library("stremr")
library("data.table")
setDTthreads(4)
library("foreach")
library("doParallel")
library("xgboost")
library("magrittr")
library("ggplot2")
library("tibble")
library("tidyr")
library("purrr")
library("dplyr")
run_test_xgb_Models <- function(seed){
data(agaricus.train, package='xgboost')
data(agaricus.test, package='xgboost')
dtrain <- xgb.DMatrix(agaricus.train$data, label = agaricus.train$label)
dtest <- xgb.DMatrix(agaricus.test$data, label = agaricus.test$label)
watchlist <- list(eval = dtest, train = dtrain)
param <- list(max_depth = 5, eta = 0.02, nthread = 2, silent = 1,
objective = "binary:logistic", eval_metric = "auc")
bst <- xgb.train(param, dtrain, nrounds = 500, watchlist, verbose = FALSE)
return(bst)
}
test.xgboost.parallel.10Kdata <- function() {
`%+%` <- function(a, b) paste0(a, b)
# options(stremr.verbose = TRUE)
options(stremr.verbose = FALSE)
options(gridisl.verbose = TRUE)
# options(gridisl.verbose = FALSE)
data(OdatDT_10K)
Odat_DT <- OdatDT_10K
# select only the first 1,000 IDs
# Odat_DT <- Odat_DT[ID %in% (1:1000), ]
setkeyv(Odat_DT, cols = c("ID", "t"))
# ---------------------------------------------------------------------------
# Define some summaries (lags C[t-1], A[t-1], N[t-1])
# ---------------------------------------------------------------------------
ID <- "ID"; t <- "t"; TRT <- "TI"; I <- "highA1c"; outcome <- "Y.tplus1";
lagnodes <- c("C", "TI", "N")
newVarnames <- lagnodes %+% ".tminus1"
Odat_DT[, (newVarnames) := shift(.SD, n=1L, fill=0L, type="lag"), by=ID, .SDcols=(lagnodes)]
# indicator that the person has never been on treatment up to current t
Odat_DT[, ("barTIm1eq0") := as.integer(c(0, cumsum(get(TRT))[-.N]) %in% 0), by = eval(ID)]
Odat_DT[, ("lastNat1.factor") := as.factor(lastNat1)]
# ------------------------------------------------------------------
# Propensity score models for Treatment, Censoring & Monitoring
# ------------------------------------------------------------------
gform_TRT <- "TI ~ CVD + highA1c + N.tminus1"
stratify_TRT <- list(
TI=c("t == 0L", # MODEL TI AT t=0
"(t > 0L) & (N.tminus1 == 1L) & (barTIm1eq0 == 1L)", # MODEL TRT INITATION WHEN MONITORED
"(t > 0L) & (N.tminus1 == 0L) & (barTIm1eq0 == 1L)", # MODEL TRT INITATION WHEN NOT MONITORED
"(t > 0L) & (barTIm1eq0 == 0L)" # MODEL TRT CONTINUATION (BOTH MONITORED AND NOT MONITORED)
))
gform_CENS <- c("C ~ highA1c + t")
gform_MONITOR <- "N ~ 1"
# ----------------------------------------------------------------
# IMPORT DATA
# ----------------------------------------------------------------
OData <- stremr::importData(Odat_DT, ID = "ID", t = "t", covars = c("highA1c", "lastNat1", "lastNat1.factor"), CENS = "C", TRT = "TI", MONITOR = "N", OUTCOME = outcome)
OData <- define_CVfolds(OData, nfolds = 5, fold_column = "fold_ID", seed = 12345)
OData$dat.sVar[]
OData$fold_column <- NULL
OData$nfolds <- NULL
fold_column <- "fold_ID"
fit_method_g <- "cv"
# ----------------------------------------------------------------
# FIT PROPENSITY SCORES WITH xgboost gbm and V fold CV
# ----------------------------------------------------------------
OData <- fitPropensity(OData, gform_CENS = gform_CENS, gform_TRT = gform_TRT,
stratify_TRT = stratify_TRT, gform_MONITOR = gform_MONITOR,
estimator = "xgboost__gbm", fit_method = "cv", fold_column = "fold_ID",
family = "quasibinomial", rounds = 1000, early_stopping_rounds = 50)
