Thank you Bert and Ivan,
I was building the SVM model in hopes of applying it to future cases and hoped
that the model would be able to deal with new words it hadn't encountered
during training. But I tried Bert's suggestion by converting all of the data
to tokens, creating a DTM, transforming the whole thing with TFI DF, and then
separating it 75%/25%. But when I began to train the SVM on the training data,
R said it needed 26GB for a vector and crashed. I tried again, it crashed
again. I don't know why this would happen. I'd just trained 4 SVM models
using my previous method without any memory trouble on my 8GB CPU. I
unsuccessfully tried to remove the new words from the new test data. Should I
try that? Is there a way to stop my system from crashing with the new method?
Thank you for any ideas.
Here is the code I used when I separated the data after converting to tokens
and applying TFI DF:
url <- "D:/test/M2.csv"
data <- read_csv(url)
text_corpus <- Corpus(VectorSource(data$Text))
tokenize_document <- function(doc) {
doc_tokens <- unlist(tokenize_words(doc))
doc_bigrams <- unlist(tokenize_ngrams(doc, n = 2))
doc_trigrams <- unlist(tokenize_ngrams(doc, n = 3))
all_tokens <- c(doc_tokens, doc_bigrams, doc_trigrams)
return(all_tokens)
}
all_tokens <- lapply(text_corpus, tokenize_document)
text_dtm <- DocumentTermMatrix(Corpus(VectorSource(all_tokens)))
text_tfidf <- weightTfIdf(text_dtm)
processed_data <- data.frame(M2 = data$M2, text_tfidf = as.matrix(text_tfidf))
indexes <- createDataPartition(processed_data$M2, p = 0.75, list = FALSE)
trainData <- processed_data[indexes,]
testData <- processed_data[-indexes,]
ctrl <- trainControl(method = "repeatedcv", number = 5, repeats = 2, classProbs
= TRUE)
model_svmRadial <- train(M2 ~ ., data = trainData, method = "svmRadial",
trControl = ctrl)
________________________________
From: Ivan Krylov <krylov.r...@gmail.com>
Sent: Saturday, August 12, 2023 12:49 AM
To: James C Schopf <jcsch...@hotmail.com>
Cc: r-help@r-project.org <r-help@r-project.org>
Subject: Re: [R] Different TFIDF settings in test set prevent testing model
� Fri, 11 Aug 2023 10:20:27 +0000
James C Schopf <jcsch...@hotmail.com> �����:
> > train_text_dtm <-
> > DocumentTermMatrix(Corpus(VectorSource(all_train_tokens)))
> > test_text_dtm <-
> > DocumentTermMatrix(Corpus(VectorSource(all_test_tokens)))
I understand the need to prepare the test dataset separately
(e.g. in order to be able to work with text that don't exist at the
time when model is trained), but since the model has no representation
for tokens it (well, the tokeniser) hasn't seen during the training
process, you have to ensure that test_text_dtm references exactly the
same tokens as train_text_dtm, in the same order of the columns.
Also, it probably makes sense to reuse the term frequency learned on
the training document set; otherwise you may be importance-weighting
different tokens than ones your SVM has learned as important if your
test set has a significantly different distribution from that of the
training set.
Bert is probably right: with the API given by the tm package, it's
seems easiest to tokenise and weight document-term matrices first, then
split them into the train and test subsets. It may be worth asking the
maintainer about applying previously "learned" transformations to new
corpora.
--
Best regards,
Ivan
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