[SPARK-13786][ML][PYSPARK] Add save/load for pyspark.ml.tuning

## What changes were proposed in this pull request?

https://issues.apache.org/jira/browse/SPARK-13786

Add save/load for Python CrossValidator/Model and TrainValidationSplit/Model.

## How was this patch tested?

Test with Python doctest.

Author: Xusen Yin <yinxusen@gmail.com>

Closes #12020 from yinxusen/SPARK-13786.

1 files changed, 54 insertions, 2 deletions

diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index f6159b2c95..e3f873e3a7 100644
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -44,7 +44,7 @@ import numpy as np
 from pyspark.ml import Estimator, Model, Pipeline, PipelineModel, Transformer
 from pyspark.ml.classification import LogisticRegression, DecisionTreeClassifier
 from pyspark.ml.clustering import KMeans
-from pyspark.ml.evaluation import RegressionEvaluator
+from pyspark.ml.evaluation import BinaryClassificationEvaluator, RegressionEvaluator
 from pyspark.ml.feature import *
 from pyspark.ml.param import Param, Params, TypeConverters
 from pyspark.ml.param.shared import HasMaxIter, HasInputCol, HasSeed
@@ -53,7 +53,7 @@ from pyspark.ml.tuning import *
 from pyspark.ml.util import keyword_only
 from pyspark.ml.util import MLWritable, MLWriter
 from pyspark.ml.wrapper import JavaWrapper
-from pyspark.mllib.linalg import DenseVector, SparseVector
+from pyspark.mllib.linalg import Vectors, DenseVector, SparseVector
 from pyspark.sql import DataFrame, SQLContext, Row
 from pyspark.sql.functions import rand
 from pyspark.tests import ReusedPySparkTestCase as PySparkTestCase
@@ -479,6 +479,32 @@ class CrossValidatorTests(PySparkTestCase):
                          "Best model should have zero induced error")
         self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
 
+    def test_save_load(self):
+        temp_path = tempfile.mkdtemp()
+        sqlContext = SQLContext(self.sc)
+        dataset = sqlContext.createDataFrame(
+            [(Vectors.dense([0.0]), 0.0),
+             (Vectors.dense([0.4]), 1.0),
+             (Vectors.dense([0.5]), 0.0),
+             (Vectors.dense([0.6]), 1.0),
+             (Vectors.dense([1.0]), 1.0)] * 10,
+            ["features", "label"])
+        lr = LogisticRegression()
+        grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
+        evaluator = BinaryClassificationEvaluator()
+        cv = CrossValidator(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
+        cvModel = cv.fit(dataset)
+        cvPath = temp_path + "/cv"
+        cv.save(cvPath)
+        loadedCV = CrossValidator.load(cvPath)
+        self.assertEqual(loadedCV.getEstimator().uid, cv.getEstimator().uid)
+        self.assertEqual(loadedCV.getEvaluator().uid, cv.getEvaluator().uid)
+        self.assertEqual(loadedCV.getEstimatorParamMaps(), cv.getEstimatorParamMaps())
+        cvModelPath = temp_path + "/cvModel"
+        cvModel.save(cvModelPath)
+        loadedModel = CrossValidatorModel.load(cvModelPath)
+        self.assertEqual(loadedModel.bestModel.uid, cvModel.bestModel.uid)
+
 
 class TrainValidationSplitTests(PySparkTestCase):
 
@@ -530,6 +556,32 @@ class TrainValidationSplitTests(PySparkTestCase):
                          "Best model should have zero induced error")
         self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
 
+    def test_save_load(self):
+        temp_path = tempfile.mkdtemp()
+        sqlContext = SQLContext(self.sc)
+        dataset = sqlContext.createDataFrame(
+            [(Vectors.dense([0.0]), 0.0),
+             (Vectors.dense([0.4]), 1.0),
+             (Vectors.dense([0.5]), 0.0),
+             (Vectors.dense([0.6]), 1.0),
+             (Vectors.dense([1.0]), 1.0)] * 10,
+            ["features", "label"])
+        lr = LogisticRegression()
+        grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
+        evaluator = BinaryClassificationEvaluator()
+        tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator)
+        tvsModel = tvs.fit(dataset)
+        tvsPath = temp_path + "/tvs"
+        tvs.save(tvsPath)
+        loadedTvs = TrainValidationSplit.load(tvsPath)
+        self.assertEqual(loadedTvs.getEstimator().uid, tvs.getEstimator().uid)
+        self.assertEqual(loadedTvs.getEvaluator().uid, tvs.getEvaluator().uid)
+        self.assertEqual(loadedTvs.getEstimatorParamMaps(), tvs.getEstimatorParamMaps())
+        tvsModelPath = temp_path + "/tvsModel"
+        tvsModel.save(tvsModelPath)
+        loadedModel = TrainValidationSplitModel.load(tvsModelPath)
+        self.assertEqual(loadedModel.bestModel.uid, tvsModel.bestModel.uid)
+
 
 class PersistenceTest(PySparkTestCase):