The fastai library
simplifies training fast and accurate neural nets using modern best
practices. See the fastai website to get started. The library is based
on research into deep learning best practices undertaken at
fast.ai, and includes “out of the box” support for
vision, text, tabular, and
collab (collaborative filtering) models.
The dataset can be downloaded from Kaggle:
library(rBayesianOptimization)
library(magrittr)
library(fastai)
df = data.table::fread('train.csv')
df$ID_code <- NULL
df$target <- as.character(df$target)
procs = list(FillMissing(),Categorify(),Normalize())
pct_80 = round(nrow(df) * .8)
dep_var = 'target'
cont_names = setdiff(names(df), dep_var)
dls = TabularDataTable(df, procs, NULL, cont_names,
y_names = dep_var, splits = list(c(1:pct_80),c(c(pct_80+1):nrow(df))
)) %>%
dataloaders(bs = 100)
fastai_fit = function(layer_1, layer_2, layer_3, lr, wd, emb_p) {
model <- dls %>% tabular_learner(layers = c(layer_1, layer_2, layer_3),
wd = wd, config = tabular_config(embed_p = emb_p,
use_bn = TRUE),
metrics=list(RocAucBinary(),accuracy()),
cbs = list(EarlyStoppingCallback(monitor='valid_loss',
patience = 2))
)
result_ <- model %>% fit_one_cycle(10,lr)
score_ <- list(Score = unlist(tail(result_$roc_auc_score,1)),
Pred = 0)
rm(model)
return(score_)
}
search_bound_fastai <- list(layer_1 = c(20,200), layer_2 = c(20,200),
layer_3 = c(20,200),
lr = c(0, 0.1), wd = c(0, 0.1),
emb_p = c(0,1)
)
set.seed(123)
search_grid_fastai <- data.frame(layer_1 = runif(30, 20, 200),
layer_2 = runif(30, 20, 200),
layer_3 = runif(30, 20, 200),
lr = runif(30, 0, 0.1),
wd = runif(30, 0, 0.1),
emb_p = runif(30, 0, 1)
)
head(search_grid_fastai)
set.seed(123)
bayes_fastai <- BayesianOptimization(FUN = fastai_fit, bounds = search_bound_fastai,
init_points = 2, init_grid_dt = search_grid_fastai,
n_iter = 5, acq = "ucb")
bayes_fastai$Best_Par