Source code for pyspark.ml.tuning

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import os
import sys
import itertools
from multiprocessing.pool import ThreadPool
from typing import (
    Any,
    Callable,
    Dict,
    Iterable,
    List,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
    cast,
    overload,
    TYPE_CHECKING,
)

import numpy as np

from pyspark import keyword_only, since, inheritable_thread_target
from pyspark.ml import Estimator, Transformer, Model
from pyspark.ml.common import inherit_doc, _py2java, _java2py
from pyspark.ml.evaluation import Evaluator, JavaEvaluator
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
from pyspark.sql.dataframe import DataFrame

if TYPE_CHECKING:
    from pyspark.ml._typing import ParamMap
    from py4j.java_gateway import JavaObject
    from py4j.java_collections import JavaArray
    from pyspark.core.context import SparkContext

__all__ = [
    "ParamGridBuilder",
    "CrossValidator",
    "CrossValidatorModel",
    "TrainValidationSplit",
    "TrainValidationSplitModel",
]


def _parallelFitTasks(
    est: Estimator,
    train: DataFrame,
    eva: Evaluator,
    validation: DataFrame,
    epm: Sequence["ParamMap"],
    collectSubModel: bool,
) -> List[Callable[[], Tuple[int, float, Transformer]]]:
    """
    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() -> Tuple[int, float, Transformer]:
        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)


[docs]class ParamGridBuilder: 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) -> None: self._param_grid: "ParamMap" = {}
[docs] @since("1.4.0") def addGrid(self, param: Param[Any], values: List[Any]) -> "ParamGridBuilder": """ 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
@overload def baseOn(self, __args: "ParamMap") -> "ParamGridBuilder": ... @overload def baseOn(self, *args: Tuple[Param, Any]) -> "ParamGridBuilder": ...
[docs] @since("1.4.0") def baseOn(self, *args: Union["ParamMap", Tuple[Param, Any]]) -> "ParamGridBuilder": """ 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
[docs] @since("1.4.0") def build(self) -> List["ParamMap"]: """ 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: Iterable[Param], values: Iterable[Any] ) -> Sequence[Tuple[Param, Any]]: 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[Estimator] = Param( Params._dummy(), "estimator", "estimator to be cross-validated" ) estimatorParamMaps: Param[List["ParamMap"]] = Param( Params._dummy(), "estimatorParamMaps", "estimator param maps" ) evaluator: Param[Evaluator] = Param( Params._dummy(), "evaluator", "evaluator used to select hyper-parameters that maximize the validator metric", ) @since("2.0.0") def getEstimator(self) -> Estimator: """ Gets the value of estimator or its default value. """ return self.getOrDefault(self.estimator) @since("2.0.0") def getEstimatorParamMaps(self) -> List["ParamMap"]: """ Gets the value of estimatorParamMaps or its default value. """ return self.getOrDefault(self.estimatorParamMaps) @since("2.0.0") def getEvaluator(self) -> Evaluator: """ Gets the value of evaluator or its default value. """ return self.getOrDefault(self.evaluator) @classmethod def _from_java_impl( cls, java_stage: "JavaObject" ) -> Tuple[Estimator, List["ParamMap"], Evaluator]: """ Return Python estimator, estimatorParamMaps, and evaluator from a Java ValidatorParams. """ # Load information from java_stage to the instance. estimator: Estimator = JavaParams._from_java(java_stage.getEstimator()) evaluator: 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) -> Tuple["JavaObject", "JavaObject", "JavaObject"]: """ Return Java estimator, estimatorParamMaps, and evaluator from this Python instance. """ from pyspark.core.context import SparkContext gateway = SparkContext._gateway assert gateway is not None and SparkContext._jvm is not None 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] = estimator._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 = cast(JavaEstimator, self.getEstimator())._to_java() java_evaluator = cast(JavaEvaluator, self.getEvaluator())._to_java() return java_estimator, java_epms, java_evaluator class _ValidatorSharedReadWrite: @staticmethod def meta_estimator_transfer_param_maps_to_java( pyEstimator: Estimator, pyParamMaps: Sequence["ParamMap"] ) -> "JavaArray": from pyspark.core.context import SparkContext pyStages = MetaAlgorithmReadWrite.getAllNestedStages(pyEstimator) stagePairs = list(map(lambda stage: (stage, cast(JavaParams, stage)._to_java()), pyStages)) sc = SparkContext._active_spark_context assert ( sc is not None and SparkContext._jvm is not None and SparkContext._gateway is not None ) 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 = cast(JavaParams, 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: Estimator, javaParamMaps: "JavaArray" ) -> List["ParamMap"]: from pyspark.core.context import SparkContext pyStages = MetaAlgorithmReadWrite.getAllNestedStages(pyEstimator) stagePairs = list(map(lambda stage: (stage, cast(JavaParams, stage)._to_java()), pyStages)) sc = SparkContext._active_spark_context assert sc is not None and sc._jvm is not None 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() pyValue: Any 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: _ValidatorParams) -> bool: 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: str, instance: _ValidatorParams, sc: "SparkContext", extraMetadata: Optional[Dict[str, Any]] = None, ) -> None: numParamsNotJson = 0 jsonEstimatorParamMaps = [] for paramMap in instance.getEstimatorParamMaps(): jsonParamMap = [] for p, v in paramMap.items(): jsonParam: Dict[str, Any] = {"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 cast(MLWritable, 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 Estimator/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") cast(MLWritable, instance.getEvaluator()).save(evaluatorPath) estimatorPath = os.path.join(path, "estimator") cast(MLWritable, instance.getEstimator()).save(estimatorPath) @staticmethod def load( path: str, sc: "SparkContext", metadata: Dict[str, Any] ) -> Tuple[Dict[str, Any], Estimator, Evaluator, List["ParamMap"]]: evaluatorPath = os.path.join(path, "evaluator") evaluator: Evaluator = DefaultParamsReader.loadParamsInstance(evaluatorPath, sc) estimatorPath = os.path.join(path, "estimator") 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: _ValidatorParams) -> None: 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 " "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: MLWriter) -> bool: 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 # type: ignore[attr-defined] _save_with_persist_submodels_no_submodels_found_err: str = ( "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["CrossValidator"]): def __init__(self, cls: Type["CrossValidator"]): super(CrossValidatorReader, self).__init__() self.cls = cls def load(self, path: str) -> "CrossValidator": metadata = DefaultParamsReader.loadMetadata(path, self.sc) if not DefaultParamsReader.isPythonParamsInstance(metadata): return JavaMLReader(self.cls).load(path) # type: ignore[arg-type] 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: "CrossValidator"): super(CrossValidatorWriter, self).__init__() self.instance = instance def saveImpl(self, path: str) -> None: _ValidatorSharedReadWrite.validateParams(self.instance) _ValidatorSharedReadWrite.saveImpl(path, self.instance, self.sc) @inherit_doc class CrossValidatorModelReader(MLReader["CrossValidatorModel"]): def __init__(self, cls: Type["CrossValidatorModel"]): super(CrossValidatorModelReader, self).__init__() self.cls = cls def load(self, path: str) -> "CrossValidatorModel": metadata = DefaultParamsReader.loadMetadata(path, self.sc) if not DefaultParamsReader.isPythonParamsInstance(metadata): return JavaMLReader(self.cls).load(path) # type: ignore[arg-type] else: metadata, estimator, evaluator, estimatorParamMaps = _ValidatorSharedReadWrite.load( path, self.sc, metadata ) numFolds = metadata["paramMap"]["numFolds"] bestModelPath = os.path.join(path, "bestModel") bestModel: Model = DefaultParamsReader.loadParamsInstance(bestModelPath, self.sc) avgMetrics = metadata["avgMetrics"] if "stdMetrics" in metadata: stdMetrics = metadata["stdMetrics"] else: stdMetrics = None 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=cast(List[List[Model]], subModels), stdMetrics=stdMetrics, ) 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: "CrossValidatorModel"): super(CrossValidatorModelWriter, self).