diff options
8 files changed, 391 insertions, 207 deletions
diff --git a/docs/mllib-isotonic-regression.md b/docs/mllib-isotonic-regression.md index f91a697b31..85f9226b43 100644 --- a/docs/mllib-isotonic-regression.md +++ b/docs/mllib-isotonic-regression.md @@ -61,42 +61,8 @@ labels and real labels in the test set. Refer to the [`IsotonicRegression` Scala docs](api/scala/index.html#org.apache.spark.mllib.regression.IsotonicRegression) and [`IsotonicRegressionModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.regression.IsotonicRegressionModel) for details on the API. -{% highlight scala %} -import org.apache.spark.mllib.regression.{IsotonicRegression, IsotonicRegressionModel} - -val data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt") - -// Create label, feature, weight tuples from input data with weight set to default value 1.0. -val parsedData = data.map { line => - val parts = line.split(',').map(_.toDouble) - (parts(0), parts(1), 1.0) -} - -// Split data into training (60%) and test (40%) sets. -val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L) -val training = splits(0) -val test = splits(1) - -// Create isotonic regression model from training data. -// Isotonic parameter defaults to true so it is only shown for demonstration -val model = new IsotonicRegression().setIsotonic(true).run(training) - -// Create tuples of predicted and real labels. -val predictionAndLabel = test.map { point => - val predictedLabel = model.predict(point._2) - (predictedLabel, point._1) -} - -// Calculate mean squared error between predicted and real labels. -val meanSquaredError = predictionAndLabel.map{case(p, l) => math.pow((p - l), 2)}.mean() -println("Mean Squared Error = " + meanSquaredError) - -// Save and load model -model.save(sc, "myModelPath") -val sameModel = IsotonicRegressionModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala %} </div> - <div data-lang="java" markdown="1"> Data are read from a file where each line has a format label,feature i.e. 4710.28,500.00. The data are split to training and testing set. @@ -105,66 +71,8 @@ labels and real labels in the test set. Refer to the [`IsotonicRegression` Java docs](api/java/org/apache/spark/mllib/regression/IsotonicRegression.html) and [`IsotonicRegressionModel` Java docs](api/java/org/apache/spark/mllib/regression/IsotonicRegressionModel.html) for details on the API. -{% highlight java %} -import org.apache.spark.SparkConf; -import org.apache.spark.api.java.JavaDoubleRDD; -import org.apache.spark.api.java.JavaPairRDD; -import org.apache.spark.api.java.JavaRDD; -import org.apache.spark.api.java.JavaSparkContext; -import org.apache.spark.api.java.function.Function; -import org.apache.spark.api.java.function.PairFunction; -import org.apache.spark.mllib.regression.IsotonicRegressionModel; -import scala.Tuple2; -import scala.Tuple3; - -JavaRDD<String> data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt"); - -// Create label, feature, weight tuples from input data with weight set to default value 1.0. -JavaRDD<Tuple3<Double, Double, Double>> parsedData = data.map( - new Function<String, Tuple3<Double, Double, Double>>() { - public Tuple3<Double, Double, Double> call(String line) { - String[] parts = line.split(","); - return new Tuple3<>(new Double(parts[0]), new Double(parts[1]), 1.0); - } - } -); - -// Split data into training (60%) and test (40%) sets. -JavaRDD<Tuple3<Double, Double, Double>>[] splits = parsedData.randomSplit(new double[] {0.6, 0.4}, 11L); -JavaRDD<Tuple3<Double, Double, Double>> training = splits[0]; -JavaRDD<Tuple3<Double, Double, Double>> test = splits[1]; - -// Create isotonic regression model from training data. -// Isotonic parameter defaults to true so it is only shown for demonstration -IsotonicRegressionModel model = new IsotonicRegression().setIsotonic(true).run(training); - -// Create tuples of predicted and real labels. -JavaPairRDD<Double, Double> predictionAndLabel = test.mapToPair( - new PairFunction<Tuple3<Double, Double, Double>, Double, Double>() { - @Override public Tuple2<Double, Double> call(Tuple3<Double, Double, Double> point) { - Double predictedLabel = model.predict(point._2()); - return new Tuple2<Double, Double>(predictedLabel, point._1()); - } - } -); - -// Calculate mean squared error between predicted and real labels. -Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map( - new