sparknlp_jsl.annotator.GenericClassifierApproach#
- class sparknlp_jsl.annotator.GenericClassifierApproach(classname='com.johnsnowlabs.nlp.annotators.generic_classifier.GenericClassifierApproach')[source]#
Bases:
AnnotatorApproach
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
- 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, no logs are generated
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 >>> 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)
Methods
__init__
([classname])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.
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.
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.
setBatchSize
(size)Size for each batch in the optimization process
setDropout
(dropout)Sets drouptup
setEpochsNumber
(epochs)Sets number of epochs for the optimization process
setFeatureScaling
(feature_scaling)Sets Feature scaling method.
setFixImbalance
(fix_imbalance)Sets A flag indicating whenther to balance the trainig set.
setInputCols
(*value)Sets column names of input annotations.
setLabelCol
(label_column)Sets Size for each batch in the optimization process
setLazyAnnotator
(value)Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
setLearningRate
(lamda)Sets learning rate for the optimization process
setModelFile
(mode_file)Sets file name to load the mode from"
setOutputCol
(value)Sets output column name of annotations.
setOutputLogsPath
(output_logs_path)Sets path to folder where logs will be saved.
setParamValue
(paramName)Sets the value of a parameter.
setValidationSplit
(validation_split)Sets validaiton split - how much data to use for validation
write
()Returns an MLWriter instance for this ML instance.
Attributes
batchSize
dropout
epochsN
featureScaling
fixImbalance
getter_attrs
inputCols
labelColumn
lazyAnnotator
learningRate
modelFile
outputCol
outputLogsPath
Returns all params ordered by name.
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 – 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 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.
- setBatchSize(size)[source]#
Size for each batch in the optimization process
- Parameters:
- sizeint
Size for each batch in the optimization process
- setDropout(dropout)[source]#
Sets drouptup
- Parameters:
- dropoutfloat
Dropout at the output of each layer
- setEpochsNumber(epochs)[source]#
Sets number of epochs for the optimization process
- Parameters:
- epochsint
Number of epochs for the optimization process
- setFeatureScaling(feature_scaling)[source]#
Sets Feature scaling method. Possible values are ‘zscore’, ‘minmax’ or empty (no scaling
- Parameters:
- feature_scalingstr
Feature scaling method. Possible values are ‘zscore’, ‘minmax’ or empty (no scaling
- setFixImbalance(fix_imbalance)[source]#
Sets A flag indicating whenther to balance the trainig set.
- Parameters:
- fix_imbalancebool
A flag indicating whenther to balance the trainig set.
- setInputCols(*value)#
Sets column names of input annotations.
- Parameters:
- *valuestr
Input columns for the annotator
- setLabelCol(label_column)[source]#
Sets Size for each batch in the optimization process
- Parameters:
- labelstr
Column with the value result we are trying to predict.
- setLazyAnnotator(value)#
Sets whether Annotator should be evaluated lazily in a RecursivePipeline.
- Parameters:
- valuebool
Whether Annotator should be evaluated lazily in a RecursivePipeline
- setLearningRate(lamda)[source]#
Sets learning rate for the optimization process
- Parameters:
- lamdafloat
Learning rate for the optimization process
- setModelFile(mode_file)[source]#
Sets file name to load the mode from”
- Parameters:
- labelstr
File name to load the mode from”
- setOutputCol(value)#
Sets output column name of annotations.
- Parameters:
- valuestr
Name of output column
- setOutputLogsPath(output_logs_path)[source]#
Sets path to folder where logs will be saved. If no path is specified, no logs are generated
- Parameters:
- labelstr
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:
- paramNamestr
Name of the parameter
- setValidationSplit(validation_split)[source]#
Sets validaiton split - how much data to use for validation
- Parameters:
- validation_splitfloat
Validaiton split - how much data to use for validation
- uid#
A unique id for the object.
- write()#
Returns an MLWriter instance for this ML instance.