sparknlp.base.RecursivePipelineModel#

class sparknlp.base.RecursivePipelineModel(pipeline_model)[source]#

Bases: pyspark.ml.pipeline.PipelineModel

Fitted RecursivePipeline.

Behaves the same as a Spark PipelineModel does. Not intended to be initialized by itself. To create a RecursivePipelineModel please fit data to a RecursivePipeline.

Methods

__init__(pipeline_model)

clear(param)

Clears a param from the param map if it has been explicitly set.

copy([extra])

Creates a 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])

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.

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.

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

params

Returns all params ordered by name.

clear(param)#

Clears a param from the param map if it has been explicitly set.

copy(extra=None)#

Creates a copy of this instance.

Parameters

extra – extra parameters

Returns

new instance

New in version 1.4.0.

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

extra – extra param values

Returns

merged param map

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.

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).

property params#

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

classmethod read()#

Returns an MLReader instance for this class.

New in version 2.0.0.

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.

Parameters
  • dataset – input dataset, which is an instance of pyspark.sql.DataFrame

  • params – an optional param map that overrides embedded params.

Returns

transformed dataset

New in version 1.3.0.

uid#

A unique id for the object.

write()#

Returns an MLWriter instance for this ML instance.

New in version 2.0.0.