Function<Tuple2<Double, Double>, Object>() { - @Override public Object call(Tuple2<Double, Double> pl) { - return Math.pow(pl._1() - pl._2(), 2); - } - } -).rdd()).mean(); - -System.out.println("Mean Squared Error = " + meanSquaredError); - -// Save and load model -model.save(sc.sc(), "myModelPath"); -IsotonicRegressionModel sameModel = IsotonicRegressionModel.load(sc.sc(), "myModelPath"); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java %} </div> - <div data-lang="python" markdown="1"> Data are read from a file where each line has a format label,feature i.e. 4710.28,500.00. The data are split to training and testing set. @@ -173,32 +81,6 @@ labels and real labels in the test set. Refer to the [`IsotonicRegression` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.regression.IsotonicRegression) and [`IsotonicRegressionModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.regression.IsotonicRegressionModel) for more details on the API. -{% highlight python %} -import math -from pyspark.mllib.regression import IsotonicRegression, IsotonicRegressionModel - -data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt") - -# Create label, feature, weight tuples from input data with weight set to default value 1.0. -parsedData = data.map(lambda line: tuple([float(x) for x in line.split(',')]) + (1.0,)) - -# Split data into training (60%) and test (40%) sets. -training, test = parsedData.randomSplit([0.6, 0.4], 11) - -# Create isotonic regression model from training data. -# Isotonic parameter defaults to true so it is only shown for demonstration -model = IsotonicRegression.train(training) - -# Create tuples of predicted and real labels. -predictionAndLabel = test.map(lambda p: (model.predict(p[1]), p[0])) - -# Calculate mean squared error between predicted and real labels. -meanSquaredError = predictionAndLabel.map(lambda pl: math.pow((pl[0] - pl[1]), 2)).mean() -print("Mean Squared Error = " + str(meanSquaredError)) - -# Save and load model -model.save(sc, "myModelPath") -sameModel = IsotonicRegressionModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example python/mllib/isotonic_regression_example.py %} </div> </div> 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 %} </div> - <div data-lang="java" markdown="1"> [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<LabeledPoint> training = ... // training set -JavaRDD<LabeledPoint> test = ... // test set - -final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0); - -JavaPairRDD<Double, Double> predictionAndLabel = - test.mapToPair(new PairFunction<LabeledPoint, Double, Double>() { - @Override public Tuple2<Double, Double> call(LabeledPoint p) { - return new Tuple2<Double, Double>(model.predict(p.features()), p.label()); - } - }); -double accuracy = predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() { - @Override public Boolean call(Tuple2<Double, Double> 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 %} </div> - <div data-lang="python" markdown="1"> [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 %} </div> </div> diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java new file mode 100644 index 0000000000..37e709b4cb --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java @@ -0,0 +1,86 @@ +/* + * 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.mllib; + +// $example on$ +import scala.Tuple2; +import scala.Tuple3; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.api.java.function.PairFunction; +import org.apache.spark.api.java.JavaDoubleRDD; +import org.apache.spark.api.java.JavaPairRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.mllib.regression.IsotonicRegression; +import org.apache.spark.mllib.regression.IsotonicRegressionModel; +// $example off$ +import org.apache.spark.SparkConf; + +public class JavaIsotonicRegressionExample { + public static void main(String[] args) { + SparkConf sparkConf = new SparkConf().setAppName("JavaIsotonicRegressionExample"); + JavaSparkContext jsc = new JavaSparkContext(sparkConf); + // $example on$ + JavaRDD<String> data = jsc.textFile("data/mllib/sample_isotonic_regression_data.txt"); + + // Create label, feature, weight tuples from input data with weight set to default value 1.0. + JavaRDD<Tuple3<Double, Double, Double>> parsedData = data.map( + new Function<String, Tuple3<Double, Double, Double>>() { + public Tuple3<Double, Double, Double> call(String line) { + String[] parts = line.split(","); + return