naive bayes classifiers

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Angela
30 Apr 2016
1# Naive Bayes Classifiers
2
3from pyspark.sql import Row
4from pyspark.ml.linalg import Vectors
5df = spark.createDataFrame([
6  Row(label=0.0, weight=0.1, features=Vectors.dense([0.0, 0.0])),
7  Row(label=0.0, weight=0.5, features=Vectors.dense([0.0, 1.0])),
8  Row(label=1.0, weight=1.0, features=Vectors.dense([1.0, 0.0]))])
9nb = NaiveBayes(smoothing=1.0, modelType="multinomial", weightCol="weight")
10model = nb.fit(df)
11model.pi
12# DenseVector([-0.81..., -0.58...])
13model.theta
14# DenseMatrix(2, 2, [-0.91..., -0.51..., -0.40..., -1.09...], 1)
15test0 = sc.parallelize([Row(features=Vectors.dense([1.0, 0.0]))]).toDF()
16result = model.transform(test0).head()
17result.prediction
18# 1.0
19result.probability
20# DenseVector([0.32..., 0.67...])
21result.rawPrediction
22# DenseVector([-1.72..., 0.99...])
23test1 = sc.parallelize([Row(features=Vectors.sparse(2, [0], [1.0]))]).toDF()
24model.transform(test1).head().prediction
25# 1.0
26nb_path = temp_path + "/nb"
27nb.save(nb_path)
28nb2 = NaiveBayes.load(nb_path)
29nb2. getSmoothing()
30# 1.0
31model_path = temp_path + "/nb_model"
32model.save(model_path)
33model2 = NaiveBayesModel.load(model_path)
34model.pi == model2.pi
35# True
36nb = nb.setThresholds([0.01, 10.00])
37model3 = nb.fit(df)
38result = model3.transform(test0).head()
39result.prediction
40# 0.0
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