├── .gitignore ├── LICENSE ├── README.md ├── build.sbt ├── lib └── search-prediction-api-1.0.jar ├── project ├── build.properties └── plugins.sbt └── src ├── main └── scala │ ├── SparkDataUtils.scala │ ├── SparkEntry.scala │ ├── SparkGeneric_Trainer.scala │ ├── SparkModelHelpers.scala │ ├── SparkPredictorEngine.scala │ └── impl │ ├── LinearRegression.scala │ ├── LogisticRegression.scala │ ├── RigeRegression.scala │ └── SVM.scala └── test ├── resources ├── mini.csv ├── prop1.conf ├── prop_clf.conf └── spark-clf-test.model └── scala ├── DataUtilSpec.scala ├── SarkPredictorEngineSpec.scala └── SparkTrainerSpec.scala /.gitignore: -------------------------------------------------------------------------------- 1 | *.class 2 | *.log 3 | 4 | # sbt specific 5 | .cache 6 | .history 7 | .lib/ 8 | dist/* 9 | target/ 10 | lib_managed/ 11 | src_managed/ 12 | project/boot/ 13 | project/plugins/project/ 14 | 15 | # Scala-IDE specific 16 | .scala_dependencies 17 | .worksheet 18 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright {yyyy} {name of copyright owner} 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | Search Prediction Using Spark Models 2 | ====================== 3 | 4 | Using the api from [elasticsearch-prediction](https://github.com/mahisoft/elasticsearch-prediction), this is the implementation that uses Spark as the backend to compute large scale models offline, and generates a plugin for elasticsearch for runtime evaluation of a trained scoring function. 5 | 6 | *This is highly experimental code as a proof of concept, so there are many many areas of improvements, and bugs. Use for fun only* 7 | 8 | Note that currently the only suppported spark models are linear, until the serialization and spark.ml API matures out of beta 9 | 10 | Some references: 11 | - [scripting-scores](http://www.elasticsearch.org/guide/en/elasticsearch/guide/current/script-score.html) 12 | - [modules-scripting](http://www.elasticsearch.org/guide/en/elasticsearch/reference/current/modules-scripting.html) 13 | 14 | ## License 15 | Code is provided under the Apache 2.0 license available at http://opensource.org/licenses/Apache-2.0, 16 | as well as in the LICENSE file. This is the same license used as Spark and [elasticsearch-prediction](https://github.com/mahisoft/elasticsearch-prediction). 17 | -------------------------------------------------------------------------------- /build.sbt: -------------------------------------------------------------------------------- 1 | organization := "com.sdhu" 2 | 3 | name := "elasticsearchprediction-spark" 4 | 5 | version := "0.1" 6 | 7 | scalaVersion := "2.10.4" 8 | 9 | val sparkV = "1.3.0" 10 | 11 | scalacOptions ++= Seq( 12 | "-deprecation", 13 | "-encoding", 14 | "UTF-8", 15 | "-feature", 16 | "-unchecked", 17 | "-Ywarn-adapted-args", 18 | "-Ywarn-value-discard", 19 | "-Xlint") 20 | 21 | resolvers ++= Seq( 22 | "Typesafe Releases" at "http://repo.typesafe.com/typesafe/releases/", 23 | "Sonatype Snapshots" at "https://oss.sonatype.org/content/repositories/snapshots/" 24 | ) 25 | 26 | libraryDependencies ++= Seq( 27 | "org.apache.spark" %% "spark-core" % sparkV, 28 | "org.apache.spark" %% "spark-mllib" % sparkV, 29 | ("org.elasticsearch" %% "elasticsearch-spark" % "2.1.0.Beta3"). 30 | exclude("org.apache.hadoop", "hadoop-yarn-api"). 31 | exclude("org.eclipse.jetty.orbit", "javax.mail.glassfish"). 32 | exclude("org.eclipse.jetty.orbit", "javax.servlet"). 33 | exclude("org.slf4j", "slf4j-api"), 34 | "com.holdenkarau" %% "spark-testing-base" % "1.3.0_0.0.5", 35 | "org.scalatest" %% "scalatest" % "2.2.4" % "test" withSources() 36 | ) 37 | 38 | 39 | //excluding all the mess that causes the build to break on spark... 40 | mergeStrategy in assembly <<= (mergeStrategy in assembly) { (old) => 41 | { 42 | case PathList("org", "apache", xs @ _*) => MergeStrategy.last 43 | case PathList("com","google", "common", xs @ _*) => MergeStrategy.last 44 | case PathList("com", "esotericsoftware", xs @ _*) => MergeStrategy.last 45 | case x => old(x) 46 | } 47 | } 48 | 49 | licenses := Seq("Apache License 2.0" -> url("http://www.apache.org/licenses/LICENSE-2.0.html")) 50 | 51 | //logLevel := Level.Warn 52 | 53 | //logLevel in compile := Level.Warn 54 | 55 | cancelable := true 56 | 57 | parallelExecution in ThisBuild := false 58 | -------------------------------------------------------------------------------- /lib/search-prediction-api-1.0.jar: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sdhu/elasticsearch-prediction-spark/ad5a15a4def3d4912abce49b9fedf00318539fb4/lib/search-prediction-api-1.0.jar -------------------------------------------------------------------------------- /project/build.properties: -------------------------------------------------------------------------------- 1 | sbt.version=0.13.7 -------------------------------------------------------------------------------- /project/plugins.sbt: -------------------------------------------------------------------------------- 1 | addSbtPlugin("com.eed3si9n" % "sbt-assembly" % "0.13.0") 2 | 3 | 4 | 5 | -------------------------------------------------------------------------------- /src/main/scala/SparkDataUtils.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | 3 | import com.mahisoft.elasticsearchprediction._ 4 | import plugin.domain.{ IndexValue, IndexAttributeDefinition } 5 | import domain.DataType 6 | 7 | import org.apache.spark._ 8 | import rdd.RDD 9 | import mllib.linalg.{ Vectors, Vector ⇒ spV } 10 | import mllib.regression.LabeledPoint 11 | 12 | import java.util.Collection 13 | 14 | import scala.util.control.Exception._ 15 | import scala.collection.JavaConversions._ 16 | import scala.util.Random 17 | 18 | object CsvUtil extends Serializable { 19 | 20 | implicit class RichArrayString(val a: Array[String]) extends Serializable { 21 | def toDoubleOpt(i: Int): Option[Double] = 22 | catching(classOf[NumberFormatException]).opt(a(i).toDouble) 23 | 24 | def toDoubleEither(i: Int): Either[Double, String] = { 25 | this.toDoubleOpt(i) match { 26 | case Some(d) ⇒ Left(d) 27 | case None ⇒ Right(a(i)) 28 | } 29 | } 30 | 31 | def toDoubleArray(cm: Map[String, Double]): Array[Double] = { 32 | a.zipWithIndex.map{ case (v,i) ⇒ { 33 | this.toDoubleOpt(i) match { 34 | case Some(d) ⇒ d 35 | case None ⇒ cm.getOrElse(v, 0.0) 36 | } 37 | }} 38 | } 39 | } 40 | } 41 | 42 | 43 | object ReadUtil extends Serializable { 44 | import CsvUtil._ 45 | 46 | /* 47 | * Reads a csv file using and selects appropiate columns for features, 48 | * and given column as label 49 | */ 50 | def csv2RDD( 51 | sc: SparkContext, 52 | path: String, 53 | cols: IndexedSeq[String], 54 | label: String, 55 | isRegression: Boolean = true, 56 | thresh: Double = 0.5 57 | ): (RDD[LabeledPoint], Map[String, Double]) = { 58 | val csv = sc.textFile(path) 59 | .map(_.split(",").map(_.trim)) 60 | val header = csv.first.zipWithIndex 61 | val feat_ind = header.filter(x ⇒ cols.contains(x._1)).map(_._2) 62 | val label_ind = header.filter(_._1 == label).map(_._2) 63 | val ind = label_ind ++ feat_ind 64 | 65 | //convert to numeric and get all categorical values 66 | val select_csv = csv.filter(_.head != header.head._1) 67 | .map(x ⇒ ind.map(i ⇒ x.toDoubleEither(i))) 68 | 69 | val categories = select_csv.flatMap(_.zipWithIndex.filter(_._1.isRight).map(x ⇒ (x._1.right.get, x._2))) 70 | .countByKey 71 | 72 | // log.debug(s"csv2RDD categories: ${categories}") 73 | 74 | //start index on 1.0 since 0.0 is left for sparseness 75 | val cats = categories.keys.zipWithIndex.map(x ⇒ (x._1, x._2.toDouble + 1.0)).toMap 76 | val bcCats = sc.broadcast(cats) 77 | 78 | val ret = select_csv.map(x ⇒ { 79 | val dv = x.map(_ match { 80 | case Left(d) ⇒ d 81 | case Right(s) ⇒ bcCats.value.getOrElse(s, 0.0) 82 | }) 83 | 84 | val lb = if (isRegression) { 85 | dv(0) 86 | } else { 87 | if (dv(0) > thresh) 1.0 else 0.0 88 | } 89 | 90 | LabeledPoint(lb, Vectors.dense(dv.drop(1))) 91 | }) 92 | 93 | 94 | (ret, cats) 95 | } 96 | 97 | 98 | /* 99 | * Take an IndexedValue from the pplugin and convert it with appropiate categorical numerical 100 | * mapp]ng into a spark vector 101 | * 102 | * Uggly need to fix type conversion 103 | */ 104 | def cIndVal2Vector(v: Collection[IndexValue], cm: Map[String, Double]): spV = { 105 | val a = v.map(x ⇒ x.getDefinition.getType match { 106 | case DataType.DOUBLE ⇒ x.getValue.asInstanceOf[Double].toString 107 | case _ ⇒ x.getValue.asInstanceOf[String] 108 | }).toArray.toDoubleArray(cm) 109 | 110 | println(s"array ${a.mkString(",")}") 111 | Vectors.dense(a) 112 | } 113 | 114 | // not using IdexAttributeDefinition ... just set it to double 115 | def arr2CIndVal(v: Array[String]): Collection[IndexValue] = { 116 | val ret = v.map(s ⇒ new IndexValue( 117 | new IndexAttributeDefinition("notUsed", DataType.STRING), 118 | s)) 119 | 120 | asJavaCollection[IndexValue](ret) 121 | } 122 | } 123 | 124 | -------------------------------------------------------------------------------- /src/main/scala/SparkEntry.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | 3 | import com.mahisoft.elasticsearchprediction._ 4 | import utils.DataProperties 5 | import classifier.GenericClassifier 6 | import plugin.engine.PredictorEngine 7 | 8 | class Spark_Trainer(dp: DataProperties) { 9 | 10 | def getSparkGenericTrainer: SparkGenericTrainer[_] = { 11 | val config = SparkClassifierConfig().readDataProperties(dp) 12 | 13 | if (config.clf_type.nonEmpty) { 14 | config.clf_type.get match { 15 | case "linear-regression" ⇒ new SparkGenericTrainer(LinearRegression_Helper) 16 | case "logistic-regression" ⇒ new SparkGenericTrainer(LogisticRegression_Helper) 17 | case "ridge-regression" ⇒ new SparkGenericTrainer(RidgeRegression_Helper) 18 | case "svm" ⇒ new SparkGenericTrainer(SVM_Helper) 19 | case _ ⇒ throw new IllegalArgumentException(s"Invalid spark.clf_type") 20 | } 21 | } else { 22 | throw new IllegalArgumentException("spark.clf_type must be provided") 23 | } 24 | } 25 | 26 | def getGenericClassifier: GenericClassifier = this.getSparkGenericTrainer 27 | } 28 | 29 | 30 | class Spark_PredictorEngine(readP: String, clf_type: String) { 31 | 32 | def getSparkPredictorEngine: SparkPredictorEngine[_] = { 33 | val predEng = clf_type match { 34 | case "spark.linear-regression" ⇒ new SparkPredictorEngine(readP, LinearRegression_Helper) 35 | case "spark.logistic-regression" ⇒ new SparkPredictorEngine(readP, LogisticRegression_Helper) 36 | case "spark.ridge-regression" ⇒ new SparkPredictorEngine(readP, RidgeRegression_Helper) 37 | case "spark.svm" ⇒ new SparkPredictorEngine(readP, SVM_Helper) 38 | case _ ⇒ throw new IllegalArgumentException(s"Invalid spark.clf_type") 39 | } 40 | predEng.readModel() 41 | predEng 42 | } 43 | 44 | def getPredictorEngine: PredictorEngine = this.getSparkPredictorEngine 45 | } 46 | -------------------------------------------------------------------------------- /src/main/scala/SparkGeneric_Trainer.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | 3 | import com.mahisoft.elasticsearchprediction._ 4 | import classifier.GenericClassifier 5 | import utils.DataProperties 6 | import domain.{ CrossDataSetResult, SimpleDataSetResult } 7 | //import exception._ 8 | 9 | import org.apache.spark._ 10 | import rdd.RDD 11 | import mllib.util.MLUtils 12 | import mllib.regression.{ LabeledPoint, GeneralizedLinearModel } 13 | 14 | import org.apache.log4j.{ Logger, LogManager } 15 | 16 | import java.io.File 17 | import java.lang.{Boolean ⇒ JBoolean, Integer ⇒ JInt} 18 | 19 | 20 | /* 21 | * Specific format for sparkClassifier properties: 22 | * 23 | * data.filename = of training data 24 | * data.type = (csv supported only as of now. Future work on json, parquet, etc...) 25 | * data.columns = feature dimensions to select from data 26 | * data.column = 27 | * 28 | * train.percentage = 0-100 29 | * validate.numFolds = 30 | * model.filename = of location to save/read serialized trained model 31 | * 32 | * spark.model.type = currently only linear models supported 33 | * spark.model.parmas = params for model training 34 | * spark.model.isregression = 35 | * spark.model.binThreshold = specify threshold to binarize target variable 36 | * spark.conf = set spark conf values such 37 | * as master, parallelism, etc... 38 | */ 39 | case class SparkClassifierConfig( 40 | data_filename: Option[String] = None, 41 | data_columns: Option[IndexedSeq[String]] = None, 42 | data_column_label: Option[String] = None, 43 | model_filename: Option[String] = None, 44 | train_percentage: Option[Int] = Some(75), 45 | validate_kfolds: Option[Int] = Some(0), 46 | clf_type: Option[String] = None, 47 | clf_params: Option[Map[String,String]] = None, 48 | clf_isRegression: Option[Boolean] = None, 49 | clf_binThreshold: Option[Double] = None, 50 | clf_numClasses: Option[Int] = None, 51 | spark_conf: Option[Map[String, String]] = None 52 | ) { 53 | def readDataProperties(dp: DataProperties) = { 54 | this.copy( 55 | data_filename = Option(dp.getValue("data.filename")), 56 | data_columns = Option(dp.getValue("data.columns")).map(_.split(",").toIndexedSeq), 57 | data_column_label = Option(dp.getValue("data.column.label")), 58 | model_filename = Option(dp.getValue("model.filename")), 59 | train_percentage = Option(dp.getValue("train.percentage")).map(_.toInt), 60 | validate_kfolds = Option(dp.getValue("validate.numFolds")).map(_.toInt), 61 | clf_type = Option(dp.getValue("spark.model.type")), 62 | clf_params = Option(dp.getValue("spark.model.params")) 63 | .map(_.split(",").map(_.split(":")).collect { case Array(p,v) ⇒ (p,v) }.toMap), 64 | clf_isRegression = Option(dp.getValue("spark.model.isregression")).map(_.toBoolean), 65 | clf_binThreshold = Option(dp.getValue("spark.model.binThreshold")).map(_.toDouble), 66 | clf_numClasses = Option(dp.getValue("spark.model.numClasses")).map(_.toInt), 67 | spark_conf = Option(dp.getValue("spark.conf")) 68 | .map(_.split(",").map(_.split(":")).collect { case Array(p,v) ⇒ (p,v) }.toMap) 69 | ) 70 | } 71 | 72 | def checkValid: Boolean = { 73 | this.data_filename.nonEmpty && 74 | this.data_columns.nonEmpty && 75 | this.model_filename.nonEmpty && 76 | this.clf_type.nonEmpty && 77 | this.spark_conf.nonEmpty 78 | } 79 | } 80 | 81 | 82 | object Labels extends Enumeration { 83 | type Labels = Value 84 | val POS, NEG, TP, TN, FN, FP = Value 85 | } 86 | 87 | 88 | case class Result(size: Int, result: String) 89 | 90 | 91 | /* 92 | * 93 | * Trait to operate with GenericClassifier Java interface with additional utils specific for 94 | * Spark classifier types in mllib 95 | * 96 | * TODO: will be cleaned out once spark.ml interface is out of beta... to support 97 | * models beyond linear 98 | * - a cleanup method to sc.stop()..... 99 | */ 100 | class SparkGenericTrainer[M <: GeneralizedLinearModel](val sparkModelHelper: SparkModelHelpers[M]) extends GenericClassifier { 101 | private var _sparkConf: SparkConf = new SparkConf() 102 | 103 | @transient 104 | var _scOpt: Option[SparkContext] = None 105 | 106 | @transient 107 | private val log: Logger = LogManager.getLogger(this.getClass.getSimpleName) 108 | 109 | //should be private for debug will make visible 110 | private var _model: ModelData[M] = ModelData() 111 | private var _data: Option[RDD[LabeledPoint]] = None 112 | private var _config: SparkClassifierConfig = SparkClassifierConfig() 113 | 114 | def cleanUp(): Unit = { 115 | if (_scOpt.nonEmpty) { 116 | _scOpt.get.stop() 117 | _scOpt = None 118 | } 119 | } 120 | 121 | def getModelData = this._model 122 | 123 | def getData = this._data 124 | 125 | def getConfig = this._config 126 | 127 | /* 128 | * Calculate simple metrics. Assume y input is tuple (truth, prediction) 129 | */ 130 | def evalResults(y: Array[(Double, Double)], isRegression: Boolean, binTr: Option[Double]): Result = { 131 | import Labels._ 132 | import scala.math._ 133 | 134 | val res = if (isRegression) { 135 | val diff = y.map{ case(a, b) ⇒ a - b} 136 | val diff2 = diff.map(x ⇒ x * x) 137 | val n = y.size.toDouble 138 | val u_truth = y.map(_._1).sum / n 139 | val u_pred = y.map(_._2).sum / n 140 | 141 | val mse = diff2.sum / n 142 | val rmse = sqrt(mse) 143 | val mae = diff.map(x ⇒ abs(x)).sum / n 144 | val rse = diff2.sum / y.map(x ⇒ pow(x._1 - u_truth, 2)).sum 145 | 146 | s"MSE: $mse, RMSE: $rmse, MAE: $mae, RSE: $rse \n N: $n, u_truth: $u_truth, u_pred: $u_pred" 147 | } else { 148 | val yBin = y.map{ case(a, b) ⇒ { 149 | val btr = binTr.getOrElse(0.5) 150 | val tr = if(a > btr) POS else NEG 151 | val pred = if(b > btr) POS else NEG 152 | (tr, pred) match { 153 | case (POS, POS) ⇒ TP 154 | case (POS, NEG) ⇒ FN 155 | case (NEG, POS) ⇒ FP 156 | case (NEG, NEG) ⇒ TN 157 | } 158 | }} 159 | 160 | val tp = yBin.filter(_ == TP).size.toDouble 161 | val tn = yBin.filter(_ == TN).size.toDouble 162 | val fp = yBin.filter(_ == FP).size.toDouble 163 | val fn = yBin.filter(_ == FN).size.toDouble 164 | val n = yBin.size 165 | val prc = if ((tp + fp) > 0.0) tp / (tp + fp) else 0.0 166 | val rec = if ((tp + fn) > 0.0) tp / (tp + fn) else 0.0 167 | val f1 = if ((prc + rec) > 0.0) (2 * prc * rec ) / (prc + rec) else 0.0 168 | 169 | s"Precision: $prc, Recall: $rec, F1-Score: $f1 \n N: $n, TP: $tp, TN: $tn, FP: $fp, FN: $fn" 170 | } 171 | Result(y.size, res) 172 | } 173 | 174 | // @throws(classOf[ModelException]) 175 | override def loadClassifier(): JBoolean = { 176 | _config.model_filename match { 177 | case Some(path) ⇒ loadClassifier(path) 178 | case None ⇒ false 179 | } 180 | } 181 | 182 | def loadClassifier(path: String): Boolean = { 183 | _model = sparkModelHelper.readSparkModel(path) 184 | _model.clf.nonEmpty 185 | } 186 | 187 | 188 | //TODO support for other formats other than CSV 189 | override def loadData(dataFile: File): JBoolean = { 190 | if (_scOpt.isEmpty) 191 | _scOpt = Some(new SparkContext(_sparkConf)) 192 | 193 | val ret = _config.data_columns.map(cols ⇒ { 194 | val lb: String = _config.data_column_label.getOrElse(cols.last) 195 | val c = if(cols.last == lb) cols.dropRight(1) else cols 196 | ReadUtil.csv2RDD( 197 | _scOpt.get, 198 | dataFile.getPath, 199 | c, 200 | lb 201 | ) 202 | }) 203 | 204 | // _config.clf_isRegression.getOrElse(true), 205 | // _config.clf_binThreshold.getOrElse(0.5))) 206 | 207 | _data = ret.map(_._1) 208 | _model = _model.setCategoriesMap(ret.map(_._2)) 209 | _data.nonEmpty 210 | } 211 | 212 | 213 | // right now jsut training on full set.... 214 | override def trainModel(): Unit = { 215 | val p = _config.clf_params.getOrElse(Map[String,String]()) 216 | _model = _model.setClf(_data.map(d ⇒ sparkModelHelper.sparkTrainClf(d, p))) 217 | 218 | if (_model.clf.nonEmpty) 219 | log.info(s"Trained Model for: ${sparkModelHelper.name}") 220 | else 221 | log.info(s"Bad Training Model for: ${sparkModelHelper.name}") 222 | } 223 | 224 | // not implemented 225 | override def splitDataSet(trainingPercentage: JInt): JBoolean = { 226 | true 227 | } 228 | 229 | override def simpleValidation(): SimpleDataSetResult = { 230 | val ropt = if (_data.nonEmpty && _model.clf.nonEmpty && _config.clf_isRegression.nonEmpty) { 231 | val data = _data.get.cache 232 | val y_truth = data.map(_.label).collect 233 | val y_pred = _model.clf.get.predict(data.map(_.features)).collect 234 | 235 | Some(evalResults(y_truth.zip(y_pred), _config.clf_isRegression.get, _config.clf_binThreshold)) 236 | } else None 237 | 238 | val res = new SimpleDataSetResult() 239 | 240 | if (ropt.nonEmpty) { 241 | val r = ropt.get 242 | res.setTrainDataSetSize(r.size) 243 | res.setTestDataSetSize(r.size) 244 | res.setResults(r.result) 245 | } else { 246 | log.error("Error validating results") 247 | } 248 | 249 | res 250 | } 251 | 252 | // try set of params???? 