sparknlp.annotator.StopWordsCleaner

class sparknlp.annotator.StopWordsCleaner(classname='com.johnsnowlabs.nlp.annotators.StopWordsCleaner', java_model=None)[source]

Bases: sparknlp.common.AnnotatorModel

This annotator takes a sequence of strings (e.g. the output of a Tokenizer, Normalizer, Lemmatizer, and Stemmer) and drops all the stop words from the input sequences.

By default, it uses stop words from MLlibs StopWordsRemover. Stop words can also be defined by explicitly setting them with setStopWords() or loaded from pretrained models using pretrained of its companion object.

>>> stopWords = StopWordsCleaner.pretrained() \
...     .setInputCols(["token"]) \
...     .setOutputCol("cleanTokens")

This will load the default pretrained model "stopwords_en".

For available pretrained models please see the Models Hub.

For extended examples of usage, see the Spark NLP Workshop.

Input Annotation types

Output Annotation type

TOKEN

TOKEN

Parameters
stopWords

The words to be filtered out, by default english stopwords from Spark ML

caseSensitive

Whether to do a case sensitive, by default False

locale

Locale of the input. ignored when case sensitive, by default locale of the JVM

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> sentenceDetector = SentenceDetector() \
...     .setInputCols(["document"]) \
...     .setOutputCol("sentence")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["sentence"]) \
...     .setOutputCol("token")
>>> stopWords = StopWordsCleaner() \
...     .setInputCols(["token"]) \
...     .setOutputCol("cleanTokens") \
...     .setCaseSensitive(False)
>>> pipeline = Pipeline().setStages([
...       documentAssembler,
...       sentenceDetector,
...       tokenizer,
...       stopWords
...     ])
>>> data = spark.createDataFrame([
...     ["This is my first sentence. This is my second."],
...     ["This is my third sentence. This is my forth."]
... ]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("cleanTokens.result").show(truncate=False)
+-------------------------------+
|result                         |
+-------------------------------+
|[first, sentence, ., second, .]|
|[third, sentence, ., forth, .] |
+-------------------------------+

Methods

__init__([classname, java_model])

Initialize this instance with a Java model object.

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.

getInputCols()

Gets current column names of input annotations.

getLazyAnnotator()

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param)

Gets the value of a param in the user-supplied param map or its default value.

getOutputCol()

Gets output column name of annotations.

getParam(paramName)

Gets a param by its name.

getParamValue(paramName)

Gets the value of a parameter.

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).

loadDefaultStopWords()

Loads the default stop words for the given language.

pretrained([name, lang, remote_loc])

Downloads and loads a pretrained model.

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.

setCaseSensitive(value)

Sets whether to do a case sensitive comparison, by default False.

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setLocale(value)

Sets locale of the input.

setOutputCol(value)

Sets output column name of annotations.

setParamValue(paramName)

Sets the value of a parameter.

setParams()

setStopWords(value)

Sets the words to be filtered out, by default english stopwords from Spark ML.

transform(dataset[, params])

Transforms the input dataset with optional parameters.

write()

Returns an MLWriter instance for this ML instance.

Attributes

caseSensitive

getter_attrs

inputCols

lazyAnnotator

locale

name

outputCol

params

Returns all params ordered by name.

stopWords

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

extra – Extra parameters to copy to the new instance

Returns

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

extra – extra param values

Returns

merged param map

getInputCols()

Gets current column names of input annotations.

getLazyAnnotator()

Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

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 output column name of annotations.

getParam(paramName)

Gets a param by its name.

getParamValue(paramName)

Gets the value of a parameter.

Parameters
paramNamestr

Name of the parameter

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).

loadDefaultStopWords()[source]

Loads the default stop words for the given language.

Supported languages: danish, dutch, english, finnish, french, german, hungarian, italian, norwegian, portuguese, russian, spanish, swedish, turkish

Parameters
languagestr, optional

Language stopwords to load, by default “english”

property params

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

static pretrained(name='stopwords_en', lang='en', remote_loc=None)[source]

Downloads and loads a pretrained model.

Parameters
namestr, optional

Name of the pretrained model, by default “stopwords_en”

langstr, optional

Language of the pretrained model, by default “en”

remote_locstr, optional

Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns
StopWordsCleaner

The restored model

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.

setCaseSensitive(value)[source]

Sets whether to do a case sensitive comparison, by default False.

Parameters
valuebool

Whether to do a case sensitive comparison

setInputCols(*value)

Sets column names of input annotations.

Parameters
*valuestr

Input columns for the annotator

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters
valuebool

Whether Annotator should be evaluated lazily in a RecursivePipeline

setLocale(value)[source]

Sets locale of the input. Ignored when case sensitive, by default locale of the JVM.

Parameters
valuestr

Locale of the input

setOutputCol(value)

Sets output column name of annotations.

Parameters
valuestr

Name of output column

setParamValue(paramName)

Sets the value of a parameter.

Parameters
paramNamestr

Name of the parameter

setStopWords(value)[source]

Sets the words to be filtered out, by default english stopwords from Spark ML.

Parameters
valueList[str]

The words to be filtered out

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.