sparknlp_jsl.annotator.ChunkFiltererApproach#

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

Bases: AnnotatorApproach

Model that Filters entities coming from CHUNK annotations. Filters can be set via a white list of terms or a regular expression.: White list criteria is enabled by default. To use regex, criteria has to be set to regex.

Input Annotation types	Output Annotation type
`DOCUMENT, CHUNK, ASSERTION`	`CHUNK`

Parameters:

whiteList

If defined, list of entities to process. The rest will be ignored

regex

If defined, list of entities to process. The rest will be ignored.

criteria

Tag representing what is the criteria to filter the chunks. possibles values (assertion|isIn|regex): isIn : Filter by the chunk regex : Filter using a regex

entitiesConfidence

Path to csv with pairs (entity,confidenceThreshold). Filter the chunks with entities which have confidence lower than the confidence threshold.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.common import *
>>> from sparknlp.annotator import *
>>> from sparknlp.training import *
>>> import sparknlp_jsl
>>> from sparknlp_jsl.base import *
>>> from sparknlp_jsl.annotator import *
>>> from pyspark.ml import Pipeline
>>> data = spark.createDataFrame([["Has a past history of gastroenteritis and stomach pain, however patient ..."]]).toDF("text")
>>> docAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> sentenceDetector = SentenceDetector().setInputCols(["document"]).setOutputCol("sentence")
>>> tokenizer = Tokenizer().setInputCols(["sentence"]).setOutputCol("token")
>>> posTagger = PerceptronModel.pretrained()     ...    .setInputCols(["sentence", "token"])     ...    .setOutputCol("pos")
>>> chunker = Chunker()     ...   .setInputCols(["pos", "sentence"])     ...   .setOutputCol("chunk")     ...   .setRegexParsers(["(<NN>)+"])
...
>>> chunkerFilter = ChunkFiltererApproach()     ...   .setInputCols(["sentence","chunk"])     ...   .setOutputCol("filtered")     ...   .setCriteria("isin")     ...   .setWhiteList(["gastroenteritis"])
...
>>> pipeline = Pipeline(stages=[
...   docAssembler,
...   sentenceDetector,
...   tokenizer,
...   posTagger,
...   chunker,
...   chunkerFilter])
...
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(chunk)").show(truncate=False)

>>> result.selectExpr("explode(chunk)").show(truncate=False)
+---------------------------------------------------------------------------------+
|col                                                                              |
+---------------------------------------------------------------------------------+
|{chunk, 11, 17, history, {sentence -> 0, chunk -> 0}, []}                        |
|{chunk, 22, 36, gastroenteritis, {sentence -> 0, chunk -> 1}, []}                |
|{chunk, 42, 53, stomach pain, {sentence -> 0, chunk -> 2}, []}                   |
|{chunk, 64, 70, patient, {sentence -> 0, chunk -> 3}, []}                        |
|{chunk, 81, 110, stomach pain now.We don't care, {sentence -> 0, chunk -> 4}, []}|
|{chunk, 118, 132, gastroenteritis, {sentence -> 0, chunk -> 5}, []}              |
+---------------------------------------------------------------------------------+

>>> result.selectExpr("explode(filtered)").show(truncate=False)
+-------------------------------------------------------------------+
|col                                                                |
+-------------------------------------------------------------------+
|{chunk, 22, 36, gastroenteritis, {sentence -> 0, chunk -> 1}, []}  |
|{chunk, 118, 132, gastroenteritis, {sentence -> 0, chunk -> 5}, []}|
+-------------------------------------------------------------------+

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.
`setCriteria`(s)	Set tag representing what is the criteria to filter the chunks.
`setEntitiesConfidenceResource`(path[, ...])
`setFilterEntity`(s)	Set tag representing what is the criteria to filter the chunks.
`setInputCols`(*value)	Sets column names of input annotations.
`setLazyAnnotator`(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setRegex`(value)	Sets llist of regex to process.
`setWhiteList`(value)	Sets list of entities to process.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`criteria`
`entitiesConfidenceResource`
`filterValue`
`getter_attrs`
`inputCols`
`lazyAnnotator`
`name`
`outputCol`
`params`	Returns all params ordered by name.
`regex`
`whiteList`

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.

setCriteria(s)[source]#

Set tag representing what is the criteria to filter the chunks. possibles values (isIn|regex)

Parameters:

sstr: List of dash-separated pairs of named entities

setFilterEntity(s)[source]#

Set tag representing what is the criteria to filter the chunks. possibles values (assertion|isIn|regex)

Parameters:

sstr: possibles values result|entity.

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

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

setRegex(value)[source]#

Sets llist of regex to process. The rest will be ignored.

Parameters:

valuelist: List of dash-separated pairs of named entities

setWhiteList(value)[source]#

Sets list of entities to process. The rest will be ignored.

Parameters:

valuelist: If defined, list of entities to process. The rest will be ignored.

uid#: A unique id for the object.

write()#: Returns an MLWriter instance for this ML instance.