sparknlp.base.RecursivePipeline

class sparknlp.base.RecursivePipeline(*args, **kwargs)[source]

Bases: pyspark.ml.pipeline.Pipeline, pyspark.ml.wrapper.JavaEstimator

Recursive pipelines are Spark NLP specific pipelines that allow a Spark ML Pipeline to know about itself on every Pipeline Stage task.

This allows annotators to utilize this same pipeline against external resources to process them in the same way the user decides.

Only some of the annotators take advantage of this. RecursivePipeline behaves exactly the same as normal Spark ML pipelines, so they can be used with the same intention.

Examples

>>> from sparknlp.annotator import *
>>> from sparknlp.base import *
>>> recursivePipeline = RecursivePipeline(stages=[
...     documentAssembler,
...     sentenceDetector,
...     tokenizer,
...     lemmatizer,
...     finisher
... ])

Methods

__init__(self[, stages])

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.

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.

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.

getStages()

Get pipeline stages.

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.

setParams(self[, stages])

Sets params for Pipeline.

setStages(value)

Set pipeline stages.

write()

Returns an MLWriter instance for this ML instance.

Attributes

params

Returns all params ordered by name.

stages

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

fit(dataset, params=None)

Fits a model to 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. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns

fitted model(s)

New in version 1.3.0.

fitMultiple(dataset, paramMaps)

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

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

  • paramMaps – A Sequence of param maps.

Returns

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.

New in version 2.3.0.

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.

getStages()

Get pipeline stages.

New in version 1.3.0.

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).

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.

setParams(self, stages=None)

Sets params for Pipeline.

New in version 1.3.0.

setStages(value)

Set pipeline stages.

Parameters

value – a list of transformers or estimators

Returns

the pipeline instance

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.