__init__() self.instance = instance def saveImpl(self, path: str) -> None: _ValidatorSharedReadWrite.validateParams(self.instance) instance = self.instance persistSubModels = _ValidatorSharedReadWrite.getValidatorModelWriterPersistSubModelsParam( self ) extraMetadata = {"avgMetrics": instance.avgMetrics, "persistSubModels": persistSubModels} if instance.stdMetrics: extraMetadata["stdMetrics"] = instance.stdMetrics _ValidatorSharedReadWrite.saveImpl(path, instance, self.sc, extraMetadata=extraMetadata) bestModelPath = os.path.join(path, "bestModel") cast(MLWritable, 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}") cast(MLWritable, instance.subModels[splitIndex][paramIndex]).save(modelPath) class _CrossValidatorParams(_ValidatorParams): """ Params for :py:class:`CrossValidator` and :py:class:`CrossValidatorModel`. .. versionadded:: 3.0.0 """ numFolds: Param[int] = Param( Params._dummy(), "numFolds", "number of folds for cross validation", typeConverter=TypeConverters.toInt, ) foldCol: Param[str] = 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: Any): super(_CrossValidatorParams, self).__init__(*args) self._setDefault(numFolds=3, foldCol="") @since("1.4.0") def getNumFolds(self) -> int: """ Gets the value of numFolds or its default value. """ return self.getOrDefault(self.numFolds) @since("3.1.0") def getFoldCol(self) -> str: """ Gets the value of foldCol or its default value. """ return self.getOrDefault(self.foldCol)
[docs]class CrossValidator( Estimator["CrossValidatorModel"], _CrossValidatorParams, HasParallelism, HasCollectSubModels, MLReadable["CrossValidator"], 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 CrossValidator, ParamGridBuilder, 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... """ _input_kwargs: Dict[str, Any] @keyword_only def __init__( self, *, estimator: Optional[Estimator] = None, estimatorParamMaps: Optional[List["ParamMap"]] = None, evaluator: Optional[Evaluator] = None, numFolds: int = 3, seed: Optional[int] = None, parallelism: int = 1, collectSubModels: bool = False, foldCol: str = "", ) -> None: """ __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)
[docs] @keyword_only @since("1.4.0") def setParams( self, *, estimator: Optional[Estimator] = None, estimatorParamMaps: Optional[List["ParamMap"]] = None, evaluator: Optional[Evaluator] = None, numFolds: int = 3, seed: Optional[int] = None, parallelism: int = 1, collectSubModels: bool = False, foldCol: str = "", ) -> "CrossValidator": """ 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)
[docs] @since("2.0.0") def setEstimator(self, value: Estimator) -> "CrossValidator": """ Sets the value of :py:attr:`estimator`. """ return self._set(estimator=value)
[docs] @since("2.0.0") def setEstimatorParamMaps(self, value: List["ParamMap"]) -> "CrossValidator": """ Sets the value of :py:attr:`estimatorParamMaps`. """ return self._set(estimatorParamMaps=value)
[docs] @since("2.0.0") def setEvaluator(self, value: Evaluator) -> "CrossValidator": """ Sets the value of :py:attr:`evaluator`. """ return self._set(evaluator=value)
[docs] @since("1.4.0") def setNumFolds(self, value: int) -> "CrossValidator": """ Sets the value of :py:attr:`numFolds`. """ return self._set(numFolds=value)
[docs] @since("3.1.0") def setFoldCol(self, value: str) -> "CrossValidator": """ Sets the value of :py:attr:`foldCol`. """ return self._set(foldCol=value)
[docs] def setSeed(self, value: int) -> "CrossValidator": """ Sets the value of :py:attr:`seed`. """ return self._set(seed=value)
[docs] def setParallelism(self, value: int) -> "CrossValidator": """ Sets the value of :py:attr:`parallelism`. """ return self._set(parallelism=value)
[docs] def setCollectSubModels(self, value: bool) -> "CrossValidator": """ Sets the value of :py:attr:`collectSubModels`. """ return self._set(collectSubModels=value)
