diff options
3 files changed, 200 insertions, 143 deletions
diff --git a/docs/mllib-optimization.md b/docs/mllib-optimization.md index a3bd130ba0..ad7bcd9bfd 100644 --- a/docs/mllib-optimization.md +++ b/docs/mllib-optimization.md @@ -220,154 +220,13 @@ L-BFGS optimizer. <div data-lang="scala" markdown="1"> Refer to the [`LBFGS` Scala docs](api/scala/index.html#org.apache.spark.mllib.optimization.LBFGS) and [`SquaredL2Updater` Scala docs](api/scala/index.html#org.apache.spark.mllib.optimization.SquaredL2Updater) for details on the API. -{% highlight scala %} -import org.apache.spark.SparkContext -import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics -import org.apache.spark.mllib.linalg.Vectors -import org.apache.spark.mllib.util.MLUtils -import org.apache.spark.mllib.classification.LogisticRegressionModel -import org.apache.spark.mllib.optimization.{LBFGS, LogisticGradient, SquaredL2Updater} - -val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") -val numFeatures = data.take(1)(0).features.size - -// Split data into training (60%) and test (40%). -val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L) - -// Append 1 into the training data as intercept. -val training = splits(0).map(x => (x.label, MLUtils.appendBias(x.features))).cache() - -val test = splits(1) - -// Run training algorithm to build the model -val numCorrections = 10 -val convergenceTol = 1e-4 -val maxNumIterations = 20 -val regParam = 0.1 -val initialWeightsWithIntercept = Vectors.dense(new Array[Double](numFeatures + 1)) - -val (weightsWithIntercept, loss) = LBFGS.runLBFGS( - training, - new LogisticGradient(), - new SquaredL2Updater(), - numCorrections, - convergenceTol, - maxNumIterations, - regParam, - initialWeightsWithIntercept) - -val model = new LogisticRegressionModel( - Vectors.dense(weightsWithIntercept.toArray.slice(0, weightsWithIntercept.size - 1)), - weightsWithIntercept(weightsWithIntercept.size - 1)) - -// Clear the default threshold. -model.clearThreshold() - -// Compute raw scores on the test set. -val scoreAndLabels = test.map { point => - val score = model.predict(point.features) - (score, point.label) -} - -// Get evaluation metrics. -val metrics = new BinaryClassificationMetrics(scoreAndLabels) -val auROC = metrics.areaUnderROC() - -println("Loss of each step in training process") -loss.foreach(println) -println("Area under ROC = " + auROC) -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/mllib/LBFGSExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [`LBFGS` Java docs](api/java/org/apache/spark/mllib/optimization/LBFGS.html) and [`SquaredL2Updater` Java docs](api/java/org/apache/spark/mllib/optimization/SquaredL2Updater.html) for details on the API. -{% highlight java %} -import java.util.Arrays; -import java.util.Random; - -import scala.Tuple2; - -import org.apache.spark.api.java.*; -import org.apache.spark.api.java.function.Function; -import org.apache.spark.mllib.classification.LogisticRegressionModel; -import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics; -import org.apache.spark.mllib.linalg.Vector; -import org.apache.spark.mllib.linalg.Vectors; -import org.apache.spark.mllib.optimization.*; -import org.apache.spark.mllib.regression.LabeledPoint; -import org.apache.spark.mllib.util.MLUtils; -import org.apache.spark.SparkConf; -import org.apache.spark.SparkContext; - -public class LBFGSExample { - public static void main(String[] args) { - SparkConf conf = new SparkConf().setAppName("L-BFGS Example"); - SparkContext sc = new SparkContext(conf); - String path = "data/mllib/sample_libsvm_data.txt"; - JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD(); - int numFeatures = data.take(1).get(0).features().size(); - - // Split initial RDD into two... [60% training data, 40% testing data]. - JavaRDD<LabeledPoint> trainingInit = data.sample(false, 0.6, 11L); - JavaRDD<LabeledPoint> test = data.subtract(trainingInit); - - // Append 1 into the training data as intercept. - JavaRDD<Tuple2<Object, Vector>> training = data.map( - new Function<LabeledPoint, Tuple2<Object, Vector>>() { - public Tuple2<Object, Vector> call(LabeledPoint p) { - return new Tuple2<Object, Vector>(p.label(), MLUtils.appendBias(p.features())); - } - }); - training.cache(); - - // Run training algorithm to build the model. - int numCorrections = 10; - double convergenceTol = 1e-4; - int maxNumIterations = 20; - double regParam = 0.1; - Vector initialWeightsWithIntercept = Vectors.dense(new double[numFeatures + 1]); - - Tuple2<Vector, double[]> result = LBFGS.runLBFGS( - training.rdd(), - new LogisticGradient(), - new SquaredL2Updater(), - numCorrections, - convergenceTol, - maxNumIterations, - regParam, - initialWeightsWithIntercept); - Vector weightsWithIntercept = result._1(); - double[] loss = result._2(); - - final LogisticRegressionModel model = new LogisticRegressionModel( - Vectors.dense(Arrays.copyOf(weightsWithIntercept.toArray(), weightsWithIntercept.size() - 1)), - (weightsWithIntercept.toArray())[weightsWithIntercept.size() - 1]); - - // Clear the default threshold. - model.clearThreshold(); - - // Compute raw scores on the test set. - JavaRDD<Tuple2<Object, Object>> scoreAndLabels = test.map( - new Function<LabeledPoint, Tuple2<Object, Object>>() { - public Tuple2<Object, Object> call(LabeledPoint p) { - Double score = model.predict(p.features()); - return new Tuple2<Object, Object>(score, p.label()); - } - }); - - // Get evaluation metrics. - BinaryClassificationMetrics metrics = - new BinaryClassificationMetrics(scoreAndLabels.rdd()); - double auROC = metrics.areaUnderROC(); - - System.out.println("Loss of each step in training process"); - for (double l : loss) - System.out.println(l); - System.out.println("Area under ROC = " + auROC); - } -} -{% endhighlight %} +{% include_example java/org/apache/spark/examples/mllib/JavaLBFGSExample.java %} </div> </div> diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaLBFGSExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLBFGSExample.java new file mode 100644 index 0000000000..355883f61b --- /dev/null +++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaLBFGSExample.java @@ -0,0 +1,108 @@ +/* + * 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 java.util.Arrays; + +import scala.Tuple2; + +import org.apache.spark.api.java.*; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.mllib.classification.LogisticRegressionModel; +import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics; +import org.apache.spark.mllib.linalg.Vector; +import org.apache.spark.mllib.linalg.Vectors; +import org.apache.spark.mllib.optimization.*; +import org.apache.spark.mllib.regression.LabeledPoint; +import org.apache.spark.mllib.util.MLUtils; +import org.apache.spark.SparkConf; +import org.apache.spark.SparkContext; +// $example off$ + +public class JavaLBFGSExample { + public static void main(String[] args) { + SparkConf conf = new SparkConf().setAppName("L-BFGS Example"); + SparkContext sc = new SparkContext(conf); + + // $example on$ + String path = "data/mllib/sample_libsvm_data.txt"; + JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc, path).toJavaRDD(); + int numFeatures = data.take(1).get(0).features().size(); + + // Split initial RDD into two... [60% training data, 40% testing data]. + JavaRDD<LabeledPoint> trainingInit = data.sample(false, 0.6, 11L); + JavaRDD<LabeledPoint> test = data.subtract(trainingInit); + + // Append 1 into the training data as intercept. + JavaRDD<Tuple2<Object, Vector>> training = data.map( + new Function<LabeledPoint, Tuple2<Object, Vector>>() { + public Tuple2<Object, Vector> call(LabeledPoint p) { + return new Tuple2<Object, Vector>(p.label(), MLUtils.appendBias(p.features())); + } + }); + training.cache(); + + // Run training algorithm to build the model. + int