sparknlp_jsl.annotator.chunker.chunk_converter#

Classes#

class ChunkConverter(classname='com.johnsnowlabs.nlp.annotators.chunker.ChunkConverter', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal, sparknlp_jsl.annotator.source_tracking_metadata_params.SourceTrackingMetadataParams

Convert chunks from regexMatcher to chunks with a entity in the metadata. Use the identifier or field as a entity.

Input Annotation types	Output Annotation type
CHUNK	CHUNK

Examples

>>> test_data = spark.createDataFrame(
...    [
...        (
...            1,
...            "My first sentence with the first rule. This is my second sentence with ceremonies rule.",
...        ),
...    ]).toDF("id", "text")
...
>>> document_assembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> sentence_detector = (
...     SentenceDetector().setInputCols(["document"]).setOutputCol("sentence")
... )
>>> regex_matcher = (
...     RegexMatcher()
...     .setInputCols("sentence")
...     .setOutputCol("regex")
...     .setExternalRules(
...         path="../src/test/resources/regex-matcher/rules.txt", delimiter=","
...     ))
>>> chunkConverter = ChunkConverter().setInputCols("regex").setOutputCol("chunk")
>>> pipeline = Pipeline(
...     stages=[
...         document_assembler,
...         sentence_detector,
...         regex_matcher,
...         regex_matcher,
...         chunkConverter,
...     ])
>>> model = pipeline.fit(test_data)
>>> outdf = model.transform(test_data)
+------------------------------------------------------------------------------------------------+
|col                                                                                             |
+------------------------------------------------------------------------------------------------+
|[chunk, 23, 31, the first, [identifier -> NAME, sentence -> 0, chunk -> 0, entity -> NAME], []] |
|[chunk, 71, 80, ceremonies, [identifier -> NAME, sentence -> 1, chunk -> 0, entity -> NAME], []]|
+------------------------------------------------------------------------------------------------+

allPossibleFieldsToStandardize#

getter_attrs = []#

includeOutputColumn#

includeStandardField#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = 'ChunkConverter'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = 'chunk'#

outputCol#

outputColumnKey#

resetSentenceIndices#

skipLPInputColsValidation = True#

standardFieldKey#

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.

setAllPossibleFieldsToStandardize(fields)#

Sets array with all possible fields containing the value to write in the standard field ordered by priority

Parameters:

fields (list) – array with all possible fields containing the value to write in the standard field ordered by priority

setForceInputTypeValidation(etfm)#

setIncludeOutputColumn(p)#

Sets whether to include a metadata key/value to specify the output column name for the annotation

Parameters:

p (bool) – whether to include a metadata key/value to specify the output column name for the annotation

setIncludeStandardField(p)#

Sets whether to include a metadata key/value to specify the output column name for the annotation

Parameters:

p (bool) – whether to include a metadata key/value to specify the output column name for the annotation

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

setOutputColumnKey(s)#

Set key name for the source column value

Parameters:

s (str) – key name for the source column value

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:

paramName (str) – Name of the parameter

setParams()#

setResetSentenceIndices(value)#

Set whether to reset sentence indices to treat the entire output as if it originates from a single document.

When set to true, the metadata of each entity will be updated by assigning the sentence key a value of 0, effectively treating the entire output as if it originates from a single document. regardless of the original sentence boundaries. Default: False.

Parameters:

value (bool) – If set to true, sentence indices will be reset to treat the entire output as if it originates from a single document.

setStandardFieldKey(s)#

Set key name for the source column value

Parameters:

s (str) – key name for the source column value

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.