@staticmethod def _gen_avg_and_std_metrics(metrics_all: List[List[float]]) -> Tuple[List[float], List[float]]: avg_metrics = np.mean(metrics_all, axis=0) std_metrics = np.std(metrics_all, axis=0) return list(avg_metrics), list(std_metrics) def _fit(self, dataset: DataFrame) -> "CrossValidatorModel": est = self.getOrDefault(self.estimator) epm = self.getOrDefault(self.estimatorParamMaps) numModels = len(epm) eva = self.getOrDefault(self.evaluator) nFolds = self.getOrDefault(self.numFolds) metrics_all = [[0.0] * numModels for i in range(nFolds)] 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 = map( inheritable_thread_target, _parallelFitTasks(est, train, eva, validation, epm, collectSubModelsParam), ) for j, metric, subModel in pool.imap_unordered(lambda f: f(), tasks): metrics_all[i][j] = metric if collectSubModelsParam: assert subModels is not None subModels[i][j] = subModel validation.unpersist() train.unpersist() metrics, std_metrics = CrossValidator._gen_avg_and_std_metrics(metrics_all) if eva.isLargerBetter(): bestIndex = np.argmax(metrics) else: bestIndex = np.argmin(metrics) bestModel = est.fit(dataset, epm[bestIndex]) return self._copyValues( CrossValidatorModel(bestModel, metrics, cast(List[List[Model]], subModels), std_metrics) ) def _kFold(self, dataset: DataFrame) -> List[Tuple[DataFrame, DataFrame]]: 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: int) -> bool: 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
[docs] def copy(self, extra: Optional["ParamMap"] = None) -> "CrossValidator": """ 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
[docs] @since("2.3.0") def write(self) -> MLWriter: """Returns an MLWriter instance for this ML instance.""" if _ValidatorSharedReadWrite.is_java_convertible(self): return JavaMLWriter(self) # type: ignore[arg-type] return CrossValidatorWriter(self)
[docs] @classmethod @since("2.3.0") def read(cls) -> CrossValidatorReader: """Returns an MLReader instance for this class.""" return CrossValidatorReader(cls)
@classmethod def _from_java(cls, java_stage: "JavaObject") -> "CrossValidator": """ 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) -> "JavaObject": """ 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
[docs]class CrossValidatorModel( Model, _CrossValidatorParams, MLReadable["CrossValidatorModel"], 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 Notes ----- Since version 3.3.0, CrossValidatorModel contains a new attribute "stdMetrics", which represent standard deviation of metrics for each paramMap in CrossValidator.estimatorParamMaps. """ def __init__( self, bestModel: Model, avgMetrics: Optional[List[float]] = None, subModels: Optional[List[List[Model]]] = None, stdMetrics: Optional[List[float]] = 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 or [] #: sub model list from cross validation self.subModels = subModels #: standard deviation of metrics for each paramMap in #: CrossValidator.estimatorParamMaps, in the corresponding order. self.stdMetrics = stdMetrics or [] def _transform(self, dataset: DataFrame) -> DataFrame: return self.bestModel.transform(dataset)
[docs] def copy(self, extra: Optional["ParamMap"] = None) -> "CrossValidatorModel": """ 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) assert self.subModels is not None subModels = [ [sub_model.copy() for sub_model in fold_sub_models] for fold_sub_models in self.subModels ] stdMetrics = list(self.stdMetrics) return self._copyValues( CrossValidatorModel(bestModel, avgMetrics, subModels, stdMetrics), extra=extra )
[docs] @since("2.3.0") def write(self) -> MLWriter: """Returns an MLWriter instance for this ML instance.""" if _ValidatorSharedReadWrite.is_java_convertible(self): return JavaMLWriter(self) # type: ignore[arg-type] return CrossValidatorModelWriter(self)
[docs] @classmethod @since("2.3.0") def read(cls) -> CrossValidatorModelReader: """Returns an MLReader instance for this class.""" return CrossValidatorModelReader(cls)
@classmethod def _from_java(cls, java_stage: "JavaObject") -> "CrossValidatorModel": """ Given a Java CrossValidatorModel, create and return a Python wrapper of it. Used for ML persistence. """ from pyspark.core.context import SparkContext sc = SparkContext._active_spark_context assert sc is not None bestModel: Model = 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) -> "JavaObject": """ Transfer this instance to a Java CrossValidatorModel. Used for ML persistence. Returns ------- py4j.java_gateway.JavaObject Java object equivalent to this instance. """ from pyspark.core.context import SparkContext sc = SparkContext._active_spark_context assert sc is not None _java_obj = JavaParams._new_java_obj( "org.apache.spark.ml.tuning.CrossValidatorModel", self.uid, cast(JavaParams, 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 = [ [cast(JavaParams, 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["TrainValidationSplit"]): def __init__(self, cls: Type["TrainValidationSplit"]): super(TrainValidationSplitReader, self).