GibbsLDA++ is a C/C++ implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling technique for parameter estimation and inference. It is very fast and is designed to analyze hidden/latent topic structures of large-scale datasets including large collections of text/Web documents. LDA was first introduced by David Blei et al [Blei03]. There have been several implementations of this model in C (using Variational Methods), Java, and Matlab. We decided to release this implementation of LDA in C/C++ using Gibbs Sampling to provide an alternative to the topic-model community. GibbsLDA++ is useful for the following potential application areas: Information retrieval and search (analyzing semantic/latent topic/concept structures of large text collection for a more intelligent information search).

Document classification/clustering, document summarization, and text/web mining community in general.

Content-based image clustering, object recognition, and other applications of computer vision in general.

Other potential applications in biological data. Contact us: all comments, suggestions, and bug reports are highly appreciated. And if you have any further problems, please contact us: Xuan-Hieu Phan (pxhieu at gmail dot com), was at Tohoku University, Japan (now at Vietnam National University, Hanoi)

Cam-Tu Nguyen (ncamtu at gmail dot com), was at Vietnam National University, Hanoi (now at Google Japan) License: GibbsLDA++ is a free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. GibbsLDA++ is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with GibbsLDA++; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA.

You can find and download source code, document, and case studies of GibbsLDA++ at the project page. You should download version 0.2 that includes bug fix and code optimization, and thus faster than the version 0.1. A Java implementation (JGibbLDA) is also available. You can download at its project page Here are some other tools developed by the same author(s): FlexCRFs: Flexible Conditional Random Fields

CRFTagger: CRF English POS Chunker

CRFChunker: CRF English Phrase Chunker

JTextPro: A Java-based Text Processing Toolkit

JWebPro: A Java-based Web Processing Toolkit

JVnSegmenter: A Java-based Vietnamese Word Segmentation Tool

JVnTextPro: A Java-based Vietnamese Text Processing Tool

Environments Unix, Linux, Cygwin, and MinGW System requirements A C/C++ compiler and the STL library. In the Makefile, we use g++ as the default compiler command, if the C/C++ compiler on your system has another name (e.g., cc, cpp, CC, CPP, etc.), you can modify the CC variable in the Makefile in order to use make utility smoothly. The computational time of GibbsLDA++ much depends on the size of input data, the CPU speed, and the memory size. If your dataset is quite large (e.g., larger than 100,000 documents or so), it is better to train GibbsLDA++ on a minimum of 2GHz CPU, 1Gb RAM system. Untar and unzip the source code $ gunzip GibbsLDA++.tar.gz $ tar -xf GibbsLDA++.tar Compile Go to the home directory of GibbsLDA++ (i.e. GibbsLDA++ directory), and type: $ make clean $ make all After compiling GibbsLDA++, we have an executable file "lda" in the GibbsLDA++/src directory.

Parameter estimation from scratch Command line: $ lda -est [-alpha <double>] [-beta <double>] [-ntopics <int>] [-niters <int>] [-savestep <int>] [-twords <int>] -dfile <string> in which (parameters in [] are optional): -est : Estimate the LDA model from scratch

: Estimate the LDA model from scratch -alpha <double> : The value of alpha, hyper-parameter of LDA. The default value of alpha is 50 / K (K is the the number of topics). See [Griffiths04] for a detailed discussion of choosing alpha and beta values.

: The value of alpha, hyper-parameter of LDA. The default value of alpha is 50 / K (K is the the number of topics). See [Griffiths04] for a detailed discussion of choosing alpha and beta values. -beta <double> : The value of beta, also the hyper-parameter of LDA. Its default value is 0.1

: The value of beta, also the hyper-parameter of LDA. Its default value is 0.1 -ntopics <int> : The number of topics. Its default value is 100. This depends on the input dataset. See [Griffiths04] and [Blei03] for a more careful discussion of selecting the number of topics.

: The number of topics. Its default value is 100. This depends on the input dataset. See [Griffiths04] and [Blei03] for a more careful discussion of selecting the number of topics. -niters <int> : The number of Gibbs sampling iterations. The default value is 2000.

