sparknlp_jsl.annotator.feature_assembler#

Classes#

class FeaturesAssembler#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

Collects features from different columns.

It can collect features from single value columns (anything which can be cast to a float. If casts fails then the value is set to 0), array columns or SparkNLP annotations (if the annotation is an embedding, it takes the embedding, otherwise tries to cast the result field).

The output of the transformer is a FEATURE_VECTOR annotation (the numeric vector is in the embeddings field).

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp_jsl.common import *
>>> from sparknlp.training import *
>>> import sparknlp_jsl
>>> from sparknlp_jsl.base import *
>>> data = spark.read.option("header", "true").option("timestampFormat", "yyyy/MM/dd HH:mm:ss ZZ") \
...            .csv("./test_jsl/resources/relfeatures.csv") \
...            .withColumn("array_column", F.array("words_in_ent1", "words_in_ent2"))
...
>>> features_asm1 = sparknlp_jsl.base.FeaturesAssembler()\
...                    .setInputCols(["words_in_ent1", "words_in_ent2", "words_between", "array_column"]) \
...                    .setOutputCol("features_t")

>>>  results = Pipeline().setStages([features_asm1]).fit(self.__data).transform(self.__data).cache()

getter_attrs = []#

inputAnnotatorTypes = [None]#

inputCols#

lazyAnnotator#

name = 'FeaturesAssembler'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType = 'feature_vector'#

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

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.

setForceInputTypeValidation(etfm)#

setInputCols(value: str)#

Sets input columns name.

Parameters:

value (str) – Name of the input column

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:

value (bool) – Whether Annotator should be evaluated lazily in a RecursivePipeline

setOutputCol(value: str)#

Sets output column name.

Parameters:

value (str) – Name of the Output Column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:

paramName (str) – Name of the parameter

setParams()#

Sets the class parameters.

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.