sparknlp_jsl.annotator.chunker.assertion_filterer
#
Module Contents#
Classes#
Filters entities coming from ASSERTION type annotations and returns the CHUNKS. |
- 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 toisin
. For regex,criteria
has to be set toregex
.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#
- filterValue#
- getter_attrs = []#
- inputAnnotatorTypes#
- inputCols#
- lazyAnnotator#
- name = 'AssertionFilterer'#
- optionalInputAnnotatorTypes = []#
- outputAnnotatorType = 'chunk'#
- outputCol#
- regex#
- skipLPInputColsValidation = True#
- uid = ''#
- whiteList#
- 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.
- 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
- 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: 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 datasetparams (dict, optional) – an optional param map that overrides embedded params.
- Returns:
transformed dataset
- Return type:
- write() JavaMLWriter #
Returns an MLWriter instance for this ML instance.