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class="divider"></li> + + + + + <li class="header">TABLE OF CONTENTS</li> + + + + <li class="chapter " data-level="1.1" data-path="../"> + + <a href="../"> + + + <b>1.1.</b> + + Introduction + + </a> + + + + </li> + + <li class="chapter " data-level="1.2" data-path="../getting_started/"> + + <a href="../getting_started/"> + + + <b>1.2.</b> + + Getting Started + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.2.1" data-path="../getting_started/installation.html"> + + <a href="../getting_started/installation.html"> + + + <b>1.2.1.</b> + + Installation + + </a> + + + + </li> + + <li class="chapter " data-level="1.2.2" data-path="../getting_started/permanent-functions.html"> + + <a href="../getting_started/permanent-functions.html"> + + + <b>1.2.2.</b> + + Install as permanent functions + + </a> + + + + </li> + + <li class="chapter " data-level="1.2.3" data-path="../getting_started/input-format.html"> + + <a href="../getting_started/input-format.html"> + + + <b>1.2.3.</b> + + Input Format + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="1.3" data-path="../tips/"> + + <a href="../tips/"> + + + <b>1.3.</b> + + Tips for Effective Hivemall + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.3.1" data-path="../tips/addbias.html"> + + <a href="../tips/addbias.html"> + + + <b>1.3.1.</b> + + Explicit addBias() for better prediction + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.2" data-path="../tips/rand_amplify.html"> + + <a href="../tips/rand_amplify.html"> + + + <b>1.3.2.</b> + + Use rand_amplify() to better prediction results + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.3" data-path="../tips/rt_prediction.html"> + + <a href="../tips/rt_prediction.html"> + + + <b>1.3.3.</b> + + Real-time Prediction on RDBMS + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.4" data-path="../tips/ensemble_learning.html"> + + <a href="../tips/ensemble_learning.html"> + + + <b>1.3.4.</b> + + Ensemble learning for stable prediction + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.5" data-path="../tips/mixserver.html"> + + <a href="../tips/mixserver.html"> + + + <b>1.3.5.</b> + + Mixing models for a better prediction convergence (MIX server) + + </a> + + + + </li> + + <li class="chapter " data-level="1.3.6" data-path="../tips/emr.html"> + + <a href="../tips/emr.html"> + + + <b>1.3.6.</b> + + Run Hivemall on Amazon Elastic MapReduce + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="1.4" data-path="../tips/general_tips.html"> + + <a href="../tips/general_tips.html"> + + + <b>1.4.</b> + + General Hive/Hadoop tips + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.4.1" data-path="../tips/rowid.html"> + + <a href="../tips/rowid.html"> + + + <b>1.4.1.</b> + + Adding rowid for each row + + </a> + + + + </li> + + <li class="chapter " data-level="1.4.2" data-path="../tips/hadoop_tuning.html"> + + <a href="../tips/hadoop_tuning.html"> + + + <b>1.4.2.</b> + + Hadoop tuning for Hivemall + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="1.5" data-path="../troubleshooting/"> + + <a href="../troubleshooting/"> + + + <b>1.5.</b> + + Troubleshooting + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="1.5.1" data-path="../troubleshooting/oom.html"> + + <a href="../troubleshooting/oom.html"> + + + <b>1.5.1.</b> + + OutOfMemoryError in training + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.2" data-path="../troubleshooting/mapjoin_task_error.html"> + + <a href="../troubleshooting/mapjoin_task_error.html"> + + + <b>1.5.2.</b> + + SemanticException Generate Map Join Task Error: Cannot serialize object + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.3" data-path="../troubleshooting/asterisk.html"> + + <a href="../troubleshooting/asterisk.html"> + + + <b>1.5.3.</b> + + Asterisk argument for UDTF does not work + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.4" data-path="../troubleshooting/num_mappers.html"> + + <a href="../troubleshooting/num_mappers.html"> + + + <b>1.5.4.</b> + + The number of mappers is less than input splits in Hadoop 2.x + + </a> + + + + </li> + + <li class="chapter " data-level="1.5.5" data-path="../troubleshooting/mapjoin_classcastex.html"> + + <a href="../troubleshooting/mapjoin_classcastex.html"> + + + <b>1.5.5.