pass test:compile

Author: mengxr

mllib/src/test/java/org/apache/spark/ml/classification/JavaLogisticRegressionSuite.java

@@ -84,7 +84,7 @@ public void logisticRegressionWithSetters() {
       .setThreshold(0.6)
       .setProbabilityCol("myProbability");
     LogisticRegressionModel model = lr.fit(dataset);
-    LogisticRegression parent = model.parent();
+    LogisticRegression parent = (LogisticRegression) model.parent();
     assert(parent.getMaxIter() == 10);
     assert(parent.getRegParam() == 1.0);
     assert(parent.getThreshold() == 0.6);
@@ -110,7 +110,7 @@ public void logisticRegressionWithSetters() {
     // Call fit() with new params, and check as many params as we can.
     LogisticRegressionModel model2 = lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1),
         lr.threshold().w(0.4), lr.probabilityCol().w("theProb"));
-    LogisticRegression parent2 = model2.parent();
+    LogisticRegression parent2 = (LogisticRegression) model2.parent();
     assert(parent2.getMaxIter() == 5);
     assert(parent2.getRegParam() == 0.1);
     assert(parent2.getThreshold() == 0.4);

mllib/src/test/java/org/apache/spark/ml/param/JavaTestParams.java

@@ -21,11 +21,30 @@
 
 import com.google.common.collect.Lists;
 
+import org.apache.spark.ml.util.Identifiable$;
+
 /**
  * A subclass of Params for testing.
  */
 public class JavaTestParams extends JavaParams {
 
+  public JavaTestParams() {
+    this._uid = Identifiable$.MODULE$.randomUID("javaTestParams");
+    _init();
+  }
+
+  public JavaTestParams(String uid) {
+    this._uid = uid;
+    _init();
+  }
+
+  private String _uid;
+
+  @Override
+  public String uid() {
+    return _uid;
+  }
+
   public IntParam myIntParam;
 
   public int getMyIntParam() { return (Integer)getOrDefault(myIntParam); }
@@ -50,7 +69,7 @@ public JavaTestParams setMyStringParam(String value) {
     set(myStringParam, value); return this;
   }
 
-  public JavaTestParams() {
+  private void _init() {
     myIntParam = new IntParam(this, "myIntParam", "this is an int param", ParamValidators.gt(0));
     myDoubleParam = new DoubleParam(this, "myDoubleParam", "this is a double param",
       ParamValidators.inRange(0.0, 1.0));

mllib/src/test/java/org/apache/spark/ml/regression/JavaLinearRegressionSuite.java

@@ -77,14 +77,14 @@ public void linearRegressionWithSetters() {
         .setMaxIter(10)
         .setRegParam(1.0);
     LinearRegressionModel model = lr.fit(dataset);
-    LinearRegression parent = model.parent();
+    LinearRegression parent = (LinearRegression) model.parent();
     assertEquals(10, parent.getMaxIter());
     assertEquals(1.0, parent.getRegParam(), 0.0);
 
     // Call fit() with new params, and check as many params as we can.
     LinearRegressionModel model2 =
         lr.fit(dataset, lr.maxIter().w(5), lr.regParam().w(0.1), lr.predictionCol().w("thePred"));
-    LinearRegression parent2 = model2.parent();
+    LinearRegression parent2 = (LinearRegression) model2.parent();
     assertEquals(5, parent2.getMaxIter());
     assertEquals(0.1, parent2.getRegParam(), 0.0);
     assertEquals("thePred", model2.getPredictionCol());

mllib/src/test/java/org/apache/spark/ml/util/IdentifiableSuite.scala

@@ -21,30 +21,20 @@ import org.scalatest.FunSuite
 
 class IdentifiableSuite extends FunSuite {
 
-  import IdentifiableSuite._
+  import IdentifiableSuite.Test
 
   test("Identifiable") {
-    val test0 = new Test0
-    assert(test0.uid.startsWith(classOf[Test0].getSimpleName + "_"))
-
-    val test1 = new Test1
-    assert(test1.uid.startsWith("test_"),
-      "simpleClassName should be the first part of the generated UID.")
-    val copied = test1.copy
-    assert(copied.uid === test1.uid, "Copied objects should be able to use the same UID.")
+    val test0 = new Test("test_0")
+    assert(test0.uid === "test_0")
+
+    val test1 = new Test
+    assert(test1.uid.startsWith("test_"))
   }
 }
 
 object IdentifiableSuite {
 
-  class Test0 extends Identifiable
-
-  class Test1 extends Identifiable {
-
-    override def simpleClassName: String = "test"
-
-    def copy: Test1 = {
-      new Test1().setUID(uid)
-    }
+  class Test(override val uid: String) extends Identifiable {
+    def this() = this(Identifiable.randomUID("test"))
   }
 }

mllib/src/test/scala/org/apache/spark/ml/classification/DecisionTreeClassifierSuite.scala

