R/_obsolete/cF2FDictionary.R
In davidmacro/frog2features: Convert Frog Object (NLP-data) to Features
cDictionary <- R6Class("F2FDictionary",
public = list(
Input = NULL,
Transformations = NULL,
Data = NULL,
initialize = function(...){
# Derive properties from whatever has been provided
private$Properties %<>% modifyList(list(...)[names(list(...)) %intersect% names(private$Properties)])
# Set the data object, if specified:
(self$setData(list(...)[["Data"]])) %if% ("Data" %in% (list(...) %>% names))
},
setData = function(x){
stop("Data should be a data.table or data.frame") %if% (class(x) %notin% c("data.table","data.frame"))
stop("Data should contain at least: docid, sent, position, and a token.") %ifnot% c("docid", "position", "sent") %allin% colnames(x)
self$Data <- x
},
setName = function(x){
private$Properties$Name <- x
},
setPrefix = function(x){
private$Properties$Prefix <- x
},
setDescription = function(x){
private$Properties$Description <- x
},
getName = function(x){
private$Properties$Name %>% return
},
getPrefix = function(x){
private$Properties$Prefix %>% return
},
getDescription = function(x){
private$Properties$Description %>% return
},
getProperties = function(x){
private$Properties %>% return
},
Make = function(){
},
Apply = function(Data = NULL, Return = NULL){
}
),
private = list(
Properties = list(
Name = NULL,
Prefix = NULL,
Description = NULL
)
)
)
#'
#'
#' create_dictionary <- function(...){
#' dict <- cDictionary$new(dictionary.name = dictionary.name ,
#' dictionary.type = dictionary.type,
#' datasource.type = "frog",
#' datasource = datasource)
#' return(dict)
#' }
#'
#' #' Dictionary class definition
#' #' @import data.table
#' #' @import magrittr
#' #' @import R6
#' cDictionary <- R6Class("dictionary",
#'
#' public = list(
#'
#' description = NULL,
#' logbook = NULL,
#'
#' dictionary.options = NULL,
#' dictionary.exclude = NULL,
#' dictionary.exclude.frequency = NULL,
#'
#' dataset.exclude = NULL,
#' dataset.exclude.frequency = NULL,
#'
#' dictionary.token = NULL,
#' dataset.token = NULL,
#' dataset.token.transformed = NULL,
#'
#' degree = NULL,
#'
#' synset.database = NULL,
#' hyperonym.database = NULL,
#'
#' entities = NULL,
#' entities_retained = NULL,
#' entities.list = NULL,
#'
#' prefix = NULL,
#' frog.columns.to.retain = NULL,
#'
#' entity_properties = NULL,
#'
#' entity_relations = NULL,
#' entity_relation_types = NULL,
#' entity_relation_properties = NULL,
#'
#' datasource = NULL,
#' datasource.type = NULL,
#'
#' initialize = function(datasource.type = "frog",
#' datasource.name = "data",
#' datasource = NULL,
#' degree = 0,
#' dataset.token = NULL,
#' dataset.token.transformed = NULL,
#' dictionary.token = NULL,
#' prefix = NULL,
#' exclude = NULL,
#' exclude.frequency = list(ll = 0, ul = 1),
#' dataset.exclude = NULL,
#' dataset.exclude.frequency = list(ll = 0, ul = 1),
#' dictionary.exclude = NULL,
#' dictionary.exclude.frequency = list(ll = 0, ul = 1),
#' description = NULL,
#' dictionary.name = NULL,
#' dictionary.options = dict.options.strict,
#' synset.database = NULL,
#' hyperonym.database = NULL){
#'
#' self$dataset.token <- dataset.token %>% unlist %>% c
#' self$dataset.token.transformed <- dataset.token %>% unlist %>% c
#'
#' self$dictionary.token <- dictionary.token %>% unlist %>% c
#'
#' self$dataset.exclude <- dataset.exclude
#' self$dataset.exclude.frequency <- dataset.exclude.frequency
#'
#' self$degree <- degree
#'
#' self$dictionary.exclude <- dictionary.exclude
#' self$dictionary.exclude.frequency <- dictionary.exclude.frequency
#'
#' self$dictionary.options <- dictionary.options
#'
#' if(self$dictionary.options$use_log){
#' self$logbook <- cLogBook$new(
#' name = "Dictionary logbook",
#' description = "Document the steps taken in the creation of the dictionary object."
