text: #
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  # contributor license agreements.  See the NOTICE file distributed with
  # this work for additional information regarding copyright ownership.
  # The ASF licenses this file to You under the Apache License, Version 2.0
  # (the "License"); you may not use this file except in compliance with
  # the License.  You may obtain a copy of the License at
  #
  #    http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  #
  
  import os
  import sys
  import itertools
  from multiprocessing.pool import ThreadPool
  
  import numpy as np
  
  from pyspark import keyword_only, since, SparkContext
  from pyspark.ml import Estimator, Transformer, Model
  from pyspark.ml.common import inherit_doc, _py2java, _java2py
  from pyspark.ml.evaluation import Evaluator
  from pyspark.ml.param import Params, Param, TypeConverters
  from pyspark.ml.param.shared import HasCollectSubModels, HasParallelism, HasSeed
  from pyspark.ml.util import DefaultParamsReader, DefaultParamsWriter, MetaAlgorithmReadWrite, \
      MLReadable, MLReader, MLWritable, MLWriter, JavaMLReader, JavaMLWriter
  from pyspark.ml.wrapper import JavaParams, JavaEstimator, JavaWrapper
  from pyspark.sql.functions import col, lit, rand, UserDefinedFunction
  from pyspark.sql.types import BooleanType
  
  __all__ = ['ParamGridBuilder', 'CrossValidator', 'CrossValidatorModel', 'TrainValidationSplit',
             'TrainValidationSplitModel']
  
  
  def _parallelFitTasks(est, train, eva, validation, epm, collectSubModel):
      """
      Creates a list of callables which can be called from different threads to fit and evaluate
      an estimator in parallel. Each callable returns an `(index, metric)` pair.
  
      Parameters
      ----------
      est : :py:class:`pyspark.ml.baseEstimator`
          he estimator to be fit.
      train : :py:class:`pyspark.sql.DataFrame`
          DataFrame, training data set, used for fitting.
      eva : :py:class:`pyspark.ml.evaluation.Evaluator`
          used to compute `metric`
      validation : :py:class:`pyspark.sql.DataFrame`
          DataFrame, validation data set, used for evaluation.
      epm : :py:class:`collections.abc.Sequence`
          Sequence of ParamMap, params maps to be used during fitting & evaluation.
      collectSubModel : bool
          Whether to collect sub model.
  
      Returns
      -------
      tuple
          (int, float, subModel), an index into `epm` and the associated metric value.
      """
      modelIter = est.fitMultiple(train, epm)
  
      def singleTask():
          index, model = next(modelIter)
          # TODO: duplicate evaluator to take extra params from input
          #  Note: Supporting tuning params in evaluator need update method
          #  `MetaAlgorithmReadWrite.getAllNestedStages`, make it return
          #  all nested stages and evaluators
          metric = eva.evaluate(model.transform(validation, epm[index]))
          return index, metric, model if collectSubModel else None
  
      return [singleTask] * len(epm)
  
  
  class ParamGridBuilder(object):
      r"""
      Builder for a param grid used in grid search-based model selection.
  
  
      .. versionadded:: 1.4.0
  
      Examples
      --------
      >>> from pyspark.ml.classification import LogisticRegression
      >>> lr = LogisticRegression()
      >>> output = ParamGridBuilder() \
      ...     .baseOn({lr.labelCol: 'l'}) \
      ...     .baseOn([lr.predictionCol, 'p']) \
      ...     .addGrid(lr.regParam, [1.0, 2.0]) \
      ...     .addGrid(lr.maxIter, [1, 5]) \
      ...     .build()
      >>> expected = [
      ...     {lr.regParam: 1.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
      ...     {lr.regParam: 2.0, lr.maxIter: 1, lr.labelCol: 'l', lr.predictionCol: 'p'},
      ...     {lr.regParam: 1.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'},
      ...     {lr.regParam: 2.0, lr.maxIter: 5, lr.labelCol: 'l', lr.predictionCol: 'p'}]
      >>> len(output) == len(expected)
      True
      >>> all([m in expected for m in output])
      True
      """
  
      def __init__(self):
          self._param_grid = {}
  
      @since("1.4.0")
      def addGrid(self, param, values):
          """
          Sets the given parameters in this grid to fixed values.
  
          param must be an instance of Param associated with an instance of Params
          (such as Estimator or Transformer).
          """
          if isinstance(param, Param):
              self._param_grid[param] = values
          else:
              raise TypeError("param must be an instance of Param")
  
          return self
  
      @since("1.4.0")
      def baseOn(self, *args):
          """
          Sets the given parameters in this grid to fixed values.
          Accepts either a parameter dictionary or a list of (parameter, value) pairs.
          """
          if isinstance(args[0], dict):
              self.baseOn(*args[0].items())
          else:
              for (param, value) in args:
                  self.addGrid(param, [value])
  
          return self
  
      @since("1.4.0")
      def build(self):
          """
          Builds and returns all combinations of parameters specified
          by the param grid.
          """
          keys = self._param_grid.keys()
          grid_values = self._param_grid.values()
  
          def to_key_value_pairs(keys, values):
              return [(key, key.typeConverter(value)) for key, value in zip(keys, values)]
  
          return [dict(to_key_value_pairs(keys, prod)) for prod in itertools.product(*grid_values)]
  
  
  class _ValidatorParams(HasSeed):
      """
      Common params for TrainValidationSplit and CrossValidator.
      """
  
      estimator = Param(Params._dummy(), "estimator", "estimator to be cross-validated")
      estimatorParamMaps = Param(Params._dummy(), "estimatorParamMaps", "estimator param maps")
      evaluator = Param(
          Params._dummy(), "evaluator",
          "evaluator used to select hyper-parameters that maximize the validator metric")
  
      @since("2.0.0")
      def getEstimator(self):
          """
          Gets the value of estimator or its default value.
          """
          return self.getOrDefault(self.estimator)
  
      @since("2.0.0")
      def getEstimatorParamMaps(self):
          """
          Gets the value of estimatorParamMaps or its default value.
          """
          return self.getOrDefault(self.estimatorParamMaps)
  