new Tuple3<>(new Double(parts[0]), new Double(parts[1]), 1.0); + } + } + ); + + // Split data into training (60%) and test (40%) sets. + JavaRDD<Tuple3<Double, Double, Double>>[] splits = parsedData.randomSplit(new double[]{0.6, 0.4}, 11L); + JavaRDD<Tuple3<Double, Double, Double>> training = splits[0]; + JavaRDD<Tuple3<Double, Double, Double>> test = splits[1]; + + // Create isotonic regression model from training data. + // Isotonic parameter defaults to true so it is only shown for demonstration + final IsotonicRegressionModel model = new IsotonicRegression().setIsotonic(true).run(training); + + // Create tuples of predicted and real labels. + JavaPairRDD<Double, Double> predictionAndLabel = test.mapToPair( + new PairFunction<Tuple3<Double, Double, Double>, Double, Double>() { + @Override + public Tuple2<Double, Double> call(Tuple3<Double, Double, Double> point) { + Double predictedLabel = model.predict(point._2()); + return new Tuple2<Double, Double>(predictedLabel, point._1()); + } + } + ); + + // Calculate mean squared error between predicted and real labels. + Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map( + new Function<Tuple2<Double, Double>, Object>() { + @Override + public Object call(Tuple2<Double, Double> pl) { + return Math.pow(pl._1() - pl._2(), 2); + } + } + ).rdd()).mean(); + System.out.println("Mean Squared Error = " + meanSquaredError); + + // Save and load model + model.save(jsc.sc(), "target/tmp/myIsotonicRegressionModel"); + IsotonicRegressionModel sameModel = IsotonicRegressionModel.load(jsc.sc(), "target/tmp/myIsotonicRegressionModel"); + // $example off$ + } +} 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 new file mode 100644 index 0000000000..e6a5904bd7 --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/mllib/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.mllib; + +// $example on$ +import scala.Tuple2; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.api.java.function.PairFunction; +import org.apache.spark.api.java.JavaPairRDD; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.mllib.classification.NaiveBayes; +import org.apache.spark.mllib.classification.NaiveBayesModel; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.mllib.util.MLUtils; +// $example off$ +import org.apache.spark.SparkConf; + +public class JavaNaiveBayesExample { + public static void main(String[] args) { + SparkConf sparkConf = new SparkConf().setAppName("JavaNaiveBayesExample"); + JavaSparkContext jsc = new JavaSparkContext(sparkConf); + // $example on$ + String path = "data/mllib/sample_naive_bayes_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> training = tmp[0]; // training set + JavaRDD<LabeledPoint> test = tmp[1]; // test set + final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0); + JavaPairRDD<Double, Double> predictionAndLabel = + test.mapToPair(new PairFunction<LabeledPoint, Double, Double>() { + @Override + public Tuple2<Double, Double> call(LabeledPoint p) { + return new Tuple2<Double, Double>(model.predict(p.features()), p.label()); + } + }); + double accuracy = predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() { + @Override + public Boolean call(Tuple2<Double, Double> pl) { + return pl._1().equals(pl._2()); + } + }).count() / (double) test.count(); + + // Save and load model + model.save(jsc.sc(), "target/tmp/myNaiveBayesModel"); + NaiveBayesModel sameModel = NaiveBayesModel.load(jsc.sc(), "target/tmp/myNaiveBayesModel"); + // $example off$ + } +} diff --git a/examples/src/main/python/mllib/isotonic_regression_example.py b/examples/src/main/python/mllib/isotonic_regression_example.py new file mode 100644 index 0000000000..89dc9f4b66 --- /dev/null +++ b/examples/src/main/python/mllib/isotonic_regression_example.py @@ -0,0 +1,56 @@ +# +# 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. +# + +""" +Isotonic Regression Example. +""" +from __future__ import print_function + +from pyspark import SparkContext +# $example on$ +import math +from pyspark.mllib.regression import IsotonicRegression, IsotonicRegressionModel +# $example off$ + +if __name__ == "__main__": + + sc = SparkContext(appName="PythonIsotonicRegressionExample") + + # $example on$ + data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt") + + # Create label, feature, weight tuples from input data with weight set to default value 1.0. + parsedData = data.map(lambda line: tuple([float(x) for