#' )
#' }
#'
#' cat("\r\n\r\n\tCreate a dictionary with the following properties: \r\n")
#' cat("\t--------------------------------------------------- \r\n")
#' cat("\tName: \t", dictionary.name, "\r\n")
#' cat("\tDescription: ", description, "\r\n")
#' cat("\tDatasource token: ", dataset.token, "\r\n")
#' cat("\tDictionary token: ", dictionary.token, "\r\n")
#' cat("\t\r\n ")
#' cat("\tExcludes:\r\n")
#' cat("\t [-] dictionary.exclude \t= ");print(dictionary.exclude);
#' cat("\t [-] dictionary.exclude.frequency = ");print(dictionary.exclude.frequency); cat("\r\n")
#' cat("\t-------------------------------------------------- \r\n\r\n")
#' self$datasource <- datasource
#'
#' # Check eerst of de dictionary wel aangemaakt kan worden:
#'
#' cat("\r\n\t\tCreating ", self$dataset.token %>% unlist %>% c, "dictionary. \r\n")
#' # Set the dictionary according to the appropriate entity type
#'
#' (private$setAsWordDictionary()) %if% (self$dataset.token == "word")
#' (private$setAsLemmaDictionary()) %if% (self$dataset.token == "lemma")
#' (private$setAsMajorposDictionary()) %if% (self$dataset.token == "majorpos")
#' (private$setAsPosDictionary()) %if% (self$dataset.token == "majorpos")
#' (private$setAsSemanticDictionary()) %if% (self$dataset.token %in% c("synset", "hypernyms"))
#'
#' if(is.data.table(datasource)){
#'
#' if(datasource.type %in% c("frog")){
#'
#' #cat("Frog data detected; can continue.\r\n")
#'
#' private$setAsFrogDataSource(datasource.type)
#'
#' if(self$dataset.token %in% c("word","lemma", "majorpos", "ngram", "majorpos", "synset", "hyperonym")){
#'
#' # All entities
#' self$extract_entities_from_frog(datasource)
#'
#' # Retained entities
#' self$entities_retained <- self$entities
#' }
#' } else {
#' if(c("docid","text") %allin% colnames(datasource)){
#'
#' }
#' }
#' } else{
#' print("Warning: no entries to enumerate. Did you intend to create an empty dictionary?")
#' }
#' invisible(self)
#' },
#'
#' get.valid.dictionary.types = function(...){
#' self$get.valid.types(...)
#' },
#'
#' get.valid.types = function(dictionary.type = NULL,
#' datasource.type = NULL,
#' print = TRUE, ...){
#'
#' filter_expresson <- list(
#' ("dictionary.type == \"" %+% dictionary.type %+% "\"") %ifnotnull% (dictionary.type),
#' ("datasource.type == \"" %+% datasource.type %+% "\"") %ifnotnull% (datasource.type)
#' ) %>% Filter(. %>% is.null %>% `!`, .) %>%
#' unlist %>% paste0( collapse = " & ") %>% as.expression %>% parse(text = .)
#'
#' if(print){
#' print(private$allowed_dictionaries[filter_expresson %>% eval,])
#' invisible(self)
#' } else {
#' return(private$allowed_dictionaries)
#' }
#' },
#'
#'
#' is.similar.to = function(y,x = NULL){
#'
#' if(is.null(x)){
#' if(exists("self")){
#' x <- self$entities
#' } else {
#' print("Not instantiated!")
