`sparknlp_jsl.annotator.assertion.assertion_chunk_converter`#

Module Contents#

Classes#

AssertionChunkConverter

Creates a chunk column with metadata for training assertion status detection models.

class AssertionChunkConverter#

Bases: sparknlp.internal.AnnotatorTransformer

Creates a chunk column with metadata for training assertion status detection models.

In some cases, there may be issues while creating the chunk column when using token indices that can lead to loss of data to train assertion status models.

The AssertionChunkConverter annotator uses both begin and end indices of the tokens as input to add a more robust metadata to the chunk column in a way that improves the reliability of the indices and avoid loss of data.

Notes

Chunk begin and end indices in the assertion status model training dataframe can be populated using the new version of ALAB module.

Input Annotation types	Output Annotation type
`TOKEN`	`CHUNK`

Parameters:

chunkTextCol (str) – Name of the column containing the text of the chunk.
chunkBeginCol (str) – Name of the column containing the start index of the chunk.
chunkEndCol (str) – Name of the column containing the end index of the chunk.
outputTokenBeginCol (str) – Name of the column containing selected token start index.
outputTokenEndCol (str) – Name of the column containing selected token end index.
metadataFields (dict) – The dictionary of metadata fields to be added to the chunk column. Add the specified data to the metadata fields of the chunk outputs in the appropriate columns. First element of the tuple is the column name to read the data from, and the second element is the name of the metadata field. The given column names must be present in the DataFrame and must be of StringType.

Examples

>>> document_assembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")

>>> sentenceDetector = SentenceDetector()\
...     .setInputCols(["document"])\
...     .setOutputCol("sentence")

>>> tokenizer = Tokenizer() \
...     .setInputCols(["sentence"]) \
...     .setOutputCol("tokens")

>>> converter = AssertionChunkConverter() \
...     .setInputCols("tokens")\
...     .setChunkTextCol("target")\
...     .setChunkBeginCol("char_begin")\
...     .setChunkEndCol("char_end")\
...     .setOutputTokenBeginCol("token_begin")\
...     .setOutputTokenEndCol("token_end")\
...     .setOutputCol("chunk")

>>> pipeline = Pipeline().setStages([document_assembler,sentenceDetector, tokenizer, converter])

>>> results = pipeline.fit(data).transform(data)

>>> results\
...     .selectExpr(
...         "target",
...         "char_begin",
...         "char_end",
...         "token_begin",
...         "token_end",
...         "tokens[token_begin].result",
...         "tokens[token_end].result",
...         "target",
...         "chunk")\
...     .show(truncate=False)
+------+----------+--------+-----------+---------+--------------------------+------------------------+------+----------------------------------------------+
|target|char_begin|char_end|token_begin|token_end|tokens[token_begin].result|tokens[token_end].result|target|chunk                                         |
+------+----------+--------+-----------+---------+--------------------------+------------------------+------+----------------------------------------------+
|Minnie|57        |63      |10         |10       |Minnie                    |Minnie                  |Minnie|[{chunk, 57, 62, Minnie, {sentence -> 0}, []}]|
|PCP   |31        |34      |5          |5        |PCP                       |PCP                     |PCP   |[{chunk, 31, 33, PCP, {sentence -> 0}, []}]   |
+------+----------+--------+-----------+---------+--------------------------+------------------------+------+----------------------------------------------+

chunkBeginCol#

chunkEndCol#

chunkTextCol#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

name = 'AssertionChunkConverter'#

outputAnnotatorType = 'chunk'#

outputCol#

outputTokenBeginCol#

outputTokenEndCol#

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

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.

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.

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.

setChunkBeginCol(col: str)#

Sets the name of the column containing the start index of the chunk

Parameters:: col (str) – Name of the column containing the start index of the chunk

setChunkEndCol(col: str)#

Sets the name of the column containing the end index of the chunk

Parameters:: col (str) – Name of the column containing the end index of the chunk

setChunkTextCol(col: str)#

Sets the name of the column containing the text of the chunk.

Parameters:: col (str) – Name of the column containing the text of the chunk

setInputCols(*value)#

Sets column names of input annotations.

Parameters:: *value (str) – Input columns for the annotator

setMetadataFields(value: dict)#

Sets the dictionary of metadata fields to be added to the chunk column. Add the specified data to the metadata fields of the chunk outputs in the appropriate columns. First element of the tuple is the column name to read the data from, and the second element is the name of the metadata field. The given column names must be present in the DataFrame and must be of StringType.

Example:#

>>> AssertionChunkConverter() \
>>>     .setMetadataFields({'label': 'entity'}) \

param value:: The dictionary of of metadata fields to be added to the chunk column.
type value:: dict[str, str]

setOutputCol(value: str)#

Sets column name of output annotations.

Parameters:: value (str) – Name of output columns

setOutputTokenBeginCol(col: str)#

Sets the name of the column containing selected token start index

Parameters:: col (str) – Name of the column containing selected token start index

setOutputTokenEndCol(col: str)#

Sets the name of the column containing selected token end index

Parameters:: col (str) – Name of the column containing selected token end index

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.assertion.assertion_chunk_converter#

Module Contents#

Classes#

Example:#

`sparknlp_jsl.annotator.assertion.assertion_chunk_converter`#