Hands on!

1. Hands on! Speakers: Ted Dunning, Robin Anil OSCON 2011, Portland

2. About Us Ted Dunning: Chief Application Architect at MapR Committer and PMC Member at Apache Mahout Previously: MusicMatch (Yahoo! Music), Veoh recommendation, ID Analytics Robin Anil: Software Engineer at Google Committer and PMC Member at Apache Mahout Previously: Yahoo! (Display ads), Minekey recommendation

3. Agenda Intro to Mahout (5 mins) Overview of Algorithms in Mahout (10 mins) Hands on Mahout! - Clustering (30 mins) - Classification (30 mins) - Advanced topics with Q&A (15 mins)

4. Mission To build a scalable machine learning library

5. Scale! Scale to large datasets - Hadoop MapReduce implementations that scales linearly with data. - Fast sequential algorithms whose runtime doesn’t depend on the size of the data - Goal: To be as fast as possible for any algorithm Scalable to support your business case - Apache Software License 2 Scalable community - Vibrant, responsive and diverse - Come to the mailing list and find out more

6. Current state of ML libraries Lack community Lack scalability Lack documentations and examples Lack Apache licensing Are not well tested Are Research oriented Not built over existing production quality libraries Lack “Deployability”

7. Algorithms and Applications

8. Clustering Call it fuzzy grouping based on a notion of similarity

9. Mahout Clustering Plenty of Algorithms: K-Means, Fuzzy K-Means, Mean Shift, Canopy, Dirichlet Group similar looking objects Notion of similarity: Distance measure: - Euclidean - Cosine - Tanimoto - Manhattan

10. Classification Predicting the type of a new object based on its features The types are predetermined Dog Cat

11. Mahout Classification Plenty of algorithms - Naïve Bayes - Complementary Naïve Bayes - Random Forests - Logistic Regression (SGD) - Support Vector Machines (patch ready) Learn a model from a manually classified data Predict the class of a new object based on its features and the learned model

12. Part 1 - Clustering

13. Understanding data - Vectors Y X = 5 , Y = 3 (5, 3) X The vector denoted by point (5, 3) is simply Array([5, 3]) or HashMap([0 => 5], [1 => 3])

14. Representing Vectors – The basics Now think 3, 4, 5, ….. n-dimensional Think of a document as a bag of words. “she sells sea shells on the sea shore” Now map them to integers she => 0 sells => 1 sea => 2 and so on The resulting vector [1.0, 1.0, 2.0, … ]

15. ? Vectors Imagine one dimension for each word. Each dimension is also called a feature Two techniques - Dictionary Based - Randomizer Based

16. Clustering Reuters dataset

17. Step 1 – Convert dataset into a Hadoop Sequence File http://www.daviddlewis.com/resources/testcollections/reuters21578/reuters21578.tar.gz Download (8.2 MB) and extract the SGML files. - $ mkdir -p mahout-work/reuters-sgm - $ cd mahout-work/reuters-sgm && tar xzf ../reuters21578.tar.gz && cd .. && cd .. Extract content from SGML to text file - $ bin/mahout org.apache.lucene.benchmark.utils.ExtractReuters mahout- work/reuters-sgm mahout-work/reuters-out

18. Step 1 – Convert dataset into a Hadoop Sequence File Use seqdirectory tool to convert text file into a Hadoop Sequence File - $ bin/mahout seqdirectory \ -i mahout-work/reuters-out \ -o mahout-work/reuters-out-seqdir \ -c UTF-8 -chunk 5

19. Hadoop Sequence File Sequence of Records, where each record is a <Key, Value> pair - <Key1, Value1> - <Key2, Value2> - … - … - … - <Keyn, Valuen> Key and Value needs to be of class org.apache.hadoop.io.Text - Key = Record name or File name or unique identifier - Value = Content as UTF-8 encoded string TIP: Dump data from your database directly into Hadoop Sequence Files (see next slide)

20. Writing to Sequence Files Configuration conf = new Configuration(); FileSystem fs = FileSystem.get(conf); Path path = new Path("testdata/part-00000"); SequenceFile.Writer writer = new SequenceFile.Writer( fs, conf, path, Text.class, Text.class); for (int i = 0; i < MAX_DOCS; i++) writer.append(new Text(documents(i).Id()), new Text(documents(i).Content())); } writer.close();

21. Generate Vectors from Sequence Files Steps 1. Compute Dictionary 2. Assign integers for words 3. Compute feature weights 4. Create vector for each document using word-integer mapping and feature-weight Or Simply run $ bin/mahout seq2sparse