#' }
#' }
#'
#' # Match on class type
#' ch1 <- ("dictionary" %in% class(x)) & (class(x) %identical% class(y))
#'
#' (print("Error. Both x and y must be dictionary objects")) %ifnot% (ch1)
#'
#' ch2 <- (x$token == y$token)
#'
#' (print("Error. Both x and y must be the same dictionary type.")) %ifnot% (ch2)
#'
#' ch3 <- (x$degree == y$degree) # Match on degrees (i.e., whether n.gram or not)
#'
#' return(ch1 & ch2)
#'
#' },
#'
#'
#' # Simple function to subset the entities of the current dictionary
#' subset = function(y, x = NULL, by = NULL, by.x = NULL, by.y = NULL, all.x = FALSE, all.y = FALSE){
#'
#' # The default subset is an inner join (only entities both in x and y are returned)
#'
#' return.subsetted <- FALSE
#'
#' if(!is.data.table(y)){
#' if(!is.vector(y)){
#' if(!is.dictionary(y)){
#' print("Error: y is not a data.table object")
#' break;
#' } else {
#' if(x %similar% y){
#' y <- y$entities
#' } else {
#' print("Dissimilar dictionaries.")
#' }
#' }
#' } else {
#' y <- NA %>% # Start with a missing value
#' c %>% # Make it a vector
#' as.character %>% # Make it (explicitly) a character vector
#' matrix(ncol = 1, nrow=length(y)) %>% # Make it a matrix
#' data.table %>% # Make it a data.table
#' set_colnames(dict.words$token) %>% # Fix column name
#' set(j = dict.words$dictionary.type, value = y) # Input the actual values
#' }
#' } else {
#'
#' if(is.null(by) & is.null(by.x) & is.null(by.y)){
#'
#' print("Error: by and by.x are both not provided; please provide the column to subset the dictionary on.")
#' break;
#'
#' } else {
#'
#' if(!is.null(x)){
#' print("Error: x is not a data.table object")
#' break;
#' }
#'
#' if(!is.data.table(self$entities)){
#' print("Error: y is not a data.table object")
#' break;
#' }
#' }
#' }
#'
#' by.column = self$dictionary.type
#'
#' retval <- data.table:::merge.data.table(x = self$entities,
#' y = y[,c(self$dictionary.type), with=F],
#' by = by.column, all.x = all.x, all.y = all.y)
#'
#' if(return.subsetted) {
#' return(retval)
#' } else {
#' self$entities <- retval
#' }
#'
#' self %>% invisible
#' },
#'
#' join = function(dictionary, type = NA, keep.x = FALSE, keep.y = FALSE){
#'
#' # x = the current dictionary;
#' # y = the subsetted dictionary
#'
#' entities_selected
#'
#' self %>% invisible %>% return
#' },
#'
#' join.left = function(dictionary){
#' self %>% invisible %>% return
#' },
#'
#' join.right = function(dictionary){
#' self %>% invisible %>% return
#' },
#'
#' extract_entities_from_data.table = function(dtable){
#'
#' # Not yet fully implemented: bag-of-words approach
#' self$source_column_namesource_column_name <- "document"
#'
#' if(!is.null(self$entity_properties)){
#' cols <- c(self$source_column_name,c(self$entity_properties)) %>% unique
#' } else {
#' cols <- c(self$source_column_name)
#' }
#'
#' self$entities <- dtable[,c(cols), with=F] %>% FixStringsFactors %>% unique
#'
#' self$enumerate_entities(prefix=prefix)
#'
#' },
#'
#' extract_entities_from_frog = function(dtfrog){
#'
#' cat("Extract entities from frog:")
#'
#' if(!is.null(self$entity_properties)){
#' cols <- union(self$dictionary.token, c(self$entity_properties)) %>% unique
#' } else {
#' cols <- self$dictionary.token %>% unlist %>% c
#' }
#'
#' if(is.null(self$frog.columns.to.retain)){
#' self$frog.columns.to.retain <- self$dictionary.token
#' }
#'
#' self$entities <- dtfrog[, c("docid",self$frog.columns.to.retain), with=F] %>% FixStringsFactors
#'
#' self$entities[, ndoc := uniqueN(docid), by = c(self$frog.columns.to.retain)][, docid := NULL]
#'
#' self$entities <- self$entities %>% unique
#'
#' self$enumerate_entities(prefix=prefix)
#'
#' },
#'
#' enumerate_entities = function(prefix = NA){