      @since("2.0.0")
      def getEvaluator(self):
          """
          Gets the value of evaluator or its default value.
          """
          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())
          if isinstance(estimator, JavaEstimator):
              epms = [estimator._transfer_param_map_from_java(epm)
                      for epm in java_stage.getEstimatorParamMaps()]
          elif MetaAlgorithmReadWrite.isMetaEstimator(estimator):
              # Meta estimator such as Pipeline, OneVsRest
              epms = _ValidatorSharedReadWrite.meta_estimator_transfer_param_maps_from_java(
                  estimator, java_stage.getEstimatorParamMaps())
          else:
              raise ValueError('Unsupported estimator used in tuning: ' + str(estimator))
  
          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
  
          estimator = self.getEstimator()
          if isinstance(estimator, JavaEstimator):
              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)
          elif MetaAlgorithmReadWrite.isMetaEstimator(estimator):
              # Meta estimator such as Pipeline, OneVsRest
              java_epms = _ValidatorSharedReadWrite.meta_estimator_transfer_param_maps_to_java(
                  estimator, self.getEstimatorParamMaps())
          else:
              raise ValueError('Unsupported estimator used in tuning: ' + str(estimator))
  
          java_estimator = self.getEstimator()._to_java()
          java_evaluator = self.getEvaluator()._to_java()
          return java_estimator, java_epms, java_evaluator
  
  
  class _ValidatorSharedReadWrite:
  
      @staticmethod
      def meta_estimator_transfer_param_maps_to_java(pyEstimator, pyParamMaps):
          pyStages = MetaAlgorithmReadWrite.getAllNestedStages(pyEstimator)
          stagePairs = list(map(lambda stage: (stage, stage._to_java()), pyStages))
          sc = SparkContext._active_spark_context
  
          paramMapCls = SparkContext._jvm.org.apache.spark.ml.param.ParamMap
          javaParamMaps = SparkContext._gateway.new_array(paramMapCls, len(pyParamMaps))
  
          for idx, pyParamMap in enumerate(pyParamMaps):
              javaParamMap = JavaWrapper._new_java_obj("org.apache.spark.ml.param.ParamMap")
              for pyParam, pyValue in pyParamMap.items():
                  javaParam = None
                  for pyStage, javaStage in stagePairs:
                      if pyStage._testOwnParam(pyParam.parent, pyParam.name):
                          javaParam = javaStage.getParam(pyParam.name)
                          break
                  if javaParam is None:
                      raise ValueError('Resolve param in estimatorParamMaps failed: ' + str(pyParam))
                  if isinstance(pyValue, Params) and hasattr(pyValue, "_to_java"):
                      javaValue = pyValue._to_java()
                  else:
                      javaValue = _py2java(sc, pyValue)
                  pair = javaParam.w(javaValue)
                  javaParamMap.put([pair])
              javaParamMaps[idx] = javaParamMap
          return javaParamMaps
  
      @staticmethod
      def meta_estimator_transfer_param_maps_from_java(pyEstimator, javaParamMaps):
          pyStages = MetaAlgorithmReadWrite.getAllNestedStages(pyEstimator)
          stagePairs = list(map(lambda stage: (stage, stage._to_java()), pyStages))
          sc = SparkContext._active_spark_context
          pyParamMaps = []
          for javaParamMap in javaParamMaps:
              pyParamMap = dict()
              for javaPair in javaParamMap.toList():
                  javaParam = javaPair.param()
                  pyParam = None
                  for pyStage, javaStage in stagePairs:
                      if pyStage._testOwnParam(javaParam.parent(), javaParam.name()):
                          pyParam = pyStage.getParam(javaParam.name())
                  if pyParam is None:
                      raise ValueError('Resolve param in estimatorParamMaps failed: ' +
                                       javaParam.parent() + '.' + javaParam.name())
                  javaValue = javaPair.value()
                  if sc._jvm.Class.forName("org.apache.spark.ml.util.DefaultParamsWritable") \
                          .isInstance(javaValue):
                      pyValue = JavaParams._from_java(javaValue)
                  else:
                      pyValue = _java2py(sc, javaValue)
                  pyParamMap[pyParam] = pyValue
              pyParamMaps.append(pyParamMap)
          return pyParamMaps
  
      @staticmethod
      def is_java_convertible(instance):
          allNestedStages = MetaAlgorithmReadWrite.getAllNestedStages(instance.getEstimator())
          evaluator_convertible = isinstance(instance.getEvaluator(), JavaParams)
          estimator_convertible = all(map(lambda stage: hasattr(stage, '_to_java'), allNestedStages))
          return estimator_convertible and evaluator_convertible
  
      @staticmethod
      def saveImpl(path, instance, sc, extraMetadata=None):
          numParamsNotJson = 0
          jsonEstimatorParamMaps = []
          for paramMap in instance.getEstimatorParamMaps():
              jsonParamMap = []
              for p, v in paramMap.items():
                  jsonParam = {'parent': p.parent, 'name': p.name}
                  if (isinstance(v, Estimator) and not MetaAlgorithmReadWrite.isMetaEstimator(v)) \
                          or isinstance(v, Transformer) or isinstance(v, Evaluator):
                      relative_path = f'epm_{p.name}{numParamsNotJson}'
                      param_path = os.path.join(path, relative_path)
                      numParamsNotJson += 1
                      v.save(param_path)
                      jsonParam['value'] = relative_path
                      jsonParam['isJson'] = False
                  elif isinstance(v, MLWritable):
                      raise RuntimeError(
                          "ValidatorSharedReadWrite.saveImpl does not handle parameters of type: "
                          "MLWritable that are not Estimaor/Evaluator/Transformer, and if parameter "
                          "is estimator, it cannot be meta estimator such as Validator or OneVsRest")
                  else:
                      jsonParam['value'] = v
                      jsonParam['isJson'] = True
                  jsonParamMap.append(jsonParam)
              jsonEstimatorParamMaps.append(jsonParamMap)
  
          skipParams = ['estimator', 'evaluator', 'estimatorParamMaps']
          jsonParams = DefaultParamsWriter.extractJsonParams(instance, skipParams)
          jsonParams['estimatorParamMaps'] = jsonEstimatorParamMaps
  
          DefaultParamsWriter.saveMetadata(instance, path, sc, extraMetadata, jsonParams)
          evaluatorPath = os.path.join(path, 'evaluator')
          instance.getEvaluator().save(evaluatorPath)
          estimatorPath = os.path.join(path, 'estimator')
          instance.getEstimator().save(estimatorPath)
  
