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It provides a number of ease of use machine learning functionalities through the Apache Hive UDF/UDAF/UDTF interface.\n\n\n\n\nApache Hivemall offers a variety of functionalities: regression, classification, recommendation, anomaly detection, k-nearest neighbor, and feature engineering. It also supports state-of-the-art machine learning algorithms such as Soft Confidence Weighted, Adaptive Regularization of Weight Vectors, Factorization Machines, and AdaDelta. \nArchitecture\nApache Hivemall is mainly designed to run on Apache Hive but it also supports Apache Pig and Apache Spark for the runtime.\nThus, it can be considered as a cross platform library for machine learning; prediction models built by a batch query of Apache Hive can be used on Apache Spark/Pig, and conversely, prediction models build by Apache Spark can be used from Apache Hive/Pig.\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"getting_started/":{"url":"getting_started/","title":"Getting Started","keywords":"","body":"\nSummary\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"getting_started/installation.html":{"url":"getting_started/installation.html","title":"Installation","keywords":"","body":"\nPrerequisites\n\nHadoop v2.4.0 or later\nHive v0.13 or later\nJava 7 or later\nhivemall-all-xxx.jar\ndefine-all.hive (of a given version, e.g., v0.5.0)\n\n Note\nInstallation\nAdd the following two lines to your $HOME/.hiverc file.\nadd jar /home/myui/tmp/hivemall-all-xxx.jar;\nsource /home/myui/tmp/define-all.hive;\nThis automatically loads all Hivemall functions every time you start a Hive session. Alternatively, you can run the following command each time.\n$ hive\nadd jar /tmp/hivemall-all-xxx.jar;\nsource /tmp/define-all.hive;\nOther choices\nYou can also run Hivemall on the following platforms:\n\nApache Spark\nApache Pig\nApache Hive on Docker for testing\n\nBuild from Source\n$ git clone https://github.com/apache/incubator-hivemall.git\n$ cd incubator-hivemall\n$ bin/build.sh\n\nThen, you can find Hivemall jars in ./target.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"getting_started/permanent-functions.html":{"url":"getting_started/permanent-functions.html","title":"Install as permanent functions","keywords":"","body":"\nHive v0.13 or later supports permanent functions that lives across sessions.\nPermanent functions are useful when you are using Hive through Hiveserver or to avoid hivemall installation for each session.\n\n\n\nPut hivemall jar to HDFS\nCreate permanent functions\nConfirm functions\n\n\n\nPut hivemall jar to HDFS\nFirst, put hivemall jar to HDFS as follows:\nhadoop fs -mkdir -p /apps/hivemall\nhadoop fs -put hivemall-all-xxx.jar /apps/hivemall\n\nCreate permanent functions\nThe following is an auxiliary step to define functions for hivemall databases, not for the default database.\nCREATE DATABASE IF NOT EXISTS hivemall;\nUSE hivemall;\n\nThen, create permanent functions using define-all-as-permanent.hive, a DDL script to define permanent UDFs.\nset hivevar:hivemall_jar=hdfs:///apps/hivemall/hivemall-all-xxx.jar.jar;\n\nsource /tmp/define-all-as-permanent.hive;\n\nConfirm functions\nshow functions \"hivemall.*\";\n\n> hivemall.adadelta\n> hivemall.adagrad\n\n CautionYou need to specify \"hivemall.\" prefix to call hivemall UDFs in your queries if UDFs are loaded into non-default scheme, in this case hivemall.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"getting_started/input-format.html":{"url":"getting_started/input-format.html","title":"Input Format","keywords":"","body":"\nThis page explains the input format of training data in Hivemall. \nHere, we use EBNF-like notation for describing the format.\n\n\n\nInput Format for Classification\nFeatures format (for classification and regression)\nQuantitative and Categorical variables\nBias/Dummy Variable in features\nFeature hashing\nFeature Normalization\n\n\nLabel format in Binary Classification\nLabel format in Multi-class Classification\nInput format in Regression\nTarget in Logistic Regression\n\n\nHelper functions\nQuantitative Features\nCategorical Features\nPreparing training data table\n\n\n\n\n\nInput Format for Classification\nThe classifiers of Hivemall takes 2 (or 3) arguments: features, label, and options (a.k.a. hyperparameters). The first two arguments of training functions represents training examples. \nIn Statistics, features and label are called Explanatory variable and Response Variable, respectively.\nFeatures format (for classification and regression)\nThe format of features is common between (binary and multi-class) classification and regression.\nHivemall accepts ARRAY for the type of features column.\nHivemall uses a sparse data format (cf. Compressed Row Storage) which is similar to LIBSVM and Vowpal Wabbit.\nThe format of each feature in an array is as follows:\nfeature ::= : or \nEach element of index or weight then accepts the following format:\nindex ::= \nweight ::= \nThe index are usually a number (INT or BIGINT) starting from 1. \nHere is an instance of a features.\n10:3.4  123:0.5  34567:0.231\nNote: As mentioned later, index \"0\" is reserved for a Bias/Dummy variable.\nIn addition to numbers, you can use a TEXT value for an index. For example, you can use array(\"height:1.5\", \"length:2.0\") for the features.\n\"height:1.5\" \"length:2.0\"\nQuantitative and Categorical variables\nA quantitative variable must have an index entry.\nHivemall (v0.3.1 or later) provides add_feature_index function which is useful for adding indexes to quantitative variables. \nselect add_feature_index(array(3,4.0,5)) from dual;\n\n\n[\"1:3.0\",\"2:4.0\",\"3:5.0\"]\n\nYou can omit specifying weight for each feature e.g. for Categorical variables as follows:\nfeature ::= \nNote 1.0 is used for the weight when omitting weight. \nBias/Dummy Variable in features\nNote that \"0\" is reserved for a Bias variable (called dummy variable in Statistics). \nThe add_bias function is Hivemall appends \"0:1.0\" as an element of array in features.\nFeature hashing\nHivemall supports feature hashing/hashing trick through mhash function.\nThe mhash function takes a feature (i.e., index) of TEXT format and generates a hash number of a range from 1 to 2^24 (=16777216) by the default setting.\nFeature hashing is useful where the dimension of feature vector (i.e., the number of elements in features) is so large. Consider applying mhash function) when a prediction model does not fit in memory and OutOfMemory exception happens.\nIn general, you don't need to use mhash when the dimension of feature vector is less than 16777216.\nIf feature index is very long TEXT (e.g., \"xxxxxxx-yyyyyy-weight:55.3\") and uses huge memory spaces, consider using mhash as follows:\n-- feature is v0.3.2 or before\nconcat(mhash(extract_feature(\"xxxxxxx-yyyyyy-weight:55.3\")), \":\", extract_weight(\"xxxxxxx-yyyyyy-weight:55.3\"))\n\n-- feature is v0.3.2-1 or later\nfeature(mhash(extract_feature(\"xxxxxxx-yyyyyy-weight:55.3\")), extract_weight(\"xxxxxxx-yyyyyy-weight:55.3\"))\n\n\n43352:55.3\n\nFeature Normalization\nFeature (weight) normalization is important in machine learning. Please refer this article for detail.\n\nLabel format in Binary Classification\nThe label must be an INT typed column and the values are positive (+1) or negative (-1) as follows:\n ::= 1 | -1\nAlternatively, you can use the following format that represents 1 for a positive example and 0 for a negative example: \n ::= 0 | 1\nLabel format in Multi-class Classification\nYou can used any PRIMITIVE type in the multi-class label.  \n ::= \nTypically, the type of label column will be INT, BIGINT, or TEXT.\n\nInput format in Regression\nIn regression, response/predictor variable (we denote it as target) is a real number.\nBefore Hivemall v0.3, we accepts only FLOAT type for target.\n ::= \nYou need to explicitly cast a double value of target to float as follows:\nCAST(target as FLOAT)\n\nOn the other hand, Hivemall v0.3 or later accepts double compatible numbers in target.\n ::= \nTarget in Logistic Regression\nLogistic regression is actually a binary classification scheme while it can produce probabilities of positive of a training example. \nA target value of a training input must be in range 0.0 to 1.0, specifically 0.0 or 1.0.\n\nHelper functions\n-- hivemall v0.3.2 and before\nselect concat(\"weight\",\":\",55.0);\n\n-- hivemall v0.3.2-1 and later\nselect feature(\"weight\", 55.0);\n\n\nweight:55.0\n\nselect extract_feature(\"weight:55.0\"), extract_weight(\"weight:55.0\");\n\n\nweight | 55.0\n\n-- hivemall v0.4.0 and later\nselect feature_index(array(\"10:0.2\",\"7:0.3\",\"9\"));\n\n\n[10,7,9]\n\nselect \n  convert_label(-1), convert_label(1), convert_label(0.0f), convert_label(1.0f)\nfrom \n  dual;\n\n\n0.0f | 1.0f | -1 | 1\n\nQuantitative Features\narray quantitative_features(array featureNames, feature1, feature2, .. [, const string options]) is a helper function to create sparse quantitative features from a table.\nselect quantitative_features(\n  array(\"apple\",\"height\",\"weight\"),\n  1,180.3,70.2\n  -- ,\"-emit_null\"\n);\n\n\n[\"apple:1.0\",\"height:180.3\",\"weight:70.2\"]\n\nselect quantitative_features(\n  array(\"apple\",\"height\",\"weight\"),\n  1,cast(null as double),70.2\n  ,\"-emit_null\"\n);\n\n\n[\"apple:1.0\",null,\"weight:70.2\"]\n\nCategorical Features\narray categorical_features(array featureNames,  feature1, feature2, .. [, const string options]) is a helper function to create sparse categorical features from a table.\nselect categorical_features(\n  array(\"is_cat\",\"is_dog\",\"is_lion\",\"is_pengin\",\"species\"),\n  1, 0, 1.0, true, \"dog\"\n  -- ,\"-emit_null\"\n);\n\n\n[\"is_cat#1\",\"is_dog#0\",\"is_lion#1.0\",\"is_pengin#true\",\"species#dog\"]\n\nselect categorical_features(\n  array(\"is_cat\",\"is_dog\",\"is_lion\",\"is_pengin\",\"species\"),\n  1, 0, 1.0, true, null\n  ,\"-emit_null\"\n);\n\n\n[\"is_cat#1\",\"is_dog#0\",\"is_lion#1.0\",\"is_pengin#true\",null]\n\nPreparing training data table\nYou can create a training data table as follows:\nselect \n  rowid() as rowid,\n  concat_array(\n    array(\"bias:1.0\"),\n    categorical_features( \n      array(\"id\", \"name\"),\n      id, name\n    ),\n    quantitative_features(\n      array(\"height\", \"weight\"),\n      height, weight\n    )\n  ) as features, \n  click_or_not as label\nfrom\n  table;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"misc/funcs.html":{"url":"misc/funcs.html","title":"List of Functions","keywords":"","body":"\nThis page describes a list of Hivemall functions. See also a list of generic Hivemall functions for more general-purpose functions such as array and map UDFs.\n\n\n\nRegression\nClassification\nBinary classification\nMulticlass classification\n\n\nMatrix factorization\nFactorization machines\nRecommendation\nAnomaly detection\nTopic modeling\nPreprocessing\nData amplification\nFeature binning\nFeature format conversion\nFeature hashing\nFeature paring\nRanking\nFeature scaling\nFeature selection\nFeature transformation and vectorization\n\n\nGeospatial functions\nDistance measures\nLocality-sensitive hashing\nSimilarity measures\nEvaluation\nSketching\nEnsemble learning\nBagging\n\n\nDecision trees and RandomForest\nXGBoost\nTerm Vector Model\nNLP\nOthers\n\n\n\nRegression\n\ntrain_arow_regr(array features, float target [, constant string options]) - a standard AROW (Adaptive Reguralization of Weight Vectors) regressor that uses y - w^Tx for the loss function.SELECT \n  feature,\n  argmin_kld(weight, covar) as weight\nFROM (\n  SELECT \n     train_arow_regr(features,label) as (feature,weight,covar)\n  FROM \n     training_data\n ) t \nGROUP BY feature\n\nReference: K. Crammer, A. Kulesza, and M. Dredze, \"Adaptive Regularization of Weight Vectors\", In Proc. NIPS, 2009.\ntrain_arowe2_regr(array features, float target [, constant string options]) - a refined version of AROW (Adaptive Reguralization of Weight Vectors) regressor that usages adaptive epsilon-insensitive hinge loss |w^t - y| - epsilon * stddev for the loss function\nSELECT \n  feature,\n  argmin_kld(weight, covar) as weight\nFROM (\n  SELECT \n     train_arowe2_regr(features,label) as (feature,weight,covar)\n  FROM \n     training_data\n ) t \nGROUP BY feature\n\n\ntrain_arowe_regr(array features, float target [, constant string options]) - a refined version of AROW (Adaptive Reguralization of Weight Vectors) regressor that usages epsilon-insensitive hinge loss |w^t - y| - epsilon for the loss function\nSELECT \n  feature,\n  argmin_kld(weight, covar) as weight\nFROM (\n  SELECT \n     train_arowe_regr(features,label) as (feature,weight,covar)\n  FROM \n     training_data\n ) t \nGROUP BY feature\n\n\ntrain_pa1_regr(array features, float target [, constant string options]) - PA-1 regressor that returns a relation consists of (int|bigint|string) feature, float weight.\nSELECT \n feature,\n avg(weight) as weight\nFROM \n (SELECT \n     train_pa1_regr(features,label) as (feature,weight)\n  FROM \n     training_data\n ) t \nGROUP BY feature\n\nReference: Koby Crammer et.al., Online Passive-Aggressive Algorithms. Journal of Machine Learning Research, 2006.\n\ntrain_pa1a_regr(array features, float target [, constant string options]) - Returns a relation consists of (int|bigint|string) feature, float weight.\n\ntrain_pa2_regr(array features, float target [, constant string options]) - Returns a relation consists of (int|bigint|string) feature, float weight.\n\ntrain_pa2a_regr(array features, float target [, constant string options]) - Returns a relation consists of (int|bigint|string) feature, float weight.\n\ntrain_regressor(list features, double label [, const string options]) - Returns a relation consists of \nBuild a prediction model by a generic regressor\n\n\nClassification\nBinary classification\n\nkpa_predict(@Nonnull double xh, @Nonnull double xk, @Nullable float w0, @Nonnull float w1, @Nonnull float w2, @Nullable float w3) - Returns a prediction value in Double\n\ntrain_arow(list features, int label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Adaptive Regularization of Weight Vectors (AROW) binary classifier\nReference: K. Crammer, A. Kulesza, and M. Dredze, \"Adaptive Regularization of Weight Vectors\", In Proc. NIPS, 2009.\n\ntrain_arowh(list features, int label [, const string options]) - Returns a relation consists of \nBuild a prediction model by AROW binary classifier using hinge loss\n\ntrain_classifier(list features, int label [, const string options]) - Returns a relation consists of \nBuild a prediction model by a generic classifier\n\ntrain_cw(list features, int label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Confidence-Weighted (CW) binary classifier\n\ntrain_kpa(array features, int label [, const string options]) - returns a relation \n\ntrain_pa(list features, int label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Passive-Aggressive (PA) binary classifier\n\ntrain_pa1(list features, int label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Passive-Aggressive 1 (PA-1) binary classifier\n\ntrain_pa2(list features, int label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Passive-Aggressive 2 (PA-2) binary classifier\n\ntrain_perceptron(list features, int label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Perceptron binary classifier\n\ntrain_scw(list features, int label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Soft Confidence-Weighted (SCW-1) binary classifier\n\ntrain_scw2(list features, int label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Soft Confidence-Weighted 2 (SCW-2) binary classifier\n\n\nMulticlass classification\n\ntrain_multiclass_arow(list features, {int|string} label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Adaptive Regularization of Weight Vectors (AROW) multiclass classifier\n\ntrain_multiclass_arowh(list features, int|string label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Adaptive Regularization of Weight Vectors (AROW) multiclass classifier using hinge loss\n\ntrain_multiclass_cw(list features, {int|string} label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Confidence-Weighted (CW) multiclass classifier\n\ntrain_multiclass_pa(list features, {int|string} label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Passive-Aggressive (PA) multiclass classifier\n\ntrain_multiclass_pa1(list features, {int|string} label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Passive-Aggressive 1 (PA-1) multiclass classifier\n\ntrain_multiclass_pa2(list features, {int|string} label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Passive-Aggressive 2 (PA-2) multiclass classifier\n\ntrain_multiclass_perceptron(list features, {int|string} label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Perceptron multiclass classifier\n\ntrain_multiclass_scw(list features, {int|string} label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Soft Confidence-Weighted (SCW-1) multiclass classifier\n\ntrain_multiclass_scw2(list features, {int|string} label [, const string options]) - Returns a relation consists of \nBuild a prediction model by Soft Confidence-Weighted 2 (SCW-2) multiclass classifier\n\n\nMatrix factorization\n\nbprmf_predict(List Pu, List Qi[, double Bi]) - Returns the prediction value\n\nmf_predict(array Pu, array Qi[, double Bu, double Bi[, double mu]]) - Returns the prediction value\n\ntrain_bprmf(INT user, INT posItem, INT negItem [, String options]) - Returns a relation \n\ntrain_mf_adagrad(INT user, INT item, FLOAT rating [, CONSTANT STRING options]) - Returns a relation consists of  Pu, array Qi [, float Bu, float Bi [, float mu]]>\n\ntrain_mf_sgd(INT user, INT item, FLOAT rating [, CONSTANT STRING options]) - Returns a relation consists of  Pu, array Qi [, float Bu, float Bi [, float mu]]>\n\n\nFactorization machines\n\nffm_predict(float Wi, array Vifj, array Vjfi, float Xi, float Xj) - Returns a prediction value in Double\n\nfm_predict(Float Wj, array Vjf, float Xj) - Returns a prediction value in Double\n\ntrain_ffm(array x, double y [, const string options]) - Returns a prediction model\n\ntrain_fm(array x, double y [, const string options]) - Returns a prediction model\n\n\nRecommendation\n\ntrain_slim( int i, map r_i, map> topKRatesOfI, int j, map r_j [, constant string options]) - Returns row index, column index and non-zero weight value of prediction model\n\nAnomaly detection\n\nchangefinder(double|array x [, const string options]) - Returns outlier/change-point scores and decisions using ChangeFinder. It will return a tuple \n\nsst(double|array x [, const string options]) - Returns change-point scores and decisions using Singular Spectrum Transformation (SST). It will return a tuple \n\n\nTopic modeling\n\nlda_predict(string word, float value, int label, float lambda[, const string options]) - Returns a list which consists of \n\nplsa_predict(string word, float value, int label, float prob[, const string options]) - Returns a list which consists of \n\ntrain_lda(array words[, const string options]) - Returns a relation consists of \n\ntrain_plsa(array words[, const string options]) - Returns a relation consists of \n\n\nPreprocessing\n\nadd_bias(feature_vector in array) - Returns features with a bias in array\n\nadd_feature_index(ARRAY[DOUBLE]: dense feature vector) - Returns a feature vector with feature indices\n\nextract_feature(feature_vector in array) - Returns features in array\n\nextract_weight(feature_vector in array) - Returns the weights of features in array\n\nfeature( feature,  value) - Returns a feature string\n\nfeature_index(feature_vector in array) - Returns feature indices in array\n\nsort_by_feature(map in map) - Returns a sorted map\n\n\nData amplification\n\namplify(const int xtimes, *) - amplify the input records x-times\n\nrand_amplify(const int xtimes [, const string options], *) - amplify the input records x-times in map-side\n\n\nFeature binning\n\nbuild_bins(number weight, const int num_of_bins[, const boolean auto_shrink = false]) - Return quantiles representing bins: array\n\nfeature_binning(array features, map> quantiles_map) - returns a binned feature vector as an array FUNC(number weight, array quantiles) - returns bin ID as int\nWITH extracted as (\n  select \n    extract_feature(feature) as index,\n    extract_weight(feature) as value\n  from\n    input l\n    LATERAL VIEW explode(features) r as feature\n),\nmapping as (\n  select\n    index, \n    build_bins(value, 5, true) as quantiles -- 5 bins with auto bin shrinking\n  from\n    extracted\n  group by\n    index\n),\nbins as (\n   select \n    to_map(index, quantiles) as quantiles \n   from\n    mapping\n)\nselect\n  l.features as original,\n  feature_binning(l.features, r.quantiles) as features\nfrom\n  input l\n  cross join bins r\n\n> [\"name#Jacob\",\"gender#Male\",\"age:20.0\"] [\"name#Jacob\",\"gender#Male\",\"age:2\"]\n> [\"name#Isabella\",\"gender#Female\",\"age:20.0\"]    [\"name#Isabella\",\"gender#Female\",\"age:2\"]\n\n\n\nFeature format conversion\n\nconv2dense(int feature, float weight, int nDims) - Return a dense model in array\n\nquantify(boolean output, col1, col2, ...) - Returns an identified features\n\nto_dense_features(array feature_vector, int dimensions) - Returns a dense feature in array\n\nto_libsvm_format(array feautres [, double/integer target, const string options]) - Returns a string representation of libsvm\nUsage:\n select to_libsvm_format(array('apple:3.4','orange:2.1'))\n > 6284535:3.4 8104713:2.1\n select to_libsvm_format(array('apple:3.4','orange:2.1'), '-features 10')\n > 3:2.1 7:3.4\n select to_libsvm_format(array('7:3.4','3:2.1'), 5.0)\n > 5.0 3:2.1 7:3.4\n\n\nto_sparse_features(array feature_vector) - Returns a sparse feature in array\n\n\nFeature hashing\n\narray_hash_values(array values, [string prefix [, int numFeatures], boolean useIndexAsPrefix]) returns hash values in array\n\nfeature_hashing(array features [, const string options]) - returns a hashed feature vector in array\nselect feature_hashing(array('aaa:1.0','aaa','bbb:2.0'), '-libsvm');\n [\"4063537:1.0\",\"4063537:1\",\"8459207:2.0\"]\n\nselect feature_hashing(array('aaa:1.0','aaa','bbb:2.0'), '-features 10');\n [\"7:1.0\",\"7\",\"1:2.0\"]\n\nselect feature_hashing(array('aaa:1.0','aaa','bbb:2.0'), '-features 10 -libsvm');\n [\"1:2.0\",\"7:1.0\",\"7:1\"]\n\n\nmhash(string word) returns a murmurhash3 INT value starting from 1\n\nprefixed_hash_values(array values, string prefix [, boolean useIndexAsPrefix]) returns array that each element has the specified prefix\n\nsha1(string word [, int numFeatures]) returns a SHA-1 value\n\n\nFeature paring\n\nfeature_pairs(feature_vector in array, [, const string options]) - Returns a relation \n\npolynomial_features(feature_vector in array) - Returns a feature vectorhaving polynomial feature space\n\npowered_features(feature_vector in array, int degree [, boolean truncate]) - Returns a feature vector having a powered feature space\n\n\nRanking\n\nbpr_sampling(int userId, List posItems [, const string options])- Returns a relation consists of \n\nitem_pairs_sampling(array pos_items, const int max_item_id [, const string options])- Returns a relation consists of \n\npopulate_not_in(list items, const int max_item_id [, const string options])- Returns a relation consists of  that item does not exist in the given items\n\n\nFeature scaling\n\nl1_normalize(ftvec string) - Returned a L1 normalized value\n\nl2_normalize(ftvec string) - Returned a L2 normalized value\n\nrescale(value, min, max) - Returns rescaled value by min-max normalization\n\nzscore(value, mean, stddev) - Returns a standard score (zscore)\n\n\nFeature selection\n\nchi2(array> observed, array> expected) - Returns chi2_val and p_val of each columns as , array>\n\nsnr(array features, array one-hot class label) - Returns Signal Noise Ratio for each feature as array\n\n\nFeature transformation and vectorization\n\nadd_field_indices(array features) - Returns arrays of string that field indices (:)* are augmented\n\nbinarize_label(int/long positive, int/long negative, ...) - Returns positive/negative records that are represented as (..., int label) where label is 0 or 1\n\ncategorical_features(array featureNames, feature1, feature2, .. [, const string options]) - Returns a feature vector array\n\nffm_features(const array featureNames, feature1, feature2, .. [, const string options]) - Takes categorical variables and returns a feature vector array in a libffm format ::\n\nindexed_features(double v1, double v2, ...) - Returns a list of features as array: [1:v1, 2:v2, ..]\n\nonehot_encoding(PRIMITIVE feature, ...) - Compute onehot encoded label for each feature\nWITH mapping as (\n  select \n    m.f1, m.f2 \n  from (\n    select onehot_encoding(species, category) m\n    from test\n  ) tmp\n)\nselect\n  array(m.f1[t.species],m.f2[t.category],feature('count',count)) as sparse_features\nfrom\n  test t\n  CROSS JOIN mapping m;\n\n[\"2\",\"8\",\"count:9\"]\n[\"5\",\"8\",\"count:10\"]\n[\"1\",\"6\",\"count:101\"]\n\n\nquantified_features(boolean output, col1, col2, ...) - Returns an identified features in a dense array\n\nquantitative_features(array featureNames, feature1, feature2, .. [, const string options]) - Returns a feature vector array\n\nvectorize_features(array featureNames, feature1, feature2, .. [, const string options]) - Returns a feature vector array\n\n\nGeospatial functions\n\nhaversine_distance(double lat1, double lon1, double lat2, double lon2, [const boolean mile=false])::double - return distance between two locations in km [or miles] using haversine formula\nUsage: select latlon_distance(lat1, lon1, lat2, lon2) from ...\n\n\nlat2tiley(double lat, int zoom)::int - Returns the tile number of the given latitude and zoom level\n\nlon2tilex(double lon, int zoom)::int - Returns the tile number of the given longitude and zoom level\n\nmap_url(double lat, double lon, int zoom [, const string option]) - Returns a URL string\nOpenStreetMap: http://tile.openstreetmap.org/${zoom}/${xtile}/${ytile}.png\nGoogle Maps: https://www.google.com/maps/@${lat},${lon},${zoom}z\n\ntile(double lat, double lon, int zoom)::bigint - Returns a tile number 2^2n where n is zoom level. FUNC(lat,lon,zoom) = xtile(lon,zoom) + ytile(lat,zoom) * 2^zoom\nrefer https://wiki.openstreetmap.org/wiki/Slippy_map_tilenames for detail\n\ntilex2lon(int x, int zoom)::double - Returns longitude of the given tile x and zoom level\n\ntiley2lat(int y, int zoom)::double - Returns latitude of the given tile y and zoom level\n\n\nDistance measures\n\nangular_distance(ftvec1, ftvec2) - Returns an angular distance of the given two vectors\nWITH docs as (\n  select 1 as docid, array('apple:1.0', 'orange:2.0', 'banana:1.0', 'kuwi:0') as features\n  union all\n  select 2 as docid, array('apple:1.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n  union all\n  select 3 as docid, array('apple:2.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n) \nselect\n  l.docid as doc1,\n  r.docid as doc2,\n  angular_distance(l.features, r.features) as distance,\n  distance2similarity(angular_distance(l.features, r.features)) as similarity\nfrom \n  docs l\n  CROSS JOIN docs r\nwhere\n  l.docid != r.docid\norder by \n  doc1 asc,\n  distance asc;\n\ndoc1    doc2    distance        similarity\n1       3       0.31678355      0.75942624\n1       2       0.33333337      0.75\n2       3       0.09841931      0.91039914\n2       1       0.33333337      0.75\n3       2       0.09841931      0.91039914\n3       1       0.31678355      0.75942624\n\n\ncosine_distance(ftvec1, ftvec2) - Returns a cosine distance of the given two vectors\nWITH docs as (\n  select 1 as docid, array('apple:1.0', 'orange:2.0', 'banana:1.0', 'kuwi:0') as features\n  union all\n  select 2 as docid, array('apple:1.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n  union all\n  select 3 as docid, array('apple:2.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n) \nselect\n  l.docid as doc1,\n  r.docid as doc2,\n  cosine_distance(l.features, r.features) as distance,\n  distance2similarity(cosine_distance(l.features, r.features)) as similarity\nfrom \n  docs l\n  CROSS JOIN docs r\nwhere\n  l.docid != r.docid\norder by \n  doc1 asc,\n  distance asc;\n\ndoc1    doc2    distance        similarity\n1       3       0.45566893      0.6869694\n1       2       0.5     0.6666667\n2       3       0.04742068      0.95472616\n2       1       0.5     0.6666667\n3       2       0.04742068      0.95472616\n3       1       0.45566893      0.6869694\n\n\neuclid_distance(ftvec1, ftvec2) - Returns the square root of the sum of the squared differences: sqrt(sum((x - y)^2))\nWITH docs as (\n  select 1 as docid, array('apple:1.0', 'orange:2.0', 'banana:1.0', 'kuwi:0') as features\n  union all\n  select 2 as docid, array('apple:1.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n  union all\n  select 3 as docid, array('apple:2.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n) \nselect\n  l.docid as doc1,\n  r.docid as doc2,\n  euclid_distance(l.features, r.features) as distance,\n  distance2similarity(euclid_distance(l.features, r.features)) as similarity\nfrom \n  docs l\n  CROSS JOIN docs r\nwhere\n  l.docid != r.docid\norder by \n  doc1 asc,\n  distance asc;\n\ndoc1    doc2    distance        similarity\n1       2       2.4494898       0.28989795\n1       3       2.6457512       0.2742919\n2       3       1.0     0.5\n2       1       2.4494898       0.28989795\n3       2       1.0     0.5\n3       1       2.6457512       0.2742919\n\n\nhamming_distance(integer A, integer B) - Returns Hamming distance between A and B\nselect \n  hamming_distance(0,3) as c1, \n  hamming_distance(\"0\",\"3\") as c2 -- 0=0x00, 3=0x11\n;\n\nc1      c2\n2       2\n\n\njaccard_distance(integer A, integer B [,int k=128]) - Returns Jaccard distance between A and B\nselect \n  jaccard_distance(0,3) as c1, \n  jaccard_distance(\"0\",\"3\") as c2, -- 0=0x00, 0=0x11\n  jaccard_distance(0,4) as c3\n;\n\nc1      c2      c3\n0.03125 0.03125 0.015625\n\n\nkld(double mu1, double sigma1, double mu2, double sigma2) - Returns KL divergence between two distributions\n\nmanhattan_distance(list x, list y) - Returns sum(|x - y|)\nWITH docs as (\n  select 1 as docid, array('apple:1.0', 'orange:2.0', 'banana:1.0', 'kuwi:0') as features\n  union all\n  select 2 as docid, array('apple:1.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n  union all\n  select 3 as docid, array('apple:2.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n) \nselect\n  l.docid as doc1,\n  r.docid as doc2,\n  manhattan_distance(l.features, r.features) as distance,\n  distance2similarity(angular_distance(l.features, r.features)) as similarity\nfrom \n  docs l\n  CROSS JOIN docs r\nwhere\n  l.docid != r.docid\norder by \n  doc1 asc,\n  distance asc;\n\ndoc1    doc2    distance        similarity\n1       2       4.0     0.75\n1       3       5.0     0.75942624\n2       3       1.0     0.91039914\n2       1       4.0     0.75\n3       2       1.0     0.91039914\n3       1       5.0     0.75942624\n\n\nminkowski_distance(list x, list y, double p) - Returns sum(|x - y|^p)^(1/p)\nWITH docs as (\n  select 1 as docid, array('apple:1.0', 'orange:2.0', 'banana:1.0', 'kuwi:0') as features\n  union all\n  select 2 as docid, array('apple:1.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n  union all\n  select 3 as docid, array('apple:2.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n) \nselect\n  l.docid as doc1,\n  r.docid as doc2,\n  minkowski_distance(l.features, r.features, 1) as distance1, -- p=1 (manhattan_distance)\n  minkowski_distance(l.features, r.features, 2) as distance2, -- p=2 (euclid_distance)\n  minkowski_distance(l.features, r.features, 3) as distance3, -- p=3\n  manhattan_distance(l.features, r.features) as manhattan_distance,\n  euclid_distance(l.features, r.features) as euclid_distance\nfrom \n  docs l\n  CROSS JOIN docs r\nwhere\n  l.docid != r.docid\norder by \n  doc1 asc,\n  distance1 asc;\n\ndoc1    doc2    distance1       distance2       distance3       manhattan_distance      euclid_distance\n1       2       4.0     2.4494898       2.1544347       4.0     2.4494898\n1       3       5.0     2.6457512       2.2239802       5.0     2.6457512\n2       3       1.0     1.0     1.0     1.0     1.0\n2       1       4.0     2.4494898       2.1544347       4.0     2.4494898\n3       2       1.0     1.0     1.0     1.0     1.0\n3       1       5.0     2.6457512       2.2239802       5.0     2.6457512\n\n\npopcnt(a [, b]) - Returns a popcount value\nselect \n  popcnt(3),\n  popcnt(\"3\"),  -- 3=0x11\n  popcnt(array(1,3));\n\n2       2       3\n\n\n\nLocality-sensitive hashing\n\nbbit_minhash(array<> features [, int numHashes]) - Returns a b-bits minhash value\n\nminhash(ANY item, array features [, constant string options]) - Returns n different k-depth signatures (i.e., clusterid) for each item \n\nminhashes(array<> features [, int numHashes, int keyGroup [, boolean noWeight]]) - Returns minhash values\n\n\nSimilarity measures\n\nangular_similarity(ftvec1, ftvec2) - Returns an angular similarity of the given two vectors\nWITH docs as (\n  select 1 as docid, array('apple:1.0', 'orange:2.0', 'banana:1.0', 'kuwi:0') as features\n  union all\n  select 2 as docid, array('apple:1.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n  union all\n  select 3 as docid, array('apple:2.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n) \nselect\n  l.docid as doc1,\n  r.docid as doc2,\n  angular_similarity(l.features, r.features) as similarity\nfrom \n  docs l\n  CROSS JOIN docs r\nwhere\n  l.docid != r.docid\norder by \n  doc1 asc,\n  similarity desc;\n\ndoc1    doc2    similarity\n1       3       0.68321645\n1       2       0.6666666\n2       3       0.9015807\n2       1       0.6666666\n3       2       0.9015807\n3       1       0.68321645\n\n\ncosine_similarity(ftvec1, ftvec2) - Returns a cosine similarity of the given two vectors\nWITH docs as (\n  select 1 as docid, array('apple:1.0', 'orange:2.0', 'banana:1.0', 'kuwi:0') as features\n  union all\n  select 2 as docid, array('apple:1.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n  union all\n  select 3 as docid, array('apple:2.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n) \nselect\n  l.docid as doc1,\n  r.docid as doc2,\n  cosine_similarity(l.features, r.features) as similarity\nfrom \n  docs l\n  CROSS JOIN docs r\nwhere\n  l.docid != r.docid\norder by \n  doc1 asc,\n  similarity desc;\n\ndoc1    doc2    similarity\n1       3       0.5443311\n1       2       0.5\n2       3       0.9525793\n2       1       0.5\n3       2       0.9525793\n3       1       0.5443311\n\n\ndimsum_mapper(array row, map colNorms [, const string options]) - Returns column-wise partial similarities\n\ndistance2similarity(float d) - Returns 1.0 / (1.0 + d)\n\neuclid_similarity(ftvec1, ftvec2) - Returns a euclid distance based similarity, which is 1.0 / (1.0 + distance), of the given two vectors\nWITH docs as (\n  select 1 as docid, array('apple:1.0', 'orange:2.0', 'banana:1.0', 'kuwi:0') as features\n  union all\n  select 2 as docid, array('apple:1.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n  union all\n  select 3 as docid, array('apple:2.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n) \nselect\n  l.docid as doc1,\n  r.docid as doc2,\n  euclid_similarity(l.features, r.features) as similarity\nfrom \n  docs l\n  CROSS JOIN docs r\nwhere\n  l.docid != r.docid\norder by \n  doc1 asc,\n  similarity desc;\n\ndoc1    doc2    similarity\n1       2       0.28989795\n1       3       0.2742919\n2       3       0.5\n2       1       0.28989795\n3       2       0.5\n3       1       0.2742919\n\n\njaccard_similarity(A, B [,int k]) - Returns Jaccard similarity coefficient of A and B\nWITH docs as (\n  select 1 as docid, array('apple:1.0', 'orange:2.0', 'banana:1.0', 'kuwi:0') as features\n  union all\n  select 2 as docid, array('apple:1.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n  union all\n  select 3 as docid, array('apple:2.0', 'orange:0', 'banana:2.0', 'kuwi:1.0') as features\n) \nselect\n  l.docid as doc1,\n  r.docid as doc2,\n  jaccard_similarity(l.features, r.features) as similarity\nfrom \n  docs l\n  CROSS JOIN docs r\nwhere\n  l.docid != r.docid\norder by \n  doc1 asc,\n  similarity desc;\n\ndoc1    doc2    similarity\n1       2       0.14285715\n1       3       0.0\n2       3       0.6\n2       1       0.14285715\n3       2       0.6\n3       1       0.0\n\n\n\nEvaluation\n\nauc(array rankItems | double score, array correctItems | int label [, const int recommendSize = rankItems.size ]) - Returns AUC\n\naverage_precision(array rankItems, array correctItems [, const int recommendSize = rankItems.size]) - Returns MAP\n\nf1score(array[int], array[int]) - Return a F1 score\n\nfmeasure(array|int|boolean actual, array|int| boolean predicted [, const string options]) - Return a F-measure (f1score is the special with beta=1.0)\n\nhitrate(array rankItems, array correctItems [, const int recommendSize = rankItems.size]) - Returns HitRate\n\nlogloss(double predicted, double actual) - Return a Logrithmic Loss\n\nmae(double predicted, double actual) - Return a Mean Absolute Error\n\nmrr(array rankItems, array correctItems [, const int recommendSize = rankItems.size]) - Returns MRR\n\nmse(double predicted, double actual) - Return a Mean Squared Error\n\nndcg(array rankItems, array correctItems [, const int recommendSize = rankItems.size]) - Returns nDCG\n\nprecision_at(array rankItems, array correctItems [, const int recommendSize = rankItems.size]) - Returns Precision\n\nr2(double predicted, double actual) - Return R Squared (coefficient of determination)\n\nrecall_at(array rankItems, array correctItems [, const int recommendSize = rankItems.size]) - Returns Recall\n\nrmse(double predicted, double actual) - Return a Root Mean Squared Error\n\n\nSketching\n\napprox_count_distinct(expr x [, const string options]) - Returns an approximation of count(DISTINCT x) using HyperLogLogPlus algorithm\n\nbloom(string key) - Constructs a BloomFilter by aggregating a set of keys\nCREATE TABLE satisfied_movies AS \n  SELECT bloom(movieid) as movies\n  FROM (\n    SELECT movieid\n    FROM ratings\n    GROUP BY movieid\n    HAVING avg(rating) >= 4.0\n  ) t;\n\n\nbloom_and(string bloom1, string bloom2) - Returns the logical AND of two bloom filters\nSELECT bloom_and(bf1, bf2) FROM xxx;\n\n\nbloom_contains(string bloom, string key) or FUNC(string bloom, array keys) - Returns true if the bloom filter contains all the given key(s). Returns false if key is null.\nWITH satisfied_movies as (\n  SELECT bloom(movieid) as movies\n  FROM (\n    SELECT movieid\n    FROM ratings\n    GROUP BY movieid\n    HAVING avg(rating) >= 4.0\n  ) t\n)\nSELECT\n  l.rating,\n  count(distinct l.userid) as cnt\nFROM\n  ratings l \n  CROSS JOIN satisfied_movies r\nWHERE\n  bloom_contains(r.movies, l.movieid) -- includes false positive\nGROUP BY \n  l.rating;\n\nl.rating        cnt\n1       1296\n2       2770\n3       5008\n4       5824\n5       5925\n\n\nbloom_contains_any(string bloom, string key) or FUNC(string bloom, array keys)- Returns true if the bloom filter contains any of the given key\nWITH data1 as (\n  SELECT explode(array(1,2,3,4,5)) as id\n),\ndata2 as (\n  SELECT explode(array(1,3,5,6,8)) as id\n),\nbloom as (\n  SELECT bloom(id) as bf\n  FROM data1\n)\nSELECT \n  l.* \nFROM \n  data2 l\n  CROSS JOIN bloom r\nWHERE\n  bloom_contains_any(r.bf, array(l.id))\n\n\nbloom_not(string bloom) - Returns the logical NOT of a bloom filters\nSELECT bloom_not(bf) FROM xxx;\n\n\nbloom_or(string bloom1, string bloom2) - Returns the logical OR of two bloom filters\nSELECT bloom_or(bf1, bf2) FROM xxx;\n\n\n\nEnsemble learning\n\nargmin_kld(float mean, float covar) - Returns mean or covar that minimize a KL-distance among distributions\nThe returned value is (1.0 / (sum(1.0 / covar))) * (sum(mean / covar)\n\nmax_label(double value, string label) - Returns a label that has the maximum value\n\nmaxrow(ANY compare, ...) - Returns a row that has maximum value in the 1st argument\n\n\nBagging\n\nvoted_avg(double value) - Returns an averaged value by bagging for classification\n\nweight_voted_avg(expr) - Returns an averaged value by considering sum of positive/negative weights\n\n\nDecision trees and RandomForest\n\ntrain_gradient_tree_boosting_classifier(array features, int label [, string options]) - Returns a relation consists of  pred_models, double intercept, double shrinkage, array var_importance, float oob_error_rate>\n\ntrain_randomforest_classifier(array features, int label [, const string options, const array classWeights])- Returns a relation consists of  var_importance, int oob_errors, int oob_tests>\n\ntrain_randomforest_regressor(array features, double target [, string options]) - Returns a relation consists of  var_importance, double oob_errors, int oob_tests>\n\ndecision_path(string modelId, string model, array features [, const string options] [, optional array featureNames=null, optional array classNames=null]) - Returns a decision path for each prediction in array\nSELECT\n  t.passengerid,\n  decision_path(m.model_id, m.model, t.features, '-classification')\nFROM\n  model_rf m\n  LEFT OUTER JOIN\n  test_rf t;\n | 892 | [\"2 [0.0] = 0.0\",\"0 [3.0] = 3.0\",\"1 [696.0] != 107.0\",\"7 [7.8292] \n\nguess_attribute_types(ANY, ...) - Returns attribute types\nselect guess_attribute_types(*) from train limit 1;\n Q,Q,C,C,C,C,Q,C,C,C,Q,C,Q,Q,Q,Q,C,Q\n\n\nrf_ensemble(int yhat [, array proba [, double model_weight=1.0]]) - Returns ensembled prediction results in  probabilities>\n\ntree_export(string model, const string options, optional array featureNames=null, optional array classNames=null) - exports a Decision Tree model as javascript/dot]\n\ntree_predict(string modelId, string model, array features [, const string options | const boolean classification=false]) - Returns a prediction result of a random forest in  a posteriori> for classification and  for regression\n\n\nXGBoost\n\ntrain_xgboost(array features,  target, const string options) - Returns a relation consists of  pred_model>\nSELECT \n  train_xgboost(features, label, '-objective binary:logistic -iters 10') \n    as (model_id, model)\nfrom (\n  select features, label\n  from xgb_input\n  cluster by rand(43) -- shuffle\n) shuffled;\n\n\nxgboost_batch_predict(PRIMITIVE rowid, array features, string model_id, array pred_model [, string options]) - Returns a prediction result as (string rowid, array predicted)\nselect\n  rowid, \n  array_avg(predicted) as predicted,\n  avg(predicted[0]) as predicted0\nfrom (\n  select\n    xgboost_batch_predict(rowid, features, model_id, model) as (rowid, predicted)\n  from\n    xgb_model l\n    LEFT OUTER JOIN xgb_input r\n) t\ngroup by rowid;\n\n\nxgboost_predict(PRIMITIVE rowid, array features, string model_id, array pred_model [, string options]) - Returns a prediction result as (string rowid, array predicted)\nselect\n  rowid, \n  array_avg(predicted) as predicted,\n  avg(predicted[0]) as predicted0\nfrom (\n  select\n    xgboost_predict(rowid, features, model_id, model) as (rowid, predicted)\n  from\n    xgb_model l\n    LEFT OUTER JOIN xgb_input r\n) t\ngroup by rowid;\n\n\nxgboost_predict_one(PRIMITIVE rowid, array features, string model_id, array pred_model [, string options]) - Returns a prediction result as (string rowid, double predicted)\nselect\n  rowid, \n  avg(predicted) as predicted\nfrom (\n  select\n    xgboost_predict_one(rowid, features, model_id, model) as (rowid, predicted)\n  from\n    xgb_model l\n    LEFT OUTER JOIN xgb_input r\n) t\ngroup by rowid;\n\n\nxgboost_predict_triple(PRIMITIVE rowid, array features, string model_id, array pred_model [, string options]) - Returns a prediction result as (string rowid, string label, double probability)\nselect\n  rowid,\n  label,\n  avg(prob) as prob\nfrom (\n  select\n    xgboost_predict_triple(rowid, features, model_id, model) as (rowid, label, prob)\n  from\n    xgb_model l\n    LEFT OUTER JOIN xgb_input r\n) t\ngroup by rowid, label;\n\n\nxgboost_version() - Returns the version of xgboost\nSELECT xgboost_version();\n\n\n\nTerm Vector Model\n\nbm25(double termFrequency, int docLength, double avgDocLength, int numDocs, int numDocsWithTerm [, const string options]) - Return an Okapi BM25 score in double. Refer http://hivemall.incubator.apache.org/userguide/ft_engineering/bm25.html for usage\n\ntf(string text) - Return a term frequency in \n\ntfidf(double termFrequency, long numDocs, const long totalNumDocs) - Return a smoothed TFIDF score in double.\n\n\nNLP\n\nstoptags_exclude(array excludeTags, [, const string lang='ja']) - Returns stoptags excluding given tags\nSELECT stoptags_exclude(array('名詞-固有名詞', '形容詞'))\n\n\ntokenize_cn(String line [, const list stopWords]) - returns tokenized strings in array\n\ntokenize_ja(String line [, const string mode = \"normal\", const array stopWords, const array stopTags, const array userDict (or string userDictURL)]) - returns tokenized strings in array\nselect tokenize_ja(\"kuromojiを使った分かち書きのテストです。第二引数にはnormal/search/extendedを指定できます。デフォルトではnormalモードです。\");\n\n> [\"kuromoji\",\"使う\",\"分かち書き\",\"テスト\",\"第\",\"二\",\"引数\",\"normal\",\"search\",\"extended\",\"指定\",\"デフォルト\",\"normal\",\" モード\"]\n\n\ntokenize_ja_neologd(String line [, const string mode = \"normal\", const array stopWords, const array stopTags, const array userDict (or string userDictURL)]) - returns tokenized strings in array\nselect tokenize_ja_neologd(\"kuromojiを使った分かち書きのテストです。第二引数にはnormal/search/extendedを指定できます。デフォルトではnormalモードです。\");\n\n> [\"kuromoji\",\"使う\",\"分かち書き\",\"テスト\",\"第\",\"二\",\"引数\",\"normal\",\"search\",\"extended\",\"指定\",\"デフォルト\",\"normal\",\" モード\"]\n\n\n\nOthers\n\nhivemall_version() - Returns the version of Hivemall\nSELECT hivemall_version();\n\n\nlr_datagen(options string) - Generates a logistic regression dataset\nWITH dual AS (SELECT 1) SELECT lr_datagen('-n_examples 1k -n_features 10') FROM dual;\n\n\nbm25(double termFrequency, int docLength, double avgDocLength, int numDocs, int numDocsWithTerm [, const string options]) - Return an Okapi BM25 score in double. Refer http://hivemall.incubator.apache.org/userguide/ft_engineering/bm25.html for usage\n\ntf(string text) - Return a term frequency in \n\ntfidf(double termFrequency, long numDocs, const long totalNumDocs) - Return a smoothed TFIDF score in double.\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"tips/":{"url":"tips/","title":"Tips for Effective Hivemall","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"tips/addbias.html":{"url":"tips/addbias.html","title":"Explicit add_bias() for better prediction","keywords":"","body":"\nA trainer learns the function f(x)=y, or weights W, of the following form to predict a label y where x is a feature vector.\ny=f(x)=Wx\nWithout a bias clause (or regularization), f(x) cannot make a hyperplane that divides (1,1) and (2,2) becuase f(x) crosses the origin point (0,0).\nWith bias clause b, a trainer learns the following f(x).\nf(x)=Wx+b \nThen, the predicted model considers bias existing in the dataset and the predicted hyperplane does not always cross the origin.\nadd_bias() of Hivemall, adds a bias to a feature vector. \nTo enable a bias clause, use addbias() for both(important!) training and test data as follows.\nThe bias _b is a feature of \"0\" (\"-1\" in before v0.3) by the default. See AddBiasUDF for the detail.\nNote that Bias is expressed as a feature that found in all training/testing examples.\nAdding a bias clause to test data\ncreate table e2006tfidf_test_exploded as\nselect \n  rowid,\n  target,\n  split(feature,\":\")[0] as feature,\n  cast(split(feature,\":\")[1] as float) as value\n  -- extract_feature(feature) as feature, -- hivemall v0.3.1 or later\n  -- extract_weight(feature) as value     -- hivemall v0.3.1 or later\nfrom \n  e2006tfidf_test LATERAL VIEW explode(add_bias(features)) t AS feature;\n\nAdding a bias clause to training data\ncreate table e2006tfidf_pa1a_model as\nselect \n feature,\n avg(weight) as weight\nfrom \n (select \n     pa1a_regress(add_bias(features),target) as (feature,weight)\n  from \n     e2006tfidf_train_x3\n ) t \ngroup by feature;\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"tips/rand_amplify.html":{"url":"tips/rand_amplify.html","title":"Use rand_amplify() to better prediction results","keywords":"","body":"\nThis article explains amplify technique that is useful for improving prediction score.\nIterations are mandatory in machine learning (e.g., in stochastic gradient descent) to get good prediction models. However, MapReduce is known to be not suited for iterative algorithms because IN/OUT of each MapReduce job is through HDFS.\nIn this example, we show how Hivemall deals with this problem. We use KDD Cup 2012, Track 2 Task as an example.\n\n\n\nAmplify training examples in Map phase and shuffle them in Reduce phase\nAmplify and shuffle training examples in each Map task\nConclusion\n\n\n\n\nAmplify training examples in Map phase and shuffle them in Reduce phase\nHivemall provides the amplify UDTF to enumerate iteration effects in machine learning without several MapReduce steps. \nThe amplify function returns multiple rows for each row.\nThe first argument ${xtimes} is the multiplication factor.In the following examples, the multiplication factor is set to 3.\nset hivevar:xtimes=3;\n\ncreate or replace view training_x3\nas\nselect \n  * \nfrom (\nselect\n   amplify(${xtimes}, *) as (rowid, label, features)\nfrom  \n   training_orcfile\n) t\nCLUSTER BY rand();\n\nIn the above example, the  CLUSTER BY clause distributes Map outputs to reducers using a random key for the distribution key. And then, the input records of the reducer is randomly shuffled.\nThe multiplication of records and  the random shuffling has a similar effect to iterations.\nSo, we recommend users to use an amplified view for training as follows:\ncreate table lr_model_x3 \nas\nselect \n feature,\n cast(avg(weight) as float) as weight\nfrom \n (select \n     logress(features,label) as (feature,weight)\n  from \n     training_x3\n ) t \ngroup by feature;\n\nThe above query is executed by 2 MapReduce jobs as shown below:\n\nUsing trainning_x3  instead of the plain training table results in higher and better AUC (0.746214) in this example.\nA problem in amplify() is that the shuffle (copy) and merge phase of the stage 1 could become a bottleneck.\nWhen the training table is so large that involves 100 Map tasks, the merge operator needs to merge at least 100 files by (external) merge sort! \nNote that the actual bottleneck is not M/R iterations but shuffling training instance. Iteration without shuffling (as in the Spark example) causes very slow convergence and results in requiring more iterations. Shuffling cannot be avoided even in iterative MapReduce variants.\n\n\nAmplify and shuffle training examples in each Map task\nTo deal with large training data, Hivemall provides rand_amplify UDTF that randomly shuffles input rows in a Map task.\nThe rand_amplify UDTF outputs rows in a random order when the local buffer specified by ${shufflebuffersize} is filled.\nWith rand_amplify(), the view definition of training_x3 becomes as follows:\nset hivevar:shufflebuffersize=1000;\n\ncreate or replace view training_x3\nas\nselect\n   rand_amplify(${xtimes}, ${shufflebuffersize}, *) as (rowid, label, features)\nfrom  \n   training_orcfile;\n\nThe training query is executed as follows:\n\nThe map-local multiplication and shuffling has no bottleneck in the merge phase and the query is efficiently executed within a single MapReduce job.\n\nUsing rand_amplify results in a better AUC (0.743392) in this example.\n\nConclusion\nWe recommend users to use amplify() for small training inputs and to use rand_amplify() for large training inputs to get a better accuracy in a reasonable training time.\n\n\n\nMethod\nELAPSED TIME (sec)\nAUC\n\n\n\n\nPlain\n89.718\n0.734805\n\n\namplifier+clustered by\n479.855\n0.746214\n\n\nrand_amplifier\n116.424\n0.743392\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"tips/rt_prediction.html":{"url":"tips/rt_prediction.html","title":"Real-time prediction on RDBMS","keywords":"","body":"\nApache Hivemall provides a batch learning scheme that builds prediction models on Apache Hive.\nThe learning process itself is a batch process; however, an online/real-time prediction can be achieved by carrying a prediction on a transactional relational DBMS.\nIn this article, we explain how to run a real-time prediction using a relational DBMS. \nWe assume that you have already run the a9a binary classification task.\n\n\n\nPrerequisites\nPreparing Model Tables on MySQL\nExporting Hive table to MySQL\nExporting test data from Hadoop to MySQL (optional step)\nOnline/realtime prediction on MySQL\n\n\n\nPrerequisites\n\nMySQL\n  Put mysql-connector-java.jar (JDBC driver) on $SQOOP_HOME/lib.\n\nSqoop\n  Sqoop 1.4.5 does not support Hadoop v2.6.0. So, you need to build packages for Hadoop 2.6.\n  To do that you need to edit build.xml and ivy.xml as shown in this patch.\n\n\nPreparing Model Tables on MySQL\ncreate database a9a;\nuse a9a;\n\ncreate user sqoop identified by 'sqoop';\ngrant all privileges on a9a.* to 'sqoop'@'%' identified by 'sqoop';\nflush privileges;\n\ncreate table a9a_model1 (\n  feature int, \n  weight double\n);\n\nDo not forget to edit bind_address in the MySQL configuration file (/etc/mysql/my.conf) accessible from master and slave nodes of Hadoop.\nExporting Hive table to MySQL\nCheck the connectivity to MySQL server using Sqoop.\nexport MYSQL_HOST=dm01\n\nexport HADOOP_HOME=/opt/hadoop\nexport HADOOP_CONF_DIR=${HADOOP_HOME}/etc/hadoop/\nexport HADOOP_COMMON_HOME=${HADOOP_HOME}\n\nbin/sqoop list-tables --connect jdbc:mysql://${MYSQL_HOST}/a9a --username sqoop --password sqoop\n\nCreate TSV table because Sqoop cannot directory read Hive tables.\ncreate table a9a_model1_tsv \n  ROW FORMAT DELIMITED \n    FIELDS TERMINATED BY \"\\t\"\n    LINES TERMINATED BY \"\\n\"\n  STORED AS TEXTFILE\nAS\nselect * from a9a_model1;\n\nCheck the location of 'a9a_model1_tsv' as follows:\ndesc extended a9a_model1_tsv;\n> location:hdfs://dm01:9000/user/hive/warehouse/a9a.db/a9a_model1_tsv\n\nbin/sqoop export \\\n--connect jdbc:mysql://${MYSQL_HOST}/a9a \\\n--username sqoop --password sqoop \\\n--table a9a_model1 \\\n--export-dir /user/hive/warehouse/a9a.db/a9a_model1_tsv \\\n--input-fields-terminated-by '\\t' --input-lines-terminated-by '\\n' \\\n--batch\n\nWhen the exporting successfully finishes, you can find entries in the model table in MySQL.\nmysql> select * from a9a_model1 limit 3;\n+---------+---------------------+\n| feature | weight              |\n+---------+---------------------+\n|       0 | -0.5761121511459351 |\n|       1 | -1.5259535312652588 |\n|      10 | 0.21053194999694824 |\n+---------+---------------------+\n3 rows in set (0.00 sec)\n\nWe recommend to create an index of model tables to boost lookups in online prediction.\nCREATE UNIQUE INDEX a9a_model1_feature_index on a9a_model1 (feature);\n-- USING BTREE;\n\nExporting test data from Hadoop to MySQL (optional step)\nPrepare a testing data table in Hive which is being exported.\ncreate table a9atest_exploded_tsv\n  ROW FORMAT DELIMITED \n    FIELDS TERMINATED BY \"\\t\"\n    LINES TERMINATED BY \"\\n\"\n  STORED AS TEXTFILE\nAS\nselect\n  rowid, \n  -- label, \n  extract_feature(feature) as feature,\n  extract_weight(feature) as value\nfrom\n  a9atest LATERAL VIEW explode(add_bias(features)) t AS feature;\n\ndesc extended a9atest_exploded_tsv;\n> location:hdfs://dm01:9000/user/hive/warehouse/a9a.db/a9atest_exploded_tsv,\n\nPrepare a test table, importing data from Hadoop.\nuse a9a;\n\ncreate table a9atest_exploded (\n  rowid bigint,\n  feature int, \n  value double\n);\n\nThen, run Sqoop to export data from HDFS to MySQL.\nexport MYSQL_HOST=dm01\n\nbin/sqoop export \\\n--connect jdbc:mysql://${MYSQL_HOST}/a9a \\\n--username sqoop --password sqoop \\\n--table a9atest_exploded \\\n--export-dir /user/hive/warehouse/a9a.db/a9atest_exploded_tsv \\\n--input-fields-terminated-by '\\t' --input-lines-terminated-by '\\n' \\\n--batch\n\nBetter to add an index to the rowid column to boost selection by rowids.\nCREATE INDEX a9atest_exploded_rowid_index on a9atest_exploded (rowid) USING BTREE;\n\nWhen the exporting successfully finishes, you can find entries in the test table in MySQL.\nmysql> select * from a9atest_exploded limit 10;\n+-------+---------+-------+\n| rowid | feature | value |\n+-------+---------+-------+\n| 12427 |      67 |     1 |\n| 12427 |      73 |     1 |\n| 12427 |      74 |     1 |\n| 12427 |      76 |     1 |\n| 12427 |      82 |     1 |\n| 12427 |      83 |     1 |\n| 12427 |       0 |     1 |\n| 12428 |       5 |     1 |\n| 12428 |       7 |     1 |\n| 12428 |      16 |     1 |\n+-------+---------+-------+\n10 rows in set (0.00 sec)\n\nOnline/realtime prediction on MySQL\nDefine sigmoid function used for a prediction of logistic regression as follows: \nDROP FUNCTION IF EXISTS sigmoid;\nDELIMITER //\nCREATE FUNCTION sigmoid(x DOUBLE)\n  RETURNS DOUBLE\n  LANGUAGE SQL\nBEGIN\n  RETURN 1.0 / (1.0 + EXP(-x));\nEND;\n//\nDELIMITER ;\n\nWe assume here that doing prediction for a 'features' having (0,1,10) and each of them is a categorical feature (i.e., the weight is 1.0). Then, you can get the probability by logistic regression simply as follows:\nselect\n  sigmoid(sum(m.weight)) as prob\nfrom\n  a9a_model1 m\nwhere\n  m.feature in (0,1,10);\n\n+--------------------+\n| prob               |\n+--------------------+\n| 0.1310696931351625 |\n+--------------------+\n1 row in set (0.00 sec)\nSimilar to the way in Hive, you can run prediction as follows:\nselect\n  sigmoid(sum(t.value * m.weight)) as prob, \n  if(sigmoid(sum(t.value * m.weight)) > 0.5, 1.0, 0.0) as predicted\nfrom\n  a9atest_exploded t LEFT OUTER JOIN\n  a9a_model1 m ON (t.feature = m.feature)\nwhere\n  t.rowid = 12427; -- prediction on a particular id\n\nAlternatively, you can use SQL views for testing target 't' in the above query.\n+---------------------+-----------+\n| prob                | predicted |\n+---------------------+-----------+\n| 0.05595205126313402 |       0.0 |\n+---------------------+-----------+\n1 row in set (0.00 sec)\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"tips/ensemble_learning.html":{"url":"tips/ensemble_learning.html","title":"Ensemble learning for stable prediction","keywords":"","body":"\nThis example explains how to run ensemble learning in Hivemall.Two heads are better than one? Let's verify it by ensemble learning.\n\n\n\n[Case1] Model ensemble/mixing\ntraining\nprediction\nevaluation\n\n\n[Case2] Prediction ensemble\nprediction\nevaluation\n\n\n\n\n\n[Case1] Model ensemble/mixing\ntraining\nSET hive.exec.parallel=true;\nSET hive.exec.parallel.thread.number=8;\nSET mapred.reduce.tasks=4;\n\ndrop table news20mc_ensemble_model1;\ncreate table news20mc_ensemble_model1 as\nselect \n label, \n -- cast(feature as int) as feature, -- hivemall v0.1\n argmin_kld(feature, covar) as feature, -- hivemall v0.2 or later\n voted_avg(weight) as weight\nfrom \n (select \n     -- train_multiclass_cw(add_bias(features),label) as (label,feature,weight)      -- hivemall v0.1\n     train_multiclass_cw(add_bias(features),label) as (label,feature,weight,covar)   -- hivemall v0.2 or later\n  from \n     news20mc_train_x3\n  union all\n  select \n     -- train_multiclass_arow(add_bias(features),label) as (label,feature,weight)    -- hivemall v0.1\n     train_multiclass_arow(add_bias(features),label) as (label,feature,weight,covar) -- hivemall v0.2 or later\n  from \n     news20mc_train_x3\n  union all\n  select \n     -- train_multiclass_scw(add_bias(features),label) as (label,feature,weight)     -- hivemall v0.1\n     train_multiclass_scw(add_bias(features),label) as (label,feature,weight,covar)  -- hivemall v0.2 or later\n  from \n     news20mc_train_x3\n ) t \ngroup by label, feature;\n\n-- reset to the default\nSET hive.exec.parallel=false;\nSET mapred.reduce.tasks=-1;\n\nprediction\ncreate or replace view news20mc_ensemble_predict1 \nas\nselect \n  rowid, \n  m.col0 as score, \n  m.col1 as label\nfrom (\nselect\n   rowid, \n   maxrow(score, label) as m\nfrom (\n  select\n    t.rowid,\n    m.label,\n    sum(m.weight * t.value) as score\n  from \n    news20mc_test_exploded t LEFT OUTER JOIN\n    news20mc_ensemble_model1 m ON (t.feature = m.feature)\n  group by\n    t.rowid, m.label\n) t1\ngroup by rowid\n) t2;\n\nevaluation\ncreate or replace view news20mc_ensemble_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20mc_test t JOIN news20mc_ensemble_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/3993 from news20mc_ensemble_submit1 \nwhere actual == predicted;\n\n0.8494866015527173\n\nUnfortunately, too many cooks spoil the broth in this case :-(\n\n\n\nAlgorithm\nAccuracy\n\n\n\n\nAROW\n0.8474830954169797\n\n\nSCW2\n0.8482344102178813\n\n\nEnsemble(model)\n0.8494866015527173\n\n\nCW\n0.850488354620586\n\n\n\n[Case2] Prediction ensemble\nprediction\ncreate or replace view news20mc_pred_ensemble_predict1 \nas\nselect \n  rowid, \n  m.col1 as label\nfrom (\n  select\n    rowid, \n    maxrow(cnt, label) as m\n  from (\n    select\n      rowid,\n      label,\n      count(1) as cnt\n    from (\n      select * from news20mc_arow_predict1\n      union all\n      select * from news20mc_scw2_predict1\n      union all\n      select * from news20mc_cw_predict1\n    ) t1\n    group by rowid, label\n  ) t2\n  group by rowid\n) t3;\n\nevaluation\ncreate or replace view news20mc_pred_ensemble_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20mc_test t JOIN news20mc_pred_ensemble_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/3993 from news20mc_pred_ensemble_submit1 \nwhere actual == predicted;\n\n0.8499874780866516\n\nUnfortunately, too many cooks spoil the broth in this case too :-(\n\n\n\nAlgorithm\nAccuracy\n\n\n\n\nAROW\n0.8474830954169797\n\n\nSCW2\n0.8482344102178813\n\n\nEnsemble(model)\n0.8494866015527173\n\n\nEnsemble(prediction)\n0.8499874780866516\n\n\nCW\n0.850488354620586\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"tips/mixserver.html":{"url":"tips/mixserver.html","title":"Mixing models for a better prediction convergence (MIX server)","keywords":"","body":"\nIn this page, we will explain how to use model mixing on Hivemall. The model mixing is useful for a better prediction performance and faster convergence in training classifiers. \nYou can find a brief explanation of the internal design of MIX protocol in this slide.