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Apache Mahout Feb 13, 2012 Shannon Quinn Cloud Computing CS 15-319

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MapReduce Review C0C1C2C3 M0M1M2M3 IO0IO1IO2IO3 R0R1 FO0FO1 chunks mappers Reducers Map Phase Reduce Phase Shuffling Data Scalable programming model Map phase Shuffle Reduce phase MapReduce Implementations Google Hadoop Figure from lecture 6: MapReduce

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MapReduce Review C0C1C2C3 M0M1M2M3 IO0IO1IO2IO3 R0R1 FO0FO1 chunks mappers Reducers Map Phase Reduce Phase Shuffling Data Scalable programming model Map phase Shuffle Reduce phase MapReduce Implementations Google Hadoop Figure from lecture 6: MapReduce This is our focus!

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Apache Mahout A scalable machine learning library

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Apache Mahout A scalable machine learning library Built on Hadoop

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Apache Mahout A scalable machine learning library Built on Hadoop Philosophy of Mahout (and Hadoop by proxy)

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What does Mahout do?

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Recommendation

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Classification

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Clustering

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Other Mahout Algorithms Dimensionality Reduction Regression Evolutionary Algorithms

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Mahout 1.Recommendation 2.Classification 3.Clustering

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Recommendation Overview Help users find items they might like based on historical preferences

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Recommendation Overview Mathematically

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Recommendation Overview Alice Bob Peter 514 ? 25 432*based on example by Sebastian Schelter

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Recommendation Overview 514 - 25 432*based on example by Sebastian Schelter

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Recommendation Overview 514 ? 25 432 Bob *based on example by Sebastian Schelter

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Recommendation Overview 514 1.5 25 432 Bob *based on example by Sebastian Schelter

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Recommendation in Mahout 1 st Map phase: process input *based on example by Sebastian Schelter

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Recommendation in Mahout 1 st Map phase: process input 1 st Reduce phase: list by user *based on example by Sebastian Schelter

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Recommendation in Mahout 2 nd Map phase: Emit co-occurred ratings *based on example by Sebastian Schelter

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Recommendation in Mahout 2 nd Map phase: Emit co-occurred ratings 2 nd Reduce phase: Compute similarities *based on example by Sebastian Schelter

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Mahout 1.Recommendation 2.Classification 3.Clustering

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Classification Overview Assigning data to discrete categories

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Classification Overview Assigning data to discrete categories Train a model on labeled data Spam Not spam

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Classification Overview Assigning data to discrete categories Train a model on labeled data Run the model on new, unlabeled data Spam Not spam ?

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Nave Bayes Example

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Prob (token | label) =

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Nave Bayes Example Not spam

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Nave Bayes Example President Obamas Nobel Prize Speech Not spam

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Nave Bayes Example Spam

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Nave Bayes Example Spam Spam email content

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Nave Bayes Example

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Nave Bayes in Mahout Complex!

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Nave Bayes in Mahout Complex! Training 1.Read the features

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Nave Bayes in Mahout Complex! Training 1.Read the features 2.Calculate per-document statistics

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Nave Bayes in Mahout Complex! Training 1.Read the features 2.Calculate per-document statistics 3.Normalize across categories

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Nave Bayes in Mahout Complex! Training 1.Read the features 2.Calculate per-document statistics 3.Normalize across categories 4.Calculate normalizing factor of each label

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Nave Bayes in Mahout Complex! Training 1.Read the features 2.Calculate per-document statistics 3.Normalize across categories 4.Calculate normalizing factor of each label Testing Classification

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Other Classification Algorithms Stochastic Gradient Descent

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Other Classification Algorithms Stochastic Gradient Descent Support Vector Machines

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Other Classification Algorithms Stochastic Gradient Descent Support Vector Machines Random Forests

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Mahout 1.Recommendation 2.Classification 3.Clustering

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Clustering Overview Grouping unstructured data

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Clustering Overview Grouping unstructured data Small intra-cluster distance

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Clustering Overview Grouping unstructured data Small intra-cluster distance Large inter-cluster distance

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K-Means Clustering Example

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Cats Dogs

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K-Means Clustering in Mahout + C0C1C2C3 M0M1M2M3 IO0IO1IO2IO3 R0R1 FO0FO1 chunks mappers Reducers Map Phase Reduce Phase Shuffling Data Figure from lecture 6: MapReduce

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K-Means Clustering in Mahout Assume: # clusters