      @staticmethod
      def load(path, sc, metadata):
          evaluatorPath = os.path.join(path, 'evaluator')
          evaluator = DefaultParamsReader.loadParamsInstance(evaluatorPath, sc)
          estimatorPath = os.path.join(path, 'estimator')
          estimator = DefaultParamsReader.loadParamsInstance(estimatorPath, sc)
  
          uidToParams = MetaAlgorithmReadWrite.getUidMap(estimator)
          uidToParams[evaluator.uid] = evaluator
  
          jsonEstimatorParamMaps = metadata['paramMap']['estimatorParamMaps']
  
          estimatorParamMaps = []
          for jsonParamMap in jsonEstimatorParamMaps:
              paramMap = {}
              for jsonParam in jsonParamMap:
                  est = uidToParams[jsonParam['parent']]
                  param = getattr(est, jsonParam['name'])
                  if 'isJson' not in jsonParam or ('isJson' in jsonParam and jsonParam['isJson']):
                      value = jsonParam['value']
                  else:
                      relativePath = jsonParam['value']
                      valueSavedPath = os.path.join(path, relativePath)
                      value = DefaultParamsReader.loadParamsInstance(valueSavedPath, sc)
                  paramMap[param] = value
              estimatorParamMaps.append(paramMap)
  
          return metadata, estimator, evaluator, estimatorParamMaps
  
      @staticmethod
      def validateParams(instance):
          estiamtor = instance.getEstimator()
          evaluator = instance.getEvaluator()
          uidMap = MetaAlgorithmReadWrite.getUidMap(estiamtor)
  
          for elem in [evaluator] + list(uidMap.values()):
              if not isinstance(elem, MLWritable):
                  raise ValueError(f'Validator write will fail because it contains {elem.uid} '
                                   f'which is not writable.')
  
          estimatorParamMaps = instance.getEstimatorParamMaps()
          paramErr = 'Validator save requires all Params in estimatorParamMaps to apply to ' \
                     f'its Estimator, An extraneous Param was found: '
          for paramMap in estimatorParamMaps:
              for param in paramMap:
                  if param.parent not in uidMap:
                      raise ValueError(paramErr + repr(param))
  
      @staticmethod
      def getValidatorModelWriterPersistSubModelsParam(writer):
          if 'persistsubmodels' in writer.optionMap:
              persistSubModelsParam = writer.optionMap['persistsubmodels'].lower()
              if persistSubModelsParam == 'true':
                  return True
              elif persistSubModelsParam == 'false':
                  return False
              else:
                  raise ValueError(
                      f'persistSubModels option value {persistSubModelsParam} is invalid, '
                      f"the possible values are True, 'True' or False, 'False'")
          else:
              return writer.instance.subModels is not None
  
  
  _save_with_persist_submodels_no_submodels_found_err = \
      'When persisting tuning models, you can only set persistSubModels to true if the tuning ' \
      'was done with collectSubModels set to true. To save the sub-models, try rerunning fitting ' \
      'with collectSubModels set to true.'
  
  
  @inherit_doc
  class CrossValidatorReader(MLReader):
  
      def __init__(self, cls):
          super(CrossValidatorReader, self).__init__()
          self.cls = cls
  
      def load(self, path):
          metadata = DefaultParamsReader.loadMetadata(path, self.sc)
          if not DefaultParamsReader.isPythonParamsInstance(metadata):
              return JavaMLReader(self.cls).load(path)
          else:
              metadata, estimator, evaluator, estimatorParamMaps = \
                  _ValidatorSharedReadWrite.load(path, self.sc, metadata)
              cv = CrossValidator(estimator=estimator,
                                  estimatorParamMaps=estimatorParamMaps,
                                  evaluator=evaluator)
              cv = cv._resetUid(metadata['uid'])
              DefaultParamsReader.getAndSetParams(cv, metadata, skipParams=['estimatorParamMaps'])
              return cv
  
  
  @inherit_doc
  class CrossValidatorWriter(MLWriter):
  
      def __init__(self, instance):
          super(CrossValidatorWriter, self).__init__()
          self.instance = instance
  
      def saveImpl(self, path):
          _ValidatorSharedReadWrite.validateParams(self.instance)
          _ValidatorSharedReadWrite.saveImpl(path, self.instance, self.sc)
  
  
  @inherit_doc
  class CrossValidatorModelReader(MLReader):
  
      def __init__(self, cls):
          super(CrossValidatorModelReader, self).__init__()
          self.cls = cls
  
      def load(self, path):
          metadata = DefaultParamsReader.loadMetadata(path, self.sc)
          if not DefaultParamsReader.isPythonParamsInstance(metadata):
              return JavaMLReader(self.cls).load(path)
          else:
              metadata, estimator, evaluator, estimatorParamMaps = \
                  _ValidatorSharedReadWrite.load(path, self.sc, metadata)
              numFolds = metadata['paramMap']['numFolds']
              bestModelPath = os.path.join(path, 'bestModel')
              bestModel = DefaultParamsReader.loadParamsInstance(bestModelPath, self.sc)
              avgMetrics = metadata['avgMetrics']
              persistSubModels = ('persistSubModels' in metadata) and metadata['persistSubModels']
  
              if persistSubModels:
                  subModels = [[None] * len(estimatorParamMaps)] * numFolds
                  for splitIndex in range(numFolds):
                      for paramIndex in range(len(estimatorParamMaps)):
                          modelPath = os.path.join(
                              path, 'subModels', f'fold{splitIndex}', f'{paramIndex}')
                          subModels[splitIndex][paramIndex] = \
                              DefaultParamsReader.loadParamsInstance(modelPath, self.sc)
              else:
                  subModels = None
  
              cvModel = CrossValidatorModel(bestModel, avgMetrics=avgMetrics, subModels=subModels)
              cvModel = cvModel._resetUid(metadata['uid'])
              cvModel.set(cvModel.estimator, estimator)
              cvModel.set(cvModel.estimatorParamMaps, estimatorParamMaps)
              cvModel.set(cvModel.evaluator, evaluator)
              DefaultParamsReader.getAndSetParams(
                  cvModel, metadata, skipParams=['estimatorParamMaps'])
              return cvModel
  
