Datascientistsymp1113
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Talk at 2nd Data Scientist Symp on Nov 13, 2015. http://www.datascientist.or.jp/symp/2015/
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Datascientistsymp1113
- 2. Ø 2015.04 Joined Treasure Data, Inc. 1st Research Engineer in Treasure Data My mission in TD is developing ML-as-a-Service Ø 2010.04-2015.03 Senior Researcher at National Institute of Advanced Industrial Science and Technology, Japan. Worked on a large-scale Machine Learning project and Parallel Databases Ø 2009.03 Ph.D. in Computer Science from NAIST Who am I ? 2014/09/17 Talk@Japan DataScientist Society 2
- 8. What is Hivemall Hadoop HDFS MapReduce (MR v1) Hive / PIG Hivemall Apache YARN Apache Tez DAG processing MR v2 Machine Learning Query Processing Parallel Data Processing Framework Resource Management Distributed File System 2015/10/20 Hivemall meetup #2 8 Scalable machine learning library built as a collection of Hive UDFs, licensed under the Apache License v2
- 10. Hivemall’s Vision: ML on SQL Classification with Mahout CREATE TABLE lr_model AS SELECT feature, -- reducers perform model averaging in parallel avg(weight) as weight FROM ( SELECT logress(features,label,..) as (feature,weight) FROM train ) t -- map-only task GROUP BY feature; -- shuffled to reducers ✓Machine Learning made easy for SQL developers (ML for the rest of us) ✓Interactive and Stable APIs w/ SQL abstraction This SQL query automatically runs in parallel on Hadoop 2014/09/17 Talk@Japan DataScientist Society 10
- 11. Ø CTR prediction of Ad click logs •Freakout Inc. and more Ø Gender prediction of Ad click logs •Scaleout Inc. Ø Churn Detection •OISIX and more Ø Item/User recommendation •Adtech Companies, ISP portal, and more Ø Value prediction of Real estates •Livesense Industry use cases of Hivemall 112015/10/20 Hivemall meetup #2
- 12. List of Features in Hivemall v0.3.2 Classification (both binary- and multi-class) ✓ Perceptron ✓ Passive Aggressive (PA) ✓ Confidence Weighted (CW) ✓ Adaptive Regularization of Weight Vectors (AROW) ✓ Soft Confidence Weighted (SCW) ✓ AdaGrad+RDA Regression ✓Logistic Regression (SGD) ✓PA Regression ✓AROW Regression ✓AdaGrad ✓AdaDELTA kNN and Recommendation ✓ Minhash and b-Bit Minhash (LSH variant) ✓ Similarity Search using K-NN (Euclid/Cosine/Jaccard/Angular) ✓ Matrix Factorization Feature engineering ✓ Feature Hashing ✓ Feature Scaling (normalization, z-score) ✓ TF-IDF vectorizer ✓ Polynomial Expansion Anomaly Detection ✓ Local Outlier Factor 2015/10/20 Hivemall meetup #2 12
- 13. Features supported in Hivemall v0.4 2015/10/20 Hivemall meetup #2 13 1.RandomForest • classification, regression 2.Factorization Machine • classification, regression (factorization) Treasure Data now supports v0.4.0-2 Factorization Machine are often used by data science competition winners (Criteo/Avazu CTR prediction)
- 17. Features to be supported in Hivemall v0.4.1 2015/10/20 Hivemall meetup #2 17 1. Gradient Tree Boosting • classifier, regression 2. Field-aware Factorization Machine • classification, regression (factorization) • Existing implementation, i.e., LibFFM, only can be applied for classification 3. NLP Tokenizer (形態素解析) Planned to release v0.4.1 in Dec.
- 18. Features to be supported in Hivemall v0.4.2 2015/10/20 Hivemall meetup #2 18 1. Online LDA • topic modeling, clustering 2. Mix server on Apache YARN • Service for parameter sharing among workers Planned to release v0.4.2 in Jan.
- 19. Conclusion and Takeaway 2015/10/20 Hivemall meetup #2 19 Hivemall provides a collection of machine learning algorithms as Hive UDFs/UDTFs Hivemall’s Positioning Treasure Data provides ML-as-a-Service using Hivemall Major development leaps in v0.4 More will follow in v0.4.1 • For SQL users that need ML • Easy-of-use and scalability in mind • Random Forest • Factorization Machine