</b> + + Map-side Join causes ClassCastException on Tez + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part II - Generic Features</li> + + + + <li class="chapter " data-level="2.1" data-path="../misc/generic_funcs.html"> + + <a href="../misc/generic_funcs.html"> + + + <b>2.1.</b> + + List of generic Hivemall functions + + </a> + + + + </li> + + <li class="chapter " data-level="2.2" data-path="../misc/topk.html"> + + <a href="../misc/topk.html"> + + + <b>2.2.</b> + + Efficient Top-K query processing + + </a> + + + + </li> + + <li class="chapter " data-level="2.3" data-path="../misc/tokenizer.html"> + + <a href="../misc/tokenizer.html"> + + + <b>2.3.</b> + + English/Japanese Text Tokenizer + + </a> + + + + </li> + + + + + <li class="header">Part III - Feature Engineering</li> + + + + <li class="chapter " data-level="3.1" data-path="../ft_engineering/scaling.html"> + + <a href="../ft_engineering/scaling.html"> + + + <b>3.1.</b> + + Feature Scaling + + </a> + + + + </li> + + <li class="chapter " data-level="3.2" data-path="../ft_engineering/hashing.html"> + + <a href="../ft_engineering/hashing.html"> + + + <b>3.2.</b> + + Feature Hashing + + </a> + + + + </li> + + <li class="chapter " data-level="3.3" data-path="../ft_engineering/tfidf.html"> + + <a href="../ft_engineering/tfidf.html"> + + + <b>3.3.</b> + + TF-IDF calculation + + </a> + + + + </li> + + <li class="chapter " data-level="3.4" data-path="../ft_engineering/ft_trans.html"> + + <a href="../ft_engineering/ft_trans.html"> + + + <b>3.4.</b> + + FEATURE TRANSFORMATION + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="3.4.1" data-path="../ft_engineering/vectorizer.html"> + + <a href="../ft_engineering/vectorizer.html"> + + + <b>3.4.1.</b> + + Vectorize Features + + </a> + + + + </li> + + <li class="chapter " data-level="3.4.2" data-path="../ft_engineering/quantify.html"> + + <a href="../ft_engineering/quantify.html"> + + + <b>3.4.2.</b> + + Quantify non-number features + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="3.5" data-path="../ft_engineering/feature_selection.html"> + + <a href="../ft_engineering/feature_selection.html"> + + + <b>3.5.</b> + + Feature selection + + </a> + + + + </li> + + + + + <li class="header">Part IV - Evaluation</li> + + + + <li class="chapter " data-level="4.1" data-path="../eval/stat_eval.html"> + + <a href="../eval/stat_eval.html"> + + + <b>4.1.</b> + + Statistical evaluation of a prediction model + + </a> + + + + </li> + + <li class="chapter " data-level="4.2" data-path="../eval/datagen.html"> + + <a href="../eval/datagen.html"> + + + <b>4.2.</b> + + Data Generation + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="4.2.1" data-path="../eval/lr_datagen.html"> + + <a href="../eval/lr_datagen.html"> + + + <b>4.2.1.</b> + + Logistic Regression data generation + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part V - Binary classification</li> + + + + <li class="chapter " data-level="5.1" data-path="../binaryclass/a9a.html"> + + <a href="../binaryclass/a9a.html"> + + + <b>5.1.</b> + + a9a Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.1.1" data-path="../binaryclass/a9a_dataset.html"> + + <a href="../binaryclass/a9a_dataset.html"> + + + <b>5.1.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.1.2" data-path="../binaryclass/a9a_lr.html"> + + <a href="../binaryclass/a9a_lr.html"> + + + <b>5.1.2.</b> + + Logistic Regression + + </a> + + + + </li> + + <li class="chapter " data-level="5.1.3" data-path="../binaryclass/a9a_minibatch.html"> + + <a href="../binaryclass/a9a_minibatch.html"> + + + <b>5.1.3.</b> + + Mini-batch Gradient Descent + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.2" data-path="../binaryclass/news20.html"> + + <a href="../binaryclass/news20.html"> + + + <b>5.2.</b> + + News20 Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.2.1" data-path="../binaryclass/news20_dataset.html"> + + <a href="../binaryclass/news20_dataset.html"> + + + <b>5.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.2.2" data-path="../binaryclass/news20_pa.html"> + + <a href="../binaryclass/news20_pa.html"> + + + <b>5.2.2.</b> + + Perceptron, Passive Aggressive + + </a> + + + + </li> + + <li class="chapter " data-level="5.2.3" data-path="../binaryclass/news20_scw.html"> + + <a href="../binaryclass/news20_scw.html"> + + + <b>5.2.3.</b> + + CW, AROW, SCW + + </a> + + + + </li> + + <li class="chapter " data-level="5.2.4" data-path="../binaryclass/news20_adagrad.html"> + + <a href="../binaryclass/news20_adagrad.html"> + + + <b>5.2.4.