sparknlp_jsl.annotator.Router#
- class sparknlp_jsl.annotator.Router(classname='com.johnsnowlabs.nlp.Router', java_model=None)[source]#
Bases:
AnnotatorModel
Convert chunks from regexMatcher to chunks with a entity in the metadata. Use the identifier or field as a entity.
Input Annotation types
Output Annotation type
ANY
ANY
- Parameters:
- inputType
The type of the entity that you want to filter by default sentence_embeddings.Possible values
document|token|wordpiece|word_embeddings|sentence_embeddings|category|date|sentiment|pos|chunk|named_entity|regex|dependency|labeled_dependency|language|keyword
- filterFieldsElements
The filterfieldsElements are the allowed values for the metadata field that is being used
- metadataField
The key in the metadata dictionary that you want to filter (by default
entity
)
Examples
>>> test_data = spark.createDataFrame(sentences).toDF("text") >>> document = DocumentAssembler().setInputCol("text").setOutputCol("document") >>> sentence = SentenceDetector().setInputCols("document").setOutputCol("sentence") >>> regexMatcher = RegexMatcher().setExternalRules("../src/test/resources/regex-matcher/rules2.txt", ",") \ ... .setInputCols("sentence") \ ... .setOutputCol("regex") \ ... .setStrategy("MATCH_ALL") >>> chunk2Doc = Chunk2Doc().setInputCols("regex").setOutputCol("doc_chunk") >>> embeddings = BertSentenceEmbeddings.pretrained("sent_small_bert_L2_128") \ ... .setInputCols("doc_chunk") \ ... .setOutputCol("bert") \ ... .setCaseSensitive(False) \ ... .setMaxSentenceLength(32) >>> router_name_embeddings = Router() \ ... .setInputType("sentence_embeddings") \ ... .setInputCols("bert") \ ... .setMetadataField("identifier") \ ... .setFilterFieldsElements(["name"]) \ ... .setOutputCol("names_embeddings") >>> router_city_embeddings = Router() \ ... .setInputType("sentence_embeddings") \ ... .setInputCols(["bert"]) \ ... .setMetadataField("identifier") \ ... .setFilterFieldsElements(["city"]) \ ... .setOutputCol("cities_embeddings") >>> router_names = Router() \ ... .setInputType("chunk") \ ... .setInputCols("regex") \ ... .setMetadataField("identifier") \ ... .setFilterFieldsElements(["name"]) \ ... .setOutputCol("names_chunks") >>> pipeline = Pipeline().setStages( >>> [document, sentence, regexMatcher, chunk2Doc, router_names, embeddings, router_name_embeddings, ... router_city_embeddings])
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.
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.
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.
setFilterFieldsElements
(value)Sets the filterfieldsElements are the allowed values for the metadata field that is being used
setInputCols
(*value)Sets column names of input annotations.
setInputType
(value)Sets the type of the entity that you want to filter by default sentence_embedding
setLazyAnnotator
(value)Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
setMetadataField
(value)Sets the key in the metadata dictionary that you want to filter (by default 'entity')
setOutputCol
(value)Sets output column name of annotations.
setParamValue
(paramName)Sets the value of a parameter.
setParams
()transform
(dataset[, params])Transforms the input dataset with optional parameters.
write
()Returns an MLWriter instance for this ML instance.
Attributes
filterFieldsElements
getter_attrs
inputCols
inputType
lazyAnnotator
metadataField
name
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
- 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.
- setFilterFieldsElements(value)[source]#
Sets the filterfieldsElements are the allowed values for the metadata field that is being used
- Parameters:
- valuelist
The filterfieldsElements are the allowed values for the metadata field that is being used
- setInputCols(*value)#
Sets column names of input annotations.
- Parameters:
- *valuestr
Input columns for the annotator
- setInputType(value)[source]#
Sets the type of the entity that you want to filter by default sentence_embedding
- Parameters:
- valueint
The type of the entity that you want to filter by default sentence_embedding
- setLazyAnnotator(value)#
Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
- Parameters:
- valuebool
Whether Annotator should be evaluated lazily in a RecursivePipeline
- setMetadataField(value)[source]#
Sets the key in the metadata dictionary that you want to filter (by default ‘entity’)
- Parameters:
- valuestr
The key in the metadata dictionary that you want to filter (by default ‘entity’)
- 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
- 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.