sparknlp_jsl.annotator.chunker.chunker_filterer#

Classes#

class ChunkFilterer(classname='com.johnsnowlabs.nlp.annotators.chunker.ChunkFilterer', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal, sparknlp_jsl.annotator.filtering_params.FilteringParams, sparknlp_jsl.annotator.handle_exception_params.HandleExceptionParams

ChunkFilterer can filter chunks coming from CHUNK annotations.

Filters can be set via white list and black list or a regular expression. White list criteria is enabled by default. To use regex, criteria has to be set to regex. Additionally, It can filter chunks according to the confidence of the chunk in the metadata.

Input Annotation types	Output Annotation type
DOCUMENT, CHUNK	CHUNK

Parameters:

• whiteList (list) – If defined, list of entities to process. The rest will be ignored.

• blackList (list) – If defined, list of entities to ignore. The rest will be processed.

• caseSensitive (bool) – Determines whether the definitions of the white listed and black listed entities are case sensitive. Default: True. If the filterValue is ‘entity’, ‘caseSensitive’ is always False.

• regex (list) – If defined, list of regex to process the chunks (Default: []).

• criteria (str) – Tag representing what is the criteria to filter the chunks. Possibles values are: - isIn: Filter by the chunk - regex: Filter by using a regex

• entitiesConfidence (dict[str, float]) – Pairs (entity,confidenceThreshold) to filter the chunks with entities which have confidence lower than the confidence threshold.

• filterValue (str) – Possible values are ‘result’ and ‘entity’. If the value is ‘result’, It filters according to the result of the Annotation. If the value is ‘entity’, It filters according to the entity field in the metadata of the Annotation.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp_jsl.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 = ChunkFilterer() \
...   .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}, []}|
+-------------------------------------------------------------------+

blackList#

caseSensitive#

criteria#

doExceptionHandling#

entitiesConfidence#

filterValue#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = ChunkFilterer#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

regex#

skipLPInputColsValidation = True#

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 (dict, optional) – Extra parameters to copy to the new instance

Returns:

Copy of this instance

Return type:

JavaParams

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 (dict, optional) – extra param values

Returns:

merged param map

Return type:

dict

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:

paramName (str) – 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.

inputColsValidation(value)#

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).

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.

setBlackList(value)#

Sets If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

Parameters:

value (List[str]) – If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

setCaseSensitive(value)#

Determines whether the definitions of the white listed and black listed entities are case sensitive or not.

Parameters:

value (bool) – Whether white listed and black listed entities are case sensitive or not. Default: True.

setCriteria(value)#

It is used to compare black and white listed values with the result of the Annotation.

Possible values are the following: ‘isin’, ‘regex’. Default: ‘isin’.

isin : Filter by the chunk regex : Filter by using a regex

Parameters:

value (string) – It is used to compare black and white listed values with the result of the Annotation. Possible values are the following: ‘isin’, ‘regex’. Default: ‘isin’.

setDenyList(value)#

Sets If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

Parameters:

value (List[str]) – If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

setDoExceptionHandling(value: bool)#

If True, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

Parameters:

value (bool) – If True, exceptions are handled.

setEntitiesConfidence(entities_confidence: dict)#

Sets the entitiesConfidence parameter.

Parameters:

entities_confidence (dict[str, float]) – Pairs (entity,confidenceThreshold) to filter the chunks with entities which have confidence lower than the confidence threshold.

setFilterEntity(filter_by: str)#

Sets the filterValue parameter.

If equal to “entity”, use the ner label to filter. If set to “result”, use the result attribute of the annotation to filter.

Parameters:

filter_by (str) – possibles values result|entity.

setFilterValue(value)#

Sets possible values ‘result’ or ‘entity’.

If the value is ‘result’, It filters according to the result of the Annotation. If the value is ‘entity’, It filters according to the entity field in the metadata of the Annotation.

Parameters:

value (string) – possible values are ‘result’ and ‘entity’.

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

*value (List[str]) – Input columns for the annotator

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:

value (bool) – Whether Annotator should be evaluated lazily in a RecursivePipeline

setOutputCol(value)#

Sets output column name of annotations.

Parameters:

value (str) – Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:

paramName (str) – Name of the parameter

setParams()#

setRegex(value)#

Sets If defined, list of regex to process the chunks.

Parameters:

value (List[str]) – If defined, list of regex to process the chunks

setWhiteList(value)#

Sets If defined, list of entities to process. The rest will be ignored. Do not include IOB prefix on labels

Parameters:

value (List[str]) – If defined, list of entities to process. The rest will be ignored. Do not include IOB prefix on labels

transform(dataset, params=None)#

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

• dataset (pyspark.sql.DataFrame) – input dataset

• params (dict, optional) – an optional param map that overrides embedded params.

Returns:

transformed dataset

Return type:

pyspark.sql.DataFrame

write()#

Returns an MLWriter instance for this ML instance.

class ChunkFiltererApproach(classname='com.johnsnowlabs.nlp.annotators.chunker.ChunkFiltererApproach')#

Bases: sparknlp_jsl.common.AnnotatorApproachInternal, sparknlp_jsl.annotator.filtering_params.FilteringParams, sparknlp_jsl.annotator.handle_exception_params.HandleExceptionParams

Trains a ChunkFilterer annotator.