253 | override def crossValidation(numFolds: JInt): CrossDataSetResult = { 254 | val res = new CrossDataSetResult(numFolds) 255 | 256 | if (_data.nonEmpty && _config.clf_isRegression.nonEmpty){ 257 | val rg = _config.clf_isRegression.get 258 | val bopt = _config.clf_binThreshold 259 | 260 | val data = _data.get.cache 261 | val kdata = MLUtils.kFold(data, numFolds, 0).zipWithIndex 262 | val p = _config.clf_params.getOrElse(Map[String,String]()) 263 | 264 | res.setDataSetSize(data.count.toInt) 265 | 266 | for (((tr,te),i) ← kdata) { 267 | val clf = sparkModelHelper.sparkTrainClf(tr, p) 268 | 269 | val y_tr_truth = tr.map(_.label).collect 270 | val y_te_truth = te.map(_.label).collect 271 | val y_tr_pred = clf.predict(tr.map(_.features)).collect 272 | val y_te_pred = clf.predict(te.map(_.features)).collect 273 | 274 | val result_tr = evalResults(y_tr_truth.zip(y_tr_pred), rg, bopt).result 275 | val result_te = evalResults(y_te_truth.zip(y_te_pred), rg, bopt).result 276 | 277 | res.addResult(i, s"Training: ${result_tr}\nTesting: ${result_te}") 278 | } 279 | } 280 | 281 | return res 282 | } 283 | 284 | override def saveModel(modelName: String): JBoolean = { 285 | _config.model_filename match { 286 | case Some(path) ⇒ saveModel(modelName, path) 287 | case None ⇒ false 288 | } 289 | } 290 | 291 | def saveModel(modelName: String, path: String): Boolean = { 292 | sparkModelHelper.saveSparkModel(path, modelName, _model) 293 | } 294 | 295 | override def setDataProperties(dataProperties: DataProperties) = { 296 | _config = _config.readDataProperties(dataProperties) 297 | _model = _model.setProperties(_config.clf_binThreshold, _config.clf_numClasses) 298 | 299 | //validate that required fields have been passed.... 300 | if (_config.checkValid){ 301 | 302 | // log.info(s"Setting ${sparkModelHelper.name} DataProperties") 303 | val spc = _config.spark_conf.get 304 | _sparkConf = _sparkConf.setAppName(sparkModelHelper.name) 305 | .setMaster(spc.getOrElse("spark.master","local[4]")) 306 | 307 | // configure all other spark settings 308 | val spc2 = if (spc.contains("spark.master")) spc - "spark.master" else spc 309 | for ( (spK, spV) ← spc2.toSeq){ 310 | _sparkConf = _sparkConf.set(spK, spV) 311 | } 312 | } 313 | else{ 314 | log.error(s"Invalid Setting ${sparkModelHelper.name} DataProperties") 315 | } 316 | } 317 | 318 | } 319 | -------------------------------------------------------------------------------- /src/main/scala/SparkModelHelpers.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | 3 | import org.apache.spark.rdd.RDD 4 | import org.apache.spark.mllib.regression.{ LabeledPoint, GeneralizedLinearModel } 5 | 6 | import org.json4s._ 7 | import org.json4s.jackson.Serialization 8 | 9 | import java.nio.file.{Paths, Files} 10 | import java.nio.charset.StandardCharsets 11 | 12 | import scala.util.Try 13 | 14 | /* 15 | * Note: This can be cleaned up a bit, and can be expanded beyond linear classifiers easily 16 | * once spark.ml graduates out of beta and useful traits like Estimator become public. 17 | * Right now only supported typed of models will be linear.... 18 | * 19 | * So everything is just inheriting off of GeneralizedLinearModel which is not Ideal 20 | * instead it should have an "sparkModel" so any algorithm can be used 21 | * as long as we can seriliaze it 22 | sealed trait SparkModel extends Serializable { 23 | 24 | def predict(d: RDD[Vector]): RDD[Double] 25 | 26 | def predict(d: Vector): Double 27 | 28 | } 29 | */ 30 | 31 | 32 | case class ModelData[M <: GeneralizedLinearModel]( 33 | clf: Option[M] = None, 34 | categoriesMap: Option[Map[String, Double]] = None, 35 | binThreshold: Option[Double] = None, 36 | numClasses: Option[Int] = None 37 | ) { 38 | def setClf(m: Option[M]) = this.copy(clf = m) 39 | def setCategoriesMap(cm: Option[Map[String, Double]]) = this.copy(categoriesMap = cm) 40 | def setProperties(tOpt: Option[Double], nOpt: Option[Int]) = this.copy(binThreshold = tOpt, numClasses = nOpt) 41 | } 42 | 43 | 44 | case class LinearModelData( 45 | modelName: String, 46 | numFeatures: Int, 47 | intercept: Double, 48 | weights: Array[Double], 49 | categoriesMap: Option[Map[String, Double]], 50 | binThreshold: Option[Double], 51 | numClasses: Option[Int] 52 | ) 53 | 54 | 55 | trait SparkModelHelpers[M <: GeneralizedLinearModel] { 56 | 57 | val name: String 58 | 59 | def sparkTrainClf(data: RDD[LabeledPoint], params: Map[String,String]): M 60 | 61 | def saveSparkModel(path: String, modelName: String, model: ModelData[M]): Boolean 62 | 63 | def readSparkModel(path: String): ModelData[M] 64 | } 65 | 66 | 67 | trait Linear_SparkModelHelpers[M <: GeneralizedLinearModel] extends SparkModelHelpers[M] { 68 | def getOptClf(lm: Option[LinearModelData]): Option[M] 69 | 70 | /* 71 | * Read into format usable by sparkGenericTrainer 72 | */ 73 | def readSparkModel(path: String): ModelData[M] = { 74 | if (Files.exists(Paths.get(path))) { 75 | val lm = readModelFromJson(path) 76 | ModelData( 77 | getOptClf(lm), 78 | lm.map(_.categoriesMap).getOrElse(None), 79 | lm.map(_.binThreshold).getOrElse(None), 80 | lm.map(_.numClasses).getOrElse(None) 81 | ) 82 | } else { 83 | ModelData() 84 | } 85 | } 86 | 87 | /* 88 | * Current support is only for Linear, so saving intercepts and weights is enough 89 | */ 90 | def saveSparkModel(path: String, modelName: String, model: ModelData[M]): Boolean = { 91 | model.clf match { 92 | case Some(m) ⇒ { 93 | saveModel2Json(path, modelName, m, model.categoriesMap, model.binThreshold, model.numClasses) 94 | Files.exists(Paths.get(path)) //check if file was created... 95 | } 96 | case None ⇒ false 97 | } 98 | } 99 | 100 | /* 101 | * Custom Serialization of linear models without using spark context 102 | * using json for the weights and intercepts 103 | */ 104 | def saveModel2Json( 105 | path: String, 106 | modelName: String, 107 | m: M, 108 | catMap: Option[Map[String, Double]], 109 | thresh: Option[Double], 110 | numClasses: Option[Int]): Unit = { 111 | implicit val formats = Serialization.formats(NoTypeHints) 112 | val jx_str = Serialization.write( 113 | LinearModelData( 114 | modelName, 115 | m.weights.size, 116 | m.intercept, 117 | m.weights.toArray, 118 | catMap, 119 | thresh, 120 | numClasses)) 121 | 122 | Files.write(Paths.get(path), jx_str.getBytes(StandardCharsets.UTF_8)) 123 | } 124 | 125 | def readModelFromJson(path: String): Option[LinearModelData] = { 126 | Try { 127 | implicit val formats = Serialization.formats(NoTypeHints) 128 | val s = scala.io.Source.fromFile(path).getLines.mkString 129 | Serialization.read[LinearModelData](s) 130 | }.toOption 131 | } 132 | } 133 | -------------------------------------------------------------------------------- /src/main/scala/SparkPredictorEngine.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | 3 | import com.mahisoft.elasticsearchprediction.plugin.engine.PredictorEngine 4 | import com.mahisoft.elasticsearchprediction.plugin.domain.IndexValue 5 | import com.mahisoft.elasticsearchprediction.plugin.exception.PredictionException 6 | 7 | import org.apache.spark.mllib.linalg.Vectors 8 | import org.apache.spark.mllib.regression.GeneralizedLinearModel 9 | 10 | import java.util.Collection 11 | 12 | class SparkPredictorEngine[M <: GeneralizedLinearModel](val readPath: String, val spHelp: SparkModelHelpers[M]) extends PredictorEngine { 13 | 14 | private var _model: ModelData[M] = ModelData[M]() 15 | 16 | override def getPrediction(values: Collection[IndexValue]): Double = { 17 | if (_model.clf.nonEmpty) { 18 | val v = ReadUtil.cIndVal2Vector( 19 | values, 20 | _model.categoriesMap.getOrElse(Map[String, Double]())) 21 | 22 | _model.clf.get.predict(v) 23 | } else { 24 | throw new PredictionException("Empty model"); 25 | } 26 | } 27 | 28 | def readModel(): ModelData[M] = { 29 | _model = spHelp.readSparkModel(readPath) 30 | _model 31 | } 32 | 33 | def getModel: ModelData[M] = _model 34 | } 35 | 36 | -------------------------------------------------------------------------------- /src/main/scala/impl/LinearRegression.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | 3 | import org.apache.spark.rdd.RDD 4 | import org.apache.spark.mllib.regression._ 5 | import org.apache.spark.mllib.linalg.Vectors 6 | 7 | object LinearRegression_Helper extends Linear_SparkModelHelpers[LinearRegressionModel]{ 8 | val name = "LinearRegression_Helper" 9 | 10 | override def sparkTrainClf(data: RDD[LabeledPoint], params: Map[String,String]): LinearRegressionModel = { 11 | LinearRegressionWithSGD.train( 12 | data, 13 | params.getOrElse("numIterations","3").toInt, 14 | params.getOrElse("stepSize","1.0").toDouble, 15 | params.getOrElse("minBatchFraction","1.0").toDouble 16 | ) 17 | } 18 | 19 | override def getOptClf(lm: Option[LinearModelData]): Option[LinearRegressionModel] = { 20 | lm.map(x ⇒ new LinearRegressionModel(Vectors.dense(x.weights), x.intercept)) 21 | } 22 | } 23 | 24 | -------------------------------------------------------------------------------- /src/main/scala/impl/LogisticRegression.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | 3 | import org.apache.spark.rdd.RDD 4 | import org.apache.spark.mllib.classification._ 5 | import org.apache.spark.mllib.regression._ 6 | import org.apache.spark.mllib.linalg.Vectors 7 | 8 | object LogisticRegression_Helper extends Linear_SparkModelHelpers[LogisticRegressionModel]{ 9 | val name = "LogisticRegression_Helper" 10 | 11 | override def sparkTrainClf(data: RDD[LabeledPoint], params: Map[String,String]): LogisticRegressionModel = { 12 | LogisticRegressionWithSGD.train( 13 | data, 14 | params.getOrElse("numIterations","3").toInt, 15 | params.getOrElse("stepSize","1.0").toDouble, 16 | params.getOrElse("minBatchFraction","1.0").toDouble 17 | ) 18 | } 19 | 20 | override def getOptClf(lm: Option[LinearModelData]): Option[LogisticRegressionModel] = { 21 | lm.map(x ⇒ new LogisticRegressionModel(Vectors.dense(x.weights), x.intercept)) 22 | } 23 | } 24 | 25 | -------------------------------------------------------------------------------- /src/main/scala/impl/RigeRegression.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | 3 | import org.apache.spark.rdd.RDD 4 | import org.apache.spark.mllib.regression._ 5 | import org.apache.spark.mllib.linalg.Vectors 6 | 7 | object RidgeRegression_Helper extends Linear_SparkModelHelpers[RidgeRegressionModel]{ 8 | val name = "RidgeRegression_Helper" 9 | 10 | override def sparkTrainClf(data: RDD[LabeledPoint], params: Map[String,String]): RidgeRegressionModel = { 11 | RidgeRegressionWithSGD.train( 12 | data, 13 | params.getOrElse("numIterations","3").toInt, 14 | params.getOrElse("stepSize","1.0").toDouble, 15 | params.getOrElse("regParam","0.01").toDouble, 16 | params.getOrElse("minBatchFraction","1.0").toDouble 17 | ) 18 | } 19 | 20 | override def getOptClf(lm: Option[LinearModelData]): Option[RidgeRegressionModel] = { 21 | lm.map(x ⇒ new RidgeRegressionModel(Vectors.dense(x.weights), x.intercept)) 22 | } 23 | } 24 | 25 | 26 | -------------------------------------------------------------------------------- /src/main/scala/impl/SVM.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | 3 | import org.apache.spark.rdd.RDD 4 | import org.apache.spark.mllib.classification._ 5 | import org.apache.spark.mllib.regression._ 6 | import org.apache.spark.mllib.linalg.Vectors 7 | 8 | object SVM_Helper extends Linear_SparkModelHelpers[SVMModel]{ 9 | val name = "SVM_Helper" 10 | 11 | override def sparkTrainClf(data: RDD[LabeledPoint], params: Map[String,String]): SVMModel = { 12 | SVMWithSGD.train( 13 | data, 14 | params.getOrElse("numIterations","3").toInt, 15 | params.getOrElse("stepSize","1.0").toDouble, 16 | params.getOrElse("regParam","0.01").toDouble, 17 | params.getOrElse("minBatchFraction","1.0").toDouble 18 | ) 19 | } 20 | 21 | override def getOptClf(lm: Option[LinearModelData]): Option[SVMModel] = { 22 | lm.map(x ⇒ new SVMModel(Vectors.dense(x.weights), x.intercept)) 23 | } 24 | } 25 | 26 | 27 | -------------------------------------------------------------------------------- /src/test/resources/mini.csv: -------------------------------------------------------------------------------- 1 | age,workclass,fnlwgt,education,education_num,marital_status,occupation,relationship,race,sex,capital_gain,capital_loss,hours-per-week,native-country,probability 2 | 39, State-gov, 77516, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 2174, 0, 40, United-States, 0 3 | 50, Self-emp-not-inc, 83311, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 13, United-States, 0 4 | 38, Private, 215646, HS-grad, 9, Divorced, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, 0 5 | 53, Private, 234721, 11th, 7, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, 0 6 | 28, Private, 338409, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, Black, Female, 0, 0, 40, Cuba, 0 7 | 37, Private, 284582, Masters, 14, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, 0 8 | 49, Private, 160187, 9th, 5, Married-spouse-absent, Other-service, Not-in-family, Black, Female, 0, 0, 16, Jamaica, 0 9 | 52, Self-emp-not-inc, 209642, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, 1 10 | 31, Private, 45781, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 14084, 0, 50, United-States, 1 11 | 42, Private, 159449, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 5178, 0, 40, United-States, 1 12 | 37, Private, 280464, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, Black, Male, 0, 0, 80, United-States, 1 13 | 30, State-gov, 141297, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, Asian-Pac-Islander, Male, 0, 0, 40, India, 1 14 | 23, Private, 122272, Bachelors, 13, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 30, United-States, 0 15 | 32, Private, 205019, Assoc-acdm, 12, Never-married, Sales, Not-in-family, Black, Male, 0, 0, 50, United-States, 0 16 | 40, Private, 121772, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, Asian-Pac-Islander, Male, 0, 0, 40, ?, 1 17 | 34, Private, 245487, 7th-8th, 4, Married-civ-spouse, Transport-moving, Husband, Amer-Indian-Eskimo, Male, 0, 0, 45, Mexico, 0 18 | 25, Self-emp-not-inc, 176756, HS-grad, 9, Never-married, Farming-fishing, Own-child, White, Male, 0, 0, 35, United-States, 0 19 | 32, Private, 186824, HS-grad, 9, Never-married, Machine-op-inspct, Unmarried, White, Male, 0, 0, 40, United-States, 0 20 | 38, Private, 28887, 11th, 7, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, 0 21 | 43, Self-emp-not-inc, 292175, Masters, 14, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 45, United-States, 1 22 | 40, Private, 193524, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 60, United-States, 1 23 | 54, Private, 302146, HS-grad, 9, Separated, Other-service, Unmarried, Black, Female, 0, 0, 20, United-States, 0 24 | 35, Federal-gov, 76845, 9th, 5, Married-civ-spouse, Farming-fishing, Husband, Black, Male, 0, 0, 40, United-States, 0 25 | 43, Private, 117037, 11th, 7, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 2042, 40, United-States, 0 26 | 59, Private, 109015, HS-grad, 9, Divorced, Tech-support, Unmarried, White, Female, 0, 0, 40, United-States, 0 27 | 56, Local-gov, 216851, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, 1 28 | 19, Private, 168294, HS-grad, 9, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 40, United-States, 0 29 | 54, ?, 180211, Some-college, 10, Married-civ-spouse, ?, Husband, Asian-Pac-Islander, Male, 0, 0, 60, South, 1 30 | 39, Private, 367260, HS-grad, 9, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 80, United-States, 0 31 | 49, Private, 193366, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 32 | 23, Local-gov, 190709, Assoc-acdm, 12, Never-married, Protective-serv, Not-in-family, White, Male, 0, 0, 52, United-States, 0 33 | 20, Private, 266015, Some-college, 10, Never-married, Sales, Own-child, Black, Male, 0, 0, 44, United-States, 0 34 | 45, Private, 386940, Bachelors, 13, Divorced, Exec-managerial, Own-child, White, Male, 0, 1408, 40, United-States, 0 35 | 30, Federal-gov, 59951, Some-college, 10, Married-civ-spouse, Adm-clerical, Own-child, White, Male, 0, 0, 40, United-States, 0 36 | 22, State-gov, 311512, Some-college, 10, Married-civ-spouse, Other-service, Husband, Black, Male, 0, 0, 15, United-States, 0 37 | 48, Private, 242406, 11th, 7, Never-married, Machine-op-inspct, Unmarried, White, Male, 0, 0, 40, Puerto-Rico, 0 38 | 21, Private, 197200, Some-college, 10, Never-married, Machine-op-inspct, Own-child, White, Male, 0, 0, 40, United-States, 0 39 | 19, Private, 544091, HS-grad, 9, Married-AF-spouse, Adm-clerical, Wife, White, Female, 0, 0, 25, United-States, 0 40 | 31, Private, 84154, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 38, ?, 1 41 | 48, Self-emp-not-inc, 265477, Assoc-acdm, 12, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 0 42 | 31, Private, 507875, 9th, 5, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 43, United-States, 0 43 | 53, Self-emp-not-inc, 88506, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 0 44 | 24, Private, 172987, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 50, United-States, 0 45 | 49, Private, 94638, HS-grad, 9, Separated, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, 0 46 | 25, Private, 289980, HS-grad, 9, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 35, United-States, 0 47 | 57, Federal-gov, 337895, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, Black, Male, 0, 0, 40, United-States, 1 48 | 53, Private, 144361, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 38, United-States, 0 49 | 44, Private, 128354, Masters, 14, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 40, United-States, 0 50 | 41, State-gov, 101603, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 51 | 29, Private, 271466, Assoc-voc, 11, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 43, United-States, 0 52 | 25, Private, 32275, Some-college, 10, Married-civ-spouse, Exec-managerial, Wife, Other, Female, 0, 0, 40, United-States, 0 53 | 18, Private, 226956, HS-grad, 9, Never-married, Other-service, Own-child, White, Female, 0, 0, 30, ?, 0 54 | 47, Private, 51835, Prof-school, 15, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 1902, 60, Honduras, 1 55 | 50, Federal-gov, 251585, Bachelors, 13, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 55, United-States, 1 56 | 47, Self-emp-inc, 109832, HS-grad, 9, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 60, United-States, 0 57 | 43, Private, 237993, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, 1 58 | 46, Private, 216666, 5th-6th, 3, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, Mexico, 0 59 | 35, Private, 56352, Assoc-voc, 11, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, Puerto-Rico, 0 60 | 41, Private, 147372, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 48, United-States, 0 61 | 30, Private, 188146, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 5013, 0, 40, United-States, 0 62 | 30, Private, 59496, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 2407, 0, 40, United-States, 0 63 | 32, ?, 293936, 7th-8th, 4, Married-spouse-absent, ?, Not-in-family, White, Male, 0, 0, 40, ?, 0 64 | 48, Private, 149640, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, 0 65 | 42, Private, 116632, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 45, United-States, 1 66 | 29, Private, 105598, Some-college, 10, Divorced, Tech-support, Not-in-family, White, Male, 0, 0, 58, United-States, 0 67 | 36, Private, 155537, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 68 | 28, Private, 183175, Some-college, 10, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, 0 69 | 53, Private, 169846, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 40, United-States, 1 70 | 