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author | Xusen Yin <yinxusen@gmail.com> | 2015-11-10 10:05:53 -0800 |
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committer | Xiangrui Meng <meng@databricks.com> | 2015-11-10 10:05:53 -0800 |
commit | a81f47ff7498e7063c855ccf75bba81ab101b43e (patch) | |
tree | b5e07052ba1b895da31ce66553355ef171b21efc /docs/mllib-decision-tree.md | |
parent | 5507a9d0935aa42d65c3a4fa65da680b5af14faf (diff) | |
download | spark-a81f47ff7498e7063c855ccf75bba81ab101b43e.tar.gz spark-a81f47ff7498e7063c855ccf75bba81ab101b43e.tar.bz2 spark-a81f47ff7498e7063c855ccf75bba81ab101b43e.zip |
[SPARK-11382] Replace example code in mllib-decision-tree.md using include_example
https://issues.apache.org/jira/browse/SPARK-11382
B.T.W. I fix an error in naive_bayes_example.py.
Author: Xusen Yin <yinxusen@gmail.com>
Closes #9596 from yinxusen/SPARK-11382.
Diffstat (limited to 'docs/mllib-decision-tree.md')
-rw-r--r-- | docs/mllib-decision-tree.md | 253 |
1 files changed, 6 insertions, 247 deletions
diff --git a/docs/mllib-decision-tree.md b/docs/mllib-decision-tree.md index b5b454bc69..77ce34e91a 100644 --- a/docs/mllib-decision-tree.md +++ b/docs/mllib-decision-tree.md @@ -194,137 +194,19 @@ maximum tree depth of 5. The test error is calculated to measure the algorithm a <div data-lang="scala" markdown="1"> Refer to the [`DecisionTree` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) and [`DecisionTreeModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.model.DecisionTreeModel) for details on the API. -{% highlight scala %} -import org.apache.spark.mllib.tree.DecisionTree -import org.apache.spark.mllib.tree.model.DecisionTreeModel -import org.apache.spark.mllib.util.MLUtils - -// Load and parse the data file. -val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") -// Split the data into training and test sets (30% held out for testing) -val splits = data.randomSplit(Array(0.7, 0.3)) -val (trainingData, testData) = (splits(0), splits(1)) - -// Train a DecisionTree model. -// Empty categoricalFeaturesInfo indicates all features are continuous. -val numClasses = 2 -val categoricalFeaturesInfo = Map[Int, Int]() -val impurity = "gini" -val maxDepth = 5 -val maxBins = 32 - -val model = DecisionTree.trainClassifier(trainingData, numClasses, categoricalFeaturesInfo, - impurity, maxDepth, maxBins) - -// Evaluate model on test instances and compute test error -val labelAndPreds = testData.map { point => - val prediction = model.predict(point.features) - (point.label, prediction) -} -val testErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / testData.count() -println("Test Error = " + testErr) -println("Learned classification tree model:\n" + model.toDebugString) - -// Save and load model -model.save(sc, "myModelPath") -val sameModel = DecisionTreeModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/mllib/DecisionTreeClassificationExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [`DecisionTree` Java docs](api/java/org/apache/spark/mllib/tree/DecisionTree.html) and [`DecisionTreeModel` Java docs](api/java/org/apache/spark/mllib/tree/model/DecisionTreeModel.html) for details on the API. -{% highlight java %} -import java.util.HashMap; -import scala.Tuple2; -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.LabeledPoint; -import org.apache.spark.mllib.tree.DecisionTree; -import org.apache.spark.mllib.tree.model.DecisionTreeModel; -import org.apache.spark.mllib.util.MLUtils; -import org.apache.spark.SparkConf; - -SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree"); -JavaSparkContext sc = new JavaSparkContext(sparkConf); - -// Load and parse the data file. -String datapath = "data/mllib/sample_libsvm_data.txt"; -JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD(); -// Split the data into training and test sets (30% held out for testing) -JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3}); -JavaRDD<LabeledPoint> trainingData = splits[0]; -JavaRDD<LabeledPoint> testData = splits[1]; - -// Set parameters. -// Empty categoricalFeaturesInfo indicates all features are continuous. -Integer numClasses = 2; -Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>(); -String impurity = "gini"; -Integer maxDepth = 5; -Integer maxBins = 32; - -// Train a DecisionTree model for classification. -final DecisionTreeModel model = DecisionTree.trainClassifier(trainingData, numClasses, - categoricalFeaturesInfo, impurity, maxDepth, maxBins); - -// Evaluate model on test instances and compute test error -JavaPairRDD<Double, Double> predictionAndLabel = - testData.