#'
#' if(!is.null(self$entities)){
#'
#' self$prefix <- (prefix) %or_if_absent% (' ')
#'
#' self$entities[, (self$dictionary.token %+% "Id") := .I ]
#'
#' self$entities[, (self$dictionary.token %+% "Name") := self$prefix %+% self$dictionary.token %+% .I ]
#'
#' cols.main <- c(self$dictionary.token %+% "Id",
#' self$dictionary.token %+% "Name",
#' self$dictionary.token )
#'
#' cols.other <- colnames(self$entities) %-% cols.main
#'
#' cols.order <- c(cols.main, cols.other)
#'
#' }
#' }
#'
#'
#' ), private = list(
#'
#' allowed_dictionaries = data.table(
#' datasource.type = c("frog", "frog", "frog", "frog", "frog.ngram", "frog.ngram","frog.ngram", "frog"),
#' dictionary.type = c("word", "lemma", "majorpos", "pos", "word", "lemma", "majorpos", "pos" )
#' ),
#'
#' setAsWordDictionary = function(){
#' self$description <- "Word dictionary"
#'
#' self$logbook$append(action = "Set dictionary type",
#' description = "dictionary type: word",
#' functioncall = sys.call())
#'
#' },
#'
#' setAsLemmaDictionary = function(){
#' self$description <- "Lemma dictionary"
#'
#' self$logbook$append(action = "Set dictionary type",
#' description = "dictionary type: lemma",
#' functioncall = sys.call())
#'
#' },
#'
#' setAsPosDictionary = function(){
#' self$description <- "Dictionary with all part-of-speech codings"
#'
#' self$logbook$append(action = "Set dictionary type",
#' description = "dictionary type: pos",
#' functioncall = sys.call())
#'
#' },
#'
#' setAsMajorposDictionary = function(){
#' self$description <- "Majorpos dictionary"
#'
#' self$logbook$append(action = "Set dictionary type",
#' description = "dictionary type: majorpos",
#' functioncall = sys.call())
#'
#' },
#'
#'
#' setAsSemanticDictionary = function(){
#' self$description <- "Semantic dictionary"
#'
#' self$logbook$append(action = "Set dictionary type",
#' description = "dictionary type: semantic",
#' functioncall = sys.call())
#'
#' },
#'
#' setAsRelationalDictionary = function(type = NA){
#'
#' (type = '') %if% (type %>% is.na)
#'
#' self$logbook$append(action = "Set as relational",
#' description = "relation type: " %+% type,
#' functioncall = sys.call())
#'
#' },
#'
#' setAsFrogDataSource = function(datasource.type){
#'
#' (datasource.type = '') %if% (datasource.type %>% is.na)
#'
#' self$logbook$append(action = "Set data source as Frog",
#' description = '',
#' functioncall = sys.call())
#'
#'
#'
#' if(datasource.type == "frog"){
#'
#' if(self$dataset.token == "word"){
#' if(self$degree > 0){
#' self$frog.columns.to.retain <- c(self$dataset.transformed.token)
#' } else {
#' self$frog.columns.to.retain <- c("word","lemma","majorpos")
#' }
#'
#' }
#'
#' if(self$dataset.token == "lemma"){
#' if(self$degree > 0){
#' self$frog.columns.to.retain <- c(self$dataset.transformed.token)
#' } else {
#' self$frog.columns.to.retain <- c("lemma","majorpos")
#' }
#' }
#'
#' if(self$dataset.token == "majorpos"){
#' if(self$degree > 0){
#' self$frog.columns.to.retain <- c(self$dataset.transformed.token)
#' } else {
#' self$frog.columns.to.retain <- c("majorpos")
#' }
#' }
#'
#' }
#'
#' if(datasource.type == "frog.ngram"){
#'
#' if(self$dataset.token %in% c("word", "lemma", "majorpos")){
#' self$frog.columns.to.retain <- c("ngram")
#' }
#'
#' }
#'
#' if(datasource.type == "frog.synset"){
#'
#' if(self$dataset.token == "lemma"){
#' self$frog.columns.to.retain <- c("synset", "lemmas")
#' }
#'
#' }
#'
#' if(datasource.type == "frog.hypernyms"){
#'
#' if(self$dictionary.type == "lemma"){
#' self$frog.columns.to.retain <- c("synset", "lemmas")
#' }
#'
#' }
#' }
#'
#'
#' )
#' )
#'
#'
davidmacro/frog2features documentation built on Oct. 27, 2021, 12:17 a.m.