# ---------------------------------------------------------------------------------------------------------
# Parallel test run of xgboost inside function
# ---------------------------------------------------------------------------------------------------------
# unregister <- function() {
# env <- foreach:::.foreachGlobals
# rm(list=ls(name=env), pos=env)
# }
# unregister()
# stopImplicitCluster()
# cl <- makeForkCluster(4, outfile = "")
# registerDoParallel(cl); Sys.sleep(2)
# cat("...running inside run_test_xgb_Models...", "\n")
# r <- foreach(n=seq.int(8), .packages=c('xgboost'), .export = "run_test_xgb_Models") %dopar% {
# run_test_xgb_Models(n)
# }
# cat("...finished inside run_test_xgb_Models...", "\n")
trunc_IPW <- 10
# tvals <- 0:8
tvals <- 0:4
tmax <- 13
nfolds <- 10 ## number of folds for CV
# tbreaks = c(1:8,12,16)-1
tbreaks = c(1:8,11,14)-1
Qforms <- rep.int("Qkplus1 ~ CVD + highA1c + N + lastNat1 + TI + TI.tminus1", (max(tvals)+1))
## ------------------------------------------------------------
## **** As a first step define a grid of all possible parameter combinations (for all estimators)
## **** This dataset is to be saved and will be later merged in with all analysis
## ------------------------------------------------------------
analysis <- list(intervened_TRT = c("gTI.dlow", "gTI.dhigh", "gTI.dlow"),
trunc_wt = c(FALSE, TRUE),
stratifyQ_by_rule = c(TRUE, FALSE)) %>%
cross_df() %>%
arrange(stratifyQ_by_rule) %>%
mutate(nfolds = as.integer(nfolds)) %>%
mutate(trunc_MSM = map_dbl(trunc_wt, ~ ifelse(.x, trunc_IPW, Inf))) %>%
mutate(trunc_TMLE = trunc_MSM*10)
## ------------------------------------------------------------
## IPW ANALYSIS
## **** For each individual analysis do filter()/subset()/etc to create a grid of parameters specific to given estimator
## ------------------------------------------------------------
IPW_time <- system.time({
IPW <- analysis %>%
rename(trunc_weight = trunc_MSM) %>%
distinct(intervened_TRT, trunc_weight) %>%
group_by(intervened_TRT) %>%
mutate(wts_data = map(first(intervened_TRT), getIPWeights, OData = OData, tmax = tmax)) %>%
## save the tables of weights summaries (sep for each regimen)
mutate(wts_tabs = map(wts_data,
~ get_wtsummary(.x, cutoffs = c(0, 0.5, 1, 10, 20, 30, 40, 50, 100, 150), by.rule = TRUE))) %>%
## save the tables with number at risk / following each rule (sep for each regimen)
mutate(FUPtimes_tabs = map(wts_data,
~ get_FUPtimes(.x, IDnode = ID, tnode = t))) %>%
ungroup() %>%
## IPW-Adjusted KM (Non-Parametric or Saturated MSM):
mutate(NPMSM = map2(wts_data, trunc_weight,
~ survNPMSM(wts_data = .x,
trunc_weights = .y,
OData = OData))) %>%
mutate(NPMSM = map(NPMSM, "estimates")) %>%
## Crude MSM for hazard (w/out IPW):
mutate(MSM.crude = map(wts_data,
~ survMSM(wts_data = .x,
OData = OData,
tbreaks = tbreaks,
use_weights = FALSE,
glm_package = "speedglm"))) %>%
mutate(MSM.crude = map(MSM.crude, "estimates")) %>%
## IPW-MSM for hazard (smoothing over time-intervals in tbreaks):
mutate(MSM = map2(wts_data, trunc_weight,
~ survMSM(wts_data = .x,
trunc_weights = .y,
OData = OData,
tbreaks = tbreaks,
glm_package = "speedglm"))) %>%
mutate(MSM = map(MSM, "estimates")) %>%
rename(trunc_MSM = trunc_weight)
})
IPW_time_hrs <- IPW_time[3]/60/60
## save IPW tables (will be later merged with main results dataset)
IPWtabs <- analysis %>%
left_join(IPW) %>%
distinct(intervened_TRT, trunc_MSM, wts_tabs, FUPtimes_tabs) %>%
nest(intervened_TRT, wts_tabs, FUPtimes_tabs, .key = "IPWtabs")
IPW <- IPW %>% select(-wts_data, -wts_tabs, -FUPtimes_tabs)
## ------------------------------------------------------------
## Parallel GCOMP / TMLE with xgboost gbm and CV
## ------------------------------------------------------------
tmle.model <- "xgb.glm"
fit_method_Q <- "cv"
models_Q <- gridisl::defModel(estimator = "xgboost__gbm",
family = "quasibinomial",
nthread = 2,
