CrossValidator¶

class pyspark.ml.tuning.CrossValidator(*, estimator: Optional[pyspark.ml.base.Estimator] = None, estimatorParamMaps: Optional[List[ParamMap]] = None, evaluator: Optional[pyspark.ml.evaluation.Evaluator] = None, numFolds: int = 3, seed: Optional[int] = None, parallelism: int = 1, collectSubModels: bool = False, foldCol: str = '')¶

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.

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: pyspark.ml.param.Param) → None¶: Clears a param from the param map if it has been explicitly set.

copy(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.

Parameters

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

Returns

CrossValidator: Copy of this instance

explainParam(param: Union[str, pyspark.ml.param.Param]) → str¶: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

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

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap¶

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: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]¶

Fits a model to the input dataset with optional parameters.

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: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]¶

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

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() → bool¶: Gets the value of collectSubModels or its default value.

getEstimator() → pyspark.ml.base.Estimator¶: Gets the value of estimator or its default value.

getEstimatorParamMaps() → List[ParamMap]¶: Gets the value of estimatorParamMaps or its default value.

getEvaluator() → pyspark.ml.evaluation.Evaluator ¶: Gets the value of evaluator or its default value.

getFoldCol() → str¶: Gets the value of foldCol or its default value.

getNumFolds() → int¶: Gets the value of numFolds or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶: Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getParallelism() → int¶: Gets the value of parallelism or its default value.

getParam(paramName: str) → pyspark.ml.param.Param ¶: Gets a param by its name.

getSeed() → int¶: Gets the value of seed or its default value.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param has a default value.

hasParam(paramName: str) → bool¶: Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL¶: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → pyspark.ml.tuning.CrossValidatorReader¶: Returns an MLReader instance for this class.

save(path: str) → None¶: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None¶: Sets a parameter in the embedded param map.

setCollectSubModels(value: bool) → pyspark.ml.tuning.CrossValidator ¶: Sets the value of collectSubModels.

setEstimator(value: pyspark.ml.base.Estimator) → pyspark.ml.tuning.CrossValidator ¶: Sets the value of estimator.

setEstimatorParamMaps(value: List[ParamMap]) → CrossValidator¶: Sets the value of estimatorParamMaps.

setEvaluator(value: pyspark.ml.evaluation.Evaluator) → pyspark.ml.tuning.CrossValidator ¶: Sets the value of evaluator.

setFoldCol(value: str) → pyspark.ml.tuning.CrossValidator ¶: Sets the value of foldCol.

setNumFolds(value: int) → pyspark.ml.tuning.CrossValidator ¶: Sets the value of numFolds.

setParallelism(value: int) → pyspark.ml.tuning.CrossValidator ¶: Sets the value of parallelism.

setParams(*, estimator: Optional[pyspark.ml.base.Estimator] = None, estimatorParamMaps: Optional[List[ParamMap]] = None, evaluator: Optional[pyspark.ml.evaluation.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.

setSeed(value: int) → pyspark.ml.tuning.CrossValidator ¶: Sets the value of seed.

write() → pyspark.ml.util.MLWriter ¶: Returns an MLWriter instance for this ML instance.

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.')¶

ParamGridBuilder

CrossValidatorModel