initial merge
This commit is contained in:
amdsc21
@@ -37,7 +37,7 @@
|
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<spark.version>3.1.1</spark.version>
|
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<scala.version>2.12.8</scala.version>
|
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<scala.binary.version>2.12</scala.binary.version>
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<hadoop.version>3.3.4</hadoop.version>
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<hadoop.version>3.3.5</hadoop.version>
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<maven.wagon.http.retryHandler.count>5</maven.wagon.http.retryHandler.count>
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<log.capi.invocation>OFF</log.capi.invocation>
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<use.cuda>OFF</use.cuda>
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@@ -118,7 +118,7 @@
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-release-plugin</artifactId>
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<version>2.5.3</version>
|
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<version>3.0.0</version>
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<configuration>
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<autoVersionSubmodules>true</autoVersionSubmodules>
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<useReleaseProfile>false</useReleaseProfile>
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@@ -427,7 +427,7 @@
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<plugin>
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<groupId>org.apache.maven.plugins</groupId>
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<artifactId>maven-surefire-plugin</artifactId>
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<version>2.22.2</version>
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<version>3.0.0</version>
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<configuration>
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<skipTests>false</skipTests>
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<useSystemClassLoader>false</useSystemClassLoader>
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@@ -1,30 +1,30 @@
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XGBoost4J Code Examples
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=======================
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## Java API
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* [Basic walkthrough of wrappers](src/main/java/ml/dmlc/xgboost4j/java/example/BasicWalkThrough.java)
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* [Customize loss function, and evaluation metric](src/main/java/ml/dmlc/xgboost4j/java/example/CustomObjective.java)
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* [Boosting from existing prediction](src/main/java/ml/dmlc/xgboost4j/java/example/BoostFromPrediction.java)
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* [Predicting using first n trees](src/main/java/ml/dmlc/xgboost4j/java/example/PredictFirstNtree.java)
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* [Generalized Linear Model](src/main/java/ml/dmlc/xgboost4j/java/example/GeneralizedLinearModel.java)
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* [Cross validation](src/main/java/ml/dmlc/xgboost4j/java/example/CrossValidation.java)
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* [Predicting leaf indices](src/main/java/ml/dmlc/xgboost4j/java/example/PredictLeafIndices.java)
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* [External Memory](src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java)
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* [Early Stopping](src/main/java/ml/dmlc/xgboost4j/java/example/EarlyStopping.java)
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## Scala API
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* [Basic walkthrough of wrappers](src/main/scala/ml/dmlc/xgboost4j/scala/example/BasicWalkThrough.scala)
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* [Customize loss function, and evaluation metric](src/main/scala/ml/dmlc/xgboost4j/scala/example/CustomObjective.scala)
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* [Boosting from existing prediction](src/main/scala/ml/dmlc/xgboost4j/scala/example/BoostFromPrediction.scala)
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* [Predicting using first n trees](src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictFirstNTree.scala)
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* [Generalized Linear Model](src/main/scala/ml/dmlc/xgboost4j/scala/example/GeneralizedLinearModel.scala)
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* [Cross validation](src/main/scala/ml/dmlc/xgboost4j/scala/example/CrossValidation.scala)
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* [Predicting leaf indices](src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictLeafIndices.scala)
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* [External Memory](src/main/scala/ml/dmlc/xgboost4j/scala/example/ExternalMemory.scala)
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|
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## Spark API
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* [Distributed Training with Spark](src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkMLlibPipeline.scala)
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|
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## Flink API
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* [Distributed Training with Flink](src/main/scala/ml/dmlc/xgboost4j/scala/example/flink/DistTrainWithFlink.scala)
