`sparknlp_jsl.annotator.annotation_merger`#

Module Contents#

Classes#

AnnotationMerger

Merges Annotations from multiple columns.

class AnnotationMerger(classname='com.johnsnowlabs.annotator.AnnotationMerger', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

Merges Annotations from multiple columns.

Input Annotation types	Output Annotation type
`ANY`	`ANY`

Parameters:: inputType – The type of the annotations that you want to merge. Possible values document|token|wordpiece|word_embeddings|sentence_embeddings|category|date|sentiment|pos|chunk|named_entity|regex|dependency|labeled_dependency|language|keyword

Examples

>>> empty_data = spark.createDataFrame([[""]]).toDF("text")
>>> document1 = DocumentAssembler().setInputCol("text").setOutputCol("document1")
>>> document2 = DocumentAssembler().setInputCol("text").setOutputCol("document2")
>>> annotation_merger = AnnotationMerger()\
...     .setInputCols("document1", "document2")\
...     .setInputType("document")\
...     .setOutputCol("all_docs")
>>>
>>> pipelineModel = Pipeline().setStages([document1, document2, annotation_merger]).fit(empty_data)
>>> lp = LightPipeline(pipelineModel)
>>> lp.fullAnnotate("one doc to be replicated")
[{'document1': [Annotation(document, 0, 23, one doc to be replicated, {})], 'document2': [Annotation(document, 0, 23, one doc to be replicated, {})], 'all_docs': [Annotation(document, 0, 23, one doc to be replicated, {}), Annotation(document, 0, 23, one doc to be replicated, {})]}]

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

inputType#

lazyAnnotator#

name = 'AnnotationMerger'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

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 (str) – Input columns for the annotator

setInputType(value)#

Sets the type of the entity that you want to filter by default sentence_embedding

Parameters:: value (int) – The type of the entity that you want to filter by default sentence_embedding

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: 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.

sparknlp_jsl.annotator.annotation_merger#

Module Contents#

Classes#

`sparknlp_jsl.annotator.annotation_merger`#