sparknlp_jsl.annotator.AssertionLogRegModel#

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

Bases: AnnotatorModel, HasStorageRef

This is a main class in AssertionLogReg family. Logarithmic Regression is used to extract Assertion Status: from extracted entities and text. AssertionLogRegModel requires DOCUMENT, CHUNK and WORD_EMBEDDINGS type annotator inputs, which can be obtained by e.g a

Excluding the label, this can be done with for example:

a SentenceDetector,
a Chunk,
a WordEmbeddingsModel.

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

Parameters:

beforeParam: Length of the context before the target
afterParam: Length of the context after the target
startCol: Column that contains the token number for the start of the target”
endCol: Column that contains the token number for the end of the target
nerCol: Column with NER type annotation output, use either nerCol or startCol and endCol
targetNerLabels

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from sparknlp_jsl.annotator import *
>>> from sparknlp.training import *
>>> from pyspark.ml import Pipeline

>>> document_assembler = DocumentAssembler() \
...    .setInputCol("text") \
...    .setOutputCol("document")
...
>>> sentence_detector = SentenceDetector() \
...    .setInputCol("document") \
...    .setOutputCol("sentence")
...
>>> tokenizer = Tokenizer() \
...    .setInputCols(["sentence"]) \
...    .setOutputCol("token")
...
>>> embeddings = WordEmbeddingsModel.pretrained("embeddings_clinical", "en", "clinical/models") \
...    .setInputCols(["sentence", "token"]) \
...    .setOutputCol("word_embeddings")     ...    .setCaseSensitive(False)
...
>>> chunk = Chunker() \
...    .setInputCols([sentence]) \
...    .setChunkCol("chunk") \
...    .setOutputCol("chunk")
...
Then the AssertionLogRegApproach model is defined. Label column is needed in the dataset for training.
>>> assertion = AssertionLogRegModel().pretrained() \
...    .setLabelCol("label") \
...    .setInputCols(["document", "chunk", "word_embeddings"]) \
...    .setOutputCol("assertion") \
...
...
>>> assertionPipeline = Pipeline(stages=[
...    document_assembler,
...    sentence_detector,
...    tokenizer,
...    embeddings,
...    chunk,
...    assertion
>>>])

>>> assertionModel = assertionPipeline.fit(dataset)
>>> assertionPretrained = assertionModel.transform(dataset)

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.
`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`()
`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

`afterParam`
`beforeParam`
`endCol`
`getter_attrs`
`inputCols`
`lazyAnnotator`
`name`
`nerCol`
`outputCol`
`params`	Returns all params ordered by name.
`startCol`
`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.

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

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.