\n\n\n\nPrerequisite\nRunning Mix Server\nUsing Mix Protocol through Hivemall\nThe effect of model mixing\n\n\n\nPrerequisite\n\nHivemall v0.3 or later\n  We recommend to use Mixing in a cluster with fast networking. The current standard GbE is enough though.\n\n\nRunning Mix Server\nFirst, put the following files on server(s) that are accessible from Hadoop worker nodes:\n\ntarget/hivemall-mixserv.jar\nbin/run_mixserv.sh\n\nCaution: hivemall-mixserv.jar is large in size and thus only used for Mix servers.\n# run a Mix Server\n./run_mixserv.sh\n\nWe assume in this example that Mix servers are running on host01, host03 and host03.\nThe default port used by Mix server is 11212 and the port is configurable through \"-port\" option of run_mixserv.sh. \nSee MixServer.java to get detail of the Mix server options.\nWe recommended to use multiple MIX servers to get better MIX throughput (3-5 or so would be enough for normal cluster size). The MIX protocol of Hivemall is horizontally scalable by adding MIX server nodes.\nUsing Mix Protocol through Hivemall\nInstall Hivemall on Hive.\nMake sure that hivemall-with-dependencies.jar is used for installation. The jar contains minimum requirement jars (netty,jsr305) for running Hivemall on Hive.\nNow, we explain that how to use mixing in an example using KDD2010a dataset.\nEnabling the mixing on Hivemall is simple as follows:\nuse kdd2010;\n\ncreate table kdd10a_pa1_model1 as\nselect \n feature,\n cast(voted_avg(weight) as float) as weight\nfrom \n (select \n     train_pa1(add_bias(features),label,\"-mix host01,host02,host03\") as (feature,weight)\n  from \n     kdd10a_train_x3\n ) t \ngroup by feature;\n\nAll you have to do is just adding \"-mix\" training option as seen in the above query.\nThe effect of model mixing\nIn my experience, the MIX improved the prediction accuracy of the above KDD2010a PA1 training on a 32 nodes cluster from 0.844835019263103 (w/o mix) to 0.8678096499719774 (w/ mix).\nThe overhead of using the MIX protocol is almost negligible because the MIX communication is efficiently handled using asynchronous non-blocking I/O. Furthermore, the training time could be improved on certain settings because of the faster convergence due to mixing.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"tips/emr.html":{"url":"tips/emr.html","title":"Run Hivemall on Amazon Elastic MapReduce","keywords":"","body":"\n\n\n\nPrerequisite\nData preparation\nAdaptive Regularization of Weight Vectors (AROW)\n\n\ntraining\nprediction\nevaluation\nCleaning\nTips\n\n\n\nPrerequisite\nLearn how to use Hive with Elastic MapReduce (EMR).https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-hive.html\nBefore launching an EMR job, \n\ncreate ${s3bucket}/emr/outputs for outputs\noptionally, create ${s3bucket}/emr/logs for logging\nput emr_hivemall_bootstrap.sh on ${s3bucket}/emr/conf\n\nThen, lunch an EMR job with hive in an interactive mode.\nI'm usually lunching EMR instances with cheap Spot instances through CLI client as follows:\n./elastic-mapreduce --create --alive \\\n --name \"Hive cluster\" \\\n --hive-interactive --hive-versions latest \\\n --hive-site=s3://${s3bucket}/emr/conf/hive-site.xml \\\n --ami-version latest \\\n --instance-group master --instance-type m1.medium --instance-count 1 --bid-price 0.175 \\\n --instance-group core --instance-type m1.large --instance-count 3 --bid-price 0.35 \\\n --enable-debugging --log-uri s3n://${s3bucket}/emr/logs \\\n --bootstrap-action s3://elasticmapreduce/bootstrap-actions/run-if \\\n   --args \"instance.isMaster=true,s3://${s3bucket}/emr/conf/emr_hivemall_bootstrap.sh\" --bootstrap-name \"hivemall setup\"\n --bootstrap-action s3://elasticmapreduce/bootstrap-actions/install-ganglia --bootstrap-name \"install ganglia\"\nTo use YARN instead of old Hadoop, specify \"--ami-version 3.0.0\". Hivemall works on both old Hadoop and YARN.\nOr, lunch an interactive EMR job using the EMR GUI wizard.\n\n\nData preparation\nPut training and test data in a TSV format on Amazon S3, e.g., on ${s3bucket}/datasets/news20b/[train|test].\ncreate database news20;\nuse news20;\n\nadd jar ./tmp/hivemall.jar;\nsource ./tmp/define-all.hive;\n\nset hivevar:s3bucket=YOUR_BUCKET_NAME;\n\n-- The default input split size is often too large for Hivemall\nset mapred.max.split.size=67108864;\n\nCreate external table news20b_train (\n  rowid int,\n  label int,\n  features ARRAY\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" \nSTORED AS TEXTFILE LOCATION 's3n://${s3bucket}/datasets/news20b/train';\n\nCreate external table news20b_test (\n  rowid int, \n  label int,\n  features ARRAY\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\"\nSTORED AS TEXTFILE LOCATION 's3n://${s3bucket}/datasets/news20b/test';\n\n-- create or replace view news20b_train_x3\n-- as\n-- select \n--  * \n-- from (\n-- select\n--   amplify(3, *) as (rowid, label, features)\n-- from  \n--   news20b_train \n-- ) t\n-- CLUSTER BY CAST(rand(47) * 100 as INT), CAST(rand(49) * 100 as INT), CAST(rand(50) * 100 as INT);\n\ncreate or replace view news20b_train_x3\nas\nselect\n   rand_amplify(3, 1000, *) as (rowid, label, features)\nfrom  \n   news20b_train;\n\ncreate table news20b_test_exploded as\nselect \n  rowid,\n  label,\n  cast(split(feature,\":\")[0] as int) as feature,\n  cast(split(feature,\":\")[1] as float) as value\nfrom \n  news20b_test LATERAL VIEW explode(add_bias(features)) t AS feature;\n\n\nAdaptive Regularization of Weight Vectors (AROW)\ntraining\nDROP TABLE news20b_arow_model1;\nCREATE EXTERNAL TABLE IF NOT EXISTS news20b_arow_model1 (\n  feature string,\n  weight float\n)\nROW FORMAT DELIMITED \n  FIELDS TERMINATED BY '\\t'\n  LINES TERMINATED BY '\\n'\nSTORED AS TEXTFILE\nLOCATION 's3://${s3bucket}/emr/outputs/news20b_arow_model1';\n\ninsert overwrite table news20b_arow_model1\nselect \n feature,\n cast(voted_avg(weight) as float) as weight\nfrom \n (select \n     train_arow(add_bias(features),label) as (feature,weight)\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view news20b_arow_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_arow_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\ncreate or replace view news20b_arow_submit1 as\nselect \n  t.rowid, \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20b_test t JOIN news20b_arow_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/4996 from news20b_arow_submit1 \nwhere actual == predicted;\n\n\n0.9659727782225781\n\nCleaning\ndrop table news20b_arow_model1;\ndrop view news20b_arow_predict1;\ndrop view news20b_arow_submit1;\n\n\nTips\nWe recommended users to use m1.xlarge running Hivemall on EMR as follows.\n./elastic-mapreduce --create --alive \\\n --name \"Hive cluster\" \\\n --hive-interactive --hive-versions latest \\\n --ami-version latest \\\n --instance-group master --instance-type m1.xlarge --instance-count 1 \\\n --instance-group core --instance-type m1.xlarge --instance-count 8 --bid-price 0.7 \\\n --instance-group task --instance-type m1.xlarge --instance-count 2 --bid-price 0.7 \\\n --enable-debugging --log-uri s3://mybucket/emr/logs \\\n --bootstrap-action s3://elasticmapreduce/bootstrap-actions/configure-hadoop \\\n   --args \"-m,mapred.child.java.opts=-Xmx1536m,-m,mapred.tasktracker.map.tasks.maximum=7,-m,mapred.tasktracker.reduce.tasks.maximum=2,-c,fs.s3n.multipart.uploads.enable=true,-c,fs.s3n.multipart.uploads.split.size=67108864\" \\\n --bootstrap-action s3://elasticmapreduce/bootstrap-actions/run-if \\\n   --args \"instance.isMaster=true,s3://mybucket/emr/conf/emr_hivemall_bootstrap.sh\" \\\n   --bootstrap-name \"hivemall setup\" \\\n --bootstrap-action s3://elasticmapreduce/bootstrap-actions/install-ganglia \\\n   --bootstrap-name \"install ganglia\" \\\n --availability-zone ap-northeast-1a\nUsing spot instance for core/task instance groups is the best way to save your money.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"tips/general_tips.html":{"url":"tips/general_tips.html","title":"General Hive/Hadoop Tips","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"tips/rowid.html":{"url":"tips/rowid.html","title":"Adding rowid for each row","keywords":"","body":"\n\n\n\nRowid generator provided in Hivemall\nOther Rowid generation schemes using SQL\n\n\n\nRowid generator provided in Hivemall\nYou can use rowid() function to generate an unique rowid in Hivemall v0.2 or later.\nselect\n  rowid() as rowid, -- returns ${task_id}-${sequence_number} as string\n  *\nfrom \n  xxx;\n\nAlso, rownum() is supported since Hivemall v0.5-rc.1 or later.\nselect\n  rownum() as rowid, -- returns sprintf(`%d%04d`,sequence,taskId) as long\n  *\nfrom\n  xxx;\n\nOther Rowid generation schemes using SQL\nCREATE TABLE xxx\nAS\nSELECT \n  regexp_replace(reflect('java.util.UUID','randomUUID'), '-', '') as rowid,\n  *\nFROM\n  ..;\n\nAnother option to generate rowid is to use row_number(). \nHowever, the query execution would become too slow for large dataset because the rowid generation is executed on a single reducer.\nCREATE TABLE xxx\nAS\nselect \n  row_number() over () as rowid, \n  * \nfrom a9atest;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"tips/hadoop_tuning.html":{"url":"tips/hadoop_tuning.html","title":"Hadoop tuning for Hivemall","keywords":"","body":"\n\n\n\nPrerequisites\nMapper-side configuration\nReducer-side configuration\nFormula to estimate consumed memory in Hivemall\nEnable CTE materialization\nExecution Engine of Hive\n\n\n\nPrerequisites\nPlease refer the following guides for Hadoop tuning:\n\nhttp://hadoopbook.com/\nhttps://www.slideshare.net/cloudera/mr-perf\n\n\nMapper-side configuration\nMapper configuration is important for hivemall when training runs on mappers (e.g., when using rand_amplify()).\nmapreduce.map.java.opts=\"-Xmx2048m -XX:+PrintGCDetails\" (YARN)\nmapred.map.child.java.opts=\"-Xmx2048m -XX:+PrintGCDetails\" (MR v1)\n\nmapreduce.task.io.sort.mb=1024 (YARN)\nio.sort.mb=1024 (MR v1)\nHivemall can use at max 1024MB in the above case.\n\nmapreduce.map.java.opts - mapreduce.task.io.sort.mb = 2048MB - 1024MB = 1024MB\n\nMoreover, other Hadoop components consumes memory spaces. It would be about 1024MB * 0.5 or so is available for Hivemall. We recommend to set at least -Xmx2048m for a mapper.\nSo, make mapreduce.map.java.opts - mapreduce.task.io.sort.mb as large as possible.\nReducer-side configuration\nReducer configuration is important for hivemall when training runs on reducers (e.g., when using amplify()).\nmapreduce.reduce.java.opts=\"-Xmx2048m -XX:+PrintGCDetails\" (YARN)\nmapred.reduce.child.java.opts=\"-Xmx2048m -XX:+PrintGCDetails\" (MR v1)\n\nmapreduce.reduce.shuffle.input.buffer.percent=0.6 (YARN)\nmapred.reduce.shuffle.input.buffer.percent=0.6 (MR v1)\n\n-- mapreduce.reduce.input.buffer.percent=0.2 (YARN)\n-- mapred.job.reduce.input.buffer.percent=0.2 (MR v1)\nHivemall can use at max 820MB in the above case.\n\nmapreduce.reduce.java.opts  (1 - mapreduce.reduce.input.buffer.percent) = 2048  (1 - 0.6) ≈ 820 MB\n\nMoreover, other Hadoop components consumes memory spaces. It would be about 820MB * 0.5 or so is available for Hivemall. We recommend to set at least -Xmx2048m for a reducer.\nSo, make mapreduce.reduce.java.opts * (1 - mapreduce.reduce.input.buffer.percent) as large as possible.\n\nFormula to estimate consumed memory in Hivemall\nFor a dense model, the consumed memory in Hivemall is as follows:\nfeature_dimensions (2^24 by the default) * 4 bytes (float) * 2 (iff covariance is calculated) * 1.2 (heuristics)\n\n2^24  4 bytes  2 * 1.2 ≈ 161MB\n\nWhen SpaceEfficientDenseModel is used, the formula changes as follows:\nfeature_dimensions (assume here 2^25) * 2 bytes (short) * 2 (iff covariance is calculated) * 1.2 (heuristics)\n\n2^25  2 bytes  2 * 1.2 ≈ 161MB\n\nNote: Hivemall uses a sparse representation of prediction model (using a hash table) by the default. Use \"-densemodel\" option to use a dense model.\nEnable CTE materialization\nHive 2.1.0 or later support CTE materialization through hive.optimize.cte.materialize.threshold option and it's recommended to set hive.optimize.cte.materialize.threshold=2 when using Hivemall.\nExecution Engine of Hive\nWe recommend to use Apache Tez for execute engine of Hive for Hivemall queries.\nset mapreduce.framework.name=yarn-tez;\nset hive.execution.engine=tez;\n\nYou can use the plain old MapReduce by setting following setting:\nset mapreduce.framework.name=yarn;\nset hive.execution.engine=mr;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"troubleshooting/":{"url":"troubleshooting/","title":"Troubleshooting","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"troubleshooting/oom.html":{"url":"troubleshooting/oom.html","title":"OutOfMemoryError in training","keywords":"","body":"\nOOM in mappers\nIn a certain setting, the default input split size is too large for Hivemall. Due to that, OutOfMemoryError cloud happen on mappers in the middle of training.\nThen, revise your a Hadoop setting (mapred.child.java.opts/mapred.map.child.java.opts) first to use a larger value as possible.\nIf an OOM error still caused after that, set smaller mapred.max.split.size value before training.\nSET mapred.max.split.size=67108864;\nThen, the number of training examples used for each trainer is reduced (as the number of mappers increases) and the trained model would fit in the memory.\nOOM in shuffle/merge\nIf OOM caused during the merge step, try setting a larger mapred.reduce.tasks value before training and revise shuffle/reduce parameters.\nSET mapred.reduce.tasks=64;\nIf your OOM happened by using amplify(), try using rand_amplify() instead.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"troubleshooting/mapjoin_task_error.html":{"url":"troubleshooting/mapjoin_task_error.html","title":"SemanticException generate map join task error: Cannot serialize object","keywords":"","body":"\nFrom Hive 0.11.0, hive.auto.convert.join is enabled by the default.\nWhen using complex queries using views, the auto conversion sometimes throws SemanticException, cannot serialize object.\nWorkaround for the exception is to disable hive.auto.convert.join before the execution as follows.\nset hive.auto.convert.join=false;\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"troubleshooting/asterisk.html":{"url":"troubleshooting/asterisk.html","title":"Asterisk argument for UDTF does not work","keywords":"","body":"\nSee HIVE-4181 that asterisk argument without table alias for UDTF is not working. It has been fixed as part of Hive v0.12 release.\nA possible workaround is to use asterisk with a table alias, or to specify names of arguments explicitly.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"troubleshooting/num_mappers.html":{"url":"troubleshooting/num_mappers.html","title":"The number of mappers is less than input splits in Hadoop 2.x","keywords":"","body":"\nThe default hive.input.format is set to org.apache.hadoop.hive.ql.io.CombineHiveInputFormat.\nThis configuration could give less number of mappers than the split size (i.e., # blocks in HDFS) of the input table.\nTry setting org.apache.hadoop.hive.ql.io.HiveInputFormat for hive.input.format.\nset hive.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat;\nNote Apache Tez uses org.apache.hadoop.hive.ql.io.HiveInputFormat by the default.\nset hive.tez.input.format;\n\nhive.tez.input.format=org.apache.hadoop.hive.ql.io.HiveInputFormat\n\n\nYou can then control the maximum number of mappers via setting:\nset mapreduce.job.maps=128;\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"troubleshooting/mapjoin_classcastex.html":{"url":"troubleshooting/mapjoin_classcastex.html","title":"Map-side join causes ClassCastException on Tez","keywords":"","body":"\nMap-side join on Tez causes ClassCastException when a serialized table contains array column(s).\n[Workaround] Try setting hive.mapjoin.optimized.hashtable off as follows:\nset hive.mapjoin.optimized.hashtable=false;\n\nCaution: Fixed in Hive 1.3.0. Refer HIVE_11051 for the detail.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"misc/generic_funcs.html":{"url":"misc/generic_funcs.html","title":"List of Generic Hivemall Functions","keywords":"","body":"\nThis page describes a list of useful Hivemall generic functions. See also a list of machine-learning-related functions.\n\n\n\nAggregation\nArray\nBitset\nCompression\nDatetime\nJSON\nMap\nMapReduce\nMath\nVector/Matrix\nSanity Checks\nText processing\nTimeseries\nOthers\n\n\n\nAggregation\n\nmajority_vote(Primitive x) - Returns the most frequent value of x\n-- see https://issues.apache.org/jira/browse/HIVE-17406 \nWITH data as (\n  select\n    explode(array('1', '2', '2', '2', '5', '4', '1', '2')) as k\n)\nselect\n  majority_vote(k) as k\nfrom \n  data;\n2\n\n\nmax_by(x, y) - Returns the value of x associated with the maximum value of y over all input values.\n-- see https://issues.apache.org/jira/browse/HIVE-17406 \nWITH data as (\n  select 'jake' as name, 18 as age\n  union all\n  select 'tom' as name, 64 as age\n  union all\n  select 'lisa' as name, 32 as age\n)\nselect\n  max_by(name, age) as name\nfrom\n  data;\ntom\n\n\nmin_by(x, y) - Returns the value of x associated with the minimum value of y over all input values.\n-- see https://issues.apache.org/jira/browse/HIVE-17406 \nWITH data as (\n  select 'jake' as name, 18 as age\n  union all\n  select 'tom' as name, 64 as age\n  union all\n  select 'lisa' as name, 32 as age\n)\nselect\n  min_by(name, age) as name\nfrom\n  data;\n\njake\n\n\n\nArray\n\narange([int start=0, ] int stop, [int step=1]) - Return evenly spaced values within a given interval\nSELECT arange(5), arange(1, 5), arange(1, 5, 1), arange(0, 5, 1);\n> [0,1,2,3,4]     [1,2,3,4]       [1,2,3,4]       [0,1,2,3,4]\n\nSELECT arange(1, 6, 2);\n> 1, 3, 5\n\nSELECT arange(-1, -6, 2);\n-1, -3, -5\n\n\nargmax(array a) - Returns the first index of the maximum value\nSELECT argmax(array(5,2,0,1));\n0\n\n\nargmin(array a) - Returns the first index of the minimum value\nSELECT argmin(array(5,2,0,1));\n2\n\n\nargrank(array a) - Returns the indices that would sort an array.\nSELECT argrank(array(5,2,0,1)), argsort(argsort(array(5,2,0,1)));\n[3, 2, 0, 1]     [3, 2, 0, 1]\n\n\nargsort(array a) - Returns the indices that would sort an array.\nSELECT argsort(array(5,2,0,1));\n2, 3, 1, 0\n\nSELECT array_slice(array(5,2,0,1), argsort(array(5,2,0,1)));\n0, 1, 2, 5\n\n\narray_append(array arr, T elem) - Append an element to the end of an array\nSELECT array_append(array(1,2),3);\n 1,2,3\n\nSELECT array_append(array('a','b'),'c');\n \"a\",\"b\",\"c\"\n\n\narray_avg(array) - Returns an array in which each element is the mean of a set of numbers\nWITH input as (\n  select array(1.0, 2.0, 3.0) as nums\n  UNION ALL\n  select array(2.0, 3.0, 4.0) as nums\n)\nselect\n  array_avg(nums)\nfrom\n  input;\n\n[\"1.5\",\"2.5\",\"3.5\"]\n\n\narray_concat(array x1, array x2, ..) - Returns a concatenated array\nSELECT array_concat(array(1),array(2,3));\n [1,2,3]\n\n\narray_flatten(array>) - Returns an array with the elements flattened.\nSELECT array_flatten(array(array(1,2,3),array(4,5),array(6,7,8)));\n [1,2,3,4,5,6,7,8]\n\n\narray_intersect(array x1, array x2, ..) - Returns an intersect of given arrays\nSELECT array_intersect(array(1,3,4),array(2,3,4),array(3,5));\n [3]\n\n\narray_remove(array values, PRIMITIVE|array target) - Returns an array that the target elements are removed from the original array\nselect array_remove(array(2.0,2.1,3.0,4.0,2.0),2), array_remove(array(2.0,3.0,4.0),array(3,2.0));\n[2.1,3,4]       [4]\n\nSELECT array_remove(array(1,null,3),null);\n[1,3]\n\nSELECT array_remove(array(1,null,3,null,5),null);\n[1,3,5]\n\nSELECT array_remove(array(1,null,3),array(null));\n[1,3]\n\nSELECT array_remove(array('aaa','bbb'),'bbb');\n[\"aaa\"]\n\nSELECT array_remove(array('aaa','bbb','ccc','bbb'), array('bbb','ccc'));\n[\"aaa\"]\n\nselect array_remove(array(null),null);\n[]\n\nselect array_remove(array(null,'bbb'),'aaa');\n[null,\"bbb\"]\n\n\narray_slice(array values, int offset [, int length]) - Slices the given array by the given offset and length parameters.\nSELECT \n  array_slice(array(1,2,3,4,5,6),2,4),\n  array_slice(\n   array(\"zero\", \"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\", \"nine\", \"ten\"),\n   0, -- offset\n   2 -- length\n  ),\n  array_slice(\n   array(\"zero\", \"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\", \"nine\", \"ten\"),\n   6, -- offset\n   3 -- length\n  ),\n  array_slice(\n   array(\"zero\", \"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\", \"nine\", \"ten\"),\n   6, -- offset\n   10 -- length\n  ),\n  array_slice(\n   array(\"zero\", \"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\", \"nine\", \"ten\"),\n   6 -- offset\n  ),\n  array_slice(\n   array(\"zero\", \"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\", \"nine\", \"ten\"),\n   -3 -- offset\n  ),\n  array_slice(\n   array(\"zero\", \"one\", \"two\", \"three\", \"four\", \"five\", \"six\", \"seven\", \"eight\", \"nine\", \"ten\"),\n   -3, -- offset\n   2 -- length\n  );\n\n [3,4]\n [\"zero\",\"one\"] \n [\"six\",\"seven\",\"eight\"]\n [\"six\",\"seven\",\"eight\",\"nine\",\"ten\"]\n [\"six\",\"seven\",\"eight\",\"nine\",\"ten\"]\n [\"eight\",\"nine\",\"ten\"]\n [\"eight\",\"nine\"]\n\n\narray_sum(array) - Returns an array in which each element is summed up\nWITH input as (\n  select array(1.0, 2.0, 3.0) as nums\n  UNION ALL\n  select array(2.0, 3.0, 4.0) as nums\n)\nselect\n  array_sum(nums)\nfrom\n  input;\n\n[\"3.0\",\"5.0\",\"7.0\"]\n\n\narray_to_str(array arr [, string sep=',']) - Convert array to string using a sperator\nSELECT array_to_str(array(1,2,3),'-');\n1-2-3\n\n\narray_union(array1, array2, ...) - Returns the union of a set of arrays\nSELECT array_union(array(1,2),array(1,2));\n[1,2]\n\nSELECT array_union(array(1,2),array(2,3),array(2,5));\n[1,2,3,5]\n\n\nconditional_emit(array conditions, array features) - Emit features of a row according to various conditions\nWITH input as (\n   select array(true, false, true) as conditions, array(\"one\", \"two\", \"three\") as features\n   UNION ALL\n   select array(true, true, false), array(\"four\", \"five\", \"six\")\n)\nSELECT\n  conditional_emit(\n     conditions, features\n  )\nFROM \n  input;\n one\n three\n four\n five\n\n\nelement_at(array list, int pos) - Returns an element at the given position\nSELECT element_at(array(1,2,3,4),0);\n 1\n\nSELECT element_at(array(1,2,3,4),-2);\n 3\n\n\nfirst_element(x) - Returns the first element in an array\nSELECT first_element(array('a','b','c'));\n a\n\nSELECT first_element(array());\n NULL\n\n\nfloat_array(nDims) - Returns an array of nDims elements\n\nlast_element(x) - Return the last element in an array\nSELECT last_element(array('a','b','c'));\n c\n\n\nselect_k_best(array array, const array importance, const int k) - Returns selected top-k elements as array\n\nsort_and_uniq_array(array) - Takes array and returns a sorted array with duplicate elements eliminated\nSELECT sort_and_uniq_array(array(3,1,1,-2,10));\n [-2,1,3,10]\n\n\nsubarray(array values, int fromIndex [, int toIndex])- Returns a slice of the original array between the inclusive fromIndex and the exclusive toIndex.\nSELECT \n  subarray(array(0,1,2,3,4,5),4),\n  subarray(array(0,1,2,3,4,5),3,4),\n  subarray(array(0,1,2,3,4,5),3,3),\n  subarray(array(0,1,2,3,4,5),3,2),\n  subarray(array(0,1,2,3,4,5),0,2),\n  subarray(array(0,1,2,3,4,5),-1,2),\n  subarray(array(1,2,3,4,5,6),4),\n  subarray(array(1,2,3,4,5,6),4,6),\n  subarray(array(1,2,3,4,5,6),2,4),\n  subarray(array(1,2,3,4,5,6),0,2),\n  subarray(array(1,2,3,4,5,6),4,6),\n  subarray(array(1,2,3,4,5,6),4,7);\n\n [4,5]\n [3]\n []\n []\n [0,1]\n [0,1]\n [5,6]\n [5,6]\n [3,4]\n [1,2]\n [5,6]\n [5,6]\n\n\nsubarray_endwith(array original, int|text key) - Returns an array that ends with the specified key\nSELECT subarray_endwith(array(1,2,3,4), 3);\n [1,2,3]\n\n\nsubarray_startwith(array original, int|text key) - Returns an array that starts with the specified key\nSELECT subarray_startwith(array(1,2,3,4), 2);\n [2,3,4]\n\n\nto_string_array(array) - Returns an array of strings\nselect to_string_array(array(1.0,2.0,3.0));\n\n[\"1.0\",\"2.0\",\"3.0\"]\n\n\nto_ordered_list(PRIMITIVE value [, PRIMITIVE key, const string options]) - Return list of values sorted by value itself or specific key\nWITH data as (\n    SELECT 5 as key, 'apple' as value\n    UNION ALL\n    SELECT 3 as key, 'banana' as value\n    UNION ALL\n    SELECT 4 as key, 'candy' as value\n    UNION ALL\n    SELECT 1 as key, 'donut' as value\n    UNION ALL\n    SELECT 2 as key, 'egg' as value \n    UNION ALL\n    SELECT 4 as key, 'candy' as value -- both key and value duplicates\n)\nSELECT                                                  -- expected output\n    to_ordered_list(value, key, '-reverse'),            -- [apple, candy, candy, (banana, egg | egg, banana), donut] (reverse order)\n    to_ordered_list(value, key, '-k 2'),                -- [apple, candy] (top-k)\n    to_ordered_list(value, key, '-k 100'),              -- [apple, candy, candy, (banana, egg | egg, banana), dunut]\n    to_ordered_list(value, key, '-k 2 -reverse'),       -- [donut, (banana | egg)] (reverse top-k = tail-k)\n    to_ordered_list(value, key),                        -- [donut, (banana, egg | egg, banana), candy, candy, apple] (natural order)\n    to_ordered_list(value, key, '-k -2'),               -- [donut, (banana | egg)] (tail-k)\n    to_ordered_list(value, key, '-k -100'),             -- [donut, (banana, egg | egg, banana), candy, candy, apple]\n    to_ordered_list(value, key, '-k -2 -reverse'),      -- [apple, candy] (reverse tail-k = top-k)\n    to_ordered_list(value, '-k 2'),                     -- [egg, donut] (alphabetically)\n    to_ordered_list(key, '-k -2 -reverse'),             -- [5, 4] (top-2 keys)\n    to_ordered_list(key),                               -- [1, 2, 3, 4, 4, 5] (natural ordered keys)\n    to_ordered_list(value, key, '-k 2 -kv_map'),        -- {5:\"apple\",4:\"candy\"}\n    to_ordered_list(value, key, '-k 2 -vk_map'),        -- {\"apple\":5,\"candy\":4}\n    to_ordered_list(value, key, '-k -2 -kv_map'),       -- {1:\"donut\",2:\"egg\"}\n    to_ordered_list(value, key, '-k -2 -vk_map'),       -- {\"donut\":1,\"egg\":2}\n    to_ordered_list(value, key, '-k 4 -dedup -vk_map'), -- {\"apple\":5,\"candy\":4,\"banana\":3,\"egg\":2}\n    to_ordered_list(value, key, '-k 4 -vk_map'),        -- {\"apple\":5,\"candy\":4,\"banana\":3}\n    to_ordered_list(value, key, '-k 4 -dedup'),         -- [\"apple\",\"candy\",\"banana\",\"egg\"]\n    to_ordered_list(value, key, '-k 4')                 -- [\"apple\",\"candy\",\"candy\",\"banana\"]\nFROM\n    data\n\n\n\nBitset\n\nbits_collect(int|long x) - Returns a bitset in array\n\nbits_or(array b1, array b2, ..) - Returns a logical OR given bitsets\nSELECT unbits(bits_or(to_bits(array(1,4)),to_bits(array(2,3))));\n [1,2,3,4]\n\n\nto_bits(int[] indexes) - Returns an bitset representation if the given indexes in long[]\nSELECT to_bits(array(1,2,3,128));\n [14,-9223372036854775808]\n\n\nunbits(long[] bitset) - Returns an long array of the give bitset representation\nSELECT unbits(to_bits(array(1,4,2,3)));\n [1,2,3,4]\n\n\n\nCompression\n\ndeflate(TEXT data [, const int compressionLevel]) - Returns a compressed BINARY object by using Deflater. The compression level must be in range [-1,9]\nSELECT base91(deflate('aaaaaaaaaaaaaaaabbbbccc'));\n AA+=kaIM|WTt!+wbGAA\n\n\ninflate(BINARY compressedData) - Returns a decompressed STRING by using Inflater\nSELECT inflate(unbase91(base91(deflate('aaaaaaaaaaaaaaaabbbbccc'))));\n aaaaaaaaaaaaaaaabbbbccc\n\n\n\nDatetime\n\nsessionize(long timeInSec, long thresholdInSec [, String subject])- Returns a UUID string of a session.SELECT \n  sessionize(time, 3600, ip_addr) as session_id, \n  time, ip_addr\nFROM (\n  SELECT time, ipaddr \n  FROM weblog \n  DISTRIBUTE BY ip_addr, time SORT BY ip_addr, time DESC\n) t1\n\n\n\nJSON\n\nfrom_json(string jsonString, const string returnTypes [, const array|const string columnNames]) - Return Hive object.\nSELECT\n  from_json(to_json(map('one',1,'two',2)), 'map'),\n  from_json(\n    '{ \"person\" : { \"name\" : \"makoto\" , \"age\" : 37 } }',\n    'struct', \n    array('person')\n  ),\n  from_json(\n    '[0.1,1.1,2.2]',\n    'array'\n  ),\n  from_json(to_json(\n    ARRAY(\n      NAMED_STRUCT(\"country\", \"japan\", \"city\", \"tokyo\"), \n      NAMED_STRUCT(\"country\", \"japan\", \"city\", \"osaka\")\n    )\n  ),'array>'),\n  from_json(to_json(\n    ARRAY(\n      NAMED_STRUCT(\"country\", \"japan\", \"city\", \"tokyo\"), \n      NAMED_STRUCT(\"country\", \"japan\", \"city\", \"osaka\")\n    ),\n    array('city')\n  ), 'array>'),\n  from_json(to_json(\n    ARRAY(\n      NAMED_STRUCT(\"country\", \"japan\", \"city\", \"tokyo\"), \n      NAMED_STRUCT(\"country\", \"japan\", \"city\", \"osaka\")\n    )\n  ),'array>');\n\n {\"one\":1,\"two\":2}\n {\"name\":\"makoto\",\"age\":37}\n [0.1,1.1,2.2]\n [{\"country\":\"japan\",\"city\":\"tokyo\"},{\"country\":\"japan\",\"city\":\"osaka\"}]\n [{\"country\":\"japan\",\"city\":\"tokyo\"},{\"country\":\"japan\",\"city\":\"osaka\"}]\n [{\"city\":\"tokyo\"},{\"city\":\"osaka\"}]\n\nto_json(ANY object [, const array|const string columnNames]) - Returns Json string\nSELECT \n  NAMED_STRUCT(\"Name\", \"John\", \"age\", 31),\n  to_json(\n     NAMED_STRUCT(\"Name\", \"John\", \"age\", 31)\n  ),\n  to_json(\n     NAMED_STRUCT(\"Name\", \"John\", \"age\", 31),\n     array('Name', 'age')\n  ),\n  to_json(\n     NAMED_STRUCT(\"Name\", \"John\", \"age\", 31),\n     array('name', 'age')\n  ),\n  to_json(\n     NAMED_STRUCT(\"Name\", \"John\", \"age\", 31),\n     array('age')\n  ),\n  to_json(\n     NAMED_STRUCT(\"Name\", \"John\", \"age\", 31),\n     array()\n  ),\n  to_json(\n     null,\n     array()\n  ),\n  to_json(\n    struct(\"123\", \"456\", 789, array(314,007)),\n    array('ti','si','i','bi')\n  ),\n  to_json(\n    struct(\"123\", \"456\", 789, array(314,007)),\n    'ti,si,i,bi'\n  ),\n  to_json(\n    struct(\"123\", \"456\", 789, array(314,007))\n  ),\n  to_json(\n    NAMED_STRUCT(\"country\", \"japan\", \"city\", \"tokyo\")\n  ),\n  to_json(\n    NAMED_STRUCT(\"country\", \"japan\", \"city\", \"tokyo\"), \n    array('city')\n  ),\n  to_json(\n    ARRAY(\n      NAMED_STRUCT(\"country\", \"japan\", \"city\", \"tokyo\"), \n      NAMED_STRUCT(\"country\", \"japan\", \"city\", \"osaka\")\n    )\n  ),\n  to_json(\n    ARRAY(\n      NAMED_STRUCT(\"country\", \"japan\", \"city\", \"tokyo\"), \n      NAMED_STRUCT(\"country\", \"japan\", \"city\", \"osaka\")\n    ),\n    array('city')\n  );\n\n {\"name\":\"John\",\"age\":31}\n {\"name\":\"John\",\"age\":31}\n {\"Name\":\"John\",\"age\":31}\n {\"name\":\"John\",\"age\":31}\n {\"age\":31}\n {}\n NULL\n {\"ti\":\"123\",\"si\":\"456\",\"i\":789,\"bi\":[314,7]}\n {\"ti\":\"123\",\"si\":\"456\",\"i\":789,\"bi\":[314,7]}\n {\"col1\":\"123\",\"col2\":\"456\",\"col3\":789,\"col4\":[314,7]}\n {\"country\":\"japan\",\"city\":\"tokyo\"}\n {\"city\":\"tokyo\"}\n [{\"country\":\"japan\",\"city\":\"tokyo\"},{\"country\":\"japan\",\"city\":\"osaka\"}]\n [{\"country\":\"japan\",\"city\":\"tokyo\"},{\"country\":\"japan\",\"city\":\"osaka\"}]\n\n\nMap\n\nmap_exclude_keys(Map map, array filteringKeys) - Returns the filtered entries of a map not having specified keys\nSELECT map_exclude_keys(map(1,'one',2,'two',3,'three'),array(2,3));\n{1:\"one\"}\n\n\nmap_get(MAP a, K n) - Returns the value corresponding to the key in the map.\nNote this is a workaround for a Hive issue that non-constant expression for map indexes not supported.\nSee https://issues.apache.org/jira/browse/HIVE-1955\n\nWITH tmp as (\n  SELECT \"one\" as key\n  UNION ALL\n  SELECT \"two\" as key\n)\nSELECT map_get(map(\"one\",1,\"two\",2),key)\nFROM tmp;\n\n> 1\n> 2\n\n\nmap_get_sum(map src, array keys) - Returns sum of values that are retrieved by keys\n\nmap_include_keys(Map map, array filteringKeys) - Returns the filtered entries of a map having specified keys\nSELECT map_include_keys(map(1,'one',2,'two',3,'three'),array(2,3));\n{2:\"two\",3:\"three\"}\n\n\nmap_key_values(MAP map) - Returns a array of key-value pairs in array>\nSELECT map_key_values(map(\"one\",1,\"two\",2));\n\n> [{\"key\":\"one\",\"value\":1},{\"key\":\"two\",\"value\":2}]\n\n\nmap_roulette(Map map [, (const) int/bigint seed]) - Returns a map key based on weighted random sampling of map values. Average of values is used for null values\n-- `map_roulette(map [, integer seed])` returns key by weighted random selection\nSELECT \n  map_roulette(to_map(a, b)) -- 25% Tom, 21% Zhang, 54% Wang\nFROM ( -- see https://issues.apache.org/jira/browse/HIVE-17406\n  select 'Wang' as a, 54 as b\n  union all\n  select 'Zhang' as a, 21 as b\n  union all\n  select 'Tom' as a, 25 as b\n) tmp;\n> Wang\n\n-- Weight random selection with using filling nulls with the average value\nSELECT\n  map_roulette(map(1, 0.5, 'Wang', null)), -- 50% Wang, 50% 1\n  map_roulette(map(1, 0.5, 'Wang', null, 'Zhang', null)) -- 1/3 Wang, 1/3 1, 1/3 Zhang\n;\n\n-- NULL will be returned if every key is null\nSELECT \n  map_roulette(map()),\n  map_roulette(map(null, null, null, null));\n> NULL    NULL\n\n-- Return NULL if all weights are zero\nSELECT\n  map_roulette(map(1, 0)),\n  map_roulette(map(1, 0, '5', 0))\n;\n> NULL    NULL\n\n-- map_roulette does not support non-numeric weights or negative weights.\nSELECT map_roulette(map('Wong', 'A string', 'Zhao', 2));\n> HiveException: Error evaluating map_roulette(map('Wong':'A string','Zhao':2))\nSELECT map_roulette(map('Wong', 'A string', 'Zhao', 2));\n> UDFArgumentException: Map value must be greather than or equals to zero: -2\n\n\nmap_tail_n(map SRC, int N) - Returns the last N elements from a sorted array of SRC\n\nmerge_maps(Map x) - Returns a map which contains the union of an aggregation of maps. Note that an existing value of a key can be replaced with the other duplicate key entry.\nSELECT \n  merge_maps(m) \nFROM (\n  SELECT map('A',10,'B',20,'C',30) \n  UNION ALL \n  SELECT map('A',10,'B',20,'C',30)\n) t\n\n\nto_map(key, value) - Convert two aggregated columns into a key-value map\nWITH input as (\n  select 'aaa' as key, 111 as value\n  UNION all\n  select 'bbb' as key, 222 as value\n)\nselect to_map(key, value)\nfrom input;\n\n> {\"bbb\":222,\"aaa\":111}\n\n\nto_ordered_map(key, value [, const int k|const boolean reverseOrder=false]) - Convert two aggregated columns into an ordered key-value map\nwith t as (\n    select 10 as key, 'apple' as value\n    union all\n    select 3 as key, 'banana' as value\n    union all\n    select 4 as key, 'candy' as value\n)\nselect\n    to_ordered_map(key, value, true),   -- {10:\"apple\",4:\"candy\",3:\"banana\"} (reverse)\n    to_ordered_map(key, value, 1),      -- {10:\"apple\"} (top-1)\n    to_ordered_map(key, value, 2),      -- {10:\"apple\",4:\"candy\"} (top-2)\n    to_ordered_map(key, value, 3),      -- {10:\"apple\",4:\"candy\",3:\"banana\"} (top-3)\n    to_ordered_map(key, value, 100),    -- {10:\"apple\",4:\"candy\",3:\"banana\"} (top-100)\n    to_ordered_map(key, value),         -- {3:\"banana\",4:\"candy\",10:\"apple\"} (natural)\n    to_ordered_map(key, value, -1),     -- {3:\"banana\"} (tail-1)\n    to_ordered_map(key, value, -2),     -- {3:\"banana\",4:\"candy\"} (tail-2)\n    to_ordered_map(key, value, -3),     -- {3:\"banana\",4:\"candy\",10:\"apple\"} (tail-3)\n    to_ordered_map(key, value, -100)    -- {3:\"banana\",4:\"candy\",10:\"apple\"} (tail-100)\nfrom t\n\n\n\nMapReduce\n\ndistcache_gets(filepath, key, default_value [, parseKey]) - Returns map|value_type\n\njobconf_gets() - Returns the value from JobConf\n\njobid() - Returns the value of mapred.job.id\n\nrowid() - Returns a generated row id of a form {TASK_ID}-{SEQUENCE_NUMBER}\n\nrownum() - Returns a generated row number sprintf(%d%04d,sequence,taskId) in long\nSELECT rownum() as rownum, xxx from ...\n\n\ntaskid() - Returns the value of mapred.task.partition\n\n\nMath\n\ninfinity() - Returns the constant representing positive infinity.\n\nis_finite(x) - Determine if x is finite.\nSELECT is_finite(333), is_finite(infinity());\ntrue false\n\n\nis_infinite(x) - Determine if x is infinite.\n\nis_nan(x) - Determine if x is not-a-number.\n\nl2_norm(double x) - Return a L2 norm of the given input x.\nWITH input as (\n  select generate_series(1,3) as v\n)\nselect l2_norm(v) as l2norm\nfrom input;\n3.7416573867739413 = sqrt(1^2+2^2+3^2))\n\n\nnan() - Returns the constant representing not-a-number.\nSELECT nan(), is_nan(nan());\nNaN true\n\n\nsigmoid(x) - Returns 1.0 / (1.0 + exp(-x))\nWITH input as (\n  SELECT 3.0 as x\n  UNION ALL\n  SELECT -3.0 as x\n)\nselect \n  1.0 / (1.0 + exp(-x)),\n  sigmoid(x)\nfrom\n  input;\n0.04742587317756678   0.04742587357759476\n0.9525741268224334    0.9525741338729858\n\n\n\nVector/Matrix\n\ntranspose_and_dot(array X, array Y) - Returns dot(X.T, Y) as array>, shape = (X.#cols, Y.#cols)\nWITH input as (\n  select array(1.0, 2.0, 3.0, 4.0) as x, array(1, 2) as y\n  UNION ALL\n  select array(2.0, 3.0, 4.0, 5.0) as x, array(1, 2) as y\n)\nselect\n  transpose_and_dot(x, y) as xy,\n  transpose_and_dot(y, x) as yx\nfrom \n  input;\n\n[[\"3.0\",\"6.0\"],[\"5.0\",\"10.0\"],[\"7.0\",\"14.0\"],[\"9.0\",\"18.0\"]]   [[\"3.0\",\"5.0\",\"7.0\",\"9.0\"],[\"6.0\",\"10.0\",\"14.0\",\"18.0\"]]\n\n\nvector_add(array x, array y) - Perform vector ADD operation.\nSELECT vector_add(array(1.0,2.0,3.0), array(2, 3, 4));\n[3.0,5.0,7.0]\n\n\nvector_dot(array x, array y) - Performs vector dot product.\nSELECT vector_dot(array(1.0,2.0,3.0),array(2.0,3.0,4.0));\n20\n\nSELECT vector_dot(array(1.0,2.0,3.0),2);\n[2.0,4.0,6.0]\n\n\n\nSanity Checks\n\nassert(boolean condition) or FUNC(boolean condition, string errMsg)- Throws HiveException if condition is not met\nSELECT count(1) FROM stock_price WHERE assert(price > 0.0);\nSELECT count(1) FROM stock_price WHERE assert(price > 0.0, 'price MUST be more than 0.0')\n\n\nraise_error() or FUNC(string msg) - Throws an error\nSELECT product_id, price, raise_error('Found an invalid record') FROM xxx WHERE price \n\n\nText processing\n\nbase91(BINARY bin) - Convert the argument from binary to a BASE91 string\nSELECT base91(deflate('aaaaaaaaaaaaaaaabbbbccc'));\n AA+=kaIM|WTt!+wbGAA\n\n\nis_stopword(string word) - Returns whether English stopword or not\n\nnormalize_unicode(string str [, string form]) - Transforms str with the specified normalization form. The form takes one of NFC (default), NFD, NFKC, or NFKD\nSELECT normalize_unicode('ﾊﾝｶｸｶﾅ','NFKC');\n ハンカクカナ\n\nSELECT normalize_unicode('㈱㌧㌦Ⅲ','NFKC');\n (株)トンドルIII\n\n\nsingularize(string word) - Returns singular form of a given English word\nSELECT singularize(lower(\"Apples\"));\n\n \"apple\"\n\n\nsplit_words(string query [, string regex]) - Returns an array containing splitted strings\n\ntokenize(string englishText [, boolean toLowerCase]) - Returns tokenized words in array\n\nunbase91(string) - Convert a BASE91 string to a binary\nSELECT inflate(unbase91(base91(deflate('aaaaaaaaaaaaaaaabbbbccc'))));\n aaaaaaaaaaaaaaaabbbbccc\n\n\nword_ngrams(array words, int minSize, int maxSize]) - Returns list of n-grams for given words, where minSize &lt;= n &lt;= maxSize\nSELECT word_ngrams(tokenize('Machine learning is fun!', true), 1, 2);\n\n [\"machine\",\"machine learning\",\"learning\",\"learning is\",\"is\",\"is fun\",\"fun\"]\n\n\nstr_contains(string query, array searchTerms [, boolean orQuery=false]) - Returns true if the given query contains search terms\nselect\n  str_contains('There are apple and orange', array('apple')), -- or=false\n  str_contains('There are apple and orange', array('apple', 'banana'), true), -- or=true\n  str_contains('There are apple and orange', array('apple', 'banana'), false); -- or=false\n> true, true, false\n\n\n\nTimeseries\n\nmoving_avg(NUMBER value, const int windowSize) - Returns moving average of a time series using a given windowSELECT moving_avg(x, 3) FROM (SELECT explode(array(1.0,2.0,3.0,4.0,5.0,6.0,7.0)) as x) series;\n 1.0\n 1.5\n 2.0\n 3.0\n 4.0\n 5.0\n 6.0\n\n\n\nOthers\n\nconvert_label(const int|const float) - Convert from -1|1 to 0.0f|1.0f, or from 0.0f|1.0f to -1|1\n\neach_top_k(int K, Object group, double cmpKey, *) - Returns top-K values (or tail-K values when k is less than 0)\n\ngenerate_series(const int|bigint start, const int|bigint end) - Generate a series of values, from start to end. A similar function to PostgreSQL's generate_serics\nSELECT generate_series(2,4);\n\n 2\n 3\n 4\n\nSELECT generate_series(5,1,-2);\n\n 5\n 3\n 1\n\nSELECT generate_series(4,3);\n\n (no return)\n\nSELECT date_add(current_date(),value),value from (SELECT generate_series(1,3)) t;\n\n 2018-04-21      1\n 2018-04-22      2\n 2018-04-23      3\n\nWITH input as (\n SELECT 1 as c1, 10 as c2, 3 as step\n UNION ALL\n SELECT 10, 2, -3\n)\nSELECT generate_series(c1, c2, step) as series\nFROM input;\n\n 1\n 4\n 7\n 10\n 10\n 7\n 4\n\n\ntry_cast(ANY src, const string typeName) - Explicitly cast a value as a type. Returns null if cast fails.\nSELECT try_cast(array(1.0,2.0,3.0), 'array')\nSELECT try_cast(map('A',10,'B',20,'C',30), 'map')\n\n\nx_rank(KEY) - Generates a pseudo sequence number starting from 1 for each key\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"misc/topk.html":{"url":"misc/topk.html","title":"Efficient Top-K Query Processing","keywords":"","body":"\neach_top_k(int k, ANY group, double value, arg1, arg2, ..., argN) returns a top-k records for each group. It returns a relation consists of (int rank, double value, arg1, arg2, .., argN).\nThis function is particularly useful for applying a similarity/distance function where the computation complexity is O(nm).\neach_top_k is very fast when compared to other methods running top-k queries (e.g., rank/distribute by) in Hive.\n\n\n\nCaution\nUsage\nEfficient Top-k Query Processing using each_top_k\ntop-k clicks\nTop-k similarity computation\nExplicit grouping using distribute by and sort by\nParallelization of similarity computation using WITH clause\n\n\ntail-K\n\n\nAlternative approaches\n\n\n\nCaution\n\neach_top_k is supported from Hivemall v0.3.2-3 or later.\nThis UDTF assumes that input records are sorted by group. Use DISTRIBUTE BY group SORT BY group to ensure that. Or, you can use LEFT OUTER JOIN for certain cases.\nIt takes variable lengths arguments in argN. \nThe third argument value is used for the comparison.\nAny number types or timestamp are accepted for the type of value.\nIf k is less than 0, reverse order is used and tail-K records are returned for each group.\nNote that this function returns a pseudo ranking for top-k. It always returns at-most K records for each group. The ranking scheme is similar to dense_rank but slightly different in certain cases.\n\nUsage\nEfficient Top-k Query Processing using each_top_k\nEfficient processing of Top-k queries is a crucial requirement in many interactive environments that involve massive amounts of data. \nOur Hive extension each_top_k helps running Top-k processing efficiently.\n\nSuppose the following table as the input\n\n\n\n\nstudent\nclass\nscore\n\n\n\n\n1\nb\n70\n\n\n2\na\n80\n\n\n3\na\n90\n\n\n4\nb\n50\n\n\n5\na\n70\n\n\n6\nb\n60\n\n\n\n\nThen, list top-2 students for each class\n\n\n\n\nstudent\nclass\nscore\nrank\n\n\n\n\n3\na\n90\n1\n\n\n2\na\n80\n2\n\n\n1\nb\n70\n1\n\n\n6\nb\n60\n2\n\n\n\nThe standard way using SQL window function would be as follows:\nSELECT \n  student, class, score, rank\nFROM (\n  SELECT\n    student, class, score, \n    rank() over (PARTITION BY class ORDER BY score DESC) as rank\n  FROM\n    table\n) t\nWHRE rank \nAn alternative and efficient way to compute top-k items using each_top_k is as follows:\nSELECT \n  each_top_k(\n    2, class, score,\n    class, student -- output other columns in addition to rank and score\n  ) as (rank, score, class, student)\nFROM (\n  SELECT * FROM table\n  CLUSTER BY class -- Mandatory for `each_top_k`\n) t\n\n NoteCLUSTER BY x is a synonym of DISTRIBUTE BY x SORT BY x and required when using each_top_k.\nThe function signature of each_top_k is each_top_k(int k, ANY group, double value, arg1, arg2, ..., argN) and it returns a relation (int rank, double value, arg1, arg2, .., argN).\nAny number types or timestamp are accepted for the type of value but it MUST be not NULL. \nDo null hanlding like if(value is null, -1, value) to avoid null.\nIf k is less than 0, reverse order is used and tail-K records are returned for each group.\nThe ranking semantics of each_top_k follows SQL's dense_rank and then limits results by k. \n Cautioneach_top_k is benefical where the number of grouping keys are large. If the number of grouping keys are not so large (e.g., less than 100), consider using rank() over instead.\ntop-k clicks\nhttps://stackoverflow.com/questions/9390698/hive-getting-top-n-records-in-group-by-query/32559050#32559050\nset hivevar:k=5;\n\nselect\n  page-id, \n  user-id,\n  clicks\nfrom (\n  select\n    each_top_k(${k}, page-id, clicks, page-id, user-id)\n      as (rank, clicks, page-id, user-id)\n  from (\n    select\n      page-id, user-id, clicks\n    from\n      mytable\n    DISTRIBUTE BY page-id SORT BY page-id\n  ) t1\n) t2\norder by page-id ASC, clicks DESC;\n\nTop-k similarity computation\nset hivevar:k=10;\n\nSELECT\n  each_top_k(\n    ${k}, t2.id, angular_similarity(t2.features, t1.features), \n    t2.id, \n    t1.id,  \n    t1.y\n  ) as (rank, similarity, base_id, neighbor_id, y)\nFROM\n  test_hivemall t2 \n  LEFT OUTER JOIN train_hivemall t1;\n\n\n\n\nrank\nsimilarity\nbase_id\nneighbor_id\ny\n\n\n\n\n1\n0.8594650626182556\n12\n10514\n0\n\n\n2\n0.8585299849510193\n12\n11719\n0\n\n\n3\n0.856602132320404\n12\n21009\n0\n\n\n4\n0.8562054634094238\n12\n17582\n0\n\n\n5\n0.8516314029693604\n12\n22006\n0\n\n\n6\n0.8499397039413452\n12\n25364\n0\n\n\n7\n0.8467264771461487\n12\n900\n0\n\n\n8\n0.8463355302810669\n12\n8018\n0\n\n\n9\n0.8439178466796875\n12\n7041\n0\n\n\n10\n0.8438876867294312\n12\n21595\n0\n\n\n1\n0.8390793800354004\n25\n21125\n0\n\n\n2\n0.8344510793685913\n25\n14073\n0\n\n\n3\n0.8340602517127991\n25\n9008\n0\n\n\n4\n0.8328862190246582\n25\n6598\n0\n\n\n5\n0.8301891088485718\n25\n943\n0\n\n\n6\n0.8271955251693726\n25\n20400\n0\n\n\n7\n0.8255619406700134\n25\n10922\n0\n\n\n8\n0.8241575956344604\n25\n8477\n0\n\n\n9\n0.822281539440155\n25\n25977\n0\n\n\n10\n0.8205751180648804\n25\n21115\n0\n\n\n1\n0.9761330485343933\n34\n2513\n0\n\n\n2\n0.9536819458007812\n34\n8697\n0\n\n\n3\n0.9531533122062683\n34\n7326\n0\n\n\n4\n0.9493276476860046\n34\n15173\n0\n\n\n5\n0.9480557441711426\n34\n19468\n0\n\n\n..\n..\n..\n..\n..\n\n\n\nExplicit grouping using distribute by and sort by\nSELECT\n  each_top_k(\n    10, id1, angular_similarity(features1, features2), \n    id1, \n    id2,  \n    y\n  ) as (rank, similarity, id, other_id, y)\nFROM (\nselect\n  t1.id as id1,\n  t2.id as id2,\n  t1.features as features1,\n  t2.features as features2,\n  t1.y\nfrom\n  train_hivemall t1\n  CROSS JOIN test_hivemall t2\nDISTRIBUTE BY id1 SORT BY id1\n) t;\n\nParallelization of similarity computation using WITH clause\ncreate table similarities\nas\nWITH test_rnd as (\nselect\n  rand(31) as rnd,\n  id,\n  features\nfrom\n  test_hivemall\n),\nt01 as (\nselect\n id,\n features\nfrom\n test_rnd\nwhere\n rnd = 0.2 and rnd = 0.4 and rnd = 0.6 and rnd = 0.8\n),\ns01 as (\nSELECT\n  each_top_k(\n    10, t2.id, angular_similarity(t2.features, t1.features), \n    t2.id, \n    t1.id,  \n    t1.y\n  ) as (rank, similarity, base_id, neighbor_id, y)\nFROM\n  t01 t2 \n  LEFT OUTER JOIN train_hivemall t1\n),\ns02 as (\nSELECT\n  each_top_k(\n    10, t2.id, angular_similarity(t2.features, t1.features), \n    t2.id, \n    t1.id,  \n    t1.y\n  ) as (rank, similarity, base_id, neighbor_id, y)\nFROM\n  t02 t2 \n  LEFT OUTER JOIN train_hivemall t1\n),\ns03 as (\nSELECT\n  each_top_k(\n    10, t2.id, angular_similarity(t2.features, t1.features), \n    t2.id, \n    t1.id,  \n    t1.y\n  ) as (rank, similarity, base_id, neighbor_id, y)\nFROM\n  t03 t2 \n  LEFT OUTER JOIN train_hivemall t1\n),\ns04 as (\nSELECT\n  each_top_k(\n    10, t2.id, angular_similarity(t2.features, t1.features), \n    t2.id, \n    t1.id,  \n    t1.y\n  ) as (rank, similarity, base_id, neighbor_id, y)\nFROM\n  t04 t2 \n  LEFT OUTER JOIN train_hivemall t1\n),\ns05 as (\nSELECT\n  each_top_k(\n    10, t2.id, angular_similarity(t2.features, t1.features), \n    t2.id, \n    t1.id,  \n    t1.y\n  ) as (rank, similarity, base_id, neighbor_id, y)\nFROM\n  t05 t2 \n  LEFT OUTER JOIN train_hivemall t1\n)\nselect * from s01\nunion all\nselect * from s02\nunion all\nselect * from s03\nunion all\nselect * from s04\nunion all\nselect * from s05;\n\ntail-K\nset hivevar:k=-10;\n\nSELECT\n  each_top_k(\n    ${k}, t2.id, angular_similarity(t2.features, t1.features), \n    t2.id, \n    t1.id,  \n    t1.y\n  ) as (rank, similarity, base_id, neighbor_id, y)\nFROM\n  test_hivemall t2 \n  LEFT OUTER JOIN train_hivemall t1\n-- limit 25\n\n\n\n\nrank\nsimilarity\nbase_id\nneighbor_id\ny\n\n\n\n\n1\n0.4383084177970886\n1\n7503\n0\n\n\n2\n0.44166821241378784\n1\n10143\n0\n\n\n3\n0.4424300789833069\n1\n11073\n0\n\n\n4\n0.44254064559936523\n1\n17782\n0\n\n\n5\n0.4442034363746643\n1\n18556\n0\n\n\n6\n0.45163780450820923\n1\n3786\n0\n\n\n7\n0.45244503021240234\n1\n10242\n0\n\n\n8\n0.4525672197341919\n1\n21657\n0\n\n\n9\n0.4527127146720886\n1\n17218\n0\n\n\n10\n0.45314133167266846\n1\n25141\n0\n\n\n1\n0.44030147790908813\n2\n3786\n0\n\n\n2\n0.4408798813819885\n2\n23386\n0\n\n\n3\n0.44112563133239746\n2\n11073\n0\n\n\n4\n0.4415401816368103\n2\n22853\n0\n\n\n5\n0.4422193765640259\n2\n21657\n0\n\n\n6\n0.4429032802581787\n2\n10143\n0\n\n\n7\n0.4435907006263733\n2\n24413\n0\n\n\n8\n0.44569307565689087\n2\n7503\n0\n\n\n9\n0.4460843801498413\n2\n25141\n0\n\n\n10\n0.4464914798736572\n2\n24289\n0\n\n\n1\n0.43862903118133545\n3\n23150\n1\n\n\n2\n0.4398220181465149\n3\n9881\n1\n\n\n3\n0.44283604621887207\n3\n27121\n0\n\n\n4\n0.4432108402252197\n3\n26220\n1\n\n\n5\n0.44323229789733887\n3\n18541\n0\n\n\n..\n..\n..\n..\n..\n\n\n\nAlternative approaches\nIn order to utilize mapper-side aggregation and reduce computational cost of shuffling, you can use to_ordered_map or to_ordered_list to get top/tail-k elements instead of each_top_k.\nAs long as key is unique in each id, the following queries return same result:\nwith t as (\n  select\n    each_top_k(\n      10, id, key,\n      id, value\n    ) as (rank, key, id, value)\n  from (\n    select\n      *\n    from \n      test\n    cluster by \n      id\n  ) t\n)\nselect \n  id, collect_list(value) as topk\nfrom \n  t\ngroup by\n  id\n\nwith t as (\n  select\n    id, to_ordered_map(key, value, 10) as m\n  from \n    test\n  group by\n    id\n)\nselect \n  id, collect_list(value) as topk\nfrom \n  t\nlateral view explode(m) t as key, value\ngroup by\n  id\n\nselect \n  id, to_ordered_list(value, key, '-k 10') as topk\nfrom \n  test\ngroup by\n  id\n\n CautionIn case that key could duplicate in id, to_ordered_map behaves differently because key K is always unique in Map.\nSimilarly to each_top_k, tail-k can also be represented as: to_ordered_map(key, value, -10) and to_ordered_list(value, key, '-k -10').\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"misc/tokenizer.html":{"url":"misc/tokenizer.html","title":"Text Tokenizer","keywords":"","body":"\n\n\n\nTokenizer for English Texts\nTokenizer for Non-English Texts\nJapanese Tokenizer\nPart-of-speech\nChinese Tokenizer\n\n\n\n\n\nTokenizer for English Texts\nHivemall provides simple English text tokenizer UDF that has following syntax:\ntokenize(text input, optional boolean toLowerCase = false)\n\nTokenizer for Non-English Texts\nJapanese Tokenizer\nJapanese text tokenizer UDF uses Kuromoji. \nThe signature of the UDF is as follows:\n-- uses Kuromoji default dictionary by the default\ntokenize_ja(text input, optional const text mode = \"normal\", optional const array stopWords, const array stopTags, const array userDict)\n\n-- tokenize_ja_neologd uses mecab-ipa-neologd for it's dictionary.\ntokenize_ja_neologd(text input, optional const text mode = \"normal\", optional const array stopWords, const array stopTags, const array userDict)\n\n Notetokenize_ja_neologd returns tokenized strings in an array by using the NEologd dictionary. mecab-ipadic-NEologd is a customized system dictionary for MeCab inclucing new vocablaries extracted from many resources on the Web. \nSee differences between with and without Neologd as follows:\nselect tokenize_ja(\"彼女はペンパイナッポーアッポーペンと恋ダンスを踊った。\");\n>[\"彼女\",\"ペンパイナッポーアッポーペン\",\"恋\",\"ダンス\",\"踊る\"]\n\nselect tokenize_ja_neologd(\"彼女はペンパイナッポーアッポーペンと恋ダンスを踊った。\");\n> [\"彼女\",\"ペンパイナッポーアッポーペン\",\"恋ダンス\",\"踊る\"]\n\nYou can print versions for Kuromoji UDFs as follows:\nselect tokenize_ja();\n> [\"8.8.2\"]\n\nselect tokenize_ja_neologd();\n> [\"8.8.2-20200910.2\"]\n\nIts basic usage is as follows:\nselect tokenize_ja(\"kuromojiを使った分かち書きのテストです。第二引数にはnormal/search/extendedを指定できます。デフォルトではnormalモードです。\");\n\n\n[\"kuromoji\",\"使う\",\"分かち書き\",\"テスト\",\"第\",\"二\",\"引数\",\"normal\",\"search\",\"extended\",\"指定\",\"デフォルト\",\"normal\",\"モード\"]\n\nIn addition, the third and fourth argument respectively allow you to use your own list of stop words and stop tags. For example, the following query simply ignores \"kuromoji\" (as a stop word) and noun word \"分かち書き\" (as a stop tag):\nselect tokenize_ja(\"kuromojiを使った分かち書きのテストです。\", \"normal\", array(\"kuromoji\"), array(\"名詞-一般\"));\n\n\n[\"を\",\"使う\",\"た\",\"の\",\"テスト\",\"です\"]\n\nselect tokenize_ja(\"kuromojiを使った分かち書きのテストです。\", \"normal\", array(\"kuromoji\"), stoptags_exclude(array(\"名詞\")));\n\n\n[\"分かち書き\",\"テスト\"]\n\nstoptags_exclude(array tags, [, const string lang='ja']) is a useful UDF for getting stoptags excluding given part-of-speech tags as seen below:\nselect stoptags_exclude(array(\"名詞-固有名詞\"));\n\n\n[\"その他\",\"その他-間投\",\"フィラー\",\"副詞\",\"副詞-一般\",\"副詞-助詞類接続\",\"助動詞\",\"助詞\",\"助詞-並立助詞\"\n,\"助詞-係助詞\",\"助詞-副助詞\",\"助詞-副助詞／並立助詞／終助詞\",\"助詞-副詞化\",\"助詞-接続助詞\",\"助詞-格助詞\n\",\"助詞-格助詞-一般\",\"助詞-格助詞-引用\",\"助詞-格助詞-連語\",\"助詞-特殊\",\"助詞-終助詞\",\"助詞-連体化\",\"助\n詞-間投助詞\",\"動詞\",\"動詞-接尾\",\"動詞-自立\",\"動詞-非自立\",\"名詞\",\"名詞-サ変接続\",\"名詞-ナイ形容詞語幹\",\n\"名詞-一般\",\"名詞-代名詞\",\"名詞-代名詞-一般\",\"名詞-代名詞-縮約\",\"名詞-副詞可能\",\"名詞-動詞非自立的\",\"名\n詞-引用文字列\",\"名詞-形容動詞語幹\",\"名詞-接尾\",\"名詞-接尾-サ変接続\",\"名詞-接尾-一般\",\"名詞-接尾-人名\",\"\n名詞-接尾-副詞可能\",\"名詞-接尾-助動詞語幹\",\"名詞-接尾-助数詞\",\"名詞-接尾-地域\",\"名詞-接尾-形容動詞語幹\"\n,\"名詞-接尾-特殊\",\"名詞-接続詞的\",\"名詞-数\",\"名詞-特殊\",\"名詞-特殊-助動詞語幹\",\"名詞-非自立\",\"名詞-非自\n立-一般\",\"名詞-非自立-副詞可能\",\"名詞-非自立-助動詞語幹\",\"名詞-非自立-形容動詞語幹\",\"形容詞\",\"形容詞-接\n尾\",\"形容詞-自立\",\"形容詞-非自立\",\"感動詞\",\"接続詞\",\"接頭詞\",\"接頭詞-動詞接続\",\"接頭詞-名詞接続\",\"接頭\n詞-形容詞接続\",\"接頭詞-数接\",\"未知語\",\"記号\",\"記号-アルファベット\",\"記号-一般\",\"記号-句点\",\"記号-括弧閉\n\",\"記号-括弧開\",\"記号-空白\",\"記号-読点\",\"語断片\",\"連体詞\",\"非言語音\"]\n\nMoreover, the fifth argument userDict enables you to register a user-defined custom dictionary in Kuromoji official format:\nselect tokenize_ja(\"日本経済新聞＆関西国際空港\", \"normal\", null, null, \n                   array(\n                     \"日本経済新聞,日本 経済 新聞,ニホン ケイザイ シンブン,カスタム名詞\", \n                     \"関西国際空港,関西 国際 空港,カンサイ コクサイ クウコウ,テスト名詞\"\n                   ));\n\n\n[\"日本\",\"経済\",\"新聞\",\"関西\",\"国際\",\"空港\"]\n\nNote that you can pass null to each of the third and fourth argument to explicitly use Kuromoji's default stop words and stop tags.\nIf you have a large custom dictionary as an external file, userDict can also be const string userDictURL which indicates URL of the external file on somewhere like Amazon S3:\nselect tokenize_ja(\"日本経済新聞＆関西国際空港\", \"normal\", null, null,\n                   \"https://raw.githubusercontent.com/atilika/kuromoji/909fd6b32bf4e9dc86b7599de5c9b50ca8f004a1/kuromoji-core/src/test/resources/userdict.txt\");\n\n> [\"日本\",\"経済\",\"新聞\",\"関西\",\"国際\",\"空港\"]\n\n NoteDictionary SHOULD be accessible through http/https protocol. And, it SHOULD be compressed using gzip with .gz suffix because the maximum dictionary size is limited to 32MB and read timeout is set to 60 sec. Also, connection must be established in 10 sec.If you want to use HTTP Basic Authentication, please use the following form: https://user:password@www.sitreurl.com/my_dict.txt.gz (see Sec 3.1 of rfc1738)\nFor detailed APIs, please refer Javadoc of JapaneseAnalyzer as well.\nPart-of-speech\nFrom Hivemall v0.6.0, the second argument can also accept the following option format:\n -mode    The tokenization mode. One of ['normal', 'search',\n               'extended', 'default' (normal)]\n -pos          Return part-of-speech information\nThen, you can get part-of-speech information as follows:\nWITH tmp as (\n  select\n    tokenize_ja('kuromojiを使った分かち書きのテストです。','-mode search -pos') as r\n)\nselect\n  r.tokens,\n  r.pos,\n  r.tokens[0] as token0,\n  r.pos[0] as pos0\nfrom\n  tmp;\n\n\n\n\ntokens\npos\ntoken0\npos0\n\n\n\n\n[\"kuromoji\",\"使う\",\"分かち書き\",\"テスト\"]\n[\"名詞-一般\",\"動詞-自立\",\"名詞-一般\",\"名詞-サ変接続\"]\nkuromoji\n名詞-一般\n\n\n\nNote that when -pos option is specified, tokenize_ja returns a struct record containing array tokens and array pos as the elements.\nChinese Tokenizer\nChinese text tokenizer UDF uses SmartChineseAnalyzer. \nThe signature of the UDF is as follows:\ntokenize_cn(string line, optional const array stopWords)\n\nIts basic usage is as follows:\nselect tokenize_cn(\"Smartcn为Apache2.0协议的开源中文分词系统，Java语言编写，修改的中科院计算所ICTCLAS分词系统。\");\n\n\n[smartcn, 为, apach, 2, 0, 协议, 的, 开源, 中文, 分词, 系统, java, 语言, 编写, 修改, 的, 中科院, 计算, 所, ictcla, 分词, 系统]\n\nFor detailed APIs, please refer Javadoc of SmartChineseAnalyzer as well.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"misc/approx.html":{"url":"misc/approx.html","title":"Approximate Aggregate Functions","keywords":"","body":"\n\n\n\nApproximate Counting using HyperLogLog\nUsage\nFunction Signature\n\n\n\n\n\nApproximate Counting using HyperLogLog\ncount(distinct value) can often cause memory exhausted errors where input data and the cardinality of value are large.\nHyperLogLog is an efficient algorithm for approximating the number of distinct elements in a multiset. \nHivemall implements HyperLogLog++ in approx_count_distinct.\nUsage\napprox_count_distinct is less accurate than COUNT(DISTINCT expression), but performs better on huge input.\nselect\n    count(distinct rowid) as actual,\n    approx_count_distinct(rowid) as default_p \nfrom\n    train;\n\n\n\n\nactual\ndefault_p\n\n\n\n\n45840617\n45567770\n\n\n\nselect\n    approx_count_distinct(rowid, '-p 4') as p4,\n    approx_count_distinct(rowid, '-p 6 -sp 6') as p6_sp6,\n    approx_count_distinct(rowid, '-p 14') as p14,\n    approx_count_distinct(rowid, '-p 15') as p15,\n    approx_count_distinct(rowid, '-p 16') as p16,\n    approx_count_distinct(rowid, '-p 24') as p24,\n    approx_count_distinct(rowid, '-p 25') as p25,\n    approx_count_distinct(rowid, '-p 15 -sp 15') as p15_sp15\nfrom\n    train;\n\n\n\n\np4\np6_sp6\np14\np15\np16\np24\np25\np15_sp15\n\n\n\n\n38033066\n49332600\n45051015\n45567770\n45614484\n45831359\n45832280\n45567770\n\n\n\n Notep controls expected precision and memory consumption tradeoff and default p=15 generally works well. Find More information on this paper.\nFunction Signature\nYou can find the function signature and options of approx_count_distinct is as follows:\nselect \n    approx_count_distinct(rowid, '-help')\nfrom\n    train;\n\nusage: HLLEvaluator [-help] [-p ] [-sp ]\n -help       Show function help\n -p     The size of registers for the normal set. `p` MUST be in the\n             range [4,sp] and 15 by the default\n -sp    The size of registers for the sparse set. `sp` MUST be in the\n             range [4,32] and 25 by the defaul\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/scaling.html":{"url":"ft_engineering/scaling.html","title":"Feature Scaling","keywords":"","body":"\n\n\n\nL1/L2 Normalization\nMin-Max Normalization\nFeature scaling by zscore\nApply Normalization to more complex feature vector\n\n\n\nL1/L2 Normalization\nL1 and L2 normalization ensures that each feature vector has unit length:\nselect l1_normalize(array('apple:1.0', 'banana:0.5'))\n\n\n[\"apple:0.6666667\",\"banana:0.33333334\"]\n\nselect l2_normalize(array('apple:1.0', 'banana:0.5'))\n\n\n[\"apple:0.8944272\",\"banana:0.4472136\"]\n\nMin-Max Normalization\nMin-max normalization converts values to range [0.0,1.0].\nselect \n  rescale(target, min(target) over (), max(target) over ()) as target\nfrom\n  e2006tfidf_train\n\nIt can also expressed without Windowing function as follows:\nselect min(target), max(target)\nfrom (\n  select target from e2006tfidf_train \n-- union all\n-- select target from e2006tfidf_test \n) t;\n\n\n-7.899578       -0.51940954\n\nset hivevar:min_target=-7.899578;\nset hivevar:max_target=-0.51940954;\n\ncreate or replace view e2006tfidf_train_scaled \nas\nselect \n  rowid,\n  rescale(target, ${min_target}, ${max_target}) as target, \n  features\nfrom \n  e2006tfidf_train;\n\nFeature scaling by zscore\nRefer this article to get details about Zscore.\nselect \n  zscore(target, avg(target) over (), stddev_pop(target) over ()) as target\nfrom \n  e2006tfidf_train;\n\nApply Normalization to more complex feature vector\nApply normalization to the following data.\ncreate table train as \nselect \n  1 as rowid, array(\"weight:69.613\",\"specific_heat:129.07\",\"reflectance:52.111\") as features\nUNION ALL\nselect \n  2 as rowid, array(\"weight:70.67\",\"specific_heat:128.161\",\"reflectance:52.446\") as features\nUNION ALL\nselect \n  3 as rowid, array(\"weight:72.303\",\"specific_heat:128.45\",\"reflectance:52.853\") as features\n\nselect rowid, features from train;\n\n1       [\"weight:69.613\",\"specific_heat:129.07\",\"reflectance:52.111\"]\n2       [\"weight:70.67\",\"specific_heat:128.161\",\"reflectance:52.446\"]\n3       [\"weight:72.303\",\"specific_heat:128.45\",\"reflectance:52.853\"]\nWe can create a normalized table as follows:\ncreate table train_normalized\nas\nWITH exploded as (\n  select \n    rowid, \n    extract_feature(feature) as feature,\n    extract_weight(feature) as value\n  from \n    train \n    LATERAL VIEW explode(features) exploded AS feature\n), \nscaled as (\n  select \n    rowid,\n    feature,\n    rescale(value, min(value) over (partition by feature), max(value) over (partition by feature)) as minmax,\n    zscore(value, avg(value) over (partition by feature), stddev_pop(value) over (partition by feature)) as zscore\n  from \n    exploded\n)\nselect\n  rowid,\n  collect_list(feature(feature, minmax)) as features\nfrom\n  scaled\ngroup by\n  rowid;\n\n1       [\"reflectance:0.0\",\"specific_heat:1.0\",\"weight:0.0\"]\n2       [\"reflectance:0.4514809\",\"specific_heat:0.0\",\"weight:0.39293614\"]\n3       [\"reflectance:1.0\",\"specific_heat:0.31792927\",\"weight:1.0\"]\n...