sparknlp.annotator.SymmetricDeleteApproach#

class sparknlp.annotator.SymmetricDeleteApproach[source]#

Bases: sparknlp.common.AnnotatorApproach

Trains a Symmetric Delete spelling correction algorithm. Retrieves tokens and utilizes distance metrics to compute possible derived words.

For instantiated/pretrained models, see SymmetricDeleteModel.

Input Annotation types	Output Annotation type
TOKEN	TOKEN

Parameters

dictionary

folder or file with text that teaches about the language

maxEditDistance

max edit distance characters to derive strings from a word, by default 3

frequencyThreshold

minimum frequency of words to be considered from training, by default 0

deletesThreshold

minimum frequency of corrections a word needs to have to be considered from training, by default 0

See also

NorvigSweetingApproach

for an alternative approach to spell checking

ContextSpellCheckerApproach

for a DL based approach

References

Inspired by SymSpell.

Examples

In this example, the dictionary "words.txt" has the form of:

...
gummy
gummic
gummier
gummiest
gummiferous
...

This dictionary is then set to be the basis of the spell checker.

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> spellChecker = SymmetricDeleteApproach() \
...     .setInputCols(["token"]) \
...     .setOutputCol("spell") \
...     .setDictionary("src/test/resources/spell/words.txt")
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     tokenizer,
...     spellChecker
... ])
>>> pipelineModel = pipeline.fit(trainingData)

Methods

__init__()
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.
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.
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.
setDeletesThreshold(v)	Sets minimum frequency of corrections a word needs to have to be considered from training, by default 0.
setDictionary(path[, token_pattern, ...])	Sets folder or file with text that teaches about the language.
setFrequencyThreshold(v)	Sets minimum frequency of words to be considered from training, by default 0.
setInputCols(*value)	Sets column names of input annotations.
setLazyAnnotator(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
setMaxEditDistance(v)	Sets max edit distance characters to derive strings from a word, by default 3.
setOutputCol(value)	Sets output column name of annotations.
setParamValue(paramName)	Sets the value of a parameter.
write()	Returns an MLWriter instance for this ML instance.

Attributes

corpus
deletesThreshold
dictionary
dupsLimit
frequencyThreshold
getter_attrs
inputCols
lazyAnnotator
maxEditDistance
outputCol
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 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

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.

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.

setDeletesThreshold(v)[source]#

Sets minimum frequency of corrections a word needs to have to be considered from training, by default 0.

Parameters

vint

Minimum frequency of corrections a word needs to have to be considered from training

setDictionary(path, token_pattern='\\S+', read_as='TEXT', options={'format': 'text'})[source]#

Sets folder or file with text that teaches about the language.

Parameters

pathstr

Path to the resource

token_patternstr, optional

Regex patttern to extract tokens, by default “S+”

read_asstr, optional

How to read the resource, by default ReadAs.TEXT

optionsdict, optional

Options for reading the resource, by default {“format”: “text”}

setFrequencyThreshold(v)[source]#

Sets minimum frequency of words to be considered from training, by default 0.

Parameters

vint

Minimum frequency of words to be considered from training

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

setMaxEditDistance(v)[source]#

Sets max edit distance characters to derive strings from a word, by default 3.

Parameters

vint

Max edit distance characters to derive strings from a word

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

uid#

A unique id for the object.

write()#

Returns an MLWriter instance for this ML instance.