sparknlp_jsl.annotator.ContextualParserApproach#

class sparknlp_jsl.annotator.ContextualParserApproach[source]#

Bases: AnnotatorApproach

Creates a model, that extracts entity from a document based on user defined rules. Rule matching is based on a RegexMatcher defined in a JSON file. It is set through the parameter setJsonPath() In this JSON file, regex is defined that you want to match along with the information that will output on metadata field. Additionally, a dictionary can be provided with setDictionary to map extracted entities to a unified representation. The first column of the dictionary file should be the representation with following columns the possible matches.

Input Annotation types	Output Annotation type
`DOCUMENT, TOKEN`	`CHUNK`

Parameters:

jsonPath: Path to json file with rules
caseSensitive: Whether to use case sensitive when matching values
prefixAndSuffixMatch: Whether to match both prefix and suffix to annotate the hit
dictionary: Path to dictionary file in tsv or csv format

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")

Define the parser (json file needs to be provided)

>>> data = spark.createDataFrame([["A patient has liver metastases pT1bN0M0 and the T5 primary site may be colon or... "]]).toDF("text")
>>> contextualParser = ContextualParserApproach()     ...   .setInputCols(["sentence", "token"])     ...   .setOutputCol("entity")     ...   .setJsonPath("/path/to/regex_token.json")     ...   .setCaseSensitive(True)
...
>>> pipeline = Pipeline(stages=[
...     documentAssembler,
...     sentenceDetector,
...     tokenizer,
...     contextualParser
...   ])

>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(entity)").show(5, truncate=False)

col

{chunk, 32, 39, pT1bN0M0, {field -> Stage, normalized -> , confidenceValue -> 1.00, sentence -> 0}, []} {chunk, 49, 50, T5, {field -> Stage, normalized -> , confidenceValue -> 1.00, sentence -> 0}, []} {chunk, 148, 156, cT4bcN2M1, {field -> Stage, normalized -> , confidenceValue -> 1.00, sentence -> 1}, []} {chunk, 189, 194, T?N3M1, {field -> Stage, normalized -> , confidenceValue -> 1.00, sentence -> 2}, []} {chunk, 316, 323, pT1bN0M0, {field -> Stage, normalized -> , confidenceValue -> 1.00, sentence -> 3}, []}

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.
`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.
`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.
`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.
`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.
`setCaseSensitive`(value)	Sets whether to use case sensitive when matching values
`setDictionary`(path[, read_as, options])	Sets if we want to use 'bow' for word embeddings or 'sentence' for sentences"
`setInputCols`(*value)	Sets column names of input annotations.
`setJsonPath`(value)	Sets path to json file with rules
`setLazyAnnotator`(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
`setOptionalContextRules`(value)
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setPrefixAndSuffixMatch`(value)	Sets whether to match both prefix and suffix to annotate the hit
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`caseSensitive`
`dictionary`
`getter_attrs`
`inputCols`
`jsonPath`
`lazyAnnotator`
`optionalContextRules`
`outputCol`
`params`	Returns all params ordered by name.
`prefixAndSuffixMatch`

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.

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 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.

setCaseSensitive(value)[source]#

Sets whether to use case sensitive when matching values

Parameters:

valuebool: Whether to use case sensitive when matching values

setDictionary(path, read_as='TEXT', options=None)[source]#

Sets if we want to use ‘bow’ for word embeddings or ‘sentence’ for sentences”

Parameters:

pathstr: Path wher is de dictionary
read_as: ReadAs: Format of the file
options: dict: Dictionary with the options to read the file.

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

*valuestr: Input columns for the annotator

setJsonPath(value)[source]#

Sets path to json file with rules

Parameters:

valuestr: Path to json file with rules

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

setPrefixAndSuffixMatch(value)[source]#

Sets whether to match both prefix and suffix to annotate the hit

Parameters:

valuebool: Whether to match both prefix and suffix to annotate the hit

uid#: A unique id for the object.

write()#: Returns an MLWriter instance for this ML instance.