R/idiomclassification_wiki1.R

library(caret)
library(tidyverse)
library(DMwR)
library(randomForest)
library(FSelector)
# library(randomForestExplainer)
# may need to: options(expressions = 5e5) to avoid stackoverflow for installing package

set.seed(42)

ngramfilegold1<-"goldstandard01_anno_ml_synfeat_nstop1"
ngramfilegold2<-"gold03_anno_ml_synfeat_nstopw"
ngramfiletest<-"wikilist_cleanup_syn"
setwd(dirname(rstudioapi::getSourceEditorContext()$path))
stopwords <- readLines(con = "../data/stopwords.txt",encoding="UTF-8")
oringramme1 <- read.csv(paste("../data/",ngramfilegold1,".csv",sep=""), header = TRUE, sep = "\t", dec=".", quote="", encoding="UTF-8",stringsAsFactors=FALSE)
oringramme2 <- read.csv(paste("../data/",ngramfilegold2,".csv",sep=""), header = TRUE, sep = "\t", dec=".", quote="", encoding="UTF-8",stringsAsFactors=FALSE)
oringrammetest <- read.csv(paste("../data/",ngramfiletest,".csv",sep=""), header = TRUE, sep = "\t", dec=".", quote="", encoding="UTF-8",stringsAsFactors=FALSE)
syfeaturenames <- read.csv("../data/syfeatures.tsv", header = TRUE, sep = "\t", dec=".", quote="", encoding="UTF-8",stringsAsFactors=FALSE)
featurenames <- read.csv("../data/features.tsv", header = TRUE, sep = "\t", dec=".", quote="", encoding="UTF-8",stringsAsFactors=FALSE)

deleteStopwords =  function(wl, stopwords = NULL) {
  wl[!(wl %in% stopwords)]
}

oringramme1 <- oringramme1 %>%
  mutate(CO_IDIOM = ifelse(is.na(CO_IDIOM),0,CO_IDIOM)) %>%  # treat NAs as 0
  filter(CO_IDIOM < 2)   # just two classes: 0 no idiom, 1 idiom

oringramme2 <- oringramme2 %>%
  mutate(CO_IDIOM = ifelse(is.na(CO_IDIOM),0,CO_IDIOM)) %>%  # treat NAs as 0
  filter(CO_IDIOM < 2)   # just two classes: 0 no idiom, 1 idiom

# feature sets

sy_w_vars <- c("SY_W_AVG","SY_W_CON","SY_W_MAX","SY_W_NSUM","SY_W_NSUM_AF")# sy_w_vars_1 <- c("SY_W_AVG","SY_W_CON","SY_W_MAX","SY_W_NSUM")
sy_r_vars <- c("SY_C1_R","SY_W_R1","SY_W_R2","SY_R_D")
sy_c1_vars_1 <- c("SY_C1_LD","SY_C1_LDAF","SY_C1_LL","SY_C1_MI","SY_C1_MI3")
o_vars_1 <- c("O_NSTOPW","O_GRAM")
sy_vars <- c(sy_c1_vars_1, sy_w_vars, sy_r_vars,o_vars_1)

sy_featurenames<-subset(featurenames,newnames %in% sy_vars)

# Reduce number of classes, treat null values, add NSTOPW, change names for SY features and rename all features
# new featurenames.