</b> + + AdaGradRDA, AdaGrad, AdaDelta + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.3" data-path="../binaryclass/kdd2010a.html"> + + <a href="../binaryclass/kdd2010a.html"> + + + <b>5.3.</b> + + KDD2010a Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.3.1" data-path="../binaryclass/kdd2010a_dataset.html"> + + <a href="../binaryclass/kdd2010a_dataset.html"> + + + <b>5.3.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.3.2" data-path="../binaryclass/kdd2010a_scw.html"> + + <a href="../binaryclass/kdd2010a_scw.html"> + + + <b>5.3.2.</b> + + PA, CW, AROW, SCW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.4" data-path="../binaryclass/kdd2010b.html"> + + <a href="../binaryclass/kdd2010b.html"> + + + <b>5.4.</b> + + KDD2010b Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.4.1" data-path="../binaryclass/kdd2010b_dataset.html"> + + <a href="../binaryclass/kdd2010b_dataset.html"> + + + <b>5.4.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.4.2" data-path="../binaryclass/kdd2010b_arow.html"> + + <a href="../binaryclass/kdd2010b_arow.html"> + + + <b>5.4.2.</b> + + AROW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.5" data-path="../binaryclass/webspam.html"> + + <a href="../binaryclass/webspam.html"> + + + <b>5.5.</b> + + Webspam Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="5.5.1" data-path="../binaryclass/webspam_dataset.html"> + + <a href="../binaryclass/webspam_dataset.html"> + + + <b>5.5.1.</b> + + Data pareparation + + </a> + + + + </li> + + <li class="chapter " data-level="5.5.2" data-path="../binaryclass/webspam_scw.html"> + + <a href="../binaryclass/webspam_scw.html"> + + + <b>5.5.2.</b> + + PA1, AROW, SCW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="5.6" data-path="../binaryclass/titanic_rf.html"> + + <a href="../binaryclass/titanic_rf.html"> + + + <b>5.6.</b> + + Kaggle Titanic Tutorial + + </a> + + + + </li> + + + + + <li class="header">Part VI - Multiclass classification</li> + + + + <li class="chapter " data-level="6.1" data-path="../multiclass/news20.html"> + + <a href="../multiclass/news20.html"> + + + <b>6.1.</b> + + News20 Multiclass Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="6.1.1" data-path="../multiclass/news20_dataset.html"> + + <a href="../multiclass/news20_dataset.html"> + + + <b>6.1.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.2" data-path="../multiclass/news20_one-vs-the-rest_dataset.html"> + + <a href="../multiclass/news20_one-vs-the-rest_dataset.html"> + + + <b>6.1.2.</b> + + Data preparation for one-vs-the-rest classifiers + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.3" data-path="../multiclass/news20_pa.html"> + + <a href="../multiclass/news20_pa.html"> + + + <b>6.1.3.</b> + + PA + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.4" data-path="../multiclass/news20_scw.html"> + + <a href="../multiclass/news20_scw.html"> + + + <b>6.1.4.</b> + + CW, AROW, SCW + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.5" data-path="../multiclass/news20_ensemble.html"> + + <a href="../multiclass/news20_ensemble.html"> + + + <b>6.1.5.</b> + + Ensemble learning + + </a> + + + + </li> + + <li class="chapter " data-level="6.1.6" data-path="../multiclass/news20_one-vs-the-rest.html"> + + <a href="../multiclass/news20_one-vs-the-rest.html"> + + + <b>6.1.6.</b> + + one-vs-the-rest classifier + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="6.2" data-path="../multiclass/iris.html"> + + <a href="../multiclass/iris.html"> + + + <b>6.2.</b> + + Iris Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="6.2.1" data-path="../multiclass/iris_dataset.html"> + + <a href="../multiclass/iris_dataset.html"> + + + <b>6.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="6.2.2" data-path="../multiclass/iris_scw.html"> + + <a href="../multiclass/iris_scw.html"> + + + <b>6.2.2.</b> + + SCW + + </a> + + + + </li> + + <li class="chapter " data-level="6.2.3" data-path="../multiclass/iris_randomforest.html"> + + <a href="../multiclass/iris_randomforest.html"> + + + <b>6.2.3.</b> + + RandomForest + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part VII - Regression</li> + + + + <li class="chapter " data-level="7.1" data-path="../regression/e2006.html"> + + <a href="../regression/e2006.html"> + + + <b>7.1.</b> + + E2006-tfidf regression Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="7.1.1" data-path="../regression/e2006_dataset.html"> + + <a href="../regression/e2006_dataset.html"> + + + <b>7.1.