NaiveBayesModel#

class pyspark.ml.classification.NaiveBayesModel(java_model=None)[source]#

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.')#

uid#: A unique id for the object.