TargetEncoderModel#

class pyspark.ml.feature.TargetEncoderModel(java_model=None)[source]#

Model fitted by TargetEncoder.

Added in version 4.0.0.

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 the same 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.
`getHandleInvalid`()	Gets the value of handleInvalid or its default value.
`getInputCol`()	Gets the value of inputCol or its default value.
`getInputCols`()	Gets the value of inputCols or its default value.
`getLabelCol`()	Gets the value of labelCol or its default value.
`getOrDefault`(param)	Gets the value of a param in the user-supplied param map or its default value.
`getOutputCol`()	Gets the value of outputCol or its default value.
`getOutputCols`()	Gets the value of outputCols or its default value.
`getParam`(paramName)	Gets a param by its name.
`getSmoothing`()	Gets the value of smoothing or its default value.
`getTargetType`()	Gets the value of targetType 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.
`setHandleInvalid`(value)	Sets the value of `handleInvalid`.
`setInputCol`(value)	Sets the value of `inputCol`.
`setInputCols`(value)	Sets the value of `inputCols`.
`setOutputCol`(value)	Sets the value of `outputCol`.
`setOutputCols`(value)	Sets the value of `outputCols`.
`setSmoothing`(value)	Sets the value of `smoothing`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`handleInvalid`
`inputCol`
`inputCols`
`labelCol`
`outputCol`
`outputCols`
`params`	Returns all params ordered by name.
`smoothing`
`targetType`

Methods Documentation

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

copy(extra=None)#

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters:

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

Returns:

JavaParams: 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

getHandleInvalid()#: Gets the value of handleInvalid or its default value.

getInputCol()#: Gets the value of inputCol or its default value.

getInputCols()#: Gets the value of inputCols or its default value.

getLabelCol()#: Gets the value of labelCol or its default value.

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.

getOutputCol()#: Gets the value of outputCol or its default value.

getOutputCols()#: Gets the value of outputCols or its default value.

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

getSmoothing()#: Gets the value of smoothing or its default value.

Added in version 4.0.0.

getTargetType()#: Gets the value of targetType or its default value.

Added in version 4.0.0.

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()#: 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.

setHandleInvalid(value)[source]#: Sets the value of handleInvalid.

Added in version 4.0.0.

setInputCol(value)[source]#: Sets the value of inputCol.

Added in version 4.0.0.

setInputCols(value)[source]#: Sets the value of inputCols.

Added in version 4.0.0.

setOutputCol(value)[source]#: Sets the value of outputCol.

Added in version 4.0.0.

setOutputCols(value)[source]#: Sets the value of outputCols.

Added in version 4.0.0.

setSmoothing(value)[source]#: Sets the value of smoothing.

Added in version 4.0.0.

transform(dataset, params=None)#

Transforms the input dataset with optional parameters.

Added 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()#: Returns an MLWriter instance for this ML instance.

Attributes Documentation

handleInvalid = Param(parent='undefined', name='handleInvalid', doc="How to handle invalid data during transform(). Options are 'keep' (invalid data presented as an extra categorical feature) or error (throw an error).")#

inputCol = Param(parent='undefined', name='inputCol', doc='input column name.')#

inputCols = Param(parent='undefined', name='inputCols', doc='input column names.')#

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')#

outputCol = Param(parent='undefined', name='outputCol', doc='output column name.')#

outputCols = Param(parent='undefined', name='outputCols', doc='output column names.')#

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

smoothing = Param(parent='undefined', name='smoothing', doc='value to smooth in-category averages with overall averages.')#

targetType = Param(parent='undefined', name='targetType', doc="whether the label is 'binary' or 'continuous'")#

uid#: A unique id for the object.