  
  @inherit_doc
  class CrossValidatorModelWriter(MLWriter):
  
      def __init__(self, instance):
          super(CrossValidatorModelWriter, self).__init__()
          self.instance = instance
  
      def saveImpl(self, path):
          _ValidatorSharedReadWrite.validateParams(self.instance)
          instance = self.instance
          persistSubModels = _ValidatorSharedReadWrite \
              .getValidatorModelWriterPersistSubModelsParam(self)
          extraMetadata = {'avgMetrics': instance.avgMetrics,
                           'persistSubModels': persistSubModels}
          _ValidatorSharedReadWrite.saveImpl(path, instance, self.sc, extraMetadata=extraMetadata)
          bestModelPath = os.path.join(path, 'bestModel')
          instance.bestModel.save(bestModelPath)
          if persistSubModels:
              if instance.subModels is None:
                  raise ValueError(_save_with_persist_submodels_no_submodels_found_err)
              subModelsPath = os.path.join(path, 'subModels')
              for splitIndex in range(instance.getNumFolds()):
                  splitPath = os.path.join(subModelsPath, f'fold{splitIndex}')
                  for paramIndex in range(len(instance.getEstimatorParamMaps())):
                      modelPath = os.path.join(splitPath, f'{paramIndex}')
                      instance.subModels[splitIndex][paramIndex].save(modelPath)
  
  
  class _CrossValidatorParams(_ValidatorParams):
      """
      Params for :py:class:`CrossValidator` and :py:class:`CrossValidatorModel`.
  
      .. versionadded:: 3.0.0
      """
  
      numFolds = Param(Params._dummy(), "numFolds", "number of folds for cross validation",
                       typeConverter=TypeConverters.toInt)
  
      foldCol = Param(Params._dummy(), "foldCol", "Param for the column name of user " +
                      "specified fold number. Once this is specified, :py:class:`CrossValidator` " +
                      "won't do random k-fold split. Note that this column should be integer type " +
                      "with range [0, numFolds) and Spark will throw exception on out-of-range " +
                      "fold numbers.", typeConverter=TypeConverters.toString)
  
      def __init__(self, *args):
          super(_CrossValidatorParams, self).__init__(*args)
          self._setDefault(numFolds=3, foldCol="")
  
      @since("1.4.0")
      def getNumFolds(self):
          """
          Gets the value of numFolds or its default value.
          """
          return self.getOrDefault(self.numFolds)
  
      @since("3.1.0")
      def getFoldCol(self):
          """
          Gets the value of foldCol or its default value.
          """
          return self.getOrDefault(self.foldCol)
  
  
  class CrossValidator(Estimator, _CrossValidatorParams, HasParallelism, HasCollectSubModels,
                       MLReadable, MLWritable):
      """
  
      K-fold cross validation performs model selection by splitting the dataset into a set of
      non-overlapping randomly partitioned folds which are used as separate training and test datasets
      e.g., with k=3 folds, K-fold cross validation will generate 3 (training, test) dataset pairs,
      each of which uses 2/3 of the data for training and 1/3 for testing. Each fold is used as the
      test set exactly once.
  
      .. versionadded:: 1.4.0
  
      Examples
      --------
      >>> from pyspark.ml.classification import LogisticRegression
      >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
      >>> from pyspark.ml.linalg import Vectors
      >>> from pyspark.ml.tuning import CrossValidatorModel
      >>> import tempfile
      >>> dataset = spark.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,
      ...     parallelism=2)
      >>> cvModel = cv.fit(dataset)
      >>> cvModel.getNumFolds()
      3
      >>> cvModel.avgMetrics[0]
      0.5
      >>> path = tempfile.mkdtemp()
      >>> model_path = path + "/model"
      >>> cvModel.write().save(model_path)
      >>> cvModelRead = CrossValidatorModel.read().load(model_path)
      >>> cvModelRead.avgMetrics
      [0.5, ...
      >>> evaluator.evaluate(cvModel.transform(dataset))
      0.8333...
      >>> evaluator.evaluate(cvModelRead.transform(dataset))
      0.8333...
      """
  
      @keyword_only
      def __init__(self, *, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,
                   seed=None, parallelism=1, collectSubModels=False, foldCol=""):
          """
          __init__(self, \\*, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\
                   seed=None, parallelism=1, collectSubModels=False, foldCol="")
          """
          super(CrossValidator, self).__init__()
          self._setDefault(parallelism=1)
          kwargs = self._input_kwargs
          self._set(**kwargs)
  
      @keyword_only
      @since("1.4.0")
      def setParams(self, *, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,
                    seed=None, parallelism=1, collectSubModels=False, foldCol=""):
          """
          setParams(self, \\*, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\
                    seed=None, parallelism=1, collectSubModels=False, foldCol=""):
          Sets params for cross validator.
          """
          kwargs = self._input_kwargs
          return self._set(**kwargs)
  
      @since("2.0.0")
      def setEstimator(self, value):
          """
          Sets the value of :py:attr:`estimator`.
          """
          return self._set(estimator=value)
  
      @since("2.0.0")
      def setEstimatorParamMaps(self, value):
          """
          Sets the value of :py:attr:`estimatorParamMaps`.
          """
          return self._set(estimatorParamMaps=value)
  
      @since("2.0.0")
      def setEvaluator(self, value):
          """
          Sets the value of :py:attr:`evaluator`.
          """
          return self._set(evaluator=value)
  
      @since("1.4.0")
      def setNumFolds(self, value):
          """
          Sets the value of :py:attr:`numFolds`.
          """
          return self._set(numFolds=value)
  
      @since("3.1.0")
      def setFoldCol(self, value):
          """
          Sets the value of :py:attr:`foldCol`.
          """
          return self._set(foldCol=value)
  
      def setSeed(self, value):
          """
          Sets the value of :py:attr:`seed`.
          """
          return self._set(seed=value)
  
      def setParallelism(self, value):
          """
          Sets the value of :py:attr:`parallelism`.
          """
          return self._set(parallelism=value)
  
      def setCollectSubModels(self, value):
          """
          Sets the value of :py:attr:`collectSubModels`.
          """
          return self._set(collectSubModels=value)
  
      def _fit(self, dataset):
          est = self.getOrDefault(self.estimator)
          epm = self.getOrDefault(self.estimatorParamMaps)
          numModels = len(epm)
          eva = self.getOrDefault(self.evaluator)
          nFolds = self.getOrDefault(self.numFolds)
          metrics = [0.0] * numModels
  
          pool = ThreadPool(processes=min(self.getParallelism(), numModels))
          subModels = None
          collectSubModelsParam = self.getCollectSubModels()
          if collectSubModelsParam:
              subModels = [[None for j in range(numModels)] for i in range(nFolds)]
  
