You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
225 lines
13 KiB
225 lines
13 KiB
#' Generate a classification model
|
|
#'
|
|
#' Generate a nested cross validated classification model based on a dfm with class labels as docvars
|
|
#' Currently only supports Naïve Bayes using quanteda's textmodel_nb
|
|
#' Hyperparemeter optimization is enabled through the grid parameter
|
|
#' A grid should be generated from vectors with the labels as described for each model, using the crossing() command
|
|
#' For Naïve Bayes, the following parameters can be used:
|
|
#' - percentiles (cutoff point for tf-idf feature selection)
|
|
#' - measures (what measure to use for determining feature importance, see textstat_keyness for options)
|
|
#' @param dfm A quanteda dfm used to train and evaluate the model, should contain the vector with class labels in docvars
|
|
#' @param cores_outer Number of cores to use for outer CV (cannot be more than the number of outer folds)
|
|
#' @param cores_grid Number of cores to use for grid search (cannot be more than the number of grid rows (i.e. possible parameter combinations), multiplies with cores_outer)
|
|
#' @param cores_inner Number of cores to use for inner CV loop (cannot be more than number of inner CV folds, multiplies with cores_outer and cores_grid)
|
|
#' @param cores_feats Number of cores to use for feature selection (multiplies with cores outer, cores_grid and cores_inner)
|
|
#' @param seed Integer to use as seed for random number generation, ensures replicability
|
|
#' @param outer_k Number of outer cross-validation folds (for performance estimation)
|
|
#' @param inner_k Number of inner cross-validation folds (for hyperparameter optimization and feature selection)
|
|
#' @param model Classification algorithm to use (currently only "nb" for Naïve Bayes using textmodel_nb)
|
|
#' @param class_type Type of classification to model ("junk", "aggregate", or "codes")
|
|
#' @param opt_measure Label of measure in confusion matrix to use as performance indicator
|
|
#' @param country Two-letter country abbreviation of the country the model is estimated for (used for filename)
|
|
#' @param grid Data frame providing all possible combinations of hyperparameters and feature selection parameters for a given model (grid search)
|
|
#' @return An .RData file in the current working directory (getwd()) containing the final model, performance estimates and the parameters used for grid search and cross-validation
|
|
#' @export
|
|
#' @examples
|
|
#' modelizer(dfm, cores_outer = 1, cores_grid = 1, cores_inner = 1, cores_feats = 1, seed = 42, outer_k = 3, inner_k = 5, model = model, class_type = class_type, opt_measure = opt_measure, country = country, grid = grid)
|
|
#################################################################################################
|
|
#################################### Function to generate classification models #################
|
|
#################################################################################################
|
|
|
|
modelizer <- function(dfm, cores_outer, cores_grid, cores_inner, cores_feats, seed, outer_k, inner_k, model, class_type, opt_measure, country, grid) {
|
|
### Functions ###
|
|
feat_select <- function (topic, dfm, class_type, percentile,measure) {
|
|
keyness <- textstat_keyness(dfm, measure = measure, docvars(dfm, class_type) == as.numeric(topic)) %>%
|
|
na.omit()
|
|
keyness <- filter(keyness, keyness[,2] > quantile(as.matrix(keyness[,2]),percentile))$feature
|
|
return(keyness)
|
|
}
|
|
### Generate inner folds for nested cv
|
|
inner_loop <- function(fold, dfm, inner_k, class_type) {
|
|
# RNG needs to be set explicitly for each fold
|
|
set.seed(seed, kind = "Mersenne-Twister", normal.kind = "Inversion")
|
|
## Either createDataPartition for simple holdout parameter optimization
|
|
## Or createFolds for proper inner CV for nested CV
|
|
# if (inner_k <= 1) {
|
|
# inner_folds <- createDataPartition(as.factor(docvars(dfm[-fold], class_type)), times = 1, p = 1-0.8)
|
|
# } else {
|
|
