[SPARK-11893] Model export/import for spark.ml: TrainValidationSplit

https://issues.apache.org/jira/browse/SPARK-11893

jkbradley In order to share read/write with `TrainValidationSplit`, I move the `SharedReadWrite` out of `CrossValidator` into a new trait `SharedReadWrite` in the tunning package.

To reduce the repeated tests, I move the complex tests from `CrossValidatorSuite` to `SharedReadWriteSuite`, and create a fake validator called `MyValidator` to test the shared code.

With `SharedReadWrite`, potential newly added `Validator` can share the read/write common part, and only need to implement their extra params save/load.

Author: Xusen Yin <yinxusen@gmail.com>
Author: Joseph K. Bradley <joseph@databricks.com>

Closes #9971 from yinxusen/SPARK-11893.

1 files changed, 43 insertions, 2 deletions

diff --git a/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala b/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
index cf8dcefebc..7cf7b3e087 100644
--- a/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
+++ b/mllib/src/test/scala/org/apache/spark/ml/tuning/TrainValidationSplitSuite.scala
@@ -19,17 +19,20 @@ package org.apache.spark.ml.tuning
 
 import org.apache.spark.SparkFunSuite
 import org.apache.spark.ml.{Estimator, Model}
-import org.apache.spark.ml.classification.LogisticRegression
+import org.apache.spark.ml.classification.{LogisticRegression, LogisticRegressionModel}
 import org.apache.spark.ml.evaluation.{BinaryClassificationEvaluator, Evaluator, RegressionEvaluator}
 import org.apache.spark.ml.param.ParamMap
 import org.apache.spark.ml.param.shared.HasInputCol
 import org.apache.spark.ml.regression.LinearRegression
+import org.apache.spark.ml.util.DefaultReadWriteTest
 import org.apache.spark.mllib.classification.LogisticRegressionSuite.generateLogisticInput
+import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.util.{LinearDataGenerator, MLlibTestSparkContext}
 import org.apache.spark.sql.DataFrame
 import org.apache.spark.sql.types.StructType
 
-class TrainValidationSplitSuite extends SparkFunSuite with MLlibTestSparkContext {
+class TrainValidationSplitSuite
+  extends SparkFunSuite with MLlibTestSparkContext with DefaultReadWriteTest {
   test("train validation with logistic regression") {
     val dataset = sqlContext.createDataFrame(
       sc.parallelize(generateLogisticInput(1.0, 1.0, 100, 42), 2))
@@ -105,6 +108,44 @@ class TrainValidationSplitSuite extends SparkFunSuite with MLlibTestSparkContext
       cv.transformSchema(new StructType())
     }
   }
+
+  test("read/write: TrainValidationSplit") {
+    val lr = new LogisticRegression().setMaxIter(3)
+    val evaluator = new BinaryClassificationEvaluator()
+    val paramMaps = new ParamGridBuilder()
+        .addGrid(lr.regParam, Array(0.1, 0.2))
+        .build()
+    val tvs = new TrainValidationSplit()
+      .setEstimator(lr)
+      .setEvaluator(evaluator)
+      .setTrainRatio(0.5)
+      .setEstimatorParamMaps(paramMaps)
+
+    val tvs2 = testDefaultReadWrite(tvs, testParams = false)
+
+    assert(tvs.getTrainRatio === tvs2.getTrainRatio)
+  }
+
+  test("read/write: TrainValidationSplitModel") {
+    val lr = new LogisticRegression()
+      .setThreshold(0.6)
+    val lrModel = new LogisticRegressionModel(lr.uid, Vectors.dense(1.0, 2.0), 1.2)
+      .setThreshold(0.6)
+    val evaluator = new BinaryClassificationEvaluator()
+    val paramMaps = new ParamGridBuilder()
+        .addGrid(lr.regParam, Array(0.1, 0.2))
+        .build()
+    val tvs = new TrainValidationSplitModel("cvUid", lrModel, Array(0.3, 0.6))
+    tvs.set(tvs.estimator, lr)
+      .set(tvs.evaluator, evaluator)
+      .set(tvs.trainRatio, 0.5)
+      .set(tvs.estimatorParamMaps, paramMaps)
+
+    val tvs2 = testDefaultReadWrite(tvs, testParams = false)
+
+    assert(tvs.getTrainRatio === tvs2.getTrainRatio)
+    assert(tvs.validationMetrics === tvs2.validationMetrics)
+  }
 }
 
 object TrainValidationSplitSuite {