ngramme1 <- oringramme1 %>%
  add_column(NSTOPW = sapply(oringramme1$tokens,function(x) length(deleteStopwords(tolower(unlist(strsplit(x," "))),stopwords)))) %>%
  add_column(nstokens = sapply(oringramme1$tokens, function(x) paste(deleteStopwords(tolower(unlist(strsplit(x," "))),stopwords),collapse=" "))) %>%
  # select(-matches("CO_TOKEN.*"), -tokens) %>%
  add_column(CO_GRAM = sapply(oringramme1$tokens, function(x) length(unlist(strsplit(x," ")))))%>%
  select(-matches("CO_TOKEN.*")) %>% # keep tokens for interpretability
  mutate(across(matches(".rank.*"), ~ replace_na(.x, 1000))) %>%
  mutate(across(c("dice", "lfmd", "llr", "ld", "pmi"), ~ replace_na(.x, min(.x) - 1))) %>%
  rename_at(syfeaturenames$innames, ~ syfeaturenames[syfeaturenames$innames==.x,]$synames ) %>%
  rename_at(featurenames$oldnames, ~ featurenames[featurenames$oldnames==.x,]$newnames ) %>%
  mutate(across(everything(), ~ replace_na(.x, 0))) %>%
  mutate(CO_IDIOM = as.factor(if_else(CO_IDIOM == 1, "idiom", "no_idiom"))) %>% # just two classes: 0 no idiom, 1 idiom
  add_column(GOLD = 1) %>%
  select(c("CO_IDIOM","tokens","nstokens","GOLD",all_of(sy_vars)))

ngramme2 <- oringramme2 %>%
  add_column(NSTOPW = sapply(oringramme2$tokens,function(x) length(deleteStopwords(tolower(unlist(strsplit(x," "))),stopwords)))) %>%
  add_column(nstokens = sapply(oringramme2$tokens, function(x) paste(deleteStopwords(tolower(unlist(strsplit(x," "))),stopwords),collapse=" "))) %>%
  # select(-matches("CO_TOKEN.*"), -tokens) %>%
  select(-matches("CO_TOKEN.*")) %>% # keep tokens for interpretability
  mutate(across(matches(".rank.*"), ~ replace_na(.x, 1000))) %>%
  mutate(across(c("dice", "lfmd", "llr", "ld", "pmi"), ~ replace_na(.x, min(.x) - 1))) %>%
  rename_at(syfeaturenames$innames, ~ syfeaturenames[syfeaturenames$innames==.x,]$synames ) %>%
  rename_at(featurenames$oldnames, ~ featurenames[featurenames$oldnames==.x,]$newnames ) %>%
  mutate(across(everything(), ~ replace_na(.x, 0))) %>%
  mutate(CO_IDIOM = as.factor(if_else(CO_IDIOM == 1, "idiom", "no_idiom"))) %>% # just two classes: 0 no idiom, 1 idiom
  add_column(GOLD = 1) %>%
  select(c("CO_IDIOM","tokens","nstokens","GOLD",all_of(sy_vars)))

# combine

ngramme <- rbind(ngramme1,ngramme2[colnames(ngramme1)])
# ngramme<-ngramme1
# ngramme <- ngramme2[colnames(ngramme1)]
ngramme<-ngramme%>% distinct(nstokens,.keep_all=T)

ngramme1<-ngramme1%>% distinct(nstokens,.keep_all=T)


# discard all ngrams with less than 2 non stopwords (no syntagmatic features possible)

ngramme1 <- ngramme1 %>%
   filter(O_NSTOPW > 1)

# wiki ngrams

wikingramme <- oringrammetest %>%
  add_column(NSTOPW = sapply(oringrammetest$tokens,function(x) length(deleteStopwords(tolower(unlist(strsplit(x," "))),stopwords)))) %>%
  add_column(nstokens = sapply(oringrammetest$tokens, function(x) paste(deleteStopwords(tolower(unlist(strsplit(x," "))),stopwords),collapse=" "))) %>%
  add_column(CO_GRAM = sapply(oringrammetest$tokens, function(x) length(unlist(strsplit(x," ")))))%>%
  select(-c(IDIOM,KERN)) %>% # keep tokens for interpretability
  mutate(across(matches(".rank.*"), ~ replace_na(.x, 1000))) %>%
  mutate(across(c("dice", "lfmd", "llr", "ld", "pmi"), ~ replace_na(.x, min(.x) - 1))) %>%
  rename_at(syfeaturenames$innames, ~ syfeaturenames[syfeaturenames$innames==.x,]$synames ) %>%
  rename_at(sy_featurenames$oldnames, ~ sy_featurenames[sy_featurenames$oldnames==.x,]$newnames ) %>%
  mutate(across(everything(), ~ replace_na(.x, 0))) %>%
  add_column(CO_IDIOM = as.factor("idiom")) %>%
  add_column(GOLD = 0) %>%
  select(c("CO_IDIOM","tokens","nstokens","GOLD",all_of(sy_vars)))

