## ----eval=FALSE---------------------------------------------------------------
# options(width=100L)
# fn <- "dbpedia_csv.tar.gz"
# 
# if ( !file.exists(fn) ) {
#     download.file("https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz",
#                   fn)
#     untar(fn)
# }

## ----eval=FALSE---------------------------------------------------------------
# library("fastTextR")
# 
# train <- sample(sprintf("__label__%s", readLines("dbpedia_csv/train.csv")))
# head(train, 2)

## ----eval=FALSE---------------------------------------------------------------
# train <- ft_normalize(train)
# writeLines(train, con = "dbpedia.train")
# 
# test <- readLines("dbpedia_csv/test.csv")
# labels <- trimws(gsub(",.*", "", test))
# table(labels)

## ----eval=FALSE---------------------------------------------------------------
# test <- ft_normalize(test)
# test <- trimws(sub(".*?,", "", test))
# head(test, 2)

## ----eval=FALSE---------------------------------------------------------------
# cntrl <- ft_control(word_vec_size = 10L, learning_rate = 0.1, max_len_ngram = 2L,
#                     min_count = 1L, nbuckets = 10000000L, epoch = 5L, nthreads = 4L)
# 
# model <- ft_train(file = "dbpedia.train", method = "supervised", control = cntrl)
# ft_save(model, "dbpedia.bin")

## ----eval=FALSE---------------------------------------------------------------
# model <- ft_load("dbpedia.bin")

## ----eval=FALSE---------------------------------------------------------------
# test_pred <- ft_predict(model, newdata=test, k = 1L)
# str(test_pred)

## ----eval=FALSE---------------------------------------------------------------
# confusion_matrix <- table(truth=as.integer(labels),
#                           predicted=as.integer(gsub("\\D", "", test_pred$label)))
# print(confusion_matrix)

## ----eval=FALSE---------------------------------------------------------------
# accuracy <- sum(diag(confusion_matrix)) / sum(confusion_matrix)
# print(sprintf("Accuracy: %0.4f", accuracy))