sparknlp.base.GraphFinisher

class sparknlp.base.GraphFinisher[source]

Bases: sparknlp.internal.AnnotatorTransformer

Helper class to convert the knowledge graph from GraphExtraction into a generic format, such as RDF.

Input Annotation types

Output Annotation type

NONE

NONE

Parameters
inputCol

Name of input annotation column

outputCol

Name of finisher output column

cleanAnnotations

Whether to remove all the existing annotation columns, by default True

outputAsArray

Whether to generate an Array with the results, by default True

Examples

This is a continuation of the example of GraphExtraction. To see how the graph is extracted, see the documentation of that class.

>>> graphFinisher = GraphFinisher() \
...     .setInputCol("graph") \
...     .setOutputCol("graph_finished")
...     .setOutputAsArray(False)
>>> finishedResult = graphFinisher.transform(result)
>>> finishedResult.select("text", "graph_finished").show(truncate=False)
+-----------------------------------------------------+-----------------------------------------------------------------------+
|text                                                 |graph_finished                                                         |
+-----------------------------------------------------+-----------------------------------------------------------------------+
|You and John prefer the morning flight through Denver|[[(prefer,nsubj,morning), (morning,flat,flight), (flight,flat,Denver)]]|
+-----------------------------------------------------+-----------------------------------------------------------------------+

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.

getOrDefault(param)

Gets the value of a param in the user-supplied param map or its default value.

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.

setCleanAnnotations(value)

Sets whether to remove all the existing annotation columns, by default True.

setInputCol(value)

Sets name of input annotation column.

setOutputAsArray(value)

Sets whether to generate an Array with the results, by default True.

setOutputCol(value)

Sets name of finisher output column.

setParamValue(paramName)

Sets the value of a parameter.

setParams()

transform(dataset[, params])

Transforms the input dataset with optional parameters.

write()

Returns an MLWriter instance for this ML instance.

Attributes

cleanAnnotations

getter_attrs

inputCol

name

outputAsArray

outputCol

params

Returns all params ordered by name.

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

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.

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.

setCleanAnnotations(value)[source]

Sets whether to remove all the existing annotation columns, by default True.

Parameters
valuebool

Whether to remove all the existing annotation columns, by default True.

setInputCol(value)[source]

Sets name of input annotation column.

Parameters
valuestr

Name of input annotation column.

setOutputAsArray(value)[source]

Sets whether to generate an Array with the results, by default True.

Parameters
valuebool

Whether to generate an Array with the results, by default True.

setOutputCol(value)[source]

Sets name of finisher output column.

Parameters
valuestr

Name of finisher output column.

setParamValue(paramName)

Sets the value of a parameter.

Parameters
paramNamestr

Name of the parameter

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.