sparknlp.annotator.TextMatcher

class sparknlp.annotator.TextMatcher[source]

Bases: sparknlp.common.AnnotatorApproach

Annotator to match exact phrases (by token) provided in a file against a Document.

A text file of predefined phrases must be provided with setEntities().

For extended examples of usage, see the Spark NLP Workshop.

Input Annotation types

Output Annotation type

DOCUMENT, TOKEN

CHUNK

Parameters
entities

ExternalResource for entities

caseSensitive

Whether to match regardless of case, by default True

mergeOverlapping

Whether to merge overlapping matched chunks, by default False

entityValue

Value for the entity metadata field

buildFromTokens

Whether the TextMatcher should take the CHUNK from TOKEN or not

Examples

In this example, the entities file is of the form:

...
dolore magna aliqua
lorem ipsum dolor. sit
laborum
...

where each line represents an entity phrase to be extracted.

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> data = spark.createDataFrame([["Hello dolore magna aliqua. Lorem ipsum dolor. sit in laborum"]]).toDF("text")
>>> entityExtractor = TextMatcher() \
...     .setInputCols(["document", "token"]) \
...     .setEntities("src/test/resources/entity-extractor/test-phrases.txt", ReadAs.TEXT) \
...     .setOutputCol("entity") \
...     .setCaseSensitive(False)
>>> pipeline = Pipeline().setStages([documentAssembler, tokenizer, entityExtractor])
>>> results = pipeline.fit(data).transform(data)
>>> results.selectExpr("explode(entity) as result").show(truncate=False)
+------------------------------------------------------------------------------------------+
|result                                                                                    |
+------------------------------------------------------------------------------------------+
|[chunk, 6, 24, dolore magna aliqua, [entity -> entity, sentence -> 0, chunk -> 0], []]    |
|[chunk, 27, 48, Lorem ipsum dolor. sit, [entity -> entity, sentence -> 0, chunk -> 1], []]|
|[chunk, 53, 59, laborum, [entity -> entity, sentence -> 0, chunk -> 2], []]               |
+------------------------------------------------------------------------------------------+

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.

setBuildFromTokens(b)

Sets whether the TextMatcher should take the CHUNK from TOKEN or not.

setCaseSensitive(b)

Sets whether to match regardless of case, by default True.

setEntities(path[, read_as, options])

Sets the external resource for the entities.

setEntityValue(b)

Sets value for the entity metadata field.

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setMergeOverlapping(b)

Sets whether to merge overlapping matched chunks, by default False.

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

buildFromTokens

caseSensitive

entities

entityValue

getter_attrs

inputCols

lazyAnnotator

mergeOverlapping

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.

setBuildFromTokens(b)[source]

Sets whether the TextMatcher should take the CHUNK from TOKEN or not.

Parameters
bbool

Whether the TextMatcher should take the CHUNK from TOKEN or not

setCaseSensitive(b)[source]

Sets whether to match regardless of case, by default True.

Parameters
bbool

Whether to match regardless of case

setEntities(path, read_as='TEXT', options={'format': 'text'})[source]

Sets the external resource for the entities.

Parameters
pathstr

Path to the external resource

read_asstr, optional

How to read the resource, by default ReadAs.TEXT

optionsdict, optional

Options for reading the resource, by default {“format”: “text”}

setEntityValue(b)[source]

Sets value for the entity metadata field.

Parameters
bstr

Value for the entity metadata field

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

setMergeOverlapping(b)[source]

Sets whether to merge overlapping matched chunks, by default False.

Parameters
bbool

Whether to merge overlapping matched chunks

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.