49, Self-emp-inc, 191681, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, 1 71 | 25, ?, 200681, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 40, United-States, 0 72 | 19, Private, 101509, Some-college, 10, Never-married, Prof-specialty, Own-child, White, Male, 0, 0, 32, United-States, 0 73 | 31, Private, 309974, Bachelors, 13, Separated, Sales, Own-child, Black, Female, 0, 0, 40, United-States, 0 74 | 29, Self-emp-not-inc, 162298, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 70, United-States, 1 75 | 23, Private, 211678, Some-college, 10, Never-married, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 40, United-States, 0 76 | 79, Private, 124744, Some-college, 10, Married-civ-spouse, Prof-specialty, Other-relative, White, Male, 0, 0, 20, United-States, 0 77 | 27, Private, 213921, HS-grad, 9, Never-married, Other-service, Own-child, White, Male, 0, 0, 40, Mexico, 0 78 | 40, Private, 32214, Assoc-acdm, 12, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, 0 79 | 67, ?, 212759, 10th, 6, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 2, United-States, 0 80 | 18, Private, 309634, 11th, 7, Never-married, Other-service, Own-child, White, Female, 0, 0, 22, United-States, 0 81 | 31, Local-gov, 125927, 7th-8th, 4, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 40, United-States, 0 82 | 18, Private, 446839, HS-grad, 9, Never-married, Sales, Not-in-family, White, Male, 0, 0, 30, United-States, 0 83 | 52, Private, 276515, Bachelors, 13, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, Cuba, 0 84 | 46, Private, 51618, HS-grad, 9, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 40, United-States, 0 85 | 59, Private, 159937, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 48, United-States, 0 86 | 44, Private, 343591, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Female, 14344, 0, 40, United-States, 1 87 | 53, Private, 346253, HS-grad, 9, Divorced, Sales, Own-child, White, Female, 0, 0, 35, United-States, 0 88 | 49, Local-gov, 268234, HS-grad, 9, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, 1 89 | 33, Private, 202051, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, 0 90 | 30, Private, 54334, 9th, 5, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, 0 91 | 43, Federal-gov, 410867, Doctorate, 16, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 50, United-States, 1 92 | 57, Private, 249977, Assoc-voc, 11, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 0 93 | 37, Private, 286730, Some-college, 10, Divorced, Craft-repair, Unmarried, White, Female, 0, 0, 40, United-States, 0 94 | 28, Private, 212563, Some-college, 10, Divorced, Machine-op-inspct, Unmarried, Black, Female, 0, 0, 25, United-States, 0 95 | 30, Private, 117747, HS-grad, 9, Married-civ-spouse, Sales, Wife, Asian-Pac-Islander, Female, 0, 1573, 35, ?, 0 96 | 34, Local-gov, 226296, Bachelors, 13, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, 1 97 | 29, Local-gov, 115585, Some-college, 10, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 50, United-States, 0 98 | 48, Self-emp-not-inc, 191277, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 1902, 60, United-States, 1 99 | 37, Private, 202683, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 48, United-States, 1 100 | 48, Private, 171095, Assoc-acdm, 12, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 40, England, 0 101 | 32, Federal-gov, 249409, HS-grad, 9, Never-married, Other-service, Own-child, Black, Male, 0, 0, 40, United-States, 0 102 | 76, Private, 124191, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 103 | 44, Private, 198282, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 60, United-States, 1 104 | 47, Self-emp-not-inc, 149116, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 50, United-States, 0 105 | 20, Private, 188300, Some-college, 10, Never-married, Tech-support, Own-child, White, Female, 0, 0, 40, United-States, 0 106 | 29, Private, 103432, HS-grad, 9, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, 0 107 | 32, Self-emp-inc, 317660, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 7688, 0, 40, United-States, 1 108 | 17, ?, 304873, 10th, 6, Never-married, ?, Own-child, White, Female, 34095, 0, 32, United-States, 0 109 | 30, Private, 194901, 11th, 7, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, 0 110 | 31, Local-gov, 189265, HS-grad, 9, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, 0 111 | 42, Private, 124692, HS-grad, 9, Married-civ-spouse, Handlers-cleaners, Husband, White, Male, 0, 0, 40, United-States, 0 112 | 24, Private, 432376, Bachelors, 13, Never-married, Sales, Other-relative, White, Male, 0, 0, 40, United-States, 0 113 | 38, Private, 65324, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 1 114 | 56, Self-emp-not-inc, 335605, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 1887, 50, Canada, 1 115 | 28, Private, 377869, Some-college, 10, Married-civ-spouse, Sales, Wife, White, Female, 4064, 0, 25, United-States, 0 116 | 36, Private, 102864, HS-grad, 9, Never-married, Machine-op-inspct, Own-child, White, Female, 0, 0, 40, United-States, 0 117 | 53, Private, 95647, 9th, 5, Married-civ-spouse, Handlers-cleaners, Husband, White, Male, 0, 0, 50, United-States, 0 118 | 56, Self-emp-inc, 303090, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, 0 119 | 49, Local-gov, 197371, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, Black, Male, 0, 0, 40, United-States, 1 120 | 55, Private, 247552, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 56, United-States, 0 121 | 22, Private, 102632, HS-grad, 9, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 41, United-States, 0 122 | 21, Private, 199915, Some-college, 10, Never-married, Other-service, Own-child, White, Female, 0, 0, 40, United-States, 0 123 | 40, Private, 118853, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 60, United-States, 0 124 | 30, Private, 77143, Bachelors, 13, Never-married, Exec-managerial, Own-child, Black, Male, 0, 0, 40, Germany, 0 125 | 29, State-gov, 267989, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, 1 126 | 19, Private, 301606, Some-college, 10, Never-married, Other-service, Own-child, Black, Male, 0, 0, 35, United-States, 0 127 | 47, Private, 287828, Bachelors, 13, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, 1 128 | 20, Private, 111697, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Female, 0, 1719, 28, United-States, 0 129 | 31, Private, 114937, Assoc-acdm, 12, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, 1 130 | 35, ?, 129305, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 40, United-States, 0 131 | 39, Private, 365739, Some-college, 10, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, 0 132 | 28, Private, 69621, Assoc-acdm, 12, Never-married, Sales, Not-in-family, White, Female, 0, 0, 60, United-States, 0 133 | 24, Private, 43323, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 1762, 40, United-States, 0 134 | 38, Self-emp-not-inc, 120985, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 4386, 0, 35, United-States, 0 135 | 37, Private, 254202, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, 0 136 | 46, Private, 146195, Assoc-acdm, 12, Divorced, Tech-support, Not-in-family, Black, Female, 0, 0, 36, United-States, 0 137 | 38, Federal-gov, 125933, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, Iran, 1 138 | 43, Self-emp-not-inc, 56920, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 60, United-States, 0 139 | 27, Private, 163127, Assoc-voc, 11, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 35, United-States, 0 140 | 20, Private, 34310, Some-college, 10, Never-married, Sales, Own-child, White, Male, 0, 0, 20, United-States, 0 141 | 49, Private, 81973, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, Asian-Pac-Islander, Male, 0, 0, 40, United-States, 1 142 | 61, Self-emp-inc, 66614, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 143 | 27, Private, 232782, Some-college, 10, Never-married, Sales, Own-child, White, Female, 0, 0, 40, United-States, 0 144 | 19, Private, 316868, Some-college, 10, Never-married, Other-service, Own-child, White, Male, 0, 0, 30, Mexico, 0 145 | 45, Private, 196584, Assoc-voc, 11, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 1564, 40, United-States, 1 146 | 70, Private, 105376, Some-college, 10, Never-married, Tech-support, Other-relative, White, Male, 0, 0, 40, United-States, 0 147 | 31, Private, 185814, HS-grad, 9, Never-married, Transport-moving, Unmarried, Black, Female, 0, 0, 30, United-States, 0 148 | 22, Private, 175374, Some-college, 10, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 24, United-States, 0 149 | 36, Private, 108293, HS-grad, 9, Widowed, Other-service, Unmarried, White, Female, 0, 0, 24, United-States, 0 150 | 64, Private, 181232, 11th, 7, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 2179, 40, United-States, 0 151 | 43, ?, 174662, Some-college, 10, Divorced, ?, Not-in-family, White, Female, 0, 0, 40, United-States, 0 152 | 47, Local-gov, 186009, Some-college, 10, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 38, Mexico, 0 153 | 34, Private, 198183, HS-grad, 9, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, 0 154 | 33, Private, 163003, Bachelors, 13, Never-married, Exec-managerial, Other-relative, Asian-Pac-Islander, Female, 0, 0, 40, Philippines, 0 155 | 21, Private, 296158, HS-grad, 9, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 35, United-States, 0 156 | 52, ?, 252903, HS-grad, 9, Divorced, ?, Not-in-family, White, Male, 0, 0, 45, United-States, 1 157 | 48, Private, 187715, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 46, United-States, 0 158 | 23, Private, 214542, Bachelors, 13, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, 0 159 | 71, Self-emp-not-inc, 494223, Some-college, 10, Separated, Sales, Unmarried, Black, Male, 0, 1816, 2, United-States, 0 160 | 29, Private, 191535, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 60, United-States, 0 161 | 42, Private, 228456, Bachelors, 13, Separated, Other-service, Other-relative, Black, Male, 0, 0, 50, United-States, 0 162 | 68, ?, 38317, 1st-4th, 2, Divorced, ?, Not-in-family, White, Female, 0, 0, 20, United-States, 0 163 | 25, Private, 252752, HS-grad, 9, Never-married, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, 0 164 | 44, Self-emp-inc, 78374, Masters, 14, Divorced, Exec-managerial, Unmarried, Asian-Pac-Islander, Female, 0, 0, 40, United-States, 0 165 | 28, Private, 88419, HS-grad, 9, Never-married, Exec-managerial, Not-in-family, Asian-Pac-Islander, Female, 0, 0, 40, England, 0 166 | 45, Self-emp-not-inc, 201080, Masters, 14, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 40, United-States, 1 167 | 36, Private, 207157, Some-college, 10, Divorced, Other-service, Unmarried, White, Female, 0, 0, 40, Mexico, 0 168 | 39, Federal-gov, 235485, Assoc-acdm, 12, Never-married, Exec-managerial, Not-in-family, White, Male, 0, 0, 42, United-States, 0 169 | 46, State-gov, 102628, Masters, 14, Widowed, Protective-serv, Unmarried, White, Male, 0, 0, 40, United-States, 0 170 | 18, Private, 25828, 11th, 7, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 16, United-States, 0 171 | 66, Local-gov, 54826, Assoc-voc, 11, Widowed, Prof-specialty, Not-in-family, White, Female, 0, 0, 20, United-States, 0 172 | 27, Private, 124953, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Male, 0, 1980, 40, United-States, 0 173 | 28, State-gov, 175325, HS-grad, 9, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, 0 174 | 51, Private, 96062, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 1977, 40, United-States, 1 175 | 27, Private, 428030, Bachelors, 13, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 50, United-States, 0 176 | 28, State-gov, 149624, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 1 177 | 27, Private, 253814, HS-grad, 9, Married-spouse-absent, Sales, Unmarried, White, Female, 0, 0, 25, United-States, 0 178 | 21, Private, 312956, HS-grad, 9, Never-married, Craft-repair, Own-child, Black, Male, 0, 0, 40, United-States, 0 179 | 34, Private, 483777, HS-grad, 9, Never-married, Handlers-cleaners, Not-in-family, Black, Male, 0, 0, 40, United-States, 0 180 | 18, Private, 183930, HS-grad, 9, Never-married, Other-service, Own-child, White, Male, 0, 0, 12, United-States, 0 181 | 33, Private, 37274, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 65, United-States, 0 182 | 44, Local-gov, 181344, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, Black, Male, 0, 0, 38, United-States, 1 183 | 43, Private, 114580, Some-college, 10, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, 0 184 | 30, Private, 633742, Some-college, 10, Never-married, Craft-repair, Not-in-family, Black, Male, 0, 0, 45, United-States, 0 185 | 40, Private, 286370, 7th-8th, 4, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, Mexico, 1 186 | 37, Federal-gov, 29054, Some-college, 10, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 42, United-States, 1 187 | 34, Private, 304030, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, Black, Male, 0, 0, 40, United-States, 0 188 | 41, Self-emp-not-inc, 143129, Bachelors, 13, Divorced, Exec-managerial, Not-in-family, White, Female, 0, 0, 40, United-States, 0 189 | 53, ?, 135105, Bachelors, 13, Divorced, ?, Not-in-family, White, Female, 0, 0, 50, United-States, 0 190 | 31, Private, 99928, Masters, 14, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 50, United-States, 0 191 | 58, State-gov, 109567, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 1, United-States, 1 192 | 38, Private, 155222, Some-college, 10, Divorced, Machine-op-inspct, Not-in-family, Black, Female, 0, 0, 28, United-States, 0 193 | 24, Private, 159567, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 194 | 41, Local-gov, 523910, Bachelors, 13, Married-civ-spouse, Craft-repair, Husband, Black, Male, 0, 0, 40, United-States, 0 195 | 47, Private, 120939, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 45, United-States, 0 196 | 41, Federal-gov, 130760, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 24, United-States, 0 197 | 23, Private, 197387, 5th-6th, 3, Married-civ-spouse, Transport-moving, Other-relative, White, Male, 0, 0, 40, Mexico, 0 198 | 36, Private, 99374, Some-college, 10, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, 0 199 | 40, Federal-gov, 56795, Masters, 14, Never-married, Exec-managerial, Not-in-family, White, Female, 14084, 0, 55, United-States, 1 200 | 35, Private, 138992, Masters, 14, Married-civ-spouse, Prof-specialty, Other-relative, White, Male, 7298, 0, 40, United-States, 1 201 | 24, Self-emp-not-inc, 32921, HS-grad, 9, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, 0 202 | 26, Private, 397317, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 1876, 40, United-States, 0 203 | 19, ?, 170653, HS-grad, 9, Never-married, ?, Own-child, White, Male, 0, 0, 40, Italy, 0 204 | 51, Private, 259323, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, 1 205 | 42, Local-gov, 254817, Some-college, 10, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 1340, 40, United-States, 0 206 | 37, State-gov, 48211, HS-grad, 9, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 35, United-States, 0 207 | 18, Private, 140164, 11th, 7, Never-married, Sales, Own-child, White, Female, 0, 0, 40, United-States, 0 208 | 36, Private, 128757, Bachelors, 13, Married-civ-spouse, Other-service, Husband, Black, Male, 7298, 0, 36, United-States, 1 209 | 35, Private, 36270, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 60, United-States, 0 210 | 58, Self-emp-inc, 210563, HS-grad, 9, Married-civ-spouse, Sales, Wife, White, Female, 15024, 0, 35, United-States, 1 211 | 17, Private, 65368, 11th, 7, Never-married, Sales, Own-child, White, Female, 0, 0, 12, United-States, 0 212 | 44, Local-gov, 160943, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, Black, Male, 0, 0, 40, United-States, 0 213 | 37, Private, 208358, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 1 214 | 35, Private, 153790, Some-college, 10, Never-married, Sales, Not-in-family, Amer-Indian-Eskimo, Female, 0, 0, 40, United-States, 0 215 | 60, Private, 85815, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, Asian-Pac-Islander, Male, 0, 0, 40, United-States, 0 216 | 54, Self-emp-inc, 125417, 7th-8th, 4, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 1 217 | 37, Private, 635913, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, Black, Male, 0, 0, 60, United-States, 1 218 | 50, Private, 313321, Assoc-acdm, 12, Divorced, Sales, Not-in-family, White, Female, 0, 0, 40, United-States, 0 219 | 38, Private, 182609, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, Poland, 0 220 | 45, Private, 109434, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 55, United-States, 0 221 | 25, Private, 255004, 10th, 6, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, 0 222 | 31, Private, 197860, Some-college, 10, Married-civ-spouse, Handlers-cleaners, Husband, White, Male, 0, 0, 40, United-States, 0 223 | 64, ?, 187656, 1st-4th, 2, Divorced, ?, Not-in-family, White, Male, 0, 0, 40, United-States, 0 224 | 90, Private, 51744, HS-grad, 9, Never-married, Other-service, Not-in-family, Black, Male, 0, 2206, 40, United-States, 0 225 | 54, Private, 176681, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, Black, Male, 0, 0, 20, United-States, 0 226 | 53, Local-gov, 140359, Preschool, 1, Never-married, Machine-op-inspct, Not-in-family, White, Female, 0, 0, 35, United-States, 0 227 | 18, Private, 243313, HS-grad, 9, Never-married, Sales, Own-child, White, Female, 0, 0, 40, United-States, 0 228 | 60, ?, 24215, 10th, 6, Divorced, ?, Not-in-family, Amer-Indian-Eskimo, Female, 0, 0, 10, United-States, 0 229 | 66, Self-emp-not-inc, 167687, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 1409, 0, 50, United-States, 0 230 | 75, Private, 314209, Assoc-voc, 11, Widowed, Adm-clerical, Not-in-family, White, Female, 0, 0, 20, Columbia, 0 231 | 65, Private, 176796, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, 0 232 | 35, Private, 538583, 11th, 7, Separated, Transport-moving, Not-in-family, Black, Male, 3674, 0, 40, United-States, 0 233 | 41, Private, 130408, HS-grad, 9, Divorced, Sales, Unmarried, Black, Female, 0, 0, 38, United-States, 0 234 | 25, Private, 159732, Some-college, 10, Never-married, Adm-clerical, Not-in-family, White, Male, 0, 0, 42, United-States, 0 235 | 33, Private, 110978, Some-college, 10, Divorced, Craft-repair, Other-relative, Other, Female, 0, 0, 40, United-States, 0 236 | 28, Private, 76714, Prof-school, 15, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 55, United-States, 1 237 | 59, State-gov, 268700, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, 0 238 | 40, State-gov, 170525, Some-college, 10, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 38, United-States, 0 239 | 41, Private, 180138, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, Iran, 1 240 | 38, Local-gov, 115076, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 70, United-States, 1 241 | 23, Private, 115458, HS-grad, 9, Never-married, Transport-moving, Own-child, White, Male, 0, 0, 40, United-States, 0 242 | 40, Private, 347890, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 1 243 | 41, Self-emp-not-inc, 196001, HS-grad, 9, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 20, United-States, 0 244 | 24, State-gov, 273905, Assoc-acdm, 12, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 50, United-States, 0 245 | 20, ?, 119156, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 20, United-States, 0 246 | 38, Private, 179488, Some-college, 