT_~J5=sZe12+NgYRQG+O{DevBeBSq zzDcElPr4qsYYp9`lD?!{18%vYn~L)ClZX!f&O?Ck>gDC0yt zr;Yn}&SG15(;2-UbiwUr_C&tV#JT@ScOJMG7$>wC7zM3 z^!!%=E@SA%s(JYwfo`Q?uXb=jGN$xXh)Yd|9-k9<4U}^IDQn| z2n(;D9?5uC561+!pBdpu#CiEg{&PO0#|wock>>f2xQLZYD(yrHhfCaFihK};BdN5r zU2p^7J|#Kwfe43rUk-!YEV(crq?C9e{~fdXFRirKh&yfPlzOWV+!=6RF~X5i>IE0v zjvU%yTf=-W!jVZuajxJfKOX>>l3W;$tYVM+Hw2D5FGA^&RmwHuMr>S49gpY(HwJF0 z)D6-j6;2e|y1k=Z7Ozt+elnJ80!%ai0Hr z!5sznrV);qa{s#E8g4{8Vw;v8DKt=VuHYy=+QB^{xgb4Kai!l#{_6$Dod`!fqu)n; 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z2G-K=ri~(dc%F}1#c#fiu2kmWi`(Ysv_|D_#xVZ`*Qj*n46ZD6oza6|`{ZV{Y45PY YWNlMtsAGOh6@1t6S}I}H*NQU#A0Of;3;+NC diff --git a/tests/testthat/test_ranger_surv.R b/tests/testthat/test_ranger_surv.R new file mode 100644 index 0000000..3912745 --- /dev/null +++ b/tests/testthat/test_ranger_surv.R @@ -0,0 +1,100 @@ +library(treeshap) + +data_colon <- data.table::data.table(survival::colon) +data_colon <- na.omit(data_colon[get("etype") == 2, ]) +surv_cols <- c("status", "time", "rx") + +feature_cols <- colnames(data_colon)[3:(ncol(data_colon) - 1)] + +x <- model.matrix( + ~ -1 + ., + data_colon[, .SD, .SDcols = setdiff(feature_cols, surv_cols[1:2])] +) +y <- survival::Surv( + event = (data_colon[, get("status")] |> + as.character() |> + as.integer()), + time = data_colon[, get("time")], + type = "right" +) + +ranger_num_model <- ranger::ranger( + x = x, + y = y, + data = data_colon, + max.depth = 10, + num.trees = 10 +) + + +test_that('ranger_surv.unify creates an object of the appropriate class', { + expect_true(is.model_unified(ranger_surv.unify(ranger_num_model, x))) +}) + +test_that('ranger_surv.unify returns an object with correct attributes', { + unified_model <- ranger_surv.unify(ranger_num_model, x) + + expect_equal(attr(unified_model, "missing_support"), FALSE) + expect_equal(attr(unified_model, "model"), "ranger") +}) + +test_that('the ranger_surv.unify function returns data frame with columns of appropriate column', { + unifier <- ranger_surv.unify(ranger_num_model, x)$model + expect_true(is.integer(unifier$Tree)) + expect_true(is.integer(unifier$Node)) + expect_true(is.character(unifier$Feature)) + expect_true(is.factor(unifier$Decision.type)) + expect_true(is.numeric(unifier$Split)) + expect_true(is.integer(unifier$Yes)) + expect_true(is.integer(unifier$No)) + expect_true(all(is.na(unifier$Missing))) + expect_true(is.numeric(unifier$Prediction)) + expect_true(is.numeric(unifier$Cover)) +}) + +test_that("ranger_surv: shap calculates without an error", { + unifier <- ranger_surv.unify(ranger_num_model, x) + expect_error(treeshap(unifier, x[1:3,], verbose = FALSE), NA) +}) + +test_that("ranger_surv: predictions from unified == original predictions", { + unifier <- ranger_surv.unify(ranger_num_model, x) + obs <- x[1:800, ] + surv_preds <- stats::predict(ranger_num_model, obs) + original <- rowSums(surv_preds$chf) + from_unified <- predict(unifier, obs) + expect_true(all(abs((from_unified - original) / original) < 10**(-14))) +}) + +test_that("ranger_surv: mean prediction calculated using predict == using covers", { + unifier <- ranger_surv.unify(ranger_num_model, x) + + intercept_predict <- mean(predict(unifier, x)) + + ntrees <- sum(unifier$model$Node == 0) + leaves <- unifier$model[is.na(unifier$model$Feature), ] + intercept_covers <- sum(leaves$Prediction * leaves$Cover) / sum(leaves$Cover) * ntrees + + #expect_true(all(abs((intercept_predict - intercept_covers) / intercept_predict) < 10**(-14))) + expect_equal(intercept_predict, intercept_covers) +}) + +test_that("ranger_surv: covers correctness", { + unifier <- ranger_surv.unify(ranger_num_model, x) + + roots <- unifier$model[unifier$model$Node == 0, ] + expect_true(all(roots$Cover == nrow(x))) + + internals <- unifier$model[!is.na(unifier$model$Feature), ] + yes_child_cover <- unifier$model[internals$Yes, ]$Cover + no_child_cover <- unifier$model[internals$No, ]$Cover + if (all(is.na(internals$Missing))) { + children_cover <- yes_child_cover + no_child_cover + } else { + missing_child_cover <- unifier$model[internals$Missing, ]$Cover + missing_child_cover[is.na(missing_child_cover)] <- 0 + missing_child_cover[internals$Missing == internals$Yes | internals$Missing == internals$No] <- 0 + children_cover <- yes_child_cover + no_child_cover + missing_child_cover + } + expect_true(all(internals$Cover == children_cover)) +}) From 35eb9c7d7997365e77955fd4120a916c9fbe0c2a Mon Sep 17 00:00:00 2001 From: kapsner Date: Mon, 28 Nov 2022 14:10:43 +0100 Subject: [PATCH 2/2] fix: 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