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- 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="scaling.html"> - - <a href="scaling.html"> - - - <b>3.1.</b> - - Feature Scaling - - </a> - - - - </li> - - <li class="chapter " data-level="3.2" data-path="hashing.html"> - - <a href="hashing.html"> - - - <b>3.2.</b> - - Feature Hashing - - </a> - - - - </li> - - <li class="chapter " data-level="3.3" data-path="tfidf.html"> - - <a href="tfidf.html"> - - - <b>3.3.</b> - - TF-IDF calculation - - </a> - - - - </li> - - <li class="chapter " data-level="3.4" data-path="ft_trans.html"> - - <a href="ft_trans.html"> - - - <b>3.4.</b> - - FEATURE TRANSFORMATION - - </a> - - - - <ul class="articles"> - - - <li class="chapter active" data-level="3.4.1" data-path="vectorizer.html"> - - <a href="vectorizer.html"> - - - <b>3.4.1.</b> - - Vectorize Features - - </a> - - - - </li> - - <li class="chapter " data-level="3.4.2" data-path="quantify.html"> - - <a href="quantify.html"> - - - <b>3.4.2.</b> - - Quantify non-number features - - </a> - - - - </li> - - - </ul> - - </li> - - <li class="chapter " data-level="3.5" data-path="feature_selection.html"> - - <a href="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> - - - - <ul class="articles"> - - - <li class="chapter " data-level="4.1.1" data-path="../eval/auc.html"> - - <a href="../eval/auc.html"> - - - <b>4.1.1.</b> - - Area Under the ROC Curve - - </a> - - - - </li> - - - </ul> - - </li> - - <li class="chapter " data-level="4.2" data-path="../eval/rank.html"> - - <a href="../eval/rank.html"> - - - <b>4.2.</b> - - Ranking Measures - - </a> - - - - </li> - - <li class="chapter " data-level="4.3" data-path="../eval/datagen.html"> - - <a href="../eval/datagen.html"> - - - <b>4.3.</b> - - Data Generation - - </a> - - - - <ul class="articles"> - - - <li class="chapter " data-level="4.3.1" data-path="../eval/lr_datagen.html"> - - <a href="../eval/lr_datagen.html"> - - - <b>4.3.1.</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="../anomaly/lof.html"> - - <a href="../anomaly/lof.html"> - - - <b>9.1.</b> - - Outlier Detection using Local Outlier Factor (LOF) - - </a> - - - - </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> - - - - - <li class="header">Part X - Clustering</li> - - - - <li class="chapter " data-level="10.1" data-path="../clustering/lda.html"> - - <a href="../clustering/lda.html"> - - - <b>10.1.</b> - - Latent Dirichlet Allocation - - </a> - - - - </li> - - <li class="chapter " data-level="10.2" data-path="../clustering/plsa.html"> - - <a href="../clustering/plsa.html"> - - - <b>10.2.</b> - - Probabilistic Latent Semantic Analysis - - </a> - - - - </li> - - - - - <li class="header">Part XI - GeoSpatial functions</li> - - - - <li class="chapter " data-level="11.1" data-path="../geospatial/latlon.html"> - - <a href="../geospatial/latlon.html"> - - - <b>11.1.</b> - - Lat/Lon functions - - </a> - - - - </li> - - - - - <li class="header">Part XII - Hivemall on Spark</li> - - - - <li class="chapter " data-level="12.1" data-path="../spark/getting_started/"> - - <a href="../spark/getting_started/"> - - - <b>12.1.</b> - - Getting Started - - </a> - - - - <ul class="articles"> - - - <li class="chapter " data-level="12.1.1" data-path="../spark/getting_started/installation.html"> - - <a href="../spark/getting_started/installation.html"> - - - <b>12.1.1.</b> - - Installation - - </a> - - - - </li> - - - </ul> - - </li> - - <li class="chapter " data-level="12.2" data-path="../spark/binaryclass/"> - - <a href="../spark/binaryclass/"> - - - <b>12.2.</b> - - Binary Classification - - </a> - - - - <ul class="articles"> - - - <li class="chapter " data-level="12.2.1" data-path="../spark/binaryclass/a9a_df.html"> - - <a href="../spark/binaryclass/a9a_df.html"> - - - <b>12.2.1.