sparknlp_jsl.annotator.ChunkMapperApproach#

class sparknlp_jsl.annotator.ChunkMapperApproach(classname='com.johnsnowlabs.nlp.annotators.chunker.ChunkMapperApproach')[source]#

Bases: AnnotatorApproach

The chunk mapper Approach load a JsonDictionary that have the relations to be mapped in the ChunkMapperModel

Input Annotation types

Output Annotation type

CHUNK

LABEL_DEPENDENCY

Input Annotation types	Output Annotation type
`CHUNK`	`LABEL_DEPENDENCY`

Parameters:

dictionary: Dictionary path where is the json that contains the mappinmgs columns
rel: Relation that we going to use to map the chunk
lowerCase: Parameter to decide if we going to use the chunk mapper or not

mappings

[{labeled_dependency, 22, 35, Analgesic, {chunk -> 0, relation -> action, confidence -> 0.56995, all_relations -> Antipyretic, entity -> Warfarina Lusa, sentence -> 0}, []}, {labeled_dependency, 41, 50, NONE, {entity -> amlodipine, sentence -> 0, chunk -> 1, confidence -> 0.9989}, []}, {labeled_dependency, 55, 56, NONE, {entity -> MG, sentence -> 0, chunk -> 2, confidence -> 0.9123}, []}]

Methods

`__init__`([classname])
`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.
`setAllowMultiTokenChunk`(mc)	Whether if we skyp relations with multitokens Parameters ---------- mc : bool "Whether to decide if we want to send multi-chunk tokens or only single token chunks
`setDictionary`(p)	Sets if we want to use 'bow' for word embeddings or 'sentence' for sentences" Parameters ---------- path : str Path where is the dictionary
`setInputCols`(*value)	Sets column names of input annotations.
`setLazyAnnotator`(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
`setLowerCase`(lc)	Set if we want to save the keys of the dictionary in lower case or not Parameters ---------- lc : bool Parameter that select if you want to use the keys in lower case or not
`setMultivaluesRelations`(mc)	Whether to decide if we want to send multi-chunk tokens or only single token chunks Parameters ---------- mc : bool "Whether to decide if we want to send multi-chunk tokens or only single token chunks
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setRel`(r)
`setRels`(rs)
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`allowMultiTokenChunk`
`dictionary`
`getter_attrs`
`inputCols`
`lazyAnnotator`
`lowerCase`
`multivaluesRelations`
`name`
`outputCol`
`params`	Returns all params ordered by name.
`rel`
`rels`

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.

setAllowMultiTokenChunk(mc)[source]#: Whether if we skyp relations with multitokens Parameters ———- mc : bool

“Whether to decide if we want to send multi-chunk tokens or only single token chunks

setDictionary(p)[source]#: Sets if we want to use ‘bow’ for word embeddings or ‘sentence’ for sentences” Parameters ———- path : str

Path where is the dictionary

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(lc)[source]#: Set if we want to save the keys of the dictionary in lower case or not Parameters ———- lc : bool

Parameter that select if you want to use the keys in lower case or not

setMultivaluesRelations(mc)[source]#

Whether to decide if we want to send multi-chunk tokens or only single token chunks Parameters ———-

mcbool
“Whether to decide if we want to send multi-chunk tokens or only single token chunks

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.