diff --git a/.github/workflows/maven.yml b/.github/workflows/maven.yml index 107eee33..d22b3c5e 100644 --- a/.github/workflows/maven.yml +++ b/.github/workflows/maven.yml @@ -22,4 +22,4 @@ jobs: path: ~/.m2 key: ${{ runner.os }}-m2-${{ hashFiles('**/pom.xml') }} restore-keys: ${{ runner.os }}-m2 - - run: mvn -B package --file pom.xml + - run: mvn -Dxgboost4j-spark.version=2.0.1 -B package --file pom.xml diff --git a/README.md b/README.md index 62ab1a53..3824a1c8 100644 --- a/README.md +++ b/README.md @@ -109,6 +109,14 @@ Java library and command-line application for converting Apache Spark ML pipelin * [`tuning.TrainValidationSplitModel`](https://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/tuning/TrainValidationSplitModel.html) +
+ JPMML-SparkML + + * Feature transformers: + * `org.jpmml.sparkml.feature.InvalidCategoryTransformer` + * `org.jpmml.sparkml.feature.SparseToDenseTransformer` +
+
LightGBM @@ -120,8 +128,6 @@ Java library and command-line application for converting Apache Spark ML pipelin
XGBoost - * Feature transformers: - * `org.jpmml.sparkml.xgboost.SparseToDenseTransformer` * Prediction models: * [`ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel`](https://xgboost.readthedocs.io/en/latest/jvm/scaladocs/xgboost4j-spark/ml/dmlc/xgboost4j/scala/spark/XGBoostClassificationModel.html) * [`ml.dmlc.xgboost4j.scala.spark.XGBoostRegressionModel`](https://xgboost.readthedocs.io/en/latest/jvm/scaladocs/xgboost4j-spark/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressionModel.html) @@ -247,6 +253,7 @@ spark-submit --master local --class org.jpmml.sparkml.example.Main pmml-sparkml- # Documentation # +* [Training PySpark LightGBM pipelines](https://openscoring.io/blog/2023/05/26/pyspark_lightgbm_pipeline/) * [Converting logistic regression models to PMML documents](https://openscoring.io/blog/2020/01/19/converting_logistic_regression_pmml/#apache-spark) * [Deploying Apache Spark ML pipeline models on Openscoring REST web service](https://openscoring.io/blog/2020/02/16/deploying_sparkml_pipeline_openscoring_rest/) * [Converting Apache Spark ML pipeline models to PMML documents](https://openscoring.io/blog/2018/07/09/converting_sparkml_pipeline_pmml/) diff --git a/pmml-sparkml-example/pom.xml b/pmml-sparkml-example/pom.xml index 6454e0fc..03663491 100644 --- a/pmml-sparkml-example/pom.xml +++ b/pmml-sparkml-example/pom.xml @@ -67,7 +67,7 @@ org.apache.maven.plugins maven-shade-plugin - 3.4.1 + 3.5.1 package diff --git a/pmml-sparkml-example/src/main/java/org/jpmml/sparkml/example/Main.java b/pmml-sparkml-example/src/main/java/org/jpmml/sparkml/example/Main.java index 83d04031..5fc1278c 100644 --- a/pmml-sparkml-example/src/main/java/org/jpmml/sparkml/example/Main.java +++ b/pmml-sparkml-example/src/main/java/org/jpmml/sparkml/example/Main.java @@ -37,6 +37,7 @@ import org.apache.spark.sql.types.StructType; import org.dmg.pmml.PMML; import org.jpmml.model.metro.MetroJAXBUtil; +import org.jpmml.sparkml.ArchiveUtil; import org.jpmml.sparkml.PMMLBuilder; import org.jpmml.sparkml.PipelineModelUtil; import org.jpmml.sparkml.model.HasPredictionModelOptions; @@ -187,7 +188,7 @@ private void run() throws Exception { logger.info("Loading pipeline model.."); if(this.pipelineInput.isFile()){ - this.pipelineInput = PipelineModelUtil.uncompress(this.pipelineInput); + this.pipelineInput = ArchiveUtil.uncompress(this.pipelineInput); } long begin = System.currentTimeMillis(); diff --git a/pmml-sparkml-lightgbm/src/test/java/org/jpmml/sparkml/lightgbm/testing/LightGBMTest.java b/pmml-sparkml-lightgbm/src/test/java/org/jpmml/sparkml/lightgbm/testing/LightGBMTest.java index a6f819b7..f7092cb7 100644 --- a/pmml-sparkml-lightgbm/src/test/java/org/jpmml/sparkml/lightgbm/testing/LightGBMTest.java +++ b/pmml-sparkml-lightgbm/src/test/java/org/jpmml/sparkml/lightgbm/testing/LightGBMTest.java @@ -21,6 +21,7 @@ import java.util.function.Predicate; import com.google.common.base.Equivalence; +import org.jpmml.converter.testing.Datasets; import org.jpmml.evaluator.ResultField; import org.jpmml.evaluator.testing.IntegrationTest; import org.jpmml.evaluator.testing.PMMLEquivalence; @@ -28,7 +29,7 @@ import org.jpmml.sparkml.testing.SparkMLEncoderBatchTest; import org.junit.Test; -public class LightGBMTest extends IntegrationTest { +public class LightGBMTest extends IntegrationTest implements Datasets { public LightGBMTest(){ super(new PMMLEquivalence(1e-14, 1e-14)); @@ -43,16 +44,26 @@ public SimpleArchiveBatch createBatch(String algorithm, String dataset, Predicat @Test public void evaluateLightGBMAudit() throws Exception { - evaluate("LightGBM", "Audit"); + evaluate("LightGBM", AUDIT); + } + + @Test + public void evaluateLightGBMAuditNA() throws Exception { + evaluate("LightGBM", AUDIT_NA); } @Test public void evaluateLightGBMAuto() throws Exception { - evaluate("LightGBM", "Auto"); + evaluate("LightGBM", AUTO); + } + + @Test + public void evaluateLightGBMAutoNA() throws Exception { + evaluate("LightGBM", AUTO_NA); } @Test public void evaluateLightGBMIris() throws Exception { - evaluate("LightGBM", "Iris"); + evaluate("LightGBM", IRIS); } } \ No newline at end of file diff --git a/pmml-sparkml-lightgbm/src/test/resources/LightGBMAuditNA.scala b/pmml-sparkml-lightgbm/src/test/resources/LightGBMAuditNA.scala new file mode 100644 index 00000000..359ad80c --- /dev/null +++ b/pmml-sparkml-lightgbm/src/test/resources/LightGBMAuditNA.scala @@ -0,0 +1,44 @@ +import java.io.File + +import com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassifier +import org.apache.spark.ml.Pipeline +import org.apache.spark.ml.feature._ +import org.apache.spark.ml.linalg.Vector +import org.apache.spark.sql.functions.{lit, udf} +import org.apache.spark.sql.types.StringType +import org.jpmml.sparkml.{DatasetUtil, PipelineModelUtil, PMMLBuilder} +import org.jpmml.sparkml.feature.InvalidCategoryTransformer + +var df = DatasetUtil.loadCsv(spark, new File("csv/AuditNA.csv")) +df = DatasetUtil.castColumn(df, "Adjusted", StringType) + +//DatasetUtil.storeSchema(df, new File("schema/AuditNA.json")) + +val cat_cols = Array("Education", "Employment", "Gender", "Marital", "Occupation") +val cont_cols = Array("Age", "Hours", "Income") + +val labelIndexer = new StringIndexer().setInputCol("Adjusted").setOutputCol("idx_Adjusted") + +val indexer = new StringIndexer().setInputCols(cat_cols).setOutputCols(cat_cols.map(cat_col => "idx_" + cat_col)).setHandleInvalid("keep") +val indexTransformer = new InvalidCategoryTransformer().setInputCols(indexer.getOutputCols).setOutputCols(cat_cols.map(cat_col => "idxTransformed_" + cat_col)) + +val assembler = new VectorAssembler().setInputCols(indexTransformer.getOutputCols ++ cont_cols).setOutputCol("featureVector").setHandleInvalid("keep") + +val classifier = new LightGBMClassifier().setObjective("binary").setNumIterations(101).setLabelCol(labelIndexer.getOutputCol).setFeaturesCol(assembler.getOutputCol) + +val pipeline = new Pipeline().setStages(Array(labelIndexer, indexer, indexTransformer, assembler, classifier)) +val pipelineModel = pipeline.fit(df) + +//PipelineModelUtil.storeZip(pipelineModel, new File("pipeline/LightGBMAuditNA.zip")) + +new PMMLBuilder(df.schema, pipelineModel).buildFile(new File("pmml/LightGBMAuditNA.pmml")) + +val predLabel = udf{ (value: Float) => value.toInt.toString } +val vectorToColumn = udf{ (vec: Vector, index: Int) => vec(index) } + +var lgbDf = pipelineModel.transform(df) +lgbDf = lgbDf.selectExpr("prediction", "probability") +lgbDf = lgbDf.withColumn("Adjusted", predLabel(lgbDf("prediction"))).drop("prediction") +lgbDf = lgbDf.withColumn("probability(0)", vectorToColumn(lgbDf("probability"), lit(0))).withColumn("probability(1)", vectorToColumn(lgbDf("probability"), lit(1))).drop("probability").drop("probability") + +DatasetUtil.storeCsv(lgbDf, new File("csv/LightGBMAuditNA.csv")) diff --git a/pmml-sparkml-lightgbm/src/test/resources/LightGBMAutoNA.scala b/pmml-sparkml-lightgbm/src/test/resources/LightGBMAutoNA.scala new file mode 100644 index 00000000..f252a884 --- /dev/null +++ b/pmml-sparkml-lightgbm/src/test/resources/LightGBMAutoNA.scala @@ -0,0 +1,33 @@ +import java.io.File + +import com.microsoft.azure.synapse.ml.lightgbm.LightGBMRegressor +import org.apache.spark.ml.Pipeline +import org.apache.spark.ml.feature._ +import org.jpmml.sparkml.{DatasetUtil, PipelineModelUtil, PMMLBuilder} +import org.jpmml.sparkml.feature.InvalidCategoryTransformer + +var df = DatasetUtil.loadCsv(spark, new File("csv/AutoNA.csv")) + +//DatasetUtil.storeSchema(df, new File("schema/AutoNA.json")) + +val cat_cols = Array("cylinders", "model_year", "origin") +val cont_cols = Array("acceleration", "displacement", "horsepower", "weight") + +val indexer = new StringIndexer().setInputCols(cat_cols).setOutputCols(cat_cols.map(cat_col => "idx_" + cat_col)).setHandleInvalid("keep") +val indexTransformer = new InvalidCategoryTransformer().setInputCols(indexer.getOutputCols).setOutputCols(cat_cols.map(cat_col => "idxTransformed_" + cat_col)) + +val assembler = new VectorAssembler().setInputCols(indexTransformer.getOutputCols ++ cont_cols).setOutputCol("featureVector").setHandleInvalid("keep") + +val regressor = new LightGBMRegressor().setNumIterations(101).setLabelCol("mpg").setFeaturesCol(assembler.getOutputCol) + +val pipeline = new Pipeline().setStages(Array(indexer, indexTransformer, assembler, regressor)) +val pipelineModel = pipeline.fit(df) + +//PipelineModelUtil.storeZip(pipelineModel, new File("pipeline/LightGBMAutoNA.zip")) + +new PMMLBuilder(df.schema, pipelineModel).buildFile(new File("pmml/LightGBMAutoNA.pmml")) + +var lgbDf = pipelineModel.transform(df) +lgbDf = lgbDf.selectExpr("prediction as mpg") + +DatasetUtil.storeCsv(lgbDf, new File("csv/LightGBMAutoNA.csv")) diff --git a/pmml-sparkml-lightgbm/src/test/resources/csv/AuditNA.csv b/pmml-sparkml-lightgbm/src/test/resources/csv/AuditNA.csv new file mode 100644 index 00000000..7d904c78 --- /dev/null +++ b/pmml-sparkml-lightgbm/src/test/resources/csv/AuditNA.csv @@ -0,0 +1,1900 @@ +Age,Employment,Education,Marital,Occupation,Income,Gender,Deductions,Hours,Adjusted +38,Private,College,Unmarried,Service,81838,N/A,0,72,0 +35,N/A,Associate,N/A,N/A,72099,N/A,0,30,0 +N/A,Private,HSgrad,Divorced,Clerical,154676.74,N/A,N/A,40,0 +45,Private,Bachelor,Married,Repair,27743.82,Male,0,N/A,1 +60,N/A,College,Married,Executive,7568.23,Male,0,N/A,1 +74,Private,HSgrad,Married,Service,33144.4,Male,N/A,N/A,0 +43,Private,Bachelor,N/A,N/A,43391.17,Male,0,50,1 +35,Private,N/A,N/A,Machinist,59906.65,Male,0,N/A,0 +25,Private,Associate,Divorced,N/A,126888.91,Female,0,40,0 +22,Private,N/A,Absent,N/A,52466.49,Female,N/A,37,0 +N/A,Private,College,Divorced,N/A,N/A,Female,N/A,N/A,0 +60,Private,N/A,N/A,N/A,24155.31,Male,0,40,0 +21,Private,N/A,Absent,Service,N/A,Female,0,35,0 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+27,N/A,N/A,Divorced,N/A,291269.15,Female,0,40,0 +49,Private,Vocational,N/A,Support,175877.19,Female,0,N/A,0 +53,Private,HSgrad,N/A,Transport,33204.38,Male,N/A,N/A,1 +N/A,PSFederal,College,Divorced,Clerical,178189.6,Female,0,40,0 +54,PSLocal,N/A,Absent,N/A,120586.35,N/A,0,40,0 +N/A,PSFederal,College,Married,Cleaner,27526.46,Male,0,N/A,0 +29,Private,N/A,Absent,N/A,69967.33,Male,0,45,0 +33,Consultant,N/A,Married,Executive,32405.19,Male,0,45,0 +38,Private,N/A,Absent,Service,141697.29,Female,N/A,30,0 +79,N/A,Bachelor,N/A,N/A,1598.95,Male,N/A,40,0 +58,Private,HSgrad,Widowed,Clerical,N/A,Female,0,40,0 +N/A,Private,N/A,Absent,Service,N/A,N/A,0,20,0 +N/A,Private,Associate,Divorced,Repair,N/A,Male,0,45,0 +24,Private,N/A,Absent,Farming,175354.11,N/A,0,50,0 +29,N/A,Bachelor,Absent,Professional,62808.8,Male,0,40,0 +17,Private,N/A,N/A,Cleaner,117629.97,Male,N/A,10,0 +31,Consultant,HSgrad,Divorced,N/A,32864.65,Male,0,35,0 +N/A,Private,College,N/A,Sales,57036.86,N/A,0,N/A,0 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-Xmx1024m - - 2.12.15 - - - - scala-compile-first - process-resources - - add-source - compile - - - - scala-test-compile - process-test-resources - - testCompile - - - - - - diff --git a/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/BoosterUtil.java b/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/BoosterUtil.java index faff9fba..8849cf3e 100644 --- a/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/BoosterUtil.java +++ b/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/BoosterUtil.java @@ -18,13 +18,14 @@ */ package org.jpmml.sparkml.xgboost; -import java.io.ByteArrayInputStream; -import java.io.IOException; +import java.io.File; +import java.io.FileInputStream; import java.io.InputStream; import java.util.LinkedHashMap; import java.util.Map; import java.util.function.Function; +import com.google.common.io.MoreFiles; import ml.dmlc.xgboost4j.scala.Booster; import ml.dmlc.xgboost4j.scala.spark.params.GeneralParams; import org.apache.spark.ml.Model; @@ -49,20 +50,20 @@ private BoosterUtil(){ public & HasPredictionCol & GeneralParams, C extends ModelConverter & HasSparkMLXGBoostOptions> MiningModel encodeBooster(C converter, Booster booster, Schema schema){ M model = converter.getModel(); - byte[] bytes; + Learner learner; try { - bytes = booster.toByteArray(); - } catch(Exception e){ - throw new RuntimeException(e); - } + File tmpBoosterFile = File.createTempFile("Booster", ".json"); - Learner learner; + booster.saveModel(tmpBoosterFile.getAbsolutePath()); + + try(InputStream is = new FileInputStream(tmpBoosterFile)){ + learner = XGBoostUtil.loadLearner(is); + } - try(InputStream is = new ByteArrayInputStream(bytes)){ - learner = XGBoostUtil.loadLearner(is); - } catch(IOException ioe){ - throw new RuntimeException(ioe); + MoreFiles.deleteRecursively(tmpBoosterFile.toPath()); + } catch(Exception e){ + throw new RuntimeException(e); } Boolean inputFloat = (Boolean)converter.getOption(HasSparkMLXGBoostOptions.OPTION_INPUT_FLOAT, null); diff --git a/pmml-sparkml-xgboost/src/main/resources/META-INF/sparkml2pmml.properties b/pmml-sparkml-xgboost/src/main/resources/META-INF/sparkml2pmml.properties index d7356f09..cc08e4e1 100644 --- a/pmml-sparkml-xgboost/src/main/resources/META-INF/sparkml2pmml.properties +++ b/pmml-sparkml-xgboost/src/main/resources/META-INF/sparkml2pmml.properties @@ -1,4 +1,2 @@ -org.jpmml.sparkml.xgboost.SparseToDenseTransformer = org.jpmml.sparkml.xgboost.SparseToDenseTransformerConverter - ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel = org.jpmml.sparkml.xgboost.XGBoostClassificationModelConverter ml.dmlc.xgboost4j.scala.spark.XGBoostRegressionModel = org.jpmml.sparkml.xgboost.XGBoostRegressionModelConverter diff --git a/pmml-sparkml-xgboost/src/test/java/org/jpmml/sparkml/xgboost/testing/XGBoostTest.java b/pmml-sparkml-xgboost/src/test/java/org/jpmml/sparkml/xgboost/testing/XGBoostTest.java index 8d3ed467..f6f65f47 100644 --- a/pmml-sparkml-xgboost/src/test/java/org/jpmml/sparkml/xgboost/testing/XGBoostTest.java +++ b/pmml-sparkml-xgboost/src/test/java/org/jpmml/sparkml/xgboost/testing/XGBoostTest.java @@ -18,21 +18,37 @@ */ package org.jpmml.sparkml.xgboost.testing; +import java.io.File; +import java.io.IOException; +import java.io.InputStream; import java.util.LinkedHashMap; import java.util.List; import java.util.Map; +import java.util.Objects; import java.util.function.Predicate; import com.google.common.base.Equivalence; +import ml.dmlc.xgboost4j.scala.spark.XGBoostClassificationModel; +import ml.dmlc.xgboost4j.scala.spark.XGBoostRegressionModel; +import org.apache.spark.ml.PipelineModel; +import org.apache.spark.ml.PredictionModel; +import org.apache.spark.ml.util.MLReader; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SparkSession; +import org.dmg.pmml.Model; import org.dmg.pmml.PMML; import org.dmg.pmml.VerificationField; import org.dmg.pmml.Visitor; import org.dmg.pmml.VisitorAction; +import org.jpmml.converter.testing.Datasets; import org.jpmml.converter.testing.Fields; import org.jpmml.converter.testing.OptionsUtil; import org.jpmml.evaluator.ResultField; import org.jpmml.evaluator.testing.FloatEquivalence; import org.jpmml.model.visitors.AbstractVisitor; +import org.jpmml.sparkml.ArchiveUtil; +import org.jpmml.sparkml.PipelineModelUtil; import org.jpmml.sparkml.testing.SparkMLEncoderBatch; import org.jpmml.sparkml.testing.SparkMLEncoderBatchTest; import org.jpmml.sparkml.xgboost.HasSparkMLXGBoostOptions; @@ -41,7 +57,7 @@ import org.junit.BeforeClass; import org.junit.Test; -public class XGBoostTest extends SparkMLEncoderBatchTest { +public class XGBoostTest extends SparkMLEncoderBatchTest implements Datasets { public XGBoostTest(){ super(new FloatEquivalence(12)); @@ -74,8 +90,20 @@ public List> getOptionsMatrix(){ public PMML getPMML() throws Exception { PMML pmml = super.getPMML(); + String dataset = getDataset(); + Visitor visitor = new AbstractVisitor(){ + @Override + public VisitorAction visit(Model model){ + + if(Objects.equals(dataset, AUDIT)){ + model.setModelVerification(null); + } + + return super.visit(model); + } + @Override public VisitorAction visit(VerificationField verificationField){ verificationField @@ -89,6 +117,86 @@ public VisitorAction visit(VerificationField verificationField){ return pmml; } + + @Override + protected PipelineModel loadPipelineModel(SparkSession sparkSession, List tmpResources) throws IOException { + String dataset = getDataset(); + + if(Objects.equals(dataset, AUDIT_NA)){ + return loadPipelineModel(sparkSession, "Transformers", "XGBoostClassificationModel", tmpResources); + } else + + if(Objects.equals(dataset, AUTO_NA)){ + return loadPipelineModel(sparkSession, "Transformers", "XGBoostRegressionModel", tmpResources); + } else + + { + return super.loadPipelineModel(sparkSession, tmpResources); + } + } + + private PipelineModel loadPipelineModel(SparkSession sparkSession, String pipelineModelName, String modelName, List tmpResources) throws IOException { + String dataset = getDataset(); + + PipelineModel pipelineModel; + + try(InputStream is = open("/pipeline/" + pipelineModelName + dataset + ".zip")){ + File tmpZipFile = toTmpFile(is, pipelineModelName + dataset, ".zip"); + + tmpResources.add(tmpZipFile); + + File tmpPipelineModelDir = ArchiveUtil.uncompress(tmpZipFile); + + tmpResources.add(tmpPipelineModelDir); + + pipelineModel = PipelineModelUtil.load(sparkSession, tmpPipelineModelDir); + } + + PredictionModel model; + + try(InputStream is = open("/pipeline/" + modelName + dataset + ".zip")){ + File tmpZipFile = toTmpFile(is, modelName + dataset, ".zip"); + + tmpResources.add(tmpZipFile); + + File tmpModelDir = ArchiveUtil.uncompress(tmpZipFile); + + tmpResources.add(tmpModelDir); + + MLReader mlReader; + + if(modelName.endsWith("ClassificationModel")){ + mlReader = XGBoostClassificationModel.read(); + } else + + if(modelName.endsWith("RegressionModel")){ + mlReader = XGBoostRegressionModel.read(); + } else + + { + throw new IllegalArgumentException(); + } + + mlReader.session(sparkSession); + + model = (PredictionModel)mlReader.load(tmpModelDir.getAbsolutePath()); + } + + PipelineModelUtil.addStage(pipelineModel, (pipelineModel.stages()).length, model); + + return pipelineModel; + } + + @Override + public Dataset getVerificationDataset(Dataset inputDataset){ + String dataset = getDataset(); + + if(Objects.equals(dataset, AUDIT_NA) || Objects.equals(dataset, AUTO_NA)){ + return null; + } + + return super.getVerificationDataset(inputDataset); + } }; return result; @@ -96,22 +204,32 @@ public VisitorAction visit(VerificationField verificationField){ @Test public void evaluateAudit() throws Exception { - evaluate("XGBoost", "Audit", excludeFields(Fields.AUDIT_PROBABILITY_FALSE)); + evaluate("XGBoost", AUDIT, excludeFields(Fields.AUDIT_PROBABILITY_FALSE), new FloatEquivalence(64 + 8)); + } + + @Test + public void evaluateAuditNA() throws Exception { + evaluate("XGBoost", AUDIT_NA, excludeFields(Fields.AUDIT_PROBABILITY_FALSE), new FloatEquivalence(64 + 8)); } @Test public void evaluateAuto() throws Exception { - evaluate("XGBoost", "Auto"); + evaluate("XGBoost", AUTO); + } + + @Test + public void evaluateAutoNA() throws Exception { + evaluate("XGBoost", AUTO_NA); } @Test public void evaluateHousing() throws Exception { - evaluate("XGBoost", "Housing"); + evaluate("XGBoost", HOUSING); } @Test public void evaluateIris() throws Exception { - evaluate("XGBoost", "Iris", new FloatEquivalence(16)); + evaluate("XGBoost", IRIS, new FloatEquivalence(16)); } @BeforeClass diff --git a/pmml-sparkml-xgboost/src/test/resources/XGBoostAudit.scala b/pmml-sparkml-xgboost/src/test/resources/XGBoostAudit.scala index 8e9d134f..09c551f2 100644 --- a/pmml-sparkml-xgboost/src/test/resources/XGBoostAudit.scala +++ b/pmml-sparkml-xgboost/src/test/resources/XGBoostAudit.scala @@ -7,7 +7,7 @@ import org.apache.spark.ml.linalg.Vector import org.apache.spark.sql.functions.