10, Divorced, Craft-repair, Not-in-family, White, Male, 0, 1741, 40, United-States, 0 247 | 56, Private, 203580, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 35, ?, 0 248 | 58, Private, 236596, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 45, United-States, 1 249 | 32, Private, 183916, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 34, United-States, 0 250 | 40, Private, 207578, Assoc-acdm, 12, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 1977, 60, United-States, 1 251 | 45, Private, 153141, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, ?, 0 252 | 41, Private, 112763, Prof-school, 15, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 40, United-States, 1 253 | 42, Private, 390781, Bachelors, 13, Married-civ-spouse, Adm-clerical, Wife, Black, Female, 0, 0, 40, United-States, 0 254 | 59, Local-gov, 171328, 10th, 6, Widowed, Other-service, Unmarried, Black, Female, 0, 0, 30, United-States, 0 255 | 19, Local-gov, 27382, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Male, 0, 0, 40, United-States, 0 256 | 58, Private, 259014, Some-college, 10, Never-married, Transport-moving, Not-in-family, White, Male, 0, 0, 20, United-States, 0 257 | 42, Self-emp-not-inc, 303044, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, Asian-Pac-Islander, Male, 0, 0, 40, Cambodia, 1 258 | 20, Private, 117789, HS-grad, 9, Never-married, Other-service, Own-child, White, Female, 0, 0, 40, United-States, 0 259 | 32, Private, 172579, HS-grad, 9, Separated, Other-service, Not-in-family, White, Female, 0, 0, 30, United-States, 0 260 | 45, Private, 187666, Assoc-voc, 11, Widowed, Exec-managerial, Not-in-family, White, Female, 0, 0, 45, United-States, 0 261 | 50, Private, 204518, 7th-8th, 4, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, 0 262 | 36, Private, 150042, Bachelors, 13, Divorced, Prof-specialty, Own-child, White, Female, 0, 0, 40, United-States, 0 263 | 45, Private, 98092, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 60, United-States, 0 264 | 17, Private, 245918, 11th, 7, Never-married, Other-service, Own-child, White, Male, 0, 0, 12, United-States, 0 265 | 59, Private, 146013, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 4064, 0, 40, United-States, 0 266 | 26, Private, 378322, 11th, 7, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 267 | 37, Self-emp-inc, 257295, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, Asian-Pac-Islander, Male, 0, 0, 75, Thailand, 1 268 | 19, ?, 218956, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 24, Canada, 0 269 | 64, Private, 21174, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 270 | 33, Private, 185480, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 45, United-States, 0 271 | 33, Private, 222205, HS-grad, 9, Married-civ-spouse, Craft-repair, Wife, White, Female, 0, 0, 40, United-States, 1 272 | 61, Private, 69867, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 273 | 17, Private, 191260, 9th, 5, Never-married, Other-service, Own-child, White, Male, 1055, 0, 24, United-States, 0 274 | 50, Self-emp-not-inc, 30653, Masters, 14, Married-civ-spouse, Farming-fishing, Husband, White, Male, 2407, 0, 98, United-States, 0 275 | 27, Local-gov, 209109, Masters, 14, Never-married, Prof-specialty, Own-child, White, Male, 0, 0, 35, United-States, 0 276 | 30, Private, 70377, HS-grad, 9, Divorced, Prof-specialty, Own-child, White, Female, 0, 0, 40, United-States, 0 277 | 43, Private, 477983, HS-grad, 9, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, 0 278 | 44, Private, 170924, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 7298, 0, 40, United-States, 1 279 | 35, Private, 190174, Some-college, 10, Never-married, Exec-managerial, Not-in-family, White, Female, 0, 0, 40, United-States, 0 280 | 25, Private, 193787, Some-college, 10, Never-married, Tech-support, Own-child, White, Female, 0, 0, 40, United-States, 0 281 | 24, Private, 279472, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Wife, White, Female, 7298, 0, 48, United-States, 1 282 | 22, Private, 34918, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 15, Germany, 0 283 | 42, Local-gov, 97688, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 5178, 0, 40, United-States, 1 284 | 34, Private, 175413, Assoc-acdm, 12, Divorced, Sales, Unmarried, Black, Female, 0, 0, 45, United-States, 0 285 | 60, Private, 173960, Bachelors, 13, Divorced, Prof-specialty, Not-in-family, White, Female, 0, 0, 42, United-States, 0 286 | 21, Private, 205759, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, 0 287 | 57, Federal-gov, 425161, Masters, 14, Married-civ-spouse, Sales, Husband, White, Male, 15024, 0, 40, United-States, 1 288 | 41, Private, 220531, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 60, United-States, 1 289 | 50, Private, 176609, Some-college, 10, Divorced, Other-service, Not-in-family, White, Male, 0, 0, 45, United-States, 0 290 | 25, Private, 371987, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, White, Female, 0, 0, 40, United-States, 0 291 | 50, Private, 193884, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, Ecuador, 0 292 | 36, Private, 200352, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 45, United-States, 0 293 | 31, Private, 127595, HS-grad, 9, Divorced, Prof-specialty, Not-in-family, White, Female, 0, 0, 40, United-States, 0 294 | 29, Local-gov, 220419, Bachelors, 13, Never-married, Protective-serv, Not-in-family, White, Male, 0, 0, 56, United-States, 0 295 | 21, Private, 231931, Some-college, 10, Never-married, Sales, Own-child, White, Male, 0, 0, 45, United-States, 0 296 | 27, Private, 248402, Bachelors, 13, Never-married, Tech-support, Unmarried, Black, Female, 0, 0, 40, United-States, 0 297 | 65, Private, 111095, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 16, United-States, 0 298 | 37, Self-emp-inc, 57424, Bachelors, 13, Divorced, Sales, Not-in-family, White, Female, 0, 0, 60, United-States, 0 299 | 39, ?, 157443, Masters, 14, Married-civ-spouse, ?, Wife, Asian-Pac-Islander, Female, 3464, 0, 40, ?, 0 300 | 24, Private, 278130, HS-grad, 9, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 40, United-States, 0 301 | 38, Private, 169469, HS-grad, 9, Divorced, Sales, Not-in-family, White, Male, 0, 0, 80, United-States, 0 302 | 48, Private, 146268, Bachelors, 13, Married-civ-spouse, Adm-clerical, Husband, White, Male, 7688, 0, 40, United-States, 1 303 | 21, Private, 153718, Some-college, 10, Never-married, Other-service, Not-in-family, Asian-Pac-Islander, Female, 0, 0, 25, United-States, 0 304 | 31, Private, 217460, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 45, United-States, 1 305 | 55, Private, 238638, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 4386, 0, 40, United-States, 1 306 | 24, Private, 303296, Some-college, 10, Married-civ-spouse, Adm-clerical, Wife, Asian-Pac-Islander, Female, 0, 0, 40, Laos, 0 307 | 43, Private, 173321, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, 0 308 | 26, Private, 193945, Assoc-acdm, 12, Never-married, Tech-support, Not-in-family, White, Male, 0, 0, 45, United-States, 0 309 | 46, Private, 83082, Assoc-acdm, 12, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 33, United-States, 0 310 | 35, Private, 193815, Assoc-acdm, 12, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, 1 311 | 41, Self-emp-inc, 34987, Some-college, 10, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 54, United-States, 1 312 | 26, Private, 59306, Bachelors, 13, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, 0 313 | 34, Private, 142897, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, Asian-Pac-Islander, Male, 7298, 0, 35, Taiwan, 1 314 | 19, ?, 860348, Some-college, 10, Never-married, ?, Own-child, Black, Female, 0, 0, 25, United-States, 0 315 | 36, Self-emp-not-inc, 205607, Bachelors, 13, Divorced, Prof-specialty, Not-in-family, Black, Female, 0, 0, 40, United-States, 1 316 | 22, Private, 199698, Some-college, 10, Never-married, Sales, Own-child, White, Male, 0, 0, 15, United-States, 0 317 | 24, Private, 191954, Some-college, 10, Never-married, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 40, United-States, 0 318 | 77, Self-emp-not-inc, 138714, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 40, United-States, 0 319 | 22, Private, 399087, 5th-6th, 3, Married-civ-spouse, Machine-op-inspct, Other-relative, White, Female, 0, 0, 40, Mexico, 0 320 | 29, Private, 423158, Some-college, 10, Never-married, Tech-support, Not-in-family, White, Female, 0, 0, 40, United-States, 0 321 | 62, Private, 159841, HS-grad, 9, Widowed, Other-service, Not-in-family, White, Female, 0, 0, 24, United-States, 0 322 | 39, Self-emp-not-inc, 174308, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 0 323 | 43, Private, 50356, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 1485, 50, United-States, 0 324 | 35, Private, 186110, HS-grad, 9, Divorced, Transport-moving, Not-in-family, White, Male, 0, 0, 45, United-States, 0 325 | 29, Private, 200381, 11th, 7, Never-married, Exec-managerial, Not-in-family, White, Female, 0, 0, 40, United-States, 0 326 | 76, Self-emp-not-inc, 174309, Masters, 14, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 10, United-States, 0 327 | 63, Self-emp-not-inc, 78383, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 45, United-States, 0 328 | 23, ?, 211601, Assoc-voc, 11, Never-married, ?, Own-child, Black, Female, 0, 0, 15, United-States, 0 329 | 43, Private, 187728, Some-college, 10, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 1887, 50, United-States, 1 330 | 58, Self-emp-not-inc, 321171, HS-grad, 9, Married-civ-spouse, Handlers-cleaners, Husband, White, Male, 0, 0, 40, United-States, 0 331 | 66, Private, 127921, HS-grad, 9, Never-married, Transport-moving, Not-in-family, White, Male, 2050, 0, 55, United-States, 0 332 | 41, Private, 206565, Some-college, 10, Never-married, Craft-repair, Not-in-family, Black, Male, 0, 0, 45, United-States, 0 333 | 26, Private, 224563, Bachelors, 13, Never-married, Adm-clerical, Not-in-family, White, Male, 0, 0, 40, United-States, 0 334 | 47, Private, 178686, Assoc-voc, 11, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 40, United-States, 0 335 | 55, Local-gov, 98545, 10th, 6, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, 0 336 | 53, Private, 242606, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, 0 337 | 17, Private, 270942, 5th-6th, 3, Never-married, Other-service, Other-relative, White, Male, 0, 0, 48, Mexico, 0 338 | 30, Private, 94235, HS-grad, 9, Never-married, Craft-repair, Other-relative, White, Male, 0, 0, 40, United-States, 0 339 | 49, Private, 71195, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 60, United-States, 0 340 | 19, Private, 104112, HS-grad, 9, Never-married, Sales, Unmarried, Black, Male, 0, 0, 30, Haiti, 0 341 | 45, Private, 261192, HS-grad, 9, Married-civ-spouse, Other-service, Husband, Black, Male, 0, 0, 40, United-States, 0 342 | 26, Private, 94936, Assoc-acdm, 12, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, 0 343 | 38, Private, 296478, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, White, Male, 7298, 0, 40, United-States, 1 344 | 36, State-gov, 119272, HS-grad, 9, Married-civ-spouse, Protective-serv, Husband, White, Male, 7298, 0, 40, United-States, 1 345 | 33, Private, 85043, HS-grad, 9, Never-married, Farming-fishing, Not-in-family, White, Male, 0, 0, 20, United-States, 0 346 | 22, State-gov, 293364, Some-college, 10, Never-married, Protective-serv, Own-child, Black, Female, 0, 0, 40, United-States, 0 347 | 43, Self-emp-not-inc, 241895, Bachelors, 13, Never-married, Sales, Not-in-family, White, Male, 0, 0, 42, United-States, 0 348 | 67, ?, 36135, 11th, 7, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 8, United-States, 0 349 | 30, ?, 151989, Assoc-voc, 11, Divorced, ?, Unmarried, White, Female, 0, 0, 40, United-States, 0 350 | 56, Private, 101128, Assoc-acdm, 12, Married-spouse-absent, Other-service, Not-in-family, White, Male, 0, 0, 25, Iran, 0 351 | 31, Private, 156464, Bachelors, 13, Never-married, Prof-specialty, Own-child, White, Male, 0, 0, 25, United-States, 0 352 | 33, Private, 117963, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 353 | 26, Private, 192262, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, 0 354 | 33, Private, 111363, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 355 | 46, Local-gov, 329752, 11th, 7, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 30, United-States, 0 356 | 59, ?, 372020, Bachelors, 13, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 40, United-States, 1 357 | 38, Federal-gov, 95432, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, 1 358 | 65, Private, 161400, 11th, 7, Widowed, Other-service, Unmarried, Other, Male, 0, 0, 40, United-States, 0 359 | 40, Private, 96129, Assoc-voc, 11, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, 1 360 | 42, Private, 111949, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 35, United-States, 0 361 | 26, Self-emp-not-inc, 117125, 9th, 5, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, Portugal, 0 362 | 36, Private, 348022, 10th, 6, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 24, United-States, 0 363 | 62, Private, 270092, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 1 364 | 43, Private, 180609, Bachelors, 13, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 45, United-States, 0 365 | 43, Private, 174575, Bachelors, 13, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 1564, 45, United-States, 1 366 | 22, Private, 410439, HS-grad, 9, Married-spouse-absent, Sales, Not-in-family, White, Male, 0, 0, 55, United-States, 0 367 | 28, Private, 92262, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 368 | 56, Self-emp-not-inc, 183081, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 45, United-States, 0 369 | 22, Private, 362589, Assoc-acdm, 12, Never-married, Sales, Not-in-family, White, Female, 0, 0, 15, United-States, 0 370 | 57, Private, 212448, Bachelors, 13, Divorced, Exec-managerial, Not-in-family, White, Female, 0, 0, 45, United-States, 1 371 | 39, Private, 481060, HS-grad, 9, Divorced, Sales, Unmarried, White, Female, 0, 0, 40, United-States, 0 372 | 26, Federal-gov, 185885, Some-college, 10, Never-married, Adm-clerical, Unmarried, White, Female, 0, 0, 15, United-States, 0 373 | 17, Private, 89821, 11th, 7, Never-married, Other-service, Own-child, White, Male, 0, 0, 10, United-States, 0 374 | 40, State-gov, 184018, Assoc-voc, 11, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 38, United-States, 1 375 | 45, Private, 256649, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 0, 0, 40, United-States, 0 376 | 44, Private, 160323, HS-grad, 9, Never-married, Craft-repair, Not-in-family, Black, Male, 0, 0, 40, United-States, 0 377 | 20, Local-gov, 350845, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 10, United-States, 0 378 | 33, Private, 267404, HS-grad, 9, Married-civ-spouse, Craft-repair, Wife, White, Female, 0, 0, 40, United-States, 0 379 | 23, Private, 35633, Some-college, 10, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, 0 380 | 46, Self-emp-not-inc, 80914, Masters, 14, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 30, United-States, 0 381 | 38, Private, 172927, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, 0 382 | 54, Private, 174319, HS-grad, 9, Divorced, Transport-moving, Not-in-family, White, Male, 0, 0, 40, United-States, 0 383 | 46, Private, 214955, 5th-6th, 3, Divorced, Craft-repair, Not-in-family, White, Female, 0, 2339, 45, United-States, 0 384 | 25, Private, 344991, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 385 | 46, Private, 108699, Some-college, 10, Divorced, Sales, Not-in-family, White, Female, 0, 0, 40, United-States, 0 386 | 36, Local-gov, 117312, Some-college, 10, Married-civ-spouse, Transport-moving, Wife, White, Female, 0, 0, 40, United-States, 0 387 | 23, Private, 396099, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 25, United-States, 0 388 | 29, Private, 134152, HS-grad, 9, Separated, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 40, United-States, 0 389 | 44, Private, 162028, Some-college, 10, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 2415, 6, United-States, 1 390 | 19, Private, 25429, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 16, United-States, 0 391 | 19, Private, 232392, HS-grad, 9, Never-married, Other-service, Other-relative, White, Female, 0, 0, 40, United-States, 0 392 | 35, Private, 220098, HS-grad, 9, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 40, United-States, 1 393 | 27, Private, 301302, Bachelors, 13, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 50, United-States, 0 394 | 46, Self-emp-not-inc, 277946, Assoc-acdm, 12, Separated, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, 0 395 | 34, State-gov, 98101, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 7688, 0, 45, ?, 1 396 | 34, Private, 196164, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 35, United-States, 0 397 | 44, Private, 115562, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, 0 398 | 45, Private, 96975, Some-college, 10, Divorced, Handlers-cleaners, Unmarried, White, Female, 0, 0, 40, United-States, 0 399 | 20, ?, 137300, HS-grad, 9, Never-married, ?, Other-relative, White, Female, 0, 0, 35, United-States, 0 400 | 25, Private, 86872, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 55, United-States, 1 401 | 52, Self-emp-inc, 132178, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, 1 402 | 20, Private, 416103, Some-college, 10, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, 0 403 | 28, Private, 108574, Some-college, 10, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 40, United-States, 0 404 | 50, State-gov, 288353, Bachelors, 13, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, 1 405 | 34, Private, 227689, Assoc-voc, 11, Divorced, Tech-support, Not-in-family, White, Female, 0, 0, 64, United-States, 0 406 | 28, Private, 166481, 7th-8th, 4, Married-civ-spouse, Handlers-cleaners, Husband, Other, Male, 0, 2179, 40, Puerto-Rico, 0 407 | 41, Private, 445382, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 1977, 65, United-States, 1 408 | 28, Private, 110145, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, 0 409 | 46, Self-emp-not-inc, 317253, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 25, United-States, 0 410 | 28, ?, 123147, Some-college, 10, Married-civ-spouse, ?, Wife, White, Female, 0, 1887, 40, United-States, 1 411 | 32, Private, 364657, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, 0 412 | 41, Local-gov, 42346, Some-college, 10, Divorced, Other-service, Not-in-family, Black, Female, 0, 0, 24, United-States, 0 413 | 24, Private, 241951, HS-grad, 9, Never-married, Handlers-cleaners, Unmarried, Black, Female, 0, 0, 40, United-States, 0 414 | 33, Private, 118500, Some-college, 10, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 40, United-States, 0 415 | 46, Private, 188386, Doctorate, 16, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 60, United-States, 1 416 | 31, State-gov, 1033222, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 417 | 35, Private, 92440, 12th, 8, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 50, United-States, 1 418 | 52, Private, 190762, 1st-4th, 2, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, Mexico, 0 419 | 30, Private, 426017, 11th, 7, Never-married, Other-service, Own-child, White, Female, 0, 0, 19, United-States, 0 420 | 34, Local-gov, 243867, 11th, 7, Separated, Machine-op-inspct, Not-in-family, Black, Male, 0, 0, 40, United-States, 0 421 | 34, State-gov, 240283, HS-grad, 9, Divorced, Transport-moving, Unmarried, White, Female, 