dfm_gen: remove multicore, update merger() code elasticizer: changed filenaming scheme for dump option merger: Fixed bug where an NA lemma would cause the entire document to become NA. Now the NA lemmas are filtered out before merging ud_update: removed parallel processing, changed script to save bulk updates in .Rds files instead of sending them straight awaymaster
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5 years ago
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#' Merges list of lemmas back into a pseudo-document
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#'
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#' Merges list of lemmas back into a pseudo-document
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#' @param row A row number form the Elasticizer-generated data frame
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#' @param words String indicating the number of words to keep from each document (maximum document length), 999 indicates the whole document
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#' @param out The elasticizer-generated data frame
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#' @param text String indicating whether the "merged" field will contain the "full" text, old-style "lemmas" (will be deprecated), new-style "ud"
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#' @param clean Boolean indicating whether the results should be cleaned by removing words matching regex (see code).
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#' @return A documentified string of lemmas, one document at a time
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#' @export
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#' @examples
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#' merger(1, words = '999', out, text)
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#################################################################################################
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#################################### Reconstructing documents from lemmas########################
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#################################################################################################
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## Only merging lemmas for now, feature selection has no impact on junk classification
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merger_old <- function(row, out, text, clean) {
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df <- out[row,]
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# Mergin lemmas into single string
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if (text == 'lemmas') {
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lemmas <- paste(str_split(df$`_source.tokens.lemmas`, "\\|")[[1]],collapse = ' ')
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}
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if (text == 'ud') {
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lemmas <- paste0(df$`_source.ud`[[1]]$lemma[[1]], collapse = ' ')
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}
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if (text == 'ud_upos') {
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df <- unnest(df,`_source.ud`)
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lemmas <- str_c(unlist(df$lemma)[which(unlist(df$upos) != 'PUNCT')], unlist(df$upos)[which(unlist(df$upos) != 'PUNCT')], sep = '_', collapse = ' ') %>%
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# Regex removes all words consisting of or containing numbers, @#$%
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# Punctuation is not taken into account, as it is already filtered out, see above
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{if(clean == T) str_replace_all(.,"\\S*?[0-9@#$%]+[^\\s]*", "") else . }
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# In the very rare but obviously occuring (CxqrOmMB4Bzg6Uhtzw0P) case that a document consists only of punctuation, return an empty string
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if (length(lemmas) == 0 ){
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lemmas <- ''
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}
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return(lemmas)
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}
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# Replacing $-marked punctuation with their regular forms
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lemmas <- str_replace_all(lemmas," \\$(.+?)", "\\1") %>%
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# Regex removes all words consisting of or containing numbers, @#$%
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# Punctuation is only filtered out when not followed by a whitespace character, and when the word contains any of the characters above
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# Regex also used in out_parser
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{if(clean == T) str_replace_all(.,"\\S*?[0-9@#$%]+([^\\s!?.,;:]|[!?.,:;]\\S)*", "") else . } %>%
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# Adding extra . at end of string to allow for strings that contain less than 150 words and do not end on ". "
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paste0(.,". ")
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return(lemmas)
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}
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@ -1,47 +1,59 @@
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#' Merges list of lemmas back into a pseudo-document
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#'
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#' Merges list of lemmas back into a pseudo-document
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#' @param row A row number form the Elasticizer-generated data frame
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#' @param words String indicating the number of words to keep from each document (maximum document length), 999 indicates the whole document
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#' @param out The elasticizer-generated data frame
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#' @param text String indicating whether the "merged" field will contain the "full" text, old-style "lemmas" (will be deprecated), new-style "ud"
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#' @param clean Boolean indicating whether the results should be cleaned by removing words matching regex (see code).
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#' @return A documentified string of lemmas, one document at a time
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#' @export
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#' @examples
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#' merger(1, words = '999', out, text)
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#' merger(out, text, clean)
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#################################################################################################
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#################################### Reconstructing documents from lemmas########################
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#################################################################################################
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## Only merging lemmas for now, feature selection has no impact on junk classification
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merger <- function(row, out, text, clean) {
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df <- out[row,]
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# Mergin lemmas into single string
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if (text == 'lemmas') {
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lemmas <- paste(str_split(df$`_source.tokens.lemmas`, "\\|")[[1]],collapse = ' ')
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}
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if (text == 'ud') {
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lemmas <- paste0(df$`_source.ud`[[1]]$lemma[[1]], collapse = ' ')
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}
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merger <- function(out, text, clean) {
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df <- unnest(out, cols = '_source.ud') %>%
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unnest(cols = c('lemma','upos')) %>%
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# This line is added in the new merger function, in the old merger function this would result in the following:
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# 1: when using ud, it would result in the string "NA" being present in place of the faulty lemma
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# 2: when using ud_upos, it would result in the entire article becoming NA, because of str_c() returning NA when any value is NA
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filter(!is.na(lemma)) %>%
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group_by(`_id`)
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if (text == 'ud_upos') {
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df <- unnest(df,`_source.ud`)
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lemmas <- str_c(unlist(df$lemma)[which(unlist(df$upos) != 'PUNCT')], unlist(df$upos)[which(unlist(df$upos) != 'PUNCT')], sep = '_', collapse = ' ') %>%
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df <- df %>%
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filter(upos != 'PUNCT') %>%
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mutate(
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lem_u = str_c(lemma,upos,sep="_")
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) %>%
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summarise(
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merged = str_c(c(lem_u), collapse= ' ')
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) %>%
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# Regex removes all words consisting of or containing numbers, @#$%
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# Punctuation is not taken into account, as it is already filtered out, see above
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{if(clean == T) str_replace_all(.,"\\S*?[0-9@#$%]+[^\\s]*", "") else . }
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# In the very rare but obviously occuring (CxqrOmMB4Bzg6Uhtzw0P) case that a document consists only of punctuation, return an empty string
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if (length(lemmas) == 0 ){
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lemmas <- ''
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}
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return(lemmas)
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{if(clean == T) mutate(.,
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merged = str_replace_all(merged,"\\S*?[0-9@#$%]+[^\\s]*", "")
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)
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else . }
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}
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if (text == 'ud') {
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df <- df %>%
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summarise(
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merged = str_c(c(lemma), collapse= ' ')
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) %>%
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mutate(
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merged = str_replace_all(merged," \\$(.+?)", "\\1")
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) %>%
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# Regex removes all words consisting of or containing numbers, @#$%
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# Punctuation is only filtered out when not followed by a whitespace character, and when the word contains any of the characters above
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# Regex also used in out_parser
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# Adding extra . at end of string to allow for strings that contain less than 150 words and do not end on ". "
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{if(clean == T) mutate(.,
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merged = str_replace_all(merged,"\\S*?[0-9@#$%]+([^\\s!?.,;:]|[!?.,:;]\\S)*", "")
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)
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else . } %>%
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mutate(.,
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merged = paste0(merged,'. '))
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}
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# Replacing $-marked punctuation with their regular forms
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lemmas <- str_replace_all(lemmas," \\$(.+?)", "\\1") %>%
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# Regex removes all words consisting of or containing numbers, @#$%
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# Punctuation is only filtered out when not followed by a whitespace character, and when the word contains any of the characters above
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# Regex also used in out_parser
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{if(clean == T) str_replace_all(.,"\\S*?[0-9@#$%]+([^\\s!?.,;:]|[!?.,:;]\\S)*", "") else . } %>%
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# Adding extra . at end of string to allow for strings that contain less than 150 words and do not end on ". "
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paste0(.,". ")
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return(lemmas)
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return(df)
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}
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