sparknlp_jsl.annotator.DrugNormalizer#

class sparknlp_jsl.annotator.DrugNormalizer[source]#

Bases: AnnotatorModel

Annotator which normalizes raw text from clinical documents, e.g. scraped web pages or xml documents, from document type columns into Sentence.: Removes all dirty characters from text following one or more input regex patterns. Can apply non wanted character removal which a specific policy. Can apply lower case normalization.

Input Annotation types	Output Annotation type
`DOCUMENT`	`DOCUMENT`

Parameters:

lowercase: whether to convert strings to lowercase
policy: policy to remove patterns from text. Defaults “all”

Examples

>>> data = spark.createDataFrame([
...   ["Sodium Chloride/Potassium Chloride 13bag"],
...   ["interferon alfa-2b 10 million unit ( 1 ml ) injec"],
...   ["aspirin 10 meq/ 5 ml oral sol"]
... ]).toDF("text")
>>> document = DocumentAssembler().setInputCol("text").setOutputCol("document")
>>> drugNormalizer = DrugNormalizer().setInputCols(["document"]).setOutputCol("document_normalized")
>>> trainingPipeline = Pipeline(stages=[document, drugNormalizer])
>>> result = trainingPipeline.fit(data).transform(data)
>>> result.selectExpr("explode(document_normalized.result) as normalized_text").show(truncate=False)
+----------------------------------------------------+
|normalized_text                                     |
+----------------------------------------------------+
|Sodium Chloride / Potassium Chloride 13 bag         |
|interferon alfa - 2b 10000000 unt ( 1 ml ) injection|
|aspirin 2 meq/ml oral solution                      |
+----------------------------------------------------+

Methods

`__init__`()	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.
`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.
`setInputCols`(*value)	Sets column names of input annotations.
`setLazyAnnotator`(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
`setLowercase`(value)	Sets whether to convert strings to lowercase
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setParams`()
`setPolicy`(value)	Sets policy to remove patterns from text.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`getter_attrs`
`inputCols`
`lazyAnnotator`
`lowercase`
`outputCol`
`params`	Returns all params ordered by name.
`policy`

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

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

setLowercase(value)[source]#

Sets whether to convert strings to lowercase

Parameters:

pbool: Whether to convert strings to lowercase

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

setPolicy(value)[source]#

Sets policy to remove patterns from text.

Parameters:

pstr: policy to remove patterns from text.

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.