Introduction to New features and Use cases of Hivemall
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Treasure Data Tech Talk 2016-03-30 http://eventdots.jp/event/583226
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Introduction to New features and Use cases of Hivemall
- 2. Ø 2015.04 Joined Treasure Data, Inc. 1st Research Engineer in Treasure Data Ø 2010.04-2015.03 Senior Researcher at National Institute of Advanced Industrial Science and Technology, Japan. Ø 2009.03 Ph.D. in Computer Science from NAIST Ø TD登山部部長 Ø 部員3名(うち幽霊部員1名） Who am I ? 2
- 3. Ø 2015.04 Joined Treasure Data, Inc. 1st Research Engineer in Treasure Data Ø 2010.04-2015.03 Senior Researcher at National Institute of Advanced Industrial Science and Technology, Japan. Ø 2009.03 Ph.D. in Computer Science from NAIST Ø TD登山部部長 Ø 部員3名(うち幽霊部員1名） Who am I ? 3
- 13. Agenda 1. Introduction to Hivemall 2. Industrial use cases 3. How to use Hivemall 4. Development roadmap 13
- 16. 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 16
- 17. List of Features in Hivemall v0.3.x 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 Top-k query processing 17
- 18. Features supported in Hivemall v0.4.0 18 1.RandomForest • classification, regression 2.Factorization Machine • classification, regression (factorization)
- 19. Features supported in Hivemall v0.4.1-alpha 19 1. NLP Tokenizer (形態素解析) • Kuromoji 2. Mini-batch Gradient Descent 3. RandomForest scalability Improvements Treasure Data is operating Hivemall v0.4.1-alpha.6 The above feature are already supported
- 20. Agenda 1. Introduction to Hivemall 2. Industrial use cases 3. How to use Hivemall 4. Development roadmap 20
- 26. 26 会員サービスの解約予測 •10万人の会員による定期購 買が会社全体の売上、利益を 左右するが、解約リスクのあ る会員を事前に把握、防止す る策を欠いていた •統計の専門知識無しで機械学習 •解約予測リストへのポイント付 与により解約率が半減 •解約リスクを伴う施策、イベン トを炙り出すと同時に、非解約 者の特徴的な行動も把握可能に •リスク度合いに応じて UI を変 更するなど間接的なサービス改 善も実現 •機械学習を行い、過去1ヶ月間 のデータをもとに未来1ヶ月間 に解約する可能性の高い顧客リ ストを作成 •具体的には、学習用テーブル作 成 -> 正規化 -> 学習モデル作成 -> ロジスティック回帰の各ス テップをTD + Hivemall を用い てクエリで簡便に実現 Web Mobile 属性情報 行動ログ クレーム情報 流入元 利用サービス情報 直接施策 間接施策 ポイント付与 ケアコール 成功体験への誘導UI 変更 予測に使うデータ
- 28. Agenda 1. Introduction to Hivemall 2. Industrial use cases 3. How to use Hivemall 4. Development roadmap 28
- 37. 37 Training of Matrix Factorization Support iterative training using local disk cache
- 41. 41 Training data for Factorization Machines Each Feature takes LibSVM-like format <feature[:weight]>
- 46. Agenda 1. Introduction to Hivemall 2. Industrial use cases 3. How to use Hivemall 4. Development roadmap 46
- 47. Features to be supported in Hivemall v0.4.1 47 1. NLP Tokenizer (形態素解析) • Kuromoji integration was requested by Company R 2. Mini-batch Gradient Descent 3. RandomForest scalability Improvements 4. Recommendation for Implicit Feedback Dataset • Useful where only positive-only feedback is available • BPR: Bayesian Personalized Ranking from Implicit Feedback, Proc. UAI, 2009. Planned to release v0.4.1 in April.
- 48. Features to be supported in Hivemall v0.4.2 48 1. Gradient Tree Boosting • classifier, regression • based on Smile https://github.com/haifengl/smile/
- 49. Features to be supported in Hivemall v0.4.2 49 1. Gradient Tree Boosting • classifier, regression • based on Smile https://github.com/haifengl/smile/ 2. Field-aware Factorization Machine • classification, regression (factorization) Planned to release v0.4.1 in June
- 50. Features to be supported in Hivemall v0.5 50 1. Mix server on Apache YARN • Service for parameter sharing among workers 学習器1 学習器2 学習器N パラメタ 交換 学習 モデル 分割された訓練例 データ並列 データ並列
- 51. Features to be supported in Hivemall v0.5 51 1. Mix server on Apache YARN • Service for parameter sharing among worker 2. Online LDA • topic modeling, clustering 3. XGBoost Integration 4.Generalized Linear Model • Ridge/Elastic net/Lasso regularization • Supports various loss functions 5. Alternating Direction Method of Multipliers (ADMM) convex optimization 6. T-sne Dimension Reduction
- 52. 52 Analytics Workflow Machine learning workflows can be simplified using our new workflow engine, named Digdag +main: +prepare: _parallel: true +train: td>: ./tasks/train_join.sql +test: td>: ./tasks/test_join.sql +quantify: td>: ./tasks/train_quantify.sql +model_test_quantify: _parallel: true +model: td>: ./tasks/make_model.sql +test_quantify: td>: ./tasks/test_quantify.sql +pred: td>: ./tasks/prediction.sql CLI version will be released soon. Stay tuned!
- 53. Conclusion and Takeaway 53 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 and later • For SQL users that need ML • Easy-of-use and scalability in mind • Random Forest • Factorization Machine