`sparknlp_jsl.annotator.generic_classifier.generic_classifier`#

Module Contents#

Classes#

`GenericClassifierApproach`	Trains a TensorFlow model for generic classification of feature vectors. It takes FEATURE_VECTOR annotations from
`GenericClassifierModel`	Generic classifier of feature vectors. It takes FEATURE_VECTOR annotations from

class GenericClassifierApproach(classname='com.johnsnowlabs.nlp.annotators.generic_classifier.GenericClassifierApproach')#

Bases: sparknlp_jsl.common.AnnotatorApproachInternal, sparknlp_jsl.common.HasEngine, sparknlp_jsl.annotator.handle_exception_params.HandleExceptionParams

Trains a TensorFlow model for generic classification of feature vectors. It takes FEATURE_VECTOR annotations from FeaturesAssembler` as input, classifies them and outputs CATEGORY annotations.

Input Annotation types	Output Annotation type
`FEATURE_VECTOR`	`CATEGORY`

Parameters:

labelColumn – Column with one label per document
batchSize – Size for each batch in the optimization process
epochsN – Number of epochs for the optimization process
learningRate – Learning rate for the optimization proces
dropou – Dropout at the output of each laye
validationSplit – Validaiton split - how much data to use for validation
modelFile – File name to load the mode from
fixImbalance – A flag indicating whenther to balance the trainig set
multiClass – Return only the label with the highest confidence score or all labels
featureScaling – Feature scaling method. Possible values are ‘zscore’, ‘minmax’ or empty (no scaling)
outputLogsPath – Path to folder where logs will be saved. If no path is specified, the logs won’t be stored in disk. The path can be a local file path, a distributed file path (HDFS, DBFS), or a cloud storage (S3).

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
>>> features_asm = FeaturesAssembler()     ...    .setInputCols(["feature_1", "feature_2", "...", "feature_n"])     ...    .setOutputCol("features")
...
>>> gen_clf = GenericClassifierApproach() \
...    .setLabelColumn("target") \
...    .setInputCols(["features"]) \
...    .setOutputCol("prediction") \
...    .setModelFile("/path/to/graph_file.pb") \
...    .setEpochsNumber(50) \
...    .setBatchSize(100) \
...    .setFeatureScaling("zscore") \
...    .setlearningRate(0.001) \
...    .setFixImbalance(True) \
...    .setOutputLogsPath("logs") \
...    .setValidationSplit(0.2) # keep 20% of the data for validation purposes
...
>>> pipeline = Pipeline().setStages([
...    features_asm,
...    gen_clf
...])
...
>>> clf_model = pipeline.fit(data)

batchSize#

doExceptionHandling#

dropout#

engine#

epochsN#

featureScaling#

fixImbalance#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

labelColumn#

lazyAnnotator#

learningRate#

modelFile#

multiClass#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

outputLogsPath#

skipLPInputColsValidation = True#

validationSplit#

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 (dict, optional) – Extra parameters to copy to the new instance
Returns:: Copy of this instance
Return type:: JavaParams

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 (dict, optional) – extra param values
Returns:: merged param map
Return type:: dict

fit(dataset, params=None)#

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, paramMaps)#

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

getEngine()#

Returns:: Deep Learning engine used for this model”
Return type:: str

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:: paramName (str) – 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.

inputColsValidation(value)#

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).

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.

setBatchSize(size: int)#

Size for each batch in the optimization process

Parameters:: size (int) – Size for each batch in the optimization process

setDoExceptionHandling(value: bool)#

If True, exceptions are handled. If exception causing data is passed to the model, a error annotation is emitted which has the exception message. Processing continues with the next one. This comes with a performance penalty.

Parameters:: value (bool) – If True, exceptions are handled.

setDropout(dropout: float)#

Sets drouptup

Parameters:: dropout (float) – Dropout at the output of each layer

setEpochsNumber(epochs: int)#

Sets number of epochs for the optimization process

Parameters:: epochs (int) – Number of epochs for the optimization process

setFeatureScaling(feature_scaling: str)#

Sets Feature scaling method. Possible values are ‘zscore’, ‘minmax’ or empty (no scaling

Parameters:: feature_scaling (str) – Feature scaling method. Possible values are ‘zscore’, ‘minmax’ or empty (no scaling

setFixImbalance(fix_imbalance: bool)#

Sets A flag indicating whenther to balance the trainig set.

