sparknlp_jsl.annotator.chunker.mapper2_chunk#

Classes#

class Mapper2Chunk#

Bases: sparknlp_jsl.annotator.AnnotatorTransformer, sparknlp_jsl.annotator.AnnotatorProperties

This annotator converts ‘LABELED_DEPENDENCY’ type annotations coming from [[ChunkMapper]] into ‘CHUNK’ type to create new chunk-type column,

compatible with annotators that use chunk type as input.

Input Annotation types	Output Annotation type
LABELED_DEPENDENCY	CHUNK

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

>>> # Sample data
>>> text = "Patient resting in bed. Patient given azithromycin without any difficulty. Patient denies nausea at this time. zofran declined. Patient is also having intermittent sweating"
>>> data = spark.createDataFrame([[text]]).toDF("text")

>>> # Define the Spark NLP pipeline stages
>>> documentAssembler = DocumentAssembler()     >>>     .setInputCol("text")     >>>     .setOutputCol("document")

>>> sentenceDetector = SentenceDetector()     >>>     .setInputCols(["document"])     >>>     .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_jsl", "en", "clinical/models")     >>>     .setInputCols(["sentence", "token", "embeddings"])     >>>     .setOutputCol("ner")

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

>>> chunkMapper = ChunkMapperModel.pretrained("drug_action_treatment_mapper", "en", "clinical/models")     >>>     .setInputCols(["ner_chunk"])     >>>     .setOutputCol("relations")     >>>     .setRels(["action"])

>>> mapper2chunk = Mapper2Chunk()     >>>     .setInputCols(["relations"])     >>>     .setOutputCol("chunk")     >>>     .setFilterNoneValues(True)

>>> # Build the pipeline
>>> pipeline = Pipeline(stages=[
>>>     documentAssembler,
>>>     sentenceDetector,
>>>     tokenizer,
>>>     word_embeddings,
>>>     clinical_ner,
>>>     ner_converter,
>>>     chunkMapper,
>>>     mapper2chunk
>>> ]).fit(data)

>>> # Transform the data using the pipeline
>>> result = pipeline.transform(data)

>>> result.selectExpr("chunk.result", "chunk.annotatorType").show(truncate=False)

result	annotatorType
[bactericidal, antiemetic]	[chunk, chunk]

filterNoneValues#

getter_attrs = []#

inputAnnotatorTypes#

name = 'Mapper2Chunk'#

outputAnnotatorType = 'chunk'#

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

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.

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.

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.

setFilterNoneValues(value)#

Whether to filter ‘NONE’ values. Default is false.

Parameters:

value (bool) – Whether to filter ‘NONE’ values.

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.