# remove all with NSTOPW > 1 (no syntagmatic context available)

wikingramme <- wikingramme %>%
   filter(O_NSTOPW > 1)

# remove duplicates after stopword exclusion

wikingramme <- wikingramme %>% distinct(nstokens,.keep_all=T)

# find duplicates by lower cased tokens without stopwords

bothngramme <- merge(ngramme[,c("tokens","nstokens","CO_IDIOM")],wikingramme[,c("tokens","nstokens","CO_IDIOM")],by="nstokens")

# 100% agreement ;)

# combine

allngramme <- rbind(ngramme1,wikingramme)


# and again remove duplicates

allngramme <- allngramme %>% distinct(nstokens,.keep_all=T)

ngramme1 <- allngramme %>% filter(GOLD==1)
wikingramme <- allngramme %>% filter(GOLD==0)

write.table(allngramme,file=paste("../data/","dataset1_wiki_noduplicates.tsv",sep=""), sep = "\t", quote=F)


# formulae for training and testing rf

sy_fmla <- as.formula(paste("CO_IDIOM ~ ", paste(sy_vars, collapse= "+")))

# Train/Test split

# Training: 80% Gold Standard, no_idiom + 100% Gold Standard, idiom
# Test: 20% Gold Standard, no_idiom + 100 % Wiki, idiom

noidiomsgold <- ngramme1 %>% filter(CO_IDIOM=="no_idiom")
idiomsgold <- ngramme1 %>% filter(CO_IDIOM=="idiom")

set.seed(1120)
trainRows <- sample(nrow(noidiomsgold), nrow(noidiomsgold)*0.8, replace = FALSE)
train <- rbind(noidiomsgold[trainRows,],idiomsgold)
test <- rbind(noidiomsgold[setdiff(1:nrow(noidiomsgold),trainRows),],wikingramme)

cat("Random Forest\n")

rf_classifier = randomForest(sy_fmla, train, importance=TRUE)

prediction_for_table <- predict(rf_classifier, test %>% select(-CO_IDIOM))

res <- confusionMatrix(prediction_for_table, test$CO_IDIOM, positive= "idiom",mode="everything")
print(res)
collected_results <- bind_cols("rf" = res$byClass)

# Sensitivity is recall of class 1
# Pos Pred Value is precision
varImpPlot(rf_classifier)

cat("Random Forest with cutoff\n")
prediction_for_table <- predict(rf_classifier,test %>% select(-CO_IDIOM), cutoff = c(0.05, 0.95))
res <- confusionMatrix(prediction_for_table,test$CO_IDIOM, positive = "idiom",mode="everything")
collected_results <- bind_cols(collected_results, "rf with cutoff" = res$byClass)
print(res)

cat("With SMOTE resampled training data\n")
smoted.data <- SMOTE(fmlasy, subset(train, select = c("CO_IDIOM", syvars)), perc.over = 1200, perc.under = 100)
rf_classifier = randomForest(fmlasy, smoted.data, importance=TRUE)
prediction_for_table <- predict(rf_classifier,test %>% select(-CO_IDIOM))
res <- confusionMatrix(prediction_for_table,test$CO_IDIOM, positive = "idiom")
collected_results <- bind_cols(collected_results, "rf with SMOTE" = res$byClass)
print(res)

cat("With SMOTE and cutoff\n")
prediction_for_table <- predict(rf_classifier,test %>% select(-CO_IDIOM), cutoff = c(0.2, 0.8))
res <- confusionMatrix(prediction_for_table,test$CO_IDIOM, positive = "idiom")
collected_results <- bind_cols(collected_results, "rf with SMOTE and cutoff" = res$byClass)
print(res)

collected_results <- collected_results %>%
  round(3) %>%
  add_column(measure = names(res$byClass)) %>%
  column_to_rownames("measure")

