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+<!DOCTYPE HTML>
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+ <title>SCW · Hivemall User Manual</title>
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+ <link rel="next" href="iris_randomforest.html" />
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+ </head>
+ <body>
+
+<div class="book">
+ <div class="book-summary">
+
+
+<div id="book-search-input" role="search">
+ <input type="text" placeholder="Type to search" />
+</div>
+
+
+ <nav role="navigation">
+
+
+
+<ul class="summary">
+
+
+
+
+ <li>
+ <a href="http://hivemall.incubator.apache.org/"; target="_blank"
class="custom-link"><i class="fa fa-home"></i> Home</a>
+ </li>
+
+
+
+
+ <li 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="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="header">Part VI - Multiclass classification</li>
+
+
+
+ <li class="chapter " data-level="6.1" data-path="news20.html">
+
+ <a href="news20.html">
+
+
+ <b>6.1.</b>
+
+ News20 Multiclass Tutorial
+
+ </a>
+
+
+
+ <ul class="articles">
+
+
+ <li class="chapter " data-level="6.1.1"
data-path="news20_dataset.html">
+
+ <a href="news20_dataset.html">
+
+
+ <b>6.1.1.</b>
+
+ Data preparation
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="6.1.2"
data-path="news20_one-vs-the-rest_dataset.html">
+
+ <a href="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="news20_pa.html">
+
+ <a href="news20_pa.html">
+
+
+ <b>6.1.3.</b>
+
+ PA
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="6.1.4" data-path="news20_scw.html">
+
+ <a href="news20_scw.html">
+
+
+ <b>6.1.4.</b>
+
+ CW, AROW, SCW
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="6.1.5"
data-path="news20_ensemble.html">
+
+ <a href="news20_ensemble.html">
+
+
+ <b>6.1.5.</b>
+
+ Ensemble learning
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="6.1.6"
data-path="news20_one-vs-the-rest.html">
+
+ <a href="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="iris.html">
+
+ <a href="iris.html">
+
+
+ <b>6.2.</b>
+
+ Iris Tutorial
+
+ </a>
+
+
+
+ <ul class="articles">
+
+
+ <li class="chapter " data-level="6.2.1" data-path="iris_dataset.html">
+
+ <a href="iris_dataset.html">
+
+
+ <b>6.2.1.</b>
+
+ Data preparation
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter active" data-level="6.2.2"
data-path="iris_scw.html">
+
+ <a href="iris_scw.html">
+
+
+ <b>6.2.2.</b>
+
+ SCW
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="6.2.3"
data-path="iris_randomforest.html">
+
+ <a href="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="news20_dataset.html">
+
+ <a href="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="../anomaly/lof.html">
+
+ <a href="../anomaly/lof.html">
+
+
+ <b>9.1.</b>
+
+ Outlier Detection using Local Outlier Factor (LOF)
+
+ </a>
+
+
+
+ </li>
+
+
+
+
+ <li class="header">Part X - External References</li>
+
+
+
+ <li class="chapter " data-level="10.1" >
+
+ <a target="_blank"
href="https://github.com/maropu/hivemall-spark";>
+
+
+ <b>10.1.</b>
+
+ Hivemall on Apache Spark
+
+ </a>
+
+
+
+ </li>
+
+ <li class="chapter " data-level="10.2" >
+
+ <a target="_blank"
href="https://github.com/daijyc/hivemall/wiki/PigHome";>
+
+
+ <b>10.2.</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=".." >SCW</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">
+
+ <p><em>NOTE: RandomForest is being supported
from Hivemall v0.4 or later.</em></p>
+<h1 id="dataset">Dataset</h1>
+<ul>
+<li><a href="https://archive.ics.uci.edu/ml/datasets/Iris";
target="_blank">https://archive.ics.uci.edu/ml/datasets/Iris</a></li>
+</ul>
+<pre><code>Attribute Information:
+ 1. sepal length in cm
