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Distributed Latent Semantic Analysis

Distributed Latent Semantic Analysis

Note

See Distributed Computing for an introduction to distributed computing in gensim.

Setting up the cluster

We will show how to run distributed Latent Semantic Analysis by means of an example. Let’s say we have 5 computers at our disposal, all on the same network segment (=reachable by network broadcast). To start with, install gensim and set up Pyro on each computer with:

$ sudo easy_install gensim[distributed]
$ export PYRO_SERIALIZERS_ACCEPTED=pickle
$ export PYRO_SERIALIZER=pickle

Then run Pyro’s name server on exactly one of the machines (doesn’t matter which one):

$ python -m Pyro4.naming -n 0.0.0.0 &

Let’s say our example cluster consists of dual-core computers with loads of memory. We will therefore run two worker scripts on four of the physical machines, creating eight logical worker nodes:

$ python -m gensim.models.lsi_worker &

This will execute gensim’s lsi_worker.py script (to be run twice on each of the four computer). This lets gensim know that it can run two jobs on each of the four computers in parallel, so that the computation will be done faster, while also taking up twice as much memory on each machine.

Next, pick one computer that will be a job scheduler in charge of worker synchronization, and on it, run LSA dispatcher. In our example, we will use the fifth computer to act as the dispatcher and from there run:

$ python -m gensim.models.lsi_dispatcher &

In general, the dispatcher can be run on the same machine as one of the worker nodes, or it can be another, distinct computer (within the same broadcast domain). The dispatcher won’t be doing much with CPU most of the time, but pick a computer with ample memory.

And that’s it! The cluster is set up and running, ready to accept jobs. To remove a worker later on, simply terminate its lsi_worker process. To add another worker, run another lsi_worker (this will not affect a computation that is already running, the additions/deletions are not dynamic). If you terminate lsi_dispatcher, you won’t be able to run computations until you run it again (surviving worker processes can be re-used though).

Running LSA

So let’s test our setup and run one computation of distributed LSA. Open a Python shell on one of the five machines (again, this can be done on any computer in the same broadcast domain, our choice is incidental) and try:

>>> from gensim import corpora, models, utils
>>> import logging
>>> logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)

>>> corpus = corpora.MmCorpus('/tmp/deerwester.mm') # load a corpus of nine documents, from the Tutorials
>>> id2word = corpora.Dictionary.load('/tmp/deerwester.dict')

>>> lsi = models.LsiModel(corpus, id2word=id2word, num_topics=200, chunksize=1, distributed=True) # run distributed LSA on nine documents

This uses the corpus and feature-token mapping created in the Corpora and Vector Spaces tutorial. If you look at the log in your Python session, you should see a line similar to:

2010-08-09 23:44:25,746 : INFO : using distributed version with 8 workers

which means all went well. You can also check the logs coming from your worker and dispatcher processes — this is especially helpful in case of problems. To check the LSA results, let’s print the first two latent topics:

>>> lsi.print_topics(num_topics=2, num_words=5)
topic #0(3.341): 0.644*"system" + 0.404*"user" + 0.301*"eps" + 0.265*"time" + 0.265*"response"
topic #1(2.542): 0.623*"graph" + 0.490*"trees" + 0.451*"minors" + 0.274*"survey" + -0.167*"system"

Success! But a corpus of nine documents is no challenge for our powerful cluster… In fact, we had to lower the job size (chunksize parameter above) to a single document at a time, otherwise all documents would be processed by a single worker all at once.

So let’s run LSA on one million documents instead:

>>> # inflate the corpus to 1M documents, by repeating its documents over&over
>>> corpus1m = utils.RepeatCorpus(corpus, 1000000)
>>> # run distributed LSA on 1 million documents
>>> lsi1m = models.LsiModel(corpus1m, id2word=id2word, num_topics=200, chunksize=10000, distributed=True)

>>> lsi1m.print_topics(num_topics=2, num_words=5)
topic #0(1113.628): 0.644*"system" + 0.404*"user" + 0.301*"eps" + 0.265*"time" + 0.265*"response"
topic #1(847.233): 0.623*"graph" + 0.490*"trees" + 0.451*"minors" + 0.274*"survey" + -0.167*"system"

The log from 1M LSA should look like:

2010-08-10 02:46:35,087 : INFO : using distributed version with 8 workers
2010-08-10 02:46:35,087 : INFO : updating SVD with new documents
2010-08-10 02:46:35,202 : INFO : dispatched documents up to #10000
2010-08-10 02:46:35,296 : INFO : dispatched documents up to #20000
...
2010-08-10 02:46:46,524 : INFO : dispatched documents up to #990000
2010-08-10 02:46:46,694 : INFO : dispatched documents up to #1000000
2010-08-10 02:46:46,694 : INFO : reached the end of input; now waiting for all remaining jobs to finish
2010-08-10 02:46:47,195 : INFO : all jobs finished, downloading final projection
2010-08-10 02:46:47,200 : INFO : decomposition complete

Due to the small vocabulary size and trivial structure of our “one-million corpus”, the computation of LSA still takes only 12 seconds. To really stress-test our cluster, let’s do Latent Semantic Analysis on the English Wikipedia.

