[SPARK-13786][ML][PYTHON] Removed save/load for python tuning

## What changes were proposed in this pull request?

Per discussion on [https://github.com/apache/spark/pull/12604], this removes ML persistence for Python tuning (TrainValidationSplit, CrossValidator, and their Models) since they do not handle nesting easily.  This support should be re-designed and added in the next release.

## How was this patch tested?

Removed unit test elements saving and loading the tuning algorithms, but kept tests to save and load their bestModel fields.

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #12782 from jkbradley/remove-python-tuning-saveload.

2 files changed, 21 insertions, 262 deletions

diff --git a/python/pyspark/ml/tests.py b/python/pyspark/ml/tests.py
index e7d4c0af45..faca148218 100644
--- a/python/pyspark/ml/tests.py
+++ b/python/pyspark/ml/tests.py
@@ -44,8 +44,7 @@ import numpy as np
 
 from pyspark import keyword_only
 from pyspark.ml import Estimator, Model, Pipeline, PipelineModel, Transformer
-from pyspark.ml.classification import (
-    LogisticRegression, DecisionTreeClassifier, OneVsRest, OneVsRestModel)
+from pyspark.ml.classification import *
 from pyspark.ml.clustering import *
 from pyspark.ml.evaluation import BinaryClassificationEvaluator, RegressionEvaluator
 from pyspark.ml.feature import *
@@ -540,6 +539,8 @@ class CrossValidatorTests(PySparkTestCase):
         self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
 
     def test_save_load(self):
+        # This tests saving and loading the trained model only.
+        # Save/load for CrossValidator will be added later: SPARK-13786
         temp_path = tempfile.mkdtemp()
         sqlContext = SQLContext(self.sc)
         dataset = sqlContext.createDataFrame(
@@ -554,18 +555,13 @@ class CrossValidatorTests(PySparkTestCase):
         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())
+        lrModel = cvModel.bestModel
+
         cvModelPath = temp_path + "/cvModel"
-        cvModel.save(cvModelPath)
-        loadedModel = CrossValidatorModel.load(cvModelPath)
-        self.assertEqual(loadedModel.bestModel.uid, cvModel.bestModel.uid)
-        for index in range(len(loadedModel.avgMetrics)):
-            self.assertTrue(abs(loadedModel.avgMetrics[index] - cvModel.avgMetrics[index]) < 0.0001)
+        lrModel.save(cvModelPath)
+        loadedLrModel = LogisticRegressionModel.load(cvModelPath)
+        self.assertEqual(loadedLrModel.uid, lrModel.uid)
+        self.assertEqual(loadedLrModel.intercept, lrModel.intercept)
 
 
 class TrainValidationSplitTests(PySparkTestCase):
@@ -619,6 +615,8 @@ class TrainValidationSplitTests(PySparkTestCase):
         self.assertEqual(1.0, bestModelMetric, "Best model has R-squared of 1")
 
     def test_save_load(self):
+        # This tests saving and loading the trained model only.
+        # Save/load for TrainValidationSplit will be added later: SPARK-13786
         temp_path = tempfile.mkdtemp()
         sqlContext = SQLContext(self.sc)
         dataset = sqlContext.createDataFrame(
@@ -633,16 +631,13 @@ class TrainValidationSplitTests(PySparkTestCase):
         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())
+        lrModel = tvsModel.bestModel
+
         tvsModelPath = temp_path + "/tvsModel"
-        tvsModel.save(tvsModelPath)
-        loadedModel = TrainValidationSplitModel.load(tvsModelPath)
-        self.assertEqual(loadedModel.bestModel.uid, tvsModel.bestModel.uid)
+        lrModel.save(tvsModelPath)
+        loadedLrModel = LogisticRegressionModel.load(tvsModelPath)
+        self.assertEqual(loadedLrModel.uid, lrModel.uid)
+        self.assertEqual(loadedLrModel.intercept, lrModel.intercept)
 
 
 class PersistenceTest(PySparkTestCase):
diff --git a/python/pyspark/ml/tuning.py b/python/pyspark/ml/tuning.py
index 22f9680cab..eb1f029ebb 100644
--- a/python/pyspark/ml/tuning.py
+++ b/python/pyspark/ml/tuning.py
@@ -23,7 +23,6 @@ from pyspark import since, keyword_only
 from pyspark.ml import Estimator, Model
 from pyspark.ml.param import Params, Param, TypeConverters
 from pyspark.ml.param.shared import HasSeed
-from pyspark.ml.util import JavaMLWriter, JavaMLReader, MLReadable, MLWritable
 from pyspark.ml.wrapper import JavaParams
 from pyspark.sql.functions import rand
 from pyspark.mllib.common import inherit_doc, _py2java
@@ -141,37 +140,8 @@ class ValidatorParams(HasSeed):
         """
         return self.getOrDefault(self.evaluator)
 
