sparknlp_jsl.annotator.RENerChunksFilter#

class sparknlp_jsl.annotator.RENerChunksFilter(classname='com.johnsnowlabs.nlp.annotators.re.RENerChunksFilter', java_model=None)[source]#

Bases: AnnotatorModel

Filters and outputs combinations of relations between extracted entities, for further processing. This annotator is especially useful to create inputs for the RelationExtractionDLModel.

Input Annotation types	Output Annotation type
CHUNK,DEPENDENCY	CHUNK

Parameters:

relationPairs

List of valid relations to encode

relationPairsCaseSensitive

Determines whether relation pairs are case sensitive

maxSyntacticDistance

Maximum syntactic distance between a pair of named entities to consider them as a relation

docLevelRelations

Include relations between entities from different sentences (Default: False)

Examples

>>> documenter = DocumentAssembler()\
...   .setInputCol("text")\
...   .setOutputCol("document")
...
>>> sentencer = SentenceDetector()\
...   .setInputCols(["document"])\
...   .setOutputCol("sentences")
...
>>> tokenizer = Tokenizer()\
...   .setInputCols(["sentences"])\
...   .setOutputCol("tokens")
...
>>> words_embedder = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")\
...   .setInputCols(["sentences", "tokens"])\
...   .setOutputCol("embeddings")
...
>>> pos_tagger = PerceptronModel.pretrained("pos_clinical", "en", "clinical/models")\
...   .setInputCols(["sentences", "tokens"])\
...   .setOutputCol("pos_tags")
...
>>> dependency_parser = DependencyParserModel.pretrained("dependency_conllu", "en")\
...   .setInputCols(["sentences", "pos_tags", "tokens"])\
...   .setOutputCol("dependencies")
...
>>> clinical_ner_tagger = MedicalNerModel.pretrained("jsl_ner_wip_greedy_clinical","en","clinical/models")\
...   .setInputCols(["sentences", "tokens", "embeddings"])\
...   .setOutputCol("ner_tags")
...
>>> ner_chunker = NerConverter()\
...   .setInputCols(["sentences", "tokens", "ner_tags"])\
...   .setOutputCol("ner_chunks")
...
... # Define the relation pairs and the filter
>>> relationPairs = [
...   "direction-external_body_part_or_region",
...   "external_body_part_or_region-direction",
...   "direction-internal_organ_or_component",
...   "internal_organ_or_component-direction"
... ]
...
>>> re_ner_chunk_filter = RENerChunksFilter()\
...   .setInputCols(["ner_chunks", "dependencies"])\
...   .setOutputCol("re_ner_chunks")\
...   .setMaxSyntacticDistance(4)\
...   .setRelationPairs(["internal_organ_or_component-direction"])
...
>>> trained_pipeline = Pipeline(stages=[
...   documenter,
...   sentencer,
...   tokenizer,
...   words_embedder,
...   pos_tagger,
...   clinical_ner_tagger,
...   ner_chunker,
...   dependency_parser,
...   re_ner_chunk_filter
... ])
...
>>> data = spark.cre>>>DataFrame([["MRI demonstrated infarction in the upper brain stem , left cerebellum and  right basil ganglia"]]).toDF("text")
>>> result = trained_pipeline.fit(data).transform(data)
...
... # Show results
>>> result.selectExpr("explode(re_ner_chunks) as re_chunks")     ...   .selectExpr("re_chunks.begin", "re_chunks.result", "re_chunks.metadata.entity", "re_chunks.metadata.paired_to")     ...   .show(6, truncate=False)
+-----+-------------+---------------------------+---------+
|begin|result       |entity                     |paired_to|
+-----+-------------+---------------------------+---------+
|35   |upper        |Direction                  |41       |
|41   |brain stem   |Internal_organ_or_component|35       |
|35   |upper        |Direction                  |59       |
|59   |cerebellum   |Internal_organ_or_component|35       |
|35   |upper        |Direction                  |81       |
|81   |basil ganglia|Internal_organ_or_component|35       |
+-----+-------------+---------------------------+---------+
+---------------------------------------------------------+

Methods

__init__([classname, java_model])	Initialize this instance with a Java model object.
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.
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.
setDocLevelRelations(docLevelRelations)	Sets whether to include relations between entities from different sentences
setInputCols(*value)	Sets column names of input annotations.
setLazyAnnotator(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
setMaxSyntacticDistance(distance)	Sets maximum syntactic distance between a pair of named entities to consider them as a relation"
setOutputCol(value)	Sets output column name of annotations.
setParamValue(paramName)	Sets the value of a parameter.
setParams()
setRelationPairs(pairs)	Sets list of dash-separated pairs of named entities
setRelationPairsCaseSensitive(value)	Sets the case sensitivity of relation pairs Parameters ---------- value : boolean whether relation pairs are case sensitive
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

docLevelRelations
getter_attrs
inputCols
lazyAnnotator
maxSyntacticDistance
name
outputCol
params	Returns all params ordered by name.
relationPairs
relationPairsCaseSensitive

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

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.

setDocLevelRelations(docLevelRelations)[source]#

Sets whether to include relations between entities from different sentences

Parameters:

docLevelRelationsbool

Whether to include relations between entities from different sentences

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

setMaxSyntacticDistance(distance)[source]#

Sets maximum syntactic distance between a pair of named entities to consider them as a relation”

Parameters:

distanceint

Maximum syntactic distance between a pair of named entities to consider them as a relation

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

setRelationPairs(pairs)[source]#

Sets list of dash-separated pairs of named entities

Parameters:

pairsstr

List of dash-separated pairs of named entities

setRelationPairsCaseSensitive(value)[source]#

Sets the case sensitivity of relation pairs Parameters ———- value : boolean

whether relation pairs are case sensitive

transform(dataset, params=None)#

Transforms 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.

Returns:

transformed dataset

New in version 1.3.0.

uid#

A unique id for the object.

write()#

Returns an MLWriter instance for this ML instance.