From 2804674a7af8f11eeb1280459bc9145815398eed Mon Sep 17 00:00:00 2001 From: Rishabh Bhardwaj Date: Mon, 2 Nov 2015 14:03:50 -0800 Subject: [SPARK-11383][DOCS] Replaced example code in mllib-naive-bayes.md/mllib-isotonic-regression.md using include_example I have made the required changes in mllib-naive-bayes.md/mllib-isotonic-regression.md and also verified them. Kindle Review it. Author: Rishabh Bhardwaj Closes #9353 from rishabhbhardwaj/SPARK-11383. --- docs/mllib-naive-bayes.md | 89 ++--------------------------------------------- 1 file changed, 3 insertions(+), 86 deletions(-) (limited to 'docs/mllib-naive-bayes.md') diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md index f4f6a10c82..60ac6c7e5b 100644 --- a/docs/mllib-naive-bayes.md +++ b/docs/mllib-naive-bayes.md @@ -40,32 +40,8 @@ can be used for evaluation and prediction. Refer to the [`NaiveBayes` Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes) and [`NaiveBayesModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel) for details on the API. -{% highlight scala %} -import org.apache.spark.mllib.classification.{NaiveBayes, NaiveBayesModel} -import org.apache.spark.mllib.linalg.Vectors -import org.apache.spark.mllib.regression.LabeledPoint - -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))) -} -// 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 model = NaiveBayes.train(training, lambda = 1.0, modelType = "multinomial") - -val predictionAndLabel = test.map(p => (model.predict(p.features), p.label)) -val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count() - -// Save and load model -model.save(sc, "myModelPath") -val sameModel = NaiveBayesModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala %} -
[NaiveBayes](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) implements @@ -77,40 +53,8 @@ can be used for evaluation and prediction. Refer to the [`NaiveBayes` Java docs](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) and [`NaiveBayesModel` Java docs](api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html) for details on the API. -{% highlight java %} -import scala.Tuple2; - -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.function.Function; -import org.apache.spark.api.java.function.PairFunction; -import org.apache.spark.mllib.classification.NaiveBayes; -import org.apache.spark.mllib.classification.NaiveBayesModel; -import org.apache.spark.mllib.regression.LabeledPoint; - -JavaRDD training = ... // training set -JavaRDD test = ... // test set - -final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0); - -JavaPairRDD predictionAndLabel = - test.mapToPair(new PairFunction() { - @Override public Tuple2 call(LabeledPoint p) { - return new Tuple2(model.predict(p.features()), p.label()); - } - }); -double accuracy = predictionAndLabel.filter(new Function, Boolean>() { - @Override public Boolean call(Tuple2 pl) { - return pl._1().equals(pl._2()); - } - }).count() / (double) test.count(); - -// Save and load model -model.save(sc.sc(), "myModelPath"); -NaiveBayesModel sameModel = NaiveBayesModel.load(sc.sc(), "myModelPath"); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java %}
-
[NaiveBayes](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes) implements multinomial @@ -124,33 +68,6 @@ Note that the Python API does not yet support model save/load but will in the fu Refer to the [`NaiveBayes` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes) and [`NaiveBayesModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayesModel) for more details on the API. -{% highlight python %} -from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel -from pyspark.mllib.linalg import Vectors -from pyspark.mllib.regression import LabeledPoint - -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) - -data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine) - -# Split data aproximately into training (60%) and test (40%) -training, test = data.randomSplit([0.6, 0.4], seed = 0) - -# Train a naive Bayes model. -model = NaiveBayes.train(training, 1.0) - -# Make prediction and test accuracy. -predictionAndLabel = test.map(lambda p : (model.predict(p.features), p.label)) -accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count() - -# Save and load model -model.save(sc, "myModelPath") -sameModel = NaiveBayesModel.load(sc, "myModelPath") -{% endhighlight %} - +{% include_example python/mllib/naive_bayes_example.py %}
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