`sparknlp_jsl.annotator.deid.replacer`#

Module Contents#

Classes#

Replacer

Replaces entities in the original text with new ones.

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

Bases: sparknlp_jsl.common.AnnotatorModelInternal

Replaces entities in the original text with new ones.

This class allows to replace entities in the original text with the ones obtained with, for example, :py:class:: `DeIdentificationModel`_ or :py:class:: `DateNormalizer`_.

useReplacement#

Specifies whether to use the replacement field from the metadata if it exists. Default: True.

Type:: bool

noneValuesTo#

Action to take when encountering a value of ‘NONE’ in the annotation.

Type:: str

placeHolder#

The value to replace ‘NONE’ values with when noneValuesTo is set to ‘place_holder’.

Type:: str

placeHolderDelimiters#

An array of two strings used as delimiters to wrap the placeholder or entity field. Default is [‘<’, ‘>’].

Type:: list

mappingsColumn#

This column maps the annotations to their corresponding chunks before the entities are replaced.

Type:: str

returnEntityMappings#

With this property you select if you want to return mapping column.

Type:: bool

staticEntityMappingsFallback#

Fallback option for static entity mappings. Allowed values: ‘entity’, ‘place_holder’, ‘skip’, ‘error’.

Type:: str

staticEntityMappings#

Static entity mappings. A dictionary with entity types as keys and replacement values as values.

Type:: dict

Examples:

>>> documentAssembler = DocumentAssembler()\
...     .setInputCol("text")\
...     .setOutputCol("sentence")

>>> tokenizer = Tokenizer()\
...     .setInputCols("sentence")\
...     .setOutputCol("token")

>>> word_embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
...         .setInputCols(["sentence", "token"])\
...         .setOutputCol("embeddings")

>>> clinical_ner = MedicalNerModel.pretrained("ner_deid_generic_augmented", "en", "clinical/models") \
...         .setInputCols(["sentence", "token", "embeddings"]) \
...         .setOutputCol("ner")

>>> ner_converter_name = NerConverterInternal()\
...         .setInputCols(["sentence","token","ner"])\
...         .setOutputCol("ner_chunk")

>>> nameChunkObfuscator = NameChunkObfuscatorApproach()\
...     .setInputCols("ner_chunk")\
...     .setOutputCol("replacement")\
...     .setRefFileFormat("csv")\
...     .setObfuscateRefFile("names_test.txt")\
...     .setRefSep("#")

>>> replacer_name = Replacer()\
...     .setInputCols("replacement","sentence")\
...     .setOutputCol("obfuscated_document_name")\
...     .setUseReplacement(True)

>>> nlpPipeline = Pipeline(stages=[
...         documentAssembler,
...         tokenizer,
...         word_embeddings,
...         clinical_ner,
...         ner_converter_name,
...         nameChunkObfuscator,
...         replacer_name,
...         ])

>>> empty_data = spark.createDataFrame([[""]]).toDF("text")
>>> model_chunck_obfuscator = nlpPipeline.fit(empty_data)
>>> sample_text = '''John Davies is a 62 y.o. patient admitted. Mr. Davies was seen by attending physician Dr. Lorand and was scheduled for emergency assessment.'''
>>> lmodel = LightPipeline(model_chunck_obfuscator)
>>> res = lmodel.fullAnnotate(sample_text)
>>> print("Original text.  : ", res[0]['sentence'][0].result)
>>> print("Obfuscated text : ", res[0]['obfuscated_document_name'][0].result)
Original text.  :  John Davies is a 62 y.o. patient admitted. Mr. Davies was seen by attending physician Dr. Lorand and was scheduled for emergency assessment.
Obfuscated text :  Fitzpatrick is a <AGE> y.o. patient admitted. Mr. Bowman was seen by attending physician Dr. Acosta and was scheduled for emergency assessment.

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

mappingsColumn#

name = 'Replacer'#

noneValuesTo#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

placeHolder#

placeHolderDelimiters#

returnEntityMappings#

skipLPInputColsValidation = True#

staticEntityMappingsFallback#

uid#

useReplacement#

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

getStaticEntityMappings()#: Gets the value of staticEntityMappings.

getUseReplacement()#: Gets the value of useReplacement or its default value.

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.

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

setMappingsColumn(value: str)#

This column maps the annotations to their corresponding chunks before the entities are replaced.

Parameters:: value (str) – Column name for mapping

setNoneValuesTo(value: str)#

Determines the action to take when encountering a value of ‘NONE’ in the annotation.: This parameter can take one of the following three string values: - “entity”: Replaces ‘NONE’ values with the entity field extracted from the annotation, if available. If the entity field is not available, it uses the string “NONE” wrapped by the specified delimiters. - “place_holder”: Replaces ‘NONE’ values with a placeholder string wrapped by the specified delimiters. - “skip”: Retains the original annotation result or uses the target_text from the annotation’s metadata if available. - “prioritizestatic_entity”: If a static entity mapping is available for the entity type, it will use this values for mapping. Allowed Values: “entity”, “place_holder”, “skip” Error Handling: If an unrecognized value is provided, an IllegalArgumentException will be thrown.

Parameters:: value (str) – Action to take when encountering a value of ‘NONE’ in the annotation.

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()#

setPlaceHolder(value: str)#

Specifies the placeholder string to use when noneValuesTo is set to “place_holder”.: This placeholder string will be wrapped by the delimiters if placeHolderDelimiters are defined.

Parameters:: value (str) – The value to replace ‘NONE’ values with when noneValuesTo is set to ‘place_holder’

setPlaceHolderDelimiters(value: list)#

An array of two strings used as delimiters to wrap the placeholder or entity field: when noneValuesTo is set to “place_holder” or “entity”. The first element of the array is the prefix delimiter,and the second element is the suffix delimiter. Default is [‘<’, ‘>’]

Parameters:: value (list) – An array of two strings used as delimiters to wrap the placeholder or entity field.

setReturnEntityMappings(value: bool)#

With this property you select if you want to return mapping column

Parameters:: value (bool) – True for returning mapping column, False otherwise.

setStaticEntityMappings(value: dict)#

Static entity mappings. A dictionary with entity types as keys and replacement values as values.

Parameters:: value (dict) – Static entity mappings.

setStaticEntityMappingsFallback(value: str)#

Fallback option for static entity mappings. Allowed values: ‘entity’, ‘place_holder’, ‘skip’, ‘error’

Parameters:: value (str) – Fallback option for static entity mappings.

setUseReplacement(value: bool)#

Specifies whether to use the replacement field from the metadata if it exists.

Parameters:: value (bool) – True for use replacement field in metadata, False otherwise.

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.deid.replacer#

Module Contents#

Classes#

`sparknlp_jsl.annotator.deid.replacer`#