          datasets = self._kFold(dataset)
          for i in range(nFolds):
              validation = datasets[i][1].cache()
              train = datasets[i][0].cache()
  
              tasks = _parallelFitTasks(est, train, eva, validation, epm, collectSubModelsParam)
              for j, metric, subModel in pool.imap_unordered(lambda f: f(), tasks):
                  metrics[j] += (metric / nFolds)
                  if collectSubModelsParam:
                      subModels[i][j] = subModel
  
              validation.unpersist()
              train.unpersist()
  
          if eva.isLargerBetter():
              bestIndex = np.argmax(metrics)
          else:
              bestIndex = np.argmin(metrics)
          bestModel = est.fit(dataset, epm[bestIndex])
          return self._copyValues(CrossValidatorModel(bestModel, metrics, subModels))
  
      def _kFold(self, dataset):
          nFolds = self.getOrDefault(self.numFolds)
          foldCol = self.getOrDefault(self.foldCol)
  
          datasets = []
          if not foldCol:
              # Do random k-fold split.
              seed = self.getOrDefault(self.seed)
              h = 1.0 / nFolds
              randCol = self.uid + "_rand"
              df = dataset.select("*", rand(seed).alias(randCol))
              for i in range(nFolds):
                  validateLB = i * h
                  validateUB = (i + 1) * h
                  condition = (df[randCol] >= validateLB) & (df[randCol] < validateUB)
                  validation = df.filter(condition)
                  train = df.filter(~condition)
                  datasets.append((train, validation))
          else:
              # Use user-specified fold numbers.
              def checker(foldNum):
                  if foldNum < 0 or foldNum >= nFolds:
                      raise ValueError(
                          "Fold number must be in range [0, %s), but got %s." % (nFolds, foldNum))
                  return True
  
              checker_udf = UserDefinedFunction(checker, BooleanType())
              for i in range(nFolds):
                  training = dataset.filter(checker_udf(dataset[foldCol]) & (col(foldCol) != lit(i)))
                  validation = dataset.filter(
                      checker_udf(dataset[foldCol]) & (col(foldCol) == lit(i)))
                  if training.rdd.getNumPartitions() == 0 or len(training.take(1)) == 0:
                      raise ValueError("The training data at fold %s is empty." % i)
                  if validation.rdd.getNumPartitions() == 0 or len(validation.take(1)) == 0:
                      raise ValueError("The validation data at fold %s is empty." % i)
                  datasets.append((training, validation))
  
          return datasets
  
      def copy(self, extra=None):
          """
          Creates a copy of this instance with a randomly generated uid
          and some extra params. This copies creates a deep copy of
          the embedded paramMap, and copies the embedded and extra parameters over.
  
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          extra : dict, optional
              Extra parameters to copy to the new instance
  
          Returns
          -------
          :py:class:`CrossValidator`
              Copy of this instance
          """
          if extra is None:
              extra = dict()
          newCV = Params.copy(self, extra)
          if self.isSet(self.estimator):
              newCV.setEstimator(self.getEstimator().copy(extra))
          # estimatorParamMaps remain the same
          if self.isSet(self.evaluator):
              newCV.setEvaluator(self.getEvaluator().copy(extra))
          return newCV
  
      @since("2.3.0")
      def write(self):
          """Returns an MLWriter instance for this ML instance."""
          if _ValidatorSharedReadWrite.is_java_convertible(self):
              return JavaMLWriter(self)
          return CrossValidatorWriter(self)
  
      @classmethod
      @since("2.3.0")
      def read(cls):
          """Returns an MLReader instance for this class."""
          return CrossValidatorReader(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()
          parallelism = java_stage.getParallelism()
          collectSubModels = java_stage.getCollectSubModels()
          foldCol = java_stage.getFoldCol()
          # Create a new instance of this stage.
          py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
                         numFolds=numFolds, seed=seed, parallelism=parallelism,
                         collectSubModels=collectSubModels, foldCol=foldCol)
          py_stage._resetUid(java_stage.uid())
          return py_stage
  
      def _to_java(self):
          """
          Transfer this instance to a Java CrossValidator. Used for ML persistence.
  
          Returns
          -------
          py4j.java_gateway.JavaObject
              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())
          _java_obj.setParallelism(self.getParallelism())
          _java_obj.setCollectSubModels(self.getCollectSubModels())
          _java_obj.setFoldCol(self.getFoldCol())
  
          return _java_obj
  
  
  class CrossValidatorModel(Model, _CrossValidatorParams, MLReadable, MLWritable):
      """
  
      CrossValidatorModel contains the model with the highest average cross-validation
      metric across folds and uses this model to transform input data. CrossValidatorModel
      also tracks the metrics for each param map evaluated.
  
      .. versionadded:: 1.4.0
      """
  
      def __init__(self, bestModel, avgMetrics=[], subModels=None):
          super(CrossValidatorModel, self).__init__()
          #: best model from cross validation
          self.bestModel = bestModel
          #: Average cross-validation metrics for each paramMap in
          #: CrossValidator.estimatorParamMaps, in the corresponding order.
          self.avgMetrics = avgMetrics
          #: sub model list from cross validation
          self.subModels = subModels
  
      def _transform(self, dataset):
          return self.bestModel.transform(dataset)
  
      def copy(self, extra=None):
          """
          Creates a copy of this instance with a randomly generated uid
          and some extra params. This copies the underlying bestModel,
          creates a deep copy of the embedded paramMap, and
          copies the embedded and extra parameters over.
          It does not copy the extra Params into the subModels.
  
          .. versionadded:: 1.4.0
  
          Parameters
          ----------
          extra : dict, optional
              Extra parameters to copy to the new instance
  
          Returns
          -------
          :py:class:`CrossValidatorModel`
              Copy of this instance
          """
          if extra is None:
              extra = dict()
          bestModel = self.bestModel.copy(extra)
          avgMetrics = list(self.avgMetrics)
          subModels = [
              [sub_model.copy() for sub_model in fold_sub_models]
              for fold_sub_models in self.subModels
          ]
          return self._copyValues(CrossValidatorModel(bestModel, avgMetrics, subModels), extra=extra)
  
      @since("2.3.0")
      def write(self):
          """Returns an MLWriter instance for this ML instance."""
          if _ValidatorSharedReadWrite.is_java_convertible(self):
              return JavaMLWriter(self)
          return CrossValidatorModelWriter(self)
  
      @classmethod
      @since("2.3.0")
      def read(cls):
          """Returns an MLReader instance for this class."""
          return CrossValidatorModelReader(cls)
  
