Intro to Apache Mahout
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Intro level talk to Apache Mahout machine learning library.
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Intro to Apache Mahout
- 1. Machine Learning with Apache Mahout Grant Ingersoll July 8, 2010
- 2. Introduction You Machine learning experience? Business Intelligence? Natural Lang. Processing? ApacheHadoop? Me Co-founder Apache Mahout Apache Lucene/Solr committer Co-founder Lucid Imagination
- 3. Topics What is Machine Learning? ML Use Cases What is Mahout? What can I do with it right now? Where’s Mahout headed?
- 4. Amazon.com What is Machine Learning? Google News
- 5. Really it’s… “Machine Learning is programming computers to optimize a performance criterion using example data or past experience” Intro. To Machine Learning by E. Alpaydin Subset of Artificial Intelligence Lots of related fields: Information Retrieval Stats Biology Linear algebra Many more
- 6. Common Use Cases Recommend friends/dates/products Classify content into predefined groups Find similar content based on object properties Find associations/patterns in actions/behaviors Identify key topics in large collections of text Detect anomalies in machine output Ranking search results Others?
- 7. Useful Terminology Vectors/Matrices Weights Sparse Dense Norms Features Feature reduction Occurrences and Co-occurrences
- 8. Getting Started with ML Get your data Decide on your features per your algorithm Prep the data Different approaches for different algorithms Run your algorithm(s) Lather, rinse, repeat Validate your results Smell test, A/B testing, more formal methods
- 9. Apache Mahout http://dictionary.reference.com/browse/mahout An Apache Software Foundation project to create scalable machine learning libraries under the Apache Software License http://mahout.apache.org Why Mahout? Many Open Source ML libraries either: Lack Community Lack Documentation and Examples Lack Scalability Lack the Apache License Or are research-oriented
- 10. Focus: Machine Learning Applications Examples Recommenders Clustering Classification Freq. Pattern Mining Genetic Math Vectors/Matrices/SVD Utilities Lucene/Vectorizer Collections (primitives) Apache Hadoop See http://cwiki.apache.org/confluence/display/MAHOUT/Algorithms
- 11. Focus: Scalable Goal: Be as fast and efficient as possible given the intrinsic design of the algorithm Some algorithms won’t scale to massive machine clusters Others fit logically on a Map Reduce framework like Apache Hadoop Still others will needalternative distributed programming models Be pragmatic Most Mahout implementations are Map Reduce enabled (Always a) Work in Progress
- 12. Prepare Data from Raw content Data Sources: Lucene integration bin/mahout lucenevector … Document Vectorizer bin/mahout seqdirectory … bin/mahout seq2sparse … Programmatically See the Utils module in Mahout Database File system
- 13. Recommendations Extensive framework for collaborative filtering Recommenders User based Item based Online and Offline support Offline can utilize Hadoop Many different Similarity measures Cosine, LLR, Tanimoto, Pearson, others
- 14. Clustering Document level Group documents based on a notion of similarity K-Means, Fuzzy K-Means, Dirichlet, Canopy, Mean-Shift Distance Measures Manhattan, Euclidean, other Topic Modeling Cluster words across documents to identify topics Latent Dirichlet Allocation
- 15. Categorization Place new items into predefined categories: Sports, politics, entertainment Mahout has several implementations Naïve Bayes Complementary Naïve Bayes Decision Forests Logistic Regression (Almost done)
- 16. Freq. Pattern Mining Identify frequently co-occurrent items Useful for: Query Recommendations Apple -> iPhone, orange, OS X Related product placement “Beer and Diapers” Spam Detection Yahoo: http://www.slideshare.net/hadoopusergroup/mail-antispam http://www.amazon.com
- 17. Evolutionary Map-Reduce ready fitness functions for genetic programming Integration with Watchmaker http://watchmaker.uncommons.org/index.php Problems solved: Traveling salesman Class discovery Many others
- 18. Singular Value Decomposition Reduces a big matrix into a much smaller matrix by amplifying the important parts while removing/reducing the less important parts Mahout has fully distributed Lanczos implementation <MAHOUT_HOME>/bin/mahout svd -Dmapred.input.dir=path/to/corpus --tempDir path/for/svd-output --rank 300 --numColumns <numcols> --numRows <num rows in the input> <MAHOUT_HOME>/bin/mahout cleansvd --eigenInput path/for/svd-output --corpusInput path/to/corpus --output path/for/cleanOutput --maxError 0.1 --minEigenvalue 10.0 https://cwiki.apache.org/confluence/display/MAHOUT/Dimensional+Reduction
- 19. How To: Recommenders Data: Users (abstract) Items (abstract) Ratings (optional) Load the data model Ask for Recommendations: User-User Item-Item
- 20. Ugly Demo I Group Lens Data: http://www.grouplens.org http://lucene.apache.org/mahout/taste.html#demo http://localhost:8080/RecommenderServlet?userID=1&debug=true In other words: the reason why I work on servers, not UIs!
