sparknlp_jsl.finance.chunk_classification.resolution.sentence_entity_resolver#

Module Contents#

Classes#

SentenceEntityResolverApproach

Contains all the parameters and methods to train a SentenceEntityResolverModel.

SentenceEntityResolverModel

Contains all the parameters and methods to train a SentenceEntityResolverModel.
class SentenceEntityResolverApproach#

Bases: sparknlp_jsl.annotator.resolution.sentence_entity_resolver.SentenceEntityResolverApproach

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. The path can be a local file path, a distributed file path (HDFS, DBFS), or a cloud storage (S3).

  • 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)
aux_label_col#
caseSensitive#
confidenceFunction#
datasetInfo#
distanceFunction#
doExceptionHandling#
dropCodesList#
engine#
getter_attrs = []#
inputAnnotatorTypes#
inputCols#
labelCol#
lazyAnnotator#
missAsEmpty#
neighbours#
normalizedCol#
optionalInputAnnotatorTypes = []#
outputAnnotatorType = 'entity'#
outputCol#
overrideExistingCodes#
pretrainedModelPath#
returnCosineDistances#
returnResolvedTextEmbeddings#
skipLPInputColsValidation = True#
threshold#
uid = ''#
useAuxLabel#
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

fit(dataset: pyspark.sql.dataframe.DataFrame, params: pyspark.ml._typing.ParamMap | None = ...) → M#
fit(dataset: pyspark.sql.dataframe.DataFrame, params: List[pyspark.ml._typing.ParamMap] | Tuple[pyspark.ml._typing.ParamMap]) → List[M]

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

  • dataset (pyspark.sql.DataFrame) – input dataset.

  • params (dict or list or tuple, optional) – 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)

Return type:

Transformer or a list of Transformer

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[pyspark.ml._typing.ParamMap]) → Iterator[Tuple[int, M]]#

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters:

  • dataset (pyspark.sql.DataFrame) – input dataset.

  • paramMaps (collections.abc.Sequence) – 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.

Return type:

_FitMultipleIterator

getCaseSensitive()#

Gets whether to ignore case in tokens for embeddings matching.

Returns:

Whether to ignore case in tokens for embeddings matching

Return type:

bool

getEngine()#
Returns:

Deep Learning engine used for this model”

Return type:

str

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.

setAuxLabelCol(name: str)#

Sets auxiliary label which maps resolved entities to additional labels

Parameters:

name (str) – Auxiliary label which maps resolved entities to additional labels

setCaseSensitive(value)#

Sets whether to ignore case in tokens for embeddings matching.

Parameters:

value (bool) – Whether to ignore case in tokens for embeddings matching

setConfidenceFunction(conf_function: str)#

What function to use to calculate confidence: Either ‘INVERSE’ or ‘SOFTMAX’.

Parameters:

conf_function (str) – What function to use to calculate confidence: Either ‘INVERSE’ or ‘SOFTMAX’.

setDatasetInfo(info: str)#

Sets descriptive information about the dataset being used.

Parameters:

info (str) – Descriptive information about the dataset being used.

setDistanceFunction(dist: str)#

Sets distance function to use for Word Mover’s Distance (WMD): ‘EUCLIDEAN’ or ‘COSINE’.

Parameters:

dist (str) – Value that selects what distance function to use for WMD: ‘EUCLIDEAN’ or ‘COSINE’.

setDoExceptionHandling(value: bool)#

If True, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

Parameters:

value (bool) – If True, exceptions are handled.

setDropCodesList(value: list)#

Sets list of codes to leave out when continue training with new data.

Parameters:

value (list) – List of codes in a pretrained model to leave out when continue training with new data.

setExtractCosineDistances(name: bool)#

Extract Cosine Distances. True or False.

Parameters:

name (bool) – Extract Cosine Distances. True or False

setForceInputTypeValidation(etfm)#
setInputCols(*value)#

Sets column names of input annotations.

