[SPARK-13089][ML] [Doc] spark.ml Naive Bayes user guide and examples
jira: https://issues.apache.org/jira/browse/SPARK-13089 Add section in ml-classification.md for NaiveBayes DataFrame-based API, plus example code (using include_example to clip code from examples/ folder files). Author: Yuhao Yang <hhbyyh@gmail.com> Closes #11015 from hhbyyh/naiveBayesDoc.
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Yuhao Yang
Joseph K. Bradley
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@ -302,6 +302,40 @@ Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/OneVsRe
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</div>
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</div>
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## Naive Bayes
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[Naive Bayes](http://en.wikipedia.org/wiki/Naive_Bayes_classifier) are a family of simple
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probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence
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assumptions between the features. The spark.ml implementation currently supports both [multinomial
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naive Bayes](http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-classification-1.html)
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and [Bernoulli naive Bayes](http://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html).
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More information can be found in the section on [Naive Bayes in MLlib](mllib-naive-bayes.html#naive-bayes-sparkmllib).
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**Example**
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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Refer to the [Scala API docs](api/scala/index.html#org.apache.spark.ml.classification.NaiveBayes) for more details.
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{% include_example scala/org/apache/spark/examples/ml/NaiveBayesExample.scala %}
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</div>
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<div data-lang="java" markdown="1">
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Refer to the [Java API docs](api/java/org/apache/spark/ml/classification/NaiveBayes.html) for more details.
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{% include_example java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java %}
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</div>
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<div data-lang="python" markdown="1">
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Refer to the [Python API docs](api/python/pyspark.ml.html#pyspark.ml.classification.NaiveBayes) for more details.
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{% include_example python/ml/naive_bayes_example.py %}
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</div>
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</div>
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# Regression
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@ -0,0 +1,64 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.examples.ml;
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import org.apache.spark.SparkConf;
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import org.apache.spark.api.java.JavaSparkContext;
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// $example on$
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import org.apache.spark.ml.classification.NaiveBayes;
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import org.apache.spark.ml.classification.NaiveBayesModel;
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import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator;
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import org.apache.spark.sql.Dataset;
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import org.apache.spark.sql.Row;
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import org.apache.spark.sql.SQLContext;
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// $example off$
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/**
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* An example for Naive Bayes Classification.
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*/
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public class JavaNaiveBayesExample {
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public static void main(String[] args) {
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SparkConf conf = new SparkConf().setAppName("JavaNaiveBayesExample");
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JavaSparkContext jsc = new JavaSparkContext(conf);
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SQLContext jsql = new SQLContext(jsc);
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// $example on$
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// Load training data
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Dataset<Row> dataFrame = jsql.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
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// Split the data into train and test
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Dataset<Row>[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L);
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Dataset<Row> train = splits[0];
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Dataset<Row> test = splits[1];
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// create the trainer and set its parameters
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NaiveBayes nb = new NaiveBayes();
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// train the model
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NaiveBayesModel model = nb.fit(train);
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// compute precision on the test set
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Dataset<Row> result = model.transform(test);
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Dataset<Row> predictionAndLabels = result.select("prediction", "label");
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MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator()
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.setMetricName("precision");
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System.out.println("Precision = " + evaluator.evaluate(predictionAndLabels));
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// $example off$
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jsc.stop();
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}
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}
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53
examples/src/main/python/ml/naive_bayes_example.py
Normal file
53
examples/src/main/python/ml/naive_bayes_example.py
Normal file
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#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from __future__ import print_function
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from pyspark import SparkContext
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from pyspark.sql import SQLContext
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# $example on$
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from pyspark.ml.classification import NaiveBayes
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from pyspark.ml.evaluation import MulticlassClassificationEvaluator
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# $example off$
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if __name__ == "__main__":
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sc = SparkContext(appName="naive_bayes_example")
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sqlContext = SQLContext(sc)
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# $example on$
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# Load training data
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data = sqlContext.read.format("libsvm") \
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.load("data/mllib/sample_libsvm_data.txt")
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# Split the data into train and test
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splits = data.randomSplit([0.6, 0.4], 1234)
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train = splits[0]
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test = splits[1]
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# create the trainer and set its parameters
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nb = NaiveBayes(smoothing=1.0, modelType="multinomial")
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# train the model
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model = nb.fit(train)
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# compute precision on the test set
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result = model.transform(test)
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predictionAndLabels = result.select("prediction", "label")
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evaluator = MulticlassClassificationEvaluator(metricName="precision")
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print("Precision:" + str(evaluator.evaluate(predictionAndLabels)))
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# $example off$
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sc.stop()
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@ -0,0 +1,58 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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// scalastyle:off println
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package org.apache.spark.examples.ml
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import org.apache.spark.{SparkConf, SparkContext}
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// $example on$
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import org.apache.spark.ml.classification.{NaiveBayes}
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import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator
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// $example off$
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import org.apache.spark.sql.SQLContext
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object NaiveBayesExample {
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def main(args: Array[String]): Unit = {
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val conf = new SparkConf().setAppName("NaiveBayesExample")
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val sc = new SparkContext(conf)
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val sqlContext = new SQLContext(sc)
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// $example on$
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// Load the data stored in LIBSVM format as a DataFrame.
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val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
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// Split the data into training and test sets (30% held out for testing)
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val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3))
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// Train a NaiveBayes model.
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val model = new NaiveBayes()
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.fit(trainingData)
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// Select example rows to display.
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val predictions = model.transform(testData)
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predictions.show()
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// Select (prediction, true label) and compute test error
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val evaluator = new MulticlassClassificationEvaluator()
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.setLabelCol("label")
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.setPredictionCol("prediction")
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.setMetricName("precision")
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val precision = evaluator.evaluate(predictions)
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println("Precision:" + precision)
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// $example off$
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}
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}
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// scalastyle:on println
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