+ 2. sepal width in cm
+ 3. petal length in cm
+ 4. petal width in cm
+ 5. class:
+ -- Iris Setosa
+ -- Iris Versicolour
+ -- Iris Virginica
+</code></pre><h1 id="table-preparation">Table preparation</h1>
+<pre><code class="lang-sql"><span class="hljs-keyword">create</span> <span
class="hljs-keyword">database</span> iris;
+<span class="hljs-keyword">use</span> iris;
+
+<span class="hljs-keyword">create</span> <span
class="hljs-keyword">external</span> <span class="hljs-keyword">table</span>
<span class="hljs-keyword">raw</span> (
+ sepal_length <span class="hljs-built_in">int</span>,
+ sepal_width <span class="hljs-built_in">int</span>,
+ petal_length <span class="hljs-built_in">int</span>,
+ petak_width <span class="hljs-built_in">int</span>,
+ <span class="hljs-keyword">class</span> <span
class="hljs-keyword">string</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">LINES</span> <span
class="hljs-keyword">TERMINATED</span> <span class="hljs-keyword">BY</span>
<span class="hljs-string">'\n'</span>
+<span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span>
TEXTFILE LOCATION <span
class="hljs-string">'/dataset/iris/raw'</span>;
+
+$ sed '/^$/d' iris.data | hadoop fs -put -
/dataset/iris/raw/iris.data
+</code></pre>
+<pre><code class="lang-sql"><span class="hljs-keyword">create</span> <span
class="hljs-keyword">table</span> label_mapping
+<span class="hljs-keyword">as</span>
+<span class="hljs-keyword">select</span>
+ <span class="hljs-keyword">class</span>,
+ <span class="hljs-keyword">rank</span> - <span class="hljs-number">1</span>
<span class="hljs-keyword">as</span> label
+<span class="hljs-keyword">from</span> (
+<span class="hljs-keyword">select</span>
+ <span class="hljs-keyword">distinct</span> <span
class="hljs-keyword">class</span>,
+ <span class="hljs-keyword">dense_rank</span>() <span
class="hljs-keyword">over</span> (<span class="hljs-keyword">order</span> <span
class="hljs-keyword">by</span> <span class="hljs-keyword">class</span>) <span
class="hljs-keyword">as</span> <span class="hljs-keyword">rank</span>
+<span class="hljs-keyword">from</span>
+ <span class="hljs-keyword">raw</span>
+) t
+;
+</code></pre>
+<pre><code class="lang-sql"><span class="hljs-keyword">create</span> <span
class="hljs-keyword">table</span> training
+<span class="hljs-keyword">as</span>
+<span class="hljs-keyword">select</span>
+ <span class="hljs-keyword">rowid</span>() <span
class="hljs-keyword">as</span> <span class="hljs-keyword">rowid</span>,
+ <span class="hljs-built_in">array</span>(t1.sepal_length, t1.sepal_width,
t1.petal_length, t1.petak_width) <span class="hljs-keyword">as</span> features,
+ t2.label
+<span class="hljs-keyword">from</span>
+ <span class="hljs-keyword">raw</span> t1
+ <span class="hljs-keyword">JOIN</span> label_mapping t2 <span
class="hljs-keyword">ON</span> (t1.<span class="hljs-keyword">class</span> =
t2.<span class="hljs-keyword">class</span>)
+;
+</code></pre>
+<h1 id="training">Training</h1>
+<p><code>train_randomforest_classifier</code> takes a dense
<code>features</code> in double[] and a <code>label</code> starting from 0.</p>
+<pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span
class="hljs-keyword">TABLE</span> <span class="hljs-keyword">model</span>
+<span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span>
SEQUENCEFILE
+<span class="hljs-keyword">AS</span>
+<span class="hljs-keyword">select</span>
+ train_randomforest_classifier(features, label)
+ <span class="hljs-comment">-- hivemall v0.4.1-alpha.2 and before</span>
+ <span class="hljs-comment">-- train_randomforest_classifier(features, label)
as (pred_model, var_importance, oob_errors, oob_tests)</span>
