sparknlp_jsl.annotator.chunker.assertion_filterer#

Classes#

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

Bases: sparknlp_jsl.common.AnnotatorModelInternal, sparknlp_jsl.annotator.filtering_params.FilteringParams, sparknlp_jsl.annotator.HandleExceptionParams

Filters entities coming from ASSERTION type annotations and returns the CHUNKS.

Filters can be set via white and black lists on the extracted chunk, the assertion or a regular expression. White and black lists for assertion are enabled by default. To use chunk white list, criteria has to be set to isin. For regex, criteria has to be set to regex.

Input Annotation types	Output Annotation type
DOCUMENT, CHUNK, ASSERTION	CHUNK

Parameters:

• whiteList (List[str]) – If defined, list of entities to process. The rest will be ignored.

• blackList (List[str]) – If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels.

• caseSensitive (bool) – Determines whether the definitions of the white listed and black listed entities are case sensitive. Default: False.

• regex (List[str]) – If defined, list of regex to process the chunks.

• criteria (str) –

  It is used to compare black and white listed values with the result of the Annotation. Possible values are the following: ‘isin’, ‘regex’ and ‘assertion’.

  • assertion: Filter by the assertion

  • isin : Filter by the chunk

  • regex : Filter by using a regex

  • Default: assertion

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp_jsl.common import *
>>> from sparknlp.annotator import *
>>> from sparknlp.training import *
>>> import sparknlp_jsl
>>> from sparknlp_jsl.base import *
>>> from sparknlp_jsl.annotator import *
>>> from pyspark.ml import Pipeline
 To see how the assertions are extracted, see the example for AssertionDLModel.
 Define an extra step where the assertions are filtered
>>> assertionFilterer = AssertionFilterer() \
...   .setInputCols(["sentence","ner_chunk","assertion"]) \
...    .setOutputCol("filtered") \
...   .setCriteria("assertion") \
...   .setWhiteList(["present"])
...
>>> assertionPipeline = Pipeline(stages=[
...   documentAssembler,
...   sentenceDetector,
...   tokenizer,
...   embeddings,
...   nerModel,
...   nerConverter,
...   clinicalAssertion,
...   assertionFilterer
... ])
...
>>> assertionModel = assertionPipeline.fit(data)
>>> result = assertionModel.transform(data)

>>> result.selectExpr("ner_chunk.result", "assertion.result").show(3, truncate=False)
+--------------------------------+--------------------------------+
|result                          |result                          |
+--------------------------------+--------------------------------+
|[severe fever, sore throat]     |[present, present]              |
|[stomach pain]                  |[absent]                        |
|[an epidural, PCA, pain control]|[present, present, hypothetical]|
+--------------------------------+--------------------------------+

>>> result.select("filtered.result").show(3, truncate=False)
+---------------------------+
|result                     |
+---------------------------+
|[severe fever, sore throat]|
|[]                         |
|[an epidural, PCA]         |
+---------------------------+

blackList#

caseSensitive#

criteria#

doExceptionHandling#

filterValue#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

name = AssertionFilterer#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

regex#

skipLPInputColsValidation = True#

whiteList#

clear(param)#

Clears a param from the param map if it has been explicitly set.

copy(extra=None)#

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)#

Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams()#

Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra=None)#

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)#

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)#

Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters:

paramName (str) – Name of the parameter

hasDefault(param)#

Checks whether a param has a default value.

hasParam(paramName)#

Tests whether this instance contains a param with a given (string) name.

inputColsValidation(value)#

isDefined(param)#

Checks whether a param is explicitly set by user or has a default value.

isSet(param)#

Checks whether a param is explicitly set by user.

classmethod load(path)#

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)#

Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param, value)#

Sets a parameter in the embedded param map.

setBlackList(value)#

Sets If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

Parameters:

value (List[str]) – If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

setCaseSensitive(value)#

Determines whether the definitions of the white listed and black listed entities are case sensitive or not.

Parameters:

value (bool) – Whether white listed and black listed entities are case sensitive or not. Default: True.

setCriteria(value)#

It is used to compare black and white listed values with the result of the Annotation.

Possible values are the following: ‘isin’, ‘regex’ and ‘assertion’. Default: ‘assertion’. assertion: Filter by the assertion isin : Filter by the chunk regex : Filter by using a regex

Parameters:

value (string) – It is used to compare black and white listed values with the result of the Annotation. Possible values are the following: ‘isin’, ‘regex’ and ‘assertion’. Default: ‘assertion’.

setDenyList(value)#

Sets If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

Parameters:

value (List[str]) – If defined, list of entities to ignore. The rest will be processed. Do not include IOB prefix on labels

setDoExceptionHandling(value: bool)#

If True, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

Parameters:

value (bool) – If True, exceptions are handled.

setFilterValue(value)#

Sets possible values ‘result’ or ‘entity’.

If the value is ‘result’, It filters according to the result of the Annotation. If the value is ‘entity’, It filters according to the entity field in the metadata of the Annotation.

Parameters:

value (string) – possible values are ‘result’ and ‘entity’.

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()#

setRegex(value)#

Sets If defined, list of regex to process the chunks.

Parameters:

value (List[str]) – If defined, list of regex to process the chunks

setWhiteList(value)#

Sets If defined, list of entities to process. The rest will be ignored. Do not include IOB prefix on labels

Parameters:

value (List[str]) – If defined, list of entities to process. The rest will be ignored. Do not include IOB prefix on labels

transform(dataset, params=None)#

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()#

Returns an MLWriter instance for this ML instance.