nrounds = 100,
early_stopping_rounds = 20)
GCOMP_time <- system.time({
GCOMP <-analysis %>%
distinct(intervened_TRT, stratifyQ_by_rule) %>%
mutate(GCOMP = map2(intervened_TRT, stratifyQ_by_rule,
~ fit_GCOMP(intervened_TRT = .x,
stratifyQ_by_rule = .y,
tvals = tvals,
OData = OData,
models = models_Q,
Qforms = Qforms,
fit_method = fit_method_Q,
fold_column = fold_column,
parallel = TRUE))) %>%
mutate(GCOMP = map(GCOMP, "estimates"))
})
GCOMP_time_hrs <- GCOMP_time[3]/60/60
## ------------------------------------------------------------
## TMLE ANALYSIS
## ------------------------------------------------------------
TMLE <- analysis %>%
rename(trunc_weight = trunc_TMLE) %>%
distinct(intervened_TRT, stratifyQ_by_rule, trunc_weight)
TMLE_time <- system.time({
TMLE <- TMLE %>%
mutate(TMLE = pmap(TMLE, fit_TMLE,
tvals = tvals,
OData = OData,
models = models_Q,
Qforms = Qforms,
fit_method = fit_method_Q,
fold_column = fold_column,
parallel = TRUE)) %>%
mutate(TMLE = map(TMLE, "estimates")) %>%
rename(trunc_TMLE = trunc_weight)
})
TMLE_time_hrs <- TMLE_time[3]/60/60
## ------------------------------------------------------------
## COMBINE ALL ANALYSES INTO A SINGLE DATASET
## ------------------------------------------------------------
results <- analysis %>%
left_join(IPW) %>%
left_join(GCOMP) %>%
left_join(TMLE)
## Nest each estimator by treatment regimen (we now only show the main analysis rows)
results <- results %>%
# nest(intervened_TRT, NPMSM, MSM.crude, MSM, .key = "estimates")
nest(intervened_TRT, NPMSM, MSM.crude, MSM, GCOMP, TMLE, .key = "estimates")
## Calculate RDs (contrasting all interventions, for each analysis row & estimator).
## The RDs data no longer needs the intervened_TRT column
results <- results %>%
mutate(RDs =
map(estimates,
~ select(.x, -intervened_TRT) %>%
map(~ get_RDs(.x)) %>%
as_tibble()
))
## Clean up by removing the subject-level IC estimates for EVERY SINGLE ESTIMATE / ANALYSIS
## WARNING: THIS IS A SIDE-EFFECT FUNCTION!
# res <- results[["estimates"]] %>%
# map(
# ~ select(.x, -intervened_TRT) %>%
# map(
# ~ map(.x,
# ~ suppressWarnings(.x[, ("IC.St") := NULL]))))
# rm(res)
## equivalent RD function above but with explicitely defined inside function::
# results_2 <- results %>%
# mutate(RDs = map(estimates, function(.df) {
# res <- select(.df, -intervened_TRT) %>%
# map(~ get_RDs(.x))
# browser()
# as_tibble(res)
# return(res)
# })
# )
## ------------------------------------------------------------
## Add models used for g and Q
## Add IPWtabs
## ------------------------------------------------------------
cat("IPW time, hrs: ", IPW_time_hrs, "\n")
cat("GCOMP time, hrs: ", GCOMP_time_hrs, "\n")
cat("TMLE time, hrs: ", TMLE_time_hrs, "\n")
results <- results %>%
left_join(IPWtabs) %>%
mutate(fit_method_g = fit_method_g) %>%
mutate(fit_method_Q = fit_method_Q) %>%
# mutate(models_g = map(fit_method_g, ~ models_g)) %>%
mutate(models_Q = map(fit_method_Q, ~ models_Q)) %>%
mutate(run_time = map(trunc_wt,
~ tibble(IPW_time_hrs = IPW_time_hrs, GCOMP_time_hrs = GCOMP_time_hrs, TMLE_time_hrs = TMLE_time_hrs)))
# as.data.table(results)
# as.data.table(results)[["models_g"]]
# as.data.table(results)[["run_time"]]
return(results)
}
# registerDoParallel(cores = 4); Sys.sleep(2)
cl <- makeForkCluster(3, outfile = "")
registerDoParallel(cl); Sys.sleep(2)
cat("...running outside run_test_xgb_Models...", "\n")
r <- foreach(n=seq.int(8), .packages=c('xgboost')) %dopar% {
run_test_xgb_Models(n)
}
cat("...finished outside with run_test_xgb_Models...", "\n")
results <- test.xgboost.parallel.10Kdata()
stopCluster(cl)
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