</b> - - a9a Tutorial for DataFrame - - </a> - - - - </li> - - - </ul> - - </li> - - <li class="chapter " data-level="12.3" data-path="../spark/binaryclass/"> - - <a href="../spark/binaryclass/"> - - - <b>12.3.</b> - - Regression - - </a> - - - - <ul class="articles"> - - - <li class="chapter " data-level="12.3.1" data-path="../spark/regression/e2006_df.html"> - - <a href="../spark/regression/e2006_df.html"> - - - <b>12.3.1.</b> - - E2006-tfidf regression Tutorial for DataFrame - - </a> - - - - </li> - - - </ul> - - </li> - - <li class="chapter " data-level="12.4" data-path="../spark/misc/misc.html"> - - <a href="../spark/misc/misc.html"> - - - <b>12.4.</b> - - Generic features - - </a> - - - - <ul class="articles"> - - - <li class="chapter " data-level="12.4.1" data-path="../spark/misc/topk_join.html"> - - <a href="../spark/misc/topk_join.html"> - - - <b>12.4.1.</b> - - Top-k Join processing - - </a> - - - - </li> - - <li class="chapter " data-level="12.4.2" data-path="../spark/misc/functions.html"> - - <a href="../spark/misc/functions.html"> - - - <b>12.4.2.</b> - - Other utility functions - - </a> - - - - </li> - - - </ul> - - </li> - - - - - <li class="header">Part XIII - Hivemall on Docker</li> - - - - <li class="chapter " data-level="13.1" data-path="../docker/getting_started.html"> - - <a href="../docker/getting_started.html"> - - - <b>13.1.</b> - - Getting Started - - </a> - - - - </li> - - - - - <li class="header">Part XIV - External References</li> - - - - <li class="chapter " data-level="14.1" > - - <a target="_blank" href="https://github.com/maropu/hivemall-spark";> - - - <b>14.1.</b> - - Hivemall on Apache Spark - - </a> - - - - </li> - - <li class="chapter " data-level="14.2" > - - <a target="_blank" href="https://github.com/daijyc/hivemall/wiki/PigHome";> - - - <b>14.2.</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=".." >Vectorize Features</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. ---> -<h2 id="feature-vectorizer">Feature Vectorizer</h2> -<p><code>array<string> vectorize_feature(array<string> featureNames, ...)</code> is useful to generate a feature vector for each row, from a table.</p> -<pre><code class="lang-sql">select vectorize_features(array("a","b"),"0.2","0.3") from dual; ->["a:0.2","b:0.3"] - --- avoid zero weight -select vectorize_features(array("a","b"),"0.2",0) from dual; -> ["a:0.2"] - --- true boolean value is treated as 1.0 (a categorical value w/ its column name) -select vectorize_features(array("a","b","bool"),0.2,0.3,true) from dual; -> ["a:0.2","b:0.3","bool:1.0"] - --- an example to generate feature vectors from table -select * from dual; -> 1 -select vectorize_features(array("a"),*) from dual; -> ["a:1.0"] - --- has categorical feature -select vectorize_features(array("a","b","wheather"),"0.2","0.3","sunny") from dual; -> ["a:0.2","b:0.3","whether#sunny"] -</code></pre> -<pre><code class="lang-sql">select - id, - vectorize_features( - array("age","job","marital","education","default","balance","housing","loan","contact","day","month","duration","campaign","pdays","previous","poutcome"), - age,job,marital,education,default,balance,housing,loan,contact,day,month,duration,campaign,pdays,previous,poutcome - ) as features, - y -from - train -limit 2; - -> 1 ["age:39.0","job#blue-collar","marital#married","education#secondary","default#no","balance:1756.0","housing#yes","loan#no","contact#cellular","day:3.0","month#apr","duration:939.0","campaign:1.0","pdays:-1.0","poutcome#unknown"] 1 -> 2 ["age:51.0","job#entrepreneur","marital#married","education#primary","default#no","balance:1443.0","housing#no","loan#no","contact#cellular","day:18.0","month#feb","duration:172.0","campaign:10.0","pdays:-1.0","poutcome#unknown"] 