Package projector. February 27, 2018

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1 Package projector February 27, 2018 Title Project Dense Vectors Representation of Texts on a 2D Plan Version Date Maintainer Michaël Benesty <michael@benesty.fr> Display dense vector representation of texts on a 2D plan to better understand embeddings by observing the neighbors of a selected text. It also includes an interactive application to change dynamically the pivot text. URL BugReports Depends R (>= 3.4) Imports methods, RcppAnnoy (>= ), Rtsne, RColorBrewer, dbscan, plotly, assertthat, shiny, shinythemes, shinyjs Suggests knitr, testthat, fastrtext, covr License MIT + file LICENSE Encoding UTF-8 LazyData true RoxygenNote NeedsCompilation no Author Michaël Benesty [aut, cre, cph] Repository CRAN Date/Publication :11:58 UTC R topics documented: get_annoy_model interactive_embedding_exploration load_annoy_model plot_texts retrieve_neighbors save_annoy_model

2 2 interactive_embedding_exploration Index 7 get_annoy_model Build Annoy model RcppAnnoy model is used to retrieve the most similar vectors to a pivot one. This function builds the RcppAnnoy model. get_annoy_model(vectors, number_trees) vectors number_trees matrix where each row is an observation. rownames should contain textual versions of the vectors. integer counting the number of trees to grow in Annoy (for neighbor search). More gives better results but is slower to compute. if (interactive()){ word_embeddings <- get_word_vectors(model, words = head(get_dictionary(model), 2e5)) interactive_embedding_exploration Interactive exploration of embeddings Shiny application to interactively play with embeddings. User provides a pivot word and the n most similar word are projected on a scatter plot.

3 load_annoy_model 3 interactive_embedding_exploration(annoy_model, default_number_neighbors = 100, default_number_rounds = 500, default_perplexity = 30) annoy_model RcppAnnoy model generated with get_annoy_model default_number_neighbors set the number of neighbors slider to this value default_number_rounds set the number of T-SNE rounds slider to this value default_perplexity set the T-SNE perplexity slider to this value Details For large list of texts, the auto-complete can be slow. Increasing the number of neighbors can make things very slow, in particular with T-SNE approach. 500 neighbors is usually a good value to have an understanding of the neighborhood of a vector. Colors in the scatter plot represents clusters found by dbscan. if(interactive()){ word_embeddings <- get_word_vectors(model, words = head(get_dictionary(model), 2e5)) interactive_embedding_exploration(annoy_model) load_annoy_model Load RcppAnnoy model

4 4 plot_texts Load the content of the model from two files: the RcppAnnoy model the dictionary (character containing texts) load_annoy_model(path_annoy, path_dictionary) path_annoy path to the RcppAnnoy model path_dictionary path to the dictionary (character containing texts) plot_texts Plot vectors on a 2D plan Plot the text on an interactive scatter plot. plot_texts(coordinates, min_cluster_size = 5) coordinates data.frame containing 2D coordinates of texts. min_cluster_size integer corresponding to the minimum size of a vector (for the colors). if(interactive()){ word_embeddings <- get_word_vectors(model, words = head(get_dictionary(model), 2e5))

5 retrieve_neighbors 5 selected_word <- "out" df <- retrieve_neighbors(text = selected_word, projection_type = "tsne", annoy_model = annoy_model, n = 1000) plot_texts(df, 3) retrieve_neighbors Retrieve a list of neighbor vectors Use RcppAnnoy to rapidly retrieve a list of vector neighbors. retrieve_neighbors(text, projection_type, annoy_model, n, search_k = max(10000, 10 * n), center_pivot = TRUE,...) text character containing the text related to the pivot vector projection_type character defining the algorithm to use to compute the coordinates. (tsne or pca) annoy_model n search_k center_pivot RcppAnnoy model number of neighbors to retrieve number of nodes to search in (RcppAnnoy parameter). Higher = ++precision & speed put pivot text in the middle of the graph... parameters passed to projection algorithm (max_iter, perplexity, verbose, transformations) Details Transform original vectors in 2D coordinates applying either: PCA T-SNE.

6 6 save_annoy_model if (interactive()){ word_embeddings <- get_word_vectors(model, words = head(get_dictionary(model), 2e5)) selected_word <- "out" df <- retrieve_neighbors(text = selected_word, projection_type = "tsne", annoy_model = annoy_model, n = 1000) save_annoy_model Save RcppAnnoy model Save the content of the model in two files: the RcppAnnoy model the dictionary (character containing texts) save_annoy_model(annoy_model, path_annoy, path_dictionary) annoy_model RcppAnnoy model path_annoy path for the RcppAnnoy model path_dictionary path for the dictionary (character containing texts)

7 Index character, 4 6 data.frame, 4 dbscan, 3 get_annoy_model, 2, 3 integer, 2, 4 interactive_embedding_exploration, 2 load_annoy_model, 3 matrix, 2 plot_texts, 4 RcppAnnoy, 2 6 retrieve_neighbors, 5 rownames, 2 save_annoy_model, 6 7

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