diff --git a/.Rbuildignore b/.Rbuildignore index 91f6187..ca60038 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,2 +1,3 @@ ^wordpredictor\.Rproj$ ^\.Rproj\.user$ +^LICENSE\.md$ diff --git a/.gitignore b/.gitignore index 565f2b6..3f32e00 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,4 @@ .Rdata .httr-oauth .DS_Store +tests/testthat/data diff --git a/DESCRIPTION b/DESCRIPTION index eadbe32..4276530 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,16 +1,32 @@ Package: wordpredictor -Title: What the Package Does (One Line, Title Case) -Version: 0.0.0.9000 +Title: Develop Text Prediction Models Based On N-grams +Version: 1.0.0 Authors@R: - person(given = "First", - family = "Last", + person(given = "Nadir", + family = "Latif", role = c("aut", "cre"), - email = "first.last@example.com", - comment = c(ORCID = "YOUR-ORCID-ID")) -Description: What the package does (one paragraph). -License: `use_mit_license()`, `use_gpl3_license()` or friends to - pick a license + email = "pakjiddat@gmail.com", + comment = c(ORCID = "0000-0002-7543-7405")) +Description: It allows developing n-gram models for predicting text. It allows + cleaning input text using various options such as removal of stop words, + profanity, non-dictionary words, punctuation, stop words and stemming. + It generates n-gram tokens of a given size from a text file. It also + generates transition probability data for the n-grams. This data is used + to predict the next word given a set of words. The model's performance can + be evaluation using Perplexity (intrinsic evaluation) and + accuracy (extrinsic evaluation). The package also provides methods for + analyzing the n-gram model using bar plots. For example it displays plots + of n-gram frequencies and plots of model performance. It provides methods + for generating the n-gram model step by step or by using a single method. + The performance of the generated model may be evaluated and displayed in + plots. The generated model can be easily exported as a R object and used + in applications. The package is implemented using R6 classes. +License: MIT + file LICENSE Encoding: UTF-8 LazyData: true Roxygen: list(markdown = TRUE) RoxygenNote: 7.1.1 +Imports: digest, ggplot2, R6, patchwork, stringr, dplyr, pryr, SnowballC, utils +Suggests: + testthat (>= 3.0.0) +Config/testthat/edition: 3 diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..586e115 --- /dev/null +++ b/LICENSE @@ -0,0 +1,2 @@ +YEAR: 2021 +COPYRIGHT HOLDER: Nadir Latif diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 0000000..c7b302f --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,21 @@ +# MIT License + +Copyright (c) 2021 Nadir Latif + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/NAMESPACE b/NAMESPACE index 6ae9268..0a8a37e 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,2 +1,30 @@ # Generated by roxygen2: do not edit by hand +export(DataAnalyzer) +export(DataCleaner) +export(ModelEvaluator) +export(TPGenerator) +export(TextFileProcessor) +export(TokenGenerator) +importFrom(SnowballC,wordStem) +importFrom(digest,digest2int) +importFrom(dplyr,group_by) +importFrom(dplyr,mutate) +importFrom(dplyr,summarize_all) +importFrom(ggplot2,aes) +importFrom(ggplot2,aes_string) +importFrom(ggplot2,coord_cartesian) +importFrom(ggplot2,coord_flip) +importFrom(ggplot2,geom_bar) +importFrom(ggplot2,geom_point) +importFrom(ggplot2,geom_smooth) +importFrom(ggplot2,ggplot) +importFrom(ggplot2,ggtitle) +importFrom(ggplot2,labs) +importFrom(ggplot2,xlab) +importFrom(ggplot2,xlim) +importFrom(ggplot2,ylab) +importFrom(patchwork,plot_annotation) +importFrom(pryr,mem_change) +importFrom(pryr,object_size) +importFrom(stringr,str_match) diff --git a/R/data-analyzer.R b/R/data-analyzer.R index 6484074..3ae411c 100644 --- a/R/data-analyzer.R +++ b/R/data-analyzer.R @@ -11,6 +11,7 @@ #' length and file size. It also provides a method that takes random samples of #' lines in an input text file. It provides a method that reads an input text #' file containing token frequencies. It displays the most occuring tokens. +#' @importFrom ggplot2 ggplot geom_bar ggtitle coord_flip ylab xlab aes DataAnalyzer <- R6::R6Class( "DataAnalyzer", inherit = TextFileProcessor, @@ -21,14 +22,15 @@ DataAnalyzer <- R6::R6Class( #' @param file_name The path to the input file. #' @param line_count The number of lines to read at a time. #' @param verbose If progress information should be displayed. - initialize = function(file_name = "./data/n1.txt", + #' @export + initialize = function(file_name = NULL, verbose = 0) { # The file name is set - self$file_name <- file_name + private$file_name <- file_name # The processed output is initialized - self$p_output <- data.frame() + private$p_output <- data.frame() # The verbose options is set - self$verbose = verbose + private$verbose = verbose }, #' @description @@ -42,23 +44,23 @@ DataAnalyzer <- R6::R6Class( #' 'top_features', 'coverage'. #' n -> For 'top_features', it is the number of top most occuring #' tokens. - plot_data = function(opts) { + plot_n_gram_stats = function(opts) { # The opts is merged with the da_opts attribute private$da_opts = modifyList(private$da_opts, opts) # The da_opts is merged with the base class opts attribute - self$opts = modifyList(self$opts, private$da_opts) + private$opts = modifyList(private$opts, private$da_opts) # The ngram data is read - df <- self$read_file(self$file_name, T) + df <- private$read_obj(private$file_name) # The information message is shown - self$display_msg("Displaying Plot...", 1) + private$display_msg("Displaying Plot...", 1) # If the coverage option was specified - if (self$opts[["type"]] == "coverage") { + if (private$opts[["type"]] == "coverage") { # The y values - y <- as.character(1:self$opts[["n"]]) + y <- as.character(1:private$opts[["n"]]) # The x values x <- numeric() # The percentage frequencies is calculated - for (i in 1:self$opts[["n"]]) { + for (i in 1:private$opts[["n"]]) { # The percentage of tokens with frequency i x[i] <- round(100*(nrow(df[df$freq == i,])/nrow(df)), 2) } @@ -74,12 +76,12 @@ DataAnalyzer <- R6::R6Class( private$display_plot(df, labels) } # If the top_features option was specified - else if (self$opts[["type"]] == "top_features") { + else if (private$opts[["type"]] == "top_features") { # The plot labels labels <- list( y = "Frequency", x = "Feature", - title = paste("Top", self$opts[["n"]], "Features")) + title = paste("Top", private$opts[["n"]], "Features")) # The chart is plotted private$display_plot(df, labels) @@ -87,53 +89,85 @@ DataAnalyzer <- R6::R6Class( }, #' @description - #' Generates and returns information about the given text files. - #' @param file_list The list of text files to check. - #' @return A data frame containing the overall file statistics. - get_file_info = function(file_list = list()) { - # If the file list is empty, then the file name passed to the - # current objet is used. - if (length(file_list) == 0) - file_list = list(self$file_name) - - # Empty list. Used to store information about each file - stats <- data.frame( - "total_line_count" = 0, - "max_line_length" = 0, - "min_line_length" = 0, - "mean_line_length" = 0, - "total_size" = 0) + #' Generates and returns information about text files. + #' @param res The name of a directory or a file name. + #' @return A data frame containing the file statistics. + get_file_info = function(res) { + # The list of files to check + file_list <- NULL + # If a directory name was passed + if (dir.exists(res)) { + # All files in the directory are fetched + file_list = dir(res, full.names = T) + } + # If a file name was passed + else if (file.exists(res)) { + # The file name is set + file_list <- res + } + + # Used to store overall information about files + ostats <- data.frame( + "total_lc" = 0, + "max_ll" = 0, + "min_ll" = 0, + "mean_ll" = 0, + "total_s" = 0 + ) + + # Used to store information about each file + fstats <- tstats <- data.frame() + # Temporary variables for calculating max, min, mean line length temp_max <- temp_min <- temp_mean <- 0 # For each file in the list - for (file_name in file_list) { + for (fn in file_list) { # The file is read - lines <- self$read_file(file_name, F) - + lines <- private$read_file(fn, F) + # The line count + lc <- length(lines) + # The file size + size <- file.size(fn) # The file stats are updated - stats["total_size"] <- - stats["total_size"] + file.size(file_name) - stats["total_line_count"] <- - stats["total_line_count"] + length(lines) + ostats[["total_s"]] <- ostats[["total_s"]] + size + ostats[["total_lc"]] <- ostats[["total_lc"]] + lc # The temporary variables are updated temp_max <- max(nchar(lines)) temp_min <- min(nchar(lines)) - temp_mean <- mean(nchar(lines)) - - if (temp_max > stats["max_line_length"]) - stats["max_line_length"] <- temp_max - if (temp_min > stats["min_line_length"]) - stats["min_line_length"] <- temp_min - if (temp_mean > stats["mean_line_length"]) - stats["mean_line_length"] <- round(temp_mean) + temp_mean <- round(mean(nchar(lines))) + + # The file stats are updated + tstats <- data.frame( + "fn" = fn, + "total_lc" = lc, + "max_ll" = temp_max, + "min_ll" = temp_min, + "mean_ll" = temp_mean, + "size" = size + ) + # The size is formatted + tstats["size"] <- + utils:::format.object_size(tstats["size"], "auto") + + # The file stats are appended + fstats <- rbind(fstats, tstats) + + if (temp_max > ostats["max_ll"]) + ostats["max_ll"] <- temp_max + if (temp_min > ostats["min_ll"]) + ostats["min_ll"] <- temp_min + if (temp_mean > ostats["mean_ll"]) + ostats["mean_ll"] <- temp_mean } # The total size is formatted - stats["total_size"] <- utils:::format.object_size( - stats["total_size"], "auto") + ostats["total_s"] <- + utils:::format.object_size(ostats["total_s"], "auto") - # The required data is returned + # The required stats + stats = list("file_stats" = fstats, "overall_stats" = ostats) + # The required stats are returned return(stats) }, @@ -149,7 +183,7 @@ DataAnalyzer <- R6::R6Class( #' @param percs The size of the training, testing and validation sets. generate_data = function(dir, percs) { # The information message is shown - self$display_msg( + private$display_msg( "Generating training, testing and validation data sets...", 1) # If the train, test and validation files already exist if (file.exists(paste0(dir, "/train.txt")) && @@ -158,11 +192,11 @@ DataAnalyzer <- R6::R6Class( # The information message msg <- "The train, test and validate files already exist" # The information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) } else { # The input file is read - data <- self$read_file(self$file_name, F) + data <- private$read_file(private$file_name, F) # The number of lines in the data lc <- length(data) # Random indexes are generated @@ -176,11 +210,11 @@ DataAnalyzer <- R6::R6Class( # The validation set data validate_ds <- rd[1:round(lc*percs[["validate"]])] # The training data is written to file - self$write_file(train_ds, paste0(dir, "/train.txt"), F) + private$write_file(train_ds, paste0(dir, "/train.txt"), F) # The testing data is written to file - self$write_file(test_ds, paste0(dir, "/test.txt"), F) + private$write_file(test_ds, paste0(dir, "/test.txt"), F) # The validation data is written to file - self$write_file(validate_ds, paste0(dir, "/validate.txt"), F) + private$write_file(validate_ds, paste0(dir, "/validate.txt"), F) } }, @@ -194,7 +228,7 @@ DataAnalyzer <- R6::R6Class( #' @param pre The ngram prefix, given as a regular expression. get_ngrams = function(fn, c = NULL, pre = NULL) { # The data is read - df <- self$read_obj(fn) + df <- private$read_obj(fn) # If the prefix is not given if (is.null(pre)) { # The sample indexes @@ -235,7 +269,7 @@ DataAnalyzer <- R6::R6Class( # The data frame is sorted in descending order df <- (df[order(df$freq, decreasing = T),]) # The top n terms are extracted - df <- df[1:self$opts[["n"]], ] + df <- df[1:private$opts[["n"]], ] # The ngram names and their frequencies are plotted g <- ggplot(data = df, aes(x = reorder(pre, freq), y = freq)) + geom_bar(stat = "identity", fill = "red") + diff --git a/R/data-cleaner.R b/R/data-cleaner.R index 61ec87c..396ae6c 100644 --- a/R/data-cleaner.R +++ b/R/data-cleaner.R @@ -11,48 +11,6 @@ DataCleaner <- R6::R6Class( "DataCleaner", inherit = TextFileProcessor, public = list( - #' @field dc_opts The options for the data cleaner object. - #' min_words -> The minimum number of words per sentence. - #' line_count -> The number of lines to read and clean at a time. - # save_data -> If the combined processed lines should be saved. - # output_file -> Name of the output file used to store the data. - #' sw_file -> The stop words file path. - # dict_file -> The dictionary file path. - #' bad_file -> The bad words file path. - #' to_lower -> If the words should be converted to lower case. - #' remove_stop -> If stop words should be removed. - #' remove_punct -> If punctuation symbols should be removed. - #' remove_non_dict -> If non dictionary words should be removed. - #' remove_non_alpha -> If non alphabet symbols should be removed. - #' remove_extra_space -> If leading, trailing and double spaces - #' should be removed. - #' remove_bad -> If bad words should be removed - dc_opts = list( - "min_words" = 2, - "line_count" = 1000, - "save_data" = T, - "output_file" = "./data/sample-clean.txt", - "sw_file" = "./data/stop-words.txt", - "dict_file" = "./data/dict-no-bad.txt", - "bad_file" = "./data/bad-words.txt", - "to_lower" = T, - "remove_stop" = F, - "remove_punct" = T, - "remove_non_dict" = T, - "remove_non_alpha" = T, - "remove_extra_space" = T, - "remove_bad" = F - ), - - #' @field sw The list of stop words. - sw = list(), - - #' @field bw The list of bad words. - bw = list(), - - #' @field dw The list of dictionary words. - dw = list(), - #' @description #' It initializes the current object. It is used to set the file name #' and verbose options. @@ -74,21 +32,33 @@ DataCleaner <- R6::R6Class( #' should be removed. #' remove_bad -> If bad words should be removed #' @param verbose Indicates if progress information should be displayed. + #' @export initialize = function(file_name = NULL, - opts = self$dc_opts, + opts = list(), verbose = 0) { + # The stop words file is checked + opts[["sw_file"]] <- private$check_file( + opts[["sw_file"]], "stop-words.txt") + # The bad words file is checked + opts[["bad_file"]] <- private$check_file( + opts[["bad_file"]], "bad-words.txt") + # The dict words file is checked + opts[["dict_file"]] <- private$check_file( + opts[["dict_file"]], "dict-no-bad.txt") # The given options are merged with the opts attribute - self$dc_opts <- modifyList(self$dc_opts, opts) - # The dc_opts is merged with the base class opts attribute - self$opts <- modifyList(self$opts, self$dc_opts) + private$dc_opts <- modifyList(private$dc_opts, opts) + # The save_data option of base class is set + private$opts[["save_data"]] <- private$dc_opts[["save_data"]] + # The output_file option of base class is set + private$opts[["output_file"]] <- private$dc_opts[["output_file"]] # The stop words file is read - self$sw <- self$read_file(self$opts[["sw_file"]], F); + private$sw <- private$read_file(private$opts[["sw_file"]], F); # The dictionary file is read - self$dw <- self$read_file(self$opts[["dict_file"]], F); + private$dw <- private$read_file(private$opts[["dict_file"]], F); # The bad word file is read - self$bw <- self$read_file(self$opts[["bad_file"]], F); + private$bw <- private$read_file(private$opts[["bad_file"]], F); # The base class is initialized - super$initialize(file_name, self$opts[['line_count']], verbose) + super$initialize(file_name, private$opts[['line_count']], verbose) }, #' @description @@ -100,12 +70,17 @@ DataCleaner <- R6::R6Class( #' number of lines, the cleaned lines are saved to the output file. clean_file = function() { # The information message - msg <- paste0("Cleaning the sample file...", self$file_name) + msg <- paste0("Cleaning the sample file...", private$file_name) # The information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # The base class process_file function is called - super$process_file(super$pre_process, private$process, - super$post_process) + private$process_file(private$pre_process, private$process, + private$post_process) + # If the data should not be saved + if (!private$opts[["save_data"]]) { + # The processed output is returned + return(private$p_output) + } }, #' @description @@ -122,41 +97,41 @@ DataCleaner <- R6::R6Class( # The "." character is replaced with the string "specialdotsep" l <- gsub("\\.", " specialdotsep ", l) # If the words should be converted to lower case - if (self$opts[["to_lower"]]) { + if (private$opts[["to_lower"]]) { # The information message - self$display_msg("Converting lines to lower case...", 3) + private$display_msg("Converting lines to lower case...", 3) # The line is converted to lower case l <- tolower(l) } # If punctuation symbols should be removed - if (self$opts[["remove_punct"]]) { + if (private$opts[["remove_punct"]]) { # The information message - self$display_msg("Removing punctuation symbols...", 3) + private$display_msg("Removing punctuation symbols...", 3) # The pattern for removing all punctuation symbols l <- gsub("[[:punct:]]+", "", l) } # If non alphabet symbols should be removed - if (self$opts[["remove_non_alpha"]]) { + if (private$opts[["remove_non_alpha"]]) { # The information message - self$display_msg("Removing non alphabet symbols...", 3) + private$display_msg("Removing non alphabet symbols...", 3) # Words containing non alphabetical characters are removed l <- gsub("([^[:alpha:]\\s])", "", l, perl = T) } # If stop words should be removed - if (self$opts[["remove_stop"]]) { + if (private$opts[["remove_stop"]]) { # The information message - self$display_msg("Removing stop words..", 3) + private$display_msg("Removing stop words..", 3) # Stop words are collapsed - sw <- paste(self$sw, collapse = "|") + sw <- paste(private$sw, collapse = "|") swp <- paste("\\b(", sw, ")\\b", sep = "") # The stop words are removed l <- gsub(swp, "", l) } # If extra spaces should be removed - if (self$opts[["remove_extra_space"]]) { + if (private$opts[["remove_extra_space"]]) { # The information message - self$display_msg("Removing extra space...", 3) + private$display_msg("Removing extra space...", 3) # Multiple spaces are replaced by single space l = gsub("\\s{2,}", " ", l) # Leading and trailing whitespaces are removed @@ -168,10 +143,10 @@ DataCleaner <- R6::R6Class( # The words are converted to an atomic list words <- unlist(words) # If non dictionary words should be removed - if (self$opts[["remove_non_dict"]]) { + if (private$opts[["remove_non_dict"]]) { # The "specialdotsep" string is added to list of dictionary # words - dw <- c(self$dw, "specialdotsep") + dw <- c(private$dw, "specialdotsep") # The non dictionary words are removed from the data words <- words[words %in% dw] # All 1 length words except for 'a' and 'i' are removed @@ -184,11 +159,11 @@ DataCleaner <- R6::R6Class( words <- words[i1 | i2] } # If bad words should be removed - if (self$opts[["remove_bad"]]) { + if (private$opts[["remove_bad"]]) { # The "specialdotsep" string is added to list of bad words - bw <- c(self$bw, "specialdotsep") + bw <- c(private$bw, "specialdotsep") # The bad words are removed from the data - words <- words[!words %in% self$bw] + words <- words[!words %in% private$bw] } # The words are combined with space l <- paste(words, collapse = " ") @@ -199,7 +174,7 @@ DataCleaner <- R6::R6Class( # The sentences are converted to an atomic list l <- unlist(l) # If extra spaces should be removed - if (self$opts[["remove_extra_space"]]) { + if (private$opts[["remove_extra_space"]]) { # Multiple spaces are replaced by single space l = gsub("\\s{2,}", " ", l) # Leading and trailing whitespaces are removed @@ -207,12 +182,12 @@ DataCleaner <- R6::R6Class( } # If each sentence should have a minimum number of words - if (self$opts[["min_words"]] > -1) { + if (private$opts[["min_words"]] > -1) { # The number of words in each sentence wc <- str_count(l, pattern = boundary("word")) # The lines containing less than min_words number of words are # removed - l <- l[wc >= self$opts[["min_words"]]] + l <- l[wc >= private$opts[["min_words"]]] } # Consecutive 'a' and 'i' are replaced with single 'a' or 'i' @@ -225,6 +200,47 @@ DataCleaner <- R6::R6Class( ), private = list( + # @field dc_opts The options for the data cleaner object. + # min_words -> The minimum number of words per sentence. + # line_count -> The number of lines to read and clean at a time. + # save_data -> If the combined processed lines should be saved. + # output_file -> Name of the output file used to store the data. + # sw_file -> The stop words file path. + # dict_file -> The dictionary file path. + # bad_file -> The bad words file path. + # to_lower -> If the words should be converted to lower case. + # remove_stop -> If stop words should be removed. + # remove_punct -> If punctuation symbols should be removed. + # remove_non_dict -> If non dictionary words should be removed. + # remove_non_alpha -> If non alphabet symbols should be removed. + # remove_extra_space -> If leading, trailing and double spaces + # should be removed. + # remove_bad -> If bad words should be removed + dc_opts = list( + "min_words" = 2, + "line_count" = 1000, + "save_data" = T, + "output_file" = "./data/sample-clean.txt", + "sw_file" = NULL, + "dict_file" = NULL, + "bad_file" = NULL, + "to_lower" = T, + "remove_stop" = F, + "remove_punct" = T, + "remove_non_dict" = T, + "remove_non_alpha" = T, + "remove_extra_space" = T, + "remove_bad" = F + ), + + # @field sw The list of stop words. + sw = list(), + + # @field bw The list of bad words. + bw = list(), + + # @field dw The list of dictionary words. + dw = list(), # @description # Performs processing for the \code{clean_files} function. @@ -239,6 +255,35 @@ DataCleaner <- R6::R6Class( cl <- self$clean_lines(lines) return(cl) + }, + + # @description + # Checks if the given file exists. If it does not exist, + # then it tried to load the file from the external data folder of the + # package. It throws an error if the file was not found + # @param fn The file name. + # @param dfn The name of the default file in the external data folder of + # the package. + # @return The name of the file if it exists, or the full path to the + # default file. + check_file = function(fn, dfn) { + # The required file name + rfn <- fn + # If the file is not given + if (is.null(fn)) { + # The file path is set to the default file + # included with the wordpredictor package + rfn <- system.file("extdata", dfn, package = "wordpredictor") + # If the file was not found + if (!file.exists(rfn)) + stop(paste0("The file: ", rfn," does not exist !")) + } + # If the file name is given but the file does not exist + else if (!file.exists(fn)) { + # An error message is shown + stop(paste0("The file: ", fn," does not exist !")) + } + return (rfn) } ) ) diff --git a/R/data-sampler.R b/R/data-sampler.R new file mode 100644 index 0000000..dc2989c --- /dev/null +++ b/R/data-sampler.R @@ -0,0 +1,196 @@ +#' It is used to generate data samples from text files. +#' +#' @description +#' It provides a method for generating training, testing and +#' validation data sets from a given input text file. It also provides a method +#' for generating a sample file of given size or number of lines from an input +#' text file. +DataSampler <- R6::R6Class( + "DataSampler", + inherit = TextFileProcessor, + public = list( + #' @description + #' It initializes the current object. It is used to set the + #' verbose option. + #' @param ddir The data directory. + #' @param mdir The model directory. + #' @param verbose If progress information should be displayed. + initialize = function(ddir = "./data", + mdir = "./models", + verbose = 0) { + # The data directory name is set + private$ddir <- ddir + # The model directory name is set + private$mdir <- mdir + # The base class is initialized + super$initialize(NULL, NULL, verbose) + }, + + #' @description Generates a sample file of given size from the given + #' input file. The file is optionally cleaned and saved. + #' @param fn The input file name. It is the short file name relative to + #' the ddir. If not given, then the file name is auto generated from + #' the type parameter. + #' @param ss The number of lines or proportion of lines to sample. + #' @param ic If the sample file should be cleaned. + #' @param t The type of sample. It can be: 'tr' -> training 'te' -> + #' testing 'va' -> validation + #' @param is If the sampled data should be saved to a file. + generate_sample = function(fn = NULL, ss, ic, t, is) { + # If the type is 'tr' + if (t == 'tr') sfn <- 'train' + # If the type is 'te' + else