`sparknlp_jsl.annotator.resolution2_chunk`#

Contains classes for Resolution2Chunk.

Module Contents#

Classes#

Resolution2Chunk

Converts Resolution type annotations into CHUNK type with the

class Resolution2Chunk#

Bases: sparknlp.internal.AnnotatorTransformer, sparknlp_jsl.common.AnnotatorProperties

Converts Resolution 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.

Input Annotation types	Output Annotation type
`Resolution`	`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

>>> documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("ner_chunk")

>>> sbert_embedder = BertSentenceEmbeddings.pretrained('sbiobert_base_cased_mli', 'en','clinical/models')    >>>  .setInputCols(["ner_chunk"])    >>>  .setOutputCol("sentence_embeddings")    >>>  .setCaseSensitive(False)

>>> rxnorm_resolver = SentenceEntityResolverModel.pretrained("sbiobertresolve_rxnorm_augmented","en", "clinical/models")    >>>  .setInputCols(["sentence_embeddings"])    >>>  .setOutputCol("rxnorm_code")
>>> data = spark.createDataFrame(
>>>    [["I'm ready!"], ["If I could put into words how much I love waking up at 6 am on Mondays I would."]]).toDF(
>>>    "text")
>>> resolver2chunk = Resolution2Chunk().setInputCols("resolution").setOutputCol("chunk")

>>> pipeline = Pipeline().setStages([documentAssembler, sbert_embedder, rxnorm_resolver, resolver2chunk]).fit(data)
>>> result = pipeline.transform(data)
>>> result.selectExpr("chunk.result", "chunk.annotatorType").show(truncate=False)
        +--------+-------------+
        |result  |annotatorType|
        +--------+-------------+
        |[219400]|[chunk]      |
        |[13369] |[chunk]      |
        +--------+-------------+

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = Resolution2Chunk#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

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

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.

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, 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.

sparknlp_jsl.annotator.resolution2_chunk#

Module Contents#

Classes#

`sparknlp_jsl.annotator.resolution2_chunk`#