View(collected_results)


# Analysing tradeoff between Fscore, Recall, Precision for various cutoffs
# full range from precision almost 100% to recall almost 100%
rf_classifier = randomForest(sy_fmla, train, importance=TRUE)
cvaluesw<-tibble()
for (c in c(seq(from=0.4,to=0.99,by=0.025),0.999)) {
  prediction_for_table <- predict(rf_classifier, test %>% select(-CO_IDIOM), cutoff = c(1-c, c))
  conf<-confusionMatrix(prediction_for_table, test$CO_IDIOM, positive = "idiom")
  cvaluesw <-bind_rows(cvaluesw, c(cutoff=c, conf$byClass))
}
cvaluesw %>%
  select(c("cutoff", "Recall", "Precision", "F1", "Specificity", "Balanced Accuracy")) %>%
  pivot_longer(!cutoff, names_to=c("measure")) %>%
  ggplot(aes(cutoff, value, colour=measure)) + geom_line(size=1) +
  scale_x_continuous(breaks = scales::pretty_breaks(n = 10)) +
  scale_y_continuous(breaks = scales::pretty_breaks(n = 10))


# Using estimates by random forest on entire dataset

library(randomForest)
rf_classifier_full = randomForest(fmlasy, data=allngramme, importance=TRUE)
rf_classifier_full
# class.error is 1 - recall
varImpPlot(rf_classifier_full)

# Feature ranking

# rf features as table

# correlated features seem to split their rankings

rfranks<-importance(rf_classifier_full)[,3:4]

# ttest

idioms<-ngramme %>% filter(CO_IDIOM == "idiom")
nonidioms<-ngramme %>% filter(CO_IDIOM != "idiom")

ttestPvalues<-sapply(syvars,
                     function(sel) t.test(idioms[sel],nonidioms[sel])$p.value)

# information gain
# multiply by 1000 to avoid undersized bins
# features are ranked individually no matter their correlation
igain<-information.gain(fmlasy, data=ngramme%>%mutate_at(syvars, ~ . * 1000),unit="log2")

featureRanks<-cbind(rfranks,igain,ttestPvalues)

#randomForestExplainer::explain_forest(rf_classifier )

# averate estimates and feature ranks over 10 runs

errrate<-0
conf<-matrix(0,2,3)
featureRanks<-matrix(0,4,length(vars))
for (i in 1:10) {
  rfc =randomForest(fmlasy, data=ngramme, importance=TRUE)
  #rfc =randomForest(fmlasy, data=ngramme, importance=TRUE, cutoff=c(0.2, 0.8))
  errrate<-errrate+rfc$err.rate[100,1]
  conf<-conf+rfc$confusion
  featureRanks<-featureRanks+
    cbind(importance(rfc)[,3:4],
          information.gain(fmlasy, data=ngramme%>%mutate_at(syvars, ~ . * 1000),unit="log2"),
          sapply(syvars,
                 function(sel) t.test(idioms[sel],nonidioms[sel])$p.value))
  print(errrate/i)
  conf1<-round(
    rbind(
      cbind(conf[,1:2]/i,(1-conf[,3]/i)*100),
      c(100*diag(conf[,1:2])/colSums(conf[,1:2]),NA),
      c(rowSums(conf[,1:2]/i),NA)),digits=2)
  colnames(conf1)<-c("1","0","rec")
  rownames(conf1)<-c("1","0","prec","sum")
  print(conf1)
}
featureRanks<-featureRanks/10
colnames(featureRanks)<-c("MeanDecreaseAccuracy","MeanDecreaseGini","InformationGain","Ttest")