\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/hashing.html":{"url":"ft_engineering/hashing.html","title":"Feature Hashing","keywords":"","body":"\nHivemall supports Feature Hashing (a.k.a. hashing trick) through feature_hashing and mhash functions. \nFind the differences in the following examples.\n\n\nfeature_hashing function\nfeature_hashing applies MurmurHash3 hashing to features. \nselect feature_hashing('aaa');\n\n\n4063537\n\nselect feature_hashing('aaa','-features 3');\n\n\n2\n\nselect feature_hashing(array('aaa','bbb'));\n\n\n[\"4063537\",\"8459207\"]\n\nselect feature_hashing(array('aaa','bbb'),'-features 10');\n\n\n[\"7\",\"1\"]\n\nselect feature_hashing(array('aaa:1.0','aaa','bbb:2.0'));\n\n\n[\"4063537:1.0\",\"4063537\",\"8459207:2.0\"]\n\nselect feature_hashing(array('aaa:1.0','aaa','bbb:2.0'), '-libsvm');\n\n\n[\"4063537:1.0\",\"4063537:1\",\"8459207:2.0\"]\n\nselect feature_hashing(array('aaa:1.0','aaa','bbb:2.0'), '-features 10');\n\n\n[\"7:1.0\",\"7\",\"1:2.0\"]\n\nselect feature_hashing(array('aaa:1.0','aaa','bbb:2.0'), '-features 10 -libsvm');\n\n\n[\"1:2.0\",\"7:1.0\",\"7:1\"]\n\nselect feature_hashing(array(1,2,3));\n\n\n[\"11293631\",\"3322224\",\"4331412\"]\n\nselect feature_hashing(array('1','2','3'));\n\n\n[\"11293631\",\"3322224\",\"4331412\"]\n\nselect feature_hashing(array('1:0.1','2:0.2','3:0.3'));\n\n\n[\"11293631:0.1\",\"3322224:0.2\",\"4331412:0.3\"]\n\nselect feature_hashing(features), features from training_fm limit 2;\n\n\n[\"1803454\",\"6630176\"]   [\"userid#5689\",\"movieid#3072\"]\n[\"1828616\",\"6238429\"]   [\"userid#4505\",\"movieid#2331\"]\n\nselect feature_hashing(array(\"userid#4505:3.3\",\"movieid#2331:4.999\", \"movieid#2331\"));\n\n\n[\"1828616:3.3\",\"6238429:4.999\",\"6238429\"]\n\nselect feature_hashing();\n\nusage: feature_hashing(array features [, const string options]) -\n       returns a hashed feature vector in array [-features ]\n       [-libsvm]\n -features,--num_features    The number of features [default:\n                                  16777217 (2^24)]\n -libsvm                          Returns in libsvm format\n                                  (:)* sorted by index\n                                  ascending order\n\n NoteThe hash value is starting from 1 and 0 is system reserved for a bias clause. The default number of features are 16777217 (2^24). \nYou can control the number of features by -num_features (or -features) option.\nmhash function\ndescribe function extended mhash;\n\n\nmhash(string word) returns a murmurhash3 INT value starting from 1\n\nselect mhash('aaa');\n\n\n4063537\n\nNote: The default number of features are 16777216 (2^24).\nset hivevar:num_features=16777216;\nselect mhash('aaa',${num_features});\n\n\n4063537\n\nNote: mhash returns a +1'd murmurhash3 value starting from 1. Never returns 0 (It's a system reserved number).\nset hivevar:num_features=1;\nselect mhash('aaa',${num_features});\n\n\n1\n\nNote: mhash does not considers feature values.\nselect mhash('aaa:2.0');\n\n\n2746618\n\nNote: mhash always returns a scalar INT value.\nselect mhash(array('aaa','bbb'));\n\n\n9566153\n\nNote: mhash value of an array is element order-sentitive.\nselect mhash(array('bbb','aaa'));\n\n\n3874068\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/selection.html":{"url":"ft_engineering/selection.html","title":"Feature Selection","keywords":"","body":"\nFeature Selection is the process of selecting a subset of relevant features for use in model construction. \nIt is a useful technique to 1) improve prediction results by omitting redundant features, 2) to shorten training time, and 3) to know important features for prediction.\nNote: This feature is supported from Hivemall v0.5-rc.1 or later.\n\n\n\nSupported Feature Selection algorithms\nUsage\nFeature Selection based on Chi-square test\nFeature Selection based on Signal Noise Ratio (SNR)\n\n\nFunction signatures\n[UDAF] transpose_and_dot(X::array, Y::array)::array>\n[UDF] select_k_best(X::array, importance_list::array, k::int)::array\n[UDF] chi2(observed::array>, expected::array>)::struct, array>\n[UDAF] snr(X::array, Y::array)::array\n\n\n\n\n\nSupported Feature Selection algorithms\n\nChi-square (Chi2)\nIn statistics, the χ2\\chi^2χ​2​​ test is applied to test the independence of two even events. Chi-square statistics between every feature variable and the target variable can be applied to Feature Selection. Refer this article for Mathematical details.\n\n\nSignal Noise Ratio (SNR)\nThe Signal Noise Ratio (SNR) is a univariate feature ranking metric, which can be used as a feature selection criterion for binary classification problems. SNR is defined as ∣μ1−μ2∣/(σ1+σ2)|\\mu_{1} - \\mu_{2}| / (\\sigma_{1} + \\sigma_{2})∣μ​1​​−μ​2​​∣/(σ​1​​+σ​2​​), where μk\\mu_{k}μ​k​​ is the mean value of the variable in classes kkk, and σk\\sigma_{k}σ​k​​ is the standard deviations of the variable in classes kkk. Clearly, features with larger SNR are useful for classification.\n\n\n\nUsage\nFeature Selection based on Chi-square test\nCREATE TABLE input (\n  X array, -- features\n  Y array -- binarized label\n);\n\nset hivevar:k=2;\n\nWITH stats AS (\n  SELECT\n    transpose_and_dot(Y, X) AS observed, -- array>, shape = (n_classes, n_features)\n    array_sum(X) AS feature_count, -- n_features col vector, shape = (1, array)\n    array_avg(Y) AS class_prob -- n_class col vector, shape = (1, array)\n  FROM\n    input\n),\ntest AS (\n  SELECT\n    transpose_and_dot(class_prob, feature_count) AS expected -- array>, shape = (n_class, n_features)\n  FROM\n    stats\n),\nchi2 AS (\n  SELECT\n    chi2(r.observed, l.expected) AS v -- struct, array>, each shape = (1, n_features)\n  FROM\n    test l\n    CROSS JOIN stats r\n)\nSELECT\n  select_k_best(l.X, r.v.chi2, ${k}) as features -- top-k feature selection based on chi2 score\nFROM\n  input l\n  CROSS JOIN chi2 r;\n\nFeature Selection based on Signal Noise Ratio (SNR)\nCREATE TABLE input (\n  X array, -- features\n  Y array -- binarized label\n);\n\nset hivevar:k=2;\n\nWITH snr AS (\n  SELECT snr(X, Y) AS snr -- aggregated SNR as array, shape = (1, #features)\n  FROM input\n)\nSELECT \n  select_k_best(X, snr, ${k}) as features\nFROM\n  input\n  CROSS JOIN snr;\n\nFunction signatures\n[UDAF] transpose_and_dot(X::array, Y::array)::array>\nInput\n\n\n\narray X\narray Y\n\n\n\n\na row of matrix\na row of matrix\n\n\n\nOutput\n\n\n\narray> dot product\n\n\n\n\ndot(X.T, Y) of shape = (X.#cols, Y.#cols)\n\n\n\n[UDF] select_k_best(X::array, importance_list::array, k::int)::array\nInput\n\n\n\narray X\narray importance_list\nint k\n\n\n\n\nfeature vector\nimportance of each feature\nthe number of features to be selected\n\n\n\nOutput\n\n\n\narray> k-best features\n\n\n\n\ntop-k elements from feature vector X based on importance list\n\n\n\n[UDF] chi2(observed::array>, expected::array>)::struct, array>\nInput\n\n\n\narray observed\narray expected\n\n\n\n\nobserved features\nexpected features dot(class_prob.T, feature_count)\n\n\n\nBoth of observed and expected have a shape (#classes, #features)\nOutput\n\n\n\nstruct, array> importance_list\n\n\n\n\nchi2-value and p-value for each feature\n\n\n\n[UDAF] snr(X::array, Y::array)::array\nInput\n\n\n\narray X\narray Y\n\n\n\n\nfeature vector\none hot label\n\n\n\nOutput\n\n\n\narray importance_list\n\n\n\n\nSignal Noise Ratio for each feature\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/binning.html":{"url":"ft_engineering/binning.html","title":"Feature Binning","keywords":"","body":"\nFeature binning is a method of dividing quantitative variables into categorical values. It groups quantitative values into a pre-defined number of bins.\nIf the number of bins is set to 3, the bin ranges become something like [-Inf, 1], (1, 10], (10, Inf].\n\n\n\nData Preparation\nUsage\nCustom rule for binning\nBinning based on Quantiles\nConcrete Example\nCreate a mapping table by Feature Binning\n\n\nFunction Signatures\nUDAF build_bins(weight num_of_bins [, auto_shrink=false])\nUDF feature_binning(features, quantiles_map)\nUDF feature_binning(weight, quantiles)\n\n\n\n\n\nData Preparation\nPrepare sample data (users table) first as follows:\nCREATE TABLE users (\n  rowid int, name string, age int, gender string\n);\nINSERT INTO users VALUES\n  (1, 'Jacob', 20, 'Male'),\n  (2, 'Mason', 22, 'Male'),\n  (3, 'Sophia', 35, 'Female'),\n  (4, 'Ethan', 55, 'Male'),\n  (5, 'Emma', 15, 'Female'),\n  (6, 'Noah', 46, 'Male'),\n  (7, 'Isabella', 20, 'Female')\n;\n\nCREATE TABLE input as\nSELECT\n  rowid,\n  array_concat(\n    categorical_features(\n      array('name', 'gender'),\n      name, gender\n    ),\n    quantitative_features(\n      array('age'),\n      age\n    )\n  ) AS features\nFROM\n  users;\n\nselect * from input limit 2;\n\n\n\n\ninput.rowid\ninput.features\n\n\n\n\n1\n[\"name#Jacob\",\"gender#Male\",\"age:20.0\"]\n\n\n2\n[\"name#Mason\",\"gender#Male\",\"age:22.0\"]\n\n\n\nUsage\nCustom rule for binning\nYou can provide a custom rule for binning as follows:\nselect \n  features as original,\n  feature_binning(\n    features,\n    -- [-INF-10.0], (10.0-20.0], (20.0-30.0], (30.0-40.0], (40.0-INF]\n    map('age', array(-infinity(), 10.0, 20.0, 30.0, 40.0, infinity()))\n  ) as binned\nfrom\n  input;\n\n\n\n\noriginal\nbinned\n\n\n\n\n[\"name#Jacob\",\"gender#Male\",\"age:20.0\"]\n[\"name#Jacob\",\"gender#Male\",\"age:1\"]\n\n\n[\"name#Mason\",\"gender#Male\",\"age:22.0\"]\n[\"name#Mason\",\"gender#Male\",\"age:2\"]\n\n\n[\"name#Sophia\",\"gender#Female\",\"age:35.0\"]\n[\"name#Sophia\",\"gender#Female\",\"age:3\"]\n\n\n[\"name#Ethan\",\"gender#Male\",\"age:55.0\"]\n[\"name#Ethan\",\"gender#Male\",\"age:4\"]\n\n\n[\"name#Emma\",\"gender#Female\",\"age:15.0\"]\n[\"name#Emma\",\"gender#Female\",\"age:1\"]\n\n\n[\"name#Noah\",\"gender#Male\",\"age:46.0\"]\n[\"name#Noah\",\"gender#Male\",\"age:4\"]\n\n\n[\"name#Isabella\",\"gender#Female\",\"age:20.0\"]\n[\"name#Isabella\",\"gender#Female\",\"age:1\"]\n\n\n\nBinning based on Quantiles\nYou can apply feature binning based on quantiles. \nSuppose converting age values into 3 bins:\nSELECT\n  map('age', build_bins(age, 3)) AS quantiles_map\nFROM\n  users\n\n\n{\"age\":[-Infinity,18.333333333333332,30.666666666666657,Infinity]}\n\nIn the above query result, you can find 4 values for age in quantiles_map. It's a threshold for 3 bins.\nWITH bins as (\n  SELECT\n    map('age', build_bins(age, 3)) AS quantiles_map\n  FROM\n    users\n)\nselect\n  feature_binning(\n    array('age:-Infinity', 'age:-1', 'age:0', 'age:1', 'age:18.333333333333331', 'age:18.333333333333332'), quantiles_map\n  ),\n  feature_binning(\n    array('age:18.3333333333333333', 'age:18.33333333333334', 'age:19', 'age:30', 'age:30.666666666666656', 'age:30.666666666666657'), quantiles_map\n  ),\n  feature_binning(\n    array('age:666666666666658', 'age:30.66666666666666', 'age:31', 'age:99', 'age:Infinity'), quantiles_map\n  ),\n  feature_binning(\n    array('age:NaN'), quantiles_map\n  ),\n  feature_binning( -- not in map\n    array('weight:60.3'), quantiles_map\n  )\nfrom\n  bins\n\n\n[\"age:0\",\"age:0\",\"age:0\",\"age:0\",\"age:0\",\"age:0\"]       [\"age:0\",\"age:1\",\"age:1\",\"age:1\",\"age:1\",\"age:1\"]       [\"age:2\",\"a\nge:2\",\"age:2\",\"age:2\",\"age:2\"]  [\"age:3\"]       [\"weight:60.3\"]\n\nThe following query shows more practical usage:\nWITH bins AS (\n  SELECT\n    map('age', build_bins(age, 3)) AS quantiles_map\n  FROM\n    users\n)\nSELECT\n  feature_binning(features, quantiles_map) AS features\nFROM\n  input\n  CROSS JOIN bins;\n\n\n\n\nfeatures: array\n\n\n\n\n[\"name#Jacob\",\"gender#Male\",\"age:1\"]\n\n\n[\"name#Mason\",\"gender#Male\",\"age:1\"]\n\n\n[\"name#Sophia\",\"gender#Female\",\"age:2\"]\n\n\n[\"name#Ethan\",\"gender#Male\",\"age:2\"]\n\n\n...\n\n\n\nConcrete Example\nHere, we show a more practical usage of feature_binning UDF that applied feature binning for given feature vectors.\nWITH extracted as (\n  select \n    extract_feature(feature) as index,\n    extract_weight(feature) as value\n  from\n    input l\n    LATERAL VIEW explode(features) r as feature\n  where\n    instr(feature, ':') > 0 -- filter out categorical features\n),\nmapping as (\n  select\n    index, \n    build_bins(value, 5, true) as quantiles -- 5 bins with auto bin shrinking\n  from\n    extracted\n  group by\n    index\n),\nbins as (\n   select \n    to_map(index, quantiles) as quantiles \n   from\n    mapping\n)\nselect\n  l.features as original,\n  feature_binning(l.features, r.quantiles) as features\nfrom\n  input l\n  cross join bins r\n-- limit 10;\n\n\n\n\noriginal\nfeatures\n\n\n\n\n[\"name#Jacob\",\"gender#Male\",\"age:20.0\"]\n[\"name#Jacob\",\"gender#Male\",\"age:2\"]\n\n\n[\"name#Isabella\",\"gender#Female\",\"age:20.0\"]\n[\"name#Isabella\",\"gender#Female\",\"age:2\"]\n\n\n...\n...\n\n\n\nCreate a mapping table by Feature Binning\nWITH bins AS (\n  SELECT build_bins(age, 3) AS quantiles\n  FROM users\n)\nSELECT\n  age, feature_binning(age, quantiles) AS bin\nFROM\n  users CROSS JOIN bins;\n\n\n\n\nage:int\nbin: int\n\n\n\n\n20\n1\n\n\n22\n1\n\n\n35\n2\n\n\n55\n2\n\n\n15\n0\n\n\n46\n2\n\n\n20\n1\n\n\n\nFunction Signatures\nUDAF build_bins(weight num_of_bins [, auto_shrink=false])\nInput\n\n\n\nweight: int|bigint|float|double\nnum_of_bins: int\n[auto_shrink: boolean = false]\n\n\n\n\nweight\ngreather than or equals to 2\nbehavior when separations are repeated: T=>skip, F=>exception\n\n\n\nOutput\n\n\n\nquantiles: array\n\n\n\n\nthresholds of bins based on quantiles\n\n\n\n NoteThere is the possibility quantiles are repeated because of too many num_of_bins or too few data.\nIf auto_shrink is set to true, skip duplicated quantiles. If not, throw an exception.\nUDF feature_binning(features, quantiles_map)\nInput\n\n\n\nfeatures: array\nquantiles_map: map>\n\n\n\n\nfeature vector\na map where key=column name and value=quantiles\n\n\n\nOutput\n\n\n\nfeatures: array\n\n\n\n\nbinned features\n\n\n\nUDF feature_binning(weight, quantiles)\nInput\n\n\n\nweight: int|bigint|float|double\nquantiles: array\n\n\n\n\nweight\narray of separation value\n\n\n\nOutput\n\n\n\nbin: int\n\n\n\n\ncategorical value (bin ID)\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/pairing.html":{"url":"ft_engineering/pairing.html","title":"Feature Paring","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/polynomial.html":{"url":"ft_engineering/polynomial.html","title":"Polynomial features","keywords":"","body":"\n\n\n\nPolynomial Features\nPowered Features\n\n\n\nPolynomial features allows you to do non-linear regression/classification with a linear model.\n CautionPolynomial Features assumes normalized inputs because x**n easily becomes INF/-INF where n is large.\nPolynomial Features\nAs a similar to one in Scikit-Learn, polynomial_feature(array features, int degree [, boolean interactionOnly=false, boolean truncate=true]) is a function to generate polynomial and interaction features.\nselect polynomial_features(array(\"a:0.5\",\"b:0.2\"), 2);\n> [\"a:0.5\",\"a^a:0.25\",\"a^b:0.1\",\"b:0.2\",\"b^b:0.040000003\"]\n\nselect polynomial_features(array(\"a:0.5\",\"b:0.2\"), 3);\n> [\"a:0.5\",\"a^a:0.25\",\"a^a^a:0.125\",\"a^a^b:0.05\",\"a^b:0.1\",\"a^b^b:0.020000001\",\"b:0.2\",\"b^b:0.040000003\",\"b^b^b:0.008\"]\n\n-- interaction only\nselect polynomial_features(array(\"a:0.5\",\"b:0.2\"), 3, true);\n> [\"a:0.5\",\"a^b:0.1\",\"b:0.2\"]\n\nselect polynomial_features(array(\"a:0.5\",\"b:0.2\",\"c:0.3\"), 3, true);\n> [\"a:0.5\",\"a^b:0.1\",\"a^b^c:0.030000001\",\"a^c:0.15\",\"b:0.2\",\"b^c:0.060000002\",\"c:0.3\"]\n\n-- interaction only + no truncate\nselect polynomial_features(array(\"a:0.5\",\"b:1.0\", \"c:0.3\"), 3, true, false);\n> [\"a:0.5\",\"a^b:0.5\",\"a^b^c:0.15\",\"a^c:0.15\",\"b:1.0\",\"b^c:0.3\",\"c:0.3\"]\n\n-- interaction only + truncate\nselect polynomial_features(array(\"a:0.5\",\"b:1.0\",\"c:0.3\"), 3, true, true);\n> [\"a:0.5\",\"a^c:0.15\",\"b:1.0\",\"c:0.3\"]\n\n-- truncate\nselect polynomial_features(array(\"a:0.5\",\"b:1.0\", \"c:0.3\"), 3, false, true);\n> [\"a:0.5\",\"a^a:0.25\",\"a^a^a:0.125\",\"a^a^c:0.075\",\"a^c:0.15\",\"a^c^c:0.045\",\"b:1.0\",\"c:0.3\",\"c^c:0.09\",\"c^c^c:0.027000003\"]\n\n-- do not truncate\nselect polynomial_features(array(\"a:0.5\",\"b:1.0\", \"c:0.3\"), 3, false, false);\n> [\"a:0.5\",\"a^a:0.25\",\"a^a^a:0.125\",\"a^a^b:0.25\",\"a^a^c:0.075\",\"a^b:0.5\",\"a^b^b:0.5\",\"a^b^c:0.15\",\"a^c:0.15\",\"a^c^c:0.045\",\"b:1.0\",\"b^b:1.0\",\"b^b^b:1.0\",\"b^b^c:0.3\",\"b^c:0.3\",\"b^c^c:0.09\",\"c:0.3\",\"c^c:0.09\",\"c^c^c:0.027000003\"]\n>\n\nNote: truncate is used to eliminate unnecessary combinations.\nPowered Features\nThe powered_features(array features, int degree [, boolean truncate=true] ) is a function to generate polynomial features.\nselect powered_features(array(\"a:0.5\",\"b:0.2\"), 3);\n> [\"a:0.5\",\"a^2:0.25\",\"a^3:0.125\",\"b:0.2\",\"b^2:0.040000003\",\"b^3:0.008\"]\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/ft_trans.html":{"url":"ft_engineering/ft_trans.html","title":"Feature Transformation","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/vectorization.html":{"url":"ft_engineering/vectorization.html","title":"Feature vectorization","keywords":"","body":"\nFeature Vectorization\narray vectorize_feature(array featureNames, ...) is useful to generate a feature vector for each row, from a table.\nselect vectorize_features(array(\"a\",\"b\"),\"0.2\",\"0.3\") from dual;\n>[\"a:0.2\",\"b:0.3\"]\n\n-- avoid zero weight\nselect vectorize_features(array(\"a\",\"b\"),\"0.2\",0) from dual;\n> [\"a:0.2\"]\n\n-- true boolean value is treated as 1.0 (a categorical value w/ its column name)\nselect vectorize_features(array(\"a\",\"b\",\"bool\"),0.2,0.3,true) from dual;\n> [\"a:0.2\",\"b:0.3\",\"bool:1.0\"]\n\n-- an example to generate feature vectors from table\nselect * from dual;\n> 1                                         \nselect vectorize_features(array(\"a\"),*) from dual;\n> [\"a:1.0\"]\n\n-- has categorical feature\nselect vectorize_features(array(\"a\",\"b\",\"weather\"),\"0.2\",\"0.3\",\"sunny\") from dual;\n> [\"a:0.2\",\"b:0.3\",\"weather#sunny\"]\n\nselect\n  id,\n  vectorize_features(\n    array(\"age\",\"job\",\"marital\",\"education\",\"default\",\"balance\",\"housing\",\"loan\",\"contact\",\"day\",\"month\",\"duration\",\"campaign\",\"pdays\",\"previous\",\"poutcome\"), \n    age,job,marital,education,default,balance,housing,loan,contact,day,month,duration,campaign,pdays,previous,poutcome\n  ) as features,\n  y\nfrom\n  train\nlimit 2;\n\n\n1       [\"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\n2       [\"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\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/quantify.html":{"url":"ft_engineering/quantify.html","title":"Quantify non-number features","keywords":"","body":"\nquantified_features is useful for transforming values of non-number columns to indexed numbers.\nNote: The feature is supported from Hivemall v0.4 or later.\ndesc train;\n\nid                      int                                         \nage                     int                                         \njob                     string                                      \nmarital                 string                                      \neducation               string                                      \ndefault                 string                                      \nbalance                 int                                         \nhousing                 string                                      \nloan                    string                                      \ncontact                 string                                      \nday                     int                                         \nmonth                   string                                      \nduration                int                                         \ncampaign                int                                         \npdays                   int                                         \nprevious                int                                         \npoutcome                string                                      \ny                       int\n\nselect * from train limit 10;\n\n1       39      blue-collar     married secondary       no      1756    yes     no      cellular        3       apr     939     1       -1      0       unknown 1\n2       51      entrepreneur    married primary no      1443    no      no      cellular        18      feb     172     10      -1      0       unknown 1\n3       36      management      single  tertiary        no      436     no      no      cellular        13      apr     567     1       595     2       failure 1\n4       63      retired married secondary       no      474     no      no      cellular        25      jan     423     1       -1      0       unknown 1\n5       31      management      single  tertiary        no      354     no      no      cellular        30      apr     502     1       9       2       success 1\n6       29      blue-collar     single  secondary       no      260     yes     no      unknown 2       jun     707     14      -1      0       unknown 1\n7       37      services        married secondary       no      52      yes     no      cellular        6       sep     908     1       185     9       success 1\n8       32      technician      single  secondary       no      230     yes     no      cellular        18      may     442     1       266     8       failure 1\n9       31      admin.  single  secondary       no      0       yes     no      cellular        7       may     895     2       295     2       failure 1\n10      32      self-employed   single  tertiary        no      1815    no      no      telephone       10      aug     235     1       102     2       failure 1\n\nset hivevar:output_row=true;\n\nselect quantify(${output_row}, *) \nfrom (\n  select * from train\n  order by id asc -- force quantify() runs on a single reducer\n) t\nlimit 10;\n\n1       39      0       0       0       0       1756    0       0       0       3       0       939     1       -1      0       0       1\n2       51      1       0       1       0       1443    1       0       0       18      1       172     10      -1      0       0       1\n3       36      2       1       2       0       436     1       0       0       13      0       567     1       595     2       1       1\n4       63      3       0       0       0       474     1       0       0       25      2       423     1       -1      0       0       1\n5       31      2       1       2       0       354     1       0       0       30      0       502     1       9       2       2       1\n6       29      0       1       0       0       260     0       0       1       2       3       707     14      -1      0       0       1\n7       37      4       0       0       0       52      0       0       0       6       4       908     1       185     9       2       1\n8       32      5       1       0       0       230     0       0       0       18      5       442     1       266     8       1       1\n9       31      6       1       0       0       0       0       0       0       7       5       895     2       295     2       1       1\n10      32      7       1       2       0       1815    1       0       2       10      6       235     1       102     2       1       1\n\nselect \n  quantify(\n    ${output_row}, id, age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, cast(pdays as string), previous, poutcome, y\n  ) as (id, age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, pdays, previous, poutcome, y)\nfrom (\n  select * from train\n  order by id asc\n) t\nlimit 10;\n\n1       39      0       0       0       0       1756    0       0       0       3       0       939     1       0       0       0       1\n2       51      1       0       1       0       1443    1       0       0       18      1       172     10      0       0       0       1\n3       36      2       1       2       0       436     1       0       0       13      0       567     1       1       2       1       1\n4       63      3       0       0       0       474     1       0       0       25      2       423     1       0       0       0       1\n5       31      2       1       2       0       354     1       0       0       30      0       502     1       2       2       2       1\n6       29      0       1       0       0       260     0       0       1       2       3       707     14      0       0       0       1\n7       37      4       0       0       0       52      0       0       0       6       4       908     1       3       9       2       1\n8       32      5       1       0       0       230     0       0       0       18      5       442     1       4       8       1       1\n9       31      6       1       0       0       0       0       0       0       7       5       895     2       5       2       1       1\n10      32      7       1       2       0       1815    1       0       2       10      6       235     1       6       2       1       1\n\nselect \n  quantified_features(\n    ${output_row}, id, age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, cast(pdays as string), previous, poutcome, y\n  ) as features\nfrom (\n  select * from train\n  order by id asc\n) t\nlimit 10;\n\n[1.0,39.0,0.0,0.0,0.0,0.0,1756.0,0.0,0.0,0.0,3.0,0.0,939.0,1.0,0.0,0.0,0.0,1.0]\n[2.0,51.0,1.0,0.0,1.0,0.0,1443.0,1.0,0.0,0.0,18.0,1.0,172.0,10.0,0.0,0.0,0.0,1.0]\n[3.0,36.0,2.0,1.0,2.0,0.0,436.0,1.0,0.0,0.0,13.0,0.0,567.0,1.0,1.0,2.0,1.0,1.0]\n[4.0,63.0,3.0,0.0,0.0,0.0,474.0,1.0,0.0,0.0,25.0,2.0,423.0,1.0,0.0,0.0,0.0,1.0]\n[5.0,31.0,2.0,1.0,2.0,0.0,354.0,1.0,0.0,0.0,30.0,0.0,502.0,1.0,2.0,2.0,2.0,1.0]\n[6.0,29.0,0.0,1.0,0.0,0.0,260.0,0.0,0.0,1.0,2.0,3.0,707.0,14.0,0.0,0.0,0.0,1.0]\n[7.0,37.0,4.0,0.0,0.0,0.0,52.0,0.0,0.0,0.0,6.0,4.0,908.0,1.0,3.0,9.0,2.0,1.0]\n[8.0,32.0,5.0,1.0,0.0,0.0,230.0,0.0,0.0,0.0,18.0,5.0,442.0,1.0,4.0,8.0,1.0,1.0]\n[9.0,31.0,6.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,7.0,5.0,895.0,2.0,5.0,2.0,1.0,1.0]\n[10.0,32.0,7.0,1.0,2.0,0.0,1815.0,1.0,0.0,2.0,10.0,6.0,235.0,1.0,6.0,2.0,1.0,1.0]\n\nQuantify test dataset\nselect * from test limit 10;\n\n1       30      management      single  tertiary        no      1028    no      no      cellular        4       feb     1294    2       -1      0       unknown\n2       39      self-employed   single  tertiary        no      426     no      no      unknown 18      jun     1029    1       -1      0       unknown\n3       38      technician      single  tertiary        no      -572    yes     yes     unknown 5       jun     26      24      -1      0       unknown\n4       34      technician      single  secondary       no      -476    yes     no      unknown 27      may     92      4       -1      0       unknown\n5       37      entrepreneur    married primary no      62      no      no      cellular        31      jul     404     2       -1      0       unknown\n6       43      services        married primary no      574     yes     no      cellular        8       may     140     1       -1      0       unknown\n7       54      technician      married secondary       no      324     yes     no      telephone       13      may     51      1       -1      0       unknown\n8       41      blue-collar     married secondary       no      121     yes     no      cellular        13      may     16      6       176     5       other\n9       52      housemaid       married primary no      1466    no      yes     cellular        20      nov     150     1       -1      0       unknown\n10      32      management      married secondary       no      6217    yes     yes     cellular        18      nov     486     2       181     2       failure\n\nselect\n  id, \n  array(age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, pdays, previous, poutcome) as features\nfrom (\n  select \n    quantify(\n      output_row, id, age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, if(pdays==-1,0,pdays), previous, poutcome\n    ) as (id, age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, pdays, previous, poutcome)\n  from (\n    select * from (\n      select\n        1 as train_first, false as output_row, id, age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, pdays, previous, poutcome\n      from\n        train\n      union all\n      select\n        2 as train_first, true as output_row, id, age, job, marital, education, default, balance, housing, loan, contact, day, month, duration, campaign, pdays, previous, poutcome\n      from\n        test\n    ) t0\n    order by train_first, id asc\n  ) t1\n) t2\nlimit 10;\n\n1       [30,2,1,2,0,1028,1,0,0,4,1,1294,2,0,0,0]\n2       [39,7,1,2,0,426,1,0,1,18,3,1029,1,0,0,0]\n3       [38,5,1,2,0,-572,0,1,1,5,3,26,24,0,0,0]\n4       [34,5,1,0,0,-476,0,0,1,27,5,92,4,0,0,0]\n5       [37,1,0,1,0,62,1,0,0,31,8,404,2,0,0,0]\n6       [43,4,0,1,0,574,0,0,0,8,5,140,1,0,0,0]\n7       [54,5,0,0,0,324,0,0,2,13,5,51,1,0,0,0]\n8       [41,0,0,0,0,121,0,0,0,13,5,16,6,176,5,3]\n9       [52,8,0,1,0,1466,1,1,0,20,9,150,1,0,0,0]\n10      [32,2,0,0,0,6217,0,1,0,18,9,486,2,181,2,1]\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/binarize.html":{"url":"ft_engineering/binarize.html","title":"Binarize label","keywords":"","body":"\nIntroduction\nExpanding numeric labels to actual count of samples can contribute to accuracy improvement in some cases. binarize_label explode a record that keeps the count of positive/negative labeled samples into corresponding actual count of samples. For example,\n\n\n\npositive\nnegative\nfeatures\n\n\n\n\n2\n3\n\"[a:1, b:2]\"\n\n\n\nis converted into \n\n\n\nfeatures\nlabel\n\n\n\n\n\"[a:1, b:2]\"\n0\n\n\n\"[a:1, b:2]\"\n0\n\n\n\"[a:1, b:2]\"\n1\n\n\n\"[a:1, b:2]\"\n1\n\n\n\"[a:1, b:2]\"\n1\n\n\n\nFunction signature\nbinarize_label(int/long positive, int/long negative, ANY arg1, ANY arg2, ..., ANY argN) \nreturns (ANY arg1, ANY arg2, ..., ANY argN, int label) where label is 0 or 1.\nUsage\nWITH input as (\n  select 2 as positive, 3 as negative, array('a:1','b:2') as features\n  UNION ALL\n  select 2 as positive, 1 as negative, array('c:3','d:4') as features\n)\nSELECT\n  binarize_label(positive, negative, features)\nfrom \n  input;\n\n\n\n\nfeatures\nlabel\n\n\n\n\n[\"a:1\",\"b:2\"]\n1\n\n\n[\"a:1\",\"b:2\"]\n1\n\n\n[\"a:1\",\"b:2\"]\n0\n\n\n[\"a:1\",\"b:2\"]\n0\n\n\n[\"a:1\",\"b:2\"]\n0\n\n\n[\"c:3\",\"d:4\"]\n1\n\n\n[\"c:3\",\"d:4\"]\n1\n\n\n[\"c:3\",\"d:4\"]\n0\n\n\n\n CautionDon't forget to shuffle converted training instances in a random order, e.g., by CLUSTER BY rand().\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/onehot.html":{"url":"ft_engineering/onehot.html","title":"One-hot encoding","keywords":"","body":"\n\n\n\nWhat's One-hot encoding?\nOne-hot encoding table\nHow to use One-hot encoding\n\n\n\nWhat's One-hot encoding?\nOnt-hot encoding is a method to encode categorical features by a 1-of-K (thus called 1-hot) encoding scheme.\nSuppose the following table:\n\n\n\nCompany\nPrice\n\n\n\n\nVW\n290\n\n\nToyota\n300\n\n\nHonda\n190\n\n\nHonda\n250\n\n\n\nA one-hot encoding output is expected as follows:\n\n\n\nCompany_VW\nCompany_Toyota\nCompany_Honda\nPrice\n\n\n\n\n1\n0\n0\n290\n\n\n0\n1\n0\n300\n\n\n0\n0\n1\n190\n\n\n0\n0\n1\n250\n\n\n\nThe above one-hot table is a dense feature format and it can be expressed as follows by a sparse format:\n\n\n\nCompany\nPrice\n\n\n\n\n{1}\n290\n\n\n{2}\n300\n\n\n{3}\n190\n\n\n{3}\n250\n\n\n\nThe mapping for company name is {VW->1, Toyota->2, Honda->3}.\nNow, suppose encoding two categorical variables as follows into a sparse vector.\n\n\n\ncategory1\ncategory2\n\n\n\n\ncat\nmammal\n\n\ndog\nmammal\n\n\nhuman\nmammal\n\n\nseahawk\nbird\n\n\nwasp\ninsect\n\n\nwasp\ninsect\n\n\ncat\nmammal\n\n\ndog\nmammal\n\n\nhuman\nmammal\n\n\n\nThe one-hot encoded feature vector could be as follows:\n\n\n\ncategory1\ncategory2\nencoded_features\n\n\n\n\ncat\nmammal\n{1,6}\n\n\ndog\nmammal\n{2,6}\n\n\nhuman\nmammal\n{3,6}\n\n\nseahawk\nbird\n{4,7}\n\n\nwasp\ninsect\n{5,8}\n\n\n\nWe use this test table for explaration.\ndrop table test;\ncreate table test (species string, category string, count int);\n\ntruncate table test;\ninsert into table test values\n  ('cat','mammal',9), \n  ('dog','mammal',10),\n  ('human','mammal',10),\n  ('seahawk','bird',101),\n  ('wasp','insect',3),\n  ('wasp','insect',9),\n  ('cat','mammal',101),\n  ('dog','mammal',1),\n  ('human','mammal',9);\n\nOne-hot encoding table\nYou can get one-hot encoding table for spieces as follows:\nWITH t as (\n  select onehot_encoding(species) m\n  from test\n)\nselect m.f1 from t;\n\n\n\n\nf1\n\n\n\n\n{\"seahawk\":1,\"cat\":2,\"human\":3,\"wasp\":4,\"dog\":5}\n\n\n\nWITH t as (\n  select onehot_encoding(species, category) m\n  from test\n)\nselect m.f1, m.f2 from t;\n\n| f1 | f2 |\n| {\"seahawk\":1,\"cat\":2,\"human\":3,\"wasp\":4,\"dog\":5} | {\"bird\":6,\"insect\":7,\"mammal\":8} |\nYou can create a mapping table as follows:\ncreate table mapping as\nWITH t as (\n  select onehot_encoding(species, category) m\n  from test\n)\nselect m.f1, m.f2 from t;\n\ndesc mapping;\n\ncol_name    | data_type\n------------|----------------\nf1          | map                             \nf2          | map\n\nHow to use One-hot encoding\nThe following query applies one-hot encoding using the mapping table.\nselect\n  t.species, m.f1[t.species],\n  t.category, m.f2[t.category]\nfrom\n  test t\n  CROSS JOIN mapping m;\n\ncat 2   mammal  8 \ndog 5   mammal  8 \nhuman   3   mammal  8 \nseahawk 1   bird    6 \nwasp    4   insect  7 \nwasp    4   insect  7 \ncat 2   mammal  8 \ndog 5   mammal  8\nhuman   3   mammal  8\n\nYou can create a sparse feature vector as follows:\nselect\n  array(m.f1[t.species],m.f2[t.category],feature('count',count)) as sparse_feature \nfrom\n  test t\n  CROSS JOIN mapping m;\n\nsparse_feature\n[\"2\",\"8\",\"count:9\"]\n[\"5\",\"8\",\"count:10\"]\n[\"3\",\"8\",\"count:10\"]\n[\"1\",\"6\",\"count:101\"]\n[\"4\",\"7\",\"count:3\"]\n[\"4\",\"7\",\"count:9\"]\n[\"2\",\"8\",\"count:101\"]\n[\"5\",\"8\",\"count:1\"]\n[\"3\",\"8\",\"count:9\"]\n\nIt also can be achieved by a single query as follows:\nWITH mapping as (\n  select \n    m.f1, m.f2 \n  from (\n    select onehot_encoding(species, category) m\n    from test\n  ) tmp\n)\nselect\n  array(m.f1[t.species],m.f2[t.category],feature('count',count)) as sparse_features\nfrom\n  test t\n  CROSS JOIN mapping m;\n\nNote that one-hot encoding is required only for categorical variables. Feature hasing is another scalable way to encode categorical variables to numerical index.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/term_vector.html":{"url":"ft_engineering/term_vector.html","title":"Term Vector Model","keywords":"","body":"\nTerm vector model or Vector space model is an algebraic model for representing text documents (and any objects, in general) as vectors of identifiers.\nIt is used in information filtering, information retrieval, relevancy rankings, and machine learning.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/tfidf.html":{"url":"ft_engineering/tfidf.html","title":"TF-IDF Term Weighting","keywords":"","body":"\nThis document explains how to compute TF-IDF with Apache Hive/Hivemall.\nWhat you need to compute TF-IDF is a table/view composing (docid, word) pair, 2 views, and 1 query.\n\n\n\nDefine macros used in the TF-IDF computation\nData preparation\nDefine views of TF/DF\nTF-IDF calculation for each docid/word pair\nFeature Vector with TF-IDF values\n\n\n\n NoteThis feature is supported since Hivemall v0.3-beta3 or later. Macro is supported since Hive 0.12 or later.\nDefine macros used in the TF-IDF computation\ncreate temporary macro max2(x INT, y INT)\nif(x>y,x,y);\n\n-- create temporary macro idf(df_t INT, n_docs INT)\n-- (log(10, CAST(n_docs as FLOAT)/max2(1,df_t)) + 1.0);\n\ncreate temporary macro tfidf(tf FLOAT, df_t INT, n_docs INT)\ntf * (log(10, CAST(n_docs as FLOAT)/max2(1,df_t)) + 1.0);\n\nData preparation\nTo calculate TF-IDF, you need to prepare a relation consists of (docid,word) tuples.\ncreate external table wikipage (\n  docid int,\n  page string\n)\nROW FORMAT DELIMITED FIELDS TERMINATED BY '|'\nSTORED AS TEXTFILE;\n\ncd ~/tmp\nwget https://gist.githubusercontent.com/myui/190b91a3a792ccfceda0/raw/327acd192da4f96da8276dcdff01b19947a4373c/tfidf_test.tsv\n\nLOAD DATA LOCAL INPATH '/home/myui/tmp/tfidf_test.tsv' INTO TABLE wikipage;\n\ncreate or replace view wikipage_exploded\nas\nselect\n  docid, \n  word\nfrom\n  wikipage LATERAL VIEW explode(tokenize(page,true)) t as word\nwhere\n  not is_stopword(word);\n\nYou can download the data of the wikipage table from this link.\nDefine views of TF/DF\ncreate or replace view term_frequency \nas\nselect\n  docid, \n  word,\n  freq\nfrom (\nselect\n  docid,\n  tf(word) as word2freq\nfrom\n  wikipage_exploded\ngroup by\n  docid\n) t \nLATERAL VIEW explode(word2freq) t2 as word, freq;\n\ncreate or replace view document_frequency\nas\nselect\n  word, \n  count(distinct docid) docs\nfrom\n  wikipage_exploded\ngroup by\n  word;\n\nTF-IDF calculation for each docid/word pair\n-- set the total number of documents\nselect count(distinct docid) from wikipage;\nset hivevar:n_docs=3;\n\ncreate or replace view tfidf\nas\nselect\n  tf.docid,\n  tf.word, \n  -- tf.freq * (log(10, CAST(${n_docs} as FLOAT)/max2(1,df.docs)) + 1.0) as tfidf\n  tfidf(tf.freq, df.docs, ${n_docs}) as tfidf\nfrom\n  term_frequency tf \n  JOIN document_frequency df ON (tf.word = df.word)\norder by \n  tfidf desc;\n\nThe result will be as follows:\ndocid  word     tfidf\n1       justice 0.1641245850805637\n3       knowledge       0.09484606645205085\n2       action  0.07033910867777095\n1       law     0.06564983513276658\n1       found   0.06564983513276658\n1       religion        0.06564983513276658\n1       discussion      0.06564983513276658\n  ...\n  ...\n2       act     0.017584777169442737\n2       virtues 0.017584777169442737\n2       well    0.017584777169442737\n2       willingness     0.017584777169442737\n2       find    0.017584777169442737\n2       1       0.014001086678120098\n2       experience      0.014001086678120098\n2       often   0.014001086678120098\nThe above result is considered to be appropriate as docid 1, 2, and 3 are the Wikipedia entries of Justice, Wisdom, and Knowledge, respectively.\nFeature Vector with TF-IDF values\nselect\n  docid, \n  -- collect_list(concat(word, \":\", tfidf)) as features -- Hive 0.13 or later\n  collect_list(feature(word, tfidf)) as features -- Hivemall v0.3.4 & Hive 0.13 or later\n  -- collect_all(concat(word, \":\", tfidf)) as features -- before Hive 0.13\nfrom \n  tfidf\ngroup by\n  docid;\n\n1       [\"justice:0.1641245850805637\",\"found:0.06564983513276658\",\"discussion:0.06564983513276658\",\"law:0.065\n64983513276658\",\"based:0.06564983513276658\",\"religion:0.06564983513276658\",\"viewpoints:0.03282491756638329\",\"\nrationality:0.03282491756638329\",\"including:0.03282491756638329\",\"context:0.03282491756638329\",\"concept:0.032\n82491756638329\",\"rightness:0.03282491756638329\",\"general:0.03282491756638329\",\"many:0.03282491756638329\",\"dif\nfering:0.03282491756638329\",\"fairness:0.03282491756638329\",\"social:0.03282491756638329\",\"broadest:0.032824917\n56638329\",\"equity:0.03282491756638329\",\"includes:0.03282491756638329\",\"theology:0.03282491756638329\",\"ethics:\n0.03282491756638329\",\"moral:0.03282491756638329\",\"numerous:0.03282491756638329\",\"philosophical:0.032824917566\n38329\",\"application:0.03282491756638329\",\"perspectives:0.03282491756638329\",\"procedural:0.03282491756638329\",\n\"realm:0.03282491756638329\",\"divided:0.03282491756638329\",\"concepts:0.03282491756638329\",\"attainment:0.032824\n91756638329\",\"fields:0.03282491756638329\",\"often:0.026135361945200226\",\"philosophy:0.026135361945200226\",\"stu\ndy:0.026135361945200226\"]\n2       [\"action:0.07033910867777095\",\"wisdom:0.05275433288400458\",\"one:0.05275433288400458\",\"understanding:0\n.04200326112968063\",\"judgement:0.035169554338885474\",\"apply:0.035169554338885474\",\"disposition:0.035169554338\n885474\",\"given:0.035169554338885474\"\n...\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"ft_engineering/bm25.html":{"url":"ft_engineering/bm25.html","title":"Okapi BM25 Term Weighting","keywords":"","body":"\nOkapi BM25 is a ranking function for documents for a given query.\nIt can also be used for a better replacement of TF-IDF and can be used for term-weight for each document.\n\n\n\nThe ranking function\nData preparation\nDefine views of term/doc frequency\nCompute Okapi BM25 score\nHyperparameters\nShow important terms for each document\n\n\nRetrive relevant documents for a given search terms\n\n\n\nThe ranking function\nGiven a query QQQ, containing keywords q1,....,qnq1,....,q_nq1,....,q​n​​, the BM25 score of a document DDD is:\nscore(Q,D)=∑i=1nIDF(qi)⋅tf(qi,D)⋅(k1+1)tf(qi,D)+k1⋅(1−b+b⋅∣D∣avgdl)\nscore(Q, D) = \\sum_{i=1}^{n}IDF(q_{i}) \\cdot \\frac{tf(q_{i},D) \\cdot (k_{1}+1)}{tf(q_{i},D) + k_{1} \\cdot (1 - b + b \\cdot \\frac{|D|}{avgdl})}\nscore(Q,D)=​i=1​∑​n​​IDF(q​i​​)⋅​tf(q​i​​,D)+k​1​​⋅(1−b+b⋅​avgdl​​∣D∣​​)​​tf(q​i​​,D)⋅(k​1​​+1)​​\nwhere tf(qi,D)tf(q_{i}, D)tf(q​i​​,D) is qiq_{i}q​i​​'s term frequency in the document DDD, ∣D∣|D|∣D∣ is the length of the document DDD in words, and avgdlavgdlavgdl is the average document length in the text collection from which documents are drawn. k1k_{1}k​1​​ and bbb are free parameters, usually chosen, in absence of an advanced optimization, as k1∈[1.2,2.0]k_{1} \\in [1.2,2.0]k​1​​∈[1.2,2.0] and b=0.75b = 0.75b=0.75.\nBM25 can also be applied for term weighing, showing how important a word is to a document in a collection or corpus, as follows:\nscore(ti,D)=IDF(ti)⋅tf(ti,D)⋅(k1+1)tf(ti,D)+k1⋅(1−b+b⋅∣D∣avgdl)\nscore(t_{i}, D) = IDF(t_{i}) \\cdot \\frac{tf(t_{i},D) \\cdot (k_{1}+1)}{tf(t_{i},D) + k_{1} \\cdot (1 - b + b \\cdot \\frac{|D|}{avgdl})}\nscore(t​i​​,D)=IDF(t​i​​)⋅​tf(t​i​​,D)+k​1​​⋅(1−b+b⋅​avgdl​​∣D∣​​)​​tf(t​i​​,D)⋅(k​1​​+1)​​\nwhere tit_{i}t​i​​ is a term appeared in document DDD.\nData preparation\nIn similar to TF-IDF, you need to prepare a relation consists of (docid,word) tuples to compute BM25 score.\ncreate external table wikipage (\n  docid int,\n  page string\n)\nROW FORMAT DELIMITED FIELDS TERMINATED BY '|'\nSTORED AS TEXTFILE;\n\ncd ~/tmp\nwget https://gist.githubusercontent.com/myui/190b91a3a792ccfceda0/raw/327acd192da4f96da8276dcdff01b19947a4373c/tfidf_test.tsv\n\nLOAD DATA LOCAL INPATH '/home/myui/tmp/tfidf_test.tsv' INTO TABLE wikipage;\n\ncreate or replace view wikipage_exploded\nas\nselect\n  docid, \n  word\nfrom\n  wikipage LATERAL VIEW explode(tokenize(page,true)) t as word\nwhere\n  not is_stopword(word);\n\nDefine views of term/doc frequency\ncreate or replace view term_frequency \nas\nselect\n  t1.docid, \n  t2.word,\n  t2.freq\nfrom (\n  select\n    docid,\n    tf(word) as word2freq\n  from\n    wikipage_exploded\n  group by\n    docid\n) t1 \nLATERAL VIEW explode(word2freq) t2 as word, freq;\n\ncreate or replace view document_frequency\nas\nselect\n  word, \n  count(distinct docid) docs\nfrom\n  wikipage_exploded\ngroup by\n  word;\n\ncreate or replace view doc_len\nas\nselect \n  docid, \n  count(1) as dl,\n  avg(count(1)) over () as avgdl,\n  count(distinct docid) over () as total_docs\nfrom\n  wikipage_exploded\ngroup by\n  docid\n;\n\nCompute Okapi BM25 score\nBM25 (and TF-IDF) score that represents importance of term for each document is useful for feature weight in feature engineering.\ncreate table scores\nas\nselect\n  tf.docid,\n  tf.word,\n  bm25(\n    tf.freq,\n    dl.dl,\n    dl.avgdl,\n    dl.total_docs,\n    df.docs\n    -- , '-k1 1.5 -b 0.75'\n  ) as bm25,\n  tfidf(tf.freq, df.docs, dl.total_docs) as tfidf\nfrom\n  term_frequency tf\n  JOIN document_frequency df ON (tf.word = df.word)\n  JOIN doc_len dl ON (tf.docid = dl.docid)\n;\n\nHyperparameters\nbm25()'s function signature and hyperparameters are as follows:\nhive> select bm25();\nFAILED: SemanticException Line 1:7 Wrong arguments 'bm25':\n\n#arguments must be greater than or equal to 5: 0\n\nusage: bm25(double termFrequency, int docLength, double avgDocLength, int\n       numDocs, int numDocsWithTerm [, const string options]) - Return an\n       Okapi BM25 score in double [-b ] [-d ] [-k1 ]\n       [-min_idf ]\n -b                    Hyperparameter with type double in range 0.0\n                            and 1.0 [default: 0.75]\n -d,--delta            Hyperparameter delta of BM25+ [default: 0.0]\n -k1                   Hyperparameter with type double, usually in\n                            range 1.2 and 2.0 [default: 1.2]\n -min_idf,--epsilon    Hyperparameter delta of BM25+ [default: 1e-8]\n\nShow important terms for each document\nselect\n  docid, \n  to_ordered_list(feature(word,bm25), bm25, '-k 10') as bm25_scores,\n  to_ordered_list(feature(word,tfidf),tfidf, '-k 10') as tfidf_scores\nfrom \n  scores\ngroup by\n  docid\nlimit 10;\n\nRetrive relevant documents for a given search terms\nYou can retrieve relevant documents for a given search query wisdom, justice, discussion as follows:\nWITH scores as (\n  select\n    tf.docid,\n    tf.word,\n    bm25(\n      tf.freq,\n      dl.dl,\n      dl.avgdl,\n      dl.total_docs,\n      df.docs\n      -- , '-k1 1.5 -b 0.75'\n    ) as bm25,\n    tfidf(tf.freq, df.docs, dl.total_docs) as tfidf\n  from\n    term_frequency tf\n    JOIN document_frequency df ON (tf.word = df.word)\n    JOIN doc_len dl ON (tf.docid = dl.docid)\n  where\n    tf.word in ('wisdom', 'justice', 'discussion')\n)\nselect\n  docid,\n  sum(bm25) as score \nfrom\n  scores \ngroup by\n  docid\norder by\n  score DESC \nLIMIT 10\n;\n\n\n\n\ndocid\nscore\n\n\n\n\n1\n0.14190456024682774\n\n\n2\n0.02197354085722251\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"eval/binary_classification_measures.html":{"url":"eval/binary_classification_measures.html","title":"Binary Classification Metrics","keywords":"","body":"\n\n\n\nBinary problems\nExample\nData\nPreliminary metrics\nRecall\nPrecision\n\n\n\n\nMetrics\nF1-score\nMicro average\nBinary average\n\n\nF-measure\n\n\n\n\n\nBinary problems\nBinary classification is a task to predict a label of each data given two categories.\nHivemall provides several tutorials to deal with binary classification problems as follows:\n\nOnline advertisement click prediction\nNews classification\n\nThis page focuses on the evaluation of such binary classification problems.\nIf your classifier outputs probability rather than 0/1 label, evaluation based on Area Under the ROC Curve would be more appropriate.\nExample\nThis page introduces toy example data and two metrics for explanation.\nData\nThe following table shows examples of binary classification's prediction.\n\n\n\ntruth label\npredicted label\ndescription\n\n\n\n\n1\n0\nFalse Negative\n\n\n0\n1\nFalse Positive\n\n\n0\n0\nTrue Negative\n\n\n1\n1\nTrue Positive\n\n\n0\n1\nFalse Positive\n\n\n0\n0\nTrue Negative\n\n\n\nIn this case, 1 means positive label and 0 means negative label.\nThe leftmost column shows truth labels, and center column includes predicted labels.\nPreliminary metrics\nSome evaluation metrics are calculated based on 4 values:\n\nTrue Positive (TP): truth label is positive and predicted label is also positive\nTrue Negative (TN): truth label is negative and predicted label is also negative\nFalse Positive (FP): truth label is negative but predicted label is positive\nFalse Negative (FN): truth label is positive but predicted label is negative\n\nTR and TN represent correct classification, and FP and FN illustrate incorrect ones.\nIn this example, we can obtain those values:\n\nTP: 1\nTN: 2\nFP: 2\nFN: 1\n\nif you want to know about those metrics, Wikipedia provides more detail information.\nRecall\nRecall indicates the true positive rate in truth positive labels.\nThe value is computed by the following equation:\nrecall=#TP#TP+#FN\n\\mathrm{recall} = \\frac{\\mathrm{\\#TP}}{\\mathrm{\\#TP} + \\mathrm{\\#FN}}\nrecall=​#TP+#FN​​#TP​​\nIn the previous example, precision=12\\mathrm{precision} = \\frac{1}{2}precision=​2​​1​​.\nPrecision\nPrecision indicates the true positive rate in positive predictive labels.\nThe value is computed by the following equation:\nprecision=#TP#TP+#FP\n\\mathrm{precision} = \\frac{\\mathrm{\\#TP}}{\\mathrm{\\#TP} + \\mathrm{\\#FP}}\nprecision=​#TP+#FP​​#TP​​\nIn the previous example, precision=13\\mathrm{precision} = \\frac{1}{3}precision=​3​​1​​.\nMetrics\nTo use metrics examples, please create the following table.\ncreate table data as \n  select 1 as truth, 0 as predicted\nunion all\n  select 0 as truth, 1 as predicted\nunion all\n  select 0 as truth, 0 as predicted\nunion all\n  select 1 as truth, 1 as predicted\nunion all\n  select 0 as truth, 1 as predicted\nunion all\n  select 0 as truth, 0 as predicted\n;\n\nF1-score\nF1-score is the harmonic mean of recall and precision.\nF1-score is computed by the following equation:\nF1=2precision∗recallprecision+recall\n\\mathrm{F}_1 = 2 \\frac{\\mathrm{precision} * \\mathrm{recall}}{\\mathrm{precision} + \\mathrm{recall}}\nF​1​​=2​precision+recall​​precision∗recall​​\nHivemall's fmeasure function provides the option which can switch micro(default) or binary by passing average argument.\n CautionHivemall also provides f1score function, but it is old function to obtain F1-score. The value of f1score is based on set operation. So, we recommend to use fmeasure function to get F1-score based on this article.\nYou can learn more about this from the following external resource:\n\nscikit-learn's F1-score\n\nMicro average\nIf micro is passed to average, \nrecall and precision are modified to consider True Negative.\nSo, micro f1score are calculated by those modified recall and precision.\nrecall=#TP+#TN#TP+#FN+#TN\n\\mathrm{recall} = \\frac{\\mathrm{\\#TP} + \\mathrm{\\#TN}}{\\mathrm{\\#TP} + \\mathrm{\\#FN} + \\mathrm{\\#TN}}\nrecall=​#TP+#FN+#TN​​#TP+#TN​​\nprecision=#TP+#TN#TP+#FP+#TN\n\\mathrm{precision} = \\frac{\\mathrm{\\#TP} + \\mathrm{\\#TN}}{\\mathrm{\\#TP} + \\mathrm{\\#FP} + \\mathrm{\\#TN}}\nprecision=​#TP+#FP+#TN​​#TP+#TN​​\nIf average argument is omitted, fmeasure use default value: '-average micro'.\nThe following query shows the example to obtain F1-score.\nEach row value has the same type (int or boolean).\nIf row value's type is int, 1 is considered as the positive label, and -1 or 0 is considered as the negative label.\nselect fmeasure(truth, predicted, '-average micro') from data;\n\n\n0.5\n\nIt should be noted that, since the old f1score(truth, predicted) function simply counts the number of \"matched\" elements between truth and predicted, the above query is equivalent to:\nselect f1score(array(truth), array(predicted)) from data;\n\nBinary average\nIf binary is passed to average, True Negative samples are ignored to get F1-score.\nThe following query shows the example to obtain F1-score with binary average.\nselect fmeasure(truth, predicted, '-average binary') from data;\n\n\n0.4\n\nF-measure\nF-measure is generalized F1-score and the weighted harmonic mean of recall and precision.\nF-measure is computed by the following equation:\nFβ=(1+β2)precision∗recallβ2precision+recall\n\\mathrm{F}_{\\beta} = (1+\\beta^2) \\frac{\\mathrm{precision} * \\mathrm{recall}}{\\beta^2 \\mathrm{precision} + \\mathrm{recall}}\nF​β​​=(1+β​2​​)​β​2​​precision+recall​​precision∗recall​​\nβ\\betaβ is the parameter to determine the weight of precision.\nSo, F1-score is the special case of F-measure given β=1\\beta=1β=1.\nIf β\\betaβ is larger positive value than 1.0, F-measure reaches recall.\nOn the other hand,\nif β\\betaβ is smaller positive value than 1.0, F-measure reaches precision.\nIf β\\betaβ is omitted, hivemall calculates F-measure with β=1\\beta=1β=1 (: equivalent to F1-score).\nHivemall's fmeasure function also provides the option which can switch micro(default) or binary by passing average argument.\nThe following query shows the example to obtain F-measure with β=2\\beta=2β=2 and micro average.\nselect fmeasure(truth, predicted, '-beta 2. -average micro') from data;\n\n\n0.5\n\nThe following query shows the example to obtain F-measure with β=2\\beta=2β=2 and binary average.\nselect fmeasure(truth, predicted, '-beta 2. -average binary') from data;\n\n\n0.45454545454545453\n\nYou can learn more about this from the following external resource:\n\nscikit-learn's FMeasure\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"eval/auc.html":{"url":"eval/auc.html","title":"Area under the ROC curve","keywords":"","body":"\n\n\n\nArea Under the ROC Curve\nCompute AUC on Hivemall\nSequential AUC computation on a single node\nParallel approximate AUC computation\n\n\nDifference between AUC and Logarithmic Loss\n\n\n\nArea Under the ROC Curve\nROC curve and Area Under the ROC Curve (AUC) are widely-used metric for binary (i.e., positive or negative) classification problems such as Logistic Regression.\nBinary classifiers generally predict how likely a sample is to be positive by computing probability. Ultimately, we can evaluate the classifiers by comparing the probabilities with truth positive/negative labels.\nNow we assume that there is a table which contains predicted scores (i.e., probabilities) and truth labels as follows:\n\n\n\nprobability(predicted score)\ntruth label\n\n\n\n\n0.5\n0\n\n\n0.3\n1\n\n\n0.2\n0\n\n\n0.8\n1\n\n\n0.7\n1\n\n\n\nOnce the rows are sorted by the probabilities in a descending order, AUC gives a metric based on how many positive (label=1) samples are ranked higher than negative (label=0) samples. If many positive rows get larger scores than negative rows, AUC would be large, and hence our classifier would perform well.\nCompute AUC on Hivemall\nIn Hivemall, a function auc(double score, int label) provides a way to compute AUC for pairs of probability and truth label.\nSequential AUC computation on a single node\nFor instance, the following query computes AUC of the table which was shown above:\nwith data as (\n  select 0.5 as prob, 0 as label\n  union all\n  select 0.3 as prob, 1 as label\n  union all\n  select 0.2 as prob, 0 as label\n  union all\n  select 0.8 as prob, 1 as label\n  union all\n  select 0.7 as prob, 1 as label\n)\nselect\n  auc(prob, label) as auc\nfrom (\n  select prob, label\n  from data\n  ORDER BY prob DESC\n) t;\n\nThis query returns 0.83333 as AUC.\nSince AUC is a metric based on ranked probability-label pairs as mentioned above, input data (rows) needs to be ordered by scores in a descending order.\nParallel approximate AUC computation\nMeanwhile, Hive's distribute by clause allows you to compute AUC in parallel:\nwith data as (\n  select 0.5 as prob, 0 as label\n  union all\n  select 0.3 as prob, 1 as label\n  union all\n  select 0.2 as prob, 0 as label\n  union all\n  select 0.8 as prob, 1 as label\n  union all\n  select 0.7 as prob, 1 as label\n)\nselect\n  auc(prob, label) as auc\nfrom (\n  select prob, label\n  from data\n  DISTRIBUTE BY floor(prob / 0.2)\n  SORT BY prob DESC\n) t;\n\nNote that floor(prob / 0.2) means that the rows are distributed to 5 bins for the AUC computation because the column prob is in a [0, 1] range.\nDifference between AUC and Logarithmic Loss\nHivemall has another metric called Logarithmic Loss for binary classification. Both AUC and Logarithmic Loss compute scores for probability-label pairs.\nScore produced by AUC is a relative metric based on sorted pairs. On the other hand, Logarithmic Loss simply gives a metric by comparing probability with its truth label one-by-one.\nTo give an example, auc(prob, label) and logloss(prob, label) respectively returns 0.83333 and 0.54001 in the above case. Note that larger AUC and smaller Logarithmic Loss are better.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"eval/multilabel_classification_measures.html":{"url":"eval/multilabel_classification_measures.html","title":"Multi-label Classification Metrics","keywords":"","body":"\n\n\n\nMulti-label classification\nExample\nData\n\n\nEvaluation metrics for multi-label classification\nMicro F1-score\nMicro F-measure\n\n\n\n\n\nMulti-label classification\nMulti-label classification problem is a task to predict labels given two or more categories.\nEach sample iii has lil_il​i​​ labels, where LLL is a set of unique labels in the dataset, and 0≤li≤∣L∣0 \\leq  l_i \\leq |L|0≤l​i​​≤∣L∣.\nThis page focuses on evaluation of such multi-label classification problems.\nExample\nThis page introduces toy example dataset for explanation.\nData\nThe following table shows examples of multi-label classification's prediction.\nSuppose that animal names represent tags of blog posts and the given task is to predict tags for blog posts.\nThe left column shows the ground truth labels and the right column shows predicted labels by a multi-label classifier.\n\n\n\ntruth labels\npredicted labels\n\n\n\n\ncat, bird\ncat, dog\n\n\ncat, dog\ncat, bird\n\n\ncat\n(no truth label)\n\n\nbird\nbird\n\n\nbird, cat\nbird, cat\n\n\ncat, dog\ncat, dog, bird\n\n\ndog, bird\ndog\n\n\n\nEvaluation metrics for multi-label classification\nHivemall provides micro F1-score and micro F-measure.\nDefine LLL is the set of the tag of blog posts, and lil_il​i​​ is a tag set of iii-th document.\nIn the same manner, pip_ip​i​​ is a predicted tag set of iii-th document.\nMicro F1-score\nF1-score is the harmonic mean of recall and precision.\nThe value is computed by the following equation:\nF1=2∑i∣li∩pi∣2∗∑i∣li∩pi∣+∑i∣li−pi∣+∑i∣pi−li∣\n\\mathrm{F}_1 = 2 \\frac\n{\\sum_i |l_i \\cap p_i |}\n{ 2* \\sum_i |l_i \\cap p_i | + \\sum_i |l_i - p_i| + \\sum_i |p_i - l_i| }\nF​1​​=2​2∗∑​i​​∣l​i​​∩p​i​​∣+∑​i​​∣l​i​​−p​i​​∣+∑​i​​∣p​i​​−l​i​​∣​​∑​i​​∣l​i​​∩p​i​​∣​​\n CautionHivemall also provides f1score function, but it is old function to obtain F1-score. The value of f1score is based on set operation. So, we recommend to use fmeasure function to get F1-score based on this article.\nThe following query shows the example to obtain F1-score.\nWITH data as (\n  select array(\"cat\", \"bird\") as actual, array(\"cat\", \"dog\")         as predicted\nunion all\n  select array(\"cat\", \"dog\")  as actual, array(\"cat\", \"bird\")        as predicted\nunion all\n  select array(\"cat\")         as actual, array()                     as predicted\nunion all\n  select array(\"bird\")        as actual, array(\"bird\")               as predicted\nunion all\n  select array(\"bird\", \"cat\") as actual, array(\"bird\", \"cat\")        as predicted\nunion all\n  select array(\"cat\", \"dog\")  as actual, array(\"cat\", \"dog\", \"bird\") as predicted\nunion all\n  select array(\"dog\", \"bird\") as actual, array(\"dog\")                as predicted\n)\nselect\n  fmeasure(actual, predicted)\nfrom data\n;\n\n\n0.6956521739130435\n\nMicro F-measure\nF-measure is generalized F1-score and the weighted harmonic mean of recall and precision.\nThe value is computed by the following equation:\nFβ=(1+β2)∑i∣li∩pi∣β2(∑i∣li∩pi∣+∑i∣li−pi∣)+∑i∣li∩pi∣+∑i∣pi−li∣\n\\mathrm{F}_{\\beta} = (1+\\beta^2) \\frac\n{\\sum_i |l_i \\cap p_i |}\n{ \\beta^2 (\\sum_i |l_i \\cap p_i | + \\sum_i |l_i - p_i|) + \\sum_i |l_i \\cap p_i | + \\sum_i |p_i - l_i|}\nF​β​​=(1+β​2​​)​β​2​​(∑​i​​∣l​i​​∩p​i​​∣+∑​i​​∣l​i​​−p​i​​∣)+∑​i​​∣l​i​​∩p​i​​∣+∑​i​​∣p​i​​−l​i​​∣​​∑​i​​∣l​i​​∩p​i​​∣​​\nβ\\betaβ is the parameter to determine the weight of precision.\nSo, F1-score is the special case of F-measure given β=1\\beta=1β=1.\nIf β\\betaβ is larger positive value than 1.0, F-measure reaches micro recall.\nOn the other hand,\nif β\\betaβ is smaller positive value than 1.0, F-measure reaches micro precision.\nIf β\\betaβ is omitted, hivemall calculates F-measure with β=1\\beta=1β=1 (: equivalent to F1-score).\nThe following query shows the example to obtain F-measure with β=2\\beta=2β=2.\nWITH data as (\n  select array(\"cat\", \"bird\") as actual, array(\"cat\", \"dog\")         as predicted\nunion all\n  select array(\"cat\", \"dog\")  as actual, array(\"cat\", \"bird\")        as predicted\nunion all\n  select array(\"cat\")         as actual, array()                     as predicted\nunion all\n  select array(\"bird\")        as actual, array(\"bird\")               as predicted\nunion all\n  select array(\"bird\", \"cat\") as actual, array(\"bird\", \"cat\")        as predicted\nunion all\n  select array(\"cat\", \"dog\")  as actual, array(\"cat\", \"dog\", \"bird\") as predicted\nunion all\n  select array(\"dog\", \"bird\") as actual, array(\"dog\")                as predicted\n)\nselect\n  fmeasure(actual, predicted, '-beta 2.')\nfrom data\n;\n\n\n0.6779661016949152\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"eval/regression.html":{"url":"eval/regression.html","title":"Regression Metrics","keywords":"","body":"\nUsing the E2006 tfidf regression example, we explain how to evaluate the prediction model on Hive.\n\n\n\nScoring by evaluation metrics\nLogarithmic Loss\nReferences\n\n\n\nScoring by evaluation metrics\nselect avg(actual), avg(predicted) from e2006tfidf_pa2a_submit;\n\n\n-3.8200363760415414     -3.9124877451612488\n\nset hivevar:mean_actual=-3.8200363760415414;\n\nselect\n-- Root Mean Squared Error\n   rmse(predicted, actual) as RMSE,\n   -- sqrt(sum(pow(predicted - actual,2.0))/count(1)) as RMSE,\n-- Mean Squared Error\n   mse(predicted, actual) as MSE,\n   -- sum(pow(predicted - actual,2.0))/count(1) as MSE,\n-- Mean Absolute Error\n   mae(predicted, actual) as MAE,\n   -- sum(abs(predicted - actual))/count(1) as MAE,\n-- coefficient of determination (R^2)\n   -- 1 - sum(pow(actual - predicted,2.0)) / sum(pow(actual - ${mean_actual},2.0)) as R2\n   r2(predicted, actual) as R2\nfrom\n   e2006tfidf_pa2a_submit;\n\n\n0.38538660838804495     0.14852283792484033     0.2466732002711477      0.48623913673053565\n\nLogarithmic Loss\nLogarithmic Loss can be computed as follows:\nWITH t as (\n  select\n    0 as actual,\n    0.01 as predicted\n  union all\n  select\n    1 as actual,\n    0.02 as predicted\n)\nselect\n   -SUM(actual*LN(predicted)+(1-actual)*LN(1-predicted))/count(1) as logloss1,\n  logloss(predicted, actual) as logloss2 -- supported since Hivemall v0.4.2-rc.1\nfrom\nfrom t;\n\n\n1.9610366706408238    1.9610366706408238\n\nReferences\n\nR2 https://en.wikipedia.org/wiki/Coefficient_of_determination\nEvaluation Metrics https://www.kaggle.com/wiki/Metrics\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"eval/rank.html":{"url":"eval/rank.html","title":"Ranking Measures","keywords":"","body":"\n\n\n\nRanking Problems\nBinary Response Measures\nRecall-At-k\nPrecision-At-k\nMean Average Precision (MAP)\nArea Under the ROC Curve (AUC)\nMean Reciprocal Rank (MRR)\nNormalized Discounted Cumulative Gain (NDCG)\n\n\nGraded Response Measures\n\n\n\nRanking Problems\nPractical machine learning applications such as information retrieval and recommendation internally solve ranking problem which generates and returns a ranked list of items. Hivemall provides a way to tackle the problems as follows:\n\nEfficient top-k query processing\nRecommendation based on item-based collaborative filtering\n\nThis page focuses on evaluation of the results from such ranking problems.