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
See also
BigTextMatcher
to match large amounts of text
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.
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.
Gets current column names of input annotations.
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.
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.
Sets whether the TextMatcher should take the CHUNK from TOKEN or not.
Sets whether to match regardless of case, by default True.
setEntities
(path[, read_as, options])Sets the external resource for the entities.
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.
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
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 typeParam
.
- 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.