NaiveBayes¶

class pyspark.ml.classification.NaiveBayes(*, featuresCol: str = 'features', labelCol: str = 'label', predictionCol: str = 'prediction', probabilityCol: str = 'probability', rawPredictionCol: str = 'rawPrediction', smoothing: float = 1.0, modelType: str = 'multinomial', thresholds: Optional[List[float]] = None, weightCol: Optional[str] = None)¶

Naive Bayes Classifiers. It supports both Multinomial and Bernoulli NB. Multinomial NB can handle finitely supported discrete data. For example, by converting documents into TF-IDF vectors, it can be used for document classification. By making every vector a binary (0/1) data, it can also be used as Bernoulli NB.

The input feature values for Multinomial NB and Bernoulli NB must be nonnegative. Since 3.0.0, it supports Complement NB which is an adaptation of the Multinomial NB. Specifically, Complement NB uses statistics from the complement of each class to compute the model’s coefficients. The inventors of Complement NB show empirically that the parameter estimates for CNB are more stable than those for Multinomial NB. Like Multinomial NB, the input feature values for Complement NB must be nonnegative. Since 3.0.0, it also supports Gaussian NB. which can handle continuous data.

Examples

>>> from pyspark.sql import Row
>>> from pyspark.ml.linalg import Vectors
>>> df = spark.createDataFrame([
...     Row(label=0.0, weight=0.1, features=Vectors.dense([0.0, 0.0])),
...     Row(label=0.0, weight=0.5, features=Vectors.dense([0.0, 1.0])),
...     Row(label=1.0, weight=1.0, features=Vectors.dense([1.0, 0.0]))])
>>> nb = NaiveBayes(smoothing=1.0, modelType="multinomial", weightCol="weight")
>>> model = nb.fit(df)
>>> model.setFeaturesCol("features")
NaiveBayesModel...
>>> model.getSmoothing()
1.0
>>> model.pi
DenseVector([-0.81..., -0.58...])
>>> model.theta
DenseMatrix(2, 2, [-0.91..., -0.51..., -0.40..., -1.09...], 1)
>>> model.sigma
DenseMatrix(0, 0, [...], ...)
>>> test0 = sc.parallelize([Row(features=Vectors.dense([1.0, 0.0]))]).toDF()
>>> model.predict(test0.head().features)
1.0
>>> model.predictRaw(test0.head().features)
DenseVector([-1.72..., -0.99...])
>>> model.predictProbability(test0.head().features)
DenseVector([0.32..., 0.67...])
>>> result = model.transform(test0).head()
>>> result.prediction
1.0
>>> result.probability
DenseVector([0.32..., 0.67...])
>>> result.rawPrediction
DenseVector([-1.72..., -0.99...])
>>> test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF()
>>> model.transform(test1).head().prediction
1.0
>>> nb_path = temp_path + "/nb"
>>> nb.save(nb_path)
>>> nb2 = NaiveBayes.load(nb_path)
>>> nb2.getSmoothing()
1.0
>>> model_path = temp_path + "/nb_model"
>>> model.save(model_path)
>>> model2 = NaiveBayesModel.load(model_path)
>>> model.pi == model2.pi
True
>>> model.theta == model2.theta
True
>>> model.transform(test0).take(1) == model2.transform(test0).take(1)
True
>>> nb = nb.setThresholds([0.01, 10.00])
>>> model3 = nb.fit(df)
>>> result = model3.transform(test0).head()
>>> result.prediction
0.0
>>> nb3 = NaiveBayes().setModelType("gaussian")
>>> model4 = nb3.fit(df)
>>> model4.getModelType()
'gaussian'
>>> model4.sigma
DenseMatrix(2, 2, [0.0, 0.25, 0.0, 0.0], 1)
>>> nb5 = NaiveBayes(smoothing=1.0, modelType="complement", weightCol="weight")
>>> model5 = nb5.fit(df)
>>> model5.getModelType()
'complement'
>>> model5.theta
DenseMatrix(2, 2, [...], 1)
>>> model5.sigma
DenseMatrix(0, 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.
`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.
`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).
`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`.
`setLabelCol`(value)	Sets the value of `labelCol`.
`setModelType`(value)	Sets the value of `modelType`.
`setParams`(self, \*[, featuresCol, labelCol, …])	Sets params for Naive Bayes.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setProbabilityCol`(value)	Sets the value of `probabilityCol`.
`setRawPredictionCol`(value)	Sets the value of `rawPredictionCol`.
`setSmoothing`(value)	Sets the value of `smoothing`.
`setThresholds`(value)	Sets the value of `thresholds`.
`setWeightCol`(value)	Sets the value of `weightCol`.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`featuresCol`
`labelCol`
`modelType`
`params`	Returns all params ordered by name.
`predictionCol`
`probabilityCol`
`rawPredictionCol`
`smoothing`
`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

fit(dataset: pyspark.sql.dataframe.DataFrame, params: Union[ParamMap, List[ParamMap], Tuple[ParamMap], None] = None) → Union[M, List[M]]¶

Fits a model to the input dataset with optional parameters.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramsdict 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

Transformer or a list of Transformer: fitted model(s)

fitMultiple(dataset: pyspark.sql.dataframe.DataFrame, paramMaps: Sequence[ParamMap]) → Iterator[Tuple[int, M]]¶

Fits a model to the input dataset for each param map in paramMaps.

Parameters

datasetpyspark.sql.DataFrame: input dataset.
paramMapscollections.abc.Sequence: A Sequence of param maps.

Returns

_FitMultipleIterator: 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.

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).

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.

setLabelCol(value: str) → P¶: Sets the value of labelCol.

setModelType(value: str) → pyspark.ml.classification.NaiveBayes ¶: Sets the value of modelType.

setParams(self, \*, featuresCol="features", labelCol="label", predictionCol="prediction", probabilityCol="probability", rawPredictionCol="rawPrediction", smoothing=1.0, modelType="multinomial", thresholds=None, weightCol=None)¶: Sets params for Naive Bayes.

setPredictionCol(value: str) → P¶: Sets the value of predictionCol.

setProbabilityCol(value: str) → P¶: Sets the value of probabilityCol.

setRawPredictionCol(value: str) → P¶: Sets the value of rawPredictionCol.

setSmoothing(value: float) → pyspark.ml.classification.NaiveBayes ¶: Sets the value of smoothing.

setThresholds(value: List[float]) → P¶: Sets the value of thresholds.

setWeightCol(value: str) → pyspark.ml.classification.NaiveBayes ¶: Sets the value of weightCol.

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.')¶

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

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.')¶

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

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.')¶

BinaryRandomForestClassificationTrainingSummary

NaiveBayesModel