annotate text_to_wordmatrix.R @ 0:dd696b179eb7 draft

"planemo upload for repository https://github.com/dlal-group/simtext commit fd3f5b7b0506fbc460f2a281f694cb57f1c90a3c-dirty"

#!/usr/bin/env Rscript
# tool: text_to_wordmatrix
#
#The tool extracts the most frequent words per entity (per row). Text of columns starting with "ABSTRACT" or "TEXT" are considered. 
#All extracted terms are used to generate a word matrix with rows = entities and columns = extracted words. 
#The resulting matrix is binary with 0= word not present in abstracts of entity and 1= word present in abstracts of entity.
#
#Input: Output of 'pubmed_by_queries' or 'abstracts_by_pmids', or tab-delimited table with entities in column called “ID_<name>”, 
#e.g. “ID_genes” and text in columns starting with "ABSTRACT" or "TEXT".
#
#Output: Binary matrix with rows = entities and columns = extracted words.
#
#usage: text_to_wordmatrix.R [-h] [-i INPUT] [-o OUTPUT] [-n NUMBER] [-r] [-l] [-w] [-s] [-p]
# 
# optional arguments:
# -h, --help                    show help message
# -i INPUT, --input INPUT       input file name. add path if file is not in working directory
# -o OUTPUT, --output OUTPUT    output file name. [default "text_to_wordmatrix_output"]
# -n NUMBER, --number NUMBER    number of most frequent words that should be extracted [default "50"]
# -r, --remove_num              remove any numbers in text
# -l, --lower_case              by default all characters are translated to lower case. otherwise use -l
# -w, --remove_stopwords        by default a set of english stopwords (e.g., 'the' or 'not') are removed. otherwise use -w
# -s, --stemDoc                 apply Porter's stemming algorithm: collapsing words to a common root to aid comparison of vocabulary
# -p, --plurals                 by default words in plural and singular are merged to the singular form. otherwise use -p

if ( '--install_packages' %in% commandArgs()) {
  print('Installing packages')
  if (!require('argparse')) install.packages('argparse', repo="http://cran.rstudio.com/");
  if (!require("PubMedWordcloud")) install.packages("PubMedWordcloud", repo="http://cran.rstudio.com/");
  if (!require('SnowballC')) install.packages('SnowballC', repo="http://cran.rstudio.com/");
  if (!require('textclean')) install.packages('textclean', repo="http://cran.rstudio.com/");
  if (!require('SemNetCleaner')) install.packages('SemNetCleaner',repo="http://cran.rstudio.com/");
  if (!require('stringi')) install.packages('stringi',repo="http://cran.rstudio.com/");
  if (!require('stringr')) install.packages('stringr',repo="http://cran.rstudio.com/");
}

suppressPackageStartupMessages(library("argparse"))
suppressPackageStartupMessages(library("PubMedWordcloud"))
suppressPackageStartupMessages(library("SnowballC"))
suppressPackageStartupMessages(library("SemNetCleaner"))
suppressPackageStartupMessages(library("textclean"))
suppressPackageStartupMessages(library("stringi"))
suppressPackageStartupMessages(library("stringr"))

parser <- ArgumentParser()
parser$add_argument("-i", "--input", 
                    help = "input fie name. add path if file is not in workind directory")
parser$add_argument("-o", "--output", default="text_to_wordmatrix_output",
                    help = "output file name. [default \"%(default)s\"]")
parser$add_argument("-n", "--number", type="integer", default=50, choices=seq(1, 500), metavar="{0..500}",
                    help="number of most frequent words used per ID in word matrix [default \"%(default)s\"]")
parser$add_argument("-r", "--remove_num", action="store_true", default=FALSE,
                    help= "remove any numbers in text")
parser$add_argument("-l", "--lower_case", action="store_false", default=TRUE,
                    help="by default all characters are translated to lower case. otherwise use -l")
parser$add_argument("-w", "--remove_stopwords", action="store_false", default=TRUE,
                    help="by default a set of English stopwords (e.g., 'the' or 'not') are removed. otherwise use -s")
parser$add_argument("-s", "--stemDoc", action="store_true", default=FALSE,
                    help="apply Porter's stemming algorithm: collapsing words to a common root to aid comparison of vocabulary")
parser$add_argument("-p", "--plurals", action="store_false", default=TRUE,
                    help="by default words in plural and singular are merged to the singular form. otherwise use -p")
parser$add_argument("--install_packages", action="store_true", default=FALSE,
                    help="If you want to auto install missing required packages.")

args <- parser$parse_args()


data = read.delim(args$input, stringsAsFactors=FALSE, header = TRUE, sep='\t')
word_matrix = data.frame()

text_cols_index <- grep(c("ABSTRACT|TEXT"), names(data))

for(row in 1:nrow(data)){
    top_words = cleanAbstracts(abstracts= data[row,text_cols_index], 
                               rmNum = args$remove_num, 
                               tolw= args$lower_case,
                               rmWords= args$remove_stopwords,
                               stemDoc= args$stemDoc)
    
    top_words$word <- as.character(top_words$word)
    
   # δ γ ε
    
    cat("Most frequent words for row", row, " are extracted.", "\n")
    
    if(args$plurals == TRUE){
      top_words$word <- sapply(top_words$word, function(x){singularize(x)})
      top_words = aggregate(freq~word,top_words,sum)
    }
    
    top_words = top_words[order(top_words$freq, decreasing = TRUE), ]
    top_words$word = as.character(top_words$word)
    
    number_extract = min(args$number, nrow(top_words))
    word_matrix[row,sapply(1:number_extract, function(x){paste0(top_words$word[x])})] <- top_words$freq[1:number_extract]
  }

  word_matrix <- as.matrix(word_matrix)
  word_matrix[is.na(word_matrix)] <- 0
  word_matrix <- (word_matrix>0) *1  #binary matrix

cat("A matrix with ", nrow(word_matrix), " rows and ", ncol(word_matrix), "columns is generated.", "\n")
  
write.table(word_matrix, args$output, row.names = FALSE, sep = '\t')