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="7.1.2" data-path="../regression/e2006_arow.html"> + + <a href="../regression/e2006_arow.html"> + + + <b>7.1.2.</b> + + Passive Aggressive, AROW + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="7.2" data-path="../regression/kddcup12tr2.html"> + + <a href="../regression/kddcup12tr2.html"> + + + <b>7.2.</b> + + KDDCup 2012 track 2 CTR prediction Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="7.2.1" data-path="../regression/kddcup12tr2_dataset.html"> + + <a href="../regression/kddcup12tr2_dataset.html"> + + + <b>7.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="7.2.2" data-path="../regression/kddcup12tr2_lr.html"> + + <a href="../regression/kddcup12tr2_lr.html"> + + + <b>7.2.2.</b> + + Logistic Regression, Passive Aggressive + + </a> + + + + </li> + + <li class="chapter " data-level="7.2.3" data-path="../regression/kddcup12tr2_lr_amplify.html"> + + <a href="../regression/kddcup12tr2_lr_amplify.html"> + + + <b>7.2.3.</b> + + Logistic Regression with Amplifier + + </a> + + + + </li> + + <li class="chapter " data-level="7.2.4" data-path="../regression/kddcup12tr2_adagrad.html"> + + <a href="../regression/kddcup12tr2_adagrad.html"> + + + <b>7.2.4.</b> + + AdaGrad, AdaDelta + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part VIII - Recommendation</li> + + + + <li class="chapter " data-level="8.1" data-path="../recommend/cf.html"> + + <a href="../recommend/cf.html"> + + + <b>8.1.</b> + + Collaborative Filtering + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="8.1.1" data-path="../recommend/item_based_cf.html"> + + <a href="../recommend/item_based_cf.html"> + + + <b>8.1.1.</b> + + Item-based Collaborative Filtering + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="8.2" data-path="../recommend/news20.html"> + + <a href="../recommend/news20.html"> + + + <b>8.2.</b> + + News20 related article recommendation Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="8.2.1" data-path="../multiclass/news20_dataset.html"> + + <a href="../multiclass/news20_dataset.html"> + + + <b>8.2.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.2" data-path="../recommend/news20_jaccard.html"> + + <a href="../recommend/news20_jaccard.html"> + + + <b>8.2.2.</b> + + LSH/Minhash and Jaccard Similarity + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.3" data-path="../recommend/news20_knn.html"> + + <a href="../recommend/news20_knn.html"> + + + <b>8.2.3.</b> + + LSH/Minhash and Brute-Force Search + + </a> + + + + </li> + + <li class="chapter " data-level="8.2.4" data-path="../recommend/news20_bbit_minhash.html"> + + <a href="../recommend/news20_bbit_minhash.html"> + + + <b>8.2.4.</b> + + kNN search using b-Bits Minhash + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="8.3" data-path="../recommend/movielens.html"> + + <a href="../recommend/movielens.html"> + + + <b>8.3.</b> + + MovieLens movie recommendation Tutorial + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="8.3.1" data-path="../recommend/movielens_dataset.html"> + + <a href="../recommend/movielens_dataset.html"> + + + <b>8.3.1.</b> + + Data preparation + + </a> + + + + </li> + + <li class="chapter " data-level="8.3.2" data-path="../recommend/movielens_mf.html"> + + <a href="../recommend/movielens_mf.html"> + + + <b>8.3.2.</b> + + Matrix Factorization + + </a> + + + + </li> + + <li class="chapter " data-level="8.3.3" data-path="../recommend/movielens_fm.html"> + + <a href="../recommend/movielens_fm.html"> + + + <b>8.3.3.</b> + + Factorization Machine + + </a> + + + + </li> + + <li class="chapter " data-level="8.3.4" data-path="../recommend/movielens_cv.html"> + + <a href="../recommend/movielens_cv.html"> + + + <b>8.3.4.</b> + + 10-fold Cross Validation (Matrix Factorization) + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part IX - Anomaly Detection</li> + + + + <li class="chapter " data-level="9.1" data-path="lof.html"> + + <a href="lof.html"> + + + <b>9.1.</b> + + Outlier Detection using Local Outlier Factor (LOF) + + </a> + + + + </li> + + <li class="chapter active" data-level="9.2" data-path="sst.html"> + + <a href="sst.html"> + + + <b>9.2.</b> + + Change-Point Detection using Singular Spectrum Transformation (SST) + + </a> + + + + </li> + + <li class="chapter " data-level="9.3" data-path="changefinder.html"> + + <a href="changefinder.html"> + + + <b>9.3.