`sparknlp_jsl.annotator.matcher.text_matcher_internal`#

Contains classes for the TextMatcherInternal.

Module Contents#

Classes#

`TextMatcherInternal`	Annotator to match exact phrases (by token) provided in a file against a
`TextMatcherInternalModel`	Instantiated model of the TextMatcherInternal.

class TextMatcherInternal#

Bases: sparknlp_jsl.common.AnnotatorApproachInternal

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().

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 TextMatcherInternal should take the CHUNK from TOKEN

Examples

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

… dolore magna aliqua, entity_name_1 lorem ipsum dolor. sit, entity_name_1 laborum, entity_name_1 …

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 = TextMatcherInternal() \
...     .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], []]               |
+------------------------------------------------------------------------------------------+

buildFromTokens#

caseSensitive#

delimiter#

entities#

entityValue#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

mergeOverlapping#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = 'chunk'#

outputCol#

skipLPInputColsValidation = True#

uid = ''#

clear(param: pyspark.ml.param.Param) → None#: Clears a param from the param map if it has been explicitly set.

copy(extra: pyspark.ml._typing.ParamMap | None = None) → JP#

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: str | Param) → str#: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str#: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: pyspark.ml._typing.ParamMap | None = None) → pyspark.ml._typing.ParamMap#

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

fit(dataset: pyspark.sql.dataframe.DataFrame, params: pyspark.ml._typing.ParamMap | None = ...) → M#

fit(dataset: pyspark.sql.dataframe.DataFrame, params: List[pyspark.ml._typing.ParamMap] | Tuple[pyspark.ml._typing.ParamMap]) → List[M]

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

dataset (pyspark.sql.DataFrame) – input dataset.
params (dict or list or tuple, optional) – 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)

Return type:

Transformer or a list of Transformer

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[pyspark.ml._typing.ParamMap]) → Iterator[Tuple[int, M]]#

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters:

dataset (pyspark.sql.DataFrame) – input dataset.
paramMaps (collections.abc.Sequence) – 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.

Return type:

_FitMultipleIterator

getInputCols()#: Gets current column names of input annotations.

getLazyAnnotator()#: Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param: str) → Any#
getOrDefault(param: Param[T]) → T: 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: str) → Param#: Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

hasDefault(param: str | Param[Any]) → bool#: Checks whether a param has a default value.

hasParam(paramName: str) → bool#: Tests whether this instance contains a param with a given (string) name.

inputColsValidation(value)#

isDefined(param: str | Param[Any]) → bool#: Checks whether a param is explicitly set by user or has a default value.

isSet(param: str | Param[Any]) → bool#: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL#: 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: str) → None#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: Param, value: Any) → None#: Sets a parameter in the embedded param map.

setBuildFromTokens(b)#

Sets whether the TextMatcherInternal should take the CHUNK from TOKEN.

Parameters:: b (bool) – Whether the TextMatcherInternal should take the CHUNK from TOKEN

setCaseSensitive(b)#

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

Parameters:: b (bool) – Whether to match regardless of case

setDelimiter(b)#

Sets Value for the delimiter between Phrase, Entity.

Parameters:: b (bool) – Whether the TextMatcherInternal should take the CHUNK from TOKEN

setEntities(path, read_as=ReadAs.TEXT, options={'format': 'text'})#

Sets the external resource for the entities.

Parameters:

path (str) – Path to the external resource
read_as (str, optional) – How to read the resource, by default ReadAs.TEXT
options (dict, optional) – Options for reading the resource, by default {“format”: “text”}

setEntityValue(b)#

Sets value for the entity metadata field.

Parameters:: b (str) – Value for the entity metadata field

setForceInputTypeValidation(etfm)#

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

setMergeOverlapping(b)#

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

Parameters:: b (bool) – Whether to merge overlapping matched chunks

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

write() → JavaMLWriter#: Returns an MLWriter instance for this ML instance.

class TextMatcherInternalModel(classname='com.johnsnowlabs.nlp.annotators.matcher.TextMatcherInternalModel', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

Instantiated model of the TextMatcherInternal.