inner_folds <- createFolds(as.factor(docvars(dfm[-fold], class_type)), k= inner_k)
|
|
# }
|
|
return(c(outer_fold = list(fold),inner_folds))
|
|
}
|
|
|
|
### Generate outer folds for nested cv
|
|
generate_folds <- function(outer_k, inner_k, dfm, class_type){
|
|
set.seed(seed, kind = "Mersenne-Twister", normal.kind = "Inversion")
|
|
folds <- createFolds(as.factor(docvars(dfm, class_type)), k= outer_k)
|
|
return(lapply(folds,inner_loop, dfm = dfm, inner_k = inner_k, class_type = class_type))
|
|
}
|
|
|
|
### Gets called for every parameter combination, and calls classifier for every inner cv fold
|
|
inner_cv <- function(row,grid,outer_fold, inner_folds, dfm, class_type, model, cores_inner, cores_feats) {
|
|
params <- grid[row,]
|
|
# For each inner fold, cross validate the specified parameters
|
|
res <-
|
|
bind_rows(mclapply(inner_folds,
|
|
classifier,
|
|
outer_fold = outer_fold,
|
|
params = params,
|
|
dfm = dfm,
|
|
class_type = class_type,
|
|
model = model,
|
|
cores_feats = cores_feats,
|
|
mc.cores = cores_inner
|
|
)
|
|
)
|
|
|
|
return(cbind(as.data.frame(t(colMeans(select(res, 1:`Balanced Accuracy`)))),params))
|
|
}
|
|
|
|
### Gets called for every outer cv fold, and calls inner_cv for all parameter combinations in grid
|
|
outer_cv <- function(fold, grid, dfm, class_type, model, cores_grid, cores_inner, cores_feats) {
|
|
# If fold contains both inner folds and outer fold
|
|
if (length(fold) == inner_k + 1) {
|
|
inner_folds <- fold[-1]
|
|
outer_fold <- fold$outer_fold
|
|
# For each row in grid, cross-validate results
|
|
res <- bind_rows(mclapply(seq(1,length(grid[[1]]),1),
|
|
inner_cv,
|
|
cores_feats= cores_feats,
|
|
grid = grid,
|
|
dfm = dfm,
|
|
class_type = class_type,
|
|
model = model,
|
|
outer_fold = outer_fold,
|
|
inner_folds = inner_folds,
|
|
cores_inner = cores_inner,
|
|
mc.cores = cores_grid)
|
|
)
|
|
# Determine optimum hyperparameters within outer fold training set
|
|
optimum <- res[which.max(res[,opt_measure]),] %>%
|
|
select(percentiles: ncol(.))
|
|
# Validate performance of optimum hyperparameters on outer fold test set
|
|
return(classifier(NULL, outer_fold = outer_fold, params = optimum, dfm = dfm, class_type = class_type, model = model, cores_feats = cores_feats))
|
|
} else {
|
|
# If no outer fold, go directly to parameter optimization using inner folds, and return performance of hyperparameters
|
|
inner_folds <- fold
|
|
outer_fold <- NULL
|
|
res <- bind_rows(mclapply(seq(1,length(grid[[1]]),1),
|
|
inner_cv,
|
|
cores_feats= cores_feats,
|
|
grid = grid,
|
|
dfm = dfm,
|
|
class_type = class_type,
|
|
model = model,
|
|
outer_fold = outer_fold,
|
|
inner_folds = inner_folds,
|
|
cores_inner = cores_inner,
|
|
mc.cores = cores_grid)
|
|
)
|
|
return(res)
|
|
}
|
|
}
|
|
|
|
### Custom tfidf function to allow same idf for different dfm's
|
|
custom_tfidf <- function(x, scheme_tf = "count", scheme_df = "inverse", base = 10, dfreq = dfreq) {
|
|
if (!nfeat(x) || !ndoc(x)) return(x)
|
|
tfreq <- dfm_weight(x, scheme = scheme_tf, base = base)
|
|
if (nfeat(x) != length(dfreq))
|
|
stop("missing some values in idf calculation")
|
|
# get the document indexes
|
|
j <- as(tfreq, "dgTMatrix")@j + 1
|
|
# replace just the non-zero values by product with idf
|
|
x@x <- tfreq@x * dfreq[j]
|
|
# record attributes
|
|
x@weightTf <- tfreq@weightTf
|
|
x@weightDf <- c(list(scheme = scheme_df, base = base), args)
|
|
return(x)
|
|
}
|
|
|
|
### Classification function
|
|
classifier <- function (inner_fold, outer_fold, params, dfm, class_type, model, cores_feats) {
|
|
# If both inner and outer folds, subset dfm to outer_fold training set, then create train and test sets according to inner fold. Evaluate performance
|
|
if (length(inner_fold) > 0 && length(outer_fold) > 0) {
|
|
dfm_train <- dfm[-outer_fold] %>%
|
|
.[-inner_fold]
|
|
dfm_test <- dfm[-outer_fold] %>%
|
|
.[inner_fold]
|
|
# If only outer folds, but no inner folds, validate performance of outer fold training data on outer fold test data
|
|
} else if (length(outer_fold) > 0 ) {
|
|
dfm_train <- dfm[-outer_fold]
|
|
dfm_test <- dfm[outer_fold]
