Apache Mahout is a scalable machine learning library built on Hadoop. It provides algorithms for recommendation engines, clustering, classification and other machine learning tasks. Some key algorithms include user-based and item-based collaborative filtering for recommendations, k-means and fuzzy k-means clustering, logistic regression for classification. Mahout is well suited for large datasets and allows machine learning tasks to be easily parallelized across a Hadoop cluster. It has advantages of being open source, scalable, and built on production-quality libraries.

1. Apache Mahout:
Scalable Machine Learning Library
Anastasiia Kornilova

2. What is Machine Learning?
“Machine learning - branch of artificial
intelligence, concerns the construction
and study of systems that can learn from
data”

3. Typical Use Cases
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Recommend products/friends …
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Classify content into predefined groups
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Computer vision
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Sentiment analysis/opinion mining
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Find patterns in users behavior/actions
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Identify key topics/summarize text
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Detect anomalies/fraud
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Ranking search results
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Speech and handwriting recognition
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Natural language processing

4. ML Algorithms (subset):
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Supervised learning
–
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Logistic regression
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Support Vector Machines
–
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Linear regression
Random Forests
Unsupervised learning
–
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Blind signal separation
–
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Clustering
Hidden Markov models
Semi-supervised

5. Many ML libraries, frameworks
and tools:
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Weka
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Python Scikit
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Pylearn/Pylearn2
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Theano
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Orange
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SSBrain :)
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More can be find here: http://mloss.org/software/

6. Typical Workflow
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Get data
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Prepare data
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Choose algorithm(s)
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Run your algorithm(s)
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Validate results

7. Every ML algorithms deals
with:
1.Data
2.Computation over this data

8. Scalability strategies:
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“Bigger” computer
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More cores
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GPU computing
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Parallel computing, MapReduce

9. What is Mahout?
●
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Scalable ML library built on Hadoop, written in Java
Driven by Ng et al's. Paper “MapReduce for Machine Learning on
Multicore”
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Started as Lucene sub-project. Became Apache TLP in April 2010
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25 July 2013 - Apache Mahout 0.8 released
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Taste Recommended Framework by Sean Owen was added in
2008

10. Who use Mahout?

11. When you need Mahout?
Data Size
Lines, Sample Data
Task
Analysis and
visualization
Tools
Whiteboard, bash, ...
KBs – low MBs,
Prototype Data
Analysis and
visualization
Octave, R, bash, ...
MBs – low Gbs,
Online Data
Storage
Data bases (MySQL,
Postgresql), ...
Analysis
NumPy, SciPy, BLAS,
Weka
Visualization
GBs – TBs – Pbs
Big Data
Protovis, D3, ...
Storage
HDFS, Hbase,
Cassandra, ...
Analysis
Mahout, Hive, Pig, ….
table from Varad Meru

12. Advantages
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Community
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Documentations and examples
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Scalability
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Apache license
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Well tested
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Built over existing production quality
libraries

13. Requirements
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Java 1.6.x or greater
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Maven 3.x to build the source code
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Hadoop 0.20.0 or greater

14. Core themes
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Recommender engines (collaborative
filtering)
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Clustering
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Classification

15. Core themes
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Recommender engines (collaborative
filtering)
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Clustering
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Classification

16. Algorithms
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User and Item based recommenders
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Matrix factorization based recommenders
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K-Means, Fuzzy K-Means clustering
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Latent Dirichlet Allocation
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Singular value decomposition
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Logistic regression based classifier
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Complementary Naive Bayes classifier
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Random forest decision tree based classifier

17. Recommender engine

18. Personalization level
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Generic / Non-Personalized: everyone
receives same recommendations
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Demographic: matches a target group
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Ephemeral: matches current activity
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Persistent: matches long-term interests

19. Content based
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User Ratings x Item Attributes => Model
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Model applied to new items via attributes
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Alternative: knowledge-based (Item
attributes form model of item space)
Example: Personalized news feeds

20. Table of ratings

21. Ratings
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Explicit (Rating, Review, Vote, Like)
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Implicit (Click, Purchase, Follow)

22. Item Item
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For every item I
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Select N similar items
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Recommend users, who work with item I
this N items

23. User user
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For every user
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Find n most similar users
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Aggregate preferences for this user
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Generate recommended items

24. Similarities metrics
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Pearson Correlation
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Tanimoto
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Cosine similarity
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Euclidean distance

25. Sparse matrix

26. Parameters
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●
●
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DataModel – FileDataModel, MySQLJDBCDataModel,
PostgreSQLJDBCDataModel, MongoDBDataModel,
CassandraDataModel
UserSimilarity – Pearson Corelation, Tanimoto, Log-Likelihood,
Euclidian Distance, Cosine Similarity
ItemSimilarity – Pearson Corelation, Tanimoto, Log-Likelihood,
Euclidian Distance, Cosine Similarity
UserNeighborhood – Nearest N-User Neighborhood, Threshold
User Neighborhood

27. Code example

28. Evaluation
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Average absolute difference
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RMSE
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Precision and recall
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Precision is the proportion of top results that are relevant, for some
definition of relevant.
Recall is the proportion of all relevant results included in the top
results.

29. Clustering

30. Mahout Clustering Algorithms
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K-Means - runs on Hadoop
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Fuzzy K-means - runs on Hadoop
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Latent Dirichlet Allocation -runs on Hadoop
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Canopy clustering - runs on Hadoop
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Minhash clustering - runs on Hadoop
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kMeans++ streaming clustering - documentation
missing

31. Classification

32. Mahout Classification
Algorithms
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Logistic regression (SGD) - model parameter
selection can be done in Hadoop
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Naive Bayes - training runs on Hadoop
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Random Forests - training is done in Hadoop
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Hidden Markov Models - training is done in
Map-Reduce

33. Resources
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Mahout in action
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Apache Mahout Cookbook
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Introduction to Apache Mahout
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http://mahout.apache.org/

34. Q&A