\n CautionIn order to obtain ranked list of items, this page introduces queries using to_ordered_map() such as map_values(to_ordered_map(score, itemid, true)). However, this kind of usage has a potential issue that multiple itemid-s (i.e., values) which have the exactly same score (i.e., key) will be aggregated to single arbitrary itemid, because to_ordered_map() creates a key-value map which uses duplicated score as key.Hence, if map key could duplicate on more then one map values, we recommend you to use to_ordered_list(value, key, '-reverse') instead of map_values(to_ordered_map(key, value, true)). The alternative approach is available from Hivemall v0.5-rc.1 or later.\nBinary Response Measures\nIn a context of ranking problem, binary response means that binary labels are assigned to items, and positive items are considered as truth observations.\nIn a dummy_truth table, we assume that there are three users (userid = 1, 2, 3) who have exactly same three truth ranked items (itemid = 1, 2, 4) chosen from existing six items:\n\n\n\nuserid\nitemid\n\n\n\n\n1\n1\n\n\n1\n2\n\n\n1\n4\n\n\n2\n1\n\n\n2\n2\n\n\n2\n4\n\n\n3\n1\n\n\n3\n2\n\n\n3\n4\n\n\n\nAdditionally, here is a dummy_rec table we obtained as a result of prediction:\n\n\n\nuserid\nitemid\nscore\n\n\n\n\n1\n1\n10.0\n\n\n1\n3\n8.0\n\n\n1\n2\n6.0\n\n\n1\n6\n2.0\n\n\n2\n1\n10.0\n\n\n2\n3\n8.0\n\n\n2\n2\n6.0\n\n\n2\n6\n2.0\n\n\n3\n1\n10.0\n\n\n3\n3\n8.0\n\n\n3\n2\n6.0\n\n\n3\n6\n2.0\n\n\n\nHow can we compare dummy_rec with dummy_truth to figure out the accuracy of dummy_rec?\nTo be more precise, in case we built a recommender system, let a target user u∈Uu \\in \\mathcal{U}u∈U, set of all items I\\mathcal{I}I, ordered set of top-k recommended items Ik(u)⊂II_k(u) \\subset \\mathcal{I}I​k​​(u)⊂I, and set of truth items Iu+\\mathcal{I}^+_uI​u​+​​. Hence, when we launch top-2 recommendation for the above tables, U={1,2,3}\\mathcal{U} = \\{1, 2, 3\\}U={1,2,3}, I={1,2,3,4,5,6}\\mathcal{I} = \\{1, 2, 3, 4, 5, 6\\}I={1,2,3,4,5,6} and I2(u)={1,3}I_2(u) = \\{1, 3\\}I​2​​(u)={1,3} which consists of two highest-scored items, and Iu+={1,2,4}\\mathcal{I}^+_u = \\{1, 2, 4\\}I​u​+​​={1,2,4}.\nEvaluation of the ordered sets can be done by the following query:\nwith truth as (\n  select userid, collect_set(itemid) as truth\n  from dummy_truth\n  group by userid\n),\nrec as (\n  select\n    userid,\n    -- map_values(to_ordered_map(score, itemid, true)) as rec,\n    to_ordered_list(itemid, score, '-reverse') as rec,\n    cast(count(itemid) as int) as max_k\n  from dummy_rec\n  group by userid\n)\nselect\n  -- rec = [1,3,2,6], truth = [1,2,4] for each user\n\n  -- Recall@k\n  recall_at(t1.rec, t2.truth, t1.max_k) as recall,\n  recall_at(t1.rec, t2.truth, 2) as recall_at_2,\n\n  -- Precision@k\n  precision_at(t1.rec, t2.truth, t1.max_k) as precision,\n  precision_at(t1.rec, t2.truth, 2) as precision_at_2,\n\n  -- MAP\n  average_precision(t1.rec, t2.truth, t1.max_k) as average_precision,\n  average_precision(t1.rec, t2.truth, 2) as average_precision_at_2,\n\n  -- AUC\n  auc(t1.rec, t2.truth, t1.max_k) as auc,\n  auc(t1.rec, t2.truth, 2) as auc_at_2,\n\n  -- MRR\n  mrr(t1.rec, t2.truth, t1.max_k) as mrr,\n  mrr(t1.rec, t2.truth, 2) as mrr_at_2,\n\n  -- NDCG\n  ndcg(t1.rec, t2.truth, t1.max_k) as ndcg,\n  ndcg(t1.rec, t2.truth, 2) as ndcg_at_2\nfrom rec t1\njoin truth t2 on (t1.userid = t2.userid)\n;\n\nWe have six different measures, and outputs will be:\n\n\n\nRanking measure\ntop-4 (max_k)\ntop-2\n\n\n\n\nRecall\n0.6666666666666666\n0.3333333333333333\n\n\nPrecision\n0.5\n0.5\n\n\nMAP\n0.5555555555555555\n0.3333333333333333\n\n\nAUC\n0.75\n1.0\n\n\nMRR\n1.0\n1.0\n\n\nNDCG\n0.7039180890341349\n0.6131471927654585\n\n\n\nHere, we introduce the six measures for evaluation of ranked list of items. Importantly, each metric has a different concept behind formulation, and the accuracy measured by the metrics shows different values even for the exactly same input as demonstrated above. Thus, evaluation using multiple ranking measures is more convincing, and it should be easy in Hivemall.\n CautionBefore Hivemall v0.5-rc.1, recall_at() and precision_at() are respectively registered as recall() and precision(). However, since precision is a reserved keyword from Hive v2.2.0, we renamed the function names. If you are still using recall() and/or precision(), we strongly recommend you to use the latest version of Hivemall and replace them with the newer function names.\nRecall-At-k\nRecall-at-k (Recall@k) indicates coverage of truth samples as a result of top-k recommendation. The value is computed by the following equation:\nRecall@k=∣Iu+∩Ik(u)∣∣Iu+∣.\n\\mathrm{Recall@}k = \\frac{|\\mathcal{I}^+_u \\cap I_k(u)|}{|\\mathcal{I}^+_u|}.\nRecall@k=​∣I​u​+​​∣​​∣I​u​+​​∩I​k​​(u)∣​​.\nHere, ∣Iu+∩Ik(u)∣|\\mathcal{I}^+_u \\cap I_k(u)|∣I​u​+​​∩I​k​​(u)∣ is the number of true positives. If I2(u)={1,3}I_2(u) = \\{1, 3\\}I​2​​(u)={1,3} and Iu+={1,2,4}\\mathcal{I}^+_u = \\{1, 2, 4\\}I​u​+​​={1,2,4}, Recall@2=1/3≈0.333\\mathrm{Recall@}2 = 1 / 3 \\approx 0.333Recall@2=1/3≈0.333.\nPrecision-At-k\nUnlike Recall@k, Precision-at-k (Precision@k) evaluates correctness of a top-k recommendation list Ik(u)I_k(u)I​k​​(u) according to the portion of true positives in the list as:\nPrecision@k=∣Iu+∩Ik(u)∣∣Ik(u)∣.\n\\mathrm{Precision@}k = \\frac{|\\mathcal{I}^+_u \\cap I_k(u)|}{|I_k(u)|}.\nPrecision@k=​∣I​k​​(u)∣​​∣I​u​+​​∩I​k​​(u)∣​​.\nIn other words, Precision@k means how much the recommendation list covers true pairs. Here, Precision@2=1/2=0.5\\mathrm{Precision@}2 = 1 / 2 = 0.5Precision@2=1/2=0.5 where I2(u)={1,3}I_2(u) = \\{1, 3\\}I​2​​(u)={1,3} and Iu+={1,2,4}\\mathcal{I}^+_u = \\{1, 2, 4\\}I​u​+​​={1,2,4}.\nMean Average Precision (MAP)\nWhile the original Precision@k provides a score for a fixed-length recommendation list Ik(u)I_k(u)I​k​​(u), mean average precision (MAP) computes an average of the scores over all recommendation sizes from 1 to ∣I∣|\\mathcal{I}|∣I∣. MAP is formulated with an indicator function for ini_ni​n​​ (the nnn-th item of I(u)I(u)I(u)), as:\nMAP=1∣Iu+∣∑n=1∣I∣Precision@n⋅[in∈Iu+].\n\\mathrm{MAP} = \\frac{1}{|\\mathcal{I}^+_u|} \\sum_{n = 1}^{|\\mathcal{I}|} \\mathrm{Precision@}n \\cdot  [ i_n \\in \\mathcal{I}^+_u ].\nMAP=​∣I​u​+​​∣​​1​​​n=1​∑​∣I∣​​Precision@n⋅[i​n​​∈I​u​+​​].\nIt should be noticed that, MAP is not a simple mean of sum of Precision@1, Precision@2, ..., Precision@∣I∣|\\mathcal{I}|∣I∣, and higher-ranked true positives lead better MAP. To give an example,\nMAP(Iu+,{1,3,2,6,4,5})=11+23+353≈0.756,\n\\mathrm{MAP}(\\mathcal{I}^+_u, \\{1, 3, 2, 6, 4 , 5\\}) = \\frac{\\frac{1}{1} + \\frac{2}{3} + \\frac{3}{5}}{3} \\approx \\mathbf{0.756},\nMAP(I​u​+​​,{1,3,2,6,4,5})=​3​​​1​​1​​+​3​​2​​+​5​​3​​​​≈0.756,\nwhere Iu+={1,2,4}\\mathcal{I}^+_u = \\{1, 2, 4\\}I​u​+​​={1,2,4}, while \nMAP(Iu+,{1,3,2,4,6,5})=11+23+343≈0.806.\n\\mathrm{MAP}(\\mathcal{I}^+_u, \\{1, 3, 2, 4, 6, 5\\}) = \\frac{\\frac{1}{1} + \\frac{2}{3} + \\frac{3}{4}}{3} \\approx \\mathbf{0.806}.\nMAP(I​u​+​​,{1,3,2,4,6,5})=​3​​​1​​1​​+​3​​2​​+​4​​3​​​​≈0.806.\nArea Under the ROC Curve (AUC)\nROC curve and area under the ROC curve (AUC) are generally used in evaluation of the classification problems as we described before. However, these concepts can also be interpreted in a context of ranking problem. \nBasically, the AUC metric for ranking considers all possible pairs of truth and other items which are respectively denoted by i+∈Iu+i^+ \\in \\mathcal{I}^+_ui​+​​∈I​u​+​​ and i−∈Iu−i^- \\in \\mathcal{I}^-_ui​−​​∈I​u​−​​, and it expects that the best recommender completely ranks i+i^+i​+​​ higher than i−i^-i​−​​. A score is finally computed as portion of the correct ordered (i+,i−)(i^+, i^-)(i​+​​,i​−​​) pairs in the all possible combinations determined by ∣Iu+∣×∣Iu−∣|\\mathcal{I}^+_u| \\times |\\mathcal{I}^-_u|∣I​u​+​​∣×∣I​u​−​​∣ in set notation. \nMean Reciprocal Rank (MRR)\nIf we are only interested in the first true positive, mean reciprocal rank (MRR) could be a reasonable choice to quantitatively assess the recommendation lists. For ntp∈[1,∣I∣]n_{\\mathrm{tp}} \\in \\left[ 1, |\\mathcal{I}| \\right]n​tp​​∈[1,∣I∣], a position of the first true positive in I(u)I(u)I(u), MRR simply returns its inverse:\nMRR=1ntp.\n  \\mathrm{MRR} = \\frac{1}{n_{\\mathrm{tp}}}.\nMRR=​n​tp​​​​1​​.\nMRR can be zero if and only if Iu+\\mathcal{I}^+_uI​u​+​​ is empty.\nIn our dummy tables depicted above, the first true positive is placed at the first place in the ranked list of items. Hence, MRR=1/1=1\\mathrm{MRR} = 1/1 = 1MRR=1/1=1, the best result on this metric.\nNormalized Discounted Cumulative Gain (NDCG)\nNormalized discounted cumulative gain (NDCG) computes a score for I(u)I(u)I(u) which places emphasis on higher-ranked true positives. In addition to being a more well-formulated measure, the difference between NDCG and MPR is that NDCG allows us to specify an expected ranking within Iu+\\mathcal{I}^+_uI​u​+​​; that is, the metric can incorporate reln\\mathrm{rel}_nrel​n​​, a relevance score which suggests how likely the nnn-th sample is to be ranked at the top of a recommendation list, and it directly corresponds to an expected ranking of the truth samples.\nAs a result of top-k recommendation, NDCG is computed by:\nNDCGk=DCGkIDCGk=∑n=1∣I∣Dk(n)[in∈Iu+]∑n=1∣I∣Dk(n),\n\\mathrm{NDCG}_k = \\frac{\\mathrm{DCG}_k}{\\mathrm{IDCG}_k} = \\frac{\\sum_{n=1}^{|\\mathcal{I}|} D_k(n) \\left[i_n \\in \\mathcal{I}^+_u\\right]}{\\sum_{n=1}^{|\\mathcal{I}|} D_k(n)},\nNDCG​k​​=​IDCG​k​​​​DCG​k​​​​=​∑​n=1​∣I∣​​D​k​​(n)​​∑​n=1​∣I∣​​D​k​​(n)[i​n​​∈I​u​+​​]​​,\nwhere\nDk(n)={(2reln−1)/log2(n+1)(1≤n≤k)0(n>k).\nD_k(n) = \\left\\{\n\\begin{array}{ll}\n  (2^{\\mathrm{rel}_n} - 1) / \\log_2(n + 1) & (1 \\leq n \\leq k) \\\\\n  0 & (n > k)\n\\end{array}\n\\right.\n.\nD​k​​(n)={​(2​rel​n​​​​−1)/log​2​​(n+1)​0​​​(1≤n≤k)​(n>k)​​.\nHere, DCGk\\mathrm{DCG}_kDCG​k​​ indicates how well I(u)I(u)I(u) fits to the truth permutation, and IDCGk\\mathrm{IDCG}_kIDCG​k​​ is the best DCGk\\mathrm{DCG}_kDCG​k​​ that I(u)I(u)I(u) exactly matches to Iu+\\mathcal{I}^+_uI​u​+​​. \nNow, we only consider binary responses, so relevance score is binary as:\nreln={1(in∈Iu+)0(otherwise).\n\\mathrm{rel}_n = \\left\\{\n\\begin{array}{ll}\n  1 & (i_n \\in \\mathcal{I}^+_u) \\\\\n  0 & (\\mathrm{otherwise})\n\\end{array}\n\\right.\n.\nrel​n​​={​1​0​​​(i​n​​∈I​u​+​​)​(otherwise)​​.\nSince our recommender launched top-2 recommendation on top of this chapter, IDCG2=1/log22+1/log23≈1.631\\mathrm{IDCG}_2 = 1/\\log_2 2 + 1/\\log_2 3 \\approx 1.631IDCG​2​​=1/log​2​​2+1/log​2​​3≈1.631. Meanwhile, only the first sample in I2(u)I_2(u)I​2​​(u) is true positive, so DCG2=1/log22=1\\mathrm{DCG}_2 = 1/\\log_2 2 = 1DCG​2​​=1/log​2​​2=1. Hence, NDCG2=DCG2/IDCG2≈0.613\\mathrm{NDCG}_2 = \\mathrm{DCG}_2 / \\mathrm{IDCG}_2 \\approx 0.613NDCG​2​​=DCG​2​​/IDCG​2​​≈0.613.\nGraded Response Measures\nWhile the binary response setting simply considers positive-only ranked list of items, graded response additionally handles expected rankings (scores) of the items. Hivemall's NDCG implementation with non-binary relevance score reln\\mathrm{rel}_nrel​n​​ enables you to evaluate based on the graded responses.\nUnlike separated dummy_truth and dummy_rec table in the binary setting, we assume the following single table named dummy_recrel which contains item-reln\\mathrm{rel}_nrel​n​​ pairs:\n\n\n\nuserid\nitemid\nscore(predicted)\nrelscore(expected)\n\n\n\n\n1\n1\n10.0\n5.0\n\n\n1\n3\n8.0\n2.0\n\n\n1\n2\n6.0\n4.0\n\n\n1\n6\n2.0\n1.0\n\n\n1\n4\n1.0\n3.0\n\n\n2\n1\n10.0\n5.0\n\n\n2\n3\n8.0\n2.0\n\n\n2\n2\n6.0\n4.0\n\n\n2\n6\n2.0\n1.0\n\n\n2\n4\n1.0\n3.0\n\n\n3\n1\n10.0\n5.0\n\n\n3\n3\n8.0\n2.0\n\n\n3\n2\n6.0\n4.0\n\n\n3\n6\n2.0\n1.0\n\n\n3\n4\n1.0\n3.0\n\n\n\nThe function ndcg() can take non-binary truth values as the second argument: \nwith truth as (\n  select\n    userid,\n    to_ordered_list(relscore, '-reverse') as truth\n  from\n    dummy_recrel\n  group by\n    userid\n),\nrec as (\n  select\n    userid,\n    to_ordered_list(struct(relscore, itemid), score, \"-reverse\") as rec,\n    count(itemid) as max_k\n  from\n    dummy_recrel\n  group by\n    userid\n)\nselect \n  -- top-2 recommendation\n  ndcg(t1.rec, t2.truth, 2), -- => 0.8128912838590544\n  -- top-3 recommendation\n  ndcg(t1.rec, t2.truth, 3)  -- => 0.9187707805346093\nfrom\n  rec t1\n  join truth t2 on (t1.userid = t2.userid)\n;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"eval/datagen.html":{"url":"eval/datagen.html","title":"Data Generation","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"eval/lr_datagen.html":{"url":"eval/lr_datagen.html","title":"Logistic Regression data generation","keywords":"","body":"\n\n\n\ncreate a dual table\nSparse dataset generation by a single task\nClassification dataset generation\nDense dataset generation\nParallel and scalable data generation using multiple reducers (RECOMMENDED)\n\n\n\ncreate a dual table\nCreate a dual table as follows:\nCREATE TABLE dual (\n  dummy int\n);\nINSERT INTO TABLE dual SELECT count(*)+1 FROM dual;\n\nSparse dataset generation by a single task\ncreate table regression_data1\nas\nselect lr_datagen('-n_examples 10k -n_features 10 -seed 100') as (label,features)\nfrom dual;\n\nFind the details of the option, run lr_datagen('-help').\nYou can generate a sparse dataset as well as a dense dataset. By the default, a sparse dataset is generated.\nhive> desc regression_data1;\nOK\nlabel                   float                   None\nfeatures                array           None\n\nhive> select * from regression_data1 limit 2;\nOK\n0.7220096       [\"140:2.8347101\",\"165:3.0056276\",\"179:4.030076\",\"112:3.3919246\",\"99:3.98914\",\"16:3.5653272\",\"128:3.046535\",\"124:2.7708225\",\"78:2.4960368\",\"6:1.7866131\"]\n0.7346627       [\"139:1.9607254\",\"110:2.958568\",\"186:3.2524762\",\"31:3.9243593\",\"167:0.72854257\",\"26:1.8355447\",\"117:2.7663715\",\"3:2.1551287\",\"179:3.1099443\",\"19:3.6411424\"]\nTime taken: 0.046 seconds, Fetched: 2 row(s)\n\nClassification dataset generation\nYou can use \"-cl\" option to generation 0/1 label.\nselect lr_datagen(\"-cl\") as (label,features)\nfrom dual \nlimit 5;\nOK\n1.0     [\"84:3.4227803\",\"80:3.8875976\",\"58:3.2909582\",\"123:3.1056073\",\"194:3.3360343\",\"199:2.20207\",\"75:3.5469763\",\"74:3.3869767\",\"126:0.9969454\",\"93:2.5352612\"]\n0.0     [\"84:-0.5568947\",\"10:0.621897\",\"6:-0.13126314\",\"190:0.18610542\",\"131:1.7232913\",\"24:-2.7551131\",\"113:-0.9842969\",\"177:0.062993184\",\"176:-0.19020283\",\"21:-0.54811275\"]\n1.0     [\"73:3.4391513\",\"198:4.42387\",\"164:4.248151\",\"66:3.5224934\",\"84:1.9026604\",\"76:0.79803777\",\"18:2.2168183\",\"163:2.248695\",\"119:1.5906067\",\"72:2.0267224\"]\n1.0     [\"34:2.9269936\",\"35:0.37033868\",\"39:3.771989\",\"47:2.2087111\",\"28:2.9445739\",\"55:4.134555\",\"14:2.4297745\",\"164:3.0913055\",\"52:2.0519433\",\"128:2.9108515\"]\n1.0     [\"98:4.2451696\",\"4:3.486905\",\"133:2.4589922\",\"26:2.7301126\",\"103:2.6827147\",\"2:3.6198254\",\"34:3.7042716\",\"47:2.5515237\",\"68:2.4294896\",\"197:4.4958663\"]\n\nDense dataset generation\ncreate table regression_data_dense\nas\nselect lr_datagen(\"-dense -n_examples 9999 -n_features 100 -n_dims 100\") as (label,features)\nfrom dual;\n\nhive> desc regression_data_dense;\nOK\nlabel                   float                   None\nfeatures                array            None\n\nhive> select * from regression_data_dense limit 1;\nOK\n0.7274741       [4.061373,3.9373128,3.5195694,3.3604698,3.7698417,4.2518,3.8796813,1.6020582,4.937072,1.5513933,3.0289552,2.6674519,3.432688,2.980945,1.8897587,2.9770515,3.3435504,1.7867403,3.4057906,1.2151588,5.0587463,2.1410913,2.8097973,2.4518871,3.175268,3.3347685,3.728993,3.1443396,3.5506077,3.6357877,4.248151,3.5224934,3.2423255,2.5188355,1.8626233,2.8432152,2.2762651,4.57472,2.2168183,2.248695,3.3636255,2.8359523,2.0327945,1.5917025,2.9269936,0.37033868,2.6151125,4.545956,2.0863252,3.7857852,2.9445739,4.134555,3.0660007,3.4279037,2.0519433,2.9108515,3.5171766,3.4708095,3.161707,2.39229,2.4589922,2.7301126,3.5303073,2.7398396,3.7042716,2.5515237,3.0943663,0.41565156,4.672767,3.1461313,3.0443575,3.4023938,2.2205734,1.8950733,2.1664586,4.8654623,2.787029,4.0460386,2.4455893,3.464298,1.062505,3.0513604,4.382525,2.771433,3.2828436,3.803544,2.178681,4.2466116,3.5440445,3.1546876,3.4248536,0.9067459,3.0134914,1.9528451,1.7175893,2.7029774,2.5759792,3.643847,3.0799,3.735559]\nTime taken: 0.044 seconds, Fetched: 1 row(s)\n\nParallel and scalable data generation using multiple reducers (RECOMMENDED)\nDataset generation using (at max) 10 reducers.\nset hivevar:n_parallel_datagen=10;\n\ncreate or replace view seq10 \nas\nselect * from (\n  select generate_series(1,${n_parallel_datagen})\n  from dual \n) t\nDISTRIBUTE BY value;\n\nset mapred.reduce.tasks=${n_parallel_datagen};\ncreate table lrdata1k\nas\nselect lr_datagen(\"-n_examples 100\")\nfrom seq10;\nset mapred.reduce.tasks=-1; -- reset to the default setting\n\nhive> select count(1) from lrdata1k;\nOK\n1000\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"supervised_learning/prediction.html":{"url":"supervised_learning/prediction.html","title":"How Prediction Works","keywords":"","body":"\n\n\n\nWhat is \"prediction problem\"?\nRegression\nClassification\nMathematical formulation of generic prediction model\n\n\n\nWhat is \"prediction problem\"?\nIn a context of machine learning, numerous tasks can be seen as prediction problem. For example, this user guide provides solutions for:\n\nspam detection\nnews article classification\nclick-through-rate estimation\n\nFor any kinds of prediction problems, we generally provide a set of input-output pairs as:\n\nInput: Set of features\ne.g., [\"1:0.001\",\"4:0.23\",\"35:0.0035\",...]\n\n\nOutput: Target value\ne.g., 1, 0, 0.54, 42.195, ...\n\n\n\nOnce a prediction model has been constructed based on the samples, the model can make prediction for unforeseen inputs. \nIn order to train prediction models, an algorithm so-called stochastic gradient descent (SGD) is normally applied. You can learn more about this from the following external resources:\n\nscikit-learn documentation\nSpark MLlib documentation\n\nImportantly, depending on types of output value, prediction problem can be categorized into regression and classification problem.\nRegression\nThe goal of regression is to predict real values as shown below:\n\n\n\nfeatures (input)\ntarget real value (output)\n\n\n\n\n[\"1:0.001\",\"4:0.23\",\"35:0.0035\",...]\n21.3\n\n\n[\"1:0.2\",\"3:0.1\",\"13:0.005\",...]\n6.2\n\n\n[\"5:1.3\",\"22:0.0.089\",\"77:0.0001\",...]\n17.1\n\n\n...\n...\n\n\n\nIn practice, target values could be any of small/large float/int negative/positive values. Our CTR prediction tutorial solves regression problem with small floating point target values in a 0-1 range, for example.\nWhile there are several ways to realize regression by using Hivemall, train_regressor() is one of the most flexible functions. This feature is explained in this page.\nClassification\nIn contrast to regression, output for classification problems should be (integer) labels:\n\n\n\nfeatures (input)\nlabel (output)\n\n\n\n\n[\"1:0.001\",\"4:0.23\",\"35:0.0035\",...]\n0\n\n\n[\"1:0.2\",\"3:0.1\",\"13:0.005\",...]\n1\n\n\n[\"5:1.3\",\"22:0.0.089\",\"77:0.0001\",...]\n1\n\n\n...\n...\n\n\n\nIn case the number of possible labels is 2 (0/1 or -1/1), the problem is binary classification, and Hivemall's train_classifier() function enables you to build binary classifiers. Binary Classification demonstrates how to use the function.\nAnother type of classification problems is multi-class classification. This task assumes that the number of possible labels is more than 2. We need to use different functions for the multi-class problems, and our news20 and iris tutorials would be helpful.\nMathematical formulation of generic prediction model\nHere, we briefly explain about how prediction model is constructed.\nFirst and foremost, we represent input and output for prediction models as follows:\n\nInput: a vector x\\mathbf{x}x\nOutput: a value yyy\n\nFor a set of samples (x1,y1),(x2,y2),⋯,(xn,yn)(\\mathbf{x}_1, y_1), (\\mathbf{x}_2, y_2), \\cdots, (\\mathbf{x}_n, y_n)(x​1​​,y​1​​),(x​2​​,y​2​​),⋯,(x​n​​,y​n​​), the goal of prediction algorithms is to find a weight vector (i.e., parameters) w\\mathbf{w}w by minimizing the following error:\nE(w):=1n∑i=1nL(w;xi,yi)+λR(w)\nE(\\mathbf{w}) := \\frac{1}{n} \\sum_{i=1}^{n} L(\\mathbf{w}; \\mathbf{x}_i, y_i) + \\lambda R(\\mathbf{w})\nE(w):=​n​​1​​​i=1​∑​n​​L(w;x​i​​,y​i​​)+λR(w)\nIn the above formulation, there are two auxiliary functions we have to know: \n\nL(w;xi,yi)L(\\mathbf{w}; \\mathbf{x}_i, y_i)L(w;x​i​​,y​i​​)\nLoss function for a single sample (xi,yi)(\\mathbf{x}_i, y_i)(x​i​​,y​i​​) and given w\\mathbf{w}w.\nIf this function produces small values, it means the parameter w\\mathbf{w}w is successfully learnt. \n\n\nR(w)R(\\mathbf{w})R(w)\nRegularization function for the current parameter w\\mathbf{w}w.\nIt prevents failing to a negative condition so-called over-fitting.\n\n\n\n(λ\\lambdaλ is a small value which controls the effect of regularization function.)\nEventually, minimizing the function E(w)E(\\mathbf{w})E(w) can be implemented by the SGD technique as described before, and w\\mathbf{w}w itself is used as a \"model\" for future prediction.\nInterestingly, depending on a choice of loss and regularization function, prediction model you obtained will behave differently; even if one combination could work as a classifier, another choice might be appropriate for regression.\nBelow we list possible options for train_regressor and train_classifier, and this is the reason why these two functions are the most flexible in Hivemall:\n\nLoss function: -loss, -loss_function\n\nFor train_regressor\nSquaredLoss (synonym: squared)\nQuantileLoss (synonym: quantile)\nEpsilonInsensitiveLoss (synonym: epsilon_insensitive)\nSquaredEpsilonInsensitiveLoss (synonym: squared_epsilon_insensitive)\nHuberLoss (synonym: huber)\n\n\nFor train_classifier\nHingeLoss (synonym: hinge)\nLogLoss (synonym: log, logistic)\nSquaredHingeLoss (synonym: squared_hinge)\nModifiedHuberLoss (synonym: modified_huber)\nThe following losses are mainly designed for regression but can sometimes be useful in classification as well:\nSquaredLoss (synonym: squared)\nQuantileLoss (synonym: quantile)\nEpsilonInsensitiveLoss (synonym: epsilon_insensitive)\nSquaredEpsilonInsensitiveLoss (synonym: squared_epsilon_insensitive)\nHuberLoss (synonym: huber)\n\n\n\n\n\n\nRegularization function: -reg, -regularization\n\nL1\nL2\nElasticNet\nRDA\n\n\n\nAdditionally, there are several variants of the SGD technique, and it is also configurable as:\n\nOptimizer: -opt, -optimizer\nSGD\nMomentum\nHyperparameters\n-alpha 1.0 Learning rate.\n-momentum 0.9 Exponential decay rate of the first order moment.\n\n\n\n\nNesterov\nSee: https://arxiv.org/abs/1212.0901\nHyperparameters\nsame as Momentum\n\n\n\n\nAdaGrad (default)\nSee: http://jmlr.org/papers/v12/duchi11a.html\nHyperparameters\n-eps 1.0 Constant for the numerical stability.\n\n\n\n\nRMSprop\nDescription: RMSprop optimizer introducing weight decay to AdaGrad.\nSee: http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf\nHyperparameters\n-decay 0.95 Weight decay rate\n-eps 1.0 Constant for numerical stability\n\n\n\n\nRMSpropGraves\nDescription: Alex Graves's RMSprop introducing weight decay and momentum.\nSee: https://arxiv.org/abs/1308.0850\nHyperparameters\n-alpha 1.0 Learning rate.\n-decay 0.95 Weight decay rate\n-momentum 0.9 Exponential decay rate of the first order moment.\n-eps 1.0 Constant for numerical stability\n\n\n\n\nAdaDelta\nSee: https://arxiv.org/abs/1212.5701\nHyperparameters\n-decay 0.95 Weight decay rate\n-eps 1e-6f Constant for numerical stability\n\n\n\n\nAdam\nSee:\nAdam: A Method for Stochastic Optimization\nFixing Weight Decay Regularization in Adam\nOn the Convergence of Adam and Beyond\n\n\nHyperparameters\n-alpha 1.0 Learning rate.\n-beta1 0.9 Exponential decay rate of the first order moment.\n-beta2 0.999 Exponential decay rate of the second order moment.\n-eps 1e-8f Constant for numerical stability\n-decay 0.0 Weight decay rate\n\n\n\n\nNadam\nDescription: Nadam is Adam optimizer with Nesterov momentum.\nSee:\nIncorporating Nesterov Momentum into Adam\nAdam report\nOn the importance of initialization and momentum in deep learning\n\n\nHyperparameters\nsame as Adam except ...\n-scheduleDecay 0.004 Scheduled decay rate (for each 250 steps by the default; 1/250=0.004)\n\n\n\n\nEve\nSee: https://openreview.net/forum?id=r1WUqIceg\nHyperparameters\nsame as Adam except ...\n-beta3 0.999 Decay rate for Eve coefficient.\n-c 10 Constant used for gradient clipping clip(val, 1/c, c)\n\n\n\n\nAdamHD\nDescription: Adam optimizer with Hypergradient Descent. Learning rate -alpha is automatically tuned.\nSee:\nOnline Learning Rate Adaptation with Hypergradient Descent\nConvergence Analysis of an Adaptive Method of Gradient Descent\n\n\nHyperparameters\nsame as Adam except ...\n-alpha 0.02 Learning rate.\n-beta -1e-6 Constant used for tuning learning rate.\n\n\n\n\n\n\n\nDefault (Adagrad+RDA), AdaDelta, Adam, and AdamHD is worth trying in my experience.\n NoteOption values are case insensitive and you can use sgd or rda, or huberloss in lower-case letters.\nFurthermore, optimizer offers to set auxiliary options such as:\n\nNumber of iterations: -iter, -iterations [default: 10]\nRepeat optimizer's learning procedure more than once to diligently find better result.\n\n\nConvergence rate: -cv_rate, -convergence_rate [default: 0.005]\nDefine a stopping criterion for the iterative training.\nIf the criterion is too small or too large, you may encounter over-fitting or under-fitting depending on value of -iter option.\n\n\nMini-batch size: -mini_batch, -mini_batch_size [default: 1]\nInstead of learning samples one-by-one, this option enables optimizer to utilize multiple samples at once to minimize the error function.\nAppropriate mini-batch size leads efficient training and effective prediction model.\n\n\n\nFor details of available options, following queries might be helpful to list all of them:\nselect train_regressor('-help');\nselect train_classifier('-help');\n\nSELECT train_regressor('-help');\n\nFAILED: UDFArgumentException\ntrain_regressor takes two or three arguments: List features, float target [, constant string options]\n\nusage: train_regressor(list features, double label [,\n       const string options]) - Returns a relation consists of\n        [-alpha ] [-amsgrad]\n       [-beta ] [-beta1 ] [-beta2 ] [-beta3 ] [-c\n       ] [-cv_rate ] [-decay] [-dense] [-dims ]\n       [-disable_cv] [-disable_halffloat] [-eps ] [-eta ] [-eta0\n       ] [-inspect_opts] [-iter ] [-iters ] [-l1_ratio\n       ] [-lambda ] [-loss ] [-mini_batch ] [-mix\n       ] [-mix_cancel] [-mix_session ] [-mix_threshold ]\n       [-opt ] [-power_t ] [-reg ] [-rho ] [-scale\n       ] [-ssl] [-t ]\n -alpha                             Coefficient of learning rate\n                                         [default: 1.0\n                                         (adam/RMSPropGraves), 0.02\n                                         (AdamHD/Nesterov)]\n -amsgrad                                Whether to use AMSGrad variant of\n                                         Adam\n -beta                              Hyperparameter for tuning alpha\n                                         in Adam-HD [default: 1e-6f]\n -beta1,--momentum                  Exponential decay rate of the\n                                         first order moment used in Adam\n                                         [default: 0.9]\n -beta2                             Exponential decay rate of the\n                                         second order moment used in Adam\n                                         [default: 0.999]\n -beta3                             Exponential decay rate of alpha\n                                         value  [default: 0.999]\n -c                                 Clipping constant of alpha used\n                                         in Eve optimizer so that clipped\n[default: 10]\n-cv_rate,--convergence_rate        Threshold to determine\n                                         convergence [default: 0.005]\n -decay                                  Weight decay rate [default: 0.0]\n -dense,--densemodel                     Use dense model or not\n -dims,--feature_dimensions         The dimension of model [default:\n                                         16777216 (2^24)]\n -disable_cv,--disable_cvtest            Whether to disable convergence\n                                         check [default: OFF]\n -disable_halffloat                      Toggle this option to disable the\n                                         use of SpaceEfficientDenseModel\n -eps                               Denominator value of\n                                         AdaDelta/AdaGrad/Adam [default:\n                                         1e-8 (AdaDelta/Adam), 1.0\n                                         (Adagrad)]\n -eta                               Learning rate scheme [default:\n                                         inverse/inv, fixed, simple]\n -eta0                              The initial learning rate\n                                         [default: 0.1]\n -inspect_opts                           Inspect Optimizer options\n -iter,--iterations                 The maximum number of iterations\n                                         [default: 10]\n -iters,--iterations                The maximum number of iterations\n                                         [default: 10]\n -l1_ratio                          Ratio of L1 regularizer as a part\n                                         of Elastic Net regularization\n                                         [default: 0.5]\n -lambda                            Regularization term [default\n                                         0.0001]\n -loss,--loss_function              Loss function [SquaredLoss\n                                         (default), QuantileLoss,\n                                         EpsilonInsensitiveLoss,\n                                         SquaredEpsilonInsensitiveLoss,\n                                         HuberLoss]\n -mini_batch,--mini_batch_size      Mini batch size [default: 1].\n                                         Expecting the value in range\n                                         [1,100] or so.\n -mix,--mix_servers                 Comma separated list of MIX\n                                         servers\n -mix_cancel,--enable_mix_canceling      Enable mix cancel requests\n -mix_session,--mix_session_name    Mix session name [default:\n                                         ${mapred.job.id}]\n -mix_threshold                     Threshold to mix local updates in\n                                         range (0,127] [default: 3]\n -opt,--optimizer                   Optimizer to update weights\n                                         [default: adagrad, sgd, momentum,\n                                         nesterov, rmsprop, rmspropgraves,\n                                         adadelta, adam, eve, adam_hd]\n -power_t                           The exponent for inverse scaling\n                                         learning rate [default: 0.1]\n -reg,--regularization              Regularization type [default:\n                                         rda, l1, l2, elasticnet]\n -rho,--decay                        Exponential decay rate of the\n                                         first and second order moments\n                                         [default 0.95 (AdaDelta,\n                                         rmsprop)]\n -scale                             Scaling factor for cumulative\n                                         weights [100.0]\n -ssl                                    Use SSL for the communication\n                                         with mix servers\n -t,--total_steps                   a total of n_samples * epochs\ntime steps\nIn practice, you can try different combinations of the options in order to achieve higher prediction accuracy.\nYou can also find the default optimizer hyperparameters by -inspect_opts option as follows:\nselect train_regressor(array(), 0, '-inspect_opts -optimizer adam -reg l1');\n\nFAILED: UDFArgumentException Inspected Optimizer options ...\n{disable_cvtest=false, regularization=L1, loss_function=SquaredLoss, eps=1.0E-8, decay=0.0, iterations=10, eta0=0.1, lambda=1.0E-4, eta=Invscaling, optimizer=adam, beta1=0.9, beta2=0.999, alpha=1.0, cv_rate=0.005, power_t=0.1}\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"supervised_learning/tutorial.html":{"url":"supervised_learning/tutorial.html","title":"Step-by-Step Tutorial on Supervised Learning","keywords":"","body":"\nStep-by-Step Tutorial on Supervised Learning\n\n\n\nWhat is Hivemall?\nBinary classification\nStep 1. Feature representation\nStep 2. Training\nStep 3. Prediction\nEvaluation\n\n\nRegression\nStep 1. Feature representation\nStep 2. Training\nStep 3. Prediction\nEvaluation\n\n\nNext steps\n\n\n\nWhat is Hivemall?\nApache Hivemall is a collection of user-defined functions (UDFs) for HiveQL which is strongly optimized for machine learning (ML) and data science. To give an example, you can efficiently build a logistic regression model with the stochastic gradient descent (SGD) optimization by issuing the following ~10 lines of query:\nSELECT\n  train_classifier(\n    features,\n    label,\n    '-loss_function logloss -optimizer SGD'\n  ) as (feature, weight)\nFROM\n  training\n;\n\nBelow we list ML and relevant problems that Hivemall can solve:\n\nBinary and multi-class classification\nRegression\nRecommendation\nAnomaly detection\nNatural language processing\nClustering (i.e., topic modeling)\nData sketching\nEvaluation\n\nOur YouTube demo video would be helpful to understand more about an overview of Hivemall.\nThis tutorial explains the basic usage of Hivemall with examples of supervised learning of simple regressor and binary classifier.\nBinary classification\nImagine a scenario that we like to build a binary classifier from the mock purchase_history data and predict unforeseen purchases to conduct a new campaign effectively:\n\n\n\nday_of_week\ngender\nprice\ncategory\nlabel\n\n\n\n\nSaturday\nmale\n600\nbook\n1\n\n\nFriday\nfemale\n4800\nsports\n0\n\n\nFriday\nother\n18000\nentertainment\n0\n\n\nThursday\nmale\n200\nfood\n0\n\n\nWednesday\nfemale\n1000\nelectronics\n1\n\n\n\nYou can create this table as follows:\ncreate table if not exists purchase_history as\nselect 1 as id, \"Saturday\" as day_of_week, \"male\" as gender, 600 as price, \"book\" as category, 1 as label\nunion all\nselect 2 as id, \"Friday\" as day_of_week, \"female\" as gender, 4800 as price, \"sports\" as category, 0 as label\nunion all\nselect 3 as id, \"Friday\" as day_of_week, \"other\" as gender, 18000 as price, \"entertainment\" as category, 0 as label\nunion all\nselect 4 as id, \"Thursday\" as day_of_week, \"male\" as gender, 200 as price, \"food\" as category, 0 as label\nunion all\nselect 5 as id, \"Wednesday\" as day_of_week, \"female\" as gender, 1000 as price, \"electronics\" as category, 1 as label\n;\n\nUse Hivemall train_classifier() UDF to tackle the problem as follows.\nStep 1. Feature representation\nFirst of all, we have to convert the records into pairs of the feature vector and corresponding target value. Here, Hivemall requires you to represent input features in a specific format.\nTo be more precise, Hivemall represents single feature in a concatenation of index (i.e., name) and its value:\n\nQuantitative feature: :\ne.g., price:600.0\n\n\nCategorical feature: #\ne.g., gender#male\n\n\n\nFeature index and feature value are separated by comma. When comma is omitted, the value is considered to be 1.0. So, a categorical feature gender#male a one-hot representation of index := gender#male and value := 1.0. Note that # is not a special character for categorical feature.\nEach of those features is a string value in Hive, and \"feature vector\" means an array of string values like:\n[\"price:600.0\", \"day of week#Saturday\", \"gender#male\", \"category#book\"]\nSee also more detailed document for input format.\nTherefore, what we first need to do is to convert the records into an array of feature strings, and Hivemall functions quantitative_features(), categorical_features() and array_concat() provide a simple way to create the pairs of feature vector and target value:\ncreate table if not exists training as\nselect\n  id,\n  array_concat( -- concatenate two arrays of quantitative and categorical features into single array\n    quantitative_features(\n      array(\"price\"), -- quantitative feature names\n      price -- corresponding column names\n    ),\n    categorical_features(\n      array(\"day of week\", \"gender\", \"category\"), -- categorical feature names\n      day_of_week, gender, category -- corresponding column names\n    )\n  ) as features,\n  label\nfrom\n  purchase_history\n;\n\nThe training table is as follows:\n\n\n\nid\nfeatures\nlabel\n\n\n\n\n1\n[\"price:600.0\",\"day of week#Saturday\",\"gender#male\",\"category#book\"]\n1\n\n\n2\n[\"price:4800.0\",\"day of week#Friday\",\"gender#female\",\"category#sports\"]\n0\n\n\n3\n[\"price:18000.0\",\"day of week#Friday\",\"gender#other\",\"category#entertainment\"]\n0\n\n\n4\n[\"price:200.0\",\"day of week#Thursday\",\"gender#male\",\"category#food\"]\n0\n\n\n5\n[\"price:1000.0\",\"day of week#Wednesday\",\"gender#female\",\"category#electronics\"]\n1\n\n\n\nThe output table training will be directly used as an input to Hivemall's ML functions in the next step.\n NoteYou can apply extra Hivemall functions (e.g., rescale(), feature_hashing(), l1_normalize()) for the features in this step to make your prediction model more accurate and stable; it is known as feature engineering in the context of ML. See our documentation for more information.\nStep 2. Training\nOnce the original table purchase_history has been converted into pairs of features and label, you can build a binary classifier by running the following query:\ncreate table if not exists classifier as\nselect\n  train_classifier(\n    features, -- feature vector\n    label, -- target value\n    '-loss_function logloss -optimizer SGD -regularization l1' -- hyper-parameters\n  ) as (feature, weight)\nfrom\n  training\n;\n\nWhat the above query does is to build a binary classifier with:\n\n-loss_function logloss\nUse logistic loss i.e., logistic regression\n\n\n-optimizer SGD\nLearn model parameters with the SGD optimization\n\n\n-regularization l1\nApply L1 regularization\n\n\n\nEventually, the output table classifier stores model parameters as:\n\n\n\nfeature\nweight\n\n\n\n\nday of week#Wednesday\n0.7443372011184692\n\n\nday of week#Thursday\n1.415687620465178e-07\n\n\nday of week#Friday\n-0.2697019577026367\n\n\nday of week#Saturday\n0.7337419390678406\n\n\ncategory#book\n0.7337419390678406\n\n\ncategory#electronics\n0.7443372011184692\n\n\ncategory#entertainment\n5.039264578954317e-07\n\n\ncategory#food\n1.415687620465178e-07\n\n\ncategory#sports\n-0.2697771489620209\n\n\ngender#male\n0.7336684465408325\n\n\ngender#female\n0.47442761063575745\n\n\ngender#other\n5.039264578954317e-07\n\n\nprice\n-110.62307739257812\n\n\n\nNotice that weight is learned for each possible value in a categorical feature, and for every single quantitative feature.\nOf course, you can optimize hyper-parameters to build more accurate prediction model. Check the output of the following query to see all available options, including learning rate, number of iterations and regularization parameters, and their default values:\nselect train_classifier('-help');\n-- Hivemall 0.5.2 and before\n-- select train_classifier(array(), 0, '-help');\n\nStep 3. Prediction\nNow, the table classifier has liner coefficients for given features, and we can predict unforeseen samples by computing a weighted sum of their features.\nHow about the probability of purchase by a male customer who sees a food product priced at 120 on Friday? Which product is more likely to be purchased by the customer on Friday?\nTo differentiate potential purchases, create a unforeseen_samples table with these unknown combinations of features:\ncreate table if not exists unforeseen_samples as\nselect 1 as id, array(\"gender#male\", \"category#food\", \"day of week#Friday\", \"price:120\") as features\nunion all\nselect 2 as id, array(\"gender#male\", \"category#sports\", \"day of week#Friday\", \"price:1000\") as features\nunion all\nselect 3 as id, array(\"gender#male\", \"category#electronics\", \"day of week#Friday\", \"price:540\") as features\n;\n\nPrediction for the feature vectors can be made by join operation between unforeseen_samples and classifier on each feature as:\nwith features_exploded as (\n  select\n    id,\n    -- split feature string into its name and value\n    -- to join with a model table\n    extract_feature(fv) as feature,\n    extract_weight(fv) as value\n  from\n    unforeseen_samples t1\n    LATERAL VIEW explode(features) t2 as fv\n)\nselect\n  t1.id,\n  sigmoid( sum(p1.weight * t1.value) ) as probability\nfrom\n  features_exploded t1\n  LEFT OUTER JOIN classifier p1 \n    ON (t1.feature = p1.feature)\ngroup by\n  t1.id\n;\n\n Notesigmoid() should be applied only for logistic loss and you can't get a probability with other loss functions for a classification. See also this video.\nOutput for single sample can be:\n\n\n\nid\nprobability\n\n\n\n\n1\n1.0261879540562902e-10\n\n\n\nEvaluation\nIf you have test samples for evaluation, use Hivemall's evaluation UDFs to measure the accuracy of prediction.\nFor instance, prediction accuracy over the training samples can be measured as:\nwith features_exploded as (\n  select\n    id,\n    extract_feature(fv) as feature,\n    extract_weight(fv) as value\n  from\n    training t1 \n    LATERAL VIEW explode(features) t2 as fv\n),\npredictions as (\n  select\n    t1.id,\n    sigmoid( sum(p1.weight * t1.value) ) as probability\n  from\n    features_exploded t1\n    LEFT OUTER JOIN classifier p1 \n      ON (t1.feature = p1.feature)\n  group by\n    t1.id\n)\nselect\n  auc(probability, label) as auc,\n  logloss(probability, label) as logloss\nfrom (\n  select \n    t1.probability, t2.label\n  from \n    predictions t1\n    join training t2 on (t1.id = t2.id)\n  ORDER BY \n    probability DESC\n) t\n;\n\n\n\n\nauc\nlogloss\n\n\n\n\n0.5\n9.200000003614099\n\n\n\nSince we are trying to solve the binary classification problem, the accuracy is measured by Area Under the ROC Curve auc() and/or Logarithmic Loss logloss().\nRegression\nIf you use train_regressor() instead of train_classifier(), you can also solve a regression problem with almost same queries.\nImagine the following customers table:\ncreate table if not exists customers as\nselect 1 as id, \"male\" as gender, 23 as age, \"Japan\" as country, 12 as num_purchases\nunion all\nselect 2 as id, \"female\" as gender, 43 as age, \"US\" as country, 4 as num_purchases\nunion all\nselect 3 as id, \"other\" as gender, 19 as age, \"UK\" as country, 2 as num_purchases\nunion all\nselect 4 as id, \"male\" as gender, 31 as age, \"US\" as country, 20 as num_purchases\nunion all\nselect 5 as id, \"female\" as gender, 37 as age, \"Australia\" as country, 9 as num_purchases\n;\n\n\n\n\ngender\nage\ncountry\nnum_purchases\n\n\n\n\nmale\n23\nJapan\n12\n\n\nfemale\n43\nUS\n4\n\n\nother\n19\nUK\n2\n\n\nmale\n31\nUS\n20\n\n\nfemale\n37\nAustralia\n9\n\n\n\nNow, our goal is to build a regression model to predict the number of purchases potentially done by new customers.\nStep 1. Feature representation\nSame as the classification example:\ninsert overwrite table training\nselect\n  id,\n  array_concat(\n    quantitative_features(\n      array(\"age\"),\n      age\n    ),\n    categorical_features(\n      array(\"country\", \"gender\"),\n      country, gender\n    )\n  ) as features,\n  num_purchases\nfrom\n  customers\n;\n\nStep 2. Training\ntrain_regressor() requires you to specify an appropriate loss function. One option is to replace the classifier-specific loss function logloss with squared as:\ncreate table if not exists regressor as\nselect\n  train_regressor(\n    features, -- feature vector\n    num_purchases, -- target value\n    '-loss_function squared -optimizer AdaGrad' -- hyper-parameters\n  ) as (feature, weight)\nfrom\n  training\n;\n\n-loss_function squared means that this query builds a simple linear regressor with the squared loss. Meanwhile, this example optimizes the parameters based on the AdaGrad optimization scheme with l2 regularization.\nRun the function with -help option to list available options:\nselect train_regressor('-help');\n-- Hivemall 0.5.2 and before\n-- select train_regressor(array(), 0, '-help');\n\nStep 3. Prediction\nPrepare dummy new customers:\ncreate table if not exists new_customers as\nselect 1 as id, array(\"gender#male\", \"age:10\", \"country#Japan\") as features\nunion all\nselect 2 as id, array(\"gender#female\", \"age:60\", \"country#US\") as features\nunion all\nselect 3 as id, array(\"gender#other\", \"age:50\", \"country#UK\") as features\n;\n\nA way of prediction is almost the same as classification, but not need to pass through the sigmoid() function:\nwith features_exploded as (\n  select\n    id,\n    extract_feature(fv) as feature,\n    extract_weight(fv) as value\n  from new_customers t1 LATERAL VIEW explode(features) t2 as fv\n)\nselect\n  t1.id,\n  sum(p1.weight * t1.value) as predicted_num_purchases\nfrom\n  features_exploded t1\n  LEFT OUTER JOIN regressor p1 ON (t1.feature = p1.feature)\ngroup by\n  t1.id\n;\n\nOutput is like:\n\n\n\nid\npredicted_num_purchases\n\n\n\n\n1\n3.645142912864685\n\n\n\nEvaluation\nUse Root Mean Square Error rmse() or Mean Absolute Error mae() UDFs for evaluation of regressors:\nwith features_exploded as (\n  select\n    id,\n    extract_feature(fv) as feature,\n    extract_weight(fv) as value\n  from\n    training t1 \n    LATERAL VIEW explode(features) t2 as fv\n),\npredictions as (\n  select\n    t1.id,\n    sum(p1.weight * t1.value) as predicted_num_purchases\n  from\n    features_exploded t1\n    LEFT OUTER JOIN regressor p1 ON (t1.feature = p1.feature)\n  group by\n    t1.id\n)\nselect\n  rmse(t1.predicted_num_purchases, t2.num_purchases) as rmse,\n  mae(t1.predicted_num_purchases, t2.num_purchases) as mae\nfrom\n  predictions t1\njoin\n  training t2 on (t1.id = t2.id)\n;\n\nOutput is like:\n\n\n\nrmse\nmae\n\n\n\n\n9.411633136764399\n7.124141833186149\n\n\n\nNext steps\nSee the following resources for further information:\n\nDetailed documentation of train_classifier and train_regressor\nQuery examples for some public datasets are also available in it.\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/general.html":{"url":"binaryclass/general.html","title":"Binary Classification","keywords":"","body":"\nHivemall has a generic function for classification: train_classifier. Compared to the other functions we will see in the later chapters, train_classifier provides simpler and configurable generic interface which can be utilized to build binary classification models in a variety of settings.\nHere, we briefly introduce usage of the function. Before trying sample queries, you first need to prepare a9a data. See our a9a tutorial page for further instructions.\n\n\n\nTraining\nPrediction & evaluation\nComparison with the other binary classifiers\n\n\n\n NoteThis feature is supported from Hivemall v0.5-rc.1 or later.\nTraining\ncreate table classification_model as\nselect\n feature,\n avg(weight) as weight\nfrom\n (\n  select\n    train_classifier(add_bias(features), label, '-loss logloss -opt SGD -reg no') as (feature, weight)\n  from\n     a9a_train\n ) t\ngroup by feature;\n\nPrediction & evaluation\nWITH test_exploded as (\n  select\n    rowid,\n    label,\n    extract_feature(feature) as feature,\n    extract_weight(feature) as value\n  from\n    a9a_test LATERAL VIEW explode(add_bias(features)) t AS feature\n),\npredict as (\n  select\n    t.rowid,\n    sigmoid(sum(m.weight * t.value)) as prob,\n    (case when sigmoid(sum(m.weight * t.value)) >= 0.5 then 1.0 else 0.0 end)as label\n  from\n    test_exploded t\n    LEFT OUTER JOIN classification_model m \n      ON (t.feature = m.feature)\n  group by\n    t.rowid\n),\nsubmit as (\n  select\n    t.label as actual,\n    p.label as predicted,\n    p.prob as probability\n  from\n    a9a_test t\n    JOIN predict p\n      on (t.rowid = p.rowid)\n)\nselect \n  sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom\n  submit;\n\n\n\n\naccuracy\n\n\n\n\n0.8461396720103188\n\n\n\nComparison with the other binary classifiers\nIn the next part of this user guide, our binary classification tutorials introduce many different functions:\n\nLogistic Regression\nand its mini-batch variant\n\n\nPerceptron\nPassive Aggressive\nCW\nAROW\nSCW\nAdaGradRDA\nAdaGrad\nAdaDelta\n\nAll of them actually have the same interface, but mathematical formulation and its implementation differ from each other.\nIn particular, the above sample queries are almost same as a9a tutorial using Logistic Regression. The difference is only in a choice of training function: logress() vs. train_classifier().\nHowever, at the same time, the options -loss logloss -opt SGD -reg no for train_classifier indicates that Hivemall uses the generic classifier as logress. Hence, the accuracy of prediction based on either logress and train_classifier would be (almost) same under the configuration.\nIn addition, train_classifier supports the -mini_batch option in a similar manner to what logress does. Thus, following two training queries show the same results:\nselect\n    logress(add_bias(features), label, '-mini_batch 10') as (feature, weight)\nfrom\n    a9a_train\n\nselect\n    train_classifier(add_bias(features), label, '-loss logloss -opt SGD -reg no -mini_batch 10') as (feature, weight)\nfrom\n    a9a_train\n\nLikewise, you can generate many different classifiers based on its options.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/a9a.html":{"url":"binaryclass/a9a.html","title":"a9a Tutorial","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/a9a_dataset.html":{"url":"binaryclass/a9a_dataset.html","title":"Data Preparation","keywords":"","body":"\na9a\nhttps://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#a9a\n\npreparation\nconv.awk\ncd /mnt/archive/datasets/classification/a9a\nawk -f conv.awk a9a | sed -e \"s/+1/1/\" | sed -e \"s/-1/0/\" > a9a.train\nawk -f conv.awk a9a.t | sed -e \"s/+1/1/\" | sed -e \"s/-1/0/\" > a9a.test\nPutting data on HDFS\nhadoop fs -mkdir -p /dataset/a9a/train\nhadoop fs -mkdir -p /dataset/a9a/test\n\nhadoop fs -copyFromLocal a9a.train /dataset/a9a/train\nhadoop fs -copyFromLocal a9a.test /dataset/a9a/test\nTraining/test data prepareation\ncreate database a9a;\nuse a9a;\n\ncreate external table a9atrain (\n  rowid int,\n  label float,\n  features ARRAY\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" STORED AS TEXTFILE LOCATION '/dataset/a9a/train';\n\ncreate external table a9atest (\n  rowid int, \n  label float,\n  features ARRAY\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" STORED AS TEXTFILE LOCATION '/dataset/a9a/test';\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/a9a_generic.html":{"url":"binaryclass/a9a_generic.html","title":"General Binary Classifier","keywords":"","body":"\nThis page shows the usage of General Binary Classifier using a9a dataset.\n\n\n\nTraining\nPrediction\nEvaluation\n\n\n\nTraining\ncreate table model\nas\nselect \n feature,\n avg(weight) as weight\nfrom (\n  select \n     train_classifier(\n       add_bias(features), label, \n       \"-loss logistic -iter 30\"\n     ) as (feature,weight)\n  from \n     a9a_train\n ) t \ngroup by feature;\n\nPrediction\ncreate table predict \nas\nWITH exploded as (\nselect \n  rowid,\n  label,\n  extract_feature(feature) as feature,\n  extract_weight(feature) as value\nfrom \n  a9a_test LATERAL VIEW explode(add_bias(features)) t AS feature\n)\nselect\n  t.rowid, \n  sigmoid(sum(m.weight * t.value)) as prob,\n  (case when sigmoid(sum(m.weight * t.value)) >= 0.5 then 1.0 else 0.0 end) as label\nfrom \n  exploded t LEFT OUTER JOIN\n  model m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nEvaluation\ncreate or replace view submit as\nselect \n  t.label as actual, \n  p.label as predicted, \n  p.prob as probability\nfrom \n  a9a_test t \n  JOIN predict p on (t.rowid = p.rowid);\n\nselect \n  sum(if(actual == predicted, 1, 0)) / count(1) as accuracy\nfrom\n  submit;\n\n\n0.8462625145875561\n\nThe following table shows accuracy for changing optimizer by -loss logistic -opt XXXXXX -reg l1 -iter 30 option:\n\n\n\nOptimizer\nAccuracy\n\n\n\n\nDefault (Adagrad+RDA)\n0.8462625145875561\n\n\nSGD\n0.8462010932989374\n\n\nMomentum\n0.8254406977458387\n\n\nNesterov\n0.8286346047540077\n\n\nAdaGrad\n0.850991953811191\n\n\nRMSprop\n0.8463239358761747\n\n\nRMSpropGraves\n0.825563540323076\n\n\nAdaDelta\n0.8492721577298692\n\n\nAdam\n0.8341625207296849\n\n\nNadam\n0.8349609974817271\n\n\nEve\n0.8348381549044899\n\n\nAdamHD\n0.8447269823720902\n\n\n\n NoteOptimizers using momentum need to tune decay rate well.\nDefault (Adagrad+RDA), AdaDelta, Adam, and AdamHD is worth trying in my experience.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/a9a_lr.html":{"url":"binaryclass/a9a_lr.html","title":"Logistic Regression","keywords":"","body":"\nThis pages shows an example of applying logistic regression for a9a binary classification task.\n Cautionlogloss() became deprecated since v0.5.0 release. Use smarter general classifier instead.\n\n\n\nUDF preparation\ntraining\nprediction\nevaluation\n\n\n\nUDF preparation\nselect count(1) from a9atrain;\n-- set total_steps ideally be \"count(1) / #map tasks\"\nset hivevar:total_steps=32561;\n\nselect count(1) from a9atest;\nset hivevar:num_test_instances=16281;\n\ntraining\ncreate table a9a_model1 \nas\nselect \n cast(feature as int) as feature,\n avg(weight) as weight\nfrom \n (select \n     logress(add_bias(features),label,\"-total_steps ${total_steps}\") as (feature,weight)\n  from \n     a9atrain\n ) t \ngroup by feature;\n\n Note-total_steps option is optional for logress() function. We recommend you NOT to use options (e.g., total_steps and eta0) if you are not familiar with those options. Hivemall then uses an autonomic ETA (learning rate) estimator.\nprediction\ncreate or replace view a9a_predict1 \nas\nWITH a9atest_exploded as (\nselect \n  rowid,\n  label,\n  extract_feature(feature) as feature,\n  extract_weight(feature) as value\nfrom \n  a9atest LATERAL VIEW explode(add_bias(features)) t AS feature\n)\nselect\n  t.rowid, \n  sigmoid(sum(m.weight * t.value)) as prob,\n  CAST((case when sigmoid(sum(m.weight * t.value)) >= 0.5 then 1.0 else 0.0 end) as FLOAT) as label\nfrom \n  a9atest_exploded t LEFT OUTER JOIN\n  a9a_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\ncreate or replace view a9a_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted, \n  pd.prob as probability\nfrom \n  a9atest t JOIN a9a_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1) / ${num_test_instances} from a9a_submit1 \nwhere actual == predicted;\n\n\n0.8430071862907684\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/a9a_minibatch.html":{"url":"binaryclass/a9a_minibatch.html","title":"Mini-batch Gradient Descent","keywords":"","body":"\nThis page explains how to apply Mini-Batch Gradient Descent for the training of logistic regression explained in this example. So, refer this page first. This content depends on it.\n Cautionlogloss() became deprecated since v0.5.0 release. Use smarter general classifier instead. You can use -mini_batch option in general classifier as well.\n\n\n\nTraining\nEvaluation\n\n\n\nTraining\nReplace a9a_model1 of this example.\nset hivevar:total_steps=32561;\nset hivevar:mini_batch_size=10;\n\ncreate table a9a_model1 \nas\nselect \n cast(feature as int) as feature,\n avg(weight) as weight\nfrom \n (select \n     logress(add_bias(features),label,\"-total_steps ${total_steps} -mini_batch ${mini_batch_size}\") as (feature,weight)\n  from \n     a9atrain\n ) t \ngroup by feature;\n\nEvaluation\nselect count(1) / ${num_test_instances} from a9a_submit1 \nwhere actual == predicted;\n\n\n\n\nStochastic Gradient Descent\nMinibatch Gradient Descent\n\n\n\n\n0.8430071862907684\n0.8463239358761747\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/news20.html":{"url":"binaryclass/news20.html","title":"News20 Tutorial","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/news20_dataset.html":{"url":"binaryclass/news20_dataset.html","title":"Data Preparation","keywords":"","body":"\nGet the news20b dataset.\nhttps://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#news20.binary\ncat  conv.awk\nBEGIN{ FS=\" \" }\n{\n    label=\\$1;\n    features=\\$2;\n    for(i=3;i news20.random\n# [mac]\n# $ brew install coreutils\n# $ gsort -R news20.binary > news20.random\nhead -15000 news20.random > news20.train\ntail -4996 news20.random > news20.test\ngawk -f conv.awk news20.train > news20.train.t\ngawk -f conv.awk news20.test > news20.test.t\n\nPutting data on HDFS\nhadoop fs -mkdir -p /dataset/news20-binary/train\nhadoop fs -mkdir -p /dataset/news20-binary/test\n\nhadoop fs -copyFromLocal news20.train.t /dataset/news20-binary/train\nhadoop fs -copyFromLocal news20.test.t /dataset/news20-binary/test\nTraining/test data prepareation\ncreate database news20;\nuse news20;\n\nCreate external table news20b_train (\n  rowid int,\n  label int,\n  features ARRAY\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" STORED AS TEXTFILE LOCATION '/dataset/news20-binary/train';\n\nCreate external table news20b_test (\n  rowid int, \n  label int,\n  features ARRAY\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" STORED AS TEXTFILE LOCATION '/dataset/news20-binary/test';\n\nset hivevar:seed=31;\ncreate or replace view news20b_train_x3\nas\nselect \n  * \nfrom (\n  select\n    amplify(3, *) as (rowid, label, features)\n  from\n    news20b_train\n) t\nCLUSTER BY rand(${seed});\n\ncreate table news20b_test_exploded as\nselect \n  rowid,\n  label,\n  extract_feature(feature) as feature,\n  extract_weight(feature) as value\nfrom \n  news20b_test LATERAL VIEW explode(add_bias(features)) t AS feature;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/news20_pa.html":{"url":"binaryclass/news20_pa.html","title":"Perceptron, Passive Aggressive","keywords":"","body":"\n[Perceptron]\nmodel building\ndrop table news20b_perceptron_model1;\ncreate table news20b_perceptron_model1 as\nselect \n feature,\n voted_avg(weight) as weight\nfrom \n (select \n     train_perceptron(add_bias(features),label) as (feature,weight)\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view news20b_perceptron_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_perceptron_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\ncreate or replace view news20b_perceptron_submit1 as\nselect \n  t.label as actual, \n  p.label as predicted\nfrom \n  news20b_test t JOIN news20b_perceptron_predict1 p\n    on (t.rowid = p.rowid);\n\nselect \n  sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom\n  news20b_perceptron_submit1;\n\n\n0.9459567654123299\n\n[Passive Aggressive]\nmodel building\ndrop table news20b_pa_model1;\ncreate table news20b_pa_model1 as\nselect \n feature,\n voted_avg(weight) as weight\nfrom \n (select \n     train_pa(add_bias(features),label) as (feature,weight)\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view news20b_pa_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_pa_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\ncreate or replace view news20b_pa_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20b_test t JOIN news20b_pa_predict1 pd \n    on (t.rowid = pd.rowid);\nselect \n  sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom\n  news20b_pa_submit1;\n\n\n0.9603682946357086\n\n[Passive Aggressive (PA1)]\nmodel building\ndrop table news20b_pa1_model1;\ncreate table news20b_pa1_model1 as\nselect \n feature,\n voted_avg(weight) as weight\nfrom \n (select \n     train_pa1(add_bias(features),label) as (feature,weight)\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view news20b_pa1_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t \n  LEFT OUTER JOIN news20b_pa1_model1 m \n    ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\ncreate or replace view news20b_pa1_submit1 as\nselect \n  t.label as actual, \n  p.label as predicted\nfrom \n  news20b_test t \n  JOIN news20b_pa1_predict1 p \n    on (t.rowid = p.rowid);\n\nselect \n  sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom \n  news20b_pa1_submit1;\n\n\n0.9601681345076061\n\n[Passive Aggressive (PA2)]\nmodel building\ndrop table news20b_pa2_model1;\ncreate table news20b_pa2_model1 as\nselect \n feature,\n voted_avg(weight) as weight\nfrom \n (select \n     train_pa2(add_bias(features),label) as (feature,weight)\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view news20b_pa2_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_pa2_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\ncreate or replace view news20b_pa2_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20b_test t JOIN news20b_pa2_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect \n  sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom \n  news20b_pa2_submit1;\n\n\n0.9597678142514011\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/news20_scw.html":{"url":"binaryclass/news20_scw.html","title":"CW, AROW, SCW","keywords":"","body":"\nConfidece Weighted (CW)\ntraining\ndrop table news20b_cw_model1;\ncreate table news20b_cw_model1 as\nselect \n feature,\n -- voted_avg(weight) as weight -- [hivemall v0.1]\n argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]\nfrom \n (select \n     -- train_cw(add_bias(features), label) as (feature, weight) -- [hivemall v0.1]\n     train_cw(add_bias(features), label) as (feature, weight, covar) -- [hivemall v0.2 or later]\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view news20b_cw_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_cw_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\nWITH submit as (\nselect \n  t.rowid,\n  t.label as actual, \n  p.label as predicted\nfrom \n  news20b_test t \n  JOIN news20b_cw_predict1 p\n    on (t.rowid = p.rowid)\n)\nselect sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom submit;\n\n\n0.9655724579663731\n\nAdaptive Regularization of Weight Vectors (AROW)\ntraining\ndrop table news20b_arow_model1;\ncreate table news20b_arow_model1 as\nselect \n feature,\n -- voted_avg(weight) as weight -- [hivemall v0.1]\n argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]\nfrom \n (select \n     -- train_arow(add_bias(features),label) as (feature,weight) -- [hivemall v0.1]\n     train_arow(add_bias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later]\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view news20b_arow_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_arow_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\nWITH submit as (\nselect\n  t.rowid, \n  t.label as actual, \n  p.label as predicted\nfrom \n  news20b_test t\n  JOIN news20b_arow_predict1 p\n    on (t.rowid = p.rowid)\n)\nselect sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom submit;\n\n\n0.9659727782225781\n\nSoft Confidence-Weighted (SCW1)\ntraining\ndrop table news20b_scw_model1;\ncreate table news20b_scw_model1 as\nselect \n feature,\n -- voted_avg(weight) as weight -- [hivemall v0.1]\n argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]\nfrom \n (select \n     -- train_scw(add_bias(features),label) as (feature,weight) -- [hivemall v0.1]\n     train_scw(add_bias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later]\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view news20b_scw_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_scw_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\nWITH submit as (\n  select \n    t.rowid, \n    t.label as actual, \n    p.label as predicted\n  from \n    news20b_test t JOIN news20b_scw_predict1 p\n      on (t.rowid = p.rowid)\n)\nselect sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom submit\n\n\n0.9661729383506805\n\nSoft Confidence-Weighted (SCW2)\ntraining\ndrop table news20b_scw2_model1;\ncreate table news20b_scw2_model1 as\nselect \n feature,\n -- voted_avg(weight) as weight -- [hivemall v0.1]\n argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]\nfrom \n (select \n     -- train_scw2(add_bias(features),label) as (feature,weight)    -- [hivemall v0.1]\n     train_scw2(add_bias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later]\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view news20b_scw2_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_scw2_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\nWITH submit as (\nselect \n  t.rowid, \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20b_test t\n  JOIN news20b_scw2_predict1 pd \n    on (t.rowid = pd.rowid)\n)\nselect sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom submit;\n\n\n0.9579663730984788\n\n--\n\n\n\nAlgorithm\nAccuracy\n\n\n\n\nPerceptron\n0.9459567654123299\n\n\nSCW2\n0.9579663730984788\n\n\nPA2\n0.9597678142514011\n\n\nPA1\n0.9601681345076061\n\n\nPA\n0.9603682946357086\n\n\nCW\n0.9655724579663731\n\n\nAROW\n0.9659727782225781\n\n\nSCW1\n0.9661729383506805\n\n\n\nMy recommendation is AROW for classification.