sparknlp_jsl.annotator.merge.re_chunk_merger#

Classes#

class REChunkMerger(classname='com.johnsnowlabs.nlp.annotators.merge.REChunkMerger', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

REChunkMerger annotator merges relation chunks to create a new chunk.

Input Annotation types	Output Annotation type
CATEGORY	CHUNK

Examples

>>> documentAssembler = DocumentAssembler().setInputCol("sentence").setOutputCol("document")
>>> tokenizer = Tokenizer()     ...     .setInputCols(["document"])     ...     .setOutputCol("tokens")     >>> words_embedder = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models")     ...     .setInputCols(["document", "tokens"])     ...     .setOutputCol("embeddings")
>>> pos_tagger = PerceptronModel.pretrained("pos_clinical", "en", "clinical/models")     ...     .setInputCols(["document", "tokens"])     ...     .setOutputCol("pos_tags")
>>> ner_tagger = MedicalNerModel.pretrained("ner_clinical", "en", "clinical/models")     ...     .setInputCols(["document", "tokens", "embeddings"])     ...     .setOutputCol("ner_tags")
>>> ner_converter = NerConverter()     ...     .setInputCols(["document", "tokens", "ner_tags"])     ...     .setOutputCol("ner_chunks")
>>> dependency_parser = DependencyParserModel.pretrained("dependency_conllu", "en")     ...     .setInputCols(["document", "pos_tags", "tokens"])     ...     .setOutputCol("dependencies")
>>> re_model = RelationExtractionModel.pretrained("re_clinical", "en", "clinical/models")     ...     .setInputCols(["embeddings", "pos_tags", "ner_chunks", "dependencies"])     ...     .setOutputCol("re_chunk")
>>> re_chunk_merger = REChunkMerger()     ...     .setInputCols(["re_chunk"])     ...     .setOutputCol("relation_chunks")     ...     .setSeparator(" ")
>>> data = spark.createDataFrame([["A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years prior to " +
...    "presentation and subsequent type two diabetes mellitus ( T2DM )."]]).toDF("sentence")
>>> pipeline = Pipeline()     ...     .setStages([
...         documentAssembler, tokenizer, words_embedder,
...         pos_tagger, ner_tagger, ner_converter, dependency_parser, re_model, re_chunk_merger
...     ])     ...     .fit(data)
>>> results = pipeline.transform(data)
>>> results.selectExpr("explode(relation_chunks.result) result").show(truncate=False)

result

gestational diabetes mellitus subsequent type two diabetes mellitus gestational diabetes mellitus T2DM subsequent type two diabetes mellitus T2DM

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = 'REChunkMerger'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

separator#

skipLPInputColsValidation = True#

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.

setForceInputTypeValidation(etfm)#

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()#

setSeparator(value: str)#

Sets the separator to add between the relations. Default: “ “.

Parameters:

value (str) – the separator to add between the relations. Default: “ “.

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.