sparknlp_jsl.annotator.ChunkFilterer#
- class sparknlp_jsl.annotator.ChunkFilterer(classname='com.johnsnowlabs.nlp.annotators.chunker.ChunkFilterer', java_model=None)[source]#
Bases:
AnnotatorModel
- Model that Filters entities coming from CHUNK annotations. Filters can be set via a white list of terms or a regular expression.
White list criteria is enabled by default. To use regex, criteria has to be set to regex.This model was trained using the ChunkFiltererApproach and has embeded the list of pairs (entity,confidenceThreshold).
Input Annotation types
Output Annotation type
DOCUMENT, CHUNK, ASSERTION
CHUNK
- Parameters:
- whiteList
If defined, list of entities to process. The rest will be ignored
- regex
If defined, list of entities to process. The rest will be ignored.
- criteria
- Tag representing what is the criteria to filter the chunks. possibles values (assertion|isIn|regex)
isIn : Filter by the chunk regex : Filter using a regex
Examples
>>> import sparknlp >>> from sparknlp.base import * >>> from sparknlp.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 >>> data = spark.createDataFrame([["Has a past history of gastroenteritis and stomach pain, however patient ..."]]).toDF("text") >>> docAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document") >>> sentenceDetector = SentenceDetector().setInputCols(["document"]).setOutputCol("sentence") >>> tokenizer = Tokenizer().setInputCols(["sentence"]).setOutputCol("token") >>> posTagger = PerceptronModel.pretrained() ... .setInputCols(["sentence", "token"]) ... .setOutputCol("pos") >>> chunker = Chunker() ... .setInputCols(["pos", "sentence"]) ... .setOutputCol("chunk") ... .setRegexParsers(["(<NN>)+"]) ... >>> chunkerFilter = ChunkFilterer() ... .setInputCols(["sentence","chunk"]) ... .setOutputCol("filtered") ... .setCriteria("isin") ... .setWhiteList(["gastroenteritis"]) ... >>> pipeline = Pipeline(stages=[ ... docAssembler, ... sentenceDetector, ... tokenizer, ... posTagger, ... chunker, ... chunkerFilter]) ... >>> result = pipeline.fit(data).transform(data) >>> result.selectExpr("explode(chunk)").show(truncate=False)
>>> result.selectExpr("explode(chunk)").show(truncate=False) +---------------------------------------------------------------------------------+ |col | +---------------------------------------------------------------------------------+ |{chunk, 11, 17, history, {sentence -> 0, chunk -> 0}, []} | |{chunk, 22, 36, gastroenteritis, {sentence -> 0, chunk -> 1}, []} | |{chunk, 42, 53, stomach pain, {sentence -> 0, chunk -> 2}, []} | |{chunk, 64, 70, patient, {sentence -> 0, chunk -> 3}, []} | |{chunk, 81, 110, stomach pain now.We don't care, {sentence -> 0, chunk -> 4}, []}| |{chunk, 118, 132, gastroenteritis, {sentence -> 0, chunk -> 5}, []} | +---------------------------------------------------------------------------------+
>>> result.selectExpr("explode(filtered)").show(truncate=False) +-------------------------------------------------------------------+ |col | +-------------------------------------------------------------------+ |{chunk, 22, 36, gastroenteritis, {sentence -> 0, chunk -> 1}, []} | |{chunk, 118, 132, gastroenteritis, {sentence -> 0, chunk -> 5}, []}| +-------------------------------------------------------------------+
Methods
__init__
([classname, java_model])Initialize this instance with a Java model object.
clear
(param)Clears a param from the param map if it has been explicitly set.
copy
([extra])Creates a copy of this instance with the same uid and some extra params.
explainParam
(param)Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap
([extra])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.
Gets current column names of input annotations.
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.
Gets output column name of annotations.
getParam
(paramName)Gets a param by its name.
getParamValue
(paramName)Gets the value of a 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.
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.
load
(path)Reads an ML instance from the input path, a shortcut of read().load(path).
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.
setCriteria
(s)setFilterEntity
(s)setInputCols
(*value)Sets column names of input annotations.
setLazyAnnotator
(value)Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
setOutputCol
(value)Sets output column name of annotations.
setParamValue
(paramName)Sets the value of a parameter.
setParams
()setRegex
(value)setWhiteList
(value)transform
(dataset[, params])Transforms the input dataset with optional parameters.
write
()Returns an MLWriter instance for this ML instance.
Attributes
criteria
filterValue
getter_attrs
inputCols
lazyAnnotator
name
outputCol
Returns all params ordered by name.
regex
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 – Extra parameters to copy to the new instance
- Returns:
Copy of this instance
- 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 – extra param values
- Returns:
merged param map
- 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:
- paramNamestr
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.
- 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).
- property params#
Returns all params ordered by name. The default implementation uses
dir()
to get all attributes of typeParam
.
- 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.
- setInputCols(*value)#
Sets column names of input annotations.
- Parameters:
- *valuestr
Input columns for the annotator
- setLazyAnnotator(value)#
Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
- Parameters:
- valuebool
Whether Annotator should be evaluated lazily in a RecursivePipeline
- setOutputCol(value)#
Sets output column name of annotations.
- Parameters:
- valuestr
Name of output column
- setParamValue(paramName)#
Sets the value of a parameter.
- Parameters:
- paramNamestr
Name of the parameter
- transform(dataset, params=None)#
Transforms the input dataset with optional parameters.
- Parameters:
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
params – an optional param map that overrides embedded params.
- Returns:
transformed dataset
New in version 1.3.0.
- uid#
A unique id for the object.
- write()#
Returns an MLWriter instance for this ML instance.