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/news20_generic.html":{"url":"binaryclass/news20_generic.html","title":"General Binary Classifier","keywords":"","body":"\nIn this tutorial, we build a binary classification model using general classifier.\n\n\n\nTraining\nprediction\nevaluation\n\n\n\nTraining\n-- set mapred.reduce.tasks=3; -- explicitly use 3 reducers\n\ndrop table news20b_generic_model;\ncreate table news20b_generic_model as\nselect \n feature,\n voted_avg(weight) as weight\nfrom \n (select \n     train_classifier(\n       add_bias(features), label, \n       '-loss logistic -opt AdamHD -reg l1 -iters 20'\n     ) as (feature,weight)\n  from\n     news20b_train_x3\n ) t \ngroup by feature;\n\n NoteDefault (Adagrad+RDA), AdaDelta, Adam, and AdamHD is worth trying in my experience.\nprediction\ncreate or replace view news20b_generic_predict\nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_generic_model m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\nWITH submit as (\nselect \n  t.label as actual, \n  p.label as predicted\nfrom \n  news20b_test t \n  JOIN news20b_generic_predict p\n    on (t.rowid = p.rowid)\n)\nselect \n  sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom\n  submit;\n\n\n0.967173738991193 (-opt AdamHD -reg l1)\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n\n"},"binaryclass/news20_generic_bagging.html":{"url":"binaryclass/news20_generic_bagging.html","title":"Baggnig classiers","keywords":"","body":"\nHivemall generally uses model averaging (i.e., model ensemble) for creating a unified prediction model.\nIn this tutorial, we show how to apply bagging (i.e., prediction ensemble) for making a prediction.\n\n\n\nTraining\nprediction\nevaluation\n\n\n\nTraining\n-- set mapred.reduce.tasks=3; -- explicitly use 3 reducers\n\nCREATE TABLE bagging_models\nas \nWITH train as (\n  select \n     train_classifier(\n       add_bias(features), label, \n       '-loss logistic -opt AdamHD -reg l1 -iters 20'\n     ) as (feature,weight)\n  from\n     news20b_train_x3\n)\nselect\n  taskid() as modelid,\n  feature,\n  weight\nfrom \n  train;\n\nprediction\ncreate table bagging_predict\nas\nWITH weights as (\n  select\n    t.rowid,\n    m.modelid,\n    sum(m.weight * t.value) as total_weight\n  from\n    news20b_test_exploded t \n    LEFT OUTER JOIN\n    bagging_models m ON (t.feature = m.feature)\n  group by\n    rowid, modelid\n),\nbagging as (\n  select\n    rowid,\n    voted_avg(total_weight) as total_weight\n  from \n    weights\n  group by\n    rowid \n)\nselect\n  rowid,\n  total_weight,\n  -- Note: sum(total_weight) > 0.0 equals to sigmoid(total_weight) > 0.5\n  -- https://en.wikipedia.org/wiki/Sigmoid_function\n  case when total_weight > 0.0 then 1 else -1 end as label\nfrom\n  bagging\ngroup by\n  rowid;\n\nevaluation\nWITH submit as (\n  select \n    t.label as actual, \n    p.label as predicted\n  from \n    news20b_test t \n    JOIN bagging_predict p on (t.rowid = p.rowid)\n)\nselect \n  sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom\n  submit;\n\n\n0.9641713370696557\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n\n"},"binaryclass/news20_adagrad.html":{"url":"binaryclass/news20_adagrad.html","title":"AdaGradRDA, AdaGrad, AdaDelta","keywords":"","body":"\n\n\n\nAdaGradRDA\nmodel building\nprediction\nevaluation\n\n\nAdaGrad\nmodel building\nprediction\nevaluation\n\n\nAdaDelta\nmodel building\nprediction\nevaluation\n\n\n\n\n\n Cautiontrain_adagrad() became deprecated since v0.5.0 release. Use smarter general classifier instead.\nAdaGradRDA\n NoteThe current AdaGradRDA implmenetation can only be applied to classification, not to regression, because it uses hinge loss for the loss function.\nmodel building\nuse news20;\n\ndrop table news20b_adagrad_rda_model1;\ncreate table news20b_adagrad_rda_model1 as\nselect \n feature,\n voted_avg(weight) as weight\nfrom \n (select \n     train_adagrad_rda(add_bias(features),label) as (feature,weight)\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view news20b_adagrad_rda_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_adagrad_rda_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\ncreate or replace view news20b_adagrad_rda_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20b_test t JOIN news20b_adagrad_rda_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/4996 from news20b_adagrad_rda_submit1 \nwhere actual == predicted;\n\n\nSCW1 0.9661729383506805 \nADAGRAD+RDA 0.9677742193755005\n\nAdaGrad\n NoteAdaGrad is better suited for a binary classification problem because the current implementation only support logistic loss.\nmodel building\ndrop table news20b_adagrad_model1;\ncreate table news20b_adagrad_model1 as\nselect \n feature,\n voted_avg(weight) as weight\nfrom \n (select \n     train_adagrad_regr(add_bias(features),convert_label(label)) as (feature,weight)\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\n Cautionadagrad takes 0/1 for a label value and convert_label(label) converts a label value from -1/+1 to 0/1.\nprediction\ncreate or replace view news20b_adagrad_predict1 \nas\nselect\n  t.rowid, \n  case when sigmoid(sum(m.weight * t.value)) >= 0.5 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_adagrad_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\ncreate or replace view news20b_adagrad_submit1 as\nselect \n  t.label as actual, \n  p.label as predicted\nfrom \n  news20b_test t JOIN news20b_adagrad_predict1 p\n    on (t.rowid = p.rowid);\n\nselect count(1)/4996 from news20b_adagrad_submit1 \nwhere actual == predicted;\n\n\n0.9549639711769415 (adagrad)\n\nAdaDelta\n CautionAdaDelta can only be applied for regression problem because the current implementation only support logistic loss.\nmodel building\ndrop table news20b_adadelta_model1;\ncreate table news20b_adadelta_model1 as\nselect \n feature,\n voted_avg(weight) as weight\nfrom \n (select \n     adadelta(add_bias(features),convert_label(label)) as (feature,weight)\n  from \n     news20b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view news20b_adadelta_predict1 \nas\nselect\n  t.rowid, \n  case when sigmoid(sum(m.weight * t.value)) >= 0.5 then 1 else -1 end as label\nfrom \n  news20b_test_exploded t LEFT OUTER JOIN\n  news20b_adadelta_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\ncreate or replace view news20b_adadelta_submit1 as\nselect \n  t.label as actual, \n  p.label as predicted\nfrom \n  news20b_test t JOIN news20b_adadelta_predict1 p\n    on (t.rowid = p.rowid);\n\nselect count(1)/4996 from news20b_adadelta_submit1 \nwhere actual == predicted;\n\nAdaDelta often performs better than AdaGrad.\n\n0.9549639711769415 (adagrad)\n0.9545636509207366 (adadelta)\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/news20_rf.html":{"url":"binaryclass/news20_rf.html","title":"Random Forest","keywords":"","body":"\nHivemall Random Forest supports libsvm-like sparse inputs. This page shows a classification example on 20-newsgroup dataset.\n NoteThis feature, i.e., Sparse input support in Random Forest, is supported since Hivemall v0.5.0 or later.\nfeature_hashing function is useful to prepare feature vectors for Random Forest.\n\n\n\nTraining\nPrediction\nEvaluation\n\n\n\nTraining\ndrop table rf_model;\ncreate table rf_model\nas\nselect\n  train_randomforest_classifier(\n    features,\n    convert_label(label),  -- convert -1/1 to 0/1\n    '-trees 50 -seed 71'   -- hyperparameters\n  )\nfrom\n  train;\n\n Cautionlabel must be in [0, k) where k is the number of classes.\nPrediction\n-- SET hivevar:classification=true;\n\ndrop table rf_predicted;\ncreate table rf_predicted\nas\nSELECT\n  rowid,\n  rf_ensemble(predicted.value, predicted.posteriori, model_weight) as predicted\n  -- rf_ensemble(predicted.value, predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., model_weight)\nFROM (\n  SELECT\n    rowid, \n    m.model_weight,\n    -- v0.5.0 and later\n    tree_predict(m.model_id, m.model, t.features, \"-classification\") as predicted\n    -- before v0.5.0\n    -- tree_predict(m.model_id, m.model, t.features, ${classification}) as predicted\n  FROM\n    rf_model m\n    LEFT OUTER JOIN -- CROSS JOIN\n    test t\n) t1\ngroup by\n  rowid\n;\n\nEvaluation\nWITH submit as (\n  select \n    convert_label(t.label) as actual, \n    p.predicted.label as predicted\n  from \n    test t \n    JOIN rf_predicted p on (t.rowid = p.rowid)\n)\nselect\n  sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom\n  submit;\n\n\n0.8112489991993594\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/news20b_xgboost.html":{"url":"binaryclass/news20b_xgboost.html","title":"XGBoost","keywords":"","body":"\nIn this tutorial, we build a binary classification model using XGBoost.\n\n\n\nFeature Vector format for XGBoost\nLabel format in Binary Classification\nUsage and Hyperparameters\nTraining\nprediction\nevaluation\n\n\n\nFeature Vector format for XGBoost\nFor feature vector, train_xgboost takes a sparse vector format (array) or a dense vector format (array).\nIn the feature vector, each feature takes a LIBSVM format:\nfeature ::= :\n\nindex ::=  (e.g., 0,1,2,...)\nweight ::= \n NoteUnlike the original libsvm format, it's not needed to sort a feature vector by ansceding order of feature index.\nTarget label format of binary classification follows this rule. Please refer xgboost document as well.\nLabel format in Binary Classification\nThe label must be an INT typed column and the values are positive (+1) or negative (-1) as follows:\n ::= 1 | -1\nAlternatively, you can use the following format that represents 1 for a positive example and 0 for a negative example:\n ::= 0 | 1\nUsage and Hyperparameters\nYou can find hyperparameters and it's default setting by running the following query:\nselect train_xgboost();\n\nusage: train_xgboost(array features, int|double target [,\n       string options]) - Returns a relation consists of  pred_model> [-alpha ] [-base_score ]\n       [-booster ] [-colsample_bylevel ] [-colsample_bynode\n       ] [-colsample_bytree ] [-disable_default_eval_metric\n       ] [-eta ] [-eval_metric ] [-feature_selector ]\n       [-gamma ] [-grow_policy ] [-lambda ] [-lambda_bias\n       ] [-max_bin ] [-max_delta_step ] [-max_depth ]\n       [-max_leaves ] [-maximize_evaluation_metrics ]\n       [-min_child_weight ] [-normalize_type ] [-num_class\n       ] [-num_early_stopping_rounds ] [-num_feature ]\n       [-num_parallel_tree ] [-num_pbuffer ] [-num_round ]\n       [-objective ] [-one_drop ] [-process_type ]\n       [-rate_drop ] [-refresh_leaf ] [-sample_type ]\n       [-scale_pos_weight ] [-seed ] [-silent ]\n       [-sketch_eps ] [-skip_drop ] [-subsample ] [-top_k\n       ] [-tree_method ] [-tweedie_variance_power ]\n       [-updater ] [-validation_ratio ] [-verbosity ]\n -alpha,--reg_alpha              L1 regularization term on weights.\n                                      Increasing this value will make\n                                      model more conservative. [default:\n                                      0.0]\n -base_score                     Initial prediction score of all\n                                      instances, global bias [default:\n                                      0.5]\n -booster                        Set a booster to use, gbtree or\n                                      gblinear or dart. [default: gbree]\n -colsample_bylevel              Subsample ratio of columns for each\n                                      level [default: 1.0]\n -colsample_bynode               Subsample ratio of columns for each\n                                      node [default: 1.0]\n -colsample_bytree               Subsample ratio of columns when\n                                      constructing each tree [default:\n                                      1.0]\n -disable_default_eval_metric    NFlag to disable default metric. Set\n                                      to >0 to disable. [default: 0]\n -eta,--learning_rate            Step size shrinkage used in update\n                                      to prevents overfitting [default:\n                                      0.3]\n -eval_metric                    Evaluation metrics for validation\n                                      data. A default metric is assigned\n                                      according to the objective:\n                                      - rmse: for regression\n                                      - error: for classification\n                                      - map: for ranking\n                                      For a list of valid inputs, see\n                                      XGBoost Parameters.\n -feature_selector               Feature selection and ordering\n                                      method. [Choices: cyclic (default),\n                                      shuffle, random, greedy, thrifty]\n -gamma,--min_split_loss         Minimum loss reduction required to\n                                      make a further partition on a leaf\n                                      node of the tree. [default: 0.0]\n -grow_policy                    Controls a way new nodes are added\n                                      to the tree. Currently supported\n                                      only if tree_method is set to hist.\n                                      [default: depthwise, Choices:\n                                      depthwise, lossguide]\n -lambda,--reg_lambda            L2 regularization term on weights.\n                                      Increasing this value will make\n                                      model more conservative. [default:\n                                      1.0 for gbtree, 0.0 for gblinear]\n -lambda_bias                    L2 regularization term on bias\n                                      [default: 0.0]\n -max_bin                        Maximum number of discrete bins to\n                                      bucket continuous features. Only\n                                      used if tree_method is set to hist.\n                                      [default: 256]\n -max_delta_step                 Maximum delta step we allow each\n                                      tree's weight estimation to be\n                                      [default: 0]\n -max_depth                      Max depth of decision tree [default:\n                                      6]\n -max_leaves                     Maximum number of nodes to be added.\n                                      Only relevant when\n                                      grow_policy=lossguide is set.\n                                      [default: 0]\n -maximize_evaluation_metrics    Maximize evaluation metrics\n                                      [default: false]\n -min_child_weight               Minimum sum of instance weight\n                                      (hessian) needed in a child\n                                      [default: 1.0]\n -normalize_type                 Type of normalization algorithm.\n                                      [Choices: tree (default), forest]\n -num_class                      Number of classes to classify\n -num_early_stopping_rounds      Minimum rounds required for early\n                                      stopping [default: 0]\n -num_feature                    Feature dimension used in boosting\n                                      [default: set automatically by\n                                      xgboost]\n -num_parallel_tree              Number of parallel trees constructed\n                                      during each iteration. This option\n                                      is used to support boosted random\n                                      forest. [default: 1]\n -num_pbuffer                    Size of prediction buffer [default:\n                                      set automatically by xgboost]\n -num_round,--iters              Number of boosting iterations\n                                      [default: 10]\n -objective                      Specifies the learning task and the\n                                      corresponding learning objective.\n                                      Examples: reg:linear, reg:logistic,\n                                      multi:softmax. For a full list of\n                                      valid inputs, refer to XGBoost\n                                      Parameters. [default: reg:linear]\n -one_drop                       When this flag is enabled, at least\n                                      one tree is always dropped during\n                                      the dropout. 0 or 1. [default: 0]\n -process_type                   A type of boosting process to run.\n                                      [Choices: default, update]\n -rate_drop                      Dropout rate in range [0.0, 1.0].\n                                      [default: 0.0]\n -refresh_leaf                   This is a parameter of the refresh\n                                      updater plugin. When this flag is 1,\n                                      tree leafs as well as tree nodes’\n                                      stats are updated. When it is 0,\n                                      only node stats are updated.\n                                      [default: 1]\n -sample_type                    Type of sampling algorithm.\n                                      [Choices: uniform (default),\n                                      weighted]\n -scale_pos_weight               ontrol the balance of positive and\n                                      negative weights, useful for\n                                      unbalanced classes. A typical value\n                                      to consider: sum(negative instances)\n                                      / sum(positive instances) [default:\n                                      1.0]\n -seed                           Random number seed. [default: 43]\n -silent                         Deprecated. Please use verbosity\n                                      instead. 0 means printing running\n                                      messages, 1 means silent mode\n                                      [default: 1]\n -sketch_eps                     This roughly translates into O(1 /\n                                      sketch_eps) number of bins.\n                                      Compared to directly select number\n                                      of bins, this comes with theoretical\n                                      guarantee with sketch accuracy.\n                                      Only used for tree_method=approx.\n                                      Usually user does not have to tune\n                                      this.  [default: 0.03]\n -skip_drop                      Probability of skipping the dropout\n                                      procedure during a boosting\n                                      iteration in range [0.0, 1.0].\n                                      [default: 0.0]\n -subsample                      Subsample ratio of the training\n                                      instance in range (0.0,1.0]\n                                      [default: 1.0]\n -top_k                          The number of top features to select\n                                      in greedy and thrifty feature\n                                      selector. The value of 0 means using\n                                      all the features. [default: 0]\n -tree_method                    The tree construction algorithm used\n                                      in XGBoost. [default: auto, Choices:\n                                      auto, exact, approx, hist]\n -tweedie_variance_power         Parameter that controls the variance\n                                      of the Tweedie distribution in range\n                                      [1.0, 2.0]. [default: 1.5]\n -updater                        A comma-separated string that\n                                      defines the sequence of tree\n                                      updaters to run. For a full list of\n                                      valid inputs, please refer to\n                                      XGBoost Parameters. [default:\n                                      'grow_colmaker,prune' for gbtree,\n                                      'shotgun' for gblinear]\n -validation_ratio               Validation ratio in range [0.0,1.0]\n                                      [default: 0.2]\n -verbosity                      Verbosity of printing messages.\n                                      Choices: 0 (silent), 1 (warning), 2\n                                      (info), 3 (debug). [default: 0]\n\nObjective function -objective SHOULD be specified though -objective reg:linear is used for Objective function by the default.\nFor the full list of objective functions, please refer this xgboost v0.90 documentation.\nThe following objectives would widely be used for regression, binary classication, and multiclass classication, respectively.\n\nreg:squarederror regression with squared loss.\nbinary:logistic logistic regression for binary classification, output probability.\nbinary:hinge hinge loss for binary classification. This makes predictions of 0 or 1, rather than producing probabilities.\nmulti:softmax set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class (number of classes).\nmulti:softprob same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata * nclass matrix. The result contains predicted probability of each data point belonging to each class.\n\nOther hyperparameters better to be tuned are:\n\n-booster gbree Which booster to use. The default gbtree (Gradient Boosting Trees) would be fine for most cases. Can be gbtree, gblinear or dart; gbtree and dart use tree based models while gblinear uses linear functions.\n-eta 0.1 The learning rate, 0.3 by the default. 0.05, 0.1, 0.3 are worth trying.\n-max_depth 6 The maximum depth of the tree. The default value 6 would be fine for most case. Recommended value range is 5-10.\n-num_class 3 The number of classes MUST be specified for multiclass classification (i.e., -objective multi:softmax or -objective multi:softprob)\n-num_round 10 The number of rounds for boosting. 10 or more would be preferred.\n-num_early_stopping_rounds 3 The number of rounds required for early stopping. Without specifying -num_early_stopping_rounds, no early stopping is NOT carried. When -num_round=100 and -num_early_stopping_rounds=5, traning could be early stopped at 15th iteration if there is no evaluation result greater than the 10th iteration's (best one). Early stopping 3 or so would be preferred. \n-validation_ratio 0.2 The ratio data used for validation (early stopping). 0.2 would be enough for most cases. Note that 80% data is used for training when validation_ratio 0.2 is set.\n\nYou can find the underlying XGBoost version by:\nselect xgboost_version();\n> 0.90\n\nTraining\ntrain_xgboost UDTF is used for training. \nThe function signature is train_xgboost(array features, double target [,string options]) and it returns a prediction model as a relation consist of  pred_model>.\n-- explicitly use 3 reducers\n-- set mapred.reduce.tasks=3;\n\ndrop table xgb_lr_model;\ncreate table xgb_lr_model as\nselect \n  train_xgboost(features, label, '-objective binary:logistic -num_round 10 -num_early_stopping_rounds 3') \n    as (model_id, model)\nfrom (\n  select features, label\n  from news20b_train\n  cluster by rand(43) -- shuffle data to reducers\n) shuffled;\n\ndrop table xgb_hinge_model;\ncreate table xgb_hinge_model as\nselect \n  train_xgboost(features, label, '-objective binary:hinge -num_round 10 -num_early_stopping_rounds 3') \n    as (model_id, model)\nfrom (\n  select features, label\n  from news20b_train\n  cluster by rand(43) -- shuffle data to reducers\n) shuffled;\n\n Cautioncluster by rand() is NOT required when training data is small and a single task is launched for XGBoost training.\ncluster by rand() shuffles data at random and divided it for multiple XGBoost instances.\nprediction\ndrop table xgb_lr_predicted;\ncreate table xgb_lr_predicted \nas\nselect\n  rowid, \n  array_avg(predicted) as predicted,\n  avg(predicted[0]) as prob\nfrom (\n  select\n    -- fast predictition by xgboost-predictor-java (https://github.com/komiya-atsushi/xgboost-predictor-java/)\n    xgboost_predict(rowid, features, model_id, model) as (rowid, predicted)\n    -- predict by  xgboost4j (https://xgboost.readthedocs.io/en/stable/jvm/)\n    -- xgboost_batch_predict(rowid, features, model_id, model) as (rowid, predicted)\n  from\n    -- for each model l \n    --   for each test r\n    --     predict\n    xgb_lr_model l\n    LEFT OUTER JOIN news20b_test r \n) t\ngroup by rowid;\n\ndrop table xgb_hinge_predicted;\ncreate table xgb_hinge_predicted \nas\nselect\n  rowid,\n  -- voting\n  -- if(sum(if(predicted[0]=1,1,0)) > sum(if(predicted[0]=0,1,0)),1,-1) as predicted\n  majority_vote(if(predicted[0]=1, 1, -1)) as predicted\nfrom (\n  select\n    -- binary:hinge is not supported in xgboost_predict\n    -- binary:hinge returns [1.0] or [0.0] for predicted\n    xgboost_batch_predict(rowid, features, model_id, model) \n      as (rowid, predicted)\n  from\n    -- for each model l \n    --   for each test r\n    --     predict\n    xgb_hinge_model l\n    LEFT OUTER JOIN news20b_test r \n) t\ngroup by\n  rowid\n\nYou can find the function signature of xgboost_predict by\nselect xgboost_predict();\n\nusage: xgboost_predict(PRIMITIVE rowid, array features,\n       string model_id, array pred_model [, string options]) -\n       Returns a prediction result as (string rowid, array\n       predicted)\n\nselect xgboost_batch_predict();\n\nusage: xgboost_batch_predict(PRIMITIVE rowid, array\n       features, string model_id, array pred_model [, string\n       options]) - Returns a prediction result as (string rowid,\n       array predicted) [-batch_size ]\n -batch_size    Number of rows to predict together [default: 128]\n\n Cautionxgboost_predict outputs probability for -objective binary:logistic while 0/1 is resulted for -objective binary:hinge.xgboost_predict only support the following models and objectives because it uses xgboost-predictor-java:\nModels: {gblinear, gbtree, dart}\nObjective functions: {binary:logistic, binary:logitraw, multi:softmax, multi:softprob, reg:linear, reg:squarederror, rank:pairwise}For other models and objectives, please use xgboost_batch_predict that uses xgboost4j insead.\nevaluation\nWITH submit as (\n  select \n    t.label as actual, \n    -- probability thresholding by 0.5\n    if(p.prob > 0.5,1,-1)  as predicted\n  from \n    news20b_test t \n    JOIN xgb_lr_predicted p\n      on (t.rowid = p.rowid)\n)\nselect \n  sum(if(actual = predicted, 1, 0)) / count(1) as accuracy\nfrom\n  submit;\n\n\n0.8372698158526821 (logistic loss)\n\nWITH submit as (\n  select \n   t.label as actual, \n   p.predicted\n  from \n    news20b_test t \n    JOIN xgb_hinge_predicted p\n      on (t.rowid = p.rowid)\n)\nselect \n  sum(if(actual=predicted,1,0)) / count(1) as accuracy\nfrom\n  submit;\n\n\n0.7752201761409128 (hinge loss)\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/kdd2010a.html":{"url":"binaryclass/kdd2010a.html","title":"KDD2010a Tutorial","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/kdd2010a_dataset.html":{"url":"binaryclass/kdd2010a_dataset.html","title":"Data Preparation","keywords":"","body":"\nhttps://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#kdd2010 (algebra))\n\nthe number of classes: 2\nthe number of data: 8,407,752 (training) / 510,302 (testing)\nthe number of features: 20,216,830 in about 2.73 GB (training) / 20,216,830 (testing) \n\n\nDefine training/testing tables\ncreate database kdd2010;\nuse kdd2010;\n\ncreate external table kdd10a_train (\n  rowid int,\n  label int,\n  features ARRAY\n) \nROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" \nSTORED AS TEXTFILE LOCATION '/dataset/kdd10a/train';\n\ncreate external table kdd10a_test (\n  rowid int, \n  label int,\n  features ARRAY\n) \nROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" \nSTORED AS TEXTFILE LOCATION '/dataset/kdd10a/test';\n\nPutting data into HDFS\nconv.awk\nawk -f conv.awk kdda | hadoop fs -put - /dataset/kdd10a/train/kdda\nawk -f conv.awk kdda.t | hadoop fs -put - /dataset/kdd10a/test/kdda.t\n\nMake auxiliary tables\ncreate table kdd10a_train_orcfile (\n rowid bigint,\n label int,\n features array\n) STORED AS orc tblproperties (\"orc.compress\"=\"SNAPPY\");\n\n-- SET mapred.reduce.tasks=64;\nINSERT OVERWRITE TABLE kdd10a_train_orcfile\nselect * from kdd10a_train\nCLUSTER BY rand();\n-- SET mapred.reduce.tasks=-1;\n\ncreate table kdd10a_test_exploded as\nselect \n  rowid,\n  label,\n  split(feature,\":\")[0] as feature,\n  cast(split(feature,\":\")[1] as float) as value\nfrom \n  kdd10a_test LATERAL VIEW explode(add_bias(features)) t AS feature;\n\nset hivevar:xtimes=3;\nset hivevar:shufflebuffersize=1000;\n-- set hivemall.amplify.seed=32;\ncreate or replace view kdd10a_train_x3\nas\nselect\n   rand_amplify(${xtimes}, ${shufflebuffersize}, *) as (rowid, label, features)\nfrom  \n   kdd10a_train_orcfile;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/kdd2010a_scw.html":{"url":"binaryclass/kdd2010a_scw.html","title":"PA, CW, AROW, SCW","keywords":"","body":"\nPA1\nTrain\n-- SET mapred.reduce.tasks=32;\ndrop table kdd10a_pa1_model1;\ncreate table kdd10a_pa1_model1 as\nselect \n feature,\n voted_avg(weight) as weight\nfrom \n (select \n     train_pa1(add_bias(features),label) as (feature,weight)\n  from \n     kdd10a_train_x3\n ) t \ngroup by feature;\n\nPredict\ncreate or replace view kdd10a_pa1_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  kdd10a_test_exploded t LEFT OUTER JOIN\n  kdd10a_pa1_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nEvaluate\ncreate or replace view kdd10a_pa1_submit1 as\nselect \n  t.rowid, \n  t.label as actual, \n  pd.label as predicted\nfrom \n  kdd10a_test t JOIN kdd10a_pa1_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/510302 from kdd10a_pa1_submit1 \nwhere actual = predicted;\n\n\n0.8677782959894337\n\nCW\n-- SET mapred.reduce.tasks=32;\ndrop table kdd10a_cw_model1;\ncreate table kdd10a_cw_model1 as\nselect \n feature,\n argmin_kld(weight, covar) as weight\nfrom \n (select \n     train_cw(add_bias(features),label) as (feature,weight,covar)\n  from \n     kdd10a_train_x3\n ) t \ngroup by feature;\n\ncreate or replace view kdd10a_cw_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  kdd10a_test_exploded t LEFT OUTER JOIN\n  kdd10a_cw_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\ncreate or replace view kdd10a_cw_submit1 as\nselect \n  t.rowid, \n  t.label as actual, \n  pd.label as predicted\nfrom \n  kdd10a_test t JOIN kdd10a_cw_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/510302 from kdd10a_cw_submit1 \nwhere actual = predicted;\n\n\n0.8678037711002504\n\nAROW\n-- SET mapred.reduce.tasks=32;\ndrop table kdd10a_arow_model1;\ncreate table kdd10a_arow_model1 as\nselect \n feature,\n -- voted_avg(weight) as weight\n argmin_kld(weight, covar) as weight -- [hivemall v0.2alpha3 or later]\nfrom \n (select \n     -- train_arow(add_bias(features),label) as (feature,weight) -- [hivemall v0.1]\n     train_arow(add_bias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later]\n  from \n     kdd10a_train_x3\n ) t \ngroup by feature;\n\ncreate or replace view kdd10a_arow_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  kdd10a_test_exploded t LEFT OUTER JOIN\n  kdd10a_arow_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\ncreate or replace view kdd10a_arow_submit1 as\nselect \n  t.rowid, \n  t.label as actual, \n  pd.label as predicted\nfrom \n  kdd10a_test t JOIN kdd10a_arow_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/510302 from kdd10a_arow_submit1 \nwhere actual = predicted;\n\n\n0.8676038894615345\n\nSCW\n-- SET mapred.reduce.tasks=32;\ndrop table kdd10a_scw_model1;\ncreate table kdd10a_scw_model1 as\nselect \n feature,\n argmin_kld(weight, covar) as weight\nfrom \n (select \n     train_scw(add_bias(features),label) as (feature,weight,covar)\n  from \n     kdd10a_train_x3\n ) t \ngroup by feature;\n\ncreate or replace view kdd10a_scw_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  kdd10a_test_exploded t LEFT OUTER JOIN\n  kdd10a_scw_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\ncreate or replace view kdd10a_scw_submit1 as\nselect \n  t.rowid, \n  t.label as actual, \n  pd.label as predicted\nfrom \n  kdd10a_test t JOIN kdd10a_scw_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/510302 from kdd10a_scw_submit1 \nwhere actual = predicted;\n\n\n0.8678096499719774\n\n\n\n\n\nAlgorithm\nAccuracy\n\n\n\n\nAROW\n0.8676038894615345\n\n\nPA1\n0.8677782959894337\n\n\nCW\n0.8678037711002504\n\n\nSCW1\n0.8678096499719774\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/kdd2010b.html":{"url":"binaryclass/kdd2010b.html","title":"KDD2010b Tutorial","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/kdd2010b_dataset.html":{"url":"binaryclass/kdd2010b_dataset.html","title":"Data Preparation","keywords":"","body":"\nhttps://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#kdd2010 (bridge to algebra))\n\nthe number of classes: 2\nthe number of examples: 19,264,097 (training) / 748,401 (testing)\nthe number of features: 29,890,095 (training) / 29,890,095 (testing)\n\n\nDefine training/testing tables\ncreate database kdd2010;\nuse kdd2010;\n\ncreate external table kdd10b_train (\n  rowid int,\n  label int,\n  features ARRAY\n) \nROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" \nSTORED AS TEXTFILE LOCATION '/dataset/kdd10b/train';\n\ncreate external table kdd10b_test (\n  rowid int, \n  label int,\n  features ARRAY\n) \nROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" \nSTORED AS TEXTFILE LOCATION '/dataset/kdd10b/test';\n\nPutting data into HDFS\nconv.awk\nawk -f conv.awk kddb | hadoop fs -put - /dataset/kdd10b/train/kddb\nawk -f conv.awk kddb.t | hadoop fs -put - /dataset/kdd10b/test/kddb.t\n\nMake auxiliary tables\ncreate table kdd10b_test_exploded as\nselect \n  rowid,\n  label,\n  split(feature,\":\")[0] as feature,\n  cast(split(feature,\":\")[1] as float) as value\nfrom \n  kdd10b_test LATERAL VIEW explode(add_bias(features)) t AS feature;\n\nset hivevar:xtimes=3;\nset hivevar:shufflebuffersize=1000;\ncreate or replace view kdd10b_train_x3\nas\nselect\n   rand_amplify(${xtimes}, ${shufflebuffersize}, *) as (rowid, label, features)\nfrom  \n   kdd10b_train;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/kdd2010b_arow.html":{"url":"binaryclass/kdd2010b_arow.html","title":"AROW","keywords":"","body":"\ntraining\n-- SET mapred.reduce.tasks=32;\ndrop table kdd10b_arow_model1;\ncreate table kdd10b_arow_model1 as\nselect \n feature,\n -- voted_avg(weight) as weight\n argmin_kld(weight, covar) as weight -- [hivemall v0.2alpha3 or later]\nfrom \n (select \n     -- train_arow(add_bias(features),label) as (feature,weight) -- [hivemall v0.1]\n     train_arow(add_bias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later]\n  from \n     kdd10b_train_x3\n ) t \ngroup by feature;\n\nprediction\ncreate or replace view kdd10b_arow_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  kdd10b_test_exploded t LEFT OUTER JOIN\n  kdd10b_arow_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\ncreate or replace view kdd10b_arow_submit1 as\nselect \n  t.rowid, \n  t.label as actual, \n  pd.label as predicted\nfrom \n  kdd10b_test t JOIN kdd10b_arow_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/748401 from kdd10b_arow_submit1 \nwhere actual = predicted;\n\n\n0.8565808971393678\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/webspam.html":{"url":"binaryclass/webspam.html","title":"Webspam Tutorial","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/webspam_dataset.html":{"url":"binaryclass/webspam_dataset.html","title":"Data Pareparation","keywords":"","body":"\nGet the dataset from \nhttps://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#webspam\nPutting data on HDFS\nhadoop fs -mkdir -p /dataset/webspam/raw\n\nawk -f conv.awk webspam_wc_normalized_trigram.svm | \\\nhadoop fs -put - /dataset/webspam/raw/\n\nTable preparation\ncreate database webspam;\nuse webspam;\n\ncreate external table webspam_raw (\n  rowid int,\n  label int,\n  features ARRAY\n) ROW FORMAT \nDELIMITED FIELDS TERMINATED BY '\\t' \nCOLLECTION ITEMS TERMINATED BY \",\" \nSTORED AS TEXTFILE LOCATION '/dataset/webspam/raw';\n\nset hive.sample.seednumber=43;\ncreate table webspam_test\nas\nselect * from webspam_raw TABLESAMPLE(1000 ROWS) s\nCLUSTER BY rand(43)\nlimit 70000;\n\nMake auxiliary tables\ncreate table webspam_train_orcfile (\n rowid int,\n label int,\n features array\n) STORED AS orc tblproperties (\"orc.compress\"=\"SNAPPY\");\n\n-- SET mapred.reduce.tasks=128;\nINSERT OVERWRITE TABLE webspam_train_orcfile\nselect\n  s.rowid, \n  label,\n  add_bias(features) as features\nfrom webspam_raw s\nwhere not exists (select rowid from webspam_test t where s.rowid = t.rowid)\nCLUSTER BY rand(43);\n-- SET mapred.reduce.tasks=-1;\n\nset hivevar:xtimes=3;\nset hivevar:shufflebuffersize=100;\nset hivemall.amplify.seed=32;\ncreate or replace view webspam_train_x3\nas\nselect\n   rand_amplify(${xtimes}, ${shufflebuffersize}, *) as (rowid, label, features)\nfrom  \n   webspam_train_orcfile;\n\ncreate table webspam_test_exploded as\nselect \n  rowid,\n  label,\n  split(feature,\":\")[0] as feature,\n  cast(split(feature,\":\")[1] as float) as value\nfrom \n  webspam_test LATERAL VIEW explode(add_bias(features)) t AS feature;\n\nCaution: For this dataset, use small shufflebuffersize because each training example has lots of features though (xtimes  shufflebuffersize  N) training examples are cached in memory.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/webspam_scw.html":{"url":"binaryclass/webspam_scw.html","title":"PA1, AROW, SCW","keywords":"","body":"\nPA1\nuse webspam;\n\ndrop table webspam_pa1_model1;\ncreate table webspam_pa1_model1 as\nselect \n feature,\n cast(voted_avg(weight) as float) as weight\nfrom \n (select \n     train_pa1(features,label) as (feature,weight) -- sparse model\n     -- train_pa1(features,label,\"-dense -dims 33554432\") as (feature,weight)\n  from \n     webspam_train_x3\n ) t \ngroup by feature;\n\ncreate or replace view webspam_pa1_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  webspam_test_exploded t LEFT OUTER JOIN\n  webspam_pa1_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\ncreate or replace view webspam_pa1_submit1 as\nselect \n  t.rowid, \n  t.label as actual, \n  pd.label as predicted\nfrom \n  webspam_test t JOIN webspam_pa1_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/70000 from webspam_pa1_submit1 \nwhere actual = predicted;\n\n\nPrediction accuracy: 0.9628428571428571\n\nAROW\ndrop table webspam_arow_model1;\ncreate table webspam_arow_model1 as\nselect \n feature,\n argmin_kld(weight,covar)as weight\nfrom \n (select \n     train_arow(features,label) as (feature,weight,covar)\n  from \n     webspam_train_x3\n ) t \ngroup by feature;\n\ncreate or replace view webspam_arow_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  webspam_test_exploded t LEFT OUTER JOIN\n  webspam_arow_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\ncreate or replace view webspam_arow_submit1 as\nselect \n  t.rowid, \n  t.label as actual, \n  pd.label as predicted\nfrom \n  webspam_test t JOIN webspam_arow_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/70000 from webspam_arow_submit1 \nwhere actual = predicted;\n\n\nPrediction accuracy: 0.9747428571428571\n\nSCW1\ndrop table webspam_scw_model1;\ncreate table webspam_scw_model1 as\nselect \n feature,\n argmin_kld(weight,covar)as weight\nfrom \n (select \n     train_scw(features,label) as (feature,weight,covar)\n  from \n     webspam_train_x3\n ) t \ngroup by feature;\n\ncreate or replace view webspam_scw_predict1 \nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as total_weight,\n  case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label\nfrom \n  webspam_test_exploded t LEFT OUTER JOIN\n  webspam_scw_model1 m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\ncreate or replace view webspam_scw_submit1 as\nselect \n  t.rowid, \n  t.label as actual, \n  pd.label as predicted\nfrom \n  webspam_test t JOIN webspam_scw_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/70000 from webspam_scw_submit1 \nwhere actual = predicted;\n\n\nPrediction accuracy: 0.9778714285714286\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/titanic_rf.html":{"url":"binaryclass/titanic_rf.html","title":"Kaggle Titanic Tutorial","keywords":"","body":"\nThis examples gives a basic usage of RandomForest on Hivemall using Kaggle Titanic dataset.\nThe example gives a baseline score without any feature engineering.\n\n\n\nData preparation\nData preparation for RandomForest\n\n\nTraining\nPrediction\nKaggle submission\nGraphviz export\nTest by dividing training dataset\nTracing predictions\n\n\n\n\n\nData preparation\ncreate database titanic;\nuse titanic;\n\ndrop table train;\ncreate external table train (\n  passengerid int, -- unique id\n  survived int, -- target label\n  pclass int,\n  name string,\n  sex string,\n  age int,\n  sibsp int, -- Number of Siblings/Spouses Aboard\n  parch int, -- Number of Parents/Children Aboard\n  ticket string,\n  fare double,\n  cabin string,\n  embarked string\n) \nROW FORMAT DELIMITED\n   FIELDS TERMINATED BY '|'\n   LINES TERMINATED BY '\\n'\nSTORED AS TEXTFILE LOCATION '/dataset/titanic/train';\n\nhadoop fs -rm /dataset/titanic/train/train.csv\nawk '{ FPAT=\"([^,]*)|(\\\"[^\\\"]+\\\")\";OFS=\"|\"; } NR >1 {$1=$1;$4=substr($4,2,length($4)-2);print $0}' train.csv | hadoop fs -put - /dataset/titanic/train/train.csv\n\ndrop table test_raw;\ncreate external table test_raw (\n  passengerid int,\n  pclass int,\n  name string,\n  sex string,\n  age int,\n  sibsp int, -- Number of Siblings/Spouses Aboard\n  parch int, -- Number of Parents/Children Aboard\n  ticket string,\n  fare double,\n  cabin string,\n  embarked string\n)\nROW FORMAT DELIMITED\n   FIELDS TERMINATED BY '|'\n   LINES TERMINATED BY '\\n'\nSTORED AS TEXTFILE LOCATION '/dataset/titanic/test_raw';\n\nhadoop fs -rm /dataset/titanic/test_raw/test.csv\nawk '{ FPAT=\"([^,]*)|(\\\"[^\\\"]+\\\")\";OFS=\"|\"; } NR >1 {$1=$1;$3=substr($3,2,length($3)-2);print $0}' test.csv | hadoop fs -put - /dataset/titanic/test_raw/test.csv\n\nData preparation for RandomForest\nset hivevar:output_row=true;\n\ndrop table train_rf;\ncreate table train_rf\nas\nWITH train_quantified as (\n  select    \n    quantify(\n      ${output_row}, passengerid, survived, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked\n    ) as (passengerid, survived, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked)\n  from (\n    select * from train\n    order by passengerid asc\n  ) t\n)\nselect\n  rand(31) as rnd,\n  passengerid, \n  array(pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked) as features,\n  survived\nfrom\n  train_quantified\n;\n\ndrop table test_rf;\ncreate table test_rf\nas\nWITH test_quantified as (\n  select \n    quantify(\n      output_row, passengerid, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked\n    ) as (passengerid, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked)\n  from (\n    -- need training data to assign consistent ids to categorical variables\n    select * from (\n      select\n        1 as train_first, false as output_row, passengerid, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked\n      from\n        train\n      union all\n      select\n        2 as train_first, true as output_row, passengerid, pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked\n      from\n        test_raw\n    ) t0\n    order by train_first asc, passengerid asc\n  ) t1\n)\nselect\n  passengerid, \n  array(pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked) as features\nfrom\n  test_quantified\n;\n\nTraining\nselect guess_attribute_types(pclass, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked) from train limit 1;\n\nQ,C,C,Q,Q,Q,C,Q,C,C\n\nQ and C represent quantitative variable and categorical variables, respectively.\n CautionNote that the output of guess_attribute_types is not perfect. Revise it by your self.\nFor example, pclass is a categorical variable.\nset hivevar:attrs=C,C,C,Q,Q,Q,C,Q,C,C;\n\ndrop table model_rf;\ncreate table model_rf\nAS\nselect\n  train_randomforest_classifier(features, survived, \"-trees 500 -attrs ${attrs}\") \nfrom\n  train_rf\n;\n\nselect\n  array_sum(var_importance) as var_importance,\n  sum(oob_errors) / sum(oob_tests) as oob_err_rate\nfrom\n  model_rf;\n\n\n[137.00242639169272,1194.2140119834373,328.78017188176966,628.2568660509628,200.31275032394072,160.12876797647078,1083.5987543408116,664.1234312561456,422.89449844090393,130.72019667694784]     0.18742985409652077\n\nPrediction\n-- SET hivevar:classification=true;\nset hive.auto.convert.join=true;\nSET hive.mapjoin.optimized.hashtable=false;\nSET mapred.reduce.tasks=16;\n\ndrop table predicted_rf;\ncreate table predicted_rf\nas\nSELECT \n  passengerid,\n  predicted.label,\n  predicted.probability,\n  predicted.probabilities\nFROM (\n  SELECT\n    passengerid,\n    rf_ensemble(predicted.value, predicted.posteriori, model_weight) as predicted\n    -- rf_ensemble(predicted.value, predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., model_weight)\n  FROM (\n    SELECT\n      t.passengerid, \n      p.model_weight,\n      tree_predict(p.model_id, p.model, t.features, \"-classification\") as predicted\n      -- tree_predict_v1(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted -- to use the old model in v0.5.0 or later\n    FROM (\n      SELECT \n        model_id, model_weight, model\n      FROM \n        model_rf \n      DISTRIBUTE BY rand(1)\n    ) p\n    LEFT OUTER JOIN test_rf t\n  ) t1\n  group by\n    passengerid\n) t2\n;\n\n Cautiontree_predict_v1 is for the backward compatibility for using prediction models built before v0.5.0 on v0.5.0 or later.\nKaggle submission\ndrop table predicted_rf_submit;\ncreate table predicted_rf_submit\n  ROW FORMAT DELIMITED \n    FIELDS TERMINATED BY \",\"\n    LINES TERMINATED BY \"\\n\"\n  STORED AS TEXTFILE\nas\nSELECT passengerid, label as survived\nFROM predicted_rf\nORDER BY passengerid ASC;\n\nhadoop fs -getmerge /user/hive/warehouse/titanic.db/predicted_rf_submit predicted_rf_submit.csv\nsed -i -e \"1i PassengerId,Survived\" predicted_rf_submit.csv\n\nAccuracy would gives 0.76555 for a Kaggle submission.\nGraphviz export\n Notetree_export feature is supported from Hivemall v0.5.0 or later.\nBetter to limit tree depth on training by -depth option to plot a Decision Tree.\nHivemall provide tree_export to export a decision tree into Graphviz or human-readable Javascript format. You can find the usage by issuing the following query:\n> select tree_export(\"\",\"-help\");\n\nusage: tree_export(string model, const string options, optional\n       array featureNames=null, optional array\n       classNames=null) - exports a Decision Tree model as javascript/dot]\n       [-help] [-output_name ] [-r] [-t ]\n -help                             Show function help\n -output_name,--outputName    output name [default: predicted]\n -r,--regression                   Is regression tree or not\n -t,--type                    Type of output [default: js,\n                                   javascript/js, graphviz/dot\nCREATE TABLE model_exported \n  STORED AS ORC tblproperties(\"orc.compress\"=\"SNAPPY\")\nAS\nselect\n  model_id,\n  tree_export(model, \"-type javascript -output_name survived\", array('pclass','name','sex','age','sibsp','parch','ticket','fare','cabin','embarked'), array('no','yes')) as js,\n  tree_export(model, \"-type graphviz -output_name survived\", array('pclass','name','sex','age','sibsp','parch','ticket','fare','cabin','embarked'), array('no','yes')) as dot\nfrom\n  model_rf\n-- limit 1\n;\n\nHere is an example plotting a decision tree using Graphviz or Vis.js.\nTest by dividing training dataset\ndrop table train_rf_07;\ncreate table train_rf_07 \nas\nselect * from train_rf \nwhere rnd = 0.7;\n\ndrop table model_rf_07;\ncreate table model_rf_07\nAS\nselect\n  train_randomforest_classifier(features, survived, \"-trees 500 -attrs ${attrs}\") \nfrom\n  train_rf_07;\n\nselect\n  array_sum(var_importance) as var_importance,\n  sum(oob_errors) / sum(oob_tests) as oob_err_rate\nfrom\n  model_rf_07;\n\n\n[116.12055542977338,960.8569891444097,291.08765260103837,469.74671636586226,163.721292772701,120.784769882858,847.9769298113661,554.4617571355476,346.3500941757221,97.42593940113392]    0.1838351822503962\n\n-- SET hivevar:classification=true;\nSET hive.mapjoin.optimized.hashtable=false;\nSET mapred.reduce.tasks=16;\n\ndrop table predicted_rf_03;\ncreate table predicted_rf_03\nas\nSELECT \n  passengerid,\n  predicted.label,\n  predicted.probability,\n  predicted.probabilities\nFROM (\n  SELECT\n    passengerid,\n    rf_ensemble(predicted.value, predicted.posteriori, model_weight) as predicted\n    -- rf_ensemble(predicted.value, predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., model_weight)\n  FROM (\n    SELECT\n      t.passengerid,\n      p.model_weight,\n      tree_predict(p.model_id, p.model, t.features, \"-classification\") as predicted\n      -- tree_predict(p.model_id, p.model, t.features, ${classification}) as predicted\n      -- tree_predict_v1(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted -- to use the old model in v0.5.0 or later\n    FROM (\n      SELECT \n        model_id, model_weight, model\n      FROM \n        model_rf_07\n      DISTRIBUTE BY rand(1)\n    ) p\n    LEFT OUTER JOIN test_rf_03 t\n  ) t1\n  group by\n    passengerid\n) t2;\n\nWITH rf_submit_03 as (\n  select \n    t.survived as actual, \n    p.label as predicted\n  from \n    test_rf_03 t \n    JOIN predicted_rf_03 p on (t.passengerid = p.passengerid)\n)\nselect sum(if(actual=predicted,1,0))/count(1) as accuracy \nfrom rf_submit_03;\n\n\n0.8153846153846154\n\nTracing predictions\nFind important attributes and conditions predicted to survive.\nWITH tmp as (\n  SELECT\n    t.survived as actual,\n    decision_path(m.model_id, m.model, t.features, '-classification -no_verbose', array('pclass','name','sex','age','sibsp','parch','ticket','fare','cabin','embarked')) as path\n  FROM\n    model_rf_07 m\n    LEFT OUTER JOIN -- CROSS JOIN\n    test_rf_03 t\n)\nselect\n  r.branch,\n  count(1) as cnt\nfrom\n  tmp l\n  LATERAL VIEW explode(array_slice(path, 0, -1)) r as branch\nwhere\n  -- actual = 1 and      -- actual is survived\n  last_element(path) = 1 -- predicted is survived\ngroup by\n  r.branch\norder by\n  cnt desc\nlimit 100;\n\n\n\n\nr.branch\ncnt\n\n\n\n\nsex != 0.0\n29786\n\n\npclass != 3.0\n18520\n\n\npclass = 3.0\n7444\n\n\nsex = 0.0\n6494\n\n\nembarked != 1.0\n6175\n\n\nticket != 22.0\n5560\n\n\n...\n...\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/criteo.html":{"url":"binaryclass/criteo.html","title":"Criteo Tutorial","keywords":"","body":"\nThis tutorial tackles Kaggle Display Advertising Challenge.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/criteo_dataset.html":{"url":"binaryclass/criteo_dataset.html","title":"Data Preparation","keywords":"","body":"\n\n\n\nDownload data\nConvert data into CSV format\nCreate tables\n\n\n\nDownload data\nGet dataset of Kaggle Display Advertising Challenge from one of the following sources:\n\nOriginal competition data (by Criteo Labs) [~20GB]\nSubset of the original competition data (by Criteo Labs) [~30MB]\nTiny sample data (by the winners of the competition) [~20bytes]\n\nIt should be noted that you must accept and agree with CRITEO LABS DATA TERM OF USE before downloading the data.\nConvert data into CSV format\nHere, you can use a script prepared by one of the Hivemall PPMC members: takuti/criteo-ffm.\nClone the repository:\ngit clone git@github.com:takuti/criteo-ffm.git\ncd criteo-ffm\n\nA script data.sh downloads the original data and converts them into CSV format:\n./data.sh  # downloads the original data and generates `train.csv` and `test.csv`\nln -s train.csv tr.csv\nln -s test.csv te.csv\n\nOr, since the original data is very huge, starting from the tiny sample data bundled into the repository would be better:\nln -s train.tiny.csv tr.csv\nln -s test.tiny.csv te.csv\n\nCreate tables\nLoad the CSV files to Hive tables as:\nhadoop fs -put tr.csv /criteo/train\nhadoop fs -put te.csv /criteo/test\n\nCREATE DATABASE IF NOT EXISTS criteo;\nuse criteo;\n\nDROP TABLE IF EXISTS train;\nCREATE EXTERNAL TABLE train (\n  id bigint,\n  label int,\n  -- quantitative features\n  i1 int,i2 int,i3 int,i4 int,i5 int,i6 int,i7 int,i8 int,i9 int,i10 int,i11 int,i12 int,i13 int,\n  -- categorical features\n  c1 string,c2 string,c3 string,c4 string,c5 string,c6 string,c7 string,c8 string,c9 string,c10 string,c11 string,c12 string,c13 string,c14 string,c15 string,c16 string,c17 string,c18 string,c19 string,c20 string,c21 string,c22 string,c23 string,c24 string,c25 string,c26 string\n) ROW FORMAT\nDELIMITED FIELDS TERMINATED BY ','\nSTORED AS TEXTFILE LOCATION '/criteo/train';\n\nDROP TABLE IF EXISTS test;\nCREATE EXTERNAL TABLE test (\n  label int,\n  -- quantitative features\n  i1 int,i2 int,i3 int,i4 int,i5 int,i6 int,i7 int,i8 int,i9 int,i10 int,i11 int,i12 int,i13 int,\n  -- categorical features\n  c1 string,c2 string,c3 string,c4 string,c5 string,c6 string,c7 string,c8 string,c9 string,c10 string,c11 string,c12 string,c13 string,c14 string,c15 string,c16 string,c17 string,c18 string,c19 string,c20 string,c21 string,c22 string,c23 string,c24 string,c25 string,c26 string\n) ROW FORMAT\nDELIMITED FIELDS TERMINATED BY ','\nSTORED AS TEXTFILE LOCATION '/criteo/test';\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"binaryclass/criteo_ffm.html":{"url":"binaryclass/criteo_ffm.html","title":"Field-Aware Factorization Machines","keywords":"","body":"\nField-aware factorization machines (FFM) is a factorization model which has been used by the #1 solution of the Criteo competition.\nThis page guides you to try the factorization technique with Hivemall's train_ffm and ffm_predict UDFs.\n\n\n\nPreprocess data and convert into LIBFFM format\nInsert preprocessed data into tables\nTraining\nPrediction and evaluation\n\n\n\n NoteThis feature is supported from Hivemall v0.5.1 or later.\nPreprocess data and convert into LIBFFM format\nSince FFM is a relatively complex factor-based model which requires us to spend a significant amount of time for feature engineering, preprocessing data outside of Hive can be a reasonable option.\nYou can again use the repository takuti/criteo-ffm cloned in the data preparation guide to preprocess the data as the winning solution did:\ncd criteo-ffm\n# create the CSV files `tr.csv` and `te.csv`\nmake preprocess\n\nTask make preprocess executes some Python scripts which are originally taken from guestwalk/kaggle-2014-criteo and chenhuang-learn/ffm.\nEventually, you will obtain the following files in so-called LIBFFM format:\n\ntr.ffm - Labeled training samples\ntr.sp - 80% of the labeled training samples randomly picked from tr.ffm\nva.sp - Remaining 20% of samples for evaluation\n\n\nte.ffm - Unlabeled test samples\n\n :: :: ...\n.\n.\n.\nSee LIBFFM official README for detail.\nIn order to evaluate the accuracy of prediction at the end of this tutorial, later sections use tr.sp and va.sp.\nInsert preprocessed data into tables\nCreate new tables used by the FFM UDFs:\nhadoop fs -put tr.sp /criteo/ffm/train\nhadoop fs -put va.sp /criteo/ffm/test\n\nuse criteo;\n\nDROP TABLE IF EXISTS train_ffm;\nCREATE EXTERNAL TABLE train_ffm (\n  label int,\n  -- quantitative features\n  i1 string,i2 string,i3 string,i4 string,i5 string,i6 string,i7 string,i8 string,i9 string,i10 string,i11 string,i12 string,i13 string,\n  -- categorical features\n  c1 string,c2 string,c3 string,c4 string,c5 string,c6 string,c7 string,c8 string,c9 string,c10 string,c11 string,c12 string,c13 string,c14 string,c15 string,c16 string,c17 string,c18 string,c19 string,c20 string,c21 string,c22 string,c23 string,c24 string,c25 string,c26 string\n) ROW FORMAT\nDELIMITED FIELDS TERMINATED BY ' '\nSTORED AS TEXTFILE LOCATION '/criteo/ffm/train';\n\nDROP TABLE IF EXISTS test_ffm;\nCREATE EXTERNAL TABLE test_ffm (\n  label int,\n  -- quantitative features\n  i1 string,i2 string,i3 string,i4 string,i5 string,i6 string,i7 string,i8 string,i9 string,i10 string,i11 string,i12 string,i13 string,\n  -- categorical features\n  c1 string,c2 string,c3 string,c4 string,c5 string,c6 string,c7 string,c8 string,c9 string,c10 string,c11 string,c12 string,c13 string,c14 string,c15 string,c16 string,c17 string,c18 string,c19 string,c20 string,c21 string,c22 string,c23 string,c24 string,c25 string,c26 string\n) ROW FORMAT\nDELIMITED FIELDS TERMINATED BY ' '\nSTORED AS TEXTFILE LOCATION '/criteo/ffm/test';\n\nVectorize the LIBFFM-formatted features with rowid:\nDROP TABLE IF EXISTS train_vectorized;\nCREATE TABLE train_vectorized AS\nSELECT\n  row_number() OVER () AS rowid,\n  array(\n    i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11, i12, i13,\n    c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16, c17, c18, c19, c20, c21, c22, c23, c24, c25, c26\n  ) AS features,\n  label\nFROM\n  train_ffm\n;\n\nDROP TABLE IF EXISTS test_vectorized;\nCREATE TABLE test_vectorized AS\nSELECT\n  row_number() OVER () AS rowid,\n  array(\n    i1, i2, i3, i4, i5, i6, i7, i8, i9, i10, i11, i12, i13,\n    c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14, c15, c16, c17, c18, c19, c20, c21, c22, c23, c24, c25, c26\n  ) AS features,\n  label\nFROM\n  test_ffm\n;\n\nTraining\nDROP TABLE IF EXISTS criteo.ffm_model;\nCREATE TABLE  criteo.ffm_model (\n  model_id int,\n  i int,\n  Wi float,\n  Vi array\n);\n\nINSERT OVERWRITE TABLE criteo.ffm_model\nSELECT\n  train_ffm(\n    features,\n    label,\n    '-init_v random -max_init_value 0.5 -classification -iterations 15 -factors 4 -eta 0.2 -optimizer adagrad -lambda 0.00002'\n  )\nFROM (\n  SELECT\n    features, label\n  FROM\n    criteo.train_vectorized\n  CLUSTER BY rand(1)\n) t\n;\n\nThe third argument of train_ffm accepts a variety of options:\nhive> SELECT train_ffm(array(), 0, '-help');\nusage: train_ffm(array x, double y [, const string options]) -\n       Returns a prediction model [-alpha ] [-auto_stop] [-beta\n       ] [-c] [-cv_rate ] [-disable_cv] [-enable_norm]\n       [-enable_wi] [-eps ] [-eta ] [-eta0 ] [-f ]\n       [-feature_hashing ] [-help] [-init_v ] [-int_feature]\n       [-iters ] [-l1 ] [-l2 ] [-lambda0 ] [-lambdaV\n       ] [-lambdaW0 ] [-lambdaWi ] [-max ] [-maxval\n       ] [-min ] [-min_init_stddev ] [-no_norm]\n       [-num_fields ] [-opt ] [-p ] [-power_t ] [-seed\n       ] [-sigma ] [-t ] [-va_ratio ] [-va_threshold\n       ] [-w0]\n -alpha,--alphaFTRL                      Alpha value (learning rate)\n                                              of\n                                              Follow-The-Regularized-Reade\n                                              r [default: 0.2]\n -auto_stop,--early_stopping                  Stop at the iteration that\n                                              achieves the best validation\n                                              on partial samples [default:\n                                              OFF]\n -beta,--betaFTRL                        Beta value (a learning\n                                              smoothing parameter) of\n                                              Follow-The-Regularized-Reade\n                                              r [default: 1.0]\n -c,--classification                          Act as classification\n -cv_rate,--convergence_rate             Threshold to determine\n                                              convergence [default: 0.005]\n -disable_cv,--disable_cvtest                 Whether to disable\n                                              convergence check [default:\n                                              OFF]\n -enable_norm,--l2norm                        Enable instance-wise L2\n                                              normalization\n -enable_wi,--linear_term                     Include linear term\n                                              [default: OFF]\n -eps                                    A constant used in the\n                                              denominator of AdaGrad\n                                              [default: 1.0]\n -eta                                    The initial learning rate\n -eta0                                   The initial learning rate\n                                              [default 0.1]\n -f,--factors                            The number of the latent\n                                              variables [default: 5]\n -feature_hashing                        The number of bits for\n                                              feature hashing in range\n                                              [18,31] [default: -1]. No\n                                              feature hashing for -1.