|
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XGBoost4J Code Examples
|
||||
=======================
|
||||
|
||||
## Java API
|
||||
* [Basic walkthrough of wrappers](src/main/java/ml/dmlc/xgboost4j/java/example/BasicWalkThrough.java)
|
||||
* [Customize loss function, and evaluation metric](src/main/java/ml/dmlc/xgboost4j/java/example/CustomObjective.java)
|
||||
* [Boosting from existing prediction](src/main/java/ml/dmlc/xgboost4j/java/example/BoostFromPrediction.java)
|
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* [Predicting using first n trees](src/main/java/ml/dmlc/xgboost4j/java/example/PredictFirstNtree.java)
|
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* [Generalized Linear Model](src/main/java/ml/dmlc/xgboost4j/java/example/GeneralizedLinearModel.java)
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* [Cross validation](src/main/java/ml/dmlc/xgboost4j/java/example/CrossValidation.java)
|
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* [Predicting leaf indices](src/main/java/ml/dmlc/xgboost4j/java/example/PredictLeafIndices.java)
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* [External Memory](src/main/java/ml/dmlc/xgboost4j/java/example/ExternalMemory.java)
|
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* [Early Stopping](src/main/java/ml/dmlc/xgboost4j/java/example/EarlyStopping.java)
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|
||||
## Scala API
|
||||
|
||||
* [Basic walkthrough of wrappers](src/main/scala/ml/dmlc/xgboost4j/scala/example/BasicWalkThrough.scala)
|
||||
* [Customize loss function, and evaluation metric](src/main/scala/ml/dmlc/xgboost4j/scala/example/CustomObjective.scala)
|
||||
* [Boosting from existing prediction](src/main/scala/ml/dmlc/xgboost4j/scala/example/BoostFromPrediction.scala)
|
||||
* [Predicting using first n trees](src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictFirstNTree.scala)
|
||||
* [Generalized Linear Model](src/main/scala/ml/dmlc/xgboost4j/scala/example/GeneralizedLinearModel.scala)
|
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* [Cross validation](src/main/scala/ml/dmlc/xgboost4j/scala/example/CrossValidation.scala)
|
||||
* [Predicting leaf indices](src/main/scala/ml/dmlc/xgboost4j/scala/example/PredictLeafIndices.scala)
|
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* [External Memory](src/main/scala/ml/dmlc/xgboost4j/scala/example/ExternalMemory.scala)
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|
||||
## Spark API
|
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* [Distributed Training with Spark](src/main/scala/ml/dmlc/xgboost4j/scala/example/spark/SparkMLlibPipeline.scala)
|
||||
|
||||
## Flink API
|
||||
* [Distributed Training with Flink](src/main/scala/ml/dmlc/xgboost4j/scala/example/flink/DistTrainWithFlink.scala)
|
||||
|
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@@ -51,7 +51,7 @@
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<dependency>
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<groupId>org.apache.hadoop</groupId>
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<artifactId>hadoop-common</artifactId>
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<version>3.3.4</version>
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<version>3.3.5</version>
|
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</dependency>
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</dependencies>
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@@ -41,13 +41,13 @@
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<dependency>
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<groupId>com.typesafe.akka</groupId>
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<artifactId>akka-actor_${scala.binary.version}</artifactId>
|
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<version>2.7.0</version>
|
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<version>2.6.20</version>
|
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<scope>compile</scope>
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</dependency>
|
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<dependency>
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<groupId>com.typesafe.akka</groupId>
|
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<artifactId>akka-testkit_${scala.binary.version}</artifactId>
|
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<version>2.7.0</version>
|
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<version>2.6.20</version>
|
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<scope>test</scope>
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</dependency>
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<dependency>
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|
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@@ -84,9 +84,10 @@ public class BoosterTest {
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};
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try (Table tmpTable = Table.readCSV(schema, opts, new File(trainingDataPath))) {
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ColumnVector[] df = new ColumnVector[12];
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for (int i = 0; i < 12; ++i) {
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df[i] = tmpTable.getColumn(i);
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ColumnVector[] df = new ColumnVector[10];
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// exclude the first two columns, they are label bounds and contain inf.
