diff options
4 files changed, 10 insertions, 33 deletions
diff --git a/data/mllib/sample_naive_bayes_data.txt b/data/mllib/sample_naive_bayes_data.txt deleted file mode 100644 index bd22bea3a5..0000000000 --- a/data/mllib/sample_naive_bayes_data.txt +++ /dev/null @@ -1,12 +0,0 @@ -0,1 0 0 -0,2 0 0 -0,3 0 0 -0,4 0 0 -1,0 1 0 -1,0 2 0 -1,0 3 0 -1,0 4 0 -2,0 0 1 -2,0 0 2 -2,0 0 3 -2,0 0 4
\ No newline at end of file diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java index 2b17dbb963..f4ec04b0c6 100644 --- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java +++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java @@ -36,9 +36,9 @@ public class JavaNaiveBayesExample { SparkConf sparkConf = new SparkConf().setAppName("JavaNaiveBayesExample"); JavaSparkContext jsc = new JavaSparkContext(sparkConf); // $example on$ - String path = "data/mllib/sample_naive_bayes_data.txt"; + String path = "data/mllib/sample_libsvm_data.txt"; JavaRDD<LabeledPoint> inputData = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD(); - JavaRDD<LabeledPoint>[] tmp = inputData.randomSplit(new double[]{0.6, 0.4}, 12345); + JavaRDD<LabeledPoint>[] tmp = inputData.randomSplit(new double[]{0.6, 0.4}); JavaRDD<LabeledPoint> training = tmp[0]; // training set JavaRDD<LabeledPoint> test = tmp[1]; // test set final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0); diff --git a/examples/src/main/python/mllib/naive_bayes_example.py b/examples/src/main/python/mllib/naive_bayes_example.py index 35724f7d6a..749353b20e 100644 --- a/examples/src/main/python/mllib/naive_bayes_example.py +++ b/examples/src/main/python/mllib/naive_bayes_example.py @@ -29,15 +29,9 @@ import shutil from pyspark import SparkContext # $example on$ from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel -from pyspark.mllib.linalg import Vectors -from pyspark.mllib.regression import LabeledPoint +from pyspark.mllib.util import MLUtils -def parseLine(line): - parts = line.split(',') - label = float(parts[0]) - features = Vectors.dense([float(x) for x in parts[1].split(' ')]) - return LabeledPoint(label, features) # $example off$ if __name__ == "__main__": @@ -45,10 +39,11 @@ if __name__ == "__main__": sc = SparkContext(appName="PythonNaiveBayesExample") # $example on$ - data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine) + # Load and parse the data file. + data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") # Split data approximately into training (60%) and test (40%) - training, test = data.randomSplit([0.6, 0.4], seed=0) + training, test = data.randomSplit([0.6, 0.4]) # Train a naive Bayes model. model = NaiveBayes.train(training, 1.0) diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala index 0187ad603a..b321d8e127 100644 --- a/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala @@ -21,8 +21,7 @@ package org.apache.spark.examples.mllib import org.apache.spark.{SparkConf, SparkContext} // $example on$ import org.apache.spark.mllib.classification.{NaiveBayes, NaiveBayesModel} -import org.apache.spark.mllib.linalg.Vectors -import org.apache.spark.mllib.regression.LabeledPoint +import org.apache.spark.mllib.util.MLUtils // $example off$ object NaiveBayesExample { @@ -31,16 +30,11 @@ object NaiveBayesExample { val conf = new SparkConf().setAppName("NaiveBayesExample") val sc = new SparkContext(conf) // $example on$ - val data = sc.textFile("data/mllib/sample_naive_bayes_data.txt") - val parsedData = data.map { line => - val parts = line.split(',') - LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble))) - } + // Load and parse the data file. + val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") // Split data into training (60%) and test (40%). - val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L) - val training = splits(0) - val test = splits(1) + val Array(training, test) = data.randomSplit(Array(0.6, 0.4)) val model = NaiveBayes.train(training, lambda = 1.0, modelType = "multinomial") |