: The number of Gibbs sampling iterations. The default value is 2000. -savestep <int> : The step (counted by the number of Gibbs sampling iterations) at which the LDA model is saved to hard disk. The default value is 200.

: The step (counted by the number of Gibbs sampling iterations) at which the LDA model is saved to hard disk. The default value is 200. -twords <int> : The number of most likely words for each topic. The default value is zero. If you set this parameter a value larger than zero, e.g., 20, GibbsLDA++ will print out the list of top 20 most likely words per each topic each time it save the model to hard disk according to the parameter savestep above.

: The number of most likely words for each topic. The default value is zero. If you set this parameter a value larger than zero, e.g., 20, GibbsLDA++ will print out the list of top 20 most likely words per each topic each time it save the model to hard disk according to the parameter savestep above. -dfile <string> : The input training data file. See section "Input data format" for a description of input data format. Parameter estimation from a previously estimated model Command line: $ lda -estc -dir <string> -model <string> [-niters <int>] [-savestep <int>] [-twords <int>] in which (parameters in [] are optional): -estc : Continue to estimate the model from a previously estimated model.

: Continue to estimate the model from a previously estimated model. -dir <string> : The directory contain the previously estimated model

: The directory contain the previously estimated model -model <string> : The name of the previously estimated model. See section "Outputs" to know how GibbsLDA++ saves outputs on hard disk.

: The name of the previously estimated model. See section "Outputs" to know how GibbsLDA++ saves outputs on hard disk. -niters <int> : The number of Gibbs sampling iterations to continue estimating. The default value is 2000.

: The number of Gibbs sampling iterations to continue estimating. The default value is 2000. -savestep <int> : The step (counted by the number of Gibbs sampling iterations) at which the LDA model is saved to hard disk. The default value is 200.

: The step (counted by the number of Gibbs sampling iterations) at which the LDA model is saved to hard disk. The default value is 200. -twords <int> : The number of most likely words for each topic. The default value is zero. If you set this parameter a value larger than zero, e.g., 20, GibbsLDA++ will print out the list of top 20 most likely words per each topic each time it save the model to hard disk according to the parameter savestep above. Inference for previously unseen (new) data Command line: $ lda -inf -dir <string> -model <string> [-niters <int>] [-twords <int>] -dfile <string> in which (parameters in [] are optional): -inf : Do inference for previously unseen (new) data using a previously estimated LDA model.

: Do inference for previously unseen (new) data using a previously estimated LDA model. -dir <string> : The directory contain the previously estimated model

: The directory contain the previously estimated model -model <string> : The name of the previously estimated model. See section "Outputs" to know how GibbsLDA++ saves outputs on hard disk.

: The name of the previously estimated model. See section "Outputs" to know how GibbsLDA++ saves outputs on hard disk. -niters <int> : The number of Gibbs sampling iterations for inference. The default value is 20.

: The number of Gibbs sampling iterations for inference. The default value is 20. -twords <int> : The number of most likely words for each topic of the new data. The default value is zero. If you set this parameter a value larger than zero, e.g., 20, GibbsLDA++ will print out the list of top 20 most likely words per each topic after inference.