This is the instantiated model of the TextMatcherInternal. For training your own model, please see the documentation of that class.

Input Annotation types	Output Annotation type
`DOCUMENT, TOKEN`	`CHUNK`

Parameters:

mergeOverlapping – Whether to merge overlapping matched chunks, by default False
entityValue – Value for the entity metadata field
buildFromTokens – Whether the TextMatcherInternal should take the CHUNK from TOKEN

buildFromTokens#

caseSensitive#

delimiter#

entityValue#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

mergeOverlapping#

name = 'TextMatcherInternalModel'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = 'chunk'#

outputCol#

searchTrie#

skipLPInputColsValidation = True#

uid = ''#

clear(param: pyspark.ml.param.Param) → None#: Clears a param from the param map if it has been explicitly set.

copy(extra: pyspark.ml._typing.ParamMap | None = None) → JP#

Parameters:: extra (dict, optional) – Extra parameters to copy to the new instance
Returns:: Copy of this instance
Return type:: JavaParams

explainParam(param: str | Param) → str#: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str#: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: pyspark.ml._typing.ParamMap | None = None) → pyspark.ml._typing.ParamMap#

Parameters:: extra (dict, optional) – extra param values
Returns:: merged param map
Return type:: dict

getCaseSensitive()#: Gets whether the model is matching regardless of case

getDelimiter()#: Gets value for the delimiter between Phrase, Entity.

getInputCols()#: Gets current column names of input annotations.

getLazyAnnotator()#: Gets whether Annotator should be evaluated lazily in a RecursivePipeline.

getOrDefault(param: str) → Any#
getOrDefault(param: Param[T]) → T: 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: str) → Param#: Gets a param by its name.

getParamValue(paramName)#

Gets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

hasDefault(param: str | Param[Any]) → bool#: Checks whether a param has a default value.

hasParam(paramName: str) → bool#: Tests whether this instance contains a param with a given (string) name.

inputColsValidation(value)#

isDefined(param: str | Param[Any]) → bool#: Checks whether a param is explicitly set by user or has a default value.

isSet(param: str | Param[Any]) → bool#: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL#: Reads an ML instance from the input path, a shortcut of read().load(path).

static pretrained(name, lang='en', remote_loc=None)#

Downloads and loads a pretrained model.

Parameters:

name (str, optional) – Name of the pretrained model
lang (str, optional) – Language of the pretrained model, by default “en”
remote_loc (str, optional) – Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:

The restored model

Return type:

TextMatcherInternalModel

classmethod read()#: Returns an MLReader instance for this class.

save(path: str) → None#: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: Param, value: Any) → None#: Sets a parameter in the embedded param map.

setBuildFromTokens(b)#

Sets whether the TextMatcherInternal should take the CHUNK from TOKEN.

Parameters:: b (bool) – Whether the TextMatcherInternal should take the CHUNK from TOKEN

setDelimiter(b)#

Sets Value for the delimiter between Phrase, Entity.

Parameters:: b (bool) – Whether the TextMatcherInternal should take the CHUNK from TOKEN

setEntityValue(b)#

Sets value for the entity metadata field.

Parameters:: b (str) – Value for the entity metadata field

setForceInputTypeValidation(etfm)#

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

setMergeOverlapping(b)#

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

Parameters:: b (bool) – Whether to merge overlapping matched chunks

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()#

transform(dataset: pyspark.sql.dataframe.DataFrame, params: pyspark.ml._typing.ParamMap | None = None) → pyspark.sql.dataframe.DataFrame#

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() → JavaMLWriter#: Returns an MLWriter instance for this ML instance.

sparknlp_jsl.annotator.matcher.text_matcher_internal#

Module Contents#

Classes#

`sparknlp_jsl.annotator.matcher.text_matcher_internal`#