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Updated elasticizer docs, created modelizer and class_update functions

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master NOJunk
Erik de Vries 6 years ago
parent c815dc7f2b
commit d203de0b2a
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4
DESCRIPTION
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@ -9,7 +9,9 @@ Depends: R (>= 3.3.1),
parallel,
tidyverse,
quanteda,
httr
httr,
caret,
e1071
License: Copyright Erik de Vries
Encoding: UTF-8
LazyData: true

2
NAMESPACE
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# Generated by roxygen2: do not edit by hand
export(bulk_writer)
export(class_update)
export(dfm_gen)
export(dupe_detect)
export(elastic_update)
export(elasticizer)
export(merger)
export(modelizer)

32
R/class_update.R
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#' Classifier function for use in combination with the elasticizer function as 'update' parameter (without brackets), see elasticizer documentation for more information
#'
#' Classifier function for use in combination with the elasticizer function as 'update' parameter (without brackets), see elasticizer documentation for more information
#' @param out Does not need to be defined explicitly! (is already parsed in the elasticizer function)
#' @param model_final The classification model (e.g. output from textstat_nb(), svm() or others)
#' @param dfm_words A dfm containing all the words and only the words used to generate the model (is used for subsetting)
#' @param varname String containing the variable name to use for the classification result, usually has the format computerCodes.varname
#' @param es_super Password for write access to ElasticSearch
#' @return As this is a nested function used within elasticizer, there is no return output
#' @export
#' @examples
#' elasticizer(query, src = T, es_pwd = es_pwd, update = class_update, model_final = model_final, dfm_words = dfm_words, varname = computerCodes.varname, es_super = es_super)
#################################################################################################
#################################### Update any kind of classification ##########################
#################################################################################################
class_update <- function(out, model_final, dfm_words, varname, es_super) {
print('updating')
dfm <- dfm_gen(out, text = 'lemmas') %>%
dfm_keep(dfm_words, valuetype="fixed", verbose=T)
pred <- data.frame(id = out$`_id`, pred = predict(model_final, newdata = dfm))
bulk <- apply(pred, 1, bulk_writer, varname = varname)
res <- elastic_update(bulk, es_super = es_super)
stop_for_status(res)
content(res, "parsed", "application/json")
appData <- content(res)
if (appData$errors == T){
print(appData)
stop("Aborting, errors found during updating")
}
print("updated")
Sys.sleep(1)
}

1
R/elasticizer.R
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#' @param src Logical (true/false) indicating whether or not the source of each document should be retrieved
#' @param index The name of the Elasticsearch index to search through
#' @param update When set, indicates an update function to use on each batch of 1000 articles
#' @param ... Parameters passed on to the update function
#' @return A data frame containing all the search results
#' @export

224
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 <- 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'))
}

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man/class_update.Rd
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/class_update.R
\name{class_update}
\alias{class_update}
\title{Classifier function for use in combination with the elasticizer function as 'update' parameter (without brackets), see elasticizer documentation for more information}
\usage{
class_update(out, model_final, dfm_words, varname, es_super)
}
\arguments{
\item{out}{Does not need to be defined explicitly! (is already parsed in the elasticizer function)}
\item{model_final}{The classification model (e.g. output from textstat_nb(), svm() or others)}
\item{dfm_words}{A dfm containing all the words and only the words used to generate the model (is used for subsetting)}
\item{varname}{String containing the variable name to use for the classification result, usually has the format computerCodes.varname}
\item{es_super}{Password for write access to ElasticSearch}
}
\value{
As this is a nested function used within elasticizer, there is no return output
}
\description{
Classifier function for use in combination with the elasticizer function as 'update' parameter (without brackets), see elasticizer documentation for more information
}
\examples{
elasticizer(query, src = T, es_pwd = es_pwd, update = class_update, model_final = model_final, dfm_words = dfm_words, varname = computerCodes.varname, es_super = es_super)
}

2
man/elasticizer.Rd
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@ -16,6 +16,8 @@ elasticizer(query, src = T, index = "maml",
\item{index}{The name of the Elasticsearch index to search through}
\item{update}{When set, indicates an update function to use on each batch of 1000 articles}
\item{...}{Parameters passed on to the update function}
}
\value{
A data frame containing all the search results

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man/modelizer.Rd
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/modelizer.R
\name{modelizer}
\alias{modelizer}
\title{Generate a classification model}
\usage{
modelizer(dfm, cores_outer, cores_grid, cores_inner, cores_feats, seed,
outer_k, inner_k, model, class_type, opt_measure, country, grid)
}
\arguments{
\item{dfm}{A quanteda dfm used to train and evaluate the model, should contain the vector with class labels in docvars}
\item{cores_outer}{Number of cores to use for outer CV (cannot be more than the number of outer folds)}
\item{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)}
\item{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)}
\item{cores_feats}{Number of cores to use for feature selection (multiplies with cores outer, cores_grid and cores_inner)}
\item{seed}{Integer to use as seed for random number generation, ensures replicability}
\item{outer_k}{Number of outer cross-validation folds (for performance estimation)}
\item{inner_k}{Number of inner cross-validation folds (for hyperparameter optimization and feature selection)}
\item{model}{Classification algorithm to use (currently only "nb" for Naïve Bayes using textmodel_nb)}
\item{class_type}{Type of classification to model ("junk", "aggregate", or "codes")}
\item{opt_measure}{Label of measure in confusion matrix to use as performance indicator}
\item{country}{Two-letter country abbreviation of the country the model is estimated for (used for filename)}
\item{grid}{Data frame providing all possible combinations of hyperparameters and feature selection parameters for a given model (grid search)}
}
\value{
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
}
\description{
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)
}
\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)
}
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