move feature size validating tests into a separate suite

Author: wbo4958

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/FeatureSizeValidatingSuite.scala

@@ -0,0 +1,71 @@
+/*
+ Copyright (c) 2014 by Contributors
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+ */
+
+package ml.dmlc.xgboost4j.scala.spark
+
+import ml.dmlc.xgboost4j.java.XGBoostError
+import org.apache.spark.Partitioner
+import org.apache.spark.ml.feature.VectorAssembler
+import org.apache.spark.sql.SparkSession
+import org.scalatest.FunSuite
+
+import scala.util.Random
+
+class FeatureSizeValidatingSuite extends FunSuite with PerTest {
+
+  test("transform throwing exception if feature size of dataset is different with model's") {
+    val modelPath = getClass.getResource("/model/0.82/model").getPath
+    val model = XGBoostClassificationModel.read.load(modelPath)
+    val r = new Random(0)
+    // 0.82/model was trained with 251 features. and transform will throw exception
+    // if feature size of data is not equal to 251
+    val df = ss.createDataFrame(Seq.fill(100)(r.nextInt(2)).map(i => (i, i))).
+      toDF("feature", "label")
+    val assembler = new VectorAssembler()
+      .setInputCols(df.columns.filter(!_.contains("label")))
+      .setOutputCol("features")
+    val thrown = intercept[Exception] {
+      model.transform(assembler.transform(df)).show()
+    }
+    assert(thrown.getMessage.contains(
+      "Number of columns does not match number of features in booster"))
+  }
+
+  test("train throwing exception if feature size of dataset is different on distributed train") {
+    val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
+      "objective" -> "binary:logistic",
+      "num_round" -> 5, "num_workers" -> 2, "use_external_memory" -> true, "missing" -> 0)
+    import DataUtils._
+    val sparkSession = SparkSession.builder().getOrCreate()
+    import sparkSession.implicits._
+    val repartitioned = sc.parallelize(Synthetic.trainWithDiffFeatureSize, 2)
+      .map(lp => (lp.label, lp)).partitionBy(
+      new Partitioner {
+        override def numPartitions: Int = 2
+
+        override def getPartition(key: Any): Int = key.asInstanceOf[Float].toInt
+      }
+    ).map(_._2).zipWithIndex().map {
+      case (lp, id) =>
+        (id, lp.label, lp.features)
+    }.toDF("id", "label", "features")
+    val xgb = new XGBoostClassifier(paramMap)
+    intercept[XGBoostError] {
+      xgb.fit(repartitioned)
+    }
+  }
+
+}

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostGeneralSuite.scala

@@ -16,19 +16,12 @@
 
 package ml.dmlc.xgboost4j.scala.spark
 
-import java.nio.file.Files
-
-import ml.dmlc.xgboost4j.java.XGBoostError
-
 import scala.util.Random
 import ml.dmlc.xgboost4j.{LabeledPoint => XGBLabeledPoint}
 import ml.dmlc.xgboost4j.scala.DMatrix
-import ml.dmlc.xgboost4j.scala.{XGBoost => SXGBoost, _}
-import org.apache.hadoop.fs.{FileSystem, Path}
-import org.apache.spark.{Partitioner, TaskContext}
+import org.apache.spark.{TaskContext}
 import org.scalatest.FunSuite
 import org.apache.spark.ml.feature.VectorAssembler
-import org.apache.spark.sql.SparkSession
 import org.apache.spark.sql.functions.lit
 
 class XGBoostGeneralSuite extends FunSuite with TmpFolderPerSuite with PerTest {
@@ -374,45 +367,4 @@ class XGBoostGeneralSuite extends FunSuite with TmpFolderPerSuite with PerTest {
     df2.collect()
   }
 
-  test("transform throwing exception when feature size of dataset is different with model's") {
-    val modelPath = getClass.getResource("/model/0.82/model").getPath
-    val model = XGBoostClassificationModel.read.load(modelPath)
-    val r = new Random(0)
-    // 0.82/model was trained with 251 features. and transform will throw exception
-    // if feature size of data is not equal to 251
-    val df = ss.createDataFrame(Seq.fill(100)(r.nextInt(2)).map(i => (i, i))).
-      toDF("feature", "label")
-    val assembler = new VectorAssembler()
-      .setInputCols(df.columns.filter(!_.contains("label")))
-      .setOutputCol("features")
-    val thrown = intercept[Exception] {
-      model.transform(assembler.transform(df)).show()
-    }
-    assert(thrown.getMessage.contains(
-      "Number of columns does not match number of features in booster"))
-  }
-
-  test("train throwing exception when feature size of dataset is different on distributed train") {
-    val paramMap = Map("eta" -> "1", "max_depth" -> "6", "silent" -> "1",
-      "objective" -> "binary:logistic",
-      "num_round" -> 5, "num_workers" -> 2, "use_external_memory" -> true, "missing" -> 0)
-    import DataUtils._
-    val sparkSession = SparkSession.builder().getOrCreate()
-    import sparkSession.implicits._
-    val repartitioned = sc.parallelize(Synthetic.trainWithDiffFeatureSize, 2)
-      .map(lp => (lp.label, lp)).partitionBy(
-      new Partitioner {
-        override def numPartitions: Int = 2
-
-        override def getPartition(key: Any): Int = key.asInstanceOf[Float].toInt
-      }
-    ).map(_._2).zipWithIndex().map {
-      case (lp, id) =>
-        (id, lp.label, lp.features)
-    }.toDF("id", "label", "features")
-    val xgb = new XGBoostClassifier(paramMap)
-    intercept[XGBoostError] {
-      xgb.fit(repartitioned)
-    }
-  }
 }