Last updated on 2025-10-31 07:52:03 CET.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.1.0 | 11.09 | 283.39 | 294.48 | NOTE | |
| r-devel-linux-x86_64-debian-gcc | 0.1.0 | 17.73 | 253.56 | 271.29 | NOTE | |
| r-devel-linux-x86_64-fedora-clang | 0.1.0 | 44.00 | 414.60 | 458.60 | NOTE | |
| r-devel-linux-x86_64-fedora-gcc | 0.1.0 | 44.00 | 407.85 | 451.85 | NOTE | |
| r-devel-windows-x86_64 | 0.1.0 | 14.00 | 108.00 | 122.00 | ERROR | |
| r-patched-linux-x86_64 | 0.1.0 | 10.78 | 275.45 | 286.23 | NOTE | |
| r-release-linux-x86_64 | 0.1.0 | 10.42 | 276.87 | 287.29 | NOTE | |
| r-release-macos-arm64 | 0.1.0 | 8.00 | 95.00 | 103.00 | NOTE | |
| r-release-macos-x86_64 | 0.1.0 | 15.00 | 193.00 | 208.00 | NOTE | |
| r-release-windows-x86_64 | 0.1.0 | 21.00 | 138.00 | 159.00 | ERROR | |
| r-oldrel-macos-arm64 | 0.1.0 | 6.00 | 41.00 | 47.00 | NOTE | |
| r-oldrel-macos-x86_64 | 0.1.0 | 7.00 | 58.00 | 65.00 | NOTE | |
| r-oldrel-windows-x86_64 | 0.1.0 | 14.00 | 179.00 | 193.00 | ERROR |
Version: 0.1.0
Check: CRAN incoming feasibility
Result: NOTE
Maintainer: ‘Jan Wijffels <jwijffels@bnosac.be>’
The Description field contains
David M. Blei (2019), available at <arXiv:1907.04907>.
Please refer to arXiv e-prints via their arXiv DOI <doi:10.48550/arXiv.YYMM.NNNNN>.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Version: 0.1.0
Check: Rd files
Result: NOTE
checkRd: (-1) ETM.Rd:33: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) ETM.Rd:34: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) ETM.Rd:35: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) ETM.Rd:36: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) ETM.Rd:37: Lost braces in \itemize; \value handles \item{}{} directly
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Version: 0.1.0
Check: examples
Result: ERROR
Running examples in 'topicmodels.etm-Ex.R' failed
The error most likely occurred in:
> ### Name: ETM
> ### Title: Topic Modelling in Semantic Embedding Spaces
> ### Aliases: ETM
>
> ### ** Examples
>
> library(torch)
> library(topicmodels.etm)
> library(word2vec)
> library(udpipe)
> data(brussels_reviews_anno, package = "udpipe")
> ##
> ## Toy example with pretrained embeddings
> ##
>
> ## a. build word2vec model
> x <- subset(brussels_reviews_anno, language %in% "nl")
> x <- paste.data.frame(x, term = "lemma", group = "doc_id")
> set.seed(4321)
> w2v <- word2vec(x = x$lemma, dim = 15, iter = 20, type = "cbow", min_count = 5)
> embeddings <- as.matrix(w2v)
>
> ## b. build document term matrix on nouns + adjectives, align with the embedding terms
> dtm <- subset(brussels_reviews_anno, language %in% "nl" & upos %in% c("NOUN", "ADJ"))
> dtm <- document_term_frequencies(dtm, document = "doc_id", term = "lemma")
