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