\n -help                                        Show function help\n -init_v                                 Initialization strategy of\n                                              matrix V [random,\n                                              gaussian](default: 'random'\n                                              for regression / 'gaussian'\n                                              for classification)\n -int_feature,--feature_as_integer            Parse a feature as integer\n                                              [default: OFF]\n -iters,--iterations                     The number of iterations\n                                              [default: 10]\n -l1,--lambda1                           L1 regularization value of\n                                              Follow-The-Regularized-Reade\n                                              r that controls model\n                                              Sparseness [default: 0.001]\n -l2,--lambda2                           L2 regularization value of\n                                              Follow-The-Regularized-Reade\n                                              r [default: 0.0001]\n -lambda0,--lambda                       The initial lambda value for\n                                              regularization [default:\n                                              0.0001]\n -lambdaV,--lambda_v                     The initial lambda value for\n                                              V regularization [default:\n                                              0.0001]\n -lambdaW0,--lambda_w0                   The initial lambda value for\n                                              W0 regularization [default:\n                                              0.0001]\n -lambdaWi,--lambda_wi                   The initial lambda value for\n                                              Wi regularization [default:\n                                              0.0001]\n -max,--max_target                       The maximum value of target\n                                              variable\n -maxval,--max_init_value                The maximum initial value in\n                                              the matrix V [default: 0.5]\n -min,--min_target                       The minimum value of target\n                                              variable\n -min_init_stddev                        The minimum standard\n                                              deviation of initial matrix\n                                              V [default: 0.1]\n -no_norm,--disable_norm                      Disable instance-wise L2\n                                              normalization\n -num_fields                             The number of fields\n                                              [default: 256]\n -opt,--optimizer                        Gradient Descent optimizer\n                                              [default: ftrl, adagrad,\n                                              sgd]\n -p,--num_features                       The size of feature\n                                              dimensions [default: -1]\n -power_t                                The exponent for inverse\n                                              scaling learning rate\n                                              [default 0.1]\n -seed                                   Seed value [default: -1\n                                              (random)]\n -sigma                                  The standard deviation for\n                                              initializing V [default:\n                                              0.1]\n -t,--total_steps                        The total number of training\n                                              examples\n -va_ratio,--validation_ratio            Ratio of training data used\n                                              for validation [default:\n                                              0.05f]\n -va_threshold,--validation_threshold    Threshold to start\n                                              validation. At least N\n                                              training examples are used\n                                              before validation [default:\n                                              1000]\n -w0,--global_bias                            Whether to include global\n                                              bias term w0 [default: OFF]\nNote that debug log describes the change of cumulative loss over iterations as follows:\nIteration #2 | average loss=0.5407147187026483, current cumulative loss=858.114258581103, previous cumulative loss=1682.1101438997914, change rate=0.48985846040280256, #trainingExamples=1587\nIteration #3 | average loss=0.5105058761578417, current cumulative loss=810.1728254624949, previous cumulative loss=858.114258581103, change rate=0.05586835626980435, #trainingExamples=1587\nIteration #4 | average loss=0.49045915570992393, current cumulative loss=778.3586801116493, previous cumulative loss=810.1728254624949, change rate=0.039268344174200345, #trainingExamples=1587\nIteration #5 | average loss=0.4752751205770395, current cumulative loss=754.2616163557617, previous cumulative loss=778.3586801116493, change rate=0.030958816766109738, #trainingExamples=1587\nIteration #6 | average loss=0.46308523885164105, current cumulative loss=734.9162740575543, previous cumulative loss=754.2616163557617, change rate=0.02564805351182389, #trainingExamples=1587\nIteration #7 | average loss=0.4529012395753083, current cumulative loss=718.7542672060143, previous cumulative loss=734.9162740575543, change rate=0.02199163009727323, #trainingExamples=1587\nIteration #8 | average loss=0.44411358945347845, current cumulative loss=704.8082664626703, previous cumulative loss=718.7542672060143, change rate=0.019403016273636577, #trainingExamples=1587\nIteration #9 | average loss=0.4363264696377158, current cumulative loss=692.450107315055, previous cumulative loss=704.8082664626703, change rate=0.017534072365012268, #trainingExamples=1587\nIteration #10 | average loss=0.4292753045556725, current cumulative loss=681.2599083298522, previous cumulative loss=692.450107315055, change rate=0.01616029641267912, #trainingExamples=1587\nIteration #11 | average loss=0.42277515600757143, current cumulative loss=670.9441725840159, previous cumulative loss=681.2599083298522, change rate=0.015142144165104322, #trainingExamples=1587\nIteration #12 | average loss=0.416689617663307, current cumulative loss=661.2864232316682, previous cumulative loss=670.9441725840159, change rate=0.014394266687126348, #trainingExamples=1587\nIteration #13 | average loss=0.4109140194740033, current cumulative loss=652.1205489052433, previous cumulative loss=661.2864232316682, change rate=0.013860672175351585, #trainingExamples=1587\nIteration #14 | average loss=0.4053667348634373, current cumulative loss=643.317008228275, previous cumulative loss=652.1205489052433, change rate=0.013499866998129951, #trainingExamples=1587\nIteration #15 | average loss=0.3999840450561501, current cumulative loss=634.7746795041102, previous cumulative loss=643.317008228275, change rate=0.013278568131893133, #trainingExamples=1587\nPerformed 15 iterations of 1,587 training examples on memory (thus 23,805 training updates in total)\nPrediction and evaluation\nDROP TABLE IF EXISTS criteo.test_exploded;\nCREATE TABLE criteo.test_exploded AS\nSELECT\n  t1.rowid,\n  t2.i,\n  t2.j,\n  t2.Xi,\n  t2.Xj\nfrom\n  criteo.test_vectorized t1\n  LATERAL VIEW feature_pairs(t1.features, '-ffm') t2 AS i, j, Xi, Xj\n;\n\nWITH predicted AS (\n  SELECT\n    rowid,\n    avg(score) AS predicted\n  FROM (\n    SELECT\n      t1.rowid,\n      p1.model_id,\n      sigmoid(ffm_predict(p1.Wi, p1.Vi, p2.Vi, t1.Xi, t1.Xj)) AS score\n    FROM\n      criteo.test_exploded t1\n      JOIN criteo.ffm_model p1 ON (p1.i = t1.i) -- at least p1.i = 0 and t1.i = 0 exists\n      LEFT OUTER JOIN criteo.ffm_model p2 ON (p2.model_id = p1.model_id and p2.i = t1.j)\n    WHERE\n      p1.Wi is not null OR p2.Vi is not null\n    GROUP BY\n      t1.rowid, p1.model_id\n  ) t\n  GROUP BY\n    rowid\n)\nSELECT\n  logloss(t1.predicted, t2.label)\nFROM\n  predicted t1\nJOIN\n  criteo.test_vectorized t2\n  ON t1.rowid = t2.rowid\n;\n\n\n0.47276208106423234\n\n\n NoteThe accuracy varies depending on the random separation of tr.sp and va.sp.\nNotice that LogLoss around 0.45 is reasonable accuracy compared to the competition leaderboard and output from LIBFFM.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/news20.html":{"url":"multiclass/news20.html","title":"News20 Multiclass Tutorial","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/news20_dataset.html":{"url":"multiclass/news20_dataset.html","title":"Data Preparation","keywords":"","body":"\nGet the news20 dataset.\nhttps://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#news20\n$ cat  conv.awk\nBEGIN{ FS=\" \" }\n{\n    label=\\$1;\n    features=\\$2;\n    for(i=3;i news20_scale.train\n$ gawk -f conv.awk news20.t.scale > news20_scale.test\n\nPutting data on HDFS\nhadoop fs -mkdir -p /dataset/news20-multiclass/train\nhadoop fs -mkdir -p /dataset/news20-multiclass/test\n\nhadoop fs -copyFromLocal news20_scale.train /dataset/news20-multiclass/train\nhadoop fs -copyFromLocal news20_scale.test /dataset/news20-multiclass/test\n\nTraining/test data prepareation\nuse news20;\n\nCreate external table news20mc_train (\n  rowid int,\n  label int,\n  features ARRAY\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" STORED AS TEXTFILE LOCATION '/dataset/news20-multiclass/train';\n\nCreate external table news20mc_test (\n  rowid int, \n  label int,\n  features ARRAY\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" STORED AS TEXTFILE LOCATION '/dataset/news20-multiclass/test';\n\nset hivevar:seed=31;\ncreate or replace view news20mc_train_x3\nas\nselect \n  * \nfrom (\nselect\n   amplify(3, *) as (rowid, label, features)\nfrom  \n   news20mc_train \n) t\nCLUSTER BY rand(${seed});\n\ncreate table news20mc_test_exploded as\nselect \n  rowid,\n  label,\n  cast(split(feature,\":\")[0] as int) as feature,\n  cast(split(feature,\":\")[1] as float) as value\n  -- hivemall v0.3.1 or later\n  -- cast(extract_feature(feature) as int) as feature,\n  -- extract_weight(feature) as value\nfrom \n  news20mc_test LATERAL VIEW explode(add_bias(features)) t AS feature;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/news20_one-vs-the-rest_dataset.html":{"url":"multiclass/news20_one-vs-the-rest_dataset.html","title":"Data Preparation for one-vs-the-rest classifiers","keywords":"","body":"\nOne-vs-the-rest is a multiclass classification method that uses binary classifiers independently for each class.\nhttp://en.wikipedia.org/wiki/Multiclass_classification#one_vs_all\nDataset preparation for one-vs-the-rest classifiers\nselect collect_set(label) from news20mc_train;\n\n\n[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,17,16,19,18,20]\n\nSET hivevar:possible_labels=\"1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,17,16,19,18,20\";\n\none-vs-rest.awk\ncreate or replace view news20_onevsrest_train\nas\nselect transform(${possible_labels}, rowid, label, add_bias(features))\n  ROW FORMAT DELIMITED\n    FIELDS TERMINATED BY \"\\t\"\n    COLLECTION ITEMS TERMINATED BY \",\"\n    LINES TERMINATED BY \"\\n\"\nusing 'gawk -f one-vs-rest.awk'\n  as (rowid BIGINT, label INT, target INT, features ARRAY)\n  ROW FORMAT DELIMITED\n    FIELDS TERMINATED BY \"\\t\"\n    COLLECTION ITEMS TERMINATED BY \",\"\n    LINES TERMINATED BY \"\\n\"\nfrom news20mc_train;\n\ncreate or replace view news20_onevsrest_train_x3\nas\nselect\n *\nfrom (\n  select\n    amplify(3, *) as (rowid, label, target, features)\n  from\n    news20_onevsrest_train\n) t\nCLUSTER BY rand();\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/news20_pa.html":{"url":"multiclass/news20_pa.html","title":"PA","keywords":"","body":"\n\n\n\nPassive Aggressive (PA2)\nTraining\nPrediction\nEvaluation\n\n\n\n\n\nPassive Aggressive (PA2)\nTraining\ndrop table news20mc_pa2_model1;\ncreate table news20mc_pa2_model1 as\nselect \n label, \n cast(feature as int) as feature,\n voted_avg(weight) as weight\nfrom \n (select \n     train_multiclass_pa2(add_bias(features),label) as (label,feature,weight)\n  from \n     news20mc_train_x3\n ) t \ngroup by label, feature;\n\nPrediction\ncreate or replace view news20mc_pa2_predict1 \nas\nselect \n  rowid, \n  m.col0 as score, \n  m.col1 as label\nfrom (\nselect\n   rowid, \n   maxrow(score, label) as m\nfrom (\n  select\n    t.rowid,\n    m.label,\n    sum(m.weight * t.value) as score\n  from \n    news20mc_test_exploded t LEFT OUTER JOIN\n    news20mc_pa2_model1 m ON (t.feature = m.feature)\n  group by\n    t.rowid, m.label\n) t1\ngroup by rowid\n) t2;\nEvaluation\ncreate or replace view news20mc_pa2_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20mc_test t JOIN news20mc_pa2_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/3993 from news20mc_pa2_submit1 \nwhere actual == predicted;\n\n\n0.7478086651640371 (plain)\n0.8204357625845229 (x3)\n0.8204357625845229 (x3 + bagging)\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/news20_scw.html":{"url":"multiclass/news20_scw.html","title":"CW, AROW, SCW","keywords":"","body":"\n\n\n\nCW\ntraining\nprediction\nevaluation\n\n\nAROW\ntraining\nprediction\nevaluation\n\n\nSCW1\ntraining\nprediction\nevaluation\n\n\nSCW2\ntraining\nprediction\nevaluation\n\n\nWrap up of evaluation results\n\n\n\nCW\ntraining\ndrop table news20mc_cw_model1;\ncreate table news20mc_cw_model1 as\nselect \n label, \n cast(feature as int) as feature,\n -- voted_avg(weight) as weight -- [hivemall v0.1]\n argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]\nfrom \n (select \n     -- train_multiclass_cw(add_bias(features),label) as (label,feature,weight) -- [hivemall v0.1]\n     train_multiclass_cw(add_bias(features),label) as (label,feature,weight,covar)    -- [hivemall v0.2 or later]\n  from \n     news20mc_train_x3\n ) t \ngroup by label, feature;\n\nprediction\ncreate or replace view news20mc_cw_predict1 \nas\nselect \n  rowid, \n  m.col0 as score, \n  m.col1 as label\nfrom (\nselect\n   rowid, \n   maxrow(score, label) as m\nfrom (\n  select\n    t.rowid,\n    m.label,\n    sum(m.weight * t.value) as score\n  from \n    news20mc_test_exploded t LEFT OUTER JOIN\n    news20mc_cw_model1 m ON (t.feature = m.feature)\n  group by\n    t.rowid, m.label\n) t1\ngroup by rowid\n) t2;\n\nevaluation\ncreate or replace view news20mc_cw_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20mc_test t JOIN news20mc_cw_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/3993 from news20mc_cw_submit1 \nwhere actual == predicted;\n\n0.850488354620586\n\nAROW\ntraining\ndrop table news20mc_arow_model1;\ncreate table news20mc_arow_model1 as\nselect \n label, \n cast(feature as int) as feature,\n -- voted_avg(weight) as weight -- [hivemall v0.1]\n argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]\nfrom \n (select \n     -- train_multiclass_arow(add_bias(features),label) as (label,feature,weight) -- [hivemall v0.1]\n     train_multiclass_arow(add_bias(features),label) as (label,feature,weight,covar) -- [hivemall v0.2 or later]\n  from \n     news20mc_train_x3\n ) t \ngroup by label, feature;\n\nprediction\ncreate or replace view news20mc_arow_predict1 \nas\nselect \n  rowid, \n  m.col0 as score, \n  m.col1 as label\nfrom (\nselect\n   rowid, \n   maxrow(score, label) as m\nfrom (\n  select\n    t.rowid,\n    m.label,\n    sum(m.weight * t.value) as score\n  from \n    news20mc_test_exploded t LEFT OUTER JOIN\n    news20mc_arow_model1 m ON (t.feature = m.feature)\n  group by\n    t.rowid, m.label\n) t1\ngroup by rowid\n) t2;\n\nevaluation\ncreate or replace view news20mc_arow_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20mc_test t JOIN news20mc_arow_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/3993 from news20mc_arow_submit1 \nwhere actual == predicted;\n\n0.8474830954169797\n\nSCW1\ntraining\ndrop table news20mc_scw_model1;\ncreate table news20mc_scw_model1 as\nselect \n label, \n cast(feature as int) as feature,\n -- voted_avg(weight) as weight -- [hivemall v0.1]\n argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]\nfrom \n (select \n     -- train_multiclass_scw(add_bias(features),label) as (label,feature,weight) -- [hivemall v0.1]\n     train_multiclass_scw(add_bias(features),label) as (label,feature,weight,covar) -- [hivemall v0.2 or later]\n  from \n     news20mc_train_x3\n ) t \ngroup by label, feature;\n\nprediction\ncreate or replace view news20mc_scw_predict1 \nas\nselect \n  rowid, \n  m.col0 as score, \n  m.col1 as label\nfrom (\nselect\n   rowid, \n   maxrow(score, label) as m\nfrom (\n  select\n    t.rowid,\n    m.label,\n    sum(m.weight * t.value) as score\n  from \n    news20mc_test_exploded t LEFT OUTER JOIN\n    news20mc_scw_model1 m ON (t.feature = m.feature)\n  group by\n    t.rowid, m.label\n) t1\ngroup by rowid\n) t2;\n\nevaluation\ncreate or replace view news20mc_scw_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20mc_test t JOIN news20mc_scw_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/3993 from news20mc_scw_submit1 \nwhere actual == predicted;\n\n0.8314550463310794\n\nSCW2\ntraining\ndrop table news20mc_scw2_model1;\ncreate table news20mc_scw2_model1 as\nselect \n label, \n cast(feature as int) as feature,\n -- voted_avg(weight) as weight -- [hivemall v0.1]\n argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]\nfrom \n (select \n     -- train_multiclass_scw2(add_bias(features),label) as (label,feature,weight) -- [hivemall v0.1]\n     train_multiclass_scw2(add_bias(features),label) as (label,feature,weight,covar) -- [hivemall v0.2 or later]\n  from \n     news20mc_train_x3\n ) t \ngroup by label, feature;\n\nprediction\ncreate or replace view news20mc_scw2_predict1 \nas\nselect \n  rowid, \n  m.col0 as score, \n  m.col1 as label\nfrom (\nselect\n   rowid, \n   maxrow(score, label) as m\nfrom (\n  select\n    t.rowid,\n    m.label,\n    sum(m.weight * t.value) as score\n  from \n    news20mc_test_exploded t LEFT OUTER JOIN\n    news20mc_scw2_model1 m ON (t.feature = m.feature)\n  group by\n    t.rowid, m.label\n) t1\ngroup by rowid\n) t2;\n\nevaluation\ncreate or replace view news20mc_scw2_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20mc_test t JOIN news20mc_scw2_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/3993 from news20mc_scw2_submit1 \nwhere actual == predicted;\n\n0.8482344102178813\n\nWrap up of evaluation results\n\n\n\nAlgorithm\nAccuracy\n\n\n\n\nPA2\n0.8204357625845229\n\n\nSCW1\n0.8314550463310794\n\n\nAROW\n0.8474830954169797\n\n\nSCW2\n0.8482344102178813\n\n\nCW\n0.850488354620586\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/news20_xgboost.html":{"url":"multiclass/news20_xgboost.html","title":"XGBoost","keywords":"","body":"\n\n\n\ntraining\nprediction\nevaluation\n\n\n\ntraining\nFor multiclass classification, the following two objects are supported in xgboost:\n\nmulti:softmax set XGBoost to do multiclass classification using the softmax objective, you also need to set num_class (number of classes).\nmulti:softprob same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata * nclass matrix. The result contains predicted probability of each data point belonging to each class.\n\nselect count(distinct label) from news20mc_train;\n> 20\n\n-- explicitly use 3 reducers\n-- set mapred.reduce.tasks=3;\n\ndrop table xgb_softmax_model;\ncreate table xgb_softmax_model as\nselect \n  train_xgboost(features, label, '-objective multi:softmax -num_class 20 -num_round 10 -num_early_stopping_rounds 3') \n    as (model_id, model)\nfrom (\n  select features, (label - 1) as label\n  from news20mc_train\n  cluster by rand(43) -- shuffle data to reducers\n) shuffled;\n\ndrop table xgb_softprob_model;\ncreate table xgb_softprob_model as\nselect \n  train_xgboost(features, label, '-objective multi:softprob -num_class 20 -num_round 10 -num_early_stopping_rounds 3') \n    as (model_id, model)\nfrom (\n  select features, (label - 1) as label\n  from news20mc_train\n  cluster by rand(43) -- shuffle data to reducers\n) shuffled;\n\n Caution-num_class is required for multiclass objectives.\nThe target label must be in range [0, num_class) (i.e., 0, 1, 2, .., num_class-1).\nprediction\ndrop table xgb_softmax_predicted;\ncreate table xgb_softmax_predicted as\nselect\n  rowid,\n  majority_vote(cast(predicted as int) + 1) as label\nfrom (\n  select\n    xgboost_predict_one(rowid, features, model_id, model) as (rowid, predicted)\n  from\n    xgb_softmax_model l\n    LEFT OUTER JOIN news20mc_test r\n) t\ngroup by rowid;\n\n\ndrop table xgb_softprob_predicted;\ncreate table xgb_softprob_predicted as\nselect\n  rowid,\n  array_avg(predicted) as prob,\n  argmax(array_avg(predicted)) + 1 as label -- convert 0 start index to 1 start index\nfrom (\n  select\n    xgboost_predict(rowid, features, model_id, model) as (rowid, predicted)\n  from\n    xgb_softprob_model l\n    LEFT OUTER JOIN news20mc_test r\n) t\ngroup by\n  rowid;\n\n CautionFor -objective softmax, xgboost predictor returns class label in double.\nFor -objective softprob, probabilities for each label is returned in array.\nevaluation\nWITH validate as (\n  select \n    t.label as actual, \n    p.label as predicted\n  from \n    news20mc_test t\n    JOIN xgb_softmax_predicted p\n      on (t.rowid = p.rowid)\n)\nselect \n  sum(if(actual=predicted,1.0,0.0))/count(1) \nfrom\n  validate;\n\nWITH validate as (\n  select \n    t.label as actual, \n    p.label as predicted\n  from \n    news20mc_test t\n    JOIN xgb_softprob_predicted p\n      on (t.rowid = p.rowid)\n)\nselect \n  sum(if(actual=predicted,1.0,0.0))/count(1) \nfrom\n  validate;\n\n\n\n\nobjective\naccuracy\n\n\n\n\nsoftmax\n0.6689206110693713999\n\n\nsoftprob\n0.6944653143000250438\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/news20_ensemble.html":{"url":"multiclass/news20_ensemble.html","title":"Ensemble learning","keywords":"","body":"\nThis example explains how to run ensemble learning in Hivemall.Two heads are better than one? Let's verify it by ensemble learning.\n[Case1] Model ensemble/mixing\ntraining\nSET hive.exec.parallel=true;\nSET hive.exec.parallel.thread.number=8;\nSET mapred.reduce.tasks=4;\n\ndrop table news20mc_ensemble_model1;\ncreate table news20mc_ensemble_model1 as\nselect \n label, \n -- cast(feature as int) as feature, -- hivemall v0.1\n argmin_kld(feature, covar) as feature, -- hivemall v0.2 or later\n voted_avg(weight) as weight\nfrom \n (select \n     -- train_multiclass_cw(add_bias(features),label) as (label,feature,weight)      -- hivemall v0.1\n     train_multiclass_cw(add_bias(features),label) as (label,feature,weight,covar)   -- hivemall v0.2 or later\n  from \n     news20mc_train_x3\n  union all\n  select \n     -- train_multiclass_arow(add_bias(features),label) as (label,feature,weight)    -- hivemall v0.1\n     train_multiclass_arow(add_bias(features),label) as (label,feature,weight,covar) -- hivemall v0.2 or later\n  from \n     news20mc_train_x3\n  union all\n  select \n     -- train_multiclass_scw(add_bias(features),label) as (label,feature,weight)     -- hivemall v0.1\n     train_multiclass_scw(add_bias(features),label) as (label,feature,weight,covar)  -- hivemall v0.2 or later\n  from \n     news20mc_train_x3\n ) t \ngroup by label, feature;\n\n-- reset to the default\nSET hive.exec.parallel=false;\nSET mapred.reduce.tasks=-1;\n\nprediction\ncreate or replace view news20mc_ensemble_predict1 \nas\nselect \n  rowid, \n  m.col0 as score, \n  m.col1 as label\nfrom (\nselect\n   rowid, \n   maxrow(score, label) as m\nfrom (\n  select\n    t.rowid,\n    m.label,\n    sum(m.weight * t.value) as score\n  from \n    news20mc_test_exploded t LEFT OUTER JOIN\n    news20mc_ensemble_model1 m ON (t.feature = m.feature)\n  group by\n    t.rowid, m.label\n) t1\ngroup by rowid\n) t2;\n\nevaluation\ncreate or replace view news20mc_ensemble_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20mc_test t JOIN news20mc_ensemble_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/3993 from news20mc_ensemble_submit1 \nwhere actual == predicted;\n\n0.8494866015527173\n\nUnfortunately, too many cooks spoil the broth in this case :-(\n\n\n\nAlgorithm\nAccuracy\n\n\n\n\nAROW\n0.8474830954169797\n\n\nSCW2\n0.8482344102178813\n\n\nEnsemble(model)\n0.8494866015527173\n\n\nCW\n0.850488354620586\n\n\n\n[Case2] Prediction ensemble\nprediction\ncreate or replace view news20mc_pred_ensemble_predict1 \nas\nselect \n  rowid, \n  m.col1 as label\nfrom (\n  select\n    rowid, \n    maxrow(cnt, label) as m\n  from (\n    select\n      rowid,\n      label,\n      count(1) as cnt\n    from (\n      select * from news20mc_arow_predict1\n      union all\n      select * from news20mc_scw2_predict1\n      union all\n      select * from news20mc_cw_predict1\n    ) t1\n    group by rowid, label\n  ) t2\n  group by rowid\n) t3;\n\nevaluation\ncreate or replace view news20mc_pred_ensemble_submit1 as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20mc_test t JOIN news20mc_pred_ensemble_predict1 pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/3993 from news20mc_pred_ensemble_submit1 \nwhere actual == predicted;\n\n0.8499874780866516\n\nUnfortunately, too many cooks spoil the broth in this case too :-(\n\n\n\nAlgorithm\nAccuracy\n\n\n\n\nAROW\n0.8474830954169797\n\n\nSCW2\n0.8482344102178813\n\n\nEnsemble(model)\n0.8494866015527173\n\n\nEnsemble(prediction)\n0.8499874780866516\n\n\nCW\n0.850488354620586\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/news20_one-vs-the-rest.html":{"url":"multiclass/news20_one-vs-the-rest.html","title":"one-vs-the-rest Classifier","keywords":"","body":"\nA one-vs-the-rest classifier use the binary classifier for each class.\ntraining\nSET mapred.reduce.tasks=4;\n\ndrop table news20_onevsrest_arow_model;\ncreate table news20_onevsrest_arow_model \nas\nselect\n  label,\n  feature,\n  -- voted_avg(weight) as weight -- [hivemall v0.1]\n  argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]\nfrom (\nselect\n  1 as label,\n  *\nfrom (\nselect \n  -- train_arow(features, target) as (feature, weight)     -- [hivemall v0.1]\n  train_arow(features, target) as (feature, weight, covar) -- [hivemall v0.2 or later]\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 1\n) t1\nunion all\nselect\n  2 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 2\n) t2\nunion all\nselect\n  3 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 3\n) t3\nunion all\nselect\n  4 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 4\n) t4\nunion all\nselect\n  5 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 5\n) t5\nunion all\nselect\n  6 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 6\n) t6\nunion all\nselect\n  7 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 7\n) t7\nunion all\nselect\n  8 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 8\n) t8\nunion all\nselect\n  9 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 9\n) t9\nunion all\nselect\n  10 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 10\n) t10\nunion all\nselect\n  11 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 11\n) t11\nunion all\nselect\n  12 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 12\n) t12\nunion all\nselect\n  13 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 13\n) t13\nunion all\nselect\n  14 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 14\n) t14\nunion all\nselect\n  15 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 15\n) t15\nunion all\nselect\n  16 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 16\n) t16\nunion all\nselect\n  17 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 17\n) t17\nunion all\nselect\n  18 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 18\n) t18\nunion all\nselect\n  19 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 19\n) t19\nunion all\nselect\n  20 as label,\n  *\nfrom (\nselect \n  train_arow(features, target) as (feature, weight, covar)\nfrom \n  news20_onevsrest_train_x3\nwhere\n  label = 20\n) t20\n) t\ngroup by \n  label, feature;\n\n-- reset to the default\nSET mapred.reduce.tasks=-1;\n\nNote that the above query is optimized to scan news20_onevsrest_train_x3 once!\nprediction\ncreate or replace view news20_onevsrest_arow_predict \nas\nselect \n  rowid, \n  m.col0 as score, \n  m.col1 as label\nfrom (\nselect\n   rowid, \n   maxrow(score, label) as m\nfrom (\n  select\n    t.rowid,\n    m.label,\n    sum(m.weight * t.value) as score\n  from \n    news20mc_test_exploded t LEFT OUTER JOIN\n    news20_onevsrest_arow_model m ON (t.feature = m.feature)\n  group by\n    t.rowid, m.label\n) t1\ngroup by rowid\n) t2;\n\nevaluation\ncreate or replace view news20_onevsrest_arow_submit as\nselect \n  t.label as actual, \n  pd.label as predicted\nfrom \n  news20mc_test t JOIN news20_onevsrest_arow_predict pd \n    on (t.rowid = pd.rowid);\n\nselect count(1)/3993 from news20_onevsrest_arow_submit\nwhere actual == predicted;\n\n0.8567493112947658\n\n\n\n\nAlgorithm\nAccuracy\n\n\n\n\nAROW(multi-class)\n0.8474830954169797\n\n\nCW\n0.850488354620586\n\n\nAROW(one-vs-rest)\n0.8567493112947658\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/iris.html":{"url":"multiclass/iris.html","title":"Iris Tutorial","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/iris_dataset.html":{"url":"multiclass/iris_dataset.html","title":"Data preparation","keywords":"","body":"\nDataset prepration\nIris Dataset: https://archive.ics.uci.edu/ml/datasets/Iris\n$ wget http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\n$ less iris.data\n\n   ...\n5.3,3.7,1.5,0.2,Iris-setosa\n5.0,3.3,1.4,0.2,Iris-setosa\n7.0,3.2,4.7,1.4,Iris-versicolor\n   ...\n\nCreate training/test table in Hive\ncreate database iris;\nuse iris;\n\ncreate external table iris_raw (\n  rowid int,\n  label string,\n  features array\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '|' COLLECTION ITEMS TERMINATED BY \",\" STORED AS TEXTFILE LOCATION '/dataset/iris/raw';\n\nLoading data into HDFS\n$ awk -F\",\" 'NF >0 {OFS=\"|\"; print NR,$5,$1\",\"$2\",\"$3\",\"$4}' iris.data | head -3\n\n1|Iris-setosa|5.1,3.5,1.4,0.2\n2|Iris-setosa|4.9,3.0,1.4,0.2\n3|Iris-setosa|4.7,3.2,1.3,0.2\n\n$ awk -F\",\" 'NF >0 {OFS=\"|\"; print NR,$5,$1\",\"$2\",\"$3\",\"$4}' iris.data | hadoop fs -put - /dataset/iris/raw/iris.data\n\nselect count(1) from iris_raw;\n\n> 150\n\nFeature scaling\nNormalization of feature weights is very important to get a good prediction in machine learning.\nselect \n  min(features[0]), max(features[0]),\n  min(features[1]), max(features[1]),\n  min(features[2]), max(features[2]),\n  min(features[3]), max(features[3])\nfrom\n  iris_raw;\n\n> 4.3     7.9     2.0     4.4     1.0     6.9     0.1     2.5\n\nset hivevar:f0_min=4.3;\nset hivevar:f0_max=7.9;\nset hivevar:f1_min=2.0;\nset hivevar:f1_max=4.4;\nset hivevar:f2_min=1.0;\nset hivevar:f2_max=6.9;\nset hivevar:f3_min=0.1;\nset hivevar:f3_max=2.5;\n\ncreate or replace view iris_scaled\nas\nselect\n  rowid, \n  label,\n  add_bias(array(\n     concat(\"1:\", rescale(features[0],${f0_min},${f0_max})), \n     concat(\"2:\", rescale(features[1],${f1_min},${f1_max})), \n     concat(\"3:\", rescale(features[2],${f2_min},${f2_max})), \n     concat(\"4:\", rescale(features[3],${f3_min},${f3_max}))\n  )) as features\nfrom \n  iris_raw;\n\nselect * from iris_scaled limit 3;\n\n> 1       Iris-setosa     [\"1:0.22222215\",\"2:0.625\",\"3:0.0677966\",\"4:0.041666664\",\"0:1.0\"]\n> 2       Iris-setosa     [\"1:0.16666664\",\"2:0.41666666\",\"3:0.0677966\",\"4:0.041666664\",\"0:1.0\"]\n> 3       Iris-setosa     [\"1:0.11111101\",\"2:0.5\",\"3:0.05084745\",\"4:0.041666664\",\"0:1.0\"]\n\nLibSVM web page provides a normalized (using ZScore) version of Iris dataset.\nCreate training/test data\nset hivevar:rand_seed=31;\n\ncreate table iris_shuffled \nas\nselect rand(${rand_seed}) as rnd, * from iris_scaled;\n\n-- 80% for training\ncreate table train80p as\nselect * from iris_shuffled \norder by rnd DESC\nlimit 120;\n\n-- 20% for testing\ncreate table test20p as\nselect * from iris_shuffled \norder by rnd ASC\nlimit 30;\n\ncreate table test20p_exploded \nas\nselect \n  rowid,\n  label,\n  extract_feature(feature) as feature,\n  extract_weight(feature) as value\nfrom \n  test20p LATERAL VIEW explode(features) t AS feature;\n\nDefine an amplified view for the training input\nset hivevar:xtimes=10;\nset hivevar:shufflebuffersize=1000;\n\ncreate or replace view training_x10\nas\nselect\n   rand_amplify(${xtimes}, ${shufflebuffersize}, rowid, label, features) as (rowid, label, features)\nfrom  \n   train80p;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/iris_scw.html":{"url":"multiclass/iris_scw.html","title":"SCW","keywords":"","body":"\nTraining (multiclass classification)\ncreate table model_scw1 as\nselect \n label, \n feature,\n argmin_kld(weight, covar) as weight\nfrom \n (select \n     train_multiclass_scw(features, label) as (label, feature, weight, covar)\n  from \n     training_x10\n ) t \ngroup by label, feature;\n\nPredict\ncreate or replace view predict_scw1\nas\nselect \n  rowid, \n  m.col0 as score, \n  m.col1 as label\nfrom (\nselect\n   rowid, \n   maxrow(score, label) as m\nfrom (\n  select\n    t.rowid,\n    m.label,\n    sum(m.weight * t.value) as score\n  from \n    test20p_exploded t LEFT OUTER JOIN\n    model_scw1 m ON (t.feature = m.feature)\n  group by\n    t.rowid, m.label\n) t1\ngroup by rowid\n) t2;\n\nEvaluation\ncreate or replace view eval_scw1 as\nselect \n  t.label as actual, \n  p.label as predicted\nfrom \n  test20p t JOIN predict_scw1 p \n    on (t.rowid = p.rowid);\n\nselect count(1)/30 from eval_scw1 \nwhere actual = predicted;\n\n\n0.9666666666666667\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/iris_randomforest.html":{"url":"multiclass/iris_randomforest.html","title":"Random Forest","keywords":"","body":"\n\n\n\nDataset\nTable preparation\nTraining\nTraining options\nParallelize Training\nLearning stats\n\n\n\n\nPrediction\nParallelize Prediction\n\n\nEvaluation\nGraphviz export\n\n\n\nDataset\n\nhttps://archive.ics.uci.edu/ml/datasets/Iris\n\nAttribute Information:\n   1. sepal length in cm\n   2. sepal width in cm\n   3. petal length in cm\n   4. petal width in cm\n   5. class: \n      -- Iris Setosa\n      -- Iris Versicolour\n      -- Iris Virginica\nTable preparation\ncreate database iris;\nuse iris;\n\ncreate external table raw (\n  sepal_length int,\n  sepal_width int,\n  petal_length int,\n  petak_width int,\n  class string\n)\nROW FORMAT DELIMITED\n  FIELDS TERMINATED BY ','\n  LINES TERMINATED BY '\\n'\nSTORED AS TEXTFILE LOCATION '/dataset/iris/raw';\n\n$ sed '/^$/d' iris.data | hadoop fs -put - /dataset/iris/raw/iris.data\n\ncreate table label_mapping \nas\nselect\n  class,\n  rank - 1 as label\nfrom (\nselect\n  distinct class,\n  dense_rank() over (order by class) as rank\nfrom \n  raw\n) t\n;\n\ncreate table training\nas\nselect\n  rowid() as rowid,\n  array(t1.sepal_length, t1.sepal_width, t1.petal_length, t1.petak_width) as features,\n  t2.label\nfrom\n  raw t1\n  JOIN label_mapping t2 ON (t1.class = t2.class)\n;\n\nTraining\ntrain_randomforest_classifier takes a dense features in double[] and a label starting from 0.\nCREATE TABLE model \n  STORED AS SEQUENCEFILE \nAS\nselect \n  train_randomforest_classifier(features, label)\n  -- v0.5.0 and later\n  -- train_randomforest_classifier(features, label) as (model_id, model_weight, model, var_importance, oob_errors, oob_tests)\n  -- v0.4.1-alpha.2 and before\n  -- train_randomforest_classifier(features, label) as (pred_model, var_importance, oob_errors, oob_tests)\n  -- from v0.4.1 to v0.4.2-rc4\n  -- train_randomforest_classifier(features, label) as (model_id, model_type, pred_model, var_importance, oob_errors, oob_tests)\nfrom\n  training;\n\n CautionNote that model storage format is different between versions as seen the above.\nhive> desc extended model;\n\n\n\n\ncol_name\ndata_type \n\n\n\n\nmodel_id\nstring\n\n\nmodel_weight\ndouble\n\n\nmodel\nstring\n\n\nvar_importance\narray\n\n\noob_errors\nint\n\n\noob_tests\nint\n\n\n\nTraining options\n-help option shows usage of the function.\nselect train_randomforest_classifier(features, label, \"-help\") from training;\n\n> FAILED: UDFArgumentException \nusage: train_randomforest_classifier(array features, int\n       label [, const array classWeights, const string options]) -\n       Returns a relation consists of  var_importance, int oob_errors,\n       int oob_tests, double weight> [-attrs ] [-depth ] [-help]\n       [-leafs ] [-min_samples_leaf ] [-rule ] [-seed\n       ] [-splits ] [-stratified] [-subsample ] [-trees\n       ] [-vars ]\n -attrs,--attribute_types       Comma separated attribute types (Q\n                                     for quantitative variable and C for\n                                     categorical variable. e.g.,\n                                     [Q,C,Q,C])\n -depth,--max_depth             The maximum number of the tree depth\n                                     [default: Integer.MAX_VALUE]\n -help                               Show function help\n -leafs,--max_leaf_nodes        The maximum number of leaf nodes\n                                     [default: Integer.MAX_VALUE]\n -min_samples_leaf              The minimum number of samples in a\n                                     leaf node [default: 1]\n -rule,--split_rule             Split algorithm [default: GINI,\n                                     ENTROPY]\n -seed                          seed value in long [default: -1\n                                     (random)]\n -splits,--min_split            A node that has greater than or\n                                     equals to `min_split` examples will\n                                     split [default: 2]\n -stratified,--stratified_sampling   Enable Stratified sampling for\n                                     unbalanced data\n -subsample                     Sampling rate in range (0.0,1.0]\n -trees,--num_trees             The number of trees for each task\n                                     [default: 50]\n -vars,--num_variables          The number of random selected\n                                     features [default:\n                                     ceil(sqrt(x[0].length))].\n                                     int(num_variables * x[0].length) is\n                                     considered if num_variable is (0,1]\n\n Caution-num_trees controls the number of trees for each task, not the total number of trees.\nParallelize Training\nTo parallelize RandomForest training, you can use UNION ALL as follows:\nCREATE TABLE model \n  STORED AS ORC tblproperties(\"orc.compress\"=\"SNAPPY\")\n  -- STORED AS SEQUENCEFILE \nAS\nselect \n  train_randomforest_classifier(features, label, '-trees 25') \nfrom\n  training\nUNION ALL\nselect \n  train_randomforest_classifier(features, label, '-trees 25')\nfrom\n  training\n;\n\nLearning stats\nVariable importance and Out Of Bag (OOB) error rate of RandomForest can be shown as follows:\nselect\n  array_sum(var_importance) as var_importance,\n  sum(oob_errors) / sum(oob_tests) as oob_err_rate\nfrom\n  model;\n\n\n[6.837674865013268,4.1317115752776665,24.331571871930226,25.677497925673062]    0.056666666666666664\n\nPrediction\n-- set hivevar:classification=true;\nset hive.auto.convert.join=true;\nset hive.mapjoin.optimized.hashtable=false;\n\ncreate table predicted\nas\nSELECT\n  rowid,\n  -- rf_ensemble(predicted) as predicted\n  -- v0.5.0 or later\n  rf_ensemble(predicted.value, predicted.posteriori, model_weight) as predicted\n  -- rf_ensemble(predicted.value, predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., model_weight)\nFROM (\n  SELECT\n    rowid, \n    -- from v0.4.1 to v0.4.2-rc4\n    -- tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted\n    -- v0.5.0 or later\n    p.model_weight,\n    tree_predict(p.model_id, p.model, t.features, \"-classification\") as predicted\n    -- tree_predict(p.model_id, p.model, t.features, ${classification}) as predicted\n    -- tree_predict_v1(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted -- to use the old model in v0.5.0 or later\n  FROM\n    model p\n    LEFT OUTER JOIN -- CROSS JOIN\n    training t\n) t1\ngroup by\n  rowid\n;\n\n NoteLeft outer join without a join condition (i.e., model p LEFT OUTER JOIN training t) is a trick to fix the left table for cross join.Cautiontree_predict_v1 is for the backward compatibility for using prediction models built before v0.5 on v0.5 or later.\nParallelize Prediction\nThe following query runs predictions in N-parallel. It would reduce elapsed time for prediction almost by N.\nSET hivevar:classification=true;\nset hive.auto.convert.join=true;\nSET hive.mapjoin.optimized.hashtable=false;\nSET mapred.reduce.tasks=8;\n\ndrop table predicted;\ncreate table predicted\nas\nSELECT\n  rowid,\n  -- rf_ensemble(predicted) as predicted\n  -- v0.5.0 or later\n  rf_ensemble(predicted.value, predicted.posteriori, model_weight) as predicted\n  -- rf_ensemble(predicted.value, predicted.posteriori) as predicted -- avoid OOB accuracy (i.e., model_weight)\nFROM (\n  SELECT\n    t.rowid, \n    -- from v0.4.1 to v0.4.2-rc4\n    -- tree_predict(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted\n    -- v0.5.0 or later\n    p.model_weight,\n    tree_predict(p.model_id, p.model, t.features, \"-classification\") as predicted\n    -- tree_predict(p.model_id, p.model, t.features, ${classification}) as predicted\n    -- tree_predict_v1(p.model_id, p.model_type, p.pred_model, t.features, ${classification}) as predicted as predicted -- to use the old model in v0.5.0 or later\n  FROM (\n    SELECT \n      -- from v0.4.1 to v0.4.2-rc4\n      -- model_id, model_type, pred_model\n      -- v0.5.0 or later\n      model_id, model_weight, model\n    FROM model\n    DISTRIBUTE BY rand(1)\n  ) p \n  LEFT OUTER JOIN training t\n) t1\ngroup by\n  rowid;\n\nEvaluation\nselect count(1) from training;\n\n\n150\n\nset hivevar:total_cnt=150;\n\nWITH t1 as (\n  SELECT\n    t.rowid,\n    t.label as actual,\n    p.predicted.label as predicted\n  FROM\n    predicted p\n    LEFT OUTER JOIN training t ON (t.rowid = p.rowid)\n)\nSELECT\n  count(1) / ${total_cnt}\nFROM\n  t1\nWHERE\n  actual = predicted\n;\n\n\n0.98\n\nGraphviz export\n Notetree_export feature is supported from Hivemall v0.5.0 or later.\nBetter to limit tree depth on training by -depth option to plot a Decision Tree.\nHivemall provide tree_export to export a decision tree into Graphviz or human-readable Javascript format. You can find the usage by issuing the following query:\n> select tree_export(\"\",\"-help\");\n\nusage: tree_export(string model, const string options, optional\n       array featureNames=null, optional array\n       classNames=null) - exports a Decision Tree model as javascript/dot]\n       [-help] [-output_name ] [-r] [-t ]\n -help                             Show function help\n -output_name,--outputName    output name [default: predicted]\n -r,--regression                   Is regression tree or not\n -t,--type                    Type of output [default: js,\n                                   javascript/js, graphviz/dot\nCREATE TABLE model_exported \n  STORED AS ORC tblproperties(\"orc.compress\"=\"SNAPPY\")\nAS\nselect\n  model_id,\n  tree_export(model, \"-type javascript\", array('sepal_length','sepal_width','petal_length','petak_width'), array('Setosa','Versicolour','Virginica')) as js,\n  tree_export(model, \"-type graphviz\", array('sepal_length','sepal_width','petal_length','petak_width'), array('Setosa','Versicolour','Virginica')) as dot\nfrom\n  model\n-- limit 1\n;\n\ndigraph Tree {\n node [shape=box, style=\"filled, rounded\", color=\"black\", fontname=helvetica];\n edge [fontname=helvetica];\n 0 [label=, fillcolor=\"#00000000\"];\n 1 [label=, fillcolor=\"0.0000,1.000,1.000\", shape=ellipse];\n 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"];\n 2 [label=, fillcolor=\"#00000000\"];\n 0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"];\n 3 [label=, fillcolor=\"#00000000\"];\n 2 -> 3;\n 4 [label=, fillcolor=\"0.3333,1.000,1.000\", shape=ellipse];\n 3 -> 4;\n 5 [label=, fillcolor=\"#00000000\"];\n 3 -> 5;\n 6 [label=, fillcolor=\"0.6667,1.000,1.000\", shape=ellipse];\n 5 -> 6;\n 7 [label=, fillcolor=\"0.3333,1.000,1.000\", shape=ellipse];\n 5 -> 7;\n 8 [label=, fillcolor=\"#00000000\"];\n 2 -> 8;\n 9 [label=, fillcolor=\"#00000000\"];\n 8 -> 9;\n 10 [label=, fillcolor=\"0.3333,1.000,1.000\", shape=ellipse];\n 9 -> 10;\n 11 [label=, fillcolor=\"0.6667,1.000,1.000\", shape=ellipse];\n 9 -> 11;\n 12 [label=, fillcolor=\"0.6667,1.000,1.000\", shape=ellipse];\n 8 -> 12;\n}\n\nYou can draw a graph by dot -Tpng iris.dot -o iris.png or using Viz.js.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"multiclass/iris_xgboost.html":{"url":"multiclass/iris_xgboost.html","title":"XGBoost","keywords":"","body":"\n\n\n\nData preparation\nTraining\nPrediction\nEvaluation\n\n\n\nData preparation\ncreate database iris;\nuse iris;\n\ncreate external table raw (\n  sepal_length int,\n  sepal_width int,\n  petal_length int,\n  petak_width int,\n  class string\n)\nROW FORMAT DELIMITED\n  FIELDS TERMINATED BY ','\n  LINES TERMINATED BY '\\n'\nSTORED AS TEXTFILE LOCATION '/dataset/iris/raw';\n\n$ sed '/^$/d' iris.data | hadoop fs -put - /dataset/iris/raw/iris.data\n\ndrop table label_mapping;\ncreate table label_mapping \nas\nselect\n  class,\n  rank - 1 as label -- zero start index\nfrom (\n  select\n    distinct class,\n    dense_rank() over (order by class) as rank\n  from \n    raw\n) t;\n\ndrop table xgb_input;\ncreate table xgb_input\nas\nselect\n  rowid() as rowid,\n  array(sepal_length, sepal_width, petal_length, petal_width) as dense_features,\n  indexed_features(sepal_length, sepal_width, petal_length, petal_width) as sparse_features,\n  t2.label\nfrom\n  raw t1\n  JOIN label_mapping t2 ON (t1.class = t2.class)\n;\n\nselect * from xgb_input limit 3;\n\n\n\n\nxgb_input.rowid\nxgb_input.dense_features\nxgb_input.sparse_features\nxgb_input.label\n\n\n\n\n1-1\n[5,3,1,0]\n[\"1:5\",\"2:3\",\"3:1\",\"4:0\"]\n0\n\n\n1-2\n[4,3,1,0]\n[\"1:4\",\"2:3\",\"3:1\",\"4:0\"]\n0\n\n\n1-3\n[4,3,1,0]\n[\"1:4\",\"2:3\",\"3:1\",\"4:0\"]\n0\n\n\n\nTraining\n-- explicitly use 3 reducers\n-- set mapred.reduce.tasks=3;\n\ndrop table xgb_softmax_model;\ncreate table xgb_softmax_model \nas\nselect \n  train_xgboost(features, label, '-objective multi:softmax -num_class 3 -num_round 10 -num_early_stopping_rounds 3') \n    as (model_id, model)\nfrom (\n  select \n    -- both sparse and dense format is supported\n    dense_features as features, label\n    -- sparse_features as features, label \n  from\n    xgb_input\n  cluster by rand(43) -- shuffle\n) shuffled;\n Caution-num_class is required for multiclass objectives.\nNote both sparse and dense vector is supported for feature vector format.\nPrediction\ndrop table xgb_softmax_predicted;\ncreate table xgb_softmax_predicted as\nselect\n  rowid,\n  majority_vote(cast(predicted as int)) as label\nfrom (\n  select\n    xgboost_predict_one(rowid, dense_features, model_id, model) as (rowid, predicted)\n    -- xgboost_predict_one(rowid, sparse_features, model_id, model) as (rowid, predicted)\n  from\n    xgb_softmax_model l\n    LEFT OUTER JOIN xgb_input r\n) t\ngroup by rowid;\n\nEvaluation\nWITH validate as (\n  select \n    t.label as actual, \n    p.label as predicted\n  from \n    xgb_input t\n    JOIN xgb_softmax_predicted p\n      on (t.rowid = p.rowid)\n)\nselect \n  sum(if(actual=predicted,1.0,0.0))/count(1) \nfrom\n  validate;\n\n\n0.9533333333333333333\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n\n"},"regression/general.html":{"url":"regression/general.html","title":"Regression","keywords":"","body":"\nIn our regression tutorials, you can tackle realistic prediction problems by using several Hivemall's regression features such as:\n\nPA1a\nPA2a\nAROW\nAROWe\n\nOur train_regressor function enables you to solve the regression problems with flexible configurable options. Let us try the function below.\nIt should be noted that the sample queries require you to prepare E2006-tfidf data. See our E2006-tfidf tutorial page for further instructions.\n\n\n\nTraining\nPrediction & evaluation\n\n\n\n NoteThis feature is supported from Hivemall v0.5-rc.1 or later.\nTraining\ncreate table e2006tfidf_regression_model as\nselect \n    feature,\n    avg(weight) as weight\nfrom (\n    select \n      train_regressor(features,target,'-loss squaredloss -opt AdaGrad -reg no') as (feature,weight)\n  from \n    e2006tfidf_train_x3\n) t \ngroup by feature;\n\nPrediction & evaluation\nWITH predict as (\n    select\n      t.rowid, \n      sum(m.weight * t.value) as predicted\n    from \n      e2006tfidf_test_exploded t LEFT OUTER JOIN\n      e2006tfidf_regression_model m ON (t.feature = m.feature)\n    group by\n      t.rowid\n),\nsubmit as (\n    select \n      t.target as actual, \n      p.predicted as predicted\n    from \n      e2006tfidf_test t JOIN predict p \n        on (t.rowid = p.rowid)\n)\nselect rmse(predicted, actual) from submit;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"regression/e2006.html":{"url":"regression/e2006.html","title":"E2006-tfidf Regression Tutorial","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"regression/e2006_dataset.html":{"url":"regression/e2006_dataset.html","title":"Data Preparation","keywords":"","body":"\nPrerequisite\n\nE2006-tfidf Dataset\nconv.awk\n\nData preparation\ncd /mnt/archive/datasets/regression/E2006-tfidf\nawk -f conv.awk E2006.train > E2006.train.tsv\nawk -f conv.awk  E2006.test > E2006.test.tsv\n\nhadoop fs -mkdir -p /dataset/E2006-tfidf/train\nhadoop fs -mkdir -p /dataset/E2006-tfidf/test\nhadoop fs -put E2006.train.tsv /dataset/E2006-tfidf/train\nhadoop fs -put E2006.test.tsv /dataset/E2006-tfidf/test\n\ncreate database E2006;\nuse E2006;\n\ncreate external table e2006tfidf_train (\n  rowid int,\n  target float,\n  features ARRAY\n) \nROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" \nSTORED AS TEXTFILE LOCATION '/dataset/E2006-tfidf/train';\n\ncreate external table e2006tfidf_test (\n  rowid int, \n  target float,\n  features ARRAY\n) \nROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' COLLECTION ITEMS TERMINATED BY \",\" \nSTORED AS TEXTFILE LOCATION '/dataset/E2006-tfidf/test';\n\ncreate table e2006tfidf_test_exploded as\nselect \n  rowid,\n  target,\n  -- split(feature,\":\")[0] as feature,\n  -- cast(split(feature,\":\")[1] as float) as value\n  -- hivemall v0.3.1 or later\n  extract_feature(feature) as feature,\n  extract_weight(feature) as value\nfrom \n  e2006tfidf_test LATERAL VIEW explode(add_bias(features)) t AS feature;\n\nAmplify training examples (global shuffle)\n-- set mapred.reduce.tasks=32;\nset hivevar:seed=31;\nset hivevar:xtimes=3;\n\ncreate or replace view e2006tfidf_train_x3 as \nselect * from (\n  select amplify(${xtimes}, *) as (rowid, target, features)\n  from e2006tfidf_train\n) t\nCLUSTER BY rand(${seed});\n\n-- set mapred.reduce.tasks=-1;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"regression/e2006_generic.html":{"url":"regression/e2006_generic.html","title":"General Regessor","keywords":"","body":"\nThis tutorial shows how to apply General Regressor for a regression problem of e2006 dataset.\n\n\n\nTraining\nprediction\nevaluation\n\n\n\nTraining\nset mapred.reduce.tasks=32;\n\ndrop table e2006tfidf_generic_model;\ncreate table e2006tfidf_generic_model as\nselect \n feature,\n avg(weight) as weight\nfrom \n (select \n     train_regressor(\n       add_bias(features), target,\n       '-loss squaredloss -opt AdamHD -reg No -iters 20'\n     ) as (feature, weight)\n  from \n     e2006tfidf_train_x3\n ) t \ngroup by feature;\n\n-- reset to the default setting\nset mapred.reduce.tasks=-1;\n\n CautionRegularization could not work well for regression problem. Then, try providing -reg No option as seen in the above query.\nAlso, do not use voted_avg() for regression. voted_avg() is for classification.\nprediction\ncreate or replace view e2006tfidf_generic_predict\nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as predicted\nfrom \n  e2006tfidf_test_exploded t LEFT OUTER JOIN\n  e2006tfidf_generic_model m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\nWITH submit as (\n  select \n    t.target as actual, \n    p.predicted as predicted\n  from \n    e2006tfidf_test t\n    JOIN e2006tfidf_generic_predict p \n      on (t.rowid = p.rowid)\n)\nselect \n   rmse(predicted, actual) as RMSE,\n   mse(predicted, actual) as MSE, \n   mae(predicted, actual) as MAE,\n   r2(predicted, actual) as R2\nfrom \n   submit;\n\n\n\n\nrmse\nmse\nmae\nr2\n\n\n\n\n0.37125069279938866\n0.13782707690402607\n0.2270351090214029\n0.5232372408076887\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"regression/e2006_arow.html":{"url":"regression/e2006_arow.html","title":"Passive Aggressive, AROW","keywords":"","body":"\n\n\n\nPA1a\nTraining\nprediction\nevaluation\n\n\nPA2a\nTraining\nprediction\nevaluation\n\n\nAROW\nTraining\nprediction\nevaluation\n\n\nAROWe\nTraining\nprediction\nevaluation\n\n\n\n\n\nPA1a\nTraining\nset mapred.reduce.tasks=64;\n\ndrop table e2006tfidf_pa1a_model ;\ncreate table e2006tfidf_pa1a_model as\nselect \n feature,\n avg(weight) as weight\nfrom \n (select \n     train_pa1a_regr(add_bias(features),target) as (feature,weight)\n  from \n     e2006tfidf_train_x3\n ) t \ngroup by feature;\n\n-- reset to the default setting\nset mapred.reduce.tasks=-1;\n\n CautionDo not use voted_avg() for regression. voted_avg() is for classification.\nprediction\ncreate or replace view e2006tfidf_pa1a_predict\nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as predicted\nfrom \n  e2006tfidf_test_exploded t LEFT OUTER JOIN\n  e2006tfidf_pa1a_model m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\nWITH submit as (\n  select \n    t.target as actual, \n    p.predicted as predicted\n  from \n    e2006tfidf_test t\n    JOIN e2006tfidf_pa1a_predict p \n      on (t.rowid = p.rowid)\n)\nselect \n   rmse(predicted, actual) as RMSE,\n   mse(predicted, actual) as MSE, \n   mae(predicted, actual) as MAE,\n   r2(predicted, actual) as R2\nfrom \n   submit;\n\n\n\n\nrmse\nmse\nmae\nr2\n\n\n\n\n0.3797959864675519\n0.14424499133686086\n0.23846059576113587\n0.5010367946980386\n\n\n\nPA2a\nTraining\nset mapred.reduce.tasks=64;\ndrop table e2006tfidf_pa2a_model;\ncreate table e2006tfidf_pa2a_model as\nselect \n feature,\n avg(weight) as weight\nfrom \n (select \n     train_pa2a_regr(add_bias(features),target) as (feature,weight)\n  from \n     e2006tfidf_train_x3\n ) t \ngroup by feature;\nset mapred.reduce.tasks=-1;\n\nprediction\ncreate or replace view e2006tfidf_pa2a_predict\nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as predicted\nfrom \n  e2006tfidf_test_exploded t LEFT OUTER JOIN\n  e2006tfidf_pa2a_model m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\nWITH submit as (\n  select \n    t.target as actual, \n    p.predicted as predicted\n  from \n    e2006tfidf_test t\n    JOIN e2006tfidf_pa2a_predict p \n      on (t.rowid = p.rowid)\n)\nselect \n   rmse(predicted, actual) as RMSE,\n   mse(predicted, actual) as MSE, \n   mae(predicted, actual) as MAE,\n   r2(predicted, actual) as R2\nfrom \n   submit;\n\n\n\n\nrmse\nmse\nmae\nr2\n\n\n\n\n0.38538660838804495\n0.14852283792484033\n0.2466732002711477\n0.48623913673053565\n\n\n\nAROW\nTraining\nset mapred.reduce.tasks=64;\ndrop table e2006tfidf_arow_model ;\ncreate table e2006tfidf_arow_model as\nselect \n feature,\n -- avg(weight) as weight -- [hivemall v0.1]\n argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]\nfrom \n (select \n     train_arow_regr(add_bias(features),target) as (feature,weight,covar)\n  from \n     e2006tfidf_train_x3\n ) t \ngroup by feature;\nset mapred.reduce.tasks=-1;\n\nprediction\ncreate or replace view e2006tfidf_arow_predict\nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as predicted\nfrom \n  e2006tfidf_test_exploded t LEFT OUTER JOIN\n  e2006tfidf_arow_model m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\nWITH submit as (\n  select \n    t.target as actual, \n    p.predicted as predicted\n  from \n    e2006tfidf_test t\n    JOIN e2006tfidf_arow_predict p \n      on (t.rowid = p.rowid)\n)\nselect \n   rmse(predicted, actual) as RMSE,\n   mse(predicted, actual) as MSE, \n   mae(predicted, actual) as MAE,\n   r2(predicted, actual) as R2\nfrom \n   submit;\n\n\n\n\nrmse\nmse\nmae\nr2\n\n\n\n\n0.37862513029019407\n0.14335698928726642\n0.2368787001269389\n0.5041085155590119\n\n\n\nAROWe\nAROWe is a modified version of AROW that uses Hinge loss (epsilion = 0.1)\nTraining\n-- set mapred.reduce.tasks=64;\n\ndrop table e2006tfidf_arowe_model ;\ncreate table e2006tfidf_arowe_model as\nselect \n feature,\n argmin_kld(weight, covar) as weight \nfrom \n (select \n     train_arowe_regr(add_bias(features),target) as (feature,weight,covar)\n  from \n     e2006tfidf_train_x3\n ) t \ngroup by feature;\nset mapred.reduce.tasks=-1;\n\nprediction\ncreate or replace view e2006tfidf_arowe_predict\nas\nselect\n  t.rowid, \n  sum(m.weight * t.value) as predicted\nfrom \n  e2006tfidf_test_exploded t LEFT OUTER JOIN\n  e2006tfidf_arowe_model m ON (t.feature = m.feature)\ngroup by\n  t.rowid;\n\nevaluation\nWITH submit as (\n  select \n    t.target as actual, \n    p.predicted as predicted\n  from \n    e2006tfidf_test t\n    JOIN e2006tfidf_arowe_predict p \n      on (t.rowid = p.rowid)\n)\nselect \n   rmse(predicted, actual) as RMSE,\n   mse(predicted, actual) as MSE, \n   mae(predicted, actual) as MAE,\n   r2(predicted, actual) as R2\nfrom \n   submit;\n\n\n\n\nrmse\nmse\nmae\nr2\n\n\n\n\n0.37789148212861856\n0.14280197226536404\n0.2357339155291536\n0.5060283955470721\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"regression/e2006_xgboost.html":{"url":"regression/e2006_xgboost.html","title":"XGBoost","keywords":"","body":"\nThis tutorial explains how to use XGBoost for regression problems.\n\n\n\nTraining\nprediction\nevaluation\n\n\n\nTraining\nThe following objective is supported in XGboost for regression:\n\nreg:squarederror regression with squared loss\nreg:logistic logistic regression\n\nreg:squarederror is widely used as the regression objective.\nuse e2006;\n\ndesc e2006tfidf_train;\n\n\n\n\ncol_name\ndata_type\n\n\n\n\nrowid\nint\n\n\ntarget\nfloat\n\n\nfeatures\narray\n\n\n\n-- explicitly use 3 reducers\n-- set mapred.reduce.tasks=3\n\ndrop table xgb_regr_model;\ncreate table xgb_regr_model as\nselect \n  train_xgboost(features, target, '-objective reg:squarederror -num_round 10 -num_early_stopping_rounds 3') \n    as (model_id, model)\nfrom (\n  select features, target\n  from e2006tfidf_train\n  cluster by rand(43) -- shuffle data to reducers\n) shuffled;\n\nprediction\ndrop table xgb_regr_predicted;\ncreate table xgb_regr_predicted as\nselect\n  rowid,\n  avg(predicted) as predicted\nfrom (\n  select\n    xgboost_predict_one(rowid, features, model_id, model) as (rowid, predicted)\n  from\n    xgb_regr_model l\n    LEFT OUTER JOIN e2006tfidf_test r\n) t\ngroup by rowid;\n\n Notexgboost_predict returns new double[1] (e.g., [-3.9760303385555744]) for -objective reg:squarederror.\nOn the other hand, xgboost_predict_one returns a scalar double value as predicted -3.9760303385555744.\nevaluation\nWITH submit as (\n  select \n    t.target as actual, \n    p.predicted as predicted\n  from \n    e2006tfidf_test t\n    JOIN xgb_regr_predicted p \n      on (t.rowid = p.rowid)\n)\nselect \n   rmse(predicted, actual) as RMSE,\n   mse(predicted, actual) as MSE, \n   mae(predicted, actual) as MAE,\n   r2(predicted, actual) as R2\nfrom \n   submit;\n\n\n\n\nrmse\nmse\nmae\nr2\n\n\n\n\n0.3949633797136429\n0.15599607131482326\n0.25367043577533693\n0.4603881976325721\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"regression/kddcup12tr2.html":{"url":"regression/kddcup12tr2.html","title":"KDDCup 2012 Track 2 CTR Prediction Tutorial","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"regression/kddcup12tr2_dataset.html":{"url":"regression/kddcup12tr2_dataset.html","title":"Data Preparation","keywords":"","body":"\nThe task is predicting the click through rate (CTR) of advertisement, meaning that we are to predict the probability of each ad being clicked. \nhttps://www.kaggle.com/c/kddcup2012-track2\n\nDataset  \n\n\n\nFile\nSize\nRecords\n\n\n\n\nKDD_Track2_solution.csv\n244MB\n20,297,595 (20,297,594 w/o header)\n\n\ndescriptionid_tokensid.txt\n268MB\n3,171,830\n\n\npurchasedkeywordid_tokensid.txt\n26MB\n1,249,785\n\n\nqueryid_tokensid.txt\n704MB\n26,243,606\n\n\ntest.txt\n1.3GB\n20,297,594\n\n\ntitleid_tokensid.txt\n171MB\n4,051,441\n\n\ntraining.txt\n9.9GB\n149,639,105\n\n\nserid_profile.txt\n283MB\n23,669,283\n\n\n\n\nTokens are actually not used in this example. Try using them on your own.