0, 0, 40, United-States, 0 422 | 20, Private, 61777, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 30, United-States, 0 423 | 17, Private, 175024, 11th, 7, Never-married, Handlers-cleaners, Own-child, White, Male, 2176, 0, 18, United-States, 0 424 | 32, State-gov, 92003, Bachelors, 13, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, 1 425 | 29, Private, 188401, HS-grad, 9, Divorced, Farming-fishing, Not-in-family, White, Male, 0, 0, 40, United-States, 0 426 | 33, Private, 228528, 10th, 6, Never-married, Craft-repair, Unmarried, White, Female, 0, 0, 35, United-States, 0 427 | 25, Private, 133373, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 60, United-States, 0 428 | 36, Federal-gov, 255191, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 1408, 40, United-States, 0 429 | 23, Private, 204653, HS-grad, 9, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 72, Dominican-Republic, 0 430 | 63, Self-emp-inc, 222289, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 431 | 47, Local-gov, 287480, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 0 432 | 80, ?, 107762, HS-grad, 9, Widowed, ?, Not-in-family, White, Male, 0, 0, 24, United-States, 0 433 | 17, ?, 202521, 11th, 7, Never-married, ?, Own-child, White, Male, 0, 0, 40, United-States, 0 434 | 40, Self-emp-not-inc, 204116, Bachelors, 13, Married-spouse-absent, Prof-specialty, Not-in-family, White, Female, 2174, 0, 40, United-States, 0 435 | 30, Private, 29662, Assoc-acdm, 12, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 25, United-States, 1 436 | 27, Private, 116358, Some-college, 10, Never-married, Craft-repair, Own-child, Asian-Pac-Islander, Male, 0, 1980, 40, Philippines, 0 437 | 33, Private, 208405, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, 1 438 | 34, Local-gov, 284843, HS-grad, 9, Never-married, Farming-fishing, Not-in-family, Black, Male, 594, 0, 60, United-States, 0 439 | 34, Local-gov, 117018, Some-college, 10, Never-married, Protective-serv, Not-in-family, White, Female, 0, 0, 40, United-States, 0 440 | 23, Private, 81281, Some-college, 10, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, 0 441 | 42, Local-gov, 340148, Some-college, 10, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 40, United-States, 0 442 | 29, Private, 363425, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, United-States, 0 443 | 45, Private, 45857, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 28, United-States, 0 444 | 24, Federal-gov, 191073, HS-grad, 9, Never-married, Armed-Forces, Own-child, White, Male, 0, 0, 40, United-States, 0 445 | 44, Private, 116632, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 1 446 | 27, Private, 405855, 9th, 5, Never-married, Craft-repair, Other-relative, White, Male, 0, 0, 40, Mexico, 0 447 | 20, Private, 298227, Some-college, 10, Never-married, Sales, Not-in-family, White, Male, 0, 0, 35, United-States, 0 448 | 44, Private, 290521, HS-grad, 9, Widowed, Exec-managerial, Unmarried, Black, Female, 0, 0, 40, United-States, 0 449 | 51, Private, 56915, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, Amer-Indian-Eskimo, Male, 0, 0, 40, United-States, 0 450 | 20, Private, 146538, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 451 | 17, ?, 258872, 11th, 7, Never-married, ?, Own-child, White, Female, 0, 0, 5, United-States, 0 452 | 19, Private, 206399, HS-grad, 9, Never-married, Machine-op-inspct, Own-child, Black, Female, 0, 0, 40, United-States, 0 453 | 45, Self-emp-inc, 197332, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 55, United-States, 1 454 | 60, Private, 245062, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 1 455 | 42, Private, 197583, Assoc-acdm, 12, Married-civ-spouse, Exec-managerial, Husband, Black, Male, 0, 0, 40, ?, 1 456 | 44, Self-emp-not-inc, 234885, HS-grad, 9, Married-civ-spouse, Sales, Wife, White, Female, 0, 0, 40, United-States, 1 457 | 40, Private, 72887, Assoc-voc, 11, Married-civ-spouse, Machine-op-inspct, Husband, Asian-Pac-Islander, Male, 0, 0, 40, United-States, 1 458 | 30, Private, 180374, HS-grad, 9, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 40, United-States, 0 459 | 38, Private, 351299, Some-college, 10, Married-civ-spouse, Transport-moving, Husband, Black, Male, 0, 0, 50, United-States, 0 460 | 23, Private, 54012, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 60, United-States, 0 461 | 32, ?, 115745, Some-college, 10, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 40, United-States, 0 462 | 44, Private, 116632, Assoc-acdm, 12, Never-married, Farming-fishing, Own-child, White, Male, 0, 0, 40, United-States, 0 463 | 54, Local-gov, 288825, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, Black, Male, 0, 0, 40, United-States, 0 464 | 32, Private, 132601, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, 0 465 | 50, Private, 193374, 1st-4th, 2, Married-spouse-absent, Craft-repair, Unmarried, White, Male, 0, 0, 40, United-States, 0 466 | 24, Private, 170070, Bachelors, 13, Never-married, Tech-support, Not-in-family, White, Female, 0, 0, 20, United-States, 0 467 | 37, Private, 126708, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 60, United-States, 0 468 | 52, Private, 35598, HS-grad, 9, Divorced, Transport-moving, Unmarried, White, Male, 0, 0, 40, United-States, 0 469 | 38, Private, 33983, Some-college, 10, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, 0 470 | 49, Private, 192776, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 1977, 45, United-States, 1 471 | 30, Private, 118551, Bachelors, 13, Married-civ-spouse, Tech-support, Wife, White, Female, 0, 0, 16, United-States, 1 472 | 60, Private, 201965, Some-college, 10, Never-married, Prof-specialty, Unmarried, White, Male, 0, 0, 40, United-States, 1 473 | 22, ?, 139883, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 40, United-States, 0 474 | 35, Private, 285020, HS-grad, 9, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, 0 475 | 30, Private, 303990, HS-grad, 9, Never-married, Transport-moving, Not-in-family, White, Male, 0, 0, 60, United-States, 0 476 | 67, Private, 49401, Assoc-voc, 11, Divorced, Other-service, Not-in-family, White, Female, 0, 0, 24, United-States, 0 477 | 46, Private, 279196, Bachelors, 13, Never-married, Craft-repair, Not-in-family, White, Female, 0, 0, 40, United-States, 0 478 | 17, Private, 211870, 9th, 5, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 6, United-States, 0 479 | 22, Private, 281432, Some-college, 10, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 30, United-States, 0 480 | 27, Private, 161155, 10th, 6, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 40, United-States, 0 481 | 23, Private, 197904, HS-grad, 9, Never-married, Other-service, Unmarried, White, Female, 0, 0, 35, United-States, 0 482 | 33, Private, 111746, Assoc-acdm, 12, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 45, Portugal, 0 483 | 43, Self-emp-not-inc, 170721, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 20, United-States, 0 484 | 28, State-gov, 70100, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 20, United-States, 0 485 | 41, Private, 193626, HS-grad, 9, Married-spouse-absent, Craft-repair, Unmarried, White, Female, 0, 0, 40, United-States, 0 486 | 52, ?, 271749, 12th, 8, Never-married, ?, Other-relative, Black, Male, 594, 0, 40, United-States, 0 487 | 25, Private, 189775, Some-college, 10, Married-spouse-absent, Adm-clerical, Own-child, Black, Female, 0, 0, 20, United-States, 0 488 | 63, ?, 401531, 1st-4th, 2, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 35, United-States, 0 489 | 59, Local-gov, 286967, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 45, United-States, 0 490 | 45, Local-gov, 164427, Bachelors, 13, Divorced, Prof-specialty, Unmarried, White, Female, 0, 0, 40, United-States, 0 491 | 38, Private, 91039, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 15024, 0, 60, United-States, 1 492 | 40, Private, 347934, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 35, United-States, 0 493 | 46, Federal-gov, 371373, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, White, Male, 0, 0, 40, United-States, 0 494 | 35, Private, 32220, Assoc-acdm, 12, Never-married, Exec-managerial, Not-in-family, White, Female, 0, 0, 60, United-States, 0 495 | 34, Private, 187251, HS-grad, 9, Divorced, Prof-specialty, Unmarried, White, Female, 0, 0, 25, United-States, 0 496 | 33, Private, 178107, Bachelors, 13, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 20, United-States, 0 497 | 41, Private, 343121, HS-grad, 9, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 36, United-States, 0 498 | 20, Private, 262749, Some-college, 10, Never-married, Machine-op-inspct, Own-child, White, Male, 0, 0, 40, United-States, 0 499 | 23, Private, 403107, 5th-6th, 3, Never-married, Other-service, Own-child, White, Male, 0, 0, 40, El-Salvador, 0 500 | 26, Private, 64293, Some-college, 10, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 35, United-States, 0 501 | 72, ?, 303588, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 20, United-States, 0 502 | 23, Local-gov, 324960, HS-grad, 9, Never-married, Farming-fishing, Not-in-family, White, Male, 0, 0, 40, Poland, 0 503 | 62, Local-gov, 114060, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, 0 504 | 52, Private, 48925, Some-college, 10, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, United-States, 0 505 | 58, Private, 180980, Some-college, 10, Divorced, Other-service, Unmarried, White, Female, 0, 0, 42, France, 0 506 | 25, Private, 181054, Bachelors, 13, Never-married, Sales, Not-in-family, White, Female, 0, 0, 40, United-States, 0 507 | 24, Private, 388093, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, Black, Male, 0, 0, 40, United-States, 0 508 | 19, Private, 249609, Some-college, 10, Never-married, Protective-serv, Own-child, White, Male, 0, 0, 8, United-States, 0 509 | 43, Private, 112131, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 0 510 | 47, Local-gov, 543162, HS-grad, 9, Separated, Adm-clerical, Unmarried, Black, Female, 0, 0, 40, United-States, 0 511 | 39, Private, 91996, HS-grad, 9, Divorced, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, 0 512 | 49, Private, 141944, Assoc-voc, 11, Married-spouse-absent, Handlers-cleaners, Unmarried, White, Male, 0, 1380, 42, United-States, 0 513 | 53, ?, 251804, 5th-6th, 3, Widowed, ?, Unmarried, Black, Female, 0, 0, 30, United-States, 0 514 | 32, Private, 37070, Assoc-voc, 11, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 45, United-States, 0 515 | 34, Private, 337587, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, 1 516 | 28, Private, 189346, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 45, United-States, 0 517 | 57, ?, 222216, Assoc-voc, 11, Widowed, ?, Unmarried, White, Female, 0, 0, 38, United-States, 0 518 | 25, Private, 267044, Some-college, 10, Never-married, Adm-clerical, Not-in-family, Amer-Indian-Eskimo, Female, 0, 0, 20, United-States, 0 519 | 20, ?, 214635, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 24, United-States, 0 520 | 21, ?, 204226, Some-college, 10, Never-married, ?, Unmarried, White, Female, 0, 0, 35, United-States, 0 521 | 34, Private, 108116, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, 1 522 | 38, Self-emp-inc, 99146, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 80, United-States, 1 523 | 50, Private, 196232, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 7688, 0, 50, United-States, 1 524 | 24, Local-gov, 248344, Some-college, 10, Divorced, Handlers-cleaners, Not-in-family, Black, Male, 0, 0, 50, United-States, 0 525 | 37, Local-gov, 186035, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 45, United-States, 1 526 | 44, Private, 177905, Some-college, 10, Divorced, Machine-op-inspct, Unmarried, White, Male, 0, 0, 58, United-States, 1 527 | 28, Private, 85812, Some-college, 10, Married-civ-spouse, Sales, Wife, White, Female, 0, 0, 40, United-States, 0 528 | 42, Private, 221172, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 529 | 74, Private, 99183, Some-college, 10, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 9, United-States, 0 530 | 38, Self-emp-not-inc, 190387, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 50, United-States, 0 531 | 44, Self-emp-not-inc, 202692, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 0 532 | 44, Private, 109339, 11th, 7, Divorced, Machine-op-inspct, Unmarried, Other, Female, 0, 0, 46, Puerto-Rico, 0 533 | 26, Private, 108658, HS-grad, 9, Never-married, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 40, United-States, 0 534 | 36, Private, 197202, HS-grad, 9, Married-civ-spouse, Other-service, Husband, Black, Male, 0, 0, 40, United-States, 0 535 | 41, Private, 101739, Assoc-acdm, 12, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 0, 50, United-States, 1 536 | 67, Private, 231559, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 20051, 0, 48, United-States, 1 537 | 39, Local-gov, 207853, 12th, 8, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 50, United-States, 0 538 | 57, Private, 190942, 1st-4th, 2, Widowed, Priv-house-serv, Not-in-family, Black, Female, 0, 0, 30, United-States, 0 539 | 29, Private, 102345, Assoc-voc, 11, Separated, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, 0 540 | 31, Self-emp-inc, 41493, Bachelors, 13, Never-married, Farming-fishing, Not-in-family, White, Female, 0, 0, 45, United-States, 0 541 | 34, ?, 190027, HS-grad, 9, Never-married, ?, Unmarried, Black, Female, 0, 0, 40, United-States, 0 542 | 44, Private, 210525, Some-college, 10, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, 0 543 | 29, Private, 133937, Doctorate, 16, Never-married, Prof-specialty, Own-child, White, Male, 0, 0, 40, United-States, 0 544 | 30, Private, 237903, Some-college, 10, Never-married, Handlers-cleaners, Unmarried, White, Female, 0, 0, 40, United-States, 0 545 | 27, Private, 163862, HS-grad, 9, Never-married, Transport-moving, Not-in-family, White, Male, 0, 0, 40, United-States, 0 546 | 27, Private, 201872, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, 0 547 | 32, Private, 84179, HS-grad, 9, Never-married, Handlers-cleaners, Not-in-family, White, Female, 0, 0, 45, United-States, 0 548 | 58, Private, 51662, 10th, 6, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 8, United-States, 0 549 | 35, Local-gov, 233327, Some-college, 10, Married-civ-spouse, Protective-serv, Husband, White, Male, 0, 0, 40, United-States, 0 550 | 21, Private, 259510, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 36, United-States, 0 551 | 28, Private, 184831, Some-college, 10, Never-married, Craft-repair, Unmarried, White, Male, 0, 0, 40, United-States, 0 552 | 46, Self-emp-not-inc, 245724, Some-college, 10, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 50, United-States, 0 553 | 36, Self-emp-not-inc, 27053, HS-grad, 9, Separated, Other-service, Unmarried, White, Female, 0, 0, 40, United-States, 0 554 | 72, Private, 205343, 11th, 7, Widowed, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, 0 555 | 35, Private, 229328, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Wife, Black, Female, 0, 0, 40, United-States, 0 556 | 33, Federal-gov, 319560, Assoc-voc, 11, Divorced, Craft-repair, Unmarried, Black, Female, 0, 0, 40, United-States, 1 557 | 69, Private, 136218, 11th, 7, Never-married, Machine-op-inspct, Not-in-family, White, Female, 0, 0, 40, United-States, 0 558 | 35, Private, 54576, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 559 | 31, Private, 323069, HS-grad, 9, Separated, Adm-clerical, Unmarried, White, Female, 0, 0, 20, ?, 0 560 | 34, Private, 148291, HS-grad, 9, Married-civ-spouse, Tech-support, Wife, White, Female, 0, 0, 32, United-States, 0 561 | 30, Private, 152453, 11th, 7, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, Mexico, 0 562 | 28, Private, 114053, Bachelors, 13, Never-married, Transport-moving, Not-in-family, White, Male, 0, 0, 55, United-States, 0 563 | 54, Private, 212960, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 35, United-States, 1 564 | 47, Private, 264052, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, 1 565 | 24, Private, 82804, HS-grad, 9, Never-married, Handlers-cleaners, Unmarried, Black, Female, 0, 0, 40, United-States, 0 566 | 52, Self-emp-not-inc, 334273, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 60, United-States, 1 567 | 20, Private, 27337, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, Amer-Indian-Eskimo, Male, 0, 0, 48, United-States, 0 568 | 43, Self-emp-inc, 188436, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 5013, 0, 45, United-States, 0 569 | 45, Private, 433665, 7th-8th, 4, Separated, Other-service, Unmarried, White, Female, 0, 0, 40, Mexico, 0 570 | 29, Self-emp-not-inc, 110663, HS-grad, 9, Separated, Craft-repair, Not-in-family, White, Male, 0, 0, 35, United-States, 0 571 | 47, Private, 87490, Masters, 14, Divorced, Exec-managerial, Unmarried, White, Male, 0, 0, 42, United-States, 0 572 | 24, Private, 354351, HS-grad, 9, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 40, United-States, 0 573 | 51, Private, 95469, HS-grad, 9, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 0 574 | 17, Private, 242718, 11th, 7, Never-married, Sales, Own-child, White, Male, 0, 0, 12, United-States, 0 575 | 37, Private, 22463, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 1977, 40, United-States, 1 576 | 27, Private, 158156, Doctorate, 16, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 70, United-States, 0 577 | 29, Private, 350162, Bachelors, 13, Married-civ-spouse, Exec-managerial, Wife, White, Male, 0, 0, 40, United-States, 1 578 | 18, ?, 165532, 12th, 8, Never-married, ?, Own-child, White, Male, 0, 0, 25, United-States, 0 579 | 36, Self-emp-not-inc, 28738, Assoc-acdm, 12, Divorced, Sales, Unmarried, White, Female, 0, 0, 35, United-States, 0 580 | 58, Local-gov, 283635, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 40, United-States, 0 581 | 26, Self-emp-not-inc, 86646, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 45, United-States, 0 582 | 65, ?, 195733, Some-college, 10, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 30, United-States, 1 583 | 57, Private, 69884, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 40, United-States, 1 584 | 59, Private, 199713, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 585 | 27, Private, 181659, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 586 | 31, Self-emp-not-inc, 340939, Bachelors, 13, Never-married, Tech-support, Not-in-family, White, Male, 0, 0, 40, United-States, 0 587 | 21, Private, 197747, Some-college, 10, Never-married, Sales, Own-child, White, Female, 0, 0, 24, United-States, 0 588 | 29, Private, 34292, Some-college, 10, Never-married, Adm-clerical, Not-in-family, White, Male, 0, 0, 60, United-States, 0 589 | 18, Private, 156764, 11th, 7, Never-married, Other-service, Own-child, White, Male, 0, 0, 40, United-States, 0 590 | 52, Private, 25826, 10th, 6, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 1887, 47, United-States, 1 591 | 57, Self-emp-inc, 103948, Bachelors, 13, Divorced, Prof-specialty, Not-in-family, White, Male, 0, 0, 80, United-States, 0 592 | 42, ?, 137390, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 40, United-States, 0 593 | 55, ?, 105138, HS-grad, 9, Married-civ-spouse, ?, Wife, Asian-Pac-Islander, Female, 0, 0, 40, United-States, 0 594 | 60, Private, 39352, 7th-8th, 4, Never-married, Transport-moving, Not-in-family, White, Male, 0, 0, 48, United-States, 1 595 | 31, Private, 168387, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 7688, 0, 40, Canada, 1 596 | 23, Private, 117789, Bachelors, 13, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 40, United-States, 0 597 | 27, Private, 267147, HS-grad, 9, Never-married, Sales, Own-child, White, Male, 0, 0, 40, United-States, 0 598 | 23, ?, 99399, Some-college, 10, Never-married, ?, Unmarried, Amer-Indian-Eskimo, Female, 0, 0, 25, United-States, 0 599 | 42, Self-emp-not-inc, 214242, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 1902, 50, United-States, 1 600 | 25, Private, 200408, Some-college, 10, Never-married, Tech-support, Not-in-family, White, Male, 2174, 0, 40, United-States, 0 601 | 49, Private, 136455, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 45, United-States, 0 602 | 32, Private, 239824, Bachelors, 13, Never-married, Tech-support, Not-in-family, White, Male, 0, 0, 40, United-States, 0 603 | 19, Private, 217039, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Male, 0, 0, 28, United-States, 0 604 | 60, Private, 51290, 7th-8th, 4, Divorced, Other-service, Not-in-family, White, Female, 0, 0, 40, United-States, 0 605 | 42, Local-gov, 175674, 9th, 5, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, 0 606 | 35, Self-emp-not-inc, 194404, Assoc-acdm, 12, Never-married, Farming-fishing, Not-in-family, White, Male, 0, 0, 40, United-States, 0 607 | 48, Private, 45612, HS-grad, 9, Never-married, Adm-clerical, Unmarried, Black, Female, 0, 0, 37, United-States, 0 608 | 51, Private, 410114, Masters, 14, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 1 609 | 29, Private, 182521, HS-grad, 9, Never-married, Craft-repair, Not-in-family, Amer-Indian-Eskimo, Male, 0, 0, 40, United-States, 0 610 | 36, Local-gov, 339772, HS-grad, 9, Separated, Exec-managerial, Not-in-family, Black, Male, 0, 0, 40, United-States, 0 611 | 17, Private, 169658, 10th, 6, Never-married, Other-service, Own-child, White, Female, 0, 0, 21, United-States, 0 612 | 52, Private, 200853, Masters, 14, Divorced, Prof-specialty, Not-in-family, White, Female, 6849, 0, 60, United-States, 0 613 | 24, Private, 247564, HS-grad, 9, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, 0 614 | 24, Private, 249909, HS-grad, 9, Married-civ-spouse, Handlers-cleaners, Husband, White, Male, 0, 0, 50, United-States, 0 615 | 26, Local-gov, 208122, Bachelors, 13, Never-married, Prof-specialty, Own-child, White, Male, 1055, 0, 40, United-States, 0 616 | 27, Private, 109881, Bachelors, 13, Never-married, Other-service, Own-child, White, Female, 0, 0, 20, United-States, 0 617 | 39, Private, 207824, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 60, United-States, 0 618 | 30, Private, 369027, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, Black, Male, 0, 0, 45, United-States, 0 619 | 50, Self-emp-not-inc, 114117, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 32, United-States, 0 620 | 52, Self-emp-inc, 51048, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 55, United-States, 0 621 | 46, Private, 102388, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 15024, 0, 45, United-States, 1 622 | 23, Private, 190483, Bachelors, 13, Never-married, Sales, Own-child, White, Female, 0, 0, 20, United-States, 0 623 | 45, Private, 462440, 11th, 7, Widowed, Other-service, Not-in-family, Black, Female, 0, 0, 20, United-States, 0 624 | 65, Private, 109351, 9th, 5, Widowed, Priv-house-serv, Unmarried, Black, Female, 0, 0, 24, United-States, 0 625 | 29, Private, 34383, Assoc-voc, 11, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 55, United-States, 0 626 | 47, Private, 241832, 9th, 5, Married-spouse-absent, Handlers-cleaners, Unmarried, White, Male, 0, 0, 40, El-Salvador, 0 627 | 30, Private, 124187, HS-grad, 9, Never-married, Farming-fishing, Own-child, Black, Male, 0, 0, 60, United-States, 0 628 | 34, Private, 153614, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 45, United-States, 1 629 | 38, Self-emp-not-inc, 267556, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 64, United-States, 0 630 | 33, Private, 205469, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 631 | 49, Private, 268090, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 26, United-States, 1 632 | 47, Self-emp-not-inc, 165039, Some-college, 10, Never-married, Other-service, Unmarried, Black, Female, 0, 0, 40, United-States, 0 633 | 49, Local-gov, 120451, 10th, 6, Separated, Other-service, Unmarried, Black, Female, 0, 0, 40, United-States, 0 634 | 43, Private, 154374, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 15024, 0, 60, United-States, 1 635 | 30, Private, 103649, Bachelors, 13, Married-civ-spouse, Adm-clerical, Wife, Black, Female, 0, 0, 40, United-States, 1 636 | 58, Self-emp-not-inc, 35723, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 60, United-States, 0 637 | 19, Private, 262601, HS-grad, 9, Never-married, Other-service, Own-child, White, Female, 0, 0, 14, United-States, 0 638 | 21, Private, 226181, Bachelors, 13, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, 0 639 | 33, Private, 175697, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 60, United-States, 1 640 | 47, Self-emp-inc, 248145, 5th-6th, 3, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 50, Cuba, 0 641 | 52, Self-emp-not-inc, 289436, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 60, United-States, 1 642 | 26, Private, 75654, HS-grad, 9, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 55, United-States, 0 643 | 60, Private, 199378, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 644 | 21, Private, 160968, Some-college, 10, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, 0 645 | 36, Private, 188563, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 5178, 0, 50, United-States, 1 646 | 31, Private, 55849, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 45, United-States, 0 647 | 50, Self-emp-inc, 195322, Doctorate, 16, Separated, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, United-States, 1 648 | 31, Local-gov, 402089, HS-grad, 9, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, 0 649 | 71, Private, 78277, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 15, United-States, 0 650 | 58, ?, 158611, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 50, United-States, 0 651 | 30, State-gov, 169496, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, 1 652 | 20, Private, 130959, Some-college, 10, Never-married, Other-service, Own-child, White, Male, 0, 0, 20, United-States, 0 653 | 24, Private, 556660, HS-grad, 9, Never-married, Exec-managerial, Other-relative, White, Male, 4101, 0, 50, United-States, 0 654 | 35, Private, 292472, Doctorate, 16, Married-civ-spouse, Prof-specialty, Husband, Asian-Pac-Islander, Male, 0, 0, 40, Taiwan, 1 655 | 38, State-gov, 143774, Some-college, 10, Separated, Exec-managerial, Not-in-family, White, Female, 0, 0, 45, United-States, 0 656 | 27, Private, 288341, HS-grad, 9, Never-married, Machine-op-inspct, Own-child, White, Female, 0, 0, 32, United-States, 0 657 | 29, State-gov, 71592, Some-college, 10, Never-married, Adm-clerical, Unmarried, Asian-Pac-Islander, Female, 0, 0, 40, Philippines, 0 658 | 70, ?, 167358, 9th, 5, Widowed, ?, Unmarried, White, Female, 1111, 0, 15, United-States, 0 659 | 34, Private, 106742, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 45, United-States, 0 660 | 44, Private, 219288, 7th-8th, 4, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 35, United-States, 0 661 | 43, Private, 174524, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 1 662 | 44, Self-emp-not-inc, 335183, 12th, 8, Married-civ-spouse, Handlers-cleaners, Husband, Black, Male, 0, 0, 40, United-States, 1 663 | 35, Private, 261293, Masters, 14, Never-married, Sales, Not-in-family, White, Male, 0, 0, 60, United-States, 0 664 | 27, Private, 111900, Some-college, 10, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, United-States, 0 665 | 43, Local-gov, 194360, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 38, United-States, 0 666 | 20, Private, 81145, Some-college, 10, Never-married, Sales, Not-in-family, White, Female, 0, 0, 25, United-States, 0 667 | 42, Private, 341204, Assoc-acdm, 12, Divorced, Prof-specialty, Unmarried, White, Female, 8614, 0, 40, United-States, 1 668 | 27, State-gov, 249362, Some-college, 10, Married-civ-spouse, Transport-moving, Husband, White, Male, 3411, 0, 40, United-States, 0 669 | 42, Private, 247019, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, Black, Male, 0, 0, 40, United-States, 1 670 | 20, ?, 114746, 11th, 7, Married-spouse-absent, ?, Own-child, Asian-Pac-Islander, Female, 0, 1762, 40, South, 0 671 | 24, Private, 172146, 9th, 5, Never-married, Machine-op-inspct, Not-in-family, White, Male, 0, 1721, 40, United-States, 0 672 | 48, Federal-gov, 110457, Some-college, 10, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 40, United-States, 0 673 | 17, ?, 80077, 11th, 7, Never-married, ?, Own-child, White, Female, 0, 0, 20, United-States, 0 674 | 17, Self-emp-not-inc, 368700, 11th, 7, Never-married, Farming-fishing, Own-child, White, Male, 0, 0, 10, United-States, 0 675 | 33, Private, 182556, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 676 | 50, Self-emp-inc, 219420, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, 1 677 | 22, Private, 240817, HS-grad, 9, Never-married, Sales, Own-child, White, Female, 2597, 0, 40, United-States, 0 678 | 17, Private, 102726, 12th, 8, Never-married, Other-service, Own-child, White, Male, 0, 0, 16, United-States, 0 679 | 32, Private, 226267, Some-college, 10, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 40, Mexico, 0 680 | 31, Private, 125457, HS-grad, 9, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 45, United-States, 0 681 | 58, Self-emp-not-inc, 204021, HS-grad, 9, Widowed, Exec-managerial, Not-in-family, White, Male, 0, 0, 50, United-States, 0 682 | 29, Local-gov, 92262, HS-grad, 9, Never-married, Protective-serv, Own-child, White, Male, 0, 0, 48, United-States, 0 683 | 37, Private, 161141, Assoc-voc, 11, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, Portugal, 1 684 | 34, Self-emp-not-inc, 190290, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 1 685 | 23, Local-gov, 430828, Some-college, 10, Separated, Exec-managerial, Unmarried, Black, Male, 0, 0, 40, United-States, 0 686 | 18, State-gov, 59342, 11th, 7, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 5, United-States, 0 687 | 34, Private, 136721, HS-grad, 9, Divorced, Exec-managerial, Not-in-family, White, Female, 0, 0, 40, United-States, 0 688 | 66, ?, 149422, 7th-8th, 4, Never-married, ?, Not-in-family, White, Male, 0, 0, 4, United-States, 0 689 | 45, Local-gov, 86644, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 55, United-States, 0 690 | 41, Private, 195124, Masters, 14, Never-married, Exec-managerial, Not-in-family, White, Male, 0, 0, 35, Dominican-Republic, 0 691 | 26, Private, 167350, HS-grad, 9, Never-married, Other-service, Other-relative, White, Male, 0, 0, 30, United-States, 0 692 | 54, Local-gov, 113000, Some-college, 10, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 40, United-States, 0 693 | 24, Private, 140027, Some-college, 10, Never-married, Machine-op-inspct, Own-child, Black, Female, 0, 0, 45, United-States, 0 694 | 42, Private, 262425, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, 0 695 | 20, Private, 316702, Some-college, 10, Never-married, Prof-specialty, Own-child, White, Male, 0, 0, 20, United-States, 0 696 | 23, State-gov, 335453, Bachelors, 13, Never-married, Tech-support, Not-in-family, White, Female, 0, 0, 20, United-States, 0 697 | 25, ?, 202480, Assoc-acdm, 12, Never-married, ?, Other-relative, White, Male, 0, 0, 45, United-States, 0 698 | 35, Private, 203628, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 60, United-States, 1 699 | 31, Private, 118710, Masters, 14, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 1902, 40, United-States, 1 700 | 30, Private, 189620, Bachelors, 13, Never-married, Prof-specialty, Own-child, White, Female, 0, 0, 40, Poland, 0 701 | 19, Private, 475028, HS-grad, 9, Never-married, Sales, Own-child, White, Female, 0, 0, 20, United-States, 0 702 | 36, Local-gov, 110866, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 50, United-States, 0 703 | 31, Private, 243605, Bachelors, 13, Widowed, Sales, Unmarried, White, Female, 0, 1380, 40, Cuba, 0 704 | 21, Private, 163870, Some-college, 10, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 30, United-States, 0 705 | 31, Self-emp-not-inc, 80145, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 706 | 46, Private, 295566, Doctorate, 16, Divorced, Prof-specialty, Unmarried, White, Female, 25236, 0, 65, United-States, 1 707 | 44, Private, 63042, Bachelors, 13, Divorced, Exec-managerial, Own-child, White, Female, 0, 0, 50, United-States, 1 708 | 40, Private, 229148, 12th, 8, Married-civ-spouse, Other-service, Husband, Black, Male, 0, 0, 40, Jamaica, 0 709 | 45, Private, 242552, Some-college, 10, Never-married, Sales, Not-in-family, Black, Male, 0, 0, 40, United-States, 0 710 | 60, Private, 177665, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 35, United-States, 0 711 | 18, Private, 208103, 11th, 7, Never-married, Other-service, Other-relative, White, Male, 0, 0, 25, United-States, 0 712 | 28, Private, 296450, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 0 713 | 36, Private, 70282, Some-college, 10, Divorced, Adm-clerical, Unmarried, Black, Female, 0, 0, 40, United-States, 0 714 | 36, Private, 271767, Bachelors, 13, Separated, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, ?, 0 715 | 40, Private, 144995, Assoc-voc, 11, Married-civ-spouse, Tech-support, Husband, White, Male, 4386, 0, 40, United-States, 0 716 | 36, Local-gov, 382635, Bachelors, 13, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 35, Honduras, 0 717 | 31, Private, 295697, HS-grad, 9, Separated, Other-service, Unmarried, Black, Female, 0, 0, 40, United-States, 0 718 | 33, Private, 194141, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 719 | 19, State-gov, 378418, HS-grad, 9, Never-married, Tech-support, Own-child, White, Female, 0, 0, 40, United-States, 0 720 | 22, Private, 214399, Some-college, 10, Never-married, Sales, Own-child, White, Female, 0, 0, 15, United-States, 0 721 | 34, Private, 217460, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, 1 722 | 33, Private, 182556, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Male, 0, 0, 40, United-States, 0 723 | 41, Private, 125831, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 2051, 60, United-States, 0 724 | 29, Private, 271328, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Male, 4650, 0, 40, United-States, 0 725 | 50, Local-gov, 50459, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 42, United-States, 1 726 | 42, Private, 162140, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 7298, 0, 45, United-States, 1 727 | 43, Private, 177937, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, ?, 1 728 | 44, Private, 111502, HS-grad, 9, Married-civ-spouse, Sales, Wife, White, Female, 0, 0, 40, United-States, 0 729 | 20, Private, 299047, Some-college, 10, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 20, United-States, 0 730 | 31, Private, 223212, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, Mexico, 0 731 | 65, Self-emp-not-inc, 118474, 11th, 7, Married-civ-spouse, Exec-managerial, Husband, White, Male, 9386, 0, 59, ?, 1 732 | 23, Private, 352139, Some-college, 10, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 24, United-States, 0 733 | 55, Private, 173093, Some-college, 10, Divorced, Adm-clerical, Not-in-family, Asian-Pac-Islander, Female, 0, 0, 40, United-States, 0 734 | 26, Private, 181655, Assoc-voc, 11, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 2377, 45, United-States, 0 735 | 25, Private, 332702, Assoc-voc, 11, Never-married, Other-service, Own-child, White, Female, 0, 0, 15, United-States, 0 736 | 45, ?, 51164, Some-college, 10, Married-civ-spouse, ?, Wife, Black, Female, 0, 0, 40, United-States, 0 737 | 35, Private, 234901, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 2407, 0, 40, United-States, 0 738 | 36, Private, 131414, Some-college, 10, Never-married, Sales, Not-in-family, Black, Female, 0, 0, 36, United-States, 0 739 | 43, State-gov, 260960, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 50, United-States, 0 740 | 56, Private, 156052, HS-grad, 9, Widowed, Other-service, Unmarried, Black, Female, 594, 0, 20, United-States, 0 741 | 42, Private, 279914, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, 1 742 | 19, Private, 192453, Some-college, 10, Never-married, Other-service, Other-relative, White, Female, 0, 0, 25, United-States, 0 743 | 55, Self-emp-not-inc, 200939, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 72, United-States, 0 744 | 42, Private, 151408, Masters, 14, Never-married, Exec-managerial, Not-in-family, White, Female, 14084, 0, 50, United-States, 1 745 | 26, Private, 112847, Assoc-voc, 11, Never-married, Tech-support, Own-child, White, Male, 0, 0, 40, United-States, 0 746 | 17, Private, 316929, 12th, 8, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 20, United-States, 0 747 | 42, Local-gov, 126319, Bachelors, 13, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 40, United-States, 1 748 | 55, Private, 197422, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 7688, 0, 40, United-States, 1 749 | 32, Private, 267736, Some-college, 10, Never-married, Adm-clerical, Own-child, Black, Female, 0, 0, 40, United-States, 0 750 | 29, Private, 267034, 11th, 7, Never-married, Craft-repair, Own-child, Black, Male, 0, 0, 40, Haiti, 0 751 | 46, State-gov, 193047, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 37, United-States, 0 752 | 29, State-gov, 356089, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 7688, 0, 40, United-States, 1 753 | 22, Private, 223515, Bachelors, 13, Never-married, Prof-specialty, Unmarried, White, Male, 0, 0, 20, United-States, 0 754 | 58, Self-emp-not-inc, 87510, 10th, 6, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 755 | 23, Private, 145111, HS-grad, 9, Never-married, Transport-moving, Unmarried, White, Male, 0, 0, 50, United-States, 0 756 | 39, Private, 48093, HS-grad, 9, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, 0 757 | 27, Private, 31757, Assoc-voc, 11, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 38, United-States, 0 758 | 54, Private, 285854, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, 1 759 | 33, Local-gov, 120064, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 45, United-States, 0 760 | 46, Federal-gov, 167381, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 40, United-States, 1 761 | 37, Private, 103408, HS-grad, 9, Never-married, Farming-fishing, Not-in-family, Black, Male, 0, 0, 40, United-States, 0 762 | 36, Private, 101460, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 18, United-States, 0 763 | 59, Local-gov, 420537, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 38, United-States, 1 764 | 34, Local-gov, 119411, HS-grad, 9, Divorced, Protective-serv, Unmarried, White, Male, 0, 0, 40, Portugal, 0 765 | 53, Self-emp-inc, 128272, Doctorate, 16, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 70, United-States, 1 766 | 51, Private, 386773, Bachelors, 13, Never-married, Sales, Not-in-family, White, Male, 0, 0, 55, United-States, 1 767 | 32, Private, 283268, 10th, 6, Separated, Other-service, Unmarried, White, Female, 0, 0, 42, United-States, 0 768 | 31, State-gov, 301526, Some-college, 10, Married-spouse-absent, Other-service, Other-relative, White, Male, 0, 0, 40, United-States, 0 769 | 22, Private, 151790, Some-college, 10, Married-civ-spouse, Sales, Wife, White, Female, 0, 0, 30, Germany, 0 770 | 47, Self-emp-not-inc, 