__init__() self.cls = cls def load(self, path: str) -> "TrainValidationSplit": metadata = DefaultParamsReader.loadMetadata(path, self.sc) if not DefaultParamsReader.isPythonParamsInstance(metadata): return JavaMLReader(self.cls).load(path) # type: ignore[arg-type] 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: "TrainValidationSplit"): super(TrainValidationSplitWriter, self).__init__() self.instance = instance def saveImpl(self, path: str) -> None: _ValidatorSharedReadWrite.validateParams(self.instance) _ValidatorSharedReadWrite.saveImpl(path, self.instance, self.sc) @inherit_doc class TrainValidationSplitModelReader(MLReader["TrainValidationSplitModel"]): def __init__(self, cls: Type["TrainValidationSplitModel"]): super(TrainValidationSplitModelReader, self).__init__() self.cls = cls def load(self, path: str) -> "TrainValidationSplitModel": metadata = DefaultParamsReader.loadMetadata(path, self.sc) if not DefaultParamsReader.isPythonParamsInstance(metadata): return JavaMLReader(self.cls).load(path) # type: ignore[arg-type] else: metadata, estimator, evaluator, estimatorParamMaps = _ValidatorSharedReadWrite.load( path, self.sc, metadata ) bestModelPath = os.path.join(path, "bestModel") bestModel: Model = 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=cast(Optional[List[Model]], 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: "TrainValidationSplitModel"): super(TrainValidationSplitModelWriter, self).__init__() self.instance = instance def saveImpl(self, path: str) -> None: _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") cast(MLWritable, 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}") cast(MLWritable, instance.subModels[paramIndex]).save(modelPath) class _TrainValidationSplitParams(_ValidatorParams): """ Params for :py:class:`TrainValidationSplit` and :py:class:`TrainValidationSplitModel`. .. versionadded:: 3.0.0 """ trainRatio: Param[float] = 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: Any): super(_TrainValidationSplitParams, self).__init__(*args) self._setDefault(trainRatio=0.75) @since("2.0.0") def getTrainRatio(self) -> float: """ Gets the value of trainRatio or its default value. """ return self.getOrDefault(self.trainRatio)
[docs]class TrainValidationSplit( Estimator["TrainValidationSplitModel"], _TrainValidationSplitParams, HasParallelism, HasCollectSubModels, MLReadable["TrainValidationSplit"], 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 TrainValidationSplit, ParamGridBuilder >>> 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... """ _input_kwargs: Dict[str, Any] @keyword_only def __init__( self, *, estimator: Optional[Estimator] = None, estimatorParamMaps: Optional[List["ParamMap"]] = None, evaluator: Optional[Evaluator] = None, trainRatio: float = 0.75, parallelism: int = 1, collectSubModels: bool = False, seed: Optional[int] = None, ) -> 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)
[docs] @since("2.0.0") @keyword_only def setParams( self, *, estimator: Optional[Estimator] = None, estimatorParamMaps: Optional[List["ParamMap"]] = None, evaluator: Optional[Evaluator] = None, trainRatio: float = 0.75, parallelism: int = 1, collectSubModels: bool = False, seed: Optional[int] = None, ) -> "TrainValidationSplit": """ 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)
[docs] @since("2.0.0") def setEstimator(self, value: Estimator) -> "TrainValidationSplit": """ Sets the value of :py:attr:`estimator`. """ return self._set(estimator=value)
[docs] @since("2.0.0") def setEstimatorParamMaps(self, value: List["ParamMap"]) -> "TrainValidationSplit": """ Sets the value of :py:attr:`estimatorParamMaps`. """ return self._set(estimatorParamMaps=value)
[docs] @since("2.0.0") def setEvaluator(self, value: Evaluator) -> "TrainValidationSplit": """ Sets the value of :py:attr:`evaluator`. """ return self._set(evaluator=value)
[docs] @since("2.0.0") def setTrainRatio(self, value: float) -> "TrainValidationSplit": """ Sets the value of :py:attr:`trainRatio`. """ return self._set(trainRatio=value)
