sparknlp.annotator.EntityRulerApproach

class sparknlp.annotator.EntityRulerApproach[source]

Bases: sparknlp.common.AnnotatorApproach, sparknlp.common.HasStorage

Fits an Annotator to match exact strings or regex patterns provided in a file against a Document and assigns them an named entity. The definitions can contain any number of named entities.

There are multiple ways and formats to set the extraction resource. It is possible to set it either as a “JSON”, “JSONL” or “CSV” file. A path to the file needs to be provided to setPatternsResource. The file format needs to be set as the “format” field in the option parameter map and depending on the file type, additional parameters might need to be set.

To enable regex extraction, setEnablePatternRegex(True) needs to be called.

If the file is in a JSON format, then the rule definitions need to be given in a list with the fields “id”, “label” and “patterns”:

 [
    {
      "id": "person-regex",
      "label": "PERSON",
      "patterns": ["\w+\s\w+", "\w+-\w+"]
    },
    {
      "id": "locations-words",
      "label": "LOCATION",
      "patterns": ["Winterfell"]
    }
]

The same fields also apply to a file in the JSONL format:

{"id": "names-with-j", "label": "PERSON", "patterns": ["Jon", "John", "John Snow"]}
{"id": "names-with-s", "label": "PERSON", "patterns": ["Stark", "Snow"]}
{"id": "names-with-e", "label": "PERSON", "patterns": ["Eddard", "Eddard Stark"]}

In order to use a CSV file, an additional parameter “delimiter” needs to be set. In this case, the delimiter might be set by using .setPatternsResource("patterns.csv", ReadAs.TEXT, {"format": "csv", "delimiter": "|")}):

PERSON|Jon
PERSON|John
PERSON|John Snow
LOCATION|Winterfell

Input Annotation types

Output Annotation type

DOCUMENT, TOKEN

CHUNK

Parameters
patternsResource

Resource in JSON or CSV format to map entities to patterns

enablePatternRegex

Enables regex pattern match

useStorage

Whether to use RocksDB storage to serialize patterns

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from sparknlp.common import *
>>> from pyspark.ml import Pipeline

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

PERSON|Jon
PERSON|John
PERSON|John Snow
LOCATION|Winterfell

where each line represents an entity and the associated string delimited by “|”.

>>> documentAssembler = DocumentAssembler() \
...     .setInputCol("text") \
...     .setOutputCol("document")
>>> tokenizer = Tokenizer() \
...     .setInputCols(["document"]) \
...     .setOutputCol("token")
>>> entityRuler = EntityRulerApproach() \
...     .setInputCols(["document", "token"]) \
...     .setOutputCol("entities") \
...     .setPatternsResource(
...       "patterns.csv",
...       ReadAs.TEXT,
...       {"format": "csv", "delimiter": "\\|"}
...     ) \
...     .setEnablePatternRegex(True)
>>> pipeline = Pipeline().setStages([
...     documentAssembler,
...     tokenizer,
...     entityRuler
... ])
>>> data = spark.createDataFrame([["Jon Snow wants to be lord of Winterfell."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.selectExpr("explode(entities)").show(truncate=False)
+--------------------------------------------------------------------+
|col                                                                 |
+--------------------------------------------------------------------+
|[chunk, 0, 2, Jon, [entity -> PERSON, sentence -> 0], []]           |
|[chunk, 29, 38, Winterfell, [entity -> LOCATION, sentence -> 0], []]|
+--------------------------------------------------------------------+

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.

getCaseSensitive()

Gets whether to ignore case in tokens for embeddings matching.

getIncludeStorage()

Gets whether to include indexed storage in trained model.

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.

getStoragePath()

Gets path to file.

getStorageRef()

Gets unique reference name for identification.

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.

setCaseSensitive(value)

Sets whether to ignore case in tokens for embeddings matching.

setEnablePatternRegex(value)

Sets whether to enable regex pattern matching.

setIncludeStorage(value)

Sets whether to include indexed storage in trained model.

setInputCols(*value)

Sets column names of input annotations.

setLazyAnnotator(value)

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

setOutputCol(value)

Sets output column name of annotations.

setParamValue(paramName)

Sets the value of a parameter.

setPatternsResource(path[, read_as, options])

Sets Resource in JSON or CSV format to map entities to patterns.

setStoragePath(path, read_as)

Sets path to file.

setStorageRef(value)

Sets unique reference name for identification.

setUseStorage(value)

Sets whether to use RocksDB storage to serialize patterns.

write()

Returns an MLWriter instance for this ML instance.

Attributes

caseSensitive

enablePatternRegex

getter_attrs

includeStorage

inputCols

lazyAnnotator

name

outputCol

params

Returns all params ordered by name.

patternsResource

storagePath

storageRef

useStorage

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.

getCaseSensitive()

Gets whether to ignore case in tokens for embeddings matching.

Returns
bool

Whether to ignore case in tokens for embeddings matching

getIncludeStorage()

Gets whether to include indexed storage in trained model.

Returns
bool

Whether to include indexed storage in trained model

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

getStoragePath()

Gets path to file.

Returns
str

path to file

getStorageRef()

Gets unique reference name for identification.

Returns
str

Unique reference name for identification

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.

setCaseSensitive(value)

Sets whether to ignore case in tokens for embeddings matching.

Parameters
valuebool

Whether to ignore case in tokens for embeddings matching

setEnablePatternRegex(value)[source]

Sets whether to enable regex pattern matching.

Parameters
valuebool

Whether to enable regex pattern matching.

setIncludeStorage(value)

Sets whether to include indexed storage in trained model.

Parameters
valuebool

Whether to include indexed storage in trained model

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

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

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

Sets Resource in JSON or CSV format to map entities to patterns.

Parameters
pathstr

Path to the resource

read_asstr, optional

How to interpret the resource, by default ReadAs.TEXT

optionsdict, optional

Options for parsing the resource, by default {“format”: “JSON”}

setStoragePath(path, read_as)

Sets path to file.

Parameters
pathstr

Path to file

read_asstr

How to interpret the file

Notes

See ReadAs for reading options.

setStorageRef(value)

Sets unique reference name for identification.

Parameters
valuestr

Unique reference name for identification

setUseStorage(value)[source]

Sets whether to use RocksDB storage to serialize patterns.

Parameters
valuebool

Whether to use RocksDB storage to serialize patterns.

uid

A unique id for the object.

write()

Returns an MLWriter instance for this ML instance.