Parameters:

*value (List[str]) – Input columns for the annotator

setLabelCol(name: str)#

Sets column name for the value we are trying to resolve

Parameters:

name (str) – Column name for the value we are trying to resolve

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:

value (bool) – Whether Annotator should be evaluated lazily in a RecursivePipeline

setMissAsEmpty(value: bool)#

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

Parameters:

value (bool) – whether or not to return an empty annotation on unmatched chunks.

setNeighbours(k: int)#

Sets the number of neighbours to consider in the KNN algorithmn.

Parameters:

k (int) – Number of neighbours to consider in the KNN query to calculate Word Mover’s Distance (WMD).

setNormalizedCol(name: str)#

Sets column name for the original, normalized description

Parameters:

name (str) – Column name for the original, normalized description

setOutputCol(value)#

Sets output column name of annotations.

Parameters:

value (str) – Name of output column

setOverrideExistingCodes(value: bool)#

Sets whether to override the existing codes with new data while continue the training from a pretrained model.

Parameters:

value (bool) – 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).

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:

paramName (str) – Name of the parameter

setPretrainedModelPath(path: str)#

Sets path to an already trained SentenceEntityResolverModel.

Parameters:

path (str) – Path to an already trained SentenceEntityResolverModel, which is used as a starting point for training the new model.

setReturnResolvedTextEmbeddings(value: bool)#

Sets whether to include embeddings for resolved text candidates.

If set to true, embeddings will be included; if set to false, embeddings will be excluded. (Default : False)

Parameters:

value (bool) – Whether to include embeddings for resolved text embeddings.

setThreshold(thres: float)#

Sets Threshold value for the last distance calculated.

Parameters:

thres (float) – Threshold value for the last distance calculated.

setUseAuxLabel(name: bool)#

Sets Use AuxLabel Col or not.

Parameters:

name (bool) – Use AuxLabel Col or not.

write() → JavaMLWriter#

Returns an MLWriter instance for this ML instance.

class SentenceEntityResolverModel(classname='com.johnsnowlabs.finance.chunk_classification.resolution.SentenceEntityResolverModel', java_model=None)#

Bases: sparknlp_jsl.annotator.resolution.sentence_entity_resolver.SentenceEntityResolverModel

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_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
>>> 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)
dimension#
getter_attrs = []#
inputAnnotatorTypes#
inputCols#
lazyAnnotator#
name = 'SentenceEntityResolverModel'#
optionalInputAnnotatorTypes = []#
outputAnnotatorType = 'entity'#
outputCol#
returnCosineDistances#
searchTree#
skipLPInputColsValidation = True#
uid = ''#
useAuxLabel#
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

getDimension()#

Gets embeddings dimension.

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).

static pretrained(name='finel_edgar_company_name', lang='en', remote_loc='finance/models')#

Download a pre-trained SentenceEntityResolverModel.

Parameters:

  • name (str) – Name of the pre-trained model, by default “finel_edgar_company_name”

  • lang (str) – Language of the pre-trained model, by default “en”

  • remote_loc (str) – Remote location of the pre-trained model. If None, use the open-source location. Other values are “clinical/models”, “finance/models”, or “legal/models”.

Returns:

A pre-trained SentenceEntityResolverModel.

Return type:

SentenceEntityResolverModel

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.

setDimension(value)#

Sets embeddings dimension.

Parameters:

value (int) – Embeddings dimension

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()#
setSearchTree(search_tree)#

Sets the search tree to use.

Usualy the user don’t need to set this parameter, as it uses an internal encapsulated search tree based on KDTree.

Parameters:

search_tree (SerializableKDTree) – The search tree to use. Should be an instance of SerializableKDTree.

setUseAuxLabel(name: bool)#

Sets Use AuxLabel Col or not.

Parameters:

name (bool) – Use AuxLabel Col or not.

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 dataset

  • params (dict, optional) – an optional param map that overrides embedded params.

Returns:

transformed dataset

Return type:

pyspark.sql.DataFrame

write() → JavaMLWriter#

Returns an MLWriter instance for this ML instance.