`sparknlp_jsl.annotator.deid.reIdentification`#

Module Contents#

Classes#

ReIdentification

Reidentifies obfuscated entities.

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

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

Reidentifies obfuscated entities.

Requires the outputs from the :py:class:: `DeIdentification`_ annotator as input. - deidentified document - deidentification mappings set with DeIdentification.setMappingsColumn.

To see how the entities are deidentified, please refer to the example of that class.

Examples: result contains the deidentified document and the mappings in a spark data frame. >>> reideintification = ( … ReIdentification() … .setInputCols(“dei”, “protectedEntities”) … .setOutputCol(“reid”) … ).transform(result)

Deidentified results:

>>> result.select("dei.result").show(truncate = false)
+--------------------------------------------------------------------------------------------------+
|result                                                                                            |
+--------------------------------------------------------------------------------------------------+
|[# 01010101 Date : 01/18/93 PCP : Dr. Gregory House , <AGE> years-old , Record date : 2079-11-14.]|
+--------------------------------------------------------------------------------------------------+

Reidentification results: >>> reideintification.selectExpr(“explode(reid.result)”).show(false) +———————————————————————————–+ |col | +-----------------------------------------------------------------------------------+ |# 7194334 Date : 01/13/93 PCP : Oliveira , 25 years-old , Record date : 2079-11-09.| +———————————————————————————–+

auxEncryptionKey#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = 'ReDeideintification'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = 'document'#

outputCol#

skipLPInputColsValidation = True#

uid = ''#

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

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

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()#

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.reIdentification#

Module Contents#

Classes#

`sparknlp_jsl.annotator.deid.reIdentification`#