[docs] def setSeed(self, value: int) -> "TrainValidationSplit": """ Sets the value of :py:attr:`seed`. """ return self._set(seed=value)
[docs] def setParallelism(self, value: int) -> "TrainValidationSplit": """ Sets the value of :py:attr:`parallelism`. """ return self._set(parallelism=value)
[docs] def setCollectSubModels(self, value: bool) -> "TrainValidationSplit": """ Sets the value of :py:attr:`collectSubModels`. """ return self._set(collectSubModels=value)
def _fit(self, dataset: DataFrame) -> "TrainValidationSplitModel": 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 = map( inheritable_thread_target, _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: assert subModels is not None subModels[j] = subModel train.unpersist() validation.unpersist() if eva.isLargerBetter(): bestIndex = np.argmax(cast(List[float], metrics)) else: bestIndex = np.argmin(cast(List[float], metrics)) bestModel = est.fit(dataset, epm[bestIndex]) return self._copyValues( TrainValidationSplitModel( bestModel, cast(List[float], metrics), subModels, # type: ignore[arg-type] ) )
[docs] def copy(self, extra: Optional["ParamMap"] = None) -> "TrainValidationSplit": """ 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
[docs] @since("2.3.0") def write(self) -> MLWriter: """Returns an MLWriter instance for this ML instance.""" if _ValidatorSharedReadWrite.is_java_convertible(self): return JavaMLWriter(self) # type: ignore[arg-type] return TrainValidationSplitWriter(self)
[docs] @classmethod @since("2.3.0") def read(cls) -> TrainValidationSplitReader: """Returns an MLReader instance for this class.""" return TrainValidationSplitReader(cls)
@classmethod def _from_java(cls, java_stage: "JavaObject") -> "TrainValidationSplit": """ 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) -> "JavaObject": """ 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
[docs]class TrainValidationSplitModel( Model, _TrainValidationSplitParams, MLReadable["TrainValidationSplitModel"], MLWritable ): """ Model from train validation split. .. versionadded:: 2.0.0 """ def __init__( self, bestModel: Model, validationMetrics: Optional[List[float]] = None, subModels: Optional[List[Model]] = None, ): super(TrainValidationSplitModel, self).__init__() #: best model from train validation split self.bestModel = bestModel #: evaluated validation metrics self.validationMetrics = validationMetrics or [] #: sub models from train validation split self.subModels = subModels def _transform(self, dataset: DataFrame) -> DataFrame: return self.bestModel.transform(dataset)
[docs] def copy(self, extra: Optional["ParamMap"] = None) -> "TrainValidationSplitModel": """ 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) assert self.subModels is not None subModels = [model.copy() for model in self.subModels] return self._copyValues( TrainValidationSplitModel(bestModel, validationMetrics, subModels), extra=extra )
[docs] @since("2.3.0") def write(self) -> MLWriter: """Returns an MLWriter instance for this ML instance.""" if _ValidatorSharedReadWrite.is_java_convertible(self): return JavaMLWriter(self) # type: ignore[arg-type] return TrainValidationSplitModelWriter(self)
[docs] @classmethod @since("2.3.0") def read(cls) -> TrainValidationSplitModelReader: """Returns an MLReader instance for this class.""" return TrainValidationSplitModelReader(cls)
@classmethod def _from_java(cls, java_stage: "JavaObject") -> "TrainValidationSplitModel": """ Given a Java TrainValidationSplitModel, create and return a Python wrapper of it. Used for ML persistence. """ from pyspark.core.context import SparkContext # Load information from java_stage to the instance. sc = SparkContext._active_spark_context assert sc is not None bestModel: Model = 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) -> "JavaObject": """ Transfer this instance to a Java TrainValidationSplitModel. Used for ML persistence. Returns ------- py4j.java_gateway.JavaObject Java object equivalent to this instance. """ from pyspark.core.context import SparkContext sc = SparkContext._active_spark_context assert sc is not None _java_obj = JavaParams._new_java_obj( "org.apache.spark.ml.tuning.TrainValidationSplitModel", self.uid, cast(JavaParams, 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 = [ cast(JavaParams, 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)