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cDictionary <- R6Class("F2FDictionary",
public = list(
Input = NULL,
Transformations = NULL,
Data = NULL,
initialize = function(...){
# Derive properties from whatever has been provided
private$Properties %<>% modifyList(list(...)[names(list(...)) %intersect% names(private$Properties)])
# Set the data object, if specified:
(self$setData(list(...)[["Data"]])) %if% ("Data" %in% (list(...) %>% names))
},
setData = function(x){
stop("Data should be a data.table or data.frame") %if% (class(x) %notin% c("data.table","data.frame"))
stop("Data should contain at least: docid, sent, position, and a token.") %ifnot% c("docid", "position", "sent") %allin% colnames(x)
self$Data <- x
},
setName = function(x){
private$Properties$Name <- x
},
setPrefix = function(x){
private$Properties$Prefix <- x
},
setDescription = function(x){
private$Properties$Description <- x
},
getName = function(x){
private$Properties$Name %>% return
},
getPrefix = function(x){
private$Properties$Prefix %>% return
},
getDescription = function(x){
private$Properties$Description %>% return
},
getProperties = function(x){
private$Properties %>% return
},
Make = function(){
},
Apply = function(Data = NULL, Return = NULL){
}
),
private = list(
Properties = list(
Name = NULL,
Prefix = NULL,
Description = NULL
)
)
)
#'
#'
#' create_dictionary <- function(...){
#' dict <- cDictionary$new(dictionary.name = dictionary.name ,
#' dictionary.type = dictionary.type,
#' datasource.type = "frog",
#' datasource = datasource)
#' return(dict)
#' }
#'
#' #' Dictionary class definition
#' #' @import data.table
#' #' @import magrittr
#' #' @import R6
#' cDictionary <- R6Class("dictionary",
#'
#' public = list(
#'
#' description = NULL,
#' logbook = NULL,
#'
#' dictionary.options = NULL,
#' dictionary.exclude = NULL,
#' dictionary.exclude.frequency = NULL,
#'
#' dataset.exclude = NULL,
#' dataset.exclude.frequency = NULL,
#'
#' dictionary.token = NULL,
#' dataset.token = NULL,
#' dataset.token.transformed = NULL,
#'
#' degree = NULL,
#'
#' synset.database = NULL,
#' hyperonym.database = NULL,
#'
#' entities = NULL,
#' entities_retained = NULL,
#' entities.list = NULL,
#'
#' prefix = NULL,
#' frog.columns.to.retain = NULL,
#'
#' entity_properties = NULL,
#'
#' entity_relations = NULL,
#' entity_relation_types = NULL,
#' entity_relation_properties = NULL,
#'
#' datasource = NULL,
#' datasource.type = NULL,
#'
#' initialize = function(datasource.type = "frog",
#' datasource.name = "data",
#' datasource = NULL,
#' degree = 0,
#' dataset.token = NULL,
#' dataset.token.transformed = NULL,
#' dictionary.token = NULL,
#' prefix = NULL,
#' exclude = NULL,
#' exclude.frequency = list(ll = 0, ul = 1),
#' dataset.exclude = NULL,
#' dataset.exclude.frequency = list(ll = 0, ul = 1),
#' dictionary.exclude = NULL,
#' dictionary.exclude.frequency = list(ll = 0, ul = 1),
#' description = NULL,
#' dictionary.name = NULL,
#' dictionary.options = dict.options.strict,
#' synset.database = NULL,
#' hyperonym.database = NULL){
#'
#' self$dataset.token <- dataset.token %>% unlist %>% c
#' self$dataset.token.transformed <- dataset.token %>% unlist %>% c
#'
#' self$dictionary.token <- dictionary.token %>% unlist %>% c
#'
#' self$dataset.exclude <- dataset.exclude
#' self$dataset.exclude.frequency <- dataset.exclude.frequency
#'
#' self$degree <- degree
#'
#' self$dictionary.exclude <- dictionary.exclude
#' self$dictionary.exclude.frequency <- dictionary.exclude.frequency
#'
#' self$dictionary.options <- dictionary.options
#'
#' if(self$dictionary.options$use_log){
#' self$logbook <- cLogBook$new(
#' name = "Dictionary logbook",
#' description = "Document the steps taken in the creation of the dictionary object."