ChunkFiltererApproach can filter chunks coming from CHUNK annotations. Filters can be set via white list and black list or a regular expression. White list criteria is enabled by default. To use regex, criteria has to be set to regex. Additionally, It can filter chunks according to the confidence of the chunk in the metadata.

Input Annotation types	Output Annotation type
DOCUMENT, CHUNK	CHUNK

Parameters:

• whiteList (list) – If defined, list of entities to process. The rest will be ignored.

• blackList (list) – If defined, list of entities to ignore. The rest will be processed.

• caseSensitive (bool) – Determines whether the definitions of the white listed and black listed entities are case sensitive. Default: True. If the filterValue is ‘entity’, ‘caseSensitive’ is always False.

• regex (str) – If defined, list of regex to process the chunks (Default: []).

• criteria (str) – Tag representing what is the criteria to filter the chunks. Possibles values are: - isIn: Filter by the chunk - regex: Filter using a regex

• filterValue (str) – If equal to “entity”, use the ner label to filter. If set to “result”, use the result attribute of the annotation to filter.

• entitiesConfidenceResource (str) – Path to a CSV file containing the entity pairs to remove chunks based on the confidance level. The CSV file should have two columns: entity and confidenceThreshold. The entity column should contain the entity name and the confidenceThreshold column should contain the confidence threshold to filter the chunks.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp_jsl.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])
...
>>> model = pipeline.fit(data)

blackList#

caseSensitive#

criteria#

doExceptionHandling#

entitiesConfidenceResource#

entitiesConfidenceResourceAsJsonString#

filterValue#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = ChunksFilterApproach#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

regex#

skipLPInputColsValidation = True#

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 (dict, optional) – Extra parameters to copy to the new instance

Returns:

Copy of this instance

Return type:

JavaParams

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 (dict, optional) – extra param values

Returns:

merged param map

Return type:

dict

fit(dataset, params=None)#

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

• dataset (pyspark.sql.DataFrame) – input dataset.

• params (dict or list or tuple, optional) – 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)

Return type:

Transformer or a list of Transformer

fitMultiple(dataset, paramMaps)#

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters:

• dataset (pyspark.sql.DataFrame) – input dataset.

• paramMaps (collections.abc.Sequence) – 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.

Return type:

_FitMultipleIterator

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:

paramName (str) – 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.

inputColsValidation(value)#

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).

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.

setBlackList(value)#

Sets If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

Parameters:

value (List[str]) – If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

setCaseSensitive(value)#

Determines whether the definitions of the white listed and black listed entities are case sensitive or not.

Parameters:

value (bool) – Whether white listed and black listed entities are case sensitive or not. Default: True.

setCriteria(value)#

It is used to compare black and white listed values with the result of the Annotation.

Possible values are the following: ‘isin’, ‘regex’. Default: ‘isin’.

isin : Filter by the chunk regex : Filter by using a regex

Parameters:

value (string) – It is used to compare black and white listed values with the result of the Annotation. Possible values are the following: ‘isin’, ‘regex’. Default: ‘isin’.

setDenyList(value)#

Sets If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

Parameters:

value (List[str]) – If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

setDoExceptionHandling(value: bool)#

If True, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

Parameters:

value (bool) – If True, exceptions are handled.

setEntitiesConfidenceResource(path: str, read_as: str = ReadAs.TEXT, options: dict = None)#

Sets the entitiesConfidenceResource parameter.

Parameters:

• path (str) – Path to csv with entity pairs to remove based on the confidence level

• read_as (str) – Read file as ‘TEXT’, ‘SPARK’, or ‘BINARY’.

• options (dict) – Options for reading the file.

setEntitiesConfidenceResourceAsJsonString(json)#

Sets the entitiesConfidenceResource parameter as JSON String.

Parameters:

json (str) – string given as JSON with entity pairs to remove chunks based on the confidence level

setFilterEntity(filter_by: str)#

Sets the filterValue parameter.

If equal to “entity”, use the ner label to filter. If set to “result”, use the result attribute of the annotation to filter.

Parameters:

filter_by (str) – possibles values result|entity.

setFilterValue(value)#

Sets possible values ‘result’ or ‘entity’.

If the value is ‘result’, It filters according to the result of the Annotation. If the value is ‘entity’, It filters according to the entity field in the metadata of the Annotation.

Parameters:

value (string) – possible values are ‘result’ and ‘entity’.

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

*value (List[str]) – Input columns for the annotator

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:

value (bool) – Whether Annotator should be evaluated lazily in a RecursivePipeline

setOutputCol(value)#

Sets output column name of annotations.

Parameters:

value (str) – Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:

paramName (str) – Name of the parameter

setRegex(value)#

Sets If defined, list of regex to process the chunks.

Parameters:

value (List[str]) – If defined, list of regex to process the chunks

setWhiteList(value)#

Sets If defined, list of entities to process. The rest will be ignored. Do not include IOB prefix on labels

Parameters:

value (List[str]) – If defined, list of entities to process. The rest will be ignored. Do not include IOB prefix on labels

write()#

Returns an MLWriter instance for this ML instance.