if (t == 'te') sfn <- 'test' + # If the type is 'va' + else if (t == 'va') sfn <- 'validate' + # If the input file name is not given + if (is.null(fn)) { + # The input file name + fn <- paste0(private$ddir, "/", sfn, ".txt") + } + # If the input file name is given + else { + # The full path to the input file + fn <- paste0(private$ddir, "/", fn) + } + # If the input file does not exist + if (!file.exists(fn)) { + # The information message + msg <- paste0("The input file: ", fn, " does not exist") + # An error is thrown + stop(msg) + } + # The sample file name + sf <- paste0(private$mdir, "/", sfn, ".txt") + # The clean sample file name + csf <- paste0(private$mdir, "/", sfn, "-clean.txt") + # If the cleaned sample file already exists + if (file.exists(csf)) { + # The information message + msg <- paste0("The cleaned sample file: ", csf, + " already exists") + # Information message is shown + private$display_msg(msg, 2) + } + else { + # If the sample file does not exist + if (!file.exists(sf)) { + # The information message + msg <- paste0("Generating sample file from the file: ", fn) + # Information message is shown + private$display_msg(msg, 2) + # The input file is read + data <- private$read_file(fn, F) + # If the sample size is less than 1 + if (ss < 1) { + # The number of lines in the main file + lc <- length(data) + # The number of lines in the sample file + lc <- round(lc*ss) + } + else { + lc <- ss + } + # The sample file data + data <- data[1:lc] + # If the data should be saved + if (is) { + # The sample file data is saved + private$write_file(data, sf, F) + } + } + # If the sample file exists + else { + # The information message + msg <- paste0("The sample file: ", sf, " already exists") + # Information message is shown + private$display_msg(msg, 2) + } + # If the sample file should be cleaned + if (ic) { + # The options for cleaning the data + opts <- private$dc_opts + # The line count is set to 5000 + opts[["line_count"]] <- 5000 + # The output file name + opts[["output_file"]] <- csf + # If the data should be saved + opts[["save_data"]] <- is + # The data cleaner object is created + dc <- DataCleaner$new(sf, opts, verbose = private$verbose) + # The sample file is cleaned + data <- dc$clean_file() + } + } + # If the data should not be saved + if (!is) { + # The data is returned + return(data) + } + }, + + #' @description + #' It generates training, testing and validation data sets + #' from the given input file. It first reads the file given as a + #' parameter to the current object. It generates random indexes for the + #' data. It partitions the data into training, testing and validation + #' sets, according to the given parameters. The files are named + #' train.txt, test.txt and va.txt. The files are saved to the given + #' output folder. + #' @param fn The input file name. It should be relative to the ddir. + #' @param dir The name of the output folder. + #' @param percs The size of the training, testing and validation sets. + generate_data = function(fn, dir, percs) { + # The information message is shown + private$display_msg( + "Generating training, testing and validation data sets...", 1) + # The input file path is generated + fn <- paste0(private$ddir, "/", fn) + # If the input file does not exist + if (!file.exists(fn)) { + # The information message + msg <- paste0("The input file: ", fn, " does not exist") + # An error is thrown + stop(msg) + } + # If the train, test and validation files already exist + if (file.exists(paste0(dir, "/train.txt")) && + file.exists(paste0(dir, "/test.txt")) && + file.exists(paste0(dir, "/validate.txt"))) { + # The information message + msg <- "The train, test and validate files already exist" + # The information message is shown + private$display_msg(msg, 1) + } + else { + # The input file is read + data <- private$read_file(fn, F) + # The number of lines in the data + lc <- length(data) + # Random indexes are generated + indexes <- sample(1:lc, lc) + # The randomized data + rd <- data[indexes] + # The training set data + train_ds <- rd[1:round(lc*percs[["train"]])] + # The testing set data + test_ds <- rd[1:round(lc*percs[["test"]])] + # The validation set data + validate_ds <- rd[1:round(lc*percs[["validate"]])] + # The training data is written to file + private$write_file(train_ds, paste0(dir, "/train.txt"), F) + # The testing data is written to file + private$write_file(test_ds, paste0(dir, "/test.txt"), F) + # The validation data is written to file + private$write_file(validate_ds, paste0(dir, "/validate.txt"), F) + } + } + ), + + private = list( + # @field ddir The folder containing the data files + ddir = "./data", + # @field mdir The folder containing the model files + mdir = "./models" + ) +) diff --git a/R/model-evaluator.R b/R/model-evaluator.R deleted file mode 100644 index ca02aa1..0000000 --- a/R/model-evaluator.R +++ /dev/null @@ -1,1136 +0,0 @@ -#' It is used to evaluate the accuracy and performance of the model. -#' -#' @description -#' It provides methods that perform extrinsic and intrinsic model -#' evaluation. It also provides methods for determining the memory and time -#' requirements for generating the model. It also provides a method for -#' determining how much memory is used by the final model. -#' -#' @details -#' It provides a method that performs intrinsic model evaluation based -#' on Perplexity. It also provides a method that performs extrinsic model -#' evalation based on accuracy. It provides a method for determining how much -#' memory and time is needed to generate a model for different input data -#' sizes. It provides a method for determining how much memory is needed by -#' the final model. -ModelEvaluator <- R6::R6Class( - "ModelEvaluator", - inherit = TextFileProcessor, - public = list( - #' @field tp The transition probabilities data frame. - tp = NULL, - - #' @field wl The list of unique words. - wl = NULL, - - #' @field dp The default probability is equal to 1/(N+V), where N is the - #' number of words in the sentence, V is the number of words in the - #' vocabulary. - dp = NULL, - - #' @field model The maximum number of ngrams supported by the model. - model = 4, - - #' @field dc_opts The options for the data cleaner object. - #' min_words -> The minimum number of words per sentence. - #' line_count -> The number of lines to read and clean at a time. - #' sw_file -> The stop words file path. - # dict_file -> The dictionary file path. - #' bad_file -> The bad words file path. - #' to_lower -> If the words should be converted to lower case. - #' remove_stop -> If stop words should be removed. - #' remove_punct -> If punctuation symbols should be removed. - #' remove_non_dict -> If non dictionary words should be removed. - #' remove_non_alpha -> If non alphabet symbols should be removed. - #' remove_extra_space -> If leading, trailing and double spaces - #' should be removed. - #' remove_bad -> If bad words should be removed - dc_opts = list( - "min_words" = 2, - "line_count" = 1000, - "sw_file" = "./data/stop-words.txt", - "dict_file" = "./data/dict-no-bad.txt", - "bad_file" = "./data/bad-words.txt", - "to_lower" = T, - "remove_stop" = F, - "remove_punc" = T, - "remove_non_dict" = T, - "remove_non_alpha" = T, - "remove_extra_space" = T, - "remove_bad" = F - ), - - #' @field tg_opts The options for the token generator obj. - #' n -> The ngram size. - #' save_ngrams -> If the ngram data should be saved. - #' min_freq -> All ngrams with frequency less than min_freq are - #' ignored. - #' line_count -> The number of lines to process at a time. - #' stem_words -> If words should be converted to their stem. - #' dir -> The dir where the output file should be saved. - #' format -> The format for the output. There are two options. - #' 'plain' -> The data is stored in plain text. - #' 'obj' -> The data is stored as a R obj. - tg_opts = list( - "min_freq" = -1, - "n" = 1, - "save_ngrams" = T, - "min_freq" = -1, - "line_count" = 5000, - "stem_words" = F, - "dir" = "./data/models", - "format" = "obj" - ), - - #' @field ssize The sample size in Mb. - ssize = 30, - - #' @field ddir The folder containing the data files - ddir = "./data", - - #' @field mdir The folder containing the model files - mdir = "./data/models", - - #' @description - #' It initializes the current object. It is used to set the - #' maximum ngram number, sample size, input file name, data cleaner - #' options and verbose option. - #' @param model The maximum ngram number supported by the model. - #' @param ssize The sample size in Mb. - #' @param ddir The data directory. - #' @param mdir The model directory. - #' @param dc_opts The data cleaner options. - #' @param tg_opts The token generator options. - #' @param verbose If progress information should be displayed. - initialize = function(model = 4, - ssize = 30, - ddir = "./data", - mdir = "./data/models", - dc_opts = list(), - tg_opts = list(), - verbose = 0) { - - # The base class is initialized - super$initialize(NULL, NULL, verbose) - - # The model number is set - self$model <- model - # The sample size is set - self$ssize <- ssize - # The data directory name is set - self$ddir <- ddir - # The model directory name is set - self$mdir <- mdir - # If the dc_opts are given - if (length(dc_opts) > 0) { - # The custom dc_opts are merged with the default dc_opts - self$dc_opts = modifyList(self$dc_opts, dc_opts) - } - # If the tg_opts are given - if (length(tg_opts) > 0) { - # The custom tg_opts are merged with the default tg_opts - self$tg_opts = modifyList(self$tg_opts, tg_opts) - } - # The transition probabilities data is initialized - self$tp <- NULL - }, - - #' @description - #' It compares the performance of the specified models by - #' plotting the performance statistics. The models are specified with - #' the type parameter. - #' @param type The models to compare. It can be: - #' 'basic' -> One model for each ngram. - #' 'grouped' -> One model for each group and ngram. For - #' e.g For data size 5 Mb, there are 3 models for the ngrams 2:4. - #' @param opts The options for plotting the data. - #' 'group' -> The field to group by. - #' 'title' -> The main plot title. - #' 'subtitle' -> The plot sub title. - #' @return The performance stats. - performance_comparision = function(type, opts) { - # If the type is 'basic' - if (type == 'basic') { - # The pstats file name - fn <- paste0(self$mdir, "/pstats.RDS") - # The stats are read - pstats <- self$read_obj(fn) - # The config file name - fn <- paste0(self$mdir, "/config.RDS") - # The config file is read - config <- self$read_obj(fn) - # The y-axis values - y <- list("m" = NULL, "t" = NULL, "p" = NULL, "a" = NULL) - # For each model the performance metrics are measured - for (i in 1:(config$model-1)) { - # The memory value - m <- self$format_size(pstats[[i]][["memory"]]) - # The y-axis values are updated - y[["m"]] <- c(y[["m"]], m) - y[["t"]] <- c(y[["t"]], pstats[[i]][["time"]]) - y[["p"]] <- c(y[["p"]], - pstats[[i]][["intrinsic"]][["mean"]]) - y[["a"]] <- c(y[["a"]], - pstats[[i]][["extrinsic"]][["valid_perc"]]) - } - # The data frame containing the data to be plotted - df <- data.frame( - "n" = 2:(length(pstats) + 1), - "m" = y[["m"]], - "t" = y[["t"]], - "p" = y[["p"]], - "a" = y[["a"]] - ) - # The options for plotting - opts <- list( - "type" = "basic", - "title" = "Variation of performance with ngram size", - "subtitle" = opts[['subtitle']]) - } - # If the type is 'grouped' - else if (type == 'grouped') { - # The average performance stats for each data size - y <- list("m" = NULL, "t" = NULL, "p" = NULL, "a" = NULL) - # The different items in the group. Each folder is a group item - f <- list.dirs(self$mdir, recursive = F, full.names = F) - # The output data frame - df <- data.frame() - # For each folder, the performance stats for all ngram models is - # calculated. - for (i in f) { - # The performance stats file name - fn <- paste0(self$mdir, "/", i, "/pstats.RDS") - # The stats are read - pstats <- self$read_obj(fn) - # The config file name - fn <- paste0(self$mdir, "/", i, "/config.RDS") - # The stats are read - config <- self$read_obj(fn) - # The memory usage for each ngram - m <- private$process_stats(pstats, "memory", config$model) - # The time taken for each ngram - t <- private$process_stats(pstats, "time", config$model) - # The perplexity for each ngram - p <- private$process_stats(pstats, "perplexity", config$model) - # The accuracy score for each ngram - a <- private$process_stats(pstats, "accuracy", config$model) - # The temprary data frame for the current data size - tdf <- data.frame( - rep(i, (config$model-1)), - m, t, p, a, 2:(length(pstats) + 1)) - # The column names are set - names(tdf) <- c(opts[["group"]], "m", "t", "p", "a", "n") - # The temprary data frame is appended to the output data - # frame - df <- rbind(df, tdf) - } - # The group name - g <- opts[["group"]] - # The options for plotting - opts <- list( - "type" = "grouped", - "group" = g, - "title" = opts[['title']], - "subtitle" = opts[['subtitle']]) - # If the group column is numeric - if (is.numeric(as.numeric(as.character(df[[g]])))) { - # The group column is converted to numeric - df[[g]] <- as.numeric(as.character(df[[g]])) - # The data is ordered by group - df <- df[order(df[[opts[["group"]]]]), ] - # The group column is converted to factor - df[[g]] <- as.factor(df[[g]]) - } - } - # The performance stats are plotted - private$plot_stats(df, opts) - # The performance stats are returned - return(df) - }, - - #' @description - #' For each model it performs intrinsic and extrinsic - #' evaluation. It also measures the memory usage and time taken. The - #' performance metrics are displayed in 5 plots on one page. Performance - #' statistics are saved to the file pstats.RDS. - performance_evaluation = function() { - # The performance stats - pstats <- list() - # The maximum model number - mmax <- self$model - # The y-axis values - y <- list("m" = NULL, "t" = NULL, "p" = NULL, "a" = NULL) - # For each model the performance metrics are measured - for (m in 2:mmax) { - # The transition probabilities data is initialized - self$tp <- NULL - # The model number is set - self$model <- m - # The intrinsic and extrinsic evaluation is performed - time_taken <- system.time({ - memory_used <- mem_change({ - # intrinsic evaluation is performed - istats <- self$intrinsic_evaluation() - # Extrinsic evaluation is performed - estats <- self$extrinsic_evaluation() - }) - }) - # The memory used - memory_used <- object_size(self$tp) - # The performance stats are updated - pstats[[m-1]] <- list( - "intrinsic" = istats, - "extrinsic" = estats, - "memory" = memory_used, - "time" = time_taken[[3]] - ) - # The y-axis values are updated - y[["m"]] <- c(y[["m"]], self$format_size(memory_used)) - y[["t"]] <- c(y[["t"]], time_taken[[3]]) - y[["p"]] <- c(y[["p"]], istats$mean) - y[["a"]] <- c(y[["a"]], estats$valid_perc) - } - - # The information message is shown - self$display_msg("Saving model performance stats...", 1) - # The performance stats file name - fn <- paste0(self$mdir, "/pstats.RDS") - # The performance stats are saved - self$save_obj(pstats, fn) - # The data to be plotted - df <- data.frame( - "n" = 2:(length(pstats)+1), - "m" = y[["m"]], - "t" = y[["t"]], - "p" = y[["p"]], - "a" = y[["a"]] - ) - # The options for plotting - opts <- list("type" = "basic") - # The performance stats are plotted - private$plot_stats(df, opts) - }, - - #' @description - #' It loads the given model configuration file. - #' @param fn The config file name. - load_config = function(fn) { - # The model configuration is read - config <- self$read_obj(fn) - # The configuration is copied to the object attributes - self$model <- config$model - self$ssize <- config$ssize - self$dc_opts <- config$dc_opts - self$ddir <- config$ddir - self$mdir <- config$mdir - }, - - #' @description - #' It loads the model located at the mdir location - load_model = function() { - # The configuration file name - fn <- paste0(self$mdir, "/config.RDS") - # The model configuration is loaded to the current object - self$load_config(fn) - # The tp data for the specified ngrams is loaded - private$read_tp_data(self$model) - }, - - #' @description - #' It saves the model configuration to the models - #' subdirectory of the given directory. - save_config = function() { - # The information message is shown - self$display_msg("Saving model configuration...", 1) - # The model configuration - config <- list( - "model" = self$model, - "ssize" = self$ssize, - "ddir" = self$ddir, - "mdir" = self$mdir, - "dc_opts" = self$dc_opts, - "tg_opts" = self$tg_opts - ) - # The configuration file - fn <- paste0(self$mdir, "/config.RDS") - # The configuration is saved - self$save_obj(config, fn) - }, - - #' @description - #' It generates the model for the given ngram number, - #' data sample size, data cleaning options and input file. - generate_model = function() { - # The data analyzer object is created - da <- DataAnalyzer$new( - paste0(self$ddir, "/train.txt"), self$verbose) - # The training, testing and validation data sets are generated - da$generate_data(self$ddir, - list(train = .8, test = .1, validate = .1)) - # The object size is formatted - obj_size <- file.size(paste0(self$ddir, "/train.txt"))/10^6 - # The proportion of data to sample - prop <- (self$ssize/obj_size) - # Random sample is taken and cleaned - self$generate_clean_sample(prop, T, 'tr') - # The model directory is set - self$tg_opts$dir <- self$mdir - # For each ngram number, the ngram token file is generated - for (i in 1:self$model) { - # The clean train data file name - fn <- paste0(self$mdir, "/train-clean.txt") - # The ngram number is set - self$tg_opts$n <- i - # The TokenGenerator object is created - tg <- TokenGenerator$new(fn, self$tg_opts, self$verbose) - # The ngram tokens are generated - tg$generate_tokens() - } - # For each ngram number, the transition probabilities are generated - for (i in 2:self$model) { - # The TPGenerator object is created - tp <- TPGenerator$new() - tp_opts <- list( - "n_range" = 1:i, - "save_tp" = T, - "format" = "obj", - "dir" = self$mdir - ) - # The transition probabilities are generated - tp$generate_tp(tp_opts) - } - # The model config is saved - self$save_config() - }, - - #' @description - #' Evaluates the model using intrinsic evaluation based on - #' Perplexity. First a validation data set containing 1000 lines is - #' generated. It is then cleaned. 20 random sentences are taken. For - #' each sentence, Perplexity of all the words is calculated. - #' @return The minumum, maximum and mean Perplexity score. - intrinsic_evaluation = function() { - # The transition probabilities data is read - private$read_tp_data(self$model) - # Random sample is taken and cleaned - self$generate_clean_sample(1000, T, 'va') - # The validation sample data is read - data <- self$read_file( - paste0(self$mdir,"/validate-clean.txt"), F) - # A sample of 20 sentences is taken - data <- data[1:20] - # The information message - msg <- paste0( - "Calculating Perplexity for ", length(data), " sentences") - # The information message is shown - self$display_msg(msg, 1) - # The list of perplexities - pl <- c(); - # The loop counter - c <- 1 - # The Perplexity of each sentence in the test data is calculated - for (line in data) { - # The line is split on space - words <- str_split(line, " ")[[1]] - # The perplexity for the line is calculated - p <- self$calc_perplexity(words) - # The information message - msg <- paste0( - "Perplexity of the sentence '", - line, "' is: ", p) - # The information message is shown - self$display_msg(msg, 2) - # The list of perplexities is updated - pl <- c(pl, p); - # If the counter is divisible by 10 - if (c %% 10 == 0) { - # The information message - msg <- paste0(c, " lines have been processed") - # The information message is shown - self$display_msg(msg, 1) - } - # The counter is increased by 1 - c <- c + 1 - } - # The perplexity stats - stats <- list( - "min" = min(pl), - "max" = max(pl), - "mean" = mean(pl)); - - return(stats); - }, - - #' @description - #' Evaluates the model using extrinsic evaluation based on - #' Accuracy. First a validation data set containing 1000 lines is - #' generated. It is then cleaned. 20 random sentences are taken. For - #' each sentence, the model is used to predict the next word. The - #' accuracy stats are returned. A prediction is considered to be correct - #' if one of the predicted words matched the actual word. - #' @return The number of correct and incorrect predictions. - extrinsic_evaluation = function() { - # The transition probabilities data is read - private$read_tp_data(self$model) - # Random sample is taken and cleaned - self$generate_clean_sample(1000, T, 'va') - # The validation sample data is read - data <- self$read_file( - paste0(self$mdir,"/validate-clean.txt"), F) - # A Random sample of 100 sentences is taken - data <- data[1:100] - # The information message - msg <- paste0( - "Predicting the next word for ", length(data), " sentences") - # The information message is shown - self$display_msg(msg, 1) - # The statistics - stats <- list("valid" = 0, "invalid" = 0) - # The loop counter - c <- 1 - # The last word for each sentence is predicted - for (line in data) { - # The line is split on space - words <- str_split(line, " ")[[1]] - # The word to predict - w <- words[length(words)] - # The previous words used to predict the word - pw <- words[1:length(words)-1] - # If the words should be stemmed - if (self$tg_opts[["stem_words"]]) { - # The previous words are stemmed - pw <- wordStem(pw) - } - # The next word is predicted - res <- self$predict_word(pw, F) - # If the predicted word matches the actual word - if (w %in% res["words"]) { - stats[["valid"]] <- stats[["valid"]] + 1; - # The information message - self$display_msg( - paste0("The word: ", w, " was predicted"), 3) - } - # If the predicted word does not match - else { - stats[["invalid"]] <- stats[["invalid"]] + 1; - # The information message - self$display_msg( - paste0("The word: ", w, " could not be predicted"), 3) - } - # The counter is increased by 1 - c <- c + 1 - # If the counter is divisible by 10 - if (c %% 10 == 0) { - # The information message - msg <- paste0(c, " sentences have been processed") - # The information message is shown - self$display_msg(msg, 1) - } - } - - # The precentage of valid - stats[["valid_perc"]] <- - (stats[["valid"]]/(stats[["valid"]] + stats[["invalid"]]))*100 - # The precentage of invalid - stats[["invalid_perc"]] <- 100-stats[["valid_perc"]] - - return(stats) - }, - - #' @description - #' Predicts the new word given a list of 1, 2 or 3 previous - #' words. It checks the given n words in the transition probabilities - #' data. If there is a match, the top 3 next words with highest - #' probabilities are returned. If there is no match, then the last n-1 - #' previous words are checked. This process is continued until the last - #' word is checked. If there is no match, then empty result is returned. - #' @param words A character vector of previous words or a single vector - #' containing the previous word text. - #' @param count The number of results to return. - #' @param dc A DataCleaner object. If it is given, then the given words - #' are cleaned. If the stem_words option was set in the TokenGenerator - #' object configuration for the current model, then the words are - #' converted to their stems. - #' @return The top 3 predicted words along with their probabilities. - predict_word = function(words, count = 3, dc = NULL) { - # The transition probabilities data is read - private$read_tp_data(self$model) - # The words are assigned to temp variable - w <- words - # If the DataCleaner obj was specified - if (!is.null(dc)) { - # If the words is a set of vectors - if (length(w) > 1) { - # The words are converted to a single line of text - w <- paste0(w, collapse = " ") - } - # The words are cleaned - w <- dc$clean_lines(w) - # If the words should be stemmed - if (self$tg_opts[["stem_words"]]) { - # The previous words are stemmed - w <- wordStem(w) - } - } - - # If the words are in the form of a line - if (length(w) == 1) { - # The words are split on space - w <- strsplit(w, " ")[[1]] - } - - # The length of previous words - pwl <- length(w) - # The loop counter - c <- 1 - # Indicates if the word was found - found <- FALSE - # The result - result <- list("found" = F, "words" = "", "probs" = "") - # If the previous words length is 0 - if (pwl == 0) - return(result) - # The last 3 words are extracted. - # If the previous word length is more than 3 - if (pwl > 3) { - pw <- w[(pwl-2):pwl] - } - else { - pw <- w - } - # The length of previous words - pwl <- length(pw) - # Each ngram in the previous word list is checked starting from - # largest ngram - for (i in pwl:1) { - # The previous words to check - tpw <- pw[c:pwl] - # The key to use for the transition probabilities data - k <- paste(tpw, collapse = "_") - # The key is converted to a numeric hash - h <- digest2int(k) - # The transition probabilities data is checked - res <- self$tp[self$tp$pre == h, ] - # If the prefix was found - if (nrow(res) > 0) { - # The word was found - found <- TRUE - # The result is sorted by probability - sres <- res[order(res$prob, decreasing = T),] - # The number of rows in the result set - rcount <- nrow(sres) - # If the number of results is more than the required number - # of results - if (rcount > count) { - # The result count is set to the required number of - # results - rc <- count - } - else { - # The result count is set to the number of results - rc <- rcount - } - # The required word probabilities - probs <- sres$prob[1:rc] - # The next words indexes - ind <- sres$nw[1:rc] - # The required words - nw <- as.character(self$wl$pre[ind]) - - # The result is updated - result[["words"]] <- nw - result[["probs"]] <- probs - result[["found"]] <- T - # The information message - msg <- paste0("The ngram key: ", k, " was found") - # Information message is shown - self$display_msg(msg, 3) - # The loop ends - break; - } - else { - # The information message - msg <- paste0("The ngram key: ", k, " was not found") - # Information message is shown - self$display_msg(msg, 3) - # The result is updated - result[["found"]] <- F - } - # The information message - msg <- paste0("Backing off to ", (i) ,"-gram") - # Information message is shown - self$display_msg(msg, 3); - # The counter is increased by 1 - c <- c + 1; - } - - return(result); - }, - - #' @description - #' Calculates the probability of the given word given the - #' previous model-1 words, where model is the maximum ngram number. It - #' looks up the probability of a word given n previous words. The - #' previous n words are converted to numeric hash using digest2int - #' function. The hash is looked up in a data frame of transition - #' probabilities. The word is converted to a number by checking its - #' position in a list of unique words. If the hash and the word position - #' were found, then the probability of the previous word and hash is - #' returned. If it was not found, then the hash of the n-1 previous - #' words is taken and the processed is repeated. If the data was not - #' found in the data frame, then the word probability is returned. This - #' is known as backoff. If the word probability could not be found then - #' the default probability is returned. The default probability is - #' calculated as 1/(N+V), Where N = number of words in corpus and V is - #' the number of dictionary words. - #' @param word The word whoose probability is to be calculated. - #' @param pw The previous words. - #' @return The probability of the word given the previous words. - get_word_prob = function(word, pw) { - # If the default probability is not set, then an error is raised - if (is.null(self$dp)) - stop("The default probability is not set !") - # The length of previous words - pwl <- length(pw) - # The probability of the word given the previous words. It is - # initialized to the default probability, which should be 1/(N+V) - prob <- self$dp - # The loop counter - c <- 1 - # Indicates if the word was found - found <- FALSE - # The next word id - nw <- match(word, self$wl$pre) - # If the next word was not found - if (is.na(nw)) { - # The information message - msg <- paste0( - "The next word: ", word, " was not found") - # Information message is shown - self$display_msg(msg, 3) - } - # If the previous word count is more than 0 - else if (pwl > 0) { - # The previous words are checked - for (i in pwl:1) { - # The previous words to check - tpw <- pw[c:pwl] - # The key to use for the transition matrix - k <- paste(tpw, collapse = "_") - # The key is converted to a numeric hash - h <- digest2int(k) - # The transition probabilities data is checked - res <- self$tp[self$tp$pre == h & self$tp$nw == nw, ] - # If the prefix was found - if (nrow(res) > 0) { - # The word was found - found <- TRUE - # The probability is set - prob <- as.numeric(res$prob) - # The information message - msg <- paste0("The ngram key: ", - k, " and the next word: ", - word, " were found") - # Information message is shown - self$display_msg(msg, 3) - # The loop ends - break - } - else { - # The information message - msg <- paste0("The ngram key: ", - k, " and the next word: ", - word, " were not found") - # Information message is shown - self$display_msg(msg, 3) - } - # The information message - msg <- paste0("Backing off to ", (i) ,"-gram") - # Information message is shown - self$display_msg(msg, 3) - # The counter is increased by 1 - c <- c + 1 - } - } - # If the word was not found then the probability of the word is - # checked in the n1-gram - if (!found) { - # If the word was not found - if (sum(self$wl$pre == word) == 0) { - # Information message is shown - self$display_msg("Using default probability", 3) - } - else { - # The word probability - prob <- as.numeric(self$wl[self$wl$pre == word, "prob"]) - } - } - - return(prob) - }, - - #' @description - #' The Perplexity for the given sentence is calculated. For - #' each word, the probability of the word given the previous words is - #' calculated. The probabilities are multiplied and then inverted. The - #' nth root of the result is the perplexity, where n is the number of - #' words in the sentence. If the stem_words tokenization option was - #' specified, then the previous words are converted to their stem. - #' @param words The list of words. - #' @return The perplexity of the given list of words. - calc_perplexity = function(words) { - # The number of words in the sentence - wl <- length(words) - # The product of the word probabilities - prob_prod <- 1 - # For each word, the probability of the word is calculated - for (i in 1:wl) { - # The word - word <- words[i] - # The list of previous words - pw <- NULL - # If i is more than 1 - if (i > 1) { - # The start index - start <- 1 - # If i > self$model - if (i > self$model) start <- i-(self$model-1) - # The list of previous words - pw <- words[start:(i-1)] - # If the words should be stemmed - if (self$tg_opts[["stem_words"]]) { - # The previous words are stemmed - pw <- wordStem(pw) - } - } - # The word probability - prob <- self$get_word_prob(word, pw) - # The probability product is updated - prob_prod <- prob_prod * prob - } - # The nth root of the inverse of the probability product is taken - p <- round((1/prob_prod)^(1/wl), 0) - - return(p) - }, - - #' @description - #' Generates a sample file of given size from the main - #' train.txt file file name. The file is cleaned and saved. - #' @param ss The number of lines or proportion of lines to sample. - #' @param ic If the sample file should be cleaned. - #' @param t The type of sample. It can be: - #' 'tr' -> training - #' 'te' -> testing - #' 'va' -> validation - generate_clean_sample = function(ss, ic, t) { - # If the type is 'tr' - if (t == 'tr') sfn <- 'train' - # If the type is 'te' - else if (t == 'te') sfn <- 'test' - # If the type is 'va' - else if (t == 'va') sfn <- 'validate' - # The sample file name - fn <- paste0(self$ddir, "/", sfn, ".txt") - # The sample file name - sf <- paste0(self$mdir, "/", sfn, ".txt") - # The clean sample file name - csf <- paste0(self$mdir, "/", sfn, "-clean.txt") - # If the cleaned sample file already exists - if (file.exists(csf)) { - # The information message - msg <- paste0("The cleaned sample file: ", csf, - " already exists") - # Information message is shown - self$display_msg(msg, 2) - } - else { - # If the sample file does not exist - if (!file.exists(sf)) { - # The information message - msg <- paste0("Generating sample file from the file: ", fn) - # Information message is shown - self$display_msg(msg, 2) - # The input file is read - data <- self$read_file(fn, F) - # If the sample size is less than 1 - if (ss < 1) { - # The number of lines in the main file - lc <- length(data) - # The number of lines in the sample file - lc <- round(lc*ss) - } - else { - lc <- ss - } - # The sample file data - data <- data[1:lc] - # The sample file data is saved - self$write_file(data, sf, F) - } - # If the sample file should be cleaned - if (ic) { - # The options for cleaning the data - opts <- self$dc_opts - # The line count is set to 5000 - opts[["line_count"]] <- 5000 - # The output file name - opts[["output_file"]] <- csf - # The data cleaner object is created - dc <- DataCleaner$new(sf, opts, verbose = self$verbose) - # The sample file is cleaned - dc$clean_file() - } - } - } - ), - - private = list( - - # @description It creates a single plot based on ggplot2. Depending on - # the opt parameter, the plot may contain groups or it may be a simple - # plot. - # @param data A data frame containing the data to be plotted. It should - # have 2 variables, x and y. - # @param opts The options for plotting the data. It contains: - # 'x_lab' -> The x-axis label. - # 'y_lab' -> The y-axis label. - # 'type' -> The type of plot. It can be 'basic' or 'grouped'. - # 'group' -> The field to group by. - # @return A ggplot object representing the plot. - plot_graph = function(data, opts) { - # y-max - y_max <- max(as.numeric(as.character(data$y))) - # If the type is basic - if (opts[['type']] == 'basic') { - # The graph is plotted - p <- ggplot(data, aes(x, y)) + geom_point() + - geom_smooth(method='lm', formula= y~x) + - labs(x = opts[["x_lab"]], y = opts[["y_lab"]]) + - xlim(min(data$x), max(data$x)) + - coord_cartesian(ylim = c(0,y_max)) - } - # If the type is grouped - else if (opts[['type']] == 'grouped') { - # The data is duplicated to prevent the warnings - data <- rbind(data, data) - # The group field data - g <- opts$group - # The graph is plotted - p <- ggplot( - data, - aes_string("x", "y", group = g, col = g)) + - geom_point() + - geom_smooth(method = "loess", formula = y ~ x, span = 1.4, se = FALSE) + - labs(x = opts[["x_lab"]], y = opts[["y_lab"]]) + - xlim(min(data$x), max(data$x)) + - coord_cartesian(ylim = c(0,y_max)) - } - return(p) - }, - - # @description - # It plots the given model performance stats. The stats - # are plotted on one page. 5 stats are plotted. The first 4 plots are - # of memory, time taken, mean Perplexity and accuracy. The fifth plot - # is of Perplexity vs accuracy. - # @param data The data to plot. - # @param opts The options for plotting the data. It contains: - # 'type' -> The type of plot. It can be 'basic' or 'grouped'. - # 'group' -> The field to group by. - # 'title' -> The main plot title. - # 'subtitle' -> The plot sub title. - plot_stats = function(data, opts) { - # The information message is shown - self$display_msg("Plotting model performance stats...", 1) - # If the type is basic - if (opts[["type"]] == 'basic') { - # The data frames - df1 <- data.frame(x = data$n, y = data$m) - df2 <- data.frame(x = data$n, y = data$t) - df3 <- data.frame(x = data$n, y = data$p) - df4 <- data.frame(x = data$n, y = data$a) - df5 <- data.frame(x = data$a, y = data$p) - # The options for plot 1 - popts <- list("x_lab" = "ngram", - "y_lab" = "memory", - "type" = opts[["type"]]) - # Plot 1 - p1 <- private$plot_graph(df1, popts) - # The options for plot 2 - popts <- list("x_lab" = "ngram", - "y_lab" = "time", - "type" = opts[["type"]]) - # Plot 2 - p2 <- private$plot_graph(df2, popts) - # The options for plot 3 - popts <- list("x_lab" = "ngram", - "y_lab" = "perplexity", - "type" = opts[["type"]]) - # Plot 3 - p3 <- private$plot_graph(df3, popts) - # The options for plot 4 - popts <- list("x_lab" = "ngram", - "y_lab" = "accuracy", - "type" = opts[["type"]]) - # Plot 4 - p4 <- private$plot_graph(df4, popts) - # The options for plot 5 - popts <- list("x_lab" = "accuracy", - "y_lab" = "perplexity", - "type" = "basic") - # Plot 5 - p5 <- private$plot_graph(df5, popts) - # The plots are displayed on a single page - patchwork <- p1 + p2 + p3 + p4 + p5 - # Main title is added - print(patchwork + plot_annotation( - "title" = opts[["title"]], - "subtitle" = opts[["subtitle"]])) - } - # If the type is grouped - else if (opts[["type"]] == 'grouped') { - # The group name - g <- opts[["group"]] - # The data frames - df1 <- data.frame(data$n, data$m, data[[g]]) - df2 <- data.frame(data$n, data$t, data[[g]]) - df3 <- data.frame(data$n, data$p, data[[g]]) - df4 <- data.frame(data$n, data$a, data[[g]]) - # The column names - n <- c("x", "y", g) - # The column names are set - names(df1) <- n - names(df2) <- n - names(df3) <- n - names(df4) <- n - # The options for plot 1 - popts <- list("x_lab" = "ngram", - "y_lab" = "memory", - "group" = opts[["group"]], - "type" = opts[["type"]]) - # Plot 1 - p1 <- private$plot_graph(df1, popts) - # The options for plot 2 - popts <- list("x_lab" = "ngram", - "y_lab" = "time", - "group" = opts[["group"]], - "type" = opts[["type"]]) - # Plot 2 - p2 <- private$plot_graph(df2, popts) - # The options for plot 3 - popts <- list("x_lab" = "ngram", - "y_lab" = "perplexity", - "group" = opts[["group"]], - "type" = opts[["type"]]) - # Plot 3 - p3 <- private$plot_graph(df3, popts) - # The options for plot 4 - popts <- list("x_lab" = "ngram", - "y_lab" = "accuracy", - "group" = opts[["group"]], - "type" = opts[["type"]]) - # Plot 4 - p4 <- private$plot_graph(df4, popts) - # The plots are displayed on a single page - patchwork <- p1 + p2 + p3 + p4 - # Main title is added - # Main title is added - print(patchwork + plot_annotation( - "title" = opts[["title"]], - "subtitle" = opts[["subtitle"]])) - } - }, - - # @description - # Reads the model file and sets the current objects attributes. - # @param model The model number. It is the maximum ngrams supported by - # the model. - read_tp_data = function(model) { - # If the model has already been loaded then function returns - if (!is.null(self$tp)) return() - # The information message - msg <- paste0("Loading model ", model, "...") - # The information message is shown - self$display_msg(msg, 1) - # The model config file name - fn <- paste0(self$mdir, "/config.RDS") - # The config file is loaded - self$load_config(fn) - # The model file name - fn <- paste0(self$mdir, "/model-", model, ".RDS") - # The model is read to a data frame - self$tp <- self$read_obj(fn) - # The words file name - fn <- paste0(self$mdir, "/words.RDS") - # The list of words is read - self$wl <- self$read_obj(fn) - # The dictionary file name - fn <- self$dc_opts[["dict_file"]] - # The file contents - dict <- self$read_file(fn, F) - # The information message is shown - self$display_msg("Calculating default probability...", 1) - # The number of words in the dictionary file. It is used to - # calculate Perplexity. - vc <- length(dict) - # The cleaned training file is read - fn <- paste0(self$mdir, "/train-clean.txt") - # The model is read to a data frame - data <- self$read_file(fn, F) - # The words are split on " " - w <- strsplit(data, " ") - # The words are converted to atomic list - w <- unlist(w) - # The number of words - n <- length(w) - # The default probability is set - self$dp <- 1/(n + vc) - }, - - # @description - # It returns the average value for the required stats, for - # the given performance stats. - # @param pstats The performance stats. - # @param type The type of stats. It can be: - # 'memory' -> The mean memory used. - # 'time_taken' -> The mean time taken by the intrinsic and - # extrinsic evaluation tests. - # 'perplexity' -> The mean Perplexity score. - # 'accuracy' -> The mean accuracy score. - # @param nmax -> The maximum ngram size. - process_stats = function(pstats, type, nmax) { - # The stats - s <- NULL - # The average memory for each ngram model - for (n in 1:(nmax-1)) { - # If the stats is memory - if (type == "memory") { - # The stats value for the current ngram number - v <- pstats[[n]][["memory"]] - # The stats are formated - v <- self$format_size(v) - } - # If the stats is perplexity - else if (type == "perplexity") { - # The stats value for the current ngram number - v <- pstats[[n]][["intrinsic"]][["mean"]] - } - # If the stats is accuracy - else if (type == "accuracy") { - # The stats value for the current ngram number - v <- pstats[[n]][["extrinsic"]][["valid_perc"]] - } - # If the stats is time - else if (type == "time") { - # The stats value for the current ngram number - v <- pstats[[n]][["time"]] - } - # The stats are updated - s <- c(s, v) - } - - return(s) - } - ) -) diff --git a/R/model/evaluator.R b/R/model/evaluator.R new file mode 100644 index 0000000..d0451cb --- /dev/null +++ b/R/model/evaluator.R @@ -0,0 +1,320 @@ +#' It is used to perform extrinsic and intrinsic evaluation of a model. +#' +#' @description +#' It provides methods for performing extrinsic and intrinsic +#' evaluation. Intrinsic evaluation is based on calculation of Perplexity. +#' Extrinsic evaluation is based on accuracy. It involves determining the +#' percentage of correct next word predictions. +#' +#' @details +#' Before performing the intrinsic and extrinsic model evaluation, a +#' validation file must be first generated. This can be done using the Generator +#' class. Each line in the validation file is evaluated. For intrinsic +#' evaluation Perplexity for the line is calculated. An overall summary of the +#' Perplexity calculations is returned. It includes the min, max and mean +#' Perplexity. For extrinsic evaluation, next word prediction is performed on +#' each line. If the actual next word is one of the three predicted next words, +#' then the prediction is considered to be accurate. The extrinsic evaluation +#' returns the percentage of correct and incorrect predictions. +Evaluator <- R6::R6Class( + "Evaluator", + inherit = TextFileProcessor, + public = list( + #' @description + #' It initializes the current object. It is used to set the + #' model file name and verbose options. + #' @param mfile The model file name. + #' @param verbose If progress information should be displayed. + #' @export + initialize = function(mfile, verbose = 0) { + # The base class is initialized + super$initialize(NULL, NULL, verbose) + # If the model file name is not valid, then an error is thrown + if (!file.exists(mfile)) + stop(paste0("Invalid model file: ", model_file)) + else { + # The model file name is set + private$mfile <- mfile + # The model object is read + private$m <- private$read_obj(model_file) + } + }, + + #' @description + #' It performs intrinsic and extrinsic evaluation for the + #' given model. It also measures the memory usage and time taken. The + #' performance metrics are displayed in 5 plots on one page. Performance + #' statistics are saved to the model object. + #' @param lc The number of lines of text in the validation file to be + #' used for the evaluation. + #' @param fn The name of the validation file. If it does not exist, then + #' the default file validation-clean.txt is checked in the models + #' folder + performance_evaluation = function(lc, fn) { + # The y-axis values + pstats <- list("m" = NULL, "t" = NULL, "p" = NULL, "a" = NULL) + time_taken <- system.time({ + memory_used <- mem_change({ + # Intrinsic evaluation is performed + istats <- self$intrinsic_evaluation(lc, fn) + # Extrinsic evaluation is performed + estats <- self$extrinsic_evaluation(lc, fn) + }) + }) + # The memory used + memory_used <- object_size(private$model) + # The y-axis values are updated + pstats[["m"]] <- private$format_size(memory_used) + pstats[["t"]] <- time_taken[[3]] + pstats[["p"]] <- istats$mean + pstats[["a"]] <- estats$valid_perc + # The performance stats are saved + private$m$pstats <- pstats + # The model is saved + private$save_obj(private$m, private$mfile) + }, + + #' @description + #' It compares the performance of the specified models by + #' plotting the performance statistics generated by the + #' performance_evaluation function. The models are specified with the + #' type parameter. + #' @param type The models to compare. It can be: 'basic' -> The models + #' for each n-gram size are compared. 'grouped' -> Each model folder + #' contains n-gram models for a given input data size. For e.g For + #' data size 5 Mb, there are 3 models for the ngrams 2:4. The + #' performance of all models in all folders is plotted on a graph. + #' @param opts A list of options for plotting the data. 'group' -> The + #' field to group by. 'title' -> The main plot title. 'subtitle' -> + #' The plot sub title. + #' @return The performance stats. + performance_comparision = function(type, opts) { + # If the type is 'basic' + if (type == 'basic') { + # The pstats file name + fn <- paste0(private$mdir, "/pstats.RDS") + # The stats are read + pstats <- private$read_obj(fn) + # The config file name + fn <- paste0(private$mdir, "/config.RDS") + # The config file is read + config <- private$read_obj(fn) + # The y-axis values + y <- list("m" = NULL, "t" = NULL, "p" = NULL, "a" = NULL) + # For each model the performance metrics are measured + for (i in 1:(config$model-1)) { + # The memory value + m <- private$format_size(pstats[[i]][["memory"]]) + # The y-axis values are updated + y[["m"]] <- c(y[["m"]], m) + y[["t"]] <- c(y[["t"]], pstats[[i]][["time"]]) + y[["p"]] <- c(y[["p"]], + pstats[[i]][["intrinsic"]][["mean"]]) + y[["a"]] <- c(y[["a"]], + pstats[[i]][["extrinsic"]][["valid_perc"]]) + } + # The data frame containing the data to be plotted + df <- data.frame( + "n" = 2:(length(pstats) + 1), + "m" = y[["m"]], + "t" = y[["t"]], + "p" = y[["p"]], + "a" = y[["a"]] + ) + # The options for plotting + opts <- list( + "type" = "basic", + "title" = "Variation of performance with ngram size", + "subtitle" = opts[['subtitle']]) + } + # If the type is 'grouped' + else if (type == 'grouped') { + # The average performance stats for each data size + y <- list("m" = NULL, "t" = NULL, "p" = NULL, "a" = NULL) + # The different items in the group. Each folder is a group item + f <- list.dirs(private$mdir, recursive = F, full.names = F) + # The output data frame + df <- data.frame() + # For each folder, the performance stats for all ngram models is + # calculated. + for (i in f) { + # The performance stats file name + fn <- paste0(private$mdir, "/", i, "/pstats.RDS") + # The stats are read + pstats <- private$read_obj(fn) + # The config file name + fn <- paste0(private$mdir, "/", i, "/config.RDS") + # The stats are read + config <- private$read_obj(fn) + # The memory usage for each ngram + m <- private$process_stats(pstats, "memory", config$model) + # The time taken for each ngram + t <- private$process_stats(pstats, "time", config$model) + # The perplexity for each ngram + p <- private$process_stats(pstats, "perplexity", config$model) + # The accuracy score for each ngram + a <- private$process_stats(pstats, "accuracy", config$model) + # The temprary data frame for the current data size + tdf <- data.frame( + rep(i, (config$model-1)), + m, t, p, a, 2:(length(pstats) + 1)) + # The column names are set + names(tdf) <- c(opts[["group"]], "m", "t", "p", "a", "n") + # The temprary data frame is appended to the output data + # frame + df <- rbind(df, tdf) + } + # The group name + g <- opts[["group"]] + # The options for plotting + opts <- list( + "type" = "grouped", + "group" = g, + "title" = opts[['title']], + "subtitle" = opts[['subtitle']]) + # If the group column is numeric + if (is.numeric(as.numeric(as.character(df[[g]])))) { + # The group column is converted to numeric + df[[g]] <- as.numeric(as.character(df[[g]])) + # The data is ordered by group + df <- df[order(df[[opts[["group"]]]]), ] + # The group column is converted to factor + df[[g]] <- as.factor(df[[g]]) + } + } + # The performance stats are plotted + private$plot_stats(df, opts) + # The performance stats are returned + return(df) + }, + + #' @description + #' Evaluates the model using intrinsic evaluation based on + #' Perplexity. The given number of sentences are taken from the + #' validation file. For each sentence, the Perplexity is calculated. + #' @param lc The number of lines of text in the validation file to be + #' used for the evaluation. + #' @param fn The name of the validation file. If it does not exist, then + #' the default file validation-clean.txt is checked in the models + #' folder + #' @return The min, max and mean Perplexity score. + intrinsic_evaluation = function(lc, fn) { + # The Predictor class object is created + pr <- Predictor$new() + # The validation data is read + data <- private$read_validation_data(lc, "I", fn) + # The list of perplexities + pl <- c(); + # The loop counter + c <- 1 + # The Perplexity of each sentence in the test data is calculated + for (line in data) { + # The line is split on space + words <- str_split(line, " ")[[1]] + # The perplexity for the line is calculated + p <- pr$calc_perplexity(words) + # The information message + msg <- paste0( + "Perplexity of the sentence '", + line, "' is: ", p) + # The information message is shown + private$display_msg(msg, 2) + # The list of perplexities is updated + pl <- c(pl, p); + # If the counter is divisible by 10 + if (c %% 10 == 0) { + # The information message + msg <- paste0(c, " lines have been processed") + # The information message is shown + private$display_msg(msg, 1) + } + # The counter is increased by 1 + c <- c + 1 + } + # The perplexity stats + stats <- list( + "min" = min(pl), + "max" = max(pl), + "mean" = mean(pl)); + + return(stats); + }, + + #' @description + #' Evaluates the model using extrinsic evaluation based on + #' Accuracy. The given number of sentences are taken from the validation + #' file. For each sentence, the model is used to predict the next word. + #' The accuracy stats are returned. A prediction is considered to be + #' correct if one of the predicted words matches the actual word. + #' @param lc The number of lines of text in the validation file to be + #' used for the evaluation. + #' @param fn The name of the validation file. If it does not exist, then + #' the default file validation-clean.txt is checked in the models + #' folder + #' @return The number of correct and incorrect predictions. + extrinsic_evaluation = function(lc, fn) { + # The Predictor class object is created + pr <- Predictor$new() + # The validation data is read + data <- private$read_validation_data(lc, "E", fn) + # The statistics + stats <- list("valid" = 0, "invalid" = 0) + # The loop counter + c <- 1 + # The last word for each sentence is predicted + for (line in data) { + # The line is split on space + words <- str_split(line, " ")[[1]] + # The word to predict + w <- words[length(words)] + # The previous words used to predict the word + pw <- words[1:length(words)-1] + # If the words should be stemmed + if (private$tg_opts[["stem_words"]]) { + # The previous words are stemmed + pw <- wordStem(pw) + } + # The next word is predicted + res <- pr$predict_word(pw, F) + # If the predicted word matches the actual word + if (w %in% res["words"]) { + stats[["valid"]] <- stats[["valid"]] + 1; + # The information message + private$display_msg( + paste0("The word: ", w, " was predicted"), 3) + } + # If the predicted word does not match + else { + stats[["invalid"]] <- stats[["invalid"]] + 1; + # The information message + private$display_msg( + paste0("The word: ", w, " could not be predicted"), 3) + } + # The counter is increased by 1 + c <- c + 1 + # If the counter is divisible by 10 + if (c %% 10 == 0) { + # The information message + msg <- paste0(c, " sentences have been processed") + # The information message is shown + private$display_msg(msg, 1) + } + } + + # The precentage of valid + stats[["valid_perc"]] <- + (stats[["valid"]]/(stats[["valid"]] + stats[["invalid"]]))*100 + # The precentage of invalid + stats[["invalid_perc"]] <- 100-stats[["valid_perc"]] + + return(stats) + } + ), + + private = list( + # @field m The model object. + m = NULL, + # @field mfile The path to the model file. + mfile = NULL + ) +) diff --git a/R/model/generator.R b/R/model/generator.R new file mode 100644 index 0000000..c8b0d3c --- /dev/null +++ b/R/model/generator.R @@ -0,0 +1,211 @@ +#' It is used to generate n-gram models for the given data file. +#' +#' @description +#' It provides methods that are used for generating n-gram models. +#' The n-gram models may be customized by specifying the data cleaning and +#' tokenization options. +#' +#' @details +#' It provides a method that generates a n-gram model. The n-gram model +#' may be customized by specifying the data cleaning and tokenization options. +#' The data cleaning options include removal of punctuation, stop words, extra +#' space, non-dictionary words and bad words. The tokenization options include +#' n-gram number and word stemming. +Generator <- R6::R6Class( + "Generator", + inherit = TextFileProcessor, + public = list( + #' @description + #' It initializes the current object. It is used to set the + #' maximum ngram number, sample size, input file name, data cleaner, + #' tokenization and verbose option. + #' @param n The n-gram size for the model. + #' @param ssize The sample size in Mb. + #' @param ddir The data directory. + #' @param mdir The model directory. + #' @param dc_opts The data cleaner options. + #' @param tg_opts The token generator options. + #' @param verbose If progress information should be displayed. + #' @export + initialize = function(n = 4, + ssize = 30, + ddir = "./data", + mdir = "./models", + dc_opts = list(), + tg_opts = list(), + verbose = 0) { + + # The base class is initialized + super$initialize(NULL, NULL, verbose) + + # The n-gram size is set + private$n <- n + # The sample size is set + private$ssize <- ssize + # The data directory name is set + private$ddir <- ddir + # The model directory name is set + private$mdir <- mdir + # If the dc_opts are given + if (length(dc_opts) > 0) { + # The custom dc_opts are merged with the default dc_opts + private$dc_opts = modifyList(private$dc_opts, dc_opts) + } + # If the tg_opts are given + if (length(tg_opts) > 0) { + # The custom tg_opts are merged with the default tg_opts + private$tg_opts = modifyList(private$tg_opts, tg_opts) + } + }, + + #' @description + #' It generates the model for the given name, description, + #' n-gram size, data sample size, data cleaning options and input file. + #' @param name The model name. + #' @param desc The model description. + #' @param ifn The input file name. If not given then the train.txt file + #' in the data directory folder is used. + #' @param ofn The output file name. If not given, then "model.RDS" is + #' used as the file name . + generate_model = function(name, desc, ifn = NULL, ofn = NULL) { + # If the input file name is not given + if (is.null(ifn)) { + # The default training data file name + ifn <- paste0(private$ddir, "/train.txt") + } + # If the input file does not exist + if (!file.exists(ifn)) { + # An error is thrown + stop("The file: ", ifn, " does not exist !") + } + # If the output file name is not given + if (is.null(ofn)) { + # The default output file name is used + ofn <- paste0(private$mdir, "/model.RDS") + } + # The data analyzer object is created + da <- DataAnalyzer$new(fn, private$verbose) + # The training, testing and validation data sets are generated + da$generate_data(private$ddir, + list(train = .8, test = .1, validate = .1)) + # The object size is formatted + obj_size <- file.size(ifn)/10^6 + # The proportion of data to sample + prop <- (private$ssize/obj_size) + # Random sample is taken and cleaned + self$generate_sample(prop, T, 'tr') + # The model directory is set + private$tg_opts$dir <- private$mdir + # The clean train data file name + fn <- paste0(private$mdir, "/train-clean.txt") + # For each ngram number, the ngram token file is generated + for (i in 1:private$n) { + # The ngram number is set + private$tg_opts$n <- i + # The TokenGenerator object is created + tg <- TokenGenerator$new(fn, private$tg_opts, private$verbose) + # The ngram tokens are generated + tg$generate_tokens() + } + # The TPGenerator object is created + tp <- TPGenerator$new() + # The options for generating combined transition probabilities + tp_opts <- list( + "n" = private$n, + "save_tp" = T, + "format" = "obj", + "dir" = private$mdir + ) + # The transition probabilities are generated + tp$generate_tp(tp_opts) + # The information message is shown + private$display_msg("Saving model...", 1) + # An object of class Model is created + m <- Model$new( + name = name, + desc = desc, + data_file = fn, + wl_file = paste0(private$mdir, "/words.RDS"), + model = private$n, + ssize = private$ssize, + ddir = private$ddir, + mdir = private$mdir, + dc_opts = private$dc_opts, + tg_opts = private$tg_opts, + verbose = private$verbose + ) + # The model object is loaded + m$load_model() + # The model object is saved to the models folder using the output + # file name + private$save_obj(m, ofn) + } + ), + + private = list( + # @field tp The transition probabilities data frame. + tp = NULL, + # @field wl The list of unique words. + wl = NULL, + # @field dp The default probability is equal to 1/(N+V), where N is the + # number of words in the sentence, V is the number of words in the + # vocabulary. + dp = NULL, + # @field n The n-gram size. + n = 4, + # @field dc_opts The options for the data cleaner object. + # min_words -> The minimum number of words per sentence. + # line_count -> The number of lines to read and clean at a time. + # sw_file -> The stop words file path. + # dict_file -> The dictionary file path. + # bad_file -> The bad words file path. + # to_lower -> If the words should be converted to lower case. + # remove_stop -> If stop words should be removed. + # remove_punct -> If punctuation symbols should be removed. + # remove_non_dict -> If non dictionary words should be removed. + # remove_non_alpha -> If non alphabet symbols should be removed. + # remove_extra_space -> If leading, trailing and double spaces + # should be removed. + # remove_bad -> If bad words should be removed + dc_opts = list( + "min_words" = 2, + "line_count" = 1000, + "sw_file" = NULL, + "dict_file" = NULL, + "bad_file" = NULL, + "to_lower" = T, + "remove_stop" = F, + "remove_punc" = T, + "remove_non_dict" = T, + "remove_non_alpha" = T, + "remove_extra_space" = T, + "remove_bad" = F + ), + # @field tg_opts The options for the token generator obj. + # n -> The ngram size. + # save_ngrams -> If the ngram data should be saved. + # min_freq -> All ngrams with frequency less than min_freq are + # ignored. + # line_count -> The number of lines to process at a time. + # stem_words -> If words should be converted to their stem. + # dir -> The dir where the output file should be saved. + # format -> The format for the output. There are two options. + # 'plain' -> The data is stored in plain text. + # 'obj' -> The data is stored as a R obj. + tg_opts = list( + "min_freq" = -1, + "n" = 1, + "save_ngrams" = T, + "min_freq" = -1, + "line_count" = 5000, + "stem_words" = F, + "dir" = "./data/models", + "format" = "obj" + ), + # @field ssize The sample size in Mb. + ssize = 30, + # @field ddir The folder containing the data files + ddir = "./data", + # @field mdir The folder containing the model files + mdir = "./models") +) diff --git a/R/model/model.R b/R/model/model.R new file mode 100644 index 0000000..f794742 --- /dev/null +++ b/R/model/model.R @@ -0,0 +1,219 @@ +#' The Model class represents n-gram models. An instance of the class is a +#' single n-gram model. +#' +#' @description +#' The attributes of this class are used to store n-gram model +#' information. The class provides methods for loading and saving the model. +#' +#' @details +#' The attributes of this class are used to store n-gram model +#' information such as model name, model description, model file name, n-gram +#' number, transition probabilities data, default probability, n-gram +#' configuration options such as data cleaning and tokenization options, word +#' list, model path, data directory path and performance stats. The model is +#' saved to a single file as a R object. A model file contains all the +#' information required by the model. The model object is used by other model +#' classes that perform operations on the model such as evaluation of model +#' performance, making word predictions based on the model and plotting model +#' performance stats. +Model <- R6::R6Class( + "Model", + inherit = TextFileProcessor, + public = list( + #' @field pstats The performance stats for the model. + pstats = list(), + #' @field name The model name. + name = NULL, + #' @field desc The model description. + desc = NULL, + + #' @description + #' It initializes the current object. It is used to set the + #' maximum ngram number, sample size, input file name, data cleaner + #' options, tokenization options, combined transition probabilities file + #' name and verbose. + #' @param name The model name. + #' @param desc The model description. + #' @param file_name The model file name. + #' @param data_file The path of the file used to generate the model. If + #' the data was cleaned, then data_file is the path to the cleaned + #' file. + #' @param wl_file The path of the word list file. + #' @param n The maximum ngram number supported by the model. + #' @param ssize The sample size in Mb. + #' @param ddir The data directory. + #' @param mdir The model directory. + #' @param dc_opts The data cleaner options. + #' @param tg_opts The token generator options. + #' @param verbose If progress information should be displayed. + #' @export + initialize = function(name = NULL, + desc = NULL, + file_name = NULL, + data_file = NULL, + wl_file = NULL, + n = 4, + ssize = 30, + ddir = "./data", + mdir = "./models", + dc_opts = list(), + tg_opts = list(), + verbose = 0) { + + # The base class is initialized + super$initialize(NULL, NULL, verbose) + + # If the wl_file does not exist, then an error is thrown + if (!file.exists(wl_file)) + stop(paste0("The file: ", wl_file, " does not exist !")) + # If the data file does not exist, then an error is thrown + if (!file.exists(data_file)) + stop(paste0("The file: ", data_file, " does not exist !")) + # If the data directory does not exist, then an error is thrown + if (!dir.exists(ddir)) + stop(paste0("The dir: ", ddir, " does not exist !")) + # If the model directory does not exist, then an error is thrown + if (!dir.exists(mdir)) + stop(paste0("The dir: ", mdir, " does not exist !")) + # If the dict_file does not exist, then an error is thrown + if (!file.exists(dc_opts$dict_file)) { + # The error message + msg <- paste0("The file: ", dc_opts$dict_file) + msg <- paste0(msg, " does not exist !") + stop(msg) + } + + # The model name is set + self$name <- name + # The model description is set + self$desc <- desc + # The ngram number is set + private$n <- n + # The sample size is set + private$ssize <- ssize + # The data directory name is set + private$ddir <- ddir + # The model directory name is set + private$mdir <- mdir + # The input file name is set + private$data_file <- data_file + # The word list file name is set + private$wl_file <- wl_file + # The model file name is set + private$file_name <- file_name + # If the dc_opts are given + if (length(dc_opts) > 0) { + # The custom dc_opts are merged with the default dc_opts + private$dc_opts = modifyList(private$dc_opts, dc_opts) + } + # If the tg_opts are given + if (length(tg_opts) > 0) { + # The custom tg_opts are merged with the default tg_opts + private$tg_opts = modifyList(private$tg_opts, tg_opts) + } + }, + + #' @description + #' It loads the model using the given information + load_model = function() { + # The tp file name + fn <- paste0(mdir, "/model-", private$n, ".RDS") + # The tp file is read + private$tp <- private$read_obj(fn) + # The wl file is read + private$wl <- private$read_obj(private$wl_file) + # The dictionary file name + fn <- private$dc_opts[["dict_file"]] + # The file contents + dict <- private$read_file(fn, F) + # The information message is shown + private$display_msg("Calculating default probability...", 1) + # The number of words in the dictionary file. It is used to + # calculate Perplexity. + vc <- length(dict) + # The data file is read + data <- private$read_file(private$data_file, F) + # The words are split on " " + w <- strsplit(data, " ") + # The words are converted to atomic list + w <- unlist(w) + # The number of words + n <- length(w) + # The default probability is set + private$dp <- 1/(n + vc) + } + ), + private = list( + # @field file_name The path to the model file. + file_name = NULL, + # @field wl_file The path to the word list file. + wl_file = NULL, + # @field data_file The path to the input file. + data_file = NULL, + # @field tp The transition probabilities data frame. + tp = NULL, + # @field wl The list of unique words. + wl = NULL, + # @field dp The default probability is equal to 1/(N+V), where N is the + # number of words in the sentence, V is the number of words in the + # vocabulary. + dp = NULL, + # @field n The maximum number of ngrams supported by the model. + n = 4, + # @field dc_opts The options for the data cleaner object. + # min_words -> The minimum number of words per sentence. + # line_count -> The number of lines to read and clean at a time. + # sw_file -> The stop words file path. + # dict_file -> The dictionary file path. + # bad_file -> The bad words file path. + # to_lower -> If the words should be converted to lower case. + # remove_stop -> If stop words should be removed. + # remove_punct -> If punctuation symbols should be removed. + # remove_non_dict -> If non dictionary words should be removed. + # remove_non_alpha -> If non alphabet symbols should be removed. + # remove_extra_space -> If leading, trailing and double spaces + # should be removed. + # remove_bad -> If bad words should be removed + dc_opts = list( + "min_words" = 2, + "line_count" = 1000, + "sw_file" = NULL, + "dict_file" = NULL, + "bad_file" = NULL, + "to_lower" = T, + "remove_stop" = F, + "remove_punc" = T, + "remove_non_dict" = T, + "remove_non_alpha" = T, + "remove_extra_space" = T, + "remove_bad" = F + ), + # @field tg_opts The options for the token generator obj. + # n -> The ngram size. + # save_ngrams -> If the ngram data should be saved. + # min_freq -> All ngrams with frequency less than min_freq are + # ignored. + # line_count -> The number of lines to process at a time. + # stem_words -> If words should be converted to their stem. + # dir -> The dir where the output file should be saved. + # format -> The format for the output. There are two options. + # 'plain' -> The data is stored in plain text. + # 'obj' -> The data is stored as a R obj. + tg_opts = list( + "min_freq" = -1, + "n" = 1, + "save_ngrams" = T, + "min_freq" = -1, + "line_count" = 5000, + "stem_words" = F, + "dir" = "./data/models", + "format" = "obj" + ), + # @field ssize The sample size in Mb. + ssize = 30, + # @field ddir The folder containing the data files + ddir = "./data", + # @field mdir The folder containing the model files + mdir = "./models" + ) +) diff --git a/R/model/predictor.R b/R/model/predictor.R new file mode 100644 index 0000000..79d0834 --- /dev/null +++ b/R/model/predictor.R @@ -0,0 +1,303 @@ +#' It is used to evaluate the accuracy and performance of the model. +#' +#' @description +#' It provides methods that perform extrinsic and intrinsic model +#' evaluation. It also provides methods for determining the memory and time +#' requirements for generating the model. It also provides a method for +#' determining how much memory is used by the final model. +#' +#' @details +#' It provides a method that performs intrinsic model evaluation based +#' on Perplexity. It also provides a method that performs extrinsic model +#' evalation based on accuracy. It provides a method for determining how much +#' memory and time is needed to generate a model for different input data sizes. +#' It provides a method for determining how much memory is needed by the final +#' model. +#' @importFrom ggplot2 ggplot aes aes_string geom_point geom_smooth labs xlim +#' coord_cartesian +#' @importFrom digest digest2int +#' @importFrom SnowballC wordStem +#' @importFrom patchwork plot_annotation +#' @importFrom pryr mem_change object_size +ModelEvaluator <- R6::R6Class( + "ModelEvaluator", + inherit = TextFileProcessor, + public = list( + #' @description + #' It initializes the current object. It is used to set the + #' maximum ngram number, sample size, input file name, data cleaner + #' options and verbose option. + #' @param model The maximum ngram number supported by the model. + #' @param ssize The sample size in Mb. + #' @param ddir The data directory. + #' @param mdir The model directory. + #' @param dc_opts The data cleaner options. + #' @param tg_opts The token generator options. + #' @param verbose If progress information should be displayed. + #' @export + initialize = function(model = 4, + ssize = 30, + ddir = "./data", + mdir = "./data/models", + dc_opts = list(), + tg_opts = list(), + verbose = 0) { + + # The base class is initialized + super$initialize(NULL, NULL, verbose) + + # The model number is set + private$model <- model + # The sample size is set + private$ssize <- ssize + # The data directory name is set + private$ddir <- ddir + # The model directory name is set + private$mdir <- mdir + # If the dc_opts are given + if (length(dc_opts) > 0) { + # The custom dc_opts are merged with the default dc_opts + private$dc_opts = modifyList(private$dc_opts, dc_opts) + } + # If the tg_opts are given + if (length(tg_opts) > 0) { + # The custom tg_opts are merged with the default tg_opts + private$tg_opts = modifyList(private$tg_opts, tg_opts) + } + # The transition probabilities data is initialized + private$tp <- NULL + }, + + #' @description + #' Predicts the new word given a list of 1, 2 or 3 previous + #' words. It checks the given n words in the transition probabilities + #' data. If there is a match, the top 3 next words with highest + #' probabilities are returned. If there is no match, then the last n-1 + #' previous words are checked. This process is continued until the last + #' word is checked. If there is no match, then empty result is returned. + #' @param words A character vector of previous words or a single vector + #' containing the previous word text. + #' @param count The number of results to return. + #' @param dc A DataCleaner object. If it is given, then the given words + #' are cleaned. If the stem_words option was set in the TokenGenerator + #' object configuration for the current model, then the words are + #' converted to their stems. + #' @return The top 3 predicted words along with their probabilities. + predict_word = function(words, count = 3, dc = NULL) { + # The transition probabilities data is read + private$read_tp_data(private$model) + # The words are assigned to temp variable + w <- words + # If the DataCleaner obj was specified + if (!is.null(dc)) { + # If the words is a set of vectors + if (length(w) > 1) { + # The words are converted to a single line of text + w <- paste0(w, collapse = " ") + } + # The words are cleaned + w <- dc$clean_lines(w) + # If the words should be stemmed + if (private$tg_opts[["stem_words"]]) { + # The previous words are stemmed + w <- wordStem(w) + } + } + + # If the words are in the form of a line + if (length(w) == 1) { + # The words are split on space + w <- strsplit(w, " ")[[1]] + } + + # The length of previous words + pwl <- length(w) + # The loop counter + c <- 1 + # Indicates if the word was found + found <- FALSE + # The result + result <- list("found" = F, "words" = "", "probs" = "") + # If the previous words length is 0 + if (pwl == 0) + return(result) + # The last 3 words are extracted. + # If the previous word length is more than 3 + if (pwl > 3) { + pw <- w[(pwl-2):pwl] + } + else { + pw <- w + } + # The length of previous words + pwl <- length(pw) + # Each ngram in the previous word list is checked starting from + # largest ngram + for (i in pwl:1) { + # The previous words to check + tpw <- pw[c:pwl] + # The key to use for the transition probabilities data + k <- paste(tpw, collapse = "_") + # The key is converted to a numeric hash + h <- digest2int(k) + # The transition probabilities data is checked + res <- private$tp[private$tp$pre == h, ] + # If the prefix was found + if (nrow(res) > 0) { + # The word was found + found <- TRUE + # The result is sorted by probability + sres <- res[order(res$prob, decreasing = T),] + # The number of rows in the result set + rcount <- nrow(sres) + # If the number of results is more than the required number + # of results + if (rcount > count) { + # The result count is set to the required number of + # results + rc <- count + } + else { + # The result count is set to the number of results + rc <- rcount + } + # The required word probabilities + probs <- sres$prob[1:rc] + # The next words indexes + ind <- sres$nw[1:rc] + # The required words + nw <- as.character(private$wl$pre[ind]) + + # The result is updated + result[["words"]] <- nw + result[["probs"]] <- probs + result[["found"]] <- T + # The information message + msg <- paste0("The ngram key: ", k, " was found") + # Information message is shown + private$display_msg(msg, 3) + # The loop ends + break; + } + else { + # The information message + msg <- paste0("The ngram key: ", k, " was not found") + # Information message is shown + private$display_msg(msg, 3) + # The result is updated + result[["found"]] <- F + } + # The information message + msg <- paste0("Backing off to ", (i) ,"-gram") + # Information message is shown + private$display_msg(msg, 