Distributed LSA on Wikipedia

First, download and prepare the Wikipedia corpus as per Experiments on the English Wikipedia, then load the corpus iterator with:

>>> import logging, gensim
>>> logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)

>>> # load id->word mapping (the dictionary)
>>> id2word = gensim.corpora.Dictionary.load_from_text('wiki_en_wordids.txt')
>>> # load corpus iterator
>>> mm = gensim.corpora.MmCorpus('wiki_en_tfidf.mm')
>>> # mm = gensim.corpora.MmCorpus('wiki_en_tfidf.mm.bz2') # use this if you compressed the TFIDF output

>>> print(mm)
MmCorpus(3199665 documents, 100000 features, 495547400 non-zero entries)

Now we’re ready to run distributed LSA on the English Wikipedia:

>>> # extract 400 LSI topics, using a cluster of nodes
>>> lsi = gensim.models.lsimodel.LsiModel(corpus=mm, id2word=id2word, num_topics=400, chunksize=20000, distributed=True)

>>> # print the most contributing words (both positively and negatively) for each of the first ten topics
>>> lsi.print_topics(10)
2010-11-03 16:08:27,602 : INFO : topic #0(200.990): -0.475*"delete" + -0.383*"deletion" + -0.275*"debate" + -0.223*"comments" + -0.220*"edits" + -0.213*"modify" + -0.208*"appropriate" + -0.194*"subsequent" + -0.155*"wp" + -0.117*"notability"
2010-11-03 16:08:27,626 : INFO : topic #1(143.129): -0.320*"diff" + -0.305*"link" + -0.199*"image" + -0.171*"www" + -0.162*"user" + 0.149*"delete" + -0.147*"undo" + -0.144*"contribs" + -0.122*"album" + 0.113*"deletion"
2010-11-03 16:08:27,651 : INFO : topic #2(135.665): -0.437*"diff" + -0.400*"link" + -0.202*"undo" + -0.192*"user" + -0.182*"www" + -0.176*"contribs" + 0.168*"image" + -0.109*"added" + 0.106*"album" + 0.097*"copyright"
2010-11-03 16:08:27,677 : INFO : topic #3(125.027): -0.354*"image" + 0.239*"age" + 0.218*"median" + -0.213*"copyright" + 0.204*"population" + -0.195*"fair" + 0.195*"income" + 0.167*"census" + 0.165*"km" + 0.162*"households"
2010-11-03 16:08:27,701 : INFO : topic #4(116.927): -0.307*"image" + 0.195*"players" + -0.184*"median" + -0.184*"copyright" + -0.181*"age" + -0.167*"fair" + -0.162*"income" + -0.151*"population" + -0.136*"households" + -0.134*"census"
2010-11-03 16:08:27,728 : INFO : topic #5(100.326): 0.501*"players" + 0.318*"football" + 0.284*"league" + 0.193*"footballers" + 0.142*"image" + 0.133*"season" + 0.119*"cup" + 0.113*"club" + 0.110*"baseball" + 0.103*"f"
2010-11-03 16:08:27,754 : INFO : topic #6(92.298): -0.411*"album" + -0.275*"albums" + -0.217*"band" + -0.214*"song" + -0.184*"chart" + -0.163*"songs" + -0.160*"singles" + -0.149*"vocals" + -0.139*"guitar" + -0.129*"track"
2010-11-03 16:08:27,780 : INFO : topic #7(83.811): -0.248*"wikipedia" + -0.182*"keep" + 0.180*"delete" + -0.167*"articles" + -0.152*"your" + -0.150*"my" + 0.144*"film" + -0.130*"we" + -0.123*"think" + -0.120*"user"
2010-11-03 16:08:27,807 : INFO : topic #8(78.981): 0.588*"film" + 0.460*"films" + -0.130*"album" + -0.127*"station" + 0.121*"television" + 0.115*"poster" + 0.112*"directed" + 0.110*"actors" + -0.096*"railway" + 0.086*"movie"
2010-11-03 16:08:27,834 : INFO : topic #9(78.620): 0.502*"kategori" + 0.282*"categoria" + 0.248*"kategorija" + 0.234*"kategorie" + 0.172*"категория" + 0.165*"categoría" + 0.161*"kategoria" + 0.148*"categorie" + 0.126*"kategória" + 0.121*"catégorie"

In serial mode, creating the LSI model of Wikipedia with this one-pass algorithm takes about 5.25h on my laptop (OS X, C2D 2.53GHz, 4GB RAM with libVec). In distributed mode with four workers (Linux, dual-core Xeons of 2Ghz, 4GB RAM with ATLAS), the wallclock time taken drops to 1 hour and 41 minutes. You can read more about various internal settings and experiments in my research paper.