106252, Bachelors, 13, Divorced, Sales, Not-in-family, White, Female, 0, 0, 50, United-States, 0 771 | 32, Private, 188557, HS-grad, 9, Never-married, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 40, United-States, 0 772 | 26, Private, 171114, Some-college, 10, Never-married, Farming-fishing, Not-in-family, White, Female, 0, 0, 38, United-States, 0 773 | 37, Private, 327323, 5th-6th, 3, Separated, Farming-fishing, Not-in-family, White, Male, 0, 0, 32, Guatemala, 0 774 | 31, Private, 244147, HS-grad, 9, Divorced, Craft-repair, Unmarried, White, Male, 0, 0, 55, United-States, 0 775 | 37, Private, 280282, Assoc-voc, 11, Married-civ-spouse, Tech-support, Wife, White, Female, 0, 0, 24, United-States, 1 776 | 55, Private, 116442, HS-grad, 9, Never-married, Sales, Not-in-family, White, Male, 0, 0, 38, United-States, 0 777 | 23, Local-gov, 282579, Assoc-voc, 11, Divorced, Tech-support, Not-in-family, White, Male, 0, 0, 56, United-States, 0 778 | 36, Private, 51838, Some-college, 10, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, 0 779 | 34, Private, 73585, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, ?, 0 780 | 43, Private, 226902, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, 1 781 | 54, Private, 279129, Some-college, 10, Never-married, Tech-support, Not-in-family, White, Male, 0, 0, 40, United-States, 0 782 | 43, State-gov, 146908, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, ?, 0 783 | 28, Private, 196690, Assoc-voc, 11, Never-married, Machine-op-inspct, Not-in-family, White, Female, 0, 1669, 42, United-States, 0 784 | 40, Private, 130760, Assoc-voc, 11, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, 1 785 | 41, Self-emp-not-inc, 49572, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 60, United-States, 0 786 | 40, Private, 237601, Bachelors, 13, Never-married, Sales, Not-in-family, Other, Female, 0, 0, 55, United-States, 1 787 | 42, Private, 169628, Some-college, 10, Divorced, Adm-clerical, Not-in-family, Black, Female, 0, 0, 38, United-States, 0 788 | 61, Self-emp-not-inc, 36671, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 2352, 50, United-States, 0 789 | 18, Private, 231193, 12th, 8, Never-married, Machine-op-inspct, Own-child, White, Male, 0, 0, 30, United-States, 0 790 | 59, ?, 192130, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 16, United-States, 0 791 | 21, ?, 149704, HS-grad, 9, Never-married, ?, Not-in-family, White, Female, 1055, 0, 40, United-States, 0 792 | 48, Private, 102102, Assoc-voc, 11, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 50, United-States, 1 793 | 41, Self-emp-inc, 32185, Bachelors, 13, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, 0 794 | 18, ?, 196061, Some-college, 10, Never-married, ?, Own-child, White, Male, 0, 0, 33, United-States, 0 795 | 23, Private, 211046, HS-grad, 9, Never-married, Sales, Not-in-family, White, Female, 2463, 0, 40, United-States, 0 796 | 60, Private, 31577, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 60, United-States, 1 797 | 22, Private, 162343, Some-college, 10, Never-married, Other-service, Other-relative, Black, Male, 0, 0, 20, United-States, 0 798 | 61, Private, 128831, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 799 | 25, Private, 316688, HS-grad, 9, Never-married, Machine-op-inspct, Not-in-family, Black, Male, 0, 0, 40, United-States, 0 800 | 46, Private, 90758, Masters, 14, Never-married, Tech-support, Not-in-family, White, Male, 0, 0, 35, United-States, 1 801 | 43, Private, 274363, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 1902, 40, England, 1 802 | 43, Private, 154538, Assoc-acdm, 12, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, 0 803 | 24, Private, 106085, HS-grad, 9, Never-married, Other-service, Own-child, Black, Male, 0, 1721, 30, United-States, 0 804 | 68, Self-emp-not-inc, 315859, 11th, 7, Never-married, Farming-fishing, Unmarried, White, Male, 0, 0, 20, United-States, 0 805 | 31, Private, 51471, HS-grad, 9, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 38, United-States, 0 806 | 17, Private, 193830, 11th, 7, Never-married, Sales, Own-child, White, Female, 0, 0, 20, United-States, 0 807 | 32, Private, 231043, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 5178, 0, 48, United-States, 1 808 | 50, ?, 23780, Masters, 14, Married-spouse-absent, ?, Other-relative, White, Male, 0, 0, 40, United-States, 0 809 | 33, Private, 169879, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 3103, 0, 47, United-States, 1 810 | 64, Private, 270333, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 1 811 | 20, Private, 138768, HS-grad, 9, Never-married, Transport-moving, Own-child, White, Male, 0, 0, 30, United-States, 0 812 | 30, Private, 191571, HS-grad, 9, Separated, Other-service, Own-child, White, Female, 0, 0, 36, United-States, 0 813 | 22, ?, 219941, Some-college, 10, Never-married, ?, Own-child, Black, Male, 0, 0, 40, United-States, 0 814 | 43, Private, 94113, Some-college, 10, Divorced, Exec-managerial, Not-in-family, White, Male, 0, 0, 45, United-States, 0 815 | 22, Private, 137510, Some-college, 10, Never-married, Adm-clerical, Own-child, White, Male, 0, 0, 40, United-States, 0 816 | 17, Private, 32607, 10th, 6, Never-married, Farming-fishing, Own-child, White, Male, 0, 0, 20, United-States, 0 817 | 47, Self-emp-not-inc, 93208, HS-grad, 9, Married-civ-spouse, Other-service, Wife, White, Female, 0, 0, 75, Italy, 0 818 | 41, Private, 254440, Assoc-voc, 11, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 60, United-States, 0 819 | 56, Private, 186556, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 50, United-States, 1 820 | 64, Private, 169871, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 45, United-States, 0 821 | 47, Private, 191277, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 50, United-States, 1 822 | 48, Private, 167159, Assoc-voc, 11, Never-married, Adm-clerical, Unmarried, White, Male, 0, 0, 40, United-States, 0 823 | 31, Private, 171871, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 46, United-States, 0 824 | 29, Private, 154411, Assoc-voc, 11, Never-married, Tech-support, Own-child, White, Male, 0, 0, 40, United-States, 0 825 | 30, Private, 129227, HS-grad, 9, Widowed, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, 0 826 | 32, Private, 110331, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 1672, 60, United-States, 0 827 | 57, Private, 34269, HS-grad, 9, Widowed, Transport-moving, Unmarried, White, Male, 0, 653, 42, United-States, 1 828 | 62, Private, 174355, HS-grad, 9, Widowed, Other-service, Not-in-family, White, Female, 0, 0, 40, United-States, 0 829 | 39, Private, 680390, HS-grad, 9, Separated, Machine-op-inspct, Unmarried, White, Female, 0, 0, 24, United-States, 0 830 | 43, Private, 233130, Some-college, 10, Never-married, Adm-clerical, Not-in-family, White, Male, 0, 0, 25, United-States, 0 831 | 24, Self-emp-inc, 165474, Bachelors, 13, Never-married, Sales, Own-child, White, Male, 0, 0, 40, United-States, 0 832 | 42, ?, 257780, 11th, 7, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 15, United-States, 0 833 | 53, Private, 194259, Some-college, 10, Married-civ-spouse, Adm-clerical, Wife, White, Female, 4386, 0, 40, United-States, 1 834 | 26, Private, 280093, Some-college, 10, Never-married, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, 0 835 | 73, Self-emp-not-inc, 177387, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 0 836 | 72, ?, 28929, 11th, 7, Widowed, ?, Not-in-family, White, Female, 0, 0, 24, United-States, 0 837 | 55, Private, 105304, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 838 | 25, Private, 499233, HS-grad, 9, Divorced, Adm-clerical, Not-in-family, White, Male, 0, 0, 40, United-States, 0 839 | 41, Private, 180572, Bachelors, 13, Divorced, Prof-specialty, Not-in-family, White, Female, 0, 0, 50, United-States, 1 840 | 24, Private, 321435, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, White, Male, 0, 0, 50, United-States, 0 841 | 63, Private, 86108, HS-grad, 9, Widowed, Farming-fishing, Not-in-family, White, Male, 0, 0, 6, United-States, 0 842 | 17, Private, 198124, 11th, 7, Never-married, Sales, Own-child, White, Male, 0, 0, 20, United-States, 0 843 | 35, Private, 135162, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 50, United-States, 0 844 | 51, Private, 146813, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 1 845 | 62, Local-gov, 291175, Bachelors, 13, Widowed, Prof-specialty, Not-in-family, White, Female, 0, 0, 48, United-States, 0 846 | 55, Private, 387569, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 4386, 0, 40, United-States, 1 847 | 43, Private, 102895, Some-college, 10, Divorced, Prof-specialty, Not-in-family, White, Male, 0, 0, 40, United-States, 0 848 | 40, Local-gov, 33274, HS-grad, 9, Divorced, Other-service, Not-in-family, White, Female, 0, 0, 50, United-States, 0 849 | 37, Private, 86551, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, 1 850 | 39, Private, 138192, Bachelors, 13, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 851 | 31, Private, 118966, HS-grad, 9, Never-married, Craft-repair, Own-child, White, Male, 0, 0, 18, United-States, 0 852 | 61, Private, 99784, Masters, 14, Widowed, Prof-specialty, Not-in-family, White, Female, 0, 0, 40, United-States, 1 853 | 26, Private, 90980, Assoc-voc, 11, Divorced, Adm-clerical, Not-in-family, White, Female, 0, 0, 55, United-States, 0 854 | 46, Self-emp-not-inc, 177407, HS-grad, 9, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, 0 855 | 26, Private, 96467, Bachelors, 13, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 40, United-States, 0 856 | 48, State-gov, 327886, Doctorate, 16, Divorced, Prof-specialty, Own-child, White, Male, 0, 0, 50, United-States, 1 857 | 34, Private, 111567, HS-grad, 9, Never-married, Transport-moving, Own-child, White, Male, 0, 0, 40, United-States, 0 858 | 34, Local-gov, 166545, HS-grad, 9, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 40, United-States, 0 859 | 59, Private, 142182, HS-grad, 9, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, 1 860 | 34, Private, 188798, Bachelors, 13, Never-married, Prof-specialty, Own-child, White, Female, 0, 0, 40, United-States, 0 861 | 49, Private, 38563, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, White, Female, 0, 0, 56, United-States, 1 862 | 18, Private, 216284, 11th, 7, Never-married, Adm-clerical, Own-child, White, Female, 0, 0, 20, United-States, 0 863 | 43, Private, 191547, HS-grad, 9, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 40, Mexico, 0 864 | 48, Private, 285335, 11th, 7, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 50, United-States, 0 865 | 28, Self-emp-inc, 142712, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 70, United-States, 0 866 | 33, Private, 80945, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 867 | 24, Private, 309055, Some-college, 10, Never-married, Sales, Not-in-family, White, Female, 0, 0, 15, United-States, 0 868 | 21, Private, 62339, 10th, 6, Never-married, Handlers-cleaners, Not-in-family, White, Male, 0, 0, 40, United-States, 0 869 | 17, Private, 368700, 11th, 7, Never-married, Sales, Own-child, White, Male, 0, 0, 28, United-States, 0 870 | 39, Private, 176186, Some-college, 10, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 50, United-States, 1 871 | 29, Self-emp-not-inc, 266855, Bachelors, 13, Separated, Prof-specialty, Own-child, White, Male, 0, 0, 40, United-States, 0 872 | 44, Private, 48087, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 873 | 24, Private, 121313, Some-college, 10, Never-married, Transport-moving, Own-child, White, Male, 0, 0, 50, United-States, 0 874 | 71, Self-emp-not-inc, 143437, Masters, 14, Married-civ-spouse, Sales, Husband, White, Male, 10605, 0, 40, United-States, 1 875 | 51, Self-emp-not-inc, 160724, Bachelors, 13, Married-civ-spouse, Sales, Husband, Asian-Pac-Islander, Male, 0, 2415, 40, China, 1 876 | 55, Private, 282753, 5th-6th, 3, Divorced, Other-service, Unmarried, Black, Male, 0, 0, 25, United-States, 0 877 | 41, Private, 194636, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 60, United-States, 1 878 | 23, Private, 153044, HS-grad, 9, Never-married, Handlers-cleaners, Unmarried, Black, Female, 0, 0, 7, United-States, 0 879 | 38, Private, 411797, Assoc-voc, 11, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, 0 880 | 39, Private, 117683, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, 1 881 | 19, Private, 376540, HS-grad, 9, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 30, United-States, 0 882 | 49, Private, 72393, 9th, 5, Divorced, Machine-op-inspct, Not-in-family, White, Female, 0, 0, 40, United-States, 0 883 | 32, Private, 270335, Bachelors, 13, Married-civ-spouse, Adm-clerical, Other-relative, White, Male, 0, 0, 40, Philippines, 1 884 | 27, Private, 96226, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 70, United-States, 0 885 | 38, Private, 95336, Bachelors, 13, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 50, United-States, 1 886 | 33, Private, 258498, Some-college, 10, Married-civ-spouse, Craft-repair, Wife, White, Female, 0, 0, 60, United-States, 0 887 | 63, ?, 149698, Some-college, 10, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 15, United-States, 0 888 | 23, Private, 205865, Bachelors, 13, Never-married, Exec-managerial, Own-child, White, Male, 0, 0, 28, United-States, 0 889 | 33, Self-emp-inc, 155781, Some-college, 10, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 60, ?, 0 890 | 54, Self-emp-not-inc, 406468, HS-grad, 9, Married-civ-spouse, Sales, Husband, Black, Male, 0, 0, 40, United-States, 0 891 | 29, Private, 177119, Assoc-voc, 11, Divorced, Tech-support, Not-in-family, White, Female, 2174, 0, 45, United-States, 0 892 | 48, ?, 144397, Some-college, 10, Divorced, ?, Unmarried, Black, Female, 0, 0, 30, United-States, 0 893 | 35, Self-emp-not-inc, 372525, Bachelors, 13, Never-married, Exec-managerial, Not-in-family, White, Male, 0, 0, 40, United-States, 0 894 | 28, Private, 164170, Assoc-voc, 11, Married-civ-spouse, Adm-clerical, Wife, Asian-Pac-Islander, Female, 0, 0, 40, India, 0 895 | 37, Private, 183800, Bachelors, 13, Married-civ-spouse, Prof-specialty, Husband, White, Male, 7688, 0, 50, United-States, 1 896 | 42, Self-emp-not-inc, 177307, Prof-school, 15, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 65, United-States, 1 897 | 40, Private, 170108, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 40, United-States, 0 898 | 47, Private, 341995, Some-college, 10, Divorced, Sales, Own-child, White, Male, 0, 0, 55, United-States, 0 899 | 22, Private, 226508, Bachelors, 13, Never-married, Exec-managerial, Own-child, White, Female, 0, 0, 50, United-States, 0 900 | 30, Private, 87418, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 45, United-States, 1 901 | 28, Private, 109165, HS-grad, 9, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, 0 902 | 63, Local-gov, 28856, 7th-8th, 4, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 55, United-States, 0 903 | 51, Self-emp-not-inc, 175897, 9th, 5, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 20, United-States, 0 904 | 22, Private, 99697, HS-grad, 9, Never-married, Handlers-cleaners, Own-child, White, Female, 0, 0, 40, United-States, 0 905 | 27, ?, 90270, Assoc-acdm, 12, Married-civ-spouse, ?, Own-child, Amer-Indian-Eskimo, Male, 0, 0, 40, United-States, 0 906 | 35, Private, 152375, HS-grad, 9, Never-married, Sales, Not-in-family, White, Male, 0, 0, 45, United-States, 0 907 | 46, Private, 171550, HS-grad, 9, Divorced, Machine-op-inspct, Not-in-family, White, Female, 0, 0, 38, United-States, 0 908 | 37, Private, 211154, Some-college, 10, Divorced, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 52, United-States, 0 909 | 24, Private, 202570, Bachelors, 13, Never-married, Prof-specialty, Own-child, Black, Male, 0, 0, 15, United-States, 0 910 | 37, Self-emp-not-inc, 168496, HS-grad, 9, Divorced, Handlers-cleaners, Own-child, White, Male, 0, 0, 10, United-States, 0 911 | 53, Private, 68898, Some-college, 10, Married-civ-spouse, Tech-support, Husband, White, Male, 0, 0, 40, United-States, 0 912 | 27, Private, 93235, HS-grad, 9, Never-married, Sales, Not-in-family, White, Male, 0, 0, 30, United-States, 0 913 | 38, Private, 278924, Some-college, 10, Divorced, Craft-repair, Not-in-family, White, Male, 0, 0, 44, United-States, 0 914 | 53, Self-emp-not-inc, 311020, 10th, 6, Married-civ-spouse, Farming-fishing, Husband, White, Male, 0, 0, 60, United-States, 0 915 | 34, Private, 175878, Some-college, 10, Never-married, Craft-repair, Not-in-family, White, Male, 0, 0, 40, United-States, 0 916 | 23, Private, 543028, HS-grad, 9, Never-married, Sales, Own-child, Black, Male, 0, 0, 40, United-States, 0 917 | 39, Private, 202027, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 45, United-States, 1 918 | 43, Private, 158926, Masters, 14, Married-civ-spouse, Prof-specialty, Wife, Asian-Pac-Islander, Female, 0, 0, 50, South, 0 919 | 67, Self-emp-inc, 76860, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, Asian-Pac-Islander, Male, 0, 0, 40, United-States, 1 920 | 81, Self-emp-not-inc, 136063, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 30, United-States, 0 921 | 21, Private, 186648, Some-college, 10, Never-married, Other-service, Own-child, White, Male, 0, 0, 20, United-States, 0 922 | 23, Private, 257509, Some-college, 10, Never-married, Sales, Not-in-family, White, Male, 0, 0, 25, United-States, 0 923 | 25, Private, 98155, Some-college, 10, Never-married, Transport-moving, Unmarried, White, Male, 0, 0, 40, United-States, 0 924 | 42, Private, 274198, 5th-6th, 3, Married-civ-spouse, Machine-op-inspct, Wife, White, Female, 0, 0, 38, Mexico, 0 925 | 38, Private, 97083, Some-college, 10, Married-civ-spouse, Adm-clerical, Wife, Black, Female, 0, 0, 40, United-States, 0 926 | 64, ?, 29825, HS-grad, 9, Married-civ-spouse, ?, Husband, White, Male, 0, 0, 5, United-States, 0 927 | 32, Private, 262153, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 928 | 37, Private, 214738, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 929 | 51, Private, 138022, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 60, United-States, 1 930 | 22, Private, 91842, Some-college, 10, Never-married, Sales, Not-in-family, White, Female, 0, 0, 42, United-States, 0 931 | 33, Private, 373662, 1st-4th, 2, Married-spouse-absent, Priv-house-serv, Not-in-family, White, Female, 0, 0, 40, Guatemala, 0 932 | 42, Private, 162003, HS-grad, 9, Divorced, Machine-op-inspct, Not-in-family, White, Male, 0, 0, 55, United-States, 0 933 | 19, ?, 52114, Some-college, 10, Never-married, ?, Own-child, White, Female, 0, 0, 10, United-States, 0 934 | 51, Local-gov, 241843, Preschool, 1, Married-civ-spouse, Other-service, Husband, White, Male, 0, 0, 40, United-States, 0 935 | 23, Private, 375871, HS-grad, 9, Married-civ-spouse, Adm-clerical, Wife, White, Female, 0, 0, 40, Mexico, 0 936 | 37, Private, 186934, 11th, 7, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 3103, 0, 44, United-States, 1 937 | 37, Private, 176900, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 99, United-States, 1 938 | 47, Private, 21906, Masters, 14, Never-married, Prof-specialty, Not-in-family, White, Female, 0, 0, 25, United-States, 0 939 | 41, Private, 132222, Prof-school, 15, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 2415, 40, United-States, 1 940 | 33, Private, 143653, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 30, United-States, 0 941 | 31, Private, 111567, Bachelors, 13, Never-married, Sales, Not-in-family, White, Male, 0, 0, 40, United-States, 1 942 | 31, Private, 78602, Assoc-acdm, 