`sparknlp_jsl.annotator.classification.document_log_classifier`#

Module Contents#

Classes#

`DocumentLogRegClassifierApproach`	Trains a model to classify documents with a Logarithmic Regression algorithm. Training data requires columns for
`DocumentLogRegClassifierModel`	Classifies documents with a Logarithmic Regression algorithm.

class DocumentLogRegClassifierApproach#

Bases: sparknlp_jsl.common.AnnotatorApproachInternal

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_jsl.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)

classificationModelPath#

fitIntercept#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

labelCol#

labels#

lazyAnnotator#

maxIter#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

skipLPInputColsValidation = True#

tol#

uid#

vectorizationModelPath#

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

fit(dataset: pyspark.sql.dataframe.DataFrame, params: pyspark.ml._typing.ParamMap | None = ...) → M#

fit(dataset: pyspark.sql.dataframe.DataFrame, params: List[pyspark.ml._typing.ParamMap] | Tuple[pyspark.ml._typing.ParamMap]) → List[M]

Fits a model to the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

dataset (pyspark.sql.DataFrame) – input dataset.
params (dict or list or tuple, optional) – 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)

Return type:

Transformer or a list of Transformer

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[pyspark.ml._typing.ParamMap]) → Iterator[Tuple[int, M]]#

Fits a model to the input dataset for each param map in paramMaps.

New in version 2.3.0.

Parameters:

dataset (pyspark.sql.DataFrame) – input dataset.
paramMaps (collections.abc.Sequence) – 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.

Return type:

_FitMultipleIterator

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.

setClassificationModelPath(value)#

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

Parameters:: label (str) – Path to the the classification model if it has been already trained.

setFitIntercept(merge)#

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

Parameters:: label (str) – Whether to fit an intercept term, default is true.

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

Parameters:: *value (List[str]) – Input columns for the annotator

setLabelColumn(label)#

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

Parameters:: label (str) – Column with the value result we are trying to predict.

setLabels(value)#

Sets array to output the label in the original form.

Parameters:: label (list) – array to output the label in the original form.

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

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

setMaxIter(k)#

Sets maximum number of iterations.

Parameters:: k (int) – Maximum number of iterations.

setOutputCol(value)#

Sets output column name of annotations.

Parameters:: value (str) – Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

setTol(dist)#

Sets convergence tolerance after each iteration.

Parameters:: dist (float) – Convergence tolerance after each iteration.

setVectorizationModelPath(value)#

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

Parameters:: label (str) – Path to the the classification model if it has been already trained.

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

class DocumentLogRegClassifierModel(classname='com.johnsnowlabs.nlp.annotators.classification.DocumentLogRegClassifierModel', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal

Classifies documents with a Logarithmic Regression algorithm.

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

Parameters:

mergeChunks – Whether to merge all chunks in a document or not (Default: false)
labels – Array to output the label in the original form.
vectorizationModel – Vectorization model if it has been already trained.
classificationModel – Classification model if it has been already trained.

classificationModel#

getter_attrs = []#

inputAnnotatorTypes = [True]#

inputCols#

labels#

lazyAnnotator#

mergeChunks#

name = 'DocumentLogRegClassifierModel'#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

skipLPInputColsValidation = True#

uid#

vectorizationModel#

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#

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#

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).

static pretrained(name='classifier_logreg_ade', lang='en', remote_loc='clinical/models')#

Downloads and loads a pretrained model.

Parameters:

name (str, optional) – Name of the pretrained model, by default “classifier_logreg_ade”
lang (str, optional) – Language of the pretrained model, by default “en”
remote_loc (str, optional) – Optional remote address of the resource, by default “clinical/models”. Will use Spark NLPs repositories otherwise.

Returns:

The restored model

Return type:

DocumentLogRegClassifierModel

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.

setClassificationModel(model)#

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

Parameters:: model (pyspark.ml.PipelineModel) – Classification model if it has been already trained.

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

Parameters:: *value (List[str]) – Input columns for the annotator

setLabels(value)#

Sets array to output the label in the original form.

Parameters:: label (list) – array to output the label in the original form.

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

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

setMergeChunks(merge)#

Sets whether to merge all chunks in a document or not (Default: false)

Parameters:: merge (list) – whether to merge all chunks in a document or not (Default: false)

setOutputCol(value)#

Sets output column name of annotations.

Parameters:: value (str) – Name of output column

setParamValue(paramName)#

Sets the value of a parameter.

Parameters:: paramName (str) – Name of the parameter

setParams()#

setVectorizationModel(model)#

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

Parameters:: model (pyspark.ml.PipelineModel) – Classification model if it has been already trained.

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.

sparknlp_jsl.annotator.classification.document_log_classifier#

Module Contents#

Classes#

`sparknlp_jsl.annotator.classification.document_log_classifier`#