{lit, udf} import org.apache.spark.sql.types.StringType import org.jpmml.sparkml.{DatasetUtil, PipelineModelUtil} -import org.jpmml.sparkml.xgboost.SparseToDenseTransformer +import org.jpmml.sparkml.feature.SparseToDenseTransformer var df = DatasetUtil.loadCsv(spark, new File("csv/Audit.csv")) df = DatasetUtil.castColumn(df, "Adjusted", StringType) diff --git a/pmml-sparkml-xgboost/src/test/resources/XGBoostAuditNA.scala b/pmml-sparkml-xgboost/src/test/resources/XGBoostAuditNA.scala new file mode 100644 index 00000000..dd9c377e --- /dev/null +++ b/pmml-sparkml-xgboost/src/test/resources/XGBoostAuditNA.scala @@ -0,0 +1,54 @@ +import java.io.File + +import ml.dmlc.xgboost4j.scala.spark.XGBoostClassifier +import org.apache.spark.ml.Pipeline +import org.apache.spark.ml.feature._ +import org.apache.spark.ml.linalg.Vector +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.sql.functions.{lit, udf} +import org.apache.spark.sql.types.StringType +import org.apache.spark.ml.util.MLWritable +import org.jpmml.sparkml.{ArchiveUtil, DatasetUtil, PipelineModelUtil} +import org.jpmml.sparkml.feature.{InvalidCategoryTransformer, SparseToDenseTransformer} + +var df = DatasetUtil.loadCsv(spark, new File("csv/AuditNA.csv")) +df = DatasetUtil.castColumn(df, "Adjusted", StringType) + +DatasetUtil.storeSchema(df, new File("schema/AuditNA.json")) + +val cat_cols = Array("Education", "Employment", "Gender", "Marital", "Occupation") +val cont_cols = Array("Age", "Hours", "Income") + +val labelIndexer = new StringIndexer().setInputCol("Adjusted").setOutputCol("idx_Adjusted") + +val indexer = new StringIndexer().setInputCols(cat_cols).setOutputCols(cat_cols.map(cat_col => "idx_" + cat_col)).setHandleInvalid("keep") +val indexTransformer = new InvalidCategoryTransformer().setInputCols(indexer.getOutputCols).setOutputCols(cat_cols.map(cat_col => "idxTransformed_" + cat_col)) + +val assembler = new VectorAssembler().setInputCols(indexTransformer.getOutputCols ++ cont_cols).setOutputCol("featureVector").setHandleInvalid("keep") + +val sparse2dense = new SparseToDenseTransformer().setInputCol(assembler.getOutputCol).setOutputCol("denseFeatureVec") + +val classifier = new XGBoostClassifier(Map("objective" -> "binary:logistic", "num_round" -> 101)).setLabelCol(labelIndexer.getOutputCol).setFeaturesCol(sparse2dense.getOutputCol).setFeatureTypes(Array("c", "c", "c", "c", "c", "q", "q", "q"))//.setHandleInvalid("keep").setMissing(Float.NaN) + +val pipeline = new Pipeline().setStages(Array(labelIndexer, indexer, indexTransformer, assembler, sparse2dense, classifier)) +val pipelineModel = pipeline.fit(df) + +//PipelineModelUtil.storeZip(pipelineModel, new File("pipeline/XGBoostAuditNA.zip")) + +val transformers = pipelineModel.copy(new ParamMap()) +val classificationModel = PipelineModelUtil.removeStage(transformers, 5) + +PipelineModelUtil.storeZip(transformers, new File("pipeline/TransformersAuditNA.zip")) + +val mlWriter = classificationModel.asInstanceOf[MLWritable].write.option("format", "json") +ArchiveUtil.storeZip(mlWriter, new File("pipeline/XGBoostClassificationModelAuditNA.zip")) + +val predLabel = udf{ (value: Float) => value.toInt.toString } +val vectorToColumn = udf{ (vec: Vector, index: Int) => vec(index).toFloat } + +var xgbDf = pipelineModel.transform(df) +xgbDf = xgbDf.selectExpr("prediction", "probability") +xgbDf = xgbDf.withColumn("Adjusted", predLabel(xgbDf("prediction"))).drop("prediction") +xgbDf = xgbDf.withColumn("probability(0)", vectorToColumn(xgbDf("probability"), lit(0))).withColumn("probability(1)", vectorToColumn(xgbDf("probability"), lit(1))).drop("probability").drop("probability") + +DatasetUtil.storeCsv(xgbDf, new File("csv/XGBoostAuditNA.csv")) diff --git a/pmml-sparkml-xgboost/src/test/resources/XGBoostAuto.scala b/pmml-sparkml-xgboost/src/test/resources/XGBoostAuto.scala index c00ee10d..c8951333 100644 --- a/pmml-sparkml-xgboost/src/test/resources/XGBoostAuto.scala +++ b/pmml-sparkml-xgboost/src/test/resources/XGBoostAuto.scala @@ -5,7 +5,7 @@ import org.apache.spark.ml.Pipeline import org.apache.spark.ml.feature._ import org.apache.spark.sql.types.{FloatType, StringType} import org.jpmml.sparkml.{DatasetUtil, PipelineModelUtil} -import org.jpmml.sparkml.xgboost.SparseToDenseTransformer +import org.jpmml.sparkml.feature.SparseToDenseTransformer var df = DatasetUtil.loadCsv(spark, new File("csv/Auto.csv")) df = DatasetUtil.castColumn(df, "origin", StringType) diff --git a/pmml-sparkml-xgboost/src/test/resources/XGBoostAutoNA.scala b/pmml-sparkml-xgboost/src/test/resources/XGBoostAutoNA.scala new file mode 100644 index 00000000..d43f6060 --- /dev/null +++ b/pmml-sparkml-xgboost/src/test/resources/XGBoostAutoNA.scala @@ -0,0 +1,43 @@ +import java.io.File + +import ml.dmlc.xgboost4j.scala.spark.XGBoostRegressor +import org.apache.spark.ml.Pipeline +import org.apache.spark.ml.feature._ +import org.apache.spark.ml.param.ParamMap +import org.apache.spark.ml.util.MLWritable +import org.apache.spark.sql.types.FloatType +import org.jpmml.sparkml.{ArchiveUtil, DatasetUtil, PipelineModelUtil} +import org.jpmml.sparkml.feature.InvalidCategoryTransformer + +var df = DatasetUtil.loadCsv(spark, new File("csv/AutoNA.csv")) + +DatasetUtil.storeSchema(df, new File("schema/AutoNA.json")) + +val cat_cols = Array("cylinders", "model_year", "origin") +val cont_cols = Array("acceleration", "displacement", "horsepower", "weight") + +val indexer = new StringIndexer().setInputCols(cat_cols).setOutputCols(cat_cols.map(cat_col => "idx_" + cat_col)).setHandleInvalid("keep") +val indexTransformer = new InvalidCategoryTransformer().setInputCols(indexer.getOutputCols).setOutputCols(cat_cols.map(cat_col => "idxTransformed_" + cat_col)) + +val assembler = new VectorAssembler().setInputCols(indexTransformer.getOutputCols ++ cont_cols).setOutputCol("featureVector").setHandleInvalid("keep") + +val regressor = new XGBoostRegressor(Map("objective" -> "reg:squarederror", "num_round" -> 101, "num_workers" -> 1, "tree_method" -> "hist")).setLabelCol("mpg").setFeaturesCol(assembler.getOutputCol).setFeatureTypes(Array("c", "c", "c", "q", "q", "q", "q")) + +val pipeline = new Pipeline().setStages(Array(indexer, indexTransformer, assembler, regressor)) +val pipelineModel = pipeline.fit(df) + +//PipelineModelUtil.storeZip(pipelineModel, new File("pipeline/XGBoostAutoNA.zip")) + +val transformers = pipelineModel.copy(new ParamMap()) +val regressionModel = PipelineModelUtil.removeStage(transformers, 3) + +PipelineModelUtil.storeZip(transformers, new File("pipeline/TransformersAutoNA.zip")) + +val mlWriter = regressionModel.asInstanceOf[MLWritable].write.option("format", "json") +ArchiveUtil.storeZip(mlWriter, new File("pipeline/XGBoostRegressionModelAutoNA.zip")) + +var xgbDf = pipelineModel.transform(df) +xgbDf = xgbDf.selectExpr("prediction as mpg") +xgbDf = DatasetUtil.castColumn(xgbDf, "mpg", FloatType) + +DatasetUtil.storeCsv(xgbDf, new File("csv/XGBoostAutoNA.csv")) diff --git a/pmml-sparkml-xgboost/src/test/resources/csv/AuditNA.csv b/pmml-sparkml-xgboost/src/test/resources/csv/AuditNA.csv new file mode 100644 index 00000000..7d904c78 --- /dev/null +++ b/pmml-sparkml-xgboost/src/test/resources/csv/AuditNA.csv @@ -0,0 +1,1900 @@ +Age,Employment,Education,Marital,Occupation,Income,Gender,Deductions,Hours,Adjusted +38,Private,College,Unmarried,Service,81838,N/A,0,72,0 +35,N/A,Associate,N/A,N/A,72099,N/A,0,30,0 +N/A,Private,HSgrad,Divorced,Clerical,154676.74,N/A,N/A,40,0 +45,Private,Bachelor,Married,Repair,27743.82,Male,0,N/A,1 +60,N/A,College,Married,Executive,7568.23,Male,0,N/A,1 +74,Private,HSgrad,Married,Service,33144.4,Male,N/A,N/A,0 +43,Private,Bachelor,N/A,N/A,43391.17,Male,0,50,1 +35,Private,N/A,N/A,Machinist,59906.65,Male,0,N/A,0 +25,Private,Associate,Divorced,N/A,126888.91,Female,0,40,0 +22,Private,N/A,Absent,N/A,52466.49,Female,N/A,37,0 +N/A,Private,College,Divorced,N/A,N/A,Female,N/A,N/A,0 +60,Private,N/A,N/A,N/A,24155.31,Male,0,40,0 +21,Private,N/A,Absent,Service,N/A,Female,0,35,0 +21,Private,College,Absent,Machinist,N/A,Male,0,40,0 +50,N/A,Master,N/A,Executive,34919.16,N/A,0,40,1 +37,Private,N/A,Divorced,N/A,N/A,N/A,0,35,0 +30,Consultant,HSgrad,N/A,Repair,9608.48,Male,0,40,0 +32,Private,HSgrad,Married,Machinist,12475.84,Male,0,40,0 +65,N/A,College,Married,Sales,32963.39,Male,0,40,0 +N/A,Private,N/A,Married,Executive,31534.97,Male,N/A,55,0 +N/A,N/A,N/A,Divorced,N/A,182165.08,N/A,N/A,40,0 +41,PSState,Bachelor,Divorced,N/A,70603.7,Male,0,40,0 +30,Private,N/A,Absent,Service,N/A,N/A,N/A,30,0 +38,Private,N/A,Married,Repair,8670.9,N/A,N/A,N/A,0 +N/A,Private,Yr11,Unmarried,Professional,260405.44,N/A,0,N/A,0 +42,PSState,College,Absent,Executive,N/A,Female,0,40,0 +26,Private,N/A,Absent,Sales,73751.48,Female,0,40,0 +32,Consultant,N/A,Married,Sales,N/A,N/A,0,60,1 +49,PSFederal,College,N/A,Support,15345.33,Male,0,N/A,1 +N/A,Private,HSgrad,Married,Repair,N/A,Male,0,40,0 +N/A,Private,Yr10,Married,Machinist,33493.89,Male,N/A,40,0 +N/A,PSFederal,N/A,Married,Support,54653.36,Male,0,24,0 +46,Private,HSgrad,Absent,Service,229077.27,Female,0,N/A,0 +42,N/A,College,Absent,Machinist,59201.06,Female,0,40,0 +39,Private,College,Divorced,N/A,31036.73,Female,N/A,40,0 +N/A,Private,Yr11,Absent,Machinist,N/A,Female,0,40,0 +47,PSLocal,Doctorate,Absent,Professional,N/A,Female,0,40,0 +24,Private,Associate,Unmarried,Repair,193135.59,Male,0,40,1 +45,Private,N/A,Married,Repair,26717.49,Male,0,N/A,0 +40,PSFederal,Associate,Absent,Clerical,N/A,Female,0,N/A,0 +51,SelfEmp,Doctorate,Married,Professional,13612.07,N/A,0,N/A,1 +77,N/A,HSgrad,Married,Service,39950.92,Male,0,N/A,0 +N/A,Private,College,Married,N/A,N/A,Male,0,45,0 +39,Private,Yr9,Divorced,Cleaner,78516.3,Male,N/A,50,0 +39,PSState,N/A,N/A,N/A,92268.68,Female,0,40,0 +63,SelfEmp,Bachelor,N/A,Farming,N/A,Male,N/A,N/A,1 +N/A,Private,HSgrad,Widowed,Service,148865.82,Female,0,38,0 +39,Private,Bachelor,Married,Professional,N/A,Male,0,N/A,1 +66,N/A,Yr5t6,Married-spouse-absent,N/A,139087.01,Female,0,40,0 +N/A,N/A,College,Married,Executive,N/A,Male,0,45,1 +31,PSLocal,N/A,N/A,Clerical,67012.13,Female,N/A,N/A,0 +31,Private,Bachelor,Married,Executive,N/A,N/A,1,45,1 +45,Private,Professional,Absent,N/A,44102.23,Male,N/A,40,1 +19,PSLocal,College,Absent,Service,N/A,Female,N/A,35,0 +32,Private,HSgrad,N/A,Machinist,14657.59,Male,0,40,0 +27,Private,N/A,Absent,Professional,89775.14,Male,0,N/A,1 +25,N/A,N/A,Married,Transport,56417.35,Male,0,30,0 +21,Private,N/A,N/A,Clerical,43362.93,Male,0,65,0 +N/A,Consultant,HSgrad,Absent,Farming,214238.62,Male,0,70,0 +N/A,Private,Bachelor,Absent,Executive,83143.19,Male,1,38,0 +56,PSFederal,N/A,Divorced,N/A,37078.8,N/A,0,40,0 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a/pmml-sparkml-xgboost/src/test/resources/pipeline/XGBoostAuto.zip and b/pmml-sparkml-xgboost/src/test/resources/pipeline/XGBoostAuto.zip differ diff --git a/pmml-sparkml-xgboost/src/test/resources/pipeline/XGBoostClassificationModelAuditNA.zip b/pmml-sparkml-xgboost/src/test/resources/pipeline/XGBoostClassificationModelAuditNA.zip new file mode 100644 index 00000000..743cda22 Binary files /dev/null and b/pmml-sparkml-xgboost/src/test/resources/pipeline/XGBoostClassificationModelAuditNA.zip differ diff --git a/pmml-sparkml-xgboost/src/test/resources/pipeline/XGBoostRegressionModelAutoNA.zip b/pmml-sparkml-xgboost/src/test/resources/pipeline/XGBoostRegressionModelAutoNA.zip new file mode 100644 index 00000000..e5aeed16 Binary files /dev/null and b/pmml-sparkml-xgboost/src/test/resources/pipeline/XGBoostRegressionModelAutoNA.zip differ diff --git a/pmml-sparkml-xgboost/src/test/resources/schema/AuditNA.json b/pmml-sparkml-xgboost/src/test/resources/schema/AuditNA.json new file mode 100644 index 00000000..f2dd0162 --- /dev/null +++ b/pmml-sparkml-xgboost/src/test/resources/schema/AuditNA.json @@ -0,0 +1 @@ +{"type":"struct","fields":[{"name":"Age","type":"integer","nullable":true,"metadata":{}},{"name":"Employment","type":"string","nullable":true,"metadata":{}},{"name":"Education","type":"string","nullable":true,"metadata":{}},{"name":"Marital","type":"string","nullable":true,"metadata":{}},{"name":"Occupation","type":"string","nullable":true,"metadata":{}},{"name":"Income","type":"double","nullable":true,"metadata":{}},{"name":"Gender","type":"string","nullable":true,"metadata":{}},{"name":"Deductions","type":"integer","nullable":true,"metadata":{}},{"name":"Hours","type":"integer","nullable":true,"metadata":{}},{"name":"Adjusted","type":"string","nullable":true,"metadata":{}}]} \ No newline at end of file diff --git a/pmml-sparkml-xgboost/src/test/resources/schema/AutoNA.json b/pmml-sparkml-xgboost/src/test/resources/schema/AutoNA.json new file mode 100644 index 00000000..e44a066a --- /dev/null +++ b/pmml-sparkml-xgboost/src/test/resources/schema/AutoNA.json @@ -0,0 +1 @@ +{"type":"struct","fields":[{"name":"cylinders","type":"integer","nullable":true,"metadata":{}},{"name":"displacement","type":"integer","nullable":true,"metadata":{}},{"name":"horsepower","type":"integer","nullable":true,"metadata":{}},{"name":"weight","type":"integer","nullable":true,"metadata":{}},{"name":"acceleration","type":"double","nullable":true,"metadata":{}},{"name":"model_year","type":"integer","nullable":true,"metadata":{}},{"name":"origin","type":"integer","nullable":true,"metadata":{}},{"name":"mpg","type":"double","nullable":true,"metadata":{}}]} \ No newline at end of file diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/ArchiveUtil.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/ArchiveUtil.java new file mode 100644 index 00000000..ce387f03 --- /dev/null +++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/ArchiveUtil.java @@ -0,0 +1,170 @@ +/* + * Copyright (c) 2023 Villu Ruusmann + * + * This file is part of JPMML-SparkML + * + * JPMML-SparkML is free software: you can redistribute it and/or modify + * it under the terms of the GNU Affero General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * JPMML-SparkML is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU Affero General Public License for more details. + * + * You should have received a copy of the GNU Affero General Public License + * along with JPMML-SparkML. If not, see . + */ +package org.jpmml.sparkml; + +import java.io.File; +import java.io.FileInputStream; +import java.io.FileOutputStream; +import java.io.IOException; +import java.io.InputStream; +import java.io.OutputStream; +import java.nio.file.FileVisitResult; +import java.nio.file.FileVisitor; +import java.nio.file.Files; +import java.nio.file.Path; +import java.nio.file.Paths; +import java.nio.file.SimpleFileVisitor; +import java.nio.file.attribute.BasicFileAttributes; +import java.util.Enumeration; +import java.util.zip.ZipEntry; +import java.util.zip.ZipFile; +import java.util.zip.ZipOutputStream; + +import com.google.common.io.ByteStreams; +import com.google.common.io.MoreFiles; +import org.apache.spark.ml.PipelineStage; +import org.apache.spark.ml.util.MLReadable; +import org.apache.spark.ml.util.MLReader; +import org.apache.spark.ml.util.MLWritable; +import org.apache.spark.ml.util.MLWriter; + +public class ArchiveUtil { + + private ArchiveUtil(){ + } + + static + public > E loadZip(MLReader mlReader, File file) throws IOException { + File tmpDir = ArchiveUtil.uncompress(file); + + E stage = mlReader.load(tmpDir.getAbsolutePath()); + + MoreFiles.deleteRecursively(tmpDir.toPath()); + + return stage; + } + + static + public void storeZip(E stage, File file) throws IOException { + storeZip(stage.write(), file); + } + + static + public void storeZip(MLWriter mlWriter, File file) throws IOException { + File tmpDir = File.createTempFile("PipelineStage", ""); + if(!tmpDir.delete()){ + throw new IOException(); + } + + mlWriter.save(tmpDir.getAbsolutePath()); + + ArchiveUtil.compress(tmpDir, file); + + MoreFiles.deleteRecursively(tmpDir.toPath()); + } + + static + public void compress(File dir, File file) throws IOException { + Path dirPath = Paths.get(dir.getAbsolutePath()); + + try(OutputStream os = new FileOutputStream(file)){ + ZipOutputStream zos = new ZipOutputStream(os); + + FileVisitor dirFileVisitor = new SimpleFileVisitor(){ + + @Override + public FileVisitResult visitFile(Path path, BasicFileAttributes mainAtts) throws IOException { + File dirFile = path.toFile(); + + Path relativePath = dirPath.relativize(path); + + ZipEntry entry = new ZipEntry(relativePath.toString()); + entry.setSize(dirFile.length()); + entry.setTime(dirFile.lastModified()); + + zos.putNextEntry(entry); + + try(InputStream is = new FileInputStream(dirFile)){ + ByteStreams.copy(is, zos); + } + + zos.closeEntry(); + + return FileVisitResult.CONTINUE; + } + }; + + Files.walkFileTree(dirPath, dirFileVisitor); + + zos.finish(); + } + } + + static + public File uncompress(File file) throws IOException { + + try(ZipFile zipFile = new ZipFile(file)){ + File tmpDir = File.createTempFile("PipelineModel", ""); + if(!tmpDir.delete()){ + throw new IOException(); + } + + tmpDir.mkdirs(); + + ArchiveUtil.uncompress(zipFile, tmpDir); + + return tmpDir; + } + } + + static + public void uncompress(File file, File dir) throws IOException { + + try(ZipFile zipFile = new ZipFile(file)){ + uncompress(zipFile, dir); + } + } + + static void uncompress(ZipFile zipFile, File dir) throws IOException { + + for(Enumeration entries = zipFile.entries(); entries.hasMoreElements(); ){ + ZipEntry entry = entries.nextElement(); + + if(entry.isDirectory()){ + continue; + } + + try(InputStream is = zipFile.getInputStream(entry)){ + File file = new File(dir, entry.getName()); + + File parentDir = file.getParentFile(); + if(!parentDir.exists()){ + + if(!parentDir.mkdirs()){ + throw new IOException(parentDir.getAbsolutePath()); + } + } + + try(OutputStream os = new FileOutputStream(file)){ + ByteStreams.copy(is, os); + } + } + } + } +} \ No newline at end of file diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/DatasetUtil.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/DatasetUtil.java index c41db0e6..101a48ab 100644 --- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/DatasetUtil.java +++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/DatasetUtil.java @@ -88,6 +88,8 @@ public Dataset loadCsv(SparkSession sparkSession, File file) throws IOExcep .format("csv") .option("header", true) .option("inferSchema", true) + .option("nullValue", "N/A") + .option("nanValue", "N/A") .load(file.getAbsolutePath()); } diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/ModelConverter.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/ModelConverter.java index cdc774a4..f9a64150 100644 --- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/ModelConverter.java +++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/ModelConverter.java @@ -19,7 +19,6 @@ package org.jpmml.sparkml; import java.util.List; -import java.util.Objects; import org.apache.spark.ml.Model; import org.apache.spark.ml.param.shared.HasPredictionCol; @@ -27,6 +26,7 @@ import org.dmg.pmml.Output; import org.dmg.pmml.OutputField; import org.jpmml.converter.Feature; +import org.jpmml.converter.FeatureUtil; import org.jpmml.converter.Label; import org.jpmml.converter.ModelUtil; import org.jpmml.converter.ScalarLabel; @@ -89,11 +89,9 @@ public void checkSchema(Schema schema){ if(label instanceof ScalarLabel){ ScalarLabel scalarLabel = (ScalarLabel)label; - for(Feature feature : features){ - - if(Objects.equals(scalarLabel.getName(), feature.getName())){ - throw new IllegalArgumentException("Label column '" + scalarLabel.getName() + "' is contained in the list of feature columns"); - } + Feature labelFeature = FeatureUtil.findLabelFeature(features, scalarLabel); + if(labelFeature != null){ + throw new IllegalArgumentException("Label column '" + scalarLabel.getName() + "' is contained in the list of feature columns"); } } } diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/PipelineModelUtil.