Params

KID_CORPUS_PATH <- here("data/processed/other/downsampled_kidbook.txt")

MIN_WORD_COUNT <- c(5, 10, 15, 20)
VECTOR_SIZE <- c(200, 300, 400, 500)
WINDOW_SIZE <- c(5, 10, 15, 20)
EPOCHS <- c(1, 5, 10, 15, 20, 50)
train_model <- function(params, corpus){
    min_word_count <- params[[1]]
    vector_size <- params[[2]]
    window_size <- params[[3]]
    epoch_size <- params[[4]] 

    tmp_file_txt <- tempfile()
    tmp_file_model <- tempfile()

    writeLines(text = corpus, con = tmp_file_txt)
    execute(commands = c("skipgram",
                         #set the input and output files
                         "-input", tmp_file_txt,
                         "-output", tmp_file_model,
                         #set the window size if needed, default is 5
                         "-ws", window_size,
                         #min length of char ngram, default is 3
                         "-minn", 1,
                         #max length of char ngram, default is 6, minn and maxn both as 1 if don’t want to use ngrams
                         "-maxn", 1,
                         #minimal number of word occurrences, default is 5, can set to 100 instead
                         "-minCount", min_word_count,
                         #max length of word ngram, default is 1
                         #”-wordNgrams",1,
                         #number of epochs, default is 5
                         "-lr", .01,
                         "-epoch", epoch_size,
                         #set the number of dimensions, default is 100
                         "-dim", vector_size,
                         "-verbose", 1))

  walk(list.files(dirname(tmp_file_model), full.names = T),
   ~try(file.remove(.x, silent = TRUE)))
  
  walk(list.files(dirname(tmp_file_txt), full.names = T),
   ~try(file.remove(.x, silent = TRUE)))
    
}

# get corpus
corpus <- read_lines(KID_CORPUS_PATH)   %>%
  str_split(" ")  %>%
  unlist()

Min word count

print(MIN_WORD_COUNT)

cross(list(MIN_WORD_COUNT,
           VECTOR_SIZE[1],
           WINDOW_SIZE[1],
           EPOCHS[1])) %>%
  map(train_model, corpus)

Vector size

print(VECTOR_SIZE)

cross(list(MIN_WORD_COUNT[1],
           VECTOR_SIZE,
           WINDOW_SIZE[1],
           EPOCHS[1])) %>%
  map(train_model, corpus)

Window size

print(WINDOW_SIZE)

cross(list(MIN_WORD_COUNT[1],
           VECTOR_SIZE[1],
           WINDOW_SIZE,
           EPOCHS[1])) %>%
  map(train_model, corpus)

Epochs

print(EPOCHS)
## [1]  1  5 10 15 20 50
cross(list(MIN_WORD_COUNT[1],
           VECTOR_SIZE[1],
           WINDOW_SIZE[1],
           EPOCHS)) %>%
  map(train_model, corpus)
## 
Read 0M words
## Number of words:  2876
## Number of labels: 0
## 
Progress: 100.0% words/sec/thread:   14338 lr:  0.000000 loss:  4.135611 ETA:   0h 0m
## 
## 
Read 0M words
## Number of words:  2876
## Number of labels: 0
## 
Progress: 100.0% words/sec/thread:   66358 lr:  0.000000 loss:  3.837582 ETA:   0h 0m
## 
## 
Read 0M words
## Number of words:  2876
## Number of labels: 0
## 
Progress: 100.0% words/sec/thread:  139513 lr:  0.000000 loss:  3.655409 ETA:   0h 0m
## 
## 
Read 0M words
## Number of words:  2876
## Number of labels: 0
## 
Progress: 100.0% words/sec/thread:  229531 lr:  0.000000 loss:  3.529545 ETA:   0h 0m
## 
## 
Read 0M words
## Number of words:  2876
## Number of labels: 0
## 
Progress: 100.0% words/sec/thread:  323230 lr:  0.000000 loss:  2.799038 ETA:   0h 0m
## 
## 
Read 0M words
## Number of words:  2876
## Number of labels: 0
## 
Progress: 100.0% words/sec/thread:  641987 lr:  0.000000 loss:  2.023787 ETA:   0h 0m
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