#' )
#' }
#'
#' cat("\r\n\r\n\tCreate a dictionary with the following properties: \r\n")
#' cat("\t--------------------------------------------------- \r\n")
#' cat("\tName: \t", dictionary.name, "\r\n")
#' cat("\tDescription: ", description, "\r\n")
#' cat("\tDatasource token: ", dataset.token, "\r\n")
#' cat("\tDictionary token: ", dictionary.token, "\r\n")
#' cat("\t\r\n ")
#' cat("\tExcludes:\r\n")
#' cat("\t [-] dictionary.exclude \t= ");print(dictionary.exclude);
#' cat("\t [-] dictionary.exclude.frequency = ");print(dictionary.exclude.frequency); cat("\r\n")
#' cat("\t-------------------------------------------------- \r\n\r\n")
#' self$datasource <- datasource
#'
#' # Check eerst of de dictionary wel aangemaakt kan worden:
#'
#' cat("\r\n\t\tCreating ", self$dataset.token %>% unlist %>% c, "dictionary. \r\n")
#' # Set the dictionary according to the appropriate entity type
#'
#' (private$setAsWordDictionary()) %if% (self$dataset.token == "word")
#' (private$setAsLemmaDictionary()) %if% (self$dataset.token == "lemma")
#' (private$setAsMajorposDictionary()) %if% (self$dataset.token == "majorpos")
#' (private$setAsPosDictionary()) %if% (self$dataset.token == "majorpos")
#' (private$setAsSemanticDictionary()) %if% (self$dataset.token %in% c("synset", "hypernyms"))
#'
#' if(is.data.table(datasource)){
#'
#' if(datasource.type %in% c("frog")){
#'
#' #cat("Frog data detected; can continue.\r\n")
#'
#' private$setAsFrogDataSource(datasource.type)
#'
#' if(self$dataset.token %in% c("word","lemma", "majorpos", "ngram", "majorpos", "synset", "hyperonym")){
#'
#' # All entities
#' self$extract_entities_from_frog(datasource)
#'
#' # Retained entities
#' self$entities_retained <- self$entities
#' }
#' } else {
#' if(c("docid","text") %allin% colnames(datasource)){
#'
#' }
#' }
#' } else{
#' print("Warning: no entries to enumerate. Did you intend to create an empty dictionary?")
#' }
#' invisible(self)
#' },
#'
#' get.valid.dictionary.types = function(...){
#' self$get.valid.types(...)
#' },
#'
#' get.valid.types = function(dictionary.type = NULL,
#' datasource.type = NULL,
#' print = TRUE, ...){
#'
#' filter_expresson <- list(
#' ("dictionary.type == \"" %+% dictionary.type %+% "\"") %ifnotnull% (dictionary.type),
#' ("datasource.type == \"" %+% datasource.type %+% "\"") %ifnotnull% (datasource.type)
#' ) %>% Filter(. %>% is.null %>% `!`, .) %>%
#' unlist %>% paste0( collapse = " & ") %>% as.expression %>% parse(text = .)
#'
#' if(print){
#' print(private$allowed_dictionaries[filter_expresson %>% eval,])
#' invisible(self)
#' } else {
#' return(private$allowed_dictionaries)
#' }
#' },
#'
#'
#' is.similar.to = function(y,x = NULL){
#'
#' if(is.null(x)){
#' if(exists("self")){
#' x <- self$entities
#' } else {
#' print("Not instantiated!")