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/PipelineModelUtil.java index 13327c9a..57eee5f1 100644 --- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/PipelineModelUtil.java +++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/PipelineModelUtil.java @@ -19,38 +19,48 @@ package org.jpmml.sparkml; import java.io.File; -import java.io.FileInputStream; -import java.io.FileOutputStream; import java.io.IOException; -import java.io.InputStream; -import java.io.OutputStream; -import java.nio.file.FileVisitResult; -import java.nio.file.FileVisitor; -import java.nio.file.Files; -import java.nio.file.Path; -import java.nio.file.Paths; -import java.nio.file.SimpleFileVisitor; -import java.nio.file.attribute.BasicFileAttributes; -import java.util.Enumeration; -import java.util.zip.ZipEntry; -import java.util.zip.ZipFile; -import java.util.zip.ZipOutputStream; - -import com.google.common.io.ByteStreams; +import java.lang.reflect.Field; +import java.util.ArrayList; +import java.util.Arrays; +import java.util.List; + import com.google.common.io.MoreFiles; import org.apache.spark.ml.PipelineModel; +import org.apache.spark.ml.Transformer; import org.apache.spark.ml.util.MLReader; import org.apache.spark.ml.util.MLWriter; import org.apache.spark.sql.SparkSession; +import org.jpmml.model.ReflectionUtil; public class PipelineModelUtil { private PipelineModelUtil(){ } + static + public void addStage(PipelineModel pipelineModel, int index, Transformer transformer){ + List stages = new ArrayList<>(Arrays.asList(pipelineModel.stages())); + + stages.add(index, transformer); + + ReflectionUtil.setFieldValue(PipelineModelUtil.FIELD_STAGES, pipelineModel, stages.toArray(new Transformer[stages.size()])); + } + + static + public Transformer removeStage(PipelineModel pipelineModel, int index){ + List stages = new ArrayList<>(Arrays.asList(pipelineModel.stages())); + + Transformer result = stages.remove(index); + + ReflectionUtil.setFieldValue(PipelineModelUtil.FIELD_STAGES, pipelineModel, stages.toArray(new Transformer[stages.size()])); + + return result; + } + static public PipelineModel load(SparkSession sparkSession, File dir) throws IOException { - MLReader mlReader = new PipelineModel.PipelineModelReader(); + MLReader mlReader = PipelineModel.read(); mlReader.session(sparkSession); return mlReader.load(dir.getAbsolutePath()); @@ -58,7 +68,7 @@ public PipelineModel load(SparkSession sparkSession, File dir) throws IOExceptio static public PipelineModel loadZip(SparkSession sparkSession, File file) throws IOException { - File tmpDir = uncompress(file); + File tmpDir = ArchiveUtil.uncompress(file); PipelineModel pipelineModel = load(sparkSession, tmpDir); @@ -83,98 +93,10 @@ public void storeZip(PipelineModel pipelineModel, File file) throws IOException store(pipelineModel, tmpDir); - compress(tmpDir, file); + ArchiveUtil.compress(tmpDir, file); MoreFiles.deleteRecursively(tmpDir.toPath()); } - static - public void compress(File dir, File file) throws IOException { - Path dirPath = Paths.get(dir.getAbsolutePath()); - - try(OutputStream os = new FileOutputStream(file)){ - ZipOutputStream zos = new ZipOutputStream(os); - - FileVisitor dirFileVisitor = new SimpleFileVisitor(){ - - @Override - public FileVisitResult visitFile(Path path, BasicFileAttributes mainAtts) throws IOException { - File dirFile = path.toFile(); - - Path relativePath = dirPath.relativize(path); - - ZipEntry entry = new ZipEntry(relativePath.toString()); - entry.setSize(dirFile.length()); - entry.setTime(dirFile.lastModified()); - - zos.putNextEntry(entry); - - try(InputStream is = new FileInputStream(dirFile)){ - ByteStreams.copy(is, zos); - } - - zos.closeEntry(); - - return FileVisitResult.CONTINUE; - } - }; - - Files.walkFileTree(dirPath, dirFileVisitor); - - zos.finish(); - } - } - - static - public File uncompress(File file) throws IOException { - - try(ZipFile zipFile = new ZipFile(file)){ - File tmpDir = File.createTempFile("PipelineModel", ""); - if(!tmpDir.delete()){ - throw new IOException(); - } - - tmpDir.mkdirs(); - - uncompress(zipFile, tmpDir); - - return tmpDir; - } - } - - static - public void uncompress(File file, File dir) throws IOException { - - try(ZipFile zipFile = new ZipFile(file)){ - uncompress(zipFile, dir); - } - } - - static - private void uncompress(ZipFile zipFile, File dir) throws IOException { - - for(Enumeration entries = zipFile.entries(); entries.hasMoreElements(); ){ - ZipEntry entry = entries.nextElement(); - - if(entry.isDirectory()){ - continue; - } - - try(InputStream is = zipFile.getInputStream(entry)){ - File file = new File(dir, entry.getName()); - - File parentDir = file.getParentFile(); - if(!parentDir.exists()){ - - if(!parentDir.mkdirs()){ - throw new IOException(parentDir.getAbsolutePath()); - } - } - - try(OutputStream os = new FileOutputStream(file)){ - ByteStreams.copy(is, os); - } - } - } - } + private static final Field FIELD_STAGES = ReflectionUtil.getField(PipelineModel.class, "stages"); } \ No newline at end of file diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/SparkMLEncoder.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/SparkMLEncoder.java index 494eaa0f..98e2ba20 100644 --- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/SparkMLEncoder.java +++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/SparkMLEncoder.java @@ -37,12 +37,10 @@ import org.dmg.pmml.Visitor; import org.dmg.pmml.VisitorAction; import org.dmg.pmml.association.Item; -import org.jpmml.converter.BooleanFeature; -import org.jpmml.converter.ContinuousFeature; import org.jpmml.converter.Feature; +import org.jpmml.converter.FeatureUtil; import org.jpmml.converter.ModelEncoder; import org.jpmml.converter.SchemaUtil; -import org.jpmml.converter.StringFeature; import org.jpmml.converter.TypeUtil; import org.jpmml.model.visitors.AbstractVisitor; @@ -164,13 +162,11 @@ public Feature createFeature(Field field){ switch(dataType){ case STRING: - return new StringFeature(this, field); case INTEGER: case FLOAT: case DOUBLE: - return new ContinuousFeature(this, field); case BOOLEAN: - return new BooleanFeature(this, field); + return FeatureUtil.createFeature(field, this); default: throw new IllegalArgumentException("Data type " + dataType + " is not supported"); } diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/InvalidCategoryTransformerConverter.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/InvalidCategoryTransformerConverter.java new file mode 100644 index 00000000..abe43282 --- /dev/null +++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/InvalidCategoryTransformerConverter.java @@ -0,0 +1,147 @@ +/* + * Copyright (c) 2023 Villu Ruusmann + * + * This file is part of JPMML-SparkML + * + * JPMML-SparkML is free software: you can redistribute it and/or modify + * it under the terms of the GNU Affero General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * JPMML-SparkML is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU Affero General Public License for more details. + * + * You should have received a copy of the GNU Affero General Public License + * along with JPMML-SparkML. If not, see . + */ +package org.jpmml.sparkml.feature; + +import java.util.ArrayList; +import java.util.Iterator; +import java.util.List; +import java.util.Map; +import java.util.Objects; + +import com.google.common.collect.ListMultimap; +import org.dmg.pmml.Apply; +import org.dmg.pmml.Constant; +import org.dmg.pmml.DataField; +import org.dmg.pmml.DerivedField; +import org.dmg.pmml.Expression; +import org.dmg.pmml.Field; +import org.dmg.pmml.InvalidValueTreatmentMethod; +import org.dmg.pmml.Model; +import org.jpmml.converter.CategoricalFeature; +import org.jpmml.converter.Decorator; +import org.jpmml.converter.Feature; +import org.jpmml.converter.InvalidValueDecorator; +import org.jpmml.sparkml.FeatureConverter.InOutMode; +import org.jpmml.sparkml.MultiFeatureConverter; +import org.jpmml.sparkml.SparkMLEncoder; + +public class InvalidCategoryTransformerConverter extends MultiFeatureConverter { + + public InvalidCategoryTransformerConverter(InvalidCategoryTransformer transformer){ + super(transformer); + } + + @Override + public List encodeFeatures(SparkMLEncoder encoder){ + InvalidCategoryTransformer transformer = getTransformer(); + + InOutMode inputMode = getInputMode(); + + List result = new ArrayList<>(); + + String[] inputCols = inputMode.getInputCols(transformer); + for(String inputCol : inputCols){ + Feature feature = encoder.getOnlyFeature(inputCol); + + if(!(feature instanceof CategoricalFeature)){ + throw new IllegalArgumentException(); + } + + CategoricalFeature categoricalFeature = (CategoricalFeature)feature; + + Field field = categoricalFeature.getField(); + List values = categoricalFeature.getValues(); + + Object invalidCategory; + + if(!values.isEmpty()){ + invalidCategory = values.get(values.size() - 1); + } else + + { + throw new IllegalArgumentException(); + } // End if + + if(Objects.equals(invalidCategory, "-999") || Objects.equals(invalidCategory, "__unknown")){ + values = values.subList(0, values.size() - 1); + } else + + { + throw new IllegalArgumentException(); + } // End if + + if(field instanceof DataField){ + DataField dataField = (DataField)field; + + replaceDecorator(dataField, new InvalidValueDecorator(InvalidValueTreatmentMethod.AS_MISSING, null), encoder); + } else + + if(field instanceof DerivedField){ + DerivedField derivedField = (DerivedField)field; + + Apply apply = (Apply)derivedField.getExpression(); + + List expressions = apply.getExpressions(); + + if(!expressions.isEmpty()){ + Constant constant = (Constant)expressions.remove(expressions.size() - 1); + + if(!Objects.equals(invalidCategory, constant.getValue())){ + throw new IllegalArgumentException(); + } + } else + + { + throw new IllegalArgumentException(); + } + } else + + { + throw new IllegalArgumentException(); + } + + result.add(new CategoricalFeature(encoder, field, values)); + } + + return result; + } + + static + private void replaceDecorator(Field field, Decorator decorator, SparkMLEncoder encoder){ + Map> modelDecorators = encoder.getDecorators(); + + ListMultimap decorators = modelDecorators.get(null); + if(decorators != null){ + List fieldDecorators = decorators.get(field.requireName()); + + if(fieldDecorators != null && !fieldDecorators.isEmpty()){ + + for(Iterator it = fieldDecorators.iterator(); it.hasNext(); ){ + Decorator fieldDecorator = it.next(); + + if(Objects.equals(fieldDecorator.getClass(), decorator.getClass())){ + it.remove(); + } + } + } + } + + decorators.put(field.requireName(), decorator); + } +} \ No newline at end of file diff --git a/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/SparseToDenseTransformerConverter.