> dtm <- document_term_matrix(dtm)
> dtm <- dtm_conform(dtm, columns = rownames(embeddings))
> dtm <- dtm[dtm_rowsums(dtm) > 0, ]
>
> ## create and fit an embedding topic model - 8 topics, theta 100-dimensional
> if (torch::torch_is_installed()) {
+
+ set.seed(4321)
+ torch_manual_seed(4321)
+ model <- ETM(k = 8, dim = 100, embeddings = embeddings, dropout = 0.5)
+ optimizer <- optim_adam(params = model$parameters, lr = 0.005, weight_decay = 0.0000012)
+ overview <- model$fit(data = dtm, optimizer = optimizer, epoch = 40, batch_size = 1000)
+ scores <- predict(model, dtm, type = "topics")
+
+ lastbatch <- subset(overview$loss, overview$loss$batch_is_last == TRUE)
+ plot(lastbatch$epoch, lastbatch$loss)
+ plot(overview$loss_test)
+
+ ## show top words in each topic
+ terminology <- predict(model, type = "terms", top_n = 7)
+ terminology
+
+ ##
+ ## Toy example without pretrained word embeddings
+ ##
+ set.seed(4321)
+ torch_manual_seed(4321)
+ model <- ETM(k = 8, dim = 100, embeddings = 15, dropout = 0.5, vocab = colnames(dtm))
+ optimizer <- optim_adam(params = model$parameters, lr = 0.005, weight_decay = 0.0000012)
+ overview <- model$fit(data = dtm, optimizer = optimizer, epoch = 40, batch_size = 1000)
+ terminology <- predict(model, type = "terms", top_n = 7)
+ terminology
+
+
+
+ ## Don't show:
+ ##
+ ## Another example using fit_original
+ ##
+ data(ng20, package = "topicmodels.etm")
+ vocab <- ng20$vocab
+ tokens <- ng20$bow_tr$tokens
+ counts <- ng20$bow_tr$counts
+
+ torch_manual_seed(123456789)
+ model <- ETM(k = 4, vocab = vocab, dim = 5, embeddings = 25)
+ model
+ optimizer <- optim_adam(params = model$parameters, lr = 0.005, weight_decay = 0.0000012)
+
+ traindata <- list(tokens = tokens, counts = counts, vocab = vocab)
+ test1 <- list(tokens = ng20$bow_ts_h1$tokens, counts = ng20$bow_ts_h1$counts, vocab = vocab)
+ test2 <- list(tokens = ng20$bow_ts_h2$tokens, counts = ng20$bow_ts_h2$counts, vocab = vocab)
+
+ out <- model$fit_original(data = traindata, test1 = test1, test2 = test2, epoch = 4,
+ optimizer = optimizer, batch_size = 1000,
+ lr_anneal_factor = 4, lr_anneal_nonmono = 10)
+ test <- subset(out$loss, out$loss$batch_is_last == TRUE)
+ plot(test$epoch, test$loss)
+
+ topic.centers <- as.matrix(model, type = "embedding", which = "topics")
+ word.embeddings <- as.matrix(model, type = "embedding", which = "words")
+ topic.terminology <- as.matrix(model, type = "beta")
+
+ terminology <- predict(model, type = "terms", top_n = 4)
+ terminology
+ ## End(Don't show)
+
+ }
Flavor: r-devel-windows-x86_64
Version: 0.1.0
Check: whether package can be installed
Result: WARN
Found the following significant warnings:
Warning: Torch libraries are installed but loading them caused a segfault.
