CrossValidatorModel

class pyspark.ml.tuning.CrossValidatorModel(bestModel: pyspark.ml.base.Model, avgMetrics: Optional[List[float]] = None, subModels: Optional[List[List[pyspark.ml.base.Model]]] = None, stdMetrics: Optional[List[float]] = None)

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

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) → 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.

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: 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

getEstimator() → pyspark.ml.base.Estimator

Gets the value of estimator or its default value.

New in version 2.0.0.

getEstimatorParamMaps() → List[ParamMap]

Gets the value of estimatorParamMaps or its default value.

New in version 2.0.0.

getEvaluator() → pyspark.ml.evaluation.Evaluator

Gets the value of evaluator or its default value.

New in version 2.0.0.

getFoldCol() → str

Gets the value of foldCol or its default value.

New in version 3.1.0.

getNumFolds() → int

Gets the value of numFolds or its default value.

New in version 1.4.0.

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.

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.CrossValidatorModelReader

Returns an MLReader instance for this class.

New in version 2.3.0.

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.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame

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() → pyspark.ml.util.MLWriter

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: pyspark.ml.param.Param[str] = 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: pyspark.ml.param.Param[int] = 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.')

CrossValidator TrainValidationSplit