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A bit of code for conducting parallelized random grid-search of randomForest hyperparameters using purrr::map() and futures (for multicore/multisession). This is a bit of a proof-of-concept as there are plenty of ways to iterate over a grid and do CV. Also, especially with randomForest, this is very memory inefficient. However, the approach may …
### ------- Load Packages ---------- ###
library("purrr")
library("future")
library("dplyr")
library("randomForest")
library("rsample")
library("ggplot2")
library("viridis")
### ------- Helper Functions for map() ---------- ###
# breaks CV splits into train (analysis) and test (assessmnet) sets
cv_helper <- function(data, params){
d <- data %>%
mutate(mtry = params$mtry,
ntree = params$ntree,
train = map(splits, analysis),
test = map(splits, assessment)) %>%
select(-splits)
}
# the main modeling helper. Trains and Tests RF model on each CV fold
rf_helper <- function(folds){
rf_model <- function(train,mtry,ntree,test){
xtest <- dplyr::select(test,-medv)
ytest <- test$medv
m1 <- randomForest(medv ~., data=train,mtry=mtry,ntree=ntree,
xtest=xtest,ytest=ytest,keep.forest=TRUE)
}
# prediction function can go here and map in below
# randomForest() has its own internal test set prediction
m <- folds %>%
mutate(model = pmap(list(train,mtry,ntree,test),rf_model))
}
# Extracts test set MSE from RF Model and averages MSE from each tree
MSE_helper <- function(model){
err_helper <- function(rf){
mse <- mean(rf$test$mse)
}
m2 <- model %>%
mutate(mse = map_dbl(model, err_helper)) %>%
dplyr::select(mse)
mean(m2$mse)
}
### ------- Load Data & Set Parameters ---------- ###
data <- MASS::Boston
searches <- 10 # number of random grid searches
max_trees <- 500
cv_folds <- 5
### -------------------- START non-parallel version --------------- ###
# Fit and evaluate all grid searches
model_fits <- seq_len(searches) %>%
tibble(
id = .,
ntree = sample(c(1,seq(25,max_trees,25)),length(id),replace = T),
mtry = sample(seq(1,ncol(data)-1,1),length(id),replace = T)
) %>%
nest(-id, .key = "params") %>%
mutate(splits = list(rsample::vfold_cv(data,cv_folds)), # same resamples in each
folds = map2(splits, params, cv_helper),
model = map(folds, rf_helper), # <- non-parallel version
mse = map_dbl(model, MSE_helper))
### -------------------- END non-parallel version --------------- ###
### -------------------- START parallel version --------------- ###
future::plan(multisession) # <- setup parallel backend, could also be "multicore"
# Fit and evaluate all grid searches with
model_fits <- seq_len(searches) %>%
tibble(id = .,
ntree = sample(c(1,seq(25,max_trees,25)),length(id),replace = T),
mtry = sample(seq(1,ncol(data)-1,1),length(id),replace = T)) %>%
nest(-id, .key = "params") %>%
mutate(folds = list(rsample::vfold_cv(data,cv_folds)),
folds = map2(folds, params, cv_helper)) %>%
mutate(model = map(folds, ~future::future(rf_helper(.x)))) %>%
mutate(model = map(model, ~future::value(.x)),
mse = map_dbl(model, MSE_helper))
### -------------------- END parallel version --------------- ###
# reshape for plotting
MSE_plot <- model_fits %>%
mutate(mtry = map_dbl(params, "mtry"),
ntree = map_dbl(params, "ntree")) %>%
dplyr::select(mtry, ntree, mse) %>%
group_by(mtry, ntree) %>%
summarise(mse = mean(mse))
# plot results
ggplot(data = MSE_plot, aes(x=mtry,y=ntree)) +
geom_point(data = filter(MSE_plot, mse == min(MSE_plot$mse)),
aes(x=mtry,y=ntree), color = "red",
size = 8, shape = 15, alpha = 0.85) +
geom_point(data = MSE_plot, aes(x=mtry,y=ntree, size = mse, color = mse)) +
scale_size(range = c(1,10)) +
scale_x_continuous(breaks = scales::pretty_breaks(n=ncol(data)-1)) +
scale_color_viridis() +
labs(title = "Random Grid Search of RandomForest Hyperparameters",
subtitle = paste0("Mean MSE from ", cv_folds, "-fold CV over ",
searches, " parameter pairs"),
caption = "(medv ~ ., data = Boston)") +
theme_bw() +
theme(
text = element_text(family = "Iosevka")
)
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