</b> + + ChangeFinder: Detecting Outlier and Change-Point Simultaneously + + </a> + + + + </li> + + + + + <li class="header">Part X - Hivemall on Spark</li> + + + + <li class="chapter " data-level="10.1" data-path="../spark/binaryclass/"> + + <a href="../spark/binaryclass/"> + + + <b>10.1.</b> + + Binary Classification + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="10.1.1" data-path="../spark/binaryclass/a9a_df.html"> + + <a href="../spark/binaryclass/a9a_df.html"> + + + <b>10.1.1.</b> + + a9a Tutorial for DataFrame + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="10.2" data-path="../spark/binaryclass/"> + + <a href="../spark/binaryclass/"> + + + <b>10.2.</b> + + Regression + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="10.2.1" data-path="../spark/regression/e2006_df.html"> + + <a href="../spark/regression/e2006_df.html"> + + + <b>10.2.1.</b> + + E2006-tfidf regression Tutorial for DataFrame + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="10.3" data-path="../spark/misc/misc.html"> + + <a href="../spark/misc/misc.html"> + + + <b>10.3.</b> + + Generic features + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="10.3.1" data-path="../spark/misc/topk_join.html"> + + <a href="../spark/misc/topk_join.html"> + + + <b>10.3.1.</b> + + Top-k Join processing + + </a> + + + + </li> + + + </ul> + + </li> + + + + + <li class="header">Part X - External References</li> + + + + <li class="chapter " data-level="11.1" > + + <a target="_blank" href="https://github.com/maropu/hivemall-spark";> + + + <b>11.1.</b> + + Hivemall on Apache Spark + + </a> + + + + </li> + + <li class="chapter " data-level="11.2" > + + <a target="_blank" href="https://github.com/daijyc/hivemall/wiki/PigHome";> + + + <b>11.2.</b> + + Hivemall on Apache Pig + + </a> + + + + </li> + + + + + <li class="divider"></li> + + <li> + <a href="https://www.gitbook.com"; target="blank" class="gitbook-link"> + Published with GitBook + </a> + </li> +</ul> + + + </nav> + + + </div> + + <div class="book-body"> + + <div class="body-inner"> + + + +<div class="book-header" role="navigation"> + + + <!-- Title --> + <h1> + <i class="fa fa-circle-o-notch fa-spin"></i> + <a href=".." >Change-Point Detection using Singular Spectrum Transformation (SST)</a> + </h1> +</div> + + + + + <div class="page-wrapper" tabindex="-1" role="main"> + <div class="page-inner"> + +<div id="book-search-results"> + <div class="search-noresults"> + + <section class="normal markdown-section"> + + <!-- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--> +<p>This page introduces how to find change-points using <strong>Singular Spectrum Transformation</strong> (SST) on Hivemall. The following papers describe the details of this technique:</p> +<ul> +<li>T. Idé and K. Inoue. <a href="http://epubs.siam.org/doi/abs/10.1137/1.9781611972757.63"; target="_blank">Knowledge Discovery from Heterogeneous Dynamic Systems using Change-Point Correlations</a>. SDM'05.</li> +<li>T. Idé and K. Tsuda. <a href="http://epubs.siam.org/doi/abs/10.1137/1.9781611972771.54"; target="_blank">Change-Point Detection using Krylov Subspace Learning</a>. SDM'07.</li> +</ul> +<!-- toc --><div id="toc" class="toc"> + +<ul> +<li><a href="#outlier-vs-change-point">Outlier vs Change-Point</a></li> +<li><a href="#data-preparation">Data Preparation</a><ul> +<li><a href="#get-twitters-data">Get Twitter's data</a></li> +<li><a href="#importing-data-as-a-hive-table">Importing data as a Hive table</a><ul> +<li><a href="#create-a-hive-table">Create a Hive table</a></li> +<li><a href="#load-data-into-the-table">Load data into the table</a></li> +</ul> +</li> +</ul> +</li> +<li><a href="#change-point-detection-using-sst">Change-Point Detection using SST</a></li> +</ul> + +</div><!-- tocstop --> +<h1 id="outlier-vs-change-point">Outlier vs Change-Point</h1> +<p>It is important that anomaly detectors are generally categorized into outlier and change-point detectors. Outliers are some spiky "local" data points which are suddenly observed in a series of normal samples, and <a href="lof.html">Local Outlier Detection</a> is an algorithm to detect outliers. On the other hand, change-points indicate "global" change on a wider scale in terms of characteristics of data points.</p> +<p>In this page, we specially focus on change-point detection. More concretely, the following sections introduce a way to detect change-points on Hivemall, by using a specific technique named Singular Spectrum Transformation (SST).