\n\ncreate database kdd12track2;\nuse kdd12track2;\n\nCreate external table training (\n  RowID BIGINT,\n  Clicks INT, \n  Impression INT, \n  DisplayURL STRING, \n  AdID INT,\n  AdvertiserID INT, \n  Depth SMALLINT, \n  Position SMALLINT, \n  QueryID INT, \n  KeywordID INT,\n  TitleID INT, \n  DescriptionID INT, \n  UserID INT\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' STORED AS TEXTFILE LOCATION '/kddcup2012/track2/training';\n\nCreate external table testing (\n  RowID BIGINT,\n  DisplayURL STRING, \n  AdID INT,\n  AdvertiserID INT, \n  Depth SMALLINT, \n  Position SMALLINT, \n  QueryID INT, \n  KeywordID INT,\n  TitleID INT, \n  DescriptionID INT, \n  UserID INT\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' STORED AS TEXTFILE LOCATION '/kddcup2012/track2/testing';\n\nCreate external table user (\n  UserID INT, \n  Gender TINYINT,\n  Age TINYINT\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' STORED AS TEXTFILE LOCATION '/kddcup2012/track2/user';\n\nCreate external table query (\n  QueryID INT,\n  Tokens STRING\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' STORED AS TEXTFILE LOCATION '/kddcup2012/track2/query';\n\nCreate external table keyword (\n  KeywordID INT,\n  Tokens STRING\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' STORED AS TEXTFILE LOCATION '/kddcup2012/track2/keyword';\n\nCreate external table title (\n  TitleID INT, \n  Tokens STRING\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' STORED AS TEXTFILE LOCATION '/kddcup2012/track2/title';\n\nCreate external table description (\n  DescriptionID INT,\n  Tokens STRING\n) ROW FORMAT DELIMITED FIELDS TERMINATED BY '\\t' STORED AS TEXTFILE LOCATION '/kddcup2012/track2/description';\n\nCreate external table solution (\n   RowID BIGINT,\n   Clicks INT,\n   Impressions INT,\n   Private BOOLEAN \n) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' STORED AS TEXTFILE LOCATION '/kddcup2012/track2/solution';\n\ngawk '{print NR\"\\t\"$0;}' training.txt | \\\nhadoop fs -put - /kddcup2012/track2/training/training.tsv\n\ngawk '{print NR\"\\t\"$0;}' test.txt | \\\nhadoop fs -put - /kddcup2012/track2/testing/test.tsv\n\nhadoop fs -put userid_profile.txt /kddcup2012/track2/user/user.tsv\n\ntail -n +2 KDD_Track2_solution.csv | sed -e 's/Public/FALSE/g' | sed -e 's/Private/TRUE/g' | gawk '{print NR\",\"$0;}' \\\nhadoop fs -put - /kddcup2012/track2/solution/solution.csv\n\nhadoop fs -put  queryid_tokensid.txt /kddcup2012/track2/query/tokensid.tsv\nhadoop fs -put purchasedkeywordid_tokensid.txt /kddcup2012/track2/keyword/tokensid.tsv\nhadoop fs -put titleid_tokensid.txt /kddcup2012/track2/title/tokensid.tsv\nhadoop fs -put descriptionid_tokensid.txt /kddcup2012/track2/description/tokensid.tsv\n\nConverting feature representation by feature hashing\nhttp://en.wikipedia.org/wiki/Feature_hashing\nmhash is the MurmurHash3 function to convert a feature vector into a hash value.\ncreate or replace view training2 as\nselect\n  rowid,\n  clicks,\n  (impression - clicks) as noclick,\n  mhash(concat(\"1:\", displayurl)) as displayurl, \n  mhash(concat(\"2:\", adid)) as adid, \n  mhash(concat(\"3:\", advertiserid)) as advertiserid, \n  mhash(concat(\"4:\", depth)) as depth, \n  mhash(concat(\"5:\", position)) as position, \n  mhash(concat(\"6:\", queryid)) as queryid, \n  mhash(concat(\"7:\", keywordid)) as keywordid, \n  mhash(concat(\"8:\", titleid)) as titleid, \n  mhash(concat(\"9:\", descriptionid)) as descriptionid, \n  mhash(concat(\"10:\", userid)) as userid, \n  mhash(concat(\"11:\", COALESCE(gender,\"0\"))) as gender, \n  mhash(concat(\"12:\", COALESCE(age,\"-1\"))) as age, \n  -1 as bias\nfrom (\n  select\n    t.*,\n    u.gender,\n    u.age\n  from \n    training t \n    LEFT OUTER JOIN user u \n      on t.userid = u.userid\n) t;\n\ncreate or replace view testing2 as\nselect\n  rowid, \n  array(displayurl, adid, advertiserid, depth, position, queryid, keywordid, titleid, descriptionid, userid, gender, age, bias) \n    as features\nfrom (\n  select\n    rowid,\n    mhash(concat(\"1:\", displayurl)) as displayurl, \n    mhash(concat(\"2:\", adid)) as adid, \n    mhash(concat(\"3:\", advertiserid)) as advertiserid, \n    mhash(concat(\"4:\", depth)) as depth, \n    mhash(concat(\"5:\", position)) as position, \n    mhash(concat(\"6:\", queryid)) as queryid, \n    mhash(concat(\"7:\", keywordid)) as keywordid, \n    mhash(concat(\"8:\", titleid)) as titleid, \n    mhash(concat(\"9:\", descriptionid)) as descriptionid, \n    mhash(concat(\"10:\", userid)) as userid, \n    mhash(concat(\"11:\", COALESCE(gender,\"0\"))) as gender, \n    mhash(concat(\"12:\", COALESCE(age,\"-1\"))) as age, \n    -1 as bias\n  from (\n    select\n      t.*,\n      u.gender,\n      u.age\n    from \n      testing t \n      LEFT OUTER JOIN user u \n        on t.userid = u.userid\n  ) t1\n) t2;\n\nCompressing large training tables\ncreate table training_orcfile (\n rowid bigint,\n features array,\n label int\n) STORED AS orc tblproperties (\"orc.compress\"=\"SNAPPY\");\n\n\nCaution: Joining between training table and user table takes a long time. Consider not to use gender and age and avoid joins if your Hadoop cluster is small.\n\n-- SET mapred.reduce.tasks=64;\n-- SET hive.auto.convert.join=false;\n\nINSERT OVERWRITE TABLE training_orcfile \nselect \n  binarize_label(clicks, noclick, rowid, features)\n    as (rowid, features, label)\nfrom\n  training2\nCLUSTER BY rand(); -- shuffle\n\n-- SET mapred.reduce.tasks=-1;\n-- SET hive.auto.convert.join=true;\n\ncreate table testing_exploded as\nselect \n  rowid,\n  feature\nfrom \n  testing2 \n  LATERAL VIEW explode(features) t AS feature;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"regression/kddcup12tr2_lr.html":{"url":"regression/kddcup12tr2_lr.html","title":"Logistic Regression, Passive Aggressive","keywords":"","body":"\nThe task is predicting the click through rate (CTR) of advertisement, meaning that we are to predict the probability of each ad being clicked.https://www.kaggle.com/c/kddcup2012-track2\nCaution: This example just shows a baseline result. Use token tables and amplifier to get better AUC score.\n\n\n\nLogistic Regression\nTraining\nPrediction\nEvaluation\n\n\nPassive Aggressive\nTraining\nPrediction\nEvaluation\n\n\n\n\n\nLogistic Regression\nTraining\nuse kdd12track2;\n\n-- set mapred.max.split.size=134217728; -- [optional] set if OOM caused at mappers on training\n-- SET mapred.max.split.size=67108864;\nselect count(1) from training_orcfile;\n\n\n235582879\n\n235582879 / 56 (mappers) = 4206837\nset hivevar:total_steps=5000000;\n-- set mapred.reduce.tasks=64; -- [optional] set the explicit number of reducers to make group-by aggregation faster\n\ndrop table lr_model;\ncreate table lr_model \nas\nselect \n feature,\n cast(avg(weight) as float) as weight\nfrom \n (select \n     logress(features, label, \"-total_steps ${total_steps}\") as (feature,weight)\n     -- logress(features, label) as (feature,weight)\n  from \n     training_orcfile\n ) t \ngroup by feature;\n\n-- set mapred.max.split.size=-1; -- reset to the default value\n\n NoteSetting the \"-total_steps\" option is optional.\nPrediction\ndrop table lr_predict;\ncreate table lr_predict\n  ROW FORMAT DELIMITED \n    FIELDS TERMINATED BY \"\\t\"\n    LINES TERMINATED BY \"\\n\"\n  STORED AS TEXTFILE\nas\nselect\n  t.rowid, \n  sigmoid(sum(m.weight)) as prob\nfrom \n  testing_exploded t\n  LEFT OUTER JOIN lr_model m \n      ON (t.feature = m.feature)\ngroup by \n  t.rowid\norder by \n  rowid ASC;\n\n Notesigmoid(sum(m.weight)) not sigmoid(sum(m.weight * t.value))) because t.value is always 1.0 for categorical variable.\nEvaluation\nYou can download scoreKDD.py from KDD Cup 2012, Track 2 site. After logging-in to Kaggle, download\nscoreKDD.py.\nhadoop fs -getmerge /user/hive/warehouse/kdd12track2.db/lr_predict lr_predict.tbl\n\ngawk -F \"\\t\" '{print $2;}' lr_predict.tbl > lr_predict.submit\n\npypy scoreKDD.py KDD_Track2_solution.csv  lr_predict.submit\n\n\n\n\nMeasure\nScore\n\n\n\n\nAUC\n0.741111\n\n\nNWMAE\n0.045493\n\n\nWRMSE\n0.142395\n\n\n\n\nPassive Aggressive\nTraining\ndrop table pa_model;\ncreate table pa_model \nas\nselect \n feature,\n cast(avg(weight) as float) as weight\nfrom \n (select \n     train_pa1a_regr(features,label) as (feature,weight)\n  from \n     training_orcfile\n ) t \ngroup by feature;\n\n NotePA1a is recommended when using PA for regression.\nPrediction\ndrop table pa_predict;\ncreate table pa_predict\n  ROW FORMAT DELIMITED \n    FIELDS TERMINATED BY \"\\t\"\n    LINES TERMINATED BY \"\\n\"\n  STORED AS TEXTFILE\nas\nselect\n  t.rowid, \n  sum(m.weight) as prob\nfrom \n  testing_exploded  t LEFT OUTER JOIN\n  pa_model m ON (t.feature = m.feature)\ngroup by \n  t.rowid\norder by \n  rowid ASC;\n\n CautionThe \"prob\" of PA can be used only for ranking and can have a negative value. A higher weight means much likely to be clicked. Note that AUC is sort a measure for evaluating ranking accuracy.\nEvaluation\nhadoop fs -getmerge /user/hive/warehouse/kdd12track2.db/pa_predict pa_predict.tbl\n\ngawk -F \"\\t\" '{print $2;}' pa_predict.tbl > pa_predict.submit\n\npypy scoreKDD.py KDD_Track2_solution.csv  pa_predict.submit\n\n\n\n\nMeasure\nScore\n\n\n\n\nAUC\n0.739722\n\n\nNWMAE\n0.049582\n\n\nWRMSE\n0.143698\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"regression/kddcup12tr2_lr_amplify.html":{"url":"regression/kddcup12tr2_lr_amplify.html","title":"Logistic Regression with amplifier","keywords":"","body":"\nThis article explains amplify technique that is useful for improving prediction score.\nIterations are mandatory in machine learning (e.g., in stochastic gradient descent) to get good prediction models. However, MapReduce is known to be not suited for iterative algorithms because IN/OUT of each MapReduce job is through HDFS.\nIn this example, we show how Hivemall deals with this problem. We use KDD Cup 2012, Track 2 Task as an example.\nWARNING: rand_amplify() is supported in v0.2-beta1 and later.\n\nAmplify training examples in Map phase and shuffle them in Reduce phase\nHivemall provides the amplify UDTF to enumerate iteration effects in machine learning without several MapReduce steps. \nThe amplify function returns multiple rows for each row.\nThe first argument ${xtimes} is the multiplication factor.In the following examples, the multiplication factor is set to 3.\nset hivevar:xtimes=3;\n\ncreate or replace view training_x3\nas\nselect \n  * \nfrom (\nselect\n   amplify(${xtimes}, *) as (rowid, label, features)\nfrom  \n   training_orcfile\n) t\nCLUSTER BY rand();\n\nIn the above example, the  CLUSTER BY clause distributes Map outputs to reducers using a random key for the distribution key. And then, the input records of the reducer is randomly shuffled.\nThe multiplication of records and  the random shuffling has a similar effect to iterations.\nSo, we recommend users to use an amplified view for training as follows:\ncreate table lr_model_x3 \nas\nselect \n feature,\n cast(avg(weight) as float) as weight\nfrom \n (select \n     logress(features,label) as (feature,weight)\n  from \n     training_x3\n ) t \ngroup by feature;\n\nThe above query is executed by 2 MapReduce jobs as shown below:\n\nUsing trainning_x3  instead of the plain training table results in higher and better AUC (0.746214) in this example.\nA problem in amplify() is that the shuffle (copy) and merge phase of the stage 1 could become a bottleneck.\nWhen the training table is so large that involves 100 Map tasks, the merge operator needs to merge at least 100 files by (external) merge sort! \nNote that the actual bottleneck is not M/R iterations but shuffling training instance. Iteration without shuffling (as in the Spark example) causes very slow convergence and results in requiring more iterations. Shuffling cannot be avoided even in iterative MapReduce variants.\n\n\nAmplify and shuffle training examples in each Map task\nTo deal with large training data, Hivemall provides rand_amplify UDTF that randomly shuffles input rows in a Map task.\nThe rand_amplify UDTF outputs rows in a random order when the local buffer specified by ${shufflebuffersize} is filled.\nWith rand_amplify(), the view definition of training_x3 becomes as follows:\nset hivevar:shufflebuffersize=1000;\n\ncreate or replace view training_x3\nas\nselect\n   rand_amplify(${xtimes}, ${shufflebuffersize}, *) as (rowid, label, features)\nfrom  \n   training_orcfile;\n\nThe training query is executed as follows:\n\nThe map-local multiplication and shuffling has no bottleneck in the merge phase and the query is efficiently executed within a single MapReduce job.\n\nUsing rand_amplify results in a better AUC (0.743392) in this example.\n\nConclusion\nWe recommend users to use amplify() for small training inputs and to use rand_amplify() for large training inputs to get a better accuracy in a reasonable training time.\n\n\n\nMethod\nELAPSED TIME (sec)\nAUC\n\n\n\n\nPlain\n89.718\n0.734805\n\n\namplifier+clustered by\n479.855\n0.746214\n\n\nrand_amplifier\n116.424\n0.743392\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"regression/kddcup12tr2_adagrad.html":{"url":"regression/kddcup12tr2_adagrad.html","title":"AdaGrad, AdaDelta","keywords":"","body":"\nPreparation\nuse kdd12track2;\n\n-- SET mapreduce.framework.name=yarn;\n-- SET hive.execution.engine=mr;\n-- SET mapreduce.framework.name=yarn-tez;\n-- SET hive.execution.engine=tez;\nSET mapred.reduce.tasks=32; -- [optional] set the explicit number of reducers to make group-by aggregation faster\n\nAdaGrad\ndrop table adagrad_model;\ncreate table adagrad_model \nas\nselect \n feature,\n avg(weight) as weight\nfrom \n (select \n     adagrad(features,label) as (feature,weight)\n  from \n     training_orcfile\n ) t \ngroup by feature;\n\ndrop table adagrad_predict;\ncreate table adagrad_predict\n  ROW FORMAT DELIMITED \n    FIELDS TERMINATED BY \"\\t\"\n    LINES TERMINATED BY \"\\n\"\n  STORED AS TEXTFILE\nas\nselect\n  t.rowid, \n  sigmoid(sum(m.weight)) as prob\nfrom \n  testing_exploded t LEFT OUTER JOIN\n  adagrad_model m ON (t.feature = m.feature)\ngroup by \n  t.rowid\norder by \n  rowid ASC;\n\n Notesigmoid(sum(m.weight)) not sigmoid(sum(m.weight * t.value))) because t.value is always 1.0 for categorical variable.\nhadoop fs -getmerge /user/hive/warehouse/kdd12track2.db/adagrad_predict adagrad_predict.tbl\n\ngawk -F \"\\t\" '{print $2;}' adagrad_predict.tbl > adagrad_predict.submit\n\npypy scoreKDD.py KDD_Track2_solution.csv adagrad_predict.submit\n\n\n\n\nAlgorithm\nAUC\n\n\n\n\nSGD\n0.739351\n\n\nADAGRAD\n0.743279\n\n\n\nAdaDelta\ndrop table adadelta_model;\ncreate table adadelta_model \nas\nselect \n feature,\n cast(avg(weight) as float) as weight\nfrom \n (select \n     adadelta(features,label) as (feature,weight)\n  from \n     training_orcfile\n ) t \ngroup by feature;\n\ndrop table adadelta_predict;\ncreate table adadelta_predict\n  ROW FORMAT DELIMITED \n    FIELDS TERMINATED BY \"\\t\"\n    LINES TERMINATED BY \"\\n\"\n  STORED AS TEXTFILE\nas\nselect\n  t.rowid, \n  sigmoid(sum(m.weight)) as prob\nfrom \n  testing_exploded  t LEFT OUTER JOIN\n  adadelta_model m ON (t.feature = m.feature)\ngroup by \n  t.rowid\norder by \n  rowid ASC;\n\nhadoop fs -getmerge /user/hive/warehouse/kdd12track2.db/adadelta_predict adadelta_predict.tbl\n\ngawk -F \"\\t\" '{print $2;}' adadelta_predict.tbl > adadelta_predict.submit\n\npypy scoreKDD.py KDD_Track2_solution.csv adadelta_predict.submit\n\n\n\n\nAlgorithm\nAUC\n\n\n\n\nSGD\n0.739351\n\n\nADAGRAD\n0.743279\n\n\nAdaDelta\n0.746878\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/cf.html":{"url":"recommend/cf.html","title":"Collaborative Filtering","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/item_based_cf.html":{"url":"recommend/item_based_cf.html","title":"Item-based Collaborative Filtering","keywords":"","body":"\nThis document describes how to do Item-based Collaborative Filtering using Hivemall.\n\n\n\nPrepare transaction table\nCreate item_features table\nStep 1: Creating user_purchased table\nStep 2: Creating cooccurrence table\nStep 2-1: Create cooccurrence table directly\nStep 2-2: Create cooccurrence table from Upper Triangular Matrix of cooccurrence\nComputing cooccurrence ratio (optional step)\n\n\nStep 3: Creating a feature vector for each item\n\n\nCompute item similarity scores\nOption 1: Parallel computation with computationally heavy shuffling\nTaking advantage of the symmetric property of item similarity matrix\n\n\nOption 2: Sequential computation\n\n\nItem-based recommendation\nStep 1: Computes top-k recently purchased items for each user\nStep 2: Recommend top-k items based on users' recently purchased items\nCooccurrence-based\nSimilarity-based\n\n\n\n\nEfficient similarity computation\nMinHash: Compute \"pseudo\" Jaccard similarity\nCompute cosine similarity by using the MinHash-based Jaccard similarity\n\n\nDIMSUM: Approximated all-pairs \"Cosine\" similarity computation\nCreate item_similarity from Upper Triangular Matrix\n\n\n\n\n\n\n\n CautionNaive similarity computation is O(n^2) to compute all item-item pair similarity. In order to accelerate the procedure, Hivemall has an efficient scheme for computing Jaccard and/or cosine similarity as mentioned later.\nPrepare transaction table\nPrepare following transaction table. We will generate feature_vector for each itemid based on cooccurrence of purchased items, a sort of bucket analysis.\n\n\n\nuserid\nitemid\npurchase_at timestamp\n\n\n\n\n1\n31231\n2015-04-9 00:29:02\n\n\n1\n13212\n2016-05-24 16:29:02\n\n\n2\n312\n2016-06-03 23:29:02\n\n\n3\n2313\n2016-06-04 19:29:02\n\n\n..\n..\n..\n\n\n\nCreate item_features table\nWhat we want for creating a feature vector for each item is the following cooccurrence relation.\n\n\n\nitemid\nother\ncnt\n\n\n\n\n583266\n621056\n9999\n\n\n583266\n583266\n18\n\n\n31231\n13212\n129\n\n\n31231\n31231\n3\n\n\n31231\n9833\n953\n\n\n...\n...\n...\n\n\n\nFeature vectors of each item will be as follows:\n\n\n\nitemid\nfeature_vector array\n\n\n\n\n583266\n621056:9999, 583266:18\n\n\n31231\n13212:129, 31231:3, 9833:953\n\n\n...\n...\n\n\n\nNote that value of feature vector should be scaled for k-NN similarity computation e.g., as follows:\n\n\n\nitemid\nfeature_vector array\n\n\n\n\n583266\n621056:ln(9999+1), 583266:ln(18+1)\n\n\n31231\n13212:ln(129+1), 31231:ln(3+1), 9833:ln(953+1)\n\n\n...\n...\n\n\n\nThe following queries results in creating the above table.\nStep 1: Creating user_purchased table\nThe following query creates a table that contains userid, itemid, and purchased_at. The table represents the last user-item contact (purchase) while the transaction table holds all contacts.\nCREATE TABLE user_purchased as\n-- INSERT OVERWRITE TABLE user_purchased\nselect \n  userid,\n  itemid,\n  max(purchased_at) as purchased_at,\n  count(1) as purchase_count\nfrom\n  transaction\n-- where purchased_at \n NoteBetter to avoid too old transactions because those information would be outdated though an enough number of transactions is required for recommendation.\nStep 2: Creating cooccurrence table\n CautionItem-item cooccurrence matrix is a symmetric matrix that has the number of total occurrence for each diagonal element. If the size of items is k, then the size of expected matrix is k * (k - 1) / 2, usually a very large one. Hence, it is better to use step 2-2 instead of step 2-1 for creating a cooccurrence table where dataset is large.\nStep 2-1: Create cooccurrence table directly\ncreate table cooccurrence as \n-- INSERT OVERWRITE TABLE cooccurrence\nselect\n  u1.itemid,\n  u2.itemid as other, \n  count(1) as cnt\nfrom\n  user_purchased u1\n  JOIN user_purchased u2 ON (u1.userid = u2.userid)\nwhere\n  u1.itemid != u2.itemid \n  -- AND u2.purchased_at >= u1.purchased_at -- the other item should be purchased with/after the base item\ngroup by\n  u1.itemid, u2.itemid\n-- having -- optional but recommended to have this condition where dataset is large\n--  cnt >= 2 -- count(1) >= 2\n;\n\n NoteNote that specifying having cnt >= 2 has a drawback that item cooccurrence is not calculated where cnt is less than 2. It could result no recommended items for certain items. Please ignore having cnt >= 2 if the following computations finish in an acceptable/reasonable time.\n\n CautionWe ignore a purchase order in the following example. It means that the occurrence counts of ItemA -> ItemB and ItemB -> ItemA are assumed to be same. It is sometimes not a good idea in terms of reasoning; for example, Camera -> SD card and SD card -> Camera need to be considered separately.\nStep 2-2: Create cooccurrence table from Upper Triangular Matrix of cooccurrence\nBetter to create Upper Triangular Matrix that has itemid > other if resulting table is very large. No need to create Upper Triangular Matrix if your Hadoop cluster can handle the following instructions without considering it.\ncreate table cooccurrence_upper_triangular as \n-- INSERT OVERWRITE TABLE cooccurrence_upper_triangular\nselect\n  u1.itemid,\n  u2.itemid as other, \n  count(1) as cnt\nfrom\n  user_purchased u1\n  JOIN user_purchased u2 ON (u1.userid = u2.userid)\nwhere\n  u1.itemid > u2.itemid \ngroup by\n  u1.itemid, u2.itemid\n;\n\ncreate table cooccurrence as \n-- INSERT OVERWRITE TABLE cooccurrence\nselect * from (\n  select itemid, other, cnt from cooccurrence_upper_triangular\n  UNION ALL\n  select other as itemid, itemid as other, cnt from cooccurrence_upper_triangular\n) t;\n\n NoteUNION ALL required to be embedded in Hive.\nLimiting size of elements in cooccurrence_upper_triangular\nUsing only top-N frequently co-occurred item pairs allows you to reduce the size of cooccurrence table:\ncreate table cooccurrence_upper_triangular as\nWITH t1 as (\n  select\n    u1.itemid,\n    u2.itemid as other, \n    count(1) as cnt\n  from\n    user_purchased u1\n    JOIN user_purchased u2 ON (u1.userid = u2.userid)\n  where\n    u1.itemid > u2.itemid \n  group by\n    u1.itemid, u2.itemid\n),\nt2 as (\n  select\n    each_top_k( -- top 1000\n      1000, itemid, cnt, \n      itemid, other, cnt\n    ) as (rank, cmpkey, itemid, other, cnt)\n  from (\n    select * from t1\n    CLUSTER BY itemid\n  ) t;\n)\n-- INSERT OVERWRITE TABLE cooccurrence_upper_triangular\nselect itemid, other, cnt\nfrom t2;\n\ncreate table cooccurrence as \nWITh t1 as (\n  select itemid, other, cnt from cooccurrence_upper_triangular\n  UNION ALL\n  select other as itemid, itemid as other, cnt from cooccurrence_upper_triangular\n),\nt2 as (\n  select\n    each_top_k(\n      1000, itemid, cnt,\n      itemid, other, cnt\n    ) as (rank, cmpkey, itemid, other, cnt)\n  from (\n    select * from t1\n    CLUSTER BY itemid\n  ) t\n)\n-- INSERT OVERWRITE TABLE cooccurrence\nselect itemid, other, cnt\nfrom t2;\n\nComputing cooccurrence ratio (optional step)\nYou can optionally compute cooccurrence ratio as follows:\nWITH stats as (\n  select \n    itemid,\n    sum(cnt) as totalcnt\n  from \n    cooccurrence\n  group by\n    itemid\n)\nINSERT OVERWRITE TABLE cooccurrence_ratio\nSELECT\n  l.itemid,\n  l.other, \n  (l.cnt / r.totalcnt) as ratio\nFROM\n  cooccurrence l\n  JOIN stats r ON (l.itemid = r.itemid)\ngroup by\n  l.itemid, l.other\n;\n\nl.cnt / r.totalcnt represents a cooccurrence ratio of range [0,1].\nStep 3: Creating a feature vector for each item\nINSERT OVERWRITE TABLE item_features\nSELECT\n  itemid,\n  -- scaling `ln(cnt+1)` to avoid large value in the feature vector\n  -- rounding to xxx.yyyyy to reduce size of feature_vector in array\n  collect_list(feature(other, round(ln(cnt+1),5))) as feature_vector\nFROM\n  cooccurrence\nGROUP BY\n  itemid\n;\n\nCompute item similarity scores\nItem-item similarity computation is known to be computational complexity O(n^2) where n is the number of items. We have two options to compute the similarities, and, depending on your cluster size and your dataset, the optimal solution differs. \n NoteIf your dataset is large enough, better to choose modified version of option 1, which utilizes the symmetric property of similarity matrix.\nOption 1: Parallel computation with computationally heavy shuffling\nThis version involves 3-way joins w/ large data shuffle; However, this version works in parallel where a cluster has enough task slots.\nWITH similarity as (\n  select\n    o.itemid,\n    o.other,\n    cosine_similarity(t1.feature_vector, t2.feature_vector) as similarity\n  from\n    cooccurrence o\n    JOIN item_features t1 ON (o.itemid = t1.itemid)\n    JOIN item_features t2 ON (o.other = t2.itemid)\n),\ntopk as (\n  select\n    each_top_k( -- get top-10 items based on similarity score\n      10, itemid, similarity,\n      itemid, other -- output items\n    ) as (rank, similarity, itemid, other)\n  from (\n    select * from similarity\n    where similarity > 0 -- similarity > 0.01\n    CLUSTER BY itemid\n  ) t\n)\nINSERT OVERWRITE TABLE item_similarity\nselect \n  itemid, other, similarity\nfrom \n  topk;\n\nTaking advantage of the symmetric property of item similarity matrix\nNotice that item_similarity is a symmetric matrix. So, you can compute it from an upper triangular matrix as follows.\nWITH cooccurrence_top100 as (\n  select\n    each_top_k(\n      100, itemid, cnt,  \n      itemid, other\n    ) as (rank, cmpkey, itemid, other)\n  from (\n    select * from cooccurrence_upper_triangular\n    CLUSTER BY itemid\n  ) t\n), \nsimilarity as (\n  select\n    o.itemid,\n    o.other,\n    cosine_similarity(t1.feature_vector, t2.feature_vector) as similarity\n  from\n    cooccurrence_top100 o\n    -- cooccurrence_upper_triangular o\n    JOIN item_features t1 ON (o.itemid = t1.itemid)\n    JOIN item_features t2 ON (o.other = t2.itemid)\n),\ntopk as (\n  select\n    each_top_k( -- get top-10 items based on similarity score\n      10, itemid, similarity,\n      itemid, other -- output items\n    ) as (rank, similarity, itemid, other)\n  from (\n    select * from similarity\n    where similarity > 0 -- similarity > 0.01\n    CLUSTER BY itemid\n  ) t\n)\nINSERT OVERWRITE TABLE item_similarity_upper_triangler\nselect \n  itemid, other, similarity\nfrom \n  topk;\n\nINSERT OVERWRITE TABLE item_similarity\nselect * from (\n  select itemid, other, similarity from item_similarity_upper_triangler\n  UNION ALL\n  select other as itemid, itemid as other, similarity from item_similarity_upper_triangler\n) t;\n\nOption 2: Sequential computation\nAlternatively, you can compute cosine similarity as follows. This version involves cross join and thus runs sequentially in a single task. However, it involves less shuffle compared to option 1.\nWITH similarity as (\n  select\n   t1.itemid,\n   t2.itemid as other,\n   cosine_similarity(t1.feature_vector, t2.feature_vector) as similarity\n  from\n   item_features t1\n   CROSS JOIN item_features t2\n  WHERE\n    t1.itemid != t2.itemid\n),\ntopk as (\n  select\n    each_top_k( -- get top-10 items based on similarity score\n      10, itemid, similarity,\n      itemid, other -- output items\n    ) as (rank, similarity, itemid, other)\n  from (\n    select * from similarity\n    where similarity > 0 -- similarity > 0.01\n    CLUSTER BY itemid\n  ) t\n)\nINSERT OVERWRITE TABLE item_similarity\nselect \n  itemid, other, similarity\nfrom \n  topk\n;\n\n\n\n\nitem\nother\nsimilarity\n\n\n\n\n583266\n621056\n0.33\n\n\n583266\n583266\n0.18\n\n\n31231\n13212\n1.29\n\n\n31231\n31231\n0.3\n\n\n31231\n9833\n0.953\n\n\n...\n...\n...\n\n\n\nItem-based recommendation\nThis section introduces item-based recommendation based on recently purchased items by each user.\n CautionIt would better to ignore recommending some of items that user already purchased (only 1 time) while items that are purchased twice or more would be okey to be included in the recommendation list (e.g., repeatedly purchased daily necessities). So, you would need an item property table showing that each item is repeatedly purchased items or not.\nStep 1: Computes top-k recently purchased items for each user\nFirst, prepare recently_purchased_items table as follows:\nINSERT OVERWRITE TABLE recently_purchased_items\nselect\n  each_top_k( -- get top-5 recently purchased items for each user\n     5, userid, purchased_at,\n     userid, itemid\n  ) as (rank, purchased_at, userid, itemid)\nfrom (\n  select\n    purchased_at, userid, itemid\n  from \n    user_purchased\n  -- where [optional filtering]\n  --  purchased_at >= xxx -- divide training/test data by time\n  CLUSTER BY\n    user_id -- Note CLUSTER BY is mandatory when using each_top_k\n) t;\n\nStep 2: Recommend top-k items based on users' recently purchased items\nIn order to generate a list of recommended items, you can use either cooccurrence count or similarity as a relevance score.\n CautionIn order to obtain ranked list of items, this section introduces queries using map_values(to_ordered_map(rank, rec_item)). However, this kind of usage has a potential issue that multiple rec_item-s which have the exactly same rank will be aggregated to single arbitrary rec_item, because to_ordered_map() creates a key-value map which uses duplicated rank as key.Since such situation is possible in case that each_top_k() is executed for different userid-s who have the same cnt or similarity, we recommend you to use to_ordered_list(rec_item, rank, '-reverse') instead of map_values(to_ordered_map(rank, rec_item, true)). The alternative approach is available from Hivemall v0.5-rc.1 or later.\nCooccurrence-based\nWITH topk as (\n  select\n    each_top_k(\n       5, userid, cnt,\n       userid, other\n    ) as (rank, cnt, userid, rec_item)\n  from (\n    select \n      t1.userid, t2.other, max(t2.cnt) as cnt\n    from\n      recently_purchased_items t1\n      JOIN cooccurrence t2 ON (t1.itemid = t2.itemid)\n    where\n      t1.itemid != t2.other -- do not include items that user already purchased\n      AND NOT EXISTS (\n        SELECT a.itemid FROM user_purchased a\n        WHERE a.userid = t1.userid AND a.itemid = t2.other\n--        AND a.purchased_count \nSimilarity-based\nWITH topk as (\n  select\n    each_top_k(\n       5, userid, similarity,\n       userid, other\n    ) as (rank, similarity, userid, rec_item)\n  from (\n    select\n      t1.userid, t2.other, max(t2.similarity) as similarity\n    from\n      recently_purchased_items t1\n      JOIN item_similarity t2 ON (t1.itemid = t2.itemid)\n    where\n      t1.itemid != t2.other -- do not include items that user already purchased\n      AND NOT EXISTS (\n        SELECT a.itemid FROM user_purchased a\n        WHERE a.userid = t1.userid AND a.itemid = t2.other\n--        AND a.purchased_count \nEfficient similarity computation\nSince naive similarity computation takes O(n^2) computational complexity, utilizing a certain approximation scheme is practically important to improve efficiency and feasibility. In particular, Hivemall enables you to use one of two sophisticated approximation schemes, MinHash and DIMSUM.\nMinHash: Compute \"pseudo\" Jaccard similarity\nRefer this article to get details about MinHash and Jarccard similarity. This blog article also explains about Hivemall's minhash.\nINSERT OVERWRITE TABLE minhash -- results in 30x records of item_features\nselect  \n  -- assign 30 minhash values for each item\n  minhash(itemid, feature_vector, \"-n 30\") as (clusterid, itemid) -- '-n' would be 10~100\nfrom\n  item_features\n;\n\nWITH t1 as (\n  select\n    l.itemid,\n    r.itemid as other,\n    count(1) / 30 as similarity -- Pseudo jaccard similarity '-n 30'\n  from\n    minhash l \n    JOIN minhash r \n      ON (l.clusterid = r.clusterid)\n  where \n    l.itemid != r.itemid\n  group by\n    l.itemid, r.itemid\n  having\n    count(1) >= 3 -- [optional] filtering equals to (count(1)/30) >= 0.1\n),\ntop100 as (\n  select\n    each_top_k(100, itemid, similarity, itemid, other)\n      as (rank, similarity, itemid, other)\n  from (\n    select * from t1 \n    -- where similarity >= 0.1 -- Optional filtering. Can be ignored.\n    CLUSTER BY itemid \n  ) t2\n)\nINSERT OVERWRITE TABLE jaccard_similarity\nselect\n  itemid, other, similarity\nfrom\n  top100\n;\n\n NoteThere might be no similar item for certain items.\nCompute cosine similarity by using the MinHash-based Jaccard similarity\nOnce the MinHash-based approach found rough top-N similar items, you can efficiently find top-k similar items in terms of cosine similarity, where k  (e.g., k=10 and N=100).\nWITH similarity as (\n  select\n    o.itemid,\n    o.other,\n    cosine_similarity(t1.feature_vector, t2.feature_vector) as similarity\n  from\n    jaccard_similarity o\n    JOIN item_features t1 ON (o.itemid = t1.itemid)\n    JOIN item_features t2 ON (o.other = t2.itemid)\n),\ntopk as (\n  select\n    each_top_k( -- get top-10 items based on similarity score\n      10, itemid, similarity,\n      itemid, other -- output items\n    ) as (rank, similarity, itemid, other)\n  from (\n    select * from similarity\n    where similarity > 0 -- similarity > 0.01\n    CLUSTER BY itemid\n  ) t\n)\nINSERT OVERWRITE TABLE cosine_similarity\nselect \n  itemid, other, similarity\nfrom \n  topk;\n\nDIMSUM: Approximated all-pairs \"Cosine\" similarity computation\n NoteThis feature is supported from Hivemall v0.5-rc.1 or later.\nDIMSUM is a technique to efficiently and approximately compute Cosine similarities for all-pairs of items. You can refer to an article in Twitter's Engineering blog to learn how DIMSUM reduces running time.\nHere, let us begin with the user_purchased table. item_similarity table can be obtained as follows:\ncreate table item_similarity as\nwith item_magnitude as ( -- compute magnitude of each item vector\n  select\n    to_map(j, mag) as mags\n  from (\n    select \n      itemid as j,\n      l2_norm(ln(purchase_count+1)) as mag -- use scaled value\n    from \n      user_purchased\n    group by\n      itemid\n  ) t0\n),\nitem_features as (\n  select\n    userid as i,\n    collect_list(\n      feature(itemid, ln(purchase_count+1)) -- use scaled value\n    ) as feature_vector\n  from\n    user_purchased\n  group by\n    userid\n),\npartial_result as ( -- launch DIMSUM in a MapReduce fashion\n  select\n    dimsum_mapper(f.feature_vector, m.mags, '-threshold 0.5')\n      as (itemid, other, s)\n  from\n    item_features f\n  left outer join item_magnitude m\n),\nsimilarity as (\n  -- reduce (i.e., sum up) mappers' partial results\n  select\n    itemid, \n    other,\n    sum(s) as similarity\n  from \n    partial_result\n  group by\n    itemid, other\n),\ntopk as (\n  select\n    each_top_k( -- get top-10 items based on similarity score\n      10, itemid, similarity,\n      itemid, other -- output items\n    ) as (rank, similarity, itemid, other)\n  from (\n    select * from similarity\n    CLUSTER BY itemid\n  ) t\n)\n-- insert overwrite table item_similarity\nselect \n  itemid, other, similarity\nfrom \n  topk\n;\n\nUltimately, using item_similarity for item-based recommendation is straightforward in a similar way to what we explained above.\nIn the above query, an important part is obviously dimsum_mapper(f.feature_vector, m.mags, '-threshold 0.5'). An option -threshold is a real value in [0, 1) range, and intuitively it illustrates \"similarities above this threshold are approximated by the DIMSUM algorithm\".\nCreate item_similarity from Upper Triangular Matrix\nThanks to the symmetric property of similarity matrix, DIMSUM enables you to utilize space-efficient Upper-Triangular-Matrix-style output by just adding an option -disable_symmetric_output:\ncreate table item_similarity as\nwith item_magnitude as (\n  ...\n),\npartial_result as (\n  select\n    dimsum_mapper(f.feature_vector, m.mags, '-threshold 0.5 -disable_symmetric_output')\n      as (itemid, other, s)\n  from\n    item_features f\n  left outer join item_magnitude m\n),\nsimilarity_upper_triangular as (\n  -- if similarity of (i1, i2) pair is in this table, (i2, i1)'s similarity is omitted\n  select\n    itemid, \n    other,\n    sum(s) as similarity\n  from \n    partial_result\n  group by\n    itemid, other\n),\nsimilarity as ( -- copy (i1, i2)'s similarity as (i2, i1)'s one\n  select itemid, other, similarity from similarity_upper_triangular\n  union all\n  select other as itemid, itemid as other, similarity from similarity_upper_triangular\n),\ntopk as (\n  ...\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/news20.html":{"url":"recommend/news20.html","title":"News20 Related Article Recommendation Tutorial","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/news20_jaccard.html":{"url":"recommend/news20_jaccard.html","title":"LSH/MinHash and Jaccard Similarity","keywords":"","body":"\nList related (similar) articles for each article.\nExtract clusters\nuse news20;\n\nset hivevar:hashes=100; -- Generate N sets of minhash values for each row (DEFAULT: 5)\nset hivevar:keygroups=2; -- Use K minhash value for generating a resulting value (DEFAULT: 2)\n\ncreate table news20_clusterid_assign\nas\nselect \n  -- minhash(rowid, features) as (clusterId, rowid)\n  minhash(rowid, features, \"-n ${hashes} -k ${keygroups}\") as (clusterId, rowid)\nfrom \n  news20mc_train;\n\n--set hivevar:min_cluster_size=5;\n\ncreate or replace view news20_cluster\nas\nselect\n  clusterId, \n  collect_set(rowid) as rowids\nfrom \n  news20_clusterid_assign\ngroup by clusterId\n-- having size(rowids) > ${min_cluster_size}\n;\n\nGet recommendations\ncreate table news20_similar_articles\nas\nWITH t1 as (\nselect\n  l.rowid,\n  r.rowid as other_id,\n  count(1) as cnt\nfrom\n  news20_clusterid_assign l \n  LEFT OUTER JOIN\n  news20_clusterid_assign r\n     ON (l.clusterid = r.clusterid)\nwhere \n  l.rowid != r.rowid\ngroup by\n  l.rowid, r.rowid\nhaving \n-- 10/${hashes}=10/100=0.1 (filter by a pseudo Jaccard similarity by Minhash is greater than or equals to 0.1)\n  cnt >= 10 \n)\nselect\n  rowid,\n  collect_set(other_id) as related_articles\nfrom \n  t1\ngroup by\n  rowid\n-- order by rowid asc\n;\n\nList all possible clusters w/o using a similarity threshold:\ncreate table news20_similar_articles2\nas\nselect\n  l.rowid,\n  collect_set(r.rowid) as related_articles\nfrom\n  news20_clusterid_assign l \n  LEFT OUTER JOIN\n  news20_clusterid_assign r\n     ON (l.clusterid = r.clusterid)\nwhere \n  l.rowid != r.rowid\ngroup by\n  l.rowid\n-- order by rowid asc\n;\n\nJaccard similarity computation using k-Minhash\ncreate table news20_jaccard_similarity\nas\nWITH t1 as (\nselect\n  l.rowid,\n  r.rowid as other_id,\n  count(1) / ${hashes} as similarity\nfrom\n  news20_clusterid_assign l \n  JOIN news20_clusterid_assign r\n     ON (l.clusterid = r.clusterid)\nwhere \n  l.rowid != r.rowid\ngroup by\n  l.rowid, r.rowid\n)\nselect\n  rowid,\n  other_id,\n  similarity,\n  1.0 - similarity as distance\nfrom\n  t1\nwhere\n  similarity >= 0.1\n;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/news20_knn.html":{"url":"recommend/news20_knn.html","title":"LSH/MinHash and Brute-force Search","keywords":"","body":"\nExtract clusters and assign N cluster IDs to each article\ncreate or replace view news20_cluster\nas\nselect \n  minhash(rowid, features) as (clusterId, rowid)\nfrom \n  news20mc_train;\n\ncreate table news20_with_clusterid\nas\nselect \n  t1.clusterid, \n  t1.rowid, \n  o1.features\nfrom \n  news20_cluster t1\n  JOIN news20mc_train o1 ON (t1.rowid = o1.rowid);\nQuery expression with cluster id\nset hivevar:noWeight=false;\n\ncreate table extract_target_cluster\nas\nselect \n  features,\n  clusterid\nfrom (\n  select \n     features,\n     minhashes(features,${noWeight}) as clusters\n  from \n     news20mc_test \n  where \n     rowid = 1\n) t1\nLATERAL VIEW explode(clusters) t2 AS clusterid;\nkNN search using minhashing\nset hivevar:topn=10;\n\nselect \n  t1.rowid, \n  cosine_similarity(t1.features, q1.features, false) as similarity\nfrom\n  news20_with_clusterid t1\n  JOIN extract_target_cluster q1 ON (t1.clusterid = q1.clusterid)\norder by\n  similarity DESC\nlimit ${topn};\n\n\nTime taken: 22.161 seconds\n\n\n\n\nrowid\nsimilarity\n\n\n\n\n2182\n0.21697778\n\n\n5622\n0.21483186\n\n\n962\n0.13240485\n\n\n12242\n0.12158953\n\n\n5102\n0.11168713\n\n\n8562\n0.107470974\n\n\n14396\n0.09949879\n\n\n2542\n0.09011547\n\n\n1645\n0.08894014\n\n\n2862\n0.08800333\n\n\n\nBrute force kNN search (based on cosine similarity)\nselect\n  t1.rowid,\n  cosine_similarity(t1.features, q1.features) as similarity -- hive v0.3.2 or later\n  -- cosine_similarity(t1.features, q1.features, false) as similarity -- hive v0.3.1 or before\nfrom \n  news20mc_train t1\n  CROSS JOIN\n  (select features from news20mc_test where rowid = 1) q1\nORDER BY\n  similarity DESC\nlimit ${topn};\n\n\nTime taken: 24.335 seconds\n\n\n\n\nrowid\nsimilarity\n\n\n\n\n12902\n0.47759432\n\n\n7922\n0.4184913\n\n\n2382\n0.21919869\n\n\n2182\n0.21697778\n\n\n5622\n0.21483186\n\n\n9562\n0.21223815\n\n\n3222\n0.164399\n\n\n11202\n0.16439897\n\n\n10122\n0.1620197\n\n\n8482\n0.15229382\n\n\n\nRefer this page for efficient top-k kNN computation.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/news20_bbit_minhash.html":{"url":"recommend/news20_bbit_minhash.html","title":"kNN search using b-Bits MinHash","keywords":"","body":"\nFunction Signature of bbit_minhash\nText bbit_minhash(array features)\nText bbit_minhash(array features, int numHashes=128)\nText bbit_minhash(array features, boolean discardWeight=false)\nText bbit_minhash(array features, int numHashes=128, boolean discardWeight=false)\nCreate a signature for each article\ncreate table new20mc_with_signature\nas\nselect\n  rowid, \n  bbit_minhash(features, false) as signature\nfrom\n  news20mc_train;\n\nkNN brute-force search using b-Bit minhash\nset hivevar:topn=10;\n\nselect\n  t1.rowid, \n  jaccard_similarity(t1.signature, q1.signature,128) as similarity\n--  , popcnt(t1.signature, q1.signature) as popcnt\nfrom\n  new20mc_with_signature t1 \n  CROSS JOIN \n  (select bbit_minhash(features,128,false) as signature from news20mc_test where rowid = 1) q1\norder by\n  similarity DESC\nlimit ${topn};\n\n\n\n\nrowid\nsimilarity\npopcnt\n\n\n\n\n11952\n0.390625\n41\n\n\n10748\n0.359375\n41\n\n\n12902\n0.34375\n45\n\n\n3087\n0.328125\n48\n\n\n3\n0.328125\n37\n\n\n11493\n0.328125\n38\n\n\n3839\n0.328125\n41\n\n\n12669\n0.328125\n37\n\n\n13604\n0.3125\n41\n\n\n6333\n0.3125\n39\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/movielens.html":{"url":"recommend/movielens.html","title":"MovieLens Movie Recommendation Tutorial","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/movielens_dataset.html":{"url":"recommend/movielens_dataset.html","title":"Data Preparation","keywords":"","body":"\nData preparation\nFirst, downlod MovieLens dataset from the following site.\n\nhttp://files.grouplens.org/datasets/movielens/ml-1m.zip\n\nGet detail about the dataset in the README.\n\nhttp://files.grouplens.org/papers/ml-1m-README.txt\n\nYou can find three dat file in the archive: \n\nmovies.dat, ratings.dat, users.dat.\n\nChange column separator as follows:\nsed 's/::/#/g' movies.dat > movies.t\nsed 's/::/#/g' ratings.dat > ratings.t\nsed 's/::/#/g' users.dat > users.t\n\nCreate a file named occupations.t with the following contents:\n0#other/not specified\n1#academic/educator\n2#artist\n3#clerical/admin\n4#college/grad student\n5#customer service\n6#doctor/health care\n7#executive/managerial\n8#farmer\n9#homemaker\n10#K-12 student\n11#lawyer\n12#programmer\n13#retired\n14#sales/marketing\n15#scientist\n16#self-employed\n17#technician/engineer\n18#tradesman/craftsman\n19#unemployed\n20#writer\nImporting data as Hive tables\ncreate tables\ncreate database movielens;\nuse movielens;\n\nCREATE EXTERNAL TABLE ratings (\n  userid INT, \n  movieid INT,\n  rating INT, \n  tstamp STRING\n) ROW FORMAT DELIMITED\nFIELDS TERMINATED BY '#'\nSTORED AS TEXTFILE\nLOCATION '/dataset/movielens/ratings';\n\nCREATE EXTERNAL TABLE movies (\n  movieid INT, \n  title STRING,\n  genres ARRAY\n) ROW FORMAT DELIMITED\nFIELDS TERMINATED BY '#'\nCOLLECTION ITEMS TERMINATED BY \"|\"\nSTORED AS TEXTFILE\nLOCATION '/dataset/movielens/movies';\n\nCREATE EXTERNAL TABLE users (\n  userid INT, \n  gender STRING, \n  age INT,\n  occupation INT,\n  zipcode STRING\n) ROW FORMAT DELIMITED\nFIELDS TERMINATED BY '#'\nSTORED AS TEXTFILE\nLOCATION '/dataset/movielens/users';\n\nCREATE EXTERNAL TABLE occupations (\n  id INT,\n  occupation STRING\n) ROW FORMAT DELIMITED\nFIELDS TERMINATED BY '#'\nSTORED AS TEXTFILE\nLOCATION '/dataset/movielens/occupations';\n\nload data into tables\nhadoop fs -put ratings.t /dataset/movielens/ratings\nhadoop fs -put movies.t /dataset/movielens/movies\nhadoop fs -put users.t /dataset/movielens/users\nhadoop fs -put occupations.t /dataset/movielens/occupations\n\nCreate a concatenated table\nCREATE TABLE rating_full\nas\nselect \n  r.*, \n  m.title as m_title,\n  concat_ws('|',sort_array(m.genres)) as m_genres, \n  u.gender as u_gender,\n  u.age as u_age,\n  u.occupation as u_occupation,\n  u.zipcode as u_zipcode\nfrom\n  ratings r \n  JOIN movies m ON (r.movieid = m.movieid)\n  JOIN users u ON (r.userid = u.userid);\n\nhive> desc rating_full;\nuserid                  int                     None\nmovieid                 int                     None\nrating                  int                     None\ntstamp                  string                  None\nm_title                 string                  None\nm_genres                string                  None\nu_gender                string                  None\nu_age                   int                     None\nu_occupation            int                     None\nu_zipcode               string                  None\n\nCreating training/testing data\nCreate a training/testing table such that each has 80%/20% of the original rating data.\n-- Adding rowids to the rating table\nSET hivevar:seed=31;\nCREATE TABLE ratings2\nas\nselect\n  rand(${seed}) as rnd, \n  userid, \n  movieid, \n  rating\nfrom \n  ratings;\n\nCREATE TABLE training\nas\nselect * from ratings2\norder by rnd DESC\nlimit 800000;\n\nCREATE TABLE testing\nas\nselect * from ratings2\norder by rnd ASC\nlimit 200209;\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/movielens_cf.html":{"url":"recommend/movielens_cf.html","title":"Item-based Collaborative Filtering","keywords":"","body":"\nOur user guide for item-based collaborative filtering (CF) introduced how to make recommendation based on item-item similarities. Here, we particularly focus on DIMSUM, an efficient and approximated similarity computation scheme, and try to make recommendation from the MovieLens data.\n\n\n\nCompute movie-movie similarity\nPrediction\nRecommendation\nEvaluation\n\n\n\n CautionIn order to obtain ranked list of items, this page introduces queries using to_ordered_map() such as map_values(to_ordered_map(rating, movieid, true)). However, this kind of usage has a potential issue that multiple movieid-s (i.e., values) which have the exactly same rating (i.e., key) will be aggregated to single arbitrary movieid, because to_ordered_map() creates a key-value map which uses duplicated rating as key.Hence, if map key could duplicate on more then one map values, we recommend you to use to_ordered_list(value, key, '-reverse') instead of map_values(to_ordered_map(key, value, true)). The alternative approach is available from Hivemall v0.5-rc.1 or later.\nCompute movie-movie similarity\nAs we explained in the general introduction of item-based CF, following query finds top-kkk nearest-neighborhood movies for each movie:\ndrop table if exists dimsum_movie_similarity;\ncreate table dimsum_movie_similarity \nas\nwith movie_magnitude as ( -- compute magnitude of each movie vector\n  select\n    to_map(j, mag) as mags\n  from (\n    select \n      movieid as j,\n      l2_norm(rating) as mag\n    from \n      training\n    group by\n      movieid\n  ) t0\n),\nmovie_features as (\n  select\n    userid as i,\n    collect_list(\n      feature(movieid, rating)\n    ) as feature_vector\n  from\n    training\n  group by\n    userid\n),\npartial_result as ( -- launch DIMSUM in a MapReduce fashion\n  select\n    dimsum_mapper(f.feature_vector, m.mags, '-threshold 0.1 -disable_symmetric_output')\n      as (movieid, other, s)\n  from\n    movie_features f\n  left outer join movie_magnitude m\n),\nsimilarity as (\n    -- reduce (i.e., sum up) mappers' partial results \n    select\n      movieid, \n      other,\n      sum(s) as similarity\n    from \n      partial_result\n    group by\n      movieid, other\n),\ntopk as (\n  select\n    each_top_k( -- get top-10 nearest neighbors based on similarity score\n      10, movieid, similarity,\n      movieid, other -- output items\n    ) as (rank, similarity, movieid, other)\n  from (\n    select * from similarity\n    CLUSTER BY movieid\n  ) t\n)\nselect \n  movieid, other, similarity\nfrom \n  topk\n;\n\n\n\n\nmovieid\nother\nsimilarity\n\n\n\n\n1\n2095\n0.9377422722094696\n\n\n1\n231\n0.9316530366756418\n\n\n1\n1407\n0.9194745656079863\n\n\n1\n3442\n0.9133853300741587\n\n\n1\n1792\n0.9072960945403309\n\n\n...\n...\n...\n\n\n\nSince we set k=10, output has 10 most-similar movies per movieid.\n NoteSince we specified an option -disable_symmetric_output, output table does not contain inverted similarities such as , , , ...\nPrediction\nNext, we predict rating for unforeseen user-movie pairs based on the top-kkk similarities:\ndrop table if exists dimsum_prediction;\ncreate table dimsum_prediction\nas\nwith similarity_all as (\n  -- copy (i1, i2)'s similarity as (i2, i1)'s one\n  select movieid, other, similarity from dimsum_movie_similarity\n  union all\n  select other as movieid, movieid as other, similarity from dimsum_movie_similarity\n)\nselect \n    -- target user\n    t1.userid,\n\n    -- recommendation candidate\n    t2.movieid,\n\n    -- predicted rating: r_{u,i} = sum(s_{i,:} * r_{u,:}) / sum(s_{i,:})\n    sum(t1.rating * t2.similarity) / sum(t2.similarity) as rating\nfrom\n    training t1 -- r_{u,}\nleft join -- s_{i,}\n    similarity_all t2 \n    on t1.movieid = t2.other\nwhere\n    -- do not include movies that user already rated\n    NOT EXISTS (\n        SELECT a.movieid FROM training a\n        WHERE a.userid = t1.userid AND a.movieid = t2.movieid\n    )\ngroup by\n    t1.userid, t2.movieid\n;\n\nThis query computes estimated rating as follows:\n\n\n\nuserid\nmovieid\nrating\n\n\n\n\n1\n1000\n5.0\n\n\n1\n1010\n5.0\n\n\n1\n1012\n4.246349332667371\n\n\n1\n1013\n5.0\n\n\n1\n1014\n5.0\n\n\n...\n...\n...\n\n\n\nTheoretically, for the ttt-th nearest-neighborhood item τ(t)\\tau(t)τ(t), prediction can be done by top-kkk weighted sum of target user's historical ratings:\nru,i=∑t=1ksi,τ(t)⋅ru,τ(t)∑t=1ksi,τ(t),\nr_{u,i} = \\frac{\\sum^k_{t=1} s_{i,\\tau(t)} \\cdot r_{u,\\tau(t)} }{ \\sum^k_{t=1} s_{i,\\tau(t)} },\nr​u,i​​=​∑​t=1​k​​s​i,τ(t)​​​​∑​t=1​k​​s​i,τ(t)​​⋅r​u,τ(t)​​​​,\nwhere ru,ir_{u,i}r​u,i​​ is user uuu's rating for item (movie) iii, and si,js_{i,j}s​i,j​​ is similarity of iii-jjj (movieid-other) pair.\n CautionSince the number of similarities and users' past ratings are limited, we cannot say this output always contains prediction for every unforeseen user-item pairs; sometimes prediction for a specific user-item pair might be missing (i.e., NULL).\nIn fact, our goal is to make recommendation, but we can evaluate the intermediate result as a rating prediction problem:\nselect\n    mae(t1.rating, t2.rating) as mae,\n    rmse(t1.rating, t2.rating) as rmse\nfrom\n    testing t1 \nleft join\n    dimsum_prediction t2\n    on t1.movieid = t2.movieid\nwhere\n    t1.userid = t2.userid\n;\n\n\n\n\nmae\nrmse\n\n\n\n\n0.7308365821230256\n0.9594799959251938\n\n\n\nRating of the MovieLens data is in [1, 5] range, so this average errors are reasonable as a predictor.\nRecommendation\nBy using the prediction table, making recommendation for each user is straightforward:\ndrop table if exists dimsum_recommendation;\ncreate table dimsum_recommendation\nas\nselect\n  userid,\n  map_values(to_ordered_map(rating, movieid, true)) as rec_movies\nfrom\n  dimsum_prediction\ngroup by\n  userid\n;\n\n\n\n\nuserid\nrec_movies\n\n\n\n\n1\n[\"2590\",\"999\",\"372\",\"1380\",\"2078\",...]\n\n\n2\n[\"580\",\"945\",\"43\",\"36\",\"1704\",...]\n\n\n3\n[\"3744\",\"852\",\"1610\",\"3740\",\"2915\",...]\n\n\n4\n[\"3379\",\"923\",\"1997\",\"2194\",\"2944\",...]\n\n\n5\n[\"998\",\"101\",\"2696\",\"2968\",\"2275\",...]\n\n\n...\n...\n\n\n\n NoteSize of rec_movies varies depending on each user's training samples and what movies he/she already rated. \nEvaluation\nEventually, you can measure the quality of recommendation by using ranking measures:\nwith truth as (\n  select \n    userid, \n    map_values(to_ordered_map(rating, cast(movieid as string), true)) as truth\n  from\n    testing\n  group by\n    userid\n)\nselect \n  recall_at(t1.rec_movies, t2.truth, 10) as recall,\n  precision_at(t1.rec_movies, t2.truth, 10) as precision,\n  average_precision(t1.rec_movies, t2.truth) as average_precision,\n  auc(t1.rec_movies, t2.truth) as auc,\n  mrr(t1.rec_movies, t2.truth) as mrr,\n  ndcg(t1.rec_movies, t2.truth) as ndcg\nfrom\n  dimsum_recommendation t1\njoin\n  truth t2 on t1.userid = t2.userid\nwhere -- at least 10 recommended items are necessary to compute recall@10 and precision@10\n  size(t1.rec_movies) >= 10\n;\n\n\n\n\nmeasure\naccuracy\n\n\n\n\nRecall@10\n0.027033598585322713\n\n\nPrecision@10\n0.009001989389920506\n\n\nAverage Precision\n0.017363681149831108\n\n\nAUC\n0.5264553136097863\n\n\nMRR\n0.03507380742291146\n\n\nNDCG\n0.15787655209987522\n\n\n\nIf you set larger value to the DIMSUM's -threshold option, similarity will be more aggressively approximated. Consequently, while efficiency is improved, the accuracy is likely to be decreased.\n CautionBefore Hivemall v0.5-rc.1, recall_at() and precision_at() are respectively registered as recall() and precision(). However, since precision is a reserved keyword from Hive v2.2.0, we renamed the function names. If you are still using recall() and/or precision(), we strongly recommend you to use the latest version of Hivemall and replace them with the newer function names.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/movielens_mf.html":{"url":"recommend/movielens_mf.html","title":"Matrix Factorization","keywords":"","body":"\nThis page explains how to run matrix factorization on MovieLens 1M dataset.\n\n\n\nCalculate the mean rating in the training dataset\nSet variables (hyperparameters) for training\nTraining\nPredict\nEvaluate (computes MAE and RMSE)\nItem Recommendation\n\n\n\n\n\nCalculate the mean rating in the training dataset\nuse movielens;\n\nselect avg(rating) from training;\n\n\n3.593565\n\nSet variables (hyperparameters) for training\n-- mean rating\nset hivevar:mu=3.593565;\n-- number of factors\nset hivevar:factor=10;\n-- maximum number of training iterations\nset hivevar:iters=50;\n\nNote that there are no need to set an exact value for $mu. It actually works without setting $mu but recommended to set one for getting a better prediction.\nDue to a bug in Hive, do not issue comments in CLI.\nTraining\ncreate table sgd_model\nas\nselect\n  idx, \n  array_avg(u_rank) as Pu, \n  array_avg(m_rank) as Qi, \n  avg(u_bias) as Bu, \n  avg(m_bias) as Bi\nfrom (\n  select \n    train_mf_sgd(userid, movieid, rating, '-factor ${factor} -mu ${mu} -iter ${iters}') as (idx, u_rank, m_rank, u_bias, m_bias)\n  from \n    training\n) t\ngroup by idx;\n\n NoteHivemall also provides train_mf_adagrad for training using AdaGrad. \n-help option shows a complete list of hyperparameters.\nPredict\nselect\n  t2.actual,\n  mf_predict(t2.Pu, p2.Qi, t2.Bu, p2.Bi, ${mu}) as predicted\nfrom (\n  select\n    t1.userid, \n    t1.movieid,\n    t1.rating as actual,\n    p1.Pu,\n    p1.Bu\n  from\n    testing t1 LEFT OUTER JOIN sgd_model p1\n    ON (t1.userid = p1.idx) \n) t2 \nLEFT OUTER JOIN sgd_model p2\nON (t2.movieid = p2.idx);\n\nEvaluate (computes MAE and RMSE)\nselect\n  mae(predicted, actual) as mae,\n  rmse(predicted, actual) as rmse\nfrom (\n  select\n    t2.actual,\n    mf_predict(t2.Pu, p2.Qi, t2.Bu, p2.Bi, ${mu}) as predicted\n  from (\n    select\n      t1.userid, \n      t1.movieid,\n      t1.rating as actual,\n      p1.Pu,\n      p1.Bu\n    from\n      testing t1 LEFT OUTER JOIN sgd_model p1\n      ON (t1.userid = p1.idx) \n  ) t2 \n  LEFT OUTER JOIN sgd_model p2\n  ON (t2.movieid = p2.idx)\n) t;\n\n\n\n\nMAE\nRMSE\n\n\n\n\n0.6728969407733578\n0.8584162122694449\n\n\n\nItem Recommendation\nRecommend top-k movies that a user have not ever seen.\nset hivevar:userid=1;\nset hivevar:topk=5;\n\nselect\n  t1.movieid, \n  mf_predict(t2.Pu, t1.Qi, t2.Bu, t1.Bi, ${mu}) as predicted\nfrom (\n  select\n    idx movieid,\n    Qi, \n    Bi\n  from\n    sgd_model p\n  where\n    p.idx NOT IN \n      (select movieid from training where userid=${userid})\n) t1 CROSS JOIN (\n  select\n    Pu,\n    Bu\n  from \n    sgd_model\n  where\n    idx = ${userid}\n) t2\norder by\n  predicted DESC\nlimit ${topk};\n\n\n\n\nmovieid\npredicted\n\n\n\n\n318\n4.8051853\n\n\n2503\n4.788541\n\n\n53\n4.7518783\n\n\n904\n4.7463417\n\n\n953\n4.732769\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/movielens_fm.html":{"url":"recommend/movielens_fm.html","title":"Factorization Machine","keywords":"","body":"\nCaution: Factorization Machine is supported from Hivemall v0.4 or later.\n\n\n\nData preparation\nTraining\nHyperparamters for Training\nBuild a prediction model by Factorization Machine\nUsage of train_fm\n\n\nPrediction\nEvaluation\nFast Factorization Machines Training using Int Features\n\n\n\nData preparation\nFirst of all, please create ratings table described in this article.\nuse movielens;\n\nSET hivevar:seed=31;\n\nDROP TABLE ratings_fm;\nCREATE TABLE ratings_fm\nas\nselect\n    rowid() as rowid,\n    categorical_features(array(\"userid\",\"movieid\"), userid, movieid) \n      as features,\n    rating,\n    rand(${seed}) as rnd\nfrom\n    ratings\nCLUSTER BY rand(43); -- shuffle training input\n\nselect * from ratings_fm limit 2;\n\n\n\n\nrowid\nfeatures\nrating\nrnd\n\n\n\n\n1-383970\n[\"userid#2244\",\"movieid#1272\"]\n5\n0.33947035987020546\n\n\n1-557913\n[\"userid#3425\",\"movieid#2791\"]\n4\n0.12344886396954391\n\n\n\n-- use 80% for training\nDROP TABLE training_fm;\nCREATE TABLE training_fm\nas\nselect * from ratings_fm\norder by rnd DESC\nlimit 800000;\n\n-- use 20% for testing\nDROP TABLE testing_fm;\nCREATE TABLE testing_fm\nas\nselect * from ratings_fm\norder by rnd ASC\nlimit 200209;\n\n-- testing table for prediction\nCREATE OR REPLACE VIEW testing_fm_exploded\nas \nselect \n  rowid,\n  extract_feature(fv) as feature,\n  extract_weight(fv) as Xi,\n  rating\nfrom\n  testing_fm t1 LATERAL VIEW explode(add_bias(features)) t2 as fv;\n\nCaution: Don't forget to call add_bias in the above query. No need to call add_bias for preparing training data in Factorization Machines because it always considers it.\nTraining\nHyperparamters for Training\n-- number of factors\nset hivevar:factor=10;\n-- maximum number of training iterations\nset hivevar:iters=50;\n\nBuild a prediction model by Factorization Machine\ndrop table fm_model;\ncreate table fm_model\nas\nselect\n  feature,\n  avg(Wi) as Wi,\n  array_avg(Vif) as Vif\nfrom (\n  select \n    train_fm(features, rating, \"-factor ${factor} -iters ${iters} -eta 0.01\") \n        as (feature, Wi, Vif)\n  from \n    training_fm\n) t\ngroup by feature;\n\nNote: setting eta option is optional. However, setting -eta 0.01 usually works well.\nUsage of train_fm\nYou can get usages of train_fm by giving -help option as follows:\nselect \n  train_fm(features, rating, \"-help\") as (feature, Wi, Vif)\nfrom \n  training_fm\n\nusage: train_fm(array x, double y [, const string options]) -\n       Returns a prediction value [-adareg] [-c] [-cv_rate ]\n       [-disable_cv] [-eta ] [-eta0 ] [-f ] [-help]\n       [-init_v ] [-int_feature] [-iters ] [-lambda ] [-max\n       ] [-maxval ] [-min ] [-min_init_stddev ] [-p\n       ] [-power_t ] [-seed ] [-sigma ] [-t ]\n       [-va_ratio ] [-va_threshold ]\n -adareg,--adaptive_regularizaion             Whether to enable adaptive\n                                              regularization [default:\n                                              OFF]\n -c,--classification                          Act as classification\n -cv_rate,--convergence_rate             Threshold to determine\n                                              convergence [default: 0.005]\n -disable_cv,--disable_cvtest                 Whether to disable\n                                              convergence check [default:\n                                              OFF]\n -eta                                    The initial learning rate\n -eta0                                   The initial learning rate\n                                              [default 0.1]\n -f,--factor                             The number of the latent\n                                              variables [default: 10]\n -help                                        Show function help\n -init_v                                 Initialization strategy of\n                                              matrix V [random, gaussian]\n                                              (default: random)\n -int_feature,--feature_as_integer            Parse a feature as integer\n                                              [default: OFF, ON if -p\n                                              option is specified]\n -iters,--iterations                     The number of iterations\n                                              [default: 1]\n -lambda,--lambda0                       The initial lambda value for\n                                              regularization [default:\n                                              0.01]\n -max,--max_target                       The maximum value of target\n                                              variable\n -maxval,--max_init_value                The maximum initial value in\n                                              the matrix V [default: 1.0]\n -min,--min_target                       The minimum value of target\n                                              variable\n -min_init_stddev                        The minimum standard\n                                              deviation of initial matrix\n                                              V [default: 0.1]\n -p,--size_x                             The size of x\n -power_t                                The exponent for inverse\n                                              scaling learning rate\n                                              [default 0.1]\n -seed                                   Seed value [default: -1\n                                              (random)]\n -sigma                                  The standard deviation for\n                                              initializing V [default:\n                                              0.1]\n -t,--total_steps                        The total number of training\n                                              examples\n -va_ratio,--validation_ratio            Ratio of training data used\n                                              for validation [default:\n                                              0.05f]\n -va_threshold,--validation_threshold    Threshold to start\n                                              validation. At least N\n                                              training examples are used\n                                              before validation [default:\n                                              1000]\nPrediction\n-- workaround for a bug \n-- https://issues.apache.org/jira/browse/HIVE-11051\nset hive.mapjoin.optimized.hashtable=false;\n\ndrop table fm_predict;\ncreate table fm_predict\nas\nselect\n  t1.rowid,\n  fm_predict(p1.Wi, p1.Vif, t1.Xi) as predicted\nfrom \n  testing_fm_exploded t1\n  LEFT OUTER JOIN fm_model p1 ON (t1.feature = p1.feature)\ngroup by\n  t1.rowid;\n\nEvaluation\nselect\n  mae(p.predicted, rating) as mae,\n  rmse(p.predicted, rating) as rmse\nfrom\n  testing_fm as t\n  JOIN fm_predict as p on (t.rowid = p.rowid);\n\n\n0.6736798239047873 (mae)     0.858938110314545 (rmse)\n\nFast Factorization Machines Training using Int Features\nTraining of Factorization Machines (FM) can be done more efficietly, in term of speed, by using INT features.