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 testErr = - 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() { - @Override - public Boolean call(Tuple2<Double, Double> pl) { - return !pl._1().equals(pl._2()); - } - }).count() / testData.count(); -System.out.println("Test Error: " + testErr); -System.out.println("Learned classification tree model:\n" + model.toDebugString()); - -// Save and load model -model.save(sc.sc(), "myModelPath"); -DecisionTreeModel sameModel = DecisionTreeModel.load(sc.sc(), "myModelPath"); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java %} </div> <div data-lang="python" markdown="1"> Refer to the [`DecisionTree` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.DecisionTree) and [`DecisionTreeModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.DecisionTreeModel) for more details on the API. -{% highlight python %} -from pyspark.mllib.regression import LabeledPoint -from pyspark.mllib.tree import DecisionTree, DecisionTreeModel -from pyspark.mllib.util import MLUtils - -# Load and parse the data file into an RDD of LabeledPoint. -data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt') -# Split the data into training and test sets (30% held out for testing) -(trainingData, testData) = data.randomSplit([0.7, 0.3]) - -# Train a DecisionTree model. -# Empty categoricalFeaturesInfo indicates all features are continuous. -model = DecisionTree.trainClassifier(trainingData, numClasses=2, categoricalFeaturesInfo={}, - impurity='gini', maxDepth=5, maxBins=32) - -# Evaluate model on test instances and compute test error -predictions = model.predict(testData.map(lambda x: x.features)) -labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) -testErr = labelsAndPredictions.filter(lambda (v, p): v != p).count() / float(testData.count()) -print('Test Error = ' + str(testErr)) -print('Learned classification tree model:') -print(model.toDebugString()) - -# Save and load model -model.save(sc, "myModelPath") -sameModel = DecisionTreeModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example python/mllib/decision_tree_classification_example.py %} </div> </div> @@ -343,142 +225,19 @@ depth of 5. The Mean Squared Error (MSE) is computed at the end to evaluate <div data-lang="scala" markdown="1"> Refer to the [`DecisionTree` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) and [`DecisionTreeModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.model.DecisionTreeModel) for details on the API. -{% highlight scala %} -import org.apache.spark.mllib.tree.DecisionTree -import org.apache.spark.mllib.tree.model.DecisionTreeModel -import org.apache.spark.mllib.util.MLUtils - -// Load and parse the data file. -val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") -// Split the data into training and test sets (30% held out for testing) -val splits = data.randomSplit(Array(0.7, 0.3)) -val (trainingData, testData) = (splits(0), splits(1)) - -// Train a DecisionTree model. -// Empty categoricalFeaturesInfo indicates all features are continuous. -val categoricalFeaturesInfo = Map[Int, Int]() -val impurity = "variance" -val maxDepth = 5 -val maxBins = 32 - -val model = DecisionTree.trainRegressor(trainingData, categoricalFeaturesInfo, impurity, - maxDepth, maxBins) - -// Evaluate model on test instances and compute test error -val labelsAndPredictions = testData.map { point => - val prediction = model.predict(point.features) - (point.label, prediction) -} -val testMSE = labelsAndPredictions.map{ case(v, p) => math.pow((v - p), 2)}.mean() -println("Test Mean Squared Error = " + testMSE) -println("Learned regression tree model:\n" + model.toDebugString) - -// Save and load model -model.save(sc, "myModelPath") -val sameModel = DecisionTreeModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example scala/org/apache/spark/examples/mllib/DecisionTreeRegressionExample.scala %} </div> <div data-lang="java" markdown="1"> Refer to the [`DecisionTree` Java docs](api/java/org/apache/spark/mllib/tree/DecisionTree.html) and [`DecisionTreeModel` Java docs](api/java/org/apache/spark/mllib/tree/model/DecisionTreeModel.html) for details on the API. -{% highlight java %} -import java.util.HashMap; -import scala.Tuple2; -import org.apache.spark.api.java.function.Function2; -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.LabeledPoint; -import org.apache.spark.mllib.tree.DecisionTree; -import org.apache.spark.mllib.tree.model.DecisionTreeModel; -import org.apache.spark.mllib.util.MLUtils; -import org.apache.spark.SparkConf; - -SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree"); -JavaSparkContext sc = new JavaSparkContext(sparkConf); - -// Load and parse the data file. -String datapath = "data/mllib/sample_libsvm_data.txt"; -JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD(); -// Split the data into training and test sets (30% held out for testing) -JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3}); -JavaRDD<LabeledPoint> trainingData = splits[0]; -JavaRDD<LabeledPoint> testData = splits[1]; - -// Set parameters. -// Empty categoricalFeaturesInfo indicates all features are continuous. -Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>(); -String impurity = "variance"; -Integer maxDepth = 5; -Integer maxBins = 32; - -// Train a DecisionTree model. -final DecisionTreeModel model = DecisionTree.trainRegressor(trainingData, - categoricalFeaturesInfo, impurity, maxDepth, maxBins); - -// Evaluate model on test instances and compute test error -JavaPairRDD<Double, Double> predictionAndLabel = - testData.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 testMSE = - predictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() { - @Override - public Double call(Tuple2<Double, Double> pl) { - Double diff = pl._1() - pl._2(); - return diff * diff; - } - }).reduce(new Function2<Double, Double, Double>() { - @Override - public Double call(Double a, Double b) { - return a + b; - } - }) / testData.count(); -System.out.println("Test Mean Squared Error: " + testMSE); -System.out.println("Learned regression tree model:\n" + model.toDebugString()); - -// Save and load model -model.save(sc.sc(), "myModelPath"); -DecisionTreeModel sameModel = DecisionTreeModel.load(sc.sc(), "myModelPath"); -{% endhighlight %} +{% include_example java/org/apache/spark/examples/mllib/JavaDecisionTreeRegressionExample.java %} </div> <div data-lang="python" markdown="1"> Refer to the [`DecisionTree` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.DecisionTree) and [`DecisionTreeModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.DecisionTreeModel) for more details on the API. -{% highlight python %} -from pyspark.mllib.regression import LabeledPoint -from pyspark.mllib.tree import DecisionTree, DecisionTreeModel -from pyspark.mllib.util import MLUtils - -# Load and parse the data file into an RDD of LabeledPoint. -data = MLUtils.loadLibSVMFile(sc, 'data/mllib/sample_libsvm_data.txt') -# Split the data into training and test sets (30% held out for testing) -(trainingData, testData) = data.randomSplit([0.7, 0.3]) - -# Train a DecisionTree model. -# Empty categoricalFeaturesInfo indicates all features are continuous. -model = DecisionTree.trainRegressor(trainingData, categoricalFeaturesInfo={}, - impurity='variance', maxDepth=5, maxBins=32) - -# Evaluate model on test instances and compute test error -predictions = model.predict(testData.map(lambda x: x.features)) -labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions) -testMSE = labelsAndPredictions.map(lambda (v, p): (v - p) * (v - p)).sum() / float(testData.count()) -print('Test Mean Squared Error = ' + str(testMSE)) -print('Learned regression tree model:') -print(model.toDebugString()) - -# Save and load model -model.save(sc, "myModelPath") -sameModel = DecisionTreeModel.load(sc, "myModelPath") -{% endhighlight %} +{% include_example python/mllib/decision_tree_regression_example.py %} </div> </div> |