sparknlp_jsl.annotator.windowed.windowed_sentence#

Classes#

class WindowedParams#

glueString#

windowSize#

setGlueString(value)#

Sets string to use to join the neighboring elements together

Parameters:

value (string) – string to use to join the neighboring elements together

setWindowSize(value)#

Sets size of the sliding window

Parameters:

value (int) – size of the sliding window

class WindowedSentenceModel(classname='com.johnsnowlabs.nlp.annotators.windowed.WindowedSentenceModel', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal, WindowedParams

The WindowedSentenceModel class is used to combine a series of sentences based on specific window configurations.

Returns the joined results after windowing its inputs

Input Annotation types	Output Annotation type
DOCUMENT	DOCUMENT

Parameters:

• windowSize – size of the sliding window

• glueString – string to use to join the neighboring elements together

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")
>>>    windowedSentence = WindowedSentenceModel()    ...       .setWindowSize(1)    ...       .setInputCols("sentence")    ...       .setOutputCol("five")    ...       .setGlueString(":::")
>>>   flattener = Flattener()    ...        .setInputCols("five")

>>>   pipeline = Pipeline(stages=[documentAssembler,sentenceDetector,windowedSentence,flattener])

>>>   data = spark.createDataFrame([["A 28-year-old female with a history of gestational diabetes mellitus diagnosed eight years.Two weeks prior to presentation respiratory tract infection.She was on for HTG .  She had been of presentation . examination benign with no  or rigidity ."]]).toDF("text")

>>>    model = pipeline.fit(data).transform(data)
>>>    model.show(truncate=False)

getter_attrs = []#

glueString#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

skipLPInputColsValidation = True#

windowSize#

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 (dict, optional) – Extra parameters to copy to the new instance

Returns:

Copy of this instance

Return type:

JavaParams

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 (dict, optional) – extra param values

Returns:

merged param map

Return type:

dict

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:

paramName (str) – 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.

inputColsValidation(value)#

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).

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.

setForceInputTypeValidation(etfm)#

setGlueString(value)#

Sets string to use to join the neighboring elements together

Parameters:

value (string) – string to use to join the neighboring elements together

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()#

setWindowSize(value)#

Sets size of the sliding window

Parameters:

value (int) – size of the sliding window

transform(dataset, params=None)#

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()#

Returns an MLWriter instance for this ML instance.