</p> +<h1 id="data-preparation">Data Preparation</h1> +<h2 id="get-twitters-data">Get Twitter's data</h2> +<p>We use time series data points provided by Twitter in the following article: <a href="https://blog.twitter.com/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series"; target="_blank">Introducing practical and robust anomaly detection in a time series</a>. In fact, the dataset is originally created for R, but we can get CSV version of the same data from <a href="https://github.com/apache/incubator-hivemall/blob/master/core/src/test/resources/hivemall/anomaly/twitter.csv.gz?raw=true"; target="_blank">HERE</a>.</p> +<p>Once you uncompressed the downloaded <code>.gz</code> file, you can see a CSV file:</p> +<pre><code>$ head twitter.csv +182.478 +176.231 +183.917 +177.798 +165.469 +181.878 +184.502 +183.303 +177.578 +171.641 +</code></pre><p>These values are sequential data points. Our goal is to detect change-points in the samples. Here, let us insert a dummy timestamp into each line as follows:</p> +<pre><code>$ awk '{printf "%d#%s\n", NR, $0}' < twitter.csv > twitter.t +</code></pre><pre><code>$ head twitter.t +1#182.478 +2#176.231 +3#183.917 +4#177.798 +5#165.469 +6#181.878 +7#184.502 +8#183.303 +9#177.578 +10#171.641 +</code></pre><p>Now, Hive can understand sequence of the samples by just looking dummy timestamp.</p> +<h2 id="importing-data-as-a-hive-table">Importing data as a Hive table</h2> +<h3 id="create-a-hive-table">Create a Hive table</h3> +<p>You first need to launch a Hive console and run the following operations:</p> +<pre><code>create database twitter; +use twitter; +</code></pre><pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span class="hljs-keyword">EXTERNAL</span> <span class="hljs-keyword">TABLE</span> timeseries ( + <span class="hljs-keyword">num</span> <span class="hljs-built_in">INT</span>, + <span class="hljs-keyword">value</span> <span class="hljs-keyword">DOUBLE</span> +) <span class="hljs-keyword">ROW</span> <span class="hljs-keyword">FORMAT</span> <span class="hljs-keyword">DELIMITED</span> +<span class="hljs-keyword">FIELDS</span> <span class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span> <span class="hljs-string">'#'</span> +<span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span> TEXTFILE +LOCATION <span class="hljs-string">'/dataset/twitter/timeseries'</span>; +</code></pre> +<h3 id="load-data-into-the-table">Load data into the table</h3> +<p>Next, the <code>.t</code> file we have generated before can be loaded to the table by:</p> +<pre><code>$ hadoop fs -put twitter.t /dataset/twitter/timeseries +</code></pre><p><code>timeseries</code> table in <code>twitter</code> database should be:</p> +<table> +<thead> +<tr> +<th style="text-align:center">num</th> +<th style="text-align:center">value</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">1</td> +<td style="text-align:center">182.478</td> +</tr> +<tr> +<td style="text-align:center">2</td> +<td style="text-align:center">176.231</td> +</tr> +<tr> +<td style="text-align:center">3</td> +<td style="text-align:center">183.917</td> +</tr> +<tr> +<td style="text-align:center">4</td> +<td style="text-align:center">177.798</td> +</tr> +<tr> +<td style="text-align:center">5</td> +<td style="text-align:center">165.469</td> +</tr> +<tr> +<td style="text-align:center">...</td> +<td style="text-align:center">...</td> +</tr> +</tbody> +</table> +<h1 id="change-point-detection-using-sst">Change-Point Detection using SST</h1> +<p>We are now ready to detect change-points. A UDF <code>sst()</code> takes a <code>double</code> value as the first argument, and you can set options in the second argument. </p> +<p>What the following query does is to detect change-points from a <code>value</code> column in the <code>timeseries</code> table. An option <code>"-threshold 0.005"</code> means that a data point is detected as a change-point if its score is greater than 0.005.