-    @classmethod
-    def _from_java_impl(cls, java_stage):
-        """
-        Return Python estimator, estimatorParamMaps, and evaluator from a Java ValidatorParams.
-        """
-
-        # Load information from java_stage to the instance.
-        estimator = JavaParams._from_java(java_stage.getEstimator())
-        evaluator = JavaParams._from_java(java_stage.getEvaluator())
-        epms = [estimator._transfer_param_map_from_java(epm)
-                for epm in java_stage.getEstimatorParamMaps()]
-        return estimator, epms, evaluator
-
-    def _to_java_impl(self):
-        """
-        Return Java estimator, estimatorParamMaps, and evaluator from this Python instance.
-        """
-
-        gateway = SparkContext._gateway
-        cls = SparkContext._jvm.org.apache.spark.ml.param.ParamMap
-
-        java_epms = gateway.new_array(cls, len(self.getEstimatorParamMaps()))
-        for idx, epm in enumerate(self.getEstimatorParamMaps()):
-            java_epms[idx] = self.getEstimator()._transfer_param_map_to_java(epm)
-
-        java_estimator = self.getEstimator()._to_java()
-        java_evaluator = self.getEvaluator()._to_java()
-        return java_estimator, java_epms, java_evaluator
-
 
-class CrossValidator(Estimator, ValidatorParams, MLReadable, MLWritable):
+class CrossValidator(Estimator, ValidatorParams):
     """
     K-fold cross validation.
 
@@ -288,58 +258,8 @@ class CrossValidator(Estimator, ValidatorParams, MLReadable, MLWritable):
             newCV.setEvaluator(self.getEvaluator().copy(extra))
         return newCV
 
-    @since("2.0.0")
-    def write(self):
-        """Returns an MLWriter instance for this ML instance."""
-        return JavaMLWriter(self)
-
-    @since("2.0.0")
-    def save(self, path):
-        """Save this ML instance to the given path, a shortcut of `write().save(path)`."""
-        self.write().save(path)
-
-    @classmethod
-    @since("2.0.0")
-    def read(cls):
-        """Returns an MLReader instance for this class."""
-        return JavaMLReader(cls)
 
-    @classmethod
-    def _from_java(cls, java_stage):
-        """
-        Given a Java CrossValidator, create and return a Python wrapper of it.
-        Used for ML persistence.
-        """
-
-        estimator, epms, evaluator = super(CrossValidator, cls)._from_java_impl(java_stage)
-        numFolds = java_stage.getNumFolds()
-        seed = java_stage.getSeed()
-        # Create a new instance of this stage.
-        py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
-                       numFolds=numFolds, seed=seed)
-        py_stage._resetUid(java_stage.uid())
-        return py_stage
-
-    def _to_java(self):
-        """
-        Transfer this instance to a Java CrossValidator. Used for ML persistence.
-
-        :return: Java object equivalent to this instance.
-        """
-
-        estimator, epms, evaluator = super(CrossValidator, self)._to_java_impl()
-
-        _java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.CrossValidator", self.uid)
-        _java_obj.setEstimatorParamMaps(epms)
-        _java_obj.setEvaluator(evaluator)
-        _java_obj.setEstimator(estimator)
-        _java_obj.setSeed(self.getSeed())
-        _java_obj.setNumFolds(self.getNumFolds())
-
-        return _java_obj
-
-
-class CrossValidatorModel(Model, ValidatorParams, MLReadable, MLWritable):
+class CrossValidatorModel(Model, ValidatorParams):
     """
     Model from k-fold cross validation.
 
@@ -372,59 +292,8 @@ class CrossValidatorModel(Model, ValidatorParams, MLReadable, MLWritable):
         avgMetrics = self.avgMetrics
         return CrossValidatorModel(bestModel, avgMetrics)
 
-    @since("2.0.0")
-    def write(self):
-        """Returns an MLWriter instance for this ML instance."""
-        return JavaMLWriter(self)
-
-    @since("2.0.0")
-    def save(self, path):
-        """Save this ML instance to the given path, a shortcut of `write().save(path)`."""
-        self.write().save(path)
 
-    @classmethod
-    @since("2.0.0")
-    def read(cls):
-        """Returns an MLReader instance for this class."""
-        return JavaMLReader(cls)
-
-    @classmethod
-    def _from_java(cls, java_stage):
-        """
-        Given a Java CrossValidatorModel, create and return a Python wrapper of it.
-        Used for ML persistence.
-        """
-
-        # Load information from java_stage to the instance.
-        bestModel = JavaParams._from_java(java_stage.bestModel())
-        avgMetrics = list(java_stage.avgMetrics())
-        estimator, epms, evaluator = super(CrossValidatorModel, cls)._from_java_impl(java_stage)
-        # Create a new instance of this stage.
-        py_stage = cls(bestModel=bestModel, avgMetrics=avgMetrics)\
-            .setEstimator(estimator).setEstimatorParamMaps(epms).setEvaluator(evaluator)
-        py_stage._resetUid(java_stage.uid())
-        return py_stage
-
-    def _to_java(self):
-        """
-        Transfer this instance to a Java CrossValidatorModel. Used for ML persistence.
-
-        :return: Java object equivalent to this instance.
-        """
-
-        _java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.CrossValidatorModel",
-                                             self.uid,
-                                             self.bestModel._to_java(),
-                                             self.avgMetrics)
-        estimator, epms, evaluator = super(CrossValidatorModel, self)._to_java_impl()
-
-        _java_obj.set("evaluator", evaluator)
-        _java_obj.set("estimator", estimator)
-        _java_obj.set("estimatorParamMaps", epms)
-        return _java_obj
-
-
-class TrainValidationSplit(Estimator, ValidatorParams, MLReadable, MLWritable):
+class TrainValidationSplit(Estimator, ValidatorParams):
     """
     Train-Validation-Split.
 