x in line.split(',')]) + (1.0,)) + + # Split data into training (60%) and test (40%) sets. + training, test = parsedData.randomSplit([0.6, 0.4], 11) + + # Create isotonic regression model from training data. + # Isotonic parameter defaults to true so it is only shown for demonstration + model = IsotonicRegression.train(training) + + # Create tuples of predicted and real labels. + predictionAndLabel = test.map(lambda p: (model.predict(p[1]), p[0])) + + # Calculate mean squared error between predicted and real labels. + meanSquaredError = predictionAndLabel.map(lambda pl: math.pow((pl[0] - pl[1]), 2)).mean() + print("Mean Squared Error = " + str(meanSquaredError)) + + # Save and load model + model.save(sc, "target/tmp/myIsotonicRegressionModel") + sameModel = IsotonicRegressionModel.load(sc, "target/tmp/myIsotonicRegressionModel") + # $example off$ diff --git a/examples/src/main/python/mllib/naive_bayes_example.py b/examples/src/main/python/mllib/naive_bayes_example.py new file mode 100644 index 0000000000..a2e7dacf25 --- /dev/null +++ b/examples/src/main/python/mllib/naive_bayes_example.py @@ -0,0 +1,56 @@ +# +# 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. +# + +""" +NaiveBayes Example. +""" +from __future__ import print_function + +# $example on$ +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) +# $example off$ + +if __name__ == "__main__": + + sc = SparkContext(appName="PythonNaiveBayesExample") + + # $example on$ + 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, "target/tmp/myNaiveBayesModel") + sameModel = NaiveBayesModel.load(sc, "target/tmp/myNaiveBayesModel") + # $example off$ diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala new file mode 100644 index 0000000000..52ac9ae7dd --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala @@ -0,0 +1,66 @@ +/* + * 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.mllib + +// $example on$ +import org.apache.spark.mllib.regression.{IsotonicRegression, IsotonicRegressionModel} +// $example off$ +import org.apache.spark.{SparkConf, SparkContext} + +object IsotonicRegressionExample { + + def main(args: Array[String]) : Unit = { + + val conf = new SparkConf().setAppName("IsotonicRegressionExample") + val sc = new SparkContext(conf) + // $example on$ + val data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt") + + // Create label, feature, weight tuples from input data with weight set to default value 1.0. + val parsedData = data.map { line => + val parts = line.split(',').map(_.toDouble) + (parts(0), parts(1), 1.0) + } + + // Split data into training (60%) and test (40%) sets. + val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L) + val training = splits(0) + val test = splits(1) + + // Create isotonic regression model from training data. + // Isotonic parameter defaults to true so it is only shown for demonstration + val model = new IsotonicRegression().setIsotonic(true).run(training) + + // Create tuples of predicted and real labels. + val predictionAndLabel = test.map { point => + val predictedLabel = model.predict(point._2) + (predictedLabel, point._1) + } + + // Calculate mean squared error between predicted and real labels. + val meanSquaredError = predictionAndLabel.map { case (p, l) => math.pow((p - l), 2) }.mean() + println("Mean Squared Error = " + meanSquaredError) + + // Save and load model + model.save(sc, "target/tmp/myIsotonicRegressionModel") + val sameModel = IsotonicRegressionModel.load(sc, "target/tmp/myIsotonicRegressionModel") + // $example off$ + } +} +// scalastyle:on println 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 new file mode 100644 index 0000000000..a7a47c2a35 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala @@ -0,0 +1,57 @@ +/* + * 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.mllib + +// $example on$ +import org.apache.spark.mllib.classification.{NaiveBayes, NaiveBayesModel} +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.regression.LabeledPoint +// $example off$ +import org.apache.spark.{SparkConf, SparkContext} + +object NaiveBayesExample { + + def main(args: Array[String]) : Unit = { + 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))) + } + + // 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, "target/tmp/myNaiveBayesModel") + val sameModel = NaiveBayesModel.load(sc, "target/tmp/myNaiveBayesModel") + // $example off$ + } +} + +// scalastyle:on println |