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Machine Learning on Spark. Shivaram Venkataraman UC Berkeley. Machine learning. Computer Science . Statistics. Spam filters. Click prediction. Machine learning. Recommendations. Search ranking. Classification. Clustering. Regression. Machine learning techniques. Active learning.

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Machine Learning on Spark


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    1. Machine Learning on Spark • ShivaramVenkataraman • UC Berkeley

    2. Machine learning Computer Science Statistics

    3. Spam filters Click prediction Machine learning Recommendations Search ranking

    4. Classification Clustering Regression Machine learning techniques Active learning Collaborative filtering

    5. Implementing Machine Learning • Machine learning algorithms are • Complex, multi-stage • Iterative • MapReduce/Hadoop unsuitable • Need efficient primitives for data sharing

    6. Machine Learning using Spark • Spark RDDs  efficient data sharing • In-memory caching accelerates performance • Up to 20x faster than Hadoop • Easy to use high-level programming interface • Express complex algorithms ~100 lines.

    7. Classification Clustering Regression Machine learning techniques Active learning Collaborative filtering

    8. K-Means Clustering using Spark Focus: Implementation and Performance

    9. Clustering E.g. archaeological dig Distance North Grouping data according to similarity Distance East

    10. Clustering E.g. archaeological dig Distance North Grouping data according to similarity Distance East

    11. K-Means Algorithm E.g. archaeological dig • Benefits • Popular • Fast • Conceptually straightforward Distance North Distance East

    12. K-Means: preliminaries Data: Collection of values data =lines.map(line=> parseVector(line)) Feature 2 Feature 1

    13. K-Means: preliminaries Dissimilarity: Squared Euclidean distance Feature 2 dist = p.squaredDist(q) Feature 1

    14. K-Means: preliminaries K = Number of clusters Feature 2 Data assignments to clusters S1, S2,. . ., SK Feature 1

    15. K-Means: preliminaries K = Number of clusters Feature 2 Data assignments to clusters S1, S2,. . ., SK Feature 1

    16. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: • Assign each data point to the cluster with the closest center. • Assign each cluster center to be the mean of its cluster’s data points. Feature 2 Feature 1

    17. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: • Assign each data point to the cluster with the closest center. • Assign each cluster center to be the mean of its cluster’s data points. Feature 2 Feature 1

    18. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: • Assign each data point to the cluster with the closest center. • Assign each cluster center to be the mean of its cluster’s data points. centers = data.takeSample( false, K, seed) Feature 2 Feature 1

    19. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: • Assign each data point to the cluster with the closest center. • Assign each cluster center to be the mean of its cluster’s data points. centers = data.takeSample( false, K, seed) Feature 2 Feature 1

    20. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: • Assign each data point to the cluster with the closest center. • Assign each cluster center to be the mean of its cluster’s data points. centers = data.takeSample( false, K, seed) Feature 2 Feature 1

    21. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: • Assign each cluster center to be the mean of its cluster’s data points. centers = data.takeSample( false, K, seed) Feature 2 closest = data.map(p => (closestPoint(p,centers),p)) Feature 1

    22. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: • Assign each cluster center to be the mean of its cluster’s data points. centers = data.takeSample( false, K, seed) Feature 2 closest = data.map(p => (closestPoint(p,centers),p)) Feature 1

    23. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: • Assign each cluster center to be the mean of its cluster’s data points. centers = data.takeSample( false, K, seed) Feature 2 closest = data.map(p => (closestPoint(p,centers),p)) Feature 1

    24. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: centers = data.takeSample( false, K, seed) Feature 2 closest = data.map(p => (closestPoint(p,centers),p)) pointsGroup = closest.groupByKey() Feature 1

    25. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: centers = data.takeSample( false, K, seed) Feature 2 closest = data.map(p => (closestPoint(p,centers),p)) pointsGroup = closest.groupByKey() newCenters= pointsGroup.mapValues( ps=> average(ps)) Feature 1

    26. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: centers = data.takeSample( false, K, seed) Feature 2 closest = data.map(p => (closestPoint(p,centers),p)) pointsGroup = closest.groupByKey() newCenters= pointsGroup.mapValues( ps=> average(ps)) Feature 1

    27. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: centers = data.takeSample( false, K, seed) Feature 2 closest = data.map(p => (closestPoint(p,centers),p)) pointsGroup = closest.groupByKey() newCenters= pointsGroup.mapValues( ps=> average(ps)) Feature 1

    28. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: centers = data.takeSample( false, K, seed) Feature 2 while (dist(centers, newCenters) > ɛ) closest = data.map(p => (closestPoint(p,centers),p)) pointsGroup = closest.groupByKey() newCenters=pointsGroup.mapValues( ps=> average(ps)) Feature 1

    29. K-Means Algorithm • • Initialize K cluster centers • • Repeat until convergence: centers = data.takeSample( false, K, seed) Feature 2 while (dist(centers, newCenters) > ɛ) closest = data.map(p => (closestPoint(p,centers),p)) pointsGroup = closest.groupByKey() newCenters=pointsGroup.mapValues( ps=> average(ps)) Feature 1

    30. K-Means Source centers = data.takeSample( false, K, seed) while (d > ɛ) { closest = data.map(p => (closestPoint(p,centers),p)) Feature 2 pointsGroup = closest.groupByKey() newCenters=pointsGroup.mapValues( ps=> average(ps)) d = distance(centers, newCenters) centers = newCenters.map(_) } Feature 1

    31. Ease of use • Interactive shell: Useful for featurization, pre-processing data • Lines of code for K-Means • Spark ~ 90 lines – (Part of hands-on tutorial !) • Hadoop/Mahout ~ 4 files, > 300 lines

    32. Performance Logistic Regression K-Means [Zaharia et. al, NSDI’12]

    33. Conclusion • K means clustering using Spark • Hands-on exercise this afternoon ! Examples and more: www.spark-project.org • Spark: Framework for cluster computing • Fast and easy machine learning programs