Running LDA Algorithm With Mahout
The following article explains the usage of Apache Mahout’ s implementation of the Latent Dirichlet Allocation learning algorithm. The idea behind the example is to extract given number of topics from a collection of text files located in a given directory. For simplicity we will use Mahout’ s ability to run locally, but there is no real difference in the procedure if it’ s applied to a Mahout build configured to use a Hadoop cluster.
The first step is to download and install Mahout. Download from here the latest stable release and extract the archive to your preferred working directory. The only thing that needs to be configured for a minimal run is the JAVA_HOME environment variable.
For ubuntu just type:
1 | export JAVA_HOME=/usr/lib/jvm/java-6-sun |
to the console or change the declared path with the appropriate path for your release / set-up. Since the export command works only for the current session, you can edit the {mahout directory}/bin/mahout file and paste the export command there.
The second step is to create the directory that will contain the text files we want to extract topics from and populate it.
1) Converting text documents to SequenceFile format
Mahout has a built in utility to convert text files contained to a given directory to SequenceFile format:
1 | seqdirectory |
The options of the command are as follows:
--input (-i) input Path to job input directory. --output (-o) output The directory pathname for output. --overwrite (-ow) If present, overwrite the output directory before running job --chunkSize (-chunk) chunkSize The chunkSize in MegaBytes. Defaults to 64 --fileFilterClass (-filter) fileFilterClass The name of the class to use for file parsing. Default: org.apache.mahout.text.PrefixAd ditionFilter --keyPrefix (-prefix) keyPrefix The prefix to be prepended to the key --charset (-c) charset The name of the character encoding of the input files. Default to UTF-8 --help (-h) Print out help --tempDir tempDir Intermediate output directory --startPhase startPhase First phase to run --endPhase endPhase Last phase to run |
but the absolutely minimum to run the utility are (every command is executed from {mahout directory}/bin directory):
1 | ./mahout seqdirectory --input {full path to the folder containing text files} --output {full path to folder that we want to save sequence files} -c UTF-8 |
or for me
1 | ./mahout seqdirectory --input /home/tgi/Desktop/ruby --output /home/tgi/Desktop/1 -c UTF-8 |
the document id generated is {prefix}{relative path from parent}/document.txt
2) Creating vectors from SequenceFile
Again, Mahout has a built in utility for this:
1 | seq2sparse |
All options of the command are:
--minSupport (-s) minSupport (Optional) Minimum Support. Default Value: 2 --analyzerName (-a) analyzerName The class name of the analyzer --chunkSize (-chunk) chunkSize The chunkSize in MegaBytes. 100-10000 MB --output (-o) output The output directory --input (-i) input input dir containing the documents in sequence file format --minDF (-md) minDF The minimum document frequency. Default is 1 --maxDFPercent (-x) maxDFPercent The max percentage of docs for the DF. Can be used to remove really high frequency terms. Expressed as an integer between 0 and 100. Default is 99. --weight (-wt) weight The kind of weight to use. Currently TF or TFIDF --norm (-n) norm The norm to use, expressed as either a float or "INF" if you want to use the Infinite norm. Must be greater or equal to 0. The default is not to normalize --minLLR (-ml) minLLR (Optional)The minimum Log Likelihood Ratio(Float) Default is 1.0 --numReducers (-nr) numReducers (Optional) Number of reduce tasks. Default Value: 1 --maxNGramSize (-ng) ngramSize (Optional) The maximum size of ngrams to create (2 = bigrams, 3 = trigrams, etc) Default Value:1 --overwrite (-ow) If set, overwrite the output directory --help (-h) Print out help --sequentialAccessVector (-seq) (Optional) Whether output vectors should be SequentialAccessVectors. If set true else false --namedVector (-nv) (Optional) Whether output vectors should be NamedVectors. If set true else false --logNormalize (-lnorm) (Optional) Whether output vectors should be logNormalize. If set true else false |