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for (int i = 2; i < 12; ++i) {
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df[i - 2] = tmpTable.getColumn(i);
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}
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try (Table X = new Table(df);) {
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ColumnVector[] labels = new ColumnVector[1];
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@@ -21,7 +21,7 @@ import java.io.File
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import ml.dmlc.xgboost4j.scala.spark.{XGBoostClassificationModel, XGBoostClassifier}
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import org.apache.spark.ml.feature.VectorAssembler
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import org.apache.spark.sql.functions.{col, udf}
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import org.apache.spark.sql.functions.{col, udf, when}
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import org.apache.spark.sql.types.{FloatType, StructField, StructType}
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class GpuXGBoostClassifierSuite extends GpuTestSuite {
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@@ -47,7 +47,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
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"num_round" -> 10, "num_workers" -> 1, "tree_method" -> "gpu_hist",
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"features_cols" -> featureNames, "label_col" -> labelName)
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val Array(originalDf, testDf) = spark.read.option("header", "true").schema(schema)
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.csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
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.csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
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.randomSplit(Array(0.7, 0.3), seed = 1)
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// Get a model
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val model = new XGBoostClassifier(xgbParam)
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.fit(originalDf)
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@@ -64,7 +65,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
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"num_round" -> 10, "num_workers" -> 1, "tree_method" -> "gpu_hist",
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"features_cols" -> featureNames, "label_col" -> labelName)
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val Array(originalDf, testDf) = spark.read.option("header", "true").schema(schema)
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.csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
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.csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
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.randomSplit(Array(0.7, 0.3), seed = 1)
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val getWeightFromF1 = udf({ f1: Float => if (f1.toInt % 2 == 0) 1.0f else 0.001f })
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val dfWithWeight = originalDf.withColumn("weight", getWeightFromF1(col("f1")))
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@@ -87,7 +89,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
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val xgbParam = Map("eta" -> 0.1f, "max_depth" -> 2, "objective" -> "binary:logistic",
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"num_round" -> 10, "num_workers" -> 1)
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val Array(rawInput, testDf) = spark.read.option("header", "true").schema(schema)