1 -</code></pre> -<p><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":"Vectorize Features","level":"3.4.1","depth":2,"next":{"title":"Quantify non-number features","level":"3.4.2","depth":2,"path":"ft_engineering/quantify.md","ref":"ft_engineering/quantify.md","articles":[]},"previous":{"title":"FEATURE TRANSFORMATION","level":"3.4","depth":1,"path":"ft_engineering/ft_trans.md","ref":"ft_engineering/ft_trans.md","articles":[{"title":"Vectorize Features","level":"3.4.1","depth":2,"path":"ft_engineering/vectorizer.md","ref":"ft_engineering/vectorizer.md","articles":[]},{"title":"Quantify non-number features","level":"3.4.2","depth":2,"path":"ft_engineering/quantify.md","ref":"ft_engineering/quantify.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":{"header":1,"maxdepth":3,"mindepth":1,"notoc":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":"; 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http://git-wip-us.apache.org/repos/asf/incubator-hivemall-site/blob/15b9991a/userguide/geospatial/latlon.html ---------------------------------------------------------------------- diff --git a/userguide/geospatial/latlon.html b/userguide/geospatial/latlon.html index c991bda..37e2eca 100644 --- a/userguide/geospatial/latlon.html +++ b/userguide/geospatial/latlon.html @@ -568,14 +568,14 @@ </li> - <li class="chapter " data-level="3.3" data-path="../ft_engineering/tfidf.html"> + <li class="chapter " data-level="3.3" data-path="../ft_engineering/selection.html"> - <a href="../ft_engineering/tfidf.html"> + <a href="../ft_engineering/selection.html"> <b>3.3.</b> - TF-IDF calculation + Feature Selection </a> @@ -583,30 +583,29 @@ </li> - <li class="chapter " data-level="3.4" data-path="../ft_engineering/ft_trans.html"> + <li class="chapter " data-level="3.4" data-path="../ft_engineering/binning.html"> - <a href="../ft_engineering/ft_trans.html"> + <a href="../ft_engineering/binning.html"> <b>3.4.</b> - FEATURE TRANSFORMATION + Feature Binning </a> - <ul class="articles"> - + </li> - <li class="chapter " data-level="3.4.1" data-path="../ft_engineering/vectorizer.html"> + <li class="chapter " data-level="3.5" data-path="../ft_engineering/tfidf.html"> - <a href="../ft_engineering/vectorizer.html"> + <a href="../ft_engineering/tfidf.html"> - <b>3.4.1.</b> + <b>3.5.</b> - Vectorize Features + TF-IDF Calculation </a> @@ -614,34 +613,45 @@ </li> - <li class="chapter " data-level="3.4.2" data-path="../ft_engineering/quantify.html"> + <li class="chapter " data-level="3.6" data-path="../ft_engineering/ft_trans.html"> - <a href="../ft_engineering/quantify.html"> + <a href="../ft_engineering/ft_trans.html"> - <b>3.4.2.</b> + <b>3.6.</b> - Quantify non-number features + FEATURE TRANSFORMATION </a> - </li> + <ul class="articles"> + + <li class="chapter " data-level="3.6.1" data-path="../ft_engineering/vectorization.html"> + + <a href="../ft_engineering/vectorization.html"> + + + <b>3.6.1.</b> + + Feature Vectorization + + </a> + - </ul> </li> - <li class="chapter " data-level="3.5" data-path="../ft_engineering/feature_selection.html"> + <li class="chapter " data-level="3.6.2" data-path="../ft_engineering/quantify.html"> - <a href="../ft_engineering/feature_selection.html"> + <a href="../ft_engineering/quantify.html"> - <b>3.5.</b> + <b>3.6.2.</b> - Feature selection + Quantify non-number features </a> @@ -650,6 +660,11 @@ </li> + </ul> + + </li> + + <li class="header">Part IV - Evaluation</li> @@ -2163,7 +2178,7 @@ Apache Hivemall is an effort undergoing incubation at The Apache Software Founda <script> var gitbook = gitbook || []; 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