+ <span class="hljs-comment">-- hivemall v0.4.1 and later</span>
+ <span class="hljs-comment">-- train_randomforest_classifier(features, label)
as (model_id, model_type, pred_model, var_importance, oob_errors,
oob_tests)</span>
+<span class="hljs-keyword">from</span>
+ training;
+</code></pre>
+<p><em>Note: The default TEXTFILE should not be used for model table when
using Javascript output through "-output javascript" option.</em></p>
+<pre><code>hive> desc model;
+model_id int
+model_type int
+pred_model string
+var_importance array<double>
+oob_errors int
+oob_tests int
+</code></pre><h2 id="training-options">Training options</h2>
+<p>"-help" option shows usage of the function.</p>
+<pre><code>select train_randomforest_classifier(features, label,
"-help") from training;
+
+> FAILED: UDFArgumentException
+usage: train_randomforest_classifier(double[] features, int label [,
+ string options]) - Returns a relation consists of <int model_id,
+ int model_type, string pred_model, array<double> var_importance,
+ int oob_errors, int oob_tests> [-attrs <arg>] [-depth
<arg>]
+ [-disable_compression] [-help] [-leafs <arg>] [-output
<arg>]
+ [-rule <arg>] [-seed <arg>] [-splits <arg>] [-trees
<arg>] [-vars
+ <arg>]
+ -attrs,--attribute_types <arg> Comma separated attribute types (Q for
+ quantitative variable and C for
+ categorical variable. e.g., [Q,C,Q,C])
+ -depth,--max_depth <arg> The maximum number of the tree depth
+ [default: Integer.MAX_VALUE]
+ -disable_compression Whether to disable compression of the
+ output script [default: false]
+ -help Show function help
+ -leafs,--max_leaf_nodes <arg> The maximum number of leaf nodes
+ [default: Integer.MAX_VALUE]
+ -output,--output_type <arg> The output type (serialization/ser or
+ opscode/vm or javascript/js) [default:
+ serialization]
+ -rule,--split_rule <arg> Split algorithm [default: GINI,
ENTROPY]
+ -seed <arg> seed value in long [default: -1
+ (random)]
+ -splits,--min_split <arg> A node that has greater than or equals
+ to `min_split` examples will split
+ [default: 2]
+ -trees,--num_trees <arg> The number of trees for each task
+ [default: 50]
+ -vars,--num_variables <arg> The number of random selected features
+ [default: ceil(sqrt(x[0].length))].
+ int(num_variables * x[0].length) is
+ considered if num_variable is (0,1]
+</code></pre><p><em>Caution: "-num_trees" controls the number of
trees for each task, not the total number of trees.</em></p>
+<h3 id="parallelize-training">Parallelize Training</h3>
+<p>To parallelize RandomForest training, you can use UNION ALL as follows:</p>
+<pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span
class="hljs-keyword">TABLE</span> <span class="hljs-keyword">model</span>
+<span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span>
SEQUENCEFILE
+<span class="hljs-keyword">AS</span>
+<span class="hljs-keyword">select</span>
+ train_randomforest_classifier(features, label, <span
class="hljs-string">'-trees 25'</span>)
+<span class="hljs-keyword">from</span>
+ training
+<span class="hljs-keyword">UNION</span> ALL
+<span class="hljs-keyword">select</span>
+ train_randomforest_classifier(features, label, <span
class="hljs-string">'-trees 25'</span>)
+<span class="hljs-keyword">from</span>
+ training
+;
+</code></pre>
+<h3 id="learning-stats">Learning stats</h3>
+<p><a
href="https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#varimp";
target="_blank"><code>Variable importance</code></a> and <a
href="https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm#ooberr";
target="_blank"><code>Out Of Bag (OOB) error rate</code></a> of RandomForest
can be shown as follows:</p>