@@ -535,59 +404,8 @@ class TrainValidationSplit(Estimator, ValidatorParams, MLReadable, MLWritable):
             newTVS.setEvaluator(self.getEvaluator().copy(extra))
         return newTVS
 
-    @since("2.0.0")
-    def write(self):
-        """Returns an MLWriter instance for this ML instance."""
-        return JavaMLWriter(self)
-
-    @since("2.0.0")
-    def save(self, path):
-        """Save this ML instance to the given path, a shortcut of `write().save(path)`."""
-        self.write().save(path)
-
-    @classmethod
-    @since("2.0.0")
-    def read(cls):
-        """Returns an MLReader instance for this class."""
-        return JavaMLReader(cls)
-
-    @classmethod
-    def _from_java(cls, java_stage):
-        """
-        Given a Java TrainValidationSplit, create and return a Python wrapper of it.
-        Used for ML persistence.
-        """
-
-        estimator, epms, evaluator = super(TrainValidationSplit, cls)._from_java_impl(java_stage)
-        trainRatio = java_stage.getTrainRatio()
-        seed = java_stage.getSeed()
-        # Create a new instance of this stage.
-        py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
-                       trainRatio=trainRatio, seed=seed)
-        py_stage._resetUid(java_stage.uid())
-        return py_stage
-
-    def _to_java(self):
-        """
-        Transfer this instance to a Java TrainValidationSplit. Used for ML persistence.
-
-        :return: Java object equivalent to this instance.
-        """
-
-        estimator, epms, evaluator = super(TrainValidationSplit, self)._to_java_impl()
-
-        _java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.TrainValidationSplit",
-                                             self.uid)
-        _java_obj.setEstimatorParamMaps(epms)
-        _java_obj.setEvaluator(evaluator)
-        _java_obj.setEstimator(estimator)
-        _java_obj.setTrainRatio(self.getTrainRatio())
-        _java_obj.setSeed(self.getSeed())
 
-        return _java_obj
-
-
-class TrainValidationSplitModel(Model, ValidatorParams, MLReadable, MLWritable):
+class TrainValidationSplitModel(Model, ValidatorParams):
     """
     Model from train validation split.
 
@@ -617,60 +435,6 @@ class TrainValidationSplitModel(Model, ValidatorParams, MLReadable, MLWritable):
             extra = dict()
         return TrainValidationSplitModel(self.bestModel.copy(extra))
 
-    @since("2.0.0")
-    def write(self):
-        """Returns an MLWriter instance for this ML instance."""
-        return JavaMLWriter(self)
-
-    @since("2.0.0")
-    def save(self, path):
-        """Save this ML instance to the given path, a shortcut of `write().save(path)`."""
-        self.write().save(path)
-
-    @classmethod
-    @since("2.0.0")
-    def read(cls):
-        """Returns an MLReader instance for this class."""
-        return JavaMLReader(cls)
-
-    @classmethod
-    def _from_java(cls, java_stage):
-        """
-        Given a Java TrainValidationSplitModel, create and return a Python wrapper of it.
-        Used for ML persistence.
-        """
-
-        # Load information from java_stage to the instance.
-        bestModel = JavaParams._from_java(java_stage.bestModel())
-        estimator, epms, evaluator = \
-            super(TrainValidationSplitModel, cls)._from_java_impl(java_stage)
-        # Create a new instance of this stage.
-        py_stage = cls(bestModel=bestModel)\
-            .setEstimator(estimator).setEstimatorParamMaps(epms).setEvaluator(evaluator)
-        py_stage._resetUid(java_stage.uid())
-        return py_stage
-
-    def _to_java(self):
-        """
-        Transfer this instance to a Java TrainValidationSplitModel. Used for ML persistence.
-
-        :return: Java object equivalent to this instance.
-        """
-
-        sc = SparkContext._active_spark_context
-
-        _java_obj = JavaParams._new_java_obj(
-            "org.apache.spark.ml.tuning.TrainValidationSplitModel",
-            self.uid,
-            self.bestModel._to_java(),
-            _py2java(sc, []))
-        estimator, epms, evaluator = super(TrainValidationSplitModel, self)._to_java_impl()
-
-        _java_obj.set("evaluator", evaluator)
-        _java_obj.set("estimator", estimator)
-        _java_obj.set("estimatorParamMaps", epms)
-        return _java_obj
-
 
 if __name__ == "__main__":
     import doctest