# Predict using a Starspace model The prediction functionality allows you to retrieve the following types of elements from a Starspace model: * `generic`: get general Starspace predictions in detail * `labels`: get similarity of your text to all the labels of the Starspace model * `embedding`: document embeddings of your text (shorthand for `starspace_embedding`) * `knn`: k-nearest neighbouring (most similar) elements of the model dictionary compared to your input text (shorthand for `starspace_knn`) ```r ## S3 method for class 'textspace' predict( object, newdata, type = c("generic", "labels", "knn", "embedding"), k = 5L, sep = " ", basedoc, ... ) ``` ## Arguments - `object`: an object of class `textspace` as returned by `starspace` or `starspace_load_model` - `newdata`: a data frame with columns `doc_id` and `text` or a character vector with text where the names of the character vector represent an identifier of that text - `type`: character string: either 'generic', 'labels', 'embedding', 'knn'. Defaults to 'generic' - `k`: integer with the number of predictions to make. Defaults to 5. Only used in case `type` is set to `'generic'` or `'knn'` - `sep`: character string used to split `newdata` using boost::split. Only used in case `type` is set to `'generic'` - `basedoc`: optional, either a character vector of possible elements to predict or the path to a file in labelDoc format, containing basedocs which are set of possible things to predict, if different than the ones from the training data. Only used in case `type` is set to `'generic'` - `...`: not used ## Returns The following is returned, depending on the argument `type`: * In case type is set to `'generic'`: a list, one for each row or element in `newdata`. Each list element is a list with elements * doc_id: the identifier of the text * text: the character string with the text * prediction: data.frame with columns label, label_starspace and similarity indicating the predicted label and the similarity of the text to the label * terms: a list with elements basedoc_index and basedoc_terms indicating the position in basedoc and the terms which are part of the dictionary which are used to find the similarity * In case type is set to `'labels'`: a data.frame is returned namely: The data.frame `newdata` where several columns are added, one for each label in the Starspace model. These columns contain the similarities of the text to the label. Similarities are computed with `embedding_similarity` indicating embedding similarities of the text compared to the labels using either cosine or dot product as was used during model training. * In case type is set to `'embedding'`: A matrix of document embeddings, one embedding for each text in `newdata` as returned by `starspace_embedding`. The rownames of this matrix are set to the document identifiers of `newdata`. * In case type is set to `'knn'`: a list of data.frames, one for each row or element in `newdata` Each of these data frames contains the columns doc_id, label, similarity and rank indicating the k-nearest neighbouring (most similar) elements of the model dictionary compared to your input text as returned by `starspace_knn` ## Examples ```r data(dekamer, package = "ruimtehol") dekamer$text <- strsplit(dekamer$question, "\\W") dekamer$text <- lapply(dekamer$text, FUN = function(x) x[x != ""]) dekamer$text <- sapply(dekamer$text, FUN = function(x) paste(x, collapse = " ")) idx <- sample(nrow(dekamer), size = round(nrow(dekamer) * 0.9)) traindata <- dekamer[idx, ] testdata <- dekamer[-idx, ] set.seed(123456789) model <- embed_tagspace(x = traindata$text, y = traindata$question_theme_main, early_stopping = 0.8, dim = 10, minCount = 5) scores <- predict(model, testdata) scores <- predict(model, testdata, type = "labels") str(scores) emb <- predict(model, testdata[, c("doc_id", "text")], type = "embedding") knn <- predict(model, testdata[1:5, c("doc_id", "text")], type = "knn", k=3) ## Not run: library(udpipe) data(dekamer, package = "ruimtehol") dekamer <- subset(dekamer, question_theme_main == "DEFENSIEBELEID") x <- udpipe(dekamer$question, "dutch", tagger = "none", parser = "none", trace = 100) x <- x[, c("doc_id", "sentence_id", "sentence", "token")] set.seed(123456789) model <- embed_sentencespace(x, dim = 15, epoch = 5, minCount = 5) scores <- predict(model, "Wat zijn de cijfers qua doorstroming van 2016?", basedoc = unique(x$sentence), k = 3) str(scores) #' ## clean up for cran file.remove(list.files(pattern = ".udpipe$")) ## End(Not run) ```

predict.textspace function