- 21. How to: Command Line Most algorithms have a Driver program Shell script in $MAHOUT_HOME/bin helps with most tasks Prepare the Data Different algorithms require different setup Run the algorithm Single Node Hadoop Print out the results Several helper classes: LDAPrintTopics, ClusterDumper, etc.
- 22. Ugly Demo II - Prep Data Set: Reuters http://www.daviddlewis.com/resources/testcollections/reuters21578/ Convert to Text via http://www.lucenebootcamp.com/lucene-boot-camp-preclass-training/ Convert to Sequence File: bin/mahout seqdirectory –input <PATH> --output <PATH> --charset UTF-8 Convert to Sparse Vector: bin/mahout seq2sparse --input <PATH>/content/reuters/seqfiles/ --norm 2 --weight TF --output <PATH>/content/reuters/seqfiles-TF/ --minDF 5 --maxDFPercent 90
- 23. Ugly Demo II: Topic Modeling Latent Dirichlet Allocation ./mahout lda --input <PATH>/content/reuters/seqfiles-TF/vectors/ --output <PATH>/content/reuters/seqfiles-TF/lda-output --numWords 34000 –numTopics 10 ./mahout org.apache.mahout.clustering.lda.LDAPrintTopics --input <PATH>/content/reuters/seqfiles-TF/lda-output/state-19 --dict <PATH>/content/reuters/seqfiles-TF/dictionary.file-0 --words 10 --output <PATH>/content/reuters/seqfiles-TF/lda-output/topics --dictionaryTypesequencefile Good feature reduction (stopword removal) required
- 24. Ugly Demo III: Clustering K-Means Same Prep as UD II, except use TFIDF weight ./mahout kmeans --input <PATH>/content/reuters/seqfiles-TFIDF/vectors/part-00000 --k 15 --output <PATH>/content/reuters/seqfiles-TFIDF/output-kmeans --clusters <PATH>/content/reuters/seqfiles-TFIDF/output-kmeans/clusters Print out the clusters: ./mahout clusterdump --seqFileDir <PATH>/content/reuters/seqfiles-TFIDF/output-kmeans/clusters-15/ --pointsDir <PATH>/content/reuters/seqfiles-TFIDF/output-kmeans/points/ --dictionary <PATH>/content/reuters/seqfiles-TFIDF/dictionary.file-0 --dictionaryTypesequencefile --substring 20
- 25. Ugly Demo IV: Frequent Pattern Mining Data: http://fimi.cs.helsinki.fi/data/ ./mahout fpg -i <PATH>/content/freqitemset/accidents.dat -o patterns -k 50 -method mapreduce -g 10 -regex [] ./mahout seqdump --seqFile patterns/fpgrowth/part-r-00000
- 26. What’s Next? Google Summer of Code (GSOC) Restricted Boltzmann Machines SVM EigenCuts (spectral clustering) Neural Networks SVD-based Recommender 0.4 release in Fall (after GSOC) Stabilize API’s for 1.0 release Benchmarking http://cwiki.apache.org/MAHOUT/howtocontribute.html
- 27. Resources Slides will be posted at http://lucene.grantingersoll.com Examples at: http://lucene.grantingersoll.com/2010/02/13/intro-to-mahout-slides-and-demo-examples/ More Examples in Mahout SVN in the examples directory
- 28. Resources http://mahout.apache.org http://cwiki.apache.org/MAHOUT {user|dev}@mahout.apache.org http://svn.apache.org/repos/asf/mahout/trunk http://hadoop.apache.org
- 29. Resources “Mahout in Action” by Owen and Anil http://lucene.li/E “Taming Text” by Ingersoll, Morton and Farris http://lucene.li/D “Introducing Apache Mahout” http://lucene.li/F “Programming Collective Intelligence” by Toby Segaran http://lucene.li/G “Data Mining - Practical Machine Learning Tools and Techniques” by Ian H. Witten and Eibe Frank http://lucene.li/H
- 30. References HAL: http://en.wikipedia.org/wiki/File:Hal-9000.jpg Terminator: http://en.wikipedia.org/wiki/File:Terminator1984movieposter.jpg Matrix: http://en.wikipedia.org/wiki/File:The_Matrix_Poster.jpg Google News: http://news.google.com Amazon.com: http://www.amazon.com Facebook: http://www.facebook.com Mahout: http://lucene.apache.org/mahout Beer and Diapers: http://www.flickr.com/photos/baubcat/2484459070/ http://www.theregister.co.uk/2006/08/15/beer_diapers/ DMOZ: http://www.dmoz.org
Editor's Notes
- Hadoop experience? ML experience?
- A few things come to mind
- Think about data differently than traditional DB
- Helps address the dimensionality problem often associated with machine learning problems