sparknlp.annotator.TypedDependencyParserApproach

class sparknlp.annotator.TypedDependencyParserApproach[source]

Bases: sparknlp.common.AnnotatorApproach

Labeled parser that finds a grammatical relation between two words in a sentence. Its input is either a CoNLL2009 or ConllU dataset.

For instantiated/pretrained models, see TypedDependencyParserModel.

Dependency parsers provide information about word relationship. For example, dependency parsing can tell you what the subjects and objects of a verb are, as well as which words are modifying (describing) the subject. This can help you find precise answers to specific questions.

The parser requires the dependant tokens beforehand with e.g. DependencyParser. The required training data can be set in two different ways (only one can be chosen for a particular model):

Apart from that, no additional training data is needed.

Input Annotation types

Output Annotation type

TOKEN, POS, DEPENDENCY

LABELED_DEPENDENCY

Parameters
conll2009

Path to file with CoNLL 2009 format

conllU

Universal Dependencies source files

numberOfIterations

Number of iterations in training, converges to better accuracy

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> sentence = SentenceDetector() \
...     .setInputCols(["document"]) \
...     .setOutputCol("sentence")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["sentence"]) \
...     .setOutputCol("token")
>>> posTagger = PerceptronModel.pretrained() \
...     .setInputCols(["sentence", "token"]) \
...     .setOutputCol("pos")
>>> dependencyParser = DependencyParserModel.pretrained() \
...     .setInputCols(["sentence", "pos", "token"]) \
...     .setOutputCol("dependency")
>>> typedDependencyParser = TypedDependencyParserApproach() \
...     .setInputCols(["dependency", "pos", "token"]) \
...     .setOutputCol("dependency_type") \
...     .setConllU("src/test/resources/parser/labeled/train_small.conllu.txt") \
...     .setNumberOfIterations(1)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     sentence,
...     tokenizer,
...     posTagger,
...     dependencyParser,
...     typedDependencyParser
... ])

Additional training data is not needed, the dependency parser relies on CoNLL-U only.

>>> emptyDataSet = spark.createDataFrame([[""]]).toDF("text")
>>> pipelineModel = pipeline.fit(emptyDataSet)

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.

setConll2009(path[, read_as, options])

Sets path to file with CoNLL 2009 format.

setConllU(path[, read_as, options])

Sets path to Universal Dependencies source files.

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setNumberOfIterations(value)

Sets Number of iterations in training, converges to better accuracy.

setOutputCol(value)

Sets output column name of annotations.

setParamValue(paramName)

Sets the value of a parameter.

write()

Returns an MLWriter instance for this ML instance.

Attributes

conll2009

conllU

getter_attrs

inputCols

lazyAnnotator

numberOfIterations

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

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.

setConll2009(path, read_as='TEXT', options={'key': 'value'})[source]

Sets path to file with CoNLL 2009 format.

Parameters
pathstr

Path to the resource

read_asstr, optional

How to read the resource, by default ReadAs.TEXT

optionsdict, optional

Options for reading the resource, by default {“key”: “value”}

setConllU(path, read_as='TEXT', options={'key': 'value'})[source]

Sets path to Universal Dependencies source files.

Parameters
pathstr

Path to the resource

read_asstr, optional

How to read the resource, by default ReadAs.TEXT

optionsdict, optional

Options for reading the resource, by default {“key”: “value”}

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

setNumberOfIterations(value)[source]

Sets Number of iterations in training, converges to better accuracy.

Parameters
valueint

Number of iterations in training

Returns
[type]

[description]

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

uid

A unique id for the object.

write()

Returns an MLWriter instance for this ML instance.