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74 lines
4.6 KiB
74 lines
4.6 KiB
#' Get ids of duplicate documents that have a cosine similarity score higher than [threshold]
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#'
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#' Get ids of duplicate documents that have a cosine similarity score higher than [threshold]
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#' @param row Row of grid to parse
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#' @param grid A cross-table of all possible combinations of doctypes and dates
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#' @param cutoff_lower Cutoff value for minimum cosine similarity above which documents are considered duplicates (inclusive)
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#' @param cutoff_upper Cutoff value for maximum cosine similarity, above which documents are not considered duplicates (for debugging and manual parameter tuning, inclusive)
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#' @param es_pwd Password for Elasticsearch read access
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#' @param es_super Password for write access to ElasticSearch
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#' @param words Document cutoff point in number of words. Documents are cut off at the last [.?!] before the cutoff (so document will be a little shorter than [words])
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#' @param localhost Defaults to true. When true, connect to a local Elasticsearch instance on the default port (9200)
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#' @param ver Short string (preferably a single word/sequence) indicating the version of the updated document (i.e. for a udpipe update this string might be 'udV2')
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#' @return dupe_objects.json and data frame containing each id and all its duplicates. remove_ids.txt and character vector with list of ids to be removed. Files are in current working directory
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#' @export
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#' @examples
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#' dupe_detect(1,grid,cutoff_lower, cutoff_upper = 1, es_pwd, es_super, words, localhost = T)
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#################################################################################################
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#################################### Duplicate detector ################################
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#################################################################################################
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dupe_detect <- function(row, grid, cutoff_lower, cutoff_upper = 1, es_pwd, es_super, words, localhost = T, ver) {
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params <- grid[row,]
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print(paste0('Parsing ',params$doctypes,' on ',params$dates ))
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query <- paste0('{"query":
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{"bool": {"filter":[{"term":{"doctype": "',params$doctypes,'"}},
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{"range" : {
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"publication_date" : {
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"gte" : "',params$dates,'T00:00:00Z",
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"lt" : "',params$dates+1,'T00:00:00Z"
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}
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}}]
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} } }')
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out <- elasticizer(query, es_pwd = es_pwd, localhost= localhost)
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if (class(out$hits$hits) != 'list') {
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dfm <- dfm_gen(out, text = "full", words = words, clean = T)
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if (sum(dfm[1,]) > 0) {
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simil <- as.matrix(textstat_simil(dfm, margin="documents", method="cosine"))
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diag(simil) <- NA
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duplicates <- which(simil >= cutoff_lower & simil <= cutoff_upper, arr.ind = TRUE)
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duplicates <- cbind(duplicates, rowid= rownames(duplicates))
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rownames(duplicates) <- seq(1:length(rownames(duplicates)))
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df <- as.data.frame(duplicates, make.names = NA) %>%
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mutate(colid = colnames(simil)[col]) %>%
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.[,c(3,4)] %>%
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group_by(rowid) %>% summarise(colid=list(colid))
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text <- capture.output(stream_out(df))
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# write(text[-length(text)], file = paste0(getwd(),'/dupe_objects.json'), append=T)
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simil[upper.tri(simil)] <- NA
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# write(unique(rownames(which(simil >= cutoff_lower & simil <= cutoff_upper, arr.ind = TRUE))),
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# file = paste0(getwd(),'/remove_ids.txt'),
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# append=T)
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dupe_delete <- data.frame(id=unique(rownames(which(simil >= cutoff_lower & simil <= cutoff_upper, arr.ind = TRUE))),
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dupe_delete = rep(1,length(unique(rownames(which(simil >= cutoff_lower & simil <= cutoff_upper, arr.ind = TRUE))))))
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bulk <- c(apply(df, 1, bulk_writer, varname='duplicates', type = 'set', ver = ver),
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apply(dupe_delete, 1, bulk_writer, varname='_delete', type = 'set', ver = ver))
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if (length(bulk) > 0) {
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res <- elastic_update(bulk, es_super = es_super, localhost = localhost)
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}
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return(paste0('Checked ',params$doctypes,' on ',params$dates ))
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} else {
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return(paste0('No results for ',params$doctypes,' on ',params$dates ))
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}
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} else {
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return(paste0('No results for ',params$doctypes,' on ',params$dates ))
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}
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### Dummy code to verify that filtering out unique ids using the bottom half of the matrix actually works
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# id_filter <- unique(rownames(which(simil >= cutoff, arr.ind = TRUE)))
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# dfm_nodupes <- dfm_subset(dfm, !(docnames(dfm) %in% id_filter))
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# simil_nodupes <- as.matrix(textstat_simil(dfm_nodupes, margin="documents", method="cosine"))
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# diag(simil_nodupes) <- NA
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# which(simil_nodupes >= cutoff, arr.ind = TRUE)
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}
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