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.csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
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.csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
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.randomSplit(Array(0.7, 0.3), seed = 1)
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val classifier = new XGBoostClassifier(xgbParam)
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.setFeaturesCol(featureNames)
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@@ -122,7 +125,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
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val xgbParam = Map("eta" -> 0.1f, "max_depth" -> 2, "objective" -> "binary:logistic",
|
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"num_round" -> 10, "num_workers" -> 1)
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val Array(rawInput, _) = spark.read.option("header", "true").schema(schema)
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.csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
|
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.csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
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.randomSplit(Array(0.7, 0.3), seed = 1)
|
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|
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val vectorAssembler = new VectorAssembler()
|
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.setHandleInvalid("keep")
|
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@@ -144,7 +148,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
|
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// transform on GPU
|
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withGpuSparkSession() { spark =>
|
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val Array(_, testDf) = spark.read.option("header", "true").schema(schema)
|
||||
.csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
|
||||
.csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
|
||||
.randomSplit(Array(0.7, 0.3), seed = 1)
|
||||
|
||||
// Since CPU model does not know the information about the features cols that GPU transform
|
||||
// pipeline requires. End user needs to setFeaturesCol(features: Array[String]) in the model
|
||||
@@ -174,7 +179,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
|
||||
val xgbParam = Map("eta" -> 0.1f, "max_depth" -> 2, "objective" -> "binary:logistic",
|
||||
"num_round" -> 10, "num_workers" -> 1)
|
||||
val Array(rawInput, _) = spark.read.option("header", "true").schema(schema)
|
||||
.csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
|
||||
.csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
|
||||
.randomSplit(Array(0.7, 0.3), seed = 1)
|
||||
|
||||
val classifier = new XGBoostClassifier(xgbParam)
|
||||
.setFeaturesCol(featureNames)
|
||||
@@ -190,7 +196,8 @@ class GpuXGBoostClassifierSuite extends GpuTestSuite {
|
||||
// transform on CPU
|
||||
withCpuSparkSession() { spark =>
|
||||
val Array(_, rawInput) = spark.read.option("header", "true").schema(schema)
|
||||
.csv(dataPath).randomSplit(Array(0.7, 0.3), seed = 1)
|
||||
.csv(dataPath).withColumn("f2", when(col("f2").isin(Float.PositiveInfinity), 0))
|
||||
.randomSplit(Array(0.7, 0.3), seed = 1)
|
||||
|
||||
val featureColName = "feature_col"
|
||||
val vectorAssembler = new VectorAssembler()
|
||||
|
||||
@@ -1,66 +1,66 @@
|
||||
0,10.0229017899,7.30178495562,0.118115020017,1
|
||||
0,9.93639621859,9.93102159291,0.0435030004396,1
|
||||
0,10.1301737265,0.00411765220572,2.4165878053,1
|
||||
1,9.87828587087,0.608588414992,0.111262590883,1
|
||||
0,10.1373430048,0.47764012225,0.991553052194,1
|
||||
0,10.0523814718,4.72152505167,0.672978832666,1
|