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/SparseToDenseTransformerConverter.java similarity index 93% rename from pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/SparseToDenseTransformerConverter.java rename to pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/SparseToDenseTransformerConverter.java index dab78953..08160456 100644 --- a/pmml-sparkml-xgboost/src/main/java/org/jpmml/sparkml/xgboost/SparseToDenseTransformerConverter.java +++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/feature/SparseToDenseTransformerConverter.java @@ -16,14 +16,14 @@ * You should have received a copy of the GNU Affero General Public License * along with JPMML-SparkML. If not, see . */ -package org.jpmml.sparkml.xgboost; +package org.jpmml.sparkml.feature; import java.util.List; import org.jpmml.converter.Feature; import org.jpmml.sparkml.FeatureConverter; import org.jpmml.sparkml.SparkMLEncoder; -import org.jpmml.sparkml.xgboost.SparseToDenseTransformer; +import org.jpmml.sparkml.feature.SparseToDenseTransformer; public class SparseToDenseTransformerConverter extends FeatureConverter { diff --git a/pmml-sparkml/src/main/java/org/jpmml/sparkml/testing/SparkMLEncoderBatch.java b/pmml-sparkml/src/main/java/org/jpmml/sparkml/testing/SparkMLEncoderBatch.java index c4c50777..61431bf4 100644 --- a/pmml-sparkml/src/main/java/org/jpmml/sparkml/testing/SparkMLEncoderBatch.java +++ b/pmml-sparkml/src/main/java/org/jpmml/sparkml/testing/SparkMLEncoderBatch.java @@ -42,6 +42,7 @@ import org.jpmml.converter.testing.ModelEncoderBatch; import org.jpmml.evaluator.ResultField; import org.jpmml.evaluator.testing.PMMLEquivalence; +import org.jpmml.sparkml.ArchiveUtil; import org.jpmml.sparkml.DatasetUtil; import org.jpmml.sparkml.PMMLBuilder; import org.jpmml.sparkml.PipelineModelUtil; @@ -71,7 +72,7 @@ public String getSchemaJsonPath(){ return "/schema/" + getDataset() + ".json"; } - public String getPipelineZipPath(){ + public String getPipelineModelZipPath(){ return "/pipeline/" + getAlgorithm() + getDataset() + ".zip"; } @@ -90,41 +91,13 @@ public PMML getPMML() throws Exception { List tmpResources = new ArrayList<>(); - StructType schema; + StructType schema = loadSchema(sparkSession, tmpResources); - try(InputStream is = open(getSchemaJsonPath())){ - File tmpSchemaFile = toTmpFile(is, getDataset(), ".json"); - - tmpResources.add(tmpSchemaFile); - - schema = DatasetUtil.loadSchema(tmpSchemaFile); - } - - PipelineModel pipelineModel; - - try(InputStream is = open(getPipelineZipPath())){ - File tmpZipFile = toTmpFile(is, getAlgorithm() + getDataset(), ".zip"); - - tmpResources.add(tmpZipFile); - - File tmpPipelineDir = PipelineModelUtil.uncompress(tmpZipFile); - - tmpResources.add(tmpPipelineDir); - - pipelineModel = PipelineModelUtil.load(sparkSession, tmpPipelineDir); - } + PipelineModel pipelineModel = loadPipelineModel(sparkSession, tmpResources); schema = updateSchema(schema, pipelineModel); - Dataset inputDataset; - - try(InputStream is = open(getInputCsvPath())){ - File tmpCsvFile = toTmpFile(is, getDataset(), ".csv"); - - tmpResources.add(tmpCsvFile); - - inputDataset = DatasetUtil.loadCsv(sparkSession, tmpCsvFile); - } + Dataset inputDataset = loadInput(sparkSession, tmpResources); inputDataset = DatasetUtil.castColumns(inputDataset, schema); @@ -161,12 +134,49 @@ public PMML getPMML() throws Exception { return pmml; } + protected StructType loadSchema(SparkSession sparkSession, List tmpResources) throws IOException { + + try(InputStream is = open(getSchemaJsonPath())){ + File tmpSchemaFile = toTmpFile(is, getDataset(), ".json"); + + tmpResources.add(tmpSchemaFile); + + return DatasetUtil.loadSchema(tmpSchemaFile); + } + } + + protected PipelineModel loadPipelineModel(SparkSession sparkSession, List tmpResources) throws IOException { + + try(InputStream is = open(getPipelineModelZipPath())){ + File tmpZipFile = toTmpFile(is, getAlgorithm() + getDataset(), ".zip"); + + tmpResources.add(tmpZipFile); + + File tmpPipelineModelDir = ArchiveUtil.uncompress(tmpZipFile); + + tmpResources.add(tmpPipelineModelDir); + + return PipelineModelUtil.load(sparkSession, tmpPipelineModelDir); + } + } + protected StructType updateSchema(StructType schema, PipelineModel pipelineModel){ return schema; } + protected Dataset loadInput(SparkSession sparkSession, List tmpResources) throws IOException { + + try(InputStream is = open(getInputCsvPath())){ + File tmpCsvFile = toTmpFile(is, getDataset(), ".csv"); + + tmpResources.add(tmpCsvFile); + + return DatasetUtil.loadCsv(sparkSession, tmpCsvFile); + } + } + static - private File toTmpFile(InputStream is, String prefix, String suffix) throws IOException { + protected File toTmpFile(InputStream is, String prefix, String suffix) throws IOException { File tmpFile = File.createTempFile(prefix, suffix); try(OutputStream os = new FileOutputStream(tmpFile)){ diff --git a/pmml-sparkml/src/main/resources/META-INF/sparkml2pmml.properties b/pmml-sparkml/src/main/resources/META-INF/sparkml2pmml.properties index 6f1bedd6..f9f4f748 100644 --- a/pmml-sparkml/src/main/resources/META-INF/sparkml2pmml.properties +++ b/pmml-sparkml/src/main/resources/META-INF/sparkml2pmml.properties @@ -26,6 +26,10 @@ org.apache.spark.ml.feature.VectorIndexerModel = org.jpmml.sparkml.feature.Vecto org.apache.spark.ml.feature.VectorSizeHint = org.jpmml.sparkml.feature.VectorSizeHintConverter org.apache.spark.ml.feature.VectorSlicer = org.jpmml.sparkml.feature.VectorSlicerConverter +# Custom features +org.jpmml.sparkml.feature.InvalidCategoryTransformer = org.jpmml.sparkml.feature.InvalidCategoryTransformerConverter +org.jpmml.sparkml.feature.SparseToDenseTransformer = org.jpmml.sparkml.feature.SparseToDenseTransformerConverter + # Prediction models org.apache.spark.ml.classification.DecisionTreeClassificationModel = org.jpmml.sparkml.model.DecisionTreeClassificationModelConverter org.apache.spark.ml.classification.GBTClassificationModel = org.jpmml.sparkml.model.GBTClassificationModelConverter diff --git a/pmml-sparkml/src/main/scala/org/jpmml/sparkml/feature/InvalidCategoryTransformer.scala b/pmml-sparkml/src/main/scala/org/jpmml/sparkml/feature/InvalidCategoryTransformer.scala new file mode 100644 index 00000000..d306a781 --- /dev/null +++ b/pmml-sparkml/src/main/scala/org/jpmml/sparkml/feature/InvalidCategoryTransformer.scala @@ -0,0 +1,132 @@ +/* + * Copyright (c) 2023 Villu Ruusmann + * + * This file is part of JPMML-SparkML + * + * JPMML-SparkML is free software: you can redistribute it and/or modify + * it under the terms of the GNU Affero General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * JPMML-SparkML is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU Affero General Public License for more details. + * + * You should have received a copy of the GNU Affero General Public License + * along with JPMML-SparkML. If not, see . + */ +package org.jpmml.sparkml.feature + +import org.apache.spark.ml.Transformer +import org.apache.spark.ml.attribute.NominalAttribute +import org.apache.spark.ml.param.{ParamMap, ParamValidators} +import org.apache.spark.ml.param.shared.{HasInputCol, HasInputCols, HasOutputCol, HasOutputCols} +import org.apache.spark.ml.util.{DefaultParamsReadable, DefaultParamsWritable, Identifiable} +import org.apache.spark.sql.{Column, Dataset, Row} +import org.apache.spark.sql.functions.udf +import org.apache.spark.sql.types.{StructField, StructType} + +class InvalidCategoryTransformer(override val uid: String) extends Transformer with HasInputCol with HasInputCols with HasOutputCol with HasOutputCols with DefaultParamsWritable { + + def this() = this(Identifiable.randomUID("invalidCat")) + + def setInputCol(value: String): this.type = set(inputCol, value) + + def setOutputCol(value: String): this.type = set(outputCol, value) + + def setInputCols(value: Array[String]): this.type = set(inputCols, value) + + def setOutputCols(value: Array[String]): this.type = set(outputCols, value) + + override + def copy(extra: ParamMap): InvalidCategoryTransformer = defaultCopy(extra) + + override + def transformSchema(schema: StructType): StructType = { + val (inputColNames, outputColNames) = getInOutCols() + + val inputFields = schema.fields + val outputFields = new Array[StructField](outputColNames.length) + + for(i <- 0 until outputColNames.length){ + val inputColName = inputColNames(i) + val outputColName = outputColNames(i) + + require(!inputFields.exists(_.name == outputColName), s"Output column $outputColName already exists") + + outputFields(i) = transformField(schema, inputColName, outputColName) + } + + StructType(inputFields ++ outputFields) + } + + override + def transform(dataset: Dataset[_]): Dataset[Row] = { + val (inputColNames, outputColNames) = getInOutCols() + + val transformedSchema = transformSchema(dataset.schema, logging = true) + + var result = dataset.asInstanceOf[Dataset[Row]] + + for(i <- 0 until outputColNames.length){ + val inputColName = inputColNames(i) + val outputColName = outputColNames(i) + + val inputField = transformedSchema(inputColName) + val outputField = transformedSchema(outputColName) + + val inputMlAttr = NominalAttribute.fromStructField(inputField).asInstanceOf[NominalAttribute] + require(inputMlAttr.values.isDefined) + + val inputLabels = inputMlAttr.values.get + require(inputLabels.last == "__unknown") + + val outputLabels = inputLabels.slice(0, inputLabels.size - 1) + val outputMlAttr = NominalAttribute.defaultAttr + .withName(outputColName) + .withValues(outputLabels.asInstanceOf[Array[String]]) + .toMetadata() + + val converter = udf { x: Double => if (x >= 0 && x < outputLabels.size) x else Double.NaN } + + val outputColumn = converter(dataset(inputColName)).as(outputColName, outputMlAttr) + + result = result.withColumn(outputColName, outputColumn) + } + + result + } + + private def getInOutCols(): (Array[String], Array[String]) = { + ParamValidators.checkSingleVsMultiColumnParams(this, Seq(outputCol), Seq(outputCols)) + + if(isSet(inputCol)){ + (Array($(inputCol)), Array($(outputCol))) + } else + + { + require($(inputCols).length == $(outputCols).length) + + ($(inputCols), $(outputCols)) + } + } + + private def transformField(schema: StructType, inputColName: String, outputColName: String): StructField = { + val inputField = schema(inputColName) + + val inputMlAttr = NominalAttribute.fromStructField(inputField).asInstanceOf[NominalAttribute] + + var outputMlAttr = NominalAttribute.defaultAttr + .withName(outputColName) + .toMetadata() + + StructField(outputColName, inputField.dataType, inputField.nullable, outputMlAttr) + } +} + +object InvalidCategoryTransformer extends DefaultParamsReadable[InvalidCategoryTransformer] { + + override + def load(path: String): InvalidCategoryTransformer = super.load(path) +} diff --git a/pmml-sparkml-xgboost/src/main/scala/org/jpmml/sparkml/xgboost/SparseToDenseTransformer.scala b/pmml-sparkml/src/main/scala/org/jpmml/sparkml/feature/SparseToDenseTransformer.scala similarity index 93% rename from pmml-sparkml-xgboost/src/main/scala/org/jpmml/sparkml/xgboost/SparseToDenseTransformer.scala rename to pmml-sparkml/src/main/scala/org/jpmml/sparkml/feature/SparseToDenseTransformer.scala index 8383cbd2..95203a6a 100644 --- a/pmml-sparkml-xgboost/src/main/scala/org/jpmml/sparkml/xgboost/SparseToDenseTransformer.scala +++ b/pmml-sparkml/src/main/scala/org/jpmml/sparkml/feature/SparseToDenseTransformer.scala @@ -16,7 +16,7 @@ * You should have received a copy of the GNU Affero General Public License * along with JPMML-SparkML. If not, see . */ -package org.jpmml.sparkml.xgboost +package org.jpmml.sparkml.feature import org.apache.spark.ml.Transformer import org.apache.spark.ml.linalg.