sparknlp_jsl.annotator.multi_annotation_splitter#

Classes#

class MultiAnnotationSplitter#

Bases: sparknlp_jsl.annotator.AnnotatorModelInternal

Splits a mixed-type annotation column into separate typed columns.

PretrainedZeroShotMultiTask emits all extraction results in a single output column as Array[Annotation] containing annotations of different annotatorType values:

• "chunk" — entity span annotations

• "category" — classification and relation annotations

• "struct" — structured-record annotations

These can be split into separate columns using MultiAnnotationSplitter by providing a output column name and the type of annotation to split for each column. For example, to split out the entity spans into a column named ner_chunk, you would set splitType to chunk and outputCol to ner_chunk.

Input Annotation types	Output Annotation type
multi	ANY

Parameters:

• splitType (str) –

  The type of annotation to split into a separate column. It should be one of the following values: - chunk — to split out entity span annotations - category — to split out classification and relation annotations - struct — to split out structured-record annotations

  Default value is chunk.

• filterByCategoryType (str) – If splitType is set to category, this parameter can be used to filter annotations by a specific category type. If provided, only category annotations with a matching category type in their metadata will be included in the output column. It should be one of the following values: - classification — to include only classification annotations - relation — to include only relation annotations - "" (empty string) — to include all category annotations (default)

Examples

>>> from sparknlp_jsl.annotator import MultiAnnotationSplitter
>>> splitter = MultiAnnotationSplitter() \
...     .setInputCol("extractions") \
...     .setOutputCol("ner_chunk") \
...     .setSplitType("chunk")

>>> result = splitter.transform(df)
>>> result.select("entities").show(truncate=False)

filterByCategoryType#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = 'MultiAnnotationSplitter'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = [True]#

outputCol#

skipLPInputColsValidation = True#

splitType#

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.

setFilterByCategoryType(value)#

Sets the category type to filter by when splitting category annotations. This parameter is only applicable when splitType is set to category. If provided, only category annotations with a matching category type in their metadata will be included in the output column. It should be one of the following values: - classification - relation

Default: “” (no filtering, include all category annotations)

Parameters:

value (str) – The category type to filter by when splitting category annotations.

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()#

setSplitType(value)#

Sets the type of annotation to split into a separate column. It should be one of the following values: - chunk - category - struct

Default value is chunk.

Parameters:

value (str) – The type of annotation to split into a separate column.

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.