NaiveBayesModel

class pyspark.ml.classification.NaiveBayesModel(java_model: Optional[JavaObject] = None)

Model fitted by NaiveBayes.

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: pyspark.ml.param.Param) → None

Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = 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
extradict, optional

Extra parameters to copy to the new instance

Returns
JavaParams

Copy of this instance

explainParam(param: Union[str, pyspark.ml.param.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: Optional[ParamMap] = None) → 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
extradict, optional

extra param values

Returns
dict

merged param map

getFeaturesCol() → str

Gets the value of featuresCol or its default value.

getLabelCol() → str

Gets the value of labelCol or its default value.

getModelType() → str

Gets the value of modelType or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]

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: str)pyspark.ml.param.Param

Gets a param by its name.

getPredictionCol() → str

Gets the value of predictionCol or its default value.

getProbabilityCol() → str

Gets the value of probabilityCol or its default value.

getRawPredictionCol() → str

Gets the value of rawPredictionCol or its default value.

getSmoothing() → float

Gets the value of smoothing or its default value.

getThresholds() → List[float]

Gets the value of thresholds or its default value.

getWeightCol() → str

Gets the value of weightCol or its default value.

hasDefault(param: Union[str, pyspark.ml.param.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.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool

Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.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).

predict(value: T) → float

Predict label for the given features.

predictProbability(value: pyspark.ml.linalg.Vector)pyspark.ml.linalg.Vector

Predict the probability of each class given the features.

predictRaw(value: pyspark.ml.linalg.Vector)pyspark.ml.linalg.Vector

Raw prediction for each possible label.

classmethod read() → pyspark.ml.util.JavaMLReader[RL]

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: pyspark.ml.param.Param, value: Any) → None

Sets a parameter in the embedded param map.

setFeaturesCol(value: str) → P

Sets the value of featuresCol.

setPredictionCol(value: str) → P

Sets the value of predictionCol.

setProbabilityCol(value: str) → CM

Sets the value of probabilityCol.

setRawPredictionCol(value: str) → P

Sets the value of rawPredictionCol.

setThresholds(value: List[float]) → CM

Sets the value of thresholds.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame

Transforms the input dataset with optional parameters.

Parameters
datasetpyspark.sql.DataFrame

input dataset

paramsdict, optional

an optional param map that overrides embedded params.

Returns
pyspark.sql.DataFrame

transformed dataset

write() → pyspark.ml.util.JavaMLWriter

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).

numFeatures

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

params

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

pi

log of class priors.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')
probabilityCol: Param[str] = 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.

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

log of class conditional probabilities.

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.')