\nIn this section, we show how to run FM training by using int features, more specifically by using feature hashing.\nset hivevar:factor=10;\nset hivevar:iters=50;\n\ndrop table fm_model;\ncreate table fm_model\nas\nselect\n  feature,\n  avg(Wi) as Wi,\n  array_avg(Vif) as Vif\nfrom (\n  select \n    train_fm(feature_hashing(features), rating, \"-factor ${factor} -iters ${iters} -eta 0.01 -int_feature\")  -- internally use a sparse map\n -- train_fm(feature_hashing(features), rating, \"-factor ${factor} -iters ${iters} -eta 0.01 -int_feature -num_features 16777216\") -- internally use a dense array \n        as (feature, Wi, Vif)\n  from \n    training_fm\n) t\ngroup by feature;\n\nset hive.mapjoin.optimized.hashtable=false; -- workaround for https://issues.apache.org/jira/browse/HIVE-11051\n\nWITH predicted as (\n  select\n    t1.rowid,\n    fm_predict(p1.Wi, p1.Vif, t1.Xi) as predicted\n  from \n    testing_fm_exploded t1\n    LEFT OUTER JOIN fm_model p1 ON (feature_hashing(t1.feature) = p1.feature)\n  group by\n    t1.rowid\n)\nselect\n  mae(p.predicted, rating) as mae,\n  rmse(p.predicted, rating) as rmse\nfrom\n  testing_fm as t\n  JOIN predicted as p on (t.rowid = p.rowid);\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/movielens_slim.html":{"url":"recommend/movielens_slim.html","title":"SLIM for fast top-k Recommendation","keywords":"","body":"\nHivemall supports a neighborhood-learning scheme using SLIM. \nSLIM is a representative of neighborhood-learning recommendation algorithm introduced in the following paper:\n\nXia Ning and George Karypis, SLIM: Sparse Linear Methods for Top-N Recommender Systems, Proc. ICDM, 2011.\n\nCaution: SLIM is supported from Hivemall v0.5-rc.1 or later.\n\n\n\nSLIM optimization objective\nData preparation\nRating binarization\nSplitting dataset\nLeave-one-out cross validation\nKKK-hold corss validation\n\n\nPre-compute item-item similarity\nCreate training input tables\n\n\nTraining\nBuild a prediction model by SLIM\nUsage of train_slim\n\n\nPrediction and recommendation\nPredict unknown ratings of a user-item matrix\nTop-KKK item recommendation for each user\n\n\nEvaluation\nTop-KKK ranking measures: Hit-Rate@K, MRR@K, and Precision@K\nLeave-one-out result\nKKK-hold result\n\n\nRanking measures: MRR\nLeave-one-out result\nKKK-hold result\n\n\n\n\n\n\n\nSLIM optimization objective\nThe optimization objective of SLIM is similar to Elastic Net (L1+L2 regularization) with additional constraints as follows:\nminimizewj12∥rj−Rwj∥22+β2∥wj∥22+λ∥wj∥1subject towj≥0diag(W)=0\n\\begin{aligned}\n& \\;{\\tiny\\begin{matrix}\\\\ \\normalsize \\text{minimize} \\\\ ^{\\scriptsize w_{j}}\\end{matrix}}\\; \n&& \\frac{1}{2}\\Vert r_{j} - Rw_{j} \\Vert_2^2 + \\frac{\\beta}{2} \\Vert w_{j} \\Vert_2^2 + \\lambda \\Vert w_{j} \\Vert_1 \\\\\n& \\text{subject to} \n&& w_{j} \\geq 0 \\\\\n&&& diag(W)= 0\n\\end{aligned}\n​​​​​​​​minimize​​w​j​​​​​​​subject to​​​​​​​​​​2​​1​​∥r​j​​−Rw​j​​∥​2​2​​+​2​​β​​∥w​j​​∥​2​2​​+λ∥w​j​​∥​1​​​w​j​​≥0​diag(W)=0​​\nData preparation\nRating binarization\nIn this article, each user-movie matrix element is binarized to reduce training samples and consider only high rated movies whose rating is 4 or 5. So, every matrix element having a lower rating than 4 is not used for training.\nSET hivevar:seed=31;\n\nDROP TABLE ratings2;\nCREATE TABLE ratings2 as\nselect\n  rand(${seed}) as rnd,\n  userid,\n  movieid as itemid,\n  cast(1.0 as float) as rating -- double is also accepted\nfrom\n  ratings\nwhere rating >= 4.\n;\n\nrnd field is appended for each record to split ratings2 into training and testing data later.\nBinarization is an optional step, and you can use raw rating values to train a SLIM model.\nSplitting dataset\nTo evaluate a recommendation model, this tutorial uses two type cross validations:\n\nLeave-one-out cross validation\nKKK-hold cross validation\n\nThe former is used in the SLIM's paper and the latter is used in Mendeley's slide.\nLeave-one-out cross validation\nFor leave-one-out cross validation, the dataset is split into a training set and a testing set by randomly selecting one of the non-zero entries of each user and placing it into the testing set.\nIn the following query, the movie has the smallest rnd value is used as test data (testing table) per a user.\nAnd, the others are used as training data (training table).\nWhen we select slim's best hyperparameters, different test data is used in evaluation section several times.\nDROP TABLE testing;\nCREATE TABLE testing\nas\nWITH top_k as (\n  select\n     each_top_k(1, userid, rnd, userid, itemid, rating)\n      as (rank, rnd, userid, itemid, rating)\n  from (\n    select * from ratings2\n    CLUSTER BY userid\n  ) t\n)\nselect\n  userid, itemid, rating\nfrom\n  top_k\n;\n\nDROP TABLE training;\nCREATE TABLE training as\nselect\n  l.*\nfrom\n  ratings2 l\n  LEFT OUTER JOIN testing r ON (l.userid=r.userid and l.itemid=r.itemid)\nwhere\n  r.itemid IS NULL -- anti join\n;\n\nKKK-hold corss validation\nWhen K=2K=2K=2, the dataset is divided into training data and testing dataset.\nThe numbers of training and testing samples roughly equal.\nWhen we select slim's best hyperparameters, you'll first train a SLIM prediction model from training data and evaluate the prediction model by testing data.\nOptionally, you can switch training data with testing data and evaluate again.\nDROP TABLE testing;\nCREATE TABLE testing\nas\nselect * from ratings2\nwhere rnd >= 0.5\n;\n\nDROP TABLE training;\nCREATE TABLE training\nas\nselect * from ratings2\nwhere rnd \n NoteIn the following section excluding evaluation section,\nwe will show the example of queries and its results based on KKK-hold cross validation case.\nBut, this article's queries are valid for leave-one-out cross validation.\nPre-compute item-item similarity\nSLIM needs top-kkk most similar movies for each movie to the approximate user-item matrix.\nHere, we particularly focus on DIMSUM,\nan efficient and approximated similarity computation scheme.\nBecause we set k=20, the output has 20 most-similar movies per itemid.\nWe can adjust trade-off between training and prediction time and precision of matrix approximation by varying k.\nLarger k is the better approximation for raw user-item matrix, but training time and memory usage tend to increase.\nAs we explained in the general introduction of item-based CF,\nfollowing query finds top-kkk nearest-neighborhood movies for each movie:\nset hivevar:k=20;\n\nDROP TABLE knn_train;\nCREATE TABLE knn_train\nas\nwith item_magnitude as (\n  select\n    to_map(j, mag) as mags\n  from (\n    select\n      itemid as j,\n      l2_norm(rating) as mag\n    from\n      training\n    group by\n      itemid\n  ) t0\n),\nitem_features as (\n  select\n    userid as i,\n    collect_list(\n      feature(itemid, rating)\n    ) as feature_vector\n  from\n    training\n  group by\n    userid\n),\npartial_result as (\n  select\n    dimsum_mapper(f.feature_vector, m.mags, '-threshold 0.1 -int_feature')\n      as (itemid, other, s)\n  from\n    item_features f\n    CROSS JOIN item_magnitude m\n),\nsimilarity as (\n  select\n    itemid,\n    other,\n    sum(s) as similarity\n  from\n    partial_result\n  group by\n    itemid, other\n),\ntopk as (\n  select\n    each_top_k(\n      ${k}, itemid, similarity, -- use top k items\n      itemid, other\n    ) as (rank, similarity, itemid, other)\n  from (\n    select * from similarity\n    CLUSTER BY itemid\n  ) t\n)\nselect\n  itemid, other, similarity\nfrom\n  topk\n;\n\n\n\n\nitemid\nother\nsimilarity\n\n\n\n\n1\n3114\n0.28432244\n\n\n1\n1265\n0.25180137\n\n\n1\n2355\n0.24781825\n\n\n1\n2396\n0.24435896\n\n\n1\n588\n0.24359442\n\n\n...\n...\n...\n\n\n\n CautionTo run the query above, you may need to run the following statements:set hive.strict.checks.cartesian.product=false;\nset hive.mapred.mode=nonstrict;\n\nCreate training input tables\nHere, we prepare input tables for SLIM training.\nSLIM input consists of the following columns in slim_training_item:\n\ni: axis item id\nRi: the user-rating vector of the axis item iii expressed as map.\nknn_i: top-KKK similar item matrix of item iii; the user-item rating matrix is expressed as map>.\nj: an item id in knn_i.\nRj: the user-rating vector of the item jjj expressed as map.\n\nDROP TABLE item_matrix;\nCREATE table item_matrix as\nselect\n  itemid as i,\n  to_map(userid, rating) as R_i\nfrom\n  training\ngroup by\n  itemid;\n\n-- Temporary set off map join because the following query does not work well for map join\nset hive.auto.convert.join=false;\n-- set mapred.reduce.tasks=64;\n\n-- Create SLIM input features\nDROP TABLE slim_training_item;\nCREATE TABLE slim_training_item as\nWITH knn_item_user_matrix as (\n  select\n    l.itemid,\n    r.userid,\n    to_map(l.other, r.rating) ratings\n  from\n    knn_train l\n    JOIN training r ON (l.other = r.itemid)\n  group by\n    l.itemid, r.userid\n),\nknn_item_matrix as (\n  select\n    itemid as i,\n    to_map(userid, ratings) as KNN_i -- map>\n  from\n    knn_item_user_matrix\n  group by\n    itemid\n)\nselect\n  l.itemid as i,\n  r1.R_i,\n  r2.knn_i,\n  l.other as j,\n  r3.R_i as R_j\nfrom\n  knn_train l\n  JOIN item_matrix r1 ON (l.itemid = r1.i)\n  JOIN knn_item_matrix r2 ON (l.itemid = r2.i)\n  JOIN item_matrix r3 ON (l.other = r3.i)\n;\n\n-- set to the default value\nset hive.auto.convert.join=true;\n\nTraining\nBuild a prediction model by SLIM\ntrain_slim function outputs the nonzero elements of an item-item matrix.\nFor item recommendation or prediction, this matrix is stored into the table named slim_model.\nDROP TABLE slim_model;\nCREATE TABLE slim_model as\nselect\n  i, nn, avg(w) as w\nfrom (\n  select\n    train_slim(i, r_i, knn_i, j, r_j) as (i, nn, w)\n  from (\n    select * from slim_training_item\n    CLUSTER BY i\n  ) t1\n) t2\ngroup by i, nn\n;\n\nUsage of train_slim\nYou can obtain information about train_slim function and its arguments by giving -help option as follows:\nselect train_slim(\"-help\");\n\nusage: train_slim( int i, map r_i, map> topKRatesOfI,\n       int j, map r_j [, constant string options])\n       - Returns row index, column index and non-zero weight value of prediction model\n       [-cv_rate ] [-disable_cv] [-help] [-iters ] [-l1 ] [-l2 ]\n -cv_rate,--convergence_rate    Threshold to determine convergence\n                                     [default: 0.005]\n -disable_cv,--disable_cvtest        Whether to disable convergence check\n                                     [default: enabled]\n -help                               Show function help\n -iters,--iterations            The number of iterations for\n                                     coordinate descent [default: 30]\n -l1,--l1coefficient            Coefficient for l1 regularizer\n                                     [default: 0.001]\n -l2,--l2coefficient            Coefficient for l2 regularizer\n                                     [default: 0.0005]\n\nPrediction and recommendation\nHere, we predict ratng values of binarized user-item rating matrix of testing dataset based on ratings in training dataset.\nBased on predicted rating scores, we can recommend top-k items for each user that he or she will be likely to put high scores.\nPredict unknown ratings of a user-item matrix\nBased on known ratings and SLIM weight matrix, we predict unknown ratings in the user-item matrix.\nSLIM predicts ratings of user-item pairs based on top-KKK similar items.\nThe predict_pair table represents candidates for recommended user-movie pairs, excluding known ratings in the training dataset.\nCREATE OR REPLACE VIEW predict_pair \nas\nWITH testing_users as (\n  select DISTINCT(userid) as userid from testing\n),\ntraining_items as (\n  select DISTINCT(itemid) as itemid from training\n),\nuser_items as (\n  select\n    l.userid,\n    r.itemid\n  from\n    testing_users l\n    CROSS JOIN training_items r\n)\nselect\n  l.userid,\n  l.itemid\nfrom\n  user_items l\n  LEFT OUTER JOIN training r ON (l.userid=r.userid and l.itemid=r.itemid)\nwhere\n  r.itemid IS NULL -- anti join\n;\n\n-- optionally set the mean/default value of prediction\nset hivevar:mu=0.0;\n\nDROP TABLE predicted;\nCREATE TABLE predicted \nas\nWITH knn_exploded as (\n  select\n    l.userid  as u,\n    l.itemid as i, -- axis\n    r1.other  as k, -- other\n    r2.rating as r_uk\n  from\n    predict_pair l\n    LEFT OUTER JOIN knn_train r1\n      ON (r1.itemid = l.itemid)\n    JOIN training r2\n      ON (r2.userid = l.userid and r2.itemid = r1.other)\n)\nselect\n  l.u as userid,\n  l.i as itemid,\n  coalesce(sum(l.r_uk * r.w), ${mu}) as predicted\n  -- coalesce(sum(l.r_uk * r.w)) as predicted\nfrom\n  knn_exploded l\n  LEFT OUTER JOIN slim_model r ON (l.i = r.i and l.k = r.nn)\ngroup by\n  l.u, l.i\n;\n\n CautionWhen kkk is small, slim predicted value may be null. Then, $mu replaces null value.\nThe mean value of item ratings is a good choice for $mu.\nTop-KKK item recommendation for each user\nHere, we recommend top-3 items for each user based on predicted values.\nSET hivevar:k=3;\n\nDROP TABLE IF EXISTS recommend;\nCREATE TABLE recommend\nas\nWITH top_n as (\n  select\n     each_top_k(${k}, userid, predicted, userid, itemid)\n      as (rank, predicted, userid, itemid)\n  from (\n    select * from predicted\n    CLUSTER BY userid\n  ) t\n)\nselect\n  userid,\n  collect_list(itemid) as items\nfrom\n  top_n\ngroup by\n  userid\n;\n\nselect * from recommend limit 5;\n\n\n\n\nuserid\nitems\n\n\n\n\n1\n[364,594,2081]\n\n\n2\n[2028,3256,589]\n\n\n3\n[260,1291,2791]\n\n\n4\n[1196,1200,1210]\n\n\n5\n[3813,1366,89]\n\n\n...\n...\n\n\n\nEvaluation\nTop-KKK ranking measures: Hit-Rate@K, MRR@K, and Precision@K\nIn this section, Hit-Rate@k, MRR@k, and Precision@k are computed based on recommended items.\nPrecision@K is a good evaluation measure for KKK-hold cross validation.\nOn the other hand, Hit-Rate and Mean Reciprocal Rank (i.e., Average Reciprocal Hit-Rate) are good evaluation measures for leave-one-out cross validation.\nSET hivevar:n=10;\n\nWITH top_k as (\n  select\n    each_top_k(${n}, userid, predicted, userid, itemid)\n      as (rank, predicted, userid, itemid)\n  from (\n    select * from predicted\n    CLUSTER BY userid\n  ) t\n),\nrec_items as (\n  select\n    userid,\n    collect_list(itemid) as items\n  from\n    top_k\n  group by\n    userid\n),\nground_truth as (\n  select\n    userid,\n    collect_list(itemid) as truth\n  from\n    testing\n  group by\n    userid\n)\nselect\n  hitrate(l.items, r.truth) as hitrate,\n  mrr(l.items, r.truth) as mrr,\n  precision_at(l.items, r.truth) as prec\nfrom\n  rec_items l\n  join ground_truth r on (l.userid=r.userid)\n;\n\nLeave-one-out result\n\n\n\nhitrate\nmrr\nprec\n\n\n\n\n0.21517309922146763\n0.09377752536606271\n0.021517309922146725\n\n\n\nHit Rate and MRR are similar to ones in the result of Table II in Slim's paper\nKKK-hold result\n\n\n\nhitrate\nmrr\nprec\n\n\n\n\n0.8952775476387739\n1.1751514972186057\n0.3564871582435789\n\n\n\nPrecision value is similar to the result of Mendeley's slide.\nRanking measures: MRR\nIn this example, whole recommended items are evaluated using MRR.\nWITH rec_items as (\n  select\n    userid,\n    to_ordered_list(itemid, predicted, '-reverse') as items\n  from\n    predicted\n  group by\n    userid\n),\nground_truth as (\n  select\n    userid,\n    collect_list(itemid) as truth\n  from\n    testing\n  group by\n    userid\n)\nselect\n  mrr(l.items, r.truth) as mrr\nfrom\n  rec_items l\n  join ground_truth r on (l.userid=r.userid)\n;\n\nLeave-one-out result\n\n\n\nmrr\n\n\n\n\n0.10782647321821472\n\n\n\nKKK-hold result\n\n\n\nmrr\n\n\n\n\n0.6179983058881773\n\n\n\nThis MRR value is similar to one in the Mendeley's slide.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"recommend/movielens_cv.html":{"url":"recommend/movielens_cv.html","title":"10-fold Cross Validation (Matrix Factorization)","keywords":"","body":"\nCross-validation is a model validation technique for assessing how a prediction model will generalize to an independent data set. This example shows a way to perform k-fold cross validation to evaluate prediction performance.\nCaution: Matrix factorization is supported in Hivemall v0.3 or later.\nData set creating for 10-folds cross validation.\nuse movielens;\n\nset hivevar:kfold=10;\nset hivevar:seed=31;\n\n-- Adding group id (gid) to each training instance\ndrop table ratings_groupded;\ncreate table ratings_groupded\nas\nselect\n  floor(rand(${seed})*${kfold}) gid, -- generates group id ranging from 1 to 10\n  userid, \n  movieid, \n  rating\nfrom\n  ratings\ncluster by gid, rand(${seed});\n\nSet training hyperparameters\n-- latent factors\nset hivevar:factor=10;\n-- maximum number of iterations\nset hivevar:iters=50;\n-- regularization parameter\nset hivevar:lambda=0.05;\n-- learning rate\nset hivevar:eta=0.005;\n-- conversion rate (if changes between iterations became less or equals to ${cv_rate}, the training will stop)\nset hivevar:cv_rate=0.001;\n\nDue to a bug in Hive, do not issue comments in CLI.\nselect avg(rating) from ratings;\n\n\n3.581564453029317\n\n-- mean rating value (Optional but recommended to set ${mu})\nset hivevar:mu=3.581564453029317;\n\nNote that it is not necessary to set an exact value for ${mu}.\nSQL-generation for 10-folds cross validation\nRun generate_cv.sh and create generate_cv.sql.\nThen, issue SQL queies in generate_cv.sql to get MAE/RMSE.\n\n0.6695442192077673 (MAE)\n0.8502739040257945 (RMSE)\n\nWe recommend to use Tez for running queries having many stages.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"anomaly/lof.html":{"url":"anomaly/lof.html","title":"Outlier Detection using Local Outlier Factor (LOF)","keywords":"","body":"\nThis article introduces how to find outliers using Local Outlier Detection (LOF) on Hivemall.\n\n\n\nData Preparation\nApply Data Normalization\n\n\nOutlier Detection using Local Outlier Facotor (LOF)\nParallelize Top-k computation\n\n\n\nData Preparation\ncreate database lof;\nuse lof;\n\ncreate external table hundred_balls (\n  rowid int, \n  weight double,\n  specific_heat double,\n  reflectance double\n)\nROW FORMAT DELIMITED\n  FIELDS TERMINATED BY ' '\nSTORED AS TEXTFILE LOCATION '/dataset/lof/hundred_balls';\n\nDownload hundred_balls.txt that is originally provides in this article.\nIn this example, Rowid 87 is apparently an outlier.\nawk '{FS=\" \"; OFS=\" \"; print NR,$0}' hundred_balls.txt | \\\nhadoop fs -put - /dataset/lof/hundred_balls/hundred_balls.txt\n\ncreate table train\nas\nselect rowid, array(concat(\"weight:\", weight), concat(\"specific_heat:\", specific_heat), concat(\"reflectance:\", reflectance)) as features\nfrom hundred_balls;\n\nApply Data Normalization\ncreate table train_normalized\nas\nWITH fv as (\nselect \n  rowid, \n  extract_feature(feature) as feature,\n  extract_weight(feature) as value\nfrom \n  train \n  LATERAL VIEW explode(features) exploded AS feature\n), \nstats as (\nselect\n  feature,\n  -- avg(value) as mean, stddev_pop(value) as stddev\n  min(value) as min, max(value) as max\nfrom\n  fv\ngroup by\n  feature\n), \nnorm as (\nselect \n  rowid, \n  t1.feature, \n  -- zscore(t1.value, t2.mean, t2.stddev) as zscore\n  rescale(t1.value, t2.min, t2.max) as minmax\nfrom \n  fv t1 JOIN\n  stats t2 ON (t1.feature = t2.feature) \n),\nnorm_fv as (\nselect\n  rowid, \n  -- concat(feature, \":\", zscore) as feature\n  concat(feature, \":\", minmax) as feature\nfrom\n  norm\n)\nselect \n  rowid, \n  collect_list(feature) as features\nfrom\n  norm_fv\ngroup by\n  rowid\n;\n\nhive> select * from train_normalized limit 3;\n\n1       [\"reflectance:0.5252967\",\"specific_heat:0.19863537\",\"weight:0.0\"]\n2       [\"reflectance:0.5950446\",\"specific_heat:0.09166764\",\"weight:0.052084323\"]\n3       [\"reflectance:0.6797837\",\"specific_heat:0.12567581\",\"weight:0.13255163\"]\nOutlier Detection using Local Outlier Facotor (LOF)\n-- workaround to deal with a bug in Hive/Tez\n-- https://issues.apache.org/jira/browse/HIVE-10729\n-- set hive.auto.convert.join=false;\nset hive.mapjoin.optimized.hashtable=false;\n\n-- parameter of LoF\nset hivevar:k=12;\n\n-- find topk outliers\nset hivevar:topk=3;\n\ncreate table list_neighbours\nas\nselect\n  each_top_k(\n    -${k}, t1.rowid, euclid_distance(t1.features, t2.features), \n    t1.rowid, \n    t2.rowid\n  ) as (rank, distance, target, neighbour)\nfrom \n  train_normalized t1\n  LEFT OUTER JOIN train_normalized t2\nwhere\n  t1.rowid != t2.rowid\n;\n\n Cautionlist_neighbours table SHOULD be created because list_neighbours is used multiple times.\nParallelize Top-k computation\n InfoTo parallelize a top-k computation, break LEFT-hand table into piece as describe in this page.\nWITH k_distance as (\nselect\n  target, \n  max(distance) as k_distance\nfrom\n  list_neighbours\ngroup by\n  target\n), \nreach_distance as (\nselect\n  t1.target,\n  max2(t2.k_distance, t1.distance) as reach_distance\nfrom\n  list_neighbours t1 JOIN \n  k_distance t2 ON (t1.neighbour = t2.target)\n), \nlrd as (\nselect\n  target,   \n  1.0 / avg(reach_distance) as lrd\nfrom\n  reach_distance\ngroup by\n  target\n), \nneighbours_lrd as (\nselect\n  t1.target, \n  t2.lrd\nfrom\n  list_neighbours t1 JOIN\n  lrd t2 on (t1.neighbour = t2.target)\n)\nselect\n  t1.target, \n  sum(t2.lrd / t1.lrd) / count(1) as lof\nfrom\n  lrd t1 JOIN\n  neighbours_lrd t2 on (t1.target = t2.target)\ngroup by\n  t1.target\norder by lof desc\nlimit ${topk};\n\n> 87      3.031143749957831\n> 16      1.9755564408378874\n> 1       1.8415763570939774\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"anomaly/sst.html":{"url":"anomaly/sst.html","title":"Change-Point Detection using Singular Spectrum Transformation (SST)","keywords":"","body":"\nThis page introduces how to find change-points using Singular Spectrum Transformation (SST) on Hivemall. The following papers describe the details of this technique:\n\nT. Idé and K. Inoue. Knowledge Discovery from Heterogeneous Dynamic Systems using Change-Point Correlations. SDM'05.\nT. Idé and K. Tsuda. Change-Point Detection using Krylov Subspace Learning. SDM'07.\n\n\n\n\nOutlier vs Change-Point\nData Preparation\nGet Twitter's data\nImporting data as a Hive table\nCreate a Hive table\nLoad data into the table\n\n\n\n\nChange-Point Detection using SST\n\n\n\nOutlier vs Change-Point\nIt is important that anomaly detectors are generally categorized into outlier and change-point detectors. Outliers are some spiky \"local\" data points which are suddenly observed in a series of normal samples, and Local Outlier Detection is an algorithm to detect outliers. On the other hand, change-points indicate \"global\" change on a wider scale in terms of characteristics of data points.\nIn this page, we specially focus on change-point detection. More concretely, the following sections introduce a way to detect change-points on Hivemall, by using a specific technique named Singular Spectrum Transformation (SST).\nData Preparation\nGet Twitter's data\nWe use time series data points provided by Twitter in the following article: Introducing practical and robust anomaly detection in a time series. In fact, the dataset is originally created for R, but we can get CSV version of the same data from HERE.\nOnce you uncompressed the downloaded .gz file, you can see a CSV file:\n$ head twitter.csv\n182.478\n176.231\n183.917\n177.798\n165.469\n181.878\n184.502\n183.303\n177.578\n171.641\nThese 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:\n$ awk '{printf \"%d#%s\\n\", NR, $0}'  twitter.t\n$ head twitter.t\n1#182.478\n2#176.231\n3#183.917\n4#177.798\n5#165.469\n6#181.878\n7#184.502\n8#183.303\n9#177.578\n10#171.641\nNow, Hive can understand sequence of the samples by just looking dummy timestamp.\nImporting data as a Hive table\nCreate a Hive table\nYou first need to launch a Hive console and run the following operations:\ncreate database twitter;\nuse twitter;\nCREATE EXTERNAL TABLE timeseries (\n  num INT,\n  value DOUBLE\n) ROW FORMAT DELIMITED\nFIELDS TERMINATED BY '#'\nSTORED AS TEXTFILE\nLOCATION '/dataset/twitter/timeseries';\n\nLoad data into the table\nNext, the .t file we have generated before can be loaded to the table by:\n$ hadoop fs -put twitter.t /dataset/twitter/timeseries\ntimeseries table in twitter database should be:\n\n\n\nnum\nvalue\n\n\n\n\n1\n182.478\n\n\n2\n176.231\n\n\n3\n183.917\n\n\n4\n177.798\n\n\n5\n165.469\n\n\n...\n...\n\n\n\nChange-Point Detection using SST\nWe are now ready to detect change-points. A UDF sst() takes a double value as the first argument, and you can set options in the second argument. \nWhat the following query does is to detect change-points from a value column in the timeseries table. An option \"-threshold 0.005\" means that a data point is detected as a change-point if its score is greater than 0.005.\nuse twitter;\nSELECT\n  num,\n  sst(value, \"-threshold 0.005\") AS result\nFROM\n  timeseries\nORDER BY num ASC\n;\n\nFor instance, partial outputs obtained as a result of this query are:\n\n\n\nnum\nresult\n\n\n\n\n...\n...\n\n\n7551\n{\"changepoint_score\":0.00453049288071683,\"is_changepoint\":false}\n\n\n7552\n{\"changepoint_score\":0.004711244102524104,\"is_changepoint\":false}\n\n\n7553\n{\"changepoint_score\":0.004814871928978115,\"is_changepoint\":false}\n\n\n7554\n{\"changepoint_score\":0.004968089640799422,\"is_changepoint\":false}\n\n\n7555\n{\"changepoint_score\":0.005709056330104878,\"is_changepoint\":true}\n\n\n7556\n{\"changepoint_score\":0.0044279766655132,\"is_changepoint\":false}\n\n\n7557\n{\"changepoint_score\":0.0034694956722586268,\"is_changepoint\":false}\n\n\n7558\n{\"changepoint_score\":0.002549056569322694,\"is_changepoint\":false}\n\n\n7559\n{\"changepoint_score\":0.0017395109108403473,\"is_changepoint\":false}\n\n\n7560\n{\"changepoint_score\":0.0010629833145070489,\"is_changepoint\":false}\n\n\n...\n...\n\n\n\nObviously, the 7555-th sample is detected as a change-point in this example.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"anomaly/changefinder.html":{"url":"anomaly/changefinder.html","title":"ChangeFinder: Detecting Outlier and Change-Point Simultaneously","keywords":"","body":"\nIn a context of anomaly detection, there are two types of anomalies, outlier and change-point, as discussed in this section. Hivemall has two functions which respectively detect outliers and change-points; the former is Local Outlier Detection, and the latter is Singular Spectrum Transformation.\nIn some cases, we might want to detect outlier and change-point simultaneously in order to figure out characteristics of a time series both in a local and global scale. ChangeFinder is an anomaly detection technique which enables us to detect both of outliers and change-points in a single framework. A key reference for the technique is:\n\nK. Yamanishi and J. Takeuchi. A Unifying Framework for Detecting Outliers and Change Points from Non-Stationary Time Series Data. KDD'02.\n\n\n\n\nOutlier and Change-Point Detection using ChangeFinder\nChangeFinder for Multi-Dimensional Data\nData preparation\nDetecting outliers and change-points of the 5-dimensional data\n\n\n\n\n\nOutlier and Change-Point Detection using ChangeFinder\nBy using Twitter's time series data we prepared in this section, let us try to use ChangeFinder on Hivemall.\nuse twitter;\nA function changefinder() can be used in a very similar way to sst(), a UDF for Singular Spectrum Transformation. The following query detects outliers and change-points with different thresholds:\nSELECT\n  num,\n  changefinder(value, \"-outlier_threshold 0.03 -changepoint_threshold 0.0035\") AS result\nFROM\n  timeseries\nORDER BY num ASC\n;\n\nAs a consequence, finding outliers and change-points in the data points should be easy:\n\n\n\nnum\nresult\n\n\n\n\n...\n...\n\n\n16\n{\"outlier_score\":0.051287243859365894,\"changepoint_score\":0.003292139657059704,\"is_outlier\":true,\"is_changepoint\":false}\n\n\n17\n{\"outlier_score\":0.03994335565212781,\"changepoint_score\":0.003484242549446824,\"is_outlier\":true,\"is_changepoint\":false}\n\n\n18\n{\"outlier_score\":0.9153515196592132,\"changepoint_score\":0.0036439645550477373,\"is_outlier\":true,\"is_changepoint\":true}\n\n\n19\n{\"outlier_score\":0.03940593403992665,\"changepoint_score\":0.0035825157392152134,\"is_outlier\":true,\"is_changepoint\":true}\n\n\n20\n{\"outlier_score\":0.27172093630215555,\"changepoint_score\":0.003542822324886785,\"is_outlier\":true,\"is_changepoint\":true}\n\n\n21\n{\"outlier_score\":0.006784031454620809,\"changepoint_score\":0.0035029441620275975,\"is_outlier\":false,\"is_changepoint\":true}\n\n\n22\n{\"outlier_score\":0.011838969816513334,\"changepoint_score\":0.003519599336202336,\"is_outlier\":false,\"is_changepoint\":true}\n\n\n23\n{\"outlier_score\":0.09609857927656007,\"changepoint_score\":0.003478729798944702,\"is_outlier\":true,\"is_changepoint\":false}\n\n\n24\n{\"outlier_score\":0.23927000145081978,\"changepoint_score\":0.0034338476757061237,\"is_outlier\":true,\"is_changepoint\":false}\n\n\n25\n{\"outlier_score\":0.04645945042821564,\"changepoint_score\":0.0034052091926036914,\"is_outlier\":true,\"is_changepoint\":false}\n\n\n...\n...\n\n\n\nChangeFinder for Multi-Dimensional Data\nChangeFinder additionally supports multi-dimensional data. Let us try this on synthetic data.\nData preparation\nYou first need to get synthetic 5-dimensional data from HERE and uncompress to a synthetic5d.t file:\n$ head synthetic5d.t\n0#71.45185411564131#54.456141290891466#71.78932846605129#76.73002575911214#81.71265594077099\n1#58.374230566196786#57.9798651697631#75.65793151143754#73.76101930504493#69.50315805346253\n2#66.3595943896099#52.866595973073295#76.7987325026338#78.95890786682095#74.67527753118893\n3#58.242560151043236#52.449574430621226#73.20383710416358#77.81502394558085#76.59077723631032\n4#55.89878019680371#52.69611781315756#75.02482987204824#74.11154526135637#75.86881583921179\n5#56.93554246767561#56.55687136423391#74.4056583421317#73.82419594611444#71.3017150863033\n6#65.55704393868689#52.136347983404974#71.14213602046532#72.87394198561904#73.40278960429114\n7#56.65735280596217#57.293605941063035#75.36713340281246#80.70254745535183#75.32423746923857\n8#61.22095211566127#53.47603728473668#77.48215321523912#80.7760107465893#74.43951386292905\n9#52.47574856682803#52.03250504263378#77.59550963025158#76.16623830860391#76.98394610743863\nThe first column indicates a dummy timestamp, and the following four columns are values in each dimension. \nSecond, the following Hive operations create a Hive table for the data:\ncreate database synthetic;\nuse synthetic;\nCREATE EXTERNAL TABLE synthetic5d (\n    num INT,\n  value1 DOUBLE,\n    value2 DOUBLE,\n    value3 DOUBLE,\n    value4 DOUBLE,\n    value5 DOUBLE\n) ROW FORMAT DELIMITED\nFIELDS TERMINATED BY '#'\nSTORED AS TEXTFILE\nLOCATION '/dataset/synthetic/synthetic5d';\n\nFinally, you can load the synthetic data to the table by:\n$ hadoop fs -put synthetic5d.t /dataset/synthetic/synthetic5d\nDetecting outliers and change-points of the 5-dimensional data\nUsing changefinder() for multi-dimensional data requires us to pass the first argument as an array. In our case, the data is 5-dimensional, so the first argument should be an array with 5 elements. Except for that point, basic usage of the function is same as the previous 1-dimensional example:\nSELECT\n  num,\n  changefinder(array(value1, value2, value3, value4, value5), \n               \"-outlier_threshold 0.015 -changepoint_threshold 0.0045\") AS result\nFROM\n  synthetic5d\nORDER BY num ASC\n;\n\nOutput might be:\n\n\n\nnum\nresult\n\n\n\n\n...\n...\n\n\n90\n{\"outlier_score\":0.014014718350674471,\"changepoint_score\":0.004520174906936474,\"is_outlier\":false,\"is_changepoint\":true}\n\n\n91\n{\"outlier_score\":0.013145554693405614,\"changepoint_score\":0.004480713237042799,\"is_outlier\":false,\"is_changepoint\":false}\n\n\n92\n{\"outlier_score\":0.011631759675989617,\"changepoint_score\":0.004442031415725316,\"is_outlier\":false,\"is_changepoint\":false}\n\n\n93\n{\"outlier_score\":0.012140065235943798,\"changepoint_score\":0.004404170732687428,\"is_outlier\":false,\"is_changepoint\":false}\n\n\n94\n{\"outlier_score\":0.012555903663657997,\"changepoint_score\":0.0043670553008087355,\"is_outlier\":false,\"is_changepoint\":false}\n\n\n95\n{\"outlier_score\":0.013503247137325314,\"changepoint_score\":0.0043306667027628466,\"is_outlier\":false,\"is_changepoint\":false}\n\n\n96\n{\"outlier_score\":0.013896893553710932,\"changepoint_score\":0.004294969164345527,\"is_outlier\":false,\"is_changepoint\":false}\n\n\n97\n{\"outlier_score\":0.01322874844578159,\"changepoint_score\":0.004259994590721001,\"is_outlier\":false,\"is_changepoint\":false}\n\n\n98\n{\"outlier_score\":0.019383618511936707,\"changepoint_score\":0.004225604978710543,\"is_outlier\":true,\"is_changepoint\":false}\n\n\n99\n{\"outlier_score\":0.01121758589038846,\"changepoint_score\":0.004191881992962213,\"is_outlier\":false,\"is_changepoint\":false}\n\n\n...\n...\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"clustering/lda.html":{"url":"clustering/lda.html","title":"Latent Dirichlet Allocation","keywords":"","body":"\nTopic modeling is a way to analyze massive documents by clustering them into some topics. In particular, Latent Dirichlet Allocation (LDA) is one of the most popular topic modeling techniques; papers introduce the method are as follows:\n\nD. M. Blei, et al. Latent Dirichlet Allocation. Journal of Machine Learning Research 3, pp. 993-1022, 2003.\nM. D. Hoffman, et al. Online Learning for Latent Dirichlet Allocation. NIPS 2010.\n\nHivemall enables you to analyze your data such as, but not limited to, documents based on LDA. This page gives usage instructions of the feature.\n\n\n\nPrepare document data\nBuilding Topic Models and Finding Topic Words\nPredicting Topic Assignments of Documents\n\n\n\n NoteThis feature is supported from Hivemall v0.5-rc.1 or later.\nPrepare document data\nAssume that we already have a table docs which contains many documents as string format:\n\n\n\ndocid\ndoc\n\n\n\n\n1\n\"Fruits and vegetables are healthy.\"\n\n\n2\n\"I like apples, oranges, and avocados. I do not like the flu or colds.\"\n\n\n...\n...\n\n\n\nHivemall has several functions which are particularly useful for text processing. More specifically, by using tokenize() and is_stopword(), you can immediately convert the documents to bag-of-words-like format:\nwith word_counts as (\n  select\n    docid,\n    feature(word, count(word)) as word_count\n  from \n    docs t1 \n    LATERAL VIEW explode(tokenize(doc, true)) t2 as word\n  where\n    not is_stopword(word)\n  group by\n    docid, word\n)\nselect docid, collect_list(word_count) as features\nfrom word_counts\ngroup by docid\n;\n\n\n\n\ndocid\nfeatures\n\n\n\n\n1\n[\"fruits:1\",\"healthy:1\",\"vegetables:1\"]\n\n\n2\n[\"apples:1\",\"avocados:1\",\"colds:1\",\"flu:1\",\"like:2\",\"oranges:1\"]\n\n\n\n NoteIt should be noted that, as long as your data can be represented as the feature format, LDA can be applied for arbitrary data as a generic clustering technique.\nBuilding Topic Models and Finding Topic Words\nEach feature vector is input to the train_lda() function:\nwith word_counts as (\n  select\n    docid,\n    feature(word, count(word)) as word_count\n  from docs t1 LATERAL VIEW explode(tokenize(doc, true)) t2 as word\n  where\n    not is_stopword(word)\n  group by\n    docid, word\n),\ninput as (\n  select docid, collect_list(word_count) as features\n  from word_counts\n  group by docid\n)\nselect\n  train_lda(features, '-topics 2 -iter 20') as (label, word, lambda)\nfrom\n  input\n;\n\nHere, an option -topics 2 specifies the number of topics we assume in the set of documents.\nNotice that order by docid ensures building a LDA model precisely in a single node. In case that you like to launch train_lda in parallel, following query hopefully returns similar (but might be slightly approximated) result:\nwith word_counts as (\n  -- same as above\n),\ninput as (\n  select docid, collect_list(f) as features\n  from word_counts\n  group by docid\n)\nselect\n  label, word, avg(lambda) as lambda\nfrom (\n  select\n    train_lda(features, '-topics 2 -iter 20') as (label, word, lambda)\n  from \n    input\n) t2\ngroup by label, word\n-- order by lambda desc -- ordering is optional\n;\n\nEventually, a new table lda_model is generated as shown below:\n\n\n\nlabel\nword\nlambda\n\n\n\n\n0\nfruits\n0.33372128\n\n\n0\nvegetables\n0.33272517\n\n\n0\nhealthy\n0.33246377\n\n\n0\nflu\n2.3617347E-4\n\n\n0\napples\n2.1898883E-4\n\n\n0\noranges\n1.8161473E-4\n\n\n0\nlike\n1.7666373E-4\n\n\n0\navocados\n1.726186E-4\n\n\n0\ncolds\n1.037139E-4\n\n\n1\ncolds\n0.16622013\n\n\n1\navocados\n0.16618845\n\n\n1\noranges\n0.1661859\n\n\n1\nlike\n0.16618414\n\n\n1\napples\n0.16616651\n\n\n1\nflu\n0.16615893\n\n\n1\nhealthy\n0.0012059759\n\n\n1\nvegetables\n0.0010818697\n\n\n1\nfruits\n6.080827E-4\n\n\n\nIn the table, label indicates a topic index, and lambda is a value which represents how each word is likely to characterize a topic. That is, we can say that, in terms of lambda, top-N words are the topic words of a topic.\nObviously, we can observe that topic 0 corresponds to document 1, and topic 1 represents words in document 2.\nPredicting Topic Assignments of Documents\nOnce you have constructed topic models as described before, a function lda_predict() allows you to predict topic assignments of documents.\nFor example, if we consider the docs table, the exactly same set of documents as used for training, probability that a document is assigned to a topic can be computed by:\nwith test as (\n  select\n    docid,\n    word,\n    count(word) as value\n  from\n    docs t1\n    LATERAL VIEW explode(tokenize(doc, true)) t2 as word\n  where\n    not is_stopword(word)\n  group by\n    docid, word\n)\nselect\n  t.docid,\n  lda_predict(t.word, t.value, m.label, m.lambda, '-topics 2') as probabilities\nfrom\n  test t\n  JOIN lda_model m ON (t.word = m.word)\ngroup by\n  t.docid\n;\n\n\n\n\ndocid\nprobabilities (sorted by probabilities)\n\n\n\n\n1\n[{\"label\":0,\"probability\":0.875},{\"label\":1,\"probability\":0.125}]\n\n\n2\n[{\"label\":1,\"probability\":0.9375},{\"label\":0,\"probability\":0.0625}]\n\n\n\nImportantly, an option -topics is expected to be the same value as you set for training.\nSince the probabilities are sorted in descending order, a label of the most promising topic is easily obtained as:\nselect docid, probabilities[0].label\nfrom topic;\n\n\n\n\ndocid\nlabel\n\n\n\n\n1\n0\n\n\n2\n1\n\n\n\nOf course, using the different set of documents for prediction is possible. Predicting topic assignments of newly observed documents should be more realistic scenario.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"clustering/plsa.html":{"url":"clustering/plsa.html","title":"Probabilistic Latent Semantic Analysis","keywords":"","body":"\nAs described in our user guide for Latent Dirichlet Allocation (LDA), Hivemall enables you to apply clustering for your data based on a topic modeling technique. While LDA is one of the most popular techniques, there is another approach named Probabilistic Latent Semantic Analysis (pLSA). In fact, pLSA is the predecessor of LDA, but it has an advantage in terms of running time.\n\nT. Hofmann. Probabilistic Latent Semantic Indexing. SIGIR 1999, pp. 50-57.\nT. Hofmann. Probabilistic Latent Semantic Analysis. UAI 1999, pp. 289-296.\n\nIn order to efficiently handle large-scale data, our pLSA implementation is based on the following incremental variant of the original pLSA algorithm:\n\nH. Wu, et al. Incremental Probabilistic Latent Semantic Analysis for Automatic Question Recommendation. RecSys 2008, pp. 99-106.\n\n\n\n\nUsage\nDifference with LDA\nSetting hyper-parameter alpha\n\n\n\n NoteThis feature is supported from Hivemall v0.5-rc.1 or later.\nUsage\nBasically, you can use our pLSA function in a similar way to LDA.\nIn particular, we have two pLSA functions, train_plsa() and plsa_predict(). These functions can be used almost interchangeably with train_lda() and lda_predict(). Thus, reading our user guide for LDA should be helpful before trying pLSA.\nIn short, for the sample docs table we introduced in the LDA tutorial:\n\n\n\ndocid\ndoc\n\n\n\n\n1\n\"Fruits and vegetables are healthy.\"\n\n\n2\n\"I like apples, oranges, and avocados. I do not like the flu or colds.\"\n\n\n...\n...\n\n\n\na pLSA model can be built as follows:\nwith word_counts as (\n  select\n    docid,\n    feature(word, count(word)) as f\n  from \n    docs t1\n    lateral view explode(tokenize(doc, true)) t2 as word\n  where\n    not is_stopword(word)\n  group by\n    docid, word\n),\ninput as (\n  select docid, collect_list(f) as features\n  from word_counts\n  group by docid\n)\nselect\n  train_plsa(features, '-topics 2 -eps 0.00001 -iter 2048 -alpha 0.01') as (label, word, prob)\nfrom \n  input\n;\n\n\n\n\nlabel\nword\nprob\n\n\n\n\n0\nlike\n0.28549945\n\n\n0\ncolds\n0.14294468\n\n\n0\napples\n0.14291435\n\n\n0\navocados\n0.1428958\n\n\n0\nflu\n0.14287639\n\n\n0\noranges\n0.1428691\n\n\n0\nhealthy\n1.2605103E-7\n\n\n0\nfruits\n4.772253E-8\n\n\n0\nvegetables\n1.929087E-8\n\n\n1\nvegetables\n0.32713377\n\n\n1\nfruits\n0.32713372\n\n\n1\nhealthy\n0.3271335\n\n\n1\nlike\n0.006977764\n\n\n1\noranges\n0.0025642214\n\n\n1\nflu\n0.002507711\n\n\n1\navocados\n0.0023572792\n\n\n1\napples\n0.002213457\n\n\n1\ncolds\n0.001978546\n\n\n\nAnd prediction can be done as:\ntest as (\n  select\n    docid,\n    word,\n    count(word) as value\n  from \n    docs t1\n    LATERAL VIEW explode(tokenize(doc, true)) t2 as word\n  where\n    not is_stopword(word)\n  group by\n    docid, word\n),\ntopic as (\n  select\n    t.docid,\n    plsa_predict(t.word, t.value, m.label, m.prob, '-topics 2') as probabilities\n  from\n    test t\n    JOIN plsa_model m ON (t.word = m.word)\n  group by\n    t.docid\n)\nselect \n  docid, \n  probabilities, \n  probabilities[0].label, \n  m.words -- topic each document should be assigned\nfrom\n  topic t \n  JOIN (\n    select label, collect_list(feature(word, prob)) as words\n    from plsa_model\n    group by label\n  ) m on t.probabilities[0].label = m.label\n;\n\n\n\n\ndocid\nprobabilities\nlabel\nm.words\n\n\n\n\n1\n[{\"label\":1,\"probability\":0.72298235},{\"label\":0,\"probability\":0.27701768}]\n1\n[\"vegetables:0.32713377\",\"fruits:0.32713372\",\"healthy:0.3271335\",\"like:0.006977764\",\"oranges:0.0025642214\",\"flu:0.002507711\",\"avocados:0.0023572792\",\"apples:0.002213457\",\"colds:0.001978546\"]\n\n\n2\n[{\"label\":0,\"probability\":0.7052526},{\"label\":1,\"probability\":0.2947474}]\n0\n[\"like:0.28549945\",\"colds:0.14294468\",\"apples:0.14291435\",\"avocados:0.1428958\",\"flu:0.14287639\",\"oranges:0.1428691\",\"healthy:1.2605103E-7\",\"fruits:4.772253E-8\",\"vegetables:1.929087E-8\"]\n\n\n\nDifference with LDA\nThe main advantage of using pLSA is its efficiency. Since mathematical formulation and optimization logic is much simpler than LDA, using pLSA generally requires much shorter running time.\nIn terms of accuracy, LDA could be better than pLSA. For example, a word like appears twice in the above sample document#2 gets larger probabilities both in topic#1 and #2, even though one document does not contain the word. By contrast, LDA results (i.e., lambda values) are more clearly separated as shown in the LDA page. Thus, a pLSA model is likely to be biased.\nFor the reasons that we mentioned above, we recommend you to first use LDA. After that, if you encountered problems such as slow running time and undesirable clustering results, let you try alternative pLSA approach.\nSetting hyper-parameter alpha\nFor training pLSA, we set a hyper-parameter alpha in the above example:\nSELECT train_plsa(feature, '-topics 2 -eps 0.00001 -iter 2048 -alpha 0.01')\n\nThis value controls how much iterative model update is affected by the old results.\nFrom an algorithmic point of view, training pLSA (and LDA) iteratively repeats certain operations and updates the target value (i.e., probability obtained as a result of train_plsa()). This iterative procedure gradually makes the probabilities more accurate. What alpha does is to control the degree of the change of probabilities in each step.\nImportantly, pLSA is likely to overfit single mini-batch. As a result, P(w∣z)P(w|z)P(w∣z) could be particularly bad values (i.e., (w∣z)=0(w|z) = 0(w∣z)=0), and train_plsa() sometimes fails with an exception like:\nPerplexity would be Infinity. Try different mini-batch size `-s`, larger `-delta` and/or larger `-alpha`.\nIn that case, you need to try different hyper-parameters to avoid overfitting as the exception suggests.\nFor instance, 20 newsgroups dataset which consists of 10906 realistic documents empirically requires the following options:\nSELECT train_plsa(features, '-topics 20 -iter 10 -s 128 -delta 0.01 -alpha 512 -eps 0.1')\n\nClearly, alpha is much larger than 0.01 which was used for the dummy data above. Let you keep in mind that an appropriate value of alpha highly depends on the number of documents and mini-batch size.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"geospatial/latlon.html":{"url":"geospatial/latlon.html","title":"Lat/Lon functions","keywords":"","body":"\nThis page introduces Geo-spatial functions that treats latitude and longitude.\n\n\n\nTile number function\nUsage\n\n\nDistance function\nMap URL function\nUsage\n\n\n\n\n\n NoteThis feature is supported from Hivemall v0.5-rc.1 or later.\nTile number function\ntile(double lat, double lon, int zoom) returns a tile number in xtile(lon,zoom) + ytile(lat,zoom) * 2^n. The tile number is in range [0,2^2z].\nFormulas to convert latitude and longitude into tile x,y coordinates are as follows:\nx=⌊lon+180360⋅2z⌋y=⌊(1−ln(tan(lat⋅π180)+1cos(lat⋅π180))π)⋅2z−1⌋\n\\begin{aligned}\nx &= \\left\\lfloor \\frac{lon + 180}{360} \\cdot 2^z \\right\\rfloor \\\\ \\\\\ny &=\n    \\left\\lfloor\n        \\left(\n            1 - \\frac{\n                \\ln \\left(\n                    \\tan \\left(\n                        lat \\cdot \\frac{\\pi}{180}\n                    \\right) + \\frac{1}{\\cos \\left( lat \\cdot \\frac{\\pi}{180} \\right)}\n                \\right)\n            }{\\pi}\n        \\right) \\cdot 2^{z - 1}\n    \\right\\rfloor\n\\end{aligned}\n​x​​y​​​=⌊​360​​lon+180​​⋅2​z​​⌋​=​⎣​⎢​⎢​⎢​⎢​⎢​​​⎝​⎜​⎜​⎛​​1−​π​​ln(tan(lat⋅​180​​π​​)+​cos(lat⋅​180​​π​​)​​1​​)​​​⎠​⎟​⎟​⎞​​⋅2​z−1​​​⎦​⎥​⎥​⎥​⎥​⎥​​​​\nRefer this page for detail. Zoom level is well described in this page.\nUsage\nWITH data as (\n  select 51.51202 as lat, 0.02435 as lon, 17 as zoom\n  union all\n  select 51.51202 as lat, 0.02435 as lon, 4 as zoom\n  union all\n  select null as lat, 0.02435 as lon, 17 as zoom\n)\nselect \n   lat, lon, zoom,\n   tile(lat, lon, zoom) as tile,\n   (lon2tilex(lon,zoom) + lat2tiley(lat,zoom) * cast(pow(2, zoom) as bigint)) as tile2, \n   lon2tilex(lon, zoom) as xtile,\n   lat2tiley(lat, zoom) as ytile,\n   tiley2lat(lat2tiley(lat, zoom), zoom) as lat2,  -- tiley2lat returns center of the tile\n   tilex2lon(lon2tilex(lon, zoom), zoom) as lon2 -- tilex2lon returns center of the tile\nfrom \n   data;\n\n\n\n\nlat\nlon\nzoom\ntile\ntile2\nxtile\nytile\nlat2\nlon2\n\n\n\n\n51.51202\n0.02435\n17\n5712445448\n5712445448\n65544\n43582\n51.512161249555156\n0.02197265625\n\n\n51.51202\n0.02435\n4\n88\n88\n8\n5\n55.77657301866768\n0.0\n\n\nNULL\n0.02435\n17\nNULL\nNULL\n65544\nNULL\nNULL\n0.02197265625\n\n\n\nDistance function\nhaversine_distance(double lat1, double lon1, double lat2, double lon2, [const boolean mile=false]) returns Haversine distance between given two Geo locations.\n-- Tokyo (lat: 35.6833, lon: 139.7667)\n-- Osaka (lat: 34.6603, lon: 135.5232)\nselect \n  haversine_distance(35.6833, 139.7667, 34.6603, 135.5232) as km,\n  haversine_distance(35.6833, 139.7667, 34.6603, 135.5232, true) as mile;\n\n\n\n\nkm\nmile\n\n\n\n\n402.09212137829684\n249.8484608500711\n\n\n\nMap URL function\nmap_url(double lat, double lon, int zoom [, const string option]) function returns a tile URL of openstreetmap.com or maps.google.com.\nThe 4th argument takes the following optional arguments:\nhive> select map_url(1,1,1,'-help');\n\nusage: map_url(double lat, double lon, int zoom [, const string option]) -\n       Returns a URL string [-help] [-t ]\n -help             Show function help\n -t,--type    Map type [default: openstreetmap|osm,\n                   googlemaps|google]\n\nUsage\nWITH data as (\n  select 51.51202 as lat, 0.02435 as lon, 17 as zoom\n  union all\n  select 51.51202 as lat, 0.02435 as lon, 4 as zoom\n  union all\n  select null, 0.02435, 17\n)\nselect \n   map_url(lat,lon,zoom) as osm_url,\n   map_url(lat,lon,zoom,'-type googlemaps') as gmap_url\nfrom\n  data;\n\n\n\n\nosm_url\ngmap_url\n\n\n\n\nhttp://tile.openstreetmap.org/17/65544/43582.png\nhttps://www.google.com/maps/@51.51202,0.02435,17z\n\n\nhttp://tile.openstreetmap.org/4/8/5.png\nhttps://www.google.com/maps/@51.51202,0.02435,4z\n\n\nNULL\nNULL\n\n\n\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"spark/getting_started/installation.html":{"url":"spark/getting_started/installation.html","title":"Installation","keywords":"","body":"\nPrerequisites\n\nSpark v2.1 or later\nJava 7 or later\nhivemall-spark-xxx-with-dependencies.jar that can be found in the ASF distribution mirror.\ndefine-all.spark\n\nInstallation\nFirst, you download a compiled Spark package from the Spark official web page and invoke spark-shell with a compiled Hivemall binary.\n$ spark-shell --jars target/hivemall-all--incubating-SNAPSHOT.jar\nInstallation via Spark Packages\nIn another way to install Hivemall, you can use a --packages option.\n$ spark-shell --packages org.apache.hivemall:hivemall-all:\nYou find available Hivemall versions on Maven repository.\n\nNotice\nIf you would like to try Hivemall functions on the latest release of Spark, you just say bin/spark-shell in a Hivemall package.\nThis command automatically downloads the latest Spark version, compiles Hivemall for the version, and invokes spark-shell with the compiled Hivemall binary.\n\nThen, you load scripts for Hivemall functions.\nscala> :load resources/ddl/define-all.spark\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"spark/binaryclass/":{"url":"spark/binaryclass/","title":"Binary Classification","keywords":"","body":"\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"spark/binaryclass/a9a_sql.html":{"url":"spark/binaryclass/a9a_sql.html","title":"a9a Tutorial for SQL","keywords":"","body":"\na9a\nhttps://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#a9a\nData preparation\n$ wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a\n$ wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary/a9a.t\n\nscala> :paste\npark.read.format(\"libsvm\").load(\"a9a\")\n  .select($\"label\", to_hivemall_features($\"features\").as(\"features\"))\n  .createOrReplaceTempView(\"rawTrainTable\")\n\nval (max, min) = sql(\"SELECT MAX(label), MIN(label) FROM rawTrainTable\").collect.map {\n  case Row(max: Double, min: Double) => (max, min)\n}.head\n\n// `label` must be [0.0, 1.0]\nsql(s\"\"\"\n  CREATE OR REPLACE TEMPORARY VIEW trainTable AS\n    SELECT rescale(label, $min, $max) AS label, features\n      FROM rawTrainTable\n\"\"\")\n\nscala> trainDf.printSchema\nroot\n |-- label: float (nullable = true)\n |-- features: vector (nullable = true)\n\nscala> :paste\nspark.read.format(\"libsvm\").load(\"a9a.t\")\n  .select($\"label\", to_hivemall_features($\"features\").as(\"features\"))\n  .createOrReplaceTempView(\"rawTestTable\")\n\nsql(s\"\"\"\n  CREATE OR REPLACE TEMPORARY VIEW testTable AS\n    SELECT\n        rowid() AS rowid,\n        rescale(label, $min, $max) AS target,\n        features\n      FROM\n        rawTestTable\n\"\"\")\n\n// Caches data to fix row IDs\nsql(\"CACHE TABLE testTable\")\n\nsql(\"\"\"\n  CREATE OR REPLACE TEMPORARY VIEW testTable_exploded AS\n    SELECT\n        rowid,\n        target,\n        extract_feature(ft) AS feature,\n        extract_weight(ft) AS value\n      FROM (\n        SELECT\n            rowid,\n            target,\n            explode(features) AS ft\n          FROM\n            testTable\n        )\n\"\"\")\n\nscala> testDf.printSchema\nroot\n |-- rowid: string (nullable = true)\n |-- target: float (nullable = true)\n |-- feature: string (nullable = true)\n |-- value: double (nullable = true)\n\nTutorials\n[Logistic Regression]\nTraining\nscala> :paste\nsql(\"\"\"\n  CREATE OR REPLACE TEMPORARY VIEW modelTable AS\n    SELECT\n        feature, AVG(weight) AS weight\n      FROM (\n        SELECT\n            train_logistic_regr(add_bias(features), label) AS (feature, weight)\n          FROM\n            trainTable\n          )\n      GROUP BY\n        feature\n\"\"\")\n\nTest\nscala> :paste\nsql(\"\"\"\n  CREATE OR REPLACE TEMPORARY VIEW predicted AS\n    SELECT\n        rowid,\n        CASE\n          WHEN sigmoid(sum(weight * value)) > 0.50 THEN 1.0\n          ELSE 0.0\n        END AS predicted\n      FROM\n        testTable_exploded t LEFT OUTER JOIN modelTable m\n          ON t.feature = m.feature\n      GROUP BY\n        rowid\n\"\"\")\n\nEvaluation\nval num_test_instances = spark.table(\"testTable\").count\n\nsql(s\"\"\"\n  SELECT\n      count(1) / $num_test_instances AS eval\n    FROM\n      predicted p INNER JOIN testTable t\n        ON p.rowid = t.rowid\n    WHERE\n      p.predicted = t.target\n\"\"\")\n\n+------------------+\n|              eval|\n+------------------+\n|0.8327921286841418|\n+------------------+\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"spark/regression/e2006_sql.html":{"url":"spark/regression/e2006_sql.html","title":"E2006-tfidf Regression Tutorial for SQL","keywords":"","body":"\nE2006\nhttps://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression.html#E2006-tfidf\nData preparation\n$ wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/E2006.train.bz2\n$ wget https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/regression/E2006.test.bz2\n\nscala> :paste\nspark.read.format(\"libsvm\").load(\"E2006.train.bz2\")\n  .select($\"label\", to_hivemall_features($\"features\").as(\"features\"))\n  .createOrReplaceTempView(\"rawTrainTable\")\n\nval (max, min) = sql(\"SELECT MAX(label), MIN(label) FROM rawTrainTable\").collect.map {\n  case Row(max: Double, min: Double) => (max, min)\n}.head\n\n// `label` must be [0.0, 1.0]\nsql(s\"\"\"\n  CREATE OR REPLACE TEMPORARY VIEW trainTable AS\n    SELECT rescale(label, $min, $max) AS label, features\n      FROM rawTrainTable\n\"\"\")\n\nscala> trainDf.printSchema\nroot\n |-- label: float (nullable = true)\n |-- features: vector (nullable = true)\n\nscala> :paste\nspark.read.format(\"libsvm\").load(\"E2006.test.bz2\")\n  .select($\"label\", to_hivemall_features($\"features\").as(\"features\"))\n  .createOrReplaceTempView(\"rawTestTable\")\n\nsql(s\"\"\"\n  CREATE OR REPLACE TEMPORARY VIEW testTable AS\n    SELECT\n        rowid() AS rowid,\n        rescale(label, $min, $max) AS target,\n        features\n      FROM\n        rawTestTable\n\"\"\")\n\n// Caches data to fix row IDs\nsql(\"CACHE TABLE testTable\")\n\nsql(\"\"\"\n  CREATE OR REPLACE TEMPORARY VIEW testTable_exploded AS\n    SELECT\n        rowid,\n        target,\n        extract_feature(ft) AS feature,\n        extract_weight(ft) AS value\n      FROM (\n        SELECT\n            rowid,\n            target,\n            explode(features) AS ft\n          FROM\n            testTable\n\"\"\")\n\nscala> df.printSchema\nroot\n |-- rowid: string (nullable = true)\n |-- target: float (nullable = true)\n |-- feature: string (nullable = true)\n |-- value: double (nullable = true)\n\nTutorials\n[AROWe2]\nTraining\nscala> :paste\nsql(\"\"\"\n  CREATE OR REPLACE TEMPORARY VIEW modelTable AS\n    SELECT\n        feature, AVG(weight) AS weight\n      FROM (\n        SELECT\n            train_arowe2_regr(add_bias(features), label) AS (feature, weight)\n          FROM\n            trainTable\n        )\n      GROUP BY\n        feature\n\"\"\")\n\nTest\nscala> :paste\nsql(\"\"\"\n  CREATE OR REPLACE TEMPORARY VIEW predicted AS\n    SELECT\n        rowid, sum(weight * value) AS predicted\n      FROM\n        testTable_exploded t LEFT OUTER JOIN modelTable m\n          ON t.feature = m.feature\n      GROUP BY\n        rowid\n\"\"\")\n\nEvaluation\nscala> :paste\nsql(s\"\"\"\n  SELECT\n      AVG(target), AVG(predicted)\n    FROM\n      predicted p INNER JOIN testTable t\n        ON p.rowid = t.rowid\n\"\"\")\n\n+------------------+------------------+\n|       avg(target)|    avg(predicted)|\n+------------------+------------------+\n|0.5489154884487879|0.6030108853227014|\n+------------------+------------------+\n\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"},"docker/getting_started.html":{"url":"docker/getting_started.html","title":"Getting Started","keywords":"","body":"\nGetting started with Hivemall on Docker\nThis page introduces how to run Hivemall on Docker.\n\n\n\nRequirements\nBuild image\nUsing docker-compose\nUsing docker command\n\n\nRun container\nBy docker-compose\nBy docker command\nRunning pre-built Docker image in Docker Hub\n\n\nRun Hivemall on Docker\nAccessing Hadoop management GUIs\nLoad data into HDFS (optional)\nBuild Hivemall (optional)\n\n\n\n\n\n CautionThis docker image contains a single-node Hadoop enviroment for evaluating Hivemall. Not suited for production uses.\nRequirements\n\nDocker Engine 1.6+\nDocker Compose 1.10+\n\nBuild image\nYou have two options in order to build a hivemall docker image:\nUsing docker-compose\n$ docker-compose -f resources/docker/docker-compose.yml build\nUsing docker command\n$ docker build -f resources/docker/Dockerfile .\n NoteYou can skip building images if you try to use a pre-build docker image from Docker Hub. However, since the Docker Hub repository is experimental one, the distributed image is NOT built on the \"latest\" commit in our master branch.\nRun container\nIf you built an image by yourself, it can be launched by either docker-compose or docker command:\nBy docker-compose\n$ docker-compose -f resources/docker/docker-compose.yml up -d && docker attach hivemall\nYou can edit resources/docker/docker-compose.yml as needed.\nFor example, setting volumes options enables to mount your local directories to the container as follows:\nvolumes:\n  - \"../../:/opt/hivemall/\" # mount current hivemall dir to `/opt/hivemall` ($HIVEMALL_PATH) on the container\n  - \"/path/to/data/:/root/data/\" # mount resources to container-side  `/root/data` directory\n\nBy docker command\nFind a local docker image by docker images, and hit:\n$ docker run -p 8088:8088 -p 50070:50070 -p 19888:19888 -it ${docker_image_id}\nRefer Docker reference for the command detail.\nSimilarly to the volumes option in the docker-compose file, docker run has --volume (-v) option: \n$ docker run ... -v /path/to/local/hivemall:/opt/hivemall\nRunning pre-built Docker image in Docker Hub\n CautionThis part is experimental. Hivemall in the pre-built image might be out-of-date compared to the latest version in our master branch.\nYou can find pre-built Hivemall docker images in this repository.\n\nCheck the latest tag first\nPull pre-build docker image from Docker Hub: $ docker pull hivemall/latest:20170517\n\nLaunch the pre-build image:$ docker run -p 8088:8088 -p 50070:50070 -p 19888:19888 -it hivemall/latest:20170517\n\n\nRun Hivemall on Docker\n\nType hive to run (.hiverc automatically loads Hivemall functions)\nTry your Hivemall queries!\n\nAccessing Hadoop management GUIs\n\nYARN http://localhost:8088/\nHDFS http://localhost:50070/\nMR jobhistory server http://localhost:19888/\n\nNote that you need to expose local ports e.g., by -p 8088:8088 -p 50070:50070 -p 19888:19888 on running docker image.\nLoad data into HDFS (optional)\nYou can find an example script to load data into HDFS in $HOME/bin/prepare_iris.sh.\n  The script loads iris dataset into iris database:\n# cd $HOME && ./bin/prepare_iris.sh\n# hive\nhive> use iris;\nhive> select * from iris_raw limit 5;\nOK\n1       Iris-setosa     [5.1,3.5,1.4,0.2]\n2       Iris-setosa     [4.9,3.0,1.4,0.2]\n3       Iris-setosa     [4.7,3.2,1.3,0.2]\n4       Iris-setosa     [4.6,3.1,1.5,0.2]\n5       Iris-setosa     [5.0,3.6,1.4,0.2]\nOnce you prepared the iris database, you are ready to move on to our multi-class classification tutorial.\nBuild Hivemall (optional)\nIn the container, Hivemall resource is stored in $HIVEMALL_PATH.\nYou can build Hivemall package by cd $HIVEMALL_PATH && ./bin/build.sh.\n\n\nApache Hivemall is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator.\n\n\n"}}}