@@ -268,7 +268,7 @@ private[ml] object DecisionTreeClassifierSuite extends FunSuite {
     val newTree = dt.fit(newData)
     // Use parent, fittingParamMap from newTree since these are not checked anyways.
     val oldTreeAsNew = DecisionTreeClassificationModel.fromOld(
-      oldTree, newTree.parent, categoricalFeatures)
+      oldTree, newTree.parent.asInstanceOf[DecisionTreeClassifier], categoricalFeatures)
     TreeTests.checkEqual(oldTreeAsNew, newTree)
   }
 }

mllib/src/test/scala/org/apache/spark/ml/classification/GBTClassifierSuite.scala

@@ -130,7 +130,7 @@ private object GBTClassifierSuite {
     val newModel = gbt.fit(newData)
     // Use parent, fittingParamMap from newTree since these are not checked anyways.
     val oldModelAsNew = GBTClassificationModel.fromOld(
-      oldModel, newModel.parent, categoricalFeatures)
+      oldModel, newModel.parent.asInstanceOf[GBTClassifier], categoricalFeatures)
     TreeTests.checkEqual(oldModelAsNew, newModel)
   }
 }

mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala

@@ -74,7 +74,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
       .setThreshold(0.6)
       .setProbabilityCol("myProbability")
     val model = lr.fit(dataset)
-    val parent = model.parent
+    val parent = model.parent.asInstanceOf[LogisticRegression]
     assert(parent.getMaxIter === 10)
     assert(parent.getRegParam === 1.0)
     assert(parent.getThreshold === 0.6)
@@ -100,7 +100,7 @@ class LogisticRegressionSuite extends FunSuite with MLlibTestSparkContext {
     // Call fit() with new params, and check as many params as we can.
     val model2 = lr.fit(dataset, lr.maxIter -> 5, lr.regParam -> 0.1, lr.threshold -> 0.4,
       lr.probabilityCol -> "theProb")
-    val parent2 = model2.parent
+    val parent2 = model2.parent.asInstanceOf[LogisticRegression]
     assert(parent2.getMaxIter === 5)
     assert(parent2.getRegParam === 0.1)
     assert(parent2.getThreshold === 0.4)

mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala

@@ -160,7 +160,7 @@ private object RandomForestClassifierSuite {
     val newModel = rf.fit(newData)
     // Use parent, fittingParamMap from newTree since these are not checked anyways.
     val oldModelAsNew = RandomForestClassificationModel.fromOld(
-      oldModel, newModel.parent, categoricalFeatures)
+      oldModel, newModel.parent.asInstanceOf[RandomForestClassifier], categoricalFeatures)
     TreeTests.checkEqual(oldModelAsNew, newModel)
   }
 }

mllib/src/test/scala/org/apache/spark/ml/param/TestParams.scala

@@ -18,9 +18,12 @@
 package org.apache.spark.ml.param
 
 import org.apache.spark.ml.param.shared.{HasInputCol, HasMaxIter}
+import org.apache.spark.ml.util.Identifiable
 
 /** A subclass of Params for testing. */
-class TestParams extends Params with HasMaxIter with HasInputCol {
+class TestParams(override val uid: String) extends Params with HasMaxIter with HasInputCol {
+
+  def this() = this(Identifiable.randomUID("testParams"))
 
   def setMaxIter(value: Int): this.type = { set(maxIter, value); this }
 

mllib/src/test/scala/org/apache/spark/ml/regression/DecisionTreeRegressorSuite.scala

@@ -85,7 +85,7 @@ private[ml] object DecisionTreeRegressorSuite extends FunSuite {
     val newTree = dt.fit(newData)
     // Use parent, fittingParamMap from newTree since these are not checked anyways.
     val oldTreeAsNew = DecisionTreeRegressionModel.fromOld(
-      oldTree, newTree.parent, categoricalFeatures)
+      oldTree, newTree.parent.asInstanceOf[DecisionTreeRegressor], categoricalFeatures)
     TreeTests.checkEqual(oldTreeAsNew, newTree)
   }
 }