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.
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.
Gets current column names of input annotations.
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.
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.
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.
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.
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
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 typeParam
.
- 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.