: The number of most likely words for each topic of the new data. The default value is zero. If you set this parameter a value larger than zero, e.g., 20, GibbsLDA++ will print out the list of top 20 most likely words per each topic after inference. -dfile <string> :The file containing new data. See section "Input data format" for a description of input data format. Input data format Both data for training/estimating the model and new data (i.e., previously unseen data) have the same format as follows: [M] [document 1 ] [document 2 ] ... [document M ] in which the first line is the total number for documents [M]. Each line after that is one document. [document i ] is the ith document of the dataset that consists of a list of N i words/terms. [document i ] = [word i1 ] [word i2 ] ... [word iNi ] in which all [word ij ] (i=1..M, j=1..N i ) are text strings and they are separated by the blank character. Note that the terms document and word here are abstract and should not only be understood as normal text documents. This is because LDA can be used to discover the underlying topic structures of any kind of discrete data. Therefore, GibbsLDA++ is not limited to text and natural language processing but can also be applied to other kinds of data like images and biological sequences. Also, keep in mind that for text/Web data collections, we should first preprocess the data (e.g., removing stop words and rare words, stemming, etc.) before estimating with GibbsLDA++. Outputs Outputs of Gibbs sampling estimation of GibbsLDA++ include the following files: <model_name>.others <model_name>.phi <model_name>.theta <model_name>.tassign <model_name>.twords in which: <model_name>: is the name of a LDA model corresponding to the time step it was saved on the hard disk. For example, the name of the model was saved at the Gibbs sampling iteration 400th will be model-00400. Similarly, the model was saved at the 1200th iteration is model-01200. The model name of the last Gibbs sampling iteration is model-final. <model_name>.others: This file contains some parameters of LDA model, such as: alpha=? beta=? ntopics=? # i.e., number of topics ndocs=? # i.e., number of documents nwords=? # i.e., the vocabulary size liter=? # i.e., the Gibbs sampling iteration at which the model was saved <model_name>.phi: This file contains the word-topic distributions, i.e., p(word w |topic t ). Each line is a topic, each column is a word in the vocabulary. <model_name>.theta: This file contains the topic-document distributions, i.e., p(topic t |document m ). Each line is a document and each column is a topic. <model_name>.tassign: This file contains the topic assignments for words in training data. Each line is a document that consists of a list of <wordij>:<topic of wordij> <model_file>.twords: This file contains twords most likely words of each topic. twords is specified in the command line. GibbsLDA++ also saves a file called wordmap.txt that contains the maps between words and word's IDs (integer). This is because GibbsLDA++ works directly with integer IDs of words/terms inside instead of text strings. Outputs of Gibbs sampling inference for previously unseen data The outputs of GibbsLDA++ inference are almost the same as those of the estimation process except that the contents of those files are of the new data. The <model_name> is exactly the same as the filename of the input (new) data.

For example, we want to estimate a LDA model for a collection of documents stored in file called models/casestudy/trndocs.dat and then use that model to do inference for new data stored in file models/casestudy/newdocs.dat. We want to estimate for 100 topics with alpha = 0.5 and beta = 0.1. We want to perform 1000 Gibbs sampling iterations, save a model at every 100 iterations, and each time a model is saved, print out the list of 20 most likely words for each topic. Supposing that we are now at the home directory of GibbsLDA++, We will execute the following command to estimate LDA model from scratch: $ src/lda -est -alpha 0.5 -beta 0.1 -ntopics 100 -niters 1000 -savestep 100 -twords 20 -dfile models/casestudy/trndocs.dat Now look into the models/casestudy directory, we can see the outputs. Now, we want to continue to perform another 800 Gibbs sampling iterations from the previously estimated model model-01000 with savestep = 100, twords = 30, we perform the following command: $ src/lda -estc -dir models/casestudy/ -model model-01000 -niters 800 -savestep 100 -twords 30 Now, look into the casestudy directory to see the outputs. Now, if we want to do inference (30 Gibbs sampling iterations) for the new data newdocs.dat (note that the new data file is stored in the same directory of the LDA models) using one of the previously estimated LDA models, for example model-01800, we perform the following command: $ src/lda -inf -dir models/casestudy/ -model model-01800 -niters 30 -twords 20 -dfile newdocs.dat Now, look into the casestudy directory, we can see the outputs of the inferences: newdocs.dat.others newdocs.dat.phi newdocs.dat.tassign newdocs.dat.theta newdocs.dat.twords Here are the outputs of two large-scale datasets: 200 topics from Wikipedia (240Mb, 71,986 docs)

200 topics from Ohsumed (a subset of MEDLINE abstracts, 156Mb, 233,442 abstracts)

60 topics, 120 topics, and 200 topics from VnExpress News Collection (in Vietnamese)