      @classmethod
      def _from_java(cls, java_stage):
          """
          Given a Java CrossValidatorModel, create and return a Python wrapper of it.
          Used for ML persistence.
          """
          sc = SparkContext._active_spark_context
          bestModel = JavaParams._from_java(java_stage.bestModel())
          avgMetrics = _java2py(sc, java_stage.avgMetrics())
          estimator, epms, evaluator = super(CrossValidatorModel, cls)._from_java_impl(java_stage)
  
          py_stage = cls(bestModel=bestModel, avgMetrics=avgMetrics)
          params = {
              "evaluator": evaluator,
              "estimator": estimator,
              "estimatorParamMaps": epms,
              "numFolds": java_stage.getNumFolds(),
              "foldCol": java_stage.getFoldCol(),
              "seed": java_stage.getSeed(),
          }
          for param_name, param_val in params.items():
              py_stage = py_stage._set(**{param_name: param_val})
  
          if java_stage.hasSubModels():
              py_stage.subModels = [[JavaParams._from_java(sub_model)
                                     for sub_model in fold_sub_models]
                                    for fold_sub_models in java_stage.subModels()]
  
          py_stage._resetUid(java_stage.uid())
          return py_stage
  
      def _to_java(self):
          """
          Transfer this instance to a Java CrossValidatorModel. Used for ML persistence.
  
          Returns
          -------
          py4j.java_gateway.JavaObject
              Java object equivalent to this instance.
          """
  
          sc = SparkContext._active_spark_context
          _java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.CrossValidatorModel",
                                               self.uid,
                                               self.bestModel._to_java(),
                                               _py2java(sc, self.avgMetrics))
          estimator, epms, evaluator = super(CrossValidatorModel, self)._to_java_impl()
  
          params = {
              "evaluator": evaluator,
              "estimator": estimator,
              "estimatorParamMaps": epms,
              "numFolds": self.getNumFolds(),
              "foldCol": self.getFoldCol(),
              "seed": self.getSeed(),
          }
          for param_name, param_val in params.items():
              java_param = _java_obj.getParam(param_name)
              pair = java_param.w(param_val)
              _java_obj.set(pair)
  
          if self.subModels is not None:
              java_sub_models = [[sub_model._to_java() for sub_model in fold_sub_models]
                                 for fold_sub_models in self.subModels]
              _java_obj.setSubModels(java_sub_models)
          return _java_obj
  
  
  @inherit_doc
  class TrainValidationSplitReader(MLReader):
  
      def __init__(self, cls):
          super(TrainValidationSplitReader, self).__init__()
          self.cls = cls
  
      def load(self, path):
          metadata = DefaultParamsReader.loadMetadata(path, self.sc)
          if not DefaultParamsReader.isPythonParamsInstance(metadata):
              return JavaMLReader(self.cls).load(path)
          else:
              metadata, estimator, evaluator, estimatorParamMaps = \
                  _ValidatorSharedReadWrite.load(path, self.sc, metadata)
              tvs = TrainValidationSplit(estimator=estimator,
                                         estimatorParamMaps=estimatorParamMaps,
                                         evaluator=evaluator)
              tvs = tvs._resetUid(metadata['uid'])
              DefaultParamsReader.getAndSetParams(tvs, metadata, skipParams=['estimatorParamMaps'])
              return tvs
  
  
  @inherit_doc
  class TrainValidationSplitWriter(MLWriter):
  
      def __init__(self, instance):
          super(TrainValidationSplitWriter, self).__init__()
          self.instance = instance
  
      def saveImpl(self, path):
          _ValidatorSharedReadWrite.validateParams(self.instance)
          _ValidatorSharedReadWrite.saveImpl(path, self.instance, self.sc)
  
  
  @inherit_doc
  class TrainValidationSplitModelReader(MLReader):
  
      def __init__(self, cls):
          super(TrainValidationSplitModelReader, self).__init__()
          self.cls = cls
  
      def load(self, path):
          metadata = DefaultParamsReader.loadMetadata(path, self.sc)
          if not DefaultParamsReader.isPythonParamsInstance(metadata):
              return JavaMLReader(self.cls).load(path)
          else:
              metadata, estimator, evaluator, estimatorParamMaps = \
                  _ValidatorSharedReadWrite.load(path, self.sc, metadata)
              bestModelPath = os.path.join(path, 'bestModel')
              bestModel = DefaultParamsReader.loadParamsInstance(bestModelPath, self.sc)
              validationMetrics = metadata['validationMetrics']
              persistSubModels = ('persistSubModels' in metadata) and metadata['persistSubModels']
  
              if persistSubModels:
                  subModels = [None] * len(estimatorParamMaps)
                  for paramIndex in range(len(estimatorParamMaps)):
                      modelPath = os.path.join(path, 'subModels', f'{paramIndex}')
                      subModels[paramIndex] = \
                          DefaultParamsReader.loadParamsInstance(modelPath, self.sc)
              else:
                  subModels = None
  
              tvsModel = TrainValidationSplitModel(
                  bestModel, validationMetrics=validationMetrics, subModels=subModels)
              tvsModel = tvsModel._resetUid(metadata['uid'])
              tvsModel.set(tvsModel.estimator, estimator)
              tvsModel.set(tvsModel.estimatorParamMaps, estimatorParamMaps)
              tvsModel.set(tvsModel.evaluator, evaluator)
              DefaultParamsReader.getAndSetParams(
                  tvsModel, metadata, skipParams=['estimatorParamMaps'])
              return tvsModel
  
  
  @inherit_doc
  class TrainValidationSplitModelWriter(MLWriter):
  
      def __init__(self, instance):
          super(TrainValidationSplitModelWriter, self).__init__()
          self.instance = instance
  
      def saveImpl(self, path):
          _ValidatorSharedReadWrite.validateParams(self.instance)
          instance = self.instance
          persistSubModels = _ValidatorSharedReadWrite \
              .getValidatorModelWriterPersistSubModelsParam(self)
  
          extraMetadata = {'validationMetrics': instance.validationMetrics,
                           'persistSubModels': persistSubModels}
          _ValidatorSharedReadWrite.saveImpl(path, instance, self.sc, extraMetadata=extraMetadata)
          bestModelPath = os.path.join(path, 'bestModel')
          instance.bestModel.save(bestModelPath)
          if persistSubModels:
              if instance.subModels is None:
                  raise ValueError(_save_with_persist_submodels_no_submodels_found_err)
              subModelsPath = os.path.join(path, 'subModels')
              for paramIndex in range(len(instance.getEstimatorParamMaps())):
                  modelPath = os.path.join(subModelsPath, f'{paramIndex}')
                  instance.subModels[paramIndex].save(modelPath)
  
  
  class _TrainValidationSplitParams(_ValidatorParams):
      """
      Params for :py:class:`TrainValidationSplit` and :py:class:`TrainValidationSplitModel`.
  