</p> +<pre><code>use twitter; +</code></pre><pre><code class="lang-sql"><span class="hljs-keyword">SELECT</span> + <span class="hljs-keyword">num</span>, + sst(<span class="hljs-keyword">value</span>, <span class="hljs-string">"-threshold 0.005"</span>) <span class="hljs-keyword">AS</span> <span class="hljs-keyword">result</span> +<span class="hljs-keyword">FROM</span> + timeseries +<span class="hljs-keyword">ORDER</span> <span class="hljs-keyword">BY</span> <span class="hljs-keyword">num</span> <span class="hljs-keyword">ASC</span> +; +</code></pre> +<p>For instance, partial outputs obtained as a result of this query are:</p> +<table> +<thead> +<tr> +<th style="text-align:center">num</th> +<th style="text-align:left">result</th> +</tr> +</thead> +<tbody> +<tr> +<td style="text-align:center">...</td> +<td style="text-align:left">...</td> +</tr> +<tr> +<td style="text-align:center">7551</td> +<td style="text-align:left">{"changepoint_score":0.00453049288071683,"is_changepoint":false}</td> +</tr> +<tr> +<td style="text-align:center">7552</td> +<td style="text-align:left">{"changepoint_score":0.004711244102524104,"is_changepoint":false}</td> +</tr> +<tr> +<td style="text-align:center">7553</td> +<td style="text-align:left">{"changepoint_score":0.004814871928978115,"is_changepoint":false}</td> +</tr> +<tr> +<td style="text-align:center">7554</td> +<td style="text-align:left">{"changepoint_score":0.004968089640799422,"is_changepoint":false}</td> +</tr> +<tr> +<td style="text-align:center">7555</td> +<td style="text-align:left">{"changepoint_score":0.005709056330104878,"is_changepoint":true}</td> +</tr> +<tr> +<td style="text-align:center">7556</td> +<td style="text-align:left">{"changepoint_score":0.0044279766655132,"is_changepoint":false}</td> +</tr> +<tr> +<td style="text-align:center">7557</td> +<td style="text-align:left">{"changepoint_score":0.0034694956722586268,"is_changepoint":false}</td> +</tr> +<tr> +<td style="text-align:center">7558</td> +<td style="text-align:left">{"changepoint_score":0.002549056569322694,"is_changepoint":false}</td> +</tr> +<tr> +<td style="text-align:center">7559</td> +<td style="text-align:left">{"changepoint_score":0.0017395109108403473,"is_changepoint":false}</td> +</tr> +<tr> +<td style="text-align:center">7560</td> +<td style="text-align:left">{"changepoint_score":0.0010629833145070489,"is_changepoint":false}</td> +</tr> +<tr> +<td style="text-align:center">...</td> +<td style="text-align:left">...</td> +</tr> +</tbody> +</table> +<p>Obviously, the 7555-th sample is detected as a change-point in this example. +<div id="page-footer"><hr><!-- + Licensed to the Apache Software Foundation (ASF) under one + or more contributor license agreements. See the NOTICE file + distributed with this work for additional information + regarding copyright ownership. The ASF licenses this file + to you under the Apache License, Version 2.0 (the + "License"); you may not use this file except in compliance + with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, + software distributed under the License is distributed on an + "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY + KIND, either express or implied. See the License for the + specific language governing permissions and limitations + under the License. +--> +<p><sub><font color="gray"> +Apache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. +</font></sub></p> +</div></p> + + + </section> + + </div> + <div class="search-results"> + <div class="has-results"> + + <h1 class="search-results-title"><span class='search-results-count'></span> results matching "<span class='search-query'></span>"</h1> + <ul class="search-results-list"></ul> + + </div> + <div class="no-results"> + + <h1 class="search-results-title">No results matching "<span class='search-query'></span>"</h1> + + </div> + </div> +</div> + + </div> + </div> + + </div> + + + + + </div> + + <script> + var gitbook = gitbook || []; + gitbook.push(function() { + gitbook.page.hasChanged({"page":{"title":"Change-Point Detection using Singular Spectrum Transformation (SST)","level":"9.2","depth":1,"next":{"title":"ChangeFinder: Detecting Outlier and Change-Point Simultaneously","level":"9.3","depth":1,"path":"anomaly/changefinder.md","ref":"anomaly/changefinder.md","articles":[]},"previous":{"title":"Outlier Detection using Local Outlier Factor (LOF)","level":"9.1","depth":1,"path":"anomaly/lof.md","ref":"anomaly/lof.md","articles":[]},"dir":"ltr"},"config":{"plugins":["theme-api","edit-link","github","splitter","sitemap","etoc","callouts","toggle-chapters","anchorjs","codeblock-filename","expandable-chapters","multipart","codeblock-filename","katex","emphasize","localized-footer"],"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css","print":"styles/print.css"},"pluginsConfig":{"emphasize":{},"callouts":{},"etoc":{"maxdepth":3,"mindepth":1,"noto