#' }
#' }
#'
#' # Match on class type
#' ch1 <- ("dictionary" %in% class(x)) & (class(x) %identical% class(y))
#'
#' (print("Error. Both x and y must be dictionary objects")) %ifnot% (ch1)
#'
#' ch2 <- (x$token == y$token)
#'
#' (print("Error. Both x and y must be the same dictionary type.")) %ifnot% (ch2)
#'
#' ch3 <- (x$degree == y$degree) # Match on degrees (i.e., whether n.gram or not)
#'
#' return(ch1 & ch2)
#'
#' },
#'
#'
#' # Simple function to subset the entities of the current dictionary
#' subset = function(y, x = NULL, by = NULL, by.x = NULL, by.y = NULL, all.x = FALSE, all.y = FALSE){
#'
#' # The default subset is an inner join (only entities both in x and y are returned)
#'
#' return.subsetted <- FALSE
#'
#' if(!is.data.table(y)){
#' if(!is.vector(y)){
#' if(!is.dictionary(y)){
#' print("Error: y is not a data.table object")
#' break;
#' } else {
#' if(x %similar% y){
#' y <- y$entities
#' } else {
#' print("Dissimilar dictionaries.")
#' }
#' }
#' } else {
#' y <- NA %>% # Start with a missing value
#' c %>% # Make it a vector
#' as.character %>% # Make it (explicitly) a character vector
#' matrix(ncol = 1, nrow=length(y)) %>% # Make it a matrix
#' data.table %>% # Make it a data.table
#' set_colnames(dict.words$token) %>% # Fix column name
#' set(j = dict.words$dictionary.type, value = y) # Input the actual values
#' }
#' } else {
#'
#' if(is.null(by) & is.null(by.x) & is.null(by.y)){
#'
#' print("Error: by and by.x are both not provided; please provide the column to subset the dictionary on.")
#' break;
#'
#' } else {
#'
#' if(!is.null(x)){
#' print("Error: x is not a data.table object")
#' break;
#' }
#'
#' if(!is.data.table(self$entities)){
#' print("Error: y is not a data.table object")
#' break;
#' }
#' }
#' }
#'
#' by.column = self$dictionary.type
#'
#' retval <- data.table:::merge.data.table(x = self$entities,
#' y = y[,c(self$dictionary.type), with=F],
#' by = by.column, all.x = all.x, all.y = all.y)
#'
#' if(return.subsetted) {
#' return(retval)
#' } else {
#' self$entities <- retval
#' }
#'
#' self %>% invisible
#' },
#'
#' join = function(dictionary, type = NA, keep.x = FALSE, keep.y = FALSE){
#'
#' # x = the current dictionary;
#' # y = the subsetted dictionary
#'
#' entities_selected
#'
#' self %>% invisible %>% return
#' },
#'
#' join.left = function(dictionary){
#' self %>% invisible %>% return
#' },
#'
#' join.right = function(dictionary){
#' self %>% invisible %>% return
#' },
#'
#' extract_entities_from_data.table = function(dtable){
#'
#' # Not yet fully implemented: bag-of-words approach
#' self$source_column_namesource_column_name <- "document"
#'
#' if(!is.null(self$entity_properties)){
#' cols <- c(self$source_column_name,c(self$entity_properties)) %>% unique
#' } else {
#' cols <- c(self$source_column_name)
#' }
#'
#' self$entities <- dtable[,c(cols), with=F] %>% FixStringsFactors %>% unique
#'
#' self$enumerate_entities(prefix=prefix)
#'
#' },
#'
#' extract_entities_from_frog = function(dtfrog){
#'
#' cat("Extract entities from frog:")
#'
#' if(!is.null(self$entity_properties)){
#' cols <- union(self$dictionary.token, c(self$entity_properties)) %>% unique
#' } else {
#' cols <- self$dictionary.token %>% unlist %>% c
#' }
#'
#' if(is.null(self$frog.columns.to.retain)){
#' self$frog.columns.to.retain <- self$dictionary.token
#' }
#'
#' self$entities <- dtfrog[, c("docid",self$frog.columns.to.retain), with=F] %>% FixStringsFactors
#'
#' self$entities[, ndoc := uniqueN(docid), by = c(self$frog.columns.to.retain)][, docid := NULL]
#'
#' self$entities <- self$entities %>% unique
#'
#' self$enumerate_entities(prefix=prefix)
#'
#' },
#'