      .. versionadded:: 3.0.0
      """
  
      trainRatio = Param(Params._dummy(), "trainRatio", "Param for ratio between train and\
       validation data. Must be between 0 and 1.", typeConverter=TypeConverters.toFloat)
  
      def __init__(self, *args):
          super(_TrainValidationSplitParams, self).__init__(*args)
          self._setDefault(trainRatio=0.75)
  
      @since("2.0.0")
      def getTrainRatio(self):
          """
          Gets the value of trainRatio or its default value.
          """
          return self.getOrDefault(self.trainRatio)
  
  
  class TrainValidationSplit(Estimator, _TrainValidationSplitParams, HasParallelism,
                             HasCollectSubModels, MLReadable, MLWritable):
      """
      Validation for hyper-parameter tuning. Randomly splits the input dataset into train and
      validation sets, and uses evaluation metric on the validation set to select the best model.
      Similar to :class:`CrossValidator`, but only splits the set once.
  
      .. versionadded:: 2.0.0
  
      Examples
      --------
      >>> from pyspark.ml.classification import LogisticRegression
      >>> from pyspark.ml.evaluation import BinaryClassificationEvaluator
      >>> from pyspark.ml.linalg import Vectors
      >>> from pyspark.ml.tuning import TrainValidationSplitModel
      >>> import tempfile
      >>> dataset = spark.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"]).repartition(1)
      >>> lr = LogisticRegression()
      >>> grid = ParamGridBuilder().addGrid(lr.maxIter, [0, 1]).build()
      >>> evaluator = BinaryClassificationEvaluator()
      >>> tvs = TrainValidationSplit(estimator=lr, estimatorParamMaps=grid, evaluator=evaluator,
      ...     parallelism=1, seed=42)
      >>> tvsModel = tvs.fit(dataset)
      >>> tvsModel.getTrainRatio()
      0.75
      >>> tvsModel.validationMetrics
      [0.5, ...
      >>> path = tempfile.mkdtemp()
      >>> model_path = path + "/model"
      >>> tvsModel.write().save(model_path)
      >>> tvsModelRead = TrainValidationSplitModel.read().load(model_path)
      >>> tvsModelRead.validationMetrics
      [0.5, ...
      >>> evaluator.evaluate(tvsModel.transform(dataset))
      0.833...
      >>> evaluator.evaluate(tvsModelRead.transform(dataset))
      0.833...
      """
  
      @keyword_only
      def __init__(self, *, estimator=None, estimatorParamMaps=None, evaluator=None,
                   trainRatio=0.75, parallelism=1, collectSubModels=False, seed=None):
          """
          __init__(self, \\*, estimator=None, estimatorParamMaps=None, evaluator=None, \
                   trainRatio=0.75, parallelism=1, collectSubModels=False, seed=None)
          """
          super(TrainValidationSplit, self).__init__()
          self._setDefault(parallelism=1)
          kwargs = self._input_kwargs
          self._set(**kwargs)
  
      @since("2.0.0")
      @keyword_only
      def setParams(self, *, estimator=None, estimatorParamMaps=None, evaluator=None,
                    trainRatio=0.75, parallelism=1, collectSubModels=False, seed=None):
          """
          setParams(self, \\*, estimator=None, estimatorParamMaps=None, evaluator=None, \
                    trainRatio=0.75, parallelism=1, collectSubModels=False, seed=None):
          Sets params for the train validation split.
          """
          kwargs = self._input_kwargs
          return self._set(**kwargs)
  
      @since("2.0.0")
      def setEstimator(self, value):
          """
          Sets the value of :py:attr:`estimator`.
          """
          return self._set(estimator=value)
  
      @since("2.0.0")
      def setEstimatorParamMaps(self, value):
          """
          Sets the value of :py:attr:`estimatorParamMaps`.
          """
          return self._set(estimatorParamMaps=value)
  
      @since("2.0.0")
      def setEvaluator(self, value):
          """
          Sets the value of :py:attr:`evaluator`.
          """
          return self._set(evaluator=value)
  
      @since("2.0.0")
      def setTrainRatio(self, value):
          """
          Sets the value of :py:attr:`trainRatio`.
          """
          return self._set(trainRatio=value)
  
      def setSeed(self, value):
          """
          Sets the value of :py:attr:`seed`.
          """
          return self._set(seed=value)
  
      def setParallelism(self, value):
          """
          Sets the value of :py:attr:`parallelism`.
          """
          return self._set(parallelism=value)
  
      def setCollectSubModels(self, value):
          """
          Sets the value of :py:attr:`collectSubModels`.
          """
          return self._set(collectSubModels=value)
  
      def _fit(self, dataset):
          est = self.getOrDefault(self.estimator)
          epm = self.getOrDefault(self.estimatorParamMaps)
          numModels = len(epm)
          eva = self.getOrDefault(self.evaluator)
          tRatio = self.getOrDefault(self.trainRatio)
          seed = self.getOrDefault(self.seed)
          randCol = self.uid + "_rand"
          df = dataset.select("*", rand(seed).alias(randCol))
          condition = (df[randCol] >= tRatio)
          validation = df.filter(condition).cache()
          train = df.filter(~condition).cache()
  
          subModels = None
          collectSubModelsParam = self.getCollectSubModels()
          if collectSubModelsParam:
              subModels = [None for i in range(numModels)]
  
          tasks = _parallelFitTasks(est, train, eva, validation, epm, collectSubModelsParam)
          pool = ThreadPool(processes=min(self.getParallelism(), numModels))
          metrics = [None] * numModels
          for j, metric, subModel in pool.imap_unordered(lambda f: f(), tasks):
              metrics[j] = metric
              if collectSubModelsParam:
                  subModels[j] = subModel
  
          train.unpersist()
          validation.unpersist()
  
          if eva.isLargerBetter():
              bestIndex = np.argmax(metrics)
          else:
              bestIndex = np.argmin(metrics)
          bestModel = est.fit(dataset, epm[bestIndex])
          return self._copyValues(TrainValidationSplitModel(bestModel, metrics, subModels))
  
      def copy(self, extra=None):
          """
          Creates a copy of this instance with a randomly generated uid
          and some extra params. This copies creates a deep copy of
          the embedded paramMap, and copies the embedded and extra parameters over.
  