{DenseVector, SparseVector, Vector} @@ -43,6 +43,7 @@ class SparseToDenseTransformer(override val uid: String) extends Transformer wit def transformSchema(schema: StructType): StructType = { val inputColName = $(inputCol) val outputColName = $(outputCol) + val inputFields = schema.fields require(!inputFields.exists(_.name == outputColName), s"Output column $outputColName already exists") @@ -55,11 +56,14 @@ class SparseToDenseTransformer(override val uid: String) extends Transformer wit override def transform(dataset: Dataset[_]): Dataset[Row] = { + val inputColName = $(inputCol) + val outputColName = $(outputCol) + transformSchema(dataset.schema, logging = true) val converter = udf { vec: Vector => vec.toDense } - dataset.withColumn($(outputCol), converter(dataset($(inputCol)))) + dataset.withColumn(outputColName, converter(dataset(inputColName))) } } diff --git a/pmml-sparkml/src/test/java/org/jpmml/sparkml/ExpressionTranslatorTest.java b/pmml-sparkml/src/test/java/org/jpmml/sparkml/ExpressionTranslatorTest.java index 25116a21..18e41855 100644 --- a/pmml-sparkml/src/test/java/org/jpmml/sparkml/ExpressionTranslatorTest.java +++ b/pmml-sparkml/src/test/java/org/jpmml/sparkml/ExpressionTranslatorTest.java @@ -22,7 +22,6 @@ import java.util.List; import java.util.Objects; -import org.apache.spark.sql.SparkSession; import org.apache.spark.sql.catalyst.InternalRow; import org.apache.spark.sql.catalyst.expressions.Expression; import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan; @@ -43,8 +42,6 @@ import org.jpmml.evaluator.FieldValueUtil; import org.jpmml.evaluator.VirtualEvaluationContext; import org.jpmml.model.ReflectionUtil; -import org.junit.AfterClass; -import org.junit.BeforeClass; import org.junit.Test; import scala.collection.JavaConversions; @@ -52,7 +49,7 @@ import static org.junit.Assert.assertTrue; import static org.junit.Assert.fail; -public class ExpressionTranslatorTest { +public class ExpressionTranslatorTest extends SparkMLTest { @Test public void translateLogicalExpression(){ @@ -295,7 +292,7 @@ private Object evaluate(String sqlExpression){ static private Expression translateInternal(String sqlStatement){ - LogicalPlan logicalPlan = DatasetUtil.createAnalyzedLogicalPlan(ExpressionTranslatorTest.sparkSession, ExpressionTranslatorTest.schema, sqlStatement); + LogicalPlan logicalPlan = DatasetUtil.createAnalyzedLogicalPlan(SparkMLTest.sparkSession, ExpressionTranslatorTest.schema, sqlStatement); List expressions = JavaConversions.seqAsJavaList(logicalPlan.expressions()); if(expressions.size() != 1){ @@ -376,20 +373,6 @@ private void checkExpression(org.dmg.pmml.Expression expected, String string){ assertTrue(ReflectionUtil.equals(expected, actual)); } - @BeforeClass - static - public void createSparkSession(){ - ExpressionTranslatorTest.sparkSession = SparkSessionUtil.createSparkSession(); - } - - @AfterClass - static - public void destroySparkSession(){ - ExpressionTranslatorTest.sparkSession = SparkSessionUtil.destroySparkSession(ExpressionTranslatorTest.sparkSession); - } - - public static SparkSession sparkSession = null; - private static final StructType schema = new StructType() .add("flag", DataTypes.BooleanType) .add("x1", DataTypes.DoubleType) diff --git a/pmml-sparkml/src/test/java/org/jpmml/sparkml/PMMLBuilderTest.java b/pmml-sparkml/src/test/java/org/jpmml/sparkml/PMMLBuilderTest.java index 9721551e..19e074b6 100644 --- a/pmml-sparkml/src/test/java/org/jpmml/sparkml/PMMLBuilderTest.java +++ b/pmml-sparkml/src/test/java/org/jpmml/sparkml/PMMLBuilderTest.java @@ -24,10 +24,7 @@ import org.apache.spark.ml.PipelineModel; import org.apache.spark.ml.classification.LogisticRegressionModel; import org.apache.spark.ml.linalg.DenseVector; -import org.apache.spark.sql.SparkSession; import org.apache.spark.sql.types.StructType; -import org.junit.AfterClass; -import org.junit.BeforeClass; import org.junit.Test; import static org.junit.Assert.fail; @@ -56,18 +53,4 @@ public void construct(){ throw iae; } } - - @BeforeClass - static - public void createSparkSession(){ - PMMLBuilderTest.sparkSession = SparkSessionUtil.createSparkSession(); - } - - @AfterClass - static - public void destroySparkSession(){ - PMMLBuilderTest.sparkSession = SparkSessionUtil.destroySparkSession(PMMLBuilderTest.sparkSession); - } - - public static SparkSession sparkSession = null; } \ No newline at end of file diff --git a/pmml-sparkml/src/test/java/org/jpmml/sparkml/SparkMLTest.java b/pmml-sparkml/src/test/java/org/jpmml/sparkml/SparkMLTest.java new file mode 100644 index 00000000..4b9e20a0 --- /dev/null +++ b/pmml-sparkml/src/test/java/org/jpmml/sparkml/SparkMLTest.java @@ -0,0 +1,41 @@ +/* + * Copyright (c) 2023 Villu Ruusmann + * + * This file is part of JPMML-SparkML + * + * JPMML-SparkML is free software: you can redistribute it and/or modify + * it under the terms of the GNU Affero General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * JPMML-SparkML is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU Affero General Public License for more details. + * + * You should have received a copy of the GNU Affero General Public License + * along with JPMML-SparkML. If not, see . + */ +package org.jpmml.sparkml; + +import org.apache.spark.sql.SparkSession; +import org.junit.AfterClass; +import org.junit.BeforeClass; + +abstract +public class SparkMLTest { + + @BeforeClass + static + public void createSparkSession(){ + SparkMLTest.sparkSession = SparkSessionUtil.createSparkSession(); + } + + @AfterClass + static + public void destroySparkSession(){ + SparkMLTest.sparkSession = SparkSessionUtil.destroySparkSession(SparkMLTest.sparkSession); + } + + public static SparkSession sparkSession = null; +} \ No newline at end of file diff --git a/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/InvalidCategoryTransformerTest.java b/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/InvalidCategoryTransformerTest.java new file mode 100644 index 00000000..8c061372 --- /dev/null +++ b/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/InvalidCategoryTransformerTest.java @@ -0,0 +1,162 @@ +/* + * Copyright (c) 2023 Villu Ruusmann + * + * This file is part of JPMML-SparkML + * + * JPMML-SparkML is free software: you can redistribute it and/or modify + * it under the terms of the GNU Affero General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * JPMML-SparkML is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU Affero General Public License for more details. + * + * You should have received a copy of the GNU Affero General Public License + * along with JPMML-SparkML. If not, see . + */ +package org.jpmml.sparkml.feature; + +import java.util.Arrays; +import java.util.List; +import java.util.Objects; + +import org.apache.spark.ml.Pipeline; +import org.apache.spark.ml.PipelineModel; +import org.apache.spark.ml.PipelineStage; +import org.apache.spark.ml.attribute.Attribute; +import org.apache.spark.ml.attribute.AttributeKeys; +import org.apache.spark.ml.attribute.AttributeType; +import org.apache.spark.ml.attribute.NominalAttribute; +import org.apache.spark.ml.feature.StringIndexer; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.types.DataTypes; +import org.apache.spark.sql.types.Metadata; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import org.dmg.pmml.PMML; +import org.jpmml.sparkml.PMMLBuilder; +import org.jpmml.sparkml.SparkMLTest; +import org.junit.Test; + +import static org.junit.Assert.assertArrayEquals; +import static org.junit.Assert.assertEquals; + +public class InvalidCategoryTransformerTest extends SparkMLTest { + + @Test + public void transform(){ + StructType schema = new StructType() + .add("fruit", DataTypes.StringType, true) + .add("color", DataTypes.StringType, true) + .add("rating", DataTypes.DoubleType, false); + + List rows = Arrays.asList( + RowFactory.create("apple", "red", 2d), + RowFactory.create("orange", "orange", 3d), + RowFactory.create("banana", "yellow", 3d), + RowFactory.create("banana", "green", 1d), + RowFactory.create("apple", "green", 2d) + ); + + Dataset ds = SparkMLTest.sparkSession.createDataFrame(rows, schema); + + StringIndexer stringIndexer = new StringIndexer() + .setStringOrderType("alphabetAsc") + .setInputCols(new String[]{"fruit", "color", "rating"}) + .setOutputCols(new String[]{"fruitIdx", "colorIdx", "ratingIdx"}) + .setHandleInvalid("keep"); + + InvalidCategoryTransformer multiInvalidColumnTransformer = new InvalidCategoryTransformer() + .setInputCols(new String[]{"fruitIdx", "colorIdx"}) + .setOutputCols(new String[]{"fruitIdxTransformed", "colorIdxTransformed"}); + + InvalidCategoryTransformer singleInvalidCategoryTransformer = new InvalidCategoryTransformer() + .setInputCol("ratingIdx") + .setOutputCol("ratingIdxTransformed"); + + Pipeline pipeline = new