The minimum command to issue, continuing the example, is:
1 | ./mahout seq2sparse -i /home/tgi/Desktop/1 -o /home/tgi/Desktop/2 -wt tf |
3) Invoking LDA algorithm
With the documents prepared we can now invoke the LDA algorithm. options to run the algorithm are:
--input (-i) input Path to job input directory. --output (-o) output The directory pathname for output. --overwrite (-ow) If present, overwrite the output directory before running job --numTopics (-k) numTopics The total number of topics in the corpus --numWords (-v) numWords The total number of words in the corpus (can be approximate, needs to exceed the actual value) --topicSmoothing (-a) topicSmoothing Topic smoothing parameter. Default is 50/numTopics. --maxIter (-x) maxIter The maximum number of iterations. --help (-h) Print out help --tempDir tempDir Intermediate output directory --startPhase startPhase First phase to run --endPhase endPhase Last phase to run |
Continuing the example we issue the command:
1 | ./mahout lda -i /home/tgi/Desktop/2/tf-vectors -o /home/tgi/Desktop/3 -k 50 -v 200000 |
choosing to compute 50 topics in our corpus. The numWords parameter must exceed the total number of words in the previously computed dictionary. The easiest way to do this is to run it with a fictional number the first time, find this in the job initialization log outputted in the console
1 | INFO: record buffer = 262144/327680 |
and re run LDA with -v > 327680 in the example.
The input directory must point to the output directory of the previous face /tf-vectors or /tfidf-vectors depending on our previous choice.
4) Output the computed topics
After running LDA you can obtain an output of the computed topics using another Mahout utility:
1 | ldatopics |
All options of the command are:
--dict (-d) dict Dictionary to read in, in the same format as one created by org.apache.mahout.utils.vectors.lucen e.Driver --output (-o) output Output directory to write top words --words (-w) words Number of words to print --input (-i) input Path to an LDA output (a state) --dictionaryType (-dt) dictionaryType The dictionary file type (text|sequencefile) |
<span class="Apple-style-span" style="font-family: Georgia, 'Times New Roman', 'Bitstream Charter', Times, serif; font-size: 13px; line-height: 19px; white-space: normal;">and to print out the topics of the example corpus we type: </span> |
1 | ./mahout ldatopics -i /home/tgi/Desktop/3/state-9/ -d /home/tgi/Desktop/2/dictionary.* -o /home/tgi/Desktop/4 --dictionaryType sequencefile |
Be careful here to use as the input directory the last state before convergence of the algorithm (sate-9 for the example). The output should be a set of files that each represent a computed topic and contain the words of that topic. Something like this:
end [p(end|topic_47) = 0.02054285450740693 class [p(class|topic_47) = 0.019173433558138983 you [p(you|topic_47) = 0.011081809083779152 ruby [p(ruby|topic_47) = 0.010939372894063723 code [p(code|topic_47) = 0.009912782417548813 pm [p(pm|topic_47) = 0.009565600712295945 07 [p(07|topic_47) = 0.008512880092545057 x [p(x|topic_47) = 0.007979504372069835 3 [p(3|topic_47) = 0.007552053207748147 from [p(from|topic_47) = 0.007501607551193776 your [p(your|topic_47) = 0.0074405804416695824 use [p(use|topic_47) = 0.007428773869438666 page [p(page|topic_47) = 0.007026473801340893 def [p(def|topic_47) = 0.006772484477458351 chapter [p(chapter|topic_47) = 0.006635243376794015 10 [p(10|topic_47) = 0.00600798063831032 have [p(have|topic_47) = 0.005999418317965101 2 [p(2|topic_47) = 0.005959277207972052 need [p(need|topic_47) = 0.005849514834292144 method [p(method|topic_47) = 0.005676919181434945 |
If you run LDA using a Hadoop cluster, you should substitute all paths used in the example with hdfs paths.
Currently (mahout version 0.5) LDA implementation is not completely integrated with the other Mahout clustering applications. There is no classification step to match an initial input document with a computed topic.
[…] http://theglassicon.com/computing/machine-learning/running-lda-algorithm-mahout […]