diff --git a/.github/workflows/maven.yml b/.github/workflows/maven.yml index d22b3c5e..7a61ed1e 100644 --- a/.github/workflows/maven.yml +++ b/.github/workflows/maven.yml @@ -2,7 +2,7 @@ name: maven on: push: - branches: [ '2.0.X', '2.1.X', '2.2.X', '2.3.X', master ] + branches: [ '2.0.X', '2.1.X', '2.2.X', '2.3.X', '2.4.X', master ] jobs: build: diff --git a/README.md b/README.md index 939f755e..39210ff7 100644 --- a/README.md +++ b/README.md @@ -45,6 +45,8 @@ Java library and command-line application for converting Apache Spark ML pipelin
Apache Spark ML + Examples: [main.py](https://github.com/jpmml/jpmml-sparkml/blob/master/pmml-sparkml/src/test/resources/main.py) + * Feature extractors, transformers and selectors: * [`feature.Binarizer`](https://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/feature/Binarizer.html) * [`feature.Bucketizer`](https://spark.apache.org/docs/latest/api/java/org/apache/spark/ml/feature/Bucketizer.html) @@ -120,6 +122,8 @@ Java library and command-line application for converting Apache Spark ML pipelin
LightGBM + Examples: [LightGBMAuditNA.scala](https://github.com/jpmml/jpmml-sparkml/blob/master/pmml-sparkml-lightgbm/src/test/resources/LightGBMAuditNA.scala), [LightGBMAutoNA.scaka](https://github.com/jpmml/jpmml-sparkml/blob/master/pmml-sparkml-lightgbm/src/test/resources/LightGBMAutoNA.scala), etc. + * Prediction models: * [`com.microsoft.azure.synapse.ml.lightgbm.LightGBMClassificationModel`](https://mmlspark.blob.core.windows.net/docs/0.9.5/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMClassificationModel.html) * [`com.microsoft.azure.synapse.ml.lightgbm.LightGBMRegressionModel`](https://mmlspark.blob.core.windows.net/docs/0.9.5/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRegressionModel.html) @@ -128,6 +132,8 @@ Java library and command-line application for converting Apache Spark ML pipelin
XGBoost + Examples: [XGBoostAuditNA.scala](https://github.com/jpmml/jpmml-sparkml/blob/master/pmml-sparkml-xgboost/src/test/resources/XGBoostAuditNA.scala), [XGBoostAutoNA.scala](https://github.com/jpmml/jpmml-sparkml/blob/master/pmml-sparkml-xgboost/src/test/resources/XGBoostAutoNA.scala), etc. + * 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) @@ -135,7 +141,7 @@ Java library and command-line application for converting Apache Spark ML pipelin # Prerequisites # -* Apache Spark 3.0.X, 3.1.X, 3.2.X, 3.3.X or 3.4.X. +* Apache Spark 3.0.X, 3.1.X, 3.2.X, 3.3.X, 3.4.X or 3.5.X. # Installation # @@ -163,7 +169,8 @@ Active development branches: | 3.1.X | [`2.1.X`](https://github.com/jpmml/jpmml-sparkml/tree/2.1.X) | | 3.2.X | [`2.2.X`](https://github.com/jpmml/jpmml-sparkml/tree/2.2.X) | | 3.3.X | [`2.3.X`](https://github.com/jpmml/jpmml-sparkml/tree/2.3.X) | -| 3.4.X | [`master`](https://github.com/jpmml/jpmml-sparkml/tree/master) | +| 3.4.X | [`2.4.X`](https://github.com/jpmml/jpmml-sparkml/tree/2.4.X) | +| 3.5.X | [`master`](https://github.com/jpmml/jpmml-sparkml/tree/master) | Archived development branches: diff --git a/pmml-sparkml-xgboost/src/test/resources/XGBoostHousing.scala b/pmml-sparkml-xgboost/src/test/resources/XGBoostHousing.scala index a878d3d8..8156a0d8 100644 --- a/pmml-sparkml-xgboost/src/test/resources/XGBoostHousing.scala +++ b/pmml-sparkml-xgboost/src/test/resources/XGBoostHousing.scala @@ -1,6 +1,6 @@ import java.io.File -import ml.dmlc.xgboost4j.scala.spark.{TrackerConf, XGBoostRegressor} +import ml.dmlc.xgboost4j.scala.spark.XGBoostRegressor import org.apache.spark.ml.Pipeline import org.apache.spark.ml.feature._ import org.apache.spark.sql.types.FloatType @@ -16,8 +16,7 @@ val cont_cols = Array("CRIM", "ZN", "INDUS", "NOX", "RM", "AGE", "DIS", "PTRATIO val assembler = new VectorAssembler().setInputCols(cat_cols ++ cont_cols).setOutputCol("featureVector") val indexer = new VectorIndexer().setInputCol(assembler.getOutputCol).setOutputCol("catFeatureVector") -val trackerConf = TrackerConf(0, "scala") -val regressor = new XGBoostRegressor(Map("objective" -> "reg:squarederror", "num_round" -> 101, "num_workers" -> 1, "tracker_conf" -> trackerConf)).setMissing(-1).setLabelCol("MEDV").setFeaturesCol(indexer.getOutputCol) +val regressor = new XGBoostRegressor(Map("objective" -> "reg:squarederror", "num_round" -> 101)).setMissing(-1).setLabelCol("MEDV").setFeaturesCol(indexer.getOutputCol) val pipeline = new Pipeline().setStages(Array(assembler, indexer, regressor)) val pipelineModel = pipeline.fit(df) diff --git a/pmml-sparkml-xgboost/src/test/resources/XGBoostIris.scala b/pmml-sparkml-xgboost/src/test/resources/XGBoostIris.scala index 7771b385..4a088e54 100644 --- a/pmml-sparkml-xgboost/src/test/resources/XGBoostIris.scala +++ b/pmml-sparkml-xgboost/src/test/resources/XGBoostIris.scala @@ -1,6 +1,6 @@ import java.io.File -import ml.dmlc.xgboost4j.scala.spark.{TrackerConf, XGBoostClassifier} +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 @@ -22,8 +22,7 @@ val labelIndexerModel = labelIndexer.fit(df) val assembler = new VectorAssembler().setInputCols(Array("Sepal_Length", "Sepal_Width", "Petal_Length", "Petal_Width")).setOutputCol("featureVector") -val trackerConf = TrackerConf(0, "scala") -val classifier = new XGBoostClassifier(Map("objective" -> "multi:softprob", "num_class" -> 3, "num_round" -> 17, "tracker_conf" -> trackerConf)).setLabelCol(labelIndexer.getOutputCol).setFeaturesCol(assembler.getOutputCol) +val classifier = new XGBoostClassifier(Map("objective" -> "multi:softprob", "num_class" -> 3)).setLabelCol(labelIndexer.getOutputCol).setFeaturesCol(assembler.getOutputCol) val pipeline = new Pipeline().setStages(Array(labelIndexer, assembler, classifier)) val pipelineModel = pipeline.fit(df)