||||
0,10.0449715742,8.40373928536,0.384457573667,1
|
||||
1,996.398498791,941.976309154,0.230269231292,2
|
||||
0,1005.11269468,900.093680877,0.265031528873,2
|
||||
0,997.160349441,891.331101688,2.19362017313,2
|
||||
0,993.754139031,44.8000165317,1.03868009875,2
|
||||
1,994.831299184,241.959208453,0.667631827024,2
|
||||
0,995.948333283,7.94326917112,0.750490877118,3
|
||||
0,989.733981273,7.52077625436,0.0126335967282,3
|
||||
0,1003.54086516,6.48177510564,1.19441696788,3
|
||||
0,996.56177804,9.71959812613,1.33082465111,3
|
||||
0,1005.61382467,0.234339369309,1.17987797356,3
|
||||
1,980.215758708,6.85554542926,2.63965085259,3
|
||||
1,987.776408872,2.23354609991,0.841885278028,3
|
||||
0,1006.54260396,8.12142049834,2.26639471174,3
|
||||
0,1009.87927639,6.40028519044,0.775155669615,3
|
||||
0,9.95006244393,928.76896718,234.948458244,4
|
||||
1,10.0749152258,255.294574476,62.9728604166,4
|
||||
1,10.1916541988,312.682867085,92.299413677,4
|
||||
0,9.95646724484,742.263188416,53.3310473654,4
|
||||
0,9.86211293222,996.237023866,2.00760301168,4
|
||||
1,9.91801019468,303.971783709,50.3147230679,4
|
||||
0,996.983996934,9.52188222766,1.33588120981,5
|
||||
0,995.704388126,9.49260524915,0.908498516541,5
|
||||
0,987.86480767,0.0870786716821,0.108859297837,5
|
||||
0,1000.99561307,2.85272694575,0.171134518956,5
|
||||
0,1011.05508066,7.55336771768,1.04950084825,5
|
||||
1,985.52199365,0.763305780608,1.7402424375,5
|
||||
0,10.0430321467,813.185427181,4.97728254185,6
|
||||
0,10.0812334228,258.297288417,0.127477670549,6
|
||||
0,9.84210504292,887.205815261,0.991689193955,6
|
||||
1,9.94625332613,0.298622762132,0.147881353231,6
|
||||
0,9.97800659954,727.619819757,0.0718361141866,6
|
||||
1,9.8037938472,957.385549617,0.0618862028941,6
|
||||
0,10.0880634741,185.024638577,1.7028095095,6
|
||||
0,9.98630799154,109.10631473,0.681117359751,6
|
||||
0,9.91671416638,166.248076588,122.538291094,7
|
||||
0,10.1206910464,88.1539468531,141.189859069,7
|
||||
1,10.1767160518,1.02960996847,172.02256237,7
|
||||
0,9.93025147233,391.196641942,58.040338247,7
|
||||
0,9.84850936037,474.63346537,17.5627875397,7
|
||||
1,9.8162731343,61.9199554213,30.6740972851,7
|
||||
0,10.0403482984,987.50416929,73.0472906209,7
|
||||
1,997.019228359,133.294717663,0.0572254083186,8
|
||||
0,973.303999107,1.79080888849,0.100478717048,8
|
||||
0,1008.28808825,342.282350685,0.409806485495,8
|
||||
0,1014.55621524,0.680510407082,0.929530602495,8
|
||||
1,1012.74370325,823.105266455,0.0894693730585,8
|
||||
0,1003.63554038,727.334432075,0.58206275756,8
|
||||
0,10.1560432436,740.35938307,11.6823378533,9
|
||||
0,9.83949099701,512.828227154,138.206666681,9
|
||||
1,10.1837395682,179.287126088,185.479062365,9
|
||||
1,9.9761881495,12.1093388336,9.1264604171,9
|
||||
1,9.77402180766,318.561317743,80.6005221355,9
|
||||
0,1011.15705381,0.215825852155,1.34429667906,10
|
||||
0,1005.60353229,727.202346126,1.47146041005,10
|
||||
1,1013.93702961,58.7312725205,0.421041560754,10
|
||||
0,1004.86813074,757.693204258,0.566055205344,10
|
||||
0,999.996324692,813.12386828,0.864428279513,10
|
||||
0,996.55255931,918.760056995,0.43365051974,10
|
||||
1,1004.1394132,464.371823646,0.312492288321,10
|
||||
0,10.0229017899,7.30178495562,0.118115020017,1
|
||||
0,9.93639621859,9.93102159291,0.0435030004396,1
|
||||
0,10.1301737265,0.00411765220572,2.4165878053,1
|
||||
1,9.87828587087,0.608588414992,0.111262590883,1
|
||||
0,10.1373430048,0.47764012225,0.991553052194,1
|
||||
0,10.0523814718,4.72152505167,0.672978832666,1
|
||||
0,10.0449715742,8.40373928536,0.384457573667,1
|
||||
1,996.398498791,941.976309154,0.230269231292,2
|
||||
0,1005.11269468,900.093680877,0.265031528873,2
|
||||
0,997.160349441,891.331101688,2.19362017313,2
|
||||
0,993.754139031,44.8000165317,1.03868009875,2
