sparknlp_jsl.annotator.DocumentLogRegClassifierApproach#

class sparknlp_jsl.annotator.DocumentLogRegClassifierApproach[source]#

Bases: AnnotatorApproach

Trains a model to classify documents with a Logarithmic Regression algorithm. Training data requires columns for text and their label. The result is a trained GenericClassifierModel.

Input Annotation types	Output Annotation type
`TOKEN` `	`CATEGORY`

Parameters:

labelCol: Column with the value result we are trying to predict.
maxIter: maximum number of iterations.
tol: convergence tolerance after each iteration.
fitIntercept: whether to fit an intercept term, default is true.
labels: array to output the label in the original form.
vectorizationModelPath: specify the vectorization model if it has been already trained.
classificationModelPath: specify the classification model if it has been already trained.

Examples

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

An example pipeline could then be defined like this

>>> tokenizer = Tokenizer() \
...    .setInputCols("document") \
...    .setOutputCol("token")
...
>>> normalizer = Normalizer() \
...    .setInputCols("token") \
...    .setOutputCol("normalized")
...
>>> stopwords_cleaner = StopWordsCleaner()\
...    .setInputCols("normalized")\
...    .setOutputCol("cleanTokens")\
...    .setCaseSensitive(False)
...
...stemmer = Stemmer()     ...    .setInputCols("cleanTokens")     ...    .setOutputCol("stem")
...
>>> gen_clf = DocumentLogRegClassifierApproach() \
...    .setLabelColumn("category") \
...    .setInputCols("stem") \
...    .setOutputCol("prediction")
...
>>> pipeline = Pipeline().setStages([
...    document_assembler,
...    tokenizer,
...    normalizer,
...    stopwords_cleaner,
...    stemmer,
...    logreg
...])
...
>>> clf_model = pipeline.fit(data)

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.
`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.
`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.
`setClassificationModelPath`(value)	Sets a path to the the classification model if it has been already trained.
`setFitIntercept`(merge)	Sets whether to fit an intercept term, default is true.
`setInputCols`(*value)	Sets column names of input annotations.
`setLabelColumn`(label)	Sets column with the value result we are trying to predict.
`setLabels`(value)	Sets array to output the label in the original form.
`setLazyAnnotator`(value)	Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
`setMaxIter`(k)	Sets maximum number of iterations.
`setOutputCol`(value)	Sets output column name of annotations.
`setParamValue`(paramName)	Sets the value of a parameter.
`setTol`(dist)	Sets convergence tolerance after each iteration.
`setVectorizationModelPath`(value)	Sets a path to the the classification model if it has been already trained.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`classificationModelPath`
`fitIntercept`
`getter_attrs`
`inputCols`
`labelCol`
`labels`
`lazyAnnotator`
`maxIter`
`outputCol`
`params`	Returns all params ordered by name.
`tol`
`vectorizationModelPath`

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.

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 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.

setClassificationModelPath(value)[source]#

Sets a path to the the classification model if it has been already trained.

Parameters:

labelstr: Path to the the classification model if it has been already trained.

setFitIntercept(merge)[source]#

Sets whether to fit an intercept term, default is true.

Parameters:

labelstr: Whether to fit an intercept term, default is true.

setInputCols(*value)#

Sets column names of input annotations.

Parameters:

*valuestr: Input columns for the annotator

setLabelColumn(label)[source]#

Sets column with the value result we are trying to predict.

Parameters:

labelstr: Column with the value result we are trying to predict.

setLabels(value)[source]#

Sets array to output the label in the original form.

Parameters:

labellist: array to output the label in the original form.

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

Parameters:

valuebool: Whether Annotator should be evaluated lazily in a RecursivePipeline

setMaxIter(k)[source]#

Sets maximum number of iterations.

Parameters:

kint: Maximum number of iterations.

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

setTol(dist)[source]#

Sets convergence tolerance after each iteration.

Parameters:

distfloat: Convergence tolerance after each iteration.

setVectorizationModelPath(value)[source]#

Sets a path to the the classification model if it has been already trained.

Parameters:

labelstr: Path to the the classification model if it has been already trained.

uid#: A unique id for the object.

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