[SPARK-12877][ML] Add train-validation-split to pyspark

## What changes were proposed in this pull request?
The changes proposed were to add train-validation-split to pyspark.ml.tuning.

## How was the this patch tested?
This patch was tested through unit tests located in pyspark/ml/test.py.

This is my original work and I license it to Spark.

Author: JeremyNixon <jnixon2@gmail.com>

Closes #11335 from JeremyNixon/tvs_pyspark.

1 files changed, 52 insertions, 1 deletions

diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index 5fcfa9e61f..8182fcfb4e 100644
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -45,7 +45,7 @@ from pyspark.ml.feature import *
 from pyspark.ml.param import Param, Params
 from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed
 from pyspark.ml.regression import LinearRegression
-from pyspark.ml.tuning import ParamGridBuilder, CrossValidator, CrossValidatorModel
+from pyspark.ml.tuning import *
 from pyspark.ml.util import keyword_only
 from pyspark.mllib.linalg import DenseVector
 from pyspark.sql import DataFrame, SQLContext, Row
@@ -423,6 +423,57 @@ class CrossValidatorTests(PySparkTestCase):
         self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
 
 
+class TrainValidationSplitTests(PySparkTestCase):
+
+    def test_fit_minimize_metric(self):
+        sqlContext = SQLContext(self.sc)
+        dataset = sqlContext.createDataFrame([
+            (10, 10.0),
+            (50, 50.0),
+            (100, 100.0),
+            (500, 500.0)] * 10,
+            ["feature", "label"])
+
+        iee = InducedErrorEstimator()
+        evaluator = RegressionEvaluator(metricName="rmse")
+
+        grid = (ParamGridBuilder()
+                .addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
+                .build())
+        tvs = TrainValidationSplit(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
+        tvsModel = tvs.fit(dataset)
+        bestModel = tvsModel.bestModel
+        bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
+
+        self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
+                         "Best model should have zero induced error")
+        self.assertEqual(0.0, bestModelMetric, "Best model has RMSE of 0")
+
+    def test_fit_maximize_metric(self):
+        sqlContext = SQLContext(self.sc)
+        dataset = sqlContext.createDataFrame([
+            (10, 10.0),
+            (50, 50.0),
+            (100, 100.0),
+            (500, 500.0)] * 10,
+            ["feature", "label"])
+
+        iee = InducedErrorEstimator()
+        evaluator = RegressionEvaluator(metricName="r2")
+
+        grid = (ParamGridBuilder()
+                .addGrid(iee.inducedError, [100.0, 0.0, 10000.0])
+                .build())
+        tvs = TrainValidationSplit(estimator=iee, estimatorParamMaps=grid, evaluator=evaluator)
+        tvsModel = tvs.fit(dataset)
+        bestModel = tvsModel.bestModel
+        bestModelMetric = evaluator.evaluate(bestModel.transform(dataset))
+
+        self.assertEqual(0.0, bestModel.getOrDefault('inducedError'),
+                         "Best model should have zero induced error")
+        self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
+
+
 class PersistenceTest(PySparkTestCase):
 
     def test_linear_regression(self):