numCorrections = 10; + double convergenceTol = 1e-4; + int maxNumIterations = 20; + double regParam = 0.1; + Vector initialWeightsWithIntercept = Vectors.dense(new double[numFeatures + 1]); + + Tuple2<Vector, double[]> result = LBFGS.runLBFGS( + training.rdd(), + new LogisticGradient(), + new SquaredL2Updater(), + numCorrections, + convergenceTol, + maxNumIterations, + regParam, + initialWeightsWithIntercept); + Vector weightsWithIntercept = result._1(); + double[] loss = result._2(); + + final LogisticRegressionModel model = new LogisticRegressionModel( + Vectors.dense(Arrays.copyOf(weightsWithIntercept.toArray(), weightsWithIntercept.size() - 1)), + (weightsWithIntercept.toArray())[weightsWithIntercept.size() - 1]); + + // Clear the default threshold. + model.clearThreshold(); + + // Compute raw scores on the test set. + JavaRDD<Tuple2<Object, Object>> scoreAndLabels = test.map( + new Function<LabeledPoint, Tuple2<Object, Object>>() { + public Tuple2<Object, Object> call(LabeledPoint p) { + Double score = model.predict(p.features()); + return new Tuple2<Object, Object>(score, p.label()); + } + }); + + // Get evaluation metrics. + BinaryClassificationMetrics metrics = + new BinaryClassificationMetrics(scoreAndLabels.rdd()); + double auROC = metrics.areaUnderROC(); + + System.out.println("Loss of each step in training process"); + for (double l : loss) + System.out.println(l); + System.out.println("Area under ROC = " + auROC); + // $example off$ + } +} + diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/LBFGSExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/LBFGSExample.scala new file mode 100644 index 0000000000..61d2e7715f --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/mllib/LBFGSExample.scala @@ -0,0 +1,90 @@ +/* + * 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.LogisticRegressionModel +import org.apache.spark.mllib.evaluation.BinaryClassificationMetrics +import org.apache.spark.mllib.linalg.Vectors +import org.apache.spark.mllib.optimization.{LBFGS, LogisticGradient, SquaredL2Updater} +import org.apache.spark.mllib.util.MLUtils +// $example off$ + +import org.apache.spark.{SparkConf, SparkContext} + +object LBFGSExample { + + def main(args: Array[String]): Unit = { + + val conf = new SparkConf().setAppName("LBFGSExample") + val sc = new SparkContext(conf) + + // $example on$ + val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") + val numFeatures = data.take(1)(0).features.size + + // Split data into training (60%) and test (40%). + val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L) + + // Append 1 into the training data as intercept. + val training = splits(0).map(x => (x.label, MLUtils.appendBias(x.features))).cache() + + val test = splits(1) + + // Run training algorithm to build the model + val numCorrections = 10 + val convergenceTol = 1e-4 + val maxNumIterations = 20 + val regParam = 0.1 + val initialWeightsWithIntercept = Vectors.dense(new Array[Double](numFeatures + 1)) + + val (weightsWithIntercept, loss) = LBFGS.runLBFGS( + training, + new LogisticGradient(), + new SquaredL2Updater(), + numCorrections, + convergenceTol, + maxNumIterations, + regParam, + initialWeightsWithIntercept) + + val model = new LogisticRegressionModel( + Vectors.dense(weightsWithIntercept.toArray.slice(0, weightsWithIntercept.size - 1)), + weightsWithIntercept(weightsWithIntercept.size - 1)) + + // Clear the default threshold. + model.clearThreshold() + + // Compute raw scores on the test set. + val scoreAndLabels = test.map { point => + val score = model.predict(point.features) + (score, point.label) + } + + // Get evaluation metrics. + val metrics = new BinaryClassificationMetrics(scoreAndLabels) + val auROC = metrics.areaUnderROC() + + println("Loss of each step in training process") + loss.foreach(println) + println("Area under ROC = " + auROC) + // $example off$ + } +} +// scalastyle:on println |