Source code for pyspark.ml.tuning

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import itertools
import sys
from multiprocessing.pool import ThreadPool

import numpy as np

from pyspark import since, keyword_only
from pyspark.ml import Estimator, Model
from pyspark.ml.common import _py2java, _java2py
from pyspark.ml.param import Params, Param, TypeConverters
from pyspark.ml.param.shared import HasCollectSubModels, HasParallelism, HasSeed
from pyspark.ml.util import *
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.

    :param est: Estimator, the estimator to be fit.
    :param train: DataFrame, training data set, used for fitting.
    :param eva: Evaluator, used to compute `metric`
    :param validation: DataFrame, validation data set, used for evaluation.
    :param epm: Sequence of ParamMap, params maps to be used during fitting & evaluation.
    :param collectSubModel: Whether to collect sub model.
    :return: (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)


[docs]class ParamGridBuilder(object): r""" Builder for a param grid used in grid search-based model selection. >>> 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 .. versionadded:: 1.4.0 """ def __init__(self): self._param_grid = {}
[docs] @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
[docs] @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
[docs] @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 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) def __init__(self, *args): super(_CrossValidatorParams, self).__init__(*args) self._setDefault(numFolds=3) @since("1.4.0") def getNumFolds(self): """ Gets the value of numFolds or its default value. """ return self.getOrDefault(self.numFolds)
[docs]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. >>> 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... .. versionadded:: 1.4.0 """ @keyword_only def __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3, seed=None, parallelism=1, collectSubModels=False): """ __init__(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\ seed=None, parallelism=1, collectSubModels=False) """ 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=None, estimatorParamMaps=None, evaluator=None, numFolds=3, seed=None, parallelism=1, collectSubModels=False): """ setParams(self, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3,\ seed=None, parallelism=1, collectSubModels=False): Sets params for cross validator. """ kwargs = self._input_kwargs return self._set(**kwargs)
[docs] @since("2.0.0") def setEstimator(self, value): """ Sets the value of :py:attr:`estimator`. """ return self._set(estimator=value)
[docs] @since("2.0.0") def setEstimatorParamMaps(self, value): """ Sets the value of :py:attr:`estimatorParamMaps`. """ return self._set(estimatorParamMaps=value)
[docs] @since("2.0.0") def setEvaluator(self, value): """ Sets the value of :py:attr:`evaluator`. """ return self._set(evaluator=value)
[docs] @since("1.4.0") def setNumFolds(self, value): """ Sets the value of :py:attr:`numFolds`. """ return self._set(numFolds=value)
[docs] def setSeed(self, value): """ Sets the value of :py:attr:`seed`. """ return self._set(seed=value)
[docs] def setParallelism(self, value): """ Sets the value of :py:attr:`parallelism`. """ return self._set(parallelism=value)
[docs] 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) seed = self.getOrDefault(self.seed) h = 1.0 / nFolds randCol = self.uid + "_rand" df = dataset.select("*", rand(seed).alias(randCol)) 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)] for i in range(nFolds): validateLB = i * h validateUB = (i + 1) * h condition = (df[randCol] >= validateLB) & (df[randCol] < validateUB) validation = df.filter(condition).cache() train = df.filter(~condition).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))
[docs] @since("1.4.0") 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. :param extra: Extra parameters to copy to the new instance :return: 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): """Returns an MLWriter instance for this ML instance.""" return JavaMLWriter(self)
[docs] @classmethod @since("2.3.0") def read(cls): """Returns an MLReader instance for this class.""" return JavaMLReader(cls)
@classmethod def _from_java(cls, java_stage): """ Given a Java CrossValidator, create and return a Python wrapper of it. Used for ML persistence. """ estimator, epms, evaluator = super(CrossValidator, cls)._from_java_impl(java_stage) numFolds = java_stage.getNumFolds() seed = java_stage.getSeed() parallelism = java_stage.getParallelism() collectSubModels = java_stage.getCollectSubModels() # Create a new instance of this stage. py_stage = cls(estimator=estimator, estimatorParamMaps=epms, evaluator=evaluator, numFolds=numFolds, 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 CrossValidator. Used for ML persistence. :return: Java object equivalent to this instance. """ estimator, epms, evaluator = super(CrossValidator, self)._to_java_impl() _java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.CrossValidator", self.uid) _java_obj.setEstimatorParamMaps(epms) _java_obj.setEvaluator(evaluator) _java_obj.setEstimator(estimator) _java_obj.setSeed(self.getSeed()) _java_obj.setNumFolds(self.getNumFolds()) _java_obj.setParallelism(self.getParallelism()) _java_obj.setCollectSubModels(self.getCollectSubModels()) return _java_obj
