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![Page 8
Sketches
• Store a “summary” of the dataset
• Querying the sketch is “almost” as good as querying the dataset
• Example: frequent items in a stream
– Initialize associative array A of size k-1
– Process: for each j
- if j is in keys(A), A[j] += 1
- else if |keys(A)| < k - 1, A[j] = 1
- else
– for each l in keys(A),
» A(l) -=1 ;
» if A(l) = 0, remove l;
– done](https://image.slidesharecdn.com/apacheconbigdata2015-datasciencefromthetrenches-150930142053-lva1-app6892/75/Apache-con-big-data-2015-Data-Science-from-the-trenches-8-2048.jpg)
Uploaded byVinay Shukla
Apache con big data 2015 - Data Science from the trenches
The document outlines key challenges and methodologies in data science, including online learning, clustering, and co-training. It discusses problems such as click prediction in search advertising, entity resolution, and login risk detection while emphasizing the importance of efficient and adaptable algorithms. Additionally, it introduces Apache Zeppelin as a tool for interactive analytics in the data science workflow, highlighting its capabilities and future developments.
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Apache con big data 2015 - Data Science from the trenches
- 1. Page 1 Data Science:A view from the trenches Ram Sriharsha Twitter: @halfbrane Vinay Shukla Twitter: @neomythos
- 2. Page 2 Agenda • Problemswe work on • Common Challenges • Reductions • Handling label sparsity – Co Training – Adaptive Learning • When you have to be fast and accurate – Online Clustering – Sketches – Online Learning • Visualization
- 3. Page 3 Some Problems • SearchAdvertising – Click Prediction: Given a query, ad and user context, how likely is the user to click on ad? – Feature Engineering: Query/ ad categorization, query -> feature vector • Entity Resolution and Disambiguation • Over / Under Payment of claims detection • Document Matching • Login Risk Detection
- 4. Page 4 Common Challenges • Labeling is expensive and not clean – Selectively ask for labels (active learning) – Co-Training to expand label set • Not enough high quality implementations of algorithms – Modular extensions of base implementations (Reductions) – Boosting • Speed of training/ scoring important – Online learning – Online clustering – Sketches • Freshness of models – Online and adaptive learning • Visualizing performance and feature importance – Zeppelin
- 5. Page 5 Reductions OVR Let R= rejection sampling algorithm For each example h, sample according to Cost of h and feed to 0/1 classifier A A … Randomize over classifiers that Output yes Importance Weighting R A R^-1 Let A = Algorithm for optimizing 0/1 loss
- 6. Page 6 Active Learning • Givena pool of examples determine which ones is the classifier least confident about • Ask those examples to be labeled, and feed to training • Choose query points that shrink the space of classifiers rapidly • Exploit natural structure in data 45% 45%2.5% 2.5%5%
- 7. Page 7 Co Training • Supposeyou have two “views” of the data – e.g, web pages have content, and hyperlinks pointing to and from them – Suppose problem is to label webpage as about literature/ or not (binary classification) • One approach: – Label web pages manually. Train classifier to use both content text and hyperlinks as features – This requires a large # of labeled pages • Other approach: – Since we have two views , try to learn two classifiers – Each classifier learns on a subset of labeled examples. – The scores of each classifier are used to label a subset of unlabeled web pages and extend the labels for the other classifier.
- 8. Page 8 Sketches • Store a“summary” of the dataset • Querying the sketch is “almost” as good as querying the dataset • Example: frequent items in a stream – Initialize associative array A of size k-1 – Process: for each j - if j is in keys(A), A[j] += 1 - else if |keys(A)| < k - 1, A[j] = 1 - else – for each l in keys(A), » A(l) -=1 ; » if A(l) = 0, remove l; – done
- 9. Page 9 Clustering isnot fast enough • Sample and then cluster • Do clusters need to dynamically adapt? – Online clustering – Streaming K Means
- 10. Page 10 K Means • Initializecluster centers somehow – random – K means ++ • Alternate – Assign each point to closest cluster center – Move cluster center to average of points assigned to center • Stop when convergence criteria reached – Points don’t move “much” – Number of iterations reached.