Pipeline() + .setStages(new PipelineStage[]{stringIndexer, multiInvalidColumnTransformer, singleInvalidCategoryTransformer}); + + PipelineModel pipelineModel = pipeline.fit(ds); + + @SuppressWarnings("unused") + PMML pmml = new PMMLBuilder(schema, pipelineModel) + .build(); + + Dataset transformedDs = pipelineModel.transform(ds); + + StructType transformedSchema = transformedDs.schema(); + + NominalAttribute fruitIdxAttr = (NominalAttribute)getAttribute(transformedSchema, "fruitIdx"); + NominalAttribute colorIdxAttr = (NominalAttribute)getAttribute(transformedSchema, "colorIdx"); + NominalAttribute ratingIdxAttr = (NominalAttribute)getAttribute(transformedSchema, "ratingIdx"); + + assertArrayEquals(new String[]{"apple", "banana", "orange", "__unknown"}, (fruitIdxAttr.values()).get()); + assertArrayEquals(new String[]{"green", "orange", "red", "yellow", "__unknown"}, (colorIdxAttr.values()).get()); + assertArrayEquals(new String[]{"1.0", "2.0", "3.0", "__unknown"}, (ratingIdxAttr.values()).get()); + + NominalAttribute fruitIdxTransformedAttr = (NominalAttribute)getAttribute(transformedSchema, "fruitIdxTransformed"); + NominalAttribute colorIdxTransformedAttr = (NominalAttribute)getAttribute(transformedSchema, "colorIdxTransformed"); + NominalAttribute ratingIdxTransformedAttr = (NominalAttribute)getAttribute(transformedSchema, "ratingIdxTransformed"); + + assertArrayEquals(new String[]{"apple", "banana", "orange"}, (fruitIdxTransformedAttr.values()).get()); + assertArrayEquals(new String[]{"green", "orange", "red", "yellow"}, (colorIdxTransformedAttr.values()).get()); + assertArrayEquals(new String[]{"1.0", "2.0", "3.0"}, (ratingIdxTransformedAttr.values()).get()); + + List testRows = Arrays.asList( + RowFactory.create(null, "yellow", 0d), + RowFactory.create("apple", "", 1d), + RowFactory.create("banana", "red", Double.NaN) + ); + + Dataset testDs = SparkMLTest.sparkSession.createDataFrame(testRows, schema); + + Dataset transformedTestDs = pipelineModel.transform(testDs); + + List transformedTestRows = transformedTestDs + .select("fruitIdxTransformed", "colorIdxTransformed", "ratingIdxTransformed") + .collectAsList(); + + assertEquals(3, transformedTestRows.size()); + + assertArrayEquals(new Object[]{Double.NaN, 3d, Double.NaN}, getValues(transformedTestRows.get(0))); + assertArrayEquals(new Object[]{0d, Double.NaN, 0d}, getValues(transformedTestRows.get(1))); + assertArrayEquals(new Object[]{1d, 2d, Double.NaN}, getValues(transformedTestRows.get(2))); + } + + static + private Attribute getAttribute(StructType schema, String name){ + StructField structField = schema.apply(name); + + Metadata metadata = structField.metadata(); + + String mlAttr = AttributeKeys.ML_ATTR(); + + if(metadata.contains(mlAttr)){ + Metadata mlAttrMetadata = metadata.getMetadata(mlAttr); + + if(!Objects.equals(mlAttrMetadata.getString("type"), AttributeType.Nominal().name())){ + throw new IllegalArgumentException(); + } + + return NominalAttribute.fromStructField(structField); + } + + return null; + } + + static + private Object[] getValues(Row row){ + Object[] result = new Object[row.size()]; + + for(int i = 0, max = row.size(); i < max; i++){ + result[i] = row.get(i); + } + + return result; + } +} \ No newline at end of file diff --git a/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/SQLTransformerConverterTest.java b/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/SQLTransformerConverterTest.java index 5373e918..64f3312f 100644 --- a/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/SQLTransformerConverterTest.java +++ b/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/SQLTransformerConverterTest.java @@ -24,7 +24,6 @@ import java.util.LinkedHashSet; import java.util.List; -import org.apache.spark.sql.SparkSession; import org.apache.spark.sql.catalyst.plans.logical.LogicalPlan; import org.apache.spark.sql.types.DataTypes; import org.apache.spark.sql.types.StructType; @@ -33,14 +32,12 @@ import org.jpmml.sparkml.ConverterFactory; import org.jpmml.sparkml.DatasetUtil; import org.jpmml.sparkml.SparkMLEncoder; -import org.jpmml.sparkml.SparkSessionUtil; -import org.junit.AfterClass; -import org.junit.BeforeClass; +import org.jpmml.sparkml.SparkMLTest; import org.junit.Test; import static org.junit.Assert.assertTrue; -public class SQLTransformerConverterTest { +public class SQLTransformerConverterTest extends SparkMLTest { @Test public void encodeLogicalPlan(){ @@ -73,7 +70,7 @@ private void checkFields(String sqlStatement, Collection dataFieldNames, SparkMLEncoder encoder = new SparkMLEncoder(SQLTransformerConverterTest.schema, converterFactory); - LogicalPlan logicalPlan = DatasetUtil.createAnalyzedLogicalPlan(SQLTransformerConverterTest.sparkSession, SQLTransformerConverterTest.schema, sqlStatement); + LogicalPlan logicalPlan = DatasetUtil.createAnalyzedLogicalPlan(SparkMLTest.sparkSession, SQLTransformerConverterTest.schema, sqlStatement); SQLTransformerConverter.encodeLogicalPlan(encoder, logicalPlan); @@ -96,20 +93,6 @@ private void checkFields(String sqlStatement, Collection dataFieldNames, assertTrue(derivedFieldNames.toString(), derivedFieldNames.isEmpty()); } - @BeforeClass - static - public void createSparkSession(){ - SQLTransformerConverterTest.sparkSession = SparkSessionUtil.createSparkSession(); - } - - @AfterClass - static - public void destroySparkSession(){ - SQLTransformerConverterTest.sparkSession = SparkSessionUtil.destroySparkSession(SQLTransformerConverterTest.sparkSession); - } - - public static SparkSession sparkSession = null; - private static final StructType schema = new StructType() .add("Sepal_Length", DataTypes.DoubleType) .add("Sepal_Width", DataTypes.DoubleType) diff --git a/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/SparseToDenseTransformerTest.java b/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/SparseToDenseTransformerTest.java new file mode 100644 index 00000000..f367179b --- /dev/null +++ b/pmml-sparkml/src/test/java/org/jpmml/sparkml/feature/SparseToDenseTransformerTest.java @@ -0,0 +1,93 @@ +/* + * Copyright (c) 2023 Villu Ruusmann + * + * This file is part of JPMML-SparkML + * + * JPMML-SparkML is free software: you can redistribute it and/or modify + * it under the terms of the GNU Affero General Public License as published by + * the Free Software Foundation, either version 3 of the License, or + * (at your option) any later version. + * + * JPMML-SparkML is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + * GNU Affero General Public License for more details. + * + * You should have received a copy of the GNU Affero General Public License + * along with JPMML-SparkML. If not, see . + */ +package org.jpmml.sparkml.feature; + +import java.util.Arrays; +import java.util.List; + +import org.apache.spark.ml.Pipeline; +import org.apache.spark.ml.PipelineModel; +import org.apache.spark.ml.PipelineStage; +import org.apache.spark.ml.Transformer; +import org.apache.spark.ml.linalg.DenseVector; +import org.apache.spark.ml.linalg.SparseVector; +import org.apache.spark.ml.linalg.Vector; +import org.apache.spark.ml.linalg.VectorUDT; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.types.StructType; +import org.jpmml.sparkml.SparkMLTest; +import org.junit.Test; + +import static org.junit.Assert.assertEquals; +import static org.junit.Assert.assertNotNull; +import static org.junit.Assert.assertTrue; + +public class SparseToDenseTransformerTest extends SparkMLTest { + + @Test + public void transform(){ + StructType schema = new StructType() + .add("featureVec", new VectorUDT(), false); + + List rows = Arrays.asList( + RowFactory.create(new SparseVector(3, new int[]{1}, new double[]{1.0})), + RowFactory.create(new DenseVector(new double[]{0.0d, 0.0d, 1.0d})), + RowFactory.create(new SparseVector(3, new int[]{0}, new double[]{1.0})) + ); + + Dataset ds = SparkMLTest.sparkSession.createDataFrame(rows, schema); + + Transformer transformer = new SparseToDenseTransformer() + .setInputCol("featureVec") + .setOutputCol("denseFeatureVec"); + + Pipeline pipeline = new Pipeline() + .setStages(new PipelineStage[]{transformer}); + + PipelineModel pipelineModel = pipeline.fit(ds); + + Dataset transformedDs = pipelineModel.transform(ds); + + assertNotNull(transformedDs.col("featureVec")); + assertNotNull(transformedDs.col("denseFeatureVec")); + + List transformedRows = transformedDs + .select("featureVec", "denseFeatureVec") + .collectAsList(); + + for(int i = 0; i < 3; i++){ + Row transformedRow = transformedRows.get(i); + + Vector vector = (Vector)transformedRow.get(0); + Vector denseVector = (Vector)transformedRow.get(1); + + assertEquals(i == 1 ? 3 : 1, vector.numActives()); + assertEquals(1, vector.numNonzeros()); + assertEquals(3, vector.size()); + + assertTrue(denseVector instanceof DenseVector); + + assertEquals(3, denseVector.numActives()); + assertEquals(1, denseVector.numNonzeros()); + assertEquals(3, denseVector.size()); + } + } +} \ No newline at end of file diff --git a/pom.xml b/pom.xml index f63abcf7..0a4f53a7 100644 --- a/pom.xml +++ b/pom.xml @@ -50,6 +50,14 @@ https://github.com/jpmml/jpmml-sparkml/issues + + [0.9.5,) + + [1.0.0, 2.0.1] + + false + + @@ -76,7 +84,7 @@ org.jpmml pmml-converter - 1.5.4 + 1.5.5 org.slf4j @@ -87,7 +95,7 @@ org.jpmml pmml-converter-testing - 1.5.4 + 1.5.5 @@ -99,19 +107,19 @@ org.jpmml pmml-lightgbm - 1.4.5 + 1.5.0 org.jpmml pmml-xgboost - 1.7.3 + 1.8.1 com.microsoft.azure synapseml-lightgbm_2.12 - [0.9.5,) + ${synapseml-lightgbm.version} @@ -123,7 +131,7 @@ ml.dmlc xgboost4j-spark_2.12 - [1.0.0,) + ${xgboost4j-spark.version} @@ -178,7 +186,7 @@ org.apache.maven.plugins maven-javadoc-plugin - 3.5.0 + 3.6.3 1.8 @@ -186,8 +194,10 @@ org.apache.maven.plugins maven-release-plugin - 3.0.0 + 3.0.1 + + -Psonatype-oss-release -DskipTests=${skipTests} true true false @@ -196,16 +206,47 @@ org.apache.maven.plugins maven-surefire-plugin - 3.0.0 + 3.2.3 ${jacoco.agent} + ${skipTests} false + + net.alchim31.maven + scala-maven-plugin + 4.8.1 + + ${project.build.sourceEncoding} + ScalaThenJava + + -Xmx1024m + + 2.12.15 + + + + scala-compile-first + process-resources + + add-source + compile + + + + scala-test-compile + process-test-resources + + testCompile + + + + org.jacoco jacoco-maven-plugin - 0.8.9 + 0.8.10 pre-unit-test