|
||||
1,994.831299184,241.959208453,0.667631827024,2
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1,9.86479307916,6.41701315844,55.1365304834,5
|
||||
0,10.0428628516,9.98466447697,0.391632812588,5
|
||||
0,9.94445884566,9.99970945878,260.438436534,5
|
||||
1,9.84641392823,225.78051312,1.00525978847,6
|
||||
1,9.86907690608,26.8971083147,0.577959255991,6
|
||||
0,10.0177314626,0.110585342313,2.30545043031,6
|
||||
0,10.0688190907,412.023866234,1.22421542264,6
|
||||
0,10.1251769646,13.8212202925,0.129171734504,6
|
||||
0,10.0840758802,407.359097187,0.477000870705,6
|
||||
0,10.1007458705,987.183625145,0.149385677415,6
|
||||
0,9.86472656059,169.559640615,0.147221652519,6
|
||||
0,9.94207419238,507.290053755,0.41996207214,6
|
||||
0,9.9671005502,1.62610457716,0.408173666788,6
|
||||
0,1010.57126596,9.06673707562,0.672092284372,7
|
||||
0,1001.6718262,9.53203990055,4.7364050044,7
|
||||
0,995.777341384,4.43847316256,2.07229073634,7
|
||||
0,1002.95701386,5.51711016665,1.24294450546,7
|
||||
0,1016.0988238,0.626468941906,0.105627919134,7
|
||||
0,1013.67571419,0.042315529666,0.717619310322,7
|
||||
1,994.747747892,6.01989364024,0.772910130015,7
|
||||
1,991.654593872,7.35575736952,1.19822091548,7
|
||||
0,1008.47101732,8.28240754909,0.229582481359,7
|
||||
0,1000.81975227,1.52448354056,0.096441660362,7
|
||||
0,10.0900922344,322.656649307,57.8149073088,8
|
||||
1,10.0868337371,2.88652339174,54.8865514572,8
|
||||
0,10.0988984137,979.483832657,52.6809830901,8
|
||||
0,9.97678959238,665.770979738,481.069628909,8
|
||||
0,9.78554312773,257.309358658,47.7324475232,8
|
||||
0,10.0985967566,935.896512941,138.937052808,8
|
||||
0,10.0522252319,876.376299607,6.00373510669,8
|
||||
1,9.88065229501,9.99979825653,0.0674603696149,9
|
||||
0,10.0483244098,0.0653852316381,0.130679349938,9
|
||||
1,9.99685215607,1.76602542774,0.2551321159,9
|
||||
0,9.99750159428,1.01591534436,0.145445506504,9
|
||||
1,9.97380908941,0.940048645571,0.411805696316,9
|
||||
0,9.99977678382,6.91329929641,5.57858201258,9
|
||||
0,978.876096381,933.775364741,0.579170824236,10
|
||||
0,998.381016406,220.940470582,2.01491778565,10
|
||||
0,987.917644594,8.74667873567,0.364006099758,10
|
||||
0,1000.20994892,25.2945450565,3.5684398964,10
|
||||
0,1014.57141264,675.593540733,0.164174055535,10
|
||||
0,998.867283535,765.452750642,0.818425293238,10
|
||||
0,10.2143092481,273.576539531,137.111774354,11
|
||||
0,10.0366658918,842.469052609,2.32134375927,11
|
||||
0,10.1281202091,395.654057342,35.4184893063,11
|
||||
0,10.1443721289,960.058461049,272.887070637,11
|
||||
0,10.1353234784,535.51304462,2.15393842032,11
|
||||
1,10.0451640374,216.733858424,55.6533298016,11
|
||||
1,9.94254592171,44.5985537358,304.614176871,11
|
||||
0,10.1319257181,613.545504487,5.42391587912,11
|
||||
0,1020.63622468,997.476744201,0.509425590461,12
|
||||
0,986.304585519,822.669937965,0.605133561808,12
|
||||
1,1012.66863221,26.7185759069,0.0875458784828,12
|
||||
0,995.387656321,81.8540176995,0.691999430068,12
|
||||
0,1020.6587198,848.826964547,0.540159430526,12
|
||||
1,1003.81573853,379.84350931,0.0083682925194,12
|
||||
0,1021.60921516,641.376951467,1.12339054807,12
|
||||
0,1000.17585041,122.107138713,1.09906375372,12
|
||||
1,987.64802348,5.98448541152,0.124241987204,12
|
||||
1,9.94610136583,346.114985897,0.387708236565,13
|
||||
0,9.96812192337,313.278109696,0.00863026595671,13
|
||||
0,10.0181739194,36.7378924562,2.92179879835,13
|
||||
0,9.89000102695,164.273723971,0.685222591968,13
|
||||
0,10.1555212436,320.451459462,2.01341536261,13
|
||||
0,10.0085727613,999.767117646,0.462294934168,13
|
||||
1,9.93099658724,5.17478203909,0.213855205032,13
|
||||
0,10.0629454957,663.088181857,0.049022351462,13
|
||||
0,10.1109732417,734.904569784,1.6998450094,13
|