Flavor: r-release-windows-x86_64
Version: 0.1.0
Check: examples
Result: ERROR
Running examples in 'topicmodels.etm-Ex.R' failed
The error most likely occurred in:
> ### Name: ETM
> ### Title: Topic Modelling in Semantic Embedding Spaces
> ### Aliases: ETM
>
> ### ** Examples
>
> library(torch)
> library(topicmodels.etm)
> library(word2vec)
> library(udpipe)
> data(brussels_reviews_anno, package = "udpipe")
> ##
> ## Toy example with pretrained embeddings
> ##
>
> ## a. build word2vec model
> x <- subset(brussels_reviews_anno, language %in% "nl")
> x <- paste.data.frame(x, term = "lemma", group = "doc_id")
> set.seed(4321)
> w2v <- word2vec(x = x$lemma, dim = 15, iter = 20, type = "cbow", min_count = 5)
> embeddings <- as.matrix(w2v)
>
> ## b. build document term matrix on nouns + adjectives, align with the embedding terms
> dtm <- subset(brussels_reviews_anno, language %in% "nl" & upos %in% c("NOUN", "ADJ"))
> dtm <- document_term_frequencies(dtm, document = "doc_id", term = "lemma")
> dtm <- document_term_matrix(dtm)
> dtm <- dtm_conform(dtm, columns = rownames(embeddings))
> dtm <- dtm[dtm_rowsums(dtm) > 0, ]
>
> ## create and fit an embedding topic model - 8 topics, theta 100-dimensional
> if (torch::torch_is_installed()) {
+
+ set.seed(4321)
+ torch_manual_seed(4321)
+ model <- ETM(k = 8, dim = 100, embeddings = embeddings, dropout = 0.5)
+ optimizer <- optim_adam(params = model$parameters, lr = 0.005, weight_decay = 0.0000012)
+ overview <- model$fit(data = dtm, optimizer = optimizer, epoch = 40, batch_size = 1000)
+ scores <- predict(model, dtm, type = "topics")
+
+ lastbatch <- subset(overview$loss, overview$loss$batch_is_last == TRUE)
+ plot(lastbatch$epoch, lastbatch$loss)
+ plot(overview$loss_test)
+
+ ## show top words in each topic
+ terminology <- predict(model, type = "terms", top_n = 7)
+ terminology
+
+ ##
+ ## Toy example without pretrained word embeddings
+ ##
+ set.seed(4321)
+ torch_manual_seed(4321)
+ model <- ETM(k = 8, dim = 100, embeddings = 15, dropout = 0.5, vocab = colnames(dtm))
+ optimizer <- optim_adam(params = model$parameters, lr = 0.005, weight_decay = 0.0000012)
+ overview <- model$fit(data = dtm, optimizer = optimizer, epoch = 40, batch_size = 1000)
+ terminology <- predict(model, type = "terms", top_n = 7)
+ terminology
+
+
+
+ ## Don't show:
+ ##
+ ## Another example using fit_original
+ ##
+ data(ng20, package = "topicmodels.etm")
+ vocab <- ng20$vocab
+ tokens <- ng20$bow_tr$tokens
+ counts <- ng20$bow_tr$counts
+
+ torch_manual_seed(123456789)
+ model <- ETM(k = 4, vocab = vocab, dim = 5, embeddings = 25)
+ model
+ optimizer <- optim_adam(params = model$parameters, lr = 0.005, weight_decay = 0.0000012)
+
+ traindata <- list(tokens = tokens, counts = counts, vocab = vocab)
+ test1 <- list(tokens = ng20$bow_ts_h1$tokens, counts = ng20$bow_ts_h1$counts, vocab = vocab)
+ test2 <- list(tokens = ng20$bow_ts_h2$tokens, counts = ng20$bow_ts_h2$counts, vocab = vocab)
+
+ out <- model$fit_original(data = traindata, test1 = test1, test2 = test2, epoch = 4,
+ optimizer = optimizer, batch_size = 1000,
+ lr_anneal_factor = 4, lr_anneal_nonmono = 10)
+ test <- subset(out$loss, out$loss$batch_is_last == TRUE)
+ plot(test$epoch, test$loss)
+
+ topic.centers <- as.matrix(model, type = "embedding", which = "topics")
+ word.embeddings <- as.matrix(model, type = "embedding", which = "words")
+ topic.terminology <- as.matrix(model, type = "beta")
+
+ terminology <- predict(model, type = "terms", top_n = 4)
+ terminology
+ ## End(Don't show)
+
+ }
Error in cpp_torch_manual_seed(as.character(seed)) :
Lantern is not loaded. Please use `install_torch()` to install additional dependencies.
Calls: torch_manual_seed -> cpp_torch_manual_seed
Execution halted
Flavors: r-release-windows-x86_64, r-oldrel-windows-x86_64