|
|
# If only inner folds, validate performance directly on inner folds (is the same as above?)
|
|
} else if (length(inner_fold) > 0 ) {
|
|
dfm_train <- dfm[-inner_fold]
|
|
dfm_test <- dfm[inner_fold]
|
|
# If both inner and outer folds are NULL, set training set to whole dataset, estimate model and return final model
|
|
} else {
|
|
final <- T ### Indicate final modeling run on whole dataset
|
|
dfm_train <- dfm
|
|
}
|
|
### Getting features from training dataset
|
|
# Getting idf from training data, and using it to normalize both training and testing feature occurence
|
|
dfm_train <- dfm_trim(dfm_train, min_termfreq = 1, min_docfreq = 0)
|
|
dfreq <- docfreq(dfm_train, scheme = "inverse", base = 10, smoothing = 0, k = 0, threshold = 0, use.names=T)
|
|
dfm_train <- custom_tfidf(dfm_train, scheme_tf = "count", scheme_df = "inverse", base = 10, dfreq = dfreq)
|
|
words <- unlist(mclapply(unique(docvars(dfm_train, class_type)),
|
|
feat_select,
|
|
dfm = dfm_train,
|
|
class_type = class_type,
|
|
percentile = params$percentiles,
|
|
measure = params$measures,
|
|
mc.cores = cores_feats
|
|
))
|
|
dfm_train <- dfm_keep(dfm_train, words, valuetype="fixed", verbose=T)
|
|
|
|
|
|
if (model == "nb") {
|
|
text_model <- textmodel_nb(dfm_train, y = docvars(dfm_train, class_type), smooth = .001, prior = "uniform", distribution = "multinomial")
|
|
}
|
|
if (model == "svm") {
|
|
text_model <- svm(x=dfm_train, y=as.factor(docvars(dfm_train, class_type)), type = "C-classification", kernel = "linear", gamma = params$gamma, cost = params$cost, epsilon = params$epsilon)
|
|
}
|
|
### Add more if statements for different models
|
|
if (exists("final") == T) {
|
|
return(text_model)
|
|
} else {
|
|
### Removing all features not in training set from test set and weighting the remaining features according to training idf
|
|
dfm_test <- dfm_keep(dfm_test, pattern = dfm_train, valuetype = "fixed", verbose = T)
|
|
dfm_test <- custom_tfidf(dfm_test, scheme_tf = "count", scheme_df = "inverse", base = 10, dfreq = dfreq[words])
|
|
pred <- predict(text_model, newdata = dfm_test)
|
|
class_table <- table(prediction = pred, trueValues = docvars(dfm_test, class_type))
|
|
conf_mat <- confusionMatrix(class_table, mode = "everything")
|
|
if (is.matrix(conf_mat$byClass) == T) {
|
|
return(cbind(as.data.frame(t(conf_mat$overall)),as.data.frame(t(colMeans(conf_mat$byClass))),params))
|
|
} else {
|
|
return(cbind(as.data.frame(t(conf_mat$overall)),as.data.frame(t(conf_mat$byClass)),params, pos_cat = conf_mat$positive))
|
|
}
|
|
}
|
|
}
|
|
|
|
## Generate nested CV folds, based on number of inner and outer folds defined (see start of script)
|
|
folds <- generate_folds(outer_k,inner_k = inner_k, dfm = dfm, class_type = class_type)
|
|
|
|
## Get performance of each outer fold validation, and add row with mean scores (This is the final performance indicator)
|
|
performance <- bind_rows(mclapply(folds, outer_cv, grid=grid, dfm=dfm, class_type=class_type, model=model, cores_grid=cores_grid, cores_inner=cores_inner, cores_feats=cores_feats, mc.cores = cores_outer)) %>%
|
|
bind_rows(., colMeans(select(., 1:`Balanced Accuracy`)))
|
|
|
|
## Set seed and generate folds for final hyperparameter optimization search (using CV)
|
|
set.seed(seed, kind = "Mersenne-Twister", normal.kind = "Inversion")
|
|
folds_final <- list(createFolds(as.factor(docvars(dfm, class_type)), k= inner_k))
|
|
|
|
## Get the final hyperparameter performance for all value combinations in grid
|
|
params_final <- bind_rows(mclapply(folds_final, outer_cv, grid=grid, dfm=dfm, class_type=class_type, model=model, cores_grid=cores_grid, cores_inner=cores_inner, cores_feats=cores_feats, mc.cores = cores_outer))
|
|
|
|
## Select optimum final hyperparameters
|
|
optimum_final <- params_final[which.max(params_final[,opt_measure]),] %>%
|
|
select(percentiles: ncol(.))
|
|
|
|
## Estimate final model on whole dataset, using optimum final hyperparameters determined above
|
|
model_final <- classifier(NULL, outer_fold = NULL, params = optimum_final, dfm = dfm, class_type = class_type, model = model, cores_feats = detectCores())
|
|
rm(list=setdiff(ls(), c("model_final", "optimum_final","params_final","performance","grid","folds","folds_final","country","model","class_type","opt_measure")), envir = environment())
|
|
save(list = ls(all.names = TRUE), file = paste0(getwd(),'/',country,'_',model,'_',class_type,'_',opt_measure,'_',Sys.time(),'.RData'), envir = environment())
|
|
return(paste0(getwd(),'/',country,'_',model,'_',class_type,'_',opt_measure,'_',Sys.time(),'.RData'))
|
|
}
|