[docs]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)
[docs] @since("1.4.0") 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. :param extra: Extra parameters to copy to the new instance :return: 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)
[docs] @since("2.3.0") def write(self): """Returns an MLWriter instance for this ML instance.""" return JavaMLWriter(self)
[docs] @classmethod @since("2.3.0") def read(cls): """Returns an MLReader instance for this class.""" return JavaMLReader(cls)
@classmethod def _from_java(cls, java_stage): """ Given a Java CrossValidatorModel, create and return a Python wrapper of it. Used for ML persistence. """ 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(), "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. :return: 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(), "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
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)
[docs]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. >>> 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... .. versionadded:: 2.0.0 """ @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)
[docs] @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)
[docs] @since("2.0.0") def setEstimator(self, value): """ Sets the value of :py:attr:`estimator`. """ return self._set(estimator=value)
[docs] @since("2.0.0") def setEstimatorParamMaps(self, value): """ Sets the value of :py:attr:`estimatorParamMaps`. """ return self._set(estimatorParamMaps=value)
[docs] @since("2.0.0") def setEvaluator(self, value): """ Sets the value of :py:attr:`evaluator`. """ return self._set(evaluator=value)
[docs] @since("2.0.0") def setTrainRatio(self, value): """ Sets the value of :py:attr:`trainRatio`. """ return self._set(trainRatio=value)
[docs] def setSeed(self, value): """ Sets the value of :py:attr:`seed`. """ return self._set(seed=value)
[docs] def setParallelism(self, value): """ Sets the value of :py:attr:`parallelism`. """ return self._set(parallelism=value)
[docs] 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))
[docs] @since("2.0.0") 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. :param extra: Extra parameters to copy to the new instance :return: 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): """Returns an MLWriter instance for this ML instance.""" return JavaMLWriter(self)
[docs] @classmethod @since("2.3.0") def read(cls): """Returns an MLReader instance for this class.""" return JavaMLReader(cls)
@classmethod def _from_java(cls, java_stage): """ Given a Java TrainValidationSplit, create and return a Python wrapper of it. Used for ML persistence. """ estimator, epms, evaluator = super(TrainValidationSplit, cls)._from_java_impl(java_stage) trainRatio = java_stage.getTrainRatio() seed = java_stage.getSeed() 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. :return: Java object equivalent to this instance. """ estimator, epms, evaluator = super(TrainValidationSplit, self)._to_java_impl() _java_obj = JavaParams._new_java_obj("org.apache.spark.ml.tuning.TrainValidationSplit", self.uid) _java_obj.setEstimatorParamMaps(epms) _java_obj.setEvaluator(evaluator) _java_obj.setEstimator(estimator) _java_obj.setTrainRatio(self.getTrainRatio()) _java_obj.setSeed(self.getSeed()) _java_obj.setParallelism(self.getParallelism()) _java_obj.setCollectSubModels(self.getCollectSubModels()) return _java_obj
[docs]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)
[docs] @since("2.0.0") 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. :param extra: Extra parameters to copy to the new instance :return: 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 )
[docs] @since("2.3.0") def write(self): """Returns an MLWriter instance for this ML instance.""" return JavaMLWriter(self)
[docs] @classmethod @since("2.3.0") def read(cls): """Returns an MLReader instance for this class.""" return JavaMLReader(cls)
@classmethod def _from_java(cls, java_stage): """ Given a Java TrainValidationSplitModel, create and return a Python wrapper of it. Used for ML persistence. """ # Load information from java_stage to the instance. 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. :return: Java object equivalent to this instance. """ sc = SparkContext._active_spark_context _java_obj = JavaParams._new_java_obj( "org.apache.spark.ml.tuning.TrainValidationSplitModel", self.uid, self.bestModel._to_java(), _py2java(sc, 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)