22. Generate Vectors from Sequence Files $ bin/mahout seq2sparse \ -i mahout-work/reuters-out-seqdir/ \ -o mahout-work/reuters-out-seqdir-sparse-kmeans Important options - Ngrams - Lucene Analyzer for tokenizing - Feature Pruning - Min support - Max Document Frequency - Min LLR (for ngrams) - Weighting Method - TF v/s TFIDF - lp-Norm - Log normalize length

23. Start K-Means clustering $ bin/mahout kmeans \ -i mahout-work/reuters-out-seqdir-sparse-kmeans/tfidf-vectors/ \ -c mahout-work/reuters-kmeans-clusters \ -o mahout-work/reuters-kmeans \ -dm org.apache.mahout.distance.CosineDistanceMeasure –cd 0.1 \ -x 10 -k 20 –ow Things to watch out for - Number of iterations - Convergence delta - Distance Measure - Creating assignments

24. K-Means clustering c2 c1 c3

25. K-Means clustering c2 c1 c3

26. K-Means clustering c2 c2 c1 c1 c3 c3

27. K-Means clustering c2 c1 c3

28. Inspect clusters $ bin/mahout clusterdump \ -s mahout-work/reuters-kmeans/clusters-9 \ -d mahout-work/reuters-out-seqdir-sparse-kmeans/dictionary.file-0 \ -dt sequencefile -b 100 -n 20 Typical output :VL-21438{n=518 c=[0.56:0.019, 00:0.154, 00.03:0.018, 00.18:0.018, … Top Terms: iran => 3.1861672217321213 strike => 2.567886952727918 iranian => 2.133417966282966 union => 2.116033937940266 said => 2.101773806290277 workers => 2.066259451354332 gulf => 1.9501374918521601 had => 1.6077752463145605 he => 1.5355078004962228

29. FAQs How to get rid of useless words How to see documents to cluster assignments How to choose appropriate weighting How to run this on a cluster How to scale How to choose k How to improve similarity measurement

30. FAQs How to get rid of useless words - Increase minSupport and or decrease dfPercent - Use StopwordsAnalyzer How to see documents to cluster assignments - Run clustering process at the end of centroid generation using –cl How to choose appropriate weighting - If its long text, go with tfidf. Use normalization if documents different in length How to run this on a cluster - Set HADOOP_CONF directory to point to your hadoop cluster conf directory How to scale - Use small value of k to partially cluster data and then do full clustering on each cluster.

31. FAQs How to choose k - Figure out based on the data you have. Trial and error - Or use Canopy Clustering and distance threshold to figure it out - Or use Spectral clustering How to improve Similarity Measurement - Not all features are equal - Small weight difference for certain types creates a large semantic difference - Use WeightedDistanceMeasure - Or write a custom DistanceMeasure

32. Interesting problems Cluster users talking about OSCON’11 and cluster them based on what they are tweeting - Can you suggest people to network with. Use user generate tags that people have given for musicians and cluster them - Use the cluster to pre-populate suggest-box to autocomplete tags when users type Cluster movies based on abstract and description and show related movies. - Note: How it can augment recommendations or collaborative filtering algorithms.

33. More clustering algorithms Canopy Fuzzy K-Means Mean Shift Dirichlet process clustering Spectral clustering.

34. Part 2 - Classification

35. Preliminaries Code is available from github: - git@github.com:tdunning/Chapter-16.git EC2 instances available Thumb drives also available Email to ted.dunning@gmail.com Twitter @ted_dunning

36. A Quick Review What is classification? - goes-ins: predictors - goes-outs: target variable What is classifiable data? - continuous, categorical, word-like, text-like - uniform schema How do we convert from classifiable data to feature vector?

37. Data Flow Not quite so simple

38. Classifiable Data Continuous - A number that represents a quantity, not an id - Blood pressure, stock price, latitude, mass Categorical - One of a known, small set (color, shape) Word-like - One of a possibly unknown, possibly large set Text-like - Many word-like things, usually unordered

39. But that isn’t quite there Learning algorithms need feature vectors - Have to convert from data to vector Can assign one location per feature - or category - or word Can assign one or more locations with hashing - scary - but safe on average

40. Data Flow

42. The pipeline Classifiable Data Vectors

43. Instance and Target Variable

44. Instance and Target Variable

45. Hashed Encoding

46. What about collisions?

47. Let’s write some code (cue relaxing background music)

48. Generating new features Sometimes the existing features are difficult to use Restating the geometry using new reference points may help Automatic reference points using k-means can be better than manual references

49. K-means using target

50. K-means features