sparknlp_jsl.annotator.deid.replacer#

Classes#

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

Bases: sparknlp_jsl.common.AnnotatorModelInternal, sparknlp_jsl.annotator.deid.aux_encryption_params.AuxEncryptionParams

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.

auxEncryptionKey#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

mappingsColumn#

name = 'Replacer'#

noneValuesTo#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = 'document'#

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

getAuxEncryptionKey()#

Returns the stored (wrapped) form of the aux encryption key, or an empty string when encryption is disabled.

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.

setAuxEncryptionKey(value: str)#

Sets the key used to encrypt and decrypt the mappings (aux) column.

When set on a de-identification annotator, the original values carried by the mappings column are encrypted. ReIdentification needs the very same key to restore the original text. When left unset, the mappings column is produced in clear text.

The key is never stored in clear text; it is wrapped before being kept in the annotator, so a saved pipeline never contains the raw value.

Examples

>>> de_identification = DeIdentification() \
...     .setReturnEntityMappings(True) \
...     .setMappingsColumn("aux") \
...     .setAuxEncryptionKey("my-secret-key")
>>> re_identification = ReIdentification() \
...     .setAuxEncryptionKey("my-secret-key")

Parameters:

value (str) – The key used to encrypt and decrypt the mappings column.

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.