sparknlp_jsl.annotator.AssertionDLModel#

class sparknlp_jsl.annotator.AssertionDLModel(classname='com.johnsnowlabs.nlp.annotators.assertion.dl.AssertionDLModel', java_model=None)[source]#

Bases: AnnotatorModel, HasStorageRef

AssertionDL is a deep Learning based approach used to extract Assertion Status from extracted entities and text. AssertionDLModel requires DOCUMENT, CHUNK and WORD_EMBEDDINGS type annotator inputs, which can be obtained by e.g a

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

Parameters:

maxSentLen

Max length for an input sentence.

targetNerLabels

List of NER labels to mark as target for assertion, must match NER output.

configProtoBytes

ConfigProto from tensorflow, serialized into byte array.

classes

Tags used to trained this AssertionDLModel

scopeWindow

The scope window of the assertion expression

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([[“Patient with severe fever and sore throat”],[“Patient shows no stomach pain”],[“She was maintained on an epidural and PCA for pain control.”]]).toDF(“text”)

>>> documentAssembler = DocumentAssembler().setInputCol(“text”).setOutputCol(“document”)

>>> sentenceDetector = SentenceDetector().setInputCols([“document”]).setOutputCol(“sentence”)

>>> tokenizer = Tokenizer().setInputCols([“sentence”]).setOutputCol(“token”)

**>>> embeddings = WordEmbeddingsModel.pretrained(“embeddings_clinical”, “en”, “clinical/models”) **

… .setOutputCol(“embeddings”)

**>>> nerModel = MedicalNerModel.pretrained(“ner_clinical”, “en”, “clinical/models”) **

… .setInputCols([“sentence”, “token”, “embeddings”]).setOutputCol(“ner”)

>>> nerConverter = NerConverter().setInputCols([“sentence”, “token”, “ner”]).setOutputCol(“ner_chunk”)

**>>> clinicalAssertion = AssertionDLModel.pretrained(“assertion_dl”, “en”, “clinical/models”) **

**… .setInputCols([“sentence”, “ner_chunk”, “embeddings”]) **

… .setOutputCol(“assertion”)

>>> assertionPipeline = Pipeline(stages=[

… documentAssembler,

… sentenceDetector,

… tokenizer,

… embeddings,

… nerModel,

… nerConverter,

… clinicalAssertion

… ])

>>> assertionModel = assertionPipeline.fit(data)

>>> result = assertionModel.transform(data)

>>> result.selectExpr(“ner_chunk.result as ner”, “assertion.result”).show(3, truncate=False)

+——————————–+——————————–+

|ner |result |

+——————————–+——————————–+

|[severe fever, sore throat] |[present, present] |

|[stomach pain] |[absent] |

|[an epidural, PCA, pain control]|[present, present, hypothetical]|

+——————————–+——————————–+

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.
explainParams()	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.
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.
getOutputCol()	Gets output column name of annotations.
getParam(paramName)	Gets a param by its name.
getParamValue(paramName)	Gets the value of a parameter.
getStorageRef()	Gets unique reference name for identification.
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).
pretrained(name[, lang, remote_loc])
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.
setConfigProtoBytes(b)
setIncludeConfidence(value)	Sets if you waht to include confidence scores in annotation metadata.
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()
setScopeWindow(value)	Sets the scope of the window of the assertion expression
setStorageRef(value)	Sets unique reference name for identification.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

classes
configProtoBytes
getter_attrs
includeConfidence
inputCols
lazyAnnotator
maxSentLen
name
outputCol
params	Returns all params ordered by name.
scopeWindow
storageRef
targetNerLabels

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

getStorageRef()#

Gets unique reference name for identification.

Returns:

str

Unique reference name for identification

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 type Param.

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.

setIncludeConfidence(value)[source]#

Sets if you waht to include confidence scores in annotation metadata.

Parameters:

pbool

Value that selects if you want to use confidence scores in annotation metadata

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

setScopeWindow(value)[source]#

Sets the scope of the window of the assertion expression

Parameters:

value[int, int]

Left and right offset if the scope window. Offsets must be non-negative values

setStorageRef(value)#

Sets unique reference name for identification.

Parameters:

valuestr

Unique reference name for identification

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.