Parameters:: fix_imbalance (bool) – A flag indicating whenther to balance the trainig set.

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

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

setLabelCol(label_column: str)#

Sets Size for each batch in the optimization process

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

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

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

setLearningRate(learning_rate: float)#

Sets learning rate for the optimization process

Parameters:: learning_rate (float) – Learning rate for the optimization process

setModelFile(mode_file: str)#

Sets file name to load the mode from”

Parameters:: label (str) – File name to load the mode from”

setMultiClass(value: bool)#

Sets the model in multi class prediction mode (Default: false)

Parameters:: value (bool) – Whether to return only the label with the highest confidence score or all labels

setOutputCol(value)#

Sets output column name of annotations.

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

setOutputLogsPath(output_logs_path: str)#

Sets path to folder where logs will be saved. If no path is specified, no logs are generated

Parameters:: output_logs_path (str) – Path to folder where logs will be saved. If no path is specified, no logs are generated

setParamValue(paramName)#

Sets the value of a parameter.

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

setValidationSplit(validation_split: float)#

Sets validaiton split - how much data to use for validation

Parameters:: validation_split (float) – Validaiton split - how much data to use for validation

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

class GenericClassifierModel(classname='com.johnsnowlabs.nlp.annotators.generic_classifier.GenericClassifierModel', java_model=None)#

Bases: sparknlp_jsl.common.AnnotatorModelInternal, sparknlp_jsl.annotator.handle_exception_params.HandleExceptionParams

Generic classifier of feature vectors. It takes FEATURE_VECTOR annotations from FeaturesAssembler` as input, classifies them and outputs CATEGORY annotations.

Input Annotation types	Output Annotation type
`FEATURE_VECTOR`	`CATEGORY`

Parameters:

multiClass – Return only the label with the highest confidence score or all labels
featureScaling – Feature scaling method. Possible values are ‘zscore’, ‘minmax’ or empty (no scaling)

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
>>> features_asm = FeaturesAssembler() \
...    .setInputCols(["feature_1", "feature_2", "...", "feature_n"]) \
...    .setOutputCol("features")
...
>>> gen_clf = GenericClassifierModel.pretrained() \
...    .setInputCols(["features"]) \
...    .setOutputCol("prediction") \
...
>>> pipeline = Pipeline().setStages([
...    features_asm,
...    gen_clf
...])
...
>>> clf_model = pipeline.fit(data)

classes#

doExceptionHandling#

featureScaling#

getter_attrs = []#

inputAnnotatorTypes#

inputCols#

lazyAnnotator#

multiClass#

name = GenericClassifierModel#

optionalInputAnnotatorTypes = []#

outputAnnotatorType#

outputCol#

skipLPInputColsValidation = True#

clear(param)#: Clears a param from the param map if it has been explicitly set.

copy(extra=None)#

Parameters:: extra (dict, optional) – Extra parameters to copy to the new instance
Returns:: Copy of this instance
Return type:: JavaParams

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)#

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)#: 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:: paramName (str) – 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.

inputColsValidation(value)#

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).

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

Downloads and loads a pretrained model.

Parameters:

name (str, optional) – Name of the pretrained model, by default “genericclassifier_sdoh_housing_insecurity_sbiobert_cased_mli”
lang (str, optional) – Language of the pretrained model, by default “en”
remote_loc (str, optional) – Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:

The restored model

Return type:

GenericClassifierModel

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.

setDoExceptionHandling(value: bool)#

Parameters:: value (bool) – If True, exceptions are handled.

setFeatureScaling(feature_scaling: str)#

Sets Feature scaling method. Possible values are ‘zscore’, ‘minmax’ or empty (no scaling)

Parameters:: feature_scaling (str) – Feature scaling method. Possible values are ‘zscore’, ‘minmax’ or empty (no scaling)

setForceInputTypeValidation(etfm)#

setInputCols(*value)#

Sets column names of input annotations.

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

setLazyAnnotator(value)#

Sets whether Annotator should be evaluated lazily in a RecursivePipeline.

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

setMultiClass(value: bool)#

Sets the model in multi class prediction mode (Default: false)

Parameters:: value (bool) – Whether to return only the label with the highest confidence score or all labels

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()#

transform(dataset, params=None)#

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

sparknlp_jsl.annotator.generic_classifier.generic_classifier#

Module Contents#

Classes#

`sparknlp_jsl.annotator.generic_classifier.generic_classifier`#