12, Divorced, Other-service, Unmarried, Amer-Indian-Eskimo, Female, 0, 0, 40, United-States, 0 943 | 35, Private, 465507, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, Black, Male, 0, 0, 40, United-States, 0 944 | 38, Self-emp-inc, 196373, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 945 | 18, Private, 293227, HS-grad, 9, Never-married, Other-service, Not-in-family, White, Female, 0, 0, 45, United-States, 0 946 | 20, Private, 241752, HS-grad, 9, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 947 | 54, Local-gov, 166398, Some-college, 10, Divorced, Exec-managerial, Unmarried, Black, Female, 0, 0, 35, United-States, 0 948 | 40, Private, 184682, Some-college, 10, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, 0 949 | 36, Self-emp-inc, 108293, Bachelors, 13, Married-civ-spouse, Exec-managerial, Wife, White, Female, 0, 1977, 45, United-States, 1 950 | 43, Private, 250802, Some-college, 10, Divorced, Craft-repair, Unmarried, White, Male, 0, 0, 35, United-States, 0 951 | 44, Self-emp-not-inc, 325159, Some-college, 10, Divorced, Farming-fishing, Unmarried, White, Male, 0, 0, 40, United-States, 0 952 | 44, State-gov, 174675, HS-grad, 9, Married-civ-spouse, Prof-specialty, Wife, White, Female, 0, 0, 40, United-States, 1 953 | 43, Private, 227065, Masters, 14, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 43, United-States, 1 954 | 51, Private, 269080, 7th-8th, 4, Widowed, Other-service, Unmarried, Black, Female, 0, 0, 40, United-States, 0 955 | 18, Private, 177722, HS-grad, 9, Never-married, Other-service, Own-child, White, Female, 0, 0, 20, United-States, 0 956 | 51, Private, 133461, Some-college, 10, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 0 957 | 41, Private, 239683, 10th, 6, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 30, ?, 0 958 | 44, Self-emp-inc, 398473, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 70, United-States, 1 959 | 33, Local-gov, 298785, 10th, 6, Divorced, Transport-moving, Not-in-family, White, Male, 0, 0, 40, United-States, 0 960 | 33, Self-emp-not-inc, 123424, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 961 | 42, Private, 176286, Assoc-acdm, 12, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 40, United-States, 1 962 | 25, Private, 150062, Some-college, 10, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 45, United-States, 0 963 | 32, Private, 169240, HS-grad, 9, Divorced, Machine-op-inspct, Not-in-family, White, Female, 0, 0, 38, United-States, 0 964 | 32, Private, 288273, Bachelors, 13, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 70, Mexico, 0 965 | 36, Private, 526968, 10th, 6, Divorced, Exec-managerial, Unmarried, White, Female, 0, 0, 40, United-States, 0 966 | 28, Private, 57066, HS-grad, 9, Married-civ-spouse, Transport-moving, Husband, White, Male, 0, 0, 40, United-States, 0 967 | 20, Private, 323573, HS-grad, 9, Never-married, Other-service, Own-child, White, Female, 0, 0, 20, United-States, 0 968 | 35, Self-emp-inc, 368825, Some-college, 10, Married-civ-spouse, Sales, Husband, White, Male, 0, 0, 60, United-States, 1 969 | 55, Self-emp-not-inc, 189721, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 20, United-States, 0 970 | 48, Private, 164966, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, Asian-Pac-Islander, Male, 0, 0, 40, India, 1 971 | 36, ?, 94954, Assoc-voc, 11, Widowed, ?, Not-in-family, White, Female, 0, 0, 20, United-States, 0 972 | 34, Private, 202046, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 35, United-States, 1 973 | 28, Private, 161538, Bachelors, 13, Never-married, Tech-support, Not-in-family, White, Female, 0, 0, 35, United-States, 0 974 | 67, Private, 105252, Bachelors, 13, Widowed, Exec-managerial, Not-in-family, White, Male, 0, 2392, 40, United-States, 1 975 | 37, Private, 200153, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 40, United-States, 0 976 | 44, Private, 32185, HS-grad, 9, Never-married, Transport-moving, Unmarried, White, Male, 0, 0, 70, United-States, 0 977 | 25, Private, 178326, Some-college, 10, Never-married, Sales, Not-in-family, White, Female, 0, 0, 40, United-States, 0 978 | 21, Private, 255957, Some-college, 10, Never-married, Exec-managerial, Not-in-family, White, Female, 4101, 0, 40, United-States, 0 979 | 40, State-gov, 188693, Masters, 14, Married-civ-spouse, Prof-specialty, Husband, White, Male, 0, 0, 35, United-States, 1 980 | 78, Private, 182977, HS-grad, 9, Widowed, Other-service, Not-in-family, Black, Female, 2964, 0, 40, United-States, 0 981 | 34, Private, 159929, HS-grad, 9, Divorced, Handlers-cleaners, Own-child, White, Male, 0, 0, 40, United-States, 0 982 | 49, Private, 123207, HS-grad, 9, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 44, United-States, 0 983 | 22, Private, 284317, Assoc-acdm, 12, Never-married, Adm-clerical, Not-in-family, White, Female, 0, 0, 40, United-States, 0 984 | 23, ?, 184699, HS-grad, 9, Never-married, ?, Unmarried, Black, Female, 0, 0, 40, United-States, 0 985 | 60, Self-emp-not-inc, 154474, HS-grad, 9, Never-married, Farming-fishing, Unmarried, White, Male, 0, 0, 42, United-States, 0 986 | 45, Local-gov, 318280, HS-grad, 9, Widowed, Protective-serv, Not-in-family, White, Male, 0, 0, 40, United-States, 1 987 | 63, Private, 254907, Assoc-voc, 11, Divorced, Other-service, Not-in-family, White, Female, 0, 0, 20, United-States, 0 988 | 41, Private, 349221, HS-grad, 9, Never-married, Craft-repair, Own-child, Black, Female, 0, 0, 35, United-States, 0 989 | 47, Private, 335973, HS-grad, 9, Divorced, Adm-clerical, Unmarried, White, Female, 0, 0, 40, United-States, 0 990 | 44, Private, 126701, HS-grad, 9, Divorced, Craft-repair, Unmarried, White, Male, 0, 0, 40, United-States, 0 991 | 51, Private, 122159, Some-college, 10, Widowed, Prof-specialty, Not-in-family, White, Female, 3325, 0, 40, United-States, 0 992 | 46, Private, 187370, Bachelors, 13, Never-married, Sales, Not-in-family, White, Male, 0, 1504, 40, United-States, 0 993 | 41, Private, 194636, Assoc-voc, 11, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 0, 0, 40, United-States, 0 994 | 50, Self-emp-not-inc, 124793, HS-grad, 9, Married-civ-spouse, Craft-repair, Husband, White, Male, 0, 0, 30, United-States, 0 995 | 47, Private, 192835, HS-grad, 9, Married-civ-spouse, Adm-clerical, Husband, White, Male, 0, 0, 50, United-States, 1 996 | 35, Private, 290226, HS-grad, 9, Never-married, Exec-managerial, Not-in-family, White, Male, 0, 0, 45, United-States, 0 997 | 56, Private, 112840, HS-grad, 9, Married-civ-spouse, Exec-managerial, Husband, White, Male, 0, 0, 55, United-States, 1 998 | 45, Private, 89325, Masters, 14, Divorced, Prof-specialty, Not-in-family, White, Male, 0, 0, 45, United-States, 0 999 | 48, Federal-gov, 33109, Bachelors, 13, Divorced, Exec-managerial, Unmarried, White, Male, 0, 0, 58, United-States, 1 1000 | 40, Private, 82465, Some-college, 10, Married-civ-spouse, Machine-op-inspct, Husband, White, Male, 2580, 0, 40, United-States, 0 1001 | 39, Self-emp-inc, 329980, Bachelors, 13, Married-civ-spouse, Exec-managerial, Husband, White, Male, 15024, 0, 50, United-States, 1 1002 | -------------------------------------------------------------------------------- /src/test/resources/prop1.conf: -------------------------------------------------------------------------------- 1 | #Generic options 2 | data.filename=./src/test/resource/mini.csv 3 | data.columns=age,sex,capital_gain,capital_loss,native-country 4 | data.column.label=probability 5 | data.type=csv 6 | 7 | #Classifier options 8 | classifier.lib=spark.logistic-regression 9 | train.percentage=80 10 | validate.options=SdCv 11 | validate.numFolds=5 12 | model.filename=./src/test/resource/sparkmodel.test 13 | 14 | #Elasticsearch options 15 | cluster.name= 16 | node.name= 17 | index.name= 18 | mapping.filename=./src/test/resource/mapping.test 19 | host=localhost 20 | port=9300 21 | 22 | #Spark options 23 | spark.model.type=logistic-regression 24 | spark.model.isregression=false 25 | spark.model.binThreshold=0.5 26 | spark.model.params=numIterations:3 27 | spark.model.numClasses=2 28 | spark.conf=spark.master:local[4],spark.driver.memory:512m 29 | -------------------------------------------------------------------------------- /src/test/resources/prop_clf.conf: -------------------------------------------------------------------------------- 1 | #Generic options 2 | data.filename=adult_num.csv 3 | data.columns=age,workclass,sex,capital_gain,capital_loss,native-country,probability 4 | data.column.label=probability 5 | data.type=csv 6 | 7 | #Classifier options 8 | classifier.lib=spark.logistic-regression 9 | train.percentage=80 10 | validate.options=SdCv 11 | validate.numFolds=5 12 | model.filename=spark-clf-test.model 13 | 14 | #Elasticsearch options 15 | cluster.name=elasticsearch_sdhu 16 | node.name=Zuras 17 | index.name=spark-clf-test 18 | mapping.filename=mapping-clf.test 19 | host=localhost 20 | port=9300 21 | 22 | #Spark options 23 | spark.model.type=logistic-regression 24 | spark.model.isregression=false 25 | spark.model.binThreshold=0.5 26 | spark.model.params=numIterations:4 27 | spark.model.numClasses=2 28 | spark.conf=spark.master:local[4],spark.driver.memory:512m 29 | -------------------------------------------------------------------------------- /src/test/resources/spark-clf-test.model: -------------------------------------------------------------------------------- 1 | {"modelName":"spark-clf-test.model","numFeatures":6,"intercept":0.0,"weights":[-2.0092054968662714,-2.9126889680930597,-3.100270747860132,142.95583475280083,-10.008631089635665,-2.2710759560934317],"categoriesMap":{"Haiti":1.0,"Self-emp-not-inc":2.0,"Poland":3.0,"State-gov":4.0,"Philippines":5.0,"South":6.0,"Yugoslavia":7.0,"Greece":8.0,"Japan":9.0,"Thailand":10.0,"Laos":11.0,"Trinadad&Tobago":12.0,"Cambodia":13.0,"Puerto-Rico":14.0,"Columbia":15.0,"China":16.0,"Mexico":17.0,"Taiwan":18.0,"Iran":19.0,"Nicaragua":20.0,"Peru":21.0,"Guatemala":22.0,"Local-gov":23.0,"Jamaica":24.0,"Hong":25.0,"Canada":26.0,"Scotland":27.0,"India":28.0,"Without-pay":29.0,"El-Salvador":30.0,"Ecuador":31.0,"Cuba":32.0,"United-States":33.0,"Federal-gov":34.0,"Vietnam":35.0,"?":36.0,"Italy":37.0,"Outlying-US(Guam-USVI-etc)":38.0,"Ireland":39.0,"Female":40.0,"Honduras":41.0,"Dominican-Republic":42.0,"France":43.0,"Never-worked":44.0,"Holand-Netherlands":45.0,"Private":46.0,"Self-emp-inc":47.0,"England":48.0,"Germany":49.0,"Male":50.0,"Portugal":51.0,"Hungary":52.0}} -------------------------------------------------------------------------------- /src/test/scala/DataUtilSpec.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | package test 3 | 4 | import org.scalatest._ 5 | import com.holdenkarau.spark.testing._ 6 | 7 | import scala.collection.JavaConversions._ 8 | 9 | class DataUtilSpec extends FlatSpec with MustMatchers with SharedSparkContext { 10 | 11 | "RichArrayString" should "get proper conversions" in { 12 | import CsvUtil._ 13 | val a = Array("3", "1.0", "boo") 14 | val cm0 = Map("boo" -> 10.0) 15 | val cm1 = Map[String, Double]() 16 | 17 | a.toDoubleOpt(0) must equal(Option(3.0)) 18 | a.toDoubleOpt(2) must equal(None) 19 | a.toDoubleEither(1) must equal(Left(1.0)) 20 | a.toDoubleEither(2) must equal(Right("boo")) 21 | a.toDoubleArray(cm0) must equal(Array(3.0, 1.0, 10.0)) 22 | a.toDoubleArray(cm1) must equal(Array(3.0, 1.0, 0.0)) 23 | } 24 | 25 | 26 | "ReadUtils" should "read CSV" in { 27 | val cols = IndexedSeq("sex","probability") 28 | val label = "native-country" 29 | val path = getClass.getResource("/mini.csv").toURI.toString 30 | println(path) 31 | 32 | sc.textFile(path) 33 | 34 | val (rdd, cm) = ReadUtil.csv2RDD(sc, path, cols, label) 35 | val data = rdd.collect 36 | println(cm.mkString(", ")) 37 | println(data(0).features.toArray.mkString(", ")) 38 | println(data(0).label) 39 | println(data.size) 40 | 41 | cm.keys must contain allOf ("Male", "Female", "United-States", "England") 42 | data.size must equal(1000) 43 | data(0).features.size must equal(2) 44 | data(0).label must equal(cm.getOrElse("United-States", 0.0)) 45 | 46 | } 47 | 48 | it should "convert arrayString to IndexValue" in { 49 | val p0 = Array("50", "Male", "0.2","me") 50 | val cind = ReadUtil.arr2CIndVal(p0) 51 | val check = cind.map(_.getValue.asInstanceOf[String]).toArray 52 | 53 | check must equal(p0) 54 | } 55 | 56 | it should "convert Collection IndexValue to Vector" in { 57 | val p0 = Array("50", "Male", "0.2","me") 58 | val cm = Map("Male" -> -1.0, "me" -> 10.0) 59 | val cind = ReadUtil.arr2CIndVal(p0) 60 | val v = ReadUtil.cIndVal2Vector(cind, cm) 61 | 62 | v.toArray must equal(Array(50.0, -1.0, 0.2, 10.0)) 63 | } 64 | } 65 | -------------------------------------------------------------------------------- /src/test/scala/SarkPredictorEngineSpec.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | package test 3 | 4 | import com.mahisoft.elasticsearchprediction._ 5 | import utils.DataProperties 6 | import plugin.domain.IndexValue 7 | import plugin.exception.PredictionException 8 | import plugin.engine.PredictorEngine 9 | 10 | import org.apache.spark._ 11 | import rdd.RDD 12 | import mllib.regression._ 13 | import mllib.classification._ 14 | 15 | import org.scalatest._ 16 | import com.holdenkarau.spark.testing._ 17 | import java.io.File 18 | import java.util.Collection 19 | 20 | import scala.collection.JavaConversions._ 21 | 22 | 23 | class SparkPredictorEngineSpec extends FlatSpec with MustMatchers { 24 | val pconf = getClass.getResource("/prop1.conf").getPath 25 | val dataP = getClass.getResource("/mini.csv").toURI.toString 26 | val dp = new DataProperties(pconf) 27 | val modelP = getClass.getResource("/spark-clf-test.model").getPath 28 | val clf_type = "spark.logistic-regression" 29 | 30 | "Predictor Engine" should "throw empty model exception" in { 31 | val eng = new SparkPredictorEngine(modelP, SVM_Helper) 32 | evaluating {eng.getPrediction(List[IndexValue]())} must produce [PredictionException] 33 | } 34 | 35 | // "Spark_PredictorEngine" should "return sparkPredictorEngine of svm type" in { 36 | // val speng = new Spark_PredictorEngine(modelP, "spark.svm") 37 | // speng.getSparkPredictorEngine mustBe a [SparkPredictorEngine[_]] 38 | // 39 | // } 40 | 41 | it should "return a generic PredictorEngine" in { 42 | val speng = new Spark_PredictorEngine(modelP, "spark.svm") 43 | speng.getPredictorEngine mustBe a [PredictorEngine] 44 | } 45 | 46 | it should "load the classifier" in { 47 | val speng = new Spark_PredictorEngine(modelP, clf_type) 48 | val eng = speng.getSparkPredictorEngine 49 | val m = eng.getModel 50 | val cm = m.categoriesMap.getOrElse(Map[String, Double]()) 51 | 52 | m.clf must not be empty 53 | //m.numClasses must be(Some(2)) 54 | //m.binThreshold must be(Some(0.5)) 55 | cm.keys must contain allOf("Female", "Male", "United-States", "China") 56 | } 57 | 58 | it should "evaluate values" in { 59 | val speng = new Spark_PredictorEngine(modelP, clf_type) 60 | val eng = speng.getSparkPredictorEngine 61 | 62 | val p0 = Array("50", "Self-emp-not-inc", "Male", "0", "0", "United-States") 63 | val cindv = ReadUtil.arr2CIndVal(p0) 64 | 65 | val check = eng.getPrediction(cindv) 66 | 67 | check must equal(0.0) 68 | check mustBe a [java.lang.Double] 69 | } 70 | 71 | it should "evaluate values using generic Predictor engine" in { 72 | val speng = new Spark_PredictorEngine(modelP, clf_type) 73 | val eng = speng.getPredictorEngine 74 | 75 | val p0 = Array("50", "Self-emp-not-inc", "Male", "0", "0", "United-States") 76 | val cindv = ReadUtil.arr2CIndVal(p0) 77 | 78 | val check = eng.getPrediction(cindv) 79 | 80 | check must equal(0.0) 81 | check mustBe a [java.lang.Double] 82 | } 83 | } 84 | -------------------------------------------------------------------------------- /src/test/scala/SparkTrainerSpec.scala: -------------------------------------------------------------------------------- 1 | package com.sdhu.elasticsearchprediction.spark 2 | package test 3 | 4 | import com.mahisoft.elasticsearchprediction.utils.DataProperties 5 | 6 | import org.apache.spark._ 7 | import rdd.RDD 8 | import mllib.regression.LabeledPoint 9 | 10 | import org.scalatest._ 11 | import com.holdenkarau.spark.testing._ 12 | import java.io.File 13 | 14 | class SparkTrainerSpec extends FlatSpec with MustMatchers with BeforeAndAfterAll { 15 | val pconf = getClass.getResource("/prop1.conf").getPath 16 | val dataP = getClass.getResource("/mini.csv").toURI.toString 17 | val dp = new DataProperties(pconf) 18 | val trainer = (new Spark_Trainer(dp)).getSparkGenericTrainer 19 | val correctSPC = SparkClassifierConfig( 20 | Some("./src/test/resource/mini.csv"), 21 | Some(IndexedSeq("age","sex","capital_gain","capital_loss","native-country")), 22 | Some("probability"), 23 | Some("./src/test/resource/sparkmodel.test"), 24 | Some(80), 25 | Some(5), 26 | Some("logistic-regression"), 27 | Some(Map("numIterations" -> "3")), 28 | Some(false), 29 | Some(0.5), 30 | Some(2), 31 | Some(Map("spark.master"->"local[4]", "spark.driver.memory"->"512m"))) 32 | 33 | val modelPath = "sparkmodel.test" 34 | 35 | override def afterAll(){ 36 | val f = new File(modelPath) 37 | f.delete() 38 | } 39 | 40 | 41 | "DataProperties" should "read into SparkClassifierConfig class" in { 42 | val spc = SparkClassifierConfig().readDataProperties(dp) 43 | 44 | spc must equal (correctSPC) 45 | 46 | spc.checkValid must equal(true) 47 | } 48 | 49 | "Spark Linear Trainer for Classification" should "select logistic-regression" in { 50 | trainer.sparkModelHelper.name must equal ("LogisticRegression_Helper") 51 | } 52 | 53 | it should "set properties" in { 54 | trainer.setDataProperties(dp) 55 | trainer.getConfig must equal(correctSPC) 56 | } 57 | 58 | it should "load data" in { 59 | trainer.setDataProperties(dp) 60 | val success = trainer.loadData(new File(dataP)) 61 | val data = trainer.getData.map(_.collect) 62 | 63 | success must equal(true) 64 | data.map(_.size) must equal(Some(1000)) 65 | data.map(_(0).features.size) must equal(Some(5)) 66 | } 67 | 68 | it should "train and do simple validation" in { 69 | trainer.setDataProperties(dp) 70 | trainer.loadData(new File(dataP)) 71 | trainer.trainModel() 72 | val res = trainer.simpleValidation() 73 | val model = trainer.getModelData 74 | val cm = model.categoriesMap.getOrElse(Map[String,Double]()) 75 | 76 | model.binThreshold must equal(Some(0.5)) 77 | model.clf must not be empty 78 | cm.keys must contain allOf("Female", "Male", "United-States", "China") 79 | 80 | res.getTrainDataSetSize must equal(1000) 81 | res.getTestDataSetSize must equal(1000) 82 | println(res.getResults) 83 | } 84 | 85 | it should "Cross Validate" in { 86 | trainer.setDataProperties(dp) 87 | trainer.loadData(new File(dataP)) 88 | trainer.trainModel() 89 | val res = trainer.crossValidation(5) 90 | 91 | res.getNumFolds must equal(5) 92 | res.getDataSetSize must equal(1000) 93 | println(res.getResults) 94 | } 95 | 96 | it should "save the Model" in { 97 | trainer.setDataProperties(dp) 98 | trainer.loadData(new File(dataP)) 99 | trainer.trainModel() 100 | 101 | val success = trainer.saveModel("testModel", modelPath) 102 | 103 | success must equal(true) 104 | } 105 | 106 | it should "load a new Model" in { 107 | val tr2 = (new Spark_Trainer(dp)).getSparkGenericTrainer 108 | tr2.setDataProperties(dp) 109 | tr2.loadClassifier(modelPath) 110 | val model = tr2.getModelData 111 | val cm = model.categoriesMap.getOrElse(Map[String,Double]()) 112 | 113 | model.binThreshold must equal(Some(0.5)) 114 | model.clf must not be empty 115 | cm.keys must contain allOf("Female", "Male", "United-States", "China") 116 | } 117 | } 118 | --------------------------------------------------------------------------------