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

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.

sparknlp.annotator.StopWordsCleaner

sparknlp.annotator.SymmetricDeleteModel