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Diffstat (limited to 'examples')
3 files changed, 175 insertions, 0 deletions
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java new file mode 100644 index 0000000000..41d7ad75b9 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaNaiveBayesExample.java @@ -0,0 +1,64 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.examples.ml; + + +import org.apache.spark.SparkConf; +import org.apache.spark.api.java.JavaSparkContext; +// $example on$ +import org.apache.spark.ml.classification.NaiveBayes; +import org.apache.spark.ml.classification.NaiveBayesModel; +import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator; +import org.apache.spark.sql.Dataset; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.SQLContext; +// $example off$ + +/** + * An example for Naive Bayes Classification. + */ +public class JavaNaiveBayesExample { + + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("JavaNaiveBayesExample"); + JavaSparkContext jsc = new JavaSparkContext(conf); + SQLContext jsql = new SQLContext(jsc); + + // $example on$ + // Load training data + Dataset<Row> dataFrame = jsql.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt"); + // Split the data into train and test + Dataset<Row>[] splits = dataFrame.randomSplit(new double[]{0.6, 0.4}, 1234L); + Dataset<Row> train = splits[0]; + Dataset<Row> test = splits[1]; + + // create the trainer and set its parameters + NaiveBayes nb = new NaiveBayes(); + // train the model + NaiveBayesModel model = nb.fit(train); + // compute precision on the test set + Dataset<Row> result = model.transform(test); + Dataset<Row> predictionAndLabels = result.select("prediction", "label"); + MulticlassClassificationEvaluator evaluator = new MulticlassClassificationEvaluator() + .setMetricName("precision"); + System.out.println("Precision = " + evaluator.evaluate(predictionAndLabels)); + // $example off$ + + jsc.stop(); + } +} diff --git a/examples/src/main/python/ml/naive_bayes_example.py b/examples/src/main/python/ml/naive_bayes_example.py new file mode 100644 index 0000000000..db8fbea9bf --- /dev/null +++ b/examples/src/main/python/ml/naive_bayes_example.py @@ -0,0 +1,53 @@ +# +# Licensed to the Apache Software Foundation (ASF) under one or more +# contributor license agreements. See the NOTICE file distributed with +# this work for additional information regarding copyright ownership. +# The ASF licenses this file to You under the Apache License, Version 2.0 +# (the "License"); you may not use this file except in compliance with +# the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# + +from __future__ import print_function + +from pyspark import SparkContext +from pyspark.sql import SQLContext +# $example on$ +from pyspark.ml.classification import NaiveBayes +from pyspark.ml.evaluation import MulticlassClassificationEvaluator +# $example off$ + +if __name__ == "__main__": + + sc = SparkContext(appName="naive_bayes_example") + sqlContext = SQLContext(sc) + + # $example on$ + # Load training data + data = sqlContext.read.format("libsvm") \ + .load("data/mllib/sample_libsvm_data.txt") + # Split the data into train and test + splits = data.randomSplit([0.6, 0.4], 1234) + train = splits[0] + test = splits[1] + + # create the trainer and set its parameters + nb = NaiveBayes(smoothing=1.0, modelType="multinomial") + + # train the model + model = nb.fit(train) + # compute precision on the test set + result = model.transform(test) + predictionAndLabels = result.select("prediction", "label") + evaluator = MulticlassClassificationEvaluator(metricName="precision") + print("Precision:" + str(evaluator.evaluate(predictionAndLabels))) + # $example off$ + + sc.stop() diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala new file mode 100644 index 0000000000..5ea1270c97 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/ml/NaiveBayesExample.scala @@ -0,0 +1,58 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +// scalastyle:off println +package org.apache.spark.examples.ml + +import org.apache.spark.{SparkConf, SparkContext} +// $example on$ +import org.apache.spark.ml.classification.{NaiveBayes} +import org.apache.spark.ml.evaluation.MulticlassClassificationEvaluator +// $example off$ +import org.apache.spark.sql.SQLContext + +object NaiveBayesExample { + def main(args: Array[String]): Unit = { + val conf = new SparkConf().setAppName("NaiveBayesExample") + val sc = new SparkContext(conf) + val sqlContext = new SQLContext(sc) + // $example on$ + // Load the data stored in LIBSVM format as a DataFrame. + val data = sqlContext.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt") + + // Split the data into training and test sets (30% held out for testing) + val Array(trainingData, testData) = data.randomSplit(Array(0.7, 0.3)) + + // Train a NaiveBayes model. + val model = new NaiveBayes() + .fit(trainingData) + + // Select example rows to display. + val predictions = model.transform(testData) + predictions.show() + + // Select (prediction, true label) and compute test error + val evaluator = new MulticlassClassificationEvaluator() + .setLabelCol("label") + .setPredictionCol("prediction") + .setMetricName("precision") + val precision = evaluator.evaluate(predictions) + println("Precision:" + precision) + // $example off$ + } +} +// scalastyle:on println |