sparknlp_jsl.annotator.SentenceEntityResolverModel#

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

Bases: AnnotatorModel, HasEmbeddingsProperties, HasStorageModel, SentenceResolverParams

Thius class contains all the parameters and methods to train a SentenceEntityResolverModel. The model transforms a dataset with Input Annotation type SENTENCE_EMBEDDINGS, coming from e.g. [BertSentenceEmbeddings](/docs/en/transformers#bertsentenceembeddings) and returns the normalized entity for a particular trained ontology / curated dataset. (e.g. ICD-10, RxNorm, SNOMED etc.)

Input Annotation types	Output Annotation type
`SENTENCE_EMBEDDINGS`	`ENTITY`

Parameters:

returnCosineDistances: Extract Cosine Distances. TRUE or False
aux_label_col: Auxiliary label which maps resolved entities to additional labels
useAuxLabel: Use AuxLabel Col or not
searchTree: Search tree for resolution

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
>>> documentAssembler = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> sentenceDetector = SentenceDetector().setInputCols(["document"]).setOutputCol("sentence")
>>> tokenizer = Tokenizer().setInputCols(["sentence"]).setOutputCol("token")
>>> bertEmbeddings = BertSentenceEmbeddings.pretrained("sent_biobert_pubmed_base_cased") \
...  .setInputCols(["sentence"]) \
...  .setOutputCol("embeddings")
>>> snomedTrainingPipeline = Pipeline(stages=[
...  documentAssembler,
...  sentenceDetector,
...  bertEmbeddings,
... ])
>>> snomedTrainingModel = snomedTrainingPipeline.fit(data)
>>> snomedData = snomedTrainingModel.transform(data).cache()
>>> assertionModel = assertionPipeline.fit(data)
>>> assertionModel = assertionPipeline.fit(data)

>>> bertExtractor = SentenceEntityResolverApproach() \
...   .setNeighbours(25) \
...   .setThreshold(1000) \
...   .setInputCols(["bert_embeddings"]) \
...   .setNormalizedCol("normalized_text") \
...   .setLabelCol("label") \
...   .setOutputCol("snomed_code") \
...   .setDistanceFunction("EUCLIDIAN") \
...   .setCaseSensitive(False)

>>> snomedModel = bertExtractor.fit(snomedData)

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.
`getCaseSensitive`()	Gets whether to ignore case in tokens for embeddings matching.
`getDimension`()	Gets embeddings dimension.
`getIncludeStorage`()	Gets whether to include indexed storage in trained model.
`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).
`loadStorage`(path, spark, storage_ref)
`loadStorages`(path, spark, storage_ref, databases)
`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)'.
`saveStorage`(path, spark)	Saves the current model to storage.
`set`(param, value)	Sets a parameter in the embedded param map.
`setAuxLabelCol`(name)	Sets auxiliary label which maps resolved entities to additional labels
`setCaseSensitive`(value)	Sets whether to ignore case in tokens for embeddings matching.
`setConfidenceFunction`(s)	What function to use to calculate confidence: INVERSE or SOFTMAX.
`setDimension`(value)	Sets embeddings dimension.
`setDistanceFunction`(dist)	Sets distance function to use for WMD: 'EUCLIDEAN' or 'COSINE'.
`setIncludeStorage`(value)	Sets whether to include indexed storage in trained model.
`setInputCols`(*value)	Sets column names of input annotations.
`setLazyAnnotator`(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
`setMissAsEmpty`(value)	Sets whether or not to return an empty annotation on unmatched chunks.
`setNeighbours`(k)	Sets number of neighbours to consider in the KNN query to calculate WMD.
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setParams`()
`setSearchTree`(s)	Sets auxiliary label which maps resolved entities to additional labels
`setStorageRef`(value)	Sets unique reference name for identification.
`setThreshold`(thres)	Sets Threshold value for the last distance calculated.
`setUseAuxLabel`(name)	Sets Use AuxLabel Col or not.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`aux_label_col`
`caseSensitive`
`confidenceFunction`
`dimension`
`distanceFunction`
`getter_attrs`
`includeStorage`
`inputCols`
`lazyAnnotator`
`missAsEmpty`
`name`
`neighbours`
`outputCol`
`params`	Returns all params ordered by name.
`returnCosineDistances`
`searchTree`
`storageRef`
`threshold`
`useAuxLabel`

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

getCaseSensitive()#

Gets whether to ignore case in tokens for embeddings matching.

Returns:

bool: Whether to ignore case in tokens for embeddings matching

getDimension()#: Gets embeddings dimension.

getIncludeStorage()#

Gets whether to include indexed storage in trained model.

Returns:

bool: Whether to include indexed storage in trained model

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)’.

saveStorage(path, spark)#

Saves the current model to storage.

Parameters:

pathstr: Path for saving the model.
sparkpyspark.sql.SparkSession: The current SparkSession

set(param, value)#: Sets a parameter in the embedded param map.

setAuxLabelCol(name)[source]#

Sets auxiliary label which maps resolved entities to additional labels

Parameters:

namestr: Auxiliary label which maps resolved entities to additional labels

setCaseSensitive(value)#

Sets whether to ignore case in tokens for embeddings matching.

Parameters:

valuebool: Whether to ignore case in tokens for embeddings matching

setConfidenceFunction(s)#

What function to use to calculate confidence: INVERSE or SOFTMAX.

Parameters:

sstr: What function to use to calculate confidence: INVERSE or SOFTMAX.

setDimension(value)#

Sets embeddings dimension.

Parameters:

valueint: Embeddings dimension

setDistanceFunction(dist)#

Sets distance function to use for WMD: ‘EUCLIDEAN’ or ‘COSINE’.

Parameters:

diststr: Value that selects what distance function to use for WMD: ‘EUCLIDEAN’ or ‘COSINE’.

setIncludeStorage(value)#

Sets whether to include indexed storage in trained model.

Parameters:

valuebool: Whether to include indexed storage in trained model

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

setMissAsEmpty(value)#

Sets whether or not to return an empty annotation on unmatched chunks.

Parameters:

valuebool: whether or not to return an empty annotation on unmatched chunks.

setNeighbours(k)#

Sets number of neighbours to consider in the KNN query to calculate WMD.

Parameters:

kint: Number of neighbours to consider in the KNN query to calculate WMD.

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

setSearchTree(s)[source]#

Sets auxiliary label which maps resolved entities to additional labels

Parameters:

namestr: Auxiliary label which maps resolved entities to additional labels

setStorageRef(value)#

Sets unique reference name for identification.

Parameters:

valuestr: Unique reference name for identification

setThreshold(thres)#

Sets Threshold value for the last distance calculated.

Parameters:

thresfloat: Threshold value for the last distance calculated.

setUseAuxLabel(name)[source]#

Sets Use AuxLabel Col or not.

Parameters:

namebool: Use AuxLabel Col or not.

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.