c":true},"github":{"url":"https://github.com/apache/incubator-hivemall/"},"splitter":{},"search":{},"downloadpdf":{"base":"https://github.com/apache/incubator-hivemall/docs/gitbook","label":"PDF","multilingual":false},"multipart":{},"localized-footer":{"filename":"FOOTER.md"},"lunr":{"maxIndexSize":1000000,"ignoreSpecialCharacters":false},"katex":{},"fontsettings":{"theme":"white","family":"sans","size":2,"font":"sans"},"highlight":{},"codeblock-filename":{},"sitemap":{"hostname":"http://hivemall.incubator.apache.org/"},"theme-api":{"languages":[],"split":false,"theme":"dark"},"sharing":{"facebook":true,"twitter":true,"google":false,"weibo":false,"instapaper":false,"vk":false,"all":["facebook","google","twitter","weibo","instapaper"]},"edit-link":{"label":"Edit","base":"https://github.com/apache/incubator-hivemall/docs/gitbook"},"theme-default":{"styles":{"website":"styles/website.css","pdf":"styles/pdf.css","epub":"styles/epub.css","mobi":"styles/mobi.css","ebook":"styles/ebook.css ","print":"styles/print.css"},"showLevel":true},"anchorjs":{"selector":"h1,h2,h3,*:not(.callout) > h4,h5"},"toggle-chapters":{},"expandable-chapters":{}},"theme":"default","pdf":{"pageNumbers":true,"fontSize":12,"fontFamily":"Arial","paperSize":"a4","chapterMark":"pagebreak","pageBreaksBefore":"/","margin":{"right":62,"left":62,"top":56,"bottom":56}},"structure":{"langs":"LANGS.md","readme":"README.md","glossary":"GLOSSARY.md","summary":"SUMMARY.md"},"variables":{},"title":"Hivemall User Manual","links":{"sidebar":{"<i class=\"fa fa-home\"></i> Home":"http://hivemall.incubator.apache.org/"}},"gitbook":"3.x.x","description":"User Manual for Apache Hivemall"},"file":{"path":"anomaly/sst.md","mtime":"2017-02-08T09:17:59.000Z","type":"markdown"},"gitbook":{"version":"3.2.2","time":"2017-02-23T13:06:39.809Z"},"basePath":"..","book":{"language":""}}); 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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/2d3fdeb2/userguide/binaryclass/a9a.html ---------------------------------------------------------------------- diff --git a/userguide/binaryclass/a9a.html b/userguide/binaryclass/a9a.html index a98dd01..4ee3ff3 100644 --- a/userguide/binaryclass/a9a.html +++ b/userguide/binaryclass/a9a.html @@ -1579,6 +1579,36 @@ </li> + <li class="chapter " data-level="9.2" data-path="../anomaly/sst.html"> + + <a href="../anomaly/sst.html"> + + + <b>9.2.</b> + + Change-Point Detection using Singular Spectrum Transformation (SST) + + </a> + + + + </li> + + <li class="chapter " data-level="9.3" data-path="../anomaly/changefinder.html"> + + <a href="../anomaly/changefinder.html"> + + + <b>9.3.</b> + + ChangeFinder: Detecting Outlier and Change-Point Simultaneously + + </a> + + + + </li> + @@ -1586,13 +1616,85 @@ - <li class="chapter " data-level="10.1" data-path="../spark/misc/misc.html"> + <li class="chapter " data-level="10.1" data-path="../spark/binaryclass/"> - <a href="../spark/misc/misc.html"> + <a href="../spark/binaryclass/"> <b>10.1.</b> + Binary Classification + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="10.1.1" data-path="../spark/binaryclass/a9a_df.html"> + + <a href="../spark/binaryclass/a9a_df.html"> + + + <b>10.1.1.</b> + + a9a Tutorial for DataFrame + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="10.2" data-path="../spark/binaryclass/"> + + <a href="../spark/binaryclass/"> + + + <b>10.2.</b> + + Regression + + </a> + + + + <ul class="articles"> + + + <li class="chapter " data-level="10.2.1" data-path="../spark/regression/e2006_df.html"> + + <a href="../spark/regression/e2006_df.html"> + + + <b>10.2.1.</b> + + E2006-tfidf regression Tutorial for DataFrame + + </a> + + + + </li> + + + </ul> + + </li> + + <li class="chapter " data-level="10.3" data-path="../spark/misc/misc.html"> + + <a href="../spark/misc/misc.html"> + + + <b>10.3.</b> + Generic features </a> @@ -1602,12 +1704,12 @@ <ul class="articles"> - <li class="chapter " data-level="10.1.1" data-path="../spark/misc/topk_join.html"> + <li class="chapter " data-level="10.3.1" data-path="../spark/misc/topk_join.html"> <a href="../spark/misc/topk_join.html"> - <b>10.1.1.</b> + <b>10.3.1.</b> Top-k Join processing @@ -1777,7 +1879,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; 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