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HILDA_model_building2.R
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HILDA_model_building2.R
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#!/usr/bin/Rscript
# Copyright (C) Abhishek Sheetal
# This file is part of HILDA methods project
#
# HILDA methods is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# HILDA methods is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with HILDA methods If not, see <http://www.gnu.org/licenses/>.
#
# This file is the main file for model building. It searches for optimal parameters
# and finally builds a best possible model
#
#
suppressPackageStartupMessages({
library(caret)
library(naniar)
library(AppliedPredictiveModeling)
set.seed(12345)
library(caret)
library(xgboost)
library(dplyr)
library(mlrMBO)
library(missRanger)
library(filenamer)
library(testit)
library(rstanarm)
library(bayestestR)
library(ggthemes)
library(mice)
library(rstan)
library(rstanarm)
library(brms)
library(parallel)
library(bnstruct)
library(glmnet)
library(purrr)
})
MODEL_VAR <- Sys.getenv("MODEL")
MODEL_VAR <- ifelse(MODEL_VAR == "", "neurotic", MODEL_VAR)
COMPLETE <- Sys.getenv("COMPLETE")
COMPLETE <- ifelse(COMPLETE == "", FALSE, as.logical(as.integer(COMPLETE)))
print(paste("Building model in ", MODEL_VAR, COMPLETE))
BASE_PATH <- "/research/dataset/HILDA/HILDA_methods" #Give proper full path name where all outpuut files will be saved
GPU_ID <- 0 #If there are multiple graphics cards in the system, use this to control which GPU will be used to run
OBJECTIVE <- "reg:squarederror"
EVAL_METRIC <- list("rmse") #for regression use rmse, Check xgboost manual for other eval_metric
TRAIN_DATA <- paste(BASE_PATH, "/all_data_2022-03-14.rds", sep="")
PREPROCESS <- c("center", "scale")
METHOD <- "xgbTree" #Do not change this unless prepared to do a major surgery of this code
TREE_METHOD <- "gpu_hist" #Options are hist for CPU based processing or gpu_hist for GPU based processing. can try others from the xgboost manual
THREADS <- 4 #change this only if you wish to explore how deep the rabbit hole goes
DEBUG_FILE <- "/research/dataset/HILDA/HILDA_methods/bayes_debug.log" #sometimes the program will crash, you can try to debug
TRIALS <- 200 #You could watch your hair get gray, so increase with caution
#this N should control all corners of bounds. Increase or decrease this N if all corners are not addressed in seeding. (10 worked for xgbTree)
big5 <- c("extrav", "agree", "consc", "neurotic", "open")
big5_subitem_wild <- paste0(big5, "*")
options(filenamer.timestamp=1)
SAVE_MODEL <- filename(paste0("xgb_bayes_completed_model_", MODEL_VAR, "_", COMPLETE),
path=BASE_PATH,
tag=NULL,
ext="RData",
subdir=FALSE) %>%
as.character() %>%
print()
df.pre <- readRDS(TRAIN_DATA) %>%
pluck("df.train.imputed") %>%
as.data.frame() %>%
filter(!is.na(get(MODEL_VAR)))
all_big5 <- grep(paste(big5_subitem_wild, collapse="|"), names(df.pre), value = TRUE)
INDEPENDANT_VARS <- names(df.pre) %>%
setdiff(all_big5)
if (COMPLETE) {
df <- df.pre %>%
na.omit() %>%
select(c(INDEPENDANT_VARS, MODEL_VAR))
} else {
df <- df.pre %>%
select(c(INDEPENDANT_VARS, MODEL_VAR))
}
#drop 1 column percentage
col_sel <- (ncol(df) - 1) / ncol(df)
#determine the weights
weight_table <- table(floor(df[,MODEL_VAR] + 0.5)) %>%
as.data.frame() %>%
mutate(weight = min(Freq)/Freq)
df_weights <- weight_table$weight[floor(df[,MODEL_VAR]+0.5)]
#bounds: Refer to xgboost manual on explanations.
#this n value must be adjusted to make sure seedgrid below covers all corners of the bounds above
SEED_N <- 10
par.set = makeParamSet(
makeIntegerParam("max_depth", lower = 3L, upper = 23L),
makeNumericParam("eta", lower = 0.000001, upper = .999999),
makeNumericParam("min_child_weight", lower= 0L, upper = 2L),
#makeNumericParam("subsample", lower = 1.0, upper = 1.0),
#makeNumericParam("colsample_bytree", lower = 1.0, upper = 1.0),
makeNumericParam("gamma", lower = 0L, upper = 20L),
makeIntegerParam("nrounds", lower = 500L, upper = 2000L))
#This is the trial model to check seed bounds. If the trial fails, then there is some issue with bounds
form <- paste(MODEL_VAR, "~", paste(INDEPENDANT_VARS, collapse="+")) %>%
as.formula()
set.seed(101)
#Should not need any more changes after this comment
fitControl <- trainControl(
method = "repeatedcv",
number = 10,
repeats = 5,
allowParallel = TRUE
)
fn <- function(x){
set.seed(101)
train_model = caret::train(form,
data = df,
na.action = na.pass,
weights = df_weights,
method = METHOD,
objective = OBJECTIVE,
eval_metric = EVAL_METRIC,
trControl = fitControl,
tuneGrid = expand.grid(max_depth = x$max_depth,
eta = x$eta,
min_child_weight = x$min_child_weight,
subsample = 1.0, #x$subsample,
colsample_bytree = col_sel, #x$colsample_bytree,
gamma = x$gamma,
nrounds = x$nrounds),
#preProcess = PREPROCESS,
tree_method=TREE_METHOD, #only for computers where GPU is enabled
gpu_id = GPU_ID,
nthread=THREADS)
getTrainPerf(train_model)[, "TrainRMSE"]
}
obj.fun <- smoof::makeSingleObjectiveFunction(
name = "xgb_cv_bayes",
has.simple.signature = FALSE,
fn = fn,
par.set = par.set,
minimize = TRUE
)
control <- makeMBOControl()
control <- setMBOControlTermination(control, iters = TRIALS)
des <- generateDesign(n = SEED_N,
par.set = getParamSet(obj.fun),
fun = lhs::randomLHS)
print("Fitting model with bayesian optimized hyperparameters..\n")
run <- mbo(fun = obj.fun,
control = control,
show.info = TRUE,
design = des)
################3
print("Completed Hyperpameter..Now building final model\n")
set.seed(101)
xgb.tuned.bayes <- caret::train(form,
df,
na.action = na.pass,
weights = df_weights,
method=METHOD,
objective = OBJECTIVE,
eval_metric = EVAL_METRIC,
tuneGrid = data.frame(max_depth = run$x["max_depth"],
eta = run$x["eta"],
min_child_weight = run$x["min_child_weight"],
subsample = 1.0, #run$x["subsample"],
colsample_bytree = col_sel, #run$x["colsample_bytree"],
gamma = run$x["gamma"],
nrounds = run$x["nrounds"]),
trControl = fitControl,
#preProcess = PREPROCESS,
tree_method=TREE_METHOD,
gpu_id = GPU_ID,
nthread=THREADS)
save.image(SAVE_MODEL)
print("XGBOOST Model building is completed")
prior <- get_prior(formula = form,
data = df,
family = gaussian())
model_bayes <- brm(formula = form,
data = df,
prior = prior,
family = gaussian(),
warmup = 1000,
iter = 3000,
chains = 6,
cores = 6,
seed = 123)
save.image(SAVE_MODEL)
print("MCMC Model building is completed")