- 11. Page 11 11 Initialize ClusterCenters k1 k2 k3 X Y Pick 3 initial cluster centers (randomly)
- 12. Page 12 Assign eachpoint k1 k2 k3 X Y Assign each point to the closest cluster center
- 13. Page 13 Recompute ClusterCenters X Y Move each cluster center to the mean of each cluster k1 k2 k2 k1 k3 k3
- 14. Page 14 Streaming KMeans • For each new point – Assign to closest cluster center – Update cluster center to incrementally move in direction of new point • Online version of Lloyd’s algorithm • Good enough in practice
- 15. Page 15 Recompute ClusterCenters X Y Move each cluster center to the mean of each cluster k2 k1 k3
- 16. Page 16 Online Clustering(Liberty, Sriharsha, Sviridenko) • Initialization Phase: – First point is its own cluster – Pick some Normalization factor f • Update Phase for point p: – Let d = distance from p to closest center so far – With probability proportional to d/ f , attach p to closest center – With probability max (1 – d/f, 1), form a new cluster center at p. • Merge Phase: – Once sufficient clusters have opened up, or sufficient cost accumulated, merge clusters
- 17. Page 17 Properties • Provably closeto optimal in online setting • Does not open more than O(log(OPT)) clusters pays O(OPT) cost • Very efficient to implement • Adaptive algorithm • Forgetfulness can be introduced in the merge process • Leaving out the merge process still produces a clustering that might be indicative of structure, i.e useful as a machine learning feature
- 18. Page 18 My classifieris not fast enough • Even for batch problems online learning might be good enough! • For real time problems, online learning or incremental learning is needed.
- 19. Page 19 What isonline learning? • Batch Learning: – Classifier sees a set of labeled examples, and trains a model – Predicts on trained model for unseen examples • Online Learning: – Classifier sees an example at a time. – Limited look back window (often 0) – Predicts on example and is revealed the cost – Learns from mistake – Yields a batch learning algorithm that is one pass: simply run online algorithm for each example in a batch.
- 20. Page 20 Challenges ofonline learning • normalization – In batch set up, can normalize data by making a pass over the full dataset – In online setting, cannot make a second pass – Solution: Adaptive normalization • Late arriving features – In Batch setting, all features are recorded in the dataset – In online setting different features may arrive at different times – Solution: Adagrad (Adaptive gradient technique) • Stochastic Gradient Descent convergence can be slow – More data helps – Adaptive normalization improves convergence – Adagrad improves convergence and reduces sensitivity to step size
- 21. Page 21 Visualization • Speed upfeature discovery • Intuitive visualization of model performance • Improve debuggability
- 22. Page 22 The DataScience Workflow… What is the question I'm answering? What data will I need? Plan Acquire the data Analyze data quality Reformat Impute etc Clean Data Analyze data Visualize Create model Evaluate results Create features Create report Deploy in Production Publish & Share Start here End here Script VisualizeScript
- 23. Page 23 Introducing ApacheZeppelin Web-based Notebook for interactive analytics Use Case Data exploration and discovery Visualization Interactive snippet-at-a-time experience “Modern Data Science Studio”
- 24. Page 24 Zeppelin todayin Data Science Workflow… What is the question I'm answering? What data will I need? Plan Acquire the data Analyze data quality Reformat Impute etc Clean Data Analyze data Visualize Create model Evaluate results Create features Create report Deploy in Production Publish & Share Start here End here Script VisualizeScript
- 25. Page 25 Zeppelin –Road Ahead Operations - Deploy to the cluster with Ambari Security - Authentication against LDAP - SSL - Run in Kerberized Cluster - Authorization of notebooks Sharing/ Collaboration - Share selected notebooks with selected users/groups - Ability to read/publish notebooks to github Data Import - Visual data import/download - Clean data as it comes Usability - Summary Data – See column summary - Keyboard shortcuts, Auto complete, syntax high light, line numbers Visualization - Pluggable visualization & more charts, maps & tables. R support - Harden SparkR interpreter Enterprise ReadyEase of Use
- 26. Page 26 Upcoming Work • EntityResolution package GA – Supports Entity Graph based resolution – Includes Random Walk algorithm for computing similarity score • Online learning and clustering Spark Packages • Contribute more Reduction algorithms to Spark ML – Cost Sensitive Classification – Filter tree based Multiclass Reduction • Zeppelin GA
- 27. Page 27 Thank you! • RamSriharsha @halfabrane • Vinay Shukla @neomythos