Subject: RE: any pointer to run wikipedia bayes example To: [email protected] From: [email protected] Date: Tue, 26 Aug 2014 18:12:52 -0400
Hello Andrew, I have given a try to NB on the medium size Wikipedia (~1GB data after decompression, roughly 1/50 of the full Wikipedia size) with two categories (US/UK) I examined the tf-idf vectors generated. I have two questions: (1) It seems there are (only) 11683 data points (i.e., documents) generated, albeit each data point has relatively high dimension. 10K data points seem not very exciting, even I multiply it by 50 ( to the full extend of Wikipedia dataset), it seems the data points are not particularly many. I suspect that many of the documents are not categorized as either US or UK, thus not included in the training set. On a 20 node (8 cores each)( cluster (albeit a quite old one, 5 years old), it took 45 minutes to label/vectorize the dataset, but only 3 minutes to train the NB. If you used option (2) from the classify-wiki.sh script, seq2sparse will be vectorizing the data using 4-grams which take much longer and give you a much larger feature set. option (1) uses bigrams. I am wondering is there a way to get a larger dataset that can stress the NB training (instead of the label/vectorization part) either by providing a more inclusive category file or choosing another dataset ? You could run on the the full country set: https://github.com/apache/mahout/blob/master/examples/src/test/resources/country.txt By editing line 101 or 107 to read: cp $MAHOUT_HOME/examples/src/test/resources/country.txt ${WORK_DIR}/country.txt However on the medium data set, this only yields ~38200 documents so it still probably will not be not be the size that you are looking for. Alternatively, you could create your own category.txt file to use and pass it to the -c argument. As well you could try turning the -all option which as we discussed before will likely skew the categories into an "unknown" category, but will not reject any documents With a more inclusive category file, I can potentially get a larger dataset, but I don't know how to handle the case where a document has two labels in that category file. Currently, the WikipediaMapper is labeling the document as the first matching category that it finds, but you can customize this however you'd like. (2) I am wondering if I use Wikipedia dataset as the input to the K-means clustering, (thus no need to label the data), then I can get a relatively large dataset, and both K-means and NB use the SequenceFileFormat. I believe this this should work- you could remove the labeling section - basically lines 79-85 from WikipediaMapper.java https://github.com/apache/mahout/blob/master/integration/src/main/java/org/apache/mahout/text/wikipedia/WikipediaMapper.java and write out something like (K=document_title,V=document) to the sequence file. and then run this sequence file through seq2sparse and kmeans as is done in the cluster-reuters.sh example(starting at line 109): https://github.com/andrewpalumbo/mahout/blob/master/examples/bin/cluster-reuters.sh It seems that I would just need to bypass the label data part and go directly to the vectorization, I am not sure if it is feasible ? Thanks a lot ! Wei Andrew Palumbo ---08/21/2014 02:28:45 PM---Hello, Yes, If you work off of the current trunk, you can use the classify-wiki.sh example. There i From: Andrew Palumbo <[email protected]> To: "[email protected]" <[email protected]> Date: 08/21/2014 02:28 PM Subject: RE: any pointer to run wikipedia bayes example Hello, Yes, If you work off of the current trunk, you can use the classify-wiki.sh example. There is currently no documentation on the Mahout site for this. You can run this script to build and test an NB classifier for option (1) 10 arbitrary countries or option (2) 2 countries (United States and United Kingdom) By defult the script is set to run on a medium sized wikipedia XML dump. To run on the full set you'll have to change the download by commenting out line 78, and uncommenting line 80 [1]. *Be sure to clean your work directory when changing datasets- option (3).* The step by step process for Creating a Naive Bayes Classifier for the wikipedia XML dump is very similar to creating the the 20 Newsgroups Classifier. The only difference being that instead of running $mahout seqdirectory on the unzipped 20 Newsgroups file, you'll run $mahout seqwiki on the unzipped wikipedia xml dump. $ mahout seqwiki invokes WikipediaToSequenceFile.java which accepts a text file of categories [2] and starts an MR job to parse the each document in the XML file. This process will seek to extract documents with category which (exactly, if the exactMatchOnly option is set) matches a line in the category file. If no match is found and the -all option is set, the document will be dumped into an "unknown" category. The documents will then be written out as a <Text,Text> sequence file of the form (K: /category/document_title , V: document) . There are 3 different example category files available to in the /examples/src/test/resources directory: country.txt, country10.txt and country2.txt. The CLI options for seqwiki are as follows: -input (-i) input pathname String -output (-o) the output pathname String -categories (-c) the file containing the Wikipedia categories -exactMatchOnly (-e) if set, then the Wikipedia category must match exactly instead of simply containing the category string -all (-all) if set select all categories >From there you just need to run seq2sparse, split, trainnb, testnb as in the >example script. Especially for the Binary classification problem you should have better results using 3 or 4-grams and a low maxDF cuttoff like 30. [1] https://github.com/apache/mahout/blob/master/examples/bin/classify-wiki.sh [2] https://github.com/apache/mahout/blob/master/examples/src/test/resources/country10.txt Subject: Re: any pointer to run wikipedia bayes example To: [email protected] From: [email protected] Date: Wed, 20 Aug 2014 09:50:42 -0400 hi, After did a bit more searching, I found https://issues.apache.org/jira/browse/MAHOUT-1527 The version of Mahout that I have been working on is Mahout 0.9 (from http://mahout.apache.org/general/downloads.html), which I downloaded in April. Albeit the latest stable release, it doesn't include the patch mentioned in https://issues.apache.org/jira/browse/MAHOUT-1527 Then I realized had I cloned the latest mahout, I would get a script that classify-wiki.sh, and probably can start from there. Sorry for the spam! Thanks, Wei Wei Zhang---08/19/2014 06:18:09 PM---Hi, I have been able to run the bayesian network 20news group example provided From: Wei Zhang/Watson/IBM@IBMUS To: [email protected] Date: 08/19/2014 06:18 PM Subject: any pointer to run wikipedia bayes example Hi, I have been able to run the bayesian network 20news group example provided at Mahout website. I am interested in running the Wikipedia bayes example, as it is a much larger dataset. >From several googling attempts, I figured it is a bit different workflow than running the 20news group example -- e.g., I would need to provide a categories.txt file, and invoke WikipediaXmlSplitter, call wikipediaDataSetCreator and etc. I am wondering is there a document somewhere that describes the process of running Wikipedia bayes example ? https://cwiki.apache.org/MAHOUT/wikipedia-bayes-example.html seems no longer work. Greatly appreciated! Wei