+<pre><code class="lang-sql"><span class="hljs-keyword">select</span>
+ array_sum(var_importance) <span class="hljs-keyword">as</span>
var_importance,
+ <span class="hljs-keyword">sum</span>(oob_errors) / <span
class="hljs-keyword">sum</span>(oob_tests) <span class="hljs-keyword">as</span>
oob_err_rate
+<span class="hljs-keyword">from</span>
+ <span class="hljs-keyword">model</span>;
+</code></pre>
+<blockquote>
+<p>[2.81010338879605,0.4970357753626371,23.790369091407698,14.315316390235273]
0.05333333333333334</p>
+</blockquote>
+<h3 id="output-prediction-model-by-javascipt">Output prediction model by
Javascipt</h3>
+<pre><code class="lang-sql"><span class="hljs-keyword">CREATE</span> <span
class="hljs-keyword">TABLE</span> model_javascript
+<span class="hljs-keyword">STORED</span> <span class="hljs-keyword">AS</span>
SEQUENCEFILE
+<span class="hljs-keyword">AS</span>
+<span class="hljs-keyword">select</span>
train_randomforest_classifier(features, label, <span
class="hljs-string">"-output_type js -disable_compression"</span>)
+<span class="hljs-keyword">from</span> training;
+
+<span class="hljs-keyword">select</span> <span
class="hljs-keyword">model</span> <span class="hljs-keyword">from</span>
model_javascript <span class="hljs-keyword">limit</span> <span
class="hljs-number">1</span>;
+</code></pre>
+<pre><code class="lang-js"><span class="hljs-keyword">if</span>(x[<span
class="hljs-number">3</span>] <= <span class="hljs-number">0.5</span>) {
+ <span class="hljs-number">0</span>;
+} <span class="hljs-keyword">else</span> {
+ <span class="hljs-keyword">if</span>(x[<span class="hljs-number">2</span>]
<= <span class="hljs-number">4.5</span>) {
+ <span class="hljs-keyword">if</span>(x[<span class="hljs-number">3</span>]
<= <span class="hljs-number">1.5</span>) {
+ <span class="hljs-keyword">if</span>(x[<span
class="hljs-number">0</span>] <= <span class="hljs-number">4.5</span>) {
+ <span class="hljs-number">1</span>;
+ } <span class="hljs-keyword">else</span> {
+ <span class="hljs-keyword">if</span>(x[<span
class="hljs-number">0</span>] <= <span class="hljs-number">5.5</span>) {
+ <span class="hljs-number">1</span>;
+ } <span class="hljs-keyword">else</span> {
+ <span class="hljs-keyword">if</span>(x[<span
class="hljs-number">1</span>] <= <span class="hljs-number">2.5</span>) {
+ <span class="hljs-number">1</span>;
+ } <span class="hljs-keyword">else</span> {
+ <span class="hljs-number">1</span>;
+ }
+ }
+ }
+ } <span class="hljs-keyword">else</span> {
+ <span class="hljs-number">2</span>;
+ }
+ } <span class="hljs-keyword">else</span> {
+ <span class="hljs-keyword">if</span>(x[<span class="hljs-number">3</span>]
<= <span class="hljs-number">1.5</span>) {
+ <span class="hljs-number">2</span>;
+ } <span class="hljs-keyword">else</span> {
+ <span class="hljs-number">2</span>;
+ }
+ }
+}
+</code></pre>
+<h1 id="prediction">Prediction</h1>
+<pre><code class="lang-sql"><span class="hljs-keyword">set</span>
hivevar:classification=<span class="hljs-literal">true</span>;
+<span class="hljs-keyword">set</span> hive.<span
class="hljs-keyword">auto</span>.<span
class="hljs-keyword">convert</span>.<span
class="hljs-keyword">join</span>=<span class="hljs-literal">true</span>;
+<span class="hljs-keyword">set</span> hive.mapjoin.optimized.hashtable=<span
class="hljs-literal">false</span>;
+
+<span class="hljs-keyword">create</span> <span
class="hljs-keyword">table</span> predicted_vm
+<span class="hljs-keyword">as</span>
+<span class="hljs-keyword">SELECT</span>
+ <span class="hljs-keyword">rowid</span>,
+ rf_ensemble(predicted) <span class="hljs-keyword">as</span> predicted