||||
0,1006.6015266,505.023453703,1.90870566777,14
|
||||
0,991.865769489,245.437343115,0.475109744256,14
|
||||
0,998.682734072,950.041057232,1.9256314201,14
|
||||
0,1005.02207209,2.9619314197,0.0517146822357,14
|
||||
0,1002.54526214,860.562681899,0.915687092848,14
|
||||
0,1000.38847359,808.416525088,0.209690673808,14
|
||||
1,992.557818382,373.889409453,0.107571728577,14
|
||||
0,1002.07722137,997.329626371,1.06504260496,14
|
||||
0,1000.40504333,949.832139189,0.539159980327,14
|
||||
0,10.1460179902,8.86082969819,135.953842715,15
|
||||
1,9.98529296553,2.87366448495,1.74249892194,15
|
||||
0,9.88942676744,9.4031821056,149.473066381,15
|
||||
1,10.0192953341,1.99685737576,1.79502473397,15
|
||||
0,10.0110654379,8.13112593726,87.7765628103,15
|
||||
0,997.148677047,733.936190093,1.49298494242,16
|
||||
0,1008.70465919,957.121652078,0.217414013634,16
|
||||
1,997.356154278,541.599587807,0.100855972216,16
|
||||
0,999.615897283,943.700501824,0.862874175879,16
|
||||
1,997.36859077,0.200859940848,0.13601892182,16
|
||||
0,10.0423255624,1.73855202168,0.956695338485,17
|
||||
1,9.88440755486,9.9994600678,0.305080529665,17
|
||||
0,10.0891026412,3.28031719474,0.364450973697,17
|
||||
0,9.90078644258,8.77839663617,0.456660574479,17
|
||||
1,9.79380029711,8.77220326156,0.527292005175,17
|
||||
0,9.93613887011,9.76270841268,1.40865693823,17
|
||||
0,10.0009239007,7.29056178263,0.498015866607,17
|
||||
0,9.96603319905,5.12498000925,0.517492532783,17
|
||||
0,10.0923827222,2.76652583955,1.56571226159,17
|
||||
1,10.0983782035,587.788120694,0.031756483687,18
|
||||
1,9.91397225464,994.527496819,3.72092164978,18
|
||||
0,10.1057472738,2.92894440088,0.683506438532,18
|
||||
0,10.1014053354,959.082038017,1.07039624129,18
|
||||
0,10.1433253044,322.515119317,0.51408278993,18
|
||||
1,9.82832510699,637.104433908,0.250272776427,18
|
||||
0,1000.49729075,2.75336888111,0.576634423274,19
|
||||
1,984.90338088,0.0295435794035,1.26273339929,19
|
||||
0,1001.53811442,4.64164410861,0.0293389959504,19
|
||||
1,995.875898395,5.08223403205,0.382330566779,19
|
||||
0,996.405937252,6.26395190757,0.453645816611,19
|
||||
0,10.0165140779,340.126072514,0.220794603312,20
|
||||
0,9.93482824816,951.672000448,0.124406293612,20
|
||||
0,10.1700278554,0.0140985961008,0.252452256311,20
|
||||
0,9.99825079542,950.382643896,0.875382402062,20
|
||||
0,9.87316410028,686.788257829,0.215886999825,20
|
||||
0,10.2893240654,89.3947931451,0.569578232133,20
|
||||
0,9.98689192703,0.430107535413,2.99869831728,20
|
||||
0,10.1365175107,972.279245093,0.0865099386744,20
|
||||
0,9.90744703306,50.810461183,3.00863325197,20
|
||||
|
||||
|
@@ -51,13 +51,13 @@ pom_template = """
|
||||
<dependency>
|
||||
<groupId>com.typesafe.akka</groupId>
|
||||
<artifactId>akka-actor_${{scala.binary.version}}</artifactId>
|
||||
<version>2.7.0</version>
|
||||
<version>2.6.20</version>
|
||||
<scope>compile</scope>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>com.typesafe.akka</groupId>
|
||||
<artifactId>akka-testkit_${{scala.binary.version}}</artifactId>
|
||||
<version>2.7.0</version>
|
||||
<version>2.6.20</version>
|
||||
<scope>test</scope>
|
||||
</dependency>
|
||||
<dependency>
|
||||
|
||||
@@ -34,13 +34,13 @@
|
||||
<dependency>
|
||||
<groupId>com.typesafe.akka</groupId>
|
||||
<artifactId>akka-actor_${scala.binary.version}</artifactId>
|
||||
<version>2.7.0</version>
|
||||
<version>2.6.20</version>
|
||||
<scope>compile</scope>
|
||||
</dependency>
|
||||
<dependency>
|
||||
<groupId>com.typesafe.akka</groupId>
|
||||
<artifactId>akka-testkit_${scala.binary.version}</artifactId>
|
||||
<version>2.7.0</version>
|
||||
<version>2.6.20</version>
|
||||
<scope>test</scope>
|
||||
</dependency>
|
||||
<dependency>
|
||||
|
||||
Reference in New Issue
Block a user