token_vector: Vectorization of tokens
In dongminjung/ttgsea: Tokenizing Text of Gene Set Enrichment Analysis

Description Usage Arguments Value Author(s) See Also Examples

View source: R/ttgsea.R

A vectorization of words or tokens of text is necessary for machine learning. Vectorized sequences are padded or truncated.

1	token_vector(text, token, length_seq)

`text`	text data
`token`	result of tokenization (output of "text_token")
`length_seq`	length of input sequences

sequences of integers

Dongmin Jung

tm::removeWords, stopwords::stopwords, textstem::lemmatize_strings, tokenizers::tokenize_ngrams, keras::pad_sequences

library(reticulate)
if (keras::is_keras_available() & reticulate::py_available()) {
  library(fgsea)
  data(examplePathways)
  data(exampleRanks)
  names(examplePathways) <- gsub("_", " ",
                            substr(names(examplePathways), 9, 1000))
  set.seed(1)
  fgseaRes <- fgsea(examplePathways, exampleRanks)
  tokens <- text_token(data.frame(fgseaRes)[,"pathway"],
            num_tokens = 1000)
  sequences <- token_vector("Cell Cycle", tokens, 10)
}