sparknlp.annotator.EntityRulerModel#

class sparknlp.annotator.EntityRulerModel(classname='com.johnsnowlabs.nlp.annotators.er.EntityRulerModel', java_model=None)[source]#

Bases: sparknlp.common.AnnotatorModel, sparknlp.common.HasStorageModel

Instantiated model of the EntityRulerApproach. For usage and examples see the documentation of the main class.

Input Annotation types	Output Annotation type
DOCUMENT, TOKEN	CHUNK

Methods

__init__([classname, java_model])	Initialize this instance with a Java model object.
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.
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.
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).
loadStorage(path, spark, storage_ref)
loadStorages(path, spark, storage_ref, databases)
pretrained(name[, lang, remote_loc])
read()	Returns an MLReader instance for this class.
save(path)	Save this ML instance to the given path, a shortcut of 'write().save(path)'.
saveStorage(path, spark)	Saves the current model to storage.
set(param, value)	Sets a parameter in the embedded param map.
setCaseSensitive(value)	Sets whether to ignore case in tokens for embeddings 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.
setParams()
setStorageRef(value)	Sets unique reference name for identification.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

caseSensitive
database
getter_attrs
includeStorage
inputCols
lazyAnnotator
name
outputCol
params	Returns all params ordered by name.
storageRef

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

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

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)’.

saveStorage(path, spark)#

Saves the current model to storage.

Parameters

pathstr

Path for saving the model.

sparkpyspark.sql.SparkSession

The current SparkSession

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

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

setStorageRef(value)#

Sets unique reference name for identification.

Parameters

valuestr

Unique reference name for identification

transform(dataset, params=None)#

Transforms 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.

Returns

transformed dataset

New in version 1.3.0.

uid#

A unique id for the object.

write()#

Returns an MLWriter instance for this ML instance.