CrossValidator#

class pyspark.ml.tuning.CrossValidator(*, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3, seed=None, parallelism=1, collectSubModels=False, foldCol='')[source]#

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.

New in version 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...

Methods

`clear`(param)	Clears a param from the param map if it has been explicitly set.
`copy`([extra])	Creates a copy of this instance with a randomly generated uid and some extra params.
`explainParam`(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
`explainParams`()	Returns the documentation of all params with their optionally default values and user-supplied values.
`extractParamMap`([extra])	Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.
`fit`(dataset[, params])	Fits a model to the input dataset with optional parameters.
`fitMultiple`(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
`getCollectSubModels`()	Gets the value of collectSubModels or its default value.
`getEstimator`()	Gets the value of estimator or its default value.
`getEstimatorParamMaps`()	Gets the value of estimatorParamMaps or its default value.
`getEvaluator`()	Gets the value of evaluator or its default value.
`getFoldCol`()	Gets the value of foldCol or its default value.
`getNumFolds`()	Gets the value of numFolds or its default value.
`getOrDefault`(param)	Gets the value of a param in the user-supplied param map or its default value.
`getParallelism`()	Gets the value of parallelism or its default value.
`getParam`(paramName)	Gets a param by its name.
`getSeed`()	Gets the value of seed or its default value.
`hasDefault`(param)	Checks whether a param has a default value.
`hasParam`(paramName)	Tests whether this instance contains a param with a given (string) name.
`isDefined`(param)	Checks whether a param is explicitly set by user or has a default value.
`isSet`(param)	Checks whether a param is explicitly set by user.
`load`(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
`read`()	Returns an MLReader instance for this class.
`save`(path)	Save this ML instance to the given path, a shortcut of 'write().save(path)'.
`set`(param, value)	Sets a parameter in the embedded param map.
`setCollectSubModels`(value)	Sets the value of `collectSubModels`.
`setEstimator`(value)	Sets the value of `estimator`.
`setEstimatorParamMaps`(value)	Sets the value of `estimatorParamMaps`.
`setEvaluator`(value)	Sets the value of `evaluator`.
`setFoldCol`(value)	Sets the value of `foldCol`.
`setNumFolds`(value)	Sets the value of `numFolds`.
`setParallelism`(value)	Sets the value of `parallelism`.
`setParams`(*[, estimator, ...])	setParams(self, *, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3, seed=None, parallelism=1, collectSubModels=False, foldCol=""): Sets params for cross validator.
`setSeed`(value)	Sets the value of `seed`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`collectSubModels`
`estimator`
`estimatorParamMaps`
`evaluator`
`foldCol`
`numFolds`
`parallelism`
`params`	Returns all params ordered by name.
`seed`

Methods Documentation

clear(param)#: Clears a param from the param map if it has been explicitly set.

copy(extra=None)[source]#

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.

New in version 1.4.0.

Parameters

extradict, optional: Extra parameters to copy to the new instance

Returns

CrossValidator: Copy of this instance

explainParam(param)#: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()#: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra=None)#

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values < user-supplied values < extra.

Parameters

extradict, optional: extra param values

Returns

dict: merged param map

fit(dataset, params=None)#

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramsdict or list or tuple, optional: an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns

Transformer or a list of Transformer: fitted model(s)

fitMultiple(dataset, paramMaps)#

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramMapscollections.abc.Sequence: A Sequence of param maps.

Returns

_FitMultipleIterator: A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

getCollectSubModels()#: Gets the value of collectSubModels or its default value.

getEstimator()#: Gets the value of estimator or its default value.

New in version 2.0.0.

getEstimatorParamMaps()#: Gets the value of estimatorParamMaps or its default value.

New in version 2.0.0.

getEvaluator()#: Gets the value of evaluator or its default value.

New in version 2.0.0.

getFoldCol()#: Gets the value of foldCol or its default value.

New in version 3.1.0.

getNumFolds()#: Gets the value of numFolds or its default value.

New in version 1.4.0.

getOrDefault(param)#: Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getParallelism()#: Gets the value of parallelism or its default value.

getParam(paramName)#: Gets a param by its name.

getSeed()#: Gets the value of seed or its default value.

hasDefault(param)#: Checks whether a param has a default value.

hasParam(paramName)#: Tests whether this instance contains a param with a given (string) name.

isDefined(param)#: Checks whether a param is explicitly set by user or has a default value.

isSet(param)#: Checks whether a param is explicitly set by user.

classmethod load(path)#: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read()[source]#: Returns an MLReader instance for this class.

New in version 2.3.0.

save(path)#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)#: Sets a parameter in the embedded param map.

setCollectSubModels(value)[source]#: Sets the value of collectSubModels.

setEstimator(value)[source]#: Sets the value of estimator.

New in version 2.0.0.

setEstimatorParamMaps(value)[source]#: Sets the value of estimatorParamMaps.

New in version 2.0.0.

setEvaluator(value)[source]#: Sets the value of evaluator.

New in version 2.0.0.

setFoldCol(value)[source]#: Sets the value of foldCol.

New in version 3.1.0.

setNumFolds(value)[source]#: Sets the value of numFolds.

New in version 1.4.0.

setParallelism(value)[source]#: Sets the value of parallelism.

setParams(*, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3, seed=None, parallelism=1, collectSubModels=False, foldCol='')[source]#: setParams(self, *, estimator=None, estimatorParamMaps=None, evaluator=None, numFolds=3, seed=None, parallelism=1, collectSubModels=False, foldCol=””): Sets params for cross validator.

New in version 1.4.0.

setSeed(value)[source]#: Sets the value of seed.

write()[source]#: Returns an MLWriter instance for this ML instance.

New in version 2.3.0.

Attributes Documentation

collectSubModels = Param(parent='undefined', name='collectSubModels', doc='Param for whether to collect a list of sub-models trained during tuning. If set to false, then only the single best sub-model will be available after fitting. If set to true, then all sub-models will be available. Warning: For large models, collecting all sub-models can cause OOMs on the Spark driver.')#

estimator = Param(parent='undefined', name='estimator', doc='estimator to be cross-validated')#

estimatorParamMaps = Param(parent='undefined', name='estimatorParamMaps', doc='estimator param maps')#

evaluator = Param(parent='undefined', name='evaluator', doc='evaluator used to select hyper-parameters that maximize the validator metric')#

foldCol = Param(parent='undefined', name='foldCol', doc="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.")#

numFolds = Param(parent='undefined', name='numFolds', doc='number of folds for cross validation')#

parallelism = Param(parent='undefined', name='parallelism', doc='the number of threads to use when running parallel algorithms (>= 1).')#

params#: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

seed = Param(parent='undefined', name='seed', doc='random seed.')#

uid#: A unique id for the object.