sparknlp_jsl.annotator.deid.replacer#

Classes#

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#

Enable or disable Replacement of entities. Default: True.

Type:

bool

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#

name = Replacer#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

skipLPInputColsValidation = True#

useReplacement#

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

getUseReplacement()#

Gets the value of useReplacement or its default value.

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.

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()#

setUseReplacement(value: bool)#

Enable or disable Replacement of entities

Parameters:

value (bool) – True for Replacing, False otherwise.

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.