NaiveBayesModel

class pyspark.ml.classification.NaiveBayesModel(java_model=None)

Model fitted by NaiveBayes.

New in version 1.5.0.

Methods

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.
getFeaturesCol()	Gets the value of featuresCol or its default value.
getLabelCol()	Gets the value of labelCol or its default value.
getModelType()	Gets the value of modelType or its default value.
getOrDefault(param)	Gets the value of a param in the user-supplied param map or its default value.
getParam(paramName)	Gets a param by its name.
getPredictionCol()	Gets the value of predictionCol or its default value.
getProbabilityCol()	Gets the value of probabilityCol or its default value.
getRawPredictionCol()	Gets the value of rawPredictionCol or its default value.
getSmoothing()	Gets the value of smoothing or its default value.
getThresholds()	Gets the value of thresholds or its default value.
getWeightCol()	Gets the value of weightCol or its default value.
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).
predict(value)	Predict label for the given features.
predictProbability(value)	Predict the probability of each class given the features.
predictRaw(value)	Raw prediction for each possible label.
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.
setFeaturesCol(value)	Sets the value of featuresCol.
setPredictionCol(value)	Sets the value of predictionCol.
setProbabilityCol(value)	Sets the value of probabilityCol.
setRawPredictionCol(value)	Sets the value of rawPredictionCol.
setThresholds(value)	Sets the value of thresholds.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

featuresCol
labelCol
modelType
numClasses	Number of classes (values which the label can take).
numFeatures	Returns the number of features the model was trained on.
params	Returns all params ordered by name.
pi	log of class priors.
predictionCol
probabilityCol
rawPredictionCol
sigma	variance of each feature.
smoothing
theta	log of class conditional probabilities.
thresholds
weightCol

Methods Documentation

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:

extradict, optional

Extra parameters to copy to the new instance

Returns:

JavaParams

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:

extradict, optional

extra param values

Returns:

dict

merged param map

getFeaturesCol()

Gets the value of featuresCol or its default value.

getLabelCol()

Gets the value of labelCol or its default value.

getModelType()

Gets the value of modelType or its default value.

New in version 1.5.0.

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.

getParam(paramName)

Gets a param by its name.

getPredictionCol()

Gets the value of predictionCol or its default value.

getProbabilityCol()

Gets the value of probabilityCol or its default value.

getRawPredictionCol()

Gets the value of rawPredictionCol or its default value.

getSmoothing()

Gets the value of smoothing or its default value.

New in version 1.5.0.

getThresholds()

Gets the value of thresholds or its default value.

getWeightCol()

Gets the value of weightCol or its default value.

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).

predict(value)

Predict label for the given features.

New in version 3.0.0.

predictProbability(value)

Predict the probability of each class given the features.

New in version 3.0.0.

predictRaw(value)

Raw prediction for each possible label.

New in version 3.0.0.

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.

setFeaturesCol(value)

Sets the value of featuresCol.

New in version 3.0.0.

setPredictionCol(value)

Sets the value of predictionCol.

New in version 3.0.0.

setProbabilityCol(value)

Sets the value of probabilityCol.

New in version 3.0.0.

setRawPredictionCol(value)

Sets the value of rawPredictionCol.

New in version 3.0.0.

setThresholds(value)

Sets the value of thresholds.

New in version 3.0.0.

transform(dataset, params=None)

Transforms the input dataset with optional parameters.

New in version 1.3.0.

Parameters:

datasetpyspark.sql.DataFrame

input dataset

paramsdict, optional

an optional param map that overrides embedded params.

Returns:

pyspark.sql.DataFrame

transformed dataset

write()

Returns an MLWriter instance for this ML instance.

Attributes Documentation

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')

labelCol = Param(parent='undefined', name='labelCol', doc='label column name.')

modelType = Param(parent='undefined', name='modelType', doc='The model type which is a string (case-sensitive). Supported options: multinomial (default), bernoulli and gaussian.')

numClasses

Number of classes (values which the label can take).

New in version 2.1.0.

numFeatures

Returns the number of features the model was trained on. If unknown, returns -1

New in version 2.1.0.

params

Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

pi

log of class priors.

New in version 2.0.0.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')

probabilityCol = Param(parent='undefined', name='probabilityCol', doc='Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.')

rawPredictionCol = Param(parent='undefined', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name.')

sigma

variance of each feature.

New in version 3.0.0.

smoothing = Param(parent='undefined', name='smoothing', doc='The smoothing parameter, should be >= 0, default is 1.0')

theta

log of class conditional probabilities.

New in version 2.0.0.

thresholds = Param(parent='undefined', name='thresholds', doc="Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.")

weightCol = Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')

NaiveBayes MultilayerPerceptronClassifier