CrossValidatorModel#

class pyspark.ml.tuning.CrossValidatorModel(bestModel, avgMetrics=None, subModels=None, stdMetrics=None)[source]#

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.

New in version 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.

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.
`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.
`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.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`estimator`
`estimatorParamMaps`
`evaluator`
`foldCol`
`numFolds`
`params`	Returns all params ordered by name.
`seed`
`bestModel`	best model from cross validation
`avgMetrics`	Average cross-validation metrics for each paramMap in CrossValidator.estimatorParamMaps, in the corresponding order.
`subModels`	sub model list from cross validation
`stdMetrics`	standard deviation of metrics for each paramMap in CrossValidator.estimatorParamMaps, in the corresponding order.

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 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.

New in version 1.4.0.

Parameters

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

Returns

CrossValidatorModel: 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

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.

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.

transform(dataset, params=None)#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters

datasetpyspark.sql.DataFrame: input dataset
paramsdict, optional: an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame: transformed dataset

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

New in version 2.3.0.

Attributes Documentation

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')#

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.')#

bestModel#: best model from cross validation

avgMetrics#: Average cross-validation metrics for each paramMap in CrossValidator.estimatorParamMaps, in the corresponding order.

subModels#: sub model list from cross validation

stdMetrics#: standard deviation of metrics for each paramMap in CrossValidator.estimatorParamMaps, in the corresponding order.

uid#: A unique id for the object.