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mamlr/R/modelizer.R

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#' 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[,2] <- abs(keyness[,2])
keyness <- filter(keyness, keyness[,2] > quantile(as.matrix(keyness[,2]),percentile))$feature
return(keyness)
}
### Generate inner folds for nested cv
inner_loop <- function(fold, dfm, inner_k, class_type) {
# RNG needs to be set explicitly for each fold
set.seed(seed, kind = "Mersenne-Twister", normal.kind = "Inversion")
## Either createDataPartition for simple holdout parameter optimization
## Or createFolds for proper inner CV for nested CV
# if (inner_k <= 1) {
# inner_folds <- createDataPartition(as.factor(docvars(dfm[-fold], class_type)), times = 1, p = 1-0.8)
# } else {
inner_folds <- createFolds(as.factor(docvars(dfm[-fold], class_type)), k= inner_k)
# }
return(c(outer_fold = list(fold),inner_folds))
}
### Generate outer folds for nested cv
generate_folds <- function(outer_k, inner_k, dfm, class_type){
set.seed(seed, kind = "Mersenne-Twister", normal.kind = "Inversion")
folds <- createFolds(as.factor(docvars(dfm, class_type)), k= outer_k)
return(lapply(folds,inner_loop, dfm = dfm, inner_k = inner_k, class_type = class_type))
}
### Gets called for every parameter combination, and calls classifier for every inner cv fold
inner_cv <- function(row,grid,outer_fold, inner_folds, dfm, class_type, model, cores_inner, cores_feats) {
params <- grid[row,]
# For each inner fold, cross validate the specified parameters
res <-
bind_rows(mclapply(inner_folds,
classifier,
outer_fold = outer_fold,
params = params,
dfm = dfm,
class_type = class_type,
model = model,
cores_feats = cores_feats,
mc.cores = cores_inner
)
)
# print(res)
# print(res[1,1])
# print('inner_cv')
return(cbind(as.data.frame(t(colMeans(select(res, 1:`Balanced Accuracy`)))),params))
}
### Gets called for every outer cv fold, and calls inner_cv for all parameter combinations in grid
outer_cv <- function(fold, grid, dfm, class_type, model, cores_grid, cores_inner, cores_feats) {
# If fold contains both inner folds and outer fold
if (length(fold) == inner_k + 1) {
inner_folds <- fold[-1]
outer_fold <- fold$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)
)
# print(res)
# print(res[1,1])
# print('outer_cv')
# 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)
)
# print(res)
# print(res[1,1])
# print('line 126, final model parameter optimization')
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
}
if (model == 'nb') {
scheme_tf <- 'count' # The 'old' way
} else {
scheme_tf <- 'prop' # The 'new' way
}
### 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 = scheme_tf, scheme_df = "inverse", base = 10, dfreq = dfreq)
# Added unique to filter out duplicate words, these are caused when there are multiple categories, and a words scores higher
# than the threshold on two or more of those categories
words <- unique(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 = params$kernel, gamma = params$gamma, cost = params$cost, epsilon = params$epsilon)
}
if (model == 'nnet') {
idC <- class.ind(as.factor(docvars(dfm_train, class_type)))
text_model <- nnet(dfm_train, idC, decay = params$decay, size=params$size, maxit=params$maxit, softmax=T, reltol = params$reltol, MaxNWts = params$size*(length(dfm_train@Dimnames$features)+1)+(params$size*2)+2)
}
# if (model == 'neuralnet') {
# dfm_test <- dfm_keep(dfm_test, pattern = dfm_train, valuetype = "fixed", verbose = T)
# dfm_test <- custom_tfidf(dfm_test, scheme_tf = "prop", scheme_df = "inverse", base = 10, dfreq = dfreq[words])
#
# idC <- class.ind(as.factor(docvars(dfm_train, class_type)))
# colnames(idC) <- NULL
# nn_train <- cbind(idC, dfm_train)
# n <- colnames(nn_train)[3:length(colnames(nn_train))]
# f <- as.formula(paste0("feat1 + feat2 ~ `", paste0(n, collapse = "` + `"),"`"))
# idC_out <- class.ind(as.factor(docvars(dfm_test, class_type)))
# colnames(idC_out) <- NULL
# nn_test <- cbind(idC_out, dfm_test)
# nn <- neuralnet(f,data=nn_train,hidden=3,linear.output=F,act.fct = 'logistic',lifesign = 'full', threshold = .005)
# pr.nn <- compute(nn,nn_test[,3:length(colnames(nn_test))])
# class_table <- table(prediction = as.matrix(round(pr.nn$net.result[,2])), trueValues = as.matrix(idC_out[,2]))
# nn_conf_mat <- confusionMatrix(class_table, mode = "everything")
# }
### 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 = scheme_tf, scheme_df = "inverse", base = 10, dfreq = dfreq[words])
pred <- predict(text_model, newdata = dfm_test, type = 'class')
### Fix for single-class 'predictions' in borderline situations
if (length(unique(pred)) == 1 & class_type == 'junk') {
if (unique(pred) == '0') {
pred[1] <- '1'
} else {
pred[1] <- '0'
}
}
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'))
}