WELCOME TO PLDA+! plda+ must be run in linux environment with mpich2-1.5. This is a beta version and you can report issues via https://code.google.com/p/plda/issues/list . plda+ is a new parallel algorithm for lda based on mpi, which is entirely different from plda. The detailed introduction to the algorithm of plda+ can be found in the paper: "PLDA+: Parallel Latent Dirichlet Allocation with Data Placement and Pipeline Processing. Zhiyuan Liu, Yuzhou Zhang, Edward Y. Chang, Maosong Sun. ACM Transactions on Intelligent Systems and Technology, special issue on Large Scale Machine Learning. 2011."
#Installation ##install mpich2 * see http://www.mpich.org/ * touch ~/.mpd.conf * cat "MPD_SECRETWORD=anypassword" > ~/.mpd.conf * export PATH=.../mpich-3.2/output/bin:${PATH} * export C_INCLUDE_PATH=.../mpich-3.2/output/include/:${C_INCLUDE_PATH} * export CPLUS_INCLUDE_PATH=.../mpich-3.2/output/include/:${CPLUS_INCLUDE_PATH} * export LD_LIBRARY_PATH=.../mpich-3.2/output/lib/:${LD_LIBRARY_PATH} * exort LIBRARY_PATH=.../mpich-3.2/output/lib/:${LIBRARY_PATH}
##install plda * tar -xvfz plda+.tar.gz * make mpi_lda infer * You will see two binary files 'mpi_lda' and 'infer' in the folder
#train and infer ##Prepare datafile * Data is stored in a sparse representation, with one document per line. Each line consists of the words and their counts. For example, <word1_count> <word2_count> ... * Each word is a string without any space, newline or other special characters.
##Training parallelly
* First, make sure that every parallel node has the same copy of data file and executable file (i.e. mpi_lda).
* Train: e.g.
mpiexec -n 6 ./mpi_lda
--num_pw 2
--num_topics 2
--alpha 0.1
--beta 0.01
--training_data_file testdata/test_data.txt
--model_file testdata/lda_model
--total_iterations 150
* After finish training, there will be num_pw model files (here named as lda_model_XXX, where XXX = 0,1,...,num_pw-1). Please collect all of the model files and concatenate them into a whole model file.
##Inferring unseen documents
* Prepare datafile in the same way of training datafile.
* Infer: e.g.
./infer
--alpha 0.1
--beta 0.01
--inference_data_file testdata/test_data.txt
--inference_result_file testdata/inference_result.txt
--model_file testdata/lda_model.txt
--total_iterations 15
--burn_in_iterations 10
#Command-line flags ##Training flags: * num_pw: The number of pw processors, which should be greater than 0 and less than the total number of processors (here is 6). Suggested to be number_of_processors/3 * num_topics: The total number of topics * alpha: Suggested to be 50/number_of_topics * beta: Suggested to be 0.01 * total_iterations: The total number of GibbsSampling iterations * training_data_filel: The training data file * model_file: The output file of the trained model ##Inferring flags: * alpha and beta should be the same with training. * total_iterations: The total number of GibbsSampling iterations for an unseen document. This number does not need to be as much as training, usually tens of iterations is enough. * burn_in_iterations: For an unseen document, we will average the document-topic distribution of the last (total_iterations - burn_in_iterations) iterations as the final document-topic distribution.
#TOOLS * get_topic_word, eg: python ./view_model.py model ./tmp/model.view_file