sparknlp.annotator.DocumentNormalizer

class sparknlp.annotator.DocumentNormalizer[source]

Bases: sparknlp.common.AnnotatorModel

Annotator which normalizes raw text from tagged text, e.g. scraped web pages or xml documents, from document type columns into Sentence.

Removes all dirty characters from text following one or more input regex patterns. Can apply not wanted character removal with a specific policy. Can apply lower case normalization.

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

Input Annotation types

Output Annotation type

DOCUMENT

DOCUMENT

Parameters
action

action to perform before applying regex patterns on text, by default “clean”

patterns

normalization regex patterns which match will be removed from document, by default [‘<[^>]*>’]

replacement

replacement string to apply when regexes match, by default ” “

lowercase

whether to convert strings to lowercase, by default False

policy

policy to remove pattern from text, by default “pretty_all”

encoding

file encoding to apply on normalized documents, by default “UTF-8”

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> cleanUpPatterns = ["<[^>]>"]
>>> documentNormalizer = DocumentNormalizer() \
...     .setInputCols("document") \
...     .setOutputCol("normalizedDocument") \
...     .setAction("clean") \
...     .setPatterns(cleanUpPatterns) \
...     .setReplacement(" ") \
...     .setPolicy("pretty_all") \
...     .setLowercase(True)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     documentNormalizer
... ])
>>> text = """
... <div id="theworldsgreatest" class='my-right my-hide-small my-wide toptext' style="font-family:'Segoe UI',Arial,sans-serif">
...     THE WORLD'S LARGEST WEB DEVELOPER SITE
...     <h1 style="font-size:300%;">THE WORLD'S LARGEST WEB DEVELOPER SITE</h1>
...     <p style="font-size:160%;">Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum..</p>
... </div>
... </div>"""
>>> data = spark.createDataFrame([[text]]).toDF("text")
>>> pipelineModel = pipeline.fit(data)
>>> result = pipelineModel.transform(data)
>>> result.selectExpr("normalizedDocument.result").show(truncate=False)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|result                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[ the world's largest web developer site the world's largest web developer site lorem ipsum is simply dummy text of the printing and typesetting industry. lorem ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. it has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. it was popularised in the 1960s with the release of letraset sheets containing lorem ipsum passages, and more recently with desktop publishing software like aldus pagemaker including versions of lorem ipsum..]|
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

Methods

__init__()

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).

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.

setAction(value)

Sets action to perform before applying regex patterns on text, by default "clean".

setEncoding(value)

Sets file encoding to apply on normalized documents, by default "UTF-8".

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setLowercase(value)

Sets whether to convert strings to lowercase, by default False.

setOutputCol(value)

Sets output column name of annotations.

setParamValue(paramName)

Sets the value of a parameter.

setParams()

setPatterns(value)

Sets normalization regex patterns which match will be removed from document, by default ['<[^>]*>'].

setPolicy(value)

Sets policy to remove pattern from text, by default "pretty_all".

setReplacement(value)

Sets replacement string to apply when regexes match, by default " ".

transform(dataset[, params])

Transforms the input dataset with optional parameters.

write()

Returns an MLWriter instance for this ML instance.

Attributes

action

encoding

getter_attrs

inputCols

lazyAnnotator

lowercase

outputCol

params

Returns all params ordered by name.

patterns

policy

replacement

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).

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.

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.

setAction(value)[source]

Sets action to perform before applying regex patterns on text, by default “clean”.

Parameters
valuestr

Action to perform before applying regex patterns

setEncoding(value)[source]

Sets file encoding to apply on normalized documents, by default “UTF-8”.

Parameters
valuestr

File encoding to apply on normalized documents, by default “UTF-8”

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

setLowercase(value)[source]

Sets whether to convert strings to lowercase, by default False.

Parameters
valuebool

Whether to convert strings to lowercase, by default False

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

setPatterns(value)[source]

Sets normalization regex patterns which match will be removed from document, by default [‘<[^>]*>’].

Parameters
valueList[str]

Normalization regex patterns which match will be removed from document

setPolicy(value)[source]

Sets policy to remove pattern from text, by default “pretty_all”.

Parameters
valuestr

Policy to remove pattern from text, by default “pretty_all”

setReplacement(value)[source]

Sets replacement string to apply when regexes match, by default ” “.

Parameters
valuestr

Replacement string to apply when regexes match

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.