NaiveBayesModel¶

class pyspark.mllib.classification.NaiveBayesModel(labels, pi, theta)[source]¶

Model for Naive Bayes classifiers.

New in version 0.9.0.

Parameters

labelsnumpy.ndarray: List of labels.
pinumpy.ndarray: Log of class priors, whose dimension is C, number of labels.
thetanumpy.ndarray: Log of class conditional probabilities, whose dimension is C-by-D, where D is number of features.

Examples

>>> from pyspark.mllib.linalg import SparseVector
>>> data = [
...     LabeledPoint(0.0, [0.0, 0.0]),
...     LabeledPoint(0.0, [0.0, 1.0]),
...     LabeledPoint(1.0, [1.0, 0.0]),
... ]
>>> model = NaiveBayes.train(sc.parallelize(data))
>>> model.predict(numpy.array([0.0, 1.0]))
0.0
>>> model.predict(numpy.array([1.0, 0.0]))
1.0
>>> model.predict(sc.parallelize([[1.0, 0.0]])).collect()
[1.0]
>>> sparse_data = [
...     LabeledPoint(0.0, SparseVector(2, {1: 0.0})),
...     LabeledPoint(0.0, SparseVector(2, {1: 1.0})),
...     LabeledPoint(1.0, SparseVector(2, {0: 1.0}))
... ]
>>> model = NaiveBayes.train(sc.parallelize(sparse_data))
>>> model.predict(SparseVector(2, {1: 1.0}))
0.0
>>> model.predict(SparseVector(2, {0: 1.0}))
1.0
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> model.save(sc, path)
>>> sameModel = NaiveBayesModel.load(sc, path)
>>> sameModel.predict(SparseVector(2, {0: 1.0})) == model.predict(SparseVector(2, {0: 1.0}))
True
>>> from shutil import rmtree
>>> try:
...     rmtree(path)
... except OSError:
...     pass

Methods

`load`(sc, path)	Load a model from the given path.
`predict`(x)	Return the most likely class for a data vector or an RDD of vectors
`save`(sc, path)	Save this model to the given path.

Methods Documentation

classmethod load(sc, path)[source]¶: Load a model from the given path.

New in version 1.4.0.

predict(x)[source]¶: Return the most likely class for a data vector or an RDD of vectors

New in version 0.9.0.

save(sc, path)[source]¶: Save this model to the given path.

SVMWithSGD

NaiveBayes