`sparknlp_jsl.annotator.document_filterer_by_ner`#

Module Contents#

Classes#

DocumentFiltererByNER

Filters documents by the entity fields of the chunks.

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

Bases: sparknlp_jsl.common.AnnotatorModelInternal, sparknlp_jsl.annotator.WhiteBlackListParams

Filters documents by the entity fields of the chunks. Documents are filtered by the white list and black list. The white list is a list of entities that are allowed to pass the filter. The black list is a list of entities that are not allowed to pass the filter. The filter is case sensitive. If the caseSensitive is set to false, the filter is case in-sensitive.

Input Annotation Types	Output Annotation Type
`DOCUMENT, CHUNK`	`DOCUMENT`

Notes

A document may contain multiple chunks. If any of the chunks in the document is in the white list, the document will pass the filter. And white list has priority over black list.

Parameters:

blackList (list) – If defined, list of entities to ignore. The rest will be processed.
whiteList (list) – If defined, list of entities to process. The rest will be ignored.
caseSensitive (bool) – Determines whether the definitions of the white listed and black listed entities are case sensitive or not.
outputAsDocument (bool) – Whether to return all sentences joined into a single document. Default is False.
joinString (str) – This parameter specifies the string that will be inserted between results of documents when combining them into a single result if outputAsDocument is set to True. Default is “ “.

Examples

>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from sparknlp_jsl.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> sentenceDetector = SentenceDetectorDLModel.pretrained("sentence_detector_dl_healthcare", "en", "clinical/models") \
...     .setInputCols("document").setOutputCol("sentence")
>>> tokenizer = Tokenizer().setInputCols("sentence").setOutputCol("token")
>>> word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models") \
...     .setInputCols(["sentence", "token"]).setOutputCol("embeddings")
>>> ner_jsl = MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models") \
...     .setInputCols(["sentence", "token", "embeddings"]).setOutputCol("ner")
>>> ner_converter = NerConverterInternal() \
...     .setInputCols(["sentence", "token", "ner"]).setOutputCol("ner_chunk")
>>> filterer = DocumentFiltererByNER() \
...     .setInputCols(["sentence", "ner_chunk"])\
...     .setOutputCol("filterer")\
...     .setWhiteList(["Disease_Syndrome_Disorder"])\
...     .setCaseSensitive(False)
>>> data = spark.createDataFrame([["Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus." +
...        "Most people infected with the virus will experience mild to moderate respiratory illness and recover without requiring special treatment." +
...        "However, some will become seriously ill and require medical attention. " +
...        "Older people and those with underlying medical conditions like cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness." +
...        "Anyone can get sick with COVID-19 and become seriously ill or die at any age." +
...        "The best way to prevent and slow down transmission is to be well informed about the disease and how the virus spreads." +
...        "Protect yourself and others from infection by staying at least 1 metre apart from others, wearing a properly fitted mask, and washing your hands or using an alcohol-based rub frequently." +
...        "Get vaccinated when it’s your turn and follow local guidance." +
...        "The virus can spread from an infected person’s mouth or nose in small liquid particles when they cough, sneeze, speak, sing or breathe. " +
...        "These particles range from larger respiratory droplets to smaller aerosols. It is important to practice respiratory etiquette, for example by coughing into a flexed elbow, and to stay home and self-isolate until you recover if you feel unwell."
...     ]]).toDF("text")
>>> pipeline = Pipeline()\
...     .setStages([documentAssembler, sentenceDetector, tokenizer, word_embeddings, ner_jsl, ner_converter, filterer])
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(filterer) as filter").show(truncate=False)
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|filter                                                                                                                                                                                                                               |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|{document, 0, 86, Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus., {sentence -> 0}, []}                                                                                                      |
|{document, 87, 223, Most people infected with the virus will experience mild to moderate respiratory illness and recover without requiring special treatment., {sentence -> 1}, []}                                                  |
|{document, 295, 473, Older people and those with underlying medical conditions like cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness., {sentence -> 3}, []}       |
|{document, 669, 854, Protect yourself and others from infection by staying at least 1 metre apart from others, wearing a properly fitted mask, and washing your hands or using an alcohol-based rub frequently., {sentence -> 6}, []}|
|{document, 916, 1050, The virus can spread from an infected person’s mouth or nose in small liquid particles when they cough, sneeze, speak, sing or breathe., {sentence -> 8}, []}                                                  |
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

blackList#

caseSensitive#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

joinString#

lazyAnnotator#

name = 'DocumentFiltererByNER'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputAsDocument#

outputCol#

skipLPInputColsValidation = True#

uid#

whiteList#

clear(param: pyspark.ml.param.Param) → None#: Clears a param from the param map if it has been explicitly set.

copy(extra: pyspark.ml._typing.ParamMap | None = None) → JP#

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: str | Param) → str#: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str#: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: pyspark.ml._typing.ParamMap | None = None) → pyspark.ml._typing.ParamMap#

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: str) → Any#
getOrDefault(param: Param[T]) → T: 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: str) → Param#: Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

hasDefault(param: str | Param[Any]) → bool#: Checks whether a param has a default value.

hasParam(paramName: str) → bool#: Tests whether this instance contains a param with a given (string) name.

inputColsValidation(value)#

isDefined(param: str | Param[Any]) → bool#: Checks whether a param is explicitly set by user or has a default value.

isSet(param: str | Param[Any]) → bool#: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL#: 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: str) → None#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: Param, value: Any) → None#: 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.

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

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()#

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: pyspark.sql.dataframe.DataFrame, params: pyspark.ml._typing.ParamMap | None = None) → pyspark.sql.dataframe.DataFrame#

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

sparknlp_jsl.annotator.document_filterer_by_ner#

Module Contents#

Classes#

`sparknlp_jsl.annotator.document_filterer_by_ner`#