#' enumerate_entities = function(prefix = NA){
#'
#' if(!is.null(self$entities)){
#'
#' self$prefix <- (prefix) %or_if_absent% (' ')
#'
#' self$entities[, (self$dictionary.token %+% "Id") := .I ]
#'
#' self$entities[, (self$dictionary.token %+% "Name") := self$prefix %+% self$dictionary.token %+% .I ]
#'
#' cols.main <- c(self$dictionary.token %+% "Id",
#' self$dictionary.token %+% "Name",
#' self$dictionary.token )
#'
#' cols.other <- colnames(self$entities) %-% cols.main
#'
#' cols.order <- c(cols.main, cols.other)
#'
#' }
#' }
#'
#'
#' ), private = list(
#'
#' allowed_dictionaries = data.table(
#' datasource.type = c("frog", "frog", "frog", "frog", "frog.ngram", "frog.ngram","frog.ngram", "frog"),
#' dictionary.type = c("word", "lemma", "majorpos", "pos", "word", "lemma", "majorpos", "pos" )
#' ),
#'
#' setAsWordDictionary = function(){
#' self$description <- "Word dictionary"
#'
#' self$logbook$append(action = "Set dictionary type",
#' description = "dictionary type: word",
#' functioncall = sys.call())
#'
#' },
#'
#' setAsLemmaDictionary = function(){
#' self$description <- "Lemma dictionary"
#'
#' self$logbook$append(action = "Set dictionary type",
#' description = "dictionary type: lemma",
#' functioncall = sys.call())
#'
#' },
#'
#' setAsPosDictionary = function(){
#' self$description <- "Dictionary with all part-of-speech codings"
#'
#' self$logbook$append(action = "Set dictionary type",
#' description = "dictionary type: pos",
#' functioncall = sys.call())
#'
#' },
#'
#' setAsMajorposDictionary = function(){
#' self$description <- "Majorpos dictionary"
#'
#' self$logbook$append(action = "Set dictionary type",
#' description = "dictionary type: majorpos",
#' functioncall = sys.call())
#'
#' },
#'
#'
#' setAsSemanticDictionary = function(){
#' self$description <- "Semantic dictionary"
#'
#' self$logbook$append(action = "Set dictionary type",
#' description = "dictionary type: semantic",
#' functioncall = sys.call())
#'
#' },
#'
#' setAsRelationalDictionary = function(type = NA){
#'
#' (type = '') %if% (type %>% is.na)
#'
#' self$logbook$append(action = "Set as relational",
#' description = "relation type: " %+% type,
#' functioncall = sys.call())
#'
#' },
#'
#' setAsFrogDataSource = function(datasource.type){
#'
#' (datasource.type = '') %if% (datasource.type %>% is.na)
#'
#' self$logbook$append(action = "Set data source as Frog",
#' description = '',
#' functioncall = sys.call())
#'
#'
#'
#' if(datasource.type == "frog"){
#'
#' if(self$dataset.token == "word"){
#' if(self$degree > 0){
#' self$frog.columns.to.retain <- c(self$dataset.transformed.token)
#' } else {
#' self$frog.columns.to.retain <- c("word","lemma","majorpos")
#' }
#'
#' }
#'
#' if(self$dataset.token == "lemma"){
#' if(self$degree > 0){
#' self$frog.columns.to.retain <- c(self$dataset.transformed.token)
#' } else {
#' self$frog.columns.to.retain <- c("lemma","majorpos")
#' }
#' }
#'
#' if(self$dataset.token == "majorpos"){
#' if(self$degree > 0){
#' self$frog.columns.to.retain <- c(self$dataset.transformed.token)
#' } else {
#' self$frog.columns.to.retain <- c("majorpos")
#' }
#' }
#'
#' }
#'
#' if(datasource.type == "frog.ngram"){
#'
#' if(self$dataset.token %in% c("word", "lemma", "majorpos")){
#' self$frog.columns.to.retain <- c("ngram")
#' }
#'
#' }
#'
#' if(datasource.type == "frog.synset"){
#'
#' if(self$dataset.token == "lemma"){
#' self$frog.columns.to.retain <- c("synset", "lemmas")
#' }
#'
#' }
#'
#' if(datasource.type == "frog.hypernyms"){
#'
#' if(self$dictionary.type == "lemma"){
#' self$frog.columns.to.retain <- c("synset", "lemmas")
#' }
#'
#' }
#' }
#'
#'
#' )
#' )
#'
#'
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