sparknlp_jsl.annotator.ChunkMergeApproach#

class sparknlp_jsl.annotator.ChunkMergeApproach[source]#

Bases: AnnotatorApproach

Merges two chunk columns coming from two annotators(NER, ContextualParser or any other annotator producing chunks). The merger of the two chunk columns is made by selecting one chunk from one of the columns according to certain criteria. The decision on which chunk to select is made according to the chunk indices in the source document. (chunks with longer lengths and highest information will be kept from each source) Labels can be changed by setReplaceDictResource.

Input Annotation types	Output Annotation type
`CHUNK,CHUNK`	`CHUNK`

Parameters:

mergeOverlapping: whether to merge overlapping matched chunks. Defaults false
falsePositivesResource: file with false positive pairs
replaceDictResource: replace dictionary pairs
chunkPrecedence: Select what is the precedence when two chunks have the same start and end indices. Possible values are [entity|identifier|field]
blackList: If defined, list of entities to ignore. The rest will be proccessed.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.common import *
>>> from sparknlp.annotator import *
>>> from sparknlp.training import *
>>> import sparknlp_jsl
>>> from sparknlp_jsl.base import *
>>> from sparknlp_jsl.annotator import *
>>> from pyspark.ml import Pipeline
Define a pipeline with 2 different NER models with a ChunkMergeApproach at the end
>>> data = spark.createDataFrame([["A 63-year-old man presents to the hospital ..."]]).toDF("text")
>>> pipeline = Pipeline(stages=[
...  DocumentAssembler().setInputCol("text").setOutputCol("document"),
...  SentenceDetector().setInputCols(["document"]).setOutputCol("sentence"),
...  Tokenizer().setInputCols(["sentence"]).setOutputCol("token"),
...   WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models").setOutputCol("embs"),
...   MedicalNerModel.pretrained("ner_jsl", "en", "clinical/models") \
...     .setInputCols(["sentence", "token", "embs"]).setOutputCol("jsl_ner"),
...  NerConverter().setInputCols(["sentence", "token", "jsl_ner"]).setOutputCol("jsl_ner_chunk"),
...   MedicalNerModel.pretrained("ner_bionlp", "en", "clinical/models") \
...     .setInputCols(["sentence", "token", "embs"]).setOutputCol("bionlp_ner"),
...  NerConverter().setInputCols(["sentence", "token", "bionlp_ner"]) \
...     .setOutputCol("bionlp_ner_chunk"),
...  ChunkMergeApproach().setInputCols(["jsl_ner_chunk", "bionlp_ner_chunk"]).setOutputCol("merged_chunk")
>>> ])
>>> result = pipeline.fit(data).transform(data).cache()
>>> result.selectExpr("explode(merged_chunk) as a") \
...   .selectExpr("a.begin","a.end","a.result as chunk","a.metadata.entity as entity") \
...   .show(5, False)
+-----+---+-----------+---------+
|begin|end|chunk      |entity   |
+-----+---+-----------+---------+
|5    |15 |63-year-old|Age      |
|17   |19 |man        |Gender   |
|64   |72 |recurrent  |Modifier |
|98   |107|cellulitis |Diagnosis|
|110  |119|pneumonias |Diagnosis|
+-----+---+-----------+---------+

Methods

`__init__`()
`clear`(param)	Clears a param from the param map if it has been explicitly set.
`copy`([extra])	Creates a copy of this instance with the same uid and some extra params.
`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])	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.
`fit`(dataset[, params])	Fits a model to the input dataset with optional parameters.
`fitMultiple`(dataset, paramMaps)	Fits a model to the input dataset for each param map in paramMaps.
`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.
`getOutputCol`()	Gets output column name of annotations.
`getParam`(paramName)	Gets a param by its name.
`getParamValue`(paramName)	Gets the value of a 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.
`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.
`load`(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
`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.
`setBlackList`(entities)	If defined, list of entities to ignore.
`setChunkPrecedence`(b)	Sets what is the precedence when two chunks have the same start and end indices.
`setFalsePositivesResource`(path[, read_as, ...])	Sets file with false positive pairs
`setInputCols`(*value)	Sets column names of input annotations.
`setLazyAnnotator`(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
`setMergeOverlapping`(b)	Sets whether to merge overlapping matched chunks.
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setReplaceDictResource`(path[, read_as, options])	Sets replace dictionary pairs
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`blackList`
`chunkPrecedence`
`falsePositivesResource`
`getter_attrs`
`inputCols`
`lazyAnnotator`
`mergeOverlapping`
`name`
`outputCol`
`params`	Returns all params ordered by name.
`replaceDictResource`

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 – Extra parameters to copy to the new instance
Returns:: Copy of this instance

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 – extra param values
Returns:: merged param map

fit(dataset, params=None)#

Fits a model to the input dataset with optional parameters.

Parameters:

dataset – input dataset, which is an instance of pyspark.sql.DataFrame
params – an optional param map that overrides embedded params. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.

Returns:

fitted model(s)

New in version 1.3.0.

fitMultiple(dataset, paramMaps)#

Fits a model to the input dataset for each param map in paramMaps.

Parameters:

dataset – input dataset, which is an instance of pyspark.sql.DataFrame.
paramMaps – A Sequence of param maps.

Returns:

A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.

New in version 2.3.0.

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:

paramNamestr: 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.

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).

property params#: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

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.

setBlackList(entities)[source]#

If defined, list of entities to ignore. The rest will be processed.

Parameters:

entitieslist: If defined, list of entities to ignore. The rest will be processed.

setChunkPrecedence(b)[source]#

Sets what is the precedence when two chunks have the same start and end indices. Possible values are [entity|identifier|field]

Parameters:

bstr: Select what is the precedence when two chunks have the same start and end indices. Possible values are [entity|identifier|field]

setFalsePositivesResource(path, read_as='TEXT', options=None)[source]#

Sets file with false positive pairs

Parameters:

pathstr: Path to the external resource
read_asstr, optional: How to read the resource, by default ReadAs.TEXT
optionsdict, optional: Options for reading the resource, by default {“format”: “text”}

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

*valuestr: Input columns for the annotator

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:

valuebool: Whether Annotator should be evaluated lazily in a RecursivePipeline

setMergeOverlapping(b)[source]#

Sets whether to merge overlapping matched chunks. Defaults false

Parameters:

bbool: whether to merge overlapping matched chunks. Defaults false

setOutputCol(value)#

Sets output column name of annotations.

Parameters:

valuestr: Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:

paramNamestr: Name of the parameter

setReplaceDictResource(path, read_as='TEXT', options={'delimiter': ','})[source]#

Sets replace dictionary pairs

Parameters:

pathstr: Path to the external resource
read_asstr, optional: How to read the resource, by default ReadAs.TEXT
optionsdict, optional: Options for reading the resource, by default {“format”: “text”}

uid#: A unique id for the object.

write()#: Returns an MLWriter instance for this ML instance.