sparknlp_jsl.annotator.SentenceEntityResolverApproach#
- class sparknlp_jsl.annotator.SentenceEntityResolverApproach[source]#
Bases:
AnnotatorApproach
,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:
- labelCol
Column name for the value we are trying to resolve
- normalizedCol
Column name for the original, normalized description
- pretrainedModelPath
Path to an already trained SentenceEntityResolverModel, which is used as a starting point for training the new model.
- overrideExistingCodes
Whether to override the existing codes with new data while continue the training from a pretrained model. Default value is false(keep all the codes).
- 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
- overrideExistingCodes
Whether to override the codes present in a pretrained model with new codes when the training process begins with a pretrained model
- dropCodesList
A list of codes in a pretrained model that will be omitted when the training process begins with a pretrained model
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__
()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.
fit
(dataset[, params])Fits a model to the input dataset with optional parameters.
fitMultiple
(dataset, paramMaps)Fits a model to the input dataset for each param map in paramMaps.
Gets whether to ignore case in tokens for embeddings matching.
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.
setAuxLabelCol
(name)Sets auxiliary label which maps resolved entities to additional labels
setCaseSensitive
(value)Sets whether to ignore case in tokens for embeddings matching.
What function to use to calculate confidence: INVERSE or SOFTMAX.
setDistanceFunction
(dist)Sets distance function to use for WMD: 'EUCLIDEAN' or 'COSINE'.
setDropCodesList
(value)Extract Cosine Distances.
setInputCols
(*value)Sets column names of input annotations.
setLabelCol
(name)Sets column name for the value we are trying to resolve
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.
Sets number of neighbours to consider in the KNN query to calculate WMD.
setNormalizedCol
(name)Sets column name for the original, normalized description
setOutputCol
(value)Sets output column name of annotations.
setOverrideExistingCodes
(value)setParamValue
(paramName)Sets the value of a parameter.
setPretrainedModelPath
(path)setThreshold
(thres)Sets Threshold value for the last distance calculated.
setUseAuxLabel
(name)Sets Use AuxLabel Col or not.
write
()Returns an MLWriter instance for this ML instance.
Attributes
aux_label_col
caseSensitive
confidenceFunction
distanceFunction
dropCodesList
getter_attrs
inputCols
labelCol
lazyAnnotator
missAsEmpty
neighbours
normalizedCol
outputCol
overrideExistingCodes
Returns all params ordered by name.
pretrainedModelPath
returnCosineDistances
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
- fit(dataset, params=None)#
Fits a model to 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. If a list/tuple of param maps is given, this calls fit on each param map and returns a list of models.
- Returns:
fitted model(s)
New in version 1.3.0.
- fitMultiple(dataset, paramMaps)#
Fits a model to the input dataset for each param map in paramMaps.
- Parameters:
dataset – input dataset, which is an instance of
pyspark.sql.DataFrame
.paramMaps – A Sequence of param maps.
- Returns:
A thread safe iterable which contains one model for each param map. Each call to next(modelIterator) will return (index, model) where model was fit using paramMaps[index]. index values may not be sequential.
New in version 2.3.0.
- getCaseSensitive()#
Gets whether to ignore case in tokens for embeddings matching.
- Returns:
- bool
Whether to ignore case in tokens for embeddings matching
- 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.
- 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.
- 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’.
- setExtractCosineDistances(name)[source]#
Extract Cosine Distances. TRUE or False.
- Parameters:
- namebool
Extract Cosine Distances. TRUE or False
- setInputCols(*value)#
Sets column names of input annotations.
- Parameters:
- *valuestr
Input columns for the annotator
- setLabelCol(name)[source]#
Sets column name for the value we are trying to resolve
- Parameters:
- sbool
Column name for the value we are trying to resolve
- 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.
- setNormalizedCol(name)[source]#
Sets column name for the original, normalized description
- Parameters:
- sbool
Column name for the original, normalized description
- 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
- 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.
- uid#
A unique id for the object.
- write()#
Returns an MLWriter instance for this ML instance.