3); + # The counter is increased by 1 + c <- c + 1; + } + + return(result); + }, + + #' @description + #' Calculates the probability of the given word given the + #' previous model-1 words, where model is the maximum ngram number. It + #' looks up the probability of a word given n previous words. The + #' previous n words are converted to numeric hash using digest2int + #' function. The hash is looked up in a data frame of transition + #' probabilities. The word is converted to a number by checking its + #' position in a list of unique words. If the hash and the word position + #' were found, then the probability of the previous word and hash is + #' returned. If it was not found, then the hash of the n-1 previous + #' words is taken and the processed is repeated. If the data was not + #' found in the data frame, then the word probability is returned. This + #' is known as backoff. If the word probability could not be found then + #' the default probability is returned. The default probability is + #' calculated as 1/(N+V), Where N = number of words in corpus and V is + #' the number of dictionary words. + #' @param word The word whoose probability is to be calculated. + #' @param pw The previous words. + #' @return The probability of the word given the previous words. + get_word_prob = function(word, pw) { + # If the default probability is not set, then an error is raised + if (is.null(private$dp)) + stop("The default probability is not set !") + # The length of previous words + pwl <- length(pw) + # The probability of the word given the previous words. It is + # initialized to the default probability, which should be 1/(N+V) + prob <- private$dp + # The loop counter + c <- 1 + # Indicates if the word was found + found <- FALSE + # The next word id + nw <- match(word, private$wl$pre) + # If the next word was not found + if (is.na(nw)) { + # The information message + msg <- paste0( + "The next word: ", word, " was not found") + # Information message is shown + private$display_msg(msg, 3) + } + # If the previous word count is more than 0 + else if (pwl > 0) { + # The previous words are checked + for (i in pwl:1) { + # The previous words to check + tpw <- pw[c:pwl] + # The key to use for the transition matrix + k <- paste(tpw, collapse = "_") + # The key is converted to a numeric hash + h <- digest2int(k) + # The transition probabilities data is checked + res <- private$tp[private$tp$pre == h & private$tp$nw == nw, ] + # If the prefix was found + if (nrow(res) > 0) { + # The word was found + found <- TRUE + # The probability is set + prob <- as.numeric(res$prob) + # The information message + msg <- paste0("The ngram key: ", + k, " and the next word: ", + word, " were found") + # Information message is shown + private$display_msg(msg, 3) + # The loop ends + break + } + else { + # The information message + msg <- paste0("The ngram key: ", + k, " and the next word: ", + word, " were not found") + # Information message is shown + private$display_msg(msg, 3) + } + # The information message + msg <- paste0("Backing off to ", (i) ,"-gram") + # Information message is shown + private$display_msg(msg, 3) + # The counter is increased by 1 + c <- c + 1 + } + } + # If the word was not found then the probability of the word is + # checked in the n1-gram + if (!found) { + # If the word was not found + if (sum(private$wl$pre == word) == 0) { + # Information message is shown + private$display_msg("Using default probability", 3) + } + else { + # The word probability + prob <- as.numeric(private$wl[private$wl$pre == word, "prob"]) + } + } + + return(prob) + } + ) +) diff --git a/R/text-file-processor.R b/R/text-file-processor.R index e47e187..0331e94 100644 --- a/R/text-file-processor.R +++ b/R/text-file-processor.R @@ -9,38 +9,17 @@ #' called before reading a file. The process function is called for processing a #' line. The post processing function is called on the processed data. It also #' provides a method for generating a sample file from an input text file +#' @importFrom pryr object_size TextFileProcessor <- R6::R6Class( "TextFileProcessor", public = list( - #' @field opts The list of file processing options. - #' save_data -> If the combined processed lines should be saved. - #' output_file -> Name of the output file used to store the data. - opts = list( - "save_data" = F, - "output_file" = "./data/sample-clean.txt" - ), - - #' @field line_count The number of lines to read and process at a time. - line_count = 100, - - #' @field pp_output The output of the pre-processing step - pp_output = NULL, - - #' @field p_output The output of the processing step - p_output = NULL, - - #' @field file_name The name of the text file to process. - file_name = "./data/sample.txt", - - #' @field verbose Indicates if progress data should be printed. - verbose = 0, - #' @description #' It initializes the current object. It is used to set the file name #' and verbose options. #' @param file_name The path to the file to clean. #' @param line_count The number of lines to read and clean at a time. #' @param verbose Indicates if progress information should be displayed. + #' @export initialize = function(file_name = "./data/sample.txt", line_count = 100, verbose = 2) { @@ -50,30 +29,96 @@ TextFileProcessor <- R6::R6Class( # The base class attributes are set # The file name is set - self$file_name <- file_name + private$file_name <- file_name # The verbose option is set - self$verbose <- verbose + private$verbose <- verbose # The line count is set - self$line_count <- line_count + private$line_count <- line_count # The processed output is set - self$p_output <- NULL + private$p_output <- NULL + } + ), + private = list( + # @field opts The list of file processing options. + # save_data -> If the combined processed lines should be saved. + # output_file -> Name of the output file used to store the data. + opts = list( + "save_data" = F, + "output_file" = "./data/sample-clean.txt" + ), + + # @field line_count The number of lines to read and process at a time. + line_count = 100, + + # @field p_output The output of the processing step + p_output = NULL, + + # @field file_name The name of the text file to process. + file_name = "./data/sample.txt", + + # @field verbose Indicates if progress data should be printed. + verbose = 0, + + # @field con The input file connection + con = NULL, + + # @description + # Reads the contents of the given file. Loads the file + # contents to a R object, a data frame or character vector. + # @param file_name The file name. + # @param format The file format. 'plain' or 'obj' + # @param opts Options for reading the file. + # @return The required data. + read_data = function(file_name, format, opts) { + # If the format is plain + if (format == "plain") { + # The file is read + data <- private$read_file(file_name, opts) + } + # If the format is obj + else if (format == "obj") { + # The file is read + data <- private$read_obj(file_name) + } + + return(data) }, - #' @description + # @description + # Writes the given data to a file. The data may be a R object, a + # character vector or a data frame. + # @param file_name The file name. + # @param format The file format. 'plain' or 'obj' + # @param opts Options for writting to the file. + # @return The required data. + write_data = function(data, file_name, format, opts) { + # If the format is plain + if (format == "plain") { + # The data is written to a file + private$write_file(data, file_name, opts) + } + # If the format is obj + if (format == "obj") { + # The R object is saved + private$save_obj(data, file_name) + } + }, + + # @description #' Reads the given file one line at a time. It runs the given #' pre-processing function before reading the file. It runs the given - #' line processing function for each line. It optionally saves the - #' output of line processing after reading the file or after processing - #' certain number of lines. - #' @param pre_process The pre-processing function. - #' @param process The function used to process each line. - #' @param post_process The function used to perform post processing. - #' @return The combined processed data + # line processing function for each line. It optionally saves the + # output of line processing after reading the file or after processing + # certain number of lines. + # @param pre_process The pre-processing function. + # @param process The function used to process each line. + # @param post_process The function used to perform post processing. + # @return The combined processed data process_file = function(pre_process, process, post_process) { # Pre-processing is done pre_process() # The file is opened - private$con <- file(self$file_name) + private$con <- file(private$file_name) # The connection is opened for reading open(private$con) # The lines to be read, @@ -83,31 +128,38 @@ TextFileProcessor <- R6::R6Class( # Indicates that data should not be appended is_app <- F # The output file name - of <- self$opts[["output_file"]] + of <- private$opts[["output_file"]] # All lines are read while (TRUE) { # The lines are read - lines <- readLines(private$con, n = self$line_count, + lines <- readLines(private$con, n = private$line_count, skipNul = TRUE) # If all the lines have been read if (length(lines) == 0) break # The lines are processed p_lines <- process(lines) + # If the processed lines are NULL + if(is.null(p_lines)) next # If the data should be saved - if (self$opts[["save_data"]] && !is.null(p_lines)) { + if (private$opts[["save_data"]]) { # The cleaned data is written to file - self$write_file(p_lines, of, is_app) + private$write_file(p_lines, of, is_app) # Debug message - self$display_msg( + private$display_msg( paste(length(p_lines), "lines were written"), 1) # Indicates that data should be appended is_app <- T } + # If the processed data should not be saved + else { + # The processed output is merged + private$p_output <- c(private$p_output, p_lines) + } # The loop counter is increased by 1 c <- c + 1 # Debug message - self$display_msg( - paste(self$line_count*c, "lines have been processed"), 1) + private$display_msg( + paste(private$line_count*c, "lines have been processed"), 1) # if (c == 2) break; } @@ -115,18 +167,23 @@ TextFileProcessor <- R6::R6Class( close(private$con) # Post processing is performed post_process() + # If the data should not be saved + if (!private$opts[["save_data"]]) { + # The processed output is returned + return(private$p_output) + } }, - #' @description - #' Reads the given file and returns its contents. - #' @param file_name The name of the file to read. - #' @param is_csv If the data is a csv file - #' @return The file data + # @description + # Reads the given file and returns its contents. + # @param file_name The name of the file to read. + # @param is_csv If the data is a csv file + # @return The file data read_file = function(file_name, is_csv) { # The information message msg <- paste0("Reading file: ", file_name) # Information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # If the file is not a csv file if (!is_csv) { # File is opened for reading @@ -143,16 +200,16 @@ TextFileProcessor <- R6::R6Class( return (data) }, - #' @description - #' Reads the given number of lines from the given file. - #' @param file_name The name of the file to read. - #' @param line_count The number of lines to read. - #' @return The file data + # @description + # Reads the given number of lines from the given file. + # @param file_name The name of the file to read. + # @param line_count The number of lines to read. + # @return The file data read_lines = function(file_name, line_count) { # The information message msg <- paste0("Reading file: ", file_name) # Information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # File is opened for reading con <- file(file_name) # The file contents are read @@ -163,17 +220,17 @@ TextFileProcessor <- R6::R6Class( return (data) }, - #' @description - #' Writes the given data to the given file. The data may be appended to - #' an existing file. - #' @param data The data to be written. - #' @param file_name The name of the file. - #' @param is_append Indicates if data should be saved. + # @description + # Writes the given data to the given file. The data may be appended to + # an existing file. + # @param data The data to be written. + # @param file_name The name of the file. + # @param is_append Indicates if data should be saved. write_file = function(data, file_name, is_append) { # The information message msg <- paste0("Saving file: ", file_name) # Information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # If the given data is a data frame if ("data.frame" %in% class(data)) { # The data frame is written to a file @@ -193,13 +250,13 @@ TextFileProcessor <- R6::R6Class( } }, - #' @description - #' Calculates the size of the given object or formats the given bytes - #' object as a number without units. The returned number is the size in - #' Mb. - #' @param obj An object of class bytes or an object whoose size is to be - #' found. - #' @return The size formatted as a string. + # @description + # Calculates the size of the given object or formats the given bytes + # object as a number without units. The returned number is the size in + # Mb. + # @param obj An object of class bytes or an object whoose size is to be + # found. + # @return The size formatted as a string. format_size = function(obj) { # If the obj is not of class bytes if (!("bytes" %in% class(obj))) { @@ -219,29 +276,29 @@ TextFileProcessor <- R6::R6Class( return(obj_size) }, - #' @description - #' Saves the given object as a file. - #' @param obj The object to save. - #' @param file_name The file name. + # @description + # Saves the given object as a file. + # @param obj The object to save. + # @param file_name The file name. save_obj = function(obj, file_name) { # The information message msg <- paste0("Saving file: ", file_name) # Information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # The object is saved to a file saveRDS(obj, file_name) }, - #' @description - #' Reads the contents of the given file. Loads the file - #' contents to a R object. - #' @param file_name The file name. - #' @return The loaded R obj. + # @description + # Reads the contents of the given file. Loads the file + # contents to a R object. + # @param file_name The file name. + # @return The loaded R obj. read_obj = function(file_name) { # The information message msg <- paste0("Reading file: ", file_name) # Information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # If the file does not exist if (!file.exists(file_name)) { # The information message @@ -257,83 +314,38 @@ TextFileProcessor <- R6::R6Class( return(obj) }, - #' @description - #' Prints the given message depending on verbose settings. - #' @param msg The message to be printed. - #' @param min_debug The minimum debugging level + # @description + # Prints the given message depending on verbose settings. + # @param msg The message to be printed. + # @param min_debug The minimum debugging level display_msg = function(msg, min_debug) { # If verbose is >= min_debug , then message is displayed - if (self$verbose >= min_debug) { + if (private$verbose >= min_debug) { print(msg) } }, - #' @description - #' Performs processing on the data. It should be - #' overriden by a derived class. - #' @param lines The lines to process + # @description + # Performs processing on the data. It should be + # overriden by a derived class. + # @param lines The lines to process process = function(lines) { }, - #' @description - #' Performs post-processing on the processed data. It should be - #' overriden by a derived class. + # @description + # Performs post-processing on the processed data. It should be + # overriden by a derived class. post_process = function() { }, - #' @description - #' Performs pre-processing on the processed data. It should be - #' overriden by a derived class. + # @description + # Performs pre-processing on the processed data. It should be + # overriden by a derived class. pre_process = function() { return(NULL) } - ), - private = list( - # @field con The input file connection - con = NULL, - - # @description - # Reads the contents of the given file. Loads the file - # contents to a R object, a data frame or character vector. - # @param file_name The file name. - # @param format The file format. 'plain' or 'obj' - # @param opts Options for reading the file. - # @return The required data. - read_data = function(file_name, format, opts) { - # If the format is plain - if (format == "plain") { - # The file is read - data <- self$read_file(file_name, opts) - } - # If the format is obj - else if (format == "obj") { - # The file is read - data <- self$read_obj(file_name) - } - - return(data) - }, - # @description - # Writes the given data to a file. The data may be a R object, a - # character vector or a data frame. - # @param file_name The file name. - # @param format The file format. 'plain' or 'obj' - # @param opts Options for writting to the file. - # @return The required data. - write_data = function(data, file_name, format, opts) { - # If the format is plain - if (format == "plain") { - # The data is written to a file - self$write_file(data, file_name, opts) - } - # If the format is obj - if (format == "obj") { - # The R object is saved - self$save_obj(data, file_name) - } - } ) ) diff --git a/R/token-generator.R b/R/token-generator.R index 6bd5a6a..6254971 100644 --- a/R/token-generator.R +++ b/R/token-generator.R @@ -1,39 +1,18 @@ #' It generates ngrams of given size from an input text file #' #' @description -#' It generates ngram tokens along with their frequencies. +#' It generates ngram tokens along with their frequencies. The data +#' may be saved to a file in plain text format or as a R object. #' -#' @details -#' It provides a method for generating ngrams of given size. It saves -#' each ngram along with its frequency to a text file. +#' @importFrom SnowballC wordStem +#' @importFrom dplyr group_by summarize_all TokenGenerator <- R6::R6Class( "TokenGenerator", inherit = TextFileProcessor, public = list( - #' @field tg_opts The options for the token generator obj. - #' n -> The ngram size. - #' save_ngrams -> If the ngram data should be saved. - #' min_freq -> All ngrams with frequency less than min_freq are - #' ignored. - #' line_count -> The number of lines to process at a time. - #' stem_words -> If words should be converted to their stem. - #' dir -> The dir where the output file should be saved. - #' format -> The format for the output. There are two options. - #' 'plain' -> The data is stored in plain text. - #' 'obj' -> The data is stored as a R obj. - tg_opts = list( - "n" = 1, - "save_ngrams" = F, - "min_freq" = -1, - "line_count" = 5000, - "stem_words" = F, - "dir" = "./data/models", - "format" = "obj" - ), - #' @description - #' It initializes the current obj. It is used to set the file name - #' and verbose options. + #' It initializes the current obj. It is used to set the file name, + #' tokenization options and verbose option. #' @param file_name The path to the input file. #' @param opts The options for generating the ngram tokens. #' n -> The ngram size. @@ -47,57 +26,76 @@ TokenGenerator <- R6::R6Class( #' 'plain' -> The data is stored in plain text. #' 'obj' -> The data is stored as a R obj. #' @param verbose Indicates if progress information should be displayed. - initialize = function(file_name = "./data/models/validate-clean.txt", - opts = self$tg_opts, - verbose = 0) { + #' @export + initialize = function(file_name = NULL, opts = list(), verbose = 0) { # The given options are merged with the opts attribute - self$tg_opts <- modifyList(self$tg_opts, opts) - # The tg_opts is merged with the base class opts attribute - self$opts <- modifyList(self$opts, self$tg_opts) + private$tg_opts <- modifyList(private$tg_opts, opts) # The base class is initialized - super$initialize(file_name, self$opts[["line_count"]], verbose) + super$initialize(file_name, private$tg_opts$line_count, verbose) # The processed output is initialized - self$p_output <- NULL + private$p_output <- NULL }, #' @description - #' It generates ngram tokens and their frequencies from the given file - #' name. The tokens may be saved to a text file as plain text or a R - #' obj. - #' @return The ngram tokens along with their frequencies. + #' It generates ngram tokens and their frequencies from the + #' given file name. The tokens may be saved to a text file as plain text + #' or a R object. + #' @return The data frame containing ngram tokens along with their + #' frequencies. generate_tokens = function() { # The processed output is initialized - self$p_output <- NULL + private$p_output <- NULL # The output file name fn <- private$get_file_name() # If the output file already exists if (file.exists(fn)) { # The information message - msg <- paste0("The ", self$opts[["n"]], + msg <- paste0("The ", private$tg_opts[["n"]], "-gram file already exists") # The information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # If the ngram data should not be saved - if (!self$opts[["save_ngrams"]]) { + if (!private$tg_opts[["save_ngrams"]]) { # The ngrams file is read - self$p_output <- self$read_data( - fn, self$opts[["format"]], T) + private$p_output <- private$read_data( + fn, private$tg_opts[["format"]], T) } } else { # The information message msg <- paste0("Generating ", - self$opts[["n"]], "-gram tokens...") + private$tg_opts[["n"]], "-gram tokens...") # The information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # The base class process_file function is called - super$process_file(super$pre_process, private$process, + private$process_file(private$pre_process, private$process, private$post_process) } } ), private = list( + # @field tg_opts The options for the token generator obj. + # n -> The ngram size. + # save_ngrams -> If the ngram data should be saved. + # min_freq -> All ngrams with frequency less than min_freq are + # ignored. + # line_count -> The number of lines to process at a time. + # stem_words -> If words should be converted to their stem. + # dir -> The dir where the output file should be saved. + # format -> The format for the output. There are two options. + # 'plain' -> The data is stored in plain text. + # 'obj' -> The data is stored as a R obj. + tg_opts = list( + "n" = 1, + "save_ngrams" = F, + "min_freq" = -1, + "line_count" = 5000, + "stem_words" = F, + "dir" = "./data/models", + "format" = "obj" + ), + # @description # Performs processing for the \code{generate_tokens} function. It # processes the given line of text. It converts each line of text into @@ -107,13 +105,13 @@ TokenGenerator <- R6::R6Class( # Ngrams are extracted from each line ngrams <- private$generate_ngrams(lines) # If the processed output is empty - if (is.null(self$p_output)) { + if (is.null(private$p_output)) { # The ngram words are set to the processed output - self$p_output <- ngrams + private$p_output <- ngrams } else { # The ngram words are appended to the processed output - self$p_output <- c(self$p_output, ngrams) + private$p_output <- c(private$p_output, ngrams) } }, @@ -121,11 +119,11 @@ TokenGenerator <- R6::R6Class( # It returns the name of the output ngram file. get_file_name = function() { # The ngram number - n <- self$opts[["n"]] + n <- private$tg_opts[["n"]] # The format - fo <- self$opts[["format"]] + fo <- private$tg_opts[["format"]] # The output directory - dir <- self$opts[["dir"]] + dir <- private$tg_opts[["dir"]] # The file extension if (fo == "plain") ext <- ".txt" else ext <- ".RDS" @@ -141,36 +139,36 @@ TokenGenerator <- R6::R6Class( post_process = function() { # The information message msg <- paste0("Calculating ", - self$opts[["n"]], "-gram frequencies...") + private$tg_opts[["n"]], "-gram frequencies...") # The information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # The output is copied to a variable - df <- data.frame("pre" = self$p_output) + df <- data.frame("pre" = private$p_output) # A frequency column is added df$freq <- 1 # Each prefix is grouped and summed df <- df %>% group_by(pre) %>% summarize_all(sum) # If the minimum ngram frequency is given - if (self$opts[["min_freq"]] > -1) { + if (private$tg_opts[["min_freq"]] > -1) { # The information message msg <- paste0("Removing low frequency ngrams...") # The information message is shown - self$display_msg(msg, 2) + private$display_msg(msg, 2) # All ngrams with frequency less than min_freq are ignored - df <- df[df$freq >= self$opts[["min_freq"]], ] + df <- df[df$freq >= private$tg_opts[["min_freq"]], ] } # The column names are set colnames(df) <- c("pre", "freq") # The output is set to the updated variable - self$p_output <- df + private$p_output <- df # If the ngram data should be saved - if (self$opts[["save_ngrams"]]) { + if (private$tg_opts[["save_ngrams"]]) { # The required file name fn <- private$get_file_name() # The format - fo <- self$opts[["format"]] + fo <- private$tg_opts[["format"]] # The n-gram data frame is written to file - private$write_data(self$p_output, fn, fo, F) + private$write_data(private$p_output, fn, fo, F) } }, @@ -179,7 +177,7 @@ TokenGenerator <- R6::R6Class( # @param lines The lines of text to process generate_ngrams = function(lines) { # The ngram number - n <- self$opts[["n"]] + n <- private$tg_opts[["n"]] # If n > 1 if (n > 1) { # Trailing and leading white space is removed @@ -199,7 +197,7 @@ TokenGenerator <- R6::R6Class( # The ngrams are generated l <- sapply(indexes, function(i) { # If the words should be stemmed - if (self$tg_opts[["stem_words"]]) { + if (private$tg_opts[["stem_words"]]) { # The ngram prefix words are stemmed. The next word is # not stemmed v <- c(wordStem(w[i:(i+n-2)]), w[(i+n-1)]) diff --git a/R/tp-generator.R b/R/tp-generator.R index 7a2ebb9..f18e7b0 100644 --- a/R/tp-generator.R +++ b/R/tp-generator.R @@ -1,84 +1,74 @@ -#' It is used to generate the transition probabilities +#' It is used to generate transition probabilities for n-grams. #' #' @description -#' It implements Markov Chains for ngrams. It generates transition -#' probabilities for ngrams. +#' It provides a method for generating transition probabilities for the given +#' n-gram size. It also provides a method for generating the combined transition +#' probabilities data for n-gram sizes from 1 to the given size. The combined +#' transition probabilities data can be used to implement back-off. #' #' @details -#' It reads ngram frequencies from an input text file. It parses each -#' ngram into a prefix, a next word and the next word frequency. The prefix is -#' converted to a numeric hash using the digest2int function from the digest -#' package. The next word is replaced with the position of the next word in the -#' list of all words. The data is stored in a data frame. It may be saved to a -#' file. +#' It provides a method for generating n-gram transition probabilities. It reads +#' ngram frequencies from an input text file that is generated by the +#' TokenGenerator class. It parses each ngram into a prefix, a next word and the +#' next word frequency. Maximum Likelihood count is used to generated the next +#' word probabilities. The n-gram prefix is converted to a numeric hash. The +#' next word is replaced with the position of the next word in the list of all +#' words. The transition probabilities data is stored in a file. Another method +#' is provided that combines the transition probabilities for n-grams of size 1 +#' to the given size. The combined transition probabilities can be saved as a +#' data frame. This file may be regarded as a n-gram model. The name of the file +#' is model-n.RDS, where n is the n-gram number. By combining the transition +#' probabilities of n-grams, back-off may be used to evaluate word probabilities +#' or predict the next word. +#' @importFrom stringr str_match +#' @importFrom digest digest2int +#' @importFrom dplyr group_by mutate TPGenerator <- R6::R6Class( "TPGenerator", inherit = TextFileProcessor, public = list( #' @description - #' It initializes the current obj. It is used to set the verbose option. + #' It initializes the current obj. It is used to set the + #' transition probabilities options and verbose option. + #' @param opts The options for generating the transition probabilities. + #' save_tp -> If the data should be saved. + #' n -> The ngram size. + #' dir -> The dir where the output file should be saved. + #' format -> The format for the output. There are two options. + #' 'plain' -> The data is stored in plain text. + #' 'obj' -> The data is stored as a R obj. #' @param verbose If progress information should be displayed. - initialize = function(verbose = 0) { + #' @export + initialize = function(opts = NULL, verbose = 0) { + # The given options are merged with the opts attribute + private$tp_opts <- modifyList(private$tp_opts, opts) # The base class is initialized super$initialize(NULL, NULL, verbose) # The processed output is initialized - self$p_output <- data.frame() + private$p_output <- data.frame() }, #' @description - #' It groups the given tp data by prefix. Each prefix has - #' the top c next words with the highest probabilities. All other next - #' words are removed. The trimmed tp data may be saved to a file or - #' returned. The file is saved with the suffix -min. - #' e.g model-4-min.RDS. - #' @param opts The options for trimming the tp data. - #' save_tp -> If the data should be saved. - #' dir -> The dir where the output file should be saved. - #' c -> The top c next words per ngrams prefix. - #' m -> The maximum ngram number supported by the model. - trim_tp = function(opts) { - # The model file name - fn <- paste0(opts$dir, "/model-", opts$m, ".RDS") - # The model file is read to a data frame - df <- self$read_obj(fn) - }, - - #' @description - #' It generates the transition probabilities for the given - #' ngram numbers It first generates the transition probabilities for - #' each ngram number. The transition probabilities are then combined - #' into a single data frame. The data frame may be saved to a file. - #' @param opts The options for generating the transition probabilities. - #' save_tp -> If the data should be saved. - #' n_range -> The range of ngram numbers. - #' dir -> The dir where the output file should be saved. - #' format -> The format for the output. There are two options. - #' 'plain' -> The data is stored in plain text. - #' 'obj' -> The data is stored as a R obj. - generate_tp = function(opts) { - # The opts is merged with the tp_opts attribute - private$tp_opts = modifyList(private$tp_opts, opts) - # The tp_opts is merged with the base class opts attribute - self$opts = modifyList(self$opts, private$tp_opts) + #' It combines the transition probabilities for n-grams + #' less than or equal to the given n-gram size. It first generates + #' the transition probabilities for each ngram size from 1 to the given + #' size. The transition probabilities are then combined into a single + #' data frame and saved to the output folder that is given as parameter + #' to the current object. By combining the transition probabilities for + #' all n-gram sizes less than n, back-off can be used to calculate next + #' word probabilities or predict the next word. + generate_tp = function() { # The information message - msg <- paste0( - "Generating Transition Probabilities for n = ", - min(self$opts[["n_range"]]), - ":", - max(self$opts[["n_range"]]) - ) + msg <- paste0("Generating Transition Probabilities for n = ") + msg <- paste0(msg, "1:", private$tp_opts[["n"]]) # Information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # The processed output is cleared - self$p_output <- data.frame() - # The ngram number range - n_range <- self$opts[["n_range"]] - # The minimum ngram number - nmin <- min(n_range) - # The maximum ngram number - nmax <- max(n_range) + private$p_output <- data.frame() # The output format - fo <- self$opts[["format"]] + fo <- private$tp_opts[["format"]] + # The n-gram number + nmax <- private$tp_opts[["n"]] # The file extension if (fo == "plain") ext <- ".txt" else ext <- ".RDS" @@ -87,17 +77,17 @@ TPGenerator <- R6::R6Class( n = 1, format = fo, save_tp = T, - dir = self$opts[["dir"]] + dir = private$tp_opts[["dir"]] ) # The combined tp data c_pre <- c_nw <- c_prob <- c() # For each ngram number, the transition probabilities data is # generated. - for (n in n_range) { + for (n in 1:nmax) { # The value of n is set tp_opts$n <- n # The transition probabilities or word list is generated - self$generate_tp_for_n(tp_opts) + self$generate_tp_for_n(n) # If n == 1, then word list data is saved if (n == 1) { # The combined tp data is saved @@ -105,22 +95,24 @@ TPGenerator <- R6::R6Class( } else { # c_pre is updated - c_pre <- c(c_pre, self$p_output$pre) + c_pre <- c(c_pre, private$p_output$pre) # c_nw is updated - c_nw <- c(c_nw, self$p_output$nw) + c_nw <- c(c_nw, private$p_output$nw) # c_prob is updated - c_prob <- c(c_prob, self$p_output$prob) + c_prob <- c(c_prob, private$p_output$prob) # The processed output is cleared - self$p_output <- data.frame() + private$p_output <- data.frame() } } # The processed output is set to the combined tp data - self$p_output <- + private$p_output <- data.frame("pre" = c_pre, "nw" = c_nw, "prob" = c_prob) + # The model file name + fn <- paste0("model-", nmax, ext) # The combined tp data is saved - private$save_data(paste0("model-", nmax, ext)) + private$save_data(fn) }, #' @description @@ -128,22 +120,9 @@ TPGenerator <- R6::R6Class( #' file. It generates a data frame containing the prefix, next word #' and next word frequency. The data frame may be saved to a file as #' plain text or as a R obj. For n = 1, the list of words is saved. - #' @param opts The options for generating the transition probabilities. - #' save_tp -> If the data should be saved. - #' n -> The ngram number - #' dir -> The location of the input and output files. - #' format -> The format of the input and output files. Options are: - #' 'plain' -> The data is stored in plain text. - #' 'obj' -> The data is stored as a R obj. - generate_tp_for_n = function(opts) { - # The opts is merged with the tp_opts attribute - private$tp_opts = modifyList(private$tp_opts, opts) - # The tp_opts is merged with the base class opts attribute - self$opts = modifyList(self$opts, private$tp_opts) + generate_tp_for_n = function(n) { # The output format - fo <- self$opts[["format"]] - # The ngram number - n <- self$opts[["n"]] + fo <- private$tp_opts[["format"]] # The output file name fn <- private$get_file_name(T) # If the output file already exists @@ -151,7 +130,7 @@ TPGenerator <- R6::R6Class( # The information message msg <- paste0("The file: ", fn, " already exists") # The information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # The file is read data <- private$read_data(fn, fo, T) # If n = 1 @@ -161,19 +140,19 @@ TPGenerator <- R6::R6Class( } else { # The processed output is set to the data - self$p_output <- data + private$p_output <- data } } else { # The information message msg <- paste0("Generating Transition Probabilities for n=", n) # Information message is shown - self$display_msg(msg, 1) + private$display_msg(msg, 1) # The input file name - self$file_name <- private$get_file_name(F) + private$file_name <- private$get_file_name(F) # The data is read - df <- private$read_data(self$file_name, fo, T) + df <- private$read_data(private$file_name, fo, T) # If n = 1 if (n == 1) { # The word list is set to the data frame @@ -201,13 +180,13 @@ TPGenerator <- R6::R6Class( "nw" = nw, "freq" = nf) # The processed output is set to the data frame - self$p_output <- df + private$p_output <- df # The next word probabilities are generated private$generate_probs() # The frequency column is removed - self$p_output$freq <- NULL + private$p_output$freq <- NULL # If the data should be saved - if (self$opts[["save_tp"]]) { + if (private$tp_opts[["save_tp"]]) { private$save_data() } } @@ -227,7 +206,7 @@ TPGenerator <- R6::R6Class( tp_opts = list( "save_tp" = F, "n" = 1, - "dir" = "./data/models", + "dir" = "./models", "format" = "obj" ), @@ -239,13 +218,13 @@ TPGenerator <- R6::R6Class( # saves the transition probability data to a file. generate_probs = function() { # Information message is shown - self$display_msg("Generating Transition Probabilities...", 1) + private$display_msg("Generating Transition Probabilities...", 1) # The ngram number - n <- self$opts[["n"]] + n <- private$tp_opts[["n"]] # If n > 1 if (n > 1) { # The output is copied to a variable - df <- self$p_output + df <- private$p_output # A new probability column is added. It is set to the sum of # frequency column for each prefix group. df <- df %>% @@ -254,7 +233,7 @@ TPGenerator <- R6::R6Class( # Each frequency is divided by the sum to give the probability. df$prob <- round(df$freq/df$prob, 8) # The output is set to the updated variable - self$p_output <- df + private$p_output <- df } }, @@ -263,11 +242,11 @@ TPGenerator <- R6::R6Class( # @param is_output If the output file name is required. get_file_name = function(is_output) { # The ngram number - n <- self$opts[["n"]] + n <- private$tp_opts[["n"]] # The directory - od <- self$opts[["dir"]] + od <- private$tp_opts[["dir"]] # The format - fo <- self$opts[["format"]] + fo <- private$tp_opts[["format"]] # The file extension if (fo == "plain") ext <- ".txt" else ext <- ".RDS" @@ -300,11 +279,11 @@ TPGenerator <- R6::R6Class( # @param file_name The file name to use. save_data = function(file_name = NULL) { # The ngram number - n <- self$opts[["n"]] + n <- private$tp_opts[["n"]] # The directory - od <- self$opts[["dir"]] + od <- private$tp_opts[["dir"]] # The format - fo <- self$opts[["format"]] + fo <- private$tp_opts[["format"]] # If n = 1 if (n == 1) { # The data to save @@ -313,7 +292,7 @@ TPGenerator <- R6::R6Class( # If n > 1 else if (n > 1) { # The data to save - data <- self$p_output + data <- private$p_output } # If the file name is given as parameter then it is used if (!is.null(file_name)) fn <- paste0(od, "/", file_name) @@ -328,14 +307,14 @@ TPGenerator <- R6::R6Class( # If the word list has not been read if (nrow(private$wl) == 0) { # The format - fo <- self$opts[["format"]] + fo <- private$tp_opts[["format"]] # The file extension if (fo == "plain") ext <- ".txt" else ext <- ".RDS" # The 1-gram words file name - fn <- paste0(self$opts[["dir"]], "/words", ext) + fn <- paste0(private$tp_opts[["dir"]], "/words", ext) # The words are read - private$wl <- private$read_data(fn, self$opts[["format"]], F) + private$wl <- private$read_data(fn, private$tp_opts[["format"]], F) } } ) diff --git a/inst/extdata/bad-words.txt b/inst/extdata/bad-words.txt new file mode 100644 index 0000000..d2dacc0 --- /dev/null +++ b/inst/extdata/bad-words.txt @@ -0,0 +1,1734 @@ +2 girls 1 cup +2g1c +4r5e +5h1t +5hit +a$$ +a$$hole +a_s_s +a2m +a54 +a55 +a55hole +aeolus +ahole +alabama hot pocket +alaskan pipeline +anal +anal impaler +anal leakage +analannie +analprobe +analsex +anilingus +anus +apeshit +ar5e +areola +areole +arian +arrse +arse +arsehole +aryan +ass +ass fuck +ass hole +assault +assbag +assbagger +assbandit +assbang +assbanged +assbanger +assbangs +assbite +assblaster +assclown +asscock +asscracker +asses +assface +assfaces +assfuck +assfucker +ass-fucker +assfukka +assgoblin +assh0le +asshat +ass-hat +asshead +assho1e +asshole +assholes +asshopper +asshore +ass-jabber +assjacker +assjockey +asskiss +asskisser +assklown +asslick +asslicker +asslover +assman +assmaster +assmonkey +assmucus +assmunch +assmuncher +assnigger +asspacker +asspirate +ass-pirate +asspuppies +assranger +assshit +assshole +asssucker +asswad +asswhole +asswhore +asswipe +asswipes +auto erotic +autoerotic +axwound +azazel +azz +b!tch +b00bs +b17ch +b1tch +babe +babeland +babes +baby batter +baby juice +badfuck +ball gag +ball gravy +ball kicking +ball licking +ball sack +ball sucking +ballbag +balllicker +balls +ballsack +bampot +bang +bang (one's) box +bangbros +banger +banging +bareback +barely legal +barenaked +barf +barface +barfface +bastard +bastardo +bastards +bastinado +batty boy +bawdy +bazongas +bazooms +bbw +bdsm +beaner +beaners +beardedclam +beastial +beastiality +beatch +beater +beatyourmeat +beaver +beaver cleaver +beaver lips +beef curtain +beef curtains +beer +beeyotch +bellend +bender +beotch +bestial +bestiality +bi+ch +biatch +bicurious +big black +big breasts +big knockers +big tits +bigbastard +bigbutt +bigger +bigtits +bimbo +bimbos +bint +birdlock +bisexual +bi-sexual +bitch +bitch tit +bitchass +bitched +bitcher +bitchers +bitches +bitchez +bitchin +bitching +bitchtits +bitchy +black cock +blonde action +blonde on blonde action +bloodclaat +bloody +bloody hell +blow +blow job +blow me +blow mud +blow your load +blowjob +blowjobs +blue waffle +blumpkin +boang +bod +bodily +bogan +bohunk +boink +boiolas +bollick +bollock +bollocks +bollok +bollox +bomd +bondage +bone +boned +boner +boners +bong +boob +boobies +boobs +booby +booger +bookie +boong +boonga +booobs +boooobs +booooobs +booooooobs +bootee +bootie +booty +booty call +booze +boozer +boozy +bosom +bosomy +bowel +bowels +bra +brassiere +breast +breastjob +breastlover +breastman +breasts +breeder +brotherfucker +brown showers +brunette action +buceta +bugger +buggered +buggery +bukkake +bull shit +bullcrap +bulldike +bulldyke +bullet vibe +bullshit +bullshits +bullshitted +bullturds +bum +bum boy +bumblefuck +bumclat +bumfuck +bummer +bung +bung hole +bunga +bunghole +bunny fucker +bust a load +busty +butchdike +butchdyke +butt +butt fuck +butt plug +buttbang +butt-bang +buttcheeks +buttface +buttfuck +butt-fuck +buttfucka +buttfucker +butt-fucker +butthead +butthole +buttman +buttmuch +buttmunch +buttmuncher +butt-pirate +buttplug +c.0.c.k +c.o.c.k. +c.u.n.t +c0ck +c-0-c-k +c0cksucker +caca +cahone +camel toe +cameltoe +camgirl +camslut +camwhore +carpet muncher +carpetmuncher +cawk +cervix +chesticle +chi-chi man +chick with a dick +child-fucker +chin +chinc +chincs +chink +chinky +choad +choade +choc ice +chocolate rosebuds +chode +chodes +chota bags +cipa +circlejerk +cl1t +cleveland steamer +climax +clit +clit licker +clitface +clitfuck +clitoris +clitorus +clits +clitty +clitty litter +clogwog +clover clamps +clunge +clusterfuck +cnut +cocain +cocaine +cock +c-o-c-k +cock pocket +cock snot +cock sucker +cockass +cockbite +cockblock +cockburger +cockeye +cockface +cockfucker +cockhead +cockholster +cockjockey +cockknocker +cockknoker +cocklicker +cocklover +cocklump +cockmaster +cockmongler +cockmongruel +cockmonkey +cockmunch +cockmuncher 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+yesteryear +yet +yhwnqc +yiddish +yield +yielded +yielding +yields +yin +yinyang +yip +yipping +yitbos +ymca +yodel +yoder +yoga +yoghurt +yogi +yogibear +yogurt +yoke +yoke's +yokel +yokes +yokohama +yokuts +yolanda +yolk +yon +yond +yonder +yonkers +yore +york +yorker +yorkers +yorktown +yosemite +yost +you +you'd +you'll +you're +you've +youknow +young +younger +youngest +youngish +youngly +youngster +youngster's +youngsters +youngstown +your +yourmom +yours +yourself +yourselves +yousuck +youth +youthful +youthfully +youthfulness +youths +yow +ypsilanti +yqlgr667 +ytterbium +yttrium +yucatan +yucca +yuck +yugoslav +yugoslavia +yuh +yuki +yukon +yule +yves +yvette +yvtte545 +ywca +yxcvbn +yxcvbnm +yy5rbfsc +yzerman +z +z's +zachary +zag +zagging +zagreb +zaire +zambia +zan +zanzibar +zap +zaphod +zapper +zapping +zaq12wsx +zaq1xsw2 +zaqwsx +zaqxsw +zardoz +zazen +zeal +zealand +zealot +zealous +zealously +zealousness +zebra +zebra's +zebras +zeiss +zellerbach +zen +zenith +zephyr +zeppelin +zero +zerocool +zeroed +zeroes +zeroing +zeros +zeroth +zest +zesty +zeta +zeus +zhuang +ziegler +zig +zigging +ziggy1 +zigzag +zigzagging +zilch +zildjian +zimmer +zimmerman +zinc +zing +zion +zionism +zip +zipper +zipping +zippy +zircon +zirconium +zloty +zlzfrh +zodiac +zodiacal +zoe +zomba +zombie +zonal +zonally +zone +zoned +zones +zoning +zoo +zoo's +zoological +zoologically +zoology +zoom +zooropa +zoos +zorn +zoroaster +zoroastrian +zounds +zsmj2v +ztmfcq +zucchini +zurich +zw6syj +zxcvbn +zxcvbnm +zygote diff --git a/inst/extdata/stop-words.txt b/inst/extdata/stop-words.txt new file mode 100644 index 0000000..f6e9200 --- /dev/null +++ b/inst/extdata/stop-words.txt @@ -0,0 +1,174 @@ +a +about +above +after +again +against +all +am +an +and +any +are +aren't +as +at +be +because +been +before +being +below +between +both +but +by +can't +cannot +could +couldn't +did +didn't +do +does +doesn't +doing +don't +down +during +each +few +for +from +further +had +hadn't +has +hasn't +have +haven't +having +he +he'd +he'll +he's +her +here +here's +hers +herself +him +himself +his +how +how's +i +i'd +i'll +i'm +i've +if +in +into +is +isn't +it +it's +its +itself +let's +me +more +most +mustn't +my +myself +no +nor +not +of +off +on +once +only +or +other +ought +our +ours +ourselves +out +over +own +same +shan't +she +she'd +she'll +she's +should +shouldn't +so +some +such +than +that +that's +the +their +theirs +them +themselves +then +there +there's +these +they +they'd +they'll +they're +they've +this +those +through +to +too +under +until +up +very +was +wasn't +we +we'd +we'll +we're +we've +were +weren't +what +what's +when +when's +where +where's +which +while +who +who's +whom +why +why's +with +won't +would +wouldn't +you +you'd +you'll +you're +you've +your +yours +yourself +yourselves diff --git a/man/DataAnalyzer.Rd b/man/DataAnalyzer.Rd new file mode 100644 index 0000000..5aba097 --- /dev/null +++ b/man/DataAnalyzer.Rd @@ -0,0 +1,182 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data-analyzer.R +\name{DataAnalyzer} +\alias{DataAnalyzer} +\title{It is used to analyze text data} +\description{ +It provides information on text files, such as number of lines +and number of words. It allows generating sample file from an input text +file. It displays bar plots showing word frequencies. +} +\details{ +It provides a method that returns text file information. The text +file information includes total number of lines, max, min and mean line +length and file size. It also provides a method that takes random samples of +lines in an input text file. It provides a method that reads an input text +file containing token frequencies. It displays the most occuring tokens. +} +\section{Super class}{ +\code{\link[wordpredictor:TextFileProcessor]{wordpredictor::TextFileProcessor}} -> \code{DataAnalyzer} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-new}{\code{DataAnalyzer$new()}} +\item \href{#method-plot_data}{\code{DataAnalyzer$plot_data()}} +\item \href{#method-get_file_info}{\code{DataAnalyzer$get_file_info()}} +\item \href{#method-generate_data}{\code{DataAnalyzer$generate_data()}} +\item \href{#method-get_ngrams}{\code{DataAnalyzer$get_ngrams()}} +\item \href{#method-clone}{\code{DataAnalyzer$clone()}} +} +} +\if{html}{ +\out{
Inherited methods} +\itemize{ +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-display_msg}{\code{wordpredictor::TextFileProcessor$display_msg()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-format_size}{\code{wordpredictor::TextFileProcessor$format_size()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-post_process}{\code{wordpredictor::TextFileProcessor$post_process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-pre_process}{\code{wordpredictor::TextFileProcessor$pre_process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-process}{\code{wordpredictor::TextFileProcessor$process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-process_file}{\code{wordpredictor::TextFileProcessor$process_file()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_file}{\code{wordpredictor::TextFileProcessor$read_file()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_lines}{\code{wordpredictor::TextFileProcessor$read_lines()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_obj}{\code{wordpredictor::TextFileProcessor$read_obj()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-save_obj}{\code{wordpredictor::TextFileProcessor$save_obj()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-write_file}{\code{wordpredictor::TextFileProcessor$write_file()}}\out{} +} +\out{
} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-new}{}}} +\subsection{Method \code{new()}}{ +It initializes the current object. It is used to set the file name +and verbose options. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{DataAnalyzer$new(file_name = NULL, verbose = 0)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{file_name}}{The path to the input file.} + +\item{\code{verbose}}{If progress information should be displayed.} + +\item{\code{line_count}}{The number of lines to read at a time.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-plot_data}{}}} +\subsection{Method \code{plot_data()}}{ +It reads ngram token frequencies from an input text file. The ngram +frequencies are then displayed in a bar plot. The type of plot is +specified by the type option. 'top_features' displays the top n most +occuring tokens along with their frequencies. 'coverage' displays +the number of words along with their frequencies. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{DataAnalyzer$plot_data(opts)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{opts}}{The options for analyzing the data. +type -> The type of plot to display. The options are: +'top_features', 'coverage'. +n -> For 'top_features', it is the number of top most occuring +tokens.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-get_file_info}{}}} +\subsection{Method \code{get_file_info()}}{ +Generates and returns information about the given text files. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{DataAnalyzer$get_file_info(file_list = list())}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{file_list}}{The list of text files to check.} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +A data frame containing the overall file statistics. +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-generate_data}{}}} +\subsection{Method \code{generate_data()}}{ +It generates training, testing and validation data sets +from the given input file. It first reads the file given as a +parameter to the current object. It generates random indexes for the +data. It partitions the data into training, testing and validation +sets, according to the given parameters. The files are named +train.txt, test.txt and va.txt. The files are saved to the given +output folder. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{DataAnalyzer$generate_data(dir, percs)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{dir}}{The name of the output folder.} + +\item{\code{percs}}{The size of the training, testing and validation sets.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-get_ngrams}{}}} +\subsection{Method \code{get_ngrams()}}{ +Returns the given number of ngrams and their +frequencies. If the prefix parameter is not given, then the ngrams +are randomly chosen. Otherise ngrams starting with the given regular +expression are returned. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{DataAnalyzer$get_ngrams(fn, c = NULL, pre = NULL)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{fn}}{The ngram file name.} + +\item{\code{c}}{The number of ngrams to return.} + +\item{\code{pre}}{The ngram prefix, given as a regular expression.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{DataAnalyzer$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/man/DataCleaner.Rd b/man/DataCleaner.Rd new file mode 100644 index 0000000..44a805c --- /dev/null +++ b/man/DataCleaner.Rd @@ -0,0 +1,155 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data-cleaner.R +\name{DataCleaner} +\alias{DataCleaner} +\title{Provides data cleaning functionality} +\description{ +Allows removing unneeded characters from text files. +} +\details{ +It provides a method for cleaning text files. It allows removing bad words, +stop words, non dictionary words, extra space, punctuation and non-alphabet +characters. Converting text to lower case. It supports large text files. +} +\section{Super class}{ +\code{\link[wordpredictor:TextFileProcessor]{wordpredictor::TextFileProcessor}} -> \code{DataCleaner} +} +\section{Public fields}{ +\if{html}{\out{
}} +\describe{ +\item{\code{dc_opts}}{The options for the data cleaner object. +min_words -> The minimum number of words per sentence. +line_count -> The number of lines to read and clean at a time. +sw_file -> The stop words file path. +bad_file -> The bad words file path. +to_lower -> If the words should be converted to lower case. +remove_stop -> If stop words should be removed. +remove_punct -> If punctuation symbols should be removed. +remove_non_dict -> If non dictionary words should be removed. +remove_non_alpha -> If non alphabet symbols should be removed. +remove_extra_space -> If leading, trailing and double spaces +should be removed. +remove_bad -> If bad words should be removed} + +\item{\code{sw}}{The list of stop words.} + +\item{\code{bw}}{The list of bad words.} + +\item{\code{dw}}{The list of dictionary words.} +} +\if{html}{\out{
}} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-new}{\code{DataCleaner$new()}} +\item \href{#method-clean_file}{\code{DataCleaner$clean_file()}} +\item \href{#method-clean_lines}{\code{DataCleaner$clean_lines()}} +\item \href{#method-clone}{\code{DataCleaner$clone()}} +} +} +\if{html}{ +\out{
Inherited methods} +\itemize{ +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-display_msg}{\code{wordpredictor::TextFileProcessor$display_msg()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-format_size}{\code{wordpredictor::TextFileProcessor$format_size()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-post_process}{\code{wordpredictor::TextFileProcessor$post_process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-pre_process}{\code{wordpredictor::TextFileProcessor$pre_process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-process}{\code{wordpredictor::TextFileProcessor$process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-process_file}{\code{wordpredictor::TextFileProcessor$process_file()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_file}{\code{wordpredictor::TextFileProcessor$read_file()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_lines}{\code{wordpredictor::TextFileProcessor$read_lines()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_obj}{\code{wordpredictor::TextFileProcessor$read_obj()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-save_obj}{\code{wordpredictor::TextFileProcessor$save_obj()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-write_file}{\code{wordpredictor::TextFileProcessor$write_file()}}\out{} +} +\out{
} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-new}{}}} +\subsection{Method \code{new()}}{ +It initializes the current object. It is used to set the file name +and verbose options. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{DataCleaner$new(file_name = NULL, opts = list(), verbose = 0)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{file_name}}{The path to the file to clean.} + +\item{\code{opts}}{The options for data cleaning. +min_words -> The minimum number of words per sentence. +line_count -> The number of lines to read and clean at a time. +sw_file -> The stop words file path. +bad_file -> The bad words file path. +to_lower -> If the words should be converted to lower case. +remove_stop -> If stop words should be removed. +remove_punct -> If punctuation symbols should be removed. +remove_non_dict -> If non dictionary words should be removed. +remove_non_alpha -> If non alphabet symbols should be removed. +remove_extra_space -> If leading, trailing and double spaces +should be removed. +remove_bad -> If bad words should be removed} + +\item{\code{verbose}}{Indicates if progress information should be displayed.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-clean_file}{}}} +\subsection{Method \code{clean_file()}}{ +It removes unneeded characters from the given text with +several options. It allows removing punctuations, numbers, symbols, +urls and separators. It allows removing bad words, stop words and +words not in the given dictionary file. It reads the given file one +line at a time, removing unneeded characters. After every line_count +number of lines, the cleaned lines are saved to the output file. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{DataCleaner$clean_file()}\if{html}{\out{
}} +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-clean_lines}{}}} +\subsection{Method \code{clean_lines()}}{ +It cleans the given lines of text using the options +passed to the current object. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{DataCleaner$clean_lines(lines)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{lines}}{The input sentences.} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +The cleaned lines of text. +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{DataCleaner$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/man/ModelEvaluator.Rd b/man/ModelEvaluator.Rd new file mode 100644 index 0000000..1f25cf0 --- /dev/null +++ b/man/ModelEvaluator.Rd @@ -0,0 +1,407 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/model-evaluator.R +\name{ModelEvaluator} +\alias{ModelEvaluator} +\title{It is used to evaluate the accuracy and performance of the model.} +\description{ +It provides methods that perform extrinsic and intrinsic model +evaluation. It also provides methods for determining the memory and time +requirements for generating the model. It also provides a method for +determining how much memory is used by the final model. +} +\details{ +It provides a method that performs intrinsic model evaluation based +on Perplexity. It also provides a method that performs extrinsic model +evalation based on accuracy. It provides a method for determining how much +memory and time is needed to generate a model for different input data +sizes. It provides a method for determining how much memory is needed by +the final model. +} +\section{Super class}{ +\code{\link[wordpredictor:TextFileProcessor]{wordpredictor::TextFileProcessor}} -> \code{ModelEvaluator} +} +\section{Public fields}{ +\if{html}{\out{
}} +\describe{ +\item{\code{tp}}{The transition probabilities data frame.} + +\item{\code{wl}}{The list of unique words.} + +\item{\code{dp}}{The default probability is equal to 1/(N+V), where N is the +number of words in the sentence, V is the number of words in the +vocabulary.} + +\item{\code{model}}{The maximum number of ngrams supported by the model.} + +\item{\code{dc_opts}}{The options for the data cleaner object. +min_words -> The minimum number of words per sentence. +line_count -> The number of lines to read and clean at a time. +sw_file -> The stop words file path. +bad_file -> The bad words file path. +to_lower -> If the words should be converted to lower case. +remove_stop -> If stop words should be removed. +remove_punct -> If punctuation symbols should be removed. +remove_non_dict -> If non dictionary words should be removed. +remove_non_alpha -> If non alphabet symbols should be removed. +remove_extra_space -> If leading, trailing and double spaces +should be removed. +remove_bad -> If bad words should be removed} + +\item{\code{tg_opts}}{The options for the token generator obj. +n -> The ngram size. +save_ngrams -> If the ngram data should be saved. +min_freq -> All ngrams with frequency less than min_freq are +ignored. +line_count -> The number of lines to process at a time. +stem_words -> If words should be converted to their stem. +dir -> The dir where the output file should be saved. +format -> The format for the output. There are two options. +'plain' -> The data is stored in plain text. +'obj' -> The data is stored as a R obj.} + +\item{\code{ssize}}{The sample size in Mb.} + +\item{\code{ddir}}{The folder containing the data files} + +\item{\code{mdir}}{The folder containing the model files} +} +\if{html}{\out{
}} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-new}{\code{ModelEvaluator$new()}} +\item \href{#method-performance_comparision}{\code{ModelEvaluator$performance_comparision()}} +\item \href{#method-performance_evaluation}{\code{ModelEvaluator$performance_evaluation()}} +\item \href{#method-load_config}{\code{ModelEvaluator$load_config()}} +\item \href{#method-load_model}{\code{ModelEvaluator$load_model()}} +\item \href{#method-save_config}{\code{ModelEvaluator$save_config()}} +\item \href{#method-generate_model}{\code{ModelEvaluator$generate_model()}} +\item \href{#method-intrinsic_evaluation}{\code{ModelEvaluator$intrinsic_evaluation()}} +\item \href{#method-extrinsic_evaluation}{\code{ModelEvaluator$extrinsic_evaluation()}} +\item \href{#method-predict_word}{\code{ModelEvaluator$predict_word()}} +\item \href{#method-get_word_prob}{\code{ModelEvaluator$get_word_prob()}} +\item \href{#method-calc_perplexity}{\code{ModelEvaluator$calc_perplexity()}} +\item \href{#method-generate_clean_sample}{\code{ModelEvaluator$generate_clean_sample()}} +\item \href{#method-clone}{\code{ModelEvaluator$clone()}} +} +} +\if{html}{ +\out{
Inherited methods} +\itemize{ +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-display_msg}{\code{wordpredictor::TextFileProcessor$display_msg()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-format_size}{\code{wordpredictor::TextFileProcessor$format_size()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-post_process}{\code{wordpredictor::TextFileProcessor$post_process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-pre_process}{\code{wordpredictor::TextFileProcessor$pre_process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-process}{\code{wordpredictor::TextFileProcessor$process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-process_file}{\code{wordpredictor::TextFileProcessor$process_file()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_file}{\code{wordpredictor::TextFileProcessor$read_file()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_lines}{\code{wordpredictor::TextFileProcessor$read_lines()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_obj}{\code{wordpredictor::TextFileProcessor$read_obj()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-save_obj}{\code{wordpredictor::TextFileProcessor$save_obj()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-write_file}{\code{wordpredictor::TextFileProcessor$write_file()}}\out{} +} +\out{
} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-new}{}}} +\subsection{Method \code{new()}}{ +It initializes the current object. It is used to set the +maximum ngram number, sample size, input file name, data cleaner +options and verbose option. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$new( + model = 4, + ssize = 30, + ddir = "./data", + mdir = "./data/models", + dc_opts = list(), + tg_opts = list(), + verbose = 0 +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{model}}{The maximum ngram number supported by the model.} + +\item{\code{ssize}}{The sample size in Mb.} + +\item{\code{ddir}}{The data directory.} + +\item{\code{mdir}}{The model directory.} + +\item{\code{dc_opts}}{The data cleaner options.} + +\item{\code{tg_opts}}{The token generator options.} + +\item{\code{verbose}}{If progress information should be displayed.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-performance_comparision}{}}} +\subsection{Method \code{performance_comparision()}}{ +It compares the performance of the specified models by +plotting the performance statistics. The models are specified with +the type parameter. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$performance_comparision(type, opts)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{type}}{The models to compare. It can be: +'basic' -> One model for each ngram. +'grouped' -> One model for each group and ngram. For +e.g For data size 5 Mb, there are 3 models for the ngrams 2:4.} + +\item{\code{opts}}{The options for plotting the data. +'group' -> The field to group by. +'title' -> The main plot title. +'subtitle' -> The plot sub title.} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +The performance stats. +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-performance_evaluation}{}}} +\subsection{Method \code{performance_evaluation()}}{ +For each model it performs intrinsic and extrinsic +evaluation. It also measures the memory usage and time taken. The +performance metrics are displayed in 5 plots on one page. Performance +statistics are saved to the file pstats.RDS. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$performance_evaluation()}\if{html}{\out{
}} +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-load_config}{}}} +\subsection{Method \code{load_config()}}{ +It loads the given model configuration file. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$load_config(fn)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{fn}}{The config file name.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-load_model}{}}} +\subsection{Method \code{load_model()}}{ +It loads the model located at the mdir location +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$load_model()}\if{html}{\out{
}} +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-save_config}{}}} +\subsection{Method \code{save_config()}}{ +It saves the model configuration to the models +subdirectory of the given directory. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$save_config()}\if{html}{\out{
}} +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-generate_model}{}}} +\subsection{Method \code{generate_model()}}{ +It generates the model for the given ngram number, +data sample size, data cleaning options and input file. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$generate_model()}\if{html}{\out{
}} +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-intrinsic_evaluation}{}}} +\subsection{Method \code{intrinsic_evaluation()}}{ +Evaluates the model using intrinsic evaluation based on +Perplexity. First a validation data set containing 1000 lines is +generated. It is then cleaned. 20 random sentences are taken. For +each sentence, Perplexity of all the words is calculated. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$intrinsic_evaluation()}\if{html}{\out{
}} +} + +\subsection{Returns}{ +The minumum, maximum and mean Perplexity score. +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-extrinsic_evaluation}{}}} +\subsection{Method \code{extrinsic_evaluation()}}{ +Evaluates the model using extrinsic evaluation based on +Accuracy. First a validation data set containing 1000 lines is +generated. It is then cleaned. 20 random sentences are taken. For +each sentence, the model is used to predict the next word. The +accuracy stats are returned. A prediction is considered to be correct +if one of the predicted words matched the actual word. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$extrinsic_evaluation()}\if{html}{\out{
}} +} + +\subsection{Returns}{ +The number of correct and incorrect predictions. +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-predict_word}{}}} +\subsection{Method \code{predict_word()}}{ +Predicts the new word given a list of 1, 2 or 3 previous +words. It checks the given n words in the transition probabilities +data. If there is a match, the top 3 next words with highest +probabilities are returned. If there is no match, then the last n-1 +previous words are checked. This process is continued until the last +word is checked. If there is no match, then empty result is returned. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$predict_word(words, count = 3, dc = NULL)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{words}}{A character vector of previous words or a single vector +containing the previous word text.} + +\item{\code{count}}{The number of results to return.} + +\item{\code{dc}}{A DataCleaner object. If it is given, then the given words +are cleaned. If the stem_words option was set in the TokenGenerator +object configuration for the current model, then the words are +converted to their stems.} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +The top 3 predicted words along with their probabilities. +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-get_word_prob}{}}} +\subsection{Method \code{get_word_prob()}}{ +Calculates the probability of the given word given the +previous model-1 words, where model is the maximum ngram number. It +looks up the probability of a word given n previous words. The +previous n words are converted to numeric hash using digest2int +function. The hash is looked up in a data frame of transition +probabilities. The word is converted to a number by checking its +position in a list of unique words. If the hash and the word position +were found, then the probability of the previous word and hash is +returned. If it was not found, then the hash of the n-1 previous +words is taken and the processed is repeated. If the data was not +found in the data frame, then the word probability is returned. This +is known as backoff. If the word probability could not be found then +the default probability is returned. The default probability is +calculated as 1/(N+V), Where N = number of words in corpus and V is +the number of dictionary words. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$get_word_prob(word, pw)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{word}}{The word whoose probability is to be calculated.} + +\item{\code{pw}}{The previous words.} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +The probability of the word given the previous words. +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-calc_perplexity}{}}} +\subsection{Method \code{calc_perplexity()}}{ +The Perplexity for the given sentence is calculated. For +each word, the probability of the word given the previous words is +calculated. The probabilities are multiplied and then inverted. The +nth root of the result is the perplexity, where n is the number of +words in the sentence. If the stem_words tokenization option was +specified, then the previous words are converted to their stem. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$calc_perplexity(words)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{words}}{The list of words.} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +The perplexity of the given list of words. +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-generate_clean_sample}{}}} +\subsection{Method \code{generate_clean_sample()}}{ +Generates a sample file of given size from the main +train.txt file file name. The file is cleaned and saved. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$generate_clean_sample(ss, ic, t)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{ss}}{The number of lines or proportion of lines to sample.} + +\item{\code{ic}}{If the sample file should be cleaned.} + +\item{\code{t}}{The type of sample. It can be: +'tr' -> training +'te' -> testing +'va' -> validation} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{ModelEvaluator$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/man/TPGenerator.Rd b/man/TPGenerator.Rd new file mode 100644 index 0000000..d2379ff --- /dev/null +++ b/man/TPGenerator.Rd @@ -0,0 +1,159 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/tp-generator.R +\name{TPGenerator} +\alias{TPGenerator} +\title{It is used to generate the transition probabilities} +\description{ +It implements Markov Chains for ngrams. It generates transition +probabilities for ngrams. +} +\details{ +It reads ngram frequencies from an input text file. It parses each +ngram into a prefix, a next word and the next word frequency. The prefix is +converted to a numeric hash using the digest2int function from the digest +package. The next word is replaced with the position of the next word in the +list of all words. The data is stored in a data frame. It may be saved to a +file. +} +\section{Super class}{ +\code{\link[wordpredictor:TextFileProcessor]{wordpredictor::TextFileProcessor}} -> \code{TPGenerator} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-new}{\code{TPGenerator$new()}} +\item \href{#method-trim_tp}{\code{TPGenerator$trim_tp()}} +\item \href{#method-generate_tp}{\code{TPGenerator$generate_tp()}} +\item \href{#method-generate_tp_for_n}{\code{TPGenerator$generate_tp_for_n()}} +\item \href{#method-clone}{\code{TPGenerator$clone()}} +} +} +\if{html}{ +\out{
Inherited methods} +\itemize{ +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-display_msg}{\code{wordpredictor::TextFileProcessor$display_msg()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-format_size}{\code{wordpredictor::TextFileProcessor$format_size()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-post_process}{\code{wordpredictor::TextFileProcessor$post_process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-pre_process}{\code{wordpredictor::TextFileProcessor$pre_process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-process}{\code{wordpredictor::TextFileProcessor$process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-process_file}{\code{wordpredictor::TextFileProcessor$process_file()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_file}{\code{wordpredictor::TextFileProcessor$read_file()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_lines}{\code{wordpredictor::TextFileProcessor$read_lines()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_obj}{\code{wordpredictor::TextFileProcessor$read_obj()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-save_obj}{\code{wordpredictor::TextFileProcessor$save_obj()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-write_file}{\code{wordpredictor::TextFileProcessor$write_file()}}\out{} +} +\out{
} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-new}{}}} +\subsection{Method \code{new()}}{ +It initializes the current obj. It is used to set the verbose option. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TPGenerator$new(verbose = 0)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{verbose}}{If progress information should be displayed.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-trim_tp}{}}} +\subsection{Method \code{trim_tp()}}{ +It groups the given tp data by prefix. Each prefix has +the top c next words with the highest probabilities. All other next +words are removed. The trimmed tp data may be saved to a file or +returned. The file is saved with the suffix -min. +e.g model-4-min.RDS. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TPGenerator$trim_tp(opts)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{opts}}{The options for trimming the tp data. +save_tp -> If the data should be saved. +dir -> The dir where the output file should be saved. +c -> The top c next words per ngrams prefix. +m -> The maximum ngram number supported by the model.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-generate_tp}{}}} +\subsection{Method \code{generate_tp()}}{ +It generates the transition probabilities for the given +ngram numbers It first generates the transition probabilities for +each ngram number. The transition probabilities are then combined +into a single data frame. The data frame may be saved to a file. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TPGenerator$generate_tp(opts)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{opts}}{The options for generating the transition probabilities. +save_tp -> If the data should be saved. +n_range -> The range of ngram numbers. +dir -> The dir where the output file should be saved. +format -> The format for the output. There are two options. +'plain' -> The data is stored in plain text. +'obj' -> The data is stored as a R obj.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-generate_tp_for_n}{}}} +\subsection{Method \code{generate_tp_for_n()}}{ +It reads ngram token frequencies from an input text +file. It generates a data frame containing the prefix, next word +and next word frequency. The data frame may be saved to a file as +plain text or as a R obj. For n = 1, the list of words is saved. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TPGenerator$generate_tp_for_n(opts)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{opts}}{The options for generating the transition probabilities. +save_tp -> If the data should be saved. +n -> The ngram number +dir -> The location of the input and output files. +format -> The format of the input and output files. Options are: +'plain' -> The data is stored in plain text. +'obj' -> The data is stored as a R obj.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TPGenerator$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/man/TextFileProcessor.Rd b/man/TextFileProcessor.Rd new file mode 100644 index 0000000..981f015 --- /dev/null +++ b/man/TextFileProcessor.Rd @@ -0,0 +1,312 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/text-file-processor.R +\name{TextFileProcessor} +\alias{TextFileProcessor} +\title{Base class for text file processing} +\description{ +Provides basic structure for processing text files. +} +\details{ +It provides pre-processing, processing and post-processing functions, which +need to be overridden by derived classes. The pre-processing function is +called before reading a file. The process function is called for processing a +line. The post processing function is called on the processed data. It also +provides a method for generating a sample file from an input text file +} +\section{Public fields}{ +\if{html}{\out{
}} +\describe{ +\item{\code{opts}}{The list of file processing options. +save_data -> If the combined processed lines should be saved. +output_file -> Name of the output file used to store the data.} + +\item{\code{line_count}}{The number of lines to read and process at a time.} + +\item{\code{pp_output}}{The output of the pre-processing step} + +\item{\code{p_output}}{The output of the processing step} + +\item{\code{file_name}}{The name of the text file to process.} + +\item{\code{verbose}}{Indicates if progress data should be printed.} +} +\if{html}{\out{
}} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-new}{\code{TextFileProcessor$new()}} +\item \href{#method-process_file}{\code{TextFileProcessor$process_file()}} +\item \href{#method-read_file}{\code{TextFileProcessor$read_file()}} +\item \href{#method-read_lines}{\code{TextFileProcessor$read_lines()}} +\item \href{#method-write_file}{\code{TextFileProcessor$write_file()}} +\item \href{#method-format_size}{\code{TextFileProcessor$format_size()}} +\item \href{#method-save_obj}{\code{TextFileProcessor$save_obj()}} +\item \href{#method-read_obj}{\code{TextFileProcessor$read_obj()}} +\item \href{#method-display_msg}{\code{TextFileProcessor$display_msg()}} +\item \href{#method-process}{\code{TextFileProcessor$process()}} +\item \href{#method-post_process}{\code{TextFileProcessor$post_process()}} +\item \href{#method-pre_process}{\code{TextFileProcessor$pre_process()}} +\item \href{#method-clone}{\code{TextFileProcessor$clone()}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-new}{}}} +\subsection{Method \code{new()}}{ +It initializes the current object. It is used to set the file name +and verbose options. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$new( + file_name = "./data/sample.txt", + line_count = 100, + verbose = 2 +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{file_name}}{The path to the file to clean.} + +\item{\code{line_count}}{The number of lines to read and clean at a time.} + +\item{\code{verbose}}{Indicates if progress information should be displayed.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-process_file}{}}} +\subsection{Method \code{process_file()}}{ +Reads the given file one line at a time. It runs the given +pre-processing function before reading the file. It runs the given +line processing function for each line. It optionally saves the +output of line processing after reading the file or after processing +certain number of lines. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$process_file(pre_process, process, post_process)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{pre_process}}{The pre-processing function.} + +\item{\code{process}}{The function used to process each line.} + +\item{\code{post_process}}{The function used to perform post processing.} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +The combined processed data +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-read_file}{}}} +\subsection{Method \code{read_file()}}{ +Reads the given file and returns its contents. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$read_file(file_name, is_csv)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{file_name}}{The name of the file to read.} + +\item{\code{is_csv}}{If the data is a csv file} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +The file data +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-read_lines}{}}} +\subsection{Method \code{read_lines()}}{ +Reads the given number of lines from the given file. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$read_lines(file_name, line_count)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{file_name}}{The name of the file to read.} + +\item{\code{line_count}}{The number of lines to read.} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +The file data +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-write_file}{}}} +\subsection{Method \code{write_file()}}{ +Writes the given data to the given file. The data may be appended to +an existing file. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$write_file(data, file_name, is_append)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{data}}{The data to be written.} + +\item{\code{file_name}}{The name of the file.} + +\item{\code{is_append}}{Indicates if data should be saved.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-format_size}{}}} +\subsection{Method \code{format_size()}}{ +Calculates the size of the given object or formats the given bytes +object as a number without units. The returned number is the size in +Mb. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$format_size(obj)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{obj}}{An object of class bytes or an object whoose size is to be +found.} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +The size formatted as a string. +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-save_obj}{}}} +\subsection{Method \code{save_obj()}}{ +Saves the given object as a file. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$save_obj(obj, file_name)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{obj}}{The object to save.} + +\item{\code{file_name}}{The file name.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-read_obj}{}}} +\subsection{Method \code{read_obj()}}{ +Reads the contents of the given file. Loads the file +contents to a R object. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$read_obj(file_name)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{file_name}}{The file name.} +} +\if{html}{\out{
}} +} +\subsection{Returns}{ +The loaded R obj. +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-display_msg}{}}} +\subsection{Method \code{display_msg()}}{ +Prints the given message depending on verbose settings. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$display_msg(msg, min_debug)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{msg}}{The message to be printed.} + +\item{\code{min_debug}}{The minimum debugging level} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-process}{}}} +\subsection{Method \code{process()}}{ +Performs processing on the data. It should be +overriden by a derived class. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$process(lines)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{lines}}{The lines to process} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-post_process}{}}} +\subsection{Method \code{post_process()}}{ +Performs post-processing on the processed data. It should be +overriden by a derived class. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$post_process()}\if{html}{\out{
}} +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-pre_process}{}}} +\subsection{Method \code{pre_process()}}{ +Performs pre-processing on the processed data. It should be +overriden by a derived class. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$pre_process()}\if{html}{\out{
}} +} + +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TextFileProcessor$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/man/TokenGenerator.Rd b/man/TokenGenerator.Rd new file mode 100644 index 0000000..4842e4a --- /dev/null +++ b/man/TokenGenerator.Rd @@ -0,0 +1,126 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/token-generator.R +\name{TokenGenerator} +\alias{TokenGenerator} +\title{It generates ngrams of given size from an input text file} +\description{ +It generates ngram tokens along with their frequencies. +} +\details{ +It provides a method for generating ngrams of given size. It saves +each ngram along with its frequency to a text file. +} +\section{Super class}{ +\code{\link[wordpredictor:TextFileProcessor]{wordpredictor::TextFileProcessor}} -> \code{TokenGenerator} +} +\section{Public fields}{ +\if{html}{\out{
}} +\describe{ +\item{\code{tg_opts}}{The options for the token generator obj. +n -> The ngram size. +save_ngrams -> If the ngram data should be saved. +min_freq -> All ngrams with frequency less than min_freq are +ignored. +line_count -> The number of lines to process at a time. +stem_words -> If words should be converted to their stem. +dir -> The dir where the output file should be saved. +format -> The format for the output. There are two options. +'plain' -> The data is stored in plain text. +'obj' -> The data is stored as a R obj.} +} +\if{html}{\out{
}} +} +\section{Methods}{ +\subsection{Public methods}{ +\itemize{ +\item \href{#method-new}{\code{TokenGenerator$new()}} +\item \href{#method-generate_tokens}{\code{TokenGenerator$generate_tokens()}} +\item \href{#method-clone}{\code{TokenGenerator$clone()}} +} +} +\if{html}{ +\out{
Inherited methods} +\itemize{ +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-display_msg}{\code{wordpredictor::TextFileProcessor$display_msg()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-format_size}{\code{wordpredictor::TextFileProcessor$format_size()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-post_process}{\code{wordpredictor::TextFileProcessor$post_process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-pre_process}{\code{wordpredictor::TextFileProcessor$pre_process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-process}{\code{wordpredictor::TextFileProcessor$process()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-process_file}{\code{wordpredictor::TextFileProcessor$process_file()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_file}{\code{wordpredictor::TextFileProcessor$read_file()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_lines}{\code{wordpredictor::TextFileProcessor$read_lines()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-read_obj}{\code{wordpredictor::TextFileProcessor$read_obj()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-save_obj}{\code{wordpredictor::TextFileProcessor$save_obj()}}\out{} +\item \out{}\href{../../wordpredictor/html/TextFileProcessor.html#method-write_file}{\code{wordpredictor::TextFileProcessor$write_file()}}\out{} +} +\out{
} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-new}{}}} +\subsection{Method \code{new()}}{ +It initializes the current obj. It is used to set the file name +and verbose options. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TokenGenerator$new( + file_name = "./data/models/validate-clean.txt", + opts = self$tg_opts, + verbose = 0 +)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{file_name}}{The path to the input file.} + +\item{\code{opts}}{The options for generating the ngram tokens. +n -> The ngram size. +save_ngrams -> If the ngram data should be saved. +min_freq -> All ngrams with frequency less than min_freq are +ignored. +line_count -> The number of lines to process at a time. +stem_words -> If words should be converted to their stem. +dir -> The dir where the output file should be saved. +format -> The format for the output. There are two options. +'plain' -> The data is stored in plain text. +'obj' -> The data is stored as a R obj.} + +\item{\code{verbose}}{Indicates if progress information should be displayed.} +} +\if{html}{\out{
}} +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-generate_tokens}{}}} +\subsection{Method \code{generate_tokens()}}{ +It generates ngram tokens and their frequencies from the given file +name. The tokens may be saved to a text file as plain text or a R +obj. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TokenGenerator$generate_tokens()}\if{html}{\out{
}} +} + +\subsection{Returns}{ +The ngram tokens along with their frequencies. +} +} +\if{html}{\out{
}} +\if{html}{\out{}} +\if{latex}{\out{\hypertarget{method-clone}{}}} +\subsection{Method \code{clone()}}{ +The objects of this class are cloneable with this method. +\subsection{Usage}{ +\if{html}{\out{
}}\preformatted{TokenGenerator$clone(deep = FALSE)}\if{html}{\out{
}} +} + +\subsection{Arguments}{ +\if{html}{\out{
}} +\describe{ +\item{\code{deep}}{Whether to make a deep clone.} +} +\if{html}{\out{
}} +} +} +} diff --git a/tests/testthat.R b/tests/testthat.R new file mode 100644 index 0000000..7fd76ea --- /dev/null +++ b/tests/testthat.R @@ -0,0 +1,4 @@ +library(testthat) +library(wordpredictor) + +test_check("wordpredictor") diff --git a/tests/testthat/test-data-analyzer.R b/tests/testthat/test-data-analyzer.R new file mode 100644 index 0000000..af5eaca --- /dev/null +++ b/tests/testthat/test-data-analyzer.R @@ -0,0 +1,45 @@ +test_that("Performance of random sample generator is acceptable", { + time_taken <- system.time({ + memory_used <- mem_change({ + # The DataAnalyzer object is created + da <- DataAnalyzer$new("./data/en_US.input.txt") + # The options + opts <- list("save_data" = T, "output_file" = "./data/sample.txt") + # The sample file is generated + data <- da$generate_sample(1000, opts) + }); + }); + # The memory used + memory_used <- da$format_size(memory_used) + # The time taken is tested + expect_lt(time_taken[[3]], 5) + # The memory usage is tested + expect_lt(memory_used, 2) + # The number of lines generated should be 1000 + expect_equal(length(data), 1000) +}); + +test_that("File information is corrected calculated", { + # The DataAnalyzer object is created + da <- DataAnalyzer$new("./data/sample.txt") + # The sample file is generated + file_info <- da$get_file_info("./data/sample.txt") + # The total line count is checked + expect_equal(file_info$total_line_count, 219680) + # The mean line length is checked + expect_equal(file_info$mean_line_length, 135) + # The total size is checked + expect_equal(file_info$total_size, "28.5 Mb") +}); + +test_that("plot_data function works", { + # The DataAnalyzer object is created + da <- DataAnalyzer$new("./data/n2.txt") + # The top features plot is checked + da$plot_data(da_opts) + # The coverage plot is checked + da_opts$type <- "coverage" + da$plot_data(da_opts) + # Checks that Rplots.pdf file exists + expect_true(file.exists("./Rplots.pdf")) +}); diff --git a/tests/testthat/test-data-cleaner.R b/tests/testthat/test-data-cleaner.R new file mode 100644 index 0000000..00397b9 --- /dev/null +++ b/tests/testthat/test-data-cleaner.R @@ -0,0 +1,52 @@ +test_that("Performance of file cleaning function is acceptable", { + time_taken <- system.time({ + memory_used <- mem_change({ + # The data cleaner object is created + dc <- DataCleaner$new("./data/models/test.txt") + # The sample input file is cleaned + dc$clean_file() + }); + }); + # The memory used + memory_used <- dc$format_size(memory_used) + # The time taken is tested + expect_lt(time_taken[[3]], 2) + # The memory usage is tested + expect_lt(memory_used, 2) +}); + +test_that("Sample line of text are cleaned as expected", { + # Test data is read + l <- c( + '"Follow me on Twitter if you want," Brown told viewers' + ) + # The expected results + res <- c( + "follow me on twitter if you want brown told viewers" + ) + # The DataCleaner object is created + dc <- DataCleaner$new() + # The line is cleaned + cl <- dc$clean_lines(l) + # The actual cleaned data is compared with expected data + expect_equal(cl, res) +}); + +test_that("Performance of line cleaning function is acceptable", { + time_taken <- system.time({ + memory_used <- mem_change({ + # The DataCleaner object is created + dc <- DataCleaner$new() + # The test file is read + lines <- dc$read_lines("./data/models/test.txt", -1) + # The sample lines are cleaned + cl <- dc$clean_lines(lines) + }); + }); + # The memory used + memory_used <- dc$format_size(memory_used) + # The time taken is tested + expect_lt(time_taken[[3]], 1) + # The memory usage is tested + expect_lt(memory_used, 2) +}); diff --git a/tests/testthat/test-data-sampler.R b/tests/testthat/test-data-sampler.R new file mode 100644 index 0000000..5ff5c3c --- /dev/null +++ b/tests/testthat/test-data-sampler.R @@ -0,0 +1,60 @@ +test_that("Sample file of correct size is generated", { + # The training file name + tfn <- "./data/model/train.txt" + # If the train.txt file exists in ./data/models, then it is removed + if (file.exists(tfn)) { + # The file is removed + file.remove(tfn) + } + # An object of class DataSampler is created + ds <- DataSampler$new(ddir = "./data", mdir = "./data/model") + # The sample file is generated + ds$generate_sample( + fn = "input.txt", + ss = 0.99, + ic = F, + t = "tr", + is = T + ) + # An object of class DataAnalyzer is created + da <- DataAnalyzer$new() + # The file info is fetched + fi <- da$get_file_info(tfn) + # The file size is checked + expect_equal(fi[["file_stats"]][1,6], "11.2 Mb") + +}) + + +test_that("Test, Train and Validation files of correct size are generated", { + # If the training file exists, then it is removed + if (file.exists("./data/model/train.txt")) + file.remove("./data/model/train.txt") + # If the test file exists, then it is removed + if (file.exists("./data/model/test.txt")) + file.remove("./data/model/test.txt") + # If the validate file exists, then it is removed + if (file.exists("./data/model/validate.txt")) + file.remove("./data/model/validate.txt") + + # An object of class DataSampler is created + ds <- DataSampler$new(ddir = "./data", mdir = "./data/model") + # The train, test and validation files are generated + ds$generate_data( + fn = "input.txt", + dir = "./data/model", + percs = list( + "train" = 0.8, + "test" = 0.1, + "validate" = 0.1 + ) + ) + # An object of class DataAnalyzer is created + da <- DataAnalyzer$new() + # The file info is fetched + fi <- da$get_file_info("./data/model") + # The file sizes are checked + expect_equal(fi[["file_stats"]][1,6], "1.1 Mb") + expect_equal(fi[["file_stats"]][2,6], "9 Mb") + expect_equal(fi[["file_stats"]][3,6], "1.1 Mb") +}) diff --git a/tests/testthat/test-model-evaluator.R b/tests/testthat/test-model-evaluator.R new file mode 100644 index 0000000..8849056 --- /dev/null +++ b/tests/testthat/test-model-evaluator.R @@ -0,0 +1,3 @@ +test_that("multiplication works", { + expect_equal(2 * 2, 4) +}) diff --git a/tests/testthat/test-model-generator.R b/tests/testthat/test-model-generator.R new file mode 100644 index 0000000..8849056 --- /dev/null +++ b/tests/testthat/test-model-generator.R @@ -0,0 +1,3 @@ +test_that("multiplication works", { + expect_equal(2 * 2, 4) +}) diff --git a/tests/testthat/test-model-predictor.R b/tests/testthat/test-model-predictor.R new file mode 100644 index 0000000..8849056 --- /dev/null +++ b/tests/testthat/test-model-predictor.R @@ -0,0 +1,3 @@ +test_that("multiplication works", { + expect_equal(2 * 2, 4) +}) diff --git a/tests/testthat/test-model.R b/tests/testthat/test-model.R new file mode 100644 index 0000000..8849056 --- /dev/null +++ b/tests/testthat/test-model.R @@ -0,0 +1,3 @@ +test_that("multiplication works", { + expect_equal(2 * 2, 4) +}) diff --git a/tests/testthat/test-token-generator.R b/tests/testthat/test-token-generator.R new file mode 100644 index 0000000..200d329 --- /dev/null +++ b/tests/testthat/test-token-generator.R @@ -0,0 +1,58 @@ +test_that("Performance of token generating function is acceptable", { + # Token generation for each ngram is checked + for (i in 1:4) { + time_taken <- system.time({ + memory_used <- mem_change({ + # The ngram number is set + tg_opts = list("n" = i) + # The TokenGenerator object is created + tg <- TokenGenerator$new("./data/models/test-clean.txt", tg_opts) + # The ngram tokens are generated + tg$generate_tokens() + }); + }); + # The memory used + memory_used <- dc$format_size(memory_used) + # The time taken is tested + expect_lt(time_taken[[3]], 20) + # The memory usage is tested + expect_lt(memory_used, 10) + } +}); + +test_that("Frequency of the generated tokens is calculated correctly", { + # The TokenGenerator object is created + tg <- TokenGenerator$new("./data/models/test-clean.txt") + # Each ngram file is checked + for (n in 1:4) { + # The ngram file name + fn <- paste0("./data/models/n", n, ".txt") + # The ngram file is read + df <- tg$read_data(fn, "plain", T) + # The input file name + fn <- "./data/models/test-clean.txt" + # The original input text file is read + lines <- tg$read_data(fn, "plain", F) + # The word frequencies + freq <- df$freq + # The ngram token prefixes + words <- df$pre + # The "_" is replaced with " " + words <- gsub("_", " ", words) + # 20 random samples are taken + indexes <- sample(1:20, 20) + # The words to check + words <- words[indexes] + # The frequencies to check + freq <- freq[indexes] + # The frequency of each word is checked + for (i in 1:length(words)) { + # The regular expression for matching the word + r <- regex(paste0("\\b",words[i],"\\b"), uword = T) + # The number of occurances of the word + count <- sum(str_count(lines, r)) + # The number of word occurances should match the frequency + expect_equal(count, freq[i], label = paste0("word: ", words[i])) + } + } +}); diff --git a/tests/testthat/test-tp-generator.R b/tests/testthat/test-tp-generator.R new file mode 100644 index 0000000..d980bad --- /dev/null +++ b/tests/testthat/test-tp-generator.R @@ -0,0 +1,23 @@ +test_that("Performance of transition probabilities generator is acceptable", { + # Token generation for each ngram is checked + for (i in 1:4) { + time_taken <- system.time({ + memory_used <- mem_change({ + # The TPGenerator object is created + tp <- TPGenerator$new() + # The ngram number is set + tp_opts$n <- i + # The output format is set to object + tp_opts$output_format <- "obj" + # The transition probabilities are generated + tp$generate_tp_for_n(tp_opts) + }); + }); + # The memory used + memory_used <- dc$format_size(memory_used) + # The time taken is tested + expect_lt(time_taken[[3]], 5) + # The memory usage is tested + expect_lt(memory_used, 5) + } +});