          .. versionadded:: 2.0.0
  
          Parameters
          ----------
          extra : dict, optional
              Extra parameters to copy to the new instance
  
          Returns
          -------
          :py:class:`TrainValidationSplit`
              Copy of this instance
          """
          if extra is None:
              extra = dict()
          newTVS = Params.copy(self, extra)
          if self.isSet(self.estimator):
              newTVS.setEstimator(self.getEstimator().copy(extra))
          # estimatorParamMaps remain the same
          if self.isSet(self.evaluator):
              newTVS.setEvaluator(self.getEvaluator().copy(extra))
          return newTVS
  
      @since("2.3.0")
      def write(self):
          """Returns an MLWriter instance for this ML instance."""
          if _ValidatorSharedReadWrite.is_java_convertible(self):
              return JavaMLWriter(self)
          return TrainValidationSplitWriter(self)
  
      @classmethod
      @since("2.3.0")
      def read(cls):
          """Returns an MLReader instance for this class."""
          return TrainValidationSplitReader(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()
          parallelism = java_stage.getParallelism()
          collectSubModels = java_stage.getCollectSubModels()
          # Create a new instance of this stage.
          py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator,
                         trainRatio=trainRatio, seed=seed, parallelism=parallelism,
                         collectSubModels=collectSubModels)
          py_stage._resetUid(java_stage.uid())
          return py_stage
  
      def _to_java(self):
          """
          Transfer this instance to a Java TrainValidationSplit. Used for ML persistence.
  
          Returns
          -------
          py4j.java_gateway.JavaObject
              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())
          _java_obj.setParallelism(self.getParallelism())
          _java_obj.setCollectSubModels(self.getCollectSubModels())
          return _java_obj
  
  
  class TrainValidationSplitModel(Model, _TrainValidationSplitParams, MLReadable, MLWritable):
      """
      Model from train validation split.
  
      .. versionadded:: 2.0.0
      """
  
      def __init__(self, bestModel, validationMetrics=[], subModels=None):
          super(TrainValidationSplitModel, self).__init__()
          #: best model from train validation split
          self.bestModel = bestModel
          #: evaluated validation metrics
          self.validationMetrics = validationMetrics
          #: sub models from train validation split
          self.subModels = subModels
  
      def _transform(self, dataset):
          return self.bestModel.transform(dataset)
  
      def copy(self, extra=None):
          """
          Creates a copy of this instance with a randomly generated uid
          and some extra params. This copies the underlying bestModel,
          creates a deep copy of the embedded paramMap, and
          copies the embedded and extra parameters over.
          And, this creates a shallow copy of the validationMetrics.
          It does not copy the extra Params into the subModels.
  
          .. versionadded:: 2.0.0
  
          Parameters
          ----------
          extra : dict, optional
              Extra parameters to copy to the new instance
  
          Returns
          -------
          :py:class:`TrainValidationSplitModel`
              Copy of this instance
          """
          if extra is None:
              extra = dict()
          bestModel = self.bestModel.copy(extra)
          validationMetrics = list(self.validationMetrics)
          subModels = [model.copy() for model in self.subModels]
          return self._copyValues(
              TrainValidationSplitModel(bestModel, validationMetrics, subModels),
              extra=extra
          )
  
      @since("2.3.0")
      def write(self):
          """Returns an MLWriter instance for this ML instance."""
          if _ValidatorSharedReadWrite.is_java_convertible(self):
              return JavaMLWriter(self)
          return TrainValidationSplitModelWriter(self)
  
      @classmethod
      @since("2.3.0")
      def read(cls):
          """Returns an MLReader instance for this class."""
          return TrainValidationSplitModelReader(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.
          sc = SparkContext._active_spark_context
          bestModel = JavaParams._from_java(java_stage.bestModel())
          validationMetrics = _java2py(sc, java_stage.validationMetrics())
          estimator, epms, evaluator = super(TrainValidationSplitModel,
                                             cls)._from_java_impl(java_stage)
          # Create a new instance of this stage.
          py_stage = cls(bestModel=bestModel,
                         validationMetrics=validationMetrics)
          params = {
              "evaluator": evaluator,
              "estimator": estimator,
              "estimatorParamMaps": epms,
              "trainRatio": java_stage.getTrainRatio(),
              "seed": java_stage.getSeed(),
          }
          for param_name, param_val in params.items():
              py_stage = py_stage._set(**{param_name: param_val})
  
          if java_stage.hasSubModels():
              py_stage.subModels = [JavaParams._from_java(sub_model)
                                    for sub_model in java_stage.subModels()]
  
          py_stage._resetUid(java_stage.uid())
          return py_stage
  
      def _to_java(self):
          """
          Transfer this instance to a Java TrainValidationSplitModel. Used for ML persistence.
  
          Returns
          -------
          py4j.java_gateway.JavaObject
              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, self.validationMetrics))
          estimator, epms, evaluator = super(TrainValidationSplitModel, self)._to_java_impl()
  
          params = {
              "evaluator": evaluator,
              "estimator": estimator,
              "estimatorParamMaps": epms,
              "trainRatio": self.getTrainRatio(),
              "seed": self.getSeed(),
          }
          for param_name, param_val in params.items():
              java_param = _java_obj.getParam(param_name)
              pair = java_param.w(param_val)
              _java_obj.set(pair)
  
          if self.subModels is not None:
              java_sub_models = [sub_model._to_java() for sub_model in self.subModels]
              _java_obj.setSubModels(java_sub_models)
  
          return _java_obj
  
  
  if __name__ == "__main__":
      import doctest
  
      from pyspark.sql import SparkSession
      globs = globals().copy()
  
      # The small batch size here ensures that we see multiple batches,
      # even in these small test examples:
      spark = SparkSession.builder\
          .master("local[2]")\
          .appName("ml.tuning tests")\
          .getOrCreate()
      sc = spark.sparkContext
      globs['sc'] = sc
      globs['spark'] = spark
      (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
      spark.stop()
      if failure_count:
          sys.exit(-1)
  