NaiveBayesModel¶
-
class
pyspark.mllib.classification.
NaiveBayesModel
(labels, pi, theta)[source]¶ Model for Naive Bayes classifiers.
- Parameters
labels – List of labels.
pi – Log of class priors, whose dimension is C, number of labels.
theta – Log of class conditional probabilities, whose dimension is C-by-D, where D is number of features.
>>> 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
New in version 0.9.0.
Methods
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