+<span class="hljs-keyword">FROM</span> (
+ <span class="hljs-keyword">SELECT</span>
+ <span class="hljs-keyword">rowid</span>,
+ <span class="hljs-comment">-- hivemall v0.4.1-alpha.2 and before</span>
+ <span class="hljs-comment">-- tree_predict(p.model, t.features,
${classification}) as predicted</span>
+ <span class="hljs-comment">-- hivemall v0.4.1 and later</span>
+ tree_predict(p.model_id, p.model_type, p.pred_model, t.features,
${classification}) <span class="hljs-keyword">as</span> predicted
+ <span class="hljs-keyword">FROM</span>
+ <span class="hljs-keyword">model</span> p
+ <span class="hljs-keyword">LEFT</span> <span
class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span> <span
class="hljs-comment">-- CROSS JOIN</span>
+ training t
+) t1
+<span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span>
+ <span class="hljs-keyword">rowid</span>
+;
+</code></pre>
+<p><em>Note: Javascript outputs can be evaluated by
<code>js_tree_predict</code>.</em></p>
+<h3 id="parallelize-prediction">Parallelize Prediction</h3>
+<p>The following query runs predictions in N-parallel. It would reduce elapsed
time for prediction almost by N.</p>
+<pre><code class="lang-sql"><span class="hljs-keyword">SET</span>
hivevar:classification=<span class="hljs-literal">true</span>;
+<span class="hljs-keyword">set</span> hive.<span
class="hljs-keyword">auto</span>.<span
class="hljs-keyword">convert</span>.<span
class="hljs-keyword">join</span>=<span class="hljs-literal">true</span>;
+<span class="hljs-keyword">SET</span> hive.mapjoin.optimized.hashtable=<span
class="hljs-literal">false</span>;
+<span class="hljs-keyword">SET</span> mapred.reduce.tasks=<span
class="hljs-number">8</span>;
+
+<span class="hljs-keyword">create</span> <span
class="hljs-keyword">table</span> predicted_vm
+<span class="hljs-keyword">as</span>
+<span class="hljs-keyword">SELECT</span>
+ <span class="hljs-keyword">rowid</span>,
+ rf_ensemble(predicted) <span class="hljs-keyword">as</span> predicted
+<span class="hljs-keyword">FROM</span> (
+ <span class="hljs-keyword">SELECT</span>
+ t.<span class="hljs-keyword">rowid</span>,
+ <span class="hljs-comment">-- hivemall v0.4.1-alpha.2 and before</span>
+ <span class="hljs-comment">-- tree_predict(p.pred_model, t.features,
${classification}) as predicted</span>
+ <span class="hljs-comment">-- hivemall v0.4.1 and later</span>
+ tree_predict(p.model_id, p.model_type, p.pred_model, t.features,
${classification}) <span class="hljs-keyword">as</span> predicted
+ <span class="hljs-keyword">FROM</span> (
+ <span class="hljs-keyword">SELECT</span> model_id, model_type, pred_model
+ <span class="hljs-keyword">FROM</span> <span
class="hljs-keyword">model</span>
+ <span class="hljs-keyword">DISTRIBUTE</span> <span
class="hljs-keyword">BY</span> <span class="hljs-keyword">rand</span>(<span
class="hljs-number">1</span>)
+ ) p
+ <span class="hljs-keyword">LEFT</span> <span
class="hljs-keyword">OUTER</span> <span class="hljs-keyword">JOIN</span>
training t
+) t1
+<span class="hljs-keyword">group</span> <span class="hljs-keyword">by</span>
+ <span class="hljs-keyword">rowid</span>
+;
+</code></pre>
+<h1 id="evaluation">Evaluation</h1>
+<pre><code class="lang-sql">select count(1) from training;
+> 150
+
+set hivevar:total_cnt=150;
+
+WITH t1 as (
+SELECT
+ t.rowid,
+ t.label as actual,
+ p.predicted.label as predicted
+FROM
+ predicted_vm p
+ LEFT OUTER JOIN training t ON (t.rowid = p.rowid)
+)
+SELECT
+ count(1) / ${total_cnt}
+FROM
+ t1
+WHERE
+ actual = predicted
+;
+</code></pre>
+<blockquote>
+<p>0.9533333333333334</p>
+</blockquote>
+
+
+ </section>
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