sparknlp_jsl.legal.chunk_classification.deid.document_hashcoder#

Classes#

class LegalDocumentHashCoder#

Bases: sparknlp_jsl.annotator.DocumentHashCoder

Converts DOCUMENT type annotations into CHUNK type with the contents of a chunkCol.

Chunk text must be contained within input DOCUMENT. May be either StringType or ArrayType[StringType] (using setIsArray). Useful for annotators that require a CHUNK type input.

For more extended examples on document pre-processing see the Spark NLP Workshop.

Input Annotation types	Output Annotation type
DOCUMENT	CHUNK

Parameters:

• chunkCol – Column that contains the string. Must be part of DOCUMENT

• startCol – Column that has a reference of where the chunk begins

• startColByTokenIndex – Whether start column is prepended by whitespace tokens

• isArray – Whether the chunkCol is an array of strings, by default False

• failOnMissing – Whether to fail the job if a chunk is not found within document. Return empty otherwise

• lowerCase – Whether to lower case for matching case

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp_jsl.common import *
>>> from sparknlp.annotator import *
>>> from sparknlp.training import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> chunkAssembler = Doc2Chunk() \
...     .setInputCols("document") \
...     .setChunkCol("target") \
...     .setOutputCol("chunk") \
...     .setIsArray(True)
>>> data = spark.createDataFrame([[
...     "Spark NLP is an open-source text processing library for advanced natural language processing.",
...     ["Spark NLP", "text processing library", "natural language processing"]
... ]]).toDF("text", "target")
>>> pipeline = Pipeline().setStages([documentAssembler, chunkAssembler]).fit(data)
>>> result = pipeline.transform(data)
>>> result.selectExpr("chunk.result", "chunk.annotatorType").show(truncate=False)
+-----------------------------------------------------------------+---------------------+
|result                                                           |annotatorType        |
+-----------------------------------------------------------------+---------------------+
|[Spark NLP, text processing library, natural language processing]|[chunk, chunk, chunk]|
+-----------------------------------------------------------------+---------------------+

See also

Chunk2Doc

for converting CHUNK annotations to DOCUMENT

dateShiftColumn#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = 'DocumentHashCoder'#

newDateShift#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = 'chunk'#

outputCol#

patientIdColumn#

rangeDays#

seed#

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

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.

setDateShiftColumn(value)#

Sets column to be used for hash or predefined shift.

Parameters:

value (str) – Name of the column to be used

setIdColumn(value)#

Sets column that contains the string.

Parameters:

value (str) – Name of the column containing ID

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

setNewDateShift(value)#

Sets column that has a reference of where chunk begins.

Parameters:

value (str) – Name of the reference column

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()#

Sets the given parameters.

setPatientIdColumn(value)#

Sets column that contains the string.

Parameters:

value (str) – Name of the column containing patient ID

setRangeDays(value)#

Sets the range of dates to be sampled from.

Parameters:

value (int) – range of days to be sampled by the random generator

setSeed(value)#

Sets the seed for random number generator.

Parameters:

value (int) – seed for the random number generator

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.